Design
Participants read an email exchange where a man (Paul) said something [sexist/generally rude]: “No idea, but it’s a leadership position so I doubt very many women will apply. And the women who do apply probably won’t be very strong.” And then another man (Jeff) responded: “Shut up, Paul. No one wants to hear what you have to say.”
Manipulations
Over the course of an email exchange, a coworker (Paul) says
something rude about:
- Sexism: the other women in your workplace.
- General: the other analysts in your workplace.
Items
Status
To what extent do you agree with the following statements?
| Item label | Item text | - 3 | 0 | 3 |
|---|---|---|---|---|
| posstat1 | After his response back to Paul, I think Jeff is worthy of…: | -3. A lot of disrespect | 0. Neither disrespect nor respect | 3. A lot of respect |
| posstat2 | After his response back to Paul, I hold Jeff… | -3. In very low regard | 0. In neither low regard nor high regard | 3. In very high regard |
| posstat3 | After his response back to Paul, in terms of being like Jeff…: | -3. I want to be very different from him | -3. A lot of disrespect | 0. I don’t want to be like him, or different from him |
Rewards
Do you think that Jeff should experience any of the following changes after his response to Paul?
| Item label | Item text | - 3 | 0 | 3 |
|---|---|---|---|---|
| reward1 | change in his salary: | -3. should definitely be decreased | 0. would keep the same | 3. should definitely be increased |
| reward2 | change in his job rank: | -3. should definitely be demoted | 0. would keep the same | 3. should definitely be promoted |
| reward3 | change in visibility of his project assignments: | -3. Should be assigned to projects with very low visibility | 0. Should remain on projects with the same visibility as before | 3. Should be assigned to projects with high visibility |
| reward4 | change in his public recognition: | -3. Should definitely be decreased | 0. Should be kept the same | 3. Should definitely be increased |
Need for significance
When Jeff responded to Paul, I felt…
| My coding | Text |
|---|---|
| nfs1 | like I am respected in this group |
| nfs2 | like I am valued in this group |
| nfs3 | like I am appreciated in this group |
| nfs4 | like I am accepted in this group |
| nfs5 | like I am cared for in this group |
| nfs6 | like I am significant in this group |
| nfs7 | like I have a place in this group |
| nfs8 | like I am granted dignity in this group |
| nfs9 | like my rights are respected in this group |
| nfs10 | like my needs are considered in this group |
Procedural/Distributive justice
| My coding | Text |
|---|---|
| justice1 | the rewards I receive in this group will be quite fair |
| justice2 | decisions in this group are fair |
| justice3 | fairness is an important objective for this group |
Team functioning
| My coding | Text |
|---|---|
| disruptive | this will be disruptive to your team’s functioning |
| harder | this will make it harder for you and your teammates to get along |
| badvibes | there will be bad vibes in your team |
deter/learn uncivil
When Jeff responded to Paul, did you think that Paul… (1 = not at all, 4 = somewhat, 7 = very much so)
- learnuncivil1: would be uncivil in the future?
- learnuncivil2: feel intimidated?
- learnuncivil3: learned his lesson?
Data Information
Need for significance
Alpha
EFA
## [1] 8.873236 0.257905 0.220680 0.138069 0.127133 0.103743 0.086512 0.071862 0.062742 0.058118
## Parallel analysis suggests that the number of factors = 1 and the number of components = NA1 factor
##
## Call:
## factanal(x = nfsefa, factors = 1, rotiation = "promax")
##
## Uniquenesses:
## nfs1 nfs2 nfs3 nfs4 nfs5 nfs6 nfs7 nfs8 nfs9 nfs10
## 0.155 0.104 0.101 0.146 0.102 0.137 0.101 0.168 0.116 0.119
##
## Loadings:
## Factor1
## nfs1 0.919
## nfs2 0.946
## nfs3 0.948
## nfs4 0.924
## nfs5 0.948
## nfs6 0.929
## nfs7 0.948
## nfs8 0.912
## nfs9 0.940
## nfs10 0.939
##
## Factor1
## SS loadings 8.750
## Proportion Var 0.875
##
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 198.61 on 35 degrees of freedom.
## The p-value is 0.0000000000000000000000009282 factor
##
## Call:
## factanal(x = nfsefa, factors = 2, rotiation = "promax")
##
## Uniquenesses:
## nfs1 nfs2 nfs3 nfs4 nfs5 nfs6 nfs7 nfs8 nfs9 nfs10
## 0.131 0.072 0.079 0.150 0.103 0.142 0.095 0.103 0.086 0.111
##
## Loadings:
## Factor1 Factor2
## nfs1 0.768 0.528
## nfs2 0.805 0.529
## nfs3 0.786 0.550
## nfs4 0.691 0.610
## nfs5 0.722 0.613
## nfs6 0.665 0.645
## nfs7 0.624 0.718
## nfs8 0.508 0.799
## nfs9 0.585 0.756
## nfs10 0.644 0.689
##
## Factor1 Factor2
## SS loadings 4.702 4.225
## Proportion Var 0.470 0.422
## Cumulative Var 0.470 0.893
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 110.2 on 26 degrees of freedom.
## The p-value is 0.000000000002443 factor
##
## Call:
## factanal(x = nfsefa, factors = 3, rotiation = "promax")
##
## Uniquenesses:
## nfs1 nfs2 nfs3 nfs4 nfs5 nfs6 nfs7 nfs8 nfs9 nfs10
## 0.125 0.062 0.082 0.123 0.100 0.104 0.061 0.106 0.070 0.099
##
## Loadings:
## Factor1 Factor2 Factor3
## nfs1 0.696 0.468 0.414
## nfs2 0.735 0.452 0.440
## nfs3 0.690 0.464 0.476
## nfs4 0.536 0.447 0.624
## nfs5 0.605 0.487 0.545
## nfs6 0.504 0.466 0.652
## nfs7 0.456 0.561 0.645
## nfs8 0.430 0.714 0.446
## nfs9 0.500 0.703 0.431
## nfs10 0.574 0.631 0.417
##
## Factor1 Factor2 Factor3
## SS loadings 3.381 3.011 2.678
## Proportion Var 0.338 0.301 0.268
## Cumulative Var 0.338 0.639 0.907
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 43.77 on 18 degrees of freedom.
## The p-value is 0.000623CFA
Lavaan
0-factor model
## lavaan 0.6-20 ended normally after 9 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 20
##
## Number of observations 200
## Number of missing patterns 1
##
## Model Test User Model:
##
## Test statistic 3537.202
## Degrees of freedom 45
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 3537.202
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.000
## Tucker-Lewis Index (TLI) 0.000
##
## Robust Comparative Fit Index (CFI) 0.000
## Robust Tucker-Lewis Index (TLI) 0.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3868.921
## Loglikelihood unrestricted model (H1) -2100.320
##
## Akaike (AIC) 7777.842
## Bayesian (BIC) 7843.808
## Sample-size adjusted Bayesian (SABIC) 7780.446
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.623
## 90 Percent confidence interval - lower 0.606
## 90 Percent confidence interval - upper 0.640
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Robust RMSEA 0.623
## 90 Percent confidence interval - lower 0.606
## 90 Percent confidence interval - upper 0.640
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.728
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## nfs1 3.585 0.114 31.547 0.000 3.585 2.231
## nfs2 3.595 0.117 30.709 0.000 3.595 2.171
## nfs3 3.610 0.117 30.967 0.000 3.610 2.190
## nfs4 3.755 0.119 31.539 0.000 3.755 2.230
## nfs5 3.570 0.120 29.827 0.000 3.570 2.109
## nfs6 3.605 0.117 30.918 0.000 3.605 2.186
## nfs7 3.650 0.116 31.546 0.000 3.650 2.231
## nfs8 3.380 0.122 27.664 0.000 3.380 1.956
## nfs9 3.520 0.121 29.084 0.000 3.520 2.057
## nfs10 3.530 0.123 28.731 0.000 3.530 2.032
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## nfs1 2.583 0.258 10.000 0.000 2.583 1.000
## nfs2 2.741 0.274 10.000 0.000 2.741 1.000
## nfs3 2.718 0.272 10.000 0.000 2.718 1.000
## nfs4 2.835 0.283 10.000 0.000 2.835 1.000
## nfs5 2.865 0.287 10.000 0.000 2.865 1.000
## nfs6 2.719 0.272 10.000 0.000 2.719 1.000
## nfs7 2.677 0.268 10.000 0.000 2.677 1.000
## nfs8 2.986 0.299 10.000 0.000 2.986 1.000
## nfs9 2.930 0.293 10.000 0.000 2.930 1.000
## nfs10 3.019 0.302 10.000 0.000 3.019 1.0001-factor model
## lavaan 0.6-20 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Number of observations 200
## Number of missing patterns 1
##
## Model Test User Model:
##
## Test statistic 204.576
## Degrees of freedom 35
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 3537.202
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.951
## Tucker-Lewis Index (TLI) 0.938
##
## Robust Comparative Fit Index (CFI) 0.951
## Robust Tucker-Lewis Index (TLI) 0.938
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2202.608
## Loglikelihood unrestricted model (H1) -2100.320
##
## Akaike (AIC) 4465.216
## Bayesian (BIC) 4564.165
## Sample-size adjusted Bayesian (SABIC) 4469.122
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.156
## 90 Percent confidence interval - lower 0.135
## 90 Percent confidence interval - upper 0.177
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Robust RMSEA 0.156
## 90 Percent confidence interval - lower 0.135
## 90 Percent confidence interval - upper 0.177
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.016
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## onecommon =~
## nfs1 1.477 0.087 17.060 0.000 1.477 0.919
## nfs2 1.567 0.087 17.978 0.000 1.567 0.946
## nfs3 1.563 0.087 18.041 0.000 1.563 0.948
## nfs4 1.556 0.090 17.232 0.000 1.556 0.924
## nfs5 1.604 0.089 18.022 0.000 1.604 0.948
## nfs6 1.532 0.088 17.389 0.000 1.532 0.929
## nfs7 1.551 0.086 18.037 0.000 1.551 0.948
## nfs8 1.576 0.094 16.832 0.000 1.576 0.912
## nfs9 1.609 0.091 17.760 0.000 1.609 0.940
## nfs10 1.631 0.092 17.720 0.000 1.631 0.939
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .nfs1 3.585 0.114 31.547 0.000 3.585 2.231
## .nfs2 3.595 0.117 30.709 0.000 3.595 2.171
## .nfs3 3.610 0.117 30.967 0.000 3.610 2.190
## .nfs4 3.755 0.119 31.539 0.000 3.755 2.230
## .nfs5 3.570 0.120 29.827 0.000 3.570 2.109
## .nfs6 3.605 0.117 30.918 0.000 3.605 2.186
## .nfs7 3.650 0.116 31.546 0.000 3.650 2.231
## .nfs8 3.380 0.122 27.664 0.000 3.380 1.956
## .nfs9 3.520 0.121 29.084 0.000 3.520 2.057
## .nfs10 3.530 0.123 28.731 0.000 3.530 2.032
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## onecommon 1.000 1.000 1.000
## .nfs1 0.401 0.044 9.183 0.000 0.401 0.155
## .nfs2 0.286 0.033 8.682 0.000 0.286 0.104
## .nfs3 0.275 0.032 8.650 0.000 0.275 0.101
## .nfs4 0.413 0.045 9.145 0.000 0.413 0.146
## .nfs5 0.293 0.033 8.759 0.000 0.293 0.102
## .nfs6 0.372 0.041 9.069 0.000 0.372 0.137
## .nfs7 0.271 0.031 8.638 0.000 0.271 0.101
## .nfs8 0.502 0.054 9.232 0.000 0.502 0.168
## .nfs9 0.341 0.039 8.854 0.000 0.341 0.116
## .nfs10 0.358 0.040 8.919 0.000 0.358 0.1192-factor model
## lavaan 0.6-20 ended normally after 57 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 31
##
## Number of observations 200
## Number of missing patterns 1
##
## Model Test User Model:
##
## Test statistic 149.386
## Degrees of freedom 34
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 3537.202
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.967
## Tucker-Lewis Index (TLI) 0.956
##
## Robust Comparative Fit Index (CFI) 0.967
## Robust Tucker-Lewis Index (TLI) 0.956
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2175.013
## Loglikelihood unrestricted model (H1) -2100.320
##
## Akaike (AIC) 4412.026
## Bayesian (BIC) 4514.274
## Sample-size adjusted Bayesian (SABIC) 4416.062
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.130
## 90 Percent confidence interval - lower 0.109
## 90 Percent confidence interval - upper 0.152
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Robust RMSEA 0.130
## 90 Percent confidence interval - lower 0.109
## 90 Percent confidence interval - upper 0.152
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.015
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## f1 =~
## nfs1 1.487 0.086 17.258 0.000 1.487 0.925
## nfs2 1.579 0.087 18.224 0.000 1.579 0.954
## nfs3 1.577 0.086 18.333 0.000 1.577 0.957
## nfs4 1.558 0.090 17.252 0.000 1.558 0.925
## nfs5 1.608 0.089 18.105 0.000 1.608 0.950
## nfs6 1.528 0.088 17.307 0.000 1.528 0.927
## f2 =~
## nfs7 1.554 0.086 18.071 0.000 1.554 0.950
## nfs8 1.609 0.092 17.434 0.000 1.609 0.931
## nfs9 1.637 0.089 18.306 0.000 1.637 0.956
## nfs10 1.647 0.091 18.018 0.000 1.647 0.948
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## f1 ~~
## f2 0.972 0.006 158.188 0.000 0.972 0.972
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .nfs1 3.585 0.114 31.547 0.000 3.585 2.231
## .nfs2 3.595 0.117 30.709 0.000 3.595 2.171
## .nfs3 3.610 0.117 30.967 0.000 3.610 2.190
## .nfs4 3.755 0.119 31.539 0.000 3.755 2.230
## .nfs5 3.570 0.120 29.827 0.000 3.570 2.109
## .nfs6 3.605 0.117 30.918 0.000 3.605 2.186
## .nfs7 3.650 0.116 31.546 0.000 3.650 2.231
## .nfs8 3.380 0.122 27.664 0.000 3.380 1.956
## .nfs9 3.520 0.121 29.084 0.000 3.520 2.057
## .nfs10 3.530 0.123 28.731 0.000 3.530 2.032
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## f1 1.000 1.000 1.000
## f2 1.000 1.000 1.000
## .nfs1 0.371 0.042 8.906 0.000 0.371 0.144
## .nfs2 0.249 0.030 8.183 0.000 0.249 0.091
## .nfs3 0.231 0.029 8.047 0.000 0.231 0.085
## .nfs4 0.409 0.046 8.933 0.000 0.409 0.144
## .nfs5 0.279 0.033 8.379 0.000 0.279 0.097
## .nfs6 0.383 0.043 8.873 0.000 0.383 0.141
## .nfs7 0.263 0.033 7.898 0.000 0.263 0.098
## .nfs8 0.397 0.047 8.471 0.000 0.397 0.133
## .nfs9 0.250 0.033 7.519 0.000 0.250 0.085
## .nfs10 0.306 0.038 8.056 0.000 0.306 0.101Lavaan CFA
## lavaan 0.6-20 ended normally after 39 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 20
##
## Number of observations 200
##
## Model Test User Model:
##
## Test statistic 204.576
## Degrees of freedom 35
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 3537.202
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.951
## Tucker-Lewis Index (TLI) 0.938
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2202.608
## Loglikelihood unrestricted model (H1) -2100.320
##
## Akaike (AIC) 4445.216
## Bayesian (BIC) 4511.182
## Sample-size adjusted Bayesian (SABIC) 4447.820
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.156
## 90 Percent confidence interval - lower 0.135
## 90 Percent confidence interval - upper 0.177
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.018
##
## 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
## factor1 =~
## nfs1 1.000 1.477 0.919
## nfs2 1.061 0.041 25.579 0.000 1.567 0.946
## nfs3 1.058 0.041 25.759 0.000 1.563 0.948
## nfs4 1.054 0.045 23.555 0.000 1.556 0.924
## nfs5 1.086 0.042 25.696 0.000 1.604 0.948
## nfs6 1.037 0.043 23.961 0.000 1.532 0.929
## nfs7 1.050 0.041 25.749 0.000 1.551 0.948
## nfs8 1.067 0.047 22.571 0.000 1.576 0.912
## nfs9 1.089 0.044 24.961 0.000 1.609 0.940
## nfs10 1.104 0.044 24.847 0.000 1.631 0.939
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .nfs1 0.401 0.043 9.249 0.000 0.401 0.155
## .nfs2 0.286 0.032 8.812 0.000 0.286 0.104
## .nfs3 0.275 0.031 8.767 0.000 0.275 0.101
## .nfs4 0.413 0.045 9.190 0.000 0.413 0.146
## .nfs5 0.293 0.033 8.783 0.000 0.293 0.102
## .nfs6 0.372 0.041 9.129 0.000 0.372 0.137
## .nfs7 0.271 0.031 8.770 0.000 0.271 0.101
## .nfs8 0.502 0.054 9.317 0.000 0.502 0.168
## .nfs9 0.341 0.038 8.949 0.000 0.341 0.116
## .nfs10 0.358 0.040 8.972 0.000 0.358 0.119
## factor1 2.181 0.256 8.532 0.000 1.000 1.000## lavaan 0.6-20 ended normally after 44 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 200
##
## Model Test User Model:
##
## Test statistic 153.618
## Degrees of freedom 34
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 3537.202
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.966
## Tucker-Lewis Index (TLI) 0.955
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2177.129
## Loglikelihood unrestricted model (H1) -2100.320
##
## Akaike (AIC) 4396.258
## Bayesian (BIC) 4465.523
## Sample-size adjusted Bayesian (SABIC) 4398.992
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.133
## 90 Percent confidence interval - lower 0.112
## 90 Percent confidence interval - upper 0.154
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.016
##
## 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
## factor1 =~
## nfs1 1.000 1.490 0.927
## nfs2 1.062 0.039 27.551 0.000 1.583 0.956
## nfs3 1.061 0.038 27.993 0.000 1.582 0.959
## nfs4 1.043 0.043 24.158 0.000 1.555 0.924
## nfs5 1.077 0.040 26.691 0.000 1.606 0.949
## factor2 =~
## nfs6 1.000 1.529 0.927
## nfs7 1.022 0.037 27.402 0.000 1.562 0.955
## nfs8 1.047 0.043 24.459 0.000 1.602 0.927
## nfs9 1.063 0.040 26.847 0.000 1.626 0.950
## nfs10 1.072 0.041 26.143 0.000 1.640 0.944
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## factor1 ~~
## factor2 2.220 0.243 9.150 0.000 0.974 0.974
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .nfs1 0.361 0.041 8.879 0.000 0.361 0.140
## .nfs2 0.236 0.029 8.051 0.000 0.236 0.086
## .nfs3 0.216 0.027 7.864 0.000 0.216 0.079
## .nfs4 0.417 0.047 8.943 0.000 0.417 0.147
## .nfs5 0.287 0.034 8.354 0.000 0.287 0.100
## .nfs6 0.380 0.043 8.817 0.000 0.380 0.140
## .nfs7 0.236 0.030 7.999 0.000 0.236 0.088
## .nfs8 0.420 0.048 8.827 0.000 0.420 0.141
## .nfs9 0.285 0.035 8.206 0.000 0.285 0.097
## .nfs10 0.330 0.039 8.427 0.000 0.330 0.109
## factor1 2.222 0.256 8.664 0.000 1.000 1.000
## factor2 2.339 0.270 8.663 0.000 1.000 1.000Analyses
Midpoint analyses
Main effects
Binary
Reprimanded
##
## Call:
## glm(formula = reprimanded ~ instigation_type, family = "binomial",
## data = uncivprej4_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.995 0.225 -4.42 0.00001 ***
## instigation_typetraditional 1.115 0.301 3.70 0.00022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 269.20 on 199 degrees of freedom
## Residual deviance: 254.92 on 198 degrees of freedom
## AIC: 258.9
##
## Number of Fisher Scoring iterations: 4Stay on team
##
## Call:
## glm(formula = stayonteam ~ instigation_type, family = "binomial",
## data = uncivprej4_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.364 0.203 1.79 0.073 .
## instigation_typetraditional -0.244 0.285 -0.85 0.393
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 274.37 on 199 degrees of freedom
## Residual deviance: 273.64 on 198 degrees of freedom
## AIC: 277.6
##
## Number of Fisher Scoring iterations: 4Voice
##
## Call:
## glm(formula = voice ~ instigation_type, family = "binomial",
## data = uncivprej4_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2819 0.2020 1.40 0.16
## instigation_typetraditional -0.0812 0.2850 -0.28 0.78
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 274.37 on 199 degrees of freedom
## Residual deviance: 274.29 on 198 degrees of freedom
## AIC: 278.3
##
## Number of Fisher Scoring iterations: 3Jeff uncivil
##
## Call:
## glm(formula = jeffuncivil ~ instigation_type, family = "binomial",
## data = uncivprej4_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.046 0.228 -4.59 0.0000045 ***
## instigation_typetraditional 0.764 0.305 2.51 0.012 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 257.72 on 199 degrees of freedom
## Residual deviance: 251.27 on 198 degrees of freedom
## AIC: 255.3
##
## Number of Fisher Scoring iterations: 4Paul uncivil
##
## Call:
## glm(formula = pauluncivil ~ instigation_type, family = "binomial",
## data = uncivprej4_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.995 0.225 4.42 0.00001 ***
## instigation_typetraditional -0.505 0.305 -1.65 0.098 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 252.23 on 199 degrees of freedom
## Residual deviance: 249.46 on 198 degrees of freedom
## AIC: 253.5
##
## Number of Fisher Scoring iterations: 4Moderation
an asterix means that it reached p > .10 level. Used this benchmark because we are otherwise underpowered for moderation.
Mediation
Binary
Reprimanded
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = reprimanded ~ instigation_type + (nfs), data = uncivprej4_clean,
## plot = FALSE)
##
## The DV (Y) was reprimanded . The IV (X) was instigation_type . The mediating variable(s) = nfs .
##
## Total effect(c) of instigation_type on reprimanded = 0.26 S.E. = 0.07 t = 3.87 df= 198 with p = 0.00015
## Direct effect (c') of instigation_type on reprimanded removing nfs = 0.18 S.E. = 0.06 t = 2.72 df= 197 with p = 0.0071
## Indirect effect (ab) of instigation_type on reprimanded through nfs = 0.08
## Mean bootstrapped indirect effect = 0.08 with standard error = 0.03 Lower CI = 0.03 Upper CI = 0.15
## R = 0.44 R2 = 0.19 F = 23.07 on 2 and 197 DF p-value: 0.000000000000763
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryStay on team
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = stayonteam ~ instigation_type + (nfs), data = uncivprej4_clean,
## plot = FALSE)
##
## The DV (Y) was stayonteam . The IV (X) was instigation_type . The mediating variable(s) = nfs .
##
## Total effect(c) of instigation_type on stayonteam = -0.06 S.E. = 0.07 t = -0.85 df= 198 with p = 0.4
## Direct effect (c') of instigation_type on stayonteam removing nfs = 0.05 S.E. = 0.07 t = 0.74 df= 197 with p = 0.46
## Indirect effect (ab) of instigation_type on stayonteam through nfs = -0.11
## Mean bootstrapped indirect effect = -0.11 with standard error = 0.03 Lower CI = -0.18 Upper CI = -0.05
## R = 0.44 R2 = 0.2 F = 24.26 on 2 and 197 DF p-value: 0.000000000000209
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryVoice
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = voice ~ instigation_type + (nfs), data = uncivprej4_clean,
## plot = FALSE)
##
## The DV (Y) was voice . The IV (X) was instigation_type . The mediating variable(s) = nfs .
##
## Total effect(c) of instigation_type on voice = -0.02 S.E. = 0.07 t = -0.28 df= 198 with p = 0.78
## Direct effect (c') of instigation_type on voice removing nfs = 0.05 S.E. = 0.07 t = 0.76 df= 197 with p = 0.45
## Indirect effect (ab) of instigation_type on voice through nfs = -0.07
## Mean bootstrapped indirect effect = -0.07 with standard error = 0.03 Lower CI = -0.13 Upper CI = -0.03
## R = 0.3 R2 = 0.09 F = 9.59 on 2 and 197 DF p-value: 0.00000612
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryJeff Uncivil
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = jeffuncivil ~ instigation_type + (nfs), data = uncivprej4_clean,
## plot = FALSE)
##
## The DV (Y) was jeffuncivil . The IV (X) was instigation_type . The mediating variable(s) = nfs .
##
## Total effect(c) of instigation_type on jeffuncivil = 0.17 S.E. = 0.07 t = 2.56 df= 198 with p = 0.011
## Direct effect (c') of instigation_type on jeffuncivil removing nfs = 0.08 S.E. = 0.06 t = 1.26 df= 197 with p = 0.21
## Indirect effect (ab) of instigation_type on jeffuncivil through nfs = 0.09
## Mean bootstrapped indirect effect = 0.09 with standard error = 0.03 Lower CI = 0.04 Upper CI = 0.15
## R = 0.42 R2 = 0.18 F = 21.59 on 2 and 197 DF p-value: 0.00000000000393
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryPaul Uncivil
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = pauluncivil ~ instigation_type + (nfs), data = uncivprej4_clean,
## plot = FALSE)
##
## The DV (Y) was pauluncivil . The IV (X) was instigation_type . The mediating variable(s) = nfs .
##
## Total effect(c) of instigation_type on pauluncivil = -0.11 S.E. = 0.07 t = -1.66 df= 198 with p = 0.098
## Direct effect (c') of instigation_type on pauluncivil removing nfs = -0.09 S.E. = 0.07 t = -1.35 df= 197 with p = 0.18
## Indirect effect (ab) of instigation_type on pauluncivil through nfs = -0.02
## Mean bootstrapped indirect effect = -0.02 with standard error = 0.02 Lower CI = -0.06 Upper CI = 0.02
## R = 0.14 R2 = 0.02 F = 1.99 on 2 and 197 DF p-value: 0.117
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryControls
Binary
Reprimanded
##
## Call:
## glm(formula = reprimanded ~ instigation_type + rudeness_1 + rudeness_2 +
## genderid_1 + genderid_2 + genderid_3 + learn_1 + learn_2 +
## learn_3, family = "binomial", data = data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.988 1.290 -1.54 0.123
## instigation_typetraditional 0.761 0.338 2.25 0.024 *
## rudeness_1 0.387 0.181 2.14 0.032 *
## rudeness_2 -0.613 0.145 -4.23 0.000023 ***
## genderid_1 -0.185 0.215 -0.86 0.391
## genderid_2 0.310 0.194 1.60 0.109
## genderid_3 0.174 0.143 1.21 0.225
## learn_1 -0.036 0.116 -0.31 0.756
## learn_2 0.102 0.125 0.81 0.416
## learn_3 -0.052 0.133 -0.39 0.696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 269.20 on 199 degrees of freedom
## Residual deviance: 222.74 on 190 degrees of freedom
## AIC: 242.7
##
## Number of Fisher Scoring iterations: 4Stay on team
##
## Call:
## glm(formula = stayonteam ~ instigation_type + rudeness_1 + rudeness_2 +
## genderid_1 + genderid_2 + genderid_3 + learn_1 + learn_2 +
## learn_3, family = "binomial", data = data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.812283 1.218985 0.67 0.50518
## instigation_typetraditional 0.100466 0.327105 0.31 0.75874
## rudeness_1 -0.040637 0.164795 -0.25 0.80523
## rudeness_2 0.519312 0.134359 3.87 0.00011 ***
## genderid_1 0.045916 0.203622 0.23 0.82159
## genderid_2 0.013044 0.178590 0.07 0.94178
## genderid_3 0.062618 0.132778 0.47 0.63721
## learn_1 -0.199624 0.110758 -1.80 0.07149 .
## learn_2 0.000587 0.118926 0.00 0.99606
## learn_3 0.157424 0.126247 1.25 0.21241
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 274.37 on 199 degrees of freedom
## Residual deviance: 244.57 on 190 degrees of freedom
## AIC: 264.6
##
## Number of Fisher Scoring iterations: 4Voice
##
## Call:
## glm(formula = voice ~ instigation_type + rudeness_1 + rudeness_2 +
## genderid_1 + genderid_2 + genderid_3 + learn_1 + learn_2 +
## learn_3, family = "binomial", data = data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.2910 1.2589 -1.82 0.0688 .
## instigation_typetraditional 0.1259 0.3246 0.39 0.6982
## rudeness_1 0.0814 0.1638 0.50 0.6193
## rudeness_2 0.3705 0.1275 2.91 0.0037 **
## genderid_1 0.3751 0.2162 1.74 0.0827 .
## genderid_2 0.1874 0.1811 1.04 0.3006
## genderid_3 -0.0463 0.1328 -0.35 0.7274
## learn_1 -0.0109 0.1094 -0.10 0.9205
## learn_2 0.0160 0.1190 0.13 0.8929
## learn_3 0.2841 0.1295 2.19 0.0283 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 274.37 on 199 degrees of freedom
## Residual deviance: 247.03 on 190 degrees of freedom
## AIC: 267
##
## Number of Fisher Scoring iterations: 3Jeff uncivil
##
## Call:
## glm(formula = jeffuncivil ~ instigation_type + rudeness_1 + rudeness_2 +
## genderid_1 + genderid_2 + genderid_3 + learn_1 + learn_2 +
## learn_3, family = "binomial", data = data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1212 1.3004 -0.09 0.92577
## instigation_typetraditional 0.3180 0.3498 0.91 0.36340
## rudeness_1 0.6176 0.1857 3.33 0.00088 ***
## rudeness_2 -0.4134 0.1456 -2.84 0.00453 **
## genderid_1 -0.4667 0.2184 -2.14 0.03264 *
## genderid_2 0.1283 0.1940 0.66 0.50852
## genderid_3 0.0275 0.1471 0.19 0.85182
## learn_1 0.2144 0.1220 1.76 0.07896 .
## learn_2 0.3424 0.1353 2.53 0.01141 *
## learn_3 -0.2854 0.1449 -1.97 0.04891 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 257.72 on 199 degrees of freedom
## Residual deviance: 213.33 on 190 degrees of freedom
## AIC: 233.3
##
## Number of Fisher Scoring iterations: 4Paul uncivil
##
## Call:
## glm(formula = pauluncivil ~ instigation_type + rudeness_1 + rudeness_2 +
## genderid_1 + genderid_2 + genderid_3 + learn_1 + learn_2 +
## learn_3, family = "binomial", data = data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.1657 1.4633 -1.48 0.13887
## instigation_typetraditional -0.5128 0.3675 -1.40 0.16291
## rudeness_1 -0.7184 0.1859 -3.86 0.00011 ***
## rudeness_2 -0.0471 0.1318 -0.36 0.72083
## genderid_1 -0.6063 0.2834 -2.14 0.03239 *
## genderid_2 0.1232 0.2177 0.57 0.57146
## genderid_3 0.3332 0.1517 2.20 0.02806 *
## learn_1 0.4820 0.1280 3.77 0.00017 ***
## learn_2 0.2144 0.1419 1.51 0.13073
## learn_3 -0.0690 0.1414 -0.49 0.62556
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 252.23 on 199 degrees of freedom
## Residual deviance: 202.15 on 190 degrees of freedom
## AIC: 222.2
##
## Number of Fisher Scoring iterations: 5Multicolinearity
## instigation_type nfs
## 1.0612 1.0612## instigation_type nfs
## 1.0612 1.0612## instigation_type nfs
## 1.0612 1.0612
Social Rewards