Study 1 (Diversity Advocacy)
Anonymous
2026-04-23
Load Packages
Study 1
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 ' ' 1CFA: 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
## 0.711Table X: Model 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 0.04113469Seven-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")Table X: 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 X: 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
##
##
##
##
##
##
##
##
##
##
##
## .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
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .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
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .35**
## [.26, .44]
##
##
## 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
Table X (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 1
## 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/Study3_Mediation_RelAlly_withControls (alt med).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 X (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 1: 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 X: Moderated Mediation (Bias-Awareness Efficacy and Relational Ally Work)
res <- readModels("Mplus Syntax/Study 3 Syntax & Output/ModMed (RelAlly).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: 0.004833## Standard error: 0.009598524## Monte Carlo 95% CI: [ -0.01374178 , 0.02464381 ]# 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 X: 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 1: Conditional Indirect Effect on Relationally Ally Work via Bias-Awareness Self-Efficacy
#NOTE: NEED TO CONFIRM PLOTS (DON’T THINK BIAS-AWARENESS PLOT WAS INCLUDED IN MPLUS SYNTAX)
Figure 2: Conditional Indirect Effect on Relationally Ally Work via Self-Regulation Self-Efficacy
Table X (Model 1): Path Model for Organizational Ally Work
## Mplus VERSION 8.11 (Mac)
## MUTHEN & MUTHEN
## 04/09/2026 4:39 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Study 1: 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 2b: Mediation Analysis (Bias-Awareness and Org Allyship)
res <- readModels("Mplus Syntax/Study 3 Syntax & Output/Study3_Mediation_OrgAlly_withControls (alt med).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 = .10, sims = 10000, method = "parametric")## Indirect effect estimate: -0.01634## Indirect effect standard error: 0.01046502## Monte Carlo 90% CI: [ -0.035636 , -0.002052143 ]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 X (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 1: 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 5a: Moderated Mediation (Bias-Awareness Efficacy and Organizational Ally Work)
res <- readModels("Mplus Syntax/Study 3 Syntax & Output/ModMed (OrgAlly).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 5b: 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 = 0.10, 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 = 0.01, 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 90% CI: [ 0.052 , 0.165 ]## 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 3: Conditional Indirect Effect on Organizational Ally Work via Intergroup-Management Self-Efficacy
Supplementary Analysis: Cross-Over (non-hypothesized) Efficacy
Efficacy Beliefs and Ally Work
med_out <- readLines("Mplus Syntax/Study 3 Syntax & Output/Study3_BASE_SRSE_IMSE_Allyship.out")
cat(paste(med_out, collapse = "\n"))## Mplus VERSION 8.11 (Mac)
## MUTHEN & MUTHEN
## 04/08/2026 7:39 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Study 1
## 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:
## Regulate ON PDB LdrDivAd;
##
## OrgAlly ON PolID PDB LdrDivAd Regulate;
##
## ! Covariances among predictors
## PDB WITH LdrDivAd PolID;
## LdrDivAd WITH PolID;
##
##
##
##
## *** WARNING in MODEL command
## Variable is uncorrelated with all other variables: AWARE
## *** WARNING in MODEL command
## Variable is uncorrelated with all other variables: MANAGE
## *** WARNING in MODEL command
## Variable is uncorrelated with all other variables: RELALLY
## *** WARNING in MODEL command
## At least one variable is uncorrelated with all other variables in the model.
## Check that this is what is intended.
## 4 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
##
##
##
## 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 25
##
## Loglikelihood
##
## H0 Value -3754.289
## H1 Value -3388.352
##
## Information Criteria
##
## Akaike (AIC) 7558.578
## Bayesian (BIC) 7654.521
## Sample-Size Adjusted BIC 7575.215
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 731.874
## Degrees of Freedom 19
## P-Value 0.0000
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.331
## 90 Percent C.I. 0.310 0.351
## Probability RMSEA <= .05 0.000
##
## CFI/TLI
##
## CFI 0.204
## TLI 0.000
##
## 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 0.291
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## REGULATE ON
## PDB 0.218 0.040 5.387 0.000
## LDRDIVAD 0.126 0.037 3.423 0.001
##
## ORGALLY ON
## POLID -0.018 0.036 -0.519 0.604
## PDB 0.328 0.079 4.148 0.000
## LDRDIVAD 0.454 0.058 7.768 0.000
## REGULATE -0.005 0.083 -0.060 0.952
##
## 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
##
## Means
## AWARE 3.995 0.044 91.088 0.000
## MANAGE 3.502 0.058 60.333 0.000
## RELALLY 3.418 0.048 70.568 0.000
## 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
## REGULATE 4.188 0.035 120.659 0.000
## ORGALLY 2.475 0.354 6.991 0.000
##
## Variances
## AWARE 0.660 0.050 13.096 0.000
## MANAGE 1.156 0.088 13.096 0.000
## RELALLY 0.805 0.061 13.096 0.000
## 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
## REGULATE 0.413 0.032 13.096 0.000
## ORGALLY 0.986 0.075 13.096 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.160E-02
## (ratio of smallest to largest eigenvalue)
##
##
## Beginning Time: 19:39:11
## Ending Time: 19:39:11
## 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