S4. Complete R Markdown
2024-01-23
Preparation
Load Dataframe
data <- read.csv2("data_project_917982_2022_11_15.csv",
na.strings = c("-66","-77","-99"),
encoding = "UFT-8")
View(data)
names(data)## [1] "lfdn" "external_lfdn" "tester"
## [4] "dispcode" "lastpage" "quality"
## [7] "duration" "c_0001" "p_0001"
## [10] "c_0002" "c_0003" "c_0004"
## [13] "v_1" "v_2" "v_3"
## [16] "v_4" "v_5" "v_7"
## [19] "v_8" "v_9" "v_10"
## [22] "v_11" "v_47" "v_48"
## [25] "v_49" "v_12" "v_14"
## [28] "v_16" "v_71" "v_17"
## [31] "v_18" "v_19" "v_20"
## [34] "v_21" "v_115" "v_116"
## [37] "v_117" "v_22" "v_23"
## [40] "v_24" "v_25" "v_26"
## [43] "v_120" "v_27" "v_28"
## [46] "v_29" "v_30" "v_31"
## [49] "v_121" "v_32" "v_33"
## [52] "v_34" "v_35" "v_36"
## [55] "v_122" "v_37" "v_38"
## [58] "v_39" "v_40" "v_41"
## [61] "v_123" "v_124" "v_42"
## [64] "v_43" "v_44" "v_45"
## [67] "v_46" "v_125" "v_72"
## [70] "v_73" "v_74" "v_75"
## [73] "v_76" "v_77" "v_79"
## [76] "v_81" "v_83" "v_126"
## [79] "v_127" "v_128" "v_129"
## [82] "v_130" "v_131" "v_132"
## [85] "v_133" "v_134" "v_135"
## [88] "v_136" "v_137" "v_138"
## [91] "v_139" "v_140" "v_141"
## [94] "v_142" "v_143" "v_144"
## [97] "v_145" "v_146" "v_147"
## [100] "v_148" "v_149" "v_150"
## [103] "v_151" "v_152" "v_153"
## [106] "v_154" "v_155" "v_156"
## [109] "v_157" "v_158" "v_159"
## [112] "v_160" "v_161" "v_162"
## [115] "v_163" "v_164" "v_50"
## [118] "v_51" "v_52" "v_53"
## [121] "v_54" "v_165" "v_166"
## [124] "v_167" "v_55" "v_56"
## [127] "v_57" "v_58" "v_401"
## [130] "v_91" "v_92" "v_93"
## [133] "v_94" "v_95" "v_96"
## [136] "v_98" "v_100" "v_102"
## [139] "v_235" "v_236" "v_237"
## [142] "v_238" "v_239" "v_240"
## [145] "v_241" "v_242" "v_243"
## [148] "v_244" "v_245" "v_246"
## [151] "v_247" "v_248" "v_249"
## [154] "v_250" "v_251" "v_252"
## [157] "v_253" "v_254" "v_255"
## [160] "v_256" "v_257" "v_258"
## [163] "v_259" "v_313" "v_314"
## [166] "v_315" "v_316" "v_317"
## [169] "v_323" "v_324" "v_325"
## [172] "v_326" "v_327" "v_328"
## [175] "v_329" "v_330" "v_331"
## [178] "v_332" "v_333" "v_334"
## [181] "v_335" "v_336" "v_337"
## [184] "v_338" "v_339" "v_340"
## [187] "v_341" "v_342" "v_343"
## [190] "v_344" "v_345" "v_103"
## [193] "v_104" "v_105" "v_106"
## [196] "v_107" "v_108" "v_110"
## [199] "v_112" "v_114" "v_274"
## [202] "v_275" "v_276" "v_277"
## [205] "v_278" "v_279" "v_280"
## [208] "v_281" "v_282" "v_283"
## [211] "v_284" "v_285" "v_286"
## [214] "v_287" "v_288" "v_289"
## [217] "v_290" "v_291" "v_292"
## [220] "v_293" "v_294" "v_295"
## [223] "v_296" "v_297" "v_298"
## [226] "v_299" "v_300" "v_301"
## [229] "v_302" "v_303" "v_304"
## [232] "v_305" "v_306" "v_307"
## [235] "v_308" "v_309" "v_310"
## [238] "v_402" "v_360" "v_361"
## [241] "v_362" "v_363" "v_364"
## [244] "v_365" "v_366" "v_367"
## [247] "v_368" "v_369" "v_370"
## [250] "v_371" "v_372" "v_373"
## [253] "v_374" "v_375" "v_376"
## [256] "v_377" "v_378" "v_379"
## [259] "v_380" "v_381" "v_382"
## [262] "v_383" "v_384" "v_385"
## [265] "v_386" "v_387" "v_388"
## [268] "browser" "referer" "device_type"
## [271] "quota" "quota_assignment" "quota_rejected_id"
## [274] "page_history" "hflip" "vflip"
## [277] "output_mode" "javascript" "flash"
## [280] "session_id" "language" "cleaned"
## [283] "ats" "datetime" "date_of_last_access"
## [286] "date_of_first_mail" "rts6018385" "rts6018739"
## [289] "rts6018818" "rts6019080" "rts6019089"
## [292] "rts6021451" "rts6021455" "rts6023513"
## [295] "rts6023515" "rts6023627" "rts6023655"
## [298] "rts6023657" "rts6023660" "rts6023667"
## [301] "rts6023676" "rts6023679" "rts6033975"str(data)## 'data.frame': 6706 obs. of 303 variables:
## $ lfdn : int 94 95 98 99 100 101 107 109 93 103 ...
## $ external_lfdn : int 0 0 0 0 0 0 0 0 0 0 ...
## $ tester : int 0 0 0 0 0 0 0 0 0 0 ...
## $ dispcode : int 37 37 37 37 37 37 37 37 31 22 ...
## $ lastpage : int 6018729 6018729 6018729 6018729 6018729 6018729 6018729 6018729 6018381 6023627 ...
## $ quality : logi NA NA NA NA NA NA ...
## $ duration : int 23 57 19 25 23 37 30 38 779 68 ...
## $ c_0001 : int NA NA NA NA NA NA NA NA 3 2 ...
## $ p_0001 : num 2.26e+14 2.26e+14 2.26e+14 2.26e+14 2.26e+14 ...
## $ c_0002 : int NA NA NA NA NA NA NA NA 3 2 ...
## $ c_0003 : int NA NA NA NA NA NA NA NA 1 2 ...
## $ c_0004 : int NA NA NA NA NA NA NA NA 2 2 ...
## $ v_1 : int 2 1 2 1 2 1 1 2 2 2 ...
## $ v_2 : int 48 19 55 55 33 44 64 43 31 42 ...
## $ v_3 : int 1 1 2 2 1 1 3 3 2 2 ...
## $ v_4 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ v_5 : int 2 2 2 2 2 2 2 2 2 2 ...
## $ v_7 : int 1 3 1 2 1 1 3 2 5 6 ...
## $ v_8 : int NA NA NA NA NA NA NA NA 1 1 ...
## $ v_9 : chr NA NA NA NA ...
## $ v_10 : int NA NA NA NA NA NA NA NA 4 NA ...
## $ v_11 : int NA NA NA NA NA NA NA NA 6 NA ...
## $ v_47 : int NA NA NA NA NA NA NA NA 3 NA ...
## $ v_48 : int NA NA NA NA NA NA NA NA 6 NA ...
## $ v_49 : int NA NA NA NA NA NA NA NA 6 NA ...
## $ v_12 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_14 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_16 : int NA NA NA NA NA NA NA NA 3 NA ...
## $ v_71 : int NA NA NA NA NA NA NA NA 3 NA ...
## $ v_17 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_18 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_19 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_20 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_21 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_115 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_116 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_117 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_22 : int NA NA NA NA NA NA NA NA 3 NA ...
## $ v_23 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_24 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_25 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_26 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_120 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_27 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_28 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_29 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_30 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_31 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_121 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_32 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_33 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_34 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_35 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_36 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_122 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_37 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_38 : int NA NA NA NA NA NA NA NA 0 NA ...
## $ v_39 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_40 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_41 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_123 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_124 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_42 : int NA NA NA NA NA NA NA NA 3 NA ...
## $ v_43 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_44 : int NA NA NA NA NA NA NA NA 3 NA ...
## $ v_45 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_46 : int NA NA NA NA NA NA NA NA 3 NA ...
## $ v_125 : int NA NA NA NA NA NA NA NA 3 NA ...
## $ v_72 : int NA NA NA NA NA NA NA NA NA 6 ...
## $ v_73 : int NA NA NA NA NA NA NA NA NA 7 ...
## $ v_74 : int NA NA NA NA NA NA NA NA NA 7 ...
## $ v_75 : int NA NA NA NA NA NA NA NA NA 7 ...
## $ v_76 : int NA NA NA NA NA NA NA NA NA 7 ...
## $ v_77 : int NA NA NA NA NA NA NA NA NA 4 ...
## $ v_79 : int NA NA NA NA NA NA NA NA NA 1 ...
## $ v_81 : int NA NA NA NA NA NA NA NA NA 1 ...
## $ v_83 : int NA NA NA NA NA NA NA NA NA 1 ...
## $ v_126 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_127 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_128 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_129 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_130 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_131 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_132 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_133 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_134 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_135 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_136 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_137 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_138 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_139 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_140 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_141 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_142 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_143 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_144 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_145 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_146 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_147 : int NA NA NA NA NA NA NA NA NA NA ...
## [list output truncated]nrow(data)## [1] 6706Check Dispcodes
table(data$dispcode)##
## 20 22 31 32 36 37
## 45 1039 1754 288 1474 2106# 20 = Not started yet --> 45
# 22 = Interrupted --> 1039
# 37,38,39,40 = Screenout --> 2106
# 35,36,41 = Quota full --> 1474
# 31,32,33,34 = Finished --> 2042
# Check for multiple participations
x <- table(data$p_0001[data$dispcode==31|
data$dispcode==32])
code <- dimnames(x)[[1]]
code <- code[x>1]
code## [1] "225951288721641"#Case 225951288721641 participated twice. Exclude second participation.
data$lfdn[data$p_0001 == "225951288721641"]## [1] NA 1732 3192data$datetime[data$p_0001 == "225951288721641"]## [1] NA "2022-10-26 07:45:35" "2022-10-27 17:38:04"data <- data[!data$lfdn == 3192,]Recode Data
data$lfdn <- rank(rnorm(nrow(data)))
names(data)[1] <- "id"
data$id <- factor(data$id,levels = c(1:6705))
str(data)## 'data.frame': 6705 obs. of 303 variables:
## $ id : Factor w/ 6705 levels "1","2","3","4",..: 759 52 4690 3401 6194 1195 5513 1286 4692 193 ...
## $ external_lfdn : int 0 0 0 0 0 0 0 0 0 0 ...
## $ tester : int 0 0 0 0 0 0 0 0 0 0 ...
## $ dispcode : int 37 37 37 37 37 37 37 37 31 22 ...
## $ lastpage : int 6018729 6018729 6018729 6018729 6018729 6018729 6018729 6018729 6018381 6023627 ...
## $ quality : logi NA NA NA NA NA NA ...
## $ duration : int 23 57 19 25 23 37 30 38 779 68 ...
## $ c_0001 : int NA NA NA NA NA NA NA NA 3 2 ...
## $ p_0001 : num 2.26e+14 2.26e+14 2.26e+14 2.26e+14 2.26e+14 ...
## $ c_0002 : int NA NA NA NA NA NA NA NA 3 2 ...
## $ c_0003 : int NA NA NA NA NA NA NA NA 1 2 ...
## $ c_0004 : int NA NA NA NA NA NA NA NA 2 2 ...
## $ v_1 : int 2 1 2 1 2 1 1 2 2 2 ...
## $ v_2 : int 48 19 55 55 33 44 64 43 31 42 ...
## $ v_3 : int 1 1 2 2 1 1 3 3 2 2 ...
## $ v_4 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ v_5 : int 2 2 2 2 2 2 2 2 2 2 ...
## $ v_7 : int 1 3 1 2 1 1 3 2 5 6 ...
## $ v_8 : int NA NA NA NA NA NA NA NA 1 1 ...
## $ v_9 : chr NA NA NA NA ...
## $ v_10 : int NA NA NA NA NA NA NA NA 4 NA ...
## $ v_11 : int NA NA NA NA NA NA NA NA 6 NA ...
## $ v_47 : int NA NA NA NA NA NA NA NA 3 NA ...
## $ v_48 : int NA NA NA NA NA NA NA NA 6 NA ...
## $ v_49 : int NA NA NA NA NA NA NA NA 6 NA ...
## $ v_12 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_14 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_16 : int NA NA NA NA NA NA NA NA 3 NA ...
## $ v_71 : int NA NA NA NA NA NA NA NA 3 NA ...
## $ v_17 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_18 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_19 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_20 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_21 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_115 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_116 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_117 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_22 : int NA NA NA NA NA NA NA NA 3 NA ...
## $ v_23 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_24 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_25 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_26 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_120 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_27 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_28 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_29 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_30 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_31 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_121 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_32 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_33 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_34 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_35 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_36 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_122 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_37 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_38 : int NA NA NA NA NA NA NA NA 0 NA ...
## $ v_39 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_40 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_41 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_123 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_124 : int NA NA NA NA NA NA NA NA 2 NA ...
## $ v_42 : int NA NA NA NA NA NA NA NA 3 NA ...
## $ v_43 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_44 : int NA NA NA NA NA NA NA NA 3 NA ...
## $ v_45 : int NA NA NA NA NA NA NA NA 1 NA ...
## $ v_46 : int NA NA NA NA NA NA NA NA 3 NA ...
## $ v_125 : int NA NA NA NA NA NA NA NA 3 NA ...
## $ v_72 : int NA NA NA NA NA NA NA NA NA 6 ...
## $ v_73 : int NA NA NA NA NA NA NA NA NA 7 ...
## $ v_74 : int NA NA NA NA NA NA NA NA NA 7 ...
## $ v_75 : int NA NA NA NA NA NA NA NA NA 7 ...
## $ v_76 : int NA NA NA NA NA NA NA NA NA 7 ...
## $ v_77 : int NA NA NA NA NA NA NA NA NA 4 ...
## $ v_79 : int NA NA NA NA NA NA NA NA NA 1 ...
## $ v_81 : int NA NA NA NA NA NA NA NA NA 1 ...
## $ v_83 : int NA NA NA NA NA NA NA NA NA 1 ...
## $ v_126 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_127 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_128 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_129 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_130 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_131 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_132 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_133 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_134 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_135 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_136 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_137 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_138 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_139 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_140 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_141 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_142 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_143 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_144 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_145 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_146 : int NA NA NA NA NA NA NA NA NA NA ...
## $ v_147 : int NA NA NA NA NA NA NA NA NA NA ...
## [list output truncated]data <- rename(data, condition = c_0001, text_order = c_0003,
METI_target = c_0004)
data$condition <- factor(data$condition)
# Text presented first, 1 = Barth et al., 2 = Faerber et al.
data$text_order <- factor(data$text_order, levels = c(1,2), labels =
c("Barth", "Faerber"))
data$METI_text <- ifelse(data$text_order == "Barth","Faerber","Barth")
data$METI_text <- factor(data$METI_text, levels = c("Barth","Faerber"))
data$summary1 <- data$text_order
data$summary2 <- data$METI_text
data$METI_target <- factor (data$METI_target, levels = c(1,2), labels =
c("Study Authors","Summary Authors"))
data <- rename(data, s_sex = v_1, s_age = v_2,
s_school = v_3, s_german = v_4,
s_psychology = v_5, s_interest = v_7,
s_contact = v_8, s_field = v_9)
data$s_sex <- factor(data$s_sex, levels = c (1,2), labels = c("female","male"))
data$s_school <- factor(data$s_school, levels = c(1,2,3),
labels = c("Haupt","Real","Abi"))
data$quota[data$quota == 0] <- NA
data$quota <- factor(data$quota)Create Factor for Experimental Condition
data$version <- case_when(data$condition == 1 ~1,
data$condition == 2 ~1,
data$condition == 3 ~1,
data$condition == 4 ~1,
data$condition == 5 ~1,
data$condition == 6 ~0)
data$version <- factor(data$version, levels = c(0,1),
labels = c("old guideline","new guideline"))
summary(data$version)## old guideline new guideline NA's
## 498 2492 3715data$causality <- case_when(data$condition == 1 ~0,
data$condition == 2 ~0,
data$condition == 3 ~1,
data$condition == 4 ~1,
data$condition == 5 ~1,
data$condition == 6 ~0)
data$causality <- factor(data$causality, levels = c(0,1),
labels = c("no causality statement",
"causality statement"))
summary(data$causality)## no causality statement causality statement NA's
## 1496 1494 3715data$disclaimer <- case_when(data$condition == 1 ~0,
data$condition == 2 ~1,
data$condition == 3 ~0,
data$condition == 4 ~1,
data$condition == 5 ~1,
data$condition == 6 ~0)
data$disclaimer <- factor(data$disclaimer, levels = c(0,1),
labels = c("no disclaimer",
"disclaimer"))
summary(data$disclaimer)## no disclaimer disclaimer NA's
## 1495 1495 3715data$CAMA <- case_when(data$condition == 1 ~0,
data$condition == 2 ~0,
data$condition == 3 ~0,
data$condition == 4 ~0,
data$condition == 5 ~1,
data$condition == 6 ~0)
data$CAMA <- factor(data$CAMA, levels = c(0,1),
labels = c("no CAMA PLS",
"CAMA PLS"))
summary(data$disclaimer)## no disclaimer disclaimer NA's
## 1495 1495 3715Drop Unneeded Dispcodes
data <- data[data$dispcode == 22|data$dispcode == 31|data$dispcode == 32,]
length(unique(data$p_0001
[data$dispcode==22|data$dispcode==31|data$dispcode==32]))## [1] 3001Dropout Analyses
By Condition
data$dropout <- data$dispcode == 22
data$dropout <- factor(data$dropout, c("FALSE","TRUE"),
labels = c("No Dropout", "Dropout"))
table(data$dropout, data$condition)##
## 1 2 3 4 5 6
## No Dropout 334 345 336 341 328 357
## Dropout 165 154 162 156 170 141table(data$dropout,data$condition)[1,]/colSums(table(
data$dropout,data$condition))*100## 1 2 3 4 5 6
## 66.93387 69.13828 67.46988 68.61167 65.86345 71.68675chisq.test(data$dropout, data$condition)##
## Pearson's Chi-squared test
##
## data: data$dropout and data$condition
## X-squared = 4.775, df = 5, p-value = 0.444By Quota
table(data$dropout, data$quota)##
## 1 2 3 4 5 6 7 8 9 10 11 12
## No Dropout 169 171 174 168 171 172 168 167 164 170 172 175
## Dropout 88 112 131 93 124 143 33 44 36 63 90 74table(data$dropout, data$quota)[1,]/colSums(table(
data$dropout, data$quota))*100## 1 2 3 4 5 6 7 8
## 65.75875 60.42403 57.04918 64.36782 57.96610 54.60317 83.58209 79.14692
## 9 10 11 12
## 82.00000 72.96137 65.64885 70.28112chisq.test(data$dropout,data$quota)##
## Pearson's Chi-squared test
##
## data: data$dropout and data$quota
## X-squared = 116.16, df = 11, p-value < 2.2e-16By Participants’ Gender
table(data$dropout, data$s_sex)##
## female male
## No Dropout 1028 1013
## Dropout 587 446table(data$dropout, data$s_sex)[1,]/colSums(table(data$dropout, data$s_sex))*100## female male
## 63.65325 69.43112chisq.test(data$dropout,data$s_sex)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: data$dropout and data$s_sex
## X-squared = 11.211, df = 1, p-value = 0.0008129By Participants’ Age
# Set single age value of 744 as NA
data$s_age[data$s_age == 744] <- NA
dropout_age <- glm(data$dropout ~ data$s_age, data = data, family = "binomial",
na.action = na.omit)
summary(dropout_age)##
## Call:
## glm(formula = data$dropout ~ data$s_age, family = "binomial",
## data = data, na.action = na.omit)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.992992 0.130678 -15.25 <2e-16 ***
## data$s_age 0.027108 0.002514 10.78 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3932.6 on 3076 degrees of freedom
## Residual deviance: 3810.8 on 3075 degrees of freedom
## (3 Beobachtungen als fehlend gelöscht)
## AIC: 3814.8
##
## Number of Fisher Scoring iterations: 4exp(dropout_age$coefficients)## (Intercept) data$s_age
## 0.136287 1.027478By Participants’ Educational Background
table(data$dropout, data$s_school)##
## Haupt Real Abi
## No Dropout 685 681 675
## Dropout 387 373 277table(data$dropout, data$s_school)[1,]/colSums(
table(data$dropout, data$s_school))*100## Haupt Real Abi
## 63.89925 64.61101 70.90336chisq.test(data$dropout,data$s_school)##
## Pearson's Chi-squared test
##
## data: data$dropout and data$s_school
## X-squared = 13.142, df = 2, p-value = 0.001401dropout_edu <- glm(data$dropout ~ data$s_school, data = data, family = "binomial",
na.action = na.omit)
summary(dropout_edu)##
## Call:
## glm(formula = data$dropout ~ data$s_school, family = "binomial",
## data = data, na.action = na.omit)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.57099 0.06359 -8.979 < 2e-16 ***
## data$s_schoolReal -0.03099 0.09052 -0.342 0.732076
## data$s_schoolAbi -0.31970 0.09558 -3.345 0.000823 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3933.5 on 3077 degrees of freedom
## Residual deviance: 3920.1 on 3075 degrees of freedom
## (2 Beobachtungen als fehlend gelöscht)
## AIC: 3926.1
##
## Number of Fisher Scoring iterations: 4exp(dropout_edu$coefficients)## (Intercept) data$s_schoolReal data$s_schoolAbi
## 0.5649635 0.9694855 0.7263662Dropout Regression Analyses
dropout_logistic_1 <- glm(formula = dropout ~ condition + quota, data = data,
family = "binomial", na.action = na.omit)
summary(dropout_logistic_1)##
## Call:
## glm(formula = dropout ~ condition + quota, family = "binomial",
## data = data, na.action = na.omit)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.653151 0.163055 -4.006 6.18e-05 ***
## condition2 -0.140337 0.138830 -1.011 0.312085
## condition3 -0.048597 0.138044 -0.352 0.724807
## condition4 -0.122653 0.138728 -0.884 0.376629
## condition5 0.007591 0.137135 0.055 0.955854
## condition6 -0.257050 0.140745 -1.826 0.067798 .
## quota2 0.220186 0.185468 1.187 0.235151
## quota3 0.352223 0.180902 1.947 0.051530 .
## quota4 0.136894 0.188245 0.727 0.467096
## quota5 0.342825 0.182117 1.882 0.059776 .
## quota6 0.487074 0.178462 2.729 0.006347 **
## quota7 -0.947738 0.238435 -3.975 7.04e-05 ***
## quota8 -0.825807 0.230417 -3.584 0.000338 ***
## quota9 -0.882218 0.236521 -3.730 0.000191 ***
## quota10 -0.334860 0.204088 -1.641 0.100846
## quota11 0.035250 0.190172 0.185 0.852947
## quota12 -0.162174 0.195749 -0.828 0.407400
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3734.5 on 2988 degrees of freedom
## Residual deviance: 3609.0 on 2972 degrees of freedom
## (91 Beobachtungen als fehlend gelöscht)
## AIC: 3643
##
## Number of Fisher Scoring iterations: 4exp(dropout_logistic_1$coefficients)## (Intercept) condition2 condition3 condition4 condition5 condition6
## 0.5204033 0.8690651 0.9525646 0.8845708 1.0076203 0.7733299
## quota2 quota3 quota4 quota5 quota6 quota7
## 1.2463090 1.4222263 1.1467070 1.4089220 1.6275464 0.3876166
## quota8 quota9 quota10 quota11 quota12
## 0.4378814 0.4138641 0.7154382 1.0358787 0.8502934dropout_logistic_2 <- glm(formula = dropout ~ s_sex + s_school + s_age,
data = data, family = "binomial", na.action = na.omit)
summary(dropout_logistic_2)##
## Call:
## glm(formula = dropout ~ s_sex + s_school + s_age, family = "binomial",
## data = data, na.action = na.omit)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.828571 0.147292 -12.415 < 2e-16 ***
## s_sexmale -0.346953 0.079185 -4.382 1.18e-05 ***
## s_schoolReal 0.031970 0.092998 0.344 0.7310
## s_schoolAbi -0.222797 0.098232 -2.268 0.0233 *
## s_age 0.028037 0.002556 10.971 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3919.6 on 3070 degrees of freedom
## Residual deviance: 3769.0 on 3066 degrees of freedom
## (9 Beobachtungen als fehlend gelöscht)
## AIC: 3779
##
## Number of Fisher Scoring iterations: 4exp(dropout_logistic_2$coefficients)## (Intercept) s_sexmale s_schoolReal s_schoolAbi s_age
## 0.1606429 0.7068385 1.0324869 0.8002773 1.0284335Merging Variables
Check for NAs in Demographic Variables
sum(is.na(data$s_sex))## [1] 6sum(is.na(data$s_age))## [1] 3sum(is.na(data$s_school))## [1] 2sum(is.na(data$s_german))## [1] 0sum(is.na(data$s_psychology))## [1] 0sum(is.na(data$s_interest))## [1] 0sum(is.na(data$s_contact))## [1] 20sum(is.na(data$s_field))## [1] 2466Merge T1 and T2 Variables
data$v_10[data$v_10 == 0] <- NA
data$v_72[data$v_72 == 0] <- NA
data$v_91[data$v_91 == 0] <- NA
data$v_103[data$v_103 == 0] <- NA
data$v_11[data$v_11 == 0] <- NA
data$v_73[data$v_73 == 0] <- NA
data$v_92[data$v_92 == 0] <- NA
data$v_104[data$v_104 == 0] <- NA
data$v_47[data$v_47 == 0] <- NA
data$v_74[data$v_74 == 0] <- NA
data$v_93[data$v_93 == 0] <- NA
data$v_105[data$v_105 == 0] <- NA
data$v_48[data$v_48 == 0] <- NA
data$v_75[data$v_75 == 0] <- NA
data$v_94[data$v_94 == 0] <- NA
data$v_106[data$v_106 == 0] <- NA
data$v_49[data$v_49 == 0] <- NA
data$v_76[data$v_76 == 0] <- NA
data$v_95[data$v_95 == 0] <- NA
data$v_107[data$v_107 == 0] <- NA
data$v_12[data$v_12 == 0] <- NA
data$v_77[data$v_77 == 0] <- NA
data$v_96[data$v_96 == 0] <- NA
data$v_108[data$v_108 == 0] <- NA
data$v_14[data$v_14 == 0] <- NA
data$v_79[data$v_79 == 0] <- NA
data$v_98[data$v_98 == 0] <- NA
data$v_110[data$v_110 == 0] <- NA
data$v_16[data$v_16 == 0] <- NA
data$v_81[data$v_81 == 0] <- NA
data$v_100[data$v_100 == 0] <- NA
data$v_112[data$v_112 == 0] <- NA
data$v_71[data$v_71 == 0] <- NA
data$v_83[data$v_83 == 0] <- NA
data$v_102[data$v_102 == 0] <- NA
data$v_114[data$v_114 == 0] <- NA
data$accessibility_1 <- coalesce(data$v_10, data$v_72)
table(data$accessibility_1)##
## 1 2 3 4 5 6 7 8
## 52 82 192 353 425 527 409 471data$accessibility_2 <- coalesce(data$v_91, data$v_103)
table(data$accessibility_2)##
## 1 2 3 4 5 6 7 8
## 67 74 177 321 353 393 321 331data$understanding_1 <- coalesce(data$v_11, data$v_73)
table(data$understanding_1)##
## 1 2 3 4 5 6 7 8
## 30 55 151 342 446 576 458 445data$understanding_2 <- coalesce(data$v_92, data$v_104)
table(data$understanding_2)##
## 1 2 3 4 5 6 7 8
## 51 76 160 304 410 459 327 251data$empowerment_1 <- coalesce(data$v_47, data$v_74)
table(data$empowerment_1)##
## 1 2 3 4 5 6 7 8
## 125 137 315 468 566 469 227 190data$empowerment_2 <- coalesce(data$v_93, data$v_105)
table(data$empowerment_2)##
## 1 2 3 4 5 6 7 8
## 131 127 232 410 449 367 182 144data$credibility_1 <- coalesce(data$v_48, data$v_75)
table(data$credibility_1)##
## 1 2 3 4 5 6 7 8
## 12 29 87 344 469 601 487 470data$credibility_2 <- coalesce(data$v_94, data$v_106)
table(data$credibility_2)##
## 1 2 3 4 5 6 7 8
## 26 25 91 296 423 465 372 341data$relevance_1 <- coalesce(data$v_49, data$v_76)
table(data$relevance_1)##
## 1 2 3 4 5 6 7 8
## 17 30 62 231 327 559 466 808data$relevance_2 <- coalesce(data$v_95, data$v_107)
table(data$relevance_2)##
## 1 2 3 4 5 6 7 8
## 32 26 77 204 298 411 397 594data$curiosity_1 <- coalesce(data$v_12, data$v_77)
table(data$curiosity_1)##
## 1 2 3 4 5
## 162 414 773 816 345data$curiosity_2 <- coalesce(data$v_96, data$v_108)
table(data$curiosity_2)##
## 1 2 3 4 5
## 168 420 642 542 273data$boredom_1 <- coalesce(data$v_14, data$v_79)
table(data$boredom_1)##
## 1 2 3 4 5
## 1045 671 542 168 76data$boredom_2 <- coalesce(data$v_98, data$v_110)
table(data$boredom_2)##
## 1 2 3 4 5
## 881 507 424 150 81data$confusion_1 <- coalesce(data$v_16, data$v_81)
table(data$confusion_1)##
## 1 2 3 4 5
## 939 751 598 162 54data$confusion_2 <- coalesce(data$v_100, data$v_112)
table(data$confusion_2)##
## 1 2 3 4 5
## 733 582 493 169 68data$frustration_1 <- coalesce(data$v_71, data$v_83)
table(data$frustration_1)##
## 1 2 3 4 5
## 1486 451 419 107 36data$frustration_2 <- coalesce(data$v_102, data$v_114)
table(data$frustration_2)##
## 1 2 3 4 5
## 1136 392 355 104 58Merge and Recode Relationship-Item
data$v_17[data$v_17 == 0] <- NA
data$v_126[data$v_126 == 0] <- NA
data$v_18[data$v_18 == 0] <- NA
data$v_127[data$v_127 == 0] <- NA
data$v_19[data$v_19 == 0] <- NA
data$v_128[data$v_128 == 0] <- NA
data$v_20[data$v_20 == 0] <- NA
data$v_129[data$v_129 == 0] <- NA
data$v_21[data$v_21 == 0] <- NA
data$v_130[data$v_130 == 0] <- NA
data$v_115[data$v_115 == 0] <- NA
data$v_131[data$v_131 == 0] <- NA
data$v_116[data$v_116 == 0] <- NA
data$v_132[data$v_132 == 0] <- NA
data$v_117[data$v_117 == 0] <- NA
data$v_133[data$v_133 == 0] <- NA
data$s_relationship_1 <- coalesce(data$v_17, data$v_126)
data$s_relationship_2 <- coalesce(data$v_18, data$v_127)
data$s_relationship_3 <- coalesce(data$v_19, data$v_128)
data$s_relationship_4 <- coalesce(data$v_20, data$v_129)
data$s_relationship_5 <- coalesce(data$v_21, data$v_130)
data$s_relationship_6 <- coalesce(data$v_115, data$v_131)
data$s_relationship_7 <- coalesce(data$v_116, data$v_132)
data$s_relationship_8 <- coalesce(data$v_117, data$v_133)
data$s_relationship_1 <- mapvalues(data$s_relationship_1, c(1,2,3), c(1,-1,0))
table(data$s_relationship_1)##
## -1 0 1
## 233 345 1690data$s_relationship_2 <- mapvalues(data$s_relationship_2, c(1,2,3), c(-1,1,0))
table(data$s_relationship_2)##
## -1 0 1
## 1175 406 683data$s_relationship_3 <- mapvalues(data$s_relationship_3, c(1,2,3), c(-1,1,0))
table(data$s_relationship_3)##
## -1 0 1
## 671 772 822data$s_relationship_4 <- mapvalues(data$s_relationship_4, c(1,2,3), c(-1,1,0))
table(data$s_relationship_4)##
## -1 0 1
## 730 736 801data$s_relationship_5 <- mapvalues(data$s_relationship_5, c(1,2,3), c(1,-1,0))
table(data$s_relationship_5)##
## -1 0 1
## 256 375 1638data$s_relationship_6 <- mapvalues(data$s_relationship_6, c(1,2,3), c(-1,1,0))
table(data$s_relationship_6)##
## -1 0 1
## 1321 426 518data$s_relationship_7 <- mapvalues(data$s_relationship_7, c(1,2,3), c(-1,1,0))
table(data$s_relationship_7)##
## -1 0 1
## 1349 448 467data$s_relationship_8 <- mapvalues(data$s_relationship_8, c(1,2,3), c(-1,1,0))
table(data$s_relationship_8)##
## -1 0 1
## 1156 464 642Merge and Recode Extent of Evaluation-Item
data$v_22[data$v_22 == 0] <- NA
data$v_134[data$v_134 == 0] <- NA
data$v_23[data$v_23 == 0] <- NA
data$v_135[data$v_135 == 0] <- NA
data$v_24[data$v_24 == 0] <- NA
data$v_136[data$v_136 == 0] <- NA
data$v_25[data$v_25 == 0] <- NA
data$v_137[data$v_137 == 0] <- NA
data$v_26[data$v_26 == 0] <- NA
data$v_138[data$v_138 == 0] <- NA
data$v_120[data$v_120 == 0] <- NA
data$v_139[data$v_139 == 0] <- NA
data$s_extent_1 <- coalesce(data$v_22, data$v_134)
data$s_extent_2 <- coalesce(data$v_23, data$v_135)
data$s_extent_3 <- coalesce(data$v_24, data$v_136)
data$s_extent_4 <- coalesce(data$v_25, data$v_137)
data$s_extent_5 <- coalesce(data$v_26, data$v_138)
data$s_extent_6 <- coalesce(data$v_120, data$v_139)
data$s_extent_1 <- mapvalues(data$s_extent_1, c(1,2,3), c(-1,1,0))
table(data$s_extent_1)##
## -1 0 1
## 663 773 832data$s_extent_2 <- mapvalues(data$s_extent_2, c(1,2,3), c(-1,1,0))
table(data$s_extent_2)##
## -1 0 1
## 931 585 750data$s_extent_3 <- mapvalues(data$s_extent_3, c(1,2,3), c(-1,1,0))
table(data$s_extent_3)##
## -1 0 1
## 1035 740 488data$s_extent_4 <- mapvalues(data$s_extent_4, c(1,2,3), c(-1,1,0))
table(data$s_extent_4)##
## -1 0 1
## 968 763 533data$s_extent_5 <- mapvalues(data$s_extent_5, c(1,2,3), c(1,-1,0))
table(data$s_extent_5)##
## -1 0 1
## 317 535 1411data$s_extent_6 <- mapvalues(data$s_extent_6, c(1,2,3), c(1,-1,0))
table(data$s_extent_6)##
## -1 0 1
## 380 497 1393Merge and Recode Differentiation-Item
# Caution: Due to an error (wrong answers provided during experiment), all values for Faerber et al. are NA. Only answers for Barth et al. can be considered for analysis
data$v_27[data$v_27 == 0] <- NA
data$v_28[data$v_28 == 0] <- NA
data$v_29[data$v_29 == 0] <- NA
data$v_30[data$v_30 == 0] <- NA
data$v_31[data$v_31 == 0] <- NA
data$v_121[data$v_121 == 0] <- NA
data$v_140 <- NA
data$v_141 <- NA
data$v_142 <- NA
data$v_143 <- NA
data$v_144 <- NA
data$v_145 <- NA
data$v_235[data$v_235 == 0] <- NA
data$v_236[data$v_236 == 0] <- NA
data$v_237[data$v_237 == 0] <- NA
data$v_238[data$v_238 == 0] <- NA
data$v_239[data$v_239 == 0] <- NA
data$v_240[data$v_240 == 0] <- NA
data$v_274 <- NA
data$v_275 <- NA
data$v_276 <- NA
data$v_277 <- NA
data$v_278 <- NA
data$v_279 <- NA
#Values only need to be mapped for the items from Barth et al.
data$v_27 <- mapvalues(data$v_27, c(1,2,3),c(-1,1,0))
table(data$v_27)##
## -1 0 1
## 529 305 305data$v_28 <- mapvalues(data$v_28, c(1,2,3),c(-1,1,0))
table(data$v_28)##
## -1 0 1
## 561 297 284data$v_29 <- mapvalues(data$v_29, c(1,2,3),c(1,-1,0))
table(data$v_29)##
## -1 0 1
## 371 321 447data$v_30 <- mapvalues(data$v_30, c(1,2,3),c(-1,1,0))
table(data$v_30)##
## -1 0 1
## 373 298 467data$v_31 <- mapvalues(data$v_31, c(1,2,3),c(1,-1,0))
table(data$v_31)##
## -1 0 1
## 262 404 474data$v_121 <- mapvalues(data$v_121, c(1,2,3),c(1,-1,0))
table(data$v_121)##
## -1 0 1
## 317 343 483data$v_235 <- mapvalues(data$v_235, c(1,2,3),c(-1,1,0))
table(data$v_235)##
## -1 0 1
## 386 229 397data$v_236 <- mapvalues(data$v_236, c(1,2,3),c(-1,1,0))
table(data$v_236)##
## -1 0 1
## 415 255 344data$v_237 <- mapvalues(data$v_237, c(1,2,3),c(1,-1,0))
table(data$v_237)##
## -1 0 1
## 376 255 377data$v_238 <- mapvalues(data$v_238, c(1,2,3),c(-1,1,0))
table(data$v_238)##
## -1 0 1
## 282 252 481data$v_239 <- mapvalues(data$v_239, c(1,2,3),c(1,-1,0))
table(data$v_239)##
## -1 0 1
## 259 250 500data$v_240 <- mapvalues(data$v_240, c(1,2,3),c(1,-1,0))
table(data$v_240)##
## -1 0 1
## 311 255 446#Merge for T1
data$s_diff_1_1 <- coalesce(data$v_27, data$v_140)
table(data$s_diff_1_1)##
## -1 0 1
## 529 305 305data$s_diff_1_2 <- coalesce(data$v_28, data$v_141)
table(data$s_diff_1_2)##
## -1 0 1
## 561 297 284data$s_diff_1_3 <- coalesce(data$v_29, data$v_142)
table(data$s_diff_1_3)##
## -1 0 1
## 371 321 447data$s_diff_1_4 <- coalesce(data$v_30, data$v_143)
table(data$s_diff_1_4)##
## -1 0 1
## 373 298 467data$s_diff_1_5 <- coalesce(data$v_31, data$v_144)
table(data$s_diff_1_5)##
## -1 0 1
## 262 404 474data$s_diff_1_6 <- coalesce(data$v_121, data$v_145)
table(data$s_diff_1_6)##
## -1 0 1
## 317 343 483#Merge for T2
data$s_diff_2_1 <- coalesce(data$v_235, data$v_274)
table(data$s_diff_2_1)##
## -1 0 1
## 386 229 397data$s_diff_2_2 <- coalesce(data$v_236, data$v_275)
table(data$s_diff_2_2)##
## -1 0 1
## 415 255 344data$s_diff_2_3 <- coalesce(data$v_237, data$v_276)
table(data$s_diff_2_3)##
## -1 0 1
## 376 255 377data$s_diff_2_4 <- coalesce(data$v_238, data$v_277)
table(data$s_diff_2_4)##
## -1 0 1
## 282 252 481data$s_diff_2_5 <- coalesce(data$v_239, data$v_278)
table(data$s_diff_2_5)##
## -1 0 1
## 259 250 500data$s_diff_2_6 <- coalesce(data$v_240, data$v_279)
table(data$s_diff_2_6)##
## -1 0 1
## 311 255 446Merge and Recode Funding-Item
data$v_32[data$v_32 == 0] <- NA
data$v_33[data$v_33 == 0] <- NA
data$v_34[data$v_34 == 0] <- NA
data$v_35[data$v_35 == 0] <- NA
data$v_36[data$v_36 == 0] <- NA
data$v_122[data$v_122 == 0] <- NA
data$v_146[data$v_146 == 0] <- NA
data$v_147[data$v_147 == 0] <- NA
data$v_148[data$v_148 == 0] <- NA
data$v_149[data$v_149 == 0] <- NA
data$v_150[data$v_150 == 0] <- NA
data$v_151[data$v_151 == 0] <- NA
data$v_241[data$v_241 == 0] <- NA
data$v_242[data$v_242 == 0] <- NA
data$v_243[data$v_243 == 0] <- NA
data$v_244[data$v_244 == 0] <- NA
data$v_245[data$v_245 == 0] <- NA
data$v_246[data$v_246 == 0] <- NA
data$v_280[data$v_280 == 0] <- NA
data$v_281[data$v_281 == 0] <- NA
data$v_282[data$v_282 == 0] <- NA
data$v_283[data$v_283 == 0] <- NA
data$v_284[data$v_284 == 0] <- NA
data$v_285[data$v_285 == 0] <- NA
data$v_32 <- mapvalues(data$v_32, c(1,2,3), c(-1,1,0))
table(data$v_32)##
## -1 0 1
## 363 418 357data$v_33 <- mapvalues(data$v_33, c(1,2,3), c(-1,1,0))
table(data$v_33)##
## -1 0 1
## 335 406 401data$v_34 <- mapvalues(data$v_34, c(1,2,3), c(-1,1,0))
table(data$v_34)##
## -1 0 1
## 283 333 524data$v_35 <- mapvalues(data$v_35, c(1,2,3), c(-1,1,0))
table(data$v_35)##
## -1 0 1
## 316 452 374data$v_36 <- mapvalues(data$v_36, c(1,2,3), c(-1,1,0))
table(data$v_36)##
## -1 0 1
## 341 434 367data$v_122 <- mapvalues(data$v_122, c(1,2,3), c(1,-1,0))
table(data$v_122)##
## -1 0 1
## 240 400 501data$v_146 <- mapvalues(data$v_146, c(1,2,3), c(-1,1,0))
table(data$v_122)##
## -1 0 1
## 240 400 501data$v_147 <- mapvalues(data$v_147, c(1,2,3), c(-1,1,0))
table(data$v_147)##
## -1 0 1
## 311 396 420data$v_148 <- mapvalues(data$v_148, c(1,2,3), c(-1,1,0))
table(data$v_148)##
## -1 0 1
## 200 443 480data$v_149 <- mapvalues(data$v_149, c(1,2,3), c(1,-1,0))
table(data$v_149)##
## -1 0 1
## 241 324 559data$v_150 <- mapvalues(data$v_150, c(1,2,3), c(-1,1,0))
table(data$v_150)##
## -1 0 1
## 226 437 460data$v_151 <- mapvalues(data$v_151, c(1,2,3), c(-1,1,0))
table(data$v_151)##
## -1 0 1
## 249 405 465data$v_241 <- mapvalues(data$v_241, c(1,2,3), c(-1,1,0))
table(data$v_241)##
## -1 0 1
## 223 257 533data$v_242 <- mapvalues(data$v_242, c(1,2,3), c(-1,1,0))
table(data$v_242)##
## -1 0 1
## 227 277 508data$v_243 <- mapvalues(data$v_243, c(1,2,3), c(-1,1,0))
table(data$v_243)##
## -1 0 1
## 193 234 590data$v_244 <- mapvalues(data$v_244, c(1,2,3), c(-1,1,0))
table(data$v_244)##
## -1 0 1
## 310 274 431data$v_245 <- mapvalues(data$v_245, c(1,2,3), c(-1,1,0))
table(data$v_245)##
## -1 0 1
## 307 257 450data$v_246 <- mapvalues(data$v_246, c(1,2,3), c(1,-1,0))
table(data$v_246)##
## -1 0 1
## 192 240 585data$v_280 <- mapvalues(data$v_280, c(1,2,3), c(-1,1,0))
table(data$v_280)##
## -1 0 1
## 263 276 487data$v_281 <- mapvalues(data$v_281, c(1,2,3), c(-1,1,0))
table(data$v_281)##
## -1 0 1
## 239 279 507data$v_282 <- mapvalues(data$v_282, c(1,2,3), c(-1,1,0))
table(data$v_282)##
## -1 0 1
## 212 307 506data$v_283 <- mapvalues(data$v_283, c(1,2,3), c(1,-1,0))
table(data$v_283)##
## -1 0 1
## 197 259 572data$v_284 <- mapvalues(data$v_284, c(1,2,3), c(-1,1,0))
table(data$v_284)##
## -1 0 1
## 230 296 501data$v_285 <- mapvalues(data$v_285, c(1,2,3), c(-1,1,0))
table(data$v_285)##
## -1 0 1
## 185 311 527# Merge for T1
data$s_funding_1_1 <- coalesce(data$v_32, data$v_146)
table(data$s_funding_1_1)##
## -1 0 1
## 716 797 749data$s_funding_1_2 <- coalesce(data$v_33, data$v_147)
table(data$s_funding_1_2)##
## -1 0 1
## 646 802 821data$s_funding_1_3 <- coalesce(data$v_34, data$v_148)
table(data$s_funding_1_3)##
## -1 0 1
## 483 776 1004data$s_funding_1_4 <- coalesce(data$v_35, data$v_149)
table(data$s_funding_1_4)##
## -1 0 1
## 557 776 933data$s_funding_1_5 <- coalesce(data$v_36, data$v_150)
table(data$s_funding_1_5)##
## -1 0 1
## 567 871 827data$s_funding_1_6 <- coalesce(data$v_122, data$v_151)
table(data$s_funding_1_6)##
## -1 0 1
## 489 805 966# Merge for T2
data$s_funding_2_1 <- coalesce(data$v_241, data$v_280)
table(data$s_funding_2_1)##
## -1 0 1
## 486 533 1020data$s_funding_2_2 <- coalesce(data$v_242, data$v_281)
table(data$s_funding_2_2)##
## -1 0 1
## 466 556 1015data$s_funding_2_3 <- coalesce(data$v_243, data$v_282)
table(data$s_funding_2_3)##
## -1 0 1
## 405 541 1096data$s_funding_2_4 <- coalesce(data$v_244, data$v_283)
table(data$s_funding_2_4)##
## -1 0 1
## 507 533 1003data$s_funding_2_5 <- coalesce(data$v_245, data$v_284)
table(data$s_funding_2_5)##
## -1 0 1
## 537 553 951data$s_funding_2_6 <- coalesce(data$v_246, data$v_285)
table(data$s_funding_2_6)##
## -1 0 1
## 377 551 1112Merge and Recode COI-Item
data$v_37[data$v_37 == 0] <- NA
data$v_38[data$v_38 == 0] <- NA
data$v_39[data$v_39 == 0] <- NA
data$v_40[data$v_40 == 0] <- NA
data$v_41[data$v_41 == 0] <- NA
data$v_123[data$v_123 == 0] <- NA
data$v_124[data$v_124 == 0] <- NA
data$v_152[data$v_152 == 0] <- NA
data$v_153[data$v_153 == 0] <- NA
data$v_154[data$v_154 == 0] <- NA
data$v_155[data$v_155 == 0] <- NA
data$v_156[data$v_156 == 0] <- NA
data$v_157[data$v_157 == 0] <- NA
data$v_158[data$v_158 == 0] <- NA
data$v_247[data$v_247 == 0] <- NA
data$v_248[data$v_248 == 0] <- NA
data$v_249[data$v_249 == 0] <- NA
data$v_250[data$v_250 == 0] <- NA
data$v_251[data$v_251 == 0] <- NA
data$v_252[data$v_252 == 0] <- NA
data$v_253[data$v_253 == 0] <- NA
data$v_286[data$v_286 == 0] <- NA
data$v_287[data$v_287 == 0] <- NA
data$v_288[data$v_288 == 0] <- NA
data$v_289[data$v_289 == 0] <- NA
data$v_290[data$v_290 == 0] <- NA
data$v_291[data$v_291 == 0] <- NA
data$v_292[data$v_292 == 0] <- NA
data$v_37 <- mapvalues(data$v_37,c(1,2,3),c(-1,1,0))
table(data$v_37)##
## -1 0 1
## 360 388 393data$v_38 <- mapvalues(data$v_38,c(1,2,3),c(-1,1,0))
table(data$v_38)##
## -1 0 1
## 356 360 417data$v_39 <- mapvalues(data$v_39,c(1,2,3),c(-1,1,0))
table(data$v_39)##
## -1 0 1
## 328 386 414data$v_40 <- mapvalues(data$v_40,c(1,2,3),c(-1,1,0))
table(data$v_40)##
## -1 0 1
## 385 389 367data$v_41 <- mapvalues(data$v_41,c(1,2,3),c(-1,1,0))
table(data$v_41)##
## -1 0 1
## 309 388 437data$v_123 <- mapvalues(data$v_123,c(1,2,3),c(1,-1,0))
table(data$v_42)##
## 0 1 2 3
## 9 542 339 257data$v_124 <- mapvalues(data$v_124,c(1,2,3),c(-1,1,0))
table(data$v_124)##
## -1 0 1
## 345 395 397data$v_152 <- mapvalues(data$v_152,c(1,2,3),c(-1,1,0))
table(data$v_152)##
## -1 0 1
## 306 371 446data$v_153 <- mapvalues(data$v_153,c(1,2,3),c(-1,1,0))
table(data$v_153)##
## -1 0 1
## 270 380 469data$v_154 <- mapvalues(data$v_154,c(1,2,3),c(-1,1,0))
table(data$v_154)##
## -1 0 1
## 302 366 452data$v_155 <- mapvalues(data$v_155,c(1,2,3),c(-1,1,0))
table(data$v_155)##
## -1 0 1
## 317 379 428data$v_156 <- mapvalues(data$v_156,c(1,2,3),c(-1,1,0))
table(data$v_156)##
## -1 0 1
## 265 375 482data$v_157 <- mapvalues(data$v_157,c(1,2,3),c(-1,1,0))
table(data$v_157)##
## -1 0 1
## 276 378 467data$v_158 <- mapvalues(data$v_158,c(1,2,3),c(1,-1,0))
table(data$v_158)##
## -1 0 1
## 293 377 453data$v_247 <- mapvalues(data$v_247,c(1,2,3),c(-1,1,0))
table(data$v_247)##
## -1 0 1
## 316 265 436data$v_248 <- mapvalues(data$v_248,c(1,2,3),c(-1,1,0))
table(data$v_248)##
## -1 0 1
## 276 287 451data$v_249 <- mapvalues(data$v_249,c(1,2,3),c(-1,1,0))
table(data$v_249)##
## -1 0 1
## 269 293 452data$v_250 <- mapvalues(data$v_250,c(1,2,3),c(-1,1,0))
table(data$v_250)##
## -1 0 1
## 312 301 403data$v_251 <- mapvalues(data$v_251,c(1,2,3),c(-1,1,0))
table(data$v_251)##
## -1 0 1
## 261 300 455data$v_252 <- mapvalues(data$v_252,c(1,2,3),c(1,-1,0))
table(data$v_252)##
## -1 0 1
## 375 301 336data$v_253 <- mapvalues(data$v_253,c(1,2,3),c(-1,1,0))
table(data$v_253)##
## -1 0 1
## 255 307 453data$v_286 <- mapvalues(data$v_286,c(1,2,3),c(-1,1,0))
table(data$v_286)##
## -1 0 1
## 246 270 511data$v_287 <- mapvalues(data$v_287,c(1,2,3),c(-1,1,0))
table(data$v_287)##
## -1 0 1
## 260 284 482data$v_288 <- mapvalues(data$v_288,c(1,2,3),c(-1,1,0))
table(data$v_288)##
## -1 0 1
## 247 260 518data$v_289 <- mapvalues(data$v_289,c(1,2,3),c(-1,1,0))
table(data$v_289)##
## -1 0 1
## 238 283 505data$v_290 <- mapvalues(data$v_290,c(1,2,3),c(-1,1,0))
table(data$v_290)##
## -1 0 1
## 241 272 513data$v_291 <- mapvalues(data$v_291,c(1,2,3),c(-1,1,0))
table(data$v_291)##
## -1 0 1
## 231 282 513data$v_292 <- mapvalues(data$v_292,c(1,2,3),c(1,-1,0))
table(data$v_292)##
## -1 0 1
## 316 266 443# Merge for T1
data$s_coi_1_1 <- coalesce(data$v_37, data$v_152)
table(data$s_coi_1_1)##
## -1 0 1
## 666 759 839data$s_coi_1_2 <- coalesce(data$v_38, data$v_153)
table(data$s_coi_1_2)##
## -1 0 1
## 626 740 886data$s_coi_1_3 <- coalesce(data$v_39, data$v_154)
table(data$s_coi_1_3)##
## -1 0 1
## 630 752 866data$s_coi_1_4 <- coalesce(data$v_40, data$v_155)
table(data$s_coi_1_4)##
## -1 0 1
## 702 768 795data$s_coi_1_5 <- coalesce(data$v_41, data$v_156)
table(data$s_coi_1_5)##
## -1 0 1
## 574 763 919data$s_coi_1_6 <- coalesce(data$v_123, data$v_157)
table(data$s_coi_1_6)##
## -1 0 1
## 644 765 854data$s_coi_1_7 <- coalesce(data$v_124, data$v_158)
table(data$s_coi_1_7)##
## -1 0 1
## 638 772 850# Merge for T2
data$s_coi_2_1 <- coalesce(data$v_247, data$v_286)
table(data$s_coi_2_1)##
## -1 0 1
## 562 535 947data$s_coi_2_2 <- coalesce(data$v_248, data$v_287)
table(data$s_coi_2_2)##
## -1 0 1
## 536 571 933data$s_coi_2_3 <- coalesce(data$v_249, data$v_288)
table(data$s_coi_2_3)##
## -1 0 1
## 516 553 970data$s_coi_2_4 <- coalesce(data$v_250, data$v_289)
table(data$s_coi_2_4)##
## -1 0 1
## 550 584 908data$s_coi_2_5 <- coalesce(data$v_251, data$v_290)
table(data$s_coi_2_5)##
## -1 0 1
## 502 572 968data$s_coi_2_6 <- coalesce(data$v_252, data$v_291)
table(data$s_coi_2_6)##
## -1 0 1
## 606 583 849data$s_coi_2_7 <- coalesce(data$v_253, data$v_292)
table(data$s_coi_2_7)##
## -1 0 1
## 571 573 896Merge and Recode Causality-Item
data$v_42[data$v_42 == 0] <- NA
data$v_43[data$v_43 == 0] <- NA
data$v_44[data$v_44 == 0] <- NA
data$v_45[data$v_45 == 0] <- NA
data$v_46[data$v_46 == 0] <- NA
data$v_125[data$v_125 == 0] <- NA
data$v_159[data$v_159 == 0] <- NA
data$v_160[data$v_160 == 0] <- NA
data$v_161[data$v_161 == 0] <- NA
data$v_162[data$v_162 == 0] <- NA
data$v_163[data$v_163 == 0] <- NA
data$v_164[data$v_164 == 0] <- NA
data$v_254[data$v_254 == 0] <- NA
data$v_255[data$v_255 == 0] <- NA
data$v_256[data$v_256 == 0] <- NA
data$v_257[data$v_257 == 0] <- NA
data$v_258[data$v_258 == 0] <- NA
data$v_259[data$v_259 == 0] <- NA
data$v_293[data$v_293 == 0] <- NA
data$v_294[data$v_294 == 0] <- NA
data$v_295[data$v_295 == 0] <- NA
data$v_296[data$v_296 == 0] <- NA
data$v_297[data$v_297 == 0] <- NA
data$v_298[data$v_298 == 0] <- NA
data$v_42 <- mapvalues(data$v_42, c(1,2,3), c(1,-1,0))
table(data$v_42)##
## -1 0 1
## 339 257 542data$v_43 <- mapvalues(data$v_43, c(1,2,3), c(-1,1,0))
table(data$v_43)##
## -1 0 1
## 512 273 358data$v_44 <- mapvalues(data$v_44, c(1,2,3), c(-1,1,0))
table(data$v_44)##
## -1 0 1
## 406 373 361data$v_45 <- mapvalues(data$v_45, c(1,2,3), c(-1,1,0))
table(data$v_45)##
## -1 0 1
## 423 382 337data$v_46 <- mapvalues(data$v_46, c(1,2,3), c(-1,1,0))
table(data$v_46)##
## -1 0 1
## 476 313 353data$v_125 <- mapvalues(data$v_125, c(1,2,3), c(-1,1,0))
table(data$v_125)##
## -1 0 1
## 505 342 294data$v_159 <- mapvalues(data$v_159, c(1,2,3), c(1,-1,0))
table(data$v_159)##
## -1 0 1
## 115 186 824data$v_160 <- mapvalues(data$v_160, c(1,2,3), c(-1,1,0))
table(data$v_160)##
## -1 0 1
## 555 266 301data$v_161 <- mapvalues(data$v_161, c(1,2,3), c(-1,1,0))
table(data$v_161)##
## -1 0 1
## 642 224 257data$v_162 <- mapvalues(data$v_162, c(1,2,3), c(-1,1,0))
table(data$v_162)##
## -1 0 1
## 686 217 219data$v_163 <- mapvalues(data$v_163, c(1,2,3), c(-1,1,0))
table(data$v_163)##
## -1 0 1
## 455 314 352data$v_164 <- mapvalues(data$v_164, c(1,2,3), c(-1,1,0))
table(data$v_164)##
## -1 0 1
## 438 314 371data$v_254 <- mapvalues(data$v_254, c(1,2,3), c(1,-1,0))
table(data$v_254)##
## -1 0 1
## 305 234 475data$v_255 <- mapvalues(data$v_255, c(1,2,3), c(-1,1,0))
table(data$v_255)##
## -1 0 1
## 416 255 341data$v_256 <- mapvalues(data$v_256, c(1,2,3), c(-1,1,0))
table(data$v_256)##
## -1 0 1
## 347 330 339data$v_257 <- mapvalues(data$v_257, c(1,2,3), c(-1,1,0))
table(data$v_257)##
## -1 0 1
## 341 344 323data$v_258 <- mapvalues(data$v_258, c(1,2,3), c(-1,1,0))
table(data$v_258)##
## -1 0 1
## 392 295 328data$v_259 <- mapvalues(data$v_259, c(1,2,3), c(-1,1,0))
table(data$v_259)##
## -1 0 1
## 392 293 330data$v_293 <- mapvalues(data$v_293, c(1,2,3), c(1,-1,0))
table(data$v_293)##
## -1 0 1
## 124 204 698data$v_294 <- mapvalues(data$v_294, c(1,2,3), c(-1,1,0))
table(data$v_294)##
## -1 0 1
## 436 295 292data$v_295 <- mapvalues(data$v_295, c(1,2,3), c(-1,1,0))
table(data$v_295)##
## -1 0 1
## 535 231 261data$v_296 <- mapvalues(data$v_296, c(1,2,3), c(-1,1,0))
table(data$v_296)##
## -1 0 1
## 597 240 185data$v_297 <- mapvalues(data$v_297, c(1,2,3), c(-1,1,0))
table(data$v_297)##
## -1 0 1
## 384 311 331data$v_298 <- mapvalues(data$v_298, c(1,2,3), c(-1,1,0))
table(data$v_298)##
## -1 0 1
## 383 312 328# Merge for T1
data$s_causality_1_1 <- coalesce(data$v_42, data$v_159)
table(data$s_causality_1_1)##
## -1 0 1
## 454 443 1366data$s_causality_1_2 <- coalesce(data$v_43, data$v_160)
table(data$s_causality_1_2)##
## -1 0 1
## 1067 539 659data$s_causality_1_3 <- coalesce(data$v_44, data$v_161)
table(data$s_causality_1_3)##
## -1 0 1
## 1048 597 618data$s_causality_1_4 <- coalesce(data$v_45, data$v_162)
table(data$s_causality_1_4)##
## -1 0 1
## 1109 599 556data$s_causality_1_5 <- coalesce(data$v_46, data$v_163)
table(data$s_causality_1_5)##
## -1 0 1
## 931 627 705data$s_causality_1_6 <- coalesce(data$v_125, data$v_164)
table(data$s_causality_1_6)##
## -1 0 1
## 943 656 665# Merge for T2
data$s_causality_2_1 <- coalesce(data$v_254, data$v_293)
table(data$s_causality_2_1)##
## -1 0 1
## 429 438 1173data$s_causality_2_2 <- coalesce(data$v_255, data$v_294)
table(data$s_causality_2_2)##
## -1 0 1
## 852 550 633data$s_causality_2_3 <- coalesce(data$v_256, data$v_295)
table(data$s_causality_2_3)##
## -1 0 1
## 882 561 600data$s_causality_2_4 <- coalesce(data$v_257, data$v_296)
table(data$s_causality_2_4)##
## -1 0 1
## 938 584 508data$s_causality_2_5 <- coalesce(data$v_258, data$v_297)
table(data$s_causality_2_5)##
## -1 0 1
## 776 606 659data$s_causality_2_6 <- coalesce(data$v_259, data$v_298)
table(data$s_causality_2_6)##
## -1 0 1
## 775 605 658Merge and Recode CAMA-Items
data$v_50[data$v_50 == 0] <- NA
data$v_51[data$v_51 == 0] <- NA
data$v_52[data$v_52 == 0] <- NA
data$v_53[data$v_53 == 0] <- NA
data$v_54[data$v_54 == 0] <- NA
data$v_165[data$v_165 == 0] <- NA
data$v_166[data$v_166 == 0] <- NA
data$v_167[data$v_167 == 0] <- NA
data$v_55[data$v_55 == 0] <- NA
data$v_56[data$v_56 == 0] <- NA
data$v_57[data$v_57 == 0] <- NA
data$v_58[data$v_58 == 0] <- NA
data$v_401[data$v_401 == 0] <- NA
data$v_299[data$v_299 == 0] <- NA
data$v_300[data$v_300 == 0] <- NA
data$v_301[data$v_301 == 0] <- NA
data$v_302[data$v_302 == 0] <- NA
data$v_303[data$v_303 == 0] <- NA
data$v_304[data$v_304 == 0] <- NA
data$v_305[data$v_305 == 0] <- NA
data$v_306[data$v_306 == 0] <- NA
data$v_307[data$v_307 == 0] <- NA
data$v_308[data$v_308 == 0] <- NA
data$v_309[data$v_309 == 0] <- NA
data$v_310[data$v_310 == 0] <- NA
data$v_402[data$v_402 == 0] <- NA
# Caution: For v_401 and v_402, coding is dependent on condition. Items is correct in condition 5, incorrect in conditions 4 and 6.
data$v_50 <- mapvalues(data$v_50, c(1,2,3), c(1,-1,0))
table(data$v_50)##
## -1 0 1
## 65 179 284data$v_51 <- mapvalues(data$v_51, c(1,2,3), c(-1,1,0))
table(data$v_51)##
## -1 0 1
## 129 188 211data$v_52 <- mapvalues(data$v_52, c(1,2,3), c(-1,1,0))
table(data$v_52)##
## -1 0 1
## 203 217 111data$v_53 <- mapvalues(data$v_53, c(1,2,3), c(-1,1,0))
table(data$v_53)##
## -1 0 1
## 221 204 100data$v_54 <- mapvalues(data$v_54, c(1,2,3), c(1,-1,0))
table(data$v_54)##
## -1 0 1
## 81 246 202data$v_165 <- mapvalues(data$v_165, c(1,2,3), c(-1,1,0))
table(data$v_165)##
## -1 0 1
## 130 297 102data$v_166 <- mapvalues(data$v_166, c(1,2,3), c(-1,1,0))
table(data$v_166)##
## -1 0 1
## 99 225 206data$v_167 <- mapvalues(data$v_167, c(1,2,3), c(-1,1,0))
table(data$v_167)##
## -1 0 1
## 98 282 150data$v_55 <- mapvalues(data$v_55, c(1,2,3), c(-1,1,0))
table(data$v_55)##
## -1 0 1
## 256 191 83data$v_56 <- mapvalues(data$v_56, c(1,2,3), c(1,-1,0))
table(data$v_56)##
## -1 0 1
## 120 243 166data$v_57 <- mapvalues(data$v_57, c(1,2,3), c(-1,1,0))
table(data$v_57)##
## -1 0 1
## 155 238 138data$v_58 <- mapvalues(data$v_58, c(1,2,3), c(-1,1,0))
table(data$v_58)##
## -1 0 1
## 171 226 133data$v_401_n <- NA
data$v_401_n <- ifelse(data$condition == 4 & data$v_401 == 1, -1, data$v_401_n)
data$v_401_n <- ifelse(data$condition == 4 & data$v_401 == 2, 1, data$v_401_n)
data$v_401_n <- ifelse(data$condition == 4 & data$v_401 == 3, 0, data$v_401_n)
data$v_401_n <- ifelse(data$condition == 5 & data$v_401 == 1, 1, data$v_401_n)
data$v_401_n <- ifelse(data$condition == 5 & data$v_401 == 2, -1, data$v_401_n)
data$v_401_n <- ifelse(data$condition == 5 & data$v_401 == 3, 0, data$v_401_n)
data$v_401_n <- ifelse(data$condition == 6 & data$v_401 == 1, -1, data$v_401_n)
data$v_401_n <- ifelse(data$condition == 6 & data$v_401 == 2, 1, data$v_401_n)
data$v_401_n <- ifelse(data$condition == 6 & data$v_401 == 3, 0, data$v_401_n)
table(data$v_401_n)##
## -1 0 1
## 176 193 163data$v_299 <- mapvalues(data$v_299, c(1,2,3), c(1,-1,0))
table(data$v_299)##
## -1 0 1
## 88 177 263data$v_300 <- mapvalues(data$v_300, c(1,2,3), c(-1,1,0))
table(data$v_300)##
## -1 0 1
## 116 209 202data$v_301 <- mapvalues(data$v_301, c(1,2,3), c(-1,1,0))
table(data$v_301)##
## -1 0 1
## 181 229 118data$v_302 <- mapvalues(data$v_302, c(1,2,3), c(-1,1,0))
table(data$v_302)##
## -1 0 1
## 185 217 126data$v_303 <- mapvalues(data$v_303, c(1,2,3), c(1,-1,0))
table(data$v_303)##
## -1 0 1
## 85 238 203data$v_304 <- mapvalues(data$v_304, c(1,2,3), c(-1,1,0))
table(data$v_304)##
## -1 0 1
## 134 266 122data$v_305 <- mapvalues(data$v_305, c(1,2,3), c(-1,1,0))
table(data$v_305)##
## -1 0 1
## 108 208 210data$v_306 <- mapvalues(data$v_306, c(1,2,3), c(-1,1,0))
table(data$v_306)##
## -1 0 1
## 113 256 159data$v_307 <- mapvalues(data$v_307, c(1,2,3), c(-1,1,0))
table(data$v_307)##
## -1 0 1
## 211 170 147data$v_308 <- mapvalues(data$v_308, c(1,2,3), c(1,-1,0))
table(data$v_308)##
## -1 0 1
## 143 219 166data$v_309 <- mapvalues(data$v_309, c(1,2,3), c(-1,1,0))
table(data$v_309)##
## -1 0 1
## 170 213 145data$v_310 <- mapvalues(data$v_310, c(1,2,3), c(-1,1,0))
table(data$v_310)##
## -1 0 1
## 193 201 135data$v_402_n <- NA
data$v_402_n <- ifelse(data$condition == 4 & data$v_402 == 1, -1, data$v_402_n)
data$v_402_n <- ifelse(data$condition == 4 & data$v_402 == 2, 1, data$v_402_n)
data$v_402_n <- ifelse(data$condition == 4 & data$v_402 == 3, 0, data$v_402_n)
data$v_402_n <- ifelse(data$condition == 5 & data$v_402 == 1, 1, data$v_402_n)
data$v_402_n <- ifelse(data$condition == 5 & data$v_402 == 2, -1, data$v_402_n)
data$v_402_n <- ifelse(data$condition == 5 & data$v_402 == 3, 0, data$v_402_n)
data$v_402_n <- ifelse(data$condition == 6 & data$v_402 == 1, -1, data$v_402_n)
data$v_402_n <- ifelse(data$condition == 6 & data$v_402 == 2, 1, data$v_402_n)
data$v_402_n <- ifelse(data$condition == 6 & data$v_402 == 3, 0, data$v_402_n)
table(data$v_402_n)##
## -1 0 1
## 141 217 172data <- rename(data, s_CAMA_1_1_1 = v_50, s_CAMA_1_1_2 = v_51, s_CAMA_1_1_3 =
v_52, s_CAMA_1_1_4 = v_53, s_CAMA_1_1_5 = v_54, s_CAMA_1_1_6 =
v_165, s_CAMA_1_1_7 = v_166, s_CAMA_1_1_8 = v_167,
s_CAMA_1_2_1 = v_55, s_CAMA_1_2_2 = v_56, s_CAMA_1_2_3 = v_57,
s_CAMA_1_2_4 = v_58, s_CAMA_1_3 = v_401_n, s_CAMA_2_1_1 = v_299,
s_CAMA_2_1_2 = v_300, s_CAMA_2_1_3 = v_301, s_CAMA_2_1_4 = v_302,
s_CAMA_2_1_5 = v_303, s_CAMA_2_1_6 = v_304, s_CAMA_2_1_7 = v_305,
s_CAMA_2_1_8 = v_306, s_CAMA_2_2_1 = v_307, s_CAMA_2_2_2 = v_308,
s_CAMA_2_2_3 = v_309, s_CAMA_2_2_4 = v_310, s_CAMA_2_3 = v_402_n)
data$s_CAMA_1_1 <- coalesce(data$s_CAMA_1_1_1, data$s_CAMA_2_1_1)
table(data$s_CAMA_1_1)##
## -1 0 1
## 153 356 547data$s_CAMA_1_2 <- coalesce(data$s_CAMA_1_1_2, data$s_CAMA_2_1_2)
table(data$s_CAMA_1_2)##
## -1 0 1
## 245 397 413data$s_CAMA_1_3 <- coalesce(data$s_CAMA_1_1_3, data$s_CAMA_2_1_3)
table(data$s_CAMA_1_3)##
## -1 0 1
## 384 446 229data$s_CAMA_1_4 <- coalesce(data$s_CAMA_1_1_4, data$s_CAMA_2_1_4)
table(data$s_CAMA_1_4)##
## -1 0 1
## 406 421 226data$s_CAMA_1_5 <- coalesce(data$s_CAMA_1_1_5, data$s_CAMA_2_1_5)
table(data$s_CAMA_1_5)##
## -1 0 1
## 166 484 405data$s_CAMA_1_6 <- coalesce(data$s_CAMA_1_1_6, data$s_CAMA_2_1_6)
table(data$s_CAMA_1_6)##
## -1 0 1
## 264 563 224data$s_CAMA_1_7 <- coalesce(data$s_CAMA_1_1_7, data$s_CAMA_2_1_7)
table(data$s_CAMA_1_7)##
## -1 0 1
## 207 433 416data$s_CAMA_1_8 <- coalesce(data$s_CAMA_1_1_8, data$s_CAMA_2_1_8)
table(data$s_CAMA_1_8)##
## -1 0 1
## 211 538 309data$s_CAMA_2_1 <- coalesce(data$s_CAMA_1_2_1,data$s_CAMA_2_2_1)
table(data$s_CAMA_2_1)##
## -1 0 1
## 467 361 230data$s_CAMA_2_2 <- coalesce(data$s_CAMA_1_2_2,data$s_CAMA_2_2_2)
table(data$s_CAMA_2_2)##
## -1 0 1
## 263 462 332data$s_CAMA_2_3 <- coalesce(data$s_CAMA_1_2_3,data$s_CAMA_2_2_3)
table(data$s_CAMA_2_3)##
## -1 0 1
## 325 451 283data$s_CAMA_2_4 <- coalesce(data$s_CAMA_1_2_1,data$s_CAMA_2_2_4)
table(data$s_CAMA_2_4)##
## -1 0 1
## 449 392 218data$s_CAMA_3 <- coalesce(data$s_CAMA_1_3, data$s_CAMA_2_3)
table(data$s_CAMA_3)##
## -1 0 1
## 385 446 231Merge and Recode METI
data$v_313[data$v_313 == 0] <- NA
data$v_314[data$v_314 == 0] <- NA
data$v_315[data$v_315 == 0] <- NA
data$v_316[data$v_316 == 0] <- NA
data$v_317[data$v_317 == 0] <- NA
data$v_323[data$v_323 == 0] <- NA
data$v_324[data$v_324 == 0] <- NA
data$v_325[data$v_325 == 0] <- NA
data$v_326[data$v_326 == 0] <- NA
data$v_327[data$v_327 == 0] <- NA
data$v_328[data$v_328 == 0] <- NA
data$v_329[data$v_329 == 0] <- NA
data$v_330[data$v_330 == 0] <- NA
data$v_331[data$v_331 == 0] <- NA
data$v_360[data$v_360 == 0] <- NA
data$v_361[data$v_361 == 0] <- NA
data$v_362[data$v_362 == 0] <- NA
data$v_363[data$v_363 == 0] <- NA
data$v_364[data$v_364 == 0] <- NA
data$v_365[data$v_365 == 0] <- NA
data$v_366[data$v_366 == 0] <- NA
data$v_367[data$v_367 == 0] <- NA
data$v_368[data$v_368 == 0] <- NA
data$v_369[data$v_369 == 0] <- NA
data$v_370[data$v_370 == 0] <- NA
data$v_371[data$v_371 == 0] <- NA
data$v_372[data$v_372 == 0] <- NA
data$v_373[data$v_373 == 0] <- NA
data$v_332[data$v_332 == 0] <- NA
data$v_333[data$v_333 == 0] <- NA
data$v_334[data$v_334 == 0] <- NA
data$v_335[data$v_335 == 0] <- NA
data$v_336[data$v_336 == 0] <- NA
data$v_337[data$v_337 == 0] <- NA
data$v_338[data$v_338 == 0] <- NA
data$v_339[data$v_339 == 0] <- NA
data$v_340[data$v_340 == 0] <- NA
data$v_341[data$v_341 == 0] <- NA
data$v_342[data$v_342 == 0] <- NA
data$v_343[data$v_343 == 0] <- NA
data$v_344[data$v_344 == 0] <- NA
data$v_345[data$v_345 == 0] <- NA
data$v_374[data$v_374 == 0] <- NA
data$v_375[data$v_375 == 0] <- NA
data$v_376[data$v_376 == 0] <- NA
data$v_377[data$v_377 == 0] <- NA
data$v_378[data$v_378 == 0] <- NA
data$v_379[data$v_379 == 0] <- NA
data$v_380[data$v_380 == 0] <- NA
data$v_381[data$v_381 == 0] <- NA
data$v_382[data$v_382 == 0] <- NA
data$v_383[data$v_383 == 0] <- NA
data$v_384[data$v_384 == 0] <- NA
data$v_385[data$v_385 == 0] <- NA
data$v_386[data$v_386 == 0] <- NA
data$v_387[data$v_387 == 0] <- NA
data <- rename(data, s_METI_1_Res_exp_1 = v_313, s_METI_1_Res_int_1 = v_314,
s_METI_1_Res_ben_1 = v_315, s_METI_1_Res_ben_2 = v_316,
s_METI_1_Res_ben_3 = v_317, s_METI_1_Res_int_2 = v_323,
s_METI_1_Res_exp_2 = v_324, s_METI_1_Res_exp_3 = v_325,
s_METI_1_Res_exp_4 = v_326, s_METI_1_Res_exp_5 = v_327,
s_METI_1_Res_ben_4 = v_328, s_METI_1_Res_int_3 = v_329,
s_METI_1_Res_exp_6 = v_330, s_METI_1_Res_int_4 = v_331)
data <- rename(data, s_METI_2_Res_exp_1 = v_360, s_METI_2_Res_int_1 = v_361,
s_METI_2_Res_ben_1 = v_362, s_METI_2_Res_ben_2 = v_363,
s_METI_2_Res_ben_3 = v_364, s_METI_2_Res_int_2 = v_365,
s_METI_2_Res_exp_2 = v_366, s_METI_2_Res_exp_3 = v_367,
s_METI_2_Res_exp_4 = v_368, s_METI_2_Res_exp_5 = v_369,
s_METI_2_Res_ben_4 = v_370, s_METI_2_Res_int_3 = v_371,
s_METI_2_Res_exp_6 = v_372, s_METI_2_Res_int_4 = v_373)
data <- rename(data, s_METI_1_Auth_exp_1 = v_332, s_METI_1_Auth_int_1 = v_333,
s_METI_1_Auth_ben_1 = v_334, s_METI_1_Auth_ben_2 = v_335,
s_METI_1_Auth_ben_3 = v_336, s_METI_1_Auth_int_2 = v_337,
s_METI_1_Auth_exp_2 = v_338, s_METI_1_Auth_exp_3 = v_339,
s_METI_1_Auth_exp_4 = v_340, s_METI_1_Auth_exp_5 = v_341,
s_METI_1_Auth_ben_4 = v_342, s_METI_1_Auth_int_3 = v_343,
s_METI_1_Auth_exp_6 = v_344, s_METI_1_Auth_int_4 = v_345)
data <- rename(data, s_METI_2_Auth_exp_1 = v_374, s_METI_2_Auth_int_1 = v_375,
s_METI_2_Auth_ben_1 = v_376, s_METI_2_Auth_ben_2 = v_377,
s_METI_2_Auth_ben_3 = v_378, s_METI_2_Auth_int_2 = v_379,
s_METI_2_Auth_exp_2 = v_380, s_METI_2_Auth_exp_3 = v_381,
s_METI_2_Auth_exp_4 = v_382, s_METI_2_Auth_exp_5 = v_383,
s_METI_2_Auth_ben_4 = v_384, s_METI_2_Auth_int_3 = v_385,
s_METI_2_Auth_exp_6 = v_386, s_METI_2_Auth_int_4 = v_387)
data$s_METI_1_exp_1 <- coalesce(data$s_METI_1_Res_exp_1,
data$s_METI_1_Auth_exp_1)
data$s_METI_1_int_1 <- coalesce(data$s_METI_1_Res_int_1,
data$s_METI_1_Auth_int_1)
data$s_METI_1_ben_1 <- coalesce(data$s_METI_1_Res_ben_1,
data$s_METI_1_Auth_ben_1)
data$s_METI_1_ben_2 <- coalesce(data$s_METI_1_Res_ben_2,
data$s_METI_1_Auth_ben_2)
data$s_METI_1_ben_3 <- coalesce(data$s_METI_1_Res_ben_3,
data$s_METI_1_Auth_ben_3)
data$s_METI_1_int_2 <- coalesce(data$s_METI_1_Res_int_2,
data$s_METI_1_Auth_int_2)
data$s_METI_1_exp_2 <- coalesce(data$s_METI_1_Res_exp_2,
data$s_METI_1_Auth_exp_2)
data$s_METI_1_exp_3 <- coalesce(data$s_METI_1_Res_exp_3,
data$s_METI_1_Auth_exp_3)
data$s_METI_1_exp_4 <- coalesce(data$s_METI_1_Res_exp_4,
data$s_METI_1_Auth_exp_4)
data$s_METI_1_exp_5 <- coalesce(data$s_METI_1_Res_exp_5,
data$s_METI_1_Auth_exp_5)
data$s_METI_1_ben_4 <- coalesce(data$s_METI_1_Res_ben_4,
data$s_METI_1_Auth_ben_4)
data$s_METI_1_int_3 <- coalesce(data$s_METI_1_Res_int_3,
data$s_METI_1_Auth_int_3)
data$s_METI_1_exp_6 <- coalesce(data$s_METI_1_Res_exp_6,
data$s_METI_1_Auth_exp_6)
data$s_METI_1_int_4 <- coalesce(data$s_METI_1_Res_int_4,
data$s_METI_1_Auth_int_4)
data$s_METI_2_exp_1 <- coalesce(data$s_METI_2_Res_exp_1,
data$s_METI_2_Auth_exp_1)
data$s_METI_2_int_1 <- coalesce(data$s_METI_2_Res_int_1,
data$s_METI_2_Auth_int_1)
data$s_METI_2_ben_1 <- coalesce(data$s_METI_2_Res_ben_1,
data$s_METI_2_Auth_ben_1)
data$s_METI_2_ben_2 <- coalesce(data$s_METI_2_Res_ben_2,
data$s_METI_2_Auth_ben_2)
data$s_METI_2_ben_3 <- coalesce(data$s_METI_2_Res_ben_3,
data$s_METI_2_Auth_ben_3)
data$s_METI_2_int_2 <- coalesce(data$s_METI_2_Res_int_2,
data$s_METI_2_Auth_int_2)
data$s_METI_2_exp_2 <- coalesce(data$s_METI_2_Res_exp_2,
data$s_METI_2_Auth_exp_2)
data$s_METI_2_exp_3 <- coalesce(data$s_METI_2_Res_exp_3,
data$s_METI_2_Auth_exp_3)
data$s_METI_2_exp_4 <- coalesce(data$s_METI_2_Res_exp_4,
data$s_METI_2_Auth_exp_4)
data$s_METI_2_exp_5 <- coalesce(data$s_METI_2_Res_exp_5,
data$s_METI_2_Auth_exp_5)
data$s_METI_2_ben_4 <- coalesce(data$s_METI_2_Res_ben_4,
data$s_METI_2_Auth_ben_4)
data$s_METI_2_int_3 <- coalesce(data$s_METI_2_Res_int_3,
data$s_METI_2_Auth_int_3)
data$s_METI_2_exp_6 <- coalesce(data$s_METI_2_Res_exp_6,
data$s_METI_2_Auth_exp_6)
data$s_METI_2_int_4 <- coalesce(data$s_METI_2_Res_int_4,
data$s_METI_2_Auth_int_4)
data$s_METI_exp_1 <- coalesce(data$s_METI_1_exp_1,data$s_METI_2_exp_1)
table(data$s_METI_exp_1)##
## 1 2 3 4 5 6 7
## 33 42 68 366 362 561 598data$s_METI_int_1 <- coalesce(data$s_METI_1_int_1,data$s_METI_2_int_1)
table(data$s_METI_int_1)##
## 1 2 3 4 5 6 7
## 27 42 62 464 400 548 490data$s_METI_ben_1 <- coalesce(data$s_METI_1_ben_1,data$s_METI_2_ben_1)
table(data$s_METI_ben_1)##
## 1 2 3 4 5 6 7
## 25 34 89 468 399 521 491data$s_METI_ben_2 <- coalesce(data$s_METI_1_ben_2,data$s_METI_2_ben_2)
table(data$s_METI_ben_2)##
## 1 2 3 4 5 6 7
## 37 31 88 444 397 530 504data$s_METI_ben_3 <- coalesce(data$s_METI_1_ben_3,data$s_METI_2_ben_3)
table(data$s_METI_ben_3)##
## 1 2 3 4 5 6 7
## 35 33 76 377 386 558 567data$s_METI_int_2 <- coalesce(data$s_METI_1_int_2,data$s_METI_2_int_2)
table(data$s_METI_int_2)##
## 1 2 3 4 5 6 7
## 37 36 84 428 375 545 523data$s_METI_exp_2 <- coalesce(data$s_METI_1_exp_2,data$s_METI_2_exp_2)
table(data$s_METI_exp_2)##
## 1 2 3 4 5 6 7
## 33 33 72 356 364 587 589data$s_METI_exp_3 <- coalesce(data$s_METI_1_exp_3,data$s_METI_2_exp_3)
table(data$s_METI_exp_3)##
## 1 2 3 4 5 6 7
## 24 51 102 427 398 525 507data$s_METI_exp_4 <- coalesce(data$s_METI_1_exp_4,data$s_METI_2_exp_4)
table(data$s_METI_exp_4)##
## 1 2 3 4 5 6 7
## 27 45 78 385 375 565 560data$s_METI_exp_5 <- coalesce(data$s_METI_1_exp_5,data$s_METI_2_exp_5)
table(data$s_METI_exp_5)##
## 1 2 3 4 5 6 7
## 28 46 72 375 359 593 556data$s_METI_ben_4 <- coalesce(data$s_METI_1_ben_4,data$s_METI_2_ben_4)
table(data$s_METI_ben_4)##
## 1 2 3 4 5 6 7
## 33 36 83 462 402 528 479data$s_METI_int_3 <- coalesce(data$s_METI_1_int_3,data$s_METI_2_int_3)
table(data$s_METI_int_3)##
## 1 2 3 4 5 6 7
## 25 44 83 385 343 581 568data$s_METI_exp_6 <- coalesce(data$s_METI_1_exp_6,data$s_METI_2_exp_6)
table(data$s_METI_exp_6)##
## 1 2 3 4 5 6 7
## 24 33 74 364 370 583 586data$s_METI_int_4 <- coalesce(data$s_METI_1_int_4,data$s_METI_2_int_4)
table(data$s_METI_int_4)##
## 1 2 3 4 5 6 7
## 30 36 86 382 386 561 543Recode Awareness Check
data <- plyr::rename(data, c("v_388" = "s_awareness"))
data$s_awareness <- mapvalues(data$s_awareness, c(0,1,2,3,4,5,6,7,8,9),
c(1,0,0,0,0,0,0,0,0,0))
data$s_awareness <- factor(data$s_awareness, c(0,1),
labels = c("fail","pass"))
table(data$s_awareness)##
## fail pass
## 658 1383prop.table(table(data$s_awareness))##
## fail pass
## 0.322391 0.677609Study Duration Analyses
data2 <- data[!data$dispcode == 22,]
length(unique(data$p_0001[data$dispcode == 31| data$dispcode == 32]))## [1] 2041View(data2)
data2$duration_minutes <- data2$duration/60
data2$duration_minutes[data2$duration_minutes <= 0] <- NA
psych::describe(data2$duration_minutes)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 1753 21.9 11.91 18.45 19.93 8.06 8.02 104.83 96.82 2.05 6.21
## se
## X1 0.28hist.duration <- ggplot (data2, aes(duration_minutes)) +
theme(legend.position = "none") + geom_histogram(aes(y = after_stat(density)),
colour = "black",
fill = "white") +
labs(x = "Duration in Minutes", y = "Density")
hist.duration + stat_function(fun = dnorm,
args = list(mean = mean(data2$duration_minutes,
na.rm = TRUE),
sd = sd(data2$duration_minutes,
na.rm = TRUE)),
colour = "blue", size = 1)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 288 rows containing non-finite values (`stat_bin()`).
Duration by Condition (Boxplot)
conditionBox <- ggplot(data2, aes(condition, duration_minutes)) +
geom_boxplot() + labs (x = "Condtion", y = "Duration in Minutes")
conditionBox## Warning: Removed 288 rows containing non-finite values (`stat_boxplot()`).
conditionModel <- lm(duration_minutes ~ condition, data = data2)
summary(conditionModel)##
## Call:
## lm(formula = duration_minutes ~ condition, data = data2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.304 -7.995 -3.432 4.294 81.029
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.3892 0.7146 28.534 < 2e-16 ***
## condition2 0.8452 0.9840 0.859 0.390484
## condition3 0.7425 0.9958 0.746 0.455988
## condition4 2.5062 0.9933 2.523 0.011722 *
## condition5 3.4150 1.0143 3.367 0.000776 ***
## condition6 1.6277 0.9840 1.654 0.098248 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.87 on 1747 degrees of freedom
## (288 Beobachtungen als fehlend gelöscht)
## Multiple R-squared: 0.008949, Adjusted R-squared: 0.006113
## F-statistic: 3.155 on 5 and 1747 DF, p-value: 0.007696Duration by Quota (Boxplot)
quotaBox <- ggplot(data2, aes(quota, duration_minutes)) +
geom_boxplot() + labs (x = "Quota", y = "Duration in Minutes")
quotaBox## Warning: Removed 288 rows containing non-finite values (`stat_boxplot()`).
quotaModel <- lm(duration_minutes ~ quota, data = data2)
summary(quotaModel)##
## Call:
## lm(formula = duration_minutes ~ quota, data = data2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.507 -7.772 -3.212 4.205 79.393
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.5913 0.9541 25.773 < 2e-16 ***
## quota2 -3.3109 1.3342 -2.481 0.013178 *
## quota3 -1.5023 1.3586 -1.106 0.268974
## quota4 0.8492 1.3810 0.615 0.538703
## quota5 0.2674 1.3610 0.196 0.844256
## quota6 -2.2524 1.3384 -1.683 0.092582 .
## quota7 -5.8959 1.3610 -4.332 1.56e-05 ***
## quota8 -4.7178 1.3494 -3.496 0.000484 ***
## quota9 -6.6560 1.3733 -4.847 1.37e-06 ***
## quota10 -1.6808 1.3758 -1.222 0.221997
## quota11 -1.3459 1.3562 -0.992 0.321162
## quota12 -6.0378 1.3682 -4.413 1.08e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.69 on 1741 degrees of freedom
## (288 Beobachtungen als fehlend gelöscht)
## Multiple R-squared: 0.04297, Adjusted R-squared: 0.03692
## F-statistic: 7.106 on 11 and 1741 DF, p-value: 6.521e-12Duration by Awareness Check (Boxplot)
summary(data2$s_awareness)## fail pass
## 658 1383awarenessBox <- ggplot(data = data2, aes(s_awareness, duration_minutes)) +
geom_boxplot() + labs(x = "Awarenes Check", y = "Duration in Minutes")
awarenessBox## Warning: Removed 288 rows containing non-finite values (`stat_boxplot()`).
awarenessModel <- lm(duration_minutes ~ s_awareness, data = data2)
summary(awarenessModel)##
## Call:
## lm(formula = duration_minutes ~ s_awareness, data = data2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.837 -7.821 -3.371 3.996 81.213
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.3032 0.4892 37.416 <2e-16 ***
## s_awarenesspass 5.3176 0.5947 8.941 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.65 on 1751 degrees of freedom
## (288 Beobachtungen als fehlend gelöscht)
## Multiple R-squared: 0.04366, Adjusted R-squared: 0.04312
## F-statistic: 79.95 on 1 and 1751 DF, p-value: < 2.2e-16Duration by Age (Scatterplot)
describe(data$s_age)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3077 47.46 15.89 48 47.47 19.27 18 90 72 -0.01 -0.99 0.29describe(data2$s_age)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2040 45.22 15.23 45 45.01 17.79 18 90 72 0.12 -0.96 0.34scatter.age <- ggplot(data2, aes(s_age,duration_minutes)) +
geom_point() + geom_smooth(method = "lm", se = F) +
labs(x = "Age", y = "Duration in minutes")
scatter.age## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 289 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 289 rows containing missing values (`geom_point()`).
cor.test(data2$s_age, data2$duration_minutes)##
## Pearson's product-moment correlation
##
## data: data2$s_age and data2$duration_minutes
## t = 6.5843, df = 1750, p-value = 6.025e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1094461 0.2008492
## sample estimates:
## cor
## 0.1554804Duration by Gender (Boxplot)
genderBox <- ggplot(data = data2, aes(s_sex, duration_minutes)) +
geom_boxplot() + labs(x = "Subject Gender", y = "Duration in Minutes")
genderBox## Warning: Removed 288 rows containing non-finite values (`stat_boxplot()`).
genderModel <- lm(duration_minutes ~ s_sex, data = data2)
summary(genderModel)##
## Call:
## lm(formula = duration_minutes ~ s_sex, data = data2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.377 -7.994 -3.410 4.273 81.956
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.8771 0.4037 56.674 < 2e-16 ***
## s_sexmale -1.9271 0.5672 -3.398 0.000694 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.87 on 1751 degrees of freedom
## (288 Beobachtungen als fehlend gelöscht)
## Multiple R-squared: 0.00655, Adjusted R-squared: 0.005983
## F-statistic: 11.55 on 1 and 1751 DF, p-value: 0.0006944Duration by Educational Background (Boxplot)
schoolBox <- ggplot(data = data2, aes(s_school, duration_minutes)) +
geom_boxplot() +labs(x = "Education Level", y = "Duration in Minutes")
schoolBox## Warning: Removed 288 rows containing non-finite values (`stat_boxplot()`).
schoolModel <- lm(duration_minutes ~ s_school, data = data2)
summary(schoolModel)##
## Call:
## lm(formula = duration_minutes ~ s_school, data = data2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.872 -8.014 -3.464 4.473 81.945
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.5473 0.4917 41.784 < 2e-16 ***
## s_schoolReal 1.7293 0.6908 2.503 0.012393 *
## s_schoolAbi 2.3414 0.6991 3.349 0.000828 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.87 on 1750 degrees of freedom
## (288 Beobachtungen als fehlend gelöscht)
## Multiple R-squared: 0.006883, Adjusted R-squared: 0.005748
## F-statistic: 6.064 on 2 and 1750 DF, p-value: 0.002374Clean Wide-Format Dataset
names(data)## [1] "id" "external_lfdn" "tester"
## [4] "dispcode" "lastpage" "quality"
## [7] "duration" "condition" "p_0001"
## [10] "c_0002" "text_order" "METI_target"
## [13] "s_sex" "s_age" "s_school"
## [16] "s_german" "s_psychology" "s_interest"
## [19] "s_contact" "s_field" "v_10"
## [22] "v_11" "v_47" "v_48"
## [25] "v_49" "v_12" "v_14"
## [28] "v_16" "v_71" "v_17"
## [31] "v_18" "v_19" "v_20"
## [34] "v_21" "v_115" "v_116"
## [37] "v_117" "v_22" "v_23"
## [40] "v_24" "v_25" "v_26"
## [43] "v_120" "v_27" "v_28"
## [46] "v_29" "v_30" "v_31"
## [49] "v_121" "v_32" "v_33"
## [52] "v_34" "v_35" "v_36"
## [55] "v_122" "v_37" "v_38"
## [58] "v_39" "v_40" "v_41"
## [61] "v_123" "v_124" "v_42"
## [64] "v_43" "v_44" "v_45"
## [67] "v_46" "v_125" "v_72"
## [70] "v_73" "v_74" "v_75"
## [73] "v_76" "v_77" "v_79"
## [76] "v_81" "v_83" "v_126"
## [79] "v_127" "v_128" "v_129"
## [82] "v_130" "v_131" "v_132"
## [85] "v_133" "v_134" "v_135"
## [88] "v_136" "v_137" "v_138"
## [91] "v_139" "v_140" "v_141"
## [94] "v_142" "v_143" "v_144"
## [97] "v_145" "v_146" "v_147"
## [100] "v_148" "v_149" "v_150"
## [103] "v_151" "v_152" "v_153"
## [106] "v_154" "v_155" "v_156"
## [109] "v_157" "v_158" "v_159"
## [112] "v_160" "v_161" "v_162"
## [115] "v_163" "v_164" "s_CAMA_1_1_1"
## [118] "s_CAMA_1_1_2" "s_CAMA_1_1_3" "s_CAMA_1_1_4"
## [121] "s_CAMA_1_1_5" "s_CAMA_1_1_6" "s_CAMA_1_1_7"
## [124] "s_CAMA_1_1_8" "s_CAMA_1_2_1" "s_CAMA_1_2_2"
## [127] "s_CAMA_1_2_3" "s_CAMA_1_2_4" "v_401"
## [130] "v_91" "v_92" "v_93"
## [133] "v_94" "v_95" "v_96"
## [136] "v_98" "v_100" "v_102"
## [139] "v_235" "v_236" "v_237"
## [142] "v_238" "v_239" "v_240"
## [145] "v_241" "v_242" "v_243"
## [148] "v_244" "v_245" "v_246"
## [151] "v_247" "v_248" "v_249"
## [154] "v_250" "v_251" "v_252"
## [157] "v_253" "v_254" "v_255"
## [160] "v_256" "v_257" "v_258"
## [163] "v_259" "s_METI_1_Res_exp_1" "s_METI_1_Res_int_1"
## [166] "s_METI_1_Res_ben_1" "s_METI_1_Res_ben_2" "s_METI_1_Res_ben_3"
## [169] "s_METI_1_Res_int_2" "s_METI_1_Res_exp_2" "s_METI_1_Res_exp_3"
## [172] "s_METI_1_Res_exp_4" "s_METI_1_Res_exp_5" "s_METI_1_Res_ben_4"
## [175] "s_METI_1_Res_int_3" "s_METI_1_Res_exp_6" "s_METI_1_Res_int_4"
## [178] "s_METI_1_Auth_exp_1" "s_METI_1_Auth_int_1" "s_METI_1_Auth_ben_1"
## [181] "s_METI_1_Auth_ben_2" "s_METI_1_Auth_ben_3" "s_METI_1_Auth_int_2"
## [184] "s_METI_1_Auth_exp_2" "s_METI_1_Auth_exp_3" "s_METI_1_Auth_exp_4"
## [187] "s_METI_1_Auth_exp_5" "s_METI_1_Auth_ben_4" "s_METI_1_Auth_int_3"
## [190] "s_METI_1_Auth_exp_6" "s_METI_1_Auth_int_4" "v_103"
## [193] "v_104" "v_105" "v_106"
## [196] "v_107" "v_108" "v_110"
## [199] "v_112" "v_114" "v_274"
## [202] "v_275" "v_276" "v_277"
## [205] "v_278" "v_279" "v_280"
## [208] "v_281" "v_282" "v_283"
## [211] "v_284" "v_285" "v_286"
## [214] "v_287" "v_288" "v_289"
## [217] "v_290" "v_291" "v_292"
## [220] "v_293" "v_294" "v_295"
## [223] "v_296" "v_297" "v_298"
## [226] "s_CAMA_2_1_1" "s_CAMA_2_1_2" "s_CAMA_2_1_3"
## [229] "s_CAMA_2_1_4" "s_CAMA_2_1_5" "s_CAMA_2_1_6"
## [232] "s_CAMA_2_1_7" "s_CAMA_2_1_8" "s_CAMA_2_2_1"
## [235] "s_CAMA_2_2_2" "s_CAMA_2_2_3" "s_CAMA_2_2_4"
## [238] "v_402" "s_METI_2_Res_exp_1" "s_METI_2_Res_int_1"
## [241] "s_METI_2_Res_ben_1" "s_METI_2_Res_ben_2" "s_METI_2_Res_ben_3"
## [244] "s_METI_2_Res_int_2" "s_METI_2_Res_exp_2" "s_METI_2_Res_exp_3"
## [247] "s_METI_2_Res_exp_4" "s_METI_2_Res_exp_5" "s_METI_2_Res_ben_4"
## [250] "s_METI_2_Res_int_3" "s_METI_2_Res_exp_6" "s_METI_2_Res_int_4"
## [253] "s_METI_2_Auth_exp_1" "s_METI_2_Auth_int_1" "s_METI_2_Auth_ben_1"
## [256] "s_METI_2_Auth_ben_2" "s_METI_2_Auth_ben_3" "s_METI_2_Auth_int_2"
## [259] "s_METI_2_Auth_exp_2" "s_METI_2_Auth_exp_3" "s_METI_2_Auth_exp_4"
## [262] "s_METI_2_Auth_exp_5" "s_METI_2_Auth_ben_4" "s_METI_2_Auth_int_3"
## [265] "s_METI_2_Auth_exp_6" "s_METI_2_Auth_int_4" "s_awareness"
## [268] "browser" "referer" "device_type"
## [271] "quota" "quota_assignment" "quota_rejected_id"
## [274] "page_history" "hflip" "vflip"
## [277] "output_mode" "javascript" "flash"
## [280] "session_id" "language" "cleaned"
## [283] "ats" "datetime" "date_of_last_access"
## [286] "date_of_first_mail" "rts6018385" "rts6018739"
## [289] "rts6018818" "rts6019080" "rts6019089"
## [292] "rts6021451" "rts6021455" "rts6023513"
## [295] "rts6023515" "rts6023627" "rts6023655"
## [298] "rts6023657" "rts6023660" "rts6023667"
## [301] "rts6023676" "rts6023679" "rts6033975"
## [304] "METI_text" "summary1" "summary2"
## [307] "version" "causality" "disclaimer"
## [310] "CAMA" "dropout" "accessibility_1"
## [313] "accessibility_2" "understanding_1" "understanding_2"
## [316] "empowerment_1" "empowerment_2" "credibility_1"
## [319] "credibility_2" "relevance_1" "relevance_2"
## [322] "curiosity_1" "curiosity_2" "boredom_1"
## [325] "boredom_2" "confusion_1" "confusion_2"
## [328] "frustration_1" "frustration_2" "s_relationship_1"
## [331] "s_relationship_2" "s_relationship_3" "s_relationship_4"
## [334] "s_relationship_5" "s_relationship_6" "s_relationship_7"
## [337] "s_relationship_8" "s_extent_1" "s_extent_2"
## [340] "s_extent_3" "s_extent_4" "s_extent_5"
## [343] "s_extent_6" "s_diff_1_1" "s_diff_1_2"
## [346] "s_diff_1_3" "s_diff_1_4" "s_diff_1_5"
## [349] "s_diff_1_6" "s_diff_2_1" "s_diff_2_2"
## [352] "s_diff_2_3" "s_diff_2_4" "s_diff_2_5"
## [355] "s_diff_2_6" "s_funding_1_1" "s_funding_1_2"
## [358] "s_funding_1_3" "s_funding_1_4" "s_funding_1_5"
## [361] "s_funding_1_6" "s_funding_2_1" "s_funding_2_2"
## [364] "s_funding_2_3" "s_funding_2_4" "s_funding_2_5"
## [367] "s_funding_2_6" "s_coi_1_1" "s_coi_1_2"
## [370] "s_coi_1_3" "s_coi_1_4" "s_coi_1_5"
## [373] "s_coi_1_6" "s_coi_1_7" "s_coi_2_1"
## [376] "s_coi_2_2" "s_coi_2_3" "s_coi_2_4"
## [379] "s_coi_2_5" "s_coi_2_6" "s_coi_2_7"
## [382] "s_causality_1_1" "s_causality_1_2" "s_causality_1_3"
## [385] "s_causality_1_4" "s_causality_1_5" "s_causality_1_6"
## [388] "s_causality_2_1" "s_causality_2_2" "s_causality_2_3"
## [391] "s_causality_2_4" "s_causality_2_5" "s_causality_2_6"
## [394] "s_CAMA_1_3" "s_CAMA_2_3" "s_CAMA_1_1"
## [397] "s_CAMA_1_2" "s_CAMA_1_4" "s_CAMA_1_5"
## [400] "s_CAMA_1_6" "s_CAMA_1_7" "s_CAMA_1_8"
## [403] "s_CAMA_2_1" "s_CAMA_2_2" "s_CAMA_2_4"
## [406] "s_CAMA_3" "s_METI_1_exp_1" "s_METI_1_int_1"
## [409] "s_METI_1_ben_1" "s_METI_1_ben_2" "s_METI_1_ben_3"
## [412] "s_METI_1_int_2" "s_METI_1_exp_2" "s_METI_1_exp_3"
## [415] "s_METI_1_exp_4" "s_METI_1_exp_5" "s_METI_1_ben_4"
## [418] "s_METI_1_int_3" "s_METI_1_exp_6" "s_METI_1_int_4"
## [421] "s_METI_2_exp_1" "s_METI_2_int_1" "s_METI_2_ben_1"
## [424] "s_METI_2_ben_2" "s_METI_2_ben_3" "s_METI_2_int_2"
## [427] "s_METI_2_exp_2" "s_METI_2_exp_3" "s_METI_2_exp_4"
## [430] "s_METI_2_exp_5" "s_METI_2_ben_4" "s_METI_2_int_3"
## [433] "s_METI_2_exp_6" "s_METI_2_int_4" "s_METI_exp_1"
## [436] "s_METI_int_1" "s_METI_ben_1" "s_METI_ben_2"
## [439] "s_METI_ben_3" "s_METI_int_2" "s_METI_exp_2"
## [442] "s_METI_exp_3" "s_METI_exp_4" "s_METI_exp_5"
## [445] "s_METI_ben_4" "s_METI_int_3" "s_METI_exp_6"
## [448] "s_METI_int_4"data_wide <- data[,!names(data) %in% c("external_lfdn","tester","lastpage",
"quality","p_0001","c_0002","browser",
"referer","device_type",
"quota_assignment","quota_rejected_id",
"page_history","hflip","vflip",
"output_mode","javascript","flash",
"session_id","language","cleaned","ats",
"datetime","date_of_last_access",
"day_of_first_mail","rts6018385",
"rts6018739","rts6018818","rts6019080",
"rts6019089","rts6021451","rts6021455",
"rts6023513","rts6023515","rts6023627",
"rts6023655","rts6023657","rts6023660",
"rts6023667","rts6023676","rts6023679",
"rts6033975")]
names(data_wide)## [1] "id" "dispcode" "duration"
## [4] "condition" "text_order" "METI_target"
## [7] "s_sex" "s_age" "s_school"
## [10] "s_german" "s_psychology" "s_interest"
## [13] "s_contact" "s_field" "v_10"
## [16] "v_11" "v_47" "v_48"
## [19] "v_49" "v_12" "v_14"
## [22] "v_16" "v_71" "v_17"
## [25] "v_18" "v_19" "v_20"
## [28] "v_21" "v_115" "v_116"
## [31] "v_117" "v_22" "v_23"
## [34] "v_24" "v_25" "v_26"
## [37] "v_120" "v_27" "v_28"
## [40] "v_29" "v_30" "v_31"
## [43] "v_121" "v_32" "v_33"
## [46] "v_34" "v_35" "v_36"
## [49] "v_122" "v_37" "v_38"
## [52] "v_39" "v_40" "v_41"
## [55] "v_123" "v_124" "v_42"
## [58] "v_43" "v_44" "v_45"
## [61] "v_46" "v_125" "v_72"
## [64] "v_73" "v_74" "v_75"
## [67] "v_76" "v_77" "v_79"
## [70] "v_81" "v_83" "v_126"
## [73] "v_127" "v_128" "v_129"
## [76] "v_130" "v_131" "v_132"
## [79] "v_133" "v_134" "v_135"
## [82] "v_136" "v_137" "v_138"
## [85] "v_139" "v_140" "v_141"
## [88] "v_142" "v_143" "v_144"
## [91] "v_145" "v_146" "v_147"
## [94] "v_148" "v_149" "v_150"
## [97] "v_151" "v_152" "v_153"
## [100] "v_154" "v_155" "v_156"
## [103] "v_157" "v_158" "v_159"
## [106] "v_160" "v_161" "v_162"
## [109] "v_163" "v_164" "s_CAMA_1_1_1"
## [112] "s_CAMA_1_1_2" "s_CAMA_1_1_3" "s_CAMA_1_1_4"
## [115] "s_CAMA_1_1_5" "s_CAMA_1_1_6" "s_CAMA_1_1_7"
## [118] "s_CAMA_1_1_8" "s_CAMA_1_2_1" "s_CAMA_1_2_2"
## [121] "s_CAMA_1_2_3" "s_CAMA_1_2_4" "v_401"
## [124] "v_91" "v_92" "v_93"
## [127] "v_94" "v_95" "v_96"
## [130] "v_98" "v_100" "v_102"
## [133] "v_235" "v_236" "v_237"
## [136] "v_238" "v_239" "v_240"
## [139] "v_241" "v_242" "v_243"
## [142] "v_244" "v_245" "v_246"
## [145] "v_247" "v_248" "v_249"
## [148] "v_250" "v_251" "v_252"
## [151] "v_253" "v_254" "v_255"
## [154] "v_256" "v_257" "v_258"
## [157] "v_259" "s_METI_1_Res_exp_1" "s_METI_1_Res_int_1"
## [160] "s_METI_1_Res_ben_1" "s_METI_1_Res_ben_2" "s_METI_1_Res_ben_3"
## [163] "s_METI_1_Res_int_2" "s_METI_1_Res_exp_2" "s_METI_1_Res_exp_3"
## [166] "s_METI_1_Res_exp_4" "s_METI_1_Res_exp_5" "s_METI_1_Res_ben_4"
## [169] "s_METI_1_Res_int_3" "s_METI_1_Res_exp_6" "s_METI_1_Res_int_4"
## [172] "s_METI_1_Auth_exp_1" "s_METI_1_Auth_int_1" "s_METI_1_Auth_ben_1"
## [175] "s_METI_1_Auth_ben_2" "s_METI_1_Auth_ben_3" "s_METI_1_Auth_int_2"
## [178] "s_METI_1_Auth_exp_2" "s_METI_1_Auth_exp_3" "s_METI_1_Auth_exp_4"
## [181] "s_METI_1_Auth_exp_5" "s_METI_1_Auth_ben_4" "s_METI_1_Auth_int_3"
## [184] "s_METI_1_Auth_exp_6" "s_METI_1_Auth_int_4" "v_103"
## [187] "v_104" "v_105" "v_106"
## [190] "v_107" "v_108" "v_110"
## [193] "v_112" "v_114" "v_274"
## [196] "v_275" "v_276" "v_277"
## [199] "v_278" "v_279" "v_280"
## [202] "v_281" "v_282" "v_283"
## [205] "v_284" "v_285" "v_286"
## [208] "v_287" "v_288" "v_289"
## [211] "v_290" "v_291" "v_292"
## [214] "v_293" "v_294" "v_295"
## [217] "v_296" "v_297" "v_298"
## [220] "s_CAMA_2_1_1" "s_CAMA_2_1_2" "s_CAMA_2_1_3"
## [223] "s_CAMA_2_1_4" "s_CAMA_2_1_5" "s_CAMA_2_1_6"
## [226] "s_CAMA_2_1_7" "s_CAMA_2_1_8" "s_CAMA_2_2_1"
## [229] "s_CAMA_2_2_2" "s_CAMA_2_2_3" "s_CAMA_2_2_4"
## [232] "v_402" "s_METI_2_Res_exp_1" "s_METI_2_Res_int_1"
## [235] "s_METI_2_Res_ben_1" "s_METI_2_Res_ben_2" "s_METI_2_Res_ben_3"
## [238] "s_METI_2_Res_int_2" "s_METI_2_Res_exp_2" "s_METI_2_Res_exp_3"
## [241] "s_METI_2_Res_exp_4" "s_METI_2_Res_exp_5" "s_METI_2_Res_ben_4"
## [244] "s_METI_2_Res_int_3" "s_METI_2_Res_exp_6" "s_METI_2_Res_int_4"
## [247] "s_METI_2_Auth_exp_1" "s_METI_2_Auth_int_1" "s_METI_2_Auth_ben_1"
## [250] "s_METI_2_Auth_ben_2" "s_METI_2_Auth_ben_3" "s_METI_2_Auth_int_2"
## [253] "s_METI_2_Auth_exp_2" "s_METI_2_Auth_exp_3" "s_METI_2_Auth_exp_4"
## [256] "s_METI_2_Auth_exp_5" "s_METI_2_Auth_ben_4" "s_METI_2_Auth_int_3"
## [259] "s_METI_2_Auth_exp_6" "s_METI_2_Auth_int_4" "s_awareness"
## [262] "quota" "date_of_first_mail" "METI_text"
## [265] "summary1" "summary2" "version"
## [268] "causality" "disclaimer" "CAMA"
## [271] "dropout" "accessibility_1" "accessibility_2"
## [274] "understanding_1" "understanding_2" "empowerment_1"
## [277] "empowerment_2" "credibility_1" "credibility_2"
## [280] "relevance_1" "relevance_2" "curiosity_1"
## [283] "curiosity_2" "boredom_1" "boredom_2"
## [286] "confusion_1" "confusion_2" "frustration_1"
## [289] "frustration_2" "s_relationship_1" "s_relationship_2"
## [292] "s_relationship_3" "s_relationship_4" "s_relationship_5"
## [295] "s_relationship_6" "s_relationship_7" "s_relationship_8"
## [298] "s_extent_1" "s_extent_2" "s_extent_3"
## [301] "s_extent_4" "s_extent_5" "s_extent_6"
## [304] "s_diff_1_1" "s_diff_1_2" "s_diff_1_3"
## [307] "s_diff_1_4" "s_diff_1_5" "s_diff_1_6"
## [310] "s_diff_2_1" "s_diff_2_2" "s_diff_2_3"
## [313] "s_diff_2_4" "s_diff_2_5" "s_diff_2_6"
## [316] "s_funding_1_1" "s_funding_1_2" "s_funding_1_3"
## [319] "s_funding_1_4" "s_funding_1_5" "s_funding_1_6"
## [322] "s_funding_2_1" "s_funding_2_2" "s_funding_2_3"
## [325] "s_funding_2_4" "s_funding_2_5" "s_funding_2_6"
## [328] "s_coi_1_1" "s_coi_1_2" "s_coi_1_3"
## [331] "s_coi_1_4" "s_coi_1_5" "s_coi_1_6"
## [334] "s_coi_1_7" "s_coi_2_1" "s_coi_2_2"
## [337] "s_coi_2_3" "s_coi_2_4" "s_coi_2_5"
## [340] "s_coi_2_6" "s_coi_2_7" "s_causality_1_1"
## [343] "s_causality_1_2" "s_causality_1_3" "s_causality_1_4"
## [346] "s_causality_1_5" "s_causality_1_6" "s_causality_2_1"
## [349] "s_causality_2_2" "s_causality_2_3" "s_causality_2_4"
## [352] "s_causality_2_5" "s_causality_2_6" "s_CAMA_1_3"
## [355] "s_CAMA_2_3" "s_CAMA_1_1" "s_CAMA_1_2"
## [358] "s_CAMA_1_4" "s_CAMA_1_5" "s_CAMA_1_6"
## [361] "s_CAMA_1_7" "s_CAMA_1_8" "s_CAMA_2_1"
## [364] "s_CAMA_2_2" "s_CAMA_2_4" "s_CAMA_3"
## [367] "s_METI_1_exp_1" "s_METI_1_int_1" "s_METI_1_ben_1"
## [370] "s_METI_1_ben_2" "s_METI_1_ben_3" "s_METI_1_int_2"
## [373] "s_METI_1_exp_2" "s_METI_1_exp_3" "s_METI_1_exp_4"
## [376] "s_METI_1_exp_5" "s_METI_1_ben_4" "s_METI_1_int_3"
## [379] "s_METI_1_exp_6" "s_METI_1_int_4" "s_METI_2_exp_1"
## [382] "s_METI_2_int_1" "s_METI_2_ben_1" "s_METI_2_ben_2"
## [385] "s_METI_2_ben_3" "s_METI_2_int_2" "s_METI_2_exp_2"
## [388] "s_METI_2_exp_3" "s_METI_2_exp_4" "s_METI_2_exp_5"
## [391] "s_METI_2_ben_4" "s_METI_2_int_3" "s_METI_2_exp_6"
## [394] "s_METI_2_int_4" "s_METI_exp_1" "s_METI_int_1"
## [397] "s_METI_ben_1" "s_METI_ben_2" "s_METI_ben_3"
## [400] "s_METI_int_2" "s_METI_exp_2" "s_METI_exp_3"
## [403] "s_METI_exp_4" "s_METI_exp_5" "s_METI_ben_4"
## [406] "s_METI_int_3" "s_METI_exp_6" "s_METI_int_4"str(data_wide)## 'data.frame': 3080 obs. of 408 variables:
## $ id : Factor w/ 6705 levels "1","2","3","4",..: 4692 193 4223 5452 5207 3121 4700 3926 6230 6074 ...
## $ dispcode : int 31 22 22 31 22 31 31 31 31 31 ...
## $ duration : int 779 68 19 1043 36 546 746 938 568 1094 ...
## $ condition : Factor w/ 6 levels "1","2","3","4",..: 3 2 NA 4 5 3 5 6 2 1 ...
## $ text_order : Factor w/ 2 levels "Barth","Faerber": 1 2 NA 2 2 1 2 1 1 2 ...
## $ METI_target : Factor w/ 2 levels "Study Authors",..: 2 2 NA 1 2 2 1 2 1 2 ...
## $ s_sex : Factor w/ 2 levels "female","male": 2 2 2 1 2 2 1 2 1 1 ...
## $ s_age : int 31 42 25 31 47 54 45 43 51 57 ...
## $ s_school : Factor w/ 3 levels "Haupt","Real",..: 2 2 1 1 1 1 2 3 2 3 ...
## $ s_german : int 1 1 1 1 1 1 1 1 1 1 ...
## $ s_psychology : int 2 2 2 2 2 2 2 2 2 2 ...
## $ s_interest : int 5 6 5 5 5 5 4 7 7 8 ...
## $ s_contact : int 1 1 2 1 1 1 2 2 5 3 ...
## $ s_field : chr NA NA NA NA ...
## $ v_10 : int 4 NA NA NA NA 3 NA 5 NA NA ...
## $ v_11 : int 6 NA NA NA NA 2 NA 7 6 NA ...
## $ v_47 : int 3 NA NA NA NA 3 NA 7 6 NA ...
## $ v_48 : int 6 NA NA NA NA 3 NA 7 NA NA ...
## $ v_49 : int 6 NA NA NA NA 5 NA 7 NA NA ...
## $ v_12 : int 2 NA NA NA NA 3 NA 4 2 NA ...
## $ v_14 : int 1 NA NA NA NA 2 NA 1 4 NA ...
## $ v_16 : int 3 NA NA NA NA 4 NA 1 2 NA ...
## $ v_71 : int 3 NA NA NA NA 2 NA 1 3 NA ...
## $ v_17 : int 1 NA NA NA NA 3 NA 1 2 NA ...
## $ v_18 : int 1 NA NA NA NA 3 NA 1 1 NA ...
## $ v_19 : int 2 NA NA NA NA 3 NA 1 3 NA ...
## $ v_20 : int 2 NA NA NA NA 3 NA 1 2 NA ...
## $ v_21 : int 1 NA NA NA NA 3 NA 1 1 NA ...
## $ v_115 : int 1 NA NA NA NA 3 NA 1 NA NA ...
## $ v_116 : int 1 NA NA NA NA 3 NA 1 1 NA ...
## $ v_117 : int 1 NA NA NA NA 3 NA 1 1 NA ...
## $ v_22 : int 3 NA NA NA NA 3 NA 1 3 NA ...
## $ v_23 : int 1 NA NA NA NA 3 NA 1 1 NA ...
## $ v_24 : int 1 NA NA NA NA 3 NA 3 2 NA ...
## $ v_25 : int 1 NA NA NA NA 3 NA 1 1 NA ...
## $ v_26 : int 1 NA NA NA NA 3 NA 1 3 NA ...
## $ v_120 : int 1 NA NA NA NA 3 NA 1 2 NA ...
## $ v_27 : num 1 NA NA NA NA 0 NA -1 -1 NA ...
## $ v_28 : num 1 NA NA NA NA 0 NA -1 1 NA ...
## $ v_29 : num -1 NA NA NA NA 0 NA 1 -1 NA ...
## $ v_30 : num 1 NA NA NA NA 0 NA -1 1 NA ...
## $ v_31 : num 1 NA NA NA NA 0 NA 1 0 NA ...
## $ v_121 : num 1 NA NA NA NA 0 NA 1 -1 NA ...
## $ v_32 : num 1 NA NA NA NA 0 NA -1 0 NA ...
## $ v_33 : num 1 NA NA NA NA 0 NA -1 1 NA ...
## $ v_34 : num 1 NA NA NA NA 0 NA -1 0 NA ...
## $ v_35 : num 1 NA NA NA NA 0 NA -1 -1 NA ...
## $ v_36 : num 1 NA NA NA NA 0 NA -1 -1 NA ...
## $ v_122 : num 1 NA NA NA NA 0 NA 1 -1 NA ...
## $ v_37 : num 1 NA NA NA NA 0 NA -1 1 NA ...
## $ v_38 : num NA NA NA NA NA 0 NA 0 1 NA ...
## $ v_39 : num 1 NA NA NA NA 0 NA 0 NA NA ...
## $ v_40 : num 1 NA NA NA NA 0 NA -1 -1 NA ...
## $ v_41 : num -1 NA NA NA NA 0 NA -1 1 NA ...
## $ v_123 : num 1 NA NA NA NA 0 NA 1 1 NA ...
## $ v_124 : num 1 NA NA NA NA 0 NA -1 1 NA ...
## $ v_42 : num 0 NA NA NA NA 0 NA 1 0 NA ...
## $ v_43 : num -1 NA NA NA NA 0 NA -1 1 NA ...
## $ v_44 : num 0 NA NA NA NA 0 NA -1 1 NA ...
## $ v_45 : num -1 NA NA NA NA 0 NA -1 -1 NA ...
## $ v_46 : num 0 NA NA NA NA 0 NA 1 -1 NA ...
## $ v_125 : num 0 NA NA NA NA 0 NA -1 -1 NA ...
## $ v_72 : int NA 6 NA 4 NA NA 4 NA NA 8 ...
## $ v_73 : int NA 7 NA 5 NA NA 3 NA NA 8 ...
## $ v_74 : int NA 7 NA 5 NA NA 3 NA NA 8 ...
## $ v_75 : int NA 7 NA 6 NA NA 4 NA NA 8 ...
## $ v_76 : int NA 7 NA 8 NA NA 6 NA NA 8 ...
## $ v_77 : int NA 4 NA 4 NA NA 3 NA NA 5 ...
## $ v_79 : int NA 1 NA 3 NA NA 2 NA NA 1 ...
## $ v_81 : int NA 1 NA 1 NA NA 4 NA NA 1 ...
## $ v_83 : int NA 1 NA 2 NA NA 2 NA NA 1 ...
## $ v_126 : int NA NA NA 1 NA NA 3 NA NA 1 ...
## $ v_127 : int NA NA NA 1 NA NA 3 NA NA 1 ...
## $ v_128 : int NA NA NA 3 NA NA 3 NA NA 2 ...
## $ v_129 : int NA NA NA 3 NA NA 3 NA NA 2 ...
## $ v_130 : int NA NA NA 2 NA NA 3 NA NA 1 ...
## $ v_131 : int NA NA NA 1 NA NA 1 NA NA 1 ...
## $ v_132 : int NA NA NA 1 NA NA 1 NA NA 1 ...
## $ v_133 : int NA NA NA 2 NA NA 3 NA NA 1 ...
## $ v_134 : int NA NA NA 1 NA NA 3 NA NA 1 ...
## $ v_135 : int NA NA NA 2 NA NA 3 NA NA 1 ...
## $ v_136 : int NA NA NA 1 NA NA 1 NA NA 1 ...
## $ v_137 : int NA NA NA 2 NA NA 2 NA NA 1 ...
## $ v_138 : int NA NA NA 1 NA NA 3 NA NA 1 ...
## $ v_139 : int NA NA NA 1 NA NA 3 NA NA 1 ...
## $ v_140 : logi NA NA NA NA NA NA ...
## $ v_141 : logi NA NA NA NA NA NA ...
## $ v_142 : logi NA NA NA NA NA NA ...
## $ v_143 : logi NA NA NA NA NA NA ...
## $ v_144 : logi NA NA NA NA NA NA ...
## $ v_145 : logi NA NA NA NA NA NA ...
## $ v_146 : num NA NA NA -1 NA NA -1 NA NA -1 ...
## $ v_147 : num NA NA NA -1 NA NA -1 NA NA -1 ...
## $ v_148 : num NA NA NA 1 NA NA -1 NA NA 0 ...
## $ v_149 : num NA NA NA -1 NA NA 0 NA NA 0 ...
## $ v_150 : num NA NA NA 0 NA NA NA NA NA 0 ...
## $ v_151 : num NA NA NA -1 NA NA 1 NA NA 1 ...
## $ v_152 : num NA NA NA -1 NA NA 0 NA NA -1 ...
## $ v_153 : num NA NA NA -1 NA NA 1 NA NA -1 ...
## [list output truncated]View(data_wide)
data2_wide <- data2[,!names(data2) %in% c("external_lfdn","tester","lastpage",
"quality","p_0001","c_0002","browser",
"referer","device_type",
"quota_assignment","quota_rejected_id",
"page_history","hflip","vflip",
"output_mode","javascript","flash",
"session_id","language","cleaned","ats",
"datetime","date_of_last_access",
"day_of_first_mail","rts6018385",
"rts6018739","rts6018818","rts6019080",
"rts6019089","rts6021451","rts6021455",
"rts6023513","rts6023515","rts6023627",
"rts6023655","rts6023657","rts6023660",
"rts6023667","rts6023676","rts6023679",
"rts6033975")]
names(data2_wide)## [1] "id" "dispcode" "duration"
## [4] "condition" "text_order" "METI_target"
## [7] "s_sex" "s_age" "s_school"
## [10] "s_german" "s_psychology" "s_interest"
## [13] "s_contact" "s_field" "v_10"
## [16] "v_11" "v_47" "v_48"
## [19] "v_49" "v_12" "v_14"
## [22] "v_16" "v_71" "v_17"
## [25] "v_18" "v_19" "v_20"
## [28] "v_21" "v_115" "v_116"
## [31] "v_117" "v_22" "v_23"
## [34] "v_24" "v_25" "v_26"
## [37] "v_120" "v_27" "v_28"
## [40] "v_29" "v_30" "v_31"
## [43] "v_121" "v_32" "v_33"
## [46] "v_34" "v_35" "v_36"
## [49] "v_122" "v_37" "v_38"
## [52] "v_39" "v_40" "v_41"
## [55] "v_123" "v_124" "v_42"
## [58] "v_43" "v_44" "v_45"
## [61] "v_46" "v_125" "v_72"
## [64] "v_73" "v_74" "v_75"
## [67] "v_76" "v_77" "v_79"
## [70] "v_81" "v_83" "v_126"
## [73] "v_127" "v_128" "v_129"
## [76] "v_130" "v_131" "v_132"
## [79] "v_133" "v_134" "v_135"
## [82] "v_136" "v_137" "v_138"
## [85] "v_139" "v_140" "v_141"
## [88] "v_142" "v_143" "v_144"
## [91] "v_145" "v_146" "v_147"
## [94] "v_148" "v_149" "v_150"
## [97] "v_151" "v_152" "v_153"
## [100] "v_154" "v_155" "v_156"
## [103] "v_157" "v_158" "v_159"
## [106] "v_160" "v_161" "v_162"
## [109] "v_163" "v_164" "s_CAMA_1_1_1"
## [112] "s_CAMA_1_1_2" "s_CAMA_1_1_3" "s_CAMA_1_1_4"
## [115] "s_CAMA_1_1_5" "s_CAMA_1_1_6" "s_CAMA_1_1_7"
## [118] "s_CAMA_1_1_8" "s_CAMA_1_2_1" "s_CAMA_1_2_2"
## [121] "s_CAMA_1_2_3" "s_CAMA_1_2_4" "v_401"
## [124] "v_91" "v_92" "v_93"
## [127] "v_94" "v_95" "v_96"
## [130] "v_98" "v_100" "v_102"
## [133] "v_235" "v_236" "v_237"
## [136] "v_238" "v_239" "v_240"
## [139] "v_241" "v_242" "v_243"
## [142] "v_244" "v_245" "v_246"
## [145] "v_247" "v_248" "v_249"
## [148] "v_250" "v_251" "v_252"
## [151] "v_253" "v_254" "v_255"
## [154] "v_256" "v_257" "v_258"
## [157] "v_259" "s_METI_1_Res_exp_1" "s_METI_1_Res_int_1"
## [160] "s_METI_1_Res_ben_1" "s_METI_1_Res_ben_2" "s_METI_1_Res_ben_3"
## [163] "s_METI_1_Res_int_2" "s_METI_1_Res_exp_2" "s_METI_1_Res_exp_3"
## [166] "s_METI_1_Res_exp_4" "s_METI_1_Res_exp_5" "s_METI_1_Res_ben_4"
## [169] "s_METI_1_Res_int_3" "s_METI_1_Res_exp_6" "s_METI_1_Res_int_4"
## [172] "s_METI_1_Auth_exp_1" "s_METI_1_Auth_int_1" "s_METI_1_Auth_ben_1"
## [175] "s_METI_1_Auth_ben_2" "s_METI_1_Auth_ben_3" "s_METI_1_Auth_int_2"
## [178] "s_METI_1_Auth_exp_2" "s_METI_1_Auth_exp_3" "s_METI_1_Auth_exp_4"
## [181] "s_METI_1_Auth_exp_5" "s_METI_1_Auth_ben_4" "s_METI_1_Auth_int_3"
## [184] "s_METI_1_Auth_exp_6" "s_METI_1_Auth_int_4" "v_103"
## [187] "v_104" "v_105" "v_106"
## [190] "v_107" "v_108" "v_110"
## [193] "v_112" "v_114" "v_274"
## [196] "v_275" "v_276" "v_277"
## [199] "v_278" "v_279" "v_280"
## [202] "v_281" "v_282" "v_283"
## [205] "v_284" "v_285" "v_286"
## [208] "v_287" "v_288" "v_289"
## [211] "v_290" "v_291" "v_292"
## [214] "v_293" "v_294" "v_295"
## [217] "v_296" "v_297" "v_298"
## [220] "s_CAMA_2_1_1" "s_CAMA_2_1_2" "s_CAMA_2_1_3"
## [223] "s_CAMA_2_1_4" "s_CAMA_2_1_5" "s_CAMA_2_1_6"
## [226] "s_CAMA_2_1_7" "s_CAMA_2_1_8" "s_CAMA_2_2_1"
## [229] "s_CAMA_2_2_2" "s_CAMA_2_2_3" "s_CAMA_2_2_4"
## [232] "v_402" "s_METI_2_Res_exp_1" "s_METI_2_Res_int_1"
## [235] "s_METI_2_Res_ben_1" "s_METI_2_Res_ben_2" "s_METI_2_Res_ben_3"
## [238] "s_METI_2_Res_int_2" "s_METI_2_Res_exp_2" "s_METI_2_Res_exp_3"
## [241] "s_METI_2_Res_exp_4" "s_METI_2_Res_exp_5" "s_METI_2_Res_ben_4"
## [244] "s_METI_2_Res_int_3" "s_METI_2_Res_exp_6" "s_METI_2_Res_int_4"
## [247] "s_METI_2_Auth_exp_1" "s_METI_2_Auth_int_1" "s_METI_2_Auth_ben_1"
## [250] "s_METI_2_Auth_ben_2" "s_METI_2_Auth_ben_3" "s_METI_2_Auth_int_2"
## [253] "s_METI_2_Auth_exp_2" "s_METI_2_Auth_exp_3" "s_METI_2_Auth_exp_4"
## [256] "s_METI_2_Auth_exp_5" "s_METI_2_Auth_ben_4" "s_METI_2_Auth_int_3"
## [259] "s_METI_2_Auth_exp_6" "s_METI_2_Auth_int_4" "s_awareness"
## [262] "quota" "date_of_first_mail" "METI_text"
## [265] "summary1" "summary2" "version"
## [268] "causality" "disclaimer" "CAMA"
## [271] "dropout" "accessibility_1" "accessibility_2"
## [274] "understanding_1" "understanding_2" "empowerment_1"
## [277] "empowerment_2" "credibility_1" "credibility_2"
## [280] "relevance_1" "relevance_2" "curiosity_1"
## [283] "curiosity_2" "boredom_1" "boredom_2"
## [286] "confusion_1" "confusion_2" "frustration_1"
## [289] "frustration_2" "s_relationship_1" "s_relationship_2"
## [292] "s_relationship_3" "s_relationship_4" "s_relationship_5"
## [295] "s_relationship_6" "s_relationship_7" "s_relationship_8"
## [298] "s_extent_1" "s_extent_2" "s_extent_3"
## [301] "s_extent_4" "s_extent_5" "s_extent_6"
## [304] "s_diff_1_1" "s_diff_1_2" "s_diff_1_3"
## [307] "s_diff_1_4" "s_diff_1_5" "s_diff_1_6"
## [310] "s_diff_2_1" "s_diff_2_2" "s_diff_2_3"
## [313] "s_diff_2_4" "s_diff_2_5" "s_diff_2_6"
## [316] "s_funding_1_1" "s_funding_1_2" "s_funding_1_3"
## [319] "s_funding_1_4" "s_funding_1_5" "s_funding_1_6"
## [322] "s_funding_2_1" "s_funding_2_2" "s_funding_2_3"
## [325] "s_funding_2_4" "s_funding_2_5" "s_funding_2_6"
## [328] "s_coi_1_1" "s_coi_1_2" "s_coi_1_3"
## [331] "s_coi_1_4" "s_coi_1_5" "s_coi_1_6"
## [334] "s_coi_1_7" "s_coi_2_1" "s_coi_2_2"
## [337] "s_coi_2_3" "s_coi_2_4" "s_coi_2_5"
## [340] "s_coi_2_6" "s_coi_2_7" "s_causality_1_1"
## [343] "s_causality_1_2" "s_causality_1_3" "s_causality_1_4"
## [346] "s_causality_1_5" "s_causality_1_6" "s_causality_2_1"
## [349] "s_causality_2_2" "s_causality_2_3" "s_causality_2_4"
## [352] "s_causality_2_5" "s_causality_2_6" "s_CAMA_1_3"
## [355] "s_CAMA_2_3" "s_CAMA_1_1" "s_CAMA_1_2"
## [358] "s_CAMA_1_4" "s_CAMA_1_5" "s_CAMA_1_6"
## [361] "s_CAMA_1_7" "s_CAMA_1_8" "s_CAMA_2_1"
## [364] "s_CAMA_2_2" "s_CAMA_2_4" "s_CAMA_3"
## [367] "s_METI_1_exp_1" "s_METI_1_int_1" "s_METI_1_ben_1"
## [370] "s_METI_1_ben_2" "s_METI_1_ben_3" "s_METI_1_int_2"
## [373] "s_METI_1_exp_2" "s_METI_1_exp_3" "s_METI_1_exp_4"
## [376] "s_METI_1_exp_5" "s_METI_1_ben_4" "s_METI_1_int_3"
## [379] "s_METI_1_exp_6" "s_METI_1_int_4" "s_METI_2_exp_1"
## [382] "s_METI_2_int_1" "s_METI_2_ben_1" "s_METI_2_ben_2"
## [385] "s_METI_2_ben_3" "s_METI_2_int_2" "s_METI_2_exp_2"
## [388] "s_METI_2_exp_3" "s_METI_2_exp_4" "s_METI_2_exp_5"
## [391] "s_METI_2_ben_4" "s_METI_2_int_3" "s_METI_2_exp_6"
## [394] "s_METI_2_int_4" "s_METI_exp_1" "s_METI_int_1"
## [397] "s_METI_ben_1" "s_METI_ben_2" "s_METI_ben_3"
## [400] "s_METI_int_2" "s_METI_exp_2" "s_METI_exp_3"
## [403] "s_METI_exp_4" "s_METI_exp_5" "s_METI_ben_4"
## [406] "s_METI_int_3" "s_METI_exp_6" "s_METI_int_4"
## [409] "duration_minutes"str(data2_wide)## 'data.frame': 2041 obs. of 409 variables:
## $ id : Factor w/ 6705 levels "1","2","3","4",..: 4692 5452 3121 4700 3926 6230 6074 2675 160 402 ...
## $ dispcode : int 31 31 31 31 31 31 31 31 31 31 ...
## $ duration : int 779 1043 546 746 938 568 1094 1246 662 1298 ...
## $ condition : Factor w/ 6 levels "1","2","3","4",..: 3 4 3 5 6 2 1 6 4 4 ...
## $ text_order : Factor w/ 2 levels "Barth","Faerber": 1 2 1 2 1 1 2 1 1 2 ...
## $ METI_target : Factor w/ 2 levels "Study Authors",..: 2 1 2 1 2 1 2 1 1 1 ...
## $ s_sex : Factor w/ 2 levels "female","male": 2 1 2 1 2 1 1 1 2 2 ...
## $ s_age : int 31 31 54 45 43 51 57 26 50 35 ...
## $ s_school : Factor w/ 3 levels "Haupt","Real",..: 2 1 1 2 3 2 3 2 1 3 ...
## $ s_german : int 1 1 1 1 1 1 1 1 1 1 ...
## $ s_psychology : int 2 2 2 2 2 2 2 2 2 2 ...
## $ s_interest : int 5 5 5 4 7 7 8 7 5 5 ...
## $ s_contact : int 1 1 1 2 2 5 3 1 5 1 ...
## $ s_field : chr NA NA NA NA ...
## $ v_10 : int 4 NA 3 NA 5 NA NA 5 7 NA ...
## $ v_11 : int 6 NA 2 NA 7 6 NA 7 6 NA ...
## $ v_47 : int 3 NA 3 NA 7 6 NA 6 5 NA ...
## $ v_48 : int 6 NA 3 NA 7 NA NA 7 5 NA ...
## $ v_49 : int 6 NA 5 NA 7 NA NA 7 7 NA ...
## $ v_12 : int 2 NA 3 NA 4 2 NA 3 4 NA ...
## $ v_14 : int 1 NA 2 NA 1 4 NA 1 3 NA ...
## $ v_16 : int 3 NA 4 NA 1 2 NA 1 3 NA ...
## $ v_71 : int 3 NA 2 NA 1 3 NA 1 4 NA ...
## $ v_17 : int 1 NA 3 NA 1 2 NA 1 3 NA ...
## $ v_18 : int 1 NA 3 NA 1 1 NA 1 1 NA ...
## $ v_19 : int 2 NA 3 NA 1 3 NA 3 3 NA ...
## $ v_20 : int 2 NA 3 NA 1 2 NA 2 1 NA ...
## $ v_21 : int 1 NA 3 NA 1 1 NA 1 1 NA ...
## $ v_115 : int 1 NA 3 NA 1 NA NA 1 1 NA ...
## $ v_116 : int 1 NA 3 NA 1 1 NA 1 1 NA ...
## $ v_117 : int 1 NA 3 NA 1 1 NA 1 1 NA ...
## $ v_22 : int 3 NA 3 NA 1 3 NA 2 1 NA ...
## $ v_23 : int 1 NA 3 NA 1 1 NA 1 1 NA ...
## $ v_24 : int 1 NA 3 NA 3 2 NA 1 1 NA ...
## $ v_25 : int 1 NA 3 NA 1 1 NA 1 1 NA ...
## $ v_26 : int 1 NA 3 NA 1 3 NA 3 1 NA ...
## $ v_120 : int 1 NA 3 NA 1 2 NA 1 1 NA ...
## $ v_27 : num 1 NA 0 NA -1 -1 NA 1 -1 NA ...
## $ v_28 : num 1 NA 0 NA -1 1 NA -1 0 NA ...
## $ v_29 : num -1 NA 0 NA 1 -1 NA 1 1 NA ...
## $ v_30 : num 1 NA 0 NA -1 1 NA -1 -1 NA ...
## $ v_31 : num 1 NA 0 NA 1 0 NA 1 1 NA ...
## $ v_121 : num 1 NA 0 NA 1 -1 NA -1 1 NA ...
## $ v_32 : num 1 NA 0 NA -1 0 NA 0 -1 NA ...
## $ v_33 : num 1 NA 0 NA -1 1 NA -1 -1 NA ...
## $ v_34 : num 1 NA 0 NA -1 0 NA 0 -1 NA ...
## $ v_35 : num 1 NA 0 NA -1 -1 NA -1 -1 NA ...
## $ v_36 : num 1 NA 0 NA -1 -1 NA 0 -1 NA ...
## $ v_122 : num 1 NA 0 NA 1 -1 NA 0 1 NA ...
## $ v_37 : num 1 NA 0 NA -1 1 NA 1 -1 NA ...
## $ v_38 : num NA NA 0 NA 0 1 NA 0 0 NA ...
## $ v_39 : num 1 NA 0 NA 0 NA NA -1 0 NA ...
## $ v_40 : num 1 NA 0 NA -1 -1 NA -1 0 NA ...
## $ v_41 : num -1 NA 0 NA -1 1 NA -1 1 NA ...
## $ v_123 : num 1 NA 0 NA 1 1 NA -1 0 NA ...
## $ v_124 : num 1 NA 0 NA -1 1 NA 1 -1 NA ...
## $ v_42 : num 0 NA 0 NA 1 0 NA 0 -1 NA ...
## $ v_43 : num -1 NA 0 NA -1 1 NA 0 0 NA ...
## $ v_44 : num 0 NA 0 NA -1 1 NA 0 -1 NA ...
## $ v_45 : num -1 NA 0 NA -1 -1 NA -1 -1 NA ...
## $ v_46 : num 0 NA 0 NA 1 -1 NA -1 0 NA ...
## $ v_125 : num 0 NA 0 NA -1 -1 NA -1 0 NA ...
## $ v_72 : int NA 4 NA 4 NA NA 8 NA NA 3 ...
## $ v_73 : int NA 5 NA 3 NA NA 8 NA NA 5 ...
## $ v_74 : int NA 5 NA 3 NA NA 8 NA NA 3 ...
## $ v_75 : int NA 6 NA 4 NA NA 8 NA NA 8 ...
## $ v_76 : int NA 8 NA 6 NA NA 8 NA NA 4 ...
## $ v_77 : int NA 4 NA 3 NA NA 5 NA NA 2 ...
## $ v_79 : int NA 3 NA 2 NA NA 1 NA NA 2 ...
## $ v_81 : int NA 1 NA 4 NA NA 1 NA NA 2 ...
## $ v_83 : int NA 2 NA 2 NA NA 1 NA NA 1 ...
## $ v_126 : int NA 1 NA 3 NA NA 1 NA NA 1 ...
## $ v_127 : int NA 1 NA 3 NA NA 1 NA NA 2 ...
## $ v_128 : int NA 3 NA 3 NA NA 2 NA NA 2 ...
## $ v_129 : int NA 3 NA 3 NA NA 2 NA NA 2 ...
## $ v_130 : int NA 2 NA 3 NA NA 1 NA NA 1 ...
## $ v_131 : int NA 1 NA 1 NA NA 1 NA NA 1 ...
## $ v_132 : int NA 1 NA 1 NA NA 1 NA NA 2 ...
## $ v_133 : int NA 2 NA 3 NA NA 1 NA NA 2 ...
## $ v_134 : int NA 1 NA 3 NA NA 1 NA NA 2 ...
## $ v_135 : int NA 2 NA 3 NA NA 1 NA NA 2 ...
## $ v_136 : int NA 1 NA 1 NA NA 1 NA NA 2 ...
## $ v_137 : int NA 2 NA 2 NA NA 1 NA NA 2 ...
## $ v_138 : int NA 1 NA 3 NA NA 1 NA NA 2 ...
## $ v_139 : int NA 1 NA 3 NA NA 1 NA NA 1 ...
## $ v_140 : logi NA NA NA NA NA NA ...
## $ v_141 : logi NA NA NA NA NA NA ...
## $ v_142 : logi NA NA NA NA NA NA ...
## $ v_143 : logi NA NA NA NA NA NA ...
## $ v_144 : logi NA NA NA NA NA NA ...
## $ v_145 : logi NA NA NA NA NA NA ...
## $ v_146 : num NA -1 NA -1 NA NA -1 NA NA 1 ...
## $ v_147 : num NA -1 NA -1 NA NA -1 NA NA 1 ...
## $ v_148 : num NA 1 NA -1 NA NA 0 NA NA 1 ...
## $ v_149 : num NA -1 NA 0 NA NA 0 NA NA 1 ...
## $ v_150 : num NA 0 NA NA NA NA 0 NA NA 1 ...
## $ v_151 : num NA -1 NA 1 NA NA 1 NA NA 1 ...
## $ v_152 : num NA -1 NA 0 NA NA -1 NA NA 1 ...
## $ v_153 : num NA -1 NA 1 NA NA -1 NA NA 1 ...
## [list output truncated]View(data2_wide)
#Wide Dataset including only complete casesMETI Scale Generation
psych::alpha(data2_wide[,c("s_METI_exp_1","s_METI_exp_2","s_METI_exp_3", "s_METI_exp_4", "s_METI_exp_5","s_METI_exp_6")])##
## Reliability analysis
## Call: psych::alpha(x = data2_wide[, c("s_METI_exp_1", "s_METI_exp_2",
## "s_METI_exp_3", "s_METI_exp_4", "s_METI_exp_5", "s_METI_exp_6")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.93 0.72 16 0.0021 5.5 1.2 0.73
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.94 0.94 0.94
## Duhachek 0.94 0.94 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## s_METI_exp_1 0.93 0.93 0.92 0.73 13 0.0024 0.00042 0.73
## s_METI_exp_2 0.93 0.93 0.91 0.72 13 0.0025 0.00057 0.72
## s_METI_exp_3 0.93 0.93 0.92 0.73 14 0.0024 0.00024 0.73
## s_METI_exp_4 0.93 0.93 0.91 0.72 13 0.0026 0.00062 0.73
## s_METI_exp_5 0.93 0.93 0.91 0.72 13 0.0026 0.00076 0.72
## s_METI_exp_6 0.93 0.93 0.91 0.72 13 0.0025 0.00082 0.73
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## s_METI_exp_1 2030 0.87 0.87 0.83 0.80 5.5 1.4
## s_METI_exp_2 2034 0.88 0.88 0.85 0.82 5.5 1.4
## s_METI_exp_3 2034 0.86 0.86 0.82 0.79 5.3 1.4
## s_METI_exp_4 2035 0.89 0.89 0.86 0.83 5.4 1.4
## s_METI_exp_5 2029 0.89 0.89 0.86 0.83 5.5 1.4
## s_METI_exp_6 2034 0.88 0.88 0.86 0.83 5.5 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## s_METI_exp_1 0.02 0.02 0.03 0.18 0.18 0.28 0.29 0.01
## s_METI_exp_2 0.02 0.02 0.04 0.18 0.18 0.29 0.29 0.00
## s_METI_exp_3 0.01 0.03 0.05 0.21 0.20 0.26 0.25 0.00
## s_METI_exp_4 0.01 0.02 0.04 0.19 0.18 0.28 0.28 0.00
## s_METI_exp_5 0.01 0.02 0.04 0.18 0.18 0.29 0.27 0.01
## s_METI_exp_6 0.01 0.02 0.04 0.18 0.18 0.29 0.29 0.00psych::alpha(data2_wide[,c("s_METI_int_1","s_METI_int_2","s_METI_int_3", "s_METI_int_4")])##
## Reliability analysis
## Call: psych::alpha(x = data2_wide[, c("s_METI_int_1", "s_METI_int_2",
## "s_METI_int_3", "s_METI_int_4")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.88 0.71 9.6 0.0034 5.4 1.2 0.71
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.9 0.91 0.91
## Duhachek 0.9 0.91 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## s_METI_int_1 0.88 0.89 0.84 0.72 7.7 0.0044 4.5e-04 0.71
## s_METI_int_2 0.88 0.88 0.83 0.70 7.1 0.0047 1.8e-03 0.71
## s_METI_int_3 0.88 0.88 0.83 0.71 7.4 0.0046 3.7e-05 0.71
## s_METI_int_4 0.87 0.87 0.82 0.69 6.8 0.0049 9.9e-04 0.71
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## s_METI_int_1 2033 0.87 0.87 0.81 0.77 5.3 1.4
## s_METI_int_2 2028 0.89 0.89 0.83 0.79 5.4 1.4
## s_METI_int_3 2029 0.88 0.88 0.83 0.78 5.5 1.4
## s_METI_int_4 2024 0.89 0.89 0.85 0.81 5.4 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## s_METI_int_1 0.01 0.02 0.03 0.23 0.20 0.27 0.24 0.00
## s_METI_int_2 0.02 0.02 0.04 0.21 0.18 0.27 0.26 0.01
## s_METI_int_3 0.01 0.02 0.04 0.19 0.17 0.29 0.28 0.01
## s_METI_int_4 0.01 0.02 0.04 0.19 0.19 0.28 0.27 0.01psych::alpha(data2_wide[,c("s_METI_ben_1","s_METI_ben_2","s_METI_ben_3", "s_METI_ben_4")])##
## Reliability analysis
## Call: psych::alpha(x = data2_wide[, c("s_METI_ben_1", "s_METI_ben_2",
## "s_METI_ben_3", "s_METI_ben_4")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.88 0.71 9.6 0.0034 5.4 1.2 0.71
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.9 0.91 0.91
## Duhachek 0.9 0.91 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## s_METI_ben_1 0.87 0.87 0.82 0.70 7.0 0.0048 1.0e-04 0.70
## s_METI_ben_2 0.88 0.88 0.83 0.71 7.2 0.0047 1.6e-04 0.70
## s_METI_ben_3 0.88 0.88 0.83 0.72 7.6 0.0045 2.7e-05 0.72
## s_METI_ben_4 0.88 0.88 0.83 0.70 7.1 0.0047 1.5e-04 0.70
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## s_METI_ben_1 2027 0.89 0.89 0.84 0.80 5.3 1.4
## s_METI_ben_2 2031 0.88 0.88 0.83 0.79 5.3 1.4
## s_METI_ben_3 2032 0.88 0.88 0.81 0.77 5.5 1.4
## s_METI_ben_4 2023 0.89 0.89 0.83 0.79 5.3 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## s_METI_ben_1 0.01 0.02 0.04 0.23 0.20 0.26 0.24 0.01
## s_METI_ben_2 0.02 0.02 0.04 0.22 0.20 0.26 0.25 0.00
## s_METI_ben_3 0.02 0.02 0.04 0.19 0.19 0.27 0.28 0.00
## s_METI_ben_4 0.02 0.02 0.04 0.23 0.20 0.26 0.24 0.01data2_wide$s_METI_exp <- rowMeans(data2_wide[,c("s_METI_exp_1","s_METI_exp_2", "s_METI_exp_3","s_METI_exp_4", "s_METI_exp_5","s_METI_exp_6")])
data2_wide$s_METI_int <- rowMeans(data2_wide[,c("s_METI_int_1","s_METI_int_2", "s_METI_int_3","s_METI_int_4")])
data2_wide$s_METI_ben <- rowMeans(data2_wide[,c("s_METI_ben_1","s_METI_ben_2",
"s_METI_ben_3","s_METI_ben_4")])
describe(data2_wide$s_METI_exp)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1997 5.45 1.22 5.67 5.54 1.48 1 7 6 -0.64 0.03 0.03describe(data2_wide$s_METI_int)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1999 5.4 1.23 5.5 5.48 1.48 1 7 6 -0.56 -0.04 0.03describe(data2_wide$s_METI_ben)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1996 5.35 1.23 5.5 5.42 1.48 1 7 6 -0.55 0.12 0.03# METI Scale Reliabilities when targeting Summary Authors
data2_wide_summary_authors <- subset(data2_wide, METI_target ==
"Summary Authors")
psych::alpha(data2_wide_summary_authors[,c("s_METI_exp_1","s_METI_exp_2",
"s_METI_exp_3", "s_METI_exp_4",
"s_METI_exp_5","s_METI_exp_6")])##
## Reliability analysis
## Call: psych::alpha(x = data2_wide_summary_authors[, c("s_METI_exp_1",
## "s_METI_exp_2", "s_METI_exp_3", "s_METI_exp_4", "s_METI_exp_5",
## "s_METI_exp_6")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.93 0.73 16 0.0028 5.5 1.2 0.74
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.94 0.94 0.95
## Duhachek 0.94 0.94 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## s_METI_exp_1 0.93 0.94 0.92 0.74 14 0.0032 0.00074 0.75
## s_METI_exp_2 0.93 0.93 0.92 0.73 13 0.0035 0.00086 0.73
## s_METI_exp_3 0.93 0.94 0.92 0.74 14 0.0032 0.00067 0.75
## s_METI_exp_4 0.93 0.93 0.92 0.72 13 0.0035 0.00100 0.73
## s_METI_exp_5 0.93 0.93 0.91 0.72 13 0.0036 0.00128 0.72
## s_METI_exp_6 0.93 0.93 0.92 0.72 13 0.0035 0.00129 0.73
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## s_METI_exp_1 1009 0.86 0.86 0.82 0.79 5.5 1.4
## s_METI_exp_2 1012 0.88 0.88 0.86 0.83 5.5 1.4
## s_METI_exp_3 1012 0.86 0.86 0.82 0.79 5.3 1.4
## s_METI_exp_4 1010 0.89 0.89 0.87 0.84 5.4 1.4
## s_METI_exp_5 1008 0.90 0.90 0.87 0.85 5.5 1.4
## s_METI_exp_6 1009 0.89 0.89 0.87 0.84 5.5 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## s_METI_exp_1 0.01 0.02 0.04 0.18 0.17 0.26 0.31 0.00
## s_METI_exp_2 0.02 0.01 0.04 0.17 0.18 0.28 0.29 0.00
## s_METI_exp_3 0.01 0.03 0.05 0.21 0.19 0.25 0.25 0.00
## s_METI_exp_4 0.01 0.02 0.04 0.19 0.18 0.27 0.28 0.00
## s_METI_exp_5 0.01 0.02 0.04 0.19 0.18 0.28 0.27 0.01
## s_METI_exp_6 0.01 0.02 0.04 0.18 0.19 0.27 0.29 0.00psych::alpha(data2_wide_summary_authors[,c("s_METI_int_1","s_METI_int_2",
"s_METI_int_3", "s_METI_int_4")])##
## Reliability analysis
## Call: psych::alpha(x = data2_wide_summary_authors[, c("s_METI_int_1",
## "s_METI_int_2", "s_METI_int_3", "s_METI_int_4")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.89 0.72 10 0.0045 5.4 1.3 0.73
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.9 0.91 0.92
## Duhachek 0.9 0.91 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## s_METI_int_1 0.89 0.89 0.84 0.73 8.1 0.0060 7.0e-05 0.73
## s_METI_int_2 0.88 0.88 0.84 0.72 7.6 0.0063 1.0e-03 0.73
## s_METI_int_3 0.89 0.89 0.84 0.73 8.0 0.0060 2.1e-05 0.73
## s_METI_int_4 0.88 0.88 0.83 0.71 7.4 0.0064 7.4e-04 0.73
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## s_METI_int_1 1012 0.88 0.88 0.83 0.79 5.4 1.4
## s_METI_int_2 1008 0.89 0.89 0.84 0.81 5.4 1.4
## s_METI_int_3 1009 0.89 0.88 0.83 0.79 5.5 1.4
## s_METI_int_4 1005 0.90 0.90 0.85 0.81 5.5 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## s_METI_int_1 0.01 0.02 0.03 0.23 0.18 0.26 0.26 0.00
## s_METI_int_2 0.02 0.02 0.05 0.21 0.17 0.24 0.29 0.01
## s_METI_int_3 0.01 0.02 0.04 0.19 0.16 0.28 0.30 0.00
## s_METI_int_4 0.01 0.02 0.04 0.18 0.19 0.26 0.29 0.01psych::alpha(data2_wide_summary_authors[,c("s_METI_ben_1","s_METI_ben_2",
"s_METI_ben_3", "s_METI_ben_4")])##
## Reliability analysis
## Call: psych::alpha(x = data2_wide_summary_authors[, c("s_METI_ben_1",
## "s_METI_ben_2", "s_METI_ben_3", "s_METI_ben_4")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.89 0.72 10 0.0045 5.4 1.2 0.73
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.9 0.91 0.92
## Duhachek 0.9 0.91 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## s_METI_ben_1 0.88 0.88 0.84 0.72 7.7 0.0063 2.3e-04 0.73
## s_METI_ben_2 0.88 0.88 0.84 0.72 7.6 0.0064 2.2e-04 0.72
## s_METI_ben_3 0.89 0.89 0.84 0.73 8.1 0.0060 2.3e-05 0.73
## s_METI_ben_4 0.89 0.89 0.84 0.73 8.0 0.0060 7.7e-05 0.73
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## s_METI_ben_1 1008 0.89 0.89 0.85 0.81 5.4 1.4
## s_METI_ben_2 1010 0.90 0.90 0.85 0.81 5.3 1.4
## s_METI_ben_3 1011 0.88 0.88 0.83 0.79 5.5 1.4
## s_METI_ben_4 1005 0.89 0.89 0.83 0.79 5.3 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## s_METI_ben_1 0.01 0.01 0.05 0.22 0.20 0.25 0.25 0.01
## s_METI_ben_2 0.01 0.02 0.05 0.21 0.19 0.26 0.26 0.00
## s_METI_ben_3 0.02 0.01 0.04 0.18 0.19 0.28 0.27 0.00
## s_METI_ben_4 0.02 0.02 0.04 0.23 0.19 0.25 0.25 0.01# METI Scale Reliabilities when targeting Study Authors
data2_wide_study_authors <- subset(data2_wide, METI_target == "Study Authors")
psych::alpha(data2_wide_study_authors[,c("s_METI_exp_1","s_METI_exp_2",
"s_METI_exp_3", "s_METI_exp_4",
"s_METI_exp_5","s_METI_exp_6")])##
## Reliability analysis
## Call: psych::alpha(x = data2_wide_study_authors[, c("s_METI_exp_1",
## "s_METI_exp_2", "s_METI_exp_3", "s_METI_exp_4", "s_METI_exp_5",
## "s_METI_exp_6")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.93 0.72 15 0.003 5.5 1.2 0.72
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.93 0.94 0.94
## Duhachek 0.93 0.94 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## s_METI_exp_1 0.93 0.93 0.91 0.72 13 0.0036 0.00032 0.72
## s_METI_exp_2 0.93 0.93 0.91 0.71 13 0.0037 0.00044 0.72
## s_METI_exp_3 0.93 0.93 0.91 0.72 13 0.0035 0.00035 0.72
## s_METI_exp_4 0.92 0.93 0.91 0.71 12 0.0037 0.00076 0.71
## s_METI_exp_5 0.93 0.93 0.91 0.71 12 0.0037 0.00074 0.71
## s_METI_exp_6 0.93 0.93 0.91 0.72 13 0.0037 0.00084 0.72
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## s_METI_exp_1 1021 0.87 0.87 0.84 0.81 5.5 1.4
## s_METI_exp_2 1022 0.88 0.88 0.85 0.82 5.5 1.4
## s_METI_exp_3 1022 0.86 0.86 0.82 0.79 5.3 1.4
## s_METI_exp_4 1025 0.88 0.88 0.85 0.83 5.4 1.4
## s_METI_exp_5 1021 0.88 0.88 0.85 0.82 5.5 1.4
## s_METI_exp_6 1025 0.87 0.87 0.84 0.82 5.5 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## s_METI_exp_1 0.02 0.02 0.03 0.18 0.19 0.29 0.28 0.01
## s_METI_exp_2 0.02 0.02 0.03 0.18 0.17 0.29 0.29 0.00
## s_METI_exp_3 0.01 0.02 0.05 0.21 0.20 0.26 0.24 0.00
## s_METI_exp_4 0.01 0.02 0.03 0.19 0.19 0.29 0.27 0.00
## s_METI_exp_5 0.01 0.03 0.03 0.18 0.17 0.30 0.27 0.01
## s_METI_exp_6 0.01 0.02 0.03 0.18 0.18 0.30 0.29 0.00psych::alpha(data2_wide_study_authors[,c("s_METI_int_1","s_METI_int_2",
"s_METI_int_3", "s_METI_int_4")])##
## Reliability analysis
## Call: psych::alpha(x = data2_wide_study_authors[, c("s_METI_int_1",
## "s_METI_int_2", "s_METI_int_3", "s_METI_int_4")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.87 0.69 8.9 0.0052 5.4 1.2 0.69
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.89 0.9 0.91
## Duhachek 0.89 0.9 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## s_METI_int_1 0.88 0.88 0.83 0.71 7.3 0.0066 0.0012 0.69
## s_METI_int_2 0.87 0.87 0.82 0.69 6.6 0.0071 0.0033 0.68
## s_METI_int_3 0.87 0.87 0.82 0.69 6.7 0.0070 0.0002 0.69
## s_METI_int_4 0.86 0.86 0.81 0.68 6.2 0.0075 0.0013 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## s_METI_int_1 1021 0.86 0.86 0.79 0.75 5.3 1.3
## s_METI_int_2 1020 0.88 0.88 0.82 0.78 5.3 1.4
## s_METI_int_3 1020 0.88 0.88 0.82 0.77 5.4 1.4
## s_METI_int_4 1019 0.89 0.89 0.84 0.80 5.4 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## s_METI_int_1 0.01 0.02 0.03 0.22 0.22 0.28 0.22 0.01
## s_METI_int_2 0.02 0.02 0.03 0.21 0.20 0.29 0.23 0.01
## s_METI_int_3 0.01 0.02 0.04 0.19 0.18 0.30 0.26 0.01
## s_METI_int_4 0.02 0.01 0.04 0.20 0.19 0.30 0.25 0.01psych::alpha(data2_wide_study_authors[,c("s_METI_ben_1","s_METI_ben_2",
"s_METI_ben_3", "s_METI_ben_4")])##
## Reliability analysis
## Call: psych::alpha(x = data2_wide_study_authors[, c("s_METI_ben_1",
## "s_METI_ben_2", "s_METI_ben_3", "s_METI_ben_4")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.87 0.69 8.9 0.0052 5.3 1.2 0.69
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.89 0.9 0.91
## Duhachek 0.89 0.9 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## s_METI_ben_1 0.86 0.86 0.81 0.68 6.4 0.0073 5.6e-04 0.69
## s_METI_ben_2 0.87 0.87 0.82 0.70 6.9 0.0068 1.3e-04 0.69
## s_METI_ben_3 0.88 0.88 0.82 0.70 7.0 0.0067 8.3e-05 0.70
## s_METI_ben_4 0.86 0.86 0.81 0.68 6.4 0.0073 5.7e-04 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## s_METI_ben_1 1019 0.88 0.88 0.83 0.79 5.3 1.4
## s_METI_ben_2 1021 0.87 0.87 0.80 0.76 5.3 1.4
## s_METI_ben_3 1021 0.87 0.87 0.80 0.76 5.5 1.4
## s_METI_ben_4 1018 0.88 0.88 0.83 0.79 5.3 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## s_METI_ben_1 0.01 0.02 0.04 0.24 0.19 0.26 0.23 0.01
## s_METI_ben_2 0.02 0.01 0.03 0.23 0.20 0.27 0.24 0.01
## s_METI_ben_3 0.02 0.02 0.04 0.19 0.19 0.27 0.28 0.01
## s_METI_ben_4 0.02 0.02 0.04 0.22 0.21 0.27 0.23 0.01CFA METI
meti_mod1 <- "trust =~ s_METI_exp_1 + s_METI_exp_2 + s_METI_exp_3 + s_METI_exp_4
+ s_METI_exp_5 + s_METI_exp_6 + s_METI_int_1 + s_METI_int_2 + s_METI_int_3 +
s_METI_int_4 + s_METI_ben_1 + s_METI_ben_2 + s_METI_ben_3 + s_METI_ben_4"
meti_fit1 <- cfa(meti_mod1, data = data2_wide)
fit_1 <- fitmeasures(meti_fit1)[c("chisq","df","tli","cfi","rmsea","srmr")]
meti_mod2 <- "exp =~ s_METI_exp_1 + s_METI_exp_2 + s_METI_exp_3 + s_METI_exp_4
+ s_METI_exp_5 + s_METI_exp_6
intben =~ s_METI_int_1 + s_METI_int_2 + s_METI_int_3 +
s_METI_int_4 + s_METI_ben_1 + s_METI_ben_2 + s_METI_ben_3 + s_METI_ben_4"
meti_fit2 <- cfa(meti_mod2, data = data2_wide)
fit_2 <- fitmeasures(meti_fit2)[c("chisq","df","tli","cfi","rmsea","srmr")]
meti_mod3 <- "exp =~ s_METI_exp_1 + s_METI_exp_2 + s_METI_exp_3 + s_METI_exp_4
+ s_METI_exp_5 + s_METI_exp_6
int =~ s_METI_int_1 + s_METI_int_2 + s_METI_int_3 + s_METI_int_4
ben =~ s_METI_ben_1 + s_METI_ben_2 + s_METI_ben_3 + s_METI_ben_4"
meti_fit3 <- cfa(meti_mod3, data = data2_wide)
fit_3 <- fitmeasures(meti_fit3)[c("chisq","df","tli","cfi","rmsea","srmr")]
anova(meti_fit1,meti_fit2,meti_fit3)##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## meti_fit3 74 69189 69361 341.85
## meti_fit2 76 69208 69369 364.96 23.11 0.07382 2 9.588e-06 ***
## meti_fit1 77 69437 69593 595.90 230.94 0.34454 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Create Overall Scores for Knowledge Items
Relationship-Item
data2_wide$s_relationship <- rowSums(data2_wide[,c("s_relationship_1",
"s_relationship_2",
"s_relationship_3",
"s_relationship_4",
"s_relationship_5",
"s_relationship_6",
"s_relationship_7",
"s_relationship_8")])
describe(data2_wide$s_relationship)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1979 0.23 3.22 0 0.02 2.97 -8 8 16 0.45 -0.37 0.07table(data2_wide$s_relationship)##
## -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8
## 4 1 20 20 247 110 251 163 425 112 192 67 141 30 135 17 44Extent of Evaluation-Item
data2_wide$s_extent <- rowSums(data2_wide[,c("s_extent_1","s_extent_2", "s_extent_3","s_extent_4", "s_extent_5","s_extent_6")])
describe(data2_wide$s_extent)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1999 0.52 2.28 0 0.41 2.97 -6 6 12 0.35 -0.28 0.05table(data2_wide$s_extent)##
## -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
## 4 2 45 52 345 180 533 179 281 118 168 35 57Differentiation-Item
data2_wide$s_diff_1 <- rowSums(data2_wide[,c("s_diff_1_1","s_diff_1_2", "s_diff_1_3","s_diff_1_4",
"s_diff_1_5","s_diff_1_6")])
describe(data2_wide$s_diff_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 997 0.07 1.83 0 0.05 1.48 -6 6 12 0.14 0.68 0.06table(data2_wide$s_diff_1)##
## -6 -4 -3 -2 -1 0 1 2 3 4 5 6
## 3 33 22 145 103 360 117 146 22 33 7 6data2_wide$s_diff_2 <- rowSums(data2_wide[,c("s_diff_2_1","s_diff_2_2", "s_diff_2_3","s_diff_2_4",
"s_diff_2_5","s_diff_2_6")])
describe(data2_wide$s_diff_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 981 0.52 2.07 0 0.46 2.97 -6 6 12 0.15 0.23 0.07table(data2_wide$s_diff_2)##
## -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
## 2 3 29 15 123 61 343 82 191 31 74 10 17Funding-Item
data2_wide$s_funding_1 <- rowSums(data2_wide[,c("s_funding_1_1","s_funding_1_2",
"s_funding_1_3","s_funding_1_4", "s_funding_1_5","s_funding_1_6")])
describe(data2_wide$s_funding_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1994 0.87 3.05 0 0.84 2.97 -6 6 12 0.26 -0.74 0.07table(data2_wide$s_funding_1)##
## -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
## 13 10 152 54 215 175 511 140 181 84 115 48 296data2_wide$s_funding_2 <- rowSums(data2_wide[,c("s_funding_2_1","s_funding_2_2",
"s_funding_2_3","s_funding_2_4",
"s_funding_2_5","s_funding_2_6")])
describe(data2_wide$s_funding_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2010 1.67 3.22 1 1.82 4.45 -6 6 12 -0.07 -1.03 0.07table(data2_wide$s_funding_2)##
## -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
## 12 12 119 46 157 103 474 118 206 59 177 48 479COI-Item
data2_wide$s_coi_1 <- rowSums(data2_wide[,c("s_coi_1_1","s_coi_1_2",
"s_coi_1_3","s_coi_1_4",
"s_coi_1_5","s_coi_1_6", "s_coi_1_7")])
describe(data2_wide$s_coi_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1955 0.72 3.33 0 0.66 2.97 -7 7 14 0.19 -0.5 0.08table(data2_wide$s_coi_1)##
## -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7
## 9 8 137 51 133 101 182 467 208 83 187 47 148 22 172data2_wide$s_coi_2 <- rowSums(data2_wide[,c("s_coi_2_1","s_coi_2_2",
"s_coi_2_3","s_coi_2_4", "s_coi_2_5","s_coi_2_6","s_coi_2_7")])
describe(data2_wide$s_coi_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1993 1.3 3.56 1 1.36 2.97 -7 7 14 -0.01 -0.75 0.08table(data2_wide$s_coi_2)##
## -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7
## 18 14 114 37 123 72 182 407 201 69 207 33 235 22 259Causality-Item
data2_wide$s_causality_1 <- rowSums(data2_wide[,c("s_causality_1_1","s_causality_1_2",
"s_causality_1_3","s_causality_1_4",
"s_causality_1_5","s_causality_1_6")])
describe(data2_wide$s_causality_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1989 -0.43 2.45 0 -0.52 2.97 -6 6 12 0.2 -0.56 0.06table(data2_wide$s_causality_1)##
## -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
## 8 7 297 112 295 173 493 116 266 68 121 14 19data2_wide$s_causality_2 <- rowSums(data2_wide[,c("s_causality_2_1","s_causality_2_2",
"s_causality_2_3","s_causality_2_4",
"s_causality_2_5","s_causality_2_6")])
describe(data2_wide$s_causality_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1995 -0.21 2.45 0 -0.29 2.97 -6 6 12 0.21 -0.31 0.05table(data2_wide$s_causality_2)##
## -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
## 8 7 247 100 267 173 541 149 263 59 131 9 41CAMA-Items
data2_wide$s_CAMA_1 <- rowSums(data2_wide[,c("s_CAMA_1_1","s_CAMA_1_2",
"s_CAMA_1_3","s_CAMA_1_4",
"s_CAMA_1_5","s_CAMA_1_6",
"s_CAMA_1_7","s_CAMA_1_8")])
describe(data2_wide$s_CAMA_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 984 0.7 2.66 0 0.68 2.97 -7 8 15 0.16 -0.02 0.08table(data2_wide$s_CAMA_1)##
## -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8
## 1 5 5 69 38 66 46 318 100 110 61 85 25 41 7 7data2_wide$s_CAMA_2 <- rowSums(data2_wide[,c("s_CAMA_2_1","s_CAMA_2_2",
"s_CAMA_2_3","s_CAMA_2_4")])
describe(data2_wide$s_CAMA_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1015 -0.4 1.62 0 -0.44 1.48 -4 4 8 0.18 0.05 0.05table(data2_wide$s_CAMA_2)##
## -4 -3 -2 -1 0 1 2 3 4
## 32 36 239 99 397 67 112 14 19describe(data2_wide$s_CAMA_3)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1026 -0.14 0.75 0 -0.17 1.48 -1 1 2 0.23 -1.21 0.02table(data2_wide$s_CAMA_3)##
## -1 0 1
## 369 428 229data2_wide$s_CAMA <- rowSums(data2_wide[,c("s_CAMA_1","s_CAMA_2","s_CAMA_3")])
describe(data2_wide$s_CAMA)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 975 0.17 3.73 0 0.17 2.97 -11 13 24 0.09 0.13 0.12table(data2_wide$s_CAMA)##
## -11 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9
## 1 4 1 50 26 43 25 49 68 93 230 89 50 67 49 55 23 26 8 12
## 10 11 12 13
## 1 3 1 1Melt Dataframe into Long Format
data2_long <- melt.data.table(setDT(data2_wide),measure.vars =
list(c("summary1","summary2"),
c("accessibility_1","accessibility_2"),
c("understanding_1","understanding_2"),
c("empowerment_1","empowerment_2"),
c("credibility_1","credibility_2"),
c("relevance_1","relevance_2"),
c("curiosity_1","curiosity_2"),
c("boredom_1","boredom_2"),
c("frustration_1","frustration_2"),
c("confusion_1","confusion_2"),
c("s_funding_1","s_funding_2"),
c("s_coi_1","s_coi_2"),
c("s_diff_1","s_diff_2"), c("s_causality_1","s_causality_2")),
value.name = c("summary","accessibility",
"understanding","empowerment",
"credibility","relevance",
"curiosity",
"boredom","frustration",
"confusion","s_funding", "s_coi","s_diff","s_causality"),
variable.name = "Time_point")
View(data2_long)
data2_long <- dplyr::select(data2_long, -c(15:260,263))
View(data2_long)
data2_long$summary <- factor(data2_long$summary, levels = c("Barth","Faerber"))User Experience Scale Generation
psych::alpha(data2_long[,c("accessibility","understanding","empowerment")])##
## Reliability analysis
## Call: psych::alpha(x = data2_long[, c("accessibility", "understanding",
## "empowerment")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.77 0.62 5 0.0046 5.3 1.5 0.66
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.82 0.83 0.84
## Duhachek 0.82 0.83 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## accessibility 0.79 0.79 0.66 0.66 3.8 0.0065 NA 0.66
## understanding 0.72 0.72 0.56 0.56 2.5 0.0089 NA 0.56
## empowerment 0.79 0.79 0.66 0.66 3.8 0.0065 NA 0.66
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## accessibility 4045 0.86 0.85 0.73 0.67 5.5 1.8
## understanding 4038 0.89 0.89 0.82 0.74 5.6 1.7
## empowerment 4039 0.85 0.85 0.73 0.67 4.8 1.8
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 8 miss
## accessibility 0.03 0.03 0.08 0.15 0.17 0.20 0.16 0.18 0.01
## understanding 0.02 0.03 0.07 0.14 0.19 0.22 0.17 0.15 0.01
## empowerment 0.06 0.06 0.12 0.19 0.22 0.19 0.09 0.08 0.01CFA User Experience
UEmodel <- "outcome =~ c(a)*accessibility + c(a)*understanding + c(a)*empowerment
accessibility ~~ c(b)*empowerment"
UEfit <- sem(UEmodel, data = data2_long, estimator = "MLR", missing = "ML",
std.lv = T, fixed.x = F, group = "summary")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
## 718 1417 1965 2985 3986## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
## 1975## Warning in lavaanify(model = FLAT, constraints = constraints, varTable = DataOV, : lavaan WARNING: using a single label per parameter in a multiple group
## setting implies imposing equality constraints across all the groups;
## If this is not intended, either remove the label(s), or use a vector
## of labels (one for each group);
## See the Multiple groups section in the man page of model.syntax.summary(UEfit, standardized = T)## lavaan 0.6.16 ended normally after 36 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 20
## Number of equality constraints 6
##
## Number of observations per group: Used Total
## Barth 2036 2041
## Faerber 2040 2041
## Number of missing patterns per group:
## Barth 6
## Faerber 7
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3.534 3.167
## Degrees of freedom 4 4
## P-value (Chi-square) 0.473 0.530
## Scaling correction factor 1.116
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## Barth 1.326 1.188
## Faerber 2.208 1.978
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [Barth]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## outcome =~
## accessblty (a) 1.428 0.018 78.985 0.000 1.428 0.782
## undrstndng (a) 1.428 0.018 78.985 0.000 1.428 0.834
## empowermnt (a) 1.428 0.018 78.985 0.000 1.428 0.789
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .accessibility ~~
## .empowermnt (b) -0.204 0.039 -5.276 0.000 -0.204 -0.161
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .accessibility 5.543 0.040 137.942 0.000 5.543 3.034
## .understanding 5.646 0.037 150.746 0.000 5.646 3.295
## .empowerment 4.839 0.040 121.599 0.000 4.839 2.674
## outcome 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .accessibility 1.299 0.072 18.046 0.000 1.299 0.389
## .understanding 0.896 0.057 15.604 0.000 0.896 0.305
## .empowerment 1.236 0.071 17.419 0.000 1.236 0.377
## outcome 1.000 1.000 1.000
##
##
## Group 2 [Faerber]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## outcome =~
## accessblty (a) 1.428 0.018 78.985 0.000 1.428 0.787
## undrstndng (a) 1.428 0.018 78.985 0.000 1.428 0.838
## empowermnt (a) 1.428 0.018 78.985 0.000 1.428 0.786
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .accessibility ~~
## .empowermnt (b) -0.204 0.039 -5.276 0.000 -0.204 -0.162
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .accessibility 5.528 0.041 135.085 0.000 5.528 3.045
## .understanding 5.528 0.038 143.776 0.000 5.528 3.242
## .empowerment 4.717 0.040 116.604 0.000 4.717 2.597
## outcome 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .accessibility 1.256 0.074 17.074 0.000 1.256 0.381
## .understanding 0.867 0.050 17.364 0.000 0.867 0.298
## .empowerment 1.261 0.068 18.628 0.000 1.261 0.382
## outcome 1.000 1.000 1.000modificationindices(UEfit)## lhs op rhs block group level mi epc sepc.lv
## 8 outcome ~~ outcome 1 1 1 1.962 -0.073 -1.000
## 20 outcome ~~ outcome 2 2 1 1.962 0.073 1.000
## 31 accessibility ~~ understanding 1 1 1 0.337 -0.034 -0.034
## 32 understanding ~~ empowerment 1 1 1 0.425 -0.038 -0.038
## 33 accessibility ~~ understanding 2 2 1 2.234 0.086 0.086
## 34 understanding ~~ empowerment 2 2 1 0.072 -0.016 -0.016
## sepc.all sepc.nox
## 8 -1.000 -1.000
## 20 1.000 1.000
## 31 -0.031 -0.031
## 32 -0.036 -0.036
## 33 0.083 0.083
## 34 -0.015 -0.015fitmeasures(UEfit)## npar fmin
## 14.000 0.000
## chisq df
## 3.534 4.000
## pvalue chisq.scaled
## 0.473 3.167
## df.scaled pvalue.scaled
## 4.000 0.530
## chisq.scaling.factor baseline.chisq
## 1.116 4732.569
## baseline.df baseline.pvalue
## 6.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 2413.601 6.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 1.961
## cfi tli
## 1.000 1.000
## cfi.scaled tli.scaled
## 1.000 1.001
## cfi.robust tli.robust
## 1.000 1.000
## nnfi rfi
## 1.000 0.999
## nfi pnfi
## 0.999 0.666
## ifi rni
## 1.000 1.000
## nnfi.scaled rfi.scaled
## 1.001 0.998
## nfi.scaled pnfi.scaled
## 0.999 0.666
## ifi.scaled rni.scaled
## 1.000 1.000
## nnfi.robust rni.robust
## 1.000 1.000
## logl unrestricted.logl
## -21827.855 -21826.088
## aic bic
## 43683.710 43772.090
## ntotal bic2
## 4076.000 43727.604
## scaling.factor.h1 scaling.factor.h0
## 1.245 0.898
## rmsea rmsea.ci.lower
## 0.000 0.000
## rmsea.ci.upper rmsea.ci.level
## 0.032 0.900
## rmsea.pvalue rmsea.close.h0
## 0.999 0.050
## rmsea.notclose.pvalue rmsea.notclose.h0
## 0.000 0.080
## rmsea.scaled rmsea.ci.lower.scaled
## 0.000 0.000
## rmsea.ci.upper.scaled rmsea.pvalue.scaled
## 0.029 1.000
## rmsea.notclose.pvalue.scaled rmsea.robust
## 0.000 0.000
## rmsea.ci.lower.robust rmsea.ci.upper.robust
## 0.000 0.032
## rmsea.pvalue.robust rmsea.notclose.pvalue.robust
## 0.999 0.000
## rmr rmr_nomean
## 0.062 0.076
## srmr srmr_bentler
## 0.020 0.020
## srmr_bentler_nomean crmr
## 0.024 0.006
## crmr_nomean srmr_mplus
## 0.009 0.027
## srmr_mplus_nomean cn_05
## 0.019 10943.881
## cn_01 gfi
## 15313.979 1.000
## agfi pgfi
## 1.000 0.222
## mfi ecvi
## 1.000 0.008Descriptive Analyses
Participants’ Gender
table(data2_wide$s_sex)##
## female male
## 1028 1013prop.table(table(data2_wide$s_sex))##
## female male
## 0.5036747 0.4963253Participants’ Age
describe(data2_wide$s_age)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2040 45.22 15.23 45 45.01 17.79 18 90 72 0.12 -0.96 0.34age.hist <- ggplot(data2_wide, aes(s_age)) + geom_histogram(colour = "black",
fill = "white")+
labs(x = "Age", y = "Frequency")
age.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (`stat_bin()`).
data2_wide$age_group <- ifelse(data2_wide$s_age < 45, "low", "high")
data2_wide$age_group <- as.factor(data2_wide$age_group)
table(data2_wide$age_group)##
## high low
## 1024 1016prop.table(table(data2_wide$age_group))##
## high low
## 0.5019608 0.4980392Participant’s Educational Background
table(data2_wide$s_school)##
## Haupt Real Abi
## 685 681 675prop.table(table(data2_wide$s_school))##
## Haupt Real Abi
## 0.3356198 0.3336600 0.3307202Quota
table(data2_wide$quota)##
## 1 2 3 4 5 6 7 8 9 10 11 12
## 169 171 174 168 171 172 168 167 164 170 172 175Awareness Check
table(data2_wide$s_awareness)##
## fail pass
## 658 1383prop.table(table(data2_wide$s_awareness))##
## fail pass
## 0.322391 0.677609table(data2_wide$s_awareness, data2_wide$condition)##
## 1 2 3 4 5 6
## fail 113 94 116 135 90 110
## pass 221 251 220 206 238 247awareness_bar <- ggplot(data2_wide, aes(x = condition, fill = s_awareness))
awareness_bar <- awareness_bar + geom_bar() + theme_classic() + theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title = element_text(face = "bold"),
text = element_text(face = "bold"),
axis.text = element_text(face = "bold"),
legend.title = element_text(face = "bold"))+
labs(x = "Condition", y = "Number of Cases", fill = "Awareness Check") +
scale_fill_brewer(palette = "Blues")
awareness_bar
CrossTable(data2_wide$condition, data2_wide$s_awareness,
chisq = TRUE, expected = TRUE, sresid = TRUE, format = "SPSS")##
## Cell Contents
## |-------------------------|
## | Count |
## | Expected Values |
## | Chi-square contribution |
## | Row Percent |
## | Column Percent |
## | Total Percent |
## | Std Residual |
## |-------------------------|
##
## Total Observations in Table: 2041
##
## | data2_wide$s_awareness
## data2_wide$condition | fail | pass | Row Total |
## ---------------------|-----------|-----------|-----------|
## 1 | 113 | 221 | 334 |
## | 107.679 | 226.321 | |
## | 0.263 | 0.125 | |
## | 33.832% | 66.168% | 16.365% |
## | 17.173% | 15.980% | |
## | 5.537% | 10.828% | |
## | 0.513 | -0.354 | |
## ---------------------|-----------|-----------|-----------|
## 2 | 94 | 251 | 345 |
## | 111.225 | 233.775 | |
## | 2.668 | 1.269 | |
## | 27.246% | 72.754% | 16.903% |
## | 14.286% | 18.149% | |
## | 4.606% | 12.298% | |
## | -1.633 | 1.127 | |
## ---------------------|-----------|-----------|-----------|
## 3 | 116 | 220 | 336 |
## | 108.323 | 227.677 | |
## | 0.544 | 0.259 | |
## | 34.524% | 65.476% | 16.463% |
## | 17.629% | 15.907% | |
## | 5.683% | 10.779% | |
## | 0.738 | -0.509 | |
## ---------------------|-----------|-----------|-----------|
## 4 | 135 | 206 | 341 |
## | 109.935 | 231.065 | |
## | 5.715 | 2.719 | |
## | 39.589% | 60.411% | 16.707% |
## | 20.517% | 14.895% | |
## | 6.614% | 10.093% | |
## | 2.391 | -1.649 | |
## ---------------------|-----------|-----------|-----------|
## 5 | 90 | 238 | 328 |
## | 105.744 | 222.256 | |
## | 2.344 | 1.115 | |
## | 27.439% | 72.561% | 16.071% |
## | 13.678% | 17.209% | |
## | 4.410% | 11.661% | |
## | -1.531 | 1.056 | |
## ---------------------|-----------|-----------|-----------|
## 6 | 110 | 247 | 357 |
## | 115.094 | 241.906 | |
## | 0.225 | 0.107 | |
## | 30.812% | 69.188% | 17.491% |
## | 16.717% | 17.860% | |
## | 5.390% | 12.102% | |
## | -0.475 | 0.327 | |
## ---------------------|-----------|-----------|-----------|
## Column Total | 658 | 1383 | 2041 |
## | 32.239% | 67.761% | |
## ---------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 17.35328 d.f. = 5 p = 0.003876309
##
##
##
## Minimum expected frequency: 105.7442fisher.test(data2_wide$condition, data2_wide$s_awareness, workspace = 2e8)##
## Fisher's Exact Test for Count Data
##
## data: data2_wide$condition and data2_wide$s_awareness
## p-value = 0.004063
## alternative hypothesis: two.sidedCrossTable(data2_wide$s_sex, data2_wide$s_awareness,
chisq = TRUE, expected = TRUE, sresid = TRUE, format = "SPSS")##
## Cell Contents
## |-------------------------|
## | Count |
## | Expected Values |
## | Chi-square contribution |
## | Row Percent |
## | Column Percent |
## | Total Percent |
## | Std Residual |
## |-------------------------|
##
## Total Observations in Table: 2041
##
## | data2_wide$s_awareness
## data2_wide$s_sex | fail | pass | Row Total |
## -----------------|-----------|-----------|-----------|
## female | 301 | 727 | 1028 |
## | 331.418 | 696.582 | |
## | 2.792 | 1.328 | |
## | 29.280% | 70.720% | 50.367% |
## | 45.745% | 52.567% | |
## | 14.748% | 35.620% | |
## | -1.671 | 1.153 | |
## -----------------|-----------|-----------|-----------|
## male | 357 | 656 | 1013 |
## | 326.582 | 686.418 | |
## | 2.833 | 1.348 | |
## | 35.242% | 64.758% | 49.633% |
## | 54.255% | 47.433% | |
## | 17.491% | 32.141% | |
## | 1.683 | -1.161 | |
## -----------------|-----------|-----------|-----------|
## Column Total | 658 | 1383 | 2041 |
## | 32.239% | 67.761% | |
## -----------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 8.30114 d.f. = 1 p = 0.003962019
##
## Pearson's Chi-squared test with Yates' continuity correction
## ------------------------------------------------------------
## Chi^2 = 8.030481 d.f. = 1 p = 0.004599664
##
##
## Minimum expected frequency: 326.5821fisher.test(data2_wide$s_sex, data2_wide$s_awareness, workspace = 2e8)##
## Fisher's Exact Test for Count Data
##
## data: data2_wide$s_sex and data2_wide$s_awareness
## p-value = 0.004472
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.6287428 0.9204853
## sample estimates:
## odds ratio
## 0.7609049data2_wide$s_age_1 <- ifelse(data2_wide$s_age < 45, 0, 1)
age_hist <- ggplot(data2_wide, aes(x = s_age, fill = s_awareness, color = s_awareness))
age_hist <- age_hist + geom_histogram(alpha = 0.1, position = "identity") + theme_classic() + theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title = element_text(face = "bold"),
text = element_text(face = "bold"),
axis.text = element_text(face = "bold"),
legend.title = element_text(face = "bold"))+
labs(x = "Age", y = "Number of Cases", fill = "Awareness Check", color = "Awareness Check") +
scale_fill_brewer(palette = "Dark2") + scale_color_brewer(palette = "Dark2")
age_hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (`stat_bin()`).
CrossTable(data2_wide$s_age_1, data2_wide$s_awareness,
chisq = TRUE, expected = TRUE, sresid = TRUE, format = "SPSS")##
## Cell Contents
## |-------------------------|
## | Count |
## | Expected Values |
## | Chi-square contribution |
## | Row Percent |
## | Column Percent |
## | Total Percent |
## | Std Residual |
## |-------------------------|
##
## Total Observations in Table: 2040
##
## | data2_wide$s_awareness
## data2_wide$s_age_1 | fail | pass | Row Total |
## -------------------|-----------|-----------|-----------|
## 0 | 411 | 605 | 1016 |
## | 327.710 | 688.290 | |
## | 21.169 | 10.079 | |
## | 40.453% | 59.547% | 49.804% |
## | 62.462% | 43.777% | |
## | 20.147% | 29.657% | |
## | 4.601 | -3.175 | |
## -------------------|-----------|-----------|-----------|
## 1 | 247 | 777 | 1024 |
## | 330.290 | 693.710 | |
## | 21.004 | 10.000 | |
## | 24.121% | 75.879% | 50.196% |
## | 37.538% | 56.223% | |
## | 12.108% | 38.088% | |
## | -4.583 | 3.162 | |
## -------------------|-----------|-----------|-----------|
## Column Total | 658 | 1382 | 2040 |
## | 32.255% | 67.745% | |
## -------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 62.25162 d.f. = 1 p = 3.022599e-15
##
## Pearson's Chi-squared test with Yates' continuity correction
## ------------------------------------------------------------
## Chi^2 = 61.50646 d.f. = 1 p = 4.412932e-15
##
##
## Minimum expected frequency: 327.7098fisher.test(data2_wide$s_age_1, data2_wide$s_awareness, workspace = 2e8)##
## Fisher's Exact Test for Count Data
##
## data: data2_wide$s_age_1 and data2_wide$s_awareness
## p-value = 2.97e-15
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1.759109 2.597653
## sample estimates:
## odds ratio
## 2.136236school_bar <- ggplot(data2_wide,aes(x = s_school, fill = s_awareness))
school_bar <- school_bar + geom_bar() + theme_classic() + theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title = element_text(face = "bold"),
text = element_text(face = "bold"),
axis.text = element_text(face = "bold"),
legend.title = element_text(face = "bold"))+
labs(x = "Schooltype", y = "Number of Cases", fill = "Awareness Check") +
scale_fill_brewer(palette = "Blues")
school_bar
CrossTable(data2_wide$s_school, data2_wide$s_awareness,
chisq = TRUE, expected = TRUE, sresid = TRUE, format = "SPSS")##
## Cell Contents
## |-------------------------|
## | Count |
## | Expected Values |
## | Chi-square contribution |
## | Row Percent |
## | Column Percent |
## | Total Percent |
## | Std Residual |
## |-------------------------|
##
## Total Observations in Table: 2041
##
## | data2_wide$s_awareness
## data2_wide$s_school | fail | pass | Row Total |
## --------------------|-----------|-----------|-----------|
## Haupt | 270 | 415 | 685 |
## | 220.838 | 464.162 | |
## | 10.944 | 5.207 | |
## | 39.416% | 60.584% | 33.562% |
## | 41.033% | 30.007% | |
## | 13.229% | 20.333% | |
## | 3.308 | -2.282 | |
## --------------------|-----------|-----------|-----------|
## Real | 220 | 461 | 681 |
## | 219.548 | 461.452 | |
## | 0.001 | 0.000 | |
## | 32.305% | 67.695% | 33.366% |
## | 33.435% | 33.333% | |
## | 10.779% | 22.587% | |
## | 0.030 | -0.021 | |
## --------------------|-----------|-----------|-----------|
## Abi | 168 | 507 | 675 |
## | 217.614 | 457.386 | |
## | 11.312 | 5.382 | |
## | 24.889% | 75.111% | 33.072% |
## | 25.532% | 36.659% | |
## | 8.231% | 24.841% | |
## | -3.363 | 2.320 | |
## --------------------|-----------|-----------|-----------|
## Column Total | 658 | 1383 | 2041 |
## | 32.239% | 67.761% | |
## --------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 32.84601 d.f. = 2 p = 7.371908e-08
##
##
##
## Minimum expected frequency: 217.6139fisher.test(data2_wide$s_school, data2_wide$s_awareness, workspace = 2e8)##
## Fisher's Exact Test for Count Data
##
## data: data2_wide$s_school and data2_wide$s_awareness
## p-value = 6.61e-08
## alternative hypothesis: two.sidedAccessibility
describe(data2_wide$accessibility_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2029 5.64 1.79 6 5.77 1.48 1 8 7 -0.45 -0.52 0.04describe(data2_wide$accessibility_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2016 5.43 1.86 6 5.54 1.48 1 8 7 -0.4 -0.55 0.04dep.access.test <- wilcox.test(data2_wide$accessibility_1,
data2_wide$accessibility_2,
paired = TRUE,
correct = TRUE)
dep.access.test##
## Wilcoxon signed rank test with continuity correction
##
## data: data2_wide$accessibility_1 and data2_wide$accessibility_2
## V = 486685, p-value = 1.394e-09
## alternative hypothesis: true location shift is not equal to 0data2_wide$accessibility <- rowMeans(data2_wide[,c("accessibility_1",
"accessibility_2")])
describe(data2_wide$accessibility)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2005 5.54 1.64 5.5 5.61 1.48 1 8 7 -0.37 -0.43 0.04access.hist <- ggplot(data2_wide, aes(accessibility)) +
geom_histogram(colour = "black", fill = "white") + labs(x = "Mean Accessibility",
y = "Frequency")
access.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 36 rows containing non-finite values (`stat_bin()`).
Understanding
describe(data2_wide$understanding_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2021 5.78 1.66 6 5.89 1.48 1 8 7 -0.47 -0.36 0.04describe(data2_wide$understanding_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2017 5.41 1.73 6 5.49 1.48 1 8 7 -0.42 -0.34 0.04dep.understand.test <- wilcox.test(data2_wide$understanding_1,
data2_wide$understanding_2,
paired = TRUE,
correct = TRUE)
dep.understand.test##
## Wilcoxon signed rank test with continuity correction
##
## data: data2_wide$understanding_1 and data2_wide$understanding_2
## V = 571930, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0data2_wide$understanding <- rowMeans(data2_wide[,c("understanding_1",
"understanding_2")])
describe(data2_wide$understanding)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1998 5.6 1.5 5.5 5.66 1.48 1 8 7 -0.4 -0.28 0.03understand.hist <- ggplot(data2_wide, aes(understanding)) +
geom_histogram(colour = "black", fill = "white") + labs(x = "Mean Understanding",
y = "Frequency")
understand.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 43 rows containing non-finite values (`stat_bin()`).
Empowerment
describe(data2_wide$empowerment_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2018 4.85 1.8 5 4.89 1.48 1 8 7 -0.19 -0.48 0.04describe(data2_wide$empowerment_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2021 4.71 1.82 5 4.75 1.48 1 8 7 -0.18 -0.48 0.04dep.emp.test <- wilcox.test(data2_wide$empowerment_1,
data2_wide$empowerment_2,
paired = TRUE,
correct = TRUE)
dep.emp.test##
## Wilcoxon signed rank test with continuity correction
##
## data: data2_wide$empowerment_1 and data2_wide$empowerment_2
## V = 475021, p-value = 0.0001433
## alternative hypothesis: true location shift is not equal to 0data2_wide$empowerment <- rowMeans(data2_wide[,c("empowerment_1",
"empowerment_2")])
describe(data2_wide$empowerment)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2000 4.77 1.63 5 4.81 1.48 1 8 7 -0.18 -0.35 0.04empower.hist <- ggplot(data2_wide, aes(empowerment)) +
geom_histogram(colour = "black", fill = "white") + labs(x = "Mean Empowerment",
y = "Frequency")
empower.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 41 rows containing non-finite values (`stat_bin()`).
Credibility
data2_wide$credibility <- rowMeans(data2_wide[,c("credibility_1",
"credibility_2")])
describe(data2_wide$credibility_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2020 5.91 1.52 6 5.97 1.48 1 8 7 -0.35 -0.48 0.03describe(data2_wide$credibility_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2018 5.77 1.58 6 5.85 1.48 1 8 7 -0.43 -0.17 0.04dep.credible.test <- wilcox.test(data2_wide$credibility_1,
data2_wide$credibility_2,
paired = TRUE,
correct = TRUE)
dep.credible.test##
## Wilcoxon signed rank test with continuity correction
##
## data: data2_wide$credibility_1 and data2_wide$credibility_2
## V = 391005, p-value = 0.0001058
## alternative hypothesis: true location shift is not equal to 0describe(data2_wide$credibility)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1997 5.84 1.37 6 5.87 1.48 1 8 7 -0.25 -0.47 0.03credible.hist <- ggplot(data2_wide, aes(credibility)) +
geom_histogram(colour = "black", fill = "white") + labs(x = "Mean Credibility",
y = "Frequency")
credible.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 44 rows containing non-finite values (`stat_bin()`).
Relevance
data2_wide$relevance <- rowMeans(data2_wide[,c("relevance_1","relevance_2")])
describe(data2_wide$relevance_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2024 6.33 1.58 7 6.51 1.48 1 8 7 -0.78 0.06 0.04describe(data2_wide$relevance_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2019 6.19 1.67 6 6.37 1.48 1 8 7 -0.8 0.15 0.04dep.relevance.test <- wilcox.test(data2_wide$relevance_1,
data2_wide$relevance_2,
paired = TRUE,
correct = TRUE)
dep.relevance.test##
## Wilcoxon signed rank test with continuity correction
##
## data: data2_wide$relevance_1 and data2_wide$relevance_2
## V = 383964, p-value = 5.793e-05
## alternative hypothesis: true location shift is not equal to 0describe(data2_wide$relevance)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2003 6.26 1.42 6.5 6.39 1.48 1 8 7 -0.62 -0.14 0.03relevance.hist <- ggplot(data2_wide, aes(relevance)) +
geom_histogram(colour = "black", fill = "white") + labs(x = "Mean Relevance",
y = "Frequency")
relevance.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 38 rows containing non-finite values (`stat_bin()`).
Curiosity
data2_wide$curiosity <- rowMeans(data2_wide[,c("curiosity_1",
"curiosity_2")])
describe(data2_wide$curiosity_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2030 3.32 1.1 3 3.36 1.48 1 5 4 -0.29 -0.58 0.02describe(data2_wide$curiosity_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2025 3.16 1.15 3 3.18 1.48 1 5 4 -0.12 -0.77 0.03dep.curiosity.test <- wilcox.test(data2_wide$curiosity_1,
data2_wide$curiosity_2,
paired = TRUE,
correct = TRUE)
dep.curiosity.test##
## Wilcoxon signed rank test with continuity correction
##
## data: data2_wide$curiosity_1 and data2_wide$curiosity_2
## V = 313831, p-value = 1.291e-12
## alternative hypothesis: true location shift is not equal to 0describe(data2_wide$curiosity)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2014 3.24 1 3 3.25 1.48 1 5 4 -0.12 -0.54 0.02curiosity.hist <- ggplot(data2_wide, aes(curiosity)) +
geom_histogram(colour = "black", fill = "white") + labs(x = "Mean Curiosity",
y = "Frequency")
curiosity.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 27 rows containing non-finite values (`stat_bin()`).
Boredom
data2_wide$boredom <- rowMeans(data2_wide[,c("boredom_1",
"boredom_2")])
describe(data2_wide$boredom_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2023 2.04 1.09 2 1.89 1.48 1 5 4 0.82 -0.08 0.02describe(data2_wide$boredom_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2022 2.04 1.14 2 1.88 1.48 1 5 4 0.87 -0.11 0.03dep.boredom.test <- wilcox.test(data2_wide$boredom_1,
data2_wide$boredom_2,
paired = TRUE,
correct = TRUE)
dep.boredom.test##
## Wilcoxon signed rank test with continuity correction
##
## data: data2_wide$boredom_1 and data2_wide$boredom_2
## V = 181847, p-value = 0.6904
## alternative hypothesis: true location shift is not equal to 0describe(data2_wide$boredom)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2006 2.04 0.99 2 1.92 1.48 1 5 4 0.81 0.12 0.02boredom.hist <- ggplot(data2_wide, aes(boredom)) +
geom_histogram(colour = "black", fill = "white") + labs(x = "Mean Boredom",
y = "Frequency")
boredom.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 35 rows containing non-finite values (`stat_bin()`).
Confusion
data2_wide$confusion <- rowMeans(data2_wide[,c("confusion_1",
"confusion_2")])
describe(data2_wide$confusion_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2025 2.1 1.05 2 1.97 1.48 1 5 4 0.67 -0.25 0.02describe(data2_wide$confusion_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2024 2.15 1.1 2 2.02 1.48 1 5 4 0.69 -0.3 0.02dep.confusion.test <- wilcox.test(data2_wide$confusion_1,
data2_wide$confusion_2,
paired = TRUE,
correct = TRUE)
dep.confusion.test##
## Wilcoxon signed rank test with continuity correction
##
## data: data2_wide$confusion_1 and data2_wide$confusion_2
## V = 246237, p-value = 0.1284
## alternative hypothesis: true location shift is not equal to 0describe(data2_wide$confusion)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2008 2.12 0.93 2 2.03 0.74 1 5 4 0.61 -0.17 0.02confusion.hist <- ggplot(data2_wide, aes(confusion)) +
geom_histogram(colour = "black", fill = "white") + labs(x = "Mean Confusion",
y = "Frequency")
confusion.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 33 rows containing non-finite values (`stat_bin()`).
Frustration
data2_wide$frustration <- rowMeans(data2_wide[,c("frustration_1",
"frustration_2")])
describe(data2_wide$frustration_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2020 1.72 1 1 1.55 0 1 5 4 1.22 0.62 0.02describe(data2_wide$frustration_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2024 1.8 1.07 1 1.62 0 1 5 4 1.2 0.58 0.02dep.frustration.test <- wilcox.test(data2_wide$frustration_1,
data2_wide$frustration_2,
paired = TRUE,
correct = TRUE)
dep.frustration.test##
## Wilcoxon signed rank test with continuity correction
##
## data: data2_wide$frustration_1 and data2_wide$frustration_2
## V = 122672, p-value = 0.001244
## alternative hypothesis: true location shift is not equal to 0describe(data2_wide$frustration)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2004 1.76 0.91 1.5 1.62 0.74 1 5 4 1.11 0.55 0.02frustration.hist <- ggplot(data2_wide, aes(frustration)) +
geom_histogram(colour = "black", fill = "white") + labs(x = "Mean Frustration",
y = "Frequency")
frustration.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 37 rows containing non-finite values (`stat_bin()`).
Relationship Knowledge-Item
describe(data2_wide$s_relationship)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1979 0.23 3.22 0 0.02 2.97 -8 8 16 0.45 -0.37 0.07table(data2_wide$s_relationship)##
## -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8
## 4 1 20 20 247 110 251 163 425 112 192 67 141 30 135 17 44relationship.hist <- ggplot(data2_wide, aes(s_relationship)) +
geom_histogram(colour = "black", fill = "white") + labs(
x = "Mean Relationship Knowledge Score", y = "Frequency") +
scale_x_continuous(breaks = seq(-8,8,1))
relationship.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 62 rows containing non-finite values (`stat_bin()`).
Extent of Evaluation Knowledge-Item
describe(data2_wide$s_extent)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1999 0.52 2.28 0 0.41 2.97 -6 6 12 0.35 -0.28 0.05extent.hist <- ggplot(data2_wide, aes(s_extent)) +
geom_histogram(colour = "black", fill = "white") + labs(
x = "Mean Extent Knowledge Score",
y = "Frequency") +
scale_x_continuous(breaks = seq(-6,6,1))
extent.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 42 rows containing non-finite values (`stat_bin()`).
Differentiation Knowledge-Item
data2_wide$s_diff <- coalesce(data2_wide$s_diff_1,data2_wide$s_diff_2)
describe(data2_wide$s_diff_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 997 0.07 1.83 0 0.05 1.48 -6 6 12 0.14 0.68 0.06describe(data2_wide$s_diff_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 981 0.52 2.07 0 0.46 2.97 -6 6 12 0.15 0.23 0.07dep.diff.test <- wilcox.test(data2_wide$s_diff_1,
data2_wide$s_diff_2,
paired = FALSE,
correct = TRUE)
dep.diff.test##
## Wilcoxon rank sum test with continuity correction
##
## data: data2_wide$s_diff_1 and data2_wide$s_diff_2
## W = 428088, p-value = 8.067e-07
## alternative hypothesis: true location shift is not equal to 0describe(data2_wide$s_diff)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1978 0.29 1.97 0 0.23 1.48 -6 6 12 0.19 0.46 0.04diff.hist <- ggplot(data2_wide, aes(s_diff)) +
geom_histogram(colour = "black", fill = "white") + labs(
x = "Mean Differentiation Knowledge Score",
y = "Frequency") +
scale_x_continuous(breaks = seq(-6,6,1))
diff.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 63 rows containing non-finite values (`stat_bin()`).
Funding Knowledge-Item
data2_wide$s_funding <- rowSums(data2_wide[,c("s_funding_1",
"s_funding_2")])
describe(data2_wide$s_funding_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1994 0.87 3.05 0 0.84 2.97 -6 6 12 0.26 -0.74 0.07describe(data2_wide$s_funding_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2010 1.67 3.22 1 1.82 4.45 -6 6 12 -0.07 -1.03 0.07dep.funding.test <- wilcox.test(data2_wide$s_funding_1,
data2_wide$s_funding_2,
paired = TRUE,
correct = TRUE)
dep.funding.test##
## Wilcoxon signed rank test with continuity correction
##
## data: data2_wide$s_funding_1 and data2_wide$s_funding_2
## V = 321887, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0describe(data2_wide$s_funding)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1965 2.55 5.42 2 2.53 5.93 -12 12 24 0.07 -0.74 0.12funding.hist <- ggplot(data2_wide, aes(s_funding)) +
geom_histogram(colour = "black", fill = "white") + labs(
x = "Mean Funding Knowledge Score",
y = "Frequency") +
scale_x_continuous(breaks = seq(-12,12,1))
funding.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 76 rows containing non-finite values (`stat_bin()`).
COI Knowledge-Item
data2_wide$s_coi <- rowSums(data2_wide[,c("s_coi_1",
"s_coi_2")])
describe(data2_wide$s_coi_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1955 0.72 3.33 0 0.66 2.97 -7 7 14 0.19 -0.5 0.08describe(data2_wide$s_coi_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1993 1.3 3.56 1 1.36 2.97 -7 7 14 -0.01 -0.75 0.08dep.coi.test <- wilcox.test(data2_wide$s_coi_1,
data2_wide$s_coi_2,
paired = TRUE,
correct = TRUE)
dep.coi.test##
## Wilcoxon signed rank test with continuity correction
##
## data: data2_wide$s_coi_1 and data2_wide$s_coi_2
## V = 401472, p-value = 1.122e-12
## alternative hypothesis: true location shift is not equal to 0describe(data2_wide$s_coi)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1914 2.02 5.88 1 2.13 5.93 -13 14 27 -0.05 -0.59 0.13coi.hist <- ggplot(data2_wide, aes(s_coi)) +
geom_histogram(colour = "black", fill = "white") + labs(
x = "Mean COI Knowledge Score",
y = "Frequency") +
scale_x_continuous(breaks = seq(-14,14,1))
coi.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 127 rows containing non-finite values (`stat_bin()`).
Causality Knowledge-Item
data2_wide$s_causality <- rowSums(data2_wide[,c("s_causality_1",
"s_causality_2")])
describe(data2_wide$s_causality_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1989 -0.43 2.45 0 -0.52 2.97 -6 6 12 0.2 -0.56 0.06describe(data2_wide$s_causality_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1995 -0.21 2.45 0 -0.29 2.97 -6 6 12 0.21 -0.31 0.05dep.causality.test <- wilcox.test(data2_wide$s_causality_1,
data2_wide$s_causality_2,
paired = TRUE,
correct = TRUE)
dep.causality.test##
## Wilcoxon signed rank test with continuity correction
##
## data: data2_wide$s_causality_1 and data2_wide$s_causality_2
## V = 486523, p-value = 0.0008355
## alternative hypothesis: true location shift is not equal to 0describe(data2_wide$s_causality)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1949 -0.64 3.9 0 -0.67 2.97 -12 12 24 0.1 -0.15 0.09causality.hist <- ggplot(data2_wide, aes(s_causality)) +
geom_histogram(colour = "black", fill = "white") + labs(
x = "Mean Causality Knowledge Score",
y = "Frequency") +
scale_x_continuous(breaks = seq(-12,12,1))
causality.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 92 rows containing non-finite values (`stat_bin()`).
CAMA Knowledge-Items
describe(data2_wide$s_CAMA_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 984 0.7 2.66 0 0.68 2.97 -7 8 15 0.16 -0.02 0.08describe(data2_wide$s_CAMA_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1015 -0.4 1.62 0 -0.44 1.48 -4 4 8 0.18 0.05 0.05describe(data2_wide$s_CAMA_3)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1026 -0.14 0.75 0 -0.17 1.48 -1 1 2 0.23 -1.21 0.02describe(data2_wide$s_CAMA)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 975 0.17 3.73 0 0.17 2.97 -11 13 24 0.09 0.13 0.12CAMA1.hist <- ggplot(data2_wide, aes(s_CAMA_1)) +
geom_histogram(colour = "black", fill = "white") + labs(
x = "Mean CAMA Knowledge Score",
y = "Frequency") +
scale_x_continuous(breaks = seq(-8,8,1))
CAMA1.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1057 rows containing non-finite values (`stat_bin()`).
CAMA2.hist <- ggplot(data2_wide, aes(s_CAMA_2)) +
geom_histogram(colour = "black", fill = "white") + labs(
x = "Mean CAMA Knowledge Score",
y = "Frequency") +
scale_x_continuous(breaks = seq(-4,4,1))
CAMA2.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1026 rows containing non-finite values (`stat_bin()`).
CAMA3.hist <- ggplot(data2_wide, aes(s_CAMA_3)) +
geom_histogram(colour = "black", fill = "white") + labs(
x = "Mean CAMA Knowledge Score",
y = "Frequency") +
scale_x_continuous(breaks = seq(-1,1,1))
CAMA3.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1015 rows containing non-finite values (`stat_bin()`).
CAMA_oa.hist <- ggplot(data2_wide, aes(s_CAMA)) +
geom_histogram(colour = "black", fill = "white") + labs(
x = "Mean CAMA Knowledge Score",
y = "Frequency") +
scale_x_continuous(breaks = seq(-13,13,1))
CAMA_oa.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1066 rows containing non-finite values (`stat_bin()`).
Hypotheses-Testing
H1
H1a
H1a <- subset(data2_wide, condition == 2|condition == 4|condition == 6)
View(H1a)
describeBy(H1a$s_relationship,H1a$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 346 0.06 2.91 0 -0.05 2.97 -8 8 16 0.28 -0.22 0.16
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 668 0.39 3.45 0 0.16 2.97 -8 8 16 0.48 -0.53 0.13wilcox.test(s_relationship~disclaimer, data = H1a, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_relationship by disclaimer
## W = 112365, p-value = 0.4656
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.622830e-05 2.762469e-05
## sample estimates:
## difference in location
## -6.597433e-05H1a post hoc
H1a_1 <- subset(data2_wide, condition ==2| condition == 6)
View(H1a_1)
describeBy(H1a_1$s_relationship, H1a_1$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 346 0.06 2.91 0 -0.05 2.97 -8 8 16 0.28 -0.22 0.16
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 336 0.24 3.44 0 -0.04 2.97 -6 8 14 0.57 -0.55 0.19wilcox.test(s_relationship~disclaimer, data = H1a_1, exaxct = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_relationship by disclaimer
## W = 58323, p-value = 0.9392
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.084067e-05 5.691002e-05
## sample estimates:
## difference in location
## 2.295351e-05H1a_2 <- subset(data2_wide, condition ==4| condition == 6)
View(H1a_2)
describeBy(H1a_2$s_relationship, H1a_2$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 346 0.06 2.91 0 -0.05 2.97 -8 8 16 0.28 -0.22 0.16
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 332 0.53 3.45 0 0.37 2.97 -8 8 16 0.4 -0.5 0.19wilcox.test(s_relationship~disclaimer, data = H1a_2, exaxct = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_relationship by disclaimer
## W = 54042, p-value = 0.1789
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -9.999601e-01 5.534757e-05
## sample estimates:
## difference in location
## -2.700176e-05H1b
H1b <- subset(data2_wide, condition == 1|condition == 2|condition == 3|
condition == 4)
View(H1b)
describeBy(H1b$s_relationship,H1b$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 646 0.07 3.08 0 -0.08 2.97 -8 8 16 0.34 -0.37 0.12
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 668 0.39 3.45 0 0.16 2.97 -8 8 16 0.48 -0.53 0.13wilcox.test(s_relationship~disclaimer, data = H1b, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_relationship by disclaimer
## W = 208967, p-value = 0.3191
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.794751e-05 3.878243e-05
## sample estimates:
## difference in location
## -6.124734e-06H1b post hoc
H1b_1 <- subset(data2_wide, condition == 1|condition == 2)
View(H1b_1)
describeBy(H1b_1$s_relationship,H1b_1$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 322 0.22 3.11 0 0.12 2.97 -8 8 16 0.18 -0.53 0.17
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 336 0.24 3.44 0 -0.04 2.97 -6 8 14 0.57 -0.55 0.19wilcox.test(s_relationship~disclaimer, data = H1b_1, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_relationship by disclaimer
## W = 55565, p-value = 0.5441
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -6.470564e-05 9.999129e-01
## sample estimates:
## difference in location
## 1.755468e-05H1b_2 <- subset(data2_wide, condition == 3|condition == 4)
View(H1b_2)
describeBy(H1b_2$s_relationship,H1b_2$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 324 -0.08 3.06 0 -0.29 2.97 -6 8 14 0.5 -0.17 0.17
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 332 0.53 3.45 0 0.37 2.97 -8 8 16 0.4 -0.5 0.19wilcox.test(s_relationship~disclaimer, data = H1b_2, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_relationship by disclaimer
## W = 48896, p-value = 0.04208
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -9.999832e-01 -7.580566e-05
## sample estimates:
## difference in location
## -2.674227e-05H1b_3 <- subset(data2_wide, condition == 1|condition == 4)
View(H1b_3)
describeBy(H1b_3$s_relationship,H1b_3$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 322 0.22 3.11 0 0.12 2.97 -8 8 16 0.18 -0.53 0.17
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 332 0.53 3.45 0 0.37 2.97 -8 8 16 0.4 -0.5 0.19wilcox.test(s_relationship~disclaimer, data = H1b_3, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_relationship by disclaimer
## W = 51756, p-value = 0.4789
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -9.999557e-01 3.933794e-05
## sample estimates:
## difference in location
## -8.397794e-06H1b_4 <- subset(data2_wide, condition == 2|condition == 3)
View(H1b_4)
describeBy(H1b_4$s_relationship,H1b_4$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 324 -0.08 3.06 0 -0.29 2.97 -6 8 14 0.5 -0.17 0.17
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 336 0.24 3.44 0 -0.04 2.97 -6 8 14 0.57 -0.55 0.19wilcox.test(s_relationship~disclaimer, data = H1b_4, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_relationship by disclaimer
## W = 52751, p-value = 0.4892
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -9.999562e-01 5.642902e-05
## sample estimates:
## difference in location
## -3.622986e-06H1 Logistic Regression
# Exclude NAs for stepwise testing
sum(is.na(data2_wide$disclaimer))## [1] 0sum(is.na(data2_wide$s_awareness))## [1] 0sum(is.na(data2_wide$text_order))## [1] 0sum(is.na(data2_wide$s_age))## [1] 1data2_wide <- data2_wide %>% drop_na(s_age)
sum(is.na(data2_wide$s_sex))## [1] 0sum(is.na(data2_wide$s_school))## [1] 0sum(is.na(data2_wide$s_interest))## [1] 0data2_wide$H1_interaction <- interaction(data2_wide$disclaimer,
data2_wide$version)
data2_wide$H1_interaction <- droplevels(data2_wide$H1_interaction)
table(data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## 357 670
## disclaimer.new guideline
## 1013data2_wide_reg <- subset(data2_wide, condition != 5)
View(data2_wide_reg)
relationship_null <- clm(as.factor(s_relationship)~1, data = data2_wide_reg,
link = "logit")
relationship_model1 <- clm(as.factor(s_relationship)~ H1_interaction,
data = data2_wide_reg, link = "logit")
anova(relationship_null,relationship_model1)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## relationship_null as.factor(s_relationship) ~ 1 logit flexible
## relationship_model1 as.factor(s_relationship) ~ H1_interaction logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## relationship_null 16 8008.9 -3988.4
## relationship_model1 18 8011.7 -3987.9 1.1372 2 0.5663relationship_model2 <- clm(as.factor(s_relationship)~ H1_interaction +
s_awareness, data = data2_wide_reg, link = "logit")
anova(relationship_null,relationship_model2)## Likelihood ratio tests of cumulative link models:
##
## formula:
## relationship_null as.factor(s_relationship) ~ 1
## relationship_model2 as.factor(s_relationship) ~ H1_interaction + s_awareness
## link: threshold:
## relationship_null logit flexible
## relationship_model2 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## relationship_null 16 8008.9 -3988.4
## relationship_model2 19 7959.4 -3960.7 55.441 3 5.529e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1relationship_model3 <- clm(as.factor(s_relationship)~
H1_interaction*s_awareness, data = data2_wide_reg,
link = "logit")
anova(relationship_model2,relationship_model3)## Likelihood ratio tests of cumulative link models:
##
## formula:
## relationship_model2 as.factor(s_relationship) ~ H1_interaction + s_awareness
## relationship_model3 as.factor(s_relationship) ~ H1_interaction * s_awareness
## link: threshold:
## relationship_model2 logit flexible
## relationship_model3 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## relationship_model2 19 7959.4 -3960.7
## relationship_model3 21 7962.5 -3960.2 0.9571 2 0.6197relationship_model4 <- clm(as.factor(s_relationship)~
H1_interaction*s_awareness + summary1,
data = data2_wide_reg, link = "logit")
anova(relationship_model3,relationship_model4)## Likelihood ratio tests of cumulative link models:
##
## formula:
## relationship_model3 as.factor(s_relationship) ~ H1_interaction * s_awareness
## relationship_model4 as.factor(s_relationship) ~ H1_interaction * s_awareness + summary1
## link: threshold:
## relationship_model3 logit flexible
## relationship_model4 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## relationship_model3 21 7962.5 -3960.2
## relationship_model4 22 7963.6 -3959.8 0.8892 1 0.3457relationship_model5 <- clm(as.factor(s_relationship)~
H1_interaction*s_awareness + summary1
+ s_age, data = data2_wide_reg,
link = "logit")
anova(relationship_model4,relationship_model5)## Likelihood ratio tests of cumulative link models:
##
## formula:
## relationship_model4 as.factor(s_relationship) ~ H1_interaction * s_awareness + summary1
## relationship_model5 as.factor(s_relationship) ~ H1_interaction * s_awareness + summary1 + s_age
## link: threshold:
## relationship_model4 logit flexible
## relationship_model5 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## relationship_model4 22 7963.6 -3959.8
## relationship_model5 23 7879.4 -3916.7 86.179 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1relationship_model6 <- clm(as.factor(s_relationship)~
H1_interaction*s_awareness + summary1
+ s_age + s_sex, data = data2_wide_reg,
link = "logit")
anova(relationship_model5,relationship_model6)## Likelihood ratio tests of cumulative link models:
##
## formula:
## relationship_model5 as.factor(s_relationship) ~ H1_interaction * s_awareness + summary1 + s_age
## relationship_model6 as.factor(s_relationship) ~ H1_interaction * s_awareness + summary1 + s_age + s_sex
## link: threshold:
## relationship_model5 logit flexible
## relationship_model6 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## relationship_model5 23 7879.4 -3916.7
## relationship_model6 24 7881.1 -3916.6 0.2543 1 0.6141relationship_model7 <- clm(as.factor(s_relationship)~
H1_interaction*s_awareness + summary1 +
s_age + s_sex + s_school,
data = data2_wide_reg, link = "logit")
anova(relationship_model5,relationship_model7)## Likelihood ratio tests of cumulative link models:
##
## formula:
## relationship_model5 as.factor(s_relationship) ~ H1_interaction * s_awareness + summary1 + s_age
## relationship_model7 as.factor(s_relationship) ~ H1_interaction * s_awareness + summary1 + s_age + s_sex + s_school
## link: threshold:
## relationship_model5 logit flexible
## relationship_model7 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## relationship_model5 23 7879.4 -3916.7
## relationship_model7 26 7821.7 -3884.8 63.735 3 9.35e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1relationship_model8 <- clm(as.factor(s_relationship)~
H1_interaction*s_awareness + summary1 +
s_age + s_sex + s_school +
as.factor(s_interest), data = data2_wide_reg,
link = "logit")
anova(relationship_model7,relationship_model8)## Likelihood ratio tests of cumulative link models:
##
## formula:
## relationship_model7 as.factor(s_relationship) ~ H1_interaction * s_awareness + summary1 + s_age + s_sex + s_school
## relationship_model8 as.factor(s_relationship) ~ H1_interaction * s_awareness + summary1 + s_age + s_sex + s_school + as.factor(s_interest)
## link: threshold:
## relationship_model7 logit flexible
## relationship_model8 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## relationship_model7 26 7821.7 -3884.8
## relationship_model8 30 7811.3 -3875.7 18.351 4 0.001054 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(relationship_model8)## formula:
## as.factor(s_relationship) ~ H1_interaction * s_awareness + summary1 + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_reg
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1660 -3875.66 7811.31 9(2) 2.45e-12 1.3e+06
##
## Coefficients:
## Estimate Std. Error
## H1_interactionno disclaimer.new guideline 0.092030 0.200646
## H1_interactiondisclaimer.new guideline 0.037610 0.200875
## s_awarenesspass 0.770701 0.200424
## summary1Faerber 0.084563 0.086509
## s_age -0.025094 0.002932
## s_sexmale -0.077937 0.087012
## s_schoolReal 0.309928 0.106545
## s_schoolAbi 0.873053 0.109980
## as.factor(s_interest)5 0.049578 0.132980
## as.factor(s_interest)6 0.072247 0.134347
## as.factor(s_interest)7 -0.029943 0.147005
## as.factor(s_interest)8 -0.442159 0.140765
## H1_interactionno disclaimer.new guideline:s_awarenesspass -0.163416 0.244856
## H1_interactiondisclaimer.new guideline:s_awarenesspass 0.078636 0.245433
## z value Pr(>|z|)
## H1_interactionno disclaimer.new guideline 0.459 0.64647
## H1_interactiondisclaimer.new guideline 0.187 0.85148
## s_awarenesspass 3.845 0.00012 ***
## summary1Faerber 0.978 0.32832
## s_age -8.560 < 2e-16 ***
## s_sexmale -0.896 0.37041
## s_schoolReal 2.909 0.00363 **
## s_schoolAbi 7.938 2.05e-15 ***
## as.factor(s_interest)5 0.373 0.70928
## as.factor(s_interest)6 0.538 0.59074
## as.factor(s_interest)7 -0.204 0.83860
## as.factor(s_interest)8 -3.141 0.00168 **
## H1_interactionno disclaimer.new guideline:s_awarenesspass -0.667 0.50452
## H1_interactiondisclaimer.new guideline:s_awarenesspass 0.320 0.74867
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -8|-7 -6.4870 0.5528 -11.734
## -7|-6 -6.2629 0.5056 -12.387
## -6|-5 -4.6803 0.3119 -15.008
## -5|-4 -4.0833 0.2807 -14.544
## -4|-3 -2.1374 0.2430 -8.797
## -3|-2 -1.7452 0.2402 -7.265
## -2|-1 -1.0550 0.2368 -4.455
## -1|0 -0.6615 0.2357 -2.807
## 0|1 0.2832 0.2352 1.204
## 1|2 0.5721 0.2355 2.429
## 2|3 1.1302 0.2372 4.766
## 3|4 1.3659 0.2383 5.731
## 4|5 1.9967 0.2432 8.211
## 5|6 2.2015 0.2454 8.970
## 6|7 3.4525 0.2726 12.665
## 7|8 3.8351 0.2887 13.284
## (53 Beobachtungen als fehlend gelöscht)exp(coef(relationship_model8))## -8|-7
## 0.001523149
## -7|-6
## 0.001905707
## -6|-5
## 0.009275961
## -5|-4
## 0.016851824
## -4|-3
## 0.117966144
## -3|-2
## 0.174614003
## -2|-1
## 0.348181844
## -1|0
## 0.516085165
## 0|1
## 1.327412450
## 1|2
## 1.772012006
## 2|3
## 3.096338894
## 3|4
## 3.919213259
## 4|5
## 7.364671907
## 5|6
## 9.038596464
## 6|7
## 31.579103646
## 7|8
## 46.299122481
## H1_interactionno disclaimer.new guideline
## 1.096397874
## H1_interactiondisclaimer.new guideline
## 1.038326697
## s_awarenesspass
## 2.161281741
## summary1Faerber
## 1.088240988
## s_age
## 0.975217751
## s_sexmale
## 0.925022568
## s_schoolReal
## 1.363327598
## s_schoolAbi
## 2.394209013
## as.factor(s_interest)5
## 1.050827415
## as.factor(s_interest)6
## 1.074920924
## as.factor(s_interest)7
## 0.970500604
## as.factor(s_interest)8
## 0.642647248
## H1_interactionno disclaimer.new guideline:s_awarenesspass
## 0.849237482
## H1_interactiondisclaimer.new guideline:s_awarenesspass
## 1.081809934exp(confint(relationship_model8))## 2.5 % 97.5 %
## H1_interactionno disclaimer.new guideline 0.7401323 1.6258461
## H1_interactiondisclaimer.new guideline 0.7005461 1.5402705
## s_awarenesspass 1.4602271 3.2048311
## summary1Faerber 0.9185364 1.2893974
## s_age 0.9696156 0.9808249
## s_sexmale 0.7799476 1.0970176
## s_schoolReal 1.1065267 1.6802455
## s_schoolAbi 1.9307086 2.9715029
## as.factor(s_interest)5 0.8096677 1.3637648
## as.factor(s_interest)6 0.8260495 1.3988328
## as.factor(s_interest)7 0.7275112 1.2946699
## as.factor(s_interest)8 0.4875840 0.8467208
## H1_interactionno disclaimer.new guideline:s_awarenesspass 0.5252687 1.3719833
## H1_interactiondisclaimer.new guideline:s_awarenesspass 0.6684317 1.7498793nagelkerke(fit = relationship_model8, null = relationship_null)## $Models
##
## Model: "clm, as.factor(s_relationship) ~ H1_interaction * s_awareness + summary1 + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_reg, logit"
## Null: "clm, as.factor(s_relationship) ~ 1, data2_wide_reg, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0282758
## Cox and Snell (ML) 0.1270480
## Nagelkerke (Cragg and Uhler) 0.1280970
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -14 -112.78 225.55 3.1725e-40
##
## $Number.of.observations
##
## Model: 1660
## Null: 1660
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H1test = emmeans(relationship_model8, ~ H1_interaction)## NOTE: Results may be misleading due to involvement in interactionspairs(H1test, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - no disclaimer.new guideline -0.0103 0.122 Inf
## no disclaimer.old guideline - disclaimer.new guideline -0.0769 0.123 Inf
## no disclaimer.new guideline - disclaimer.new guideline -0.0666 0.102 Inf
## z.ratio p.value
## -0.084 0.9961
## -0.627 0.8053
## -0.652 0.7914
##
## Results are averaged over the levels of: s_awareness, summary1, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimatescld(H1test, Letters = letters)## H1_interaction emmean SE df asymp.LCL asymp.UCL .group
## no disclaimer.old guideline 0.341 0.122 Inf 0.102 0.580 a
## no disclaimer.new guideline 0.351 0.101 Inf 0.153 0.549 a
## disclaimer.new guideline 0.418 0.101 Inf 0.220 0.616 a
##
## Results are averaged over the levels of: s_awareness, summary1, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimates
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.Logistic Regression by Group
data2_wide_reg1 <- subset(data2_wide_reg, condition == 2 | condition == 6)
View(data2_wide_reg1)
relationship_null_1 <- clm(as.factor(s_relationship)~1, data = data2_wide_reg1, link = "logit")
relationship_model8_1 <- clm(as.factor(s_relationship)~
H1_interaction*s_awareness + summary1 +
s_age + s_sex + s_school +
as.factor(s_interest), data = data2_wide_reg1, link = "logit")
summary(relationship_model8_1)## formula:
## as.factor(s_relationship) ~ H1_interaction * s_awareness + summary1 + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_reg1
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 682 -1576.31 3208.62 8(1) 9.08e-08 1.0e+06
##
## Coefficients:
## Estimate Std. Error
## H1_interactiondisclaimer.new guideline -0.020159 0.245631
## s_awarenesspass 0.783550 0.204978
## summary1Faerber 0.042646 0.135944
## s_age -0.028157 0.004545
## s_sexmale -0.113214 0.137419
## s_schoolReal 0.173119 0.168318
## s_schoolAbi 0.831686 0.170260
## as.factor(s_interest)5 0.189489 0.208166
## as.factor(s_interest)6 0.311041 0.213707
## as.factor(s_interest)7 -0.015489 0.226762
## as.factor(s_interest)8 -0.362711 0.216145
## H1_interactiondisclaimer.new guideline:s_awarenesspass -0.047810 0.294064
## z value Pr(>|z|)
## H1_interactiondisclaimer.new guideline -0.082 0.934592
## s_awarenesspass 3.823 0.000132 ***
## summary1Faerber 0.314 0.753748
## s_age -6.196 5.80e-10 ***
## s_sexmale -0.824 0.410022
## s_schoolReal 1.029 0.303706
## s_schoolAbi 4.885 1.04e-06 ***
## as.factor(s_interest)5 0.910 0.362676
## as.factor(s_interest)6 1.455 0.145544
## as.factor(s_interest)7 -0.068 0.945544
## as.factor(s_interest)8 -1.678 0.093329 .
## H1_interactiondisclaimer.new guideline:s_awarenesspass -0.163 0.870846
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -8|-7 -7.1682 1.0459 -6.853
## -7|-6 -6.4740 0.7707 -8.400
## -6|-5 -5.3712 0.5105 -10.521
## -5|-4 -4.5115 0.4064 -11.100
## -4|-3 -2.2391 0.3170 -7.064
## -3|-2 -1.9020 0.3124 -6.088
## -2|-1 -1.2149 0.3053 -3.979
## -1|0 -0.7608 0.3024 -2.516
## 0|1 0.1479 0.3015 0.491
## 1|2 0.4680 0.3026 1.546
## 2|3 0.9964 0.3059 3.257
## 3|4 1.2663 0.3084 4.106
## 4|5 1.9165 0.3179 6.029
## 5|6 2.0589 0.3208 6.418
## 6|7 3.2745 0.3675 8.910
## 7|8 3.6994 0.3988 9.277
## (20 Beobachtungen als fehlend gelöscht)exp(coef(relationship_model8_1))## -8|-7
## 7.706807e-04
## -7|-6
## 1.543060e-03
## -6|-5
## 4.648423e-03
## -5|-4
## 1.098187e-02
## -4|-3
## 1.065539e-01
## -3|-2
## 1.492630e-01
## -2|-1
## 2.967463e-01
## -1|0
## 4.672734e-01
## 0|1
## 1.159442e+00
## 1|2
## 1.596801e+00
## 2|3
## 2.708518e+00
## 3|4
## 3.547668e+00
## 4|5
## 6.797181e+00
## 5|6
## 7.837718e+00
## 6|7
## 2.643053e+01
## 7|8
## 4.042181e+01
## H1_interactiondisclaimer.new guideline
## 9.800432e-01
## s_awarenesspass
## 2.189229e+00
## summary1Faerber
## 1.043568e+00
## s_age
## 9.722360e-01
## s_sexmale
## 8.929600e-01
## s_schoolReal
## 1.189007e+00
## s_schoolAbi
## 2.297188e+00
## as.factor(s_interest)5
## 1.208631e+00
## as.factor(s_interest)6
## 1.364845e+00
## as.factor(s_interest)7
## 9.846307e-01
## as.factor(s_interest)8
## 6.957877e-01
## H1_interactiondisclaimer.new guideline:s_awarenesspass
## 9.533150e-01exp(confint(relationship_model8_1))## 2.5 % 97.5 %
## H1_interactiondisclaimer.new guideline 0.6051809 1.5861761
## s_awarenesspass 1.4669237 3.2777771
## summary1Faerber 0.7994342 1.3623371
## s_age 0.9635723 0.9808995
## s_sexmale 0.6820071 1.1689774
## s_schoolReal 0.8550335 1.6544106
## s_schoolAbi 1.6467673 3.2106536
## as.factor(s_interest)5 0.8034120 1.8176163
## as.factor(s_interest)6 0.8978204 2.0758112
## as.factor(s_interest)7 0.6311712 1.5360710
## as.factor(s_interest)8 0.4551834 1.0625466
## H1_interactiondisclaimer.new guideline:s_awarenesspass 0.5355740 1.6968891nagelkerke(fit = relationship_model8_1, null = relationship_null_1)## $Models
##
## Model: "clm, as.factor(s_relationship) ~ H1_interaction * s_awareness + summary1 + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_reg1, logit"
## Null: "clm, as.factor(s_relationship) ~ 1, data2_wide_reg1, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0314367
## Cox and Snell (ML) 0.1393230
## Nagelkerke (Cragg and Uhler) 0.1405110
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -12 -51.162 102.32 1.9488e-16
##
## $Number.of.observations
##
## Model: 682
## Null: 682
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H1test_1 = emmeans(relationship_model8_1, ~ H1_interaction)## NOTE: Results may be misleading due to involvement in interactionspairs(H1test_1, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - disclaimer.new guideline 0.0441 0.147 Inf
## z.ratio p.value
## 0.299 0.7648
##
## Results are averaged over the levels of: s_awareness, summary1, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H1test_1, Letters = letters)## H1_interaction emmean SE df asymp.LCL asymp.UCL .group
## disclaimer.new guideline 0.369 0.165 Inf 0.0460 0.691 a
## no disclaimer.old guideline 0.413 0.160 Inf 0.0991 0.726 a
##
## Results are averaged over the levels of: s_awareness, summary1, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.data2_wide_reg2 <- subset(data2_wide_reg, condition == 4 | condition == 6)
View(data2_wide_reg2)
relationship_null_2 <- clm(as.factor(s_relationship)~1, data = data2_wide_reg2, link = "logit")
relationship_model8_2 <- clm(as.factor(s_relationship)~
H1_interaction*s_awareness + summary1 +
s_age + s_sex + s_school +
as.factor(s_interest), data = data2_wide_reg2, link = "logit")
summary(relationship_model8_2)## formula:
## as.factor(s_relationship) ~ H1_interaction * s_awareness + summary1 + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_reg2
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 678 -1541.94 3139.88 10(3) 7.66e-13 8.9e+05
##
## Coefficients:
## Estimate Std. Error
## H1_interactiondisclaimer.new guideline 0.084544 0.226310
## s_awarenesspass 0.765877 0.205953
## summary1Faerber -0.144396 0.136669
## s_age -0.024623 0.004634
## s_sexmale -0.293743 0.136998
## s_schoolReal 0.316078 0.170422
## s_schoolAbi 0.827959 0.172419
## as.factor(s_interest)5 0.384884 0.206182
## as.factor(s_interest)6 0.168480 0.210609
## as.factor(s_interest)7 0.127651 0.229816
## as.factor(s_interest)8 -0.221542 0.215441
## H1_interactiondisclaimer.new guideline:s_awarenesspass 0.273848 0.284203
## z value Pr(>|z|)
## H1_interactiondisclaimer.new guideline 0.374 0.7087
## s_awarenesspass 3.719 0.0002 ***
## summary1Faerber -1.057 0.2907
## s_age -5.313 1.08e-07 ***
## s_sexmale -2.144 0.0320 *
## s_schoolReal 1.855 0.0636 .
## s_schoolAbi 4.802 1.57e-06 ***
## as.factor(s_interest)5 1.867 0.0619 .
## as.factor(s_interest)6 0.800 0.4237
## as.factor(s_interest)7 0.555 0.5786
## as.factor(s_interest)8 -1.028 0.3038
## H1_interactiondisclaimer.new guideline:s_awarenesspass 0.964 0.3353
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -8|-7 -5.8942 0.6563 -8.981
## -7|-6 -5.6047 0.5894 -9.509
## -6|-5 -4.6779 0.4443 -10.528
## -5|-4 -4.1980 0.4000 -10.494
## -4|-3 -2.2286 0.3259 -6.838
## -3|-2 -1.8900 0.3209 -5.890
## -2|-1 -1.2040 0.3145 -3.828
## -1|0 -0.7364 0.3122 -2.359
## 0|1 0.3383 0.3112 1.087
## 1|2 0.6017 0.3119 1.929
## 2|3 1.1157 0.3143 3.550
## 3|4 1.3163 0.3158 4.168
## 4|5 1.9935 0.3242 6.149
## 5|6 2.1967 0.3280 6.697
## 6|7 3.6948 0.3900 9.475
## 7|8 3.7583 0.3947 9.523
## (20 Beobachtungen als fehlend gelöscht)exp(coef(relationship_model8_2))## -8|-7
## 0.002755475
## -7|-6
## 0.003680616
## -6|-5
## 0.009298112
## -5|-4
## 0.015025709
## -4|-3
## 0.107679996
## -3|-2
## 0.151078243
## -2|-1
## 0.300002446
## -1|0
## 0.478824031
## 0|1
## 1.402616547
## 1|2
## 1.825187195
## 2|3
## 3.051692428
## 3|4
## 3.729733831
## 4|5
## 7.340845989
## 5|6
## 8.994906282
## 6|7
## 40.236028259
## 7|8
## 42.875244825
## H1_interactiondisclaimer.new guideline
## 1.088220611
## s_awarenesspass
## 2.150879910
## summary1Faerber
## 0.865544592
## s_age
## 0.975677234
## s_sexmale
## 0.745467766
## s_schoolReal
## 1.371737722
## s_schoolAbi
## 2.288643582
## as.factor(s_interest)5
## 1.469444424
## as.factor(s_interest)6
## 1.183504958
## as.factor(s_interest)7
## 1.136155948
## as.factor(s_interest)8
## 0.801281937
## H1_interactiondisclaimer.new guideline:s_awarenesspass
## 1.315015242exp(confint(relationship_model8_2))## 2.5 % 97.5 %
## H1_interactiondisclaimer.new guideline 0.6982941 1.6964609
## s_awarenesspass 1.4382646 3.2260124
## summary1Faerber 0.6619509 1.1312632
## s_age 0.9668233 0.9845555
## s_sexmale 0.5696816 0.9748339
## s_schoolReal 0.9825409 1.9168710
## s_schoolAbi 1.6338480 3.2125670
## as.factor(s_interest)5 0.9808999 2.2019295
## as.factor(s_interest)6 0.7832071 1.7889544
## as.factor(s_interest)7 0.7240196 1.7832371
## as.factor(s_interest)8 0.5250900 1.2223317
## H1_interactiondisclaimer.new guideline:s_awarenesspass 0.7533457 2.2961469nagelkerke(fit = relationship_model8_2, null = relationship_null_2)## $Models
##
## Model: "clm, as.factor(s_relationship) ~ H1_interaction * s_awareness + summary1 + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_reg2, logit"
## Null: "clm, as.factor(s_relationship) ~ 1, data2_wide_reg2, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0343714
## Cox and Snell (ML) 0.1494760
## Nagelkerke (Cragg and Uhler) 0.1508340
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -12 -54.885 109.77 6.6442e-18
##
## $Number.of.observations
##
## Model: 678
## Null: 678
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H1test_2 = emmeans(relationship_model8_2, ~ H1_interaction)## NOTE: Results may be misleading due to involvement in interactionspairs(H1test_2, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - disclaimer.new guideline -0.221 0.142 Inf
## z.ratio p.value
## -1.558 0.1191
##
## Results are averaged over the levels of: s_awareness, summary1, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H1test_2, Letters = letters)## H1_interaction emmean SE df asymp.LCL asymp.UCL .group
## no disclaimer.old guideline 0.222 0.137 Inf -0.0456 0.49 a
## disclaimer.new guideline 0.444 0.136 Inf 0.1774 0.71 a
##
## Results are averaged over the levels of: s_awareness, summary1, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.H2
H2a
H2a <- subset(data2_wide, condition == 2|condition == 4|condition == 6)
View(H2a)
describeBy(H2a$s_extent,H2a$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 349 0.52 2.23 0 0.42 2.97 -6 6 12 0.33 -0.23 0.12
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 676 0.62 2.4 0 0.54 2.97 -6 6 12 0.26 -0.5 0.09wilcox.test(s_extent~disclaimer, data = H2a, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_extent by disclaimer
## W = 115603, p-value = 0.5948
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.469212e-05 8.124448e-05
## sample estimates:
## difference in location
## -4.862757e-05H2a post hoc
H2a_1 <- subset(data2_wide, condition == 2|condition == 6)
View(H2a_1)
describeBy(H2a_1$s_extent,H2a_1$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 349 0.52 2.23 0 0.42 2.97 -6 6 12 0.33 -0.23 0.12
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 340 0.52 2.38 0 0.44 2.97 -6 6 12 0.22 -0.49 0.13wilcox.test(s_extent~disclaimer, data = H2a_1, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_extent by disclaimer
## W = 59589, p-value = 0.9201
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.344322e-05 2.640857e-05
## sample estimates:
## difference in location
## 7.842257e-05H2a_2 <- subset(data2_wide, condition == 4|condition == 6)
View(H2a_2)
describeBy(H2a_2$s_extent,H2a_2$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 349 0.52 2.23 0 0.42 2.97 -6 6 12 0.33 -0.23 0.12
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 336 0.73 2.42 0 0.63 2.97 -4 6 10 0.31 -0.54 0.13wilcox.test(s_extent~disclaimer, data = H2a_2, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_extent by disclaimer
## W = 56014, p-value = 0.3053
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.918730e-05 3.332259e-05
## sample estimates:
## difference in location
## -3.546894e-05H2b
H2b <- subset(data2_wide, condition == 1| condition == 2| condition == 3|
condition == 4)
View(H2b)
describeBy(H2b$s_extent,H2b$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 654 0.41 2.15 0 0.29 2.97 -6 6 12 0.33 -0.19 0.08
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 676 0.62 2.4 0 0.54 2.97 -6 6 12 0.26 -0.5 0.09wilcox.test(s_extent~disclaimer, data = H2b, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_extent by disclaimer
## W = 211224, p-value = 0.1547
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -5.113093e-05 3.383239e-05
## sample estimates:
## difference in location
## -9.912606e-05H2b post hoc
H2b_1 <- subset(data2_wide, condition == 1| condition == 2)
View(H2b_1)
describeBy(H2b_1$s_extent,H2b_1$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 327 0.51 2.21 0 0.43 2.97 -4 6 10 0.25 -0.46 0.12
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 340 0.52 2.38 0 0.44 2.97 -6 6 12 0.22 -0.49 0.13wilcox.test(s_extent~disclaimer, data = H2b_1, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_extent by disclaimer
## W = 55838, p-value = 0.9198
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.851467e-05 3.167506e-05
## sample estimates:
## difference in location
## 4.181901e-05H2b_2 <- subset(data2_wide, condition == 3| condition == 4)
View(H2b_2)
describeBy(H2b_2$s_extent,H2b_2$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 327 0.3 2.08 0 0.17 2.97 -6 6 12 0.41 0.14 0.11
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 336 0.73 2.42 0 0.63 2.97 -4 6 10 0.31 -0.54 0.13wilcox.test(s_extent~disclaimer, data = H2b_2, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_extent by disclaimer
## W = 49672, p-value = 0.02996
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -9.999459e-01 -1.088538e-05
## sample estimates:
## difference in location
## -2.476233e-05H2b_3 <- subset(data2_wide, condition == 1| condition == 4)
View(H2b_3)
describeBy(H2b_3$s_extent,H2b_3$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 327 0.51 2.21 0 0.43 2.97 -4 6 10 0.25 -0.46 0.12
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 336 0.73 2.42 0 0.63 2.97 -4 6 10 0.31 -0.54 0.13wilcox.test(s_extent~disclaimer, data = H2b_3, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_extent by disclaimer
## W = 52652, p-value = 0.3482
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.863540e-05 3.467322e-05
## sample estimates:
## difference in location
## -3.262128e-05H2b_4 <- subset(data2_wide, condition == 2| condition == 3)
View(H2b_4)
describeBy(H2b_4$s_extent,H2b_4$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 327 0.3 2.08 0 0.17 2.97 -6 6 12 0.41 0.14 0.11
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 340 0.52 2.38 0 0.44 2.97 -6 6 12 0.22 -0.49 0.13wilcox.test(s_extent~disclaimer, data = H2b_4, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_extent by disclaimer
## W = 53062, p-value = 0.3021
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -5.449799e-05 3.884504e-05
## sample estimates:
## difference in location
## -6.112496e-05H2 Logistic Regression
data2_wide$H2_interaction <- data2_wide$H1_interaction
table(data2_wide$H2_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## 357 670
## disclaimer.new guideline
## 1013data2_wide_reg <- subset(data2_wide, condition != 5)
View(data2_wide_reg)
extent_null <- clm(as.factor(s_extent) ~ 1, data = data2_wide_reg, link = "logit")
extent_model1 <- clm(as.factor(s_extent) ~ H2_interaction, data = data2_wide_reg,
link = "logit")
anova(extent_null,extent_model1)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## extent_null as.factor(s_extent) ~ 1 logit flexible
## extent_model1 as.factor(s_extent) ~ H2_interaction logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## extent_null 12 7114.2 -3545.1
## extent_model1 14 7116.2 -3544.1 2.0399 2 0.3606extent_model2 <- clm(as.factor(s_extent) ~ H2_interaction + s_awareness,
data = data2_wide_reg, link = "logit")
anova(extent_null,extent_model2)## Likelihood ratio tests of cumulative link models:
##
## formula: link:
## extent_null as.factor(s_extent) ~ 1 logit
## extent_model2 as.factor(s_extent) ~ H2_interaction + s_awareness logit
## threshold:
## extent_null flexible
## extent_model2 flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## extent_null 12 7114.2 -3545.1
## extent_model2 15 7028.5 -3499.3 91.681 3 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1extent_model3 <- clm(as.factor(s_extent) ~ H2_interaction*s_awareness,
data = data2_wide_reg, link = "logit")
anova(extent_model2,extent_model3)## Likelihood ratio tests of cumulative link models:
##
## formula: link:
## extent_model2 as.factor(s_extent) ~ H2_interaction + s_awareness logit
## extent_model3 as.factor(s_extent) ~ H2_interaction * s_awareness logit
## threshold:
## extent_model2 flexible
## extent_model3 flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## extent_model2 15 7028.5 -3499.3
## extent_model3 17 7028.8 -3497.4 3.7023 2 0.1571extent_model4 <- clm(as.factor(s_extent) ~ H2_interaction*s_awareness + summary1,
data = data2_wide_reg, link = "logit")
anova(extent_model2,extent_model4)## Likelihood ratio tests of cumulative link models:
##
## formula:
## extent_model2 as.factor(s_extent) ~ H2_interaction + s_awareness
## extent_model4 as.factor(s_extent) ~ H2_interaction * s_awareness + summary1
## link: threshold:
## extent_model2 logit flexible
## extent_model4 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## extent_model2 15 7028.5 -3499.3
## extent_model4 18 7030.6 -3497.3 3.9089 3 0.2715extent_model5 <- clm(as.factor(s_extent) ~ H2_interaction*s_awareness +
summary1 + s_age, data = data2_wide_reg,
link = "logit")
anova(extent_model2,extent_model5)## Likelihood ratio tests of cumulative link models:
##
## formula:
## extent_model2 as.factor(s_extent) ~ H2_interaction + s_awareness
## extent_model5 as.factor(s_extent) ~ H2_interaction * s_awareness + summary1 + s_age
## link: threshold:
## extent_model2 logit flexible
## extent_model5 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## extent_model2 15 7028.5 -3499.3
## extent_model5 19 7007.7 -3484.8 28.866 4 8.323e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1extent_model6 <- clm(as.factor(s_extent) ~ H2_interaction*s_awareness +
summary1 + s_age + s_sex,
data = data2_wide_reg, link = "logit")
anova(extent_model5,extent_model6)## Likelihood ratio tests of cumulative link models:
##
## formula:
## extent_model5 as.factor(s_extent) ~ H2_interaction * s_awareness + summary1 + s_age
## extent_model6 as.factor(s_extent) ~ H2_interaction * s_awareness + summary1 + s_age + s_sex
## link: threshold:
## extent_model5 logit flexible
## extent_model6 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## extent_model5 19 7007.7 -3484.8
## extent_model6 20 7006.4 -3483.2 3.2613 1 0.07093 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1extent_model7 <- clm(as.factor(s_extent) ~ H2_interaction*s_awareness +
summary1 + s_age + s_sex +
s_school, data = data2_wide_reg,
link = "logit")
anova(extent_model5,extent_model7)## Likelihood ratio tests of cumulative link models:
##
## formula:
## extent_model5 as.factor(s_extent) ~ H2_interaction * s_awareness + summary1 + s_age
## extent_model7 as.factor(s_extent) ~ H2_interaction * s_awareness + summary1 + s_age + s_sex + s_school
## link: threshold:
## extent_model5 logit flexible
## extent_model7 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## extent_model5 19 7007.7 -3484.8
## extent_model7 22 6949.4 -3452.7 64.252 3 7.249e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1extent_model8 <- clm(as.factor(s_extent) ~ H2_interaction*s_awareness +
summary1 + s_age + s_sex +
s_school + as.factor(s_interest), data = data2_wide_reg,
link = "logit")
anova(extent_model7,extent_model8)## Likelihood ratio tests of cumulative link models:
##
## formula:
## extent_model7 as.factor(s_extent) ~ H2_interaction * s_awareness + summary1 + s_age + s_sex + s_school
## extent_model8 as.factor(s_extent) ~ H2_interaction * s_awareness + summary1 + s_age + s_sex + s_school + as.factor(s_interest)
## link: threshold:
## extent_model7 logit flexible
## extent_model8 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## extent_model7 22 6949.4 -3452.7
## extent_model8 26 6931.2 -3439.6 26.217 4 2.861e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(extent_model8)## formula:
## as.factor(s_extent) ~ H2_interaction * s_awareness + summary1 + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_reg
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1679 -3439.60 6931.20 8(2) 5.73e-08 1.1e+06
##
## Coefficients:
## Estimate Std. Error
## H2_interactionno disclaimer.new guideline 0.182468 0.198635
## H2_interactiondisclaimer.new guideline 0.036018 0.200742
## s_awarenesspass 0.966097 0.201849
## summary1Faerber -0.023117 0.086318
## s_age -0.013140 0.002876
## s_sexmale 0.119397 0.086817
## s_schoolReal 0.333643 0.106849
## s_schoolAbi 0.894282 0.110627
## as.factor(s_interest)5 -0.053340 0.133711
## as.factor(s_interest)6 -0.084149 0.134700
## as.factor(s_interest)7 -0.244367 0.146398
## as.factor(s_interest)8 -0.631426 0.142375
## H2_interactionno disclaimer.new guideline:s_awarenesspass -0.349353 0.244601
## H2_interactiondisclaimer.new guideline:s_awarenesspass 0.091748 0.246248
## z value Pr(>|z|)
## H2_interactionno disclaimer.new guideline 0.919 0.35830
## H2_interactiondisclaimer.new guideline 0.179 0.85760
## s_awarenesspass 4.786 1.70e-06 ***
## summary1Faerber -0.268 0.78884
## s_age -4.568 4.92e-06 ***
## s_sexmale 1.375 0.16905
## s_schoolReal 3.123 0.00179 **
## s_schoolAbi 8.084 6.28e-16 ***
## as.factor(s_interest)5 -0.399 0.68995
## as.factor(s_interest)6 -0.625 0.53216
## as.factor(s_interest)7 -1.669 0.09508 .
## as.factor(s_interest)8 -4.435 9.21e-06 ***
## H2_interactionno disclaimer.new guideline:s_awarenesspass -1.428 0.15322
## H2_interactiondisclaimer.new guideline:s_awarenesspass 0.373 0.70946
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -5.9015 0.5508 -10.715
## -5|-4 -5.6779 0.5034 -11.280
## -4|-3 -3.4992 0.2769 -12.636
## -3|-2 -2.7627 0.2548 -10.841
## -2|-1 -1.0233 0.2359 -4.338
## -1|0 -0.5311 0.2341 -2.268
## 0|1 0.6439 0.2349 2.742
## 1|2 1.0904 0.2363 4.615
## 2|3 1.9107 0.2397 7.971
## 3|4 2.3852 0.2429 9.818
## 4|5 3.6795 0.2631 13.984
## 5|6 4.1761 0.2793 14.953
## (34 Beobachtungen als fehlend gelöscht)exp(coef(extent_model8))## -6|-5
## 0.002735333
## -5|-4
## 0.003420693
## -4|-3
## 0.030222339
## -3|-2
## 0.063122584
## -2|-1
## 0.359412666
## -1|0
## 0.587975715
## 0|1
## 1.903977695
## 1|2
## 2.975582789
## 2|3
## 6.757910660
## 3|4
## 10.860749671
## 4|5
## 39.624729456
## 5|6
## 65.110563255
## H2_interactionno disclaimer.new guideline
## 1.200175969
## H2_interactiondisclaimer.new guideline
## 1.036674672
## s_awarenesspass
## 2.627668410
## summary1Faerber
## 0.977148237
## s_age
## 0.986945602
## s_sexmale
## 1.126816965
## s_schoolReal
## 1.396044342
## s_schoolAbi
## 2.445580033
## as.factor(s_interest)5
## 0.948057705
## as.factor(s_interest)6
## 0.919294267
## as.factor(s_interest)7
## 0.783199790
## as.factor(s_interest)8
## 0.531833048
## H2_interactionno disclaimer.new guideline:s_awarenesspass
## 0.705144234
## H2_interactiondisclaimer.new guideline:s_awarenesspass
## 1.096088417exp(confint(extent_model8))## 2.5 % 97.5 %
## H2_interactionno disclaimer.new guideline 0.8132562 1.7724428
## H2_interactiondisclaimer.new guideline 0.6994857 1.5371276
## s_awarenesspass 1.7699782 3.9063695
## summary1Faerber 0.8250335 1.1572776
## s_age 0.9813900 0.9925205
## s_sexmale 0.9505479 1.3359513
## s_schoolReal 1.1324070 1.7216000
## s_schoolAbi 1.9696324 3.0391186
## as.factor(s_interest)5 0.7294092 1.2321070
## as.factor(s_interest)6 0.7058946 1.1970182
## as.factor(s_interest)7 0.5877056 1.0433855
## as.factor(s_interest)8 0.4021808 0.7028348
## H2_interactionno disclaimer.new guideline:s_awarenesspass 0.4363978 1.1387123
## H2_interactiondisclaimer.new guideline:s_awarenesspass 0.6762817 1.7760872nagelkerke(fit = extent_model8, null = extent_null)## $Models
##
## Model: "clm, as.factor(s_extent) ~ H2_interaction * s_awareness + summary1 + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_reg, logit"
## Null: "clm, as.factor(s_extent) ~ 1, data2_wide_reg, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0297617
## Cox and Snell (ML) 0.1181030
## Nagelkerke (Cragg and Uhler) 0.1198600
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -14 -105.51 211.02 3.061e-37
##
## $Number.of.observations
##
## Model: 1679
## Null: 1679
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H2test = emmeans(extent_model8, ~ H2_interaction)## NOTE: Results may be misleading due to involvement in interactionspairs(H2test, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - no disclaimer.new guideline -0.00779 0.122 Inf
## no disclaimer.old guideline - disclaimer.new guideline -0.08189 0.123 Inf
## no disclaimer.new guideline - disclaimer.new guideline -0.07410 0.102 Inf
## z.ratio p.value
## -0.064 0.9978
## -0.665 0.7837
## -0.730 0.7459
##
## Results are averaged over the levels of: s_awareness, summary1, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimatescld(H2test, Letters = letters)## H2_interaction emmean SE df asymp.LCL asymp.UCL .group
## no disclaimer.old guideline 0.599 0.131 Inf 0.343 0.856 a
## no disclaimer.new guideline 0.607 0.111 Inf 0.389 0.825 a
## disclaimer.new guideline 0.681 0.111 Inf 0.463 0.900 a
##
## Results are averaged over the levels of: s_awareness, summary1, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimates
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.Logistic Regression by Group
data2_wide_reg3 <- subset(data2_wide, condition == 2| condition == 6)
View(data2_wide_reg3)
extent_null_1 <- clm(as.factor(s_extent) ~ 1, data = data2_wide_reg3, link = "logit")
extent_model8_1 <- clm(as.factor(s_extent) ~ H2_interaction*s_awareness +
summary1 + s_age + s_sex +
s_school + as.factor(s_interest), data = data2_wide_reg3,
link = "logit")
summary(extent_model8_1)## formula:
## as.factor(s_extent) ~ H2_interaction * s_awareness + summary1 + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_reg3
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 689 -1418.80 2883.60 7(0) 1.27e-12 6.0e+05
##
## Coefficients:
## Estimate Std. Error
## H2_interactiondisclaimer.new guideline -0.090892 0.246319
## s_awarenesspass 1.000818 0.205758
## summary1Faerber 0.002191 0.136022
## s_age -0.016205 0.004450
## s_sexmale 0.217967 0.136897
## s_schoolReal 0.388165 0.170128
## s_schoolAbi 0.924417 0.171772
## as.factor(s_interest)5 -0.403292 0.214225
## as.factor(s_interest)6 -0.219034 0.214824
## as.factor(s_interest)7 -0.532376 0.231988
## as.factor(s_interest)8 -1.015751 0.221343
## H2_interactiondisclaimer.new guideline:s_awarenesspass 0.056550 0.293925
## z value Pr(>|z|)
## H2_interactiondisclaimer.new guideline -0.369 0.712125
## s_awarenesspass 4.864 1.15e-06 ***
## summary1Faerber 0.016 0.987147
## s_age -3.642 0.000271 ***
## s_sexmale 1.592 0.111342
## s_schoolReal 2.282 0.022513 *
## s_schoolAbi 5.382 7.38e-08 ***
## as.factor(s_interest)5 -1.883 0.059760 .
## as.factor(s_interest)6 -1.020 0.307918
## as.factor(s_interest)7 -2.295 0.021742 *
## as.factor(s_interest)8 -4.589 4.45e-06 ***
## H2_interactiondisclaimer.new guideline:s_awarenesspass 0.192 0.847432
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-4 -5.6172 0.6516 -8.620
## -4|-3 -3.8535 0.3873 -9.949
## -3|-2 -2.9814 0.3416 -8.728
## -2|-1 -1.3050 0.3084 -4.231
## -1|0 -0.7459 0.3040 -2.453
## 0|1 0.3914 0.3045 1.285
## 1|2 0.7784 0.3068 2.537
## 2|3 1.5571 0.3129 4.977
## 3|4 2.1805 0.3201 6.813
## 4|5 3.4604 0.3552 9.741
## 5|6 3.9278 0.3818 10.288
## (13 Beobachtungen als fehlend gelöscht)exp(coef(extent_model8_1))## -6|-4
## 0.003634913
## -4|-3
## 0.021204999
## -3|-2
## 0.050720376
## -2|-1
## 0.271181252
## -1|0
## 0.474330351
## 0|1
## 1.479098750
## 1|2
## 2.177951598
## 2|3
## 4.744824003
## 3|4
## 8.850299130
## 4|5
## 31.829325891
## 5|6
## 50.794024402
## H2_interactiondisclaimer.new guideline
## 0.913115874
## s_awarenesspass
## 2.720506151
## summary1Faerber
## 1.002193681
## s_age
## 0.983925341
## s_sexmale
## 1.243545409
## s_schoolReal
## 1.474273412
## s_schoolAbi
## 2.520399443
## as.factor(s_interest)5
## 0.668117009
## as.factor(s_interest)6
## 0.803294277
## as.factor(s_interest)7
## 0.587208322
## as.factor(s_interest)8
## 0.362130527
## H2_interactiondisclaimer.new guideline:s_awarenesspass
## 1.058179674exp(confint(extent_model8_1))## 2.5 % 97.5 %
## H2_interactiondisclaimer.new guideline 0.5629969 1.4795765
## s_awarenesspass 1.8197702 4.0786198
## summary1Faerber 0.7676102 1.3085061
## s_age 0.9753589 0.9925287
## s_sexmale 0.9510733 1.6268243
## s_schoolReal 1.0566895 2.0591538
## s_schoolAbi 1.8018552 3.5339262
## as.factor(s_interest)5 0.4386213 1.0161700
## as.factor(s_interest)6 0.5268458 1.2234120
## as.factor(s_interest)7 0.3723557 0.9249113
## as.factor(s_interest)8 0.2342871 0.5581544
## H2_interactiondisclaimer.new guideline:s_awarenesspass 0.5947637 1.8833968nagelkerke(fit = extent_model8_1, null = extent_null_1)## $Models
##
## Model: "clm, as.factor(s_extent) ~ H2_interaction * s_awareness + summary1 + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_reg3, logit"
## Null: "clm, as.factor(s_extent) ~ 1, data2_wide_reg3, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0359274
## Cox and Snell (ML) 0.1422810
## Nagelkerke (Cragg and Uhler) 0.1442950
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -12 -52.873 105.75 4.1364e-17
##
## $Number.of.observations
##
## Model: 689
## Null: 689
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H2test_1 = emmeans(extent_model8_1, ~ H2_interaction)## NOTE: Results may be misleading due to involvement in interactionspairs(H2test_1, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - disclaimer.new guideline 0.0626 0.148 Inf
## z.ratio p.value
## 0.424 0.6715
##
## Results are averaged over the levels of: s_awareness, summary1, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H2test_1, Letters = letters)## H2_interaction emmean SE df asymp.LCL asymp.UCL .group
## disclaimer.new guideline 0.00715 0.132 Inf -0.251 0.266 a
## no disclaimer.old guideline 0.06977 0.125 Inf -0.176 0.316 a
##
## Results are averaged over the levels of: s_awareness, summary1, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.data2_wide_reg4 <- subset(data2_wide, condition == 4| condition == 6)
View(data2_wide_reg4)
extent_null_2 <- clm(as.factor(s_extent) ~ 1, data = data2_wide_reg4, link = "logit")
extent_model8_2 <- clm(as.factor(s_extent) ~ H2_interaction*s_awareness +
summary1 + s_age + s_sex +
s_school + as.factor(s_interest), data = data2_wide_reg4,
link = "logit")
summary(extent_model8_2)## formula:
## as.factor(s_extent) ~ H2_interaction * s_awareness + summary1 + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_reg4
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 685 -1403.90 2853.80 7(0) 7.90e-09 7.1e+05
##
## Coefficients:
## Estimate Std. Error
## H2_interactiondisclaimer.new guideline 0.127107 0.223031
## s_awarenesspass 0.938388 0.205758
## summary1Faerber -0.162547 0.136466
## s_age -0.008477 0.004532
## s_sexmale -0.019523 0.136157
## s_schoolReal 0.121688 0.171059
## s_schoolAbi 0.801180 0.169996
## as.factor(s_interest)5 -0.062240 0.208630
## as.factor(s_interest)6 0.081036 0.211852
## as.factor(s_interest)7 -0.314890 0.228084
## as.factor(s_interest)8 -0.876385 0.220413
## H2_interactiondisclaimer.new guideline:s_awarenesspass 0.179769 0.281711
## z value Pr(>|z|)
## H2_interactiondisclaimer.new guideline 0.570 0.5687
## s_awarenesspass 4.561 5.10e-06 ***
## summary1Faerber -1.191 0.2336
## s_age -1.870 0.0614 .
## s_sexmale -0.143 0.8860
## s_schoolReal 0.711 0.4768
## s_schoolAbi 4.713 2.44e-06 ***
## as.factor(s_interest)5 -0.298 0.7655
## as.factor(s_interest)6 0.383 0.7021
## as.factor(s_interest)7 -1.381 0.1674
## as.factor(s_interest)8 -3.976 7.01e-05 ***
## H2_interactiondisclaimer.new guideline:s_awarenesspass 0.638 0.5234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-4 -6.4502 1.0459 -6.167
## -4|-3 -3.5783 0.3906 -9.162
## -3|-2 -2.7572 0.3471 -7.945
## -2|-1 -1.1149 0.3123 -3.570
## -1|0 -0.6131 0.3079 -1.992
## 0|1 0.6156 0.3089 1.993
## 1|2 1.0569 0.3119 3.389
## 2|3 1.8305 0.3178 5.760
## 3|4 2.3326 0.3234 7.212
## 4|5 3.5177 0.3526 9.976
## 5|6 3.9199 0.3710 10.565
## (13 Beobachtungen als fehlend gelöscht)exp(coef(extent_model8_2))## -6|-4
## 0.001580161
## -4|-3
## 0.027922329
## -3|-2
## 0.063466214
## -2|-1
## 0.327932501
## -1|0
## 0.541656975
## 0|1
## 1.850831433
## 1|2
## 2.877462036
## 2|3
## 6.236700238
## 3|4
## 10.304665335
## 4|5
## 33.707327868
## 5|6
## 50.397042812
## H2_interactiondisclaimer.new guideline
## 1.135538128
## s_awarenesspass
## 2.555859088
## summary1Faerber
## 0.849976130
## s_age
## 0.991558983
## s_sexmale
## 0.980666181
## s_schoolReal
## 1.129401904
## s_schoolAbi
## 2.228168254
## as.factor(s_interest)5
## 0.939657635
## as.factor(s_interest)6
## 1.084409468
## as.factor(s_interest)7
## 0.729868975
## as.factor(s_interest)8
## 0.416285213
## H2_interactiondisclaimer.new guideline:s_awarenesspass
## 1.196941256exp(confint(extent_model8_2))## 2.5 % 97.5 %
## H2_interactiondisclaimer.new guideline 0.7334265 1.7590529
## s_awarenesspass 1.7093048 3.8310222
## summary1Faerber 0.6502987 1.1104647
## s_age 0.9827759 1.0003991
## s_sexmale 0.7509191 1.2807341
## s_schoolReal 0.8076441 1.5796077
## s_schoolAbi 1.5980104 3.1124011
## as.factor(s_interest)5 0.6241000 1.4144816
## as.factor(s_interest)6 0.7158071 1.6429696
## as.factor(s_interest)7 0.4665580 1.1413396
## as.factor(s_interest)8 0.2699372 0.6407366
## H2_interactiondisclaimer.new guideline:s_awarenesspass 0.6889916 2.0795712nagelkerke(fit = extent_model8_2, null = extent_null_2)## $Models
##
## Model: "clm, as.factor(s_extent) ~ H2_interaction * s_awareness + summary1 + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_reg4, logit"
## Null: "clm, as.factor(s_extent) ~ 1, data2_wide_reg4, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0378383
## Cox and Snell (ML) 0.1488760
## Nagelkerke (Cragg and Uhler) 0.1510090
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -12 -55.21 110.42 4.9409e-18
##
## $Number.of.observations
##
## Model: 685
## Null: 685
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H2test_2 = emmeans(extent_model8_2, ~ H2_interaction)## NOTE: Results may be misleading due to involvement in interactionspairs(H2test_2, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - disclaimer.new guideline -0.217 0.14 Inf
## z.ratio p.value
## -1.546 0.1222
##
## Results are averaged over the levels of: s_awareness, summary1, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H2test_2, Letters = letters)## H2_interaction emmean SE df asymp.LCL asymp.UCL .group
## no disclaimer.old guideline 0.176 0.145 Inf -0.108 0.460 a
## disclaimer.new guideline 0.393 0.144 Inf 0.111 0.674 a
##
## Results are averaged over the levels of: s_awareness, summary1, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.H3
H3a
H3a <- subset(data2_wide, condition == 2|condition == 4|condition == 6)
View(H3a)
describeBy(H3a$s_diff,H3a$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 345 0.37 1.93 0 0.37 1.48 -6 6 12 -0.1 0.46 0.1
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 660 0.27 1.94 0 0.18 1.48 -6 6 12 0.39 0.61 0.08wilcox.test(s_diff~disclaimer, data = H3a, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_diff by disclaimer
## W = 120378, p-value = 0.1249
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.590539e-05 2.125303e-05
## sample estimates:
## difference in location
## 5.661916e-06H3a post hoc
H3a_1 <- subset(data2_wide, condition == 2|condition == 6)
View(H3a_1)
describeBy(H3a_1$s_diff,H3a_1$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 345 0.37 1.93 0 0.37 1.48 -6 6 12 -0.1 0.46 0.1
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 330 0.12 1.94 0 0.03 1.48 -4 6 10 0.34 0.13 0.11wilcox.test(s_diff~disclaimer, data = H3a_1, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_diff by disclaimer
## W = 62522, p-value = 0.02371
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.0000133882 0.9999363469
## sample estimates:
## difference in location
## 6.73205e-06H3a_2 <- subset(data2_wide, condition == 4|condition == 6)
View(H3a_2)
describeBy(H3a_2$s_diff,H3a_2$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 345 0.37 1.93 0 0.37 1.48 -6 6 12 -0.1 0.46 0.1
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 330 0.42 1.94 0 0.32 1.48 -6 6 12 0.44 1.04 0.11wilcox.test(s_diff~disclaimer, data = H3a_2, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_diff by disclaimer
## W = 57857, p-value = 0.7051
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.302679e-05 3.720242e-05
## sample estimates:
## difference in location
## 2.559738e-05H3b
H3b <- subset(data2_wide, condition == 1| condition == 2| condition == 3|
condition == 4)
View(H3b)
describeBy(H3b$s_diff,H3b$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 652 0.27 2.02 0 0.22 1.48 -6 6 12 0.12 0.33 0.08
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 660 0.27 1.94 0 0.18 1.48 -6 6 12 0.39 0.61 0.08wilcox.test(s_diff~disclaimer, data = H3b, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_diff by disclaimer
## W = 217553, p-value = 0.72
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.205325e-06 3.457267e-05
## sample estimates:
## difference in location
## 3.252272e-05H3b post hoc
H3b_1 <- subset(data2_wide, condition == 1| condition == 2)
View(H3b_1)
describeBy(H3b_1$s_diff,H3b_1$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 321 0.19 2.04 0 0.17 1.48 -6 6 12 0.02 0.16 0.11
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 330 0.12 1.94 0 0.03 1.48 -4 6 10 0.34 0.13 0.11wilcox.test(s_diff~disclaimer, data = H3b_1, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_diff by disclaimer
## W = 54841, p-value = 0.4232
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.356082e-05 2.575015e-05
## sample estimates:
## difference in location
## 3.936834e-05H3b_2 <- subset(data2_wide, condition == 3| condition == 4)
View(H3b_2)
describeBy(H3b_2$s_diff,H3b_2$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 331 0.35 2.01 0 0.27 1.48 -6 6 12 0.21 0.47 0.11
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 330 0.42 1.94 0 0.32 1.48 -6 6 12 0.44 1.04 0.11wilcox.test(s_diff~disclaimer, data = H3b_2, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_diff by disclaimer
## W = 53775, p-value = 0.7238
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.910076e-05 3.441754e-05
## sample estimates:
## difference in location
## -5.465709e-05H3b_3 <- subset(data2_wide, condition == 2| condition == 3)
View(H3b_3)
describeBy(H3b_3$s_diff,H3b_3$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 331 0.35 2.01 0 0.27 1.48 -6 6 12 0.21 0.47 0.11
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 330 0.12 1.94 0 0.03 1.48 -4 6 10 0.34 0.13 0.11wilcox.test(s_diff~disclaimer, data = H3b_3, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_diff by disclaimer
## W = 58499, p-value = 0.1045
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.456446e-05 6.645169e-05
## sample estimates:
## difference in location
## 4.662093e-05H3b_4 <- subset(data2_wide, condition == 1| condition == 4)
View(H3b_4)
describeBy(H3b_4$s_diff,H3b_4$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 321 0.19 2.04 0 0.17 1.48 -6 6 12 0.02 0.16 0.11
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 330 0.42 1.94 0 0.32 1.48 -6 6 12 0.44 1.04 0.11wilcox.test(s_diff~disclaimer, data = H3b_4, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_diff by disclaimer
## W = 50439, p-value = 0.2779
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.565384e-05 5.297947e-05
## sample estimates:
## difference in location
## -1.49841e-07H3 Logistic Regression
data2_wide$H3_interaction <- data2_wide$H2_interaction
table(data2_wide$H3_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## 357 670
## disclaimer.new guideline
## 1013data2_wide_reg <- subset(data2_wide, condition != 5)
View(data2_wide_reg)
diff_null <- clm(as.factor(s_diff) ~ 1, data = data2_wide_reg, link = "logit")
diff_model1 <- clm(as.factor(s_diff) ~ H3_interaction, data = data2_wide_reg,
link = "logit")
anova(diff_null,diff_model1)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## diff_null as.factor(s_diff) ~ 1 logit flexible
## diff_model1 as.factor(s_diff) ~ H3_interaction logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## diff_null 12 6444.3 -3210.2
## diff_model1 14 6445.9 -3209.0 2.381 2 0.3041diff_model2 <- clm(as.factor(s_diff) ~ H3_interaction + s_awareness,
data = data2_wide_reg, link = "logit")
anova(diff_null,diff_model2)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## diff_null as.factor(s_diff) ~ 1 logit flexible
## diff_model2 as.factor(s_diff) ~ H3_interaction + s_awareness logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## diff_null 12 6444.3 -3210.2
## diff_model2 15 6427.8 -3198.9 22.545 3 5.023e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1diff_model3 <- clm(as.factor(s_diff) ~ H3_interaction*s_awareness,
data = data2_wide_reg, link = "logit")
anova(diff_model2,diff_model3)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## diff_model2 as.factor(s_diff) ~ H3_interaction + s_awareness logit flexible
## diff_model3 as.factor(s_diff) ~ H3_interaction * s_awareness logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## diff_model2 15 6427.8 -3198.9
## diff_model3 17 6430.8 -3198.4 0.9476 2 0.6226diff_model4 <- clm(as.factor(s_diff) ~ H3_interaction*s_awareness + text_order, data = data2_wide_reg, link = "logit")
anova(diff_model2,diff_model4)## Likelihood ratio tests of cumulative link models:
##
## formula: link:
## diff_model2 as.factor(s_diff) ~ H3_interaction + s_awareness logit
## diff_model4 as.factor(s_diff) ~ H3_interaction * s_awareness + text_order logit
## threshold:
## diff_model2 flexible
## diff_model4 flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## diff_model2 15 6427.8 -3198.9
## diff_model4 18 6411.5 -3187.7 22.271 3 5.73e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1diff_model5 <- clm(as.factor(s_diff) ~ H3_interaction*s_awareness + text_order + s_age, data = data2_wide_reg,
link = "logit")
anova(diff_model4,diff_model5)## Likelihood ratio tests of cumulative link models:
##
## formula:
## diff_model4 as.factor(s_diff) ~ H3_interaction * s_awareness + text_order
## diff_model5 as.factor(s_diff) ~ H3_interaction * s_awareness + text_order + s_age
## link: threshold:
## diff_model4 logit flexible
## diff_model5 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## diff_model4 18 6411.5 -3187.7
## diff_model5 19 6407.8 -3184.9 5.7455 1 0.01653 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1diff_model6 <- clm(as.factor(s_diff) ~ H3_interaction*s_awareness + text_order + s_age + s_sex,
data = data2_wide_reg,
link = "logit")
anova(diff_model5,diff_model6)## Likelihood ratio tests of cumulative link models:
##
## formula:
## diff_model5 as.factor(s_diff) ~ H3_interaction * s_awareness + text_order + s_age
## diff_model6 as.factor(s_diff) ~ H3_interaction * s_awareness + text_order + s_age + s_sex
## link: threshold:
## diff_model5 logit flexible
## diff_model6 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## diff_model5 19 6407.8 -3184.9
## diff_model6 20 6407.1 -3183.6 2.6343 1 0.1046diff_model7 <- clm(as.factor(s_diff) ~ H3_interaction*s_awareness + text_order + s_age + s_sex +
s_school, data = data2_wide_reg,
link = "logit")
anova(diff_model5,diff_model7)## Likelihood ratio tests of cumulative link models:
##
## formula:
## diff_model5 as.factor(s_diff) ~ H3_interaction * s_awareness + text_order + s_age
## diff_model7 as.factor(s_diff) ~ H3_interaction * s_awareness + text_order + s_age + s_sex + s_school
## link: threshold:
## diff_model5 logit flexible
## diff_model7 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## diff_model5 19 6407.8 -3184.9
## diff_model7 22 6409.2 -3182.6 4.5365 3 0.2091diff_model8 <- clm(as.factor(s_diff) ~ H3_interaction*s_awareness + text_order + s_age + s_sex +
s_school + as.factor(s_interest), data = data2_wide_reg,
link = "logit")
anova(diff_model5,diff_model8)## Likelihood ratio tests of cumulative link models:
##
## formula:
## diff_model5 as.factor(s_diff) ~ H3_interaction * s_awareness + text_order + s_age
## diff_model8 as.factor(s_diff) ~ H3_interaction * s_awareness + text_order + s_age + s_sex + s_school + as.factor(s_interest)
## link: threshold:
## diff_model5 logit flexible
## diff_model8 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## diff_model5 19 6407.8 -3184.9
## diff_model8 26 6413.1 -3180.6 8.6496 7 0.2788summary(diff_model8)## formula:
## as.factor(s_diff) ~ H3_interaction * s_awareness + text_order + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_reg
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1657 -3180.55 6413.10 7(0) 1.10e-09 1.0e+06
##
## Coefficients:
## Estimate Std. Error
## H3_interactionno disclaimer.new guideline -0.004203 0.205288
## H3_interactiondisclaimer.new guideline -0.095444 0.203037
## s_awarenesspass 0.540993 0.205231
## text_orderFaerber 0.410684 0.088321
## s_age -0.006246 0.002890
## s_sexmale -0.143024 0.088332
## s_schoolReal 0.026193 0.108341
## s_schoolAbi 0.134485 0.110133
## as.factor(s_interest)5 0.175235 0.135269
## as.factor(s_interest)6 0.100804 0.137713
## as.factor(s_interest)7 0.120144 0.148907
## as.factor(s_interest)8 -0.074686 0.142485
## H3_interactionno disclaimer.new guideline:s_awarenesspass -0.207895 0.251160
## H3_interactiondisclaimer.new guideline:s_awarenesspass -0.120352 0.248928
## z value Pr(>|z|)
## H3_interactionno disclaimer.new guideline -0.020 0.98367
## H3_interactiondisclaimer.new guideline -0.470 0.63830
## s_awarenesspass 2.636 0.00839 **
## text_orderFaerber 4.650 3.32e-06 ***
## s_age -2.162 0.03066 *
## s_sexmale -1.619 0.10541
## s_schoolReal 0.242 0.80897
## s_schoolAbi 1.221 0.22205
## as.factor(s_interest)5 1.295 0.19516
## as.factor(s_interest)6 0.732 0.46418
## as.factor(s_interest)7 0.807 0.41976
## as.factor(s_interest)8 -0.524 0.60016
## H3_interactionno disclaimer.new guideline:s_awarenesspass -0.828 0.40782
## H3_interactiondisclaimer.new guideline:s_awarenesspass -0.483 0.62875
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -5.8680 0.5520 -10.630
## -5|-4 -5.3066 0.4445 -11.939
## -4|-3 -3.1082 0.2669 -11.648
## -3|-2 -2.7127 0.2571 -10.550
## -2|-1 -1.2796 0.2412 -5.306
## -1|0 -0.8168 0.2393 -3.413
## 0|1 0.7005 0.2387 2.935
## 1|2 1.2036 0.2399 5.017
## 2|3 2.4419 0.2482 9.840
## 3|4 2.7840 0.2526 11.020
## 4|5 4.1303 0.2919 14.152
## 5|6 4.6722 0.3253 14.364
## (56 Beobachtungen als fehlend gelöscht)exp(coef(diff_model8))## -6|-5
## 2.828484e-03
## -5|-4
## 4.958672e-03
## -4|-3
## 4.468047e-02
## -3|-2
## 6.635460e-02
## -2|-1
## 2.781520e-01
## -1|0
## 4.418229e-01
## 0|1
## 2.014721e+00
## 1|2
## 3.332141e+00
## 2|3
## 1.149435e+01
## 3|4
## 1.618335e+01
## 4|5
## 6.219571e+01
## 5|6
## 1.069331e+02
## H3_interactionno disclaimer.new guideline
## 9.958063e-01
## H3_interactiondisclaimer.new guideline
## 9.089693e-01
## s_awarenesspass
## 1.717712e+00
## text_orderFaerber
## 1.507849e+00
## s_age
## 9.937735e-01
## s_sexmale
## 8.667330e-01
## s_schoolReal
## 1.026539e+00
## s_schoolAbi
## 1.143947e+00
## as.factor(s_interest)5
## 1.191526e+00
## as.factor(s_interest)6
## 1.106059e+00
## as.factor(s_interest)7
## 1.127659e+00
## as.factor(s_interest)8
## 9.280352e-01
## H3_interactionno disclaimer.new guideline:s_awarenesspass
## 8.122921e-01
## H3_interactiondisclaimer.new guideline:s_awarenesspass
## 8.866080e-01exp(confint((diff_model8)))## 2.5 % 97.5 %
## H3_interactionno disclaimer.new guideline 0.6660817 1.4899877
## H3_interactiondisclaimer.new guideline 0.6106643 1.3540269
## s_awarenesspass 1.1494258 2.5706104
## text_orderFaerber 1.2684280 1.7932661
## s_age 0.9881554 0.9994142
## s_sexmale 0.7288709 1.0305012
## s_schoolReal 0.8301181 1.2694337
## s_schoolAbi 0.9219047 1.4197396
## as.factor(s_interest)5 0.9140985 1.5535360
## as.factor(s_interest)6 0.8444120 1.4489208
## as.factor(s_interest)7 0.8422136 1.5100043
## as.factor(s_interest)8 0.7017732 1.2269163
## H3_interactionno disclaimer.new guideline:s_awarenesspass 0.4962519 1.3286122
## H3_interactiondisclaimer.new guideline:s_awarenesspass 0.5440616 1.4439228nagelkerke(fit = diff_model8, null = diff_null)## $Models
##
## Model: "clm, as.factor(s_diff) ~ H3_interaction * s_awareness + text_order + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_reg, logit"
## Null: "clm, as.factor(s_diff) ~ 1, data2_wide_reg, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.00922245
## Cox and Snell (ML) 0.03510290
## Nagelkerke (Cragg and Uhler) 0.03584720
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -14 -29.605 59.211 1.6126e-07
##
## $Number.of.observations
##
## Model: 1657
## Null: 1657
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H3test = emmeans(diff_model8, ~ H3_interaction)## NOTE: Results may be misleading due to involvement in interactionspairs(H3test, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - no disclaimer.new guideline 0.1082 0.126 Inf
## no disclaimer.old guideline - disclaimer.new guideline 0.1556 0.125 Inf
## no disclaimer.new guideline - disclaimer.new guideline 0.0475 0.103 Inf
## z.ratio p.value
## 0.861 0.6649
## 1.248 0.4251
## 0.459 0.8904
##
## Results are averaged over the levels of: s_awareness, text_order, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimatescld(H3test, Letters = letters)## H3_interaction emmean SE df asymp.LCL asymp.UCL .group
## disclaimer.new guideline 0.347 0.109 Inf 0.134 0.560 a
## no disclaimer.new guideline 0.394 0.110 Inf 0.179 0.610 a
## no disclaimer.old guideline 0.502 0.130 Inf 0.247 0.758 a
##
## Results are averaged over the levels of: s_awareness, text_order, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimates
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.Logistic Regression by Group
data2_wide_reg5 <- subset(data2_wide, condition == 2 | condition == 6)
View(data2_wide_reg5)
diff_null_1 <- clm(as.factor(s_diff) ~ 1, data = data2_wide_reg5, link = "logit")
diff_model8_1 <- clm(as.factor(s_diff) ~ H3_interaction*s_awareness + text_order + s_age + s_sex +
s_school + as.factor(s_interest), data = data2_wide_reg5,
link = "logit")
summary(diff_model8_1)## formula:
## as.factor(s_diff) ~ H3_interaction * s_awareness + text_order + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_reg5
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 675 -1297.88 2643.77 7(0) 6.50e-08 9.0e+05
##
## Coefficients:
## Estimate Std. Error
## H3_interactiondisclaimer.new guideline -0.162461 0.248468
## s_awarenesspass 0.511903 0.208378
## text_orderFaerber 0.427983 0.139018
## s_age -0.004515 0.004535
## s_sexmale -0.178657 0.139614
## s_schoolReal 0.237719 0.171886
## s_schoolAbi 0.469633 0.173103
## as.factor(s_interest)5 0.097727 0.212803
## as.factor(s_interest)6 -0.265737 0.219007
## as.factor(s_interest)7 0.126662 0.233023
## as.factor(s_interest)8 -0.145705 0.222113
## H3_interactiondisclaimer.new guideline:s_awarenesspass -0.250421 0.297342
## z value Pr(>|z|)
## H3_interactiondisclaimer.new guideline -0.654 0.51321
## s_awarenesspass 2.457 0.01403 *
## text_orderFaerber 3.079 0.00208 **
## s_age -0.996 0.31941
## s_sexmale -1.280 0.20067
## s_schoolReal 1.383 0.16666
## s_schoolAbi 2.713 0.00667 **
## as.factor(s_interest)5 0.459 0.64606
## as.factor(s_interest)6 -1.213 0.22499
## as.factor(s_interest)7 0.544 0.58674
## as.factor(s_interest)8 -0.656 0.51183
## H3_interactiondisclaimer.new guideline:s_awarenesspass -0.842 0.39968
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -6.2882 1.0444 -6.021
## -5|-4 -5.5936 0.7686 -7.278
## -4|-3 -3.0738 0.3640 -8.445
## -3|-2 -2.5910 0.3423 -7.568
## -2|-1 -1.1443 0.3136 -3.648
## -1|0 -0.6486 0.3103 -2.090
## 0|1 0.7799 0.3108 2.509
## 1|2 1.3353 0.3141 4.251
## 2|3 2.6480 0.3318 7.982
## 3|4 3.0441 0.3420 8.900
## 4|5 4.5942 0.4407 10.424
## 5|6 5.2972 0.5425 9.765
## (27 Beobachtungen als fehlend gelöscht)exp(coef(diff_model8_1))## -6|-5
## 1.858041e-03
## -5|-4
## 3.721608e-03
## -4|-3
## 4.624488e-02
## -3|-2
## 7.494329e-02
## -2|-1
## 3.184457e-01
## -1|0
## 5.227530e-01
## 0|1
## 2.181147e+00
## 1|2
## 3.801060e+00
## 2|3
## 1.412593e+01
## 3|4
## 2.099076e+01
## 4|5
## 9.891049e+01
## 5|6
## 1.997747e+02
## H3_interactiondisclaimer.new guideline
## 8.500493e-01
## s_awarenesspass
## 1.668464e+00
## text_orderFaerber
## 1.534160e+00
## s_age
## 9.954951e-01
## s_sexmale
## 8.363931e-01
## s_schoolReal
## 1.268353e+00
## s_schoolAbi
## 1.599407e+00
## as.factor(s_interest)5
## 1.102662e+00
## as.factor(s_interest)6
## 7.666406e-01
## as.factor(s_interest)7
## 1.135033e+00
## as.factor(s_interest)8
## 8.644129e-01
## H3_interactiondisclaimer.new guideline:s_awarenesspass
## 7.784728e-01exp(confint((diff_model8_1)))## 2.5 % 97.5 %
## H3_interactiondisclaimer.new guideline 0.5222945 1.384156
## s_awarenesspass 1.1098621 2.513249
## text_orderFaerber 1.1688067 2.015979
## s_age 0.9866743 1.004379
## s_sexmale 0.6359648 1.099494
## s_schoolReal 0.9056480 1.777042
## s_schoolAbi 1.1398880 2.247363
## as.factor(s_interest)5 0.7266725 1.674175
## as.factor(s_interest)6 0.4988425 1.177568
## as.factor(s_interest)7 0.7188252 1.792827
## as.factor(s_interest)8 0.5588914 1.335544
## H3_interactiondisclaimer.new guideline:s_awarenesspass 0.4342797 1.393749nagelkerke(fit = diff_model8_1, null = diff_null_1)## $Models
##
## Model: "clm, as.factor(s_diff) ~ H3_interaction * s_awareness + text_order + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_reg5, logit"
## Null: "clm, as.factor(s_diff) ~ 1, data2_wide_reg5, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0137674
## Cox and Snell (ML) 0.0522672
## Nagelkerke (Cragg and Uhler) 0.0533478
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -12 -18.118 36.236 0.00029689
##
## $Number.of.observations
##
## Model: 675
## Null: 675
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H3test_1 = emmeans(diff_model8_1, ~ H3_interaction)## NOTE: Results may be misleading due to involvement in interactionspairs(H3test_1, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - disclaimer.new guideline 0.288 0.15 Inf
## z.ratio p.value
## 1.914 0.0556
##
## Results are averaged over the levels of: s_awareness, text_order, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H3test_1, Letters = letters)## H3_interaction emmean SE df asymp.LCL asymp.UCL .group
## disclaimer.new guideline 0.221 0.187 Inf -0.146 0.589 a
## no disclaimer.old guideline 0.509 0.184 Inf 0.148 0.870 a
##
## Results are averaged over the levels of: s_awareness, text_order, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.data2_wide_reg6 <- subset(data2_wide, condition == 4 | condition == 6)
View(data2_wide_reg6)
diff_null_2 <- clm(as.factor(s_diff) ~ 1, data = data2_wide_reg6, link = "logit")
diff_model8_2 <- clm(as.factor(s_diff) ~ H3_interaction*s_awareness + text_order + s_age + s_sex +
s_school + as.factor(s_interest), data = data2_wide_reg6,
link = "logit")
summary(diff_model8_2)## formula:
## as.factor(s_diff) ~ H3_interaction * s_awareness + text_order + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_reg6
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 675 -1265.62 2579.23 7(0) 5.63e-12 7.2e+05
##
## Coefficients:
## Estimate Std. Error
## H3_interactiondisclaimer.new guideline -0.018587 0.227338
## s_awarenesspass 0.587635 0.211885
## text_orderFaerber 0.448846 0.139860
## s_age -0.003001 0.004543
## s_sexmale -0.092249 0.139324
## s_schoolReal 0.154994 0.176163
## s_schoolAbi 0.271576 0.171963
## as.factor(s_interest)5 0.136431 0.213510
## as.factor(s_interest)6 -0.153997 0.221819
## as.factor(s_interest)7 -0.263025 0.234743
## as.factor(s_interest)8 -0.404837 0.220637
## H3_interactiondisclaimer.new guideline:s_awarenesspass 0.065429 0.288117
## z value Pr(>|z|)
## H3_interactiondisclaimer.new guideline -0.082 0.93484
## s_awarenesspass 2.773 0.00555 **
## text_orderFaerber 3.209 0.00133 **
## s_age -0.661 0.50888
## s_sexmale -0.662 0.50789
## s_schoolReal 0.880 0.37895
## s_schoolAbi 1.579 0.11427
## as.factor(s_interest)5 0.639 0.52283
## as.factor(s_interest)6 -0.694 0.48753
## as.factor(s_interest)7 -1.120 0.26251
## as.factor(s_interest)8 -1.835 0.06653 .
## H3_interactiondisclaimer.new guideline:s_awarenesspass 0.227 0.82035
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -5.4581 0.7708 -7.081
## -5|-4 -4.7623 0.5865 -8.119
## -4|-3 -3.0741 0.3768 -8.158
## -3|-2 -2.6679 0.3561 -7.491
## -2|-1 -1.2626 0.3236 -3.902
## -1|0 -0.7669 0.3200 -2.397
## 0|1 0.8411 0.3199 2.629
## 1|2 1.4047 0.3229 4.350
## 2|3 2.7948 0.3407 8.203
## 3|4 3.0315 0.3463 8.754
## 4|5 4.2642 0.4040 10.555
## 5|6 4.7466 0.4482 10.591
## (23 Beobachtungen als fehlend gelöscht)exp(coef(diff_model8_2))## -6|-5
## 4.261512e-03
## -5|-4
## 8.546240e-03
## -4|-3
## 4.623109e-02
## -3|-2
## 6.939741e-02
## -2|-1
## 2.829156e-01
## -1|0
## 4.644447e-01
## 0|1
## 2.319009e+00
## 1|2
## 4.074329e+00
## 2|3
## 1.635989e+01
## 3|4
## 2.072832e+01
## 4|5
## 7.110448e+01
## 5|6
## 1.151946e+02
## H3_interactiondisclaimer.new guideline
## 9.815845e-01
## s_awarenesspass
## 1.799726e+00
## text_orderFaerber
## 1.566504e+00
## s_age
## 9.970034e-01
## s_sexmale
## 9.118778e-01
## s_schoolReal
## 1.167651e+00
## s_schoolAbi
## 1.312031e+00
## as.factor(s_interest)5
## 1.146176e+00
## as.factor(s_interest)6
## 8.572742e-01
## as.factor(s_interest)7
## 7.687225e-01
## as.factor(s_interest)8
## 6.670855e-01
## H3_interactiondisclaimer.new guideline:s_awarenesspass
## 1.067616e+00exp(confint((diff_model8_2)))## 2.5 % 97.5 %
## H3_interactiondisclaimer.new guideline 0.6286427 1.533369
## s_awarenesspass 1.1892117 2.730148
## text_orderFaerber 1.1915187 2.061965
## s_age 0.9881505 1.005915
## s_sexmale 0.6938016 1.198123
## s_schoolReal 0.8267911 1.649756
## s_schoolAbi 0.9369872 1.839099
## as.factor(s_interest)5 0.7542635 1.742525
## as.factor(s_interest)6 0.5546588 1.323845
## as.factor(s_interest)7 0.4849169 1.217616
## as.factor(s_interest)8 0.4324511 1.027397
## H3_interactiondisclaimer.new guideline:s_awarenesspass 0.6067614 1.877996nagelkerke(fit = diff_model8_2, null = diff_null_1)## $Models
##
## Model: "clm, as.factor(s_diff) ~ H3_interaction * s_awareness + text_order + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_reg6, logit"
## Null: "clm, as.factor(s_diff) ~ 1, data2_wide_reg5, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0382867
## Cox and Snell (ML) 0.1386810
## Nagelkerke (Cragg and Uhler) 0.1415480
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -12 -50.385 100.77 3.9323e-16
##
## $Number.of.observations
##
## Model: 675
## Null: 675
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H3test_2 = emmeans(diff_model8_2, ~ H3_interaction)## NOTE: Results may be misleading due to involvement in interactionspairs(H3test_2, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - disclaimer.new guideline -0.0141 0.144 Inf
## z.ratio p.value
## -0.098 0.9218
##
## Results are averaged over the levels of: s_awareness, text_order, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H3test_2, Letters = letters)## H3_interaction emmean SE df asymp.LCL asymp.UCL .group
## no disclaimer.old guideline 0.416 0.156 Inf 0.110 0.722 a
## disclaimer.new guideline 0.430 0.153 Inf 0.129 0.731 a
##
## Results are averaged over the levels of: s_awareness, text_order, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.H4
H4a
H4a <- subset(data2_wide, condition == 3| condition == 4| condition == 6)
View(H4a)
describeBy(H4a$s_causality, H4a$causality)##
## Descriptive statistics by group
## group: no causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 342 -0.43 3.91 0 -0.46 4.45 -10 10 20 0.12 -0.23 0.21
## ------------------------------------------------------------
## group: causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 645 -0.49 3.95 0 -0.54 2.97 -10 12 22 0.1 -0.19 0.16wilcox.test(s_causality~causality, data = H4a, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_causality by causality
## W = 110914, p-value = 0.8841
## alternative hypothesis: true location shift is not equal to 0H4a post hoc
H4a_1 <- subset(data2_wide, condition == 3| condition == 6)
View(H4a_1)
describeBy(H4a_1$s_causality, H4a_1$causality)##
## Descriptive statistics by group
## group: no causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 342 -0.43 3.91 0 -0.46 4.45 -10 10 20 0.12 -0.23 0.21
## ------------------------------------------------------------
## group: causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 320 -0.6 3.9 0 -0.67 2.97 -9 10 19 0.16 -0.21 0.22wilcox.test(s_causality~causality, data = H4a_1, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_causality by causality
## W = 56246, p-value = 0.5331
## alternative hypothesis: true location shift is not equal to 0H4a_2 <- subset(data2_wide, condition == 4| condition == 6)
View(H4a_2)
describeBy(H4a_2$s_causality, H4a_2$causality)##
## Descriptive statistics by group
## group: no causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 342 -0.43 3.91 0 -0.46 4.45 -10 10 20 0.12 -0.23 0.21
## ------------------------------------------------------------
## group: causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 325 -0.39 4.01 0 -0.41 2.97 -10 12 22 0.04 -0.19 0.22wilcox.test(s_causality~causality, data = H4a_2, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_causality by causality
## W = 54668, p-value = 0.7139
## alternative hypothesis: true location shift is not equal to 0H4b
H4b <- subset(data2_wide, condition == 1| condition == 2|
condition == 3| condition == 4)
View(H4b)
describeBy(H4b$s_causality, H4b$causality)##
## Descriptive statistics by group
## group: no causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 652 -0.65 3.84 0 -0.69 2.97 -10 10 20 0.11 -0.23 0.15
## ------------------------------------------------------------
## group: causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 645 -0.49 3.95 0 -0.54 2.97 -10 12 22 0.1 -0.19 0.16wilcox.test(s_causality~causality, data = H4b, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_causality by causality
## W = 205540, p-value = 0.481
## alternative hypothesis: true location shift is not equal to 0H4b post hoc
H4b_1 <- subset(data2_wide, condition == 1|condition == 3)
View(H4b_1)
describeBy(H4b_1$s_causality, H4b_1$causality)##
## Descriptive statistics by group
## group: no causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 323 -0.28 3.79 0 -0.33 2.97 -8 10 18 0.15 -0.21 0.21
## ------------------------------------------------------------
## group: causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 320 -0.6 3.9 0 -0.67 2.97 -9 10 19 0.16 -0.21 0.22wilcox.test(s_causality~causality, data = H4b_1, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_causality by causality
## W = 54201, p-value = 0.2822
## alternative hypothesis: true location shift is not equal to 0H4b_2 <- subset(data2_wide, condition == 2|condition == 4)
View(H4b_2)
describeBy(H4b_2$s_causality, H4b_2$causality)##
## Descriptive statistics by group
## group: no causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 329 -1.02 3.87 -1 -1.05 4.45 -10 10 20 0.09 -0.3 0.21
## ------------------------------------------------------------
## group: causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 325 -0.39 4.01 0 -0.41 2.97 -10 12 22 0.04 -0.19 0.22wilcox.test(s_causality~causality, data = H4b_2, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_causality by causality
## W = 48566, p-value = 0.0416
## alternative hypothesis: true location shift is not equal to 0H4b_3 <- subset(data2_wide, condition == 1|condition == 4)
View(H4b_3)
describeBy(H4b_3$s_causality, H4b_3$causality)##
## Descriptive statistics by group
## group: no causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 323 -0.28 3.79 0 -0.33 2.97 -8 10 18 0.15 -0.21 0.21
## ------------------------------------------------------------
## group: causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 325 -0.39 4.01 0 -0.41 2.97 -10 12 22 0.04 -0.19 0.22wilcox.test(s_causality~causality, data = H4b_3, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_causality by causality
## W = 52827, p-value = 0.8862
## alternative hypothesis: true location shift is not equal to 0H4b_4 <- subset(data2_wide, condition == 2|condition == 3)
View(H4b_4)
describeBy(H4b_4$s_causality, H4b_4$causality)##
## Descriptive statistics by group
## group: no causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 329 -1.02 3.87 -1 -1.05 4.45 -10 10 20 0.09 -0.3 0.21
## ------------------------------------------------------------
## group: causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 320 -0.6 3.9 0 -0.67 2.97 -9 10 19 0.16 -0.21 0.22wilcox.test(s_causality~causality, data = H4b_4, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_causality by causality
## W = 49946, p-value = 0.2572
## alternative hypothesis: true location shift is not equal to 0H4 Mixed Model
set.seed(288659)
data2_long$id <- as.numeric(data2_long$id)
data2_long$H4_interaction <- interaction(data2_long$causality,
data2_long$version)
data2_long$H4_interaction <- droplevels(data2_long$H4_interaction)
table(data2_long$H4_interaction)##
## no causality statement.old guideline no causality statement.new guideline
## 714 1358
## causality statement.new guideline
## 2010data2_long_reg <- subset(data2_long, condition != 5)
View(data2_long_reg)
causality_null <- clm(as.factor(s_causality) ~ 1, data = data2_long_reg,
link = "logit")
causality_model1 <- clmm(as.factor(s_causality) ~ 1 + (1|id),
data = data2_long_reg)
anova(causality_null,causality_model1)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## causality_null as.factor(s_causality) ~ 1 logit flexible
## causality_model1 as.factor(s_causality) ~ 1 + (1 | id) logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_null 12 14339 -7157.5
## causality_model1 13 14253 -7113.4 88.32 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1causality_model2 <- clmm(as.factor(s_causality) ~ H4_interaction + (1|id),
data = data2_long_reg)
anova(causality_model1,causality_model2)## Likelihood ratio tests of cumulative link models:
##
## formula: link:
## causality_model1 as.factor(s_causality) ~ 1 + (1 | id) logit
## causality_model2 as.factor(s_causality) ~ H4_interaction + (1 | id) logit
## threshold:
## causality_model1 flexible
## causality_model2 flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_model1 13 14253 -7113.4
## causality_model2 15 14256 -7112.8 1.1257 2 0.5696causality_model3 <- clmm(as.factor(s_causality) ~ H4_interaction + s_awareness +
(1|id), data = data2_long_reg)
anova(causality_model2,causality_model3)## Likelihood ratio tests of cumulative link models:
##
## formula:
## causality_model2 as.factor(s_causality) ~ H4_interaction + (1 | id)
## causality_model3 as.factor(s_causality) ~ H4_interaction + s_awareness + (1 | id)
## link: threshold:
## causality_model2 logit flexible
## causality_model3 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_model2 15 14256 -7112.8
## causality_model3 16 14229 -7098.3 28.983 1 7.302e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1causality_model4 <- clmm(as.factor(s_causality) ~ H4_interaction*s_awareness +
(1|id), data = data2_long_reg)## Warning in update.uC(rho): Non finite negative log-likelihood
## at iteration 119anova(causality_model3,causality_model4)## Likelihood ratio tests of cumulative link models:
##
## formula:
## causality_model3 as.factor(s_causality) ~ H4_interaction + s_awareness + (1 | id)
## causality_model4 as.factor(s_causality) ~ H4_interaction * s_awareness + (1 | id)
## link: threshold:
## causality_model3 logit flexible
## causality_model4 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_model3 16 14229 -7098.3
## causality_model4 18 14226 -7095.2 6.1135 2 0.04704 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1causality_model5 <- clmm(as.factor(s_causality) ~ H4_interaction*s_awareness +
summary + (1|id), data = data2_long_reg)
anova(causality_model3,causality_model5)## Likelihood ratio tests of cumulative link models:
##
## formula:
## causality_model3 as.factor(s_causality) ~ H4_interaction + s_awareness + (1 | id)
## causality_model5 as.factor(s_causality) ~ H4_interaction * s_awareness + summary + (1 | id)
## link: threshold:
## causality_model3 logit flexible
## causality_model5 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_model3 16 14229 -7098.3
## causality_model5 19 14221 -7091.3 14.036 3 0.002857 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1causality_model6 <- clmm(as.factor(s_causality) ~ H4_interaction*s_awareness +
summary + text_order + (1|id), data = data2_long_reg)## Warning in update.uC(rho): Non finite negative log-likelihood
## at iteration 131anova(causality_model5,causality_model6)## Likelihood ratio tests of cumulative link models:
##
## formula:
## causality_model5 as.factor(s_causality) ~ H4_interaction * s_awareness + summary + (1 | id)
## causality_model6 as.factor(s_causality) ~ H4_interaction * s_awareness + summary + text_order + (1 | id)
## link: threshold:
## causality_model5 logit flexible
## causality_model6 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_model5 19 14221 -7091.3
## causality_model6 20 14219 -7089.4 3.6952 1 0.05457 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1causality_model7 <- clmm(as.factor(s_causality) ~ H4_interaction*s_awareness +
summary + text_order + s_age + (1|id),
data = data2_long_reg)
anova(causality_model5,causality_model7)## Likelihood ratio tests of cumulative link models:
##
## formula:
## causality_model5 as.factor(s_causality) ~ H4_interaction * s_awareness + summary + (1 | id)
## causality_model7 as.factor(s_causality) ~ H4_interaction * s_awareness + summary + text_order + s_age + (1 | id)
## link: threshold:
## causality_model5 logit flexible
## causality_model7 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_model5 19 14221 -7091.3
## causality_model7 21 14136 -7047.0 88.5 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1causality_model8 <- clmm(as.factor(s_causality) ~ H4_interaction*s_awareness +
summary + text_order + s_age + s_sex + (1|id),
data = data2_long_reg)
anova(causality_model7,causality_model8)## Likelihood ratio tests of cumulative link models:
##
## formula:
## causality_model7 as.factor(s_causality) ~ H4_interaction * s_awareness + summary + text_order + s_age + (1 | id)
## causality_model8 as.factor(s_causality) ~ H4_interaction * s_awareness + summary + text_order + s_age + s_sex + (1 | id)
## link: threshold:
## causality_model7 logit flexible
## causality_model8 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_model7 21 14136 -7047.0
## causality_model8 22 14133 -7044.4 5.18 1 0.02285 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1causality_model9 <- clmm(as.factor(s_causality) ~ H4_interaction*s_awareness +
summary + text_order + s_age + s_sex + s_school +
(1|id), data = data2_long_reg)
anova(causality_model7,causality_model9)## Likelihood ratio tests of cumulative link models:
##
## formula:
## causality_model7 as.factor(s_causality) ~ H4_interaction * s_awareness + summary + text_order + s_age + (1 | id)
## causality_model9 as.factor(s_causality) ~ H4_interaction * s_awareness + summary + text_order + s_age + s_sex + s_school + (1 | id)
## link: threshold:
## causality_model7 logit flexible
## causality_model9 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_model7 21 14136 -7047.0
## causality_model9 24 14096 -7023.9 46.309 3 4.876e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1causality_model10 <- clmm(as.factor(s_causality) ~ H4_interaction*s_awareness +
summary + text_order + s_age + s_sex + s_school +
as.factor(s_interest) + (1|id), data = data2_long_reg)
anova(causality_model9,causality_model10)## Likelihood ratio tests of cumulative link models:
##
## formula:
## causality_model9 as.factor(s_causality) ~ H4_interaction * s_awareness + summary + text_order + s_age + s_sex + s_school + (1 | id)
## causality_model10 as.factor(s_causality) ~ H4_interaction * s_awareness + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id)
## link: threshold:
## causality_model9 logit flexible
## causality_model10 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_model9 24 14096 -7023.9
## causality_model10 28 14072 -7008.1 31.653 4 2.252e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(causality_model10)## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: as.factor(s_causality) ~ H4_interaction * s_awareness + summary +
## text_order + s_age + s_sex + s_school + as.factor(s_interest) +
## (1 | id)
## data: data2_long_reg
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 3346 -7008.06 14072.11 4864(14592) 3.12e-02 1.0e+06
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.6998 0.8365
## Number of groups: id 1707
##
## Coefficients:
## Estimate
## H4_interactionno causality statement.new guideline 0.128746
## H4_interactioncausality statement.new guideline -0.014037
## s_awarenesspass 0.560054
## summaryFaerber -0.167427
## text_orderFaerber 0.157517
## s_age -0.022849
## s_sexmale 0.137656
## s_schoolReal 0.200122
## s_schoolAbi 0.635815
## as.factor(s_interest)5 0.026437
## as.factor(s_interest)6 -0.100110
## as.factor(s_interest)7 -0.210662
## as.factor(s_interest)8 -0.573066
## H4_interactionno causality statement.new guideline:s_awarenesspass -0.320219
## H4_interactioncausality statement.new guideline:s_awarenesspass 0.118785
## Std. Error
## H4_interactionno causality statement.new guideline 0.180151
## H4_interactioncausality statement.new guideline 0.174423
## s_awarenesspass 0.177426
## summaryFaerber 0.062329
## text_orderFaerber 0.074599
## s_age 0.002525
## s_sexmale 0.075034
## s_schoolReal 0.092225
## s_schoolAbi 0.094534
## as.factor(s_interest)5 0.114523
## as.factor(s_interest)6 0.116301
## as.factor(s_interest)7 0.127171
## as.factor(s_interest)8 0.123157
## H4_interactionno causality statement.new guideline:s_awarenesspass 0.217091
## H4_interactioncausality statement.new guideline:s_awarenesspass 0.214019
## z value
## H4_interactionno causality statement.new guideline 0.715
## H4_interactioncausality statement.new guideline -0.080
## s_awarenesspass 3.157
## summaryFaerber -2.686
## text_orderFaerber 2.112
## s_age -9.049
## s_sexmale 1.835
## s_schoolReal 2.170
## s_schoolAbi 6.726
## as.factor(s_interest)5 0.231
## as.factor(s_interest)6 -0.861
## as.factor(s_interest)7 -1.657
## as.factor(s_interest)8 -4.653
## H4_interactionno causality statement.new guideline:s_awarenesspass -1.475
## H4_interactioncausality statement.new guideline:s_awarenesspass 0.555
## Pr(>|z|)
## H4_interactionno causality statement.new guideline 0.47482
## H4_interactioncausality statement.new guideline 0.93586
## s_awarenesspass 0.00160 **
## summaryFaerber 0.00723 **
## text_orderFaerber 0.03473 *
## s_age < 2e-16 ***
## s_sexmale 0.06657 .
## s_schoolReal 0.03001 *
## s_schoolAbi 1.75e-11 ***
## as.factor(s_interest)5 0.81744
## as.factor(s_interest)6 0.38935
## as.factor(s_interest)7 0.09761 .
## as.factor(s_interest)8 3.27e-06 ***
## H4_interactionno causality statement.new guideline:s_awarenesspass 0.14020
## H4_interactioncausality statement.new guideline:s_awarenesspass 0.57888
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -6.7316 0.3751 -17.945
## -5|-4 -6.0785 0.3120 -19.485
## -4|-3 -2.6165 0.2193 -11.931
## -3|-2 -2.1636 0.2161 -10.012
## -2|-1 -1.3085 0.2118 -6.177
## -1|0 -0.8773 0.2105 -4.167
## 0|1 0.3906 0.2100 1.860
## 1|2 0.7958 0.2110 3.772
## 2|3 1.8228 0.2156 8.456
## 3|4 2.2169 0.2184 10.151
## 4|5 3.8264 0.2441 15.674
## 5|6 4.2208 0.2574 16.397
## (80 Beobachtungen als fehlend gelöscht)exp(coef(causality_model10))## -6|-5
## 0.001192662
## -5|-4
## 0.002291605
## -4|-3
## 0.073059762
## -3|-2
## 0.114905968
## -2|-1
## 0.270213031
## -1|0
## 0.415898556
## 0|1
## 1.477834658
## 1|2
## 2.216168478
## 2|3
## 6.188854547
## 3|4
## 9.178383300
## 4|5
## 45.895289519
## 5|6
## 68.089209945
## H4_interactionno causality statement.new guideline
## 1.137401433
## H4_interactioncausality statement.new guideline
## 0.986061512
## s_awarenesspass
## 1.750766673
## summaryFaerber
## 0.845838072
## text_orderFaerber
## 1.170600718
## s_age
## 0.977410071
## s_sexmale
## 1.147580946
## s_schoolReal
## 1.221552276
## s_schoolAbi
## 1.888560451
## as.factor(s_interest)5
## 1.026789415
## as.factor(s_interest)6
## 0.904737468
## as.factor(s_interest)7
## 0.810047938
## as.factor(s_interest)8
## 0.563794369
## H4_interactionno causality statement.new guideline:s_awarenesspass
## 0.725990156
## H4_interactioncausality statement.new guideline:s_awarenesspass
## 1.126128325exp(confint(causality_model10))## 2.5 %
## -6|-5 0.000571748
## -5|-4 0.001243362
## -4|-3 0.047535094
## -3|-2 0.075229893
## -2|-1 0.178399335
## -1|0 0.275294354
## 0|1 0.979161888
## 1|2 1.465559546
## 2|3 4.056332112
## 3|4 5.982364104
## 4|5 28.442310254
## 5|6 41.112481197
## H4_interactionno causality statement.new guideline 0.799041199
## H4_interactioncausality statement.new guideline 0.700542974
## s_awarenesspass 1.236524653
## summaryFaerber 0.748570411
## text_orderFaerber 1.011369247
## s_age 0.972584762
## s_sexmale 0.990636499
## s_schoolReal 1.019553274
## s_schoolAbi 1.569146432
## as.factor(s_interest)5 0.820349456
## as.factor(s_interest)6 0.720322608
## as.factor(s_interest)7 0.631339351
## as.factor(s_interest)8 0.442883331
## H4_interactionno causality statement.new guideline:s_awarenesspass 0.474398106
## H4_interactioncausality statement.new guideline:s_awarenesspass 0.740311909
## 97.5 %
## -6|-5 2.487884e-03
## -5|-4 4.223590e-03
## -4|-3 1.122903e-01
## -3|-2 1.755071e-01
## -2|-1 4.092789e-01
## -1|0 6.283151e-01
## 0|1 2.230474e+00
## 1|2 3.351213e+00
## 2|3 9.442501e+00
## 3|4 1.408184e+01
## 4|5 7.405789e+01
## 5|6 1.127672e+02
## H4_interactionno causality statement.new guideline 1.619043e+00
## H4_interactioncausality statement.new guideline 1.387948e+00
## s_awarenesspass 2.478870e+00
## summaryFaerber 9.557445e-01
## text_orderFaerber 1.354902e+00
## s_age 9.822593e-01
## s_sexmale 1.329390e+00
## s_schoolReal 1.463572e+00
## s_schoolAbi 2.272994e+00
## as.factor(s_interest)5 1.285180e+00
## as.factor(s_interest)6 1.136366e+00
## as.factor(s_interest)7 1.039342e+00
## as.factor(s_interest)8 7.177152e-01
## H4_interactionno causality statement.new guideline:s_awarenesspass 1.111011e+00
## H4_interactioncausality statement.new guideline:s_awarenesspass 1.713014e+00nagelkerke(fit = causality_model10, null = causality_null)## $Models
##
## Model: "clmm, as.factor(s_causality) ~ H4_interaction * s_awareness + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id), data2_long_reg"
## Null: "clm, as.factor(s_causality) ~ 1, data2_long_reg, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0208820
## Cox and Snell (ML) 0.0854639
## Nagelkerke (Cragg and Uhler) 0.0866657
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -16 -149.46 298.93 4.2561e-54
##
## $Number.of.observations
##
## Model: 3346
## Null: 3346
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H4test = emmeans(causality_model10, ~ H4_interaction)## NOTE: Results may be misleading due to involvement in interactionspairs(H4test, adjust = "tukey")## contrast
## no causality statement.old guideline - no causality statement.new guideline
## no causality statement.old guideline - causality statement.new guideline
## no causality statement.new guideline - causality statement.new guideline
## estimate SE df z.ratio p.value
## 0.0314 0.109 Inf 0.289 0.9551
## -0.0454 0.107 Inf -0.424 0.9056
## -0.0767 0.088 Inf -0.871 0.6583
##
## Results are averaged over the levels of: s_awareness, summary, text_order, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimatescld(H4test, Letters = letters)## H4_interaction emmean SE df asymp.LCL asymp.UCL
## no causality statement.new guideline -0.07559 0.0783 Inf -0.229 0.0778
## no causality statement.old guideline -0.04423 0.0989 Inf -0.238 0.1496
## causality statement.new guideline 0.00113 0.0756 Inf -0.147 0.1493
## .group
## a
## a
## a
##
## Results are averaged over the levels of: s_awareness, summary, text_order, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimates
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.Mixed Model by Groups
set.seed(288659)
data2_long_reg1 <- subset(data2_long, condition == 3 | condition == 6)
View(data2_long_reg1)
causality_null_1 <- clm(as.factor(s_causality) ~ 1, data = data2_long_reg1, link = "logit")
causality_model10_1 <- clmm(as.factor(s_causality) ~ H4_interaction*s_awareness +
summary + text_order + s_age + s_sex + s_school +
as.factor(s_interest) + (1|id), data = data2_long_reg1)
summary(causality_model10_1)## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: as.factor(s_causality) ~ H4_interaction * s_awareness + summary +
## text_order + s_age + s_sex + s_school + as.factor(s_interest) +
## (1 | id)
## data: data2_long_reg1
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1352 -2809.00 5670.00 3788(11364) 1.15e-01 7.0e+05
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.6495 0.8059
## Number of groups: id 690
##
## Coefficients:
## Estimate
## H4_interactioncausality statement.new guideline -0.195033
## s_awarenesspass 0.525671
## summaryFaerber -0.124229
## text_orderFaerber 0.165380
## s_age -0.022736
## s_sexmale 0.104350
## s_schoolReal 0.171260
## s_schoolAbi 0.690237
## as.factor(s_interest)5 0.100506
## as.factor(s_interest)6 0.025818
## as.factor(s_interest)7 0.003071
## as.factor(s_interest)8 -0.460653
## H4_interactioncausality statement.new guideline:s_awarenesspass 0.279298
## Std. Error
## H4_interactioncausality statement.new guideline 0.201537
## s_awarenesspass 0.178008
## summaryFaerber 0.098035
## text_orderFaerber 0.117260
## s_age 0.003837
## s_sexmale 0.117628
## s_schoolReal 0.144088
## s_schoolAbi 0.148007
## as.factor(s_interest)5 0.179311
## as.factor(s_interest)6 0.187244
## as.factor(s_interest)7 0.197449
## as.factor(s_interest)8 0.195923
## H4_interactioncausality statement.new guideline:s_awarenesspass 0.247362
## z value
## H4_interactioncausality statement.new guideline -0.968
## s_awarenesspass 2.953
## summaryFaerber -1.267
## text_orderFaerber 1.410
## s_age -5.925
## s_sexmale 0.887
## s_schoolReal 1.189
## s_schoolAbi 4.664
## as.factor(s_interest)5 0.561
## as.factor(s_interest)6 0.138
## as.factor(s_interest)7 0.016
## as.factor(s_interest)8 -2.351
## H4_interactioncausality statement.new guideline:s_awarenesspass 1.129
## Pr(>|z|)
## H4_interactioncausality statement.new guideline 0.33318
## s_awarenesspass 0.00315 **
## summaryFaerber 0.20509
## text_orderFaerber 0.15843
## s_age 3.12e-09 ***
## s_sexmale 0.37501
## s_schoolReal 0.23461
## s_schoolAbi 3.11e-06 ***
## as.factor(s_interest)5 0.57513
## as.factor(s_interest)6 0.89033
## as.factor(s_interest)7 0.98759
## as.factor(s_interest)8 0.01871 *
## H4_interactioncausality statement.new guideline:s_awarenesspass 0.25885
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -7.0313 0.6489 -10.836
## -5|-4 -5.9224 0.4453 -13.299
## -4|-3 -2.5221 0.2891 -8.723
## -3|-2 -2.1210 0.2838 -7.474
## -2|-1 -1.2425 0.2757 -4.506
## -1|0 -0.7586 0.2731 -2.778
## 0|1 0.5464 0.2730 2.001
## 1|2 0.8846 0.2748 3.219
## 2|3 1.8181 0.2826 6.434
## 3|4 2.2503 0.2880 7.813
## 4|5 4.0591 0.3431 11.829
## 5|6 4.4009 0.3654 12.045
## (34 Beobachtungen als fehlend gelöscht)exp(coef(causality_model10_1))## -6|-5
## 8.837417e-04
## -5|-4
## 2.678673e-03
## -4|-3
## 8.028737e-02
## -3|-2
## 1.199086e-01
## -2|-1
## 2.886637e-01
## -1|0
## 4.683080e-01
## 0|1
## 1.727057e+00
## 1|2
## 2.422000e+00
## 2|3
## 6.159836e+00
## 3|4
## 9.490937e+00
## 4|5
## 5.792149e+01
## 5|6
## 8.152514e+01
## H4_interactioncausality statement.new guideline
## 8.228071e-01
## s_awarenesspass
## 1.691593e+00
## summaryFaerber
## 8.831771e-01
## text_orderFaerber
## 1.179841e+00
## s_age
## 9.775209e-01
## s_sexmale
## 1.109989e+00
## s_schoolReal
## 1.186799e+00
## s_schoolAbi
## 1.994189e+00
## as.factor(s_interest)5
## 1.105730e+00
## as.factor(s_interest)6
## 1.026154e+00
## as.factor(s_interest)7
## 1.003076e+00
## as.factor(s_interest)8
## 6.308716e-01
## H4_interactioncausality statement.new guideline:s_awarenesspass
## 1.322202e+00exp(confint(causality_model10_1))## 2.5 %
## -6|-5 2.477461e-04
## -5|-4 1.119027e-03
## -4|-3 4.555433e-02
## -3|-2 6.875255e-02
## -2|-1 1.681485e-01
## -1|0 2.741834e-01
## 0|1 1.011331e+00
## 1|2 1.413301e+00
## 2|3 3.540390e+00
## 3|4 5.396776e+00
## 4|5 2.956341e+01
## 5|6 3.983717e+01
## H4_interactioncausality statement.new guideline 5.543062e-01
## s_awarenesspass 1.193371e+00
## summaryFaerber 7.287866e-01
## text_orderFaerber 9.375867e-01
## s_age 9.701968e-01
## s_sexmale 8.814420e-01
## s_schoolReal 8.948070e-01
## s_schoolAbi 1.492048e+00
## as.factor(s_interest)5 7.780710e-01
## as.factor(s_interest)6 7.109350e-01
## as.factor(s_interest)7 6.811849e-01
## as.factor(s_interest)8 4.297055e-01
## H4_interactioncausality statement.new guideline:s_awarenesspass 8.142224e-01
## 97.5 %
## -6|-5 3.152419e-03
## -5|-4 6.412075e-03
## -4|-3 1.415027e-01
## -3|-2 2.091279e-01
## -2|-1 4.955544e-01
## -1|0 7.998750e-01
## 0|1 2.949307e+00
## 1|2 4.150626e+00
## 2|3 1.071735e+01
## 3|4 1.669105e+01
## 4|5 1.134814e+02
## 5|6 1.668379e+02
## H4_interactioncausality statement.new guideline 1.221368e+00
## s_awarenesspass 2.397819e+00
## summaryFaerber 1.070275e+00
## text_orderFaerber 1.484690e+00
## s_age 9.849002e-01
## s_sexmale 1.397796e+00
## s_schoolReal 1.574074e+00
## s_schoolAbi 2.665322e+00
## as.factor(s_interest)5 1.571372e+00
## as.factor(s_interest)6 1.481138e+00
## as.factor(s_interest)7 1.477076e+00
## as.factor(s_interest)8 9.262134e-01
## H4_interactioncausality statement.new guideline:s_awarenesspass 2.147101e+00nagelkerke(fit = causality_model10_1, null = causality_null_1)## $Models
##
## Model: "clmm, as.factor(s_causality) ~ H4_interaction * s_awareness + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id), data2_long_reg1"
## Null: "clm, as.factor(s_causality) ~ 1, data2_long_reg1, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0239547
## Cox and Snell (ML) 0.0969546
## Nagelkerke (Cragg and Uhler) 0.0983473
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -14 -68.94 137.88 1.871e-22
##
## $Number.of.observations
##
## Model: 1352
## Null: 1352
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H4test_1 = emmeans(causality_model10_1, ~ H4_interaction)## NOTE: Results may be misleading due to involvement in interactionspairs(H4test_1, adjust = "tukey")## contrast
## no causality statement.old guideline - causality statement.new guideline
## estimate SE df z.ratio p.value
## 0.0554 0.123 Inf 0.449 0.6532
##
## Results are averaged over the levels of: s_awareness, summary, text_order, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H4test_1, Letters = letters)## H4_interaction emmean SE df asymp.LCL asymp.UCL
## causality statement.new guideline -0.06402 0.115 Inf -0.290 0.162
## no causality statement.old guideline -0.00864 0.116 Inf -0.235 0.218
## .group
## a
## a
##
## Results are averaged over the levels of: s_awareness, summary, text_order, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.data2_long_reg2 <- subset(data2_long, condition == 4 | condition == 6)
View(data2_long_reg2)
causality_null_2 <- clm(as.factor(s_causality) ~ 1, data = data2_long_reg2, link = "logit")
causality_model10_2 <- clmm(as.factor(s_causality) ~ H4_interaction*s_awareness +
summary + text_order + s_age + s_sex + s_school +
as.factor(s_interest) + (1|id), data = data2_long_reg2)
summary(causality_model10_2)## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: as.factor(s_causality) ~ H4_interaction * s_awareness + summary +
## text_order + s_age + s_sex + s_school + as.factor(s_interest) +
## (1 | id)
## data: data2_long_reg2
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1362 -2827.31 5706.61 4094(12283) 5.43e-02 7.1e+05
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.7421 0.8614
## Number of groups: id 695
##
## Coefficients:
## Estimate
## H4_interactioncausality statement.new guideline 0.117033
## s_awarenesspass 0.580314
## summaryFaerber -0.267177
## text_orderFaerber 0.059018
## s_age -0.025479
## s_sexmale -0.009739
## s_schoolReal -0.047985
## s_schoolAbi 0.461570
## as.factor(s_interest)5 0.136535
## as.factor(s_interest)6 -0.086801
## as.factor(s_interest)7 -0.231873
## as.factor(s_interest)8 -0.547667
## H4_interactioncausality statement.new guideline:s_awarenesspass 0.027179
## Std. Error
## H4_interactioncausality statement.new guideline 0.197819
## s_awarenesspass 0.181472
## summaryFaerber 0.098288
## text_orderFaerber 0.118469
## s_age 0.004035
## s_sexmale 0.119068
## s_schoolReal 0.148525
## s_schoolAbi 0.147938
## as.factor(s_interest)5 0.180805
## as.factor(s_interest)6 0.185835
## as.factor(s_interest)7 0.199484
## as.factor(s_interest)8 0.190699
## H4_interactioncausality statement.new guideline:s_awarenesspass 0.247568
## z value
## H4_interactioncausality statement.new guideline 0.592
## s_awarenesspass 3.198
## summaryFaerber -2.718
## text_orderFaerber 0.498
## s_age -6.315
## s_sexmale -0.082
## s_schoolReal -0.323
## s_schoolAbi 3.120
## as.factor(s_interest)5 0.755
## as.factor(s_interest)6 -0.467
## as.factor(s_interest)7 -1.162
## as.factor(s_interest)8 -2.872
## H4_interactioncausality statement.new guideline:s_awarenesspass 0.110
## Pr(>|z|)
## H4_interactioncausality statement.new guideline 0.55411
## s_awarenesspass 0.00138 **
## summaryFaerber 0.00656 **
## text_orderFaerber 0.61837
## s_age 2.71e-10 ***
## s_sexmale 0.93481
## s_schoolReal 0.74664
## s_schoolAbi 0.00181 **
## as.factor(s_interest)5 0.45016
## as.factor(s_interest)6 0.64044
## as.factor(s_interest)7 0.24509
## as.factor(s_interest)8 0.00408 **
## H4_interactioncausality statement.new guideline:s_awarenesspass 0.91258
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -7.2097 0.5897 -12.226
## -5|-4 -6.3910 0.4566 -13.998
## -4|-3 -2.9928 0.3023 -9.902
## -3|-2 -2.5490 0.2958 -8.619
## -2|-1 -1.7132 0.2868 -5.973
## -1|0 -1.2982 0.2837 -4.576
## 0|1 0.0470 0.2803 0.168
## 1|2 0.4178 0.2815 1.484
## 2|3 1.4883 0.2888 5.154
## 3|4 1.8330 0.2927 6.262
## 4|5 3.3668 0.3316 10.152
## 5|6 3.5796 0.3419 10.471
## (34 Beobachtungen als fehlend gelöscht)exp(coef(causality_model10_2))## -6|-5
## 7.393669e-04
## -5|-4
## 1.676534e-03
## -4|-3
## 5.014476e-02
## -3|-2
## 7.815815e-02
## -2|-1
## 1.802860e-01
## -1|0
## 2.730271e-01
## 0|1
## 1.048123e+00
## 1|2
## 1.518603e+00
## 2|3
## 4.429337e+00
## 3|4
## 6.252702e+00
## 4|5
## 2.898481e+01
## 5|6
## 3.585869e+01
## H4_interactioncausality statement.new guideline
## 1.124157e+00
## s_awarenesspass
## 1.786599e+00
## summaryFaerber
## 7.655374e-01
## text_orderFaerber
## 1.060794e+00
## s_age
## 9.748431e-01
## s_sexmale
## 9.903081e-01
## s_schoolReal
## 9.531479e-01
## s_schoolAbi
## 1.586564e+00
## as.factor(s_interest)5
## 1.146294e+00
## as.factor(s_interest)6
## 9.168595e-01
## as.factor(s_interest)7
## 7.930467e-01
## as.factor(s_interest)8
## 5.782974e-01
## H4_interactioncausality statement.new guideline:s_awarenesspass
## 1.027552e+00exp(confint(causality_model10_2))## 2.5 %
## -6|-5 2.327497e-04
## -5|-4 6.851536e-04
## -4|-3 2.773011e-02
## -3|-2 4.377483e-02
## -2|-1 1.027599e-01
## -1|0 1.565818e-01
## 0|1 6.051013e-01
## 1|2 8.746617e-01
## 2|3 2.515087e+00
## 3|4 3.522799e+00
## 4|5 1.513151e+01
## 5|6 1.834876e+01
## H4_interactioncausality statement.new guideline 7.628575e-01
## s_awarenesspass 1.251866e+00
## summaryFaerber 6.313982e-01
## text_orderFaerber 8.409881e-01
## s_age 9.671643e-01
## s_sexmale 7.841869e-01
## s_schoolReal 7.124192e-01
## s_schoolAbi 1.187224e+00
## as.factor(s_interest)5 8.042560e-01
## as.factor(s_interest)6 6.369707e-01
## as.factor(s_interest)7 5.364113e-01
## as.factor(s_interest)8 3.979495e-01
## H4_interactioncausality statement.new guideline:s_awarenesspass 6.325191e-01
## 97.5 %
## -6|-5 0.002348718
## -5|-4 0.004102390
## -4|-3 0.090677503
## -3|-2 0.139548162
## -2|-1 0.316300961
## -1|0 0.476069393
## 0|1 1.815499305
## 1|2 2.636625235
## 2|3 7.800538673
## 3|4 11.098075410
## 4|5 55.521185306
## 5|6 70.078075025
## H4_interactioncausality statement.new guideline 1.656571980
## s_awarenesspass 2.549744390
## summaryFaerber 0.928174174
## text_orderFaerber 1.338049310
## s_age 0.982582860
## s_sexmale 1.250607730
## s_schoolReal 1.275219565
## s_schoolAbi 2.120227033
## as.factor(s_interest)5 1.633797015
## as.factor(s_interest)6 1.319733082
## as.factor(s_interest)7 1.172464270
## as.factor(s_interest)8 0.840377778
## H4_interactioncausality statement.new guideline:s_awarenesspass 1.669298576nagelkerke(fit = causality_model10_2, null = causality_null_2)## $Models
##
## Model: "clmm, as.factor(s_causality) ~ H4_interaction * s_awareness + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id), data2_long_reg2"
## Null: "clm, as.factor(s_causality) ~ 1, data2_long_reg2, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0226533
## Cox and Snell (ML) 0.0917444
## Nagelkerke (Cragg and Uhler) 0.0930748
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -14 -65.532 131.06 4.1889e-21
##
## $Number.of.observations
##
## Model: 1362
## Null: 1362
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H4test_2 = emmeans(causality_model10_2, ~ H4_interaction)## NOTE: Results may be misleading due to involvement in interactionspairs(H4test_2, adjust = "tukey")## contrast
## no causality statement.old guideline - causality statement.new guideline
## estimate SE df z.ratio p.value
## -0.131 0.124 Inf -1.057 0.2906
##
## Results are averaged over the levels of: s_awareness, summary, text_order, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H4test_2, Letters = letters)## H4_interaction emmean SE df asymp.LCL asymp.UCL
## no causality statement.old guideline -0.0398 0.113 Inf -0.261 0.182
## causality statement.new guideline 0.0908 0.110 Inf -0.125 0.307
## .group
## a
## a
##
## Results are averaged over the levels of: s_awareness, summary, text_order, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.H5
H5a
H5a <- subset(data2_wide, condition == 5|condition == 6)
describeBy(H5a$s_CAMA,H5a$CAMA)##
## Descriptive statistics by group
## group: no CAMA PLS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 344 0.01 3.59 0 0.07 2.97 -9 11 20 -0.04 -0.13 0.19
## ------------------------------------------------------------
## group: CAMA PLS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 308 0.82 4.2 0 0.81 3.71 -11 13 24 0.09 0 0.24wilcox.test(s_CAMA~CAMA, data = H5a, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_CAMA by CAMA
## W = 47200, p-value = 0.01543
## alternative hypothesis: true location shift is not equal to 0H5b
H5b <- subset(data2_wide, condition == 4| condition == 5)
describeBy(H5b$s_CAMA, H5b$CAMA)##
## Descriptive statistics by group
## group: no CAMA PLS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 322 -0.25 3.29 0 -0.24 2.97 -9 9 18 -0.05 0 0.18
## ------------------------------------------------------------
## group: CAMA PLS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 308 0.82 4.2 0 0.81 3.71 -11 13 24 0.09 0 0.24wilcox.test(s_CAMA~CAMA, data = H5b, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_CAMA by CAMA
## W = 41998, p-value = 0.0008061
## alternative hypothesis: true location shift is not equal to 0H5 Logistic Regression
data2_wide$H5_interaction <- interaction(data2_wide$CAMA, data2_wide$version)
data2_wide$H5_interaction <- droplevels(data2_wide$H5_interaction)
data2_wide_H5 <- subset(data2_wide, condition == 4| condition == 5 |condition == 6)
table(data2_wide_H5$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## 357 341 327cama_null <- clm(as.factor(s_CAMA) ~ 1,
data = data2_wide_H5,
link = "logit")
cama_model1 <- clm(as.factor(s_CAMA) ~ H5_interaction, data = data2_wide_H5,
link = "logit")
anova(cama_null,cama_model1)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## cama_null as.factor(s_CAMA) ~ 1 logit flexible
## cama_model1 as.factor(s_CAMA) ~ H5_interaction logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## cama_null 23 5115.9 -2534.9
## cama_model1 25 5107.6 -2528.8 12.235 2 0.002204 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cama_model2 <- clm(as.factor(s_CAMA) ~ H5_interaction + s_awareness,
data = data2_wide_H5, link = "logit")
anova(cama_model1,cama_model2)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## cama_model1 as.factor(s_CAMA) ~ H5_interaction logit flexible
## cama_model2 as.factor(s_CAMA) ~ H5_interaction + s_awareness logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## cama_model1 25 5107.6 -2528.8
## cama_model2 26 5033.3 -2490.6 76.369 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cama_model3 <- clm(as.factor(s_CAMA) ~ H5_interaction*s_awareness,
data = data2_wide_H5, link = "logit")
anova(cama_model2,cama_model3)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## cama_model2 as.factor(s_CAMA) ~ H5_interaction + s_awareness logit flexible
## cama_model3 as.factor(s_CAMA) ~ H5_interaction * s_awareness logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## cama_model2 26 5033.3 -2490.6
## cama_model3 28 5030.2 -2487.1 7.0659 2 0.02922 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cama_model4 <- clm(as.factor(s_CAMA) ~ H5_interaction*s_awareness +
text_order, data = data2_wide_H5, link = "logit")
anova(cama_model3,cama_model4)## Likelihood ratio tests of cumulative link models:
##
## formula: link:
## cama_model3 as.factor(s_CAMA) ~ H5_interaction * s_awareness logit
## cama_model4 as.factor(s_CAMA) ~ H5_interaction * s_awareness + text_order logit
## threshold:
## cama_model3 flexible
## cama_model4 flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## cama_model3 28 5030.2 -2487.1
## cama_model4 29 5028.4 -2485.2 3.8341 1 0.05022 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cama_model5 <- clm(as.factor(s_CAMA) ~ H5_interaction*s_awareness +
text_order + s_age, data = data2_wide_H5, link = "logit")
anova(cama_model3,cama_model5)## Likelihood ratio tests of cumulative link models:
##
## formula:
## cama_model3 as.factor(s_CAMA) ~ H5_interaction * s_awareness
## cama_model5 as.factor(s_CAMA) ~ H5_interaction * s_awareness + text_order + s_age
## link: threshold:
## cama_model3 logit flexible
## cama_model5 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## cama_model3 28 5030.2 -2487.1
## cama_model5 30 5017.5 -2478.7 16.724 2 0.0002336 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cama_model6 <- clm(as.factor(s_CAMA) ~ H5_interaction*s_awareness +
text_order + s_age + s_sex, data = data2_wide_H5,
link = "logit")
anova(cama_model5,cama_model6)## Likelihood ratio tests of cumulative link models:
##
## formula:
## cama_model5 as.factor(s_CAMA) ~ H5_interaction * s_awareness + text_order + s_age
## cama_model6 as.factor(s_CAMA) ~ H5_interaction * s_awareness + text_order + s_age + s_sex
## link: threshold:
## cama_model5 logit flexible
## cama_model6 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## cama_model5 30 5017.5 -2478.7
## cama_model6 31 5019.0 -2478.5 0.4983 1 0.4803cama_model7 <- clm(as.factor(s_CAMA) ~ H5_interaction*s_awareness +
text_order + s_age + s_sex + s_school, data = data2_wide_H5,
link = "logit")
anova(cama_model5,cama_model7)## Likelihood ratio tests of cumulative link models:
##
## formula:
## cama_model5 as.factor(s_CAMA) ~ H5_interaction * s_awareness + text_order + s_age
## cama_model7 as.factor(s_CAMA) ~ H5_interaction * s_awareness + text_order + s_age + s_sex + s_school
## link: threshold:
## cama_model5 logit flexible
## cama_model7 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## cama_model5 30 5017.5 -2478.7
## cama_model7 33 4987.2 -2460.6 36.291 3 6.499e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cama_model8 <- clm(as.factor(s_CAMA) ~ H5_interaction*s_awareness +
text_order + s_age + s_sex + s_school +
as.factor(s_interest), data = data2_wide_H5, link = "logit")
anova(cama_model7,cama_model8)## Likelihood ratio tests of cumulative link models:
##
## formula:
## cama_model7 as.factor(s_CAMA) ~ H5_interaction * s_awareness + text_order + s_age + s_sex + s_school
## cama_model8 as.factor(s_CAMA) ~ H5_interaction * s_awareness + text_order + s_age + s_sex + s_school + as.factor(s_interest)
## link: threshold:
## cama_model7 logit flexible
## cama_model8 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## cama_model7 33 4987.2 -2460.6
## cama_model8 37 4985.3 -2455.7 9.8634 4 0.04279 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(cama_model8)## formula:
## as.factor(s_CAMA) ~ H5_interaction * s_awareness + text_order + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_H5
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 974 -2455.66 4985.33 8(1) 1.67e-08 1.7e+06
##
## Coefficients:
## Estimate Std. Error
## H5_interactionno CAMA PLS.new guideline 0.324783 0.230019
## H5_interactionCAMA PLS.new guideline 0.256503 0.256379
## s_awarenesspass 1.252284 0.210605
## text_orderFaerber -0.163419 0.113787
## s_age -0.013606 0.003899
## s_sexmale 0.053372 0.113759
## s_schoolReal 0.105607 0.139628
## s_schoolAbi 0.829416 0.144582
## as.factor(s_interest)5 -0.213281 0.173145
## as.factor(s_interest)6 -0.232508 0.176000
## as.factor(s_interest)7 -0.244103 0.188818
## as.factor(s_interest)8 -0.568490 0.182575
## H5_interactionno CAMA PLS.new guideline:s_awarenesspass -0.586188 0.285307
## H5_interactionCAMA PLS.new guideline:s_awarenesspass 0.213211 0.306523
## z value Pr(>|z|)
## H5_interactionno CAMA PLS.new guideline 1.412 0.157955
## H5_interactionCAMA PLS.new guideline 1.000 0.317076
## s_awarenesspass 5.946 2.75e-09 ***
## text_orderFaerber -1.436 0.150949
## s_age -3.490 0.000484 ***
## s_sexmale 0.469 0.638949
## s_schoolReal 0.756 0.449445
## s_schoolAbi 5.737 9.66e-09 ***
## as.factor(s_interest)5 -1.232 0.218021
## as.factor(s_interest)6 -1.321 0.186479
## as.factor(s_interest)7 -1.293 0.196082
## as.factor(s_interest)8 -3.114 0.001847 **
## H5_interactionno CAMA PLS.new guideline:s_awarenesspass -2.055 0.039919 *
## H5_interactionCAMA PLS.new guideline:s_awarenesspass 0.696 0.486693
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -11|-9 -6.7914 1.0379 -6.543
## -9|-8 -5.1790 0.5266 -9.835
## -8|-7 -4.9955 0.4939 -10.115
## -7|-6 -2.6850 0.3084 -8.707
## -6|-5 -2.2489 0.2985 -7.533
## -5|-4 -1.7458 0.2908 -6.004
## -4|-3 -1.5155 0.2884 -5.256
## -3|-2 -1.1399 0.2859 -3.987
## -2|-1 -0.7217 0.2843 -2.539
## -1|0 -0.2458 0.2834 -0.867
## 0|1 0.8123 0.2850 2.850
## 1|2 1.2593 0.2870 4.387
## 2|3 1.5438 0.2886 5.349
## 3|4 1.9899 0.2917 6.822
## 4|5 2.4026 0.2956 8.128
## 5|6 3.0583 0.3055 10.011
## 6|7 3.4683 0.3152 11.004
## 7|8 4.2089 0.3443 12.223
## 8|9 4.5915 0.3685 12.461
## 9|10 5.7127 0.4972 11.489
## 10|11 5.8971 0.5297 11.133
## 11|12 6.8204 0.7620 8.950
## 12|13 7.5166 1.0396 7.230
## (51 Beobachtungen als fehlend gelöscht)exp(coef(cama_model8))## -11|-9
## 1.123446e-03
## -9|-8
## 5.633819e-03
## -8|-7
## 6.768362e-03
## -7|-6
## 6.822442e-02
## -6|-5
## 1.055106e-01
## -5|-4
## 1.745017e-01
## -4|-3
## 2.196893e-01
## -3|-2
## 3.198537e-01
## -2|-1
## 4.859392e-01
## -1|0
## 7.820880e-01
## 0|1
## 2.252987e+00
## 1|2
## 3.523066e+00
## 2|3
## 4.682308e+00
## 3|4
## 7.314730e+00
## 4|5
## 1.105160e+01
## 5|6
## 2.129028e+01
## 6|7
## 3.208148e+01
## 7|8
## 6.728468e+01
## 8|9
## 9.863891e+01
## 9|10
## 3.026971e+02
## 10|11
## 3.639738e+02
## 11|12
## 9.163658e+02
## 12|13
## 1.838318e+03
## H5_interactionno CAMA PLS.new guideline
## 1.383731e+00
## H5_interactionCAMA PLS.new guideline
## 1.292403e+00
## s_awarenesspass
## 3.498324e+00
## text_orderFaerber
## 8.492354e-01
## s_age
## 9.864860e-01
## s_sexmale
## 1.054822e+00
## s_schoolReal
## 1.111385e+00
## s_schoolAbi
## 2.291981e+00
## as.factor(s_interest)5
## 8.079293e-01
## as.factor(s_interest)6
## 7.925435e-01
## as.factor(s_interest)7
## 7.834067e-01
## as.factor(s_interest)8
## 5.663799e-01
## H5_interactionno CAMA PLS.new guideline:s_awarenesspass
## 5.564445e-01
## H5_interactionCAMA PLS.new guideline:s_awarenesspass
## 1.237646e+00exp(confint(cama_model8))## 2.5 % 97.5 %
## H5_interactionno CAMA PLS.new guideline 0.8815927 2.1730441
## H5_interactionCAMA PLS.new guideline 0.7813097 2.1358281
## s_awarenesspass 2.3169359 5.2922804
## text_orderFaerber 0.6793523 1.0613211
## s_age 0.9789612 0.9940429
## s_sexmale 0.8440127 1.3184195
## s_schoolReal 0.8452418 1.4613253
## s_schoolAbi 1.7275020 3.0452379
## as.factor(s_interest)5 0.5752706 1.1343118
## as.factor(s_interest)6 0.5610761 1.1187813
## as.factor(s_interest)7 0.5408258 1.1340259
## as.factor(s_interest)8 0.3957564 0.8097473
## H5_interactionno CAMA PLS.new guideline:s_awarenesspass 0.3178883 0.9730524
## H5_interactionCAMA PLS.new guideline:s_awarenesspass 0.6789073 2.2585905nagelkerke(fit = cama_model8, null = cama_null)## $Models
##
## Model: "clm, as.factor(s_CAMA) ~ H5_interaction * s_awareness + text_order + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_H5, logit"
## Null: "clm, as.factor(s_CAMA) ~ 1, data2_wide_H5, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0312726
## Cox and Snell (ML) 0.1502220
## Nagelkerke (Cragg and Uhler) 0.1510510
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -14 -79.274 158.55 1.3894e-26
##
## $Number.of.observations
##
## Model: 974
## Null: 974
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H5test = emmeans(cama_model8, ~ H5_interaction)## NOTE: Results may be misleading due to involvement in interactionspairs(H5test, adjust = "none")## contrast estimate SE df
## no CAMA PLS.old guideline - no CAMA PLS.new guideline -0.0317 0.142 Inf
## no CAMA PLS.old guideline - CAMA PLS.new guideline -0.3631 0.153 Inf
## no CAMA PLS.new guideline - CAMA PLS.new guideline -0.3314 0.150 Inf
## z.ratio p.value
## -0.223 0.8238
## -2.368 0.0179
## -2.202 0.0276
##
## Results are averaged over the levels of: s_awareness, text_order, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H5test, Letters = letters)## H5_interaction emmean SE df asymp.LCL asymp.UCL .group
## no CAMA PLS.old guideline -0.936 0.162 Inf -1.254 -0.619 a
## no CAMA PLS.new guideline -0.905 0.161 Inf -1.220 -0.589 ab
## CAMA PLS.new guideline -0.573 0.168 Inf -0.903 -0.244 b
##
## Results are averaged over the levels of: s_awareness, text_order, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimates
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.H6
data2_wide$user_experience <- rowMeans(data2_wide[,c("accessibility",
"understanding",
"empowerment")])
psych::describe(data2_wide$user_experience)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1944 5.31 1.41 5.33 5.35 1.48 1 8 7 -0.27 -0.25 0.03data2_wide$version <- relevel(data2_wide$version, ref = "new guideline")
# Prep long dataset and separate datasets for Faerber and Barth
data2_long$user_experience <- rowMeans(data2_long[,c("accessibility",
"understanding",
"empowerment")])
psych::describe(data2_long$user_experience)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3983 5.31 1.54 5.33 5.36 1.48 1 8 7 -0.32 -0.3 0.02data2_long$version <- relevel(data2_long$version, ref = "new guideline")
data2_long_faerber <- filter(data2_long, summary == "Faerber")
data2_long_barth <- filter(data2_long, summary == "Barth")Overall User Experience
describeBy(data2_wide$user_experience, data2_wide$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1604 5.3 1.41 5.33 5.33 1.48 1 8 7 -0.27 -0.25 0.04
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 340 5.36 1.44 5.33 5.4 1.48 1 8 7 -0.28 -0.25 0.08equiv.test(user_experience~version, data = data2_wide, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: user_experience by version
## t = -0.72935, df = 1942.0000, ncp = 3.3498, p-value = 0.004391
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.04354523# For Faerber
describeBy(data2_long_faerber$user_experience,data2_long_faerber$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1638 5.27 1.55 5.33 5.32 1.48 1 8 7 -0.32 -0.24 0.04
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 347 5.25 1.6 5.33 5.32 1.48 1 8 7 -0.36 -0.36 0.09equiv.test(user_experience~version, data = data2_long_faerber, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: user_experience by version
## t = 0.14658, df = 1983.0000, ncp = 3.3843, p-value = 0.0002071
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.008662275# For Barth
describeBy(data2_long_barth$user_experience,data2_long_barth$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1648 5.33 1.52 5.33 5.38 1.48 1 8 7 -0.31 -0.34 0.04
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 350 5.43 1.5 5.33 5.47 1.48 1 8 7 -0.23 -0.45 0.08equiv.test(user_experience~version, data = data2_long_barth, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: user_experience by version
## t = -1.1461, df = 1996.0000, ncp = 3.3982, p-value = 0.01217
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.06745572# Post Hoc Tests Overall User Experience
data2_wide_1 <- subset(data2_wide, condition == 1 | condition == 6)
data2_wide_2 <- subset(data2_wide, condition == 2 | condition == 6)
data2_wide_3 <- subset(data2_wide, condition == 3 | condition == 6)
data2_wide_4 <- subset(data2_wide, condition == 4 | condition == 6)
data2_wide_5 <- subset(data2_wide, condition == 5 | condition == 6)
table(data2_wide_1$condition)##
## 1 2 3 4 5 6
## 334 0 0 0 0 357table(data2_wide_2$condition)##
## 1 2 3 4 5 6
## 0 345 0 0 0 357table(data2_wide_3$condition)##
## 1 2 3 4 5 6
## 0 0 336 0 0 357table(data2_wide_4$condition)##
## 1 2 3 4 5 6
## 0 0 0 341 0 357table(data2_wide_5$condition)##
## 1 2 3 4 5 6
## 0 0 0 0 327 357equiv.test(user_experience~version, data = data2_wide_1, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: user_experience by version
## t = -1.4838, df = 655.0000, ncp = 2.5616, p-value = 0.1406
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.1158457equiv.test(user_experience~version, data = data2_wide_2, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: user_experience by version
## t = 0.51775, df = 672.000, ncp = 2.596, p-value = 0.0009251
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.03988743equiv.test(user_experience~version, data = data2_wide_3, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: user_experience by version
## t = 0.38989, df = 649.0000, ncp = 2.5489, p-value = 0.001649
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.03059216equiv.test(user_experience~version, data = data2_wide_4, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: user_experience by version
## t = -0.47762, df = 668.0000, ncp = 2.5881, p-value = 0.01741
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.03690814equiv.test(user_experience~version, data = data2_wide_5, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: user_experience by version
## t = -1.7543, df = 650.0000, ncp = 2.5511, p-value = 0.2129
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.1375369Accessibility
describeBy(data2_wide$accessibility,data2_wide$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1651 5.53 1.63 5.5 5.6 1.48 1 8 7 -0.37 -0.44 0.04
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 353 5.57 1.64 5.5 5.65 1.48 1 8 7 -0.35 -0.4 0.09equiv.test(accessibility~version, data = data2_wide, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: accessibility by version
## t = -0.4836, df = 2002.0000, ncp = 3.4107, p-value = 0.001711
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.02835807# For Faerber
describeBy(data2_long_faerber$accessibility,data2_long_faerber$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1667 5.53 1.84 6 5.65 1.48 1 8 7 -0.44 -0.49 0.04
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 355 5.53 1.89 6 5.67 1.48 1 8 7 -0.47 -0.46 0.1equiv.test(accessibility~version, data = data2_long_faerber, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: accessibility by version
## t = -0.01559, df = 2020.0000, ncp = 3.4215, p-value = 0.0003297
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.0009113073# For Barth
describeBy(data2_long_barth$accessibility,data2_long_barth$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1668 5.53 1.82 6 5.64 1.48 1 8 7 -0.42 -0.58 0.04
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 355 5.61 1.75 6 5.71 1.48 1 8 7 -0.3 -0.69 0.09equiv.test(accessibility~version, data = data2_long_barth, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: accessibility by version
## t = -0.79426, df = 2021.0000, ncp = 3.4217, p-value = 0.004303
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.04642451# Post Hoc Tests Accessibility
equiv.test(accessibility~version, data = data2_wide_1, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: accessibility by version
## t = -0.977, df = 676.0000, ncp = 2.6016, p-value = 0.05214
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.0751067equiv.test(accessibility~version, data = data2_wide_2, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: accessibility by version
## t = 1.0276, df = 692.000, ncp = 2.634, p-value = 0.0001262
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.07802283equiv.test(accessibility~version, data = data2_wide_3, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: accessibility by version
## t = 0.60163, df = 674.0000, ncp = 2.5974, p-value = 0.0006909
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.0463245equiv.test(accessibility~version, data = data2_wide_4, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: accessibility by version
## t = -0.56817, df = 688.0000, ncp = 2.6261, p-value = 0.0198
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.04327161equiv.test(accessibility~version, data = data2_wide_5, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: accessibility by version
## t = -1.98, df = 676.0000, ncp = 2.6016, p-value = 0.2672
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.1522127Understanding
describeBy(data2_wide$understanding,data2_wide$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1647 5.58 1.49 5.5 5.64 1.48 1 8 7 -0.37 -0.34 0.04
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 350 5.7 1.55 6 5.78 1.48 1 8 7 -0.54 -0.02 0.08equiv.test(understanding~version, data = data2_wide, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: understanding by version
## t = -1.3633, df = 1995.000, ncp = 3.398, p-value = 0.02095
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.08023921# For Faerber
describeBy(data2_long_faerber$understanding,data2_long_faerber$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1665 5.53 1.73 6 5.63 1.48 1 8 7 -0.45 -0.34 0.04
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 354 5.55 1.73 6 5.64 1.48 1 8 7 -0.47 -0.36 0.09equiv.test(understanding~version, data = data2_long_faerber, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: understanding by version
## t = -0.13493, df = 2017.0000, ncp = 3.4172, p-value = 0.0005149
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.007897128# For Barth
describeBy(data2_long_barth$understanding,data2_long_barth$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1666 5.62 1.68 6 5.72 1.48 1 8 7 -0.43 -0.36 0.04
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 353 5.81 1.72 6 5.94 1.48 1 8 7 -0.54 -0.22 0.09equiv.test(understanding~version, data = data2_long_barth, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: understanding by version
## t = -1.9398, df = 2017.0000, ncp = 3.4134, p-value = 0.07036
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.113658# Post Hoc Tests Understanding
equiv.test(understanding~version, data = data2_wide_1, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: understanding by version
## t = -2.0366, df = 673.0000, ncp = 2.5963, p-value = 0.2879
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.1568885equiv.test(understanding~version, data = data2_wide_2, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: understanding by version
## t = -0.3228, df = 690.0000, ncp = 2.6304, p-value = 0.01051
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.02454351equiv.test(understanding~version, data = data2_wide_3, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: understanding by version
## t = -0.064833, df = 672.0000, ncp = 2.5942, p-value = 0.005713
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.004998248equiv.test(understanding~version, data = data2_wide_4, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: understanding by version
## t = -0.61154, df = 682.0000, ncp = 2.6146, p-value = 0.02259
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.04677854equiv.test(understanding~version, data = data2_wide_5, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: understanding by version
## t = -2.121, df = 670.00, ncp = 2.59, p-value = 0.3195
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.1637779Empowerment
describeBy(data2_wide$empowerment,data2_wide$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1651 4.78 1.61 5 4.81 1.48 1 8 7 -0.16 -0.34 0.04
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 348 4.74 1.74 5 4.79 1.48 1 8 7 -0.22 -0.44 0.09equiv.test(empowerment~version, data = data2_wide, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: empowerment by version
## t = 0.42973, df = 1997.0000, ncp = 3.3907, p-value = 6.665e-05
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.02534755# For Faerber
describeBy(data2_long_faerber$empowerment,data2_long_faerber$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1664 4.74 1.8 5 4.77 1.48 1 8 7 -0.15 -0.48 0.04
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 352 4.62 1.92 5 4.7 1.48 1 8 7 -0.24 -0.64 0.1equiv.test(empowerment~version, data = data2_long_faerber, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: empowerment by version
## t = 1.0462, df = 2014.000, ncp = 3.409, p-value = 4.204e-06
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.06137999# For Barth
describeBy(data2_long_barth$empowerment,data2_long_barth$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1670 4.84 1.78 5 4.88 1.48 1 8 7 -0.2 -0.44 0.04
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 353 4.86 1.86 5 4.9 1.48 1 8 7 -0.19 -0.56 0.1equiv.test(empowerment~version, data = data2_long_barth, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: empowerment by version
## t = -0.18637, df = 2021.0000, ncp = 3.4141, p-value = 0.0006238
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.01091782# Post Hoc Tests Empowerment
equiv.test(empowerment~version, data = data2_wide_1, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: empowerment by version
## t = -0.68759, df = 673.0000, ncp = 2.5968, p-value = 0.02812
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.05295612equiv.test(empowerment~version, data = data2_wide_2, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: empowerment by version
## t = 1.0572, df = 686.0000, ncp = 2.6228, p-value = 0.0001175
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.08061926equiv.test(empowerment~version, data = data2_wide_3, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: empowerment by version
## t = 1.07, df = 672.0000, ncp = 2.5948, p-value = 0.0001247
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.08247223equiv.test(empowerment~version, data = data2_wide_4, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: empowerment by version
## t = 0.34666, df = 685.0000, ncp = 2.6208, p-value = 0.001502
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.02645423equiv.test(empowerment~version, data = data2_wide_5, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: empowerment by version
## t = -0.20839, df = 665.0000, ncp = 2.5802, p-value = 0.00885
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.01615317Research Questions
RQ1
describeBy(data2_wide$s_funding,data2_wide$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1620 2.52 5.4 2 2.5 5.93 -12 12 24 0.06 -0.73 0.13
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 344 2.72 5.52 2 2.7 5.93 -10 12 22 0.14 -0.81 0.3wilcox.test(s_funding~version, data = data2_wide, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_funding by version
## W = 273670, p-value = 0.6017
## alternative hypothesis: true location shift is not equal to 0RQ1 Mixed Model
sum(is.na(data2_long$disclaimer))## [1] 0sum(is.na(data2_long$s_awareness))## [1] 0sum(is.na(data2_long$text_order))## [1] 0sum(is.na(data2_long$s_age))## [1] 2data2_long <- data2_long %>% drop_na(s_age)
sum(is.na(data2_long$s_sex))## [1] 0sum(is.na(data2_long$s_school))## [1] 0sum(is.na(data2_long$s_interest))## [1] 0set.seed(288659)
funding_null <- clm(as.factor(s_funding) ~ 1,
data = data2_long,
link = "logit")
funding_model1 <- clmm(as.factor(s_funding) ~ 1 + (1|id),
data = data2_long)
anova(funding_null,funding_model1)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## funding_null as.factor(s_funding) ~ 1 logit flexible
## funding_model1 as.factor(s_funding) ~ 1 + (1 | id) logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_null 12 17675 -8825.3
## funding_model1 13 17280 -8627.0 396.78 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1funding_model2 <- clmm(as.factor(s_funding) ~ version + (1|id),
data = data2_long)
anova(funding_model1,funding_model2)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## funding_model1 as.factor(s_funding) ~ 1 + (1 | id) logit flexible
## funding_model2 as.factor(s_funding) ~ version + (1 | id) logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_model1 13 17280 -8627.0
## funding_model2 14 17281 -8626.7 0.4621 1 0.4966funding_model3 <- clmm(as.factor(s_funding) ~ version + s_awareness + (1|id),
data = data2_long)
anova(funding_model1,funding_model3)## Likelihood ratio tests of cumulative link models:
##
## formula: link:
## funding_model1 as.factor(s_funding) ~ 1 + (1 | id) logit
## funding_model3 as.factor(s_funding) ~ version + s_awareness + (1 | id) logit
## threshold:
## funding_model1 flexible
## funding_model3 flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_model1 13 17280 -8627.0
## funding_model3 15 16960 -8464.9 324.16 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1funding_model4 <- clmm(as.factor(s_funding) ~ version*s_awareness + (1|id),
data = data2_long)
anova(funding_model3, funding_model4)## Likelihood ratio tests of cumulative link models:
##
## formula: link:
## funding_model3 as.factor(s_funding) ~ version + s_awareness + (1 | id) logit
## funding_model4 as.factor(s_funding) ~ version * s_awareness + (1 | id) logit
## threshold:
## funding_model3 flexible
## funding_model4 flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_model3 15 16960 -8464.9
## funding_model4 16 16962 -8464.8 0.0616 1 0.8039funding_model5 <- clmm(as.factor(s_funding) ~ version*s_awareness + summary +
(1|id), data = data2_long)
anova(funding_model3, funding_model5)## Likelihood ratio tests of cumulative link models:
##
## formula:
## funding_model3 as.factor(s_funding) ~ version + s_awareness + (1 | id)
## funding_model5 as.factor(s_funding) ~ version * s_awareness + summary + (1 | id)
## link: threshold:
## funding_model3 logit flexible
## funding_model5 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_model3 15 16960 -8464.9
## funding_model5 17 16951 -8458.5 12.798 2 0.001663 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1funding_model6 <- clmm(as.factor(s_funding) ~ version*s_awareness + summary +
text_order + (1|id), data = data2_long)
anova(funding_model5, funding_model6)## Likelihood ratio tests of cumulative link models:
##
## formula:
## funding_model5 as.factor(s_funding) ~ version * s_awareness + summary + (1 | id)
## funding_model6 as.factor(s_funding) ~ version * s_awareness + summary + text_order + (1 | id)
## link: threshold:
## funding_model5 logit flexible
## funding_model6 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_model5 17 16951 -8458.5
## funding_model6 18 16949 -8456.5 4.0466 1 0.04426 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1funding_model7 <- clmm(as.factor(s_funding) ~ version*s_awareness + summary +
text_order + s_age + (1|id), data = data2_long)
anova(funding_model6, funding_model7)## Likelihood ratio tests of cumulative link models:
##
## formula:
## funding_model6 as.factor(s_funding) ~ version * s_awareness + summary + text_order + (1 | id)
## funding_model7 as.factor(s_funding) ~ version * s_awareness + summary + text_order + s_age + (1 | id)
## link: threshold:
## funding_model6 logit flexible
## funding_model7 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_model6 18 16949 -8456.5
## funding_model7 19 16918 -8440.0 32.825 1 1.009e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1funding_model8 <- clmm(as.factor(s_funding) ~ version*s_awareness + summary +
text_order + s_age + s_sex + (1|id), data = data2_long)
anova(funding_model7, funding_model8)## Likelihood ratio tests of cumulative link models:
##
## formula:
## funding_model7 as.factor(s_funding) ~ version * s_awareness + summary + text_order + s_age + (1 | id)
## funding_model8 as.factor(s_funding) ~ version * s_awareness + summary + text_order + s_age + s_sex + (1 | id)
## link: threshold:
## funding_model7 logit flexible
## funding_model8 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_model7 19 16918 -8440.0
## funding_model8 20 16920 -8439.8 0.5412 1 0.4619funding_model9 <- clmm(as.factor(s_funding) ~ version*s_awareness + summary +
text_order + s_age + s_school + (1|id),
data = data2_long)
anova(funding_model8, funding_model9)## Likelihood ratio tests of cumulative link models:
##
## formula:
## funding_model8 as.factor(s_funding) ~ version * s_awareness + summary + text_order + s_age + s_sex + (1 | id)
## funding_model9 as.factor(s_funding) ~ version * s_awareness + summary + text_order + s_age + s_school + (1 | id)
## link: threshold:
## funding_model8 logit flexible
## funding_model9 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_model8 20 16920 -8439.8
## funding_model9 21 16841 -8399.4 80.741 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1funding_model10 <- clmm(as.factor(s_funding) ~ version*s_awareness + summary +
text_order + s_age+ s_sex + s_school +
as.factor(s_interest) + (1|id),
data = data2_long)
anova(funding_model9, funding_model10)## Likelihood ratio tests of cumulative link models:
##
## formula:
## funding_model9 as.factor(s_funding) ~ version * s_awareness + summary + text_order + s_age + s_school + (1 | id)
## funding_model10 as.factor(s_funding) ~ version * s_awareness + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id)
## link: threshold:
## funding_model9 logit flexible
## funding_model10 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_model9 21 16841 -8399.4
## funding_model10 26 16822 -8385.2 28.396 5 3.045e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(funding_model10)## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: as.factor(s_funding) ~ version * s_awareness + summary + text_order +
## s_age + s_sex + s_school + as.factor(s_interest) + (1 | id)
## data: data2_long
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 4002 -8385.20 16822.40 4698(16804) 8.57e-02 5.7e+05
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 1.682 1.297
## Number of groups: id 2038
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## versionold guideline 0.115971 0.190277 0.609 0.542204
## s_awarenesspass 1.622291 0.103350 15.697 < 2e-16 ***
## summaryFaerber 0.203732 0.058682 3.472 0.000517 ***
## text_orderFaerber 0.182176 0.082240 2.215 0.026749 *
## s_age -0.013948 0.002788 -5.003 5.63e-07 ***
## s_sexmale -0.099259 0.082722 -1.200 0.230173
## s_schoolReal 0.526756 0.101438 5.193 2.07e-07 ***
## s_schoolAbi 0.942365 0.105173 8.960 < 2e-16 ***
## as.factor(s_interest)5 0.084974 0.126684 0.671 0.502377
## as.factor(s_interest)6 0.113073 0.128249 0.882 0.377957
## as.factor(s_interest)7 0.091911 0.140078 0.656 0.511733
## as.factor(s_interest)8 -0.479298 0.135241 -3.544 0.000394 ***
## versionold guideline:s_awarenesspass -0.094120 0.231326 -0.407 0.684102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -5.2691 0.2812 -18.740
## -5|-4 -4.6088 0.2444 -18.856
## -4|-3 -2.3121 0.1943 -11.903
## -3|-2 -1.9199 0.1906 -10.073
## -2|-1 -0.9231 0.1850 -4.990
## -1|0 -0.3882 0.1836 -2.114
## 0|1 1.1382 0.1849 6.157
## 1|2 1.5227 0.1862 8.177
## 2|3 2.1337 0.1891 11.281
## 3|4 2.3794 0.1906 12.482
## 4|5 2.9345 0.1944 15.092
## 5|6 3.1405 0.1960 16.023
## (78 Beobachtungen als fehlend gelöscht)exp(coef(funding_model10))## -6|-5 -5|-4
## 0.005148401 0.009963453
## -4|-3 -3|-2
## 0.099050398 0.146626844
## -2|-1 -1|0
## 0.397275799 0.678259796
## 0|1 1|2
## 3.121294614 4.584692407
## 2|3 3|4
## 8.445723094 10.798390191
## 4|5 5|6
## 18.811286389 23.116434962
## versionold guideline s_awarenesspass
## 1.122963356 5.064680238
## summaryFaerber text_orderFaerber
## 1.225970093 1.199825102
## s_age s_sexmale
## 0.986148686 0.905508178
## s_schoolReal s_schoolAbi
## 1.693430179 2.566042042
## as.factor(s_interest)5 as.factor(s_interest)6
## 1.088688615 1.119713275
## as.factor(s_interest)7 as.factor(s_interest)8
## 1.096267345 0.619217953
## versionold guideline:s_awarenesspass
## 0.910173646exp(confint(funding_model10))## 2.5 % 97.5 %
## -6|-5 0.002967161 0.008933129
## -5|-4 0.006170975 0.016086664
## -4|-3 0.067687724 0.144944767
## -3|-2 0.100918818 0.213036893
## -2|-1 0.276447088 0.570915980
## -1|0 0.473270157 0.972037521
## 0|1 2.172628201 4.484191113
## 1|2 3.182743171 6.604178638
## 2|3 5.829566463 12.235942250
## 3|4 7.431971267 15.689677279
## 4|5 12.850290519 27.537470464
## 5|6 15.742802193 33.943738784
## versionold guideline 0.773394668 1.630534515
## s_awarenesspass 4.135996005 6.201888465
## summaryFaerber 1.092772394 1.375403219
## text_orderFaerber 1.021209750 1.409681287
## s_age 0.980775129 0.991551684
## s_sexmale 0.769980086 1.064891254
## s_schoolReal 1.388106338 2.065912165
## s_schoolAbi 2.088049299 3.153456083
## as.factor(s_interest)5 0.849317557 1.395523843
## as.factor(s_interest)6 0.870846098 1.439700792
## as.factor(s_interest)7 0.833070502 1.442617507
## as.factor(s_interest)8 0.475035657 0.807162299
## versionold guideline:s_awarenesspass 0.578388408 1.432283314nagelkerke(fit = funding_model10, null = funding_null)## $Models
##
## Model: "clmm, as.factor(s_funding) ~ version * s_awareness + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id), data2_long"
## Null: "clm, as.factor(s_funding) ~ 1, data2_long, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0498729
## Cox and Snell (ML) 0.1974510
## Nagelkerke (Cragg and Uhler) 0.1998800
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -14 -440.15 880.29 7.2038e-179
##
## $Number.of.observations
##
## Model: 4002
## Null: 4002
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"RQ2
describeBy(data2_wide$s_coi,data2_wide$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1579 1.94 5.88 1 2.04 5.93 -13 14 27 -0.02 -0.57 0.15
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 334 2.38 5.88 2 2.56 5.93 -11 14 25 -0.19 -0.64 0.32wilcox.test(s_coi~version, data = data2_wide, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_coi by version
## W = 250867, p-value = 0.1606
## alternative hypothesis: true location shift is not equal to 0RQ2 Mixed Model
set.seed(288659)
coi_null <- clm(as.factor(s_coi) ~ 1, data = data2_long, link = "logit")
coi_model1 <- clmm(as.factor(s_coi) ~ 1 + (1|id), data = data2_long)## Warning in update.uC(rho): Non finite negative log-likelihood
## at iteration 101anova(coi_null, coi_model1)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## coi_null as.factor(s_coi) ~ 1 logit flexible
## coi_model1 as.factor(s_coi) ~ 1 + (1 | id) logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_null 14 18800 -9385.8
## coi_model1 15 18439 -9204.3 363.02 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coi_model2 <- clmm(as.factor(s_coi) ~ version + (1|id), data = data2_long)## Warning in update.uC(rho): Non finite negative log-likelihood
## at iteration 107anova(coi_model1, coi_model2)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## coi_model1 as.factor(s_coi) ~ 1 + (1 | id) logit flexible
## coi_model2 as.factor(s_coi) ~ version + (1 | id) logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_model1 15 18439 -9204.3
## coi_model2 16 18440 -9203.9 0.9507 1 0.3295coi_model3 <- clmm(as.factor(s_coi) ~ version + s_awareness + (1|id),
data = data2_long)## Warning in update.uC(rho): Non finite negative log-likelihood
## at iteration 113anova(coi_model2, coi_model3)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## coi_model2 as.factor(s_coi) ~ version + (1 | id) logit flexible
## coi_model3 as.factor(s_coi) ~ version + s_awareness + (1 | id) logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_model2 16 18440 -9203.9
## coi_model3 17 18191 -9078.3 251.03 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coi_model4 <- clmm(as.factor(s_coi) ~ version*s_awareness + (1|id),
data = data2_long)## Warning in update.uC(rho): Non finite negative log-likelihood
## at iteration 119anova(coi_model2, coi_model4)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## coi_model2 as.factor(s_coi) ~ version + (1 | id) logit flexible
## coi_model4 as.factor(s_coi) ~ version * s_awareness + (1 | id) logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_model2 16 18440 -9203.9
## coi_model4 18 18190 -9077.2 253.39 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coi_model5 <- clmm(as.factor(s_coi) ~ version*s_awareness + summary + (1|id),
data = data2_long)## Warning in update.uC(rho): Non finite negative log-likelihood
## at iteration 125anova(coi_model4, coi_model5)## Likelihood ratio tests of cumulative link models:
##
## formula: link:
## coi_model4 as.factor(s_coi) ~ version * s_awareness + (1 | id) logit
## coi_model5 as.factor(s_coi) ~ version * s_awareness + summary + (1 | id) logit
## threshold:
## coi_model4 flexible
## coi_model5 flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_model4 18 18190 -9077.2
## coi_model5 19 18114 -9037.9 78.51 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coi_model6 <- clmm(as.factor(s_coi) ~ version*s_awareness + summary +
text_order + (1|id),
data = data2_long)## Warning in update.uC(rho): Non finite negative log-likelihood
## at iteration 131anova(coi_model4, coi_model6)## Likelihood ratio tests of cumulative link models:
##
## formula:
## coi_model4 as.factor(s_coi) ~ version * s_awareness + (1 | id)
## coi_model6 as.factor(s_coi) ~ version * s_awareness + summary + text_order + (1 | id)
## link: threshold:
## coi_model4 logit flexible
## coi_model6 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_model4 18 18190 -9077.2
## coi_model6 20 18115 -9037.7 78.84 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coi_model7 <- clmm(as.factor(s_coi) ~ version*s_awareness + summary +
text_order + s_age + (1|id),
data = data2_long)
anova(coi_model6, coi_model7)## Likelihood ratio tests of cumulative link models:
##
## formula:
## coi_model6 as.factor(s_coi) ~ version * s_awareness + summary + text_order + (1 | id)
## coi_model7 as.factor(s_coi) ~ version * s_awareness + summary + text_order + s_age + (1 | id)
## link: threshold:
## coi_model6 logit flexible
## coi_model7 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_model6 20 18115 -9037.7
## coi_model7 21 18102 -9030.1 15.355 1 8.911e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coi_model8 <- clmm(as.factor(s_coi) ~ version*s_awareness + summary +
text_order + s_age + s_sex + (1|id),
data = data2_long)
anova(coi_model6, coi_model8)## Likelihood ratio tests of cumulative link models:
##
## formula:
## coi_model6 as.factor(s_coi) ~ version * s_awareness + summary + text_order + (1 | id)
## coi_model8 as.factor(s_coi) ~ version * s_awareness + summary + text_order + s_age + s_sex + (1 | id)
## link: threshold:
## coi_model6 logit flexible
## coi_model8 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_model6 20 18115 -9037.7
## coi_model8 22 18103 -9029.6 16.355 2 0.0002809 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coi_model9 <- clmm(as.factor(s_coi) ~ version*s_awareness + summary +
text_order + s_age + s_sex + s_school + (1|id),
data = data2_long)
anova(coi_model8, coi_model9)## Likelihood ratio tests of cumulative link models:
##
## formula:
## coi_model8 as.factor(s_coi) ~ version * s_awareness + summary + text_order + s_age + s_sex + (1 | id)
## coi_model9 as.factor(s_coi) ~ version * s_awareness + summary + text_order + s_age + s_sex + s_school + (1 | id)
## link: threshold:
## coi_model8 logit flexible
## coi_model9 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_model8 22 18103 -9029.6
## coi_model9 24 17984 -8968.2 122.65 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coi_model10 <- clmm(as.factor(s_coi) ~ version*s_awareness + summary +
text_order + s_age + s_sex + s_school +
as.factor(s_interest) + (1|id), data = data2_long)
anova(coi_model9, coi_model10)## Likelihood ratio tests of cumulative link models:
##
## formula:
## coi_model9 as.factor(s_coi) ~ version * s_awareness + summary + text_order + s_age + s_sex + s_school + (1 | id)
## coi_model10 as.factor(s_coi) ~ version * s_awareness + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id)
## link: threshold:
## coi_model9 logit flexible
## coi_model10 logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_model9 24 17984 -8968.2
## coi_model10 28 17973 -8958.4 19.593 4 0.0006009 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(coi_model10)## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: as.factor(s_coi) ~ version * s_awareness + summary + text_order +
## s_age + s_sex + s_school + as.factor(s_interest) + (1 | id)
## data: data2_long
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 3946 -8958.45 17972.89 5273(19537) 1.26e-02 6.7e+05
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 1.703 1.305
## Number of groups: id 2033
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## versionold guideline -0.137491 0.191305 -0.719 0.47232
## s_awarenesspass 1.333718 0.101978 13.078 < 2e-16 ***
## summaryFaerber 0.522540 0.059016 8.854 < 2e-16 ***
## text_orderFaerber 0.053404 0.082020 0.651 0.51498
## s_age -0.008624 0.002779 -3.103 0.00191 **
## s_sexmale 0.038925 0.082597 0.471 0.63745
## s_schoolReal 0.570161 0.101711 5.606 2.07e-08 ***
## s_schoolAbi 1.167466 0.105807 11.034 < 2e-16 ***
## as.factor(s_interest)5 0.081718 0.126791 0.645 0.51925
## as.factor(s_interest)6 0.044759 0.128542 0.348 0.72769
## as.factor(s_interest)7 -0.071007 0.139836 -0.508 0.61160
## as.factor(s_interest)8 -0.433832 0.135075 -3.212 0.00132 **
## versionold guideline:s_awarenesspass 0.329187 0.231729 1.421 0.15544
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -7|-6 -4.8812 0.2745 -17.780
## -6|-5 -4.2588 0.2416 -17.629
## -5|-4 -2.1063 0.1948 -10.813
## -4|-3 -1.7480 0.1913 -9.138
## -3|-2 -0.9814 0.1861 -5.274
## -2|-1 -0.5821 0.1845 -3.156
## -1|0 0.1040 0.1832 0.568
## 0|1 1.4534 0.1856 7.832
## 1|2 2.0811 0.1887 11.031
## 2|3 2.3298 0.1902 12.248
## 3|4 3.0372 0.1954 15.547
## 4|5 3.1994 0.1966 16.271
## 5|6 4.1542 0.2049 20.274
## 6|7 4.2959 0.2062 20.829
## (134 Beobachtungen als fehlend gelöscht)exp(coef(coi_model10))## -7|-6 -6|-5
## 0.007587795 0.014139607
## -5|-4 -4|-3
## 0.121683785 0.174127292
## -3|-2 -2|-1
## 0.374788856 0.558707240
## -1|0 0|1
## 1.109618408 4.277712935
## 1|2 2|3
## 8.013127538 10.275464669
## 3|4 4|5
## 20.847670547 24.516698205
## 5|6 6|7
## 63.699775943 73.395477379
## versionold guideline s_awarenesspass
## 0.871542315 3.795128846
## summaryFaerber text_orderFaerber
## 1.686304678 1.054855315
## s_age s_sexmale
## 0.991413339 1.039692569
## s_schoolReal s_schoolAbi
## 1.768550931 3.213836927
## as.factor(s_interest)5 as.factor(s_interest)6
## 1.085149549 1.045775331
## as.factor(s_interest)7 as.factor(s_interest)8
## 0.931455343 0.648021124
## versionold guideline:s_awarenesspass
## 1.389838113exp(confint(coi_model10))## 2.5 % 97.5 %
## -7|-6 0.004430339 0.01299554
## -6|-5 0.008806611 0.02270209
## -5|-4 0.083065405 0.17825644
## -4|-3 0.119684439 0.25333547
## -3|-2 0.260246752 0.53974425
## -2|-1 0.389201754 0.80203590
## -1|0 0.774921272 1.58887497
## 0|1 2.973429019 6.15411629
## 1|2 5.536341176 11.59795087
## 2|3 7.077760968 14.91787793
## 3|4 14.215819982 30.57335896
## 4|5 16.676004938 36.04391418
## 5|6 42.631368537 95.18018291
## 6|7 48.991083629 109.95666355
## versionold guideline 0.599031569 1.26802333
## s_awarenesspass 3.107582202 4.63479388
## summaryFaerber 1.502107964 1.89308860
## text_orderFaerber 0.898208413 1.23882132
## s_age 0.986028490 0.99682760
## s_sexmale 0.884298016 1.22239406
## s_schoolReal 1.448907812 2.15871042
## s_schoolAbi 2.611926855 3.95445522
## as.factor(s_interest)5 0.846378340 1.39128034
## as.factor(s_interest)6 0.812874307 1.34540609
## as.factor(s_interest)7 0.708163354 1.22515384
## as.factor(s_interest)8 0.497293826 0.84443312
## versionold guideline:s_awarenesspass 0.882502575 2.18883212nagelkerke(fit = coi_model10, null = coi_null)## $Models
##
## Model: "clmm, as.factor(s_coi) ~ version * s_awareness + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id), data2_long"
## Null: "clm, as.factor(s_coi) ~ 1, data2_long, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0455369
## Cox and Snell (ML) 0.1947690
## Nagelkerke (Cragg and Uhler) 0.1964560
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -14 -427.4 854.8 2.0669e-173
##
## $Number.of.observations
##
## Model: 3946
## Null: 3946
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"RQ3
psych::describeBy(data2_wide$s_METI_exp,data2_wide$METI_target)##
## Descriptive statistics by group
## group: Study Authors
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1007 5.46 1.21 5.67 5.55 1.24 1 7 6 -0.69 0.2 0.04
## ------------------------------------------------------------
## group: Summary Authors
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 989 5.45 1.23 5.67 5.53 1.48 1 7 6 -0.59 -0.13 0.04psych::describeBy(data2_wide$s_METI_exp,data2_wide$condition)##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 325 5.45 1.17 5.67 5.53 1.24 1.83 7 5.17 -0.58 -0.27 0.07
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 342 5.43 1.23 5.67 5.51 1.48 1 7 6 -0.65 0.12 0.07
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 326 5.54 1.28 5.83 5.66 1.48 1 7 6 -0.78 0.09 0.07
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 336 5.45 1.17 5.67 5.5 1.48 1.67 7 5.33 -0.4 -0.69 0.06
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 320 5.43 1.21 5.58 5.5 1.36 1 7 6 -0.54 -0.14 0.07
## ------------------------------------------------------------
## group: 6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 347 5.44 1.26 5.83 5.53 1.48 1 7 6 -0.82 0.67 0.07psych::describeBy(data2_wide$s_METI_int,data2_wide$METI_target)##
## Descriptive statistics by group
## group: Study Authors
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1004 5.37 1.2 5.5 5.44 1.3 1 7 6 -0.58 0.09 0.04
## ------------------------------------------------------------
## group: Summary Authors
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 995 5.43 1.26 5.5 5.51 1.48 1 7 6 -0.56 -0.17 0.04psych::describeBy(data2_wide$s_METI_int,data2_wide$condition)##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 326 5.38 1.2 5.5 5.45 1.48 1 7 6 -0.57 0.03 0.07
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 339 5.38 1.26 5.5 5.48 1.48 1 7 6 -0.67 0.21 0.07
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 327 5.47 1.28 5.75 5.58 1.48 1 7 6 -0.79 0.36 0.07
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 338 5.38 1.2 5.5 5.43 1.48 2 7 5 -0.29 -0.84 0.07
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 319 5.41 1.2 5.5 5.47 1.48 1 7 6 -0.47 -0.17 0.07
## ------------------------------------------------------------
## group: 6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 350 5.37 1.22 5.5 5.44 1.48 1 7 6 -0.54 -0.04 0.07psych::describeBy(data2_wide$s_METI_ben,data2_wide$METI_target)##
## Descriptive statistics by group
## group: Study Authors
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1003 5.34 1.21 5.5 5.4 1.48 1 7 6 -0.57 0.21 0.04
## ------------------------------------------------------------
## group: Summary Authors
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 992 5.36 1.24 5.5 5.43 1.48 1 7 6 -0.53 0.04 0.04psych::describeBy(data2_wide$s_METI_ben,data2_wide$condition)##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 326 5.34 1.17 5.5 5.39 1.11 1.5 7 5.5 -0.43 -0.25 0.07
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 341 5.35 1.25 5.5 5.44 1.48 1 7 6 -0.66 0.36 0.07
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 322 5.46 1.25 5.75 5.56 1.48 1 7 6 -0.72 0.28 0.07
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 334 5.33 1.19 5.5 5.36 1.48 1.25 7 5.75 -0.27 -0.66 0.06
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 324 5.33 1.22 5.5 5.39 1.48 1 7 6 -0.49 0.11 0.07
## ------------------------------------------------------------
## group: 6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 348 5.29 1.27 5.5 5.37 1.48 1 7 6 -0.65 0.55 0.07RQ3: Expertise
expMETIModel <- lm(s_METI_exp ~ version*s_awareness + summary2 +
METI_target + s_sex + s_age + s_school + s_interest,
data = data2_wide)
summary(expMETIModel)##
## Call:
## lm(formula = s_METI_exp ~ version * s_awareness + summary2 +
## METI_target + s_sex + s_age + s_school + s_interest, data = data2_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2143 -0.7035 0.1406 0.8334 2.3495
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.616980 0.152992 23.642 < 2e-16 ***
## versionold guideline -0.027082 0.120289 -0.225 0.8219
## s_awarenesspass 0.577994 0.061116 9.457 < 2e-16 ***
## summary2Faerber 0.102042 0.050900 2.005 0.0451 *
## METI_targetSummary Authors -0.024576 0.050930 -0.483 0.6295
## s_sexmale -0.259712 0.051324 -5.060 4.57e-07 ***
## s_age 0.013098 0.001718 7.623 3.82e-14 ***
## s_schoolReal -0.045886 0.062292 -0.737 0.4614
## s_schoolAbi -0.006324 0.063957 -0.099 0.9212
## s_interest 0.163536 0.018697 8.747 < 2e-16 ***
## versionold guideline:s_awarenesspass -0.003286 0.144941 -0.023 0.9819
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.135 on 1985 degrees of freedom
## (44 Beobachtungen als fehlend gelöscht)
## Multiple R-squared: 0.1366, Adjusted R-squared: 0.1322
## F-statistic: 31.4 on 10 and 1985 DF, p-value: < 2.2e-16dwt(expMETIModel)## lag Autocorrelation D-W Statistic p-value
## 1 -0.02456489 2.048477 0.242
## Alternative hypothesis: rho != 0vif(expMETIModel)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## GVIF Df GVIF^(1/(2*Df))
## version 3.222049 1 1.795007
## s_awareness 1.263551 1 1.124078
## summary2 1.004172 1 1.002084
## METI_target 1.005315 1 1.002654
## s_sex 1.020980 1 1.010435
## s_age 1.056900 1 1.028056
## s_school 1.057583 2 1.014095
## s_interest 1.042409 1 1.020985
## version:s_awareness 3.445448 1 1.8561921/vif(expMETIModel)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## GVIF Df GVIF^(1/(2*Df))
## version 0.3103615 1.0 0.5571009
## s_awareness 0.7914202 1.0 0.8896180
## summary2 0.9958454 1.0 0.9979205
## METI_target 0.9947131 1.0 0.9973531
## s_sex 0.9794516 1.0 0.9896725
## s_age 0.9461632 1.0 0.9727092
## s_school 0.9455524 0.5 0.9861010
## s_interest 0.9593160 1.0 0.9794468
## version:s_awareness 0.2902380 1.0 0.5387374mean(vif(expMETIModel))## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## [1] 1.295259RQ3: Integrity
intMETIModel <- lm(s_METI_int ~ version*s_awareness + summary2 +
METI_target + s_sex + s_age + s_school + s_interest,
data = data2_wide)
summary(intMETIModel)##
## Call:
## lm(formula = s_METI_int ~ version * s_awareness + summary2 +
## METI_target + s_sex + s_age + s_school + s_interest, data = data2_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0182 -0.7592 0.1491 0.8767 2.3842
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.647474 0.155570 23.446 < 2e-16 ***
## versionold guideline 0.039990 0.122005 0.328 0.743
## s_awarenesspass 0.511788 0.062256 8.221 3.61e-16 ***
## summary2Faerber 0.063990 0.051748 1.237 0.216
## METI_targetSummary Authors 0.034274 0.051783 0.662 0.508
## s_sexmale -0.288823 0.052122 -5.541 3.40e-08 ***
## s_age 0.013094 0.001743 7.513 8.68e-14 ***
## s_schoolReal -0.091183 0.063398 -1.438 0.151
## s_schoolAbi -0.077160 0.065019 -1.187 0.235
## s_interest 0.164069 0.019065 8.606 < 2e-16 ***
## versionold guideline:s_awarenesspass -0.118094 0.146961 -0.804 0.422
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.154 on 1988 degrees of freedom
## (41 Beobachtungen als fehlend gelöscht)
## Multiple R-squared: 0.1197, Adjusted R-squared: 0.1153
## F-statistic: 27.03 on 10 and 1988 DF, p-value: < 2.2e-16dwt(intMETIModel)## lag Autocorrelation D-W Statistic p-value
## 1 -0.01308015 2.025996 0.586
## Alternative hypothesis: rho != 0vif(intMETIModel)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## GVIF Df GVIF^(1/(2*Df))
## version 3.225666 1 1.796014
## s_awareness 1.264672 1 1.124576
## summary2 1.004414 1 1.002205
## METI_target 1.005782 1 1.002887
## s_sex 1.018884 1 1.009398
## s_age 1.056227 1 1.027729
## s_school 1.058289 2 1.014264
## s_interest 1.042882 1 1.021216
## version:s_awareness 3.448031 1 1.8568871/vif(intMETIModel)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## GVIF Df GVIF^(1/(2*Df))
## version 0.3100135 1.0 0.5567885
## s_awareness 0.7907187 1.0 0.8892237
## summary2 0.9956050 1.0 0.9978001
## METI_target 0.9942510 1.0 0.9971214
## s_sex 0.9814663 1.0 0.9906898
## s_age 0.9467661 1.0 0.9730191
## s_school 0.9449215 0.5 0.9859365
## s_interest 0.9588816 1.0 0.9792250
## version:s_awareness 0.2900206 1.0 0.5385356mean(vif(intMETIModel))## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## [1] 1.295556RQ3: Benevolence
benMETIModel <- lm(s_METI_ben ~ version*s_awareness + summary2 +
METI_target + s_sex + s_age + s_school + s_interest,
data = data2_wide)
summary(benMETIModel)##
## Call:
## lm(formula = s_METI_ben ~ version * s_awareness + summary2 +
## METI_target + s_sex + s_age + s_school + s_interest, data = data2_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9371 -0.7358 0.1280 0.8514 2.4009
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.564248 0.155305 22.950 < 2e-16 ***
## versionold guideline -0.059166 0.122432 -0.483 0.6290
## s_awarenesspass 0.487666 0.061987 7.867 5.91e-15 ***
## summary2Faerber 0.097960 0.051575 1.899 0.0577 .
## METI_targetSummary Authors -0.003628 0.051632 -0.070 0.9440
## s_sexmale -0.301682 0.052008 -5.801 7.67e-09 ***
## s_age 0.013642 0.001744 7.820 8.48e-15 ***
## s_schoolReal -0.065244 0.063222 -1.032 0.3022
## s_schoolAbi -0.083552 0.064793 -1.290 0.1974
## s_interest 0.169806 0.019052 8.913 < 2e-16 ***
## versionold guideline:s_awarenesspass -0.032837 0.147140 -0.223 0.8234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.15 on 1984 degrees of freedom
## (45 Beobachtungen als fehlend gelöscht)
## Multiple R-squared: 0.1255, Adjusted R-squared: 0.1211
## F-statistic: 28.47 on 10 and 1984 DF, p-value: < 2.2e-16dwt(benMETIModel)## lag Autocorrelation D-W Statistic p-value
## 1 0.02635584 1.946987 0.238
## Alternative hypothesis: rho != 0vif(benMETIModel)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## GVIF Df GVIF^(1/(2*Df))
## version 3.257897 1 1.804965
## s_awareness 1.262538 1 1.123627
## summary2 1.003548 1 1.001772
## METI_target 1.005832 1 1.002912
## s_sex 1.020511 1 1.010204
## s_age 1.056892 1 1.028052
## s_school 1.058993 2 1.014433
## s_interest 1.045170 1 1.022335
## version:s_awareness 3.482841 1 1.8662371/vif(benMETIModel)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## GVIF Df GVIF^(1/(2*Df))
## version 0.3069465 1.0 0.5540275
## s_awareness 0.7920554 1.0 0.8899749
## summary2 0.9964647 1.0 0.9982308
## METI_target 0.9942021 1.0 0.9970969
## s_sex 0.9799010 1.0 0.9898995
## s_age 0.9461709 1.0 0.9727132
## s_school 0.9442935 0.5 0.9857726
## s_interest 0.9567825 1.0 0.9781526
## version:s_awareness 0.2871219 1.0 0.5358376mean(vif(benMETIModel))## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## [1] 1.298843Additional Analyses: Awareness-Check Pass - Subset
data2_wide_pass <- subset(data2_wide, s_awareness == "pass")
length(unique(data2_wide_pass$id))## [1] 1382data2_long_pass <- subset(data2_long, s_awareness == "pass")
length(unique(data2_long_pass$id))## [1] 1382H1
H1a
H1a_pass <- subset(data2_wide_pass, condition == 2|condition == 4|condition == 6)
View(H1a_pass)
describeBy(H1a_pass$s_relationship,H1a_pass$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 240 0.51 3.02 0 0.45 2.97 -7 8 15 0.19 -0.46 0.2
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 450 0.91 3.64 0 0.74 2.97 -6 8 14 0.38 -0.94 0.17wilcox.test(s_relationship~disclaimer, data = H1a_pass, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_relationship by disclaimer
## W = 52152, p-value = 0.4557
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -9.999659e-01 6.563517e-05
## sample estimates:
## difference in location
## -2.086247e-05H1a post hoc
H1a_pass_1 <- subset(data2_wide_pass, condition ==2| condition == 6)
View(H1a_pass_1)
describeBy(H1a_pass_1$s_relationship, H1a_pass_1$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 240 0.51 3.02 0 0.45 2.97 -7 8 15 0.19 -0.46 0.2
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 246 0.61 3.66 0 0.38 2.97 -6 8 14 0.45 -0.86 0.23wilcox.test(s_relationship~disclaimer, data = H1a_pass_1, exaxct = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_relationship by disclaimer
## W = 30093, p-value = 0.71
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -6.923148e-05 9.999839e-01
## sample estimates:
## difference in location
## 5.003085e-05H1a_pass_2 <- subset(data2_wide_pass, condition ==4| condition == 6)
View(H1a_pass_2)
describeBy(H1a_pass_2$s_relationship, H1a_pass_2$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 240 0.51 3.02 0 0.45 2.97 -7 8 15 0.19 -0.46 0.2
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 204 1.27 3.59 0 1.16 2.97 -5 8 13 0.3 -1.03 0.25wilcox.test(s_relationship~disclaimer, data = H1a_pass_2, exaxct = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_relationship by disclaimer
## W = 22059, p-value = 0.07029
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.000003e+00 7.173675e-05
## sample estimates:
## difference in location
## -1.098109e-05H1b
H1b_pass <- subset(data2_wide_pass, condition == 1|condition == 2|condition == 3|
condition == 4)
View(H1b_pass)
describeBy(H1b_pass$s_relationship,H1b_pass$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 424 0.45 3.21 0 0.32 2.97 -6 8 14 0.25 -0.63 0.16
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 450 0.91 3.64 0 0.74 2.97 -6 8 14 0.38 -0.94 0.17wilcox.test(s_relationship~disclaimer, data = H1b_pass, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_relationship by disclaimer
## W = 90501, p-value = 0.1862
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -9.999266e-01 2.889107e-05
## sample estimates:
## difference in location
## -3.904304e-05H1b post hoc
H1b_pass_1 <- subset(data2_wide_pass, condition == 1|condition == 2)
View(H1b_pass_1)
describeBy(H1b_pass_1$s_relationship,H1b_pass_1$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 213 0.62 3.21 0 0.56 2.97 -6 8 14 0.09 -0.68 0.22
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 246 0.61 3.66 0 0.38 2.97 -6 8 14 0.45 -0.86 0.23wilcox.test(s_relationship~disclaimer, data = H1b_pass_1, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_relationship by disclaimer
## W = 27041, p-value = 0.5505
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.730705e-05 9.999690e-01
## sample estimates:
## difference in location
## 5.618701e-05H1b_pass_2 <- subset(data2_wide_pass, condition == 3|condition == 4)
View(H1b_pass_2)
describeBy(H1b_pass_2$s_relationship,H1b_pass_2$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 211 0.27 3.21 0 0.09 2.97 -6 8 14 0.41 -0.54 0.22
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 204 1.27 3.59 0 1.16 2.97 -5 8 13 0.3 -1.03 0.25wilcox.test(s_relationship~disclaimer, data = H1b_pass_2, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_relationship by disclaimer
## W = 18358, p-value = 0.009108
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.999996e+00 -2.505375e-05
## sample estimates:
## difference in location
## -0.9999673H1b_pass_3 <- subset(data2_wide_pass, condition == 1|condition == 4)
View(H1b_pass_3)
describeBy(H1b_pass_3$s_relationship,H1b_pass_3$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 213 0.62 3.21 0 0.56 2.97 -6 8 14 0.09 -0.68 0.22
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 204 1.27 3.59 0 1.16 2.97 -5 8 13 0.3 -1.03 0.25wilcox.test(s_relationship~disclaimer, data = H1b_pass_3, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_relationship by disclaimer
## W = 19958, p-value = 0.1479
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.000021e+00 3.034113e-05
## sample estimates:
## difference in location
## -6.483228e-05H1b_pass_4 <- subset(data2_wide_pass, condition == 2|condition == 3)
View(H1b_pass_4)
describeBy(H1b_pass_4$s_relationship,H1b_pass_4$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 211 0.27 3.21 0 0.09 2.97 -6 8 14 0.41 -0.54 0.22
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 246 0.61 3.66 0 0.38 2.97 -6 8 14 0.45 -0.86 0.23wilcox.test(s_relationship~disclaimer, data = H1b_pass_4, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_relationship by disclaimer
## W = 25145, p-value = 0.5635
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -9.999563e-01 4.130311e-05
## sample estimates:
## difference in location
## -4.90988e-05H1 Logistic Regression
sum(is.na(data2_wide_pass$disclaimer))## [1] 0sum(is.na(data2_wide_pass$s_awareness))## [1] 0sum(is.na(data2_wide_pass$text_order))## [1] 0sum(is.na(data2_wide_pass$s_age))## [1] 0data2_wide_pass <- data2_wide_pass %>% drop_na(s_age)
sum(is.na(data2_wide_pass$s_sex))## [1] 0sum(is.na(data2_wide_pass$s_school))## [1] 0sum(is.na(data2_wide_pass$s_interest))## [1] 0data2_wide_pass$H1_interaction <- interaction(data2_wide_pass$disclaimer,
data2_wide_pass$version)
data2_wide_pass$H1_interaction <- droplevels(data2_wide_pass$H1_interaction)
data2_wide_pass$H1_interaction <- factor(data2_wide_pass$H1_interaction,
levels = c(
"no disclaimer.old guideline",
"no disclaimer.new guideline",
"disclaimer.new guideline"))
table(data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## 247 441
## disclaimer.new guideline
## 694data2_wide_pass_reg <- subset(data2_wide_pass, condition != 5)
View(data2_wide_pass_reg)
relationship_null_pass <- clm(as.factor(s_relationship)~1,
data = data2_wide_pass_reg, link = "logit")
relationship_model1_pass <- clm(as.factor(s_relationship)~ H1_interaction,
data = data2_wide_pass_reg, link = "logit")
anova(relationship_null_pass,relationship_model1_pass)## Likelihood ratio tests of cumulative link models:
##
## formula: link:
## relationship_null_pass as.factor(s_relationship) ~ 1 logit
## relationship_model1_pass as.factor(s_relationship) ~ H1_interaction logit
## threshold:
## relationship_null_pass flexible
## relationship_model1_pass flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## relationship_null_pass 15 5463.4 -2716.7
## relationship_model1_pass 17 5465.6 -2715.8 1.8388 2 0.3988relationship_model2_pass <- clm(as.factor(s_relationship)~
H1_interaction + summary1,
data = data2_wide_pass_reg,
link = "logit")
anova(relationship_null_pass,relationship_model2_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## relationship_null_pass as.factor(s_relationship) ~ 1
## relationship_model2_pass as.factor(s_relationship) ~ H1_interaction + summary1
## link: threshold:
## relationship_null_pass logit flexible
## relationship_model2_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## relationship_null_pass 15 5463.4 -2716.7
## relationship_model2_pass 18 5467.4 -2715.7 2.0697 3 0.5581relationship_model3_pass <- clm(as.factor(s_relationship)~
H1_interaction + summary1
, data = data2_wide_pass_reg,
link = "logit")
anova(relationship_null_pass,relationship_model3_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## relationship_null_pass as.factor(s_relationship) ~ 1
## relationship_model3_pass as.factor(s_relationship) ~ H1_interaction + summary1
## link: threshold:
## relationship_null_pass logit flexible
## relationship_model3_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## relationship_null_pass 15 5463.4 -2716.7
## relationship_model3_pass 18 5467.4 -2715.7 2.0697 3 0.5581relationship_model4_pass <- clm(as.factor(s_relationship)~
H1_interaction + summary1 +
s_age, data = data2_wide_pass_reg,
link = "logit")
anova(relationship_null_pass,relationship_model4_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## relationship_null_pass as.factor(s_relationship) ~ 1
## relationship_model4_pass as.factor(s_relationship) ~ H1_interaction + summary1 + s_age
## link: threshold:
## relationship_null_pass logit flexible
## relationship_model4_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## relationship_null_pass 15 5463.4 -2716.7
## relationship_model4_pass 19 5400.5 -2681.2 70.945 4 1.433e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1relationship_model5_pass <- clm(as.factor(s_relationship)~
H1_interaction + summary1 +
s_age + s_sex,
data = data2_wide_pass_reg,
link = "logit")
anova(relationship_model4_pass,relationship_model5_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## relationship_model4_pass as.factor(s_relationship) ~ H1_interaction + summary1 + s_age
## relationship_model5_pass as.factor(s_relationship) ~ H1_interaction + summary1 + s_age + s_sex
## link: threshold:
## relationship_model4_pass logit flexible
## relationship_model5_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## relationship_model4_pass 19 5400.5 -2681.2
## relationship_model5_pass 20 5399.3 -2679.6 3.1887 1 0.07415 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1relationship_model6_pass <- clm(as.factor(s_relationship)~
H1_interaction + summary1 +
s_age + s_sex + s_school,
data = data2_wide_pass_reg, link = "logit")
anova(relationship_model4_pass,relationship_model6_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## relationship_model4_pass as.factor(s_relationship) ~ H1_interaction + summary1 + s_age
## relationship_model6_pass as.factor(s_relationship) ~ H1_interaction + summary1 + s_age + s_sex + s_school
## link: threshold:
## relationship_model4_pass logit flexible
## relationship_model6_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## relationship_model4_pass 19 5400.5 -2681.2
## relationship_model6_pass 22 5333.5 -2644.7 72.996 3 9.742e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1relationship_model7_pass <- clm(as.factor(s_relationship)~
H1_interaction + summary1 +
s_age + s_sex + s_school +
as.factor(s_interest), data = data2_wide_pass_reg,
link = "logit")
anova(relationship_model6_pass,relationship_model7_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## relationship_model6_pass as.factor(s_relationship) ~ H1_interaction + summary1 + s_age + s_sex + s_school
## relationship_model7_pass as.factor(s_relationship) ~ H1_interaction + summary1 + s_age + s_sex + s_school + as.factor(s_interest)
## link: threshold:
## relationship_model6_pass logit flexible
## relationship_model7_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## relationship_model6_pass 22 5333.5 -2644.7
## relationship_model7_pass 26 5338.7 -2643.4 2.7427 4 0.6018summary(relationship_model7_pass)## formula:
## as.factor(s_relationship) ~ H1_interaction + summary1 + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_pass_reg
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1114 -2643.37 5338.74 8(1) 1.31e-12 1.2e+06
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## H1_interactionno disclaimer.new guideline -0.06035 0.13830 -0.436 0.66260
## H1_interactiondisclaimer.new guideline 0.11472 0.13879 0.827 0.40846
## summary1Faerber 0.02985 0.10508 0.284 0.77634
## s_age -0.02585 0.00353 -7.322 2.44e-13
## s_sexmale -0.19652 0.10678 -1.840 0.06572
## s_schoolReal 0.45967 0.13360 3.441 0.00058
## s_schoolAbi 1.07333 0.13350 8.040 9.00e-16
## as.factor(s_interest)5 0.02395 0.16480 0.145 0.88447
## as.factor(s_interest)6 0.13498 0.16600 0.813 0.41612
## as.factor(s_interest)7 -0.04526 0.17902 -0.253 0.80041
## as.factor(s_interest)8 -0.12919 0.17824 -0.725 0.46857
##
## H1_interactionno disclaimer.new guideline
## H1_interactiondisclaimer.new guideline
## summary1Faerber
## s_age ***
## s_sexmale .
## s_schoolReal ***
## s_schoolAbi ***
## as.factor(s_interest)5
## as.factor(s_interest)6
## as.factor(s_interest)7
## as.factor(s_interest)8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -7|-6 -7.9237 1.0343 -7.661
## -6|-5 -5.3487 0.3831 -13.963
## -5|-4 -4.7245 0.3338 -14.156
## -4|-3 -2.8220 0.2746 -10.276
## -3|-2 -2.3623 0.2691 -8.778
## -2|-1 -1.6553 0.2631 -6.291
## -1|0 -1.2762 0.2610 -4.889
## 0|1 -0.4453 0.2586 -1.722
## 1|2 -0.2039 0.2583 -0.789
## 2|3 0.3402 0.2589 1.314
## 3|4 0.5520 0.2596 2.126
## 4|5 1.2115 0.2639 4.592
## 5|6 1.3670 0.2655 5.150
## 6|7 2.6058 0.2911 8.951
## 7|8 2.9495 0.3049 9.674
## (31 Beobachtungen als fehlend gelöscht)exp(coef(relationship_model7_pass))## -7|-6
## 3.620686e-04
## -6|-5
## 4.754510e-03
## -5|-4
## 8.875414e-03
## -4|-3
## 5.948701e-02
## -3|-2
## 9.420078e-02
## -2|-1
## 1.910288e-01
## -1|0
## 2.790939e-01
## 0|1
## 6.406466e-01
## 1|2
## 8.155315e-01
## 2|3
## 1.405292e+00
## 3|4
## 1.736693e+00
## 4|5
## 3.358526e+00
## 5|6
## 3.923682e+00
## 6|7
## 1.354198e+01
## 7|8
## 1.909619e+01
## H1_interactionno disclaimer.new guideline
## 9.414391e-01
## H1_interactiondisclaimer.new guideline
## 1.121562e+00
## summary1Faerber
## 1.030301e+00
## s_age
## 9.744805e-01
## s_sexmale
## 8.215881e-01
## s_schoolReal
## 1.583553e+00
## s_schoolAbi
## 2.925090e+00
## as.factor(s_interest)5
## 1.024235e+00
## as.factor(s_interest)6
## 1.144519e+00
## as.factor(s_interest)7
## 9.557482e-01
## as.factor(s_interest)8
## 8.788072e-01exp(confint(relationship_model7_pass))## 2.5 % 97.5 %
## H1_interactionno disclaimer.new guideline 0.7179040 1.2347526
## H1_interactiondisclaimer.new guideline 0.8545192 1.4725154
## summary1Faerber 0.8385192 1.2659907
## s_age 0.9677420 0.9812311
## s_sexmale 0.6663281 1.0127647
## s_schoolReal 1.2190899 2.0584310
## s_schoolAbi 2.2533018 3.8031841
## as.factor(s_interest)5 0.7414271 1.4148866
## as.factor(s_interest)6 0.8265856 1.5848085
## as.factor(s_interest)7 0.6728448 1.3576500
## as.factor(s_interest)8 0.6195866 1.2463580nagelkerke(fit = relationship_model7_pass, null = relationship_null_pass)## $Models
##
## Model: "clm, as.factor(s_relationship) ~ H1_interaction + summary1 + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_pass_reg, logit"
## Null: "clm, as.factor(s_relationship) ~ 1, data2_wide_pass_reg, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0269966
## Cox and Snell (ML) 0.1233720
## Nagelkerke (Cragg and Uhler) 0.1243190
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -11 -73.342 146.68 7.0852e-26
##
## $Number.of.observations
##
## Model: 1114
## Null: 1114
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H1test_pass = emmeans(relationship_model7_pass, ~ H1_interaction)
pairs(H1test_pass, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - no disclaimer.new guideline 0.0603 0.138 Inf
## no disclaimer.old guideline - disclaimer.new guideline -0.1147 0.139 Inf
## no disclaimer.new guideline - disclaimer.new guideline -0.1751 0.120 Inf
## z.ratio p.value
## 0.436 0.9004
## -0.827 0.6865
## -1.461 0.3100
##
## Results are averaged over the levels of: summary1, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimatescld(H1test_pass, Letters = letters)## H1_interaction emmean SE df asymp.LCL asymp.UCL .group
## no disclaimer.new guideline 0.328 0.117 Inf 0.0978 0.558 a
## no disclaimer.old guideline 0.388 0.137 Inf 0.1190 0.657 a
## disclaimer.new guideline 0.503 0.117 Inf 0.2731 0.733 a
##
## Results are averaged over the levels of: summary1, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimates
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.Logistic Regression by Group
data2_wide_pass_reg1 <- subset(data2_wide_pass_reg, condition == 2 | condition == 6)
View(data2_wide_pass_reg1)
relationship_null_pass1 <- clm(as.factor(s_relationship)~1, data = data2_wide_pass_reg1, link = "logit")
relationship_model8_pass1 <- clm(as.factor(s_relationship)~
H1_interaction + summary1 +
s_age + s_sex + s_school +
as.factor(s_interest), data = data2_wide_pass_reg1, link = "logit")
summary(relationship_model8_pass1)## formula:
## as.factor(s_relationship) ~ H1_interaction + summary1 + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_pass_reg1
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 486 -1147.25 2344.50 8(1) 1.95e-12 8.8e+05
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## H1_interactiondisclaimer.new guideline -0.065661 0.159779 -0.411 0.6811
## summary1Faerber 0.011016 0.160189 0.069 0.9452
## s_age -0.031365 0.005283 -5.937 2.90e-09
## s_sexmale -0.212001 0.164079 -1.292 0.1963
## s_schoolReal 0.481969 0.204975 2.351 0.0187
## s_schoolAbi 1.005286 0.199581 5.037 4.73e-07
## as.factor(s_interest)5 0.089178 0.248484 0.359 0.7197
## as.factor(s_interest)6 0.438808 0.258044 1.701 0.0890
## as.factor(s_interest)7 -0.072975 0.274101 -0.266 0.7901
## as.factor(s_interest)8 -0.158705 0.263502 -0.602 0.5470
##
## H1_interactiondisclaimer.new guideline
## summary1Faerber
## s_age ***
## s_sexmale
## s_schoolReal *
## s_schoolAbi ***
## as.factor(s_interest)5
## as.factor(s_interest)6 .
## as.factor(s_interest)7
## as.factor(s_interest)8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -7|-6 -7.4248 1.0679 -6.953
## -6|-5 -5.8077 0.5834 -9.955
## -5|-4 -5.0036 0.4805 -10.412
## -4|-3 -3.0118 0.3864 -7.795
## -3|-2 -2.6811 0.3794 -7.067
## -2|-1 -1.9338 0.3675 -5.262
## -1|0 -1.4740 0.3627 -4.064
## 0|1 -0.6758 0.3585 -1.885
## 1|2 -0.3784 0.3585 -1.056
## 2|3 0.1258 0.3601 0.349
## 3|4 0.3354 0.3611 0.929
## 4|5 0.9917 0.3672 2.700
## 5|6 1.1274 0.3694 3.052
## 6|7 2.3418 0.4092 5.722
## 7|8 2.7719 0.4377 6.333
## (12 Beobachtungen als fehlend gelöscht)exp(coef(relationship_model8_pass1))## -7|-6 -6|-5
## 5.962604e-04 3.004315e-03
## -5|-4 -4|-3
## 6.713648e-03 4.920516e-02
## -3|-2 -2|-1
## 6.849028e-02 1.445920e-01
## -1|0 0|1
## 2.290127e-01 5.087602e-01
## 1|2 2|3
## 6.849830e-01 1.134024e+00
## 3|4 4|5
## 1.398514e+00 2.695847e+00
## 5|6 6|7
## 3.087526e+00 1.039951e+01
## 7|8 H1_interactiondisclaimer.new guideline
## 1.598859e+01 9.364482e-01
## summary1Faerber s_age
## 1.011076e+00 9.691219e-01
## s_sexmale s_schoolReal
## 8.089638e-01 1.619259e+00
## s_schoolAbi as.factor(s_interest)5
## 2.732688e+00 1.093276e+00
## as.factor(s_interest)6 as.factor(s_interest)7
## 1.550858e+00 9.296238e-01
## as.factor(s_interest)8
## 8.532480e-01exp(confint(relationship_model8_pass1))## 2.5 % 97.5 %
## H1_interactiondisclaimer.new guideline 0.6845099 1.2808557
## summary1Faerber 0.7385515 1.3842098
## s_age 0.9590856 0.9791665
## s_sexmale 0.5862197 1.1156221
## s_schoolReal 1.0843282 2.4227437
## s_schoolAbi 1.8506928 4.0483615
## as.factor(s_interest)5 0.6713715 1.7795245
## as.factor(s_interest)6 0.9349611 2.5728632
## as.factor(s_interest)7 0.5430250 1.5915346
## as.factor(s_interest)8 0.5085288 1.4297325nagelkerke(fit = relationship_model8_pass1, null = relationship_null_pass1)## $Models
##
## Model: "clm, as.factor(s_relationship) ~ H1_interaction + summary1 + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_pass_reg1, logit"
## Null: "clm, as.factor(s_relationship) ~ 1, data2_wide_pass_reg1, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0322679
## Cox and Snell (ML) 0.1456570
## Nagelkerke (Cragg and Uhler) 0.1467740
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -10 -38.254 76.508 2.4192e-12
##
## $Number.of.observations
##
## Model: 486
## Null: 486
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H1test_pass1 = emmeans(relationship_model8_pass1, ~ H1_interaction)
pairs(H1test_pass1, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - disclaimer.new guideline 0.0657 0.16 Inf
## z.ratio p.value
## 0.411 0.6811
##
## Results are averaged over the levels of: summary1, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H1test_pass1, Letters = letters)## H1_interaction emmean SE df asymp.LCL asymp.UCL .group
## disclaimer.new guideline 0.274 0.150 Inf -0.0201 0.569 a
## no disclaimer.old guideline 0.340 0.149 Inf 0.0473 0.632 a
##
## Results are averaged over the levels of: summary1, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.data2_wide_pass_reg2 <- subset(data2_wide_pass_reg, condition == 4 | condition == 6)
View(data2_wide_pass_reg2)
relationship_null_pass2 <- clm(as.factor(s_relationship)~1, data = data2_wide_pass_reg2, link = "logit")
relationship_model8_pass2 <- clm(as.factor(s_relationship)~
H1_interaction + summary1 +
s_age + s_sex + s_school +
as.factor(s_interest), data = data2_wide_pass_reg2, link = "logit")
summary(relationship_model8_pass2)## formula:
## as.factor(s_relationship) ~ H1_interaction + summary1 + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_pass_reg2
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 444 -1031.59 2113.19 9(3) 3.60e-09 9.9e+05
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## H1_interactiondisclaimer.new guideline 0.352494 0.169991 2.074 0.0381
## summary1Faerber -0.188076 0.167999 -1.120 0.2629
## s_age -0.024956 0.005708 -4.372 1.23e-05
## s_sexmale -0.433739 0.171931 -2.523 0.0116
## s_schoolReal 0.502953 0.220156 2.285 0.0223
## s_schoolAbi 0.941977 0.214734 4.387 1.15e-05
## as.factor(s_interest)5 0.471621 0.261108 1.806 0.0709
## as.factor(s_interest)6 0.190710 0.266937 0.714 0.4750
## as.factor(s_interest)7 0.181755 0.283032 0.642 0.5208
## as.factor(s_interest)8 0.273741 0.280349 0.976 0.3289
##
## H1_interactiondisclaimer.new guideline *
## summary1Faerber
## s_age ***
## s_sexmale *
## s_schoolReal *
## s_schoolAbi ***
## as.factor(s_interest)5 .
## as.factor(s_interest)6
## as.factor(s_interest)7
## as.factor(s_interest)8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -7|-6 -6.88286 1.08026 -6.372
## -6|-5 -5.48822 0.64543 -8.503
## -5|-4 -4.78372 0.53975 -8.863
## -4|-3 -2.92696 0.43038 -6.801
## -3|-2 -2.47213 0.41996 -5.887
## -2|-1 -1.74864 0.41001 -4.265
## -1|0 -1.25725 0.40655 -3.093
## 0|1 -0.31747 0.40275 -0.788
## 1|2 -0.08492 0.40226 -0.211
## 2|3 0.37662 0.40244 0.936
## 3|4 0.55056 0.40290 1.366
## 4|5 1.25390 0.40822 3.072
## 5|6 1.45013 0.41116 3.527
## 6|7 2.87853 0.46015 6.256
## 7|8 2.94284 0.46417 6.340
## (9 Beobachtungen als fehlend gelöscht)exp(coef(relationship_model8_pass2))## -7|-6 -6|-5
## 0.001025204 0.004135188
## -5|-4 -4|-3
## 0.008364854 0.053559424
## -3|-2 -2|-1
## 0.084404961 0.174011274
## -1|0 0|1
## 0.284434790 0.727991812
## 1|2 2|3
## 0.918581584 1.457353505
## 3|4 4|5
## 1.734226224 3.503976489
## 5|6 6|7
## 4.263680510 17.788192626
## 7|8 H1_interactiondisclaimer.new guideline
## 18.969570287 1.422611563
## summary1Faerber s_age
## 0.828551806 0.975352831
## s_sexmale s_schoolReal
## 0.648081388 1.653597700
## s_schoolAbi as.factor(s_interest)5
## 2.565046537 1.602589467
## as.factor(s_interest)6 as.factor(s_interest)7
## 1.210108923 1.199320176
## as.factor(s_interest)8
## 1.314873638exp(confint(relationship_model8_pass2))## 2.5 % 97.5 %
## H1_interactiondisclaimer.new guideline 1.0200037 1.9866959
## summary1Faerber 0.5957644 1.1513567
## s_age 0.9644536 0.9862922
## s_sexmale 0.4622383 0.9072056
## s_schoolReal 1.0750819 2.5496399
## s_schoolAbi 1.6867743 3.9159907
## as.factor(s_interest)5 0.9607293 2.6758901
## as.factor(s_interest)6 0.7169060 2.0429677
## as.factor(s_interest)7 0.6887823 2.0907663
## as.factor(s_interest)8 0.7593988 2.2809793nagelkerke(fit = relationship_model8_pass2, null = relationship_null_pass2)## $Models
##
## Model: "clm, as.factor(s_relationship) ~ H1_interaction + summary1 + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_pass_reg2, logit"
## Null: "clm, as.factor(s_relationship) ~ 1, data2_wide_pass_reg2, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0309605
## Cox and Snell (ML) 0.1379690
## Nagelkerke (Cragg and Uhler) 0.1391200
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -10 -32.959 65.918 2.704e-10
##
## $Number.of.observations
##
## Model: 444
## Null: 444
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H1test_pass2 = emmeans(relationship_model8_pass2, ~ H1_interaction)
pairs(H1test_pass2, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - disclaimer.new guideline -0.352 0.17 Inf
## z.ratio p.value
## -2.074 0.0381
##
## Results are averaged over the levels of: summary1, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H1test_pass2, Letters = letters)## H1_interaction emmean SE df asymp.LCL asymp.UCL .group
## no disclaimer.old guideline 0.295 0.155 Inf -0.00851 0.598 a
## disclaimer.new guideline 0.647 0.166 Inf 0.32274 0.972 b
##
## Results are averaged over the levels of: summary1, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.H1 Graphical
describeBy(data2_wide_pass_reg$s_relationship,
data2_wide_pass_reg$H1_interaction)##
## Descriptive statistics by group
## group: no disclaimer.old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 240 0.51 3.02 0 0.45 2.97 -7 8 15 0.19 -0.46 0.2
## ------------------------------------------------------------
## group: no disclaimer.new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 424 0.45 3.21 0 0.32 2.97 -6 8 14 0.25 -0.63 0.16
## ------------------------------------------------------------
## group: disclaimer.new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 450 0.91 3.64 0 0.74 2.97 -6 8 14 0.38 -0.94 0.17H1_bar <- ggplot(data2_wide_pass_reg, aes(H1_interaction,
s_relationship)) +
stat_summary(fun = mean, geom = "bar", fill = "White",
colour = "Black") + stat_summary(fun.data =
mean_cl_normal,
geom = "pointrange") +
labs(x = "Condition", y = "Relationship Knowledge Score")
H1_bar## Warning: Removed 31 rows containing non-finite values (`stat_summary()`).
## Removed 31 rows containing non-finite values (`stat_summary()`).
data2_wide_pass_reg$H1_interaction <- mapvalues(data2_wide_pass_reg$H1_interaction,
c("no disclaimer.old guideline",
"no disclaimer.new guideline",
"disclaimer.new guideline"),
c("old, no disclaimer",
"new, no disclaimer",
"new, disclaimer"))
H1_boxplot <- ggplot(data2_wide_pass_reg, aes(H1_interaction, s_relationship,
fill = H1_interaction))
H1_boxplot <- H1_boxplot + geom_boxplot() +
theme_classic() + theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title = element_text(face = "bold"),
text = element_text(face = "bold"),
axis.text = element_text(face = "bold"),
legend.title = element_text(face = "bold"))+
labs(x = "Condition", y = "Realtionship Knowledge Score") +
scale_fill_brewer(palette = "Blues")
H1_boxplot## Warning: Removed 31 rows containing non-finite values (`stat_boxplot()`).
ggsave("H1_boxplot.png", plot = H1_boxplot,
scale = 1, dpi = 600)## Saving 7 x 5 in image## Warning: Removed 31 rows containing non-finite values (`stat_boxplot()`).H2
H2a
H2a_pass <- subset(data2_wide_pass, condition == 2|condition == 4|condition == 6)
View(H2a_pass)
describeBy(H2a_pass$s_extent,H2a_pass$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 241 0.93 2.27 1 0.87 2.97 -4 6 10 0.22 -0.63 0.15
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 453 1.07 2.45 1 1.02 2.97 -6 6 12 0.12 -0.77 0.12wilcox.test(s_extent~disclaimer, data = H2a_pass, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_extent by disclaimer
## W = 52872, p-value = 0.4913
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.072637e-05 8.141003e-05
## sample estimates:
## difference in location
## -4.522336e-05H2a post hoc
H2a_pass_1 <- subset(data2_wide_pass, condition == 2|condition == 6)
View(H2a_pass_1)
describeBy(H2a_pass_1$s_extent,H2a_pass_1$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 241 0.93 2.27 1 0.87 2.97 -4 6 10 0.22 -0.63 0.15
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 249 0.87 2.43 0 0.81 2.97 -6 6 12 0.15 -0.69 0.15wilcox.test(s_extent~disclaimer, data = H2a_pass_1, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_extent by disclaimer
## W = 30495, p-value = 0.7521
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.699256e-05 1.121775e-05
## sample estimates:
## difference in location
## 2.572533e-05H2a_pass_2 <- subset(data2_wide_pass, condition == 4|condition == 6)
View(H2a_pass_2)
describeBy(H2a_pass_2$s_extent,H2a_pass_2$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 241 0.93 2.27 1 0.87 2.97 -4 6 10 0.22 -0.63 0.15
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 204 1.31 2.47 1 1.27 2.97 -4 6 10 0.07 -0.89 0.17wilcox.test(s_extent~disclaimer, data = H2a_pass_2, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_extent by disclaimer
## W = 22377, p-value = 0.09958
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -9.999351e-01 3.470513e-05
## sample estimates:
## difference in location
## -4.93409e-05H2b
H2b_pass <- subset(data2_wide_pass, condition == 1| condition == 2| condition == 3|
condition == 4)
View(H2b_pass)
describeBy(H2b_pass$s_extent,H2b_pass$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 430 0.69 2.24 0 0.64 2.97 -6 6 12 0.15 -0.39 0.11
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 453 1.07 2.45 1 1.02 2.97 -6 6 12 0.12 -0.77 0.12wilcox.test(s_extent~disclaimer, data = H2b_pass, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_extent by disclaimer
## W = 89460, p-value = 0.03434
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -9.999829e-01 -3.957081e-05
## sample estimates:
## difference in location
## -4.508197e-05H2b post hoc
H2b_pass_1 <- subset(data2_wide, condition == 1| condition == 2)
View(H2b_pass_1)
describeBy(H2b_pass_1$s_extent,H2b_pass_1$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 327 0.51 2.21 0 0.43 2.97 -4 6 10 0.25 -0.46 0.12
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 340 0.52 2.38 0 0.44 2.97 -6 6 12 0.22 -0.49 0.13wilcox.test(s_extent~disclaimer, data = H2b_pass_1, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_extent by disclaimer
## W = 55838, p-value = 0.9198
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.851467e-05 3.167506e-05
## sample estimates:
## difference in location
## 4.181901e-05H2b_pass_2 <- subset(data2_wide_pass, condition == 3| condition == 4)
View(H2b_pass_2)
describeBy(H2b_pass_2$s_extent,H2b_pass_2$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 213 0.53 2.16 0 0.47 2.97 -6 6 12 0.12 -0.09 0.15
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 204 1.31 2.47 1 1.27 2.97 -4 6 10 0.07 -0.89 0.17wilcox.test(s_extent~disclaimer, data = H2b_pass_2, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_extent by disclaimer
## W = 17879, p-value = 0.001565
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.000023e+00 -4.802819e-05
## sample estimates:
## difference in location
## -0.9999337H2b_pass_3 <- subset(data2_wide_pass, condition == 1| condition == 4)
View(H2b_pass_3)
describeBy(H2b_pass_3$s_extent,H2b_pass_3$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 217 0.85 2.31 1 0.8 2.97 -4 6 10 0.14 -0.68 0.16
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 204 1.31 2.47 1 1.27 2.97 -4 6 10 0.07 -0.89 0.17wilcox.test(s_extent~disclaimer, data = H2b_pass_3, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_extent by disclaimer
## W = 19853, p-value = 0.06502
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -9.999654e-01 2.904643e-05
## sample estimates:
## difference in location
## -2.018815e-05H2b_pass_4 <- subset(data2_wide_pass, condition == 2| condition == 3)
View(H2b_pass_4)
describeBy(H2b_pass_4$s_extent,H2b_pass_4$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 213 0.53 2.16 0 0.47 2.97 -6 6 12 0.12 -0.09 0.15
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 249 0.87 2.43 0 0.81 2.97 -6 6 12 0.15 -0.69 0.15wilcox.test(s_extent~disclaimer, data = H2b_pass_4, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_extent by disclaimer
## W = 24692, p-value = 0.1965
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -9.999545e-01 2.824539e-05
## sample estimates:
## difference in location
## -7.797653e-05H2 Logistic Regression
data2_wide_pass$H2_interaction <- data2_wide_pass$H1_interaction
table(data2_wide_pass$H2_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## 247 441
## disclaimer.new guideline
## 694data2_wide_pass_reg <- subset(data2_wide_pass, condition != 5)
View(data2_wide_pass_reg)
extent_null_pass <- clm(as.factor(s_extent) ~ 1, data = data2_wide_pass_reg,
link = "logit")
extent_model1_pass <- clm(as.factor(s_extent) ~ H2_interaction,
data = data2_wide_pass_reg, link = "logit")
anova(extent_null_pass,extent_model1_pass)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## extent_null_pass as.factor(s_extent) ~ 1 logit flexible
## extent_model1_pass as.factor(s_extent) ~ H2_interaction logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## extent_null_pass 12 4901.9 -2438.9
## extent_model1_pass 14 4901.3 -2436.6 4.5981 2 0.1004extent_model2_pass <- clm(as.factor(s_extent) ~ H2_interaction + summary1,
data = data2_wide_pass_reg, link = "logit")
anova(extent_model1_pass,extent_model2_pass)## Likelihood ratio tests of cumulative link models:
##
## formula: link:
## extent_model1_pass as.factor(s_extent) ~ H2_interaction logit
## extent_model2_pass as.factor(s_extent) ~ H2_interaction + summary1 logit
## threshold:
## extent_model1_pass flexible
## extent_model2_pass flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## extent_model1_pass 14 4901.3 -2436.6
## extent_model2_pass 15 4903.3 -2436.6 0.002 1 0.9641extent_model3_pass <- clm(as.factor(s_extent) ~ H2_interaction + summary1 +
s_age, data = data2_wide_pass_reg,
link = "logit")
anova(extent_model1_pass,extent_model3_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## extent_model1_pass as.factor(s_extent) ~ H2_interaction
## extent_model3_pass as.factor(s_extent) ~ H2_interaction + summary1 + s_age
## link: threshold:
## extent_model1_pass logit flexible
## extent_model3_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## extent_model1_pass 14 4901.3 -2436.6
## extent_model3_pass 16 4889.2 -2428.6 16.109 2 0.0003177 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1extent_model4_pass <- clm(as.factor(s_extent) ~ H2_interaction +
summary1 + s_age + s_sex,
data = data2_wide_pass_reg, link = "logit")
anova(extent_model3_pass,extent_model4_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## extent_model3_pass as.factor(s_extent) ~ H2_interaction + summary1 + s_age
## extent_model4_pass as.factor(s_extent) ~ H2_interaction + summary1 + s_age + s_sex
## link: threshold:
## extent_model3_pass logit flexible
## extent_model4_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## extent_model3_pass 16 4889.2 -2428.6
## extent_model4_pass 17 4890.0 -2428.0 1.1912 1 0.2751extent_model5_pass <- clm(as.factor(s_extent) ~ H2_interaction + summary1 +
s_age + s_sex + s_school,
data = data2_wide_pass_reg, link = "logit")
anova(extent_model3_pass,extent_model5_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## extent_model3_pass as.factor(s_extent) ~ H2_interaction + summary1 + s_age
## extent_model5_pass as.factor(s_extent) ~ H2_interaction + summary1 + s_age + s_sex + s_school
## link: threshold:
## extent_model3_pass logit flexible
## extent_model5_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## extent_model3_pass 16 4889.2 -2428.6
## extent_model5_pass 19 4827.2 -2394.6 67.932 3 1.183e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1extent_model6_pass <- clm(as.factor(s_extent) ~ H2_interaction + summary1 +
s_age + s_sex + s_school +
as.factor(s_interest), data = data2_wide_pass_reg,
link = "logit")
anova(extent_model5_pass,extent_model6_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## extent_model5_pass as.factor(s_extent) ~ H2_interaction + summary1 + s_age + s_sex + s_school
## extent_model6_pass as.factor(s_extent) ~ H2_interaction + summary1 + s_age + s_sex + s_school + as.factor(s_interest)
## link: threshold:
## extent_model5_pass logit flexible
## extent_model6_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## extent_model5_pass 19 4827.2 -2394.6
## extent_model6_pass 23 4824.1 -2389.1 11.137 4 0.02507 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(extent_model6_pass)## formula:
## as.factor(s_extent) ~ H2_interaction + summary1 + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_pass_reg
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1124 -2389.05 4824.11 7(0) 7.79e-08 1.1e+06
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## H2_interactionno disclaimer.new guideline -0.147638 0.140490 -1.051 0.29331
## H2_interactiondisclaimer.new guideline 0.117724 0.140369 0.839 0.40165
## summary1Faerber 0.020479 0.104929 0.195 0.84526
## s_age -0.011816 0.003469 -3.406 0.00066
## s_sexmale 0.078822 0.106339 0.741 0.45855
## s_schoolReal 0.565252 0.134680 4.197 2.7e-05
## s_schoolAbi 1.101721 0.133843 8.231 < 2e-16
## as.factor(s_interest)5 -0.153299 0.166794 -0.919 0.35805
## as.factor(s_interest)6 -0.142009 0.165998 -0.855 0.39228
## as.factor(s_interest)7 -0.295302 0.181130 -1.630 0.10303
## as.factor(s_interest)8 -0.561386 0.182576 -3.075 0.00211
##
## H2_interactionno disclaimer.new guideline
## H2_interactiondisclaimer.new guideline
## summary1Faerber
## s_age ***
## s_sexmale
## s_schoolReal ***
## s_schoolAbi ***
## as.factor(s_interest)5
## as.factor(s_interest)6
## as.factor(s_interest)7
## as.factor(s_interest)8 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -6.6085 0.7542 -8.762
## -5|-4 -6.2020 0.6342 -9.779
## -4|-3 -4.2889 0.3449 -12.436
## -3|-2 -3.3854 0.2997 -11.294
## -2|-1 -1.7076 0.2704 -6.315
## -1|0 -1.1692 0.2669 -4.381
## 0|1 -0.1881 0.2651 -0.710
## 1|2 0.2877 0.2657 1.083
## 2|3 1.0299 0.2677 3.848
## 3|4 1.5009 0.2702 5.554
## 4|5 2.8465 0.2905 9.799
## 5|6 3.4037 0.3093 11.004
## (21 Beobachtungen als fehlend gelöscht)exp(coef(extent_model6_pass))## -6|-5
## 0.001348841
## -5|-4
## 0.002025423
## -4|-3
## 0.013720079
## -3|-2
## 0.033863684
## -2|-1
## 0.181304316
## -1|0
## 0.310600539
## 0|1
## 0.828508464
## 1|2
## 1.333313271
## 2|3
## 2.800707691
## 3|4
## 4.485916176
## 4|5
## 17.226841649
## 5|6
## 30.074089560
## H2_interactionno disclaimer.new guideline
## 0.862743658
## H2_interactiondisclaimer.new guideline
## 1.124933886
## summary1Faerber
## 1.020690062
## s_age
## 0.988253475
## s_sexmale
## 1.082011416
## s_schoolReal
## 1.759891565
## s_schoolAbi
## 3.009341248
## as.factor(s_interest)5
## 0.857873532
## as.factor(s_interest)6
## 0.867613364
## as.factor(s_interest)7
## 0.744306662
## as.factor(s_interest)8
## 0.570417685exp(confint(extent_model6_pass))## 2.5 % 97.5 %
## H2_interactionno disclaimer.new guideline 0.6550016 1.1362689
## H2_interactiondisclaimer.new guideline 0.8544239 1.4815155
## summary1Faerber 0.8309360 1.2538099
## s_age 0.9815466 0.9949903
## s_sexmale 0.8784657 1.3328799
## s_schoolReal 1.3520751 2.2926483
## s_schoolAbi 2.3166961 3.9154156
## as.factor(s_interest)5 0.6184745 1.1895039
## as.factor(s_interest)6 0.6264859 1.2011560
## as.factor(s_interest)7 0.5216627 1.0613352
## as.factor(s_interest)8 0.3985866 0.8155419nagelkerke(fit = extent_model6_pass, null = extent_null_pass)## $Models
##
## Model: "clm, as.factor(s_extent) ~ H2_interaction + summary1 + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_pass_reg, logit"
## Null: "clm, as.factor(s_extent) ~ 1, data2_wide_pass_reg, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0204548
## Cox and Snell (ML) 0.0849428
## Nagelkerke (Cragg and Uhler) 0.0860651
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -11 -49.888 99.776 1.9778e-16
##
## $Number.of.observations
##
## Model: 1124
## Null: 1124
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H2test_pass = emmeans(extent_model6_pass, ~ H2_interaction)
pairs(H2test_pass, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - no disclaimer.new guideline 0.148 0.140 Inf
## no disclaimer.old guideline - disclaimer.new guideline -0.118 0.140 Inf
## no disclaimer.new guideline - disclaimer.new guideline -0.265 0.119 Inf
## z.ratio p.value
## 1.051 0.5447
## -0.839 0.6789
## -2.236 0.0653
##
## Results are averaged over the levels of: summary1, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimatescld(H2test_pass, Letters = letters)## H2_interaction emmean SE df asymp.LCL asymp.UCL .group
## no disclaimer.new guideline 0.875 0.142 Inf 0.597 1.15 a
## no disclaimer.old guideline 1.023 0.161 Inf 0.708 1.34 a
## disclaimer.new guideline 1.141 0.141 Inf 0.863 1.42 a
##
## Results are averaged over the levels of: summary1, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimates
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.Logistic Regression by Group
data2_wide_pass_reg3 <- subset(data2_wide_pass, condition == 2| condition == 6)
View(data2_wide_pass_reg3)
extent_null_pass1 <- clm(as.factor(s_extent) ~ 1, data = data2_wide_pass_reg3, link = "logit")
extent_model8_pass1 <- clm(as.factor(s_extent) ~ H2_interaction +
summary1 + s_age + s_sex +
s_school + as.factor(s_interest), data = data2_wide_pass_reg3,
link = "logit")
summary(extent_model8_pass1)## formula:
## as.factor(s_extent) ~ H2_interaction + summary1 + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_pass_reg3
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 490 -1034.81 2111.63 7(0) 3.06e-13 7.5e+05
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## H2_interactiondisclaimer.new guideline -0.044804 0.159616 -0.281 0.77894
## summary1Faerber 0.095749 0.160578 0.596 0.55099
## s_age -0.014622 0.005158 -2.835 0.00459
## s_sexmale 0.145365 0.162996 0.892 0.37248
## s_schoolReal 0.624281 0.208019 3.001 0.00269
## s_schoolAbi 1.223665 0.202689 6.037 1.57e-09
## as.factor(s_interest)5 -0.686079 0.261132 -2.627 0.00861
## as.factor(s_interest)6 -0.346777 0.261641 -1.325 0.18504
## as.factor(s_interest)7 -0.610305 0.285482 -2.138 0.03253
## as.factor(s_interest)8 -1.078512 0.273982 -3.936 8.27e-05
##
## H2_interactiondisclaimer.new guideline
## summary1Faerber
## s_age **
## s_sexmale
## s_schoolReal **
## s_schoolAbi ***
## as.factor(s_interest)5 **
## as.factor(s_interest)6
## as.factor(s_interest)7 *
## as.factor(s_interest)8 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-4 -6.86459 1.06688 -6.434
## -4|-3 -5.06113 0.55190 -9.170
## -3|-2 -3.67533 0.42583 -8.631
## -2|-1 -2.06712 0.38233 -5.407
## -1|0 -1.49388 0.37557 -3.978
## 0|1 -0.49938 0.37124 -1.345
## 1|2 -0.08956 0.37196 -0.241
## 2|3 0.64829 0.37515 1.728
## 3|4 1.23557 0.37937 3.257
## 4|5 2.53649 0.40768 6.222
## 5|6 3.03144 0.43258 7.008
## (8 Beobachtungen als fehlend gelöscht)exp(coef(extent_model8_pass1))## -6|-4 -4|-3
## 0.001044105 0.006338367
## -3|-2 -2|-1
## 0.025341049 0.126550148
## -1|0 0|1
## 0.224499522 0.606904850
## 1|2 2|3
## 0.914330938 1.912260879
## 3|4 4|5
## 3.440345754 12.635257566
## 5|6 H2_interactiondisclaimer.new guideline
## 20.727031668 0.956185263
## summary1Faerber s_age
## 1.100482296 0.985484742
## s_sexmale s_schoolReal
## 1.156461331 1.866902863
## s_schoolAbi as.factor(s_interest)5
## 3.399625174 0.503546744
## as.factor(s_interest)6 as.factor(s_interest)7
## 0.706962760 0.543185084
## as.factor(s_interest)8
## 0.340101191exp(confint(extent_model8_pass1))## 2.5 % 97.5 %
## H2_interactiondisclaimer.new guideline 0.6991790 1.3074693
## summary1Faerber 0.8033906 1.5080332
## s_age 0.9755443 0.9954825
## s_sexmale 0.8402997 1.5923638
## s_schoolReal 1.2430276 2.8106958
## s_schoolAbi 2.2897012 5.0701958
## as.factor(s_interest)5 0.3011995 0.8389337
## as.factor(s_interest)6 0.4227658 1.1798470
## as.factor(s_interest)7 0.3099438 0.9498413
## as.factor(s_interest)8 0.1982278 0.5806786nagelkerke(fit = extent_model8_pass1, null = extent_null_pass1)## $Models
##
## Model: "clm, as.factor(s_extent) ~ H2_interaction + summary1 + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_pass_reg3, logit"
## Null: "clm, as.factor(s_extent) ~ 1, data2_wide_pass_reg3, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0282131
## Cox and Snell (ML) 0.1154040
## Nagelkerke (Cragg and Uhler) 0.1169180
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -10 -30.043 60.086 3.4913e-09
##
## $Number.of.observations
##
## Model: 490
## Null: 490
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H2test_pass1 = emmeans(extent_model8_pass1, ~ H2_interaction)
pairs(H2test_pass1, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - disclaimer.new guideline 0.0448 0.16 Inf
## z.ratio p.value
## 0.281 0.7789
##
## Results are averaged over the levels of: summary1, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H2test_pass1, Letters = letters)## H2_interaction emmean SE df asymp.LCL asymp.UCL .group
## disclaimer.new guideline 0.568 0.163 Inf 0.248 0.888 a
## no disclaimer.old guideline 0.613 0.166 Inf 0.288 0.938 a
##
## Results are averaged over the levels of: summary1, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.data2_wide_pass_reg4 <- subset(data2_wide_pass, condition == 4| condition == 6)
View(data2_wide_pass_reg4)
extent_null_pass2 <- clm(as.factor(s_extent) ~ 1, data = data2_wide_pass_reg4, link = "logit")
extent_model8_pass2 <- clm(as.factor(s_extent) ~ H2_interaction +
summary1 + s_age + s_sex +
s_school + as.factor(s_interest), data = data2_wide_pass_reg4,
link = "logit")
summary(extent_model8_pass2)## formula:
## as.factor(s_extent) ~ H2_interaction + summary1 + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_pass_reg4
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 445 -943.66 1927.33 6(0) 4.78e-08 6.5e+05
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## H2_interactiondisclaimer.new guideline 0.278387 0.168382 1.653 0.09827
## summary1Faerber -0.108853 0.167853 -0.649 0.51666
## s_age -0.003981 0.005547 -0.718 0.47291
## s_sexmale -0.011885 0.169055 -0.070 0.94395
## s_schoolReal 0.474719 0.219729 2.160 0.03074
## s_schoolAbi 1.073030 0.212220 5.056 4.28e-07
## as.factor(s_interest)5 -0.124291 0.265590 -0.468 0.63980
## as.factor(s_interest)6 -0.024801 0.267356 -0.093 0.92609
## as.factor(s_interest)7 -0.349794 0.285345 -1.226 0.22025
## as.factor(s_interest)8 -0.862544 0.291118 -2.963 0.00305
##
## H2_interactiondisclaimer.new guideline .
## summary1Faerber
## s_age
## s_sexmale
## s_schoolReal *
## s_schoolAbi ***
## as.factor(s_interest)5
## as.factor(s_interest)6
## as.factor(s_interest)7
## as.factor(s_interest)8 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -4|-3 -4.42779 0.60056 -7.373
## -3|-2 -3.16934 0.46954 -6.750
## -2|-1 -1.49384 0.41582 -3.593
## -1|0 -0.95047 0.40910 -2.323
## 0|1 0.03591 0.40670 0.088
## 1|2 0.47841 0.40851 1.171
## 2|3 1.20354 0.41197 2.921
## 3|4 1.68563 0.41575 4.054
## 4|5 2.97797 0.44392 6.708
## 5|6 3.41373 0.46278 7.377
## (8 Beobachtungen als fehlend gelöscht)exp(coef(extent_model8_pass2))## -4|-3 -3|-2
## 0.01194086 0.04203126
## -2|-1 -1|0
## 0.22450841 0.38656013
## 0|1 1|2
## 1.03656299 1.61350067
## 2|3 3|4
## 3.33189615 5.39586770
## 4|5 5|6
## 19.64792867 30.37828672
## H2_interactiondisclaimer.new guideline summary1Faerber
## 1.32099742 0.89686220
## s_age s_sexmale
## 0.99602656 0.98818499
## s_schoolReal s_schoolAbi
## 1.60756289 2.92422687
## as.factor(s_interest)5 as.factor(s_interest)6
## 0.88312240 0.97550415
## as.factor(s_interest)7 as.factor(s_interest)8
## 0.70483297 0.42208709exp(confint(extent_model8_pass2))## 2.5 % 97.5 %
## H2_interactiondisclaimer.new guideline 0.9499940 1.8387032
## summary1Faerber 0.6452066 1.2461931
## s_age 0.9852479 1.0069212
## s_sexmale 0.7093950 1.3766550
## s_schoolReal 1.0456603 2.4757086
## s_schoolAbi 1.9324547 4.4424361
## as.factor(s_interest)5 0.5243658 1.4864294
## as.factor(s_interest)6 0.5773738 1.6480811
## as.factor(s_interest)7 0.4025247 1.2330482
## as.factor(s_interest)8 0.2380862 0.7460035nagelkerke(fit = extent_model8_pass2, null = extent_null_pass2)## $Models
##
## Model: "clm, as.factor(s_extent) ~ H2_interaction + summary1 + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_pass_reg4, logit"
## Null: "clm, as.factor(s_extent) ~ 1, data2_wide_pass_reg4, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0227249
## Cox and Snell (ML) 0.0939145
## Nagelkerke (Cragg and Uhler) 0.0951552
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -10 -21.943 43.887 3.4497e-06
##
## $Number.of.observations
##
## Model: 445
## Null: 445
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H2test_pass2 = emmeans(extent_model8_pass2, ~ H2_interaction)
pairs(H2test_pass2, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - disclaimer.new guideline -0.278 0.168 Inf
## z.ratio p.value
## -1.653 0.0983
##
## Results are averaged over the levels of: summary1, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H2test_pass2, Letters = letters)## H2_interaction emmean SE df asymp.LCL asymp.UCL .group
## no disclaimer.old guideline 0.0166 0.131 Inf -0.2403 0.273 a
## disclaimer.new guideline 0.2950 0.140 Inf 0.0203 0.570 a
##
## Results are averaged over the levels of: summary1, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.H2 Graphical
describeBy(data2_wide_pass_reg$s_extent,
data2_wide_pass_reg$H2_interaction)##
## Descriptive statistics by group
## group: no disclaimer.old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 241 0.93 2.27 1 0.87 2.97 -4 6 10 0.22 -0.63 0.15
## ------------------------------------------------------------
## group: no disclaimer.new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 430 0.69 2.24 0 0.64 2.97 -6 6 12 0.15 -0.39 0.11
## ------------------------------------------------------------
## group: disclaimer.new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 453 1.07 2.45 1 1.02 2.97 -6 6 12 0.12 -0.77 0.12H2_bar <- ggplot(data2_wide_pass_reg, aes(H2_interaction,
s_extent)) +
stat_summary(fun = mean, geom = "bar", fill = "White",
colour = "Black") + stat_summary(fun.data =
mean_cl_normal,
geom = "pointrange") +
labs(x = "Condition", y = "Extent of Evaluation Knowledge Score")
H2_bar## Warning: Removed 21 rows containing non-finite values (`stat_summary()`).
## Removed 21 rows containing non-finite values (`stat_summary()`).
data2_wide_pass_reg$H2_interaction <- mapvalues(data2_wide_pass_reg$H2_interaction,
c("no disclaimer.old guideline",
"no disclaimer.new guideline",
"disclaimer.new guideline"),
c("old, no disclaimer",
"new, no disclaimer",
"new, disclaimer"))
H2_boxplot <- ggplot(data2_wide_pass_reg, aes(H2_interaction, s_extent,
fill = H2_interaction))
H2_boxplot <- H2_boxplot + geom_boxplot() + theme_classic() + theme(
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title = element_text(face = "bold"),
axis.text = element_text(face = "bold"),
legend.title = element_text(face = "bold"))+
labs(x = "Condition", y = "Extent of Evaluation Knowledge Score") +
scale_fill_brewer(palette = "Blues")
H2_boxplot## Warning: Removed 21 rows containing non-finite values (`stat_boxplot()`).
H3
H3a
H3a_pass <- subset(data2_wide_pass, condition == 2|condition == 4|condition == 6)
View(H3a_pass)
describeBy(H3a_pass$s_diff,H3a_pass$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 239 0.58 2 0 0.56 1.48 -6 6 12 -0.07 0.24 0.13
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 438 0.43 2.1 0 0.32 2.97 -6 6 12 0.31 0.21 0.1wilcox.test(s_diff~disclaimer, data = H3a_pass, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_diff by disclaimer
## W = 55496, p-value = 0.1862
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -5.555652e-05 3.627647e-05
## sample estimates:
## difference in location
## 6.347909e-05H3a post hoc
H3a_pass1 <- subset(data2_wide_pass, condition == 2|condition == 6)
View(H3a_pass1)
describeBy(H3a_pass1$s_diff,H3a_pass1$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 239 0.58 2 0 0.56 1.48 -6 6 12 -0.07 0.24 0.13
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 238 0.19 2.03 0 0.1 2.97 -4 6 10 0.31 0.08 0.13wilcox.test(s_diff~disclaimer, data = H3a_pass1, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_diff by disclaimer
## W = 31996, p-value = 0.01615
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 5.293119e-05 9.999779e-01
## sample estimates:
## difference in location
## 6.013123e-05H3a_pass2 <- subset(data2_wide_pass, condition == 4|condition == 6)
View(H3a_pass2)
describeBy(H3a_pass2$s_diff,H3a_pass2$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 239 0.58 2 0 0.56 1.48 -6 6 12 -0.07 0.24 0.13
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 200 0.72 2.14 0 0.61 2.97 -6 6 12 0.28 0.3 0.15wilcox.test(s_diff~disclaimer, data = H3a_pass2, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_diff by disclaimer
## W = 23501, p-value = 0.7588
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.626346e-05 2.603510e-05
## sample estimates:
## difference in location
## -5.60431e-05H3b
H3b_pass <- subset(data2_wide_pass, condition == 1| condition == 2| condition == 3|
condition == 4)
View(H3b_pass)
describeBy(H3b_pass$s_diff,H3b_pass$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 432 0.39 2.11 0 0.34 2.22 -6 6 12 0.07 0.3 0.1
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 438 0.43 2.1 0 0.32 2.97 -6 6 12 0.31 0.21 0.1wilcox.test(s_diff~disclaimer, data = H3b_pass, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_diff by disclaimer
## W = 94509, p-value = 0.9783
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.612500e-05 4.959797e-05
## sample estimates:
## difference in location
## -3.882985e-05H3b post hoc
H3b_pass1 <- subset(data2_wide_pass, condition == 1| condition == 2)
View(H3b_pass1)
describeBy(H3b_pass1$s_diff,H3b_pass1$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 216 0.26 2.05 0 0.27 1.48 -6 6 12 -0.06 0.29 0.14
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 238 0.19 2.03 0 0.1 2.97 -4 6 10 0.31 0.08 0.13wilcox.test(s_diff~disclaimer, data = H3b_pass1, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_diff by disclaimer
## W = 26715, p-value = 0.4599
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.963525e-06 8.116125e-06
## sample estimates:
## difference in location
## 3.549421e-05H3b_pass2 <- subset(data2_wide_pass, condition == 3| condition == 4)
View(H3b_pass2)
describeBy(H3b_pass2$s_diff,H3b_pass2$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 216 0.51 2.16 0 0.45 2.97 -6 6 12 0.17 0.22 0.15
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 200 0.72 2.14 0 0.61 2.97 -6 6 12 0.28 0.3 0.15wilcox.test(s_diff~disclaimer, data = H3b_pass2, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_diff by disclaimer
## W = 20468, p-value = 0.3454
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.811281e-05 1.060134e-05
## sample estimates:
## difference in location
## -5.52108e-05H3b_pass3 <- subset(data2_wide_pass, condition == 2| condition == 3)
View(H3b_pass3)
describeBy(H3b_pass3$s_diff,H3b_pass3$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 216 0.51 2.16 0 0.45 2.97 -6 6 12 0.17 0.22 0.15
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 238 0.19 2.03 0 0.1 2.97 -4 6 10 0.31 0.08 0.13wilcox.test(s_diff~disclaimer, data = H3b_pass3, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_diff by disclaimer
## W = 27860, p-value = 0.1147
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.027259e-05 9.999986e-01
## sample estimates:
## difference in location
## 5.850487e-05H3b_pass4 <- subset(data2_wide_pass, condition == 1| condition == 4)
View(H3b_pass4)
describeBy(H3b_pass4$s_diff,H3b_pass4$disclaimer)##
## Descriptive statistics by group
## group: no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 216 0.26 2.05 0 0.27 1.48 -6 6 12 -0.06 0.29 0.14
## ------------------------------------------------------------
## group: disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 200 0.72 2.14 0 0.61 2.97 -6 6 12 0.28 0.3 0.15wilcox.test(s_diff~disclaimer, data = H3b_pass4, exact = FALSE,
conf.int = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_diff by disclaimer
## W = 19467, p-value = 0.07539
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.9999553527 0.0000525234
## sample estimates:
## difference in location
## -4.526937e-05H3 Logistic Regression
data2_wide_pass$H3_interaction <- data2_wide_pass$H2_interaction
table(data2_wide_pass$H3_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## 247 441
## disclaimer.new guideline
## 694data2_wide_pass_reg <- subset(data2_wide_pass, condition != 5)
View(data2_wide_pass_reg)
diff_null_pass <- clm(as.factor(s_diff) ~ 1, data = data2_wide_pass_reg, link = "logit")
diff_model1_pass <- clm(as.factor(s_diff) ~ H3_interaction, data = data2_wide_pass_reg,
link = "logit")
anova(diff_null_pass,diff_model1_pass)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## diff_null_pass as.factor(s_diff) ~ 1 logit flexible
## diff_model1_pass as.factor(s_diff) ~ H3_interaction logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## diff_null_pass 12 4455.7 -2215.8
## diff_model1_pass 14 4457.4 -2214.7 2.2258 2 0.3286diff_model2_pass <- clm(as.factor(s_diff) ~ H3_interaction + text_order, data = data2_wide_pass_reg, link = "logit")
anova(diff_null_pass,diff_model2_pass)## Likelihood ratio tests of cumulative link models:
##
## formula: link:
## diff_null_pass as.factor(s_diff) ~ 1 logit
## diff_model2_pass as.factor(s_diff) ~ H3_interaction + text_order logit
## threshold:
## diff_null_pass flexible
## diff_model2_pass flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## diff_null_pass 12 4455.7 -2215.8
## diff_model2_pass 15 4436.4 -2203.2 25.314 3 1.328e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1diff_model3_pass <- clm(as.factor(s_diff) ~ H3_interaction + text_order + s_age, data = data2_wide_pass_reg,
link = "logit")
anova(diff_model2_pass,diff_model3_pass)## Likelihood ratio tests of cumulative link models:
##
## formula: link:
## diff_model2_pass as.factor(s_diff) ~ H3_interaction + text_order logit
## diff_model3_pass as.factor(s_diff) ~ H3_interaction + text_order + s_age logit
## threshold:
## diff_model2_pass flexible
## diff_model3_pass flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## diff_model2_pass 15 4436.4 -2203.2
## diff_model3_pass 16 4431.5 -2199.8 6.8496 1 0.008866 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1diff_model4_pass <- clm(as.factor(s_diff) ~ H3_interaction + text_order + s_age + s_sex,
data = data2_wide_pass_reg,
link = "logit")
anova(diff_model3_pass,diff_model4_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## diff_model3_pass as.factor(s_diff) ~ H3_interaction + text_order + s_age
## diff_model4_pass as.factor(s_diff) ~ H3_interaction + text_order + s_age + s_sex
## link: threshold:
## diff_model3_pass logit flexible
## diff_model4_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## diff_model3_pass 16 4431.5 -2199.8
## diff_model4_pass 17 4428.3 -2197.2 5.1723 1 0.02295 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1diff_model5_pass <- clm(as.factor(s_diff) ~ H3_interaction + text_order + s_age + s_sex +
s_school, data = data2_wide_pass_reg,
link = "logit")
anova(diff_model4_pass,diff_model5_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## diff_model4_pass as.factor(s_diff) ~ H3_interaction + text_order + s_age + s_sex
## diff_model5_pass as.factor(s_diff) ~ H3_interaction + text_order + s_age + s_sex + s_school
## link: threshold:
## diff_model4_pass logit flexible
## diff_model5_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## diff_model4_pass 17 4428.3 -2197.2
## diff_model5_pass 19 4429.3 -2195.6 3.05 2 0.2176diff_model6_pass <- clm(as.factor(s_diff) ~ H3_interaction + text_order + s_age + s_sex +
s_school + as.factor(s_interest), data = data2_wide_pass_reg,
link = "logit")
anova(diff_model4_pass,diff_model6_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## diff_model4_pass as.factor(s_diff) ~ H3_interaction + text_order + s_age + s_sex
## diff_model6_pass as.factor(s_diff) ~ H3_interaction + text_order + s_age + s_sex + s_school + as.factor(s_interest)
## link: threshold:
## diff_model4_pass logit flexible
## diff_model6_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## diff_model4_pass 17 4428.3 -2197.2
## diff_model6_pass 23 4432.6 -2193.3 7.6897 6 0.2617summary(diff_model6_pass)## formula:
## as.factor(s_diff) ~ H3_interaction + text_order + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_pass_reg
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 1109 -2193.32 4432.64 8(2) 2.77e-09 8.9e+05
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## H3_interactionno disclaimer.new guideline -0.194153 0.142134 -1.366 0.1719
## H3_interactiondisclaimer.new guideline -0.194669 0.141730 -1.374 0.1696
## text_orderFaerber 0.516386 0.107709 4.794 1.63e-06
## s_age -0.007849 0.003501 -2.242 0.0250
## s_sexmale -0.247799 0.108325 -2.288 0.0222
## s_schoolReal 0.005230 0.136029 0.038 0.9693
## s_schoolAbi 0.192445 0.132190 1.456 0.1454
## as.factor(s_interest)5 0.124630 0.168940 0.738 0.4607
## as.factor(s_interest)6 0.089459 0.170234 0.526 0.5992
## as.factor(s_interest)7 0.103742 0.182736 0.568 0.5702
## as.factor(s_interest)8 -0.200823 0.182186 -1.102 0.2703
##
## H3_interactionno disclaimer.new guideline
## H3_interactiondisclaimer.new guideline
## text_orderFaerber ***
## s_age *
## s_sexmale *
## s_schoolReal
## s_schoolAbi
## as.factor(s_interest)5
## as.factor(s_interest)6
## as.factor(s_interest)7
## as.factor(s_interest)8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -5.952747 0.564623 -10.543
## -5|-4 -5.728589 0.518428 -11.050
## -4|-3 -3.617525 0.306116 -11.817
## -3|-2 -3.210759 0.292886 -10.962
## -2|-1 -1.759454 0.271141 -6.489
## -1|0 -1.313645 0.268326 -4.896
## 0|1 -0.003668 0.264491 -0.014
## 1|2 0.502327 0.264802 1.897
## 2|3 1.780497 0.272814 6.526
## 3|4 2.053815 0.276476 7.429
## 4|5 3.304792 0.312878 10.563
## 5|6 3.841515 0.345440 11.121
## (36 Beobachtungen als fehlend gelöscht)exp(coef(diff_model6_pass))## -6|-5
## 0.002598693
## -5|-4
## 0.003251662
## -4|-3
## 0.026849048
## -3|-2
## 0.040325981
## -2|-1
## 0.172138759
## -1|0
## 0.268838330
## 0|1
## 0.996338650
## 1|2
## 1.652562935
## 2|3
## 5.932805631
## 3|4
## 7.797591811
## 4|5
## 27.242875959
## 5|6
## 46.595999285
## H3_interactionno disclaimer.new guideline
## 0.823531544
## H3_interactiondisclaimer.new guideline
## 0.823107147
## text_orderFaerber
## 1.675959468
## s_age
## 0.992181668
## s_sexmale
## 0.780516664
## s_schoolReal
## 1.005243926
## s_schoolAbi
## 1.212209291
## as.factor(s_interest)5
## 1.132729009
## as.factor(s_interest)6
## 1.093582519
## as.factor(s_interest)7
## 1.109314599
## as.factor(s_interest)8
## 0.818056919exp(confint(diff_model6_pass))## 2.5 % 97.5 %
## H3_interactionno disclaimer.new guideline 0.6231962 1.0880869
## H3_interactiondisclaimer.new guideline 0.6233792 1.0866853
## text_orderFaerber 1.3575010 2.0708138
## s_age 0.9853886 0.9990093
## s_sexmale 0.6310609 0.9649871
## s_schoolReal 0.7699230 1.3124440
## s_schoolAbi 0.9356472 1.5711167
## as.factor(s_interest)5 0.8134840 1.5777806
## as.factor(s_interest)6 0.7832952 1.5269502
## as.factor(s_interest)7 0.7753767 1.5874767
## as.factor(s_interest)8 0.5721892 1.1689666nagelkerke(fit = diff_model6_pass, null = diff_null_pass)## $Models
##
## Model: "clm, as.factor(s_diff) ~ H3_interaction + text_order + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_pass_reg, logit"
## Null: "clm, as.factor(s_diff) ~ 1, data2_wide_pass_reg, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0101598
## Cox and Snell (ML) 0.0397866
## Nagelkerke (Cragg and Uhler) 0.0405318
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -11 -22.513 45.025 4.8032e-06
##
## $Number.of.observations
##
## Model: 1109
## Null: 1109
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H3test_pass = emmeans(diff_model6_pass, ~ H3_interaction)
pairs(H3test_pass, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - no disclaimer.new guideline 0.194153 0.142 Inf
## no disclaimer.old guideline - disclaimer.new guideline 0.194669 0.142 Inf
## no disclaimer.new guideline - disclaimer.new guideline 0.000515 0.121 Inf
## z.ratio p.value
## 1.366 0.3589
## 1.374 0.3548
## 0.004 1.0000
##
## Results are averaged over the levels of: text_order, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimatescld(H3test_pass, Letters = letters)## H3_interaction emmean SE df asymp.LCL asymp.UCL .group
## disclaimer.new guideline 0.502 0.124 Inf 0.258 0.746 a
## no disclaimer.new guideline 0.502 0.125 Inf 0.257 0.747 a
## no disclaimer.old guideline 0.696 0.146 Inf 0.411 0.982 a
##
## Results are averaged over the levels of: text_order, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimates
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.Logistic Regression by Group
data2_wide_pass_reg5 <- subset(data2_wide_pass, condition == 2 | condition == 6)
View(data2_wide_pass_reg5)
diff_null_pass1 <- clm(as.factor(s_diff) ~ 1, data = data2_wide_pass_reg5, link = "logit")
diff_model8_pass1 <- clm(as.factor(s_diff) ~ H3_interaction + text_order + s_age + s_sex +
s_school + as.factor(s_interest), data = data2_wide_pass_reg5,
link = "logit")
summary(diff_model8_pass1)## formula:
## as.factor(s_diff) ~ H3_interaction + text_order + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_pass_reg5
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 477 -932.58 1907.17 7(0) 3.80e-11 7.1e+05
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## H3_interactiondisclaimer.new guideline -0.388193 0.164305 -2.363 0.01815 *
## text_orderFaerber 0.486639 0.165159 2.946 0.00321 **
## s_age -0.006172 0.005239 -1.178 0.23874
## s_sexmale -0.345801 0.167879 -2.060 0.03942 *
## s_schoolReal 0.198953 0.211035 0.943 0.34581
## s_schoolAbi 0.539629 0.203704 2.649 0.00807 **
## as.factor(s_interest)5 -0.094372 0.260464 -0.362 0.71711
## as.factor(s_interest)6 -0.313121 0.267101 -1.172 0.24108
## as.factor(s_interest)7 0.068689 0.284515 0.241 0.80923
## as.factor(s_interest)8 -0.532610 0.274367 -1.941 0.05223 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-4 -6.58513 1.06629 -6.176
## -4|-3 -3.77559 0.44636 -8.459
## -3|-2 -3.26554 0.41875 -7.798
## -2|-1 -1.77227 0.38316 -4.625
## -1|0 -1.32142 0.37890 -3.488
## 0|1 -0.05228 0.37387 -0.140
## 1|2 0.55506 0.37512 1.480
## 2|3 1.86651 0.38929 4.795
## 3|4 2.13559 0.39532 5.402
## 4|5 3.63270 0.48029 7.564
## 5|6 4.33950 0.57506 7.546
## (21 Beobachtungen als fehlend gelöscht)exp(coef(diff_model8_pass1))## -6|-4 -4|-3
## 0.001380745 0.022923453
## -3|-2 -2|-1
## 0.038176286 0.169946912
## -1|0 0|1
## 0.266755290 0.949067024
## 1|2 2|3
## 1.742043392 6.465663507
## 3|4 4|5
## 8.462045053 37.814938028
## 5|6 H3_interactiondisclaimer.new guideline
## 76.669164452 0.678281411
## text_orderFaerber s_age
## 1.626839914 0.993846724
## s_sexmale s_schoolReal
## 0.707653180 1.220124945
## s_schoolAbi as.factor(s_interest)5
## 1.715370987 0.909944074
## as.factor(s_interest)6 as.factor(s_interest)7
## 0.731161557 1.071103294
## as.factor(s_interest)8
## 0.587070492exp(confint((diff_model8_pass1)))## 2.5 % 97.5 %
## H3_interactiondisclaimer.new guideline 0.4911004 0.9354306
## text_orderFaerber 1.1778298 2.2510215
## s_age 0.9836779 1.0041017
## s_sexmale 0.5088386 0.9829124
## s_schoolReal 0.8067329 1.8458995
## s_schoolAbi 1.1517100 2.5605504
## as.factor(s_interest)5 0.5460993 1.5171487
## as.factor(s_interest)6 0.4328520 1.2341965
## as.factor(s_interest)7 0.6130843 1.8717818
## as.factor(s_interest)8 0.3422971 1.0043133nagelkerke(fit = diff_model8_pass1, null = diff_null_pass1)## $Models
##
## Model: "clm, as.factor(s_diff) ~ H3_interaction + text_order + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_pass_reg5, logit"
## Null: "clm, as.factor(s_diff) ~ 1, data2_wide_pass_reg5, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0168509
## Cox and Snell (ML) 0.0648234
## Nagelkerke (Cragg and Uhler) 0.0660612
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -10 -15.984 31.968 0.00040531
##
## $Number.of.observations
##
## Model: 477
## Null: 477
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H3test_pass1 = emmeans(diff_model8_pass1, ~ H3_interaction)
pairs(H3test_pass1, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - disclaimer.new guideline 0.388 0.164 Inf
## z.ratio p.value
## 2.363 0.0181
##
## Results are averaged over the levels of: text_order, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H3test_pass1, Letters = letters)## H3_interaction emmean SE df asymp.LCL asymp.UCL .group
## disclaimer.new guideline -0.153 0.170 Inf -0.486 0.179 a
## no disclaimer.old guideline 0.235 0.169 Inf -0.096 0.566 b
##
## Results are averaged over the levels of: text_order, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.data2_wide_pass_reg6 <- subset(data2_wide_pass, condition == 4 | condition == 6)
View(data2_wide_pass_reg6)
diff_null_pass2 <- clm(as.factor(s_diff) ~ 1, data = data2_wide_pass_reg6, link = "logit")
diff_model8_pass2 <- clm(as.factor(s_diff) ~ H3_interaction + text_order + s_age + s_sex +
s_school + as.factor(s_interest), data = data2_wide_pass_reg6,
link = "logit")
summary(diff_model8_pass2)## formula:
## as.factor(s_diff) ~ H3_interaction + text_order + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_pass_reg6
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 439 -863.63 1769.26 6(0) 2.79e-07 7.4e+05
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## H3_interactiondisclaimer.new guideline 0.040609 0.171091 0.237 0.81238
## text_orderFaerber 0.688554 0.173445 3.970 7.19e-05
## s_age -0.005643 0.005587 -1.010 0.31245
## s_sexmale -0.186012 0.172343 -1.079 0.28045
## s_schoolReal 0.084964 0.224419 0.379 0.70499
## s_schoolAbi 0.250525 0.211117 1.187 0.23536
## as.factor(s_interest)5 -0.081958 0.273645 -0.300 0.76456
## as.factor(s_interest)6 -0.275165 0.284954 -0.966 0.33422
## as.factor(s_interest)7 -0.352366 0.289682 -1.216 0.22384
## as.factor(s_interest)8 -0.768699 0.290873 -2.643 0.00822
##
## H3_interactiondisclaimer.new guideline
## text_orderFaerber ***
## s_age
## s_sexmale
## s_schoolReal
## s_schoolAbi
## as.factor(s_interest)5
## as.factor(s_interest)6
## as.factor(s_interest)7
## as.factor(s_interest)8 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-4 -5.62529 0.81663 -6.888
## -4|-3 -3.89968 0.50775 -7.680
## -3|-2 -3.33395 0.46892 -7.110
## -2|-1 -1.86714 0.42576 -4.385
## -1|0 -1.35600 0.41965 -3.231
## 0|1 -0.09764 0.41315 -0.236
## 1|2 0.47915 0.41409 1.157
## 2|3 1.84211 0.42647 4.319
## 3|4 2.04630 0.43012 4.757
## 4|5 3.28624 0.47948 6.854
## 5|6 3.81460 0.52408 7.279
## (14 Beobachtungen als fehlend gelöscht)exp(coef(diff_model8_pass2))## -6|-4 -4|-3
## 0.003605502 0.020248427
## -3|-2 -2|-1
## 0.035652068 0.154564650
## -1|0 0|1
## 0.257689089 0.906974807
## 1|2 2|3
## 1.614697910 6.309817279
## 3|4 4|5
## 7.739192246 26.742070059
## 5|6 H3_interactiondisclaimer.new guideline
## 45.358523588 1.041445037
## text_orderFaerber s_age
## 1.990835127 0.994372655
## s_sexmale s_schoolReal
## 0.830263764 1.088678216
## s_schoolAbi as.factor(s_interest)5
## 1.284699639 0.921310933
## as.factor(s_interest)6 as.factor(s_interest)7
## 0.759446499 0.703022865
## as.factor(s_interest)8
## 0.463616005exp(confint((diff_model8_pass2)))## 2.5 % 97.5 %
## H3_interactiondisclaimer.new guideline 0.7446871 1.4567482
## text_orderFaerber 1.4188033 2.8012153
## s_age 0.9835143 1.0053083
## s_sexmale 0.5919237 1.1636173
## s_schoolReal 0.7011513 1.6908710
## s_schoolAbi 0.8496957 1.9449402
## as.factor(s_interest)5 0.5387405 1.5762248
## as.factor(s_interest)6 0.4339542 1.3272051
## as.factor(s_interest)7 0.3980580 1.2402486
## as.factor(s_interest)8 0.2616050 0.8189529nagelkerke(fit = diff_model8_pass2, null = diff_null_pass1)## $Models
##
## Model: "clm, as.factor(s_diff) ~ H3_interaction + text_order + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_pass_reg6, logit"
## Null: "clm, as.factor(s_diff) ~ 1, data2_wide_pass_reg5, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0895446
## Cox and Snell (ML) 0.3208860
## Nagelkerke (Cragg and Uhler) 0.3252050
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -10 -84.939 169.88 2.9397e-31
##
## $Number.of.observations
##
## Model: 439
## Null: 477
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "WARNING: Fitted and null models have different numbers of observations"H3test_pass2 = emmeans(diff_model8_pass2, ~ H3_interaction)
pairs(H3test_pass2, adjust = "tukey")## contrast estimate SE df
## no disclaimer.old guideline - disclaimer.new guideline -0.0406 0.171 Inf
## z.ratio p.value
## -0.237 0.8124
##
## Results are averaged over the levels of: text_order, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H3test_pass2, Letters = letters)## H3_interaction emmean SE df asymp.LCL asymp.UCL .group
## no disclaimer.old guideline 0.225 0.154 Inf -0.0770 0.528 a
## disclaimer.new guideline 0.266 0.163 Inf -0.0542 0.586 a
##
## Results are averaged over the levels of: text_order, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.H3 Graphical
describeBy(data2_wide_pass_reg$s_diff,
data2_wide_pass_reg$H3_interaction)##
## Descriptive statistics by group
## group: no disclaimer.old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 239 0.58 2 0 0.56 1.48 -6 6 12 -0.07 0.24 0.13
## ------------------------------------------------------------
## group: no disclaimer.new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 432 0.39 2.11 0 0.34 2.22 -6 6 12 0.07 0.3 0.1
## ------------------------------------------------------------
## group: disclaimer.new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 438 0.43 2.1 0 0.32 2.97 -6 6 12 0.31 0.21 0.1H3_bar <- ggplot(data2_wide_pass_reg, aes(H3_interaction, s_diff)) +
stat_summary(fun = mean, geom = "bar", fill = "White",
colour = "Black") + stat_summary(fun.data =
mean_cl_normal,
geom = "pointrange") +
labs(x = "Condition", y = "Differentiation Knowledge Score")
H3_bar## Warning: Removed 36 rows containing non-finite values (`stat_summary()`).
## Removed 36 rows containing non-finite values (`stat_summary()`).
data2_wide_pass_reg$H3_interaction <- mapvalues(data2_wide_pass_reg$H3_interaction,
c("no disclaimer.old guideline",
"no disclaimer.new guideline",
"disclaimer.new guideline"),
c("old, no disclaimer",
"new, no disclaimer",
"new, disclaimer"))
H3_boxplot <- ggplot(data2_wide_pass_reg, aes(H3_interaction, s_diff,
fill = H3_interaction))
H3_boxplot <- H3_boxplot + geom_boxplot() + theme_classic() + theme(
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title = element_text(face = "bold"),
axis.text = element_text(face = "bold"),
legend.title = element_text(face = "bold"))+
labs(x = "Condition", y = "Differentiation Knowledge Score") +
scale_fill_brewer(palette = "Blues")
H3_boxplot## Warning: Removed 36 rows containing non-finite values (`stat_boxplot()`).
by(data2_wide_pass_reg$s_diff, data2_wide_pass_reg$H3_interaction, describe)## data2_wide_pass_reg$H3_interaction: old, no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 239 0.58 2 0 0.56 1.48 -6 6 12 -0.07 0.24 0.13
## ------------------------------------------------------------
## data2_wide_pass_reg$H3_interaction: new, no disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 432 0.39 2.11 0 0.34 2.22 -6 6 12 0.07 0.3 0.1
## ------------------------------------------------------------
## data2_wide_pass_reg$H3_interaction: new, disclaimer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 438 0.43 2.1 0 0.32 2.97 -6 6 12 0.31 0.21 0.1data2_wide_pass_reg_old <- subset(data2_wide_pass_reg, H3_interaction ==
"old, no disclaimer")
quantile(data2_wide_pass_reg_old$s_diff, c(0.25, 0.75), na.rm = TRUE)## 25% 75%
## 0 2H4
H4a
H4a_pass <- subset(data2_wide_pass, condition == 3| condition == 4| condition == 6)
View(H4a_pass)
describeBy(H4a_pass$s_causality, H4a_pass$causality)##
## Descriptive statistics by group
## group: no causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 239 0 3.85 0 -0.06 2.97 -8 10 18 0.15 -0.27 0.25
## ------------------------------------------------------------
## group: causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 405 0.11 3.95 0 0.08 2.97 -9 12 21 0.11 -0.2 0.2wilcox.test(s_causality~causality, data = H4a_pass, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_causality by causality
## W = 47508, p-value = 0.6952
## alternative hypothesis: true location shift is not equal to 0# T-Test was computed to double-check results. Careful:
# Requirements are not met.
t.test(s_causality~causality, data = H4a_pass, confint = TRUE)##
## Welch Two Sample t-test
##
## data: s_causality by causality
## t = -0.32162, df = 508.95, p-value = 0.7479
## alternative hypothesis: true difference in means between group no causality statement and group causality statement is not equal to 0
## 95 percent confidence interval:
## -0.7249870 0.5210096
## sample estimates:
## mean in group no causality statement mean in group causality statement
## 0.0041841 0.1061728H4a post hoc
H4a_pass1 <- subset(data2_wide_pass, condition == 3| condition == 6)
View(H4a_pass1)
describeBy(H4a_pass1$s_causality, H4a_pass1$causality)##
## Descriptive statistics by group
## group: no causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 239 0 3.85 0 -0.06 2.97 -8 10 18 0.15 -0.27 0.25
## ------------------------------------------------------------
## group: causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 209 0.08 3.92 0 0.05 2.97 -8 10 18 0.11 -0.23 0.27wilcox.test(s_causality~causality, data = H4a_pass1, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_causality by causality
## W = 24716, p-value = 0.8491
## alternative hypothesis: true location shift is not equal to 0H4a_pass2 <- subset(data2_wide_pass, condition == 4| condition == 6)
View(H4a_pass2)
describeBy(H4a_pass2$s_causality, H4a_pass2$causality)##
## Descriptive statistics by group
## group: no causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 239 0 3.85 0 -0.06 2.97 -8 10 18 0.15 -0.27 0.25
## ------------------------------------------------------------
## group: causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 196 0.14 3.98 0 0.11 2.97 -9 12 21 0.1 -0.2 0.28wilcox.test(s_causality~causality, data = H4a_pass2, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_causality by causality
## W = 22792, p-value = 0.6275
## alternative hypothesis: true location shift is not equal to 0H4b
H4b_pass <- subset(data2_wide_pass, condition == 1| condition == 2|
condition == 3| condition == 4)
View(H4b_pass)
describeBy(H4b_pass$s_causality, H4b_pass$causality)##
## Descriptive statistics by group
## group: no causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 457 -0.51 3.88 0 -0.57 2.97 -10 10 20 0.13 -0.24 0.18
## ------------------------------------------------------------
## group: causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 405 0.11 3.95 0 0.08 2.97 -9 12 21 0.11 -0.2 0.2wilcox.test(s_causality~causality, data = H4b_pass, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_causality by causality
## W = 84435, p-value = 0.02563
## alternative hypothesis: true location shift is not equal to 0# T-Test was computed to double-check results. Careful:
# Requirements are not met.
t.test(s_causality~causality, data = H4b_pass, confint = TRUE)##
## Welch Two Sample t-test
##
## data: s_causality by causality
## t = -2.3053, df = 844.03, p-value = 0.02139
## alternative hypothesis: true difference in means between group no causality statement and group causality statement is not equal to 0
## 95 percent confidence interval:
## -1.14050223 -0.09153711
## sample estimates:
## mean in group no causality statement mean in group causality statement
## -0.5098468 0.1061728H4b post hoc
H4b_pass1 <- subset(data2_wide_pass, condition == 1|condition == 3)
View(H4b_pass1)
describeBy(H4b_pass1$s_causality, H4b_pass1$causality)##
## Descriptive statistics by group
## group: no causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 215 -0.12 3.85 0 -0.19 4.45 -8 10 18 0.15 -0.39 0.26
## ------------------------------------------------------------
## group: causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 209 0.08 3.92 0 0.05 2.97 -8 10 18 0.11 -0.23 0.27wilcox.test(s_causality~causality, data = H4b_pass1, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_causality by causality
## W = 21834, p-value = 0.6144
## alternative hypothesis: true location shift is not equal to 0H4b_pass2 <- subset(data2_wide_pass, condition == 2|condition == 4)
View(H4b_pass2)
describeBy(H4b_pass2$s_causality, H4b_pass2$causality)##
## Descriptive statistics by group
## group: no causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 242 -0.86 3.88 -1 -0.91 4.45 -10 10 20 0.12 -0.16 0.25
## ------------------------------------------------------------
## group: causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 196 0.14 3.98 0 0.11 2.97 -9 12 21 0.1 -0.2 0.28wilcox.test(s_causality~causality, data = H4b_pass2, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_causality by causality
## W = 20336, p-value = 0.009955
## alternative hypothesis: true location shift is not equal to 0H4b_pass3 <- subset(data2_wide_pass, condition == 1|condition == 4)
View(H4b_pass3)
describeBy(H4b_pass3$s_causality, H4b_pass3$causality)##
## Descriptive statistics by group
## group: no causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 215 -0.12 3.85 0 -0.19 4.45 -8 10 18 0.15 -0.39 0.26
## ------------------------------------------------------------
## group: causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 196 0.14 3.98 0 0.11 2.97 -9 12 21 0.1 -0.2 0.28wilcox.test(s_causality~causality, data = H4b_pass3, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_causality by causality
## W = 20235, p-value = 0.4859
## alternative hypothesis: true location shift is not equal to 0H4b_pass4 <- subset(data2_wide_pass, condition == 2|condition == 3)
View(H4b_pass4)
describeBy(H4b_pass4$s_causality, H4b_pass4$causality)##
## Descriptive statistics by group
## group: no causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 242 -0.86 3.88 -1 -0.91 4.45 -10 10 20 0.12 -0.16 0.25
## ------------------------------------------------------------
## group: causality statement
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 209 0.08 3.92 0 0.05 2.97 -8 10 18 0.11 -0.23 0.27wilcox.test(s_causality~causality, data = H4b_pass4, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_causality by causality
## W = 22030, p-value = 0.01772
## alternative hypothesis: true location shift is not equal to 0H4 Mixed Model
sum(is.na(data2_long_pass$disclaimer))## [1] 0sum(is.na(data2_long_pass$s_awareness))## [1] 0sum(is.na(data2_long_pass$text_order))## [1] 0sum(is.na(data2_long_pass$s_age))## [1] 0data2_long_pass <- data2_long_pass %>% drop_na(s_age)
sum(is.na(data2_long_pass$s_sex))## [1] 0sum(is.na(data2_long_pass$s_school))## [1] 0sum(is.na(data2_long_pass$s_interest))## [1] 0data2_long_pass$H4_interaction <- interaction(data2_long_pass$causality,
data2_long_pass$version)
data2_long_pass$H4_interaction <- droplevels(data2_long_pass$H4_interaction)
table(data2_long_pass$H4_interaction)##
## no causality statement.new guideline causality statement.new guideline
## 944 1326
## no causality statement.old guideline
## 494data2_long_pass_reg <- subset(data2_long_pass, condition != 5)
View(data2_long_pass_reg)
data2_long_pass_reg$H4_interaction <- relevel(data2_long_pass_reg$H4_interaction,
ref = "no causality statement.old guideline")
set.seed(288659)
causality_null_pass <- clm(as.factor(s_causality) ~ 1,
data = data2_long_pass_reg,
link = "logit")
causality_model1_pass <- clmm(as.factor(s_causality) ~ 1 + (1|id),
data = data2_long_pass_reg)## Warning in update.uC(rho): Non finite negative log-likelihood
## at iteration 75anova(causality_null_pass,causality_model1_pass)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## causality_null_pass as.factor(s_causality) ~ 1 logit flexible
## causality_model1_pass as.factor(s_causality) ~ 1 + (1 | id) logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_null_pass 12 9757.2 -4866.6
## causality_model1_pass 13 9727.4 -4850.7 31.761 1 1.744e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1causality_model2_pass <- clmm(as.factor(s_causality) ~ H4_interaction + (1|id),
data = data2_long_pass_reg)## Warning in update.uC(rho): Non finite negative log-likelihood
## at iteration 85anova(causality_model1_pass,causality_model2_pass)## Likelihood ratio tests of cumulative link models:
##
## formula: link:
## causality_model1_pass as.factor(s_causality) ~ 1 + (1 | id) logit
## causality_model2_pass as.factor(s_causality) ~ H4_interaction + (1 | id) logit
## threshold:
## causality_model1_pass flexible
## causality_model2_pass flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_model1_pass 13 9727.4 -4850.7
## causality_model2_pass 15 9725.0 -4847.5 6.3853 2 0.04106 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1causality_model3_pass <- clmm(as.factor(s_causality) ~ H4_interaction +
summary + (1|id), data = data2_long_pass_reg)## Warning in update.uC(rho): Non finite negative log-likelihood
## at iteration 90anova(causality_model2_pass,causality_model3_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## causality_model2_pass as.factor(s_causality) ~ H4_interaction + (1 | id)
## causality_model3_pass as.factor(s_causality) ~ H4_interaction + summary + (1 | id)
## link: threshold:
## causality_model2_pass logit flexible
## causality_model3_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_model2_pass 15 9725.0 -4847.5
## causality_model3_pass 16 9723.8 -4845.9 3.2427 1 0.07174 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1causality_model4_pass <- clmm(as.factor(s_causality) ~ H4_interaction +
summary + text_order + (1|id),
data = data2_long_pass_reg)## Warning in update.uC(rho): Non finite negative log-likelihood
## at iteration 95anova(causality_model2_pass,causality_model4_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## causality_model2_pass as.factor(s_causality) ~ H4_interaction + (1 | id)
## causality_model4_pass as.factor(s_causality) ~ H4_interaction + summary + text_order + (1 | id)
## link: threshold:
## causality_model2_pass logit flexible
## causality_model4_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_model2_pass 15 9725.0 -4847.5
## causality_model4_pass 17 9722.2 -4844.1 6.866 2 0.03229 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1causality_model5_pass <- clmm(as.factor(s_causality) ~ H4_interaction +
summary + text_order + s_age + (1|id),
data = data2_long_pass_reg)
anova(causality_model2_pass,causality_model5_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## causality_model2_pass as.factor(s_causality) ~ H4_interaction + (1 | id)
## causality_model5_pass as.factor(s_causality) ~ H4_interaction + summary + text_order + s_age + (1 | id)
## link: threshold:
## causality_model2_pass logit flexible
## causality_model5_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_model2_pass 15 9725.0 -4847.5
## causality_model5_pass 18 9681.1 -4822.6 49.889 3 8.434e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1causality_model6_pass <- clmm(as.factor(s_causality) ~ H4_interaction +
summary + text_order + s_age + s_sex + (1|id),
data = data2_long_pass_reg)
anova(causality_model5_pass,causality_model6_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## causality_model5_pass as.factor(s_causality) ~ H4_interaction + summary + text_order + s_age + (1 | id)
## causality_model6_pass as.factor(s_causality) ~ H4_interaction + summary + text_order + s_age + s_sex + (1 | id)
## link: threshold:
## causality_model5_pass logit flexible
## causality_model6_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_model5_pass 18 9681.1 -4822.6
## causality_model6_pass 19 9681.5 -4821.8 1.6077 1 0.2048causality_model7_pass <- clmm(as.factor(s_causality) ~ H4_interaction +
summary + text_order + s_age + s_sex + s_school +
(1|id), data = data2_long_pass_reg)
anova(causality_model5_pass,causality_model7_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## causality_model5_pass as.factor(s_causality) ~ H4_interaction + summary + text_order + s_age + (1 | id)
## causality_model7_pass as.factor(s_causality) ~ H4_interaction + summary + text_order + s_age + s_sex + s_school + (1 | id)
## link: threshold:
## causality_model5_pass logit flexible
## causality_model7_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_model5_pass 18 9681.1 -4822.6
## causality_model7_pass 21 9641.4 -4799.7 45.772 3 6.34e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1causality_model8_pass <- clmm(as.factor(s_causality) ~ H4_interaction +
summary + text_order + s_age + s_sex + s_school +
as.factor(s_interest) + (1|id),
data = data2_long_pass_reg)
anova(causality_model7_pass,causality_model8_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## causality_model7_pass as.factor(s_causality) ~ H4_interaction + summary + text_order + s_age + s_sex + s_school + (1 | id)
## causality_model8_pass as.factor(s_causality) ~ H4_interaction + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id)
## link: threshold:
## causality_model7_pass logit flexible
## causality_model8_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## causality_model7_pass 21 9641.4 -4799.7
## causality_model8_pass 25 9639.9 -4794.9 9.5015 4 0.04972 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(causality_model8_pass)## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: as.factor(s_causality) ~ H4_interaction + summary + text_order +
## s_age + s_sex + s_school + as.factor(s_interest) + (1 | id)
## data: data2_long_pass_reg
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 2241 -4794.94 9639.87 4028(11718) 1.40e+03 8.4e+06
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.497 0.705
## Number of groups: id 1140
##
## Coefficients:
## Estimate Std. Error
## H4_interactionno causality statement.new guideline -0.1815985 0.0001996
## H4_interactioncausality statement.new guideline 0.1087917 0.0863757
## summaryFaerber -0.1255491 0.0001894
## text_orderFaerber 0.1757052 0.0788898
## s_age -0.0177713 0.0001884
## s_sexmale 0.0929959 0.0789162
## s_schoolReal 0.2094841 0.0903320
## s_schoolAbi 0.6934230 0.0001996
## as.factor(s_interest)5 0.0983129 0.1070160
## as.factor(s_interest)6 -0.0129118 0.1047526
## as.factor(s_interest)7 -0.1677846 0.1168966
## as.factor(s_interest)8 -0.3113103 0.0001996
## z value Pr(>|z|)
## H4_interactionno causality statement.new guideline -909.819 <2e-16 ***
## H4_interactioncausality statement.new guideline 1.260 0.2078
## summaryFaerber -663.023 <2e-16 ***
## text_orderFaerber 2.227 0.0259 *
## s_age -94.343 <2e-16 ***
## s_sexmale 1.178 0.2386
## s_schoolReal 2.319 0.0204 *
## s_schoolAbi 3474.084 <2e-16 ***
## as.factor(s_interest)5 0.919 0.3583
## as.factor(s_interest)6 -0.123 0.9019
## as.factor(s_interest)7 -1.435 0.1512
## as.factor(s_interest)8 -1559.681 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -6.7945533 0.4042384 -16.808
## -5|-4 -5.9460333 0.2631006 -22.600
## -4|-3 -2.6712206 0.0001894 -14106.773
## -3|-2 -2.2408979 0.0001894 -11834.255
## -2|-1 -1.3892263 0.0434762 -31.954
## -1|0 -0.9576527 0.0492953 -19.427
## 0|1 0.1050367 0.0594889 1.766
## 1|2 0.4723382 0.0631892 7.475
## 2|3 1.4633246 0.0766954 19.080
## 3|4 1.7932872 0.0831083 21.578
## 4|5 3.4485920 0.1449077 23.799
## 5|6 3.7781135 0.1664454 22.699
## (49 Beobachtungen als fehlend gelöscht)exp(coef(causality_model8_pass))## -6|-5
## 0.001119858
## -5|-4
## 0.002616198
## -4|-3
## 0.069167747
## -3|-2
## 0.106362954
## -2|-1
## 0.249268093
## -1|0
## 0.383792709
## 0|1
## 1.110751353
## 1|2
## 1.603739714
## 2|3
## 4.320299055
## 3|4
## 6.009173409
## 4|5
## 31.456072357
## 5|6
## 43.733459723
## H4_interactionno causality statement.new guideline
## 0.833936077
## H4_interactioncausality statement.new guideline
## 1.114930086
## summaryFaerber
## 0.882012473
## text_orderFaerber
## 1.192086603
## s_age
## 0.982385704
## s_sexmale
## 1.097457252
## s_schoolReal
## 1.233041825
## s_schoolAbi
## 2.000551646
## as.factor(s_interest)5
## 1.103307994
## as.factor(s_interest)6
## 0.987171202
## as.factor(s_interest)7
## 0.845535951
## as.factor(s_interest)8
## 0.732486541exp(confint(causality_model8_pass))## 2.5 % 97.5 %
## -6|-5 5.070779e-04 0.002473155
## -5|-4 1.562136e-03 0.004381494
## -4|-3 6.914208e-02 0.069193422
## -3|-2 1.063235e-01 0.106402436
## -2|-1 2.289073e-01 0.271439888
## -1|0 3.484468e-01 0.422724064
## 0|1 9.885071e-01 1.248112957
## 1|2 1.416926e+00 1.815183812
## 2|3 3.717325e+00 5.021079185
## 3|4 5.105906e+00 7.072234837
## 4|5 2.367875e+01 41.787863810
## 5|6 3.155987e+01 60.602763239
## H4_interactionno causality statement.new guideline 8.336099e-01 0.834262382
## H4_interactioncausality statement.new guideline 9.412923e-01 1.320598366
## summaryFaerber 8.816852e-01 0.882339880
## text_orderFaerber 1.021308e+00 1.391422192
## s_age 9.820231e-01 0.982748463
## s_sexmale 9.401866e-01 1.281035515
## s_schoolReal 1.032968e+00 1.471867006
## s_schoolAbi 1.999769e+00 2.001334428
## as.factor(s_interest)5 8.945499e-01 1.360783190
## as.factor(s_interest)6 8.039460e-01 1.212154755
## as.factor(s_interest)7 6.724027e-01 1.063248253
## as.factor(s_interest)8 7.322000e-01 0.732773150nagelkerke(fit = causality_model8_pass, null = causality_null_pass)## $Models
##
## Model: "clmm, as.factor(s_causality) ~ H4_interaction + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id), data2_long_pass_reg"
## Null: "clm, as.factor(s_causality) ~ 1, data2_long_pass_reg, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0147238
## Cox and Snell (ML) 0.0619471
## Nagelkerke (Cragg and Uhler) 0.0627627
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -13 -71.655 143.31 4.5656e-24
##
## $Number.of.observations
##
## Model: 2241
## Null: 2241
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H4test_pass = emmeans(causality_model8_pass, ~ H4_interaction)
pairs(H4test_pass, adjust = "tukey")## contrast
## no causality statement.old guideline - no causality statement.new guideline
## no causality statement.old guideline - causality statement.new guideline
## no causality statement.new guideline - causality statement.new guideline
## estimate SE df z.ratio p.value
## 0.1816 0.0001996 Inf 909.819 <.0001
## -0.1088 0.0863757 Inf -1.260 0.4182
## -0.2904 0.0863756 Inf -3.362 0.0022
##
## Results are averaged over the levels of: summary, text_order, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimatescld(H4test_pass, Letters = letters)## H4_interaction emmean SE df asymp.LCL asymp.UCL
## no causality statement.new guideline 0.0186 0.0726 Inf -0.1237 0.161
## no causality statement.old guideline 0.2002 0.0726 Inf 0.0579 0.343
## causality statement.new guideline 0.3090 0.0900 Inf 0.1326 0.485
## .group
## a
## b
## b
##
## Results are averaged over the levels of: summary, text_order, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimates
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.#Changing reference group for H4_interaction to new_no causality" for further testing
data2_long_pass_reg$H4_interaction <- relevel(data2_long_pass_reg$H4_interaction,
ref = "no causality statement.new guideline")
set.seed(288659)
causality_null_pass <- clm(as.factor(s_causality) ~ 1,
data = data2_long_pass_reg,
link = "logit")
causality_model8_pass <- clmm(as.factor(s_causality) ~ H4_interaction +
summary + text_order + s_age + s_sex + s_school +
as.factor(s_interest) + (1|id),
data = data2_long_pass_reg)
summary(causality_model8_pass)## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: as.factor(s_causality) ~ H4_interaction + summary + text_order +
## s_age + s_sex + s_school + as.factor(s_interest) + (1 | id)
## data: data2_long_pass_reg
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 2241 -4794.89 9639.77 4881(14309) 1.39e+03 7.0e+06
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.4961 0.7044
## Number of groups: id 1140
##
## Coefficients:
## Estimate Std. Error
## H4_interactionno causality statement.old guideline 0.1786773 0.1078775
## H4_interactioncausality statement.new guideline 0.2788990 0.0912867
## summaryFaerber -0.1259396 0.0001896
## text_orderFaerber 0.1735413 0.0796119
## s_age -0.0177474 0.0001886
## s_sexmale 0.0884027 0.0795611
## s_schoolReal 0.2068055 0.0906082
## s_schoolAbi 0.6943650 0.0001958
## as.factor(s_interest)5 0.0918543 0.1083552
## as.factor(s_interest)6 -0.0175016 0.1055569
## as.factor(s_interest)7 -0.1572082 0.1184559
## as.factor(s_interest)8 -0.3082542 0.0001958
## z value Pr(>|z|)
## H4_interactionno causality statement.old guideline 1.656 0.09766 .
## H4_interactioncausality statement.new guideline 3.055 0.00225 **
## summaryFaerber -664.358 < 2e-16 ***
## text_orderFaerber 2.180 0.02927 *
## s_age -94.082 < 2e-16 ***
## s_sexmale 1.111 0.26651
## s_schoolReal 2.282 0.02246 *
## s_schoolAbi 3546.635 < 2e-16 ***
## as.factor(s_interest)5 0.848 0.39660
## as.factor(s_interest)6 -0.166 0.86831
## as.factor(s_interest)7 -1.327 0.18446
## as.factor(s_interest)8 -1574.483 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -6.6246147 0.4049569 -16.359
## -5|-4 -5.7731911 0.2629285 -21.957
## -4|-3 -2.5008761 0.0001896 -13192.732
## -3|-2 -2.0710260 0.0001896 -10925.191
## -2|-1 -1.2171565 0.0438267 -27.772
## -1|0 -0.7854026 0.0497442 -15.789
## 0|1 0.2811440 0.0601178 4.677
## 1|2 0.6473634 0.0637883 10.149
## 2|3 1.6422486 0.0773354 21.235
## 3|4 1.9721463 0.0837088 23.560
## 4|5 3.6194797 0.1447421 25.006
## 5|6 3.9433068 0.1656134 23.810
## (49 Beobachtungen als fehlend gelöscht)exp(coef(causality_model8_pass))## -6|-5
## 0.001327292
## -5|-4
## 0.003109818
## -4|-3
## 0.082013118
## -3|-2
## 0.126056386
## -2|-1
## 0.296070835
## -1|0
## 0.455936086
## 0|1
## 1.324644327
## 1|2
## 1.910497003
## 2|3
## 5.166774512
## 3|4
## 7.186083677
## 4|5
## 37.318146840
## 5|6
## 51.588914574
## H4_interactionno causality statement.old guideline
## 1.195634906
## H4_interactioncausality statement.new guideline
## 1.321673814
## summaryFaerber
## 0.881668115
## text_orderFaerber
## 1.189509807
## s_age
## 0.982409174
## s_sexmale
## 1.092427931
## s_schoolReal
## 1.229743348
## s_schoolAbi
## 2.002437155
## as.factor(s_interest)5
## 1.096205068
## as.factor(s_interest)6
## 0.982650625
## as.factor(s_interest)7
## 0.854526113
## as.factor(s_interest)8
## 0.734728535exp(confint(causality_model8_pass))## 2.5 % 97.5 %
## -6|-5 6.001592e-04 0.002935394
## -5|-4 1.857504e-03 0.005206431
## -4|-3 8.198265e-02 0.082043594
## -3|-2 1.260096e-01 0.126103230
## -2|-1 2.717004e-01 0.322627193
## -1|0 4.135820e-01 0.502627580
## 0|1 1.177408e+00 1.490292874
## 1|2 1.685969e+00 2.164926000
## 2|3 4.440087e+00 6.012394891
## 3|4 6.098727e+00 8.467308255
## 4|5 2.810058e+01 49.559259014
## 5|6 3.728945e+01 71.371826410
## H4_interactionno causality statement.old guideline 9.677721e-01 1.477148271
## H4_interactioncausality statement.new guideline 1.105149e+00 1.580620724
## summaryFaerber 8.813406e-01 0.881995753
## text_orderFaerber 1.017659e+00 1.390380860
## s_age 9.820460e-01 0.982772461
## s_sexmale 9.346958e-01 1.276777796
## s_schoolReal 1.029648e+00 1.468724567
## s_schoolAbi 2.001669e+00 2.003205686
## as.factor(s_interest)5 8.864611e-01 1.355576221
## as.factor(s_interest)6 7.990040e-01 1.208507447
## as.factor(s_interest)7 6.774784e-01 1.077842263
## as.factor(s_interest)8 7.344467e-01 0.735010522nagelkerke(fit = causality_model8_pass, null = causality_null_pass)## $Models
##
## Model: "clmm, as.factor(s_causality) ~ H4_interaction + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id), data2_long_pass_reg"
## Null: "clm, as.factor(s_causality) ~ 1, data2_long_pass_reg, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0147338
## Cox and Snell (ML) 0.0619876
## Nagelkerke (Cragg and Uhler) 0.0628037
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -13 -71.703 143.41 4.3658e-24
##
## $Number.of.observations
##
## Model: 2241
## Null: 2241
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H4test_pass = emmeans(causality_model8_pass, ~ H4_interaction)
pairs(H4test_pass, adjust = "tukey")## contrast
## no causality statement.new guideline - no causality statement.old guideline
## no causality statement.new guideline - causality statement.new guideline
## no causality statement.old guideline - causality statement.new guideline
## estimate SE df z.ratio p.value
## -0.179 0.1079 Inf -1.656 0.2222
## -0.279 0.0913 Inf -3.055 0.0064
## -0.100 0.1166 Inf -0.859 0.6660
##
## Results are averaged over the levels of: summary, text_order, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimatescld(H4test_pass, Letters = letters)## H4_interaction emmean SE df asymp.LCL asymp.UCL
## no causality statement.new guideline 0.025 0.0779 Inf -0.12761 0.178
## no causality statement.old guideline 0.204 0.1078 Inf -0.00763 0.415
## causality statement.new guideline 0.304 0.0900 Inf 0.12750 0.480
## .group
## a
## ab
## b
##
## Results are averaged over the levels of: summary, text_order, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimates
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.Mixed Model by Groups
set.seed(288659)
data2_long_pass_reg1 <- subset(data2_long_pass, condition == 3 | condition == 6)
View(data2_long_pass_reg1)
causality_null_pass1 <- clm(as.factor(s_causality) ~ 1, data = data2_long_pass_reg1, link = "logit")
causality_model10_pass1 <- clmm(as.factor(s_causality) ~ H4_interaction +
summary + text_order + s_age + s_sex + s_school +
as.factor(s_interest) + (1|id), data = data2_long_pass_reg1)
summary(causality_model10_pass1)## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: as.factor(s_causality) ~ H4_interaction + summary + text_order +
## s_age + s_sex + s_school + as.factor(s_interest) + (1 | id)
## data: data2_long_pass_reg1
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 913 -1946.71 3941.42 2900(8398) 5.87e+02 8.5e+06
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.4964 0.7046
## Number of groups: id 465
##
## Coefficients:
## Estimate Std. Error
## H4_interactionno causality statement.old guideline -0.0997313 0.1290745
## summaryFaerber -0.0152518 0.0002921
## text_orderFaerber 0.1497657 0.1266031
## s_age -0.0190058 0.0002909
## s_sexmale 0.0724975 0.0003017
## s_schoolReal 0.0333418 0.0003017
## s_schoolAbi 0.7230952 0.1446164
## as.factor(s_interest)5 0.1390184 0.1659623
## as.factor(s_interest)6 0.0651620 0.1743074
## as.factor(s_interest)7 -0.0183709 0.1927326
## as.factor(s_interest)8 -0.1665107 0.2091752
## z value Pr(>|z|)
## H4_interactionno causality statement.old guideline -0.773 0.440
## summaryFaerber -52.214 < 2e-16 ***
## text_orderFaerber 1.183 0.237
## s_age -65.342 < 2e-16 ***
## s_sexmale 240.313 < 2e-16 ***
## s_schoolReal 110.521 < 2e-16 ***
## s_schoolAbi 5.000 5.73e-07 ***
## as.factor(s_interest)5 0.838 0.402
## as.factor(s_interest)6 0.374 0.709
## as.factor(s_interest)7 -0.095 0.924
## as.factor(s_interest)8 -0.796 0.426
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -7.0209208 0.7127743 -9.850
## -5|-4 -5.9130827 0.4151365 -14.244
## -4|-3 -2.8117318 0.1298472 -21.654
## -3|-2 -2.4156564 0.1188085 -20.332
## -2|-1 -1.5168817 0.1016805 -14.918
## -1|0 -1.0494706 0.0953385 -11.008
## 0|1 0.0573920 0.0802807 0.715
## 1|2 0.3940832 0.0734972 5.362
## 2|3 1.3367872 0.0002921 4576.484
## 3|4 1.7048392 0.0002921 5836.530
## 4|5 3.5134056 0.2026555 17.337
## 5|6 3.8168416 0.2415576 15.801
## (21 Beobachtungen als fehlend gelöscht)exp(coef(causality_model10_pass1))## -6|-5
## 8.930028e-04
## -5|-4
## 2.703839e-03
## -4|-3
## 6.010082e-02
## -3|-2
## 8.930869e-02
## -2|-1
## 2.193950e-01
## -1|0
## 3.501230e-01
## 0|1
## 1.059071e+00
## 1|2
## 1.483024e+00
## 2|3
## 3.806793e+00
## 3|4
## 5.500501e+00
## 4|5
## 3.356237e+01
## 5|6
## 4.546040e+01
## H4_interactionno causality statement.old guideline
## 9.050806e-01
## summaryFaerber
## 9.848639e-01
## text_orderFaerber
## 1.161562e+00
## s_age
## 9.811737e-01
## s_sexmale
## 1.075190e+00
## s_schoolReal
## 1.033904e+00
## s_schoolAbi
## 2.060802e+00
## as.factor(s_interest)5
## 1.149145e+00
## as.factor(s_interest)6
## 1.067332e+00
## as.factor(s_interest)7
## 9.817968e-01
## as.factor(s_interest)8
## 8.466138e-01exp(confint(causality_model10_pass1))## 2.5 % 97.5 %
## -6|-5 2.208708e-04 0.003610501
## -5|-4 1.198439e-03 0.006100222
## -4|-3 4.659661e-02 0.077518709
## -3|-2 7.075608e-02 0.112725906
## -2|-1 1.797530e-01 0.267779418
## -1|0 2.904480e-01 0.422058833
## 0|1 9.048778e-01 1.239538731
## 1|2 1.284066e+00 1.712809466
## 2|3 3.804614e+00 3.808973298
## 3|4 5.497353e+00 5.503651203
## 4|5 2.256067e+01 49.929060470
## 5|6 2.831516e+01 72.987320143
## H4_interactionno causality statement.old guideline 7.027791e-01 1.165616398
## summaryFaerber 9.843003e-01 0.985427946
## text_orderFaerber 9.063118e-01 1.488699939
## s_age 9.806145e-01 0.981733172
## s_sexmale 1.074555e+00 1.075826028
## s_schoolReal 1.033293e+00 1.034515370
## s_schoolAbi 1.552167e+00 2.736112499
## as.factor(s_interest)5 8.300564e-01 1.590897651
## as.factor(s_interest)6 7.584531e-01 1.502001105
## as.factor(s_interest)7 6.729261e-01 1.432438156
## as.factor(s_interest)8 5.618690e-01 1.275661908nagelkerke(fit = causality_model10_pass1, null = causality_null_pass1)## $Models
##
## Model: "clmm, as.factor(s_causality) ~ H4_interaction + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id), data2_long_pass_reg1"
## Null: "clm, as.factor(s_causality) ~ 1, data2_long_pass_reg1, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0149519
## Cox and Snell (ML) 0.0626788
## Nagelkerke (Cragg and Uhler) 0.0635159
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -12 -29.549 59.098 3.2947e-08
##
## $Number.of.observations
##
## Model: 913
## Null: 913
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H4test_pass1 = emmeans(causality_model10_pass1, ~ H4_interaction)
pairs(H4test_pass1, adjust = "tukey")## contrast
## causality statement.new guideline - no causality statement.old guideline
## estimate SE df z.ratio p.value
## 0.0997 0.129 Inf 0.773 0.4397
##
## Results are averaged over the levels of: summary, text_order, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H4test_pass1, Letters = letters)## H4_interaction emmean SE df asymp.LCL asymp.UCL
## no causality statement.old guideline 0.194 0.129 Inf -0.0593 0.447
## causality statement.new guideline 0.294 0.123 Inf 0.0525 0.535
## .group
## a
## a
##
## Results are averaged over the levels of: summary, text_order, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.data2_long_pass_reg2 <- subset(data2_long_pass, condition == 4 | condition == 6)
View(data2_long_pass_reg2)
causality_null_pass2 <- clm(as.factor(s_causality) ~ 1, data = data2_long_pass_reg2, link = "logit")
causality_model10_pass2 <- clmm(as.factor(s_causality) ~ H4_interaction +
summary + text_order + s_age + s_sex + s_school +
as.factor(s_interest) + (1|id), data = data2_long_pass_reg2)
summary(causality_model10_pass2)## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: as.factor(s_causality) ~ H4_interaction + summary + text_order +
## s_age + s_sex + s_school + as.factor(s_interest) + (1 | id)
## data: data2_long_pass_reg2
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 886 -1887.79 3823.58 3341(7710) 2.22e-02 9.9e+05
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.3295 0.574
## Number of groups: id 451
##
## Coefficients:
## Estimate Std. Error z value
## H4_interactionno causality statement.old guideline -0.118928 0.132703 -0.896
## summaryFaerber -0.148873 0.119944 -1.241
## text_orderFaerber 0.115044 0.132548 0.868
## s_age -0.018639 0.004464 -4.176
## s_sexmale 0.020535 0.134037 0.153
## s_schoolReal 0.044700 0.172999 0.258
## s_schoolAbi 0.601577 0.165542 3.634
## as.factor(s_interest)5 0.266951 0.209249 1.276
## as.factor(s_interest)6 0.076552 0.213502 0.359
## as.factor(s_interest)7 -0.041883 0.220704 -0.190
## as.factor(s_interest)8 -0.123130 0.227445 -0.541
## Pr(>|z|)
## H4_interactionno causality statement.old guideline 0.370147
## summaryFaerber 0.214536
## text_orderFaerber 0.385425
## s_age 2.97e-05 ***
## s_sexmale 0.878236
## s_schoolReal 0.796112
## s_schoolAbi 0.000279 ***
## as.factor(s_interest)5 0.202041
## as.factor(s_interest)6 0.719930
## as.factor(s_interest)7 0.849488
## as.factor(s_interest)8 0.588259
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -7.7090 1.0620 -7.259
## -5|-4 -5.9094 0.5425 -10.893
## -4|-3 -2.8459 0.3586 -7.936
## -3|-2 -2.4231 0.3514 -6.896
## -2|-1 -1.5457 0.3412 -4.531
## -1|0 -1.1540 0.3380 -3.414
## 0|1 -0.1024 0.3345 -0.306
## 1|2 0.2402 0.3354 0.716
## 2|3 1.2434 0.3418 3.638
## 3|4 1.5105 0.3447 4.383
## 4|5 3.0561 0.3839 7.960
## 5|6 3.1802 0.3899 8.156
## (20 Beobachtungen als fehlend gelöscht)exp(coef(causality_model10_pass2))## -6|-5
## 4.487809e-04
## -5|-4
## 2.713847e-03
## -4|-3
## 5.808065e-02
## -3|-2
## 8.864492e-02
## -2|-1
## 2.131572e-01
## -1|0
## 3.153692e-01
## 0|1
## 9.027076e-01
## 1|2
## 1.271515e+00
## 2|3
## 3.467490e+00
## 3|4
## 4.529133e+00
## 4|5
## 2.124499e+01
## 5|6
## 2.405164e+01
## H4_interactionno causality statement.old guideline
## 8.878716e-01
## summaryFaerber
## 8.616788e-01
## text_orderFaerber
## 1.121923e+00
## s_age
## 9.815333e-01
## s_sexmale
## 1.020748e+00
## s_schoolReal
## 1.045714e+00
## s_schoolAbi
## 1.824995e+00
## as.factor(s_interest)5
## 1.305976e+00
## as.factor(s_interest)6
## 1.079558e+00
## as.factor(s_interest)7
## 9.589815e-01
## as.factor(s_interest)8
## 8.841487e-01exp(confint(causality_model10_pass2))## 2.5 % 97.5 %
## -6|-5 5.598198e-05 0.003597664
## -5|-4 9.371317e-04 0.007859052
## -4|-3 2.876044e-02 0.117291749
## -3|-2 4.452233e-02 0.176493965
## -2|-1 1.092200e-01 0.416004133
## -1|0 1.625917e-01 0.611702454
## 0|1 4.686053e-01 1.738949582
## 1|2 6.588962e-01 2.453725326
## 2|3 1.774458e+00 6.775863591
## 3|4 2.304823e+00 8.900053074
## 4|5 1.001070e+01 45.086698909
## 5|6 1.120072e+01 51.646816739
## H4_interactionno causality statement.old guideline 6.845311e-01 1.151614486
## summaryFaerber 6.811601e-01 1.090037973
## text_orderFaerber 8.652430e-01 1.454748889
## s_age 9.729837e-01 0.990157946
## s_sexmale 7.849213e-01 1.327426775
## s_schoolReal 7.449995e-01 1.467809717
## s_schoolAbi 1.319326e+00 2.524476263
## as.factor(s_interest)5 8.666075e-01 1.968104048
## as.factor(s_interest)6 7.104154e-01 1.640512872
## as.factor(s_interest)7 6.222240e-01 1.477997579
## as.factor(s_interest)8 5.661396e-01 1.380788253nagelkerke(fit = causality_model10_pass2, null = causality_null_pass2)## $Models
##
## Model: "clmm, as.factor(s_causality) ~ H4_interaction + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id), data2_long_pass_reg2"
## Null: "clm, as.factor(s_causality) ~ 1, data2_long_pass_reg2, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0142506
## Cox and Snell (ML) 0.0597460
## Nagelkerke (Cragg and Uhler) 0.0605489
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -12 -27.291 54.582 2.1503e-07
##
## $Number.of.observations
##
## Model: 886
## Null: 886
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H4test_pass2 = emmeans(causality_model10_pass2, ~ H4_interaction)
pairs(H4test_pass2, adjust = "tukey")## contrast
## causality statement.new guideline - no causality statement.old guideline
## estimate SE df z.ratio p.value
## 0.119 0.133 Inf 0.896 0.3701
##
## Results are averaged over the levels of: summary, text_order, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H4test_pass2, Letters = letters)## H4_interaction emmean SE df asymp.LCL asymp.UCL
## no causality statement.old guideline 0.270 0.139 Inf -0.00207 0.542
## causality statement.new guideline 0.389 0.145 Inf 0.10435 0.673
## .group
## a
## a
##
## Results are averaged over the levels of: summary, text_order, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.H4 Graphical
describeBy(data2_long_pass_reg$s_causality,
data2_long_pass_reg$H4_interaction)##
## Descriptive statistics by group
## group: no causality statement.new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 928 -0.26 2.5 0 -0.36 2.97 -6 6 12 0.18 -0.61 0.08
## ------------------------------------------------------------
## group: no causality statement.old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 486 -0.02 2.52 0 -0.07 2.97 -5 6 11 0.18 -0.57 0.11
## ------------------------------------------------------------
## group: causality statement.new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 827 0.06 2.55 0 0.02 2.97 -6 6 12 0.14 -0.56 0.09H4_bar <- ggplot(data2_long_pass_reg, aes(H4_interaction,
s_causality)) +
stat_summary(fun = mean, geom = "bar", fill = "White",
colour = "Black") + stat_summary(fun.data =
mean_cl_normal,
geom = "pointrange") +
labs(x = "Condition", y = "Causality Knowledge Score")
H4_bar## Warning: Removed 49 rows containing non-finite values (`stat_summary()`).
## Removed 49 rows containing non-finite values (`stat_summary()`).
data2_long_pass_reg$H4_interaction <- mapvalues(data2_long_pass_reg$H4_interaction,
c("no causality statement.old guideline",
"no causality statement.new guideline",
"causality statement.new guideline"),
c("old, no causality",
"new, no causality",
"new, causality"))
H4_boxplot <- ggplot(data2_long_pass_reg, aes(H4_interaction, s_causality,
fill = H4_interaction))
H4_boxplot <- H4_boxplot + geom_boxplot() + theme_classic() + theme(
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title = element_text(face = "bold"),
axis.text = element_text(face = "bold"),
legend.title = element_text(face = "bold"))+
labs(x = "Condition", y = "Causality Knowledge Score") +
scale_fill_brewer(palette = "Blues")
H4_boxplot## Warning: Removed 49 rows containing non-finite values (`stat_boxplot()`).
H5
H5a
H5a_pass <- subset(data2_wide_pass, condition == 5|condition == 6)
describeBy(H5a_pass$s_CAMA,H5a_pass$CAMA)##
## Descriptive statistics by group
## group: no CAMA PLS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 239 0.77 3.36 0 0.83 2.97 -7 11 18 -0.04 0.12 0.22
## ------------------------------------------------------------
## group: CAMA PLS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 225 1.66 4.2 1 1.7 4.45 -11 13 24 -0.01 -0.05 0.28wilcox.test(s_CAMA~CAMA, data = H5a_pass, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_CAMA by CAMA
## W = 23613, p-value = 0.02249
## alternative hypothesis: true location shift is not equal to 0H5b
H5b_pass <- subset(data2_wide_pass, condition == 4| condition == 5)
describeBy(H5b_pass$s_CAMA, H5b_pass$CAMA)##
## Descriptive statistics by group
## group: no CAMA PLS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 193 0.3 3.01 0 0.32 2.97 -9 7 16 -0.09 0.12 0.22
## ------------------------------------------------------------
## group: CAMA PLS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 225 1.66 4.2 1 1.7 4.45 -11 13 24 -0.01 -0.05 0.28wilcox.test(s_CAMA~CAMA, data = H5b_pass, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_CAMA by CAMA
## W = 17375, p-value = 0.0003894
## alternative hypothesis: true location shift is not equal to 0H5 Logistic Regression
data2_wide_pass$H5_interaction <- interaction(data2_wide_pass$CAMA,
data2_wide_pass$version)
data2_wide_pass$H5_interaction <- droplevels(data2_wide_pass$H5_interaction)
data2_wide_pass$H5_interaction <- factor(data2_wide_pass$H5_interaction,
levels = c(
"no CAMA PLS.old guideline",
"no CAMA PLS.new guideline",
"CAMA PLS.new guideline"))
data2_wide_pass_H5 <- subset(data2_wide_pass, condition == 4| condition == 5 |
condition == 6)
table(data2_wide_pass_H5$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## 247 206 237cama_null_pass <- clm(as.factor(s_CAMA) ~ 1, data = data2_wide_pass_H5,
link = "logit")
cama_model1_pass <- clm(as.factor(s_CAMA) ~ H5_interaction,
data = data2_wide_pass_H5, link = "logit")
anova(cama_null_pass,cama_model1_pass)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## cama_null_pass as.factor(s_CAMA) ~ 1 logit flexible
## cama_model1_pass as.factor(s_CAMA) ~ H5_interaction logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## cama_null_pass 22 3459.0 -1707.5
## cama_model1_pass 24 3449.2 -1700.6 13.834 2 0.0009909 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cama_model2_pass <- clm(as.factor(s_CAMA) ~ H5_interaction + text_order,
data = data2_wide_pass_H5, link = "logit")
anova(cama_model1_pass,cama_model2_pass)## Likelihood ratio tests of cumulative link models:
##
## formula: link:
## cama_model1_pass as.factor(s_CAMA) ~ H5_interaction logit
## cama_model2_pass as.factor(s_CAMA) ~ H5_interaction + text_order logit
## threshold:
## cama_model1_pass flexible
## cama_model2_pass flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## cama_model1_pass 24 3449.2 -1700.6
## cama_model2_pass 25 3449.6 -1699.8 1.6002 1 0.2059cama_model3_pass <- clm(as.factor(s_CAMA) ~ H5_interaction +
text_order + s_age, data = data2_wide_pass_H5, link = "logit")
anova(cama_model1_pass,cama_model3_pass)## Likelihood ratio tests of cumulative link models:
##
## formula: link:
## cama_model1_pass as.factor(s_CAMA) ~ H5_interaction logit
## cama_model3_pass as.factor(s_CAMA) ~ H5_interaction + text_order + s_age logit
## threshold:
## cama_model1_pass flexible
## cama_model3_pass flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## cama_model1_pass 24 3449.2 -1700.6
## cama_model3_pass 26 3446.5 -1697.2 6.7211 2 0.03472 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cama_model4_pass <- clm(as.factor(s_CAMA) ~ H5_interaction +
text_order + s_age + s_sex, data = data2_wide_pass_H5,
link = "logit")
anova(cama_model3_pass,cama_model4_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## cama_model3_pass as.factor(s_CAMA) ~ H5_interaction + text_order + s_age
## cama_model4_pass as.factor(s_CAMA) ~ H5_interaction + text_order + s_age + s_sex
## link: threshold:
## cama_model3_pass logit flexible
## cama_model4_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## cama_model3_pass 26 3446.5 -1697.2
## cama_model4_pass 27 3448.4 -1697.2 3e-04 1 0.9873cama_model5_pass <- clm(as.factor(s_CAMA) ~ H5_interaction +
text_order + s_age + s_sex + s_school,
data = data2_wide_pass_H5, link = "logit")
anova(cama_model3_pass,cama_model5_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## cama_model3_pass as.factor(s_CAMA) ~ H5_interaction + text_order + s_age
## cama_model5_pass as.factor(s_CAMA) ~ H5_interaction + text_order + s_age + s_sex + s_school
## link: threshold:
## cama_model3_pass logit flexible
## cama_model5_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## cama_model3_pass 26 3446.5 -1697.2
## cama_model5_pass 29 3419.7 -1680.8 32.779 3 3.585e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cama_model6_pass <- clm(as.factor(s_CAMA) ~ H5_interaction +
text_order + s_age + s_sex + s_school +
as.factor(s_interest), data = data2_wide_pass_H5,
link = "logit")
anova(cama_model5_pass,cama_model6_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## cama_model5_pass as.factor(s_CAMA) ~ H5_interaction + text_order + s_age + s_sex + s_school
## cama_model6_pass as.factor(s_CAMA) ~ H5_interaction + text_order + s_age + s_sex + s_school + as.factor(s_interest)
## link: threshold:
## cama_model5_pass logit flexible
## cama_model6_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## cama_model5_pass 29 3419.7 -1680.8
## cama_model6_pass 33 3426.9 -1680.5 0.7545 4 0.9444summary(cama_model6_pass)## formula:
## as.factor(s_CAMA) ~ H5_interaction + text_order + s_age + s_sex + s_school + as.factor(s_interest)
## data: data2_wide_pass_H5
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 657 -1680.46 3426.92 7(0) 2.98e-11 1.6e+06
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## H5_interactionno CAMA PLS.new guideline -0.262374 0.167304 -1.568 0.11682
## H5_interactionCAMA PLS.new guideline 0.478083 0.167268 2.858 0.00426
## text_orderFaerber -0.110927 0.138257 -0.802 0.42236
## s_age -0.007547 0.004675 -1.614 0.10645
## s_sexmale 0.008042 0.138852 0.058 0.95382
## s_schoolReal 0.170208 0.175719 0.969 0.33273
## s_schoolAbi 0.929699 0.177237 5.246 1.56e-07
## as.factor(s_interest)5 -0.147259 0.216936 -0.679 0.49726
## as.factor(s_interest)6 -0.135124 0.216552 -0.624 0.53264
## as.factor(s_interest)7 -0.137275 0.225313 -0.609 0.54235
## as.factor(s_interest)8 -0.032987 0.237320 -0.139 0.88945
##
## H5_interactionno CAMA PLS.new guideline
## H5_interactionCAMA PLS.new guideline **
## text_orderFaerber
## s_age
## s_sexmale
## s_schoolReal
## s_schoolAbi ***
## as.factor(s_interest)5
## as.factor(s_interest)6
## as.factor(s_interest)7
## as.factor(s_interest)8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -11|-9 -6.60905 1.05323 -6.275
## -9|-7 -5.50745 0.66523 -8.279
## -7|-6 -3.58099 0.39864 -8.983
## -6|-5 -3.12509 0.37560 -8.320
## -5|-4 -2.63725 0.35910 -7.344
## -4|-3 -2.41480 0.35352 -6.831
## -3|-2 -1.93911 0.34468 -5.626
## -2|-1 -1.43488 0.33871 -4.236
## -1|0 -0.91807 0.33519 -2.739
## 0|1 0.07168 0.33416 0.215
## 1|2 0.45977 0.33548 1.370
## 2|3 0.74358 0.33675 2.208
## 3|4 1.16960 0.33917 3.448
## 4|5 1.59541 0.34272 4.655
## 5|6 2.25738 0.35233 6.407
## 6|7 2.71030 0.36286 7.469
## 7|8 3.43429 0.39099 8.784
## 8|9 3.75083 0.41029 9.142
## 9|10 4.81709 0.52608 9.157
## 10|11 5.00191 0.55689 8.982
## 11|12 5.92668 0.78121 7.587
## 12|13 6.62309 1.05373 6.285
## (33 Beobachtungen als fehlend gelöscht)exp(coef(cama_model6_pass))## -11|-9 -9|-7
## 1.348114e-03 4.056438e-03
## -7|-6 -6|-5
## 2.784807e-02 4.393306e-02
## -5|-4 -4|-3
## 7.155746e-02 8.938497e-02
## -3|-2 -2|-1
## 1.438326e-01 2.381433e-01
## -1|0 0|1
## 3.992886e-01 1.074310e+00
## 1|2 2|3
## 1.583707e+00 2.103444e+00
## 3|4 4|5
## 3.220700e+00 4.930355e+00
## 5|6 6|7
## 9.557989e+00 1.503382e+01
## 7|8 8|9
## 3.100929e+01 4.255629e+01
## 9|10 10|11
## 1.236049e+02 1.486973e+02
## 11|12 12|13
## 3.749073e+02 7.522626e+02
## H5_interactionno CAMA PLS.new guideline H5_interactionCAMA PLS.new guideline
## 7.692232e-01 1.612979e+00
## text_orderFaerber s_age
## 8.950039e-01 9.924813e-01
## s_sexmale s_schoolReal
## 1.008074e+00 1.185551e+00
## s_schoolAbi as.factor(s_interest)5
## 2.533746e+00 8.630705e-01
## as.factor(s_interest)6 as.factor(s_interest)7
## 8.736073e-01 8.717303e-01
## as.factor(s_interest)8
## 9.675508e-01exp(confint(cama_model6_pass))## 2.5 % 97.5 %
## H5_interactionno CAMA PLS.new guideline 0.5539004 1.067482
## H5_interactionCAMA PLS.new guideline 1.1626234 2.240295
## text_orderFaerber 0.6824211 1.173535
## s_age 0.9834147 1.001612
## s_sexmale 0.7678170 1.323472
## s_schoolReal 0.8401177 1.673439
## s_schoolAbi 1.7921677 3.591132
## as.factor(s_interest)5 0.5640010 1.320657
## as.factor(s_interest)6 0.5711298 1.335344
## as.factor(s_interest)7 0.5602173 1.355618
## as.factor(s_interest)8 0.6072331 1.540302nagelkerke(fit = cama_model6_pass, null = cama_null_pass)## $Models
##
## Model: "clm, as.factor(s_CAMA) ~ H5_interaction + text_order + s_age + s_sex + s_school + as.factor(s_interest), data2_wide_pass_H5, logit"
## Null: "clm, as.factor(s_CAMA) ~ 1, data2_wide_pass_H5, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0158385
## Cox and Snell (ML) 0.0790289
## Nagelkerke (Cragg and Uhler) 0.0794682
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -11 -27.044 54.089 1.1373e-07
##
## $Number.of.observations
##
## Model: 657
## Null: 657
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H5test_pass = emmeans(cama_model6_pass, ~ H5_interaction)
pairs(H5test_pass, adjust = "none")## contrast estimate SE df
## no CAMA PLS.old guideline - no CAMA PLS.new guideline 0.262 0.167 Inf
## no CAMA PLS.old guideline - CAMA PLS.new guideline -0.478 0.167 Inf
## no CAMA PLS.new guideline - CAMA PLS.new guideline -0.740 0.176 Inf
## z.ratio p.value
## 1.568 0.1168
## -2.858 0.0043
## -4.211 <.0001
##
## Results are averaged over the levels of: text_order, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H5test_pass, Letters = letters)## H5_interaction emmean SE df asymp.LCL asymp.UCL .group
## no CAMA PLS.new guideline -0.864 0.182 Inf -1.220 -0.507 a
## no CAMA PLS.old guideline -0.601 0.174 Inf -0.943 -0.260 a
## CAMA PLS.new guideline -0.123 0.177 Inf -0.469 0.223 b
##
## Results are averaged over the levels of: text_order, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimates
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.H5 Graphical
describeBy(data2_wide_pass_H5$s_CAMA,
data2_wide_pass_H5$H5_interaction)##
## Descriptive statistics by group
## group: no CAMA PLS.old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 239 0.77 3.36 0 0.83 2.97 -7 11 18 -0.04 0.12 0.22
## ------------------------------------------------------------
## group: no CAMA PLS.new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 193 0.3 3.01 0 0.32 2.97 -9 7 16 -0.09 0.12 0.22
## ------------------------------------------------------------
## group: CAMA PLS.new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 225 1.66 4.2 1 1.7 4.45 -11 13 24 -0.01 -0.05 0.28H5_bar <- ggplot(data2_wide_pass_H5, aes(H5_interaction,
s_CAMA)) +
stat_summary(fun = mean, geom = "bar", fill = "White",
colour = "Black") + stat_summary(fun.data =
mean_cl_normal,
geom = "pointrange") +
labs(x = "Condition", y = "CAMA Knowledge Score")
H5_bar## Warning: Removed 33 rows containing non-finite values (`stat_summary()`).
## Removed 33 rows containing non-finite values (`stat_summary()`).
data2_wide_pass_H5$H5_interaction <- mapvalues(data2_wide_pass_H5$H5_interaction,
c("no CAMA PLS.old guideline",
"no CAMA PLS.new guideline",
"CAMA PLS.new guideline"),
c("old, no CAMA PLS",
"new, no CAMA PLS",
"new, CAMA PLS"))
H5_boxplot <- ggplot(data2_wide_pass_H5, aes(H5_interaction, s_CAMA,
fill = H5_interaction))
H5_boxplot <- H5_boxplot + geom_boxplot() + theme_classic() + theme(
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title = element_text(face = "bold"),
axis.text = element_text(face = "bold"),
legend.title = element_text(face = "bold"))+
labs(x = "Condition", y = "CAMA Knowledge Score") +
scale_fill_brewer(palette = "Blues")
H5_boxplot## Warning: Removed 33 rows containing non-finite values (`stat_boxplot()`).
H6
data2_wide_pass$user_experience <- rowMeans(data2_wide_pass[,c("accessibility",
"understanding",
"empowerment")])
psych::describe(data2_wide_pass$user_experience)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1341 5.29 1.42 5.5 5.36 1.48 1 8 7 -0.39 -0.29 0.04data2_wide_pass$version <- relevel(data2_wide_pass$version, ref =
"new guideline")
# Prep long dataset and seperate datasets for Faerber and Barth
data2_long_pass$user_experience <- rowMeans(data2_long_pass[,c("accessibility",
"understanding",
"empowerment")])
psych::describe(data2_long_pass$user_experience)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2722 5.29 1.56 5.33 5.36 1.48 1 8 7 -0.42 -0.29 0.03data2_long_pass$version <- relevel(data2_long_pass$version, ref = "new guideline")
data2_long_pass_faerber <- filter(data2_long_pass, summary == "Faerber")
data2_long_pass_barth <- filter(data2_long_pass, summary == "Barth")Overall User Experience
describeBy(data2_wide_pass$user_experience, data2_wide_pass$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1102 5.28 1.42 5.5 5.35 1.48 1 8 7 -0.42 -0.27 0.04
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 239 5.37 1.43 5.5 5.41 1.48 1.5 8 6.5 -0.26 -0.41 0.09equiv.test(user_experience~version, data = data2_wide_pass, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: user_experience by version
## t = -0.87214, df = 1339.0000, ncp = 2.8029, p-value = 0.02676
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.06223154H6ue_bar <- ggplot(data2_wide_pass, aes(version,
user_experience)) +
stat_summary(fun = mean, geom = "bar", fill = "White",
colour = "Black") + stat_summary(fun.data =
mean_cl_normal,
geom = "pointrange") +
labs(x = "Guideline Version", y = "User Experience Score")
H6ue_bar## Warning: Removed 41 rows containing non-finite values (`stat_summary()`).
## Removed 41 rows containing non-finite values (`stat_summary()`).
H6ue_boxplot <- ggplot(data2_wide_pass, aes(version, user_experience,
fill = version))
H6ue_boxplot <- H6ue_boxplot + geom_boxplot() + theme_classic() + theme(
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title = element_text(face = "bold"),
axis.text = element_text(face = "bold"),
legend.title = element_text(face = "bold"))+
labs(x = "Guideline Version", y = "User Experience Score") +
scale_fill_brewer(palette = "Blues")
H6ue_boxplot## Warning: Removed 41 rows containing non-finite values (`stat_boxplot()`).
# For Faerber
describeBy(data2_long_pass_faerber$user_experience,data2_long_pass_faerber$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1115 5.23 1.59 5.33 5.31 1.48 1 8 7 -0.44 -0.27 0.05
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 242 5.27 1.59 5.33 5.34 1.48 1 8 7 -0.38 -0.43 0.1equiv.test(user_experience~version, data = data2_long_pass_faerber, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: user_experience by version
## t = -0.31758, df = 1355.0000, ncp = 2.8202, p-value = 0.006163
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.02252122# For Barth
describeBy(data2_long_pass_barth$user_experience,data2_long_pass_barth$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1121 5.32 1.53 5.67 5.4 1.48 1 8 7 -0.45 -0.27 0.05
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 244 5.42 1.49 5.33 5.45 1.48 1.67 8 6.33 -0.2 -0.56 0.1equiv.test(user_experience~version, data = data2_long_pass_barth, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: user_experience by version
## t = -0.91421, df = 1363.0000, ncp = 2.8311, p-value = 0.02763
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.06458235# Post Hoc Tests Overall User Experience
data2_wide_pass1 <- subset(data2_wide_pass, condition == 1 | condition == 6)
data2_wide_pass2 <- subset(data2_wide_pass, condition == 2 | condition == 6)
data2_wide_pass3 <- subset(data2_wide_pass, condition == 3 | condition == 6)
data2_wide_pass4 <- subset(data2_wide_pass, condition == 4 | condition == 6)
data2_wide_pass5 <- subset(data2_wide_pass, condition == 5 | condition == 6)
table(data2_wide_pass1$condition)##
## 1 2 3 4 5 6
## 221 0 0 0 0 247table(data2_wide_pass2$condition)##
## 1 2 3 4 5 6
## 0 251 0 0 0 247table(data2_wide_pass3$condition)##
## 1 2 3 4 5 6
## 0 0 220 0 0 247table(data2_wide_pass4$condition)##
## 1 2 3 4 5 6
## 0 0 0 206 0 247table(data2_wide_pass5$condition)##
## 1 2 3 4 5 6
## 0 0 0 0 237 247equiv.test(user_experience~version, data = data2_wide_pass1, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: user_experience by version
## t = -0.82184, df = 454.0000, ncp = 2.1329, p-value = 0.09492
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.07706175equiv.test(user_experience~version, data = data2_wide_pass2, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: user_experience by version
## t = 0.28578, df = 484.0000, ncp = 2.2042, p-value = 0.006391
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.02592987equiv.test(user_experience~version, data = data2_wide_pass3, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: user_experience by version
## t = -0.027427, df = 444.0000, ncp = 2.1064, p-value = 0.01881
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.002604127equiv.test(user_experience~version, data = data2_wide_pass4, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: user_experience by version
## t = -0.69832, df = 441.0000, ncp = 2.0982, p-value = 0.08078
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.06656408equiv.test(user_experience~version, data = data2_wide_pass5, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: user_experience by version
## t = -2.0866, df = 464.000, ncp = 2.158, p-value = 0.4712
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.1933815Accessibility
describeBy(data2_wide_pass$accessibility,data2_wide_pass$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1125 5.55 1.66 6 5.64 1.48 1 8 7 -0.46 -0.41 0.05
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 5.6 1.62 5.5 5.66 2.22 1 8 7 -0.26 -0.68 0.1equiv.test(accessibility~version, data = data2_wide_pass, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: accessibility by version
## t = -0.40254, df = 1370.0000, ncp = 2.8463, p-value = 0.007268
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.02828507H6accessibility_bar <- ggplot(data2_wide_pass, aes(version,
accessibility)) +
stat_summary(fun = mean, geom = "bar", fill = "White",
colour = "Black") + stat_summary(fun.data =
mean_cl_normal,
geom = "pointrange") +
labs(x = "Guideline Version", y = "Accessibility Score")
H6accessibility_bar## Warning: Removed 10 rows containing non-finite values (`stat_summary()`).
## Removed 10 rows containing non-finite values (`stat_summary()`).
H6accessibility_boxplot <- ggplot(data2_wide_pass, aes(version, accessibility,
fill = version))
H6accessibility_boxplot <- H6accessibility_boxplot + geom_boxplot() + theme_classic() + theme(
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title = element_text(face = "bold"),
axis.text = element_text(face = "bold"),
legend.title = element_text(face = "bold"))+
labs(x = "Guideline Version", y = "Accessibility Score") +
scale_fill_brewer(palette = "Blues")
H6accessibility_boxplot## Warning: Removed 10 rows containing non-finite values (`stat_boxplot()`).
# For Faerber
describeBy(data2_long_pass_faerber$accessibility,data2_long_pass_faerber$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1129 5.55 1.86 6 5.69 1.48 1 8 7 -0.52 -0.46 0.06
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 5.54 1.89 6 5.67 1.48 1 8 7 -0.45 -0.57 0.12equiv.test(accessibility~version, data = data2_long_pass_faerber, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: accessibility by version
## t = 0.070917, df = 1374.0000, ncp = 2.8472, p-value = 0.001761
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.004981581# For Barth
describeBy(data2_long_pass_barth$accessibility,data2_long_pass_barth$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1131 5.55 1.84 6 5.67 1.48 1 8 7 -0.51 -0.52 0.05
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 5.65 1.68 6 5.72 1.48 1 8 7 -0.22 -0.85 0.11equiv.test(accessibility~version, data = data2_long_pass_barth, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: accessibility by version
## t = -0.79634, df = 1376.0000, ncp = 2.8476, p-value = 0.02012
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.0559301# Post Hoc Tests Accessibility
equiv.test(accessibility~version, data = data2_wide_pass1, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: accessibility by version
## t = 0.020547, df = 464.0000, ncp = 2.1548, p-value = 0.0148
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.001907115equiv.test(accessibility~version, data = data2_wide_pass2, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: accessibility by version
## t = 1.0123, df = 496.0000, ncp = 2.2315, p-value = 0.0005943
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.09072331equiv.test(accessibility~version, data = data2_wide_pass3, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: accessibility by version
## t = 0.27681, df = 460.0000, ncp = 2.1443, p-value = 0.007743
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.02581846equiv.test(accessibility~version, data = data2_wide_pass4, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: accessibility by version
## t = -0.70293, df = 450.0000, ncp = 2.1168, p-value = 0.07869
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.06641314equiv.test(accessibility~version, data = data2_wide_pass5, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: accessibility by version
## t = -2.1988, df = 480.0000, ncp = 2.1948, p-value = 0.5011
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.2003628Understanding
describeBy(data2_wide_pass$understanding,data2_wide_pass$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1121 5.64 1.5 6 5.73 1.48 1 8 7 -0.53 -0.22 0.04
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 242 5.79 1.53 6 5.89 1.48 1 8 7 -0.65 0.08 0.1equiv.test(understanding~version, data = data2_wide_pass, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: understanding by version
## t = -1.3934, df = 1361.0000, ncp = 2.8216, p-value = 0.07666
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.09876733H6understanding_bar <- ggplot(data2_wide_pass, aes(version,
understanding)) +
stat_summary(fun = mean, geom = "bar", fill = "White",
colour = "Black") + stat_summary(fun.data =
mean_cl_normal,
geom = "pointrange") +
labs(x = "Guideline Version", y = "Understanding Score")
H6understanding_bar## Warning: Removed 19 rows containing non-finite values (`stat_summary()`).
## Removed 19 rows containing non-finite values (`stat_summary()`).
H6understanding_boxplot <- ggplot(data2_wide_pass, aes(version, understanding,
fill = version))
H6understanding_boxplot <- H6understanding_boxplot + geom_boxplot() + theme_classic() + theme(
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title = element_text(face = "bold"),
axis.text = element_text(face = "bold"),
legend.title = element_text(face = "bold"))+
labs(x = "Guideline Version", y = "Understanding Score") +
scale_fill_brewer(palette = "Blues")
H6understanding_boxplot## Warning: Removed 19 rows containing non-finite values (`stat_boxplot()`).
# For Faerber
describeBy(data2_long_pass_faerber$understanding,data2_long_pass_faerber$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1127 5.58 1.75 6 5.7 1.48 1 8 7 -0.56 -0.26 0.05
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 244 5.66 1.71 6 5.78 1.48 1 8 7 -0.58 -0.24 0.11equiv.test(understanding~version, data = data2_long_pass_faerber, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: understanding by version
## t = -0.62025, df = 1369.0000, ncp = 2.8325, p-value = 0.01348
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.04379567# For Barth
describeBy(data2_long_pass_barth$understanding,data2_long_pass_barth$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1128 5.69 1.67 6 5.81 1.48 1 8 7 -0.58 -0.17 0.05
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 245 5.87 1.68 6 5.99 1.48 1 8 7 -0.58 -0.14 0.11equiv.test(understanding~version, data = data2_long_pass_barth, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: understanding by version
## t = -1.5116, df = 1371.0000, ncp = 2.8375, p-value = 0.09249
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.1065482# Post Hoc Tests Understanding
equiv.test(understanding~version, data = data2_wide_pass1, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: understanding by version
## t = -1.071, df = 458.0000, ncp = 2.1418, p-value = 0.1421
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.1000102equiv.test(understanding~version, data = data2_wide_pass2, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: understanding by version
## t = -0.63113, df = 489.0000, ncp = 2.2156, p-value = 0.05654
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.05697061equiv.test(understanding~version, data = data2_wide_pass3, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: understanding by version
## t = -0.42938, df = 455.000, ncp = 2.134, p-value = 0.04412
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.04024149equiv.test(understanding~version, data = data2_wide_pass4, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: understanding by version
## t = -0.59774, df = 445.000, ncp = 2.107, p-value = 0.06561
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.05673865equiv.test(understanding~version, data = data2_wide_pass5, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: understanding by version
## t = -2.465, df = 474.0000, ncp = 2.1814, p-value = 0.6108
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.2259951Empowerment
describeBy(data2_wide_pass$empowerment,data2_wide_pass$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1122 4.65 1.64 5 4.68 1.48 1 8 7 -0.2 -0.48 0.05
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 243 4.61 1.74 4.5 4.65 1.48 1 8 7 -0.17 -0.48 0.11equiv.test(empowerment~version, data = data2_wide_pass, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: empowerment by version
## t = 0.2917, df = 1363.0000, ncp = 2.8266, p-value = 0.0009098
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.02063939H6empowerment_bar <- ggplot(data2_wide_pass, aes(version,
empowerment)) +
stat_summary(fun = mean, geom = "bar", fill = "White",
colour = "Black") + stat_summary(fun.data =
mean_cl_normal,
geom = "pointrange") +
labs(x = "Guideline Version", y = "Empowerment Score")
H6empowerment_bar## Warning: Removed 17 rows containing non-finite values (`stat_summary()`).
## Removed 17 rows containing non-finite values (`stat_summary()`).
H6empowerment_boxplot <- ggplot(data2_wide_pass, aes(version, empowerment,
fill = version))
H6empowerment_boxplot <- H6empowerment_boxplot + geom_boxplot() + theme_classic() + theme(
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title = element_text(face = "bold"),
axis.text = element_text(face = "bold"),
legend.title = element_text(face = "bold"))+
labs(x = "Guideline Version", y = "Empowerment Score") +
scale_fill_brewer(palette = "Blues")
H6empowerment_boxplot## Warning: Removed 17 rows containing non-finite values (`stat_boxplot()`).
# For Faerber
describeBy(data2_long_pass_faerber$empowerment,data2_long_pass_faerber$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1128 4.57 1.83 5 4.61 1.48 1 8 7 -0.13 -0.6 0.05
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 245 4.5 1.89 5 4.56 1.48 1 8 7 -0.22 -0.66 0.12equiv.test(empowerment~version, data = data2_long_pass_faerber, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: empowerment by version
## t = 0.54422, df = 1371.0000, ncp = 2.8375, p-value = 0.0003606
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.03835926# For Barth
describeBy(data2_long_pass_barth$empowerment,data2_long_pass_barth$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1129 4.73 1.79 5 4.78 1.48 1 8 7 -0.25 -0.49 0.05
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 245 4.73 1.87 5 4.75 1.48 1 8 7 -0.15 -0.62 0.12equiv.test(empowerment~version, data = data2_long_pass_barth, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: empowerment by version
## t = -0.0087268, df = 1372.0000, ncp = 2.8377, p-value = 0.002335
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.000615064# Post Hoc Tests Empowerment
equiv.test(empowerment~version, data = data2_wide_pass1, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: empowerment by version
## t = -0.54525, df = 461.0000, ncp = 2.1491, p-value = 0.05437
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.05074275equiv.test(empowerment~version, data = data2_wide_pass2, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: empowerment by version
## t = 0.98251, df = 490.0000, ncp = 2.2179, p-value = 0.000691
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.08859657equiv.test(empowerment~version, data = data2_wide_pass3, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: empowerment by version
## t = 0.90395, df = 456.0000, ncp = 2.1361, p-value = 0.00119
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.08463598equiv.test(empowerment~version, data = data2_wide_pass4, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: empowerment by version
## t = 0.20321, df = 447.0000, ncp = 2.1118, p-value = 0.01031
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## 0.01924514equiv.test(empowerment~version, data = data2_wide_pass5, eps = 0.2,
alternative = "greater")##
## Two sample non-inferiority test
##
## data: empowerment by version
## t = -0.51378, df = 473.0000, ncp = 2.1789, p-value = 0.04794
## alternative hypothesis: non-inferiority
## null values:
## lower upper
## -Inf -0.2
## sample estimates:
## d
## -0.04716043Overall plot
complete_boxplot <- ggarrange(H1_boxplot, H2_boxplot, H3_boxplot, H4_boxplot,
H5_boxplot, H6ue_boxplot, nrow = 2)## Warning: Removed 31 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 21 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 36 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 49 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 33 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 41 rows containing non-finite values (`stat_boxplot()`).
complete_boxplot
ggsave("complete_boxplot.png", plot = complete_boxplot, width = 25, height = 15,
units = "cm", scale = 1.5, dpi = 600)
ggsave("complete_boxplot.jpeg", plot = complete_boxplot, width = 25, height = 15,
units = "cm", scale = 1.5, dpi = 600)
ggsave("complete_boxplot.pdf", plot = complete_boxplot, width = 25, height = 15,
units = "cm", scale = 1.5, dpi = 600)
ggsave("complete_boxplot.tiff", plot = complete_boxplot, width = 25, height = 15,
units = "cm", scale = 1.5, dpi = 600)
ggsave("complete_boxplot.tiff", plot = complete_boxplot, width = 25, height = 15,
units = "cm", scale = 1.5, dpi = 300)RQ1
describeBy(data2_wide_pass$s_funding,data2_wide_pass$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1092 4.01 5.28 4 4.16 5.93 -10 12 22 -0.17 -0.84 0.16
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 240 4.04 5.51 4 4.24 5.93 -10 12 22 -0.12 -0.89 0.36wilcox.test(s_funding~version, data = data2_wide_pass, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_funding by version
## W = 130325, p-value = 0.8943
## alternative hypothesis: true location shift is not equal to 0RQ1 Mixed Model
data2_long_pass$version <- relevel(data2_long_pass$version, ref = "old guideline")
set.seed(288659)
funding_null_pass <- clm(as.factor(s_funding) ~ 1,
data = data2_long_pass,
link = "logit")
funding_model1_pass <- clmm(as.factor(s_funding) ~ 1 + (1|id),
data = data2_long_pass)
anova(funding_null_pass,funding_model1_pass)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## funding_null_pass as.factor(s_funding) ~ 1 logit flexible
## funding_model1_pass as.factor(s_funding) ~ 1 + (1 | id) logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_null_pass 12 11646 -5810.8
## funding_model1_pass 13 11460 -5716.9 187.69 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1funding_model2_pass <- clmm(as.factor(s_funding) ~ version + (1|id),
data = data2_long_pass)
anova(funding_model1_pass,funding_model2_pass)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## funding_model1_pass as.factor(s_funding) ~ 1 + (1 | id) logit flexible
## funding_model2_pass as.factor(s_funding) ~ version + (1 | id) logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_model1_pass 13 11460 -5716.9
## funding_model2_pass 14 11462 -5716.9 0.0512 1 0.8209funding_model3_pass <- clmm(as.factor(s_funding) ~ version + summary + (1|id),
data = data2_long_pass)
anova(funding_model2_pass,funding_model3_pass)## Likelihood ratio tests of cumulative link models:
##
## formula: link:
## funding_model2_pass as.factor(s_funding) ~ version + (1 | id) logit
## funding_model3_pass as.factor(s_funding) ~ version + summary + (1 | id) logit
## threshold:
## funding_model2_pass flexible
## funding_model3_pass flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_model2_pass 14 11462 -5716.9
## funding_model3_pass 15 11442 -5706.2 21.377 1 3.774e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1funding_model4_pass <- clmm(as.factor(s_funding) ~ version + summary +
text_order + (1|id), data = data2_long_pass)
anova(funding_model3_pass,funding_model4_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## funding_model3_pass as.factor(s_funding) ~ version + summary + (1 | id)
## funding_model4_pass as.factor(s_funding) ~ version + summary + text_order + (1 | id)
## link: threshold:
## funding_model3_pass logit flexible
## funding_model4_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_model3_pass 15 11442 -5706.2
## funding_model4_pass 16 11442 -5705.1 2.3288 1 0.127funding_model5_pass <- clmm(as.factor(s_funding) ~ version + summary +
text_order + s_age + (1|id), data =
data2_long_pass)
anova(funding_model3_pass,funding_model5_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## funding_model3_pass as.factor(s_funding) ~ version + summary + (1 | id)
## funding_model5_pass as.factor(s_funding) ~ version + summary + text_order + s_age + (1 | id)
## link: threshold:
## funding_model3_pass logit flexible
## funding_model5_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_model3_pass 15 11442 -5706.2
## funding_model5_pass 17 11412 -5689.1 34.192 2 3.761e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1funding_model6_pass <- clmm(as.factor(s_funding) ~ version + summary +
text_order + s_age + s_sex + (1|id), data =
data2_long_pass)
anova(funding_model5_pass,funding_model6_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## funding_model5_pass as.factor(s_funding) ~ version + summary + text_order + s_age + (1 | id)
## funding_model6_pass as.factor(s_funding) ~ version + summary + text_order + s_age + s_sex + (1 | id)
## link: threshold:
## funding_model5_pass logit flexible
## funding_model6_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_model5_pass 17 11412 -5689.1
## funding_model6_pass 18 11412 -5688.2 1.8037 1 0.1793funding_model7_pass <- clmm(as.factor(s_funding) ~ version + summary +
text_order + s_age + s_sex + s_school + (1|id),
data = data2_long_pass)
anova(funding_model6_pass,funding_model7_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## funding_model6_pass as.factor(s_funding) ~ version + summary + text_order + s_age + s_sex + (1 | id)
## funding_model7_pass as.factor(s_funding) ~ version + summary + text_order + s_age + s_sex + s_school + (1 | id)
## link: threshold:
## funding_model6_pass logit flexible
## funding_model7_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_model6_pass 18 11412 -5688.2
## funding_model7_pass 20 11340 -5649.9 76.639 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1funding_model8_pass <- clmm(as.factor(s_funding) ~ version + summary +
text_order + s_age + s_sex + s_school +
as.factor(s_interest)+ (1|id), data =
data2_long_pass)
anova(funding_model6_pass,funding_model8_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## funding_model6_pass as.factor(s_funding) ~ version + summary + text_order + s_age + s_sex + (1 | id)
## funding_model8_pass as.factor(s_funding) ~ version + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id)
## link: threshold:
## funding_model6_pass logit flexible
## funding_model8_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_model6_pass 18 11412 -5688.2
## funding_model8_pass 24 11338 -5645.1 86.197 6 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1funding_model9_pass <- clmm(as.factor(s_funding) ~ version*summary +
text_order + s_age + s_sex + s_school +
as.factor(s_interest)+ (1|id), data =
data2_long_pass)
anova(funding_model8_pass,funding_model9_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## funding_model8_pass as.factor(s_funding) ~ version + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id)
## funding_model9_pass as.factor(s_funding) ~ version * summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id)
## link: threshold:
## funding_model8_pass logit flexible
## funding_model9_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## funding_model8_pass 24 11338 -5645.1
## funding_model9_pass 25 11337 -5643.4 3.4739 1 0.06235 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(funding_model8_pass)## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: as.factor(s_funding) ~ version + summary + text_order + s_age +
## s_sex + s_school + as.factor(s_interest) + (1 | id)
## data: data2_long_pass
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 2712 -5645.14 11338.28 3987(14464) 3.54e-01 8.2e+05
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 1.636 1.279
## Number of groups: id 1380
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## versionnew guideline -0.012676 0.130101 -0.097 0.9224
## summaryFaerber 0.326449 0.072132 4.526 6.02e-06 ***
## text_orderFaerber 0.176627 0.099779 1.770 0.0767 .
## s_age -0.015095 0.003352 -4.503 6.68e-06 ***
## s_sexmale -0.151315 0.100877 -1.500 0.1336
## s_schoolReal 0.595373 0.126764 4.697 2.64e-06 ***
## s_schoolAbi 1.062530 0.127444 8.337 < 2e-16 ***
## as.factor(s_interest)5 0.258381 0.157433 1.641 0.1008
## as.factor(s_interest)6 0.231295 0.157766 1.466 0.1426
## as.factor(s_interest)7 0.285743 0.169754 1.683 0.0923 .
## as.factor(s_interest)8 -0.122145 0.172482 -0.708 0.4788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -6.0651 0.3829 -15.840
## -5|-4 -5.4246 0.3362 -16.136
## -4|-3 -3.6322 0.2790 -13.017
## -3|-2 -3.1546 0.2719 -11.603
## -2|-1 -2.2396 0.2627 -8.525
## -1|0 -1.6774 0.2592 -6.472
## 0|1 -0.2710 0.2552 -1.062
## 1|2 0.1036 0.2551 0.406
## 2|3 0.6583 0.2558 2.574
## 3|4 0.8615 0.2563 3.362
## 4|5 1.3917 0.2579 5.396
## 5|6 1.5920 0.2587 6.153
## (52 Beobachtungen als fehlend gelöscht)exp(coef(funding_model8_pass))## -6|-5 -5|-4 -4|-3
## 0.002322568 0.004406736 0.026458544
## -3|-2 -2|-1 -1|0
## 0.042655030 0.106503875 0.186867164
## 0|1 1|2 2|3
## 0.762590787 1.109193326 1.931535253
## 3|4 4|5 5|6
## 2.366670986 4.021741107 4.913765099
## versionnew guideline summaryFaerber text_orderFaerber
## 0.987404363 1.386037645 1.193186087
## s_age s_sexmale s_schoolReal
## 0.985018490 0.859577315 1.813707109
## s_schoolAbi as.factor(s_interest)5 as.factor(s_interest)6
## 2.893684162 1.294832373 1.260230797
## as.factor(s_interest)7 as.factor(s_interest)8
## 1.330749924 0.885019928exp(confint(funding_model8_pass))## 2.5 % 97.5 %
## -6|-5 0.001096572 0.004919259
## -5|-4 0.002280166 0.008516627
## -4|-3 0.015312917 0.045716605
## -3|-2 0.025035452 0.072675003
## -2|-1 0.063642630 0.178230776
## -1|0 0.112444803 0.310546476
## 0|1 0.462493585 1.257411402
## 1|2 0.672708372 1.828890329
## 2|3 1.169934523 3.188920714
## 3|4 1.432190493 3.910884467
## 4|5 2.425830087 6.667573964
## 5|6 2.959288166 8.159086273
## versionnew guideline 0.765161148 1.274198747
## summaryFaerber 1.203305963 1.596518603
## text_orderFaerber 0.981242205 1.450908890
## s_age 0.978568680 0.991510811
## s_sexmale 0.705372171 1.047494062
## s_schoolReal 1.414703039 2.325246632
## s_schoolAbi 2.254084565 3.714771023
## as.factor(s_interest)5 0.951056857 1.762871337
## as.factor(s_interest)6 0.925037200 1.716884101
## as.factor(s_interest)7 0.954116030 1.856058702
## as.factor(s_interest)8 0.631155380 1.240994371nagelkerke(fit = funding_model8_pass, null = funding_null_pass)## $Models
##
## Model: "clmm, as.factor(s_funding) ~ version + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id), data2_long_pass"
## Null: "clm, as.factor(s_funding) ~ 1, data2_long_pass, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0285082
## Cox and Snell (ML) 0.1149970
## Nagelkerke (Cragg and Uhler) 0.1166030
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -12 -165.66 331.31 1.2216e-63
##
## $Number.of.observations
##
## Model: 2712
## Null: 2712
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"RQ2
describeBy(data2_wide_pass$s_coi,data2_wide_pass$version)##
## Descriptive statistics by group
## group: new guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1067 3.3 5.86 3 3.48 7.41 -12 14 26 -0.19 -0.68 0.18
## ------------------------------------------------------------
## group: old guideline
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 231 3.97 5.53 5 4.26 5.93 -10 14 24 -0.42 -0.37 0.36wilcox.test(s_coi~version, data = data2_wide_pass, exact = FALSE,
confint = TRUE)##
## Wilcoxon rank sum test with continuity correction
##
## data: s_coi by version
## W = 114768, p-value = 0.1
## alternative hypothesis: true location shift is not equal to 0RQ2 Mixed Model
set.seed(288659)
coi_null_pass <- clm(as.factor(s_coi) ~ 1, data = data2_long_pass,
link = "logit")
coi_model1_pass <- clmm(as.factor(s_coi) ~ 1 + (1|id), data = data2_long_pass)## Warning in update.uC(rho): Non finite negative log-likelihood
## at iteration 101anova(coi_null_pass, coi_model1_pass)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## coi_null_pass as.factor(s_coi) ~ 1 logit flexible
## coi_model1_pass as.factor(s_coi) ~ 1 + (1 | id) logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_null_pass 14 12565 -6268.7
## coi_model1_pass 15 12397 -6183.6 170.18 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coi_model2_pass <- clmm(as.factor(s_coi) ~ version + (1|id),
data = data2_long_pass)## Warning in update.uC(rho): Non finite negative log-likelihood
## at iteration 107anova(coi_model1_pass, coi_model2_pass)## Likelihood ratio tests of cumulative link models:
##
## formula: link: threshold:
## coi_model1_pass as.factor(s_coi) ~ 1 + (1 | id) logit flexible
## coi_model2_pass as.factor(s_coi) ~ version + (1 | id) logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_model1_pass 15 12397 -6183.6
## coi_model2_pass 16 12397 -6182.5 2.2212 1 0.1361coi_model3_pass <- clmm(as.factor(s_coi) ~ version + summary + (1|id),
data = data2_long_pass)## Warning in update.uC(rho): Non finite negative log-likelihood
## at iteration 113anova(coi_model1_pass, coi_model3_pass)## Likelihood ratio tests of cumulative link models:
##
## formula: link:
## coi_model1_pass as.factor(s_coi) ~ 1 + (1 | id) logit
## coi_model3_pass as.factor(s_coi) ~ version + summary + (1 | id) logit
## threshold:
## coi_model1_pass flexible
## coi_model3_pass flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_model1_pass 15 12397 -6183.6
## coi_model3_pass 17 12288 -6127.2 112.73 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coi_model4_pass <- clmm(as.factor(s_coi) ~ version + summary + text_order +
(1|id), data = data2_long_pass)## Warning in update.uC(rho): Non finite negative log-likelihood
## at iteration 119anova(coi_model3_pass, coi_model4_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## coi_model3_pass as.factor(s_coi) ~ version + summary + (1 | id)
## coi_model4_pass as.factor(s_coi) ~ version + summary + text_order + (1 | id)
## link: threshold:
## coi_model3_pass logit flexible
## coi_model4_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_model3_pass 17 12288 -6127.2
## coi_model4_pass 18 12290 -6127.1 0.2999 1 0.5839coi_model5_pass <- clmm(as.factor(s_coi) ~ version + summary + text_order +
s_age + (1|id), data = data2_long_pass)
anova(coi_model3_pass, coi_model5_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## coi_model3_pass as.factor(s_coi) ~ version + summary + (1 | id)
## coi_model5_pass as.factor(s_coi) ~ version + summary + text_order + s_age + (1 | id)
## link: threshold:
## coi_model3_pass logit flexible
## coi_model5_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_model3_pass 17 12288 -6127.2
## coi_model5_pass 19 12284 -6122.9 8.6968 2 0.01293 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coi_model6_pass <- clmm(as.factor(s_coi) ~ version + summary + text_order +
s_age + s_sex + (1|id), data = data2_long_pass)
anova(coi_model5_pass, coi_model6_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## coi_model5_pass as.factor(s_coi) ~ version + summary + text_order + s_age + (1 | id)
## coi_model6_pass as.factor(s_coi) ~ version + summary + text_order + s_age + s_sex + (1 | id)
## link: threshold:
## coi_model5_pass logit flexible
## coi_model6_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_model5_pass 19 12284 -6122.9
## coi_model6_pass 20 12286 -6122.9 0.0208 1 0.8853coi_model7_pass <- clmm(as.factor(s_coi) ~ version + summary +
text_order + s_age + s_sex + s_school + (1|id),
data = data2_long_pass)
anova(coi_model5_pass, coi_model7_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## coi_model5_pass as.factor(s_coi) ~ version + summary + text_order + s_age + (1 | id)
## coi_model7_pass as.factor(s_coi) ~ version + summary + text_order + s_age + s_sex + s_school + (1 | id)
## link: threshold:
## coi_model5_pass logit flexible
## coi_model7_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_model5_pass 19 12284 -6122.9
## coi_model7_pass 22 12156 -6056.2 133.36 3 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coi_model8_pass <- clmm(as.factor(s_coi) ~ version + summary + text_order +
s_age + s_sex + s_school + as.factor(s_interest) +
(1|id), data = data2_long_pass)
anova(coi_model7_pass, coi_model8_pass)## Likelihood ratio tests of cumulative link models:
##
## formula:
## coi_model7_pass as.factor(s_coi) ~ version + summary + text_order + s_age + s_sex + s_school + (1 | id)
## coi_model8_pass as.factor(s_coi) ~ version + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id)
## link: threshold:
## coi_model7_pass logit flexible
## coi_model8_pass logit flexible
##
## no.par AIC logLik LR.stat df Pr(>Chisq)
## coi_model7_pass 22 12156 -6056.2
## coi_model8_pass 26 12152 -6050.1 12.131 4 0.01641 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(coi_model8_pass)## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: as.factor(s_coi) ~ version + summary + text_order + s_age + s_sex +
## s_school + as.factor(s_interest) + (1 | id)
## data: data2_long_pass
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 2675 -6050.12 12152.25 4310(15646) 5.38e-03 9.4e+05
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 1.561 1.25
## Number of groups: id 1377
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## versionnew guideline -0.169686 0.127420 -1.332 0.1830
## summaryFaerber 0.756996 0.072909 10.383 < 2e-16 ***
## text_orderFaerber 0.062410 0.097660 0.639 0.5228
## s_age -0.005239 0.003260 -1.607 0.1081
## s_sexmale -0.048385 0.098815 -0.490 0.6244
## s_schoolReal 0.664333 0.124803 5.323 1.02e-07 ***
## s_schoolAbi 1.394269 0.126524 11.020 < 2e-16 ***
## as.factor(s_interest)5 0.288074 0.155304 1.855 0.0636 .
## as.factor(s_interest)6 0.321365 0.155103 2.072 0.0383 *
## as.factor(s_interest)7 0.116880 0.166546 0.702 0.4828
## as.factor(s_interest)8 -0.133703 0.169150 -0.790 0.4293
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -7|-6 -5.5660 0.3903 -14.259
## -6|-5 -4.9020 0.3351 -14.627
## -5|-4 -2.9191 0.2694 -10.836
## -4|-3 -2.5253 0.2640 -9.565
## -3|-2 -1.8114 0.2570 -7.048
## -2|-1 -1.4046 0.2543 -5.523
## -1|0 -0.7605 0.2514 -3.025
## 0|1 0.5108 0.2506 2.039
## 1|2 1.0704 0.2521 4.245
## 2|3 1.2790 0.2530 5.056
## 3|4 1.9867 0.2565 7.746
## 4|5 2.1324 0.2573 8.287
## 5|6 3.0917 0.2637 11.725
## 6|7 3.2405 0.2648 12.236
## (89 Beobachtungen als fehlend gelöscht)exp(coef(coi_model8_pass))## -7|-6 -6|-5 -5|-4
## 0.003825663 0.007431350 0.053979554
## -4|-3 -3|-2 -2|-1
## 0.080032401 0.163420568 0.245476823
## -1|0 0|1 1|2
## 0.467431568 1.666686705 2.916498052
## 2|3 3|4 4|5
## 3.593041804 7.291465754 8.434956274
## 5|6 6|7 versionnew guideline
## 22.015011894 25.547155227 0.843929640
## summaryFaerber text_orderFaerber s_age
## 2.131861654 1.064399022 0.994775120
## s_sexmale s_schoolReal s_schoolAbi
## 0.952766612 1.943193740 4.032027844
## as.factor(s_interest)5 as.factor(s_interest)6 as.factor(s_interest)7
## 1.333855947 1.379008819 1.123984451
## as.factor(s_interest)8
## 0.874849431exp(confint(coi_model8_pass))## 2.5 % 97.5 %
## -7|-6 0.001780108 0.008221805
## -6|-5 0.003852944 0.014333187
## -5|-4 0.031835431 0.091526710
## -4|-3 0.047702097 0.134274710
## -3|-2 0.098748217 0.270448246
## -2|-1 0.149128696 0.404072939
## -1|0 0.285564667 0.765123617
## 0|1 1.019948486 2.723514580
## 1|2 1.779321331 4.780452377
## 2|3 2.188497941 5.899000023
## 3|4 4.410540061 12.054186588
## 4|5 5.093982438 13.967163846
## 5|6 13.129899515 36.912753836
## 6|7 15.202175962 42.931823826
## versionnew guideline 0.657424865 1.083343931
## summaryFaerber 1.847987829 2.459342015
## text_orderFaerber 0.878973361 1.288941540
## s_age 0.988438537 1.001152325
## s_sexmale 0.785010033 1.156372757
## s_schoolReal 1.521541266 2.481695366
## s_schoolAbi 3.146486806 5.166793803
## as.factor(s_interest)5 0.983815619 1.808440174
## as.factor(s_interest)6 1.017519555 1.868922629
## as.factor(s_interest)7 0.810953291 1.557846870
## as.factor(s_interest)8 0.627989743 1.218748451nagelkerke(fit = coi_model8_pass, null = coi_null_pass)## $Models
##
## Model: "clmm, as.factor(s_coi) ~ version + summary + text_order + s_age + s_sex + s_school + as.factor(s_interest) + (1 | id), data2_long_pass"
## Null: "clm, as.factor(s_coi) ~ 1, data2_long_pass, logit"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0348641
## Cox and Snell (ML) 0.1507510
## Nagelkerke (Cragg and Uhler) 0.1521530
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -12 -218.55 437.1 5.1623e-86
##
## $Number.of.observations
##
## Model: 2675
## Null: 2675
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"RQ3
psych::describeBy(data2_wide_pass$s_METI_exp,data2_wide_pass$METI_target)##
## Descriptive statistics by group
## group: Study Authors
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 689 5.64 1.15 5.83 5.76 1.24 1 7 6 -1 1.03 0.04
## ------------------------------------------------------------
## group: Summary Authors
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 665 5.7 1.13 6 5.81 1.24 1.17 7 5.83 -0.92 0.68 0.04psych::describeBy(data2_wide_pass$s_METI_exp,data2_wide_pass$condition)##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 216 5.67 1.13 5.83 5.8 0.99 1.83 7 5.17 -1.08 1.14 0.08
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 249 5.61 1.16 6 5.71 1.24 1 7 6 -0.8 0.19 0.07
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 215 5.79 1.16 6 5.93 1.24 1.83 7 5.17 -1.07 0.77 0.08
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 203 5.67 1.1 5.83 5.76 1.24 1.67 7 5.33 -0.81 0.45 0.08
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 231 5.66 1.11 5.83 5.75 1.24 1.17 7 5.83 -0.74 0.21 0.07
## ------------------------------------------------------------
## group: 6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 240 5.65 1.16 5.83 5.78 1.24 1 7 6 -1.22 2.17 0.08psych::describeBy(data2_wide_pass$s_METI_int,data2_wide_pass$METI_target)##
## Descriptive statistics by group
## group: Study Authors
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 692 5.51 1.16 5.75 5.6 1.11 1 7 6 -0.84 0.77 0.04
## ------------------------------------------------------------
## group: Summary Authors
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 668 5.66 1.16 6 5.77 1.48 1 7 6 -0.84 0.56 0.05psych::describeBy(data2_wide_pass$s_METI_int,data2_wide_pass$condition)##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 217 5.61 1.16 5.75 5.72 1.11 1 7 6 -1.04 1.44 0.08
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 246 5.52 1.23 5.75 5.63 1.48 1 7 6 -0.86 0.64 0.08
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 216 5.71 1.14 6 5.82 1.11 1 7 6 -0.94 0.76 0.08
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 206 5.55 1.13 5.75 5.62 1.48 2.25 7 4.75 -0.52 -0.44 0.08
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 233 5.62 1.11 5.75 5.7 1.11 1.25 7 5.75 -0.64 0.16 0.07
## ------------------------------------------------------------
## group: 6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 242 5.52 1.19 5.75 5.62 1.11 1 7 6 -0.89 0.91 0.08psych::describeBy(data2_wide_pass$s_METI_ben,data2_wide_pass$METI_target)##
## Descriptive statistics by group
## group: Study Authors
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 689 5.48 1.19 5.75 5.56 1.11 1 7 6 -0.77 0.69 0.05
## ------------------------------------------------------------
## group: Summary Authors
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 667 5.59 1.15 5.75 5.68 1.11 1 7 6 -0.69 0.32 0.04psych::describeBy(data2_wide_pass$s_METI_ben,data2_wide_pass$condition)##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 216 5.54 1.11 5.5 5.61 1.11 1.5 7 5.5 -0.69 0.5 0.08
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 5.49 1.23 5.75 5.6 1.48 1 7 6 -0.82 0.45 0.08
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 215 5.68 1.15 6 5.78 1.48 1 7 6 -0.86 0.76 0.08
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 201 5.5 1.16 5.75 5.57 1.11 1.25 7 5.75 -0.54 -0.23 0.08
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 235 5.55 1.1 5.75 5.6 1.11 1.25 7 5.75 -0.42 -0.22 0.07
## ------------------------------------------------------------
## group: 6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 242 5.46 1.23 5.5 5.57 1.11 1 7 6 -0.91 1.19 0.08data2_wide_pass$version <- relevel(data2_wide_pass$version, ref = "old guideline")RQ3 Expertise
expMETIModel_pass <- lm(s_METI_exp ~ version + summary2 +
METI_target + s_sex + s_age + s_school + s_interest,
data = data2_wide_pass)
summary(expMETIModel_pass)##
## Call:
## lm(formula = s_METI_exp ~ version + summary2 + METI_target +
## s_sex + s_age + s_school + s_interest, data = data2_wide_pass)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0744 -0.6022 0.2386 0.8486 1.8455
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.510531 0.199191 22.644 < 2e-16 ***
## versionnew guideline 0.031712 0.079199 0.400 0.688921
## summary2Faerber 0.074637 0.060592 1.232 0.218244
## METI_targetSummary Authors 0.014361 0.060908 0.236 0.813640
## s_sexmale -0.229680 0.061492 -3.735 0.000195 ***
## s_age 0.009097 0.002021 4.502 7.33e-06 ***
## s_schoolReal -0.077091 0.076153 -1.012 0.311564
## s_schoolAbi -0.004158 0.075576 -0.055 0.956138
## s_interest 0.134974 0.023126 5.836 6.68e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.112 on 1345 degrees of freedom
## (28 Beobachtungen als fehlend gelöscht)
## Multiple R-squared: 0.05037, Adjusted R-squared: 0.04472
## F-statistic: 8.918 on 8 and 1345 DF, p-value: 5.721e-12dwt(expMETIModel_pass)## lag Autocorrelation D-W Statistic p-value
## 1 -0.01642422 2.031437 0.582
## Alternative hypothesis: rho != 0vif(expMETIModel_pass)## GVIF Df GVIF^(1/(2*Df))
## version 1.002313 1 1.001156
## summary2 1.005707 1 1.002849
## METI_target 1.015929 1 1.007933
## s_sex 1.033046 1 1.016389
## s_age 1.038588 1 1.019111
## s_school 1.037644 2 1.009281
## s_interest 1.047061 1 1.0232601/vif(expMETIModel_pass)## GVIF Df GVIF^(1/(2*Df))
## version 0.9976919 1.0 0.9988453
## summary2 0.9943256 1.0 0.9971588
## METI_target 0.9843211 1.0 0.9921296
## s_sex 0.9680108 1.0 0.9838754
## s_age 0.9628455 1.0 0.9812469
## s_school 0.9637219 0.5 0.9908044
## s_interest 0.9550545 1.0 0.9772689mean(vif(expMETIModel_pass))## [1] 1.060013RQ3 Integrity
intMETIModel_pass <- lm(s_METI_int ~ version + summary2 +
METI_target + s_sex + s_age + s_school +
s_interest, data = data2_wide_pass)
summary(intMETIModel_pass)##
## Call:
## lm(formula = s_METI_int ~ version + summary2 + METI_target +
## s_sex + s_age + s_school + s_interest, data = data2_wide_pass)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9951 -0.6616 0.2203 0.8394 1.9745
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.385025 0.202376 21.668 < 2e-16 ***
## versionnew guideline 0.077256 0.080342 0.962 0.336
## summary2Faerber 0.040920 0.061589 0.664 0.507
## METI_targetSummary Authors 0.099614 0.061928 1.609 0.108
## s_sexmale -0.267241 0.062480 -4.277 2.03e-05 ***
## s_age 0.010112 0.002056 4.918 9.81e-07 ***
## s_schoolReal -0.121783 0.077544 -1.571 0.117
## s_schoolAbi -0.095256 0.076759 -1.241 0.215
## s_interest 0.134181 0.023531 5.702 1.45e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.132 on 1351 degrees of freedom
## (22 Beobachtungen als fehlend gelöscht)
## Multiple R-squared: 0.05814, Adjusted R-squared: 0.05257
## F-statistic: 10.42 on 8 and 1351 DF, p-value: 2.928e-14dwt(intMETIModel_pass)## lag Autocorrelation D-W Statistic p-value
## 1 -0.02030847 2.039929 0.436
## Alternative hypothesis: rho != 0vif(intMETIModel_pass)## GVIF Df GVIF^(1/(2*Df))
## version 1.001984 1 1.000992
## summary2 1.006353 1 1.003172
## METI_target 1.017150 1 1.008539
## s_sex 1.032782 1 1.016259
## s_age 1.037378 1 1.018518
## s_school 1.037355 2 1.009211
## s_interest 1.048584 1 1.0240041/vif(intMETIModel_pass)## GVIF Df GVIF^(1/(2*Df))
## version 0.9980196 1.0 0.9990093
## summary2 0.9936870 1.0 0.9968385
## METI_target 0.9831392 1.0 0.9915338
## s_sex 0.9682590 1.0 0.9840015
## s_age 0.9639684 1.0 0.9818189
## s_school 0.9639905 0.5 0.9908734
## s_interest 0.9536674 1.0 0.9765590mean(vif(intMETIModel_pass))## [1] 1.060108RQ3 Benevolence
benMETIModel_pass <- lm(s_METI_ben ~ version + summary2 +
METI_target + s_sex + s_age + s_school +
s_interest, data = data2_wide_pass)
summary(benMETIModel_pass)##
## Call:
## lm(formula = s_METI_ben ~ version + summary2 + METI_target +
## s_sex + s_age + s_school + s_interest, data = data2_wide_pass)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9380 -0.7093 0.1893 0.8631 2.0990
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.251784 0.202849 20.960 < 2e-16 ***
## versionnew guideline 0.091636 0.080411 1.140 0.255
## summary2Faerber 0.067799 0.061658 1.100 0.272
## METI_targetSummary Authors 0.059676 0.062014 0.962 0.336
## s_sexmale -0.293757 0.062613 -4.692 2.99e-06 ***
## s_age 0.010599 0.002062 5.140 3.14e-07 ***
## s_schoolReal -0.097845 0.077660 -1.260 0.208
## s_schoolAbi -0.091808 0.076916 -1.194 0.233
## s_interest 0.143717 0.023606 6.088 1.49e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.133 on 1347 degrees of freedom
## (26 Beobachtungen als fehlend gelöscht)
## Multiple R-squared: 0.06438, Adjusted R-squared: 0.05883
## F-statistic: 11.59 on 8 and 1347 DF, p-value: 5.007e-16dwt(benMETIModel_pass)## lag Autocorrelation D-W Statistic p-value
## 1 -0.006280475 2.012405 0.778
## Alternative hypothesis: rho != 0vif(benMETIModel_pass)## GVIF Df GVIF^(1/(2*Df))
## version 1.001589 1 1.000794
## summary2 1.004128 1 1.002062
## METI_target 1.015520 1 1.007730
## s_sex 1.032738 1 1.016237
## s_age 1.036920 1 1.018293
## s_school 1.037557 2 1.009260
## s_interest 1.049584 1 1.0244921/vif(benMETIModel_pass)## GVIF Df GVIF^(1/(2*Df))
## version 0.9984138 1.0 0.9992066
## summary2 0.9958893 1.0 0.9979426
## METI_target 0.9847176 1.0 0.9923294
## s_sex 0.9682996 1.0 0.9840222
## s_age 0.9643945 1.0 0.9820359
## s_school 0.9638026 0.5 0.9908252
## s_interest 0.9527585 1.0 0.9760935mean(vif(benMETIModel_pass))## [1] 1.059852Appendix: Percentage Tables for Knowledge Items
Preparation
data2_wide_old <- subset(data2_wide, H1_interaction == "no disclaimer.old guideline")
length(unique(data2_wide_old$id))## [1] 357data2_wide_new <- subset(data2_wide, H1_interaction == "no disclaimer.new guideline")
length(unique(data2_wide_new$id))## [1] 670data2_wide_disclaimer_new <- subset(data2_wide, H1_interaction == "disclaimer.new guideline")
length(unique(data2_wide_disclaimer_new$id))## [1] 1013data2_wide_old_pass <- subset(data2_wide_pass, H1_interaction == "no disclaimer.old guideline")
length(unique(data2_wide_old_pass$id))## [1] 247data2_wide_new_pass <- subset(data2_wide_pass, H1_interaction == "no disclaimer.new guideline")
length(unique(data2_wide_new_pass$id))## [1] 441data2_wide_disclaimer_new_pass <- subset(data2_wide, H1_interaction == "disclaimer.new guideline")
length(unique(data2_wide_disclaimer_new_pass$id))## [1] 1013data2_long_old1 <- subset(data2_long, H4_interaction ==
"no causality statement.old guideline")
length(unique(data2_long_old1$id))## [1] 357data2_long_new1 <- subset(data2_long, H4_interaction ==
"no causality statement.new guideline")
length(unique(data2_long_new1$id))## [1] 679data2_long_statement_new <- subset(data2_long,
H4_interaction ==
"causality statement.new guideline")
length(unique(data2_long_statement_new$id))## [1] 1004data2_long_old1_pass <- subset(data2_long_pass,
H4_interaction == "no causality statement.old guideline")
length(unique(data2_long_old1_pass$id))## [1] 247data2_long_new1_pass <- subset(data2_long_pass,
H4_interaction == "no causality statement.new guideline")
length(unique(data2_long_new1_pass$id))## [1] 472data2_long_statement_new_pass <- subset(data2_long_pass,
H4_interaction ==
"causality statement.new guideline")
length(unique(data2_long_statement_new_pass$id))## [1] 663data2_wide_old2 <- subset(data2_wide, H5_interaction ==
"no CAMA PLS.old guideline")
length(unique(data2_wide_old2$id))## [1] 357data2_wide_new2 <- subset(data2_wide, H5_interaction ==
"no CAMA PLS.new guideline")
data2_wide_CAMA_new <- subset(data2_wide, H5_interaction
== "CAMA PLS.new guideline")
length(unique(data2_wide_new2$id))## [1] 1356data2_wide_old2_pass <- subset(data2_wide_pass,
H5_interaction ==
"no CAMA PLS.old guideline")
length(unique(data2_wide_old2_pass$id))## [1] 247data2_wide_new2_pass <- subset(data2_wide_pass,
H5_interaction ==
"no CAMA PLS.new guideline")
length(unique(data2_wide_new2_pass$id))## [1] 898data2_wide_CAMA_new_pass <- subset(data2_wide_pass,
H5_interaction
== "CAMA PLS.new guideline")
length(unique(data2_wide_CAMA_new_pass$id))## [1] 237data2_long_version_old <- subset(data2_long, version == "old guideline")
length(unique(data2_long_version_old$id))## [1] 357data2_long_version_new <- subset(data2_long, version == "new guideline")
length(unique(data2_long_version_new$id))## [1] 1683data2_long_version_old_pass <- subset(data2_long_pass, version == "old guideline")
length(unique(data2_long_version_old_pass$id))## [1] 247data2_long_version_new_pass <- subset(data2_long_pass, version == "new guideline")
length(unique(data2_long_version_new_pass$id))## [1] 1135Relationship-Item
Item 1
# "Der KLaRtext fasst die Übersichtsarbeit zusammen."
table(data2_wide$s_relationship_1,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 39 89
## 0 60 91
## 1 257 487
##
## disclaimer.new guideline
## -1 85
## 0 160
## 1 766chisq.test(data2_wide$s_relationship_1, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_relationship_1 and data2_wide$H1_interaction
## X-squared = 12.042, df = 4, p-value = 0.01704prop.table(table(data2_wide_old$s_relationship_1))##
## -1 0 1
## 0.1095506 0.1685393 0.7219101prop.table(table(data2_wide_new$s_relationship_1))##
## -1 0 1
## 0.1334333 0.1364318 0.7301349prop.table(table(data2_wide_disclaimer_new$s_relationship_1))##
## -1 0 1
## 0.08407517 0.15825915 0.75766568Awareness Check Pass
table(data2_wide_pass$s_relationship_1,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 24 43
## 0 31 40
## 1 191 356
##
## disclaimer.new guideline
## -1 40
## 0 73
## 1 581chisq.test(data2_wide_pass$s_relationship_1, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_relationship_1 and data2_wide_pass$H1_interaction
## X-squared = 9.8761, df = 4, p-value = 0.04257prop.table(table(data2_wide_old_pass$s_relationship_1))##
## -1 0 1
## 0.09756098 0.12601626 0.77642276prop.table(table(data2_wide_new_pass$s_relationship_1))##
## -1 0 1
## 0.09794989 0.09111617 0.81093394prop.table(table(data2_wide_disclaimer_new_pass$s_relationship_1))##
## -1 0 1
## 0.08407517 0.15825915 0.75766568Item 2
# "Die Übersichtsarbeit fasst den KLARtext zusammen."
table(data2_wide$s_relationship_2,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 190 332
## 0 75 122
## 1 91 209
##
## disclaimer.new guideline
## -1 524
## 0 166
## 1 321chisq.test(data2_wide$s_relationship_2, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_relationship_2 and data2_wide$H1_interaction
## X-squared = 7.4299, df = 4, p-value = 0.1148prop.table(table(data2_wide_old$s_relationship_2))##
## -1 0 1
## 0.5337079 0.2106742 0.2556180prop.table(table(data2_wide_new$s_relationship_2))##
## -1 0 1
## 0.5007541 0.1840121 0.3152338prop.table(table(data2_wide_disclaimer_new$s_relationship_2))##
## -1 0 1
## 0.5182987 0.1641939 0.3175074Awareness Check Pass
table(data2_wide_pass$s_relationship_2,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 123 214
## 0 51 74
## 1 72 150
##
## disclaimer.new guideline
## -1 353
## 0 91
## 1 250chisq.test(data2_wide_pass$s_relationship_2, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_relationship_2 and data2_wide_pass$H1_interaction
## X-squared = 9.9718, df = 4, p-value = 0.04091prop.table(table(data2_wide_old_pass$s_relationship_2))##
## -1 0 1
## 0.5000000 0.2073171 0.2926829prop.table(table(data2_wide_new_pass$s_relationship_2))##
## -1 0 1
## 0.4885845 0.1689498 0.3424658prop.table(table(data2_wide_disclaimer_new_pass$s_relationship_2))##
## -1 0 1
## 0.5182987 0.1641939 0.3175074Item 3
# "Die Autor:innen des KLARtextes waren auch die Autor:innen der Übersichtsarbeit."
table(data2_wide$s_relationship_3,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 92 207
## 0 132 240
## 1 128 221
##
## disclaimer.new guideline
## -1 307
## 0 306
## 1 397chisq.test(data2_wide$s_relationship_3, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_relationship_3 and data2_wide$H1_interaction
## X-squared = 12.233, df = 4, p-value = 0.0157prop.table(table(data2_wide_old$s_relationship_3))##
## -1 0 1
## 0.2613636 0.3750000 0.3636364prop.table(table(data2_wide_new$s_relationship_3))##
## -1 0 1
## 0.3098802 0.3592814 0.3308383prop.table(table(data2_wide_disclaimer_new$s_relationship_3))##
## -1 0 1
## 0.3039604 0.3029703 0.3930693Awareness Check Pass
table(data2_wide_pass$s_relationship_3,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 46 113
## 0 96 169
## 1 100 157
##
## disclaimer.new guideline
## -1 171
## 0 204
## 1 317chisq.test(data2_wide_pass$s_relationship_3, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_relationship_3 and data2_wide_pass$H1_interaction
## X-squared = 18.713, df = 4, p-value = 0.000895prop.table(table(data2_wide_old_pass$s_relationship_3))##
## -1 0 1
## 0.1900826 0.3966942 0.4132231prop.table(table(data2_wide_new_pass$s_relationship_3))##
## -1 0 1
## 0.2574032 0.3849658 0.3576310prop.table(table(data2_wide_disclaimer_new_pass$s_relationship_3))##
## -1 0 1
## 0.3039604 0.3029703 0.3930693Item 4
# "Die Herausgeber:innen des KLARtextes sind auch die Herausgeber:innen der Übersichtsarbeit."
table(data2_wide$s_relationship_4,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 111 218
## 0 133 235
## 1 112 214
##
## disclaimer.new guideline
## -1 323
## 0 286
## 1 400chisq.test(data2_wide$s_relationship_4, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_relationship_4 and data2_wide$H1_interaction
## X-squared = 18.299, df = 4, p-value = 0.001079prop.table(table(data2_wide_old$s_relationship_4))##
## -1 0 1
## 0.3117978 0.3735955 0.3146067prop.table(table(data2_wide_new$s_relationship_4))##
## -1 0 1
## 0.3268366 0.3523238 0.3208396prop.table(table(data2_wide_disclaimer_new$s_relationship_4))##
## -1 0 1
## 0.3201189 0.2834490 0.3964321Awareness Check Pass
table(data2_wide_pass$s_relationship_4,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 64 131
## 0 96 161
## 1 87 147
##
## disclaimer.new guideline
## -1 187
## 0 191
## 1 314chisq.test(data2_wide_pass$s_relationship_4, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_relationship_4 and data2_wide_pass$H1_interaction
## X-squared = 22.778, df = 4, p-value = 0.0001402prop.table(table(data2_wide_old_pass$s_relationship_4))##
## -1 0 1
## 0.2591093 0.3886640 0.3522267prop.table(table(data2_wide_new_pass$s_relationship_4))##
## -1 0 1
## 0.2984055 0.3667426 0.3348519prop.table(table(data2_wide_disclaimer_new_pass$s_relationship_4))##
## -1 0 1
## 0.3201189 0.2834490 0.3964321Item 5
# "Der KLARtext gibt die Durchführung und die Ergebnisse der Übersichtsarbeit allgemeinverständlich wieder."
table(data2_wide$s_relationship_5,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 43 92
## 0 56 106
## 1 256 472
##
## disclaimer.new guideline
## -1 97
## 0 170
## 1 742chisq.test(data2_wide$s_relationship_5, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_relationship_5 and data2_wide$H1_interaction
## X-squared = 7.0471, df = 4, p-value = 0.1334prop.table(table(data2_wide_old$s_relationship_5))##
## -1 0 1
## 0.1211268 0.1577465 0.7211268prop.table(table(data2_wide_new$s_relationship_5))##
## -1 0 1
## 0.1373134 0.1582090 0.7044776prop.table(table(data2_wide_disclaimer_new$s_relationship_5))##
## -1 0 1
## 0.09613479 0.16848365 0.73538157Awareness Check Pass
table(data2_wide_pass$s_relationship_5,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 26 41
## 0 33 53
## 1 187 347
##
## disclaimer.new guideline
## -1 57
## 0 88
## 1 546chisq.test(data2_wide_pass$s_relationship_5, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_relationship_5 and data2_wide_pass$H1_interaction
## X-squared = 1.6225, df = 4, p-value = 0.8047prop.table(table(data2_wide_old_pass$s_relationship_5))##
## -1 0 1
## 0.1056911 0.1341463 0.7601626prop.table(table(data2_wide_new_pass$s_relationship_5))##
## -1 0 1
## 0.09297052 0.12018141 0.78684807prop.table(table(data2_wide_disclaimer_new_pass$s_relationship_5))##
## -1 0 1
## 0.09613479 0.16848365 0.73538157Item 6
# "Die Übersichtsarbeit gibt die Durchführung und die Ergebnisse des KLARtextes allgemeinverständlich wieder."
table(data2_wide$s_relationship_6,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 198 396
## 0 79 119
## 1 79 153
##
## disclaimer.new guideline
## -1 573
## 0 184
## 1 250chisq.test(data2_wide$s_relationship_6, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_relationship_6 and data2_wide$H1_interaction
## X-squared = 4.3783, df = 4, p-value = 0.3572prop.table(table(data2_wide_old$s_relationship_6))##
## -1 0 1
## 0.5561798 0.2219101 0.2219101prop.table(table(data2_wide_new$s_relationship_6))##
## -1 0 1
## 0.5928144 0.1781437 0.2290419prop.table(table(data2_wide_disclaimer_new$s_relationship_6))##
## -1 0 1
## 0.5690169 0.1827210 0.2482622Awareness Check Pass
table(data2_wide_pass$s_relationship_6,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 142 278
## 0 48 63
## 1 56 98
##
## disclaimer.new guideline
## -1 405
## 0 95
## 1 192chisq.test(data2_wide_pass$s_relationship_6, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_relationship_6 and data2_wide_pass$H1_interaction
## X-squared = 9.2989, df = 4, p-value = 0.05405prop.table(table(data2_wide_old_pass$s_relationship_6))##
## -1 0 1
## 0.5772358 0.1951220 0.2276423prop.table(table(data2_wide_new_pass$s_relationship_6))##
## -1 0 1
## 0.6332574 0.1435080 0.2232346prop.table(table(data2_wide_disclaimer_new_pass$s_relationship_6))##
## -1 0 1
## 0.5690169 0.1827210 0.2482622Item 7
# "Der KLARtext gibt die Durchführung und die Ergebnisse der Übersichtsarbiet für Wussenschaftler:innen wieder."
table(data2_wide$s_relationship_7,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 211 391
## 0 78 130
## 1 66 144
##
## disclaimer.new guideline
## -1 603
## 0 193
## 1 213chisq.test(data2_wide$s_relationship_7, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_relationship_7 and data2_wide$H1_interaction
## X-squared = 2.2683, df = 4, p-value = 0.6865prop.table(table(data2_wide_old$s_relationship_7))##
## -1 0 1
## 0.5943662 0.2197183 0.1859155prop.table(table(data2_wide_new$s_relationship_7))##
## -1 0 1
## 0.5879699 0.1954887 0.2165414prop.table(table(data2_wide_disclaimer_new$s_relationship_7))##
## -1 0 1
## 0.5976214 0.1912785 0.2111001Awareness Check Pass
table(data2_wide_pass$s_relationship_7,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 154 267
## 0 43 72
## 1 48 98
##
## disclaimer.new guideline
## -1 412
## 0 111
## 1 168chisq.test(data2_wide_pass$s_relationship_7, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_relationship_7 and data2_wide_pass$H1_interaction
## X-squared = 2.392, df = 4, p-value = 0.6641prop.table(table(data2_wide_old_pass$s_relationship_7))##
## -1 0 1
## 0.6285714 0.1755102 0.1959184prop.table(table(data2_wide_new_pass$s_relationship_7))##
## -1 0 1
## 0.6109840 0.1647597 0.2242563prop.table(table(data2_wide_disclaimer_new_pass$s_relationship_7))##
## -1 0 1
## 0.5976214 0.1912785 0.2111001Item 8
# "Die Übersichtsarbeit gibt die Durchführung und die Ergebnisse des KLARtextes für Wissenschaftler:innen wieder."
table(data2_wide$s_relationship_8,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 180 335
## 0 88 141
## 1 86 189
##
## disclaimer.new guideline
## -1 504
## 0 194
## 1 311chisq.test(data2_wide$s_relationship_8, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_relationship_8 and data2_wide$H1_interaction
## X-squared = 8.0295, df = 4, p-value = 0.0905prop.table(table(data2_wide_old$s_relationship_8))##
## -1 0 1
## 0.5084746 0.2485876 0.2429379prop.table(table(data2_wide_new$s_relationship_8))##
## -1 0 1
## 0.5037594 0.2120301 0.2842105prop.table(table(data2_wide_disclaimer_new$s_relationship_8))##
## -1 0 1
## 0.4995045 0.1922696 0.3082260Awareness Check Pass
table(data2_wide_pass$s_relationship_8,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 116 222
## 0 58 83
## 1 72 131
##
## disclaimer.new guideline
## -1 337
## 0 114
## 1 240chisq.test(data2_wide_pass$s_relationship_8, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_relationship_8 and data2_wide_pass$H1_interaction
## X-squared = 8.12, df = 4, p-value = 0.08728prop.table(table(data2_wide_old_pass$s_relationship_8))##
## -1 0 1
## 0.4715447 0.2357724 0.2926829prop.table(table(data2_wide_new_pass$s_relationship_8))##
## -1 0 1
## 0.5091743 0.1903670 0.3004587prop.table(table(data2_wide_disclaimer_new_pass$s_relationship_8))##
## -1 0 1
## 0.4995045 0.1922696 0.3082260Extent of Evaluation-Item
Item 1
# "KLARtexte werden nur für besonders hochwertige Übersichtsarbeiten geschrieben."
table(data2_wide$s_extent_1,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 96 202
## 0 130 215
## 1 130 251
##
## disclaimer.new guideline
## -1 289
## 0 334
## 1 386chisq.test(data2_wide$s_extent_1, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_extent_1 and data2_wide$H1_interaction
## X-squared = 2.4732, df = 4, p-value = 0.6494prop.table(table(data2_wide_old$s_extent_1))##
## -1 0 1
## 0.2696629 0.3651685 0.3651685prop.table(table(data2_wide_new$s_extent_1))##
## -1 0 1
## 0.3023952 0.3218563 0.3757485prop.table(table(data2_wide_disclaimer_new$s_extent_1))##
## -1 0 1
## 0.2864222 0.3310208 0.3825570Awareness Check Pass
table(data2_wide_pass$s_extent_1,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 56 112
## 0 85 142
## 1 106 187
##
## disclaimer.new guideline
## -1 164
## 0 225
## 1 303chisq.test(data2_wide_pass$s_extent_1, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_extent_1 and data2_wide_pass$H1_interaction
## X-squared = 0.94823, df = 4, p-value = 0.9175prop.table(table(data2_wide_old_pass$s_extent_1))##
## -1 0 1
## 0.2267206 0.3441296 0.4291498prop.table(table(data2_wide_new_pass$s_extent_1))##
## -1 0 1
## 0.2539683 0.3219955 0.4240363prop.table(table(data2_wide_disclaimer_new_pass$s_extent_1))##
## -1 0 1
## 0.2864222 0.3310208 0.3825570Item 2
# "KLARtexte prüfen alle Aussagen der Übersichtsarbeit auf ihre Korrektheit."
table(data2_wide$s_extent_2,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 139 281
## 0 97 180
## 1 120 207
##
## disclaimer.new guideline
## -1 406
## 0 233
## 1 369chisq.test(data2_wide$s_extent_2, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_extent_2 and data2_wide$H1_interaction
## X-squared = 7.4736, df = 4, p-value = 0.1129prop.table(table(data2_wide_old$s_extent_2))##
## -1 0 1
## 0.3904494 0.2724719 0.3370787prop.table(table(data2_wide_new$s_extent_2))##
## -1 0 1
## 0.4206587 0.2694611 0.3098802prop.table(table(data2_wide_disclaimer_new$s_extent_2))##
## -1 0 1
## 0.4027778 0.2311508 0.3660714Awareness Check Pass
table(data2_wide_pass$s_extent_2,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 89 174
## 0 70 122
## 1 88 145
##
## disclaimer.new guideline
## -1 261
## 0 136
## 1 295chisq.test(data2_wide_pass$s_extent_2, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_extent_2 and data2_wide_pass$H1_interaction
## X-squared = 17.669, df = 4, p-value = 0.001432prop.table(table(data2_wide_old_pass$s_extent_2))##
## -1 0 1
## 0.3603239 0.2834008 0.3562753prop.table(table(data2_wide_new_pass$s_extent_2))##
## -1 0 1
## 0.3945578 0.2766440 0.3287982prop.table(table(data2_wide_disclaimer_new_pass$s_extent_2))##
## -1 0 1
## 0.4027778 0.2311508 0.3660714Item 3
# "Die Inhalte der Übersichtsarbeit wurden durch das Leibniz-Institut für Psychologie geprüft."
table(data2_wide$s_extent_3,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 160 309
## 0 118 218
## 1 77 137
##
## disclaimer.new guideline
## -1 453
## 0 322
## 1 237chisq.test(data2_wide$s_extent_3, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_extent_3 and data2_wide$H1_interaction
## X-squared = 1.9623, df = 4, p-value = 0.7427prop.table(table(data2_wide_old$s_extent_3))##
## -1 0 1
## 0.4507042 0.3323944 0.2169014prop.table(table(data2_wide_new$s_extent_3))##
## -1 0 1
## 0.4653614 0.3283133 0.2063253prop.table(table(data2_wide_disclaimer_new$s_extent_3))##
## -1 0 1
## 0.4476285 0.3181818 0.2341897Awareness Check Pass
table(data2_wide_pass$s_extent_3,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 112 195
## 0 77 151
## 1 57 92
##
## disclaimer.new guideline
## -1 305
## 0 217
## 1 172chisq.test(data2_wide_pass$s_extent_3, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_extent_3 and data2_wide_pass$H1_interaction
## X-squared = 2.6987, df = 4, p-value = 0.6094prop.table(table(data2_wide_old_pass$s_extent_3))##
## -1 0 1
## 0.4552846 0.3130081 0.2317073prop.table(table(data2_wide_new_pass$s_extent_3))##
## -1 0 1
## 0.4452055 0.3447489 0.2100457prop.table(table(data2_wide_disclaimer_new_pass$s_extent_3))##
## -1 0 1
## 0.4476285 0.3181818 0.2341897Item 4
# "Aussagen in KLARtexten werden durch Studien des Leibniz-Instituts für Psychologie abgesichert."
table(data2_wide$s_extent_4,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 144 286
## 0 133 229
## 1 77 150
##
## disclaimer.new guideline
## -1 429
## 0 326
## 1 255chisq.test(data2_wide$s_extent_4, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_extent_4 and data2_wide$H1_interaction
## X-squared = 4.5001, df = 4, p-value = 0.3425prop.table(table(data2_wide_old$s_extent_4))##
## -1 0 1
## 0.4067797 0.3757062 0.2175141prop.table(table(data2_wide_new$s_extent_4))##
## -1 0 1
## 0.4300752 0.3443609 0.2255639prop.table(table(data2_wide_disclaimer_new$s_extent_4))##
## -1 0 1
## 0.4247525 0.3227723 0.2524752Awareness Check Pass
table(data2_wide_pass$s_extent_4,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 90 193
## 0 93 153
## 1 62 91
##
## disclaimer.new guideline
## -1 273
## 0 222
## 1 197chisq.test(data2_wide_pass$s_extent_4, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_extent_4 and data2_wide_pass$H1_interaction
## X-squared = 10.65, df = 4, p-value = 0.03079prop.table(table(data2_wide_old_pass$s_extent_4))##
## -1 0 1
## 0.3673469 0.3795918 0.2530612prop.table(table(data2_wide_new_pass$s_extent_4))##
## -1 0 1
## 0.4416476 0.3501144 0.2082380prop.table(table(data2_wide_disclaimer_new_pass$s_extent_4))##
## -1 0 1
## 0.4247525 0.3227723 0.2524752Item 5
# "KLARtexte geben die Aussagen der Autor:innen einer Übersichtsarbeit wieder."
table(data2_wide$s_extent_5,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 50 101
## 0 86 155
## 1 218 409
##
## disclaimer.new guideline
## -1 142
## 0 235
## 1 633chisq.test(data2_wide$s_extent_5, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_extent_5 and data2_wide$H1_interaction
## X-squared = 0.61761, df = 4, p-value = 0.9611prop.table(table(data2_wide_old$s_extent_5))##
## -1 0 1
## 0.1412429 0.2429379 0.6158192prop.table(table(data2_wide_new$s_extent_5))##
## -1 0 1
## 0.1518797 0.2330827 0.6150376prop.table(table(data2_wide_disclaimer_new$s_extent_5))##
## -1 0 1
## 0.1405941 0.2326733 0.6267327Awareness Check Pass
table(data2_wide_pass$s_extent_5,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 27 53
## 0 53 89
## 1 165 295
##
## disclaimer.new guideline
## -1 87
## 0 131
## 1 474chisq.test(data2_wide_pass$s_extent_5, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_extent_5 and data2_wide_pass$H1_interaction
## X-squared = 1.1596, df = 4, p-value = 0.8847prop.table(table(data2_wide_old_pass$s_extent_5))##
## -1 0 1
## 0.1102041 0.2163265 0.6734694prop.table(table(data2_wide_new_pass$s_extent_5))##
## -1 0 1
## 0.1212815 0.2036613 0.6750572prop.table(table(data2_wide_disclaimer_new_pass$s_extent_5))##
## -1 0 1
## 0.1405941 0.2326733 0.6267327Item 6
# "KLARtexte geben den Stand der Forschung zu einem bestimmten Zeitpunkt wieder."
table(data2_wide$s_extent_6,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 60 124
## 0 80 126
## 1 214 419
##
## disclaimer.new guideline
## -1 166
## 0 235
## 1 611chisq.test(data2_wide$s_extent_6, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_extent_6 and data2_wide$H1_interaction
## X-squared = 5.1949, df = 4, p-value = 0.2679prop.table(table(data2_wide_old$s_extent_6))##
## -1 0 1
## 0.1694915 0.2259887 0.6045198prop.table(table(data2_wide_new$s_extent_6))##
## -1 0 1
## 0.1853513 0.1883408 0.6263079prop.table(table(data2_wide_disclaimer_new$s_extent_6))##
## -1 0 1
## 0.1640316 0.2322134 0.6037549Awareness Check Pass
table(data2_wide_pass$s_extent_6,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 36 76
## 0 52 68
## 1 158 297
##
## disclaimer.new guideline
## -1 109
## 0 138
## 1 446chisq.test(data2_wide_pass$s_extent_6, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_extent_6 and data2_wide_pass$H1_interaction
## X-squared = 5.0243, df = 4, p-value = 0.2848prop.table(table(data2_wide_old_pass$s_extent_6))##
## -1 0 1
## 0.1463415 0.2113821 0.6422764prop.table(table(data2_wide_new_pass$s_extent_6))##
## -1 0 1
## 0.1723356 0.1541950 0.6734694prop.table(table(data2_wide_disclaimer_new_pass$s_extent_6))##
## -1 0 1
## 0.1640316 0.2322134 0.6037549Differentiation-Item
Item 1
# "Mitarbeiter:innen des Leibniz-Instituts für Psychologie."T1
table(data2_wide$s_diff_1_1,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 79 153
## 0 56 91
## 1 47 81
##
## disclaimer.new guideline
## -1 237
## 0 123
## 1 150chisq.test(data2_wide$s_diff_1_1, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_diff_1_1 and data2_wide$H1_interaction
## X-squared = 4.6313, df = 4, p-value = 0.3273prop.table(table(data2_wide_old$s_diff_1_1))##
## -1 0 1
## 0.4340659 0.3076923 0.2582418prop.table(table(data2_wide_new$s_diff_1_1))##
## -1 0 1
## 0.4707692 0.2800000 0.2492308prop.table(table(data2_wide_disclaimer_new$s_diff_1_1))##
## -1 0 1
## 0.4647059 0.2411765 0.2941176Awareness Check Pass
table(data2_wide_pass$s_diff_1_1,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 44 96
## 0 42 59
## 1 38 61
##
## disclaimer.new guideline
## -1 156
## 0 80
## 1 114chisq.test(data2_wide_pass$s_diff_1_1, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_diff_1_1 and data2_wide_pass$H1_interaction
## X-squared = 7.1264, df = 4, p-value = 0.1294prop.table(table(data2_wide_old_pass$s_diff_1_1))##
## -1 0 1
## 0.3548387 0.3387097 0.3064516prop.table(table(data2_wide_new_pass$s_diff_1_1))##
## -1 0 1
## 0.4444444 0.2731481 0.2824074prop.table(table(data2_wide_disclaimer_new_pass$s_diff_1_1))##
## -1 0 1
## 0.4647059 0.2411765 0.2941176T2
table(data2_wide$s_diff_2_1,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 54 134
## 0 39 74
## 1 79 130
##
## disclaimer.new guideline
## -1 198
## 0 115
## 1 187chisq.test(data2_wide$s_diff_2_1, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_diff_2_1 and data2_wide$H1_interaction
## X-squared = 5.0824, df = 4, p-value = 0.2789prop.table(table(data2_wide_old$s_diff_2_1))##
## -1 0 1
## 0.3139535 0.2267442 0.4593023prop.table(table(data2_wide_new$s_diff_2_1))##
## -1 0 1
## 0.3964497 0.2189349 0.3846154prop.table(table(data2_wide_disclaimer_new$s_diff_2_1))##
## -1 0 1
## 0.396 0.230 0.374Awareness Ckeck Pass
table(data2_wide_pass$s_diff_2_1,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 32 81
## 0 24 45
## 1 65 95
##
## disclaimer.new guideline
## -1 124
## 0 63
## 1 154chisq.test(data2_wide_pass$s_diff_2_1, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_diff_2_1 and data2_wide_pass$H1_interaction
## X-squared = 5.2136, df = 4, p-value = 0.2661prop.table(table(data2_wide_old_pass$s_diff_2_1))##
## -1 0 1
## 0.2644628 0.1983471 0.5371901prop.table(table(data2_wide_new_pass$s_diff_2_1))##
## -1 0 1
## 0.3665158 0.2036199 0.4298643prop.table(table(data2_wide_disclaimer_new_pass$s_diff_2_1))##
## -1 0 1
## 0.396 0.230 0.374Item 2
# "Die Autor:innen, deren Einzelstudien in der Übersichtsarbeit berücksichtigt wurden."T1
table(data2_wide$s_diff_1_2,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 86 163
## 0 59 80
## 1 39 87
##
## disclaimer.new guideline
## -1 249
## 0 124
## 1 133chisq.test(data2_wide$s_diff_1_2, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_diff_1_2 and data2_wide$H1_interaction
## X-squared = 5.1988, df = 4, p-value = 0.2675prop.table(table(data2_wide_old$s_diff_1_2))##
## -1 0 1
## 0.4673913 0.3206522 0.2119565prop.table(table(data2_wide_new$s_diff_1_2))##
## -1 0 1
## 0.4939394 0.2424242 0.2636364prop.table(table(data2_wide_disclaimer_new$s_diff_1_2))##
## -1 0 1
## 0.4920949 0.2450593 0.2628458Awareness Check Pass
table(data2_wide_pass$s_diff_1_2,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 65 114
## 0 37 51
## 1 23 53
##
## disclaimer.new guideline
## -1 173
## 0 81
## 1 95chisq.test(data2_wide_pass$s_diff_1_2, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_diff_1_2 and data2_wide_pass$H1_interaction
## X-squared = 4.8292, df = 4, p-value = 0.3053prop.table(table(data2_wide_old_pass$s_diff_1_2))##
## -1 0 1
## 0.520 0.296 0.184prop.table(table(data2_wide_new_pass$s_diff_1_2))##
## -1 0 1
## 0.5229358 0.2339450 0.2431193prop.table(table(data2_wide_disclaimer_new_pass$s_diff_1_2))##
## -1 0 1
## 0.4920949 0.2450593 0.2628458T2
table(data2_wide$s_diff_2_2,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 78 128
## 0 41 85
## 1 53 126
##
## disclaimer.new guideline
## -1 208
## 0 127
## 1 165chisq.test(data2_wide$s_diff_2_2, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_diff_2_2 and data2_wide$H1_interaction
## X-squared = 3.493, df = 4, p-value = 0.4789prop.table(table(data2_wide_old$s_diff_2_2))##
## -1 0 1
## 0.4534884 0.2383721 0.3081395prop.table(table(data2_wide_new$s_diff_2_2))##
## -1 0 1
## 0.3775811 0.2507375 0.3716814prop.table(table(data2_wide_disclaimer_new$s_diff_2_2))##
## -1 0 1
## 0.416 0.254 0.330Awareness Check Pass
table(data2_wide_pass$s_diff_2_2,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 52 85
## 0 28 49
## 1 41 88
##
## disclaimer.new guideline
## -1 143
## 0 70
## 1 128chisq.test(data2_wide_pass$s_diff_2_2, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_diff_2_2 and data2_wide_pass$H1_interaction
## X-squared = 1.6122, df = 4, p-value = 0.8066prop.table(table(data2_wide_old_pass$s_diff_2_2))##
## -1 0 1
## 0.4297521 0.2314050 0.3388430prop.table(table(data2_wide_new_pass$s_diff_2_2))##
## -1 0 1
## 0.3828829 0.2207207 0.3963964prop.table(table(data2_wide_disclaimer_new_pass$s_diff_2_2))##
## -1 0 1
## 0.416 0.254 0.330Item 3
# "Die Autor:innen der Übersichtsarbeit."T1
table(data2_wide$s_diff_1_3,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 53 102
## 0 59 96
## 1 70 131
##
## disclaimer.new guideline
## -1 174
## 0 130
## 1 203chisq.test(data2_wide$s_diff_1_3, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_diff_1_3 and data2_wide$H1_interaction
## X-squared = 3.9061, df = 4, p-value = 0.4189prop.table(table(data2_wide_old$s_diff_1_3))##
## -1 0 1
## 0.2912088 0.3241758 0.3846154prop.table(table(data2_wide_new$s_diff_1_3))##
## -1 0 1
## 0.3100304 0.2917933 0.3981763prop.table(table(data2_wide_disclaimer_new$s_diff_1_3))##
## -1 0 1
## 0.3431953 0.2564103 0.4003945Awareness Check Pass
table(data2_wide_pass$s_diff_1_3,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 38 70
## 0 35 58
## 1 50 89
##
## disclaimer.new guideline
## -1 129
## 0 83
## 1 137chisq.test(data2_wide_pass$s_diff_1_3, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_diff_1_3 and data2_wide_pass$H1_interaction
## X-squared = 2.463, df = 4, p-value = 0.6513prop.table(table(data2_wide_old_pass$s_diff_1_3))##
## -1 0 1
## 0.3089431 0.2845528 0.4065041prop.table(table(data2_wide_new_pass$s_diff_1_3))##
## -1 0 1
## 0.3225806 0.2672811 0.4101382prop.table(table(data2_wide_disclaimer_new_pass$s_diff_1_3))##
## -1 0 1
## 0.3431953 0.2564103 0.4003945T2
table(data2_wide$s_diff_2_3,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 60 127
## 0 42 84
## 1 67 125
##
## disclaimer.new guideline
## -1 189
## 0 127
## 1 184chisq.test(data2_wide$s_diff_2_3, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_diff_2_3 and data2_wide$H1_interaction
## X-squared = 0.49848, df = 4, p-value = 0.9736prop.table(table(data2_wide_old$s_diff_2_3))##
## -1 0 1
## 0.3550296 0.2485207 0.3964497prop.table(table(data2_wide_new$s_diff_2_3))##
## -1 0 1
## 0.3779762 0.2500000 0.3720238prop.table(table(data2_wide_disclaimer_new$s_diff_2_3))##
## -1 0 1
## 0.378 0.254 0.368Awareness Check Pass
table(data2_wide_pass$s_diff_2_3,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 49 98
## 0 25 49
## 1 45 73
##
## disclaimer.new guideline
## -1 148
## 0 69
## 1 125chisq.test(data2_wide_pass$s_diff_2_3, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_diff_2_3 and data2_wide_pass$H1_interaction
## X-squared = 1.0924, df = 4, p-value = 0.8955prop.table(table(data2_wide_old_pass$s_diff_2_3))##
## -1 0 1
## 0.4117647 0.2100840 0.3781513prop.table(table(data2_wide_new_pass$s_diff_2_3))##
## -1 0 1
## 0.4454545 0.2227273 0.3318182prop.table(table(data2_wide_disclaimer_new_pass$s_diff_2_3))##
## -1 0 1
## 0.378 0.254 0.368Item 4
"Die Autor:innen des KLARtextes."## [1] "Die Autor:innen des KLARtextes."T1
table(data2_wide$s_diff_1_4,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 58 111
## 0 53 94
## 1 73 124
##
## disclaimer.new guideline
## -1 168
## 0 116
## 1 222chisq.test(data2_wide$s_diff_1_4, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_diff_1_4 and data2_wide$H1_interaction
## X-squared = 5.3916, df = 4, p-value = 0.2494prop.table(table(data2_wide_old$s_diff_1_4))##
## -1 0 1
## 0.3152174 0.2880435 0.3967391prop.table(table(data2_wide_new$s_diff_1_4))##
## -1 0 1
## 0.3373860 0.2857143 0.3768997prop.table(table(data2_wide_disclaimer_new$s_diff_1_4))##
## -1 0 1
## 0.3320158 0.2292490 0.4387352Awareness Check Pass
table(data2_wide_pass$s_diff_1_4,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 35 71
## 0 31 57
## 1 59 90
##
## disclaimer.new guideline
## -1 98
## 0 68
## 1 180chisq.test(data2_wide_pass$s_diff_1_4, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_diff_1_4 and data2_wide_pass$H1_interaction
## X-squared = 6.9918, df = 4, p-value = 0.1363prop.table(table(data2_wide_old_pass$s_diff_1_4))##
## -1 0 1
## 0.280 0.248 0.472prop.table(table(data2_wide_new_pass$s_diff_1_4))##
## -1 0 1
## 0.3256881 0.2614679 0.4128440prop.table(table(data2_wide_disclaimer_new_pass$s_diff_1_4))##
## -1 0 1
## 0.3320158 0.2292490 0.4387352T2
table(data2_wide$s_diff_2_4,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 55 92
## 0 46 84
## 1 72 163
##
## disclaimer.new guideline
## -1 135
## 0 119
## 1 246chisq.test(data2_wide$s_diff_2_4, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_diff_2_4 and data2_wide$H1_interaction
## X-squared = 3.1557, df = 4, p-value = 0.5321prop.table(table(data2_wide_old$s_diff_2_4))##
## -1 0 1
## 0.3179191 0.2658960 0.4161850prop.table(table(data2_wide_new$s_diff_2_4))##
## -1 0 1
## 0.2713864 0.2477876 0.4808260prop.table(table(data2_wide_disclaimer_new$s_diff_2_4))##
## -1 0 1
## 0.270 0.238 0.492Awareness Check Pass
table(data2_wide_pass$s_diff_2_4,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 32 53
## 0 28 48
## 1 62 121
##
## disclaimer.new guideline
## -1 79
## 0 60
## 1 202chisq.test(data2_wide_pass$s_diff_2_4, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_diff_2_4 and data2_wide_pass$H1_interaction
## X-squared = 3.4692, df = 4, p-value = 0.4826prop.table(table(data2_wide_old_pass$s_diff_2_4))##
## -1 0 1
## 0.2622951 0.2295082 0.5081967prop.table(table(data2_wide_new_pass$s_diff_2_4))##
## -1 0 1
## 0.2387387 0.2162162 0.5450450prop.table(table(data2_wide_disclaimer_new_pass$s_diff_2_4))##
## -1 0 1
## 0.270 0.238 0.492Item 5
# "Jürgen Barth und sieben weitere Forschende."T1
table(data2_wide$s_diff_1_5,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 42 70
## 0 71 119
## 1 69 138
##
## disclaimer.new guideline
## -1 120
## 0 171
## 1 218chisq.test(data2_wide$s_diff_1_5, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_diff_1_5 and data2_wide$H1_interaction
## X-squared = 2.4453, df = 4, p-value = 0.6545prop.table(table(data2_wide_old$s_diff_1_5))##
## -1 0 1
## 0.2307692 0.3901099 0.3791209prop.table(table(data2_wide_new$s_diff_1_5))##
## -1 0 1
## 0.2140673 0.3639144 0.4220183prop.table(table(data2_wide_disclaimer_new$s_diff_1_5))##
## -1 0 1
## 0.2357564 0.3359528 0.4282908Awareness Check Pass
table(data2_wide_pass$s_diff_1_5,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 30 47
## 0 46 77
## 1 48 93
##
## disclaimer.new guideline
## -1 87
## 0 117
## 1 146chisq.test(data2_wide_pass$s_diff_1_5, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_diff_1_5 and data2_wide_pass$H1_interaction
## X-squared = 1.3293, df = 4, p-value = 0.8564prop.table(table(data2_wide_old_pass$s_diff_1_5))##
## -1 0 1
## 0.2419355 0.3709677 0.3870968prop.table(table(data2_wide_new_pass$s_diff_1_5))##
## -1 0 1
## 0.2165899 0.3548387 0.4285714prop.table(table(data2_wide_disclaimer_new_pass$s_diff_1_5))##
## -1 0 1
## 0.2357564 0.3359528 0.4282908T2
table(data2_wide$s_diff_2_5,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 40 96
## 0 42 71
## 1 89 172
##
## disclaimer.new guideline
## -1 123
## 0 136
## 1 237chisq.test(data2_wide$s_diff_2_5, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_diff_2_5 and data2_wide$H1_interaction
## X-squared = 5.4515, df = 4, p-value = 0.244prop.table(table(data2_wide_old$s_diff_2_5))##
## -1 0 1
## 0.2339181 0.2456140 0.5204678prop.table(table(data2_wide_new$s_diff_2_5))##
## -1 0 1
## 0.2831858 0.2094395 0.5073746prop.table(table(data2_wide_disclaimer_new$s_diff_2_5))##
## -1 0 1
## 0.2479839 0.2741935 0.4778226Awareness Check Pass
table(data2_wide_pass$s_diff_2_5,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 29 57
## 0 25 34
## 1 67 131
##
## disclaimer.new guideline
## -1 92
## 0 72
## 1 177chisq.test(data2_wide_pass$s_diff_2_5, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_diff_2_5 and data2_wide_pass$H1_interaction
## X-squared = 4.1079, df = 4, p-value = 0.3916prop.table(table(data2_wide_old_pass$s_diff_2_5))##
## -1 0 1
## 0.2396694 0.2066116 0.5537190prop.table(table(data2_wide_new_pass$s_diff_2_5))##
## -1 0 1
## 0.2567568 0.1531532 0.5900901prop.table(table(data2_wide_disclaimer_new_pass$s_diff_2_5))##
## -1 0 1
## 0.2479839 0.2741935 0.4778226Item 6
# "Mitarbeiter:innen von der Universität Bern und zwei weiteren Instituten."T1
table(data2_wide$s_diff_1_6,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 45 99
## 0 56 98
## 1 82 131
##
## disclaimer.new guideline
## -1 139
## 0 152
## 1 219chisq.test(data2_wide$s_diff_1_6, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_diff_1_6 and data2_wide$H1_interaction
## X-squared = 2.1882, df = 4, p-value = 0.7012prop.table(table(data2_wide_old$s_diff_1_6))##
## -1 0 1
## 0.2459016 0.3060109 0.4480874prop.table(table(data2_wide_new$s_diff_1_6))##
## -1 0 1
## 0.3018293 0.2987805 0.3993902prop.table(table(data2_wide_disclaimer_new$s_diff_1_6))##
## -1 0 1
## 0.2725490 0.2980392 0.4294118Awareness Check Pass
table(data2_wide_pass$s_diff_1_6,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 29 68
## 0 37 61
## 1 58 88
##
## disclaimer.new guideline
## -1 107
## 0 104
## 1 139chisq.test(data2_wide_pass$s_diff_1_6, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_diff_1_6 and data2_wide_pass$H1_interaction
## X-squared = 3.2333, df = 4, p-value = 0.5196prop.table(table(data2_wide_old_pass$s_diff_1_6))##
## -1 0 1
## 0.2338710 0.2983871 0.4677419prop.table(table(data2_wide_new_pass$s_diff_1_6))##
## -1 0 1
## 0.3133641 0.2811060 0.4055300prop.table(table(data2_wide_disclaimer_new_pass$s_diff_1_6))##
## -1 0 1
## 0.2725490 0.2980392 0.4294118T2
table(data2_wide$s_diff_2_6,data2_wide$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 49 104
## 0 34 86
## 1 89 148
##
## disclaimer.new guideline
## -1 158
## 0 134
## 1 207chisq.test(data2_wide$s_diff_2_6, data2_wide$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_diff_2_6 and data2_wide$H1_interaction
## X-squared = 6.0511, df = 4, p-value = 0.1954prop.table(table(data2_wide_old$s_diff_2_6))##
## -1 0 1
## 0.2848837 0.1976744 0.5174419prop.table(table(data2_wide_new$s_diff_2_6))##
## -1 0 1
## 0.3076923 0.2544379 0.4378698prop.table(table(data2_wide_disclaimer_new$s_diff_2_6))##
## -1 0 1
## 0.3166333 0.2685371 0.4148297Awareness Check Pass
table(data2_wide_pass$s_diff_2_6,data2_wide_pass$H1_interaction)##
## no disclaimer.old guideline no disclaimer.new guideline
## -1 39 75
## 0 20 49
## 1 62 99
##
## disclaimer.new guideline
## -1 118
## 0 75
## 1 148chisq.test(data2_wide_pass$s_diff_2_6, data2_wide_pass$H1_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_diff_2_6 and data2_wide_pass$H1_interaction
## X-squared = 2.8189, df = 4, p-value = 0.5886prop.table(table(data2_wide_old_pass$s_diff_2_6))##
## -1 0 1
## 0.3223140 0.1652893 0.5123967prop.table(table(data2_wide_new_pass$s_diff_2_6))##
## -1 0 1
## 0.3363229 0.2197309 0.4439462prop.table(table(data2_wide_disclaimer_new_pass$s_diff_2_6))##
## -1 0 1
## 0.3166333 0.2685371 0.4148297Causality-Item
Item 1
T1
table(data2_long$s_causality_1_1,data2_long$H4_interaction)##
## no causality statement.old guideline no causality statement.new guideline
## -1 150 286
## 0 132 256
## 1 428 806
##
## causality statement.new guideline
## -1 392
## 0 402
## 1 1204chisq.test(data2_long$s_causality_1_1, data2_long$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long$s_causality_1_1 and data2_long$H4_interaction
## X-squared = 2.1268, df = 4, p-value = 0.7125prop.table(table(data2_long_old1$s_causality_1_1))##
## -1 0 1
## 0.2112676 0.1859155 0.6028169prop.table(table(data2_long_new1$s_causality_1_1))##
## -1 0 1
## 0.2121662 0.1899110 0.5979228prop.table(table(data2_long_statement_new$s_causality_1_1))##
## -1 0 1
## 0.1961962 0.2012012 0.6026026Awareness Check Pass
table(data2_long_pass$s_causality_1_1,data2_long_pass$H4_interaction)##
## no causality statement.new guideline causality statement.new guideline
## -1 192 264
## 0 150 226
## 1 598 830
##
## no causality statement.old guideline
## -1 114
## 0 74
## 1 304chisq.test(data2_long_pass$s_causality_1_1, data2_long_pass$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_causality_1_1 and data2_long_pass$H4_interaction
## X-squared = 3.0538, df = 4, p-value = 0.5489prop.table(table(data2_long_old1_pass$s_causality_1_1))##
## -1 0 1
## 0.2317073 0.1504065 0.6178862prop.table(table(data2_long_new1_pass$s_causality_1_1))##
## -1 0 1
## 0.2042553 0.1595745 0.6361702prop.table(table(data2_long_statement_new_pass$s_causality_1_1))##
## -1 0 1
## 0.2000000 0.1712121 0.6287879T2
table(data2_long_pass$s_causality_2_1,data2_long_pass$H4_interaction)##
## no causality statement.new guideline causality statement.new guideline
## -1 208 242
## 0 180 228
## 1 554 850
##
## no causality statement.old guideline
## -1 104
## 0 84
## 1 302chisq.test(data2_long_pass$s_causality_2_1, data2_long_pass$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_causality_2_1 and data2_long_pass$H4_interaction
## X-squared = 8.1812, df = 4, p-value = 0.08516prop.table(table(data2_long_old1_pass$s_causality_2_1))##
## -1 0 1
## 0.2122449 0.1714286 0.6163265prop.table(table(data2_long_new1_pass$s_causality_2_1))##
## -1 0 1
## 0.2208068 0.1910828 0.5881104prop.table(table(data2_long_statement_new_pass$s_causality_2_1))##
## -1 0 1
## 0.1833333 0.1727273 0.6439394Awareness Check Pass
table(data2_long_pass$s_causality_1_1,data2_long_pass$H4_interaction)##
## no causality statement.new guideline causality statement.new guideline
## -1 192 264
## 0 150 226
## 1 598 830
##
## no causality statement.old guideline
## -1 114
## 0 74
## 1 304chisq.test(data2_long_pass$s_causality_2_1, data2_long_pass$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_causality_2_1 and data2_long_pass$H4_interaction
## X-squared = 8.1812, df = 4, p-value = 0.08516prop.table(table(data2_long_old1_pass$s_causality_2_1))##
## -1 0 1
## 0.2122449 0.1714286 0.6163265prop.table(table(data2_long_new1_pass$s_causality_2_1))##
## -1 0 1
## 0.2208068 0.1910828 0.5881104prop.table(table(data2_long_statement_new_pass$s_causality_2_1))##
## -1 0 1
## 0.1833333 0.1727273 0.6439394Item 2
T1
table(data2_long$s_causality_1_2,data2_long$H4_interaction)##
## no causality statement.old guideline no causality statement.new guideline
## -1 348 616
## 0 178 320
## 1 186 416
##
## causality statement.new guideline
## -1 932
## 0 476
## 1 590chisq.test(data2_long$s_causality_1_2, data2_long$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long$s_causality_1_2 and data2_long$H4_interaction
## X-squared = 4.9578, df = 4, p-value = 0.2917prop.table(table(data2_long_old1$s_causality_1_2))##
## -1 0 1
## 0.488764 0.250000 0.261236prop.table(table(data2_long_new1$s_causality_1_2))##
## -1 0 1
## 0.4556213 0.2366864 0.3076923prop.table(table(data2_long_statement_new$s_causality_1_2))##
## -1 0 1
## 0.4664665 0.2382382 0.2952953Awareness Check Pass
table(data2_long_pass$s_causality_1_2,data2_long_pass$H4_interaction)##
## no causality statement.new guideline causality statement.new guideline
## -1 452 640
## 0 208 268
## 1 278 414
##
## no causality statement.old guideline
## -1 248
## 0 110
## 1 136chisq.test(data2_long_pass$s_causality_1_2, data2_long_pass$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_causality_1_2 and data2_long_pass$H4_interaction
## X-squared = 3.3124, df = 4, p-value = 0.507prop.table(table(data2_long_old1_pass$s_causality_1_2))##
## -1 0 1
## 0.5020243 0.2226721 0.2753036prop.table(table(data2_long_new1_pass$s_causality_1_2))##
## -1 0 1
## 0.4818763 0.2217484 0.2963753prop.table(table(data2_long_statement_new_pass$s_causality_1_2))##
## -1 0 1
## 0.4841150 0.2027231 0.3131619T2
table(data2_long$s_causality_2_2,data2_long$H4_interaction)##
## no causality statement.old guideline no causality statement.new guideline
## -1 250 614
## 0 210 328
## 1 252 404
##
## causality statement.new guideline
## -1 830
## 0 558
## 1 608chisq.test(data2_long$s_causality_2_2, data2_long$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long$s_causality_2_2 and data2_long$H4_interaction
## X-squared = 23.065, df = 4, p-value = 0.0001229prop.table(table(data2_long_old1$s_causality_2_2))##
## -1 0 1
## 0.3511236 0.2949438 0.3539326prop.table(table(data2_long_new1$s_causality_2_2))##
## -1 0 1
## 0.4561664 0.2436850 0.3001486prop.table(table(data2_long_statement_new$s_causality_2_2))##
## -1 0 1
## 0.4158317 0.2795591 0.3046092Awareness Check Pass
table(data2_long_pass$s_causality_2_2,data2_long_pass$H4_interaction)##
## no causality statement.new guideline causality statement.new guideline
## -1 434 544
## 0 220 332
## 1 286 440
##
## no causality statement.old guideline
## -1 166
## 0 130
## 1 198chisq.test(data2_long_pass$s_causality_2_2, data2_long_pass$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_causality_2_2 and data2_long_pass$H4_interaction
## X-squared = 22.634, df = 4, p-value = 0.0001498prop.table(table(data2_long_old1_pass$s_causality_2_2))##
## -1 0 1
## 0.3360324 0.2631579 0.4008097prop.table(table(data2_long_new1_pass$s_causality_2_2))##
## -1 0 1
## 0.4617021 0.2340426 0.3042553prop.table(table(data2_long_statement_new_pass$s_causality_2_2))##
## -1 0 1
## 0.4133739 0.2522796 0.3343465Item 3
T1
table(data2_long$s_causality_1_3,data2_long$H4_interaction)##
## no causality statement.old guideline no causality statement.new guideline
## -1 328 638
## 0 188 324
## 1 192 388
##
## causality statement.new guideline
## -1 910
## 0 552
## 1 538chisq.test(data2_long$s_causality_1_3, data2_long$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long$s_causality_1_3 and data2_long$H4_interaction
## X-squared = 5.6109, df = 4, p-value = 0.2302prop.table(table(data2_long_old1$s_causality_1_3))##
## -1 0 1
## 0.4632768 0.2655367 0.2711864prop.table(table(data2_long_new1$s_causality_1_3))##
## -1 0 1
## 0.4725926 0.2400000 0.2874074prop.table(table(data2_long_statement_new$s_causality_1_3))##
## -1 0 1
## 0.455 0.276 0.269Awareness Check Pass
table(data2_long_pass$s_causality_1_3,data2_long_pass$H4_interaction)##
## no causality statement.new guideline causality statement.new guideline
## -1 438 606
## 0 230 334
## 1 270 382
##
## no causality statement.old guideline
## -1 226
## 0 120
## 1 146chisq.test(data2_long_pass$s_causality_1_3, data2_long_pass$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_causality_1_3 and data2_long_pass$H4_interaction
## X-squared = 0.36449, df = 4, p-value = 0.9853prop.table(table(data2_long_old1_pass$s_causality_1_3))##
## -1 0 1
## 0.4593496 0.2439024 0.2967480prop.table(table(data2_long_new1_pass$s_causality_1_3))##
## -1 0 1
## 0.4669510 0.2452026 0.2878465prop.table(table(data2_long_statement_new_pass$s_causality_1_3))##
## -1 0 1
## 0.4583964 0.2526475 0.2889561T2
table(data2_long$s_causality_2_3,data2_long$H4_interaction)##
## no causality statement.old guideline no causality statement.new guideline
## -1 308 554
## 0 186 390
## 1 218 414
##
## causality statement.new guideline
## -1 898
## 0 540
## 1 562chisq.test(data2_long$s_causality_2_3, data2_long$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long$s_causality_2_3 and data2_long$H4_interaction
## X-squared = 6.6043, df = 4, p-value = 0.1583prop.table(table(data2_long_old1$s_causality_2_3))##
## -1 0 1
## 0.4325843 0.2612360 0.3061798prop.table(table(data2_long_new1$s_causality_2_3))##
## -1 0 1
## 0.4079529 0.2871870 0.3048601prop.table(table(data2_long_statement_new$s_causality_2_3))##
## -1 0 1
## 0.449 0.270 0.281Awareness Check Pass
table(data2_long_pass$s_causality_2_3,data2_long_pass$H4_interaction)##
## no causality statement.new guideline causality statement.new guideline
## -1 380 592
## 0 258 318
## 1 306 408
##
## no causality statement.old guideline
## -1 208
## 0 116
## 1 170chisq.test(data2_long_pass$s_causality_2_3, data2_long_pass$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_causality_2_3 and data2_long_pass$H4_interaction
## X-squared = 7.1359, df = 4, p-value = 0.1289prop.table(table(data2_long_old1_pass$s_causality_2_3))##
## -1 0 1
## 0.4210526 0.2348178 0.3441296prop.table(table(data2_long_new1_pass$s_causality_2_3))##
## -1 0 1
## 0.4025424 0.2733051 0.3241525prop.table(table(data2_long_statement_new_pass$s_causality_2_3))##
## -1 0 1
## 0.4491654 0.2412747 0.3095599Item 4
T1
table(data2_long$s_causality_1_4,data2_long$H4_interaction)##
## no causality statement.old guideline no causality statement.new guideline
## -1 354 694
## 0 182 344
## 1 176 316
##
## causality statement.new guideline
## -1 954
## 0 526
## 1 516chisq.test(data2_long$s_causality_1_4, data2_long$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long$s_causality_1_4 and data2_long$H4_interaction
## X-squared = 4.3662, df = 4, p-value = 0.3587prop.table(table(data2_long_old1$s_causality_1_4))##
## -1 0 1
## 0.497191 0.255618 0.247191prop.table(table(data2_long_new1$s_causality_1_4))##
## -1 0 1
## 0.5125554 0.2540620 0.2333826prop.table(table(data2_long_statement_new$s_causality_1_4))##
## -1 0 1
## 0.4779559 0.2635271 0.2585170Awareness Check Pass
table(data2_long_pass$s_causality_1_4,data2_long_pass$H4_interaction)##
## no causality statement.new guideline causality statement.new guideline
## -1 504 632
## 0 234 332
## 1 206 354
##
## no causality statement.old guideline
## -1 264
## 0 120
## 1 110chisq.test(data2_long_pass$s_causality_1_4, data2_long_pass$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_causality_1_4 and data2_long_pass$H4_interaction
## X-squared = 10.927, df = 4, p-value = 0.0274prop.table(table(data2_long_old1_pass$s_causality_1_4))##
## -1 0 1
## 0.5344130 0.2429150 0.2226721prop.table(table(data2_long_new1_pass$s_causality_1_4))##
## -1 0 1
## 0.5338983 0.2478814 0.2182203prop.table(table(data2_long_statement_new_pass$s_causality_1_4))##
## -1 0 1
## 0.4795144 0.2518968 0.2685888T2
table(data2_long$s_causality_2_4,data2_long$H4_interaction)##
## no causality statement.old guideline no causality statement.new guideline
## -1 330 620
## 0 206 388
## 1 172 342
##
## causality statement.new guideline
## -1 916
## 0 570
## 1 500chisq.test(data2_long$s_causality_2_4, data2_long$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long$s_causality_2_4 and data2_long$H4_interaction
## X-squared = 0.29341, df = 4, p-value = 0.9902prop.table(table(data2_long_old1$s_causality_2_4))##
## -1 0 1
## 0.4661017 0.2909605 0.2429379prop.table(table(data2_long_new1$s_causality_2_4))##
## -1 0 1
## 0.4592593 0.2874074 0.2533333prop.table(table(data2_long_statement_new$s_causality_2_4))##
## -1 0 1
## 0.4612286 0.2870091 0.2517623Awareness Check Pass
table(data2_long_pass$s_causality_2_4,data2_long_pass$H4_interaction)##
## no causality statement.new guideline causality statement.new guideline
## -1 442 590
## 0 276 348
## 1 220 366
##
## no causality statement.old guideline
## -1 246
## 0 124
## 1 122chisq.test(data2_long_pass$s_causality_2_4, data2_long_pass$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_causality_2_4 and data2_long_pass$H4_interaction
## X-squared = 9.048, df = 4, p-value = 0.05991prop.table(table(data2_long_old1_pass$s_causality_2_4))##
## -1 0 1
## 0.5000000 0.2520325 0.2479675prop.table(table(data2_long_new1_pass$s_causality_2_4))##
## -1 0 1
## 0.4712154 0.2942431 0.2345416prop.table(table(data2_long_statement_new_pass$s_causality_2_4))##
## -1 0 1
## 0.4524540 0.2668712 0.2806748Item 5
T1
table(data2_long$s_causality_1_5,data2_long$H4_interaction)##
## no causality statement.old guideline no causality statement.new guideline
## -1 270 566
## 0 208 316
## 1 230 468
##
## causality statement.new guideline
## -1 828
## 0 592
## 1 578chisq.test(data2_long$s_causality_1_5, data2_long$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long$s_causality_1_5 and data2_long$H4_interaction
## X-squared = 22.929, df = 4, p-value = 0.0001308prop.table(table(data2_long_old1$s_causality_1_5))##
## -1 0 1
## 0.3813559 0.2937853 0.3248588prop.table(table(data2_long_new1$s_causality_1_5))##
## -1 0 1
## 0.4192593 0.2340741 0.3466667prop.table(table(data2_long_statement_new$s_causality_1_5))##
## -1 0 1
## 0.4144144 0.2962963 0.2892893Awareness Check Pass
table(data2_long_pass$s_causality_1_5,data2_long_pass$H4_interaction)##
## no causality statement.new guideline causality statement.new guideline
## -1 392 500
## 0 208 374
## 1 340 446
##
## no causality statement.old guideline
## -1 168
## 0 140
## 1 184chisq.test(data2_long_pass$s_causality_1_5, data2_long_pass$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_causality_1_5 and data2_long_pass$H4_interaction
## X-squared = 15.91, df = 4, p-value = 0.003142prop.table(table(data2_long_old1_pass$s_causality_1_5))##
## -1 0 1
## 0.3414634 0.2845528 0.3739837prop.table(table(data2_long_new1_pass$s_causality_1_5))##
## -1 0 1
## 0.4170213 0.2212766 0.3617021prop.table(table(data2_long_statement_new_pass$s_causality_1_5))##
## -1 0 1
## 0.3787879 0.2833333 0.3378788T2
table(data2_long$s_causality_2_5,data2_long$H4_interaction)##
## no causality statement.old guideline no causality statement.new guideline
## -1 266 536
## 0 202 398
## 1 246 422
##
## causality statement.new guideline
## -1 746
## 0 606
## 1 644chisq.test(data2_long$s_causality_2_5, data2_long$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long$s_causality_2_5 and data2_long$H4_interaction
## X-squared = 3.5703, df = 4, p-value = 0.4673prop.table(table(data2_long_old1$s_causality_2_5))##
## -1 0 1
## 0.3725490 0.2829132 0.3445378prop.table(table(data2_long_new1$s_causality_2_5))##
## -1 0 1
## 0.3952802 0.2935103 0.3112094prop.table(table(data2_long_statement_new$s_causality_2_5))##
## -1 0 1
## 0.3737475 0.3036072 0.3226453Awareness Check Pass
table(data2_long_pass$s_causality_2_5,data2_long_pass$H4_interaction)##
## no causality statement.new guideline causality statement.new guideline
## -1 354 448
## 0 274 380
## 1 316 492
##
## no causality statement.old guideline
## -1 170
## 0 138
## 1 186chisq.test(data2_long_pass$s_causality_2_5, data2_long_pass$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_causality_2_5 and data2_long_pass$H4_interaction
## X-squared = 4.8728, df = 4, p-value = 0.3006prop.table(table(data2_long_old1_pass$s_causality_2_5))##
## -1 0 1
## 0.3441296 0.2793522 0.3765182prop.table(table(data2_long_new1_pass$s_causality_2_5))##
## -1 0 1
## 0.3750000 0.2902542 0.3347458prop.table(table(data2_long_statement_new_pass$s_causality_2_5))##
## -1 0 1
## 0.3393939 0.2878788 0.3727273Item 6
T1
table(data2_long$s_causality_1_6,data2_long$H4_interaction)##
## no causality statement.old guideline no causality statement.new guideline
## -1 276 572
## 0 206 370
## 1 226 412
##
## causality statement.new guideline
## -1 840
## 0 600
## 1 558chisq.test(data2_long$s_causality_1_6, data2_long$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long$s_causality_1_6 and data2_long$H4_interaction
## X-squared = 6.9153, df = 4, p-value = 0.1404prop.table(table(data2_long_old1$s_causality_1_6))##
## -1 0 1
## 0.3898305 0.2909605 0.3192090prop.table(table(data2_long_new1$s_causality_1_6))##
## -1 0 1
## 0.4224520 0.2732644 0.3042836prop.table(table(data2_long_statement_new$s_causality_1_6))##
## -1 0 1
## 0.4204204 0.3003003 0.2792793Awareness Check Pass
table(data2_long_pass$s_causality_1_6,data2_long_pass$H4_interaction)##
## no causality statement.new guideline causality statement.new guideline
## -1 386 546
## 0 242 370
## 1 316 404
##
## no causality statement.old guideline
## -1 180
## 0 134
## 1 178chisq.test(data2_long_pass$s_causality_1_6, data2_long_pass$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_causality_1_6 and data2_long_pass$H4_interaction
## X-squared = 7.0708, df = 4, p-value = 0.1322prop.table(table(data2_long_old1_pass$s_causality_1_6))##
## -1 0 1
## 0.3658537 0.2723577 0.3617886prop.table(table(data2_long_new1_pass$s_causality_1_6))##
## -1 0 1
## 0.4088983 0.2563559 0.3347458prop.table(table(data2_long_statement_new_pass$s_causality_1_6))##
## -1 0 1
## 0.4136364 0.2803030 0.3060606T2
table(data2_long$s_causality_2_6,data2_long$H4_interaction)##
## no causality statement.old guideline no causality statement.new guideline
## -1 258 520
## 0 208 382
## 1 244 450
##
## causality statement.new guideline
## -1 764
## 0 616
## 1 618chisq.test(data2_long$s_causality_2_6, data2_long$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long$s_causality_2_6 and data2_long$H4_interaction
## X-squared = 4.9736, df = 4, p-value = 0.29prop.table(table(data2_long_old1$s_causality_2_6))##
## -1 0 1
## 0.3633803 0.2929577 0.3436620prop.table(table(data2_long_new1$s_causality_2_6))##
## -1 0 1
## 0.3846154 0.2825444 0.3328402prop.table(table(data2_long_statement_new$s_causality_2_6))##
## -1 0 1
## 0.3823824 0.3083083 0.3093093Awareness Check Pass
table(data2_long_pass$s_causality_2_6,data2_long_pass$H4_interaction)##
## no causality statement.new guideline causality statement.new guideline
## -1 342 460
## 0 272 396
## 1 326 460
##
## no causality statement.old guideline
## -1 160
## 0 142
## 1 190chisq.test(data2_long_pass$s_causality_2_6, data2_long_pass$H4_interaction)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_causality_2_6 and data2_long_pass$H4_interaction
## X-squared = 3.3377, df = 4, p-value = 0.503prop.table(table(data2_long_old1_pass$s_causality_2_6))##
## -1 0 1
## 0.3252033 0.2886179 0.3861789prop.table(table(data2_long_new1_pass$s_causality_2_6))##
## -1 0 1
## 0.3638298 0.2893617 0.3468085prop.table(table(data2_long_statement_new_pass$s_causality_2_6))##
## -1 0 1
## 0.3495441 0.3009119 0.3495441CAMA-Item
Item 1
# "Lebeindige Evidenz" bedeutet, dass fortlaufend neue Ergebnisse in eine Metaanalyse aufgenommen werden können."
table(data2_wide$s_CAMA_1_1,data2_wide$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 65 50 36
## 0 116 140 86
## 1 173 150 203chisq.test(data2_wide$s_CAMA_1_1, data2_wide$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_CAMA_1_1 and data2_wide$H5_interaction
## X-squared = 28.521, df = 4, p-value = 9.781e-06prop.table(table(data2_wide_old2$s_CAMA_1_1))##
## -1 0 1
## 0.1836158 0.3276836 0.4887006prop.table(table(data2_wide_new2$s_CAMA_1_1))##
## -1 0 1
## 0.1470588 0.4117647 0.4411765prop.table(table(data2_wide_CAMA_new$s_CAMA_1_1))##
## -1 0 1
## 0.1107692 0.2646154 0.6246154Awareness Check Pass
table(data2_wide_pass$s_CAMA_1_1,data2_wide_pass$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 36 24 21
## 0 87 94 57
## 1 123 88 157chisq.test(data2_wide_pass$s_CAMA_1_1, data2_wide_pass$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_CAMA_1_1 and data2_wide_pass$H5_interaction
## X-squared = 30.653, df = 4, p-value = 3.603e-06prop.table(table(data2_wide_old2_pass$s_CAMA_1_1))##
## -1 0 1
## 0.1463415 0.3536585 0.5000000prop.table(table(data2_wide_new2_pass$s_CAMA_1_1))##
## -1 0 1
## 0.1165049 0.4563107 0.4271845prop.table(table(data2_wide_CAMA_new_pass$s_CAMA_1_1))##
## -1 0 1
## 0.0893617 0.2425532 0.6680851Item 2
# "Lebendige Evidenz" bedeutet, dass die Darstellung der Ergebnisse spannend formuliert ist."
table(data2_wide$s_CAMA_1_2,data2_wide$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 89 83 66
## 0 139 139 98
## 1 128 116 160chisq.test(data2_wide$s_CAMA_1_2, data2_wide$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_CAMA_1_2 and data2_wide$H5_interaction
## X-squared = 19.192, df = 4, p-value = 0.0007206prop.table(table(data2_wide_old2$s_CAMA_1_2))##
## -1 0 1
## 0.2500000 0.3904494 0.3595506prop.table(table(data2_wide_new2$s_CAMA_1_2))##
## -1 0 1
## 0.2455621 0.4112426 0.3431953prop.table(table(data2_wide_CAMA_new$s_CAMA_1_2))##
## -1 0 1
## 0.2037037 0.3024691 0.4938272Awareness Check Pass
table(data2_wide_pass$s_CAMA_1_2,data2_wide_pass$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 44 31 36
## 0 100 87 67
## 1 102 87 133chisq.test(data2_wide_pass$s_CAMA_1_2, data2_wide_pass$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_CAMA_1_2 and data2_wide_pass$H5_interaction
## X-squared = 14.893, df = 4, p-value = 0.004929prop.table(table(data2_wide_old2_pass$s_CAMA_1_2))##
## -1 0 1
## 0.1788618 0.4065041 0.4146341prop.table(table(data2_wide_new2_pass$s_CAMA_1_2))##
## -1 0 1
## 0.1512195 0.4243902 0.4243902prop.table(table(data2_wide_CAMA_new_pass$s_CAMA_1_2))##
## -1 0 1
## 0.1525424 0.2838983 0.5635593Item 3
# "Lebendige Evidenz" bedeutet, dass ein Zwischenbericht zu allerersten Ergebnissen einer noch in Arbeit befindlichen Metaanalyse gegeben wird."
table(data2_wide$s_CAMA_1_3,data2_wide$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 122 111 134
## 0 157 169 102
## 1 78 61 88chisq.test(data2_wide$s_CAMA_1_3, data2_wide$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_CAMA_1_3 and data2_wide$H5_interaction
## X-squared = 24.055, df = 4, p-value = 7.786e-05prop.table(table(data2_wide_old2$s_CAMA_1_3))##
## -1 0 1
## 0.3417367 0.4397759 0.2184874prop.table(table(data2_wide_new2$s_CAMA_1_3))##
## -1 0 1
## 0.3255132 0.4956012 0.1788856prop.table(table(data2_wide_CAMA_new$s_CAMA_1_3))##
## -1 0 1
## 0.4135802 0.3148148 0.2716049Awareness Check Pass
table(data2_wide_pass$s_CAMA_1_3,data2_wide_pass$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 67 55 89
## 0 121 117 72
## 1 59 34 73chisq.test(data2_wide_pass$s_CAMA_1_3, data2_wide_pass$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_CAMA_1_3 and data2_wide_pass$H5_interaction
## X-squared = 33.721, df = 4, p-value = 8.503e-07prop.table(table(data2_wide_old2_pass$s_CAMA_1_3))##
## -1 0 1
## 0.2712551 0.4898785 0.2388664prop.table(table(data2_wide_new2_pass$s_CAMA_1_3))##
## -1 0 1
## 0.2669903 0.5679612 0.1650485prop.table(table(data2_wide_CAMA_new_pass$s_CAMA_1_3))##
## -1 0 1
## 0.3803419 0.3076923 0.3119658Item 4
# "Lebendige Evidenz" bedeutet, dass es sich um Metaanalysen handelt, die besonders alltagsnahe Themen behandeln."
table(data2_wide$s_CAMA_1_4,data2_wide$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 137 130 121
## 0 150 146 109
## 1 70 61 94chisq.test(data2_wide$s_CAMA_1_4, data2_wide$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_CAMA_1_4 and data2_wide$H5_interaction
## X-squared = 15.102, df = 4, p-value = 0.004494prop.table(table(data2_wide_old2$s_CAMA_1_4))##
## -1 0 1
## 0.3837535 0.4201681 0.1960784prop.table(table(data2_wide_new2$s_CAMA_1_4))##
## -1 0 1
## 0.3857567 0.4332344 0.1810089prop.table(table(data2_wide_CAMA_new$s_CAMA_1_4))##
## -1 0 1
## 0.3734568 0.3364198 0.2901235Awareness Check Pass
table(data2_wide_pass$s_CAMA_1_4,data2_wide_pass$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 89 72 84
## 0 110 100 76
## 1 48 30 76chisq.test(data2_wide_pass$s_CAMA_1_4, data2_wide_pass$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_CAMA_1_4 and data2_wide_pass$H5_interaction
## X-squared = 24.701, df = 4, p-value = 5.778e-05prop.table(table(data2_wide_old2_pass$s_CAMA_1_4))##
## -1 0 1
## 0.3603239 0.4453441 0.1943320prop.table(table(data2_wide_new2_pass$s_CAMA_1_4))##
## -1 0 1
## 0.3564356 0.4950495 0.1485149prop.table(table(data2_wide_CAMA_new_pass$s_CAMA_1_4))##
## -1 0 1
## 0.3559322 0.3220339 0.3220339Item 5
# "In PsychOpen CAMA kann man die Ergebnisse einer Metaanalyse auslesen."
table(data2_wide$s_CAMA_1_5,data2_wide$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 61 51 52
## 0 172 177 115
## 1 122 110 158chisq.test(data2_wide$s_CAMA_1_5, data2_wide$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_CAMA_1_5 and data2_wide$H5_interaction
## X-squared = 25.328, df = 4, p-value = 4.322e-05prop.table(table(data2_wide_old2$s_CAMA_1_5))##
## -1 0 1
## 0.171831 0.484507 0.343662prop.table(table(data2_wide_new2$s_CAMA_1_5))##
## -1 0 1
## 0.1508876 0.5236686 0.3254438prop.table(table(data2_wide_CAMA_new$s_CAMA_1_5))##
## -1 0 1
## 0.1600000 0.3538462 0.4861538Awareness Check Pass
table(data2_wide_pass$s_CAMA_1_5,data2_wide_pass$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 28 19 27
## 0 133 120 84
## 1 85 65 125chisq.test(data2_wide_pass$s_CAMA_1_5, data2_wide_pass$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_CAMA_1_5 and data2_wide_pass$H5_interaction
## X-squared = 29.596, df = 4, p-value = 5.915e-06prop.table(table(data2_wide_old2_pass$s_CAMA_1_5))##
## -1 0 1
## 0.1138211 0.5406504 0.3455285prop.table(table(data2_wide_new2_pass$s_CAMA_1_5))##
## -1 0 1
## 0.09313725 0.58823529 0.31862745prop.table(table(data2_wide_CAMA_new_pass$s_CAMA_1_5))##
## -1 0 1
## 0.1144068 0.3559322 0.5296610Item 6
# "In PsychOpen CAMA kann man Metaanalysen in einem Peer-ReView-Verfahren begutachten lassen."
table(data2_wide$s_CAMA_1_6,data2_wide$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 86 88 81
## 0 202 189 150
## 1 69 61 88chisq.test(data2_wide$s_CAMA_1_6, data2_wide$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_CAMA_1_6 and data2_wide$H5_interaction
## X-squared = 11.937, df = 4, p-value = 0.01782prop.table(table(data2_wide_old2$s_CAMA_1_6))##
## -1 0 1
## 0.2408964 0.5658263 0.1932773prop.table(table(data2_wide_new2$s_CAMA_1_6))##
## -1 0 1
## 0.2603550 0.5591716 0.1804734prop.table(table(data2_wide_CAMA_new$s_CAMA_1_6))##
## -1 0 1
## 0.2539185 0.4702194 0.2758621Awareness Check Pass
table(data2_wide_pass$s_CAMA_1_6,data2_wide_pass$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 42 46 46
## 0 161 132 118
## 1 44 25 67chisq.test(data2_wide_pass$s_CAMA_1_6, data2_wide_pass$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_CAMA_1_6 and data2_wide_pass$H5_interaction
## X-squared = 22.772, df = 4, p-value = 0.0001406prop.table(table(data2_wide_old2_pass$s_CAMA_1_6))##
## -1 0 1
## 0.1700405 0.6518219 0.1781377prop.table(table(data2_wide_new2_pass$s_CAMA_1_6))##
## -1 0 1
## 0.2266010 0.6502463 0.1231527prop.table(table(data2_wide_CAMA_new_pass$s_CAMA_1_6))##
## -1 0 1
## 0.1991342 0.5108225 0.2900433Item 7
# "In PsychOpen CAMA werden ausschließlich Metaanalysen aufgenommen, die im weitesten Sinne etwas mit dem Thema Schlafqualität zu tun haben."
table(data2_wide$s_CAMA_1_7,data2_wide$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 72 60 69
## 0 151 169 96
## 1 132 111 159chisq.test(data2_wide$s_CAMA_1_7, data2_wide$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_CAMA_1_7 and data2_wide$H5_interaction
## X-squared = 30.034, df = 4, p-value = 4.818e-06prop.table(table(data2_wide_old2$s_CAMA_1_7))##
## -1 0 1
## 0.2028169 0.4253521 0.3718310prop.table(table(data2_wide_new2$s_CAMA_1_7))##
## -1 0 1
## 0.1764706 0.4970588 0.3264706prop.table(table(data2_wide_CAMA_new$s_CAMA_1_7))##
## -1 0 1
## 0.2129630 0.2962963 0.4907407Awareness Check Pass
table(data2_wide_pass$s_CAMA_1_7,data2_wide_pass$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 29 25 31
## 0 115 111 65
## 1 103 70 139chisq.test(data2_wide_pass$s_CAMA_1_7, data2_wide_pass$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_CAMA_1_7 and data2_wide_pass$H5_interaction
## X-squared = 36.184, df = 4, p-value = 2.652e-07prop.table(table(data2_wide_old2_pass$s_CAMA_1_7))##
## -1 0 1
## 0.1174089 0.4655870 0.4170040prop.table(table(data2_wide_new2_pass$s_CAMA_1_7))##
## -1 0 1
## 0.1213592 0.5388350 0.3398058prop.table(table(data2_wide_CAMA_new_pass$s_CAMA_1_7))##
## -1 0 1
## 0.1319149 0.2765957 0.5914894Item 8
# "In PsychOpen CAMA werden ausschließlich narrative Übersichtsarbeiten aufgenommen."
table(data2_wide$s_CAMA_1_8,data2_wide$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 69 62 72
## 0 188 188 139
## 1 100 88 115chisq.test(data2_wide$s_CAMA_1_8, data2_wide$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_CAMA_1_8 and data2_wide$H5_interaction
## X-squared = 12.624, df = 4, p-value = 0.01327prop.table(table(data2_wide_old2$s_CAMA_1_8))##
## -1 0 1
## 0.1932773 0.5266106 0.2801120prop.table(table(data2_wide_new2$s_CAMA_1_8))##
## -1 0 1
## 0.183432 0.556213 0.260355prop.table(table(data2_wide_CAMA_new$s_CAMA_1_8))##
## -1 0 1
## 0.2208589 0.4263804 0.3527607Awareness Check Pass
table(data2_wide_pass$s_CAMA_1_8,data2_wide_pass$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 28 22 43
## 0 148 128 101
## 1 71 54 92chisq.test(data2_wide_pass$s_CAMA_1_8, data2_wide_pass$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_CAMA_1_8 and data2_wide_pass$H5_interaction
## X-squared = 21.981, df = 4, p-value = 0.0002022prop.table(table(data2_wide_old2_pass$s_CAMA_1_8))##
## -1 0 1
## 0.1133603 0.5991903 0.2874494prop.table(table(data2_wide_new2_pass$s_CAMA_1_8))##
## -1 0 1
## 0.1078431 0.6274510 0.2647059prop.table(table(data2_wide_CAMA_new_pass$s_CAMA_1_8))##
## -1 0 1
## 0.1822034 0.4279661 0.3898305Item 9
# "Die in der Übersichtsarbeit zu einer Metaanalyse zusammengefassten Studien stammen aus einer Recherche des Leibniz-Instituts für Psychologie"
table(data2_wide$s_CAMA_2_1,data2_wide$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 141 157 149
## 0 136 104 106
## 1 77 80 71chisq.test(data2_wide$s_CAMA_2_1, data2_wide$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_CAMA_2_1 and data2_wide$H5_interaction
## X-squared = 5.709, df = 4, p-value = 0.222prop.table(table(data2_wide_old2$s_CAMA_2_1))##
## -1 0 1
## 0.3983051 0.3841808 0.2175141prop.table(table(data2_wide_new2$s_CAMA_2_1))##
## -1 0 1
## 0.4604106 0.3049853 0.2346041prop.table(table(data2_wide_CAMA_new$s_CAMA_2_1))##
## -1 0 1
## 0.4570552 0.3251534 0.2177914Awareness Check Pass
table(data2_wide_pass$s_CAMA_2_1,data2_wide_pass$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 94 94 104
## 0 94 58 77
## 1 56 54 55chisq.test(data2_wide_pass$s_CAMA_2_1, data2_wide_pass$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_CAMA_2_1 and data2_wide_pass$H5_interaction
## X-squared = 5.753, df = 4, p-value = 0.2184prop.table(table(data2_wide_old2_pass$s_CAMA_2_1))##
## -1 0 1
## 0.3852459 0.3852459 0.2295082prop.table(table(data2_wide_new2_pass$s_CAMA_2_1))##
## -1 0 1
## 0.4563107 0.2815534 0.2621359prop.table(table(data2_wide_CAMA_new_pass$s_CAMA_2_1))##
## -1 0 1
## 0.4406780 0.3262712 0.2330508Item 10
# "PsychOpen CAMA beinhaltet die Ergebnisse der Übersichtsarbeiten von Francesca Färber und Jenny Rosendahl und erweitert diese Ergebnisse."
table(data2_wide$s_CAMA_2_2,data2_wide$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 92 91 73
## 0 173 147 123
## 1 92 100 129chisq.test(data2_wide$s_CAMA_2_2, data2_wide$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_CAMA_2_2 and data2_wide$H5_interaction
## X-squared = 16.874, df = 4, p-value = 0.002045prop.table(table(data2_wide_old2$s_CAMA_2_2))##
## -1 0 1
## 0.2577031 0.4845938 0.2577031prop.table(table(data2_wide_new2$s_CAMA_2_2))##
## -1 0 1
## 0.2692308 0.4349112 0.2958580prop.table(table(data2_wide_CAMA_new$s_CAMA_2_2))##
## -1 0 1
## 0.2246154 0.3784615 0.3969231Awareness Check Pass
table(data2_wide_pass$s_CAMA_2_2,data2_wide_pass$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 72 58 53
## 0 133 93 86
## 1 42 53 96chisq.test(data2_wide_pass$s_CAMA_2_2, data2_wide_pass$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_CAMA_2_2 and data2_wide_pass$H5_interaction
## X-squared = 35.138, df = 4, p-value = 4.352e-07prop.table(table(data2_wide_old2_pass$s_CAMA_2_2))##
## -1 0 1
## 0.2914980 0.5384615 0.1700405prop.table(table(data2_wide_new2_pass$s_CAMA_2_2))##
## -1 0 1
## 0.2843137 0.4558824 0.2598039prop.table(table(data2_wide_CAMA_new_pass$s_CAMA_2_2))##
## -1 0 1
## 0.2255319 0.3659574 0.4085106Item 11
# "Die Übersichtsarbeit von Francesca Färber und Jenny Rosendahl beinhaltet die Ergebnisse aus PsychOpen CAMA und erweitert diese Ergebnisse."
table(data2_wide$s_CAMA_2_3,data2_wide$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 90 105 121
## 0 155 151 125
## 1 110 84 81chisq.test(data2_wide$s_CAMA_2_3, data2_wide$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_CAMA_2_3 and data2_wide$H5_interaction
## X-squared = 12.633, df = 4, p-value = 0.01321prop.table(table(data2_wide_old2$s_CAMA_2_3))##
## -1 0 1
## 0.2535211 0.4366197 0.3098592prop.table(table(data2_wide_new2$s_CAMA_2_3))##
## -1 0 1
## 0.3088235 0.4441176 0.2470588prop.table(table(data2_wide_CAMA_new$s_CAMA_2_3))##
## -1 0 1
## 0.3700306 0.3822630 0.2477064Awareness Check Pass
table(data2_wide_pass$s_CAMA_2_3,data2_wide_pass$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 48 53 82
## 0 119 97 93
## 1 78 56 62chisq.test(data2_wide_pass$s_CAMA_2_3, data2_wide_pass$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_CAMA_2_3 and data2_wide_pass$H5_interaction
## X-squared = 14.467, df = 4, p-value = 0.005945prop.table(table(data2_wide_old2_pass$s_CAMA_2_3))##
## -1 0 1
## 0.1959184 0.4857143 0.3183673prop.table(table(data2_wide_new2_pass$s_CAMA_2_3))##
## -1 0 1
## 0.2572816 0.4708738 0.2718447prop.table(table(data2_wide_CAMA_new_pass$s_CAMA_2_3))##
## -1 0 1
## 0.3459916 0.3924051 0.2616034Item 12
# Der KLARtext bezieht sich vorrangig auf Ergebnisse der Übersichtsarbeit von Francesca Färber und Jenny Rosendahl unabhängig von den Ergebnissen in PsychOpen CAMA
table(data2_wide$s_CAMA_2_4,data2_wide$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 147 147 136
## 0 133 128 115
## 1 75 66 75chisq.test(data2_wide$s_CAMA_2_4, data2_wide$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_CAMA_2_4 and data2_wide$H5_interaction
## X-squared = 1.484, df = 4, p-value = 0.8295prop.table(table(data2_wide_old2$s_CAMA_2_4))##
## -1 0 1
## 0.4140845 0.3746479 0.2112676prop.table(table(data2_wide_new2$s_CAMA_2_4))##
## -1 0 1
## 0.4310850 0.3753666 0.1935484prop.table(table(data2_wide_CAMA_new$s_CAMA_2_4))##
## -1 0 1
## 0.4171779 0.3527607 0.2300613Awareness Check Pass
table(data2_wide_pass$s_CAMA_2_4,data2_wide_pass$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 97 91 100
## 0 93 71 83
## 1 55 44 53chisq.test(data2_wide_pass$s_CAMA_2_4, data2_wide_pass$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_CAMA_2_4 and data2_wide_pass$H5_interaction
## X-squared = 1.0974, df = 4, p-value = 0.8947prop.table(table(data2_wide_old2_pass$s_CAMA_2_4))##
## -1 0 1
## 0.3959184 0.3795918 0.2244898prop.table(table(data2_wide_new2_pass$s_CAMA_2_4))##
## -1 0 1
## 0.4417476 0.3446602 0.2135922prop.table(table(data2_wide_CAMA_new_pass$s_CAMA_2_4))##
## -1 0 1
## 0.4237288 0.3516949 0.2245763Item 13
# Der KLARtext, den ich gerade gelesen habe, beruht auf lebendiger Evidenz
table(data2_wide$s_CAMA_3,data2_wide$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 122 111 135
## 0 157 169 102
## 1 78 61 90chisq.test(data2_wide$s_CAMA_3, data2_wide$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide$s_CAMA_3 and data2_wide$H5_interaction
## X-squared = 25.131, df = 4, p-value = 4.735e-05prop.table(table(data2_wide_old2$s_CAMA_3))##
## -1 0 1
## 0.3417367 0.4397759 0.2184874prop.table(table(data2_wide_new2$s_CAMA_3))##
## -1 0 1
## 0.3255132 0.4956012 0.1788856prop.table(table(data2_wide_CAMA_new$s_CAMA_3))##
## -1 0 1
## 0.4128440 0.3119266 0.2752294Awareness Check Pass
table(data2_wide_pass$s_CAMA_3,data2_wide_pass$H5_interaction)##
## no CAMA PLS.old guideline no CAMA PLS.new guideline CAMA PLS.new guideline
## -1 67 55 90
## 0 121 117 72
## 1 59 34 75chisq.test(data2_wide_pass$s_CAMA_3, data2_wide_pass$H5_interaction)##
## Pearson's Chi-squared test
##
## data: data2_wide_pass$s_CAMA_3 and data2_wide_pass$H5_interaction
## X-squared = 35.122, df = 4, p-value = 4.385e-07prop.table(table(data2_wide_old2_pass$s_CAMA_3))##
## -1 0 1
## 0.2712551 0.4898785 0.2388664prop.table(table(data2_wide_new2_pass$s_CAMA_3))##
## -1 0 1
## 0.2669903 0.5679612 0.1650485prop.table(table(data2_wide_CAMA_new_pass$s_CAMA_3))##
## -1 0 1
## 0.3797468 0.3037975 0.3164557Funding Item
Item 1
T1
table(data2_long$s_funding_1_1,data2_long$version)##
## new guideline old guideline
## -1 1042 222
## 0 1172 242
## 1 1132 246chisq.test(data2_long$s_funding_1_1, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_funding_1_1 and data2_long$version
## X-squared = 0.26711, df = 2, p-value = 0.875prop.table(table(data2_long_version_old$s_funding_1_1))##
## -1 0 1
## 0.3126761 0.3408451 0.3464789prop.table(table(data2_long_version_new$s_funding_1_1))##
## -1 0 1
## 0.3114166 0.3502690 0.3383144Awareness Check Pass
table(data2_long_pass$s_funding_1_1,data2_long_pass$version)##
## old guideline new guideline
## -1 138 600
## 0 156 774
## 1 198 882chisq.test(data2_long_pass$s_funding_1_1, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_funding_1_1 and data2_long_pass$version
## X-squared = 1.2606, df = 2, p-value = 0.5324prop.table(table(data2_long_version_old_pass$s_funding_1_1))##
## -1 0 1
## 0.2804878 0.3170732 0.4024390prop.table(table(data2_long_version_new_pass$s_funding_1_1))##
## -1 0 1
## 0.2659574 0.3430851 0.3909574T1, Barth et al.
subset_barth <- subset(data2_long, summary == "Barth")
subset_barth_version_old <- subset(data2_long_version_old, summary == "Barth")
subset_barth_version_new <- subset(data2_long_version_new, summary == "Barth")
table(subset_barth$s_funding_1_1, subset_barth$version)##
## new guideline old guideline
## -1 521 111
## 0 586 121
## 1 566 123chisq.test(subset_barth$s_funding_1_1, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_funding_1_1 and subset_barth$version
## X-squared = 0.13355, df = 2, p-value = 0.9354prop.table(table(subset_barth_version_old$s_funding_1_1))##
## -1 0 1
## 0.3126761 0.3408451 0.3464789prop.table(table(subset_barth_version_new$s_funding_1_1))##
## -1 0 1
## 0.3114166 0.3502690 0.3383144Awareness Check Pass
subset_barth_pass <- subset(data2_long_pass, summary == "Barth")
subset_barth_version_old_pass <- subset(data2_long_version_old_pass, summary == "Barth")
subset_barth_version_new_pass <- subset(data2_long_version_new_pass, summary == "Barth")
table(subset_barth_pass$s_funding_1_1, subset_barth_pass$version)##
## old guideline new guideline
## -1 69 300
## 0 78 387
## 1 99 441chisq.test(subset_barth_pass$s_funding_1_1, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_funding_1_1 and subset_barth_pass$version
## X-squared = 0.63028, df = 2, p-value = 0.7297prop.table(table(subset_barth_version_old_pass$s_funding_1_1))##
## -1 0 1
## 0.2804878 0.3170732 0.4024390prop.table(table(subset_barth_version_new_pass$s_funding_1_1))##
## -1 0 1
## 0.2659574 0.3430851 0.3909574T1, Faerber et al.
subset_faerber <- subset(data2_long, summary == "Faerber")
subset_faerber_version_old <- subset(data2_long_version_old, summary == "Faerber")
subset_faerber_version_new <- subset(data2_long_version_new, summary == "Faerber")
table(subset_faerber$s_funding_1_1, subset_faerber$version)##
## new guideline old guideline
## -1 521 111
## 0 586 121
## 1 566 123chisq.test(subset_faerber$s_funding_1_1, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_funding_1_1 and subset_faerber$version
## X-squared = 0.13355, df = 2, p-value = 0.9354prop.table(table(subset_faerber_version_old$s_funding_1_1))##
## -1 0 1
## 0.3126761 0.3408451 0.3464789prop.table(table(subset_faerber_version_new$s_funding_1_1))##
## -1 0 1
## 0.3114166 0.3502690 0.3383144Awareness Check Pass
subset_faerber_pass <- subset(data2_long_pass, summary == "Faerber")
subset_faerber_version_old_pass <- subset(data2_long_version_old_pass, summary == "Faerber")
subset_faerber_version_new_pass <- subset(data2_long_version_new_pass, summary == "Faerber")
table(subset_faerber_pass$s_funding_1_1, subset_faerber_pass$version)##
## old guideline new guideline
## -1 69 300
## 0 78 387
## 1 99 441chisq.test(subset_faerber_pass$s_funding_1_1, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_funding_1_1 and subset_faerber_pass$version
## X-squared = 0.63028, df = 2, p-value = 0.7297prop.table(table(subset_faerber_version_old_pass$s_funding_1_1))##
## -1 0 1
## 0.2804878 0.3170732 0.4024390prop.table(table(subset_faerber_version_new_pass$s_funding_1_1))##
## -1 0 1
## 0.2659574 0.3430851 0.3909574T2
table(data2_long$s_funding_2_1,data2_long$version)##
## new guideline old guideline
## -1 816 152
## 0 880 184
## 1 1654 376chisq.test(data2_long$s_funding_2_1, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_funding_2_1 and data2_long$version
## X-squared = 3.66, df = 2, p-value = 0.1604prop.table(table(data2_long_version_old$s_funding_2_1))##
## -1 0 1
## 0.2134831 0.2584270 0.5280899prop.table(table(data2_long_version_new$s_funding_2_1))##
## -1 0 1
## 0.2435821 0.2626866 0.4937313Awareness Check Pass
table(data2_long_pass$s_funding_2_1,data2_long_pass$version)##
## old guideline new guideline
## -1 74 410
## 0 114 512
## 1 306 1336chisq.test(data2_long_pass$s_funding_2_1, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_funding_2_1 and data2_long_pass$version
## X-squared = 2.8797, df = 2, p-value = 0.237prop.table(table(data2_long_version_old_pass$s_funding_2_1))##
## -1 0 1
## 0.1497976 0.2307692 0.6194332prop.table(table(data2_long_version_new_pass$s_funding_2_1))##
## -1 0 1
## 0.1815766 0.2267493 0.5916740T2 Barth et al.
table(subset_barth$s_funding_2_1,subset_barth$version)##
## new guideline old guideline
## -1 408 76
## 0 440 92
## 1 827 188chisq.test(subset_barth$s_funding_2_1, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_funding_2_1 and subset_barth$version
## X-squared = 1.83, df = 2, p-value = 0.4005prop.table(table(subset_barth_version_old$s_funding_2_1))##
## -1 0 1
## 0.2134831 0.2584270 0.5280899prop.table(table(subset_barth_version_new$s_funding_2_1))##
## -1 0 1
## 0.2435821 0.2626866 0.4937313Awarness Check Pass
table(subset_barth_pass$s_funding_2_1,subset_barth_pass$version)##
## old guideline new guideline
## -1 37 205
## 0 57 256
## 1 153 668chisq.test(subset_barth_pass$s_funding_2_1, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_funding_2_1 and subset_barth_pass$version
## X-squared = 1.4399, df = 2, p-value = 0.4868prop.table(table(subset_barth_version_old_pass$s_funding_2_1))##
## -1 0 1
## 0.1497976 0.2307692 0.6194332prop.table(table(subset_barth_version_new_pass$s_funding_2_1))##
## -1 0 1
## 0.1815766 0.2267493 0.5916740T2 Faerber et al.
table(subset_faerber$s_funding_2_1,subset_faerber$version)##
## new guideline old guideline
## -1 408 76
## 0 440 92
## 1 827 188chisq.test(subset_faerber$s_funding_2_1, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_funding_2_1 and subset_faerber$version
## X-squared = 1.83, df = 2, p-value = 0.4005prop.table(table(subset_faerber_version_old$s_funding_2_1))##
## -1 0 1
## 0.2134831 0.2584270 0.5280899prop.table(table(subset_faerber_version_new$s_funding_2_1))##
## -1 0 1
## 0.2435821 0.2626866 0.4937313Awareness Check Pass
table(subset_faerber_pass$s_funding_2_1,subset_faerber_pass$version)##
## old guideline new guideline
## -1 37 205
## 0 57 256
## 1 153 668chisq.test(subset_faerber_pass$s_funding_2_1, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_funding_2_1 and subset_faerber_pass$version
## X-squared = 1.4399, df = 2, p-value = 0.4868prop.table(table(subset_faerber_version_old_pass$s_funding_2_1))##
## -1 0 1
## 0.1497976 0.2307692 0.6194332prop.table(table(subset_faerber_version_new_pass$s_funding_2_1))##
## -1 0 1
## 0.1815766 0.2267493 0.5916740Item 2
T1
table(data2_long$s_funding_1_2,data2_long$version)##
## new guideline old guideline
## -1 946 194
## 0 1180 250
## 1 1234 264chisq.test(data2_long$s_funding_1_2, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_funding_1_2 and data2_long$version
## X-squared = 0.17478, df = 2, p-value = 0.9163prop.table(table(data2_long_version_old$s_funding_1_2))##
## -1 0 1
## 0.2740113 0.3531073 0.3728814prop.table(table(data2_long_version_new$s_funding_1_2))##
## -1 0 1
## 0.2815476 0.3511905 0.3672619Awareness Check Pass
table(data2_long_pass$s_funding_1_2,data2_long_pass$version)##
## old guideline new guideline
## -1 98 492
## 0 180 808
## 1 214 966chisq.test(data2_long_pass$s_funding_1_2, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_funding_1_2 and data2_long_pass$version
## X-squared = 0.77582, df = 2, p-value = 0.6785prop.table(table(data2_long_version_old_pass$s_funding_1_2))##
## -1 0 1
## 0.1991870 0.3658537 0.4349593prop.table(table(data2_long_version_new_pass$s_funding_1_2))##
## -1 0 1
## 0.2171227 0.3565755 0.4263019T1, Barth et al.
table(subset_barth$s_funding_1_2, subset_barth$version)##
## new guideline old guideline
## -1 473 97
## 0 590 125
## 1 617 132chisq.test(subset_barth$s_funding_1_2, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_funding_1_2 and subset_barth$version
## X-squared = 0.087389, df = 2, p-value = 0.9572prop.table(table(subset_barth_version_old$s_funding_1_2))##
## -1 0 1
## 0.2740113 0.3531073 0.3728814prop.table(table(subset_barth_version_new$s_funding_1_2))##
## -1 0 1
## 0.2815476 0.3511905 0.3672619Awareness Check Pass
table(subset_barth_pass$s_funding_1_2, subset_barth_pass$version)##
## old guideline new guideline
## -1 49 246
## 0 90 404
## 1 107 483chisq.test(subset_barth_pass$s_funding_1_2, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_funding_1_2 and subset_barth_pass$version
## X-squared = 0.38791, df = 2, p-value = 0.8237prop.table(table(subset_barth_version_old_pass$s_funding_1_2))##
## -1 0 1
## 0.1991870 0.3658537 0.4349593prop.table(table(subset_barth_version_new_pass$s_funding_1_2))##
## -1 0 1
## 0.2171227 0.3565755 0.4263019T1, Faerber et al.
table(subset_faerber$s_funding_1_2, subset_faerber$version)##
## new guideline old guideline
## -1 473 97
## 0 590 125
## 1 617 132chisq.test(subset_faerber$s_funding_1_2, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_funding_1_2 and subset_faerber$version
## X-squared = 0.087389, df = 2, p-value = 0.9572prop.table(table(subset_faerber_version_old$s_funding_1_2))##
## -1 0 1
## 0.2740113 0.3531073 0.3728814prop.table(table(subset_faerber_version_new$s_funding_1_2))##
## -1 0 1
## 0.2815476 0.3511905 0.3672619Awareness Check Pass
table(subset_faerber_pass$s_funding_1_2, subset_faerber_pass$version)##
## old guideline new guideline
## -1 49 246
## 0 90 404
## 1 107 483chisq.test(subset_faerber_pass$s_funding_1_2, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_funding_1_2 and subset_faerber_pass$version
## X-squared = 0.38791, df = 2, p-value = 0.8237prop.table(table(subset_faerber_version_old_pass$s_funding_1_2))##
## -1 0 1
## 0.1991870 0.3658537 0.4349593prop.table(table(subset_faerber_version_new_pass$s_funding_1_2))##
## -1 0 1
## 0.2171227 0.3565755 0.4263019T2
table(data2_long$s_funding_2_2,data2_long$version)##
## new guideline old guideline
## -1 768 160
## 0 922 190
## 1 1656 362chisq.test(data2_long$s_funding_2_2, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_funding_2_2 and data2_long$version
## X-squared = 0.43688, df = 2, p-value = 0.8038prop.table(table(data2_long_version_old$s_funding_2_2))##
## -1 0 1
## 0.2247191 0.2668539 0.5084270prop.table(table(data2_long_version_new$s_funding_2_2))##
## -1 0 1
## 0.2295278 0.2755529 0.4949193Awareness Check Pass
table(data2_long_pass$s_funding_2_2,data2_long_pass$version)##
## old guideline new guideline
## -1 80 378
## 0 124 542
## 1 288 1336chisq.test(data2_long_pass$s_funding_2_2, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_funding_2_2 and data2_long_pass$version
## X-squared = 0.32277, df = 2, p-value = 0.851prop.table(table(data2_long_version_old_pass$s_funding_2_2))##
## -1 0 1
## 0.1626016 0.2520325 0.5853659prop.table(table(data2_long_version_new_pass$s_funding_2_2))##
## -1 0 1
## 0.1675532 0.2402482 0.5921986T2, Barth et al.
table(subset_barth$s_funding_2_2, subset_barth$version)##
## new guideline old guideline
## -1 384 80
## 0 461 95
## 1 828 181chisq.test(subset_barth$s_funding_2_2, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_funding_2_2 and subset_barth$version
## X-squared = 0.21844, df = 2, p-value = 0.8965prop.table(table(subset_barth_version_old$s_funding_2_2))##
## -1 0 1
## 0.2247191 0.2668539 0.5084270prop.table(table(subset_barth_version_new$s_funding_2_2))##
## -1 0 1
## 0.2295278 0.2755529 0.4949193Awareness Check Pass
table(subset_barth_pass$s_funding_2_2, subset_barth_pass$version)##
## old guideline new guideline
## -1 40 189
## 0 62 271
## 1 144 668chisq.test(subset_barth_pass$s_funding_2_2, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_funding_2_2 and subset_barth_pass$version
## X-squared = 0.16138, df = 2, p-value = 0.9225prop.table(table(subset_barth_version_old_pass$s_funding_2_2))##
## -1 0 1
## 0.1626016 0.2520325 0.5853659prop.table(table(subset_barth_version_new_pass$s_funding_2_2))##
## -1 0 1
## 0.1675532 0.2402482 0.5921986T2, Faerber et al.
table(subset_faerber$s_funding_2_2, subset_faerber$version)##
## new guideline old guideline
## -1 384 80
## 0 461 95
## 1 828 181chisq.test(subset_faerber$s_funding_2_2, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_funding_2_2 and subset_faerber$version
## X-squared = 0.21844, df = 2, p-value = 0.8965prop.table(table(subset_faerber_version_old$s_funding_2_2))##
## -1 0 1
## 0.2247191 0.2668539 0.5084270prop.table(table(subset_faerber_version_new$s_funding_2_2))##
## -1 0 1
## 0.2295278 0.2755529 0.4949193Awareness Check Pass
table(subset_faerber_pass$s_funding_2_2, subset_faerber_pass$version)##
## old guideline new guideline
## -1 40 189
## 0 62 271
## 1 144 668chisq.test(subset_faerber_pass$s_funding_2_2, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_funding_2_2 and subset_faerber_pass$version
## X-squared = 0.16138, df = 2, p-value = 0.9225prop.table(table(subset_faerber_version_old_pass$s_funding_2_2))##
## -1 0 1
## 0.1626016 0.2520325 0.5853659prop.table(table(subset_faerber_version_new_pass$s_funding_2_2))##
## -1 0 1
## 0.1675532 0.2402482 0.5921986Item 3
T1
table(data2_long$s_funding_1_3,data2_long$version)##
## new guideline old guideline
## -1 720 142
## 0 1140 252
## 1 1488 314chisq.test(data2_long$s_funding_1_3, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_funding_1_3 and data2_long$version
## X-squared = 0.98388, df = 2, p-value = 0.6114prop.table(table(data2_long_version_old$s_funding_1_3))##
## -1 0 1
## 0.2005650 0.3559322 0.4435028prop.table(table(data2_long_version_new$s_funding_1_3))##
## -1 0 1
## 0.2150538 0.3405018 0.4444444Awareness Check Pass
table(data2_long_pass$s_funding_1_3,data2_long_pass$version)##
## old guideline new guideline
## -1 80 380
## 0 166 756
## 1 244 1122chisq.test(data2_long_pass$s_funding_1_3, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_funding_1_3 and data2_long_pass$version
## X-squared = 0.080523, df = 2, p-value = 0.9605prop.table(table(data2_long_version_old_pass$s_funding_1_3))##
## -1 0 1
## 0.1632653 0.3387755 0.4979592prop.table(table(data2_long_version_new_pass$s_funding_1_3))##
## -1 0 1
## 0.1682905 0.3348096 0.4968999T1, Barth et al.
table(subset_barth$s_funding_1_3, subset_barth$version)##
## new guideline old guideline
## -1 360 71
## 0 570 126
## 1 744 157chisq.test(subset_barth$s_funding_1_3, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_funding_1_3 and subset_barth$version
## X-squared = 0.49194, df = 2, p-value = 0.7819prop.table(table(subset_barth_version_old$s_funding_1_3))##
## -1 0 1
## 0.2005650 0.3559322 0.4435028prop.table(table(subset_barth_version_new$s_funding_1_3))##
## -1 0 1
## 0.2150538 0.3405018 0.4444444Awareness Check Pass
table(subset_barth_pass$s_funding_1_3, subset_barth_pass$version)##
## old guideline new guideline
## -1 40 190
## 0 83 378
## 1 122 561chisq.test(subset_barth_pass$s_funding_1_3, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_funding_1_3 and subset_barth_pass$version
## X-squared = 0.040262, df = 2, p-value = 0.9801prop.table(table(subset_barth_version_old_pass$s_funding_1_3))##
## -1 0 1
## 0.1632653 0.3387755 0.4979592prop.table(table(subset_barth_version_new_pass$s_funding_1_3))##
## -1 0 1
## 0.1682905 0.3348096 0.4968999T1, Faerber et al.
table(subset_faerber$s_funding_1_3, subset_faerber$version)##
## new guideline old guideline
## -1 360 71
## 0 570 126
## 1 744 157chisq.test(subset_faerber$s_funding_1_3, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_funding_1_3 and subset_faerber$version
## X-squared = 0.49194, df = 2, p-value = 0.7819prop.table(table(subset_faerber_version_old$s_funding_1_3))##
## -1 0 1
## 0.2005650 0.3559322 0.4435028prop.table(table(subset_faerber_version_new$s_funding_1_3))##
## -1 0 1
## 0.2150538 0.3405018 0.4444444Awareness Check Pass
table(subset_faerber_pass$s_funding_1_3, subset_faerber_pass$version)##
## old guideline new guideline
## -1 40 190
## 0 83 378
## 1 122 561chisq.test(subset_faerber_pass$s_funding_1_3, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_funding_1_3 and subset_faerber_pass$version
## X-squared = 0.040262, df = 2, p-value = 0.9801prop.table(table(subset_faerber_version_old_pass$s_funding_1_3))##
## -1 0 1
## 0.1632653 0.3387755 0.4979592prop.table(table(subset_faerber_version_new_pass$s_funding_1_3))##
## -1 0 1
## 0.1682905 0.3348096 0.4968999T2
table(data2_long$s_funding_2_3,data2_long$version)##
## new guideline old guideline
## -1 674 134
## 0 880 202
## 1 1802 376chisq.test(data2_long$s_funding_2_3, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_funding_2_3 and data2_long$version
## X-squared = 1.578, df = 2, p-value = 0.4543prop.table(table(data2_long_version_old$s_funding_2_3))##
## -1 0 1
## 0.1882022 0.2837079 0.5280899prop.table(table(data2_long_version_new$s_funding_2_3))##
## -1 0 1
## 0.2008343 0.2622169 0.5369487Awareness Check Pass
table(data2_long_pass$s_funding_2_3,data2_long_pass$version)##
## old guideline new guideline
## -1 60 302
## 0 130 520
## 1 304 1438chisq.test(data2_long_pass$s_funding_2_3, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_funding_2_3 and data2_long_pass$version
## X-squared = 2.6151, df = 2, p-value = 0.2705prop.table(table(data2_long_version_old_pass$s_funding_2_3))##
## -1 0 1
## 0.1214575 0.2631579 0.6153846prop.table(table(data2_long_version_new_pass$s_funding_2_3))##
## -1 0 1
## 0.1336283 0.2300885 0.6362832T2, Barth et al.
table(subset_barth$s_funding_2_3, subset_barth$version)##
## new guideline old guideline
## -1 337 67
## 0 440 101
## 1 901 188chisq.test(subset_barth$s_funding_2_3, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_funding_2_3 and subset_barth$version
## X-squared = 0.78898, df = 2, p-value = 0.674prop.table(table(subset_barth_version_old$s_funding_2_3))##
## -1 0 1
## 0.1882022 0.2837079 0.5280899prop.table(table(subset_barth_version_new$s_funding_2_3))##
## -1 0 1
## 0.2008343 0.2622169 0.5369487Awareness Check Pass
table(subset_barth_pass$s_funding_2_3, subset_barth_pass$version)##
## old guideline new guideline
## -1 30 151
## 0 65 260
## 1 152 719chisq.test(subset_barth_pass$s_funding_2_3, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_funding_2_3 and subset_barth_pass$version
## X-squared = 1.3075, df = 2, p-value = 0.5201prop.table(table(subset_barth_version_old_pass$s_funding_2_3))##
## -1 0 1
## 0.1214575 0.2631579 0.6153846prop.table(table(subset_barth_version_new_pass$s_funding_2_3))##
## -1 0 1
## 0.1336283 0.2300885 0.6362832T2, Faerber et al.
table(subset_faerber$s_funding_2_3, subset_faerber$version)##
## new guideline old guideline
## -1 337 67
## 0 440 101
## 1 901 188chisq.test(subset_faerber$s_funding_2_3, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_funding_2_3 and subset_faerber$version
## X-squared = 0.78898, df = 2, p-value = 0.674prop.table(table(subset_faerber_version_old$s_funding_2_3))##
## -1 0 1
## 0.1882022 0.2837079 0.5280899prop.table(table(subset_faerber_version_new$s_funding_2_3))##
## -1 0 1
## 0.2008343 0.2622169 0.5369487Awareness Check Pass
table(subset_faerber_pass$s_funding_2_3, subset_faerber_pass$version)##
## old guideline new guideline
## -1 30 151
## 0 65 260
## 1 152 719chisq.test(subset_faerber_pass$s_funding_2_3, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_funding_2_3 and subset_faerber_pass$version
## X-squared = 1.3075, df = 2, p-value = 0.5201prop.table(table(subset_faerber_version_old_pass$s_funding_2_3))##
## -1 0 1
## 0.1214575 0.2631579 0.6153846prop.table(table(subset_faerber_version_new_pass$s_funding_2_3))##
## -1 0 1
## 0.1336283 0.2300885 0.6362832Item 4
T1
table(data2_long$s_funding_1_4,data2_long$version)##
## new guideline old guideline
## -1 832 164
## 0 1150 228
## 1 1370 318chisq.test(data2_long$s_funding_1_4, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_funding_1_4 and data2_long$version
## X-squared = 3.705, df = 2, p-value = 0.1568prop.table(table(data2_long_version_old$s_funding_1_4))##
## -1 0 1
## 0.2309859 0.3211268 0.4478873prop.table(table(data2_long_version_new$s_funding_1_4))##
## -1 0 1
## 0.2482100 0.3430788 0.4087112Awareness Check Pass
table(data2_long_pass$s_funding_1_4,data2_long_pass$version)##
## old guideline new guideline
## -1 96 436
## 0 152 770
## 1 244 1050chisq.test(data2_long_pass$s_funding_1_4, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_funding_1_4 and data2_long_pass$version
## X-squared = 2.067, df = 2, p-value = 0.3558prop.table(table(data2_long_version_old_pass$s_funding_1_4))##
## -1 0 1
## 0.1951220 0.3089431 0.4959350prop.table(table(data2_long_version_new_pass$s_funding_1_4))##
## -1 0 1
## 0.1932624 0.3413121 0.4654255T1, Barth et al.
table(subset_barth$s_funding_1_4, subset_barth$version)##
## new guideline old guideline
## -1 416 82
## 0 575 114
## 1 685 159chisq.test(subset_barth$s_funding_1_4, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_funding_1_4 and subset_barth$version
## X-squared = 1.8525, df = 2, p-value = 0.396prop.table(table(subset_barth_version_old$s_funding_1_4))##
## -1 0 1
## 0.2309859 0.3211268 0.4478873prop.table(table(subset_barth_version_new$s_funding_1_4))##
## -1 0 1
## 0.2482100 0.3430788 0.4087112Awareness Check Pass
table(subset_barth_pass$s_funding_1_4, subset_barth_pass$version)##
## old guideline new guideline
## -1 48 218
## 0 76 385
## 1 122 525chisq.test(subset_barth_pass$s_funding_1_4, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_funding_1_4 and subset_barth_pass$version
## X-squared = 1.0335, df = 2, p-value = 0.5965prop.table(table(subset_barth_version_old_pass$s_funding_1_4))##
## -1 0 1
## 0.1951220 0.3089431 0.4959350prop.table(table(subset_barth_version_new_pass$s_funding_1_4))##
## -1 0 1
## 0.1932624 0.3413121 0.4654255T1, Faerber et al.
table(subset_faerber$s_funding_1_4, subset_faerber$version)##
## new guideline old guideline
## -1 416 82
## 0 575 114
## 1 685 159chisq.test(subset_faerber$s_funding_1_4, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_funding_1_4 and subset_faerber$version
## X-squared = 1.8525, df = 2, p-value = 0.396prop.table(table(subset_faerber_version_old$s_funding_1_4))##
## -1 0 1
## 0.2309859 0.3211268 0.4478873prop.table(table(subset_faerber_version_new$s_funding_1_4))##
## -1 0 1
## 0.2482100 0.3430788 0.4087112Awareness Check Pass
table(subset_faerber_pass$s_funding_1_4, subset_faerber_pass$version)##
## old guideline new guideline
## -1 48 218
## 0 76 385
## 1 122 525chisq.test(subset_faerber_pass$s_funding_1_4, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_funding_1_4 and subset_faerber_pass$version
## X-squared = 1.0335, df = 2, p-value = 0.5965prop.table(table(subset_faerber_version_old_pass$s_funding_1_4))##
## -1 0 1
## 0.1951220 0.3089431 0.4959350prop.table(table(subset_faerber_version_new_pass$s_funding_1_4))##
## -1 0 1
## 0.1932624 0.3413121 0.4654255T2
table(data2_long$s_funding_2_4,data2_long$version)##
## new guideline old guideline
## -1 826 182
## 0 882 180
## 1 1648 352chisq.test(data2_long$s_funding_2_4, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_funding_2_4 and data2_long$version
## X-squared = 0.44647, df = 2, p-value = 0.7999prop.table(table(data2_long_version_old$s_funding_2_4))##
## -1 0 1
## 0.2549020 0.2521008 0.4929972prop.table(table(data2_long_version_new$s_funding_2_4))##
## -1 0 1
## 0.2461263 0.2628129 0.4910608Awareness Check Pass
table(data2_long_pass$s_funding_2_4,data2_long_pass$version)##
## old guideline new guideline
## -1 110 472
## 0 110 508
## 1 274 1284chisq.test(data2_long_pass$s_funding_2_4, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_funding_2_4 and data2_long_pass$version
## X-squared = 0.50415, df = 2, p-value = 0.7772prop.table(table(data2_long_version_old_pass$s_funding_2_4))##
## -1 0 1
## 0.2226721 0.2226721 0.5546559prop.table(table(data2_long_version_new_pass$s_funding_2_4))##
## -1 0 1
## 0.2084806 0.2243816 0.5671378T2, Barth et al.
table(subset_barth$s_funding_2_4, subset_barth$version)##
## new guideline old guideline
## -1 413 91
## 0 441 90
## 1 824 176chisq.test(subset_barth$s_funding_2_4, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_funding_2_4 and subset_barth$version
## X-squared = 0.22323, df = 2, p-value = 0.8944prop.table(table(subset_barth_version_old$s_funding_2_4))##
## -1 0 1
## 0.2549020 0.2521008 0.4929972prop.table(table(subset_barth_version_new$s_funding_2_4))##
## -1 0 1
## 0.2461263 0.2628129 0.4910608Awareness Check Pass
table(subset_barth_pass$s_funding_2_4, subset_barth_pass$version)##
## old guideline new guideline
## -1 55 236
## 0 55 254
## 1 137 642chisq.test(subset_barth_pass$s_funding_2_4, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_funding_2_4 and subset_barth_pass$version
## X-squared = 0.25208, df = 2, p-value = 0.8816prop.table(table(subset_barth_version_old_pass$s_funding_2_4))##
## -1 0 1
## 0.2226721 0.2226721 0.5546559prop.table(table(subset_barth_version_new_pass$s_funding_2_4))##
## -1 0 1
## 0.2084806 0.2243816 0.5671378T2, Faerber et al.
table(subset_faerber$s_funding_2_4, subset_faerber$version)##
## new guideline old guideline
## -1 413 91
## 0 441 90
## 1 824 176chisq.test(subset_faerber$s_funding_2_4, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_funding_2_4 and subset_faerber$version
## X-squared = 0.22323, df = 2, p-value = 0.8944prop.table(table(subset_faerber_version_old$s_funding_2_4))##
## -1 0 1
## 0.2549020 0.2521008 0.4929972prop.table(table(subset_faerber_version_new$s_funding_2_4))##
## -1 0 1
## 0.2461263 0.2628129 0.4910608Awareness Check Pass
table(subset_faerber_pass$s_funding_2_4, subset_faerber_pass$version)##
## old guideline new guideline
## -1 55 236
## 0 55 254
## 1 137 642chisq.test(subset_faerber_pass$s_funding_2_4, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_funding_2_4 and subset_faerber_pass$version
## X-squared = 0.25208, df = 2, p-value = 0.8816prop.table(table(subset_faerber_version_old_pass$s_funding_2_4))##
## -1 0 1
## 0.2226721 0.2226721 0.5546559prop.table(table(subset_faerber_version_new_pass$s_funding_2_4))##
## -1 0 1
## 0.2084806 0.2243816 0.5671378Item 5
T1
table(data2_long$s_funding_1_5,data2_long$version)##
## new guideline old guideline
## -1 844 180
## 0 1288 262
## 1 1220 266chisq.test(data2_long$s_funding_1_5, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_funding_1_5 and data2_long$version
## X-squared = 0.54254, df = 2, p-value = 0.7624prop.table(table(data2_long_version_old$s_funding_1_5))##
## -1 0 1
## 0.2542373 0.3700565 0.3757062prop.table(table(data2_long_version_new$s_funding_1_5))##
## -1 0 1
## 0.2517900 0.3842482 0.3639618Awareness Check Pass
table(data2_long_pass$s_funding_1_5,data2_long_pass$version)##
## old guideline new guideline
## -1 102 474
## 0 178 878
## 1 212 910chisq.test(data2_long_pass$s_funding_1_5, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_funding_1_5 and data2_long_pass$version
## X-squared = 1.5532, df = 2, p-value = 0.46prop.table(table(data2_long_version_old_pass$s_funding_1_5))##
## -1 0 1
## 0.2073171 0.3617886 0.4308943prop.table(table(data2_long_version_new_pass$s_funding_1_5))##
## -1 0 1
## 0.2095491 0.3881521 0.4022989T1, Barth et al.
table(subset_barth$s_funding_1_5, subset_barth$version)##
## new guideline old guideline
## -1 422 90
## 0 644 131
## 1 610 133chisq.test(subset_barth$s_funding_1_5, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_funding_1_5 and subset_barth$version
## X-squared = 0.27127, df = 2, p-value = 0.8732prop.table(table(subset_barth_version_old$s_funding_1_5))##
## -1 0 1
## 0.2542373 0.3700565 0.3757062prop.table(table(subset_barth_version_new$s_funding_1_5))##
## -1 0 1
## 0.2517900 0.3842482 0.3639618Awareness Check Pass
table(subset_barth_pass$s_funding_1_5, subset_barth_pass$version)##
## old guideline new guideline
## -1 51 237
## 0 89 439
## 1 106 455chisq.test(subset_barth_pass$s_funding_1_5, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_funding_1_5 and subset_barth_pass$version
## X-squared = 0.77659, df = 2, p-value = 0.6782prop.table(table(subset_barth_version_old_pass$s_funding_1_5))##
## -1 0 1
## 0.2073171 0.3617886 0.4308943prop.table(table(subset_barth_version_new_pass$s_funding_1_5))##
## -1 0 1
## 0.2095491 0.3881521 0.4022989T1, Faerber et al.
table(subset_faerber$s_funding_1_5, subset_faerber$version)##
## new guideline old guideline
## -1 422 90
## 0 644 131
## 1 610 133chisq.test(subset_faerber$s_funding_1_5, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_funding_1_5 and subset_faerber$version
## X-squared = 0.27127, df = 2, p-value = 0.8732prop.table(table(subset_faerber_version_old$s_funding_1_5))##
## -1 0 1
## 0.2542373 0.3700565 0.3757062prop.table(table(subset_faerber_version_new$s_funding_1_5))##
## -1 0 1
## 0.2517900 0.3842482 0.3639618Awareness Check Pass
table(subset_faerber_pass$s_funding_1_5, subset_faerber_pass$version)##
## old guideline new guideline
## -1 51 237
## 0 89 439
## 1 106 455chisq.test(subset_faerber_pass$s_funding_1_5, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_funding_1_5 and subset_faerber_pass$version
## X-squared = 0.77659, df = 2, p-value = 0.6782prop.table(table(subset_faerber_version_old_pass$s_funding_1_5))##
## -1 0 1
## 0.2073171 0.3617886 0.4308943prop.table(table(subset_faerber_version_new_pass$s_funding_1_5))##
## -1 0 1
## 0.2095491 0.3881521 0.4022989T2
table(data2_long$s_funding_2_5,data2_long$version)##
## new guideline old guideline
## -1 864 204
## 0 912 190
## 1 1576 320chisq.test(data2_long$s_funding_2_5, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_funding_2_5 and data2_long$version
## X-squared = 2.4393, df = 2, p-value = 0.2953prop.table(table(data2_long_version_old$s_funding_2_5))##
## -1 0 1
## 0.2857143 0.2661064 0.4481793prop.table(table(data2_long_version_new$s_funding_2_5))##
## -1 0 1
## 0.2577566 0.2720764 0.4701671Awareness Check Pass
table(data2_long_pass$s_funding_2_5,data2_long_pass$version)##
## old guideline new guideline
## -1 132 466
## 0 112 540
## 1 250 1254chisq.test(data2_long_pass$s_funding_2_5, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_funding_2_5 and data2_long_pass$version
## X-squared = 8.9726, df = 2, p-value = 0.01126prop.table(table(data2_long_version_old_pass$s_funding_2_5))##
## -1 0 1
## 0.2672065 0.2267206 0.5060729prop.table(table(data2_long_version_new_pass$s_funding_2_5))##
## -1 0 1
## 0.2061947 0.2389381 0.5548673T2, Barth et al.
table(subset_barth$s_funding_2_5, subset_barth$version)##
## new guideline old guideline
## -1 432 102
## 0 456 95
## 1 788 160chisq.test(subset_barth$s_funding_2_5, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_funding_2_5 and subset_barth$version
## X-squared = 1.2196, df = 2, p-value = 0.5434prop.table(table(subset_barth_version_old$s_funding_2_5))##
## -1 0 1
## 0.2857143 0.2661064 0.4481793prop.table(table(subset_barth_version_new$s_funding_2_5))##
## -1 0 1
## 0.2577566 0.2720764 0.4701671Awareness Check Pass
table(subset_barth_pass$s_funding_2_5, subset_barth_pass$version)##
## old guideline new guideline
## -1 66 233
## 0 56 270
## 1 125 627chisq.test(subset_barth_pass$s_funding_2_5, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_funding_2_5 and subset_barth_pass$version
## X-squared = 4.4863, df = 2, p-value = 0.1061prop.table(table(subset_barth_version_old_pass$s_funding_2_5))##
## -1 0 1
## 0.2672065 0.2267206 0.5060729prop.table(table(subset_barth_version_new_pass$s_funding_2_5))##
## -1 0 1
## 0.2061947 0.2389381 0.5548673T2, Faerber et al.
table(subset_faerber$s_funding_2_5, subset_faerber$version)##
## new guideline old guideline
## -1 432 102
## 0 456 95
## 1 788 160chisq.test(subset_faerber$s_funding_2_5, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_funding_2_5 and subset_faerber$version
## X-squared = 1.2196, df = 2, p-value = 0.5434prop.table(table(subset_faerber_version_old$s_funding_2_5))##
## -1 0 1
## 0.2857143 0.2661064 0.4481793prop.table(table(subset_faerber_version_new$s_funding_2_5))##
## -1 0 1
## 0.2577566 0.2720764 0.4701671Awareness Check Pass
table(subset_faerber_pass$s_funding_2_5, subset_faerber_pass$version)##
## old guideline new guideline
## -1 66 233
## 0 56 270
## 1 125 627chisq.test(subset_faerber_pass$s_funding_2_5, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_funding_2_5 and subset_faerber_pass$version
## X-squared = 4.4863, df = 2, p-value = 0.1061prop.table(table(subset_faerber_version_old_pass$s_funding_2_5))##
## -1 0 1
## 0.2672065 0.2267206 0.5060729prop.table(table(subset_faerber_version_new_pass$s_funding_2_5))##
## -1 0 1
## 0.2061947 0.2389381 0.5548673Item 6
T1
table(data2_long$s_funding_1_6,data2_long$version)##
## new guideline old guideline
## -1 702 152
## 0 1186 244
## 1 1460 308chisq.test(data2_long$s_funding_1_6, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_funding_1_6 and data2_long$version
## X-squared = 0.20633, df = 2, p-value = 0.902prop.table(table(data2_long_version_old$s_funding_1_6))##
## -1 0 1
## 0.2159091 0.3465909 0.4375000prop.table(table(data2_long_version_new$s_funding_1_6))##
## -1 0 1
## 0.2096774 0.3542413 0.4360812Awareness Check Pass
table(data2_long_pass$s_funding_1_6,data2_long_pass$version)##
## old guideline new guideline
## -1 92 376
## 0 162 774
## 1 234 1104chisq.test(data2_long_pass$s_funding_1_6, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_funding_1_6 and data2_long_pass$version
## X-squared = 1.3481, df = 2, p-value = 0.5096prop.table(table(data2_long_version_old_pass$s_funding_1_6))##
## -1 0 1
## 0.1885246 0.3319672 0.4795082prop.table(table(data2_long_version_new_pass$s_funding_1_6))##
## -1 0 1
## 0.1668146 0.3433895 0.4897959T1, Barth et al.
table(subset_barth$s_funding_1_6, subset_barth$version)##
## new guideline old guideline
## -1 351 76
## 0 593 122
## 1 730 154chisq.test(subset_barth$s_funding_1_6, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_funding_1_6 and subset_barth$version
## X-squared = 0.10317, df = 2, p-value = 0.9497prop.table(table(subset_barth_version_old$s_funding_1_6))##
## -1 0 1
## 0.2159091 0.3465909 0.4375000prop.table(table(subset_barth_version_new$s_funding_1_6))##
## -1 0 1
## 0.2096774 0.3542413 0.4360812Awareness Check Pass
table(subset_barth_pass$s_funding_1_6, subset_barth_pass$version)##
## old guideline new guideline
## -1 46 188
## 0 81 387
## 1 117 552chisq.test(subset_barth_pass$s_funding_1_6, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_funding_1_6 and subset_barth_pass$version
## X-squared = 0.67405, df = 2, p-value = 0.7139prop.table(table(subset_barth_version_old_pass$s_funding_1_6))##
## -1 0 1
## 0.1885246 0.3319672 0.4795082prop.table(table(subset_barth_version_new_pass$s_funding_1_6))##
## -1 0 1
## 0.1668146 0.3433895 0.4897959T1, Faerber et al.
table(subset_faerber$s_funding_1_6, subset_faerber$version)##
## new guideline old guideline
## -1 351 76
## 0 593 122
## 1 730 154chisq.test(subset_faerber$s_funding_1_6, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_funding_1_6 and subset_faerber$version
## X-squared = 0.10317, df = 2, p-value = 0.9497prop.table(table(subset_faerber_version_old$s_funding_1_6))##
## -1 0 1
## 0.2159091 0.3465909 0.4375000prop.table(table(subset_faerber_version_new$s_funding_1_6))##
## -1 0 1
## 0.2096774 0.3542413 0.4360812Awareness Check Pass
table(subset_faerber_pass$s_funding_1_6, subset_faerber_pass$version)##
## old guideline new guideline
## -1 46 188
## 0 81 387
## 1 117 552chisq.test(subset_faerber_pass$s_funding_1_6, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_funding_1_6 and subset_faerber_pass$version
## X-squared = 0.67405, df = 2, p-value = 0.7139prop.table(table(subset_faerber_version_old_pass$s_funding_1_6))##
## -1 0 1
## 0.1885246 0.3319672 0.4795082prop.table(table(subset_faerber_version_new_pass$s_funding_1_6))##
## -1 0 1
## 0.1668146 0.3433895 0.4897959T2
table(data2_long$s_funding_1_6,data2_long$version)##
## new guideline old guideline
## -1 702 152
## 0 1186 244
## 1 1460 308chisq.test(data2_long$s_funding_2_6, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_funding_2_6 and data2_long$version
## X-squared = 1.9534, df = 2, p-value = 0.3766prop.table(table(data2_long_version_old$s_funding_2_6))##
## -1 0 1
## 0.1680672 0.2689076 0.5630252prop.table(table(data2_long_version_new$s_funding_2_6))##
## -1 0 1
## 0.1886567 0.2716418 0.5397015Awareness Check Pass
table(data2_long_pass$s_funding_2_6,data2_long_pass$version)##
## old guideline new guideline
## -1 60 312
## 0 116 548
## 1 318 1400chisq.test(data2_long_pass$s_funding_2_6, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_funding_2_6 and data2_long_pass$version
## X-squared = 1.3076, df = 2, p-value = 0.5201prop.table(table(data2_long_version_old_pass$s_funding_2_6))##
## -1 0 1
## 0.1214575 0.2348178 0.6437247prop.table(table(data2_long_version_new_pass$s_funding_2_6))##
## -1 0 1
## 0.1380531 0.2424779 0.6194690T2, Barth et al.
table(subset_barth$s_funding_2_6, subset_barth$version)##
## new guideline old guideline
## -1 316 60
## 0 455 96
## 1 904 201chisq.test(subset_barth$s_funding_2_6, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_funding_2_6 and subset_barth$version
## X-squared = 0.9767, df = 2, p-value = 0.6136prop.table(table(subset_barth_version_old$s_funding_2_6))##
## -1 0 1
## 0.1680672 0.2689076 0.5630252prop.table(table(subset_barth_version_new$s_funding_2_6))##
## -1 0 1
## 0.1886567 0.2716418 0.5397015Awareness Check Pass
table(subset_barth_pass$s_funding_2_6, subset_barth_pass$version)##
## old guideline new guideline
## -1 30 156
## 0 58 274
## 1 159 700chisq.test(subset_barth_pass$s_funding_2_6, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_funding_2_6 and subset_barth_pass$version
## X-squared = 0.65378, df = 2, p-value = 0.7212prop.table(table(subset_barth_version_old_pass$s_funding_2_6))##
## -1 0 1
## 0.1214575 0.2348178 0.6437247prop.table(table(subset_barth_version_new_pass$s_funding_2_6))##
## -1 0 1
## 0.1380531 0.2424779 0.6194690T2, Faerber et al.
table(subset_faerber$s_funding_2_6, subset_faerber$version)##
## new guideline old guideline
## -1 316 60
## 0 455 96
## 1 904 201chisq.test(subset_faerber$s_funding_2_6, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_funding_2_6 and subset_faerber$version
## X-squared = 0.9767, df = 2, p-value = 0.6136prop.table(table(subset_faerber_version_old$s_funding_2_6))##
## -1 0 1
## 0.1680672 0.2689076 0.5630252prop.table(table(subset_faerber_version_new$s_funding_2_6))##
## -1 0 1
## 0.1886567 0.2716418 0.5397015Awareness Check Pass
table(subset_faerber_pass$s_funding_2_6, subset_faerber_pass$version)##
## old guideline new guideline
## -1 30 156
## 0 58 274
## 1 159 700chisq.test(subset_faerber_pass$s_funding_2_6, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_funding_2_6 and subset_faerber_pass$version
## X-squared = 0.65378, df = 2, p-value = 0.7212prop.table(table(subset_faerber_version_old_pass$s_funding_2_6))##
## -1 0 1
## 0.1214575 0.2348178 0.6437247prop.table(table(subset_faerber_version_new_pass$s_funding_2_6))##
## -1 0 1
## 0.1380531 0.2424779 0.6194690COI-Item
Item 1
T1
table(data2_long$s_coi_1_1,data2_long$version)##
## new guideline old guideline
## -1 998 198
## 0 1138 218
## 1 1220 286chisq.test(data2_long$s_coi_1_1, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_coi_1_1 and data2_long$version
## X-squared = 4.8912, df = 2, p-value = 0.08668prop.table(table(data2_long_version_old$s_coi_1_1))##
## -1 0 1
## 0.2820513 0.3105413 0.4074074prop.table(table(data2_long_version_new$s_coi_1_1))##
## -1 0 1
## 0.2973778 0.3390942 0.3635280Awareness Check Pass
table(data2_long_pass$s_coi_1_1,data2_long_pass$version)##
## old guideline new guideline
## -1 100 554
## 0 162 756
## 1 226 954chisq.test(data2_long_pass$s_coi_1_1, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_coi_1_1 and data2_long_pass$version
## X-squared = 4.3091, df = 2, p-value = 0.116prop.table(table(data2_long_version_old_pass$s_coi_1_1))##
## -1 0 1
## 0.2049180 0.3319672 0.4631148prop.table(table(data2_long_version_new_pass$s_coi_1_1))##
## -1 0 1
## 0.2446996 0.3339223 0.4213781T1 Barth et al.
table(subset_barth$s_coi_1_1, subset_barth$version)##
## new guideline old guideline
## -1 499 99
## 0 569 109
## 1 610 143chisq.test(subset_barth$s_coi_1_1, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_coi_1_1 and subset_barth$version
## X-squared = 2.4456, df = 2, p-value = 0.2944prop.table(table(subset_barth_version_old$s_coi_1_1))##
## -1 0 1
## 0.2820513 0.3105413 0.4074074prop.table(table(subset_barth_version_new$s_coi_1_1))##
## -1 0 1
## 0.2973778 0.3390942 0.3635280Awareness Check Pass
table(subset_barth_pass$s_coi_1_1, subset_barth_pass$version)##
## old guideline new guideline
## -1 50 277
## 0 81 378
## 1 113 477chisq.test(subset_barth_pass$s_coi_1_1, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_coi_1_1 and subset_barth_pass$version
## X-squared = 2.1546, df = 2, p-value = 0.3405prop.table(table(subset_barth_version_old_pass$s_coi_1_1))##
## -1 0 1
## 0.2049180 0.3319672 0.4631148prop.table(table(subset_barth_version_new_pass$s_coi_1_1))##
## -1 0 1
## 0.2446996 0.3339223 0.4213781T1 Faerber et al.
table(subset_faerber$s_coi_1_1, subset_faerber$version)##
## new guideline old guideline
## -1 499 99
## 0 569 109
## 1 610 143chisq.test(subset_faerber$s_coi_1_1, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_coi_1_1 and subset_faerber$version
## X-squared = 2.4456, df = 2, p-value = 0.2944prop.table(table(subset_faerber_version_old$s_coi_1_1))##
## -1 0 1
## 0.2820513 0.3105413 0.4074074prop.table(table(subset_faerber_version_new$s_coi_1_1))##
## -1 0 1
## 0.2973778 0.3390942 0.3635280Awareness Check Pass
table(subset_faerber_pass$s_coi_1_1, subset_faerber_pass$version)##
## old guideline new guideline
## -1 50 277
## 0 81 378
## 1 113 477chisq.test(subset_faerber_pass$s_coi_1_1, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_coi_1_1 and subset_faerber_pass$version
## X-squared = 2.1546, df = 2, p-value = 0.3405prop.table(table(subset_faerber_version_old_pass$s_coi_1_1))##
## -1 0 1
## 0.2049180 0.3319672 0.4631148prop.table(table(subset_faerber_version_new_pass$s_coi_1_1))##
## -1 0 1
## 0.2446996 0.3339223 0.4213781T2
table(data2_long$s_coi_2_1,data2_long$version)##
## new guideline old guideline
## -1 946 176
## 0 902 158
## 1 1510 380chisq.test(data2_long$s_coi_2_1, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_coi_2_1 and data2_long$version
## X-squared = 16.359, df = 2, p-value = 0.0002804prop.table(table(data2_long_version_old$s_coi_2_1))##
## -1 0 1
## 0.2464986 0.2212885 0.5322129prop.table(table(data2_long_version_new$s_coi_2_1))##
## -1 0 1
## 0.2817153 0.2686123 0.4496724Awareness Check Pass
table(data2_long_pass$s_coi_2_1,data2_long_pass$version)##
## old guideline new guideline
## -1 92 528
## 0 98 560
## 1 304 1178chisq.test(data2_long_pass$s_coi_2_1, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_coi_2_1 and data2_long_pass$version
## X-squared = 14.886, df = 2, p-value = 0.0005855prop.table(table(data2_long_version_old_pass$s_coi_2_1))##
## -1 0 1
## 0.1862348 0.1983806 0.6153846prop.table(table(data2_long_version_new_pass$s_coi_2_1))##
## -1 0 1
## 0.2330097 0.2471315 0.5198588T2 Barth et al.
table(subset_barth$s_coi_2_1, subset_barth$version)##
## new guideline old guideline
## -1 473 88
## 0 451 79
## 1 755 190chisq.test(subset_barth$s_coi_2_1, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_coi_2_1 and subset_barth$version
## X-squared = 8.1793, df = 2, p-value = 0.01675prop.table(table(subset_barth_version_old$s_coi_2_1))##
## -1 0 1
## 0.2464986 0.2212885 0.5322129prop.table(table(subset_barth_version_new$s_coi_2_1))##
## -1 0 1
## 0.2817153 0.2686123 0.4496724Awareness Check Pass
table(subset_barth_pass$s_coi_2_1, subset_barth_pass$version)##
## old guideline new guideline
## -1 46 264
## 0 49 280
## 1 152 589chisq.test(subset_barth_pass$s_coi_2_1, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_coi_2_1 and subset_barth_pass$version
## X-squared = 7.443, df = 2, p-value = 0.0242prop.table(table(subset_barth_version_old_pass$s_coi_2_1))##
## -1 0 1
## 0.1862348 0.1983806 0.6153846prop.table(table(subset_barth_version_new_pass$s_coi_2_1))##
## -1 0 1
## 0.2330097 0.2471315 0.5198588T2 Faerber et al.
table(subset_faerber$s_coi_2_1, subset_faerber$version)##
## new guideline old guideline
## -1 473 88
## 0 451 79
## 1 755 190chisq.test(subset_faerber$s_coi_2_1, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_coi_2_1 and subset_faerber$version
## X-squared = 8.1793, df = 2, p-value = 0.01675prop.table(table(subset_faerber_version_old$s_coi_2_1))##
## -1 0 1
## 0.2464986 0.2212885 0.5322129prop.table(table(subset_faerber_version_new$s_coi_2_1))##
## -1 0 1
## 0.2817153 0.2686123 0.4496724Awareness Check Pass
table(subset_faerber_pass$s_coi_2_1, subset_faerber_pass$version)##
## old guideline new guideline
## -1 46 264
## 0 49 280
## 1 152 589chisq.test(subset_faerber_pass$s_coi_2_1, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_coi_2_1 and subset_faerber_pass$version
## X-squared = 7.443, df = 2, p-value = 0.0242prop.table(table(subset_faerber_version_old_pass$s_coi_2_1))##
## -1 0 1
## 0.1862348 0.1983806 0.6153846prop.table(table(subset_faerber_version_new_pass$s_coi_2_1))##
## -1 0 1
## 0.2330097 0.2471315 0.5198588Item 2
T1
table(data2_long$s_coi_1_2,data2_long$version)##
## new guideline old guideline
## -1 932 194
## 0 1110 204
## 1 1294 304chisq.test(data2_long$s_coi_1_2, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_coi_1_2 and data2_long$version
## X-squared = 6.1718, df = 2, p-value = 0.04569prop.table(table(data2_long_version_old$s_coi_1_2))##
## -1 0 1
## 0.2763533 0.2905983 0.4330484prop.table(table(data2_long_version_new$s_coi_1_2))##
## -1 0 1
## 0.2793765 0.3327338 0.3878897Awareness Check Pass
table(data2_long_pass$s_coi_1_2,data2_long_pass$version)##
## old guideline new guideline
## -1 100 538
## 0 146 710
## 1 242 1000chisq.test(data2_long_pass$s_coi_1_2, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_coi_1_2 and data2_long_pass$version
## X-squared = 4.6939, df = 2, p-value = 0.09566prop.table(table(data2_long_version_old_pass$s_coi_1_2))##
## -1 0 1
## 0.2049180 0.2991803 0.4959016prop.table(table(data2_long_version_new_pass$s_coi_1_2))##
## -1 0 1
## 0.2393238 0.3158363 0.4448399T1 Barth et al.
table(subset_barth$s_coi_1_2, subset_barth$version)##
## new guideline old guideline
## -1 466 97
## 0 555 102
## 1 647 152chisq.test(subset_barth$s_coi_1_2, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_coi_1_2 and subset_barth$version
## X-squared = 3.0859, df = 2, p-value = 0.2137prop.table(table(subset_barth_version_old$s_coi_1_2))##
## -1 0 1
## 0.2763533 0.2905983 0.4330484prop.table(table(subset_barth_version_new$s_coi_1_2))##
## -1 0 1
## 0.2793765 0.3327338 0.3878897Awareness Check Pass
table(subset_barth_pass$s_coi_1_2, subset_barth_pass$version)##
## old guideline new guideline
## -1 50 269
## 0 73 355
## 1 121 500chisq.test(subset_barth_pass$s_coi_1_2, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_coi_1_2 and subset_barth_pass$version
## X-squared = 2.347, df = 2, p-value = 0.3093prop.table(table(subset_barth_version_old_pass$s_coi_1_2))##
## -1 0 1
## 0.2049180 0.2991803 0.4959016prop.table(table(subset_barth_version_new_pass$s_coi_1_2))##
## -1 0 1
## 0.2393238 0.3158363 0.4448399T1 Faerber et al.
table(subset_faerber$s_coi_1_2, subset_faerber$version)##
## new guideline old guideline
## -1 466 97
## 0 555 102
## 1 647 152chisq.test(subset_faerber$s_coi_1_2, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_coi_1_2 and subset_faerber$version
## X-squared = 3.0859, df = 2, p-value = 0.2137prop.table(table(subset_faerber_version_old$s_coi_1_2))##
## -1 0 1
## 0.2763533 0.2905983 0.4330484prop.table(table(subset_faerber_version_new$s_coi_1_2))##
## -1 0 1
## 0.2793765 0.3327338 0.3878897Awareness Check Pass
table(subset_faerber_pass$s_coi_1_2, subset_faerber_pass$version)##
## old guideline new guideline
## -1 50 269
## 0 73 355
## 1 121 500chisq.test(subset_faerber_pass$s_coi_1_2, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_coi_1_2 and subset_faerber_pass$version
## X-squared = 2.347, df = 2, p-value = 0.3093prop.table(table(subset_faerber_version_old_pass$s_coi_1_2))##
## -1 0 1
## 0.2049180 0.2991803 0.4959016prop.table(table(subset_faerber_version_new_pass$s_coi_1_2))##
## -1 0 1
## 0.2393238 0.3158363 0.4448399T2
table(data2_long$s_coi_2_2,data2_long$version)##
## new guideline old guideline
## -1 874 198
## 0 976 156
## 1 1502 358chisq.test(data2_long$s_coi_2_2, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_coi_2_2 and data2_long$version
## X-squared = 15.462, df = 2, p-value = 0.0004389prop.table(table(data2_long_version_old$s_coi_2_2))##
## -1 0 1
## 0.2780899 0.2191011 0.5028090prop.table(table(data2_long_version_new$s_coi_2_2))##
## -1 0 1
## 0.2607399 0.2911695 0.4480907Awareness Check Pass
table(data2_long_pass$s_coi_2_2,data2_long_pass$version)##
## old guideline new guideline
## -1 98 442
## 0 96 600
## 1 298 1218chisq.test(data2_long_pass$s_coi_2_2, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_coi_2_2 and data2_long_pass$version
## X-squared = 11.205, df = 2, p-value = 0.003688prop.table(table(data2_long_version_old_pass$s_coi_2_2))##
## -1 0 1
## 0.1991870 0.1951220 0.6056911prop.table(table(data2_long_version_new_pass$s_coi_2_2))##
## -1 0 1
## 0.1955752 0.2654867 0.5389381T2 Barth et al.
table(subset_barth$s_coi_2_2, subset_barth$version)##
## new guideline old guideline
## -1 437 99
## 0 488 78
## 1 751 179chisq.test(subset_barth$s_coi_2_2, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_coi_2_2 and subset_barth$version
## X-squared = 7.7312, df = 2, p-value = 0.02095prop.table(table(subset_barth_version_old$s_coi_2_2))##
## -1 0 1
## 0.2780899 0.2191011 0.5028090prop.table(table(subset_barth_version_new$s_coi_2_2))##
## -1 0 1
## 0.2607399 0.2911695 0.4480907Awareness Check Pass
table(subset_barth_pass$s_coi_2_2, subset_barth_pass$version)##
## old guideline new guideline
## -1 49 221
## 0 48 300
## 1 149 609chisq.test(subset_barth_pass$s_coi_2_2, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_coi_2_2 and subset_barth_pass$version
## X-squared = 5.6025, df = 2, p-value = 0.06073prop.table(table(subset_barth_version_old_pass$s_coi_2_2))##
## -1 0 1
## 0.1991870 0.1951220 0.6056911prop.table(table(subset_barth_version_new_pass$s_coi_2_2))##
## -1 0 1
## 0.1955752 0.2654867 0.5389381T2 Faerber et al.
table(subset_faerber$s_coi_2_2, subset_faerber$version)##
## new guideline old guideline
## -1 437 99
## 0 488 78
## 1 751 179chisq.test(subset_faerber$s_coi_2_2, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_coi_2_2 and subset_faerber$version
## X-squared = 7.7312, df = 2, p-value = 0.02095prop.table(table(subset_faerber_version_old$s_coi_2_2))##
## -1 0 1
## 0.2780899 0.2191011 0.5028090prop.table(table(subset_faerber_version_new$s_coi_2_2))##
## -1 0 1
## 0.2607399 0.2911695 0.4480907Awareness Check Pass
table(subset_faerber_pass$s_coi_2_2, subset_faerber_pass$version)##
## old guideline new guideline
## -1 49 221
## 0 48 300
## 1 149 609chisq.test(subset_faerber_pass$s_coi_2_2, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_coi_2_2 and subset_faerber_pass$version
## X-squared = 5.6025, df = 2, p-value = 0.06073prop.table(table(subset_faerber_version_old_pass$s_coi_2_2))##
## -1 0 1
## 0.1991870 0.1951220 0.6056911prop.table(table(subset_faerber_version_new_pass$s_coi_2_2))##
## -1 0 1
## 0.1955752 0.2654867 0.5389381Item 3
T1
table(data2_long$s_coi_1_3,data2_long$version)##
## new guideline old guideline
## -1 930 188
## 0 1106 234
## 1 1286 286chisq.test(data2_long$s_coi_1_3, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_coi_1_3 and data2_long$version
## X-squared = 0.87168, df = 2, p-value = 0.6467prop.table(table(data2_long_version_old$s_coi_1_3))##
## -1 0 1
## 0.2655367 0.3305085 0.4039548prop.table(table(data2_long_version_new$s_coi_1_3))##
## -1 0 1
## 0.2799518 0.3329320 0.3871162Awareness Check Pass
table(data2_long_pass$s_coi_1_3,data2_long_pass$version)##
## old guideline new guideline
## -1 92 518
## 0 164 710
## 1 234 1014chisq.test(data2_long_pass$s_coi_1_3, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_coi_1_3 and data2_long_pass$version
## X-squared = 4.345, df = 2, p-value = 0.1139prop.table(table(data2_long_version_old_pass$s_coi_1_3))##
## -1 0 1
## 0.1877551 0.3346939 0.4775510prop.table(table(data2_long_version_new_pass$s_coi_1_3))##
## -1 0 1
## 0.2310437 0.3166815 0.4522748T1 Barth et al.
table(subset_barth$s_coi_1_3, subset_barth$version)##
## new guideline old guideline
## -1 465 94
## 0 553 117
## 1 643 143chisq.test(subset_barth$s_coi_1_3, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_coi_1_3 and subset_barth$version
## X-squared = 0.43584, df = 2, p-value = 0.8042prop.table(table(subset_barth_version_old$s_coi_1_3))##
## -1 0 1
## 0.2655367 0.3305085 0.4039548prop.table(table(subset_barth_version_new$s_coi_1_3))##
## -1 0 1
## 0.2799518 0.3329320 0.3871162Awareness Check Pass
table(subset_barth_pass$s_coi_1_3, subset_barth_pass$version)##
## old guideline new guideline
## -1 46 259
## 0 82 355
## 1 117 507chisq.test(subset_barth_pass$s_coi_1_3, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_coi_1_3 and subset_barth_pass$version
## X-squared = 2.1725, df = 2, p-value = 0.3375prop.table(table(subset_barth_version_old_pass$s_coi_1_3))##
## -1 0 1
## 0.1877551 0.3346939 0.4775510prop.table(table(subset_barth_version_new_pass$s_coi_1_3))##
## -1 0 1
## 0.2310437 0.3166815 0.4522748T1 Faerber et al.
table(subset_faerber$s_coi_1_3, subset_faerber$version)##
## new guideline old guideline
## -1 465 94
## 0 553 117
## 1 643 143chisq.test(subset_faerber$s_coi_1_3, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_coi_1_3 and subset_faerber$version
## X-squared = 0.43584, df = 2, p-value = 0.8042prop.table(table(subset_faerber_version_old$s_coi_1_3))##
## -1 0 1
## 0.2655367 0.3305085 0.4039548prop.table(table(subset_faerber_version_new$s_coi_1_3))##
## -1 0 1
## 0.2799518 0.3329320 0.3871162Awareness Check Pass
table(subset_faerber_pass$s_coi_1_3, subset_faerber_pass$version)##
## old guideline new guideline
## -1 46 259
## 0 82 355
## 1 117 507chisq.test(subset_faerber_pass$s_coi_1_3, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_coi_1_3 and subset_faerber_pass$version
## X-squared = 2.1725, df = 2, p-value = 0.3375prop.table(table(subset_faerber_version_old_pass$s_coi_1_3))##
## -1 0 1
## 0.1877551 0.3346939 0.4775510prop.table(table(subset_faerber_version_new_pass$s_coi_1_3))##
## -1 0 1
## 0.2310437 0.3166815 0.4522748T2
table(data2_long$s_coi_2_3,data2_long$version)##
## new guideline old guideline
## -1 842 190
## 0 922 174
## 1 1588 346chisq.test(data2_long$s_coi_2_3, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_coi_2_3 and data2_long$version
## X-squared = 2.8009, df = 2, p-value = 0.2465prop.table(table(data2_long_version_old$s_coi_2_3))##
## -1 0 1
## 0.2676056 0.2450704 0.4873239prop.table(table(data2_long_version_new$s_coi_2_3))##
## -1 0 1
## 0.2511933 0.2750597 0.4737470Awareness Check Pass
table(data2_long_pass$s_coi_2_3,data2_long_pass$version)##
## old guideline new guideline
## -1 92 412
## 0 106 560
## 1 292 1292chisq.test(data2_long_pass$s_coi_2_3, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_coi_2_3 and data2_long_pass$version
## X-squared = 2.123, df = 2, p-value = 0.3459prop.table(table(data2_long_version_old_pass$s_coi_2_3))##
## -1 0 1
## 0.1877551 0.2163265 0.5959184prop.table(table(data2_long_version_new_pass$s_coi_2_3))##
## -1 0 1
## 0.1819788 0.2473498 0.5706714T2 Barth et al.
table(subset_barth$s_coi_2_3, subset_barth$version)##
## new guideline old guideline
## -1 421 95
## 0 461 87
## 1 794 173chisq.test(subset_barth$s_coi_2_3, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_coi_2_3 and subset_barth$version
## X-squared = 1.4005, df = 2, p-value = 0.4965prop.table(table(subset_barth_version_old$s_coi_2_3))##
## -1 0 1
## 0.2676056 0.2450704 0.4873239prop.table(table(subset_barth_version_new$s_coi_2_3))##
## -1 0 1
## 0.2511933 0.2750597 0.4737470Awareness Check Pass
table(subset_barth_pass$s_coi_2_3, subset_barth_pass$version)##
## old guideline new guideline
## -1 46 206
## 0 53 280
## 1 146 646chisq.test(subset_barth_pass$s_coi_2_3, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_coi_2_3 and subset_barth_pass$version
## X-squared = 1.0615, df = 2, p-value = 0.5882prop.table(table(subset_barth_version_old_pass$s_coi_2_3))##
## -1 0 1
## 0.1877551 0.2163265 0.5959184prop.table(table(subset_barth_version_new_pass$s_coi_2_3))##
## -1 0 1
## 0.1819788 0.2473498 0.5706714T2 Faerber et al.
table(subset_faerber$s_coi_2_3, subset_faerber$version)##
## new guideline old guideline
## -1 421 95
## 0 461 87
## 1 794 173chisq.test(subset_faerber$s_coi_2_3, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_coi_2_3 and subset_faerber$version
## X-squared = 1.4005, df = 2, p-value = 0.4965prop.table(table(subset_faerber_version_old$s_coi_2_3))##
## -1 0 1
## 0.2676056 0.2450704 0.4873239prop.table(table(subset_faerber_version_new$s_coi_2_3))##
## -1 0 1
## 0.2511933 0.2750597 0.4737470Awareness Check Pass
table(subset_faerber_pass$s_coi_2_3, subset_faerber_pass$version)##
## old guideline new guideline
## -1 46 206
## 0 53 280
## 1 146 646chisq.test(subset_faerber_pass$s_coi_2_3, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_coi_2_3 and subset_faerber_pass$version
## X-squared = 1.0615, df = 2, p-value = 0.5882prop.table(table(subset_faerber_version_old_pass$s_coi_2_3))##
## -1 0 1
## 0.1877551 0.2163265 0.5959184prop.table(table(subset_faerber_version_new_pass$s_coi_2_3))##
## -1 0 1
## 0.1819788 0.2473498 0.5706714Item 4
T1
table(data2_long$s_coi_1_4,data2_long$version)##
## new guideline old guideline
## -1 1042 216
## 0 1120 230
## 1 1192 262chisq.test(data2_long$s_coi_1_4, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_coi_1_4 and data2_long$version
## X-squared = 0.55469, df = 2, p-value = 0.7578prop.table(table(data2_long_version_old$s_coi_1_4))##
## -1 0 1
## 0.3050847 0.3248588 0.3700565prop.table(table(data2_long_version_new$s_coi_1_4))##
## -1 0 1
## 0.3106738 0.3339296 0.3553965Awareness Check Pass
table(data2_long_pass$s_coi_1_4,data2_long_pass$version)##
## old guideline new guideline
## -1 132 640
## 0 152 730
## 1 206 892chisq.test(data2_long_pass$s_coi_1_4, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_coi_1_4 and data2_long_pass$version
## X-squared = 1.1464, df = 2, p-value = 0.5637prop.table(table(data2_long_version_old_pass$s_coi_1_4))##
## -1 0 1
## 0.2693878 0.3102041 0.4204082prop.table(table(data2_long_version_new_pass$s_coi_1_4))##
## -1 0 1
## 0.2829355 0.3227233 0.3943413T1 Barth et al.
table(subset_barth$s_coi_1_4, subset_barth$version)##
## new guideline old guideline
## -1 521 108
## 0 560 115
## 1 596 131chisq.test(subset_barth$s_coi_1_4, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_coi_1_4 and subset_barth$version
## X-squared = 0.27734, df = 2, p-value = 0.8705prop.table(table(subset_barth_version_old$s_coi_1_4))##
## -1 0 1
## 0.3050847 0.3248588 0.3700565prop.table(table(subset_barth_version_new$s_coi_1_4))##
## -1 0 1
## 0.3106738 0.3339296 0.3553965Awareness Check Pass
table(subset_barth_pass$s_coi_1_4, subset_barth_pass$version)##
## old guideline new guideline
## -1 66 320
## 0 76 365
## 1 103 446chisq.test(subset_barth_pass$s_coi_1_4, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_coi_1_4 and subset_barth_pass$version
## X-squared = 0.57319, df = 2, p-value = 0.7508prop.table(table(subset_barth_version_old_pass$s_coi_1_4))##
## -1 0 1
## 0.2693878 0.3102041 0.4204082prop.table(table(subset_barth_version_new_pass$s_coi_1_4))##
## -1 0 1
## 0.2829355 0.3227233 0.3943413T1 Faerber et al.
table(subset_faerber$s_coi_1_4, subset_faerber$version)##
## new guideline old guideline
## -1 521 108
## 0 560 115
## 1 596 131chisq.test(subset_faerber$s_coi_1_4, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_coi_1_4 and subset_faerber$version
## X-squared = 0.27734, df = 2, p-value = 0.8705prop.table(table(subset_faerber_version_old$s_coi_1_4))##
## -1 0 1
## 0.3050847 0.3248588 0.3700565prop.table(table(subset_faerber_version_new$s_coi_1_4))##
## -1 0 1
## 0.3106738 0.3339296 0.3553965Awareness Check Pass
table(subset_faerber_pass$s_coi_1_4, subset_faerber_pass$version)##
## old guideline new guideline
## -1 66 320
## 0 76 365
## 1 103 446chisq.test(subset_faerber_pass$s_coi_1_4, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_coi_1_4 and subset_faerber_pass$version
## X-squared = 0.57319, df = 2, p-value = 0.7508prop.table(table(subset_faerber_version_old_pass$s_coi_1_4))##
## -1 0 1
## 0.2693878 0.3102041 0.4204082prop.table(table(subset_faerber_version_new_pass$s_coi_1_4))##
## -1 0 1
## 0.2829355 0.3227233 0.3943413T2
table(data2_long$s_coi_2_4,data2_long$version)##
## new guideline old guideline
## -1 928 168
## 0 972 190
## 1 1454 356chisq.test(data2_long$s_coi_2_4, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_coi_2_4 and data2_long$version
## X-squared = 10.506, df = 2, p-value = 0.005233prop.table(table(data2_long_version_old$s_coi_2_4))##
## -1 0 1
## 0.2352941 0.2661064 0.4985994prop.table(table(data2_long_version_new$s_coi_2_4))##
## -1 0 1
## 0.2766846 0.2898032 0.4335122Awareness Check Pass
table(data2_long_pass$s_coi_2_4,data2_long_pass$version)##
## old guideline new guideline
## -1 84 528
## 0 122 598
## 1 288 1136chisq.test(data2_long_pass$s_coi_2_4, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_coi_2_4 and data2_long_pass$version
## X-squared = 12.926, df = 2, p-value = 0.00156prop.table(table(data2_long_version_old_pass$s_coi_2_4))##
## -1 0 1
## 0.1700405 0.2469636 0.5829960prop.table(table(data2_long_version_new_pass$s_coi_2_4))##
## -1 0 1
## 0.2334218 0.2643678 0.5022104T2 Barth et al.
table(subset_barth$s_coi_2_4, subset_barth$version)##
## new guideline old guideline
## -1 464 84
## 0 486 95
## 1 727 178chisq.test(subset_barth$s_coi_2_4, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_coi_2_4 and subset_barth$version
## X-squared = 5.2528, df = 2, p-value = 0.07234prop.table(table(subset_barth_version_old$s_coi_2_4))##
## -1 0 1
## 0.2352941 0.2661064 0.4985994prop.table(table(subset_barth_version_new$s_coi_2_4))##
## -1 0 1
## 0.2766846 0.2898032 0.4335122Awareness Check Pass
table(subset_barth_pass$s_coi_2_4, subset_barth_pass$version)##
## old guideline new guideline
## -1 42 264
## 0 61 299
## 1 144 568chisq.test(subset_barth_pass$s_coi_2_4, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_coi_2_4 and subset_barth_pass$version
## X-squared = 6.4631, df = 2, p-value = 0.0395prop.table(table(subset_barth_version_old_pass$s_coi_2_4))##
## -1 0 1
## 0.1700405 0.2469636 0.5829960prop.table(table(subset_barth_version_new_pass$s_coi_2_4))##
## -1 0 1
## 0.2334218 0.2643678 0.5022104T2 Faerber et al.
table(subset_faerber$s_coi_2_4, subset_faerber$version)##
## new guideline old guideline
## -1 464 84
## 0 486 95
## 1 727 178chisq.test(subset_faerber$s_coi_2_4, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_coi_2_4 and subset_faerber$version
## X-squared = 5.2528, df = 2, p-value = 0.07234prop.table(table(subset_faerber_version_old$s_coi_2_4))##
## -1 0 1
## 0.2352941 0.2661064 0.4985994prop.table(table(subset_faerber_version_new$s_coi_2_4))##
## -1 0 1
## 0.2766846 0.2898032 0.4335122Awareness Check Pass
table(subset_faerber_pass$s_coi_2_4, subset_faerber_pass$version)##
## old guideline new guideline
## -1 42 264
## 0 61 299
## 1 144 568chisq.test(subset_faerber_pass$s_coi_2_4, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_coi_2_4 and subset_faerber_pass$version
## X-squared = 6.4631, df = 2, p-value = 0.0395prop.table(table(subset_faerber_version_old_pass$s_coi_2_4))##
## -1 0 1
## 0.1700405 0.2469636 0.5829960prop.table(table(subset_faerber_version_new_pass$s_coi_2_4))##
## -1 0 1
## 0.2334218 0.2643678 0.5022104Item 5
T1
table(data2_long$s_coi_1_5,data2_long$version)##
## new guideline old guideline
## -1 858 166
## 0 1092 246
## 1 1390 294chisq.test(data2_long$s_coi_1_5, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_coi_1_5 and data2_long$version
## X-squared = 1.9047, df = 2, p-value = 0.3858prop.table(table(data2_long_version_old$s_coi_1_5))##
## -1 0 1
## 0.2351275 0.3484419 0.4164306prop.table(table(data2_long_version_new$s_coi_1_5))##
## -1 0 1
## 0.2568862 0.3269461 0.4161677Awareness Check Pass
table(data2_long_pass$s_coi_1_5,data2_long_pass$version)##
## old guideline new guideline
## -1 90 492
## 0 154 694
## 1 246 1066chisq.test(data2_long_pass$s_coi_1_5, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_coi_1_5 and data2_long_pass$version
## X-squared = 3.0367, df = 2, p-value = 0.2191prop.table(table(data2_long_version_old_pass$s_coi_1_5))##
## -1 0 1
## 0.1836735 0.3142857 0.5020408prop.table(table(data2_long_version_new_pass$s_coi_1_5))##
## -1 0 1
## 0.2184725 0.3081705 0.4733570T1 Barth et al.
table(subset_barth$s_coi_1_5, subset_barth$version)##
## new guideline old guideline
## -1 429 83
## 0 546 123
## 1 695 147chisq.test(subset_barth$s_coi_1_5, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_coi_1_5 and subset_barth$version
## X-squared = 0.95233, df = 2, p-value = 0.6212prop.table(table(subset_barth_version_old$s_coi_1_5))##
## -1 0 1
## 0.2351275 0.3484419 0.4164306prop.table(table(subset_barth_version_new$s_coi_1_5))##
## -1 0 1
## 0.2568862 0.3269461 0.4161677Awareness Check Pass
table(subset_barth_pass$s_coi_1_5, subset_barth_pass$version)##
## old guideline new guideline
## -1 45 246
## 0 77 347
## 1 123 533chisq.test(subset_barth_pass$s_coi_1_5, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_coi_1_5 and subset_barth_pass$version
## X-squared = 1.5183, df = 2, p-value = 0.4681prop.table(table(subset_barth_version_old_pass$s_coi_1_5))##
## -1 0 1
## 0.1836735 0.3142857 0.5020408prop.table(table(subset_barth_version_new_pass$s_coi_1_5))##
## -1 0 1
## 0.2184725 0.3081705 0.4733570T1 Faerber et al.
table(subset_faerber$s_coi_1_5, subset_faerber$version)##
## new guideline old guideline
## -1 429 83
## 0 546 123
## 1 695 147chisq.test(subset_faerber$s_coi_1_5, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_coi_1_5 and subset_faerber$version
## X-squared = 0.95233, df = 2, p-value = 0.6212prop.table(table(subset_faerber_version_old$s_coi_1_5))##
## -1 0 1
## 0.2351275 0.3484419 0.4164306prop.table(table(subset_faerber_version_new$s_coi_1_5))##
## -1 0 1
## 0.2568862 0.3269461 0.4161677Awareness Check Pass
table(subset_faerber_pass$s_coi_1_5, subset_faerber_pass$version)##
## old guideline new guideline
## -1 45 246
## 0 77 347
## 1 123 533chisq.test(subset_faerber_pass$s_coi_1_5, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_coi_1_5 and subset_faerber_pass$version
## X-squared = 1.5183, df = 2, p-value = 0.4681prop.table(table(subset_faerber_version_old_pass$s_coi_1_5))##
## -1 0 1
## 0.1836735 0.3142857 0.5020408prop.table(table(subset_faerber_version_new_pass$s_coi_1_5))##
## -1 0 1
## 0.2184725 0.3081705 0.4733570T2
table(data2_long$s_coi_2_5,data2_long$version)##
## new guideline old guideline
## -1 830 172
## 0 958 180
## 1 1568 360chisq.test(data2_long$s_coi_2_5, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_coi_2_5 and data2_long$version
## X-squared = 4.1441, df = 2, p-value = 0.1259prop.table(table(data2_long_version_old$s_coi_2_5))##
## -1 0 1
## 0.241573 0.252809 0.505618prop.table(table(data2_long_version_new$s_coi_2_5))##
## -1 0 1
## 0.2473182 0.2854589 0.4672229Awareness Check Pass
table(data2_long_pass$s_coi_2_5,data2_long_pass$version)##
## old guideline new guideline
## -1 98 436
## 0 108 562
## 1 286 1264chisq.test(data2_long_pass$s_coi_2_5, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_coi_2_5 and data2_long_pass$version
## X-squared = 1.8412, df = 2, p-value = 0.3983prop.table(table(data2_long_version_old_pass$s_coi_2_5))##
## -1 0 1
## 0.1991870 0.2195122 0.5813008prop.table(table(data2_long_version_new_pass$s_coi_2_5))##
## -1 0 1
## 0.1927498 0.2484527 0.5587975T2 Barth et al.
table(subset_barth$s_coi_2_5, subset_barth$version)##
## new guideline old guideline
## -1 415 86
## 0 479 90
## 1 784 180chisq.test(subset_barth$s_coi_2_5, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_coi_2_5 and subset_barth$version
## X-squared = 2.072, df = 2, p-value = 0.3549prop.table(table(subset_barth_version_old$s_coi_2_5))##
## -1 0 1
## 0.241573 0.252809 0.505618prop.table(table(subset_barth_version_new$s_coi_2_5))##
## -1 0 1
## 0.2473182 0.2854589 0.4672229Awareness Check Pass
table(subset_barth_pass$s_coi_2_5, subset_barth_pass$version)##
## old guideline new guideline
## -1 49 218
## 0 54 281
## 1 143 632chisq.test(subset_barth_pass$s_coi_2_5, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_coi_2_5 and subset_barth_pass$version
## X-squared = 0.92059, df = 2, p-value = 0.6311prop.table(table(subset_barth_version_old_pass$s_coi_2_5))##
## -1 0 1
## 0.1991870 0.2195122 0.5813008prop.table(table(subset_barth_version_new_pass$s_coi_2_5))##
## -1 0 1
## 0.1927498 0.2484527 0.5587975T2 Faerber et al.
table(subset_faerber$s_coi_2_5, subset_faerber$version)##
## new guideline old guideline
## -1 415 86
## 0 479 90
## 1 784 180chisq.test(subset_faerber$s_coi_2_5, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_coi_2_5 and subset_faerber$version
## X-squared = 2.072, df = 2, p-value = 0.3549prop.table(table(subset_faerber_version_old$s_coi_2_5))##
## -1 0 1
## 0.241573 0.252809 0.505618prop.table(table(subset_faerber_version_new$s_coi_2_5))##
## -1 0 1
## 0.2473182 0.2854589 0.4672229Awareness Check Pass
table(subset_faerber_pass$s_coi_2_5, subset_faerber_pass$version)##
## old guideline new guideline
## -1 49 218
## 0 54 281
## 1 143 632chisq.test(subset_faerber_pass$s_coi_2_5, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_coi_2_5 and subset_faerber_pass$version
## X-squared = 0.92059, df = 2, p-value = 0.6311prop.table(table(subset_faerber_version_old_pass$s_coi_2_5))##
## -1 0 1
## 0.1991870 0.2195122 0.5813008prop.table(table(subset_faerber_version_new_pass$s_coi_2_5))##
## -1 0 1
## 0.1927498 0.2484527 0.5587975Item 6
T1
table(data2_long$s_coi_1_6, data2_long$version)##
## new guideline old guideline
## -1 928 206
## 0 1116 226
## 1 1304 276chisq.test(data2_long$s_coi_1_6, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_coi_1_6 and data2_long$version
## X-squared = 0.74947, df = 2, p-value = 0.6875prop.table(table(data2_long_version_old$s_coi_1_6))##
## -1 0 1
## 0.2909605 0.3192090 0.3898305prop.table(table(data2_long_version_new$s_coi_1_6))##
## -1 0 1
## 0.2771804 0.3333333 0.3894863Awareness Check Pass
table(data2_long_pass$s_coi_1_6,data2_long_pass$version)##
## old guideline new guideline
## -1 150 608
## 0 144 712
## 1 198 936chisq.test(data2_long_pass$s_coi_1_6, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_coi_1_6 and data2_long_pass$version
## X-squared = 2.6654, df = 2, p-value = 0.2638prop.table(table(data2_long_version_old_pass$s_coi_1_6))##
## -1 0 1
## 0.3048780 0.2926829 0.4024390prop.table(table(data2_long_version_new_pass$s_coi_1_6))##
## -1 0 1
## 0.2695035 0.3156028 0.4148936T1 Barth et al.
table(subset_barth$s_coi_1_6, subset_barth$version)##
## new guideline old guideline
## -1 464 103
## 0 558 113
## 1 652 138chisq.test(subset_barth$s_coi_1_6, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_coi_1_6 and subset_barth$version
## X-squared = 0.37474, df = 2, p-value = 0.8291prop.table(table(subset_barth_version_old$s_coi_1_6))##
## -1 0 1
## 0.2909605 0.3192090 0.3898305prop.table(table(subset_barth_version_new$s_coi_1_6))##
## -1 0 1
## 0.2771804 0.3333333 0.3894863Awareness Check Pass
table(subset_barth_pass$s_coi_1_6, subset_barth_pass$version)##
## old guideline new guideline
## -1 75 304
## 0 72 356
## 1 99 468chisq.test(subset_barth_pass$s_coi_1_6, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_coi_1_6 and subset_barth_pass$version
## X-squared = 1.3327, df = 2, p-value = 0.5136prop.table(table(subset_barth_version_old_pass$s_coi_1_6))##
## -1 0 1
## 0.3048780 0.2926829 0.4024390prop.table(table(subset_barth_version_new_pass$s_coi_1_6))##
## -1 0 1
## 0.2695035 0.3156028 0.4148936T1 Faerber et al.
table(subset_faerber$s_coi_1_6, subset_faerber$version)##
## new guideline old guideline
## -1 464 103
## 0 558 113
## 1 652 138chisq.test(subset_faerber$s_coi_1_6, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_coi_1_6 and subset_faerber$version
## X-squared = 0.37474, df = 2, p-value = 0.8291prop.table(table(subset_faerber_version_old$s_coi_1_6))##
## -1 0 1
## 0.2909605 0.3192090 0.3898305prop.table(table(subset_faerber_version_new$s_coi_1_6))##
## -1 0 1
## 0.2771804 0.3333333 0.3894863Awareness Check Pass
table(subset_faerber_pass$s_coi_1_6, subset_faerber_pass$version)##
## old guideline new guideline
## -1 75 304
## 0 72 356
## 1 99 468chisq.test(subset_faerber_pass$s_coi_1_6, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_coi_1_6 and subset_faerber_pass$version
## X-squared = 1.3327, df = 2, p-value = 0.5136prop.table(table(subset_faerber_version_old_pass$s_coi_1_6))##
## -1 0 1
## 0.3048780 0.2926829 0.4024390prop.table(table(subset_faerber_version_new_pass$s_coi_1_6))##
## -1 0 1
## 0.2695035 0.3156028 0.4148936T2
table(data2_long$s_coi_2_6, data2_long$version)##
## new guideline old guideline
## -1 988 222
## 0 972 188
## 1 1390 300chisq.test(data2_long$s_coi_2_6, data2_long$version)##
## Pearson's Chi-squared test
##
## data: data2_long$s_coi_2_6 and data2_long$version
## X-squared = 2.0196, df = 2, p-value = 0.3643prop.table(table(data2_long_version_old$s_coi_2_6))##
## -1 0 1
## 0.3126761 0.2647887 0.4225352prop.table(table(data2_long_version_new$s_coi_2_6))##
## -1 0 1
## 0.2949254 0.2901493 0.4149254Awareness Check Pass
table(data2_long_pass$s_coi_2_6,data2_long_pass$version)##
## old guideline new guideline
## -1 146 628
## 0 114 608
## 1 230 1024chisq.test(data2_long_pass$s_coi_2_6, data2_long_pass$version)##
## Pearson's Chi-squared test
##
## data: data2_long_pass$s_coi_2_6 and data2_long_pass$version
## X-squared = 2.8407, df = 2, p-value = 0.2416prop.table(table(data2_long_version_old_pass$s_coi_2_6))##
## -1 0 1
## 0.2979592 0.2326531 0.4693878prop.table(table(data2_long_version_new_pass$s_coi_2_6))##
## -1 0 1
## 0.2778761 0.2690265 0.4530973T2 Barth et al.
table(subset_barth$s_coi_2_6, subset_barth$version)##
## new guideline old guideline
## -1 494 111
## 0 486 94
## 1 695 150chisq.test(subset_barth$s_coi_2_6, subset_barth$version)##
## Pearson's Chi-squared test
##
## data: subset_barth$s_coi_2_6 and subset_barth$version
## X-squared = 1.0098, df = 2, p-value = 0.6036prop.table(table(subset_barth_version_old$s_coi_2_6))##
## -1 0 1
## 0.3126761 0.2647887 0.4225352prop.table(table(subset_barth_version_new$s_coi_2_6))##
## -1 0 1
## 0.2949254 0.2901493 0.4149254Awareness Check Pass
table(subset_barth_pass$s_coi_2_6, subset_barth_pass$version)##
## old guideline new guideline
## -1 73 314
## 0 57 304
## 1 115 512chisq.test(subset_barth_pass$s_coi_2_6, subset_barth_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_barth_pass$s_coi_2_6 and subset_barth_pass$version
## X-squared = 1.4203, df = 2, p-value = 0.4916prop.table(table(subset_barth_version_old_pass$s_coi_2_6))##
## -1 0 1
## 0.2979592 0.2326531 0.4693878prop.table(table(subset_barth_version_new_pass$s_coi_2_6))##
## -1 0 1
## 0.2778761 0.2690265 0.4530973T2 Faerber et al.
table(subset_faerber$s_coi_2_6, subset_faerber$version)##
## new guideline old guideline
## -1 494 111
## 0 486 94
## 1 695 150chisq.test(subset_faerber$s_coi_2_6, subset_faerber$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber$s_coi_2_6 and subset_faerber$version
## X-squared = 1.0098, df = 2, p-value = 0.6036prop.table(table(subset_faerber_version_old$s_coi_2_6))##
## -1 0 1
## 0.3126761 0.2647887 0.4225352prop.table(table(subset_faerber_version_new$s_coi_2_6))##
## -1 0 1
## 0.2949254 0.2901493 0.4149254Awareness Check Pass
table(subset_faerber_pass$s_coi_2_6, subset_faerber_pass$version)##
## old guideline new guideline
## -1 73 314
## 0 57 304
## 1 115 512chisq.test(subset_faerber_pass$s_coi_2_6, subset_faerber_pass$version)##
## Pearson's Chi-squared test
##
## data: subset_faerber_pass$s_coi_2_6 and subset_faerber_pass$version
## X-squared = 1.4203, df = 2, p-value = 0.4916prop.table(table(subset_faerber_version_old_pass$s_coi_2_6))##
## -1 0 1
## 0.2979592 0.2326531 0.4693878prop.table(table(subset_faerber_version_new_pass$s_coi_2_6))##
## -1 0 1
## 0.2778761 0.2690265 0.4530973Save Dataframes
#write.csv2(data, "data.csv", row.names = FALSE)
#write.csv2(data_wide, "data_wide.csv", row.names = FALSE)
#write.csv2(data2_wide, "data2_wide.csv", row.names = FALSE)
#write.csv2(data2_wide_pass, "data2_wide_pass.csv", #row.names = FALSE)
#write.csv2(data_long, "data_long.csv", row.names = FALSE)
#write.csv2(data2_long, "data2_long.csv", row.names = FALSE)
#write.csv2(data2_long_pass, "data2_long_pass.csv", #row.names = FALSE)Appendix: Descriptive Statistics by Experimental Group
describeBy(data2_wide_pass$s_extent, group = data2_wide_pass$condition)##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 217 0.85 2.31 1 0.8 2.97 -4 6 10 0.14 -0.68 0.16
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 249 0.87 2.43 0 0.81 2.97 -6 6 12 0.15 -0.69 0.15
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 213 0.53 2.16 0 0.47 2.97 -6 6 12 0.12 -0.09 0.15
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 204 1.31 2.47 1 1.27 2.97 -4 6 10 0.07 -0.89 0.17
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 232 0.85 2.46 0 0.72 2.97 -5 6 11 0.37 -0.4 0.16
## ------------------------------------------------------------
## group: 6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 241 0.93 2.27 1 0.87 2.97 -4 6 10 0.22 -0.63 0.15a <- data2_wide_pass %>%
group_by(condition) %>%
reframe(quantile(s_extent, na.rm = TRUE))
a <- data.frame(a)
a## condition quantile.s_extent..na.rm...TRUE.
## 1 1 -4.00
## 2 1 -1.00
## 3 1 1.00
## 4 1 2.00
## 5 1 6.00
## 6 2 -6.00
## 7 2 -1.00
## 8 2 0.00
## 9 2 3.00
## 10 2 6.00
## 11 3 -6.00
## 12 3 -1.00
## 13 3 0.00
## 14 3 2.00
## 15 3 6.00
## 16 4 -4.00
## 17 4 0.00
## 18 4 1.00
## 19 4 3.25
## 20 4 6.00
## 21 5 -5.00
## 22 5 -1.00
## 23 5 0.00
## 24 5 2.00
## 25 5 6.00
## 26 6 -4.00
## 27 6 -1.00
## 28 6 1.00
## 29 6 3.00
## 30 6 6.00describeBy(data2_wide_pass$s_diff, group = data2_wide_pass$condition)##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 216 0.26 2.05 0 0.27 1.48 -6 6 12 -0.06 0.29 0.14
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 238 0.19 2.03 0 0.1 2.97 -4 6 10 0.31 0.08 0.13
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 216 0.51 2.16 0 0.45 2.97 -6 6 12 0.17 0.22 0.15
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 200 0.72 2.14 0 0.61 2.97 -6 6 12 0.28 0.3 0.15
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 234 0.42 2 0 0.35 1.48 -6 6 12 0.19 0.29 0.13
## ------------------------------------------------------------
## group: 6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 239 0.58 2 0 0.56 1.48 -6 6 12 -0.07 0.24 0.13b <- data2_wide_pass %>%
group_by(condition) %>%
reframe(quantile(s_diff, na.rm = TRUE))
b <- data.frame(b)
b## condition quantile.s_diff..na.rm...TRUE.
## 1 1 -6.00
## 2 1 -1.00
## 3 1 0.00
## 4 1 2.00
## 5 1 6.00
## 6 2 -4.00
## 7 2 -1.00
## 8 2 0.00
## 9 2 2.00
## 10 2 6.00
## 11 3 -6.00
## 12 3 -1.00
## 13 3 0.00
## 14 3 2.00
## 15 3 6.00
## 16 4 -6.00
## 17 4 -0.25
## 18 4 0.00
## 19 4 2.00
## 20 4 6.00
## 21 5 -6.00
## 22 5 -1.00
## 23 5 0.00
## 24 5 2.00
## 25 5 6.00
## 26 6 -6.00
## 27 6 0.00
## 28 6 0.00
## 29 6 2.00
## 30 6 6.00describeBy(data2_wide_pass$s_causality, group = data2_wide_pass$condition)##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 215 -0.12 3.85 0 -0.19 4.45 -8 10 18 0.15 -0.39 0.26
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 242 -0.86 3.88 -1 -0.91 4.45 -10 10 20 0.12 -0.16 0.25
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 209 0.08 3.92 0 0.05 2.97 -8 10 18 0.11 -0.23 0.27
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 196 0.14 3.98 0 0.11 2.97 -9 12 21 0.1 -0.2 0.28
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 224 -0.68 3.73 0 -0.69 2.97 -10 12 22 0.16 0.25 0.25
## ------------------------------------------------------------
## group: 6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 239 0 3.85 0 -0.06 2.97 -8 10 18 0.15 -0.27 0.25c <- data2_wide_pass %>%
group_by(condition) %>%
reframe(quantile(s_causality, na.rm = TRUE))
c <- data.frame(c)
c## condition quantile.s_causality..na.rm...TRUE.
## 1 1 -8.00
## 2 1 -3.00
## 3 1 0.00
## 4 1 2.00
## 5 1 10.00
## 6 2 -10.00
## 7 2 -3.75
## 8 2 -1.00
## 9 2 2.00
## 10 2 10.00
## 11 3 -8.00
## 12 3 -2.00
## 13 3 0.00
## 14 3 2.00
## 15 3 10.00
## 16 4 -9.00
## 17 4 -2.00
## 18 4 0.00
## 19 4 2.00
## 20 4 12.00
## 21 5 -10.00
## 22 5 -3.00
## 23 5 0.00
## 24 5 2.00
## 25 5 12.00
## 26 6 -8.00
## 27 6 -2.00
## 28 6 0.00
## 29 6 2.50
## 30 6 10.00describeBy(data2_wide_pass$s_CAMA, group = data2_wide_pass$condition)## Warning in min(x, na.rm = na.rm): kein nicht-fehlendes Argument für min; gebe
## Inf zurück## Warning in max(x, na.rm = na.rm): kein nicht-fehlendes Argument für max; gebe
## -Inf zurück## Warning in min(x, na.rm = na.rm): kein nicht-fehlendes Argument für min; gebe
## Inf zurück## Warning in max(x, na.rm = na.rm): kein nicht-fehlendes Argument für max; gebe
## -Inf zurück## Warning in min(x, na.rm = na.rm): kein nicht-fehlendes Argument für min; gebe
## Inf zurück## Warning in max(x, na.rm = na.rm): kein nicht-fehlendes Argument für max; gebe
## -Inf zurück##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 0 NaN NA NA NaN NA Inf -Inf -Inf NA NA NA
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 0 NaN NA NA NaN NA Inf -Inf -Inf NA NA NA
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 0 NaN NA NA NaN NA Inf -Inf -Inf NA NA NA
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 193 0.3 3.01 0 0.32 2.97 -9 7 16 -0.09 0.12 0.22
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 225 1.66 4.2 1 1.7 4.45 -11 13 24 -0.01 -0.05 0.28
## ------------------------------------------------------------
## group: 6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 239 0.77 3.36 0 0.83 2.97 -7 11 18 -0.04 0.12 0.22d <- data2_wide_pass %>%
group_by(condition) %>%
reframe(quantile(s_CAMA, na.rm = TRUE))
d <- data.frame(d)
d## condition quantile.s_CAMA..na.rm...TRUE.
## 1 1 NA
## 2 1 NA
## 3 1 NA
## 4 1 NA
## 5 1 NA
## 6 2 NA
## 7 2 NA
## 8 2 NA
## 9 2 NA
## 10 2 NA
## 11 3 NA
## 12 3 NA
## 13 3 NA
## 14 3 NA
## 15 3 NA
## 16 4 -9
## 17 4 -1
## 18 4 0
## 19 4 2
## 20 4 7
## 21 5 -11
## 22 5 -1
## 23 5 1
## 24 5 5
## 25 5 13
## 26 6 -7
## 27 6 -1
## 28 6 0
## 29 6 3
## 30 6 11describeBy(data2_wide_pass$user_experience, group = data2_wide_pass$condition)##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 217 5.26 1.37 5.33 5.28 1.48 1.67 8 6.33 -0.17 -0.55 0.09
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 5.4 1.45 5.67 5.48 1.48 1 8 7 -0.47 -0.33 0.09
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 207 5.36 1.4 5.67 5.48 1.24 1.17 8 6.83 -0.73 0.33 0.1
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 204 5.27 1.39 5.33 5.33 1.48 1.5 8 6.5 -0.37 -0.34 0.1
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 227 5.09 1.46 5.17 5.15 1.73 1.33 8 6.67 -0.35 -0.44 0.1
## ------------------------------------------------------------
## group: 6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 239 5.37 1.43 5.5 5.41 1.48 1.5 8 6.5 -0.26 -0.41 0.09Session Info
sessionInfo()## R version 4.3.2 (2023-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8
## [3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C
## [5] LC_TIME=German_Germany.utf8
##
## time zone: Europe/Berlin
## tzcode source: internal
##
## attached base packages:
## [1] splines stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] gmodels_2.18.1.1 equivUMP_0.1.1 egg_0.4.5
## [4] gridExtra_2.3 regclass_1.6 randomForest_4.7-1.1
## [7] rpart_4.1.21 VGAM_1.1-8 bestglm_0.37.3
## [10] leaps_3.1 car_3.1-2 carData_3.0-5
## [13] emmeans_1.8.6 multcomp_1.4-24 TH.data_1.1-2
## [16] MASS_7.3-60 survival_3.5-7 mvtnorm_1.1-3
## [19] tidyr_1.3.0 rcompanion_2.4.30 ordinal_2022.11-16
## [22] semTools_0.5-6 lavaan_0.6-16 data.table_1.14.8
## [25] ggplot2_3.4.2 pastecs_1.3.21 psych_2.3.6
## [28] dplyr_1.1.2 plyr_1.8.8
##
## loaded via a namespace (and not attached):
## [1] mnormt_2.1.1 gld_2.6.6 sandwich_3.0-2
## [4] readxl_1.4.3 rlang_1.1.1 magrittr_2.0.3
## [7] rpart.plot_3.1.1 matrixStats_1.0.0 e1071_1.7-13
## [10] compiler_4.3.2 mgcv_1.9-0 gdata_3.0.0
## [13] systemfonts_1.0.4 vctrs_0.6.2 stringr_1.5.0
## [16] quadprog_1.5-8 crayon_1.5.2 pkgconfig_2.0.3
## [19] shape_1.4.6 fastmap_1.1.1 backports_1.4.1
## [22] labeling_0.4.2 pbivnorm_0.6.0 utf8_1.2.3
## [25] rmarkdown_2.23 ragg_1.2.5 purrr_1.0.1
## [28] xfun_0.39 glmnet_4.1-7 modeltools_0.2-23
## [31] cachem_1.0.8 jsonlite_1.8.7 highr_0.10
## [34] cluster_2.1.4 parallel_4.3.2 DescTools_0.99.49
## [37] R6_2.5.1 stringi_1.7.12 RColorBrewer_1.1-3
## [40] coin_1.4-2 bslib_0.5.0 boot_1.3-28.1
## [43] lmtest_0.9-40 jquerylib_0.1.4 cellranger_1.1.0
## [46] numDeriv_2016.8-1.1 estimability_1.4.1 Rcpp_1.0.10
## [49] iterators_1.0.14 knitr_1.43 zoo_1.8-12
## [52] base64enc_0.1-3 nnet_7.3-19 Matrix_1.6-0
## [55] tidyselect_1.2.0 rstudioapi_0.15.0 abind_1.4-5
## [58] yaml_2.3.7 codetools_0.2-19 lattice_0.21-9
## [61] tibble_3.2.1 withr_2.5.0 coda_0.19-4
## [64] evaluate_0.21 foreign_0.8-85 proxy_0.4-27
## [67] grpreg_3.4.0 pillar_1.9.0 checkmate_2.2.0
## [70] nortest_1.0-4 foreach_1.5.2 generics_0.1.3
## [73] munsell_0.5.0 scales_1.2.1 rootSolve_1.8.2.3
## [76] gtools_3.9.5 xtable_1.8-4 class_7.3-22
## [79] glue_1.6.2 Hmisc_5.1-0 lmom_2.9
## [82] tools_4.3.2 Exact_3.2 grid_4.3.2
## [85] libcoin_1.0-9 colorspace_2.1-0 nlme_3.1-162
## [88] htmlTable_2.4.1 Formula_1.2-5 cli_3.6.1
## [91] textshaping_0.3.6 fansi_1.0.4 expm_0.999-7
## [94] gtable_0.3.3 pls_2.8-2 sass_0.4.7
## [97] digest_0.6.31 ucminf_1.2.0 htmlwidgets_1.6.2
## [100] farver_2.1.1 htmltools_0.5.5 lifecycle_1.0.3
## [103] httr_1.4.6 multcompView_0.1-9