Going over filters
Only 201 out of 400 participants passed our pre-registered exclusion criteria, which was unexpected. Below is the breakdown of how many people missed each question. It looks like the biggest culprit was the attention check. This may be because we launched this friday afternoon.
Columns are the manipulation check. 0 means participant failed. 1 means the participant passed.
Racial vs. Moral Manip Check.
## rac_mor_manip
## cond_label 0 1
## Moral 3 193
## Racial 62 141Amount Manip Check.
## amt_manip
## cond_amt_label 1 2 3
## 1. Low Diversity - Majority 127 5 3
## 2. Low Diversity - Minority 9 114 10
## 3. High Diversity 10 6 117NZ/AUS res.
## amt_manip
## cond_amt_label 1 2 3
## 1. Low Diversity - Majority 127 5 3
## 2. Low Diversity - Minority 9 114 10
## 3. High Diversity 10 6 117Attn_check (6 is the right answer)
## attn_chk
## 1 2 3 4 6
## 2 14 54 3 328Main Effects
Means and SDs
With pre-registered exclusions
## $Tightness
## cond_label
## 1 Moral Racial
## 2 cond_amt_label M SD M SD
## 3 1. Low Diversity - Majority 5.06 0.76 4.91 0.74
## 4 2. Low Diversity - Minority 4.61 0.94 4.73 0.41
## 5 3. High Diversity 3.95 0.85 4.39 0.70
##
## $`NormVio.\nAccept.`
## cond_label
## 1 Moral Racial
## 2 cond_amt_label M SD M SD
## 3 1. Low Diversity - Majority 1.85 0.98 1.85 0.87
## 4 2. Low Diversity - Minority 2.86 1.51 1.79 0.55
## 5 3. High Diversity 2.56 1.05 1.74 0.77
##
## $Punish
## cond_label
## 1 Moral Racial
## 2 cond_amt_label M SD M SD
## 3 1. Low Diversity - Majority 4.30 0.47 4.48 0.50
## 4 2. Low Diversity - Minority 3.60 0.97 4.23 0.56
## 5 3. High Diversity 3.72 0.63 4.08 0.77
##
## $OCBs
## cond_label
## 1 Moral Racial
## 2 cond_amt_label M SD M SD
## 3 1. Low Diversity - Majority 2.31 0.71 2.42 0.63
## 4 2. Low Diversity - Minority 2.21 0.65 2.10 0.35
## 5 3. High Diversity 2.30 0.58 2.18 0.81Full dataset
## $Tightness
## cond_label
## 1 Moral Racial
## 2 cond_amt_label M SD M SD
## 3 1. Low Diversity - Majority 5.03 0.76 4.86 0.71
## 4 2. Low Diversity - Minority 4.61 0.89 4.85 0.61
## 5 3. High Diversity 4.13 0.85 4.54 0.78
##
## $`NormVio.\nAccept.`
## cond_label
## 1 Moral Racial
## 2 cond_amt_label M SD M SD
## 3 1. Low Diversity - Majority 1.91 1.01 1.83 0.83
## 4 2. Low Diversity - Minority 2.71 1.39 2.01 0.95
## 5 3. High Diversity 2.52 1.15 1.88 1.02
##
## $Punish
## cond_label
## 1 Moral Racial
## 2 cond_amt_label M SD M SD
## 3 1. Low Diversity - Majority 4.28 0.48 4.21 0.66
## 4 2. Low Diversity - Minority 3.70 0.87 4.17 0.58
## 5 3. High Diversity 3.81 0.68 4.11 0.75
##
## $OCBs
## cond_label
## 1 Moral Racial
## 2 cond_amt_label M SD M SD
## 3 1. Low Diversity - Majority 2.34 0.76 2.35 0.73
## 4 2. Low Diversity - Minority 2.20 0.66 2.17 0.64
## 5 3. High Diversity 2.30 0.63 2.19 0.72Anovas
With pre-registered exclusions
## [1] "Tightness"## Df Sum Sq Mean Sq F value Pr(>F)
## cond_label 1 0.05 0.049 0.075 0.784
## cond_amt_label 2 31.21 15.603 24.193 4.12e-10 ***
## cond_label:cond_amt_label 2 2.10 1.048 1.626 0.199
## Residuals 195 125.76 0.645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "Norm violation acceptance"## Df Sum Sq Mean Sq F value Pr(>F)
## cond_label 1 12.41 12.414 10.085 0.00174 **
## cond_amt_label 2 23.30 11.648 9.462 0.00012 ***
## cond_label:cond_amt_label 2 6.78 3.388 2.752 0.06628 .
## Residuals 195 240.05 1.231
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "Punishment"## Df Sum Sq Mean Sq F value Pr(>F)
## cond_label 1 3.81 3.813 7.863 0.00556 **
## cond_amt_label 2 15.92 7.962 16.421 2.57e-07 ***
## cond_label:cond_amt_label 2 1.11 0.557 1.148 0.31936
## Residuals 195 94.55 0.485
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "OCB"## Df Sum Sq Mean Sq F value Pr(>F)
## cond_label 1 0.10 0.0966 0.225 0.636
## cond_amt_label 2 0.74 0.3676 0.856 0.426
## cond_label:cond_amt_label 2 0.38 0.1915 0.446 0.641
## Residuals 195 83.74 0.4294With full dataset
## [1] "Tightness"## Df Sum Sq Mean Sq F value Pr(>F)
## cond_label 1 1.51 1.510 2.522 0.11309
## cond_amt_label 2 24.77 12.387 20.688 2.85e-09 ***
## cond_label:cond_amt_label 2 5.71 2.853 4.765 0.00902 **
## Residuals 395 236.50 0.599
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "norm violation acceptance"## Df Sum Sq Mean Sq F value Pr(>F)
## cond_label 1 22.4 22.355 19.342 1.41e-05 ***
## cond_amt_label 2 17.5 8.735 7.557 0.000601 ***
## cond_label:cond_amt_label 2 7.7 3.854 3.334 0.036645 *
## Residuals 395 456.5 1.156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "Punishment"## Df Sum Sq Mean Sq F value Pr(>F)
## cond_label 1 4.99 4.993 10.589 0.001235 **
## cond_amt_label 2 8.44 4.222 8.953 0.000158 ***
## cond_label:cond_amt_label 2 5.12 2.562 5.433 0.004702 **
## Residuals 395 186.27 0.472
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "OCB"## Df Sum Sq Mean Sq F value Pr(>F)
## cond_label 1 0.19 0.1922 0.401 0.527
## cond_amt_label 2 1.84 0.9175 1.915 0.149
## cond_label:cond_amt_label 2 0.24 0.1208 0.252 0.777
## Residuals 395 189.22 0.4790Graphs
With pre-registered exclusions
Full dataset
