Summary of Idea

We want to understand the psychology behind selecting people for diversity initiatives. My lay theory is that people think that disadvantaged group members (DGM) are best-suited for taking on diversity initiatives. However, it is not clear why.

Predictions

Managers are more likely to solicit voice from a marginalized (vs. non-marginalized) person about ``DEI work’’ - i.e., work that is meant to improve DEI in the organization.
They make this choice because they believe that DGM would provide more useful input. By useful input, we mean:

2a. Creativity
2b. Expertise
2c. Perceptions of Solicitee’s Interest

Contributions

1. Voice Solicitation

This allows us to also develop theory about who people choose to solicit voice from.

1a. This allows us to show that people seek to solicit voice from people who they think are most likely to provide meaningful input.
1b. Past work has focused on why we would (or would) not solicit voice, and the outcomes of soliciting voice, but not who we choose to solicit voice from.

Moreover, we are able to identify a misperception between what CAN be contributed, and the target’s actual ability to contribute.

2. Diversity Work

We propose an under-theorized prescriptive norm: That DGM should do diversity work.

1a. We identify what drives this norm: primarily, beliefs about:
++ Creativity.
++ Expertise.
++ Perceptions of Solicitee’s Interest.

We can also illustrate how there is a possible misperception between what CAN be contributed, and the target’s actual ability to contribute.

Study Summaries

Pilot 1

I asked participants to imagine that they are going to make recommendations to a CEO about “leading teams into the 21st century”. I manipulated if the team was focused on making strategic marketing improvements or gender diversity improvements.

I presented four candidates: Bill, Thomas, Jessica, and Kathleen. Each candidate also had a piece of information: e.g., “leads new recruit day”, and a position e.g., “market specialist”. I counterbalanced these across candidates.

For each candidate, I asked participants to:
- Indicate if they saw the candidate has having the right job fit - Indicate if they think it would be helpful to solicit the candidate’s voice

Finally, I asked participants to indicate which candidate they would want to select.

Measures

Job Fit Items. 1. [Female Candidate’s Name Here]’s abilities fit the demands of this task force. 2. [Female Candidate’s Name Here] has the right skills and abilities for participating in this task force. 3. There is a good match between the requirements of this task force and [Feale Candidate’s Name Here]’s skills. 4. [Female Candidate’s Name Here]’s abilities and training are a good fit with the requirements of the task force. 5. [Female Candidate’s Name Here]’s personal abilities and education provide a good match with the demands that this task force would place on him.

Voice Solicitation Items. 1. I would ask [Male Candidate’s Name Here] to tell me about things that he thinks would be helpful for this task force. 2. I would ask [Male Candidate’s Name Here] personally to tell me about what he has done in a similar task force. 3. I would seek out advice related to this task force from [Male Candidate’s Name Here]. 4. I would ask [Male Candidate’s Name Here] personally what skills he has that I may not know about which could contribute to this task force.

Link to full report is here

Graphs

Likert Variables - VS and JF

Likert Variables - Considerations

Ordinal Variables

Correlations

		Woman		Man		Bill		Thomas		Kathleen		Jessica		Considerations
	Condition	Woman VS	Woman JF	Man VS	Man JF	Bill JF	Bill VS	Thomas JF	Thomas VS	Kathleen JF	Kathleen VS	Jessica JF	Jessica VS	Qualif.	Skills	Fit w/ Needs	Div.	Gend.	Workloads	Standing
Condition
Woman VS	0.18*
Woman JF	0.19**	0.67***
Man VS	-0.19*	0.32***	0.02
Man JF	-0.20**	-0.01	0.11	0.55***
Bill JF	-0.10	-0.09	0.04	0.45***	0.81***
Bill VS	-0.12	0.20**	-0.03	0.83***	0.52***	0.68***
Thomas JF	-0.23**	0.08	0.13	0.43***	0.77***	0.26***	0.12
Thomas VS	-0.19**	0.33***	0.07	0.82***	0.39***	0.05	0.35***	0.60***
Kathleen JF	0.14	0.49***	0.71***	0.03	0.14	0.00	-0.03	0.23**	0.08
Kathleen VS	0.09	0.85***	0.50***	0.35***	0.02	-0.10	0.20**	0.14	0.38***	0.63***
Jessica JF	0.15*	0.52***	0.78***	0.01	0.03	0.06	-0.01	-0.01	0.02	0.12	0.15*
Jessica VS	0.22**	0.86***	0.64***	0.20**	-0.03	-0.05	0.14	0.00	0.19*	0.21**	0.45***	0.72***
Qualifications	-0.17*	0.24**	0.20**	0.23**	0.08	0.08	0.17*	0.04	0.21**	0.14	0.24**	0.16*	0.17*
Skills	-0.17*	0.28***	0.22**	0.19*	0.08	0.07	0.15*	0.06	0.16*	0.16*	0.22**	0.17*	0.25***	0.69***
Fit w/ Needs	-0.08	0.21**	0.18*	0.18*	0.06	0.11	0.19*	-0.01	0.12	0.07	0.15*	0.19**	0.21**	0.75***	0.61***
Diversity	0.41***	0.27***	0.26***	-0.21**	-0.23**	-0.28***	-0.31***	-0.07	-0.04	0.24**	0.21**	0.15*	0.24**	0.01	0.01	0.09
Gender	0.55***	0.24**	0.25***	-0.28***	-0.24**	-0.26***	-0.33***	-0.12	-0.12	0.19*	0.16*	0.19*	0.24***	-0.17*	-0.13	-0.05	0.76***
Workloads	0.06	0.21**	0.16*	0.19*	0.13	0.06	0.09	0.15*	0.22**	0.07	0.17*	0.16*	0.18*	0.25***	0.23**	0.19**	0.25***	0.21**
Standing	0.03	0.16*	0.13	0.16*	0.15*	0.10	0.10	0.15*	0.17*	0.14	0.16*	0.07	0.11	0.36***	0.26***	0.24**	0.21**	0.15*	0.60***
Interest in Goals	0.11	0.29***	0.22**	0.16*	0.02	0.00	0.11	0.03	0.16*	0.15*	0.19**	0.18*	0.30***	0.40***	0.36***	0.41***	0.34***	0.20**	0.54***	0.49***

psych::mediate(chose_woman_bin~cond_num+(woman_jf)+(standing), data=dei1_clean)

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = chose_woman_bin ~ cond_num + (woman_jf) + 
##     (standing), data = dei1_clean)
## 
## The DV (Y) was  chose_woman_bin . The IV (X) was  cond_num . The mediating variable(s) =  woman_jf standing .
## 
## Total effect(c) of  cond_num  on  chose_woman_bin  =  0.27   S.E. =  0.06  t  =  4.26  df=  179   with p =  3.3e-05
## Direct effect (c') of  cond_num  on  chose_woman_bin  removing  woman_jf standing  =  0.2   S.E. =  0.06  t  =  3.45  df=  177   with p =  0.00069
## Indirect effect (ab) of  cond_num  on  chose_woman_bin  through  woman_jf standing   =  0.07 
## Mean bootstrapped indirect effect =  0.07  with standard error =  0.03  Lower CI =  0.01    Upper CI =  0.14
## R = 0.55 R2 = 0.3   F = 25.55 on 3 and 177 DF   p-value:  1.02e-16 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

psych::mediate(chose_woman_bin~cond_num+(woman_jf)+(gender), data=dei1_clean)

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = chose_woman_bin ~ cond_num + (woman_jf) + 
##     (gender), data = dei1_clean)
## 
## The DV (Y) was  chose_woman_bin . The IV (X) was  cond_num . The mediating variable(s) =  woman_jf gender .
## 
## Total effect(c) of  cond_num  on  chose_woman_bin  =  0.27   S.E. =  0.06  t  =  4.26  df=  179   with p =  3.3e-05
## Direct effect (c') of  cond_num  on  chose_woman_bin  removing  woman_jf gender  =  0.06   S.E. =  0.07  t  =  0.87  df=  177   with p =  0.38
## Indirect effect (ab) of  cond_num  on  chose_woman_bin  through  woman_jf gender   =  0.22 
## Mean bootstrapped indirect effect =  0.22  with standard error =  0.05  Lower CI =  0.12    Upper CI =  0.33
## R = 0.59 R2 = 0.34   F = 30.8 on 3 and 177 DF   p-value:  1.85e-19 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

psych::mediate(chose_woman_bin~cond_num+(woman_jf)+(diversity), data=dei1_clean)

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = chose_woman_bin ~ cond_num + (woman_jf) + 
##     (diversity), data = dei1_clean)
## 
## The DV (Y) was  chose_woman_bin . The IV (X) was  cond_num . The mediating variable(s) =  woman_jf diversity .
## 
## Total effect(c) of  cond_num  on  chose_woman_bin  =  0.27   S.E. =  0.06  t  =  4.26  df=  179   with p =  3.3e-05
## Direct effect (c') of  cond_num  on  chose_woman_bin  removing  woman_jf diversity  =  0.13   S.E. =  0.06  t  =  2.14  df=  177   with p =  0.034
## Indirect effect (ab) of  cond_num  on  chose_woman_bin  through  woman_jf diversity   =  0.14 
## Mean bootstrapped indirect effect =  0.14  with standard error =  0.04  Lower CI =  0.06    Upper CI =  0.23
## R = 0.56 R2 = 0.31   F = 26.59 on 3 and 177 DF   p-value:  2.79e-17 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

psych::mediate(woman_vs~cond_num+(woman_jf)+(gender), data=dei1_clean)

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = woman_vs ~ cond_num + (woman_jf) + (gender), 
##     data = dei1_clean)
## 
## The DV (Y) was  woman_vs . The IV (X) was  cond_num . The mediating variable(s) =  woman_jf gender .
## 
## Total effect(c) of  cond_num  on  woman_vs  =  0.34   S.E. =  0.15  t  =  2.35  df=  179   with p =  0.02
## Direct effect (c') of  cond_num  on  woman_vs  removing  woman_jf gender  =  0.03   S.E. =  0.13  t  =  0.22  df=  177   with p =  0.82
## Indirect effect (ab) of  cond_num  on  woman_vs  through  woman_jf gender   =  0.31 
## Mean bootstrapped indirect effect =  0.31  with standard error =  0.12  Lower CI =  0.09    Upper CI =  0.54
## R = 0.68 R2 = 0.46   F = 50.15 on 3 and 177 DF   p-value:  3.62e-28 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

psych::mediate(woman_vs~cond_num+(woman_jf)+(diversity), data=dei1_clean)

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = woman_vs ~ cond_num + (woman_jf) + (diversity), 
##     data = dei1_clean)
## 
## The DV (Y) was  woman_vs . The IV (X) was  cond_num . The mediating variable(s) =  woman_jf diversity .
## 
## Total effect(c) of  cond_num  on  woman_vs  =  0.34   S.E. =  0.15  t  =  2.35  df=  179   with p =  0.02
## Direct effect (c') of  cond_num  on  woman_vs  removing  woman_jf diversity  =  0.02   S.E. =  0.12  t  =  0.19  df=  177   with p =  0.85
## Indirect effect (ab) of  cond_num  on  woman_vs  through  woman_jf diversity   =  0.32 
## Mean bootstrapped indirect effect =  0.32  with standard error =  0.11  Lower CI =  0.11    Upper CI =  0.54
## R = 0.68 R2 = 0.46   F = 51.08 on 3 and 177 DF   p-value:  1.54e-28 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

Important Analyses

Participants in the diversity condition were more likely to choose a woman than participants in the strategy condition.

summary(lm(chose_woman_bin~condition, dei1_clean))

## 
## Call:
## lm(formula = chose_woman_bin ~ condition, data = dei1_clean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.8478 -0.5730  0.1522  0.4270  0.4270 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.84783    0.04520  18.757  < 2e-16 ***
## conditionstrategy -0.27479    0.06446  -4.263 3.26e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4335 on 179 degrees of freedom
## Multiple R-squared:  0.09217,    Adjusted R-squared:  0.0871 
## F-statistic: 18.17 on 1 and 179 DF,  p-value: 3.258e-05

with(dei1_clean, table(chose_woman,condition)/nrow(dei1_clean))

##            condition
## chose_woman  diversity   strategy
##         No  0.07734807 0.20994475
##         Yes 0.43093923 0.28176796

Pilot 2 - Gender Typed Tasks

I asked participants to evaluate four roles. I adapted each job role from Heilman et al. (2004) - financial planning analyst (which they found was considered to be masculine), employee assistant analyst (which they found was considered to be feminine), and training analyst (which they found was considered to be neutral: i.e., in between masculine and feminine). Using these three roles as templates, I made a “gender diversity training analyst” role.

Goal

I predicted that people think a candidate’s gender is more relevant for the DEI position than other positions. The other items were filler to distract participants from the DEI items.

Study Description

For each role, I asked participants to imagine that they were hiring for that role

Then, I asked participants the following:
1. Nationwide, what percentage of people in each role do you think are women?
2. To what extent do you think that other people see each role as more masculine or feminine (1 = Very Masculine, 3 = Slightly Masculine, 4 = Neither masculine nor feminine, 5 = Slightly feminine, 7 = Very Feminine; I adapted this from Heilman et al., 2004)?
3. What is the ideal gender that you think others have in mind when hiring for these positions (1 = Male, 2 = Female, 3 = Non-binary, 4 = Not listed, 5 = Does Not Matter)?
4. How important do you think it is that the person in this position is a woman?

Here are the Roles (and their descriptions which I included):

Financial Planning Analyst:.
Providing financial planning information to employees. Informing employees about within-company benefit options through individual appointments and in-house workshops.
Locating out-of-company sources that can aid them in mapping out long-term financial strategies for themselves and their families.
Being good with numbers and knowledgeable about banking, insurance, accounting, and bond and equity investment.
Staying abreast of programs and practices within the industry concerning life insurance and mortgage assistance.

Employee Assistance Analyst:.
Providing assistance to employees with personal and family problems.
Counseling employees about mental health problems through individual appointments and in-house workshop.
Referring employees to professionals who can aid them in coping with issues affecting their work performance.
Having good interpersonal skills, sensitivity to the concerns of others, and the ability to build trusting relationships.
Staying updated of programs and practices within the industry concerning on-site day care.

Training Analyst:.
Providing skill training to employees who seek to upgrade their roles within the company.
Informing employees about job advancement opportunities through individual appointments and in-house workshops.
Referring employees to professionals who can aid them in developing longterm career goals.
Good communication skills and knowledgeable about job and career planning.
Is up-to-date with programs and practices within the industry concerning paid leave for taking courses.

Gender Diversity, Equity, and Inclusion Analyst:.
Providing training to employees about how to make the workplace better for women.
Giving information to employees about upcoming company-wide events intended to honor, or promote, women.
Referring employees to professionals who can offer additional information about creating a diverse and inclusive environment.
Good communication skills, ability to navigate difficult conversations, and knowledgeable about the issues that women face in the workplace.
Stays current on programs and practices within the industry concerning practices for enhancing gender equity.

Qualifications:
I also asked them to report how important it was that the candidate possess the following characteristics (scale was 1 = Not at all important to 7 = Very important):
- Prior Experience.
- Time in similar roles.
- Prior performance in similar roles.
- The candidate’s gender.
- The candidate’s ethnicity.
- The candidate’s ability to connect with other people on the team.
- The candidate’s ability to connect with other people in the organization.
- The candidate’s comfort working with numbers.
- The candidate’s creativity.
- The candidate’s interpersonal warmth.

Next step:

Use these findings to justify the following study design:
1. I tell participants that their organization recently decided a new initiative, and I would like to know who they will solicit voice from. 2. I present three candidates, each with different experiences.
3. I manipulate:
- The focus of the initiative (DEI vs. Financial planning).
4. I measure:
- If participants pick the woman to so solicit voice in the DEI (vs. finacical planning) condition.
- How interested participants think the woman is in each condition (I expect people to assume she is more interested in the DEI condition, relative to the financial planning condition).

Works Cited/Studies Referenced

Heilman, M. E., Wallen, A. S., Fuchs, D., & Tamkins, M. M. (2004). Penalties for success: reactions to women who succeed at male gender-typed tasks. Journal of applied psychology, 89(3), 416.

Participant Summary

I collected data from 102 part-/and full-time workers on Prolific

Nationwide, what percentage of people in each role do you think are women?

## ANOVA Table (type III tests)
## 
## $ANOVA
##   Effect DFn DFd     F        p p<.05   ges
## 1   Role   3 297 81.85 1.29e-38     * 0.279
## 
## $`Mauchly's Test for Sphericity`
##   Effect     W        p p<.05
## 1   Role 0.766 8.99e-05     *
## 
## $`Sphericity Corrections`
##   Effect   GGe      DF[GG]    p[GG] p[GG]<.05   HFe       DF[HF]    p[HF]
## 1   Role 0.866 2.6, 257.22 8.35e-34         * 0.891 2.67, 264.77 1.02e-34
##   p[HF]<.05
## 1         *

Graph of “nationwide, what percentage of people in each role do you think are women?”

To what extent do you think that other people see each role as more masculine or feminine?

To what extent do you think that other people see each role as more masculine or feminine (1 = Very Masculine, 3 = Slightly Masculine, 4 = Neither masculine nor feminine, 5 = Slightly feminine, 7 = Very Feminine; I adapted this from Heilman et al., 2004)

## ANOVA Table (type III tests)
## 
## $ANOVA
##   Effect DFn DFd       F        p p<.05   ges
## 1   Role   3 297 143.128 2.23e-57     * 0.499
## 
## $`Mauchly's Test for Sphericity`
##   Effect     W        p p<.05
## 1   Role 0.798 0.000507     *
## 
## $`Sphericity Corrections`
##   Effect  GGe       DF[GG]    p[GG] p[GG]<.05   HFe       DF[HF]    p[HF]
## 1   Role 0.86 2.58, 255.52 9.34e-50         * 0.885 2.66, 262.96 4.01e-51
##   p[HF]<.05
## 1         *

Graphs of “to what extent do you think that other people see each role as more masculine or feminine?”

What is the ideal gender that you think others have in mind when hiring for these positions?

As a note: In the below analyses, I coded “1” if the participant said that women were the ideal candidates, and “0” if they did not select “woman”.

## ANOVA Table (type III tests)
## 
## $ANOVA
##   Effect DFn DFd      F        p p<.05   ges
## 1   Role   3 297 64.352 4.39e-32     * 0.277
## 
## $`Mauchly's Test for Sphericity`
##   Effect     W        p p<.05
## 1   Role 0.612 3.57e-09     *
## 
## $`Sphericity Corrections`
##   Effect   GGe       DF[GG]    p[GG] p[GG]<.05   HFe       DF[HF]    p[HF]
## 1   Role 0.814 2.44, 241.87 1.29e-26         * 0.837 2.51, 248.44 2.88e-27
##   p[HF]<.05
## 1         *

Graphs

As a note: In the below analyses, I coded “1” if the participant said that women were the ideal candidates, and “0” if they did not select “woman”.

## ANOVA Table (type III tests)
## 
## $ANOVA
##   Effect DFn DFd      F        p p<.05   ges
## 1   Role   3 297 58.201 1.27e-29     * 0.241
## 
## $`Mauchly's Test for Sphericity`
##   Effect     W        p p<.05
## 1   Role 0.291 2.29e-24     *
## 
## $`Sphericity Corrections`
##   Effect  GGe       DF[GG]    p[GG] p[GG]<.05   HFe      DF[HF]    p[HF]
## 1   Role 0.68 2.04, 201.92 5.78e-21         * 0.694 2.08, 206.2 2.35e-21
##   p[HF]<.05
## 1         *

Graphs

How important do you think it is that the person in this position is a woman?

## ANOVA Table (type III tests)
## 
## $ANOVA
##   Effect DFn DFd      F        p p<.05   ges
## 1   Role   3 297 53.492 1.13e-27     * 0.145
## 
## $`Mauchly's Test for Sphericity`
##   Effect     W        p p<.05
## 1   Role 0.206 1.65e-31     *
## 
## $`Sphericity Corrections`
##   Effect   GGe      DF[GG]    p[GG] p[GG]<.05   HFe       DF[HF]    p[HF]
## 1   Role 0.501 1.5, 148.65 4.09e-15         * 0.507 1.52, 150.43 2.89e-15
##   p[HF]<.05
## 1         *

Graphs

Candidate Qualifications

I asked all participants, for each position, to indicate how much they think the following qualifications are important for the position:
- Prior Experience.
- Time in similar roles.
- Prior performance in similar roles.
- The candidate’s gender.
- The candidate’s ethnicity.
- The candidate’s ability to connect with other people on the team.
- The candidate’s ability to connect with other people in the organization.
- The candidate’s comfort working with numbers.
- The candidate’s creativity.
- The candidate’s interpersonal warmth.

## $`Educ. Experience Means + SDs`
## # A tibble: 4 × 5
##   condition                 n  mean    sd    se
##   <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 1. dei                  100  5.01  1.52 0.152
## 2 2. emp_assist (fem)     100  5.18  1.39 0.139
## 3 3. financial (masc)     100  5.79  1.2  0.12 
## 4 4. training (neutral)   100  5.34  1.24 0.124
## 
## $`Educ. Experience Anova Table`
## $`Educ. Experience Anova Table`$ANOVA
##      Effect DFn DFd      F        p p<.05   ges
## 1 condition   3 297 10.322 1.75e-06     * 0.045
## 
## 
## $`Educ. Experience T-test Results`
##     .y.              group1                group2 statistic    p.adj
## 1 Value              1. dei   2. emp_assist (fem) -1.220370 1.000000
## 2 Value              1. dei   3. financial (masc) -4.502120 0.000110
## 3 Value              1. dei 4. training (neutral) -2.309605 0.138000
## 4 Value 2. emp_assist (fem)   3. financial (masc) -4.151010 0.000421
## 5 Value 2. emp_assist (fem) 4. training (neutral) -1.237705 1.000000
## 6 Value 3. financial (masc) 4. training (neutral)  3.013559 0.020000
##   p.adj.signif
## 1           ns
## 2          ***
## 3           ns
## 4          ***
## 5           ns
## 6            *
## 
## $`Graph of Educ. Experience`

## 
## $`Time in Similar Roles Means + SDs`
## # A tibble: 4 × 5
##   condition                 n  mean    sd     se
##   <fct>                 <dbl> <dbl> <dbl>  <dbl>
## 1 1. dei                  100  5.44 1.29  0.129 
## 2 2. emp_assist (fem)     100  5.66 1.10  0.110 
## 3 3. financial (masc)     100  6    0.974 0.0974
## 4 4. training (neutral)   100  5.78 1.02  0.102 
## 
## $`Time in Similar Roles Anova Table`
## $`Time in Similar Roles Anova Table`$ANOVA
##      Effect DFn DFd     F        p p<.05   ges
## 1 condition   3 297 9.065 9.24e-06     * 0.033
## 
## 
## $`Time in Similar Roles T-test Results`
##     .y.              group1                group2 statistic    p.adj
## 1 Value              1. dei   2. emp_assist (fem) -1.911347 0.353000
## 2 Value              1. dei   3. financial (masc) -4.265453 0.000274
## 3 Value              1. dei 4. training (neutral) -2.985014 0.021000
## 4 Value 2. emp_assist (fem)   3. financial (masc) -3.279020 0.009000
## 5 Value 2. emp_assist (fem) 4. training (neutral) -1.330268 1.000000
## 6 Value 3. financial (masc) 4. training (neutral)  2.198224 0.182000
##   p.adj.signif
## 1           ns
## 2          ***
## 3            *
## 4           **
## 5           ns
## 6           ns
## 
## $`Graph of Time in Similar Roles`

## 
## $`Prior Performance Means + SDs`
## # A tibble: 4 × 5
##   condition                 n  mean    sd     se
##   <fct>                 <dbl> <dbl> <dbl>  <dbl>
## 1 1. dei                  100  5.74 1.28  0.128 
## 2 2. emp_assist (fem)     100  5.92 1.02  0.102 
## 3 3. financial (masc)     100  6.14 0.932 0.0932
## 4 4. training (neutral)   100  5.93 1.02  0.102 
## 
## $`Prior Performance Anova Table`
## $`Prior Performance Anova Table`$ANOVA
##      Effect DFn DFd     F     p p<.05   ges
## 1 condition   3 297 4.724 0.003     * 0.017
## 
## 
## $`Prior Performance T-test Results`
##     .y.              group1                group2  statistic p.adj p.adj.signif
## 1 Value              1. dei   2. emp_assist (fem) -1.7173369 0.534           ns
## 2 Value              1. dei   3. financial (masc) -3.1078186 0.015            *
## 3 Value              1. dei 4. training (neutral) -1.6619611 0.598           ns
## 4 Value 2. emp_assist (fem)   3. financial (masc) -2.3182232 0.135           ns
## 5 Value 2. emp_assist (fem) 4. training (neutral) -0.1119519 1.000           ns
## 6 Value 3. financial (masc) 4. training (neutral)  2.0629295 0.250           ns
## 
## $`Graph of Prior Performance`

## 
## $`Gender Means + SDs`
## # A tibble: 4 × 5
##   condition                 n  mean    sd    se
##   <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 1. dei                  100  3.86  2.23 0.223
## 2 2. emp_assist (fem)     100  2.07  1.55 0.155
## 3 3. financial (masc)     100  1.77  1.30 0.130
## 4 4. training (neutral)   100  1.81  1.38 0.138
## 
## $`Gender Anova Table`
## $`Gender Anova Table`$ANOVA
##      Effect DFn DFd      F        p p<.05   ges
## 1 condition   3 297 74.417 6.37e-36     * 0.215
## 
## 
## $`Gender T-test Results`
##     .y.              group1                group2  statistic    p.adj
## 1 Value              1. dei   2. emp_assist (fem)  8.3168398 3.00e-12
## 2 Value              1. dei   3. financial (masc)  9.5295797 6.96e-15
## 3 Value              1. dei 4. training (neutral)  9.5232113 7.20e-15
## 4 Value 2. emp_assist (fem)   3. financial (masc)  3.1640537 1.20e-02
## 5 Value 2. emp_assist (fem) 4. training (neutral)  3.6139853 3.00e-03
## 6 Value 3. financial (masc) 4. training (neutral) -0.5424067 1.00e+00
##   p.adj.signif
## 1         ****
## 2         ****
## 3         ****
## 4            *
## 5           **
## 6           ns
## 
## $`Graph of Gender`

## 
## $`Ethnicity Means + SDs`
## # A tibble: 4 × 5
##   condition                 n  mean    sd    se
##   <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 1. dei                  100  2.76  1.98 0.198
## 2 2. emp_assist (fem)     100  1.89  1.49 0.149
## 3 3. financial (masc)     100  1.69  1.31 0.131
## 4 4. training (neutral)   100  1.71  1.27 0.127
## 
## $`Ethnicity Anova Table`
## $`Ethnicity Anova Table`$ANOVA
##      Effect DFn DFd    F        p p<.05   ges
## 1 condition   3 297 33.8 8.01e-19     * 0.076
## 
## 
## $`Ethnicity T-test Results`
##     .y.              group1                group2  statistic    p.adj
## 1 Value              1. dei   2. emp_assist (fem)  5.5495385 1.44e-06
## 2 Value              1. dei   3. financial (masc)  6.4024464 3.09e-08
## 3 Value              1. dei 4. training (neutral)  6.8187394 4.40e-09
## 4 Value 2. emp_assist (fem)   3. financial (masc)  2.5272706 7.90e-02
## 5 Value 2. emp_assist (fem) 4. training (neutral)  2.6191046 6.10e-02
## 6 Value 3. financial (masc) 4. training (neutral) -0.3229866 1.00e+00
##   p.adj.signif
## 1         ****
## 2         ****
## 3         ****
## 4           ns
## 5           ns
## 6           ns
## 
## $`Graph of Ethnicity`

## 
## $`Ability to connect with others on the team Means + SDs`
## # A tibble: 4 × 5
##   condition                 n  mean    sd    se
##   <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 1. dei                  100  5.99  1.18 0.119
## 2 2. emp_assist (fem)     100  6.15  1.09 0.109
## 3 3. financial (masc)     100  4.96  1.38 0.138
## 4 4. training (neutral)   100  5.98  1.12 0.112
## 
## $`Ability to connect with others on the team Anova Table`
## $`Ability to connect with others on the team Anova Table`$ANOVA
##      Effect DFn DFd      F        p p<.05   ges
## 1 condition   3 297 34.847 2.52e-19     * 0.136
## 
## 
## $`Ability to connect with others on the team T-test Results`
##     .y.              group1                group2   statistic    p.adj
## 1 Value              1. dei   2. emp_assist (fem) -1.16905750 1.00e+00
## 2 Value              1. dei   3. financial (masc)  6.99079408 1.94e-09
## 3 Value              1. dei 4. training (neutral)  0.08439677 1.00e+00
## 4 Value 2. emp_assist (fem)   3. financial (masc)  8.26180445 3.95e-12
## 5 Value 2. emp_assist (fem) 4. training (neutral)  2.02011705 2.77e-01
## 6 Value 3. financial (masc) 4. training (neutral) -7.17705385 7.98e-10
##   p.adj.signif
## 1           ns
## 2         ****
## 3           ns
## 4         ****
## 5           ns
## 6         ****
## 
## $`Graph of Ability to connect with others on the team`

## 
## $`Ability to connect with others in the org. Means + SDs`
## # A tibble: 4 × 5
##   condition                 n  mean    sd    se
##   <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 1. dei                  100  6.04  1.20 0.120
## 2 2. emp_assist (fem)     100  6.14  1.05 0.105
## 3 3. financial (masc)     100  5     1.41 0.141
## 4 4. training (neutral)   100  6.04  1.05 0.105
## 
## $`Ability to connect with others in the org. Anova Table`
## $`Ability to connect with others in the org. Anova Table`$ANOVA
##      Effect DFn DFd     F        p p<.05   ges
## 1 condition   3 297 36.77 3.09e-20     * 0.135
## 
## 
## $`Ability to connect with others in the org. T-test Results`
##     .y.              group1                group2  statistic    p.adj
## 1 Value              1. dei   2. emp_assist (fem) -0.8500751 1.00e+00
## 2 Value              1. dei   3. financial (masc)  6.8857382 3.20e-09
## 3 Value              1. dei 4. training (neutral)  0.0000000 1.00e+00
## 4 Value 2. emp_assist (fem)   3. financial (masc)  7.6466574 8.16e-11
## 5 Value 2. emp_assist (fem) 4. training (neutral)  1.2953633 1.00e+00
## 6 Value 3. financial (masc) 4. training (neutral) -7.5501729 1.31e-10
##   p.adj.signif
## 1           ns
## 2         ****
## 3           ns
## 4         ****
## 5           ns
## 6         ****
## 
## $`Graph of Ability to connect with others in the org.`

## 
## $`Comfort with numbers Means + SDs`
## # A tibble: 4 × 5
##   condition                 n  mean    sd    se
##   <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 1. dei                  100  3.55  2.02 0.202
## 2 2. emp_assist (fem)     100  3.52  1.87 0.187
## 3 3. financial (masc)     100  6.26  1.18 0.118
## 4 4. training (neutral)   100  4.04  1.81 0.181
## 
## $`Comfort with numbers Anova Table`
## $`Comfort with numbers Anova Table`$ANOVA
##      Effect DFn DFd      F        p p<.05   ges
## 1 condition   3 297 86.297 3.54e-40     * 0.295
## 
## 
## $`Comfort with numbers T-test Results`
##     .y.              group1                group2   statistic    p.adj
## 1 Value              1. dei   2. emp_assist (fem)   0.2382942 1.00e+00
## 2 Value              1. dei   3. financial (masc) -10.2458686 1.91e-16
## 3 Value              1. dei 4. training (neutral)  -3.7068343 2.00e-03
## 4 Value 2. emp_assist (fem)   3. financial (masc) -11.1743458 1.84e-18
## 5 Value 2. emp_assist (fem) 4. training (neutral)  -3.8461804 1.00e-03
## 6 Value 3. financial (masc) 4. training (neutral)   9.6023397 4.84e-15
##   p.adj.signif
## 1           ns
## 2         ****
## 3           **
## 4         ****
## 5           **
## 6         ****
## 
## $`Graph of Comfort with numbers`

## 
## $`Creativity Means + SDs`
## # A tibble: 4 × 5
##   condition                 n  mean    sd    se
##   <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 1. dei                  100  5.18  1.43 0.143
## 2 2. emp_assist (fem)     100  4.88  1.40 0.140
## 3 3. financial (masc)     100  4.29  1.52 0.152
## 4 4. training (neutral)   100  4.99  1.37 0.137
## 
## $`Creativity Anova Table`
## $`Creativity Anova Table`$ANOVA
##      Effect DFn DFd      F        p p<.05   ges
## 1 condition   3 297 12.031 1.87e-07     * 0.052
## 
## 
## $`Creativity T-test Results`
##     .y.              group1                group2  statistic    p.adj
## 1 Value              1. dei   2. emp_assist (fem)  1.9389414 3.32e-01
## 2 Value              1. dei   3. financial (masc)  4.7899163 3.52e-05
## 3 Value              1. dei 4. training (neutral)  1.2941487 1.00e+00
## 4 Value 2. emp_assist (fem)   3. financial (masc)  3.8525462 1.00e-03
## 5 Value 2. emp_assist (fem) 4. training (neutral) -0.8883594 1.00e+00
## 6 Value 3. financial (masc) 4. training (neutral) -4.1697838 3.92e-04
##   p.adj.signif
## 1           ns
## 2         ****
## 3           ns
## 4           **
## 5           ns
## 6          ***
## 
## $`Graph of Creativity`

## 
## $`Interpersonal warmth Means + SDs`
## # A tibble: 4 × 5
##   condition                 n  mean    sd    se
##   <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 1. dei                  100  6.01  1.23 0.123
## 2 2. emp_assist (fem)     100  6.27  1.14 0.114
## 3 3. financial (masc)     100  4.46  1.44 0.145
## 4 4. training (neutral)   100  5.88  1.17 0.117
## 
## $`Interpersonal warmth Anova Table`
## $`Interpersonal warmth Anova Table`$ANOVA
##      Effect DFn DFd      F        p p<.05   ges
## 1 condition   3 297 76.769 8.69e-37     * 0.243
## 
## 
## $`Interpersonal warmth T-test Results`
##     .y.              group1                group2 statistic    p.adj
## 1 Value              1. dei   2. emp_assist (fem) -2.596854 6.50e-02
## 2 Value              1. dei   3. financial (masc)  9.739324 2.43e-15
## 3 Value              1. dei 4. training (neutral)  1.260452 1.00e+00
## 4 Value 2. emp_assist (fem)   3. financial (masc) 11.403985 5.86e-19
## 5 Value 2. emp_assist (fem) 4. training (neutral)  4.006858 7.14e-04
## 6 Value 3. financial (masc) 4. training (neutral) -9.432639 1.13e-14
##   p.adj.signif
## 1           ns
## 2         ****
## 3           ns
## 4         ****
## 5          ***
## 6         ****
## 
## $`Graph of Interpersonal warmth`

Does participant’s gender matter?

In the following analyses, I tested if my findings varied by the participant’s gender.

(By Participant’s Gender): Nationwide, what percentage of people in each role do you think are women?

## ANOVA Table (type II tests)
## 
## $ANOVA
##                   Effect DFn DFd      F        p p<.05   ges
## 1      ParticipantGender   1  98  1.794 1.83e-01       0.010
## 2                   Role   3 294 81.283 2.55e-38     * 0.281
## 3 ParticipantGender:Role   3 294  0.314 8.15e-01       0.002
## 
## $`Mauchly's Test for Sphericity`
##                   Effect     W        p p<.05
## 1                   Role 0.765 9.41e-05     *
## 2 ParticipantGender:Role 0.765 9.41e-05     *
## 
## $`Sphericity Corrections`
##                   Effect   GGe      DF[GG]    p[GG] p[GG]<.05   HFe
## 1                   Role 0.865 2.59, 254.3 1.66e-33         * 0.891
## 2 ParticipantGender:Role 0.865 2.59, 254.3 7.86e-01           0.891
##         DF[HF]    p[HF] p[HF]<.05
## 1 2.67, 261.83 2.03e-34         *
## 2 2.67, 261.83 7.92e-01

(By Participant’s Gender): To what extent do you think that other people see each role as more masculine or feminine?

To what extent do you think that other people see each role as more masculine or feminine (1 = Very Masculine, 3 = Slightly Masculine, 4 = Neither masculine nor feminine, 5 = Slightly feminine, 7 = Very Feminine; I adapted this from Heilman et al., 2004)

(By Participant’s Gender): What is the ideal gender that you think others have in mind when hiring for these positions?

If the woman is selected, DV is coded as 1. If any of the others were selected, they were coded as 0.

If the man is selected, DV is coded as 1. If any of the others were selected, they were coded as 0.

(By Participant’s Gender): How important do you think it is that the person in this position is a woman?

## ANOVA Table (type II tests)
## 
## $ANOVA
##                   Effect DFn DFd      F        p p<.05      ges
## 1      ParticipantGender   1  98  0.001 9.75e-01       7.19e-06
## 2                   Role   3 294 54.370 5.50e-28     * 1.46e-01
## 3 ParticipantGender:Role   3 294  2.625 5.10e-02       8.00e-03
## 
## $`Mauchly's Test for Sphericity`
##                   Effect     W        p p<.05
## 1                   Role 0.205 2.97e-31     *
## 2 ParticipantGender:Role 0.205 2.97e-31     *
## 
## $`Sphericity Corrections`
##                   Effect   GGe      DF[GG]    p[GG] p[GG]<.05   HFe
## 1                   Role 0.501 1.5, 147.21 2.81e-15         * 0.507
## 2 ParticipantGender:Role 0.501 1.5, 147.21 9.10e-02           0.507
##         DF[HF]    p[HF] p[HF]<.05
## 1 1.52, 148.99 1.96e-15         *
## 2 1.52, 148.99 9.00e-02

Candidate Qualifications

## $`Educ. Experience Means + SDs`
## # A tibble: 8 × 6
##   ParticipantGender condition                 n  mean    sd    se
##   <chr>             <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 Female            1. dei                   50  5.04  1.43 0.202
## 2 Male              1. dei                   50  4.98  1.62 0.229
## 3 Female            2. emp_assist (fem)      50  5.24  1.45 0.205
## 4 Male              2. emp_assist (fem)      50  5.12  1.34 0.189
## 5 Female            3. financial (masc)      50  5.9   1.20 0.170
## 6 Male              3. financial (masc)      50  5.68  1.20 0.170
## 7 Female            4. training (neutral)    50  5.32  1.30 0.184
## 8 Male              4. training (neutral)    50  5.36  1.19 0.168
## 
## $`Educ. Experience Anova Table`
## $`Educ. Experience Anova Table`$ANOVA
##                        Effect DFn DFd      F        p p<.05   ges
## 1           ParticipantGender   1  98  0.203 6.53e-01       0.001
## 2                   condition   3 294 10.246 1.95e-06     * 0.045
## 3 ParticipantGender:condition   3 294  0.271 8.46e-01       0.001
## 
## 
## $`Educ. Experience T-test Results`
##               condition   .y. group1 group2  statistic p.adj p.adj.signif
## 1                1. dei Value Female   Male  0.1929857 0.848           ns
## 2   2. emp_assist (fem) Value Female   Male  0.4565217 0.650           ns
## 3   3. financial (masc) Value Female   Male  0.9119613 0.366           ns
## 4 4. training (neutral) Value Female   Male -0.1565639 0.876           ns
## 
## $`Graph of Educ. Experience`

## 
## $`Time in Similar Roles Means + SDs`
## # A tibble: 8 × 6
##   ParticipantGender condition                 n  mean    sd    se
##   <chr>             <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 Female            1. dei                   50  5.54 1.22  0.172
## 2 Male              1. dei                   50  5.34 1.36  0.193
## 3 Female            2. emp_assist (fem)      50  5.68 1.04  0.147
## 4 Male              2. emp_assist (fem)      50  5.64 1.17  0.166
## 5 Female            3. financial (masc)      50  6.02 0.958 0.135
## 6 Male              3. financial (masc)      50  5.98 1     0.141
## 7 Female            4. training (neutral)    50  5.66 1.10  0.155
## 8 Male              4. training (neutral)    50  5.9  0.931 0.132
## 
## $`Time in Similar Roles Anova Table`
## $`Time in Similar Roles Anova Table`$ANOVA
##                        Effect DFn DFd     F        p p<.05      ges
## 1           ParticipantGender   1  98 0.003 9.55e-01       2.08e-05
## 2                   condition   3 294 9.101 8.86e-06     * 3.30e-02
## 3 ParticipantGender:condition   3 294 1.393 2.45e-01       5.00e-03
## 
## 
## $`Time in Similar Roles T-test Results`
##               condition   .y. group1 group2  statistic p.adj p.adj.signif
## 1                1. dei Value Female   Male  0.6931033 0.492           ns
## 2   2. emp_assist (fem) Value Female   Male  0.1871497 0.852           ns
## 3   3. financial (masc) Value Female   Male  0.2021568 0.841           ns
## 4 4. training (neutral) Value Female   Male -1.2310418 0.224           ns
## 
## $`Graph of Time in Similar Roles`

## 
## $`Prior Performance Means + SDs`
## # A tibble: 8 × 6
##   ParticipantGender condition                 n  mean    sd    se
##   <chr>             <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 Female            1. dei                   50  6    1.11  0.157
## 2 Male              1. dei                   50  5.48 1.40  0.198
## 3 Female            2. emp_assist (fem)      50  6.02 1     0.141
## 4 Male              2. emp_assist (fem)      50  5.82 1.04  0.148
## 5 Female            3. financial (masc)      50  6.26 0.853 0.121
## 6 Male              3. financial (masc)      50  6.02 1     0.141
## 7 Female            4. training (neutral)    50  5.92 1.14  0.161
## 8 Male              4. training (neutral)    50  5.94 0.89  0.126
## 
## $`Prior Performance Anova Table`
## $`Prior Performance Anova Table`$ANOVA
##                        Effect DFn DFd     F     p p<.05   ges
## 1           ParticipantGender   1  98 1.923 0.169       0.012
## 2                   condition   3 294 4.781 0.003     * 0.018
## 3 ParticipantGender:condition   3 294 2.196 0.089       0.008
## 
## 
## $`Prior Performance T-test Results`
##               condition   .y. group1 group2   statistic p.adj p.adj.signif
## 1                1. dei Value Female   Male  1.91589562 0.061           ns
## 2   2. emp_assist (fem) Value Female   Male  0.95257934 0.345           ns
## 3   3. financial (masc) Value Female   Male  1.28759261 0.204           ns
## 4 4. training (neutral) Value Female   Male -0.09571098 0.924           ns
## 
## $`Graph of Prior Performance`

## 
## $`Gender Means + SDs`
## # A tibble: 8 × 6
##   ParticipantGender condition                 n  mean    sd    se
##   <chr>             <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 Female            1. dei                   50  3.82 2.23  0.315
## 2 Male              1. dei                   50  3.9  2.25  0.318
## 3 Female            2. emp_assist (fem)      50  1.88 1.44  0.203
## 4 Male              2. emp_assist (fem)      50  2.26 1.65  0.233
## 5 Female            3. financial (masc)      50  1.54 1.18  0.167
## 6 Male              3. financial (masc)      50  2    1.38  0.196
## 7 Female            4. training (neutral)    50  1.44 0.993 0.140
## 8 Male              4. training (neutral)    50  2.18 1.61  0.228
## 
## $`Gender Anova Table`
## $`Gender Anova Table`$ANOVA
##                        Effect DFn DFd      F        p p<.05   ges
## 1           ParticipantGender   1  98  2.506 1.17e-01       0.016
## 2                   condition   3 294 74.706 6.07e-36     * 0.219
## 3 ParticipantGender:condition   3 294  1.384 2.48e-01       0.005
## 
## 
## $`Gender T-test Results`
##               condition   .y. group1 group2  statistic p.adj p.adj.signif
## 1                1. dei Value Female   Male -0.1928331 0.848           ns
## 2   2. emp_assist (fem) Value Female   Male -1.2517682 0.217           ns
## 3   3. financial (masc) Value Female   Male -1.9638528 0.055           ns
## 4 4. training (neutral) Value Female   Male -2.9746544 0.005           **
## 
## $`Graph of Gender`

## 
## $`Ethnicity Means + SDs`
## # A tibble: 8 × 6
##   ParticipantGender condition                 n  mean    sd    se
##   <chr>             <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 Female            1. dei                   50  2.58 1.88  0.265
## 2 Male              1. dei                   50  2.94 2.07  0.293
## 3 Female            2. emp_assist (fem)      50  1.68 1.27  0.179
## 4 Male              2. emp_assist (fem)      50  2.1  1.67  0.236
## 5 Female            3. financial (masc)      50  1.46 1.11  0.157
## 6 Male              3. financial (masc)      50  1.92 1.46  0.206
## 7 Female            4. training (neutral)    50  1.44 0.993 0.140
## 8 Male              4. training (neutral)    50  1.98 1.45  0.205
## 
## $`Ethnicity Anova Table`
## $`Ethnicity Anova Table`$ANOVA
##                        Effect DFn DFd      F        p p<.05      ges
## 1           ParticipantGender   1  98  2.817 9.60e-02       0.021000
## 2                   condition   3 294 33.522 1.15e-18     * 0.078000
## 3 ParticipantGender:condition   3 294  0.186 9.06e-01       0.000468
## 
## 
## $`Ethnicity T-test Results`
##               condition   .y. group1 group2  statistic p.adj p.adj.signif
## 1                1. dei Value Female   Male -0.9294907 0.357           ns
## 2   2. emp_assist (fem) Value Female   Male -1.5074743 0.138           ns
## 3   3. financial (masc) Value Female   Male -1.9786277 0.054           ns
## 4 4. training (neutral) Value Female   Male -2.4157424 0.020            *
## 
## $`Graph of Ethnicity`

## 
## $`Ability to connect with others on the team Means + SDs`
## # A tibble: 8 × 6
##   ParticipantGender condition                 n  mean    sd    se
##   <chr>             <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 Female            1. dei                   50  6.2  0.969 0.137
## 2 Male              1. dei                   50  5.78 1.34  0.190
## 3 Female            2. emp_assist (fem)      50  6.4  0.99  0.140
## 4 Male              2. emp_assist (fem)      50  5.9  1.13  0.160
## 5 Female            3. financial (masc)      50  5.22 1.34  0.190
## 6 Male              3. financial (masc)      50  4.7  1.39  0.196
## 7 Female            4. training (neutral)    50  6.12 1.08  0.153
## 8 Male              4. training (neutral)    50  5.84 1.15  0.162
## 
## $`Ability to connect with others on the team Anova Table`
## $`Ability to connect with others on the team Anova Table`$ANOVA
##                        Effect DFn DFd      F        p p<.05   ges
## 1           ParticipantGender   1  98  6.094 1.50e-02     * 0.033
## 2                   condition   3 294 34.617 3.45e-19     * 0.140
## 3 ParticipantGender:condition   3 294  0.345 7.93e-01       0.002
## 
## 
## $`Ability to connect with others on the team T-test Results`
##               condition   .y. group1 group2 statistic p.adj p.adj.signif
## 1                1. dei Value Female   Male  1.632427 0.109           ns
## 2   2. emp_assist (fem) Value Female   Male  2.200431 0.032            *
## 3   3. financial (masc) Value Female   Male  1.733412 0.089           ns
## 4 4. training (neutral) Value Female   Male  1.154620 0.254           ns
## 
## $`Graph of Ability to connect with others on the team`

## 
## $`Ability to connect with others in the org. Means + SDs`
## # A tibble: 8 × 6
##   ParticipantGender condition                 n  mean    sd    se
##   <chr>             <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 Female            1. dei                   50  6.36 0.921 0.130
## 2 Male              1. dei                   50  5.72 1.36  0.192
## 3 Female            2. emp_assist (fem)      50  6.44 0.884 0.125
## 4 Male              2. emp_assist (fem)      50  5.84 1.13  0.160
## 5 Female            3. financial (masc)      50  5.32 1.32  0.186
## 6 Male              3. financial (masc)      50  4.68 1.45  0.205
## 7 Female            4. training (neutral)    50  6.22 0.932 0.132
## 8 Male              4. training (neutral)    50  5.86 1.14  0.162
## 
## $`Ability to connect with others in the org. Anova Table`
## $`Ability to connect with others in the org. Anova Table`$ANOVA
##                        Effect DFn DFd      F        p p<.05   ges
## 1           ParticipantGender   1  98 10.456 2.00e-03     * 0.056
## 2                   condition   3 294 36.611 3.93e-20     * 0.142
## 3 ParticipantGender:condition   3 294  0.572 6.34e-01       0.003
## 
## 
## $`Ability to connect with others in the org. T-test Results`
##               condition   .y. group1 group2 statistic p.adj p.adj.signif
## 1                1. dei Value Female   Male  2.762276 0.008           **
## 2   2. emp_assist (fem) Value Female   Male  2.884572 0.006           **
## 3   3. financial (masc) Value Female   Male  2.199040 0.033            *
## 4 4. training (neutral) Value Female   Male  1.786201 0.080           ns
## 
## $`Graph of Ability to connect with others in the org.`

## 
## $`Comfort with numbers Means + SDs`
## # A tibble: 8 × 6
##   ParticipantGender condition                 n  mean    sd    se
##   <chr>             <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 Female            1. dei                   50  3.5  1.92  0.271
## 2 Male              1. dei                   50  3.6  2.13  0.301
## 3 Female            2. emp_assist (fem)      50  3.42 1.94  0.274
## 4 Male              2. emp_assist (fem)      50  3.62 1.82  0.257
## 5 Female            3. financial (masc)      50  6.54 0.838 0.119
## 6 Male              3. financial (masc)      50  5.98 1.39  0.197
## 7 Female            4. training (neutral)    50  4.04 1.83  0.259
## 8 Male              4. training (neutral)    50  4.04 1.81  0.256
## 
## $`Comfort with numbers Anova Table`
## $`Comfort with numbers Anova Table`$ANOVA
##                        Effect DFn DFd      F        p p<.05      ges
## 1           ParticipantGender   1  98  0.066 7.98e-01       0.000351
## 2                   condition   3 294 86.717 3.23e-40     * 0.297000
## 3 ParticipantGender:condition   3 294  1.482 2.20e-01       0.007000
## 
## 
## $`Comfort with numbers T-test Results`
##               condition   .y. group1 group2  statistic p.adj p.adj.signif
## 1                1. dei Value Female   Male -0.2431200 0.809           ns
## 2   2. emp_assist (fem) Value Female   Male -0.5400617 0.592           ns
## 3   3. financial (masc) Value Female   Male  2.1493089 0.037            *
## 4 4. training (neutral) Value Female   Male  0.0000000 1.000           ns
## 
## $`Graph of Comfort with numbers`

## 
## $`Creativity Means + SDs`
## # A tibble: 8 × 6
##   ParticipantGender condition                 n  mean    sd    se
##   <chr>             <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 Female            1. dei                   50  5.36  1.47 0.207
## 2 Male              1. dei                   50  5     1.38 0.196
## 3 Female            2. emp_assist (fem)      50  5.14  1.50 0.212
## 4 Male              2. emp_assist (fem)      50  4.62  1.24 0.176
## 5 Female            3. financial (masc)      50  4.58  1.37 0.194
## 6 Male              3. financial (masc)      50  4     1.62 0.229
## 7 Female            4. training (neutral)    50  5.16  1.38 0.195
## 8 Male              4. training (neutral)    50  4.82  1.35 0.191
## 
## $`Creativity Anova Table`
## $`Creativity Anova Table`$ANOVA
##                        Effect DFn DFd      F        p p<.05   ges
## 1           ParticipantGender   1  98  4.671 3.30e-02     * 0.025
## 2                   condition   3 294 11.944 2.11e-07     * 0.053
## 3 ParticipantGender:condition   3 294  0.284 8.37e-01       0.001
## 
## 
## $`Creativity T-test Results`
##               condition   .y. group1 group2 statistic p.adj p.adj.signif
## 1                1. dei Value Female   Male  1.242964 0.220           ns
## 2   2. emp_assist (fem) Value Female   Male  1.791214 0.079           ns
## 3   3. financial (masc) Value Female   Male  1.837198 0.072           ns
## 4 4. training (neutral) Value Female   Male  1.111567 0.272           ns
## 
## $`Graph of Creativity`

## 
## $`Interpersonal warmth Means + SDs`
## # A tibble: 8 × 6
##   ParticipantGender condition                 n  mean    sd    se
##   <chr>             <fct>                 <dbl> <dbl> <dbl> <dbl>
## 1 Female            1. dei                   50  6.44 0.837 0.118
## 2 Male              1. dei                   50  5.58 1.40  0.198
## 3 Female            2. emp_assist (fem)      50  6.66 0.745 0.105
## 4 Male              2. emp_assist (fem)      50  5.88 1.32  0.187
## 5 Female            3. financial (masc)      50  4.88 1.36  0.193
## 6 Male              3. financial (masc)      50  4.04 1.41  0.200
## 7 Female            4. training (neutral)    50  6.08 1.21  0.171
## 8 Male              4. training (neutral)    50  5.68 1.10  0.155
## 
## $`Interpersonal warmth Anova Table`
## $`Interpersonal warmth Anova Table`$ANOVA
##                        Effect DFn DFd      F        p p<.05   ges
## 1           ParticipantGender   1  98 16.359 1.05e-04     * 0.084
## 2                   condition   3 294 77.049 8.45e-37     * 0.261
## 3 ParticipantGender:condition   3 294  1.360 2.55e-01       0.006
## 
## 
## $`Interpersonal warmth T-test Results`
##               condition   .y. group1 group2 statistic    p.adj p.adj.signif
## 1                1. dei Value Female   Male  3.822396 0.000374          ***
## 2   2. emp_assist (fem) Value Female   Male  3.575737 0.000797          ***
## 3   3. financial (masc) Value Female   Male  2.864240 0.006000           **
## 4 4. training (neutral) Value Female   Male  1.595448 0.117000           ns
## 
## $`Graph of Interpersonal warmth`

Pilot 3 - DEI Leader Evaluations

We followed the protocol in Study 3 of Harrison & Martin (2022):
We told participants that there were increasing demands to improve:
- diversity equity and inclusion for women at work (Diversity condition)
- general support for employees in the workplace (Employee Assistance Condition)
- efficiency of financial accounting practices (Financial Planning Condition)

We used the materials developed in the previous study to describe the roles. We asked participants to evaluate how likely they would be to solicit voice from three people: Peter Abignale, Phillip Dunn, and Judith Clark.

Along with Voice Solicitation, I collected the following:
> Exploratory items.

I think that employee is probably curious about this role (“curious”)
I think that employee probably has some good ideas about this role (“ideas”)
I think that employee is probably interested in learning more about this role (“learning”)
I think that employee probably would want to have this role (“have”)

Support for social justice.

I got this measure from here:
Saguy, T., Fernández, S., Branscombe, N. R., & Shany, A. (2020). Justice Agents: Discriminated Group Members Are Perceived to be Highly Committed to Social Justice. Personality and Social Psychology Bulletin, 46(1), 155–167. https://doi.org/10.1177/0146167219845922

I think that employee supports the idea of conditions.
I think that employee is likely to talk to their coworkers about conditions.
I think that employee probably worries about conditions language.
I think that issues related to conditions language are present in employee’s mind and heart.

H1a: Do people solicit voice from the woman more in the DEI condition, compared to the other conditions?

DV	IV	Analyses
Voice solicitation to Judith Clark	Focus of initiative	One-way between subjects ANOVA

##              Df Sum Sq Mean Sq F value   Pr(>F)    
## condition     2   52.9  26.456   14.93 9.18e-07 ***
## Residuals   197  349.1   1.772                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = jc_vs_tot ~ condition, data = deileader1_clean)
## 
## $condition
##                           diff        lwr       upr     p adj
## fin_plan-emp_assist -0.2245687 -0.7578774 0.3087401 0.5812541
## gdei-emp_assist      0.9870414  0.4364029 1.5376799 0.0001043
## gdei-fin_plan        1.2116100  0.6591167 1.7641034 0.0000016

H1b: In the DEI condition, do people solicit voice more from the woman than the men?

DV	IV	Filter	Analyses
Voice solicitation (across employees)	Specific Employee	Only data in the “Gender Diversity Initiative” Condition	One-way within subjects ANOVA

H2a: Do people see the woman as supporting social justice more in the DEI condition, compared to the other conditions?

DV	IV	Analyses
Perceptions of Judith Clark’s interest in social justice	Focus of initiative	One-way between subjects ANOVA

##              Df Sum Sq Mean Sq F value   Pr(>F)    
## condition     2  20.32  10.160    7.18 0.000978 ***
## Residuals   197 278.76   1.415                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = jc_sj_tot ~ condition, data = deileader1_clean)
## 
## $condition
##                           diff         lwr       upr     p adj
## fin_plan-emp_assist -0.1874396 -0.66400151 0.2891223 0.6227221
## gdei-emp_assist      0.5781421  0.08609439 1.0701898 0.0166059
## gdei-fin_plan        0.7655817  0.27187650 1.2592869 0.0009343

H2b: In the DEI condition, do people see the woman as supporting social justice more than the men?

DV	IV	Filter	Analyses
Perceptions of social justice (across employees)	Specific Employee	Only data in the “Gender Diversity Initiative” Condition	One-way within subjects ANOVA

Who would participants select for the position?

Using the “select” variable (which is coded “1” if participants selected the woman, and “0” if participants did not) I tested if participants were more likely to choose the woman.

DV	IV	Analyses
Selected a woman (vs. not)	Manipulation	Binary logistic regression

## 
## Call:
## lm(formula = select ~ condition_recoded, data = deileader1_clean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.80328 -0.22857 -0.07246  0.19672  0.92754 
## 
## Coefficients:
##                                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                             0.80328    0.04707  17.067  < 2e-16 ***
## condition_recoded2. Financial Planning -0.73081    0.06460 -11.312  < 2e-16 ***
## condition_recoded3. Employee Assist    -0.57471    0.06439  -8.926 3.09e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3676 on 197 degrees of freedom
## Multiple R-squared:  0.4149, Adjusted R-squared:  0.409 
## F-statistic: 69.86 on 2 and 197 DF,  p-value: < 2.2e-16

H4: These effects are driven by social justice

I created a binary variable: 1 if participants were in the GDEI condition, 0 if not.

DV	Mediator	IV	Analyses
Selected a woman (vs. not)	Perceptions of social justice (jc_sj_total)	Binary condition variable	Mediation

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = select ~ cond_num_bin + (jc_sj_tot), data = deileader1_clean)
## 
## The DV (Y) was  select . The IV (X) was  cond_num_bin . The mediating variable(s) =  jc_sj_tot .
## 
## Total effect(c) of  cond_num_bin  on  select  =  0.65   S.E. =  0.06  t  =  11.4  df=  198   with p =  1.8e-23
## Direct effect (c') of  cond_num_bin  on  select  removing  jc_sj_tot  =  0.6   S.E. =  0.06  t  =  10.43  df=  197   with p =  1.4e-20
## Indirect effect (ab) of  cond_num_bin  on  select  through  jc_sj_tot   =  0.06 
## Mean bootstrapped indirect effect =  0.06  with standard error =  0.02  Lower CI =  0.02    Upper CI =  0.1
## R = 0.66 R2 = 0.44   F = 77.44 on 2 and 197 DF   p-value:  3.94e-33 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

Note: want help interpreting odds ratios.

##                            (Intercept) condition_recoded2. Financial Planning 
##                              2.2328498                              0.4815164 
##    condition_recoded3. Employee Assist 
##                              0.5628696

Exploratory - Selection

Curious

DV	Mediator	IV
Selected a woman (vs. not)	I think that Judith Clark is probably curious about this role	Binary condition variable

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = select ~ cond_num_bin + (jc_curious), data = deileader1_clean)
## 
## The DV (Y) was  select . The IV (X) was  cond_num_bin . The mediating variable(s) =  jc_curious .
## 
## Total effect(c) of  cond_num_bin  on  select  =  0.65   S.E. =  0.06  t  =  11.4  df=  198   with p =  1.8e-23
## Direct effect (c') of  cond_num_bin  on  select  removing  jc_curious  =  0.6   S.E. =  0.06  t  =  10.25  df=  197   with p =  5.1e-20
## Indirect effect (ab) of  cond_num_bin  on  select  through  jc_curious   =  0.05 
## Mean bootstrapped indirect effect =  0.05  with standard error =  0.02  Lower CI =  0.01    Upper CI =  0.09
## R = 0.65 R2 = 0.42   F = 71.05 on 2 and 197 DF   p-value:  3.48e-31 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

Ideas

DV	Mediator	IV
Selected a woman (vs. not)	I think that Judith Clark probably has some good ideas about this role	Binary condition variable

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = select ~ cond_num_bin + (jc_ideas), data = deileader1_clean)
## 
## The DV (Y) was  select . The IV (X) was  cond_num_bin . The mediating variable(s) =  jc_ideas .
## 
## Total effect(c) of  cond_num_bin  on  select  =  0.65   S.E. =  0.06  t  =  11.4  df=  198   with p =  1.8e-23
## Direct effect (c') of  cond_num_bin  on  select  removing  jc_ideas  =  0.55   S.E. =  0.06  t  =  9.81  df=  197   with p =  9.7e-19
## Indirect effect (ab) of  cond_num_bin  on  select  through  jc_ideas   =  0.11 
## Mean bootstrapped indirect effect =  0.11  with standard error =  0.03  Lower CI =  0.06    Upper CI =  0.16
## R = 0.7 R2 = 0.49   F = 94.59 on 2 and 197 DF   p-value:  5.9e-38 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

Learning

DV	Mediator	IV
Selected a woman (vs. not)	I think that Judith Clark is probably interested in learning more about this role	Binary condition variable

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = select ~ cond_num_bin + (jc_learning), data = deileader1_clean)
## 
## The DV (Y) was  select . The IV (X) was  cond_num_bin . The mediating variable(s) =  jc_learning .
## 
## Total effect(c) of  cond_num_bin  on  select  =  0.65   S.E. =  0.06  t  =  11.4  df=  198   with p =  1.8e-23
## Direct effect (c') of  cond_num_bin  on  select  removing  jc_learning  =  0.6   S.E. =  0.06  t  =  10.55  df=  197   with p =  6.4e-21
## Indirect effect (ab) of  cond_num_bin  on  select  through  jc_learning   =  0.05 
## Mean bootstrapped indirect effect =  0.05  with standard error =  0.02  Lower CI =  0.02    Upper CI =  0.09
## R = 0.66 R2 = 0.43   F = 75.01 on 2 and 197 DF   p-value:  2.12e-32 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

Have

DV	Mediator	IV
Selected a woman (vs. not)	I think that Judith Clark probably would want to have this role	Binary condition variable

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = select ~ cond_num_bin + (jc_have), data = deileader1_clean)
## 
## The DV (Y) was  select . The IV (X) was  cond_num_bin . The mediating variable(s) =  jc_have .
## 
## Total effect(c) of  cond_num_bin  on  select  =  0.65   S.E. =  0.06  t  =  11.4  df=  198   with p =  1.8e-23
## Direct effect (c') of  cond_num_bin  on  select  removing  jc_have  =  0.61   S.E. =  0.06  t  =  10.65  df=  197   with p =  3.3e-21
## Indirect effect (ab) of  cond_num_bin  on  select  through  jc_have   =  0.04 
## Mean bootstrapped indirect effect =  0.04  with standard error =  0.02  Lower CI =  0.01    Upper CI =  0.08
## R = 0.65 R2 = 0.42   F = 72.53 on 2 and 197 DF   p-value:  1.21e-31 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

All on Selection

psych::mediate(select~cond_num_bin+(jc_sj_tot)+(jc_curious)+(jc_learning)+(jc_ideas)+(jc_have), data = deileader1_clean)

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = select ~ cond_num_bin + (jc_sj_tot) + (jc_curious) + 
##     (jc_learning) + (jc_ideas) + (jc_have), data = deileader1_clean)
## 
## The DV (Y) was  select . The IV (X) was  cond_num_bin . The mediating variable(s) =  jc_sj_tot jc_curious jc_learning jc_ideas jc_have .
## 
## Total effect(c) of  cond_num_bin  on  select  =  0.65   S.E. =  0.06  t  =  11.4  df=  198   with p =  1.8e-23
## Direct effect (c') of  cond_num_bin  on  select  removing  jc_sj_tot jc_curious jc_learning jc_ideas jc_have  =  0.55   S.E. =  0.06  t  =  9.88  df=  193   with p =  6.8e-19
## Indirect effect (ab) of  cond_num_bin  on  select  through  jc_sj_tot jc_curious jc_learning jc_ideas jc_have   =  0.1 
## Mean bootstrapped indirect effect =  0.1  with standard error =  0.03  Lower CI =  0.04    Upper CI =  0.16
## R = 0.71 R2 = 0.5   F = 32.47 on 6 and 193 DF   p-value:  1.53e-29 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

Exploratory - Voice Solicitation

Curious

DV	Mediator	IV
Solicited Voice (Likert)	I think that Judith Clark is probably curious about this role	Binary condition variable

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = jc_vs_tot ~ cond_num_bin + (jc_curious), data = deileader1_clean)
## 
## The DV (Y) was  jc_vs_tot . The IV (X) was  cond_num_bin . The mediating variable(s) =  jc_curious .
## 
## Total effect(c) of  cond_num_bin  on  jc_vs_tot  =  1.1   S.E. =  0.2  t  =  5.37  df=  198   with p =  2.2e-07
## Direct effect (c') of  cond_num_bin  on  jc_vs_tot  removing  jc_curious  =  0.56   S.E. =  0.17  t  =  3.27  df=  197   with p =  0.0013
## Indirect effect (ab) of  cond_num_bin  on  jc_vs_tot  through  jc_curious   =  0.54 
## Mean bootstrapped indirect effect =  0.54  with standard error =  0.14  Lower CI =  0.27    Upper CI =  0.83
## R = 0.67 R2 = 0.44   F = 78.19 on 2 and 197 DF   p-value:  2.36e-33 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

Ideas

DV	Mediator	IV
Solicited Voice (Likert)	I think that Judith Clark probably has some good ideas about this role	Binary condition variable

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = jc_vs_tot ~ cond_num_bin + (jc_ideas), data = deileader1_clean)
## 
## The DV (Y) was  jc_vs_tot . The IV (X) was  cond_num_bin . The mediating variable(s) =  jc_ideas .
## 
## Total effect(c) of  cond_num_bin  on  jc_vs_tot  =  1.1   S.E. =  0.2  t  =  5.37  df=  198   with p =  2.2e-07
## Direct effect (c') of  cond_num_bin  on  jc_vs_tot  removing  jc_ideas  =  0.34   S.E. =  0.13  t  =  2.53  df=  197   with p =  0.012
## Indirect effect (ab) of  cond_num_bin  on  jc_vs_tot  through  jc_ideas   =  0.76 
## Mean bootstrapped indirect effect =  0.75  with standard error =  0.16  Lower CI =  0.45    Upper CI =  1.08
## R = 0.81 R2 = 0.66   F = 191.58 on 2 and 197 DF   p-value:  3.77e-58 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

Learning

DV	Mediator	IV
Solicited Voice (Likert)	I think that Judith Clark is probably interested in learning more about this role	Binary condition variable

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = jc_vs_tot ~ cond_num_bin + (jc_learning), 
##     data = deileader1_clean)
## 
## The DV (Y) was  jc_vs_tot . The IV (X) was  cond_num_bin . The mediating variable(s) =  jc_learning .
## 
## Total effect(c) of  cond_num_bin  on  jc_vs_tot  =  1.1   S.E. =  0.2  t  =  5.37  df=  198   with p =  2.2e-07
## Direct effect (c') of  cond_num_bin  on  jc_vs_tot  removing  jc_learning  =  0.65   S.E. =  0.16  t  =  4  df=  197   with p =  9.1e-05
## Indirect effect (ab) of  cond_num_bin  on  jc_vs_tot  through  jc_learning   =  0.45 
## Mean bootstrapped indirect effect =  0.45  with standard error =  0.14  Lower CI =  0.2    Upper CI =  0.72
## R = 0.7 R2 = 0.48   F = 92.72 on 2 and 197 DF   p-value:  1.87e-37 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

Have

DV	Mediator	IV
Solicited Voice (Likert)	I think that Judith Clark probably would want to have this role	Binary condition variable

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = jc_vs_tot ~ cond_num_bin + (jc_have), data = deileader1_clean)
## 
## The DV (Y) was  jc_vs_tot . The IV (X) was  cond_num_bin . The mediating variable(s) =  jc_have .
## 
## Total effect(c) of  cond_num_bin  on  jc_vs_tot  =  1.1   S.E. =  0.2  t  =  5.37  df=  198   with p =  2.2e-07
## Direct effect (c') of  cond_num_bin  on  jc_vs_tot  removing  jc_have  =  0.72   S.E. =  0.17  t  =  4.16  df=  197   with p =  4.7e-05
## Indirect effect (ab) of  cond_num_bin  on  jc_vs_tot  through  jc_have   =  0.38 
## Mean bootstrapped indirect effect =  0.38  with standard error =  0.12  Lower CI =  0.14    Upper CI =  0.63
## R = 0.64 R2 = 0.41   F = 67.1 on 2 and 197 DF   p-value:  6.16e-30 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

All on Voice Solicitation

psych::mediate(jc_vs_tot~cond_num_bin+(jc_sj_tot)+(jc_curious)+(jc_learning)+(jc_ideas)+(jc_have), data = deileader1_clean)

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = jc_vs_tot ~ cond_num_bin + (jc_sj_tot) + (jc_curious) + 
##     (jc_learning) + (jc_ideas) + (jc_have), data = deileader1_clean)
## 
## The DV (Y) was  jc_vs_tot . The IV (X) was  cond_num_bin . The mediating variable(s) =  jc_sj_tot jc_curious jc_learning jc_ideas jc_have .
## 
## Total effect(c) of  cond_num_bin  on  jc_vs_tot  =  1.1   S.E. =  0.2  t  =  5.37  df=  198   with p =  2.2e-07
## Direct effect (c') of  cond_num_bin  on  jc_vs_tot  removing  jc_sj_tot jc_curious jc_learning jc_ideas jc_have  =  0.33   S.E. =  0.13  t  =  2.45  df=  193   with p =  0.015
## Indirect effect (ab) of  cond_num_bin  on  jc_vs_tot  through  jc_sj_tot jc_curious jc_learning jc_ideas jc_have   =  0.77 
## Mean bootstrapped indirect effect =  0.77  with standard error =  0.17  Lower CI =  0.43    Upper CI =  1.1
## R = 0.82 R2 = 0.68   F = 68.09 on 6 and 193 DF   p-value:  9.1e-49 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

Mixed Models ANOVAs

Voice Solicitation

DV	Between-subjects IV	Within-subjects IV
Selected a woman (vs. not)	Initiatives Manipulation	Employee

## ANOVA Table (type III tests)
## 
## $ANOVA
##                   Effect DFn DFd      F        p p<.05   ges
## 1            condition_f   2 197  1.730 1.80e-01       0.008
## 2             employee_f   2 394  5.951 3.00e-03     * 0.015
## 3 condition_f:employee_f   4 394 23.503 1.92e-17     * 0.110
## 
## $`Mauchly's Test for Sphericity`
##                   Effect     W       p p<.05
## 1             employee_f 0.844 6.4e-08     *
## 2 condition_f:employee_f 0.844 6.4e-08     *
## 
## $`Sphericity Corrections`
##                   Effect   GGe       DF[GG]    p[GG] p[GG]<.05   HFe
## 1             employee_f 0.865 1.73, 340.98 4.00e-03         * 0.872
## 2 condition_f:employee_f 0.865 3.46, 340.98 2.05e-15         * 0.872
##         DF[HF]    p[HF] p[HF]<.05
## 1 1.74, 343.73 4.00e-03         *
## 2 3.49, 343.73 1.61e-15         *

Justice

## ANOVA Table (type III tests)
## 
## $ANOVA
##                   Effect DFn DFd      F        p p<.05   ges
## 1            condition_f   2 197  6.824 1.00e-03     * 0.034
## 2             employee_f   2 394 21.290 1.66e-09     * 0.050
## 3 condition_f:employee_f   4 394 23.736 1.33e-17     * 0.105
## 
## $`Mauchly's Test for Sphericity`
##                   Effect     W        p p<.05
## 1             employee_f 0.925 0.000467     *
## 2 condition_f:employee_f 0.925 0.000467     *
## 
## $`Sphericity Corrections`
##                   Effect  GGe       DF[GG]    p[GG] p[GG]<.05   HFe
## 1             employee_f 0.93 1.86, 366.42 5.32e-09         * 0.938
## 2 condition_f:employee_f 0.93 3.72, 366.42 1.54e-16         * 0.938
##         DF[HF]    p[HF] p[HF]<.05
## 1 1.88, 369.77 4.62e-09         *
## 2 3.75, 369.77 1.15e-16         *

Exploratory Analyses

## $`ideas  anova`
## ANOVA Table (type III tests)
## 
## $ANOVA
##                   Effect DFn DFd      F        p p<.05   ges
## 1            condition_f   2 197  6.781 1.00e-03     * 0.030
## 2             employee_f   2 394  9.153 1.30e-04     * 0.025
## 3 condition_f:employee_f   4 394 26.466 1.86e-19     * 0.130
## 
## $`Mauchly's Test for Sphericity`
##                   Effect     W        p p<.05
## 1             employee_f 0.877 2.54e-06     *
## 2 condition_f:employee_f 0.877 2.54e-06     *
## 
## $`Sphericity Corrections`
##                   Effect  GGe       DF[GG]    p[GG] p[GG]<.05   HFe
## 1             employee_f 0.89 1.78, 350.79 2.54e-04         * 0.898
## 2 condition_f:employee_f 0.89 3.56, 350.79 1.38e-17         * 0.898
##         DF[HF]    p[HF] p[HF]<.05
## 1  1.8, 353.77 2.43e-04         *
## 2 3.59, 353.77 1.02e-17         *
## 
## 
## $`ideas  posthoc`
## # A tibble: 6 × 8
##   condition_f         group1 group2 statistic    df       p   p.adj p.adj.signif
##   <fct>               <chr>  <chr>      <dbl> <dbl>   <dbl>   <dbl> <chr>       
## 1 Employee Assistanc… Judit… Peter…    -1.28     69 2.06e-1 6.18e-1 ns          
## 2 Employee Assistanc… Judit… Phill…    -1.17     69 2.47e-1 7.41e-1 ns          
## 3 Financial Planning… Judit… Peter…    -0.785    68 4.35e-1 1   e+0 ns          
## 4 Financial Planning… Judit… Phill…    -6.74     68 4.16e-9 1.25e-8 ****        
## 5 Gender Diversity C… Judit… Peter…     6.20     60 5.64e-8 1.69e-7 ****        
## 6 Gender Diversity C… Judit… Phill…     6.78     60 5.99e-9 1.80e-8 ****        
## 
## $`ideas  graph`

## 
## $`curious  anova`
## ANOVA Table (type III tests)
## 
## $ANOVA
##                   Effect DFn DFd      F        p p<.05   ges
## 1            condition_f   2 197  4.030 1.90e-02     * 0.021
## 2             employee_f   2 394 30.004 7.44e-13     * 0.068
## 3 condition_f:employee_f   4 394 20.648 1.84e-15     * 0.092
## 
## $`Mauchly's Test for Sphericity`
##                   Effect    W     p p<.05
## 1             employee_f 0.95 0.007     *
## 2 condition_f:employee_f 0.95 0.007     *
## 
## $`Sphericity Corrections`
##                   Effect   GGe       DF[GG]    p[GG] p[GG]<.05   HFe
## 1             employee_f 0.953 1.91, 375.32 2.33e-12         * 0.962
## 2 condition_f:employee_f 0.953 3.81, 375.32 7.70e-15         * 0.962
##         DF[HF]    p[HF] p[HF]<.05
## 1 1.92, 378.89 1.87e-12         *
## 2 3.85, 378.89 5.86e-15         *
## 
## 
## $`curious  posthoc`
## # A tibble: 6 × 8
##   condition_f       group1 group2 statistic    df        p    p.adj p.adj.signif
##   <fct>             <chr>  <chr>      <dbl> <dbl>    <dbl>    <dbl> <chr>       
## 1 Employee Assista… Judit… Peter…     1.20     69 2.35e- 1 7.05e- 1 ns          
## 2 Employee Assista… Judit… Phill…    -0.597    69 5.52e- 1 1   e+ 0 ns          
## 3 Financial Planni… Judit… Peter…     1.23     68 2.22e- 1 6.66e- 1 ns          
## 4 Financial Planni… Judit… Phill…    -4.92     68 5.73e- 6 1.72e- 5 ****        
## 5 Gender Diversity… Judit… Peter…     7.54     60 3.04e-10 9.12e-10 ****        
## 6 Gender Diversity… Judit… Phill…     6.49     60 1.86e- 8 5.58e- 8 ****        
## 
## $`curious  graph`

## 
## $`learning  anova`
## ANOVA Table (type III tests)
## 
## $ANOVA
##                   Effect DFn DFd      F        p p<.05   ges
## 1            condition_f   2 197  7.361 8.26e-04     * 0.036
## 2             employee_f   2 394 21.074 2.02e-09     * 0.050
## 3 condition_f:employee_f   4 394 19.141 2.12e-14     * 0.088
## 
## $`Mauchly's Test for Sphericity`
##                   Effect     W        p p<.05
## 1             employee_f 0.924 0.000437     *
## 2 condition_f:employee_f 0.924 0.000437     *
## 
## $`Sphericity Corrections`
##                   Effect   GGe       DF[GG]    p[GG] p[GG]<.05   HFe
## 1             employee_f 0.929 1.86, 366.21 6.44e-09         * 0.938
## 2 condition_f:employee_f 0.929 3.72, 366.21 1.51e-13         * 0.938
##         DF[HF]   p[HF] p[HF]<.05
## 1 1.88, 369.55 5.6e-09         *
## 2 3.75, 369.55 1.2e-13         *
## 
## 
## $`learning  posthoc`
## # A tibble: 6 × 8
##   condition_f         group1 group2 statistic    df       p   p.adj p.adj.signif
##   <fct>               <chr>  <chr>      <dbl> <dbl>   <dbl>   <dbl> <chr>       
## 1 Employee Assistanc… Judit… Peter…     0.668    69 5.06e-1 1   e+0 ns          
## 2 Employee Assistanc… Judit… Phill…     0        69 1   e+0 1   e+0 ns          
## 3 Financial Planning… Judit… Peter…     1.11     68 2.73e-1 8.19e-1 ns          
## 4 Financial Planning… Judit… Phill…    -4.79     68 9.54e-6 2.86e-5 ****        
## 5 Gender Diversity C… Judit… Peter…     6.83     60 4.94e-9 1.48e-8 ****        
## 6 Gender Diversity C… Judit… Phill…     6.74     60 6.92e-9 2.08e-8 ****        
## 
## $`learning  graph`

## 
## $`have  anova`
## ANOVA Table (type III tests)
## 
## $ANOVA
##                   Effect DFn DFd      F        p p<.05   ges
## 1            condition_f   2 197  5.716 4.00e-03     * 0.031
## 2             employee_f   2 394 30.257 5.97e-13     * 0.065
## 3 condition_f:employee_f   4 394 20.817 1.40e-15     * 0.088
## 
## $`Mauchly's Test for Sphericity`
##                   Effect     W     p p<.05
## 1             employee_f 0.933 0.001     *
## 2 condition_f:employee_f 0.933 0.001     *
## 
## $`Sphericity Corrections`
##                   Effect   GGe       DF[GG]    p[GG] p[GG]<.05   HFe
## 1             employee_f 0.937 1.87, 369.36 2.73e-12         * 0.946
## 2 condition_f:employee_f 0.937 3.75, 369.36 9.42e-15         * 0.946
##         DF[HF]    p[HF] p[HF]<.05
## 1 1.89, 372.79 2.21e-12         *
## 2 3.78, 372.79 7.22e-15         *
## 
## 
## $`have  posthoc`
## # A tibble: 6 × 8
##   condition_f       group1 group2 statistic    df        p    p.adj p.adj.signif
##   <fct>             <chr>  <chr>      <dbl> <dbl>    <dbl>    <dbl> <chr>       
## 1 Employee Assista… Judit… Peter…     1.13     69 2.64e- 1 7.92e- 1 ns          
## 2 Employee Assista… Judit… Phill…     0.674    69 5.03e- 1 1   e+ 0 ns          
## 3 Financial Planni… Judit… Peter…     1.61     68 1.11e- 1 3.33e- 1 ns          
## 4 Financial Planni… Judit… Phill…    -4.61     68 1.81e- 5 5.43e- 5 ****        
## 5 Gender Diversity… Judit… Peter…     8.00     60 4.88e-11 1.46e-10 ****        
## 6 Gender Diversity… Judit… Phill…     6.74     60 6.85e- 9 2.06e- 8 ****        
## 
## $`have  graph`

DEI Leader Summary

Summary of Idea

Predictions

Contributions

1. Voice Solicitation

2. Diversity Work

Study Summaries

Pilot 1

Measures

Link to full report is here

Graphs

Likert Variables - VS and JF

Likert Variables - Considerations

Ordinal Variables

Correlations

Important Analyses

Pilot 2 - Gender Typed Tasks

Goal

Study Description

Here are the Roles (and their descriptions which I included):

Next step:

Works Cited/Studies Referenced

Participant Summary

Nationwide, what percentage of people in each role do you think are women?

Graph of “nationwide, what percentage of people in each role do you think are women?”

To what extent do you think that other people see each role as more masculine or feminine?

Graphs of “to what extent do you think that other people see each role as more masculine or feminine?”

What is the ideal gender that you think others have in mind when hiring for these positions?

As a note: In the below analyses, I coded “1” if the participant said that women were the ideal candidates, and “0” if they did not select “woman”.

Graphs

As a note: In the below analyses, I coded “1” if the participant said that women were the ideal candidates, and “0” if they did not select “woman”.

Graphs

How important do you think it is that the person in this position is a woman?

Graphs

Candidate Qualifications

Does participant’s gender matter?

(By Participant’s Gender): Nationwide, what percentage of people in each role do you think are women?

(By Participant’s Gender): To what extent do you think that other people see each role as more masculine or feminine?

(By Participant’s Gender): What is the ideal gender that you think others have in mind when hiring for these positions?

If the woman is selected, DV is coded as 1. If any of the others were selected, they were coded as 0.

If the man is selected, DV is coded as 1. If any of the others were selected, they were coded as 0.

(By Participant’s Gender): How important do you think it is that the person in this position is a woman?

Candidate Qualifications

Pilot 3 - DEI Leader Evaluations

H1a: Do people solicit voice from the woman more in the DEI condition, compared to the other conditions?

H1b: In the DEI condition, do people solicit voice more from the woman than the men?

H2a: Do people see the woman as supporting social justice more in the DEI condition, compared to the other conditions?

H2b: In the DEI condition, do people see the woman as supporting social justice more than the men?

Who would participants select for the position?

H4: These effects are driven by social justice

Exploratory - Selection

Curious

Ideas

Learning

Have

All on Selection

Exploratory - Voice Solicitation

Curious

Ideas

Learning

Have

All on Voice Solicitation

Mixed Models ANOVAs

Voice Solicitation

Justice

Exploratory Analyses