Are universities the same?

## # A tibble: 3 × 6
##     uni     n pre_median priming_median intervention_median post_median
##   <int> <int>      <dbl>          <dbl>               <dbl>       <dbl>
## 1     1    59          5              3                   0           0
## 2     2    69          6              4                   0           0
## 3     3   333          6              4                   0           0
## # A tibble: 3 × 6
##     uni     n pre_IQR priming_IQR intervention_IQR post_IQR
##   <int> <int>   <dbl>       <dbl>            <dbl>    <dbl>
## 1     1    59       4           1                0        0
## 2     2    69       3           1                0        1
## 3     3   333       2           2                0        0
## # A tibble: 1 × 6
##   .y.       n statistic    df         p method        
## * <chr> <int>     <dbl> <int>     <dbl> <chr>         
## 1 pre     461      22.8     2 0.0000109 Kruskal-Wallis
## # A tibble: 1 × 6
##   .y.         n statistic    df      p method        
## * <chr>   <int>     <dbl> <int>  <dbl> <chr>         
## 1 priming   461      5.19     2 0.0747 Kruskal-Wallis
## # A tibble: 1 × 6
##   .y.              n statistic    df            p method        
## * <chr>        <int>     <dbl> <int>        <dbl> <chr>         
## 1 intervention   461      36.6     2 0.0000000114 Kruskal-Wallis
## # A tibble: 1 × 6
##   .y.       n statistic    df        p method        
## * <chr> <int>     <dbl> <int>    <dbl> <chr>         
## 1 post    461      76.4     2 2.53e-17 Kruskal-Wallis
## # A tibble: 3 × 9
##   .y.   group1 group2    n1    n2 statistic         p     p.adj p.adj.signif
## * <chr> <chr>  <chr>  <int> <int>     <dbl>     <dbl>     <dbl> <chr>       
## 1 pre   1      2         59    69      1.29 0.199     0.199     ns          
## 2 pre   1      3         59   333      4.26 0.0000205 0.0000614 ****        
## 3 pre   2      3         69   333      2.83 0.00472   0.00945   **
## # A tibble: 3 × 9
##   .y.     group1 group2    n1    n2 statistic      p  p.adj p.adj.signif
## * <chr>   <chr>  <chr>  <int> <int>     <dbl>  <dbl>  <dbl> <chr>       
## 1 priming 1      2         59    69     2.16  0.0307 0.0922 ns          
## 2 priming 1      3         59   333     0.930 0.352  0.352  ns          
## 3 priming 2      3         69   333    -1.90  0.0570 0.114  ns
## # A tibble: 3 × 9
##   .y.          group1 group2    n1    n2 statistic            p    p.adj p.adj…¹
## * <chr>        <chr>  <chr>  <int> <int>     <dbl>        <dbl>    <dbl> <chr>  
## 1 intervention 1      2         59    69      1.24 0.217         2.17e-1 ns     
## 2 intervention 1      3         59   333     -3.52 0.000427      8.55e-4 ***    
## 3 intervention 2      3         69   333     -5.42 0.0000000601  1.80e-7 ****   
## # … with abbreviated variable name ¹​p.adj.signif
## # A tibble: 3 × 9
##   .y.   group1 group2    n1    n2 statistic        p    p.adj p.adj.signif
## * <chr> <chr>  <chr>  <int> <int>     <dbl>    <dbl>    <dbl> <chr>       
## 1 post  1      2         59    69      1.84 6.56e- 2 6.56e- 2 ns          
## 2 post  1      3         59   333     -5.05 4.50e- 7 9.00e- 7 ****        
## 3 post  2      3         69   333     -7.86 3.92e-15 1.18e-14 ****

Participants

## [1] 461
## # A tibble: 3 × 2
##     uni     n
##   <int> <int>
## 1     1    59
## 2     2    69
## 3     3   333
## # A tibble: 3 × 2
##   group     n
##   <int> <int>
## 1     1   156
## 2     2   145
## 3     3   160

Descriptives

Normality checks

## [1] "pre-test"

## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?

## 
##  Shapiro-Wilk normality test
## 
## data:  df$pre
## W = 0.88707, p-value < 2.2e-16
## [1] "priming-test"

## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?

## 
##  Shapiro-Wilk normality test
## 
## data:  df$priming
## W = 0.70067, p-value < 2.2e-16
## [1] "intervention-test"

## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?

## 
##  Shapiro-Wilk normality test
## 
## data:  df$intervention
## W = 0.18199, p-value < 2.2e-16
## [1] "post-test"

## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?

## 
##  Shapiro-Wilk normality test
## 
## data:  df$post
## W = 0.29061, p-value < 2.2e-16
##   median iqrL iqrH
## 1      6    5    7
## # A tibble: 5 × 3
##   priming     n percent
##     <int> <int>   <dbl>
## 1       0    25     5.4
## 2       1    39     8.5
## 3       2    54    11.7
## 4       3    60    13  
## 5       4   283    61.4
## # A tibble: 4 × 3
##   intervention     n percent
##          <int> <int>   <dbl>
## 1            0   443    96.1
## 2            1     9     2  
## 3            2     7     1.5
## 4            3     2     0.4
## [1] 0.01952278
## [1] 0.9219089

Group differences

## # A tibble: 3 × 6
##   group     n pre_median priming_median intervention_median post_median
##   <int> <int>      <dbl>          <dbl>               <dbl>       <dbl>
## 1     1   156          6              4                   0           0
## 2     2   145          6              4                   0           0
## 3     3   160          6              4                   0           0
## # A tibble: 3 × 6
##   group     n pre_IQR priming_IQR intervention_IQR post_IQR
##   <int> <int>   <dbl>       <dbl>            <dbl>    <dbl>
## 1     1   156       2           1                0        0
## 2     2   145       3           2                0        0
## 3     3   160       2           1                0        0
## # A tibble: 1 × 6
##   .y.       n statistic    df     p method        
## * <chr> <int>     <dbl> <int> <dbl> <chr>         
## 1 pre     461      2.01     2 0.367 Kruskal-Wallis
## # A tibble: 1 × 6
##   .y.       n statistic    df     p method        
## * <chr> <int>     <dbl> <int> <dbl> <chr>         
## 1 post    461      1.07     2 0.586 Kruskal-Wallis

Intervention misconception answers

## [1] 1529
## [1] 287
## [1] 1816
## [1] 0.8419604
## `summarise()` has grouped output by 'group'. You can override using the
## `.groups` argument.

## # A tibble: 4 × 2
##   int_misc_count  post
##            <int> <int>
## 1              1     8
## 2              2    14
## 3              3     9
## 4              4    38

Post-test misconception answers

## `summarise()` has grouped output by 'group'. You can override using the
## `.groups` argument.
## [1] 1010
## [1] 305
## [1] 1315
## [1] 0.7680608
## # A tibble: 1 × 6
##       n statistic           p    df method          p.signif
## * <int>     <dbl>       <dbl> <int> <chr>           <chr>   
## 1  3131      26.7 0.000000239     1 Chi-square test ****

## # A tibble: 3 × 2
##   group  mean
##   <int> <dbl>
## 1     1  1.02
## 2     2  1.08
## 3     3  1.27

Post-score counts by group

## `summarise()` has grouped output by 'post'. You can override using the
## `.groups` argument.

Comment coding analysis

## # A tibble: 4 × 3
##    post     n  perc
##   <int> <int> <dbl>
## 1     0    18 56.2 
## 2     1     3  9.38
## 3     2     5 15.6 
## 4     3     6 18.8
## # A tibble: 4 × 3
##   post_misc_count     n  perc
##             <int> <int> <dbl>
## 1               0    11  34.4
## 2               1     5  15.6
## 3               2     5  15.6
## 4               3    11  34.4
## # A tibble: 4 × 3
##    post     n  perc
##   <int> <int> <dbl>
## 1     0   407   Inf
## 2     1     9   Inf
## 3     2    10   Inf
## 4     3     3   Inf
## # A tibble: 1 × 7
##   .y.   group1 group2    n1    n2 statistic        p
## * <chr> <chr>  <chr>  <int> <int>     <dbl>    <dbl>
## 1 post  0      1        429    32     4160. 1.32e-15
## # A tibble: 3 × 3
##   group     n  perc
##   <int> <int> <dbl>
## 1     1    11  34.4
## 2     2     9  28.1
## 3     3    12  37.5
## # A tibble: 3 × 3
##   intervention     n  perc
##          <int> <int> <dbl>
## 1            0    26 81.2 
## 2            1     2  6.25
## 3            2     4 12.5
## # A tibble: 1 × 3
##    post     n  perc
##   <int> <int> <dbl>
## 1     0     9   100
## # A tibble: 3 × 3
##   group     n  perc
##   <int> <int> <dbl>
## 1     1     5 1.11 
## 2     2     2 0.442
## 3     3     2 0.442
## # A tibble: 1 × 7
##   .y.   group1 group2    n1    n2 statistic     p
## * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>
## 1 post  0      1        452     9      2196  0.38
## # A tibble: 1 × 3
##    post     n  perc
##   <int> <int> <dbl>
## 1     0    38   100
## # A tibble: 2 × 3
##   post_misc_count     n  perc
##             <int> <int> <dbl>
## 1               2     8  21.1
## 2               3    30  78.9
## # A tibble: 3 × 3
##   group     n  perc
##   <int> <int> <dbl>
## 1     1    13  34.2
## 2     2    17  44.7
## 3     3     8  21.1