Feedback Study
2023-08-29
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 ¹p.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 160Descriptives
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] 0.9219089Group 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-WallisIntervention 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 38Post-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 1.27Post-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