Person
| Manipulation text | explanation |
|---|---|
| Jeff | Man was confronter/seeker |
| Jane | Woman was confronter/seeker |
Solicitation (cond_person)
All participants saw “Your comment about women is pretty messed up” before the text below.
| Manipulation label | |
|---|---|
| noseeking | [participants didn’t see any additional text |
| indirect | [participant] probably doesn’t appreciate it either. |
| general | What did others think about it? |
| direct_think | “[participant]…what do you think of Paul’s comment?” |
| direct_describe | “[participant]…will you describe how Paul’s comment made you feel?” |
| direct_explain | “[participant]…could you explain why Paul’s comment was discriminatory?” |
Items
Competence
When {he/she} responded to Paul, did you think that {Jeff/Jane} was…(1 = Not at all, 4 = Somewhat, 7 = Very much so)
| Item label | Item text |
|---|---|
| Comp1 | competent at reducing sexism? |
| Comp2 | competent in managing difficult conversations? |
Tokenism
{Jeff/Jane} led me to feel…(1 = Not at all, 4 = Somewhat, 7 = Very much so)
| Item label | Item text |
|---|---|
| token_1 | worried that I stood out because I am a woman |
| token_2 | feel like my skills and knowledge as a woman were made salient |
| token_3 | feel like a “token” representative of women |
Empowerment
{Jeff/Jane} led me to feel…(1 = Not at all, 4 = Somewhat, 7 = Very much so)
| Item label | Item text |
|---|---|
| empower_exp_2 | supported |
| empower_exp_3 | empowered |
| empower_exp_4 | like I have influence |
| empower_exp_4.1 | inspired |
Agency
When {Jeff/Jane} responded to Paul, did you think that {Jeff/Jane} was…(1 = Not at all, 4 = Somewhat, 7 = Very much so)
| Item label | Item text |
|---|---|
| agency_1 | powerful |
| agency_2 | capable |
| agency_3 | agentic |
Warm
When {Jeff/Jane} responded to Paul, did you think that {Jeff/Jane} was…(1 = Not at all, 4 = Somewhat, 7 = Very much so)
| Item label | Item text |
|---|---|
| warm_1 | warm |
| warm_2 | friendly |
| warm_3 | caring |
Status
Do you think that {Jeff/Jane} should experience any of the following changes after {his/her} response to Paul?
| Item label | Item text | - 3 | 0 | 3 |
|---|---|---|---|---|
| j_posstat1 | After {his/her} response back to Paul, I think{Jeff/Jane} is worthy of…: | -3. A lot of disrespect | 0. Neither disrespect nor respect | 3. A lot of respect |
| j_posstat2 | After {his/her} response back to Paul, I hold{Jeff/Jane}… | -3. In very low regard | 0. In neither low regard nor high regard | 3. In very high regard |
| j_posstat3 | After {his/her} response back to Paul, in terms of being like{Jeff/Jane}…: | -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/Jane} should experience any of the following changes after {his/her} response to Paul?
| Item label | Item text | - 3 | 0 | 3 |
|---|---|---|---|---|
| j_reward1 | change in {his/her} salary: | -3. should definitely be decreased | 0. would keep the same | 3. should definitely be increased |
| j_reward2 | change in {his/her} job rank: | -3. should definitely be demoted | 0. would keep the same | 3. should definitely be promoted |
| j_reward3 | change in visibility of {his/her} 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/her} public recognition: | -3. Should definitely be decreased | 0. Should be kept the same | 3. Should definitely be increased |
Manipulations
##
## Direct General Indirect NoSeeking
## 30 73 84 82##
## ManConfronter WomanConfronter
## 132 137Analyses
Means and SDs
Significant interactions?
Because this is exploratory, I included all items in each scale.
Coded all interactions @ p<.05 w/ “”, all interactions @ p< .01 w/ ””, all interactions @ p< .001 w/ ””
Significant comparisons (Interactions)
effect that correspond to a significant estiimate are coded with asterices
Graphs
Main effects (controlling)
Responses
All comparisons
##
## Call:
## glm(formula = response ~ manipulation * condition, family = "binomial",
## data = diffidp2raw)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.0606 0.3483 0.17 0.86
## manipulationDirect -0.5715 0.6229 -0.92 0.36
## manipulationGeneral -0.3791 0.4788 -0.79 0.43
## manipulationIndirect -0.2838 0.4597 -0.62 0.54
## conditionWomanConfronter 0.1442 0.4515 0.32 0.75
## manipulationDirect:conditionWomanConfronter 0.0790 0.8730 0.09 0.93
## manipulationGeneral:conditionWomanConfronter 0.4620 0.6546 0.71 0.48
## manipulationIndirect:conditionWomanConfronter 0.8899 0.6436 1.38 0.17
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 372.61 on 268 degrees of freedom
## Residual deviance: 362.93 on 261 degrees of freedom
## AIC: 378.9
##
## Number of Fisher Scoring iterations: 4Controls
##
## Call:
## glm(formula = response ~ manipulation + condition, family = "binomial",
## data = diffidp2raw)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.182 0.268 -0.68 0.496
## manipulationDirect -0.489 0.438 -1.12 0.264
## manipulationGeneral -0.111 0.326 -0.34 0.734
## manipulationIndirect 0.169 0.317 0.53 0.594
## conditionWomanConfronter 0.554 0.249 2.22 0.026 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 372.61 on 268 degrees of freedom
## Residual deviance: 365.08 on 264 degrees of freedom
## AIC: 375.1
##
## Number of Fisher Scoring iterations: 4## (Intercept) manipulationDirect manipulationGeneral manipulationIndirect conditionWomanConfronter
## 0.8334 0.6132 0.8949 1.1843 1.7396##
## Call:
## glm(formula = response ~ manipulation, family = "binomial", data = diffidp2raw %>%
## filter(condition == "WomanConfronter"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2048 0.2872 0.71 0.48
## manipulationDirect -0.4925 0.6117 -0.81 0.42
## manipulationGeneral 0.0829 0.4463 0.19 0.85
## manipulationIndirect 0.6061 0.4504 1.35 0.18
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 186.04 on 136 degrees of freedom
## Residual deviance: 182.49 on 133 degrees of freedom
## AIC: 190.5
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = response ~ manipulation, family = "binomial", data = diffidp2raw %>%
## filter(condition == "ManConfronter"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.0606 0.3483 0.17 0.86
## manipulationDirect -0.5715 0.6229 -0.92 0.36
## manipulationGeneral -0.3791 0.4788 -0.79 0.43
## manipulationIndirect -0.2838 0.4597 -0.62 0.54
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 181.50 on 131 degrees of freedom
## Residual deviance: 180.44 on 128 degrees of freedom
## AIC: 188.4
##
## Number of Fisher Scoring iterations: 4
Social Rewards