Study 1 (Diversity Advocacy)
Anonymous
2026-05-20
Load Packages
Study 3
Confirmatory Factor Analysis
Three-Factor Model (Efficacy Scales)
ThreeFactor_model <- '
# Factor 1: Bias-Awareness Efficacy
Awareness =~ DSE_awareness_1 + DSE_awareness_2 + DSE_awareness_3
# Factor 2: Self-Regulation Efficacy
Regulation =~ DSE_selfRegulation_1 + DSE_selfRegulation_2 + DSE_selfRegulation_3 +
DSE_selfRegulation_4 + DSE_selfRegulation_5
# Factor 3: Intergroup-Management Efficacy
Management =~ DSE_management_1 + DSE_management_2 + DSE_management_3 +
DSE_management_4 + DSE_management_5
'
ThreeFactor_fit <- cfa(ThreeFactor_model, Study_3_CFA, estimator = "ML")
# plot CFA results
semPaths(ThreeFactor_fit, "std", weighted = FALSE, nCharNodes = 7,
shapeMan = "rectangle", sizeMan = 8, sizeMan2 = 5)
## Fit Indices:
##
## chisq df pvalue cfi srmr
## 261.156 62.000 0.000 0.945 0.043## Standardized Factor Loadings:
##
## Awrnss Regltn Mngmnt
## DSE_awareness_1 0.825 0.000 0.000
## DSE_awareness_2 0.775 0.000 0.000
## DSE_awareness_3 0.888 0.000 0.000
## DSE_selfRegulation_1 0.000 0.784 0.000
## DSE_selfRegulation_2 0.000 0.835 0.000
## DSE_selfRegulation_3 0.000 0.866 0.000
## DSE_selfRegulation_4 0.000 0.750 0.000
## DSE_selfRegulation_5 0.000 0.834 0.000
## DSE_management_1 0.000 0.000 0.915
## DSE_management_2 0.000 0.000 0.936
## DSE_management_3 0.000 0.000 0.896
## DSE_management_4 0.000 0.000 0.814
## DSE_management_5 0.000 0.000 0.816
##
##
## Explained Variance (R²):
##
## DSE_awareness_1 DSE_awareness_2 DSE_awareness_3
## 0.681 0.601 0.789
## DSE_selfRegulation_1 DSE_selfRegulation_2 DSE_selfRegulation_3
## 0.615 0.697 0.749
## DSE_selfRegulation_4 DSE_selfRegulation_5 DSE_management_1
## 0.562 0.696 0.837
## DSE_management_2 DSE_management_3 DSE_management_4
## 0.876 0.803 0.663
## DSE_management_5
## 0.666One-Factor Model (Efficacy Scales)
OneFactor_model <- '
# One Factor: Bias-Awareness, Self-Regulation, & Intergroup-Management Efficacy
OneFactor =~ DSE_awareness_1 + DSE_awareness_2 + DSE_awareness_3 +
DSE_selfRegulation_1 + DSE_selfRegulation_2 + DSE_selfRegulation_3 +
DSE_selfRegulation_4 + DSE_selfRegulation_5 +
DSE_management_1 + DSE_management_2 + DSE_management_3 +
DSE_management_4 + DSE_management_5
'
OneFactor_fit <- cfa(OneFactor_model, Study_3_CFA, estimator = "ML")## Fit Indices:
##
## chisq df pvalue cfi srmr
## 1366.215 65.000 0.000 0.641 0.152## Standardized Factor Loadings:
##
## OnFctr
## DSE_awareness_1 0.495
## DSE_awareness_2 0.520
## DSE_awareness_3 0.582
## DSE_selfRegulation_1 0.569
## DSE_selfRegulation_2 0.600
## DSE_selfRegulation_3 0.592
## DSE_selfRegulation_4 0.561
## DSE_selfRegulation_5 0.601
## DSE_management_1 0.877
## DSE_management_2 0.880
## DSE_management_3 0.868
## DSE_management_4 0.824
## DSE_management_5 0.831
##
##
## Explained Variance (R²):
##
## DSE_awareness_1 DSE_awareness_2 DSE_awareness_3
## 0.245 0.271 0.339
## DSE_selfRegulation_1 DSE_selfRegulation_2 DSE_selfRegulation_3
## 0.324 0.361 0.350
## DSE_selfRegulation_4 DSE_selfRegulation_5 DSE_management_1
## 0.314 0.361 0.768
## DSE_management_2 DSE_management_3 DSE_management_4
## 0.774 0.753 0.678
## DSE_management_5
## 0.690##
##
## Three-Factor vs One-Factor Model:
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## ThreeFactor_fit 62 8987.7 9099 261.16
## OneFactor_fit 65 10086.7 10186 1366.21 1105.1 1.0349 3 < 2.2e-16
##
## ThreeFactor_fit
## OneFactor_fit ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Seven-Factor Measurement Model (All Seven Multi-Item Scales)
SevenFactor_model <- '
# Factor 1: Bias-Awareness Efficacy
Awareness =~ DSE_awareness_1 + DSE_awareness_2 + DSE_awareness_3
# Factor 2: Self-Regulation Efficacy
Regulation =~ DSE_selfRegulation_1 + DSE_selfRegulation_2 + DSE_selfRegulation_3 +
DSE_selfRegulation_4 + DSE_selfRegulation_5
# Factor 3: Intergroup-Management Efficacy
Management =~ DSE_management_1 + DSE_management_2 + DSE_management_3 +
DSE_management_4 + DSE_management_5
# Factor 4: Leader Diversity Advocacy
LdrDivAd =~ LdrDivAd_1 + LdrDivAd_2 +
LdrDivAd_3 + LdrDivAd_4
# Factor 5: Pro-Diversity Attitudes
ProDiversity =~ ProDiversity_1 + ProDiversity_2 +
ProDiversity_3 + ProDiversity_4
# Factor 6: Relational Allyship
RelationalAlly =~ Relational_Allyship_1 + Relational_Allyship_2 +
Relational_Allyship_3 + Relational_Allyship_4 +
Relational_Allyship_5 + Relational_Allyship_6 +
Relational_Allyship_7
# Factor 7: Organizational Allyship
OrgAlly =~ Org_Allyship_1 + Org_Allyship_2 + Org_Allyship_3 +
Org_Allyship_4 + Org_Allyship_5 + Org_Allyship_6 +
Org_Allyship_7 + Org_Allyship_8 + Org_Allyship_9
'
SevenFactor_fit <- cfa(SevenFactor_model, Study_3_CFA, estimator = "ML")## Fit Indices:
##
## chisq df pvalue cfi srmr
## 1371.618 608.000 0.000 0.944 0.047## Standardized Factor Loadings:
##
## Awrnss Regltn Mngmnt LdrDvA PrDvrs RltnlA OrgAll
## DSE_awareness_1 0.824 0.000 0.000 0.000 0.000 0.000 0.000
## DSE_awareness_2 0.775 0.000 0.000 0.000 0.000 0.000 0.000
## DSE_awareness_3 0.890 0.000 0.000 0.000 0.000 0.000 0.000
## DSE_selfRegulation_1 0.000 0.783 0.000 0.000 0.000 0.000 0.000
## DSE_selfRegulation_2 0.000 0.833 0.000 0.000 0.000 0.000 0.000
## DSE_selfRegulation_3 0.000 0.863 0.000 0.000 0.000 0.000 0.000
## DSE_selfRegulation_4 0.000 0.753 0.000 0.000 0.000 0.000 0.000
## DSE_selfRegulation_5 0.000 0.837 0.000 0.000 0.000 0.000 0.000
## DSE_management_1 0.000 0.000 0.913 0.000 0.000 0.000 0.000
## DSE_management_2 0.000 0.000 0.936 0.000 0.000 0.000 0.000
## DSE_management_3 0.000 0.000 0.900 0.000 0.000 0.000 0.000
## DSE_management_4 0.000 0.000 0.813 0.000 0.000 0.000 0.000
## DSE_management_5 0.000 0.000 0.816 0.000 0.000 0.000 0.000
## LdrDivAd_1 0.000 0.000 0.000 0.799 0.000 0.000 0.000
## LdrDivAd_2 0.000 0.000 0.000 0.915 0.000 0.000 0.000
## LdrDivAd_3 0.000 0.000 0.000 0.890 0.000 0.000 0.000
## LdrDivAd_4 0.000 0.000 0.000 0.861 0.000 0.000 0.000
## ProDiversity_1 0.000 0.000 0.000 0.000 0.911 0.000 0.000
## ProDiversity_2 0.000 0.000 0.000 0.000 0.913 0.000 0.000
## ProDiversity_3 0.000 0.000 0.000 0.000 0.921 0.000 0.000
## ProDiversity_4 0.000 0.000 0.000 0.000 0.900 0.000 0.000
## Relational_Allyship_1 0.000 0.000 0.000 0.000 0.000 0.873 0.000
## Relational_Allyship_2 0.000 0.000 0.000 0.000 0.000 0.850 0.000
## Relational_Allyship_3 0.000 0.000 0.000 0.000 0.000 0.919 0.000
## Relational_Allyship_4 0.000 0.000 0.000 0.000 0.000 0.901 0.000
## Relational_Allyship_5 0.000 0.000 0.000 0.000 0.000 0.843 0.000
## Relational_Allyship_6 0.000 0.000 0.000 0.000 0.000 0.788 0.000
## Relational_Allyship_7 0.000 0.000 0.000 0.000 0.000 0.764 0.000
## Org_Allyship_1 0.000 0.000 0.000 0.000 0.000 0.000 0.918
## Org_Allyship_2 0.000 0.000 0.000 0.000 0.000 0.000 0.919
## Org_Allyship_3 0.000 0.000 0.000 0.000 0.000 0.000 0.928
## Org_Allyship_4 0.000 0.000 0.000 0.000 0.000 0.000 0.885
## Org_Allyship_5 0.000 0.000 0.000 0.000 0.000 0.000 0.920
## Org_Allyship_6 0.000 0.000 0.000 0.000 0.000 0.000 0.927
## Org_Allyship_7 0.000 0.000 0.000 0.000 0.000 0.000 0.855
## Org_Allyship_8 0.000 0.000 0.000 0.000 0.000 0.000 0.922
## Org_Allyship_9 0.000 0.000 0.000 0.000 0.000 0.000 0.927
##
##
## Explained Variance (R²):
##
## DSE_awareness_1 DSE_awareness_2 DSE_awareness_3
## 0.679 0.601 0.792
## DSE_selfRegulation_1 DSE_selfRegulation_2 DSE_selfRegulation_3
## 0.614 0.694 0.745
## DSE_selfRegulation_4 DSE_selfRegulation_5 DSE_management_1
## 0.567 0.700 0.834
## DSE_management_2 DSE_management_3 DSE_management_4
## 0.875 0.809 0.661
## DSE_management_5 LdrDivAd_1 LdrDivAd_2
## 0.666 0.639 0.837
## LdrDivAd_3 LdrDivAd_4 ProDiversity_1
## 0.792 0.741 0.830
## ProDiversity_2 ProDiversity_3 ProDiversity_4
## 0.834 0.849 0.811
## Relational_Allyship_1 Relational_Allyship_2 Relational_Allyship_3
## 0.763 0.723 0.844
## Relational_Allyship_4 Relational_Allyship_5 Relational_Allyship_6
## 0.813 0.710 0.620
## Relational_Allyship_7 Org_Allyship_1 Org_Allyship_2
## 0.584 0.843 0.844
## Org_Allyship_3 Org_Allyship_4 Org_Allyship_5
## 0.862 0.782 0.846
## Org_Allyship_6 Org_Allyship_7 Org_Allyship_8
## 0.860 0.731 0.851
## Org_Allyship_9
## 0.860Six-Factor Alternative Model #1 (Combine outcomes into single factor)
SixFactor_model <- '
# Factor 1: Bias-Awareness Efficacy
Awareness =~ DSE_awareness_1 + DSE_awareness_2 + DSE_awareness_3
# Factor 2: Self-Regulation Efficacy
Regulation =~ DSE_selfRegulation_1 + DSE_selfRegulation_2 + DSE_selfRegulation_3 +
DSE_selfRegulation_4 + DSE_selfRegulation_5
# Factor 3: Intergroup-Management Efficacy
Management =~ DSE_management_1 + DSE_management_2 + DSE_management_3 +
DSE_management_4 + DSE_management_5
# Factor 4: Leader Diversity Advocacy
LdrDivAd =~ LdrDivAd_1 + LdrDivAd_2 +
LdrDivAd_3 + LdrDivAd_4
# Factor 5: Pro-Diversity Attitudes
ProDiversity =~ ProDiversity_1 + ProDiversity_2 +
ProDiversity_3 + ProDiversity_4
# Factor 6: Relational Allyship & Organizational Allyship
Outcomes =~ Relational_Allyship_1 + Relational_Allyship_2 +
Relational_Allyship_3 + Relational_Allyship_4 +
Relational_Allyship_5 + Relational_Allyship_6 +
Relational_Allyship_7 +
Org_Allyship_1 + Org_Allyship_2 + Org_Allyship_3 +
Org_Allyship_4 + Org_Allyship_5 + Org_Allyship_6 +
Org_Allyship_7 + Org_Allyship_8 + Org_Allyship_9
'
SixFactor_fit1 <- cfa(SixFactor_model, Study_3_CFA, estimator = "ML")Six-Factor Alternative Model #2 (Combine predictors into single factor)
SixFactor2_model <- '
# Factor 1: Bias-Awareness Efficacy
Awareness =~ DSE_awareness_1 + DSE_awareness_2 + DSE_awareness_3
# Factor 2: Self-Regulation Efficacy
Regulation =~ DSE_selfRegulation_1 + DSE_selfRegulation_2 + DSE_selfRegulation_3 +
DSE_selfRegulation_4 + DSE_selfRegulation_5
# Factor 3: Intergroup-Management Efficacy
Management =~ DSE_management_1 + DSE_management_2 + DSE_management_3 +
DSE_management_4 + DSE_management_5
# Factor 4: Leader Diversity Advocacy & Pro-Diversity Attitudes
Predictors =~ LdrDivAd_1 + LdrDivAd_2 +
LdrDivAd_3 + LdrDivAd_4 +
ProDiversity_1 + ProDiversity_2 +
ProDiversity_3 + ProDiversity_4
# Factor 5: Relational Allyship
RelationalAlly =~ Relational_Allyship_1 + Relational_Allyship_2 +
Relational_Allyship_3 + Relational_Allyship_4 +
Relational_Allyship_5 + Relational_Allyship_6 +
Relational_Allyship_7
# Factor 6: Organizational Allyship
OrgAlly =~ Org_Allyship_1 + Org_Allyship_2 + Org_Allyship_3 +
Org_Allyship_4 + Org_Allyship_5 + Org_Allyship_6 +
Org_Allyship_7 + Org_Allyship_8 + Org_Allyship_9
'
SixFactor_fit2 <- cfa(SixFactor2_model, Study_3_CFA, estimator = "ML")Five-Factor Alternative Model #3 (Combine predictors/outcomes into single factor)
FiveFactor_model <- '
# Factor 1: Bias-Awareness Efficacy
Awareness =~ DSE_awareness_1 + DSE_awareness_2 + DSE_awareness_3
# Factor 2: Self-Regulation Efficacy
Regulation =~ DSE_selfRegulation_1 + DSE_selfRegulation_2 + DSE_selfRegulation_3 +
DSE_selfRegulation_4 + DSE_selfRegulation_5
# Factor 3: Intergroup-Management Efficacy
Management =~ DSE_management_1 + DSE_management_2 + DSE_management_3 +
DSE_management_4 + DSE_management_5
# Factor 4: Leader Diversity Advocacy & Pro-Diversity Attitudes
Predictors =~ LdrDivAd_1 + LdrDivAd_2 +
LdrDivAd_3 + LdrDivAd_4 +
ProDiversity_1 + ProDiversity_2 +
ProDiversity_3 + ProDiversity_4
# Factor 5: Relational Allyship & Organizational Allyship
Outcomes =~ Relational_Allyship_1 + Relational_Allyship_2 +
Relational_Allyship_3 + Relational_Allyship_4 +
Relational_Allyship_5 + Relational_Allyship_6 +
Relational_Allyship_7 +
Org_Allyship_1 + Org_Allyship_2 + Org_Allyship_3 +
Org_Allyship_4 + Org_Allyship_5 + Org_Allyship_6 +
Org_Allyship_7 + Org_Allyship_8 + Org_Allyship_9
'
FiveFactor_fit <- cfa(FiveFactor_model, Study_3_CFA, estimator = "ML")Model Comparison
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## SevenFactor_fit 608 25072 25437 1371.6
## SixFactor_fit1 614 26549 26890 2860.2 1488.6 0.84876 6 < 2.2e-16
## SixFactor_fit2 614 26015 26357 2326.7 955.1 0.67910 6 < 2.2e-16
## FiveFactor_fit 619 27487 27809 3808.4 2436.8 0.80183 11 < 2.2e-16
##
## SevenFactor_fit
## SixFactor_fit1 ***
## SixFactor_fit2 ***
## FiveFactor_fit ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## cfi srmr
## SevenFactor 0.9441521 0.04686290
## SixFactor_Alt1 0.8357234 0.10986568
## SixFactor_Alt2 0.8747385 0.09841021
## FiveFactor_Alt3 0.7667416 0.14268708Reliability Estimates (Cronbach Alpha) - Study 1 Measures
## Alpha for bias-awareness efficacy: 0.859664
## Alpha for self-regulation efficacy: 0.906099
## Alpha for intergroup-management efficacy: 0.9434502## Alpha for relational ally work: 0.9467444
## Alpha for organizational ally work: 0.9775725## Alpha for leader diversity advocacy: 0.9228933
## Alpha for pro-diversity attitudes: 0.948369Table 8: Descriptives & Correlations
##
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2 3
## 1. Male 0.50 0.50
##
## 2. White 0.68 0.47 -.09
## [-.19, .02]
##
## 3. Political_Ideology 3.33 1.87 .09 .06
## [-.02, .19] [-.05, .16]
##
## 4. LdrDivAd 3.30 1.03 .06 -.05 -.11*
## [-.05, .17] [-.15, .06] [-.22, -.01]
##
## 5. PDB 4.22 0.94 -.07 -.05 -.58**
## [-.18, .03] [-.16, .06] [-.64, -.50]
##
## 6. Aware 4.00 0.81 .03 -.08 -.29**
## [-.07, .14] [-.19, .02] [-.38, -.19]
##
## 7. Regulate 4.19 0.70 .07 .02 -.19**
## [-.04, .17] [-.09, .12] [-.29, -.09]
##
## 8. Manage 3.50 1.08 .05 -.10 -.24**
## [-.06, .15] [-.20, .01] [-.34, -.14]
##
## 9. OrgAlly 2.47 1.20 -.00 -.05 -.22**
## [-.11, .10] [-.15, .06] [-.32, -.12]
##
## 10. RelAlly 3.42 0.90 -.04 .01 -.28**
## [-.14, .07] [-.09, .12] [-.38, -.18]
##
## 11. InclusiveLdr 4.07 0.96 .02 .02 -.01
## [-.08, .13] [-.08, .13] [-.11, .10]
##
## 12. DVB 4.14 0.90 .03 .03 -.05
## [-.08, .13] [-.08, .13] [-.15, .06]
##
## 13. SupIntegrity 3.87 0.87 .12* .05 .03
## [.00, .23] [-.06, .17] [-.08, .14]
##
## 14. intergroupAnxiety 3.12 0.76 -.19** .00 .00
## [-.30, -.08] [-.11, .11] [-.11, .11]
##
## 15. empathicConcern 3.92 0.84 -.19** .08 -.30**
## [-.29, -.08] [-.03, .18] [-.39, -.20]
##
## 16. perspectiveTaking 3.84 0.77 -.03 .09 -.13*
## [-.14, .08] [-.02, .19] [-.24, -.03]
##
## 17. diversityClimate 3.84 0.84 .08 -.04 -.08
## [-.02, .19] [-.14, .07] [-.18, .03]
##
## 4 5 6 7 8 9
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##
##
##
##
##
##
##
##
##
##
## .40**
## [.31, .49]
##
## .24** .35**
## [.14, .34] [.25, .44]
##
## .30** .37** .55**
## [.20, .40] [.27, .45] [.47, .62]
##
## .47** .48** .49** .54**
## [.38, .54] [.40, .56] [.40, .56] [.47, .62]
##
## .50** .43** .23** .21** .59**
## [.41, .57] [.34, .51] [.13, .33] [.11, .31] [.51, .65]
##
## .52** .59** .36** .43** .57** .62**
## [.44, .60] [.52, .65] [.26, .44] [.34, .51] [.50, .64] [.55, .68]
##
## .59** .21** .17** .28** .28** .22**
## [.51, .65] [.11, .31] [.06, .27] [.18, .38] [.17, .37] [.12, .32]
##
## .65** .24** .18** .28** .27** .17**
## [.58, .70] [.14, .34] [.08, .28] [.18, .38] [.17, .37] [.07, .27]
##
## .52** .18** .24** .32** .20** .14*
## [.43, .60] [.07, .29] [.13, .34] [.22, .42] [.09, .31] [.03, .25]
##
## -.33** -.28** -.09 -.20** -.34** -.30**
## [-.43, -.23] [-.38, -.17] [-.21, .02] [-.31, -.09] [-.44, -.23] [-.40, -.19]
##
## .30** .54** .38** .40** .49** .42**
## [.21, .40] [.47, .61] [.29, .47] [.30, .48] [.41, .57] [.33, .50]
##
## .37** .44** .33** .43** .46** .37**
## [.27, .45] [.35, .52] [.24, .42] [.33, .51] [.37, .54] [.28, .46]
##
## .74** .35** .24** .34** .45** .36**
## [.69, .78] [.26, .44] [.14, .34] [.24, .43] [.36, .53] [.27, .45]
##
## 10 11 12 13 14 15
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
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## .27**
## [.17, .37]
##
## .26** .78**
## [.15, .35] [.73, .82]
##
## .19** .70** .75**
## [.08, .30] [.63, .75] [.69, .79]
##
## -.26** -.18** -.20** -.21**
## [-.36, -.15] [-.28, -.06] [-.30, -.08] [-.31, -.10]
##
## .54** .29** .30** .22** -.23**
## [.46, .61] [.19, .38] [.20, .39] [.11, .33] [-.33, -.12]
##
## .46** .32** .34** .25** -.25** .62**
## [.37, .54] [.22, .41] [.24, .43] [.14, .35] [-.35, -.14] [.56, .69]
##
## .42** .60** .67** .56** -.24** .29**
## [.33, .50] [.53, .66] [.61, .73] [.48, .63] [-.35, -.13] [.19, .38]
##
## 16
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## .35**
## [.26, .44]
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## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
## Run Mplus Path Models
Hypotheses 1a-b: Direct Effect Analysis
## Mplus VERSION 8.11 (Mac)
## MUTHEN & MUTHEN
## 03/11/2026 2:53 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Study 3: Mediation Models
## DATA: FILE = "Study3.dat";
## VARIABLE:
## NAMES = Male White Ideology LdrDivAd PDB Aware Regulate Manage OrgAlly RelAlly
## IncluLdr DVB SupInt Anxiety empathy
## PT Climate;
## MISSING = ALL (999);
##
## USEVARIABLES LdrDivAd PDB OrgAlly RelAlly
## PolID;
##
## DEFINE:
## ! Political Ideology centered at the scale midpoint (= 4)
## PolID = Ideology - 4;
##
## ! Grand-mean center Leader Diversity Advocacy and Pro-Diversity Attitudes
## CENTER LdrDivAd PDB (GRANDMEAN);
##
## ANALYSIS:
## ESTIMATOR = ML;
##
## MODEL:
## OrgAlly RelAlly ON PolID PDB LdrDivAd;
##
##
## ! Covariances among predictors
## PDB WITH LdrDivAd PolID;
## LdrDivAd WITH PolID;
##
##
##
## INPUT READING TERMINATED NORMALLY
##
##
##
## Study 2: Mediation Models
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 343
##
## Number of dependent variables 2
## Number of independent variables 3
## Number of continuous latent variables 0
##
## Observed dependent variables
##
## Continuous
## ORGALLY RELALLY
##
## Observed independent variables
## LDRDIVAD PDB POLID
##
## Variables with special functions
##
## Centering (GRANDMEAN)
## LDRDIVAD PDB
##
##
## Estimator ML
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Study3.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 1
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.100
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## ORGALLY RELALLY LDRDIVAD PDB POLID
## ________ ________ ________ ________ ________
## ORGALLY 1.000
## RELALLY 1.000 1.000
## LDRDIVAD 1.000 1.000 1.000
## PDB 1.000 1.000 1.000 1.000
## POLID 1.000 1.000 1.000 1.000 1.000
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## ORGALLY 2.467 0.367 1.000 19.24% 1.111 2.000 2.333
## 343.000 1.431 -0.960 5.000 3.50% 2.889 3.556
## RELALLY 3.418 -0.423 1.000 1.75% 2.857 3.143 3.429
## 343.000 0.805 -0.013 5.000 6.12% 3.714 4.143
## LDRDIVAD 0.000 -0.308 -2.296 4.37% -0.796 -0.296 -0.046
## 343.000 1.066 -0.374 1.704 7.87% 0.204 0.704
## PDB 0.000 -1.408 -3.216 2.04% -0.716 -0.216 0.284
## 343.000 0.878 1.815 0.784 39.94% 0.534 0.784
## POLID -0.673 0.390 -3.000 19.24% -2.000 -2.000 -1.000
## 343.000 3.479 -1.090 3.000 4.96% 0.000 1.000
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 20
##
## Loglikelihood
##
## H0 Value -2358.856
## H1 Value -2358.856
##
## Information Criteria
##
## Akaike (AIC) 4757.712
## Bayesian (BIC) 4834.466
## Sample-Size Adjusted BIC 4771.022
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 0.000
## Degrees of Freedom 0
## P-Value 0.0000
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.000
## 90 Percent C.I. 0.000 0.000
## Probability RMSEA <= .05 0.000
##
## CFI/TLI
##
## CFI 1.000
## TLI 1.000
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 394.494
## Degrees of Freedom 7
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.000
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## ORGALLY ON
## POLID -0.018 0.036 -0.519 0.604
## PDB 0.327 0.077 4.246 0.000
## LDRDIVAD 0.453 0.057 7.886 0.000
##
## RELALLY ON
## POLID 0.011 0.024 0.475 0.634
## PDB 0.448 0.052 8.647 0.000
## LDRDIVAD 0.293 0.039 7.574 0.000
##
## PDB WITH
## LDRDIVAD 0.389 0.056 6.909 0.000
## POLID -1.008 0.109 -9.251 0.000
##
## LDRDIVAD WITH
## POLID -0.216 0.105 -2.066 0.039
##
## RELALLY WITH
## ORGALLY 0.275 0.039 7.090 0.000
##
## Means
## LDRDIVAD 0.000 0.056 0.000 1.000
## PDB 0.000 0.051 0.000 1.000
## POLID -0.673 0.101 -6.687 0.000
##
## Intercepts
## ORGALLY 2.455 0.059 41.788 0.000
## RELALLY 3.425 0.040 86.675 0.000
##
## Variances
## LDRDIVAD 1.066 0.081 13.096 0.000
## PDB 0.878 0.067 13.096 0.000
## POLID 3.479 0.266 13.096 0.000
##
## Residual Variances
## ORGALLY 0.986 0.075 13.096 0.000
## RELALLY 0.446 0.034 13.096 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.602E-02
## (ratio of smallest to largest eigenvalue)
##
##
## Beginning Time: 14:53:24
## Ending Time: 14:53:24
## Elapsed Time: 00:00:00
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2024 Muthen & MuthenTable 9 (Model 1): Path Model for Relational Ally Work
## Mplus VERSION 8.11 (Mac)
## MUTHEN & MUTHEN
## 04/09/2026 2:43 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Study 3
## DATA: FILE = "Study3.dat";
## VARIABLE:
## NAMES = Male White Ideology LdrDivAd PDB Aware Regulate Manage OrgAlly RelAlly
## IncluLdr DVB SupInt Anxiety empathy
## PT Climate;
##
## MISSING = ALL (999);
##
## USEVARIABLES LdrDivAd PDB Aware Regulate RelAlly
## Anxiety Empathy PT PolID;
##
## DEFINE:
## ! Political Ideology centered at the scale midpoint (= 4)
## PolID = Ideology - 4;
##
## ! Grand-mean center Leader Diversity Advocacy and Pro-Diversity Attitudes
## CENTER LdrDivAd PDB (GRANDMEAN);
##
## ANALYSIS:
## ESTIMATOR = ML;
##
## MODEL:
## Aware ON PDB LdrDivAd;
## Regulate ON PDB LdrDivAd;
## Anxiety ON PDB LdrDivAd;
## Empathy ON PDB LdrDivAd;
## PT ON PDB LdrDivAd;
##
## RelAlly ON PolID PDB LdrDivAd Aware Regulate Anxiety Empathy PT;
##
## ! Covariances among predictors
## PDB WITH LdrDivAd PolID;
## LdrDivAd WITH PolID;
## Aware WITH Regulate Anxiety Empathy PT;
## Regulate WITH Anxiety Empathy PT;
## Anxiety WITH Empathy PT;
## PT WITH Empathy;
##
##
##
## INPUT READING TERMINATED NORMALLY
##
##
##
## Study 1
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 343
##
## Number of dependent variables 6
## Number of independent variables 3
## Number of continuous latent variables 0
##
## Observed dependent variables
##
## Continuous
## AWARE REGULATE RELALLY ANXIETY EMPATHY PT
##
## Observed independent variables
## LDRDIVAD PDB POLID
##
## Variables with special functions
##
## Centering (GRANDMEAN)
## LDRDIVAD PDB
##
##
## Estimator ML
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Study3.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 2
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.100
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## AWARE REGULATE RELALLY ANXIETY EMPATHY
## ________ ________ ________ ________ ________
## AWARE 1.000
## REGULATE 1.000 1.000
## RELALLY 1.000 1.000 1.000
## ANXIETY 0.872 0.872 0.872 0.872
## EMPATHY 1.000 1.000 1.000 0.872 1.000
## PT 1.000 1.000 1.000 0.872 1.000
## LDRDIVAD 1.000 1.000 1.000 0.872 1.000
## PDB 1.000 1.000 1.000 0.872 1.000
## POLID 1.000 1.000 1.000 0.872 1.000
##
##
## Covariance Coverage
## PT LDRDIVAD PDB POLID
## ________ ________ ________ ________
## PT 1.000
## LDRDIVAD 1.000 1.000
## PDB 1.000 1.000 1.000
## POLID 1.000 1.000 1.000 1.000
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## AWARE 3.995 -0.511 1.667 0.58% 3.333 4.000 4.000
## 343.000 0.660 -0.474 5.000 22.45% 4.333 5.000
## REGULATE 4.188 -0.817 1.200 0.29% 3.600 4.000 4.200
## 343.000 0.493 0.633 5.000 22.45% 4.400 5.000
## RELALLY 3.418 -0.423 1.000 1.75% 2.857 3.143 3.429
## 343.000 0.805 -0.013 5.000 6.12% 3.714 4.143
## ANXIETY 3.118 -0.677 1.000 2.01% 2.857 3.000 3.000
## 299.000 0.575 0.820 5.000 0.67% 3.143 3.714
## EMPATHY 3.923 -0.705 1.000 0.29% 3.143 3.857 4.000
## 343.000 0.708 0.074 5.000 11.66% 4.286 4.714
## PT 3.843 -0.609 1.000 0.29% 3.286 3.714 3.857
## 343.000 0.585 0.374 5.000 8.75% 4.143 4.429
## LDRDIVAD 0.000 -0.308 -2.296 4.37% -0.796 -0.296 -0.046
## 343.000 1.066 -0.374 1.704 7.87% 0.204 0.704
## PDB 0.000 -1.408 -3.216 2.04% -0.716 -0.216 0.284
## 343.000 0.878 1.815 0.784 39.94% 0.534 0.784
## POLID -0.673 0.390 -3.000 19.24% -2.000 -2.000 -1.000
## 343.000 3.479 -1.090 3.000 4.96% 0.000 1.000
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 49
##
## Loglikelihood
##
## H0 Value -3526.775
## H1 Value -3514.751
##
## Information Criteria
##
## Akaike (AIC) 7151.551
## Bayesian (BIC) 7339.599
## Sample-Size Adjusted BIC 7184.160
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 24.049
## Degrees of Freedom 5
## P-Value 0.0002
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.105
## 90 Percent C.I. 0.066 0.149
## Probability RMSEA <= .05 0.013
##
## CFI/TLI
##
## CFI 0.977
## TLI 0.851
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 875.548
## Degrees of Freedom 33
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.026
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## AWARE ON
## PDB 0.261 0.048 5.476 0.000
## LDRDIVAD 0.095 0.043 2.195 0.028
##
## REGULATE ON
## PDB 0.218 0.040 5.387 0.000
## LDRDIVAD 0.126 0.037 3.423 0.001
##
## ANXIETY ON
## PDB -0.128 0.047 -2.705 0.007
## LDRDIVAD -0.200 0.045 -4.449 0.000
##
## EMPATHY ON
## PDB 0.452 0.044 10.228 0.000
## LDRDIVAD 0.084 0.040 2.089 0.037
##
## PT ON
## PDB 0.282 0.042 6.701 0.000
## LDRDIVAD 0.168 0.038 4.408 0.000
##
## RELALLY ON
## POLID 0.012 0.023 0.515 0.606
## PDB 0.294 0.055 5.363 0.000
## LDRDIVAD 0.248 0.038 6.463 0.000
## AWARE 0.027 0.052 0.524 0.600
## REGULATE 0.166 0.062 2.690 0.007
## ANXIETY 0.019 0.053 0.367 0.714
## EMPATHY 0.230 0.057 4.072 0.000
## PT 0.034 0.060 0.565 0.572
##
## PDB WITH
## LDRDIVAD 0.389 0.056 6.909 0.000
## POLID -1.008 0.109 -9.251 0.000
##
## LDRDIVAD WITH
## POLID -0.216 0.105 -2.066 0.039
##
## AWARE WITH
## REGULATE 0.231 0.029 7.964 0.000
## ANXIETY 0.018 0.031 0.593 0.553
## EMPATHY 0.123 0.029 4.183 0.000
## PT 0.098 0.028 3.529 0.000
##
## REGULATE WITH
## ANXIETY -0.030 0.026 -1.170 0.242
## EMPATHY 0.107 0.025 4.263 0.000
## PT 0.124 0.024 5.128 0.000
##
## ANXIETY WITH
## EMPATHY -0.034 0.029 -1.151 0.250
## PT -0.044 0.027 -1.650 0.099
##
## PT WITH
## EMPATHY 0.236 0.028 8.329 0.000
##
## Means
## LDRDIVAD 0.000 0.056 0.000 1.000
## PDB 0.000 0.051 0.000 1.000
## POLID -0.673 0.101 -6.687 0.000
##
## Intercepts
## AWARE 3.995 0.041 97.879 0.000
## REGULATE 4.188 0.035 120.660 0.000
## RELALLY 1.528 0.339 4.513 0.000
## ANXIETY 3.114 0.041 76.286 0.000
## EMPATHY 3.923 0.038 103.533 0.000
## PT 3.843 0.036 106.386 0.000
##
## Variances
## LDRDIVAD 1.066 0.081 13.096 0.000
## PDB 0.878 0.067 13.096 0.000
## POLID 3.479 0.266 13.096 0.000
##
## Residual Variances
## AWARE 0.571 0.044 13.096 0.000
## REGULATE 0.413 0.032 13.096 0.000
## RELALLY 0.391 0.030 13.095 0.000
## ANXIETY 0.498 0.041 12.231 0.000
## EMPATHY 0.492 0.038 13.096 0.000
## PT 0.448 0.034 13.096 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.128E-03
## (ratio of smallest to largest eigenvalue)
##
##
## Beginning Time: 14:43:45
## Ending Time: 14:43:45
## Elapsed Time: 00:00:00
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2024 Muthen & MuthenHypothesis 2a: Mediation Analysis (Bias-Awareness - Relational Allyship)
res <- readModels("Mplus Syntax/Study 3 Syntax & Output/Hannah_Study 3/Study3_Mediation_RelAlly (H2a_H3).out")
# Extract coefficients
a_est <- res$parameters$unstandardized$est[2] # Effect of X on M (a-path)
b_est <- res$parameters$unstandardized$est[14] # Effect of M on Y (b-path)
# Extract standard errors
a_se <- res$parameters$unstandardized$se[2]
b_se <- res$parameters$unstandardized$se[14]
# Compute Monte Carlo confidence interval
mc_ci <- medci(mu.x = a_est, mu.y = b_est, se.x = a_se, se.y = b_se,
rho = 0, alpha = .05, sims = 10000, method = "parametric")## Indirect effect estimate: 0.002565## Indirect effect standard error: 0.005545378## Monte Carlo 95% CI: [ -0.007877536 , 0.01501881 ]Hypothesis 3: Mediation Analysis (Self-Regulation - Relational Allyship)
# Extract coefficients
a_est <- res$parameters$unstandardized$est[4] # Effect of X on M (a-path)
b_est <- res$parameters$unstandardized$est[15] # Effect of M on Y (b-path)
# Extract standard errors
a_se <- res$parameters$unstandardized$se[4]
b_se <- res$parameters$unstandardized$se[15]
# Compute Monte Carlo confidence interval
mc_ci <- medci(mu.x = a_est, mu.y = b_est, se.x = a_se, se.y = b_se,
rho = 0, alpha = .05, sims = 10000, method = "parametric")## Indirect effect estimate: 0.020916## Indirect effect standard error: 0.01019872## Monte Carlo 95% CI: [ 0.004206751 , 0.04375455 ]Table 9 (Model 2): Conditional Path Model for Relational Ally Work
## Mplus VERSION 8.11 (Mac)
## MUTHEN & MUTHEN
## 04/09/2026 4:40 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Study 3: Moderated Mediation Models
## DATA: FILE = "Study3.dat";
## VARIABLE:
## NAMES = Male White Ideology LdrDivAd PDB Aware Regulate Manage OrgAlly RelAlly
## IncluLdr DVB SupInt Anxiety Empathy
## PT Climate;
## MISSING = ALL (999);
##
## USEVARIABLES LdrDivAd PDB Aware Regulate RelAlly
## Anxiety Empathy PT
## PolID LDA_PDB;
##
## DEFINE:
## ! Political Ideology centered at the scale midpoint (= 4)
## PolID = Ideology - 4;
##
## ! Grand-mean center Leader Diversity Advocacy and Pro-Diversity Attitudes
## CENTER LdrDivAd PDB (GRANDMEAN);
##
## ! Create interaction term
## LDA_PDB = LdrDivAd * PDB;
##
## ANALYSIS:
## ESTIMATOR = ML;
##
## MODEL:
## Aware ON PDB (a2)
## LdrDivAd (a1)
## LDA_PDB (a3);
##
## Regulate ON PDB (b2)
## LdrDivAd (b1)
## LDA_PDB (b3);
##
## RelAlly ON PolID
## PDB
## LdrDivAd
## Aware (aw)
## Regulate (rg);
##
## Anxiety ON PDB LdrDivAd;
## Empathy ON PDB LdrDivAd;
## PT ON PDB LdrDivAd;
##
## RelAlly ON Anxiety Empathy PT;
##
## ! Covariances among predictors
## PDB WITH LdrDivAd PolID;
## LdrDivAd WITH PolID;
## Aware WITH Regulate Anxiety Empathy PT;
## Regulate WITH Anxiety Empathy PT;
## Anxiety WITH Empathy PT;
## PT WITH Empathy;
##
## MODEL CONSTRAINT:
##
## LOOP(PDB, -1.88, 1.88, .94); ! Two Standard Deviations Below/Above Mean
## PLOT(
## ! LdrDivAd_aw
## LdrDivAd_rg
## );
## ! LdrDivAd_aw = (a1 + a3*PDB)*aw;
## LdrDivAd_rg = (b1 + b3*PDB)*rg;
##
## PLOT: TYPE = PLOT2
##
##
##
##
##
##
##
## INPUT READING TERMINATED NORMALLY
##
##
##
## Study 1: Moderated Mediation Models
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 343
##
## Number of dependent variables 6
## Number of independent variables 4
## Number of continuous latent variables 0
##
## Observed dependent variables
##
## Continuous
## AWARE REGULATE RELALLY ANXIETY EMPATHY PT
##
## Observed independent variables
## LDRDIVAD PDB POLID LDA_PDB
##
## Variables with special functions
##
## Centering (GRANDMEAN)
## LDRDIVAD PDB
##
##
## Estimator ML
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Study3.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 2
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.100
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## AWARE REGULATE RELALLY ANXIETY EMPATHY
## ________ ________ ________ ________ ________
## AWARE 1.000
## REGULATE 1.000 1.000
## RELALLY 1.000 1.000 1.000
## ANXIETY 0.872 0.872 0.872 0.872
## EMPATHY 1.000 1.000 1.000 0.872 1.000
## PT 1.000 1.000 1.000 0.872 1.000
## LDRDIVAD 1.000 1.000 1.000 0.872 1.000
## PDB 1.000 1.000 1.000 0.872 1.000
## POLID 1.000 1.000 1.000 0.872 1.000
## LDA_PDB 1.000 1.000 1.000 0.872 1.000
##
##
## Covariance Coverage
## PT LDRDIVAD PDB POLID LDA_PDB
## ________ ________ ________ ________ ________
## PT 1.000
## LDRDIVAD 1.000 1.000
## PDB 1.000 1.000 1.000
## POLID 1.000 1.000 1.000 1.000
## LDA_PDB 1.000 1.000 1.000 1.000 1.000
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## AWARE 3.995 -0.511 1.667 0.58% 3.333 4.000 4.000
## 343.000 0.660 -0.474 5.000 22.45% 4.333 5.000
## REGULATE 4.188 -0.817 1.200 0.29% 3.600 4.000 4.200
## 343.000 0.493 0.633 5.000 22.45% 4.400 5.000
## RELALLY 3.418 -0.423 1.000 1.75% 2.857 3.143 3.429
## 343.000 0.805 -0.013 5.000 6.12% 3.714 4.143
## ANXIETY 3.118 -0.677 1.000 2.01% 2.857 3.000 3.000
## 299.000 0.575 0.820 5.000 0.67% 3.143 3.714
## EMPATHY 3.923 -0.705 1.000 0.29% 3.143 3.857 4.000
## 343.000 0.708 0.074 5.000 11.66% 4.286 4.714
## PT 3.843 -0.609 1.000 0.29% 3.286 3.714 3.857
## 343.000 0.585 0.374 5.000 8.75% 4.143 4.429
## LDRDIVAD 0.000 -0.308 -2.296 4.37% -0.796 -0.296 -0.046
## 343.000 1.066 -0.374 1.704 7.87% 0.204 0.704
## PDB 0.000 -1.408 -3.216 2.04% -0.716 -0.216 0.284
## 343.000 0.878 1.815 0.784 39.94% 0.534 0.784
## POLID -0.673 0.390 -3.000 19.24% -2.000 -2.000 -1.000
## 343.000 3.479 -1.090 3.000 4.96% 0.000 1.000
## LDA_PDB 0.389 2.769 -1.801 1.46% -0.211 0.021 0.138
## 343.000 1.327 12.377 7.383 0.58% 0.356 0.944
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 51
##
## Loglikelihood
##
## H0 Value -3516.536
## H1 Value -3440.849
##
## Information Criteria
##
## Akaike (AIC) 7135.073
## Bayesian (BIC) 7330.797
## Sample-Size Adjusted BIC 7169.013
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 151.374
## Degrees of Freedom 12
## P-Value 0.0000
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.184
## 90 Percent C.I. 0.158 0.211
## Probability RMSEA <= .05 0.000
##
## CFI/TLI
##
## CFI 0.837
## TLI 0.472
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 896.613
## Degrees of Freedom 39
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.112
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## AWARE ON
## PDB 0.393 0.055 7.138 0.000
## LDRDIVAD 0.052 0.043 1.200 0.230
## LDA_PDB 0.179 0.040 4.467 0.000
##
## REGULATE ON
## PDB 0.290 0.047 6.130 0.000
## LDRDIVAD 0.102 0.037 2.749 0.006
## LDA_PDB 0.097 0.034 2.856 0.004
##
## RELALLY ON
## POLID 0.012 0.023 0.514 0.607
## PDB 0.294 0.055 5.363 0.000
## LDRDIVAD 0.248 0.038 6.461 0.000
## AWARE 0.027 0.052 0.524 0.601
## REGULATE 0.166 0.062 2.690 0.007
## ANXIETY 0.019 0.053 0.363 0.717
## EMPATHY 0.230 0.057 4.072 0.000
## PT 0.034 0.060 0.565 0.572
##
## ANXIETY ON
## PDB -0.127 0.047 -2.696 0.007
## LDRDIVAD -0.201 0.045 -4.463 0.000
##
## EMPATHY ON
## PDB 0.452 0.044 10.228 0.000
## LDRDIVAD 0.084 0.040 2.089 0.037
##
## PT ON
## PDB 0.282 0.042 6.701 0.000
## LDRDIVAD 0.168 0.038 4.408 0.000
##
## PDB WITH
## LDRDIVAD 0.389 0.056 6.909 0.000
## POLID -1.008 0.109 -9.251 0.000
##
## LDRDIVAD WITH
## POLID -0.216 0.105 -2.066 0.039
##
## AWARE WITH
## REGULATE 0.216 0.028 7.741 0.000
## ANXIETY 0.024 0.030 0.809 0.418
## EMPATHY 0.126 0.029 4.390 0.000
## PT 0.096 0.027 3.543 0.000
##
## REGULATE WITH
## ANXIETY -0.027 0.026 -1.046 0.295
## EMPATHY 0.108 0.025 4.373 0.000
## PT 0.123 0.024 5.137 0.000
##
## ANXIETY WITH
## EMPATHY -0.034 0.029 -1.158 0.247
## PT -0.044 0.027 -1.657 0.097
##
## PT WITH
## EMPATHY 0.236 0.028 8.329 0.000
##
## Means
## LDRDIVAD 0.000 0.056 0.000 1.000
## PDB 0.000 0.051 0.000 1.000
## POLID -0.673 0.101 -6.687 0.000
##
## Intercepts
## AWARE 3.926 0.043 91.818 0.000
## REGULATE 4.150 0.037 112.774 0.000
## RELALLY 1.528 0.338 4.516 0.000
## ANXIETY 3.114 0.041 76.292 0.000
## EMPATHY 3.923 0.038 103.533 0.000
## PT 3.843 0.036 106.386 0.000
##
## Variances
## LDRDIVAD 1.066 0.081 13.096 0.000
## PDB 0.878 0.067 13.096 0.000
## POLID 3.479 0.266 13.096 0.000
##
## Residual Variances
## AWARE 0.544 0.042 13.095 0.000
## REGULATE 0.404 0.031 13.096 0.000
## RELALLY 0.391 0.030 13.095 0.000
## ANXIETY 0.498 0.041 12.230 0.000
## EMPATHY 0.492 0.038 13.096 0.000
## PT 0.448 0.034 13.096 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.150E-04
## (ratio of smallest to largest eigenvalue)
##
##
## PLOT INFORMATION
##
## The following plots are available:
##
## Loop plots
##
## Beginning Time: 16:40:05
## Ending Time: 16:40:05
## Elapsed Time: 00:00:00
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2024 Muthen & MuthenHypothesis 5a: Moderated Mediation (Bias-Awareness Efficacy and Relational Ally Work)
res <- readModels("Mplus Syntax/Study 3 Syntax & Output/Hannah_Study 3/Study3_Moderated Mediation_RelAlly (H5a-b).out")
# Extract coefficients
a_int <- res$parameters$unstandardized$est[3] # Effect of XW on M (a-path)
b_est <- res$parameters$unstandardized$est[10] # Effect of M on Y (b-path)
# Extract standard errors
a_int_se <- res$parameters$unstandardized$se[3]
b_se <- res$parameters$unstandardized$se[10]
# Compute Monte Carlo confidence interval
mc_ci <- medci(mu.x = a_int, mu.y = b_est, se.x = a_int_se, se.y = b_se,
rho = 0, alpha = .10, sims = 10000, method = "parametric")##
## --- Index of Moderated Mediation ---## Estimate: 0.004833## Standard error: 0.009598524## Monte Carlo 90% CI: [ -0.01048743 , 0.0209896 ]# Conditional Indirect Effect for High and Low Pro-Diversity Attitudes
# Set moderator values (e.g., ±1 SD)
PDB_low <- -0.94 # 1 SD below mean
PDB_high <- 0.94 # 1 SD above mean
# Extract coefficient and standard error for leader diversity advocacy
a_main <- res$parameters$unstandardized$est[2] # Effect of X on M (a-path)
a_main_se <- res$parameters$unstandardized$se[2]
# Compute conditional a-paths
a_low <- a_main + a_int * PDB_low
a_high <- a_main + a_int * PDB_high
# Compute SEs of conditional a-paths
a_low_se <- sqrt(a_main_se^2 + (PDB_low^2 * a_int_se^2))
a_high_se <- sqrt(a_main_se^2 + (PDB_high^2 * a_int_se^2))
# Compute Monte Carlo CI for indirect effect at low PDB
mc_ci_low <- medci(mu.x = a_low, mu.y = b_est,
se.x = a_low_se, se.y = b_se,
rho = 0, alpha = 0.10, sims = 10000, method = "parametric")
# Compute Monte Carlo CI for indirect effect at high PDB
mc_ci_high <- medci(mu.x = a_high, mu.y = b_est,
se.x = a_high_se, se.y = b_se,
rho = 0, alpha = 0.05, sims = 10000, method = "parametric")##
## --- Conditional Indirect Effects ---## At LOW PDB (-1 SD):## Indirect effect estimate: -0.003## Standard error (LOW PDB): 0.007## Monte Carlo 90% CI: [ -0.015 , 0.007 ]## At HIGH PDB (+1 SD):## Indirect effect estimate: 0.006## Standard error (HIGH PDB): 0.012## Monte Carlo 95% CI: [ -0.017 , 0.031 ]Hypothesis 5b: Moderated Mediation (Self-Regulation Efficacy and Relational Ally Work)
# Extract coefficients
a_int <- res$parameters$unstandardized$est[6] # Effect of XW on M (a-path)
b_est <- res$parameters$unstandardized$est[11] # Effect of M on Y (b-path)
# Extract standard errors
a_int_se <- res$parameters$unstandardized$se[6]
b_se <- res$parameters$unstandardized$se[11]
# Compute Monte Carlo confidence interval (alpha = .10)
mc_ci <- medci(mu.x = a_int, mu.y = b_est, se.x = a_int_se, se.y = b_se,
rho = 0, alpha = .05, sims = 10000, method = "parametric")##
## --- Index of Moderated Mediation ---## Estimate: 0.016102## Standard error: 0.008512731## Monte Carlo 95% CI: [ 0.002512161 , 0.03541562 ]# Conditional Indirect Effect for High and Low Pro-Diversity Attitudes
# Set moderator values (e.g., ±1 SD)
PDB_low <- -0.94 # 1 SD below mean
PDB_high <- 0.94 # 1 SD above mean
# Extract coefficient and standard error for leader diversity advocacy
a_main <- res$parameters$unstandardized$est[5] # Effect of X on M (a-path)
a_main_se <- res$parameters$unstandardized$se[5]
# Compute conditional a-paths
a_low <- a_main + a_int * PDB_low
a_high <- a_main + a_int * PDB_high
# Compute SEs of conditional a-paths
a_low_se <- sqrt(a_main_se^2 + (PDB_low^2 * a_int_se^2))
a_high_se <- sqrt(a_main_se^2 + (PDB_high^2 * a_int_se^2))
# Compute Monte Carlo CI for indirect effect at low PDB
mc_ci_low <- medci(mu.x = a_low, mu.y = b_est,
se.x = a_low_se, se.y = b_se,
rho = 0, alpha = 0.10, sims = 10000, method = "parametric")
# Compute Monte Carlo CI for indirect effect at high PDB
mc_ci_high <- medci(mu.x = a_high, mu.y = b_est,
se.x = a_high_se, se.y = b_se,
rho = 0, alpha = 0.05, sims = 10000, method = "parametric")##
## --- Conditional Indirect Effects ---## At LOW PDB (-1 SD):## Indirect effect estimate: 0.0018## Standard error (LOW PDB): 0.009## Monte Carlo 90% CI: [ -0.012 , 0.016 ]## At HIGH PDB (+1 SD):## Indirect effect estimate: 0.032## Standard error (HIGH PDB): 0.015## Monte Carlo 95% CI: [ 0.007 , 0.065 ]View Johnson-Neyman Plots for Conditional Indirect Effects
Figure 6: Conditional Indirect Effect on Relationally Ally Work via Self-Regulation Self-Efficacy
Table 10 (Model 1): Path Model for Organizational Ally Work
modmed_out <- readLines(“Mplus Syntax/Study 3 Syntax & Output/Hannah_Study 3/Study3_Moderated Mediation_RelAlly (H5a-b).out”) cat(paste(modmed_out, collapse = “”))
## Mplus VERSION 8.11 (Mac)
## MUTHEN & MUTHEN
## 04/09/2026 2:43 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Study 3
## DATA: FILE = "Study3.dat";
## VARIABLE:
## NAMES = Male White Ideology LdrDivAd PDB Aware Regulate Manage OrgAlly RelAlly
## IncluLdr DVB SupInt Anxiety empathy
## PT Climate;
##
## MISSING = ALL (999);
##
## USEVARIABLES LdrDivAd PDB Aware Manage OrgAlly
## Anxiety Empathy PT PolID;
##
## DEFINE:
## ! Political Ideology centered at the scale midpoint (= 4)
## PolID = Ideology - 4;
##
## ! Grand-mean center Leader Diversity Advocacy and Pro-Diversity Attitudes
## CENTER LdrDivAd PDB (GRANDMEAN);
##
## ANALYSIS:
## ESTIMATOR = ML;
##
## MODEL:
## Aware ON PDB LdrDivAd;
## Manage ON PDB LdrDivAd;
## Anxiety ON PDB LdrDivAd;
## Empathy ON PDB LdrDivAd;
## PT ON PDB LdrDivAd;
##
## OrgAlly ON PolID PDB LdrDivAd Aware Manage Anxiety Empathy PT;
##
## ! Covariances among predictors
## PDB WITH LdrDivAd PolID;
## LdrDivAd WITH PolID;
## Aware WITH Manage Anxiety Empathy PT;
## Manage WITH Anxiety Empathy PT;
## Anxiety WITH Empathy PT;
## PT WITH Empathy;
##
##
##
## INPUT READING TERMINATED NORMALLY
##
##
##
## Study 1
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 343
##
## Number of dependent variables 6
## Number of independent variables 3
## Number of continuous latent variables 0
##
## Observed dependent variables
##
## Continuous
## AWARE MANAGE ORGALLY ANXIETY EMPATHY PT
##
## Observed independent variables
## LDRDIVAD PDB POLID
##
## Variables with special functions
##
## Centering (GRANDMEAN)
## LDRDIVAD PDB
##
##
## Estimator ML
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Study3.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 2
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.100
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## AWARE MANAGE ORGALLY ANXIETY EMPATHY
## ________ ________ ________ ________ ________
## AWARE 1.000
## MANAGE 1.000 1.000
## ORGALLY 1.000 1.000 1.000
## ANXIETY 0.872 0.872 0.872 0.872
## EMPATHY 1.000 1.000 1.000 0.872 1.000
## PT 1.000 1.000 1.000 0.872 1.000
## LDRDIVAD 1.000 1.000 1.000 0.872 1.000
## PDB 1.000 1.000 1.000 0.872 1.000
## POLID 1.000 1.000 1.000 0.872 1.000
##
##
## Covariance Coverage
## PT LDRDIVAD PDB POLID
## ________ ________ ________ ________
## PT 1.000
## LDRDIVAD 1.000 1.000
## PDB 1.000 1.000 1.000
## POLID 1.000 1.000 1.000 1.000
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## AWARE 3.995 -0.511 1.667 0.58% 3.333 4.000 4.000
## 343.000 0.660 -0.474 5.000 22.45% 4.333 5.000
## MANAGE 3.502 -0.531 1.000 3.50% 2.600 3.400 3.600
## 343.000 1.156 -0.501 5.000 10.20% 4.000 4.400
## ORGALLY 2.467 0.367 1.000 19.24% 1.111 2.000 2.333
## 343.000 1.431 -0.960 5.000 3.50% 2.889 3.556
## ANXIETY 3.118 -0.677 1.000 2.01% 2.857 3.000 3.000
## 299.000 0.575 0.820 5.000 0.67% 3.143 3.714
## EMPATHY 3.923 -0.705 1.000 0.29% 3.143 3.857 4.000
## 343.000 0.708 0.074 5.000 11.66% 4.286 4.714
## PT 3.843 -0.609 1.000 0.29% 3.286 3.714 3.857
## 343.000 0.585 0.374 5.000 8.75% 4.143 4.429
## LDRDIVAD 0.000 -0.308 -2.296 4.37% -0.796 -0.296 -0.046
## 343.000 1.066 -0.374 1.704 7.87% 0.204 0.704
## PDB 0.000 -1.408 -3.216 2.04% -0.716 -0.216 0.284
## 343.000 0.878 1.815 0.784 39.94% 0.534 0.784
## POLID -0.673 0.390 -3.000 19.24% -2.000 -2.000 -1.000
## 343.000 3.479 -1.090 3.000 4.96% 0.000 1.000
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 49
##
## Loglikelihood
##
## H0 Value -3772.288
## H1 Value -3760.136
##
## Information Criteria
##
## Akaike (AIC) 7642.576
## Bayesian (BIC) 7830.625
## Sample-Size Adjusted BIC 7675.185
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 24.305
## Degrees of Freedom 5
## P-Value 0.0002
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.106
## 90 Percent C.I. 0.066 0.150
## Probability RMSEA <= .05 0.012
##
## CFI/TLI
##
## CFI 0.977
## TLI 0.849
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 874.357
## Degrees of Freedom 33
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.026
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## AWARE ON
## PDB 0.261 0.048 5.476 0.000
## LDRDIVAD 0.095 0.043 2.195 0.028
##
## MANAGE ON
## PDB 0.405 0.056 7.260 0.000
## LDRDIVAD 0.337 0.051 6.660 0.000
##
## ANXIETY ON
## PDB -0.133 0.047 -2.818 0.005
## LDRDIVAD -0.194 0.045 -4.314 0.000
##
## EMPATHY ON
## PDB 0.452 0.044 10.228 0.000
## LDRDIVAD 0.084 0.040 2.089 0.037
##
## PT ON
## PDB 0.282 0.042 6.701 0.000
## LDRDIVAD 0.168 0.038 4.408 0.000
##
## ORGALLY ON
## POLID -0.037 0.033 -1.106 0.269
## PDB 0.074 0.079 0.940 0.347
## LDRDIVAD 0.292 0.056 5.196 0.000
## AWARE -0.172 0.071 -2.428 0.015
## MANAGE 0.454 0.062 7.335 0.000
## ANXIETY -0.049 0.079 -0.618 0.537
## EMPATHY 0.166 0.082 2.029 0.042
## PT 0.033 0.086 0.389 0.697
##
## PDB WITH
## LDRDIVAD 0.389 0.056 6.909 0.000
## POLID -1.008 0.109 -9.251 0.000
##
## LDRDIVAD WITH
## POLID -0.216 0.105 -2.066 0.039
##
## AWARE WITH
## MANAGE 0.249 0.039 6.444 0.000
## ANXIETY 0.020 0.031 0.645 0.519
## EMPATHY 0.123 0.029 4.183 0.000
## PT 0.098 0.028 3.529 0.000
##
## MANAGE WITH
## ANXIETY -0.106 0.036 -2.961 0.003
## EMPATHY 0.181 0.035 5.178 0.000
## PT 0.151 0.033 4.564 0.000
##
## ANXIETY WITH
## EMPATHY -0.032 0.029 -1.089 0.276
## PT -0.043 0.027 -1.616 0.106
##
## PT WITH
## EMPATHY 0.236 0.028 8.329 0.000
##
## Means
## LDRDIVAD 0.000 0.056 0.000 1.000
## PDB 0.000 0.051 0.000 1.000
## POLID -0.673 0.101 -6.687 0.000
##
## Intercepts
## AWARE 3.995 0.041 97.879 0.000
## MANAGE 3.502 0.048 73.218 0.000
## ORGALLY 0.914 0.459 1.991 0.046
## ANXIETY 3.111 0.041 76.374 0.000
## EMPATHY 3.923 0.038 103.533 0.000
## PT 3.843 0.036 106.386 0.000
##
## Variances
## LDRDIVAD 1.066 0.081 13.096 0.000
## PDB 0.878 0.067 13.096 0.000
## POLID 3.479 0.266 13.096 0.000
##
## Residual Variances
## AWARE 0.571 0.044 13.096 0.000
## MANAGE 0.785 0.060 13.096 0.000
## ORGALLY 0.800 0.061 13.091 0.000
## ANXIETY 0.497 0.041 12.250 0.000
## EMPATHY 0.492 0.038 13.096 0.000
## PT 0.448 0.034 13.096 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.153E-03
## (ratio of smallest to largest eigenvalue)
##
##
## Beginning Time: 14:43:44
## Ending Time: 14:43:44
## Elapsed Time: 00:00:00
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2024 Muthen & MuthenHypothesis 2b: Mediation Analysis (Bias-Awareness and Org Allyship)
res <- readModels("Mplus Syntax/Study 3 Syntax & Output/Hannah_Study 3/Study3_Mediation_OrgAlly (H2b_H4).out")
# Extract coefficients
a_est <- res$parameters$unstandardized$est[2] # Effect of X on M (a-path)
b_est <- res$parameters$unstandardized$est[14] # Effect of M on Y (b-path)
# Extract standard errors
a_se <- res$parameters$unstandardized$se[2]
b_se <- res$parameters$unstandardized$se[14]
# Compute Monte Carlo confidence interval
mc_ci <- medci(mu.x = a_est, mu.y = b_est, se.x = a_se, se.y = b_se,
rho = 0, alpha = .05, sims = 10000, method = "parametric")## Indirect effect estimate: -0.01634## Indirect effect standard error: 0.01046502## Monte Carlo 95% CI: [ -0.04053756 , -0.0003549086 ]Hypothesis 4: Mediation Analysis (Intergroup-Management and Org Allyship)
# Extract coefficients
a_est <- res$parameters$unstandardized$est[4] # Effect of X on M (a-path)
b_est <- res$parameters$unstandardized$est[15] # Effect of M on Y (b-path)
# Extract standard errors
a_se <- res$parameters$unstandardized$se[4]
b_se <- res$parameters$unstandardized$se[15]
# Compute Monte Carlo confidence interval
mc_ci <- medci(mu.x = a_est, mu.y = b_est, se.x = a_se, se.y = b_se,
rho = 0, alpha = .01, sims = 10000, method = "parametric")## Indirect effect estimate: 0.152998## Indirect effect standard error: 0.03134749## Monte Carlo 99% CI: [ 0.08118917 , 0.2423572 ]Table 10 (Model 2): Conditional Path Model for Organizational Ally Work
## Mplus VERSION 8.11 (Mac)
## MUTHEN & MUTHEN
## 04/09/2026 4:39 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Study 3: Moderated Mediation Models
## DATA: FILE = "Study3.dat";
## VARIABLE: NAMES = Male White Ideology LdrDivAd PDB Aware
## Regulate Manage OrgAlly RelAlly
## IncluLdr DVB SupInt Anxiety empathy
## PT Climate;
## MISSING = ALL (999);
##
## USEVARIABLES LdrDivAd PDB Aware Manage OrgAlly
## Anxiety Empathy PT
## PolID LDA_PDB;
##
## DEFINE:
## ! Political Ideology centered at the scale midpoint (= 4)
## PolID = Ideology - 4;
##
## ! Grand-mean center Leader Diversity Advocacy and Pro-Diversity Attitudes
## CENTER LdrDivAd PDB (GRANDMEAN);
##
## ! Create interaction term
## LDA_PDB = LdrDivAd * PDB;
##
## ANALYSIS:
## ESTIMATOR = ML;
##
## MODEL:
## Aware ON PDB (a2)
## LdrDivAd (a1)
## LDA_PDB (a3);
##
## Manage ON PDB (b2)
## LdrDivAd (b1)
## LDA_PDB (b3);
##
## OrgAlly ON PolID
## PDB
## LdrDivAd
## Aware (aw)
## Manage (rg);
##
## Anxiety ON PDB LdrDivAd;
## Empathy ON PDB LdrDivAd;
## PT ON PDB LdrDivAd;
##
## OrgAlly ON Anxiety Empathy PT;
##
## ! Covariances among predictors
## PDB WITH LdrDivAd PolID;
## LdrDivAd WITH PolID;
## Aware WITH Manage Anxiety Empathy PT;
## Manage WITH Anxiety Empathy PT;
## Anxiety WITH Empathy PT;
## PT WITH Empathy;
##
## MODEL CONSTRAINT:
##
## LOOP(PDB, -1.88, 1.88, .94); ! Two Standard Deviations Below/Above Mean
## PLOT(
## ! LdrDivAd_aw
## LdrDivAd_rg
## );
## ! LdrDivAd_aw = (a1 + a3*PDB)*aw;
## LdrDivAd_rg = (b1 + b3*PDB)*rg;
##
## PLOT: TYPE = PLOT2
##
##
##
##
##
##
## INPUT READING TERMINATED NORMALLY
##
##
##
## Study 1: Moderated Mediation Models
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 343
##
## Number of dependent variables 6
## Number of independent variables 4
## Number of continuous latent variables 0
##
## Observed dependent variables
##
## Continuous
## AWARE MANAGE ORGALLY ANXIETY EMPATHY PT
##
## Observed independent variables
## LDRDIVAD PDB POLID LDA_PDB
##
## Variables with special functions
##
## Centering (GRANDMEAN)
## LDRDIVAD PDB
##
##
## Estimator ML
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Study3.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 2
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.100
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## AWARE MANAGE ORGALLY ANXIETY EMPATHY
## ________ ________ ________ ________ ________
## AWARE 1.000
## MANAGE 1.000 1.000
## ORGALLY 1.000 1.000 1.000
## ANXIETY 0.872 0.872 0.872 0.872
## EMPATHY 1.000 1.000 1.000 0.872 1.000
## PT 1.000 1.000 1.000 0.872 1.000
## LDRDIVAD 1.000 1.000 1.000 0.872 1.000
## PDB 1.000 1.000 1.000 0.872 1.000
## POLID 1.000 1.000 1.000 0.872 1.000
## LDA_PDB 1.000 1.000 1.000 0.872 1.000
##
##
## Covariance Coverage
## PT LDRDIVAD PDB POLID LDA_PDB
## ________ ________ ________ ________ ________
## PT 1.000
## LDRDIVAD 1.000 1.000
## PDB 1.000 1.000 1.000
## POLID 1.000 1.000 1.000 1.000
## LDA_PDB 1.000 1.000 1.000 1.000 1.000
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## AWARE 3.995 -0.511 1.667 0.58% 3.333 4.000 4.000
## 343.000 0.660 -0.474 5.000 22.45% 4.333 5.000
## MANAGE 3.502 -0.531 1.000 3.50% 2.600 3.400 3.600
## 343.000 1.156 -0.501 5.000 10.20% 4.000 4.400
## ORGALLY 2.467 0.367 1.000 19.24% 1.111 2.000 2.333
## 343.000 1.431 -0.960 5.000 3.50% 2.889 3.556
## ANXIETY 3.118 -0.677 1.000 2.01% 2.857 3.000 3.000
## 299.000 0.575 0.820 5.000 0.67% 3.143 3.714
## EMPATHY 3.923 -0.705 1.000 0.29% 3.143 3.857 4.000
## 343.000 0.708 0.074 5.000 11.66% 4.286 4.714
## PT 3.843 -0.609 1.000 0.29% 3.286 3.714 3.857
## 343.000 0.585 0.374 5.000 8.75% 4.143 4.429
## LDRDIVAD 0.000 -0.308 -2.296 4.37% -0.796 -0.296 -0.046
## 343.000 1.066 -0.374 1.704 7.87% 0.204 0.704
## PDB 0.000 -1.408 -3.216 2.04% -0.716 -0.216 0.284
## 343.000 0.878 1.815 0.784 39.94% 0.534 0.784
## POLID -0.673 0.390 -3.000 19.24% -2.000 -2.000 -1.000
## 343.000 3.479 -1.090 3.000 4.96% 0.000 1.000
## LDA_PDB 0.389 2.769 -1.801 1.46% -0.211 0.021 0.138
## 343.000 1.327 12.377 7.383 0.58% 0.356 0.944
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 51
##
## Loglikelihood
##
## H0 Value -3762.433
## H1 Value -3684.495
##
## Information Criteria
##
## Akaike (AIC) 7626.866
## Bayesian (BIC) 7822.591
## Sample-Size Adjusted BIC 7660.807
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 155.877
## Degrees of Freedom 12
## P-Value 0.0000
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.187
## 90 Percent C.I. 0.161 0.214
## Probability RMSEA <= .05 0.000
##
## CFI/TLI
##
## CFI 0.833
## TLI 0.456
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 898.901
## Degrees of Freedom 39
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.106
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## AWARE ON
## PDB 0.394 0.055 7.142 0.000
## LDRDIVAD 0.052 0.043 1.198 0.231
## LDA_PDB 0.179 0.040 4.476 0.000
##
## MANAGE ON
## PDB 0.470 0.065 7.193 0.000
## LDRDIVAD 0.316 0.052 6.121 0.000
## LDA_PDB 0.088 0.047 1.895 0.058
##
## ORGALLY ON
## POLID -0.037 0.033 -1.106 0.269
## PDB 0.074 0.079 0.940 0.347
## LDRDIVAD 0.292 0.056 5.193 0.000
## AWARE -0.172 0.071 -2.425 0.015
## MANAGE 0.454 0.062 7.334 0.000
## ANXIETY -0.049 0.079 -0.626 0.532
## EMPATHY 0.166 0.082 2.029 0.042
## PT 0.033 0.086 0.388 0.698
##
## ANXIETY ON
## PDB -0.132 0.047 -2.807 0.005
## LDRDIVAD -0.194 0.045 -4.329 0.000
##
## EMPATHY ON
## PDB 0.452 0.044 10.228 0.000
## LDRDIVAD 0.084 0.040 2.089 0.037
##
## PT ON
## PDB 0.282 0.042 6.701 0.000
## LDRDIVAD 0.168 0.038 4.408 0.000
##
## PDB WITH
## LDRDIVAD 0.389 0.056 6.909 0.000
## POLID -1.008 0.109 -9.251 0.000
##
## LDRDIVAD WITH
## POLID -0.216 0.105 -2.066 0.039
##
## AWARE WITH
## MANAGE 0.234 0.037 6.271 0.000
## ANXIETY 0.027 0.030 0.881 0.378
## EMPATHY 0.126 0.029 4.390 0.000
## PT 0.096 0.027 3.543 0.000
##
## MANAGE WITH
## ANXIETY -0.103 0.035 -2.891 0.004
## EMPATHY 0.183 0.035 5.243 0.000
## PT 0.150 0.033 4.556 0.000
##
## ANXIETY WITH
## EMPATHY -0.032 0.029 -1.095 0.273
## PT -0.044 0.027 -1.624 0.104
##
## PT WITH
## EMPATHY 0.236 0.028 8.329 0.000
##
## Means
## LDRDIVAD 0.000 0.056 0.000 1.000
## PDB 0.000 0.051 0.000 1.000
## POLID -0.673 0.101 -6.687 0.000
##
## Intercepts
## AWARE 3.925 0.043 91.815 0.000
## MANAGE 3.468 0.051 68.073 0.000
## ORGALLY 0.916 0.459 1.996 0.046
## ANXIETY 3.111 0.041 76.374 0.000
## EMPATHY 3.923 0.038 103.533 0.000
## PT 3.843 0.036 106.386 0.000
##
## Variances
## LDRDIVAD 1.066 0.081 13.096 0.000
## PDB 0.878 0.067 13.096 0.000
## POLID 3.479 0.266 13.096 0.000
##
## Residual Variances
## AWARE 0.544 0.042 13.095 0.000
## MANAGE 0.777 0.059 13.096 0.000
## ORGALLY 0.800 0.061 13.092 0.000
## ANXIETY 0.497 0.041 12.247 0.000
## EMPATHY 0.492 0.038 13.096 0.000
## PT 0.448 0.034 13.096 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.328E-04
## (ratio of smallest to largest eigenvalue)
##
##
## PLOT INFORMATION
##
## The following plots are available:
##
## Loop plots
##
## Beginning Time: 16:39:37
## Ending Time: 16:39:37
## Elapsed Time: 00:00:00
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2024 Muthen & MuthenHypothesis 6a: Moderated Mediation (Bias-Awareness Efficacy and Organizational Ally Work)
res <- readModels("Mplus Syntax/Study 3 Syntax & Output/Hannah_Study 3/Study3_Moderated Mediation (OrgAlly) (H6a-b).out")
# Extract coefficients
a_int <- res$parameters$unstandardized$est[3] # Effect of XW on M (a-path)
b_est <- res$parameters$unstandardized$est[10] # Effect of M on Y (b-path)
# Extract standard errors
a_int_se <- res$parameters$unstandardized$se[3]
b_se <- res$parameters$unstandardized$se[10]
# Compute Monte Carlo confidence interval
mc_ci <- medci(mu.x = a_int, mu.y = b_est, se.x = a_int_se, se.y = b_se,
rho = 0, alpha = .05, sims = 10000, method = "parametric")##
## --- Index of Moderated Mediation ---## Estimate: -0.030788## Standard error: 0.01472816## Monte Carlo 95% CI: [ -0.06287483 , -0.005328748 ]Hypothesis 6b: Moderated Mediation (Intergroup-Management Efficacy and Organizational Ally Work)
# Extract coefficients
a_int <- res$parameters$unstandardized$est[6] # Effect of XW on M (a-path)
b_est <- res$parameters$unstandardized$est[11] # Effect of M on Y (b-path)
# Extract standard errors
a_int_se <- res$parameters$unstandardized$se[6]
b_se <- res$parameters$unstandardized$se[11]
# Compute Monte Carlo confidence interval
mc_ci <- medci(mu.x = a_int, mu.y = b_est, se.x = a_int_se, se.y = b_se,
rho = 0, alpha = .10, sims = 10000, method = "parametric")##
## --- Index of Moderated Mediation ---## Estimate: 0.039952## Standard error: 0.02221643## Monte Carlo 90% CI: [ 0.00474096 , 0.07761517 ]# Conditional Indirect Effect for High and Low Pro-Diversity Attitudes
# Set moderator values (e.g., ±1 SD)
PDB_low <- -0.94 # 1 SD below mean
PDB_high <- 0.94 # 1 SD above mean
# Extract coefficient and standard error for leader diversity advocacy
a_main <- res$parameters$unstandardized$est[5] # Effect of X on M (a-path)
a_main_se <- res$parameters$unstandardized$se[5]
# Compute conditional a-paths
a_low <- a_main + a_int * PDB_low
a_high <- a_main + a_int * PDB_high
# Compute SEs of conditional a-paths
a_low_se <- sqrt(a_main_se^2 + (PDB_low^2 * a_int_se^2))
a_high_se <- sqrt(a_main_se^2 + (PDB_high^2 * a_int_se^2))
# Compute Monte Carlo CI for indirect effect at low PDB
mc_ci_low <- medci(mu.x = a_low, mu.y = b_est,
se.x = a_low_se, se.y = b_se,
rho = 0, alpha = 0.05, sims = 10000, method = "parametric")
# Compute Monte Carlo CI for indirect effect at high PDB
mc_ci_high <- medci(mu.x = a_high, mu.y = b_est,
se.x = a_high_se, se.y = b_se,
rho = 0, alpha = 0.01, sims = 10000, method = "parametric")##
## --- Conditional Indirect Effects ---## At LOW PDB (-1 SD):## Indirect effect estimate: 0.10591## Standard error (LOW PDB): 0.03445## Monte Carlo 95% CI: [ 0.043 , 0.178 ]## At HIGH PDB (+1 SD):## Indirect effect estimate: 0.181## Standard error (HIGH PDB): 0.03986## Monte Carlo 99% CI: [ 0.09 , 0.295 ]View Johnson-Neyman Plots for Conditional Indirect Effects
Figure 7: Conditional Indirect Effect on Organizational Ally Work via Intergroup-Management Self-Efficacy
Exploratory Serial Mediation Analysis: Relational Allyship
## Mplus VERSION 8.11 (Mac)
## MUTHEN & MUTHEN
## 04/08/2026 8:59 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Study 3
## DATA: FILE = "Study3.dat";
## VARIABLE:
## NAMES = Male White Ideology LdrDivAd PDB Aware Regulate Manage OrgAlly RelAlly
## IncluLdr DVB SupInt Anxiety empathy
## PT Climate;
## MISSING = ALL (999);
##
## USEVARIABLES LdrDivAd PDB Aware Regulate RelAlly
## PolID;
##
## DEFINE:
## ! Political Ideology centered at the scale midpoint (= 4)
## PolID = Ideology - 4;
##
## ! Grand-mean center Leader Diversity Advocacy and Pro-Diversity Attitudes
## CENTER LdrDivAd PDB (GRANDMEAN);
##
## ANALYSIS:
## ESTIMATOR = ML;
##
## MODEL:
## PDB ON LdrDivAd;
## Aware ON LdrDivAd;
## Regulate ON LdrDivAd;
##
## RelAlly ON LdrDivAd PolID;
##
## RelAlly ON PDB ;
## RelAlly ON Aware;
## RelAlly ON Regulate ;
##
## Aware Regulate ON PDB ;
##
## ! Covariances among predictors
## Aware WITH Regulate;
##
## MODEL INDIRECT:
## RelAlly IND LdrDivAd;
##
## OUTPUT: CINTERVAL;
##
##
##
## INPUT READING TERMINATED NORMALLY
##
##
##
## Study 3
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 343
##
## Number of dependent variables 4
## Number of independent variables 2
## Number of continuous latent variables 0
##
## Observed dependent variables
##
## Continuous
## PDB AWARE REGULATE RELALLY
##
## Observed independent variables
## LDRDIVAD POLID
##
## Variables with special functions
##
## Centering (GRANDMEAN)
## LDRDIVAD PDB
##
##
## Estimator ML
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Study3.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 1
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.100
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## PDB AWARE REGULATE RELALLY LDRDIVAD
## ________ ________ ________ ________ ________
## PDB 1.000
## AWARE 1.000 1.000
## REGULATE 1.000 1.000 1.000
## RELALLY 1.000 1.000 1.000 1.000
## LDRDIVAD 1.000 1.000 1.000 1.000 1.000
## POLID 1.000 1.000 1.000 1.000 1.000
##
##
## Covariance Coverage
## POLID
## ________
## POLID 1.000
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## PDB 0.000 -1.408 -3.216 2.04% -0.716 -0.216 0.284
## 343.000 0.878 1.815 0.784 39.94% 0.534 0.784
## AWARE 3.995 -0.511 1.667 0.58% 3.333 4.000 4.000
## 343.000 0.660 -0.474 5.000 22.45% 4.333 5.000
## REGULATE 4.188 -0.817 1.200 0.29% 3.600 4.000 4.200
## 343.000 0.493 0.633 5.000 22.45% 4.400 5.000
## RELALLY 3.418 -0.423 1.000 1.75% 2.857 3.143 3.429
## 343.000 0.805 -0.013 5.000 6.12% 3.714 4.143
## LDRDIVAD 0.000 -0.308 -2.296 4.37% -0.796 -0.296 -0.046
## 343.000 1.066 -0.374 1.704 7.87% 0.204 0.704
## POLID -0.673 0.390 -3.000 19.24% -2.000 -2.000 -1.000
## 343.000 3.479 -1.090 3.000 4.96% 0.000 1.000
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 19
##
## Loglikelihood
##
## H0 Value -1453.309
## H1 Value -1377.745
##
## Information Criteria
##
## Akaike (AIC) 2944.618
## Bayesian (BIC) 3017.535
## Sample-Size Adjusted BIC 2957.262
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 151.128
## Degrees of Freedom 3
## P-Value 0.0000
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.379
## 90 Percent C.I. 0.329 0.432
## Probability RMSEA <= .05 0.000
##
## CFI/TLI
##
## CFI 0.761
## TLI 0.000
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 633.784
## Degrees of Freedom 14
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.142
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## PDB ON
## LDRDIVAD 0.365 0.045 8.133 0.000
##
## AWARE ON
## LDRDIVAD 0.095 0.043 2.195 0.028
## PDB 0.261 0.048 5.476 0.000
##
## REGULATE ON
## LDRDIVAD 0.126 0.037 3.423 0.001
## PDB 0.218 0.040 5.387 0.000
##
## RELALLY ON
## LDRDIVAD 0.259 0.038 6.783 0.000
## POLID 0.016 0.023 0.689 0.491
## PDB 0.391 0.052 7.567 0.000
## AWARE 0.065 0.053 1.231 0.218
## REGULATE 0.213 0.062 3.432 0.001
##
## AWARE WITH
## REGULATE 0.231 0.029 7.964 0.000
##
## Intercepts
## PDB 0.000 0.046 0.000 1.000
## AWARE 3.995 0.041 97.879 0.000
## REGULATE 4.188 0.035 120.659 0.000
## RELALLY 2.276 0.246 9.250 0.000
##
## Residual Variances
## PDB 0.736 0.056 13.096 0.000
## AWARE 0.571 0.044 13.096 0.000
## REGULATE 0.413 0.032 13.096 0.000
## RELALLY 0.419 0.032 13.096 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.688E-03
## (ratio of smallest to largest eigenvalue)
##
##
## TOTAL, TOTAL INDIRECT, SPECIFIC INDIRECT, AND DIRECT EFFECTS
##
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## Effects from LDRDIVAD to RELALLY
##
## Total 0.457 0.041 11.227 0.000
## Total indirect 0.199 0.030 6.689 0.000
##
## Specific indirect 1
## RELALLY
## PDB
## LDRDIVAD 0.143 0.026 5.540 0.000
##
## Specific indirect 2
## RELALLY
## AWARE
## LDRDIVAD 0.006 0.006 1.073 0.283
##
## Specific indirect 3
## RELALLY
## REGULATE
## LDRDIVAD 0.027 0.011 2.424 0.015
##
## Specific indirect 4
## RELALLY
## AWARE
## PDB
## LDRDIVAD 0.006 0.005 1.188 0.235
##
## Specific indirect 5
## RELALLY
## REGULATE
## PDB
## LDRDIVAD 0.017 0.006 2.727 0.006
##
## Direct
## RELALLY
## LDRDIVAD 0.259 0.038 6.783 0.000
##
##
##
## CONFIDENCE INTERVALS OF MODEL RESULTS
##
## Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5%
##
## PDB ON
## LDRDIVAD 0.249 0.277 0.291 0.365 0.439 0.453 0.481
##
## AWARE ON
## LDRDIVAD -0.016 0.010 0.024 0.095 0.166 0.179 0.206
## PDB 0.138 0.167 0.182 0.261 0.339 0.354 0.383
##
## REGULATE ON
## LDRDIVAD 0.031 0.054 0.065 0.126 0.186 0.198 0.220
## PDB 0.114 0.139 0.151 0.218 0.285 0.297 0.322
##
## RELALLY ON
## LDRDIVAD 0.160 0.184 0.196 0.259 0.321 0.333 0.357
## POLID -0.044 -0.030 -0.022 0.016 0.055 0.062 0.077
## PDB 0.258 0.289 0.306 0.391 0.475 0.492 0.523
## AWARE -0.072 -0.039 -0.022 0.065 0.153 0.170 0.202
## REGULATE 0.053 0.091 0.111 0.213 0.315 0.334 0.372
##
## AWARE WITH
## REGULATE 0.157 0.175 0.184 0.231 0.279 0.288 0.306
##
## Intercepts
## PDB -0.119 -0.091 -0.076 0.000 0.076 0.091 0.119
## AWARE 3.890 3.915 3.928 3.995 4.062 4.075 4.100
## REGULATE 4.099 4.120 4.131 4.188 4.245 4.256 4.278
## RELALLY 1.643 1.794 1.872 2.276 2.681 2.759 2.910
##
## Residual Variances
## PDB 0.591 0.626 0.644 0.736 0.829 0.846 0.881
## AWARE 0.459 0.486 0.500 0.571 0.643 0.657 0.684
## REGULATE 0.332 0.351 0.361 0.413 0.465 0.475 0.495
## RELALLY 0.336 0.356 0.366 0.419 0.471 0.481 0.501
##
##
## CONFIDENCE INTERVALS OF TOTAL, TOTAL INDIRECT, SPECIFIC INDIRECT, AND DIRECT EFFECTS
##
##
## Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5%
##
## Effects from LDRDIVAD to RELALLY
##
## Total 0.352 0.377 0.390 0.457 0.524 0.537 0.562
## Total indirect 0.122 0.140 0.150 0.199 0.247 0.257 0.275
##
## Specific indirect 1
## RELALLY
## PDB
## LDRDIVAD 0.076 0.092 0.100 0.143 0.185 0.193 0.209
##
## Specific indirect 2
## RELALLY
## AWARE
## LDRDIVAD -0.009 -0.005 -0.003 0.006 0.016 0.018 0.021
##
## Specific indirect 3
## RELALLY
## REGULATE
## LDRDIVAD -0.002 0.005 0.009 0.027 0.045 0.048 0.055
##
## Specific indirect 4
## RELALLY
## AWARE
## PDB
## LDRDIVAD -0.007 -0.004 -0.002 0.006 0.015 0.016 0.020
##
## Specific indirect 5
## RELALLY
## REGULATE
## PDB
## LDRDIVAD 0.001 0.005 0.007 0.017 0.027 0.029 0.033
##
## Direct
## RELALLY
## LDRDIVAD 0.160 0.184 0.196 0.259 0.321 0.333 0.357
##
##
##
## Beginning Time: 20:59:40
## Ending Time: 20:59:40
## Elapsed Time: 00:00:00
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2024 Muthen & Muthen# Serial mediators: pro-diversity beliefs and bias-awareness self-efficacy
res$indirect$ci.unstandardized$specific[4, ]## pred intervening outcome low.5 low2.5 low5 est up5 up2.5 up.5
## 4 LDRDIVAD AWARE.PDB RELALLY -0.007 -0.004 -0.002 0.006 0.015 0.016 0.020# Serial mediators: pro-diversity beliefs and self-regulation self-efficacy
res$indirect$ci.unstandardized$specific[5, ]## pred intervening outcome low.5 low2.5 low5 est up5 up2.5 up.5
## 5 LDRDIVAD REGULATE.PDB RELALLY 0.001 0.005 0.007 0.017 0.027 0.029 0.033Exploratory Serial Mediation Analysis: Organizational Allyship
## Mplus VERSION 8.11 (Mac)
## MUTHEN & MUTHEN
## 04/08/2026 8:59 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Study 3
## DATA: FILE = "Study3.dat";
## VARIABLE:
## NAMES = Male White Ideology LdrDivAd PDB Aware Regulate Manage OrgAlly RelAlly
## IncluLdr DVB SupInt Anxiety empathy
## PT Climate;
## MISSING = ALL (999);
##
## USEVARIABLES LdrDivAd PDB Aware Manage OrgAlly
## PolID;
##
## DEFINE:
## ! Political Ideology centered at the scale midpoint (= 4)
## PolID = Ideology - 4;
##
## ! Grand-mean center Leader Diversity Advocacy and Pro-Diversity Attitudes
## CENTER LdrDivAd PDB (GRANDMEAN);
##
## ANALYSIS:
## ESTIMATOR = ML;
##
## MODEL:
## PDB ON LdrDivAd;
## Aware ON LdrDivAd;
## Manage ON LdrDivAd;
##
## OrgAlly ON LdrDivAd PolID;
##
## OrgAlly ON PDB ;
## OrgAlly ON Aware;
## OrgAlly ON Manage ;
##
## Aware Manage ON PDB ;
##
## ! Covariances among predictors
## Aware WITH Manage;
##
## MODEL INDIRECT:
## OrgAlly IND LdrDivAd;
##
## OUTPUT: CINTERVAL;
##
##
##
##
## INPUT READING TERMINATED NORMALLY
##
##
##
## Study 3
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 343
##
## Number of dependent variables 4
## Number of independent variables 2
## Number of continuous latent variables 0
##
## Observed dependent variables
##
## Continuous
## PDB AWARE MANAGE ORGALLY
##
## Observed independent variables
## LDRDIVAD POLID
##
## Variables with special functions
##
## Centering (GRANDMEAN)
## LDRDIVAD PDB
##
##
## Estimator ML
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Study3.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 1
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.100
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## PDB AWARE MANAGE ORGALLY LDRDIVAD
## ________ ________ ________ ________ ________
## PDB 1.000
## AWARE 1.000 1.000
## MANAGE 1.000 1.000 1.000
## ORGALLY 1.000 1.000 1.000 1.000
## LDRDIVAD 1.000 1.000 1.000 1.000 1.000
## POLID 1.000 1.000 1.000 1.000 1.000
##
##
## Covariance Coverage
## POLID
## ________
## POLID 1.000
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## PDB 0.000 -1.408 -3.216 2.04% -0.716 -0.216 0.284
## 343.000 0.878 1.815 0.784 39.94% 0.534 0.784
## AWARE 3.995 -0.511 1.667 0.58% 3.333 4.000 4.000
## 343.000 0.660 -0.474 5.000 22.45% 4.333 5.000
## MANAGE 3.502 -0.531 1.000 3.50% 2.600 3.400 3.600
## 343.000 1.156 -0.501 5.000 10.20% 4.000 4.400
## ORGALLY 2.467 0.367 1.000 19.24% 1.111 2.000 2.333
## 343.000 1.431 -0.960 5.000 3.50% 2.889 3.556
## LDRDIVAD 0.000 -0.308 -2.296 4.37% -0.796 -0.296 -0.046
## 343.000 1.066 -0.374 1.704 7.87% 0.204 0.704
## POLID -0.673 0.390 -3.000 19.24% -2.000 -2.000 -1.000
## 343.000 3.479 -1.090 3.000 4.96% 0.000 1.000
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 19
##
## Loglikelihood
##
## H0 Value -1696.414
## H1 Value -1621.143
##
## Information Criteria
##
## Akaike (AIC) 3430.828
## Bayesian (BIC) 3503.745
## Sample-Size Adjusted BIC 3443.472
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 150.542
## Degrees of Freedom 3
## P-Value 0.0000
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.379
## 90 Percent C.I. 0.328 0.431
## Probability RMSEA <= .05 0.000
##
## CFI/TLI
##
## CFI 0.763
## TLI 0.000
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 636.566
## Degrees of Freedom 14
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.135
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## PDB ON
## LDRDIVAD 0.365 0.045 8.133 0.000
##
## AWARE ON
## LDRDIVAD 0.095 0.043 2.195 0.028
## PDB 0.261 0.048 5.477 0.000
##
## MANAGE ON
## LDRDIVAD 0.337 0.051 6.660 0.000
## PDB 0.405 0.056 7.260 0.000
##
## ORGALLY ON
## LDRDIVAD 0.302 0.055 5.450 0.000
## POLID -0.029 0.033 -0.886 0.376
## PDB 0.151 0.074 2.053 0.040
## AWARE -0.149 0.070 -2.126 0.033
## MANAGE 0.498 0.059 8.379 0.000
##
## AWARE WITH
## MANAGE 0.248 0.039 6.444 0.000
##
## Intercepts
## PDB 0.000 0.046 0.000 1.000
## AWARE 3.995 0.041 97.879 0.000
## MANAGE 3.502 0.048 73.219 0.000
## ORGALLY 1.301 0.283 4.606 0.000
##
## Residual Variances
## PDB 0.736 0.056 13.096 0.000
## AWARE 0.571 0.044 13.096 0.000
## MANAGE 0.785 0.060 13.096 0.000
## ORGALLY 0.816 0.062 13.096 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.915E-03
## (ratio of smallest to largest eigenvalue)
##
##
## TOTAL, TOTAL INDIRECT, SPECIFIC INDIRECT, AND DIRECT EFFECTS
##
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## Effects from LDRDIVAD to ORGALLY
##
## Total 0.570 0.054 10.518 0.000
## Total indirect 0.268 0.041 6.573 0.000
##
## Specific indirect 1
## ORGALLY
## PDB
## LDRDIVAD 0.055 0.028 1.990 0.047
##
## Specific indirect 2
## ORGALLY
## AWARE
## LDRDIVAD -0.014 0.009 -1.527 0.127
##
## Specific indirect 3
## ORGALLY
## MANAGE
## LDRDIVAD 0.168 0.032 5.213 0.000
##
## Specific indirect 4
## ORGALLY
## AWARE
## PDB
## LDRDIVAD -0.014 0.007 -1.926 0.054
##
## Specific indirect 5
## ORGALLY
## MANAGE
## PDB
## LDRDIVAD 0.074 0.016 4.549 0.000
##
## Direct
## ORGALLY
## LDRDIVAD 0.302 0.055 5.450 0.000
##
##
##
## CONFIDENCE INTERVALS OF MODEL RESULTS
##
## Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5%
##
## PDB ON
## LDRDIVAD 0.249 0.277 0.291 0.365 0.439 0.453 0.481
##
## AWARE ON
## LDRDIVAD -0.016 0.010 0.024 0.095 0.166 0.179 0.206
## PDB 0.138 0.167 0.182 0.261 0.339 0.354 0.383
##
## MANAGE ON
## LDRDIVAD 0.207 0.238 0.254 0.337 0.420 0.436 0.467
## PDB 0.261 0.295 0.313 0.405 0.496 0.514 0.548
##
## ORGALLY ON
## LDRDIVAD 0.159 0.194 0.211 0.302 0.394 0.411 0.445
## POLID -0.113 -0.093 -0.083 -0.029 0.025 0.035 0.055
## PDB -0.038 0.007 0.030 0.151 0.272 0.295 0.341
## AWARE -0.330 -0.287 -0.265 -0.149 -0.034 -0.012 0.032
## MANAGE 0.345 0.381 0.400 0.498 0.595 0.614 0.651
##
## AWARE WITH
## MANAGE 0.149 0.173 0.185 0.248 0.312 0.324 0.348
##
## Intercepts
## PDB -0.119 -0.091 -0.076 0.000 0.076 0.091 0.119
## AWARE 3.890 3.915 3.928 3.995 4.062 4.075 4.100
## MANAGE 3.379 3.408 3.423 3.502 3.581 3.596 3.625
## ORGALLY 0.574 0.748 0.837 1.301 1.766 1.855 2.029
##
## Residual Variances
## PDB 0.591 0.626 0.644 0.736 0.829 0.846 0.881
## AWARE 0.459 0.486 0.500 0.571 0.643 0.657 0.684
## MANAGE 0.630 0.667 0.686 0.785 0.883 0.902 0.939
## ORGALLY 0.656 0.694 0.714 0.816 0.919 0.938 0.977
##
##
## CONFIDENCE INTERVALS OF TOTAL, TOTAL INDIRECT, SPECIFIC INDIRECT, AND DIRECT EFFECTS
##
##
## Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5%
##
## Effects from LDRDIVAD to ORGALLY
##
## Total 0.431 0.464 0.481 0.570 0.659 0.677 0.710
## Total indirect 0.163 0.188 0.201 0.268 0.335 0.348 0.373
##
## Specific indirect 1
## ORGALLY
## PDB
## LDRDIVAD -0.016 0.001 0.010 0.055 0.101 0.109 0.126
##
## Specific indirect 2
## ORGALLY
## AWARE
## LDRDIVAD -0.038 -0.032 -0.029 -0.014 0.001 0.004 0.010
##
## Specific indirect 3
## ORGALLY
## MANAGE
## LDRDIVAD 0.085 0.105 0.115 0.168 0.221 0.231 0.251
##
## Specific indirect 4
## ORGALLY
## AWARE
## PDB
## LDRDIVAD -0.033 -0.029 -0.026 -0.014 -0.002 0.000 0.005
##
## Specific indirect 5
## ORGALLY
## MANAGE
## PDB
## LDRDIVAD 0.032 0.042 0.047 0.074 0.100 0.105 0.115
##
## Direct
## ORGALLY
## LDRDIVAD 0.159 0.194 0.211 0.302 0.394 0.411 0.445
##
##
##
## Beginning Time: 20:59:18
## Ending Time: 20:59:18
## Elapsed Time: 00:00:00
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2024 Muthen & Muthen# Serial mediators: pro-diversity beliefs and bias-awareness self-efficacy
res$indirect$ci.unstandardized$specific[4, ]## pred intervening outcome low.5 low2.5 low5 est up5 up2.5 up.5
## 4 LDRDIVAD AWARE.PDB ORGALLY -0.033 -0.029 -0.026 -0.014 -0.002 0.000 0.005# Serial mediators: pro-diversity beliefs and self-regulation self-efficacy
res$indirect$ci.unstandardized$specific[5, ]## pred intervening outcome low.5 low2.5 low5 est up5 up2.5 up.5
## 5 LDRDIVAD MANAGE.PDB ORGALLY 0.032 0.042 0.047 0.074 0.100 0.105 0.115Supplementary CFA Analysis: Comparing Leader Diversity Advocacy and Inclusive Leadership
Three-Factor Model
ThreeFactor_model <- '
# Factor 1: Leader Diversity Advocacy
LdrDivAd =~ LdrDivAd_1 + LdrDivAd_2 + LdrDivAd_3 + LdrDivAd_4
# Factor 2: Inclusive Leadership
InclusiveLdr =~ InclusiveLeadership_1 + InclusiveLeadership_2 + InclusiveLeadership_3
# Factor 3: Diversity Valuing Behavior
DVB =~ DVB_1 + DVB_2 + DVB_3
'
ThreeFactor_fit <- cfa(ThreeFactor_model, Study_3_CFA, estimator = "ML")
# plot CFA results
semPaths(ThreeFactor_fit, "std", weighted = FALSE, nCharNodes = 7,
shapeMan = "rectangle", sizeMan = 8, sizeMan2 = 5)
## Fit Indices:
##
## chisq df pvalue cfi srmr
## 105.520 32.000 0.000 0.975 0.033## Standardized Factor Loadings:
##
## LdrDvA InclsL DVB
## LdrDivAd_1 0.780 0.000 0.000
## LdrDivAd_2 0.914 0.000 0.000
## LdrDivAd_3 0.901 0.000 0.000
## LdrDivAd_4 0.863 0.000 0.000
## InclusiveLeadership_1 0.000 0.791 0.000
## InclusiveLeadership_2 0.000 0.799 0.000
## InclusiveLeadership_3 0.000 0.927 0.000
## DVB_1 0.000 0.000 0.866
## DVB_2 0.000 0.000 0.909
## DVB_3 0.000 0.000 0.859
##
##
## Explained Variance (R²):
##
## LdrDivAd_1 LdrDivAd_2 LdrDivAd_3
## 0.608 0.835 0.811
## LdrDivAd_4 InclusiveLeadership_1 InclusiveLeadership_2
## 0.745 0.626 0.639
## InclusiveLeadership_3 DVB_1 DVB_2
## 0.860 0.750 0.827
## DVB_3
## 0.738Two-Factor Model #1
TwoFactor_model1 <- '
# Factor 1: Leader Diversity Advocacy & Inclusive Leadership
LdrDivAd =~ LdrDivAd_1 + LdrDivAd_2 + LdrDivAd_3 + LdrDivAd_4 +
InclusiveLeadership_1 + InclusiveLeadership_2 + InclusiveLeadership_3
# Factor 2: Diversity Valuing Behavior
DVB =~ DVB_1 + DVB_2 + DVB_3
'
TwoFactor_fit1 <- cfa(TwoFactor_model1, Study_3_CFA, estimator = "ML")
# plot CFA results
semPaths(TwoFactor_fit1, "std", weighted = FALSE, nCharNodes = 7,
shapeMan = "rectangle", sizeMan = 8, sizeMan2 = 5)
## Fit Indices:
##
## chisq df pvalue cfi srmr
## 543.052 34.000 0.000 0.825 0.110## Standardized Factor Loadings:
##
## LdrDvA DVB
## LdrDivAd_1 0.746 0.000
## LdrDivAd_2 0.892 0.000
## LdrDivAd_3 0.880 0.000
## LdrDivAd_4 0.841 0.000
## InclusiveLeadership_1 0.610 0.000
## InclusiveLeadership_2 0.672 0.000
## InclusiveLeadership_3 0.704 0.000
## DVB_1 0.000 0.856
## DVB_2 0.000 0.923
## DVB_3 0.000 0.850
##
##
## Explained Variance (R²):
##
## LdrDivAd_1 LdrDivAd_2 LdrDivAd_3
## 0.557 0.796 0.774
## LdrDivAd_4 InclusiveLeadership_1 InclusiveLeadership_2
## 0.707 0.373 0.452
## InclusiveLeadership_3 DVB_1 DVB_2
## 0.495 0.733 0.852
## DVB_3
## 0.723Two-Factor Model #2
TwoFactor_model2 <- '
# Factor 1: Leader Diversity Advocacy & Diversity Valuing Behavior
LdrDivAd =~ LdrDivAd_1 + LdrDivAd_2 + LdrDivAd_3 + LdrDivAd_4 +
DVB_1 + DVB_2 + DVB_3
# Factor 2: Inclusive Leadership
InclusiveLdr =~ InclusiveLeadership_1 + InclusiveLeadership_2 + InclusiveLeadership_3
'
TwoFactor_fit2 <- cfa(TwoFactor_model2, Study_3_CFA, estimator = "ML")
# plot CFA results
semPaths(TwoFactor_fit2, "std", weighted = FALSE, nCharNodes = 7,
shapeMan = "rectangle", sizeMan = 8, sizeMan2 = 5)
## Fit Indices:
##
## chisq df pvalue cfi srmr
## 564.030 34.000 0.000 0.818 0.093## Standardized Factor Loadings:
##
## LdrDvA InclsL
## LdrDivAd_1 0.714 0.000
## LdrDivAd_2 0.864 0.000
## LdrDivAd_3 0.850 0.000
## LdrDivAd_4 0.818 0.000
## DVB_1 0.759 0.000
## DVB_2 0.826 0.000
## DVB_3 0.739 0.000
## InclusiveLeadership_1 0.000 0.805
## InclusiveLeadership_2 0.000 0.806
## InclusiveLeadership_3 0.000 0.913
##
##
## Explained Variance (R²):
##
## LdrDivAd_1 LdrDivAd_2 LdrDivAd_3
## 0.510 0.746 0.723
## LdrDivAd_4 DVB_1 DVB_2
## 0.669 0.575 0.682
## DVB_3 InclusiveLeadership_1 InclusiveLeadership_2
## 0.547 0.648 0.649
## InclusiveLeadership_3
## 0.834Two-Factor Model #3
TwoFactor_model3 <- '
# Factor 1: Leader Diversity Advocacy
LdrDivAd =~ LdrDivAd_1 + LdrDivAd_2 + LdrDivAd_3 + LdrDivAd_4
# Factor 2: Inclusive Leadership & Diversity Valuing Behavior
InclusiveLdr =~ InclusiveLeadership_1 + InclusiveLeadership_2 + InclusiveLeadership_3 +
DVB_1 + DVB_2 + DVB_3
'
TwoFactor_fit3 <- cfa(TwoFactor_model3, Study_3_CFA, estimator = "ML")
# plot CFA results
semPaths(TwoFactor_fit3, "std", weighted = FALSE, nCharNodes = 7,
shapeMan = "rectangle", sizeMan = 8, sizeMan2 = 5)
## Fit Indices:
##
## chisq df pvalue cfi srmr
## 199.676 34.000 0.000 0.943 0.041## Standardized Factor Loadings:
##
## LdrDvA InclsL
## LdrDivAd_1 0.780 0.000
## LdrDivAd_2 0.913 0.000
## LdrDivAd_3 0.901 0.000
## LdrDivAd_4 0.863 0.000
## InclusiveLeadership_1 0.000 0.721
## InclusiveLeadership_2 0.000 0.778
## InclusiveLeadership_3 0.000 0.864
## DVB_1 0.000 0.855
## DVB_2 0.000 0.880
## DVB_3 0.000 0.843
##
##
## Explained Variance (R²):
##
## LdrDivAd_1 LdrDivAd_2 LdrDivAd_3
## 0.608 0.834 0.812
## LdrDivAd_4 InclusiveLeadership_1 InclusiveLeadership_2
## 0.745 0.519 0.606
## InclusiveLeadership_3 DVB_1 DVB_2
## 0.746 0.731 0.774
## DVB_3
## 0.711Model Comparison
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## ThreeFactor_fit 32 7375.7 7463.9 105.52
## TwoFactor_fit1 34 7809.2 7889.8 543.05 437.53 0.79680 2 < 2.2e-16
## TwoFactor_fit2 34 7830.2 7910.8 564.03 458.51 0.81576 2 < 2.2e-16
## TwoFactor_fit3 34 7465.8 7546.4 199.68 94.16 0.36652 2 < 2.2e-16
##
## ThreeFactor_fit
## TwoFactor_fit1 ***
## TwoFactor_fit2 ***
## TwoFactor_fit3 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## cfi srmr
## ThreeFactor 0.9747574 0.03267602
## TwoFactor_Alt1 0.8252194 0.10953596
## TwoFactor_Alt2 0.8180167 0.09335403
## TwoFactor_Alt3 0.9431159 0.04113469Supplemental Regression and Commonality Analyses: Comparing Diversity-Supportive Leadership Constructs
# Regression Analyses: Relational Allyship
model_RelAlly <- lm(RelAlly ~ Political_Ideology + PDB + LdrDivAd + InclusiveLdr + DVB, data = Study3_vars)
summary(model_RelAlly)##
## Call:
## lm(formula = RelAlly ~ Political_Ideology + PDB + LdrDivAd +
## InclusiveLdr + DVB, data = Study3_vars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.52377 -0.44430 0.01295 0.44142 1.40384
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.79467 0.28370 2.801 0.00539 **
## Political_Ideology 0.01137 0.02400 0.474 0.63613
## PDB 0.44614 0.05174 8.622 2.60e-16 ***
## LdrDivAd 0.35794 0.04953 7.227 3.32e-12 ***
## InclusiveLdr 0.05805 0.06077 0.955 0.34017
## DVB -0.17167 0.06909 -2.485 0.01346 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6672 on 337 degrees of freedom
## Multiple R-squared: 0.4564, Adjusted R-squared: 0.4483
## F-statistic: 56.59 on 5 and 337 DF, p-value: < 2.2e-16# Commonality Analyses: Relational Allyship
commonality_RelAlly <- commonalityCoefficients(Study3_vars,"RelAlly", list("LdrDivAd","InclusiveLdr","DVB"))
commonality_RelAlly## $CC
## Coefficient % Total
## Unique to LdrDivAd 0.2075 72.65
## Unique to InclusiveLdr 0.0009 0.32
## Unique to DVB 0.0105 3.67
## Common to LdrDivAd, and InclusiveLdr 0.0118 4.15
## Common to LdrDivAd, and DVB -0.0052 -1.82
## Common to InclusiveLdr, and DVB 0.0012 0.44
## Common to LdrDivAd, InclusiveLdr, and DVB 0.0588 20.60
## Total 0.2856 100.00
##
## $CCTotalbyVar
## Unique Common Total
## LdrDivAd 0.2075 0.0655 0.2730
## InclusiveLdr 0.0009 0.0720 0.0729
## DVB 0.0105 0.0549 0.0654# Regression Analyses: Organizational Allyship
model_OrgAlly <- lm(OrgAlly ~ Political_Ideology + PDB + LdrDivAd + InclusiveLdr + DVB, data = Study3_vars)
summary(model_OrgAlly)##
## Call:
## lm(formula = OrgAlly ~ Political_Ideology + PDB + LdrDivAd +
## InclusiveLdr + DVB, data = Study3_vars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.63351 -0.70629 0.02063 0.68995 2.38917
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.37405 0.41333 0.905 0.366
## Political_Ideology -0.01765 0.03497 -0.505 0.614
## PDB 0.32286 0.07538 4.283 2.41e-05 ***
## LdrDivAd 0.62847 0.07216 8.710 < 2e-16 ***
## InclusiveLdr 0.11534 0.08853 1.303 0.194
## DVB -0.42224 0.10066 -4.195 3.50e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.972 on 337 degrees of freedom
## Multiple R-squared: 0.3512, Adjusted R-squared: 0.3416
## F-statistic: 36.49 on 5 and 337 DF, p-value: < 2.2e-16# Commonality Analyses: Organizational Allyship
commonality_OrgAlly <- commonalityCoefficients(Study3_vars,"OrgAlly", list("LdrDivAd","InclusiveLdr","DVB"))
commonality_OrgAlly## $CC
## Coefficient % Total
## Unique to LdrDivAd 0.2412 83.29
## Unique to InclusiveLdr 0.0025 0.87
## Unique to DVB 0.0342 11.80
## Common to LdrDivAd, and InclusiveLdr 0.0170 5.86
## Common to LdrDivAd, and DVB -0.0342 -11.80
## Common to InclusiveLdr, and DVB 0.0056 1.93
## Common to LdrDivAd, InclusiveLdr, and DVB 0.0233 8.04
## Total 0.2896 100.00
##
## $CCTotalbyVar
## Unique Common Total
## LdrDivAd 0.2412 0.0061 0.2473
## InclusiveLdr 0.0025 0.0459 0.0484
## DVB 0.0342 -0.0053 0.0289Supplemental Regression and Commonality Analyses: Comparing Psychological Diversity Climate
# Regression Analyses: Relational Allyship
model_RelAlly <- lm(RelAlly ~ Political_Ideology + PDB + LdrDivAd + diversityClimate, data = Study3_vars)
summary(model_RelAlly)##
## Call:
## lm(formula = RelAlly ~ Political_Ideology + PDB + LdrDivAd +
## diversityClimate, data = Study3_vars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.63127 -0.41526 0.03481 0.45057 1.45045
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.50242 0.27489 1.828 0.0685 .
## Political_Ideology 0.01094 0.02422 0.451 0.6519
## PDB 0.44633 0.05255 8.494 6.46e-16 ***
## LdrDivAd 0.28339 0.05381 5.266 2.49e-07 ***
## diversityClimate 0.01649 0.06513 0.253 0.8002
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6729 on 338 degrees of freedom
## Multiple R-squared: 0.4454, Adjusted R-squared: 0.4388
## F-statistic: 67.86 on 4 and 338 DF, p-value: < 2.2e-16# Commonality Analyses: Relational Allyship
commonality_RelAlly <- commonalityCoefficients(Study3_vars,"RelAlly", list("LdrDivAd","diversityClimate"))
commonality_RelAlly## $CC
## Coefficient % Total
## Unique to LdrDivAd 0.0987 35.83
## Unique to diversityClimate 0.0025 0.90
## Common to LdrDivAd, and diversityClimate 0.1743 63.27
## Total 0.2755 100.00
##
## $CCTotalbyVar
## Unique Common Total
## LdrDivAd 0.0987 0.1743 0.2730
## diversityClimate 0.0025 0.1743 0.1768# Regression Analyses: Organizational Allyship
model_OrgAlly <- lm(OrgAlly ~ Political_Ideology + PDB + LdrDivAd + diversityClimate, data = Study3_vars)
summary(model_OrgAlly)##
## Call:
## lm(formula = OrgAlly ~ Political_Ideology + PDB + LdrDivAd +
## diversityClimate, data = Study3_vars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.22531 -0.69221 0.04567 0.72577 2.73707
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.25377 0.40839 -0.621 0.535
## Political_Ideology -0.01676 0.03598 -0.466 0.642
## PDB 0.33279 0.07807 4.263 2.62e-05 ***
## LdrDivAd 0.48799 0.07995 6.104 2.84e-09 ***
## diversityClimate -0.06113 0.09676 -0.632 0.528
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9997 on 338 degrees of freedom
## Multiple R-squared: 0.3117, Adjusted R-squared: 0.3036
## F-statistic: 38.27 on 4 and 338 DF, p-value: < 2.2e-16# Commonality Analyses: Organizational Allyship
commonality_OrgAlly <- commonalityCoefficients(Study3_vars,"OrgAlly", list("LdrDivAd","diversityClimate"))
commonality_OrgAlly## $CC
## Coefficient % Total
## Unique to LdrDivAd 0.1151 46.54
## Unique to diversityClimate 0.0000 0.02
## Common to LdrDivAd, and diversityClimate 0.1322 53.44
## Total 0.2473 100.00
##
## $CCTotalbyVar
## Unique Common Total
## LdrDivAd 0.1151 0.1322 0.2473
## diversityClimate 0.0000 0.1322 0.1322Supplementary Analysis
All Efficacy Beliefs Predicting Organizational Allyship
med_out <- readLines("Mplus Syntax/Study 3 Syntax & Output/Hannah_Study 3/Study3_All Efficacy Constructs Predicting Allyship.out")
cat(paste(med_out, collapse = "\n"))## Mplus VERSION 8.11 (Mac)
## MUTHEN & MUTHEN
## 04/08/2026 8:03 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Study 3
## DATA: FILE = "Study3.dat";
## VARIABLE:
## NAMES = Male White Ideology LdrDivAd PDB Aware Regulate Manage OrgAlly RelAlly
## IncluLdr DVB SupInt Anxiety empathy
## PT Climate;
## MISSING = ALL (999);
##
## USEVARIABLES LdrDivAd PDB Aware Regulate Manage OrgAlly RelAlly
## PolID;
##
## DEFINE:
## ! Political Ideology centered at the scale midpoint (= 4)
## PolID = Ideology - 4;
##
## ! Grand-mean center Leader Diversity Advocacy and Pro-Diversity Attitudes
## CENTER LdrDivAd PDB (GRANDMEAN);
##
## ANALYSIS:
## ESTIMATOR = ML;
##
## MODEL:
## Aware Regulate Manage ON PDB LdrDivAd;
##
## RelAlly OrgAlly ON PolID PDB LdrDivAd Aware Regulate Manage;
##
## ! Covariances among predictors
## PDB WITH LdrDivAd PolID;
## LdrDivAd WITH PolID;
## Aware WITH Manage;
## Aware WITH Regulate;
## Regulate WITH Manage;
## RelAlly WITH OrgAlly;
##
## MODEL INDIRECT:
## RelAlly IND LdrDivAd;
##
##
##
## INPUT READING TERMINATED NORMALLY
##
##
##
## Study 1
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 343
##
## Number of dependent variables 5
## Number of independent variables 3
## Number of continuous latent variables 0
##
## Observed dependent variables
##
## Continuous
## AWARE REGULATE MANAGE ORGALLY RELALLY
##
## Observed independent variables
## LDRDIVAD PDB POLID
##
## Variables with special functions
##
## Centering (GRANDMEAN)
## LDRDIVAD PDB
##
##
## Estimator ML
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Study3.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 1
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.100
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## AWARE REGULATE MANAGE ORGALLY RELALLY
## ________ ________ ________ ________ ________
## AWARE 1.000
## REGULATE 1.000 1.000
## MANAGE 1.000 1.000 1.000
## ORGALLY 1.000 1.000 1.000 1.000
## RELALLY 1.000 1.000 1.000 1.000 1.000
## LDRDIVAD 1.000 1.000 1.000 1.000 1.000
## PDB 1.000 1.000 1.000 1.000 1.000
## POLID 1.000 1.000 1.000 1.000 1.000
##
##
## Covariance Coverage
## LDRDIVAD PDB POLID
## ________ ________ ________
## LDRDIVAD 1.000
## PDB 1.000 1.000
## POLID 1.000 1.000 1.000
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## AWARE 3.995 -0.511 1.667 0.58% 3.333 4.000 4.000
## 343.000 0.660 -0.474 5.000 22.45% 4.333 5.000
## REGULATE 4.188 -0.817 1.200 0.29% 3.600 4.000 4.200
## 343.000 0.493 0.633 5.000 22.45% 4.400 5.000
## MANAGE 3.502 -0.531 1.000 3.50% 2.600 3.400 3.600
## 343.000 1.156 -0.501 5.000 10.20% 4.000 4.400
## ORGALLY 2.467 0.367 1.000 19.24% 1.111 2.000 2.333
## 343.000 1.431 -0.960 5.000 3.50% 2.889 3.556
## RELALLY 3.418 -0.423 1.000 1.75% 2.857 3.143 3.429
## 343.000 0.805 -0.013 5.000 6.12% 3.714 4.143
## LDRDIVAD 0.000 -0.308 -2.296 4.37% -0.796 -0.296 -0.046
## 343.000 1.066 -0.374 1.704 7.87% 0.204 0.704
## PDB 0.000 -1.408 -3.216 2.04% -0.716 -0.216 0.284
## 343.000 0.878 1.815 0.784 39.94% 0.534 0.784
## POLID -0.673 0.390 -3.000 19.24% -2.000 -2.000 -1.000
## 343.000 3.479 -1.090 3.000 4.96% 0.000 1.000
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 41
##
## Loglikelihood
##
## H0 Value -3392.578
## H1 Value -3388.352
##
## Information Criteria
##
## Akaike (AIC) 6867.157
## Bayesian (BIC) 7024.504
## Sample-Size Adjusted BIC 6894.442
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 8.453
## Degrees of Freedom 3
## P-Value 0.0375
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.073
## 90 Percent C.I. 0.016 0.133
## Probability RMSEA <= .05 0.203
##
## CFI/TLI
##
## CFI 0.994
## TLI 0.949
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 920.217
## Degrees of Freedom 25
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.015
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## AWARE ON
## PDB 0.261 0.048 5.476 0.000
## LDRDIVAD 0.095 0.043 2.195 0.028
##
## REGULATE ON
## PDB 0.218 0.040 5.387 0.000
## LDRDIVAD 0.126 0.037 3.423 0.001
##
## MANAGE ON
## PDB 0.405 0.056 7.260 0.000
## LDRDIVAD 0.337 0.051 6.660 0.000
##
## RELALLY ON
## POLID 0.012 0.023 0.540 0.589
## PDB 0.338 0.051 6.573 0.000
## LDRDIVAD 0.209 0.039 5.407 0.000
## AWARE 0.013 0.053 0.248 0.804
## REGULATE 0.127 0.063 2.014 0.044
## MANAGE 0.198 0.043 4.574 0.000
##
## ORGALLY ON
## POLID -0.023 0.032 -0.712 0.476
## PDB 0.174 0.073 2.395 0.017
## LDRDIVAD 0.309 0.055 5.653 0.000
## AWARE -0.053 0.075 -0.705 0.481
## REGULATE -0.300 0.089 -3.371 0.001
## MANAGE 0.559 0.061 9.134 0.000
##
## PDB WITH
## LDRDIVAD 0.389 0.056 6.909 0.000
## POLID -1.008 0.109 -9.251 0.000
##
## LDRDIVAD WITH
## POLID -0.216 0.105 -2.066 0.039
##
## AWARE WITH
## MANAGE 0.249 0.039 6.444 0.000
## REGULATE 0.231 0.029 7.964 0.000
##
## REGULATE WITH
## MANAGE 0.240 0.033 7.199 0.000
##
## RELALLY WITH
## ORGALLY 0.205 0.032 6.374 0.000
##
## Means
## LDRDIVAD 0.000 0.056 0.000 1.000
## PDB 0.000 0.051 0.000 1.000
## POLID -0.673 0.101 -6.687 0.000
##
## Intercepts
## AWARE 3.995 0.041 97.879 0.000
## REGULATE 4.188 0.035 120.659 0.000
## MANAGE 3.502 0.048 73.218 0.000
## ORGALLY 1.963 0.340 5.769 0.000
## RELALLY 2.149 0.241 8.935 0.000
##
## Variances
## LDRDIVAD 1.066 0.081 13.096 0.000
## PDB 0.878 0.067 13.096 0.000
## POLID 3.479 0.266 13.096 0.000
##
## Residual Variances
## AWARE 0.571 0.044 13.096 0.000
## REGULATE 0.413 0.032 13.096 0.000
## MANAGE 0.785 0.060 13.096 0.000
## ORGALLY 0.790 0.060 13.096 0.000
## RELALLY 0.395 0.030 13.096 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.141E-03
## (ratio of smallest to largest eigenvalue)
##
##
## TOTAL, TOTAL INDIRECT, SPECIFIC INDIRECT, AND DIRECT EFFECTS
##
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## Effects from LDRDIVAD to RELALLY
##
## Total 0.293 0.039 7.577 0.000
## Total indirect 0.084 0.018 4.562 0.000
##
## Specific indirect 1
## RELALLY
## AWARE
## LDRDIVAD 0.001 0.005 0.246 0.806
##
## Specific indirect 2
## RELALLY
## REGULATE
## LDRDIVAD 0.016 0.009 1.736 0.083
##
## Specific indirect 3
## RELALLY
## MANAGE
## LDRDIVAD 0.067 0.018 3.770 0.000
##
## Direct
## RELALLY
## LDRDIVAD 0.209 0.039 5.407 0.000
##
##
##
## Beginning Time: 20:03:46
## Ending Time: 20:03:46
## Elapsed Time: 00:00:00
##
##
##
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## Los Angeles, CA 90066
##
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## Web: www.StatModel.com
## Support: Support@StatModel.com
##
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