Study 1
Anonymous
2026-05-07
Load Packages
Study 1
Confirmatory Factor Analysis
Three-Factor Model (Efficacy Scales)
ThreeFactor_model <- '
# Factor 1: Bias-Awareness Efficacy
Awareness =~ DSE_Aware_1_t2 + DSE_Aware_2_t2 + DSE_Aware_3_t2
# Factor 2: Self-Regulation Efficacy
Regulation =~ DSE_Regulate_1_t2 + DSE_Regulate_2_t2 + DSE_Regulate_3_t2 +
DSE_Regulate_4_t2 + DSE_Regulate_5_t2
# Factor 3: Intergroup-Management Efficacy
Management =~ DSE_Management_1_t2 + DSE_Management_2_t2 + DSE_Management_3_t2 +
DSE_Management_4_t2 + DSE_Management_5_t2
'
ThreeFactor_fit <- cfa(ThreeFactor_model, Study_1_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
## 178.794 62.000 0.000 0.957 0.042## Standardized Factor Loadings:
##
## Awrnss Regltn Mngmnt
## DSE_Aware_1_t2 0.894 0.000 0.000
## DSE_Aware_2_t2 0.866 0.000 0.000
## DSE_Aware_3_t2 0.912 0.000 0.000
## DSE_Regulate_1_t2 0.000 0.784 0.000
## DSE_Regulate_2_t2 0.000 0.847 0.000
## DSE_Regulate_3_t2 0.000 0.807 0.000
## DSE_Regulate_4_t2 0.000 0.814 0.000
## DSE_Regulate_5_t2 0.000 0.870 0.000
## DSE_Management_1_t2 0.000 0.000 0.891
## DSE_Management_2_t2 0.000 0.000 0.879
## DSE_Management_3_t2 0.000 0.000 0.912
## DSE_Management_4_t2 0.000 0.000 0.916
## DSE_Management_5_t2 0.000 0.000 0.866
##
##
## Explained Variance (R²):
##
## DSE_Aware_1_t2 DSE_Aware_2_t2 DSE_Aware_3_t2 DSE_Regulate_1_t2
## 0.799 0.750 0.832 0.614
## DSE_Regulate_2_t2 DSE_Regulate_3_t2 DSE_Regulate_4_t2 DSE_Regulate_5_t2
## 0.717 0.651 0.662 0.757
## DSE_Management_1_t2 DSE_Management_2_t2 DSE_Management_3_t2 DSE_Management_4_t2
## 0.793 0.773 0.831 0.839
## DSE_Management_5_t2
## 0.751One-Factor Model (Efficacy Scales)
OneFactor_model <- '
# One Factor: Bias-Awareness, Self-Regulation, & Intergroup-Management Efficacy
OneFactor =~ DSE_Aware_1_t2 + DSE_Aware_2_t2 + DSE_Aware_3_t2 +
DSE_Regulate_1_t2 + DSE_Regulate_2_t2 + DSE_Regulate_3_t2 +
DSE_Regulate_4_t2 + DSE_Regulate_5_t2 +
DSE_Management_1_t2 + DSE_Management_2_t2 + DSE_Management_3_t2 +
DSE_Management_4_t2 + DSE_Management_5_t2
'
OneFactor_fit <- cfa(OneFactor_model, Study_1_CFA, estimator = "ML")## Fit Indices:
##
## chisq df pvalue cfi srmr
## 783.767 65.000 0.000 0.738 0.125## Standardized Factor Loadings:
##
## OnFctr
## DSE_Aware_1_t2 0.782
## DSE_Aware_2_t2 0.799
## DSE_Aware_3_t2 0.794
## DSE_Regulate_1_t2 0.495
## DSE_Regulate_2_t2 0.580
## DSE_Regulate_3_t2 0.640
## DSE_Regulate_4_t2 0.589
## DSE_Regulate_5_t2 0.645
## DSE_Management_1_t2 0.885
## DSE_Management_2_t2 0.864
## DSE_Management_3_t2 0.871
## DSE_Management_4_t2 0.881
## DSE_Management_5_t2 0.865
##
##
## Explained Variance (R²):
##
## DSE_Aware_1_t2 DSE_Aware_2_t2 DSE_Aware_3_t2 DSE_Regulate_1_t2
## 0.611 0.639 0.631 0.245
## DSE_Regulate_2_t2 DSE_Regulate_3_t2 DSE_Regulate_4_t2 DSE_Regulate_5_t2
## 0.337 0.410 0.347 0.416
## DSE_Management_1_t2 DSE_Management_2_t2 DSE_Management_3_t2 DSE_Management_4_t2
## 0.783 0.746 0.758 0.777
## DSE_Management_5_t2
## 0.748##
##
## Three-Factor vs One-Factor Model:
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## ThreeFactor_fit 62 6180.3 6279.1 178.79
## OneFactor_fit 65 6779.3 6867.8 783.77 604.97 0.94858 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_Aware_1_t2 + DSE_Aware_2_t2 + DSE_Aware_3_t2
# Factor 2: Self-Regulation Efficacy
Regulation =~ DSE_Regulate_1_t2 + DSE_Regulate_2_t2 + DSE_Regulate_3_t2 +
DSE_Regulate_4_t2 + DSE_Regulate_5_t2
# Factor 3: Intergroup-Management Efficacy
Management =~ DSE_Management_1_t2 + DSE_Management_2_t2 + DSE_Management_3_t2 +
DSE_Management_4_t2 + DSE_Management_5_t2
# Factor 4: Leader Diversity Advocacy
InclusiveLeadership =~ Inclusive_Leader_1_t1 + Inclusive_Leader_2_t1 +
Inclusive_Leader_3_t1 + Inclusive_Leader_4_t1
# Factor 5: Pro-Diversity Attitudes
ProDiversity =~ ProDiversity_1_t1 + ProDiversity_2_t1 +
ProDiversity_3_t1 + ProDiversity_4_t1
# Factor 6: Relational Allyship
RelationalAlly =~ Relational_Allyship_1_t3 + Relational_Allyship_2_t3 +
Relational_Allyship_3_t3 + Relational_Allyship_4_t3 +
Relational_Allyship_5_t3 + Relational_Allyship_6_t3 +
Relational_Allyship_7_t3
# Factor 7: Organizational Allyship
OrgAlly =~ Org_Allyship_1_t3 + Org_Allyship_2_t3 + Org_Allyship_3_t3 +
Org_Allyship_4_t3 + Org_Allyship_5_t3 + Org_Allyship_6_t3 +
Org_Allyship_7_t3 + Org_Allyship_8_t3 + Org_Allyship_9_t3
'
SevenFactor_fit <- cfa(SevenFactor_model, Study_1_CFA, estimator = "ML")## Fit Indices:
##
## chisq df pvalue cfi srmr
## 1263.032 608.000 0.000 0.936 0.046## Standardized Factor Loadings:
##
## Awrnss Regltn Mngmnt InclsL PrDvrs RltnlA OrgAll
## DSE_Aware_1_t2 0.895 0.000 0.000 0.000 0.000 0.000 0.000
## DSE_Aware_2_t2 0.865 0.000 0.000 0.000 0.000 0.000 0.000
## DSE_Aware_3_t2 0.912 0.000 0.000 0.000 0.000 0.000 0.000
## DSE_Regulate_1_t2 0.000 0.791 0.000 0.000 0.000 0.000 0.000
## DSE_Regulate_2_t2 0.000 0.855 0.000 0.000 0.000 0.000 0.000
## DSE_Regulate_3_t2 0.000 0.790 0.000 0.000 0.000 0.000 0.000
## DSE_Regulate_4_t2 0.000 0.805 0.000 0.000 0.000 0.000 0.000
## DSE_Regulate_5_t2 0.000 0.878 0.000 0.000 0.000 0.000 0.000
## DSE_Management_1_t2 0.000 0.000 0.890 0.000 0.000 0.000 0.000
## DSE_Management_2_t2 0.000 0.000 0.878 0.000 0.000 0.000 0.000
## DSE_Management_3_t2 0.000 0.000 0.915 0.000 0.000 0.000 0.000
## DSE_Management_4_t2 0.000 0.000 0.919 0.000 0.000 0.000 0.000
## DSE_Management_5_t2 0.000 0.000 0.870 0.000 0.000 0.000 0.000
## Inclusive_Leader_1_t1 0.000 0.000 0.000 0.884 0.000 0.000 0.000
## Inclusive_Leader_2_t1 0.000 0.000 0.000 0.948 0.000 0.000 0.000
## Inclusive_Leader_3_t1 0.000 0.000 0.000 0.932 0.000 0.000 0.000
## Inclusive_Leader_4_t1 0.000 0.000 0.000 0.936 0.000 0.000 0.000
## ProDiversity_1_t1 0.000 0.000 0.000 0.000 0.901 0.000 0.000
## ProDiversity_2_t1 0.000 0.000 0.000 0.000 0.932 0.000 0.000
## ProDiversity_3_t1 0.000 0.000 0.000 0.000 0.864 0.000 0.000
## ProDiversity_4_t1 0.000 0.000 0.000 0.000 0.902 0.000 0.000
## Relational_Allyship_1_t3 0.000 0.000 0.000 0.000 0.000 0.879 0.000
## Relational_Allyship_2_t3 0.000 0.000 0.000 0.000 0.000 0.877 0.000
## Relational_Allyship_3_t3 0.000 0.000 0.000 0.000 0.000 0.918 0.000
## Relational_Allyship_4_t3 0.000 0.000 0.000 0.000 0.000 0.924 0.000
## Relational_Allyship_5_t3 0.000 0.000 0.000 0.000 0.000 0.907 0.000
## Relational_Allyship_6_t3 0.000 0.000 0.000 0.000 0.000 0.820 0.000
## Relational_Allyship_7_t3 0.000 0.000 0.000 0.000 0.000 0.852 0.000
## Org_Allyship_1_t3 0.000 0.000 0.000 0.000 0.000 0.000 0.929
## Org_Allyship_2_t3 0.000 0.000 0.000 0.000 0.000 0.000 0.920
## Org_Allyship_3_t3 0.000 0.000 0.000 0.000 0.000 0.000 0.933
## Org_Allyship_4_t3 0.000 0.000 0.000 0.000 0.000 0.000 0.917
## Org_Allyship_5_t3 0.000 0.000 0.000 0.000 0.000 0.000 0.930
## Org_Allyship_6_t3 0.000 0.000 0.000 0.000 0.000 0.000 0.930
## Org_Allyship_7_t3 0.000 0.000 0.000 0.000 0.000 0.000 0.918
## Org_Allyship_8_t3 0.000 0.000 0.000 0.000 0.000 0.000 0.921
## Org_Allyship_9_t3 0.000 0.000 0.000 0.000 0.000 0.000 0.934
##
##
## Explained Variance (R²):
##
## DSE_Aware_1_t2 DSE_Aware_2_t2 DSE_Aware_3_t2
## 0.801 0.748 0.831
## DSE_Regulate_1_t2 DSE_Regulate_2_t2 DSE_Regulate_3_t2
## 0.625 0.732 0.624
## DSE_Regulate_4_t2 DSE_Regulate_5_t2 DSE_Management_1_t2
## 0.648 0.771 0.792
## DSE_Management_2_t2 DSE_Management_3_t2 DSE_Management_4_t2
## 0.770 0.837 0.844
## DSE_Management_5_t2 Inclusive_Leader_1_t1 Inclusive_Leader_2_t1
## 0.758 0.782 0.899
## Inclusive_Leader_3_t1 Inclusive_Leader_4_t1 ProDiversity_1_t1
## 0.869 0.875 0.812
## ProDiversity_2_t1 ProDiversity_3_t1 ProDiversity_4_t1
## 0.868 0.746 0.813
## Relational_Allyship_1_t3 Relational_Allyship_2_t3 Relational_Allyship_3_t3
## 0.773 0.769 0.843
## Relational_Allyship_4_t3 Relational_Allyship_5_t3 Relational_Allyship_6_t3
## 0.853 0.823 0.672
## Relational_Allyship_7_t3 Org_Allyship_1_t3 Org_Allyship_2_t3
## 0.725 0.864 0.846
## Org_Allyship_3_t3 Org_Allyship_4_t3 Org_Allyship_5_t3
## 0.870 0.841 0.865
## Org_Allyship_6_t3 Org_Allyship_7_t3 Org_Allyship_8_t3
## 0.865 0.842 0.848
## Org_Allyship_9_t3
## 0.872Six-Factor Alternative Model #1 (Combine outcomes into single factor)
SixFactor_model <- '
# Factor 1: Bias-Awareness Efficacy
Awareness =~ DSE_Aware_1_t2 + DSE_Aware_2_t2 + DSE_Aware_3_t2
# Factor 2: Self-Regulation Efficacy
Regulation =~ DSE_Regulate_1_t2 + DSE_Regulate_2_t2 + DSE_Regulate_3_t2 +
DSE_Regulate_4_t2 + DSE_Regulate_5_t2
# Factor 3: Intergroup-Management Efficacy
Management =~ DSE_Management_1_t2 + DSE_Management_2_t2 + DSE_Management_3_t2 +
DSE_Management_4_t2 + DSE_Management_5_t2
# Factor 4: Leader Diversity Advocacy
InclusiveLeadership =~ Inclusive_Leader_1_t1 + Inclusive_Leader_2_t1 +
Inclusive_Leader_3_t1 + Inclusive_Leader_4_t1
# Factor 5: Pro-Diversity Attitudes
ProDiversity =~ ProDiversity_1_t1 + ProDiversity_2_t1 +
ProDiversity_3_t1 + ProDiversity_4_t1
# Factor 6: Relational Allyship & Organizational Allyship
Outcomes =~ Relational_Allyship_1_t3 + Relational_Allyship_2_t3 +
Relational_Allyship_3_t3 + Relational_Allyship_4_t3 +
Relational_Allyship_5_t3 + Relational_Allyship_6_t3 +
Relational_Allyship_7_t3 +
Org_Allyship_1_t3 + Org_Allyship_2_t3 + Org_Allyship_3_t3 +
Org_Allyship_4_t3 + Org_Allyship_5_t3 + Org_Allyship_6_t3 +
Org_Allyship_7_t3 + Org_Allyship_8_t3 + Org_Allyship_9_t3
'
SixFactor_fit1 <- cfa(SixFactor_model, Study_1_CFA, estimator = "ML")Six-Factor Alternative Model #2 (Combine predictors into single factor)
SixFactor2_model <- '
# Factor 1: Bias-Awareness Efficacy
Awareness =~ DSE_Aware_1_t2 + DSE_Aware_2_t2 + DSE_Aware_3_t2
# Factor 2: Self-Regulation Efficacy
Regulation =~ DSE_Regulate_1_t2 + DSE_Regulate_2_t2 + DSE_Regulate_3_t2 +
DSE_Regulate_4_t2 + DSE_Regulate_5_t2
# Factor 3: Intergroup-Management Efficacy
Management =~ DSE_Management_1_t2 + DSE_Management_2_t2 + DSE_Management_3_t2 +
DSE_Management_4_t2 + DSE_Management_5_t2
# Factor 4: Leader Diversity Advocacy & Pro-Diversity Attitudes
Predictors =~ Inclusive_Leader_1_t1 + Inclusive_Leader_2_t1 +
Inclusive_Leader_3_t1 + Inclusive_Leader_4_t1 +
ProDiversity_1_t1 + ProDiversity_2_t1 +
ProDiversity_3_t1 + ProDiversity_4_t1
# Factor 5: Relational Allyship
RelationalAlly =~ Relational_Allyship_1_t3 + Relational_Allyship_2_t3 +
Relational_Allyship_3_t3 + Relational_Allyship_4_t3 +
Relational_Allyship_5_t3 + Relational_Allyship_6_t3 +
Relational_Allyship_7_t3
# Factor 6: Organizational Allyship
OrgAlly =~ Org_Allyship_1_t3 + Org_Allyship_2_t3 + Org_Allyship_3_t3 +
Org_Allyship_4_t3 + Org_Allyship_5_t3 + Org_Allyship_6_t3 +
Org_Allyship_7_t3 + Org_Allyship_8_t3 + Org_Allyship_9_t3
'
SixFactor_fit2 <- cfa(SixFactor2_model, Study_1_CFA, estimator = "ML")Five-Factor Alternative Model #3 (Combine predictors/outcomes into single factor)
FiveFactor_model <- '
# Factor 1: Bias-Awareness Efficacy
Awareness =~ DSE_Aware_1_t2 + DSE_Aware_2_t2 + DSE_Aware_3_t2
# Factor 2: Self-Regulation Efficacy
Regulation =~ DSE_Regulate_1_t2 + DSE_Regulate_2_t2 + DSE_Regulate_3_t2 +
DSE_Regulate_4_t2 + DSE_Regulate_5_t2
# Factor 3: Intergroup-Management Efficacy
Management =~ DSE_Management_1_t2 + DSE_Management_2_t2 + DSE_Management_3_t2 +
DSE_Management_4_t2 + DSE_Management_5_t2
# Factor 4: Leader Diversity Advocacy & Pro-Diversity Attitudes
Predictors =~ Inclusive_Leader_1_t1 + Inclusive_Leader_2_t1 +
Inclusive_Leader_3_t1 + Inclusive_Leader_4_t1 +
ProDiversity_1_t1 + ProDiversity_2_t1 +
ProDiversity_3_t1 + ProDiversity_4_t1
# Factor 5: Relational Allyship & Organizational Allyship
Outcomes =~ Relational_Allyship_1_t3 + Relational_Allyship_2_t3 +
Relational_Allyship_3_t3 + Relational_Allyship_4_t3 +
Relational_Allyship_5_t3 + Relational_Allyship_6_t3 +
Relational_Allyship_7_t3 +
Org_Allyship_1_t3 + Org_Allyship_2_t3 + Org_Allyship_3_t3 +
Org_Allyship_4_t3 + Org_Allyship_5_t3 + Org_Allyship_6_t3 +
Org_Allyship_7_t3 + Org_Allyship_8_t3 + Org_Allyship_9_t3
'
FiveFactor_fit <- cfa(FiveFactor_model, Study_1_CFA, estimator = "ML")Table 1: Model Comparison
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## SevenFactor_fit 608 16468 16790 1263.0
## SixFactor_fit1 614 17510 17812 2317.5 1054.45 0.89122 6 < 2.2e-16
## SixFactor_fit2 614 17177 17479 1984.4 721.39 0.73618 6 < 2.2e-16
## FiveFactor_fit 619 18201 18486 3018.2 1755.13 0.84895 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 0.9360565 0.04608307
## SixFactor_Alt1 0.8337080 0.10531765
## SixFactor_Alt2 0.8662205 0.09155365
## FiveFactor_Alt3 0.7657961 0.13010189Reliability Estimates (Cronbach Alpha) - Study 1 Measures
## Alpha for bias-awareness efficacy: 0.9173952
## Alpha for self-regulation efficacy: 0.9139933
## Alpha for intergroup-management efficacy: 0.9512536## Alpha for relational ally work: 0.9608815
## Alpha for organizational ally work: 0.9817662## Alpha for leader diversity advocacy: 0.9596095
## Alpha for pro-diversity attitudes: 0.9409475Table 2: Descriptives & Correlations
##
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2 3 4
## 1. Male 0.59 0.49
##
## 2. White 0.71 0.46 -.06
## [-.19, .07]
##
## 3. Ideology 3.31 1.88 .15* .10
## [.02, .28] [-.03, .23]
##
## 4. LdrDivAd 3.27 1.23 -.11 -.11 -.06
## [-.24, .02] [-.24, .02] [-.19, .07]
##
## 5. PDB 4.32 0.98 -.24** -.08 -.48** .45**
## [-.36, -.12] [-.20, .06] [-.57, -.37] [.34, .55]
##
## 6. Aware 3.69 1.08 -.14* .00 -.23** .38**
## [-.26, -.01] [-.13, .13] [-.35, -.10] [.26, .48]
##
## 7. Regulate 4.28 0.77 -.15* .08 -.16* .29**
## [-.27, -.02] [-.05, .21] [-.28, -.03] [.17, .41]
##
## 8. Manage 3.41 1.20 -.12 -.03 -.19** .51**
## [-.24, .01] [-.16, .11] [-.31, -.06] [.41, .61]
##
## 9. OrgAlly 2.56 1.28 -.09 -.03 -.12 .56**
## [-.22, .04] [-.17, .10] [-.25, .01] [.46, .64]
##
## 10. RelAlly 3.55 0.95 -.16* -.03 -.27** .55**
## [-.29, -.03] [-.16, .10] [-.38, -.14] [.45, .63]
##
## 5 6 7 8 9
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .41**
## [.30, .51]
##
## .37** .58**
## [.26, .48] [.49, .66]
##
## .41** .77** .57**
## [.30, .52] [.72, .82] [.48, .65]
##
## .41** .48** .25** .59**
## [.30, .52] [.38, .58] [.12, .37] [.50, .67]
##
## .61** .51** .51** .54** .68**
## [.51, .68] [.41, .60] [.41, .60] [.44, .63] [.61, .75]
##
##
## 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
## 04/08/2026 10:49 AM
##
## INPUT INSTRUCTIONS
##
## TITLE: Study 1
## DATA: FILE = "Study1.dat";
## VARIABLE:
## NAMES = Male White Ideology LdrDivAd PDB Aware Regulate Manage OrgAlly RelAlly;
## 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 1
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 224
##
## 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)
## Study1.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 2.558 0.306 1.000 21.43% 1.000 2.000 2.556
## 224.000 1.627 -1.041 5.000 6.70% 3.000 3.778
## RELALLY 3.548 -0.668 1.250 3.12% 2.875 3.375 3.750
## 224.000 0.890 -0.233 5.000 0.45% 3.875 4.500
## LDRDIVAD 0.000 -0.436 -2.269 12.05% -1.019 -0.269 -0.019
## 224.000 1.503 -0.730 1.731 12.05% 0.481 0.981
## PDB 0.000 -1.748 -3.318 1.34% -0.568 0.182 0.432
## 224.000 0.947 2.453 0.682 45.98% 0.682 0.682
## POLID -0.688 0.488 -3.000 19.20% -2.000 -2.000 -1.000
## 224.000 3.509 -0.878 3.000 8.04% 0.000 1.000
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 20
##
## Loglikelihood
##
## H0 Value -1601.200
## H1 Value -1601.200
##
## Information Criteria
##
## Akaike (AIC) 3242.399
## Bayesian (BIC) 3310.632
## Sample-Size Adjusted BIC 3247.249
## (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 302.496
## 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 0.005 0.043 0.113 0.910
## PDB 0.271 0.092 2.945 0.003
## LDRDIVAD 0.484 0.064 7.533 0.000
##
## RELALLY ON
## POLID -0.019 0.029 -0.662 0.508
## PDB 0.417 0.062 6.775 0.000
## LDRDIVAD 0.269 0.043 6.253 0.000
##
## PDB WITH
## LDRDIVAD 0.536 0.087 6.131 0.000
## POLID -0.870 0.135 -6.444 0.000
##
## LDRDIVAD WITH
## POLID -0.149 0.154 -0.968 0.333
##
## RELALLY WITH
## ORGALLY 0.371 0.054 6.881 0.000
##
## Means
## LDRDIVAD 0.000 0.082 0.000 1.000
## PDB 0.000 0.065 0.000 1.000
## POLID -0.688 0.125 -5.492 0.000
##
## Intercepts
## ORGALLY 2.561 0.075 34.119 0.000
## RELALLY 3.535 0.050 70.302 0.000
##
## Variances
## LDRDIVAD 1.503 0.142 10.583 0.000
## PDB 0.947 0.089 10.583 0.000
## POLID 3.510 0.332 10.583 0.000
##
## Residual Variances
## ORGALLY 1.069 0.101 10.583 0.000
## RELALLY 0.480 0.045 10.583 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.559E-02
## (ratio of smallest to largest eigenvalue)
##
##
## Beginning Time: 10:49:55
## Ending Time: 10:49:55
## 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
res <- readModels("Mplus Syntax/Study 1 Syntax & Output/Hannah_Study 1/Study1_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[8] # Effect of M on Y (b-path)
# Extract standard errors
a_se <- res$parameters$unstandardized$se[2]
b_se <- res$parameters$unstandardized$se[8]
# 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: 0.02438## Indirect effect standard error: 0.0130437## Monte Carlo 95% CI: [ 0.002680119 , 0.05340348 ]Hypothesis 3: Mediation Analysis
# Extract coefficients
a_est <- res$parameters$unstandardized$est[4] # Effect of X on M (a-path)
b_est <- res$parameters$unstandardized$est[9] # Effect of M on Y (b-path)
# Extract standard errors
a_se <- res$parameters$unstandardized$se[4]
b_se <- res$parameters$unstandardized$se[9]
# 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: 0.027819## Indirect effect standard error: 0.01419382## Monte Carlo 95% CI: [ 0.003669805 , 0.05903483 ]Table 3: Conditional Path Model for Relational Ally Work
## Mplus VERSION 8.11 (Mac)
## MUTHEN & MUTHEN
## 09/04/2025 11:42 AM
##
## INPUT INSTRUCTIONS
##
## TITLE: Study 1: Moderated Mediation Models
## DATA: FILE = "Study1.dat";
## VARIABLE:
## NAMES = Male White Ideology LdrDivAd PDB Aware Regulate Manage OrgAlly RelAlly;
## MISSING = ALL (999);
##
## USEVARIABLES LdrDivAd PDB Aware Regulate RelAlly
## 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);
##
## PDB WITH LdrDivAd PolID;
## LdrDivAd WITH PolID;
## Aware WITH Regulate;
##
## MODEL CONSTRAINT:
##
## LOOP(PDB, -1.96, 1.96, .98); ! 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 224
##
## Number of dependent variables 3
## Number of independent variables 4
## Number of continuous latent variables 0
##
## Observed dependent variables
##
## Continuous
## AWARE REGULATE RELALLY
##
## 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)
## Study1.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 RELALLY LDRDIVAD PDB
## ________ ________ ________ ________ ________
## AWARE 1.000
## REGULATE 1.000 1.000
## RELALLY 1.000 1.000 1.000
## LDRDIVAD 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
## LDA_PDB 1.000 1.000 1.000 1.000 1.000
##
##
## Covariance Coverage
## POLID LDA_PDB
## ________ ________
## POLID 1.000
## LDA_PDB 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 3.690 -0.610 1.000 1.79% 2.667 3.667 4.000
## 224.000 1.164 -0.498 5.000 20.09% 4.000 4.667
## REGULATE 4.278 -1.277 1.400 0.45% 3.800 4.200 4.400
## 224.000 0.591 1.382 5.000 29.91% 4.600 5.000
## RELALLY 3.548 -0.668 1.250 3.12% 2.875 3.375 3.750
## 224.000 0.890 -0.233 5.000 0.45% 3.875 4.500
## LDRDIVAD 0.000 -0.436 -2.269 12.05% -1.019 -0.269 -0.019
## 224.000 1.503 -0.730 1.731 12.05% 0.481 0.981
## PDB 0.000 -1.748 -3.318 1.34% -0.568 0.182 0.432
## 224.000 0.947 2.453 0.682 45.98% 0.682 0.682
## POLID -0.688 0.488 -3.000 19.20% -2.000 -2.000 -1.000
## 224.000 3.509 -0.878 3.000 8.04% 0.000 1.000
## LDA_PDB 0.536 2.556 -2.282 0.45% -0.183 0.088 0.222
## 224.000 2.016 8.309 7.529 0.45% 0.437 1.010
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 27
##
## Loglikelihood
##
## H0 Value -1803.499
## H1 Value -1744.261
##
## Information Criteria
##
## Akaike (AIC) 3660.998
## Bayesian (BIC) 3753.113
## Sample-Size Adjusted BIC 3667.545
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 118.476
## Degrees of Freedom 6
## P-Value 0.0000
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.289
## 90 Percent C.I. 0.245 0.336
## Probability RMSEA <= .05 0.000
##
## CFI/TLI
##
## CFI 0.658
## TLI 0.146
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 344.071
## Degrees of Freedom 15
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.170
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## AWARE ON
## PDB 0.531 0.092 5.791 0.000
## LDRDIVAD 0.172 0.058 2.968 0.003
## LDA_PDB 0.194 0.057 3.425 0.001
##
## REGULATE ON
## PDB 0.325 0.069 4.723 0.000
## LDRDIVAD 0.082 0.043 1.878 0.060
## LDA_PDB 0.084 0.042 1.969 0.049
##
## RELALLY ON
## POLID -0.013 0.027 -0.475 0.635
## PDB 0.318 0.059 5.401 0.000
## LDRDIVAD 0.215 0.041 5.254 0.000
## AWARE 0.115 0.051 2.234 0.026
## REGULATE 0.281 0.069 4.054 0.000
##
## PDB WITH
## LDRDIVAD 0.536 0.087 6.131 0.000
## POLID -0.869 0.135 -6.444 0.000
##
## LDRDIVAD WITH
## POLID -0.149 0.154 -0.968 0.333
##
## AWARE WITH
## REGULATE 0.309 0.048 6.417 0.000
##
## Means
## LDRDIVAD 0.000 0.082 0.000 1.000
## PDB 0.000 0.065 0.000 1.000
## POLID -0.688 0.125 -5.493 0.000
##
## Intercepts
## AWARE 3.587 0.069 51.772 0.000
## REGULATE 4.233 0.052 81.540 0.000
## RELALLY 1.913 0.266 7.182 0.000
##
## Variances
## LDRDIVAD 1.503 0.142 10.583 0.000
## PDB 0.947 0.089 10.583 0.000
## POLID 3.509 0.332 10.583 0.000
##
## Residual Variances
## AWARE 0.869 0.082 10.583 0.000
## REGULATE 0.488 0.046 10.583 0.000
## RELALLY 0.407 0.038 10.583 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.522E-04
## (ratio of smallest to largest eigenvalue)
##
##
## PLOT INFORMATION
##
## The following plots are available:
##
## Loop plots
##
## Beginning Time: 11:42:56
## Ending Time: 11:42:56
## 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 1 Syntax & Output/Hannah_Study 1/Study1_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 = .05, sims = 10000, method = "parametric")##
## --- Index of Moderated Mediation ---## Estimate: 0.02231## Standard error: 0.01221924## Monte Carlo 95% CI: [ 0.002302669 , 0.04970842 ]# Conditional Indirect Effect for High and Low Pro-Diversity Attitudes
# Set moderator values (e.g., ±1 SD)
PDB_low <- -0.98 # 1 SD below mean
PDB_high <- 0.98 # 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: -0.002## Standard error (LOW PDB): 0.01## Monte Carlo 90% CI: [ -0.019 , 0.014 ]## At HIGH PDB (+1 SD):## Indirect effect estimate: 0.042## Standard error (HIGH PDB): 0.021## Monte Carlo 95% CI: [ 0.005 , 0.087 ]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: 0.023604## Standard error: 0.013464## Monte Carlo 95% CI: [ 0.0004302779 , 0.05311739 ]# Conditional Indirect Effect for High and Low Pro-Diversity Attitudes
# Set moderator values (e.g., ±1 SD)
PDB_low <- -0.98 # 1 SD below mean
PDB_high <- 0.98 # 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: -9e-05## Standard error (LOW PDB): 0.017## Monte Carlo 90% CI: [ -0.028 , 0.028 ]## At HIGH PDB (+1 SD):## Indirect effect estimate: 0.046## Standard error (HIGH PDB): 0.021## Monte Carlo 95% CI: [ 0.011 , 0.092 ]View Johnson-Neyman Plots for Conditional Indirect Effects
Figure 1: Conditional Indirect Effect on Relationally Ally Work via Bias-Awareness Self-Efficacy
Figure 2: Conditional Indirect Effect on Relationally Ally Work via Self-Regulation Self-Efficacy
Table 3: Path Model for Organizational Ally Work
## Mplus VERSION 8.11 (Mac)
## MUTHEN & MUTHEN
## 09/04/2025 11:42 AM
##
## INPUT INSTRUCTIONS
##
## TITLE: Study 1
## DATA: FILE = "Study1.dat";
## VARIABLE:
## NAMES = Male White Ideology LdrDivAd PDB Aware Regulate Manage OrgAlly RelAlly;
## 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:
## Aware ON PDB LdrDivAd;
## Manage ON PDB LdrDivAd;
##
## OrgAlly ON PolID PDB LdrDivAd Aware Manage;
##
##
## ! Covariances among predictors
## PDB WITH LdrDivAd PolID;
## LdrDivAd WITH PolID;
## Aware WITH Manage;
##
##
##
## INPUT READING TERMINATED NORMALLY
##
##
##
## Study 1
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 224
##
## Number of dependent variables 3
## Number of independent variables 3
## Number of continuous latent variables 0
##
## Observed dependent variables
##
## Continuous
## AWARE MANAGE ORGALLY
##
## 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)
## Study1.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 MANAGE ORGALLY LDRDIVAD PDB
## ________ ________ ________ ________ ________
## AWARE 1.000
## MANAGE 1.000 1.000
## ORGALLY 1.000 1.000 1.000
## LDRDIVAD 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
## 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
##
## AWARE 3.690 -0.610 1.000 1.79% 2.667 3.667 4.000
## 224.000 1.164 -0.498 5.000 20.09% 4.000 4.667
## MANAGE 3.409 -0.422 1.000 4.02% 2.200 3.200 3.600
## 224.000 1.439 -0.893 5.000 13.84% 4.000 4.600
## ORGALLY 2.558 0.306 1.000 21.43% 1.000 2.000 2.556
## 224.000 1.627 -1.041 5.000 6.70% 3.000 3.778
## LDRDIVAD 0.000 -0.436 -2.269 12.05% -1.019 -0.269 -0.019
## 224.000 1.503 -0.730 1.731 12.05% 0.481 0.981
## PDB 0.000 -1.748 -3.318 1.34% -0.568 0.182 0.432
## 224.000 0.947 2.453 0.682 45.98% 0.682 0.682
## POLID -0.688 0.488 -3.000 19.20% -2.000 -2.000 -1.000
## 224.000 3.509 -0.878 3.000 8.04% 0.000 1.000
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 25
##
## Loglikelihood
##
## H0 Value -1927.517
## H1 Value -1926.653
##
## Information Criteria
##
## Akaike (AIC) 3905.035
## Bayesian (BIC) 3990.326
## Sample-Size Adjusted BIC 3911.097
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 1.728
## Degrees of Freedom 2
## P-Value 0.4214
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.000
## 90 Percent C.I. 0.000 0.127
## Probability RMSEA <= .05 0.596
##
## CFI/TLI
##
## CFI 1.000
## TLI 1.000
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 428.964
## Degrees of Freedom 12
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.017
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## AWARE ON
## PDB 0.335 0.073 4.559 0.000
## LDRDIVAD 0.212 0.058 3.636 0.000
##
## MANAGE ON
## PDB 0.281 0.077 3.661 0.000
## LDRDIVAD 0.404 0.061 6.623 0.000
##
## ORGALLY ON
## POLID 0.024 0.039 0.622 0.534
## PDB 0.165 0.086 1.916 0.055
## LDRDIVAD 0.318 0.065 4.900 0.000
## AWARE 0.077 0.094 0.812 0.417
## MANAGE 0.358 0.090 3.964 0.000
##
## PDB WITH
## LDRDIVAD 0.536 0.087 6.131 0.000
## POLID -0.869 0.135 -6.444 0.000
##
## LDRDIVAD WITH
## POLID -0.149 0.154 -0.968 0.333
##
## AWARE WITH
## MANAGE 0.680 0.078 8.679 0.000
##
## Means
## LDRDIVAD 0.000 0.082 0.000 1.000
## PDB 0.000 0.065 0.000 1.000
## POLID -0.688 0.125 -5.493 0.000
##
## Intercepts
## AWARE 3.690 0.064 57.767 0.000
## MANAGE 3.409 0.067 51.076 0.000
## ORGALLY 1.071 0.260 4.125 0.000
##
## Variances
## LDRDIVAD 1.503 0.142 10.583 0.000
## PDB 0.947 0.089 10.583 0.000
## POLID 3.509 0.332 10.583 0.000
##
## Residual Variances
## AWARE 0.914 0.086 10.583 0.000
## MANAGE 0.998 0.094 10.583 0.000
## ORGALLY 0.899 0.085 10.583 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.194E-02
## (ratio of smallest to largest eigenvalue)
##
##
## Beginning Time: 11:42:55
## Ending Time: 11:42:55
## 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
res <- readModels("Mplus Syntax/Study 1 Syntax & Output/Hannah_Study 1/Study1_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[8] # Effect of M on Y (b-path)
# Extract standard errors
a_se <- res$parameters$unstandardized$se[2]
b_se <- res$parameters$unstandardized$se[8]
# 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: 0.016324## Indirect effect standard error: 0.02113752## Monte Carlo 90% CI: [ -0.01618396 , 0.05287173 ]Hypothesis 4: Mediation Analysis
# Extract coefficients
a_est <- res$parameters$unstandardized$est[4] # Effect of X on M (a-path)
b_est <- res$parameters$unstandardized$est[9] # Effect of M on Y (b-path)
# Extract standard errors
a_se <- res$parameters$unstandardized$se[4]
b_se <- res$parameters$unstandardized$se[9]
# 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: 0.144632## Indirect effect standard error: 0.04276784## Monte Carlo 99% CI: [ 0.04717184 , 0.268454 ]Table 3: Conditional Path Model for Organizational Ally Work
## Mplus VERSION 8.11 (Mac)
## MUTHEN & MUTHEN
## 09/04/2025 11:42 AM
##
## INPUT INSTRUCTIONS
##
## TITLE: Study 1: Moderated Mediation Models
## DATA: FILE = "Study1.dat";
## VARIABLE:
## NAMES = Male White Ideology LdrDivAd PDB Aware Regulate Manage OrgAlly RelAlly;
## MISSING = ALL (999);
##
## USEVARIABLES LdrDivAd PDB Aware Manage OrgAlly
## 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 (c2)
## LdrDivAd (c1)
## LDA_PDB (c3);
##
## OrgAlly ON PolID
## PDB
## LdrDivAd
## Aware
## Manage (mg);
##
## PDB WITH LdrDivAd PolID;
## LdrDivAd WITH PolID;
## Aware WITH Manage;
##
## MODEL CONSTRAINT:
##
## LOOP(PDB, -1.96, 1.96, .98); ! Two Standard Deviations Below/Above Mean
## PLOT(LdrDivAd_mg);
## LdrDivAd_mg = (c1 + c3*PDB)*mg;
##
## PLOT: TYPE = PLOT2;
##
##
##
##
##
##
##
## INPUT READING TERMINATED NORMALLY
##
##
##
## Study 1: Moderated Mediation Models
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 224
##
## Number of dependent variables 3
## Number of independent variables 4
## Number of continuous latent variables 0
##
## Observed dependent variables
##
## Continuous
## AWARE MANAGE ORGALLY
##
## 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)
## Study1.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 MANAGE ORGALLY LDRDIVAD PDB
## ________ ________ ________ ________ ________
## AWARE 1.000
## MANAGE 1.000 1.000
## ORGALLY 1.000 1.000 1.000
## LDRDIVAD 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
## LDA_PDB 1.000 1.000 1.000 1.000 1.000
##
##
## Covariance Coverage
## POLID LDA_PDB
## ________ ________
## POLID 1.000
## LDA_PDB 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 3.690 -0.610 1.000 1.79% 2.667 3.667 4.000
## 224.000 1.164 -0.498 5.000 20.09% 4.000 4.667
## MANAGE 3.409 -0.422 1.000 4.02% 2.200 3.200 3.600
## 224.000 1.439 -0.893 5.000 13.84% 4.000 4.600
## ORGALLY 2.558 0.306 1.000 21.43% 1.000 2.000 2.556
## 224.000 1.627 -1.041 5.000 6.70% 3.000 3.778
## LDRDIVAD 0.000 -0.436 -2.269 12.05% -1.019 -0.269 -0.019
## 224.000 1.503 -0.730 1.731 12.05% 0.481 0.981
## PDB 0.000 -1.748 -3.318 1.34% -0.568 0.182 0.432
## 224.000 0.947 2.453 0.682 45.98% 0.682 0.682
## POLID -0.688 0.488 -3.000 19.20% -2.000 -2.000 -1.000
## 224.000 3.509 -0.878 3.000 8.04% 0.000 1.000
## LDA_PDB 0.536 2.556 -2.282 0.45% -0.183 0.088 0.222
## 224.000 2.016 8.309 7.529 0.45% 0.437 1.010
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 27
##
## Loglikelihood
##
## H0 Value -1920.363
## H1 Value -1861.599
##
## Information Criteria
##
## Akaike (AIC) 3894.726
## Bayesian (BIC) 3986.840
## Sample-Size Adjusted BIC 3901.273
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 117.528
## Degrees of Freedom 6
## P-Value 0.0000
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.288
## 90 Percent C.I. 0.244 0.335
## Probability RMSEA <= .05 0.000
##
## CFI/TLI
##
## CFI 0.740
## TLI 0.350
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 443.910
## Degrees of Freedom 15
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.167
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## AWARE ON
## PDB 0.531 0.092 5.791 0.000
## LDRDIVAD 0.172 0.058 2.968 0.003
## LDA_PDB 0.194 0.057 3.425 0.001
##
## MANAGE ON
## PDB 0.498 0.096 5.214 0.000
## LDRDIVAD 0.360 0.060 5.949 0.000
## LDA_PDB 0.215 0.059 3.636 0.000
##
## ORGALLY ON
## POLID 0.024 0.039 0.621 0.535
## PDB 0.165 0.086 1.916 0.055
## LDRDIVAD 0.317 0.065 4.900 0.000
## AWARE 0.077 0.094 0.812 0.417
## MANAGE 0.358 0.090 3.964 0.000
##
## PDB WITH
## LDRDIVAD 0.536 0.087 6.131 0.000
## POLID -0.870 0.135 -6.444 0.000
##
## LDRDIVAD WITH
## POLID -0.149 0.154 -0.968 0.333
##
## AWARE WITH
## MANAGE 0.630 0.074 8.548 0.000
##
## Means
## LDRDIVAD 0.000 0.082 0.000 1.000
## PDB 0.000 0.065 0.000 1.000
## POLID -0.688 0.125 -5.493 0.000
##
## Intercepts
## AWARE 3.587 0.069 51.772 0.000
## MANAGE 3.294 0.072 45.658 0.000
## ORGALLY 1.071 0.260 4.125 0.000
##
## Variances
## LDRDIVAD 1.503 0.142 10.583 0.000
## PDB 0.947 0.089 10.583 0.000
## POLID 3.510 0.332 10.583 0.000
##
## Residual Variances
## AWARE 0.869 0.082 10.583 0.000
## MANAGE 0.942 0.089 10.583 0.000
## ORGALLY 0.899 0.085 10.583 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.138E-03
## (ratio of smallest to largest eigenvalue)
##
##
## PLOT INFORMATION
##
## The following plots are available:
##
## Loop plots
##
## Beginning Time: 11:42:55
## Ending Time: 11:42:56
## Elapsed Time: 00:00:01
##
##
##
## 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 1 Syntax & Output/Hannah_Study 1/Study1_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 = .10, sims = 10000, method = "parametric")##
## --- Index of Moderated Mediation ---## Estimate: 0.014938## Standard error: 0.019507## Monte Carlo 90% CI: [ -0.01479185 , 0.04886504 ]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 = .01, sims = 10000, method = "parametric")##
## --- Index of Moderated Mediation ---## Estimate: 0.07697## Standard error: 0.02913344## Monte Carlo 99% CI: [ 0.01663242 , 0.1660568 ]# Conditional Indirect Effect for High and Low Pro-Diversity Attitudes
# Set moderator values (e.g., ±1 SD)
PDB_low <- -0.98 # 1 SD below mean
PDB_high <- 0.98 # 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: 0.05345## Standard error (LOW PDB): 0.03357## Monte Carlo 90% CI: [ 0.004 , 0.113 ]## At HIGH PDB (+1 SD):## Indirect effect estimate: 0.204## Standard error (HIGH PDB): 0.05987## Monte Carlo 99% CI: [ 0.067 , 0.377 ]View Johnson-Neyman Plots for Conditional Indirect Effects
Figure 3: 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
## 04/09/2026 9:55 AM
##
## INPUT INSTRUCTIONS
##
## TITLE: Study 1
## DATA: FILE = "Study1.dat";
## VARIABLE:
## NAMES = Male White Ideology LdrDivAd PDB Aware Regulate Manage OrgAlly RelAlly;
## 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;
## BOOTSTRAP = 20000;
##
## 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(BOOTSTRAP);
##
##
##
## INPUT READING TERMINATED NORMALLY
##
##
##
## Study 1
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 224
##
## 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
## Number of bootstrap draws
## Requested 20000
## Completed 20000
##
## Input data file(s)
## Study1.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 -1.748 -3.318 1.34% -0.568 0.182 0.432
## 224.000 0.947 2.453 0.682 45.98% 0.682 0.682
## AWARE 3.690 -0.610 1.000 1.79% 2.667 3.667 4.000
## 224.000 1.164 -0.498 5.000 20.09% 4.000 4.667
## REGULATE 4.278 -1.277 1.400 0.45% 3.800 4.200 4.400
## 224.000 0.591 1.382 5.000 29.91% 4.600 5.000
## RELALLY 3.548 -0.668 1.250 3.12% 2.875 3.375 3.750
## 224.000 0.890 -0.233 5.000 0.45% 3.875 4.500
## LDRDIVAD 0.000 -0.436 -2.269 12.05% -1.019 -0.269 -0.019
## 224.000 1.503 -0.730 1.731 12.05% 0.481 0.981
## POLID -0.688 0.488 -3.000 19.20% -2.000 -2.000 -1.000
## 224.000 3.509 -0.878 3.000 8.04% 0.000 1.000
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 19
##
## Loglikelihood
##
## H0 Value -1020.430
## H1 Value -986.784
##
## Information Criteria
##
## Akaike (AIC) 2078.860
## Bayesian (BIC) 2143.681
## Sample-Size Adjusted BIC 2083.467
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 67.293
## Degrees of Freedom 3
## P-Value 0.0000
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.309
## 90 Percent C.I. 0.248 0.376
## Probability RMSEA <= .05 0.000
##
## CFI/TLI
##
## CFI 0.851
## TLI 0.307
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 446.915
## Degrees of Freedom 14
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.117
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## PDB ON
## LDRDIVAD 0.356 0.059 5.998 0.000
##
## AWARE ON
## LDRDIVAD 0.212 0.063 3.379 0.001
## PDB 0.335 0.101 3.324 0.001
##
## REGULATE ON
## LDRDIVAD 0.099 0.048 2.072 0.038
## PDB 0.240 0.077 3.123 0.002
##
## RELALLY ON
## LDRDIVAD 0.215 0.059 3.677 0.000
## POLID -0.013 0.029 -0.438 0.661
## PDB 0.318 0.080 3.979 0.000
## AWARE 0.115 0.064 1.786 0.074
## REGULATE 0.281 0.083 3.400 0.001
##
## AWARE WITH
## REGULATE 0.329 0.059 5.553 0.000
##
## Intercepts
## PDB 0.000 0.058 0.000 1.000
## AWARE 3.690 0.064 57.376 0.000
## REGULATE 4.278 0.048 88.916 0.000
## RELALLY 1.913 0.290 6.585 0.000
##
## Residual Variances
## PDB 0.756 0.094 8.047 0.000
## AWARE 0.914 0.093 9.830 0.000
## REGULATE 0.496 0.059 8.365 0.000
## RELALLY 0.407 0.046 8.856 0.000
##
##
## TOTAL, TOTAL INDIRECT, SPECIFIC INDIRECT, AND DIRECT EFFECTS
##
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## Effects from LDRDIVAD to RELALLY
##
## Total 0.418 0.052 8.123 0.000
## Total indirect 0.203 0.039 5.228 0.000
##
## Specific indirect 1
## RELALLY
## PDB
## LDRDIVAD 0.113 0.034 3.315 0.001
##
## Specific indirect 2
## RELALLY
## AWARE
## LDRDIVAD 0.024 0.015 1.632 0.103
##
## Specific indirect 3
## RELALLY
## REGULATE
## LDRDIVAD 0.028 0.018 1.536 0.125
##
## Specific indirect 4
## RELALLY
## AWARE
## PDB
## LDRDIVAD 0.014 0.010 1.416 0.157
##
## Specific indirect 5
## RELALLY
## REGULATE
## PDB
## LDRDIVAD 0.024 0.009 2.540 0.011
##
## Direct
## RELALLY
## LDRDIVAD 0.215 0.059 3.677 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 0.212 0.244 0.261 0.356 0.457 0.476 0.512
##
## AWARE ON
## LDRDIVAD 0.049 0.092 0.110 0.212 0.317 0.338 0.375
## PDB 0.080 0.140 0.174 0.335 0.506 0.536 0.598
##
## REGULATE ON
## LDRDIVAD -0.016 0.010 0.024 0.099 0.180 0.198 0.233
## PDB 0.045 0.090 0.116 0.240 0.368 0.394 0.440
##
## RELALLY ON
## LDRDIVAD 0.064 0.099 0.119 0.215 0.310 0.327 0.366
## POLID -0.086 -0.069 -0.060 -0.013 0.035 0.045 0.063
## PDB 0.123 0.167 0.191 0.318 0.454 0.481 0.535
## AWARE -0.042 -0.005 0.013 0.115 0.225 0.246 0.285
## REGULATE 0.050 0.107 0.136 0.281 0.410 0.433 0.476
##
## AWARE WITH
## REGULATE 0.179 0.210 0.227 0.329 0.422 0.443 0.483
##
## Intercepts
## PDB -0.151 -0.116 -0.096 0.000 0.095 0.113 0.147
## AWARE 3.525 3.563 3.583 3.690 3.795 3.815 3.855
## REGULATE 4.152 4.181 4.197 4.278 4.356 4.370 4.395
## RELALLY 1.210 1.368 1.458 1.913 2.414 2.510 2.729
##
## Residual Variances
## PDB 0.515 0.567 0.594 0.756 0.903 0.935 0.996
## AWARE 0.660 0.713 0.746 0.914 1.051 1.080 1.142
## REGULATE 0.344 0.375 0.392 0.496 0.587 0.607 0.649
## RELALLY 0.285 0.308 0.319 0.407 0.471 0.487 0.520
##
##
## 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 0.282 0.317 0.335 0.418 0.504 0.519 0.548
## Total indirect 0.113 0.134 0.144 0.203 0.271 0.286 0.315
##
## Specific indirect 1
## RELALLY
## PDB
## LDRDIVAD 0.038 0.053 0.062 0.113 0.174 0.187 0.213
##
## Specific indirect 2
## RELALLY
## AWARE
## LDRDIVAD -0.010 -0.001 0.003 0.024 0.051 0.057 0.072
##
## Specific indirect 3
## RELALLY
## REGULATE
## LDRDIVAD -0.003 0.002 0.004 0.028 0.062 0.071 0.089
##
## Specific indirect 4
## RELALLY
## AWARE
## PDB
## LDRDIVAD -0.005 -0.001 0.001 0.014 0.032 0.037 0.047
##
## Specific indirect 5
## RELALLY
## REGULATE
## PDB
## LDRDIVAD 0.003 0.007 0.009 0.024 0.040 0.044 0.053
##
## Direct
## RELALLY
## LDRDIVAD 0.064 0.099 0.119 0.215 0.310 0.327 0.366
##
##
##
## Beginning Time: 09:55:37
## Ending Time: 09:55:50
## Elapsed Time: 00:00:13
##
##
##
## 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$specific[4, ]## pred intervening outcome low.5 low2.5 low5 est up5 up2.5 up.5
## 4 LDRDIVAD AWARE.PDB RELALLY -0.005 -0.001 0.001 0.014 0.032 0.037 0.047# Serial mediators: pro-diversity beliefs and self-regulation self-efficacy
res$indirect$ci.unstandardized$specific[5, ]## pred intervening outcome low.5 low2.5 low5 est up5 up2.5 up.5
## 5 LDRDIVAD REGULATE.PDB RELALLY 0.003 0.007 0.009 0.024 0.040 0.044 0.053## Mplus VERSION 8.11 (Mac)
## MUTHEN & MUTHEN
## 04/08/2026 9:00 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Study 1
## DATA: FILE = "Study1.dat";
## VARIABLE:
## NAMES = Male White Ideology LdrDivAd PDB Aware Regulate Manage OrgAlly RelAlly;
## 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 1
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 224
##
## 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)
## Study1.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 -1.748 -3.318 1.34% -0.568 0.182 0.432
## 224.000 0.947 2.453 0.682 45.98% 0.682 0.682
## AWARE 3.690 -0.610 1.000 1.79% 2.667 3.667 4.000
## 224.000 1.164 -0.498 5.000 20.09% 4.000 4.667
## MANAGE 3.409 -0.422 1.000 4.02% 2.200 3.200 3.600
## 224.000 1.439 -0.893 5.000 13.84% 4.000 4.600
## ORGALLY 2.558 0.306 1.000 21.43% 1.000 2.000 2.556
## 224.000 1.627 -1.041 5.000 6.70% 3.000 3.778
## LDRDIVAD 0.000 -0.436 -2.269 12.05% -1.019 -0.269 -0.019
## 224.000 1.503 -0.730 1.731 12.05% 0.481 0.981
## POLID -0.688 0.488 -3.000 19.20% -2.000 -2.000 -1.000
## 224.000 3.509 -0.878 3.000 8.04% 0.000 1.000
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 19
##
## Loglikelihood
##
## H0 Value -1138.659
## H1 Value -1105.213
##
## Information Criteria
##
## Akaike (AIC) 2315.318
## Bayesian (BIC) 2380.140
## Sample-Size Adjusted BIC 2319.926
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 66.892
## Degrees of Freedom 3
## P-Value 0.0000
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.308
## 90 Percent C.I. 0.247 0.375
## Probability RMSEA <= .05 0.000
##
## CFI/TLI
##
## CFI 0.880
## TLI 0.438
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 544.571
## Degrees of Freedom 14
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.113
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## PDB ON
## LDRDIVAD 0.356 0.047 7.522 0.000
##
## AWARE ON
## LDRDIVAD 0.212 0.058 3.636 0.000
## PDB 0.335 0.073 4.559 0.000
##
## MANAGE ON
## LDRDIVAD 0.404 0.061 6.623 0.000
## PDB 0.281 0.077 3.661 0.000
##
## ORGALLY ON
## LDRDIVAD 0.318 0.065 4.900 0.000
## POLID 0.024 0.039 0.621 0.535
## PDB 0.165 0.086 1.916 0.055
## AWARE 0.077 0.094 0.812 0.417
## MANAGE 0.358 0.090 3.964 0.000
##
## AWARE WITH
## MANAGE 0.680 0.078 8.679 0.000
##
## Intercepts
## PDB 0.000 0.058 0.000 1.000
## AWARE 3.690 0.064 57.767 0.000
## MANAGE 3.409 0.067 51.076 0.000
## ORGALLY 1.071 0.260 4.125 0.000
##
## Residual Variances
## PDB 0.756 0.071 10.583 0.000
## AWARE 0.914 0.086 10.583 0.000
## MANAGE 0.998 0.094 10.583 0.000
## ORGALLY 0.899 0.085 10.583 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.194E-02
## (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 0.582 0.058 10.008 0.000
## Total indirect 0.265 0.048 5.472 0.000
##
## Specific indirect 1
## ORGALLY
## PDB
## LDRDIVAD 0.059 0.032 1.857 0.063
##
## Specific indirect 2
## ORGALLY
## AWARE
## LDRDIVAD 0.016 0.021 0.792 0.428
##
## Specific indirect 3
## ORGALLY
## MANAGE
## LDRDIVAD 0.144 0.042 3.401 0.001
##
## Specific indirect 4
## ORGALLY
## AWARE
## PDB
## LDRDIVAD 0.009 0.012 0.795 0.427
##
## Specific indirect 5
## ORGALLY
## MANAGE
## PDB
## LDRDIVAD 0.036 0.014 2.533 0.011
##
## Direct
## ORGALLY
## LDRDIVAD 0.318 0.065 4.900 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 0.234 0.264 0.278 0.356 0.434 0.449 0.478
##
## AWARE ON
## LDRDIVAD 0.062 0.098 0.116 0.212 0.308 0.326 0.362
## PDB 0.146 0.191 0.214 0.335 0.456 0.479 0.524
##
## MANAGE ON
## LDRDIVAD 0.247 0.284 0.303 0.404 0.504 0.523 0.561
## PDB 0.083 0.131 0.155 0.281 0.407 0.432 0.479
##
## ORGALLY ON
## LDRDIVAD 0.151 0.190 0.211 0.318 0.424 0.445 0.484
## POLID -0.077 -0.053 -0.040 0.024 0.089 0.102 0.126
## PDB -0.057 -0.004 0.023 0.165 0.307 0.335 0.388
## AWARE -0.167 -0.108 -0.079 0.077 0.232 0.262 0.320
## MANAGE 0.125 0.181 0.209 0.358 0.507 0.535 0.591
##
## AWARE WITH
## MANAGE 0.478 0.526 0.551 0.680 0.809 0.833 0.882
##
## Intercepts
## PDB -0.150 -0.114 -0.096 0.000 0.096 0.114 0.150
## AWARE 3.526 3.565 3.585 3.690 3.796 3.816 3.855
## MANAGE 3.237 3.278 3.299 3.409 3.519 3.540 3.581
## ORGALLY 0.402 0.562 0.644 1.071 1.498 1.580 1.740
##
## Residual Variances
## PDB 0.572 0.616 0.638 0.756 0.873 0.896 0.940
## AWARE 0.692 0.745 0.772 0.914 1.056 1.084 1.137
## MANAGE 0.755 0.813 0.843 0.998 1.153 1.183 1.241
## ORGALLY 0.680 0.733 0.759 0.899 1.039 1.066 1.118
##
##
## 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 0.432 0.468 0.487 0.582 0.678 0.696 0.732
## Total indirect 0.140 0.170 0.185 0.265 0.344 0.360 0.389
##
## Specific indirect 1
## ORGALLY
## PDB
## LDRDIVAD -0.023 -0.003 0.007 0.059 0.111 0.121 0.141
##
## Specific indirect 2
## ORGALLY
## AWARE
## LDRDIVAD -0.037 -0.024 -0.018 0.016 0.050 0.056 0.069
##
## Specific indirect 3
## ORGALLY
## MANAGE
## LDRDIVAD 0.035 0.061 0.075 0.144 0.214 0.228 0.254
##
## Specific indirect 4
## ORGALLY
## AWARE
## PDB
## LDRDIVAD -0.021 -0.013 -0.010 0.009 0.028 0.032 0.039
##
## Specific indirect 5
## ORGALLY
## MANAGE
## PDB
## LDRDIVAD -0.001 0.008 0.013 0.036 0.059 0.064 0.072
##
## Direct
## ORGALLY
## LDRDIVAD 0.151 0.190 0.211 0.318 0.424 0.445 0.484
##
##
##
## Beginning Time: 21:00:55
## Ending Time: 21:00:55
## 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$specific[4, ]## pred intervening outcome low.5 low2.5 low5 est up5 up2.5 up.5
## 4 LDRDIVAD AWARE.PDB ORGALLY -0.021 -0.013 -0.010 0.009 0.028 0.032 0.039# Serial mediators: pro-diversity beliefs and self-regulation self-efficacy
res$indirect$ci.unstandardized$specific[5, ]## pred intervening outcome low.5 low2.5 low5 est up5 up2.5 up.5
## 5 LDRDIVAD MANAGE.PDB ORGALLY -0.001 0.008 0.013 0.036 0.059 0.064 0.072Supplementary Analysis
All Efficacy Beliefs Predicting Relational and Organizational Ally Work
med_out <- readLines("Mplus Syntax/Study 1 Syntax & Output/Hannah_Study 1/Study1_All Efficacy Constructs Predicting Allyship (Supplemental).out")
cat(paste(med_out, collapse = "\n"))## Mplus VERSION 8.11 (Mac)
## MUTHEN & MUTHEN
## 04/08/2026 7:55 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Study 1
## DATA: FILE = "Study1.dat";
## VARIABLE:
## NAMES = Male White Ideology LdrDivAd PDB Aware Regulate Manage OrgAlly RelAlly;
## MISSING = ALL (999);
##
## USEVARIABLES LdrDivAd PDB Aware Regulate Manage RelAlly 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:
## Aware Regulate Manage ON PDB LdrDivAd;
##
## RelAlly OrgAlly ON PolID PDB LdrDivAd Aware Regulate Manage;
## ! Aware only significant when PolID and PDB are not included as controls
##
##
## ! Covariances among predictors
## PDB WITH LdrDivAd PolID;
## LdrDivAd WITH PolID;
## Aware WITH Manage;
## Aware WITH Regulate;
## Regulate WITH Manage;
##
##
##
## INPUT READING TERMINATED NORMALLY
##
##
##
## Study 1
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 224
##
## Number of dependent variables 5
## Number of independent variables 3
## Number of continuous latent variables 0
##
## Observed dependent variables
##
## Continuous
## AWARE REGULATE MANAGE RELALLY ORGALLY
##
## 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)
## Study1.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 RELALLY ORGALLY
## ________ ________ ________ ________ ________
## AWARE 1.000
## REGULATE 1.000 1.000
## MANAGE 1.000 1.000 1.000
## RELALLY 1.000 1.000 1.000 1.000
## ORGALLY 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 3.690 -0.610 1.000 1.79% 2.667 3.667 4.000
## 224.000 1.164 -0.498 5.000 20.09% 4.000 4.667
## REGULATE 4.278 -1.277 1.400 0.45% 3.800 4.200 4.400
## 224.000 0.591 1.382 5.000 29.91% 4.600 5.000
## MANAGE 3.409 -0.422 1.000 4.02% 2.200 3.200 3.600
## 224.000 1.439 -0.893 5.000 13.84% 4.000 4.600
## RELALLY 3.548 -0.668 1.250 3.12% 2.875 3.375 3.750
## 224.000 0.890 -0.233 5.000 0.45% 3.875 4.500
## ORGALLY 2.558 0.306 1.000 21.43% 1.000 2.000 2.556
## 224.000 1.627 -1.041 5.000 6.70% 3.000 3.778
## LDRDIVAD 0.000 -0.436 -2.269 12.05% -1.019 -0.269 -0.019
## 224.000 1.503 -0.730 1.731 12.05% 0.481 0.981
## PDB 0.000 -1.748 -3.318 1.34% -0.568 0.182 0.432
## 224.000 0.947 2.453 0.682 45.98% 0.682 0.682
## POLID -0.688 0.488 -3.000 19.20% -2.000 -2.000 -1.000
## 224.000 3.509 -0.878 3.000 8.04% 0.000 1.000
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 41
##
## Loglikelihood
##
## H0 Value -2307.119
## H1 Value -2305.995
##
## Information Criteria
##
## Akaike (AIC) 4696.239
## Bayesian (BIC) 4836.116
## Sample-Size Adjusted BIC 4706.181
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 2.249
## Degrees of Freedom 3
## P-Value 0.5223
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.000
## 90 Percent C.I. 0.000 0.101
## Probability RMSEA <= .05 0.717
##
## CFI/TLI
##
## CFI 1.000
## TLI 1.000
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 797.663
## Degrees of Freedom 25
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.013
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## AWARE ON
## PDB 0.335 0.073 4.559 0.000
## LDRDIVAD 0.212 0.058 3.635 0.000
##
## REGULATE ON
## PDB 0.240 0.054 4.435 0.000
## LDRDIVAD 0.099 0.043 2.300 0.021
##
## MANAGE ON
## PDB 0.281 0.077 3.662 0.000
## LDRDIVAD 0.404 0.061 6.623 0.000
##
## RELALLY ON
## POLID -0.012 0.026 -0.454 0.650
## PDB 0.318 0.059 5.420 0.000
## LDRDIVAD 0.201 0.044 4.607 0.000
## AWARE 0.076 0.065 1.164 0.244
## REGULATE 0.267 0.071 3.758 0.000
## MANAGE 0.059 0.062 0.951 0.342
##
## ORGALLY ON
## POLID 0.030 0.039 0.772 0.440
## PDB 0.205 0.086 2.391 0.017
## LDRDIVAD 0.308 0.064 4.844 0.000
## AWARE 0.143 0.095 1.498 0.134
## REGULATE -0.301 0.104 -2.905 0.004
## MANAGE 0.415 0.091 4.574 0.000
##
## PDB WITH
## LDRDIVAD 0.536 0.087 6.131 0.000
## POLID -0.869 0.135 -6.444 0.000
##
## LDRDIVAD WITH
## POLID -0.149 0.154 -0.968 0.333
##
## AWARE WITH
## MANAGE 0.680 0.078 8.679 0.000
## REGULATE 0.329 0.050 6.561 0.000
##
## REGULATE WITH
## MANAGE 0.338 0.052 6.477 0.000
##
## ORGALLY WITH
## RELALLY 0.313 0.045 6.987 0.000
##
## Means
## LDRDIVAD 0.000 0.082 0.000 1.000
## PDB 0.000 0.065 0.000 1.000
## POLID -0.688 0.125 -5.493 0.000
##
## Intercepts
## AWARE 3.690 0.064 57.767 0.000
## REGULATE 4.278 0.047 90.881 0.000
## MANAGE 3.409 0.067 51.076 0.000
## RELALLY 1.917 0.266 7.210 0.000
## ORGALLY 1.923 0.389 4.949 0.000
##
## Variances
## LDRDIVAD 1.503 0.142 10.583 0.000
## PDB 0.947 0.089 10.583 0.000
## POLID 3.509 0.332 10.583 0.000
##
## Residual Variances
## AWARE 0.914 0.086 10.583 0.000
## REGULATE 0.496 0.047 10.583 0.000
## MANAGE 0.998 0.094 10.583 0.000
## RELALLY 0.406 0.038 10.583 0.000
## ORGALLY 0.866 0.082 10.583 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.125E-03
## (ratio of smallest to largest eigenvalue)
##
##
## Beginning Time: 19:55:21
## Ending Time: 19:55:21
## 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