Load Packages

Study 2

Confirmatory Factor Analysis

Three-Factor Model (Efficacy Scales)

ThreeFactor_model <- '
  # Factor 1: Bias-Awareness Efficacy
  Awareness =~ DSE_Aware_1 + DSE_Aware_2 + DSE_Aware_3

  # Factor 2: Self-Regulation Efficacy
  Regulation =~ DSE_Regulate_1 + DSE_Regulate_2 + DSE_Regulate_3 +
                DSE_Regulate_4 + DSE_Regulate_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_2_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 
## 145.078  62.000   0.000   0.953   0.044

## Standardized Factor Loadings:
## 
##                  Awrnss Regltn Mngmnt
## DSE_Aware_1       0.816  0.000  0.000
## DSE_Aware_2       0.762  0.000  0.000
## DSE_Aware_3       0.748  0.000  0.000
## DSE_Regulate_1    0.000  0.840  0.000
## DSE_Regulate_2    0.000  0.777  0.000
## DSE_Regulate_3    0.000  0.743  0.000
## DSE_Regulate_4    0.000  0.824  0.000
## DSE_Regulate_5    0.000  0.762  0.000
## DSE_Management_1  0.000  0.000  0.840
## DSE_Management_2  0.000  0.000  0.816
## DSE_Management_3  0.000  0.000  0.788
## DSE_Management_4  0.000  0.000  0.787
## DSE_Management_5  0.000  0.000  0.766
## 
## 
## Explained Variance (R²):
## 
##      DSE_Aware_1      DSE_Aware_2      DSE_Aware_3   DSE_Regulate_1 
##            0.666            0.580            0.559            0.706 
##   DSE_Regulate_2   DSE_Regulate_3   DSE_Regulate_4   DSE_Regulate_5 
##            0.604            0.553            0.680            0.581 
## DSE_Management_1 DSE_Management_2 DSE_Management_3 DSE_Management_4 
##            0.706            0.666            0.621            0.620 
## DSE_Management_5 
##            0.586

One-Factor Model (Efficacy Scales)

OneFactor_model <- '
  # One Factor: Bias-Awareness, Self-Regulation, & Intergroup-Management Efficacy
  OneFactor =~ DSE_Aware_1 + DSE_Aware_2 + DSE_Aware_3 + 
               DSE_Regulate_1 + DSE_Regulate_2 + DSE_Regulate_3 +
               DSE_Regulate_4 + DSE_Regulate_5 +
               DSE_Management_1 + DSE_Management_2 + DSE_Management_3 +
                DSE_Management_4 + DSE_Management_5
'

OneFactor_fit <- cfa(OneFactor_model, Study_2_CFA, estimator = "ML")

## Fit Indices:
## 
##   chisq      df  pvalue     cfi    srmr 
## 430.646  65.000   0.000   0.794   0.089

## Standardized Factor Loadings:
## 
##                  OnFctr
## DSE_Aware_1       0.558
## DSE_Aware_2       0.479
## DSE_Aware_3       0.536
## DSE_Regulate_1    0.761
## DSE_Regulate_2    0.706
## DSE_Regulate_3    0.714
## DSE_Regulate_4    0.792
## DSE_Regulate_5    0.686
## DSE_Management_1  0.808
## DSE_Management_2  0.779
## DSE_Management_3  0.710
## DSE_Management_4  0.738
## DSE_Management_5  0.703
## 
## 
## Explained Variance (R²):
## 
##      DSE_Aware_1      DSE_Aware_2      DSE_Aware_3   DSE_Regulate_1 
##            0.311            0.229            0.288            0.579 
##   DSE_Regulate_2   DSE_Regulate_3   DSE_Regulate_4   DSE_Regulate_5 
##            0.498            0.510            0.627            0.471 
## DSE_Management_1 DSE_Management_2 DSE_Management_3 DSE_Management_4 
##            0.653            0.607            0.504            0.545 
## DSE_Management_5 
##            0.494

## 
## 
##  Three-Factor vs One-Factor Model:
## 
## Chi-Squared Difference Test
## 
##                 Df    AIC    BIC  Chisq Chisq diff   RMSEA Df diff Pr(>Chisq)
## ThreeFactor_fit 62 6405.9 6505.0 145.08                                      
## OneFactor_fit   65 6685.5 6774.3 430.65     285.57 0.64701       3  < 2.2e-16
##                    
## ThreeFactor_fit    
## OneFactor_fit   ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Six-Factor Measurement Model (All Six Multi-Item Scales)

SixFactor_model <- '
  # Factor 1: Bias-Awareness Efficacy
  Awareness =~ DSE_Aware_1 + DSE_Aware_2 + DSE_Aware_3

  # Factor 2: Self-Regulation Efficacy
  Regulation =~ DSE_Regulate_1 + DSE_Regulate_2 + DSE_Regulate_3 +
                DSE_Regulate_4 + DSE_Regulate_5

  # Factor 3: Intergroup-Management Efficacy
  Management =~ DSE_Management_1 + DSE_Management_2 + DSE_Management_3 +
                DSE_Management_4 + DSE_Management_5

  # Factor 4: Inclusive Leadership
  InclusiveLeadership =~ Inclusive_Leader_1 + Inclusive_Leader_2 +
                         Inclusive_Leader_3 + Inclusive_Leader_4

  # Factor 5: Pro-Diversity Attitudes
  ProDiversity =~ ProDiversity_1 + ProDiversity_2 +
                  ProDiversity_3 + ProDiversity_4

  # 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_fit <- cfa(SixFactor_model, Study_2_CFA, estimator = "ML")

## Fit Indices:
## 
##   chisq      df  pvalue     cfi    srmr 
## 738.576 390.000   0.000   0.933   0.047

## Standardized Factor Loadings:
## 
##                    Awrnss Regltn Mngmnt InclsL PrDvrs OrgAll
## DSE_Aware_1         0.820  0.000  0.000  0.000  0.000  0.000
## DSE_Aware_2         0.769  0.000  0.000  0.000  0.000  0.000
## DSE_Aware_3         0.749  0.000  0.000  0.000  0.000  0.000
## DSE_Regulate_1      0.000  0.842  0.000  0.000  0.000  0.000
## DSE_Regulate_2      0.000  0.776  0.000  0.000  0.000  0.000
## DSE_Regulate_3      0.000  0.750  0.000  0.000  0.000  0.000
## DSE_Regulate_4      0.000  0.821  0.000  0.000  0.000  0.000
## DSE_Regulate_5      0.000  0.775  0.000  0.000  0.000  0.000
## DSE_Management_1    0.000  0.000  0.834  0.000  0.000  0.000
## DSE_Management_2    0.000  0.000  0.817  0.000  0.000  0.000
## DSE_Management_3    0.000  0.000  0.793  0.000  0.000  0.000
## DSE_Management_4    0.000  0.000  0.797  0.000  0.000  0.000
## DSE_Management_5    0.000  0.000  0.771  0.000  0.000  0.000
## Inclusive_Leader_1  0.000  0.000  0.000  0.818  0.000  0.000
## Inclusive_Leader_2  0.000  0.000  0.000  0.872  0.000  0.000
## Inclusive_Leader_3  0.000  0.000  0.000  0.852  0.000  0.000
## Inclusive_Leader_4  0.000  0.000  0.000  0.901  0.000  0.000
## ProDiversity_1      0.000  0.000  0.000  0.000  0.899  0.000
## ProDiversity_2      0.000  0.000  0.000  0.000  0.890  0.000
## ProDiversity_3      0.000  0.000  0.000  0.000  0.833  0.000
## ProDiversity_4      0.000  0.000  0.000  0.000  0.830  0.000
## Org_Allyship_1      0.000  0.000  0.000  0.000  0.000  0.682
## Org_Allyship_2      0.000  0.000  0.000  0.000  0.000  0.841
## Org_Allyship_3      0.000  0.000  0.000  0.000  0.000  0.794
## Org_Allyship_4      0.000  0.000  0.000  0.000  0.000  0.814
## Org_Allyship_5      0.000  0.000  0.000  0.000  0.000  0.822
## Org_Allyship_6      0.000  0.000  0.000  0.000  0.000  0.871
## Org_Allyship_7      0.000  0.000  0.000  0.000  0.000  0.830
## Org_Allyship_8      0.000  0.000  0.000  0.000  0.000  0.870
## Org_Allyship_9      0.000  0.000  0.000  0.000  0.000  0.873
## 
## 
## Explained Variance (R²):
## 
##        DSE_Aware_1        DSE_Aware_2        DSE_Aware_3     DSE_Regulate_1 
##              0.672              0.592              0.561              0.708 
##     DSE_Regulate_2     DSE_Regulate_3     DSE_Regulate_4     DSE_Regulate_5 
##              0.602              0.563              0.675              0.601 
##   DSE_Management_1   DSE_Management_2   DSE_Management_3   DSE_Management_4 
##              0.696              0.667              0.629              0.635 
##   DSE_Management_5 Inclusive_Leader_1 Inclusive_Leader_2 Inclusive_Leader_3 
##              0.594              0.670              0.761              0.726 
## Inclusive_Leader_4     ProDiversity_1     ProDiversity_2     ProDiversity_3 
##              0.811              0.809              0.792              0.695 
##     ProDiversity_4     Org_Allyship_1     Org_Allyship_2     Org_Allyship_3 
##              0.688              0.465              0.707              0.630 
##     Org_Allyship_4     Org_Allyship_5     Org_Allyship_6     Org_Allyship_7 
##              0.663              0.675              0.759              0.689 
##     Org_Allyship_8     Org_Allyship_9 
##              0.757              0.762

Reliability Estimates (Cronbach Alpha) - Study 1 Measures

# Reliability estimates for self-efficacy scales
awareness_alpha <- alpha(Study_2_CFA[, c("DSE_Aware_1", "DSE_Aware_2", "DSE_Aware_3")])

regulation_alpha <- alpha(Study_2_CFA[, c("DSE_Regulate_1", "DSE_Regulate_2", "DSE_Regulate_3", "DSE_Regulate_4", "DSE_Regulate_5")])
                                           
management_alpha <- alpha(Study_2_CFA[, c("DSE_Management_1", "DSE_Management_2", "DSE_Management_3", "DSE_Management_4", "DSE_Management_5")]) 
                                           
cat("Alpha for bias-awareness efficacy:", awareness_alpha$total$raw_alpha, "\n",
    "Alpha for self-regulation efficacy:", regulation_alpha$total$raw_alpha, "\n",
    "Alpha for intergroup-management efficacy:", management_alpha$total$raw_alpha, "\n")

## Alpha for bias-awareness efficacy: 0.8230046 
##  Alpha for self-regulation efficacy: 0.8899173 
##  Alpha for intergroup-management efficacy: 0.9015689

org_ally_alpha <- alpha(Study_2_CFA[, c("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")])
                                         
cat("Alpha for organizational ally work:", org_ally_alpha$total$raw_alpha, "\n")

## Alpha for organizational ally work: 0.9499809

# Reliability estimates for leader diversity advocacy and pro-diversity
incl_leader_alpha <- alpha(Study_2_CFA[, c("Inclusive_Leader_1", "Inclusive_Leader_2", "Inclusive_Leader_3", "Inclusive_Leader_4")])
                                            
prodiversity_alpha <- alpha(Study_2_CFA[, c("ProDiversity_1", "ProDiversity_2", "ProDiversity_3", "ProDiversity_4")])

cat("Alpha for leader diversity advocacy:", incl_leader_alpha$total$raw_alpha, "\n",
    "Alpha for pro-diversity attitudes:", prodiversity_alpha$total$raw_alpha, "\n")

## Alpha for leader diversity advocacy: 0.917747 
##  Alpha for pro-diversity attitudes: 0.9107846

Table 1: Descriptives & Correlations

## 
## 
## Means, standard deviations, and correlations with confidence intervals
##  
## 
##   Variable                M    SD   1           2            3           
##   1. Male                 0.50 0.50                                      
##                                                                          
##   2. White                0.59 0.49 -.00                                 
##                                     [-.13, .13]                          
##                                                                          
##   3. Ideology             3.79 1.59 .01         .22**                    
##                                     [-.12, .14] [.09, .34]               
##                                                                          
##   4. LdrAdvocacy          3.75 1.09 .03         -.14*        -.24**      
##                                     [-.10, .15] [-.26, -.01] [-.36, -.11]
##                                                                          
##   5. PDB                  5.63 1.19 -.03        -.10         -.23**      
##                                     [-.16, .10] [-.23, .03]  [-.35, -.10]
##                                                                          
##   6. AWARE                3.83 0.93 .04         -.20**       -.23**      
##                                     [-.09, .17] [-.32, -.07] [-.34, -.10]
##                                                                          
##   7. REGULATE             4.12 0.72 .09         -.09         -.12        
##                                     [-.04, .22] [-.21, .04]  [-.25, .00] 
##                                                                          
##   8. MANAGE               3.85 0.85 .05         -.16*        -.24**      
##                                     [-.08, .17] [-.29, -.04] [-.36, -.12]
##                                                                          
##   9. OrgAlly              3.11 1.05 .02         -.04         -.25**      
##                                     [-.10, .15] [-.17, .09]  [-.37, -.13]
##                                                                          
##   10. Diversity_Voice     4.82 1.42 .05         -.05         -.15*       
##                                     [-.08, .18] [-.18, .08]  [-.27, -.02]
##                                                                          
##   11. Interracial_Contact 4.16 0.76 .00         .02          -.01        
##                                     [-.12, .13] [-.11, .14]  [-.14, .12] 
##                                                                          
##   12. Familiarity         3.18 0.86 .17*        -.01         .05         
##                                     [.04, .29]  [-.14, .12]  [-.08, .18] 
##                                                                          
##   4          5           6          7          8          9          
##                                                                      
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##                                                                      
##   .47**                                                              
##   [.36, .56]                                                         
##                                                                      
##   .29**      .36**                                                   
##   [.17, .40] [.24, .46]                                              
##                                                                      
##   .38**      .46**       .52**                                       
##   [.26, .48] [.35, .56]  [.42, .61]                                  
##                                                                      
##   .35**      .44**       .51**      .69**                            
##   [.23, .46] [.33, .54]  [.41, .60] [.62, .75]                       
##                                                                      
##   .50**      .27**       .22**      .19**      .40**                 
##   [.40, .59] [.15, .39]  [.10, .34] [.07, .31] [.29, .51]            
##                                                                      
##   .60**      .31**       .31**      .33**      .46**      .76**      
##   [.51, .68] [.19, .43]  [.19, .42] [.21, .44] [.35, .56] [.70, .81] 
##                                                                      
##   .25**      .50**       .27**      .56**      .45**      .08        
##   [.13, .37] [.40, .59]  [.15, .38] [.46, .64] [.34, .54] [-.05, .21]
##                                                                      
##   .15*       .08         .22**      .24**      .25**      .13*       
##   [.02, .27] [-.05, .21] [.09, .34] [.11, .35] [.13, .37] [.00, .25] 
##                                                                      
##   10         11         
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##   .18**                 
##   [.06, .30]            
##                         
##   .24**      .09        
##   [.12, .36] [-.04, .22]
##                         
## 
## 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.
##

Grand-Mean Center Predictor Variables

Table 2 (Step 1, A-Path): Leader Diversity Advocacy and Efficacy Beliefs

###effects of leader diversity advocacy on employee efficacy beliefs
model.AWARE <- lm(AWARE ~ PDB_c + LdrAdvocacy_c, Study2_vars)
model.REGULATE <- lm(REGULATE ~ PDB_c + LdrAdvocacy_c, Study2_vars)
model.MANAGE <- lm(MANAGE ~ PDB_c + LdrAdvocacy_c, Study2_vars)

tab_model(model.AWARE, model.REGULATE, model.MANAGE, 
          show.p = TRUE,
          show.se = TRUE,
          show.stat = TRUE,
          digits = 3,
          title = "Leader Diversity Advocacy and Efficacy Beliefs")

Leader Diversity Advocacy and Efficacy Beliefs
	AWARE					REGULATE					MANAGE
Predictors	Estimates	std. Error	CI	Statistic	p	Estimates	std. Error	CI	Statistic	p	Estimates	std. Error	CI	Statistic	p
(Intercept)	3.833	0.056	3.721 – 3.944	67.946	<0.001	4.118	0.041	4.036 – 4.199	99.758	<0.001	3.852	0.049	3.755 – 3.948	78.424	<0.001
PDB c	0.220	0.054	0.114 – 0.326	4.096	<0.001	0.221	0.039	0.143 – 0.298	5.613	<0.001	0.250	0.047	0.158 – 0.343	5.345	<0.001
LdrAdvocacy c	0.134	0.058	0.019 – 0.249	2.292	0.023	0.138	0.043	0.054 – 0.222	3.226	0.001	0.141	0.051	0.041 – 0.241	2.772	0.006
Observations	234					234					234
R² / R² adjusted	0.146 / 0.139					0.246 / 0.239					0.217 / 0.211

Table 2 (B-Path): Efficacy Beliefs and Ally Work

##effects of efficacy beliefs on organizational ally work
model.ALLY_O <- lm(OrgAlly ~ Ideology_c + PDB_c + LdrAdvocacy_c + AWARE + MANAGE, Study2_vars)

tab_model(model.ALLY_O, 
          show.p = TRUE,
          show.se = TRUE,
          show.stat = TRUE,
          digits = 3,
          title = "Efficacy Beliefs and Allyship Behavior")

Efficacy Beliefs and Allyship Behavior
	OrgAlly
Predictors	Estimates	std. Error	CI	Statistic	p
(Intercept)	1.963	0.336	1.301 – 2.626	5.840	<0.001
Ideology c	-0.070	0.038	-0.144 – 0.005	-1.844	0.067
PDB c	-0.050	0.058	-0.165 – 0.064	-0.865	0.388
LdrAdvocacy c	0.403	0.061	0.284 – 0.522	6.657	<0.001
AWARE	-0.052	0.073	-0.196 – 0.092	-0.716	0.475
MANAGE	0.347	0.084	0.182 – 0.512	4.142	<0.001
Observations	234
R² / R² adjusted	0.325 / 0.311

Hypothesis 1b: Mediation Analysis (Not Supported)

# Extract coefficients
a_est <- coef(model.AWARE)["LdrAdvocacy_c"]  # Effect of X on M (a-path)
b_est <- coef(model.ALLY_O)["AWARE"]  # Effect of M on Y (b-path)

# Extract standard errors
a_se <- summary(model.AWARE)$coefficients["LdrAdvocacy_c", "Std. Error"]
b_se <- summary(model.ALLY_O)$coefficients["AWARE", "Std. Error"]

# 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")

# Coefficients for Indirect Effect
cat("Indirect effect estimate:", mc_ci$Estimate, "\n")

## Indirect effect estimate: -0.007003258

cat("Indirect effect standard error:", mc_ci$SE, "\n")

## Indirect effect standard error: 0.01110463

# Print Monte Carlo CI: Advocacy -> Bias-Awareness -> Organizational Allyship
cat("Monte Carlo 90% CI: [", mc_ci$`90% CI`[1], ",", mc_ci$`90% CI`[2], "]\n")

## Monte Carlo 90% CI: [ -0.02689876 , 0.009086329 ]

Hypothesis 3: Mediation Analysis

# Extract coefficients
a_est <- coef(model.MANAGE)["LdrAdvocacy_c"]  # Effect of X on M (a-path)
b_est <- coef(model.ALLY_O)["MANAGE"]  # Effect of M on Y (b-path)

# Extract standard errors
a_se <- summary(model.MANAGE)$coefficients["LdrAdvocacy_c", "Std. Error"]
b_se <- summary(model.ALLY_O)$coefficients["MANAGE", "Std. Error"]

# 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")

# Coefficients for Indirect Effect
cat("Indirect effect estimate:", mc_ci$Estimate, "\n")

## Indirect effect estimate: 0.04897704

cat("Indirect effect standard error:", mc_ci$SE, "\n")

## Indirect effect standard error: 0.02168688

# Print Monte Carlo CI: Advocacy -> Diversity Management -> Organizational Allyship
cat("Monte Carlo 99% CI: [", mc_ci$`99% CI`[1], ",", mc_ci$`99% CI`[2], "]\n")

## Monte Carlo 99% CI: [ 0.003065107 , 0.1155806 ]

Table 2 (Step 2, A-Path): Moderation Analyses

Leader Diversity Advocacy & Pro-Diversity Attitudes

###moderation
model_AWARE <- lm(AWARE ~ LdrAdvocacy_c*PDB_c, Study2_vars)
model_REGULATE <- lm(REGULATE ~ LdrAdvocacy_c*PDB_c, Study2_vars)
model_MANAGE <- lm(MANAGE ~ LdrAdvocacy_c*PDB_c, Study2_vars)

tab_model(model_AWARE, model_REGULATE, model_MANAGE,
          show.p = TRUE,
          show.se = TRUE,
          show.stat = TRUE,
          digits = 3,
          title = "Regression Results (Interaction)")

Regression Results (Interaction)
	AWARE					REGULATE					MANAGE
Predictors	Estimates	std. Error	CI	Statistic	p	Estimates	std. Error	CI	Statistic	p	Estimates	std. Error	CI	Statistic	p
(Intercept)	3.783	0.062	3.662 – 3.904	61.384	<0.001	4.079	0.045	3.990 – 4.168	90.547	<0.001	3.784	0.053	3.680 – 3.889	71.370	<0.001
LdrAdvocacy c	0.176	0.062	0.054 – 0.298	2.837	0.005	0.171	0.045	0.081 – 0.260	3.766	<0.001	0.198	0.053	0.093 – 0.303	3.716	<0.001
PDB c	0.224	0.054	0.119 – 0.330	4.194	<0.001	0.224	0.039	0.147 – 0.301	5.730	<0.001	0.256	0.046	0.165 – 0.347	5.558	<0.001
LdrAdvocacy c × PDB c	0.082	0.042	-0.001 – 0.165	1.938	0.054	0.064	0.031	0.003 – 0.125	2.067	0.040	0.112	0.036	0.040 – 0.183	3.073	0.002
Observations	234					234					234
R² / R² adjusted	0.160 / 0.149					0.260 / 0.250					0.248 / 0.238

# Compare Models: Significant change in R²?
anova(model.AWARE, model_AWARE)

## Analysis of Variance Table
## 
## Model 1: AWARE ~ PDB_c + LdrAdvocacy_c
## Model 2: AWARE ~ LdrAdvocacy_c * PDB_c
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1    231 171.98                              
## 2    230 169.22  1     2.764 3.7568 0.05382 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(model.REGULATE, model_REGULATE)

## Analysis of Variance Table
## 
## Model 1: REGULATE ~ PDB_c + LdrAdvocacy_c
## Model 2: REGULATE ~ LdrAdvocacy_c * PDB_c
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1    231 92.088                              
## 2    230 90.409  1    1.6798 4.2733 0.03983 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(model.MANAGE, model_MANAGE)

## Analysis of Variance Table
## 
## Model 1: MANAGE ~ PDB_c + LdrAdvocacy_c
## Model 2: MANAGE ~ LdrAdvocacy_c * PDB_c
##   Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
## 1    231 130.39                                
## 2    230 125.25  1    5.1418 9.4422 0.002376 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Simple Slopes Analyses

sim_slopes(model_AWARE, pred = "LdrAdvocacy_c", modx = "PDB_c", modx.values = c(-1.19, 0, 1.19))

## JOHNSON-NEYMAN INTERVAL
## 
## When PDB_c is INSIDE the interval [-0.73, 97.91], the slope of
## LdrAdvocacy_c is p < .05.
## 
## Note: The range of observed values of PDB_c is [-3.63, 1.37]
## 
## SIMPLE SLOPES ANALYSIS
## 
## Slope of LdrAdvocacy_c when PDB_c = -1.19: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.08   0.06     1.21   0.23
## 
## Slope of LdrAdvocacy_c when PDB_c =  0.00: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.18   0.06     2.84   0.00
## 
## Slope of LdrAdvocacy_c when PDB_c =  1.19: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.27   0.09     2.96   0.00

sim_slopes(model_REGULATE, pred = "LdrAdvocacy_c", modx = "PDB_c", modx.values = c(-1.19, 0, 1.19))

## JOHNSON-NEYMAN INTERVAL
## 
## When PDB_c is OUTSIDE the interval [-47.13, -1.21], the slope of
## LdrAdvocacy_c is p < .05.
## 
## Note: The range of observed values of PDB_c is [-3.63, 1.37]
## 
## SIMPLE SLOPES ANALYSIS
## 
## Slope of LdrAdvocacy_c when PDB_c = -1.19: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.09   0.05     2.00   0.05
## 
## Slope of LdrAdvocacy_c when PDB_c =  0.00: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.17   0.05     3.77   0.00
## 
## Slope of LdrAdvocacy_c when PDB_c =  1.19: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.25   0.07     3.65   0.00

sim_slopes(model_MANAGE, pred = "LdrAdvocacy_c", modx = "PDB_c", modx.values = c(-1.19, 0, 1.19))

## JOHNSON-NEYMAN INTERVAL
## 
## When PDB_c is OUTSIDE the interval [-4.46, -0.87], the slope of
## LdrAdvocacy_c is p < .05.
## 
## Note: The range of observed values of PDB_c is [-3.63, 1.37]
## 
## SIMPLE SLOPES ANALYSIS
## 
## Slope of LdrAdvocacy_c when PDB_c = -1.19: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.07   0.06     1.18   0.24
## 
## Slope of LdrAdvocacy_c when PDB_c =  0.00: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.20   0.05     3.72   0.00
## 
## Slope of LdrAdvocacy_c when PDB_c =  1.19: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.33   0.08     4.16   0.00

Figures 3 & 4: Create J-N interaction plots

## Not significant at .05 alpha level
plot1 <- johnson_neyman(model_AWARE, pred = "LdrAdvocacy_c", modx = "PDB_c", plot = TRUE)$plot +
  labs(x = "Leader Diversity Advocacy", y = "Bias-Awareness Efficacy",title = NULL)

## Figure 3
plot2 <- johnson_neyman(model_REGULATE, pred = "LdrAdvocacy_c", modx = "PDB_c", plot = TRUE)$plot + labs(x = "Leader Diversity Advocacy", y = "Self-Regulation Efficacy",title = NULL)

## Figure 4
plot3 <- johnson_neyman(model_MANAGE, pred = "LdrAdvocacy_c", modx = "PDB_c", plot = TRUE)$plot +
  labs(x = "Leader Diversity Advocacy", y = "Intergroup-Management Efficacy",title = NULL)

# Arrange in 2×2 grid (with one blank space if only 3 plots)
gridExtra::grid.arrange(plot1, plot2, plot3, ncol = 2)

Hypothesis 5a: Moderated Mediation (Bias-Awareness Efficacy and Organizational Ally Work)

# Extract coefficients
a_int <- coef(model_AWARE)["LdrAdvocacy_c:PDB_c"]  # Effect of XW on M (a-path)
b_est <- coef(model.ALLY_O)["AWARE"]  # Effect of M on Y (b-path)

# Extract standard errors
a_int_se <- summary(model_AWARE)$coefficients["LdrAdvocacy_c:PDB_c", "Std. Error"]
b_se <- summary(model.ALLY_O)$coefficients["AWARE", "Std. Error"]

# 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: -0.004273348

## Standard error: 0.007071479

## Monte Carlo 90% CI: [ -0.01713979 , 0.005632251 ]

Hypothesis 5b: Moderated Mediation (Intergroup-Management Efficacy and Organizational Ally Work)

# Extract coefficients
a_int <- coef(model_MANAGE)["LdrAdvocacy_c:PDB_c"]  # Effect of XW on M (a-path)
b_est <- coef(model.ALLY_O)["MANAGE"]  # Effect of M on Y (b-path)

# Extract standard errors
a_int_se <- summary(model_MANAGE)$coefficients["LdrAdvocacy_c:PDB_c", "Std. Error"]
b_se <- summary(model.ALLY_O)$coefficients["MANAGE", "Std. Error"]

# 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: 0.03871976

## Standard error: 0.01598252

## Monte Carlo 99% CI: [ 0.005279465 , 0.08772113 ]

## 
## --- Conditional Indirect Effects ---

## At LOW PDB (-1 SD):

## Indirect effect estimate: 0.02273

## Standard error (LOW PDB): 0.02512

## Monte Carlo 90% CI: [ -0.016 , 0.066 ]

## At HIGH PDB (+1 SD):

## Indirect effect estimate: 0.115

## Standard error (HIGH PDB): 0.03702

## Monte Carlo 99% CI: [ 0.035 , 0.226 ]

Supplementary Analyses #1: Past Positive Interracial Contact

### Predicting Efficacy Beliefs, Controlling for Past Positive Interracial Contact
model.AWARE <- lm(AWARE ~ PDB + LdrAdvocacy + Interracial_Contact, Study2_vars)
model.REGULATE <- lm(REGULATE ~ PDB + LdrAdvocacy + Interracial_Contact, Study2_vars)
model.MANAGE <- lm(MANAGE ~ PDB + LdrAdvocacy + Interracial_Contact, Study2_vars)

tab_model(model.AWARE, model.REGULATE, model.MANAGE, 
          show.p = TRUE,
          show.se = TRUE,
          show.stat = TRUE,
          digits = 3,
          title = "Leader Diversity Advocacy and Efficacy Beliefs")

Leader Diversity Advocacy and Efficacy Beliefs
	AWARE					REGULATE					MANAGE
Predictors	Estimates	std. Error	CI	Statistic	p	Estimates	std. Error	CI	Statistic	p	Estimates	std. Error	CI	Statistic	p
(Intercept)	1.749	0.347	1.064 – 2.433	5.034	<0.001	1.392	0.231	0.937 – 1.847	6.025	<0.001	1.132	0.291	0.559 – 1.706	3.890	<0.001
PDB	0.175	0.060	0.057 – 0.293	2.914	0.004	0.092	0.040	0.013 – 0.171	2.307	0.022	0.146	0.050	0.047 – 0.245	2.903	0.004
LdrAdvocacy	0.132	0.058	0.017 – 0.247	2.265	0.024	0.132	0.039	0.056 – 0.208	3.409	0.001	0.136	0.049	0.040 – 0.232	2.792	0.006
Interracial Contact	0.146	0.086	-0.024 – 0.315	1.696	0.091	0.412	0.057	0.299 – 0.524	7.205	<0.001	0.333	0.072	0.192 – 0.475	4.634	<0.001
Observations	234					234					234
R² / R² adjusted	0.156 / 0.145					0.385 / 0.377					0.284 / 0.275

Supplementary Analyses #2: Diversity Voice

###effects of leader diversity advocacy on employee efficacy beliefs
model.AWARE <- lm(AWARE ~ PDB_c + LdrAdvocacy_c, Study2_vars)
model.REGULATE <- lm(REGULATE ~ PDB_c + LdrAdvocacy_c, Study2_vars)
model.MANAGE <- lm(MANAGE ~ PDB_c + LdrAdvocacy_c, Study2_vars)

tab_model(model.AWARE, model.REGULATE, model.MANAGE, 
          show.p = TRUE,
          show.se = TRUE,
          show.stat = TRUE,
          digits = 3,
          title = "Leader Diversity Advocacy and Efficacy Beliefs")

Leader Diversity Advocacy and Efficacy Beliefs
	AWARE					REGULATE					MANAGE
Predictors	Estimates	std. Error	CI	Statistic	p	Estimates	std. Error	CI	Statistic	p	Estimates	std. Error	CI	Statistic	p
(Intercept)	3.833	0.056	3.721 – 3.944	67.946	<0.001	4.118	0.041	4.036 – 4.199	99.758	<0.001	3.852	0.049	3.755 – 3.948	78.424	<0.001
PDB c	0.220	0.054	0.114 – 0.326	4.096	<0.001	0.221	0.039	0.143 – 0.298	5.613	<0.001	0.250	0.047	0.158 – 0.343	5.345	<0.001
LdrAdvocacy c	0.134	0.058	0.019 – 0.249	2.292	0.023	0.138	0.043	0.054 – 0.222	3.226	0.001	0.141	0.051	0.041 – 0.241	2.772	0.006
Observations	234					234					234
R² / R² adjusted	0.146 / 0.139					0.246 / 0.239					0.217 / 0.211

##effects of efficacy beliefs on diversity voice
model.VOICE <- lm(Diversity_Voice ~ Ideology_c + PDB_c + LdrAdvocacy_c + AWARE + MANAGE, Study2_vars)

tab_model(model.VOICE, 
          show.p = TRUE,
          show.se = TRUE,
          show.stat = TRUE,
          digits = 3,
          title = "Efficacy Beliefs and Diversity Voice")

Efficacy Beliefs and Diversity Voice
	Diversity_Voice
Predictors	Estimates	std. Error	CI	Statistic	p
(Intercept)	2.697	0.415	1.878 – 3.515	6.494	<0.001
Ideology c	0.036	0.047	-0.056 – 0.128	0.766	0.444
PDB c	-0.080	0.072	-0.221 – 0.062	-1.108	0.269
LdrAdvocacy c	0.685	0.075	0.538 – 0.832	9.163	<0.001
AWARE	0.056	0.090	-0.122 – 0.234	0.621	0.535
MANAGE	0.499	0.104	0.295 – 0.703	4.816	<0.001
Observations	234
R² / R² adjusted	0.439 / 0.427

Hypothesis 1b

# Extract coefficients
a_est <- coef(model.AWARE)["LdrAdvocacy_c"]  # Effect of X on M (a-path)
b_est <- coef(model.VOICE)["AWARE"]  # Effect of M on Y (b-path)

# Extract standard errors
a_se <- summary(model.AWARE)$coefficients["LdrAdvocacy_c", "Std. Error"]
b_se <- summary(model.VOICE)$coefficients["AWARE", "Std. Error"]

# 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")

# Coefficients for Indirect Effect
cat("Indirect effect estimate:", mc_ci$Estimate, "\n")

## Indirect effect estimate: 0.007501663

cat("Indirect effect standard error:", mc_ci$SE, "\n")

## Indirect effect standard error: 0.01358523

# Print Monte Carlo CI: Advocacy -> Bias-Awareness -> Diversity Voice
cat("Monte Carlo 90% CI: [", mc_ci$`90% CI`[1], ",", mc_ci$`90% CI`[2], "]\n")

## Monte Carlo 90% CI: [ -0.01254505 , 0.03165335 ]

Hypothesis 3

# Extract coefficients
a_est <- coef(model.MANAGE)["LdrAdvocacy_c"]  # Effect of X on M (a-path)
b_est <- coef(model.VOICE)["MANAGE"]  # Effect of M on Y (b-path)

# Extract standard errors
a_se <- summary(model.MANAGE)$coefficients["LdrAdvocacy_c", "Std. Error"]
b_se <- summary(model.VOICE)$coefficients["MANAGE", "Std. Error"]

# 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")

# Coefficients for Indirect Effect
cat("Indirect effect estimate:", mc_ci$Estimate, "\n")

## Indirect effect estimate: 0.07034473

cat("Indirect effect standard error:", mc_ci$SE, "\n")

## Indirect effect standard error: 0.02975314

# Print Monte Carlo CI: Advocacy -> Diversity Management -> Diversity Voice
cat("Monte Carlo 99% CI: [", mc_ci$`99% CI`[1], ",", mc_ci$`99% CI`[2], "]\n")

## Monte Carlo 99% CI: [ 0.004644486 , 0.1598071 ]

Hypothesis 5a

# Extract coefficients
a_int <- coef(model_AWARE)["LdrAdvocacy_c:PDB_c"]  # Effect of XW on M (a-path)
b_est <- coef(model.VOICE)["AWARE"]  # Effect of M on Y (b-path)

# Extract standard errors
a_int_se <- summary(model_AWARE)$coefficients["LdrAdvocacy_c:PDB_c", "Std. Error"]
b_se <- summary(model.VOICE)$coefficients["AWARE", "Std. Error"]

# 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: 0.004577472

## Standard error: 0.008627602

## Monte Carlo 90% CI: [ -0.007786407 , 0.0201442 ]

Hypothesis 5b

# Extract coefficients
a_int <- coef(model_MANAGE)["LdrAdvocacy_c:PDB_c"]  # Effect of XW on M (a-path)
b_est <- coef(model.VOICE)["MANAGE"]  # Effect of M on Y (b-path)

# Extract standard errors
a_int_se <- summary(model_MANAGE)$coefficients["LdrAdvocacy_c:PDB_c", "Std. Error"]
b_se <- summary(model.VOICE)$coefficients["MANAGE", "Std. Error"]

# 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: 0.05561241

## Standard error: 0.02179405

## Monte Carlo 99% CI: [ 0.008163945 , 0.1211712 ]

## 
## --- Conditional Indirect Effects ---

## At LOW PDB (-1 SD):

## Indirect effect estimate: 0.03265

## Standard error (LOW PDB): 0.0356

## Monte Carlo 90% CI: [ -0.023 , 0.093 ]

## At HIGH PDB (+1 SD):

## Indirect effect estimate: 0.165

## Standard error (HIGH PDB): 0.04894

## Monte Carlo 99% CI: [ 0.059 , 0.31 ]

Study 1 (Diversity Advocacy)

Anonymous

2025-08-14

Load Packages

Study 2

Confirmatory Factor Analysis

Three-Factor Model (Efficacy Scales)

One-Factor Model (Efficacy Scales)

Six-Factor Measurement Model (All Six Multi-Item Scales)

Reliability Estimates (Cronbach Alpha) - Study 1 Measures

Table 1: Descriptives & Correlations

Grand-Mean Center Predictor Variables

Table 2 (Step 1, A-Path): Leader Diversity Advocacy and Efficacy Beliefs

Table 2 (B-Path): Efficacy Beliefs and Ally Work

Hypothesis 1b: Mediation Analysis (Not Supported)

Hypothesis 3: Mediation Analysis

Table 2 (Step 2, A-Path): Moderation Analyses

Simple Slopes Analyses

Figures 3 & 4: Create J-N interaction plots

Hypothesis 5a: Moderated Mediation (Bias-Awareness Efficacy and Organizational Ally Work)

Hypothesis 5b: Moderated Mediation (Intergroup-Management Efficacy and Organizational Ally Work)

Supplementary Analyses #1: Past Positive Interracial Contact

Supplementary Analyses #2: Diversity Voice

Hypothesis 1b

Hypothesis 3

Hypothesis 5a

Hypothesis 5b