## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4
## ✓ tibble 3.1.0 ✓ dplyr 1.0.5
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
The manuscript builds on three data sets, two of them are generated by the authors. These two data sets are integrated into this report. They can be extracted by clicking here (Pilot Study) and here (Main Study).
We preregistered our hypotheses prior to data analyses and will repeat them in the following.
Concerning their reasons for trusting educational scientists, teachers will score
Concerning their reasons for mistrusting educational scientists, teachers will score
Concerning their reasons for trusting scientists in general, teachers will score
Concerning their reasons for mistrusting scientists in general, teachers will score
Concerning their reasons for trusting scientists in general, a general population sample matched for age and SES will score
Concerning their reasons for mistrusting scientists in general, a general population sample matched for age and SES will score
library(tidyverse)
library(haven)
data_wb <- read_spss("data/wiss_baro_fr.sav")
data_mtmm <- read_spss("data/data_mtmm_fr.sav")
data_ttest <- read_spss("data/data_ttest_fr.sav")
data_ttest
(Main Study
)knitr::kable(tibble(labels = sjlabelled::get_label(data_ttest),
names = names(data_ttest)))
labels | names |
---|---|
Alter numerisch | altq |
Welchem Geschlecht ordnen Sie sich zu? | d1 |
F1.1: Politik | f1_1 |
F1.2: Sport | f1_2 |
F1.3: Wissenschaft und Forschung | f1_3 |
F3: Wie sehr vertrauen Sie in Wissenschaft und Forschung? | f3 |
F4.1: Weil Wissenschaftler/innen Experten auf ihrem Feld sind. | f4_1 |
F4.2: Weil Wissenschaftler/innen nach Regeln und Standards arbeiten. | f4_2 |
F4.3: Weil Wissenschaftler/innen im Interesse der Öffentlichkeit forschen. | f4_3 |
F5.1: Weil Wissenschaftler/innen häufig Fehler machen. | f5_1 |
F5.2: Weil Wissenschaftler/innen oft Ergebnisse ihren eigenen Erwartungen anpassen. | f5_2 |
F5.3: Weil Wissenschaftler/innen stark abhängig von ihren Geldgebern sind. | f5_3 |
F8: Wie sehr vertrauen Sie in Bildungswissenschaften und Bildungsforschung? | f8 |
F9.1: Weil Bildungswissenschaftler/innen Experten auf ihrem Feld sind. | f9_1 |
F9.2: Weil Bildungswissenschaftler/innen nach Regeln und Standards arbeiten. | f9_2 |
F9.3: Weil Bildungswissenschaftler/innen im Interesse der Öffentlichkeit forschen. | f9_3 |
F10.1: Weil Bildungswissenschaftler/innen häufig Fehler machen. | f10_1 |
F10.2: Weil Bildungswissenschaftler/innen oft Ergebnisse ihren eigenen Erwartungen anpassen. | f10_2 |
F10.3: Weil Bildungswissenschaftler/innen stark abhängig von ihren Geldgebern sind. | f10_3 |
Berufsjahre | berj |
Pilot Study
# Define misc skimming functions
library(skimr)
skewn <- function(x) DescTools::Skew(x, na.rm = T)
kurto <- function(x) DescTools::Kurt(x, na.rm = T)
maxabszvalue <- function(x) max(abs(scale(na.omit(x))))
my_skim <- skim_with(numeric = sfl(skewn, kurto, maxabszvalue))
data_mtmm%>%
skim()
Name | Piped data |
Number of rows | 504 |
Number of columns | 29 |
_______________________ | |
Column type frequency: | |
numeric | 29 |
________________________ | |
Group variables | None |
Variable type: numeric
skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
---|---|---|---|---|---|---|---|---|---|---|
reas_exp | 18 | 0.96 | 4.06 | 0.86 | 1 | 4 | 4.0 | 5 | 5 | ▁▁▃▇▇ |
reas_int | 19 | 0.96 | 4.04 | 0.87 | 1 | 4 | 4.0 | 5 | 5 | ▁▁▃▇▇ |
reas_ben | 17 | 0.97 | 3.77 | 0.92 | 1 | 3 | 4.0 | 4 | 5 | ▁▂▆▇▅ |
reas_exp_r | 31 | 0.94 | 2.78 | 0.95 | 1 | 2 | 3.0 | 3 | 5 | ▂▇▇▃▁ |
reas_int_r | 33 | 0.93 | 3.15 | 1.04 | 1 | 2 | 3.0 | 4 | 5 | ▁▆▇▇▂ |
reas_ben_r | 31 | 0.94 | 3.77 | 0.94 | 1 | 3 | 4.0 | 4 | 5 | ▁▂▅▇▅ |
reas_alt_exp | 20 | 0.96 | 4.13 | 0.78 | 1 | 4 | 4.0 | 5 | 5 | ▁▁▃▇▆ |
reas_alt_int | 24 | 0.95 | 4.06 | 0.82 | 1 | 4 | 4.0 | 5 | 5 | ▁▁▃▇▆ |
reas_alt_ben | 22 | 0.96 | 3.74 | 0.85 | 1 | 3 | 4.0 | 4 | 5 | ▁▁▆▇▃ |
reas_alt_exp_r | 45 | 0.91 | 2.96 | 0.86 | 1 | 2 | 3.0 | 3 | 5 | ▁▅▇▂▁ |
reas_alt_int_r | 48 | 0.90 | 2.95 | 0.98 | 1 | 2 | 3.0 | 4 | 5 | ▁▆▇▅▁ |
reas_alt_ben_r | 32 | 0.94 | 3.87 | 0.93 | 1 | 3 | 4.0 | 5 | 5 | ▁▁▅▇▅ |
meti_exp_01 | 0 | 1.00 | 3.93 | 0.91 | 1 | 3 | 4.0 | 5 | 5 | ▁▁▅▇▅ |
meti_exp_02 | 0 | 1.00 | 4.10 | 0.89 | 1 | 4 | 4.0 | 5 | 5 | ▁▁▃▇▇ |
meti_exp_03 | 0 | 1.00 | 4.04 | 0.90 | 1 | 4 | 4.0 | 5 | 5 | ▁▁▃▇▇ |
meti_exp_04 | 0 | 1.00 | 3.97 | 0.94 | 0 | 3 | 4.0 | 5 | 5 | ▁▁▃▇▆ |
meti_exp_05 | 0 | 1.00 | 3.87 | 0.86 | 1 | 3 | 4.0 | 4 | 5 | ▁▁▅▇▃ |
meti_exp_06 | 0 | 1.00 | 4.00 | 0.88 | 1 | 3 | 4.0 | 5 | 5 | ▁▁▃▇▆ |
meti_int_01 | 0 | 1.00 | 3.61 | 0.83 | 1 | 3 | 4.0 | 4 | 5 | ▁▁▆▇▂ |
meti_int_02 | 0 | 1.00 | 3.60 | 0.84 | 1 | 3 | 4.0 | 4 | 5 | ▁▁▇▇▂ |
meti_int_03 | 0 | 1.00 | 3.57 | 0.82 | 1 | 3 | 4.0 | 4 | 5 | ▁▁▇▇▂ |
meti_int_04 | 0 | 1.00 | 3.58 | 0.83 | 1 | 3 | 4.0 | 4 | 5 | ▁▁▇▇▂ |
meti_ben_01 | 0 | 1.00 | 3.45 | 0.85 | 1 | 3 | 3.0 | 4 | 5 | ▁▁▇▆▂ |
meti_ben_02 | 0 | 1.00 | 3.53 | 0.88 | 1 | 3 | 3.0 | 4 | 5 | ▁▁▇▆▂ |
meti_ben_03 | 0 | 1.00 | 3.76 | 0.90 | 1 | 3 | 4.0 | 4 | 5 | ▁▁▆▇▃ |
meti_ben_04 | 0 | 1.00 | 3.40 | 0.87 | 1 | 3 | 3.0 | 4 | 5 | ▁▁▇▅▂ |
education | 0 | 1.00 | 2.48 | 0.73 | 1 | 2 | 3.0 | 3 | 5 | ▂▅▇▁▁ |
sex | 0 | 1.00 | 1.50 | 0.50 | 1 | 1 | 1.5 | 2 | 2 | ▇▁▁▁▇ |
age | 0 | 1.00 | 5.16 | 1.96 | 2 | 3 | 5.0 | 7 | 8 | ▆▃▃▃▇ |
sjlabelled::get_labels(data_mtmm$meti_exp_01)
## NULL
library(corrplot)
## corrplot 0.84 loaded
corrplot(cor(data_mtmm%>%
na.omit(.),
method = "kendall"))
reactable::reactable(round(
cor(data_mtmm%>%
na.omit(.),
method = "kendall"),
2))
Main Study
data_ttest%>%
mutate_all(as.numeric)%>%
my_skim(.)
Name | Piped data |
Number of rows | 414 |
Number of columns | 20 |
_______________________ | |
Column type frequency: | |
numeric | 20 |
________________________ | |
Group variables | None |
Variable type: numeric
skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | skewn | kurto | maxabszvalue |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
altq | 0 | 1.00 | 47.70 | 10.82 | 27 | 38.25 | 48.5 | 57 | 65 | ▅▇▆▇▇ | -0.11 | -1.15 | 1.91 |
d1 | 0 | 1.00 | 1.67 | 0.47 | 1 | 1.00 | 2.0 | 2 | 2 | ▃▁▁▁▇ | -0.72 | -1.49 | 1.42 |
f1_1 | 0 | 1.00 | 4.01 | 0.89 | 1 | 3.00 | 4.0 | 5 | 5 | ▁▁▅▇▆ | -0.69 | 0.18 | 3.38 |
f1_2 | 0 | 1.00 | 2.98 | 1.27 | 1 | 2.00 | 3.0 | 4 | 5 | ▅▇▇▆▅ | 0.09 | -1.03 | 1.59 |
f1_3 | 0 | 1.00 | 3.81 | 0.84 | 2 | 3.00 | 4.0 | 4 | 5 | ▁▅▁▇▃ | -0.25 | -0.56 | 2.16 |
f3 | 2 | 1.00 | 3.89 | 0.57 | 1 | 4.00 | 4.0 | 4 | 5 | ▁▁▂▇▁ | -0.40 | 1.56 | 5.06 |
f4_1 | 1 | 1.00 | 4.20 | 0.64 | 1 | 4.00 | 4.0 | 5 | 5 | ▁▁▁▇▅ | -0.64 | 1.65 | 5.01 |
f4_2 | 6 | 0.99 | 4.06 | 0.78 | 1 | 4.00 | 4.0 | 5 | 5 | ▁▁▂▇▃ | -0.91 | 1.48 | 3.93 |
f4_3 | 1 | 1.00 | 3.10 | 0.96 | 1 | 2.00 | 3.0 | 4 | 5 | ▁▅▇▇▁ | -0.15 | -0.53 | 2.18 |
f5_1 | 7 | 0.98 | 2.44 | 0.80 | 1 | 2.00 | 2.0 | 3 | 5 | ▂▇▆▁▁ | 0.41 | 0.22 | 3.21 |
f5_2 | 8 | 0.98 | 3.20 | 0.97 | 1 | 3.00 | 3.0 | 4 | 5 | ▁▅▇▇▂ | -0.24 | -0.56 | 2.28 |
f5_3 | 1 | 1.00 | 4.07 | 0.82 | 1 | 4.00 | 4.0 | 5 | 5 | ▁▁▂▇▅ | -0.86 | 0.76 | 3.73 |
f8 | 2 | 1.00 | 3.38 | 0.78 | 1 | 3.00 | 4.0 | 4 | 5 | ▁▂▆▇▁ | -0.89 | 0.69 | 3.04 |
f9_1 | 6 | 0.99 | 3.63 | 0.89 | 1 | 3.00 | 4.0 | 4 | 5 | ▁▂▃▇▂ | -0.80 | 0.53 | 2.97 |
f9_2 | 7 | 0.98 | 3.66 | 0.90 | 1 | 3.00 | 4.0 | 4 | 5 | ▁▁▃▇▂ | -0.93 | 0.97 | 2.96 |
f9_3 | 3 | 0.99 | 3.38 | 0.98 | 1 | 3.00 | 4.0 | 4 | 5 | ▁▂▅▇▁ | -0.60 | -0.24 | 2.42 |
f10_1 | 11 | 0.97 | 2.82 | 0.94 | 1 | 2.00 | 3.0 | 3 | 5 | ▁▇▇▅▁ | 0.28 | -0.39 | 2.31 |
f10_2 | 10 | 0.98 | 3.51 | 1.00 | 1 | 3.00 | 4.0 | 4 | 5 | ▁▃▃▇▂ | -0.48 | -0.46 | 2.51 |
f10_3 | 8 | 0.98 | 3.32 | 1.14 | 1 | 2.00 | 3.0 | 4 | 5 | ▁▆▆▇▅ | -0.18 | -0.93 | 2.03 |
berj | 0 | 1.00 | 17.37 | 11.13 | 0 | 8.00 | 15.0 | 26 | 45 | ▇▇▆▅▂ | 0.38 | -0.87 | 2.48 |
corrplot(cor(data_ttest%>%
select(f3:f5_3, f8:f10_3)%>%
na.omit(.),
method = "kendall"))
reactable::reactable(round(
cor(data_ttest%>%
select(f3:f5_3, f8:f10_3)%>%
na.omit(.),
method = "kendall"),
2))
Pilot Study
library(lavaan)
## This is lavaan 0.6-8
## lavaan is FREE software! Please report any bugs.
library(semTools)
##
## ###############################################################################
## This is semTools 0.5-4
## All users of R (or SEM) are invited to submit functions or ideas for functions.
## ###############################################################################
##
## Attaching package: 'semTools'
## The following object is masked from 'package:readr':
##
## clipboard
meti_mod1 <-
"trust =~ meti_exp_01 + meti_exp_02 + meti_exp_03 + meti_exp_04 + meti_exp_05 + meti_exp_06 +
meti_int_01 + meti_int_02 + meti_int_03 + meti_int_04 +
meti_ben_01 + meti_ben_02 + meti_ben_03 + meti_ben_04"
meti_fit1 <- cfa(meti_mod1,
data = data_mtmm)
fit1 <-
fitmeasures(meti_fit1)[c("chisq", "df", "tli", "cfi", "rmsea", "srmr")]
meti_mod2 <-
"exp =~ meti_exp_01 + meti_exp_02 + meti_exp_03 + meti_exp_04 + meti_exp_05 + meti_exp_06
intben =~ meti_int_01 + meti_int_02 + meti_int_03 + meti_int_04 +
meti_ben_01 + meti_ben_02 + meti_ben_03 + meti_ben_04"
meti_fit2 <- cfa(meti_mod2,
data = data_mtmm)
fit2 <-
fitmeasures(meti_fit2)[c("chisq", "df", "tli", "cfi", "rmsea", "srmr")]
meti_mod <-
"exp =~ meti_exp_01 + meti_exp_02 + meti_exp_03 + meti_exp_04 + meti_exp_05 + meti_exp_06
int =~ meti_int_01 + meti_int_02 + meti_int_03 + meti_int_04
ben =~ meti_ben_01 + meti_ben_02 + meti_ben_03 + meti_ben_04"
meti_fit <- cfa(meti_mod,
data = data_mtmm)
fit3 <-
fitmeasures(meti_fit)[c("chisq", "df", "tli", "cfi", "rmsea", "srmr")]
Model | \(\chi^2\) | df | p-value | TLI | CFI | RMSEA | SRMR | \(\chi^2\)-difference Test |
---|---|---|---|---|---|---|---|---|
one-dim | 643.4882662 | 77 | NA | 0.8837476 | 0.9016326 | 0.1208189 | 0.0519676 | |
two-dim | 276.7440084 | 74 | NA | 0.9586698 | 0.9663909 | 0.072039 | 0.0328895 | |
three-dim | 267.5517437 | 76 | NA | 0.9582621 | 0.965142 | 0.0723934 | 0.0329938 |
anova(meti_fit1, meti_fit2, meti_fit)
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## meti_fit 74 12538 12668 267.55
## meti_fit2 76 12543 12665 276.74 9.19 2 0.01009 *
## meti_fit1 77 12907 13026 643.49 366.74 1 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
semTools::reliability(meti_fit)
## exp int ben
## alpha 0.9222729 0.9013068 0.8781069
## omega 0.9236665 0.9016438 0.8782946
## omega2 0.9236665 0.9016438 0.8782946
## omega3 0.9250794 0.9015949 0.8777437
## avevar 0.6696746 0.6964667 0.6437551
utum_trus_mod <-
"exp =~ meti_exp_01 + meti_exp_02 + meti_exp_03 + meti_exp_04 + meti_exp_05 + meti_exp_06 + reas_exp + reas_alt_exp
int =~ meti_int_01 + meti_int_02 + meti_int_03 + meti_int_04 + reas_int + reas_alt_int
ben =~ meti_ben_01 + meti_ben_02 + meti_ben_03 + meti_ben_04 + reas_ben + reas_alt_ben
meti =~ meti_exp_01 + meti_exp_02 + meti_exp_03 + meti_exp_04 + meti_exp_05 + meti_exp_06 +
meti_int_01 + meti_int_02 + meti_int_03 + meti_int_04 +
meti_ben_01 + meti_ben_02 + meti_ben_03 + meti_ben_04
si =~ reas_exp + reas_alt_exp + reas_int + reas_alt_int + reas_ben + reas_alt_ben
exp ~~ 0*int + 0*ben
ben ~~ 0*int
si ~~ 0*meti + 0*exp + 0*int + 0*ben
meti ~~ 0*exp + 0*int + 0*ben"
utum_trus_fit <- sem(utum_trus_mod,
data = data_mtmm,
missing = "fiml",
std.lv = T)
## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative
fitmeasures(utum_trus_fit)[c("chisq", "df", "tli", "cfi", "rmsea", "srmr")]
## chisq df tli cfi rmsea srmr
## 647.5301909 150.0000000 0.9144309 0.9324454 0.0811239 0.2283480
semPlot::semPaths(utum_trus_fit)
parameterEstimates(utum_trus_fit, standardized = T) %>%
mutate_if(is.numeric, function(x) round(x, 3))
## lhs op rhs est se z pvalue ci.lower
## 1 exp =~ meti_exp_01 0.362 0.030 12.228 0.000 0.304
## 2 exp =~ meti_exp_02 0.369 0.033 11.040 0.000 0.303
## 3 exp =~ meti_exp_03 0.355 0.031 11.303 0.000 0.294
## 4 exp =~ meti_exp_04 0.381 0.033 11.546 0.000 0.317
## 5 exp =~ meti_exp_05 0.249 0.033 7.428 0.000 0.183
## 6 exp =~ meti_exp_06 0.480 0.031 15.622 0.000 0.420
## 7 exp =~ reas_exp 0.115 0.034 3.369 0.001 0.048
## 8 exp =~ reas_alt_exp 0.164 0.032 5.062 0.000 0.100
## 9 int =~ meti_int_01 15.361 5.945 2.584 0.010 3.710
## 10 int =~ meti_int_02 0.002 0.001 1.942 0.052 0.000
## 11 int =~ meti_int_03 -0.001 0.001 -0.942 0.346 -0.002
## 12 int =~ meti_int_04 0.001 0.001 1.303 0.192 -0.001
## 13 int =~ reas_int -0.001 0.001 -1.027 0.305 -0.003
## 14 int =~ reas_alt_int -0.001 0.001 -1.046 0.296 -0.003
## 15 ben =~ meti_ben_01 0.162 0.040 4.056 0.000 0.084
## 16 ben =~ meti_ben_02 0.172 0.042 4.049 0.000 0.089
## 17 ben =~ meti_ben_03 -0.050 0.040 -1.261 0.207 -0.128
## 18 ben =~ meti_ben_04 0.147 0.043 3.449 0.001 0.063
## 19 ben =~ reas_ben 0.303 0.051 5.958 0.000 0.203
## 20 ben =~ reas_alt_ben 0.416 0.061 6.827 0.000 0.296
## 21 meti =~ meti_exp_01 0.673 0.035 19.479 0.000 0.605
## 22 meti =~ meti_exp_02 0.594 0.036 16.697 0.000 0.524
## 23 meti =~ meti_exp_03 0.624 0.035 17.822 0.000 0.555
## 24 meti =~ meti_exp_04 0.648 0.037 17.663 0.000 0.576
## 25 meti =~ meti_exp_05 0.559 0.034 16.260 0.000 0.492
## 26 meti =~ meti_exp_06 0.583 0.035 16.750 0.000 0.514
## 27 meti =~ meti_int_01 0.710 0.031 23.055 0.000 0.650
## 28 meti =~ meti_int_02 0.704 0.031 22.918 0.000 0.644
## 29 meti =~ meti_int_03 0.669 0.031 21.823 0.000 0.609
## 30 meti =~ meti_int_04 0.671 0.031 21.680 0.000 0.610
## 31 meti =~ meti_ben_01 0.685 0.031 21.930 0.000 0.624
## 32 meti =~ meti_ben_02 0.663 0.033 19.863 0.000 0.598
## 33 meti =~ meti_ben_03 0.749 0.034 22.070 0.000 0.683
## 34 meti =~ meti_ben_04 0.633 0.034 18.765 0.000 0.567
## 35 si =~ reas_exp 0.663 0.035 19.024 0.000 0.595
## 36 si =~ reas_alt_exp 0.559 0.032 17.369 0.000 0.496
## 37 si =~ reas_int 0.691 0.035 19.481 0.000 0.622
## 38 si =~ reas_alt_int 0.600 0.035 17.216 0.000 0.532
## 39 si =~ reas_ben 0.576 0.040 14.488 0.000 0.498
## 40 si =~ reas_alt_ben 0.525 0.038 13.952 0.000 0.451
## 41 exp ~~ int 0.000 0.000 NA NA 0.000
## 42 exp ~~ ben 0.000 0.000 NA NA 0.000
## 43 int ~~ ben 0.000 0.000 NA NA 0.000
## 44 meti ~~ si 0.000 0.000 NA NA 0.000
## 45 exp ~~ si 0.000 0.000 NA NA 0.000
## 46 int ~~ si 0.000 0.000 NA NA 0.000
## 47 ben ~~ si 0.000 0.000 NA NA 0.000
## 48 exp ~~ meti 0.000 0.000 NA NA 0.000
## 49 int ~~ meti 0.000 0.000 NA NA 0.000
## 50 ben ~~ meti 0.000 0.000 NA NA 0.000
## 51 meti_exp_01 ~~ meti_exp_01 0.212 0.016 13.022 0.000 0.180
## 52 meti_exp_02 ~~ meti_exp_02 0.286 0.021 13.564 0.000 0.245
## 53 meti_exp_03 ~~ meti_exp_03 0.262 0.019 13.724 0.000 0.225
## 54 meti_exp_04 ~~ meti_exp_04 0.283 0.021 13.579 0.000 0.243
## 55 meti_exp_05 ~~ meti_exp_05 0.343 0.023 14.983 0.000 0.298
## 56 meti_exp_06 ~~ meti_exp_06 0.175 0.018 9.651 0.000 0.139
## 57 reas_exp ~~ reas_exp 0.270 0.023 11.483 0.000 0.224
## 58 reas_alt_exp ~~ reas_alt_exp 0.249 0.021 12.050 0.000 0.209
## 59 meti_int_01 ~~ meti_int_01 -235.777 182.634 -1.291 0.197 -593.733
## 60 meti_int_02 ~~ meti_int_02 0.209 0.016 13.312 0.000 0.178
## 61 meti_int_03 ~~ meti_int_03 0.230 0.017 13.731 0.000 0.197
## 62 meti_int_04 ~~ meti_int_04 0.237 0.017 13.797 0.000 0.204
## 63 reas_int ~~ reas_int 0.287 0.026 11.190 0.000 0.237
## 64 reas_alt_int ~~ reas_alt_int 0.321 0.026 12.584 0.000 0.271
## 65 meti_ben_01 ~~ meti_ben_01 0.208 0.017 12.223 0.000 0.175
## 66 meti_ben_02 ~~ meti_ben_02 0.279 0.021 13.258 0.000 0.238
## 67 meti_ben_03 ~~ meti_ben_03 0.261 0.020 12.884 0.000 0.221
## 68 meti_ben_04 ~~ meti_ben_04 0.317 0.023 14.067 0.000 0.273
## 69 reas_ben ~~ reas_ben 0.415 0.036 11.669 0.000 0.345
## 70 reas_alt_ben ~~ reas_alt_ben 0.278 0.046 6.057 0.000 0.188
## 71 exp ~~ exp 1.000 0.000 NA NA 1.000
## 72 int ~~ int 1.000 0.000 NA NA 1.000
## 73 ben ~~ ben 1.000 0.000 NA NA 1.000
## 74 meti ~~ meti 1.000 0.000 NA NA 1.000
## 75 si ~~ si 1.000 0.000 NA NA 1.000
## 76 meti_exp_01 ~1 3.929 0.040 98.915 0.000 3.851
## 77 meti_exp_02 ~1 4.101 0.039 104.617 0.000 4.024
## 78 meti_exp_03 ~1 4.036 0.039 102.764 0.000 3.959
## 79 meti_exp_04 ~1 3.966 0.041 96.682 0.000 3.886
## 80 meti_exp_05 ~1 3.871 0.038 102.635 0.000 3.797
## 81 meti_exp_06 ~1 4.000 0.038 104.056 0.000 3.925
## 82 reas_exp ~1 4.052 0.038 105.516 0.000 3.977
## 83 reas_alt_exp ~1 4.115 0.035 118.558 0.000 4.047
## 84 meti_int_01 ~1 3.605 0.037 96.896 0.000 3.532
## 85 meti_int_02 ~1 3.595 0.037 96.113 0.000 3.522
## 86 meti_int_03 ~1 3.573 0.037 97.489 0.000 3.502
## 87 meti_int_04 ~1 3.581 0.037 96.944 0.000 3.509
## 88 reas_int ~1 4.029 0.040 101.905 0.000 3.951
## 89 reas_alt_int ~1 4.051 0.037 108.171 0.000 3.977
## 90 meti_ben_01 ~1 3.450 0.037 92.358 0.000 3.377
## 91 meti_ben_02 ~1 3.528 0.039 91.560 0.000 3.452
## 92 meti_ben_03 ~1 3.760 0.040 92.976 0.000 3.681
## 93 meti_ben_04 ~1 3.401 0.038 88.811 0.000 3.326
## 94 reas_ben ~1 3.765 0.041 91.043 0.000 3.684
## 95 reas_alt_ben ~1 3.740 0.039 96.862 0.000 3.664
## 96 exp ~1 0.000 0.000 NA NA 0.000
## 97 int ~1 0.000 0.000 NA NA 0.000
## 98 ben ~1 0.000 0.000 NA NA 0.000
## 99 meti ~1 0.000 0.000 NA NA 0.000
## 100 si ~1 0.000 0.000 NA NA 0.000
## ci.upper std.lv std.all std.nox
## 1 0.420 0.362 0.405 0.405
## 2 0.434 0.369 0.419 0.419
## 3 0.417 0.355 0.403 0.403
## 4 0.446 0.381 0.414 0.414
## 5 0.314 0.249 0.294 0.294
## 6 0.540 0.480 0.556 0.556
## 7 0.182 0.115 0.135 0.135
## 8 0.227 0.164 0.213 0.213
## 9 27.013 15.361 18.390 18.390
## 10 0.005 0.002 0.003 0.003
## 11 0.001 -0.001 -0.001 -0.001
## 12 0.003 0.001 0.001 0.001
## 13 0.001 -0.001 -0.001 -0.001
## 14 0.001 -0.001 -0.001 -0.001
## 15 0.240 0.162 0.193 0.193
## 16 0.255 0.172 0.199 0.199
## 17 0.028 -0.050 -0.055 -0.055
## 18 0.230 0.147 0.171 0.171
## 19 0.402 0.303 0.330 0.330
## 20 0.535 0.416 0.488 0.488
## 21 0.741 0.673 0.755 0.755
## 22 0.664 0.594 0.675 0.675
## 23 0.692 0.624 0.707 0.707
## 24 0.719 0.648 0.703 0.703
## 25 0.626 0.559 0.660 0.660
## 26 0.651 0.583 0.675 0.675
## 27 0.771 0.710 0.851 0.851
## 28 0.764 0.704 0.839 0.839
## 29 0.729 0.669 0.813 0.813
## 30 0.732 0.671 0.809 0.809
## 31 0.746 0.685 0.817 0.817
## 32 0.728 0.663 0.766 0.766
## 33 0.816 0.749 0.825 0.825
## 34 0.699 0.633 0.736 0.736
## 35 0.731 0.663 0.780 0.780
## 36 0.622 0.559 0.729 0.729
## 37 0.761 0.691 0.790 0.790
## 38 0.669 0.600 0.727 0.727
## 39 0.654 0.576 0.629 0.629
## 40 0.598 0.525 0.616 0.616
## 41 0.000 0.000 0.000 0.000
## 42 0.000 0.000 0.000 0.000
## 43 0.000 0.000 0.000 0.000
## 44 0.000 0.000 0.000 0.000
## 45 0.000 0.000 0.000 0.000
## 46 0.000 0.000 0.000 0.000
## 47 0.000 0.000 0.000 0.000
## 48 0.000 0.000 0.000 0.000
## 49 0.000 0.000 0.000 0.000
## 50 0.000 0.000 0.000 0.000
## 51 0.243 0.212 0.266 0.266
## 52 0.327 0.286 0.369 0.369
## 53 0.300 0.262 0.338 0.338
## 54 0.324 0.283 0.334 0.334
## 55 0.388 0.343 0.478 0.478
## 56 0.211 0.175 0.235 0.235
## 57 0.316 0.270 0.373 0.373
## 58 0.290 0.249 0.423 0.423
## 59 122.179 -235.777 -337.933 -337.933
## 60 0.240 0.209 0.297 0.297
## 61 0.263 0.230 0.339 0.339
## 62 0.271 0.237 0.345 0.345
## 63 0.337 0.287 0.375 0.375
## 64 0.371 0.321 0.471 0.471
## 65 0.241 0.208 0.296 0.296
## 66 0.320 0.279 0.373 0.373
## 67 0.300 0.261 0.316 0.316
## 68 0.362 0.317 0.429 0.429
## 69 0.485 0.415 0.495 0.495
## 70 0.368 0.278 0.383 0.383
## 71 1.000 1.000 1.000 1.000
## 72 1.000 1.000 1.000 1.000
## 73 1.000 1.000 1.000 1.000
## 74 1.000 1.000 1.000 1.000
## 75 1.000 1.000 1.000 1.000
## 76 4.006 3.929 4.406 4.406
## 77 4.178 4.101 4.660 4.660
## 78 4.113 4.036 4.577 4.577
## 79 4.047 3.966 4.307 4.307
## 80 3.945 3.871 4.572 4.572
## 81 4.075 4.000 4.635 4.635
## 82 4.127 4.052 4.767 4.767
## 83 4.183 4.115 5.362 5.362
## 84 3.678 3.605 4.316 4.316
## 85 3.669 3.595 4.281 4.281
## 86 3.645 3.573 4.343 4.343
## 87 3.654 3.581 4.318 4.318
## 88 4.106 4.029 4.606 4.606
## 89 4.124 4.051 4.907 4.907
## 90 3.524 3.450 4.114 4.114
## 91 3.603 3.528 4.078 4.078
## 92 3.839 3.760 4.141 4.141
## 93 3.476 3.401 3.956 3.956
## 94 3.846 3.765 4.112 4.112
## 95 3.815 3.740 4.388 4.388
## 96 0.000 0.000 0.000 0.000
## 97 0.000 0.000 0.000 0.000
## 98 0.000 0.000 0.000 0.000
## 99 0.000 0.000 0.000 0.000
## 100 0.000 0.000 0.000 0.000
ctum_trus_mod <-
"exp =~ meti_exp_01 + meti_exp_02 + meti_exp_03 + meti_exp_04 + meti_exp_05 + meti_exp_06 + reas_exp + reas_alt_exp
int =~ meti_int_01 + meti_int_02 + meti_int_03 + meti_int_04 + reas_int + reas_alt_int
ben =~ meti_ben_01 + meti_ben_02 + meti_ben_03 + meti_ben_04 + reas_ben + reas_alt_ben
meti =~ meti_exp_01 + meti_exp_02 + meti_exp_03 + meti_exp_04 + meti_exp_05 + meti_exp_06 +
meti_int_01 + meti_int_02 + meti_int_03 + meti_int_04 +
meti_ben_01 + meti_ben_02 + meti_ben_03 + meti_ben_04
si =~ reas_exp + reas_alt_exp + reas_int + reas_alt_int + reas_ben + reas_alt_ben
si ~~ 0*meti + 0*exp + 0*int + 0*ben
meti ~~ 0*exp + 0*int + 0*ben"
ctum_trus_fit <- sem(ctum_trus_mod,
data = data_mtmm,
missing = "fiml",
std.lv = T)
fitmeasures(ctum_trus_fit)[c("chisq", "df", "tli", "cfi", "rmsea", "srmr")]
## chisq df tli cfi rmsea srmr
## 424.68914246 147.00000000 0.95126617 0.96229540 0.06122172 0.02881346
semPlot::semPaths(ctum_trus_fit)
parameterEstimates(ctum_trus_fit, standardized = T) %>%
mutate_if(is.numeric, function(x) round(x, 3))
## lhs op rhs est se z pvalue ci.lower ci.upper
## 1 exp =~ meti_exp_01 0.779 0.033 23.720 0.000 0.715 0.844
## 2 exp =~ meti_exp_02 0.712 0.034 21.177 0.000 0.647 0.778
## 3 exp =~ meti_exp_03 0.726 0.034 21.068 0.000 0.659 0.794
## 4 exp =~ meti_exp_04 0.761 0.036 20.963 0.000 0.689 0.832
## 5 exp =~ meti_exp_05 0.628 0.033 18.812 0.000 0.563 0.694
## 6 exp =~ meti_exp_06 0.743 0.038 19.609 0.000 0.669 0.817
## 7 exp =~ reas_exp 0.487 0.037 13.272 0.000 0.415 0.559
## 8 exp =~ reas_alt_exp 0.438 0.033 13.096 0.000 0.373 0.504
## 9 int =~ meti_int_01 0.701 0.033 21.411 0.000 0.637 0.765
## 10 int =~ meti_int_02 0.698 0.033 21.085 0.000 0.633 0.763
## 11 int =~ meti_int_03 0.643 0.035 18.294 0.000 0.574 0.712
## 12 int =~ meti_int_04 0.640 0.037 17.475 0.000 0.568 0.712
## 13 int =~ reas_int 0.459 0.038 12.026 0.000 0.384 0.533
## 14 int =~ reas_alt_int 0.404 0.036 11.207 0.000 0.333 0.475
## 15 ben =~ meti_ben_01 0.679 0.040 17.182 0.000 0.601 0.756
## 16 ben =~ meti_ben_02 0.684 0.036 19.060 0.000 0.614 0.754
## 17 ben =~ meti_ben_03 0.759 0.034 22.237 0.000 0.692 0.826
## 18 ben =~ meti_ben_04 0.627 0.040 15.516 0.000 0.547 0.706
## 19 ben =~ reas_ben 0.423 0.041 10.394 0.000 0.343 0.502
## 20 ben =~ reas_alt_ben 0.432 0.038 11.486 0.000 0.358 0.505
## 21 meti =~ meti_exp_01 -0.047 0.076 -0.615 0.539 -0.195 0.102
## 22 meti =~ meti_exp_02 -0.032 0.070 -0.466 0.641 -0.169 0.104
## 23 meti =~ meti_exp_03 -0.102 0.073 -1.400 0.161 -0.244 0.041
## 24 meti =~ meti_exp_04 -0.119 0.074 -1.610 0.107 -0.264 0.026
## 25 meti =~ meti_exp_05 0.043 0.067 0.632 0.527 -0.089 0.174
## 26 meti =~ meti_exp_06 -0.234 0.076 -3.066 0.002 -0.383 -0.084
## 27 meti =~ meti_int_01 0.150 0.065 2.309 0.021 0.023 0.278
## 28 meti =~ meti_int_02 0.147 0.065 2.251 0.024 0.019 0.275
## 29 meti =~ meti_int_03 0.191 0.065 2.939 0.003 0.064 0.319
## 30 meti =~ meti_int_04 0.222 0.064 3.448 0.001 0.096 0.349
## 31 meti =~ meti_ben_01 0.259 0.072 3.591 0.000 0.118 0.401
## 32 meti =~ meti_ben_02 0.138 0.069 2.011 0.044 0.003 0.273
## 33 meti =~ meti_ben_03 -0.017 0.074 -0.224 0.823 -0.162 0.129
## 34 meti =~ meti_ben_04 0.250 0.063 3.959 0.000 0.126 0.374
## 35 si =~ reas_exp 0.505 0.033 15.177 0.000 0.440 0.570
## 36 si =~ reas_alt_exp 0.421 0.031 13.433 0.000 0.359 0.482
## 37 si =~ reas_int 0.515 0.036 14.406 0.000 0.445 0.585
## 38 si =~ reas_alt_int 0.436 0.035 12.436 0.000 0.367 0.505
## 39 si =~ reas_ben 0.446 0.040 11.278 0.000 0.369 0.524
## 40 si =~ reas_alt_ben 0.374 0.037 10.028 0.000 0.301 0.447
## 41 meti ~~ si 0.000 0.000 NA NA 0.000 0.000
## 42 exp ~~ si 0.000 0.000 NA NA 0.000 0.000
## 43 int ~~ si 0.000 0.000 NA NA 0.000 0.000
## 44 ben ~~ si 0.000 0.000 NA NA 0.000 0.000
## 45 exp ~~ meti 0.000 0.000 NA NA 0.000 0.000
## 46 int ~~ meti 0.000 0.000 NA NA 0.000 0.000
## 47 ben ~~ meti 0.000 0.000 NA NA 0.000 0.000
## 48 meti_exp_01 ~~ meti_exp_01 0.210 0.016 13.227 0.000 0.179 0.241
## 49 meti_exp_02 ~~ meti_exp_02 0.288 0.020 14.195 0.000 0.248 0.328
## 50 meti_exp_03 ~~ meti_exp_03 0.261 0.019 13.657 0.000 0.224 0.298
## 51 meti_exp_04 ~~ meti_exp_04 0.279 0.021 13.229 0.000 0.238 0.321
## 52 meti_exp_05 ~~ meti_exp_05 0.335 0.023 14.525 0.000 0.290 0.381
## 53 meti_exp_06 ~~ meti_exp_06 0.163 0.022 7.588 0.000 0.121 0.205
## 54 reas_exp ~~ reas_exp 0.261 0.024 10.953 0.000 0.214 0.307
## 55 reas_alt_exp ~~ reas_alt_exp 0.251 0.021 11.992 0.000 0.210 0.292
## 56 meti_int_01 ~~ meti_int_01 0.178 0.013 13.220 0.000 0.152 0.204
## 57 meti_int_02 ~~ meti_int_02 0.197 0.015 13.453 0.000 0.168 0.225
## 58 meti_int_03 ~~ meti_int_03 0.228 0.017 13.551 0.000 0.195 0.261
## 59 meti_int_04 ~~ meti_int_04 0.230 0.018 12.640 0.000 0.195 0.266
## 60 reas_int ~~ reas_int 0.292 0.026 11.308 0.000 0.242 0.343
## 61 reas_alt_int ~~ reas_alt_int 0.329 0.026 12.840 0.000 0.279 0.380
## 62 meti_ben_01 ~~ meti_ben_01 0.195 0.018 10.873 0.000 0.160 0.230
## 63 meti_ben_02 ~~ meti_ben_02 0.282 0.020 13.899 0.000 0.242 0.322
## 64 meti_ben_03 ~~ meti_ben_03 0.240 0.023 10.598 0.000 0.196 0.285
## 65 meti_ben_04 ~~ meti_ben_04 0.301 0.022 13.371 0.000 0.256 0.345
## 66 reas_ben ~~ reas_ben 0.464 0.035 13.408 0.000 0.396 0.532
## 67 reas_alt_ben ~~ reas_alt_ben 0.404 0.030 13.467 0.000 0.346 0.463
## 68 exp ~~ exp 1.000 0.000 NA NA 1.000 1.000
## 69 int ~~ int 1.000 0.000 NA NA 1.000 1.000
## 70 ben ~~ ben 1.000 0.000 NA NA 1.000 1.000
## 71 meti ~~ meti 1.000 0.000 NA NA 1.000 1.000
## 72 si ~~ si 1.000 0.000 NA NA 1.000 1.000
## 73 exp ~~ int 0.936 0.016 59.750 0.000 0.906 0.967
## 74 exp ~~ ben 0.884 0.020 44.489 0.000 0.845 0.923
## 75 int ~~ ben 0.974 0.009 107.374 0.000 0.957 0.992
## 76 meti_exp_01 ~1 3.929 0.040 97.424 0.000 3.850 4.008
## 77 meti_exp_02 ~1 4.101 0.040 103.144 0.000 4.023 4.179
## 78 meti_exp_03 ~1 4.036 0.040 101.373 0.000 3.958 4.114
## 79 meti_exp_04 ~1 3.966 0.042 95.355 0.000 3.885 4.048
## 80 meti_exp_05 ~1 3.871 0.038 101.579 0.000 3.796 3.946
## 81 meti_exp_06 ~1 4.000 0.039 102.322 0.000 3.923 4.077
## 82 reas_exp ~1 4.042 0.039 103.385 0.000 3.966 4.119
## 83 reas_alt_exp ~1 4.107 0.036 115.637 0.000 4.037 4.177
## 84 meti_int_01 ~1 3.605 0.037 97.269 0.000 3.533 3.678
## 85 meti_int_02 ~1 3.595 0.037 96.044 0.000 3.522 3.669
## 86 meti_int_03 ~1 3.573 0.037 97.402 0.000 3.502 3.645
## 87 meti_int_04 ~1 3.581 0.037 96.846 0.000 3.509 3.654
## 88 reas_int ~1 4.019 0.040 101.665 0.000 3.942 4.097
## 89 reas_alt_int ~1 4.043 0.037 108.059 0.000 3.969 4.116
## 90 meti_ben_01 ~1 3.450 0.038 91.074 0.000 3.376 3.525
## 91 meti_ben_02 ~1 3.528 0.039 90.320 0.000 3.451 3.604
## 92 meti_ben_03 ~1 3.760 0.040 93.364 0.000 3.681 3.839
## 93 meti_ben_04 ~1 3.401 0.039 87.823 0.000 3.325 3.477
## 94 reas_ben ~1 3.757 0.041 90.773 0.000 3.676 3.838
## 95 reas_alt_ben ~1 3.728 0.039 96.408 0.000 3.653 3.804
## 96 exp ~1 0.000 0.000 NA NA 0.000 0.000
## 97 int ~1 0.000 0.000 NA NA 0.000 0.000
## 98 ben ~1 0.000 0.000 NA NA 0.000 0.000
## 99 meti ~1 0.000 0.000 NA NA 0.000 0.000
## 100 si ~1 0.000 0.000 NA NA 0.000 0.000
## std.lv std.all std.nox
## 1 0.779 0.861 0.861
## 2 0.712 0.798 0.798
## 3 0.726 0.813 0.813
## 4 0.761 0.815 0.815
## 5 0.628 0.734 0.734
## 6 0.743 0.847 0.847
## 7 0.487 0.561 0.561
## 8 0.438 0.557 0.557
## 9 0.701 0.843 0.843
## 10 0.698 0.831 0.831
## 11 0.643 0.781 0.781
## 12 0.640 0.771 0.771
## 13 0.459 0.523 0.523
## 14 0.404 0.489 0.489
## 15 0.679 0.798 0.798
## 16 0.684 0.780 0.780
## 17 0.759 0.840 0.840
## 18 0.627 0.721 0.721
## 19 0.423 0.461 0.461
## 20 0.432 0.505 0.505
## 21 -0.047 -0.051 -0.051
## 22 -0.032 -0.036 -0.036
## 23 -0.102 -0.114 -0.114
## 24 -0.119 -0.128 -0.128
## 25 0.043 0.050 0.050
## 26 -0.234 -0.266 -0.266
## 27 0.150 0.180 0.180
## 28 0.147 0.175 0.175
## 29 0.191 0.232 0.232
## 30 0.222 0.268 0.268
## 31 0.259 0.305 0.305
## 32 0.138 0.158 0.158
## 33 -0.017 -0.018 -0.018
## 34 0.250 0.288 0.288
## 35 0.505 0.582 0.582
## 36 0.421 0.534 0.534
## 37 0.515 0.588 0.588
## 38 0.436 0.528 0.528
## 39 0.446 0.486 0.486
## 40 0.374 0.437 0.437
## 41 0.000 0.000 0.000
## 42 0.000 0.000 0.000
## 43 0.000 0.000 0.000
## 44 0.000 0.000 0.000
## 45 0.000 0.000 0.000
## 46 0.000 0.000 0.000
## 47 0.000 0.000 0.000
## 48 0.210 0.256 0.256
## 49 0.288 0.362 0.362
## 50 0.261 0.327 0.327
## 51 0.279 0.320 0.320
## 52 0.335 0.458 0.458
## 53 0.163 0.212 0.212
## 54 0.261 0.346 0.346
## 55 0.251 0.405 0.405
## 56 0.178 0.257 0.257
## 57 0.197 0.279 0.279
## 58 0.228 0.336 0.336
## 59 0.230 0.334 0.334
## 60 0.292 0.381 0.381
## 61 0.329 0.482 0.482
## 62 0.195 0.270 0.270
## 63 0.282 0.367 0.367
## 64 0.240 0.294 0.294
## 65 0.301 0.398 0.398
## 66 0.464 0.551 0.551
## 67 0.404 0.554 0.554
## 68 1.000 1.000 1.000
## 69 1.000 1.000 1.000
## 70 1.000 1.000 1.000
## 71 1.000 1.000 1.000
## 72 1.000 1.000 1.000
## 73 0.936 0.936 0.936
## 74 0.884 0.884 0.884
## 75 0.974 0.974 0.974
## 76 3.929 4.340 4.340
## 77 4.101 4.594 4.594
## 78 4.036 4.516 4.516
## 79 3.966 4.247 4.247
## 80 3.871 4.525 4.525
## 81 4.000 4.558 4.558
## 82 4.042 4.657 4.657
## 83 4.107 5.217 5.217
## 84 3.605 4.333 4.333
## 85 3.595 4.278 4.278
## 86 3.573 4.339 4.339
## 87 3.581 4.314 4.314
## 88 4.019 4.585 4.585
## 89 4.043 4.893 4.893
## 90 3.450 4.057 4.057
## 91 3.528 4.023 4.023
## 92 3.760 4.159 4.159
## 93 3.401 3.912 3.912
## 94 3.757 4.095 4.095
## 95 3.728 4.362 4.362
## 96 0.000 0.000 0.000
## 97 0.000 0.000 0.000
## 98 0.000 0.000 0.000
## 99 0.000 0.000 0.000
## 100 0.000 0.000 0.000
library(bain)
res_bain_subload_trust <-
bain(ctum_trus_fit, standardize = T,
"exp=~reas_exp > .4 & int=~reas_int > .4 & ben=~reas_ben > .4")
res_bain_eqload_trust <-
bain(ctum_trus_fit, standardize = T,
"-0.15 < exp=~reas_exp - exp=~reas_alt_exp < 0.15 &
-0.15 < int=~reas_int - int=~reas_alt_int < 0.15 &
-0.15 < ben=~reas_ben - ben=~reas_alt_ben < 0.15")
res_bain_subload_trust
## Bayesian informative hypothesis testing for an object of class lavaan:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.944 0.235 4.008 54.389 1.000 0.800
## Hu 0.200
##
## Hypotheses:
## H1: exp=~reas_exp>.4&int=~reas_int>.4&ben=~reas_ben>.4
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
res_bain_eqload_trust
## Bayesian informative hypothesis testing for an object of class lavaan:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.993 0.023 44.005 6506.543 1.000 0.978
## Hu 0.022
##
## Hypotheses:
## H1: -0.15<exp=~reas_exp-exp=~reas_alt_exp<0.15&-0.15<int=~reas_int-int=~reas_alt_int<0.15&-0.15<ben=~reas_ben-ben=~reas_alt_ben<0.15
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
# Recode Items
data_mtmm_rec <-
data_mtmm %>%
mutate(reas_exp_r_rec = 6 - reas_exp_r,
reas_alt_exp_r_rec = 6 - reas_alt_exp_r,
reas_int_r_rec = 6 - reas_int_r,
reas_alt_int_r_rec = 6 - reas_alt_int_r,
reas_ben_r_rec = 6 - reas_ben_r,
reas_alt_ben_r_rec = 6 - reas_alt_ben_r)
utum_dis_mod <-
"exp =~ meti_exp_01 + meti_exp_02 + meti_exp_03 + meti_exp_04 + meti_exp_05 + meti_exp_06 + reas_exp_r_rec + reas_alt_exp_r_rec
int =~ meti_int_01 + meti_int_02 + meti_int_03 + meti_int_04 + reas_int_r_rec + reas_alt_int_r_rec
ben =~ meti_ben_01 + meti_ben_02 + meti_ben_03 + meti_ben_04 + reas_ben_r_rec + reas_alt_ben_r_rec
meti =~ meti_exp_01 + meti_exp_02 + meti_exp_03 + meti_exp_04 + meti_exp_05 + meti_exp_06 +
meti_int_01 + meti_int_02 + meti_int_03 + meti_int_04 +
meti_ben_01 + meti_ben_02 + meti_ben_03 + meti_ben_04
si =~ reas_exp_r_rec + reas_alt_exp_r_rec + reas_int_r_rec + reas_alt_int_r_rec + reas_ben_r_rec + reas_alt_ben_r_rec
exp ~~ 0*int + 0*ben
int ~~ 0*ben
si ~~ 0*meti + 0*exp + 0*int + 0*ben
meti ~~ 0*exp + 0*int + 0*ben"
utum_dis_fit <-
cfa(utum_dis_mod,
data = data_mtmm_rec,
missing = "fiml",
std.lv = T)
fitmeasures(utum_dis_fit)[c("chisq", "df", "tli", "cfi", "rmsea", "srmr")]
## chisq df tli cfi rmsea srmr
## 504.82031073 150.00000000 0.93383784 0.94776672 0.06850832 0.12850829
semPlot::semPaths(utum_dis_fit)
parameterEstimates(utum_dis_fit, standardized = T) %>%
mutate_if(is.numeric, function(x) round(x, 3))
## lhs op rhs est se z pvalue
## 1 exp =~ meti_exp_01 0.360 0.029 12.330 0.000
## 2 exp =~ meti_exp_02 0.370 0.033 11.233 0.000
## 3 exp =~ meti_exp_03 0.356 0.031 11.436 0.000
## 4 exp =~ meti_exp_04 0.398 0.033 12.194 0.000
## 5 exp =~ meti_exp_05 0.248 0.033 7.527 0.000
## 6 exp =~ meti_exp_06 0.487 0.030 15.993 0.000
## 7 exp =~ reas_exp_r_rec 0.141 0.042 3.383 0.001
## 8 exp =~ reas_alt_exp_r_rec 0.095 0.041 2.336 0.019
## 9 int =~ meti_int_01 0.082 0.052 1.571 0.116
## 10 int =~ meti_int_02 0.136 0.071 1.918 0.055
## 11 int =~ meti_int_03 -0.156 0.071 -2.208 0.027
## 12 int =~ meti_int_04 -0.139 0.061 -2.285 0.022
## 13 int =~ reas_int_r_rec 0.045 0.087 0.519 0.604
## 14 int =~ reas_alt_int_r_rec 0.207 0.075 2.752 0.006
## 15 ben =~ meti_ben_01 0.071 0.034 2.076 0.038
## 16 ben =~ meti_ben_02 0.041 0.040 1.020 0.308
## 17 ben =~ meti_ben_03 -0.028 0.035 -0.801 0.423
## 18 ben =~ meti_ben_04 0.157 0.040 3.895 0.000
## 19 ben =~ reas_ben_r_rec 0.531 0.079 6.739 0.000
## 20 ben =~ reas_alt_ben_r_rec 0.495 0.074 6.671 0.000
## 21 meti =~ meti_exp_01 0.686 0.035 19.887 0.000
## 22 meti =~ meti_exp_02 0.609 0.035 17.196 0.000
## 23 meti =~ meti_exp_03 0.637 0.035 18.213 0.000
## 24 meti =~ meti_exp_04 0.658 0.037 17.960 0.000
## 25 meti =~ meti_exp_05 0.569 0.034 16.632 0.000
## 26 meti =~ meti_exp_06 0.601 0.035 17.273 0.000
## 27 meti =~ meti_int_01 0.714 0.030 23.785 0.000
## 28 meti =~ meti_int_02 0.711 0.031 23.162 0.000
## 29 meti =~ meti_int_03 0.676 0.031 21.738 0.000
## 30 meti =~ meti_int_04 0.680 0.031 21.665 0.000
## 31 meti =~ meti_ben_01 0.699 0.031 22.432 0.000
## 32 meti =~ meti_ben_02 0.678 0.033 20.384 0.000
## 33 meti =~ meti_ben_03 0.737 0.034 21.942 0.000
## 34 meti =~ meti_ben_04 0.639 0.033 19.262 0.000
## 35 si =~ reas_exp_r_rec 0.673 0.042 16.056 0.000
## 36 si =~ reas_alt_exp_r_rec 0.550 0.040 13.776 0.000
## 37 si =~ reas_int_r_rec 0.747 0.047 15.909 0.000
## 38 si =~ reas_alt_int_r_rec 0.715 0.045 15.889 0.000
## 39 si =~ reas_ben_r_rec 0.497 0.045 11.066 0.000
## 40 si =~ reas_alt_ben_r_rec 0.371 0.046 8.067 0.000
## 41 exp ~~ int 0.000 0.000 NA NA
## 42 exp ~~ ben 0.000 0.000 NA NA
## 43 int ~~ ben 0.000 0.000 NA NA
## 44 meti ~~ si 0.000 0.000 NA NA
## 45 exp ~~ si 0.000 0.000 NA NA
## 46 int ~~ si 0.000 0.000 NA NA
## 47 ben ~~ si 0.000 0.000 NA NA
## 48 exp ~~ meti 0.000 0.000 NA NA
## 49 int ~~ meti 0.000 0.000 NA NA
## 50 ben ~~ meti 0.000 0.000 NA NA
## 51 meti_exp_01 ~~ meti_exp_01 0.216 0.016 13.174 0.000
## 52 meti_exp_02 ~~ meti_exp_02 0.287 0.021 13.549 0.000
## 53 meti_exp_03 ~~ meti_exp_03 0.265 0.019 13.767 0.000
## 54 meti_exp_04 ~~ meti_exp_04 0.279 0.021 13.313 0.000
## 55 meti_exp_05 ~~ meti_exp_05 0.344 0.023 15.008 0.000
## 56 meti_exp_06 ~~ meti_exp_06 0.171 0.018 9.397 0.000
## 57 reas_exp_r_rec ~~ reas_exp_r_rec 0.417 0.038 10.877 0.000
## 58 reas_alt_exp_r_rec ~~ reas_alt_exp_r_rec 0.419 0.034 12.335 0.000
## 59 meti_int_01 ~~ meti_int_01 0.171 0.015 11.532 0.000
## 60 meti_int_02 ~~ meti_int_02 0.174 0.023 7.715 0.000
## 61 meti_int_03 ~~ meti_int_03 0.203 0.026 7.889 0.000
## 62 meti_int_04 ~~ meti_int_04 0.213 0.023 9.252 0.000
## 63 reas_int_r_rec ~~ reas_int_r_rec 0.505 0.045 11.156 0.000
## 64 reas_alt_int_r_rec ~~ reas_alt_int_r_rec 0.389 0.047 8.356 0.000
## 65 meti_ben_01 ~~ meti_ben_01 0.224 0.016 13.863 0.000
## 66 meti_ben_02 ~~ meti_ben_02 0.304 0.021 14.570 0.000
## 67 meti_ben_03 ~~ meti_ben_03 0.275 0.020 14.039 0.000
## 68 meti_ben_04 ~~ meti_ben_04 0.312 0.023 13.789 0.000
## 69 reas_ben_r_rec ~~ reas_ben_r_rec 0.365 0.079 4.635 0.000
## 70 reas_alt_ben_r_rec ~~ reas_alt_ben_r_rec 0.487 0.071 6.871 0.000
## 71 exp ~~ exp 1.000 0.000 NA NA
## 72 int ~~ int 1.000 0.000 NA NA
## 73 ben ~~ ben 1.000 0.000 NA NA
## 74 meti ~~ meti 1.000 0.000 NA NA
## 75 si ~~ si 1.000 0.000 NA NA
## 76 meti_exp_01 ~1 3.929 0.040 97.609 0.000
## 77 meti_exp_02 ~1 4.101 0.040 103.342 0.000
## 78 meti_exp_03 ~1 4.036 0.040 101.501 0.000
## 79 meti_exp_04 ~1 3.966 0.042 95.476 0.000
## 80 meti_exp_05 ~1 3.871 0.038 101.772 0.000
## 81 meti_exp_06 ~1 4.000 0.039 102.387 0.000
## 82 reas_exp_r_rec ~1 3.206 0.043 74.375 0.000
## 83 reas_alt_exp_r_rec ~1 3.040 0.040 76.902 0.000
## 84 meti_int_01 ~1 3.605 0.037 97.641 0.000
## 85 meti_int_02 ~1 3.595 0.037 96.594 0.000
## 86 meti_int_03 ~1 3.573 0.037 96.941 0.000
## 87 meti_int_04 ~1 3.581 0.037 96.462 0.000
## 88 reas_int_r_rec ~1 2.847 0.047 60.181 0.000
## 89 reas_alt_int_r_rec ~1 3.050 0.045 67.937 0.000
## 90 meti_ben_01 ~1 3.450 0.038 91.398 0.000
## 91 meti_ben_02 ~1 3.528 0.039 90.489 0.000
## 92 meti_ben_03 ~1 3.760 0.040 93.234 0.000
## 93 meti_ben_04 ~1 3.401 0.038 88.458 0.000
## 94 reas_ben_r_rec ~1 2.242 0.043 51.832 0.000
## 95 reas_alt_ben_r_rec ~1 2.144 0.043 50.132 0.000
## 96 exp ~1 0.000 0.000 NA NA
## 97 int ~1 0.000 0.000 NA NA
## 98 ben ~1 0.000 0.000 NA NA
## 99 meti ~1 0.000 0.000 NA NA
## 100 si ~1 0.000 0.000 NA NA
## ci.lower ci.upper std.lv std.all std.nox
## 1 0.302 0.417 0.360 0.398 0.398
## 2 0.305 0.434 0.370 0.415 0.415
## 3 0.295 0.417 0.356 0.399 0.399
## 4 0.334 0.462 0.398 0.426 0.426
## 5 0.184 0.313 0.248 0.291 0.291
## 6 0.427 0.547 0.487 0.555 0.555
## 7 0.059 0.222 0.141 0.149 0.149
## 8 0.015 0.175 0.095 0.112 0.112
## 9 -0.020 0.184 0.082 0.099 0.099
## 10 -0.003 0.274 0.136 0.162 0.162
## 11 -0.295 -0.018 -0.156 -0.189 -0.189
## 12 -0.259 -0.020 -0.139 -0.167 -0.167
## 13 -0.125 0.216 0.045 0.044 0.044
## 14 0.060 0.354 0.207 0.213 0.213
## 15 0.004 0.137 0.071 0.083 0.083
## 16 -0.037 0.119 0.041 0.046 0.046
## 17 -0.098 0.041 -0.028 -0.031 -0.031
## 18 0.078 0.236 0.157 0.182 0.182
## 19 0.376 0.685 0.531 0.561 0.561
## 20 0.349 0.640 0.495 0.531 0.531
## 21 0.619 0.754 0.686 0.760 0.760
## 22 0.539 0.678 0.609 0.683 0.683
## 23 0.568 0.705 0.637 0.713 0.713
## 24 0.586 0.730 0.658 0.705 0.705
## 25 0.502 0.636 0.569 0.666 0.666
## 26 0.533 0.669 0.601 0.685 0.685
## 27 0.655 0.772 0.714 0.861 0.861
## 28 0.651 0.771 0.711 0.851 0.851
## 29 0.615 0.737 0.676 0.817 0.817
## 30 0.618 0.741 0.680 0.816 0.816
## 31 0.638 0.760 0.699 0.825 0.825
## 32 0.613 0.744 0.678 0.775 0.775
## 33 0.671 0.803 0.737 0.814 0.814
## 34 0.574 0.704 0.639 0.741 0.741
## 35 0.590 0.755 0.673 0.713 0.713
## 36 0.472 0.628 0.550 0.643 0.643
## 37 0.655 0.839 0.747 0.724 0.724
## 38 0.627 0.803 0.715 0.736 0.736
## 39 0.409 0.585 0.497 0.526 0.526
## 40 0.281 0.461 0.371 0.398 0.398
## 41 0.000 0.000 0.000 0.000 0.000
## 42 0.000 0.000 0.000 0.000 0.000
## 43 0.000 0.000 0.000 0.000 0.000
## 44 0.000 0.000 0.000 0.000 0.000
## 45 0.000 0.000 0.000 0.000 0.000
## 46 0.000 0.000 0.000 0.000 0.000
## 47 0.000 0.000 0.000 0.000 0.000
## 48 0.000 0.000 0.000 0.000 0.000
## 49 0.000 0.000 0.000 0.000 0.000
## 50 0.000 0.000 0.000 0.000 0.000
## 51 0.184 0.248 0.216 0.265 0.265
## 52 0.245 0.328 0.287 0.361 0.361
## 53 0.227 0.302 0.265 0.332 0.332
## 54 0.238 0.320 0.279 0.321 0.321
## 55 0.299 0.389 0.344 0.471 0.471
## 56 0.135 0.207 0.171 0.222 0.222
## 57 0.342 0.492 0.417 0.469 0.469
## 58 0.353 0.486 0.419 0.574 0.574
## 59 0.142 0.200 0.171 0.249 0.249
## 60 0.130 0.218 0.174 0.249 0.249
## 61 0.153 0.254 0.203 0.297 0.297
## 62 0.168 0.258 0.213 0.306 0.306
## 63 0.417 0.594 0.505 0.474 0.474
## 64 0.298 0.480 0.389 0.413 0.413
## 65 0.193 0.256 0.224 0.312 0.312
## 66 0.263 0.345 0.304 0.397 0.397
## 67 0.237 0.314 0.275 0.336 0.336
## 68 0.267 0.356 0.312 0.418 0.418
## 69 0.210 0.519 0.365 0.408 0.408
## 70 0.348 0.626 0.487 0.560 0.560
## 71 1.000 1.000 1.000 1.000 1.000
## 72 1.000 1.000 1.000 1.000 1.000
## 73 1.000 1.000 1.000 1.000 1.000
## 74 1.000 1.000 1.000 1.000 1.000
## 75 1.000 1.000 1.000 1.000 1.000
## 76 3.850 4.007 3.929 4.348 4.348
## 77 4.023 4.179 4.101 4.603 4.603
## 78 3.958 4.114 4.036 4.521 4.521
## 79 3.885 4.048 3.966 4.253 4.253
## 80 3.796 3.946 3.871 4.533 4.533
## 81 3.923 4.077 4.000 4.561 4.561
## 82 3.121 3.290 3.206 3.401 3.401
## 83 2.963 3.118 3.040 3.557 3.557
## 84 3.533 3.678 3.605 4.349 4.349
## 85 3.522 3.668 3.595 4.303 4.303
## 86 3.501 3.646 3.573 4.318 4.318
## 87 3.509 3.654 3.581 4.297 4.297
## 88 2.754 2.939 2.847 2.757 2.757
## 89 2.962 3.138 3.050 3.141 3.141
## 90 3.376 3.524 3.450 4.071 4.071
## 91 3.451 3.604 3.528 4.031 4.031
## 92 3.681 3.839 3.760 4.153 4.153
## 93 3.325 3.476 3.401 3.940 3.940
## 94 2.157 2.327 2.242 2.372 2.372
## 95 2.060 2.227 2.144 2.299 2.299
## 96 0.000 0.000 0.000 0.000 0.000
## 97 0.000 0.000 0.000 0.000 0.000
## 98 0.000 0.000 0.000 0.000 0.000
## 99 0.000 0.000 0.000 0.000 0.000
## 100 0.000 0.000 0.000 0.000 0.000
ctum_dis_mod <-
"exp =~ meti_exp_01 + meti_exp_02 + meti_exp_03 + meti_exp_04 + meti_exp_05 + meti_exp_06 + reas_exp_r_rec + reas_alt_exp_r_rec
int =~ meti_int_01 + meti_int_02 + meti_int_03 + meti_int_04 + reas_int_r_rec + reas_alt_int_r_rec
ben =~ meti_ben_01 + meti_ben_02 + meti_ben_03 + meti_ben_04 + reas_ben_r_rec + reas_alt_ben_r_rec
meti =~ meti_exp_01 + meti_exp_02 + meti_exp_03 + meti_exp_04 + meti_exp_05 + meti_exp_06 +
meti_int_01 + meti_int_02 + meti_int_03 + meti_int_04 +
meti_ben_01 + meti_ben_02 + meti_ben_03 + meti_ben_04
si =~ reas_exp_r_rec + reas_alt_exp_r_rec + reas_int_r_rec + reas_alt_int_r_rec + reas_ben_r_rec + reas_alt_ben_r_rec
si ~~ 0*meti + 0*exp + 0*int + 0*ben
meti ~~ 0*exp + 0*int + 0*ben"
ctum_dis_fit <-
cfa(ctum_dis_mod,
data = data_mtmm_rec,
estimator = "ML",
std.lv = T)
fitmeasures(ctum_dis_fit)[c("chisq", "df", "tli", "cfi", "rmsea", "srmr")]
## chisq df tli cfi rmsea srmr
## 417.30717242 147.00000000 0.93828740 0.95225393 0.06577724 0.08707447
semPlot::semPaths(ctum_dis_fit)
parameterEstimates(ctum_dis_fit, standardized = T) %>%
mutate_if(is.numeric, function(x) round(x, 3))
## lhs op rhs est se z pvalue ci.lower
## 1 exp =~ meti_exp_01 0.406 0.035 11.554 0.000 0.337
## 2 exp =~ meti_exp_02 0.400 0.037 10.803 0.000 0.327
## 3 exp =~ meti_exp_03 0.426 0.037 11.516 0.000 0.354
## 4 exp =~ meti_exp_04 0.407 0.039 10.469 0.000 0.331
## 5 exp =~ meti_exp_05 0.311 0.038 8.131 0.000 0.236
## 6 exp =~ meti_exp_06 0.550 0.035 15.817 0.000 0.482
## 7 exp =~ reas_exp_r_rec 0.251 0.050 4.963 0.000 0.152
## 8 exp =~ reas_alt_exp_r_rec 0.112 0.046 2.418 0.016 0.021
## 9 int =~ meti_int_01 0.303 0.040 7.527 0.000 0.224
## 10 int =~ meti_int_02 0.250 0.041 6.124 0.000 0.170
## 11 int =~ meti_int_03 0.226 0.041 5.451 0.000 0.144
## 12 int =~ meti_int_04 0.250 0.042 5.941 0.000 0.168
## 13 int =~ reas_int_r_rec 0.441 0.060 7.382 0.000 0.324
## 14 int =~ reas_alt_int_r_rec 0.331 0.056 5.946 0.000 0.222
## 15 ben =~ meti_ben_01 0.214 0.042 5.045 0.000 0.131
## 16 ben =~ meti_ben_02 0.205 0.044 4.656 0.000 0.119
## 17 ben =~ meti_ben_03 0.182 0.045 4.057 0.000 0.094
## 18 ben =~ meti_ben_04 0.240 0.045 5.360 0.000 0.153
## 19 ben =~ reas_ben_r_rec 0.775 0.067 11.555 0.000 0.643
## 20 ben =~ reas_alt_ben_r_rec 0.485 0.057 8.526 0.000 0.373
## 21 meti =~ meti_exp_01 0.622 0.038 16.299 0.000 0.547
## 22 meti =~ meti_exp_02 0.540 0.039 13.987 0.000 0.464
## 23 meti =~ meti_exp_03 0.570 0.039 14.508 0.000 0.493
## 24 meti =~ meti_exp_04 0.610 0.041 14.946 0.000 0.530
## 25 meti =~ meti_exp_05 0.517 0.038 13.510 0.000 0.442
## 26 meti =~ meti_exp_06 0.494 0.038 12.852 0.000 0.419
## 27 meti =~ meti_int_01 0.633 0.033 18.939 0.000 0.568
## 28 meti =~ meti_int_02 0.648 0.034 19.296 0.000 0.582
## 29 meti =~ meti_int_03 0.601 0.034 17.703 0.000 0.534
## 30 meti =~ meti_int_04 0.607 0.035 17.478 0.000 0.539
## 31 meti =~ meti_ben_01 0.704 0.034 20.409 0.000 0.636
## 32 meti =~ meti_ben_02 0.674 0.037 18.417 0.000 0.602
## 33 meti =~ meti_ben_03 0.711 0.037 18.958 0.000 0.637
## 34 meti =~ meti_ben_04 0.626 0.037 16.813 0.000 0.553
## 35 si =~ reas_exp_r_rec 0.621 0.046 13.420 0.000 0.531
## 36 si =~ reas_alt_exp_r_rec 0.624 0.043 14.393 0.000 0.539
## 37 si =~ reas_int_r_rec 0.587 0.054 10.923 0.000 0.482
## 38 si =~ reas_alt_int_r_rec 0.641 0.049 12.952 0.000 0.544
## 39 si =~ reas_ben_r_rec 0.232 0.054 4.312 0.000 0.126
## 40 si =~ reas_alt_ben_r_rec 0.229 0.053 4.295 0.000 0.124
## 41 meti ~~ si 0.000 0.000 NA NA 0.000
## 42 exp ~~ si 0.000 0.000 NA NA 0.000
## 43 int ~~ si 0.000 0.000 NA NA 0.000
## 44 ben ~~ si 0.000 0.000 NA NA 0.000
## 45 exp ~~ meti 0.000 0.000 NA NA 0.000
## 46 int ~~ meti 0.000 0.000 NA NA 0.000
## 47 ben ~~ meti 0.000 0.000 NA NA 0.000
## 48 meti_exp_01 ~~ meti_exp_01 0.222 0.018 12.283 0.000 0.186
## 49 meti_exp_02 ~~ meti_exp_02 0.282 0.022 12.872 0.000 0.239
## 50 meti_exp_03 ~~ meti_exp_03 0.266 0.021 12.501 0.000 0.224
## 51 meti_exp_04 ~~ meti_exp_04 0.308 0.024 12.942 0.000 0.261
## 52 meti_exp_05 ~~ meti_exp_05 0.343 0.025 13.698 0.000 0.294
## 53 meti_exp_06 ~~ meti_exp_06 0.161 0.020 8.213 0.000 0.123
## 54 reas_exp_r_rec ~~ reas_exp_r_rec 0.429 0.041 10.420 0.000 0.348
## 55 reas_alt_exp_r_rec ~~ reas_alt_exp_r_rec 0.329 0.038 8.740 0.000 0.255
## 56 meti_int_01 ~~ meti_int_01 0.182 0.015 11.773 0.000 0.152
## 57 meti_int_02 ~~ meti_int_02 0.196 0.016 12.449 0.000 0.165
## 58 meti_int_03 ~~ meti_int_03 0.239 0.018 13.112 0.000 0.204
## 59 meti_int_04 ~~ meti_int_04 0.249 0.019 13.034 0.000 0.211
## 60 reas_int_r_rec ~~ reas_int_r_rec 0.518 0.046 11.148 0.000 0.427
## 61 reas_alt_int_r_rec ~~ reas_alt_int_r_rec 0.417 0.039 10.591 0.000 0.340
## 62 meti_ben_01 ~~ meti_ben_01 0.209 0.018 11.729 0.000 0.174
## 63 meti_ben_02 ~~ meti_ben_02 0.287 0.023 12.703 0.000 0.243
## 64 meti_ben_03 ~~ meti_ben_03 0.293 0.023 12.508 0.000 0.247
## 65 meti_ben_04 ~~ meti_ben_04 0.326 0.025 13.126 0.000 0.278
## 66 reas_ben_r_rec ~~ reas_ben_r_rec 0.197 0.086 2.276 0.023 0.027
## 67 reas_alt_ben_r_rec ~~ reas_alt_ben_r_rec 0.574 0.051 11.265 0.000 0.474
## 68 exp ~~ exp 1.000 0.000 NA NA 1.000
## 69 int ~~ int 1.000 0.000 NA NA 1.000
## 70 ben ~~ ben 1.000 0.000 NA NA 1.000
## 71 meti ~~ meti 1.000 0.000 NA NA 1.000
## 72 si ~~ si 1.000 0.000 NA NA 1.000
## 73 exp ~~ int 0.553 0.067 8.193 0.000 0.420
## 74 exp ~~ ben 0.275 0.077 3.574 0.000 0.124
## 75 int ~~ ben 0.759 0.060 12.618 0.000 0.641
## ci.upper std.lv std.all std.nox
## 1 0.475 0.406 0.462 0.462
## 2 0.472 0.400 0.467 0.467
## 3 0.499 0.426 0.485 0.485
## 4 0.483 0.407 0.443 0.443
## 5 0.386 0.311 0.370 0.370
## 6 0.618 0.550 0.654 0.654
## 7 0.350 0.251 0.267 0.267
## 8 0.203 0.112 0.131 0.131
## 9 0.382 0.303 0.369 0.369
## 10 0.330 0.250 0.304 0.304
## 11 0.307 0.226 0.279 0.279
## 12 0.333 0.250 0.304 0.304
## 13 0.558 0.441 0.429 0.429
## 14 0.440 0.331 0.342 0.342
## 15 0.296 0.214 0.247 0.247
## 16 0.292 0.205 0.232 0.232
## 17 0.271 0.182 0.200 0.200
## 18 0.328 0.240 0.273 0.273
## 19 0.906 0.775 0.840 0.840
## 20 0.596 0.485 0.523 0.523
## 21 0.697 0.622 0.707 0.707
## 22 0.616 0.540 0.631 0.631
## 23 0.647 0.570 0.648 0.648
## 24 0.691 0.610 0.664 0.664
## 25 0.592 0.517 0.615 0.615
## 26 0.570 0.494 0.588 0.588
## 27 0.699 0.633 0.771 0.771
## 28 0.714 0.648 0.787 0.787
## 29 0.668 0.601 0.745 0.745
## 30 0.675 0.607 0.736 0.736
## 31 0.771 0.704 0.813 0.813
## 32 0.745 0.674 0.761 0.761
## 33 0.784 0.711 0.779 0.779
## 34 0.699 0.626 0.710 0.710
## 35 0.712 0.621 0.663 0.663
## 36 0.709 0.624 0.730 0.730
## 37 0.693 0.587 0.571 0.571
## 38 0.738 0.641 0.662 0.662
## 39 0.337 0.232 0.251 0.251
## 40 0.333 0.229 0.246 0.246
## 41 0.000 0.000 0.000 0.000
## 42 0.000 0.000 0.000 0.000
## 43 0.000 0.000 0.000 0.000
## 44 0.000 0.000 0.000 0.000
## 45 0.000 0.000 0.000 0.000
## 46 0.000 0.000 0.000 0.000
## 47 0.000 0.000 0.000 0.000
## 48 0.257 0.222 0.287 0.287
## 49 0.325 0.282 0.385 0.385
## 50 0.308 0.266 0.344 0.344
## 51 0.354 0.308 0.364 0.364
## 52 0.393 0.343 0.485 0.485
## 53 0.199 0.161 0.227 0.227
## 54 0.510 0.429 0.489 0.489
## 55 0.403 0.329 0.450 0.450
## 56 0.212 0.182 0.269 0.269
## 57 0.227 0.196 0.289 0.289
## 58 0.275 0.239 0.367 0.367
## 59 0.286 0.249 0.366 0.366
## 60 0.609 0.518 0.490 0.490
## 61 0.494 0.417 0.445 0.445
## 62 0.244 0.209 0.279 0.279
## 63 0.331 0.287 0.367 0.367
## 64 0.339 0.293 0.352 0.352
## 65 0.375 0.326 0.421 0.421
## 66 0.366 0.197 0.231 0.231
## 67 0.673 0.574 0.666 0.666
## 68 1.000 1.000 1.000 1.000
## 69 1.000 1.000 1.000 1.000
## 70 1.000 1.000 1.000 1.000
## 71 1.000 1.000 1.000 1.000
## 72 1.000 1.000 1.000 1.000
## 73 0.685 0.553 0.553 0.553
## 74 0.425 0.275 0.275 0.275
## 75 0.877 0.759 0.759 0.759
modificationindices(ctum_dis_fit, sort. = T)
## lhs op rhs mi epc sepc.lv sepc.all
## 84 exp =~ meti_ben_03 49.209 0.263 0.263 0.289
## 98 int =~ meti_ben_03 37.356 0.502 0.502 0.550
## 94 int =~ reas_exp_r_rec 28.223 0.374 0.374 0.399
## 41 meti ~~ si 27.199 0.338 0.338 0.338
## 108 ben =~ reas_exp_r_rec 25.277 0.252 0.252 0.269
## 246 reas_exp_r_rec ~~ reas_ben_r_rec 21.155 0.134 0.134 0.462
## 309 reas_alt_int_r_rec ~~ reas_ben_r_rec 20.292 -0.158 -0.158 -0.553
## 241 reas_exp_r_rec ~~ reas_alt_int_r_rec 17.015 -0.155 -0.155 -0.366
## 185 meti_exp_03 ~~ meti_ben_02 16.478 0.063 0.063 0.227
## 304 reas_int_r_rec ~~ reas_alt_ben_r_rec 14.557 -0.133 -0.133 -0.245
## 130 si =~ meti_int_03 13.496 -0.109 -0.109 -0.135
## 202 meti_exp_04 ~~ meti_ben_03 13.205 0.061 0.061 0.203
## 253 reas_alt_exp_r_rec ~~ reas_alt_int_r_rec 12.981 0.141 0.141 0.380
## 228 meti_exp_06 ~~ reas_alt_int_r_rec 12.066 0.062 0.062 0.240
## 281 meti_int_03 ~~ meti_int_04 11.373 0.046 0.046 0.187
## 312 meti_ben_01 ~~ meti_ben_03 10.727 -0.051 -0.051 -0.206
## 81 exp =~ reas_alt_int_r_rec 10.546 0.183 0.183 0.189
## 226 meti_exp_06 ~~ meti_int_04 10.338 -0.041 -0.041 -0.205
## 158 meti_exp_02 ~~ meti_exp_06 10.309 -0.054 -0.054 -0.254
## 325 reas_ben_r_rec ~~ reas_alt_ben_r_rec 10.190 0.359 0.359 1.069
## 310 reas_alt_int_r_rec ~~ reas_alt_ben_r_rec 10.159 0.096 0.096 0.197
## 79 exp =~ meti_int_04 9.942 -0.122 -0.122 -0.148
## 129 si =~ meti_int_02 9.654 0.086 0.086 0.104
## 46 int ~~ meti 9.572 0.271 0.271 0.271
## 252 reas_alt_exp_r_rec ~~ reas_int_r_rec 8.924 -0.103 -0.103 -0.249
## 150 meti_exp_01 ~~ meti_ben_02 8.863 -0.043 -0.043 -0.169
## 87 exp =~ reas_alt_ben_r_rec 8.721 -0.142 -0.142 -0.153
## 240 reas_exp_r_rec ~~ reas_int_r_rec 8.546 0.100 0.100 0.213
## 113 ben =~ meti_int_04 8.535 0.178 0.178 0.215
## 215 meti_exp_05 ~~ meti_ben_01 8.282 0.044 0.044 0.163
## 311 meti_ben_01 ~~ meti_ben_02 8.169 0.043 0.043 0.177
## 206 meti_exp_05 ~~ meti_exp_06 7.552 0.045 0.045 0.193
## 168 meti_exp_02 ~~ meti_ben_02 7.468 -0.043 -0.043 -0.151
## 234 meti_exp_06 ~~ reas_alt_ben_r_rec 7.045 -0.051 -0.051 -0.168
## 187 meti_exp_03 ~~ meti_ben_04 6.728 -0.042 -0.042 -0.142
## 196 meti_exp_04 ~~ meti_int_03 6.692 -0.038 -0.038 -0.140
## 161 meti_exp_02 ~~ meti_int_01 6.616 0.033 0.033 0.145
## 239 reas_exp_r_rec ~~ meti_int_04 6.416 0.048 0.048 0.147
## 119 meti =~ reas_alt_int_r_rec 6.391 0.097 0.097 0.100
## 283 meti_int_03 ~~ reas_alt_int_r_rec 6.194 -0.046 -0.046 -0.145
## 302 reas_int_r_rec ~~ meti_ben_04 6.123 -0.058 -0.058 -0.142
## 275 meti_int_02 ~~ meti_ben_01 5.909 0.030 0.030 0.148
## 91 int =~ meti_exp_04 5.501 -0.108 -0.108 -0.117
## 272 meti_int_02 ~~ meti_int_04 5.498 -0.030 -0.030 -0.135
## 301 reas_int_r_rec ~~ meti_ben_03 5.235 0.052 0.052 0.134
## 175 meti_exp_03 ~~ meti_exp_06 5.024 0.039 0.039 0.187
## 164 meti_exp_02 ~~ meti_int_04 5.019 0.032 0.032 0.121
## 162 meti_exp_02 ~~ meti_int_02 4.934 -0.029 -0.029 -0.123
## 146 meti_exp_01 ~~ meti_int_04 4.905 -0.029 -0.029 -0.123
## 231 meti_exp_06 ~~ meti_ben_03 4.823 0.031 0.031 0.143
## 136 meti_exp_01 ~~ meti_exp_02 4.810 0.032 0.032 0.129
## 145 meti_exp_01 ~~ meti_int_03 4.748 0.028 0.028 0.121
## 306 reas_alt_int_r_rec ~~ meti_ben_02 4.712 0.044 0.044 0.128
## 279 meti_int_02 ~~ reas_ben_r_rec 4.607 -0.045 -0.045 -0.228
## 291 meti_int_04 ~~ reas_alt_int_r_rec 4.597 -0.040 -0.040 -0.125
## 220 meti_exp_05 ~~ reas_alt_ben_r_rec 4.451 -0.049 -0.049 -0.111
## 85 exp =~ meti_ben_04 4.333 -0.079 -0.079 -0.089
## 258 reas_alt_exp_r_rec ~~ reas_ben_r_rec 4.316 -0.062 -0.062 -0.243
## 103 ben =~ meti_exp_02 4.022 -0.066 -0.066 -0.077
## 260 meti_int_01 ~~ meti_int_02 3.933 0.024 0.024 0.125
## 221 meti_exp_06 ~~ reas_exp_r_rec 3.904 -0.038 -0.038 -0.145
## 230 meti_exp_06 ~~ meti_ben_02 3.849 0.027 0.027 0.127
## 261 meti_int_01 ~~ meti_int_03 3.847 -0.024 -0.024 -0.117
## 133 si =~ meti_ben_02 3.730 0.063 0.063 0.072
## 182 meti_exp_03 ~~ reas_int_r_rec 3.612 0.041 0.041 0.109
## 101 int =~ reas_alt_ben_r_rec 3.599 -0.202 -0.202 -0.218
## 115 ben =~ reas_alt_int_r_rec 3.559 -0.169 -0.169 -0.174
## 139 meti_exp_01 ~~ meti_exp_05 3.544 -0.029 -0.029 -0.105
## 273 meti_int_02 ~~ reas_int_r_rec 3.373 0.035 0.035 0.110
## 247 reas_exp_r_rec ~~ reas_alt_ben_r_rec 3.363 -0.055 -0.055 -0.111
## 154 meti_exp_01 ~~ reas_alt_ben_r_rec 3.357 0.036 0.036 0.101
## 99 int =~ meti_ben_04 3.278 -0.148 -0.148 -0.168
## 159 meti_exp_02 ~~ reas_exp_r_rec 3.183 0.036 0.036 0.104
## 42 exp ~~ si 3.084 0.144 0.144 0.144
## 47 ben ~~ meti 3.057 -0.127 -0.127 -0.127
## 82 exp =~ meti_ben_01 3.025 -0.059 -0.059 -0.068
## 135 si =~ meti_ben_04 2.991 -0.060 -0.060 -0.068
## 219 meti_exp_05 ~~ reas_ben_r_rec 2.877 0.038 0.038 0.146
## 170 meti_exp_02 ~~ meti_ben_04 2.580 0.026 0.026 0.087
## 288 meti_int_03 ~~ reas_ben_r_rec 2.566 0.036 0.036 0.164
## 303 reas_int_r_rec ~~ reas_ben_r_rec 2.490 0.069 0.069 0.216
## 194 meti_exp_04 ~~ meti_int_01 2.477 -0.021 -0.021 -0.089
## 152 meti_exp_01 ~~ meti_ben_04 2.454 0.023 0.023 0.087
## 86 exp =~ reas_ben_r_rec 2.424 0.110 0.110 0.119
## 105 ben =~ meti_exp_04 2.410 -0.054 -0.054 -0.058
## 121 meti =~ reas_alt_ben_r_rec 2.297 -0.069 -0.069 -0.074
## 285 meti_int_03 ~~ meti_ben_02 2.284 -0.022 -0.022 -0.084
## 205 meti_exp_04 ~~ reas_alt_ben_r_rec 2.271 0.034 0.034 0.081
## 109 ben =~ reas_alt_exp_r_rec 2.267 -0.074 -0.074 -0.086
## 134 si =~ meti_ben_03 2.265 0.050 0.050 0.055
## 137 meti_exp_01 ~~ meti_exp_03 2.236 -0.022 -0.022 -0.091
## 249 reas_alt_exp_r_rec ~~ meti_int_02 2.234 0.024 0.024 0.094
## 167 meti_exp_02 ~~ meti_ben_01 2.232 -0.021 -0.021 -0.086
## 177 meti_exp_03 ~~ reas_alt_exp_r_rec 2.188 -0.027 -0.027 -0.091
## 250 reas_alt_exp_r_rec ~~ meti_int_03 2.153 -0.025 -0.025 -0.090
## 318 meti_ben_02 ~~ reas_ben_r_rec 2.142 -0.043 -0.043 -0.183
## 127 si =~ meti_exp_06 2.127 0.044 0.044 0.052
## 95 int =~ reas_alt_exp_r_rec 2.124 -0.098 -0.098 -0.114
## 266 meti_int_01 ~~ meti_ben_02 2.119 -0.019 -0.019 -0.085
## 287 meti_int_03 ~~ meti_ben_04 2.098 0.022 0.022 0.079
## 149 meti_exp_01 ~~ meti_ben_01 2.013 -0.018 -0.018 -0.084
## 151 meti_exp_01 ~~ meti_ben_03 1.995 0.021 0.021 0.081
## 112 ben =~ meti_int_03 1.995 0.083 0.083 0.103
## 156 meti_exp_02 ~~ meti_exp_04 1.806 0.022 0.022 0.076
## 259 reas_alt_exp_r_rec ~~ reas_alt_ben_r_rec 1.789 0.040 0.040 0.092
## 322 meti_ben_03 ~~ reas_alt_ben_r_rec 1.788 -0.030 -0.030 -0.074
## 211 meti_exp_05 ~~ meti_int_03 1.758 0.020 0.020 0.070
## 45 exp ~~ meti 1.750 0.216 0.216 0.216
## 217 meti_exp_05 ~~ meti_ben_03 1.741 -0.023 -0.023 -0.072
## 122 si =~ meti_exp_01 1.729 -0.038 -0.038 -0.044
## 118 meti =~ reas_int_r_rec 1.711 0.056 0.056 0.054
## 181 meti_exp_03 ~~ meti_int_04 1.710 -0.018 -0.018 -0.071
## 186 meti_exp_03 ~~ meti_ben_03 1.690 0.020 0.020 0.073
## 268 meti_int_01 ~~ meti_ben_04 1.678 0.018 0.018 0.074
## 204 meti_exp_04 ~~ reas_ben_r_rec 1.655 -0.028 -0.028 -0.114
## 213 meti_exp_05 ~~ reas_int_r_rec 1.606 -0.030 -0.030 -0.070
## 198 meti_exp_04 ~~ reas_int_r_rec 1.549 -0.028 -0.028 -0.071
## 315 meti_ben_01 ~~ reas_alt_ben_r_rec 1.527 -0.024 -0.024 -0.070
## 193 meti_exp_04 ~~ reas_alt_exp_r_rec 1.511 0.024 0.024 0.074
## 171 meti_exp_02 ~~ reas_ben_r_rec 1.476 -0.025 -0.025 -0.107
## 197 meti_exp_04 ~~ meti_int_04 1.474 0.018 0.018 0.066
## 286 meti_int_03 ~~ meti_ben_03 1.474 -0.018 -0.018 -0.069
## 218 meti_exp_05 ~~ meti_ben_04 1.408 -0.021 -0.021 -0.063
## 144 meti_exp_01 ~~ meti_int_02 1.408 0.014 0.014 0.067
## 212 meti_exp_05 ~~ meti_int_04 1.357 0.018 0.018 0.061
## 169 meti_exp_02 ~~ meti_ben_03 1.315 -0.018 -0.018 -0.064
## 225 meti_exp_06 ~~ meti_int_03 1.302 -0.014 -0.014 -0.072
## 297 meti_int_04 ~~ reas_alt_ben_r_rec 1.292 0.023 0.023 0.061
## 44 ben ~~ si 1.266 0.122 0.122 0.122
## 296 meti_int_04 ~~ reas_ben_r_rec 1.243 0.026 0.026 0.115
## 163 meti_exp_02 ~~ meti_int_03 1.237 0.016 0.016 0.060
## 114 ben =~ reas_int_r_rec 1.180 -0.114 -0.114 -0.111
## 263 meti_int_01 ~~ reas_int_r_rec 1.157 -0.021 -0.021 -0.068
## 203 meti_exp_04 ~~ meti_ben_04 1.138 -0.018 -0.018 -0.058
## 141 meti_exp_01 ~~ reas_exp_r_rec 1.103 -0.019 -0.019 -0.062
## 292 meti_int_04 ~~ meti_ben_01 1.063 -0.014 -0.014 -0.061
## 243 reas_exp_r_rec ~~ meti_ben_02 1.062 -0.021 -0.021 -0.061
## 90 int =~ meti_exp_03 1.059 0.044 0.044 0.050
## 117 meti =~ reas_alt_exp_r_rec 1.051 0.037 0.037 0.043
## 298 reas_int_r_rec ~~ reas_alt_int_r_rec 1.024 0.037 0.037 0.080
## 178 meti_exp_03 ~~ meti_int_01 1.012 -0.013 -0.013 -0.057
## 271 meti_int_02 ~~ meti_int_03 1.012 -0.012 -0.012 -0.058
## 317 meti_ben_02 ~~ meti_ben_04 0.990 -0.017 -0.017 -0.056
## 320 meti_ben_03 ~~ meti_ben_04 0.980 -0.017 -0.017 -0.056
## 308 reas_alt_int_r_rec ~~ meti_ben_04 0.948 -0.021 -0.021 -0.056
## 305 reas_alt_int_r_rec ~~ meti_ben_01 0.935 0.018 0.018 0.059
## 324 meti_ben_04 ~~ reas_alt_ben_r_rec 0.909 0.023 0.023 0.053
## 245 reas_exp_r_rec ~~ meti_ben_04 0.890 0.020 0.020 0.055
## 148 meti_exp_01 ~~ reas_alt_int_r_rec 0.879 -0.017 -0.017 -0.055
## 232 meti_exp_06 ~~ meti_ben_04 0.877 -0.014 -0.014 -0.059
## 210 meti_exp_05 ~~ meti_int_02 0.856 -0.013 -0.013 -0.050
## 104 ben =~ meti_exp_03 0.804 0.029 0.029 0.033
## 89 int =~ meti_exp_02 0.797 -0.039 -0.039 -0.045
## 102 ben =~ meti_exp_01 0.788 0.027 0.027 0.031
## 251 reas_alt_exp_r_rec ~~ meti_int_04 0.756 -0.015 -0.015 -0.053
## 293 meti_int_04 ~~ meti_ben_02 0.741 0.013 0.013 0.048
## 294 meti_int_04 ~~ meti_ben_03 0.727 0.013 0.013 0.048
## 264 meti_int_01 ~~ reas_alt_int_r_rec 0.704 0.014 0.014 0.052
## 277 meti_int_02 ~~ meti_ben_03 0.691 -0.012 -0.012 -0.048
## 174 meti_exp_03 ~~ meti_exp_05 0.661 -0.014 -0.014 -0.045
## 88 int =~ meti_exp_01 0.637 0.032 0.032 0.037
## 242 reas_exp_r_rec ~~ meti_ben_01 0.603 -0.014 -0.014 -0.048
## 256 reas_alt_exp_r_rec ~~ meti_ben_03 0.596 -0.015 -0.015 -0.048
## 116 meti =~ reas_exp_r_rec 0.575 0.030 0.030 0.032
## 93 int =~ meti_exp_06 0.535 -0.032 -0.032 -0.038
## 147 meti_exp_01 ~~ reas_int_r_rec 0.529 -0.014 -0.014 -0.042
## 172 meti_exp_02 ~~ reas_alt_ben_r_rec 0.527 -0.016 -0.016 -0.039
## 190 meti_exp_04 ~~ meti_exp_05 0.521 -0.013 -0.013 -0.039
## 43 int ~~ si 0.485 0.051 0.051 0.051
## 200 meti_exp_04 ~~ meti_ben_01 0.483 -0.010 -0.010 -0.040
## 289 meti_int_03 ~~ reas_alt_ben_r_rec 0.458 0.013 0.013 0.036
## 262 meti_int_01 ~~ meti_int_04 0.458 0.009 0.009 0.041
## 111 ben =~ meti_int_02 0.443 -0.037 -0.037 -0.045
## 125 si =~ meti_exp_04 0.432 0.022 0.022 0.024
## 280 meti_int_02 ~~ reas_alt_ben_r_rec 0.428 0.012 0.012 0.036
## 233 meti_exp_06 ~~ reas_ben_r_rec 0.420 0.013 0.013 0.073
## 316 meti_ben_02 ~~ meti_ben_03 0.419 0.011 0.011 0.038
## 323 meti_ben_04 ~~ reas_ben_r_rec 0.391 0.020 0.020 0.080
## 106 ben =~ meti_exp_05 0.386 0.022 0.022 0.026
## 255 reas_alt_exp_r_rec ~~ meti_ben_02 0.385 0.012 0.012 0.038
## 184 meti_exp_03 ~~ meti_ben_01 0.382 -0.009 -0.009 -0.036
## 138 meti_exp_01 ~~ meti_exp_04 0.381 0.009 0.009 0.036
## 267 meti_int_01 ~~ meti_ben_03 0.346 -0.008 -0.008 -0.035
## 142 meti_exp_01 ~~ reas_alt_exp_r_rec 0.338 0.010 0.010 0.036
## 313 meti_ben_01 ~~ meti_ben_04 0.337 0.009 0.009 0.035
## 123 si =~ meti_exp_02 0.331 0.018 0.018 0.022
## 110 ben =~ meti_int_01 0.324 -0.033 -0.033 -0.041
## 157 meti_exp_02 ~~ meti_exp_05 0.316 -0.009 -0.009 -0.030
## 248 reas_alt_exp_r_rec ~~ meti_int_01 0.311 0.009 0.009 0.036
## 180 meti_exp_03 ~~ meti_int_03 0.308 0.008 0.008 0.030
## 143 meti_exp_01 ~~ meti_int_01 0.305 0.006 0.006 0.032
## 295 meti_int_04 ~~ meti_ben_04 0.304 0.009 0.009 0.030
## 179 meti_exp_03 ~~ meti_int_02 0.296 0.007 0.007 0.031
## 128 si =~ meti_int_01 0.294 0.015 0.015 0.018
## 92 int =~ meti_exp_05 0.281 0.025 0.025 0.030
## 120 meti =~ reas_ben_r_rec 0.269 -0.026 -0.026 -0.029
## 83 exp =~ meti_ben_02 0.262 0.019 0.019 0.021
## 321 meti_ben_03 ~~ reas_ben_r_rec 0.251 0.015 0.015 0.063
## 300 reas_int_r_rec ~~ meti_ben_02 0.250 0.011 0.011 0.029
## 216 meti_exp_05 ~~ meti_ben_02 0.244 -0.008 -0.008 -0.027
## 189 meti_exp_03 ~~ reas_alt_ben_r_rec 0.244 -0.010 -0.010 -0.027
## 107 ben =~ meti_exp_06 0.239 -0.016 -0.016 -0.019
## 183 meti_exp_03 ~~ reas_alt_int_r_rec 0.222 0.009 0.009 0.028
## 97 int =~ meti_ben_02 0.210 0.037 0.037 0.041
## 274 meti_int_02 ~~ reas_alt_int_r_rec 0.198 0.008 0.008 0.027
## 192 meti_exp_04 ~~ reas_exp_r_rec 0.175 0.009 0.009 0.024
## 195 meti_exp_04 ~~ meti_int_02 0.168 0.006 0.006 0.023
## 173 meti_exp_03 ~~ meti_exp_04 0.164 -0.007 -0.007 -0.023
## 131 si =~ meti_int_04 0.156 0.012 0.012 0.015
## 176 meti_exp_03 ~~ reas_exp_r_rec 0.155 -0.008 -0.008 -0.023
## 244 reas_exp_r_rec ~~ meti_ben_03 0.155 0.008 0.008 0.023
## 290 meti_int_04 ~~ reas_int_r_rec 0.113 0.007 0.007 0.020
## 214 meti_exp_05 ~~ reas_alt_int_r_rec 0.112 0.007 0.007 0.019
## 126 si =~ meti_exp_05 0.104 -0.011 -0.011 -0.013
## 77 exp =~ meti_int_02 0.096 -0.011 -0.011 -0.013
## 76 exp =~ meti_int_01 0.095 0.011 0.011 0.014
## 307 reas_alt_int_r_rec ~~ meti_ben_03 0.089 0.006 0.006 0.018
## 269 meti_int_01 ~~ reas_ben_r_rec 0.087 -0.006 -0.006 -0.034
## 227 meti_exp_06 ~~ reas_int_r_rec 0.086 0.006 0.006 0.020
## 132 si =~ meti_ben_01 0.086 0.009 0.009 0.010
## 208 meti_exp_05 ~~ reas_alt_exp_r_rec 0.086 0.006 0.006 0.017
## 165 meti_exp_02 ~~ reas_int_r_rec 0.072 -0.006 -0.006 -0.015
## 96 int =~ meti_ben_01 0.070 -0.020 -0.020 -0.023
## 155 meti_exp_02 ~~ meti_exp_03 0.064 -0.004 -0.004 -0.015
## 299 reas_int_r_rec ~~ meti_ben_01 0.064 0.005 0.005 0.015
## 224 meti_exp_06 ~~ meti_int_02 0.058 -0.003 -0.003 -0.016
## 254 reas_alt_exp_r_rec ~~ meti_ben_01 0.055 0.004 0.004 0.015
## 166 meti_exp_02 ~~ reas_alt_int_r_rec 0.053 -0.005 -0.005 -0.013
## 207 meti_exp_05 ~~ reas_exp_r_rec 0.049 0.005 0.005 0.012
## 153 meti_exp_01 ~~ reas_ben_r_rec 0.047 0.004 0.004 0.020
## 223 meti_exp_06 ~~ meti_int_01 0.047 -0.002 -0.002 -0.014
## 188 meti_exp_03 ~~ reas_ben_r_rec 0.043 0.004 0.004 0.018
## 209 meti_exp_05 ~~ meti_int_01 0.038 -0.003 -0.003 -0.011
## 140 meti_exp_01 ~~ meti_exp_06 0.037 0.003 0.003 0.016
## 235 reas_exp_r_rec ~~ reas_alt_exp_r_rec 0.032 -0.009 -0.009 -0.023
## 238 reas_exp_r_rec ~~ meti_int_03 0.030 -0.003 -0.003 -0.010
## 80 exp =~ reas_int_r_rec 0.028 0.010 0.010 0.010
## 278 meti_int_02 ~~ meti_ben_04 0.023 -0.002 -0.002 -0.009
## 201 meti_exp_04 ~~ meti_ben_02 0.019 -0.002 -0.002 -0.008
## 191 meti_exp_04 ~~ meti_exp_06 0.009 0.002 0.002 0.007
## 237 reas_exp_r_rec ~~ meti_int_02 0.009 -0.002 -0.002 -0.006
## 284 meti_int_03 ~~ meti_ben_01 0.007 0.001 0.001 0.005
## 222 meti_exp_06 ~~ reas_alt_exp_r_rec 0.006 0.001 0.001 0.006
## 282 meti_int_03 ~~ reas_int_r_rec 0.006 -0.002 -0.002 -0.004
## 276 meti_int_02 ~~ meti_ben_02 0.005 -0.001 -0.001 -0.004
## 236 reas_exp_r_rec ~~ meti_int_01 0.005 0.001 0.001 0.004
## 124 si =~ meti_exp_03 0.004 0.002 0.002 0.002
## 265 meti_int_01 ~~ meti_ben_01 0.004 -0.001 -0.001 -0.004
## 314 meti_ben_01 ~~ reas_ben_r_rec 0.003 0.001 0.001 0.007
## 199 meti_exp_04 ~~ reas_alt_int_r_rec 0.003 -0.001 -0.001 -0.003
## 78 exp =~ meti_int_03 0.001 -0.001 -0.001 -0.001
## 270 meti_int_01 ~~ reas_alt_ben_r_rec 0.001 -0.001 -0.001 -0.002
## 160 meti_exp_02 ~~ reas_alt_exp_r_rec 0.001 0.000 0.000 -0.001
## 319 meti_ben_02 ~~ reas_alt_ben_r_rec 0.000 0.000 0.000 -0.001
## 229 meti_exp_06 ~~ meti_ben_01 0.000 0.000 0.000 0.001
## 100 int =~ reas_ben_r_rec 0.000 -0.001 -0.001 -0.001
## 257 reas_alt_exp_r_rec ~~ meti_ben_04 0.000 0.000 0.000 0.000
## sepc.nox
## 84 0.289
## 98 0.550
## 94 0.399
## 41 0.338
## 108 0.269
## 246 0.462
## 309 -0.553
## 241 -0.366
## 185 0.227
## 304 -0.245
## 130 -0.135
## 202 0.203
## 253 0.380
## 228 0.240
## 281 0.187
## 312 -0.206
## 81 0.189
## 226 -0.205
## 158 -0.254
## 325 1.069
## 310 0.197
## 79 -0.148
## 129 0.104
## 46 0.271
## 252 -0.249
## 150 -0.169
## 87 -0.153
## 240 0.213
## 113 0.215
## 215 0.163
## 311 0.177
## 206 0.193
## 168 -0.151
## 234 -0.168
## 187 -0.142
## 196 -0.140
## 161 0.145
## 239 0.147
## 119 0.100
## 283 -0.145
## 302 -0.142
## 275 0.148
## 91 -0.117
## 272 -0.135
## 301 0.134
## 175 0.187
## 164 0.121
## 162 -0.123
## 146 -0.123
## 231 0.143
## 136 0.129
## 145 0.121
## 306 0.128
## 279 -0.228
## 291 -0.125
## 220 -0.111
## 85 -0.089
## 258 -0.243
## 103 -0.077
## 260 0.125
## 221 -0.145
## 230 0.127
## 261 -0.117
## 133 0.072
## 182 0.109
## 101 -0.218
## 115 -0.174
## 139 -0.105
## 273 0.110
## 247 -0.111
## 154 0.101
## 99 -0.168
## 159 0.104
## 42 0.144
## 47 -0.127
## 82 -0.068
## 135 -0.068
## 219 0.146
## 170 0.087
## 288 0.164
## 303 0.216
## 194 -0.089
## 152 0.087
## 86 0.119
## 105 -0.058
## 121 -0.074
## 285 -0.084
## 205 0.081
## 109 -0.086
## 134 0.055
## 137 -0.091
## 249 0.094
## 167 -0.086
## 177 -0.091
## 250 -0.090
## 318 -0.183
## 127 0.052
## 95 -0.114
## 266 -0.085
## 287 0.079
## 149 -0.084
## 151 0.081
## 112 0.103
## 156 0.076
## 259 0.092
## 322 -0.074
## 211 0.070
## 45 0.216
## 217 -0.072
## 122 -0.044
## 118 0.054
## 181 -0.071
## 186 0.073
## 268 0.074
## 204 -0.114
## 213 -0.070
## 198 -0.071
## 315 -0.070
## 193 0.074
## 171 -0.107
## 197 0.066
## 286 -0.069
## 218 -0.063
## 144 0.067
## 212 0.061
## 169 -0.064
## 225 -0.072
## 297 0.061
## 44 0.122
## 296 0.115
## 163 0.060
## 114 -0.111
## 263 -0.068
## 203 -0.058
## 141 -0.062
## 292 -0.061
## 243 -0.061
## 90 0.050
## 117 0.043
## 298 0.080
## 178 -0.057
## 271 -0.058
## 317 -0.056
## 320 -0.056
## 308 -0.056
## 305 0.059
## 324 0.053
## 245 0.055
## 148 -0.055
## 232 -0.059
## 210 -0.050
## 104 0.033
## 89 -0.045
## 102 0.031
## 251 -0.053
## 293 0.048
## 294 0.048
## 264 0.052
## 277 -0.048
## 174 -0.045
## 88 0.037
## 242 -0.048
## 256 -0.048
## 116 0.032
## 93 -0.038
## 147 -0.042
## 172 -0.039
## 190 -0.039
## 43 0.051
## 200 -0.040
## 289 0.036
## 262 0.041
## 111 -0.045
## 125 0.024
## 280 0.036
## 233 0.073
## 316 0.038
## 323 0.080
## 106 0.026
## 255 0.038
## 184 -0.036
## 138 0.036
## 267 -0.035
## 142 0.036
## 313 0.035
## 123 0.022
## 110 -0.041
## 157 -0.030
## 248 0.036
## 180 0.030
## 143 0.032
## 295 0.030
## 179 0.031
## 128 0.018
## 92 0.030
## 120 -0.029
## 83 0.021
## 321 0.063
## 300 0.029
## 216 -0.027
## 189 -0.027
## 107 -0.019
## 183 0.028
## 97 0.041
## 274 0.027
## 192 0.024
## 195 0.023
## 173 -0.023
## 131 0.015
## 176 -0.023
## 244 0.023
## 290 0.020
## 214 0.019
## 126 -0.013
## 77 -0.013
## 76 0.014
## 307 0.018
## 269 -0.034
## 227 0.020
## 132 0.010
## 208 0.017
## 165 -0.015
## 96 -0.023
## 155 -0.015
## 299 0.015
## 224 -0.016
## 254 0.015
## 166 -0.013
## 207 0.012
## 153 0.020
## 223 -0.014
## 188 0.018
## 209 -0.011
## 140 0.016
## 235 -0.023
## 238 -0.010
## 80 0.010
## 278 -0.009
## 201 -0.008
## 191 0.007
## 237 -0.006
## 284 0.005
## 222 0.006
## 282 -0.004
## 276 -0.004
## 236 0.004
## 124 0.002
## 265 -0.004
## 314 0.007
## 199 -0.003
## 78 -0.001
## 270 -0.002
## 160 -0.001
## 319 -0.001
## 229 0.001
## 100 -0.001
## 257 0.000
library(bain)
res_bain_subload_distrust <-
bain(ctum_dis_fit, standardize = T,
"int=~reas_int_r_rec > .3 & ben=~reas_ben_r_rec > .3")
res_bain_eqload_distrust <-
bain(ctum_dis_fit, standardize = T,
"-0.15 < exp=~reas_exp_r_rec - exp=~reas_alt_exp_r_rec < 0.15 &
-0.15 < int=~reas_int_r_rec - int=~reas_alt_int_r_rec < 0.15 &
-0.15 < ben=~reas_ben_r_rec - ben=~reas_alt_ben_r_rec < 0.15")
res_bain_subload_distrust
## Bayesian informative hypothesis testing for an object of class lavaan:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.991 0.240 4.129 333.960 1.000 0.805
## Hu 0.195
##
## Hypotheses:
## H1: int=~reas_int_r_rec>.3&ben=~reas_ben_r_rec>.3
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
res_bain_eqload_distrust
## Bayesian informative hypothesis testing for an object of class lavaan:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.026 0.004 6.104 6.239 1.000 0.859
## Hu 0.141
##
## Hypotheses:
## H1: -0.15<exp=~reas_exp_r_rec-exp=~reas_alt_exp_r_rec<0.15&-0.15<int=~reas_int_r_rec-int=~reas_alt_int_r_rec<0.15&-0.15<ben=~reas_ben_r_rec-ben=~reas_alt_ben_r_rec<0.15
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
Main Study
data_hyp1_2 <- data_ttest%>%
select(f9_1, f9_2, f9_3, f10_1, f10_2, f10_3)%>%
gather(variable, value)
library(patchwork)
library(hrbrthemes)
## NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
## Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
## if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow
data_hyp1_2_freq <- as.data.frame(table(data_hyp1_2$variable, data_hyp1_2$value))%>%
mutate(variable = fct_recode(Var1,
`Expertise\n(Trust)` = "f9_1",
`Integrity\n(Trust)` = "f9_2",
`Benevolence\n(Trust)` = "f9_3",
`Expertise\n(Distrust)` = "f10_1",
`Integrity\n(Distrust)` = "f10_2",
`Benevolence\n(Distrust)` = "f10_3"),
variable = fct_relevel(variable,
"Expertise\n(Trust)",
"Integrity\n(Trust)",
"Benevolence\n(Trust)",
"Expertise\n(Distrust)",
"Integrity\n(Distrust)",
"Benevolence\n(Distrust)"),
value = as.numeric(Var2),
Frequency = Freq)
plot1 <- ggplot(data = data_hyp1_2_freq,
aes(variable, value)) +
geom_point(aes(size = Frequency, color = Frequency, stat = "identity",
position = "identity"), shape = 15) +
scale_size_continuous(range = c(3,15)) +
scale_color_gradient(low = "grey95", high = "grey65") +
stat_summary(data = data_hyp1_2%>%
mutate(variable = fct_recode(variable,
`Expertise\n(Trust)` = "f9_1",
`Integrity\n(Trust)` = "f9_2",
`Benevolence\n(Trust)` = "f9_3",
`Expertise\n(Distrust)` = "f10_1",
`Integrity\n(Distrust)` = "f10_2",
`Benevolence\n(Distrust)` = "f10_3"),
variable = fct_relevel(variable,
"Expertise\n(Trust)",
"Integrity\n(Trust)",
"Benevolence\n(Trust)",
"Expertise\n(Distrust)",
"Integrity\n(Distrust)",
"Benevolence\n(Distrust)")),
aes(variable, value),
fun.data = "mean_sdl", fun.args = list(mult = 1),
color = "black") +
theme_ipsum_ps() +
theme(panel.grid.major.x = element_blank(), panel.grid.minor.y = element_blank()) +
guides(color=guide_legend(), size = guide_legend()) +
labs(title = "Reasons for trust and distrust in educational science",
subtitle = "Product plot of the raw data and means ± 1*SD") +
xlab("") +
ylab("")
## Warning: Ignoring unknown aesthetics: stat, position
#ggsave("plot1x.svg", plot1, width = 9.42, height = 5.71)
plot1
## Warning: Removed 45 rows containing non-finite values (stat_summary).
Vargha & Delaney’s A | Exp |
---|---|
Int | 0.49 |
ben | 0.57 |
Vargha & Delaney’s A | Exp |
---|---|
Int | 0.31 |
ben | 0.37 |
library(bain)
within_h1 <- lm(cbind(f9_1, f9_2, f9_3)~1, data=data_ttest)
estimate_h1 <- coef(within_h1)[1:3]
names(estimate_h1) <- c("exp","int", "ben")
ngroup_h1 <- nrow(data_ttest)
covmatr_h1 <- list(vcov(within_h1))
results_h1 <- bain(estimate_h1,"exp=int=ben;exp>(int,ben);exp>int=ben",
n=ngroup_h1, Sigma=covmatr_h1,
group_parameters=3,
joint_parameters = 0)
print(results_h1)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.373 0.000 0.000 0.000 0.000
## H2 0.287 0.322 0.891 0.847 1.000 0.471
## H3 0.000 0.270 0.000 0.000 0.000 0.000
## Hu 0.529
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp>(int,ben)
## H3: exp>int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h1$BFmatrix
## H1 H2 H3
## H1 1.000000e+00 3.842251e-05 1.533643
## H2 2.602641e+04 1.000000e+00 39915.218050
## H3 6.520423e-01 2.505310e-05 1.000000
results_h1f2 <- bain(estimate_h1,"exp=int=ben;exp>(int,ben);exp>int=ben",
n=ngroup_h1, Sigma=covmatr_h1,
group_parameters=3,
joint_parameters = 0,
fraction = 2)
print(results_h1f2)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.747 0.000 0.000 0.000 0.000
## H2 0.284 0.325 0.874 0.823 1.000 0.466
## H3 0.000 0.382 0.000 0.000 0.000 0.000
## Hu 0.534
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp>(int,ben)
## H3: exp>int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h1f2$BFmatrix
## H1 H2 H3
## H1 1.000000e+00 1.959713e-05 1.084449
## H2 5.102789e+04 1.000000e+00 55337.154693
## H3 9.221271e-01 1.807104e-05 1.000000
results_h1f3 <- bain(estimate_h1,"exp=int=ben;exp>(int,ben);exp>int=ben",
n=ngroup_h1, Sigma=covmatr_h1,
group_parameters=3,
joint_parameters = 0,
fraction = 3)
print(results_h1f3)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 1.120 0.000 0.000 0.000 0.000
## H2 0.292 0.318 0.915 0.881 1.000 0.478
## H3 0.000 0.468 0.000 0.000 0.000 0.000
## Hu 0.522
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp>(int,ben)
## H3: exp>int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h1f3$BFmatrix
## H1 H2 H3
## H1 1.00000 1.246693e-05 0.885449
## H2 80212.23153 1.000000e+00 71023.843500
## H3 1.12937 1.407978e-05 1.000000
within_h1a <- lm(cbind(f9_1, f9_2)~1, data=data_ttest)
estimate_h1a <- coef(within_h1a)[1:2]
names(estimate_h1a) <- c("exp","int")
ngroup_h1a <- nrow(data_ttest)
covmatr_h1a <- list(vcov(within_h1a))
results_h1a <- bain(estimate_h1a,"exp=int;exp>int",
n=ngroup_h1a, Sigma=covmatr_h1a,
group_parameters=2,
joint_parameters = 0)
print(results_h1a)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 7.738 0.445 17.391 17.391 0.968 0.917
## H2 0.288 0.500 0.575 0.404 0.032 0.030
## Hu 0.053
##
## Hypotheses:
## H1: exp=int
## H2: exp>int
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h1a$BFmatrix
## H1 H2
## H1 1.00000000 30.23108
## H2 0.03307854 1.00000
results_h1af2 <- bain(estimate_h1a,"exp=int;exp>int",
n=ngroup_h1a, Sigma=covmatr_h1a,
group_parameters=2,
joint_parameters = 0,
fraction = 2)
print(results_h1af2)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 7.738 0.629 12.297 12.297 0.955 0.886
## H2 0.288 0.500 0.575 0.404 0.045 0.041
## Hu 0.072
##
## Hypotheses:
## H1: exp=int
## H2: exp>int
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h1af2$BFmatrix
## H1 H2
## H1 1.00000000 21.3766
## H2 0.04678012 1.0000
results_h1af3 <- bain(estimate_h1a,"exp=int;exp>int",
n=ngroup_h1a, Sigma=covmatr_h1a,
group_parameters=2,
joint_parameters = 0,
fraction = 3)
print(results_h1af3)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 7.738 0.771 10.041 10.041 0.946 0.864
## H2 0.288 0.500 0.575 0.404 0.054 0.050
## Hu 0.086
##
## Hypotheses:
## H1: exp=int
## H2: exp>int
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h1af3$BFmatrix
## H1 H2
## H1 1.00000000 17.45392
## H2 0.05729372 1.00000
library(bain)
within_h1b <- lm(cbind(f9_1, f9_3)~1, data=data_ttest)
estimate_h1b <- coef(within_h1b)[1:2]
names(estimate_h1b) <- c("exp","ben")
ngroup_h1b <- nrow(data_ttest)
covmatr_h1b <- list(vcov(within_h1b))
results_h1b <- bain(estimate_h1b,"exp=ben;exp>ben",
n=ngroup_h1b, Sigma=covmatr_h1b,
group_parameters=2,
joint_parameters = 0)
print(results_h1b)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.374 0.001 0.001 0.000 0.000
## H2 1.000 0.500 2.000 285136.432 1.000 0.666
## Hu 0.333
##
## Hypotheses:
## H1: exp=ben
## H2: exp>ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h1b$BFmatrix
## H1 H2
## H1 1.000 0.0004201512
## H2 2380.095 1.0000000000
results_h1bf2 <- bain(estimate_h1b,"exp=ben;exp>ben",
n=ngroup_h1b, Sigma=covmatr_h1b,
group_parameters=2,
joint_parameters = 0,
fraction = 2)
print(results_h1bf2)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.528 0.001 0.001 0.000 0.000
## H2 1.000 0.500 2.000 285136.432 1.000 0.667
## Hu 0.333
##
## Hypotheses:
## H1: exp=ben
## H2: exp>ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h1bf2$BFmatrix
## H1 H2
## H1 1.000 0.0002970918
## H2 3365.963 1.0000000000
results_h1bf3 <- bain(estimate_h1b,"exp=ben;exp>ben",
n=ngroup_h1b, Sigma=covmatr_h1b,
group_parameters=2,
joint_parameters = 0,
fraction = 3)
print(results_h1bf3)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.647 0.000 0.000 0.000 0.000
## H2 1.000 0.500 2.000 285136.432 1.000 0.667
## Hu 0.333
##
## Hypotheses:
## H1: exp=ben
## H2: exp>ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h1bf3$BFmatrix
## H1 H2
## H1 1.000 0.0002425744
## H2 4122.446 1.0000000000
within_h2 <- lm(cbind(f10_1, f10_2, f10_3)~1, data=data_ttest)
estimate_h2 <- coef(within_h2)[1:3]
names(estimate_h2) <- c("exp","int", "ben")
ngroup_h2 <- nrow(data_ttest)
covmatr_h2 <- list(vcov(within_h2))
results_h2 <- bain(estimate_h2,"exp=int=ben;exp<(int,ben);exp<int=ben",
n=ngroup_h2, Sigma=covmatr_h2,
group_parameters=3,
joint_parameters = 0)
print(results_h2)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.273 0.000 0.000 0.000 0.000
## H2 1.000 0.330 3.032 22878074224672.238 0.983 0.742
## H3 0.013 0.240 0.052 0.052 0.017 0.013
## Hu 0.245
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp<(int,ben)
## H3: exp<int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h2$BFmatrix
## H1 H2 H3
## H1 1.000000e+00 1.296213e-38 7.532838e-37
## H2 7.714782e+37 1.000000e+00 5.811421e+01
## H3 1.327521e+36 1.720750e-02 1.000000e+00
results_h2f2 <- bain(estimate_h2,"exp=int=ben;exp<(int,ben);exp<int=ben",
n=ngroup_h2, Sigma=covmatr_h2,
group_parameters=3,
joint_parameters = 0,
fraction = 2)
print(results_h2f2)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.546 0.000 0.000 0.000 0.000
## H2 1.000 0.335 2.982 22372085646463.426 0.988 0.742
## H3 0.013 0.339 0.037 0.037 0.012 0.009
## Hu 0.249
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp<(int,ben)
## H3: exp<int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h2f2$BFmatrix
## H1 H2 H3
## H1 1.000000e+00 6.589533e-39 5.326521e-37
## H2 1.517558e+38 1.000000e+00 8.083306e+01
## H3 1.877398e+36 1.237118e-02 1.000000e+00
results_h2f3 <- bain(estimate_h2,"exp=int=ben;exp<(int,ben);exp<int=ben",
n=ngroup_h2, Sigma=covmatr_h2,
group_parameters=3,
joint_parameters = 0,
fraction = 3)
print(results_h2f3)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.819 0.000 0.000 0.000 0.000
## H2 1.000 0.331 3.020 21583946898072.125 0.990 0.746
## H3 0.013 0.415 0.030 0.030 0.010 0.007
## Hu 0.247
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp<(int,ben)
## H3: exp<int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h2f3$BFmatrix
## H1 H2 H3
## H1 1.000000e+00 4.337736e-39 4.349086e-37
## H2 2.305350e+38 1.000000e+00 1.002617e+02
## H3 2.299334e+36 9.973901e-03 1.000000e+00
Main Study
data_hyp3_4 <- data_ttest%>%
select(f4_1, f4_2, f4_3, f5_1, f5_2, f5_3)%>%
gather(variable, value)
data_hyp3_4_freq <- as.data.frame(table(data_hyp3_4$variable, data_hyp3_4$value))%>%
mutate(variable = fct_recode(Var1,
`Expertise\n(Trust)` = "f4_1",
`Integrity\n(Trust)` = "f4_2",
`Benevolence\n(Trust)` = "f4_3",
`Expertise\n(Distrust)` = "f5_1",
`Integrity\n(Distrust)` = "f5_2",
`Benevolence\n(Distrust)` = "f5_3"),
variable = fct_relevel(variable,
"Expertise\n(Trust)",
"Integrity\n(Trust)",
"Benevolence\n(Trust)",
"Expertise\n(Distrust)",
"Integrity\n(Distrust)",
"Benevolence\n(Distrust)"),
value = as.numeric(Var2),
Frequency = Freq)
plot2 <- ggplot(data = data_hyp3_4_freq,
aes(variable, value)) +
geom_point(aes(size = Frequency, color = Frequency, stat = "identity",
position = "identity"), shape = 15) +
scale_size_continuous(range = c(3,15)) +
scale_color_gradient(low = "grey95", high = "grey65") +
stat_summary(data = data_hyp3_4%>%
mutate(variable = fct_recode(variable,
`Expertise\n(Trust)` = "f4_1",
`Integrity\n(Trust)` = "f4_2",
`Benevolence\n(Trust)` = "f4_3",
`Expertise\n(Distrust)` = "f5_1",
`Integrity\n(Distrust)` = "f5_2",
`Benevolence\n(Distrust)` = "f5_3"),
variable = fct_relevel(variable,
"Expertise\n(Trust)",
"Integrity\n(Trust)",
"Benevolence\n(Trust)",
"Expertise\n(Distrust)",
"Integrity\n(Distrust)",
"Benevolence\n(Distrust)")),
aes(variable, value),
fun.data = "mean_sdl", fun.args = list(mult = 1),
color = "black") +
theme_ipsum_ps() +
theme(panel.grid.major.x = element_blank(), panel.grid.minor.y = element_blank()) +
guides(color=guide_legend(), size = guide_legend()) +
labs(title = "Reasons for trust and distrust in science",
subtitle = "Product plot of the raw data and means ± 1*SD") +
xlab("") +
ylab("")
## Warning: Ignoring unknown aesthetics: stat, position
#ggsave("plot2.svg", plot2, width = 9.42, height = 5.71)
plot2
## Warning: Removed 24 rows containing non-finite values (stat_summary).
Vargha & Delaney’s A | Exp |
---|---|
Int | 0.54 |
ben | 0.81 |
Vargha & Delaney’s A | Exp |
---|---|
Int | 0.28 |
ben | 0.1 |
within_h3 <- lm(cbind(f4_1, f4_2, f4_3)~1, data=data_ttest)
estimate_h3 <- coef(within_h3)[1:3]
names(estimate_h3) <- c("exp","int", "ben")
ngroup_h3 <- nrow(data_ttest)
covmatr_h3 <- list(vcov(within_h3))
results_h3 <- bain(estimate_h3,"exp=int=ben;exp>(int,ben);exp>int=ben",
n=ngroup_h3, Sigma=covmatr_h3,
group_parameters=3,
joint_parameters = 0)
print(results_h3)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.447 0.000 0.000 0.000 0.000
## H2 1.000 0.293 3.415 69578.818 1.000 0.773
## H3 0.000 0.264 0.000 0.000 0.000 0.000
## Hu 0.227
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp>(int,ben)
## H3: exp>int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h3$BFmatrix
## H1 H2 H3
## H1 1.000000e+00 1.929058e-112 2.526982e-42
## H2 5.183878e+111 1.000000e+00 1.309957e+70
## H3 3.957290e+41 7.633841e-71 1.000000e+00
results_h3f2 <- bain(estimate_h3,"exp=int=ben;exp>(int,ben);exp>int=ben",
n=ngroup_h3, Sigma=covmatr_h3,
group_parameters=3,
joint_parameters = 0,
fraction = 2)
print(results_h3f2)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.893 0.000 0.000 0.000 0.000
## H2 1.000 0.292 3.421 66094.458 1.000 0.774
## H3 0.000 0.373 0.000 0.000 0.000 0.000
## Hu 0.226
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp>(int,ben)
## H3: exp>int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h3f2$BFmatrix
## H1 H2 H3
## H1 1.000000e+00 9.628475e-113 1.786846e-42
## H2 1.038586e+112 1.000000e+00 1.855793e+70
## H3 5.596453e+41 5.388531e-71 1.000000e+00
results_h3f3 <- bain(estimate_h3,"exp=int=ben;exp>(int,ben);exp>int=ben",
n=ngroup_h3, Sigma=covmatr_h3,
group_parameters=3,
joint_parameters = 0,
fraction = 3)
print(results_h3f3)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 1.340 0.000 0.000 0.000 0.000
## H2 1.000 0.289 3.461 69204.031 1.000 0.776
## H3 0.000 0.457 0.000 0.000 0.000 0.000
## Hu 0.224
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp>(int,ben)
## H3: exp>int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h3f3$BFmatrix
## H1 H2 H3
## H1 1.000000e+00 6.344082e-113 1.458954e-42
## H2 1.576272e+112 1.000000e+00 2.299708e+70
## H3 6.854228e+41 4.348378e-71 1.000000e+00
within_h4 <- lm(cbind(f5_1, f5_2, f5_3)~1, data=data_ttest)
estimate_h4 <- coef(within_h4)[1:3]
names(estimate_h4) <- c("exp","int", "ben")
ngroup_h4 <- nrow(data_ttest)
covmatr_h4 <- list(vcov(within_h4))
results_h4 <- bain(estimate_h4,"exp=int=ben;exp<(int,ben);exp<int=ben",
n=ngroup_h4, Sigma=covmatr_h4,
group_parameters=3,
joint_parameters = 0)
print(results_h4)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.366 0.000 0.000 0.000 0.000
## H2 1.000 0.331 3.022 23171390218021.137 1.000 0.751
## H3 0.000 0.280 0.000 0.000 0.000 0.000
## Hu 0.249
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp<(int,ben)
## H3: exp<int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h4$BFmatrix
## H1 H2 H3
## H1 1.000000e+00 1.247116e-234 1.166343e-169
## H2 8.018498e+233 1.000000e+00 9.352321e+64
## H3 8.573806e+168 1.069253e-65 1.000000e+00
results_h4f2 <- bain(estimate_h4,"exp=int=ben;exp<(int,ben);exp<int=ben",
n=ngroup_h4, Sigma=covmatr_h4,
group_parameters=3,
joint_parameters = 0,
fraction = 2)
print(results_h4f2)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.731 0.000 0.000 0.000 0.000
## H2 1.000 0.332 3.013 23070728595198.426 1.000 0.751
## H3 0.000 0.396 0.000 0.000 0.000 0.000
## Hu 0.249
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp<(int,ben)
## H3: exp<int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h4f2$BFmatrix
## H1 H2 H3
## H1 1.000000e+00 6.253759e-235 8.247292e-170
## H2 1.599038e+234 1.000000e+00 1.318773e+65
## H3 1.212519e+169 7.582804e-66 1.000000e+00
results_h4f3 <- bain(estimate_h4,"exp=int=ben;exp<(int,ben);exp<int=ben",
n=ngroup_h4, Sigma=covmatr_h4,
group_parameters=3,
joint_parameters = 0,
fraction = 3)
print(results_h4f3)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 1.097 0.000 0.000 0.000 0.000
## H2 1.000 0.332 3.015 23087751457239.219 1.000 0.751
## H3 0.000 0.485 0.000 0.000 0.000 0.000
## Hu 0.249
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp<(int,ben)
## H3: exp<int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h4f3$BFmatrix
## H1 H2 H3
## H1 1.000000e+00 4.167119e-235 6.733885e-170
## H2 2.399740e+234 1.000000e+00 1.615957e+65
## H3 1.485027e+169 6.188283e-66 1.000000e+00
Main Study
data_merged <- data_wb%>%
# filter(bild == 16)%>% # only those with higher education
mutate(altq = alter,
f1_1 = V1_1, # Interesse an Politik | recoded below
f1_2 = V1_4, # Interesse an Sport | recoded below
f1_3 = V1_5, # Interesse an Wissenschaft und Forschung |
f4_1 = V9_1, # Trust: Exp | recoded below
f4_2 = V9_2, # Trust: Int | recoded below
f4_3 = V9_3, # Trust: Ben | recoded below
f5_1 = V10_1, # Distrust: Exp | recoded below
f5_2 = V10_2, # Distrust: Int | recoded below
f5_3 = V10_3, # Distrust: Ben | recoded below
population = "general",
bild = as.factor(bild))%>%
mutate_at(vars(c("f1_1", "f1_2", "f1_3",
"f4_1", "f4_2", "f4_3", "f5_1", "f5_2", "f5_3")),
list(~ifelse(. == 9, NA, .)))%>% # 9 = weiß nicht/keine Angabe
select(altq, f1_1, f1_2, f1_3, f4_1, f4_2,
f4_3, f5_1, f5_2, f5_3, population, bild)%>%
mutate(f1_1 = 6 - f1_1, # RECODING
f1_2 = 6 - f1_2,
f1_3 = 6 - f1_3,
f4_1 = 6 - f4_1,
f4_2 = 6 - f4_2,
f4_3 = 6 - f4_3,
f5_1 = 6 - f5_1,
f5_2 = 6 - f5_2,
f5_3 = 6 - f5_3,
bild = as.factor(bild))%>%
full_join(.,
data_ttest%>%
mutate(population = "teachers",
bild = as.factor("16")))%>%
mutate(population_n = as.numeric(as.factor(population)) - 1)%>%
select(altq, f1_1, f1_2, f1_3, f4_1, f4_2,
f4_3, f5_1, f5_2, f5_3, population, bild, population_n)%>%
mutate(bild = as.factor(bild))
## Joining, by = c("altq", "f1_1", "f1_2", "f1_3", "f4_1", "f4_2", "f4_3", "f5_1", "f5_2", "f5_3", "population", "bild")
set.seed(31415)
match1 <- MatchIt::matchit(population_n ~ altq + bild + f1_1 + f1_2 + f1_3,
replace = T,
data = na.omit(data_merged),
method = "genetic",
pop.size = 250)
summary(match1)
summary_match1 <- summary(match1, standardize = T)
knitr::kable(summary_match1$sum.all, digits = 3)
Means Treated | Means Control | Std. Mean Diff. | Var. Ratio | eCDF Mean | eCDF Max | Std. Pair Dist. | |
---|---|---|---|---|---|---|---|
distance | 0.587 | 0.181 | 2.861 | 0.311 | 0.375 | 0.690 | NA |
altq | 47.690 | 54.565 | -0.637 | 0.379 | 0.116 | 0.294 | NA |
bild11 | 0.000 | 0.014 | -0.144 | NA | 0.014 | 0.014 | NA |
bild12 | 0.000 | 0.159 | -0.507 | NA | 0.159 | 0.159 | NA |
bild14 | 0.000 | 0.277 | -0.702 | NA | 0.277 | 0.277 | NA |
bild15 | 0.000 | 0.159 | -0.507 | NA | 0.159 | 0.159 | NA |
bild16 | 1.000 | 0.391 | 1.233 | NA | 0.609 | 0.609 | NA |
f1_1 | 4.020 | 3.773 | 0.280 | 0.620 | 0.049 | 0.113 | NA |
f1_2 | 2.982 | 3.207 | -0.176 | 0.993 | 0.045 | 0.085 | NA |
f1_3 | 3.822 | 3.787 | 0.042 | 0.644 | 0.034 | 0.068 | NA |
knitr::kable(summary_match1$sum.matched, digits = 3)
Means Treated | Means Control | Std. Mean Diff. | Var. Ratio | eCDF Mean | eCDF Max | Std. Pair Dist. | |
---|---|---|---|---|---|---|---|
distance | 0.587 | 0.587 | -0.002 | 0.993 | 0.004 | 0.033 | 0.107 |
altq | 47.690 | 47.635 | 0.005 | 0.959 | 0.006 | 0.035 | 0.094 |
bild11 | 0.000 | 0.000 | 0.000 | NA | 0.000 | 0.000 | 0.000 |
bild12 | 0.000 | 0.000 | 0.000 | NA | 0.000 | 0.000 | 0.000 |
bild14 | 0.000 | 0.000 | 0.000 | NA | 0.000 | 0.000 | 0.000 |
bild15 | 0.000 | 0.000 | 0.000 | NA | 0.000 | 0.000 | 0.000 |
bild16 | 1.000 | 1.000 | 0.000 | NA | 0.000 | 0.000 | 0.000 |
f1_1 | 4.020 | 4.058 | -0.042 | 1.084 | 0.021 | 0.060 | 0.902 |
f1_2 | 2.982 | 3.000 | -0.014 | 1.078 | 0.018 | 0.035 | 0.159 |
f1_3 | 3.822 | 3.823 | 0.000 | 0.993 | 0.000 | 0.000 | 0.000 |
data_hyp5_6 <- na.omit(data_merged)%>%
mutate(matching_weights = match1$weights,
PID = 1:n())%>%
filter(population == "general" &
matching_weights > 0)
data_hyp5_6_long <- data_hyp5_6%>%
gather(variable, value, f4_1, f4_2, f4_3, f5_1, f5_2, f5_3)
# create a {survey}-design-object which allows to compute
# frequency tables with weighted data
wght_design <- survey::svydesign(id = ~PID, weights = ~matching_weights, data = data_hyp5_6_long)
data_hyp5_6_freq <- survey::svytable(~variable+value, wght_design)%>%
as.data.frame(.)%>%
mutate(variable = fct_recode(variable,
`Expertise\n(Trust)` = "f4_1",
`Integrity\n(Trust)` = "f4_2",
`Benevolence\n(Trust)` = "f4_3",
`Expertise\n(Distrust)` = "f5_1",
`Integrity\n(Distrust)` = "f5_2",
`Benevolence\n(Distrust)` = "f5_3"),
variable = fct_relevel(variable,
"Expertise\n(Trust)",
"Integrity\n(Trust)",
"Benevolence\n(Trust)",
"Expertise\n(Distrust)",
"Integrity\n(Distrust)",
"Benevolence\n(Distrust)"),
value = as.numeric(value),
Frequency = Freq)
plot3 <- data_hyp5_6_long%>%
group_by(variable)%>%
summarize(mean = weighted.mean(value, w = matching_weights),
sd = sqrt(Hmisc::wtd.var(value, weights = matching_weights)))%>%
mutate(variable = fct_recode(variable,
`Expertise\n(Trust)` = "f4_1",
`Integrity\n(Trust)` = "f4_2",
`Benevolence\n(Trust)` = "f4_3",
`Expertise\n(Distrust)` = "f5_1",
`Integrity\n(Distrust)` = "f5_2",
`Benevolence\n(Distrust)` = "f5_3"),
variable = fct_relevel(variable,
"Expertise\n(Trust)",
"Integrity\n(Trust)",
"Benevolence\n(Trust)",
"Expertise\n(Distrust)",
"Integrity\n(Distrust)",
"Benevolence\n(Distrust)"))%>%
ggplot(.) +
geom_point(data = data_hyp5_6_freq, aes(variable, value, size = Frequency, color = Frequency, stat = "identity",
position = "identity"), shape = 15) +
scale_size_continuous(range = c(3,15)) +
scale_color_gradient(low = "grey95", high = "grey65") +
geom_point(aes(variable, mean), shape = 19, size = 2.5) +
geom_errorbar(aes(variable, mean, ymin=mean-sd, ymax=mean+sd), width=.0) +
labs(subtitle = "Means ±1SD in matched general population") +
coord_cartesian(ylim = c(1,5)) +
theme_ipsum_ps() +
theme(panel.grid.major.x = element_blank(), panel.grid.minor.y = element_blank()) +
guides(color=guide_legend(), size = guide_legend()) +
labs(title = "Reasons for trust and distrust in science",
subtitle = "Product plot of the raw data and means ± 1*SD (matched general Population)") +
xlab("") +
ylab("")
## Warning: Ignoring unknown aesthetics: stat, position
# ggsave("plot3.svg", plot3, width = 9.42, height = 5.71)
plot3
library(RProbSup)
weightedA_trust_Exp_Int <-
A2(data_hyp5_6$f4_1,
data_hyp5_6$f4_2,
weights = T,
w = data_hyp5_6$matching_weights)
weightedA_trust_Exp_Ben <-
A2(data_hyp5_6$f4_1,
data_hyp5_6$f4_3,
weights = T,
w = data_hyp5_6$matching_weights)
weightedA_distrust_Exp_Int <-
A2(data_hyp5_6$f5_1,
data_hyp5_6$f5_2,
weights = T,
w = data_hyp5_6$matching_weights)
weightedA_distrust_Exp_Ben <-
A2(data_hyp5_6$f5_1,
data_hyp5_6$f5_3,
weights = T,
w = data_hyp5_6$matching_weights)
Vargha & Delaney’s A | Exp |
---|---|
Int | 0.58 |
ben | 0.85 |
Vargha & Delaney’s A | Exp |
---|---|
Int | 0.24 |
ben | 0.09 |
library(heplots)
## Loading required package: car
## Loading required package: carData
## Registered S3 methods overwritten by 'car':
## method from
## influence.merMod lme4
## cooks.distance.influence.merMod lme4
## dfbeta.influence.merMod lme4
## dfbetas.influence.merMod lme4
##
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
##
## recode
## The following object is masked from 'package:purrr':
##
## some
within_h5 <- robmlm(cbind(f4_1, f4_2, f4_3)~1,
data=data_hyp5_6,
weights = matching_weights)
estimate_h5 <- coef(within_h5)[1:3]
names(estimate_h5) <- c("exp","int", "ben")
ngroup_h5 <- nrow(data_hyp5_6)
covmatr_h5 <- list(heplots:::vcov.mlm(within_h5))
results_h5 <- bain(estimate_h5,"exp=int=ben;exp>(int,ben);exp>int=ben",
n=ngroup_h5, Sigma=covmatr_h5,
group_parameters=3,
joint_parameters = 0)
print(results_h5)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.323 0.000 0.000 0.000 0.000
## H2 0.999 0.297 3.361 3404.742 1.000 0.771
## H3 0.000 0.232 0.000 0.000 0.000 0.000
## Hu 0.229
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp>(int,ben)
## H3: exp>int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h5$BFmatrix
## H1 H2 H3
## H1 1.000000e+00 1.179326e-36 2.401340e-19
## H2 8.479423e+35 1.000000e+00 2.036198e+17
## H3 4.164342e+18 4.911115e-18 1.000000e+00
results_h5f2 <- bain(estimate_h5,"exp=int=ben;exp>(int,ben);exp>int=ben",
n=ngroup_h5, Sigma=covmatr_h5,
group_parameters=3,
joint_parameters = 0,
fraction = 2)
print(results_h5f2)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.647 0.000 0.000 0.000 0.000
## H2 0.999 0.298 3.357 3180.731 1.000 0.770
## H3 0.000 0.328 0.000 0.000 0.000 0.000
## Hu 0.230
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp>(int,ben)
## H3: exp>int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h5f2$BFmatrix
## H1 H2 H3
## H1 1.000000e+00 5.904281e-37 1.698004e-19
## H2 1.693686e+36 1.000000e+00 2.875885e+17
## H3 5.889269e+18 3.477190e-18 1.000000e+00
results_h5f3 <- bain(estimate_h5,"exp=int=ben;exp>(int,ben);exp>int=ben",
n=ngroup_h5, Sigma=covmatr_h5,
group_parameters=3,
joint_parameters = 0,
fraction = 3)
print(results_h5f3)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.970 0.000 0.000 0.000 0.000
## H2 0.999 0.298 3.356 3327.421 1.000 0.770
## H3 0.000 0.402 0.000 0.000 0.000 0.000
## Hu 0.230
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp>(int,ben)
## H3: exp>int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h5f3$BFmatrix
## H1 H2 H3
## H1 1.000000e+00 3.937193e-37 1.386414e-19
## H2 2.539880e+36 1.000000e+00 3.521326e+17
## H3 7.212852e+18 2.839839e-18 1.000000e+00
within_h6 <- robmlm(cbind(f5_1, f5_2, f5_3)~1,
data=data_hyp5_6,
weights = matching_weights)
estimate_h6 <- coef(within_h6)[1:3]
names(estimate_h6) <- c("exp","int", "ben")
ngroup_h6 <- nrow(data_hyp5_6)
covmatr_h6 <- list(heplots:::vcov.mlm(within_h6))
results_h6 <- bain(estimate_h6,"exp=int=ben;exp<(int,ben);exp<int=ben",
n=ngroup_h6, Sigma=covmatr_h6,
group_parameters=3,
joint_parameters = 0)
print(results_h6)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.307 0.000 0.000 0.000 0.000
## H2 1.000 0.332 3.012 23061462699880.293 1.000 0.751
## H3 0.000 0.256 0.000 0.000 0.000 0.000
## Hu 0.249
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp<(int,ben)
## H3: exp<int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h6$BFmatrix
## H1 H2 H3
## H1 1.000000e+00 2.320264e-86 1.665417e-59
## H2 4.309855e+85 1.000000e+00 7.177707e+26
## H3 6.004501e+58 1.393202e-27 1.000000e+00
results_h6f2 <- bain(estimate_h6,"exp=int=ben;exp<(int,ben);exp<int=ben",
n=ngroup_h6, Sigma=covmatr_h6,
group_parameters=3,
joint_parameters = 0,
fraction = 2)
print(results_h6f2)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.614 0.000 0.000 0.000 0.000
## H2 1.000 0.329 3.036 23333554981422.871 1.000 0.752
## H3 0.000 0.362 0.000 0.000 0.000 0.000
## Hu 0.248
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp<(int,ben)
## H3: exp<int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h6f2$BFmatrix
## H1 H2 H3
## H1 1.000000e+00 1.151059e-86 1.177628e-59
## H2 8.687650e+85 1.000000e+00 1.023082e+27
## H3 8.491646e+58 9.774388e-28 1.000000e+00
results_h6f3 <- bain(estimate_h6,"exp=int=ben;exp<(int,ben);exp<int=ben",
n=ngroup_h6, Sigma=covmatr_h6,
group_parameters=3,
joint_parameters = 0,
fraction = 3)
print(results_h6f3)
## Bayesian informative hypothesis testing for an object of class numeric:
##
## Fit Com BF.u BF.c PMPa PMPb
## H1 0.000 0.921 0.000 0.000 0.000 0.000
## H2 1.000 0.337 2.971 22589850575949.270 1.000 0.748
## H3 0.000 0.444 0.000 0.000 0.000 0.000
## Hu 0.252
##
## Hypotheses:
## H1: exp=int=ben
## H2: exp<(int,ben)
## H3: exp<int=ben
##
## Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement.
results_h6f3$BFmatrix
## H1 H2 H3
## H1 1.000000e+00 7.841337e-87 9.615292e-60
## H2 1.275293e+86 1.000000e+00 1.226231e+27
## H3 1.040010e+59 8.155070e-28 1.000000e+00
sessionInfo()
## R version 4.0.4 (2021-02-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] de_DE.UTF-8/de_DE.UTF-8/de_DE.UTF-8/C/de_DE.UTF-8/de_DE.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] heplots_1.3-8 car_3.0-10 carData_3.0-4 RProbSup_3.0
## [5] hrbrthemes_0.8.0 patchwork_1.1.1 bain_0.2.4 semTools_0.5-4
## [9] lavaan_0.6-8 corrplot_0.84 skimr_2.1.3 haven_2.3.1
## [13] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.5 purrr_0.3.4
## [17] readr_1.4.0 tidyr_1.1.3 tibble_3.1.0 ggplot2_3.3.3
## [21] tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.2.1 Hmisc_4.5-0
## [4] semPlot_1.1.2 systemfonts_1.0.1 igraph_1.2.6
## [7] plyr_1.8.6 repr_1.1.3 splines_4.0.4
## [10] crosstalk_1.1.1 digest_0.6.27 htmltools_0.5.1.1
## [13] matrixcalc_1.0-3 fansi_0.4.2 magrittr_2.0.1
## [16] Rsolnp_1.16 checkmate_2.0.0 lisrelToR_0.1.4
## [19] cluster_2.1.0 openxlsx_4.2.3 extrafont_0.17
## [22] modelr_0.1.8 extrafontdb_1.0 jpeg_0.1-8.1
## [25] sem_3.1-11 colorspace_2.0-0 rvest_1.0.0
## [28] mitools_2.4 xfun_0.22 crayon_1.4.1
## [31] jsonlite_1.7.2 Exact_2.1 lme4_1.1-26
## [34] regsem_1.6.2 survival_3.2-7 glue_1.4.2
## [37] gtable_0.3.0 mi_1.0 Rttf2pt1_1.3.8
## [40] abind_1.4-5 scales_1.1.1 mvtnorm_1.1-1
## [43] DBI_1.1.1 Rcpp_1.0.6 xtable_1.8-4
## [46] htmlTable_2.1.0 tmvnsim_1.0-2 effsize_0.8.1
## [49] foreign_0.8-81 proxy_0.4-25 Formula_1.2-4
## [52] survey_4.0 stats4_4.0.4 truncnorm_1.0-8
## [55] htmlwidgets_1.5.3 httr_1.4.2 RColorBrewer_1.1-2
## [58] ellipsis_0.3.1 farver_2.1.0 pkgconfig_2.0.3
## [61] XML_3.99-0.6 nnet_7.3-15 kutils_1.70
## [64] dbplyr_2.1.0 utf8_1.1.4 labeling_0.4.2
## [67] reshape2_1.4.4 tidyselect_1.1.0 rlang_0.4.10
## [70] munsell_0.5.0 reactR_0.4.4 cellranger_1.1.0
## [73] tools_4.0.4 cli_2.3.1 generics_0.1.0
## [76] sjlabelled_1.1.7 broom_0.7.5 fdrtool_1.2.16
## [79] evaluate_0.14 arm_1.11-2 yaml_2.2.1
## [82] knitr_1.31 fs_1.5.0 zip_2.1.1
## [85] glasso_1.11 rootSolve_1.8.2.1 pbapply_1.4-3
## [88] nlme_3.1-152 reactable_0.2.3 mime_0.10
## [91] xml2_1.3.2 compiler_4.0.4 rstudioapi_0.13
## [94] curl_4.3 png_0.1-7 e1071_1.7-6
## [97] reprex_1.0.0 statmod_1.4.35 pbivnorm_0.6.0
## [100] DescTools_0.99.40 stringi_1.5.3 highr_0.8
## [103] qgraph_1.6.9 rockchalk_1.8.144 gdtools_0.2.3
## [106] lattice_0.20-41 Matrix_1.3-2 psych_2.1.3
## [109] nloptr_1.2.2.2 vctrs_0.3.6 pillar_1.5.1
## [112] lifecycle_1.0.0 OpenMx_2.19.1 corpcor_1.6.9
## [115] data.table_1.14.0 insight_0.13.1 lmom_2.8
## [118] R6_2.5.0 latticeExtra_0.6-29 rio_0.5.26
## [121] gridExtra_2.3 gld_2.6.2 gtools_3.8.2
## [124] boot_1.3-26 MASS_7.3-53 assertthat_0.2.1
## [127] withr_2.4.1 mnormt_2.0.2 expm_0.999-6
## [130] parallel_4.0.4 hms_1.0.0 grid_4.0.4
## [133] rpart_4.1-15 coda_0.19-4 class_7.3-18
## [136] minqa_1.2.4 rmarkdown_2.7 lubridate_1.7.10
## [139] base64enc_0.1-3