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 |
|---|---|---|---|---|
| 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 |
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 |
|---|---|---|---|---|
| socreward1 | at the next work event: | -3. I would avoid Jeff | 0. I would neither avoid nor approach Jeff | 3. I would approach Jeff |
| 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 |
| 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 |
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
Inhibition
- Uninhibited
- not scared to initiate conflict with wrongdoers
Perceptions of Paul
When Jeff responded to Paul, did you feel…
- Uncomfortable
- Protected
- Empowered
- Worried
- Embarrassed
- Nervous
- Anxious
- Distracted
- Nervous
- Anxious
- Conspicuous
- Lonely
- Self-conscious
- Singled out
- Uncomfortable
- Bored
- Interested
- Confused
- Happy
- Proud
- Activated
- Stimulated
- Stirred up
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?
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
Focused analyses: comparing civility, incivility, and nonresponses within traditional and prejudice conditions
NS differences btwn uncivil/civil conditions
(+) differences btwn uncivil/civil conditions (ref: uncivil)
NS differences btwn uncivil/nonresponse conditions
(+) differences btwn uncivil/nonresponse conditions (ref: uncivil)
Comparing Prejudice to General
Within prejudice condition
Controlling for instigator’s rudeness
Effect sizes and differences
Graphs
## $status
##
## $rewards
##
## $socialrewards
##
## $agency
##
## $deter
##
## $confident
##
## $skillful
##
## $competitive
##
## $powerful
##
## $capable
##
## $agentic
##
## $uninhibited
##
## $conflictinit
##
## $uncomfortable
##
## $protected
##
## $empowered
##
## $worried
##
## $embarrassed
##
## $nervous
##
## $anxious
##
## $distracted
##
## $conspicuous
##
## $lonely
##
## $selfconscious
##
## $singledout
Interacting condition with instigator’s rudeness
Effect sizes and differences
Exploratory
Rudeness: 3-way
Status
##
## Call:
## lm(formula = status ~ manipulation_label * rudeness_2 * instigation_type,
## data = uncivilpilot3_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.620 -0.678 0.068 0.857 3.439
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3538 0.2720 1.30 0.19420
## manipulation_labelCivil 0.1693 0.4563 0.37 0.71089
## manipulation_labelNonResponse -0.8962 0.3331 -2.69 0.00747 **
## rudeness_2 0.4222 0.1218 3.47 0.00059 ***
## instigation_typeprejudice 0.8510 0.3255 2.61 0.00931 **
## manipulation_labelCivil:rudeness_2 -0.0165 0.1943 -0.09 0.93216
## manipulation_labelNonResponse:rudeness_2 0.1166 0.1736 0.67 0.50220
## manipulation_labelCivil:instigation_typeprejudice -0.8980 0.6593 -1.36 0.17400
## manipulation_labelNonResponse:instigation_typeprejudice -1.3492 0.4062 -3.32 0.00099 ***
## rudeness_2:instigation_typeprejudice 0.1044 0.1569 0.67 0.50629
## manipulation_labelCivil:rudeness_2:instigation_typeprejudice 0.1432 0.2795 0.51 0.60867
## manipulation_labelNonResponse:rudeness_2:instigation_typeprejudice -0.1772 0.2250 -0.79 0.43162
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.23 on 361 degrees of freedom
## Multiple R-squared: 0.519, Adjusted R-squared: 0.504
## F-statistic: 35.4 on 11 and 361 DF, p-value: <0.0000000000000002
Agency
##
## Call:
## lm(formula = agency ~ manipulation_label * rudeness_2 * instigation_type,
## data = uncivilpilot3_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.123 -0.638 0.051 0.716 3.661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6297 0.2523 18.35 < 0.0000000000000002 ***
## manipulation_labelCivil -0.9463 0.4233 -2.24 0.0260 *
## manipulation_labelNonResponse -0.9660 0.3090 -3.13 0.0019 **
## rudeness_2 0.5064 0.1130 4.48 0.00001 ***
## instigation_typeprejudice 0.1481 0.3019 0.49 0.6241
## manipulation_labelCivil:rudeness_2 -0.0280 0.1802 -0.16 0.8768
## manipulation_labelNonResponse:rudeness_2 -0.0957 0.1610 -0.59 0.5529
## manipulation_labelCivil:instigation_typeprejudice -0.0166 0.6115 -0.03 0.9783
## manipulation_labelNonResponse:instigation_typeprejudice -0.4732 0.3768 -1.26 0.2100
## rudeness_2:instigation_typeprejudice -0.1753 0.1455 -1.20 0.2291
## manipulation_labelCivil:rudeness_2:instigation_typeprejudice 0.1823 0.2593 0.70 0.4824
## manipulation_labelNonResponse:rudeness_2:instigation_typeprejudice 0.0641 0.2087 0.31 0.7591
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.14 on 361 degrees of freedom
## Multiple R-squared: 0.328, Adjusted R-squared: 0.308
## F-statistic: 16 on 11 and 361 DF, p-value: <0.0000000000000002
Deter
##
## Call:
## lm(formula = deter ~ manipulation_label * rudeness_2 * instigation_type,
## data = uncivilpilot3_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0913 -0.4502 -0.0642 0.4224 2.8816
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.0919 0.1734 23.60 <0.0000000000000002 ***
## manipulation_labelCivil -0.5802 0.2909 -1.99 0.0468 *
## manipulation_labelNonResponse -0.6417 0.2124 -3.02 0.0027 **
## rudeness_2 0.1914 0.0777 2.47 0.0142 *
## instigation_typeprejudice -0.3214 0.2075 -1.55 0.1223
## manipulation_labelCivil:rudeness_2 -0.0930 0.1238 -0.75 0.4529
## manipulation_labelNonResponse:rudeness_2 -0.2944 0.1107 -2.66 0.0082 **
## manipulation_labelCivil:instigation_typeprejudice 0.1832 0.4203 0.44 0.6631
## manipulation_labelNonResponse:instigation_typeprejudice -0.0917 0.2590 -0.35 0.7233
## rudeness_2:instigation_typeprejudice -0.1178 0.1000 -1.18 0.2397
## manipulation_labelCivil:rudeness_2:instigation_typeprejudice 0.1725 0.1782 0.97 0.3338
## manipulation_labelNonResponse:rudeness_2:instigation_typeprejudice 0.1936 0.1435 1.35 0.1780
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.786 on 361 degrees of freedom
## Multiple R-squared: 0.137, Adjusted R-squared: 0.11
## F-statistic: 5.2 on 11 and 361 DF, p-value: 0.000000133
Rudeness as a mediator
Moderated mediation
DV: Status
##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model ID : 7
## Model Type : Moderated Mediation
## - Outcome (Y) : status
## - Predictor (X) : instigation_type (recoded: =0, traditional=1, prejudice=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 be interpreted as "main effect"!
##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────
## (1) status (2) rudeness_2 (3) status
## ────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 0.405 *** -1.317 *** 1.124 ***
## (0.091) (0.116) (0.141)
## instigation_type 0.185 1.050 *** 0.224
## (0.181) (0.233) (0.130)
## manipulation_labelCivil 3.593 *** -0.433
## (0.164) (0.236)
## manipulation_labelNonResponse 1.676 *** -1.727 ***
## (0.165) (0.178)
## instigation_type:manipulation_labelCivil -0.900 **
## (0.329)
## instigation_type:manipulation_labelNonResponse -1.889 ***
## (0.330)
## rudeness_2 0.548 ***
## (0.048)
## ────────────────────────────────────────────────────────────────────────────────────────
## R^2 0.003 0.587 0.495
## Adj. R^2 0.000 0.581 0.489
## Num. obs. 373 373 373
## ────────────────────────────────────────────────────────────────────────────────────────
## 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 : 373
## Random Seed : set.seed()
## Simulations : 5000 (Bootstrap)
##
## Direct Effect: "instigation_type" (X) ==> "status" (Y)
## ────────────────────────────────────────────────────────────
## Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────
## Direct (c') 0.224 (0.130) 1.724 .086 . [-0.032, 0.480]
## ────────────────────────────────────────────────────────────
##
## Interaction Effect on "rudeness_2" (M)
## ──────────────────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────────────────
## instigation_type * manipulation_label 16.39 2 367 <.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 0.150 (0.232) 0.647 .518 [-0.306, 0.606]
## NonResponse -0.840 (0.234) -3.588 <.001 *** [-1.300, -0.380]
## Uncivil 1.050 (0.233) 4.508 <.001 *** [ 0.592, 1.507]
## ──────────────────────────────────────────────────────────────────────
##
## Running 5000 * 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 (0.096) 0.856 .392 [-0.110, 0.275]
## NonResponse -0.460 (0.152) -3.034 .002 ** [-0.766, -0.179]
## Uncivil 0.575 (0.169) 3.399 <.001 *** [ 0.267, 0.932]
## ──────────────────────────────────────────────────────────────────────
## 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 ID : 7
## Model Type : Moderated Mediation
## - Outcome (Y) : status
## - Predictor (X) : manipulation_label (recoded: Uncivil=0, Civil=1, NonResponse=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 be interpreted as "main effect"!
##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────
## (1) status (2) rudeness_2 (3) status
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 0.929 *** 0.185 0.641 ***
## (0.091) (0.108) (0.109)
## manipulation_label 1.551 *** 4.046 *** -0.259
## (0.183) (0.217) (0.273)
## instigation_typeprejudice 0.598 *** 0.603 ***
## (0.157) (0.160)
## manipulation_label:instigation_typeprejudice -0.900 **
## (0.314)
## rudeness_2 0.500 ***
## (0.062)
## ──────────────────────────────────────────────────────────────────────────────────────
## R^2 0.226 0.694 0.447
## Adj. R^2 0.222 0.691 0.440
## Num. obs. 249 249 249
## ──────────────────────────────────────────────────────────────────────────────────────
## 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 : 249
## Random Seed : set.seed()
## Simulations : 5000 (Bootstrap)
##
## Direct Effect: "manipulation_label" (X) ==> "status" (Y)
## ─────────────────────────────────────────────────────────────
## Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────
## Direct (c') -0.259 (0.273) -0.949 .344 [-0.798, 0.279]
## ─────────────────────────────────────────────────────────────
##
## Interaction Effect on "rudeness_2" (M)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## manipulation_label * instigation_type 8.23 1 245 .004 **
## ─────────────────────────────────────────────────────────────
##
## 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 3.147 (0.227) 13.889 <.001 *** [2.700, 3.593]
## traditional 4.046 (0.217) 18.668 <.001 *** [3.619, 4.473]
## ──────────────────────────────────────────────────────────────────
##
## Running 5000 * 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 1.572 (0.281) 5.597 <.001 *** [ 1.034, 2.138]
## traditional 2.021 (0.372) 5.426 <.001 *** [ 1.280, 2.746]
## ──────────────────────────────────────────────────────────────────
## 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. :)Controls
Controlling for rudeness, response, age
Reference for uncivil factor: Uncivil; reference for context: prejudice
Social Rewards