Checking Randomization
Distribution of responses
Note: “focalman” and “focalwoman” indicate the which columns the focal man and focal woman are in.
Labeling notes:
pers_simplified references a variable coded for EVERY
candidate.
targ_gend_f references a variable coded for the candidate’s
gender.
deiexpert_f references a variable our between-subjects
manipulations.
Snapshot of the dataset is below:
Items
Ranking:
I reverse-coded the ranking variable. So I coded ‘1’ ranking as ‘4’, ‘2’ ranking as ‘3’, and ‘4’ as ‘1’ So higher #’s means person was more likely to be picked.
Voice Quality
- I think they can offer useful ideas for this initiative.
- I think their ideas will likely have a lot of value for improving this initiative.
Voice Solicitation
- I would ask for help/advice from them on this initiative.
- I would encourage them to speak out on this initiative.
Interest
How interested do you think each candidate would be in offering their perspectives or insights on this initiative?
We will now ask for you to imagine that each candidate had their voice solicited. We are curious to know how you think each candidate would experience their voice being solicited.
Each candidate was shown the following items:
| Scale | VS Citation | Justification |
|---|---|---|
| Positive Affect | Tangirala, S., & Ramanujam, R. (2012). | Controlled for in their studies (which found that voice solicitation was POSITIVELY associated with PA), and there was a positive association. |
| Negative Affect | Tangirala, S., & Ramanujam, R. (2012). | Controlled for in their studies (which found that voice solicitation was NEGATIVELY associated with NA), and there was a negative association. |
| Voice | Tangirala, S., & Ramanujam, R. (2012). | Hypothesis was that voice solicitation leads to greater subsequent upward voice. |
| Impact | Tangirala, S., & Ramanujam, R. (2012). | Hypothesis was that voice solicitation leads to greater subsequent upward voice because voicer feels like they are making more of an impact. |
| Rewards for voicer | Park, H., Tangirala, S., Hussain, I., & Ekkirala, S. (2022). | Table 3: voice solicitation and manager-rated job rewards are positively correlated*. |
- This paper shows that managers reward employees less when they solicit voice (vs. expressing voice). But they show that there is a moderate correlation (r = .22, p < .01, n = 385) between managerial voice solicitation and manager-rated job rewards
Affect
If you asked Laura Moffett to provide her perspective or insight on this position, she would be….
- inspired (PA)
- determined (PA)
- attentive (PA)
- active (PA)
- upset (NA)
- hostile (NA)
- alert (NA)
- ashamed (NA)
- nervous (NA)
- afraid (NA)
Voice
After asking Laura Moffett to provide her perspective or insight on this position, how likely would she be to engage in the following behaviors more generally at work?
- make recommendations to you for improving work procedures in your
unit
- speak up to you with ideas for change in work procedures in your
unit
- express her opinions on work-related issues to you even when you disagree with her
Rewards
For offering her perspectives or insights on this initiative, how likely would Laura Moffett be to receive the following?
- a salary increase.
- a promotion.
- more high-profile projects.
- more public recognition.
Analyses - Men Vs. Women
Ranking
Estimated marginal means
## Call:
## clm2(location = rank_rf ~ deiexpert_f * gendertarget + (1 | pid),
## data = sig_clean_long2)
##
## Location coefficients:
## Estimate Std. Error z value Pr(>|z|)
## deiexpert_fDEI-None -0.045 0.157 -0.286 0.7752
## gendertargetWoman 1.926 0.173 11.105 < 0.0000000000000002
## deiexpert_fDEI-None:gendertargetWoman 0.077 0.221 0.347 0.7282
##
## No scale coefficients
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -0.392 0.121 -3.243
## 2|3 0.973 0.126 7.741
## 3|4 2.326 0.142 16.423
##
## log-likelihood: -1392.55
## AIC: 2797.10
## Condition number of Hessian: 53.90
## (12 observations deleted due to missingness)Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Graphs
## Call:
## clm2(location = rank_rf ~ deiexpert_f * gendertarget + (1 | pid),
## data = sig_clean_long2)
##
## Location coefficients:
## Estimate Std. Error z value Pr(>|z|)
## deiexpert_fDEI-None -0.045 0.157 -0.286 0.7752
## gendertargetWoman 1.926 0.173 11.105 < 0.0000000000000002
## deiexpert_fDEI-None:gendertargetWoman 0.077 0.221 0.347 0.7282
##
## No scale coefficients
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -0.392 0.121 -3.243
## 2|3 0.973 0.126 7.741
## 3|4 2.326 0.142 16.423
##
## log-likelihood: -1392.55
## AIC: 2797.10
## Condition number of Hessian: 53.90
## (12 observations deleted due to missingness)
Voice Solicitation
Estimated marginal means
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: vs ~ deiexpert_f * gendertarget + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3967
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.242 -0.536 0.016 0.597 3.729
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.986 0.993
## Residual 1.447 1.203
## Number of obs: 1120, groups: pid, 280
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.1371 0.1174 436.7695 35.23 <0.0000000000000002 ***
## deiexpert_fDEI-None 0.0648 0.1573 436.7695 0.41 0.68
## gendertargetWoman 1.0726 0.1080 838.0000 9.93 <0.0000000000000002 ***
## deiexpert_fDEI-None:gendertargetWoman 0.1358 0.1447 838.0000 0.94 0.35
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N gndrtW
## dxprt_DEI-N -0.746
## gndrtrgtWmn -0.460 0.343
## dxp_DEI-N:W 0.343 -0.460 -0.746Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
Voice Quality
Estimated marginal Means
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: vq ~ deiexpert_f * gendertarget + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3614
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.659 -0.548 -0.004 0.529 3.326
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.032 1.016
## Residual 0.967 0.984
## Number of obs: 1120, groups: pid, 280
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.1452 0.1105 388.9528 37.50 <0.0000000000000002 ***
## deiexpert_fDEI-None 0.2811 0.1481 388.9529 1.90 0.058 .
## gendertargetWoman 1.1290 0.0883 838.0000 12.78 <0.0000000000000002 ***
## deiexpert_fDEI-None:gendertargetWoman -0.2588 0.1183 838.0000 -2.19 0.029 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N gndrtW
## dxprt_DEI-N -0.746
## gndrtrgtWmn -0.399 0.298
## dxp_DEI-N:W 0.298 -0.399 -0.746Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
Interest
Estimated marginal means
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: interest ~ deiexpert_f * gendertarget + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3710
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.005 -0.496 -0.039 0.543 2.409
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.03 1.02
## Residual 1.08 1.04
## Number of obs: 1120, groups: pid, 280
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.2419 0.1126 399.1689 37.69 <0.0000000000000002 ***
## deiexpert_fDEI-None 0.2869 0.1508 399.1689 1.90 0.058 .
## gendertargetWoman 1.0242 0.0932 838.0000 10.99 <0.0000000000000002 ***
## deiexpert_fDEI-None:gendertargetWoman -0.0466 0.1248 838.0000 -0.37 0.709
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N gndrtW
## dxprt_DEI-N -0.746
## gndrtrgtWmn -0.414 0.309
## dxp_DEI-N:W 0.309 -0.414 -0.746Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
Positive Affect
Estimated marginal means
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: pa ~ deiexpert_f * gendertarget + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3237
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.205 -0.472 0.013 0.547 3.028
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.982 0.991
## Residual 0.639 0.800
## Number of obs: 1120, groups: pid, 280
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.1613 0.1025 358.9492 40.62 <0.0000000000000002 ***
## deiexpert_fDEI-None 0.2698 0.1373 358.9492 1.97 0.05 .
## gendertargetWoman 0.8317 0.0718 838.0000 11.58 <0.0000000000000002 ***
## deiexpert_fDEI-None:gendertargetWoman -0.0296 0.0962 838.0000 -0.31 0.76
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N gndrtW
## dxprt_DEI-N -0.746
## gndrtrgtWmn -0.350 0.262
## dxp_DEI-N:W 0.262 -0.350 -0.746Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
Negative Affect
Estimated marginal means
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: na ~ deiexpert_f * gendertarget + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 2336
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.431 -0.447 -0.068 0.409 5.355
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.550 0.742
## Residual 0.268 0.517
## Number of obs: 1120, groups: pid, 280
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.6633 0.0743 340.2555 35.86 < 0.0000000000000002 ***
## deiexpert_fDEI-None -0.3139 0.0995 340.2555 -3.16 0.00175 **
## gendertargetWoman -0.1794 0.0465 838.0000 -3.86 0.00012 ***
## deiexpert_fDEI-None:gendertargetWoman 0.1175 0.0623 838.0000 1.89 0.05950 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N gndrtW
## dxprt_DEI-N -0.746
## gndrtrgtWmn -0.313 0.233
## dxp_DEI-N:W 0.233 -0.313 -0.746Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
Voice
Estimated marginal means
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: voice ~ deiexpert_f * gendertarget + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3492
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.443 -0.478 0.013 0.572 3.358
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.814 0.902
## Residual 0.894 0.946
## Number of obs: 1120, groups: pid, 280
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.6169 0.1009 404.4392 45.77 < 0.0000000000000002 ***
## deiexpert_fDEI-None 0.0284 0.1351 404.4392 0.21 0.83
## gendertargetWoman 0.3522 0.0849 838.0000 4.15 0.000037 ***
## deiexpert_fDEI-None:gendertargetWoman 0.1062 0.1138 838.0000 0.93 0.35
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N gndrtW
## dxprt_DEI-N -0.746
## gndrtrgtWmn -0.421 0.314
## dxp_DEI-N:W 0.314 -0.421 -0.746Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
Impact
Estimated marginal means
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: impact ~ deiexpert_f * gendertarget + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3601
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.326 -0.506 0.031 0.525 3.781
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.858 0.926
## Residual 0.997 0.999
## Number of obs: 1120, groups: pid, 280
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.3898 0.1046 410.4038 41.97 <0.0000000000000002 ***
## deiexpert_fDEI-None 0.0760 0.1401 410.4038 0.54 0.588
## gendertargetWoman 0.1707 0.0897 838.0000 1.90 0.057 .
## deiexpert_fDEI-None:gendertargetWoman 0.2919 0.1202 838.0000 2.43 0.015 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N gndrtW
## dxprt_DEI-N -0.746
## gndrtrgtWmn -0.429 0.320
## dxp_DEI-N:W 0.320 -0.429 -0.746Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
Rewards
Estimated marginal means
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: rewards ~ deiexpert_f * gendertarget + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3591
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.160 -0.481 -0.057 0.455 3.643
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.622 1.273
## Residual 0.834 0.913
## Number of obs: 1120, groups: pid, 280
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.6300 0.1282 343.4696 28.31 <0.0000000000000002 ***
## deiexpert_fDEI-None 0.1696 0.1718 343.4696 0.99 0.3241
## gendertargetWoman 0.2681 0.0820 838.0000 3.27 0.0011 **
## deiexpert_fDEI-None:gendertargetWoman 0.0628 0.1099 838.0000 0.57 0.5678
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N gndrtW
## dxprt_DEI-N -0.746
## gndrtrgtWmn -0.320 0.239
## dxp_DEI-N:W 0.239 -0.320 -0.746rewardsirwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
Analyses - All targets
Ranking
Estimated marginal means
## Call:
## clm2(location = rank_rf ~ deiexpert_f * person + (1 | pid), data = sig_clean_long2)
##
## Location coefficients:
## Estimate Std. Error z value Pr(>|z|)
## deiexpert_fDEI-None -0.053 0.222 -0.240 0.8100
## personotherman -0.196 0.234 -0.838 0.4022
## personwoman1 1.812 0.239 7.588 0.0000000000000326
## personwoman2 1.849 0.238 7.783 0.0000000000000071
## deiexpert_fDEI-None:personotherman 0.024 0.314 0.078 0.9381
## deiexpert_fDEI-None:personwoman1 0.181 0.313 0.578 0.5633
## deiexpert_fDEI-None:personwoman2 -0.012 0.313 -0.038 0.9697
##
## No scale coefficients
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -0.490 0.168 -2.926
## 2|3 0.877 0.170 5.159
## 3|4 2.232 0.182 12.263
##
## log-likelihood: -1391.56
## AIC: 2803.13
## Condition number of Hessian: 179.27
## (12 observations deleted due to missingness)Mixed-Model Anova comparing EVERY candidate to each other
Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Graphs
## Call:
## clm2(location = rank_rf ~ deiexpert_f * person + (1 | pid), data = sig_clean_long2)
##
## Location coefficients:
## Estimate Std. Error z value Pr(>|z|)
## deiexpert_fDEI-None -0.053 0.222 -0.240 0.8100
## personotherman -0.196 0.234 -0.838 0.4022
## personwoman1 1.812 0.239 7.588 0.0000000000000326
## personwoman2 1.849 0.238 7.783 0.0000000000000071
## deiexpert_fDEI-None:personotherman 0.024 0.314 0.078 0.9381
## deiexpert_fDEI-None:personwoman1 0.181 0.313 0.578 0.5633
## deiexpert_fDEI-None:personwoman2 -0.012 0.313 -0.038 0.9697
##
## No scale coefficients
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -0.490 0.168 -2.926
## 2|3 0.877 0.170 5.159
## 3|4 2.232 0.182 12.263
##
## log-likelihood: -1391.56
## AIC: 2803.13
## Condition number of Hessian: 179.27
## (12 observations deleted due to missingness)
Voice Solicitation
Estimated marginal means
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: vs ~ deiexpert_f * person + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3973
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.172 -0.529 0.026 0.588 3.684
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.985 0.993
## Residual 1.450 1.204
## Number of obs: 1120, groups: pid, 280
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.1290 0.1401 745.6871 29.46 < 0.0000000000000002 ***
## deiexpert_fDEI-None 0.1210 0.1878 745.6871 0.64 0.52
## personotherman 0.0161 0.1529 834.0000 0.11 0.92
## personwoman1 1.0000 0.1529 834.0000 6.54 0.000000000107581 ***
## personwoman2 1.1613 0.1529 834.0000 7.59 0.000000000000083 ***
## deiexpert_fDEI-None:personotherman -0.1123 0.2049 834.0000 -0.55 0.58
## deiexpert_fDEI-None:personwoman1 0.2115 0.2049 834.0000 1.03 0.30
## deiexpert_fDEI-None:personwoman2 -0.0523 0.2049 834.0000 -0.26 0.80
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N prsnth prsnw1 prsnw2 dx_DEI-N: d_DEI-N:1
## dxprt_DEI-N -0.746
## personthrmn -0.546 0.407
## personwomn1 -0.546 0.407 0.500
## personwomn2 -0.546 0.407 0.500 0.500
## dxpr_DEI-N: 0.407 -0.546 -0.746 -0.373 -0.373
## dxp_DEI-N:1 0.407 -0.546 -0.373 -0.746 -0.373 0.500
## dxp_DEI-N:2 0.407 -0.546 -0.373 -0.373 -0.746 0.500 0.500Mixed-Model Anova comparing EVERY candidate to each other
Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
Voice Quality
Estimated marginal Means
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: vq ~ deiexpert_f * person + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3622
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.588 -0.558 0.028 0.539 3.341
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.03 1.015
## Residual 0.97 0.985
## Number of obs: 1120, groups: pid, 280
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.14113 0.12703 618.91202 32.60 <0.0000000000000002 ***
## deiexpert_fDEI-None 0.26592 0.17018 618.91202 1.56 0.119
## personotherman 0.00806 0.12506 834.00001 0.06 0.949
## personwoman1 1.09274 0.12506 834.00001 8.74 <0.0000000000000002 ***
## personwoman2 1.17339 0.12506 834.00001 9.38 <0.0000000000000002 ***
## deiexpert_fDEI-None:personotherman 0.03040 0.16755 834.00001 0.18 0.856
## deiexpert_fDEI-None:personwoman1 -0.13761 0.16755 834.00001 -0.82 0.412
## deiexpert_fDEI-None:personwoman2 -0.34967 0.16755 834.00001 -2.09 0.037 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N prsnth prsnw1 prsnw2 dx_DEI-N: d_DEI-N:1
## dxprt_DEI-N -0.746
## personthrmn -0.492 0.367
## personwomn1 -0.492 0.367 0.500
## personwomn2 -0.492 0.367 0.500 0.500
## dxpr_DEI-N: 0.367 -0.492 -0.746 -0.373 -0.373
## dxp_DEI-N:1 0.367 -0.492 -0.373 -0.746 -0.373 0.500
## dxp_DEI-N:2 0.367 -0.492 -0.373 -0.373 -0.746 0.500 0.500Mixed-Model Anova comparing EVERY candidate to each other
Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
Interest
Estimated marginal means
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: interest ~ deiexpert_f * person + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3716
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0195 -0.5298 0.0393 0.6045 2.3888
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.03 1.02
## Residual 1.08 1.04
## Number of obs: 1120, groups: pid, 280
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.1694 0.1304 647.1691 31.96 < 0.0000000000000002 ***
## deiexpert_fDEI-None 0.3755 0.1748 647.1691 2.15 0.032 *
## personotherman 0.1452 0.1318 834.0000 1.10 0.271
## personwoman1 1.0081 0.1318 834.0000 7.65 0.000000000000057 ***
## personwoman2 1.1855 0.1318 834.0000 8.99 < 0.0000000000000002 ***
## deiexpert_fDEI-None:personotherman -0.1772 0.1766 834.0000 -1.00 0.316
## deiexpert_fDEI-None:personwoman1 -0.0273 0.1766 834.0000 -0.15 0.877
## deiexpert_fDEI-None:personwoman2 -0.2432 0.1766 834.0000 -1.38 0.169
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N prsnth prsnw1 prsnw2 dx_DEI-N: d_DEI-N:1
## dxprt_DEI-N -0.746
## personthrmn -0.505 0.377
## personwomn1 -0.505 0.377 0.500
## personwomn2 -0.505 0.377 0.500 0.500
## dxpr_DEI-N: 0.377 -0.505 -0.746 -0.373 -0.373
## dxp_DEI-N:1 0.377 -0.505 -0.373 -0.746 -0.373 0.500
## dxp_DEI-N:2 0.377 -0.505 -0.373 -0.373 -0.746 0.500 0.500Mixed-Model Anova comparing EVERY candidate to each other
Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
Positive Affect
Estimated marginal means
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: pa ~ deiexpert_f * person + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3244
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.198 -0.477 0.017 0.537 3.116
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.982 0.991
## Residual 0.639 0.799
## Number of obs: 1120, groups: pid, 280
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.0948 0.1143 529.2272 35.82 < 0.0000000000000002 ***
## deiexpert_fDEI-None 0.3732 0.1532 529.2272 2.44 0.015 *
## personotherman 0.1331 0.1015 834.0000 1.31 0.190
## personwoman1 0.8226 0.1015 834.0000 8.10 0.0000000000000019 ***
## personwoman2 0.9738 0.1015 834.0000 9.59 < 0.0000000000000002 ***
## deiexpert_fDEI-None:personotherman -0.2068 0.1360 834.0000 -1.52 0.129
## deiexpert_fDEI-None:personwoman1 -0.0614 0.1360 834.0000 -0.45 0.652
## deiexpert_fDEI-None:personwoman2 -0.2046 0.1360 834.0000 -1.50 0.133
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N prsnth prsnw1 prsnw2 dx_DEI-N: d_DEI-N:1
## dxprt_DEI-N -0.746
## personthrmn -0.444 0.331
## personwomn1 -0.444 0.331 0.500
## personwomn2 -0.444 0.331 0.500 0.500
## dxpr_DEI-N: 0.331 -0.444 -0.746 -0.373 -0.373
## dxp_DEI-N:1 0.331 -0.444 -0.373 -0.746 -0.373 0.500
## dxp_DEI-N:2 0.331 -0.444 -0.373 -0.373 -0.746 0.500 0.500Mixed-Model Anova comparing EVERY candidate to each other
Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
Negative Affect
Estimated marginal means
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: na ~ deiexpert_f * person + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 2348
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.399 -0.438 -0.065 0.413 5.456
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.550 0.742
## Residual 0.268 0.518
## Number of obs: 1120, groups: pid, 280
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.6102 0.0812 471.8322 32.14 <0.0000000000000002 ***
## deiexpert_fDEI-None -0.2854 0.1088 471.8322 -2.62 0.009 **
## personotherman 0.1062 0.0657 834.0000 1.62 0.107
## personwoman1 -0.1425 0.0657 834.0000 -2.17 0.030 *
## personwoman2 -0.1102 0.0657 834.0000 -1.68 0.094 .
## deiexpert_fDEI-None:personotherman -0.0570 0.0881 834.0000 -0.65 0.517
## deiexpert_fDEI-None:personwoman1 0.1061 0.0881 834.0000 1.21 0.228
## deiexpert_fDEI-None:personwoman2 0.0718 0.0881 834.0000 0.81 0.415
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N prsnth prsnw1 prsnw2 dx_DEI-N: d_DEI-N:1
## dxprt_DEI-N -0.746
## personthrmn -0.405 0.302
## personwomn1 -0.405 0.302 0.500
## personwomn2 -0.405 0.302 0.500 0.500
## dxpr_DEI-N: 0.302 -0.405 -0.746 -0.373 -0.373
## dxp_DEI-N:1 0.302 -0.405 -0.373 -0.746 -0.373 0.500
## dxp_DEI-N:2 0.302 -0.405 -0.373 -0.373 -0.746 0.500 0.500Mixed-Model Anova comparing EVERY candidate to each other
Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
Voice
Estimated marginal means
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: voice ~ deiexpert_f * person + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3500
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.364 -0.467 0.021 0.572 3.407
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.814 0.902
## Residual 0.896 0.947
## Number of obs: 1120, groups: pid, 280
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.545699 0.117442 662.124417 38.71 < 0.0000000000000002 ***
## deiexpert_fDEI-None 0.086780 0.157341 662.124418 0.55 0.581
## personotherman 0.142473 0.120239 834.000012 1.18 0.236
## personwoman1 0.473118 0.120239 834.000012 3.93 0.00009 ***
## personwoman2 0.373656 0.120239 834.000012 3.11 0.002 **
## deiexpert_fDEI-None:personotherman -0.116832 0.161088 834.000012 -0.73 0.468
## deiexpert_fDEI-None:personwoman1 -0.000896 0.161088 834.000012 -0.01 0.996
## deiexpert_fDEI-None:personwoman2 0.096430 0.161088 834.000012 0.60 0.550
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N prsnth prsnw1 prsnw2 dx_DEI-N: d_DEI-N:1
## dxprt_DEI-N -0.746
## personthrmn -0.512 0.382
## personwomn1 -0.512 0.382 0.500
## personwomn2 -0.512 0.382 0.500 0.500
## dxpr_DEI-N: 0.382 -0.512 -0.746 -0.373 -0.373
## dxp_DEI-N:1 0.382 -0.512 -0.373 -0.746 -0.373 0.500
## dxp_DEI-N:2 0.382 -0.512 -0.373 -0.373 -0.746 0.500 0.500Mixed-Model Anova comparing EVERY candidate to each other
Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
Impact
Estimated marginal means
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: impact ~ deiexpert_f * person + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3607
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.241 -0.496 0.025 0.532 3.696
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.858 0.926
## Residual 0.998 0.999
## Number of obs: 1120, groups: pid, 280
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.34140 0.12233 677.51639 35.49 <0.0000000000000002 ***
## deiexpert_fDEI-None 0.07741 0.16388 677.51639 0.47 0.637
## personotherman 0.09677 0.12685 834.00001 0.76 0.446
## personwoman1 0.13441 0.12685 834.00001 1.06 0.290
## personwoman2 0.30376 0.12685 834.00001 2.39 0.017 *
## deiexpert_fDEI-None:personotherman -0.00276 0.16994 834.00001 -0.02 0.987
## deiexpert_fDEI-None:personwoman1 0.32499 0.16994 834.00001 1.91 0.056 .
## deiexpert_fDEI-None:personwoman2 0.25607 0.16994 834.00001 1.51 0.132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N prsnth prsnw1 prsnw2 dx_DEI-N: d_DEI-N:1
## dxprt_DEI-N -0.746
## personthrmn -0.518 0.387
## personwomn1 -0.518 0.387 0.500
## personwomn2 -0.518 0.387 0.500 0.500
## dxpr_DEI-N: 0.387 -0.518 -0.746 -0.373 -0.373
## dxp_DEI-N:1 0.387 -0.518 -0.373 -0.746 -0.373 0.500
## dxp_DEI-N:2 0.387 -0.518 -0.373 -0.373 -0.746 0.500 0.500Mixed-Model Anova comparing EVERY candidate to each other
Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
Rewards
Estimated marginal means
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: rewards ~ deiexpert_f * person + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3600
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.197 -0.452 -0.055 0.461 3.619
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.621 1.273
## Residual 0.837 0.915
## Number of obs: 1120, groups: pid, 280
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.61492 0.14079 482.58944 25.68 <0.0000000000000002 ***
## deiexpert_fDEI-None 0.18156 0.18863 482.58944 0.96 0.3363
## personotherman 0.03024 0.11621 834.00000 0.26 0.7947
## personwoman1 0.24395 0.11621 834.00000 2.10 0.0361 *
## personwoman2 0.32258 0.11621 834.00000 2.78 0.0056 **
## deiexpert_fDEI-None:personotherman -0.02383 0.15569 834.00000 -0.15 0.8784
## deiexpert_fDEI-None:personwoman1 0.10380 0.15569 834.00000 0.67 0.5051
## deiexpert_fDEI-None:personwoman2 -0.00207 0.15569 834.00000 -0.01 0.9894
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N prsnth prsnw1 prsnw2 dx_DEI-N: d_DEI-N:1
## dxprt_DEI-N -0.746
## personthrmn -0.413 0.308
## personwomn1 -0.413 0.308 0.500
## personwomn2 -0.413 0.308 0.500 0.500
## dxpr_DEI-N: 0.308 -0.413 -0.746 -0.373 -0.373
## dxp_DEI-N:1 0.308 -0.413 -0.373 -0.746 -0.373 0.500
## dxp_DEI-N:2 0.308 -0.413 -0.373 -0.373 -0.746 0.500 0.500Mixed-Model Anova comparing EVERY candidate to each other
rewardsirwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
Exploratory analyses
Part. gender as a control
Ranking
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: rank ~ deiexpert_f * person + part_gend_f + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3071
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1483 -0.9833 0.0202 0.9413 2.1416
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.000 0.000
## Residual 0.967 0.983
## Number of obs: 1088, groups: pid, 272
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.9752066115702482385 0.0922451748390111792 1078.9999999957651652949 32.25 < 0.0000000000000002 ***
## deiexpert_fDEI-None 0.0049258387608769293 0.1199983014661786390 1078.9999999863302946324 0.04 0.97
## personotherman 0.0991735537190080868 0.1264259073858125804 1078.9999999875099092606 0.78 0.43
## personwoman1 -0.9917355371900827832 0.1264259073858125804 1078.9999999875099092606 -7.84 0.0000000000000104 ***
## personwoman2 -1.0082644628099171058 0.1264259073858125526 1078.9999999875094545132 -7.98 0.0000000000000039 ***
## part_gend_fMale Participants 0.0000000000000000282 0.0611636379724988730 1078.9999999867852693569 0.00 1.00
## deiexpert_fDEI-None:personotherman 0.0332767774068198283 0.1696805478450158222 1078.9999999863277935219 0.20 0.84
## deiexpert_fDEI-None:personwoman1 -0.0943571780417051115 0.1696805478450158222 1078.9999999863277935219 -0.56 0.58
## deiexpert_fDEI-None:personwoman2 0.0413770455913741800 0.1696805478450157667 1078.9999999863264292799 0.24 0.81
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N prsnth prsnw1 prsnw2 pr__MP dx_DEI-N: d_DEI-N:1
## dxprt_DEI-N -0.718
## personthrmn -0.685 0.527
## personwomn1 -0.685 0.527 0.500
## personwomn2 -0.685 0.527 0.500 0.500
## prt_gnd_fMP -0.247 -0.016 0.000 0.000 0.000
## dxpr_DEI-N: 0.511 -0.707 -0.745 -0.373 -0.373 0.000
## dxp_DEI-N:1 0.511 -0.707 -0.373 -0.745 -0.373 0.000 0.500
## dxp_DEI-N:2 0.511 -0.707 -0.373 -0.373 -0.745 0.000 0.500 0.500
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Voice Solicitation
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: vs ~ deiexpert_f * person + part_gend_f + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3911
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.147 -0.518 0.034 0.579 3.653
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.99 0.995
## Residual 1.46 1.209
## Number of obs: 1100, groups: pid, 275
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.1354 0.1514 646.1461 27.31 < 0.0000000000000002 ***
## deiexpert_fDEI-None 0.1437 0.1901 730.4544 0.76 0.45
## personotherman 0.0164 0.1548 819.0001 0.11 0.92
## personwoman1 0.9795 0.1548 819.0001 6.33 0.0000000004058 ***
## personwoman2 1.1475 0.1548 819.0001 7.41 0.0000000000003 ***
## part_gend_fMale Participants -0.0116 0.1443 271.9999 -0.08 0.94
## deiexpert_fDEI-None:personotherman -0.1210 0.2075 819.0001 -0.58 0.56
## deiexpert_fDEI-None:personwoman1 0.2068 0.2075 819.0001 1.00 0.32
## deiexpert_fDEI-None:personwoman2 -0.0658 0.2075 819.0001 -0.32 0.75
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N prsnth prsnw1 prsnw2 pr__MP dx_DEI-N: d_DEI-N:1
## dxprt_DEI-N -0.690
## personthrmn -0.511 0.407
## personwomn1 -0.511 0.407 0.500
## personwomn2 -0.511 0.407 0.500 0.500
## prt_gnd_fMP -0.352 -0.023 0.000 0.000 0.000
## dxpr_DEI-N: 0.381 -0.546 -0.746 -0.373 -0.373 0.000
## dxp_DEI-N:1 0.381 -0.546 -0.373 -0.746 -0.373 0.000 0.500
## dxp_DEI-N:2 0.381 -0.546 -0.373 -0.373 -0.746 0.000 0.500 0.500Voice Quality
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: vq ~ deiexpert_f * person + part_gend_f + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3563
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.556 -0.545 0.025 0.542 3.320
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.023 1.011
## Residual 0.976 0.988
## Number of obs: 1100, groups: pid, 275
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.1540 0.1380 542.0235 30.11 <0.0000000000000002 ***
## deiexpert_fDEI-None 0.2855 0.1717 609.3325 1.66 0.097 .
## personotherman 0.0082 0.1265 819.0000 0.06 0.948
## personwoman1 1.0779 0.1265 819.0000 8.52 <0.0000000000000002 ***
## personwoman2 1.1598 0.1265 819.0000 9.17 <0.0000000000000002 ***
## part_gend_fMale Participants -0.0288 0.1395 272.0000 -0.21 0.837
## deiexpert_fDEI-None:personotherman 0.0310 0.1696 819.0000 0.18 0.855
## deiexpert_fDEI-None:personwoman1 -0.1302 0.1696 819.0000 -0.77 0.443
## deiexpert_fDEI-None:personwoman2 -0.3657 0.1696 819.0000 -2.16 0.031 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N prsnth prsnw1 prsnw2 pr__MP dx_DEI-N: d_DEI-N:1
## dxprt_DEI-N -0.683
## personthrmn -0.458 0.368
## personwomn1 -0.458 0.368 0.500
## personwomn2 -0.458 0.368 0.500 0.500
## prt_gnd_fMP -0.373 -0.024 0.000 0.000 0.000
## dxpr_DEI-N: 0.342 -0.494 -0.746 -0.373 -0.373 0.000
## dxp_DEI-N:1 0.342 -0.494 -0.373 -0.746 -0.373 0.000 0.500
## dxp_DEI-N:2 0.342 -0.494 -0.373 -0.373 -0.746 0.000 0.500 0.500Interest
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: interest ~ deiexpert_f * person + part_gend_f + rank + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3481
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.139 -0.537 -0.003 0.557 3.267
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.095 1.046
## Residual 0.908 0.953
## Number of obs: 1088, groups: pid, 272
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.20620 0.16458 794.82972 31.63 < 0.0000000000000002 ***
## deiexpert_fDEI-None 0.36156 0.17274 567.12723 2.09 0.037 *
## personotherman 0.17425 0.12254 809.00000 1.42 0.155
## personwoman1 0.68722 0.12595 809.00000 5.46 0.00000006465 ***
## personwoman2 0.82209 0.12606 809.00000 6.52 0.00000000012 ***
## part_gend_fMale Participants -0.00914 0.14303 269.00000 -0.06 0.949
## rank -0.34039 0.02950 809.00000 -11.54 < 0.0000000000000002 ***
## deiexpert_fDEI-None:personotherman -0.16890 0.16442 809.00000 -1.03 0.305
## deiexpert_fDEI-None:personwoman1 -0.07678 0.16444 809.00000 -0.47 0.641
## deiexpert_fDEI-None:personwoman2 -0.17107 0.16442 809.00000 -1.04 0.298
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N prsnth prsnw1 prsnw2 pr__MP rank dx_DEI-N: d_DEI-N:1
## dxprt_DEI-N -0.573
## personthrmn -0.359 0.355
## personwomn1 -0.486 0.345 0.481
## personwomn2 -0.487 0.344 0.480 0.527
## prt_gnd_fMP -0.323 -0.027 0.000 0.000 0.000
## rank -0.533 -0.001 -0.024 0.232 0.236 0.000
## dxpr_DEI-N: 0.280 -0.476 -0.745 -0.364 -0.363 0.000 -0.006
## dxp_DEI-N:1 0.268 -0.476 -0.373 -0.721 -0.358 0.000 0.017 0.500
## dxp_DEI-N:2 0.281 -0.476 -0.372 -0.364 -0.726 0.000 -0.007 0.500 0.500Positive Affect
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: pa ~ deiexpert_f * person + part_gend_f + rank + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3112
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.630 -0.498 0.014 0.549 3.217
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.993 0.996
## Residual 0.604 0.777
## Number of obs: 1088, groups: pid, 272
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.5745 0.1441 691.6694 31.74 < 0.0000000000000002 ***
## deiexpert_fDEI-None 0.3625 0.1543 498.0933 2.35 0.019 *
## personotherman 0.1460 0.1000 809.0000 1.46 0.144
## personwoman1 0.6759 0.1027 809.0000 6.58 0.000000000085584 ***
## personwoman2 0.7993 0.1028 809.0000 7.77 0.000000000000023 ***
## part_gend_fMale Participants 0.0588 0.1330 269.0000 0.44 0.659
## rank -0.1602 0.0241 809.0000 -6.66 0.000000000051953 ***
## deiexpert_fDEI-None:personotherman -0.1960 0.1341 809.0000 -1.46 0.144
## deiexpert_fDEI-None:personwoman1 -0.0932 0.1341 809.0000 -0.69 0.487
## deiexpert_fDEI-None:personwoman2 -0.1909 0.1341 809.0000 -1.42 0.155
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N prsnth prsnw1 prsnw2 pr__MP rank dx_DEI-N: d_DEI-N:1
## dxprt_DEI-N -0.584
## personthrmn -0.335 0.324
## personwomn1 -0.453 0.315 0.481
## personwomn2 -0.454 0.315 0.480 0.527
## prt_gnd_fMP -0.343 -0.028 0.000 0.000 0.000
## rank -0.497 -0.001 -0.024 0.232 0.236 0.000
## dxpr_DEI-N: 0.261 -0.435 -0.745 -0.364 -0.363 0.000 -0.006
## dxp_DEI-N:1 0.250 -0.435 -0.373 -0.721 -0.358 0.000 0.017 0.500
## dxp_DEI-N:2 0.262 -0.435 -0.372 -0.364 -0.726 0.000 -0.007 0.500 0.500Negative Affect
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: na ~ deiexpert_f * person + part_gend_f + rank + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 2292
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.348 -0.444 -0.063 0.408 5.400
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.547 0.740
## Residual 0.269 0.519
## Number of obs: 1088, groups: pid, 272
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.66770 0.10173 625.31700 26.22 <0.0000000000000002 ***
## deiexpert_fDEI-None -0.25569 0.11030 458.35038 -2.32 0.021 *
## personotherman 0.10115 0.06676 809.00000 1.52 0.130
## personwoman1 -0.16302 0.06861 809.00000 -2.38 0.018 *
## personwoman2 -0.11904 0.06867 809.00000 -1.73 0.083 .
## part_gend_fMale Participants -0.11741 0.09751 269.00000 -1.20 0.230
## rank -0.00604 0.01607 809.00000 -0.38 0.707
## deiexpert_fDEI-None:personotherman -0.04847 0.08957 809.00000 -0.54 0.589
## deiexpert_fDEI-None:personwoman1 0.12445 0.08958 809.00000 1.39 0.165
## deiexpert_fDEI-None:personwoman2 0.06574 0.08957 809.00000 0.73 0.463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N prsnth prsnw1 prsnw2 pr__MP rank dx_DEI-N: d_DEI-N:1
## dxprt_DEI-N -0.591
## personthrmn -0.317 0.302
## personwomn1 -0.428 0.294 0.481
## personwomn2 -0.430 0.294 0.480 0.527
## prt_gnd_fMP -0.356 -0.028 0.000 0.000 0.000
## rank -0.470 -0.001 -0.024 0.232 0.236 0.000
## dxpr_DEI-N: 0.247 -0.406 -0.745 -0.364 -0.363 0.000 -0.006
## dxp_DEI-N:1 0.236 -0.406 -0.373 -0.721 -0.358 0.000 0.017 0.500
## dxp_DEI-N:2 0.248 -0.406 -0.372 -0.364 -0.726 0.000 -0.007 0.500 0.500Voice
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: voice ~ deiexpert_f * person + part_gend_f + rank + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3390
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.358 -0.518 0.027 0.535 3.317
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.840 0.917
## Residual 0.874 0.935
## Number of obs: 1088, groups: pid, 272
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.9974 0.1544 868.6744 32.36 < 0.0000000000000002 ***
## deiexpert_fDEI-None 0.0919 0.1598 625.0657 0.57 0.5655
## personotherman 0.1491 0.1202 809.0000 1.24 0.2154
## personwoman1 0.3579 0.1236 809.0000 2.90 0.0039 **
## personwoman2 0.2399 0.1237 809.0000 1.94 0.0528 .
## part_gend_fMale Participants -0.0435 0.1280 269.0000 -0.34 0.7343
## rank -0.1418 0.0289 809.0000 -4.90 0.0000011 ***
## deiexpert_fDEI-None:personotherman -0.1281 0.1613 809.0000 -0.79 0.4275
## deiexpert_fDEI-None:personwoman1 -0.0749 0.1613 809.0000 -0.46 0.6425
## deiexpert_fDEI-None:personwoman2 0.0931 0.1613 809.0000 0.58 0.5638
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N prsnth prsnw1 prsnw2 pr__MP rank dx_DEI-N: d_DEI-N:1
## dxprt_DEI-N -0.566
## personthrmn -0.376 0.376
## personwomn1 -0.508 0.366 0.481
## personwomn2 -0.510 0.365 0.480 0.527
## prt_gnd_fMP -0.308 -0.026 0.000 0.000 0.000
## rank -0.558 -0.001 -0.024 0.232 0.236 0.000
## dxpr_DEI-N: 0.293 -0.505 -0.745 -0.364 -0.363 0.000 -0.006
## dxp_DEI-N:1 0.280 -0.505 -0.373 -0.721 -0.358 0.000 0.017 0.500
## dxp_DEI-N:2 0.294 -0.505 -0.372 -0.364 -0.726 0.000 -0.007 0.500 0.500Impact
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: impact ~ deiexpert_f * person + part_gend_f + rank + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3504
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.405 -0.504 0.033 0.525 3.626
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.884 0.940
## Residual 0.984 0.992
## Number of obs: 1088, groups: pid, 272
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.6995 0.1619 889.6804 29.03 < 0.0000000000000002 ***
## deiexpert_fDEI-None 0.0839 0.1668 643.4888 0.50 0.61526
## personotherman 0.1134 0.1276 809.0000 0.89 0.37447
## personwoman1 0.0562 0.1311 809.0000 0.43 0.66825
## personwoman2 0.1921 0.1313 809.0000 1.46 0.14383
## part_gend_fMale Participants -0.0160 0.1323 269.0000 -0.12 0.90368
## rank -0.1155 0.0307 809.0000 -3.76 0.00018 ***
## deiexpert_fDEI-None:personotherman -0.0208 0.1712 809.0000 -0.12 0.90323
## deiexpert_fDEI-None:personwoman1 0.2488 0.1712 809.0000 1.45 0.14666
## deiexpert_fDEI-None:personwoman2 0.2503 0.1712 809.0000 1.46 0.14410
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N prsnth prsnw1 prsnw2 pr__MP rank dx_DEI-N: d_DEI-N:1
## dxprt_DEI-N -0.563
## personthrmn -0.380 0.382
## personwomn1 -0.514 0.372 0.481
## personwomn2 -0.516 0.371 0.480 0.527
## prt_gnd_fMP -0.304 -0.025 0.000 0.000 0.000
## rank -0.564 -0.001 -0.024 0.232 0.236 0.000
## dxpr_DEI-N: 0.297 -0.513 -0.745 -0.364 -0.363 0.000 -0.006
## dxp_DEI-N:1 0.284 -0.513 -0.373 -0.721 -0.358 0.000 0.017 0.500
## dxp_DEI-N:2 0.298 -0.513 -0.372 -0.364 -0.726 0.000 -0.007 0.500 0.500Rewards
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: rewards ~ deiexpert_f * person + part_gend_f + rank + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3460
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.272 -0.479 -0.027 0.478 3.915
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.647 1.28
## Residual 0.792 0.89
## Number of obs: 1088, groups: pid, 272
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.12092 0.17558 618.08425 23.47 < 0.0000000000000002 ***
## deiexpert_fDEI-None 0.19684 0.19065 454.17826 1.03 0.30
## personotherman 0.01945 0.11445 809.00000 0.17 0.87
## personwoman1 0.12990 0.11763 809.00000 1.10 0.27
## personwoman2 0.19139 0.11774 809.00000 1.63 0.10
## part_gend_fMale Participants -0.10631 0.16899 269.00000 -0.63 0.53
## rank -0.15444 0.02755 809.00000 -5.61 0.000000028 ***
## deiexpert_fDEI-None:personotherman 0.00597 0.15356 809.00000 0.04 0.97
## deiexpert_fDEI-None:personwoman1 0.02522 0.15358 809.00000 0.16 0.87
## deiexpert_fDEI-None:personwoman2 -0.02118 0.15357 809.00000 -0.14 0.89
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N prsnth prsnw1 prsnw2 pr__MP rank dx_DEI-N: d_DEI-N:1
## dxprt_DEI-N -0.592
## personthrmn -0.315 0.300
## personwomn1 -0.425 0.292 0.481
## personwomn2 -0.427 0.291 0.480 0.527
## prt_gnd_fMP -0.358 -0.028 0.000 0.000 0.000
## rank -0.467 -0.001 -0.024 0.232 0.236 0.000
## dxpr_DEI-N: 0.246 -0.403 -0.745 -0.364 -0.363 0.000 -0.006
## dxp_DEI-N:1 0.235 -0.403 -0.373 -0.721 -0.358 0.000 0.017 0.500
## dxp_DEI-N:2 0.246 -0.403 -0.372 -0.364 -0.726 0.000 -0.007 0.500 0.500Part. gender as a moderator
VQ
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: vq ~ threewaycond * part_gend_f * deiexpert_f + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3556
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.448 -0.557 0.005 0.548 3.166
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.030 1.015
## Residual 0.966 0.983
## Number of obs: 1100, groups: pid, 275
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.1299 0.1610 602.7270 25.65 < 0.0000000000000002 ***
## threewaycondother\nman -0.1169 0.1584 813.0000 -0.74 0.46
## threewaycondwoman1 1.1169 0.1584 813.0000 7.05 0.000000000003802 ***
## threewaycondwoman2 1.2468 0.1584 813.0000 7.87 0.000000000000011 ***
## part_gend_fMale Participants 0.0368 0.2651 602.7270 0.14 0.89
## deiexpert_fDEI-None 0.2125 0.2182 602.7270 0.97 0.33
## threewaycondother\nman:part_gend_fMale Participants 0.3391 0.2608 813.0000 1.30 0.19
## threewaycondwoman1:part_gend_fMale Participants -0.1058 0.2608 813.0000 -0.41 0.69
## threewaycondwoman2:part_gend_fMale Participants -0.2356 0.2608 813.0000 -0.90 0.37
## threewaycondother\nman:deiexpert_fDEI-None 0.2256 0.2147 813.0000 1.05 0.29
## threewaycondwoman1:deiexpert_fDEI-None -0.0408 0.2147 813.0000 -0.19 0.85
## threewaycondwoman2:deiexpert_fDEI-None -0.1815 0.2147 813.0000 -0.85 0.40
## part_gend_fMale Participants:deiexpert_fDEI-None 0.1782 0.3531 602.7270 0.50 0.61
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fDEI-None -0.5134 0.3474 813.0000 -1.48 0.14
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fDEI-None -0.2162 0.3474 813.0000 -0.62 0.53
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fDEI-None -0.4443 0.3474 813.0000 -1.28 0.20
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VS
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: vs ~ threewaycond * part_gend_f * deiexpert_f + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3902
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.966 -0.523 0.024 0.597 3.763
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.999 1.0
## Residual 1.446 1.2
## Number of obs: 1100, groups: pid, 275
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.09091 0.17819 722.12625 22.96 < 0.0000000000000002 ***
## threewaycondother\nman -0.10390 0.19378 813.00000 -0.54 0.59
## threewaycondwoman1 1.15584 0.19378 813.00000 5.96 0.00000000365 ***
## threewaycondwoman2 1.23377 0.19378 813.00000 6.37 0.00000000032 ***
## part_gend_fMale Participants 0.10909 0.29340 722.12624 0.37 0.71
## deiexpert_fDEI-None 0.09387 0.24151 722.12624 0.39 0.70
## threewaycondother\nman:part_gend_fMale Participants 0.32612 0.31906 813.00000 1.02 0.31
## threewaycondwoman1:part_gend_fMale Participants -0.47807 0.31906 813.00000 -1.50 0.13
## threewaycondwoman2:part_gend_fMale Participants -0.23377 0.31906 813.00000 -0.73 0.46
## threewaycondother\nman:deiexpert_fDEI-None -0.01567 0.26263 813.00000 -0.06 0.95
## threewaycondwoman1:deiexpert_fDEI-None 0.21916 0.26263 813.00000 0.83 0.40
## threewaycondwoman2:deiexpert_fDEI-None 0.08145 0.26263 813.00000 0.31 0.76
## part_gend_fMale Participants:deiexpert_fDEI-None 0.11596 0.39081 722.12624 0.30 0.77
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fDEI-None -0.28852 0.42500 813.00000 -0.68 0.50
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fDEI-None 0.00471 0.42500 813.00000 0.01 0.99
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fDEI-None -0.35194 0.42500 813.00000 -0.83 0.41
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Ranking
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: rank_r ~ threewaycond * part_gend_f * deiexpert_f + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3070
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2409 -0.9114 0.0226 0.8980 2.1730
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.000 0.000
## Residual 0.964 0.982
## Number of obs: 1088, groups: pid, 272
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.0526 0.1126 1072.0000 18.23 < 0.0000000000000002 ***
## threewaycondother\nman -0.1579 0.1593 1072.0000 -0.99 0.322
## threewaycondwoman1 1.0658 0.1593 1072.0000 6.69 0.000000000035 ***
## threewaycondwoman2 0.8816 0.1593 1072.0000 5.54 0.000000039028 ***
## part_gend_fMale Participants -0.0749 0.1847 1072.0000 -0.41 0.685
## deiexpert_fDEI-None -0.1526 0.1529 1072.0000 -1.00 0.319
## threewaycondother\nman:part_gend_fMale Participants 0.1579 0.2612 1072.0000 0.60 0.546
## threewaycondwoman1:part_gend_fMale Participants -0.1991 0.2612 1072.0000 -0.76 0.446
## threewaycondwoman2:part_gend_fMale Participants 0.3406 0.2612 1072.0000 1.30 0.192
## threewaycondother\nman:deiexpert_fDEI-None 0.1246 0.2163 1072.0000 0.58 0.565
## threewaycondwoman1:deiexpert_fDEI-None 0.1898 0.2163 1072.0000 0.88 0.380
## threewaycondwoman2:deiexpert_fDEI-None 0.2962 0.2163 1072.0000 1.37 0.171
## part_gend_fMale Participants:deiexpert_fDEI-None 0.3716 0.2462 1072.0000 1.51 0.132
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fDEI-None -0.4032 0.3482 1072.0000 -1.16 0.247
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fDEI-None -0.2204 0.3482 1072.0000 -0.63 0.527
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fDEI-None -0.8627 0.3482 1072.0000 -2.48 0.013 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Interest
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: interest ~ threewaycond * part_gend_f * deiexpert_f + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3651
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0526 -0.5310 0.0412 0.5579 2.3813
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.05 1.03
## Residual 1.07 1.04
## Number of obs: 1100, groups: pid, 275
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.2078 0.1661 624.1997 25.33 < 0.0000000000000002 ***
## threewaycondother\nman 0.0519 0.1668 813.0000 0.31 0.756
## threewaycondwoman1 0.8701 0.1668 813.0000 5.22 0.000000233136 ***
## threewaycondwoman2 1.2078 0.1668 813.0000 7.24 0.000000000001 ***
## part_gend_fMale Participants -0.0745 0.2735 624.1997 -0.27 0.786
## deiexpert_fDEI-None 0.3792 0.2251 624.1997 1.68 0.093 .
## threewaycondother\nman:part_gend_fMale Participants 0.2592 0.2747 813.0000 0.94 0.346
## threewaycondwoman1:part_gend_fMale Participants 0.3743 0.2747 813.0000 1.36 0.173
## threewaycondwoman2:part_gend_fMale Participants -0.0522 0.2747 813.0000 -0.19 0.849
## threewaycondother\nman:deiexpert_fDEI-None -0.1824 0.2261 813.0000 -0.81 0.420
## threewaycondwoman1:deiexpert_fDEI-None 0.0647 0.2261 813.0000 0.29 0.775
## threewaycondwoman2:deiexpert_fDEI-None -0.2187 0.2261 813.0000 -0.97 0.334
## part_gend_fMale Participants:deiexpert_fDEI-None -0.0371 0.3643 624.1997 -0.10 0.919
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fDEI-None -0.0632 0.3659 813.0000 -0.17 0.863
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fDEI-None -0.2599 0.3659 813.0000 -0.71 0.478
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fDEI-None 0.0139 0.3659 813.0000 0.04 0.970
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Positive Affect
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: pa ~ threewaycond * part_gend_f * deiexpert_f + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3202
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1510 -0.4842 0.0125 0.5461 2.9141
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.997 0.999
## Residual 0.645 0.803
## Number of obs: 1100, groups: pid, 275
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.07143 0.14602 514.61436 27.88 < 0.0000000000000002 ***
## threewaycondother\nman 0.13636 0.12941 813.00001 1.05 0.292
## threewaycondwoman1 0.70455 0.12941 813.00001 5.44 0.0000000688909 ***
## threewaycondwoman2 0.91883 0.12941 813.00001 7.10 0.0000000000027 ***
## part_gend_fMale Participants 0.08413 0.24043 514.61436 0.35 0.727
## deiexpert_fDEI-None 0.34433 0.19791 514.61436 1.74 0.082 .
## threewaycondother\nman:part_gend_fMale Participants 0.00253 0.21308 813.00001 0.01 0.991
## threewaycondwoman1:part_gend_fMale Participants 0.33434 0.21308 813.00001 1.57 0.117
## threewaycondwoman2:part_gend_fMale Participants 0.17561 0.21308 813.00001 0.82 0.410
## threewaycondother\nman:deiexpert_fDEI-None -0.19886 0.17539 813.00001 -1.13 0.257
## threewaycondwoman1:deiexpert_fDEI-None 0.15415 0.17539 813.00001 0.88 0.380
## threewaycondwoman2:deiexpert_fDEI-None -0.03296 0.17539 813.00001 -0.19 0.851
## part_gend_fMale Participants:deiexpert_fDEI-None 0.02880 0.32026 514.61437 0.09 0.928
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fDEI-None -0.02609 0.28383 813.00001 -0.09 0.927
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fDEI-None -0.55779 0.28383 813.00001 -1.97 0.050 *
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fDEI-None -0.45493 0.28383 813.00001 -1.60 0.109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Negative Affect
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: na ~ threewaycond * part_gend_f * deiexpert_f + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 2320
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.228 -0.443 -0.082 0.415 5.490
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.552 0.743
## Residual 0.268 0.518
## Number of obs: 1100, groups: pid, 275
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.59091 0.10324 459.54902 25.10 <0.0000000000000002 ***
## threewaycondother\nman 0.06926 0.08349 813.00000 0.83 0.41
## threewaycondwoman1 -0.08658 0.08349 813.00000 -1.04 0.30
## threewaycondwoman2 -0.04762 0.08349 813.00000 -0.57 0.57
## part_gend_fMale Participants 0.02761 0.16998 459.54903 0.16 0.87
## deiexpert_fDEI-None -0.19779 0.13992 459.54903 -1.41 0.16
## threewaycondother\nman:part_gend_fMale Participants 0.10481 0.13746 813.00000 0.76 0.45
## threewaycondwoman1:part_gend_fMale Participants -0.18749 0.13746 813.00000 -1.36 0.17
## threewaycondwoman2:part_gend_fMale Participants -0.19683 0.13746 813.00000 -1.43 0.15
## threewaycondother\nman:deiexpert_fDEI-None -0.03122 0.11315 813.00000 -0.28 0.78
## threewaycondwoman1:deiexpert_fDEI-None 0.06122 0.11315 813.00000 0.54 0.59
## threewaycondwoman2:deiexpert_fDEI-None -0.00129 0.11315 813.00000 -0.01 0.99
## part_gend_fMale Participants:deiexpert_fDEI-None -0.17756 0.22642 459.54903 -0.78 0.43
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fDEI-None -0.07455 0.18311 813.00000 -0.41 0.68
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fDEI-None 0.16914 0.18311 813.00000 0.92 0.36
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fDEI-None 0.20202 0.18311 813.00000 1.10 0.27
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Voice
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: voice ~ threewaycond * part_gend_f * deiexpert_f + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3441
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.481 -0.464 0.028 0.539 3.160
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.835 0.914
## Residual 0.892 0.944
## Number of obs: 1100, groups: pid, 275
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.64935 0.14975 636.96079 31.05 <0.0000000000000002 ***
## threewaycondother\nman 0.16017 0.15217 813.00000 1.05 0.2928
## threewaycondwoman1 0.27706 0.15217 813.00000 1.82 0.0690 .
## threewaycondwoman2 0.13420 0.15217 813.00000 0.88 0.3781
## part_gend_fMale Participants -0.27898 0.24656 636.96079 -1.13 0.2583
## deiexpert_fDEI-None 0.05355 0.20296 636.96079 0.26 0.7920
## threewaycondother\nman:part_gend_fMale Participants -0.01943 0.25056 813.00000 -0.08 0.9382
## threewaycondwoman1:part_gend_fMale Participants 0.57480 0.25056 813.00000 2.29 0.0220 *
## threewaycondwoman2:part_gend_fMale Participants 0.68802 0.25056 813.00000 2.75 0.0062 **
## threewaycondother\nman:deiexpert_fDEI-None -0.15293 0.20624 813.00000 -0.74 0.4586
## threewaycondwoman1:deiexpert_fDEI-None 0.07077 0.20624 813.00000 0.34 0.7316
## threewaycondwoman2:deiexpert_fDEI-None 0.30783 0.20624 813.00000 1.49 0.1359
## part_gend_fMale Participants:deiexpert_fDEI-None 0.12253 0.32843 636.96079 0.37 0.7092
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fDEI-None 0.00672 0.33375 813.00000 0.02 0.9839
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fDEI-None -0.31606 0.33375 813.00000 -0.95 0.3439
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fDEI-None -0.62732 0.33375 813.00000 -1.88 0.0605 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Impact
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: impact ~ threewaycond * part_gend_f * deiexpert_f + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3529
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.188 -0.516 0.006 0.543 3.457
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.890 0.944
## Residual 0.971 0.985
## Number of obs: 1100, groups: pid, 275
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.4978 0.1555 642.7394 28.93 < 0.0000000000000002 ***
## threewaycondother\nman 0.0563 0.1588 813.0000 0.35 0.72313
## threewaycondwoman1 -0.1126 0.1588 813.0000 -0.71 0.47865
## threewaycondwoman2 -0.0260 0.1588 813.0000 -0.16 0.87011
## part_gend_fMale Participants -0.4312 0.2560 642.7394 -1.68 0.09260 .
## deiexpert_fDEI-None -0.0268 0.2107 642.7394 -0.13 0.89875
## threewaycondother\nman:part_gend_fMale Participants 0.1215 0.2615 813.0000 0.46 0.64228
## threewaycondwoman1:part_gend_fMale Participants 0.7422 0.2615 813.0000 2.84 0.00464 **
## threewaycondwoman2:part_gend_fMale Participants 0.9223 0.2615 813.0000 3.53 0.00044 ***
## threewaycondother\nman:deiexpert_fDEI-None 0.0778 0.2152 813.0000 0.36 0.71790
## threewaycondwoman1:deiexpert_fDEI-None 0.3553 0.2152 813.0000 1.65 0.09915 .
## threewaycondwoman2:deiexpert_fDEI-None 0.5912 0.2152 813.0000 2.75 0.00615 **
## part_gend_fMale Participants:deiexpert_fDEI-None 0.3044 0.3410 642.7394 0.89 0.37232
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fDEI-None -0.2337 0.3483 813.0000 -0.67 0.50240
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fDEI-None -0.2254 0.3483 813.0000 -0.65 0.51774
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fDEI-None -0.9137 0.3483 813.0000 -2.62 0.00887 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Rewards
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: rewards ~ threewaycond * part_gend_f * deiexpert_f + (1 | pid)
## Data: sig_clean_long2
##
## REML criterion at convergence: 3515
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.227 -0.476 -0.022 0.458 3.610
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.635 1.279
## Residual 0.812 0.901
## Number of obs: 1100, groups: pid, 275
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.6948 0.1783 463.4500 20.72 <0.0000000000000002 ***
## threewaycondother\nman 0.0325 0.1453 813.0000 0.22 0.823
## threewaycondwoman1 0.1006 0.1453 813.0000 0.69 0.489
## threewaycondwoman2 0.2500 0.1453 813.0000 1.72 0.086 .
## part_gend_fMale Participants -0.2115 0.2935 463.4500 -0.72 0.472
## deiexpert_fDEI-None 0.2970 0.2416 463.4500 1.23 0.220
## threewaycondother\nman:part_gend_fMale Participants -0.0269 0.2392 813.0000 -0.11 0.910
## threewaycondwoman1:part_gend_fMale Participants 0.4882 0.2392 813.0000 2.04 0.042 *
## threewaycondwoman2:part_gend_fMale Participants 0.3000 0.2392 813.0000 1.25 0.210
## threewaycondother\nman:deiexpert_fDEI-None -0.0678 0.1969 813.0000 -0.34 0.731
## threewaycondwoman1:deiexpert_fDEI-None -0.0436 0.1969 813.0000 -0.22 0.825
## threewaycondwoman2:deiexpert_fDEI-None -0.0815 0.1969 813.0000 -0.41 0.679
## part_gend_fMale Participants:deiexpert_fDEI-None -0.2558 0.3910 463.4500 -0.65 0.513
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fDEI-None 0.1032 0.3186 813.0000 0.32 0.746
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fDEI-None 0.2293 0.3186 813.0000 0.72 0.472
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fDEI-None 0.1053 0.3186 813.0000 0.33 0.741
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Rank woman as #1
Here, I compare whether participants ranked a woman as Number 1
##
## Call:
## lm(formula = rank_wom ~ deiexpert, data = sig_clean2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.789 0.211 0.211 0.221 0.221
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7886 0.0373 21.16 <0.0000000000000002 ***
## deiexpertnone -0.0094 0.0500 -0.19 0.85
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.413 on 275 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.000128, Adjusted R-squared: -0.00351
## F-statistic: 0.0353 on 1 and 275 DF, p-value: 0.851
