Gender Incivility 1

with(girecall1_clean, table(s_gender_text))
## s_gender_text
##   Man Woman 
##   183   119
with(girecall1_clean, table(pgender_text))
## pgender_text
##   Man Woman 
##   182   120
with(girecall1_clean, table(s_gender_text, pgender_text))
##              pgender_text
## s_gender_text Man Woman
##         Man   140    43
##         Woman  42    77
with(girecall1_clean, table(pgender_text, s_gender_text, Gender))
## , , Gender = 1
## 
##             s_gender_text
## pgender_text Man Woman
##        Man   100    13
##        Woman  18    23
## 
## , , Gender = 2
## 
##             s_gender_text
## pgender_text Man Woman
##        Man    39    26
##        Woman  24    53
## 
## , , Gender = 3
## 
##             s_gender_text
## pgender_text Man Woman
##        Man     1     2
##        Woman   1     1

Analyses

Main effects and scale information

Graphs

Full scale measures

Controls

Full list

## $`LMX-Aff`
## 
## Call:
## lm(formula = formula_list[[variable]], data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3693 -0.9770 -0.0019  1.0879  5.0111 
## 
## Coefficients: (1 not defined because of singularities)
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         4.372855   0.430375  10.161  < 2e-16 ***
## s_gender_textWoman  0.077297   0.229520   0.337  0.73655    
## partgenderWoman    -0.460707   0.216955  -2.124  0.03463 *  
## pgender_textWoman  -0.222279   0.223899  -0.993  0.32172    
## Age                -0.027428   0.009909  -2.768  0.00603 ** 
## race1,2             0.208918   0.859270   0.243  0.80809    
## race1,2,3           2.976570   1.659593   1.794  0.07402 .  
## race1,4            -0.678004   0.640889  -1.058  0.29105    
## race1,5            -0.648301   0.757447  -0.856  0.39282    
## race1,8            -4.619947   1.668637  -2.769  0.00602 ** 
## race10              0.027372   1.660593   0.016  0.98686    
## race11             -0.342871   1.183880  -0.290  0.77233    
## race2               1.599316   0.310110   5.157 4.89e-07 ***
## race2,3             1.718401   0.521982   3.292  0.00113 ** 
## race2,3,11          2.334377   1.672084   1.396  0.16385    
## race3              -0.153560   0.387119  -0.397  0.69193    
## race3,4             2.768433   1.676381   1.651  0.09983 .  
## race4              -1.017617   0.520068  -1.957  0.05142 .  
## race4,11           -0.381657   1.676464  -0.228  0.82009    
## race4,5            -1.180842   1.673579  -0.706  0.48107    
## race4,8            -1.489207   1.674217  -0.889  0.37454    
## race5               0.115208   0.434519   0.265  0.79111    
## race6              -0.057371   0.847838  -0.068  0.94610    
## race7              -2.722556   1.183692  -2.300  0.02222 *  
## race8               0.388709   1.697480   0.229  0.81905    
## sentperp_1         -0.154646   0.060709  -2.547  0.01142 *  
## sentconf_1          0.641576   0.061470  10.437  < 2e-16 ***
## ladder_partconf     0.003338   0.031454   0.106  0.91556    
## ladder_partperp     0.041179   0.048196   0.854  0.39365    
## ladder_confperp           NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.643 on 267 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.4196, Adjusted R-squared:  0.3587 
## F-statistic: 6.893 on 28 and 267 DF,  p-value: < 2.2e-16
## 
## 
## $Protection
## 
## Call:
## lm(formula = formula_list[[variable]], data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.688 -0.984  0.063  1.218  4.412 
## 
## Coefficients: (1 not defined because of singularities)
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         4.00209    0.46515   8.604  6.7e-16 ***
## s_gender_textWoman  0.05412    0.24806   0.218  0.82747    
## partgenderWoman    -0.34006    0.23448  -1.450  0.14816    
## pgender_textWoman  -0.07900    0.24199  -0.326  0.74433    
## Age                -0.01001    0.01071  -0.935  0.35073    
## race1,2             0.28355    0.92869   0.305  0.76036    
## race1,2,3           2.49549    1.79368   1.391  0.16530    
## race1,4            -0.32987    0.69267  -0.476  0.63430    
## race1,5            -1.26675    0.81864  -1.547  0.12296    
## race1,8            -1.13927    1.80345  -0.632  0.52811    
## race10             -1.46746    1.79476  -0.818  0.41429    
## race11             -0.13529    1.27953  -0.106  0.91587    
## race2               1.30839    0.33516   3.904  0.00012 ***
## race2,3             1.59636    0.56415   2.830  0.00501 ** 
## race2,3,11          1.47314    1.80718   0.815  0.41571    
## race3               0.02071    0.41840   0.050  0.96056    
## race3,4             2.97077    1.81182   1.640  0.10225    
## race4               0.21250    0.56209   0.378  0.70569    
## race4,11           -1.00587    1.81191  -0.555  0.57926    
## race4,5            -1.72696    1.80879  -0.955  0.34056    
## race4,8            -3.51708    1.80948  -1.944  0.05298 .  
## race5              -0.13637    0.46963  -0.290  0.77175    
## race6              -0.01280    0.91634  -0.014  0.98886    
## race7              -2.34996    1.27933  -1.837  0.06734 .  
## race8               1.63891    1.83463   0.893  0.37249    
## sentperp_1         -0.19163    0.06561  -2.921  0.00379 ** 
## sentconf_1          0.61354    0.06644   9.235  < 2e-16 ***
## ladder_partconf    -0.06704    0.03400  -1.972  0.04964 *  
## ladder_partperp     0.08510    0.05209   1.634  0.10350    
## ladder_confperp          NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.776 on 267 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.3713, Adjusted R-squared:  0.3054 
## F-statistic: 5.632 on 28 and 267 DF,  p-value: 4.987e-15
## 
## 
## $Comp
## 
## Call:
## lm(formula = formula_list[[variable]], data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0782 -0.7063  0.1194  1.0289  3.4863 
## 
## Coefficients: (1 not defined because of singularities)
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         4.5576518  0.4041651  11.277  < 2e-16 ***
## s_gender_textWoman  0.1412449  0.2155426   0.655   0.5128    
## partgenderWoman    -0.3285572  0.2037423  -1.613   0.1080    
## pgender_textWoman  -0.0808420  0.2102631  -0.384   0.7009    
## Age                -0.0097531  0.0093051  -1.048   0.2955    
## race1,2            -0.1143003  0.8069398  -0.142   0.8875    
## race1,2,3           2.3901985  1.5585233   1.534   0.1263    
## race1,4            -0.6936861  0.6018585  -1.153   0.2501    
## race1,5            -0.5477053  0.7113186  -0.770   0.4420    
## race1,8            -1.9190541  1.5670161  -1.225   0.2218    
## race10             -0.9184384  1.5594622  -0.589   0.5564    
## race11             -0.7396684  1.1117813  -0.665   0.5064    
## race2               1.1742959  0.2912239   4.032 7.21e-05 ***
## race2,3             1.6176513  0.4901931   3.300   0.0011 ** 
## race2,3,11          1.5230644  1.5702538   0.970   0.3330    
## race3              -0.2772344  0.3635436  -0.763   0.4464    
## race3,4             2.7727769  1.5742892   1.761   0.0793 .  
## race4              -0.1265374  0.4883957  -0.259   0.7958    
## race4,11           -1.7240532  1.5743673  -1.095   0.2745    
## race4,5             0.5862517  1.5716579   0.373   0.7094    
## race4,8            -2.7678703  1.5722564  -1.760   0.0795 .  
## race5              -0.2967149  0.4080569  -0.727   0.4678    
## race6              -0.0668010  0.7962042  -0.084   0.9332    
## race7              -2.8521732  1.1116046  -2.566   0.0108 *  
## race8               0.2572757  1.5941033   0.161   0.8719    
## sentperp_1         -0.0755031  0.0570122  -1.324   0.1865    
## sentconf_1          0.4503935  0.0577265   7.802 1.38e-13 ***
## ladder_partconf    -0.0025696  0.0295385  -0.087   0.9307    
## ladder_partperp     0.0005255  0.0452609   0.012   0.9907    
## ladder_confperp            NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.543 on 267 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.3256, Adjusted R-squared:  0.2549 
## F-statistic: 4.604 on 28 and 267 DF,  p-value: 1.104e-11
## 
## 
## $Warm
## 
## Call:
## lm(formula = formula_list[[variable]], data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5462 -0.8529  0.0000  0.9794  3.5522 
## 
## Coefficients: (1 not defined because of singularities)
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         3.625268   0.414327   8.750 2.47e-16 ***
## s_gender_textWoman  0.137325   0.220962   0.621  0.53481    
## partgenderWoman    -0.635903   0.208865  -3.045  0.00256 ** 
## pgender_textWoman  -0.040061   0.215550  -0.186  0.85270    
## Age                -0.010095   0.009539  -1.058  0.29088    
## race1,2            -1.167791   0.827228  -1.412  0.15921    
## race1,2,3           3.793910   1.597708   2.375  0.01828 *  
## race1,4             0.320392   0.616991   0.519  0.60399    
## race1,5            -1.161788   0.729203  -1.593  0.11229    
## race1,8            -2.464853   1.606414  -1.534  0.12612    
## race10             -0.614417   1.598671  -0.384  0.70104    
## race11             -1.111599   1.139734  -0.975  0.33029    
## race2               1.857733   0.298546   6.223 1.88e-09 ***
## race2,3             2.338036   0.502518   4.653 5.16e-06 ***
## race2,3,11          2.278419   1.609734   1.415  0.15812    
## race3               0.331206   0.372684   0.889  0.37496    
## race3,4             2.366672   1.613870   1.466  0.14370    
## race4              -0.278622   0.500675  -0.556  0.57834    
## race4,11           -0.462189   1.613950  -0.286  0.77482    
## race4,5            -0.397593   1.611173  -0.247  0.80527    
## race4,8            -0.232776   1.611786  -0.144  0.88528    
## race5               0.443520   0.418316   1.060  0.28999    
## race6               0.300871   0.816223   0.369  0.71271    
## race7              -1.809060   1.139553  -1.588  0.11358    
## race8               0.867055   1.634183   0.531  0.59616    
## sentperp_1         -0.179517   0.058446  -3.072  0.00235 ** 
## sentconf_1          0.557338   0.059178   9.418  < 2e-16 ***
## ladder_partconf    -0.018213   0.030281  -0.601  0.54804    
## ladder_partperp     0.044898   0.046399   0.968  0.33409    
## ladder_confperp           NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.582 on 267 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.4302, Adjusted R-squared:  0.3705 
## F-statistic:   7.2 on 28 and 267 DF,  p-value: < 2.2e-16
## 
## 
## $`Virt/Ad`
## 
## Call:
## lm(formula = formula_list[[variable]], data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.459 -1.125  0.000  1.145  5.100 
## 
## Coefficients: (1 not defined because of singularities)
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         3.93079    0.45838   8.575 8.14e-16 ***
## s_gender_textWoman -0.03454    0.24446  -0.141 0.887730    
## partgenderWoman    -0.27206    0.23107  -1.177 0.240086    
## pgender_textWoman  -0.07265    0.23847  -0.305 0.760878    
## Age                -0.02398    0.01055  -2.272 0.023883 *  
## race1,2             0.31748    0.91519   0.347 0.728935    
## race1,2,3           3.50308    1.76759   1.982 0.048523 *  
## race1,4            -0.78461    0.68260  -1.149 0.251399    
## race1,5            -1.09912    0.80674  -1.362 0.174212    
## race1,8            -4.11607    1.77723  -2.316 0.021316 *  
## race10             -0.57800    1.76866  -0.327 0.744075    
## race11             -0.22191    1.26092  -0.176 0.860436    
## race2               2.09180    0.33029   6.333 1.01e-09 ***
## race2,3             1.95043    0.55595   3.508 0.000529 ***
## race2,3,11          2.45521    1.78090   1.379 0.169161    
## race3               0.36734    0.41231   0.891 0.373771    
## race3,4             1.62522    1.78547   0.910 0.363515    
## race4               0.05728    0.55391   0.103 0.917719    
## race4,11           -0.70021    1.78556  -0.392 0.695259    
## race4,5             0.21135    1.78249   0.119 0.905706    
## race4,8            -3.00245    1.78317  -1.684 0.093394 .  
## race5               0.02492    0.46280   0.054 0.957096    
## race6               0.49920    0.90301   0.553 0.580851    
## race7              -2.16982    1.26072  -1.721 0.086392 .  
## race8               1.04895    1.80795   0.580 0.562275    
## sentperp_1         -0.17484    0.06466  -2.704 0.007292 ** 
## sentconf_1          0.51597    0.06547   7.881 8.28e-14 ***
## ladder_partconf    -0.06350    0.03350  -1.896 0.059105 .  
## ladder_partperp     0.08036    0.05133   1.566 0.118637    
## ladder_confperp          NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.75 on 267 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.3663, Adjusted R-squared:  0.2998 
## F-statistic: 5.512 on 28 and 267 DF,  p-value: 1.21e-14
## 
## 
## $StatusConf
## 
## Call:
## lm(formula = formula_list[[variable]], data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3862 -1.3006  0.0505  1.3000  4.7833 
## 
## Coefficients: (1 not defined because of singularities)
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         3.82057    0.46841   8.156 1.36e-14 ***
## s_gender_textWoman -0.03633    0.24981  -0.145  0.88447    
## partgenderWoman    -0.33713    0.23613  -1.428  0.15453    
## pgender_textWoman  -0.05140    0.24369  -0.211  0.83312    
## Age                -0.01955    0.01078  -1.813  0.07098 .  
## race1,2             0.56015    0.93521   0.599  0.54971    
## race1,2,3           3.06446    1.80627   1.697  0.09094 .  
## race1,4            -1.43292    0.69753  -2.054  0.04092 *  
## race1,5            -1.00389    0.82439  -1.218  0.22440    
## race1,8            -4.44801    1.81611  -2.449  0.01496 *  
## race10             -0.70323    1.80736  -0.389  0.69752    
## race11              0.13337    1.28851   0.104  0.91764    
## race2               1.85624    0.33752   5.500 8.90e-08 ***
## race2,3             1.61181    0.56812   2.837  0.00490 ** 
## race2,3,11          2.28625    1.81986   1.256  0.21011    
## race3               0.19680    0.42133   0.467  0.64082    
## race3,4             2.73614    1.82454   1.500  0.13489    
## race4              -0.68472    0.56603  -1.210  0.22747    
## race4,11           -0.54578    1.82463  -0.299  0.76508    
## race4,5            -1.53191    1.82149  -0.841  0.40109    
## race4,8            -2.39183    1.82218  -1.313  0.19044    
## race5               0.04042    0.47292   0.085  0.93195    
## race6               0.58585    0.92277   0.635  0.52605    
## race7              -2.98700    1.28831  -2.319  0.02118 *  
## race8               1.61610    1.84750   0.875  0.38250    
## sentperp_1         -0.18917    0.06607  -2.863  0.00453 ** 
## sentconf_1          0.58091    0.06690   8.683 3.90e-16 ***
## ladder_partconf    -0.06531    0.03423  -1.908  0.05751 .  
## ladder_partperp     0.09253    0.05246   1.764  0.07887 .  
## ladder_confperp          NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.788 on 267 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.3826, Adjusted R-squared:  0.3179 
## F-statistic: 5.909 on 28 and 267 DF,  p-value: 6.492e-16
## 
## 
## $FNVPre
## 
## Call:
## lm(formula = formula_list[[variable]], data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4344 -0.5787  0.0000  0.5193  2.2703 
## 
## Coefficients: (1 not defined because of singularities)
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         2.379040   0.220576  10.786  < 2e-16 ***
## s_gender_textWoman  0.001270   0.117634   0.011 0.991391    
## partgenderWoman    -0.171513   0.111194  -1.542 0.124144    
## pgender_textWoman  -0.083289   0.114752  -0.726 0.468586    
## Age                -0.004843   0.005078  -0.954 0.341085    
## race1,2            -0.233495   0.440392  -0.530 0.596416    
## race1,2,3           2.342134   0.850574   2.754 0.006299 ** 
## race1,4            -0.056426   0.328468  -0.172 0.863736    
## race1,5            -0.134609   0.388206  -0.347 0.729055    
## race1,8            -1.614758   0.855209  -1.888 0.060091 .  
## race10             -0.264580   0.851086  -0.311 0.756139    
## race11             -0.464817   0.606761  -0.766 0.444316    
## race2               1.216594   0.158937   7.655 3.56e-13 ***
## race2,3             0.947214   0.267526   3.541 0.000471 ***
## race2,3,11          1.515641   0.856976   1.769 0.078104 .  
## race3               0.084442   0.198406   0.426 0.670743    
## race3,4             1.296913   0.859178   1.509 0.132358    
## race4              -0.169966   0.266545  -0.638 0.524238    
## race4,11           -0.059136   0.859221  -0.069 0.945181    
## race4,5            -0.733961   0.857742  -0.856 0.392937    
## race4,8            -0.923773   0.858069  -1.077 0.282643    
## race5              -0.046813   0.222700  -0.210 0.833668    
## race6               0.048232   0.434533   0.111 0.911702    
## race7              -0.729449   0.606665  -1.202 0.230277    
## race8               0.381219   0.869992   0.438 0.661605    
## sentperp_1         -0.017294   0.031115  -0.556 0.578799    
## sentconf_1          0.226017   0.031505   7.174 7.18e-12 ***
## ladder_partconf    -0.018001   0.016121  -1.117 0.265151    
## ladder_partperp     0.029379   0.024701   1.189 0.235348    
## ladder_confperp           NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8422 on 267 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.4058, Adjusted R-squared:  0.3435 
## F-statistic: 6.513 on 28 and 267 DF,  p-value: < 2.2e-16
## 
## 
## $FNVPro
## 
## Call:
## lm(formula = formula_list[[variable]], data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1315 -0.5746 -0.0026  0.4423  3.3942 
## 
## Coefficients: (1 not defined because of singularities)
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         2.459582   0.240518  10.226  < 2e-16 ***
## s_gender_textWoman -0.112560   0.128269  -0.878   0.3810    
## partgenderWoman     0.078445   0.121247   0.647   0.5182    
## pgender_textWoman  -0.160933   0.125127  -1.286   0.1995    
## Age                 0.004219   0.005537   0.762   0.4468    
## race1,2            -0.193386   0.480208  -0.403   0.6875    
## race1,2,3          -1.241416   0.927474  -1.338   0.1819    
## race1,4             0.011025   0.358165   0.031   0.9755    
## race1,5             0.200541   0.423304   0.474   0.6361    
## race1,8             0.396686   0.932528   0.425   0.6709    
## race10             -0.560130   0.928032  -0.604   0.5466    
## race11             -0.634473   0.661618  -0.959   0.3384    
## race2               0.021431   0.173307   0.124   0.9017    
## race2,3            -0.088427   0.291713  -0.303   0.7620    
## race2,3,11          0.805181   0.934454   0.862   0.3896    
## race3               0.472753   0.216344   2.185   0.0297 *  
## race3,4            -1.131700   0.936856  -1.208   0.2281    
## race4              -0.157693   0.290643  -0.543   0.5879    
## race4,11            1.350378   0.936902   1.441   0.1507    
## race4,5             0.475558   0.935290   0.508   0.6116    
## race4,8            -0.447397   0.935646  -0.478   0.6329    
## race5              -0.410843   0.242834  -1.692   0.0918 .  
## race6               0.145760   0.473819   0.308   0.7586    
## race7              -0.646641   0.661513  -0.978   0.3292    
## race8              -0.714277   0.948647  -0.753   0.4521    
## sentperp_1          0.181840   0.033928   5.360 1.80e-07 ***
## sentconf_1         -0.230862   0.034353  -6.720 1.09e-10 ***
## ladder_partconf     0.001496   0.017578   0.085   0.9322    
## ladder_partperp     0.001928   0.026935   0.072   0.9430    
## ladder_confperp           NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9183 on 267 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.2804, Adjusted R-squared:  0.2049 
## F-statistic: 3.715 on 28 and 267 DF,  p-value: 9.744e-09
## 
## 
## $MNVPre
## 
## Call:
## lm(formula = formula_list[[variable]], data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.00022 -0.45309  0.01628  0.50060  2.15729 
## 
## Coefficients: (1 not defined because of singularities)
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         2.849258   0.219946  12.954  < 2e-16 ***
## s_gender_textWoman  0.053266   0.117298   0.454   0.6501    
## partgenderWoman    -0.181503   0.110876  -1.637   0.1028    
## pgender_textWoman  -0.143143   0.114425  -1.251   0.2120    
## Age                -0.001143   0.005064  -0.226   0.8217    
## race1,2             0.163270   0.439136   0.372   0.7103    
## race1,2,3           0.360319   0.848147   0.425   0.6713    
## race1,4            -0.664303   0.327531  -2.028   0.0435 *  
## race1,5            -0.055697   0.387099  -0.144   0.8857    
## race1,8            -0.145521   0.852769  -0.171   0.8646    
## race10              0.409357   0.848658   0.482   0.6299    
## race11             -0.867592   0.605030  -1.434   0.1528    
## race2               0.656057   0.158484   4.140 4.67e-05 ***
## race2,3             0.358747   0.266763   1.345   0.1798    
## race2,3,11          0.294534   0.854531   0.345   0.7306    
## race3              -0.007911   0.197840  -0.040   0.9681    
## race3,4             1.284950   0.856727   1.500   0.1348    
## race4               0.160555   0.265785   0.604   0.5463    
## race4,11            0.353826   0.856769   0.413   0.6800    
## race4,5             0.713894   0.855295   0.835   0.4046    
## race4,8            -1.863207   0.855621  -2.178   0.0303 *  
## race5              -0.350708   0.222064  -1.579   0.1154    
## race6              -0.577723   0.433294  -1.333   0.1836    
## race7              -1.401963   0.604934  -2.318   0.0212 *  
## race8               0.290089   0.867510   0.334   0.7383    
## sentperp_1         -0.001646   0.031026  -0.053   0.9577    
## sentconf_1          0.127319   0.031415   4.053 6.64e-05 ***
## ladder_partconf    -0.020594   0.016075  -1.281   0.2013    
## ladder_partperp     0.004572   0.024631   0.186   0.8529    
## ladder_confperp           NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8398 on 267 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.2378, Adjusted R-squared:  0.1579 
## F-statistic: 2.975 on 28 and 267 DF,  p-value: 2.655e-06
## 
## 
## $MNVPro
## 
## Call:
## lm(formula = formula_list[[variable]], data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.62589 -0.44488 -0.02948  0.36082  2.49857 
## 
## Coefficients: (1 not defined because of singularities)
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         2.157076   0.182344  11.830  < 2e-16 ***
## s_gender_textWoman -0.040983   0.097347  -0.421 0.674094    
## partgenderWoman    -0.037511   0.091965  -0.408 0.683692    
## pgender_textWoman  -0.086787   0.094977  -0.914 0.361664    
## Age                -0.001541   0.004201  -0.367 0.714014    
## race1,2            -0.143210   0.364210  -0.393 0.694481    
## race1,2,3          -0.548503   0.703206  -0.780 0.436082    
## race1,4             0.092162   0.271524   0.339 0.734557    
## race1,5             0.415029   0.320934   1.293 0.197067    
## race1,8             0.192486   0.706976   0.272 0.785628    
## race10              0.040616   0.703543   0.058 0.954007    
## race11             -0.522725   0.501633  -1.042 0.298336    
## race2               0.516823   0.133829   3.862 0.000141 ***
## race2,3             0.453501   0.221232   2.050 0.041355 *  
## race2,3,11          0.406635   0.708624   0.574 0.566563    
## race3               0.393825   0.164079   2.400 0.017073 *  
## race3,4            -0.843131   0.710303  -1.187 0.236286    
## race4              -0.393491   0.220346  -1.786 0.075273 .  
## race4,11            0.721539   0.710413   1.016 0.310714    
## race4,5            -0.483302   0.709043  -0.682 0.496069    
## race4,8             0.270333   0.709346   0.381 0.703432    
## race5              -0.107799   0.184122  -0.585 0.558724    
## race6              -0.267631   0.359201  -0.745 0.456886    
## race7               0.945670   0.501577   1.885 0.060466 .  
## race8              -0.555116   0.719312  -0.772 0.440958    
## sentperp_1          0.087006   0.025833   3.368 0.000869 ***
## sentconf_1         -0.099401   0.026215  -3.792 0.000185 ***
## ladder_partconf    -0.008619   0.013326  -0.647 0.518327    
## ladder_partperp    -0.005418   0.020424  -0.265 0.790985    
## ladder_confperp           NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6962 on 266 degrees of freedom
##   (7 observations deleted due to missingness)
## Multiple R-squared:  0.2322, Adjusted R-squared:  0.1513 
## F-statistic: 2.872 on 28 and 266 DF,  p-value: 5.766e-06
## 
## 
## $RespSeverity
## 
## Call:
## lm(formula = formula_list[[variable]], data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9060 -0.7851  0.0034  0.9691  3.0739 
## 
## Coefficients: (1 not defined because of singularities)
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         0.630224   0.393456   1.602   0.1104  
## s_gender_textWoman -0.291741   0.209831  -1.390   0.1656  
## partgenderWoman     0.005772   0.198344   0.029   0.9768  
## pgender_textWoman   0.222206   0.204692   1.086   0.2787  
## Age                -0.016558   0.009059  -1.828   0.0687 .
## race1,2             0.203313   0.785558   0.259   0.7960  
## race1,2,3           0.151980   1.517226   0.100   0.9203  
## race1,4            -0.639251   0.585911  -1.091   0.2762  
## race1,5             1.117544   0.692470   1.614   0.1077  
## race1,8             1.148369   1.525494   0.753   0.4522  
## race10             -0.240542   1.518140  -0.158   0.8742  
## race11             -0.959412   1.082322  -0.886   0.3762  
## race2               0.536201   0.283507   1.891   0.0597 .
## race2,3             1.197657   0.477204   2.510   0.0127 *
## race2,3,11          3.031854   1.528646   1.983   0.0484 *
## race3               0.425764   0.353910   1.203   0.2300  
## race3,4            -0.223074   1.532574  -0.146   0.8844  
## race4               0.076803   0.475454   0.162   0.8718  
## race4,11            3.486228   1.532650   2.275   0.0237 *
## race4,5            -0.972799   1.530013  -0.636   0.5254  
## race4,8            -3.247459   1.530595  -2.122   0.0348 *
## race5              -0.265232   0.397244  -0.668   0.5049  
## race6              -1.111550   0.775107  -1.434   0.1527  
## race7              -2.787041   1.082150  -2.575   0.0105 *
## race8               0.572421   1.551863   0.369   0.7125  
## sentperp_1          0.090684   0.055501   1.634   0.1035  
## sentconf_1          0.068131   0.056197   1.212   0.2264  
## ladder_partconf    -0.008452   0.028756  -0.294   0.7690  
## ladder_partperp    -0.007374   0.044062  -0.167   0.8672  
## ladder_confperp           NA         NA      NA       NA  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.502 on 267 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.1652, Adjusted R-squared:  0.07761 
## F-statistic: 1.886 on 28 and 267 DF,  p-value: 0.005704
## 
## 
## $MoralGood
## 
## Call:
## lm(formula = formula_list[[variable]], data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8580 -1.0995  0.1691  1.1953  3.8428 
## 
## Coefficients: (1 not defined because of singularities)
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         4.557494   0.427595  10.658  < 2e-16 ***
## s_gender_textWoman  0.050651   0.228038   0.222  0.82439    
## partgenderWoman    -0.242797   0.215554  -1.126  0.26101    
## pgender_textWoman   0.081048   0.222452   0.364  0.71589    
## Age                -0.016570   0.009845  -1.683  0.09350 .  
## race1,2             0.401187   0.853719   0.470  0.63879    
## race1,2,3           2.969026   1.648874   1.801  0.07289 .  
## race1,4            -0.044260   0.636749  -0.070  0.94464    
## race1,5            -1.230542   0.752555  -1.635  0.10320    
## race1,8            -1.824554   1.657859  -1.101  0.27208    
## race10             -1.162926   1.649867  -0.705  0.48151    
## race11             -0.126639   1.176233  -0.108  0.91434    
## race2               1.505404   0.308107   4.886 1.78e-06 ***
## race2,3             1.634670   0.518610   3.152  0.00181 ** 
## race2,3,11          1.923893   1.661284   1.158  0.24787    
## race3               0.070299   0.384619   0.183  0.85511    
## race3,4             2.603279   1.665553   1.563  0.11923    
## race4               0.119812   0.516709   0.232  0.81681    
## race4,11           -1.721205   1.665636  -1.033  0.30237    
## race4,5            -0.373055   1.662770  -0.224  0.82265    
## race4,8            -1.986725   1.663403  -1.194  0.23339    
## race5               0.126099   0.431713   0.292  0.77044    
## race6               1.523572   0.842362   1.809  0.07162 .  
## race7              -1.745190   1.176046  -1.484  0.13900    
## race8               0.963685   1.686516   0.571  0.56821    
## sentperp_1         -0.160286   0.060317  -2.657  0.00835 ** 
## sentconf_1          0.476035   0.061073   7.795 1.45e-13 ***
## ladder_partconf    -0.030106   0.031251  -0.963  0.33623    
## ladder_partperp     0.040520   0.047885   0.846  0.39820    
## ladder_confperp           NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.633 on 267 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.3246, Adjusted R-squared:  0.2538 
## F-statistic: 4.583 on 28 and 267 DF,  p-value: 1.291e-11

Moderations

Just significant moderations

warm. ~ ladderpartper*supervisor_gender

sjPlot::plot_model(
  lm(warm~ladder_partperp*s_gender_text, girecall1_clean),
  type = "int", mdrt.values = "meansd", legend.title = "Power diff. between participant and perp"
)

virtad ~ ladderpartper*supervisor_gender

sjPlot::plot_model(
  lm(virtad~ladder_partperp*s_gender_text, girecall1_clean),
  type = "int", mdrt.values = "meansd", legend.title = "Power diff. between participant and perp"
)

interactions::sim_slopes(lm(virtad~ladder_partperp*s_gender_text, girecall1_clean),
                         pred = "ladder_partperp",
                         modx = "s_gender_text")
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of ladder_partperp when s_gender_text = Man: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.02   0.07     0.30   0.76
## 
## Slope of ladder_partperp when s_gender_text = Woman: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.25   0.08     2.93   0.00

fnvpro ~ ladderpartper*supervisor_gender

sjPlot::plot_model(
  lm(fnvpro~ladder_partperp*s_gender_text, girecall1_clean),
  type = "int", mdrt.values = "meansd", legend.title = "Power diff. between participant and perp"
)

interactions::sim_slopes(lm(fnvpro~ladder_partperp*s_gender_text, girecall1_clean),
                         pred = "ladder_partperp",
                         modx = "s_gender_text")
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of ladder_partperp when s_gender_text = Man: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.01   0.04     0.26   0.80
## 
## Slope of ladder_partperp when s_gender_text = Woman: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.10   0.04    -2.49   0.01

prot ~ pgender_text*supervisor_gender

sjPlot::plot_model(
  lm(prot ~ pgender_text*s_gender_text, girecall1_clean),
  type = "int", mdrt.values = "meansd", legend.title = "Gender of manager"
)

prot ~ mnvpro*supervisor_gender

sjPlot::plot_model(
  lm(prot~mnvpro*s_gender_text, girecall1_clean),
  type = "int", mdrt.values = "meansd", legend.title = "MNV Pro"
)

Marginal moderations

statusc ~ Supervisor gender * ladder_partperp

sjPlot::plot_model(
  lm(statusc~ladder_partperp*s_gender_text, girecall1_clean),
  type = "int", mdrt.values = "meansd", legend.title = "Lad. dif btwn part and perp"
)

interactions::sim_slopes(lm(statusc~ladder_partperp*s_gender_text, girecall1_clean),
                         pred = "ladder_partperp",
                         modx = "s_gender_text")
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of ladder_partperp when s_gender_text = Man: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.04   0.08     0.56   0.58
## 
## Slope of ladder_partperp when s_gender_text = Woman: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.26   0.09     2.94   0.00

statusc ~ Supervisor gender * ladder_partperp

sjPlot::plot_model(
  lm(statusc~pgender_text*s_gender_text, girecall1_clean),
  type = "int", mdrt.values = "meansd", legend.title = "Supervisor gender"
)

moralgood ~ Supervisor gender * ladder_partperp

sjPlot::plot_model(
  lm(moralgood~ladder_partperp*s_gender_text, girecall1_clean),
  type = "int", mdrt.values = "meansd", legend.title = "Ladder dif. btwen part-perp"
)

interactions::sim_slopes(lm(moralgood~ladder_partperp*s_gender_text, girecall1_clean),
                         pred = "ladder_partperp",
                         modx = "s_gender_text")
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of ladder_partperp when s_gender_text = Man: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.02   0.07     0.25   0.80
## 
## Slope of ladder_partperp when s_gender_text = Woman: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.19   0.08     2.49   0.01

prot ~ Supervisor gender * response_app_23

sjPlot::plot_model(
  lm(prot ~ s_gender_text*response_app_23, girecall1_clean),
  type = "int", mdrt.values = "meansd", legend.title = "Severity of response"
)

interactions::sim_slopes(lm(prot ~ s_gender_text*response_app_23, girecall1_clean),
                         pred = "response_app_23",
                         modx = "s_gender_text")
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of response_app_23 when s_gender_text = Man: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.30   0.10     3.12   0.00
## 
## Slope of response_app_23 when s_gender_text = Woman: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.56   0.12     4.72   0.00

statusc ~ Supervisor gender * sentperp_1

sjPlot::plot_model(
  lm(statusc ~ sentperp_1*s_gender_text, girecall1_clean),
  type = "int", mdrt.values = "meansd", legend.title = "Sentiment towards perp."
)

interactions::sim_slopes(lm(statusc ~ sentperp_1*s_gender_text, girecall1_clean),
                         pred = "sentperp_1",
                         modx = "s_gender_text")
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of sentperp_1 when s_gender_text = Man: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.16   0.09     1.85   0.06
## 
## Slope of sentperp_1 when s_gender_text = Woman: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.11   0.13    -0.84   0.40

virtad ~ Supervisor gender * sentperp_1

sjPlot::plot_model(
  lm(virtad ~ sentperp_1*s_gender_text, girecall1_clean),
  type = "int", mdrt.values = "meansd", legend.title = "Sentiment towards perp."
)

interactions::sim_slopes(lm(virtad ~ sentperp_1*s_gender_text, girecall1_clean),
                         pred = "sentperp_1",
                         modx = "s_gender_text")
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of sentperp_1 when s_gender_text = Man: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.16   0.08     1.85   0.06
## 
## Slope of sentperp_1 when s_gender_text = Woman: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.09   0.12    -0.76   0.45