Uncivil Microaggression Response Pilot 2 - 25_10.02-UncivilPrejudicePilot (Prej versus Uncivil)

## 
##     Uncivil NonResponse       Civil 
##          96          94          97

Design

Participants read an email exchange where a man (Paul) said something sexist: “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.

Manipulations

Manipulation Text
Uncivil (retinciv2) Shut up, Paul. No one wants to hear what you have to say.
Neutral (nonresponse) Keep me updated
Civil (civil) Please don’t say that, Paul.

Items

status

Item label Item text - 3 0 3
j_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
j_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
j_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
j_reward1 change in his salary: -3. should definitely be decreased 0. would keep the same 3. should definitely be increased
j_reward2 change in his job rank: -3. should definitely be demoted 0. would keep the same 3. should definitely be promoted
j_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
j_reward4 change in his public recognition: -3. Should definitely be decreased 0. Should be kept the same 3. Should definitely be increased

socrewards

Do you think that Jeff should experience any of the following changes after his response to Paul?

Item label Item text - 3 0 3
j_socreward1 at the next work event: -3. I would avoid Jeff 0. I would neither avoid nor approach Jeff 3. I would approach Jeff
j_socreward2 how much closer did you feel to Jeff?: -3. I felt much more distant from him 0. The amount of closeness I felt towards him did not change 3. I felt much closer to him
j_socreward3 how would the amount of time that you want to spend with Jeff change?: -3. I would want to spend much less time with him 0. I would not want to change the amount of time I spend with him 3. I would want to spend much more time with him

auth

When Jeff responded to Paul, did you think that Jeff was… (1 = not at all, 4 = somewhat, 7 = very much so)

  • acting authentically?
  • acting true to himself?

agency

When Jeff responded to Paul, did you think that Jeff was… (1 = not at all, 4 = somewhat, 7 = very much so)

  • confident
  • skillful
  • competitive
  • powerful
  • capable
  • agentic

comm

When Jeff responded to Paul, did you think that Jeff was… (1 = not at all, 4 = somewhat, 7 = very much so)

  • warm
  • good natured
  • friendly
  • considerate
  • caring
  • understanding

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?

Results

Rudeness evaluations

I asked participants the extent to which they saw the instigator’s comment as rude, and the respondent’s comment as rude (even though I didn’t tell them the exact language of the comment)

Respondent rudeness

Means of RESPONDENT rudeness across conditions

Comparing within context, but between response types

Comparing between contexts, but within response types

Instigator

Means of instigator rudeness across conditions

Comparing within context, but between response types

Comparing between contexts, but within response types

Analyses

Across all conditions

Correlations

Means

Effect sizes and differences

Graphs

## $status

## 
## $rewards

## 
## $socialrewards

## 
## $auth

## 
## $agency

## 
## $comm

## 
## $learn_1

## 
## $learn_2

## 
## $learn_3

Within prejudice condition

Controlling for instigator’s rudeness

Effect sizes and differences

Graphs

## $auth_1

## 
## $auth_2

## 
## $agency_1

## 
## $agency_2

## 
## $agency_3

## 
## $agency_4

## 
## $agency_5

## 
## $agency_6

## 
## $comm_1

## 
## $comm_2

## 
## $comm_3

## 
## $comm_4

## 
## $comm_5

## 
## $comm_6

## 
## $status

## 
## $rewards

## 
## $socialrewards

## 
## $auth

## 
## $agency

## 
## $comm

## 
## $deter

## 
## $learn_1

## 
## $learn_2

## 
## $learn_3

Interacting condition with instigator’s rudeness

Effect sizes and differences

Graphs

## $status

## 
## $rewards

## 
## $socialrewards

## 
## $auth

## 
## $agency

## 
## $comm

## 
## $learn_1

## 
## $learn_2

## 
## $learn_3

Interacting with Learn 1

Effect sizes and differences

Graphs

## $status

## 
## $rewards

## 
## $socialrewards

## 
## $auth

## 
## $agency

## 
## $comm

## 
## $<NA>
## NULL
## 
## $learn_2

## 
## $learn_3

Interacting with Learn 2

Effect sizes and differences

Graphs

## $status

## 
## $rewards

## 
## $socialrewards

## 
## $auth

## 
## $agency

## 
## $comm

## 
## $learn_1

## 
## $<NA>
## NULL
## 
## $learn_3

Interacting with Learn 3

Effect sizes and differences

Graphs

## $status

## 
## $rewards

## 
## $socialrewards

## 
## $auth

## 
## $agency

## 
## $comm

## 
## $learn_1

## 
## $learn_2

## 
## $<NA>
## NULL

Exploratory

Rudeness: 3-way

Status

## 
## Call:
## lm(formula = status ~ manipulation_label * rudeness_2 * instigation_type, 
##     data = uncivilpilot2_clean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.8472 -0.6744  0.0088  0.6755  3.0977 
## 
## Coefficients:
##                                                                      Estimate Std. Error t value  Pr(>|t|)    
## (Intercept)                                                           2.30614    0.47396    4.87 0.0000019 ***
## manipulation_labelNonResponse                                        -2.50552    0.53896   -4.65 0.0000052 ***
## manipulation_labelCivil                                               0.70775    0.57304    1.24   0.21787    
## rudeness_2                                                           -0.37229    0.09605   -3.88   0.00013 ***
## instigation_typetraditional                                          -0.16844    0.75129   -0.22   0.82277    
## manipulation_labelNonResponse:rudeness_2                              0.21359    0.12112    1.76   0.07896 .  
## manipulation_labelCivil:rudeness_2                                   -0.37771    0.24939   -1.51   0.13106    
## manipulation_labelNonResponse:instigation_typetraditional             0.75994    0.84966    0.89   0.37190    
## manipulation_labelCivil:instigation_typetraditional                  -1.33675    0.85777   -1.56   0.12030    
## rudeness_2:instigation_typetraditional                               -0.08991    0.14219   -0.63   0.52774    
## manipulation_labelNonResponse:rudeness_2:instigation_typetraditional -0.00321    0.19829   -0.02   0.98710    
## manipulation_labelCivil:rudeness_2:instigation_typetraditional        0.77546    0.30565    2.54   0.01174 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1 on 271 degrees of freedom
##   (296 observations deleted due to missingness)
## Multiple R-squared:  0.558,  Adjusted R-squared:  0.54 
## F-statistic: 31.1 on 11 and 271 DF,  p-value: <0.0000000000000002

Social Rewards

## 
## Call:
## lm(formula = socialrewards ~ manipulation_label * rudeness_2 * 
##     instigation_type, data = uncivilpilot2_clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.931 -0.597  0.110  0.776  3.127 
## 
## Coefficients:
##                                                                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                                                            1.9615     0.5586    3.51  0.00052 ***
## manipulation_labelNonResponse                                         -2.2729     0.6353   -3.58  0.00041 ***
## manipulation_labelCivil                                               -0.2809     0.6754   -0.42  0.67777    
## rudeness_2                                                            -0.3304     0.1132   -2.92  0.00381 ** 
## instigation_typetraditional                                           -0.5529     0.8855   -0.62  0.53292    
## manipulation_labelNonResponse:rudeness_2                               0.1490     0.1428    1.04  0.29760    
## manipulation_labelCivil:rudeness_2                                    -0.0862     0.2940   -0.29  0.76947    
## manipulation_labelNonResponse:instigation_typetraditional              0.9338     1.0015    0.93  0.35194    
## manipulation_labelCivil:instigation_typetraditional                   -0.3552     1.0110   -0.35  0.72560    
## rudeness_2:instigation_typetraditional                                 0.0107     0.1676    0.06  0.94921    
## manipulation_labelNonResponse:rudeness_2:instigation_typetraditional  -0.0257     0.2337   -0.11  0.91246    
## manipulation_labelCivil:rudeness_2:instigation_typetraditional         0.1904     0.3603    0.53  0.59756    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.18 on 271 degrees of freedom
##   (296 observations deleted due to missingness)
## Multiple R-squared:  0.304,  Adjusted R-squared:  0.276 
## F-statistic: 10.8 on 11 and 271 DF,  p-value: <0.0000000000000002

Authenticity

## 
## Call:
## lm(formula = auth ~ manipulation_label * rudeness_2 * instigation_type, 
##     data = uncivilpilot2_clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.620 -0.560  0.102  0.793  2.793 
## 
## Coefficients:
##                                                                      Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)                                                            6.2894     0.5778   10.89 < 0.0000000000000002 ***
## manipulation_labelNonResponse                                         -2.1945     0.6570   -3.34              0.00096 ***
## manipulation_labelCivil                                                0.9990     0.6986    1.43              0.15386    
## rudeness_2                                                            -0.0784     0.1171   -0.67              0.50387    
## instigation_typetraditional                                           -1.1270     0.9159   -1.23              0.21955    
## manipulation_labelNonResponse:rudeness_2                               0.1908     0.1477    1.29              0.19743    
## manipulation_labelCivil:rudeness_2                                    -0.7440     0.3040   -2.45              0.01503 *  
## manipulation_labelNonResponse:instigation_typetraditional              1.7543     1.0358    1.69              0.09146 .  
## manipulation_labelCivil:instigation_typetraditional                    0.2270     1.0457    0.22              0.82828    
## rudeness_2:instigation_typetraditional                                 0.1375     0.1733    0.79              0.42844    
## manipulation_labelNonResponse:rudeness_2:instigation_typetraditional  -0.3516     0.2417   -1.45              0.14692    
## manipulation_labelCivil:rudeness_2:instigation_typetraditional         0.4137     0.3726    1.11              0.26783    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.22 on 271 degrees of freedom
##   (296 observations deleted due to missingness)
## Multiple R-squared:  0.289,  Adjusted R-squared:  0.261 
## F-statistic:   10 on 11 and 271 DF,  p-value: 0.00000000000000263

Agency

## 
## Call:
## lm(formula = agency ~ manipulation_label * rudeness_2 * instigation_type, 
##     data = uncivilpilot2_clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.815 -0.830 -0.022  0.811  3.762 
## 
## Coefficients:
##                                                                      Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)                                                            5.3103     0.5725    9.28 <0.0000000000000002 ***
## manipulation_labelNonResponse                                         -2.1367     0.6522   -3.28              0.0012 ** 
## manipulation_labelCivil                                                0.3201     0.6922    0.46              0.6441    
## rudeness_2                                                            -0.1626     0.1160   -1.40              0.1622    
## instigation_typetraditional                                           -0.9930     0.9076   -1.09              0.2749    
## manipulation_labelNonResponse:rudeness_2                               0.1787     0.1478    1.21              0.2276    
## manipulation_labelCivil:rudeness_2                                    -0.1334     0.3013   -0.44              0.6582    
## manipulation_labelNonResponse:instigation_typetraditional              1.9946     1.0271    1.94              0.0532 .  
## manipulation_labelCivil:instigation_typetraditional                    0.2456     1.0362    0.24              0.8128    
## rudeness_2:instigation_typetraditional                                 0.1304     0.1718    0.76              0.4485    
## manipulation_labelNonResponse:rudeness_2:instigation_typetraditional  -0.2689     0.2404   -1.12              0.2645    
## manipulation_labelCivil:rudeness_2:instigation_typetraditional         0.0975     0.3692    0.26              0.7919    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.21 on 270 degrees of freedom
##   (297 observations deleted due to missingness)
## Multiple R-squared:  0.251,  Adjusted R-squared:  0.22 
## F-statistic: 8.22 on 11 and 270 DF,  p-value: 0.00000000000209

Communality

## 
## Call:
## lm(formula = comm ~ manipulation_label * rudeness_2 * instigation_type, 
##     data = uncivilpilot2_clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -2.888 -0.927 -0.222  0.935  5.099 
## 
## Coefficients:
##                                                                      Estimate Std. Error t value       Pr(>|t|)    
## (Intercept)                                                            4.3436     0.6270    6.93 0.000000000031 ***
## manipulation_labelNonResponse                                         -1.1969     0.7130   -1.68         0.0944 .  
## manipulation_labelCivil                                                1.6363     0.7581    2.16         0.0318 *  
## rudeness_2                                                            -0.3490     0.1271   -2.75         0.0064 ** 
## instigation_typetraditional                                           -0.5501     0.9939   -0.55         0.5804    
## manipulation_labelNonResponse:rudeness_2                               0.1451     0.1602    0.91         0.3661    
## manipulation_labelCivil:rudeness_2                                    -0.0655     0.3299   -0.20         0.8428    
## manipulation_labelNonResponse:instigation_typetraditional              1.6420     1.1240    1.46         0.1452    
## manipulation_labelCivil:instigation_typetraditional                   -1.0576     1.1348   -0.93         0.3522    
## rudeness_2:instigation_typetraditional                                 0.0384     0.1881    0.20         0.8383    
## manipulation_labelNonResponse:rudeness_2:instigation_typetraditional  -0.1847     0.2623   -0.70         0.4820    
## manipulation_labelCivil:rudeness_2:instigation_typetraditional         0.4912     0.4044    1.21         0.2255    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.33 on 271 degrees of freedom
##   (296 observations deleted due to missingness)
## Multiple R-squared:  0.49,   Adjusted R-squared:  0.469 
## F-statistic: 23.7 on 11 and 271 DF,  p-value: <0.0000000000000002

Deter

## 
## Call:
## lm(formula = deter ~ manipulation_label * rudeness_2 * instigation_type, 
##     data = uncivilpilot2_clean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5672 -0.6421  0.0183  0.5331  2.9174 
## 
## Coefficients:
##                                                                      Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)                                                            4.5962     0.4417   10.40 < 0.0000000000000002 ***
## manipulation_labelNonResponse                                         -2.1936     0.5023   -4.37             0.000018 ***
## manipulation_labelCivil                                               -0.7154     0.5341   -1.34                 0.18    
## rudeness_2                                                            -0.0906     0.0895   -1.01                 0.31    
## instigation_typetraditional                                           -0.6569     0.7002   -0.94                 0.35    
## manipulation_labelNonResponse:rudeness_2                               0.2054     0.1129    1.82                 0.07 .  
## manipulation_labelCivil:rudeness_2                                     0.1915     0.2324    0.82                 0.41    
## manipulation_labelNonResponse:instigation_typetraditional              0.7894     0.7919    1.00                 0.32    
## manipulation_labelCivil:instigation_typetraditional                    0.8980     0.7995    1.12                 0.26    
## rudeness_2:instigation_typetraditional                                 0.1634     0.1325    1.23                 0.22    
## manipulation_labelNonResponse:rudeness_2:instigation_typetraditional  -0.0132     0.1848   -0.07                 0.94    
## manipulation_labelCivil:rudeness_2:instigation_typetraditional        -0.4107     0.2849   -1.44                 0.15    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.935 on 271 degrees of freedom
##   (296 observations deleted due to missingness)
## Multiple R-squared:  0.319,  Adjusted R-squared:  0.291 
## F-statistic: 11.5 on 11 and 271 DF,  p-value: <0.0000000000000002

Rudeness as a mediator

Moderated mediation

DV: Status

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 7 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## -    Outcome (Y) : status
## -  Predictor (X) : instigation_type (recoded: =0, prejudice=1, traditional=0)
## -  Mediators (M) : rudeness_2
## - Moderators (W) : manipulation_label
## - Covariates (C) : -
## -   HLM Clusters : -
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    rudeness_2 ~ instigation_type*manipulation_label
## Formula of Outcome:
## -    status ~ instigation_type + manipulation_label + rudeness_2
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ────────────────────────────────────────────────────────────────────────────────────────
##                                                 (1) status   (2) rudeness_2  (3) status 
## ────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                                       0.448 ***    5.029 ***       0.703 ***
##                                                  (0.088)      (0.143)         (0.139)   
## instigation_type                                 -0.327        0.761 **       -0.348 ** 
##                                                  (0.175)      (0.286)         (0.125)   
## manipulation_labelNonResponse                                 -2.463 ***      -1.320 ***
##                                                               (0.204)         (0.187)   
## manipulation_labelCivil                                       -3.649 ***       0.518 *  
##                                                               (0.201)         (0.222)   
## instigation_type:manipulation_labelNonResponse                -1.521 ***                
##                                                               (0.409)                   
## instigation_type:manipulation_labelCivil                      -0.500                    
##                                                               (0.402)                   
## rudeness_2                                                                    -0.315 ***
##                                                                               (0.044)   
## ────────────────────────────────────────────────────────────────────────────────────────
## R^2                                               0.012        0.574           0.515    
## Adj. R^2                                          0.009        0.566           0.508    
## Num. obs.                                       283          283             283        
## ────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.1), ‘interactions’ (v1.2.0)
## Effect Type : Moderated Mediation (Model 7)
## Sample Size : 283 (296 missing observations deleted)
## Random Seed : set.seed()
## Simulations : 100 (Bootstrap)
## 
## Direct Effect: "instigation_type" (X) ==> "status" (Y)
## ─────────────────────────────────────────────────────────────
##              Effect    S.E.      t     p             [95% CI]
## ─────────────────────────────────────────────────────────────
## Direct (c')  -0.348 (0.125) -2.790  .006 **  [-0.593, -0.102]
## ─────────────────────────────────────────────────────────────
## 
## Interaction Effect on "rudeness_2" (M)
## ─────────────────────────────────────────────────────────────
##                                           F df1 df2     p    
## ─────────────────────────────────────────────────────────────
## instigation_type * manipulation_label  7.16   2 277 <.001 ***
## ─────────────────────────────────────────────────────────────
## 
## Simple Slopes: "instigation_type" (X) ==> "rudeness_2" (M)
## (Conditional Effects [a] of X on M)
## ──────────────────────────────────────────────────────────────────────
##  "manipulation_label" Effect    S.E.      t     p             [95% CI]
## ──────────────────────────────────────────────────────────────────────
##  Civil                 0.261 (0.283)  0.920  .358     [-0.297,  0.818]
##  NonResponse          -0.760 (0.292) -2.601  .010 **  [-1.336, -0.185]
##  Uncivil               0.761 (0.286)  2.662  .008 **  [ 0.198,  1.323]
## ──────────────────────────────────────────────────────────────────────
## 
## Running 100 * 3 simulations...
## Indirect Path: "instigation_type" (X) ==> "rudeness_2" (M) ==> "status" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ──────────────────────────────────────────────────────────────────────
##  "manipulation_label" Effect    S.E.      z     p        [Boot 95% CI]
## ──────────────────────────────────────────────────────────────────────
##  Civil                -0.082 (0.057) -1.438  .151     [-0.184,  0.035]
##  NonResponse           0.240 (0.118)  2.034  .042 *   [ 0.036,  0.506]
##  Uncivil              -0.240 (0.106) -2.254  .024 *   [-0.469, -0.060]
## ──────────────────────────────────────────────────────────────────────
## Percentile Bootstrap Confidence Interval
## (SE and CI are estimated based on 100 Bootstrap samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

DV: Social rewards

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 7 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## -    Outcome (Y) : socialrewards
## -  Predictor (X) : instigation_type (recoded: =0, prejudice=1, traditional=0)
## -  Mediators (M) : rudeness_2
## - Moderators (W) : manipulation_label
## - Covariates (C) : -
## -   HLM Clusters : -
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    rudeness_2 ~ instigation_type*manipulation_label
## Formula of Outcome:
## -    socialrewards ~ instigation_type + manipulation_label + rudeness_2
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ────────────────────────────────────────────────────────────────────────────────────────────────────
##                                                 (1) socialrewards  (2) rudeness_2  (3) socialrewards
## ────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                                       0.058              5.029 ***       0.590 ***      
##                                                  (0.082)            (0.143)         (0.160)         
## instigation_type                                 -0.266              0.761 **       -0.311 *        
##                                                  (0.165)            (0.286)         (0.143)         
## manipulation_labelNonResponse                                       -2.463 ***      -1.377 ***      
##                                                                     (0.204)         (0.214)         
## manipulation_labelCivil                                             -3.649 ***      -0.251          
##                                                                     (0.201)         (0.255)         
## instigation_type:manipulation_labelNonResponse                      -1.521 ***                      
##                                                                     (0.409)                         
## instigation_type:manipulation_labelCivil                            -0.500                          
##                                                                     (0.402)                         
## rudeness_2                                                                          -0.280 ***      
##                                                                                     (0.050)         
## ────────────────────────────────────────────────────────────────────────────────────────────────────
## R^2                                               0.009              0.574           0.277          
## Adj. R^2                                          0.006              0.566           0.267          
## Num. obs.                                       283                283             283              
## ────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.1), ‘interactions’ (v1.2.0)
## Effect Type : Moderated Mediation (Model 7)
## Sample Size : 283 (296 missing observations deleted)
## Random Seed : set.seed()
## Simulations : 5000 (Bootstrap)
## 
## Direct Effect: "instigation_type" (X) ==> "socialrewards" (Y)
## ─────────────────────────────────────────────────────────────
##              Effect    S.E.      t     p             [95% CI]
## ─────────────────────────────────────────────────────────────
## Direct (c')  -0.311 (0.143) -2.173  .031 *   [-0.592, -0.029]
## ─────────────────────────────────────────────────────────────
## 
## Interaction Effect on "rudeness_2" (M)
## ─────────────────────────────────────────────────────────────
##                                           F df1 df2     p    
## ─────────────────────────────────────────────────────────────
## instigation_type * manipulation_label  7.16   2 277 <.001 ***
## ─────────────────────────────────────────────────────────────
## 
## Simple Slopes: "instigation_type" (X) ==> "rudeness_2" (M)
## (Conditional Effects [a] of X on M)
## ──────────────────────────────────────────────────────────────────────
##  "manipulation_label" Effect    S.E.      t     p             [95% CI]
## ──────────────────────────────────────────────────────────────────────
##  Civil                 0.261 (0.283)  0.920  .358     [-0.297,  0.818]
##  NonResponse          -0.760 (0.292) -2.601  .010 **  [-1.336, -0.185]
##  Uncivil               0.761 (0.286)  2.662  .008 **  [ 0.198,  1.323]
## ──────────────────────────────────────────────────────────────────────
## 
## Running 5000 * 3 simulations...
## Indirect Path: "instigation_type" (X) ==> "rudeness_2" (M) ==> "socialrewards" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ──────────────────────────────────────────────────────────────────────
##  "manipulation_label" Effect    S.E.      z     p        [Boot 95% CI]
## ──────────────────────────────────────────────────────────────────────
##  Civil                -0.073 (0.051) -1.430  .153     [-0.179,  0.020]
##  NonResponse           0.213 (0.110)  1.942  .052 .   [ 0.019,  0.449]
##  Uncivil              -0.213 (0.097) -2.192  .028 *   [-0.418, -0.039]
## ──────────────────────────────────────────────────────────────────────
## Percentile Bootstrap Confidence Interval
## (SE and CI are estimated based on 5000 Bootstrap samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

Filtering on uncivil (versus civil) responses as the moderator

DV: Status

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 7 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## -    Outcome (Y) : status
## -  Predictor (X) : manipulation_label (recoded: Uncivil=0, NonResponse=1, Civil=0)
## -  Mediators (M) : rudeness_2
## - Moderators (W) : instigation_type
## - Covariates (C) : -
## -   HLM Clusters : -
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    rudeness_2 ~ manipulation_label*instigation_type
## Formula of Outcome:
## -    status ~ manipulation_label + instigation_type + rudeness_2
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ────────────────────────────────────────────────────────────────────────────────────────
##                                                 (1) status   (2) rudeness_2  (3) status 
## ────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                                       0.883 ***    2.959 ***       1.212 ***
##                                                  (0.088)      (0.129)         (0.115)   
## manipulation_label                                0.860 ***   -1.700 ***       0.137    
##                                                  (0.088)      (0.128)         (0.142)   
## instigation_typetraditional                                    0.512 **       -0.610 ***
##                                                               (0.175)         (0.159)   
## manipulation_label:instigation_typetraditional                -0.250                    
##                                                               (0.175)                   
## rudeness_2                                                                    -0.375 ***
##                                                                               (0.065)   
## ────────────────────────────────────────────────────────────────────────────────────────
## R^2                                               0.334        0.714           0.496    
## Adj. R^2                                          0.331        0.710           0.488    
## Num. obs.                                       191          191             191        
## ────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.1), ‘interactions’ (v1.2.0)
## Effect Type : Moderated Mediation (Model 7)
## Sample Size : 191 (2 missing observations deleted)
## Random Seed : set.seed()
## Simulations : 100 (Bootstrap)
## 
## Direct Effect: "manipulation_label" (X) ==> "status" (Y)
## ────────────────────────────────────────────────────────────
##              Effect    S.E.     t     p             [95% CI]
## ────────────────────────────────────────────────────────────
## Direct (c')   0.137 (0.142) 0.969  .334     [-0.142,  0.417]
## ────────────────────────────────────────────────────────────
## 
## Interaction Effect on "rudeness_2" (M)
## ─────────────────────────────────────────────────────────────
##                                           F df1 df2     p    
## ─────────────────────────────────────────────────────────────
## manipulation_label * instigation_type  2.04   1 187  .155    
## ─────────────────────────────────────────────────────────────
## 
## Simple Slopes: "manipulation_label" (X) ==> "rudeness_2" (M)
## (Conditional Effects [a] of X on M)
## ─────────────────────────────────────────────────────────────────────
##  "instigation_type" Effect    S.E.       t     p             [95% CI]
## ─────────────────────────────────────────────────────────────────────
##  prejudice          -1.700 (0.128) -13.232 <.001 *** [-1.953, -1.447]
##  traditional        -1.950 (0.119) -16.426 <.001 *** [-2.184, -1.716]
## ─────────────────────────────────────────────────────────────────────
## 
## Running 100 * 2 simulations...
## Indirect Path: "manipulation_label" (X) ==> "rudeness_2" (M) ==> "status" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ──────────────────────────────────────────────────────────────────
##  "instigation_type" Effect    S.E.     z     p       [Boot 95% CI]
## ──────────────────────────────────────────────────────────────────
##  prejudice           0.637 (0.146) 4.349 <.001 *** [ 0.391, 0.968]
##  traditional         0.730 (0.177) 4.125 <.001 *** [ 0.379, 1.057]
## ──────────────────────────────────────────────────────────────────
## Percentile Bootstrap Confidence Interval
## (SE and CI are estimated based on 100 Bootstrap samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

DV: Social rewards

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 7 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## -    Outcome (Y) : socialrewards
## -  Predictor (X) : manipulation_label (recoded: Uncivil=0, NonResponse=1, Civil=0)
## -  Mediators (M) : rudeness_2
## - Moderators (W) : instigation_type
## - Covariates (C) : -
## -   HLM Clusters : -
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    rudeness_2 ~ manipulation_label*instigation_type
## Formula of Outcome:
## -    socialrewards ~ manipulation_label + instigation_type + rudeness_2
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ────────────────────────────────────────────────────────────────────────────────────────────────────
##                                                 (1) socialrewards  (2) rudeness_2  (3) socialrewards
## ────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                                       0.396 ***          2.959 ***       0.705 ***      
##                                                  (0.098)            (0.129)         (0.136)         
## manipulation_label                                0.409 ***         -1.700 ***      -0.191          
##                                                  (0.098)            (0.128)         (0.167)         
## instigation_typetraditional                                          0.512 **       -0.572 **       
##                                                                     (0.175)         (0.187)         
## manipulation_label:instigation_typetraditional                      -0.250                          
##                                                                     (0.175)                         
## rudeness_2                                                                          -0.309 ***      
##                                                                                     (0.076)         
## ────────────────────────────────────────────────────────────────────────────────────────────────────
## R^2                                               0.085              0.714           0.220          
## Adj. R^2                                          0.080              0.710           0.207          
## Num. obs.                                       191                191             191              
## ────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.1), ‘interactions’ (v1.2.0)
## Effect Type : Moderated Mediation (Model 7)
## Sample Size : 191 (2 missing observations deleted)
## Random Seed : set.seed()
## Simulations : 100 (Bootstrap)
## 
## Direct Effect: "manipulation_label" (X) ==> "socialrewards" (Y)
## ─────────────────────────────────────────────────────────────
##              Effect    S.E.      t     p             [95% CI]
## ─────────────────────────────────────────────────────────────
## Direct (c')  -0.191 (0.167) -1.144  .254     [-0.519,  0.138]
## ─────────────────────────────────────────────────────────────
## 
## Interaction Effect on "rudeness_2" (M)
## ─────────────────────────────────────────────────────────────
##                                           F df1 df2     p    
## ─────────────────────────────────────────────────────────────
## manipulation_label * instigation_type  2.04   1 187  .155    
## ─────────────────────────────────────────────────────────────
## 
## Simple Slopes: "manipulation_label" (X) ==> "rudeness_2" (M)
## (Conditional Effects [a] of X on M)
## ─────────────────────────────────────────────────────────────────────
##  "instigation_type" Effect    S.E.       t     p             [95% CI]
## ─────────────────────────────────────────────────────────────────────
##  prejudice          -1.700 (0.128) -13.232 <.001 *** [-1.953, -1.447]
##  traditional        -1.950 (0.119) -16.426 <.001 *** [-2.184, -1.716]
## ─────────────────────────────────────────────────────────────────────
## 
## Running 100 * 2 simulations...
## Indirect Path: "manipulation_label" (X) ==> "rudeness_2" (M) ==> "socialrewards" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ──────────────────────────────────────────────────────────────────
##  "instigation_type" Effect    S.E.     z     p       [Boot 95% CI]
## ──────────────────────────────────────────────────────────────────
##  prejudice           0.525 (0.128) 4.089 <.001 *** [ 0.304, 0.766]
##  traditional         0.603 (0.117) 5.162 <.001 *** [ 0.389, 0.834]
## ──────────────────────────────────────────────────────────────────
## Percentile Bootstrap Confidence Interval
## (SE and CI are estimated based on 100 Bootstrap samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

Response

Would you respond to Jeff’s email? If so, what would you say? (0 = No, 1 = Yes)

## , , response = 0
## 
##                   instigation_type
## manipulation_label    prejudice traditional
##        Uncivil      0        26          37
##        NonResponse  0        41          32
##        Civil        0        30          38
## 
## , , response = 1
## 
##                   instigation_type
## manipulation_label    prejudice traditional
##        Uncivil      0        14          19
##        NonResponse  0        14           7
##        Civil        0        20           9
## 
## Call:
## glm(formula = response ~ manipulation_label * instigation_type, 
##     family = "binomial", data = uncivilpilot2_clean)
## 
## Coefficients:
##                                                           Estimate Std. Error z value Pr(>|z|)  
## (Intercept)                                                -0.6190     0.3315   -1.87    0.062 .
## manipulation_labelNonResponse                              -0.4555     0.4536   -1.00    0.315  
## manipulation_labelCivil                                     0.2136     0.4396    0.49    0.627  
## instigation_typetraditional                                -0.0474     0.4354   -0.11    0.913  
## manipulation_labelNonResponse:instigation_typetraditional  -0.3979     0.6778   -0.59    0.557  
## manipulation_labelCivil:instigation_typetraditional        -0.9875     0.6406   -1.54    0.123  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 345.22  on 286  degrees of freedom
## Residual deviance: 335.86  on 281  degrees of freedom
##   (292 observations deleted due to missingness)
## AIC: 347.9
## 
## Number of Fisher Scoring iterations: 4

For those who did respond, how did they rate their response?

Means

Effect sizes and differences

Controls

Controlling for rudeness, response, age

Reference for uncivil factor: Uncivil; reference for context: prejudice

Interacting with rudeness

bind_rows(dlist) %>% select(-p.value) %>% pivot_wider(names_from = variable, values_from = asterix)