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:
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.
Analyses
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_long1
##
## REML criterion at convergence: 9575
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.572 -0.561 0.028 0.603 4.258
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.804 0.897
## Residual 1.351 1.162
## Number of obs: 2772, groups: pid, 693
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.1772 0.1303 1941.5914 32.07 < 0.0000000000000002 ***
## deiexpert_fDEI-None 0.1050 0.1799 1941.5914 0.58 0.56
## deiexpert_fInterest1 1.4382 0.1790 1941.5914 8.03 0.0000000000000016 ***
## deiexpert_fInterest2 1.7014 0.1799 1941.5914 9.46 < 0.0000000000000002 ***
## deiexpert_fInterest3 0.8858 0.1790 1941.5914 4.95 0.0000008124891750 ***
## personotherman 0.1142 0.1459 2064.0000 0.78 0.43
## personwoman1 0.6772 0.1459 2064.0000 4.64 0.0000036664070652 ***
## personwoman2 0.8189 0.1459 2064.0000 5.61 0.0000000224828709 ***
## deiexpert_fDEI-None:personotherman -0.0606 0.2015 2064.0000 -0.30 0.76
## deiexpert_fInterest1:personotherman -1.0058 0.2004 2064.0000 -5.02 0.0000005676712042 ***
## deiexpert_fInterest2:personotherman -0.9856 0.2015 2064.0000 -4.89 0.0000010730010780 ***
## deiexpert_fInterest3:personotherman -1.8205 0.2004 2064.0000 -9.08 < 0.0000000000000002 ***
## deiexpert_fDEI-None:personwoman1 0.2800 0.2015 2064.0000 1.39 0.16
## deiexpert_fInterest1:personwoman1 -1.4359 0.2004 2064.0000 -7.16 0.0000000000010891 ***
## deiexpert_fInterest2:personwoman1 -2.6415 0.2015 2064.0000 -13.11 < 0.0000000000000002 ***
## deiexpert_fInterest3:personwoman1 -1.8310 0.2004 2064.0000 -9.13 < 0.0000000000000002 ***
## deiexpert_fDEI-None:personwoman2 0.1597 0.2015 2064.0000 0.79 0.43
## deiexpert_fInterest1:personwoman2 -2.8364 0.2004 2064.0000 -14.15 < 0.0000000000000002 ***
## deiexpert_fInterest2:personwoman2 -3.2046 0.2015 2064.0000 -15.91 < 0.0000000000000002 ***
## deiexpert_fInterest3:personwoman2 -2.5427 0.2004 2064.0000 -12.69 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Mixed-Model Anova comparing EVERY candidate to each other
Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
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_long1
##
## REML criterion at convergence: 10142
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.260 -0.588 0.044 0.610 3.723
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.733 0.856
## Residual 1.762 1.327
## Number of obs: 2772, groups: pid, 693
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.952756 0.140165 2185.925846 28.20 < 0.0000000000000002 ***
## deiexpert_fDEI-None 0.161530 0.193567 2185.925846 0.83 0.40
## deiexpert_fInterest1 1.753538 0.192599 2185.925846 9.10 < 0.0000000000000002 ***
## deiexpert_fInterest2 2.068673 0.193567 2185.925846 10.69 < 0.0000000000000002 ***
## deiexpert_fInterest3 1.333957 0.192599 2185.925846 6.93 0.00000000000567 ***
## personotherman 0.078740 0.166578 2064.000001 0.47 0.64
## personwoman1 1.003937 0.166578 2064.000001 6.03 0.00000000197497 ***
## personwoman2 1.196850 0.166578 2064.000001 7.18 0.00000000000093 ***
## deiexpert_fDEI-None:personotherman -0.000169 0.230043 2064.000001 0.00 1.00
## deiexpert_fInterest1:personotherman -1.099719 0.228892 2064.000001 -4.80 0.00000166280849 ***
## deiexpert_fInterest2:personotherman -1.207312 0.230043 2064.000001 -5.25 0.00000016933264 ***
## deiexpert_fInterest3:personotherman -2.001817 0.228892 2064.000001 -8.75 < 0.0000000000000002 ***
## deiexpert_fDEI-None:personwoman1 0.017492 0.230043 2064.000001 0.08 0.94
## deiexpert_fInterest1:personwoman1 -1.598343 0.228892 2064.000001 -6.98 0.00000000000388 ***
## deiexpert_fInterest2:personwoman1 -3.318223 0.230043 2064.000001 -14.42 < 0.0000000000000002 ***
## deiexpert_fInterest3:personwoman1 -2.385056 0.228892 2064.000001 -10.42 < 0.0000000000000002 ***
## deiexpert_fDEI-None:personwoman2 -0.050422 0.230043 2064.000001 -0.22 0.83
## deiexpert_fInterest1:personwoman2 -3.445102 0.228892 2064.000001 -15.05 < 0.0000000000000002 ***
## deiexpert_fInterest2:personwoman2 -3.936136 0.230043 2064.000001 -17.11 < 0.0000000000000002 ***
## deiexpert_fInterest3:personwoman2 -3.298249 0.228892 2064.000001 -14.41 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Mixed-Model Anova comparing EVERY candidate to each other
Pairwise comparisons (comparing conditions across candidates)
Pairwise comparisons (comparing candidates across conditions)
Simple slopes
Graphs
Ranking
Estimated marginal means
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_long1)
##
## Location coefficients:
## Estimate Std. Error z value Pr(>|z|)
## deiexpert_fDEI-None 0.136 0.234 0.580 0.5620
## deiexpert_fInterest1 3.269 0.249 13.138 < 0.0000000000000002
## deiexpert_fInterest2 4.155 0.285 14.567 < 0.0000000000000002
## deiexpert_fInterest3 4.095 0.279 14.654 < 0.0000000000000002
## personotherman 0.318 0.241 1.320 0.1869
## personwoman1 2.057 0.243 8.473 < 0.0000000000000002
## personwoman2 1.974 0.240 8.222 < 0.0000000000000002
## deiexpert_fDEI-None:personotherman -0.338 0.334 -1.013 0.3112
## deiexpert_fInterest1:personotherman -3.165 0.333 -9.504 < 0.0000000000000002
## deiexpert_fInterest2:personotherman -2.862 0.357 -8.017 0.0000000000000011
## deiexpert_fInterest3:personotherman -4.237 0.358 -11.843 < 0.0000000000000002
## deiexpert_fDEI-None:personwoman1 -0.260 0.329 -0.790 0.4293
## deiexpert_fInterest1:personwoman1 -3.421 0.335 -10.225 < 0.0000000000000002
## deiexpert_fInterest2:personwoman1 -5.712 0.367 -15.560 < 0.0000000000000002
## deiexpert_fInterest3:personwoman1 -4.539 0.358 -12.688 < 0.0000000000000002
## deiexpert_fDEI-None:personwoman2 0.000 0.328 0.000 1.0000
## deiexpert_fInterest1:personwoman2 -6.739 0.370 -18.214 < 0.0000000000000002
## deiexpert_fInterest2:personwoman2 -7.714 0.396 -19.458 < 0.0000000000000002
## deiexpert_fInterest3:personwoman2 -7.025 0.379 -18.547 < 0.0000000000000002
##
## No scale coefficients
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -0.479 0.171 -2.804
## 2|3 1.098 0.173 6.340
## 3|4 2.704 0.181 14.982
##
## log-likelihood: -3115.00
## AIC: 6274.00
## Condition number of Hessian: 863.81
## (12 observations deleted due to missingness)
Exploratory analyses
Part. gender as a control
## 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_long1
##
## REML criterion at convergence: 9339
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.588 -0.562 0.030 0.590 4.282
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.795 0.892
## Residual 1.344 1.159
## Number of obs: 2708, groups: pid, 677
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.2313 0.1353 1819.5138 31.28 < 0.0000000000000002 ***
## deiexpert_fDEI-None 0.0399 0.1822 1894.6447 0.22 0.83
## deiexpert_fInterest1 1.3992 0.1812 1897.4378 7.72 0.000000000000018 ***
## deiexpert_fInterest2 1.6957 0.1822 1896.0105 9.31 < 0.0000000000000002 ***
## deiexpert_fInterest3 0.8400 0.1809 1896.2017 4.64 0.000003676482307 ***
## personotherman 0.1107 0.1484 2016.0000 0.75 0.46
## personwoman1 0.6598 0.1484 2016.0000 4.45 0.000009245752159 ***
## personwoman2 0.8197 0.1484 2016.0000 5.52 0.000000037775515 ***
## part_gend_fMale Participants -0.0554 0.0845 671.0000 -0.66 0.51
## deiexpert_fDEI-None:personotherman -0.0559 0.2041 2016.0000 -0.27 0.78
## deiexpert_fInterest1:personotherman -0.9999 0.2031 2016.0000 -4.92 0.000000914112446 ***
## deiexpert_fInterest2:personotherman -0.9829 0.2041 2016.0000 -4.82 0.000001571588045 ***
## deiexpert_fInterest3:personotherman -1.8305 0.2027 2016.0000 -9.03 < 0.0000000000000002 ***
## deiexpert_fDEI-None:personwoman1 0.3146 0.2041 2016.0000 1.54 0.12
## deiexpert_fInterest1:personwoman1 -1.4313 0.2031 2016.0000 -7.05 0.000000000002468 ***
## deiexpert_fInterest2:personwoman1 -2.6708 0.2041 2016.0000 -13.09 < 0.0000000000000002 ***
## deiexpert_fInterest3:personwoman1 -1.8230 0.2027 2016.0000 -8.99 < 0.0000000000000002 ***
## deiexpert_fDEI-None:personwoman2 0.1803 0.2041 2016.0000 0.88 0.38
## deiexpert_fInterest1:personwoman2 -2.8447 0.2031 2016.0000 -14.01 < 0.0000000000000002 ***
## deiexpert_fInterest2:personwoman2 -3.2613 0.2041 2016.0000 -15.98 < 0.0000000000000002 ***
## deiexpert_fInterest3:personwoman2 -2.5466 0.2027 2016.0000 -12.56 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 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_long1
##
## REML criterion at convergence: 9892
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.276 -0.591 0.032 0.611 3.735
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.704 0.839
## Residual 1.762 1.327
## Number of obs: 2708, groups: pid, 677
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.02772 0.14485 2078.69629 27.81 < 0.0000000000000002 ***
## deiexpert_fDEI-None 0.08976 0.19559 2154.73628 0.46 0.646
## deiexpert_fInterest1 1.74355 0.19448 2157.50670 8.97 < 0.0000000000000002 ***
## deiexpert_fInterest2 2.07896 0.19553 2156.09155 10.63 < 0.0000000000000002 ***
## deiexpert_fInterest3 1.30402 0.19421 2156.28119 6.71 0.0000000000241 ***
## personotherman 0.07377 0.16994 2016.00000 0.43 0.664
## personwoman1 0.99590 0.16994 2016.00000 5.86 0.0000000053807 ***
## personwoman2 1.19672 0.16994 2016.00000 7.04 0.0000000000026 ***
## part_gend_fMale Participants -0.15956 0.08494 671.00000 -1.88 0.061 .
## deiexpert_fDEI-None:personotherman 0.00652 0.23366 2016.00000 0.03 0.978
## deiexpert_fInterest1:personotherman -1.10948 0.23247 2016.00000 -4.77 0.0000019502629 ***
## deiexpert_fInterest2:personotherman -1.19056 0.23366 2016.00000 -5.10 0.0000003806066 ***
## deiexpert_fInterest3:personotherman -2.01703 0.23209 2016.00000 -8.69 < 0.0000000000000002 ***
## deiexpert_fDEI-None:personwoman1 0.04789 0.23366 2016.00000 0.20 0.838
## deiexpert_fInterest1:personwoman1 -1.61733 0.23247 2016.00000 -6.96 0.0000000000047 ***
## deiexpert_fInterest2:personwoman1 -3.34627 0.23366 2016.00000 -14.32 < 0.0000000000000002 ***
## deiexpert_fInterest3:personwoman1 -2.38597 0.23209 2016.00000 -10.28 < 0.0000000000000002 ***
## deiexpert_fDEI-None:personwoman2 -0.02519 0.23366 2016.00000 -0.11 0.914
## deiexpert_fInterest1:personwoman2 -3.45386 0.23247 2016.00000 -14.86 < 0.0000000000000002 ***
## deiexpert_fInterest2:personwoman2 -3.96679 0.23366 2016.00000 -16.98 < 0.0000000000000002 ***
## deiexpert_fInterest3:personwoman2 -3.29247 0.23209 2016.00000 -14.19 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 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_long1
##
## REML criterion at convergence: 6988
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0078 -0.4857 -0.0405 0.6557 3.0763
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.000 0.000
## Residual 0.768 0.876
## Number of obs: 2696, groups: pid, 674
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.99999999999998757 0.08047142879632566 2674.99999994834251993 37.28 < 0.0000000000000002 ***
## deiexpert_fDEI-None -0.05185185185183889 0.10971993010530003 2674.99999996473843566 -0.47 0.64
## deiexpert_fInterest1 -1.48571428571427466 0.10874779670644637 2674.99999996336237018 -13.66 < 0.0000000000000002 ***
## deiexpert_fInterest2 -1.68613138686130104 0.10931688290575162 2674.99999996001315594 -15.42 < 0.0000000000000002 ***
## deiexpert_fInterest3 -1.69503546099289371 0.10858471920662210 2674.99999996802671376 -15.61 < 0.0000000000000002 ***
## personotherman -0.11570247933882902 0.11263244301114265 2674.99999994881045495 -1.03 0.30
## personwoman1 -0.94214876033056560 0.11263244301114268 2674.99999997341956259 -8.36 < 0.0000000000000002 ***
## personwoman2 -0.94214876033056438 0.11263244301114265 2674.99999994870131559 -8.36 < 0.0000000000000002 ***
## part_gend_fMale Participants 0.00000000000000094 0.03483576697867593 2674.99999995989128365 0.00 1.00
## deiexpert_fDEI-None:personotherman 0.11570247933882874 0.15510171591993305 2674.99999996352471499 0.75 0.46
## deiexpert_fInterest1:personotherman 1.47284533648168758 0.15378704069658469 2674.99999997190343493 9.58 < 0.0000000000000002 ***
## deiexpert_fInterest2:personotherman 1.00621342824393745 0.15456568165225620 2674.99999995992311597 6.51 0.000000000089 ***
## deiexpert_fInterest3:personotherman 1.84619893323953588 0.15353401016921847 2674.99999997156965037 12.02 < 0.0000000000000002 ***
## deiexpert_fDEI-None:personwoman1 0.09029690847871277 0.15510171591993308 2674.99999993768733475 0.58 0.56
## deiexpert_fInterest1:personwoman1 1.47786304604485297 0.15378704069658472 2674.99999997190525391 9.61 < 0.0000000000000002 ***
## deiexpert_fInterest2:personwoman1 2.47499547565903200 0.15456568165225623 2674.99999995992493496 16.01 < 0.0000000000000002 ***
## deiexpert_fInterest3:personwoman1 1.87122677451496022 0.15353401016921847 2674.99999995255166141 12.19 < 0.0000000000000002 ***
## deiexpert_fDEI-None:personwoman2 0.00140801958982255 0.15510171591993305 2674.99999995998132363 0.01 0.99
## deiexpert_fInterest1:personwoman2 2.99214876033056587 0.15378704069658469 2674.99999997190343493 19.46 < 0.0000000000000002 ***
## deiexpert_fInterest2:personwoman2 3.26331664354224182 0.15456568165225618 2674.99999995992038748 21.11 < 0.0000000000000002 ***
## deiexpert_fInterest3:personwoman2 3.06271613621708605 0.15353401016921842 2674.99999994881500243 19.95 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Part. 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_long1
##
## REML criterion at convergence: 9339
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.543 -0.566 0.031 0.582 4.309
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.793 0.89
## Residual 1.346 1.16
## Number of obs: 2708, groups: pid, 677
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.1524 0.1615 1889.5160 25.71 < 0.0000000000000002 ***
## threewaycondother\nman 0.1463 0.1812 2001.0000 0.81 0.41938
## threewaycondwoman1 0.8110 0.1812 2001.0000 4.48 0.0000080418330476 ***
## threewaycondwoman2 0.9268 0.1812 2001.0000 5.12 0.0000003434039030 ***
## part_gend_fMale Participants 0.1851 0.2820 1889.5160 0.66 0.51180
## deiexpert_fDEI-None 0.1913 0.2298 1889.5160 0.83 0.40525
## deiexpert_fInterest1 1.5087 0.2233 1889.5160 6.76 0.0000000000186213 ***
## deiexpert_fInterest2 1.7933 0.2277 1889.5160 7.88 0.0000000000000057 ***
## deiexpert_fInterest3 0.8650 0.2257 1889.5160 3.83 0.00013 ***
## threewaycondother\nman:part_gend_fMale Participants -0.1088 0.3164 2001.0000 -0.34 0.73091
## threewaycondwoman1:part_gend_fMale Participants -0.4610 0.3164 2001.0000 -1.46 0.14534
## threewaycondwoman2:part_gend_fMale Participants -0.3268 0.3164 2001.0000 -1.03 0.30181
## threewaycondother\nman:deiexpert_fDEI-None -0.1963 0.2578 2001.0000 -0.76 0.44646
## threewaycondwoman1:deiexpert_fDEI-None 0.1328 0.2578 2001.0000 0.51 0.60665
## threewaycondwoman2:deiexpert_fDEI-None 0.0607 0.2578 2001.0000 0.24 0.81400
## threewaycondother\nman:deiexpert_fInterest1 -0.9575 0.2505 2001.0000 -3.82 0.00014 ***
## threewaycondwoman1:deiexpert_fInterest1 -1.3999 0.2505 2001.0000 -5.59 0.0000000260241764 ***
## threewaycondwoman2:deiexpert_fInterest1 -2.8824 0.2505 2001.0000 -11.51 < 0.0000000000000002 ***
## threewaycondother\nman:deiexpert_fInterest2 -1.0861 0.2555 2001.0000 -4.25 0.0000222263174504 ***
## threewaycondwoman1:deiexpert_fInterest2 -2.9857 0.2555 2001.0000 -11.69 < 0.0000000000000002 ***
## threewaycondwoman2:deiexpert_fInterest2 -3.6015 0.2555 2001.0000 -14.10 < 0.0000000000000002 ***
## threewaycondother\nman:deiexpert_fInterest3 -1.8615 0.2532 2001.0000 -7.35 0.0000000000002866 ***
## threewaycondwoman1:deiexpert_fInterest3 -1.9854 0.2532 2001.0000 -7.84 0.0000000000000073 ***
## threewaycondwoman2:deiexpert_fInterest3 -2.6419 0.2532 2001.0000 -10.43 < 0.0000000000000002 ***
## part_gend_fMale Participants:deiexpert_fDEI-None -0.4148 0.3792 1889.5160 -1.09 0.27418
## part_gend_fMale Participants:deiexpert_fInterest1 -0.3262 0.3822 1889.5160 -0.85 0.39355
## part_gend_fMale Participants:deiexpert_fInterest2 -0.2883 0.3807 1889.5160 -0.76 0.44902
## part_gend_fMale Participants:deiexpert_fInterest3 -0.1025 0.3785 1889.5160 -0.27 0.78659
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fDEI-None 0.3606 0.4255 2001.0000 0.85 0.39679
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fDEI-None 0.5348 0.4255 2001.0000 1.26 0.20893
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fDEI-None 0.3569 0.4255 2001.0000 0.84 0.40168
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fInterest1 -0.1100 0.4288 2001.0000 -0.26 0.79749
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fInterest1 -0.0501 0.4288 2001.0000 -0.12 0.90694
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fInterest1 0.1324 0.4288 2001.0000 0.31 0.75757
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fInterest2 0.2801 0.4271 2001.0000 0.66 0.51205
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fInterest2 0.8764 0.4271 2001.0000 2.05 0.04030 *
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fInterest2 0.9182 0.4271 2001.0000 2.15 0.03169 *
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fInterest3 0.0967 0.4247 2001.0000 0.23 0.81995
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fInterest3 0.4899 0.4247 2001.0000 1.15 0.24882
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fInterest3 0.2965 0.4247 2001.0000 0.70 0.48520
## ---
## 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_long1
##
## REML criterion at convergence: 9893
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.302 -0.583 0.033 0.610 3.724
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.702 0.838
## Residual 1.768 1.330
## Number of obs: 2708, groups: pid, 677
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.02439 0.17356 2147.09200 23.19 < 0.0000000000000002 ***
## threewaycondother\nman 0.03659 0.20764 2001.00000 0.18 0.86016
## threewaycondwoman1 1.14634 0.20764 2001.00000 5.52 0.000000038131398 ***
## threewaycondwoman2 1.21951 0.20764 2001.00000 5.87 0.000000004992606 ***
## part_gend_fMale Participants -0.14939 0.30311 2147.09200 -0.49 0.62216
## deiexpert_fDEI-None 0.03811 0.24698 2147.09200 0.15 0.87739
## deiexpert_fInterest1 1.86450 0.23994 2147.09200 7.77 0.000000000000012 ***
## deiexpert_fInterest2 2.10814 0.24471 2147.09200 8.61 < 0.0000000000000002 ***
## deiexpert_fInterest3 1.30119 0.24258 2147.09200 5.36 0.000000090182052 ***
## threewaycondother\nman:part_gend_fMale Participants 0.11341 0.36263 2001.00000 0.31 0.75450
## threewaycondwoman1:part_gend_fMale Participants -0.45884 0.36263 2001.00000 -1.27 0.20590
## threewaycondwoman2:part_gend_fMale Participants -0.06951 0.36263 2001.00000 -0.19 0.84800
## threewaycondother\nman:deiexpert_fDEI-None 0.01341 0.29548 2001.00000 0.05 0.96379
## threewaycondwoman1:deiexpert_fDEI-None -0.07759 0.29548 2001.00000 -0.26 0.79289
## threewaycondwoman2:deiexpert_fDEI-None 0.06799 0.29548 2001.00000 0.23 0.81804
## threewaycondother\nman:deiexpert_fInterest1 -1.09214 0.28705 2001.00000 -3.80 0.00015 ***
## threewaycondwoman1:deiexpert_fInterest1 -1.76856 0.28705 2001.00000 -6.16 0.000000000870253 ***
## threewaycondwoman2:deiexpert_fInterest1 -3.53618 0.28705 2001.00000 -12.32 < 0.0000000000000002 ***
## threewaycondother\nman:deiexpert_fInterest2 -1.14502 0.29276 2001.00000 -3.91 0.000094936317677 ***
## threewaycondwoman1:deiexpert_fInterest2 -3.54996 0.29276 2001.00000 -12.13 < 0.0000000000000002 ***
## threewaycondwoman2:deiexpert_fInterest2 -4.15325 0.29276 2001.00000 -14.19 < 0.0000000000000002 ***
## threewaycondother\nman:deiexpert_fInterest3 -2.04821 0.29021 2001.00000 -7.06 0.000000000002325 ***
## threewaycondwoman1:deiexpert_fInterest3 -2.61727 0.29021 2001.00000 -9.02 < 0.0000000000000002 ***
## threewaycondwoman2:deiexpert_fInterest3 -3.46370 0.29021 2001.00000 -11.94 < 0.0000000000000002 ***
## part_gend_fMale Participants:deiexpert_fDEI-None 0.12198 0.40754 2147.09200 0.30 0.76474
## part_gend_fMale Participants:deiexpert_fInterest1 -0.33950 0.41076 2147.09200 -0.83 0.40861
## part_gend_fMale Participants:deiexpert_fInterest2 -0.07573 0.40912 2147.09200 -0.19 0.85316
## part_gend_fMale Participants:deiexpert_fInterest3 0.00563 0.40683 2147.09200 0.01 0.98897
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fDEI-None -0.04061 0.48756 2001.00000 -0.08 0.93363
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fDEI-None 0.39886 0.48756 2001.00000 0.82 0.41341
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fDEI-None -0.20922 0.48756 2001.00000 -0.43 0.66789
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fInterest1 -0.05786 0.49141 2001.00000 -0.12 0.90629
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fInterest1 0.46106 0.49141 2001.00000 0.94 0.34823
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fInterest1 0.23618 0.49141 2001.00000 0.48 0.63084
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fInterest2 -0.13461 0.48945 2001.00000 -0.28 0.78333
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fInterest2 0.59394 0.48945 2001.00000 1.21 0.22509
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fInterest2 0.48473 0.48945 2001.00000 0.99 0.32212
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fInterest3 0.06185 0.48671 2001.00000 0.13 0.89889
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fInterest3 0.66614 0.48671 2001.00000 1.37 0.17126
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fInterest3 0.45006 0.48671 2001.00000 0.92 0.35523
## ---
## 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_long1
##
## REML criterion at convergence: 6992
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1272 -0.6098 0.0399 0.4658 3.1410
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.000 0.000
## Residual 0.765 0.875
## Number of obs: 2696, groups: pid, 674
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.9753 0.0972 2656.0000 20.33 < 0.0000000000000002 ***
## threewaycondother\nman 0.0741 0.1374 2656.0000 0.54 0.590
## threewaycondwoman1 1.1235 0.1374 2656.0000 8.18 0.00000000000000045 ***
## threewaycondwoman2 0.9012 0.1374 2656.0000 6.56 0.00000000006530207 ***
## part_gend_fMale Participants 0.0747 0.1690 2656.0000 0.44 0.659
## deiexpert_fDEI-None -0.0266 0.1387 2656.0000 -0.19 0.848
## deiexpert_fInterest1 1.5580 0.1339 2656.0000 11.63 < 0.0000000000000002 ***
## deiexpert_fInterest2 1.7596 0.1366 2656.0000 12.88 < 0.0000000000000002 ***
## deiexpert_fInterest3 1.7456 0.1354 2656.0000 12.89 < 0.0000000000000002 ***
## threewaycondother\nman:part_gend_fMale Participants 0.1259 0.2390 2656.0000 0.53 0.598
## threewaycondwoman1:part_gend_fMale Participants -0.5485 0.2390 2656.0000 -2.29 0.022 *
## threewaycondwoman2:part_gend_fMale Participants 0.1238 0.2390 2656.0000 0.52 0.605
## threewaycondother\nman:deiexpert_fDEI-None 0.1567 0.1962 2656.0000 0.80 0.425
## threewaycondwoman1:deiexpert_fDEI-None -0.1876 0.1962 2656.0000 -0.96 0.339
## threewaycondwoman2:deiexpert_fDEI-None 0.1372 0.1962 2656.0000 0.70 0.484
## threewaycondother\nman:deiexpert_fInterest1 -1.5185 0.1894 2656.0000 -8.02 0.00000000000000162 ***
## threewaycondwoman1:deiexpert_fInterest1 -1.7123 0.1894 2656.0000 -9.04 < 0.0000000000000002 ***
## threewaycondwoman2:deiexpert_fInterest1 -3.0012 0.1894 2656.0000 -15.84 < 0.0000000000000002 ***
## threewaycondother\nman:deiexpert_fInterest2 -0.9536 0.1932 2656.0000 -4.94 0.00000084488080638 ***
## threewaycondwoman1:deiexpert_fInterest2 -2.7018 0.1932 2656.0000 -13.99 < 0.0000000000000002 ***
## threewaycondwoman2:deiexpert_fInterest2 -3.3832 0.1932 2656.0000 -17.51 < 0.0000000000000002 ***
## threewaycondother\nman:deiexpert_fInterest3 -1.8299 0.1915 2656.0000 -9.56 < 0.0000000000000002 ***
## threewaycondwoman1:deiexpert_fInterest3 -2.0072 0.1915 2656.0000 -10.48 < 0.0000000000000002 ***
## threewaycondwoman2:deiexpert_fInterest3 -3.1454 0.1915 2656.0000 -16.42 < 0.0000000000000002 ***
## part_gend_fMale Participants:deiexpert_fDEI-None 0.1696 0.2276 2656.0000 0.75 0.456
## part_gend_fMale Participants:deiexpert_fInterest1 -0.2080 0.2288 2656.0000 -0.91 0.363
## part_gend_fMale Participants:deiexpert_fInterest2 -0.1985 0.2279 2656.0000 -0.87 0.384
## part_gend_fMale Participants:deiexpert_fInterest3 -0.1411 0.2266 2656.0000 -0.62 0.534
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fDEI-None -0.6725 0.3218 2656.0000 -2.09 0.037 *
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fDEI-None 0.3494 0.3218 2656.0000 1.09 0.278
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fDEI-None -0.3552 0.3218 2656.0000 -1.10 0.270
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fInterest1 0.1185 0.3236 2656.0000 0.37 0.714
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fInterest1 0.6973 0.3236 2656.0000 2.15 0.031 *
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fInterest1 0.0162 0.3236 2656.0000 0.05 0.960
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fInterest2 -0.1538 0.3223 2656.0000 -0.48 0.633
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fInterest2 0.6638 0.3223 2656.0000 2.06 0.040 *
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fInterest2 0.2841 0.3223 2656.0000 0.88 0.378
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fInterest3 -0.0610 0.3205 2656.0000 -0.19 0.849
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fInterest3 0.4322 0.3205 2656.0000 1.35 0.178
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fInterest3 0.1931 0.3205 2656.0000 0.60 0.547
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Rank woman as #1
Here, I compare whether participants ranked a woman as Number 1
##
## Call:
## lm(formula = rank_wom ~ deiexpert, data = sig_clean1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.722 -0.301 -0.114 0.297 0.886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7222 0.0371 19.45 < 0.0000000000000002 ***
## deiexpertinterest1 -0.4215 0.0509 -8.28 0.00000000000000065 ***
## deiexpertinterest2 -0.6079 0.0512 -11.88 < 0.0000000000000002 ***
## deiexpertinterest3 -0.5474 0.0509 -10.75 < 0.0000000000000002 ***
## deiexpertnone -0.0193 0.0514 -0.38 0.71
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.417 on 685 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.278, Adjusted R-squared: 0.274
## F-statistic: 65.9 on 4 and 685 DF, p-value: <0.0000000000000002
Mediation
## This is lavaan 0.6-19
## lavaan is FREE software! Please report any bugs.
##
## Attaching package: 'lavaan'
## The following object is masked from 'package:JWileymisc':
##
## cor2cov
deitraddata <- sig_clean_long1 %>%
filter(deiexpert == "Trad")
deidata <- sig_clean_long1 %>%
filter(deiexpert == "DEI")
Voice Quality
DEI
library(lavaan)
deivq_mediationdat <- data.frame(
X = deidata$gendertarget,
M = scale(deidata$vq, scale = F),
Y = scale(deidata$rank_r, scale = F),
pid = deidata$pid
)
med_mod_deivq <-
'
# Direct Effect
Y ~ c*X
# Mediator
M~a*X
Y~b*M
# Indirect effect
ab:= a*b
# Total effect
total:= c+ (a*b)
'
sem_med_deivq <- lavaan::sem(med_mod_deivq, cluster = "pid", data = deivq_mediationdat)
summary(sem_med_deivq)
as.data.frame(lavaan::parameterEstimates(sem_med_deivq, ci = TRUE, level = 0.95)) %>%
filter(lhs == "ab") %>%
dplyr::select(est, ci.lower, ci.upper)
Trad
library(lavaan)
tradvq_mediationdat <- data.frame(
X = deitraddata$gendertarget,
M = scale(deitraddata$vq, scale = F),
Y = scale(deitraddata$rank_r, scale = F),
pid = deitraddata$pid
)
med_mod_tradvq <-
'
# Direct Effect
Y ~ c*X
# Mediator
M~a*X
Y~b*M
# Indirect effect
ab:= a*b
# Total effect
total:= c+ (a*b)
'
sem_med_tradvq <- lavaan::sem(med_mod_tradvq, cluster = "pid", data = tradvq_mediationdat)
summary(sem_med_tradvq)
as.data.frame(lavaan::parameterEstimates(sem_med_tradvq, ci = TRUE, level = 0.95)) %>%
filter(lhs == "ab") %>%
dplyr::select(est, ci.lower, ci.upper)
Interest
DEI
library(lavaan)
deiinterest_mediationdat <- data.frame(
X = deidata$gendertarget,
M = scale(deidata$interest, scale = F),
Y = scale(deidata$rank_r, scale = F),
pid = deidata$pid
)
med_mod_deiinterest <-
'
# Direct Effect
Y ~ c*X
# Mediator
M~a*X
Y~b*M
# Indirect effect
ab:= a*b
# Total effect
total:= c+ (a*b)
'
sem_med_deiinterest <- lavaan::sem(med_mod_deiinterest, cluster = "pid", data = deiinterest_mediationdat)
summary(sem_med_deiinterest)
as.data.frame(lavaan::parameterEstimates(sem_med_deiinterest, ci = TRUE, level = 0.95)) %>%
filter(lhs == "ab") %>%
dplyr::select(est, ci.lower, ci.upper)
Trad
library(lavaan)
tradinterest_mediationdat <- data.frame(
X = deitraddata$gendertarget,
M = scale(deitraddata$interest, scale = F),
Y = scale(deitraddata$rank_r, scale = F),
pid = deitraddata$pid
)
med_mod_tradinterest <-
'
# Direct Effect
Y ~ c*X
# Mediator
M~a*X
Y~b*M
# Indirect effect
ab:= a*b
# Total effect
total:= c+ (a*b)
'
sem_med_tradinterest <- lavaan::sem(med_mod_tradinterest, cluster = "pid", data = tradinterest_mediationdat)
summary(sem_med_tradinterest)
as.data.frame(lavaan::parameterEstimates(sem_med_tradinterest, ci = TRUE, level = 0.95)) %>%
filter(lhs == "ab") %>%
dplyr::select(est, ci.lower, ci.upper)