S5. Shortened 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",..: 4207 77 2660 3420 1541 2601 2041 2234 3600 4018 ...
## $ 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()`).
Clean 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",..: 3600 4018 1355 2457 4593 1215 3269 2531 5304 2352 ...
## $ 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",..: 3600 2457 1215 3269 2531 5304 2352 170 1052 1611 ...
## $ 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$relevance)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2003 6.26 1.42 6.5 6.39 1.48 1 8 7 -0.62 -0.14 0.03relevance.hist <- ggplot(data2_wide, aes(relevance)) +
geom_histogram(colour = "black", fill = "white") + labs(x = "Mean Relevance",
y = "Frequency")
relevance.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 38 rows containing non-finite values (`stat_bin()`).
Relationship Knowledge-Item
describe(data2_wide$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 (Awareness Check Pass)
data2_wide_pass <- subset(data2_wide, s_awareness == "pass")
length(unique(data2_wide_pass$id))## [1] 1383data2_long_pass <- subset(data2_long, s_awareness == "pass")
length(unique(data2_long_pass$id))## [1] 1383H1
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] 1data2_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$s_age))## [1] 2data2_long_pass <- data2_long_pass %>% drop_na(s_age)
sum(is.na(data2_long_pass$s_sex))## [1] 0sum(is.na(data2_long_pass$s_school))## [1] 0sum(is.na(data2_long_pass$s_interest))## [1] 0data2_long_pass$id <- as.numeric(data2_long_pass$id)
data2_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.old guideline no causality statement.new guideline
## 494 944
## causality statement.new guideline
## 1326data2_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 0.2048# Note: The number of model iterations for causality_model7_pass &
# causality_model_8_pass can deviate slightly from their counterparts in
# the original, longer version of the Markdown.
# Therefore, analysis results can vary from the third and forth digit onward.
# The overall significance patterns of effects remain unchanged.
causality_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 45.778 3 6.324e-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 9639.8 -4794.9 9.5524 4 0.04868 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Model parameters can vary compared to the original markdown due to deviations
# in the number of model iterations
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 -4794.91 9639.81 4258(12411) 1.39e+03 8.4e+06
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.496 0.7043
## Number of groups: id 1140
##
## Coefficients:
## Estimate Std. Error
## H4_interactionno causality statement.new guideline -0.1767424 0.0001998
## H4_interactioncausality statement.new guideline 0.1066658 0.0863799
## summaryFaerber -0.1252846 0.0001896
## text_orderFaerber 0.1860169 0.0789123
## s_age -0.0179132 0.0001886
## s_sexmale 0.0986956 0.0789299
## s_schoolReal 0.2083232 0.0903357
## s_schoolAbi 0.6916899 0.0001998
## as.factor(s_interest)5 0.0778646 0.1070096
## as.factor(s_interest)6 -0.0244182 0.1047462
## as.factor(s_interest)7 -0.1531524 0.1169174
## as.factor(s_interest)8 -0.2994093 0.0001998
## z value Pr(>|z|)
## H4_interactionno causality statement.new guideline -884.514 <2e-16 ***
## H4_interactioncausality statement.new guideline 1.235 0.2169
## summaryFaerber -660.898 <2e-16 ***
## text_orderFaerber 2.357 0.0184 *
## s_age -94.993 <2e-16 ***
## s_sexmale 1.250 0.2111
## s_schoolReal 2.306 0.0211 *
## s_schoolAbi 3461.587 <2e-16 ***
## as.factor(s_interest)5 0.728 0.4668
## as.factor(s_interest)6 -0.233 0.8157
## as.factor(s_interest)7 -1.310 0.1902
## as.factor(s_interest)8 -1498.405 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -6.8001518 0.4056923 -16.762
## -5|-4 -5.9451852 0.2624667 -22.651
## -4|-3 -2.6763651 0.0001896 -14118.386
## -3|-2 -2.2462298 0.0001896 -11849.355
## -2|-1 -1.3935607 0.0435217 -32.020
## -1|0 -0.9610444 0.0493534 -19.473
## 0|1 0.1046717 0.0595704 1.757
## 1|2 0.4721950 0.0632637 7.464
## 2|3 1.4655003 0.0768068 19.080
## 3|4 1.7967658 0.0832537 21.582
## 4|5 3.4448469 0.1444983 23.840
## 5|6 3.7689982 0.1654350 22.782
## (49 Beobachtungen als fehlend gelöscht)exp(coef(causality_model8_pass))## -6|-5
## 0.001113606
## -5|-4
## 0.002618417
## -4|-3
## 0.068812828
## -3|-2
## 0.105797349
## -2|-1
## 0.248189992
## -1|0
## 0.382493205
## 0|1
## 1.110345985
## 1|2
## 1.603510042
## 2|3
## 4.329708860
## 3|4
## 6.030113219
## 4|5
## 31.338486170
## 5|6
## 43.336628924
## H4_interactionno causality statement.new guideline
## 0.837995655
## H4_interactioncausality statement.new guideline
## 1.112562377
## summaryFaerber
## 0.882245790
## text_orderFaerber
## 1.204442603
## s_age
## 0.982246284
## s_sexmale
## 1.103730219
## s_schoolReal
## 1.231611114
## s_schoolAbi
## 1.997087626
## as.factor(s_interest)5
## 1.080976261
## as.factor(s_interest)6
## 0.975877487
## as.factor(s_interest)7
## 0.857998979
## as.factor(s_interest)8
## 0.741255974exp(confint(causality_model8_pass))## 2.5 % 97.5 %
## -6|-5 0.000502812 0.002466366
## -5|-4 0.001565405 0.004379767
## -4|-3 0.068787266 0.068838399
## -3|-2 0.105758048 0.105836664
## -2|-1 0.227896956 0.270290018
## -1|0 0.347227429 0.421340711
## 0|1 0.987988610 1.247856699
## 1|2 1.416516121 1.815188981
## 2|3 3.724608720 5.033113604
## 3|4 5.122238855 7.098900786
## 4|5 23.609177459 41.598260555
## 5|6 31.335499538 59.934050332
## H4_interactionno causality statement.new guideline 0.837667529 0.838323909
## H4_interactioncausality statement.new guideline 0.939285546 1.317804843
## summaryFaerber 0.881918057 0.882573645
## text_orderFaerber 1.031848331 1.405906218
## s_age 0.981883314 0.982609389
## s_sexmale 0.945535172 1.288392470
## s_schoolReal 1.031762453 1.470169738
## s_schoolAbi 1.996305645 1.997869914
## as.factor(s_interest)5 0.876454587 1.333223300
## as.factor(s_interest)6 0.794758457 1.198272080
## as.factor(s_interest)7 0.682286093 1.078964169
## as.factor(s_interest)8 0.740965727 0.741546334nagelkerke(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 0.0147296
## Cox and Snell (ML) 0.0619706
## Nagelkerke (Cragg and Uhler) 0.0627865
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -13 -71.683 143.37 4.4487e-24
##
## $Number.of.observations
##
## Model: 2241
## Null: 2241
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H4test_pass = emmeans(causality_model8_pass, ~ H4_interaction)
pairs(H4test_pass, adjust = "tukey")## contrast
## no causality statement.old guideline - no causality statement.new guideline
## no causality statement.old guideline - causality statement.new guideline
## no causality statement.new guideline - causality statement.new guideline
## estimate SE df z.ratio p.value
## 0.1767 0.0001998 Inf 884.514 <.0001
## -0.1067 0.0863799 Inf -1.235 0.4326
## -0.2834 0.0863799 Inf -3.281 0.0030
##
## Results are averaged over the levels of: summary, text_order, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimatescld(H4test_pass, Letters = letters)## H4_interaction emmean SE df asymp.LCL asymp.UCL
## no causality statement.new guideline 0.0254 0.0726 Inf -0.1169 0.168
## no causality statement.old guideline 0.2022 0.0726 Inf 0.0599 0.344
## causality statement.new guideline 0.3088 0.0900 Inf 0.1324 0.485
## .group
## a
## b
## b
##
## Results are averaged over the levels of: summary, text_order, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimates
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.# Changing reference group for H4_interaction to new_no causality"
# for further testing. Once again, deviations in model iterations can cause
# slightly different results compared to the original Markdown.
data2_long_pass_reg$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 3858(11292) 1.39e+03 8.4e+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 0.1784537 0.1078954
## H4_interactioncausality statement.new guideline 0.2788842 0.0913016
## summaryFaerber -0.1259872 0.0001896
## text_orderFaerber 0.1734859 0.0796439
## s_age -0.0177471 0.0001886
## s_sexmale 0.0883798 0.0795865
## s_schoolReal 0.2067943 0.0906230
## s_schoolAbi 0.6943997 0.0001998
## as.factor(s_interest)5 0.0918665 0.1083699
## as.factor(s_interest)6 -0.0173139 0.1055820
## as.factor(s_interest)7 -0.1572691 0.1185124
## as.factor(s_interest)8 -0.3082968 0.0001998
## z value Pr(>|z|)
## H4_interactionno causality statement.old guideline 1.654 0.09814 .
## H4_interactioncausality statement.new guideline 3.055 0.00225 **
## summaryFaerber -664.607 < 2e-16 ***
## text_orderFaerber 2.178 0.02939 *
## s_age -94.078 < 2e-16 ***
## s_sexmale 1.110 0.26679
## s_schoolReal 2.282 0.02249 *
## s_schoolAbi 3475.160 < 2e-16 ***
## as.factor(s_interest)5 0.848 0.39660
## as.factor(s_interest)6 -0.164 0.86974
## as.factor(s_interest)7 -1.327 0.18450
## as.factor(s_interest)8 -1542.888 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -6.6247530 0.4062325 -16.308
## -5|-4 -5.7735176 0.2649810 -21.788
## -4|-3 -2.5009338 0.0393879 -63.495
## -3|-2 -2.0710706 0.0001998 -10364.765
## -2|-1 -1.2171552 0.0438892 -27.732
## -1|0 -0.7854131 0.0498319 -15.761
## 0|1 0.2811497 0.0602391 4.667
## 1|2 0.6473577 0.0639129 10.129
## 2|3 1.6422687 0.0774550 21.203
## 3|4 1.9721289 0.0838210 23.528
## 4|5 3.6194489 0.1448116 24.994
## 5|6 3.9433681 0.1656867 23.800
## (49 Beobachtungen als fehlend gelöscht)exp(coef(causality_model8_pass))## -6|-5
## 0.001327108
## -5|-4
## 0.003108803
## -4|-3
## 0.082008382
## -3|-2
## 0.126050756
## -2|-1
## 0.296071234
## -1|0
## 0.455931338
## 0|1
## 1.324651929
## 1|2
## 1.910486034
## 2|3
## 5.166878189
## 3|4
## 7.185958480
## 4|5
## 37.316996650
## 5|6
## 51.592073783
## H4_interactionno causality statement.old guideline
## 1.195367547
## H4_interactioncausality statement.new guideline
## 1.321654242
## summaryFaerber
## 0.881626133
## text_orderFaerber
## 1.189443906
## s_age
## 0.982409426
## s_sexmale
## 1.092402891
## s_schoolReal
## 1.229729564
## s_schoolAbi
## 2.002506687
## as.factor(s_interest)5
## 1.096218473
## as.factor(s_interest)6
## 0.982835138
## as.factor(s_interest)7
## 0.854474073
## as.factor(s_interest)8
## 0.734697224exp(confint(causality_model8_pass))## 2.5 % 97.5 %
## -6|-5 5.985778e-04 0.002942335
## -5|-4 1.849443e-03 0.005225711
## -4|-3 7.591563e-02 0.088590121
## -3|-2 1.260014e-01 0.126100132
## -2|-1 2.716675e-01 0.322667156
## -1|0 4.135066e-01 0.502708728
## 0|1 1.177135e+00 1.490655898
## 1|2 1.685548e+00 2.165442363
## 2|3 4.439135e+00 6.013926005
## 3|4 6.097280e+00 8.469022221
## 4|5 2.809589e+01 49.564478505
## 5|6 3.728638e+01 71.386451629
## H4_interactionno causality statement.old guideline 9.675218e-01 1.476869642
## H4_interactioncausality statement.new guideline 1.105101e+00 1.580643411
## summaryFaerber 8.812986e-01 0.881953756
## text_orderFaerber 1.017539e+00 1.390391110
## s_age 9.820463e-01 0.982772722
## s_sexmale 9.346279e-01 1.276811995
## s_schoolReal 1.029606e+00 1.468750587
## s_schoolAbi 2.001723e+00 2.003291095
## as.factor(s_interest)5 8.864464e-01 1.355631781
## as.factor(s_interest)6 7.991147e-01 1.208793809
## as.factor(s_interest)7 6.773622e-01 1.077895867
## as.factor(s_interest)8 7.344095e-01 0.734985014nagelkerke(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) 0.0619877
## Nagelkerke (Cragg and Uhler) 0.0628038
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -13 -71.703 143.41 4.3654e-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
## -0.178 0.1079 Inf -1.654 0.2232
## -0.279 0.0913 Inf -3.055 0.0064
## -0.100 0.1166 Inf -0.861 0.6648
##
## 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 0.0782 Inf -0.12822 0.178
## no causality statement.old guideline 0.203 0.1079 Inf -0.00808 0.415
## causality statement.new guideline 0.304 0.0902 Inf 0.12717 0.481
## .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
# Once again, deviations in model iterations compared to the original Markdown
# can cause slightly different results.
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 -1946.70 3941.41 2742(7893) 5.83e+02 1.0e+07
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.4968 0.7048
## Number of groups: id 465
##
## Coefficients:
## Estimate Std. Error z value
## H4_interactioncausality statement.new guideline 0.1030131 0.0003087 333.680
## summaryFaerber -0.0137720 0.0002929 -47.023
## text_orderFaerber 0.1533856 0.1252755 1.224
## s_age -0.0187952 0.0002916 -64.467
## s_sexmale 0.0692701 0.0003087 224.379
## s_schoolReal 0.0396559 0.0003087 128.453
## s_schoolAbi 0.7117213 0.1426882 4.988
## as.factor(s_interest)5 0.1420950 0.1602276 0.887
## as.factor(s_interest)6 0.0695439 0.1709242 0.407
## as.factor(s_interest)7 -0.0276561 0.1895659 -0.146
## as.factor(s_interest)8 -0.1519852 0.2047080 -0.742
## Pr(>|z|)
## H4_interactioncausality statement.new guideline < 2e-16 ***
## summaryFaerber < 2e-16 ***
## text_orderFaerber 0.221
## s_age < 2e-16 ***
## s_sexmale < 2e-16 ***
## s_schoolReal < 2e-16 ***
## s_schoolAbi 6.1e-07 ***
## as.factor(s_interest)5 0.375
## as.factor(s_interest)6 0.684
## as.factor(s_interest)7 0.884
## as.factor(s_interest)8 0.458
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -6.9056679 0.7116693 -9.703
## -5|-4 -5.8028048 0.4172264 -13.908
## -4|-3 -2.6876127 0.1282342 -20.959
## -3|-2 -2.2936112 0.1172157 -19.567
## -2|-1 -1.3970174 0.1001405 -13.951
## -1|0 -0.9333214 0.0939286 -9.936
## 0|1 0.1627729 0.0795859 2.045
## 1|2 0.4996759 0.0731177 6.834
## 2|3 1.4471730 0.0002929 4941.213
## 3|4 1.8132166 0.0002929 6191.050
## 4|5 3.6380499 0.2047215 17.771
## 5|6 3.9117980 0.2365570 16.536
## (21 Beobachtungen als fehlend gelöscht)exp(coef(causality_model10_pass1))## -6|-5
## 0.001002090
## -5|-4
## 0.003019075
## -4|-3
## 0.068043185
## -3|-2
## 0.100901430
## -2|-1
## 0.247333552
## -1|0
## 0.393245397
## 0|1
## 1.176769375
## 1|2
## 1.648186979
## 2|3
## 4.251079634
## 3|4
## 6.130133711
## 4|5
## 38.017626594
## 5|6
## 49.988751993
## H4_interactioncausality statement.new guideline
## 1.108505925
## summaryFaerber
## 0.986322408
## text_orderFaerber
## 1.165774459
## s_age
## 0.981380279
## s_sexmale
## 1.071725621
## s_schoolReal
## 1.040452703
## s_schoolAbi
## 2.037495337
## as.factor(s_interest)5
## 1.152686098
## as.factor(s_interest)6
## 1.072019121
## as.factor(s_interest)7
## 0.972722843
## as.factor(s_interest)8
## 0.859000973exp(confint(causality_model10_pass1))## 2.5 % 97.5 %
## -6|-5 2.483891e-04 0.004042784
## -5|-4 1.332693e-03 0.006839394
## -4|-3 5.292142e-02 0.087485848
## -3|-2 8.019053e-02 0.126961355
## -2|-1 2.032560e-01 0.300969675
## -1|0 3.271233e-01 0.472732925
## 0|1 1.006810e+00 1.375418897
## 1|2 1.428133e+00 1.902148505
## 2|3 4.248640e+00 4.253520584
## 3|4 6.126616e+00 6.133653593
## 4|5 2.545222e+01 56.786395131
## 5|6 3.144232e+01 79.474905310
## H4_interactioncausality statement.new guideline 1.107835e+00 1.109176860
## summaryFaerber 9.857564e-01 0.986888754
## text_orderFaerber 9.119684e-01 1.490216178
## s_age 9.808197e-01 0.981941227
## s_sexmale 1.071077e+00 1.072374296
## s_schoolReal 1.039823e+00 1.041082447
## s_schoolAbi 1.540424e+00 2.694964597
## as.factor(s_interest)5 8.420252e-01 1.577963827
## as.factor(s_interest)6 7.668520e-01 1.498626803
## as.factor(s_interest)7 6.708575e-01 1.410418301
## as.factor(s_interest)8 5.751034e-01 1.283043591nagelkerke(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 0.0149556
## Cox and Snell (ML) 0.0626937
## Nagelkerke (Cragg and Uhler) 0.0635310
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -12 -29.556 59.112 3.2748e-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
## no causality statement.old guideline - causality statement.new guideline
## estimate SE df z.ratio p.value
## -0.103 0.000309 Inf -333.680 <.0001
##
## 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 0.192 0.108 Inf -0.0202 0.404
## causality statement.new guideline 0.295 0.108 Inf 0.0828 0.507
## .group
## a
## 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
## 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 -1888.59 3825.17 2734(7833) 5.28e+02 1.8e+07
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.5044 0.7102
## Number of groups: id 451
##
## Coefficients:
## Estimate Std. Error z value
## H4_interactioncausality statement.new guideline 0.1257058 0.0003288 382.266
## summaryFaerber -0.1468639 0.0003076 -477.434
## text_orderFaerber 0.1254237 0.0003288 381.409
## s_age -0.0188970 0.0003052 -61.911
## s_sexmale 0.0199916 0.1205199 0.166
## s_schoolReal 0.0433673 0.1420353 0.305
## s_schoolAbi 0.6290552 0.0003288 1912.933
## as.factor(s_interest)5 0.2701188 0.1624932 1.662
## as.factor(s_interest)6 0.0849287 0.0003288 258.265
## as.factor(s_interest)7 -0.0314668 0.1701220 -0.185
## as.factor(s_interest)8 -0.1163818 0.1769264 -0.658
## Pr(>|z|)
## H4_interactioncausality statement.new guideline <2e-16 ***
## summaryFaerber <2e-16 ***
## text_orderFaerber <2e-16 ***
## s_age <2e-16 ***
## s_sexmale 0.8683
## s_schoolReal 0.7601
## s_schoolAbi <2e-16 ***
## as.factor(s_interest)5 0.0964 .
## as.factor(s_interest)6 <2e-16 ***
## as.factor(s_interest)7 0.8533
## as.factor(s_interest)8 0.5107
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## -6|-5 -7.5479671 0.9737526 -7.751
## -5|-4 -5.7848992 0.3987859 -14.506
## -4|-3 -2.7182867 0.0640073 -42.468
## -3|-2 -2.2906987 0.0003076 -7446.848
## -2|-1 -1.4298641 0.0003076 -4648.335
## -1|0 -1.0263013 0.0450119 -22.801
## 0|1 0.0351914 0.0741094 0.475
## 1|2 0.3687718 0.0807433 4.567
## 2|3 1.3933938 0.1061048 13.132
## 3|4 1.6574412 0.1145196 14.473
## 4|5 3.2154967 0.2024812 15.880
## 5|6 3.3274795 0.2114865 15.734
## (20 Beobachtungen als fehlend gelöscht)exp(coef(causality_model10_pass2))## -6|-5
## 5.271807e-04
## -5|-4
## 3.073620e-03
## -4|-3
## 6.598771e-02
## -3|-2
## 1.011957e-01
## -2|-1
## 2.393414e-01
## -1|0
## 3.583299e-01
## 0|1
## 1.035818e+00
## 1|2
## 1.445958e+00
## 2|3
## 4.028499e+00
## 3|4
## 5.245871e+00
## 4|5
## 2.491567e+01
## 5|6
## 2.786801e+01
## H4_interactioncausality statement.new guideline
## 1.133949e+00
## summaryFaerber
## 8.634115e-01
## text_orderFaerber
## 1.133629e+00
## s_age
## 9.812805e-01
## s_sexmale
## 1.020193e+00
## s_schoolReal
## 1.044321e+00
## s_schoolAbi
## 1.875837e+00
## as.factor(s_interest)5
## 1.310120e+00
## as.factor(s_interest)6
## 1.088639e+00
## as.factor(s_interest)7
## 9.690231e-01
## as.factor(s_interest)8
## 8.901353e-01exp(confint(causality_model10_pass2))## 2.5 % 97.5 %
## -6|-5 7.818074e-05 0.003554834
## -5|-4 1.406705e-03 0.006715794
## -4|-3 5.820763e-02 0.074807686
## -3|-2 1.011347e-01 0.101256761
## -2|-1 2.391972e-01 0.239485791
## -1|0 3.280717e-01 0.391378742
## 0|1 8.957800e-01 1.197748021
## 1|2 1.234317e+00 1.693886605
## 2|3 3.272100e+00 4.959750528
## 3|4 4.191200e+00 6.565938307
## 4|5 1.675407e+01 37.053119016
## 5|6 1.841146e+01 42.181654180
## H4_interactioncausality statement.new guideline 1.133218e+00 1.134679614
## summaryFaerber 8.628911e-01 0.863932190
## text_orderFaerber 1.132898e+00 1.134359604
## s_age 9.806936e-01 0.981867670
## s_sexmale 8.055557e-01 1.292019127
## s_schoolReal 7.905575e-01 1.379541770
## s_schoolAbi 1.874629e+00 1.877046791
## as.factor(s_interest)5 9.527887e-01 1.801464081
## as.factor(s_interest)6 1.087938e+00 1.089341364
## as.factor(s_interest)7 6.942662e-01 1.352515403
## as.factor(s_interest)8 6.292975e-01 1.259087763nagelkerke(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 0.0138339
## Cox and Snell (ML) 0.0580506
## Nagelkerke (Cragg and Uhler) 0.0588307
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -12 -26.493 52.986 4.143e-07
##
## $Number.of.observations
##
## Model: 886
## Null: 886
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"H4test_pass2 = emmeans(causality_model10_pass2, ~ H4_interaction)
pairs(H4test_pass2, adjust = "tukey")## contrast
## no causality statement.old guideline - causality statement.new guideline
## estimate SE df z.ratio p.value
## -0.126 0.000329 Inf -382.266 <.0001
##
## Results are averaged over the levels of: summary, text_order, s_sex, s_school, s_interest
## Note: contrasts are still on the as.factor scalecld(H4test_pass2, Letters = letters)## H4_interaction emmean SE df asymp.LCL asymp.UCL
## no causality statement.old guideline 0.259 0.121 Inf 0.0216 0.496
## causality statement.new guideline 0.384 0.121 Inf 0.1473 0.621
## .group
## a
## b
##
## Results are averaged over the levels of: summary, text_order, s_sex, s_school, s_interest
## Results are given on the as.factor (not the response) scale.
## Confidence level used: 0.95
## Note: contrasts are still on the as.factor scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.H4 Graphical
describeBy(data2_long_pass_reg$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 3834(13862) 1.42e-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 -0.01269 0.13007 -0.098 0.9223
## summaryFaerber 0.32644 0.07213 4.526 6.02e-06 ***
## text_orderFaerber 0.17663 0.09977 1.770 0.0767 .
## s_age -0.01509 0.00335 -4.506 6.62e-06 ***
## s_sexmale -0.15135 0.10087 -1.500 0.1335
## s_schoolReal 0.59538 0.12676 4.697 2.64e-06 ***
## s_schoolAbi 1.06255 0.12743 8.338 < 2e-16 ***
## as.factor(s_interest)5 0.25838 0.15740 1.641 0.1007
## as.factor(s_interest)6 0.23129 0.15773 1.466 0.1426
## as.factor(s_interest)7 0.28575 0.16973 1.684 0.0923 .
## as.factor(s_interest)8 -0.12213 0.17245 -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 0.3827 -15.850
## -5|-4 -5.4247 0.3359 -16.149
## -4|-3 -3.6322 0.2787 -13.031
## -3|-2 -3.1546 0.2716 -11.616
## -2|-1 -2.2396 0.2624 -8.534
## -1|0 -1.6774 0.2589 -6.479
## 0|1 -0.2711 0.2549 -1.063
## 1|2 0.1036 0.2549 0.407
## 2|3 0.6583 0.2555 2.576
## 3|4 0.8615 0.2560 3.365
## 4|5 1.3917 0.2577 5.401
## 5|6 1.5920 0.2585 6.159
## (52 Beobachtungen als fehlend gelöscht)exp(coef(funding_model8_pass))## -6|-5 -5|-4 -4|-3
## 0.002322492 0.004406519 0.026458062
## -3|-2 -2|-1 -1|0
## 0.042654264 0.106498881 0.186859242
## 0|1 1|2 2|3
## 0.762575887 1.109183155 1.931517266
## 3|4 4|5 5|6
## 2.366640905 4.021686987 4.913729478
## versionnew guideline summaryFaerber text_orderFaerber
## 0.987393173 1.386029205 1.193189593
## s_age s_sexmale s_schoolReal
## 0.985018957 0.859549122 1.813716203
## s_schoolAbi as.factor(s_interest)5 as.factor(s_interest)6
## 2.893747937 1.294824189 1.260220748
## as.factor(s_interest)7 as.factor(s_interest)8
## 1.330755494 0.885034169exp(confint(funding_model8_pass))## 2.5 % 97.5 %
## -6|-5 0.001097048 0.004916801
## -5|-4 0.002281235 0.008511798
## -4|-3 0.015321201 0.045690222
## -3|-2 0.025049043 0.072632966
## -2|-1 0.063674548 0.178124731
## -1|0 0.112500938 0.310365202
## 0|1 0.462727235 1.256727375
## 1|2 0.673050736 1.827926493
## 2|3 1.170516364 3.187276198
## 3|4 1.432891935 3.908870611
## 4|5 2.426994812 6.664194805
## 5|6 2.960716303 8.155032404
## versionnew guideline 0.765202612 1.274100825
## summaryFaerber 1.203306667 1.596498224
## text_orderFaerber 0.981256336 1.450896523
## s_age 0.978572299 0.991508085
## s_sexmale 0.705353898 1.047452484
## s_schoolReal 1.414736096 2.325215617
## s_schoolAbi 2.254189092 3.714762506
## as.factor(s_interest)5 0.951107949 1.762754358
## as.factor(s_interest)6 0.925087447 1.716763467
## as.factor(s_interest)7 0.954170858 1.855967587
## as.factor(s_interest)8 0.631206150 1.240934488nagelkerke(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 4254(15270) 1.67e-02 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 -0.169678 0.127424 -1.332 0.1830
## summaryFaerber 0.757005 0.072908 10.383 < 2e-16 ***
## text_orderFaerber 0.062416 0.097659 0.639 0.5227
## s_age -0.005239 0.003260 -1.607 0.1081
## s_sexmale -0.048376 0.098816 -0.490 0.6244
## s_schoolReal 0.664331 0.124804 5.323 1.02e-07 ***
## s_schoolAbi 1.394265 0.126523 11.020 < 2e-16 ***
## as.factor(s_interest)5 0.288072 0.155310 1.855 0.0636 .
## as.factor(s_interest)6 0.321372 0.155098 2.072 0.0383 *
## as.factor(s_interest)7 0.116883 0.166549 0.702 0.4828
## as.factor(s_interest)8 -0.133687 0.169154 -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 -4.9021 0.3351 -14.627
## -5|-4 -2.9192 0.2694 -10.835
## -4|-3 -2.5253 0.2640 -9.564
## -3|-2 -1.8114 0.2570 -7.047
## -2|-1 -1.4045 0.2543 -5.523
## -1|0 -0.7605 0.2515 -3.024
## 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 7.745
## 4|5 2.1324 0.2573 8.287
## 5|6 3.0917 0.2637 11.724
## 6|7 3.2405 0.2649 12.235
## (89 Beobachtungen als fehlend gelöscht)exp(coef(coi_model8_pass))## -7|-6 -6|-5 -5|-4
## 0.003825571 0.007431182 0.053979153
## -4|-3 -3|-2 -2|-1
## 0.080032269 0.163420748 0.245478106
## -1|0 0|1 1|2
## 0.467437836 1.666693108 2.916509733
## 2|3 3|4 4|5
## 3.593049896 7.291493433 8.434991184
## 5|6 6|7 versionnew guideline
## 22.015030128 25.547245358 0.843936571
## summaryFaerber text_orderFaerber s_age
## 2.131880601 1.064404886 0.994774804
## s_sexmale s_schoolReal s_schoolAbi
## 0.952775412 1.943189618 4.032008559
## as.factor(s_interest)5 as.factor(s_interest)6 as.factor(s_interest)7
## 1.333853436 1.379019169 1.123987704
## as.factor(s_interest)8
## 0.874863501exp(confint(coi_model8_pass))## 2.5 % 97.5 %
## -7|-6 0.001780065 0.008221605
## -6|-5 0.003852812 0.014333027
## -5|-4 0.031833922 0.091529689
## -4|-3 0.047699972 0.134280249
## -3|-2 0.098743585 0.270461527
## -2|-1 0.149122035 0.404095214
## -1|0 0.285553768 0.765173341
## 0|1 1.019902939 2.723657137
## 1|2 1.779245352 4.780694812
## 2|3 2.188403273 5.899281778
## 3|4 4.410385960 12.054699285
## 4|5 5.093812203 13.967746247
## 5|6 13.129535685 36.913837869
## 6|7 15.201804654 42.933175387
## versionnew guideline 0.657424545 1.083362254
## summaryFaerber 1.848005688 2.459361963
## text_orderFaerber 0.878980704 1.288944974
## s_age 0.988438182 1.001152049
## s_sexmale 0.785016059 1.156385242
## s_schoolReal 1.521534723 2.481695511
## s_schoolAbi 3.146480790 5.166754257
## as.factor(s_interest)5 0.983801813 1.808458743
## as.factor(s_interest)6 1.017538826 1.868915287
## as.factor(s_interest)7 0.810950957 1.557860370
## as.factor(s_interest)8 0.627995279 1.218776910nagelkerke(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] 1.059852Session 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 gdata_3.0.0 systemfonts_1.0.4
## [13] vctrs_0.6.2 stringr_1.5.0 quadprog_1.5-8
## [16] crayon_1.5.2 pkgconfig_2.0.3 shape_1.4.6
## [19] fastmap_1.1.1 backports_1.4.1 labeling_0.4.2
## [22] pbivnorm_0.6.0 utf8_1.2.3 rmarkdown_2.23
## [25] ragg_1.2.5 purrr_1.0.1 xfun_0.39
## [28] glmnet_4.1-7 modeltools_0.2-23 cachem_1.0.8
## [31] jsonlite_1.8.7 highr_0.10 cluster_2.1.4
## [34] parallel_4.3.2 DescTools_0.99.49 R6_2.5.1
## [37] stringi_1.7.12 RColorBrewer_1.1-3 coin_1.4-2
## [40] bslib_0.5.0 boot_1.3-28.1 lmtest_0.9-40
## [43] jquerylib_0.1.4 cellranger_1.1.0 numDeriv_2016.8-1.1
## [46] estimability_1.4.1 Rcpp_1.0.10 iterators_1.0.14
## [49] knitr_1.43 zoo_1.8-12 base64enc_0.1-3
## [52] nnet_7.3-19 Matrix_1.6-0 tidyselect_1.2.0
## [55] rstudioapi_0.15.0 abind_1.4-5 yaml_2.3.7
## [58] codetools_0.2-19 lattice_0.21-9 tibble_3.2.1
## [61] withr_2.5.0 coda_0.19-4 evaluate_0.21
## [64] foreign_0.8-85 proxy_0.4-27 grpreg_3.4.0
## [67] pillar_1.9.0 checkmate_2.2.0 nortest_1.0-4
## [70] foreach_1.5.2 generics_0.1.3 munsell_0.5.0
## [73] scales_1.2.1 rootSolve_1.8.2.3 gtools_3.9.5
## [76] xtable_1.8-4 class_7.3-22 glue_1.6.2
## [79] Hmisc_5.1-0 lmom_2.9 tools_4.3.2
## [82] Exact_3.2 grid_4.3.2 libcoin_1.0-9
## [85] colorspace_2.1-0 nlme_3.1-162 htmlTable_2.4.1
## [88] Formula_1.2-5 cli_3.6.1 textshaping_0.3.6
## [91] fansi_1.0.4 expm_0.999-7 gtable_0.3.3
## [94] pls_2.8-2 sass_0.4.7 digest_0.6.31
## [97] ucminf_1.2.0 htmlwidgets_1.6.2 farver_2.1.1
## [100] htmltools_0.5.5 lifecycle_1.0.3 httr_1.4.6
## [103] multcompView_0.1-9