Checking Randomization
Distribution of responses
Note: “focalman” and “focalwoman” indicate the which columns the focal man and focal woman are in.
(as.data.frame(with(qual_test, table(worked, deiexpert, focal_man, focalman, focal_woman, focalwoman))) %>%
filter(Freq > 0))%>%
arrange(worked, desc(Freq), deiexpert, focalman, focalwoman)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: vq ~ deiexpert_f * targgender + (1 | person) + (1 | pid)
## Data: qual_clean_long4
##
## REML criterion at convergence: 6944
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.454 -0.546 0.023 0.628 2.979
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.800 0.895
## person (Intercept) 0.393 0.627
## Residual 1.417 1.191
## Number of obs: 1988, groups: pid, 497; person, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.123 0.455 2.187 11.27 0.00561 **
## deiexpert_fDEI-None -0.639 0.135 818.069 -4.73 0.0000027 ***
## deiexpert_fTrad 0.221 0.141 818.069 1.56 0.11812
## targgenderWoman -0.332 0.634 2.071 -0.52 0.65173
## deiexpert_fDEI-None:targgenderWoman 1.134 0.131 1486.000 8.66 < 0.0000000000000002 ***
## deiexpert_fTrad:targgenderWoman -0.489 0.137 1486.000 -3.57 0.00037 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N dxpr_T trggnW d_DEI-N:
## dxprt_DEI-N -0.171
## dexprt_fTrd -0.163 0.549
## targgndrWmn -0.698 0.057 0.055
## dxp_DEI-N:W 0.083 -0.485 -0.266 -0.118
## dxprt_fTr:W 0.079 -0.266 -0.485 -0.113 0.549## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: vs ~ deiexpert_f * targgender + (1 | person) + (1 | pid)
## Data: qual_clean_long4
##
## REML criterion at convergence: 7162
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.339 -0.561 0.049 0.600 2.722
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.011 1.006
## person (Intercept) 0.365 0.604
## Residual 1.541 1.241
## Number of obs: 1988, groups: pid, 497; person, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.066 0.441 2.242 11.48 0.00490 **
## deiexpert_fDEI-None -0.623 0.147 784.251 -4.24 0.000025 ***
## deiexpert_fTrad 0.293 0.154 784.251 1.90 0.05715 .
## targgenderWoman -0.264 0.613 2.083 -0.43 0.70728
## deiexpert_fDEI-None:targgenderWoman 1.247 0.137 1486.000 9.13 < 0.0000000000000002 ***
## deiexpert_fTrad:targgenderWoman -0.524 0.143 1486.000 -3.67 0.00025 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N dxpr_T trggnW d_DEI-N:
## dxprt_DEI-N -0.191
## dexprt_fTrd -0.183 0.549
## targgndrWmn -0.694 0.059 0.057
## dxp_DEI-N:W 0.089 -0.465 -0.255 -0.128
## dxprt_fTr:W 0.085 -0.255 -0.465 -0.122 0.549## Call:
## clm2(location = rank_rf ~ deiexpert_f * targgender + (1 | person_num) +
## (1 | pid), data = qual_clean_long4)
##
## Location coefficients:
## Estimate Std. Error z value Pr(>|z|)
## deiexpert_fDEI-None -1.100 0.151 -7.279 0.00000000000034
## deiexpert_fTrad 0.260 0.162 1.606 0.10835
## targgenderWoman -0.759 0.154 -4.912 0.00000090118380
## deiexpert_fDEI-None:targgenderWoman 2.058 0.204 10.085 < 0.0000000000000002
## deiexpert_fTrad:targgenderWoman -0.513 0.212 -2.422 0.01542
##
## No scale coefficients
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -1.623 0.126 -12.879
## 2|3 -0.470 0.122 -3.836
## 3|4 0.730 0.122 5.990
##
## log-likelihood: -2668.41
## AIC: 5352.82
## Condition number of Hessian: 168.56
## (8 observations deleted due to missingness)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:
Manipulation Check
Within the “None” condition, was there a difference between people’s selections?
No.
##
## Chi-squared test for given probabilities
##
## data: table(manip_check_items)
## X-squared = 3.7, df = 3, p-value = 0.3
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: qual_clean_long4
##
## REML criterion at convergence: 6794
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.304 -0.551 0.049 0.628 3.160
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.834 0.913
## Residual 1.283 1.133
## Number of obs: 1988, groups: pid, 497
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.094 0.121 1348.115 50.26 <0.0000000000000002 ***
## deiexpert_fDEI-None -1.547 0.160 1348.115 -9.67 <0.0000000000000002 ***
## deiexpert_fTrad 0.262 0.167 1348.115 1.56 0.1183
## personotherman -1.941 0.134 1482.000 -14.54 <0.0000000000000002 ***
## personwoman1 -1.285 0.134 1482.000 -9.62 <0.0000000000000002 ***
## personwoman2 -1.319 0.134 1482.000 -9.89 <0.0000000000000002 ***
## deiexpert_fDEI-None:personotherman 1.817 0.176 1482.000 10.31 <0.0000000000000002 ***
## deiexpert_fTrad:personotherman -0.081 0.184 1482.000 -0.44 0.6601
## deiexpert_fDEI-None:personwoman1 2.006 0.176 1482.000 11.39 <0.0000000000000002 ***
## deiexpert_fTrad:personwoman1 -0.571 0.184 1482.000 -3.10 0.0020 **
## deiexpert_fDEI-None:personwoman2 2.080 0.176 1482.000 11.80 <0.0000000000000002 ***
## deiexpert_fTrad:personwoman2 -0.489 0.184 1482.000 -2.65 0.0081 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N dxpr_T prsnth prsnw1 prsnw2 dx_DEI-N: dxp_T: d_DEI-N:1 dx_T:1 d_DEI-N:2
## dxprt_DEI-N -0.758
## dexprt_fTrd -0.724 0.549
## personthrmn -0.550 0.417 0.399
## personwomn1 -0.550 0.417 0.399 0.500
## personwomn2 -0.550 0.417 0.399 0.500 0.500
## dxpr_DEI-N: 0.417 -0.550 -0.302 -0.758 -0.379 -0.379
## dxprt_fTrd: 0.399 -0.302 -0.550 -0.724 -0.362 -0.362 0.549
## dxp_DEI-N:1 0.417 -0.550 -0.302 -0.379 -0.758 -0.379 0.500 0.274
## dxprt_fTr:1 0.399 -0.302 -0.550 -0.362 -0.724 -0.362 0.274 0.500 0.549
## dxp_DEI-N:2 0.417 -0.550 -0.302 -0.379 -0.379 -0.758 0.500 0.274 0.500 0.274
## dxprt_fTr:2 0.399 -0.302 -0.550 -0.362 -0.362 -0.724 0.274 0.500 0.274 0.500 0.549Mixed-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: qual_clean_long4
##
## REML criterion at convergence: 7020
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.196 -0.560 0.044 0.609 2.676
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.05 1.02
## Residual 1.40 1.18
## Number of obs: 1988, groups: pid, 497
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.0417 0.1304 1276.8118 46.33 <0.0000000000000002 ***
## deiexpert_fDEI-None -1.5674 0.1721 1276.8118 -9.11 <0.0000000000000002 ***
## deiexpert_fTrad 0.2917 0.1800 1276.8118 1.62 0.1054
## personotherman -1.9514 0.1396 1482.0000 -13.98 <0.0000000000000002 ***
## personwoman1 -1.2431 0.1396 1482.0000 -8.91 <0.0000000000000002 ***
## personwoman2 -1.2361 0.1396 1482.0000 -8.86 <0.0000000000000002 ***
## deiexpert_fDEI-None:personotherman 1.8895 0.1842 1482.0000 10.26 <0.0000000000000002 ***
## deiexpert_fTrad:personotherman 0.0017 0.1926 1482.0000 0.01 0.9929
## deiexpert_fDEI-None:personwoman1 2.1606 0.1842 1482.0000 11.73 <0.0000000000000002 ***
## deiexpert_fTrad:personwoman1 -0.5022 0.1926 1482.0000 -2.61 0.0092 **
## deiexpert_fDEI-None:personwoman2 2.2232 0.1842 1482.0000 12.07 <0.0000000000000002 ***
## deiexpert_fTrad:personwoman2 -0.5438 0.1926 1482.0000 -2.82 0.0048 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dx_DEI-N dxpr_T prsnth prsnw1 prsnw2 dx_DEI-N: dxp_T: d_DEI-N:1 dx_T:1 d_DEI-N:2
## dxprt_DEI-N -0.758
## dexprt_fTrd -0.724 0.549
## personthrmn -0.535 0.405 0.388
## personwomn1 -0.535 0.405 0.388 0.500
## personwomn2 -0.535 0.405 0.388 0.500 0.500
## dxpr_DEI-N: 0.405 -0.535 -0.294 -0.758 -0.379 -0.379
## dxprt_fTrd: 0.388 -0.294 -0.535 -0.724 -0.362 -0.362 0.549
## dxp_DEI-N:1 0.405 -0.535 -0.294 -0.379 -0.758 -0.379 0.500 0.274
## dxprt_fTr:1 0.388 -0.294 -0.535 -0.362 -0.724 -0.362 0.274 0.500 0.549
## dxp_DEI-N:2 0.405 -0.535 -0.294 -0.379 -0.379 -0.758 0.500 0.274 0.500 0.274
## dxprt_fTr:2 0.388 -0.294 -0.535 -0.362 -0.362 -0.724 0.274 0.500 0.274 0.500 0.549Mixed-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_num + (1 | pid),
## data = qual_clean_long4)
##
## Location coefficients:
## Estimate Std. Error z value Pr(>|z|)
## deiexpert_fDEI-None 1.226 0.236 5.204 0.0000001955442
## deiexpert_fTrad -0.639 0.241 -2.648 0.0081
## person_num -0.050 0.067 -0.749 0.4540
## deiexpert_fDEI-None:person_num -0.488 0.089 -5.504 0.0000000371085
## deiexpert_fTrad:person_num 0.265 0.092 2.889 0.0039
##
## No scale coefficients
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 -1.256 0.178 -7.067
## 2|3 -0.116 0.174 -0.670
## 3|4 1.021 0.177 5.774
##
## log-likelihood: -2717.56
## AIC: 5451.13
## Condition number of Hessian: 1059.94
## (8 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: qual_clean_long4
##
## REML criterion at convergence: 6636
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.309 -0.548 0.053 0.624 3.159
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.841 0.917
## Residual 1.273 1.128
## Number of obs: 1944, groups: pid, 486
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.0951 0.1276 1200.8007 47.76 <0.0000000000000002 ***
## deiexpert_fDEI-None -1.5421 0.1615 1306.7307 -9.55 <0.0000000000000002 ***
## deiexpert_fTrad 0.2397 0.1689 1307.6868 1.42 0.1561
## personotherman -1.9366 0.1339 1449.0000 -14.46 <0.0000000000000002 ***
## personwoman1 -1.2746 0.1339 1449.0000 -9.52 <0.0000000000000002 ***
## personwoman2 -1.3028 0.1339 1449.0000 -9.73 <0.0000000000000002 ***
## part_gend_fMale Participants 0.0189 0.1001 482.0000 0.19 0.8504
## deiexpert_fDEI-None:personotherman 1.8096 0.1772 1449.0000 10.21 <0.0000000000000002 ***
## deiexpert_fTrad:personotherman -0.0795 0.1853 1449.0000 -0.43 0.6680
## deiexpert_fDEI-None:personwoman1 1.9440 0.1772 1449.0000 10.97 <0.0000000000000002 ***
## deiexpert_fTrad:personwoman1 -0.5576 0.1853 1449.0000 -3.01 0.0027 **
## deiexpert_fDEI-None:personwoman2 2.0118 0.1772 1449.0000 11.35 <0.0000000000000002 ***
## deiexpert_fTrad:personwoman2 -0.4746 0.1853 1449.0000 -2.56 0.0105 *
## ---
## 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: qual_clean_long4
##
## REML criterion at convergence: 6826
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.211 -0.558 0.044 0.600 2.688
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.02 1.01
## Residual 1.37 1.17
## Number of obs: 1944, groups: pid, 486
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.9965 0.1361 1145.0911 44.06 <0.0000000000000002 ***
## deiexpert_fDEI-None -1.5819 0.1720 1246.4973 -9.20 <0.0000000000000002 ***
## deiexpert_fTrad 0.2636 0.1798 1247.4205 1.47 0.1430
## personotherman -1.9366 0.1391 1449.0000 -13.92 <0.0000000000000002 ***
## personwoman1 -1.2324 0.1391 1449.0000 -8.86 <0.0000000000000002 ***
## personwoman2 -1.2289 0.1391 1449.0000 -8.83 <0.0000000000000002 ***
## part_gend_fMale Participants 0.1981 0.1086 482.0000 1.82 0.0689 .
## deiexpert_fDEI-None:personotherman 1.8731 0.1841 1449.0000 10.17 <0.0000000000000002 ***
## deiexpert_fTrad:personotherman -0.0118 0.1926 1449.0000 -0.06 0.9513
## deiexpert_fDEI-None:personwoman1 2.0842 0.1841 1449.0000 11.32 <0.0000000000000002 ***
## deiexpert_fTrad:personwoman1 -0.4870 0.1926 1449.0000 -2.53 0.0116 *
## deiexpert_fDEI-None:personwoman2 2.1574 0.1841 1449.0000 11.72 <0.0000000000000002 ***
## deiexpert_fTrad:personwoman2 -0.5614 0.1926 1449.0000 -2.92 0.0036 **
## ---
## 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: qual_clean_long4
##
## REML criterion at convergence: 5233
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5093 -0.7780 -0.0779 0.9079 3.0808
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.000 0.000
## Residual 0.841 0.917
## Number of obs: 1952, groups: pid, 488
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.272727272727269821 0.078277386224828729 1938.999999974564843797 16.26 <0.0000000000000002 ***
## deiexpert_fDEI-None 1.481199428843411159 0.101406493873103076 1938.999999953044152790 14.61 <0.0000000000000002 ***
## deiexpert_fTrad -0.097402597402594646 0.106478479717951879 1938.999999976005028657 -0.91 0.3604
## personotherman 2.027972027972029245 0.108431026165928618 1938.999999973324293023 18.70 <0.0000000000000002 ***
## personwoman1 1.440559440559442628 0.108431026165928646 1938.999999976123717715 13.29 <0.0000000000000002 ***
## personwoman2 1.440559440559443738 0.108431026165928673 1938.999999976124627210 13.29 <0.0000000000000002 ***
## part_gend_fMale Participants 0.000000000000000463 0.042550321135031835 1938.999999960017930789 0.00 1.0000
## deiexpert_fDEI-None:personotherman -1.771427525354229093 0.143387112015911949 1938.999999972486193656 -12.35 <0.0000000000000002 ***
## deiexpert_fTrad:personotherman -0.131868131868133315 0.150581435195070223 1938.999999972144905769 -0.88 0.3813
## deiexpert_fDEI-None:personwoman1 -2.026946875114417068 0.143387112015911977 1938.999999975493437887 -14.14 <0.0000000000000002 ***
## deiexpert_fTrad:personwoman1 0.410089910089908471 0.150581435195070251 1938.999999972146724758 2.72 0.0065 **
## deiexpert_fDEI-None:personwoman2 -2.126423314904994477 0.143387112015912005 1938.999999972489376887 -14.83 <0.0000000000000002 ***
## deiexpert_fTrad:personwoman2 0.111388611388608130 0.150581435195070307 1938.999999972148771121 0.74 0.4596
## ---
## 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: qual_clean_long4
##
## REML criterion at convergence: 6641
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.287 -0.552 0.047 0.617 3.105
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.844 0.919
## Residual 1.275 1.129
## Number of obs: 1944, groups: pid, 486
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.1798 0.1543 1300.9993 40.05 < 0.0000000000000002 ***
## threewaycondother\nman -2.0169 0.1693 1440.0000 -11.92 < 0.0000000000000002 ***
## threewaycondwoman1 -1.4101 0.1693 1440.0000 -8.33 < 0.0000000000000002 ***
## threewaycondwoman2 -1.4382 0.1693 1440.0000 -8.50 < 0.0000000000000002 ***
## part_gend_fMale Participants -0.2081 0.2526 1300.9993 -0.82 0.410
## deiexpert_fDEI-None -1.7252 0.2075 1300.9993 -8.31 0.00000000000000023 ***
## deiexpert_fTrad 0.2942 0.2142 1300.9993 1.37 0.170
## threewaycondother\nman:part_gend_fMale Participants 0.2150 0.2771 1440.0000 0.78 0.438
## threewaycondwoman1:part_gend_fMale Participants 0.3629 0.2771 1440.0000 1.31 0.190
## threewaycondwoman2:part_gend_fMale Participants 0.3627 0.2771 1440.0000 1.31 0.191
## threewaycondother\nman:deiexpert_fDEI-None 1.9259 0.2277 1440.0000 8.46 < 0.0000000000000002 ***
## threewaycondwoman1:deiexpert_fDEI-None 2.1874 0.2277 1440.0000 9.61 < 0.0000000000000002 ***
## threewaycondwoman2:deiexpert_fDEI-None 2.2564 0.2277 1440.0000 9.91 < 0.0000000000000002 ***
## threewaycondother\nman:deiexpert_fTrad -0.1290 0.2350 1440.0000 -0.55 0.583
## threewaycondwoman1:deiexpert_fTrad -0.5326 0.2350 1440.0000 -2.27 0.024 *
## threewaycondwoman2:deiexpert_fTrad -0.4837 0.2350 1440.0000 -2.06 0.040 *
## part_gend_fMale Participants:deiexpert_fDEI-None 0.4624 0.3315 1300.9993 1.40 0.163
## part_gend_fMale Participants:deiexpert_fTrad -0.1388 0.3489 1300.9993 -0.40 0.691
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fDEI-None -0.3013 0.3636 1440.0000 -0.83 0.408
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fDEI-None -0.6212 0.3636 1440.0000 -1.71 0.088 .
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fDEI-None -0.6240 0.3636 1440.0000 -1.72 0.086 .
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fTrad 0.1258 0.3828 1440.0000 0.33 0.743
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fTrad -0.0728 0.3828 1440.0000 -0.19 0.849
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fTrad 0.0168 0.3828 1440.0000 0.04 0.965
## ---
## 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: qual_clean_long4
##
## REML criterion at convergence: 6824
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.182 -0.554 0.064 0.620 2.620
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.03 1.01
## Residual 1.37 1.17
## Number of obs: 1944, groups: pid, 486
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.1348 0.1642 1237.3124 37.37 < 0.0000000000000002 ***
## threewaycondother\nman -2.1461 0.1755 1440.0000 -12.23 < 0.0000000000000002 ***
## threewaycondwoman1 -1.4944 0.1755 1440.0000 -8.52 < 0.0000000000000002 ***
## threewaycondwoman2 -1.4494 0.1755 1440.0000 -8.26 0.00000000000000032 ***
## part_gend_fMale Participants -0.1726 0.2687 1237.3124 -0.64 0.5209
## deiexpert_fDEI-None -1.7712 0.2208 1237.3124 -8.02 0.00000000000000240 ***
## deiexpert_fTrad 0.2298 0.2279 1237.3124 1.01 0.3136
## threewaycondother\nman:part_gend_fMale Participants 0.5612 0.2872 1440.0000 1.95 0.0509 .
## threewaycondwoman1:part_gend_fMale Participants 0.7019 0.2872 1440.0000 2.44 0.0146 *
## threewaycondwoman2:part_gend_fMale Participants 0.5909 0.2872 1440.0000 2.06 0.0398 *
## threewaycondother\nman:deiexpert_fDEI-None 2.0733 0.2360 1440.0000 8.79 < 0.0000000000000002 ***
## threewaycondwoman1:deiexpert_fDEI-None 2.4807 0.2360 1440.0000 10.51 < 0.0000000000000002 ***
## threewaycondwoman2:deiexpert_fDEI-None 2.5222 0.2360 1440.0000 10.69 < 0.0000000000000002 ***
## threewaycondother\nman:deiexpert_fTrad 0.0627 0.2436 1440.0000 0.26 0.7968
## threewaycondwoman1:deiexpert_fTrad -0.3077 0.2436 1440.0000 -1.26 0.2067
## threewaycondwoman2:deiexpert_fTrad -0.4881 0.2436 1440.0000 -2.00 0.0453 *
## part_gend_fMale Participants:deiexpert_fDEI-None 0.4925 0.3527 1237.3124 1.40 0.1628
## part_gend_fMale Participants:deiexpert_fTrad 0.0961 0.3713 1237.3124 0.26 0.7958
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fDEI-None -0.5391 0.3769 1440.0000 -1.43 0.1529
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fDEI-None -1.0237 0.3769 1440.0000 -2.72 0.0067 **
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fDEI-None -0.9358 0.3769 1440.0000 -2.48 0.0131 *
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fTrad -0.2066 0.3968 1440.0000 -0.52 0.6026
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fTrad -0.4846 0.3968 1440.0000 -1.22 0.2222
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fTrad -0.2043 0.3968 1440.0000 -0.51 0.6067
## ---
## 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: qual_clean_long4
##
## REML criterion at convergence: 5237
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.115 -0.886 0.117 0.776 2.584
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.000 0.000
## Residual 0.839 0.916
## Number of obs: 1952, groups: pid, 488
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.75556 0.09653 1928.00000 38.90 <0.0000000000000002 ***
## threewaycondother\nman -2.12222 0.13652 1928.00000 -15.55 <0.0000000000000002 ***
## threewaycondwoman1 -1.46667 0.13652 1928.00000 -10.74 <0.0000000000000002 ***
## threewaycondwoman2 -1.43333 0.13652 1928.00000 -10.50 <0.0000000000000002 ***
## part_gend_fMale Participants -0.07631 0.15857 1928.00000 -0.48 0.630
## deiexpert_fDEI-None -1.55913 0.12964 1928.00000 -12.03 <0.0000000000000002 ***
## deiexpert_fTrad 0.09708 0.13471 1928.00000 0.72 0.471
## threewaycondother\nman:part_gend_fMale Participants 0.25430 0.22425 1928.00000 1.13 0.257
## threewaycondwoman1:part_gend_fMale Participants 0.07044 0.22425 1928.00000 0.31 0.753
## threewaycondwoman2:part_gend_fMale Participants -0.01950 0.22425 1928.00000 -0.09 0.931
## threewaycondother\nman:deiexpert_fDEI-None 1.81865 0.18334 1928.00000 9.92 <0.0000000000000002 ***
## threewaycondwoman1:deiexpert_fDEI-None 2.08274 0.18334 1928.00000 11.36 <0.0000000000000002 ***
## threewaycondwoman2:deiexpert_fDEI-None 2.33512 0.18334 1928.00000 12.74 <0.0000000000000002 ***
## threewaycondother\nman:deiexpert_fTrad 0.16433 0.19051 1928.00000 0.86 0.388
## threewaycondwoman1:deiexpert_fTrad -0.42807 0.19051 1928.00000 -2.25 0.025 *
## threewaycondwoman2:deiexpert_fTrad -0.12456 0.19051 1928.00000 -0.65 0.513
## part_gend_fMale Participants:deiexpert_fDEI-None 0.19634 0.20796 1928.00000 0.94 0.345
## part_gend_fMale Participants:deiexpert_fTrad 0.00334 0.21951 1928.00000 0.02 0.988
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fDEI-None -0.14060 0.29410 1928.00000 -0.48 0.633
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fDEI-None -0.14221 0.29410 1928.00000 -0.48 0.629
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fDEI-None -0.50254 0.29410 1928.00000 -1.71 0.088 .
## threewaycondother\nman:part_gend_fMale Participants:deiexpert_fTrad -0.09301 0.31044 1928.00000 -0.30 0.765
## threewaycondwoman1:part_gend_fMale Participants:deiexpert_fTrad 0.04464 0.31044 1928.00000 0.14 0.886
## threewaycondwoman2:part_gend_fMale Participants:deiexpert_fTrad 0.03502 0.31044 1928.00000 0.11 0.910
## ---
## 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 = qual_clean4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7347 -0.0966 -0.0759 0.2653 0.9241
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0966 0.0293 3.29 0.0011 **
## deiexpertnone 0.6381 0.0387 16.49 <0.0000000000000002 ***
## deiexperttrad -0.0206 0.0406 -0.51 0.6124
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.353 on 496 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.447, Adjusted R-squared: 0.445
## F-statistic: 201 on 2 and 496 DF, p-value: <0.0000000000000002
