knitr::opts_chunk$set(echo = FALSE)
knitr::opts_chunk$set(warning = FALSE)

rm(list=ls())

#install.packages(c("openxlsx","tidyverse", "mosaic", "plotly","plyr","naniar","ggrepel", "EnvStats", "VIM", "mice", "psych", "sjPlot", "lavaan", "lavaanPlot", "semPlot", "lme4", "lmerTest", "jtools", "robustlmm", "Hmisc", "reshape2", "car", "effects", "glmmTMB", "ggpubr"))

library(openxlsx) # reading
library(tidyverse) # data manipulation
## ── Attaching packages ──────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.3     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ── Conflicts ─────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(mosaic) # descriptive analysis (inspect/favstats, gf_plots)
## Registered S3 method overwritten by 'mosaic':
##   method                           from   
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## The 'mosaic' package masks several functions from core packages in order to add 
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##     quantile, sd, t.test, var
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##     max, mean, min, prod, range, sample, sum
library(plotly)
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library(plyr)
## ------------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## ------------------------------------------------------------------------------
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library(naniar) # NA handling
library(ggrepel)
library(EnvStats)
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library(VIM) # MVA
## Loading required package: colorspace
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## VIM is ready to use.
## Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues
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library(mice) # MVA
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library(psych) #Item & scale analysis
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library(sjPlot) #Item & scale analysis, model diagnostics
## Registered S3 methods overwritten by 'lme4':
##   method                          from
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##   influence.merMod                car 
##   dfbeta.influence.merMod         car 
##   dfbetas.influence.merMod        car
## Learn more about sjPlot with 'browseVignettes("sjPlot")'.
library(lavaan) # CFA
## This is lavaan 0.6-7
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library(lavaanPlot) # CFA
library(semPlot) # CFA
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library(lme4) # multilevel modeling
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library(lmerTest)# multilevel modeling
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library(jtools)# multilevel modeling
library(robustlmm)# multilevel modeling
library(Hmisc) # correlation matrices
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library(reshape2) # data wrangling (wide/long)
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library(car) # multico
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library(effects) # model diagnostics
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library(glmmTMB) # model diagnostics
library(ggpubr) # arrange plots
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setwd("/Users/Alex/Documents/Projekte/W&E zu Evolution")
set.seed(2308)

data_long <- read.xlsx("data_long_PK_AB.xlsx")
str(data_long)
## 'data.frame':    9367 obs. of  43 variables:
##  $ questionnaire_number: num  271 272 273 274 275 276 277 278 279 280 ...
##  $ ID                  : chr  "GR_AL_271" "GR_AL_272" "GR_AL_273" "GR_AL_274" ...
##  $ country             : num  11 11 11 11 11 11 11 11 11 11 ...
##  $ bio                 : num  2 2 2 2 2 2 2 2 2 2 ...
##  $ course              : num  3 3 3 3 3 3 3 3 3 3 ...
##  $ age                 : num  17 18 18 18 19 18 18 18 99 18 ...
##  $ sex                 : num  2 1 2 2 1 1 2 2 2 2 ...
##  $ bio_classes         : num  5 5 5 5 5 4 5 4 4 5 ...
##  $ interest_bio        : num  3 5 7 7 5 7 5 99 99 6 ...
##  $ meaning_evo         : num  3 5 5 5 5 5 4 4 4 5 ...
##  $ learn_evo           : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ denomination        : num  4 4 4 4 4 4 4 4 4 4 ...
##  $ denom_summarized    : num  4 4 4 4 4 4 4 4 4 4 ...
##  $ KAEVO.A1            : num  0 1 0 0 1 0 1 0 0 0 ...
##  $ KAEVO.A2            : num  1 0 0 0 0 0 0 0 0 1 ...
##  $ KAEVO.A3            : num  0 1 0 1 1 1 1 0 0 0 ...
##  $ KAEVO.A4            : num  0 0 0 1 0 0 0 0 0 0 ...
##  $ KAEVO.A5            : num  0 0 0 1 1 1 1 0 0 0 ...
##  $ KAEVO.A6            : num  0 1 0 0 1 0 0 1 1 0 ...
##  $ KAEVO.A7            : num  0 1 1 1 1 1 1 1 1 1 ...
##  $ KAEVO.A8            : num  1 1 1 1 0 1 0 1 0 1 ...
##  $ KAEVO.A9.1          : num  1 0 0 0 0 0 0 0 0 0 ...
##  $ KAEVO.A9.2          : num  0 0 0 1 0 1 0 0 0 0 ...
##  $ KAEVO.A10           : num  1 0 0 0 0 0 0 0 0 0 ...
##  $ KAEVO.A11           : num  0 1 1 0 0 0 0 1 1 0 ...
##  $ ATEVO.E1            : num  5 4 1 5 5 5 5 5 5 5 ...
##  $ ATEVO.E2            : num  5 2 5 3 1 4 4 5 5 4 ...
##  $ ATEVO.E3            : num  4 4 1 5 4 5 5 4 4 4 ...
##  $ ATEVO.E4            : num  3 4 5 4 1 5 4 5 5 1 ...
##  $ ATEVO.E5            : num  3 4 4 5 5 5 5 4 4 5 ...
##  $ ATEVO.E6            : num  3 4 1 4 4 1 4 4 4 1 ...
##  $ ATEVO.E7            : num  3 4 2 5 4 5 5 1 1 5 ...
##  $ ATEVO.E8            : num  4 4 1 3 1 1 3 2 2 4 ...
##  $ PERF.F1             : num  4 4 5 5 5 1 5 5 5 3 ...
##  $ PERF.F2             : num  5 4 5 5 5 1 5 5 4 3 ...
##  $ PERF.F3             : num  2 4 5 5 5 1 5 4 4 1 ...
##  $ PERF.F4             : num  5 3 99 1 5 2 4 5 4 1 ...
##  $ PERF.F5             : num  3 2 3 3 4 1 4 3 3 1 ...
##  $ PERF.F6             : num  3 3 3 3 5 1 5 5 5 1 ...
##  $ PERF.F7             : num  5 2 2 4 5 2 5 4 4 1 ...
##  $ PERF.F8             : num  4 4 2 4 5 1 4 4 3 1 ...
##  $ PERF.F9             : num  5 2 1 4 5 1 5 4 3 1 ...
##  $ PERF.F10            : num  3 1 2 2 4 1 3 3 4 1 ...



0. Data Preparation and Cleaning

## The following `from` values were not present in `x`: 15
## The following `from` values were not present in `x`: 0



1. Missing Value Analysis

## 
##  Variables sorted by number of missings: 
##              Variable        Count
##              PERF.F10 0.0718478261
##               PERF.F8 0.0709782609
##               PERF.F3 0.0706521739
##               PERF.F5 0.0706521739
##               PERF.F7 0.0698913043
##               PERF.F6 0.0694565217
##               PERF.F9 0.0682608696
##               PERF.F2 0.0664130435
##               PERF.F4 0.0661956522
##               PERF.F1 0.0639130435
##              ATEVO.E4 0.0483695652
##              ATEVO.E6 0.0466304348
##              ATEVO.E3 0.0465217391
##            KAEVO.A9.1 0.0464130435
##              ATEVO.E5 0.0461956522
##              ATEVO.E8 0.0459782609
##              ATEVO.E7 0.0444565217
##              ATEVO.E2 0.0425000000
##              ATEVO.E1 0.0415217391
##          interest_bio 0.0397826087
##             KAEVO.A10 0.0392391304
##            KAEVO.A9.2 0.0371739130
##             KAEVO.A11 0.0343478261
##              KAEVO.A8 0.0223913043
##              KAEVO.A7 0.0221739130
##      denom_summarized 0.0191304348
##              KAEVO.A1 0.0186956522
##          denomination 0.0181521739
##              KAEVO.A5 0.0160869565
##              KAEVO.A4 0.0147826087
##              KAEVO.A6 0.0145652174
##              KAEVO.A3 0.0144565217
##              KAEVO.A2 0.0134782609
##                   sex 0.0077173913
##                course 0.0072826087
##           bio_classes 0.0066304348
##                   age 0.0060869565
##           meaning_evo 0.0046739130
##             learn_evo 0.0043478261
##                   bio 0.0001086957
##  questionnaire_number 0.0000000000
##                    ID 0.0000000000
##               country 0.0000000000
## 
##    0    1    2    3    4    5    6    7    8    9   10   11   12 
## 7806  910  248   84   45   29   17   10    7    5    6    9   24
## 
##    0    1    2    3    4    5    6    7    8    9   10 
## 8353  167   33   17   18    9    6   11   15   12  559
## 
##    0    1    2    3    4    5    6    7    8 
## 8527  226   49   10   11   11   12   25  329
## 
##    0    1    2    3    4    5    6    7    8    9   10   11   12   13   14   15 
## 7046  961  277   83   50   36   27   19   44   31  241   56   22   18   10    6 
##   16   17   18   19   20   21   22   23   24   25   26   27   28   29   30 
##    9    9  104   54   23   15    9    5    7    4    4    1    2    5   22
## 
##    0    1    2    3    4    5    6    7    8    9   10   11   12   13   14   15 
## 6579 1235  403  120   67   43   29   19   43   30  224   58   34   20   12    9 
##   16   17   18   19   20   21   22   23   24   25   26   27   28   29   30   36 
##    9    8   94   49   35   15    8    7   10    6    3    1    3    5    6    3 
##   38 
##   13

## 
##  Variables sorted by number of missings: 
##              Variable       Count
##               PERF.F7 0.928571429
##               PERF.F8 0.928571429
##               PERF.F9 0.928571429
##              PERF.F10 0.928571429
##               PERF.F3 0.924107143
##               PERF.F5 0.919642857
##               PERF.F6 0.919642857
##               PERF.F4 0.915178571
##               PERF.F2 0.910714286
##               PERF.F1 0.901785714
##              ATEVO.E7 0.071428571
##              ATEVO.E8 0.066964286
##              ATEVO.E6 0.062500000
##              ATEVO.E2 0.058035714
##              ATEVO.E3 0.058035714
##              ATEVO.E4 0.058035714
##              ATEVO.E5 0.058035714
##              ATEVO.E1 0.049107143
##             KAEVO.A10 0.035714286
##              KAEVO.A7 0.031250000
##              KAEVO.A8 0.031250000
##             KAEVO.A11 0.031250000
##              KAEVO.A5 0.026785714
##              KAEVO.A4 0.022321429
##            KAEVO.A9.1 0.022321429
##            KAEVO.A9.2 0.022321429
##              KAEVO.A3 0.017857143
##              KAEVO.A6 0.017857143
##              KAEVO.A2 0.013392857
##          interest_bio 0.008928571
##          denomination 0.008928571
##      denom_summarized 0.008928571
##              KAEVO.A1 0.008928571
##           meaning_evo 0.004464286
##  questionnaire_number 0.000000000
##                    ID 0.000000000
##               country 0.000000000
##                   bio 0.000000000
##                course 0.000000000
##                   age 0.000000000
##                   sex 0.000000000
##           bio_classes 0.000000000
##             learn_evo 0.000000000
##          na_count_all 0.000000000



2. Item- and scale analysis

2.1. EFA: Religious faith & acceptance of evolution

## Parallel analysis suggests that the number of factors =  3  and the number of components =  1



## R was not square, finding R from data
## $chisq
## [1] 106308
## 
## $p.value
## [1] 0
## 
## $df
## [1] 45
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = perf_fa)
## Overall MSA =  0.96
## MSA for each item = 
##  PERF.F1  PERF.F2  PERF.F3  PERF.F4  PERF.F5  PERF.F6  PERF.F7  PERF.F8 
##     0.93     0.94     0.98     0.97     0.97     0.97     0.98     0.95 
##  PERF.F9 PERF.F10 
##     0.97     0.96





## Factor Analysis using method =  pa
## Call: fa(r = perf_fa, nfactors = 1, rotate = "varimax", SMC = TRUE, 
##     fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
##           PA1   h2   u2 com
## PERF.F1  0.90 0.81 0.19   1
## PERF.F2  0.91 0.83 0.17   1
## PERF.F3  0.85 0.71 0.29   1
## PERF.F4  0.90 0.81 0.19   1
## PERF.F5  0.83 0.69 0.31   1
## PERF.F6  0.82 0.67 0.33   1
## PERF.F7  0.87 0.76 0.24   1
## PERF.F8  0.90 0.82 0.18   1
## PERF.F9  0.84 0.71 0.29   1
## PERF.F10 0.86 0.74 0.26   1
## 
##                 PA1
## SS loadings    7.55
## Proportion Var 0.76
## 
## Mean item complexity =  1
## Test of the hypothesis that 1 factor is sufficient.
## 
## The degrees of freedom for the null model are  45  and the objective function was  11.56 with Chi Square of  106308
## The degrees of freedom for the model are 35  and the objective function was  0.74 
## 
## The root mean square of the residuals (RMSR) is  0.03 
## The df corrected root mean square of the residuals is  0.04 
## 
## The harmonic number of observations is  8529 with the empirical chi square  766.01  with prob <  2.4e-138 
## The total number of observations was  9200  with Likelihood Chi Square =  6800.25  with prob <  0 
## 
## Tucker Lewis Index of factoring reliability =  0.918
## RMSEA index =  0.145  and the 90 % confidence intervals are  0.142 0.148
## BIC =  6480.81
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy             
##                                                    PA1
## Correlation of (regression) scores with factors   0.99
## Multiple R square of scores with factors          0.97
## Minimum correlation of possible factor scores     0.94





## Factor Analysis using method =  pa
## Call: fa(r = perf_fa, nfactors = 2, rotate = "varimax", SMC = TRUE, 
##     fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
##           PA1  PA2   h2    u2 com
## PERF.F1  0.44 0.85 0.92 0.079 1.5
## PERF.F2  0.52 0.78 0.87 0.128 1.7
## PERF.F3  0.52 0.67 0.73 0.273 1.9
## PERF.F4  0.57 0.70 0.82 0.183 1.9
## PERF.F5  0.73 0.44 0.74 0.264 1.6
## PERF.F6  0.55 0.60 0.66 0.335 2.0
## PERF.F7  0.67 0.56 0.76 0.238 1.9
## PERF.F8  0.77 0.51 0.85 0.150 1.7
## PERF.F9  0.66 0.53 0.72 0.283 1.9
## PERF.F10 0.80 0.43 0.81 0.185 1.5
## 
##                        PA1  PA2
## SS loadings           4.00 3.88
## Proportion Var        0.40 0.39
## Cumulative Var        0.40 0.79
## Proportion Explained  0.51 0.49
## Cumulative Proportion 0.51 1.00
## 
## Mean item complexity =  1.8
## Test of the hypothesis that 2 factors are sufficient.
## 
## The degrees of freedom for the null model are  45  and the objective function was  11.56 with Chi Square of  106308
## The degrees of freedom for the model are 26  and the objective function was  0.18 
## 
## The root mean square of the residuals (RMSR) is  0.02 
## The df corrected root mean square of the residuals is  0.02 
## 
## The harmonic number of observations is  8529 with the empirical chi square  178.38  with prob <  1.1e-24 
## The total number of observations was  9200  with Likelihood Chi Square =  1672.74  with prob <  0 
## 
## Tucker Lewis Index of factoring reliability =  0.973
## RMSEA index =  0.083  and the 90 % confidence intervals are  0.08 0.086
## BIC =  1435.44
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy             
##                                                    PA1  PA2
## Correlation of (regression) scores with factors   0.90 0.92
## Multiple R square of scores with factors          0.82 0.84
## Minimum correlation of possible factor scores     0.63 0.69
Component 1
Row Missings Mean SD Skew Item Difficulty Item Discrimination α if deleted
PERF.F1 6.39 % 3.17 1.64 -0.18 0.63 0.887 0.964
PERF.F2 6.64 % 2.97 1.59 -0.01 0.59 0.894 0.964
PERF.F3 7.07 % 2.9 1.51 0.06 0.58 0.832 0.966
PERF.F4 6.62 % 2.83 1.57 0.09 0.57 0.884 0.964
PERF.F5 7.07 % 2.29 1.45 0.68 0.46 0.817 0.967
PERF.F6 6.95 % 2.93 1.55 0.02 0.59 0.804 0.967
PERF.F7 6.99 % 2.58 1.54 0.34 0.52 0.857 0.965
PERF.F8 7.10 % 2.38 1.49 0.57 0.48 0.887 0.964
PERF.F9 6.83 % 2.61 1.57 0.34 0.52 0.833 0.966
PERF.F10 7.18 % 2.27 1.47 0.69 0.45 0.846 0.966
Mean inter-item-correlation=0.756 · Cronbach’s α=0.969

## Parallel analysis suggests that the number of factors =  5  and the number of components =  3
## R was not square, finding R from data
## $chisq
## [1] 18164.61
## 
## $p.value
## [1] 0
## 
## $df
## [1] 66
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = kaevo_fa)
## Overall MSA =  0.83
## MSA for each item = 
##   KAEVO.A1   KAEVO.A2   KAEVO.A3   KAEVO.A4   KAEVO.A5   KAEVO.A6   KAEVO.A7 
##       0.88       0.91       0.84       0.90       0.83       0.83       0.73 
##   KAEVO.A8 KAEVO.A9.1 KAEVO.A9.2  KAEVO.A10  KAEVO.A11 
##       0.69       0.86       0.70       0.91       0.93
## Factor Analysis using method =  pa
## Call: fa(r = kaevo_fa, nfactors = 4, rotate = "varimax", SMC = TRUE, 
##     fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
##             PA1  PA2  PA3   PA4    h2   u2 com
## KAEVO.A1   0.60 0.13 0.18  0.23 0.466 0.53 1.6
## KAEVO.A2   0.19 0.07 0.13  0.11 0.070 0.93 2.9
## KAEVO.A3   0.74 0.11 0.12  0.05 0.576 0.42 1.1
## KAEVO.A4   0.14 0.04 0.15  0.08 0.050 0.95 2.7
## KAEVO.A5   0.79 0.13 0.10 -0.10 0.654 0.35 1.1
## KAEVO.A6   0.74 0.11 0.17  0.23 0.649 0.35 1.4
## KAEVO.A7   0.18 0.61 0.03 -0.06 0.409 0.59 1.2
## KAEVO.A8   0.09 0.58 0.07  0.10 0.358 0.64 1.1
## KAEVO.A9.1 0.16 0.07 0.42  0.02 0.206 0.79 1.3
## KAEVO.A9.2 0.03 0.00 0.37  0.00 0.136 0.86 1.0
## KAEVO.A10  0.27 0.09 0.07 -0.05 0.087 0.91 1.4
## KAEVO.A11  0.12 0.05 0.07  0.02 0.023 0.98 2.2
## 
##                        PA1  PA2  PA3  PA4
## SS loadings           2.29 0.79 0.46 0.15
## Proportion Var        0.19 0.07 0.04 0.01
## Cumulative Var        0.19 0.26 0.29 0.31
## Proportion Explained  0.62 0.21 0.12 0.04
## Cumulative Proportion 0.62 0.83 0.96 1.00
## 
## Mean item complexity =  1.6
## Test of the hypothesis that 4 factors are sufficient.
## 
## The degrees of freedom for the null model are  66  and the objective function was  1.98 with Chi Square of  18164.61
## The degrees of freedom for the model are 24  and the objective function was  0.01 
## 
## The root mean square of the residuals (RMSR) is  0.01 
## The df corrected root mean square of the residuals is  0.01 
## 
## The harmonic number of observations is  8825 with the empirical chi square  72.31  with prob <  9.7e-07 
## The total number of observations was  9200  with Likelihood Chi Square =  59  with prob <  8.8e-05 
## 
## Tucker Lewis Index of factoring reliability =  0.995
## RMSEA index =  0.013  and the 90 % confidence intervals are  0.009 0.017
## BIC =  -160.04
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy             
##                                                    PA1  PA2   PA3   PA4
## Correlation of (regression) scores with factors   0.90 0.72  0.54  0.46
## Multiple R square of scores with factors          0.81 0.52  0.29  0.21
## Minimum correlation of possible factor scores     0.61 0.04 -0.41 -0.59
## Factor Analysis using method =  pa
## Call: fa(r = kaevo_fa, nfactors = 3, rotate = "varimax", SMC = TRUE, 
##     fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
##             PA1  PA2  PA3    h2   u2 com
## KAEVO.A1   0.62 0.13 0.20 0.439 0.56 1.3
## KAEVO.A2   0.20 0.07 0.15 0.065 0.94 2.1
## KAEVO.A3   0.75 0.11 0.11 0.586 0.41 1.1
## KAEVO.A4   0.15 0.04 0.16 0.049 0.95 2.1
## KAEVO.A5   0.75 0.13 0.09 0.593 0.41 1.1
## KAEVO.A6   0.76 0.11 0.20 0.625 0.37 1.2
## KAEVO.A7   0.18 0.60 0.03 0.388 0.61 1.2
## KAEVO.A8   0.10 0.58 0.08 0.352 0.65 1.1
## KAEVO.A9.1 0.16 0.06 0.41 0.197 0.80 1.3
## KAEVO.A9.2 0.03 0.00 0.36 0.131 0.87 1.0
## KAEVO.A10  0.26 0.09 0.06 0.081 0.92 1.3
## KAEVO.A11  0.12 0.05 0.07 0.023 0.98 2.1
## 
##                        PA1  PA2  PA3
## SS loadings           2.30 0.77 0.46
## Proportion Var        0.19 0.06 0.04
## Cumulative Var        0.19 0.26 0.29
## Proportion Explained  0.65 0.22 0.13
## Cumulative Proportion 0.65 0.87 1.00
## 
## Mean item complexity =  1.4
## Test of the hypothesis that 3 factors are sufficient.
## 
## The degrees of freedom for the null model are  66  and the objective function was  1.98 with Chi Square of  18164.61
## The degrees of freedom for the model are 33  and the objective function was  0.02 
## 
## The root mean square of the residuals (RMSR) is  0.01 
## The df corrected root mean square of the residuals is  0.02 
## 
## The harmonic number of observations is  8825 with the empirical chi square  147.53  with prob <  2e-16 
## The total number of observations was  9200  with Likelihood Chi Square =  187.92  with prob <  1.4e-23 
## 
## Tucker Lewis Index of factoring reliability =  0.983
## RMSEA index =  0.023  and the 90 % confidence intervals are  0.02 0.026
## BIC =  -113.27
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy             
##                                                    PA1  PA2   PA3
## Correlation of (regression) scores with factors   0.89 0.71  0.55
## Multiple R square of scores with factors          0.80 0.51  0.30
## Minimum correlation of possible factor scores     0.60 0.02 -0.40
## Factor Analysis using method =  pa
## Call: fa(r = kaevo_fa, nfactors = 2, rotate = "varimax", SMC = TRUE, 
##     fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
##             PA1  PA2    h2   u2 com
## KAEVO.A1   0.65 0.15 0.441 0.56 1.1
## KAEVO.A2   0.23 0.08 0.057 0.94 1.2
## KAEVO.A3   0.75 0.12 0.573 0.43 1.1
## KAEVO.A4   0.18 0.05 0.036 0.96 1.2
## KAEVO.A5   0.74 0.14 0.572 0.43 1.1
## KAEVO.A6   0.78 0.13 0.630 0.37 1.1
## KAEVO.A7   0.17 0.59 0.373 0.63 1.2
## KAEVO.A8   0.11 0.59 0.360 0.64 1.1
## KAEVO.A9.1 0.25 0.09 0.069 0.93 1.2
## KAEVO.A9.2 0.11 0.02 0.014 0.99 1.1
## KAEVO.A10  0.27 0.10 0.081 0.92 1.3
## KAEVO.A11  0.13 0.06 0.021 0.98 1.4
## 
##                        PA1  PA2
## SS loadings           2.43 0.80
## Proportion Var        0.20 0.07
## Cumulative Var        0.20 0.27
## Proportion Explained  0.75 0.25
## Cumulative Proportion 0.75 1.00
## 
## Mean item complexity =  1.2
## Test of the hypothesis that 2 factors are sufficient.
## 
## The degrees of freedom for the null model are  66  and the objective function was  1.98 with Chi Square of  18164.61
## The degrees of freedom for the model are 43  and the objective function was  0.05 
## 
## The root mean square of the residuals (RMSR) is  0.02 
## The df corrected root mean square of the residuals is  0.03 
## 
## The harmonic number of observations is  8825 with the empirical chi square  607  with prob <  1.2e-100 
## The total number of observations was  9200  with Likelihood Chi Square =  495.26  with prob <  3.3e-78 
## 
## Tucker Lewis Index of factoring reliability =  0.962
## RMSEA index =  0.034  and the 90 % confidence intervals are  0.031 0.037
## BIC =  102.8
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy             
##                                                    PA1  PA2
## Correlation of (regression) scores with factors   0.91 0.71
## Multiple R square of scores with factors          0.82 0.51
## Minimum correlation of possible factor scores     0.64 0.02
## Factor Analysis using method =  pa
## Call: fa(r = kaevo_fa, nfactors = 1, rotate = "varimax", SMC = TRUE, 
##     fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
##             PA1    h2   u2 com
## KAEVO.A1   0.67 0.443 0.56   1
## KAEVO.A2   0.24 0.058 0.94   1
## KAEVO.A3   0.75 0.558 0.44   1
## KAEVO.A4   0.19 0.037 0.96   1
## KAEVO.A5   0.75 0.565 0.44   1
## KAEVO.A6   0.78 0.612 0.39   1
## KAEVO.A7   0.31 0.098 0.90   1
## KAEVO.A8   0.26 0.067 0.93   1
## KAEVO.A9.1 0.26 0.069 0.93   1
## KAEVO.A9.2 0.12 0.014 0.99   1
## KAEVO.A10  0.29 0.082 0.92   1
## KAEVO.A11  0.15 0.021 0.98   1
## 
##                 PA1
## SS loadings    2.62
## Proportion Var 0.22
## 
## Mean item complexity =  1
## Test of the hypothesis that 1 factor is sufficient.
## 
## The degrees of freedom for the null model are  66  and the objective function was  1.98 with Chi Square of  18164.61
## The degrees of freedom for the model are 54  and the objective function was  0.17 
## 
## The root mean square of the residuals (RMSR) is  0.04 
## The df corrected root mean square of the residuals is  0.05 
## 
## The harmonic number of observations is  8825 with the empirical chi square  2218.3  with prob <  0 
## The total number of observations was  9200  with Likelihood Chi Square =  1577.83  with prob <  1.3e-294 
## 
## Tucker Lewis Index of factoring reliability =  0.897
## RMSEA index =  0.055  and the 90 % confidence intervals are  0.053 0.058
## BIC =  1084.98
## Fit based upon off diagonal values = 0.96
## Measures of factor score adequacy             
##                                                    PA1
## Correlation of (regression) scores with factors   0.92
## Multiple R square of scores with factors          0.84
## Minimum correlation of possible factor scores     0.68

## Parallel analysis suggests that the number of factors =  3  and the number of components =  2
## R was not square, finding R from data
## $chisq
## [1] 14032.44
## 
## $p.value
## [1] 0
## 
## $df
## [1] 28
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = atevo_fa)
## Overall MSA =  0.79
## MSA for each item = 
## ATEVO.E1 ATEVO.E2 ATEVO.E3 ATEVO.E4 ATEVO.E5 ATEVO.E6 ATEVO.E7 ATEVO.E8 
##     0.79     0.79     0.85     0.75     0.82     0.83     0.78     0.72
## Factor Analysis using method =  pa
## Call: fa(r = atevo_fa, nfactors = 1, rotate = "varimax", SMC = TRUE, 
##     fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
##           PA1   h2   u2 com
## ATEVO.E1 0.49 0.24 0.76   1
## ATEVO.E2 0.53 0.28 0.72   1
## ATEVO.E3 0.60 0.36 0.64   1
## ATEVO.E4 0.45 0.20 0.80   1
## ATEVO.E5 0.55 0.30 0.70   1
## ATEVO.E6 0.38 0.15 0.85   1
## ATEVO.E7 0.68 0.46 0.54   1
## ATEVO.E8 0.49 0.24 0.76   1
## 
##                 PA1
## SS loadings    2.23
## Proportion Var 0.28
## 
## Mean item complexity =  1
## Test of the hypothesis that 1 factor is sufficient.
## 
## The degrees of freedom for the null model are  28  and the objective function was  1.53 with Chi Square of  14032.44
## The degrees of freedom for the model are 20  and the objective function was  0.3 
## 
## The root mean square of the residuals (RMSR) is  0.08 
## The df corrected root mean square of the residuals is  0.09 
## 
## The harmonic number of observations is  8737 with the empirical chi square  2978.77  with prob <  0 
## The total number of observations was  9200  with Likelihood Chi Square =  2716.47  with prob <  0 
## 
## Tucker Lewis Index of factoring reliability =  0.73
## RMSEA index =  0.121  and the 90 % confidence intervals are  0.117 0.125
## BIC =  2533.93
## Fit based upon off diagonal values = 0.93
## Measures of factor score adequacy             
##                                                    PA1
## Correlation of (regression) scores with factors   0.88
## Multiple R square of scores with factors          0.77
## Minimum correlation of possible factor scores     0.53



## Factor Analysis using method =  pa
## Call: fa(r = atevo_fa, nfactors = 2, rotate = "varimax", SMC = TRUE, 
##     fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
##           PA1  PA2   h2   u2 com
## ATEVO.E1 0.53 0.12 0.30 0.70 1.1
## ATEVO.E2 0.33 0.43 0.29 0.71 1.9
## ATEVO.E3 0.55 0.25 0.37 0.63 1.4
## ATEVO.E4 0.18 0.50 0.29 0.71 1.3
## ATEVO.E5 0.55 0.18 0.34 0.66 1.2
## ATEVO.E6 0.25 0.29 0.14 0.86 2.0
## ATEVO.E7 0.75 0.18 0.60 0.40 1.1
## ATEVO.E8 0.09 0.82 0.68 0.32 1.0
## 
##                        PA1  PA2
## SS loadings           1.67 1.34
## Proportion Var        0.21 0.17
## Cumulative Var        0.21 0.38
## Proportion Explained  0.55 0.45
## Cumulative Proportion 0.55 1.00
## 
## Mean item complexity =  1.4
## Test of the hypothesis that 2 factors are sufficient.
## 
## The degrees of freedom for the null model are  28  and the objective function was  1.53 with Chi Square of  14032.44
## The degrees of freedom for the model are 13  and the objective function was  0.07 
## 
## The root mean square of the residuals (RMSR) is  0.04 
## The df corrected root mean square of the residuals is  0.05 
## 
## The harmonic number of observations is  8737 with the empirical chi square  641  with prob <  1.4e-128 
## The total number of observations was  9200  with Likelihood Chi Square =  684.75  with prob <  6.2e-138 
## 
## Tucker Lewis Index of factoring reliability =  0.897
## RMSEA index =  0.075  and the 90 % confidence intervals are  0.07 0.08
## BIC =  566.1
## Fit based upon off diagonal values = 0.98
## Measures of factor score adequacy             
##                                                    PA1  PA2
## Correlation of (regression) scores with factors   0.85 0.85
## Multiple R square of scores with factors          0.72 0.72
## Minimum correlation of possible factor scores     0.44 0.44
Component 1
Row Missings Mean SD Skew Item Difficulty Item Discrimination α if deleted
ATEVO.E1 4.15 % 4.47 0.93 -2.07 0.89 0.386 0.719
ATEVO.E2 4.25 % 3.85 1.1 -0.85 0.77 0.481 0.7
ATEVO.E3 4.65 % 4.3 0.85 -1.39 0.86 0.484 0.704
ATEVO.E4 4.84 % 3.69 1.2 -0.64 0.74 0.402 0.719
ATEVO.E5 4.62 % 4.44 0.87 -1.86 0.89 0.434 0.712
ATEVO.E6 4.66 % 3.51 1.13 -0.5 0.7 0.337 0.731
ATEVO.E7 4.45 % 4.37 0.94 -1.65 0.87 0.529 0.694
ATEVO.E8 4.60 % 3.47 1.26 -0.4 0.69 0.455 0.707
Mean inter-item-correlation=0.270 · Cronbach’s α=0.738


2.2. CFA: Knowledge about evolution

## lavaan 0.6-7 ended normally after 71 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of free parameters                         26
##                                                       
##                                                   Used       Total
##   Number of observations                          7992        9200
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                               189.379
##   Degrees of freedom                                29
##   P-value (Chi-square)                           0.000
## 
## Model Test Baseline Model:
## 
##   Test statistic                             15021.093
##   Degrees of freedom                                45
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    0.989
##   Tucker-Lewis Index (TLI)                       0.983
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)             -38620.493
##   Loglikelihood unrestricted model (H1)     -38525.803
##                                                       
##   Akaike (AIC)                               77292.985
##   Bayesian (BIC)                             77474.626
##   Sample-size adjusted Bayesian (BIC)        77392.003
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.026
##   90 Percent confidence interval - lower         0.023
##   90 Percent confidence interval - upper         0.030
##   P-value RMSEA <= 0.05                          1.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.014
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   ada =~                                                                
##     KAEVO.A1          0.331    0.005   61.882    0.000    0.331    0.662
##     KAEVO.A3          0.376    0.005   73.767    0.000    0.376    0.757
##     KAEVO.A5          0.370    0.005   74.010    0.000    0.370    0.759
##     KAEVO.A6          0.398    0.005   78.982    0.000    0.398    0.797
##   her =~                                                                
##     KAEVO.A7          0.248    0.008   29.760    0.000    0.248    0.684
##     KAEVO.A8          0.264    0.010   27.642    0.000    0.264    0.529
##   tre =~                                                                
##     KAEVO.A9.1        0.221    0.015   15.004    0.000    0.221    0.658
##     KAEVO.A9.2        0.075    0.006   12.742    0.000    0.075    0.250
##   spe =~                                                                
##     KAEVO.A4          0.093    0.008   12.241    0.000    0.093    0.236
##     KAEVO.A10         0.174    0.013   13.527    0.000    0.174    0.349
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   ada ~~                                                                
##     her               0.371    0.016   23.585    0.000    0.371    0.371
##     tre               0.374    0.027   14.002    0.000    0.374    0.374
##     spe               0.768    0.052   14.707    0.000    0.768    0.768
##   her ~~                                                                
##     tre               0.182    0.024    7.501    0.000    0.182    0.182
##     spe               0.404    0.043    9.337    0.000    0.404    0.404
##   tre ~~                                                                
##     spe               0.406    0.052    7.851    0.000    0.406    0.406
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .KAEVO.A1          0.140    0.003   53.937    0.000    0.140    0.562
##    .KAEVO.A3          0.105    0.002   46.901    0.000    0.105    0.426
##    .KAEVO.A5          0.101    0.002   46.703    0.000    0.101    0.423
##    .KAEVO.A6          0.091    0.002   42.073    0.000    0.091    0.365
##    .KAEVO.A7          0.070    0.004   17.945    0.000    0.070    0.532
##    .KAEVO.A8          0.180    0.005   35.178    0.000    0.180    0.720
##    .KAEVO.A9.1        0.064    0.006    9.984    0.000    0.064    0.568
##    .KAEVO.A9.2        0.085    0.002   55.487    0.000    0.085    0.938
##    .KAEVO.A4          0.147    0.003   56.487    0.000    0.147    0.944
##    .KAEVO.A10         0.217    0.005   40.984    0.000    0.217    0.878
##     ada               1.000                               1.000    1.000
##     her               1.000                               1.000    1.000
##     tre               1.000                               1.000    1.000
##     spe               1.000                               1.000    1.000
##           lhs op        rhs   est    se      z pvalue ci.lower ci.upper
## 1         ada =~   KAEVO.A1 0.331 0.005 61.882      0    0.320    0.341
## 2         ada =~   KAEVO.A3 0.376 0.005 73.767      0    0.366    0.386
## 3         ada =~   KAEVO.A5 0.370 0.005 74.010      0    0.360    0.380
## 4         ada =~   KAEVO.A6 0.398 0.005 78.982      0    0.388    0.408
## 5         her =~   KAEVO.A7 0.248 0.008 29.760      0    0.231    0.264
## 6         her =~   KAEVO.A8 0.264 0.010 27.642      0    0.246    0.283
## 7         tre =~ KAEVO.A9.1 0.221 0.015 15.004      0    0.192    0.250
## 8         tre =~ KAEVO.A9.2 0.075 0.006 12.742      0    0.064    0.087
## 9         spe =~   KAEVO.A4 0.093 0.008 12.241      0    0.078    0.108
## 10        spe =~  KAEVO.A10 0.174 0.013 13.527      0    0.148    0.199
## 11   KAEVO.A1 ~~   KAEVO.A1 0.140 0.003 53.937      0    0.135    0.145
## 12   KAEVO.A3 ~~   KAEVO.A3 0.105 0.002 46.901      0    0.100    0.109
## 13   KAEVO.A5 ~~   KAEVO.A5 0.101 0.002 46.703      0    0.096    0.105
## 14   KAEVO.A6 ~~   KAEVO.A6 0.091 0.002 42.073      0    0.087    0.095
## 15   KAEVO.A7 ~~   KAEVO.A7 0.070 0.004 17.945      0    0.062    0.077
## 16   KAEVO.A8 ~~   KAEVO.A8 0.180 0.005 35.178      0    0.170    0.190
## 17 KAEVO.A9.1 ~~ KAEVO.A9.1 0.064 0.006  9.984      0    0.052    0.077
## 18 KAEVO.A9.2 ~~ KAEVO.A9.2 0.085 0.002 55.487      0    0.082    0.089
## 19   KAEVO.A4 ~~   KAEVO.A4 0.147 0.003 56.487      0    0.142    0.152
## 20  KAEVO.A10 ~~  KAEVO.A10 0.217 0.005 40.984      0    0.207    0.228
## 21        ada ~~        ada 1.000 0.000     NA     NA    1.000    1.000
## 22        her ~~        her 1.000 0.000     NA     NA    1.000    1.000
## 23        tre ~~        tre 1.000 0.000     NA     NA    1.000    1.000
## 24        spe ~~        spe 1.000 0.000     NA     NA    1.000    1.000
## 25        ada ~~        her 0.371 0.016 23.585      0    0.340    0.402
## 26        ada ~~        tre 0.374 0.027 14.002      0    0.322    0.426
## 27        ada ~~        spe 0.768 0.052 14.707      0    0.666    0.871
## 28        her ~~        tre 0.182 0.024  7.501      0    0.135    0.230
## 29        her ~~        spe 0.404 0.043  9.337      0    0.320    0.489
## 30        tre ~~        spe 0.406 0.052  7.851      0    0.305    0.507


2.3. Indexing


3. Descriptive statistics

Data preparation



Cats religios faith

## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 5 x 3
##   perf_cat             count  prop
##   <fct>                <int> <dbl>
## 1 not religious at all  2986 0.357
## 2 not religious         1091 0.131
## 3 neutral               1539 0.184
## 4 religious             1246 0.149
## 5 very religious        1491 0.178
## `summarise()` regrouping output by 'bio' (override with `.groups` argument)
## # A tibble: 10 x 4
## # Groups:   bio [2]
##    bio     perf_cat             count  prop
##    <fct>   <fct>                <int> <dbl>
##  1 Bio     not religious at all  2394 0.405
##  2 Bio     not religious          835 0.141
##  3 Bio     neutral               1036 0.175
##  4 Bio     religious              760 0.129
##  5 Bio     very religious         887 0.150
##  6 Non-Bio not religious at all   592 0.243
##  7 Non-Bio not religious          256 0.105
##  8 Non-Bio neutral                503 0.206
##  9 Non-Bio religious              486 0.199
## 10 Non-Bio very religious         604 0.247
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 5 x 3
##   perf_cat             count  prop
##   <fct>                <int> <dbl>
## 1 not religious at all  2394 0.405
## 2 not religious          835 0.141
## 3 neutral               1036 0.175
## 4 religious              760 0.129
## 5 very religious         887 0.150
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 5 x 3
##   perf_cat             count  prop
##   <fct>                <int> <dbl>
## 1 not religious at all   592 0.243
## 2 not religious          256 0.105
## 3 neutral                503 0.206
## 4 religious              486 0.199
## 5 very religious         604 0.247



Cats knowledge

## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 5 x 3
##   kaevo_cat   count    prop
##   <fct>       <int>   <dbl>
## 1 very low     4332 0.555  
## 2 low          1885 0.241  
## 3 moderate     1328 0.170  
## 4 rather high   252 0.0323 
## 5 high            9 0.00115
## `summarise()` regrouping output by 'bio' (override with `.groups` argument)
## # A tibble: 10 x 4
## # Groups:   bio [3]
##    bio     kaevo_cat   count    prop
##    <fct>   <fct>       <int>   <dbl>
##  1 Bio     very low     2661 0.474  
##  2 Bio     low          1522 0.271  
##  3 Bio     moderate     1191 0.212  
##  4 Bio     rather high   233 0.0415 
##  5 Bio     high            9 0.00160
##  6 Non-Bio very low     1670 0.763  
##  7 Non-Bio low           363 0.166  
##  8 Non-Bio moderate      137 0.0626 
##  9 Non-Bio rather high    19 0.00868
## 10 <NA>    very low        1 1
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 5 x 3
##   kaevo_cat   count    prop
##   <fct>       <int>   <dbl>
## 1 very low     2661 0.474  
## 2 low          1522 0.271  
## 3 moderate     1191 0.212  
## 4 rather high   233 0.0415 
## 5 high            9 0.00160
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 4 x 3
##   kaevo_cat   count    prop
##   <fct>       <int>   <dbl>
## 1 very low     1670 0.763  
## 2 low           363 0.166  
## 3 moderate      137 0.0626 
## 4 rather high    19 0.00868



Cats acceptance

## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 5 x 3
##   atevo_cat     count    prop
##   <fct>         <int>   <dbl>
## 1 reject           36 0.00422
## 2 rather reject    87 0.0102 
## 3 neutral        1697 0.199  
## 4 rather accept  3811 0.447  
## 5 accept         2896 0.340
## `summarise()` regrouping output by 'bio' (override with `.groups` argument)
## # A tibble: 11 x 4
## # Groups:   bio [3]
##    bio     atevo_cat     count    prop
##    <fct>   <fct>         <int>   <dbl>
##  1 Bio     reject           23 0.00380
##  2 Bio     rather reject    57 0.00941
##  3 Bio     neutral        1069 0.177  
##  4 Bio     rather accept  2697 0.445  
##  5 Bio     accept         2210 0.365  
##  6 Non-Bio reject           13 0.00526
##  7 Non-Bio rather reject    30 0.0121 
##  8 Non-Bio neutral         628 0.254  
##  9 Non-Bio rather accept  1114 0.451  
## 10 Non-Bio accept          685 0.277  
## 11 <NA>    accept            1 1
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 5 x 3
##   atevo_cat     count    prop
##   <fct>         <int>   <dbl>
## 1 reject           23 0.00380
## 2 rather reject    57 0.00941
## 3 neutral        1069 0.177  
## 4 rather accept  2697 0.445  
## 5 accept         2210 0.365
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 5 x 3
##   atevo_cat     count    prop
##   <fct>         <int>   <dbl>
## 1 reject           13 0.00526
## 2 rather reject    30 0.0121 
## 3 neutral         628 0.254  
## 4 rather accept  1114 0.451  
## 5 accept          685 0.277
##       bio min Q1 median Q3 max     mean       sd    n missing
## 1     Bio   8 30     33 36  40 32.52361 4.863158 6056       0
## 2 Non-Bio   8 28     32 35  40 31.28259 5.011156 2470       0
##       bio min Q1 median Q3 max     mean       sd    n missing
## 1     Bio   0  3      6  8  12 5.532407 2.542186 5616       0
## 2 Non-Bio   0  2      3  5  11 3.850617 2.218436 2189       0
##       bio min Q1 median Q3 max     mean       sd    n missing
## 1     Bio  10 12     23 36  50 25.11316 13.36340 5912       0
## 2 Non-Bio  10 18     33 42  50 30.81852 13.30005 2441       0
##  min Q1 median Q3 max     mean       sd    n missing
##    8 29     32 36  40 32.16501 4.938833 8527       0
##  min Q1 median Q3 max     mean      sd    n missing
##    0  3      5  7  12 5.060594 2.56903 7806       0
##  min Q1 median Q3 max     mean       sd    n missing
##   10 13     26 39  50 26.78044 13.59408 8353       0
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 2 x 3
##   bio     count  prop
##   <fct>   <int> <dbl>
## 1 Bio        23 0.639
## 2 Non-Bio    13 0.361
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 2 x 3
##   bio     count  prop
##   <fct>   <int> <dbl>
## 1 Bio        57 0.655
## 2 Non-Bio    30 0.345



Favstats & Frequencies

## bio
##     Bio Non-Bio    <NA> 
##    6455    2744       1
## bio
##          Bio      Non-Bio         <NA> 
## 0.7016304348 0.2982608696 0.0001086957
## sex
##    1    2    3 <NA> 
## 2552 6537   40   71
## country
##            Austria            Belgium Bosnia-Herzegovina           Bulgaria 
##                159                399                277                196 
##            Croatia             Cyprus     Czech Republic            Finland 
##                394                 82                400                214 
##             France            Germany             Greece            Hungary 
##                748               1049                161                230 
##              Italy             Latvia        Netherlands             Poland 
##                733                176                444                460 
##           Portugal            Romania             Serbia           Slovakia 
##                149                675               1246                196 
##           Slovenia              Spain             Sweden        Switzerland 
##                322                212                 34                 68 
##             Turkey            Ukraine 
##                 85                 91
##          country
## bio       Austria Belgium Bosnia-Herzegovina Bulgaria Croatia Cyprus
##   Bio         159     308                 87      188     265     55
##   Non-Bio       0      91                190        8     129     27
##   <NA>          0       0                  0        0       0      0
##          country
## bio       Czech Republic Finland France Germany Greece Hungary Italy Latvia
##   Bio                400     214    748     659     36     196   558    170
##   Non-Bio              0       0      0     390    125      34   175      6
##   <NA>                 0       0      0       0      0       0     0      0
##          country
## bio       Netherlands Poland Portugal Romania Serbia Slovakia Slovenia Spain
##   Bio             444    124       59     264    727      188      266   201
##   Non-Bio           0    335       90     411    519        8       56    11
##   <NA>              0      1        0       0      0        0        0     0
##          country
## bio       Sweden Switzerland Turkey Ukraine
##   Bio          5          67     67       0
##   Non-Bio     29           1     18      91
##   <NA>         0           0      0       0
##          country
## bio           Austria     Belgium Bosnia-Herzegovina    Bulgaria     Croatia
##   Bio     1.000000000 0.771929825        0.314079422 0.959183673 0.672588832
##   Non-Bio 0.000000000 0.228070175        0.685920578 0.040816327 0.327411168
##   <NA>    0.000000000 0.000000000        0.000000000 0.000000000 0.000000000
##          country
## bio            Cyprus Czech Republic     Finland      France     Germany
##   Bio     0.670731707    1.000000000 1.000000000 1.000000000 0.628217350
##   Non-Bio 0.329268293    0.000000000 0.000000000 0.000000000 0.371782650
##   <NA>    0.000000000    0.000000000 0.000000000 0.000000000 0.000000000
##          country
## bio            Greece     Hungary       Italy      Latvia Netherlands
##   Bio     0.223602484 0.852173913 0.761255116 0.965909091 1.000000000
##   Non-Bio 0.776397516 0.147826087 0.238744884 0.034090909 0.000000000
##   <NA>    0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
##          country
## bio            Poland    Portugal     Romania      Serbia    Slovakia
##   Bio     0.269565217 0.395973154 0.391111111 0.583467095 0.959183673
##   Non-Bio 0.728260870 0.604026846 0.608888889 0.416532905 0.040816327
##   <NA>    0.002173913 0.000000000 0.000000000 0.000000000 0.000000000
##          country
## bio          Slovenia       Spain      Sweden Switzerland      Turkey
##   Bio     0.826086957 0.948113208 0.147058824 0.985294118 0.788235294
##   Non-Bio 0.173913043 0.051886792 0.852941176 0.014705882 0.211764706
##   <NA>    0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
##          country
## bio           Ukraine
##   Bio     0.000000000
##   Non-Bio 1.000000000
##   <NA>    0.000000000
##       country
## sex    Austria Belgium Bosnia-Herzegovina Bulgaria Croatia Cyprus
##   1         36     125                 50       51     107      9
##   2        122     269                226      145     283     73
##   3          0       2                  1        0       2      0
##   <NA>       1       3                  0        0       2      0
##       country
## sex    Czech Republic Finland France Germany Greece Hungary Italy Latvia
##   1                63      40    282     243     22      89   244     26
##   2               337     171    458     798    138     139   477    150
##   3                 0       3      6       7      1       2     4      0
##   <NA>              0       0      2       1      0       0     8      0
##       country
## sex    Netherlands Poland Portugal Romania Serbia Slovakia Slovenia Spain
##   1            153    187       26     155    384       24       41    73
##   2            290    264      121     477    854      172      280   139
##   3              1      5        0       1      4        0        0     0
##   <NA>           0      4        2      42      4        0        1     0
##       country
## sex    Sweden Switzerland Turkey Ukraine
##   1        22          39     23      38
##   2        12          29     61      52
##   3         0           0      0       1
##   <NA>      0           0      1       0
##  min Q1 median Q3 max     mean       sd    n missing
##    8 29     32 36  40 32.16501 4.938833 8527     673
##   sex min Q1 median Q3 max     mean       sd    n missing
## 1   1   8 30     34 37  40 33.01745 5.243568 2349     203
## 2   2   8 29     32 35  40 31.84739 4.775032 6094     443
## 3   3  18 27     32 35  40 31.05405 5.617166   37       3
##       bio min Q1 median Q3 max     mean       sd    n missing
## 1     Bio   8 30     33 36  40 32.52361 4.863158 6056     399
## 2 Non-Bio   8 28     32 35  40 31.28259 5.011156 2470     274
##               country min    Q1 median    Q3 max     mean       sd    n missing
## 1             Austria  16 30.75   34.0 37.00  40 33.36538 4.753385  156       3
## 2             Belgium  10 31.00   34.0 38.00  40 33.46875 5.585234  384      15
## 3  Bosnia-Herzegovina   8 27.00   30.0 33.00  40 29.94144 4.922126  222      55
## 4            Bulgaria  15 30.00   33.0 37.00  40 32.95109 4.801275  184      12
## 5             Croatia  10 29.00   33.0 36.00  40 31.91957 4.974555  373      21
## 6              Cyprus  18 27.50   31.0 35.00  40 31.12000 4.989069   75       7
## 7      Czech Republic   8 27.00   30.0 33.00  40 30.30028 4.871348  363      37
## 8             Finland  23 34.00   37.0 39.00  40 36.08500 3.375611  200      14
## 9              France  16 29.00   32.0 35.00  40 31.67107 4.005570  681      67
## 10            Germany  11 30.00   33.0 36.00  40 32.95301 4.454018  979      70
## 11             Greece  13 28.00   31.0 34.25  40 31.21053 4.447831  152       9
## 12            Hungary   8 30.00   33.0 36.00  40 32.63514 5.298103  222       8
## 13              Italy  13 30.00   32.0 35.00  40 32.26619 4.053986  695      38
## 14             Latvia  11 28.00   31.0 34.00  40 30.16867 5.238134  166      10
## 15        Netherlands   9 32.50   36.0 39.00  40 35.01856 5.301569  431      13
## 16             Poland  11 31.00   34.0 37.00  40 33.28571 4.822955  427      33
## 17           Portugal  12 30.00   33.0 35.00  40 32.44286 4.389771  140       9
## 18            Romania   8 28.00   31.0 34.00  40 30.82810 4.978118  605      70
## 19             Serbia   8 28.00   31.0 35.00  40 30.99648 5.107485 1135     111
## 20           Slovakia  16 28.00   31.0 34.00  40 30.68085 4.550872  188       8
## 21           Slovenia  17 28.00   31.0 34.00  40 31.16443 4.308375  298      24
## 22              Spain  16 33.00   35.0 38.00  40 34.70588 3.965587  204       8
## 23             Sweden  21 29.75   34.0 36.00  40 33.18750 4.638113   32       2
## 24        Switzerland  23 29.75   33.0 36.25  40 32.70312 4.263837   64       4
## 25             Turkey   8 28.00   33.0 38.00  40 32.63768 6.331460   69      16
## 26            Ukraine  19 28.00   32.5 35.00  40 31.40244 5.295840   82       9
##  min Q1 median Q3 max     mean      sd    n missing
##    0  3      5  7  12 5.060594 2.56903 7806    1394
##   sex min   Q1 median Q3 max     mean       sd    n missing
## 1   1   0 3.00      6  8  12 5.702039 2.645262 2158     394
## 2   2   0 3.00      5  7  12 4.813374 2.492259 5578     959
## 3   3   2 3.25      6  8  11 5.833333 2.464693   30      10
##       bio min Q1 median Q3 max     mean       sd    n missing
## 1     Bio   0  3      6  8  12 5.532407 2.542186 5616     839
## 2 Non-Bio   0  2      3  5  11 3.850617 2.218436 2189     555
##               country min Q1 median   Q3 max     mean       sd   n missing
## 1             Austria   1  4    6.0 8.00  11 5.979310 2.405055 145      14
## 2             Belgium   0  5    6.0 8.00  10 6.127321 2.159768 377      22
## 3  Bosnia-Herzegovina   0  2    3.0 4.00   9 2.885870 1.781206 184      93
## 4            Bulgaria   0  2    3.5 5.00   8 3.608108 1.883098 148      48
## 5             Croatia   0  3    5.0 7.00  11 5.152975 2.298554 353      41
## 6              Cyprus   0  2    3.5 6.00  10 4.200000 2.505646  70      12
## 7      Czech Republic   0  3    4.0 6.00  10 4.500000 2.044082 360      40
## 8             Finland   2  7    8.0 9.00  12 7.806283 1.685593 191      23
## 9              France   0  5    7.0 8.00  12 6.375806 2.449964 620     128
## 10            Germany   0  4    7.0 8.00  12 6.127418 2.407214 879     170
## 11             Greece   0  2    3.0 4.00   8 3.125000 1.777383 144      17
## 12            Hungary   0  3    4.0 6.00  11 4.551887 2.239699 212      18
## 13              Italy   0  4    6.0 7.00  12 5.620427 2.319017 656      77
## 14             Latvia   0  2    3.0 3.00   7 2.801418 1.498665 141      35
## 15        Netherlands   1  6    8.0 9.00  12 7.513447 1.710819 409      35
## 16             Poland   0  3    5.0 7.00  12 4.967254 2.479499 397      63
## 17           Portugal   0  3    4.0 7.00  11 4.588235 2.486916 136      13
## 18            Romania   0  2    3.0 4.00   9 3.034926 1.560526 544     131
## 19             Serbia   0  2    4.0 6.00  12 3.944444 2.252500 990     256
## 20           Slovakia   0  2    3.0 4.00   7 3.032258 1.488345 186      10
## 21           Slovenia   0  2    4.0 6.00  10 4.120623 2.203744 257      65
## 22              Spain   0  6    7.0 8.75  11 7.126316 1.980017 190      22
## 23             Sweden   1  4    6.0 7.00  10 5.451613 2.188435  31       3
## 24        Switzerland   2  6    7.0 8.00  11 6.830508 2.001315  59       9
## 25             Turkey   0  2    3.0 6.00  11 3.916667 2.352724  60      25
## 26            Ukraine   0  2    2.0 4.00   7 2.865672 1.516530  67      24
##  min Q1 median Q3 max     mean       sd    n missing
##   10 13     26 39  50 26.78044 13.59408 8353     847
##   sex min Q1 median Q3 max     mean       sd    n missing
## 1   1  10 11     21 35  50 24.07463 13.22551 2318     234
## 2   2  10 14     28 40  50 27.80762 13.58220 5957     580
## 3   3  10 11     22 36  50 24.24242 13.76597   33       7
##       bio min Q1 median Q3 max     mean       sd    n missing
## 1     Bio  10 12     23 36  50 25.11316 13.36340 5912     543
## 2 Non-Bio  10 18     33 42  50 30.81852 13.30005 2441     303
##               country min    Q1 median    Q3 max     mean        sd    n
## 1             Austria  10 11.00   23.0 33.50  50 24.47097 12.644649  155
## 2             Belgium  10 11.00   15.0 28.00  50 20.63010 12.354459  392
## 3  Bosnia-Herzegovina  10 31.00   43.0 49.00  50 38.12857 12.729713  210
## 4            Bulgaria  10 15.00   30.0 37.75  50 27.76966 12.786282  178
## 5             Croatia  10 19.25   36.0 45.00  50 32.73429 14.007191  350
## 6              Cyprus  10 35.00   40.0 45.00  50 38.01351  9.945734   74
## 7      Czech Republic  10 11.00   16.0 26.25  50 20.11702 10.675936  376
## 8             Finland  10 10.00   14.0 24.00  50 18.54187  9.980772  203
## 9              France  10 10.00   16.0 30.00  50 21.06175 12.308409  664
## 10            Germany  10 13.00   23.0 33.00  50 24.35802 12.032372  972
## 11             Greece  10 32.00   40.0 47.00  50 37.75163 10.685874  153
## 12            Hungary  10 15.00   29.0 40.50  50 28.03865 13.758262  207
## 13              Italy  10 13.00   25.0 35.00  50 25.18210 11.841855  648
## 14             Latvia  10 21.00   33.0 42.00  50 31.54651 12.382932  172
## 15        Netherlands  10 10.00   13.0 19.00  50 17.10698 10.230112  430
## 16             Poland  10 13.50   29.0 41.00  50 28.41531 14.262217  431
## 17           Portugal  10 14.50   29.0 37.00  50 27.83453 12.809981  139
## 18            Romania  10 25.00   36.0 45.00  50 33.67736 12.603506  592
## 19             Serbia  10 20.00   33.0 41.00  50 30.96067 12.747874 1068
## 20           Slovakia  10 25.00   37.0 46.00  50 34.48087 13.039602  183
## 21           Slovenia  10 16.00   31.0 40.00  50 29.53333 13.201382  300
## 22              Spain  10 10.00   14.0 27.00  50 20.01961 12.161459  204
## 23             Sweden  10 10.00   13.5 18.00  50 17.56250 10.933634   32
## 24        Switzerland  10 10.00   15.0 26.75  47 18.87879 10.005406   66
## 25             Turkey  10 25.00   40.0 48.00  50 35.25352 13.862301   71
## 26            Ukraine  10 11.50   28.0 37.50  50 27.03614 13.458560   83
##    missing
## 1        4
## 2        7
## 3       67
## 4       18
## 5       44
## 6        8
## 7       24
## 8       11
## 9       84
## 10      77
## 11       8
## 12      23
## 13      85
## 14       4
## 15      14
## 16      29
## 17      10
## 18      83
## 19     178
## 20      13
## 21      22
## 22       8
## 23       2
## 24       2
## 25      14
## 26       8



Correlations

##        perf atevo kaevo
## perf   1.00 -0.37 -0.36
## atevo -0.37  1.00  0.29
## kaevo -0.36  0.29  1.00
## 
## n
##       perf atevo kaevo
## perf  8353  8032  7260
## atevo 8032  8527  7434
## kaevo 7260  7434  7806
## 
## P
##       perf atevo kaevo
## perf        0     0   
## atevo  0          0   
## kaevo  0    0

t-tests (multilevel)

## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: atevo ~ 1 + (1 | country)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  50822.5  50843.7 -25408.3  50816.5     8524 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.4761 -0.6150  0.0675  0.7005  2.1005 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  country  (Intercept)  2.185   1.478   
##  Residual             22.464   4.740   
## Number of obs: 8527, groups:  country, 26
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)  32.2681     0.2987 26.5050     108   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Observations 8527
Dependent variable atevo
Type Mixed effects linear regression
AIC 50822.51
BIC 50843.66
Pseudo-R² (fixed effects) 0.00
Pseudo-R² (total) 0.09
Fixed Effects
Est. S.E. t val. d.f. p
(Intercept) 32.27 0.30 108.02 26.50 0.00
p values calculated using Satterthwaite d.f.
Random Effects
Group Parameter Std. Dev.
country (Intercept) 1.48
Residual 4.74
Grouping Variables
Group # groups ICC
country 26 0.09
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: atevo ~ bio + (1 | country)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  50749.0  50777.2 -25370.5  50741.0     8522 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.4986 -0.5689  0.0890  0.7348  2.1786 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  country  (Intercept)  2.017   1.420   
##  Residual             22.286   4.721   
## Number of obs: 8526, groups:  country, 26
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   32.5896     0.2902   27.3085 112.306   <2e-16 ***
## bioNon-Bio    -1.1049     0.1331 7835.8272  -8.299   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## bioNon-Bio -0.134
Observations 8526
Dependent variable atevo
Type Mixed effects linear regression
AIC 50749.00
BIC 50777.21
Pseudo-R² (fixed effects) 0.01
Pseudo-R² (total) 0.09
Fixed Effects
Est. S.E. t val. d.f. p
(Intercept) 32.59 0.29 112.31 27.31 0.00
bioNon-Bio -1.10 0.13 -8.30 7835.83 0.00
p values calculated using Satterthwaite d.f.
Random Effects
Group Parameter Std. Dev.
country (Intercept) 1.42
Residual 4.72
Grouping Variables
Group # groups ICC
country 26 0.08
Observations 7806
Dependent variable kaevo
Type Mixed effects linear regression
AIC 34285.99
BIC 34306.88
Pseudo-R² (fixed effects) 0.00
Pseudo-R² (total) 0.32
Fixed Effects
Est. S.E. t val. d.f. p
(Intercept) 4.86 0.30 16.44 26.05 0.00
p values calculated using Satterthwaite d.f.
Random Effects
Group Parameter Std. Dev.
country (Intercept) 1.50
Residual 2.16
Grouping Variables
Group # groups ICC
country 26 0.32
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: kaevo ~ bio + (1 | country)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  33934.4  33962.2 -16963.2  33926.4     7801 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3948 -0.7109  0.0490  0.7075  3.6797 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  country  (Intercept) 1.893    1.376   
##  Residual             4.454    2.110   
## Number of obs: 7805, groups:  country, 26
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)    5.20733    0.27262   26.24395   19.10   <2e-16 ***
## bioNon-Bio    -1.18690    0.06271 7799.74431  -18.93   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## bioNon-Bio -0.068
Observations 7805
Dependent variable kaevo
Type Mixed effects linear regression
AIC 33934.37
BIC 33962.22
Pseudo-R² (fixed effects) 0.04
Pseudo-R² (total) 0.33
Fixed Effects
Est. S.E. t val. d.f. p
(Intercept) 5.21 0.27 19.10 26.24 0.00
bioNon-Bio -1.19 0.06 -18.93 7799.74 0.00
p values calculated using Satterthwaite d.f.
Random Effects
Group Parameter Std. Dev.
country (Intercept) 1.38
Residual 2.11
Grouping Variables
Group # groups ICC
country 26 0.30
Observations 8353
Dependent variable perf
Type Mixed effects linear regression
AIC 65837.13
BIC 65858.22
Pseudo-R² (fixed effects) 0.00
Pseudo-R² (total) 0.21
Fixed Effects
Est. S.E. t val. d.f. p
(Intercept) 27.29 1.28 21.35 25.65 0.00
p values calculated using Satterthwaite d.f.
Random Effects
Group Parameter Std. Dev.
country (Intercept) 6.44
Residual 12.37
Grouping Variables
Group # groups ICC
country 26 0.21
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: perf ~ bio + (1 | country)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  65800.7  65828.8 -32896.3  65792.7     8349 
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.34200 -0.81326 -0.07268  0.79230  2.68673 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  country  (Intercept)  38.77    6.226  
##  Residual             152.31   12.341  
## Number of obs: 8353, groups:  country, 26
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   26.6307     1.2404   25.8115  21.470  < 2e-16 ***
## bioNon-Bio     2.1970     0.3537 8296.6646   6.212 5.49e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## bioNon-Bio -0.085
Observations 8353
Dependent variable perf
Type Mixed effects linear regression
AIC 65800.69
BIC 65828.81
Pseudo-R² (fixed effects) 0.01
Pseudo-R² (total) 0.21
Fixed Effects
Est. S.E. t val. d.f. p
(Intercept) 26.63 1.24 21.47 25.81 0.00
bioNon-Bio 2.20 0.35 6.21 8296.66 0.00
p values calculated using Satterthwaite d.f.
Random Effects
Group Parameter Std. Dev.
country (Intercept) 6.23
Residual 12.34
Grouping Variables
Group # groups ICC
country 26 0.20



4. Descriptive Plotting (main scales)

## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)





5. Scale Comparison

##   variable min     Q1   median       Q3 max     mean       sd    n missing
## 1  atevo_t   0 65.625 75.00000 87.50000 100 75.51564 15.43385 8527     673
## 2  kaevo_t   0 25.000 41.66667 58.33333 100 42.17162 21.40858 7806    1394
## 3   perf_t   0  7.500 40.00000 72.50000 100 41.95110 33.98520 8353     847
##               bio min       Q1   median       Q3       max     mean       sd
## 1     atevo_t.Bio   0 68.75000 78.12500 87.50000 100.00000 76.63629 15.19737
## 2     kaevo_t.Bio   0 25.00000 50.00000 66.66667 100.00000 46.10340 21.18488
## 3      perf_t.Bio   0  5.00000 32.50000 65.00000 100.00000 37.78290 33.40850
## 4 atevo_t.Non-Bio   0 62.50000 75.00000 84.37500 100.00000 72.75810 15.65986
## 5 kaevo_t.Non-Bio   0 16.66667 25.00000 41.66667  91.66667 32.08847 18.48697
## 6  perf_t.Non-Bio   0 20.00000 57.50000 80.00000 100.00000 52.04629 33.25012
## 7             Bio   0 30.00000 58.33333 78.12500 100.00000 53.82157 29.71309
## 8         Non-Bio   0 25.00000 57.50000 77.50000 100.00000 53.09847 29.01026
##       n missing
## 1  6056     399
## 2  5616     839
## 3  5912     543
## 4  2470     274
## 5  2189     555
## 6  2441     303
## 7 17584    1781
## 8  7100    1132
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 3 x 2
##   variable     n
##   <fct>    <int>
## 1 atevo_t   8526
## 2 kaevo_t   7805
## 3 perf_t    8353





6. Descriptive analysis for modelling procedure + data cleaning 2.0.

## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'


## `geom_smooth()` using formula 'y ~ x'


## country
##            Austria            Belgium Bosnia-Herzegovina           Bulgaria 
##                 81                340                128                126 
##            Croatia             Cyprus     Czech Republic            Finland 
##                296                  0                328                174 
##             France            Germany             Greece            Hungary 
##                530                786                101                185 
##              Italy             Latvia        Netherlands             Poland 
##                500                134                380                356 
##           Portugal            Romania             Serbia           Slovakia 
##                  0                429                782                165 
##           Slovenia              Spain             Sweden        Switzerland 
##                227                179                  0                  0 
##             Turkey            Ukraine 
##                  0                  0
## `geom_smooth()` using formula 'y ~ x'


## `geom_smooth()` using formula 'y ~ x'


## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'




7. Multilevel Modeling

##  [1] "ID"               "country"          "bio"              "course"          
##  [5] "age"              "sex"              "bio_classes"      "interest_bio"    
##  [9] "meaning_evo"      "learn_evo"        "denomination"     "denom_summarized"
## [13] "perf"             "kaevo"            "atevo"
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: atevo ~ 1 + (1 | country)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  36918.6  36938.8 -18456.3  36912.6     6224 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.6266 -0.5902  0.0607  0.7213  2.1054 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  country  (Intercept)  2.703   1.644   
##  Residual             21.731   4.662   
## Number of obs: 6227, groups:  country, 20
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)  32.5127     0.3746 19.8237    86.8   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Observations 6227
Dependent variable atevo
Type Mixed effects linear regression
AIC 36918.61
BIC 36938.82
Pseudo-R² (fixed effects) 0.00
Pseudo-R² (total) 0.11
Fixed Effects
Est. S.E. t val. d.f. p
(Intercept) 32.51 0.37 86.80 19.82 0.00
p values calculated using Satterthwaite d.f.
Random Effects
Group Parameter Std. Dev.
country (Intercept) 1.64
Residual 4.66
Grouping Variables
Group # groups ICC
country 20 0.11
## ANOVA-like table for random-effects: Single term deletions
## 
## Model:
## atevo ~ (1 | country)
##               npar logLik   AIC    LRT Df Pr(>Chisq)    
## <none>           3 -18456 36919                         
## (1 | country)    2 -18717 37437 520.65  1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1



## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: atevo ~ 1 + age + sex + (1 | country)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  36849.1  36882.8 -18419.6  36839.1     6222 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.5550 -0.5939  0.0765  0.7007  2.1523 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  country  (Intercept)  2.528   1.590   
##  Residual             21.480   4.635   
## Number of obs: 6227, groups:  country, 20
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)   34.18601    1.30595 2098.81982  26.177   <2e-16 ***
## age           -0.04243    0.06532 6218.08557  -0.650    0.516    
## sex2          -1.15224    0.13407 6219.85718  -8.595   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##      (Intr) age   
## age  -0.958       
## sex2 -0.115  0.040
Observations 6227
Dependent variable atevo
Type Mixed effects linear regression
AIC 36849.12
BIC 36882.80
Pseudo-R² (fixed effects) 0.01
Pseudo-R² (total) 0.12
Fixed Effects
Est. S.E. t val. d.f. p
(Intercept) 34.19 1.31 26.18 2098.82 0.00
age -0.04 0.07 -0.65 6218.09 0.52
sex2 -1.15 0.13 -8.59 6219.86 0.00
p values calculated using Satterthwaite d.f.
Random Effects
Group Parameter Std. Dev.
country (Intercept) 1.59
Residual 4.63
Grouping Variables
Group # groups ICC
country 20 0.11
## ANOVA-like table for random-effects: Single term deletions
## 
## Model:
## atevo ~ age + sex + (1 | country)
##               npar logLik   AIC    LRT Df Pr(>Chisq)    
## <none>           5 -18420 36849                         
## (1 | country)    4 -18667 37341 494.02  1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Computing bootstrap confidence intervals ...
## 
## 1 warning(s): Model failed to converge with max|grad| = 0.00367418 (tol = 0.002, component 1)
##                  2.5 %      97.5 %
## .sig01       1.0239220  2.04200414
## .sigma       4.5520494  4.71412205
## (Intercept) 31.6427575 36.72584424
## age         -0.1692713  0.08272452
## sex2        -1.4124695 -0.89325284
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: atevo ~ 1 + age + sex + bio + interest_bio + (1 | country)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  36697.8  36745.0 -18341.9  36683.8     6220 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.4571 -0.5695  0.0787  0.7049  2.4013 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  country  (Intercept)  1.755   1.325   
##  Residual             20.973   4.580   
## Number of obs: 6227, groups:  country, 20
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)    31.72269    1.29933 3047.85176  24.415   <2e-16 ***
## age            -0.03913    0.06451 6188.69672  -0.607   0.5441    
## sex2           -1.15262    0.13245 6223.24678  -8.702   <2e-16 ***
## bioNon-Bio     -0.33401    0.17587 5914.08954  -1.899   0.0576 .  
## interest_bio    0.47233    0.04740 6132.52956   9.964   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) age    sex2   biNn-B
## age         -0.947                     
## sex2        -0.113  0.040              
## bioNon-Bio  -0.093 -0.031 -0.013       
## interest_bi -0.192 -0.015 -0.006  0.485
Observations 6227
Dependent variable atevo
Type Mixed effects linear regression
AIC 36697.82
BIC 36744.97
Pseudo-R² (fixed effects) 0.04
Pseudo-R² (total) 0.11
Fixed Effects
Est. S.E. t val. d.f. p
(Intercept) 31.72 1.30 24.41 3047.85 0.00
age -0.04 0.06 -0.61 6188.70 0.54
sex2 -1.15 0.13 -8.70 6223.25 0.00
bioNon-Bio -0.33 0.18 -1.90 5914.09 0.06
interest_bio 0.47 0.05 9.96 6132.53 0.00
p values calculated using Satterthwaite d.f.
Random Effects
Group Parameter Std. Dev.
country (Intercept) 1.32
Residual 4.58
Grouping Variables
Group # groups ICC
country 20 0.08
## ANOVA-like table for random-effects: Single term deletions
## 
## Model:
## atevo ~ age + sex + bio + interest_bio + (1 | country)
##               npar logLik   AIC    LRT Df Pr(>Chisq)    
## <none>           7 -18342 36698                         
## (1 | country)    6 -18526 37065 368.92  1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Computing bootstrap confidence intervals ...
##                   2.5 %       97.5 %
## .sig01        0.8510339  1.723700208
## .sigma        4.4981034  4.658434123
## (Intercept)  29.1648316 34.263214655
## age          -0.1641135  0.086199328
## sex2         -1.4152628 -0.896742605
## bioNon-Bio   -0.6781684  0.008898831
## interest_bio  0.3775007  0.563078774
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: atevo ~ 1 + age + sex + bio + interest_bio + kaevo + (1 | country)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  36530.0  36583.9 -18257.0  36514.0     6219 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.5085 -0.5680  0.0969  0.7112  2.4597 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  country  (Intercept)  1.022   1.011   
##  Residual             20.440   4.521   
## Number of obs: 6227, groups:  country, 20
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)   2.937e+01  1.279e+00  4.069e+03  22.961  < 2e-16 ***
## age           3.814e-03  6.362e-02  6.073e+03   0.060    0.952    
## sex2         -8.809e-01  1.324e-01  6.225e+03  -6.655 3.08e-11 ***
## bioNon-Bio   -5.953e-02  1.740e-01  5.390e+03  -0.342    0.732    
## interest_bio  3.651e-01  4.745e-02  6.074e+03   7.694 1.65e-14 ***
## kaevo         3.690e-01  2.790e-02  5.569e+03  13.225  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) age    sex2   biNn-B intrs_
## age         -0.953                            
## sex2        -0.132  0.047                     
## bioNon-Bio  -0.111 -0.022  0.006              
## interest_bi -0.165 -0.022 -0.034  0.452       
## kaevo       -0.137  0.051  0.159  0.118 -0.182
Observations 6227
Dependent variable atevo
Type Mixed effects linear regression
AIC 36530.00
BIC 36583.89
Pseudo-R² (fixed effects) 0.08
Pseudo-R² (total) 0.13
Fixed Effects
Est. S.E. t val. d.f. p
(Intercept) 29.37 1.28 22.96 4069.01 0.00
age 0.00 0.06 0.06 6072.91 0.95
sex2 -0.88 0.13 -6.65 6224.57 0.00
bioNon-Bio -0.06 0.17 -0.34 5390.14 0.73
interest_bio 0.37 0.05 7.69 6073.68 0.00
kaevo 0.37 0.03 13.23 5569.16 0.00
p values calculated using Satterthwaite d.f.
Random Effects
Group Parameter Std. Dev.
country (Intercept) 1.01
Residual 4.52
Grouping Variables
Group # groups ICC
country 20 0.05
## ANOVA-like table for random-effects: Single term deletions
## 
## Model:
## atevo ~ age + sex + bio + interest_bio + kaevo + (1 | country)
##               npar logLik   AIC    LRT Df Pr(>Chisq)    
## <none>           8 -18257 36530                         
## (1 | country)    7 -18363 36739 211.12  1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Computing bootstrap confidence intervals ...
##                   2.5 %     97.5 %
## .sig01        0.6304516  1.3177717
## .sigma        4.4400892  4.5977240
## (Intercept)  26.8599124 31.8499461
## age          -0.1175033  0.1277037
## sex2         -1.1395876 -0.6298100
## bioNon-Bio   -0.4031302  0.2784970
## interest_bio  0.2745867  0.4581668
## kaevo         0.3141320  0.4237331
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: atevo ~ 1 + age + sex + bio + interest_bio + kaevo + perf + (1 |  
##     country)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  35920.4  35981.0 -17951.2  35902.4     6218 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.1937 -0.5773  0.0910  0.7042  2.7808 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  country  (Intercept)  0.9824  0.9911  
##  Residual             18.5246  4.3040  
## Number of obs: 6227, groups:  country, 20
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)   3.336e+01  1.229e+00  4.088e+03  27.137  < 2e-16 ***
## age          -9.645e-03  6.059e-02  6.097e+03  -0.159    0.874    
## sex2         -5.937e-01  1.265e-01  6.224e+03  -4.692 2.76e-06 ***
## bioNon-Bio   -3.454e-02  1.657e-01  5.497e+03  -0.208    0.835    
## interest_bio  3.179e-01  4.522e-02  6.101e+03   7.029 2.31e-12 ***
## kaevo         2.420e-01  2.704e-02  5.846e+03   8.949  < 2e-16 ***
## perf         -1.155e-01  4.556e-03  6.188e+03 -25.351  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) age    sex2   biNn-B intrs_ kaevo 
## age         -0.946                                   
## sex2        -0.119  0.046                            
## bioNon-Bio  -0.109 -0.022  0.007                     
## interest_bi -0.169 -0.022 -0.038  0.451              
## kaevo       -0.158  0.052  0.139  0.115 -0.171       
## perf        -0.128  0.009 -0.089 -0.006  0.041  0.185
Observations 6227
Dependent variable atevo
Type Mixed effects linear regression
AIC 35920.38
BIC 35981.01
Pseudo-R² (fixed effects) 0.19
Pseudo-R² (total) 0.23
Fixed Effects
Est. S.E. t val. d.f. p
(Intercept) 33.36 1.23 27.14 4087.66 0.00
age -0.01 0.06 -0.16 6097.50 0.87
sex2 -0.59 0.13 -4.69 6224.23 0.00
bioNon-Bio -0.03 0.17 -0.21 5496.70 0.83
interest_bio 0.32 0.05 7.03 6101.31 0.00
kaevo 0.24 0.03 8.95 5845.95 0.00
perf -0.12 0.00 -25.35 6188.44 0.00
p values calculated using Satterthwaite d.f.
Random Effects
Group Parameter Std. Dev.
country (Intercept) 0.99
Residual 4.30
Grouping Variables
Group # groups ICC
country 20 0.05
## ANOVA-like table for random-effects: Single term deletions
## 
## Model:
## atevo ~ age + sex + bio + interest_bio + kaevo + perf + (1 | country)
##               npar logLik   AIC    LRT Df Pr(>Chisq)    
## <none>           9 -17951 35920                         
## (1 | country)    8 -18079 36174 255.62  1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Computing bootstrap confidence intervals ...
## 
## 1 warning(s): Model failed to converge with max|grad| = 0.00433788 (tol = 0.002, component 1)
##                   2.5 %     97.5 %
## .sig01        0.6197575  1.2935430
## .sigma        4.2271690  4.3782596
## (Intercept)  30.9085716 35.7253763
## age          -0.1283796  0.1120428
## sex2         -0.8418351 -0.3470480
## bioNon-Bio   -0.3625076  0.2911774
## interest_bio  0.2299356  0.4062896
## kaevo         0.1888873  0.2951896
## perf         -0.1244022 -0.1062548
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: 
## atevo ~ 1 + age + sex + bio + interest_bio + kaevo + perf + denom_summarized +  
##     (1 | country)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  35888.9  35989.9 -17929.4  35858.9     6212 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.1024 -0.5887  0.0876  0.7032  3.1290 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  country  (Intercept)  1.039   1.019   
##  Residual             18.392   4.289   
## Number of obs: 6227, groups:  country, 20
## 
## Fixed effects:
##                                           Estimate Std. Error         df
## (Intercept)                              3.312e+01  1.229e+00  3.988e+03
## age                                      3.691e-03  6.043e-02  6.115e+03
## sex2                                    -6.096e-01  1.263e-01  6.224e+03
## bioNon-Bio                              -1.106e-02  1.654e-01  5.482e+03
## interest_bio                             3.193e-01  4.511e-02  6.106e+03
## kaevo                                    2.340e-01  2.706e-02  5.964e+03
## perf                                    -1.132e-01  5.307e-03  6.218e+03
## denom_summarizedProtestant              -6.699e-01  2.505e-01  5.218e+03
## denom_summarizedCatholic                 2.909e-01  1.793e-01  6.043e+03
## denom_summarizedOrthodox                 9.459e-02  2.585e-01  3.064e+03
## denom_summarizedChristian free churches -5.280e-01  2.758e-01  6.206e+03
## denom_summarizedMuslim                  -1.909e+00  4.505e-01  6.217e+03
## denom_summarizedOther                   -2.681e-01  2.589e-01  6.227e+03
##                                         t value Pr(>|t|)    
## (Intercept)                              26.949  < 2e-16 ***
## age                                       0.061  0.95130    
## sex2                                     -4.827 1.42e-06 ***
## bioNon-Bio                               -0.067  0.94672    
## interest_bio                              7.078 1.62e-12 ***
## kaevo                                     8.649  < 2e-16 ***
## perf                                    -21.333  < 2e-16 ***
## denom_summarizedProtestant               -2.674  0.00752 ** 
## denom_summarizedCatholic                  1.623  0.10470    
## denom_summarizedOrthodox                  0.366  0.71446    
## denom_summarizedChristian free churches  -1.915  0.05558 .  
## denom_summarizedMuslim                   -4.237 2.29e-05 ***
## denom_summarizedOther                    -1.036  0.30036    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it
Observations 6227
Dependent variable atevo
Type Mixed effects linear regression
AIC 35888.87
BIC 35989.92
Pseudo-R² (fixed effects) 0.19
Pseudo-R² (total) 0.23
Fixed Effects
Est. S.E. t val. d.f. p
(Intercept) 33.12 1.23 26.95 3988.09 0.00
age 0.00 0.06 0.06 6114.88 0.95
sex2 -0.61 0.13 -4.83 6224.15 0.00
bioNon-Bio -0.01 0.17 -0.07 5482.24 0.95
interest_bio 0.32 0.05 7.08 6106.27 0.00
kaevo 0.23 0.03 8.65 5964.43 0.00
perf -0.11 0.01 -21.33 6217.89 0.00
denom_summarizedProtestant -0.67 0.25 -2.67 5218.29 0.01
denom_summarizedCatholic 0.29 0.18 1.62 6043.22 0.10
denom_summarizedOrthodox 0.09 0.26 0.37 3064.01 0.71
denom_summarizedChristian free churches -0.53 0.28 -1.91 6206.11 0.06
denom_summarizedMuslim -1.91 0.45 -4.24 6216.64 0.00
denom_summarizedOther -0.27 0.26 -1.04 6226.97 0.30
p values calculated using Satterthwaite d.f.
Random Effects
Group Parameter Std. Dev.
country (Intercept) 1.02
Residual 4.29
Grouping Variables
Group # groups ICC
country 20 0.05
## Computing bootstrap confidence intervals ...
##                                               2.5 %      97.5 %
## .sig01                                   0.62994682  1.33271074
## .sigma                                   4.21017585  4.35861310
## (Intercept)                             30.69268959 35.53544916
## age                                     -0.11674080  0.11934902
## sex2                                    -0.85751287 -0.36216954
## bioNon-Bio                              -0.33504931  0.32271115
## interest_bio                             0.23183212  0.40821531
## kaevo                                    0.18059716  0.28628525
## perf                                    -0.12363372 -0.10269157
## denom_summarizedProtestant              -1.17617909 -0.19121693
## denom_summarizedCatholic                -0.06437905  0.64719025
## denom_summarizedOrthodox                -0.41473536  0.60074236
## denom_summarizedChristian free churches -1.06946346 -0.01138611
## denom_summarizedMuslim                  -2.78942688 -1.00405685
## denom_summarizedOther                   -0.77499052  0.23447296
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: atevo ~ 1 + age + sex + bio + interest_bio + kaevo * perf + (1 |  
##     country)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  35874.2  35941.6 -17927.1  35854.2     6217 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.1782 -0.5775  0.0879  0.6961  2.6843 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  country  (Intercept)  0.933   0.9659  
##  Residual             18.384   4.2877  
## Number of obs: 6227, groups:  country, 20
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)   3.163e+01  1.248e+00  4.282e+03  25.344  < 2e-16 ***
## age          -4.318e-03  6.035e-02  6.084e+03  -0.072    0.943    
## sex2         -5.661e-01  1.261e-01  6.225e+03  -4.489 7.29e-06 ***
## bioNon-Bio   -3.421e-02  1.650e-01  5.438e+03  -0.207    0.836    
## interest_bio  3.144e-01  4.505e-02  6.088e+03   6.980 3.26e-12 ***
## kaevo         5.398e-01  5.058e-02  6.194e+03  10.673  < 2e-16 ***
## perf         -5.764e-02  9.479e-03  6.215e+03  -6.081 1.27e-09 ***
## kaevo:perf   -1.179e-02  1.696e-03  6.227e+03  -6.952 3.96e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) age    sex2   biNn-B intrs_ kaevo  perf  
## age         -0.930                                          
## sex2        -0.123  0.046                                   
## bioNon-Bio  -0.107 -0.022  0.007                            
## interest_bi -0.163 -0.022 -0.038  0.451                     
## kaevo       -0.250  0.038  0.101  0.060 -0.101              
## perf        -0.234  0.015 -0.015 -0.004  0.009  0.790       
## kaevo:perf   0.198 -0.013 -0.032  0.001  0.012 -0.846 -0.878
Observations 6227
Dependent variable atevo
Type Mixed effects linear regression
AIC 35874.25
BIC 35941.61
Pseudo-R² (fixed effects) 0.19
Pseudo-R² (total) 0.23
Fixed Effects
Est. S.E. t val. d.f. p
(Intercept) 31.63 1.25 25.34 4282.36 0.00
age -0.00 0.06 -0.07 6083.92 0.94
sex2 -0.57 0.13 -4.49 6224.65 0.00
bioNon-Bio -0.03 0.17 -0.21 5438.11 0.84
interest_bio 0.31 0.05 6.98 6088.16 0.00
kaevo 0.54 0.05 10.67 6193.81 0.00
perf -0.06 0.01 -6.08 6214.85 0.00
kaevo:perf -0.01 0.00 -6.95 6226.99 0.00
p values calculated using Satterthwaite d.f.
Random Effects
Group Parameter Std. Dev.
country (Intercept) 0.97
Residual 4.29
Grouping Variables
Group # groups ICC
country 20 0.05
## ANOVA-like table for random-effects: Single term deletions
## 
## Model:
## atevo ~ age + sex + bio + interest_bio + kaevo + perf + (1 | country) + kaevo:perf
##               npar logLik   AIC    LRT Df Pr(>Chisq)    
## <none>          10 -17927 35874                         
## (1 | country)    9 -18048 36114 242.03  1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Modelselection via LRT

## Data: data
## Models:
## model_0: atevo ~ 1 + (1 | country)
## model_1: atevo ~ 1 + age + sex + (1 | country)
## model_2: atevo ~ 1 + age + sex + bio + interest_bio + (1 | country)
## model_3: atevo ~ 1 + age + sex + bio + interest_bio + kaevo + (1 | country)
## model_4: atevo ~ 1 + age + sex + bio + interest_bio + kaevo + perf + (1 | 
## model_4:     country)
## model_4_denom: atevo ~ 1 + age + sex + bio + interest_bio + kaevo + perf + denom_summarized + 
## model_4_denom:     (1 | country)
##               npar   AIC   BIC logLik deviance   Chisq Df Pr(>Chisq)    
## model_0          3 36919 36939 -18456    36913                          
## model_1          5 36849 36883 -18420    36839  73.496  2  < 2.2e-16 ***
## model_2          7 36698 36745 -18342    36684 155.304  2  < 2.2e-16 ***
## model_3          8 36530 36584 -18257    36514 169.814  1  < 2.2e-16 ***
## model_4          9 35920 35981 -17951    35902 611.620  1  < 2.2e-16 ***
## model_4_denom   15 35889 35990 -17929    35859  43.508  6  9.251e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Multicollinearity and model diagnostics

##          age          sex          bio interest_bio        kaevo         perf 
##     1.005060     1.035916     1.326216     1.353695     1.159215     1.061456
## [[1]]
## `geom_smooth()` using formula 'y ~ x'

## 
## [[2]]
## [[2]]$country
## `geom_smooth()` using formula 'y ~ x'

## 
## 
## [[3]]

## 
## [[4]]
## `geom_smooth()` using formula 'y ~ x'


Robust Alternative

## Robust linear mixed model fit by DAStau 
## Formula: atevo ~ 1 + age + sex + bio + interest_bio + perf + kaevo + (1 |      country) 
##    Data: data 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.6161 -0.6589  0.0410  0.6782  2.8410 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  country  (Intercept)  0.7581  0.8707  
##  Residual             16.8672  4.1070  
## Number of obs: 6227, groups: country, 20
## 
## Fixed effects:
##               Estimate Std. Error t value
## (Intercept)  33.558996   1.199061  27.988
## age          -0.025343   0.059239  -0.428
## sex2         -0.719781   0.123813  -5.813
## bioNon-Bio    0.036723   0.161893   0.227
## interest_bio  0.362012   0.044219   8.187
## perf         -0.106058   0.004456 -23.799
## kaevo         0.253523   0.026428   9.593
## 
## Correlation of Fixed Effects:
##             (Intr) age    sex2   biNn-B intrs_ perf  
## age         -0.948                                   
## sex2        -0.119  0.045                            
## bioNon-Bio  -0.110 -0.021  0.007                     
## interest_bi -0.170 -0.021 -0.038  0.451              
## perf        -0.128  0.009 -0.089 -0.007  0.041       
## kaevo       -0.158  0.052  0.139  0.115 -0.172  0.186
## 
## Robustness weights for the residuals: 
##  4977 weights are ~= 1. The remaining 1250 ones are summarized as
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.239   0.687   0.829   0.787   0.938   0.999 
## 
## Robustness weights for the random effects: 
##  16 weights are ~= 1. The remaining 4 ones are
##     6     7     8    15 
## 0.431 0.982 0.494 0.946 
## 
## Rho functions used for fitting:
##   Residuals:
##     eff: smoothed Huber (k = 1.345, s = 10) 
##     sig: smoothed Huber, Proposal II (k = 1.345, s = 10) 
##   Random Effects, variance component 1 (country):
##     eff: smoothed Huber (k = 1.345, s = 10) 
##     vcp: smoothed Huber, Proposal II (k = 1.345, s = 10)
##                   2.5 %      97.5 %
## (Intercept)  31.2088804 35.90911132
## age          -0.1414495  0.09076394
## sex2         -0.9624502 -0.47711181
## bioNon-Bio   -0.2805809  0.35402705
## interest_bio  0.2753449  0.44867851
## perf         -0.1147921 -0.09732328
## kaevo         0.2017256  0.30532033


Additional tests: Random Intercept + Random Slope

## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: atevo ~ 1 + age_s + sex + bio + interest_bio_s + perf_s + kaevo_s +  
##     (perf_s | country)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  35773.7  35847.8 -17875.8  35751.7     6216 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.2080 -0.5857  0.0802  0.6832  2.8368 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr
##  country  (Intercept)  1.0139  1.0069       
##           perf_s       0.9503  0.9749   0.12
##  Residual             17.9332  4.2348       
## Number of obs: 6227, groups:  country, 20
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     3.293e+01  2.565e-01  2.803e+01 128.381  < 2e-16 ***
## age_s           7.171e-03  5.863e-02  6.107e+03   0.122    0.903    
## sex2           -6.304e-01  1.250e-01  6.208e+03  -5.045 4.67e-07 ***
## bioNon-Bio     -1.225e-01  1.643e-01  5.607e+03  -0.746    0.456    
## interest_bio_s  4.699e-01  6.799e-02  6.054e+03   6.911 5.30e-12 ***
## perf_s         -1.537e+00  2.296e-01  1.964e+01  -6.696 1.78e-06 ***
## kaevo_s         6.180e-01  6.845e-02  5.825e+03   9.030  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) age_s  sex2   biNn-B intr__ perf_s
## age_s       -0.016                                   
## sex2        -0.360  0.045                            
## bioNon-Bio  -0.142 -0.025  0.009                     
## interst_b_s -0.051 -0.022 -0.035  0.452              
## perf_s       0.103  0.006 -0.022 -0.004  0.008       
## kaevo_s     -0.050  0.052  0.140  0.114 -0.167  0.045
Observations 6227
Dependent variable atevo
Type Mixed effects linear regression
AIC 35773.66
BIC 35847.77
Pseudo-R² (fixed effects) 0.18
Pseudo-R² (total) 0.26
Fixed Effects
Est. S.E. t val. d.f. p
(Intercept) 32.93 0.26 128.38 28.03 0.00
age_s 0.01 0.06 0.12 6106.59 0.90
sex2 -0.63 0.12 -5.04 6207.64 0.00
bioNon-Bio -0.12 0.16 -0.75 5607.39 0.46
interest_bio_s 0.47 0.07 6.91 6053.94 0.00
perf_s -1.54 0.23 -6.70 19.64 0.00
kaevo_s 0.62 0.07 9.03 5825.38 0.00
p values calculated using Satterthwaite d.f.
Random Effects
Group Parameter Std. Dev.
country (Intercept) 1.01
country perf_s 0.97
Residual 4.23
Grouping Variables
Group # groups ICC
country 20 0.05
## [[1]]
## `geom_smooth()` using formula 'y ~ x'

## 
## [[2]]
## [[2]]$country
## `geom_smooth()` using formula 'y ~ x'

## 
## 
## [[3]]

## 
## [[4]]
## `geom_smooth()` using formula 'y ~ x'

## Data: data
## Models:
## model_0: atevo ~ 1 + (1 | country)
## model_1: atevo ~ 1 + age + sex + (1 | country)
## model_2: atevo ~ 1 + age + sex + bio + interest_bio + (1 | country)
## model_3: atevo ~ 1 + age + sex + bio + interest_bio + kaevo + (1 | country)
## model_4: atevo ~ 1 + age + sex + bio + interest_bio + kaevo + perf + (1 | 
## model_4:     country)
## model_4_rs: atevo ~ 1 + age_s + sex + bio + interest_bio_s + perf_s + kaevo_s + 
## model_4_rs:     (perf_s | country)
##            npar   AIC   BIC logLik deviance   Chisq Df Pr(>Chisq)    
## model_0       3 36919 36939 -18456    36913                          
## model_1       5 36849 36883 -18420    36839  73.496  2  < 2.2e-16 ***
## model_2       7 36698 36745 -18342    36684 155.304  2  < 2.2e-16 ***
## model_3       8 36530 36584 -18257    36514 169.814  1  < 2.2e-16 ***
## model_4       9 35920 35981 -17951    35902 611.620  1  < 2.2e-16 ***
## model_4_rs   11 35774 35848 -17876    35752 150.717  2  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
## 
## 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] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] ggpubr_0.4.0      glmmTMB_1.0.2.1   effects_4.2-0     car_3.0-9        
##  [5] carData_3.0-4     reshape2_1.4.4    Hmisc_4.4-2       Formula_1.2-4    
##  [9] survival_3.1-12   robustlmm_2.3     jtools_2.1.0      lmerTest_3.1-3   
## [13] lme4_1.1-23       semPlot_1.1.2     lavaanPlot_0.5.1  lavaan_0.6-7     
## [17] sjPlot_2.8.4      psych_2.0.8       mice_3.11.0       VIM_6.0.0        
## [21] colorspace_1.4-1  EnvStats_2.3.1    ggrepel_0.8.2     naniar_0.6.0     
## [25] plyr_1.8.6        plotly_4.9.2.1    mosaic_1.8.2      ggridges_0.5.2   
## [29] mosaicData_0.20.1 ggformula_0.9.4   ggstance_0.3.4    Matrix_1.2-18    
## [33] lattice_0.20-41   forcats_0.5.0     stringr_1.4.0     dplyr_1.0.2      
## [37] purrr_0.3.4       readr_1.3.1       tidyr_1.1.2       tibble_3.0.3     
## [41] ggplot2_3.3.2     tidyverse_1.3.0   openxlsx_4.2.2   
## 
## loaded via a namespace (and not attached):
##   [1] estimability_1.3    coda_0.19-3         visdat_0.5.3       
##   [4] knitr_1.29          data.table_1.13.0   rpart_4.1-15       
##   [7] generics_0.0.2      leaflet_2.0.3       cowplot_1.1.0      
##  [10] DiagrammeR_1.0.6.1  webshot_0.5.2       xml2_1.3.2         
##  [13] lubridate_1.7.9     assertthat_0.2.1    d3Network_0.5.2.1  
##  [16] xfun_0.17           hms_0.5.3           evaluate_0.14      
##  [19] DEoptimR_1.0-8      fansi_0.4.1         dbplyr_1.4.4       
##  [22] readxl_1.3.1        igraph_1.2.5        DBI_1.1.0          
##  [25] tmvnsim_1.0-2       Rsolnp_1.16         htmlwidgets_1.5.1  
##  [28] stats4_4.0.2        ellipsis_0.3.1      crosstalk_1.1.0.1  
##  [31] backports_1.1.10    pbivnorm_0.6.0      insight_0.9.5      
##  [34] survey_4.0          vctrs_0.3.4         sjlabelled_1.1.6   
##  [37] abind_1.4-5         withr_2.2.0         ggforce_0.3.2      
##  [40] robustbase_0.93-6   checkmate_2.0.0     emmeans_1.5.1      
##  [43] vcd_1.4-8           fdrtool_1.2.15      fastGHQuad_1.0     
##  [46] mnormt_2.0.2        cluster_2.1.0       mi_1.0             
##  [49] lazyeval_0.2.2      laeken_0.5.1        crayon_1.3.4       
##  [52] pkgconfig_2.0.3     labeling_0.3        tweenr_1.0.1       
##  [55] nlme_3.1-148        nnet_7.3-14         rlang_0.4.7        
##  [58] lifecycle_0.2.0     kutils_1.70         modelr_0.1.8       
##  [61] cellranger_1.1.0    polyclip_1.10-0     lmtest_0.9-38      
##  [64] regsem_1.6.2        boot_1.3-25         zoo_1.8-8          
##  [67] reprex_0.3.0        base64enc_0.1-3     whisker_0.4        
##  [70] png_0.1-7           viridisLite_0.3.0   rjson_0.2.20       
##  [73] parameters_0.8.5    visNetwork_2.0.9    pander_0.6.3       
##  [76] blob_1.2.1          arm_1.11-2          jpeg_0.1-8.1       
##  [79] rockchalk_1.8.144   rstatix_0.6.0       ggeffects_0.16.0   
##  [82] ggsignif_0.6.0      scales_1.1.1        magrittr_1.5       
##  [85] compiler_4.0.2      kableExtra_1.3.1    RColorBrewer_1.1-2 
##  [88] cli_2.0.2           pbapply_1.4-3       TMB_1.7.18         
##  [91] htmlTable_2.1.0     mgcv_1.8-31         MASS_7.3-51.6      
##  [94] tidyselect_1.1.0    stringi_1.5.3       lisrelToR_0.1.4    
##  [97] sem_3.1-11          mitools_2.4         yaml_2.2.1         
## [100] OpenMx_2.18.1       latticeExtra_0.6-29 tools_4.0.2        
## [103] parallel_4.0.2      rio_0.5.16          matrixcalc_1.0-3   
## [106] rstudioapi_0.11     foreign_0.8-80      gridExtra_2.3      
## [109] farver_2.0.3        BDgraph_2.63        digest_0.6.25      
## [112] Rcpp_1.0.5          broom_0.7.0         performance_0.5.0  
## [115] httr_1.4.2          ggdendro_0.1.22     effectsize_0.3.3   
## [118] sjstats_0.18.0      rvest_0.3.6         XML_3.99-0.5       
## [121] fs_1.5.0            ranger_0.12.1       truncnorm_1.0-8    
## [124] splines_4.0.2       statmod_1.4.34      sp_1.4-2           
## [127] xtable_1.8-4        jsonlite_1.7.1      nloptr_1.2.2.2     
## [130] corpcor_1.6.9       glasso_1.11         R6_2.4.1           
## [133] pillar_1.4.6        htmltools_0.5.0     glue_1.4.2         
## [136] minqa_1.2.4         class_7.3-17        codetools_0.2-16   
## [139] mvtnorm_1.1-1       utf8_1.1.4          numDeriv_2016.8-1.1
## [142] pbkrtest_0.4-8.6    huge_1.3.4.1        curl_4.3           
## [145] gtools_3.8.2        zip_2.1.1           rmarkdown_2.3      
## [148] qgraph_1.6.5        munsell_0.5.0       e1071_1.7-3        
## [151] sjmisc_2.8.5        haven_2.3.1         mosaicCore_0.8.0   
## [154] gtable_0.3.0        bayestestR_0.7.2