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\\Rtmpc5V5JN"

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)               3.22 ***     3.09 ***     3.41 ***     3.29 ***     2.86 ***
##                          (0.06)       (0.07)       (0.06)       (0.08)       (0.08)   
## Manipulation              0.11 **      0.08         0.06        -0.05         0.02    
##                          (0.05)       (0.05)       (0.05)       (0.06)       (0.05)   
## --------------------------------------------------------------------------------------
## AIC                     470.57       545.06       500.33       640.40       573.62    
## BIC                     484.70       559.19       514.73       654.79       587.75    
## Log Likelihood         -231.28      -268.53      -246.17      -316.20      -282.81    
## Num. obs.               253          253          270          270          253       
## Num. groups: B.ID       140          140          147          147          140       
## Var: B.ID (Intercept)     0.41         0.58         0.28         0.54         0.66    
## Var: Residual             0.12         0.16         0.17         0.25         0.17    
## ======================================================================================
## *** 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 49.5
## 1  139 50.5
## ───────────
## Total N = 275

Freq(data$W.Day)

## Frequency Statistics:
## ────────────
##       N    %
## ────────────
## 11  136 49.5
## 14  139 50.5
## ────────────
## Total N = 275

1.3.1 ICC and RWG

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

## 
## ------ Sample Size Information ------
## 
## Level 1: N = 275 observations ("Manipulation")
## Level 2: K = 147 groups ("B.ID")
## 
##        n (group sizes)
## Min.             1.000
## Median           2.000
## Mean             1.871
## 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 19.000
## ────────────────────────────────────────────────────

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

## 
## ------ Sample Size Information ------
## 
## Level 1: N = 253 observations ("WP.InformationSearchV")
## Level 2: K = 140 groups ("B.ID")
## 
##        n (group sizes)
## Min.             1.000
## Median           2.000
## Mean             1.807
## Max.             2.000
## 
## ------ ICC(1), ICC(2), and rWG ------
## 
## ICC variable: "WP.InformationSearchV"
## 
## ICC(1) = 0.670 (non-independence of data)
## ICC(2) = 0.777 (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  1.000 0.850   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 = 270
## Level-2 Groups/Clusters: B.ID, 147
## 
## Model Fit:
## AIC = 519.916
## BIC = 534.309
## R_(m)² = 0.00190  (Marginal R²: fixed effects)
## R_(c)² = 0.77774  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.28    0.28  1.00 124.97 2.14  .146    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.WorkReflectionV
## ──────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ──────────────────────────────────────────────────────────────────
## (Intercept)    3.152 (0.064) 49.18 184.3 <.001 *** [ 3.026, 3.279]
## Manipulation  -0.066 (0.045) -1.46 125.0  .146     [-0.156, 0.023]
## ──────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.WorkReflectionV
## ──────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p       [95% CI of β]
## ──────────────────────────────────────────────────────────────────
## Manipulation  -0.044 (0.030) -1.46 125.0  .146     [-0.104, 0.016]
## ──────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     147 (Intercept)  0.45190 0.77732
##  Residual                  0.12946        
## ──────────────────────────────────────────

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"))

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                            -0.016                                     0.013                                    -0.039               -0.053           0.021                         0.050                     0.075                 0.080              0.005              0.012                     0.011             0.011                     -0.063            0.099             -0.088                     -0.096                     -0.049          0.011              0.104             -0.107           0.070             -0.047                      0.002              -0.099          -0.014    -0.064       -0.005           -0.021      -0.022         0.005    -0.031            -0.019
## WP.SupervisoryBehavioralFeedbackV                 0.036                             1.000                                     0.926                                     0.944                0.473           0.431                         0.525                     0.445                 0.319              0.544              0.333                     0.343             0.241                      0.467            0.426              0.576                      0.591                      0.358          0.627              0.510             -0.095           0.598              0.522                      0.584               0.502           0.565     0.461        0.106           -0.007       0.164         0.112     0.145             0.022
## WP.SupervisoryPositiveBehavioralFeedbackV         0.113                             0.681                                     1.000                                     0.750                0.533           0.450                         0.504                     0.437                 0.281              0.530              0.312                     0.316             0.234                      0.482            0.453              0.530                      0.594                      0.281          0.633              0.554             -0.139           0.654              0.508                      0.609               0.526           0.565     0.439        0.112           -0.012       0.127         0.100     0.104            -0.008
## WP.SupervisoryNegativeBehavioralFeedbackV        -0.056                             0.737                                     0.008                                     1.000                0.364           0.363                         0.480                     0.398                 0.314              0.490              0.311                     0.326             0.217                      0.398            0.352              0.546                      0.517                      0.382          0.546              0.409             -0.046           0.477              0.470                      0.492               0.420           0.496     0.424        0.088           -0.001       0.176         0.109     0.164             0.045
## WP.learningBehaviorV                              0.067                            -0.024                                    -0.025                                    -0.010                1.000           0.637                         0.631                     0.595                 0.447              0.584              0.501                     0.491             0.401                      0.598            0.631              0.610                      0.650                      0.357          0.712              0.510             -0.036           0.680              0.567                      0.702               0.610           0.706     0.425        0.172            0.053       0.111         0.035     0.120            -0.084
## WP.JobCraftingV                                   0.044                             0.091                                     0.141                                    -0.007                0.188           1.000                         0.623                     0.587                 0.406              0.596              0.577                     0.603             0.405                      0.662            0.697              0.666                      0.579                      0.517          0.618              0.520             -0.034           0.638              0.521                      0.554               0.529           0.568     0.306        0.260            0.031       0.220         0.202     0.170             0.049
## WP.CreativeProcessEngagementV                     0.211                             0.152                                     0.065                                     0.147                0.342           0.313                         1.000                     0.855                 0.793              0.928              0.587                     0.581             0.462                      0.691            0.691              0.625                      0.568                      0.461          0.713              0.595             -0.021           0.723              0.598                      0.627               0.581           0.590     0.449        0.211            0.113       0.219         0.166     0.188            -0.028
## WP.ProblemIdentificationV                         0.240                             0.047                                    -0.003                                     0.067                0.300           0.397                         0.736                     1.000                 0.575              0.698              0.566                     0.559             0.447                      0.636            0.618              0.535                      0.523                      0.358          0.656              0.509              0.018           0.641              0.540                      0.557               0.532           0.543     0.309        0.232            0.097       0.169         0.137     0.141            -0.090
## WP.InformationSearchV                             0.101                             0.060                                     0.023                                     0.061                0.209           0.230                         0.710                     0.325                 1.000              0.584              0.505                     0.421             0.511                      0.443            0.439              0.414                      0.356                      0.326          0.490              0.435             -0.050           0.473              0.413                      0.396               0.330           0.361     0.443        0.128            0.211       0.230         0.170     0.203            -0.077
## WP.IdeaGenerationV                                0.127                             0.213                                     0.114                                     0.185                0.254           0.093                         0.793                     0.317                 0.383              1.000              0.486                     0.526             0.317                      0.675            0.687              0.624                      0.559                      0.469          0.677              0.572             -0.022           0.715              0.573                      0.627               0.593           0.586     0.410        0.189            0.035       0.182         0.135     0.156             0.042
## WP.SocialLearningV                                0.065                             0.133                                     0.106                                     0.083                0.172           0.211                         0.423                     0.299                 0.193              0.424              1.000                     0.937             0.861                      0.681            0.606              0.484                      0.457                      0.340          0.478              0.377              0.073           0.446              0.334                      0.504               0.309           0.537     0.432        0.210            0.152       0.282         0.155     0.273            -0.000
## WP.ObservationalLearningV                         0.087                            -0.011                                     0.013                                    -0.026                0.089           0.227                         0.379                     0.332                 0.204              0.302              0.856                     1.000             0.630                      0.663            0.620              0.524                      0.494                      0.369          0.479              0.404              0.126           0.472              0.378                      0.553               0.362           0.554     0.376        0.199            0.106       0.266         0.162     0.250            -0.004
## WP.AdviceSeekingV                                 0.005                             0.262                                     0.182                                     0.190                0.202           0.091                         0.282                     0.114                 0.088              0.388              0.723                     0.262             1.000                      0.552            0.447              0.314                      0.299                      0.219          0.366              0.250             -0.021           0.307              0.193                      0.316               0.162           0.388     0.416        0.178            0.184       0.240         0.110     0.244             0.005
## WP.PerformanceImprovementV                       -0.068                            -0.096                                    -0.051                                    -0.083                0.210           0.273                         0.224                     0.129                 0.198              0.182              0.219                     0.265             0.055                      1.000            0.705              0.536                      0.490                      0.393          0.569              0.464             -0.017           0.608              0.438                      0.596               0.456           0.629     0.335        0.221            0.145       0.286         0.216     0.247             0.017
## WP.TakingChargeV                                  0.024                             0.078                                     0.022                                     0.086                0.255           0.163                         0.217                     0.205                 0.143              0.141              0.171                     0.165             0.099                      0.299            1.000              0.556                      0.546                      0.370          0.609              0.518             -0.018           0.631              0.451                      0.578               0.515           0.645     0.277        0.247            0.012       0.253         0.170     0.230            -0.021
## WA.WorkReflectionV                               -0.121                             0.179                                     0.060                                     0.187                0.207           0.238                         0.129                     0.151                -0.097              0.185              0.045                     0.029             0.046                      0.052            0.190              1.000                      0.823                      0.827          0.721              0.480              0.010           0.593              0.569                      0.647               0.595           0.609     0.477        0.262            0.021       0.192         0.178     0.149             0.146
## WA.PositiveWorkReflectionV                       -0.190                             0.093                                     0.029                                     0.100                0.273           0.254                         0.165                     0.204                 0.024              0.119              0.048                     0.038             0.038                      0.148            0.185              0.676                      1.000                      0.362          0.762              0.627             -0.084           0.591              0.691                      0.702               0.723           0.679     0.502        0.218            0.040       0.211         0.189     0.168             0.137
## WA.NegativeWorkReflectionV                        0.007                             0.157                                     0.054                                     0.162                0.032           0.092                         0.025                     0.019                -0.152              0.142              0.017                     0.004             0.027                     -0.064            0.089              0.744                      0.011                      1.000          0.430              0.168              0.100           0.388              0.250                      0.367               0.260           0.328     0.287        0.215           -0.005       0.106         0.106     0.079             0.105
## WA.RuminationV                                   -0.178                             0.251                                     0.181                                     0.176                0.240           0.111                         0.249                     0.205                 0.022              0.284              0.195                     0.175             0.129                      0.043            0.086              0.424                      0.347                      0.261          1.000              0.642             -0.156           0.702              0.669                      0.690               0.656           0.693     0.533        0.203            0.043       0.243         0.181     0.215             0.062
## WA.PositiveAffectV                                0.061                             0.171                                     0.036                                     0.199                0.027           0.142                         0.093                     0.139                 0.029              0.033              0.184                     0.118             0.184                     -0.147            0.036              0.183                      0.140                      0.122          0.242              1.000             -0.223           0.717              0.746                      0.557               0.698           0.571     0.395        0.180           -0.079       0.140         0.157     0.097             0.023
## WA.NegativeAffectV                               -0.012                             0.086                                     0.107                                     0.021               -0.064           0.002                         0.068                     0.031                 0.038              0.077             -0.013                    -0.030             0.016                      0.017           -0.024              0.134                     -0.014                      0.195          0.083             -0.060              1.000          -0.114             -0.097                     -0.019              -0.070          -0.073    -0.108       -0.069            0.136       0.096         0.104     0.067            -0.033
## WA.ImprovisionV                                   0.105                             0.374                                     0.196                                     0.329                0.060           0.198                         0.168                     0.209                 0.023              0.122              0.066                     0.023             0.094                     -0.044            0.132              0.382                      0.225                      0.314          0.256              0.272              0.132           1.000              0.632                      0.673               0.653           0.727     0.379        0.161           -0.030       0.144         0.149     0.104            -0.104
## WA.WorkAbsorptionV                               -0.061                             0.101                                     0.033                                     0.106                0.030           0.182                         0.011                     0.004                 0.061             -0.026             -0.104                    -0.130            -0.020                      0.005            0.050              0.218                      0.196                      0.118          0.294              0.201             -0.049           0.120              1.000                      0.597               0.820           0.577     0.341        0.160            0.073       0.148         0.098     0.138             0.066
## WA.ThrivingAtWorkLearningV                        0.213                             0.210                                     0.129                                     0.168                0.129           0.219                         0.091                     0.226                -0.010             -0.021              0.141                     0.169             0.037                      0.008            0.117              0.322                      0.197                      0.258          0.272              0.307             -0.029           0.390              0.317                      1.000               0.639           0.816     0.456        0.156            0.046       0.220         0.158     0.193             0.142
## WA.WorkRelatedFlowV                              -0.115                             0.204                                     0.090                                     0.195                0.035           0.021                        -0.071                    -0.107                -0.020             -0.027             -0.009                    -0.020             0.011                      0.094            0.240              0.287                      0.317                      0.102          0.267              0.131             -0.015           0.101              0.547                      0.310               1.000           0.618     0.349        0.238           -0.119       0.140         0.141     0.106             0.153
## WA.InspirationV                                   0.077                             0.131                                     0.102                                     0.086                0.112           0.127                         0.106                     0.112                 0.104              0.030             -0.009                     0.059            -0.096                      0.123            0.021              0.244                      0.163                      0.183          0.265              0.179             -0.049           0.325              0.224                      0.604               0.239           1.000     0.452        0.134           -0.054       0.243         0.220     0.190             0.103
## WA.GraceV                                         0.056                             0.002                                    -0.040                                     0.038                0.207          -0.064                         0.117                     0.038                 0.061              0.150              0.084                     0.037             0.107                      0.079            0.118              0.065                      0.064                      0.029          0.286              0.037              0.081           0.113              0.138                      0.100               0.009           0.017     1.000        0.047            0.079       0.184         0.155     0.152             0.101
## WA.ExerciseV                                     -0.334                             0.197                                     0.028                                     0.240               -0.045          -0.057                        -0.148                    -0.124                -0.057             -0.135             -0.089                    -0.095            -0.039                      0.091           -0.076             -0.042                      0.043                     -0.096          0.011              0.107              0.023          -0.108              0.120                      0.026               0.138           0.013    -0.043        1.000           -0.027       0.193         0.306     0.085            -0.007
## WA.SleepQualityV                                  0.031                             0.042                                    -0.060                                     0.111                0.035           0.146                         0.213                     0.235                 0.149              0.093              0.068                     0.108            -0.019                      0.216            0.135              0.182                      0.122                      0.137          0.122              0.100             -0.026           0.200              0.071                      0.304               0.107           0.243     0.027        0.010            1.000       0.134         0.146     0.093            -0.235
## WA.ReadingV                                      -0.227                             0.172                                    -0.038                                     0.266               -0.065           0.180                        -0.010                    -0.164                -0.071              0.182              0.061                     0.029             0.076                      0.027            0.037              0.155                      0.042                      0.171          0.129              0.132             -0.054           0.016              0.037                      0.071               0.214           0.178    -0.073        0.199            0.078       1.000         0.669     0.920            -0.040
## WA.PaperReadV                                    -0.043                             0.047                                    -0.081                                     0.137               -0.013           0.085                         0.031                    -0.057                 0.150              0.001              0.170                     0.189             0.064                      0.038            0.004             -0.025                     -0.030                     -0.008          0.048              0.086             -0.029          -0.108             -0.036                      0.020               0.094           0.088    -0.075        0.203           -0.033       0.568         1.000     0.326            -0.092
## WA.EReadV                                        -0.238                             0.174                                     0.001                                     0.233               -0.057           0.167                        -0.026                    -0.148                -0.180              0.217             -0.030                    -0.073             0.043                      0.011            0.042              0.174                      0.050                      0.192          0.137              0.108             -0.048           0.073              0.060                      0.065               0.194           0.163    -0.034        0.122            0.114       0.873         0.098     1.000            -0.000
## WA.SleepQuantityV                                 0.063                            -0.037                                    -0.016                                    -0.036               -0.043          -0.202                        -0.101                    -0.188                 0.088             -0.088             -0.197                    -0.232            -0.068                     -0.226           -0.123             -0.064                     -0.121                      0.022         -0.160             -0.187              0.152          -0.202              0.079                     -0.167               0.032          -0.233    -0.036        0.057           -0.377      -0.132        -0.135    -0.095             1.000
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 
## Within-Level Correlation [95% CI]:
## ────────────────────────────────────────────────────────
##                             r         [95% CI]     p    
## ────────────────────────────────────────────────────────
## Mnpl.-WP.SB             0.036 [-0.088,  0.158]  .574    
## Mnpl.-WP.SP             0.113 [-0.011,  0.233]  .074 .  
## Mnpl.-WP.SN            -0.056 [-0.178,  0.068]  .380    
## Mnpl.-WP.BV             0.067 [-0.056,  0.189]  .287    
## Mnpl.-WP.JC             0.044 [-0.080,  0.166]  .491    
## Mnpl.-WP.CP             0.211 [ 0.090,  0.326] <.001 ***
## Mnpl.-WP.PrbIV.         0.240 [ 0.121,  0.353] <.001 ***
## Mnpl.-WP.IS             0.101 [-0.023,  0.221]  .112    
## Mnpl.-WP.IG             0.127 [ 0.004,  0.247]  .044 *  
## Mnpl.-WP.SL             0.065 [-0.059,  0.187]  .304    
## Mnpl.-WP.OL             0.087 [-0.037,  0.208]  .170    
## Mnpl.-WP.AS             0.005 [-0.118,  0.129]  .933    
## Mnpl.-WP.PrfIV.        -0.068 [-0.189,  0.056]  .284    
## Mnpl.-WP.TC             0.024 [-0.099,  0.147]  .700    
## Mnpl.-WA.WRV           -0.121 [-0.237, -0.002]  .047 *  
## Mnpl.-WA.PW            -0.190 [-0.302, -0.072]  .002 ** 
## Mnpl.-WA.NW             0.007 [-0.112,  0.126]  .907    
## Mnpl.-WA.RmV.          -0.178 [-0.291, -0.060]  .004 ** 
## Mnpl.-WA.PA             0.061 [-0.059,  0.179]  .319    
## Mnpl.-WA.NA            -0.012 [-0.131,  0.108]  .848    
## Mnpl.-WA.ImV.           0.105 [-0.015,  0.221]  .088 .  
## Mnpl.-WA.WA            -0.061 [-0.179,  0.059]  .318    
## Mnpl.-WA.TA             0.213 [ 0.096,  0.324] <.001 ***
## Mnpl.-WA.WRF           -0.115 [-0.232,  0.004]  .060 .  
## Mnpl.-WA.InV.           0.077 [-0.043,  0.194]  .210    
## Mnpl.-WA.GV             0.056 [-0.064,  0.174]  .358    
## Mnpl.-WA.EV            -0.334 [-0.436, -0.224] <.001 ***
## Mnpl.-WA.SlpQlV.        0.031 [-0.089,  0.150]  .611    
## Mnpl.-WA.RdV.          -0.227 [-0.339, -0.109] <.001 ***
## Mnpl.-WA.PR            -0.043 [-0.163,  0.078]  .489    
## Mnpl.-WA.ER            -0.238 [-0.349, -0.121] <.001 ***
## Mnpl.-WA.SlpQnV.        0.063 [-0.062,  0.186]  .326    
## WP.SB-WP.SP             0.681 [ 0.609,  0.742] <.001 ***
## WP.SB-WP.SN             0.737 [ 0.675,  0.789] <.001 ***
## WP.SB-WP.BV            -0.024 [-0.147,  0.099]  .702    
## WP.SB-WP.JC             0.091 [-0.033,  0.212]  .152    
## WP.SB-WP.CP             0.152 [ 0.029,  0.270]  .017 *  
## WP.SB-WP.PrbIV.         0.047 [-0.077,  0.169]  .461    
## WP.SB-WP.IS             0.060 [-0.063,  0.182]  .340    
## WP.SB-WP.IG             0.213 [ 0.092,  0.328] <.001 ***
## WP.SB-WP.SL             0.133 [ 0.009,  0.252]  .036 *  
## WP.SB-WP.OL            -0.011 [-0.134,  0.113]  .863    
## WP.SB-WP.AS             0.262 [ 0.144,  0.374] <.001 ***
## WP.SB-WP.PrfIV.        -0.096 [-0.216,  0.028]  .131    
## WP.SB-WP.TC             0.078 [-0.046,  0.199]  .220    
## WP.SB-WA.WRV            0.179 [ 0.056,  0.297]  .005 ** 
## WP.SB-WA.PW             0.093 [-0.032,  0.215]  .145    
## WP.SB-WA.NW             0.157 [ 0.033,  0.276]  .014 *  
## WP.SB-WA.RmV.           0.251 [ 0.130,  0.364] <.001 ***
## WP.SB-WA.PA             0.171 [ 0.048,  0.290]  .007 ** 
## WP.SB-WA.NA             0.086 [-0.039,  0.209]  .176    
## WP.SB-WA.ImV.           0.374 [ 0.262,  0.477] <.001 ***
## WP.SB-WA.WA             0.101 [-0.024,  0.222]  .116    
## WP.SB-WA.TA             0.210 [ 0.088,  0.326] <.001 ***
## WP.SB-WA.WRF            0.204 [ 0.082,  0.321]  .001 ** 
## WP.SB-WA.InV.           0.131 [ 0.006,  0.251]  .040 *  
## WP.SB-WA.GV             0.002 [-0.123,  0.126]  .981    
## WP.SB-WA.EV             0.197 [ 0.074,  0.314]  .002 ** 
## WP.SB-WA.SlpQlV.        0.042 [-0.083,  0.166]  .508    
## WP.SB-WA.RdV.           0.172 [ 0.047,  0.292]  .008 ** 
## WP.SB-WA.PR             0.047 [-0.080,  0.173]  .466    
## WP.SB-WA.ER             0.174 [ 0.049,  0.293]  .007 ** 
## WP.SB-WA.SlpQnV.       -0.037 [-0.166,  0.093]  .580    
## WP.SP-WP.SN             0.008 [-0.116,  0.131]  .901    
## WP.SP-WP.BV            -0.025 [-0.148,  0.098]  .690    
## WP.SP-WP.JC             0.141 [ 0.018,  0.260]  .026 *  
## WP.SP-WP.CP             0.065 [-0.059,  0.187]  .304    
## WP.SP-WP.PrbIV.        -0.003 [-0.126,  0.120]  .961    
## WP.SP-WP.IS             0.023 [-0.101,  0.146]  .718    
## WP.SP-WP.IG             0.114 [-0.009,  0.234]  .071 .  
## WP.SP-WP.SL             0.106 [-0.017,  0.227]  .093 .  
## WP.SP-WP.OL             0.013 [-0.111,  0.136]  .843    
## WP.SP-WP.AS             0.182 [ 0.060,  0.299]  .004 ** 
## WP.SP-WP.PrfIV.        -0.051 [-0.173,  0.073]  .418    
## WP.SP-WP.TC             0.022 [-0.101,  0.145]  .727    
## WP.SP-WA.WRV            0.060 [-0.065,  0.183]  .347    
## WP.SP-WA.PW             0.029 [-0.096,  0.153]  .647    
## WP.SP-WA.NW             0.054 [-0.071,  0.178]  .394    
## WP.SP-WA.RmV.           0.181 [ 0.058,  0.299]  .005 ** 
## WP.SP-WA.PA             0.036 [-0.089,  0.160]  .576    
## WP.SP-WA.NA             0.107 [-0.018,  0.229]  .094 .  
## WP.SP-WA.ImV.           0.196 [ 0.073,  0.313]  .002 ** 
## WP.SP-WA.WA             0.033 [-0.092,  0.157]  .605    
## WP.SP-WA.TA             0.129 [ 0.004,  0.249]  .044 *  
## WP.SP-WA.WRF            0.090 [-0.035,  0.212]  .159    
## WP.SP-WA.InV.           0.102 [-0.023,  0.223]  .112    
## WP.SP-WA.GV            -0.040 [-0.164,  0.085]  .532    
## WP.SP-WA.EV             0.028 [-0.097,  0.152]  .658    
## WP.SP-WA.SlpQlV.       -0.060 [-0.183,  0.065]  .347    
## WP.SP-WA.RdV.          -0.038 [-0.163,  0.089]  .560    
## WP.SP-WA.PR            -0.081 [-0.206,  0.045]  .209    
## WP.SP-WA.ER             0.001 [-0.125,  0.127]  .986    
## WP.SP-WA.SlpQnV.       -0.016 [-0.146,  0.114]  .807    
## WP.SN-WP.BV            -0.010 [-0.133,  0.114]  .877    
## WP.SN-WP.JC            -0.007 [-0.130,  0.117]  .918    
## WP.SN-WP.CP             0.147 [ 0.024,  0.266]  .020 *  
## WP.SN-WP.PrbIV.         0.067 [-0.057,  0.188]  .293    
## WP.SN-WP.IS             0.061 [-0.062,  0.183]  .332    
## WP.SN-WP.IG             0.185 [ 0.063,  0.302]  .003 ** 
## WP.SN-WP.SL             0.083 [-0.041,  0.204]  .190    
## WP.SN-WP.OL            -0.026 [-0.149,  0.097]  .676    
## WP.SN-WP.AS             0.190 [ 0.069,  0.306]  .003 ** 
## WP.SN-WP.PrfIV.        -0.083 [-0.205,  0.040]  .187    
## WP.SN-WP.TC             0.086 [-0.038,  0.207]  .176    
## WP.SN-WA.WRV            0.187 [ 0.064,  0.305]  .003 ** 
## WP.SN-WA.PW             0.100 [-0.025,  0.221]  .119    
## WP.SN-WA.NW             0.162 [ 0.039,  0.281]  .011 *  
## WP.SN-WA.RmV.           0.176 [ 0.053,  0.294]  .006 ** 
## WP.SN-WA.PA             0.199 [ 0.077,  0.316]  .002 ** 
## WP.SN-WA.NA             0.021 [-0.104,  0.145]  .748    
## WP.SN-WA.ImV.           0.329 [ 0.214,  0.436] <.001 ***
## WP.SN-WA.WA             0.106 [-0.019,  0.228]  .097 .  
## WP.SN-WA.TA             0.168 [ 0.045,  0.287]  .008 ** 
## WP.SN-WA.WRF            0.195 [ 0.072,  0.312]  .002 ** 
## WP.SN-WA.InV.           0.086 [-0.039,  0.208]  .181    
## WP.SN-WA.GV             0.038 [-0.087,  0.162]  .553    
## WP.SN-WA.EV             0.240 [ 0.119,  0.354] <.001 ***
## WP.SN-WA.SlpQlV.        0.111 [-0.014,  0.232]  .082 .  
## WP.SN-WA.RdV.           0.266 [ 0.144,  0.379] <.001 ***
## WP.SN-WA.PR             0.137 [ 0.010,  0.258]  .035 *  
## WP.SN-WA.ER             0.233 [ 0.110,  0.349] <.001 ***
## WP.SN-WA.SlpQnV.       -0.036 [-0.165,  0.094]  .590    
## WP.BV-WP.JC             0.188 [ 0.066,  0.304]  .003 ** 
## WP.BV-WP.CP             0.342 [ 0.229,  0.447] <.001 ***
## WP.BV-WP.PrbIV.         0.300 [ 0.183,  0.408] <.001 ***
## WP.BV-WP.IS             0.209 [ 0.088,  0.324] <.001 ***
## WP.BV-WP.IG             0.254 [ 0.135,  0.366] <.001 ***
## WP.BV-WP.SL             0.172 [ 0.049,  0.289]  .007 ** 
## WP.BV-WP.OL             0.089 [-0.034,  0.210]  .159    
## WP.BV-WP.AS             0.202 [ 0.080,  0.317]  .001 ** 
## WP.BV-WP.PrfIV.         0.210 [ 0.089,  0.325] <.001 ***
## WP.BV-WP.TC             0.255 [ 0.136,  0.367] <.001 ***
## WP.BV-WA.WRV            0.207 [ 0.085,  0.323]  .001 ** 
## WP.BV-WA.PW             0.273 [ 0.154,  0.385] <.001 ***
## WP.BV-WA.NW             0.032 [-0.093,  0.156]  .616    
## WP.BV-WA.RmV.           0.240 [ 0.119,  0.354] <.001 ***
## WP.BV-WA.PA             0.027 [-0.098,  0.151]  .672    
## WP.BV-WA.NA            -0.064 [-0.187,  0.061]  .319    
## WP.BV-WA.ImV.           0.060 [-0.065,  0.183]  .350    
## WP.BV-WA.WA             0.030 [-0.095,  0.154]  .637    
## WP.BV-WA.TA             0.129 [ 0.005,  0.250]  .043 *  
## WP.BV-WA.WRF            0.035 [-0.090,  0.159]  .579    
## WP.BV-WA.InV.           0.112 [-0.012,  0.234]  .078 .  
## WP.BV-WA.GV             0.207 [ 0.084,  0.323]  .001 ** 
## WP.BV-WA.EV            -0.045 [-0.169,  0.080]  .478    
## WP.BV-WA.SlpQlV.        0.035 [-0.090,  0.159]  .580    
## WP.BV-WA.RdV.          -0.065 [-0.190,  0.062]  .318    
## WP.BV-WA.PR            -0.013 [-0.139,  0.114]  .841    
## WP.BV-WA.ER            -0.057 [-0.181,  0.070]  .382    
## WP.BV-WA.SlpQnV.       -0.043 [-0.172,  0.087]  .515    
## WP.JC-WP.CP             0.313 [ 0.197,  0.420] <.001 ***
## WP.JC-WP.PrbIV.         0.397 [ 0.288,  0.496] <.001 ***
## WP.JC-WP.IS             0.230 [ 0.109,  0.343] <.001 ***
## WP.JC-WP.IG             0.093 [-0.030,  0.214]  .140    
## WP.JC-WP.SL             0.211 [ 0.090,  0.326] <.001 ***
## WP.JC-WP.OL             0.227 [ 0.106,  0.341] <.001 ***
## WP.JC-WP.AS             0.091 [-0.032,  0.212]  .149    
## WP.JC-WP.PrfIV.         0.273 [ 0.154,  0.383] <.001 ***
## WP.JC-WP.TC             0.163 [ 0.040,  0.281]  .010 *  
## WP.JC-WA.WRV            0.238 [ 0.117,  0.352] <.001 ***
## WP.JC-WA.PW             0.254 [ 0.133,  0.367] <.001 ***
## WP.JC-WA.NW             0.092 [-0.033,  0.214]  .152    
## WP.JC-WA.RmV.           0.111 [-0.014,  0.232]  .082 .  
## WP.JC-WA.PA             0.142 [ 0.017,  0.261]  .027 *  
## WP.JC-WA.NA             0.002 [-0.123,  0.127]  .975    
## WP.JC-WA.ImV.           0.198 [ 0.076,  0.315]  .002 ** 
## WP.JC-WA.WA             0.182 [ 0.059,  0.300]  .004 ** 
## WP.JC-WA.TA             0.219 [ 0.098,  0.335] <.001 ***
## WP.JC-WA.WRF            0.021 [-0.103,  0.146]  .736    
## WP.JC-WA.InV.           0.127 [ 0.002,  0.247]  .048 *  
## WP.JC-WA.GV            -0.064 [-0.187,  0.061]  .319    
## WP.JC-WA.EV            -0.057 [-0.180,  0.068]  .376    
## WP.JC-WA.SlpQlV.        0.146 [ 0.022,  0.266]  .022 *  
## WP.JC-WA.RdV.           0.180 [ 0.055,  0.299]  .006 ** 
## WP.JC-WA.PR             0.085 [-0.042,  0.209]  .189    
## WP.JC-WA.ER             0.167 [ 0.042,  0.287]  .010 ** 
## WP.JC-WA.SlpQnV.       -0.202 [-0.323, -0.074]  .002 ** 
## WP.CP-WP.PrbIV.         0.736 [ 0.674,  0.788] <.001 ***
## WP.CP-WP.IS             0.710 [ 0.644,  0.767] <.001 ***
## WP.CP-WP.IG             0.793 [ 0.743,  0.835] <.001 ***
## WP.CP-WP.SL             0.423 [ 0.316,  0.519] <.001 ***
## WP.CP-WP.OL             0.379 [ 0.269,  0.480] <.001 ***
## WP.CP-WP.AS             0.282 [ 0.164,  0.392] <.001 ***
## WP.CP-WP.PrfIV.         0.224 [ 0.103,  0.338] <.001 ***
## WP.CP-WP.TC             0.217 [ 0.097,  0.332] <.001 ***
## WP.CP-WA.WRV            0.129 [ 0.004,  0.250]  .044 *  
## WP.CP-WA.PW             0.165 [ 0.041,  0.284]  .010 ** 
## WP.CP-WA.NW             0.025 [-0.100,  0.149]  .699    
## WP.CP-WA.RmV.           0.249 [ 0.129,  0.363] <.001 ***
## WP.CP-WA.PA             0.093 [-0.032,  0.215]  .147    
## WP.CP-WA.NA             0.068 [-0.057,  0.191]  .287    
## WP.CP-WA.ImV.           0.168 [ 0.045,  0.287]  .008 ** 
## WP.CP-WA.WA             0.011 [-0.113,  0.136]  .859    
## WP.CP-WA.TA             0.091 [-0.034,  0.213]  .155    
## WP.CP-WA.WRF           -0.071 [-0.194,  0.054]  .265    
## WP.CP-WA.InV.           0.106 [-0.018,  0.228]  .096 .  
## WP.CP-WA.GV             0.117 [-0.008,  0.238]  .067 .  
## WP.CP-WA.EV            -0.148 [-0.268, -0.024]  .020 *  
## WP.CP-WA.SlpQlV.        0.213 [ 0.090,  0.328] <.001 ***
## WP.CP-WA.RdV.          -0.010 [-0.136,  0.116]  .875    
## WP.CP-WA.PR             0.031 [-0.095,  0.157]  .630    
## WP.CP-WA.ER            -0.026 [-0.152,  0.100]  .683    
## WP.CP-WA.SlpQnV.       -0.101 [-0.227,  0.030]  .131    
## WP.PrbIV.-WP.IS         0.325 [ 0.210,  0.431] <.001 ***
## WP.PrbIV.-WP.IG         0.317 [ 0.201,  0.423] <.001 ***
## WP.PrbIV.-WP.SL         0.299 [ 0.182,  0.407] <.001 ***
## WP.PrbIV.-WP.OL         0.332 [ 0.217,  0.437] <.001 ***
## WP.PrbIV.-WP.AS         0.114 [-0.009,  0.234]  .071 .  
## WP.PrbIV.-WP.PrfIV.     0.129 [ 0.006,  0.248]  .042 *  
## WP.PrbIV.-WP.TC         0.205 [ 0.084,  0.320]  .001 ** 
## WP.PrbIV.-WA.WRV        0.151 [ 0.027,  0.270]  .018 *  
## WP.PrbIV.-WA.PW         0.204 [ 0.081,  0.320]  .001 ** 
## WP.PrbIV.-WA.NW         0.019 [-0.106,  0.143]  .770    
## WP.PrbIV.-WA.RmV.       0.205 [ 0.082,  0.321]  .001 ** 
## WP.PrbIV.-WA.PA         0.139 [ 0.015,  0.259]  .029 *  
## WP.PrbIV.-WA.NA         0.031 [-0.094,  0.155]  .632    
## WP.PrbIV.-WA.ImV.       0.209 [ 0.087,  0.325]  .001 ** 
## WP.PrbIV.-WA.WA         0.004 [-0.121,  0.128]  .951    
## WP.PrbIV.-WA.TA         0.226 [ 0.105,  0.341] <.001 ***
## WP.PrbIV.-WA.WRF       -0.107 [-0.229,  0.018]  .094 .  
## WP.PrbIV.-WA.InV.       0.112 [-0.013,  0.233]  .080 .  
## WP.PrbIV.-WA.GV         0.038 [-0.087,  0.161]  .557    
## WP.PrbIV.-WA.EV        -0.124 [-0.245,  0.001]  .053 .  
## WP.PrbIV.-WA.SlpQlV.    0.235 [ 0.113,  0.349] <.001 ***
## WP.PrbIV.-WA.RdV.      -0.164 [-0.284, -0.038]  .011 *  
## WP.PrbIV.-WA.PR        -0.057 [-0.182,  0.070]  .381    
## WP.PrbIV.-WA.ER        -0.148 [-0.269, -0.022]  .022 *  
## WP.PrbIV.-WA.SlpQnV.   -0.188 [-0.310, -0.060]  .005 ** 
## WP.IS-WP.IG             0.383 [ 0.273,  0.484] <.001 ***
## WP.IS-WP.SL             0.193 [ 0.071,  0.309]  .002 ** 
## WP.IS-WP.OL             0.204 [ 0.082,  0.319]  .001 ** 
## WP.IS-WP.AS             0.088 [-0.036,  0.209]  .164    
## WP.IS-WP.PrfIV.         0.198 [ 0.076,  0.313]  .002 ** 
## WP.IS-WP.TC             0.143 [ 0.020,  0.262]  .024 *  
## WP.IS-WA.WRV           -0.097 [-0.219,  0.028]  .129    
## WP.IS-WA.PW             0.024 [-0.101,  0.148]  .710    
## WP.IS-WA.NW            -0.152 [-0.272, -0.028]  .017 *  
## WP.IS-WA.RmV.           0.022 [-0.103,  0.146]  .728    
## WP.IS-WA.PA             0.029 [-0.096,  0.153]  .649    
## WP.IS-WA.NA             0.038 [-0.087,  0.162]  .550    
## WP.IS-WA.ImV.           0.023 [-0.102,  0.147]  .719    
## WP.IS-WA.WA             0.061 [-0.064,  0.184]  .338    
## WP.IS-WA.TA            -0.010 [-0.135,  0.115]  .874    
## WP.IS-WA.WRF           -0.020 [-0.144,  0.105]  .758    
## WP.IS-WA.InV.           0.104 [-0.021,  0.226]  .103    
## WP.IS-WA.GV             0.061 [-0.064,  0.184]  .340    
## WP.IS-WA.EV            -0.057 [-0.181,  0.068]  .369    
## WP.IS-WA.SlpQlV.        0.149 [ 0.025,  0.269]  .019 *  
## WP.IS-WA.RdV.          -0.071 [-0.196,  0.056]  .274    
## WP.IS-WA.PR             0.150 [ 0.024,  0.271]  .021 *  
## WP.IS-WA.ER            -0.180 [-0.299, -0.055]  .005 ** 
## WP.IS-WA.SlpQnV.        0.088 [-0.043,  0.215]  .188    
## WP.IG-WP.SL             0.424 [ 0.317,  0.520] <.001 ***
## WP.IG-WP.OL             0.302 [ 0.185,  0.410] <.001 ***
## WP.IG-WP.AS             0.388 [ 0.278,  0.488] <.001 ***
## WP.IG-WP.PrfIV.         0.182 [ 0.060,  0.298]  .004 ** 
## WP.IG-WP.TC             0.141 [ 0.018,  0.260]  .026 *  
## WP.IG-WA.WRV            0.185 [ 0.062,  0.303]  .004 ** 
## WP.IG-WA.PW             0.119 [-0.005,  0.240]  .062 .  
## WP.IG-WA.NW             0.142 [ 0.018,  0.262]  .026 *  
## WP.IG-WA.RmV.           0.284 [ 0.166,  0.395] <.001 ***
## WP.IG-WA.PA             0.033 [-0.092,  0.157]  .602    
## WP.IG-WA.NA             0.077 [-0.048,  0.200]  .227    
## WP.IG-WA.ImV.           0.122 [-0.002,  0.243]  .056 .  
## WP.IG-WA.WA            -0.026 [-0.150,  0.099]  .686    
## WP.IG-WA.TA            -0.021 [-0.145,  0.104]  .740    
## WP.IG-WA.WRF           -0.027 [-0.151,  0.098]  .669    
## WP.IG-WA.InV.           0.030 [-0.095,  0.154]  .638    
## WP.IG-WA.GV             0.150 [ 0.026,  0.269]  .019 *  
## WP.IG-WA.EV            -0.135 [-0.255, -0.011]  .035 *  
## WP.IG-WA.SlpQlV.        0.093 [-0.032,  0.215]  .145    
## WP.IG-WA.RdV.           0.182 [ 0.057,  0.301]  .005 ** 
## WP.IG-WA.PR             0.001 [-0.125,  0.128]  .982    
## WP.IG-WA.ER             0.217 [ 0.094,  0.334] <.001 ***
## WP.IG-WA.SlpQnV.       -0.088 [-0.216,  0.042]  .184    
## WP.SL-WP.OL             0.856 [ 0.819,  0.886] <.001 ***
## WP.SL-WP.AS             0.723 [ 0.658,  0.777] <.001 ***
## WP.SL-WP.PrfIV.         0.219 [ 0.099,  0.334] <.001 ***
## WP.SL-WP.TC             0.171 [ 0.049,  0.288]  .007 ** 
## WP.SL-WA.WRV            0.045 [-0.080,  0.169]  .480    
## WP.SL-WA.PW             0.048 [-0.077,  0.172]  .450    
## WP.SL-WA.NW             0.017 [-0.108,  0.141]  .789    
## WP.SL-WA.RmV.           0.195 [ 0.073,  0.312]  .002 ** 
## WP.SL-WA.PA             0.184 [ 0.060,  0.301]  .004 ** 
## WP.SL-WA.NA            -0.013 [-0.137,  0.112]  .844    
## WP.SL-WA.ImV.           0.066 [-0.059,  0.189]  .298    
## WP.SL-WA.WA            -0.104 [-0.226,  0.021]  .103    
## WP.SL-WA.TA             0.141 [ 0.017,  0.261]  .027 *  
## WP.SL-WA.WRF           -0.009 [-0.133,  0.116]  .893    
## WP.SL-WA.InV.          -0.009 [-0.133,  0.116]  .891    
## WP.SL-WA.GV             0.084 [-0.041,  0.206]  .189    
## WP.SL-WA.EV            -0.089 [-0.212,  0.036]  .162    
## WP.SL-WA.SlpQlV.        0.068 [-0.057,  0.191]  .286    
## WP.SL-WA.RdV.           0.061 [-0.066,  0.186]  .346    
## WP.SL-WA.PR             0.170 [ 0.045,  0.290]  .009 ** 
## WP.SL-WA.ER            -0.030 [-0.155,  0.097]  .646    
## WP.SL-WA.SlpQnV.       -0.197 [-0.319, -0.070]  .003 ** 
## WP.OL-WP.AS             0.262 [ 0.143,  0.373] <.001 ***
## WP.OL-WP.PrfIV.         0.265 [ 0.147,  0.376] <.001 ***
## WP.OL-WP.TC             0.165 [ 0.042,  0.282]  .009 ** 
## WP.OL-WA.WRV            0.029 [-0.096,  0.153]  .655    
## WP.OL-WA.PW             0.038 [-0.087,  0.162]  .547    
## WP.OL-WA.NW             0.004 [-0.121,  0.128]  .954    
## WP.OL-WA.RmV.           0.175 [ 0.052,  0.293]  .006 ** 
## WP.OL-WA.PA             0.118 [-0.006,  0.240]  .064 .  
## WP.OL-WA.NA            -0.030 [-0.154,  0.095]  .643    
## WP.OL-WA.ImV.           0.023 [-0.102,  0.147]  .724    
## WP.OL-WA.WA            -0.130 [-0.250, -0.005]  .042 *  
## WP.OL-WA.TA             0.169 [ 0.045,  0.287]  .008 ** 
## WP.OL-WA.WRF           -0.020 [-0.144,  0.105]  .753    
## WP.OL-WA.InV.           0.059 [-0.066,  0.182]  .357    
## WP.OL-WA.GV             0.037 [-0.088,  0.161]  .559    
## WP.OL-WA.EV            -0.095 [-0.217,  0.030]  .136    
## WP.OL-WA.SlpQlV.        0.108 [-0.016,  0.230]  .090 .  
## WP.OL-WA.RdV.           0.029 [-0.098,  0.154]  .659    
## WP.OL-WA.PR             0.189 [ 0.064,  0.308]  .004 ** 
## WP.OL-WA.ER            -0.073 [-0.197,  0.053]  .258    
## WP.OL-WA.SlpQnV.       -0.232 [-0.351, -0.105] <.001 ***
## WP.AS-WP.PrfIV.         0.055 [-0.069,  0.177]  .384    
## WP.AS-WP.TC             0.099 [-0.024,  0.220]  .116    
## WP.AS-WA.WRV            0.046 [-0.079,  0.169]  .473    
## WP.AS-WA.PW             0.038 [-0.087,  0.162]  .548    
## WP.AS-WA.NW             0.027 [-0.098,  0.151]  .671    
## WP.AS-WA.RmV.           0.129 [ 0.005,  0.250]  .043 *  
## WP.AS-WA.PA             0.184 [ 0.061,  0.301]  .004 ** 
## WP.AS-WA.NA             0.016 [-0.108,  0.141]  .797    
## WP.AS-WA.ImV.           0.094 [-0.031,  0.216]  .141    
## WP.AS-WA.WA            -0.020 [-0.144,  0.105]  .752    
## WP.AS-WA.TA             0.037 [-0.088,  0.161]  .560    
## WP.AS-WA.WRF            0.011 [-0.114,  0.136]  .861    
## WP.AS-WA.InV.          -0.096 [-0.218,  0.029]  .133    
## WP.AS-WA.GV             0.107 [-0.018,  0.228]  .095 .  
## WP.AS-WA.EV            -0.039 [-0.163,  0.086]  .544    
## WP.AS-WA.SlpQlV.       -0.019 [-0.143,  0.106]  .768    
## WP.AS-WA.RdV.           0.076 [-0.051,  0.200]  .242    
## WP.AS-WA.PR             0.064 [-0.063,  0.189]  .322    
## WP.AS-WA.ER             0.043 [-0.084,  0.168]  .508    
## WP.AS-WA.SlpQnV.       -0.068 [-0.196,  0.062]  .308    
## WP.PrfIV.-WP.TC         0.299 [ 0.182,  0.407] <.001 ***
## WP.PrfIV.-WA.WRV        0.052 [-0.073,  0.175]  .419    
## WP.PrfIV.-WA.PW         0.148 [ 0.024,  0.268]  .021 *  
## WP.PrfIV.-WA.NW        -0.064 [-0.187,  0.061]  .315    
## WP.PrfIV.-WA.RmV.       0.043 [-0.082,  0.167]  .502    
## WP.PrfIV.-WA.PA        -0.147 [-0.267, -0.023]  .021 *  
## WP.PrfIV.-WA.NA         0.017 [-0.108,  0.141]  .788    
## WP.PrfIV.-WA.ImV.      -0.044 [-0.168,  0.081]  .487    
## WP.PrfIV.-WA.WA         0.005 [-0.119,  0.130]  .932    
## WP.PrfIV.-WA.TA         0.008 [-0.117,  0.132]  .900    
## WP.PrfIV.-WA.WRF        0.094 [-0.031,  0.216]  .143    
## WP.PrfIV.-WA.InV.       0.123 [-0.001,  0.244]  .054 .  
## WP.PrfIV.-WA.GV         0.079 [-0.046,  0.202]  .215    
## WP.PrfIV.-WA.EV         0.091 [-0.034,  0.213]  .155    
## WP.PrfIV.-WA.SlpQlV.    0.216 [ 0.094,  0.332] <.001 ***
## WP.PrfIV.-WA.RdV.       0.027 [-0.100,  0.153]  .680    
## WP.PrfIV.-WA.PR         0.038 [-0.089,  0.163]  .562    
## WP.PrfIV.-WA.ER         0.011 [-0.116,  0.137]  .870    
## WP.PrfIV.-WA.SlpQnV.   -0.226 [-0.346, -0.100] <.001 ***
## WP.TC-WA.WRV            0.190 [ 0.067,  0.307]  .003 ** 
## WP.TC-WA.PW             0.185 [ 0.062,  0.302]  .004 ** 
## WP.TC-WA.NW             0.089 [-0.036,  0.211]  .163    
## WP.TC-WA.RmV.           0.086 [-0.039,  0.208]  .180    
## WP.TC-WA.PA             0.036 [-0.089,  0.159]  .577    
## WP.TC-WA.NA            -0.024 [-0.148,  0.101]  .708    
## WP.TC-WA.ImV.           0.132 [ 0.007,  0.252]  .039 *  
## WP.TC-WA.WA             0.050 [-0.075,  0.174]  .431    
## WP.TC-WA.TA             0.117 [-0.008,  0.238]  .068 .  
## WP.TC-WA.WRF            0.240 [ 0.119,  0.354] <.001 ***
## WP.TC-WA.InV.           0.021 [-0.104,  0.145]  .741    
## WP.TC-WA.GV             0.118 [-0.007,  0.239]  .066 .  
## WP.TC-WA.EV            -0.076 [-0.198,  0.049]  .237    
## WP.TC-WA.SlpQlV.        0.135 [ 0.010,  0.255]  .035 *  
## WP.TC-WA.RdV.           0.037 [-0.090,  0.163]  .567    
## WP.TC-WA.PR             0.004 [-0.122,  0.131]  .946    
## WP.TC-WA.ER             0.042 [-0.085,  0.167]  .517    
## WP.TC-WA.SlpQnV.       -0.123 [-0.249,  0.006]  .064 .  
## WA.WRV-WA.PW            0.676 [ 0.605,  0.736] <.001 ***
## WA.WRV-WA.NW            0.744 [ 0.686,  0.793] <.001 ***
## WA.WRV-WA.RmV.          0.424 [ 0.321,  0.517] <.001 ***
## WA.WRV-WA.PA            0.183 [ 0.065,  0.296]  .003 ** 
## WA.WRV-WA.NA            0.134 [ 0.015,  0.250]  .028 *  
## WA.WRV-WA.ImV.          0.382 [ 0.276,  0.480] <.001 ***
## WA.WRV-WA.WA            0.218 [ 0.101,  0.329] <.001 ***
## WA.WRV-WA.TA            0.322 [ 0.211,  0.425] <.001 ***
## WA.WRV-WA.WRF           0.287 [ 0.174,  0.393] <.001 ***
## WA.WRV-WA.InV.          0.244 [ 0.128,  0.353] <.001 ***
## WA.WRV-WA.GV            0.065 [-0.055,  0.183]  .292    
## WA.WRV-WA.EV           -0.042 [-0.161,  0.078]  .491    
## WA.WRV-WA.SlpQlV.       0.182 [ 0.064,  0.295]  .003 ** 
## WA.WRV-WA.RdV.          0.155 [ 0.034,  0.270]  .013 *  
## WA.WRV-WA.PR           -0.025 [-0.146,  0.096]  .682    
## WA.WRV-WA.ER            0.174 [ 0.055,  0.289]  .005 ** 
## WA.WRV-WA.SlpQnV.      -0.064 [-0.187,  0.061]  .317    
## WA.PW-WA.NW             0.011 [-0.109,  0.130]  .862    
## WA.PW-WA.RmV.           0.347 [ 0.237,  0.448] <.001 ***
## WA.PW-WA.PA             0.140 [ 0.021,  0.255]  .022 *  
## WA.PW-WA.NA            -0.014 [-0.133,  0.106]  .825    
## WA.PW-WA.ImV.           0.225 [ 0.109,  0.336] <.001 ***
## WA.PW-WA.WA             0.196 [ 0.079,  0.309]  .001 ** 
## WA.PW-WA.TA             0.197 [ 0.080,  0.309]  .001 ** 
## WA.PW-WA.WRF            0.317 [ 0.206,  0.421] <.001 ***
## WA.PW-WA.InV.           0.163 [ 0.044,  0.277]  .008 ** 
## WA.PW-WA.GV             0.064 [-0.055,  0.182]  .293    
## WA.PW-WA.EV             0.043 [-0.076,  0.162]  .478    
## WA.PW-WA.SlpQlV.        0.122 [ 0.002,  0.237]  .047 *  
## WA.PW-WA.RdV.           0.042 [-0.079,  0.162]  .496    
## WA.PW-WA.PR            -0.030 [-0.150,  0.091]  .630    
## WA.PW-WA.ER             0.050 [-0.071,  0.170]  .421    
## WA.PW-WA.SlpQnV.       -0.121 [-0.241,  0.004]  .060 .  
## WA.NW-WA.RmV.           0.261 [ 0.146,  0.369] <.001 ***
## WA.NW-WA.PA             0.122 [ 0.002,  0.238]  .047 *  
## WA.NW-WA.NA             0.195 [ 0.077,  0.307]  .002 ** 
## WA.NW-WA.ImV.           0.314 [ 0.203,  0.418] <.001 ***
## WA.NW-WA.WA             0.118 [-0.002,  0.234]  .054 .  
## WA.NW-WA.TA             0.258 [ 0.143,  0.366] <.001 ***
## WA.NW-WA.WRF            0.102 [-0.017,  0.219]  .095 .  
## WA.NW-WA.InV.           0.183 [ 0.065,  0.296]  .003 ** 
## WA.NW-WA.GV             0.029 [-0.091,  0.148]  .634    
## WA.NW-WA.EV            -0.096 [-0.213,  0.023]  .115    
## WA.NW-WA.SlpQlV.        0.137 [ 0.018,  0.252]  .025 *  
## WA.NW-WA.RdV.           0.171 [ 0.051,  0.286]  .006 ** 
## WA.NW-WA.PR            -0.008 [-0.128,  0.113]  .902    
## WA.NW-WA.ER             0.192 [ 0.073,  0.306]  .002 ** 
## WA.NW-WA.SlpQnV.        0.022 [-0.102,  0.147]  .725    
## WA.RmV.-WA.PA           0.242 [ 0.126,  0.351] <.001 ***
## WA.RmV.-WA.NA           0.083 [-0.037,  0.200]  .178    
## WA.RmV.-WA.ImV.         0.256 [ 0.141,  0.364] <.001 ***
## WA.RmV.-WA.WA           0.294 [ 0.181,  0.399] <.001 ***
## WA.RmV.-WA.TA           0.272 [ 0.158,  0.379] <.001 ***
## WA.RmV.-WA.WRF          0.267 [ 0.152,  0.374] <.001 ***
## WA.RmV.-WA.InV.         0.265 [ 0.150,  0.372] <.001 ***
## WA.RmV.-WA.GV           0.286 [ 0.172,  0.392] <.001 ***
## WA.RmV.-WA.EV           0.011 [-0.108,  0.131]  .853    
## WA.RmV.-WA.SlpQlV.      0.122 [ 0.002,  0.238]  .047 *  
## WA.RmV.-WA.RdV.         0.129 [ 0.008,  0.246]  .038 *  
## WA.RmV.-WA.PR           0.048 [-0.073,  0.168]  .435    
## WA.RmV.-WA.ER           0.137 [ 0.016,  0.253]  .028 *  
## WA.RmV.-WA.SlpQnV.     -0.160 [-0.279, -0.036]  .012 *  
## WA.PA-WA.NA            -0.060 [-0.178,  0.060]  .329    
## WA.PA-WA.ImV.           0.272 [ 0.158,  0.379] <.001 ***
## WA.PA-WA.WA             0.201 [ 0.084,  0.313]  .001 ** 
## WA.PA-WA.TA             0.307 [ 0.195,  0.411] <.001 ***
## WA.PA-WA.WRF            0.131 [ 0.012,  0.246]  .033 *  
## WA.PA-WA.InV.           0.179 [ 0.061,  0.292]  .004 ** 
## WA.PA-WA.GV             0.037 [-0.082,  0.156]  .542    
## WA.PA-WA.EV             0.107 [-0.013,  0.223]  .082 .  
## WA.PA-WA.SlpQlV.        0.100 [-0.019,  0.217]  .101    
## WA.PA-WA.RdV.           0.132 [ 0.011,  0.249]  .034 *  
## WA.PA-WA.PR             0.086 [-0.035,  0.205]  .165    
## WA.PA-WA.ER             0.108 [-0.013,  0.226]  .082 .  
## WA.PA-WA.SlpQnV.       -0.187 [-0.304, -0.063]  .004 ** 
## WA.NA-WA.ImV.           0.132 [ 0.013,  0.247]  .031 *  
## WA.NA-WA.WA            -0.049 [-0.167,  0.071]  .426    
## WA.NA-WA.TA            -0.029 [-0.148,  0.090]  .632    
## WA.NA-WA.WRF           -0.015 [-0.134,  0.105]  .812    
## WA.NA-WA.InV.          -0.049 [-0.168,  0.071]  .421    
## WA.NA-WA.GV             0.081 [-0.039,  0.198]  .188    
## WA.NA-WA.EV             0.023 [-0.096,  0.142]  .703    
## WA.NA-WA.SlpQlV.       -0.026 [-0.145,  0.093]  .666    
## WA.NA-WA.RdV.          -0.054 [-0.174,  0.068]  .386    
## WA.NA-WA.PR            -0.029 [-0.149,  0.092]  .640    
## WA.NA-WA.ER            -0.048 [-0.167,  0.073]  .441    
## WA.NA-WA.SlpQnV.        0.152 [ 0.028,  0.271]  .018 *  
## WA.ImV.-WA.WA           0.120 [ 0.001,  0.236]  .049 *  
## WA.ImV.-WA.TA           0.390 [ 0.283,  0.486] <.001 ***
## WA.ImV.-WA.WRF          0.101 [-0.019,  0.217]  .100    
## WA.ImV.-WA.InV.         0.325 [ 0.214,  0.428] <.001 ***
## WA.ImV.-WA.GV           0.113 [-0.007,  0.229]  .066 .  
## WA.ImV.-WA.EV          -0.108 [-0.225,  0.011]  .077 .  
## WA.ImV.-WA.SlpQlV.      0.200 [ 0.082,  0.312]  .001 ** 
## WA.ImV.-WA.RdV.         0.016 [-0.105,  0.137]  .792    
## WA.ImV.-WA.PR          -0.108 [-0.226,  0.013]  .081 .  
## WA.ImV.-WA.ER           0.073 [-0.048,  0.192]  .236    
## WA.ImV.-WA.SlpQnV.     -0.202 [-0.318, -0.079]  .002 ** 
## WA.WA-WA.TA             0.317 [ 0.206,  0.421] <.001 ***
## WA.WA-WA.WRF            0.547 [ 0.457,  0.625] <.001 ***
## WA.WA-WA.InV.           0.224 [ 0.107,  0.334] <.001 ***
## WA.WA-WA.GV             0.138 [ 0.019,  0.253]  .025 *  
## WA.WA-WA.EV             0.120 [ 0.000,  0.236]  .051 .  
## WA.WA-WA.SlpQlV.        0.071 [-0.049,  0.189]  .248    
## WA.WA-WA.RdV.           0.037 [-0.084,  0.158]  .547    
## WA.WA-WA.PR            -0.036 [-0.156,  0.085]  .562    
## WA.WA-WA.ER             0.060 [-0.061,  0.180]  .329    
## WA.WA-WA.SlpQnV.        0.079 [-0.046,  0.201]  .218    
## WA.TA-WA.WRF            0.310 [ 0.198,  0.414] <.001 ***
## WA.TA-WA.InV.           0.604 [ 0.522,  0.674] <.001 ***
## WA.TA-WA.GV             0.100 [-0.019,  0.217]  .102    
## WA.TA-WA.EV             0.026 [-0.093,  0.145]  .668    
## WA.TA-WA.SlpQlV.        0.304 [ 0.192,  0.409] <.001 ***
## WA.TA-WA.RdV.           0.071 [-0.050,  0.190]  .253    
## WA.TA-WA.PR             0.020 [-0.101,  0.140]  .749    
## WA.TA-WA.ER             0.065 [-0.056,  0.185]  .292    
## WA.TA-WA.SlpQnV.       -0.167 [-0.286, -0.043]  .009 ** 
## WA.WRF-WA.InV.          0.239 [ 0.123,  0.348] <.001 ***
## WA.WRF-WA.GV            0.009 [-0.111,  0.128]  .884    
## WA.WRF-WA.EV            0.138 [ 0.019,  0.253]  .024 *  
## WA.WRF-WA.SlpQlV.       0.107 [-0.013,  0.223]  .082 .  
## WA.WRF-WA.RdV.          0.214 [ 0.095,  0.326] <.001 ***
## WA.WRF-WA.PR            0.094 [-0.027,  0.212]  .130    
## WA.WRF-WA.ER            0.194 [ 0.075,  0.308]  .002 ** 
## WA.WRF-WA.SlpQnV.       0.032 [-0.093,  0.156]  .615    
## WA.InV.-WA.GV           0.017 [-0.102,  0.136]  .778    
## WA.InV.-WA.EV           0.013 [-0.106,  0.132]  .829    
## WA.InV.-WA.SlpQlV.      0.243 [ 0.127,  0.352] <.001 ***
## WA.InV.-WA.RdV.         0.178 [ 0.059,  0.293]  .004 ** 
## WA.InV.-WA.PR           0.088 [-0.034,  0.206]  .159    
## WA.InV.-WA.ER           0.163 [ 0.043,  0.278]  .009 ** 
## WA.InV.-WA.SlpQnV.     -0.233 [-0.348, -0.112] <.001 ***
## WA.GV-WA.EV            -0.043 [-0.162,  0.077]  .480    
## WA.GV-WA.SlpQlV.        0.027 [-0.093,  0.146]  .658    
## WA.GV-WA.RdV.          -0.073 [-0.192,  0.048]  .240    
## WA.GV-WA.PR            -0.075 [-0.194,  0.047]  .229    
## WA.GV-WA.ER            -0.034 [-0.154,  0.087]  .581    
## WA.GV-WA.SlpQnV.       -0.036 [-0.160,  0.089]  .570    
## WA.EV-WA.SlpQlV.        0.010 [-0.109,  0.130]  .866    
## WA.EV-WA.RdV.           0.199 [ 0.080,  0.313]  .001 ** 
## WA.EV-WA.PR             0.203 [ 0.084,  0.316]  .001 ** 
## WA.EV-WA.ER             0.122 [ 0.001,  0.239]  .050 *  
## WA.EV-WA.SlpQnV.        0.057 [-0.068,  0.181]  .369    
## WA.SlpQlV.-WA.RdV.      0.078 [-0.043,  0.197]  .207    
## WA.SlpQlV.-WA.PR       -0.033 [-0.153,  0.088]  .596    
## WA.SlpQlV.-WA.ER        0.114 [-0.007,  0.231]  .067 .  
## WA.SlpQlV.-WA.SlpQnV.  -0.377 [-0.479, -0.265] <.001 ***
## WA.RdV.-WA.PR           0.568 [ 0.480,  0.645] <.001 ***
## WA.RdV.-WA.ER           0.873 [ 0.840,  0.899] <.001 ***
## WA.RdV.-WA.SlpQnV.     -0.132 [-0.254, -0.006]  .042 *  
## WA.PR-WA.ER             0.098 [-0.023,  0.217]  .114    
## WA.PR-WA.SlpQnV.       -0.135 [-0.257, -0.008]  .038 *  
## WA.ER-WA.SlpQnV.       -0.095 [-0.219,  0.031]  .141    
## ────────────────────────────────────────────────────────
## 
## Between-Level Correlation [95% CI]:
## ────────────────────────────────────────────────────────
##                             r         [95% CI]     p    
## ────────────────────────────────────────────────────────
## Mnpl.-WP.SB            -0.016 [-0.181,  0.151]  .853    
## Mnpl.-WP.SP             0.013 [-0.153,  0.178]  .879    
## Mnpl.-WP.SN            -0.039 [-0.203,  0.128]  .649    
## Mnpl.-WP.BV            -0.053 [-0.217,  0.114]  .535    
## Mnpl.-WP.JC             0.021 [-0.146,  0.186]  .807    
## Mnpl.-WP.CP             0.050 [-0.117,  0.214]  .557    
## Mnpl.-WP.PrbIV.         0.075 [-0.092,  0.238]  .381    
## Mnpl.-WP.IS             0.080 [-0.087,  0.243]  .345    
## Mnpl.-WP.IG             0.005 [-0.161,  0.171]  .950    
## Mnpl.-WP.SL             0.012 [-0.154,  0.178]  .888    
## Mnpl.-WP.OL             0.011 [-0.156,  0.176]  .901    
## Mnpl.-WP.AS             0.011 [-0.155,  0.177]  .894    
## Mnpl.-WP.PrfIV.        -0.063 [-0.226,  0.104]  .463    
## Mnpl.-WP.TC             0.099 [-0.068,  0.261]  .244    
## Mnpl.-WA.WRV           -0.088 [-0.246,  0.075]  .290    
## Mnpl.-WA.PW            -0.096 [-0.254,  0.067]  .247    
## Mnpl.-WA.NW            -0.049 [-0.209,  0.114]  .555    
## Mnpl.-WA.RmV.           0.011 [-0.151,  0.173]  .893    
## Mnpl.-WA.PA             0.104 [-0.059,  0.261]  .211    
## Mnpl.-WA.NA            -0.107 [-0.264,  0.056]  .198    
## Mnpl.-WA.ImV.           0.070 [-0.093,  0.229]  .400    
## Mnpl.-WA.WA            -0.047 [-0.208,  0.115]  .569    
## Mnpl.-WA.TA             0.002 [-0.160,  0.164]  .977    
## Mnpl.-WA.WRF           -0.099 [-0.257,  0.064]  .231    
## Mnpl.-WA.InV.          -0.014 [-0.176,  0.148]  .864    
## Mnpl.-WA.GV            -0.064 [-0.224,  0.099]  .440    
## Mnpl.-WA.EV            -0.005 [-0.167,  0.157]  .953    
## Mnpl.-WA.SlpQlV.       -0.021 [-0.183,  0.141]  .797    
## Mnpl.-WA.RdV.          -0.022 [-0.184,  0.141]  .790    
## Mnpl.-WA.PR             0.005 [-0.158,  0.167]  .955    
## Mnpl.-WA.ER            -0.031 [-0.192,  0.132]  .712    
## Mnpl.-WA.SlpQnV.       -0.019 [-0.186,  0.148]  .820    
## WP.SB-WP.SP             0.926 [ 0.898,  0.946] <.001 ***
## WP.SB-WP.SN             0.944 [ 0.923,  0.960] <.001 ***
## WP.SB-WP.BV             0.473 [ 0.333,  0.592] <.001 ***
## WP.SB-WP.JC             0.431 [ 0.286,  0.557] <.001 ***
## WP.SB-WP.CP             0.525 [ 0.393,  0.635] <.001 ***
## WP.SB-WP.PrbIV.         0.445 [ 0.301,  0.569] <.001 ***
## WP.SB-WP.IS             0.319 [ 0.161,  0.460] <.001 ***
## WP.SB-WP.IG             0.544 [ 0.415,  0.651] <.001 ***
## WP.SB-WP.SL             0.333 [ 0.177,  0.473] <.001 ***
## WP.SB-WP.OL             0.343 [ 0.188,  0.482] <.001 ***
## WP.SB-WP.AS             0.241 [ 0.078,  0.391]  .004 ** 
## WP.SB-WP.PrfIV.         0.467 [ 0.327,  0.587] <.001 ***
## WP.SB-WP.TC             0.426 [ 0.280,  0.553] <.001 ***
## WP.SB-WA.WRV            0.576 [ 0.453,  0.677] <.001 ***
## WP.SB-WA.PW             0.591 [ 0.471,  0.689] <.001 ***
## WP.SB-WA.NW             0.358 [ 0.204,  0.495] <.001 ***
## WP.SB-WA.RmV.           0.627 [ 0.515,  0.718] <.001 ***
## WP.SB-WA.PA             0.510 [ 0.375,  0.623] <.001 ***
## WP.SB-WA.NA            -0.095 [-0.257,  0.072]  .253    
## WP.SB-WA.ImV.           0.598 [ 0.479,  0.695] <.001 ***
## WP.SB-WA.WA             0.522 [ 0.389,  0.633] <.001 ***
## WP.SB-WA.TA             0.584 [ 0.463,  0.684] <.001 ***
## WP.SB-WA.WRF            0.502 [ 0.366,  0.616] <.001 ***
## WP.SB-WA.InV.           0.565 [ 0.440,  0.668] <.001 ***
## WP.SB-WA.GV             0.461 [ 0.319,  0.582] <.001 ***
## WP.SB-WA.EV             0.106 [-0.061,  0.267]  .201    
## WP.SB-WA.SlpQlV.       -0.007 [-0.172,  0.159]  .935    
## WP.SB-WA.RdV.           0.164 [-0.003,  0.321]  .048 *  
## WP.SB-WA.PR             0.112 [-0.055,  0.273]  .178    
## WP.SB-WA.ER             0.145 [-0.022,  0.304]  .080 .  
## WP.SB-WA.SlpQnV.        0.022 [-0.150,  0.192]  .801    
## WP.SP-WP.SN             0.750 [ 0.667,  0.815] <.001 ***
## WP.SP-WP.BV             0.533 [ 0.403,  0.642] <.001 ***
## WP.SP-WP.JC             0.450 [ 0.307,  0.573] <.001 ***
## WP.SP-WP.CP             0.504 [ 0.368,  0.618] <.001 ***
## WP.SP-WP.PrbIV.         0.437 [ 0.292,  0.562] <.001 ***
## WP.SP-WP.IS             0.281 [ 0.120,  0.427] <.001 ***
## WP.SP-WP.IG             0.530 [ 0.400,  0.640] <.001 ***
## WP.SP-WP.SL             0.312 [ 0.154,  0.454] <.001 ***
## WP.SP-WP.OL             0.316 [ 0.158,  0.458] <.001 ***
## WP.SP-WP.AS             0.234 [ 0.071,  0.385]  .005 ** 
## WP.SP-WP.PrfIV.         0.482 [ 0.343,  0.600] <.001 ***
## WP.SP-WP.TC             0.453 [ 0.310,  0.575] <.001 ***
## WP.SP-WA.WRV            0.530 [ 0.400,  0.640] <.001 ***
## WP.SP-WA.PW             0.594 [ 0.475,  0.692] <.001 ***
## WP.SP-WA.NW             0.281 [ 0.121,  0.427] <.001 ***
## WP.SP-WA.RmV.           0.633 [ 0.522,  0.723] <.001 ***
## WP.SP-WA.PA             0.554 [ 0.428,  0.660] <.001 ***
## WP.SP-WA.NA            -0.139 [-0.298,  0.028]  .094 .  
## WP.SP-WA.ImV.           0.654 [ 0.548,  0.740] <.001 ***
## WP.SP-WA.WA             0.508 [ 0.374,  0.622] <.001 ***
## WP.SP-WA.TA             0.609 [ 0.493,  0.704] <.001 ***
## WP.SP-WA.WRF            0.526 [ 0.395,  0.637] <.001 ***
## WP.SP-WA.InV.           0.565 [ 0.441,  0.669] <.001 ***
## WP.SP-WA.GV             0.439 [ 0.295,  0.564] <.001 ***
## WP.SP-WA.EV             0.112 [-0.055,  0.273]  .175    
## WP.SP-WA.SlpQlV.       -0.012 [-0.178,  0.154]  .881    
## WP.SP-WA.RdV.           0.127 [-0.041,  0.287]  .128    
## WP.SP-WA.PR             0.100 [-0.067,  0.263]  .228    
## WP.SP-WA.ER             0.104 [-0.064,  0.266]  .213    
## WP.SP-WA.SlpQnV.       -0.008 [-0.179,  0.163]  .923    
## WP.SN-WP.BV             0.364 [ 0.211,  0.500] <.001 ***
## WP.SN-WP.JC             0.363 [ 0.210,  0.499] <.001 ***
## WP.SN-WP.CP             0.480 [ 0.341,  0.598] <.001 ***
## WP.SN-WP.PrbIV.         0.398 [ 0.248,  0.529] <.001 ***
## WP.SN-WP.IS             0.314 [ 0.156,  0.456] <.001 ***
## WP.SN-WP.IG             0.490 [ 0.353,  0.607] <.001 ***
## WP.SN-WP.SL             0.311 [ 0.153,  0.453] <.001 ***
## WP.SN-WP.OL             0.326 [ 0.169,  0.467] <.001 ***
## WP.SN-WP.AS             0.217 [ 0.053,  0.370]  .010 ** 
## WP.SN-WP.PrfIV.         0.398 [ 0.249,  0.529] <.001 ***
## WP.SN-WP.TC             0.352 [ 0.198,  0.489] <.001 ***
## WP.SN-WA.WRV            0.546 [ 0.418,  0.653] <.001 ***
## WP.SN-WA.PW             0.517 [ 0.384,  0.629] <.001 ***
## WP.SN-WA.NW             0.382 [ 0.231,  0.515] <.001 ***
## WP.SN-WA.RmV.           0.546 [ 0.418,  0.653] <.001 ***
## WP.SN-WA.PA             0.409 [ 0.261,  0.539] <.001 ***
## WP.SN-WA.NA            -0.046 [-0.210,  0.121]  .584    
## WP.SN-WA.ImV.           0.477 [ 0.338,  0.596] <.001 ***
## WP.SN-WA.WA             0.470 [ 0.330,  0.590] <.001 ***
## WP.SN-WA.TA             0.492 [ 0.355,  0.608] <.001 ***
## WP.SN-WA.WRF            0.420 [ 0.273,  0.548] <.001 ***
## WP.SN-WA.InV.           0.496 [ 0.360,  0.612] <.001 ***
## WP.SN-WA.GV             0.424 [ 0.277,  0.551] <.001 ***
## WP.SN-WA.EV             0.088 [-0.079,  0.250]  .289    
## WP.SN-WA.SlpQlV.       -0.001 [-0.167,  0.165]  .990    
## WP.SN-WA.RdV.           0.176 [ 0.010,  0.333]  .033 *  
## WP.SN-WA.PR             0.109 [-0.059,  0.270]  .192    
## WP.SN-WA.ER             0.164 [-0.003,  0.322]  .048 *  
## WP.SN-WA.SlpQnV.        0.045 [-0.127,  0.214]  .599    
## WP.BV-WP.JC             0.637 [ 0.526,  0.726] <.001 ***
## WP.BV-WP.CP             0.631 [ 0.520,  0.721] <.001 ***
## WP.BV-WP.PrbIV.         0.595 [ 0.476,  0.693] <.001 ***
## WP.BV-WP.IS             0.447 [ 0.304,  0.571] <.001 ***
## WP.BV-WP.IG             0.584 [ 0.463,  0.683] <.001 ***
## WP.BV-WP.SL             0.501 [ 0.366,  0.616] <.001 ***
## WP.BV-WP.OL             0.491 [ 0.354,  0.608] <.001 ***
## WP.BV-WP.AS             0.401 [ 0.252,  0.531] <.001 ***
## WP.BV-WP.PrfIV.         0.598 [ 0.479,  0.695] <.001 ***
## WP.BV-WP.TC             0.631 [ 0.519,  0.721] <.001 ***
## WP.BV-WA.WRV            0.610 [ 0.495,  0.705] <.001 ***
## WP.BV-WA.PW             0.650 [ 0.542,  0.736] <.001 ***
## WP.BV-WA.NW             0.357 [ 0.203,  0.493] <.001 ***
## WP.BV-WA.RmV.           0.712 [ 0.619,  0.785] <.001 ***
## WP.BV-WA.PA             0.510 [ 0.376,  0.623] <.001 ***
## WP.BV-WA.NA            -0.036 [-0.201,  0.131]  .666    
## WP.BV-WA.ImV.           0.680 [ 0.579,  0.760] <.001 ***
## WP.BV-WA.WA             0.567 [ 0.443,  0.670] <.001 ***
## WP.BV-WA.TA             0.702 [ 0.607,  0.778] <.001 ***
## WP.BV-WA.WRF            0.610 [ 0.494,  0.704] <.001 ***
## WP.BV-WA.InV.           0.706 [ 0.612,  0.780] <.001 ***
## WP.BV-WA.GV             0.425 [ 0.278,  0.552] <.001 ***
## WP.BV-WA.EV             0.172 [ 0.006,  0.328]  .038 *  
## WP.BV-WA.SlpQlV.        0.053 [-0.114,  0.217]  .523    
## WP.BV-WA.RdV.           0.111 [-0.057,  0.272]  .184    
## WP.BV-WA.PR             0.035 [-0.132,  0.201]  .673    
## WP.BV-WA.ER             0.120 [-0.048,  0.281]  .150    
## WP.BV-WA.SlpQnV.       -0.084 [-0.252,  0.088]  .326    
## WP.JC-WP.CP             0.623 [ 0.509,  0.715] <.001 ***
## WP.JC-WP.PrbIV.         0.587 [ 0.466,  0.686] <.001 ***
## WP.JC-WP.IS             0.406 [ 0.258,  0.536] <.001 ***
## WP.JC-WP.IG             0.596 [ 0.477,  0.693] <.001 ***
## WP.JC-WP.SL             0.577 [ 0.454,  0.678] <.001 ***
## WP.JC-WP.OL             0.603 [ 0.486,  0.699] <.001 ***
## WP.JC-WP.AS             0.405 [ 0.256,  0.535] <.001 ***
## WP.JC-WP.PrfIV.         0.662 [ 0.558,  0.746] <.001 ***
## WP.JC-WP.TC             0.697 [ 0.600,  0.773] <.001 ***
## WP.JC-WA.WRV            0.666 [ 0.562,  0.749] <.001 ***
## WP.JC-WA.PW             0.579 [ 0.458,  0.680] <.001 ***
## WP.JC-WA.NW             0.517 [ 0.384,  0.629] <.001 ***
## WP.JC-WA.RmV.           0.618 [ 0.504,  0.711] <.001 ***
## WP.JC-WA.PA             0.520 [ 0.388,  0.632] <.001 ***
## WP.JC-WA.NA            -0.034 [-0.199,  0.132]  .681    
## WP.JC-WA.ImV.           0.638 [ 0.528,  0.727] <.001 ***
## WP.JC-WA.WA             0.521 [ 0.388,  0.632] <.001 ***
## WP.JC-WA.TA             0.554 [ 0.428,  0.659] <.001 ***
## WP.JC-WA.WRF            0.529 [ 0.398,  0.638] <.001 ***
## WP.JC-WA.InV.           0.568 [ 0.444,  0.671] <.001 ***
## WP.JC-WA.GV             0.306 [ 0.148,  0.449] <.001 ***
## WP.JC-WA.EV             0.260 [ 0.098,  0.408]  .001 ** 
## WP.JC-WA.SlpQlV.        0.031 [-0.136,  0.196]  .711    
## WP.JC-WA.RdV.           0.220 [ 0.056,  0.373]  .008 ** 
## WP.JC-WA.PR             0.202 [ 0.036,  0.356]  .015 *  
## WP.JC-WA.ER             0.170 [ 0.004,  0.327]  .040 *  
## WP.JC-WA.SlpQnV.        0.049 [-0.123,  0.218]  .567    
## WP.CP-WP.PrbIV.         0.855 [ 0.803,  0.894] <.001 ***
## WP.CP-WP.IS             0.793 [ 0.722,  0.847] <.001 ***
## WP.CP-WP.IG             0.928 [ 0.900,  0.948] <.001 ***
## WP.CP-WP.SL             0.587 [ 0.467,  0.686] <.001 ***
## WP.CP-WP.OL             0.581 [ 0.459,  0.681] <.001 ***
## WP.CP-WP.AS             0.462 [ 0.321,  0.583] <.001 ***
## WP.CP-WP.PrfIV.         0.691 [ 0.593,  0.769] <.001 ***
## WP.CP-WP.TC             0.691 [ 0.593,  0.769] <.001 ***
## WP.CP-WA.WRV            0.625 [ 0.512,  0.716] <.001 ***
## WP.CP-WA.PW             0.568 [ 0.444,  0.671] <.001 ***
## WP.CP-WA.NW             0.461 [ 0.319,  0.582] <.001 ***
## WP.CP-WA.RmV.           0.713 [ 0.620,  0.786] <.001 ***
## WP.CP-WA.PA             0.595 [ 0.476,  0.693] <.001 ***
## WP.CP-WA.NA            -0.021 [-0.187,  0.145]  .797    
## WP.CP-WA.ImV.           0.723 [ 0.633,  0.794] <.001 ***
## WP.CP-WA.WA             0.598 [ 0.480,  0.695] <.001 ***
## WP.CP-WA.TA             0.627 [ 0.515,  0.718] <.001 ***
## WP.CP-WA.WRF            0.581 [ 0.460,  0.681] <.001 ***
## WP.CP-WA.InV.           0.590 [ 0.470,  0.688] <.001 ***
## WP.CP-WA.GV             0.449 [ 0.306,  0.572] <.001 ***
## WP.CP-WA.EV             0.211 [ 0.047,  0.364]  .010 *  
## WP.CP-WA.SlpQlV.        0.113 [-0.054,  0.273]  .174    
## WP.CP-WA.RdV.           0.219 [ 0.054,  0.372]  .008 ** 
## WP.CP-WA.PR             0.166 [-0.000,  0.324]  .045 *  
## WP.CP-WA.ER             0.188 [ 0.023,  0.344]  .023 *  
## WP.CP-WA.SlpQnV.       -0.028 [-0.197,  0.144]  .749    
## WP.PrbIV.-WP.IS         0.575 [ 0.452,  0.676] <.001 ***
## WP.PrbIV.-WP.IG         0.698 [ 0.602,  0.774] <.001 ***
## WP.PrbIV.-WP.SL         0.566 [ 0.442,  0.669] <.001 ***
## WP.PrbIV.-WP.OL         0.559 [ 0.433,  0.663] <.001 ***
## WP.PrbIV.-WP.AS         0.447 [ 0.303,  0.570] <.001 ***
## WP.PrbIV.-WP.PrfIV.     0.636 [ 0.525,  0.725] <.001 ***
## WP.PrbIV.-WP.TC         0.618 [ 0.504,  0.711] <.001 ***
## WP.PrbIV.-WA.WRV        0.535 [ 0.405,  0.644] <.001 ***
## WP.PrbIV.-WA.PW         0.523 [ 0.391,  0.634] <.001 ***
## WP.PrbIV.-WA.NW         0.358 [ 0.205,  0.495] <.001 ***
## WP.PrbIV.-WA.RmV.       0.656 [ 0.550,  0.741] <.001 ***
## WP.PrbIV.-WA.PA         0.509 [ 0.375,  0.622] <.001 ***
## WP.PrbIV.-WA.NA         0.018 [-0.148,  0.184]  .826    
## WP.PrbIV.-WA.ImV.       0.641 [ 0.531,  0.729] <.001 ***
## WP.PrbIV.-WA.WA         0.540 [ 0.411,  0.648] <.001 ***
## WP.PrbIV.-WA.TA         0.557 [ 0.431,  0.662] <.001 ***
## WP.PrbIV.-WA.WRF        0.532 [ 0.402,  0.642] <.001 ***
## WP.PrbIV.-WA.InV.       0.543 [ 0.414,  0.650] <.001 ***
## WP.PrbIV.-WA.GV         0.309 [ 0.150,  0.451] <.001 ***
## WP.PrbIV.-WA.EV         0.232 [ 0.069,  0.383]  .005 ** 
## WP.PrbIV.-WA.SlpQlV.    0.097 [-0.070,  0.259]  .242    
## WP.PrbIV.-WA.RdV.       0.169 [ 0.003,  0.327]  .041 *  
## WP.PrbIV.-WA.PR         0.137 [-0.030,  0.297]  .099 .  
## WP.PrbIV.-WA.ER         0.141 [-0.026,  0.300]  .090 .  
## WP.PrbIV.-WA.SlpQnV.   -0.090 [-0.257,  0.082]  .295    
## WP.IS-WP.IG             0.584 [ 0.463,  0.684] <.001 ***
## WP.IS-WP.SL             0.505 [ 0.370,  0.619] <.001 ***
## WP.IS-WP.OL             0.421 [ 0.274,  0.549] <.001 ***
## WP.IS-WP.AS             0.511 [ 0.377,  0.624] <.001 ***
## WP.IS-WP.PrfIV.         0.443 [ 0.300,  0.568] <.001 ***
## WP.IS-WP.TC             0.439 [ 0.294,  0.564] <.001 ***
## WP.IS-WA.WRV            0.414 [ 0.267,  0.543] <.001 ***
## WP.IS-WA.PW             0.356 [ 0.202,  0.493] <.001 ***
## WP.IS-WA.NW             0.326 [ 0.169,  0.467] <.001 ***
## WP.IS-WA.RmV.           0.490 [ 0.352,  0.606] <.001 ***
## WP.IS-WA.PA             0.435 [ 0.290,  0.561] <.001 ***
## WP.IS-WA.NA            -0.050 [-0.214,  0.117]  .546    
## WP.IS-WA.ImV.           0.473 [ 0.333,  0.592] <.001 ***
## WP.IS-WA.WA             0.413 [ 0.265,  0.542] <.001 ***
## WP.IS-WA.TA             0.396 [ 0.246,  0.527] <.001 ***
## WP.IS-WA.WRF            0.330 [ 0.174,  0.470] <.001 ***
## WP.IS-WA.InV.           0.361 [ 0.207,  0.497] <.001 ***
## WP.IS-WA.GV             0.443 [ 0.299,  0.567] <.001 ***
## WP.IS-WA.EV             0.128 [-0.038,  0.288]  .122    
## WP.IS-WA.SlpQlV.        0.211 [ 0.046,  0.364]  .010 *  
## WP.IS-WA.RdV.           0.230 [ 0.066,  0.382]  .005 ** 
## WP.IS-WA.PR             0.170 [ 0.004,  0.327]  .040 *  
## WP.IS-WA.ER             0.203 [ 0.038,  0.357]  .014 *  
## WP.IS-WA.SlpQnV.       -0.077 [-0.244,  0.095]  .371    
## WP.IG-WP.SL             0.486 [ 0.349,  0.604] <.001 ***
## WP.IG-WP.OL             0.526 [ 0.394,  0.636] <.001 ***
## WP.IG-WP.AS             0.317 [ 0.160,  0.459] <.001 ***
## WP.IG-WP.PrfIV.         0.675 [ 0.573,  0.756] <.001 ***
## WP.IG-WP.TC             0.687 [ 0.588,  0.766] <.001 ***
## WP.IG-WA.WRV            0.624 [ 0.511,  0.716] <.001 ***
## WP.IG-WA.PW             0.559 [ 0.433,  0.663] <.001 ***
## WP.IG-WA.NW             0.469 [ 0.329,  0.589] <.001 ***
## WP.IG-WA.RmV.           0.677 [ 0.576,  0.758] <.001 ***
## WP.IG-WA.PA             0.572 [ 0.449,  0.674] <.001 ***
## WP.IG-WA.NA            -0.022 [-0.187,  0.145]  .793    
## WP.IG-WA.ImV.           0.715 [ 0.622,  0.787] <.001 ***
## WP.IG-WA.WA             0.573 [ 0.450,  0.675] <.001 ***
## WP.IG-WA.TA             0.627 [ 0.515,  0.718] <.001 ***
## WP.IG-WA.WRF            0.593 [ 0.473,  0.691] <.001 ***
## WP.IG-WA.InV.           0.586 [ 0.465,  0.685] <.001 ***
## WP.IG-WA.GV             0.410 [ 0.262,  0.539] <.001 ***
## WP.IG-WA.EV             0.189 [ 0.024,  0.344]  .022 *  
## WP.IG-WA.SlpQlV.        0.035 [-0.131,  0.200]  .670    
## WP.IG-WA.RdV.           0.182 [ 0.015,  0.338]  .028 *  
## WP.IG-WA.PR             0.135 [-0.032,  0.295]  .104    
## WP.IG-WA.ER             0.156 [-0.011,  0.314]  .060 .  
## WP.IG-WA.SlpQnV.        0.042 [-0.130,  0.212]  .623    
## WP.SL-WP.OL             0.937 [ 0.914,  0.955] <.001 ***
## WP.SL-WP.AS             0.861 [ 0.811,  0.899] <.001 ***
## WP.SL-WP.PrfIV.         0.681 [ 0.581,  0.761] <.001 ***
## WP.SL-WP.TC             0.606 [ 0.490,  0.702] <.001 ***
## WP.SL-WA.WRV            0.484 [ 0.346,  0.602] <.001 ***
## WP.SL-WA.PW             0.457 [ 0.315,  0.579] <.001 ***
## WP.SL-WA.NW             0.340 [ 0.184,  0.479] <.001 ***
## WP.SL-WA.RmV.           0.478 [ 0.339,  0.596] <.001 ***
## WP.SL-WA.PA             0.377 [ 0.225,  0.511] <.001 ***
## WP.SL-WA.NA             0.073 [-0.094,  0.236]  .379    
## WP.SL-WA.ImV.           0.446 [ 0.303,  0.570] <.001 ***
## WP.SL-WA.WA             0.334 [ 0.178,  0.474] <.001 ***
## WP.SL-WA.TA             0.504 [ 0.369,  0.618] <.001 ***
## WP.SL-WA.WRF            0.309 [ 0.151,  0.452] <.001 ***
## WP.SL-WA.InV.           0.537 [ 0.407,  0.645] <.001 ***
## WP.SL-WA.GV             0.432 [ 0.287,  0.558] <.001 ***
## WP.SL-WA.EV             0.210 [ 0.046,  0.364]  .011 *  
## WP.SL-WA.SlpQlV.        0.152 [-0.015,  0.310]  .067 .  
## WP.SL-WA.RdV.           0.282 [ 0.121,  0.428] <.001 ***
## WP.SL-WA.PR             0.155 [-0.012,  0.314]  .061 .  
## WP.SL-WA.ER             0.273 [ 0.112,  0.420] <.001 ***
## WP.SL-WA.SlpQnV.       -0.000 [-0.171,  0.171]  .997    
## WP.OL-WP.AS             0.630 [ 0.519,  0.721] <.001 ***
## WP.OL-WP.PrfIV.         0.663 [ 0.558,  0.746] <.001 ***
## WP.OL-WP.TC             0.620 [ 0.506,  0.712] <.001 ***
## WP.OL-WA.WRV            0.524 [ 0.392,  0.635] <.001 ***
## WP.OL-WA.PW             0.494 [ 0.357,  0.610] <.001 ***
## WP.OL-WA.NW             0.369 [ 0.216,  0.504] <.001 ***
## WP.OL-WA.RmV.           0.479 [ 0.340,  0.597] <.001 ***
## WP.OL-WA.PA             0.404 [ 0.255,  0.534] <.001 ***
## WP.OL-WA.NA             0.126 [-0.041,  0.286]  .128    
## WP.OL-WA.ImV.           0.472 [ 0.332,  0.591] <.001 ***
## WP.OL-WA.WA             0.378 [ 0.226,  0.512] <.001 ***
## WP.OL-WA.TA             0.553 [ 0.426,  0.658] <.001 ***
## WP.OL-WA.WRF            0.362 [ 0.208,  0.498] <.001 ***
## WP.OL-WA.InV.           0.554 [ 0.428,  0.659] <.001 ***
## WP.OL-WA.GV             0.376 [ 0.224,  0.510] <.001 ***
## WP.OL-WA.EV             0.199 [ 0.034,  0.353]  .016 *  
## WP.OL-WA.SlpQlV.        0.106 [-0.061,  0.267]  .202    
## WP.OL-WA.RdV.           0.266 [ 0.104,  0.414]  .001 ** 
## WP.OL-WA.PR             0.162 [-0.005,  0.320]  .051 .  
## WP.OL-WA.ER             0.250 [ 0.087,  0.400]  .002 ** 
## WP.OL-WA.SlpQnV.       -0.004 [-0.175,  0.167]  .964    
## WP.AS-WP.PrfIV.         0.552 [ 0.425,  0.658] <.001 ***
## WP.AS-WP.TC             0.447 [ 0.304,  0.571] <.001 ***
## WP.AS-WA.WRV            0.314 [ 0.157,  0.456] <.001 ***
## WP.AS-WA.PW             0.299 [ 0.140,  0.443] <.001 ***
## WP.AS-WA.NW             0.219 [ 0.055,  0.371]  .008 ** 
## WP.AS-WA.RmV.           0.366 [ 0.213,  0.502] <.001 ***
## WP.AS-WA.PA             0.250 [ 0.088,  0.399]  .002 ** 
## WP.AS-WA.NA            -0.021 [-0.186,  0.145]  .799    
## WP.AS-WA.ImV.           0.307 [ 0.148,  0.450] <.001 ***
## WP.AS-WA.WA             0.193 [ 0.028,  0.348]  .019 *  
## WP.AS-WA.TA             0.316 [ 0.159,  0.458] <.001 ***
## WP.AS-WA.WRF            0.162 [-0.004,  0.319]  .050 .  
## WP.AS-WA.InV.           0.388 [ 0.238,  0.521] <.001 ***
## WP.AS-WA.GV             0.416 [ 0.268,  0.544] <.001 ***
## WP.AS-WA.EV             0.178 [ 0.013,  0.334]  .031 *  
## WP.AS-WA.SlpQlV.        0.184 [ 0.018,  0.339]  .026 *  
## WP.AS-WA.RdV.           0.240 [ 0.076,  0.391]  .004 ** 
## WP.AS-WA.PR             0.110 [-0.058,  0.271]  .188    
## WP.AS-WA.ER             0.244 [ 0.081,  0.394]  .003 ** 
## WP.AS-WA.SlpQnV.        0.005 [-0.166,  0.176]  .955    
## WP.PrfIV.-WP.TC         0.705 [ 0.610,  0.779] <.001 ***
## WP.PrfIV.-WA.WRV        0.536 [ 0.407,  0.645] <.001 ***
## WP.PrfIV.-WA.PW         0.490 [ 0.353,  0.607] <.001 ***
## WP.PrfIV.-WA.NW         0.393 [ 0.243,  0.525] <.001 ***
## WP.PrfIV.-WA.RmV.       0.569 [ 0.445,  0.672] <.001 ***
## WP.PrfIV.-WA.PA         0.464 [ 0.323,  0.585] <.001 ***
## WP.PrfIV.-WA.NA        -0.017 [-0.183,  0.149]  .833    
## WP.PrfIV.-WA.ImV.       0.608 [ 0.492,  0.703] <.001 ***
## WP.PrfIV.-WA.WA         0.438 [ 0.294,  0.563] <.001 ***
## WP.PrfIV.-WA.TA         0.596 [ 0.477,  0.693] <.001 ***
## WP.PrfIV.-WA.WRF        0.456 [ 0.313,  0.578] <.001 ***
## WP.PrfIV.-WA.InV.       0.629 [ 0.518,  0.720] <.001 ***
## WP.PrfIV.-WA.GV         0.335 [ 0.179,  0.474] <.001 ***
## WP.PrfIV.-WA.EV         0.221 [ 0.057,  0.373]  .007 ** 
## WP.PrfIV.-WA.SlpQlV.    0.145 [-0.021,  0.304]  .080 .  
## WP.PrfIV.-WA.RdV.       0.286 [ 0.126,  0.432] <.001 ***
## WP.PrfIV.-WA.PR         0.216 [ 0.051,  0.369]  .009 ** 
## WP.PrfIV.-WA.ER         0.247 [ 0.084,  0.397]  .003 ** 
## WP.PrfIV.-WA.SlpQnV.    0.017 [-0.154,  0.187]  .842    
## WP.TC-WA.WRV            0.556 [ 0.429,  0.661] <.001 ***
## WP.TC-WA.PW             0.546 [ 0.418,  0.653] <.001 ***
## WP.TC-WA.NW             0.370 [ 0.217,  0.505] <.001 ***
## WP.TC-WA.RmV.           0.609 [ 0.492,  0.703] <.001 ***
## WP.TC-WA.PA             0.518 [ 0.385,  0.629] <.001 ***
## WP.TC-WA.NA            -0.018 [-0.183,  0.149]  .832    
## WP.TC-WA.ImV.           0.631 [ 0.519,  0.721] <.001 ***
## WP.TC-WA.WA             0.451 [ 0.308,  0.574] <.001 ***
## WP.TC-WA.TA             0.578 [ 0.456,  0.679] <.001 ***
## WP.TC-WA.WRF            0.515 [ 0.382,  0.627] <.001 ***
## WP.TC-WA.InV.           0.645 [ 0.537,  0.733] <.001 ***
## WP.TC-WA.GV             0.277 [ 0.117,  0.424] <.001 ***
## WP.TC-WA.EV             0.247 [ 0.085,  0.397]  .003 ** 
## WP.TC-WA.SlpQlV.        0.012 [-0.154,  0.178]  .885    
## WP.TC-WA.RdV.           0.253 [ 0.090,  0.403]  .002 ** 
## WP.TC-WA.PR             0.170 [ 0.003,  0.327]  .040 *  
## WP.TC-WA.ER             0.230 [ 0.066,  0.382]  .005 ** 
## WP.TC-WA.SlpQnV.       -0.021 [-0.191,  0.151]  .809    
## WA.WRV-WA.PW            0.823 [ 0.763,  0.869] <.001 ***
## WA.WRV-WA.NW            0.827 [ 0.768,  0.872] <.001 ***
## WA.WRV-WA.RmV.          0.721 [ 0.633,  0.791] <.001 ***
## WA.WRV-WA.PA            0.480 [ 0.345,  0.596] <.001 ***
## WA.WRV-WA.NA            0.010 [-0.152,  0.172]  .900    
## WA.WRV-WA.ImV.          0.593 [ 0.477,  0.689] <.001 ***
## WA.WRV-WA.WA            0.569 [ 0.448,  0.669] <.001 ***
## WA.WRV-WA.TA            0.647 [ 0.542,  0.732] <.001 ***
## WA.WRV-WA.WRF           0.595 [ 0.479,  0.690] <.001 ***
## WA.WRV-WA.InV.          0.609 [ 0.496,  0.702] <.001 ***
## WA.WRV-WA.GV            0.477 [ 0.341,  0.593] <.001 ***
## WA.WRV-WA.EV            0.262 [ 0.105,  0.407]  .001 ** 
## WA.WRV-WA.SlpQlV.       0.021 [-0.142,  0.182]  .803    
## WA.WRV-WA.RdV.          0.192 [ 0.030,  0.344]  .020 *  
## WA.WRV-WA.PR            0.178 [ 0.016,  0.331]  .031 *  
## WA.WRV-WA.ER            0.149 [-0.014,  0.304]  .073 .  
## WA.WRV-WA.SlpQnV.       0.146 [-0.021,  0.306]  .087 .  
## WA.PW-WA.NW             0.362 [ 0.213,  0.495] <.001 ***
## WA.PW-WA.RmV.           0.762 [ 0.684,  0.822] <.001 ***
## WA.PW-WA.PA             0.627 [ 0.518,  0.716] <.001 ***
## WA.PW-WA.NA            -0.084 [-0.242,  0.079]  .314    
## WA.PW-WA.ImV.           0.591 [ 0.475,  0.687] <.001 ***
## WA.PW-WA.WA             0.691 [ 0.596,  0.767] <.001 ***
## WA.PW-WA.TA             0.702 [ 0.609,  0.776] <.001 ***
## WA.PW-WA.WRF            0.723 [ 0.636,  0.792] <.001 ***
## WA.PW-WA.InV.           0.679 [ 0.581,  0.758] <.001 ***
## WA.PW-WA.GV             0.502 [ 0.370,  0.614] <.001 ***
## WA.PW-WA.EV             0.218 [ 0.058,  0.367]  .008 ** 
## WA.PW-WA.SlpQlV.        0.040 [-0.123,  0.200]  .635    
## WA.PW-WA.RdV.           0.211 [ 0.051,  0.361]  .011 *  
## WA.PW-WA.PR             0.189 [ 0.027,  0.341]  .022 *  
## WA.PW-WA.ER             0.168 [ 0.005,  0.321]  .043 *  
## WA.PW-WA.SlpQnV.        0.137 [-0.031,  0.297]  .109    
## WA.NW-WA.RmV.           0.430 [ 0.288,  0.553] <.001 ***
## WA.NW-WA.PA             0.168 [ 0.006,  0.321]  .042 *  
## WA.NW-WA.NA             0.100 [-0.063,  0.258]  .229    
## WA.NW-WA.ImV.           0.388 [ 0.241,  0.517] <.001 ***
## WA.NW-WA.WA             0.250 [ 0.092,  0.396]  .002 ** 
## WA.NW-WA.TA             0.367 [ 0.218,  0.499] <.001 ***
## WA.NW-WA.WRF            0.260 [ 0.103,  0.405]  .001 ** 
## WA.NW-WA.InV.           0.328 [ 0.175,  0.465] <.001 ***
## WA.NW-WA.GV             0.287 [ 0.131,  0.429] <.001 ***
## WA.NW-WA.EV             0.215 [ 0.055,  0.364]  .009 ** 
## WA.NW-WA.SlpQlV.       -0.005 [-0.167,  0.157]  .952    
## WA.NW-WA.RdV.           0.106 [-0.057,  0.264]  .202    
## WA.NW-WA.PR             0.106 [-0.058,  0.264]  .204    
## WA.NW-WA.ER             0.079 [-0.085,  0.238]  .345    
## WA.NW-WA.SlpQnV.        0.105 [-0.063,  0.268]  .218    
## WA.RmV.-WA.PA           0.642 [ 0.536,  0.728] <.001 ***
## WA.RmV.-WA.NA          -0.156 [-0.310,  0.006]  .059 .  
## WA.RmV.-WA.ImV.         0.702 [ 0.610,  0.776] <.001 ***
## WA.RmV.-WA.WA           0.669 [ 0.569,  0.750] <.001 ***
## WA.RmV.-WA.TA           0.690 [ 0.595,  0.767] <.001 ***
## WA.RmV.-WA.WRF          0.656 [ 0.553,  0.739] <.001 ***
## WA.RmV.-WA.InV.         0.693 [ 0.598,  0.769] <.001 ***
## WA.RmV.-WA.GV           0.533 [ 0.407,  0.640] <.001 ***
## WA.RmV.-WA.EV           0.203 [ 0.042,  0.353]  .014 *  
## WA.RmV.-WA.SlpQlV.      0.043 [-0.119,  0.204]  .602    
## WA.RmV.-WA.RdV.         0.243 [ 0.084,  0.390]  .003 ** 
## WA.RmV.-WA.PR           0.181 [ 0.019,  0.334]  .029 *  
## WA.RmV.-WA.ER           0.215 [ 0.055,  0.365]  .009 ** 
## WA.RmV.-WA.SlpQnV.      0.062 [-0.107,  0.226]  .473    
## WA.PA-WA.NA            -0.223 [-0.372, -0.064]  .007 ** 
## WA.PA-WA.ImV.           0.717 [ 0.628,  0.787] <.001 ***
## WA.PA-WA.WA             0.746 [ 0.664,  0.810] <.001 ***
## WA.PA-WA.TA             0.557 [ 0.434,  0.659] <.001 ***
## WA.PA-WA.WRF            0.698 [ 0.605,  0.773] <.001 ***
## WA.PA-WA.InV.           0.571 [ 0.450,  0.671] <.001 ***
## WA.PA-WA.GV             0.395 [ 0.249,  0.523] <.001 ***
## WA.PA-WA.EV             0.180 [ 0.019,  0.332]  .029 *  
## WA.PA-WA.SlpQlV.       -0.079 [-0.238,  0.084]  .341    
## WA.PA-WA.RdV.           0.140 [-0.023,  0.295]  .093 .  
## WA.PA-WA.PR             0.157 [-0.006,  0.311]  .059 .  
## WA.PA-WA.ER             0.097 [-0.066,  0.256]  .243    
## WA.PA-WA.SlpQnV.        0.023 [-0.145,  0.189]  .788    
## WA.NA-WA.ImV.          -0.114 [-0.270,  0.049]  .171    
## WA.NA-WA.WA            -0.097 [-0.255,  0.066]  .241    
## WA.NA-WA.TA            -0.019 [-0.181,  0.143]  .815    
## WA.NA-WA.WRF           -0.070 [-0.230,  0.093]  .397    
## WA.NA-WA.InV.          -0.073 [-0.233,  0.090]  .377    
## WA.NA-WA.GV            -0.108 [-0.265,  0.055]  .193    
## WA.NA-WA.EV            -0.069 [-0.229,  0.094]  .405    
## WA.NA-WA.SlpQlV.        0.136 [-0.026,  0.292]  .099 .  
## WA.NA-WA.RdV.           0.096 [-0.067,  0.255]  .249    
## WA.NA-WA.PR             0.104 [-0.059,  0.262]  .210    
## WA.NA-WA.ER             0.067 [-0.096,  0.227]  .421    
## WA.NA-WA.SlpQnV.       -0.033 [-0.199,  0.135]  .702    
## WA.ImV.-WA.WA           0.632 [ 0.524,  0.720] <.001 ***
## WA.ImV.-WA.TA           0.673 [ 0.573,  0.753] <.001 ***
## WA.ImV.-WA.WRF          0.653 [ 0.550,  0.737] <.001 ***
## WA.ImV.-WA.InV.         0.727 [ 0.641,  0.795] <.001 ***
## WA.ImV.-WA.GV           0.379 [ 0.231,  0.509] <.001 ***
## WA.ImV.-WA.EV           0.161 [-0.001,  0.315]  .051 .  
## WA.ImV.-WA.SlpQlV.     -0.030 [-0.191,  0.133]  .722    
## WA.ImV.-WA.RdV.         0.144 [-0.019,  0.300]  .082 .  
## WA.ImV.-WA.PR           0.149 [-0.014,  0.304]  .072 .  
## WA.ImV.-WA.ER           0.104 [-0.059,  0.262]  .211    
## WA.ImV.-WA.SlpQnV.     -0.104 [-0.266,  0.064]  .226    
## WA.WA-WA.TA             0.597 [ 0.482,  0.692] <.001 ***
## WA.WA-WA.WRF            0.820 [ 0.759,  0.867] <.001 ***
## WA.WA-WA.InV.           0.577 [ 0.458,  0.676] <.001 ***
## WA.WA-WA.GV             0.341 [ 0.190,  0.477] <.001 ***
## WA.WA-WA.EV             0.160 [-0.002,  0.313]  .053 .  
## WA.WA-WA.SlpQlV.        0.073 [-0.090,  0.232]  .383    
## WA.WA-WA.RdV.           0.148 [-0.015,  0.303]  .075 .  
## WA.WA-WA.PR             0.098 [-0.066,  0.256]  .241    
## WA.WA-WA.ER             0.138 [-0.025,  0.294]  .096 .  
## WA.WA-WA.SlpQnV.        0.066 [-0.103,  0.230]  .444    
## WA.TA-WA.WRF            0.639 [ 0.532,  0.725] <.001 ***
## WA.TA-WA.InV.           0.816 [ 0.754,  0.864] <.001 ***
## WA.TA-WA.GV             0.456 [ 0.318,  0.576] <.001 ***
## WA.TA-WA.EV             0.156 [-0.006,  0.310]  .059 .  
## WA.TA-WA.SlpQlV.        0.046 [-0.116,  0.207]  .577    
## WA.TA-WA.RdV.           0.220 [ 0.060,  0.369]  .008 ** 
## WA.TA-WA.PR             0.158 [-0.004,  0.313]  .057 .  
## WA.TA-WA.ER             0.193 [ 0.031,  0.344]  .020 *  
## WA.TA-WA.SlpQnV.        0.142 [-0.026,  0.302]  .097 .  
## WA.WRF-WA.InV.          0.618 [ 0.507,  0.709] <.001 ***
## WA.WRF-WA.GV            0.349 [ 0.199,  0.484] <.001 ***
## WA.WRF-WA.EV            0.238 [ 0.079,  0.385]  .004 ** 
## WA.WRF-WA.SlpQlV.      -0.119 [-0.276,  0.044]  .151    
## WA.WRF-WA.RdV.          0.140 [-0.023,  0.295]  .093 .  
## WA.WRF-WA.PR            0.141 [-0.022,  0.296]  .090 .  
## WA.WRF-WA.ER            0.106 [-0.057,  0.264]  .203    
## WA.WRF-WA.SlpQnV.       0.153 [-0.015,  0.312]  .074 .  
## WA.InV.-WA.GV           0.452 [ 0.313,  0.572] <.001 ***
## WA.InV.-WA.EV           0.134 [-0.029,  0.289]  .107    
## WA.InV.-WA.SlpQlV.     -0.054 [-0.214,  0.109]  .517    
## WA.InV.-WA.RdV.         0.243 [ 0.084,  0.390]  .003 ** 
## WA.InV.-WA.PR           0.220 [ 0.059,  0.369]  .008 ** 
## WA.InV.-WA.ER           0.190 [ 0.029,  0.342]  .021 *  
## WA.InV.-WA.SlpQnV.      0.103 [-0.065,  0.266]  .228    
## WA.GV-WA.EV             0.047 [-0.116,  0.207]  .572    
## WA.GV-WA.SlpQlV.        0.079 [-0.084,  0.238]  .339    
## WA.GV-WA.RdV.           0.184 [ 0.022,  0.336]  .027 *  
## WA.GV-WA.PR             0.155 [-0.007,  0.310]  .061 .  
## WA.GV-WA.ER             0.152 [-0.011,  0.307]  .068 .  
## WA.GV-WA.SlpQnV.        0.101 [-0.067,  0.263]  .239    
## WA.EV-WA.SlpQlV.       -0.027 [-0.188,  0.135]  .745    
## WA.EV-WA.RdV.           0.193 [ 0.032,  0.345]  .019 *  
## WA.EV-WA.PR             0.306 [ 0.151,  0.446] <.001 ***
## WA.EV-WA.ER             0.085 [-0.079,  0.244]  .310    
## WA.EV-WA.SlpQnV.       -0.007 [-0.174,  0.161]  .938    
## WA.SlpQlV.-WA.RdV.      0.134 [-0.029,  0.291]  .106    
## WA.SlpQlV.-WA.PR        0.146 [-0.016,  0.302]  .078 .  
## WA.SlpQlV.-WA.ER        0.093 [-0.071,  0.251]  .267    
## WA.SlpQlV.-WA.SlpQnV.  -0.235 [-0.386, -0.070]  .006 ** 
## WA.RdV.-WA.PR           0.669 [ 0.569,  0.750] <.001 ***
## WA.RdV.-WA.ER           0.920 [ 0.891,  0.942] <.001 ***
## WA.RdV.-WA.SlpQnV.     -0.040 [-0.206,  0.128]  .640    
## WA.PR-WA.ER             0.326 [ 0.173,  0.464] <.001 ***
## WA.PR-WA.SlpQnV.       -0.092 [-0.256,  0.077]  .282    
## WA.ER-WA.SlpQnV.       -0.000 [-0.168,  0.167]  .996    
## ────────────────────────────────────────────────────────
## 
## 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                             0.888                                     0.803                                     0.824                0.637           0.803                         0.765                     0.541                 0.671              0.788              0.739                     0.693             0.645                      0.653            0.793              0.773                      0.730                      0.681          0.577              0.608              0.559           0.629              0.679                      0.645               0.613           0.657     0.698        0.665            0.531       0.797         0.727     0.777             0.558
## ICC2       -14.395                             0.935                                     0.880                                     0.894                0.761           0.881                         0.855                     0.681                 0.787              0.870              0.837                     0.803             0.766                      0.773            0.874              0.862                      0.833                      0.797          0.714              0.741              0.700           0.757              0.796                      0.770               0.744           0.779     0.810        0.785            0.675       0.876         0.827     0.863             0.694
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

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                             0.461                                     0.439                                     0.424                0.425           0.306                         0.449                     0.309                 0.443              0.410              0.432                     0.376             0.416                      0.335            0.277              0.477                      0.502                      0.287          0.533              0.395             -0.108           0.379              0.341                      0.456               0.349           0.452        0.047            0.079       0.184         0.155     0.152             0.101
## WP.SupervisoryBehavioralFeedbackV              0.002                             1.000                                     0.926                                     0.944                0.473           0.431                         0.525                     0.445                 0.319              0.544              0.333                     0.343             0.241                      0.467            0.426              0.576                      0.591                      0.358          0.627              0.510             -0.095           0.598              0.522                      0.584               0.502           0.565        0.106           -0.007       0.164         0.112     0.145             0.022
## WP.SupervisoryPositiveBehavioralFeedbackV     -0.040                             0.681                                     1.000                                     0.750                0.533           0.450                         0.504                     0.437                 0.281              0.530              0.312                     0.316             0.234                      0.482            0.453              0.530                      0.594                      0.281          0.633              0.554             -0.139           0.654              0.508                      0.609               0.526           0.565        0.112           -0.012       0.127         0.100     0.104            -0.008
## WP.SupervisoryNegativeBehavioralFeedbackV      0.038                             0.737                                     0.008                                     1.000                0.364           0.363                         0.480                     0.398                 0.314              0.490              0.311                     0.326             0.217                      0.398            0.352              0.546                      0.517                      0.382          0.546              0.409             -0.046           0.477              0.470                      0.492               0.420           0.496        0.088           -0.001       0.176         0.109     0.164             0.045
## WP.learningBehaviorV                           0.207                            -0.024                                    -0.025                                    -0.010                1.000           0.637                         0.631                     0.595                 0.447              0.584              0.501                     0.491             0.401                      0.598            0.631              0.610                      0.650                      0.357          0.712              0.510             -0.036           0.680              0.567                      0.702               0.610           0.706        0.172            0.053       0.111         0.035     0.120            -0.084
## WP.JobCraftingV                               -0.064                             0.091                                     0.141                                    -0.007                0.188           1.000                         0.623                     0.587                 0.406              0.596              0.577                     0.603             0.405                      0.662            0.697              0.666                      0.579                      0.517          0.618              0.520             -0.034           0.638              0.521                      0.554               0.529           0.568        0.260            0.031       0.220         0.202     0.170             0.049
## WP.CreativeProcessEngagementV                  0.117                             0.152                                     0.065                                     0.147                0.342           0.313                         1.000                     0.855                 0.793              0.928              0.587                     0.581             0.462                      0.691            0.691              0.625                      0.568                      0.461          0.713              0.595             -0.021           0.723              0.598                      0.627               0.581           0.590        0.211            0.113       0.219         0.166     0.188            -0.028
## WP.ProblemIdentificationV                      0.038                             0.047                                    -0.003                                     0.067                0.300           0.397                         0.736                     1.000                 0.575              0.698              0.566                     0.559             0.447                      0.636            0.618              0.535                      0.523                      0.358          0.656              0.509              0.018           0.641              0.540                      0.557               0.532           0.543        0.232            0.097       0.169         0.137     0.141            -0.090
## WP.InformationSearchV                          0.061                             0.060                                     0.023                                     0.061                0.209           0.230                         0.710                     0.325                 1.000              0.584              0.505                     0.421             0.511                      0.443            0.439              0.414                      0.356                      0.326          0.490              0.435             -0.050           0.473              0.413                      0.396               0.330           0.361        0.128            0.211       0.230         0.170     0.203            -0.077
## WP.IdeaGenerationV                             0.150                             0.213                                     0.114                                     0.185                0.254           0.093                         0.793                     0.317                 0.383              1.000              0.486                     0.526             0.317                      0.675            0.687              0.624                      0.559                      0.469          0.677              0.572             -0.022           0.715              0.573                      0.627               0.593           0.586        0.189            0.035       0.182         0.135     0.156             0.042
## WP.SocialLearningV                             0.084                             0.133                                     0.106                                     0.083                0.172           0.211                         0.423                     0.299                 0.193              0.424              1.000                     0.937             0.861                      0.681            0.606              0.484                      0.457                      0.340          0.478              0.377              0.073           0.446              0.334                      0.504               0.309           0.537        0.210            0.152       0.282         0.155     0.273            -0.000
## WP.ObservationalLearningV                      0.037                            -0.011                                     0.013                                    -0.026                0.089           0.227                         0.379                     0.332                 0.204              0.302              0.856                     1.000             0.630                      0.663            0.620              0.524                      0.494                      0.369          0.479              0.404              0.126           0.472              0.378                      0.553               0.362           0.554        0.199            0.106       0.266         0.162     0.250            -0.004
## WP.AdviceSeekingV                              0.107                             0.262                                     0.182                                     0.190                0.202           0.091                         0.282                     0.114                 0.088              0.388              0.723                     0.262             1.000                      0.552            0.447              0.314                      0.299                      0.219          0.366              0.250             -0.021           0.307              0.193                      0.316               0.162           0.388        0.178            0.184       0.240         0.110     0.244             0.005
## WP.PerformanceImprovementV                     0.079                            -0.096                                    -0.051                                    -0.083                0.210           0.273                         0.224                     0.129                 0.198              0.182              0.219                     0.265             0.055                      1.000            0.705              0.536                      0.490                      0.393          0.569              0.464             -0.017           0.608              0.438                      0.596               0.456           0.629        0.221            0.145       0.286         0.216     0.247             0.017
## WP.TakingChargeV                               0.118                             0.078                                     0.022                                     0.086                0.255           0.163                         0.217                     0.205                 0.143              0.141              0.171                     0.165             0.099                      0.299            1.000              0.556                      0.546                      0.370          0.609              0.518             -0.018           0.631              0.451                      0.578               0.515           0.645        0.247            0.012       0.253         0.170     0.230            -0.021
## WA.WorkReflectionV                             0.065                             0.179                                     0.060                                     0.187                0.207           0.238                         0.129                     0.151                -0.097              0.185              0.045                     0.029             0.046                      0.052            0.190              1.000                      0.823                      0.827          0.721              0.480              0.010           0.593              0.569                      0.647               0.595           0.609        0.262            0.021       0.192         0.178     0.149             0.146
## WA.PositiveWorkReflectionV                     0.064                             0.093                                     0.029                                     0.100                0.273           0.254                         0.165                     0.204                 0.024              0.119              0.048                     0.038             0.038                      0.148            0.185              0.676                      1.000                      0.362          0.762              0.627             -0.084           0.591              0.691                      0.702               0.723           0.679        0.218            0.040       0.211         0.189     0.168             0.137
## WA.NegativeWorkReflectionV                     0.029                             0.157                                     0.054                                     0.162                0.032           0.092                         0.025                     0.019                -0.152              0.142              0.017                     0.004             0.027                     -0.064            0.089              0.744                      0.011                      1.000          0.430              0.168              0.100           0.388              0.250                      0.367               0.260           0.328        0.215           -0.005       0.106         0.106     0.079             0.105
## WA.RuminationV                                 0.286                             0.251                                     0.181                                     0.176                0.240           0.111                         0.249                     0.205                 0.022              0.284              0.195                     0.175             0.129                      0.043            0.086              0.424                      0.347                      0.261          1.000              0.642             -0.156           0.702              0.669                      0.690               0.656           0.693        0.203            0.043       0.243         0.181     0.215             0.062
## WA.PositiveAffectV                             0.037                             0.171                                     0.036                                     0.199                0.027           0.142                         0.093                     0.139                 0.029              0.033              0.184                     0.118             0.184                     -0.147            0.036              0.183                      0.140                      0.122          0.242              1.000             -0.223           0.717              0.746                      0.557               0.698           0.571        0.180           -0.079       0.140         0.157     0.097             0.023
## WA.NegativeAffectV                             0.081                             0.086                                     0.107                                     0.021               -0.064           0.002                         0.068                     0.031                 0.038              0.077             -0.013                    -0.030             0.016                      0.017           -0.024              0.134                     -0.014                      0.195          0.083             -0.060              1.000          -0.114             -0.097                     -0.019              -0.070          -0.073       -0.069            0.136       0.096         0.104     0.067            -0.033
## WA.ImprovisionV                                0.113                             0.374                                     0.196                                     0.329                0.060           0.198                         0.168                     0.209                 0.023              0.122              0.066                     0.023             0.094                     -0.044            0.132              0.382                      0.225                      0.314          0.256              0.272              0.132           1.000              0.632                      0.673               0.653           0.727        0.161           -0.030       0.144         0.149     0.104            -0.104
## WA.WorkAbsorptionV                             0.138                             0.101                                     0.033                                     0.106                0.030           0.182                         0.011                     0.004                 0.061             -0.026             -0.104                    -0.130            -0.020                      0.005            0.050              0.218                      0.196                      0.118          0.294              0.201             -0.049           0.120              1.000                      0.597               0.820           0.577        0.160            0.073       0.148         0.098     0.138             0.066
## WA.ThrivingAtWorkLearningV                     0.100                             0.210                                     0.129                                     0.168                0.129           0.219                         0.091                     0.226                -0.010             -0.021              0.141                     0.169             0.037                      0.008            0.117              0.322                      0.197                      0.258          0.272              0.307             -0.029           0.390              0.317                      1.000               0.639           0.816        0.156            0.046       0.220         0.158     0.193             0.142
## WA.WorkRelatedFlowV                            0.009                             0.204                                     0.090                                     0.195                0.035           0.021                        -0.071                    -0.107                -0.020             -0.027             -0.009                    -0.020             0.011                      0.094            0.240              0.287                      0.317                      0.102          0.267              0.131             -0.015           0.101              0.547                      0.310               1.000           0.618        0.238           -0.119       0.140         0.141     0.106             0.153
## WA.InspirationV                                0.017                             0.131                                     0.102                                     0.086                0.112           0.127                         0.106                     0.112                 0.104              0.030             -0.009                     0.059            -0.096                      0.123            0.021              0.244                      0.163                      0.183          0.265              0.179             -0.049           0.325              0.224                      0.604               0.239           1.000        0.134           -0.054       0.243         0.220     0.190             0.103
## WA.ExerciseV                                  -0.043                             0.197                                     0.028                                     0.240               -0.045          -0.057                        -0.148                    -0.124                -0.057             -0.135             -0.089                    -0.095            -0.039                      0.091           -0.076             -0.042                      0.043                     -0.096          0.011              0.107              0.023          -0.108              0.120                      0.026               0.138           0.013        1.000           -0.027       0.193         0.306     0.085            -0.007
## WA.SleepQualityV                               0.027                             0.042                                    -0.060                                     0.111                0.035           0.146                         0.213                     0.235                 0.149              0.093              0.068                     0.108            -0.019                      0.216            0.135              0.182                      0.122                      0.137          0.122              0.100             -0.026           0.200              0.071                      0.304               0.107           0.243        0.010            1.000       0.134         0.146     0.093            -0.235
## WA.ReadingV                                   -0.073                             0.172                                    -0.038                                     0.266               -0.065           0.180                        -0.010                    -0.164                -0.071              0.182              0.061                     0.029             0.076                      0.027            0.037              0.155                      0.042                      0.171          0.129              0.132             -0.054           0.016              0.037                      0.071               0.214           0.178        0.199            0.078       1.000         0.669     0.920            -0.040
## WA.PaperReadV                                 -0.075                             0.047                                    -0.081                                     0.137               -0.013           0.085                         0.031                    -0.057                 0.150              0.001              0.170                     0.189             0.064                      0.038            0.004             -0.025                     -0.030                     -0.008          0.048              0.086             -0.029          -0.108             -0.036                      0.020               0.094           0.088        0.203           -0.033       0.568         1.000     0.326            -0.092
## WA.EReadV                                     -0.034                             0.174                                     0.001                                     0.233               -0.057           0.167                        -0.026                    -0.148                -0.180              0.217             -0.030                    -0.073             0.043                      0.011            0.042              0.174                      0.050                      0.192          0.137              0.108             -0.048           0.073              0.060                      0.065               0.194           0.163        0.122            0.114       0.873         0.098     1.000            -0.000
## WA.SleepQuantityV                             -0.036                            -0.037                                    -0.016                                    -0.036               -0.043          -0.202                        -0.101                    -0.188                 0.088             -0.088             -0.197                    -0.232            -0.068                     -0.226           -0.123             -0.064                     -0.121                      0.022         -0.160             -0.187              0.152          -0.202              0.079                     -0.167               0.032          -0.233        0.057           -0.377      -0.132        -0.135    -0.095             1.000
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 
## Within-Level Correlation [95% CI]:
## ────────────────────────────────────────────────────────
##                             r         [95% CI]     p    
## ────────────────────────────────────────────────────────
## WA.GV-WP.SB             0.002 [-0.123,  0.126]  .981    
## WA.GV-WP.SP            -0.040 [-0.164,  0.085]  .532    
## WA.GV-WP.SN             0.038 [-0.087,  0.162]  .553    
## WA.GV-WP.BV             0.207 [ 0.084,  0.323]  .001 ** 
## WA.GV-WP.JC            -0.064 [-0.187,  0.061]  .319    
## WA.GV-WP.CP             0.117 [-0.008,  0.238]  .067 .  
## WA.GV-WP.PrbIV.         0.038 [-0.087,  0.161]  .557    
## WA.GV-WP.IS             0.061 [-0.064,  0.184]  .340    
## WA.GV-WP.IG             0.150 [ 0.026,  0.269]  .019 *  
## WA.GV-WP.SL             0.084 [-0.041,  0.206]  .189    
## WA.GV-WP.OL             0.037 [-0.088,  0.161]  .559    
## WA.GV-WP.AS             0.107 [-0.018,  0.228]  .095 .  
## WA.GV-WP.PrfIV.         0.079 [-0.046,  0.202]  .215    
## WA.GV-WP.TC             0.118 [-0.007,  0.239]  .066 .  
## WA.GV-WA.WRV            0.065 [-0.055,  0.183]  .292    
## WA.GV-WA.PW             0.064 [-0.055,  0.182]  .293    
## WA.GV-WA.NW             0.029 [-0.091,  0.148]  .634    
## WA.GV-WA.RmV.           0.286 [ 0.172,  0.392] <.001 ***
## WA.GV-WA.PA             0.037 [-0.082,  0.156]  .542    
## WA.GV-WA.NA             0.081 [-0.039,  0.198]  .188    
## WA.GV-WA.ImV.           0.113 [-0.007,  0.229]  .066 .  
## WA.GV-WA.WA             0.138 [ 0.019,  0.253]  .025 *  
## WA.GV-WA.TA             0.100 [-0.019,  0.217]  .102    
## WA.GV-WA.WRF            0.009 [-0.111,  0.128]  .884    
## WA.GV-WA.InV.           0.017 [-0.102,  0.136]  .778    
## WA.GV-WA.EV            -0.043 [-0.162,  0.077]  .480    
## WA.GV-WA.SlpQlV.        0.027 [-0.093,  0.146]  .658    
## WA.GV-WA.RdV.          -0.073 [-0.192,  0.048]  .240    
## WA.GV-WA.PR            -0.075 [-0.194,  0.047]  .229    
## WA.GV-WA.ER            -0.034 [-0.154,  0.087]  .581    
## WA.GV-WA.SlpQnV.       -0.036 [-0.160,  0.089]  .570    
## WP.SB-WP.SP             0.681 [ 0.609,  0.742] <.001 ***
## WP.SB-WP.SN             0.737 [ 0.675,  0.789] <.001 ***
## WP.SB-WP.BV            -0.024 [-0.147,  0.099]  .702    
## WP.SB-WP.JC             0.091 [-0.033,  0.212]  .152    
## WP.SB-WP.CP             0.152 [ 0.029,  0.270]  .017 *  
## WP.SB-WP.PrbIV.         0.047 [-0.077,  0.169]  .461    
## WP.SB-WP.IS             0.060 [-0.063,  0.182]  .340    
## WP.SB-WP.IG             0.213 [ 0.092,  0.328] <.001 ***
## WP.SB-WP.SL             0.133 [ 0.009,  0.252]  .036 *  
## WP.SB-WP.OL            -0.011 [-0.134,  0.113]  .863    
## WP.SB-WP.AS             0.262 [ 0.144,  0.374] <.001 ***
## WP.SB-WP.PrfIV.        -0.096 [-0.216,  0.028]  .131    
## WP.SB-WP.TC             0.078 [-0.046,  0.199]  .220    
## WP.SB-WA.WRV            0.179 [ 0.056,  0.297]  .005 ** 
## WP.SB-WA.PW             0.093 [-0.032,  0.215]  .145    
## WP.SB-WA.NW             0.157 [ 0.033,  0.276]  .014 *  
## WP.SB-WA.RmV.           0.251 [ 0.130,  0.364] <.001 ***
## WP.SB-WA.PA             0.171 [ 0.048,  0.290]  .007 ** 
## WP.SB-WA.NA             0.086 [-0.039,  0.209]  .176    
## WP.SB-WA.ImV.           0.374 [ 0.262,  0.477] <.001 ***
## WP.SB-WA.WA             0.101 [-0.024,  0.222]  .116    
## WP.SB-WA.TA             0.210 [ 0.088,  0.326] <.001 ***
## WP.SB-WA.WRF            0.204 [ 0.082,  0.321]  .001 ** 
## WP.SB-WA.InV.           0.131 [ 0.006,  0.251]  .040 *  
## WP.SB-WA.EV             0.197 [ 0.074,  0.314]  .002 ** 
## WP.SB-WA.SlpQlV.        0.042 [-0.083,  0.166]  .508    
## WP.SB-WA.RdV.           0.172 [ 0.047,  0.292]  .008 ** 
## WP.SB-WA.PR             0.047 [-0.080,  0.173]  .466    
## WP.SB-WA.ER             0.174 [ 0.049,  0.293]  .007 ** 
## WP.SB-WA.SlpQnV.       -0.037 [-0.166,  0.093]  .580    
## WP.SP-WP.SN             0.008 [-0.116,  0.131]  .901    
## WP.SP-WP.BV            -0.025 [-0.148,  0.098]  .690    
## WP.SP-WP.JC             0.141 [ 0.018,  0.260]  .026 *  
## WP.SP-WP.CP             0.065 [-0.059,  0.187]  .304    
## WP.SP-WP.PrbIV.        -0.003 [-0.126,  0.120]  .961    
## WP.SP-WP.IS             0.023 [-0.101,  0.146]  .718    
## WP.SP-WP.IG             0.114 [-0.009,  0.234]  .071 .  
## WP.SP-WP.SL             0.106 [-0.017,  0.227]  .093 .  
## WP.SP-WP.OL             0.013 [-0.111,  0.136]  .843    
## WP.SP-WP.AS             0.182 [ 0.060,  0.299]  .004 ** 
## WP.SP-WP.PrfIV.        -0.051 [-0.173,  0.073]  .418    
## WP.SP-WP.TC             0.022 [-0.101,  0.145]  .727    
## WP.SP-WA.WRV            0.060 [-0.065,  0.183]  .347    
## WP.SP-WA.PW             0.029 [-0.096,  0.153]  .647    
## WP.SP-WA.NW             0.054 [-0.071,  0.178]  .394    
## WP.SP-WA.RmV.           0.181 [ 0.058,  0.299]  .005 ** 
## WP.SP-WA.PA             0.036 [-0.089,  0.160]  .576    
## WP.SP-WA.NA             0.107 [-0.018,  0.229]  .094 .  
## WP.SP-WA.ImV.           0.196 [ 0.073,  0.313]  .002 ** 
## WP.SP-WA.WA             0.033 [-0.092,  0.157]  .605    
## WP.SP-WA.TA             0.129 [ 0.004,  0.249]  .044 *  
## WP.SP-WA.WRF            0.090 [-0.035,  0.212]  .159    
## WP.SP-WA.InV.           0.102 [-0.023,  0.223]  .112    
## WP.SP-WA.EV             0.028 [-0.097,  0.152]  .658    
## WP.SP-WA.SlpQlV.       -0.060 [-0.183,  0.065]  .347    
## WP.SP-WA.RdV.          -0.038 [-0.163,  0.089]  .560    
## WP.SP-WA.PR            -0.081 [-0.206,  0.045]  .209    
## WP.SP-WA.ER             0.001 [-0.125,  0.127]  .986    
## WP.SP-WA.SlpQnV.       -0.016 [-0.146,  0.114]  .807    
## WP.SN-WP.BV            -0.010 [-0.133,  0.114]  .877    
## WP.SN-WP.JC            -0.007 [-0.130,  0.117]  .918    
## WP.SN-WP.CP             0.147 [ 0.024,  0.266]  .020 *  
## WP.SN-WP.PrbIV.         0.067 [-0.057,  0.188]  .293    
## WP.SN-WP.IS             0.061 [-0.062,  0.183]  .332    
## WP.SN-WP.IG             0.185 [ 0.063,  0.302]  .003 ** 
## WP.SN-WP.SL             0.083 [-0.041,  0.204]  .190    
## WP.SN-WP.OL            -0.026 [-0.149,  0.097]  .676    
## WP.SN-WP.AS             0.190 [ 0.069,  0.306]  .003 ** 
## WP.SN-WP.PrfIV.        -0.083 [-0.205,  0.040]  .187    
## WP.SN-WP.TC             0.086 [-0.038,  0.207]  .176    
## WP.SN-WA.WRV            0.187 [ 0.064,  0.305]  .003 ** 
## WP.SN-WA.PW             0.100 [-0.025,  0.221]  .119    
## WP.SN-WA.NW             0.162 [ 0.039,  0.281]  .011 *  
## WP.SN-WA.RmV.           0.176 [ 0.053,  0.294]  .006 ** 
## WP.SN-WA.PA             0.199 [ 0.077,  0.316]  .002 ** 
## WP.SN-WA.NA             0.021 [-0.104,  0.145]  .748    
## WP.SN-WA.ImV.           0.329 [ 0.214,  0.436] <.001 ***
## WP.SN-WA.WA             0.106 [-0.019,  0.228]  .097 .  
## WP.SN-WA.TA             0.168 [ 0.045,  0.287]  .008 ** 
## WP.SN-WA.WRF            0.195 [ 0.072,  0.312]  .002 ** 
## WP.SN-WA.InV.           0.086 [-0.039,  0.208]  .181    
## WP.SN-WA.EV             0.240 [ 0.119,  0.354] <.001 ***
## WP.SN-WA.SlpQlV.        0.111 [-0.014,  0.232]  .082 .  
## WP.SN-WA.RdV.           0.266 [ 0.144,  0.379] <.001 ***
## WP.SN-WA.PR             0.137 [ 0.010,  0.258]  .035 *  
## WP.SN-WA.ER             0.233 [ 0.110,  0.349] <.001 ***
## WP.SN-WA.SlpQnV.       -0.036 [-0.165,  0.094]  .590    
## WP.BV-WP.JC             0.188 [ 0.066,  0.304]  .003 ** 
## WP.BV-WP.CP             0.342 [ 0.229,  0.447] <.001 ***
## WP.BV-WP.PrbIV.         0.300 [ 0.183,  0.408] <.001 ***
## WP.BV-WP.IS             0.209 [ 0.088,  0.324] <.001 ***
## WP.BV-WP.IG             0.254 [ 0.135,  0.366] <.001 ***
## WP.BV-WP.SL             0.172 [ 0.049,  0.289]  .007 ** 
## WP.BV-WP.OL             0.089 [-0.034,  0.210]  .159    
## WP.BV-WP.AS             0.202 [ 0.080,  0.317]  .001 ** 
## WP.BV-WP.PrfIV.         0.210 [ 0.089,  0.325] <.001 ***
## WP.BV-WP.TC             0.255 [ 0.136,  0.367] <.001 ***
## WP.BV-WA.WRV            0.207 [ 0.085,  0.323]  .001 ** 
## WP.BV-WA.PW             0.273 [ 0.154,  0.385] <.001 ***
## WP.BV-WA.NW             0.032 [-0.093,  0.156]  .616    
## WP.BV-WA.RmV.           0.240 [ 0.119,  0.354] <.001 ***
## WP.BV-WA.PA             0.027 [-0.098,  0.151]  .672    
## WP.BV-WA.NA            -0.064 [-0.187,  0.061]  .319    
## WP.BV-WA.ImV.           0.060 [-0.065,  0.183]  .350    
## WP.BV-WA.WA             0.030 [-0.095,  0.154]  .637    
## WP.BV-WA.TA             0.129 [ 0.005,  0.250]  .043 *  
## WP.BV-WA.WRF            0.035 [-0.090,  0.159]  .579    
## WP.BV-WA.InV.           0.112 [-0.012,  0.234]  .078 .  
## WP.BV-WA.EV            -0.045 [-0.169,  0.080]  .478    
## WP.BV-WA.SlpQlV.        0.035 [-0.090,  0.159]  .580    
## WP.BV-WA.RdV.          -0.065 [-0.190,  0.062]  .318    
## WP.BV-WA.PR            -0.013 [-0.139,  0.114]  .841    
## WP.BV-WA.ER            -0.057 [-0.181,  0.070]  .382    
## WP.BV-WA.SlpQnV.       -0.043 [-0.172,  0.087]  .515    
## WP.JC-WP.CP             0.313 [ 0.197,  0.420] <.001 ***
## WP.JC-WP.PrbIV.         0.397 [ 0.288,  0.496] <.001 ***
## WP.JC-WP.IS             0.230 [ 0.109,  0.343] <.001 ***
## WP.JC-WP.IG             0.093 [-0.030,  0.214]  .140    
## WP.JC-WP.SL             0.211 [ 0.090,  0.326] <.001 ***
## WP.JC-WP.OL             0.227 [ 0.106,  0.341] <.001 ***
## WP.JC-WP.AS             0.091 [-0.032,  0.212]  .149    
## WP.JC-WP.PrfIV.         0.273 [ 0.154,  0.383] <.001 ***
## WP.JC-WP.TC             0.163 [ 0.040,  0.281]  .010 *  
## WP.JC-WA.WRV            0.238 [ 0.117,  0.352] <.001 ***
## WP.JC-WA.PW             0.254 [ 0.133,  0.367] <.001 ***
## WP.JC-WA.NW             0.092 [-0.033,  0.214]  .152    
## WP.JC-WA.RmV.           0.111 [-0.014,  0.232]  .082 .  
## WP.JC-WA.PA             0.142 [ 0.017,  0.261]  .027 *  
## WP.JC-WA.NA             0.002 [-0.123,  0.127]  .975    
## WP.JC-WA.ImV.           0.198 [ 0.076,  0.315]  .002 ** 
## WP.JC-WA.WA             0.182 [ 0.059,  0.300]  .004 ** 
## WP.JC-WA.TA             0.219 [ 0.098,  0.335] <.001 ***
## WP.JC-WA.WRF            0.021 [-0.103,  0.146]  .736    
## WP.JC-WA.InV.           0.127 [ 0.002,  0.247]  .048 *  
## WP.JC-WA.EV            -0.057 [-0.180,  0.068]  .376    
## WP.JC-WA.SlpQlV.        0.146 [ 0.022,  0.266]  .022 *  
## WP.JC-WA.RdV.           0.180 [ 0.055,  0.299]  .006 ** 
## WP.JC-WA.PR             0.085 [-0.042,  0.209]  .189    
## WP.JC-WA.ER             0.167 [ 0.042,  0.287]  .010 ** 
## WP.JC-WA.SlpQnV.       -0.202 [-0.323, -0.074]  .002 ** 
## WP.CP-WP.PrbIV.         0.736 [ 0.674,  0.788] <.001 ***
## WP.CP-WP.IS             0.710 [ 0.644,  0.767] <.001 ***
## WP.CP-WP.IG             0.793 [ 0.743,  0.835] <.001 ***
## WP.CP-WP.SL             0.423 [ 0.316,  0.519] <.001 ***
## WP.CP-WP.OL             0.379 [ 0.269,  0.480] <.001 ***
## WP.CP-WP.AS             0.282 [ 0.164,  0.392] <.001 ***
## WP.CP-WP.PrfIV.         0.224 [ 0.103,  0.338] <.001 ***
## WP.CP-WP.TC             0.217 [ 0.097,  0.332] <.001 ***
## WP.CP-WA.WRV            0.129 [ 0.004,  0.250]  .044 *  
## WP.CP-WA.PW             0.165 [ 0.041,  0.284]  .010 ** 
## WP.CP-WA.NW             0.025 [-0.100,  0.149]  .699    
## WP.CP-WA.RmV.           0.249 [ 0.129,  0.363] <.001 ***
## WP.CP-WA.PA             0.093 [-0.032,  0.215]  .147    
## WP.CP-WA.NA             0.068 [-0.057,  0.191]  .287    
## WP.CP-WA.ImV.           0.168 [ 0.045,  0.287]  .008 ** 
## WP.CP-WA.WA             0.011 [-0.113,  0.136]  .859    
## WP.CP-WA.TA             0.091 [-0.034,  0.213]  .155    
## WP.CP-WA.WRF           -0.071 [-0.194,  0.054]  .265    
## WP.CP-WA.InV.           0.106 [-0.018,  0.228]  .096 .  
## WP.CP-WA.EV            -0.148 [-0.268, -0.024]  .020 *  
## WP.CP-WA.SlpQlV.        0.213 [ 0.090,  0.328] <.001 ***
## WP.CP-WA.RdV.          -0.010 [-0.136,  0.116]  .875    
## WP.CP-WA.PR             0.031 [-0.095,  0.157]  .630    
## WP.CP-WA.ER            -0.026 [-0.152,  0.100]  .683    
## WP.CP-WA.SlpQnV.       -0.101 [-0.227,  0.030]  .131    
## WP.PrbIV.-WP.IS         0.325 [ 0.210,  0.431] <.001 ***
## WP.PrbIV.-WP.IG         0.317 [ 0.201,  0.423] <.001 ***
## WP.PrbIV.-WP.SL         0.299 [ 0.182,  0.407] <.001 ***
## WP.PrbIV.-WP.OL         0.332 [ 0.217,  0.437] <.001 ***
## WP.PrbIV.-WP.AS         0.114 [-0.009,  0.234]  .071 .  
## WP.PrbIV.-WP.PrfIV.     0.129 [ 0.006,  0.248]  .042 *  
## WP.PrbIV.-WP.TC         0.205 [ 0.084,  0.320]  .001 ** 
## WP.PrbIV.-WA.WRV        0.151 [ 0.027,  0.270]  .018 *  
## WP.PrbIV.-WA.PW         0.204 [ 0.081,  0.320]  .001 ** 
## WP.PrbIV.-WA.NW         0.019 [-0.106,  0.143]  .770    
## WP.PrbIV.-WA.RmV.       0.205 [ 0.082,  0.321]  .001 ** 
## WP.PrbIV.-WA.PA         0.139 [ 0.015,  0.259]  .029 *  
## WP.PrbIV.-WA.NA         0.031 [-0.094,  0.155]  .632    
## WP.PrbIV.-WA.ImV.       0.209 [ 0.087,  0.325]  .001 ** 
## WP.PrbIV.-WA.WA         0.004 [-0.121,  0.128]  .951    
## WP.PrbIV.-WA.TA         0.226 [ 0.105,  0.341] <.001 ***
## WP.PrbIV.-WA.WRF       -0.107 [-0.229,  0.018]  .094 .  
## WP.PrbIV.-WA.InV.       0.112 [-0.013,  0.233]  .080 .  
## WP.PrbIV.-WA.EV        -0.124 [-0.245,  0.001]  .053 .  
## WP.PrbIV.-WA.SlpQlV.    0.235 [ 0.113,  0.349] <.001 ***
## WP.PrbIV.-WA.RdV.      -0.164 [-0.284, -0.038]  .011 *  
## WP.PrbIV.-WA.PR        -0.057 [-0.182,  0.070]  .381    
## WP.PrbIV.-WA.ER        -0.148 [-0.269, -0.022]  .022 *  
## WP.PrbIV.-WA.SlpQnV.   -0.188 [-0.310, -0.060]  .005 ** 
## WP.IS-WP.IG             0.383 [ 0.273,  0.484] <.001 ***
## WP.IS-WP.SL             0.193 [ 0.071,  0.309]  .002 ** 
## WP.IS-WP.OL             0.204 [ 0.082,  0.319]  .001 ** 
## WP.IS-WP.AS             0.088 [-0.036,  0.209]  .164    
## WP.IS-WP.PrfIV.         0.198 [ 0.076,  0.313]  .002 ** 
## WP.IS-WP.TC             0.143 [ 0.020,  0.262]  .024 *  
## WP.IS-WA.WRV           -0.097 [-0.219,  0.028]  .129    
## WP.IS-WA.PW             0.024 [-0.101,  0.148]  .710    
## WP.IS-WA.NW            -0.152 [-0.272, -0.028]  .017 *  
## WP.IS-WA.RmV.           0.022 [-0.103,  0.146]  .728    
## WP.IS-WA.PA             0.029 [-0.096,  0.153]  .649    
## WP.IS-WA.NA             0.038 [-0.087,  0.162]  .550    
## WP.IS-WA.ImV.           0.023 [-0.102,  0.147]  .719    
## WP.IS-WA.WA             0.061 [-0.064,  0.184]  .338    
## WP.IS-WA.TA            -0.010 [-0.135,  0.115]  .874    
## WP.IS-WA.WRF           -0.020 [-0.144,  0.105]  .758    
## WP.IS-WA.InV.           0.104 [-0.021,  0.226]  .103    
## WP.IS-WA.EV            -0.057 [-0.181,  0.068]  .369    
## WP.IS-WA.SlpQlV.        0.149 [ 0.025,  0.269]  .019 *  
## WP.IS-WA.RdV.          -0.071 [-0.196,  0.056]  .274    
## WP.IS-WA.PR             0.150 [ 0.024,  0.271]  .021 *  
## WP.IS-WA.ER            -0.180 [-0.299, -0.055]  .005 ** 
## WP.IS-WA.SlpQnV.        0.088 [-0.043,  0.215]  .188    
## WP.IG-WP.SL             0.424 [ 0.317,  0.520] <.001 ***
## WP.IG-WP.OL             0.302 [ 0.185,  0.410] <.001 ***
## WP.IG-WP.AS             0.388 [ 0.278,  0.488] <.001 ***
## WP.IG-WP.PrfIV.         0.182 [ 0.060,  0.298]  .004 ** 
## WP.IG-WP.TC             0.141 [ 0.018,  0.260]  .026 *  
## WP.IG-WA.WRV            0.185 [ 0.062,  0.303]  .004 ** 
## WP.IG-WA.PW             0.119 [-0.005,  0.240]  .062 .  
## WP.IG-WA.NW             0.142 [ 0.018,  0.262]  .026 *  
## WP.IG-WA.RmV.           0.284 [ 0.166,  0.395] <.001 ***
## WP.IG-WA.PA             0.033 [-0.092,  0.157]  .602    
## WP.IG-WA.NA             0.077 [-0.048,  0.200]  .227    
## WP.IG-WA.ImV.           0.122 [-0.002,  0.243]  .056 .  
## WP.IG-WA.WA            -0.026 [-0.150,  0.099]  .686    
## WP.IG-WA.TA            -0.021 [-0.145,  0.104]  .740    
## WP.IG-WA.WRF           -0.027 [-0.151,  0.098]  .669    
## WP.IG-WA.InV.           0.030 [-0.095,  0.154]  .638    
## WP.IG-WA.EV            -0.135 [-0.255, -0.011]  .035 *  
## WP.IG-WA.SlpQlV.        0.093 [-0.032,  0.215]  .145    
## WP.IG-WA.RdV.           0.182 [ 0.057,  0.301]  .005 ** 
## WP.IG-WA.PR             0.001 [-0.125,  0.128]  .982    
## WP.IG-WA.ER             0.217 [ 0.094,  0.334] <.001 ***
## WP.IG-WA.SlpQnV.       -0.088 [-0.216,  0.042]  .184    
## WP.SL-WP.OL             0.856 [ 0.819,  0.886] <.001 ***
## WP.SL-WP.AS             0.723 [ 0.658,  0.777] <.001 ***
## WP.SL-WP.PrfIV.         0.219 [ 0.099,  0.334] <.001 ***
## WP.SL-WP.TC             0.171 [ 0.049,  0.288]  .007 ** 
## WP.SL-WA.WRV            0.045 [-0.080,  0.169]  .480    
## WP.SL-WA.PW             0.048 [-0.077,  0.172]  .450    
## WP.SL-WA.NW             0.017 [-0.108,  0.141]  .789    
## WP.SL-WA.RmV.           0.195 [ 0.073,  0.312]  .002 ** 
## WP.SL-WA.PA             0.184 [ 0.060,  0.301]  .004 ** 
## WP.SL-WA.NA            -0.013 [-0.137,  0.112]  .844    
## WP.SL-WA.ImV.           0.066 [-0.059,  0.189]  .298    
## WP.SL-WA.WA            -0.104 [-0.226,  0.021]  .103    
## WP.SL-WA.TA             0.141 [ 0.017,  0.261]  .027 *  
## WP.SL-WA.WRF           -0.009 [-0.133,  0.116]  .893    
## WP.SL-WA.InV.          -0.009 [-0.133,  0.116]  .891    
## WP.SL-WA.EV            -0.089 [-0.212,  0.036]  .162    
## WP.SL-WA.SlpQlV.        0.068 [-0.057,  0.191]  .286    
## WP.SL-WA.RdV.           0.061 [-0.066,  0.186]  .346    
## WP.SL-WA.PR             0.170 [ 0.045,  0.290]  .009 ** 
## WP.SL-WA.ER            -0.030 [-0.155,  0.097]  .646    
## WP.SL-WA.SlpQnV.       -0.197 [-0.319, -0.070]  .003 ** 
## WP.OL-WP.AS             0.262 [ 0.143,  0.373] <.001 ***
## WP.OL-WP.PrfIV.         0.265 [ 0.147,  0.376] <.001 ***
## WP.OL-WP.TC             0.165 [ 0.042,  0.282]  .009 ** 
## WP.OL-WA.WRV            0.029 [-0.096,  0.153]  .655    
## WP.OL-WA.PW             0.038 [-0.087,  0.162]  .547    
## WP.OL-WA.NW             0.004 [-0.121,  0.128]  .954    
## WP.OL-WA.RmV.           0.175 [ 0.052,  0.293]  .006 ** 
## WP.OL-WA.PA             0.118 [-0.006,  0.240]  .064 .  
## WP.OL-WA.NA            -0.030 [-0.154,  0.095]  .643    
## WP.OL-WA.ImV.           0.023 [-0.102,  0.147]  .724    
## WP.OL-WA.WA            -0.130 [-0.250, -0.005]  .042 *  
## WP.OL-WA.TA             0.169 [ 0.045,  0.287]  .008 ** 
## WP.OL-WA.WRF           -0.020 [-0.144,  0.105]  .753    
## WP.OL-WA.InV.           0.059 [-0.066,  0.182]  .357    
## WP.OL-WA.EV            -0.095 [-0.217,  0.030]  .136    
## WP.OL-WA.SlpQlV.        0.108 [-0.016,  0.230]  .090 .  
## WP.OL-WA.RdV.           0.029 [-0.098,  0.154]  .659    
## WP.OL-WA.PR             0.189 [ 0.064,  0.308]  .004 ** 
## WP.OL-WA.ER            -0.073 [-0.197,  0.053]  .258    
## WP.OL-WA.SlpQnV.       -0.232 [-0.351, -0.105] <.001 ***
## WP.AS-WP.PrfIV.         0.055 [-0.069,  0.177]  .384    
## WP.AS-WP.TC             0.099 [-0.024,  0.220]  .116    
## WP.AS-WA.WRV            0.046 [-0.079,  0.169]  .473    
## WP.AS-WA.PW             0.038 [-0.087,  0.162]  .548    
## WP.AS-WA.NW             0.027 [-0.098,  0.151]  .671    
## WP.AS-WA.RmV.           0.129 [ 0.005,  0.250]  .043 *  
## WP.AS-WA.PA             0.184 [ 0.061,  0.301]  .004 ** 
## WP.AS-WA.NA             0.016 [-0.108,  0.141]  .797    
## WP.AS-WA.ImV.           0.094 [-0.031,  0.216]  .141    
## WP.AS-WA.WA            -0.020 [-0.144,  0.105]  .752    
## WP.AS-WA.TA             0.037 [-0.088,  0.161]  .560    
## WP.AS-WA.WRF            0.011 [-0.114,  0.136]  .861    
## WP.AS-WA.InV.          -0.096 [-0.218,  0.029]  .133    
## WP.AS-WA.EV            -0.039 [-0.163,  0.086]  .544    
## WP.AS-WA.SlpQlV.       -0.019 [-0.143,  0.106]  .768    
## WP.AS-WA.RdV.           0.076 [-0.051,  0.200]  .242    
## WP.AS-WA.PR             0.064 [-0.063,  0.189]  .322    
## WP.AS-WA.ER             0.043 [-0.084,  0.168]  .508    
## WP.AS-WA.SlpQnV.       -0.068 [-0.196,  0.062]  .308    
## WP.PrfIV.-WP.TC         0.299 [ 0.182,  0.407] <.001 ***
## WP.PrfIV.-WA.WRV        0.052 [-0.073,  0.175]  .419    
## WP.PrfIV.-WA.PW         0.148 [ 0.024,  0.268]  .021 *  
## WP.PrfIV.-WA.NW        -0.064 [-0.187,  0.061]  .315    
## WP.PrfIV.-WA.RmV.       0.043 [-0.082,  0.167]  .502    
## WP.PrfIV.-WA.PA        -0.147 [-0.267, -0.023]  .021 *  
## WP.PrfIV.-WA.NA         0.017 [-0.108,  0.141]  .788    
## WP.PrfIV.-WA.ImV.      -0.044 [-0.168,  0.081]  .487    
## WP.PrfIV.-WA.WA         0.005 [-0.119,  0.130]  .932    
## WP.PrfIV.-WA.TA         0.008 [-0.117,  0.132]  .900    
## WP.PrfIV.-WA.WRF        0.094 [-0.031,  0.216]  .143    
## WP.PrfIV.-WA.InV.       0.123 [-0.001,  0.244]  .054 .  
## WP.PrfIV.-WA.EV         0.091 [-0.034,  0.213]  .155    
## WP.PrfIV.-WA.SlpQlV.    0.216 [ 0.094,  0.332] <.001 ***
## WP.PrfIV.-WA.RdV.       0.027 [-0.100,  0.153]  .680    
## WP.PrfIV.-WA.PR         0.038 [-0.089,  0.163]  .562    
## WP.PrfIV.-WA.ER         0.011 [-0.116,  0.137]  .870    
## WP.PrfIV.-WA.SlpQnV.   -0.226 [-0.346, -0.100] <.001 ***
## WP.TC-WA.WRV            0.190 [ 0.067,  0.307]  .003 ** 
## WP.TC-WA.PW             0.185 [ 0.062,  0.302]  .004 ** 
## WP.TC-WA.NW             0.089 [-0.036,  0.211]  .163    
## WP.TC-WA.RmV.           0.086 [-0.039,  0.208]  .180    
## WP.TC-WA.PA             0.036 [-0.089,  0.159]  .577    
## WP.TC-WA.NA            -0.024 [-0.148,  0.101]  .708    
## WP.TC-WA.ImV.           0.132 [ 0.007,  0.252]  .039 *  
## WP.TC-WA.WA             0.050 [-0.075,  0.174]  .431    
## WP.TC-WA.TA             0.117 [-0.008,  0.238]  .068 .  
## WP.TC-WA.WRF            0.240 [ 0.119,  0.354] <.001 ***
## WP.TC-WA.InV.           0.021 [-0.104,  0.145]  .741    
## WP.TC-WA.EV            -0.076 [-0.198,  0.049]  .237    
## WP.TC-WA.SlpQlV.        0.135 [ 0.010,  0.255]  .035 *  
## WP.TC-WA.RdV.           0.037 [-0.090,  0.163]  .567    
## WP.TC-WA.PR             0.004 [-0.122,  0.131]  .946    
## WP.TC-WA.ER             0.042 [-0.085,  0.167]  .517    
## WP.TC-WA.SlpQnV.       -0.123 [-0.249,  0.006]  .064 .  
## WA.WRV-WA.PW            0.676 [ 0.605,  0.736] <.001 ***
## WA.WRV-WA.NW            0.744 [ 0.686,  0.793] <.001 ***
## WA.WRV-WA.RmV.          0.424 [ 0.321,  0.517] <.001 ***
## WA.WRV-WA.PA            0.183 [ 0.065,  0.296]  .003 ** 
## WA.WRV-WA.NA            0.134 [ 0.015,  0.250]  .028 *  
## WA.WRV-WA.ImV.          0.382 [ 0.276,  0.480] <.001 ***
## WA.WRV-WA.WA            0.218 [ 0.101,  0.329] <.001 ***
## WA.WRV-WA.TA            0.322 [ 0.211,  0.425] <.001 ***
## WA.WRV-WA.WRF           0.287 [ 0.174,  0.393] <.001 ***
## WA.WRV-WA.InV.          0.244 [ 0.128,  0.353] <.001 ***
## WA.WRV-WA.EV           -0.042 [-0.161,  0.078]  .491    
## WA.WRV-WA.SlpQlV.       0.182 [ 0.064,  0.295]  .003 ** 
## WA.WRV-WA.RdV.          0.155 [ 0.034,  0.270]  .013 *  
## WA.WRV-WA.PR           -0.025 [-0.146,  0.096]  .682    
## WA.WRV-WA.ER            0.174 [ 0.055,  0.289]  .005 ** 
## WA.WRV-WA.SlpQnV.      -0.064 [-0.187,  0.061]  .317    
## WA.PW-WA.NW             0.011 [-0.109,  0.130]  .862    
## WA.PW-WA.RmV.           0.347 [ 0.237,  0.448] <.001 ***
## WA.PW-WA.PA             0.140 [ 0.021,  0.255]  .022 *  
## WA.PW-WA.NA            -0.014 [-0.133,  0.106]  .825    
## WA.PW-WA.ImV.           0.225 [ 0.109,  0.336] <.001 ***
## WA.PW-WA.WA             0.196 [ 0.079,  0.309]  .001 ** 
## WA.PW-WA.TA             0.197 [ 0.080,  0.309]  .001 ** 
## WA.PW-WA.WRF            0.317 [ 0.206,  0.421] <.001 ***
## WA.PW-WA.InV.           0.163 [ 0.044,  0.277]  .008 ** 
## WA.PW-WA.EV             0.043 [-0.076,  0.162]  .478    
## WA.PW-WA.SlpQlV.        0.122 [ 0.002,  0.237]  .047 *  
## WA.PW-WA.RdV.           0.042 [-0.079,  0.162]  .496    
## WA.PW-WA.PR            -0.030 [-0.150,  0.091]  .630    
## WA.PW-WA.ER             0.050 [-0.071,  0.170]  .421    
## WA.PW-WA.SlpQnV.       -0.121 [-0.241,  0.004]  .060 .  
## WA.NW-WA.RmV.           0.261 [ 0.146,  0.369] <.001 ***
## WA.NW-WA.PA             0.122 [ 0.002,  0.238]  .047 *  
## WA.NW-WA.NA             0.195 [ 0.077,  0.307]  .002 ** 
## WA.NW-WA.ImV.           0.314 [ 0.203,  0.418] <.001 ***
## WA.NW-WA.WA             0.118 [-0.002,  0.234]  .054 .  
## WA.NW-WA.TA             0.258 [ 0.143,  0.366] <.001 ***
## WA.NW-WA.WRF            0.102 [-0.017,  0.219]  .095 .  
## WA.NW-WA.InV.           0.183 [ 0.065,  0.296]  .003 ** 
## WA.NW-WA.EV            -0.096 [-0.213,  0.023]  .115    
## WA.NW-WA.SlpQlV.        0.137 [ 0.018,  0.252]  .025 *  
## WA.NW-WA.RdV.           0.171 [ 0.051,  0.286]  .006 ** 
## WA.NW-WA.PR            -0.008 [-0.128,  0.113]  .902    
## WA.NW-WA.ER             0.192 [ 0.073,  0.306]  .002 ** 
## WA.NW-WA.SlpQnV.        0.022 [-0.102,  0.147]  .725    
## WA.RmV.-WA.PA           0.242 [ 0.126,  0.351] <.001 ***
## WA.RmV.-WA.NA           0.083 [-0.037,  0.200]  .178    
## WA.RmV.-WA.ImV.         0.256 [ 0.141,  0.364] <.001 ***
## WA.RmV.-WA.WA           0.294 [ 0.181,  0.399] <.001 ***
## WA.RmV.-WA.TA           0.272 [ 0.158,  0.379] <.001 ***
## WA.RmV.-WA.WRF          0.267 [ 0.152,  0.374] <.001 ***
## WA.RmV.-WA.InV.         0.265 [ 0.150,  0.372] <.001 ***
## WA.RmV.-WA.EV           0.011 [-0.108,  0.131]  .853    
## WA.RmV.-WA.SlpQlV.      0.122 [ 0.002,  0.238]  .047 *  
## WA.RmV.-WA.RdV.         0.129 [ 0.008,  0.246]  .038 *  
## WA.RmV.-WA.PR           0.048 [-0.073,  0.168]  .435    
## WA.RmV.-WA.ER           0.137 [ 0.016,  0.253]  .028 *  
## WA.RmV.-WA.SlpQnV.     -0.160 [-0.279, -0.036]  .012 *  
## WA.PA-WA.NA            -0.060 [-0.178,  0.060]  .329    
## WA.PA-WA.ImV.           0.272 [ 0.158,  0.379] <.001 ***
## WA.PA-WA.WA             0.201 [ 0.084,  0.313]  .001 ** 
## WA.PA-WA.TA             0.307 [ 0.195,  0.411] <.001 ***
## WA.PA-WA.WRF            0.131 [ 0.012,  0.246]  .033 *  
## WA.PA-WA.InV.           0.179 [ 0.061,  0.292]  .004 ** 
## WA.PA-WA.EV             0.107 [-0.013,  0.223]  .082 .  
## WA.PA-WA.SlpQlV.        0.100 [-0.019,  0.217]  .101    
## WA.PA-WA.RdV.           0.132 [ 0.011,  0.249]  .034 *  
## WA.PA-WA.PR             0.086 [-0.035,  0.205]  .165    
## WA.PA-WA.ER             0.108 [-0.013,  0.226]  .082 .  
## WA.PA-WA.SlpQnV.       -0.187 [-0.304, -0.063]  .004 ** 
## WA.NA-WA.ImV.           0.132 [ 0.013,  0.247]  .031 *  
## WA.NA-WA.WA            -0.049 [-0.167,  0.071]  .426    
## WA.NA-WA.TA            -0.029 [-0.148,  0.090]  .632    
## WA.NA-WA.WRF           -0.015 [-0.134,  0.105]  .812    
## WA.NA-WA.InV.          -0.049 [-0.168,  0.071]  .421    
## WA.NA-WA.EV             0.023 [-0.096,  0.142]  .703    
## WA.NA-WA.SlpQlV.       -0.026 [-0.145,  0.093]  .666    
## WA.NA-WA.RdV.          -0.054 [-0.174,  0.068]  .386    
## WA.NA-WA.PR            -0.029 [-0.149,  0.092]  .640    
## WA.NA-WA.ER            -0.048 [-0.167,  0.073]  .441    
## WA.NA-WA.SlpQnV.        0.152 [ 0.028,  0.271]  .018 *  
## WA.ImV.-WA.WA           0.120 [ 0.001,  0.236]  .049 *  
## WA.ImV.-WA.TA           0.390 [ 0.283,  0.486] <.001 ***
## WA.ImV.-WA.WRF          0.101 [-0.019,  0.217]  .100    
## WA.ImV.-WA.InV.         0.325 [ 0.214,  0.428] <.001 ***
## WA.ImV.-WA.EV          -0.108 [-0.225,  0.011]  .077 .  
## WA.ImV.-WA.SlpQlV.      0.200 [ 0.082,  0.312]  .001 ** 
## WA.ImV.-WA.RdV.         0.016 [-0.105,  0.137]  .792    
## WA.ImV.-WA.PR          -0.108 [-0.226,  0.013]  .081 .  
## WA.ImV.-WA.ER           0.073 [-0.048,  0.192]  .236    
## WA.ImV.-WA.SlpQnV.     -0.202 [-0.318, -0.079]  .002 ** 
## WA.WA-WA.TA             0.317 [ 0.206,  0.421] <.001 ***
## WA.WA-WA.WRF            0.547 [ 0.457,  0.625] <.001 ***
## WA.WA-WA.InV.           0.224 [ 0.107,  0.334] <.001 ***
## WA.WA-WA.EV             0.120 [ 0.000,  0.236]  .051 .  
## WA.WA-WA.SlpQlV.        0.071 [-0.049,  0.189]  .248    
## WA.WA-WA.RdV.           0.037 [-0.084,  0.158]  .547    
## WA.WA-WA.PR            -0.036 [-0.156,  0.085]  .562    
## WA.WA-WA.ER             0.060 [-0.061,  0.180]  .329    
## WA.WA-WA.SlpQnV.        0.079 [-0.046,  0.201]  .218    
## WA.TA-WA.WRF            0.310 [ 0.198,  0.414] <.001 ***
## WA.TA-WA.InV.           0.604 [ 0.522,  0.674] <.001 ***
## WA.TA-WA.EV             0.026 [-0.093,  0.145]  .668    
## WA.TA-WA.SlpQlV.        0.304 [ 0.192,  0.409] <.001 ***
## WA.TA-WA.RdV.           0.071 [-0.050,  0.190]  .253    
## WA.TA-WA.PR             0.020 [-0.101,  0.140]  .749    
## WA.TA-WA.ER             0.065 [-0.056,  0.185]  .292    
## WA.TA-WA.SlpQnV.       -0.167 [-0.286, -0.043]  .009 ** 
## WA.WRF-WA.InV.          0.239 [ 0.123,  0.348] <.001 ***
## WA.WRF-WA.EV            0.138 [ 0.019,  0.253]  .024 *  
## WA.WRF-WA.SlpQlV.       0.107 [-0.013,  0.223]  .082 .  
## WA.WRF-WA.RdV.          0.214 [ 0.095,  0.326] <.001 ***
## WA.WRF-WA.PR            0.094 [-0.027,  0.212]  .130    
## WA.WRF-WA.ER            0.194 [ 0.075,  0.308]  .002 ** 
## WA.WRF-WA.SlpQnV.       0.032 [-0.093,  0.156]  .615    
## WA.InV.-WA.EV           0.013 [-0.106,  0.132]  .829    
## WA.InV.-WA.SlpQlV.      0.243 [ 0.127,  0.352] <.001 ***
## WA.InV.-WA.RdV.         0.178 [ 0.059,  0.293]  .004 ** 
## WA.InV.-WA.PR           0.088 [-0.034,  0.206]  .159    
## WA.InV.-WA.ER           0.163 [ 0.043,  0.278]  .009 ** 
## WA.InV.-WA.SlpQnV.     -0.233 [-0.348, -0.112] <.001 ***
## WA.EV-WA.SlpQlV.        0.010 [-0.109,  0.130]  .866    
## WA.EV-WA.RdV.           0.199 [ 0.080,  0.313]  .001 ** 
## WA.EV-WA.PR             0.203 [ 0.084,  0.316]  .001 ** 
## WA.EV-WA.ER             0.122 [ 0.001,  0.239]  .050 *  
## WA.EV-WA.SlpQnV.        0.057 [-0.068,  0.181]  .369    
## WA.SlpQlV.-WA.RdV.      0.078 [-0.043,  0.197]  .207    
## WA.SlpQlV.-WA.PR       -0.033 [-0.153,  0.088]  .596    
## WA.SlpQlV.-WA.ER        0.114 [-0.007,  0.231]  .067 .  
## WA.SlpQlV.-WA.SlpQnV.  -0.377 [-0.479, -0.265] <.001 ***
## WA.RdV.-WA.PR           0.568 [ 0.480,  0.645] <.001 ***
## WA.RdV.-WA.ER           0.873 [ 0.840,  0.899] <.001 ***
## WA.RdV.-WA.SlpQnV.     -0.132 [-0.254, -0.006]  .042 *  
## WA.PR-WA.ER             0.098 [-0.023,  0.217]  .114    
## WA.PR-WA.SlpQnV.       -0.135 [-0.257, -0.008]  .038 *  
## WA.ER-WA.SlpQnV.       -0.095 [-0.219,  0.031]  .141    
## ────────────────────────────────────────────────────────
## 
## Between-Level Correlation [95% CI]:
## ────────────────────────────────────────────────────────
##                             r         [95% CI]     p    
## ────────────────────────────────────────────────────────
## WA.GV-WP.SB             0.461 [ 0.319,  0.582] <.001 ***
## WA.GV-WP.SP             0.439 [ 0.295,  0.564] <.001 ***
## WA.GV-WP.SN             0.424 [ 0.277,  0.551] <.001 ***
## WA.GV-WP.BV             0.425 [ 0.278,  0.552] <.001 ***
## WA.GV-WP.JC             0.306 [ 0.148,  0.449] <.001 ***
## WA.GV-WP.CP             0.449 [ 0.306,  0.572] <.001 ***
## WA.GV-WP.PrbIV.         0.309 [ 0.150,  0.451] <.001 ***
## WA.GV-WP.IS             0.443 [ 0.299,  0.567] <.001 ***
## WA.GV-WP.IG             0.410 [ 0.262,  0.539] <.001 ***
## WA.GV-WP.SL             0.432 [ 0.287,  0.558] <.001 ***
## WA.GV-WP.OL             0.376 [ 0.224,  0.510] <.001 ***
## WA.GV-WP.AS             0.416 [ 0.268,  0.544] <.001 ***
## WA.GV-WP.PrfIV.         0.335 [ 0.179,  0.474] <.001 ***
## WA.GV-WP.TC             0.277 [ 0.117,  0.424] <.001 ***
## WA.GV-WA.WRV            0.477 [ 0.341,  0.593] <.001 ***
## WA.GV-WA.PW             0.502 [ 0.370,  0.614] <.001 ***
## WA.GV-WA.NW             0.287 [ 0.131,  0.429] <.001 ***
## WA.GV-WA.RmV.           0.533 [ 0.407,  0.640] <.001 ***
## WA.GV-WA.PA             0.395 [ 0.249,  0.523] <.001 ***
## WA.GV-WA.NA            -0.108 [-0.265,  0.055]  .193    
## WA.GV-WA.ImV.           0.379 [ 0.231,  0.509] <.001 ***
## WA.GV-WA.WA             0.341 [ 0.190,  0.477] <.001 ***
## WA.GV-WA.TA             0.456 [ 0.318,  0.576] <.001 ***
## WA.GV-WA.WRF            0.349 [ 0.199,  0.484] <.001 ***
## WA.GV-WA.InV.           0.452 [ 0.313,  0.572] <.001 ***
## WA.GV-WA.EV             0.047 [-0.116,  0.207]  .572    
## WA.GV-WA.SlpQlV.        0.079 [-0.084,  0.238]  .339    
## WA.GV-WA.RdV.           0.184 [ 0.022,  0.336]  .027 *  
## WA.GV-WA.PR             0.155 [-0.007,  0.310]  .061 .  
## WA.GV-WA.ER             0.152 [-0.011,  0.307]  .068 .  
## WA.GV-WA.SlpQnV.        0.101 [-0.067,  0.263]  .239    
## WP.SB-WP.SP             0.926 [ 0.898,  0.946] <.001 ***
## WP.SB-WP.SN             0.944 [ 0.923,  0.960] <.001 ***
## WP.SB-WP.BV             0.473 [ 0.333,  0.592] <.001 ***
## WP.SB-WP.JC             0.431 [ 0.286,  0.557] <.001 ***
## WP.SB-WP.CP             0.525 [ 0.393,  0.635] <.001 ***
## WP.SB-WP.PrbIV.         0.445 [ 0.301,  0.569] <.001 ***
## WP.SB-WP.IS             0.319 [ 0.161,  0.460] <.001 ***
## WP.SB-WP.IG             0.544 [ 0.415,  0.651] <.001 ***
## WP.SB-WP.SL             0.333 [ 0.177,  0.473] <.001 ***
## WP.SB-WP.OL             0.343 [ 0.188,  0.482] <.001 ***
## WP.SB-WP.AS             0.241 [ 0.078,  0.391]  .004 ** 
## WP.SB-WP.PrfIV.         0.467 [ 0.327,  0.587] <.001 ***
## WP.SB-WP.TC             0.426 [ 0.280,  0.553] <.001 ***
## WP.SB-WA.WRV            0.576 [ 0.453,  0.677] <.001 ***
## WP.SB-WA.PW             0.591 [ 0.471,  0.689] <.001 ***
## WP.SB-WA.NW             0.358 [ 0.204,  0.495] <.001 ***
## WP.SB-WA.RmV.           0.627 [ 0.515,  0.718] <.001 ***
## WP.SB-WA.PA             0.510 [ 0.375,  0.623] <.001 ***
## WP.SB-WA.NA            -0.095 [-0.257,  0.072]  .253    
## WP.SB-WA.ImV.           0.598 [ 0.479,  0.695] <.001 ***
## WP.SB-WA.WA             0.522 [ 0.389,  0.633] <.001 ***
## WP.SB-WA.TA             0.584 [ 0.463,  0.684] <.001 ***
## WP.SB-WA.WRF            0.502 [ 0.366,  0.616] <.001 ***
## WP.SB-WA.InV.           0.565 [ 0.440,  0.668] <.001 ***
## WP.SB-WA.EV             0.106 [-0.061,  0.267]  .201    
## WP.SB-WA.SlpQlV.       -0.007 [-0.172,  0.159]  .935    
## WP.SB-WA.RdV.           0.164 [-0.003,  0.321]  .048 *  
## WP.SB-WA.PR             0.112 [-0.055,  0.273]  .178    
## WP.SB-WA.ER             0.145 [-0.022,  0.304]  .080 .  
## WP.SB-WA.SlpQnV.        0.022 [-0.150,  0.192]  .801    
## WP.SP-WP.SN             0.750 [ 0.667,  0.815] <.001 ***
## WP.SP-WP.BV             0.533 [ 0.403,  0.642] <.001 ***
## WP.SP-WP.JC             0.450 [ 0.307,  0.573] <.001 ***
## WP.SP-WP.CP             0.504 [ 0.368,  0.618] <.001 ***
## WP.SP-WP.PrbIV.         0.437 [ 0.292,  0.562] <.001 ***
## WP.SP-WP.IS             0.281 [ 0.120,  0.427] <.001 ***
## WP.SP-WP.IG             0.530 [ 0.400,  0.640] <.001 ***
## WP.SP-WP.SL             0.312 [ 0.154,  0.454] <.001 ***
## WP.SP-WP.OL             0.316 [ 0.158,  0.458] <.001 ***
## WP.SP-WP.AS             0.234 [ 0.071,  0.385]  .005 ** 
## WP.SP-WP.PrfIV.         0.482 [ 0.343,  0.600] <.001 ***
## WP.SP-WP.TC             0.453 [ 0.310,  0.575] <.001 ***
## WP.SP-WA.WRV            0.530 [ 0.400,  0.640] <.001 ***
## WP.SP-WA.PW             0.594 [ 0.475,  0.692] <.001 ***
## WP.SP-WA.NW             0.281 [ 0.121,  0.427] <.001 ***
## WP.SP-WA.RmV.           0.633 [ 0.522,  0.723] <.001 ***
## WP.SP-WA.PA             0.554 [ 0.428,  0.660] <.001 ***
## WP.SP-WA.NA            -0.139 [-0.298,  0.028]  .094 .  
## WP.SP-WA.ImV.           0.654 [ 0.548,  0.740] <.001 ***
## WP.SP-WA.WA             0.508 [ 0.374,  0.622] <.001 ***
## WP.SP-WA.TA             0.609 [ 0.493,  0.704] <.001 ***
## WP.SP-WA.WRF            0.526 [ 0.395,  0.637] <.001 ***
## WP.SP-WA.InV.           0.565 [ 0.441,  0.669] <.001 ***
## WP.SP-WA.EV             0.112 [-0.055,  0.273]  .175    
## WP.SP-WA.SlpQlV.       -0.012 [-0.178,  0.154]  .881    
## WP.SP-WA.RdV.           0.127 [-0.041,  0.287]  .128    
## WP.SP-WA.PR             0.100 [-0.067,  0.263]  .228    
## WP.SP-WA.ER             0.104 [-0.064,  0.266]  .213    
## WP.SP-WA.SlpQnV.       -0.008 [-0.179,  0.163]  .923    
## WP.SN-WP.BV             0.364 [ 0.211,  0.500] <.001 ***
## WP.SN-WP.JC             0.363 [ 0.210,  0.499] <.001 ***
## WP.SN-WP.CP             0.480 [ 0.341,  0.598] <.001 ***
## WP.SN-WP.PrbIV.         0.398 [ 0.248,  0.529] <.001 ***
## WP.SN-WP.IS             0.314 [ 0.156,  0.456] <.001 ***
## WP.SN-WP.IG             0.490 [ 0.353,  0.607] <.001 ***
## WP.SN-WP.SL             0.311 [ 0.153,  0.453] <.001 ***
## WP.SN-WP.OL             0.326 [ 0.169,  0.467] <.001 ***
## WP.SN-WP.AS             0.217 [ 0.053,  0.370]  .010 ** 
## WP.SN-WP.PrfIV.         0.398 [ 0.249,  0.529] <.001 ***
## WP.SN-WP.TC             0.352 [ 0.198,  0.489] <.001 ***
## WP.SN-WA.WRV            0.546 [ 0.418,  0.653] <.001 ***
## WP.SN-WA.PW             0.517 [ 0.384,  0.629] <.001 ***
## WP.SN-WA.NW             0.382 [ 0.231,  0.515] <.001 ***
## WP.SN-WA.RmV.           0.546 [ 0.418,  0.653] <.001 ***
## WP.SN-WA.PA             0.409 [ 0.261,  0.539] <.001 ***
## WP.SN-WA.NA            -0.046 [-0.210,  0.121]  .584    
## WP.SN-WA.ImV.           0.477 [ 0.338,  0.596] <.001 ***
## WP.SN-WA.WA             0.470 [ 0.330,  0.590] <.001 ***
## WP.SN-WA.TA             0.492 [ 0.355,  0.608] <.001 ***
## WP.SN-WA.WRF            0.420 [ 0.273,  0.548] <.001 ***
## WP.SN-WA.InV.           0.496 [ 0.360,  0.612] <.001 ***
## WP.SN-WA.EV             0.088 [-0.079,  0.250]  .289    
## WP.SN-WA.SlpQlV.       -0.001 [-0.167,  0.165]  .990    
## WP.SN-WA.RdV.           0.176 [ 0.010,  0.333]  .033 *  
## WP.SN-WA.PR             0.109 [-0.059,  0.270]  .192    
## WP.SN-WA.ER             0.164 [-0.003,  0.322]  .048 *  
## WP.SN-WA.SlpQnV.        0.045 [-0.127,  0.214]  .599    
## WP.BV-WP.JC             0.637 [ 0.526,  0.726] <.001 ***
## WP.BV-WP.CP             0.631 [ 0.520,  0.721] <.001 ***
## WP.BV-WP.PrbIV.         0.595 [ 0.476,  0.693] <.001 ***
## WP.BV-WP.IS             0.447 [ 0.304,  0.571] <.001 ***
## WP.BV-WP.IG             0.584 [ 0.463,  0.683] <.001 ***
## WP.BV-WP.SL             0.501 [ 0.366,  0.616] <.001 ***
## WP.BV-WP.OL             0.491 [ 0.354,  0.608] <.001 ***
## WP.BV-WP.AS             0.401 [ 0.252,  0.531] <.001 ***
## WP.BV-WP.PrfIV.         0.598 [ 0.479,  0.695] <.001 ***
## WP.BV-WP.TC             0.631 [ 0.519,  0.721] <.001 ***
## WP.BV-WA.WRV            0.610 [ 0.495,  0.705] <.001 ***
## WP.BV-WA.PW             0.650 [ 0.542,  0.736] <.001 ***
## WP.BV-WA.NW             0.357 [ 0.203,  0.493] <.001 ***
## WP.BV-WA.RmV.           0.712 [ 0.619,  0.785] <.001 ***
## WP.BV-WA.PA             0.510 [ 0.376,  0.623] <.001 ***
## WP.BV-WA.NA            -0.036 [-0.201,  0.131]  .666    
## WP.BV-WA.ImV.           0.680 [ 0.579,  0.760] <.001 ***
## WP.BV-WA.WA             0.567 [ 0.443,  0.670] <.001 ***
## WP.BV-WA.TA             0.702 [ 0.607,  0.778] <.001 ***
## WP.BV-WA.WRF            0.610 [ 0.494,  0.704] <.001 ***
## WP.BV-WA.InV.           0.706 [ 0.612,  0.780] <.001 ***
## WP.BV-WA.EV             0.172 [ 0.006,  0.328]  .038 *  
## WP.BV-WA.SlpQlV.        0.053 [-0.114,  0.217]  .523    
## WP.BV-WA.RdV.           0.111 [-0.057,  0.272]  .184    
## WP.BV-WA.PR             0.035 [-0.132,  0.201]  .673    
## WP.BV-WA.ER             0.120 [-0.048,  0.281]  .150    
## WP.BV-WA.SlpQnV.       -0.084 [-0.252,  0.088]  .326    
## WP.JC-WP.CP             0.623 [ 0.509,  0.715] <.001 ***
## WP.JC-WP.PrbIV.         0.587 [ 0.466,  0.686] <.001 ***
## WP.JC-WP.IS             0.406 [ 0.258,  0.536] <.001 ***
## WP.JC-WP.IG             0.596 [ 0.477,  0.693] <.001 ***
## WP.JC-WP.SL             0.577 [ 0.454,  0.678] <.001 ***
## WP.JC-WP.OL             0.603 [ 0.486,  0.699] <.001 ***
## WP.JC-WP.AS             0.405 [ 0.256,  0.535] <.001 ***
## WP.JC-WP.PrfIV.         0.662 [ 0.558,  0.746] <.001 ***
## WP.JC-WP.TC             0.697 [ 0.600,  0.773] <.001 ***
## WP.JC-WA.WRV            0.666 [ 0.562,  0.749] <.001 ***
## WP.JC-WA.PW             0.579 [ 0.458,  0.680] <.001 ***
## WP.JC-WA.NW             0.517 [ 0.384,  0.629] <.001 ***
## WP.JC-WA.RmV.           0.618 [ 0.504,  0.711] <.001 ***
## WP.JC-WA.PA             0.520 [ 0.388,  0.632] <.001 ***
## WP.JC-WA.NA            -0.034 [-0.199,  0.132]  .681    
## WP.JC-WA.ImV.           0.638 [ 0.528,  0.727] <.001 ***
## WP.JC-WA.WA             0.521 [ 0.388,  0.632] <.001 ***
## WP.JC-WA.TA             0.554 [ 0.428,  0.659] <.001 ***
## WP.JC-WA.WRF            0.529 [ 0.398,  0.638] <.001 ***
## WP.JC-WA.InV.           0.568 [ 0.444,  0.671] <.001 ***
## WP.JC-WA.EV             0.260 [ 0.098,  0.408]  .001 ** 
## WP.JC-WA.SlpQlV.        0.031 [-0.136,  0.196]  .711    
## WP.JC-WA.RdV.           0.220 [ 0.056,  0.373]  .008 ** 
## WP.JC-WA.PR             0.202 [ 0.036,  0.356]  .015 *  
## WP.JC-WA.ER             0.170 [ 0.004,  0.327]  .040 *  
## WP.JC-WA.SlpQnV.        0.049 [-0.123,  0.218]  .567    
## WP.CP-WP.PrbIV.         0.855 [ 0.803,  0.894] <.001 ***
## WP.CP-WP.IS             0.793 [ 0.722,  0.847] <.001 ***
## WP.CP-WP.IG             0.928 [ 0.900,  0.948] <.001 ***
## WP.CP-WP.SL             0.587 [ 0.467,  0.686] <.001 ***
## WP.CP-WP.OL             0.581 [ 0.459,  0.681] <.001 ***
## WP.CP-WP.AS             0.462 [ 0.321,  0.583] <.001 ***
## WP.CP-WP.PrfIV.         0.691 [ 0.593,  0.769] <.001 ***
## WP.CP-WP.TC             0.691 [ 0.593,  0.769] <.001 ***
## WP.CP-WA.WRV            0.625 [ 0.512,  0.716] <.001 ***
## WP.CP-WA.PW             0.568 [ 0.444,  0.671] <.001 ***
## WP.CP-WA.NW             0.461 [ 0.319,  0.582] <.001 ***
## WP.CP-WA.RmV.           0.713 [ 0.620,  0.786] <.001 ***
## WP.CP-WA.PA             0.595 [ 0.476,  0.693] <.001 ***
## WP.CP-WA.NA            -0.021 [-0.187,  0.145]  .797    
## WP.CP-WA.ImV.           0.723 [ 0.633,  0.794] <.001 ***
## WP.CP-WA.WA             0.598 [ 0.480,  0.695] <.001 ***
## WP.CP-WA.TA             0.627 [ 0.515,  0.718] <.001 ***
## WP.CP-WA.WRF            0.581 [ 0.460,  0.681] <.001 ***
## WP.CP-WA.InV.           0.590 [ 0.470,  0.688] <.001 ***
## WP.CP-WA.EV             0.211 [ 0.047,  0.364]  .010 *  
## WP.CP-WA.SlpQlV.        0.113 [-0.054,  0.273]  .174    
## WP.CP-WA.RdV.           0.219 [ 0.054,  0.372]  .008 ** 
## WP.CP-WA.PR             0.166 [-0.000,  0.324]  .045 *  
## WP.CP-WA.ER             0.188 [ 0.023,  0.344]  .023 *  
## WP.CP-WA.SlpQnV.       -0.028 [-0.197,  0.144]  .749    
## WP.PrbIV.-WP.IS         0.575 [ 0.452,  0.676] <.001 ***
## WP.PrbIV.-WP.IG         0.698 [ 0.602,  0.774] <.001 ***
## WP.PrbIV.-WP.SL         0.566 [ 0.442,  0.669] <.001 ***
## WP.PrbIV.-WP.OL         0.559 [ 0.433,  0.663] <.001 ***
## WP.PrbIV.-WP.AS         0.447 [ 0.303,  0.570] <.001 ***
## WP.PrbIV.-WP.PrfIV.     0.636 [ 0.525,  0.725] <.001 ***
## WP.PrbIV.-WP.TC         0.618 [ 0.504,  0.711] <.001 ***
## WP.PrbIV.-WA.WRV        0.535 [ 0.405,  0.644] <.001 ***
## WP.PrbIV.-WA.PW         0.523 [ 0.391,  0.634] <.001 ***
## WP.PrbIV.-WA.NW         0.358 [ 0.205,  0.495] <.001 ***
## WP.PrbIV.-WA.RmV.       0.656 [ 0.550,  0.741] <.001 ***
## WP.PrbIV.-WA.PA         0.509 [ 0.375,  0.622] <.001 ***
## WP.PrbIV.-WA.NA         0.018 [-0.148,  0.184]  .826    
## WP.PrbIV.-WA.ImV.       0.641 [ 0.531,  0.729] <.001 ***
## WP.PrbIV.-WA.WA         0.540 [ 0.411,  0.648] <.001 ***
## WP.PrbIV.-WA.TA         0.557 [ 0.431,  0.662] <.001 ***
## WP.PrbIV.-WA.WRF        0.532 [ 0.402,  0.642] <.001 ***
## WP.PrbIV.-WA.InV.       0.543 [ 0.414,  0.650] <.001 ***
## WP.PrbIV.-WA.EV         0.232 [ 0.069,  0.383]  .005 ** 
## WP.PrbIV.-WA.SlpQlV.    0.097 [-0.070,  0.259]  .242    
## WP.PrbIV.-WA.RdV.       0.169 [ 0.003,  0.327]  .041 *  
## WP.PrbIV.-WA.PR         0.137 [-0.030,  0.297]  .099 .  
## WP.PrbIV.-WA.ER         0.141 [-0.026,  0.300]  .090 .  
## WP.PrbIV.-WA.SlpQnV.   -0.090 [-0.257,  0.082]  .295    
## WP.IS-WP.IG             0.584 [ 0.463,  0.684] <.001 ***
## WP.IS-WP.SL             0.505 [ 0.370,  0.619] <.001 ***
## WP.IS-WP.OL             0.421 [ 0.274,  0.549] <.001 ***
## WP.IS-WP.AS             0.511 [ 0.377,  0.624] <.001 ***
## WP.IS-WP.PrfIV.         0.443 [ 0.300,  0.568] <.001 ***
## WP.IS-WP.TC             0.439 [ 0.294,  0.564] <.001 ***
## WP.IS-WA.WRV            0.414 [ 0.267,  0.543] <.001 ***
## WP.IS-WA.PW             0.356 [ 0.202,  0.493] <.001 ***
## WP.IS-WA.NW             0.326 [ 0.169,  0.467] <.001 ***
## WP.IS-WA.RmV.           0.490 [ 0.352,  0.606] <.001 ***
## WP.IS-WA.PA             0.435 [ 0.290,  0.561] <.001 ***
## WP.IS-WA.NA            -0.050 [-0.214,  0.117]  .546    
## WP.IS-WA.ImV.           0.473 [ 0.333,  0.592] <.001 ***
## WP.IS-WA.WA             0.413 [ 0.265,  0.542] <.001 ***
## WP.IS-WA.TA             0.396 [ 0.246,  0.527] <.001 ***
## WP.IS-WA.WRF            0.330 [ 0.174,  0.470] <.001 ***
## WP.IS-WA.InV.           0.361 [ 0.207,  0.497] <.001 ***
## WP.IS-WA.EV             0.128 [-0.038,  0.288]  .122    
## WP.IS-WA.SlpQlV.        0.211 [ 0.046,  0.364]  .010 *  
## WP.IS-WA.RdV.           0.230 [ 0.066,  0.382]  .005 ** 
## WP.IS-WA.PR             0.170 [ 0.004,  0.327]  .040 *  
## WP.IS-WA.ER             0.203 [ 0.038,  0.357]  .014 *  
## WP.IS-WA.SlpQnV.       -0.077 [-0.244,  0.095]  .371    
## WP.IG-WP.SL             0.486 [ 0.349,  0.604] <.001 ***
## WP.IG-WP.OL             0.526 [ 0.394,  0.636] <.001 ***
## WP.IG-WP.AS             0.317 [ 0.160,  0.459] <.001 ***
## WP.IG-WP.PrfIV.         0.675 [ 0.573,  0.756] <.001 ***
## WP.IG-WP.TC             0.687 [ 0.588,  0.766] <.001 ***
## WP.IG-WA.WRV            0.624 [ 0.511,  0.716] <.001 ***
## WP.IG-WA.PW             0.559 [ 0.433,  0.663] <.001 ***
## WP.IG-WA.NW             0.469 [ 0.329,  0.589] <.001 ***
## WP.IG-WA.RmV.           0.677 [ 0.576,  0.758] <.001 ***
## WP.IG-WA.PA             0.572 [ 0.449,  0.674] <.001 ***
## WP.IG-WA.NA            -0.022 [-0.187,  0.145]  .793    
## WP.IG-WA.ImV.           0.715 [ 0.622,  0.787] <.001 ***
## WP.IG-WA.WA             0.573 [ 0.450,  0.675] <.001 ***
## WP.IG-WA.TA             0.627 [ 0.515,  0.718] <.001 ***
## WP.IG-WA.WRF            0.593 [ 0.473,  0.691] <.001 ***
## WP.IG-WA.InV.           0.586 [ 0.465,  0.685] <.001 ***
## WP.IG-WA.EV             0.189 [ 0.024,  0.344]  .022 *  
## WP.IG-WA.SlpQlV.        0.035 [-0.131,  0.200]  .670    
## WP.IG-WA.RdV.           0.182 [ 0.015,  0.338]  .028 *  
## WP.IG-WA.PR             0.135 [-0.032,  0.295]  .104    
## WP.IG-WA.ER             0.156 [-0.011,  0.314]  .060 .  
## WP.IG-WA.SlpQnV.        0.042 [-0.130,  0.212]  .623    
## WP.SL-WP.OL             0.937 [ 0.914,  0.955] <.001 ***
## WP.SL-WP.AS             0.861 [ 0.811,  0.899] <.001 ***
## WP.SL-WP.PrfIV.         0.681 [ 0.581,  0.761] <.001 ***
## WP.SL-WP.TC             0.606 [ 0.490,  0.702] <.001 ***
## WP.SL-WA.WRV            0.484 [ 0.346,  0.602] <.001 ***
## WP.SL-WA.PW             0.457 [ 0.315,  0.579] <.001 ***
## WP.SL-WA.NW             0.340 [ 0.184,  0.479] <.001 ***
## WP.SL-WA.RmV.           0.478 [ 0.339,  0.596] <.001 ***
## WP.SL-WA.PA             0.377 [ 0.225,  0.511] <.001 ***
## WP.SL-WA.NA             0.073 [-0.094,  0.236]  .379    
## WP.SL-WA.ImV.           0.446 [ 0.303,  0.570] <.001 ***
## WP.SL-WA.WA             0.334 [ 0.178,  0.474] <.001 ***
## WP.SL-WA.TA             0.504 [ 0.369,  0.618] <.001 ***
## WP.SL-WA.WRF            0.309 [ 0.151,  0.452] <.001 ***
## WP.SL-WA.InV.           0.537 [ 0.407,  0.645] <.001 ***
## WP.SL-WA.EV             0.210 [ 0.046,  0.364]  .011 *  
## WP.SL-WA.SlpQlV.        0.152 [-0.015,  0.310]  .067 .  
## WP.SL-WA.RdV.           0.282 [ 0.121,  0.428] <.001 ***
## WP.SL-WA.PR             0.155 [-0.012,  0.314]  .061 .  
## WP.SL-WA.ER             0.273 [ 0.112,  0.420] <.001 ***
## WP.SL-WA.SlpQnV.       -0.000 [-0.171,  0.171]  .997    
## WP.OL-WP.AS             0.630 [ 0.519,  0.721] <.001 ***
## WP.OL-WP.PrfIV.         0.663 [ 0.558,  0.746] <.001 ***
## WP.OL-WP.TC             0.620 [ 0.506,  0.712] <.001 ***
## WP.OL-WA.WRV            0.524 [ 0.392,  0.635] <.001 ***
## WP.OL-WA.PW             0.494 [ 0.357,  0.610] <.001 ***
## WP.OL-WA.NW             0.369 [ 0.216,  0.504] <.001 ***
## WP.OL-WA.RmV.           0.479 [ 0.340,  0.597] <.001 ***
## WP.OL-WA.PA             0.404 [ 0.255,  0.534] <.001 ***
## WP.OL-WA.NA             0.126 [-0.041,  0.286]  .128    
## WP.OL-WA.ImV.           0.472 [ 0.332,  0.591] <.001 ***
## WP.OL-WA.WA             0.378 [ 0.226,  0.512] <.001 ***
## WP.OL-WA.TA             0.553 [ 0.426,  0.658] <.001 ***
## WP.OL-WA.WRF            0.362 [ 0.208,  0.498] <.001 ***
## WP.OL-WA.InV.           0.554 [ 0.428,  0.659] <.001 ***
## WP.OL-WA.EV             0.199 [ 0.034,  0.353]  .016 *  
## WP.OL-WA.SlpQlV.        0.106 [-0.061,  0.267]  .202    
## WP.OL-WA.RdV.           0.266 [ 0.104,  0.414]  .001 ** 
## WP.OL-WA.PR             0.162 [-0.005,  0.320]  .051 .  
## WP.OL-WA.ER             0.250 [ 0.087,  0.400]  .002 ** 
## WP.OL-WA.SlpQnV.       -0.004 [-0.175,  0.167]  .964    
## WP.AS-WP.PrfIV.         0.552 [ 0.425,  0.658] <.001 ***
## WP.AS-WP.TC             0.447 [ 0.304,  0.571] <.001 ***
## WP.AS-WA.WRV            0.314 [ 0.157,  0.456] <.001 ***
## WP.AS-WA.PW             0.299 [ 0.140,  0.443] <.001 ***
## WP.AS-WA.NW             0.219 [ 0.055,  0.371]  .008 ** 
## WP.AS-WA.RmV.           0.366 [ 0.213,  0.502] <.001 ***
## WP.AS-WA.PA             0.250 [ 0.088,  0.399]  .002 ** 
## WP.AS-WA.NA            -0.021 [-0.186,  0.145]  .799    
## WP.AS-WA.ImV.           0.307 [ 0.148,  0.450] <.001 ***
## WP.AS-WA.WA             0.193 [ 0.028,  0.348]  .019 *  
## WP.AS-WA.TA             0.316 [ 0.159,  0.458] <.001 ***
## WP.AS-WA.WRF            0.162 [-0.004,  0.319]  .050 .  
## WP.AS-WA.InV.           0.388 [ 0.238,  0.521] <.001 ***
## WP.AS-WA.EV             0.178 [ 0.013,  0.334]  .031 *  
## WP.AS-WA.SlpQlV.        0.184 [ 0.018,  0.339]  .026 *  
## WP.AS-WA.RdV.           0.240 [ 0.076,  0.391]  .004 ** 
## WP.AS-WA.PR             0.110 [-0.058,  0.271]  .188    
## WP.AS-WA.ER             0.244 [ 0.081,  0.394]  .003 ** 
## WP.AS-WA.SlpQnV.        0.005 [-0.166,  0.176]  .955    
## WP.PrfIV.-WP.TC         0.705 [ 0.610,  0.779] <.001 ***
## WP.PrfIV.-WA.WRV        0.536 [ 0.407,  0.645] <.001 ***
## WP.PrfIV.-WA.PW         0.490 [ 0.353,  0.607] <.001 ***
## WP.PrfIV.-WA.NW         0.393 [ 0.243,  0.525] <.001 ***
## WP.PrfIV.-WA.RmV.       0.569 [ 0.445,  0.672] <.001 ***
## WP.PrfIV.-WA.PA         0.464 [ 0.323,  0.585] <.001 ***
## WP.PrfIV.-WA.NA        -0.017 [-0.183,  0.149]  .833    
## WP.PrfIV.-WA.ImV.       0.608 [ 0.492,  0.703] <.001 ***
## WP.PrfIV.-WA.WA         0.438 [ 0.294,  0.563] <.001 ***
## WP.PrfIV.-WA.TA         0.596 [ 0.477,  0.693] <.001 ***
## WP.PrfIV.-WA.WRF        0.456 [ 0.313,  0.578] <.001 ***
## WP.PrfIV.-WA.InV.       0.629 [ 0.518,  0.720] <.001 ***
## WP.PrfIV.-WA.EV         0.221 [ 0.057,  0.373]  .007 ** 
## WP.PrfIV.-WA.SlpQlV.    0.145 [-0.021,  0.304]  .080 .  
## WP.PrfIV.-WA.RdV.       0.286 [ 0.126,  0.432] <.001 ***
## WP.PrfIV.-WA.PR         0.216 [ 0.051,  0.369]  .009 ** 
## WP.PrfIV.-WA.ER         0.247 [ 0.084,  0.397]  .003 ** 
## WP.PrfIV.-WA.SlpQnV.    0.017 [-0.154,  0.187]  .842    
## WP.TC-WA.WRV            0.556 [ 0.429,  0.661] <.001 ***
## WP.TC-WA.PW             0.546 [ 0.418,  0.653] <.001 ***
## WP.TC-WA.NW             0.370 [ 0.217,  0.505] <.001 ***
## WP.TC-WA.RmV.           0.609 [ 0.492,  0.703] <.001 ***
## WP.TC-WA.PA             0.518 [ 0.385,  0.629] <.001 ***
## WP.TC-WA.NA            -0.018 [-0.183,  0.149]  .832    
## WP.TC-WA.ImV.           0.631 [ 0.519,  0.721] <.001 ***
## WP.TC-WA.WA             0.451 [ 0.308,  0.574] <.001 ***
## WP.TC-WA.TA             0.578 [ 0.456,  0.679] <.001 ***
## WP.TC-WA.WRF            0.515 [ 0.382,  0.627] <.001 ***
## WP.TC-WA.InV.           0.645 [ 0.537,  0.733] <.001 ***
## WP.TC-WA.EV             0.247 [ 0.085,  0.397]  .003 ** 
## WP.TC-WA.SlpQlV.        0.012 [-0.154,  0.178]  .885    
## WP.TC-WA.RdV.           0.253 [ 0.090,  0.403]  .002 ** 
## WP.TC-WA.PR             0.170 [ 0.003,  0.327]  .041 *  
## WP.TC-WA.ER             0.230 [ 0.066,  0.382]  .005 ** 
## WP.TC-WA.SlpQnV.       -0.021 [-0.191,  0.151]  .809    
## WA.WRV-WA.PW            0.823 [ 0.763,  0.869] <.001 ***
## WA.WRV-WA.NW            0.827 [ 0.768,  0.872] <.001 ***
## WA.WRV-WA.RmV.          0.721 [ 0.633,  0.791] <.001 ***
## WA.WRV-WA.PA            0.480 [ 0.345,  0.596] <.001 ***
## WA.WRV-WA.NA            0.010 [-0.152,  0.172]  .900    
## WA.WRV-WA.ImV.          0.593 [ 0.477,  0.689] <.001 ***
## WA.WRV-WA.WA            0.569 [ 0.448,  0.669] <.001 ***
## WA.WRV-WA.TA            0.647 [ 0.542,  0.732] <.001 ***
## WA.WRV-WA.WRF           0.595 [ 0.479,  0.690] <.001 ***
## WA.WRV-WA.InV.          0.609 [ 0.496,  0.702] <.001 ***
## WA.WRV-WA.EV            0.262 [ 0.105,  0.407]  .001 ** 
## WA.WRV-WA.SlpQlV.       0.021 [-0.142,  0.182]  .803    
## WA.WRV-WA.RdV.          0.192 [ 0.030,  0.344]  .020 *  
## WA.WRV-WA.PR            0.178 [ 0.016,  0.331]  .031 *  
## WA.WRV-WA.ER            0.149 [-0.014,  0.304]  .073 .  
## WA.WRV-WA.SlpQnV.       0.146 [-0.021,  0.306]  .087 .  
## WA.PW-WA.NW             0.362 [ 0.213,  0.495] <.001 ***
## WA.PW-WA.RmV.           0.762 [ 0.684,  0.822] <.001 ***
## WA.PW-WA.PA             0.627 [ 0.518,  0.716] <.001 ***
## WA.PW-WA.NA            -0.084 [-0.242,  0.079]  .314    
## WA.PW-WA.ImV.           0.591 [ 0.475,  0.687] <.001 ***
## WA.PW-WA.WA             0.691 [ 0.596,  0.767] <.001 ***
## WA.PW-WA.TA             0.702 [ 0.609,  0.776] <.001 ***
## WA.PW-WA.WRF            0.723 [ 0.636,  0.792] <.001 ***
## WA.PW-WA.InV.           0.679 [ 0.581,  0.758] <.001 ***
## WA.PW-WA.EV             0.218 [ 0.058,  0.367]  .008 ** 
## WA.PW-WA.SlpQlV.        0.040 [-0.123,  0.200]  .635    
## WA.PW-WA.RdV.           0.211 [ 0.051,  0.361]  .011 *  
## WA.PW-WA.PR             0.189 [ 0.027,  0.341]  .022 *  
## WA.PW-WA.ER             0.168 [ 0.005,  0.321]  .043 *  
## WA.PW-WA.SlpQnV.        0.137 [-0.031,  0.297]  .109    
## WA.NW-WA.RmV.           0.430 [ 0.288,  0.553] <.001 ***
## WA.NW-WA.PA             0.168 [ 0.006,  0.321]  .042 *  
## WA.NW-WA.NA             0.100 [-0.063,  0.258]  .229    
## WA.NW-WA.ImV.           0.388 [ 0.241,  0.517] <.001 ***
## WA.NW-WA.WA             0.250 [ 0.092,  0.396]  .002 ** 
## WA.NW-WA.TA             0.367 [ 0.218,  0.499] <.001 ***
## WA.NW-WA.WRF            0.260 [ 0.103,  0.405]  .001 ** 
## WA.NW-WA.InV.           0.328 [ 0.175,  0.465] <.001 ***
## WA.NW-WA.EV             0.215 [ 0.055,  0.364]  .009 ** 
## WA.NW-WA.SlpQlV.       -0.005 [-0.167,  0.157]  .952    
## WA.NW-WA.RdV.           0.106 [-0.057,  0.264]  .202    
## WA.NW-WA.PR             0.106 [-0.058,  0.264]  .204    
## WA.NW-WA.ER             0.079 [-0.085,  0.238]  .345    
## WA.NW-WA.SlpQnV.        0.105 [-0.063,  0.268]  .218    
## WA.RmV.-WA.PA           0.642 [ 0.536,  0.728] <.001 ***
## WA.RmV.-WA.NA          -0.156 [-0.310,  0.006]  .059 .  
## WA.RmV.-WA.ImV.         0.702 [ 0.610,  0.776] <.001 ***
## WA.RmV.-WA.WA           0.669 [ 0.569,  0.750] <.001 ***
## WA.RmV.-WA.TA           0.690 [ 0.595,  0.767] <.001 ***
## WA.RmV.-WA.WRF          0.656 [ 0.553,  0.739] <.001 ***
## WA.RmV.-WA.InV.         0.693 [ 0.598,  0.769] <.001 ***
## WA.RmV.-WA.EV           0.203 [ 0.042,  0.353]  .014 *  
## WA.RmV.-WA.SlpQlV.      0.043 [-0.119,  0.204]  .602    
## WA.RmV.-WA.RdV.         0.243 [ 0.084,  0.390]  .003 ** 
## WA.RmV.-WA.PR           0.181 [ 0.019,  0.334]  .029 *  
## WA.RmV.-WA.ER           0.215 [ 0.055,  0.365]  .009 ** 
## WA.RmV.-WA.SlpQnV.      0.062 [-0.107,  0.226]  .473    
## WA.PA-WA.NA            -0.223 [-0.372, -0.064]  .007 ** 
## WA.PA-WA.ImV.           0.717 [ 0.628,  0.787] <.001 ***
## WA.PA-WA.WA             0.746 [ 0.664,  0.810] <.001 ***
## WA.PA-WA.TA             0.557 [ 0.434,  0.659] <.001 ***
## WA.PA-WA.WRF            0.698 [ 0.605,  0.773] <.001 ***
## WA.PA-WA.InV.           0.571 [ 0.450,  0.671] <.001 ***
## WA.PA-WA.EV             0.180 [ 0.019,  0.332]  .029 *  
## WA.PA-WA.SlpQlV.       -0.079 [-0.238,  0.084]  .341    
## WA.PA-WA.RdV.           0.140 [-0.023,  0.295]  .093 .  
## WA.PA-WA.PR             0.157 [-0.006,  0.311]  .059 .  
## WA.PA-WA.ER             0.097 [-0.066,  0.256]  .243    
## WA.PA-WA.SlpQnV.        0.023 [-0.145,  0.189]  .788    
## WA.NA-WA.ImV.          -0.114 [-0.270,  0.049]  .171    
## WA.NA-WA.WA            -0.097 [-0.255,  0.066]  .241    
## WA.NA-WA.TA            -0.019 [-0.181,  0.143]  .815    
## WA.NA-WA.WRF           -0.070 [-0.230,  0.093]  .397    
## WA.NA-WA.InV.          -0.073 [-0.233,  0.090]  .377    
## WA.NA-WA.EV            -0.069 [-0.229,  0.094]  .405    
## WA.NA-WA.SlpQlV.        0.136 [-0.026,  0.292]  .099 .  
## WA.NA-WA.RdV.           0.096 [-0.067,  0.255]  .249    
## WA.NA-WA.PR             0.104 [-0.059,  0.262]  .210    
## WA.NA-WA.ER             0.067 [-0.096,  0.227]  .421    
## WA.NA-WA.SlpQnV.       -0.033 [-0.199,  0.135]  .702    
## WA.ImV.-WA.WA           0.632 [ 0.524,  0.720] <.001 ***
## WA.ImV.-WA.TA           0.673 [ 0.573,  0.753] <.001 ***
## WA.ImV.-WA.WRF          0.653 [ 0.550,  0.737] <.001 ***
## WA.ImV.-WA.InV.         0.727 [ 0.641,  0.795] <.001 ***
## WA.ImV.-WA.EV           0.161 [-0.001,  0.315]  .051 .  
## WA.ImV.-WA.SlpQlV.     -0.030 [-0.191,  0.133]  .722    
## WA.ImV.-WA.RdV.         0.144 [-0.019,  0.300]  .082 .  
## WA.ImV.-WA.PR           0.149 [-0.014,  0.304]  .072 .  
## WA.ImV.-WA.ER           0.104 [-0.059,  0.262]  .211    
## WA.ImV.-WA.SlpQnV.     -0.104 [-0.266,  0.064]  .226    
## WA.WA-WA.TA             0.597 [ 0.482,  0.692] <.001 ***
## WA.WA-WA.WRF            0.820 [ 0.759,  0.867] <.001 ***
## WA.WA-WA.InV.           0.577 [ 0.458,  0.676] <.001 ***
## WA.WA-WA.EV             0.160 [-0.002,  0.313]  .053 .  
## WA.WA-WA.SlpQlV.        0.073 [-0.090,  0.232]  .383    
## WA.WA-WA.RdV.           0.148 [-0.015,  0.303]  .075 .  
## WA.WA-WA.PR             0.098 [-0.066,  0.256]  .241    
## WA.WA-WA.ER             0.138 [-0.025,  0.294]  .096 .  
## WA.WA-WA.SlpQnV.        0.066 [-0.103,  0.230]  .444    
## WA.TA-WA.WRF            0.639 [ 0.532,  0.725] <.001 ***
## WA.TA-WA.InV.           0.816 [ 0.754,  0.864] <.001 ***
## WA.TA-WA.EV             0.156 [-0.006,  0.310]  .059 .  
## WA.TA-WA.SlpQlV.        0.046 [-0.116,  0.207]  .577    
## WA.TA-WA.RdV.           0.220 [ 0.060,  0.369]  .008 ** 
## WA.TA-WA.PR             0.158 [-0.004,  0.313]  .057 .  
## WA.TA-WA.ER             0.193 [ 0.031,  0.344]  .020 *  
## WA.TA-WA.SlpQnV.        0.142 [-0.026,  0.302]  .097 .  
## WA.WRF-WA.InV.          0.618 [ 0.507,  0.709] <.001 ***
## WA.WRF-WA.EV            0.238 [ 0.079,  0.385]  .004 ** 
## WA.WRF-WA.SlpQlV.      -0.119 [-0.276,  0.044]  .151    
## WA.WRF-WA.RdV.          0.140 [-0.023,  0.295]  .093 .  
## WA.WRF-WA.PR            0.141 [-0.022,  0.296]  .090 .  
## WA.WRF-WA.ER            0.106 [-0.057,  0.264]  .203    
## WA.WRF-WA.SlpQnV.       0.153 [-0.015,  0.312]  .074 .  
## WA.InV.-WA.EV           0.134 [-0.029,  0.289]  .106    
## WA.InV.-WA.SlpQlV.     -0.054 [-0.214,  0.109]  .517    
## WA.InV.-WA.RdV.         0.243 [ 0.084,  0.390]  .003 ** 
## WA.InV.-WA.PR           0.220 [ 0.059,  0.369]  .008 ** 
## WA.InV.-WA.ER           0.190 [ 0.029,  0.342]  .021 *  
## WA.InV.-WA.SlpQnV.      0.103 [-0.065,  0.266]  .228    
## WA.EV-WA.SlpQlV.       -0.027 [-0.188,  0.135]  .745    
## WA.EV-WA.RdV.           0.193 [ 0.032,  0.345]  .019 *  
## WA.EV-WA.PR             0.306 [ 0.151,  0.446] <.001 ***
## WA.EV-WA.ER             0.085 [-0.079,  0.244]  .310    
## WA.EV-WA.SlpQnV.       -0.007 [-0.174,  0.161]  .938    
## WA.SlpQlV.-WA.RdV.      0.134 [-0.029,  0.291]  .106    
## WA.SlpQlV.-WA.PR        0.146 [-0.016,  0.302]  .078 .  
## WA.SlpQlV.-WA.ER        0.093 [-0.071,  0.251]  .267    
## WA.SlpQlV.-WA.SlpQnV.  -0.235 [-0.386, -0.070]  .006 ** 
## WA.RdV.-WA.PR           0.669 [ 0.569,  0.750] <.001 ***
## WA.RdV.-WA.ER           0.920 [ 0.891,  0.942] <.001 ***
## WA.RdV.-WA.SlpQnV.     -0.040 [-0.206,  0.128]  .640    
## WA.PR-WA.ER             0.326 [ 0.173,  0.464] <.001 ***
## WA.PR-WA.SlpQnV.       -0.092 [-0.256,  0.077]  .282    
## WA.ER-WA.SlpQnV.       -0.000 [-0.168,  0.167]  .996    
## ────────────────────────────────────────────────────────
## 
## 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      0.698                             0.888                                     0.803                                     0.824                0.637           0.803                         0.765                     0.541                 0.671              0.788              0.739                     0.693             0.645                      0.653            0.793              0.773                      0.730                      0.681          0.577              0.608              0.559           0.629              0.679                      0.645               0.613           0.657        0.665            0.531       0.797         0.727     0.777             0.558
## ICC2      0.810                             0.935                                     0.880                                     0.894                0.761           0.881                         0.855                     0.681                 0.787              0.870              0.837                     0.803             0.766                      0.773            0.874              0.862                      0.833                      0.797          0.714              0.741              0.700           0.757              0.796                      0.770               0.744           0.779        0.785            0.675       0.876         0.827     0.863             0.694
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

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)                           3.530 ***                  3.264 ***                          3.529 ***              
##                                      (0.042)                    (0.018)                            (0.040)                 
## WP.CreativeProcessEngagementV_mean    0.914 ***                  1.029 ***                         -0.062                  
##                                      (0.064)                    (0.027)                            (0.108)                 
## Manipulation                          0.062                      0.102 **                          -0.036                  
##                                      (0.060)                    (0.035)                            (0.045)                 
## WP.CreativeProcessEngagementV                                                                       0.949 ***              
##                                                                                                    (0.086)                 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.517                      0.857                              0.612                  
## Conditional R^2                       0.687                      0.857                              0.841                  
## AIC                                 450.395                     94.658                            367.954                  
## BIC                                 468.062                    112.325                            389.155                  
## Num. obs.                           253                        253                                253                      
## Num. groups: B.ID                   140                        140                                140                      
## Var: B.ID (Intercept)                 0.120                      0.000                              0.163                  
## Var: Residual                         0.222                      0.078                              0.113                  
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 253 (22 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)   0.097 (0.036)  2.674  .007 **  [ 0.037, 0.172]
## Direct (c')    -0.038 (0.040) -0.945  .345     [-0.106, 0.041]
## Total (c)       0.059 (0.056)  1.059  .290     [-0.043, 0.171]
## ──────────────────────────────────────────────────────────────
## 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)                  1.860 ***                 -0.047                     1.869 ***              
##                             (0.263)                    (0.126)                   (0.265)                 
## WP.learningBehaviorV_mean    0.511 ***                  1.006 ***                 0.365 **               
##                             (0.079)                    (0.037)                   (0.113)                 
## Manipulation                 0.070                      0.031                     0.064                  
##                             (0.062)                    (0.055)                   (0.061)                 
## WP.learningBehaviorV                                                              0.145                  
##                                                                                  (0.080)                 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                 0.202                      0.742                     0.206                  
## Conditional R^2              0.678                      0.742                     0.690                  
## AIC                        540.449                    318.781                   542.439                  
## BIC                        558.116                    336.448                   563.639                  
## Num. obs.                  253                        253                       253                      
## Num. groups: B.ID          140                        140                       140                      
## Var: B.ID (Intercept)        0.335                      0.000                     0.343                  
## Var: Residual                0.227                      0.190                     0.220                  
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 253 (22 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)   0.004 (0.009) 0.462  .644     [-0.010, 0.024]
## Direct (c')     0.062 (0.059) 1.045  .296     [-0.043, 0.166]
## Total (c)       0.066 (0.060) 1.101  .271     [-0.039, 0.184]
## ─────────────────────────────────────────────────────────────
## 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)                           3.380 ***                              3.264 ***                          3.380 ***                          
##                                      (0.055)                                (0.018)                            (0.055)                             
## WP.CreativeProcessEngagementV_mean    0.629 ***                              1.029 ***                          0.521 ***                          
##                                      (0.084)                                (0.027)                            (0.111)                             
## Manipulation                          0.010                                  0.102 **                          -0.001                              
##                                      (0.035)                                (0.035)                            (0.036)                             
## WP.CreativeProcessEngagementV                                                                                   0.105                              
##                                                                                                                (0.070)                             
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.277                                  0.857                              0.277                              
## Conditional R^2                       0.887                                  0.857                              0.889                              
## AIC                                 394.812                                 94.658                            398.066                              
## BIC                                 412.479                                112.325                            419.267                              
## Num. obs.                           253                                    253                                253                                  
## Num. groups: B.ID                   140                                    140                                140                                  
## Var: B.ID (Intercept)                 0.380                                  0.000                              0.382                              
## Var: Residual                         0.070                                  0.078                              0.070                              
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 253 (22 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)   0.011 (0.008)  1.455  .146     [ 0.001, 0.027]
## Direct (c')    -0.003 (0.032) -0.083  .934     [-0.056, 0.060]
## Total (c)       0.009 (0.032)  0.273  .785     [-0.045, 0.070]
## ──────────────────────────────────────────────────────────────
## 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)                           3.327 ***                                      3.264 ***                          3.327 ***                                  
##                                      (0.062)                                        (0.018)                            (0.062)                                     
## WP.CreativeProcessEngagementV_mean    0.701 ***                                      1.029 ***                          0.694 ***                                  
##                                      (0.095)                                        (0.027)                            (0.144)                                     
## Manipulation                          0.061                                          0.102 **                           0.060                                      
##                                      (0.052)                                        (0.035)                            (0.054)                                     
## WP.CreativeProcessEngagementV                                                                                           0.006                                      
##                                                                                                                        (0.105)                                     
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.263                                          0.857                              0.263                                      
## Conditional R^2                       0.804                                          0.857                              0.803                                      
## AIC                                 520.269                                         94.658                            524.943                                      
## BIC                                 537.936                                        112.325                            546.143                                      
## Num. obs.                           253                                            253                                253                                          
## Num. groups: B.ID                   140                                            140                                140                                          
## Var: B.ID (Intercept)                 0.443                                          0.000                              0.443                                      
## Var: Residual                         0.160                                          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 : 253 (22 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)   0.002 (0.011) 0.158  .874     [-0.019, 0.022]
## Direct (c')     0.057 (0.049) 1.176  .240     [-0.025, 0.152]
## Total (c)       0.059 (0.047) 1.255  .210     [-0.021, 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="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)                           3.422 ***                                      3.264 ***                          3.422 ***                                  
##                                      (0.059)                                        (0.018)                            (0.059)                                     
## WP.CreativeProcessEngagementV_mean    0.572 ***                                      1.029 ***                          0.397 **                                   
##                                      (0.091)                                        (0.027)                            (0.130)                                     
## Manipulation                         -0.033                                          0.102 **                          -0.051                                      
##                                      (0.046)                                        (0.035)                            (0.046)                                     
## WP.CreativeProcessEngagementV                                                                                           0.170                                      
##                                                                                                                        (0.091)                                     
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.209                                          0.857                              0.212                                      
## Conditional R^2                       0.822                                          0.857                              0.825                                      
## AIC                                 476.741                                         94.658                            478.214                                      
## BIC                                 494.408                                        112.325                            499.415                                      
## Num. obs.                           253                                            253                                253                                          
## Num. groups: B.ID                   140                                            140                                140                                          
## Var: B.ID (Intercept)                 0.418                                          0.000                              0.418                                      
## Var: Residual                         0.122                                          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 : 253 (22 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)   0.018 (0.011)  1.700  .089 .   [ 0.003, 0.041]
## Direct (c')    -0.054 (0.042) -1.274  .203     [-0.124, 0.028]
## Total (c)      -0.035 (0.042) -0.850  .395     [-0.107, 0.046]
## ──────────────────────────────────────────────────────────────
## 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)                           3.195 ***                 3.264 ***                          3.194 ***             
##                                      (0.050)                   (0.018)                            (0.051)                
## WP.CreativeProcessEngagementV_mean    0.797 ***                 1.029 ***                          0.435 **              
##                                      (0.076)                   (0.027)                            (0.148)                
## Manipulation                          0.039                     0.102 **                           0.003                 
##                                      (0.066)                   (0.035)                            (0.065)                
## WP.CreativeProcessEngagementV                                                                      0.351 **              
##                                                                                                   (0.123)                
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.377                     0.857                              0.384                 
## Conditional R^2                       0.640                     0.857                              0.679                 
## AIC                                 516.672                    94.658                            513.589                 
## BIC                                 534.339                   112.325                            534.789                 
## Num. obs.                           253                       253                                253                     
## Num. groups: B.ID                   140                       140                                140                     
## Var: B.ID (Intercept)                 0.193                     0.000                              0.220                 
## Var: Residual                         0.265                     0.078                              0.240                 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 253 (22 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)   0.037 (0.017) 2.138  .032 *   [ 0.010, 0.075]
## Direct (c')     0.001 (0.058) 0.009  .993     [-0.098, 0.115]
## Total (c)       0.037 (0.059) 0.631  .528     [-0.069, 0.155]
## ─────────────────────────────────────────────────────────────
## 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)                           3.029 ***            3.264 ***                          3.028 ***        
##                                      (0.045)              (0.018)                            (0.045)           
## WP.CreativeProcessEngagementV_mean    0.696 ***            1.029 ***                          0.436 ***        
##                                      (0.068)              (0.027)                            (0.108)           
## Manipulation                          0.017                0.102 **                          -0.010            
##                                      (0.043)              (0.035)                            (0.042)           
## WP.CreativeProcessEngagementV                                                                 0.252 **         
##                                                                                              (0.081)           
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.394                0.857                              0.399            
## Conditional R^2                       0.802                0.857                              0.821            
## AIC                                 384.381               94.658                            380.353            
## BIC                                 402.048              112.325                            401.553            
## Num. obs.                           253                  253                                253                
## Num. groups: B.ID                   140                  140                                140                
## Var: B.ID (Intercept)                 0.218                0.000                              0.228            
## Var: Residual                         0.106                0.078                              0.097            
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 253 (22 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)   0.026 (0.012)  2.211  .027 *   [ 0.008, 0.053]
## Direct (c')    -0.012 (0.038) -0.313  .755     [-0.075, 0.062]
## Total (c)       0.015 (0.038)  0.380  .704     [-0.055, 0.092]
## ──────────────────────────────────────────────────────────────
## 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)                           3.241 ***                      3.264 ***                          3.243 ***                  
##                                      (0.038)                        (0.018)                            (0.035)                     
## WP.CreativeProcessEngagementV_mean    0.991 ***                      1.029 ***                         -0.189                      
##                                      (0.058)                        (0.027)                            (0.113)                     
## Manipulation                          0.194 **                       0.102 **                           0.078                      
##                                      (0.071)                        (0.035)                            (0.052)                     
## WP.CreativeProcessEngagementV                                                                           1.146 ***                  
##                                                                                                        (0.097)                     
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.564                          0.857                              0.690                      
## Conditional R^2                       0.597                          0.857                              0.802                      
## AIC                                 463.067                         94.658                            367.953                      
## BIC                                 480.734                        112.325                            389.154                      
## Num. obs.                           253                            253                                253                          
## Num. groups: B.ID                   140                            140                                140                          
## Var: B.ID (Intercept)                 0.026                          0.000                              0.087                      
## Var: Residual                         0.314                          0.078                              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 : 253 (22 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)   0.117 (0.044) 2.679  .007 **  [ 0.044, 0.207]
## Direct (c')     0.076 (0.046) 1.639  .101     [-0.003, 0.168]
## Total (c)       0.193 (0.066) 2.932  .003 **  [ 0.073, 0.325]
## ─────────────────────────────────────────────────────────────
## 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)                           3.530 ***                  3.264 ***                          3.529 ***              
##                                      (0.042)                    (0.018)                            (0.040)                 
## WP.CreativeProcessEngagementV_mean    0.914 ***                  1.029 ***                         -0.062                  
##                                      (0.064)                    (0.027)                            (0.108)                 
## Manipulation                          0.062                      0.102 **                          -0.036                  
##                                      (0.060)                    (0.035)                            (0.045)                 
## WP.CreativeProcessEngagementV                                                                       0.949 ***              
##                                                                                                    (0.086)                 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.517                      0.857                              0.612                  
## Conditional R^2                       0.687                      0.857                              0.841                  
## AIC                                 450.395                     94.658                            367.954                  
## BIC                                 468.062                    112.325                            389.155                  
## Num. obs.                           253                        253                                253                      
## Num. groups: B.ID                   140                        140                                140                      
## Var: B.ID (Intercept)                 0.120                      0.000                              0.163                  
## Var: Residual                         0.222                      0.078                              0.113                  
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 253 (22 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)   0.097 (0.036)  2.674  .007 **  [ 0.037, 0.172]
## Direct (c')    -0.038 (0.040) -0.945  .345     [-0.106, 0.041]
## Total (c)       0.059 (0.056)  1.059  .290     [-0.043, 0.171]
## ──────────────────────────────────────────────────────────────
## 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)                           3.120 ***               3.264 ***                          3.116 ***           
##                                      (0.031)                 (0.018)                            (0.026)              
## WP.CreativeProcessEngagementV_mean    1.123 ***               1.029 ***                          0.156 *             
##                                      (0.047)                 (0.027)                            (0.075)              
## Manipulation                          0.074                   0.102 **                          -0.025               
##                                      (0.050)                 (0.035)                            (0.032)              
## WP.CreativeProcessEngagementV                                                                    0.938 ***           
##                                                                                                 (0.061)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.734                   0.857                              0.834               
## Conditional R^2                       0.796                   0.857                              0.920               
## AIC                                 324.403                  94.658                            178.396               
## BIC                                 342.069                 112.325                            199.596               
## Num. obs.                           253                     253                                253                   
## Num. groups: B.ID                   140                     140                                140                   
## Var: B.ID (Intercept)                 0.046                   0.000                              0.064               
## Var: Residual                         0.153                   0.078                              0.059               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 253 (22 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)   0.096 (0.036)  2.684  .007 **  [ 0.035, 0.171]
## Direct (c')    -0.027 (0.029) -0.920  .358     [-0.076, 0.030]
## Total (c)       0.069 (0.048)  1.453  .146     [-0.023, 0.163]
## ──────────────────────────────────────────────────────────────
## 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)                           3.203 ***               3.264 ***                          3.201 ***           
##                                      (0.053)                 (0.018)                            (0.053)              
## WP.CreativeProcessEngagementV_mean    0.711 ***               1.029 ***                          0.214               
##                                      (0.082)                 (0.027)                            (0.133)              
## Manipulation                          0.030                   0.102 **                          -0.020               
##                                      (0.056)                 (0.035)                            (0.052)              
## WP.CreativeProcessEngagementV                                                                    0.481 ***           
##                                                                                                 (0.101)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.317                   0.857                              0.339               
## Conditional R^2                       0.737                   0.857                              0.782               
## AIC                                 494.658                  94.658                            478.725               
## BIC                                 512.325                 112.325                            499.925               
## Num. obs.                           253                     253                                253                   
## Num. groups: B.ID                   140                     140                                140                   
## Var: B.ID (Intercept)                 0.292                   0.000                              0.311               
## Var: Residual                         0.184                   0.078                              0.153               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 253 (22 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)   0.050 (0.020)  2.490  .013 *   [ 0.019, 0.092]
## Direct (c')    -0.022 (0.047) -0.471  .638     [-0.102, 0.070]
## Total (c)       0.028 (0.051)  0.544  .587     [-0.067, 0.127]
## ──────────────────────────────────────────────────────────────
## 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)                           3.024 ***                      3.264 ***                          3.021 ***                  
##                                      (0.058)                        (0.018)                            (0.058)                     
## WP.CreativeProcessEngagementV_mean    0.768 ***                      1.029 ***                          0.241                      
##                                      (0.089)                        (0.027)                            (0.154)                     
## Manipulation                          0.054                          0.102 **                           0.001                      
##                                      (0.066)                        (0.035)                            (0.064)                     
## WP.CreativeProcessEngagementV                                                                           0.510 ***                  
##                                                                                                        (0.122)                     
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.305                          0.857                              0.325                      
## Conditional R^2                       0.691                          0.857                              0.731                      
## AIC                                 559.644                         94.658                            547.700                      
## BIC                                 577.311                        112.325                            568.900                      
## Num. obs.                           253                            253                                253                          
## Num. groups: B.ID                   140                            140                                140                          
## Var: B.ID (Intercept)                 0.327                          0.000                              0.345                      
## Var: Residual                         0.262                          0.078                              0.229                      
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 253 (22 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)   0.053 (0.022)  2.422  .015 *   [ 0.019, 0.099]
## Direct (c')    -0.002 (0.057) -0.038  .970     [-0.099, 0.111]
## Total (c)       0.051 (0.061)  0.839  .401     [-0.061, 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.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)                           3.472 ***              3.264 ***                          3.471 ***          
##                                      (0.066)                (0.018)                            (0.066)             
## WP.CreativeProcessEngagementV_mean    0.630 ***              1.029 ***                          0.196              
##                                      (0.101)                (0.027)                            (0.177)             
## Manipulation                         -0.009                  0.102 **                          -0.053              
##                                      (0.074)                (0.035)                            (0.073)             
## WP.CreativeProcessEngagementV                                                                   0.421 **           
##                                                                                                (0.141)             
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.188                  0.857                              0.201              
## Conditional R^2                       0.644                  0.857                              0.671              
## AIC                                 618.362                 94.658                            613.838              
## BIC                                 636.029                112.325                            635.039              
## Num. obs.                           253                    253                                253                  
## Num. groups: B.ID                   140                    140                                140                  
## Var: B.ID (Intercept)                 0.420                  0.000                              0.436              
## Var: Residual                         0.328                  0.078                              0.305              
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 253 (22 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)   0.044 (0.020)  2.177  .029 *   [ 0.013, 0.089]
## Direct (c')    -0.056 (0.066) -0.850  .395     [-0.168, 0.074]
## Total (c)      -0.012 (0.067) -0.179  .858     [-0.133, 0.123]
## ──────────────────────────────────────────────────────────────
## 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)                           2.977 ***                       3.264 ***                          2.977 ***                   
##                                      (0.051)                         (0.018)                            (0.051)                      
## WP.CreativeProcessEngagementV_mean    0.870 ***                       1.029 ***                          0.442 **                    
##                                      (0.078)                         (0.027)                            (0.156)                      
## Manipulation                         -0.087                           0.102 **                          -0.130                       
##                                      (0.070)                         (0.035)                            (0.069)                      
## WP.CreativeProcessEngagementV                                                                            0.416 **                    
##                                                                                                         (0.131)                      
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.401                           0.857                              0.417                       
## Conditional R^2                       0.643                           0.857                              0.663                       
## AIC                                 537.388                          94.658                            531.756                       
## BIC                                 555.055                         112.325                            552.957                       
## Num. obs.                           253                             253                                253                           
## Num. groups: B.ID                   140                             140                                140                           
## Var: B.ID (Intercept)                 0.200                           0.000                              0.203                       
## Var: Residual                         0.294                           0.078                              0.278                       
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 253 (22 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)   0.043 (0.019)  2.231  .026 *   [ 0.013,  0.087]
## Direct (c')    -0.133 (0.062) -2.129  .033 *   [-0.239, -0.009]
## Total (c)      -0.089 (0.064) -1.395  .163     [-0.205,  0.038]
## ───────────────────────────────────────────────────────────────
## 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)                           2.858 ***             3.264 ***                          2.857 ***         
##                                      (0.053)               (0.018)                            (0.053)            
## WP.CreativeProcessEngagementV_mean    0.923 ***             1.029 ***                          0.631 ***         
##                                      (0.082)               (0.027)                            (0.135)            
## Manipulation                          0.021                 0.102 **                          -0.009             
##                                      (0.054)               (0.035)                            (0.054)            
## WP.CreativeProcessEngagementV                                                                  0.284 **          
##                                                                                               (0.105)            
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.441                 0.857                              0.450             
## Conditional R^2                       0.796                 0.857                              0.802             
## AIC                                 487.312                94.658                            484.728             
## BIC                                 504.979               112.325                            505.929             
## Num. obs.                           253                   253                                253                 
## Num. groups: B.ID                   140                   140                                140                 
## Var: B.ID (Intercept)                 0.298                 0.000                              0.296             
## Var: Residual                         0.172                 0.078                              0.166             
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 253 (22 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)   0.030 (0.014)  2.087  .037 *   [ 0.007, 0.062]
## Direct (c')    -0.012 (0.049) -0.236  .813     [-0.094, 0.085]
## Total (c)       0.018 (0.050)  0.369  .712     [-0.070, 0.117]
## ──────────────────────────────────────────────────────────────
## 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)                           3.120 ***               3.258 ***                          3.119 ***           
##                                      (0.047)                 (0.017)                            (0.047)              
## WP.CreativeProcessEngagementV_mean    0.667 ***               1.026 ***                          0.471 ***           
##                                      (0.072)                 (0.027)                            (0.121)              
## Manipulation                         -0.068                   0.095 **                          -0.086               
##                                      (0.047)                 (0.035)                            (0.047)              
## WP.CreativeProcessEngagementV                                                                    0.191 *             
##                                                                                                 (0.095)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.349                   0.858                              0.353               
## Conditional R^2                       0.775                   0.858                              0.780               
## AIC                                 407.739                  87.508                            408.569               
## BIC                                 425.306                 105.075                            429.650               
## Num. obs.                           248                     248                                248                   
## Num. groups: B.ID                   139                     139                                139                   
## Var: B.ID (Intercept)                 0.235                   0.000                              0.235               
## Var: Residual                         0.124                   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 : 248 (27 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 (0.011)  1.725  .085 .   [ 0.002,  0.043]
## Direct (c')    -0.088 (0.042) -2.066  .039 *   [-0.159, -0.005]
## Total (c)      -0.069 (0.042) -1.617  .106     [-0.143,  0.014]
## ───────────────────────────────────────────────────────────────
## 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)                           3.211 ***                       3.258 ***                          3.210 ***                   
##                                      (0.059)                         (0.017)                            (0.060)                      
## WP.CreativeProcessEngagementV_mean    0.754 ***                       1.026 ***                          0.470 **                    
##                                      (0.091)                         (0.027)                            (0.157)                      
## Manipulation                         -0.156 *                         0.095 **                          -0.181 **                    
##                                      (0.062)                         (0.035)                            (0.062)                      
## WP.CreativeProcessEngagementV                                                                            0.276 *                     
##                                                                                                         (0.124)                      
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.300                           0.858                              0.305                       
## Conditional R^2                       0.733                           0.858                              0.747                       
## AIC                                 534.167                          87.508                            533.680                       
## BIC                                 551.735                         105.075                            554.761                       
## Num. obs.                           248                             248                                248                           
## Num. groups: B.ID                   139                             139                                139                           
## Var: B.ID (Intercept)                 0.360                           0.000                              0.370                       
## Var: Residual                         0.222                           0.076                              0.212                       
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 248 (27 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 (0.015)  1.818  .069 .   [ 0.004,  0.060]
## Direct (c')    -0.184 (0.056) -3.282  .001 **  [-0.277, -0.074]
## Total (c)      -0.156 (0.056) -2.777  .005 **  [-0.255, -0.046]
## ───────────────────────────────────────────────────────────────
## 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)                           3.028 ***                       3.258 ***                          3.027 ***                   
##                                      (0.065)                         (0.017)                            (0.065)                      
## WP.CreativeProcessEngagementV_mean    0.580 ***                       1.026 ***                          0.465 **                    
##                                      (0.100)                         (0.027)                            (0.177)                      
## Manipulation                          0.021                           0.095 **                           0.011                       
##                                      (0.070)                         (0.035)                            (0.071)                      
## WP.CreativeProcessEngagementV                                                                            0.112                       
##                                                                                                         (0.143)                      
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.170                           0.858                              0.171                       
## Conditional R^2                       0.672                           0.858                              0.669                       
## AIC                                 585.658                          87.508                            589.106                       
## BIC                                 603.225                         105.075                            610.187                       
## Num. obs.                           248                             248                                248                           
## Num. groups: B.ID                   139                             139                                139                           
## Var: B.ID (Intercept)                 0.428                           0.000                              0.425                       
## Var: Residual                         0.280                           0.076                              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 : 248 (27 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)   0.012 (0.014) 0.855  .393     [-0.010, 0.040]
## Direct (c')     0.007 (0.064) 0.116  .908     [-0.101, 0.134]
## Total (c)       0.019 (0.063) 0.306  .760     [-0.090, 0.138]
## ─────────────────────────────────────────────────────────────
## 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)                           3.383 ***           3.258 ***                          3.382 ***       
##                                      (0.039)             (0.017)                            (0.040)          
## WP.CreativeProcessEngagementV_mean    0.768 ***           1.026 ***                          0.436 **        
##                                      (0.060)             (0.027)                            (0.135)          
## Manipulation                         -0.102               0.095 **                          -0.134 *         
##                                      (0.063)             (0.035)                            (0.062)          
## WP.CreativeProcessEngagementV                                                                0.324 **        
##                                                                                             (0.118)          
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.448               0.858                              0.459           
## Conditional R^2                       0.580               0.858                              0.616           
## AIC                                 430.246              87.508                            427.594           
## BIC                                 447.813             105.075                            448.674           
## Num. obs.                           248                 248                                248               
## Num. groups: B.ID                   139                 139                                139               
## Var: B.ID (Intercept)                 0.075               0.000                              0.091           
## Var: Residual                         0.240               0.076                              0.221           
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 248 (27 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)   0.032 (0.016)  2.016  .044 *   [ 0.008,  0.067]
## Direct (c')    -0.136 (0.056) -2.457  .014 *   [-0.231, -0.026]
## Total (c)      -0.105 (0.057) -1.848  .065 .   [-0.206,  0.008]
## ───────────────────────────────────────────────────────────────
## 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)                           3.485 ***               3.258 ***                          3.485 ***           
##                                      (0.048)                 (0.017)                            (0.049)              
## WP.CreativeProcessEngagementV_mean    0.665 ***               1.026 ***                          0.625 ***           
##                                      (0.074)                 (0.027)                            (0.152)              
## Manipulation                          0.033                   0.095 **                           0.029               
##                                      (0.065)                 (0.035)                            (0.066)              
## WP.CreativeProcessEngagementV                                                                    0.039               
##                                                                                                 (0.129)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.308                   0.858                              0.307               
## Conditional R^2                       0.602                   0.858                              0.601               
## AIC                                 491.020                  87.508                            495.191               
## BIC                                 508.587                 105.075                            516.271               
## Num. obs.                           248                     248                                248                   
## Num. groups: B.ID                   139                     139                                139                   
## Var: B.ID (Intercept)                 0.183                   0.000                              0.183               
## Var: Residual                         0.247                   0.076                              0.248               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 248 (27 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)   0.005 (0.012) 0.391  .695     [-0.018, 0.029]
## Direct (c')     0.026 (0.060) 0.443  .657     [-0.074, 0.144]
## Total (c)       0.031 (0.058) 0.537  .591     [-0.071, 0.143]
## ─────────────────────────────────────────────────────────────
## 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 ***               3.258 ***                          2.004 ***           
##                                      (0.052)                 (0.017)                            (0.051)              
## WP.CreativeProcessEngagementV_mean   -0.034                   1.026 ***                         -0.190               
##                                      (0.079)                 (0.027)                            (0.155)              
## Manipulation                          0.048                   0.095 **                           0.034               
##                                      (0.065)                 (0.035)                            (0.066)              
## WP.CreativeProcessEngagementV                                                                    0.152               
##                                                                                                 (0.130)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.002                   0.858                              0.006               
## Conditional R^2                       0.479                   0.858                              0.476               
## AIC                                 506.878                  87.508                            509.755               
## BIC                                 524.445                 105.075                            530.835               
## Num. obs.                           248                     248                                248                   
## Num. groups: B.ID                   139                     139                                139                   
## Var: B.ID (Intercept)                 0.225                   0.000                              0.222               
## Var: Residual                         0.246                   0.076                              0.247               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 248 (27 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)   0.016 (0.013) 1.187  .235     [-0.002, 0.043]
## Direct (c')     0.031 (0.060) 0.521  .602     [-0.070, 0.149]
## Total (c)       0.047 (0.059) 0.793  .428     [-0.055, 0.158]
## ─────────────────────────────────────────────────────────────
## 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)                           3.408 ***            3.258 ***                          3.408 ***        
##                                      (0.034)              (0.017)                            (0.034)           
## WP.CreativeProcessEngagementV_mean    0.683 ***            1.026 ***                          0.607 ***        
##                                      (0.052)              (0.027)                            (0.117)           
## Manipulation                          0.068                0.095 **                           0.061            
##                                      (0.053)              (0.035)                            (0.053)           
## WP.CreativeProcessEngagementV                                                                 0.074            
##                                                                                              (0.102)           
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.466                0.858                              0.466            
## Conditional R^2                       0.617                0.858                              0.621            
## AIC                                 352.516               87.508                            356.725            
## BIC                                 370.083              105.075                            377.806            
## Num. obs.                           248                  248                                248                
## Num. groups: B.ID                   139                  139                                139                
## Var: B.ID (Intercept)                 0.066                0.000                              0.068            
## Var: Residual                         0.166                0.076                              0.165            
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 248 (27 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)   0.008 (0.010) 0.804  .421     [-0.008, 0.028]
## Direct (c')     0.059 (0.048) 1.221  .222     [-0.023, 0.154]
## Total (c)       0.066 (0.047) 1.411  .158     [-0.017, 0.156]
## ─────────────────────────────────────────────────────────────
## 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)                           3.264 ***               3.258 ***                          3.263 ***           
##                                      (0.055)                 (0.017)                            (0.055)              
## WP.CreativeProcessEngagementV_mean    0.740 ***               1.026 ***                          0.660 ***           
##                                      (0.084)                 (0.027)                            (0.162)              
## Manipulation                         -0.060                   0.095 **                          -0.067               
##                                      (0.067)                 (0.035)                            (0.068)              
## WP.CreativeProcessEngagementV                                                                    0.078               
##                                                                                                 (0.135)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.310                   0.858                              0.310               
## Conditional R^2                       0.657                   0.858                              0.655               
## AIC                                 530.232                  87.508                            534.073               
## BIC                                 547.799                 105.075                            555.153               
## Num. obs.                           248                     248                                248                   
## Num. groups: B.ID                   139                     139                                139                   
## Var: B.ID (Intercept)                 0.264                   0.000                              0.263               
## Var: Residual                         0.262                   0.076                              0.263               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 248 (27 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)   0.009 (0.013)  0.661  .509     [-0.013, 0.034]
## Direct (c')    -0.070 (0.062) -1.136  .256     [-0.174, 0.051]
## Total (c)      -0.062 (0.060) -1.021  .307     [-0.167, 0.053]
## ──────────────────────────────────────────────────────────────
## 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)                           3.349 ***                       3.258 ***                          3.349 ***                   
##                                      (0.048)                         (0.017)                            (0.048)                      
## WP.CreativeProcessEngagementV_mean    0.708 ***                       1.026 ***                          0.618 ***                   
##                                      (0.073)                         (0.027)                            (0.148)                      
## Manipulation                          0.157 *                         0.095 **                           0.149 *                     
##                                      (0.063)                         (0.035)                            (0.064)                      
## WP.CreativeProcessEngagementV                                                                            0.088                       
##                                                                                                         (0.125)                      
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.351                           0.858                              0.351                       
## Conditional R^2                       0.636                           0.858                              0.635                       
## AIC                                 480.245                          87.508                            484.074                       
## BIC                                 497.812                         105.075                            505.154                       
## Num. obs.                           248                             248                                248                           
## Num. groups: B.ID                   139                             139                                139                           
## Var: B.ID (Intercept)                 0.182                           0.000                              0.182                       
## Var: Residual                         0.232                           0.076                              0.233                       
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 248 (27 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)   0.009 (0.012) 0.778  .436     [-0.010, 0.033]
## Direct (c')     0.146 (0.058) 2.529  .011 *   [ 0.048, 0.260]
## Total (c)       0.155 (0.057) 2.747  .006 **  [ 0.056, 0.263]
## ─────────────────────────────────────────────────────────────
## 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)                           3.015 ***                3.258 ***                          3.015 ***            
##                                      (0.057)                  (0.017)                            (0.057)               
## WP.CreativeProcessEngagementV_mean    0.736 ***                1.026 ***                          0.796 ***            
##                                      (0.087)                  (0.027)                            (0.179)               
## Manipulation                         -0.128                    0.095 **                          -0.122                
##                                      (0.077)                  (0.035)                            (0.078)               
## WP.CreativeProcessEngagementV                                                                    -0.059                
##                                                                                                  (0.152)               
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.284                    0.858                              0.284                
## Conditional R^2                       0.584                    0.858                              0.583                
## AIC                                 571.132                   87.508                            574.911                
## BIC                                 588.700                  105.075                            595.992                
## Num. obs.                           248                      248                                248                    
## Num. groups: B.ID                   139                      139                                139                    
## Var: B.ID (Intercept)                 0.250                    0.000                              0.250                
## Var: Residual                         0.345                    0.076                              0.347                
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 248 (27 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)  -0.004 (0.015) -0.287  .774     [-0.035, 0.023]
## Direct (c')    -0.126 (0.070) -1.787  .074 .   [-0.245, 0.013]
## Total (c)      -0.130 (0.068) -1.906  .057 .   [-0.247, 0.003]
## ──────────────────────────────────────────────────────────────
## 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)                           3.282 ***            3.258 ***                          3.281 ***        
##                                      (0.058)              (0.017)                            (0.058)           
## WP.CreativeProcessEngagementV_mean    0.758 ***            1.026 ***                          0.583 ***        
##                                      (0.089)              (0.027)                            (0.171)           
## Manipulation                          0.057                0.095 **                           0.041            
##                                      (0.071)              (0.035)                            (0.072)           
## WP.CreativeProcessEngagementV                                                                 0.171            
##                                                                                              (0.142)           
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.297                0.858                              0.299            
## Conditional R^2                       0.650                0.858                              0.652            
## AIC                                 557.954               87.508                            560.573            
## BIC                                 575.521              105.075                            581.653            
## Num. obs.                           248                  248                                248                
## Num. groups: B.ID                   139                  139                                139                
## Var: B.ID (Intercept)                 0.296                0.000                              0.296            
## Var: Residual                         0.293                0.076                              0.292            
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 248 (27 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)   0.017 (0.014) 1.212  .226     [-0.002, 0.047]
## Direct (c')     0.038 (0.065) 0.586  .558     [-0.072, 0.166]
## Total (c)       0.055 (0.064) 0.866  .386     [-0.055, 0.177]
## ─────────────────────────────────────────────────────────────
## 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)                           3.807 ***      3.258 ***                          3.807 ***  
##                                      (0.054)        (0.017)                            (0.055)     
## WP.CreativeProcessEngagementV_mean    0.493 ***      1.026 ***                          0.364 *    
##                                      (0.083)        (0.027)                            (0.149)     
## Manipulation                          0.019          0.095 **                           0.007      
##                                      (0.059)        (0.035)                            (0.060)     
## WP.CreativeProcessEngagementV                                                           0.126      
##                                                                                        (0.120)     
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.175          0.858                              0.176      
## Conditional R^2                       0.662          0.858                              0.665      
## AIC                                 500.503         87.508                            503.806      
## BIC                                 518.070        105.075                            524.887      
## Num. obs.                           248            248                                248          
## Num. groups: B.ID                   139            139                                139          
## Var: B.ID (Intercept)                 0.292          0.000                              0.294      
## Var: Residual                         0.203          0.076                              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 : 248 (27 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)   0.013 (0.012) 1.085  .278     [-0.004, 0.038]
## Direct (c')     0.005 (0.054) 0.086  .931     [-0.087, 0.111]
## Total (c)       0.018 (0.053) 0.331  .741     [-0.074, 0.119]
## ─────────────────────────────────────────────────────────────
## 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)                           1.225 ***         3.258 ***                          1.226 ***     
##                                      (0.048)           (0.017)                            (0.048)        
## WP.CreativeProcessEngagementV_mean    0.182 *           1.026 ***                          0.273 *       
##                                      (0.073)           (0.027)                            (0.117)        
## Manipulation                         -0.146 ***         0.095 **                          -0.138 **      
##                                      (0.043)           (0.035)                            (0.044)        
## WP.CreativeProcessEngagementV                                                             -0.089         
##                                                                                           (0.090)        
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.051             0.858                              0.052         
## Conditional R^2                       0.717             0.858                              0.718         
## AIC                                 394.720            87.508                            398.724         
## BIC                                 412.287           105.075                            419.805         
## Num. obs.                           248               248                                248             
## Num. groups: B.ID                   139               139                                139             
## Var: B.ID (Intercept)                 0.252             0.000                              0.253         
## Var: Residual                         0.107             0.076                              0.107         
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 248 (27 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)  -0.008 (0.010) -0.796  .426     [-0.028,  0.009]
## Direct (c')    -0.140 (0.040) -3.498 <.001 *** [-0.206, -0.062]
## Total (c)      -0.147 (0.039) -3.823 <.001 *** [-0.212, -0.075]
## ───────────────────────────────────────────────────────────────
## 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)                           2.546 ***             3.258 ***                          2.543 ***         
##                                      (0.078)               (0.017)                            (0.078)            
## WP.CreativeProcessEngagementV_mean    0.159                 1.026 ***                         -0.300             
##                                      (0.119)               (0.027)                            (0.222)            
## Manipulation                          0.025                 0.095 **                          -0.018             
##                                      (0.092)               (0.035)                            (0.092)            
## WP.CreativeProcessEngagementV                                                                  0.446 *           
##                                                                                               (0.182)            
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.010                 0.858                              0.024             
## Conditional R^2                       0.534                 0.858                              0.560             
## AIC                                 695.155                87.508                            692.852             
## BIC                                 712.722               105.075                            713.933             
## Num. obs.                           248                   248                                248                 
## Num. groups: B.ID                   139                   139                                139                 
## Var: B.ID (Intercept)                 0.554                 0.000                              0.571             
## Var: Residual                         0.493                 0.076                              0.470             
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 248 (27 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)   0.044 (0.023)  1.911  .056 .   [ 0.007, 0.095]
## Direct (c')    -0.022 (0.083) -0.260  .795     [-0.161, 0.141]
## Total (c)       0.022 (0.084)  0.266  .790     [-0.126, 0.188]
## ──────────────────────────────────────────────────────────────
## 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)                           1.367 ***        3.250 ***                          1.367 ***    
##                                      (0.115)          (0.018)                            (0.115)       
## WP.CreativeProcessEngagementV_mean    0.457 **         1.021 ***                          0.409        
##                                      (0.176)          (0.027)                            (0.264)       
## Manipulation                         -0.265 **         0.087 *                           -0.269 **     
##                                      (0.091)          (0.035)                            (0.092)       
## WP.CreativeProcessEngagementV                                                             0.048        
##                                                                                          (0.194)       
## ───────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.051            0.857                              0.051        
## Conditional R^2                       0.791            0.857                              0.790        
## AIC                                 767.868           84.963                            771.253        
## BIC                                 785.292          102.387                            792.162        
## Num. obs.                           241              241                                241            
## Num. groups: B.ID                   138              138                                138            
## Var: B.ID (Intercept)                 1.557            0.000                              1.557        
## Var: Residual                         0.438            0.076                              0.442        
## ───────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 (34 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)   0.006 (0.017)  0.328  .743     [-0.028,  0.038]
## Direct (c')    -0.273 (0.083) -3.277  .001 **  [-0.410, -0.112]
## Total (c)      -0.267 (0.081) -3.282  .001 **  [-0.406, -0.114]
## ───────────────────────────────────────────────────────────────
## 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)                           0.319 ***          3.250 ***                          0.319 ***      
##                                      (0.048)            (0.018)                            (0.048)         
## WP.CreativeProcessEngagementV_mean    0.145              1.021 ***                          0.117          
##                                      (0.074)            (0.027)                            (0.118)         
## Manipulation                         -0.030              0.087 *                           -0.032          
##                                      (0.043)            (0.035)                            (0.043)         
## WP.CreativeProcessEngagementV                                                               0.027          
##                                                                                            (0.091)         
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.025              0.857                              0.025          
## Conditional R^2                       0.735              0.857                              0.734          
## AIC                                 379.569             84.963                            384.449          
## BIC                                 396.993            102.387                            405.358          
## Num. obs.                           241                241                                241              
## Num. groups: B.ID                   138                138                                138              
## Var: B.ID (Intercept)                 0.263              0.000                              0.263          
## Var: Residual                         0.098              0.076                              0.099          
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 (34 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)   0.003 (0.008)  0.379  .705     [-0.012, 0.018]
## Direct (c')    -0.034 (0.039) -0.857  .391     [-0.098, 0.042]
## Total (c)      -0.031 (0.038) -0.797  .426     [-0.096, 0.042]
## ──────────────────────────────────────────────────────────────
## 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)                           1.051 ***      3.248 ***                          1.051 ***  
##                                      (0.090)        (0.018)                            (0.090)     
## WP.CreativeProcessEngagementV_mean    0.307 *        1.021 ***                          0.285      
##                                      (0.137)        (0.027)                            (0.214)     
## Manipulation                         -0.227 **       0.087 *                           -0.229 **   
##                                      (0.076)        (0.035)                            (0.077)     
## WP.CreativeProcessEngagementV                                                           0.021      
##                                                                                        (0.162)     
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.041          0.858                              0.041      
## Conditional R^2                       0.760          0.858                              0.758      
## AIC                                 668.033         84.244                            671.824      
## BIC                                 685.478        101.689                            692.758      
## Num. obs.                           242            242                                242          
## Num. groups: B.ID                   138            138                                138          
## Var: B.ID (Intercept)                 0.928          0.000                              0.927      
## Var: Residual                         0.310          0.075                              0.313      
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 242 (33 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)   0.003 (0.014)  0.213  .832     [-0.026,  0.030]
## Direct (c')    -0.232 (0.070) -3.338 <.001 *** [-0.348, -0.097]
## Total (c)      -0.229 (0.068) -3.373 <.001 *** [-0.345, -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="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)                           6.784 ***              3.276 ***                          6.785 ***          
##                                      (0.100)                (0.018)                            (0.100)             
## WP.CreativeProcessEngagementV_mean   -0.113                  1.032 ***                          0.090              
##                                      (0.150)                (0.026)                            (0.297)             
## Manipulation                          0.046                  0.085 *                            0.062              
##                                      (0.120)                (0.035)                            (0.121)             
## WP.CreativeProcessEngagementV                                                                  -0.198              
##                                                                                                (0.248)             
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.004                  0.870                              0.006              
## Conditional R^2                       0.529                  0.870                              0.535              
## AIC                                 740.356                 69.180                            742.675              
## BIC                                 757.524                 86.348                            763.278              
## Num. obs.                           229                    229                                229                  
## Num. groups: B.ID                   131                    131                                131                  
## Var: B.ID (Intercept)                 0.846                  0.000                              0.859              
## Var: Residual                         0.758                  0.071                              0.753              
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 229 (46 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)  -0.015 (0.023) -0.637  .524     [-0.067, 0.025]
## Direct (c')     0.057 (0.109)  0.523  .601     [-0.127, 0.271]
## Total (c)       0.042 (0.106)  0.398  .691     [-0.138, 0.247]
## ──────────────────────────────────────────────────────────────
## 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. :)