Reflection

set.wd()

## ✔ Set working directory to "G:/我的云端硬盘/R/Reflection"

tempdir()

## [1] "C:\\Users\\EASONZ~1\\AppData\\Local\\Temp\\Rtmpshde8J"

1 PREPARATION

1.1 Loading Data

#Week 1
data=rio::import("BWGra3.ABBA.sav")%>%as.data.table()
## Delete for making 12,11
data=data[!(W.Day==13|W.Day==14|W.Day==15)]
## Delete for making 14,11
#data=data[!(W.Day==13|W.Day==12|W.Day==15)]
## Delete for making 12,11,14
#data=data[!(W.Day==13|W.Day==15)]
#data=rio::import("BWGra3.sav")%>%as.data.table()
#data=data[!(W.Day==13)]#|W.Day==13)]

#Week 2
#data=rio::import("BWReflect2.ESMw2.sav")%>%as.data.table()
##-Choose X
#data=data[,!"Manipulation"]
#data <- rename(data, c(WA.GraceV = "Manipulation"))

CreativeProcessEngagementV= lmer(WP.CreativeProcessEngagementV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))

IdeaGenerationV= lmer(WP.IdeaGenerationV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))

ImprovisionV= lmer(WA.ImprovisionV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))

WorkAbsorptionV= lmer(WA.WorkAbsorptionV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))

TakingChargeV= lmer(WP.TakingChargeV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
screenreg(list(CreativeProcessEngagementV,IdeaGenerationV,ImprovisionV,WorkAbsorptionV,TakingChargeV), stars = c(0.01, 0.05, 0.1))

## 
## ======================================================================================
##                        Model 1      Model 2      Model 3      Model 4      Model 5    
## --------------------------------------------------------------------------------------
## (Intercept)               3.19 ***     3.06 ***     3.42 ***     3.28 ***     2.83 ***
##                          (0.06)       (0.07)       (0.06)       (0.07)       (0.08)   
## Manipulation              0.10 ***     0.06         0.07         0.07         0.08    
##                          (0.04)       (0.05)       (0.05)       (0.06)       (0.05)   
## --------------------------------------------------------------------------------------
## AIC                     427.06       511.91       479.26       588.71       560.13    
## BIC                     441.13       525.98       493.54       602.98       574.20    
## Log Likelihood         -209.53      -251.96      -235.63      -290.36      -276.07    
## Num. obs.               249          249          262          262          249       
## Num. groups: B.ID       138          138          146          146          138       
## Var: B.ID (Intercept)     0.42         0.57         0.30         0.48         0.71    
## Var: Residual             0.09         0.13         0.15         0.22         0.15    
## ======================================================================================
## *** p < 0.01; ** p < 0.05; * p < 0.1

#stargazer(CreativeProcessEngagementV),IdeaGenerationV,ImprovisionV,WorkAbsorptionV,TakingChargeV)
#HLM_summary(CreativeProcessEngagementV)
#HLM_summary(IdeaGenerationV)
#HLM_summary(ImprovisionV)
#HLM_summary(WorkAbsorptionV)
#HLM_summary(TakingChargeV)

1.2 Theoretical model

#covar=list(names=c("C"),site=list(c("M","Y")))
pmacroModel(4,labels=list(X="Gratitude", M="Mediators", Y="Outcomes", W="Instability of SBF"))#covar=covar,

1.3 Primary analysis

Freq(data$Manipulation)

## Frequency Statistics:
## ───────────
##      N    %
## ───────────
## 0  136 50.4
## 1  134 49.6
## ───────────
## Total N = 270

Freq(data$W.Day)

## Frequency Statistics:
## ────────────
##       N    %
## ────────────
## 11  136 50.4
## 12  134 49.6
## ────────────
## Total N = 270

1.3.1 ICC and RWG

HLM_ICC_rWG(data, group="B.ID", icc.var="Manipulation")

## 
## ------ Sample Size Information ------
## 
## Level 1: N = 270 observations ("Manipulation")
## Level 2: K = 147 groups ("B.ID")
## 
##        n (group sizes)
## Min.             1.000
## Median           2.000
## Mean             1.837
## Max.             2.000
## 
## ------ ICC(1), ICC(2), and rWG ------
## 
## ICC variable: "Manipulation"
## 
## ICC(1) = 0.000 (non-independence of data)
## ICC(2) = 0.000 (reliability of group means)
## 
## rWG variable: "Manipulation"
## 
## rWG (within-group agreement for single-item measures)
## ────────────────────────────────────────────────────
##       Min. 1st Qu. Median  Mean 3rd Qu.  Max.   NA's
## ────────────────────────────────────────────────────
## rWG  0.000   0.000  0.000 0.000   0.000 0.000 24.000
## ────────────────────────────────────────────────────

HLM_ICC_rWG(data, group="B.ID",  icc.var="WP.InformationSearchV")

## 
## ------ Sample Size Information ------
## 
## Level 1: N = 249 observations ("WP.InformationSearchV")
## Level 2: K = 138 groups ("B.ID")
## 
##        n (group sizes)
## Min.             1.000
## Median           2.000
## Mean             1.804
## Max.             2.000
## 
## ------ ICC(1), ICC(2), and rWG ------
## 
## ICC variable: "WP.InformationSearchV"
## 
## ICC(1) = 0.600 (non-independence of data)
## ICC(2) = 0.721 (reliability of group means)
## 
## rWG variable: "WP.InformationSearchV"
## 
## rWG (within-group agreement for single-item measures)
## ────────────────────────────────────────────────────
##       Min. 1st Qu. Median  Mean 3rd Qu.  Max.   NA's
## ────────────────────────────────────────────────────
## rWG  0.000   0.833  0.958 0.849   1.000 1.000 27.000
## ────────────────────────────────────────────────────

1.4 Manipulation check

1.4.1 MLM

CEM11RF.a1= lmer(WA.WorkReflectionV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
HLM_summary(CEM11RF.a1)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.WorkReflectionV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 262
## Level-2 Groups/Clusters: B.ID, 146
## 
## Model Fit:
## AIC = 515.974
## BIC = 530.248
## R_(m)² = 0.00012  (Marginal R²: fixed effects)
## R_(c)² = 0.70928  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.02    0.02  1.00 120.49 0.10  .756    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.WorkReflectionV
## ──────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ──────────────────────────────────────────────────────────────────
## (Intercept)    3.170 (0.062) 50.90 191.5 <.001 *** [ 3.047, 3.293]
## Manipulation  -0.016 (0.051) -0.31 120.5  .756     [-0.117, 0.085]
## ──────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.WorkReflectionV
## ──────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p       [95% CI of β]
## ──────────────────────────────────────────────────────────────────
## Manipulation  -0.011 (0.035) -0.31 120.5  .756     [-0.081, 0.059]
## ──────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     146 (Intercept)  0.38447 0.70925
##  Residual                  0.15761        
## ──────────────────────────────────────────

Model1= lmer(WP.SupervisoryBehavioralFeedbackV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model2= lmer(WP.SupervisoryPositiveBehavioralFeedbackV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model3= lmer(WP.SupervisoryNegativeBehavioralFeedbackV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model4= lmer(WP.learningBehaviorV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model5= lmer(WP.JobCraftingV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model6= lmer(WP.CreativeProcessEngagementV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model7= lmer(WP.ProblemIdentificationV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model8= lmer(WP.InformationSearchV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model9= lmer(WP.IdeaGenerationV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model10= lmer(WP.SocialLearningV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model11= lmer(WP.ObservationalLearningV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model12= lmer(WP.AdviceSeekingV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model13= lmer(WP.PerformanceImprovementV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model14= lmer(WP.TakingChargeV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model15= lmer(WA.WorkReflectionV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model16= lmer(WA.PositiveWorkReflectionV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model17= lmer(WA.NegativeWorkReflectionV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model18= lmer(WA.RuminationV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model19= lmer(WA.PositiveAffectV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model20= lmer(WA.NegativeAffectV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model21= lmer(WA.ImprovisionV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model22= lmer(WA.WorkAbsorptionV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model23= lmer(WA.ThrivingAtWorkLearningV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model24= lmer(WA.WorkRelatedFlowV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model25= lmer(WA.InspirationV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
#Model26= lmer(WA.GraceV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model27= lmer(WA.ExerciseV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model28= lmer(WA.SleepQualityV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model29= lmer(WA.ReadingV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model30= lmer(WA.PaperReadV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model31= lmer(WA.EReadV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
Model32= lmer(WA.SleepQuantityV~Manipulation + (1|B.ID), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))

HLM_summary(Model1)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WP.SupervisoryBehavioralFeedbackV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 249
## Level-2 Groups/Clusters: B.ID, 138
## 
## Model Fit:
## AIC = 499.103
## BIC = 513.172
## R_(m)² = 0.00136  (Marginal R²: fixed effects)
## R_(c)² = 0.81252  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.20    0.20  1.00 114.64 1.64  .203    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WP.SupervisoryBehavioralFeedbackV
## ──────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ──────────────────────────────────────────────────────────────────
## (Intercept)    3.373 (0.071) 47.54 170.5 <.001 *** [ 3.233, 3.513]
## Manipulation  -0.060 (0.047) -1.28 114.6  .203     [-0.153, 0.033]
## ──────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WP.SupervisoryBehavioralFeedbackV
## ──────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p       [95% CI of β]
## ──────────────────────────────────────────────────────────────────
## Manipulation  -0.037 (0.029) -1.28 114.6  .203     [-0.094, 0.020]
## ──────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     138 (Intercept)  0.53987 0.81226
##  Residual                  0.12478        
## ──────────────────────────────────────────

HLM_summary(Model2)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WP.SupervisoryPositiveBehavioralFeedbackV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 249
## Level-2 Groups/Clusters: B.ID, 138
## 
## Model Fit:
## AIC = 570.772
## BIC = 584.841
## R_(m)² = 0.00000  (Marginal R²: fixed effects)
## R_(c)² = 0.74685  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.00    0.00  1.00 115.99 0.00 1.000    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WP.SupervisoryPositiveBehavioralFeedbackV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   3.300 (0.078) 42.39 181.3 <.001 *** [ 3.147, 3.454]
## Manipulation  0.000 (0.059)  0.00 116.0 1.000     [-0.117, 0.117]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WP.SupervisoryPositiveBehavioralFeedbackV
## ────────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p       [95% CI of β]
## ────────────────────────────────────────────────────────────────
## Manipulation  0.000 (0.033) 0.00 116.0 1.000     [-0.066, 0.066]
## ────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     138 (Intercept)  0.59038 0.74685
##  Residual                  0.20011        
## ──────────────────────────────────────────

HLM_summary(Model3)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WP.SupervisoryNegativeBehavioralFeedbackV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 249
## Level-2 Groups/Clusters: B.ID, 138
## 
## Model Fit:
## AIC = 550.584
## BIC = 564.654
## R_(m)² = 0.00389  (Marginal R²: fixed effects)
## R_(c)² = 0.77690  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.68    0.68  1.00 115.49 3.96  .049 *  
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WP.SupervisoryNegativeBehavioralFeedbackV
## ───────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p      [95% CI of b/γ]
## ───────────────────────────────────────────────────────────────────
## (Intercept)    3.430 (0.076) 44.95 176.6 <.001 *** [ 3.280,  3.581]
## Manipulation  -0.109 (0.055) -1.99 115.5  .049 *   [-0.217, -0.000]
## ───────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WP.SupervisoryNegativeBehavioralFeedbackV
## ───────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p        [95% CI of β]
## ───────────────────────────────────────────────────────────────────
## Manipulation  -0.062 (0.031) -1.99 115.5  .049 *   [-0.125, -0.000]
## ───────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     138 (Intercept)  0.59261 0.77603
##  Residual                  0.17103        
## ──────────────────────────────────────────

HLM_summary(Model4)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WP.learningBehaviorV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 249
## Level-2 Groups/Clusters: B.ID, 138
## 
## Model Fit:
## AIC = 589.676
## BIC = 603.746
## R_(m)² = 0.00055  (Marginal R²: fixed effects)
## R_(c)² = 0.62761  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.09    0.09  1.00 120.59 0.34  .561    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WP.learningBehaviorV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   3.172 (0.076) 41.64 201.0 <.001 *** [ 3.022, 3.322]
## Manipulation  0.040 (0.069)  0.58 120.6  .561     [-0.096, 0.177]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WP.learningBehaviorV
## ────────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p       [95% CI of β]
## ────────────────────────────────────────────────────────────────
## Manipulation  0.023 (0.040) 0.58 120.6  .561     [-0.056, 0.103]
## ────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     138 (Intercept)  0.46518 0.62740
##  Residual                  0.27626        
## ──────────────────────────────────────────

HLM_summary(Model5)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WP.JobCraftingV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 249
## Level-2 Groups/Clusters: B.ID, 138
## 
## Model Fit:
## AIC = 460.095
## BIC = 474.165
## R_(m)² = 0.00016  (Marginal R²: fixed effects)
## R_(c)² = 0.76481  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.02    0.02  1.00 115.24 0.16  .692    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WP.JobCraftingV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   3.017 (0.063) 47.87 178.1 <.001 *** [ 2.892, 3.141]
## Manipulation  0.018 (0.046)  0.40 115.2  .692     [-0.073, 0.110]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WP.JobCraftingV
## ────────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p       [95% CI of β]
## ────────────────────────────────────────────────────────────────
## Manipulation  0.013 (0.032) 0.40 115.2  .692     [-0.051, 0.077]
## ────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     138 (Intercept)  0.39724 0.76477
##  Residual                  0.12218        
## ──────────────────────────────────────────

HLM_summary(Model6)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WP.CreativeProcessEngagementV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 249
## Level-2 Groups/Clusters: B.ID, 138
## 
## Model Fit:
## AIC = 427.060
## BIC = 441.129
## R_(m)² = 0.00524  (Marginal R²: fixed effects)
## R_(c)² = 0.82247  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.60    0.60  1.00 112.53 6.67  .011 *  
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WP.CreativeProcessEngagementV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   3.191 (0.062) 51.57 167.2 <.001 ***  [3.069, 3.313]
## Manipulation  0.103 (0.040)  2.58 112.5  .011 *    [0.024, 0.182]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WP.CreativeProcessEngagementV
## ───────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p      [95% CI of β]
## ───────────────────────────────────────────────────────────────
## Manipulation  0.074 (0.029) 2.58 112.5  .011 *   [0.017, 0.130]
## ───────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     138 (Intercept)  0.41625 0.82154
##  Residual                  0.09042        
## ──────────────────────────────────────────

HLM_summary(Model7)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WP.ProblemIdentificationV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 249
## Level-2 Groups/Clusters: B.ID, 138
## 
## Model Fit:
## AIC = 602.053
## BIC = 616.122
## R_(m)² = 0.01563  (Marginal R²: fixed effects)
## R_(c)² = 0.55649  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    2.71    2.71  1.00 119.93 8.22  .005 ** 
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WP.ProblemIdentificationV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   3.131 (0.076) 41.13 210.3 <.001 ***  [2.980, 3.281]
## Manipulation  0.215 (0.075)  2.87 119.9  .005 **   [0.067, 0.364]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WP.ProblemIdentificationV
## ───────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p      [95% CI of β]
## ───────────────────────────────────────────────────────────────
## Manipulation  0.125 (0.044) 2.87 119.9  .005 **  [0.039, 0.212]
## ───────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     138 (Intercept)  0.40227 0.54945
##  Residual                  0.32986        
## ──────────────────────────────────────────

HLM_summary(Model8)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WP.InformationSearchV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 249
## Level-2 Groups/Clusters: B.ID, 138
## 
## Model Fit:
## AIC = 560.167
## BIC = 574.237
## R_(m)² = 0.00106  (Marginal R²: fixed effects)
## R_(c)² = 0.60175  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.16    0.16  1.00 116.40 0.62  .433    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WP.InformationSearchV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   3.471 (0.071) 48.84 201.8 <.001 *** [ 3.331, 3.611]
## Manipulation  0.052 (0.066)  0.79 116.4  .433     [-0.079, 0.184]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WP.InformationSearchV
## ────────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p       [95% CI of β]
## ────────────────────────────────────────────────────────────────
## Manipulation  0.033 (0.042) 0.79 116.4  .433     [-0.050, 0.116]
## ────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     138 (Intercept)  0.38667 0.60132
##  Residual                  0.25636        
## ──────────────────────────────────────────

HLM_summary(Model9)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WP.IdeaGenerationV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 249
## Level-2 Groups/Clusters: B.ID, 138
## 
## Model Fit:
## AIC = 511.911
## BIC = 525.981
## R_(m)² = 0.00136  (Marginal R²: fixed effects)
## R_(c)² = 0.81096  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.22    0.22  1.00 113.08 1.63  .204    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WP.IdeaGenerationV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   3.062 (0.073) 42.12 169.4 <.001 *** [ 2.919, 3.206]
## Manipulation  0.062 (0.048)  1.28 113.1  .204     [-0.034, 0.157]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WP.IdeaGenerationV
## ────────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p       [95% CI of β]
## ────────────────────────────────────────────────────────────────
## Manipulation  0.038 (0.029) 1.28 113.1  .204     [-0.021, 0.096]
## ────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     138 (Intercept)  0.56563 0.81070
##  Residual                  0.13208        
## ──────────────────────────────────────────

HLM_summary(Model10)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WP.SocialLearningV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 249
## Level-2 Groups/Clusters: B.ID, 138
## 
## Model Fit:
## AIC = 545.141
## BIC = 559.211
## R_(m)² = 0.00001  (Marginal R²: fixed effects)
## R_(c)² = 0.76120  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.00    0.00  1.00 114.26 0.01  .926    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WP.SocialLearningV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   3.185 (0.075) 42.67 177.8 <.001 *** [ 3.038, 3.333]
## Manipulation  0.005 (0.055)  0.09 114.3  .926     [-0.104, 0.114]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WP.SocialLearningV
## ────────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p       [95% CI of β]
## ────────────────────────────────────────────────────────────────
## Manipulation  0.003 (0.033) 0.09 114.3  .926     [-0.062, 0.068]
## ────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     138 (Intercept)  0.55466 0.76119
##  Residual                  0.17401        
## ──────────────────────────────────────────

HLM_summary(Model11)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WP.ObservationalLearningV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 249
## Level-2 Groups/Clusters: B.ID, 138
## 
## Model Fit:
## AIC = 619.965
## BIC = 634.034
## R_(m)² = 0.00025  (Marginal R²: fixed effects)
## R_(c)² = 0.70051  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.05    0.05  1.00 115.48 0.19  .664    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WP.ObservationalLearningV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   3.002 (0.084) 35.87 187.6 <.001 *** [ 2.837, 3.167]
## Manipulation  0.030 (0.069)  0.44 115.5  .664     [-0.106, 0.166]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WP.ObservationalLearningV
## ────────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p       [95% CI of β]
## ────────────────────────────────────────────────────────────────
## Manipulation  0.016 (0.036) 0.44 115.5  .664     [-0.056, 0.088]
## ────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     138 (Intercept)  0.63447 0.70044
##  Residual                  0.27135        
## ──────────────────────────────────────────

HLM_summary(Model12)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WP.AdviceSeekingV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 249
## Level-2 Groups/Clusters: B.ID, 138
## 
## Model Fit:
## AIC = 629.413
## BIC = 643.482
## R_(m)² = 0.00032  (Marginal R²: fixed effects)
## R_(c)² = 0.69529  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.07    0.07  1.00 116.69 0.24  .624    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WP.AdviceSeekingV
## ──────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ──────────────────────────────────────────────────────────────────
## (Intercept)    3.462 (0.085) 40.69 189.2 <.001 *** [ 3.294, 3.630]
## Manipulation  -0.035 (0.070) -0.49 116.7  .624     [-0.174, 0.105]
## ──────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WP.AdviceSeekingV
## ──────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p       [95% CI of β]
## ──────────────────────────────────────────────────────────────────
## Manipulation  -0.018 (0.037) -0.49 116.7  .624     [-0.091, 0.055]
## ──────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     138 (Intercept)  0.65013 0.69519
##  Residual                  0.28505        
## ──────────────────────────────────────────

HLM_summary(Model13)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WP.PerformanceImprovementV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 249
## Level-2 Groups/Clusters: B.ID, 138
## 
## Model Fit:
## AIC = 609.577
## BIC = 623.647
## R_(m)² = 0.00013  (Marginal R²: fixed effects)
## R_(c)² = 0.70993  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.03    0.03  1.00 115.64 0.10  .752    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WP.PerformanceImprovementV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   3.010 (0.082) 36.55 186.4 <.001 *** [ 2.848, 3.173]
## Manipulation  0.021 (0.067)  0.32 115.6  .752     [-0.111, 0.153]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WP.PerformanceImprovementV
## ────────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p       [95% CI of β]
## ────────────────────────────────────────────────────────────────
## Manipulation  0.011 (0.036) 0.32 115.6  .752     [-0.060, 0.082]
## ────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     138 (Intercept)  0.62388 0.70990
##  Residual                  0.25495        
## ──────────────────────────────────────────

HLM_summary(Model14)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WP.TakingChargeV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 249
## Level-2 Groups/Clusters: B.ID, 138
## 
## Model Fit:
## AIC = 560.130
## BIC = 574.200
## R_(m)² = 0.00203  (Marginal R²: fixed effects)
## R_(c)² = 0.82244  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.40    0.40  1.00 113.29 2.59  .110    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WP.TakingChargeV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   2.826 (0.081) 34.87 167.8 <.001 *** [ 2.666, 2.986]
## Manipulation  0.084 (0.052)  1.61 113.3  .110     [-0.019, 0.188]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WP.TakingChargeV
## ────────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p       [95% CI of β]
## ────────────────────────────────────────────────────────────────
## Manipulation  0.046 (0.028) 1.61 113.3  .110     [-0.011, 0.102]
## ────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     138 (Intercept)  0.71474 0.82208
##  Residual                  0.15469        
## ──────────────────────────────────────────

HLM_summary(Model15)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.WorkReflectionV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 262
## Level-2 Groups/Clusters: B.ID, 146
## 
## Model Fit:
## AIC = 515.974
## BIC = 530.248
## R_(m)² = 0.00012  (Marginal R²: fixed effects)
## R_(c)² = 0.70928  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.02    0.02  1.00 120.49 0.10  .756    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.WorkReflectionV
## ──────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ──────────────────────────────────────────────────────────────────
## (Intercept)    3.170 (0.062) 50.90 191.5 <.001 *** [ 3.047, 3.293]
## Manipulation  -0.016 (0.051) -0.31 120.5  .756     [-0.117, 0.085]
## ──────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.WorkReflectionV
## ──────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p       [95% CI of β]
## ──────────────────────────────────────────────────────────────────
## Manipulation  -0.011 (0.035) -0.31 120.5  .756     [-0.081, 0.059]
## ──────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     146 (Intercept)  0.38447 0.70925
##  Residual                  0.15761        
## ──────────────────────────────────────────

HLM_summary(Model16)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.PositiveWorkReflectionV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 262
## Level-2 Groups/Clusters: B.ID, 146
## 
## Model Fit:
## AIC = 609.782
## BIC = 624.056
## R_(m)² = 0.00094  (Marginal R²: fixed effects)
## R_(c)² = 0.71043  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.18    0.18  1.00 122.22 0.78  .379    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.PositiveWorkReflectionV
## ──────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ──────────────────────────────────────────────────────────────────
## (Intercept)    3.308 (0.075) 44.32 192.7 <.001 *** [ 3.161, 3.455]
## Manipulation  -0.054 (0.061) -0.88 122.2  .379     [-0.175, 0.067]
## ──────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.PositiveWorkReflectionV
## ──────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p       [95% CI of β]
## ──────────────────────────────────────────────────────────────────
## Manipulation  -0.031 (0.035) -0.88 122.2  .379     [-0.100, 0.038]
## ──────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     146 (Intercept)  0.55286 0.71016
##  Residual                  0.22564        
## ──────────────────────────────────────────

HLM_summary(Model17)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.NegativeWorkReflectionV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 262
## Level-2 Groups/Clusters: B.ID, 146
## 
## Model Fit:
## AIC = 647.841
## BIC = 662.114
## R_(m)² = 0.00011  (Marginal R²: fixed effects)
## R_(c)² = 0.60930  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.02    0.02  1.00 124.95 0.07  .791    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.NegativeWorkReflectionV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   3.031 (0.077) 39.56 208.6 <.001 *** [ 2.880, 3.182]
## Manipulation  0.019 (0.072)  0.26 124.9  .791     [-0.124, 0.162]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.NegativeWorkReflectionV
## ────────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p       [95% CI of β]
## ────────────────────────────────────────────────────────────────
## Manipulation  0.011 (0.040) 0.26 124.9  .791     [-0.069, 0.090]
## ────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     146 (Intercept)  0.49423 0.60925
##  Residual                  0.31698        
## ──────────────────────────────────────────

HLM_summary(Model18)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.RuminationV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 262
## Level-2 Groups/Clusters: B.ID, 146
## 
## Model Fit:
## AIC = 524.662
## BIC = 538.936
## R_(m)² = 0.00521  (Marginal R²: fixed effects)
## R_(c)² = 0.70801  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.70    0.70  1.00 122.16 4.28  .041 *  
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.RuminationV
## ───────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p      [95% CI of b/γ]
## ───────────────────────────────────────────────────────────────────
## (Intercept)    3.462 (0.063) 54.76 193.2 <.001 *** [ 3.338,  3.587]
## Manipulation  -0.108 (0.052) -2.07 122.2  .041 *   [-0.211, -0.005]
## ───────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.RuminationV
## ───────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p        [95% CI of β]
## ───────────────────────────────────────────────────────────────────
## Manipulation  -0.073 (0.035) -2.07 122.2  .041 *   [-0.142, -0.003]
## ───────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     146 (Intercept)  0.39462 0.70648
##  Residual                  0.16395        
## ──────────────────────────────────────────

HLM_summary(Model19)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.PositiveAffectV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 262
## Level-2 Groups/Clusters: B.ID, 146
## 
## Model Fit:
## AIC = 586.249
## BIC = 600.523
## R_(m)² = 0.00526  (Marginal R²: fixed effects)
## R_(c)² = 0.63481  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.84    0.84  1.00 123.09 3.49  .064 .  
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.PositiveAffectV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   3.493 (0.069) 50.85 204.2 <.001 *** [ 3.358, 3.629]
## Manipulation  0.117 (0.063)  1.87 123.1  .064 .   [-0.007, 0.242]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.PositiveAffectV
## ────────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p       [95% CI of β]
## ────────────────────────────────────────────────────────────────
## Manipulation  0.073 (0.039) 1.87 123.1  .064 .   [-0.004, 0.150]
## ────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     146 (Intercept)  0.41376 0.63288
##  Residual                  0.24002        
## ──────────────────────────────────────────

HLM_summary(Model20)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.NegativeAffectV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 262
## Level-2 Groups/Clusters: B.ID, 146
## 
## Model Fit:
## AIC = 533.006
## BIC = 547.279
## R_(m)² = 0.00005  (Marginal R²: fixed effects)
## R_(c)² = 0.58525  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.01    0.01  1.00 126.59 0.03  .864    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.NegativeAffectV
## ──────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ──────────────────────────────────────────────────────────────────
## (Intercept)    2.039 (0.061) 33.48 212.8 <.001 *** [ 1.919, 2.159]
## Manipulation  -0.010 (0.059) -0.17 126.6  .864     [-0.127, 0.107]
## ──────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.NegativeAffectV
## ──────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p       [95% CI of β]
## ──────────────────────────────────────────────────────────────────
## Manipulation  -0.007 (0.041) -0.17 126.6  .864     [-0.089, 0.075]
## ──────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     146 (Intercept)  0.29927 0.58523
##  Residual                  0.21211        
## ──────────────────────────────────────────

HLM_summary(Model21)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.ImprovisionV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 262
## Level-2 Groups/Clusters: B.ID, 146
## 
## Model Fit:
## AIC = 479.264
## BIC = 493.537
## R_(m)² = 0.00235  (Marginal R²: fixed effects)
## R_(c)² = 0.67330  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.26    0.26  1.00 120.96 1.74  .190    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.ImprovisionV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   3.416 (0.057) 60.01 197.1 <.001 *** [ 3.304, 3.528]
## Manipulation  0.065 (0.049)  1.32 121.0  .190     [-0.033, 0.163]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.ImprovisionV
## ────────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p       [95% CI of β]
## ────────────────────────────────────────────────────────────────
## Manipulation  0.049 (0.037) 1.32 121.0  .190     [-0.025, 0.123]
## ────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     146 (Intercept)  0.30324 0.67253
##  Residual                  0.14765        
## ──────────────────────────────────────────

HLM_summary(Model22)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.WorkAbsorptionV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 262
## Level-2 Groups/Clusters: B.ID, 146
## 
## Model Fit:
## AIC = 588.711
## BIC = 602.984
## R_(m)² = 0.00180  (Marginal R²: fixed effects)
## R_(c)² = 0.69151  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.30    0.30  1.00 123.52 1.40  .239    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.WorkAbsorptionV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   3.281 (0.071) 46.26 196.4 <.001 *** [ 3.141, 3.421]
## Manipulation  0.071 (0.060)  1.18 123.5  .239     [-0.048, 0.190]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.WorkAbsorptionV
## ────────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p       [95% CI of β]
## ────────────────────────────────────────────────────────────────
## Manipulation  0.043 (0.036) 1.18 123.5  .239     [-0.029, 0.114]
## ────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     146 (Intercept)  0.48457 0.69095
##  Residual                  0.21674        
## ──────────────────────────────────────────

HLM_summary(Model23)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.ThrivingAtWorkLearningV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 262
## Level-2 Groups/Clusters: B.ID, 146
## 
## Model Fit:
## AIC = 599.177
## BIC = 613.450
## R_(m)² = 0.00133  (Marginal R²: fixed effects)
## R_(c)² = 0.58423  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.21    0.21  1.00 128.80 0.78  .380    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.ThrivingAtWorkLearningV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   3.292 (0.069) 47.62 214.3 <.001 *** [ 3.156, 3.429]
## Manipulation  0.059 (0.067)  0.88 128.8  .380     [-0.074, 0.192]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.ThrivingAtWorkLearningV
## ────────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p       [95% CI of β]
## ────────────────────────────────────────────────────────────────
## Manipulation  0.036 (0.041) 0.88 128.8  .380     [-0.045, 0.118]
## ────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     146 (Intercept)  0.38452 0.58368
##  Residual                  0.27426        
## ──────────────────────────────────────────

HLM_summary(Model24)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.WorkRelatedFlowV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 262
## Level-2 Groups/Clusters: B.ID, 146
## 
## Model Fit:
## AIC = 620.067
## BIC = 634.341
## R_(m)² = 0.00017  (Marginal R²: fixed effects)
## R_(c)² = 0.70023  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.03    0.03  1.00 122.53 0.14  .712    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.WorkRelatedFlowV
## ──────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ──────────────────────────────────────────────────────────────────
## (Intercept)    3.080 (0.076) 40.69 194.4 <.001 *** [ 2.931, 3.230]
## Manipulation  -0.023 (0.063) -0.37 122.5  .712     [-0.148, 0.102]
## ──────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.WorkRelatedFlowV
## ──────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p       [95% CI of β]
## ──────────────────────────────────────────────────────────────────
## Manipulation  -0.013 (0.035) -0.37 122.5  .712     [-0.083, 0.057]
## ──────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     146 (Intercept)  0.56011 0.70018
##  Residual                  0.23984        
## ──────────────────────────────────────────

HLM_summary(Model25)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.InspirationV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 262
## Level-2 Groups/Clusters: B.ID, 146
## 
## Model Fit:
## AIC = 648.513
## BIC = 662.786
## R_(m)² = 0.00012  (Marginal R²: fixed effects)
## R_(c)² = 0.69300  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.02    0.02  1.00 124.92 0.09  .764    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.InspirationV
## ──────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ──────────────────────────────────────────────────────────────────
## (Intercept)    3.263 (0.080) 40.97 197.2 <.001 *** [ 3.106, 3.420]
## Manipulation  -0.020 (0.067) -0.30 124.9  .764     [-0.153, 0.113]
## ──────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.InspirationV
## ──────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p       [95% CI of β]
## ──────────────────────────────────────────────────────────────────
## Manipulation  -0.011 (0.036) -0.30 124.9  .764     [-0.081, 0.060]
## ──────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     146 (Intercept)  0.61310 0.69296
##  Residual                  0.27166        
## ──────────────────────────────────────────

#HLM_summary(Model26)
HLM_summary(Model27)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.ExerciseV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 262
## Level-2 Groups/Clusters: B.ID, 146
## 
## Model Fit:
## AIC = 461.454
## BIC = 475.727
## R_(m)² = 0.00384  (Marginal R²: fixed effects)
## R_(c)² = 0.83851  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.50    0.50  1.00 110.63 5.61  .020 *  
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.ExerciseV
## ───────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p      [95% CI of b/γ]
## ───────────────────────────────────────────────────────────────────
## (Intercept)    1.344 (0.062) 21.59 164.0 <.001 *** [ 1.221,  1.467]
## Manipulation  -0.092 (0.039) -2.37 110.6  .020 *   [-0.169, -0.015]
## ───────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.ExerciseV
## ───────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p        [95% CI of β]
## ───────────────────────────────────────────────────────────────────
## Manipulation  -0.067 (0.028) -2.37 110.6  .020 *   [-0.124, -0.011]
## ───────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     146 (Intercept)  0.46181 0.83789
##  Residual                  0.08935        
## ──────────────────────────────────────────

HLM_summary(Model28)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.SleepQualityV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 262
## Level-2 Groups/Clusters: B.ID, 146
## 
## Model Fit:
## AIC = 685.211
## BIC = 699.484
## R_(m)² = 0.00623  (Marginal R²: fixed effects)
## R_(c)² = 0.68014  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    1.51    1.51  1.00 124.42 4.69  .032 *  
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.SleepQualityV
## ───────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p      [95% CI of b/γ]
## ───────────────────────────────────────────────────────────────────
## (Intercept)    2.538 (0.085) 29.92 198.8 <.001 *** [ 2.370,  2.705]
## Manipulation  -0.158 (0.073) -2.17 124.4  .032 *   [-0.303, -0.014]
## ───────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.SleepQualityV
## ───────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p        [95% CI of β]
## ───────────────────────────────────────────────────────────────────
## Manipulation  -0.079 (0.036) -2.17 124.4  .032 *   [-0.151, -0.007]
## ───────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     146 (Intercept)  0.67931 0.67813
##  Residual                  0.32243        
## ──────────────────────────────────────────

HLM_summary(Model29)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.ReadingV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 256
## Level-2 Groups/Clusters: B.ID, 145
## 
## Model Fit:
## AIC = 847.277
## BIC = 861.458
## R_(m)² = 0.00065  (Marginal R²: fixed effects)
## R_(c)² = 0.74394  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.33    0.33  1.00 118.02 0.59  .445    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.ReadingV
## ──────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ──────────────────────────────────────────────────────────────────
## (Intercept)    1.435 (0.126) 11.42 187.9 <.001 *** [ 1.187, 1.683]
## Manipulation  -0.075 (0.098) -0.77 118.0  .445     [-0.270, 0.119]
## ──────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.ReadingV
## ──────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p       [95% CI of β]
## ──────────────────────────────────────────────────────────────────
## Manipulation  -0.026 (0.033) -0.77 118.0  .445     [-0.092, 0.041]
## ──────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     145 (Intercept)  1.61783 0.74378
##  Residual                  0.55733        
## ──────────────────────────────────────────

HLM_summary(Model30)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.PaperReadV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 256
## Level-2 Groups/Clusters: B.ID, 145
## 
## Model Fit:
## AIC = 442.776
## BIC = 456.957
## R_(m)² = 0.00059  (Marginal R²: fixed effects)
## R_(c)² = 0.64110  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.05    0.05  1.00 109.94 0.38  .538    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.PaperReadV
## ──────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ──────────────────────────────────────────────────────────────────
## (Intercept)    0.333 (0.054)  6.19 196.3 <.001 *** [ 0.227, 0.439]
## Manipulation  -0.030 (0.049) -0.62 109.9  .538     [-0.128, 0.067]
## ──────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.PaperReadV
## ──────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p       [95% CI of β]
## ──────────────────────────────────────────────────────────────────
## Manipulation  -0.025 (0.040) -0.62 109.9  .538     [-0.105, 0.055]
## ──────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     145 (Intercept)  0.25153 0.64089
##  Residual                  0.14094        
## ──────────────────────────────────────────

HLM_summary(Model31)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.EReadV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 256
## Level-2 Groups/Clusters: B.ID, 145
## 
## Model Fit:
## AIC = 729.309
## BIC = 743.489
## R_(m)² = 0.00041  (Marginal R²: fixed effects)
## R_(c)² = 0.74510  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.13    0.13  1.00 120.68 0.37  .543    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.EReadV
## ──────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ──────────────────────────────────────────────────────────────────
## (Intercept)    1.100 (0.100) 11.04 189.8 <.001 *** [ 0.903, 1.297]
## Manipulation  -0.047 (0.078) -0.61 120.7  .543     [-0.202, 0.107]
## ──────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.EReadV
## ──────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p       [95% CI of β]
## ──────────────────────────────────────────────────────────────────
## Manipulation  -0.020 (0.033) -0.61 120.7  .543     [-0.085, 0.045]
## ──────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     145 (Intercept)  1.02026 0.74500
##  Residual                  0.34922        
## ──────────────────────────────────────────

HLM_summary(Model32)

## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WA.SleepQuantityV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 234
## Level-2 Groups/Clusters: B.ID, 133
## 
## Model Fit:
## AIC = 723.549
## BIC = 737.371
## R_(m)² = 0.00035  (Marginal R²: fixed effects)
## R_(c)² = 0.40239  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.10    0.10  1.00 105.09 0.13  .720    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.SleepQuantityV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   6.778 (0.104) 65.24 208.5 <.001 *** [ 6.573, 6.982]
## Manipulation  0.043 (0.120)  0.36 105.1  .720     [-0.194, 0.280]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.SleepQuantityV
## ────────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p       [95% CI of β]
## ────────────────────────────────────────────────────────────────
## Manipulation  0.019 (0.053) 0.36 105.1  .720     [-0.085, 0.123]
## ────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     133 (Intercept)  0.53288 0.40218
##  Residual                  0.79210        
## ──────────────────────────────────────────

1.4.2 T-test

#MANOVA(data=data, subID="B.ID", dv="WA.WorkReflectionV", within=c("Manipulation"))

1.5 Multilevel correlation

cor_multilevel(data[,c(1, 146, 114:145)], "B.ID", digits = 3)

## Correlations below and above the diagonal represent
## within-level and between-level correlations, respectively:
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                            Manipulation WP.SupervisoryBehavioralFeedbackV WP.SupervisoryPositiveBehavioralFeedbackV WP.SupervisoryNegativeBehavioralFeedbackV WP.learningBehaviorV WP.JobCraftingV WP.CreativeProcessEngagementV WP.ProblemIdentificationV WP.InformationSearchV WP.IdeaGenerationV WP.SocialLearningV WP.ObservationalLearningV WP.AdviceSeekingV WP.PerformanceImprovementV WP.TakingChargeV WA.WorkReflectionV WA.PositiveWorkReflectionV WA.NegativeWorkReflectionV WA.RuminationV WA.PositiveAffectV WA.NegativeAffectV WA.ImprovisionV WA.WorkAbsorptionV WA.ThrivingAtWorkLearningV WA.WorkRelatedFlowV WA.InspirationV WA.GraceV WA.ExerciseV WA.SleepQualityV WA.ReadingV WA.PaperReadV WA.EReadV WA.SleepQuantityV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Manipulation                                      1.000                            -0.046                                     0.004                                    -0.080               -0.009           0.085                         0.082                     0.119                 0.025              0.070              0.068                     0.084             0.028                     -0.044            0.026             -0.053                     -0.001                     -0.087          0.000              0.096             -0.105           0.038             -0.047                      0.050              -0.055          -0.040     0.061        0.062           -0.076      -0.096        -0.038    -0.101            -0.021
## WP.SupervisoryBehavioralFeedbackV                -0.111                             1.000                                     0.922                                     0.950                0.503           0.480                         0.523                     0.446                 0.321              0.545              0.377                     0.374             0.294                      0.513            0.479              0.627                      0.644                      0.404          0.646              0.477             -0.022           0.574              0.578                      0.581               0.575           0.551     0.495        0.163           -0.022       0.138         0.025     0.160            -0.028
## WP.SupervisoryPositiveBehavioralFeedbackV        -0.003                             0.789                                     1.000                                     0.757                0.535           0.491                         0.483                     0.422                 0.244              0.526              0.335                     0.334             0.259                      0.516            0.467              0.602                      0.680                      0.327          0.649              0.488             -0.040           0.619              0.601                      0.592               0.581           0.530     0.454        0.149           -0.041       0.073        -0.008     0.096            -0.027
## WP.SupervisoryNegativeBehavioralFeedbackV        -0.166                             0.852                                     0.352                                     1.000                0.421           0.417                         0.497                     0.415                 0.347              0.499              0.369                     0.364             0.290                      0.452            0.435              0.579                      0.546                      0.423          0.575              0.418             -0.005           0.477              0.497                      0.509               0.508           0.509     0.475        0.157           -0.003       0.175         0.048     0.195            -0.026
## WP.learningBehaviorV                              0.056                             0.042                                    -0.026                                     0.086                1.000           0.644                         0.629                     0.601                 0.418              0.599              0.538                     0.535             0.415                      0.665            0.679              0.591                      0.629                      0.359          0.605              0.432              0.038           0.650              0.533                      0.704               0.572           0.679     0.463        0.237            0.130       0.133         0.026     0.154            -0.041
## WP.JobCraftingV                                   0.027                            -0.073                                    -0.076                                    -0.046               -0.082           1.000                         0.723                     0.653                 0.459              0.723              0.582                     0.607             0.408                      0.719            0.764              0.646                      0.563                      0.519          0.568              0.509             -0.041           0.670              0.581                      0.610               0.580           0.580     0.303        0.299            0.080       0.253         0.198     0.212            -0.005
## WP.CreativeProcessEngagementV                     0.239                             0.200                                     0.121                                     0.202               -0.045           0.089                         1.000                     0.844                 0.811              0.937              0.617                     0.607             0.488                      0.703            0.691              0.630                      0.583                      0.471          0.668              0.541             -0.083           0.695              0.607                      0.628               0.571           0.562     0.414        0.324            0.165       0.219         0.110     0.218            -0.126
## WP.ProblemIdentificationV                         0.246                            -0.017                                    -0.109                                     0.067                0.042           0.016                         0.775                     1.000                 0.559              0.693              0.524                     0.510             0.421                      0.585            0.616              0.531                      0.535                      0.353          0.575              0.436             -0.052           0.589              0.480                      0.560               0.492           0.484     0.293        0.319            0.157       0.117         0.058     0.117            -0.165
## WP.InformationSearchV                             0.099                             0.153                                     0.062                                     0.180               -0.124           0.148                         0.586                     0.321                 1.000              0.642              0.512                     0.461             0.467                      0.447            0.441              0.393                      0.337                      0.321          0.434              0.387             -0.041           0.433              0.434                      0.397               0.344           0.345     0.408        0.256            0.263       0.268         0.166     0.249            -0.146
## WP.IdeaGenerationV                                0.123                             0.261                                     0.279                                     0.160               -0.022           0.027                         0.620                     0.202                -0.051              1.000              0.570                     0.587             0.411                      0.733            0.694              0.653                      0.594                      0.499          0.677              0.547             -0.103           0.719              0.618                      0.631               0.592           0.581     0.380        0.280            0.071       0.195         0.081     0.202            -0.059
## WP.SocialLearningV                                0.015                            -0.062                                    -0.059                                    -0.045                0.154           0.133                         0.004                     0.059                -0.036             -0.020              1.000                     0.938             0.857                      0.703            0.625              0.411                      0.387                      0.301          0.429              0.322              0.014           0.427              0.376                      0.463               0.360           0.510     0.331        0.265            0.183       0.232         0.039     0.272            -0.059
## WP.ObservationalLearningV                         0.044                            -0.114                                    -0.064                                    -0.120                0.166           0.167                         0.094                     0.156                 0.029             -0.002              0.866                     1.000             0.626                      0.710            0.666              0.456                      0.414                      0.349          0.457              0.362              0.088           0.480              0.416                      0.497               0.420           0.565     0.301        0.246            0.134       0.237         0.084     0.253            -0.048
## WP.AdviceSeekingV                                -0.035                             0.045                                    -0.021                                     0.087                0.058           0.014                        -0.129                    -0.113                -0.113             -0.037              0.684                     0.227             1.000                      0.528            0.417              0.247                      0.254                      0.159          0.286              0.186             -0.099           0.246              0.227                      0.302               0.185           0.307     0.297        0.231            0.212       0.170        -0.038     0.235            -0.061
## WP.PerformanceImprovementV                        0.050                             0.119                                    -0.019                                     0.197                0.000           0.136                         0.257                     0.137                 0.171              0.203             -0.041                     0.064            -0.172                      1.000            0.752              0.538                      0.506                      0.394          0.501              0.463              0.000           0.644              0.537                      0.621               0.509           0.710     0.266        0.256            0.150       0.218         0.112     0.215            -0.002
## WP.TakingChargeV                                  0.142                             0.301                                     0.167                                     0.317                0.090           0.095                         0.412                     0.265                 0.263              0.291              0.007                    -0.010             0.029                      0.081            1.000              0.596                      0.580                      0.417          0.582              0.456              0.055           0.609              0.505                      0.613               0.563           0.659     0.310        0.293            0.052       0.263         0.142     0.256            -0.061
## WA.WorkReflectionV                               -0.025                             0.194                                     0.164                                     0.158               -0.173           0.299                         0.151                     0.072                 0.063              0.157              0.134                     0.089             0.132                      0.145            0.032              1.000                      0.837                      0.836          0.721              0.559             -0.010           0.671              0.640                      0.674               0.694           0.548     0.485        0.303            0.023       0.107         0.059     0.103            -0.029
## WA.PositiveWorkReflectionV                       -0.089                             0.240                                     0.077                                     0.304               -0.024           0.344                         0.182                     0.149                 0.090              0.117              0.081                     0.086             0.033                      0.279            0.048              0.710                      1.000                      0.399          0.775              0.696             -0.086           0.692              0.719                      0.656               0.733           0.607     0.453        0.239            0.014       0.088         0.058     0.080            -0.023
## WA.NegativeWorkReflectionV                        0.039                             0.071                                     0.165                                    -0.034               -0.223           0.130                         0.059                    -0.024                 0.013              0.122              0.120                     0.053             0.158                     -0.030            0.005              0.803                      0.150                      1.000          0.431              0.238              0.069           0.431              0.351                      0.471               0.428           0.309     0.359        0.268            0.024       0.091         0.041     0.092            -0.025
## WA.RuminationV                                   -0.199                             0.160                                     0.164                                     0.103                0.004           0.149                        -0.053                     0.016                -0.089             -0.041              0.203                     0.166             0.155                      0.012            0.047              0.421                      0.298                      0.339          1.000              0.694             -0.114           0.707              0.653                      0.647               0.710           0.612     0.518        0.249            0.026       0.165         0.070     0.170            -0.050
## WA.PositiveAffectV                                0.139                            -0.050                                    -0.022                                    -0.058                0.130           0.072                         0.100                     0.144                 0.056             -0.002              0.034                     0.063            -0.024                      0.013            0.062              0.058                      0.155                     -0.049          0.034              1.000             -0.270           0.701              0.732                      0.530               0.701           0.558     0.424        0.220           -0.047       0.142         0.114     0.118             0.038
## WA.NegativeAffectV                                0.026                             0.004                                    -0.037                                     0.039               -0.034           0.024                        -0.037                    -0.006                -0.029             -0.040             -0.092                    -0.063            -0.087                      0.090            0.055              0.068                      0.044                      0.058          0.030             -0.038              1.000          -0.106             -0.074                      0.067              -0.052          -0.011    -0.050       -0.143            0.135       0.078         0.096     0.046            -0.018
## WA.ImprovisionV                                   0.106                             0.227                                     0.243                                     0.139                0.110           0.008                        -0.057                     0.077                -0.043             -0.144             -0.071                    -0.064            -0.046                     -0.036            0.189              0.270                      0.223                      0.190          0.173              0.320              0.075           1.000              0.720                      0.722               0.679           0.734     0.439        0.194            0.047       0.209         0.179     0.168            -0.079
## WA.WorkAbsorptionV                                0.121                            -0.002                                    -0.027                                     0.020                0.083          -0.076                         0.067                     0.031                -0.116              0.190              0.012                     0.008             0.013                      0.041            0.135              0.081                      0.132                      0.001         -0.004              0.202              0.134           0.127              1.000                      0.618               0.813           0.582     0.401        0.200            0.087       0.186         0.138     0.160            -0.065
## WA.ThrivingAtWorkLearningV                        0.057                             0.167                                     0.186                                     0.096                0.174          -0.124                         0.038                     0.049                -0.077              0.087             -0.056                    -0.046            -0.042                     -0.105            0.094              0.087                      0.131                      0.011         -0.012              0.264             -0.075           0.403              0.289                      1.000               0.634           0.759     0.453        0.207            0.113       0.185         0.130     0.163             0.019
## WA.WorkRelatedFlowV                              -0.029                             0.242                                     0.263                                     0.145                0.073          -0.105                        -0.012                    -0.112                -0.103              0.172              0.086                     0.045             0.101                      0.140            0.061              0.018                      0.064                     -0.030          0.046              0.235              0.021           0.209              0.320                      0.452               1.000           0.601     0.411        0.263           -0.068       0.157         0.114     0.137            -0.005
## WA.InspirationV                                  -0.020                             0.013                                    -0.083                                     0.092                0.257          -0.149                        -0.162                    -0.056                -0.006             -0.240              0.001                    -0.033             0.049                      0.036            0.032              0.008                      0.029                     -0.014          0.128              0.198              0.017           0.359              0.361                      0.395               0.262           1.000     0.378        0.187            0.093       0.254         0.223     0.200             0.047
## WA.GraceV                                         0.044                             0.204                                     0.258                                     0.089               -0.189           0.202                         0.111                     0.079                -0.017              0.143             -0.014                    -0.010            -0.014                      0.107            0.083              0.297                      0.389                      0.088          0.167              0.189              0.013           0.042             -0.101                      0.095               0.005          -0.186     1.000        0.106            0.039       0.089         0.001     0.112            -0.055
## WA.ExerciseV                                     -0.240                             0.076                                     0.103                                     0.028               -0.095          -0.016                        -0.045                    -0.172                -0.059              0.133              0.015                    -0.000             0.030                      0.149            0.021              0.109                      0.141                      0.034          0.017              0.195              0.062           0.048              0.083                      0.112               0.098           0.003     0.070        1.000            0.083       0.146         0.267     0.040            -0.192
## WA.SleepQualityV                                 -0.185                             0.137                                     0.042                                     0.175               -0.003           0.025                         0.049                     0.036                 0.119             -0.043             -0.053                     0.035            -0.155                      0.172            0.020             -0.033                      0.038                     -0.079          0.050             -0.067              0.067           0.177              0.035                      0.029              -0.037           0.175    -0.023       -0.084            1.000       0.150         0.109     0.130            -0.389
## WA.ReadingV                                      -0.051                            -0.024                                    -0.129                                     0.075                0.034          -0.000                        -0.053                    -0.088                -0.055              0.032              0.082                     0.092             0.028                      0.070           -0.107              0.029                      0.120                     -0.061          0.014             -0.024             -0.064          -0.065              0.146                      0.143               0.092           0.150    -0.054        0.069            0.257       1.000         0.650     0.912            -0.094
## WA.PaperReadV                                    -0.041                            -0.076                                    -0.129                                    -0.005                0.046          -0.007                        -0.114                    -0.056                -0.134             -0.045              0.038                     0.061            -0.013                      0.045           -0.010             -0.021                      0.125                     -0.137          0.042             -0.011              0.023          -0.062              0.179                      0.110               0.016           0.116     0.044        0.117            0.092       0.619         1.000     0.280            -0.145
## WA.EReadV                                        -0.039                             0.017                                    -0.080                                     0.096                0.013           0.004                         0.004                    -0.074                 0.015              0.067              0.077                     0.076             0.042                      0.058           -0.126              0.049                      0.074                      0.007         -0.009             -0.023             -0.095          -0.044              0.073                      0.113               0.105           0.118    -0.095        0.014            0.266       0.876         0.162     1.000            -0.038
## WA.SleepQuantityV                                 0.053                             0.247                                     0.279                                     0.133               -0.144          -0.128                        -0.074                    -0.167                -0.120              0.126             -0.160                    -0.240             0.035                     -0.046           -0.015              0.067                      0.109                     -0.001         -0.026             -0.139              0.137          -0.044              0.051                      0.030               0.052          -0.120     0.108        0.025           -0.183      -0.110         0.056    -0.176             1.000
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 
## Within-Level Correlation [95% CI]:
## ────────────────────────────────────────────────────────
##                             r         [95% CI]     p    
## ────────────────────────────────────────────────────────
## Mnpl.-WP.SB            -0.111 [-0.232,  0.013]  .082 .  
## Mnpl.-WP.SP            -0.003 [-0.128,  0.121]  .956    
## Mnpl.-WP.SN            -0.166 [-0.285, -0.043]  .009 ** 
## Mnpl.-WP.BV             0.056 [-0.069,  0.179]  .380    
## Mnpl.-WP.JC             0.027 [-0.098,  0.151]  .674    
## Mnpl.-WP.CP             0.239 [ 0.118,  0.353] <.001 ***
## Mnpl.-WP.PrbIV.         0.246 [ 0.125,  0.359] <.001 ***
## Mnpl.-WP.IS             0.099 [-0.026,  0.220]  .121    
## Mnpl.-WP.IG             0.123 [-0.001,  0.244]  .054 .  
## Mnpl.-WP.SL             0.015 [-0.110,  0.139]  .814    
## Mnpl.-WP.OL             0.044 [-0.081,  0.167]  .490    
## Mnpl.-WP.AS            -0.035 [-0.159,  0.090]  .582    
## Mnpl.-WP.PrfIV.         0.050 [-0.075,  0.173]  .438    
## Mnpl.-WP.TC             0.142 [ 0.018,  0.261]  .027 *  
## Mnpl.-WA.WRV           -0.025 [-0.146,  0.096]  .682    
## Mnpl.-WA.PW            -0.089 [-0.208,  0.033]  .154    
## Mnpl.-WA.NW             0.039 [-0.082,  0.160]  .527    
## Mnpl.-WA.RmV.          -0.199 [-0.313, -0.080]  .001 ** 
## Mnpl.-WA.PA             0.139 [ 0.018,  0.256]  .026 *  
## Mnpl.-WA.NA             0.026 [-0.095,  0.147]  .674    
## Mnpl.-WA.ImV.           0.106 [-0.016,  0.224]  .090 .  
## Mnpl.-WA.WA             0.121 [-0.000,  0.239]  .052 .  
## Mnpl.-WA.TA             0.057 [-0.065,  0.177]  .358    
## Mnpl.-WA.WRF           -0.029 [-0.149,  0.093]  .643    
## Mnpl.-WA.InV.          -0.020 [-0.141,  0.101]  .745    
## Mnpl.-WA.GV             0.044 [-0.077,  0.165]  .477    
## Mnpl.-WA.EV            -0.240 [-0.351, -0.122] <.001 ***
## Mnpl.-WA.SlpQlV.       -0.185 [-0.299, -0.065]  .003 ** 
## Mnpl.-WA.RdV.          -0.051 [-0.173,  0.072]  .417    
## Mnpl.-WA.PR            -0.041 [-0.163,  0.082]  .516    
## Mnpl.-WA.ER            -0.039 [-0.161,  0.084]  .535    
## Mnpl.-WA.SlpQnV.        0.053 [-0.076,  0.180]  .424    
## WP.SB-WP.SP             0.789 [ 0.737,  0.832] <.001 ***
## WP.SB-WP.SN             0.852 [ 0.814,  0.883] <.001 ***
## WP.SB-WP.BV             0.042 [-0.083,  0.165]  .513    
## WP.SB-WP.JC            -0.073 [-0.195,  0.052]  .255    
## WP.SB-WP.CP             0.200 [ 0.077,  0.316]  .002 ** 
## WP.SB-WP.PrbIV.        -0.017 [-0.141,  0.108]  .792    
## WP.SB-WP.IS             0.153 [ 0.029,  0.272]  .017 *  
## WP.SB-WP.IG             0.261 [ 0.141,  0.373] <.001 ***
## WP.SB-WP.SL            -0.062 [-0.185,  0.063]  .329    
## WP.SB-WP.OL            -0.114 [-0.235,  0.010]  .074 .  
## WP.SB-WP.AS             0.045 [-0.079,  0.169]  .477    
## WP.SB-WP.PrfIV.         0.119 [-0.006,  0.239]  .063 .  
## WP.SB-WP.TC             0.301 [ 0.184,  0.410] <.001 ***
## WP.SB-WA.WRV            0.194 [ 0.070,  0.313]  .003 ** 
## WP.SB-WA.PW             0.240 [ 0.118,  0.356] <.001 ***
## WP.SB-WA.NW             0.071 [-0.056,  0.196]  .274    
## WP.SB-WA.RmV.           0.160 [ 0.034,  0.280]  .014 *  
## WP.SB-WA.PA            -0.050 [-0.175,  0.077]  .442    
## WP.SB-WA.NA             0.004 [-0.122,  0.131]  .947    
## WP.SB-WA.ImV.           0.227 [ 0.104,  0.344] <.001 ***
## WP.SB-WA.WA            -0.002 [-0.128,  0.125]  .977    
## WP.SB-WA.TA             0.167 [ 0.042,  0.288]  .010 ** 
## WP.SB-WA.WRF            0.242 [ 0.119,  0.358] <.001 ***
## WP.SB-WA.InV.           0.013 [-0.114,  0.139]  .842    
## WP.SB-WA.GV             0.204 [ 0.079,  0.322]  .002 ** 
## WP.SB-WA.EV             0.076 [-0.051,  0.201]  .240    
## WP.SB-WA.SlpQlV.        0.137 [ 0.011,  0.259]  .035 *  
## WP.SB-WA.RdV.          -0.024 [-0.152,  0.104]  .710    
## WP.SB-WA.PR            -0.076 [-0.202,  0.053]  .249    
## WP.SB-WA.ER             0.017 [-0.112,  0.144]  .800    
## WP.SB-WA.SlpQnV.        0.247 [ 0.117,  0.368] <.001 ***
## WP.SP-WP.SN             0.352 [ 0.238,  0.456] <.001 ***
## WP.SP-WP.BV            -0.026 [-0.150,  0.098]  .678    
## WP.SP-WP.JC            -0.076 [-0.199,  0.048]  .231    
## WP.SP-WP.CP             0.121 [-0.004,  0.241]  .059 .  
## WP.SP-WP.PrbIV.        -0.109 [-0.230,  0.016]  .088 .  
## WP.SP-WP.IS             0.062 [-0.063,  0.185]  .330    
## WP.SP-WP.IG             0.279 [ 0.160,  0.390] <.001 ***
## WP.SP-WP.SL            -0.059 [-0.182,  0.066]  .357    
## WP.SP-WP.OL            -0.064 [-0.187,  0.061]  .318    
## WP.SP-WP.AS            -0.021 [-0.145,  0.103]  .736    
## WP.SP-WP.PrfIV.        -0.019 [-0.143,  0.106]  .772    
## WP.SP-WP.TC             0.167 [ 0.043,  0.285]  .009 ** 
## WP.SP-WA.WRV            0.164 [ 0.038,  0.284]  .012 *  
## WP.SP-WA.PW             0.077 [-0.049,  0.202]  .233    
## WP.SP-WA.NW             0.165 [ 0.039,  0.285]  .011 *  
## WP.SP-WA.RmV.           0.164 [ 0.039,  0.285]  .011 *  
## WP.SP-WA.PA            -0.022 [-0.148,  0.105]  .738    
## WP.SP-WA.NA            -0.037 [-0.163,  0.090]  .567    
## WP.SP-WA.ImV.           0.243 [ 0.120,  0.358] <.001 ***
## WP.SP-WA.WA            -0.027 [-0.152,  0.100]  .681    
## WP.SP-WA.TA             0.186 [ 0.061,  0.305]  .004 ** 
## WP.SP-WA.WRF            0.263 [ 0.142,  0.377] <.001 ***
## WP.SP-WA.InV.          -0.083 [-0.207,  0.044]  .200    
## WP.SP-WA.GV             0.258 [ 0.137,  0.373] <.001 ***
## WP.SP-WA.EV             0.103 [-0.023,  0.227]  .111    
## WP.SP-WA.SlpQlV.        0.042 [-0.085,  0.168]  .515    
## WP.SP-WA.RdV.          -0.129 [-0.253, -0.002]  .049 *  
## WP.SP-WA.PR            -0.129 [-0.253, -0.001]  .050 *  
## WP.SP-WA.ER            -0.080 [-0.206,  0.048]  .222    
## WP.SP-WA.SlpQnV.        0.279 [ 0.152,  0.398] <.001 ***
## WP.SN-WP.BV             0.086 [-0.039,  0.208]  .177    
## WP.SN-WP.JC            -0.046 [-0.169,  0.079]  .475    
## WP.SN-WP.CP             0.202 [ 0.079,  0.318]  .002 ** 
## WP.SN-WP.PrbIV.         0.067 [-0.058,  0.190]  .293    
## WP.SN-WP.IS             0.180 [ 0.057,  0.298]  .005 ** 
## WP.SN-WP.IG             0.160 [ 0.037,  0.279]  .012 *  
## WP.SN-WP.SL            -0.045 [-0.168,  0.080]  .482    
## WP.SN-WP.OL            -0.120 [-0.240,  0.005]  .061 .  
## WP.SN-WP.AS             0.087 [-0.037,  0.209]  .171    
## WP.SN-WP.PrfIV.         0.197 [ 0.074,  0.313]  .002 ** 
## WP.SN-WP.TC             0.317 [ 0.201,  0.425] <.001 ***
## WP.SN-WA.WRV            0.158 [ 0.032,  0.278]  .015 *  
## WP.SN-WA.PW             0.304 [ 0.185,  0.414] <.001 ***
## WP.SN-WA.NW            -0.034 [-0.160,  0.093]  .600    
## WP.SN-WA.RmV.           0.103 [-0.024,  0.227]  .112    
## WP.SN-WA.PA            -0.058 [-0.183,  0.069]  .370    
## WP.SN-WA.NA             0.039 [-0.088,  0.165]  .547    
## WP.SN-WA.ImV.           0.139 [ 0.013,  0.261]  .032 *  
## WP.SN-WA.WA             0.020 [-0.106,  0.146]  .754    
## WP.SN-WA.TA             0.096 [-0.030,  0.220]  .137    
## WP.SN-WA.WRF            0.145 [ 0.019,  0.266]  .026 *  
## WP.SN-WA.InV.           0.092 [-0.035,  0.216]  .155    
## WP.SN-WA.GV             0.089 [-0.038,  0.213]  .169    
## WP.SN-WA.EV             0.028 [-0.099,  0.154]  .669    
## WP.SN-WA.SlpQlV.        0.175 [ 0.049,  0.294]  .007 ** 
## WP.SN-WA.RdV.           0.075 [-0.053,  0.201]  .251    
## WP.SN-WA.PR            -0.005 [-0.133,  0.123]  .944    
## WP.SN-WA.ER             0.096 [-0.033,  0.221]  .145    
## WP.SN-WA.SlpQnV.        0.133 [-0.000,  0.262]  .052 .  
## WP.BV-WP.JC            -0.082 [-0.204,  0.043]  .198    
## WP.BV-WP.CP            -0.045 [-0.168,  0.080]  .481    
## WP.BV-WP.PrbIV.         0.042 [-0.082,  0.166]  .506    
## WP.BV-WP.IS            -0.124 [-0.244,  0.001]  .053 .  
## WP.BV-WP.IG            -0.022 [-0.146,  0.102]  .725    
## WP.BV-WP.SL             0.154 [ 0.031,  0.273]  .016 *  
## WP.BV-WP.OL             0.166 [ 0.042,  0.284]  .009 ** 
## WP.BV-WP.AS             0.058 [-0.066,  0.181]  .359    
## WP.BV-WP.PrfIV.         0.000 [-0.124,  0.124] 1.000    
## WP.BV-WP.TC             0.090 [-0.034,  0.212]  .156    
## WP.BV-WA.WRV           -0.173 [-0.293, -0.048]  .008 ** 
## WP.BV-WA.PW            -0.024 [-0.150,  0.102]  .707    
## WP.BV-WA.NW            -0.223 [-0.340, -0.099] <.001 ***
## WP.BV-WA.RmV.           0.004 [-0.122,  0.131]  .948    
## WP.BV-WA.PA             0.130 [ 0.004,  0.252]  .045 *  
## WP.BV-WA.NA            -0.034 [-0.160,  0.092]  .595    
## WP.BV-WA.ImV.           0.110 [-0.016,  0.233]  .089 .  
## WP.BV-WA.WA             0.083 [-0.044,  0.207]  .202    
## WP.BV-WA.TA             0.174 [ 0.049,  0.294]  .007 ** 
## WP.BV-WA.WRF            0.073 [-0.054,  0.198]  .258    
## WP.BV-WA.InV.           0.257 [ 0.135,  0.371] <.001 ***
## WP.BV-WA.GV            -0.189 [-0.308, -0.065]  .004 ** 
## WP.BV-WA.EV            -0.095 [-0.219,  0.032]  .143    
## WP.BV-WA.SlpQlV.       -0.003 [-0.129,  0.124]  .967    
## WP.BV-WA.RdV.           0.034 [-0.094,  0.161]  .603    
## WP.BV-WA.PR             0.046 [-0.082,  0.173]  .479    
## WP.BV-WA.ER             0.013 [-0.115,  0.141]  .837    
## WP.BV-WA.SlpQnV.       -0.144 [-0.272, -0.010]  .036 *  
## WP.JC-WP.CP             0.089 [-0.036,  0.211]  .164    
## WP.JC-WP.PrbIV.         0.016 [-0.109,  0.140]  .803    
## WP.JC-WP.IS             0.148 [ 0.024,  0.267]  .021 *  
## WP.JC-WP.IG             0.027 [-0.097,  0.151]  .668    
## WP.JC-WP.SL             0.133 [ 0.009,  0.253]  .038 *  
## WP.JC-WP.OL             0.167 [ 0.044,  0.286]  .009 ** 
## WP.JC-WP.AS             0.014 [-0.110,  0.138]  .823    
## WP.JC-WP.PrfIV.         0.136 [ 0.012,  0.256]  .033 *  
## WP.JC-WP.TC             0.095 [-0.030,  0.217]  .137    
## WP.JC-WA.WRV            0.299 [ 0.179,  0.410] <.001 ***
## WP.JC-WA.PW             0.344 [ 0.228,  0.451] <.001 ***
## WP.JC-WA.NW             0.130 [ 0.004,  0.253]  .044 *  
## WP.JC-WA.RmV.           0.149 [ 0.023,  0.271]  .021 *  
## WP.JC-WA.PA             0.072 [-0.055,  0.196]  .269    
## WP.JC-WA.NA             0.024 [-0.103,  0.150]  .712    
## WP.JC-WA.ImV.           0.008 [-0.119,  0.134]  .907    
## WP.JC-WA.WA            -0.076 [-0.200,  0.051]  .243    
## WP.JC-WA.TA            -0.124 [-0.246,  0.003]  .057 .  
## WP.JC-WA.WRF           -0.105 [-0.228,  0.022]  .105    
## WP.JC-WA.InV.          -0.149 [-0.270, -0.023]  .022 *  
## WP.JC-WA.GV             0.202 [ 0.077,  0.320]  .002 ** 
## WP.JC-WA.EV            -0.016 [-0.142,  0.111]  .809    
## WP.JC-WA.SlpQlV.        0.025 [-0.102,  0.151]  .698    
## WP.JC-WA.RdV.          -0.000 [-0.128,  0.128]  .995    
## WP.JC-WA.PR            -0.007 [-0.135,  0.121]  .916    
## WP.JC-WA.ER             0.004 [-0.124,  0.131]  .956    
## WP.JC-WA.SlpQnV.       -0.128 [-0.257,  0.005]  .062 .  
## WP.CP-WP.PrbIV.         0.775 [ 0.720,  0.820] <.001 ***
## WP.CP-WP.IS             0.586 [ 0.498,  0.662] <.001 ***
## WP.CP-WP.IG             0.620 [ 0.537,  0.691] <.001 ***
## WP.CP-WP.SL             0.004 [-0.121,  0.128]  .952    
## WP.CP-WP.OL             0.094 [-0.031,  0.216]  .142    
## WP.CP-WP.AS            -0.129 [-0.250, -0.005]  .043 *  
## WP.CP-WP.PrfIV.         0.257 [ 0.137,  0.370] <.001 ***
## WP.CP-WP.TC             0.412 [ 0.303,  0.510] <.001 ***
## WP.CP-WA.WRV            0.151 [ 0.025,  0.272]  .020 *  
## WP.CP-WA.PW             0.182 [ 0.057,  0.301]  .005 ** 
## WP.CP-WA.NW             0.059 [-0.068,  0.184]  .363    
## WP.CP-WA.RmV.          -0.053 [-0.178,  0.074]  .413    
## WP.CP-WA.PA             0.100 [-0.026,  0.224]  .122    
## WP.CP-WA.NA            -0.037 [-0.163,  0.089]  .563    
## WP.CP-WA.ImV.          -0.057 [-0.182,  0.070]  .382    
## WP.CP-WA.WA             0.067 [-0.059,  0.192]  .299    
## WP.CP-WA.TA             0.038 [-0.088,  0.164]  .553    
## WP.CP-WA.WRF           -0.012 [-0.138,  0.114]  .851    
## WP.CP-WA.InV.          -0.162 [-0.283, -0.036]  .012 *  
## WP.CP-WA.GV             0.111 [-0.015,  0.234]  .086 .  
## WP.CP-WA.EV            -0.045 [-0.171,  0.082]  .484    
## WP.CP-WA.SlpQlV.        0.049 [-0.078,  0.174]  .449    
## WP.CP-WA.RdV.          -0.053 [-0.180,  0.075]  .416    
## WP.CP-WA.PR            -0.114 [-0.239,  0.014]  .082 .  
## WP.CP-WA.ER             0.004 [-0.124,  0.132]  .948    
## WP.CP-WA.SlpQnV.       -0.074 [-0.206,  0.060]  .279    
## WP.PrbIV.-WP.IS         0.321 [ 0.205,  0.428] <.001 ***
## WP.PrbIV.-WP.IG         0.202 [ 0.080,  0.318]  .002 ** 
## WP.PrbIV.-WP.SL         0.059 [-0.066,  0.182]  .354    
## WP.PrbIV.-WP.OL         0.156 [ 0.033,  0.275]  .014 *  
## WP.PrbIV.-WP.AS        -0.113 [-0.234,  0.012]  .077 .  
## WP.PrbIV.-WP.PrfIV.     0.137 [ 0.013,  0.257]  .032 *  
## WP.PrbIV.-WP.TC         0.265 [ 0.145,  0.377] <.001 ***
## WP.PrbIV.-WA.WRV        0.072 [-0.055,  0.196]  .268    
## WP.PrbIV.-WA.PW         0.149 [ 0.023,  0.270]  .022 *  
## WP.PrbIV.-WA.NW        -0.024 [-0.150,  0.102]  .707    
## WP.PrbIV.-WA.RmV.       0.016 [-0.110,  0.142]  .801    
## WP.PrbIV.-WA.PA         0.144 [ 0.018,  0.266]  .026 *  
## WP.PrbIV.-WA.NA        -0.006 [-0.132,  0.121]  .932    
## WP.PrbIV.-WA.ImV.       0.077 [-0.050,  0.201]  .238    
## WP.PrbIV.-WA.WA         0.031 [-0.095,  0.157]  .628    
## WP.PrbIV.-WA.TA         0.049 [-0.078,  0.174]  .451    
## WP.PrbIV.-WA.WRF       -0.112 [-0.235,  0.014]  .083 .  
## WP.PrbIV.-WA.InV.      -0.056 [-0.181,  0.071]  .389    
## WP.PrbIV.-WA.GV         0.079 [-0.048,  0.203]  .225    
## WP.PrbIV.-WA.EV        -0.172 [-0.292, -0.047]  .008 ** 
## WP.PrbIV.-WA.SlpQlV.    0.036 [-0.091,  0.162]  .578    
## WP.PrbIV.-WA.RdV.      -0.088 [-0.214,  0.040]  .180    
## WP.PrbIV.-WA.PR        -0.056 [-0.183,  0.073]  .394    
## WP.PrbIV.-WA.ER        -0.074 [-0.200,  0.054]  .258    
## WP.PrbIV.-WA.SlpQnV.   -0.167 [-0.294, -0.034]  .015 *  
## WP.IS-WP.IG            -0.051 [-0.174,  0.074]  .424    
## WP.IS-WP.SL            -0.036 [-0.160,  0.089]  .571    
## WP.IS-WP.OL             0.029 [-0.096,  0.153]  .650    
## WP.IS-WP.AS            -0.113 [-0.234,  0.012]  .077 .  
## WP.IS-WP.PrfIV.         0.171 [ 0.048,  0.289]  .007 ** 
## WP.IS-WP.TC             0.263 [ 0.143,  0.375] <.001 ***
## WP.IS-WA.WRV            0.063 [-0.064,  0.188]  .332    
## WP.IS-WA.PW             0.090 [-0.037,  0.214]  .165    
## WP.IS-WA.NW             0.013 [-0.114,  0.139]  .846    
## WP.IS-WA.RmV.          -0.089 [-0.213,  0.038]  .169    
## WP.IS-WA.PA             0.056 [-0.071,  0.181]  .387    
## WP.IS-WA.NA            -0.029 [-0.155,  0.098]  .657    
## WP.IS-WA.ImV.          -0.043 [-0.169,  0.084]  .505    
## WP.IS-WA.WA            -0.116 [-0.239,  0.010]  .073 .  
## WP.IS-WA.TA            -0.077 [-0.201,  0.050]  .238    
## WP.IS-WA.WRF           -0.103 [-0.227,  0.024]  .112    
## WP.IS-WA.InV.          -0.006 [-0.132,  0.120]  .926    
## WP.IS-WA.GV            -0.017 [-0.143,  0.109]  .792    
## WP.IS-WA.EV            -0.059 [-0.184,  0.068]  .364    
## WP.IS-WA.SlpQlV.        0.119 [-0.008,  0.241]  .067 .  
## WP.IS-WA.RdV.          -0.055 [-0.181,  0.074]  .405    
## WP.IS-WA.PR            -0.134 [-0.258, -0.006]  .041 *  
## WP.IS-WA.ER             0.015 [-0.113,  0.143]  .820    
## WP.IS-WA.SlpQnV.       -0.120 [-0.249,  0.014]  .081 .  
## WP.IG-WP.SL            -0.020 [-0.144,  0.104]  .749    
## WP.IG-WP.OL            -0.002 [-0.126,  0.123]  .980    
## WP.IG-WP.AS            -0.037 [-0.161,  0.087]  .558    
## WP.IG-WP.PrfIV.         0.203 [ 0.081,  0.319]  .002 ** 
## WP.IG-WP.TC             0.291 [ 0.173,  0.401] <.001 ***
## WP.IG-WA.WRV            0.157 [ 0.031,  0.278]  .015 *  
## WP.IG-WA.PW             0.117 [-0.010,  0.240]  .071 .  
## WP.IG-WA.NW             0.122 [-0.004,  0.245]  .059 .  
## WP.IG-WA.RmV.          -0.041 [-0.166,  0.086]  .532    
## WP.IG-WA.PA            -0.002 [-0.128,  0.125]  .979    
## WP.IG-WA.NA            -0.040 [-0.166,  0.087]  .536    
## WP.IG-WA.ImV.          -0.144 [-0.265, -0.018]  .027 *  
## WP.IG-WA.WA             0.190 [ 0.065,  0.309]  .003 ** 
## WP.IG-WA.TA             0.087 [-0.040,  0.211]  .181    
## WP.IG-WA.WRF            0.172 [ 0.047,  0.292]  .008 ** 
## WP.IG-WA.InV.          -0.240 [-0.356, -0.118] <.001 ***
## WP.IG-WA.GV             0.143 [ 0.017,  0.265]  .027 *  
## WP.IG-WA.EV             0.133 [ 0.007,  0.255]  .040 *  
## WP.IG-WA.SlpQlV.       -0.043 [-0.168,  0.084]  .511    
## WP.IG-WA.RdV.           0.032 [-0.096,  0.159]  .627    
## WP.IG-WA.PR            -0.045 [-0.172,  0.083]  .491    
## WP.IG-WA.ER             0.067 [-0.062,  0.193]  .309    
## WP.IG-WA.SlpQnV.        0.126 [-0.008,  0.255]  .066 .  
## WP.SL-WP.OL             0.866 [ 0.831,  0.894] <.001 ***
## WP.SL-WP.AS             0.684 [ 0.611,  0.745] <.001 ***
## WP.SL-WP.PrfIV.        -0.041 [-0.164,  0.084]  .525    
## WP.SL-WP.TC             0.007 [-0.117,  0.132]  .908    
## WP.SL-WA.WRV            0.134 [ 0.008,  0.256]  .039 *  
## WP.SL-WA.PW             0.081 [-0.046,  0.205]  .212    
## WP.SL-WA.NW             0.120 [-0.006,  0.243]  .063 .  
## WP.SL-WA.RmV.           0.203 [ 0.078,  0.321]  .002 ** 
## WP.SL-WA.PA             0.034 [-0.092,  0.160]  .596    
## WP.SL-WA.NA            -0.092 [-0.216,  0.035]  .158    
## WP.SL-WA.ImV.          -0.071 [-0.195,  0.056]  .274    
## WP.SL-WA.WA             0.012 [-0.114,  0.139]  .848    
## WP.SL-WA.TA            -0.056 [-0.181,  0.071]  .391    
## WP.SL-WA.WRF            0.086 [-0.041,  0.210]  .187    
## WP.SL-WA.InV.           0.001 [-0.125,  0.127]  .988    
## WP.SL-WA.GV            -0.014 [-0.140,  0.112]  .827    
## WP.SL-WA.EV             0.015 [-0.111,  0.142]  .812    
## WP.SL-WA.SlpQlV.       -0.053 [-0.179,  0.073]  .410    
## WP.SL-WA.RdV.           0.082 [-0.047,  0.208]  .213    
## WP.SL-WA.PR             0.038 [-0.090,  0.165]  .561    
## WP.SL-WA.ER             0.077 [-0.051,  0.203]  .239    
## WP.SL-WA.SlpQnV.       -0.160 [-0.287, -0.027]  .020 *  
## WP.OL-WP.AS             0.227 [ 0.106,  0.342] <.001 ***
## WP.OL-WP.PrfIV.         0.064 [-0.061,  0.187]  .317    
## WP.OL-WP.TC            -0.010 [-0.134,  0.115]  .878    
## WP.OL-WA.WRV            0.089 [-0.037,  0.213]  .168    
## WP.OL-WA.PW             0.086 [-0.040,  0.210]  .183    
## WP.OL-WA.NW             0.053 [-0.074,  0.178]  .414    
## WP.OL-WA.RmV.           0.166 [ 0.041,  0.287]  .010 *  
## WP.OL-WA.PA             0.063 [-0.063,  0.188]  .328    
## WP.OL-WA.NA            -0.063 [-0.188,  0.064]  .329    
## WP.OL-WA.ImV.          -0.064 [-0.189,  0.063]  .322    
## WP.OL-WA.WA             0.008 [-0.119,  0.134]  .905    
## WP.OL-WA.TA            -0.046 [-0.172,  0.081]  .475    
## WP.OL-WA.WRF            0.045 [-0.081,  0.171]  .483    
## WP.OL-WA.InV.          -0.033 [-0.158,  0.094]  .615    
## WP.OL-WA.GV            -0.010 [-0.136,  0.117]  .883    
## WP.OL-WA.EV            -0.000 [-0.127,  0.126]  .997    
## WP.OL-WA.SlpQlV.        0.035 [-0.091,  0.161]  .585    
## WP.OL-WA.RdV.           0.092 [-0.037,  0.217]  .162    
## WP.OL-WA.PR             0.061 [-0.068,  0.187]  .354    
## WP.OL-WA.ER             0.076 [-0.053,  0.202]  .248    
## WP.OL-WA.SlpQnV.       -0.240 [-0.362, -0.110] <.001 ***
## WP.AS-WP.PrfIV.        -0.172 [-0.290, -0.049]  .007 ** 
## WP.AS-WP.TC             0.029 [-0.096,  0.152]  .653    
## WP.AS-WA.WRV            0.132 [ 0.006,  0.254]  .041 *  
## WP.AS-WA.PW             0.033 [-0.093,  0.159]  .607    
## WP.AS-WA.NW             0.158 [ 0.032,  0.279]  .015 *  
## WP.AS-WA.RmV.           0.155 [ 0.029,  0.276]  .017 *  
## WP.AS-WA.PA            -0.024 [-0.150,  0.102]  .708    
## WP.AS-WA.NA            -0.087 [-0.211,  0.039]  .178    
## WP.AS-WA.ImV.          -0.046 [-0.171,  0.081]  .482    
## WP.AS-WA.WA             0.013 [-0.113,  0.139]  .840    
## WP.AS-WA.TA            -0.042 [-0.167,  0.085]  .521    
## WP.AS-WA.WRF            0.101 [-0.026,  0.225]  .119    
## WP.AS-WA.InV.           0.049 [-0.078,  0.174]  .451    
## WP.AS-WA.GV            -0.014 [-0.140,  0.113]  .831    
## WP.AS-WA.EV             0.030 [-0.096,  0.156]  .639    
## WP.AS-WA.SlpQlV.       -0.155 [-0.276, -0.029]  .017 *  
## WP.AS-WA.RdV.           0.028 [-0.101,  0.155]  .674    
## WP.AS-WA.PR            -0.013 [-0.141,  0.115]  .843    
## WP.AS-WA.ER             0.042 [-0.086,  0.169]  .522    
## WP.AS-WA.SlpQnV.        0.035 [-0.099,  0.168]  .610    
## WP.PrfIV.-WP.TC         0.081 [-0.044,  0.203]  .204    
## WP.PrfIV.-WA.WRV        0.145 [ 0.019,  0.267]  .025 *  
## WP.PrfIV.-WA.PW         0.279 [ 0.158,  0.392] <.001 ***
## WP.PrfIV.-WA.NW        -0.030 [-0.156,  0.096]  .638    
## WP.PrfIV.-WA.RmV.       0.012 [-0.115,  0.138]  .855    
## WP.PrfIV.-WA.PA         0.013 [-0.114,  0.139]  .843    
## WP.PrfIV.-WA.NA         0.090 [-0.037,  0.214]  .164    
## WP.PrfIV.-WA.ImV.      -0.036 [-0.161,  0.091]  .580    
## WP.PrfIV.-WA.WA         0.041 [-0.086,  0.166]  .530    
## WP.PrfIV.-WA.TA        -0.105 [-0.229,  0.021]  .104    
## WP.PrfIV.-WA.WRF        0.140 [ 0.014,  0.262]  .031 *  
## WP.PrfIV.-WA.InV.       0.036 [-0.091,  0.161]  .581    
## WP.PrfIV.-WA.GV         0.107 [-0.020,  0.230]  .099 .  
## WP.PrfIV.-WA.EV         0.149 [ 0.023,  0.270]  .022 *  
## WP.PrfIV.-WA.SlpQlV.    0.172 [ 0.046,  0.292]  .008 ** 
## WP.PrfIV.-WA.RdV.       0.070 [-0.058,  0.196]  .286    
## WP.PrfIV.-WA.PR         0.045 [-0.083,  0.172]  .490    
## WP.PrfIV.-WA.ER         0.058 [-0.070,  0.185]  .375    
## WP.PrfIV.-WA.SlpQnV.   -0.046 [-0.178,  0.088]  .506    
## WP.TC-WA.WRV            0.032 [-0.095,  0.158]  .622    
## WP.TC-WA.PW             0.048 [-0.079,  0.173]  .463    
## WP.TC-WA.NW             0.005 [-0.121,  0.131]  .939    
## WP.TC-WA.RmV.           0.047 [-0.080,  0.172]  .473    
## WP.TC-WA.PA             0.062 [-0.065,  0.187]  .338    
## WP.TC-WA.NA             0.055 [-0.072,  0.180]  .395    
## WP.TC-WA.ImV.           0.189 [ 0.064,  0.308]  .004 ** 
## WP.TC-WA.WA             0.135 [ 0.008,  0.257]  .038 *  
## WP.TC-WA.TA             0.094 [-0.033,  0.218]  .148    
## WP.TC-WA.WRF            0.061 [-0.066,  0.185]  .351    
## WP.TC-WA.InV.           0.032 [-0.095,  0.157]  .626    
## WP.TC-WA.GV             0.083 [-0.044,  0.207]  .201    
## WP.TC-WA.EV             0.021 [-0.105,  0.147]  .742    
## WP.TC-WA.SlpQlV.        0.020 [-0.106,  0.146]  .754    
## WP.TC-WA.RdV.          -0.107 [-0.232,  0.021]  .102    
## WP.TC-WA.PR            -0.010 [-0.137,  0.119]  .884    
## WP.TC-WA.ER            -0.126 [-0.250,  0.002]  .055 .  
## WP.TC-WA.SlpQnV.       -0.015 [-0.148,  0.119]  .829    
## WA.WRV-WA.PW            0.710 [ 0.644,  0.765] <.001 ***
## WA.WRV-WA.NW            0.803 [ 0.755,  0.842] <.001 ***
## WA.WRV-WA.RmV.          0.421 [ 0.316,  0.516] <.001 ***
## WA.WRV-WA.PA            0.058 [-0.063,  0.178]  .347    
## WA.WRV-WA.NA            0.068 [-0.054,  0.187]  .276    
## WA.WRV-WA.ImV.          0.270 [ 0.154,  0.379] <.001 ***
## WA.WRV-WA.WA            0.081 [-0.041,  0.200]  .195    
## WA.WRV-WA.TA            0.087 [-0.035,  0.206]  .162    
## WA.WRV-WA.WRF           0.018 [-0.104,  0.138]  .778    
## WA.WRV-WA.InV.          0.008 [-0.113,  0.129]  .899    
## WA.WRV-WA.GV            0.297 [ 0.183,  0.404] <.001 ***
## WA.WRV-WA.EV            0.109 [-0.012,  0.228]  .079 .  
## WA.WRV-WA.SlpQlV.      -0.033 [-0.154,  0.088]  .592    
## WA.WRV-WA.RdV.          0.029 [-0.094,  0.151]  .646    
## WA.WRV-WA.PR           -0.021 [-0.143,  0.102]  .737    
## WA.WRV-WA.ER            0.049 [-0.074,  0.171]  .434    
## WA.WRV-WA.SlpQnV.       0.067 [-0.061,  0.194]  .307    
## WA.PW-WA.NW             0.150 [ 0.029,  0.266]  .016 *  
## WA.PW-WA.RmV.           0.298 [ 0.184,  0.405] <.001 ***
## WA.PW-WA.PA             0.155 [ 0.034,  0.271]  .013 *  
## WA.PW-WA.NA             0.044 [-0.078,  0.164]  .479    
## WA.PW-WA.ImV.           0.223 [ 0.105,  0.335] <.001 ***
## WA.PW-WA.WA             0.132 [ 0.011,  0.250]  .033 *  
## WA.PW-WA.TA             0.131 [ 0.010,  0.249]  .035 *  
## WA.PW-WA.WRF            0.064 [-0.057,  0.184]  .301    
## WA.PW-WA.InV.           0.029 [-0.092,  0.150]  .640    
## WA.PW-WA.GV             0.389 [ 0.281,  0.487] <.001 ***
## WA.PW-WA.EV             0.141 [ 0.020,  0.258]  .023 *  
## WA.PW-WA.SlpQlV.        0.038 [-0.083,  0.159]  .540    
## WA.PW-WA.RdV.           0.120 [-0.003,  0.239]  .057 .  
## WA.PW-WA.PR             0.125 [ 0.002,  0.243]  .048 *  
## WA.PW-WA.ER             0.074 [-0.049,  0.195]  .241    
## WA.PW-WA.SlpQnV.        0.109 [-0.020,  0.234]  .099 .  
## WA.NW-WA.RmV.           0.339 [ 0.227,  0.442] <.001 ***
## WA.NW-WA.PA            -0.049 [-0.169,  0.073]  .432    
## WA.NW-WA.NA             0.058 [-0.064,  0.178]  .353    
## WA.NW-WA.ImV.           0.190 [ 0.070,  0.304]  .002 ** 
## WA.NW-WA.WA             0.001 [-0.120,  0.122]  .986    
## WA.NW-WA.TA             0.011 [-0.111,  0.132]  .863    
## WA.NW-WA.WRF           -0.030 [-0.150,  0.092]  .631    
## WA.NW-WA.InV.          -0.014 [-0.135,  0.108]  .827    
## WA.NW-WA.GV             0.088 [-0.033,  0.207]  .157    
## WA.NW-WA.EV             0.034 [-0.087,  0.155]  .583    
## WA.NW-WA.SlpQlV.       -0.079 [-0.198,  0.043]  .204    
## WA.NW-WA.RdV.          -0.061 [-0.183,  0.062]  .330    
## WA.NW-WA.PR            -0.137 [-0.255, -0.014]  .030 *  
## WA.NW-WA.ER             0.007 [-0.116,  0.129]  .913    
## WA.NW-WA.SlpQnV.       -0.001 [-0.129,  0.127]  .987    
## WA.RmV.-WA.PA           0.034 [-0.087,  0.155]  .582    
## WA.RmV.-WA.NA           0.030 [-0.091,  0.151]  .624    
## WA.RmV.-WA.ImV.         0.173 [ 0.053,  0.288]  .006 ** 
## WA.RmV.-WA.WA          -0.004 [-0.125,  0.118]  .954    
## WA.RmV.-WA.TA          -0.012 [-0.133,  0.109]  .847    
## WA.RmV.-WA.WRF          0.046 [-0.076,  0.166]  .459    
## WA.RmV.-WA.InV.         0.128 [ 0.007,  0.245]  .040 *  
## WA.RmV.-WA.GV           0.167 [ 0.046,  0.282]  .007 ** 
## WA.RmV.-WA.EV           0.017 [-0.104,  0.138]  .779    
## WA.RmV.-WA.SlpQlV.      0.050 [-0.072,  0.170]  .420    
## WA.RmV.-WA.RdV.         0.014 [-0.109,  0.136]  .829    
## WA.RmV.-WA.PR           0.042 [-0.081,  0.164]  .505    
## WA.RmV.-WA.ER          -0.009 [-0.131,  0.114]  .890    
## WA.RmV.-WA.SlpQnV.     -0.026 [-0.154,  0.102]  .688    
## WA.PA-WA.NA            -0.038 [-0.159,  0.083]  .538    
## WA.PA-WA.ImV.           0.320 [ 0.207,  0.425] <.001 ***
## WA.PA-WA.WA             0.202 [ 0.083,  0.315]  .001 ** 
## WA.PA-WA.TA             0.264 [ 0.148,  0.373] <.001 ***
## WA.PA-WA.WRF            0.235 [ 0.117,  0.346] <.001 ***
## WA.PA-WA.InV.           0.198 [ 0.079,  0.312]  .001 ** 
## WA.PA-WA.GV             0.189 [ 0.070,  0.304]  .002 ** 
## WA.PA-WA.EV             0.195 [ 0.075,  0.309]  .002 ** 
## WA.PA-WA.SlpQlV.       -0.067 [-0.187,  0.054]  .279    
## WA.PA-WA.RdV.          -0.024 [-0.146,  0.099]  .708    
## WA.PA-WA.PR            -0.011 [-0.134,  0.112]  .860    
## WA.PA-WA.ER            -0.023 [-0.145,  0.100]  .716    
## WA.PA-WA.SlpQnV.       -0.139 [-0.262, -0.011]  .035 *  
## WA.NA-WA.ImV.           0.075 [-0.047,  0.194]  .228    
## WA.NA-WA.WA             0.134 [ 0.013,  0.251]  .031 *  
## WA.NA-WA.TA            -0.075 [-0.194,  0.047]  .228    
## WA.NA-WA.WRF            0.021 [-0.100,  0.142]  .730    
## WA.NA-WA.InV.           0.017 [-0.105,  0.137]  .790    
## WA.NA-WA.GV             0.013 [-0.109,  0.134]  .837    
## WA.NA-WA.EV             0.062 [-0.059,  0.182]  .315    
## WA.NA-WA.SlpQlV.        0.067 [-0.055,  0.186]  .285    
## WA.NA-WA.RdV.          -0.064 [-0.185,  0.059]  .310    
## WA.NA-WA.PR             0.023 [-0.100,  0.145]  .714    
## WA.NA-WA.ER            -0.095 [-0.215,  0.028]  .131    
## WA.NA-WA.SlpQnV.        0.137 [ 0.008,  0.260]  .038 *  
## WA.ImV.-WA.WA           0.127 [ 0.006,  0.244]  .041 *  
## WA.ImV.-WA.TA           0.403 [ 0.297,  0.500] <.001 ***
## WA.ImV.-WA.WRF          0.209 [ 0.090,  0.322] <.001 ***
## WA.ImV.-WA.InV.         0.359 [ 0.249,  0.460] <.001 ***
## WA.ImV.-WA.GV           0.042 [-0.079,  0.163]  .497    
## WA.ImV.-WA.EV           0.048 [-0.073,  0.168]  .438    
## WA.ImV.-WA.SlpQlV.      0.177 [ 0.057,  0.292]  .005 ** 
## WA.ImV.-WA.RdV.        -0.065 [-0.186,  0.058]  .300    
## WA.ImV.-WA.PR          -0.062 [-0.183,  0.061]  .322    
## WA.ImV.-WA.ER          -0.044 [-0.165,  0.079]  .487    
## WA.ImV.-WA.SlpQnV.     -0.044 [-0.171,  0.085]  .507    
## WA.WA-WA.TA             0.289 [ 0.174,  0.396] <.001 ***
## WA.WA-WA.WRF            0.320 [ 0.207,  0.424] <.001 ***
## WA.WA-WA.InV.           0.361 [ 0.250,  0.462] <.001 ***
## WA.WA-WA.GV            -0.101 [-0.220,  0.020]  .104    
## WA.WA-WA.EV             0.083 [-0.039,  0.202]  .183    
## WA.WA-WA.SlpQlV.        0.035 [-0.086,  0.156]  .571    
## WA.WA-WA.RdV.           0.146 [ 0.024,  0.264]  .021 *  
## WA.WA-WA.PR             0.179 [ 0.058,  0.296]  .004 ** 
## WA.WA-WA.ER             0.073 [-0.050,  0.193]  .249    
## WA.WA-WA.SlpQnV.        0.051 [-0.078,  0.178]  .440    
## WA.TA-WA.WRF            0.452 [ 0.350,  0.544] <.001 ***
## WA.TA-WA.InV.           0.395 [ 0.288,  0.493] <.001 ***
## WA.TA-WA.GV             0.095 [-0.026,  0.214]  .126    
## WA.TA-WA.EV             0.112 [-0.010,  0.230]  .072 .  
## WA.TA-WA.SlpQlV.        0.029 [-0.092,  0.150]  .639    
## WA.TA-WA.RdV.           0.143 [ 0.021,  0.261]  .023 *  
## WA.TA-WA.PR             0.110 [-0.013,  0.229]  .082 .  
## WA.TA-WA.ER             0.113 [-0.010,  0.232]  .074 .  
## WA.TA-WA.SlpQnV.        0.030 [-0.099,  0.157]  .652    
## WA.WRF-WA.InV.          0.262 [ 0.145,  0.371] <.001 ***
## WA.WRF-WA.GV            0.005 [-0.116,  0.126]  .938    
## WA.WRF-WA.EV            0.098 [-0.023,  0.217]  .115    
## WA.WRF-WA.SlpQlV.      -0.037 [-0.158,  0.084]  .550    
## WA.WRF-WA.RdV.          0.092 [-0.031,  0.212]  .146    
## WA.WRF-WA.PR            0.016 [-0.107,  0.138]  .801    
## WA.WRF-WA.ER            0.105 [-0.018,  0.225]  .095 .  
## WA.WRF-WA.SlpQnV.       0.052 [-0.077,  0.179]  .428    
## WA.InV.-WA.GV          -0.186 [-0.300, -0.066]  .003 ** 
## WA.InV.-WA.EV           0.003 [-0.118,  0.125]  .956    
## WA.InV.-WA.SlpQlV.      0.175 [ 0.055,  0.290]  .005 ** 
## WA.InV.-WA.RdV.         0.150 [ 0.028,  0.268]  .017 *  
## WA.InV.-WA.PR           0.116 [-0.007,  0.235]  .066 .  
## WA.InV.-WA.ER           0.118 [-0.005,  0.237]  .062 .  
## WA.InV.-WA.SlpQnV.     -0.120 [-0.244,  0.009]  .069 .  
## WA.GV-WA.EV             0.070 [-0.051,  0.190]  .258    
## WA.GV-WA.SlpQlV.       -0.023 [-0.144,  0.099]  .712    
## WA.GV-WA.RdV.          -0.054 [-0.175,  0.069]  .393    
## WA.GV-WA.PR             0.044 [-0.079,  0.166]  .483    
## WA.GV-WA.ER            -0.095 [-0.215,  0.028]  .131    
## WA.GV-WA.SlpQnV.        0.108 [-0.020,  0.233]  .100 .  
## WA.EV-WA.SlpQlV.       -0.084 [-0.203,  0.038]  .177    
## WA.EV-WA.RdV.           0.069 [-0.054,  0.190]  .274    
## WA.EV-WA.PR             0.117 [-0.005,  0.237]  .062 .  
## WA.EV-WA.ER             0.014 [-0.109,  0.137]  .822    
## WA.EV-WA.SlpQnV.        0.025 [-0.104,  0.153]  .705    
## WA.SlpQlV.-WA.RdV.      0.257 [ 0.139,  0.368] <.001 ***
## WA.SlpQlV.-WA.PR        0.092 [-0.031,  0.213]  .142    
## WA.SlpQlV.-WA.ER        0.266 [ 0.149,  0.377] <.001 ***
## WA.SlpQlV.-WA.SlpQnV.  -0.183 [-0.304, -0.056]  .006 ** 
## WA.RdV.-WA.PR           0.619 [ 0.537,  0.689] <.001 ***
## WA.RdV.-WA.ER           0.876 [ 0.844,  0.902] <.001 ***
## WA.RdV.-WA.SlpQnV.     -0.110 [-0.236,  0.020]  .100    
## WA.PR-WA.ER             0.162 [ 0.040,  0.279]  .010 *  
## WA.PR-WA.SlpQnV.        0.056 [-0.075,  0.184]  .403    
## WA.ER-WA.SlpQnV.       -0.176 [-0.299, -0.047]  .008 ** 
## ────────────────────────────────────────────────────────
## 
## Between-Level Correlation [95% CI]:
## ────────────────────────────────────────────────────────
##                             r         [95% CI]     p    
## ────────────────────────────────────────────────────────
## Mnpl.-WP.SB            -0.046 [-0.211,  0.122]  .593    
## Mnpl.-WP.SP             0.004 [-0.164,  0.171]  .967    
## Mnpl.-WP.SN            -0.080 [-0.244,  0.088]  .348    
## Mnpl.-WP.BV            -0.009 [-0.176,  0.158]  .915    
## Mnpl.-WP.JC             0.085 [-0.083,  0.249]  .321    
## Mnpl.-WP.CP             0.082 [-0.086,  0.246]  .339    
## Mnpl.-WP.PrbIV.         0.119 [-0.049,  0.281]  .164    
## Mnpl.-WP.IS             0.025 [-0.142,  0.192]  .768    
## Mnpl.-WP.IG             0.070 [-0.098,  0.235]  .411    
## Mnpl.-WP.SL             0.068 [-0.101,  0.232]  .431    
## Mnpl.-WP.OL             0.084 [-0.084,  0.247]  .328    
## Mnpl.-WP.AS             0.028 [-0.140,  0.194]  .748    
## Mnpl.-WP.PrfIV.        -0.044 [-0.209,  0.124]  .612    
## Mnpl.-WP.TC             0.026 [-0.142,  0.192]  .762    
## Mnpl.-WA.WRV           -0.053 [-0.213,  0.111]  .529    
## Mnpl.-WA.PW            -0.001 [-0.163,  0.162]  .992    
## Mnpl.-WA.NW            -0.087 [-0.246,  0.076]  .295    
## Mnpl.-WA.RmV.           0.000 [-0.162,  0.163]  .998    
## Mnpl.-WA.PA             0.096 [-0.068,  0.254]  .251    
## Mnpl.-WA.NA            -0.105 [-0.263,  0.059]  .208    
## Mnpl.-WA.ImV.           0.038 [-0.125,  0.199]  .647    
## Mnpl.-WA.WA            -0.047 [-0.208,  0.117]  .575    
## Mnpl.-WA.TA             0.050 [-0.113,  0.211]  .548    
## Mnpl.-WA.WRF           -0.055 [-0.215,  0.109]  .513    
## Mnpl.-WA.InV.          -0.040 [-0.201,  0.123]  .632    
## Mnpl.-WA.GV             0.061 [-0.102,  0.221]  .463    
## Mnpl.-WA.EV             0.062 [-0.102,  0.222]  .459    
## Mnpl.-WA.SlpQlV.       -0.076 [-0.236,  0.087]  .359    
## Mnpl.-WA.RdV.          -0.096 [-0.255,  0.068]  .251    
## Mnpl.-WA.PR            -0.038 [-0.199,  0.126]  .654    
## Mnpl.-WA.ER            -0.101 [-0.260,  0.063]  .227    
## Mnpl.-WA.SlpQnV.       -0.021 [-0.191,  0.150]  .809    
## WP.SB-WP.SP             0.922 [ 0.893,  0.944] <.001 ***
## WP.SB-WP.SN             0.950 [ 0.931,  0.964] <.001 ***
## WP.SB-WP.BV             0.503 [ 0.367,  0.618] <.001 ***
## WP.SB-WP.JC             0.480 [ 0.340,  0.599] <.001 ***
## WP.SB-WP.CP             0.523 [ 0.390,  0.635] <.001 ***
## WP.SB-WP.PrbIV.         0.446 [ 0.301,  0.571] <.001 ***
## WP.SB-WP.IS             0.321 [ 0.162,  0.463] <.001 ***
## WP.SB-WP.IG             0.545 [ 0.415,  0.652] <.001 ***
## WP.SB-WP.SL             0.377 [ 0.224,  0.512] <.001 ***
## WP.SB-WP.OL             0.374 [ 0.220,  0.509] <.001 ***
## WP.SB-WP.AS             0.294 [ 0.134,  0.440] <.001 ***
## WP.SB-WP.PrfIV.         0.513 [ 0.378,  0.626] <.001 ***
## WP.SB-WP.TC             0.479 [ 0.339,  0.598] <.001 ***
## WP.SB-WA.WRV            0.627 [ 0.513,  0.719] <.001 ***
## WP.SB-WA.PW             0.644 [ 0.534,  0.733] <.001 ***
## WP.SB-WA.NW             0.404 [ 0.254,  0.536] <.001 ***
## WP.SB-WA.RmV.           0.646 [ 0.537,  0.734] <.001 ***
## WP.SB-WA.PA             0.477 [ 0.337,  0.597] <.001 ***
## WP.SB-WA.NA            -0.022 [-0.189,  0.146]  .792    
## WP.SB-WA.ImV.           0.574 [ 0.450,  0.677] <.001 ***
## WP.SB-WA.WA             0.578 [ 0.454,  0.680] <.001 ***
## WP.SB-WA.TA             0.581 [ 0.457,  0.682] <.001 ***
## WP.SB-WA.WRF            0.575 [ 0.451,  0.677] <.001 ***
## WP.SB-WA.InV.           0.551 [ 0.423,  0.658] <.001 ***
## WP.SB-WA.GV             0.495 [ 0.357,  0.612] <.001 ***
## WP.SB-WA.EV             0.163 [-0.005,  0.322]  .049 *  
## WP.SB-WA.SlpQlV.       -0.022 [-0.189,  0.147]  .797    
## WP.SB-WA.RdV.           0.138 [-0.031,  0.299]  .098 .  
## WP.SB-WA.PR             0.025 [-0.144,  0.192]  .770    
## WP.SB-WA.ER             0.160 [-0.008,  0.320]  .054 .  
## WP.SB-WA.SlpQnV.       -0.028 [-0.203,  0.148]  .749    
## WP.SP-WP.SN             0.757 [ 0.675,  0.820] <.001 ***
## WP.SP-WP.BV             0.535 [ 0.404,  0.645] <.001 ***
## WP.SP-WP.JC             0.491 [ 0.353,  0.608] <.001 ***
## WP.SP-WP.CP             0.483 [ 0.343,  0.601] <.001 ***
## WP.SP-WP.PrbIV.         0.422 [ 0.274,  0.550] <.001 ***
## WP.SP-WP.IS             0.244 [ 0.080,  0.395]  .004 ** 
## WP.SP-WP.IG             0.526 [ 0.393,  0.637] <.001 ***
## WP.SP-WP.SL             0.335 [ 0.178,  0.476] <.001 ***
## WP.SP-WP.OL             0.334 [ 0.177,  0.475] <.001 ***
## WP.SP-WP.AS             0.259 [ 0.096,  0.408]  .002 ** 
## WP.SP-WP.PrfIV.         0.516 [ 0.382,  0.629] <.001 ***
## WP.SP-WP.TC             0.467 [ 0.325,  0.588] <.001 ***
## WP.SP-WA.WRV            0.602 [ 0.483,  0.699] <.001 ***
## WP.SP-WA.PW             0.680 [ 0.578,  0.761] <.001 ***
## WP.SP-WA.NW             0.327 [ 0.168,  0.469] <.001 ***
## WP.SP-WA.RmV.           0.649 [ 0.540,  0.736] <.001 ***
## WP.SP-WA.PA             0.488 [ 0.348,  0.606] <.001 ***
## WP.SP-WA.NA            -0.040 [-0.207,  0.128]  .628    
## WP.SP-WA.ImV.           0.619 [ 0.504,  0.713] <.001 ***
## WP.SP-WA.WA             0.601 [ 0.482,  0.698] <.001 ***
## WP.SP-WA.TA             0.592 [ 0.471,  0.691] <.001 ***
## WP.SP-WA.WRF            0.581 [ 0.458,  0.683] <.001 ***
## WP.SP-WA.InV.           0.530 [ 0.398,  0.641] <.001 ***
## WP.SP-WA.GV             0.454 [ 0.309,  0.577] <.001 ***
## WP.SP-WA.EV             0.149 [-0.020,  0.309]  .074 .  
## WP.SP-WA.SlpQlV.       -0.041 [-0.208,  0.127]  .621    
## WP.SP-WA.RdV.           0.073 [-0.097,  0.238]  .384    
## WP.SP-WA.PR            -0.008 [-0.176,  0.161]  .928    
## WP.SP-WA.ER             0.096 [-0.074,  0.260]  .252    
## WP.SP-WA.SlpQnV.       -0.027 [-0.201,  0.150]  .759    
## WP.SN-WP.BV             0.421 [ 0.273,  0.549] <.001 ***
## WP.SN-WP.JC             0.417 [ 0.269,  0.546] <.001 ***
## WP.SN-WP.CP             0.497 [ 0.360,  0.613] <.001 ***
## WP.SN-WP.PrbIV.         0.415 [ 0.267,  0.545] <.001 ***
## WP.SN-WP.IS             0.347 [ 0.191,  0.486] <.001 ***
## WP.SN-WP.IG             0.499 [ 0.362,  0.615] <.001 ***
## WP.SN-WP.SL             0.369 [ 0.215,  0.505] <.001 ***
## WP.SN-WP.OL             0.364 [ 0.209,  0.500] <.001 ***
## WP.SN-WP.AS             0.290 [ 0.129,  0.436] <.001 ***
## WP.SN-WP.PrfIV.         0.452 [ 0.308,  0.576] <.001 ***
## WP.SN-WP.TC             0.435 [ 0.289,  0.561] <.001 ***
## WP.SN-WA.WRV            0.579 [ 0.456,  0.681] <.001 ***
## WP.SN-WA.PW             0.546 [ 0.417,  0.654] <.001 ***
## WP.SN-WA.NW             0.423 [ 0.274,  0.551] <.001 ***
## WP.SN-WA.RmV.           0.575 [ 0.451,  0.677] <.001 ***
## WP.SN-WA.PA             0.418 [ 0.269,  0.547] <.001 ***
## WP.SN-WA.NA            -0.005 [-0.172,  0.163]  .953    
## WP.SN-WA.ImV.           0.477 [ 0.336,  0.597] <.001 ***
## WP.SN-WA.WA             0.497 [ 0.360,  0.614] <.001 ***
## WP.SN-WA.TA             0.509 [ 0.373,  0.623] <.001 ***
## WP.SN-WA.WRF            0.508 [ 0.372,  0.623] <.001 ***
## WP.SN-WA.InV.           0.509 [ 0.373,  0.623] <.001 ***
## WP.SN-WA.GV             0.475 [ 0.333,  0.595] <.001 ***
## WP.SN-WA.EV             0.157 [-0.011,  0.317]  .058 .  
## WP.SN-WA.SlpQlV.       -0.003 [-0.171,  0.164]  .968    
## WP.SN-WA.RdV.           0.175 [ 0.007,  0.333]  .035 *  
## WP.SN-WA.PR             0.048 [-0.122,  0.214]  .570    
## WP.SN-WA.ER             0.195 [ 0.028,  0.352]  .019 *  
## WP.SN-WA.SlpQnV.       -0.026 [-0.201,  0.150]  .767    
## WP.BV-WP.JC             0.644 [ 0.534,  0.732] <.001 ***
## WP.BV-WP.CP             0.629 [ 0.516,  0.720] <.001 ***
## WP.BV-WP.PrbIV.         0.601 [ 0.482,  0.698] <.001 ***
## WP.BV-WP.IS             0.418 [ 0.270,  0.547] <.001 ***
## WP.BV-WP.IG             0.599 [ 0.480,  0.697] <.001 ***
## WP.BV-WP.SL             0.538 [ 0.407,  0.647] <.001 ***
## WP.BV-WP.OL             0.535 [ 0.404,  0.645] <.001 ***
## WP.BV-WP.AS             0.415 [ 0.267,  0.545] <.001 ***
## WP.BV-WP.PrfIV.         0.665 [ 0.560,  0.749] <.001 ***
## WP.BV-WP.TC             0.679 [ 0.578,  0.760] <.001 ***
## WP.BV-WA.WRV            0.591 [ 0.470,  0.690] <.001 ***
## WP.BV-WA.PW             0.629 [ 0.516,  0.721] <.001 ***
## WP.BV-WA.NW             0.359 [ 0.204,  0.497] <.001 ***
## WP.BV-WA.RmV.           0.605 [ 0.486,  0.701] <.001 ***
## WP.BV-WA.PA             0.432 [ 0.285,  0.559] <.001 ***
## WP.BV-WA.NA             0.038 [-0.130,  0.205]  .645    
## WP.BV-WA.ImV.           0.650 [ 0.541,  0.737] <.001 ***
## WP.BV-WA.WA             0.533 [ 0.401,  0.643] <.001 ***
## WP.BV-WA.TA             0.704 [ 0.608,  0.780] <.001 ***
## WP.BV-WA.WRF            0.572 [ 0.447,  0.675] <.001 ***
## WP.BV-WA.InV.           0.679 [ 0.577,  0.760] <.001 ***
## WP.BV-WA.GV             0.463 [ 0.321,  0.586] <.001 ***
## WP.BV-WA.EV             0.237 [ 0.072,  0.389]  .004 ** 
## WP.BV-WA.SlpQlV.        0.130 [-0.038,  0.292]  .117    
## WP.BV-WA.RdV.           0.133 [-0.036,  0.295]  .109    
## WP.BV-WA.PR             0.026 [-0.143,  0.194]  .752    
## WP.BV-WA.ER             0.154 [-0.015,  0.314]  .064 .  
## WP.BV-WA.SlpQnV.       -0.041 [-0.215,  0.136]  .641    
## WP.JC-WP.CP             0.723 [ 0.632,  0.794] <.001 ***
## WP.JC-WP.PrbIV.         0.653 [ 0.545,  0.739] <.001 ***
## WP.JC-WP.IS             0.459 [ 0.316,  0.581] <.001 ***
## WP.JC-WP.IG             0.723 [ 0.632,  0.794] <.001 ***
## WP.JC-WP.SL             0.582 [ 0.460,  0.683] <.001 ***
## WP.JC-WP.OL             0.607 [ 0.490,  0.703] <.001 ***
## WP.JC-WP.AS             0.408 [ 0.259,  0.538] <.001 ***
## WP.JC-WP.PrfIV.         0.719 [ 0.627,  0.791] <.001 ***
## WP.JC-WP.TC             0.764 [ 0.685,  0.826] <.001 ***
## WP.JC-WA.WRV            0.646 [ 0.536,  0.734] <.001 ***
## WP.JC-WA.PW             0.563 [ 0.436,  0.667] <.001 ***
## WP.JC-WA.NW             0.519 [ 0.384,  0.632] <.001 ***
## WP.JC-WA.RmV.           0.568 [ 0.443,  0.672] <.001 ***
## WP.JC-WA.PA             0.509 [ 0.373,  0.623] <.001 ***
## WP.JC-WA.NA            -0.041 [-0.208,  0.127]  .620    
## WP.JC-WA.ImV.           0.670 [ 0.566,  0.753] <.001 ***
## WP.JC-WA.WA             0.581 [ 0.458,  0.682] <.001 ***
## WP.JC-WA.TA             0.610 [ 0.493,  0.706] <.001 ***
## WP.JC-WA.WRF            0.580 [ 0.457,  0.681] <.001 ***
## WP.JC-WA.InV.           0.580 [ 0.456,  0.681] <.001 ***
## WP.JC-WA.GV             0.303 [ 0.142,  0.448] <.001 ***
## WP.JC-WA.EV             0.299 [ 0.139,  0.445] <.001 ***
## WP.JC-WA.SlpQlV.        0.080 [-0.089,  0.244]  .340    
## WP.JC-WA.RdV.           0.253 [ 0.088,  0.404]  .002 ** 
## WP.JC-WA.PR             0.198 [ 0.031,  0.355]  .017 *  
## WP.JC-WA.ER             0.212 [ 0.046,  0.368]  .010 *  
## WP.JC-WA.SlpQnV.       -0.005 [-0.180,  0.171]  .955    
## WP.CP-WP.PrbIV.         0.844 [ 0.788,  0.886] <.001 ***
## WP.CP-WP.IS             0.811 [ 0.744,  0.861] <.001 ***
## WP.CP-WP.IG             0.937 [ 0.912,  0.954] <.001 ***
## WP.CP-WP.SL             0.617 [ 0.502,  0.711] <.001 ***
## WP.CP-WP.OL             0.607 [ 0.489,  0.703] <.001 ***
## WP.CP-WP.AS             0.488 [ 0.349,  0.605] <.001 ***
## WP.CP-WP.PrfIV.         0.703 [ 0.608,  0.779] <.001 ***
## WP.CP-WP.TC             0.691 [ 0.592,  0.769] <.001 ***
## WP.CP-WA.WRV            0.630 [ 0.516,  0.721] <.001 ***
## WP.CP-WA.PW             0.583 [ 0.460,  0.684] <.001 ***
## WP.CP-WA.NW             0.471 [ 0.329,  0.592] <.001 ***
## WP.CP-WA.RmV.           0.668 [ 0.563,  0.751] <.001 ***
## WP.CP-WA.PA             0.541 [ 0.411,  0.650] <.001 ***
## WP.CP-WA.NA            -0.083 [-0.248,  0.086]  .318    
## WP.CP-WA.ImV.           0.695 [ 0.596,  0.772] <.001 ***
## WP.CP-WA.WA             0.607 [ 0.489,  0.703] <.001 ***
## WP.CP-WA.TA             0.628 [ 0.514,  0.720] <.001 ***
## WP.CP-WA.WRF            0.571 [ 0.445,  0.674] <.001 ***
## WP.CP-WA.InV.           0.562 [ 0.435,  0.667] <.001 ***
## WP.CP-WA.GV             0.414 [ 0.265,  0.544] <.001 ***
## WP.CP-WA.EV             0.324 [ 0.165,  0.466] <.001 ***
## WP.CP-WA.SlpQlV.        0.165 [-0.003,  0.323]  .047 *  
## WP.CP-WA.RdV.           0.219 [ 0.053,  0.374]  .008 ** 
## WP.CP-WA.PR             0.110 [-0.059,  0.273]  .188    
## WP.CP-WA.ER             0.218 [ 0.051,  0.372]  .009 ** 
## WP.CP-WA.SlpQnV.       -0.126 [-0.295,  0.051]  .149    
## WP.PrbIV.-WP.IS         0.559 [ 0.432,  0.664] <.001 ***
## WP.PrbIV.-WP.IG         0.693 [ 0.594,  0.771] <.001 ***
## WP.PrbIV.-WP.SL         0.524 [ 0.391,  0.635] <.001 ***
## WP.PrbIV.-WP.OL         0.510 [ 0.375,  0.624] <.001 ***
## WP.PrbIV.-WP.AS         0.421 [ 0.273,  0.549] <.001 ***
## WP.PrbIV.-WP.PrfIV.     0.585 [ 0.464,  0.685] <.001 ***
## WP.PrbIV.-WP.TC         0.616 [ 0.500,  0.710] <.001 ***
## WP.PrbIV.-WA.WRV        0.531 [ 0.399,  0.641] <.001 ***
## WP.PrbIV.-WA.PW         0.535 [ 0.403,  0.645] <.001 ***
## WP.PrbIV.-WA.NW         0.353 [ 0.197,  0.492] <.001 ***
## WP.PrbIV.-WA.RmV.       0.575 [ 0.450,  0.677] <.001 ***
## WP.PrbIV.-WA.PA         0.436 [ 0.289,  0.562] <.001 ***
## WP.PrbIV.-WA.NA        -0.052 [-0.218,  0.117]  .531    
## WP.PrbIV.-WA.ImV.       0.589 [ 0.468,  0.689] <.001 ***
## WP.PrbIV.-WA.WA         0.480 [ 0.340,  0.600] <.001 ***
## WP.PrbIV.-WA.TA         0.560 [ 0.433,  0.665] <.001 ***
## WP.PrbIV.-WA.WRF        0.492 [ 0.354,  0.610] <.001 ***
## WP.PrbIV.-WA.InV.       0.484 [ 0.344,  0.603] <.001 ***
## WP.PrbIV.-WA.GV         0.293 [ 0.132,  0.439] <.001 ***
## WP.PrbIV.-WA.EV         0.319 [ 0.160,  0.462] <.001 ***
## WP.PrbIV.-WA.SlpQlV.    0.157 [-0.011,  0.317]  .058 .  
## WP.PrbIV.-WA.RdV.       0.117 [-0.052,  0.280]  .160    
## WP.PrbIV.-WA.PR         0.058 [-0.112,  0.224]  .491    
## WP.PrbIV.-WA.ER         0.117 [-0.053,  0.280]  .162    
## WP.PrbIV.-WA.SlpQnV.   -0.165 [-0.331,  0.011]  .058 .  
## WP.IS-WP.IG             0.642 [ 0.532,  0.731] <.001 ***
## WP.IS-WP.SL             0.512 [ 0.377,  0.625] <.001 ***
## WP.IS-WP.OL             0.461 [ 0.318,  0.583] <.001 ***
## WP.IS-WP.AS             0.467 [ 0.325,  0.588] <.001 ***
## WP.IS-WP.PrfIV.         0.447 [ 0.302,  0.571] <.001 ***
## WP.IS-WP.TC             0.441 [ 0.295,  0.566] <.001 ***
## WP.IS-WA.WRV            0.393 [ 0.241,  0.526] <.001 ***
## WP.IS-WA.PW             0.337 [ 0.179,  0.478] <.001 ***
## WP.IS-WA.NW             0.321 [ 0.162,  0.464] <.001 ***
## WP.IS-WA.RmV.           0.434 [ 0.287,  0.561] <.001 ***
## WP.IS-WA.PA             0.387 [ 0.235,  0.521] <.001 ***
## WP.IS-WA.NA            -0.041 [-0.207,  0.128]  .626    
## WP.IS-WA.ImV.           0.433 [ 0.286,  0.560] <.001 ***
## WP.IS-WA.WA             0.434 [ 0.287,  0.561] <.001 ***
## WP.IS-WA.TA             0.397 [ 0.246,  0.530] <.001 ***
## WP.IS-WA.WRF            0.344 [ 0.187,  0.483] <.001 ***
## WP.IS-WA.InV.           0.345 [ 0.188,  0.485] <.001 ***
## WP.IS-WA.GV             0.408 [ 0.258,  0.539] <.001 ***
## WP.IS-WA.EV             0.256 [ 0.092,  0.406]  .002 ** 
## WP.IS-WA.SlpQlV.        0.263 [ 0.100,  0.413]  .001 ** 
## WP.IS-WA.RdV.           0.268 [ 0.104,  0.418]  .001 ** 
## WP.IS-WA.PR             0.166 [-0.002,  0.325]  .046 *  
## WP.IS-WA.ER             0.249 [ 0.084,  0.401]  .003 ** 
## WP.IS-WA.SlpQnV.       -0.146 [-0.313,  0.031]  .094 .  
## WP.IG-WP.SL             0.570 [ 0.445,  0.673] <.001 ***
## WP.IG-WP.OL             0.587 [ 0.465,  0.686] <.001 ***
## WP.IG-WP.AS             0.411 [ 0.262,  0.541] <.001 ***
## WP.IG-WP.PrfIV.         0.733 [ 0.644,  0.802] <.001 ***
## WP.IG-WP.TC             0.694 [ 0.596,  0.772] <.001 ***
## WP.IG-WA.WRV            0.653 [ 0.545,  0.740] <.001 ***
## WP.IG-WA.PW             0.594 [ 0.474,  0.693] <.001 ***
## WP.IG-WA.NW             0.499 [ 0.362,  0.616] <.001 ***
## WP.IG-WA.RmV.           0.677 [ 0.574,  0.758] <.001 ***
## WP.IG-WA.PA             0.547 [ 0.417,  0.654] <.001 ***
## WP.IG-WA.NA            -0.103 [-0.266,  0.066]  .215    
## WP.IG-WA.ImV.           0.719 [ 0.627,  0.791] <.001 ***
## WP.IG-WA.WA             0.618 [ 0.502,  0.712] <.001 ***
## WP.IG-WA.TA             0.631 [ 0.518,  0.722] <.001 ***
## WP.IG-WA.WRF            0.592 [ 0.472,  0.691] <.001 ***
## WP.IG-WA.InV.           0.581 [ 0.457,  0.682] <.001 ***
## WP.IG-WA.GV             0.380 [ 0.226,  0.514] <.001 ***
## WP.IG-WA.EV             0.280 [ 0.118,  0.427] <.001 ***
## WP.IG-WA.SlpQlV.        0.071 [-0.098,  0.236]  .393    
## WP.IG-WA.RdV.           0.195 [ 0.027,  0.352]  .019 *  
## WP.IG-WA.PR             0.081 [-0.089,  0.246]  .334    
## WP.IG-WA.ER             0.202 [ 0.035,  0.358]  .015 *  
## WP.IG-WA.SlpQnV.       -0.059 [-0.233,  0.117]  .497    
## WP.SL-WP.OL             0.938 [ 0.914,  0.955] <.001 ***
## WP.SL-WP.AS             0.857 [ 0.805,  0.896] <.001 ***
## WP.SL-WP.PrfIV.         0.703 [ 0.607,  0.779] <.001 ***
## WP.SL-WP.TC             0.625 [ 0.511,  0.717] <.001 ***
## WP.SL-WA.WRV            0.411 [ 0.261,  0.541] <.001 ***
## WP.SL-WA.PW             0.387 [ 0.234,  0.520] <.001 ***
## WP.SL-WA.NW             0.301 [ 0.141,  0.447] <.001 ***
## WP.SL-WA.RmV.           0.429 [ 0.282,  0.557] <.001 ***
## WP.SL-WA.PA             0.322 [ 0.163,  0.465] <.001 ***
## WP.SL-WA.NA             0.014 [-0.154,  0.181]  .868    
## WP.SL-WA.ImV.           0.427 [ 0.279,  0.555] <.001 ***
## WP.SL-WA.WA             0.376 [ 0.222,  0.511] <.001 ***
## WP.SL-WA.TA             0.463 [ 0.320,  0.585] <.001 ***
## WP.SL-WA.WRF            0.360 [ 0.205,  0.498] <.001 ***
## WP.SL-WA.InV.           0.510 [ 0.374,  0.624] <.001 ***
## WP.SL-WA.GV             0.331 [ 0.173,  0.473] <.001 ***
## WP.SL-WA.EV             0.265 [ 0.102,  0.414]  .001 ** 
## WP.SL-WA.SlpQlV.        0.183 [ 0.016,  0.340]  .027 *  
## WP.SL-WA.RdV.           0.232 [ 0.066,  0.385]  .005 ** 
## WP.SL-WA.PR             0.039 [-0.130,  0.206]  .643    
## WP.SL-WA.ER             0.272 [ 0.108,  0.421] <.001 ***
## WP.SL-WA.SlpQnV.       -0.059 [-0.232,  0.118]  .500    
## WP.OL-WP.AS             0.626 [ 0.512,  0.718] <.001 ***
## WP.OL-WP.PrfIV.         0.710 [ 0.615,  0.784] <.001 ***
## WP.OL-WP.TC             0.666 [ 0.561,  0.749] <.001 ***
## WP.OL-WA.WRV            0.456 [ 0.312,  0.579] <.001 ***
## WP.OL-WA.PW             0.414 [ 0.265,  0.544] <.001 ***
## WP.OL-WA.NW             0.349 [ 0.192,  0.488] <.001 ***
## WP.OL-WA.RmV.           0.457 [ 0.313,  0.580] <.001 ***
## WP.OL-WA.PA             0.362 [ 0.207,  0.500] <.001 ***
## WP.OL-WA.NA             0.088 [-0.081,  0.252]  .293    
## WP.OL-WA.ImV.           0.480 [ 0.339,  0.599] <.001 ***
## WP.OL-WA.WA             0.416 [ 0.267,  0.546] <.001 ***
## WP.OL-WA.TA             0.497 [ 0.359,  0.614] <.001 ***
## WP.OL-WA.WRF            0.420 [ 0.272,  0.549] <.001 ***
## WP.OL-WA.InV.           0.565 [ 0.439,  0.670] <.001 ***
## WP.OL-WA.GV             0.301 [ 0.141,  0.446] <.001 ***
## WP.OL-WA.EV             0.246 [ 0.081,  0.397]  .003 ** 
## WP.OL-WA.SlpQlV.        0.134 [-0.034,  0.295]  .106    
## WP.OL-WA.RdV.           0.237 [ 0.071,  0.389]  .004 ** 
## WP.OL-WA.PR             0.084 [-0.085,  0.249]  .313    
## WP.OL-WA.ER             0.253 [ 0.088,  0.404]  .002 ** 
## WP.OL-WA.SlpQnV.       -0.048 [-0.222,  0.129]  .582    
## WP.AS-WP.PrfIV.         0.528 [ 0.396,  0.639] <.001 ***
## WP.AS-WP.TC             0.417 [ 0.268,  0.546] <.001 ***
## WP.AS-WA.WRV            0.247 [ 0.083,  0.398]  .003 ** 
## WP.AS-WA.PW             0.254 [ 0.090,  0.404]  .002 ** 
## WP.AS-WA.NW             0.159 [-0.009,  0.319]  .055 .  
## WP.AS-WA.RmV.           0.286 [ 0.124,  0.433] <.001 ***
## WP.AS-WA.PA             0.186 [ 0.018,  0.343]  .025 *  
## WP.AS-WA.NA            -0.099 [-0.263,  0.069]  .232    
## WP.AS-WA.ImV.           0.246 [ 0.082,  0.397]  .003 ** 
## WP.AS-WA.WA             0.227 [ 0.061,  0.380]  .006 ** 
## WP.AS-WA.TA             0.302 [ 0.142,  0.447] <.001 ***
## WP.AS-WA.WRF            0.185 [ 0.018,  0.342]  .026 *  
## WP.AS-WA.InV.           0.307 [ 0.146,  0.451] <.001 ***
## WP.AS-WA.GV             0.297 [ 0.136,  0.443] <.001 ***
## WP.AS-WA.EV             0.231 [ 0.066,  0.384]  .005 ** 
## WP.AS-WA.SlpQlV.        0.212 [ 0.046,  0.367]  .010 *  
## WP.AS-WA.RdV.           0.170 [ 0.002,  0.329]  .041 *  
## WP.AS-WA.PR            -0.038 [-0.205,  0.131]  .649    
## WP.AS-WA.ER             0.235 [ 0.070,  0.388]  .004 ** 
## WP.AS-WA.SlpQnV.       -0.061 [-0.234,  0.115]  .482    
## WP.PrfIV.-WP.TC         0.752 [ 0.670,  0.817] <.001 ***
## WP.PrfIV.-WA.WRV        0.538 [ 0.407,  0.647] <.001 ***
## WP.PrfIV.-WA.PW         0.506 [ 0.369,  0.621] <.001 ***
## WP.PrfIV.-WA.NW         0.394 [ 0.243,  0.527] <.001 ***
## WP.PrfIV.-WA.RmV.       0.501 [ 0.364,  0.617] <.001 ***
## WP.PrfIV.-WA.PA         0.463 [ 0.320,  0.585] <.001 ***
## WP.PrfIV.-WA.NA         0.000 [-0.167,  0.168]  .996    
## WP.PrfIV.-WA.ImV.       0.644 [ 0.534,  0.733] <.001 ***
## WP.PrfIV.-WA.WA         0.537 [ 0.406,  0.647] <.001 ***
## WP.PrfIV.-WA.TA         0.621 [ 0.506,  0.714] <.001 ***
## WP.PrfIV.-WA.WRF        0.509 [ 0.373,  0.623] <.001 ***
## WP.PrfIV.-WA.InV.       0.710 [ 0.615,  0.784] <.001 ***
## WP.PrfIV.-WA.GV         0.266 [ 0.103,  0.415]  .001 ** 
## WP.PrfIV.-WA.EV         0.256 [ 0.092,  0.406]  .002 ** 
## WP.PrfIV.-WA.SlpQlV.    0.150 [-0.018,  0.310]  .071 .  
## WP.PrfIV.-WA.RdV.       0.218 [ 0.052,  0.373]  .008 ** 
## WP.PrfIV.-WA.PR         0.112 [-0.057,  0.275]  .180    
## WP.PrfIV.-WA.ER         0.215 [ 0.049,  0.370]  .009 ** 
## WP.PrfIV.-WA.SlpQnV.   -0.002 [-0.178,  0.174]  .981    
## WP.TC-WA.WRV            0.596 [ 0.476,  0.694] <.001 ***
## WP.TC-WA.PW             0.580 [ 0.456,  0.681] <.001 ***
## WP.TC-WA.NW             0.417 [ 0.268,  0.547] <.001 ***
## WP.TC-WA.RmV.           0.582 [ 0.459,  0.683] <.001 ***
## WP.TC-WA.PA             0.456 [ 0.312,  0.579] <.001 ***
## WP.TC-WA.NA             0.055 [-0.114,  0.221]  .509    
## WP.TC-WA.ImV.           0.609 [ 0.492,  0.705] <.001 ***
## WP.TC-WA.WA             0.505 [ 0.369,  0.621] <.001 ***
## WP.TC-WA.TA             0.613 [ 0.496,  0.708] <.001 ***
## WP.TC-WA.WRF            0.563 [ 0.437,  0.668] <.001 ***
## WP.TC-WA.InV.           0.659 [ 0.553,  0.745] <.001 ***
## WP.TC-WA.GV             0.310 [ 0.150,  0.454] <.001 ***
## WP.TC-WA.EV             0.293 [ 0.132,  0.439] <.001 ***
## WP.TC-WA.SlpQlV.        0.052 [-0.117,  0.217]  .536    
## WP.TC-WA.RdV.           0.263 [ 0.099,  0.413]  .001 ** 
## WP.TC-WA.PR             0.142 [-0.027,  0.303]  .088 .  
## WP.TC-WA.ER             0.256 [ 0.091,  0.407]  .002 ** 
## WP.TC-WA.SlpQnV.       -0.061 [-0.234,  0.116]  .486    
## WA.WRV-WA.PW            0.837 [ 0.780,  0.880] <.001 ***
## WA.WRV-WA.NW            0.836 [ 0.779,  0.879] <.001 ***
## WA.WRV-WA.RmV.          0.721 [ 0.633,  0.791] <.001 ***
## WA.WRV-WA.PA            0.559 [ 0.436,  0.661] <.001 ***
## WA.WRV-WA.NA           -0.010 [-0.172,  0.152]  .902    
## WA.WRV-WA.ImV.          0.671 [ 0.571,  0.752] <.001 ***
## WA.WRV-WA.WA            0.640 [ 0.533,  0.727] <.001 ***
## WA.WRV-WA.TA            0.674 [ 0.574,  0.754] <.001 ***
## WA.WRV-WA.WRF           0.694 [ 0.599,  0.770] <.001 ***
## WA.WRV-WA.InV.          0.548 [ 0.423,  0.652] <.001 ***
## WA.WRV-WA.GV            0.485 [ 0.350,  0.600] <.001 ***
## WA.WRV-WA.EV            0.303 [ 0.148,  0.444] <.001 ***
## WA.WRV-WA.SlpQlV.       0.023 [-0.140,  0.185]  .783    
## WA.WRV-WA.RdV.          0.107 [-0.057,  0.265]  .201    
## WA.WRV-WA.PR            0.059 [-0.105,  0.220]  .480    
## WA.WRV-WA.ER            0.103 [-0.061,  0.261]  .219    
## WA.WRV-WA.SlpQnV.      -0.029 [-0.198,  0.142]  .742    
## WA.PW-WA.NW             0.399 [ 0.253,  0.527] <.001 ***
## WA.PW-WA.RmV.           0.775 [ 0.701,  0.832] <.001 ***
## WA.PW-WA.PA             0.696 [ 0.601,  0.771] <.001 ***
## WA.PW-WA.NA            -0.086 [-0.245,  0.078]  .303    
## WA.PW-WA.ImV.           0.692 [ 0.596,  0.768] <.001 ***
## WA.PW-WA.WA             0.719 [ 0.630,  0.789] <.001 ***
## WA.PW-WA.TA             0.656 [ 0.553,  0.740] <.001 ***
## WA.PW-WA.WRF            0.733 [ 0.647,  0.800] <.001 ***
## WA.PW-WA.InV.           0.607 [ 0.493,  0.700] <.001 ***
## WA.PW-WA.GV             0.453 [ 0.313,  0.573] <.001 ***
## WA.PW-WA.EV             0.239 [ 0.079,  0.386]  .004 ** 
## WA.PW-WA.SlpQlV.        0.014 [-0.149,  0.176]  .867    
## WA.PW-WA.RdV.           0.088 [-0.076,  0.248]  .292    
## WA.PW-WA.PR             0.058 [-0.106,  0.219]  .489    
## WA.PW-WA.ER             0.080 [-0.084,  0.240]  .339    
## WA.PW-WA.SlpQnV.       -0.023 [-0.192,  0.148]  .793    
## WA.NW-WA.RmV.           0.431 [ 0.289,  0.555] <.001 ***
## WA.NW-WA.PA             0.238 [ 0.079,  0.386]  .004 ** 
## WA.NW-WA.NA             0.069 [-0.095,  0.229]  .409    
## WA.NW-WA.ImV.           0.431 [ 0.289,  0.555] <.001 ***
## WA.NW-WA.WA             0.351 [ 0.200,  0.486] <.001 ***
## WA.NW-WA.TA             0.471 [ 0.334,  0.588] <.001 ***
## WA.NW-WA.WRF            0.428 [ 0.285,  0.552] <.001 ***
## WA.NW-WA.InV.           0.309 [ 0.154,  0.449] <.001 ***
## WA.NW-WA.GV             0.359 [ 0.208,  0.492] <.001 ***
## WA.NW-WA.EV             0.268 [ 0.111,  0.413]  .001 ** 
## WA.NW-WA.SlpQlV.        0.024 [-0.139,  0.186]  .769    
## WA.NW-WA.RdV.           0.091 [-0.073,  0.250]  .278    
## WA.NW-WA.PR             0.041 [-0.123,  0.203]  .624    
## WA.NW-WA.ER             0.092 [-0.072,  0.251]  .271    
## WA.NW-WA.SlpQnV.       -0.025 [-0.194,  0.146]  .776    
## WA.RmV.-WA.PA           0.694 [ 0.599,  0.770] <.001 ***
## WA.RmV.-WA.NA          -0.114 [-0.271,  0.050]  .172    
## WA.RmV.-WA.ImV.         0.707 [ 0.615,  0.780] <.001 ***
## WA.RmV.-WA.WA           0.653 [ 0.549,  0.737] <.001 ***
## WA.RmV.-WA.TA           0.647 [ 0.542,  0.733] <.001 ***
## WA.RmV.-WA.WRF          0.710 [ 0.619,  0.783] <.001 ***
## WA.RmV.-WA.InV.         0.612 [ 0.499,  0.704] <.001 ***
## WA.RmV.-WA.GV           0.518 [ 0.388,  0.628] <.001 ***
## WA.RmV.-WA.EV           0.249 [ 0.090,  0.395]  .002 ** 
## WA.RmV.-WA.SlpQlV.      0.026 [-0.137,  0.188]  .755    
## WA.RmV.-WA.RdV.         0.165 [ 0.002,  0.319]  .048 *  
## WA.RmV.-WA.PR           0.070 [-0.094,  0.230]  .402    
## WA.RmV.-WA.ER           0.170 [ 0.007,  0.324]  .041 *  
## WA.RmV.-WA.SlpQnV.     -0.050 [-0.218,  0.121]  .567    
## WA.PA-WA.NA            -0.270 [-0.414, -0.112] <.001 ***
## WA.PA-WA.ImV.           0.701 [ 0.607,  0.775] <.001 ***
## WA.PA-WA.WA             0.732 [ 0.647,  0.799] <.001 ***
## WA.PA-WA.TA             0.530 [ 0.402,  0.638] <.001 ***
## WA.PA-WA.WRF            0.701 [ 0.608,  0.775] <.001 ***
## WA.PA-WA.InV.           0.558 [ 0.435,  0.661] <.001 ***
## WA.PA-WA.GV             0.424 [ 0.281,  0.549] <.001 ***
## WA.PA-WA.EV             0.220 [ 0.059,  0.369]  .008 ** 
## WA.PA-WA.SlpQlV.       -0.047 [-0.208,  0.116]  .573    
## WA.PA-WA.RdV.           0.142 [-0.022,  0.298]  .089 .  
## WA.PA-WA.PR             0.114 [-0.050,  0.272]  .173    
## WA.PA-WA.ER             0.118 [-0.046,  0.275]  .159    
## WA.PA-WA.SlpQnV.        0.038 [-0.134,  0.206]  .668    
## WA.NA-WA.ImV.          -0.106 [-0.264,  0.057]  .201    
## WA.NA-WA.WA            -0.074 [-0.234,  0.089]  .374    
## WA.NA-WA.TA             0.067 [-0.097,  0.227]  .422    
## WA.NA-WA.WRF           -0.052 [-0.212,  0.112]  .536    
## WA.NA-WA.InV.          -0.011 [-0.173,  0.152]  .895    
## WA.NA-WA.GV            -0.050 [-0.210,  0.114]  .552    
## WA.NA-WA.EV            -0.143 [-0.299,  0.019]  .084 .  
## WA.NA-WA.SlpQlV.        0.135 [-0.028,  0.291]  .105    
## WA.NA-WA.RdV.           0.078 [-0.086,  0.238]  .350    
## WA.NA-WA.PR             0.096 [-0.068,  0.255]  .249    
## WA.NA-WA.ER             0.046 [-0.117,  0.208]  .579    
## WA.NA-WA.SlpQnV.       -0.018 [-0.188,  0.153]  .836    
## WA.ImV.-WA.WA           0.720 [ 0.632,  0.790] <.001 ***
## WA.ImV.-WA.TA           0.722 [ 0.634,  0.792] <.001 ***
## WA.ImV.-WA.WRF          0.679 [ 0.580,  0.758] <.001 ***
## WA.ImV.-WA.InV.         0.734 [ 0.649,  0.801] <.001 ***
## WA.ImV.-WA.GV           0.439 [ 0.298,  0.562] <.001 ***
## WA.ImV.-WA.EV           0.194 [ 0.033,  0.346]  .019 *  
## WA.ImV.-WA.SlpQlV.      0.047 [-0.116,  0.208]  .572    
## WA.ImV.-WA.RdV.         0.209 [ 0.048,  0.360]  .012 *  
## WA.ImV.-WA.PR           0.179 [ 0.016,  0.332]  .032 *  
## WA.ImV.-WA.ER           0.168 [ 0.005,  0.322]  .044 *  
## WA.ImV.-WA.SlpQnV.     -0.079 [-0.246,  0.092]  .363    
## WA.WA-WA.TA             0.618 [ 0.506,  0.709] <.001 ***
## WA.WA-WA.WRF            0.813 [ 0.750,  0.862] <.001 ***
## WA.WA-WA.InV.           0.582 [ 0.464,  0.680] <.001 ***
## WA.WA-WA.GV             0.401 [ 0.256,  0.529] <.001 ***
## WA.WA-WA.EV             0.200 [ 0.039,  0.351]  .015 *  
## WA.WA-WA.SlpQlV.        0.087 [-0.076,  0.246]  .296    
## WA.WA-WA.RdV.           0.186 [ 0.023,  0.338]  .026 *  
## WA.WA-WA.PR             0.138 [-0.026,  0.294]  .099 .  
## WA.WA-WA.ER             0.160 [-0.003,  0.315]  .055 .  
## WA.WA-WA.SlpQnV.       -0.065 [-0.233,  0.107]  .458    
## WA.TA-WA.WRF            0.634 [ 0.525,  0.722] <.001 ***
## WA.TA-WA.InV.           0.759 [ 0.681,  0.821] <.001 ***
## WA.TA-WA.GV             0.453 [ 0.313,  0.573] <.001 ***
## WA.TA-WA.EV             0.207 [ 0.046,  0.358]  .012 *  
## WA.TA-WA.SlpQlV.        0.113 [-0.051,  0.270]  .175    
## WA.TA-WA.RdV.           0.185 [ 0.023,  0.338]  .026 *  
## WA.TA-WA.PR             0.130 [-0.034,  0.287]  .120    
## WA.TA-WA.ER             0.163 [ 0.000,  0.318]  .050 *  
## WA.TA-WA.SlpQnV.        0.019 [-0.152,  0.189]  .828    
## WA.WRF-WA.InV.          0.601 [ 0.486,  0.695] <.001 ***
## WA.WRF-WA.GV            0.411 [ 0.267,  0.538] <.001 ***
## WA.WRF-WA.EV            0.263 [ 0.105,  0.408]  .001 ** 
## WA.WRF-WA.SlpQlV.      -0.068 [-0.228,  0.096]  .417    
## WA.WRF-WA.RdV.          0.157 [-0.006,  0.312]  .059 .  
## WA.WRF-WA.PR            0.114 [-0.050,  0.272]  .174    
## WA.WRF-WA.ER            0.137 [-0.027,  0.293]  .101    
## WA.WRF-WA.SlpQnV.      -0.005 [-0.175,  0.166]  .957    
## WA.InV.-WA.GV           0.378 [ 0.230,  0.509] <.001 ***
## WA.InV.-WA.EV           0.187 [ 0.026,  0.339]  .024 *  
## WA.InV.-WA.SlpQlV.      0.093 [-0.071,  0.252]  .264    
## WA.InV.-WA.RdV.         0.254 [ 0.095,  0.401]  .002 ** 
## WA.InV.-WA.PR           0.223 [ 0.063,  0.373]  .007 ** 
## WA.InV.-WA.ER           0.200 [ 0.038,  0.352]  .016 *  
## WA.InV.-WA.SlpQnV.      0.047 [-0.124,  0.216]  .590    
## WA.GV-WA.EV             0.106 [-0.057,  0.264]  .203    
## WA.GV-WA.SlpQlV.        0.039 [-0.124,  0.200]  .642    
## WA.GV-WA.RdV.           0.089 [-0.075,  0.248]  .287    
## WA.GV-WA.PR             0.001 [-0.162,  0.164]  .987    
## WA.GV-WA.ER             0.112 [-0.052,  0.270]  .181    
## WA.GV-WA.SlpQnV.       -0.055 [-0.223,  0.117]  .533    
## WA.EV-WA.SlpQlV.        0.083 [-0.081,  0.242]  .321    
## WA.EV-WA.RdV.           0.146 [-0.017,  0.302]  .080 .  
## WA.EV-WA.PR             0.267 [ 0.109,  0.412]  .001 ** 
## WA.EV-WA.ER             0.040 [-0.124,  0.202]  .634    
## WA.EV-WA.SlpQnV.       -0.192 [-0.351, -0.023]  .026 *  
## WA.SlpQlV.-WA.RdV.      0.150 [-0.014,  0.305]  .073 .  
## WA.SlpQlV.-WA.PR        0.109 [-0.055,  0.267]  .192    
## WA.SlpQlV.-WA.ER        0.130 [-0.034,  0.287]  .119    
## WA.SlpQlV.-WA.SlpQnV.  -0.389 [-0.525, -0.234] <.001 ***
## WA.RdV.-WA.PR           0.650 [ 0.545,  0.735] <.001 ***
## WA.RdV.-WA.ER           0.912 [ 0.879,  0.936] <.001 ***
## WA.RdV.-WA.SlpQnV.     -0.094 [-0.261,  0.078]  .281    
## WA.PR-WA.ER             0.280 [ 0.123,  0.424] <.001 ***
## WA.PR-WA.SlpQnV.       -0.145 [-0.308,  0.026]  .095 .  
## WA.ER-WA.SlpQnV.       -0.038 [-0.207,  0.134]  .666    
## ────────────────────────────────────────────────────────
## 
## Intraclass Correlation:
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##       Manipulation WP.SupervisoryBehavioralFeedbackV WP.SupervisoryPositiveBehavioralFeedbackV WP.SupervisoryNegativeBehavioralFeedbackV WP.learningBehaviorV WP.JobCraftingV WP.CreativeProcessEngagementV WP.ProblemIdentificationV WP.InformationSearchV WP.IdeaGenerationV WP.SocialLearningV WP.ObservationalLearningV WP.AdviceSeekingV WP.PerformanceImprovementV WP.TakingChargeV WA.WorkReflectionV WA.PositiveWorkReflectionV WA.NegativeWorkReflectionV WA.RuminationV WA.PositiveAffectV WA.NegativeAffectV WA.ImprovisionV WA.WorkAbsorptionV WA.ThrivingAtWorkLearningV WA.WorkRelatedFlowV WA.InspirationV WA.GraceV WA.ExerciseV WA.SleepQualityV WA.ReadingV WA.PaperReadV WA.EReadV WA.SleepQuantityV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## ICC1        -0.999                             0.811                                     0.748                                     0.772                0.629           0.765                         0.808                     0.531                 0.602              0.806              0.760                     0.701             0.697                      0.710            0.817              0.709                      0.709                      0.611          0.696              0.631              0.588           0.671              0.689                      0.585               0.702           0.696     0.720        0.807            0.671       0.745         0.643     0.750             0.435
## ICC2       -11.174                             0.886                                     0.843                                     0.859                0.753           0.855                         0.884                     0.671                 0.731              0.882              0.851                     0.809             0.806                      0.815            0.890              0.814                      0.814                      0.738          0.805              0.754              0.719           0.785              0.799                      0.716               0.808           0.804     0.822        0.883            0.786       0.838         0.761     0.841             0.575
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

cor_multilevel(data[,c(1, 139, 114:138, 140:145)], "B.ID", digits = 3)

## Correlations below and above the diagonal represent
## within-level and between-level correlations, respectively:
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                            WA.GraceV WP.SupervisoryBehavioralFeedbackV WP.SupervisoryPositiveBehavioralFeedbackV WP.SupervisoryNegativeBehavioralFeedbackV WP.learningBehaviorV WP.JobCraftingV WP.CreativeProcessEngagementV WP.ProblemIdentificationV WP.InformationSearchV WP.IdeaGenerationV WP.SocialLearningV WP.ObservationalLearningV WP.AdviceSeekingV WP.PerformanceImprovementV WP.TakingChargeV WA.WorkReflectionV WA.PositiveWorkReflectionV WA.NegativeWorkReflectionV WA.RuminationV WA.PositiveAffectV WA.NegativeAffectV WA.ImprovisionV WA.WorkAbsorptionV WA.ThrivingAtWorkLearningV WA.WorkRelatedFlowV WA.InspirationV WA.ExerciseV WA.SleepQualityV WA.ReadingV WA.PaperReadV WA.EReadV WA.SleepQuantityV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## WA.GraceV                                      1.000                             0.495                                     0.454                                     0.475                0.463           0.303                         0.414                     0.293                 0.408              0.380              0.331                     0.301             0.297                      0.266            0.310              0.485                      0.453                      0.359          0.518              0.424             -0.050           0.439              0.401                      0.453               0.411           0.378        0.106            0.039       0.089         0.001     0.112            -0.055
## WP.SupervisoryBehavioralFeedbackV              0.204                             1.000                                     0.922                                     0.950                0.503           0.480                         0.523                     0.446                 0.321              0.545              0.377                     0.374             0.294                      0.513            0.479              0.627                      0.644                      0.404          0.646              0.477             -0.022           0.574              0.578                      0.581               0.575           0.551        0.163           -0.022       0.138         0.025     0.160            -0.028
## WP.SupervisoryPositiveBehavioralFeedbackV      0.258                             0.789                                     1.000                                     0.757                0.535           0.491                         0.483                     0.422                 0.244              0.526              0.335                     0.334             0.259                      0.516            0.467              0.602                      0.680                      0.327          0.649              0.488             -0.040           0.619              0.601                      0.592               0.581           0.530        0.149           -0.041       0.073        -0.008     0.096            -0.027
## WP.SupervisoryNegativeBehavioralFeedbackV      0.089                             0.852                                     0.352                                     1.000                0.421           0.417                         0.497                     0.415                 0.347              0.499              0.369                     0.364             0.290                      0.452            0.435              0.579                      0.546                      0.423          0.575              0.418             -0.005           0.477              0.497                      0.509               0.508           0.509        0.157           -0.003       0.175         0.048     0.195            -0.026
## WP.learningBehaviorV                          -0.189                             0.042                                    -0.026                                     0.086                1.000           0.644                         0.629                     0.601                 0.418              0.599              0.538                     0.535             0.415                      0.665            0.679              0.591                      0.629                      0.359          0.605              0.432              0.038           0.650              0.533                      0.704               0.572           0.679        0.237            0.130       0.133         0.026     0.154            -0.041
## WP.JobCraftingV                                0.202                            -0.073                                    -0.076                                    -0.046               -0.082           1.000                         0.723                     0.653                 0.459              0.723              0.582                     0.607             0.408                      0.719            0.764              0.646                      0.563                      0.519          0.568              0.509             -0.041           0.670              0.581                      0.610               0.580           0.580        0.299            0.080       0.253         0.198     0.212            -0.005
## WP.CreativeProcessEngagementV                  0.111                             0.200                                     0.121                                     0.202               -0.045           0.089                         1.000                     0.844                 0.811              0.937              0.617                     0.607             0.488                      0.703            0.691              0.630                      0.583                      0.471          0.668              0.541             -0.083           0.695              0.607                      0.628               0.571           0.562        0.324            0.165       0.219         0.110     0.218            -0.126
## WP.ProblemIdentificationV                      0.079                            -0.017                                    -0.109                                     0.067                0.042           0.016                         0.775                     1.000                 0.559              0.693              0.524                     0.510             0.421                      0.585            0.616              0.531                      0.535                      0.353          0.575              0.436             -0.052           0.589              0.480                      0.560               0.492           0.484        0.319            0.157       0.117         0.058     0.117            -0.165
## WP.InformationSearchV                         -0.017                             0.153                                     0.062                                     0.180               -0.124           0.148                         0.586                     0.321                 1.000              0.642              0.512                     0.461             0.467                      0.447            0.441              0.393                      0.337                      0.321          0.434              0.387             -0.041           0.433              0.434                      0.397               0.344           0.345        0.256            0.263       0.268         0.166     0.249            -0.146
## WP.IdeaGenerationV                             0.143                             0.261                                     0.279                                     0.160               -0.022           0.027                         0.620                     0.202                -0.051              1.000              0.570                     0.587             0.411                      0.733            0.694              0.653                      0.594                      0.499          0.677              0.547             -0.103           0.719              0.618                      0.631               0.592           0.581        0.280            0.071       0.195         0.081     0.202            -0.059
## WP.SocialLearningV                            -0.014                            -0.062                                    -0.059                                    -0.045                0.154           0.133                         0.004                     0.059                -0.036             -0.020              1.000                     0.938             0.857                      0.703            0.625              0.411                      0.387                      0.301          0.429              0.322              0.014           0.427              0.376                      0.463               0.360           0.510        0.265            0.183       0.232         0.039     0.272            -0.059
## WP.ObservationalLearningV                     -0.010                            -0.114                                    -0.064                                    -0.120                0.166           0.167                         0.094                     0.156                 0.029             -0.002              0.866                     1.000             0.626                      0.710            0.666              0.456                      0.414                      0.349          0.457              0.362              0.088           0.480              0.416                      0.497               0.420           0.565        0.246            0.134       0.237         0.084     0.253            -0.048
## WP.AdviceSeekingV                             -0.014                             0.045                                    -0.021                                     0.087                0.058           0.014                        -0.129                    -0.113                -0.113             -0.037              0.684                     0.227             1.000                      0.528            0.417              0.247                      0.254                      0.159          0.286              0.186             -0.099           0.246              0.227                      0.302               0.185           0.307        0.231            0.212       0.170        -0.038     0.235            -0.061
## WP.PerformanceImprovementV                     0.107                             0.119                                    -0.019                                     0.197                0.000           0.136                         0.257                     0.137                 0.171              0.203             -0.041                     0.064            -0.172                      1.000            0.752              0.538                      0.506                      0.394          0.501              0.463              0.000           0.644              0.537                      0.621               0.509           0.710        0.256            0.150       0.218         0.112     0.215            -0.002
## WP.TakingChargeV                               0.083                             0.301                                     0.167                                     0.317                0.090           0.095                         0.412                     0.265                 0.263              0.291              0.007                    -0.010             0.029                      0.081            1.000              0.596                      0.580                      0.417          0.582              0.456              0.055           0.609              0.505                      0.613               0.563           0.659        0.293            0.052       0.263         0.142     0.256            -0.061
## WA.WorkReflectionV                             0.297                             0.194                                     0.164                                     0.158               -0.173           0.299                         0.151                     0.072                 0.063              0.157              0.134                     0.089             0.132                      0.145            0.032              1.000                      0.837                      0.836          0.721              0.559             -0.010           0.671              0.640                      0.674               0.694           0.548        0.303            0.023       0.107         0.059     0.103            -0.029
## WA.PositiveWorkReflectionV                     0.389                             0.240                                     0.077                                     0.304               -0.024           0.344                         0.182                     0.149                 0.090              0.117              0.081                     0.086             0.033                      0.279            0.048              0.710                      1.000                      0.399          0.775              0.696             -0.086           0.692              0.719                      0.656               0.733           0.607        0.239            0.014       0.088         0.058     0.080            -0.023
## WA.NegativeWorkReflectionV                     0.088                             0.071                                     0.165                                    -0.034               -0.223           0.130                         0.059                    -0.024                 0.013              0.122              0.120                     0.053             0.158                     -0.030            0.005              0.803                      0.150                      1.000          0.431              0.238              0.069           0.431              0.351                      0.471               0.428           0.309        0.268            0.024       0.091         0.041     0.092            -0.025
## WA.RuminationV                                 0.167                             0.160                                     0.164                                     0.103                0.004           0.149                        -0.053                     0.016                -0.089             -0.041              0.203                     0.166             0.155                      0.012            0.047              0.421                      0.298                      0.339          1.000              0.694             -0.114           0.707              0.653                      0.647               0.710           0.612        0.249            0.026       0.165         0.070     0.170            -0.050
## WA.PositiveAffectV                             0.189                            -0.050                                    -0.022                                    -0.058                0.130           0.072                         0.100                     0.144                 0.056             -0.002              0.034                     0.063            -0.024                      0.013            0.062              0.058                      0.155                     -0.049          0.034              1.000             -0.270           0.701              0.732                      0.530               0.701           0.558        0.220           -0.047       0.142         0.114     0.118             0.038
## WA.NegativeAffectV                             0.013                             0.004                                    -0.037                                     0.039               -0.034           0.024                        -0.037                    -0.006                -0.029             -0.040             -0.092                    -0.063            -0.087                      0.090            0.055              0.068                      0.044                      0.058          0.030             -0.038              1.000          -0.106             -0.074                      0.067              -0.052          -0.011       -0.143            0.135       0.078         0.096     0.046            -0.018
## WA.ImprovisionV                                0.042                             0.227                                     0.243                                     0.139                0.110           0.008                        -0.057                     0.077                -0.043             -0.144             -0.071                    -0.064            -0.046                     -0.036            0.189              0.270                      0.223                      0.190          0.173              0.320              0.075           1.000              0.720                      0.722               0.679           0.734        0.194            0.047       0.209         0.179     0.168            -0.079
## WA.WorkAbsorptionV                            -0.101                            -0.002                                    -0.027                                     0.020                0.083          -0.076                         0.067                     0.031                -0.116              0.190              0.012                     0.008             0.013                      0.041            0.135              0.081                      0.132                      0.001         -0.004              0.202              0.134           0.127              1.000                      0.618               0.813           0.582        0.200            0.087       0.186         0.138     0.160            -0.065
## WA.ThrivingAtWorkLearningV                     0.095                             0.167                                     0.186                                     0.096                0.174          -0.124                         0.038                     0.049                -0.077              0.087             -0.056                    -0.046            -0.042                     -0.105            0.094              0.087                      0.131                      0.011         -0.012              0.264             -0.075           0.403              0.289                      1.000               0.634           0.759        0.207            0.113       0.185         0.130     0.163             0.019
## WA.WorkRelatedFlowV                            0.005                             0.242                                     0.263                                     0.145                0.073          -0.105                        -0.012                    -0.112                -0.103              0.172              0.086                     0.045             0.101                      0.140            0.061              0.018                      0.064                     -0.030          0.046              0.235              0.021           0.209              0.320                      0.452               1.000           0.601        0.263           -0.068       0.157         0.114     0.137            -0.005
## WA.InspirationV                               -0.186                             0.013                                    -0.083                                     0.092                0.257          -0.149                        -0.162                    -0.056                -0.006             -0.240              0.001                    -0.033             0.049                      0.036            0.032              0.008                      0.029                     -0.014          0.128              0.198              0.017           0.359              0.361                      0.395               0.262           1.000        0.187            0.093       0.254         0.223     0.200             0.047
## WA.ExerciseV                                   0.070                             0.076                                     0.103                                     0.028               -0.095          -0.016                        -0.045                    -0.172                -0.059              0.133              0.015                    -0.000             0.030                      0.149            0.021              0.109                      0.141                      0.034          0.017              0.195              0.062           0.048              0.083                      0.112               0.098           0.003        1.000            0.083       0.146         0.267     0.040            -0.192
## WA.SleepQualityV                              -0.023                             0.137                                     0.042                                     0.175               -0.003           0.025                         0.049                     0.036                 0.119             -0.043             -0.053                     0.035            -0.155                      0.172            0.020             -0.033                      0.038                     -0.079          0.050             -0.067              0.067           0.177              0.035                      0.029              -0.037           0.175       -0.084            1.000       0.150         0.109     0.130            -0.389
## WA.ReadingV                                   -0.054                            -0.024                                    -0.129                                     0.075                0.034          -0.000                        -0.053                    -0.088                -0.055              0.032              0.082                     0.092             0.028                      0.070           -0.107              0.029                      0.120                     -0.061          0.014             -0.024             -0.064          -0.065              0.146                      0.143               0.092           0.150        0.069            0.257       1.000         0.650     0.912            -0.094
## WA.PaperReadV                                  0.044                            -0.076                                    -0.129                                    -0.005                0.046          -0.007                        -0.114                    -0.056                -0.134             -0.045              0.038                     0.061            -0.013                      0.045           -0.010             -0.021                      0.125                     -0.137          0.042             -0.011              0.023          -0.062              0.179                      0.110               0.016           0.116        0.117            0.092       0.619         1.000     0.280            -0.145
## WA.EReadV                                     -0.095                             0.017                                    -0.080                                     0.096                0.013           0.004                         0.004                    -0.074                 0.015              0.067              0.077                     0.076             0.042                      0.058           -0.126              0.049                      0.074                      0.007         -0.009             -0.023             -0.095          -0.044              0.073                      0.113               0.105           0.118        0.014            0.266       0.876         0.162     1.000            -0.038
## WA.SleepQuantityV                              0.108                             0.247                                     0.279                                     0.133               -0.144          -0.128                        -0.074                    -0.167                -0.120              0.126             -0.160                    -0.240             0.035                     -0.046           -0.015              0.067                      0.109                     -0.001         -0.026             -0.139              0.137          -0.044              0.051                      0.030               0.052          -0.120        0.025           -0.183      -0.110         0.056    -0.176             1.000
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 
## Within-Level Correlation [95% CI]:
## ────────────────────────────────────────────────────────
##                             r         [95% CI]     p    
## ────────────────────────────────────────────────────────
## WA.GV-WP.SB             0.204 [ 0.079,  0.322]  .002 ** 
## WA.GV-WP.SP             0.258 [ 0.137,  0.373] <.001 ***
## WA.GV-WP.SN             0.089 [-0.038,  0.213]  .169    
## WA.GV-WP.BV            -0.189 [-0.308, -0.065]  .004 ** 
## WA.GV-WP.JC             0.202 [ 0.077,  0.320]  .002 ** 
## WA.GV-WP.CP             0.111 [-0.015,  0.234]  .086 .  
## WA.GV-WP.PrbIV.         0.079 [-0.048,  0.203]  .225    
## WA.GV-WP.IS            -0.017 [-0.143,  0.109]  .792    
## WA.GV-WP.IG             0.143 [ 0.017,  0.265]  .027 *  
## WA.GV-WP.SL            -0.014 [-0.140,  0.112]  .827    
## WA.GV-WP.OL            -0.010 [-0.136,  0.117]  .883    
## WA.GV-WP.AS            -0.014 [-0.140,  0.113]  .831    
## WA.GV-WP.PrfIV.         0.107 [-0.020,  0.230]  .099 .  
## WA.GV-WP.TC             0.083 [-0.044,  0.207]  .201    
## WA.GV-WA.WRV            0.297 [ 0.183,  0.404] <.001 ***
## WA.GV-WA.PW             0.389 [ 0.281,  0.487] <.001 ***
## WA.GV-WA.NW             0.088 [-0.033,  0.207]  .157    
## WA.GV-WA.RmV.           0.167 [ 0.046,  0.282]  .007 ** 
## WA.GV-WA.PA             0.189 [ 0.070,  0.304]  .002 ** 
## WA.GV-WA.NA             0.013 [-0.109,  0.134]  .837    
## WA.GV-WA.ImV.           0.042 [-0.079,  0.163]  .497    
## WA.GV-WA.WA            -0.101 [-0.220,  0.020]  .104    
## WA.GV-WA.TA             0.095 [-0.026,  0.214]  .126    
## WA.GV-WA.WRF            0.005 [-0.116,  0.126]  .938    
## WA.GV-WA.InV.          -0.186 [-0.300, -0.066]  .003 ** 
## WA.GV-WA.EV             0.070 [-0.051,  0.190]  .258    
## WA.GV-WA.SlpQlV.       -0.023 [-0.144,  0.099]  .712    
## WA.GV-WA.RdV.          -0.054 [-0.175,  0.069]  .393    
## WA.GV-WA.PR             0.044 [-0.079,  0.166]  .483    
## WA.GV-WA.ER            -0.095 [-0.215,  0.028]  .131    
## WA.GV-WA.SlpQnV.        0.108 [-0.020,  0.233]  .100 .  
## WP.SB-WP.SP             0.789 [ 0.737,  0.832] <.001 ***
## WP.SB-WP.SN             0.852 [ 0.814,  0.883] <.001 ***
## WP.SB-WP.BV             0.042 [-0.083,  0.165]  .513    
## WP.SB-WP.JC            -0.073 [-0.195,  0.052]  .255    
## WP.SB-WP.CP             0.200 [ 0.077,  0.316]  .002 ** 
## WP.SB-WP.PrbIV.        -0.017 [-0.141,  0.108]  .792    
## WP.SB-WP.IS             0.153 [ 0.029,  0.272]  .017 *  
## WP.SB-WP.IG             0.261 [ 0.141,  0.373] <.001 ***
## WP.SB-WP.SL            -0.062 [-0.185,  0.063]  .329    
## WP.SB-WP.OL            -0.114 [-0.235,  0.010]  .074 .  
## WP.SB-WP.AS             0.045 [-0.079,  0.169]  .477    
## WP.SB-WP.PrfIV.         0.119 [-0.006,  0.239]  .063 .  
## WP.SB-WP.TC             0.301 [ 0.184,  0.410] <.001 ***
## WP.SB-WA.WRV            0.194 [ 0.070,  0.313]  .003 ** 
## WP.SB-WA.PW             0.240 [ 0.118,  0.356] <.001 ***
## WP.SB-WA.NW             0.071 [-0.056,  0.196]  .274    
## WP.SB-WA.RmV.           0.160 [ 0.034,  0.280]  .014 *  
## WP.SB-WA.PA            -0.050 [-0.175,  0.077]  .442    
## WP.SB-WA.NA             0.004 [-0.122,  0.131]  .947    
## WP.SB-WA.ImV.           0.227 [ 0.104,  0.344] <.001 ***
## WP.SB-WA.WA            -0.002 [-0.128,  0.125]  .977    
## WP.SB-WA.TA             0.167 [ 0.042,  0.288]  .010 ** 
## WP.SB-WA.WRF            0.242 [ 0.119,  0.358] <.001 ***
## WP.SB-WA.InV.           0.013 [-0.114,  0.139]  .842    
## WP.SB-WA.EV             0.076 [-0.051,  0.201]  .240    
## WP.SB-WA.SlpQlV.        0.137 [ 0.011,  0.259]  .035 *  
## WP.SB-WA.RdV.          -0.024 [-0.152,  0.104]  .710    
## WP.SB-WA.PR            -0.076 [-0.202,  0.053]  .249    
## WP.SB-WA.ER             0.017 [-0.112,  0.144]  .800    
## WP.SB-WA.SlpQnV.        0.247 [ 0.117,  0.368] <.001 ***
## WP.SP-WP.SN             0.352 [ 0.238,  0.456] <.001 ***
## WP.SP-WP.BV            -0.026 [-0.150,  0.098]  .678    
## WP.SP-WP.JC            -0.076 [-0.199,  0.048]  .231    
## WP.SP-WP.CP             0.121 [-0.004,  0.241]  .059 .  
## WP.SP-WP.PrbIV.        -0.109 [-0.230,  0.016]  .088 .  
## WP.SP-WP.IS             0.062 [-0.063,  0.185]  .330    
## WP.SP-WP.IG             0.279 [ 0.160,  0.390] <.001 ***
## WP.SP-WP.SL            -0.059 [-0.182,  0.066]  .357    
## WP.SP-WP.OL            -0.064 [-0.187,  0.061]  .318    
## WP.SP-WP.AS            -0.021 [-0.145,  0.103]  .736    
## WP.SP-WP.PrfIV.        -0.019 [-0.143,  0.106]  .772    
## WP.SP-WP.TC             0.167 [ 0.043,  0.285]  .009 ** 
## WP.SP-WA.WRV            0.164 [ 0.038,  0.284]  .012 *  
## WP.SP-WA.PW             0.077 [-0.049,  0.202]  .233    
## WP.SP-WA.NW             0.165 [ 0.039,  0.285]  .011 *  
## WP.SP-WA.RmV.           0.164 [ 0.039,  0.285]  .011 *  
## WP.SP-WA.PA            -0.022 [-0.148,  0.105]  .738    
## WP.SP-WA.NA            -0.037 [-0.163,  0.090]  .567    
## WP.SP-WA.ImV.           0.243 [ 0.120,  0.358] <.001 ***
## WP.SP-WA.WA            -0.027 [-0.152,  0.100]  .681    
## WP.SP-WA.TA             0.186 [ 0.061,  0.305]  .004 ** 
## WP.SP-WA.WRF            0.263 [ 0.142,  0.377] <.001 ***
## WP.SP-WA.InV.          -0.083 [-0.207,  0.044]  .200    
## WP.SP-WA.EV             0.103 [-0.023,  0.227]  .111    
## WP.SP-WA.SlpQlV.        0.042 [-0.085,  0.168]  .515    
## WP.SP-WA.RdV.          -0.129 [-0.253, -0.002]  .049 *  
## WP.SP-WA.PR            -0.129 [-0.253, -0.001]  .050 *  
## WP.SP-WA.ER            -0.080 [-0.206,  0.048]  .222    
## WP.SP-WA.SlpQnV.        0.279 [ 0.152,  0.398] <.001 ***
## WP.SN-WP.BV             0.086 [-0.039,  0.208]  .177    
## WP.SN-WP.JC            -0.046 [-0.169,  0.079]  .475    
## WP.SN-WP.CP             0.202 [ 0.079,  0.318]  .002 ** 
## WP.SN-WP.PrbIV.         0.067 [-0.058,  0.190]  .293    
## WP.SN-WP.IS             0.180 [ 0.057,  0.298]  .005 ** 
## WP.SN-WP.IG             0.160 [ 0.037,  0.279]  .012 *  
## WP.SN-WP.SL            -0.045 [-0.168,  0.080]  .482    
## WP.SN-WP.OL            -0.120 [-0.240,  0.005]  .061 .  
## WP.SN-WP.AS             0.087 [-0.037,  0.209]  .171    
## WP.SN-WP.PrfIV.         0.197 [ 0.074,  0.313]  .002 ** 
## WP.SN-WP.TC             0.317 [ 0.201,  0.425] <.001 ***
## WP.SN-WA.WRV            0.158 [ 0.032,  0.278]  .015 *  
## WP.SN-WA.PW             0.304 [ 0.185,  0.414] <.001 ***
## WP.SN-WA.NW            -0.034 [-0.160,  0.093]  .600    
## WP.SN-WA.RmV.           0.103 [-0.024,  0.227]  .112    
## WP.SN-WA.PA            -0.058 [-0.183,  0.069]  .370    
## WP.SN-WA.NA             0.039 [-0.088,  0.165]  .547    
## WP.SN-WA.ImV.           0.139 [ 0.013,  0.261]  .032 *  
## WP.SN-WA.WA             0.020 [-0.106,  0.146]  .754    
## WP.SN-WA.TA             0.096 [-0.030,  0.220]  .137    
## WP.SN-WA.WRF            0.145 [ 0.019,  0.266]  .026 *  
## WP.SN-WA.InV.           0.092 [-0.035,  0.216]  .155    
## WP.SN-WA.EV             0.028 [-0.099,  0.154]  .669    
## WP.SN-WA.SlpQlV.        0.175 [ 0.049,  0.294]  .007 ** 
## WP.SN-WA.RdV.           0.075 [-0.053,  0.201]  .251    
## WP.SN-WA.PR            -0.005 [-0.133,  0.123]  .944    
## WP.SN-WA.ER             0.096 [-0.033,  0.221]  .145    
## WP.SN-WA.SlpQnV.        0.133 [-0.000,  0.262]  .052 .  
## WP.BV-WP.JC            -0.082 [-0.204,  0.043]  .198    
## WP.BV-WP.CP            -0.045 [-0.168,  0.080]  .481    
## WP.BV-WP.PrbIV.         0.042 [-0.082,  0.166]  .506    
## WP.BV-WP.IS            -0.124 [-0.244,  0.001]  .053 .  
## WP.BV-WP.IG            -0.022 [-0.146,  0.102]  .725    
## WP.BV-WP.SL             0.154 [ 0.031,  0.273]  .016 *  
## WP.BV-WP.OL             0.166 [ 0.042,  0.284]  .009 ** 
## WP.BV-WP.AS             0.058 [-0.066,  0.181]  .359    
## WP.BV-WP.PrfIV.         0.000 [-0.124,  0.124] 1.000    
## WP.BV-WP.TC             0.090 [-0.034,  0.212]  .156    
## WP.BV-WA.WRV           -0.173 [-0.293, -0.048]  .008 ** 
## WP.BV-WA.PW            -0.024 [-0.150,  0.102]  .707    
## WP.BV-WA.NW            -0.223 [-0.340, -0.099] <.001 ***
## WP.BV-WA.RmV.           0.004 [-0.122,  0.131]  .948    
## WP.BV-WA.PA             0.130 [ 0.004,  0.252]  .045 *  
## WP.BV-WA.NA            -0.034 [-0.160,  0.092]  .595    
## WP.BV-WA.ImV.           0.110 [-0.016,  0.233]  .089 .  
## WP.BV-WA.WA             0.083 [-0.044,  0.207]  .202    
## WP.BV-WA.TA             0.174 [ 0.049,  0.294]  .007 ** 
## WP.BV-WA.WRF            0.073 [-0.054,  0.198]  .258    
## WP.BV-WA.InV.           0.257 [ 0.135,  0.371] <.001 ***
## WP.BV-WA.EV            -0.095 [-0.219,  0.032]  .143    
## WP.BV-WA.SlpQlV.       -0.003 [-0.129,  0.124]  .967    
## WP.BV-WA.RdV.           0.034 [-0.094,  0.161]  .603    
## WP.BV-WA.PR             0.046 [-0.082,  0.173]  .479    
## WP.BV-WA.ER             0.013 [-0.115,  0.141]  .837    
## WP.BV-WA.SlpQnV.       -0.144 [-0.272, -0.010]  .036 *  
## WP.JC-WP.CP             0.089 [-0.036,  0.211]  .164    
## WP.JC-WP.PrbIV.         0.016 [-0.109,  0.140]  .803    
## WP.JC-WP.IS             0.148 [ 0.024,  0.267]  .021 *  
## WP.JC-WP.IG             0.027 [-0.097,  0.151]  .668    
## WP.JC-WP.SL             0.133 [ 0.009,  0.253]  .038 *  
## WP.JC-WP.OL             0.167 [ 0.044,  0.286]  .009 ** 
## WP.JC-WP.AS             0.014 [-0.110,  0.138]  .823    
## WP.JC-WP.PrfIV.         0.136 [ 0.012,  0.256]  .033 *  
## WP.JC-WP.TC             0.095 [-0.030,  0.217]  .137    
## WP.JC-WA.WRV            0.299 [ 0.179,  0.410] <.001 ***
## WP.JC-WA.PW             0.344 [ 0.228,  0.451] <.001 ***
## WP.JC-WA.NW             0.130 [ 0.004,  0.253]  .044 *  
## WP.JC-WA.RmV.           0.149 [ 0.023,  0.271]  .021 *  
## WP.JC-WA.PA             0.072 [-0.055,  0.196]  .269    
## WP.JC-WA.NA             0.024 [-0.103,  0.150]  .712    
## WP.JC-WA.ImV.           0.008 [-0.119,  0.134]  .907    
## WP.JC-WA.WA            -0.076 [-0.200,  0.051]  .243    
## WP.JC-WA.TA            -0.124 [-0.246,  0.003]  .057 .  
## WP.JC-WA.WRF           -0.105 [-0.228,  0.022]  .105    
## WP.JC-WA.InV.          -0.149 [-0.270, -0.023]  .022 *  
## WP.JC-WA.EV            -0.016 [-0.142,  0.111]  .809    
## WP.JC-WA.SlpQlV.        0.025 [-0.102,  0.151]  .698    
## WP.JC-WA.RdV.          -0.000 [-0.128,  0.128]  .995    
## WP.JC-WA.PR            -0.007 [-0.135,  0.121]  .916    
## WP.JC-WA.ER             0.004 [-0.124,  0.131]  .956    
## WP.JC-WA.SlpQnV.       -0.128 [-0.257,  0.005]  .062 .  
## WP.CP-WP.PrbIV.         0.775 [ 0.720,  0.820] <.001 ***
## WP.CP-WP.IS             0.586 [ 0.498,  0.662] <.001 ***
## WP.CP-WP.IG             0.620 [ 0.537,  0.691] <.001 ***
## WP.CP-WP.SL             0.004 [-0.121,  0.128]  .952    
## WP.CP-WP.OL             0.094 [-0.031,  0.216]  .142    
## WP.CP-WP.AS            -0.129 [-0.250, -0.005]  .043 *  
## WP.CP-WP.PrfIV.         0.257 [ 0.137,  0.370] <.001 ***
## WP.CP-WP.TC             0.412 [ 0.303,  0.510] <.001 ***
## WP.CP-WA.WRV            0.151 [ 0.025,  0.272]  .020 *  
## WP.CP-WA.PW             0.182 [ 0.057,  0.301]  .005 ** 
## WP.CP-WA.NW             0.059 [-0.068,  0.184]  .363    
## WP.CP-WA.RmV.          -0.053 [-0.178,  0.074]  .413    
## WP.CP-WA.PA             0.100 [-0.026,  0.224]  .122    
## WP.CP-WA.NA            -0.037 [-0.163,  0.089]  .563    
## WP.CP-WA.ImV.          -0.057 [-0.182,  0.070]  .382    
## WP.CP-WA.WA             0.067 [-0.059,  0.192]  .299    
## WP.CP-WA.TA             0.038 [-0.088,  0.164]  .553    
## WP.CP-WA.WRF           -0.012 [-0.138,  0.114]  .851    
## WP.CP-WA.InV.          -0.162 [-0.283, -0.036]  .012 *  
## WP.CP-WA.EV            -0.045 [-0.171,  0.082]  .484    
## WP.CP-WA.SlpQlV.        0.049 [-0.078,  0.174]  .449    
## WP.CP-WA.RdV.          -0.053 [-0.180,  0.075]  .416    
## WP.CP-WA.PR            -0.114 [-0.239,  0.014]  .082 .  
## WP.CP-WA.ER             0.004 [-0.124,  0.132]  .948    
## WP.CP-WA.SlpQnV.       -0.074 [-0.206,  0.060]  .279    
## WP.PrbIV.-WP.IS         0.321 [ 0.205,  0.428] <.001 ***
## WP.PrbIV.-WP.IG         0.202 [ 0.080,  0.318]  .002 ** 
## WP.PrbIV.-WP.SL         0.059 [-0.066,  0.182]  .354    
## WP.PrbIV.-WP.OL         0.156 [ 0.033,  0.275]  .014 *  
## WP.PrbIV.-WP.AS        -0.113 [-0.234,  0.012]  .077 .  
## WP.PrbIV.-WP.PrfIV.     0.137 [ 0.013,  0.257]  .032 *  
## WP.PrbIV.-WP.TC         0.265 [ 0.145,  0.377] <.001 ***
## WP.PrbIV.-WA.WRV        0.072 [-0.055,  0.196]  .268    
## WP.PrbIV.-WA.PW         0.149 [ 0.023,  0.270]  .022 *  
## WP.PrbIV.-WA.NW        -0.024 [-0.150,  0.102]  .707    
## WP.PrbIV.-WA.RmV.       0.016 [-0.110,  0.142]  .801    
## WP.PrbIV.-WA.PA         0.144 [ 0.018,  0.266]  .026 *  
## WP.PrbIV.-WA.NA        -0.006 [-0.132,  0.121]  .932    
## WP.PrbIV.-WA.ImV.       0.077 [-0.050,  0.201]  .238    
## WP.PrbIV.-WA.WA         0.031 [-0.095,  0.157]  .628    
## WP.PrbIV.-WA.TA         0.049 [-0.078,  0.174]  .451    
## WP.PrbIV.-WA.WRF       -0.112 [-0.235,  0.014]  .083 .  
## WP.PrbIV.-WA.InV.      -0.056 [-0.181,  0.071]  .389    
## WP.PrbIV.-WA.EV        -0.172 [-0.292, -0.047]  .008 ** 
## WP.PrbIV.-WA.SlpQlV.    0.036 [-0.091,  0.162]  .578    
## WP.PrbIV.-WA.RdV.      -0.088 [-0.214,  0.040]  .180    
## WP.PrbIV.-WA.PR        -0.056 [-0.183,  0.073]  .394    
## WP.PrbIV.-WA.ER        -0.074 [-0.200,  0.054]  .258    
## WP.PrbIV.-WA.SlpQnV.   -0.167 [-0.294, -0.034]  .015 *  
## WP.IS-WP.IG            -0.051 [-0.174,  0.074]  .424    
## WP.IS-WP.SL            -0.036 [-0.160,  0.089]  .571    
## WP.IS-WP.OL             0.029 [-0.096,  0.153]  .650    
## WP.IS-WP.AS            -0.113 [-0.234,  0.012]  .077 .  
## WP.IS-WP.PrfIV.         0.171 [ 0.048,  0.289]  .007 ** 
## WP.IS-WP.TC             0.263 [ 0.143,  0.375] <.001 ***
## WP.IS-WA.WRV            0.063 [-0.064,  0.188]  .332    
## WP.IS-WA.PW             0.090 [-0.037,  0.214]  .165    
## WP.IS-WA.NW             0.013 [-0.114,  0.139]  .846    
## WP.IS-WA.RmV.          -0.089 [-0.213,  0.038]  .169    
## WP.IS-WA.PA             0.056 [-0.071,  0.181]  .387    
## WP.IS-WA.NA            -0.029 [-0.155,  0.098]  .657    
## WP.IS-WA.ImV.          -0.043 [-0.169,  0.084]  .505    
## WP.IS-WA.WA            -0.116 [-0.239,  0.010]  .073 .  
## WP.IS-WA.TA            -0.077 [-0.201,  0.050]  .238    
## WP.IS-WA.WRF           -0.103 [-0.227,  0.024]  .112    
## WP.IS-WA.InV.          -0.006 [-0.132,  0.120]  .926    
## WP.IS-WA.EV            -0.059 [-0.184,  0.068]  .364    
## WP.IS-WA.SlpQlV.        0.119 [-0.008,  0.241]  .067 .  
## WP.IS-WA.RdV.          -0.055 [-0.181,  0.074]  .405    
## WP.IS-WA.PR            -0.134 [-0.258, -0.006]  .041 *  
## WP.IS-WA.ER             0.015 [-0.113,  0.143]  .820    
## WP.IS-WA.SlpQnV.       -0.120 [-0.249,  0.014]  .081 .  
## WP.IG-WP.SL            -0.020 [-0.144,  0.104]  .749    
## WP.IG-WP.OL            -0.002 [-0.126,  0.123]  .980    
## WP.IG-WP.AS            -0.037 [-0.161,  0.087]  .558    
## WP.IG-WP.PrfIV.         0.203 [ 0.081,  0.319]  .002 ** 
## WP.IG-WP.TC             0.291 [ 0.173,  0.401] <.001 ***
## WP.IG-WA.WRV            0.157 [ 0.031,  0.278]  .015 *  
## WP.IG-WA.PW             0.117 [-0.010,  0.240]  .071 .  
## WP.IG-WA.NW             0.122 [-0.004,  0.245]  .059 .  
## WP.IG-WA.RmV.          -0.041 [-0.166,  0.086]  .532    
## WP.IG-WA.PA            -0.002 [-0.128,  0.125]  .979    
## WP.IG-WA.NA            -0.040 [-0.166,  0.087]  .536    
## WP.IG-WA.ImV.          -0.144 [-0.265, -0.018]  .027 *  
## WP.IG-WA.WA             0.190 [ 0.065,  0.309]  .003 ** 
## WP.IG-WA.TA             0.087 [-0.040,  0.211]  .181    
## WP.IG-WA.WRF            0.172 [ 0.047,  0.292]  .008 ** 
## WP.IG-WA.InV.          -0.240 [-0.356, -0.118] <.001 ***
## WP.IG-WA.EV             0.133 [ 0.007,  0.255]  .040 *  
## WP.IG-WA.SlpQlV.       -0.043 [-0.168,  0.084]  .511    
## WP.IG-WA.RdV.           0.032 [-0.096,  0.159]  .627    
## WP.IG-WA.PR            -0.045 [-0.172,  0.083]  .491    
## WP.IG-WA.ER             0.067 [-0.062,  0.193]  .309    
## WP.IG-WA.SlpQnV.        0.126 [-0.008,  0.255]  .066 .  
## WP.SL-WP.OL             0.866 [ 0.831,  0.894] <.001 ***
## WP.SL-WP.AS             0.684 [ 0.611,  0.745] <.001 ***
## WP.SL-WP.PrfIV.        -0.041 [-0.164,  0.084]  .525    
## WP.SL-WP.TC             0.007 [-0.117,  0.132]  .908    
## WP.SL-WA.WRV            0.134 [ 0.008,  0.256]  .039 *  
## WP.SL-WA.PW             0.081 [-0.046,  0.205]  .212    
## WP.SL-WA.NW             0.120 [-0.006,  0.243]  .063 .  
## WP.SL-WA.RmV.           0.203 [ 0.078,  0.321]  .002 ** 
## WP.SL-WA.PA             0.034 [-0.092,  0.160]  .596    
## WP.SL-WA.NA            -0.092 [-0.216,  0.035]  .158    
## WP.SL-WA.ImV.          -0.071 [-0.195,  0.056]  .274    
## WP.SL-WA.WA             0.012 [-0.114,  0.139]  .848    
## WP.SL-WA.TA            -0.056 [-0.181,  0.071]  .391    
## WP.SL-WA.WRF            0.086 [-0.041,  0.210]  .187    
## WP.SL-WA.InV.           0.001 [-0.125,  0.127]  .988    
## WP.SL-WA.EV             0.015 [-0.111,  0.142]  .812    
## WP.SL-WA.SlpQlV.       -0.053 [-0.179,  0.073]  .410    
## WP.SL-WA.RdV.           0.082 [-0.047,  0.208]  .213    
## WP.SL-WA.PR             0.038 [-0.090,  0.165]  .561    
## WP.SL-WA.ER             0.077 [-0.051,  0.203]  .239    
## WP.SL-WA.SlpQnV.       -0.160 [-0.287, -0.027]  .020 *  
## WP.OL-WP.AS             0.227 [ 0.106,  0.342] <.001 ***
## WP.OL-WP.PrfIV.         0.064 [-0.061,  0.187]  .317    
## WP.OL-WP.TC            -0.010 [-0.134,  0.115]  .878    
## WP.OL-WA.WRV            0.089 [-0.037,  0.213]  .168    
## WP.OL-WA.PW             0.086 [-0.040,  0.210]  .183    
## WP.OL-WA.NW             0.053 [-0.074,  0.178]  .414    
## WP.OL-WA.RmV.           0.166 [ 0.041,  0.287]  .010 *  
## WP.OL-WA.PA             0.063 [-0.063,  0.188]  .328    
## WP.OL-WA.NA            -0.063 [-0.188,  0.064]  .329    
## WP.OL-WA.ImV.          -0.064 [-0.189,  0.063]  .322    
## WP.OL-WA.WA             0.008 [-0.119,  0.134]  .905    
## WP.OL-WA.TA            -0.046 [-0.172,  0.081]  .475    
## WP.OL-WA.WRF            0.045 [-0.081,  0.171]  .483    
## WP.OL-WA.InV.          -0.033 [-0.158,  0.094]  .615    
## WP.OL-WA.EV            -0.000 [-0.127,  0.126]  .997    
## WP.OL-WA.SlpQlV.        0.035 [-0.091,  0.161]  .585    
## WP.OL-WA.RdV.           0.092 [-0.037,  0.217]  .162    
## WP.OL-WA.PR             0.061 [-0.068,  0.187]  .354    
## WP.OL-WA.ER             0.076 [-0.053,  0.202]  .248    
## WP.OL-WA.SlpQnV.       -0.240 [-0.362, -0.110] <.001 ***
## WP.AS-WP.PrfIV.        -0.172 [-0.290, -0.049]  .007 ** 
## WP.AS-WP.TC             0.029 [-0.096,  0.152]  .653    
## WP.AS-WA.WRV            0.132 [ 0.006,  0.254]  .041 *  
## WP.AS-WA.PW             0.033 [-0.093,  0.159]  .607    
## WP.AS-WA.NW             0.158 [ 0.032,  0.279]  .015 *  
## WP.AS-WA.RmV.           0.155 [ 0.029,  0.276]  .017 *  
## WP.AS-WA.PA            -0.024 [-0.150,  0.102]  .708    
## WP.AS-WA.NA            -0.087 [-0.211,  0.039]  .178    
## WP.AS-WA.ImV.          -0.046 [-0.171,  0.081]  .482    
## WP.AS-WA.WA             0.013 [-0.113,  0.139]  .840    
## WP.AS-WA.TA            -0.042 [-0.167,  0.085]  .521    
## WP.AS-WA.WRF            0.101 [-0.026,  0.225]  .119    
## WP.AS-WA.InV.           0.049 [-0.078,  0.174]  .451    
## WP.AS-WA.EV             0.030 [-0.096,  0.156]  .639    
## WP.AS-WA.SlpQlV.       -0.155 [-0.276, -0.029]  .017 *  
## WP.AS-WA.RdV.           0.028 [-0.101,  0.155]  .674    
## WP.AS-WA.PR            -0.013 [-0.141,  0.115]  .843    
## WP.AS-WA.ER             0.042 [-0.086,  0.169]  .522    
## WP.AS-WA.SlpQnV.        0.035 [-0.099,  0.168]  .610    
## WP.PrfIV.-WP.TC         0.081 [-0.044,  0.203]  .204    
## WP.PrfIV.-WA.WRV        0.145 [ 0.019,  0.267]  .025 *  
## WP.PrfIV.-WA.PW         0.279 [ 0.158,  0.392] <.001 ***
## WP.PrfIV.-WA.NW        -0.030 [-0.156,  0.096]  .638    
## WP.PrfIV.-WA.RmV.       0.012 [-0.115,  0.138]  .855    
## WP.PrfIV.-WA.PA         0.013 [-0.114,  0.139]  .843    
## WP.PrfIV.-WA.NA         0.090 [-0.037,  0.214]  .164    
## WP.PrfIV.-WA.ImV.      -0.036 [-0.161,  0.091]  .580    
## WP.PrfIV.-WA.WA         0.041 [-0.086,  0.166]  .530    
## WP.PrfIV.-WA.TA        -0.105 [-0.229,  0.021]  .104    
## WP.PrfIV.-WA.WRF        0.140 [ 0.014,  0.262]  .031 *  
## WP.PrfIV.-WA.InV.       0.036 [-0.091,  0.161]  .581    
## WP.PrfIV.-WA.EV         0.149 [ 0.023,  0.270]  .022 *  
## WP.PrfIV.-WA.SlpQlV.    0.172 [ 0.046,  0.292]  .008 ** 
## WP.PrfIV.-WA.RdV.       0.070 [-0.058,  0.196]  .286    
## WP.PrfIV.-WA.PR         0.045 [-0.083,  0.172]  .490    
## WP.PrfIV.-WA.ER         0.058 [-0.070,  0.185]  .375    
## WP.PrfIV.-WA.SlpQnV.   -0.046 [-0.178,  0.088]  .506    
## WP.TC-WA.WRV            0.032 [-0.095,  0.158]  .622    
## WP.TC-WA.PW             0.048 [-0.079,  0.173]  .463    
## WP.TC-WA.NW             0.005 [-0.121,  0.131]  .939    
## WP.TC-WA.RmV.           0.047 [-0.080,  0.172]  .473    
## WP.TC-WA.PA             0.062 [-0.065,  0.187]  .338    
## WP.TC-WA.NA             0.055 [-0.072,  0.180]  .395    
## WP.TC-WA.ImV.           0.189 [ 0.064,  0.308]  .004 ** 
## WP.TC-WA.WA             0.135 [ 0.008,  0.257]  .038 *  
## WP.TC-WA.TA             0.094 [-0.033,  0.218]  .148    
## WP.TC-WA.WRF            0.061 [-0.066,  0.185]  .351    
## WP.TC-WA.InV.           0.032 [-0.095,  0.157]  .626    
## WP.TC-WA.EV             0.021 [-0.105,  0.147]  .742    
## WP.TC-WA.SlpQlV.        0.020 [-0.106,  0.146]  .754    
## WP.TC-WA.RdV.          -0.107 [-0.232,  0.021]  .102    
## WP.TC-WA.PR            -0.010 [-0.137,  0.119]  .884    
## WP.TC-WA.ER            -0.126 [-0.250,  0.002]  .055 .  
## WP.TC-WA.SlpQnV.       -0.015 [-0.148,  0.119]  .829    
## WA.WRV-WA.PW            0.710 [ 0.644,  0.765] <.001 ***
## WA.WRV-WA.NW            0.803 [ 0.755,  0.842] <.001 ***
## WA.WRV-WA.RmV.          0.421 [ 0.316,  0.516] <.001 ***
## WA.WRV-WA.PA            0.058 [-0.063,  0.178]  .347    
## WA.WRV-WA.NA            0.068 [-0.054,  0.187]  .276    
## WA.WRV-WA.ImV.          0.270 [ 0.154,  0.379] <.001 ***
## WA.WRV-WA.WA            0.081 [-0.041,  0.200]  .195    
## WA.WRV-WA.TA            0.087 [-0.035,  0.206]  .162    
## WA.WRV-WA.WRF           0.018 [-0.104,  0.138]  .778    
## WA.WRV-WA.InV.          0.008 [-0.113,  0.129]  .899    
## WA.WRV-WA.EV            0.109 [-0.012,  0.228]  .079 .  
## WA.WRV-WA.SlpQlV.      -0.033 [-0.154,  0.088]  .592    
## WA.WRV-WA.RdV.          0.029 [-0.094,  0.151]  .646    
## WA.WRV-WA.PR           -0.021 [-0.143,  0.102]  .737    
## WA.WRV-WA.ER            0.049 [-0.074,  0.171]  .434    
## WA.WRV-WA.SlpQnV.       0.067 [-0.061,  0.194]  .307    
## WA.PW-WA.NW             0.150 [ 0.029,  0.266]  .016 *  
## WA.PW-WA.RmV.           0.298 [ 0.184,  0.405] <.001 ***
## WA.PW-WA.PA             0.155 [ 0.034,  0.271]  .013 *  
## WA.PW-WA.NA             0.044 [-0.078,  0.164]  .479    
## WA.PW-WA.ImV.           0.223 [ 0.105,  0.335] <.001 ***
## WA.PW-WA.WA             0.132 [ 0.011,  0.250]  .033 *  
## WA.PW-WA.TA             0.131 [ 0.010,  0.249]  .035 *  
## WA.PW-WA.WRF            0.064 [-0.057,  0.184]  .301    
## WA.PW-WA.InV.           0.029 [-0.092,  0.150]  .640    
## WA.PW-WA.EV             0.141 [ 0.020,  0.258]  .023 *  
## WA.PW-WA.SlpQlV.        0.038 [-0.083,  0.159]  .540    
## WA.PW-WA.RdV.           0.120 [-0.003,  0.239]  .057 .  
## WA.PW-WA.PR             0.125 [ 0.002,  0.243]  .048 *  
## WA.PW-WA.ER             0.074 [-0.049,  0.195]  .241    
## WA.PW-WA.SlpQnV.        0.109 [-0.020,  0.234]  .099 .  
## WA.NW-WA.RmV.           0.339 [ 0.227,  0.442] <.001 ***
## WA.NW-WA.PA            -0.049 [-0.169,  0.073]  .432    
## WA.NW-WA.NA             0.058 [-0.064,  0.178]  .353    
## WA.NW-WA.ImV.           0.190 [ 0.070,  0.304]  .002 ** 
## WA.NW-WA.WA             0.001 [-0.120,  0.122]  .986    
## WA.NW-WA.TA             0.011 [-0.111,  0.132]  .863    
## WA.NW-WA.WRF           -0.030 [-0.150,  0.092]  .631    
## WA.NW-WA.InV.          -0.014 [-0.135,  0.108]  .827    
## WA.NW-WA.EV             0.034 [-0.087,  0.155]  .583    
## WA.NW-WA.SlpQlV.       -0.079 [-0.198,  0.043]  .204    
## WA.NW-WA.RdV.          -0.061 [-0.183,  0.062]  .330    
## WA.NW-WA.PR            -0.137 [-0.255, -0.014]  .030 *  
## WA.NW-WA.ER             0.007 [-0.116,  0.129]  .913    
## WA.NW-WA.SlpQnV.       -0.001 [-0.129,  0.127]  .987    
## WA.RmV.-WA.PA           0.034 [-0.087,  0.155]  .582    
## WA.RmV.-WA.NA           0.030 [-0.091,  0.151]  .624    
## WA.RmV.-WA.ImV.         0.173 [ 0.053,  0.288]  .006 ** 
## WA.RmV.-WA.WA          -0.004 [-0.125,  0.118]  .954    
## WA.RmV.-WA.TA          -0.012 [-0.133,  0.109]  .847    
## WA.RmV.-WA.WRF          0.046 [-0.076,  0.166]  .459    
## WA.RmV.-WA.InV.         0.128 [ 0.007,  0.245]  .040 *  
## WA.RmV.-WA.EV           0.017 [-0.104,  0.138]  .779    
## WA.RmV.-WA.SlpQlV.      0.050 [-0.072,  0.170]  .420    
## WA.RmV.-WA.RdV.         0.014 [-0.109,  0.136]  .829    
## WA.RmV.-WA.PR           0.042 [-0.081,  0.164]  .505    
## WA.RmV.-WA.ER          -0.009 [-0.131,  0.114]  .890    
## WA.RmV.-WA.SlpQnV.     -0.026 [-0.154,  0.102]  .688    
## WA.PA-WA.NA            -0.038 [-0.159,  0.083]  .538    
## WA.PA-WA.ImV.           0.320 [ 0.207,  0.425] <.001 ***
## WA.PA-WA.WA             0.202 [ 0.083,  0.315]  .001 ** 
## WA.PA-WA.TA             0.264 [ 0.148,  0.373] <.001 ***
## WA.PA-WA.WRF            0.235 [ 0.117,  0.346] <.001 ***
## WA.PA-WA.InV.           0.198 [ 0.079,  0.312]  .001 ** 
## WA.PA-WA.EV             0.195 [ 0.075,  0.309]  .002 ** 
## WA.PA-WA.SlpQlV.       -0.067 [-0.187,  0.054]  .279    
## WA.PA-WA.RdV.          -0.024 [-0.146,  0.099]  .708    
## WA.PA-WA.PR            -0.011 [-0.134,  0.112]  .860    
## WA.PA-WA.ER            -0.023 [-0.145,  0.100]  .716    
## WA.PA-WA.SlpQnV.       -0.139 [-0.262, -0.011]  .035 *  
## WA.NA-WA.ImV.           0.075 [-0.047,  0.194]  .228    
## WA.NA-WA.WA             0.134 [ 0.013,  0.251]  .031 *  
## WA.NA-WA.TA            -0.075 [-0.194,  0.047]  .228    
## WA.NA-WA.WRF            0.021 [-0.100,  0.142]  .730    
## WA.NA-WA.InV.           0.017 [-0.105,  0.137]  .790    
## WA.NA-WA.EV             0.062 [-0.059,  0.182]  .315    
## WA.NA-WA.SlpQlV.        0.067 [-0.055,  0.186]  .285    
## WA.NA-WA.RdV.          -0.064 [-0.185,  0.059]  .310    
## WA.NA-WA.PR             0.023 [-0.100,  0.145]  .714    
## WA.NA-WA.ER            -0.095 [-0.215,  0.028]  .131    
## WA.NA-WA.SlpQnV.        0.137 [ 0.008,  0.260]  .038 *  
## WA.ImV.-WA.WA           0.127 [ 0.006,  0.244]  .041 *  
## WA.ImV.-WA.TA           0.403 [ 0.297,  0.500] <.001 ***
## WA.ImV.-WA.WRF          0.209 [ 0.090,  0.322] <.001 ***
## WA.ImV.-WA.InV.         0.359 [ 0.249,  0.460] <.001 ***
## WA.ImV.-WA.EV           0.048 [-0.073,  0.168]  .438    
## WA.ImV.-WA.SlpQlV.      0.177 [ 0.057,  0.292]  .005 ** 
## WA.ImV.-WA.RdV.        -0.065 [-0.186,  0.058]  .300    
## WA.ImV.-WA.PR          -0.062 [-0.183,  0.061]  .322    
## WA.ImV.-WA.ER          -0.044 [-0.165,  0.079]  .487    
## WA.ImV.-WA.SlpQnV.     -0.044 [-0.171,  0.085]  .507    
## WA.WA-WA.TA             0.289 [ 0.174,  0.396] <.001 ***
## WA.WA-WA.WRF            0.320 [ 0.207,  0.424] <.001 ***
## WA.WA-WA.InV.           0.361 [ 0.250,  0.462] <.001 ***
## WA.WA-WA.EV             0.083 [-0.039,  0.202]  .183    
## WA.WA-WA.SlpQlV.        0.035 [-0.086,  0.156]  .571    
## WA.WA-WA.RdV.           0.146 [ 0.024,  0.264]  .021 *  
## WA.WA-WA.PR             0.179 [ 0.058,  0.296]  .004 ** 
## WA.WA-WA.ER             0.073 [-0.050,  0.193]  .249    
## WA.WA-WA.SlpQnV.        0.051 [-0.078,  0.178]  .440    
## WA.TA-WA.WRF            0.452 [ 0.350,  0.544] <.001 ***
## WA.TA-WA.InV.           0.395 [ 0.288,  0.493] <.001 ***
## WA.TA-WA.EV             0.112 [-0.010,  0.230]  .072 .  
## WA.TA-WA.SlpQlV.        0.029 [-0.092,  0.150]  .639    
## WA.TA-WA.RdV.           0.143 [ 0.021,  0.261]  .023 *  
## WA.TA-WA.PR             0.110 [-0.013,  0.229]  .082 .  
## WA.TA-WA.ER             0.113 [-0.010,  0.232]  .074 .  
## WA.TA-WA.SlpQnV.        0.030 [-0.099,  0.157]  .652    
## WA.WRF-WA.InV.          0.262 [ 0.145,  0.371] <.001 ***
## WA.WRF-WA.EV            0.098 [-0.023,  0.217]  .115    
## WA.WRF-WA.SlpQlV.      -0.037 [-0.158,  0.084]  .550    
## WA.WRF-WA.RdV.          0.092 [-0.031,  0.212]  .146    
## WA.WRF-WA.PR            0.016 [-0.107,  0.138]  .801    
## WA.WRF-WA.ER            0.105 [-0.018,  0.225]  .095 .  
## WA.WRF-WA.SlpQnV.       0.052 [-0.077,  0.179]  .428    
## WA.InV.-WA.EV           0.003 [-0.118,  0.125]  .956    
## WA.InV.-WA.SlpQlV.      0.175 [ 0.055,  0.290]  .005 ** 
## WA.InV.-WA.RdV.         0.150 [ 0.028,  0.268]  .017 *  
## WA.InV.-WA.PR           0.116 [-0.007,  0.235]  .066 .  
## WA.InV.-WA.ER           0.118 [-0.005,  0.237]  .062 .  
## WA.InV.-WA.SlpQnV.     -0.120 [-0.244,  0.009]  .069 .  
## WA.EV-WA.SlpQlV.       -0.084 [-0.203,  0.038]  .177    
## WA.EV-WA.RdV.           0.069 [-0.054,  0.190]  .274    
## WA.EV-WA.PR             0.117 [-0.005,  0.237]  .062 .  
## WA.EV-WA.ER             0.014 [-0.109,  0.137]  .822    
## WA.EV-WA.SlpQnV.        0.025 [-0.104,  0.153]  .705    
## WA.SlpQlV.-WA.RdV.      0.257 [ 0.139,  0.368] <.001 ***
## WA.SlpQlV.-WA.PR        0.092 [-0.031,  0.213]  .142    
## WA.SlpQlV.-WA.ER        0.266 [ 0.149,  0.377] <.001 ***
## WA.SlpQlV.-WA.SlpQnV.  -0.183 [-0.304, -0.056]  .006 ** 
## WA.RdV.-WA.PR           0.619 [ 0.537,  0.689] <.001 ***
## WA.RdV.-WA.ER           0.876 [ 0.844,  0.902] <.001 ***
## WA.RdV.-WA.SlpQnV.     -0.110 [-0.236,  0.020]  .100    
## WA.PR-WA.ER             0.162 [ 0.040,  0.279]  .010 *  
## WA.PR-WA.SlpQnV.        0.056 [-0.075,  0.184]  .403    
## WA.ER-WA.SlpQnV.       -0.176 [-0.299, -0.047]  .008 ** 
## ────────────────────────────────────────────────────────
## 
## Between-Level Correlation [95% CI]:
## ────────────────────────────────────────────────────────
##                             r         [95% CI]     p    
## ────────────────────────────────────────────────────────
## WA.GV-WP.SB             0.495 [ 0.357,  0.612] <.001 ***
## WA.GV-WP.SP             0.454 [ 0.309,  0.577] <.001 ***
## WA.GV-WP.SN             0.475 [ 0.333,  0.595] <.001 ***
## WA.GV-WP.BV             0.463 [ 0.321,  0.586] <.001 ***
## WA.GV-WP.JC             0.303 [ 0.142,  0.448] <.001 ***
## WA.GV-WP.CP             0.414 [ 0.265,  0.544] <.001 ***
## WA.GV-WP.PrbIV.         0.293 [ 0.132,  0.439] <.001 ***
## WA.GV-WP.IS             0.408 [ 0.258,  0.539] <.001 ***
## WA.GV-WP.IG             0.380 [ 0.226,  0.514] <.001 ***
## WA.GV-WP.SL             0.331 [ 0.173,  0.473] <.001 ***
## WA.GV-WP.OL             0.301 [ 0.141,  0.446] <.001 ***
## WA.GV-WP.AS             0.297 [ 0.136,  0.443] <.001 ***
## WA.GV-WP.PrfIV.         0.266 [ 0.103,  0.415]  .002 ** 
## WA.GV-WP.TC             0.310 [ 0.150,  0.454] <.001 ***
## WA.GV-WA.WRV            0.485 [ 0.350,  0.600] <.001 ***
## WA.GV-WA.PW             0.453 [ 0.313,  0.573] <.001 ***
## WA.GV-WA.NW             0.359 [ 0.208,  0.492] <.001 ***
## WA.GV-WA.RmV.           0.518 [ 0.388,  0.628] <.001 ***
## WA.GV-WA.PA             0.424 [ 0.281,  0.549] <.001 ***
## WA.GV-WA.NA            -0.050 [-0.210,  0.114]  .551    
## WA.GV-WA.ImV.           0.439 [ 0.298,  0.562] <.001 ***
## WA.GV-WA.WA             0.401 [ 0.256,  0.529] <.001 ***
## WA.GV-WA.TA             0.453 [ 0.313,  0.573] <.001 ***
## WA.GV-WA.WRF            0.411 [ 0.267,  0.538] <.001 ***
## WA.GV-WA.InV.           0.378 [ 0.230,  0.509] <.001 ***
## WA.GV-WA.EV             0.106 [-0.057,  0.264]  .203    
## WA.GV-WA.SlpQlV.        0.039 [-0.124,  0.200]  .642    
## WA.GV-WA.RdV.           0.089 [-0.075,  0.248]  .287    
## WA.GV-WA.PR             0.001 [-0.162,  0.164]  .987    
## WA.GV-WA.ER             0.112 [-0.052,  0.270]  .181    
## WA.GV-WA.SlpQnV.       -0.055 [-0.223,  0.117]  .533    
## WP.SB-WP.SP             0.922 [ 0.893,  0.944] <.001 ***
## WP.SB-WP.SN             0.950 [ 0.931,  0.964] <.001 ***
## WP.SB-WP.BV             0.503 [ 0.367,  0.618] <.001 ***
## WP.SB-WP.JC             0.480 [ 0.340,  0.599] <.001 ***
## WP.SB-WP.CP             0.523 [ 0.390,  0.635] <.001 ***
## WP.SB-WP.PrbIV.         0.446 [ 0.301,  0.571] <.001 ***
## WP.SB-WP.IS             0.321 [ 0.162,  0.463] <.001 ***
## WP.SB-WP.IG             0.545 [ 0.415,  0.652] <.001 ***
## WP.SB-WP.SL             0.377 [ 0.224,  0.512] <.001 ***
## WP.SB-WP.OL             0.374 [ 0.220,  0.509] <.001 ***
## WP.SB-WP.AS             0.294 [ 0.134,  0.440] <.001 ***
## WP.SB-WP.PrfIV.         0.513 [ 0.378,  0.626] <.001 ***
## WP.SB-WP.TC             0.479 [ 0.339,  0.598] <.001 ***
## WP.SB-WA.WRV            0.627 [ 0.513,  0.719] <.001 ***
## WP.SB-WA.PW             0.644 [ 0.534,  0.733] <.001 ***
## WP.SB-WA.NW             0.404 [ 0.254,  0.536] <.001 ***
## WP.SB-WA.RmV.           0.646 [ 0.537,  0.734] <.001 ***
## WP.SB-WA.PA             0.477 [ 0.337,  0.597] <.001 ***
## WP.SB-WA.NA            -0.022 [-0.189,  0.146]  .792    
## WP.SB-WA.ImV.           0.574 [ 0.450,  0.677] <.001 ***
## WP.SB-WA.WA             0.578 [ 0.454,  0.680] <.001 ***
## WP.SB-WA.TA             0.581 [ 0.457,  0.682] <.001 ***
## WP.SB-WA.WRF            0.575 [ 0.451,  0.677] <.001 ***
## WP.SB-WA.InV.           0.551 [ 0.423,  0.658] <.001 ***
## WP.SB-WA.EV             0.163 [-0.005,  0.322]  .049 *  
## WP.SB-WA.SlpQlV.       -0.022 [-0.189,  0.147]  .797    
## WP.SB-WA.RdV.           0.138 [-0.031,  0.299]  .098 .  
## WP.SB-WA.PR             0.025 [-0.144,  0.192]  .770    
## WP.SB-WA.ER             0.160 [-0.008,  0.320]  .054 .  
## WP.SB-WA.SlpQnV.       -0.028 [-0.203,  0.148]  .749    
## WP.SP-WP.SN             0.757 [ 0.675,  0.820] <.001 ***
## WP.SP-WP.BV             0.535 [ 0.404,  0.645] <.001 ***
## WP.SP-WP.JC             0.491 [ 0.353,  0.608] <.001 ***
## WP.SP-WP.CP             0.483 [ 0.343,  0.601] <.001 ***
## WP.SP-WP.PrbIV.         0.422 [ 0.274,  0.550] <.001 ***
## WP.SP-WP.IS             0.244 [ 0.080,  0.395]  .004 ** 
## WP.SP-WP.IG             0.526 [ 0.393,  0.637] <.001 ***
## WP.SP-WP.SL             0.335 [ 0.178,  0.476] <.001 ***
## WP.SP-WP.OL             0.334 [ 0.177,  0.475] <.001 ***
## WP.SP-WP.AS             0.259 [ 0.096,  0.408]  .002 ** 
## WP.SP-WP.PrfIV.         0.516 [ 0.382,  0.629] <.001 ***
## WP.SP-WP.TC             0.467 [ 0.325,  0.588] <.001 ***
## WP.SP-WA.WRV            0.602 [ 0.483,  0.699] <.001 ***
## WP.SP-WA.PW             0.680 [ 0.578,  0.761] <.001 ***
## WP.SP-WA.NW             0.327 [ 0.168,  0.469] <.001 ***
## WP.SP-WA.RmV.           0.649 [ 0.540,  0.736] <.001 ***
## WP.SP-WA.PA             0.488 [ 0.348,  0.606] <.001 ***
## WP.SP-WA.NA            -0.040 [-0.207,  0.128]  .628    
## WP.SP-WA.ImV.           0.619 [ 0.504,  0.713] <.001 ***
## WP.SP-WA.WA             0.601 [ 0.482,  0.698] <.001 ***
## WP.SP-WA.TA             0.592 [ 0.471,  0.691] <.001 ***
## WP.SP-WA.WRF            0.581 [ 0.458,  0.683] <.001 ***
## WP.SP-WA.InV.           0.530 [ 0.398,  0.641] <.001 ***
## WP.SP-WA.EV             0.149 [-0.020,  0.309]  .074 .  
## WP.SP-WA.SlpQlV.       -0.041 [-0.208,  0.127]  .621    
## WP.SP-WA.RdV.           0.073 [-0.097,  0.238]  .384    
## WP.SP-WA.PR            -0.008 [-0.176,  0.161]  .928    
## WP.SP-WA.ER             0.096 [-0.074,  0.260]  .252    
## WP.SP-WA.SlpQnV.       -0.027 [-0.201,  0.150]  .759    
## WP.SN-WP.BV             0.421 [ 0.273,  0.549] <.001 ***
## WP.SN-WP.JC             0.417 [ 0.269,  0.546] <.001 ***
## WP.SN-WP.CP             0.497 [ 0.360,  0.613] <.001 ***
## WP.SN-WP.PrbIV.         0.415 [ 0.267,  0.545] <.001 ***
## WP.SN-WP.IS             0.347 [ 0.191,  0.486] <.001 ***
## WP.SN-WP.IG             0.499 [ 0.362,  0.615] <.001 ***
## WP.SN-WP.SL             0.369 [ 0.215,  0.505] <.001 ***
## WP.SN-WP.OL             0.364 [ 0.209,  0.500] <.001 ***
## WP.SN-WP.AS             0.290 [ 0.129,  0.436] <.001 ***
## WP.SN-WP.PrfIV.         0.452 [ 0.308,  0.576] <.001 ***
## WP.SN-WP.TC             0.435 [ 0.289,  0.561] <.001 ***
## WP.SN-WA.WRV            0.579 [ 0.456,  0.681] <.001 ***
## WP.SN-WA.PW             0.546 [ 0.417,  0.654] <.001 ***
## WP.SN-WA.NW             0.423 [ 0.274,  0.551] <.001 ***
## WP.SN-WA.RmV.           0.575 [ 0.451,  0.677] <.001 ***
## WP.SN-WA.PA             0.418 [ 0.269,  0.547] <.001 ***
## WP.SN-WA.NA            -0.005 [-0.172,  0.163]  .953    
## WP.SN-WA.ImV.           0.477 [ 0.336,  0.597] <.001 ***
## WP.SN-WA.WA             0.497 [ 0.360,  0.614] <.001 ***
## WP.SN-WA.TA             0.509 [ 0.373,  0.623] <.001 ***
## WP.SN-WA.WRF            0.508 [ 0.372,  0.623] <.001 ***
## WP.SN-WA.InV.           0.509 [ 0.373,  0.623] <.001 ***
## WP.SN-WA.EV             0.157 [-0.011,  0.317]  .058 .  
## WP.SN-WA.SlpQlV.       -0.003 [-0.171,  0.164]  .968    
## WP.SN-WA.RdV.           0.175 [ 0.007,  0.333]  .035 *  
## WP.SN-WA.PR             0.048 [-0.122,  0.214]  .570    
## WP.SN-WA.ER             0.195 [ 0.028,  0.352]  .019 *  
## WP.SN-WA.SlpQnV.       -0.026 [-0.201,  0.150]  .767    
## WP.BV-WP.JC             0.644 [ 0.534,  0.732] <.001 ***
## WP.BV-WP.CP             0.629 [ 0.516,  0.720] <.001 ***
## WP.BV-WP.PrbIV.         0.601 [ 0.482,  0.698] <.001 ***
## WP.BV-WP.IS             0.418 [ 0.270,  0.547] <.001 ***
## WP.BV-WP.IG             0.599 [ 0.480,  0.697] <.001 ***
## WP.BV-WP.SL             0.538 [ 0.407,  0.647] <.001 ***
## WP.BV-WP.OL             0.535 [ 0.404,  0.645] <.001 ***
## WP.BV-WP.AS             0.415 [ 0.267,  0.545] <.001 ***
## WP.BV-WP.PrfIV.         0.665 [ 0.560,  0.749] <.001 ***
## WP.BV-WP.TC             0.679 [ 0.578,  0.760] <.001 ***
## WP.BV-WA.WRV            0.591 [ 0.470,  0.690] <.001 ***
## WP.BV-WA.PW             0.629 [ 0.516,  0.721] <.001 ***
## WP.BV-WA.NW             0.359 [ 0.204,  0.497] <.001 ***
## WP.BV-WA.RmV.           0.605 [ 0.486,  0.701] <.001 ***
## WP.BV-WA.PA             0.432 [ 0.285,  0.559] <.001 ***
## WP.BV-WA.NA             0.038 [-0.130,  0.205]  .645    
## WP.BV-WA.ImV.           0.650 [ 0.541,  0.737] <.001 ***
## WP.BV-WA.WA             0.533 [ 0.401,  0.643] <.001 ***
## WP.BV-WA.TA             0.704 [ 0.608,  0.780] <.001 ***
## WP.BV-WA.WRF            0.572 [ 0.447,  0.675] <.001 ***
## WP.BV-WA.InV.           0.679 [ 0.577,  0.760] <.001 ***
## WP.BV-WA.EV             0.237 [ 0.072,  0.389]  .004 ** 
## WP.BV-WA.SlpQlV.        0.130 [-0.038,  0.292]  .117    
## WP.BV-WA.RdV.           0.133 [-0.036,  0.295]  .109    
## WP.BV-WA.PR             0.026 [-0.143,  0.194]  .752    
## WP.BV-WA.ER             0.154 [-0.015,  0.314]  .064 .  
## WP.BV-WA.SlpQnV.       -0.041 [-0.215,  0.136]  .641    
## WP.JC-WP.CP             0.723 [ 0.632,  0.794] <.001 ***
## WP.JC-WP.PrbIV.         0.653 [ 0.545,  0.739] <.001 ***
## WP.JC-WP.IS             0.459 [ 0.316,  0.581] <.001 ***
## WP.JC-WP.IG             0.723 [ 0.632,  0.794] <.001 ***
## WP.JC-WP.SL             0.582 [ 0.460,  0.683] <.001 ***
## WP.JC-WP.OL             0.607 [ 0.490,  0.703] <.001 ***
## WP.JC-WP.AS             0.408 [ 0.259,  0.538] <.001 ***
## WP.JC-WP.PrfIV.         0.719 [ 0.627,  0.791] <.001 ***
## WP.JC-WP.TC             0.764 [ 0.685,  0.826] <.001 ***
## WP.JC-WA.WRV            0.646 [ 0.536,  0.734] <.001 ***
## WP.JC-WA.PW             0.563 [ 0.436,  0.667] <.001 ***
## WP.JC-WA.NW             0.519 [ 0.384,  0.632] <.001 ***
## WP.JC-WA.RmV.           0.568 [ 0.443,  0.672] <.001 ***
## WP.JC-WA.PA             0.509 [ 0.373,  0.623] <.001 ***
## WP.JC-WA.NA            -0.041 [-0.208,  0.127]  .620    
## WP.JC-WA.ImV.           0.670 [ 0.566,  0.753] <.001 ***
## WP.JC-WA.WA             0.581 [ 0.458,  0.682] <.001 ***
## WP.JC-WA.TA             0.610 [ 0.493,  0.706] <.001 ***
## WP.JC-WA.WRF            0.580 [ 0.457,  0.681] <.001 ***
## WP.JC-WA.InV.           0.580 [ 0.456,  0.681] <.001 ***
## WP.JC-WA.EV             0.299 [ 0.139,  0.445] <.001 ***
## WP.JC-WA.SlpQlV.        0.080 [-0.089,  0.244]  .340    
## WP.JC-WA.RdV.           0.253 [ 0.088,  0.404]  .002 ** 
## WP.JC-WA.PR             0.198 [ 0.031,  0.355]  .017 *  
## WP.JC-WA.ER             0.212 [ 0.046,  0.368]  .010 *  
## WP.JC-WA.SlpQnV.       -0.005 [-0.180,  0.171]  .955    
## WP.CP-WP.PrbIV.         0.844 [ 0.788,  0.886] <.001 ***
## WP.CP-WP.IS             0.811 [ 0.744,  0.861] <.001 ***
## WP.CP-WP.IG             0.937 [ 0.912,  0.954] <.001 ***
## WP.CP-WP.SL             0.617 [ 0.502,  0.711] <.001 ***
## WP.CP-WP.OL             0.607 [ 0.489,  0.703] <.001 ***
## WP.CP-WP.AS             0.488 [ 0.349,  0.605] <.001 ***
## WP.CP-WP.PrfIV.         0.703 [ 0.608,  0.779] <.001 ***
## WP.CP-WP.TC             0.691 [ 0.592,  0.769] <.001 ***
## WP.CP-WA.WRV            0.630 [ 0.516,  0.721] <.001 ***
## WP.CP-WA.PW             0.583 [ 0.460,  0.684] <.001 ***
## WP.CP-WA.NW             0.471 [ 0.329,  0.592] <.001 ***
## WP.CP-WA.RmV.           0.668 [ 0.563,  0.751] <.001 ***
## WP.CP-WA.PA             0.541 [ 0.411,  0.650] <.001 ***
## WP.CP-WA.NA            -0.083 [-0.248,  0.086]  .318    
## WP.CP-WA.ImV.           0.695 [ 0.596,  0.772] <.001 ***
## WP.CP-WA.WA             0.607 [ 0.489,  0.703] <.001 ***
## WP.CP-WA.TA             0.628 [ 0.514,  0.720] <.001 ***
## WP.CP-WA.WRF            0.571 [ 0.445,  0.674] <.001 ***
## WP.CP-WA.InV.           0.562 [ 0.435,  0.667] <.001 ***
## WP.CP-WA.EV             0.324 [ 0.165,  0.466] <.001 ***
## WP.CP-WA.SlpQlV.        0.165 [-0.003,  0.323]  .047 *  
## WP.CP-WA.RdV.           0.219 [ 0.053,  0.374]  .008 ** 
## WP.CP-WA.PR             0.110 [-0.059,  0.273]  .188    
## WP.CP-WA.ER             0.218 [ 0.051,  0.372]  .009 ** 
## WP.CP-WA.SlpQnV.       -0.126 [-0.295,  0.051]  .149    
## WP.PrbIV.-WP.IS         0.559 [ 0.432,  0.664] <.001 ***
## WP.PrbIV.-WP.IG         0.693 [ 0.594,  0.771] <.001 ***
## WP.PrbIV.-WP.SL         0.524 [ 0.391,  0.635] <.001 ***
## WP.PrbIV.-WP.OL         0.510 [ 0.375,  0.624] <.001 ***
## WP.PrbIV.-WP.AS         0.421 [ 0.273,  0.549] <.001 ***
## WP.PrbIV.-WP.PrfIV.     0.585 [ 0.464,  0.685] <.001 ***
## WP.PrbIV.-WP.TC         0.616 [ 0.500,  0.710] <.001 ***
## WP.PrbIV.-WA.WRV        0.531 [ 0.399,  0.641] <.001 ***
## WP.PrbIV.-WA.PW         0.535 [ 0.403,  0.645] <.001 ***
## WP.PrbIV.-WA.NW         0.353 [ 0.197,  0.492] <.001 ***
## WP.PrbIV.-WA.RmV.       0.575 [ 0.450,  0.677] <.001 ***
## WP.PrbIV.-WA.PA         0.436 [ 0.289,  0.562] <.001 ***
## WP.PrbIV.-WA.NA        -0.052 [-0.218,  0.117]  .531    
## WP.PrbIV.-WA.ImV.       0.589 [ 0.468,  0.689] <.001 ***
## WP.PrbIV.-WA.WA         0.480 [ 0.340,  0.600] <.001 ***
## WP.PrbIV.-WA.TA         0.560 [ 0.433,  0.665] <.001 ***
## WP.PrbIV.-WA.WRF        0.492 [ 0.354,  0.610] <.001 ***
## WP.PrbIV.-WA.InV.       0.484 [ 0.344,  0.603] <.001 ***
## WP.PrbIV.-WA.EV         0.319 [ 0.160,  0.462] <.001 ***
## WP.PrbIV.-WA.SlpQlV.    0.157 [-0.011,  0.317]  .058 .  
## WP.PrbIV.-WA.RdV.       0.117 [-0.052,  0.280]  .160    
## WP.PrbIV.-WA.PR         0.058 [-0.112,  0.224]  .491    
## WP.PrbIV.-WA.ER         0.117 [-0.053,  0.280]  .162    
## WP.PrbIV.-WA.SlpQnV.   -0.165 [-0.331,  0.011]  .058 .  
## WP.IS-WP.IG             0.642 [ 0.532,  0.731] <.001 ***
## WP.IS-WP.SL             0.512 [ 0.377,  0.625] <.001 ***
## WP.IS-WP.OL             0.461 [ 0.318,  0.583] <.001 ***
## WP.IS-WP.AS             0.467 [ 0.325,  0.588] <.001 ***
## WP.IS-WP.PrfIV.         0.447 [ 0.302,  0.571] <.001 ***
## WP.IS-WP.TC             0.441 [ 0.295,  0.566] <.001 ***
## WP.IS-WA.WRV            0.393 [ 0.241,  0.526] <.001 ***
## WP.IS-WA.PW             0.337 [ 0.179,  0.478] <.001 ***
## WP.IS-WA.NW             0.321 [ 0.162,  0.464] <.001 ***
## WP.IS-WA.RmV.           0.434 [ 0.287,  0.561] <.001 ***
## WP.IS-WA.PA             0.387 [ 0.235,  0.521] <.001 ***
## WP.IS-WA.NA            -0.041 [-0.207,  0.128]  .626    
## WP.IS-WA.ImV.           0.433 [ 0.286,  0.560] <.001 ***
## WP.IS-WA.WA             0.434 [ 0.287,  0.561] <.001 ***
## WP.IS-WA.TA             0.397 [ 0.246,  0.530] <.001 ***
## WP.IS-WA.WRF            0.344 [ 0.187,  0.483] <.001 ***
## WP.IS-WA.InV.           0.345 [ 0.188,  0.485] <.001 ***
## WP.IS-WA.EV             0.256 [ 0.092,  0.406]  .002 ** 
## WP.IS-WA.SlpQlV.        0.263 [ 0.100,  0.413]  .001 ** 
## WP.IS-WA.RdV.           0.268 [ 0.104,  0.418]  .001 ** 
## WP.IS-WA.PR             0.166 [-0.002,  0.325]  .046 *  
## WP.IS-WA.ER             0.249 [ 0.084,  0.401]  .003 ** 
## WP.IS-WA.SlpQnV.       -0.146 [-0.313,  0.031]  .094 .  
## WP.IG-WP.SL             0.570 [ 0.445,  0.673] <.001 ***
## WP.IG-WP.OL             0.587 [ 0.465,  0.686] <.001 ***
## WP.IG-WP.AS             0.411 [ 0.262,  0.541] <.001 ***
## WP.IG-WP.PrfIV.         0.733 [ 0.644,  0.802] <.001 ***
## WP.IG-WP.TC             0.694 [ 0.596,  0.772] <.001 ***
## WP.IG-WA.WRV            0.653 [ 0.545,  0.740] <.001 ***
## WP.IG-WA.PW             0.594 [ 0.474,  0.693] <.001 ***
## WP.IG-WA.NW             0.499 [ 0.362,  0.616] <.001 ***
## WP.IG-WA.RmV.           0.677 [ 0.574,  0.758] <.001 ***
## WP.IG-WA.PA             0.547 [ 0.417,  0.654] <.001 ***
## WP.IG-WA.NA            -0.103 [-0.266,  0.066]  .215    
## WP.IG-WA.ImV.           0.719 [ 0.627,  0.791] <.001 ***
## WP.IG-WA.WA             0.618 [ 0.502,  0.712] <.001 ***
## WP.IG-WA.TA             0.631 [ 0.518,  0.722] <.001 ***
## WP.IG-WA.WRF            0.592 [ 0.472,  0.691] <.001 ***
## WP.IG-WA.InV.           0.581 [ 0.457,  0.682] <.001 ***
## WP.IG-WA.EV             0.280 [ 0.118,  0.427] <.001 ***
## WP.IG-WA.SlpQlV.        0.071 [-0.098,  0.236]  .393    
## WP.IG-WA.RdV.           0.195 [ 0.027,  0.352]  .019 *  
## WP.IG-WA.PR             0.081 [-0.089,  0.246]  .334    
## WP.IG-WA.ER             0.202 [ 0.035,  0.358]  .015 *  
## WP.IG-WA.SlpQnV.       -0.059 [-0.233,  0.117]  .497    
## WP.SL-WP.OL             0.938 [ 0.914,  0.955] <.001 ***
## WP.SL-WP.AS             0.857 [ 0.805,  0.896] <.001 ***
## WP.SL-WP.PrfIV.         0.703 [ 0.607,  0.779] <.001 ***
## WP.SL-WP.TC             0.625 [ 0.511,  0.717] <.001 ***
## WP.SL-WA.WRV            0.411 [ 0.261,  0.541] <.001 ***
## WP.SL-WA.PW             0.387 [ 0.234,  0.520] <.001 ***
## WP.SL-WA.NW             0.301 [ 0.141,  0.447] <.001 ***
## WP.SL-WA.RmV.           0.429 [ 0.282,  0.557] <.001 ***
## WP.SL-WA.PA             0.322 [ 0.163,  0.465] <.001 ***
## WP.SL-WA.NA             0.014 [-0.154,  0.181]  .868    
## WP.SL-WA.ImV.           0.427 [ 0.279,  0.555] <.001 ***
## WP.SL-WA.WA             0.376 [ 0.222,  0.511] <.001 ***
## WP.SL-WA.TA             0.463 [ 0.320,  0.585] <.001 ***
## WP.SL-WA.WRF            0.360 [ 0.205,  0.498] <.001 ***
## WP.SL-WA.InV.           0.510 [ 0.374,  0.624] <.001 ***
## WP.SL-WA.EV             0.265 [ 0.102,  0.414]  .001 ** 
## WP.SL-WA.SlpQlV.        0.183 [ 0.016,  0.340]  .027 *  
## WP.SL-WA.RdV.           0.232 [ 0.066,  0.385]  .005 ** 
## WP.SL-WA.PR             0.039 [-0.130,  0.206]  .643    
## WP.SL-WA.ER             0.272 [ 0.108,  0.421] <.001 ***
## WP.SL-WA.SlpQnV.       -0.059 [-0.232,  0.118]  .500    
## WP.OL-WP.AS             0.626 [ 0.512,  0.718] <.001 ***
## WP.OL-WP.PrfIV.         0.710 [ 0.615,  0.784] <.001 ***
## WP.OL-WP.TC             0.666 [ 0.561,  0.749] <.001 ***
## WP.OL-WA.WRV            0.456 [ 0.312,  0.579] <.001 ***
## WP.OL-WA.PW             0.414 [ 0.265,  0.544] <.001 ***
## WP.OL-WA.NW             0.349 [ 0.192,  0.488] <.001 ***
## WP.OL-WA.RmV.           0.457 [ 0.313,  0.580] <.001 ***
## WP.OL-WA.PA             0.362 [ 0.207,  0.500] <.001 ***
## WP.OL-WA.NA             0.088 [-0.081,  0.252]  .293    
## WP.OL-WA.ImV.           0.480 [ 0.339,  0.599] <.001 ***
## WP.OL-WA.WA             0.416 [ 0.267,  0.546] <.001 ***
## WP.OL-WA.TA             0.497 [ 0.359,  0.614] <.001 ***
## WP.OL-WA.WRF            0.420 [ 0.272,  0.549] <.001 ***
## WP.OL-WA.InV.           0.565 [ 0.439,  0.670] <.001 ***
## WP.OL-WA.EV             0.246 [ 0.081,  0.397]  .003 ** 
## WP.OL-WA.SlpQlV.        0.134 [-0.034,  0.295]  .106    
## WP.OL-WA.RdV.           0.237 [ 0.071,  0.389]  .004 ** 
## WP.OL-WA.PR             0.084 [-0.085,  0.249]  .313    
## WP.OL-WA.ER             0.253 [ 0.088,  0.404]  .002 ** 
## WP.OL-WA.SlpQnV.       -0.048 [-0.222,  0.129]  .582    
## WP.AS-WP.PrfIV.         0.528 [ 0.396,  0.639] <.001 ***
## WP.AS-WP.TC             0.417 [ 0.268,  0.546] <.001 ***
## WP.AS-WA.WRV            0.247 [ 0.083,  0.398]  .003 ** 
## WP.AS-WA.PW             0.254 [ 0.090,  0.404]  .002 ** 
## WP.AS-WA.NW             0.159 [-0.009,  0.319]  .055 .  
## WP.AS-WA.RmV.           0.286 [ 0.124,  0.433] <.001 ***
## WP.AS-WA.PA             0.186 [ 0.018,  0.343]  .025 *  
## WP.AS-WA.NA            -0.099 [-0.263,  0.069]  .232    
## WP.AS-WA.ImV.           0.246 [ 0.082,  0.397]  .003 ** 
## WP.AS-WA.WA             0.227 [ 0.061,  0.380]  .006 ** 
## WP.AS-WA.TA             0.302 [ 0.142,  0.447] <.001 ***
## WP.AS-WA.WRF            0.185 [ 0.018,  0.342]  .026 *  
## WP.AS-WA.InV.           0.307 [ 0.146,  0.451] <.001 ***
## WP.AS-WA.EV             0.231 [ 0.066,  0.384]  .005 ** 
## WP.AS-WA.SlpQlV.        0.212 [ 0.046,  0.367]  .010 *  
## WP.AS-WA.RdV.           0.170 [ 0.002,  0.329]  .041 *  
## WP.AS-WA.PR            -0.038 [-0.205,  0.131]  .649    
## WP.AS-WA.ER             0.235 [ 0.070,  0.388]  .004 ** 
## WP.AS-WA.SlpQnV.       -0.061 [-0.234,  0.115]  .482    
## WP.PrfIV.-WP.TC         0.752 [ 0.670,  0.817] <.001 ***
## WP.PrfIV.-WA.WRV        0.538 [ 0.407,  0.647] <.001 ***
## WP.PrfIV.-WA.PW         0.506 [ 0.369,  0.621] <.001 ***
## WP.PrfIV.-WA.NW         0.394 [ 0.243,  0.527] <.001 ***
## WP.PrfIV.-WA.RmV.       0.501 [ 0.364,  0.617] <.001 ***
## WP.PrfIV.-WA.PA         0.463 [ 0.320,  0.585] <.001 ***
## WP.PrfIV.-WA.NA         0.000 [-0.167,  0.168]  .996    
## WP.PrfIV.-WA.ImV.       0.644 [ 0.534,  0.733] <.001 ***
## WP.PrfIV.-WA.WA         0.537 [ 0.406,  0.647] <.001 ***
## WP.PrfIV.-WA.TA         0.621 [ 0.506,  0.714] <.001 ***
## WP.PrfIV.-WA.WRF        0.509 [ 0.373,  0.623] <.001 ***
## WP.PrfIV.-WA.InV.       0.710 [ 0.615,  0.784] <.001 ***
## WP.PrfIV.-WA.EV         0.256 [ 0.092,  0.406]  .002 ** 
## WP.PrfIV.-WA.SlpQlV.    0.150 [-0.018,  0.310]  .071 .  
## WP.PrfIV.-WA.RdV.       0.218 [ 0.052,  0.373]  .008 ** 
## WP.PrfIV.-WA.PR         0.112 [-0.057,  0.275]  .180    
## WP.PrfIV.-WA.ER         0.215 [ 0.049,  0.370]  .009 ** 
## WP.PrfIV.-WA.SlpQnV.   -0.002 [-0.178,  0.174]  .981    
## WP.TC-WA.WRV            0.596 [ 0.476,  0.694] <.001 ***
## WP.TC-WA.PW             0.580 [ 0.456,  0.681] <.001 ***
## WP.TC-WA.NW             0.417 [ 0.268,  0.547] <.001 ***
## WP.TC-WA.RmV.           0.582 [ 0.459,  0.683] <.001 ***
## WP.TC-WA.PA             0.456 [ 0.312,  0.579] <.001 ***
## WP.TC-WA.NA             0.055 [-0.114,  0.221]  .509    
## WP.TC-WA.ImV.           0.609 [ 0.492,  0.705] <.001 ***
## WP.TC-WA.WA             0.505 [ 0.369,  0.621] <.001 ***
## WP.TC-WA.TA             0.613 [ 0.496,  0.708] <.001 ***
## WP.TC-WA.WRF            0.563 [ 0.437,  0.668] <.001 ***
## WP.TC-WA.InV.           0.659 [ 0.553,  0.745] <.001 ***
## WP.TC-WA.EV             0.293 [ 0.132,  0.439] <.001 ***
## WP.TC-WA.SlpQlV.        0.052 [-0.117,  0.217]  .536    
## WP.TC-WA.RdV.           0.263 [ 0.099,  0.413]  .001 ** 
## WP.TC-WA.PR             0.142 [-0.027,  0.303]  .089 .  
## WP.TC-WA.ER             0.256 [ 0.091,  0.407]  .002 ** 
## WP.TC-WA.SlpQnV.       -0.061 [-0.234,  0.116]  .486    
## WA.WRV-WA.PW            0.837 [ 0.780,  0.880] <.001 ***
## WA.WRV-WA.NW            0.836 [ 0.779,  0.879] <.001 ***
## WA.WRV-WA.RmV.          0.721 [ 0.633,  0.791] <.001 ***
## WA.WRV-WA.PA            0.559 [ 0.436,  0.661] <.001 ***
## WA.WRV-WA.NA           -0.010 [-0.172,  0.152]  .902    
## WA.WRV-WA.ImV.          0.671 [ 0.571,  0.752] <.001 ***
## WA.WRV-WA.WA            0.640 [ 0.533,  0.727] <.001 ***
## WA.WRV-WA.TA            0.674 [ 0.574,  0.754] <.001 ***
## WA.WRV-WA.WRF           0.694 [ 0.599,  0.770] <.001 ***
## WA.WRV-WA.InV.          0.548 [ 0.423,  0.652] <.001 ***
## WA.WRV-WA.EV            0.303 [ 0.148,  0.444] <.001 ***
## WA.WRV-WA.SlpQlV.       0.023 [-0.140,  0.185]  .783    
## WA.WRV-WA.RdV.          0.107 [-0.057,  0.265]  .201    
## WA.WRV-WA.PR            0.059 [-0.105,  0.220]  .480    
## WA.WRV-WA.ER            0.103 [-0.061,  0.261]  .219    
## WA.WRV-WA.SlpQnV.      -0.029 [-0.198,  0.142]  .742    
## WA.PW-WA.NW             0.399 [ 0.253,  0.527] <.001 ***
## WA.PW-WA.RmV.           0.775 [ 0.701,  0.832] <.001 ***
## WA.PW-WA.PA             0.696 [ 0.601,  0.771] <.001 ***
## WA.PW-WA.NA            -0.086 [-0.245,  0.078]  .303    
## WA.PW-WA.ImV.           0.692 [ 0.596,  0.768] <.001 ***
## WA.PW-WA.WA             0.719 [ 0.630,  0.789] <.001 ***
## WA.PW-WA.TA             0.656 [ 0.553,  0.740] <.001 ***
## WA.PW-WA.WRF            0.733 [ 0.647,  0.800] <.001 ***
## WA.PW-WA.InV.           0.607 [ 0.493,  0.700] <.001 ***
## WA.PW-WA.EV             0.239 [ 0.079,  0.386]  .004 ** 
## WA.PW-WA.SlpQlV.        0.014 [-0.149,  0.176]  .867    
## WA.PW-WA.RdV.           0.088 [-0.076,  0.248]  .292    
## WA.PW-WA.PR             0.058 [-0.106,  0.219]  .489    
## WA.PW-WA.ER             0.080 [-0.084,  0.240]  .339    
## WA.PW-WA.SlpQnV.       -0.023 [-0.192,  0.148]  .793    
## WA.NW-WA.RmV.           0.431 [ 0.289,  0.555] <.001 ***
## WA.NW-WA.PA             0.238 [ 0.079,  0.386]  .004 ** 
## WA.NW-WA.NA             0.069 [-0.095,  0.229]  .409    
## WA.NW-WA.ImV.           0.431 [ 0.289,  0.555] <.001 ***
## WA.NW-WA.WA             0.351 [ 0.200,  0.486] <.001 ***
## WA.NW-WA.TA             0.471 [ 0.334,  0.588] <.001 ***
## WA.NW-WA.WRF            0.428 [ 0.285,  0.552] <.001 ***
## WA.NW-WA.InV.           0.309 [ 0.154,  0.449] <.001 ***
## WA.NW-WA.EV             0.268 [ 0.111,  0.413]  .001 ** 
## WA.NW-WA.SlpQlV.        0.024 [-0.139,  0.186]  .769    
## WA.NW-WA.RdV.           0.091 [-0.073,  0.250]  .278    
## WA.NW-WA.PR             0.041 [-0.123,  0.203]  .624    
## WA.NW-WA.ER             0.092 [-0.072,  0.251]  .271    
## WA.NW-WA.SlpQnV.       -0.025 [-0.194,  0.146]  .776    
## WA.RmV.-WA.PA           0.694 [ 0.599,  0.770] <.001 ***
## WA.RmV.-WA.NA          -0.114 [-0.271,  0.050]  .172    
## WA.RmV.-WA.ImV.         0.707 [ 0.615,  0.780] <.001 ***
## WA.RmV.-WA.WA           0.653 [ 0.549,  0.737] <.001 ***
## WA.RmV.-WA.TA           0.647 [ 0.542,  0.733] <.001 ***
## WA.RmV.-WA.WRF          0.710 [ 0.619,  0.783] <.001 ***
## WA.RmV.-WA.InV.         0.612 [ 0.499,  0.704] <.001 ***
## WA.RmV.-WA.EV           0.249 [ 0.090,  0.395]  .002 ** 
## WA.RmV.-WA.SlpQlV.      0.026 [-0.137,  0.188]  .755    
## WA.RmV.-WA.RdV.         0.165 [ 0.002,  0.319]  .048 *  
## WA.RmV.-WA.PR           0.070 [-0.094,  0.230]  .402    
## WA.RmV.-WA.ER           0.170 [ 0.007,  0.324]  .041 *  
## WA.RmV.-WA.SlpQnV.     -0.050 [-0.218,  0.121]  .567    
## WA.PA-WA.NA            -0.270 [-0.414, -0.112] <.001 ***
## WA.PA-WA.ImV.           0.701 [ 0.607,  0.775] <.001 ***
## WA.PA-WA.WA             0.732 [ 0.647,  0.799] <.001 ***
## WA.PA-WA.TA             0.530 [ 0.402,  0.638] <.001 ***
## WA.PA-WA.WRF            0.701 [ 0.608,  0.775] <.001 ***
## WA.PA-WA.InV.           0.558 [ 0.435,  0.661] <.001 ***
## WA.PA-WA.EV             0.220 [ 0.059,  0.369]  .008 ** 
## WA.PA-WA.SlpQlV.       -0.047 [-0.208,  0.116]  .573    
## WA.PA-WA.RdV.           0.142 [-0.022,  0.298]  .089 .  
## WA.PA-WA.PR             0.114 [-0.050,  0.272]  .173    
## WA.PA-WA.ER             0.118 [-0.046,  0.275]  .159    
## WA.PA-WA.SlpQnV.        0.038 [-0.134,  0.206]  .668    
## WA.NA-WA.ImV.          -0.106 [-0.264,  0.057]  .201    
## WA.NA-WA.WA            -0.074 [-0.234,  0.089]  .374    
## WA.NA-WA.TA             0.067 [-0.097,  0.227]  .422    
## WA.NA-WA.WRF           -0.052 [-0.212,  0.112]  .536    
## WA.NA-WA.InV.          -0.011 [-0.173,  0.152]  .895    
## WA.NA-WA.EV            -0.143 [-0.299,  0.019]  .084 .  
## WA.NA-WA.SlpQlV.        0.135 [-0.028,  0.291]  .105    
## WA.NA-WA.RdV.           0.078 [-0.086,  0.238]  .350    
## WA.NA-WA.PR             0.096 [-0.068,  0.255]  .249    
## WA.NA-WA.ER             0.046 [-0.117,  0.208]  .579    
## WA.NA-WA.SlpQnV.       -0.018 [-0.188,  0.153]  .836    
## WA.ImV.-WA.WA           0.720 [ 0.632,  0.790] <.001 ***
## WA.ImV.-WA.TA           0.722 [ 0.634,  0.792] <.001 ***
## WA.ImV.-WA.WRF          0.679 [ 0.580,  0.758] <.001 ***
## WA.ImV.-WA.InV.         0.734 [ 0.649,  0.801] <.001 ***
## WA.ImV.-WA.EV           0.194 [ 0.033,  0.346]  .019 *  
## WA.ImV.-WA.SlpQlV.      0.047 [-0.116,  0.208]  .572    
## WA.ImV.-WA.RdV.         0.209 [ 0.048,  0.360]  .012 *  
## WA.ImV.-WA.PR           0.179 [ 0.016,  0.332]  .032 *  
## WA.ImV.-WA.ER           0.168 [ 0.005,  0.322]  .044 *  
## WA.ImV.-WA.SlpQnV.     -0.079 [-0.246,  0.092]  .363    
## WA.WA-WA.TA             0.618 [ 0.506,  0.709] <.001 ***
## WA.WA-WA.WRF            0.813 [ 0.750,  0.862] <.001 ***
## WA.WA-WA.InV.           0.582 [ 0.464,  0.680] <.001 ***
## WA.WA-WA.EV             0.200 [ 0.039,  0.351]  .015 *  
## WA.WA-WA.SlpQlV.        0.087 [-0.076,  0.246]  .296    
## WA.WA-WA.RdV.           0.186 [ 0.023,  0.338]  .026 *  
## WA.WA-WA.PR             0.138 [-0.026,  0.294]  .099 .  
## WA.WA-WA.ER             0.160 [-0.003,  0.315]  .055 .  
## WA.WA-WA.SlpQnV.       -0.065 [-0.233,  0.107]  .458    
## WA.TA-WA.WRF            0.634 [ 0.525,  0.722] <.001 ***
## WA.TA-WA.InV.           0.759 [ 0.681,  0.821] <.001 ***
## WA.TA-WA.EV             0.207 [ 0.046,  0.358]  .012 *  
## WA.TA-WA.SlpQlV.        0.113 [-0.051,  0.270]  .175    
## WA.TA-WA.RdV.           0.185 [ 0.023,  0.338]  .026 *  
## WA.TA-WA.PR             0.130 [-0.034,  0.287]  .120    
## WA.TA-WA.ER             0.163 [ 0.000,  0.318]  .050 *  
## WA.TA-WA.SlpQnV.        0.019 [-0.152,  0.189]  .828    
## WA.WRF-WA.InV.          0.601 [ 0.486,  0.695] <.001 ***
## WA.WRF-WA.EV            0.263 [ 0.105,  0.408]  .001 ** 
## WA.WRF-WA.SlpQlV.      -0.068 [-0.228,  0.096]  .417    
## WA.WRF-WA.RdV.          0.157 [-0.006,  0.312]  .059 .  
## WA.WRF-WA.PR            0.114 [-0.050,  0.272]  .174    
## WA.WRF-WA.ER            0.137 [-0.027,  0.293]  .101    
## WA.WRF-WA.SlpQnV.      -0.005 [-0.175,  0.166]  .957    
## WA.InV.-WA.EV           0.187 [ 0.026,  0.339]  .024 *  
## WA.InV.-WA.SlpQlV.      0.093 [-0.071,  0.252]  .264    
## WA.InV.-WA.RdV.         0.254 [ 0.095,  0.401]  .002 ** 
## WA.InV.-WA.PR           0.223 [ 0.063,  0.373]  .007 ** 
## WA.InV.-WA.ER           0.200 [ 0.038,  0.352]  .016 *  
## WA.InV.-WA.SlpQnV.      0.047 [-0.124,  0.216]  .590    
## WA.EV-WA.SlpQlV.        0.083 [-0.081,  0.242]  .321    
## WA.EV-WA.RdV.           0.146 [-0.017,  0.302]  .080 .  
## WA.EV-WA.PR             0.267 [ 0.109,  0.412]  .001 ** 
## WA.EV-WA.ER             0.040 [-0.124,  0.202]  .634    
## WA.EV-WA.SlpQnV.       -0.192 [-0.351, -0.023]  .026 *  
## WA.SlpQlV.-WA.RdV.      0.150 [-0.014,  0.305]  .073 .  
## WA.SlpQlV.-WA.PR        0.109 [-0.055,  0.267]  .192    
## WA.SlpQlV.-WA.ER        0.130 [-0.034,  0.287]  .119    
## WA.SlpQlV.-WA.SlpQnV.  -0.389 [-0.525, -0.234] <.001 ***
## WA.RdV.-WA.PR           0.650 [ 0.545,  0.735] <.001 ***
## WA.RdV.-WA.ER           0.912 [ 0.879,  0.936] <.001 ***
## WA.RdV.-WA.SlpQnV.     -0.094 [-0.261,  0.078]  .281    
## WA.PR-WA.ER             0.280 [ 0.123,  0.424] <.001 ***
## WA.PR-WA.SlpQnV.       -0.145 [-0.308,  0.026]  .095 .  
## WA.ER-WA.SlpQnV.       -0.038 [-0.207,  0.134]  .666    
## ────────────────────────────────────────────────────────
## 
## Intraclass Correlation:
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##       WA.GraceV WP.SupervisoryBehavioralFeedbackV WP.SupervisoryPositiveBehavioralFeedbackV WP.SupervisoryNegativeBehavioralFeedbackV WP.learningBehaviorV WP.JobCraftingV WP.CreativeProcessEngagementV WP.ProblemIdentificationV WP.InformationSearchV WP.IdeaGenerationV WP.SocialLearningV WP.ObservationalLearningV WP.AdviceSeekingV WP.PerformanceImprovementV WP.TakingChargeV WA.WorkReflectionV WA.PositiveWorkReflectionV WA.NegativeWorkReflectionV WA.RuminationV WA.PositiveAffectV WA.NegativeAffectV WA.ImprovisionV WA.WorkAbsorptionV WA.ThrivingAtWorkLearningV WA.WorkRelatedFlowV WA.InspirationV WA.ExerciseV WA.SleepQualityV WA.ReadingV WA.PaperReadV WA.EReadV WA.SleepQuantityV
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## ICC1      0.720                             0.811                                     0.748                                     0.772                0.629           0.765                         0.808                     0.531                 0.602              0.806              0.760                     0.701             0.697                      0.710            0.817              0.709                      0.709                      0.611          0.696              0.631              0.588           0.671              0.689                      0.585               0.702           0.696        0.807            0.671       0.745         0.643     0.750             0.435
## ICC2      0.822                             0.886                                     0.843                                     0.859                0.753           0.855                         0.884                     0.671                 0.731              0.882              0.851                     0.809             0.806                      0.815            0.890              0.814                      0.814                      0.738          0.805              0.754              0.719           0.785              0.799                      0.716               0.808           0.804        0.883            0.786       0.838         0.761     0.841             0.575
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

2 MULTILEVEL MEDIATION EFFECT

#cor_multilevel(data[,.(B.ID, WP.InformationSearchV, Manipulation, WP.learningBehaviorV,WP.SupervisoryBehavioralFeedbackV_sd)], "B.ID", digits = 3)
PROCESS(data, y="WP.InformationSearchV", x="Manipulation", meds="WP.CreativeProcessEngagementV", 
           covs=c("WP.CreativeProcessEngagementV_mean"),
           cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)#, center=FALSE)#, file="D2.doc")hlm.re.y = "(1|B.ID)", 

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WP.InformationSearchV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WP.InformationSearchV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WP.InformationSearchV  (2) WP.CreativeProcessEngagementV  (3) WP.InformationSearchV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.498 ***                  3.245 ***                          3.499 ***              
##                                      (0.040)                    (0.018)                            (0.036)                 
## WP.CreativeProcessEngagementV_mean    0.824 ***                  0.962 ***                         -0.143                  
##                                      (0.060)                    (0.027)                            (0.116)                 
## Manipulation                          0.041                      0.098 **                          -0.056                  
##                                      (0.065)                    (0.035)                            (0.054)                 
## WP.CreativeProcessEngagementV                                                                       1.005 ***              
##                                                                                                    (0.107)                 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.474                      0.841                              0.599                  
## Conditional R^2                       0.602                      0.841                              0.732                  
## AIC                                 446.381                     95.736                            375.815                  
## BIC                                 463.969                    113.323                            396.920                  
## Num. obs.                           249                        249                                249                      
## Num. groups: B.ID                   138                        138                                138                      
## Var: B.ID (Intercept)                 0.081                      0.000                              0.084                  
## Var: Residual                         0.253                      0.078                              0.170                  
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 249 (21 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WP.InformationSearchV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)   0.098 (0.039)  2.509  .012 *   [ 0.033, 0.179]
## Direct (c')    -0.059 (0.049) -1.203  .229     [-0.142, 0.038]
## Total (c)       0.040 (0.064)  0.619  .536     [-0.077, 0.170]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WP.InformationSearchV", x="Manipulation", meds="WP.learningBehaviorV", 
           covs=c("WP.learningBehaviorV_mean"),
           cluster ="B.ID", center=FALSE)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WP.InformationSearchV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.learningBehaviorV
## - Moderators (W) : -
## - Covariates (C) : WP.learningBehaviorV_mean
## -   HLM Clusters : B.ID
## 
## Formula of Mediator:
## -    WP.learningBehaviorV ~ WP.learningBehaviorV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WP.InformationSearchV ~ WP.learningBehaviorV_mean + Manipulation + WP.learningBehaviorV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
##                            (1) WP.InformationSearchV  (2) WP.learningBehaviorV  (3) WP.InformationSearchV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                  2.062 ***                 -0.068                     2.056 ***              
##                             (0.257)                    (0.135)                   (0.258)                 
## WP.learningBehaviorV_mean    0.440 ***                  1.013 ***                 0.538 ***              
##                             (0.078)                    (0.040)                   (0.114)                 
## Manipulation                 0.049                      0.038                     0.053                  
##                             (0.066)                    (0.058)                   (0.066)                 
## WP.learningBehaviorV                                                             -0.097                  
##                                                                                  (0.082)                 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                 0.158                      0.719                     0.160                  
## Conditional R^2              0.603                      0.719                     0.609                  
## AIC                        536.310                    338.302                   540.096                  
## BIC                        553.897                    355.890                   561.201                  
## Num. obs.                  249                        249                       249                      
## Num. groups: B.ID          138                        138                       138                      
## Var: B.ID (Intercept)        0.288                      0.000                     0.291                  
## Var: Residual                0.256                      0.210                     0.254                  
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 249 (21 missing observations deleted)
## Random Seed : set.seed()
## Simulations : 100 (Bootstrap)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.learningBehaviorV" (M) ==> "WP.InformationSearchV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)  -0.006 (0.009) -0.637  .524     [-0.030, 0.005]
## Direct (c')     0.055 (0.071)  0.775  .438     [-0.073, 0.179]
## Total (c)       0.049 (0.070)  0.705  .481     [-0.073, 0.176]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WP.SupervisoryBehavioralFeedbackV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WP.SupervisoryBehavioralFeedbackV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WP.SupervisoryBehavioralFeedbackV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WP.SupervisoryBehavioralFeedbackV  (2) WP.CreativeProcessEngagementV  (3) WP.SupervisoryBehavioralFeedbackV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.339 ***                              3.245 ***                          3.340 ***                          
##                                      (0.055)                                (0.018)                            (0.056)                             
## WP.CreativeProcessEngagementV_mean    0.650 ***                              0.962 ***                          0.445 ***                          
##                                      (0.082)                                (0.027)                            (0.128)                             
## Manipulation                         -0.060                                  0.098 **                          -0.082                              
##                                      (0.047)                                (0.035)                            (0.047)                             
## WP.CreativeProcessEngagementV                                                                                   0.212 *                            
##                                                                                                                (0.102)                             
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.284                                  0.841                              0.286                              
## Conditional R^2                       0.812                                  0.841                              0.822                              
## AIC                                 451.862                                 95.736                            452.395                              
## BIC                                 469.450                                113.323                            473.499                              
## Num. obs.                           249                                    249                                249                                  
## Num. groups: B.ID                   138                                    138                                138                                  
## Var: B.ID (Intercept)                 0.350                                  0.000                              0.358                              
## Var: Residual                         0.124                                  0.078                              0.119                              
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 249 (21 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WP.SupervisoryBehavioralFeedbackV" (Y)
## ───────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p        [MCMC 95% CI]
## ───────────────────────────────────────────────────────────────
## Indirect (ab)   0.022 (0.012)  1.749  .080 .   [ 0.003,  0.049]
## Direct (c')    -0.084 (0.042) -1.981  .048 *   [-0.156, -0.001]
## Total (c)      -0.062 (0.042) -1.476  .140     [-0.138,  0.021]
## ───────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WP.SupervisoryPositiveBehavioralFeedbackV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WP.SupervisoryPositiveBehavioralFeedbackV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WP.SupervisoryPositiveBehavioralFeedbackV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WP.SupervisoryPositiveBehavioralFeedbackV  (2) WP.CreativeProcessEngagementV  (3) WP.SupervisoryPositiveBehavioralFeedbackV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.298 ***                                      3.245 ***                          3.299 ***                                  
##                                      (0.061)                                        (0.018)                            (0.062)                                     
## WP.CreativeProcessEngagementV_mean    0.645 ***                                      0.962 ***                          0.527 ***                                  
##                                      (0.091)                                        (0.027)                            (0.154)                                     
## Manipulation                          0.003                                          0.098 **                          -0.010                                      
##                                      (0.059)                                        (0.035)                            (0.060)                                     
## WP.CreativeProcessEngagementV                                                                                           0.122                                      
##                                                                                                                        (0.129)                                     
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.234                                          0.841                              0.234                                      
## Conditional R^2                       0.748                                          0.841                              0.750                                      
## AIC                                 532.498                                         95.736                            535.865                                      
## BIC                                 550.085                                        113.323                            556.970                                      
## Num. obs.                           249                                            249                                249                                          
## Num. groups: B.ID                   138                                            138                                138                                          
## Var: B.ID (Intercept)                 0.405                                          0.000                              0.409                                      
## Var: Residual                         0.199                                          0.078                              0.198                                      
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 249 (21 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WP.SupervisoryPositiveBehavioralFeedbackV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)   0.013 (0.013)  1.000  .317     [-0.006, 0.039]
## Direct (c')    -0.013 (0.054) -0.239  .811     [-0.105, 0.094]
## Total (c)       0.000 (0.053)  0.002  .998     [-0.091, 0.100]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WP.SupervisoryNegativeBehavioralFeedbackV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WP.SupervisoryNegativeBehavioralFeedbackV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WP.SupervisoryNegativeBehavioralFeedbackV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WP.SupervisoryNegativeBehavioralFeedbackV  (2) WP.CreativeProcessEngagementV  (3) WP.SupervisoryNegativeBehavioralFeedbackV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.372 ***                                      3.245 ***                          3.373 ***                                  
##                                      (0.060)                                        (0.018)                            (0.061)                                     
## WP.CreativeProcessEngagementV_mean    0.654 ***                                      0.962 ***                          0.400 **                                   
##                                      (0.089)                                        (0.027)                            (0.145)                                     
## Manipulation                         -0.110 *                                        0.098 **                          -0.137 *                                    
##                                      (0.054)                                        (0.035)                            (0.054)                                     
## WP.CreativeProcessEngagementV                                                                                           0.263 *                                    
##                                                                                                                        (0.118)                                     
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.252                                          0.841                              0.256                                      
## Conditional R^2                       0.777                                          0.841                              0.791                                      
## AIC                                 509.766                                         95.736                            509.390                                      
## BIC                                 527.353                                        113.323                            530.494                                      
## Num. obs.                           249                                            249                                249                                          
## Num. groups: B.ID                   138                                            138                                138                                          
## Var: B.ID (Intercept)                 0.401                                          0.000                              0.413                                      
## Var: Residual                         0.171                                          0.078                              0.162                                      
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 249 (21 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WP.SupervisoryNegativeBehavioralFeedbackV" (Y)
## ───────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p        [MCMC 95% CI]
## ───────────────────────────────────────────────────────────────
## Indirect (ab)   0.027 (0.015)  1.820  .069 .   [ 0.004,  0.059]
## Direct (c')    -0.139 (0.049) -2.825  .005 **  [-0.223, -0.043]
## Total (c)      -0.113 (0.049) -2.279  .023 *   [-0.201, -0.015]
## ───────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WP.learningBehaviorV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WP.learningBehaviorV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WP.learningBehaviorV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WP.learningBehaviorV  (2) WP.CreativeProcessEngagementV  (3) WP.learningBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.190 ***                 3.245 ***                          3.190 ***             
##                                      (0.053)                   (0.018)                            (0.053)                
## WP.CreativeProcessEngagementV_mean    0.737 ***                 0.962 ***                          0.743 ***             
##                                      (0.078)                   (0.027)                            (0.157)                
## Manipulation                          0.043                     0.098 **                           0.043                 
##                                      (0.068)                   (0.035)                            (0.070)                
## WP.CreativeProcessEngagementV                                                                     -0.006                 
##                                                                                                   (0.141)                
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.325                     0.841                              0.325                 
## Conditional R^2                       0.633                     0.841                              0.631                 
## AIC                                 526.146                    95.736                            530.225                 
## BIC                                 543.733                   113.323                            551.329                 
## Num. obs.                           249                       249                                249                     
## Num. groups: B.ID                   138                       138                                138                     
## Var: B.ID (Intercept)                 0.228                     0.000                              0.228                 
## Var: Residual                         0.272                     0.078                              0.274                 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 249 (21 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WP.learningBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────
##                Effect    S.E.     z     p       [MCMC 95% CI]
## ─────────────────────────────────────────────────────────────
## Indirect (ab)   0.001 (0.014) 0.054  .957     [-0.028, 0.027]
## Direct (c')     0.040 (0.063) 0.644  .520     [-0.067, 0.164]
## Total (c)       0.041 (0.060) 0.679  .497     [-0.064, 0.160]
## ─────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WP.JobCraftingV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WP.JobCraftingV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WP.JobCraftingV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WP.JobCraftingV  (2) WP.CreativeProcessEngagementV  (3) WP.JobCraftingV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.024 ***            3.245 ***                          3.025 ***        
##                                      (0.042)              (0.018)                            (0.041)           
## WP.CreativeProcessEngagementV_mean    0.702 ***            0.962 ***                          0.482 ***        
##                                      (0.062)              (0.027)                            (0.113)           
## Manipulation                          0.024                0.098 **                           0.001            
##                                      (0.046)              (0.035)                            (0.047)           
## WP.CreativeProcessEngagementV                                                                 0.229 *          
##                                                                                              (0.098)           
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.426                0.841                              0.438            
## Conditional R^2                       0.765                0.841                              0.760            
## AIC                                 375.145               95.736                            374.625            
## BIC                                 392.732              113.323                            395.730            
## Num. obs.                           249                  249                                249                
## Num. groups: B.ID                   138                  138                                138                
## Var: B.ID (Intercept)                 0.175                0.000                              0.164            
## Var: Residual                         0.121                0.078                              0.122            
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 249 (21 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WP.JobCraftingV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)   0.023 (0.012)  1.863  .062 .   [ 0.004, 0.051]
## Direct (c')    -0.001 (0.042) -0.019  .985     [-0.073, 0.083]
## Total (c)       0.022 (0.043)  0.524  .600     [-0.054, 0.106]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WP.ProblemIdentificationV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WP.ProblemIdentificationV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WP.ProblemIdentificationV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WP.ProblemIdentificationV  (2) WP.CreativeProcessEngagementV  (3) WP.ProblemIdentificationV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.240 ***                      3.245 ***                          3.242 ***                  
##                                      (0.041)                        (0.018)                            (0.034)                     
## WP.CreativeProcessEngagementV_mean    0.891 ***                      0.962 ***                         -0.486 ***                  
##                                      (0.062)                        (0.027)                            (0.107)                     
## Manipulation                          0.226 **                       0.098 **                           0.078                      
##                                      (0.072)                        (0.035)                            (0.049)                     
## WP.CreativeProcessEngagementV                                                                           1.429 ***                  
##                                                                                                        (0.097)                     
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.491                          0.841                              0.702                      
## Conditional R^2                       0.569                          0.841                              0.817                      
## AIC                                 480.181                         95.736                            336.471                      
## BIC                                 497.768                        113.323                            357.576                      
## Num. obs.                           249                            249                                249                          
## Num. groups: B.ID                   138                            138                                138                          
## Var: B.ID (Intercept)                 0.058                          0.000                              0.085                      
## Var: Residual                         0.320                          0.078                              0.135                      
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 249 (21 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WP.ProblemIdentificationV" (Y)
## ────────────────────────────────────────────────────────────
##                Effect    S.E.     z     p      [MCMC 95% CI]
## ────────────────────────────────────────────────────────────
## Indirect (ab)   0.139 (0.055) 2.528  .011 *   [0.044, 0.255]
## Direct (c')     0.075 (0.044) 1.726  .084 .   [0.001, 0.162]
## Total (c)       0.215 (0.073) 2.938  .003 **  [0.073, 0.359]
## ────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WP.InformationSearchV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WP.InformationSearchV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WP.InformationSearchV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WP.InformationSearchV  (2) WP.CreativeProcessEngagementV  (3) WP.InformationSearchV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.498 ***                  3.245 ***                          3.499 ***              
##                                      (0.040)                    (0.018)                            (0.036)                 
## WP.CreativeProcessEngagementV_mean    0.824 ***                  0.962 ***                         -0.143                  
##                                      (0.060)                    (0.027)                            (0.116)                 
## Manipulation                          0.041                      0.098 **                          -0.056                  
##                                      (0.065)                    (0.035)                            (0.054)                 
## WP.CreativeProcessEngagementV                                                                       1.005 ***              
##                                                                                                    (0.107)                 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.474                      0.841                              0.599                  
## Conditional R^2                       0.602                      0.841                              0.732                  
## AIC                                 446.381                     95.736                            375.815                  
## BIC                                 463.969                    113.323                            396.920                  
## Num. obs.                           249                        249                                249                      
## Num. groups: B.ID                   138                        138                                138                      
## Var: B.ID (Intercept)                 0.081                      0.000                              0.084                  
## Var: Residual                         0.253                      0.078                              0.170                  
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 249 (21 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WP.InformationSearchV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)   0.098 (0.039)  2.509  .012 *   [ 0.033, 0.179]
## Direct (c')    -0.059 (0.049) -1.203  .229     [-0.142, 0.038]
## Total (c)       0.040 (0.064)  0.619  .536     [-0.077, 0.170]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WP.IdeaGenerationV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WP.IdeaGenerationV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WP.IdeaGenerationV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WP.IdeaGenerationV  (2) WP.CreativeProcessEngagementV  (3) WP.IdeaGenerationV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.093 ***               3.245 ***                          3.094 ***           
##                                      (0.026)                 (0.018)                            (0.023)              
## WP.CreativeProcessEngagementV_mean    1.089 ***               0.962 ***                          0.376 ***           
##                                      (0.040)                 (0.027)                            (0.077)              
## Manipulation                          0.059                   0.098 **                          -0.014               
##                                      (0.046)                 (0.035)                            (0.037)              
## WP.CreativeProcessEngagementV                                                                    0.740 ***           
##                                                                                                 (0.072)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.775                   0.841                              0.838               
## Conditional R^2                       0.811                   0.841                              0.883               
## AIC                                 257.187                  95.736                            175.451               
## BIC                                 274.774                 113.323                            196.556               
## Num. obs.                           249                     249                                249                   
## Num. groups: B.ID                   138                     138                                138                   
## Var: B.ID (Intercept)                 0.024                   0.000                              0.031               
## Var: Residual                         0.128                   0.078                              0.080               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 249 (21 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WP.IdeaGenerationV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)   0.072 (0.029)  2.516  .012 *   [ 0.024, 0.131]
## Direct (c')    -0.015 (0.033) -0.464  .643     [-0.072, 0.051]
## Total (c)       0.057 (0.045)  1.257  .209     [-0.025, 0.148]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WP.SocialLearningV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WP.SocialLearningV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WP.SocialLearningV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WP.SocialLearningV  (2) WP.CreativeProcessEngagementV  (3) WP.SocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.186 ***               3.245 ***                          3.187 ***           
##                                      (0.055)                 (0.018)                            (0.055)              
## WP.CreativeProcessEngagementV_mean    0.736 ***               0.962 ***                          0.632 ***           
##                                      (0.081)                 (0.027)                            (0.140)              
## Manipulation                          0.006                   0.098 **                          -0.005               
##                                      (0.054)                 (0.035)                            (0.056)              
## WP.CreativeProcessEngagementV                                                                    0.108               
##                                                                                                 (0.119)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.331                   0.841                              0.333               
## Conditional R^2                       0.764                   0.841                              0.761               
## AIC                                 484.683                  95.736                            488.294               
## BIC                                 502.270                 113.323                            509.398               
## Num. obs.                           249                     249                                249                   
## Num. groups: B.ID                   138                     138                                138                   
## Var: B.ID (Intercept)                 0.314                   0.000                              0.310               
## Var: Residual                         0.171                   0.078                              0.173               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 249 (21 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WP.SocialLearningV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)   0.012 (0.012)  0.963  .336     [-0.007, 0.036]
## Direct (c')    -0.008 (0.051) -0.161  .872     [-0.094, 0.092]
## Total (c)       0.003 (0.049)  0.070  .944     [-0.082, 0.097]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WP.ObservationalLearningV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WP.ObservationalLearningV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WP.ObservationalLearningV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WP.ObservationalLearningV  (2) WP.CreativeProcessEngagementV  (3) WP.ObservationalLearningV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.015 ***                      3.245 ***                          3.015 ***                  
##                                      (0.061)                        (0.018)                            (0.061)                     
## WP.CreativeProcessEngagementV_mean    0.796 ***                      0.962 ***                          0.572 ***                  
##                                      (0.090)                        (0.027)                            (0.166)                     
## Manipulation                          0.032                          0.098 **                           0.008                      
##                                      (0.068)                        (0.035)                            (0.069)                     
## WP.CreativeProcessEngagementV                                                                           0.232                      
##                                                                                                        (0.144)                     
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.311                          0.841                              0.316                      
## Conditional R^2                       0.706                          0.841                              0.705                      
## AIC                                 562.142                         95.736                            563.596                      
## BIC                                 579.729                        113.323                            584.700                      
## Num. obs.                           249                            249                                249                          
## Num. groups: B.ID                   138                            138                                138                          
## Var: B.ID (Intercept)                 0.357                          0.000                              0.351                      
## Var: Residual                         0.266                          0.078                              0.266                      
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 249 (21 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WP.ObservationalLearningV" (Y)
## ─────────────────────────────────────────────────────────────
##                Effect    S.E.     z     p       [MCMC 95% CI]
## ─────────────────────────────────────────────────────────────
## Indirect (ab)   0.024 (0.016) 1.492  .136     [ 0.002, 0.058]
## Direct (c')     0.005 (0.062) 0.075  .940     [-0.102, 0.128]
## Total (c)       0.029 (0.062) 0.463  .643     [-0.079, 0.148]
## ─────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WP.AdviceSeekingV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WP.AdviceSeekingV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WP.AdviceSeekingV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WP.AdviceSeekingV  (2) WP.CreativeProcessEngagementV  (3) WP.AdviceSeekingV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.443 ***              3.245 ***                          3.443 ***          
##                                      (0.068)                (0.018)                            (0.068)             
## WP.CreativeProcessEngagementV_mean    0.642 ***              0.962 ***                          0.695 ***          
##                                      (0.101)                (0.027)                            (0.177)             
## Manipulation                         -0.034                  0.098 **                          -0.028              
##                                      (0.070)                (0.035)                            (0.071)             
## WP.CreativeProcessEngagementV                                                                  -0.055              
##                                                                                                (0.151)             
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.195                  0.841                              0.195              
## Conditional R^2                       0.699                  0.841                              0.700              
## AIC                                 598.003                 95.736                            601.813              
## BIC                                 615.591                113.323                            622.918              
## Num. obs.                           249                    249                                249                  
## Num. groups: B.ID                   138                    138                                138                  
## Var: B.ID (Intercept)                 0.473                  0.000                              0.476              
## Var: Residual                         0.282                  0.078                              0.282              
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 249 (21 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WP.AdviceSeekingV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)  -0.004 (0.015) -0.249  .803     [-0.036, 0.024]
## Direct (c')    -0.032 (0.065) -0.493  .622     [-0.142, 0.095]
## Total (c)      -0.036 (0.062) -0.575  .565     [-0.141, 0.085]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WP.PerformanceImprovementV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WP.PerformanceImprovementV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WP.PerformanceImprovementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WP.PerformanceImprovementV  (2) WP.CreativeProcessEngagementV  (3) WP.PerformanceImprovementV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.017 ***                       3.245 ***                          3.018 ***                   
##                                      (0.053)                         (0.018)                            (0.052)                      
## WP.CreativeProcessEngagementV_mean    0.916 ***                       0.962 ***                          0.521 ***                   
##                                      (0.078)                         (0.027)                            (0.152)                      
## Manipulation                          0.014                           0.098 **                          -0.027                       
##                                      (0.066)                         (0.035)                            (0.066)                      
## WP.CreativeProcessEngagementV                                                                            0.409 **                    
##                                                                                                         (0.135)                      
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.431                           0.841                              0.443                       
## Conditional R^2                       0.700                           0.841                              0.719                       
## AIC                                 520.165                          95.736                            515.363                       
## BIC                                 537.752                         113.323                            536.468                       
## Num. obs.                           249                             249                                249                           
## Num. groups: B.ID                   138                             138                                138                           
## Var: B.ID (Intercept)                 0.233                           0.000                              0.240                       
## Var: Residual                         0.260                           0.078                              0.244                       
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 249 (21 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WP.PerformanceImprovementV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)   0.041 (0.020)  2.091  .037 *   [ 0.011, 0.084]
## Direct (c')    -0.030 (0.059) -0.503  .615     [-0.131, 0.088]
## Total (c)       0.011 (0.061)  0.183  .855     [-0.101, 0.133]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WP.TakingChargeV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WP.TakingChargeV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WP.TakingChargeV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WP.TakingChargeV  (2) WP.CreativeProcessEngagementV  (3) WP.TakingChargeV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           2.868 ***             3.245 ***                          2.869 ***         
##                                      (0.055)               (0.018)                            (0.054)            
## WP.CreativeProcessEngagementV_mean    0.929 ***             0.962 ***                          0.455 ***         
##                                      (0.081)               (0.027)                            (0.130)            
## Manipulation                          0.089                 0.098 **                           0.037             
##                                      (0.051)               (0.035)                            (0.049)            
## WP.CreativeProcessEngagementV                                                                  0.492 ***         
##                                                                                               (0.106)            
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.446                 0.841                              0.465             
## Conditional R^2                       0.823                 0.841                              0.849             
## AIC                                 471.757                95.736                            456.268             
## BIC                                 489.344               113.323                            477.373             
## Num. obs.                           249                   249                                249                 
## Num. groups: B.ID                   138                   138                                138                 
## Var: B.ID (Intercept)                 0.324                 0.000                              0.332             
## Var: Residual                         0.153                 0.078                              0.130             
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 249 (21 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WP.TakingChargeV" (Y)
## ─────────────────────────────────────────────────────────────
##                Effect    S.E.     z     p       [MCMC 95% CI]
## ─────────────────────────────────────────────────────────────
## Indirect (ab)   0.049 (0.021) 2.348  .019 *   [ 0.017, 0.092]
## Direct (c')     0.035 (0.044) 0.782  .434     [-0.040, 0.121]
## Total (c)       0.083 (0.048) 1.724  .085 .   [-0.007, 0.178]
## ─────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.WorkReflectionV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.WorkReflectionV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.WorkReflectionV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.WorkReflectionV  (2) WP.CreativeProcessEngagementV  (3) WA.WorkReflectionV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.127 ***               3.242 ***                          3.127 ***           
##                                      (0.046)                 (0.018)                            (0.046)              
## WP.CreativeProcessEngagementV_mean    0.627 ***               0.959 ***                          0.434 ***           
##                                      (0.068)                 (0.027)                            (0.129)              
## Manipulation                         -0.024                   0.089 *                           -0.045               
##                                      (0.053)                 (0.035)                            (0.054)              
## WP.CreativeProcessEngagementV                                                                    0.201               
##                                                                                                 (0.113)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.334                   0.844                              0.338               
## Conditional R^2                       0.693                   0.844                              0.701               
## AIC                                 409.078                  83.367                            410.469               
## BIC                                 426.502                 100.791                            431.378               
## Num. obs.                           241                     241                                241                   
## Num. groups: B.ID                   134                     134                                134                   
## Var: B.ID (Intercept)                 0.186                   0.000                              0.188               
## Var: Residual                         0.159                   0.075                              0.155               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 241 (29 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.WorkReflectionV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)   0.019 (0.012)  1.528  .126     [ 0.001, 0.046]
## Direct (c')    -0.048 (0.048) -0.981  .326     [-0.130, 0.048]
## Total (c)      -0.029 (0.048) -0.599  .549     [-0.114, 0.066]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.PositiveWorkReflectionV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.PositiveWorkReflectionV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.PositiveWorkReflectionV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.PositiveWorkReflectionV  (2) WP.CreativeProcessEngagementV  (3) WA.PositiveWorkReflectionV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.256 ***                       3.242 ***                          3.255 ***                   
##                                      (0.058)                         (0.018)                            (0.058)                      
## WP.CreativeProcessEngagementV_mean    0.681 ***                       0.959 ***                          0.403 **                    
##                                      (0.087)                         (0.027)                            (0.155)                      
## Manipulation                         -0.078                           0.089 *                           -0.108                       
##                                      (0.062)                         (0.035)                            (0.062)                      
## WP.CreativeProcessEngagementV                                                                            0.289 *                     
##                                                                                                         (0.133)                      
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.274                           0.844                              0.279                       
## Conditional R^2                       0.710                           0.844                              0.725                       
## AIC                                 505.702                          83.367                            505.331                       
## BIC                                 523.126                         100.791                            526.239                       
## Num. obs.                           241                             241                                241                           
## Num. groups: B.ID                   134                             134                                134                           
## Var: B.ID (Intercept)                 0.325                           0.000                              0.335                       
## Var: Residual                         0.216                           0.075                              0.206                       
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 241 (29 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.PositiveWorkReflectionV" (Y)
## ───────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p        [MCMC 95% CI]
## ───────────────────────────────────────────────────────────────
## Indirect (ab)   0.027 (0.016)  1.706  .088 .   [ 0.003,  0.062]
## Direct (c')    -0.111 (0.056) -1.979  .048 *   [-0.207, -0.001]
## Total (c)      -0.085 (0.056) -1.502  .133     [-0.186,  0.027]
## ───────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.NegativeWorkReflectionV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.NegativeWorkReflectionV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.NegativeWorkReflectionV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.NegativeWorkReflectionV  (2) WP.CreativeProcessEngagementV  (3) WA.NegativeWorkReflectionV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           2.998 ***                       3.242 ***                          2.998 ***                   
##                                      (0.063)                         (0.018)                            (0.063)                      
## WP.CreativeProcessEngagementV_mean    0.571 ***                       0.959 ***                          0.449 *                     
##                                      (0.094)                         (0.027)                            (0.180)                      
## Manipulation                          0.027                           0.089 *                            0.014                       
##                                      (0.074)                         (0.035)                            (0.076)                      
## WP.CreativeProcessEngagementV                                                                            0.128                       
##                                                                                                         (0.159)                      
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.179                           0.844                              0.180                       
## Conditional R^2                       0.613                           0.844                              0.612                       
## AIC                                 565.243                          83.367                            568.438                       
## BIC                                 582.667                         100.791                            589.347                       
## Num. obs.                           241                             241                                241                           
## Num. groups: B.ID                   134                             134                                134                           
## Var: B.ID (Intercept)                 0.349                           0.000                              0.347                       
## Var: Residual                         0.310                           0.075                              0.312                       
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 241 (29 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.NegativeWorkReflectionV" (Y)
## ─────────────────────────────────────────────────────────────
##                Effect    S.E.     z     p       [MCMC 95% CI]
## ─────────────────────────────────────────────────────────────
## Indirect (ab)   0.013 (0.015) 0.851  .395     [-0.011, 0.043]
## Direct (c')     0.010 (0.069) 0.149  .881     [-0.106, 0.145]
## Total (c)       0.023 (0.067) 0.342  .732     [-0.094, 0.150]
## ─────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.RuminationV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.RuminationV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.RuminationV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.RuminationV  (2) WP.CreativeProcessEngagementV  (3) WA.RuminationV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.382 ***           3.242 ***                          3.382 ***       
##                                      (0.044)             (0.018)                            (0.044)          
## WP.CreativeProcessEngagementV_mean    0.695 ***           0.959 ***                          0.673 ***       
##                                      (0.065)             (0.027)                            (0.126)          
## Manipulation                         -0.091               0.089 *                           -0.093           
##                                      (0.052)             (0.035)                            (0.054)          
## WP.CreativeProcessEngagementV                                                                0.023           
##                                                                                             (0.112)          
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.401               0.844                              0.400           
## Conditional R^2                       0.710               0.844                              0.708           
## AIC                                 394.884              83.367                            399.384           
## BIC                                 412.308             100.791                            420.293           
## Num. obs.                           241                 241                                241               
## Num. groups: B.ID                   134                 134                                134               
## Var: B.ID (Intercept)                 0.165               0.000                              0.164           
## Var: Residual                         0.155               0.075                              0.156           
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 241 (29 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.RuminationV" (Y)
## ───────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p        [MCMC 95% CI]
## ───────────────────────────────────────────────────────────────
## Indirect (ab)   0.003 (0.010)  0.289  .773     [-0.017,  0.022]
## Direct (c')    -0.096 (0.048) -1.976  .048 *   [-0.178, -0.000]
## Total (c)      -0.093 (0.047) -1.981  .048 *   [-0.174, -0.002]
## ───────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.PositiveAffectV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.PositiveAffectV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.PositiveAffectV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.PositiveAffectV  (2) WP.CreativeProcessEngagementV  (3) WA.PositiveAffectV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.527 ***               3.242 ***                          3.527 ***           
##                                      (0.055)                 (0.018)                            (0.055)              
## WP.CreativeProcessEngagementV_mean    0.576 ***               0.959 ***                          0.505 ***           
##                                      (0.083)                 (0.027)                            (0.150)              
## Manipulation                          0.083                   0.089 *                            0.076               
##                                      (0.059)                 (0.035)                            (0.061)              
## WP.CreativeProcessEngagementV                                                                    0.074               
##                                                                                                 (0.130)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.232                   0.844                              0.232               
## Conditional R^2                       0.687                   0.844                              0.688               
## AIC                                 482.626                  83.367                            486.543               
## BIC                                 500.050                 100.791                            507.452               
## Num. obs.                           241                     241                                241                   
## Num. groups: B.ID                   134                     134                                134                   
## Var: B.ID (Intercept)                 0.289                   0.000                              0.291               
## Var: Residual                         0.199                   0.075                              0.199               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 241 (29 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.PositiveAffectV" (Y)
## ─────────────────────────────────────────────────────────────
##                Effect    S.E.     z     p       [MCMC 95% CI]
## ─────────────────────────────────────────────────────────────
## Indirect (ab)   0.008 (0.012) 0.645  .519     [-0.013, 0.031]
## Direct (c')     0.073 (0.055) 1.316  .188     [-0.021, 0.181]
## Total (c)       0.080 (0.054) 1.501  .133     [-0.014, 0.182]
## ─────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.NegativeAffectV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.NegativeAffectV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.NegativeAffectV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.NegativeAffectV  (2) WP.CreativeProcessEngagementV  (3) WA.NegativeAffectV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           2.004 ***               3.242 ***                          2.004 ***           
##                                      (0.053)                 (0.018)                            (0.053)              
## WP.CreativeProcessEngagementV_mean   -0.018                   0.959 ***                          0.096               
##                                      (0.080)                 (0.027)                            (0.148)              
## Manipulation                          0.048                   0.089 *                            0.060               
##                                      (0.060)                 (0.035)                            (0.061)              
## WP.CreativeProcessEngagementV                                                                   -0.119               
##                                                                                                 (0.129)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.002                   0.844                              0.004               
## Conditional R^2                       0.568                   0.844                              0.568               
## AIC                                 474.555                  83.367                            477.962               
## BIC                                 491.979                 100.791                            498.871               
## Num. obs.                           241                     241                                241                   
## Num. groups: B.ID                   134                     134                                134                   
## Var: B.ID (Intercept)                 0.262                   0.000                              0.262               
## Var: Residual                         0.200                   0.075                              0.201               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 241 (29 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.NegativeAffectV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)  -0.009 (0.013) -0.723  .470     [-0.038, 0.013]
## Direct (c')     0.057 (0.055)  1.039  .299     [-0.036, 0.166]
## Total (c)       0.048 (0.053)  0.906  .365     [-0.042, 0.149]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.ImprovisionV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.ImprovisionV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.ImprovisionV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.ImprovisionV  (2) WP.CreativeProcessEngagementV  (3) WA.ImprovisionV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.405 ***            3.242 ***                          3.405 ***        
##                                      (0.038)              (0.018)                            (0.038)           
## WP.CreativeProcessEngagementV_mean    0.623 ***            0.959 ***                          0.700 ***        
##                                      (0.057)              (0.027)                            (0.109)           
## Manipulation                          0.058                0.089 *                            0.067            
##                                      (0.045)              (0.035)                            (0.046)           
## WP.CreativeProcessEngagementV                                                                -0.081            
##                                                                                              (0.097)           
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.417                0.844                              0.417            
## Conditional R^2                       0.716                0.844                              0.716            
## AIC                                 326.478               83.367                            330.615            
## BIC                                 343.902              100.791                            351.524            
## Num. obs.                           241                  241                                241                
## Num. groups: B.ID                   134                  134                                134                
## Var: B.ID (Intercept)                 0.123                0.000                              0.123            
## Var: Residual                         0.117                0.075                              0.117            
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 241 (29 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.ImprovisionV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)  -0.006 (0.010) -0.656  .512     [-0.027, 0.010]
## Direct (c')     0.064 (0.042)  1.537  .124     [-0.007, 0.147]
## Total (c)       0.058 (0.040)  1.444  .149     [-0.010, 0.136]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.WorkAbsorptionV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.WorkAbsorptionV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.WorkAbsorptionV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.WorkAbsorptionV  (2) WP.CreativeProcessEngagementV  (3) WA.WorkAbsorptionV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.292 ***               3.242 ***                          3.292 ***           
##                                      (0.052)                 (0.018)                            (0.052)              
## WP.CreativeProcessEngagementV_mean    0.702 ***               0.959 ***                          0.682 ***           
##                                      (0.079)                 (0.027)                            (0.147)              
## Manipulation                          0.030                   0.089 *                            0.028               
##                                      (0.060)                 (0.035)                            (0.061)              
## WP.CreativeProcessEngagementV                                                                    0.021               
##                                                                                                 (0.129)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.324                   0.844                              0.323               
## Conditional R^2                       0.700                   0.844                              0.699               
## AIC                                 470.826                  83.367                            475.056               
## BIC                                 488.250                 100.791                            495.965               
## Num. obs.                           241                     241                                241                   
## Num. groups: B.ID                   134                     134                                134                   
## Var: B.ID (Intercept)                 0.251                   0.000                              0.251               
## Var: Residual                         0.200                   0.075                              0.202               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 241 (29 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.WorkAbsorptionV" (Y)
## ─────────────────────────────────────────────────────────────
##                Effect    S.E.     z     p       [MCMC 95% CI]
## ─────────────────────────────────────────────────────────────
## Indirect (ab)   0.003 (0.012) 0.252  .801     [-0.021, 0.025]
## Direct (c')     0.025 (0.055) 0.444  .657     [-0.069, 0.133]
## Total (c)       0.028 (0.053) 0.516  .606     [-0.065, 0.132]
## ─────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.ThrivingAtWorkLearningV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.ThrivingAtWorkLearningV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.ThrivingAtWorkLearningV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.ThrivingAtWorkLearningV  (2) WP.CreativeProcessEngagementV  (3) WA.ThrivingAtWorkLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.295 ***                       3.242 ***                          3.295 ***                   
##                                      (0.051)                         (0.018)                            (0.051)                      
## WP.CreativeProcessEngagementV_mean    0.662 ***                       0.959 ***                          0.608 ***                   
##                                      (0.077)                         (0.027)                            (0.155)                      
## Manipulation                          0.037                           0.089 *                            0.031                       
##                                      (0.066)                         (0.035)                            (0.068)                      
## WP.CreativeProcessEngagementV                                                                            0.056                       
##                                                                                                         (0.140)                      
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.296                           0.844                              0.296                       
## Conditional R^2                       0.611                           0.844                              0.609                       
## AIC                                 489.519                          83.367                            493.451                       
## BIC                                 506.943                         100.791                            514.360                       
## Num. obs.                           241                             241                                241                           
## Num. groups: B.ID                   134                             134                                134                           
## Var: B.ID (Intercept)                 0.204                           0.000                              0.204                       
## Var: Residual                         0.253                           0.075                              0.254                       
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 241 (29 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.ThrivingAtWorkLearningV" (Y)
## ─────────────────────────────────────────────────────────────
##                Effect    S.E.     z     p       [MCMC 95% CI]
## ─────────────────────────────────────────────────────────────
## Indirect (ab)   0.006 (0.013) 0.482  .630     [-0.018, 0.031]
## Direct (c')     0.028 (0.061) 0.455  .649     [-0.076, 0.149]
## Total (c)       0.034 (0.060) 0.572  .567     [-0.071, 0.149]
## ─────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.WorkRelatedFlowV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.WorkRelatedFlowV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.WorkRelatedFlowV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.WorkRelatedFlowV  (2) WP.CreativeProcessEngagementV  (3) WA.WorkRelatedFlowV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.065 ***                3.242 ***                          3.065 ***            
##                                      (0.059)                  (0.018)                            (0.059)               
## WP.CreativeProcessEngagementV_mean    0.662 ***                0.959 ***                          0.659 ***            
##                                      (0.089)                  (0.027)                            (0.160)               
## Manipulation                         -0.044                    0.089 *                           -0.045                
##                                      (0.063)                  (0.035)                            (0.065)               
## WP.CreativeProcessEngagementV                                                                     0.003                
##                                                                                                  (0.138)               
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.254                    0.844                              0.253                
## Conditional R^2                       0.708                    0.844                              0.706                
## AIC                                 514.767                   83.367                            518.890                
## BIC                                 532.191                  100.791                            539.799                
## Num. obs.                           241                      241                                241                    
## Num. groups: B.ID                   134                      134                                134                    
## Var: B.ID (Intercept)                 0.344                    0.000                              0.344                
## Var: Residual                         0.222                    0.075                              0.223                
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 241 (29 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.WorkRelatedFlowV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)   0.001 (0.013)  0.116  .908     [-0.025, 0.025]
## Direct (c')    -0.048 (0.059) -0.818  .414     [-0.147, 0.067]
## Total (c)      -0.046 (0.056) -0.824  .410     [-0.143, 0.064]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.InspirationV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.InspirationV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.InspirationV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.InspirationV  (2) WP.CreativeProcessEngagementV  (3) WA.InspirationV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.199 ***            3.242 ***                          3.199 ***        
##                                      (0.063)              (0.018)                            (0.063)           
## WP.CreativeProcessEngagementV_mean    0.715 ***            0.959 ***                          0.938 ***        
##                                      (0.094)              (0.027)                            (0.165)           
## Manipulation                         -0.079                0.089 *                           -0.054            
##                                      (0.064)              (0.035)                            (0.066)           
## WP.CreativeProcessEngagementV                                                                -0.232            
##                                                                                              (0.141)           
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.265                0.844                              0.269            
## Conditional R^2                       0.727                0.844                              0.732            
## AIC                                 533.776               83.367                            535.166            
## BIC                                 551.200              100.791                            556.075            
## Num. obs.                           241                  241                                241                
## Num. groups: B.ID                   134                  134                                134                
## Var: B.ID (Intercept)                 0.393                0.000                              0.394            
## Var: Residual                         0.232                0.075                              0.228            
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 241 (29 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.InspirationV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)  -0.019 (0.016) -1.181  .237     [-0.057, 0.004]
## Direct (c')    -0.057 (0.059) -0.969  .333     [-0.158, 0.059]
## Total (c)      -0.077 (0.057) -1.352  .176     [-0.172, 0.029]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.GraceV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.GraceV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.GraceV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ───────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.GraceV  (2) WP.CreativeProcessEngagementV  (3) WA.GraceV
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           3.831 ***      3.242 ***                          3.831 ***  
##                                      (0.057)        (0.018)                            (0.057)     
## WP.CreativeProcessEngagementV_mean    0.460 ***      0.959 ***                          0.368 *    
##                                      (0.085)        (0.027)                            (0.146)     
## Manipulation                          0.023          0.089 *                            0.013      
##                                      (0.056)        (0.035)                            (0.057)     
## WP.CreativeProcessEngagementV                                                           0.096      
##                                                                                        (0.123)     
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.157          0.844                              0.157      
## Conditional R^2                       0.709          0.844                              0.711      
## AIC                                 475.075         83.367                            478.824      
## BIC                                 492.499        100.791                            499.732      
## Num. obs.                           241            241                                241          
## Num. groups: B.ID                   134            134                                134          
## Var: B.ID (Intercept)                 0.328          0.000                              0.330      
## Var: Residual                         0.173          0.075                              0.172      
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 241 (29 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.GraceV" (Y)
## ─────────────────────────────────────────────────────────────
##                Effect    S.E.     z     p       [MCMC 95% CI]
## ─────────────────────────────────────────────────────────────
## Indirect (ab)   0.010 (0.011) 0.829  .407     [-0.009, 0.033]
## Direct (c')     0.010 (0.052) 0.199  .843     [-0.077, 0.111]
## Total (c)       0.020 (0.050) 0.394  .694     [-0.068, 0.115]
## ─────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.ExerciseV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.ExerciseV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.ExerciseV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.ExerciseV  (2) WP.CreativeProcessEngagementV  (3) WA.ExerciseV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           1.257 ***         3.242 ***                          1.257 ***     
##                                      (0.053)           (0.018)                            (0.053)        
## WP.CreativeProcessEngagementV_mean    0.272 ***         0.959 ***                          0.235 *       
##                                      (0.080)           (0.027)                            (0.111)        
## Manipulation                         -0.084 *           0.089 *                           -0.088 *       
##                                      (0.034)           (0.035)                            (0.036)        
## WP.CreativeProcessEngagementV                                                              0.039         
##                                                                                           (0.080)        
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.078             0.844                              0.078         
## Conditional R^2                       0.853             0.844                              0.851         
## AIC                                 354.929            83.367                            359.909         
## BIC                                 372.353           100.791                            380.817         
## Num. obs.                           241               241                                241             
## Num. groups: B.ID                   134               134                                134             
## Var: B.ID (Intercept)                 0.342             0.000                              0.340         
## Var: Residual                         0.065             0.075                              0.065         
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 241 (29 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.ExerciseV" (Y)
## ───────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p        [MCMC 95% CI]
## ───────────────────────────────────────────────────────────────
## Indirect (ab)   0.004 (0.007)  0.563  .574     [-0.009,  0.018]
## Direct (c')    -0.090 (0.033) -2.776  .006 **  [-0.145, -0.027]
## Total (c)      -0.086 (0.031) -2.759  .006 **  [-0.141, -0.027]
## ───────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.SleepQualityV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.SleepQualityV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.SleepQualityV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.SleepQualityV  (2) WP.CreativeProcessEngagementV  (3) WA.SleepQualityV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           2.443 ***             3.242 ***                          2.443 ***         
##                                      (0.081)               (0.018)                            (0.081)            
## WP.CreativeProcessEngagementV_mean    0.280 *               0.959 ***                          0.106             
##                                      (0.121)               (0.027)                            (0.205)            
## Manipulation                         -0.172 *               0.089 *                           -0.192 *           
##                                      (0.078)               (0.035)                            (0.080)            
## WP.CreativeProcessEngagementV                                                                  0.181             
##                                                                                               (0.173)            
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.040                 0.844                              0.042             
## Conditional R^2                       0.679                 0.844                              0.682             
## AIC                                 639.257                83.367                            641.835             
## BIC                                 656.681               100.791                            662.744             
## Num. obs.                           241                   241                                241                 
## Num. groups: B.ID                   134                   134                                134                 
## Var: B.ID (Intercept)                 0.672                 0.000                              0.676             
## Var: Residual                         0.337                 0.075                              0.335             
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 241 (29 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.SleepQualityV" (Y)
## ───────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p        [MCMC 95% CI]
## ───────────────────────────────────────────────────────────────
## Indirect (ab)   0.017 (0.017)  1.055  .292     [-0.006,  0.052]
## Direct (c')    -0.196 (0.072) -2.710  .007 **  [-0.318, -0.055]
## Total (c)      -0.178 (0.070) -2.531  .011 *   [-0.301, -0.043]
## ───────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.ReadingV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.ReadingV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.ReadingV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.ReadingV  (2) WP.CreativeProcessEngagementV  (3) WA.ReadingV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           1.406 ***        3.235 ***                          1.406 ***    
##                                      (0.123)          (0.018)                            (0.123)       
## WP.CreativeProcessEngagementV_mean    0.367 *          0.958 ***                          0.437        
##                                      (0.184)          (0.027)                            (0.272)       
## Manipulation                         -0.163            0.089 *                           -0.156        
##                                      (0.091)          (0.036)                            (0.094)       
## WP.CreativeProcessEngagementV                                                            -0.073        
##                                                                                          (0.208)       
## ───────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.029            0.842                              0.029        
## Conditional R^2                       0.805            0.842                              0.803        
## AIC                                 760.718           85.959                            763.899        
## BIC                                 778.015          103.257                            784.656        
## Num. obs.                           235              235                                235            
## Num. groups: B.ID                   133              133                                133            
## Var: B.ID (Intercept)                 1.744            0.000                              1.743        
## Var: Residual                         0.440            0.076                              0.443        
## ───────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 235 (35 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.ReadingV" (Y)
## ───────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p        [MCMC 95% CI]
## ───────────────────────────────────────────────────────────────
## Indirect (ab)  -0.005 (0.019) -0.241  .809     [-0.045,  0.030]
## Direct (c')    -0.161 (0.086) -1.876  .061 .   [-0.304,  0.006]
## Total (c)      -0.165 (0.082) -2.018  .044 *   [-0.303, -0.009]
## ───────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.PaperReadV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.PaperReadV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.PaperReadV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.PaperReadV  (2) WP.CreativeProcessEngagementV  (3) WA.PaperReadV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           0.315 ***          3.235 ***                          0.315 ***      
##                                      (0.052)            (0.018)                            (0.052)         
## WP.CreativeProcessEngagementV_mean    0.041              0.958 ***                          0.133          
##                                      (0.078)            (0.027)                            (0.128)         
## Manipulation                         -0.060              0.089 *                           -0.051          
##                                      (0.048)            (0.036)                            (0.049)         
## WP.CreativeProcessEngagementV                                                              -0.096          
##                                                                                            (0.105)         
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.004              0.842                              0.006          
## Conditional R^2                       0.705              0.842                              0.707          
## AIC                                 404.878             85.959                            408.713          
## BIC                                 422.176            103.257                            429.470          
## Num. obs.                           235                235                                235              
## Num. groups: B.ID                   133                133                                133              
## Var: B.ID (Intercept)                 0.287              0.000                              0.288          
## Var: Residual                         0.121              0.076                              0.121          
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 235 (35 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.PaperReadV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)  -0.008 (0.011) -0.711  .477     [-0.031, 0.011]
## Direct (c')    -0.053 (0.044) -1.203  .229     [-0.128, 0.033]
## Total (c)      -0.061 (0.042) -1.435  .151     [-0.132, 0.020]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.EReadV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.EReadV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.EReadV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ───────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.EReadV  (2) WP.CreativeProcessEngagementV  (3) WA.EReadV
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           1.088 ***      3.235 ***                          1.088 ***  
##                                      (0.097)        (0.018)                            (0.097)     
## WP.CreativeProcessEngagementV_mean    0.329 *        0.958 ***                          0.304      
##                                      (0.145)        (0.027)                            (0.220)     
## Manipulation                         -0.106          0.089 *                           -0.109      
##                                      (0.076)        (0.036)                            (0.079)     
## WP.CreativeProcessEngagementV                                                           0.026      
##                                                                                        (0.173)     
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.035          0.842                              0.035      
## Conditional R^2                       0.785          0.842                              0.784      
## AIC                                 662.460         85.959                            666.118      
## BIC                                 679.758        103.257                            686.875      
## Num. obs.                           235            235                                235          
## Num. groups: B.ID                   133            133                                133          
## Var: B.ID (Intercept)                 1.070          0.000                              1.070      
## Var: Residual                         0.306          0.076                              0.309      
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 235 (35 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.EReadV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)   0.004 (0.016)  0.236  .814     [-0.028, 0.034]
## Direct (c')    -0.113 (0.071) -1.583  .113     [-0.232, 0.026]
## Total (c)      -0.109 (0.069) -1.592  .111     [-0.227, 0.023]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.SleepQuantityV", x="Manipulation", meds="WP.CreativeProcessEngagementV", covs=c("WP.CreativeProcessEngagementV_mean"), cluster ="B.ID",ci="mcmc", nsim=100, seed=1223)

## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## -    Outcome (Y) : WA.SleepQuantityV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.CreativeProcessEngagementV
## - Moderators (W) : -
## - Covariates (C) : WP.CreativeProcessEngagementV_mean
## -   HLM Clusters : B.ID
## 
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
## 
## Formula of Mediator:
## -    WP.CreativeProcessEngagementV ~ WP.CreativeProcessEngagementV_mean + Manipulation + (1 | B.ID)
## Formula of Outcome:
## -    WA.SleepQuantityV ~ WP.CreativeProcessEngagementV_mean + Manipulation + WP.CreativeProcessEngagementV + (1 | B.ID)
## 
## CAUTION:
##   Fixed effect (coef.) of a predictor involved in an interaction
##   denotes its "simple effect/slope" at the other predictor = 0.
##   Only when all predictors in an interaction are mean-centered
##   can the fixed effect denote the "main effect"!
##   
## Model Summary
## 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##                                     (1) WA.SleepQuantityV  (2) WP.CreativeProcessEngagementV  (3) WA.SleepQuantityV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                           6.852 ***              3.271 ***                          6.854 ***          
##                                      (0.090)                (0.018)                            (0.089)             
## WP.CreativeProcessEngagementV_mean   -0.066                  0.962 ***                          0.284              
##                                      (0.133)                (0.026)                            (0.290)             
## Manipulation                          0.092                  0.092 **                           0.128              
##                                      (0.124)                (0.035)                            (0.126)             
## WP.CreativeProcessEngagementV                                                                  -0.363              
##                                                                                                (0.268)             
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.003                  0.864                              0.010              
## Conditional R^2                       0.395                  0.864                              0.397              
## AIC                                 668.021                 51.866                            668.980              
## BIC                                 684.898                 68.743                            689.232              
## Num. obs.                           216                    216                                216                  
## Num. groups: B.ID                   120                    120                                120                  
## Var: B.ID (Intercept)                 0.512                  0.000                              0.505              
## Var: Residual                         0.788                  0.067                              0.788              
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 216 (54 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WA.SleepQuantityV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)  -0.031 (0.030) -1.030  .303     [-0.098, 0.015]
## Direct (c')     0.122 (0.114)  1.071  .284     [-0.072, 0.347]
## Total (c)       0.091 (0.109)  0.835  .404     [-0.094, 0.300]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)