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\\RtmpG2szHq"

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)               3.21 ***     3.08 ***     3.42 ***     3.29 ***     2.85 ***
##                          (0.06)       (0.07)       (0.06)       (0.07)       (0.08)   
## Manipulation              0.11 ***     0.08 *       0.06         0.01         0.06    
##                          (0.04)       (0.05)       (0.04)       (0.05)       (0.05)   
## --------------------------------------------------------------------------------------
## AIC                     601.37       740.51       631.38       846.95       808.03    
## BIC                     617.13       756.27       647.33       862.90       823.79    
## Log Likelihood         -296.69      -366.25      -311.69      -419.47      -400.02    
## Num. obs.               380          380          398          398          380       
## Num. groups: B.ID       144          144          151          151          144       
## Var: B.ID (Intercept)     0.42         0.57         0.31         0.51         0.64    
## Var: Residual             0.11         0.17         0.13         0.23         0.21    
## ======================================================================================
## *** 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 33.3
## 1  273 66.7
## ───────────
## Total N = 409

Freq(data$W.Day)

## Frequency Statistics:
## ────────────
##       N    %
## ────────────
## 11  136 33.3
## 12  134 32.8
## 14  139 34.0
## ────────────
## Total N = 409

1.3.1 ICC and RWG

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

## 
## ------ Sample Size Information ------
## 
## Level 1: N = 409 observations ("Manipulation")
## Level 2: K = 151 groups ("B.ID")
## 
##        n (group sizes)
## Min.             1.000
## Median           3.000
## Mean             2.709
## Max.             3.000
## 
## ------ 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 0.051   0.000 1.000 13.000
## ────────────────────────────────────────────────────

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

## 
## ------ Sample Size Information ------
## 
## Level 1: N = 380 observations ("WP.InformationSearchV")
## Level 2: K = 144 groups ("B.ID")
## 
##        n (group sizes)
## Min.             1.000
## Median           3.000
## Mean             2.639
## Max.             3.000
## 
## ------ ICC(1), ICC(2), and rWG ------
## 
## ICC variable: "WP.InformationSearchV"
## 
## ICC(1) = 0.614 (non-independence of data)
## ICC(2) = 0.795 (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   0.778  0.972 0.831   1.000 1.000 13.000
## ────────────────────────────────────────────────────

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 = 398
## Level-2 Groups/Clusters: B.ID, 151
## 
## Model Fit:
## AIC = 713.723
## BIC = 729.669
## R_(m)² = 0.00066  (Marginal R²: fixed effects)
## R_(c)² = 0.72941  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.14    0.14  1.00 252.26 0.91  .340    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.WorkReflectionV
## ──────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ──────────────────────────────────────────────────────────────────
## (Intercept)    3.169 (0.063) 50.04 222.1 <.001 *** [ 3.044, 3.293]
## Manipulation  -0.041 (0.043) -0.96 252.3  .340     [-0.126, 0.044]
## ──────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.WorkReflectionV
## ──────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p       [95% CI of β]
## ──────────────────────────────────────────────────────────────────
## Manipulation  -0.026 (0.027) -0.96 252.3  .340     [-0.080, 0.028]
## ──────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     151 (Intercept)  0.41877 0.72923
##  Residual                  0.15550        
## ──────────────────────────────────────────

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                            -0.079                                    -0.039                                    -0.104               -0.073           0.020                         0.007                     0.055                 0.006             -0.021             -0.021                     0.012            -0.064                     -0.100            0.022             -0.082                     -0.067                     -0.070          0.009              0.124             -0.113           0.075             -0.058                      0.013              -0.079          -0.041    -0.015        0.052           -0.061      -0.076        -0.030    -0.080            -0.079
## WP.SupervisoryBehavioralFeedbackV                -0.033                             1.000                                     0.929                                     0.952                0.533           0.500                         0.562                     0.498                 0.367              0.578              0.395                     0.394             0.313                      0.531            0.481              0.633                      0.646                      0.404          0.649              0.498             -0.039           0.597              0.567                      0.619               0.546           0.602     0.506        0.154           -0.013       0.168         0.074     0.170             0.015
## WP.SupervisoryPositiveBehavioralFeedbackV         0.049                             0.731                                     1.000                                     0.771                0.583           0.508                         0.529                     0.484                 0.315              0.552              0.348                     0.344             0.281                      0.526            0.472              0.586                      0.653                      0.319          0.641              0.534             -0.078           0.646              0.578                      0.628               0.564           0.585     0.467        0.151           -0.023       0.132         0.065     0.130            -0.019
## WP.SupervisoryNegativeBehavioralFeedbackV        -0.089                             0.820                                     0.208                                     1.000                0.436           0.440                         0.529                     0.456                 0.372              0.538              0.392                     0.393             0.307                      0.479            0.438              0.604                      0.571                      0.431          0.586              0.415             -0.003           0.493              0.499                      0.546               0.473           0.551     0.483        0.140           -0.004       0.180         0.074     0.186             0.042
## WP.learningBehaviorV                              0.049                             0.084                                     0.042                                     0.086                1.000           0.697                         0.693                     0.648                 0.496              0.672              0.545                     0.548             0.426                      0.681            0.702              0.675                      0.698                      0.422          0.719              0.502             -0.008           0.703              0.610                      0.754               0.640           0.740     0.478        0.219            0.124       0.154         0.053     0.164            -0.065
## WP.JobCraftingV                                   0.024                             0.051                                     0.087                                    -0.000                0.132           1.000                         0.684                     0.640                 0.458              0.679              0.566                     0.589             0.411                      0.699            0.756              0.686                      0.605                      0.533          0.623              0.536             -0.041           0.680              0.591                      0.636               0.616           0.622     0.321        0.308            0.042       0.242         0.191     0.203             0.065
## WP.CreativeProcessEngagementV                     0.180                             0.156                                     0.112                                     0.129                0.172           0.207                         1.000                     0.868                 0.829              0.946              0.602                     0.593             0.487                      0.713            0.711              0.686                      0.619                      0.518          0.727              0.578             -0.023           0.749              0.653                      0.685               0.609           0.638     0.469        0.272            0.138       0.232         0.141     0.217            -0.011
## WP.ProblemIdentificationV                         0.212                             0.000                                    -0.032                                     0.027                0.195           0.201                         0.722                     1.000                 0.617              0.739              0.538                     0.529             0.437                      0.617            0.640              0.610                      0.581                      0.432          0.667              0.497              0.000           0.671              0.575                      0.615               0.555           0.564     0.356        0.284            0.135       0.165         0.098     0.155            -0.045
## WP.InformationSearchV                             0.074                             0.098                                     0.057                                     0.092                0.097           0.187                         0.662                     0.325                 1.000              0.675              0.534                     0.467             0.521                      0.463            0.470              0.470                      0.397                      0.383          0.524              0.446             -0.025           0.524              0.475                      0.467               0.377           0.413     0.481        0.209            0.219       0.260         0.166     0.241            -0.050
## WP.IdeaGenerationV                                0.103                             0.212                                     0.188                                     0.146                0.084           0.079                         0.754                     0.268                 0.213              1.000              0.542                     0.566             0.390                      0.744            0.724              0.693                      0.624                      0.526          0.709              0.567             -0.030           0.749              0.648                      0.690               0.630           0.660     0.424        0.239            0.067       0.204         0.121     0.192             0.031
## WP.SocialLearningV                                0.037                            -0.039                                    -0.036                                    -0.025                0.150           0.209                         0.228                     0.158                 0.074              0.234              1.000                     0.944             0.871                      0.717            0.651              0.484                      0.440                      0.363          0.470              0.338              0.061           0.440              0.373                      0.536               0.360           0.576     0.401        0.257            0.195       0.290         0.108     0.308             0.057
## WP.ObservationalLearningV                         0.055                            -0.105                                    -0.064                                    -0.097                0.121           0.199                         0.235                     0.220                 0.099              0.178              0.851                     1.000             0.662                      0.711            0.682              0.520                      0.474                      0.388          0.480              0.380              0.128           0.482              0.415                      0.570               0.424           0.608     0.349        0.241            0.137       0.285         0.126     0.292             0.049
## WP.AdviceSeekingV                                -0.004                             0.067                                     0.017                                     0.082                0.118           0.124                         0.111                     0.001                 0.007              0.197              0.723                     0.254             1.000                      0.574            0.467              0.328                      0.295                      0.249          0.356              0.203             -0.052           0.285              0.232                      0.373               0.188           0.408     0.393        0.227            0.242       0.237         0.058     0.266             0.059
## WP.PerformanceImprovementV                       -0.019                             0.054                                     0.028                                     0.055                0.093           0.238                         0.222                     0.138                 0.175              0.166              0.122                     0.172            -0.001                      1.000            0.766              0.576                      0.540                      0.415          0.577              0.458              0.013           0.629              0.530                      0.650               0.524           0.727     0.325        0.253            0.121       0.281         0.186     0.255             0.065
## WP.TakingChargeV                                  0.061                             0.172                                     0.120                                     0.146                0.124           0.146                         0.289                     0.189                 0.228              0.206              0.040                     0.037             0.025                      0.237            1.000              0.608                      0.599                      0.410          0.635              0.498              0.031           0.640              0.526                      0.650               0.577           0.711     0.320        0.285            0.050       0.287         0.174     0.269             0.021
## WA.WorkReflectionV                               -0.060                             0.138                                     0.121                                     0.098               -0.038           0.279                         0.104                     0.062                 0.004              0.134              0.113                     0.046             0.146                      0.073            0.109              1.000                      0.830                      0.829          0.742              0.523              0.004           0.644              0.652                      0.702               0.678           0.610     0.504        0.317            0.017       0.146         0.113     0.124             0.092
## WA.PositiveWorkReflectionV                       -0.108                             0.141                                     0.081                                     0.135                0.066           0.272                         0.133                     0.112                 0.049              0.113              0.110                     0.078             0.101                      0.159            0.129              0.729                      1.000                      0.376          0.785              0.663             -0.096           0.645              0.768                      0.702               0.779           0.674     0.490        0.246            0.023       0.166         0.126     0.143             0.072
## WA.NegativeWorkReflectionV                        0.011                             0.072                                     0.100                                     0.019               -0.114           0.155                         0.029                    -0.012                -0.039              0.090              0.062                    -0.004             0.120                     -0.040            0.040              0.782                      0.143                      1.000          0.446              0.205              0.103           0.422              0.313                      0.463               0.346           0.338     0.346        0.279            0.005       0.077         0.062     0.064             0.080
## WA.RuminationV                                   -0.163                             0.140                                     0.147                                     0.079                0.094           0.159                         0.109                     0.123                -0.015              0.107              0.194                     0.143             0.169                      0.035            0.051              0.467                      0.376                      0.332          1.000              0.654             -0.152           0.697              0.714                      0.687               0.711           0.661     0.537        0.249            0.055       0.205         0.120     0.196             0.041
## WA.PositiveAffectV                                0.086                             0.018                                    -0.027                                     0.049                0.065           0.060                         0.065                     0.092                 0.037              0.014              0.087                     0.012             0.143                     -0.084            0.045              0.105                      0.107                      0.055          0.152              1.000             -0.260           0.738              0.743                      0.537               0.702           0.567     0.430        0.198           -0.096       0.125         0.112     0.100             0.012
## WA.NegativeAffectV                                0.006                             0.006                                     0.035                                    -0.021                0.034           0.044                        -0.004                    -0.001                -0.023              0.011              0.044                     0.036             0.034                      0.031           -0.031              0.071                      0.001                      0.102          0.058             -0.062              1.000          -0.106             -0.082                      0.031              -0.041          -0.035    -0.100       -0.134            0.141       0.089         0.092     0.063            -0.004
## WA.ImprovisionV                                   0.093                             0.181                                     0.150                                     0.135                0.113           0.081                         0.050                     0.121                -0.014              0.001             -0.007                    -0.044             0.045                     -0.057            0.117              0.219                      0.146                      0.183          0.154              0.244              0.095           1.000              0.694                      0.731               0.683           0.748     0.408        0.210           -0.008       0.190         0.173     0.148            -0.149
## WA.WorkAbsorptionV                                0.011                             0.038                                     0.004                                     0.052                0.112           0.042                         0.076                     0.042                 0.043              0.071             -0.071                    -0.094            -0.007                      0.001            0.046              0.038                      0.098                     -0.034          0.078              0.204             -0.018           0.107              1.000                      0.632               0.846           0.592     0.416        0.192            0.088       0.162         0.113     0.146             0.009
## WA.ThrivingAtWorkLearningV                        0.109                             0.118                                     0.134                                     0.058                0.213           0.011                         0.094                     0.139                 0.017              0.044             -0.008                     0.011            -0.029                     -0.054            0.095              0.123                      0.142                      0.048          0.123              0.235             -0.046           0.315              0.221                      1.000               0.650           0.818     0.477        0.208            0.072       0.214         0.162     0.184             0.086
## WA.WorkRelatedFlowV                              -0.068                             0.160                                     0.124                                     0.128                0.061           0.042                         0.012                    -0.070                 0.008              0.073              0.021                    -0.007             0.047                      0.094            0.220              0.142                      0.196                      0.026          0.170              0.198             -0.037           0.123              0.424                      0.348               1.000           0.620     0.396        0.269           -0.088       0.155         0.134     0.127             0.079
## WA.InspirationV                                   0.026                             0.059                                     0.012                                     0.075                0.182          -0.015                        -0.025                     0.025                 0.069             -0.117             -0.040                    -0.020            -0.046                      0.048            0.009              0.100                      0.089                      0.063          0.176              0.140              0.010           0.318              0.247                      0.424               0.222           1.000     0.445        0.192            0.022       0.271         0.240     0.213             0.075
## WA.GraceV                                         0.043                             0.081                                     0.096                                     0.035                0.030           0.011                         0.090                     0.000                 0.004              0.159              0.114                     0.088             0.094                      0.126            0.039              0.203                      0.266                      0.051          0.218              0.089              0.049           0.057             -0.076                      0.091              -0.006          -0.104     1.000        0.095            0.065       0.136         0.076     0.131             0.065
## WA.ExerciseV                                     -0.270                             0.132                                     0.065                                     0.137               -0.068          -0.023                        -0.074                    -0.124                -0.058              0.011             -0.057                    -0.077            -0.005                      0.077           -0.014              0.026                      0.070                     -0.027          0.009              0.136              0.048          -0.047              0.112                      0.049               0.130          -0.036    -0.013        1.000            0.028       0.164         0.288     0.056            -0.070
## WA.SleepQualityV                                 -0.050                             0.063                                     0.005                                     0.087                0.021           0.101                         0.120                     0.155                 0.079              0.032             -0.028                     0.047            -0.111                      0.136            0.047              0.046                      0.041                      0.029          0.059             -0.039             -0.035           0.138              0.041                      0.117              -0.020           0.153    -0.046       -0.026            1.000       0.149         0.146     0.111            -0.249
## WA.ReadingV                                      -0.103                             0.094                                    -0.043                                     0.171                0.001           0.101                        -0.031                    -0.095                -0.064              0.069              0.042                     0.017             0.056                      0.028           -0.031              0.137                      0.120                      0.089          0.091              0.042             -0.012          -0.038              0.077                      0.103               0.096           0.132    -0.038        0.115            0.098       1.000         0.652     0.918            -0.071
## WA.PaperReadV                                    -0.032                             0.006                                    -0.060                                     0.060                0.012           0.028                        -0.046                    -0.043                -0.034             -0.023              0.080                     0.091             0.030                      0.040            0.007              0.035                      0.091                     -0.032          0.063              0.016              0.034          -0.074              0.031                      0.050              -0.016           0.070     0.032        0.115            0.048       0.585         1.000     0.297            -0.104
## WA.EReadV                                        -0.104                             0.109                                    -0.016                                     0.171                0.000           0.104                        -0.011                    -0.081                -0.062              0.093             -0.003                    -0.039             0.045                      0.011           -0.049              0.135                      0.086                      0.119          0.077              0.041             -0.035          -0.007              0.073                      0.089               0.122           0.120    -0.063        0.073            0.092       0.878         0.125     1.000            -0.033
## WA.SleepQuantityV                                 0.049                             0.159                                     0.137                                     0.112               -0.004          -0.133                        -0.054                    -0.139                 0.010              0.011             -0.152                    -0.194            -0.030                     -0.134           -0.030             -0.018                     -0.013                     -0.013         -0.114             -0.144              0.146          -0.083              0.083                     -0.059               0.035          -0.111     0.004        0.054           -0.262      -0.058        -0.018    -0.064             1.000
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 
## Within-Level Correlation [95% CI]:
## ────────────────────────────────────────────────────────
##                             r         [95% CI]     p    
## ────────────────────────────────────────────────────────
## Mnpl.-WP.SB            -0.033 [-0.133,  0.068]  .518    
## Mnpl.-WP.SP             0.049 [-0.052,  0.149]  .342    
## Mnpl.-WP.SN            -0.089 [-0.188,  0.012]  .085 .  
## Mnpl.-WP.BV             0.049 [-0.052,  0.148]  .346    
## Mnpl.-WP.JC             0.024 [-0.076,  0.125]  .635    
## Mnpl.-WP.CP             0.180 [ 0.081,  0.276] <.001 ***
## Mnpl.-WP.PrbIV.         0.212 [ 0.114,  0.306] <.001 ***
## Mnpl.-WP.IS             0.074 [-0.026,  0.174]  .148    
## Mnpl.-WP.IG             0.103 [ 0.002,  0.201]  .046 *  
## Mnpl.-WP.SL             0.037 [-0.064,  0.137]  .475    
## Mnpl.-WP.OL             0.055 [-0.046,  0.154]  .288    
## Mnpl.-WP.AS            -0.004 [-0.105,  0.096]  .935    
## Mnpl.-WP.PrfIV.        -0.019 [-0.120,  0.081]  .707    
## Mnpl.-WP.TC             0.061 [-0.039,  0.161]  .233    
## Mnpl.-WA.WRV           -0.060 [-0.158,  0.038]  .229    
## Mnpl.-WA.PW            -0.108 [-0.204, -0.009]  .032 *  
## Mnpl.-WA.NW             0.011 [-0.088,  0.109]  .832    
## Mnpl.-WA.RmV.          -0.163 [-0.257, -0.065]  .001 ** 
## Mnpl.-WA.PA             0.086 [-0.012,  0.183]  .087 .  
## Mnpl.-WA.NA             0.006 [-0.092,  0.105]  .897    
## Mnpl.-WA.ImV.           0.093 [-0.005,  0.190]  .064 .  
## Mnpl.-WA.WA             0.011 [-0.087,  0.109]  .825    
## Mnpl.-WA.TA             0.109 [ 0.011,  0.205]  .031 *  
## Mnpl.-WA.WRF           -0.068 [-0.165,  0.031]  .176    
## Mnpl.-WA.InV.           0.026 [-0.073,  0.124]  .611    
## Mnpl.-WA.GV             0.043 [-0.055,  0.141]  .391    
## Mnpl.-WA.EV            -0.270 [-0.359, -0.177] <.001 ***
## Mnpl.-WA.SlpQlV.       -0.050 [-0.148,  0.048]  .318    
## Mnpl.-WA.RdV.          -0.103 [-0.200, -0.003]  .043 *  
## Mnpl.-WA.PR            -0.032 [-0.131,  0.068]  .529    
## Mnpl.-WA.ER            -0.104 [-0.201, -0.005]  .041 *  
## Mnpl.-WA.SlpQnV.        0.049 [-0.055,  0.151]  .355    
## WP.SB-WP.SP             0.731 [ 0.680,  0.775] <.001 ***
## WP.SB-WP.SN             0.820 [ 0.784,  0.850] <.001 ***
## WP.SB-WP.BV             0.084 [-0.016,  0.183]  .101    
## WP.SB-WP.JC             0.051 [-0.050,  0.151]  .324    
## WP.SB-WP.CP             0.156 [ 0.056,  0.252]  .002 ** 
## WP.SB-WP.PrbIV.         0.000 [-0.100,  0.101]  .995    
## WP.SB-WP.IS             0.098 [-0.003,  0.197]  .057 .  
## WP.SB-WP.IG             0.212 [ 0.114,  0.306] <.001 ***
## WP.SB-WP.SL            -0.039 [-0.139,  0.062]  .453    
## WP.SB-WP.OL            -0.105 [-0.203, -0.004]  .041 *  
## WP.SB-WP.AS             0.067 [-0.034,  0.166]  .194    
## WP.SB-WP.PrfIV.         0.054 [-0.046,  0.154]  .290    
## WP.SB-WP.TC             0.172 [ 0.072,  0.268] <.001 ***
## WP.SB-WA.WRV            0.138 [ 0.037,  0.237]  .008 ** 
## WP.SB-WA.PW             0.141 [ 0.039,  0.239]  .007 ** 
## WP.SB-WA.NW             0.072 [-0.031,  0.172]  .170    
## WP.SB-WA.RmV.           0.140 [ 0.038,  0.239]  .007 ** 
## WP.SB-WA.PA             0.018 [-0.084,  0.120]  .731    
## WP.SB-WA.NA             0.006 [-0.097,  0.108]  .915    
## WP.SB-WA.ImV.           0.181 [ 0.080,  0.278] <.001 ***
## WP.SB-WA.WA             0.038 [-0.064,  0.140]  .466    
## WP.SB-WA.TA             0.118 [ 0.016,  0.218]  .023 *  
## WP.SB-WA.WRF            0.160 [ 0.059,  0.258]  .002 ** 
## WP.SB-WA.InV.           0.059 [-0.044,  0.160]  .261    
## WP.SB-WA.GV             0.081 [-0.022,  0.181]  .123    
## WP.SB-WA.EV             0.132 [ 0.030,  0.231]  .011 *  
## WP.SB-WA.SlpQlV.        0.063 [-0.039,  0.164]  .228    
## WP.SB-WA.RdV.           0.094 [-0.010,  0.195]  .077 .  
## WP.SB-WA.PR             0.006 [-0.097,  0.110]  .904    
## WP.SB-WA.ER             0.109 [ 0.006,  0.210]  .039 *  
## WP.SB-WA.SlpQnV.        0.159 [ 0.052,  0.261]  .004 ** 
## WP.SP-WP.SN             0.208 [ 0.110,  0.303] <.001 ***
## WP.SP-WP.BV             0.042 [-0.059,  0.142]  .415    
## WP.SP-WP.JC             0.087 [-0.014,  0.186]  .091 .  
## WP.SP-WP.CP             0.112 [ 0.011,  0.210]  .030 *  
## WP.SP-WP.PrbIV.        -0.032 [-0.132,  0.069]  .531    
## WP.SP-WP.IS             0.057 [-0.044,  0.157]  .267    
## WP.SP-WP.IG             0.188 [ 0.089,  0.283] <.001 ***
## WP.SP-WP.SL            -0.036 [-0.136,  0.065]  .482    
## WP.SP-WP.OL            -0.064 [-0.163,  0.037]  .217    
## WP.SP-WP.AS             0.017 [-0.084,  0.117]  .743    
## WP.SP-WP.PrfIV.         0.028 [-0.073,  0.128]  .585    
## WP.SP-WP.TC             0.120 [ 0.019,  0.218]  .020 *  
## WP.SP-WA.WRV            0.121 [ 0.019,  0.220]  .021 *  
## WP.SP-WA.PW             0.081 [-0.022,  0.181]  .122    
## WP.SP-WA.NW             0.100 [-0.002,  0.200]  .054 .  
## WP.SP-WA.RmV.           0.147 [ 0.045,  0.245]  .005 ** 
## WP.SP-WA.PA            -0.027 [-0.128,  0.076]  .610    
## WP.SP-WA.NA             0.035 [-0.068,  0.136]  .508    
## WP.SP-WA.ImV.           0.150 [ 0.048,  0.248]  .004 ** 
## WP.SP-WA.WA             0.004 [-0.099,  0.106]  .947    
## WP.SP-WA.TA             0.134 [ 0.032,  0.232]  .010 *  
## WP.SP-WA.WRF            0.124 [ 0.022,  0.223]  .018 *  
## WP.SP-WA.InV.           0.012 [-0.090,  0.114]  .816    
## WP.SP-WA.GV             0.096 [-0.006,  0.196]  .066 .  
## WP.SP-WA.EV             0.065 [-0.038,  0.166]  .216    
## WP.SP-WA.SlpQlV.        0.005 [-0.097,  0.107]  .918    
## WP.SP-WA.RdV.          -0.043 [-0.145,  0.061]  .421    
## WP.SP-WA.PR            -0.060 [-0.163,  0.043]  .255    
## WP.SP-WA.ER            -0.016 [-0.119,  0.087]  .756    
## WP.SP-WA.SlpQnV.        0.137 [ 0.030,  0.240]  .012 *  
## WP.SN-WP.BV             0.086 [-0.015,  0.185]  .096 .  
## WP.SN-WP.JC            -0.000 [-0.101,  0.100]  .997    
## WP.SN-WP.CP             0.129 [ 0.029,  0.227]  .012 *  
## WP.SN-WP.PrbIV.         0.027 [-0.073,  0.128]  .594    
## WP.SN-WP.IS             0.092 [-0.008,  0.191]  .073 .  
## WP.SN-WP.IG             0.146 [ 0.046,  0.243]  .005 ** 
## WP.SN-WP.SL            -0.025 [-0.125,  0.076]  .627    
## WP.SN-WP.OL            -0.097 [-0.196,  0.004]  .059 .  
## WP.SN-WP.AS             0.082 [-0.019,  0.181]  .113    
## WP.SN-WP.PrfIV.         0.055 [-0.046,  0.154]  .290    
## WP.SN-WP.TC             0.146 [ 0.046,  0.243]  .005 ** 
## WP.SN-WA.WRV            0.098 [-0.004,  0.198]  .060 .  
## WP.SN-WA.PW             0.135 [ 0.034,  0.234]  .009 ** 
## WP.SN-WA.NW             0.019 [-0.083,  0.121]  .717    
## WP.SN-WA.RmV.           0.079 [-0.024,  0.179]  .132    
## WP.SN-WA.PA             0.049 [-0.054,  0.150]  .353    
## WP.SN-WA.NA            -0.021 [-0.123,  0.081]  .686    
## WP.SN-WA.ImV.           0.135 [ 0.034,  0.234]  .009 ** 
## WP.SN-WA.WA             0.052 [-0.050,  0.154]  .317    
## WP.SN-WA.TA             0.058 [-0.044,  0.159]  .264    
## WP.SN-WA.WRF            0.128 [ 0.026,  0.227]  .014 *  
## WP.SN-WA.InV.           0.075 [-0.028,  0.176]  .152    
## WP.SN-WA.GV             0.035 [-0.067,  0.137]  .498    
## WP.SN-WA.EV             0.137 [ 0.035,  0.235]  .009 ** 
## WP.SN-WA.SlpQlV.        0.087 [-0.016,  0.187]  .097 .  
## WP.SN-WA.RdV.           0.171 [ 0.069,  0.270]  .001 ** 
## WP.SN-WA.PR             0.060 [-0.044,  0.162]  .256    
## WP.SN-WA.ER             0.171 [ 0.069,  0.270]  .001 ** 
## WP.SN-WA.SlpQnV.        0.112 [ 0.005,  0.217]  .040 *  
## WP.BV-WP.JC             0.132 [ 0.032,  0.230]  .010 *  
## WP.BV-WP.CP             0.172 [ 0.073,  0.268] <.001 ***
## WP.BV-WP.PrbIV.         0.195 [ 0.097,  0.290] <.001 ***
## WP.BV-WP.IS             0.097 [-0.003,  0.196]  .058 .  
## WP.BV-WP.IG             0.084 [-0.016,  0.183]  .101    
## WP.BV-WP.SL             0.150 [ 0.050,  0.247]  .004 ** 
## WP.BV-WP.OL             0.121 [ 0.020,  0.218]  .019 *  
## WP.BV-WP.AS             0.118 [ 0.017,  0.216]  .022 *  
## WP.BV-WP.PrfIV.         0.093 [-0.008,  0.192]  .072 .  
## WP.BV-WP.TC             0.124 [ 0.024,  0.222]  .016 *  
## WP.BV-WA.WRV           -0.038 [-0.139,  0.065]  .470    
## WP.BV-WA.PW             0.066 [-0.036,  0.167]  .206    
## WP.BV-WA.NW            -0.114 [-0.214, -0.012]  .029 *  
## WP.BV-WA.RmV.           0.094 [-0.008,  0.194]  .073 .  
## WP.BV-WA.PA             0.065 [-0.037,  0.166]  .211    
## WP.BV-WA.NA             0.034 [-0.069,  0.135]  .521    
## WP.BV-WA.ImV.           0.113 [ 0.011,  0.212]  .031 *  
## WP.BV-WA.WA             0.112 [ 0.010,  0.212]  .031 *  
## WP.BV-WA.TA             0.213 [ 0.113,  0.308] <.001 ***
## WP.BV-WA.WRF            0.061 [-0.042,  0.162]  .245    
## WP.BV-WA.InV.           0.182 [ 0.081,  0.279] <.001 ***
## WP.BV-WA.GV             0.030 [-0.072,  0.132]  .562    
## WP.BV-WA.EV            -0.068 [-0.169,  0.035]  .195    
## WP.BV-WA.SlpQlV.        0.021 [-0.081,  0.123]  .686    
## WP.BV-WA.RdV.           0.001 [-0.103,  0.104]  .992    
## WP.BV-WA.PR             0.012 [-0.091,  0.115]  .819    
## WP.BV-WA.ER             0.000 [-0.103,  0.104]  .996    
## WP.BV-WA.SlpQnV.       -0.004 [-0.111,  0.103]  .939    
## WP.JC-WP.CP             0.207 [ 0.109,  0.302] <.001 ***
## WP.JC-WP.PrbIV.         0.201 [ 0.102,  0.296] <.001 ***
## WP.JC-WP.IS             0.187 [ 0.088,  0.282] <.001 ***
## WP.JC-WP.IG             0.079 [-0.022,  0.178]  .125    
## WP.JC-WP.SL             0.209 [ 0.111,  0.304] <.001 ***
## WP.JC-WP.OL             0.199 [ 0.101,  0.294] <.001 ***
## WP.JC-WP.AS             0.124 [ 0.023,  0.221]  .016 *  
## WP.JC-WP.PrfIV.         0.238 [ 0.141,  0.331] <.001 ***
## WP.JC-WP.TC             0.146 [ 0.046,  0.243]  .004 ** 
## WP.JC-WA.WRV            0.279 [ 0.182,  0.370] <.001 ***
## WP.JC-WA.PW             0.272 [ 0.175,  0.364] <.001 ***
## WP.JC-WA.NW             0.155 [ 0.053,  0.253]  .003 ** 
## WP.JC-WA.RmV.           0.159 [ 0.057,  0.256]  .002 ** 
## WP.JC-WA.PA             0.060 [-0.042,  0.162]  .247    
## WP.JC-WA.NA             0.044 [-0.059,  0.145]  .402    
## WP.JC-WA.ImV.           0.081 [-0.021,  0.181]  .122    
## WP.JC-WA.WA             0.042 [-0.060,  0.144]  .421    
## WP.JC-WA.TA             0.011 [-0.091,  0.113]  .831    
## WP.JC-WA.WRF            0.042 [-0.060,  0.144]  .416    
## WP.JC-WA.InV.          -0.015 [-0.116,  0.088]  .781    
## WP.JC-WA.GV             0.011 [-0.091,  0.113]  .829    
## WP.JC-WA.EV            -0.023 [-0.124,  0.080]  .665    
## WP.JC-WA.SlpQlV.        0.101 [-0.001,  0.201]  .053 .  
## WP.JC-WA.RdV.           0.101 [-0.002,  0.203]  .055 .  
## WP.JC-WA.PR             0.028 [-0.076,  0.131]  .597    
## WP.JC-WA.ER             0.104 [ 0.001,  0.205]  .049 *  
## WP.JC-WA.SlpQnV.       -0.133 [-0.236, -0.026]  .015 *  
## WP.CP-WP.PrbIV.         0.722 [ 0.670,  0.767] <.001 ***
## WP.CP-WP.IS             0.662 [ 0.601,  0.715] <.001 ***
## WP.CP-WP.IG             0.754 [ 0.707,  0.794] <.001 ***
## WP.CP-WP.SL             0.228 [ 0.131,  0.321] <.001 ***
## WP.CP-WP.OL             0.235 [ 0.138,  0.328] <.001 ***
## WP.CP-WP.AS             0.111 [ 0.011,  0.210]  .030 *  
## WP.CP-WP.PrfIV.         0.222 [ 0.125,  0.316] <.001 ***
## WP.CP-WP.TC             0.289 [ 0.194,  0.378] <.001 ***
## WP.CP-WA.WRV            0.104 [ 0.002,  0.204]  .047 *  
## WP.CP-WA.PW             0.133 [ 0.032,  0.232]  .011 *  
## WP.CP-WA.NW             0.029 [-0.074,  0.130]  .585    
## WP.CP-WA.RmV.           0.109 [ 0.007,  0.209]  .036 *  
## WP.CP-WA.PA             0.065 [-0.038,  0.166]  .217    
## WP.CP-WA.NA            -0.004 [-0.106,  0.099]  .946    
## WP.CP-WA.ImV.           0.050 [-0.053,  0.151]  .342    
## WP.CP-WA.WA             0.076 [-0.026,  0.177]  .146    
## WP.CP-WA.TA             0.094 [-0.008,  0.195]  .070 .  
## WP.CP-WA.WRF            0.012 [-0.090,  0.114]  .820    
## WP.CP-WA.InV.          -0.025 [-0.127,  0.077]  .629    
## WP.CP-WA.GV             0.090 [-0.012,  0.190]  .085 .  
## WP.CP-WA.EV            -0.074 [-0.175,  0.028]  .158    
## WP.CP-WA.SlpQlV.        0.120 [ 0.018,  0.220]  .021 *  
## WP.CP-WA.RdV.          -0.031 [-0.134,  0.073]  .561    
## WP.CP-WA.PR            -0.046 [-0.148,  0.058]  .388    
## WP.CP-WA.ER            -0.011 [-0.114,  0.093]  .840    
## WP.CP-WA.SlpQnV.       -0.054 [-0.161,  0.053]  .320    
## WP.PrbIV.-WP.IS         0.325 [ 0.232,  0.412] <.001 ***
## WP.PrbIV.-WP.IG         0.268 [ 0.172,  0.359] <.001 ***
## WP.PrbIV.-WP.SL         0.158 [ 0.058,  0.254]  .002 ** 
## WP.PrbIV.-WP.OL         0.220 [ 0.122,  0.314] <.001 ***
## WP.PrbIV.-WP.AS         0.001 [-0.100,  0.101]  .989    
## WP.PrbIV.-WP.PrfIV.     0.138 [ 0.038,  0.235]  .007 ** 
## WP.PrbIV.-WP.TC         0.189 [ 0.090,  0.284] <.001 ***
## WP.PrbIV.-WA.WRV        0.062 [-0.040,  0.163]  .236    
## WP.PrbIV.-WA.PW         0.112 [ 0.010,  0.212]  .032 *  
## WP.PrbIV.-WA.NW        -0.012 [-0.114,  0.090]  .814    
## WP.PrbIV.-WA.RmV.       0.123 [ 0.021,  0.222]  .019 *  
## WP.PrbIV.-WA.PA         0.092 [-0.010,  0.192]  .078 .  
## WP.PrbIV.-WA.NA        -0.001 [-0.103,  0.101]  .991    
## WP.PrbIV.-WA.ImV.       0.121 [ 0.020,  0.221]  .020 *  
## WP.PrbIV.-WA.WA         0.042 [-0.060,  0.144]  .417    
## WP.PrbIV.-WA.TA         0.139 [ 0.037,  0.237]  .008 ** 
## WP.PrbIV.-WA.WRF       -0.070 [-0.171,  0.032]  .179    
## WP.PrbIV.-WA.InV.       0.025 [-0.077,  0.127]  .632    
## WP.PrbIV.-WA.GV         0.000 [-0.102,  0.102]  .995    
## WP.PrbIV.-WA.EV        -0.124 [-0.223, -0.022]  .017 *  
## WP.PrbIV.-WA.SlpQlV.    0.155 [ 0.054,  0.253]  .003 ** 
## WP.PrbIV.-WA.RdV.      -0.095 [-0.197,  0.008]  .072 .  
## WP.PrbIV.-WA.PR        -0.043 [-0.145,  0.061]  .420    
## WP.PrbIV.-WA.ER        -0.081 [-0.183,  0.022]  .125    
## WP.PrbIV.-WA.SlpQnV.   -0.139 [-0.243, -0.033]  .011 *  
## WP.IS-WP.IG             0.213 [ 0.115,  0.307] <.001 ***
## WP.IS-WP.SL             0.074 [-0.027,  0.173]  .149    
## WP.IS-WP.OL             0.099 [-0.002,  0.197]  .055 .  
## WP.IS-WP.AS             0.007 [-0.094,  0.107]  .893    
## WP.IS-WP.PrfIV.         0.175 [ 0.076,  0.271] <.001 ***
## WP.IS-WP.TC             0.228 [ 0.131,  0.321] <.001 ***
## WP.IS-WA.WRV            0.004 [-0.099,  0.106]  .946    
## WP.IS-WA.PW             0.049 [-0.053,  0.150]  .347    
## WP.IS-WA.NW            -0.039 [-0.141,  0.063]  .451    
## WP.IS-WA.RmV.          -0.015 [-0.117,  0.087]  .775    
## WP.IS-WA.PA             0.037 [-0.065,  0.138]  .480    
## WP.IS-WA.NA            -0.023 [-0.124,  0.080]  .664    
## WP.IS-WA.ImV.          -0.014 [-0.116,  0.088]  .782    
## WP.IS-WA.WA             0.043 [-0.060,  0.144]  .415    
## WP.IS-WA.TA             0.017 [-0.085,  0.119]  .741    
## WP.IS-WA.WRF            0.008 [-0.094,  0.110]  .871    
## WP.IS-WA.InV.           0.069 [-0.033,  0.170]  .183    
## WP.IS-WA.GV             0.004 [-0.098,  0.106]  .939    
## WP.IS-WA.EV            -0.058 [-0.159,  0.044]  .266    
## WP.IS-WA.SlpQlV.        0.079 [-0.023,  0.180]  .129    
## WP.IS-WA.RdV.          -0.064 [-0.166,  0.040]  .228    
## WP.IS-WA.PR            -0.034 [-0.137,  0.070]  .520    
## WP.IS-WA.ER            -0.062 [-0.164,  0.042]  .242    
## WP.IS-WA.SlpQnV.        0.010 [-0.098,  0.116]  .862    
## WP.IG-WP.SL             0.234 [ 0.136,  0.327] <.001 ***
## WP.IG-WP.OL             0.178 [ 0.078,  0.273] <.001 ***
## WP.IG-WP.AS             0.197 [ 0.099,  0.292] <.001 ***
## WP.IG-WP.PrfIV.         0.166 [ 0.066,  0.262]  .001 ** 
## WP.IG-WP.TC             0.206 [ 0.108,  0.301] <.001 ***
## WP.IG-WA.WRV            0.134 [ 0.032,  0.233]  .010 *  
## WP.IG-WA.PW             0.113 [ 0.011,  0.213]  .030 *  
## WP.IG-WA.NW             0.090 [-0.012,  0.191]  .084 .  
## WP.IG-WA.RmV.           0.107 [ 0.005,  0.207]  .040 *  
## WP.IG-WA.PA             0.014 [-0.088,  0.116]  .784    
## WP.IG-WA.NA             0.011 [-0.092,  0.113]  .838    
## WP.IG-WA.ImV.           0.001 [-0.102,  0.103]  .991    
## WP.IG-WA.WA             0.071 [-0.031,  0.172]  .173    
## WP.IG-WA.TA             0.044 [-0.058,  0.146]  .396    
## WP.IG-WA.WRF            0.073 [-0.030,  0.174]  .163    
## WP.IG-WA.InV.          -0.117 [-0.216, -0.015]  .025 *  
## WP.IG-WA.GV             0.159 [ 0.058,  0.257]  .002 ** 
## WP.IG-WA.EV             0.011 [-0.091,  0.113]  .835    
## WP.IG-WA.SlpQlV.        0.032 [-0.070,  0.134]  .535    
## WP.IG-WA.RdV.           0.069 [-0.034,  0.172]  .190    
## WP.IG-WA.PR            -0.023 [-0.126,  0.081]  .670    
## WP.IG-WA.ER             0.093 [-0.011,  0.194]  .079 .  
## WP.IG-WA.SlpQnV.        0.011 [-0.096,  0.118]  .845    
## WP.SL-WP.OL             0.851 [ 0.821,  0.877] <.001 ***
## WP.SL-WP.AS             0.723 [ 0.672,  0.768] <.001 ***
## WP.SL-WP.PrfIV.         0.122 [ 0.022,  0.220]  .017 *  
## WP.SL-WP.TC             0.040 [-0.061,  0.140]  .437    
## WP.SL-WA.WRV            0.113 [ 0.011,  0.212]  .031 *  
## WP.SL-WA.PW             0.110 [ 0.008,  0.210]  .035 *  
## WP.SL-WA.NW             0.062 [-0.040,  0.163]  .232    
## WP.SL-WA.RmV.           0.194 [ 0.094,  0.290] <.001 ***
## WP.SL-WA.PA             0.087 [-0.016,  0.187]  .097 .  
## WP.SL-WA.NA             0.044 [-0.059,  0.145]  .401    
## WP.SL-WA.ImV.          -0.007 [-0.109,  0.095]  .897    
## WP.SL-WA.WA            -0.071 [-0.171,  0.032]  .177    
## WP.SL-WA.TA            -0.008 [-0.110,  0.094]  .874    
## WP.SL-WA.WRF            0.021 [-0.081,  0.123]  .687    
## WP.SL-WA.InV.          -0.040 [-0.141,  0.063]  .448    
## WP.SL-WA.GV             0.114 [ 0.012,  0.213]  .029 *  
## WP.SL-WA.EV            -0.057 [-0.159,  0.045]  .272    
## WP.SL-WA.SlpQlV.       -0.028 [-0.129,  0.075]  .597    
## WP.SL-WA.RdV.           0.042 [-0.061,  0.145]  .425    
## WP.SL-WA.PR             0.080 [-0.023,  0.182]  .128    
## WP.SL-WA.ER            -0.003 [-0.107,  0.100]  .950    
## WP.SL-WA.SlpQnV.       -0.152 [-0.255, -0.045]  .005 ** 
## WP.OL-WP.AS             0.254 [ 0.157,  0.346] <.001 ***
## WP.OL-WP.PrfIV.         0.172 [ 0.072,  0.268] <.001 ***
## WP.OL-WP.TC             0.037 [-0.063,  0.137]  .468    
## WP.OL-WA.WRV            0.046 [-0.056,  0.148]  .377    
## WP.OL-WA.PW             0.078 [-0.024,  0.179]  .136    
## WP.OL-WA.NW            -0.004 [-0.106,  0.098]  .939    
## WP.OL-WA.RmV.           0.143 [ 0.042,  0.242]  .006 ** 
## WP.OL-WA.PA             0.012 [-0.090,  0.114]  .816    
## WP.OL-WA.NA             0.036 [-0.066,  0.137]  .493    
## WP.OL-WA.ImV.          -0.044 [-0.146,  0.058]  .396    
## WP.OL-WA.WA            -0.094 [-0.195,  0.008]  .070 .  
## WP.OL-WA.TA             0.011 [-0.091,  0.113]  .835    
## WP.OL-WA.WRF           -0.007 [-0.109,  0.095]  .899    
## WP.OL-WA.InV.          -0.020 [-0.122,  0.082]  .695    
## WP.OL-WA.GV             0.088 [-0.014,  0.189]  .091 .  
## WP.OL-WA.EV            -0.077 [-0.177,  0.026]  .141    
## WP.OL-WA.SlpQlV.        0.047 [-0.056,  0.148]  .371    
## WP.OL-WA.RdV.           0.017 [-0.087,  0.120]  .755    
## WP.OL-WA.PR             0.091 [-0.013,  0.193]  .085 .  
## WP.OL-WA.ER            -0.039 [-0.142,  0.064]  .459    
## WP.OL-WA.SlpQnV.       -0.194 [-0.295, -0.089] <.001 ***
## WP.AS-WP.PrfIV.        -0.001 [-0.101,  0.100]  .987    
## WP.AS-WP.TC             0.025 [-0.076,  0.125]  .633    
## WP.AS-WA.WRV            0.146 [ 0.045,  0.245]  .005 ** 
## WP.AS-WA.PW             0.101 [-0.001,  0.201]  .054 .  
## WP.AS-WA.NW             0.120 [ 0.018,  0.219]  .022 *  
## WP.AS-WA.RmV.           0.169 [ 0.068,  0.266]  .001 ** 
## WP.AS-WA.PA             0.143 [ 0.042,  0.242]  .006 ** 
## WP.AS-WA.NA             0.034 [-0.069,  0.135]  .517    
## WP.AS-WA.ImV.           0.045 [-0.057,  0.147]  .387    
## WP.AS-WA.WA            -0.007 [-0.109,  0.096]  .900    
## WP.AS-WA.TA            -0.029 [-0.131,  0.073]  .574    
## WP.AS-WA.WRF            0.047 [-0.055,  0.149]  .366    
## WP.AS-WA.InV.          -0.046 [-0.148,  0.056]  .377    
## WP.AS-WA.GV             0.094 [-0.008,  0.194]  .071 .  
## WP.AS-WA.EV            -0.005 [-0.107,  0.097]  .919    
## WP.AS-WA.SlpQlV.       -0.111 [-0.211, -0.009]  .033 *  
## WP.AS-WA.RdV.           0.056 [-0.047,  0.159]  .286    
## WP.AS-WA.PR             0.030 [-0.073,  0.133]  .568    
## WP.AS-WA.ER             0.045 [-0.059,  0.147]  .397    
## WP.AS-WA.SlpQnV.       -0.030 [-0.136,  0.078]  .588    
## WP.PrfIV.-WP.TC         0.237 [ 0.140,  0.330] <.001 ***
## WP.PrfIV.-WA.WRV        0.073 [-0.030,  0.173]  .165    
## WP.PrfIV.-WA.PW         0.159 [ 0.058,  0.257]  .002 ** 
## WP.PrfIV.-WA.NW        -0.040 [-0.141,  0.063]  .447    
## WP.PrfIV.-WA.RmV.       0.035 [-0.068,  0.136]  .505    
## WP.PrfIV.-WA.PA        -0.084 [-0.185,  0.018]  .107    
## WP.PrfIV.-WA.NA         0.031 [-0.072,  0.132]  .559    
## WP.PrfIV.-WA.ImV.      -0.057 [-0.158,  0.046]  .279    
## WP.PrfIV.-WA.WA         0.001 [-0.101,  0.103]  .988    
## WP.PrfIV.-WA.TA        -0.054 [-0.155,  0.048]  .301    
## WP.PrfIV.-WA.WRF        0.094 [-0.008,  0.195]  .071 .  
## WP.PrfIV.-WA.InV.       0.048 [-0.055,  0.149]  .362    
## WP.PrfIV.-WA.GV         0.126 [ 0.025,  0.225]  .015 *  
## WP.PrfIV.-WA.EV         0.077 [-0.025,  0.178]  .138    
## WP.PrfIV.-WA.SlpQlV.    0.136 [ 0.035,  0.235]  .009 ** 
## WP.PrfIV.-WA.RdV.       0.028 [-0.075,  0.131]  .594    
## WP.PrfIV.-WA.PR         0.040 [-0.064,  0.142]  .453    
## WP.PrfIV.-WA.ER         0.011 [-0.092,  0.114]  .835    
## WP.PrfIV.-WA.SlpQnV.   -0.134 [-0.238, -0.027]  .014 *  
## WP.TC-WA.WRV            0.109 [ 0.007,  0.209]  .037 *  
## WP.TC-WA.PW             0.129 [ 0.027,  0.228]  .013 *  
## WP.TC-WA.NW             0.040 [-0.062,  0.141]  .445    
## WP.TC-WA.RmV.           0.051 [-0.051,  0.152]  .328    
## WP.TC-WA.PA             0.045 [-0.057,  0.147]  .386    
## WP.TC-WA.NA            -0.031 [-0.133,  0.071]  .552    
## WP.TC-WA.ImV.           0.117 [ 0.015,  0.217]  .025 *  
## WP.TC-WA.WA             0.046 [-0.057,  0.147]  .382    
## WP.TC-WA.TA             0.095 [-0.007,  0.195]  .069 .  
## WP.TC-WA.WRF            0.220 [ 0.121,  0.315] <.001 ***
## WP.TC-WA.InV.           0.009 [-0.093,  0.111]  .864    
## WP.TC-WA.GV             0.039 [-0.064,  0.140]  .457    
## WP.TC-WA.EV            -0.014 [-0.116,  0.088]  .784    
## WP.TC-WA.SlpQlV.        0.047 [-0.055,  0.149]  .367    
## WP.TC-WA.RdV.          -0.031 [-0.134,  0.072]  .555    
## WP.TC-WA.PR             0.007 [-0.096,  0.111]  .890    
## WP.TC-WA.ER            -0.049 [-0.152,  0.054]  .349    
## WP.TC-WA.SlpQnV.       -0.030 [-0.137,  0.077]  .582    
## WA.WRV-WA.PW            0.729 [ 0.680,  0.772] <.001 ***
## WA.WRV-WA.NW            0.782 [ 0.740,  0.817] <.001 ***
## WA.WRV-WA.RmV.          0.467 [ 0.386,  0.540] <.001 ***
## WA.WRV-WA.PA            0.105 [ 0.007,  0.202]  .036 *  
## WA.WRV-WA.NA            0.071 [-0.027,  0.168]  .156    
## WA.WRV-WA.ImV.          0.219 [ 0.123,  0.310] <.001 ***
## WA.WRV-WA.WA            0.038 [-0.060,  0.136]  .445    
## WA.WRV-WA.TA            0.123 [ 0.025,  0.219]  .014 *  
## WA.WRV-WA.WRF           0.142 [ 0.044,  0.237]  .005 ** 
## WA.WRV-WA.InV.          0.100 [ 0.002,  0.196]  .046 *  
## WA.WRV-WA.GV            0.203 [ 0.107,  0.295] <.001 ***
## WA.WRV-WA.EV            0.026 [-0.073,  0.124]  .608    
## WA.WRV-WA.SlpQlV.       0.046 [-0.052,  0.144]  .356    
## WA.WRV-WA.RdV.          0.137 [ 0.038,  0.233]  .007 ** 
## WA.WRV-WA.PR            0.035 [-0.064,  0.134]  .488    
## WA.WRV-WA.ER            0.135 [ 0.036,  0.231]  .008 ** 
## WA.WRV-WA.SlpQnV.      -0.018 [-0.121,  0.086]  .739    
## WA.PW-WA.NW             0.143 [ 0.046,  0.238]  .004 ** 
## WA.PW-WA.RmV.           0.376 [ 0.289,  0.458] <.001 ***
## WA.PW-WA.PA             0.107 [ 0.009,  0.203]  .033 *  
## WA.PW-WA.NA             0.001 [-0.097,  0.099]  .985    
## WA.PW-WA.ImV.           0.146 [ 0.048,  0.241]  .004 ** 
## WA.PW-WA.WA             0.098 [-0.000,  0.194]  .051 .  
## WA.PW-WA.TA             0.142 [ 0.045,  0.237]  .005 ** 
## WA.PW-WA.WRF            0.196 [ 0.100,  0.289] <.001 ***
## WA.PW-WA.InV.           0.089 [-0.009,  0.186]  .075 .  
## WA.PW-WA.GV             0.266 [ 0.172,  0.355] <.001 ***
## WA.PW-WA.EV             0.070 [-0.028,  0.167]  .162    
## WA.PW-WA.SlpQlV.        0.041 [-0.057,  0.139]  .409    
## WA.PW-WA.RdV.           0.120 [ 0.021,  0.217]  .018 *  
## WA.PW-WA.PR             0.091 [-0.008,  0.189]  .073 .  
## WA.PW-WA.ER             0.086 [-0.014,  0.183]  .092 .  
## WA.PW-WA.SlpQnV.       -0.013 [-0.116,  0.090]  .805    
## WA.NW-WA.RmV.           0.332 [ 0.241,  0.417] <.001 ***
## WA.NW-WA.PA             0.055 [-0.044,  0.152]  .277    
## WA.NW-WA.NA             0.102 [ 0.004,  0.199]  .042 *  
## WA.NW-WA.ImV.           0.183 [ 0.087,  0.277] <.001 ***
## WA.NW-WA.WA            -0.034 [-0.132,  0.065]  .502    
## WA.NW-WA.TA             0.048 [-0.050,  0.146]  .337    
## WA.NW-WA.WRF            0.026 [-0.072,  0.124]  .603    
## WA.NW-WA.InV.           0.063 [-0.035,  0.161]  .208    
## WA.NW-WA.GV             0.051 [-0.047,  0.149]  .307    
## WA.NW-WA.EV            -0.027 [-0.125,  0.072]  .595    
## WA.NW-WA.SlpQlV.        0.029 [-0.069,  0.127]  .560    
## WA.NW-WA.RdV.           0.089 [-0.011,  0.187]  .080 .  
## WA.NW-WA.PR            -0.032 [-0.131,  0.068]  .531    
## WA.NW-WA.ER             0.119 [ 0.019,  0.215]  .020 *  
## WA.NW-WA.SlpQnV.       -0.013 [-0.116,  0.090]  .803    
## WA.RmV.-WA.PA           0.152 [ 0.054,  0.246]  .003 ** 
## WA.RmV.-WA.NA           0.058 [-0.040,  0.155]  .248    
## WA.RmV.-WA.ImV.         0.154 [ 0.057,  0.249]  .002 ** 
## WA.RmV.-WA.WA           0.078 [-0.020,  0.175]  .119    
## WA.RmV.-WA.TA           0.123 [ 0.025,  0.219]  .014 *  
## WA.RmV.-WA.WRF          0.170 [ 0.073,  0.264] <.001 ***
## WA.RmV.-WA.InV.         0.176 [ 0.079,  0.269] <.001 ***
## WA.RmV.-WA.GV           0.218 [ 0.123,  0.310] <.001 ***
## WA.RmV.-WA.EV           0.009 [-0.089,  0.107]  .860    
## WA.RmV.-WA.SlpQlV.      0.059 [-0.040,  0.156]  .241    
## WA.RmV.-WA.RdV.         0.091 [-0.008,  0.189]  .073 .  
## WA.RmV.-WA.PR           0.063 [-0.037,  0.162]  .215    
## WA.RmV.-WA.ER           0.077 [-0.023,  0.175]  .130    
## WA.RmV.-WA.SlpQnV.     -0.114 [-0.215, -0.011]  .030 *  
## WA.PA-WA.NA            -0.062 [-0.160,  0.036]  .215    
## WA.PA-WA.ImV.           0.244 [ 0.149,  0.334] <.001 ***
## WA.PA-WA.WA             0.204 [ 0.108,  0.296] <.001 ***
## WA.PA-WA.TA             0.235 [ 0.140,  0.326] <.001 ***
## WA.PA-WA.WRF            0.198 [ 0.102,  0.291] <.001 ***
## WA.PA-WA.InV.           0.140 [ 0.042,  0.235]  .005 ** 
## WA.PA-WA.GV             0.089 [-0.010,  0.185]  .077 .  
## WA.PA-WA.EV             0.136 [ 0.038,  0.231]  .007 ** 
## WA.PA-WA.SlpQlV.       -0.039 [-0.136,  0.060]  .442    
## WA.PA-WA.RdV.           0.042 [-0.058,  0.140]  .414    
## WA.PA-WA.PR             0.016 [-0.084,  0.115]  .758    
## WA.PA-WA.ER             0.041 [-0.058,  0.140]  .416    
## WA.PA-WA.SlpQnV.       -0.144 [-0.244, -0.042]  .006 ** 
## WA.NA-WA.ImV.           0.095 [-0.003,  0.192]  .058 .  
## WA.NA-WA.WA            -0.018 [-0.116,  0.080]  .716    
## WA.NA-WA.TA            -0.046 [-0.143,  0.053]  .365    
## WA.NA-WA.WRF           -0.037 [-0.135,  0.062]  .466    
## WA.NA-WA.InV.           0.010 [-0.088,  0.108]  .843    
## WA.NA-WA.GV             0.049 [-0.050,  0.146]  .332    
## WA.NA-WA.EV             0.048 [-0.050,  0.146]  .338    
## WA.NA-WA.SlpQlV.       -0.035 [-0.133,  0.064]  .489    
## WA.NA-WA.RdV.          -0.012 [-0.111,  0.088]  .821    
## WA.NA-WA.PR             0.034 [-0.065,  0.133]  .498    
## WA.NA-WA.ER            -0.035 [-0.133,  0.065]  .497    
## WA.NA-WA.SlpQnV.        0.146 [ 0.044,  0.246]  .006 ** 
## WA.ImV.-WA.WA           0.107 [ 0.009,  0.203]  .033 *  
## WA.ImV.-WA.TA           0.315 [ 0.223,  0.401] <.001 ***
## WA.ImV.-WA.WRF          0.123 [ 0.025,  0.219]  .014 *  
## WA.ImV.-WA.InV.         0.318 [ 0.226,  0.403] <.001 ***
## WA.ImV.-WA.GV           0.057 [-0.042,  0.154]  .259    
## WA.ImV.-WA.EV          -0.047 [-0.144,  0.052]  .354    
## WA.ImV.-WA.SlpQlV.      0.138 [ 0.041,  0.233]  .006 ** 
## WA.ImV.-WA.RdV.        -0.038 [-0.137,  0.062]  .456    
## WA.ImV.-WA.PR          -0.074 [-0.173,  0.025]  .143    
## WA.ImV.-WA.ER          -0.007 [-0.106,  0.093]  .898    
## WA.ImV.-WA.SlpQnV.     -0.083 [-0.185,  0.020]  .115    
## WA.WA-WA.TA             0.221 [ 0.126,  0.313] <.001 ***
## WA.WA-WA.WRF            0.424 [ 0.340,  0.501] <.001 ***
## WA.WA-WA.InV.           0.247 [ 0.153,  0.338] <.001 ***
## WA.WA-WA.GV            -0.076 [-0.173,  0.022]  .130    
## WA.WA-WA.EV             0.112 [ 0.014,  0.208]  .025 *  
## WA.WA-WA.SlpQlV.        0.041 [-0.057,  0.139]  .412    
## WA.WA-WA.RdV.           0.077 [-0.023,  0.175]  .130    
## WA.WA-WA.PR             0.031 [-0.069,  0.130]  .549    
## WA.WA-WA.ER             0.073 [-0.027,  0.171]  .151    
## WA.WA-WA.SlpQnV.        0.083 [-0.020,  0.185]  .115    
## WA.TA-WA.WRF            0.348 [ 0.259,  0.432] <.001 ***
## WA.TA-WA.InV.           0.424 [ 0.340,  0.501] <.001 ***
## WA.TA-WA.GV             0.091 [-0.008,  0.187]  .072 .  
## WA.TA-WA.EV             0.049 [-0.050,  0.146]  .334    
## WA.TA-WA.SlpQlV.        0.117 [ 0.019,  0.213]  .020 *  
## WA.TA-WA.RdV.           0.103 [ 0.003,  0.200]  .043 *  
## WA.TA-WA.PR             0.050 [-0.049,  0.149]  .324    
## WA.TA-WA.ER             0.089 [-0.010,  0.187]  .080 .  
## WA.TA-WA.SlpQnV.       -0.059 [-0.161,  0.044]  .263    
## WA.WRF-WA.InV.          0.222 [ 0.126,  0.313] <.001 ***
## WA.WRF-WA.GV           -0.006 [-0.105,  0.092]  .901    
## WA.WRF-WA.EV            0.130 [ 0.032,  0.225]  .010 ** 
## WA.WRF-WA.SlpQlV.      -0.020 [-0.118,  0.079]  .694    
## WA.WRF-WA.RdV.          0.096 [-0.003,  0.194]  .058 .  
## WA.WRF-WA.PR           -0.016 [-0.115,  0.083]  .752    
## WA.WRF-WA.ER            0.122 [ 0.023,  0.219]  .016 *  
## WA.WRF-WA.SlpQnV.       0.035 [-0.068,  0.138]  .504    
## WA.InV.-WA.GV          -0.104 [-0.201, -0.006]  .038 *  
## WA.InV.-WA.EV          -0.036 [-0.134,  0.062]  .473    
## WA.InV.-WA.SlpQlV.      0.153 [ 0.056,  0.248]  .002 ** 
## WA.InV.-WA.RdV.         0.132 [ 0.033,  0.229]  .009 ** 
## WA.InV.-WA.PR           0.070 [-0.029,  0.169]  .166    
## WA.InV.-WA.ER           0.120 [ 0.021,  0.216]  .018 *  
## WA.InV.-WA.SlpQnV.     -0.111 [-0.212, -0.008]  .035 *  
## WA.GV-WA.EV            -0.013 [-0.111,  0.086]  .799    
## WA.GV-WA.SlpQlV.       -0.046 [-0.144,  0.052]  .358    
## WA.GV-WA.RdV.          -0.038 [-0.137,  0.061]  .453    
## WA.GV-WA.PR             0.032 [-0.068,  0.131]  .529    
## WA.GV-WA.ER            -0.063 [-0.161,  0.036]  .213    
## WA.GV-WA.SlpQnV.        0.004 [-0.099,  0.108]  .934    
## WA.EV-WA.SlpQlV.       -0.026 [-0.124,  0.073]  .611    
## WA.EV-WA.RdV.           0.115 [ 0.016,  0.212]  .024 *  
## WA.EV-WA.PR             0.115 [ 0.016,  0.212]  .023 *  
## WA.EV-WA.ER             0.073 [-0.027,  0.171]  .152    
## WA.EV-WA.SlpQnV.        0.054 [-0.050,  0.156]  .307    
## WA.SlpQlV.-WA.RdV.      0.098 [-0.001,  0.196]  .054 .  
## WA.SlpQlV.-WA.PR        0.048 [-0.052,  0.146]  .350    
## WA.SlpQlV.-WA.ER        0.092 [-0.008,  0.189]  .071 .  
## WA.SlpQlV.-WA.SlpQnV.  -0.262 [-0.356, -0.163] <.001 ***
## WA.RdV.-WA.PR           0.585 [ 0.515,  0.647] <.001 ***
## WA.RdV.-WA.ER           0.878 [ 0.853,  0.899] <.001 ***
## WA.RdV.-WA.SlpQnV.     -0.058 [-0.162,  0.046]  .275    
## WA.PR-WA.ER             0.125 [ 0.026,  0.222]  .014 *  
## WA.PR-WA.SlpQnV.       -0.018 [-0.122,  0.087]  .743    
## WA.ER-WA.SlpQnV.       -0.064 [-0.167,  0.041]  .231    
## ────────────────────────────────────────────────────────
## 
## Between-Level Correlation [95% CI]:
## ────────────────────────────────────────────────────────
##                             r         [95% CI]     p    
## ────────────────────────────────────────────────────────
## Mnpl.-WP.SB            -0.079 [-0.240,  0.085]  .344    
## Mnpl.-WP.SP            -0.039 [-0.201,  0.126]  .644    
## Mnpl.-WP.SN            -0.104 [-0.263,  0.060]  .214    
## Mnpl.-WP.BV            -0.073 [-0.234,  0.092]  .384    
## Mnpl.-WP.JC             0.020 [-0.144,  0.183]  .811    
## Mnpl.-WP.CP             0.007 [-0.157,  0.170]  .935    
## Mnpl.-WP.PrbIV.         0.055 [-0.109,  0.217]  .510    
## Mnpl.-WP.IS             0.006 [-0.158,  0.169]  .943    
## Mnpl.-WP.IG            -0.021 [-0.184,  0.143]  .799    
## Mnpl.-WP.SL            -0.021 [-0.184,  0.144]  .807    
## Mnpl.-WP.OL             0.012 [-0.152,  0.175]  .891    
## Mnpl.-WP.AS            -0.064 [-0.225,  0.100]  .444    
## Mnpl.-WP.PrfIV.        -0.100 [-0.259,  0.065]  .232    
## Mnpl.-WP.TC             0.022 [-0.142,  0.185]  .795    
## Mnpl.-WA.WRV           -0.082 [-0.239,  0.078]  .314    
## Mnpl.-WA.PW            -0.067 [-0.224,  0.094]  .413    
## Mnpl.-WA.NW            -0.070 [-0.227,  0.091]  .395    
## Mnpl.-WA.RmV.           0.009 [-0.151,  0.168]  .917    
## Mnpl.-WA.PA             0.124 [-0.036,  0.278]  .129    
## Mnpl.-WA.NA            -0.113 [-0.268,  0.048]  .167    
## Mnpl.-WA.ImV.           0.075 [-0.086,  0.232]  .361    
## Mnpl.-WA.WA            -0.058 [-0.216,  0.102]  .478    
## Mnpl.-WA.TA             0.013 [-0.147,  0.172]  .874    
## Mnpl.-WA.WRF           -0.079 [-0.236,  0.081]  .333    
## Mnpl.-WA.InV.          -0.041 [-0.199,  0.120]  .619    
## Mnpl.-WA.GV            -0.015 [-0.174,  0.145]  .855    
## Mnpl.-WA.EV             0.052 [-0.109,  0.210]  .528    
## Mnpl.-WA.SlpQlV.       -0.061 [-0.218,  0.100]  .459    
## Mnpl.-WA.RdV.          -0.076 [-0.234,  0.085]  .354    
## Mnpl.-WA.PR            -0.030 [-0.189,  0.131]  .715    
## Mnpl.-WA.ER            -0.080 [-0.238,  0.081]  .329    
## Mnpl.-WA.SlpQnV.       -0.079 [-0.241,  0.087]  .351    
## WP.SB-WP.SP             0.929 [ 0.902,  0.948] <.001 ***
## WP.SB-WP.SN             0.952 [ 0.934,  0.966] <.001 ***
## WP.SB-WP.BV             0.533 [ 0.405,  0.641] <.001 ***
## WP.SB-WP.JC             0.500 [ 0.366,  0.613] <.001 ***
## WP.SB-WP.CP             0.562 [ 0.438,  0.664] <.001 ***
## WP.SB-WP.PrbIV.         0.498 [ 0.364,  0.612] <.001 ***
## WP.SB-WP.IS             0.367 [ 0.217,  0.501] <.001 ***
## WP.SB-WP.IG             0.578 [ 0.458,  0.678] <.001 ***
## WP.SB-WP.SL             0.395 [ 0.248,  0.525] <.001 ***
## WP.SB-WP.OL             0.394 [ 0.246,  0.524] <.001 ***
## WP.SB-WP.AS             0.313 [ 0.158,  0.454] <.001 ***
## WP.SB-WP.PrfIV.         0.531 [ 0.403,  0.639] <.001 ***
## WP.SB-WP.TC             0.481 [ 0.345,  0.598] <.001 ***
## WP.SB-WA.WRV            0.633 [ 0.523,  0.722] <.001 ***
## WP.SB-WA.PW             0.646 [ 0.539,  0.732] <.001 ***
## WP.SB-WA.NW             0.404 [ 0.258,  0.533] <.001 ***
## WP.SB-WA.RmV.           0.649 [ 0.543,  0.734] <.001 ***
## WP.SB-WA.PA             0.498 [ 0.364,  0.611] <.001 ***
## WP.SB-WA.NA            -0.039 [-0.201,  0.125]  .633    
## WP.SB-WA.ImV.           0.597 [ 0.480,  0.693] <.001 ***
## WP.SB-WA.WA             0.567 [ 0.445,  0.669] <.001 ***
## WP.SB-WA.TA             0.619 [ 0.507,  0.711] <.001 ***
## WP.SB-WA.WRF            0.546 [ 0.420,  0.652] <.001 ***
## WP.SB-WA.InV.           0.602 [ 0.486,  0.697] <.001 ***
## WP.SB-WA.GV             0.506 [ 0.373,  0.618] <.001 ***
## WP.SB-WA.EV             0.154 [-0.010,  0.310]  .059 .  
## WP.SB-WA.SlpQlV.       -0.013 [-0.177,  0.151]  .871    
## WP.SB-WA.RdV.           0.168 [ 0.004,  0.323]  .040 *  
## WP.SB-WA.PR             0.074 [-0.091,  0.236]  .366    
## WP.SB-WA.ER             0.170 [ 0.006,  0.325]  .037 *  
## WP.SB-WA.SlpQnV.        0.015 [-0.154,  0.183]  .858    
## WP.SP-WP.SN             0.771 [ 0.695,  0.830] <.001 ***
## WP.SP-WP.BV             0.583 [ 0.463,  0.681] <.001 ***
## WP.SP-WP.JC             0.508 [ 0.376,  0.620] <.001 ***
## WP.SP-WP.CP             0.529 [ 0.400,  0.637] <.001 ***
## WP.SP-WP.PrbIV.         0.484 [ 0.348,  0.600] <.001 ***
## WP.SP-WP.IS             0.315 [ 0.159,  0.455] <.001 ***
## WP.SP-WP.IG             0.552 [ 0.427,  0.656] <.001 ***
## WP.SP-WP.SL             0.348 [ 0.196,  0.484] <.001 ***
## WP.SP-WP.OL             0.344 [ 0.191,  0.481] <.001 ***
## WP.SP-WP.AS             0.281 [ 0.123,  0.425] <.001 ***
## WP.SP-WP.PrfIV.         0.526 [ 0.396,  0.635] <.001 ***
## WP.SP-WP.TC             0.472 [ 0.334,  0.590] <.001 ***
## WP.SP-WA.WRV            0.586 [ 0.467,  0.684] <.001 ***
## WP.SP-WA.PW             0.653 [ 0.548,  0.738] <.001 ***
## WP.SP-WA.NW             0.319 [ 0.164,  0.459] <.001 ***
## WP.SP-WA.RmV.           0.641 [ 0.533,  0.728] <.001 ***
## WP.SP-WA.PA             0.534 [ 0.406,  0.641] <.001 ***
## WP.SP-WA.NA            -0.078 [-0.239,  0.087]  .341    
## WP.SP-WA.ImV.           0.646 [ 0.540,  0.732] <.001 ***
## WP.SP-WA.WA             0.578 [ 0.457,  0.677] <.001 ***
## WP.SP-WA.TA             0.628 [ 0.518,  0.718] <.001 ***
## WP.SP-WA.WRF            0.564 [ 0.442,  0.666] <.001 ***
## WP.SP-WA.InV.           0.585 [ 0.467,  0.684] <.001 ***
## WP.SP-WA.GV             0.467 [ 0.329,  0.586] <.001 ***
## WP.SP-WA.EV             0.151 [-0.013,  0.307]  .064 .  
## WP.SP-WA.SlpQlV.       -0.023 [-0.186,  0.141]  .776    
## WP.SP-WA.RdV.           0.132 [-0.033,  0.290]  .108    
## WP.SP-WA.PR             0.065 [-0.101,  0.226]  .432    
## WP.SP-WA.ER             0.130 [-0.035,  0.288]  .114    
## WP.SP-WA.SlpQnV.       -0.019 [-0.187,  0.150]  .823    
## WP.SN-WP.BV             0.436 [ 0.293,  0.560] <.001 ***
## WP.SN-WP.JC             0.440 [ 0.297,  0.563] <.001 ***
## WP.SN-WP.CP             0.529 [ 0.400,  0.638] <.001 ***
## WP.SN-WP.PrbIV.         0.456 [ 0.316,  0.577] <.001 ***
## WP.SN-WP.IS             0.372 [ 0.221,  0.504] <.001 ***
## WP.SN-WP.IG             0.538 [ 0.411,  0.645] <.001 ***
## WP.SN-WP.SL             0.392 [ 0.244,  0.522] <.001 ***
## WP.SN-WP.OL             0.393 [ 0.246,  0.523] <.001 ***
## WP.SN-WP.AS             0.307 [ 0.151,  0.448] <.001 ***
## WP.SN-WP.PrfIV.         0.479 [ 0.342,  0.596] <.001 ***
## WP.SN-WP.TC             0.438 [ 0.296,  0.562] <.001 ***
## WP.SN-WA.WRV            0.604 [ 0.489,  0.699] <.001 ***
## WP.SN-WA.PW             0.571 [ 0.450,  0.672] <.001 ***
## WP.SN-WA.NW             0.431 [ 0.288,  0.555] <.001 ***
## WP.SN-WA.RmV.           0.586 [ 0.467,  0.684] <.001 ***
## WP.SN-WA.PA             0.415 [ 0.270,  0.542] <.001 ***
## WP.SN-WA.NA            -0.003 [-0.166,  0.161]  .971    
## WP.SN-WA.ImV.           0.493 [ 0.358,  0.607] <.001 ***
## WP.SN-WA.WA             0.499 [ 0.365,  0.612] <.001 ***
## WP.SN-WA.TA             0.546 [ 0.420,  0.652] <.001 ***
## WP.SN-WA.WRF            0.473 [ 0.336,  0.591] <.001 ***
## WP.SN-WA.InV.           0.551 [ 0.426,  0.656] <.001 ***
## WP.SN-WA.GV             0.483 [ 0.347,  0.600] <.001 ***
## WP.SN-WA.EV             0.140 [-0.024,  0.297]  .086 .  
## WP.SN-WA.SlpQlV.       -0.004 [-0.167,  0.160]  .963    
## WP.SN-WA.RdV.           0.180 [ 0.016,  0.334]  .027 *  
## WP.SN-WA.PR             0.074 [-0.091,  0.236]  .366    
## WP.SN-WA.ER             0.186 [ 0.022,  0.340]  .023 *  
## WP.SN-WA.SlpQnV.        0.042 [-0.128,  0.209]  .622    
## WP.BV-WP.JC             0.697 [ 0.603,  0.773] <.001 ***
## WP.BV-WP.CP             0.693 [ 0.597,  0.769] <.001 ***
## WP.BV-WP.PrbIV.         0.648 [ 0.542,  0.734] <.001 ***
## WP.BV-WP.IS             0.496 [ 0.362,  0.610] <.001 ***
## WP.BV-WP.IG             0.672 [ 0.571,  0.753] <.001 ***
## WP.BV-WP.SL             0.545 [ 0.419,  0.651] <.001 ***
## WP.BV-WP.OL             0.548 [ 0.422,  0.653] <.001 ***
## WP.BV-WP.AS             0.426 [ 0.282,  0.551] <.001 ***
## WP.BV-WP.PrfIV.         0.681 [ 0.582,  0.760] <.001 ***
## WP.BV-WP.TC             0.702 [ 0.608,  0.776] <.001 ***
## WP.BV-WA.WRV            0.675 [ 0.575,  0.755] <.001 ***
## WP.BV-WA.PW             0.698 [ 0.603,  0.773] <.001 ***
## WP.BV-WA.NW             0.422 [ 0.278,  0.548] <.001 ***
## WP.BV-WA.RmV.           0.719 [ 0.629,  0.790] <.001 ***
## WP.BV-WA.PA             0.502 [ 0.369,  0.615] <.001 ***
## WP.BV-WA.NA            -0.008 [-0.171,  0.156]  .924    
## WP.BV-WA.ImV.           0.703 [ 0.609,  0.777] <.001 ***
## WP.BV-WA.WA             0.610 [ 0.495,  0.703] <.001 ***
## WP.BV-WA.TA             0.754 [ 0.674,  0.817] <.001 ***
## WP.BV-WA.WRF            0.640 [ 0.532,  0.728] <.001 ***
## WP.BV-WA.InV.           0.740 [ 0.655,  0.806] <.001 ***
## WP.BV-WA.GV             0.478 [ 0.341,  0.595] <.001 ***
## WP.BV-WA.EV             0.219 [ 0.058,  0.370]  .007 ** 
## WP.BV-WA.SlpQlV.        0.124 [-0.040,  0.282]  .129    
## WP.BV-WA.RdV.           0.154 [-0.011,  0.310]  .060 .  
## WP.BV-WA.PR             0.053 [-0.112,  0.215]  .522    
## WP.BV-WA.ER             0.164 [-0.000,  0.320]  .045 *  
## WP.BV-WA.SlpQnV.       -0.065 [-0.231,  0.105]  .444    
## WP.JC-WP.CP             0.684 [ 0.585,  0.762] <.001 ***
## WP.JC-WP.PrbIV.         0.640 [ 0.532,  0.727] <.001 ***
## WP.JC-WP.IS             0.458 [ 0.318,  0.578] <.001 ***
## WP.JC-WP.IG             0.679 [ 0.580,  0.759] <.001 ***
## WP.JC-WP.SL             0.566 [ 0.443,  0.667] <.001 ***
## WP.JC-WP.OL             0.589 [ 0.471,  0.687] <.001 ***
## WP.JC-WP.AS             0.411 [ 0.265,  0.538] <.001 ***
## WP.JC-WP.PrfIV.         0.699 [ 0.605,  0.774] <.001 ***
## WP.JC-WP.TC             0.756 [ 0.677,  0.819] <.001 ***
## WP.JC-WA.WRV            0.686 [ 0.589,  0.764] <.001 ***
## WP.JC-WA.PW             0.605 [ 0.490,  0.700] <.001 ***
## WP.JC-WA.NW             0.533 [ 0.405,  0.641] <.001 ***
## WP.JC-WA.RmV.           0.623 [ 0.511,  0.713] <.001 ***
## WP.JC-WA.PA             0.536 [ 0.409,  0.643] <.001 ***
## WP.JC-WA.NA            -0.041 [-0.203,  0.124]  .620    
## WP.JC-WA.ImV.           0.680 [ 0.581,  0.759] <.001 ***
## WP.JC-WA.WA             0.591 [ 0.473,  0.688] <.001 ***
## WP.JC-WA.TA             0.636 [ 0.527,  0.724] <.001 ***
## WP.JC-WA.WRF            0.616 [ 0.503,  0.708] <.001 ***
## WP.JC-WA.InV.           0.622 [ 0.510,  0.713] <.001 ***
## WP.JC-WA.GV             0.321 [ 0.166,  0.460] <.001 ***
## WP.JC-WA.EV             0.308 [ 0.152,  0.449] <.001 ***
## WP.JC-WA.SlpQlV.        0.042 [-0.123,  0.204]  .611    
## WP.JC-WA.RdV.           0.242 [ 0.082,  0.391]  .003 ** 
## WP.JC-WA.PR             0.191 [ 0.027,  0.344]  .019 *  
## WP.JC-WA.ER             0.203 [ 0.040,  0.356]  .013 *  
## WP.JC-WA.SlpQnV.        0.065 [-0.104,  0.231]  .440    
## WP.CP-WP.PrbIV.         0.868 [ 0.822,  0.904] <.001 ***
## WP.CP-WP.IS             0.829 [ 0.770,  0.874] <.001 ***
## WP.CP-WP.IG             0.946 [ 0.926,  0.961] <.001 ***
## WP.CP-WP.SL             0.602 [ 0.486,  0.697] <.001 ***
## WP.CP-WP.OL             0.593 [ 0.476,  0.690] <.001 ***
## WP.CP-WP.AS             0.487 [ 0.351,  0.602] <.001 ***
## WP.CP-WP.PrfIV.         0.713 [ 0.622,  0.785] <.001 ***
## WP.CP-WP.TC             0.711 [ 0.619,  0.783] <.001 ***
## WP.CP-WA.WRV            0.686 [ 0.588,  0.763] <.001 ***
## WP.CP-WA.PW             0.619 [ 0.507,  0.711] <.001 ***
## WP.CP-WA.NW             0.518 [ 0.388,  0.629] <.001 ***
## WP.CP-WA.RmV.           0.727 [ 0.639,  0.796] <.001 ***
## WP.CP-WA.PA             0.578 [ 0.457,  0.677] <.001 ***
## WP.CP-WA.NA            -0.023 [-0.186,  0.141]  .781    
## WP.CP-WA.ImV.           0.749 [ 0.667,  0.813] <.001 ***
## WP.CP-WA.WA             0.653 [ 0.548,  0.738] <.001 ***
## WP.CP-WA.TA             0.685 [ 0.587,  0.763] <.001 ***
## WP.CP-WA.WRF            0.609 [ 0.495,  0.703] <.001 ***
## WP.CP-WA.InV.           0.638 [ 0.530,  0.726] <.001 ***
## WP.CP-WA.GV             0.469 [ 0.331,  0.588] <.001 ***
## WP.CP-WA.EV             0.272 [ 0.114,  0.417] <.001 ***
## WP.CP-WA.SlpQlV.        0.138 [-0.026,  0.295]  .091 .  
## WP.CP-WA.RdV.           0.232 [ 0.071,  0.382]  .004 ** 
## WP.CP-WA.PR             0.141 [-0.024,  0.298]  .086 .  
## WP.CP-WA.ER             0.217 [ 0.055,  0.368]  .008 ** 
## WP.CP-WA.SlpQnV.       -0.011 [-0.179,  0.158]  .897    
## WP.PrbIV.-WP.IS         0.617 [ 0.505,  0.709] <.001 ***
## WP.PrbIV.-WP.IG         0.739 [ 0.654,  0.805] <.001 ***
## WP.PrbIV.-WP.SL         0.538 [ 0.410,  0.645] <.001 ***
## WP.PrbIV.-WP.OL         0.529 [ 0.400,  0.638] <.001 ***
## WP.PrbIV.-WP.AS         0.437 [ 0.294,  0.560] <.001 ***
## WP.PrbIV.-WP.PrfIV.     0.617 [ 0.504,  0.709] <.001 ***
## WP.PrbIV.-WP.TC         0.640 [ 0.532,  0.727] <.001 ***
## WP.PrbIV.-WA.WRV        0.610 [ 0.496,  0.704] <.001 ***
## WP.PrbIV.-WA.PW         0.581 [ 0.461,  0.680] <.001 ***
## WP.PrbIV.-WA.NW         0.432 [ 0.289,  0.556] <.001 ***
## WP.PrbIV.-WA.RmV.       0.667 [ 0.566,  0.749] <.001 ***
## WP.PrbIV.-WA.PA         0.497 [ 0.363,  0.611] <.001 ***
## WP.PrbIV.-WA.NA         0.000 [-0.163,  0.164]  .999    
## WP.PrbIV.-WA.ImV.       0.671 [ 0.570,  0.752] <.001 ***
## WP.PrbIV.-WA.WA         0.575 [ 0.454,  0.675] <.001 ***
## WP.PrbIV.-WA.TA         0.615 [ 0.502,  0.707] <.001 ***
## WP.PrbIV.-WA.WRF        0.555 [ 0.431,  0.659] <.001 ***
## WP.PrbIV.-WA.InV.       0.564 [ 0.441,  0.666] <.001 ***
## WP.PrbIV.-WA.GV         0.356 [ 0.204,  0.491] <.001 ***
## WP.PrbIV.-WA.EV         0.284 [ 0.126,  0.428] <.001 ***
## WP.PrbIV.-WA.SlpQlV.    0.135 [-0.030,  0.292]  .099 .  
## WP.PrbIV.-WA.RdV.       0.165 [ 0.001,  0.321]  .043 *  
## WP.PrbIV.-WA.PR         0.098 [-0.067,  0.258]  .232    
## WP.PrbIV.-WA.ER         0.155 [-0.010,  0.311]  .059 .  
## WP.PrbIV.-WA.SlpQnV.   -0.045 [-0.212,  0.124]  .592    
## WP.IS-WP.IG             0.675 [ 0.575,  0.755] <.001 ***
## WP.IS-WP.SL             0.534 [ 0.406,  0.642] <.001 ***
## WP.IS-WP.OL             0.467 [ 0.329,  0.586] <.001 ***
## WP.IS-WP.AS             0.521 [ 0.391,  0.631] <.001 ***
## WP.IS-WP.PrfIV.         0.463 [ 0.324,  0.582] <.001 ***
## WP.IS-WP.TC             0.470 [ 0.332,  0.588] <.001 ***
## WP.IS-WA.WRV            0.470 [ 0.332,  0.588] <.001 ***
## WP.IS-WA.PW             0.397 [ 0.249,  0.526] <.001 ***
## WP.IS-WA.NW             0.383 [ 0.234,  0.514] <.001 ***
## WP.IS-WA.RmV.           0.524 [ 0.394,  0.633] <.001 ***
## WP.IS-WA.PA             0.446 [ 0.304,  0.568] <.001 ***
## WP.IS-WA.NA            -0.025 [-0.188,  0.139]  .760    
## WP.IS-WA.ImV.           0.524 [ 0.395,  0.634] <.001 ***
## WP.IS-WA.WA             0.475 [ 0.337,  0.592] <.001 ***
## WP.IS-WA.TA             0.467 [ 0.328,  0.586] <.001 ***
## WP.IS-WA.WRF            0.377 [ 0.227,  0.509] <.001 ***
## WP.IS-WA.InV.           0.413 [ 0.268,  0.540] <.001 ***
## WP.IS-WA.GV             0.481 [ 0.344,  0.597] <.001 ***
## WP.IS-WA.EV             0.209 [ 0.047,  0.360]  .010 ** 
## WP.IS-WA.SlpQlV.        0.219 [ 0.057,  0.369]  .007 ** 
## WP.IS-WA.RdV.           0.260 [ 0.100,  0.407]  .001 ** 
## WP.IS-WA.PR             0.166 [ 0.001,  0.321]  .043 *  
## WP.IS-WA.ER             0.241 [ 0.080,  0.390]  .003 ** 
## WP.IS-WA.SlpQnV.       -0.050 [-0.216,  0.120]  .558    
## WP.IG-WP.SL             0.542 [ 0.415,  0.648] <.001 ***
## WP.IG-WP.OL             0.566 [ 0.444,  0.668] <.001 ***
## WP.IG-WP.AS             0.390 [ 0.242,  0.520] <.001 ***
## WP.IG-WP.PrfIV.         0.744 [ 0.661,  0.809] <.001 ***
## WP.IG-WP.TC             0.724 [ 0.635,  0.793] <.001 ***
## WP.IG-WA.WRV            0.693 [ 0.597,  0.769] <.001 ***
## WP.IG-WA.PW             0.624 [ 0.513,  0.714] <.001 ***
## WP.IG-WA.NW             0.526 [ 0.396,  0.635] <.001 ***
## WP.IG-WA.RmV.           0.709 [ 0.617,  0.782] <.001 ***
## WP.IG-WA.PA             0.567 [ 0.445,  0.669] <.001 ***
## WP.IG-WA.NA            -0.030 [-0.192,  0.135]  .719    
## WP.IG-WA.ImV.           0.749 [ 0.667,  0.813] <.001 ***
## WP.IG-WA.WA             0.648 [ 0.542,  0.734] <.001 ***
## WP.IG-WA.TA             0.690 [ 0.594,  0.767] <.001 ***
## WP.IG-WA.WRF            0.630 [ 0.520,  0.719] <.001 ***
## WP.IG-WA.InV.           0.660 [ 0.556,  0.743] <.001 ***
## WP.IG-WA.GV             0.424 [ 0.280,  0.549] <.001 ***
## WP.IG-WA.EV             0.239 [ 0.079,  0.388]  .003 ** 
## WP.IG-WA.SlpQlV.        0.067 [-0.098,  0.228]  .416    
## WP.IG-WA.RdV.           0.204 [ 0.041,  0.356]  .012 *  
## WP.IG-WA.PR             0.121 [-0.044,  0.279]  .141    
## WP.IG-WA.ER             0.192 [ 0.029,  0.345]  .019 *  
## WP.IG-WA.SlpQnV.        0.031 [-0.138,  0.198]  .714    
## WP.SL-WP.OL             0.944 [ 0.924,  0.960] <.001 ***
## WP.SL-WP.AS             0.871 [ 0.825,  0.906] <.001 ***
## WP.SL-WP.PrfIV.         0.717 [ 0.627,  0.788] <.001 ***
## WP.SL-WP.TC             0.651 [ 0.546,  0.737] <.001 ***
## WP.SL-WA.WRV            0.484 [ 0.348,  0.600] <.001 ***
## WP.SL-WA.PW             0.440 [ 0.298,  0.563] <.001 ***
## WP.SL-WA.NW             0.363 [ 0.212,  0.497] <.001 ***
## WP.SL-WA.RmV.           0.470 [ 0.332,  0.589] <.001 ***
## WP.SL-WA.PA             0.338 [ 0.184,  0.475] <.001 ***
## WP.SL-WA.NA             0.061 [-0.104,  0.222]  .457    
## WP.SL-WA.ImV.           0.440 [ 0.298,  0.563] <.001 ***
## WP.SL-WA.WA             0.373 [ 0.223,  0.506] <.001 ***
## WP.SL-WA.TA             0.536 [ 0.409,  0.643] <.001 ***
## WP.SL-WA.WRF            0.360 [ 0.209,  0.494] <.001 ***
## WP.SL-WA.InV.           0.576 [ 0.456,  0.676] <.001 ***
## WP.SL-WA.GV             0.401 [ 0.254,  0.530] <.001 ***
## WP.SL-WA.EV             0.257 [ 0.097,  0.403]  .001 ** 
## WP.SL-WA.SlpQlV.        0.195 [ 0.033,  0.348]  .016 *  
## WP.SL-WA.RdV.           0.290 [ 0.132,  0.434] <.001 ***
## WP.SL-WA.PR             0.108 [-0.057,  0.267]  .190    
## WP.SL-WA.ER             0.308 [ 0.151,  0.449] <.001 ***
## WP.SL-WA.SlpQnV.        0.057 [-0.112,  0.224]  .497    
## WP.OL-WP.AS             0.662 [ 0.558,  0.745] <.001 ***
## WP.OL-WP.PrfIV.         0.711 [ 0.620,  0.784] <.001 ***
## WP.OL-WP.TC             0.682 [ 0.584,  0.761] <.001 ***
## WP.OL-WA.WRV            0.520 [ 0.389,  0.630] <.001 ***
## WP.OL-WA.PW             0.474 [ 0.337,  0.592] <.001 ***
## WP.OL-WA.NW             0.388 [ 0.240,  0.519] <.001 ***
## WP.OL-WA.RmV.           0.480 [ 0.344,  0.597] <.001 ***
## WP.OL-WA.PA             0.380 [ 0.231,  0.512] <.001 ***
## WP.OL-WA.NA             0.128 [-0.036,  0.285]  .118    
## WP.OL-WA.ImV.           0.482 [ 0.345,  0.598] <.001 ***
## WP.OL-WA.WA             0.415 [ 0.270,  0.542] <.001 ***
## WP.OL-WA.TA             0.570 [ 0.448,  0.671] <.001 ***
## WP.OL-WA.WRF            0.424 [ 0.279,  0.549] <.001 ***
## WP.OL-WA.InV.           0.608 [ 0.493,  0.702] <.001 ***
## WP.OL-WA.GV             0.349 [ 0.196,  0.485] <.001 ***
## WP.OL-WA.EV             0.241 [ 0.080,  0.389]  .003 ** 
## WP.OL-WA.SlpQlV.        0.137 [-0.027,  0.294]  .094 .  
## WP.OL-WA.RdV.           0.285 [ 0.127,  0.429] <.001 ***
## WP.OL-WA.PR             0.126 [-0.039,  0.284]  .126    
## WP.OL-WA.ER             0.292 [ 0.134,  0.435] <.001 ***
## WP.OL-WA.SlpQnV.        0.049 [-0.121,  0.215]  .566    
## WP.AS-WP.PrfIV.         0.574 [ 0.453,  0.674] <.001 ***
## WP.AS-WP.TC             0.467 [ 0.329,  0.586] <.001 ***
## WP.AS-WA.WRV            0.328 [ 0.174,  0.467] <.001 ***
## WP.AS-WA.PW             0.295 [ 0.138,  0.438] <.001 ***
## WP.AS-WA.NW             0.249 [ 0.089,  0.397]  .002 ** 
## WP.AS-WA.RmV.           0.356 [ 0.204,  0.491] <.001 ***
## WP.AS-WA.PA             0.203 [ 0.040,  0.355]  .013 *  
## WP.AS-WA.NA            -0.052 [-0.214,  0.113]  .527    
## WP.AS-WA.ImV.           0.285 [ 0.127,  0.428] <.001 ***
## WP.AS-WA.WA             0.232 [ 0.071,  0.381]  .004 ** 
## WP.AS-WA.TA             0.373 [ 0.223,  0.505] <.001 ***
## WP.AS-WA.WRF            0.188 [ 0.026,  0.341]  .021 *  
## WP.AS-WA.InV.           0.408 [ 0.261,  0.535] <.001 ***
## WP.AS-WA.GV             0.393 [ 0.246,  0.523] <.001 ***
## WP.AS-WA.EV             0.227 [ 0.066,  0.376]  .005 ** 
## WP.AS-WA.SlpQlV.        0.242 [ 0.081,  0.390]  .003 ** 
## WP.AS-WA.RdV.           0.237 [ 0.075,  0.386]  .004 ** 
## WP.AS-WA.PR             0.058 [-0.107,  0.220]  .480    
## WP.AS-WA.ER             0.266 [ 0.107,  0.412] <.001 ***
## WP.AS-WA.SlpQnV.        0.059 [-0.111,  0.225]  .487    
## WP.PrfIV.-WP.TC         0.766 [ 0.689,  0.826] <.001 ***
## WP.PrfIV.-WA.WRV        0.576 [ 0.455,  0.676] <.001 ***
## WP.PrfIV.-WA.PW         0.540 [ 0.413,  0.647] <.001 ***
## WP.PrfIV.-WA.NW         0.415 [ 0.270,  0.542] <.001 ***
## WP.PrfIV.-WA.RmV.       0.577 [ 0.456,  0.676] <.001 ***
## WP.PrfIV.-WA.PA         0.458 [ 0.318,  0.578] <.001 ***
## WP.PrfIV.-WA.NA         0.013 [-0.151,  0.176]  .875    
## WP.PrfIV.-WA.ImV.       0.629 [ 0.519,  0.719] <.001 ***
## WP.PrfIV.-WA.WA         0.530 [ 0.401,  0.638] <.001 ***
## WP.PrfIV.-WA.TA         0.650 [ 0.544,  0.735] <.001 ***
## WP.PrfIV.-WA.WRF        0.524 [ 0.395,  0.634] <.001 ***
## WP.PrfIV.-WA.InV.       0.727 [ 0.639,  0.796] <.001 ***
## WP.PrfIV.-WA.GV         0.325 [ 0.171,  0.464] <.001 ***
## WP.PrfIV.-WA.EV         0.253 [ 0.093,  0.400]  .002 ** 
## WP.PrfIV.-WA.SlpQlV.    0.121 [-0.043,  0.279]  .139    
## WP.PrfIV.-WA.RdV.       0.281 [ 0.123,  0.426] <.001 ***
## WP.PrfIV.-WA.PR         0.186 [ 0.022,  0.339]  .023 *  
## WP.PrfIV.-WA.ER         0.255 [ 0.095,  0.402]  .002 ** 
## WP.PrfIV.-WA.SlpQnV.    0.065 [-0.104,  0.231]  .441    
## WP.TC-WA.WRV            0.608 [ 0.494,  0.702] <.001 ***
## WP.TC-WA.PW             0.599 [ 0.482,  0.694] <.001 ***
## WP.TC-WA.NW             0.410 [ 0.264,  0.538] <.001 ***
## WP.TC-WA.RmV.           0.635 [ 0.526,  0.723] <.001 ***
## WP.TC-WA.PA             0.498 [ 0.364,  0.612] <.001 ***
## WP.TC-WA.NA             0.031 [-0.133,  0.194]  .704    
## WP.TC-WA.ImV.           0.640 [ 0.532,  0.727] <.001 ***
## WP.TC-WA.WA             0.526 [ 0.396,  0.635] <.001 ***
## WP.TC-WA.TA             0.650 [ 0.544,  0.735] <.001 ***
## WP.TC-WA.WRF            0.577 [ 0.457,  0.677] <.001 ***
## WP.TC-WA.InV.           0.711 [ 0.620,  0.784] <.001 ***
## WP.TC-WA.GV             0.320 [ 0.165,  0.460] <.001 ***
## WP.TC-WA.EV             0.285 [ 0.127,  0.428] <.001 ***
## WP.TC-WA.SlpQlV.        0.050 [-0.114,  0.212]  .538    
## WP.TC-WA.RdV.           0.287 [ 0.129,  0.431] <.001 ***
## WP.TC-WA.PR             0.174 [ 0.010,  0.329]  .033 *  
## WP.TC-WA.ER             0.269 [ 0.110,  0.415] <.001 ***
## WP.TC-WA.SlpQnV.        0.021 [-0.148,  0.189]  .803    
## WA.WRV-WA.PW            0.830 [ 0.772,  0.874] <.001 ***
## WA.WRV-WA.NW            0.829 [ 0.772,  0.873] <.001 ***
## WA.WRV-WA.RmV.          0.742 [ 0.661,  0.806] <.001 ***
## WA.WRV-WA.PA            0.523 [ 0.397,  0.630] <.001 ***
## WA.WRV-WA.NA            0.004 [-0.155,  0.164]  .958    
## WA.WRV-WA.ImV.          0.644 [ 0.539,  0.729] <.001 ***
## WA.WRV-WA.WA            0.652 [ 0.549,  0.735] <.001 ***
## WA.WRV-WA.TA            0.702 [ 0.611,  0.775] <.001 ***
## WA.WRV-WA.WRF           0.678 [ 0.581,  0.756] <.001 ***
## WA.WRV-WA.InV.          0.610 [ 0.498,  0.701] <.001 ***
## WA.WRV-WA.GV            0.504 [ 0.374,  0.614] <.001 ***
## WA.WRV-WA.EV            0.317 [ 0.165,  0.453] <.001 ***
## WA.WRV-WA.SlpQlV.       0.017 [-0.143,  0.176]  .840    
## WA.WRV-WA.RdV.          0.146 [-0.014,  0.300]  .074 .  
## WA.WRV-WA.PR            0.113 [-0.048,  0.268]  .168    
## WA.WRV-WA.ER            0.124 [-0.037,  0.279]  .130    
## WA.WRV-WA.SlpQnV.       0.092 [-0.074,  0.253]  .277    
## WA.PW-WA.NW             0.376 [ 0.230,  0.505] <.001 ***
## WA.PW-WA.RmV.           0.785 [ 0.715,  0.840] <.001 ***
## WA.PW-WA.PA             0.663 [ 0.563,  0.744] <.001 ***
## WA.PW-WA.NA            -0.096 [-0.252,  0.065]  .242    
## WA.PW-WA.ImV.           0.645 [ 0.542,  0.730] <.001 ***
## WA.PW-WA.WA             0.768 [ 0.693,  0.826] <.001 ***
## WA.PW-WA.TA             0.702 [ 0.610,  0.775] <.001 ***
## WA.PW-WA.WRF            0.779 [ 0.707,  0.835] <.001 ***
## WA.PW-WA.InV.           0.674 [ 0.576,  0.752] <.001 ***
## WA.PW-WA.GV             0.490 [ 0.358,  0.602] <.001 ***
## WA.PW-WA.EV             0.246 [ 0.090,  0.390]  .002 ** 
## WA.PW-WA.SlpQlV.        0.023 [-0.138,  0.182]  .782    
## WA.PW-WA.RdV.           0.166 [ 0.006,  0.318]  .042 *  
## WA.PW-WA.PR             0.126 [-0.035,  0.281]  .124    
## WA.PW-WA.ER             0.143 [-0.018,  0.296]  .082 .  
## WA.PW-WA.SlpQnV.        0.072 [-0.094,  0.234]  .395    
## WA.NW-WA.RmV.           0.446 [ 0.308,  0.565] <.001 ***
## WA.NW-WA.PA             0.205 [ 0.047,  0.353]  .012 *  
## WA.NW-WA.NA             0.103 [-0.058,  0.259]  .208    
## WA.NW-WA.ImV.           0.422 [ 0.282,  0.545] <.001 ***
## WA.NW-WA.WA             0.313 [ 0.162,  0.451] <.001 ***
## WA.NW-WA.TA             0.463 [ 0.328,  0.580] <.001 ***
## WA.NW-WA.WRF            0.346 [ 0.197,  0.479] <.001 ***
## WA.NW-WA.InV.           0.338 [ 0.188,  0.472] <.001 ***
## WA.NW-WA.GV             0.346 [ 0.197,  0.479] <.001 ***
## WA.NW-WA.EV             0.279 [ 0.125,  0.420] <.001 ***
## WA.NW-WA.SlpQlV.        0.005 [-0.155,  0.164]  .953    
## WA.NW-WA.RdV.           0.077 [-0.084,  0.234]  .348    
## WA.NW-WA.PR             0.062 [-0.099,  0.220]  .450    
## WA.NW-WA.ER             0.064 [-0.097,  0.222]  .434    
## WA.NW-WA.SlpQnV.        0.080 [-0.085,  0.242]  .341    
## WA.RmV.-WA.PA           0.654 [ 0.552,  0.737] <.001 ***
## WA.RmV.-WA.NA          -0.152 [-0.304,  0.008]  .062 .  
## WA.RmV.-WA.ImV.         0.697 [ 0.605,  0.771] <.001 ***
## WA.RmV.-WA.WA           0.714 [ 0.625,  0.784] <.001 ***
## WA.RmV.-WA.TA           0.687 [ 0.592,  0.763] <.001 ***
## WA.RmV.-WA.WRF          0.711 [ 0.622,  0.782] <.001 ***
## WA.RmV.-WA.InV.         0.661 [ 0.561,  0.742] <.001 ***
## WA.RmV.-WA.GV           0.537 [ 0.413,  0.642] <.001 ***
## WA.RmV.-WA.EV           0.249 [ 0.093,  0.393]  .002 ** 
## WA.RmV.-WA.SlpQlV.      0.055 [-0.105,  0.213]  .499    
## WA.RmV.-WA.RdV.         0.205 [ 0.046,  0.353]  .012 *  
## WA.RmV.-WA.PR           0.120 [-0.041,  0.275]  .145    
## WA.RmV.-WA.ER           0.196 [ 0.037,  0.345]  .016 *  
## WA.RmV.-WA.SlpQnV.      0.041 [-0.124,  0.205]  .624    
## WA.PA-WA.NA            -0.260 [-0.403, -0.104]  .001 ** 
## WA.PA-WA.ImV.           0.738 [ 0.655,  0.803] <.001 ***
## WA.PA-WA.WA             0.743 [ 0.662,  0.807] <.001 ***
## WA.PA-WA.TA             0.537 [ 0.413,  0.642] <.001 ***
## WA.PA-WA.WRF            0.702 [ 0.611,  0.775] <.001 ***
## WA.PA-WA.InV.           0.567 [ 0.448,  0.667] <.001 ***
## WA.PA-WA.GV             0.430 [ 0.290,  0.552] <.001 ***
## WA.PA-WA.EV             0.198 [ 0.039,  0.346]  .015 *  
## WA.PA-WA.SlpQlV.       -0.096 [-0.252,  0.065]  .241    
## WA.PA-WA.RdV.           0.125 [-0.036,  0.279]  .128    
## WA.PA-WA.PR             0.112 [-0.049,  0.267]  .173    
## WA.PA-WA.ER             0.100 [-0.062,  0.256]  .225    
## WA.PA-WA.SlpQnV.        0.012 [-0.153,  0.176]  .888    
## WA.NA-WA.ImV.          -0.106 [-0.261,  0.055]  .196    
## WA.NA-WA.WA            -0.082 [-0.239,  0.079]  .317    
## WA.NA-WA.TA             0.031 [-0.129,  0.190]  .703    
## WA.NA-WA.WRF           -0.041 [-0.199,  0.120]  .621    
## WA.NA-WA.InV.          -0.035 [-0.194,  0.125]  .666    
## WA.NA-WA.GV            -0.100 [-0.255,  0.061]  .224    
## WA.NA-WA.EV            -0.134 [-0.287,  0.026]  .101    
## WA.NA-WA.SlpQlV.        0.141 [-0.019,  0.294]  .085 .  
## WA.NA-WA.RdV.           0.089 [-0.073,  0.246]  .280    
## WA.NA-WA.PR             0.092 [-0.069,  0.249]  .262    
## WA.NA-WA.ER             0.063 [-0.098,  0.221]  .443    
## WA.NA-WA.SlpQnV.       -0.004 [-0.168,  0.161]  .967    
## WA.ImV.-WA.WA           0.694 [ 0.601,  0.769] <.001 ***
## WA.ImV.-WA.TA           0.731 [ 0.647,  0.797] <.001 ***
## WA.ImV.-WA.WRF          0.683 [ 0.587,  0.760] <.001 ***
## WA.ImV.-WA.InV.         0.748 [ 0.668,  0.811] <.001 ***
## WA.ImV.-WA.GV           0.408 [ 0.266,  0.533] <.001 ***
## WA.ImV.-WA.EV           0.210 [ 0.052,  0.358]  .010 ** 
## WA.ImV.-WA.SlpQlV.     -0.008 [-0.167,  0.152]  .924    
## WA.ImV.-WA.RdV.         0.190 [ 0.031,  0.340]  .020 *  
## WA.ImV.-WA.PR           0.173 [ 0.013,  0.324]  .035 *  
## WA.ImV.-WA.ER           0.148 [-0.013,  0.301]  .071 .  
## WA.ImV.-WA.SlpQnV.     -0.149 [-0.306,  0.016]  .076 .  
## WA.WA-WA.TA             0.632 [ 0.525,  0.719] <.001 ***
## WA.WA-WA.WRF            0.846 [ 0.793,  0.886] <.001 ***
## WA.WA-WA.InV.           0.592 [ 0.477,  0.687] <.001 ***
## WA.WA-WA.GV             0.416 [ 0.275,  0.540] <.001 ***
## WA.WA-WA.EV             0.192 [ 0.033,  0.341]  .018 *  
## WA.WA-WA.SlpQlV.        0.088 [-0.072,  0.245]  .280    
## WA.WA-WA.RdV.           0.162 [ 0.002,  0.314]  .048 *  
## WA.WA-WA.PR             0.113 [-0.048,  0.268]  .168    
## WA.WA-WA.ER             0.146 [-0.015,  0.299]  .075 .  
## WA.WA-WA.SlpQnV.        0.009 [-0.156,  0.174]  .915    
## WA.TA-WA.WRF            0.650 [ 0.547,  0.734] <.001 ***
## WA.TA-WA.InV.           0.818 [ 0.757,  0.865] <.001 ***
## WA.TA-WA.GV             0.477 [ 0.344,  0.592] <.001 ***
## WA.TA-WA.EV             0.208 [ 0.050,  0.356]  .011 *  
## WA.TA-WA.SlpQlV.        0.072 [-0.089,  0.229]  .381    
## WA.TA-WA.RdV.           0.214 [ 0.056,  0.362]  .009 ** 
## WA.TA-WA.PR             0.162 [ 0.001,  0.314]  .048 *  
## WA.TA-WA.ER             0.184 [ 0.024,  0.334]  .025 *  
## WA.TA-WA.SlpQnV.        0.086 [-0.080,  0.247]  .310    
## WA.WRF-WA.InV.          0.620 [ 0.511,  0.710] <.001 ***
## WA.WRF-WA.GV            0.396 [ 0.252,  0.523] <.001 ***
## WA.WRF-WA.EV            0.269 [ 0.114,  0.411] <.001 ***
## WA.WRF-WA.SlpQlV.      -0.088 [-0.244,  0.073]  .285    
## WA.WRF-WA.RdV.          0.155 [-0.005,  0.308]  .058 .  
## WA.WRF-WA.PR            0.134 [-0.027,  0.288]  .102    
## WA.WRF-WA.ER            0.127 [-0.034,  0.281]  .122    
## WA.WRF-WA.SlpQnV.       0.079 [-0.087,  0.241]  .348    
## WA.InV.-WA.GV           0.445 [ 0.307,  0.565] <.001 ***
## WA.InV.-WA.EV           0.192 [ 0.033,  0.341]  .018 *  
## WA.InV.-WA.SlpQlV.      0.022 [-0.138,  0.181]  .786    
## WA.InV.-WA.RdV.         0.271 [ 0.116,  0.413] <.001 ***
## WA.InV.-WA.PR           0.240 [ 0.083,  0.385]  .003 ** 
## WA.InV.-WA.ER           0.213 [ 0.055,  0.361]  .009 ** 
## WA.InV.-WA.SlpQnV.      0.075 [-0.091,  0.237]  .377    
## WA.GV-WA.EV             0.095 [-0.066,  0.251]  .245    
## WA.GV-WA.SlpQlV.        0.065 [-0.096,  0.222]  .430    
## WA.GV-WA.RdV.           0.136 [-0.025,  0.290]  .098 .  
## WA.GV-WA.PR             0.076 [-0.086,  0.233]  .357    
## WA.GV-WA.ER             0.131 [-0.030,  0.285]  .110    
## WA.GV-WA.SlpQnV.        0.065 [-0.101,  0.227]  .442    
## WA.EV-WA.SlpQlV.        0.028 [-0.132,  0.187]  .731    
## WA.EV-WA.RdV.           0.164 [ 0.004,  0.316]  .045 *  
## WA.EV-WA.PR             0.288 [ 0.134,  0.429] <.001 ***
## WA.EV-WA.ER             0.056 [-0.106,  0.214]  .500    
## WA.EV-WA.SlpQnV.       -0.070 [-0.232,  0.096]  .411    
## WA.SlpQlV.-WA.RdV.      0.149 [-0.011,  0.302]  .068 .  
## WA.SlpQlV.-WA.PR        0.146 [-0.015,  0.299]  .075 .  
## WA.SlpQlV.-WA.ER        0.111 [-0.050,  0.266]  .177    
## WA.SlpQlV.-WA.SlpQnV.  -0.249 [-0.397, -0.088]  .003 ** 
## WA.RdV.-WA.PR           0.652 [ 0.549,  0.735] <.001 ***
## WA.RdV.-WA.ER           0.918 [ 0.888,  0.940] <.001 ***
## WA.RdV.-WA.SlpQnV.     -0.071 [-0.234,  0.096]  .402    
## WA.PR-WA.ER             0.297 [ 0.144,  0.437] <.001 ***
## WA.PR-WA.SlpQnV.       -0.104 [-0.265,  0.062]  .217    
## WA.ER-WA.SlpQnV.       -0.033 [-0.197,  0.133]  .696    
## ────────────────────────────────────────────────────────
## 
## 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        -0.493                             0.846                                     0.782                                     0.788                0.597           0.756                         0.778                     0.585                 0.613              0.766              0.746                     0.707             0.643                      0.705            0.750              0.725                      0.705                      0.656          0.629              0.666              0.577           0.697              0.689                      0.590               0.688           0.663     0.682        0.796            0.625       0.771         0.698     0.761             0.597
## ICC2        -8.419                             0.935                                     0.904                                     0.908                0.796           0.891                         0.902                     0.788                 0.807              0.896              0.886                     0.864             0.826                      0.863            0.888              0.874                      0.863                      0.834          0.817              0.840              0.782           0.858              0.854                      0.791               0.853           0.838     0.849        0.912            0.815       0.897         0.857     0.892             0.790
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

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                             0.506                                     0.467                                     0.483                0.478           0.321                         0.469                     0.356                 0.481              0.424              0.401                     0.349             0.393                      0.325            0.320              0.504                      0.490                      0.346          0.537              0.430             -0.100           0.408              0.416                      0.477               0.396           0.445        0.095            0.065       0.136         0.076     0.131             0.065
## WP.SupervisoryBehavioralFeedbackV              0.081                             1.000                                     0.929                                     0.952                0.533           0.500                         0.562                     0.498                 0.367              0.578              0.395                     0.394             0.313                      0.531            0.481              0.633                      0.646                      0.404          0.649              0.498             -0.039           0.597              0.567                      0.619               0.546           0.602        0.154           -0.013       0.168         0.074     0.170             0.015
## WP.SupervisoryPositiveBehavioralFeedbackV      0.096                             0.731                                     1.000                                     0.771                0.583           0.508                         0.529                     0.484                 0.315              0.552              0.348                     0.344             0.281                      0.526            0.472              0.586                      0.653                      0.319          0.641              0.534             -0.078           0.646              0.578                      0.628               0.564           0.585        0.151           -0.023       0.132         0.065     0.130            -0.019
## WP.SupervisoryNegativeBehavioralFeedbackV      0.035                             0.820                                     0.208                                     1.000                0.436           0.440                         0.529                     0.456                 0.372              0.538              0.392                     0.393             0.307                      0.479            0.438              0.604                      0.571                      0.431          0.586              0.415             -0.003           0.493              0.499                      0.546               0.473           0.551        0.140           -0.004       0.180         0.074     0.186             0.042
## WP.learningBehaviorV                           0.030                             0.084                                     0.042                                     0.086                1.000           0.697                         0.693                     0.648                 0.496              0.672              0.545                     0.548             0.426                      0.681            0.702              0.675                      0.698                      0.422          0.719              0.502             -0.008           0.703              0.610                      0.754               0.640           0.740        0.219            0.124       0.154         0.053     0.164            -0.065
## WP.JobCraftingV                                0.011                             0.051                                     0.087                                    -0.000                0.132           1.000                         0.684                     0.640                 0.458              0.679              0.566                     0.589             0.411                      0.699            0.756              0.686                      0.605                      0.533          0.623              0.536             -0.041           0.680              0.591                      0.636               0.616           0.622        0.308            0.042       0.242         0.191     0.203             0.065
## WP.CreativeProcessEngagementV                  0.090                             0.156                                     0.112                                     0.129                0.172           0.207                         1.000                     0.868                 0.829              0.946              0.602                     0.593             0.487                      0.713            0.711              0.686                      0.619                      0.518          0.727              0.578             -0.023           0.749              0.653                      0.685               0.609           0.638        0.272            0.138       0.232         0.141     0.217            -0.011
## WP.ProblemIdentificationV                      0.000                             0.000                                    -0.032                                     0.027                0.195           0.201                         0.722                     1.000                 0.617              0.739              0.538                     0.529             0.437                      0.617            0.640              0.610                      0.581                      0.432          0.667              0.497              0.000           0.671              0.575                      0.615               0.555           0.564        0.284            0.135       0.165         0.098     0.155            -0.045
## WP.InformationSearchV                          0.004                             0.098                                     0.057                                     0.092                0.097           0.187                         0.662                     0.325                 1.000              0.675              0.534                     0.467             0.521                      0.463            0.470              0.470                      0.397                      0.383          0.524              0.446             -0.025           0.524              0.475                      0.467               0.377           0.413        0.209            0.219       0.260         0.166     0.241            -0.050
## WP.IdeaGenerationV                             0.159                             0.212                                     0.188                                     0.146                0.084           0.079                         0.754                     0.268                 0.213              1.000              0.542                     0.566             0.390                      0.744            0.724              0.693                      0.624                      0.526          0.709              0.567             -0.030           0.749              0.648                      0.690               0.630           0.660        0.239            0.067       0.204         0.121     0.192             0.031
## WP.SocialLearningV                             0.114                            -0.039                                    -0.036                                    -0.025                0.150           0.209                         0.228                     0.158                 0.074              0.234              1.000                     0.944             0.871                      0.717            0.651              0.484                      0.440                      0.363          0.470              0.338              0.061           0.440              0.373                      0.536               0.360           0.576        0.257            0.195       0.290         0.108     0.308             0.057
## WP.ObservationalLearningV                      0.088                            -0.105                                    -0.064                                    -0.097                0.121           0.199                         0.235                     0.220                 0.099              0.178              0.851                     1.000             0.662                      0.711            0.682              0.520                      0.474                      0.388          0.480              0.380              0.128           0.482              0.415                      0.570               0.424           0.608        0.241            0.137       0.285         0.126     0.292             0.049
## WP.AdviceSeekingV                              0.094                             0.067                                     0.017                                     0.082                0.118           0.124                         0.111                     0.001                 0.007              0.197              0.723                     0.254             1.000                      0.574            0.467              0.328                      0.295                      0.249          0.356              0.203             -0.052           0.285              0.232                      0.373               0.188           0.408        0.227            0.242       0.237         0.058     0.266             0.059
## WP.PerformanceImprovementV                     0.126                             0.054                                     0.028                                     0.055                0.093           0.238                         0.222                     0.138                 0.175              0.166              0.122                     0.172            -0.001                      1.000            0.766              0.576                      0.540                      0.415          0.577              0.458              0.013           0.629              0.530                      0.650               0.524           0.727        0.253            0.121       0.281         0.186     0.255             0.065
## WP.TakingChargeV                               0.039                             0.172                                     0.120                                     0.146                0.124           0.146                         0.289                     0.189                 0.228              0.206              0.040                     0.037             0.025                      0.237            1.000              0.608                      0.599                      0.410          0.635              0.498              0.031           0.640              0.526                      0.650               0.577           0.711        0.285            0.050       0.287         0.174     0.269             0.021
## WA.WorkReflectionV                             0.203                             0.138                                     0.121                                     0.098               -0.038           0.279                         0.104                     0.062                 0.004              0.134              0.113                     0.046             0.146                      0.073            0.109              1.000                      0.830                      0.829          0.742              0.523              0.004           0.644              0.652                      0.702               0.678           0.610        0.317            0.017       0.146         0.113     0.124             0.092
## WA.PositiveWorkReflectionV                     0.266                             0.141                                     0.081                                     0.135                0.066           0.272                         0.133                     0.112                 0.049              0.113              0.110                     0.078             0.101                      0.159            0.129              0.729                      1.000                      0.376          0.785              0.663             -0.096           0.645              0.768                      0.702               0.779           0.674        0.246            0.023       0.166         0.126     0.143             0.072
## WA.NegativeWorkReflectionV                     0.051                             0.072                                     0.100                                     0.019               -0.114           0.155                         0.029                    -0.012                -0.039              0.090              0.062                    -0.004             0.120                     -0.040            0.040              0.782                      0.143                      1.000          0.446              0.205              0.103           0.422              0.313                      0.463               0.346           0.338        0.279            0.005       0.077         0.062     0.064             0.080
## WA.RuminationV                                 0.218                             0.140                                     0.147                                     0.079                0.094           0.159                         0.109                     0.123                -0.015              0.107              0.194                     0.143             0.169                      0.035            0.051              0.467                      0.376                      0.332          1.000              0.654             -0.152           0.697              0.714                      0.687               0.711           0.661        0.249            0.055       0.205         0.120     0.196             0.041
## WA.PositiveAffectV                             0.089                             0.018                                    -0.027                                     0.049                0.065           0.060                         0.065                     0.092                 0.037              0.014              0.087                     0.012             0.143                     -0.084            0.045              0.105                      0.107                      0.055          0.152              1.000             -0.260           0.738              0.743                      0.537               0.702           0.567        0.198           -0.096       0.125         0.112     0.100             0.012
## WA.NegativeAffectV                             0.049                             0.006                                     0.035                                    -0.021                0.034           0.044                        -0.004                    -0.001                -0.023              0.011              0.044                     0.036             0.034                      0.031           -0.031              0.071                      0.001                      0.102          0.058             -0.062              1.000          -0.106             -0.082                      0.031              -0.041          -0.035       -0.134            0.141       0.089         0.092     0.063            -0.004
## WA.ImprovisionV                                0.057                             0.181                                     0.150                                     0.135                0.113           0.081                         0.050                     0.121                -0.014              0.001             -0.007                    -0.044             0.045                     -0.057            0.117              0.219                      0.146                      0.183          0.154              0.244              0.095           1.000              0.694                      0.731               0.683           0.748        0.210           -0.008       0.190         0.173     0.148            -0.149
## WA.WorkAbsorptionV                            -0.076                             0.038                                     0.004                                     0.052                0.112           0.042                         0.076                     0.042                 0.043              0.071             -0.071                    -0.094            -0.007                      0.001            0.046              0.038                      0.098                     -0.034          0.078              0.204             -0.018           0.107              1.000                      0.632               0.846           0.592        0.192            0.088       0.162         0.113     0.146             0.009
## WA.ThrivingAtWorkLearningV                     0.091                             0.118                                     0.134                                     0.058                0.213           0.011                         0.094                     0.139                 0.017              0.044             -0.008                     0.011            -0.029                     -0.054            0.095              0.123                      0.142                      0.048          0.123              0.235             -0.046           0.315              0.221                      1.000               0.650           0.818        0.208            0.072       0.214         0.162     0.184             0.086
## WA.WorkRelatedFlowV                           -0.006                             0.160                                     0.124                                     0.128                0.061           0.042                         0.012                    -0.070                 0.008              0.073              0.021                    -0.007             0.047                      0.094            0.220              0.142                      0.196                      0.026          0.170              0.198             -0.037           0.123              0.424                      0.348               1.000           0.620        0.269           -0.088       0.155         0.134     0.127             0.079
## WA.InspirationV                               -0.104                             0.059                                     0.012                                     0.075                0.182          -0.015                        -0.025                     0.025                 0.069             -0.117             -0.040                    -0.020            -0.046                      0.048            0.009              0.100                      0.089                      0.063          0.176              0.140              0.010           0.318              0.247                      0.424               0.222           1.000        0.192            0.022       0.271         0.240     0.213             0.075
## WA.ExerciseV                                  -0.013                             0.132                                     0.065                                     0.137               -0.068          -0.023                        -0.074                    -0.124                -0.058              0.011             -0.057                    -0.077            -0.005                      0.077           -0.014              0.026                      0.070                     -0.027          0.009              0.136              0.048          -0.047              0.112                      0.049               0.130          -0.036        1.000            0.028       0.164         0.288     0.056            -0.070
## WA.SleepQualityV                              -0.046                             0.063                                     0.005                                     0.087                0.021           0.101                         0.120                     0.155                 0.079              0.032             -0.028                     0.047            -0.111                      0.136            0.047              0.046                      0.041                      0.029          0.059             -0.039             -0.035           0.138              0.041                      0.117              -0.020           0.153       -0.026            1.000       0.149         0.146     0.111            -0.249
## WA.ReadingV                                   -0.038                             0.094                                    -0.043                                     0.171                0.001           0.101                        -0.031                    -0.095                -0.064              0.069              0.042                     0.017             0.056                      0.028           -0.031              0.137                      0.120                      0.089          0.091              0.042             -0.012          -0.038              0.077                      0.103               0.096           0.132        0.115            0.098       1.000         0.652     0.918            -0.071
## WA.PaperReadV                                  0.032                             0.006                                    -0.060                                     0.060                0.012           0.028                        -0.046                    -0.043                -0.034             -0.023              0.080                     0.091             0.030                      0.040            0.007              0.035                      0.091                     -0.032          0.063              0.016              0.034          -0.074              0.031                      0.050              -0.016           0.070        0.115            0.048       0.585         1.000     0.297            -0.104
## WA.EReadV                                     -0.063                             0.109                                    -0.016                                     0.171                0.000           0.104                        -0.011                    -0.081                -0.062              0.093             -0.003                    -0.039             0.045                      0.011           -0.049              0.135                      0.086                      0.119          0.077              0.041             -0.035          -0.007              0.073                      0.089               0.122           0.120        0.073            0.092       0.878         0.125     1.000            -0.033
## WA.SleepQuantityV                              0.004                             0.159                                     0.137                                     0.112               -0.004          -0.133                        -0.054                    -0.139                 0.010              0.011             -0.152                    -0.194            -0.030                     -0.134           -0.030             -0.018                     -0.013                     -0.013         -0.114             -0.144              0.146          -0.083              0.083                     -0.059               0.035          -0.111        0.054           -0.262      -0.058        -0.018    -0.064             1.000
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 
## Within-Level Correlation [95% CI]:
## ────────────────────────────────────────────────────────
##                             r         [95% CI]     p    
## ────────────────────────────────────────────────────────
## WA.GV-WP.SB             0.081 [-0.022,  0.181]  .123    
## WA.GV-WP.SP             0.096 [-0.006,  0.196]  .066 .  
## WA.GV-WP.SN             0.035 [-0.067,  0.137]  .498    
## WA.GV-WP.BV             0.030 [-0.072,  0.132]  .562    
## WA.GV-WP.JC             0.011 [-0.091,  0.113]  .829    
## WA.GV-WP.CP             0.090 [-0.012,  0.190]  .085 .  
## WA.GV-WP.PrbIV.         0.000 [-0.102,  0.102]  .995    
## WA.GV-WP.IS             0.004 [-0.098,  0.106]  .939    
## WA.GV-WP.IG             0.159 [ 0.058,  0.257]  .002 ** 
## WA.GV-WP.SL             0.114 [ 0.012,  0.213]  .029 *  
## WA.GV-WP.OL             0.088 [-0.014,  0.189]  .091 .  
## WA.GV-WP.AS             0.094 [-0.008,  0.194]  .071 .  
## WA.GV-WP.PrfIV.         0.126 [ 0.025,  0.225]  .015 *  
## WA.GV-WP.TC             0.039 [-0.064,  0.140]  .458    
## WA.GV-WA.WRV            0.203 [ 0.107,  0.295] <.001 ***
## WA.GV-WA.PW             0.266 [ 0.172,  0.355] <.001 ***
## WA.GV-WA.NW             0.051 [-0.047,  0.149]  .307    
## WA.GV-WA.RmV.           0.218 [ 0.123,  0.310] <.001 ***
## WA.GV-WA.PA             0.089 [-0.010,  0.185]  .077 .  
## WA.GV-WA.NA             0.049 [-0.050,  0.146]  .332    
## WA.GV-WA.ImV.           0.057 [-0.042,  0.154]  .259    
## WA.GV-WA.WA            -0.076 [-0.173,  0.022]  .130    
## WA.GV-WA.TA             0.091 [-0.008,  0.187]  .072 .  
## WA.GV-WA.WRF           -0.006 [-0.105,  0.092]  .901    
## WA.GV-WA.InV.          -0.104 [-0.201, -0.006]  .038 *  
## WA.GV-WA.EV            -0.013 [-0.111,  0.086]  .799    
## WA.GV-WA.SlpQlV.       -0.046 [-0.144,  0.052]  .358    
## WA.GV-WA.RdV.          -0.038 [-0.137,  0.061]  .453    
## WA.GV-WA.PR             0.032 [-0.068,  0.131]  .529    
## WA.GV-WA.ER            -0.063 [-0.161,  0.036]  .213    
## WA.GV-WA.SlpQnV.        0.004 [-0.099,  0.108]  .934    
## WP.SB-WP.SP             0.731 [ 0.680,  0.775] <.001 ***
## WP.SB-WP.SN             0.820 [ 0.784,  0.850] <.001 ***
## WP.SB-WP.BV             0.084 [-0.016,  0.183]  .101    
## WP.SB-WP.JC             0.051 [-0.050,  0.151]  .324    
## WP.SB-WP.CP             0.156 [ 0.056,  0.252]  .002 ** 
## WP.SB-WP.PrbIV.         0.000 [-0.100,  0.101]  .995    
## WP.SB-WP.IS             0.098 [-0.003,  0.197]  .057 .  
## WP.SB-WP.IG             0.212 [ 0.114,  0.306] <.001 ***
## WP.SB-WP.SL            -0.039 [-0.139,  0.062]  .453    
## WP.SB-WP.OL            -0.105 [-0.203, -0.004]  .041 *  
## WP.SB-WP.AS             0.067 [-0.034,  0.166]  .194    
## WP.SB-WP.PrfIV.         0.054 [-0.046,  0.154]  .290    
## WP.SB-WP.TC             0.172 [ 0.072,  0.268] <.001 ***
## WP.SB-WA.WRV            0.138 [ 0.037,  0.237]  .008 ** 
## WP.SB-WA.PW             0.141 [ 0.039,  0.239]  .007 ** 
## WP.SB-WA.NW             0.072 [-0.031,  0.172]  .170    
## WP.SB-WA.RmV.           0.140 [ 0.038,  0.239]  .007 ** 
## WP.SB-WA.PA             0.018 [-0.084,  0.120]  .731    
## WP.SB-WA.NA             0.006 [-0.097,  0.108]  .915    
## WP.SB-WA.ImV.           0.181 [ 0.080,  0.278] <.001 ***
## WP.SB-WA.WA             0.038 [-0.064,  0.140]  .466    
## WP.SB-WA.TA             0.118 [ 0.016,  0.218]  .023 *  
## WP.SB-WA.WRF            0.160 [ 0.059,  0.258]  .002 ** 
## WP.SB-WA.InV.           0.059 [-0.044,  0.160]  .261    
## WP.SB-WA.EV             0.132 [ 0.030,  0.231]  .011 *  
## WP.SB-WA.SlpQlV.        0.063 [-0.039,  0.164]  .228    
## WP.SB-WA.RdV.           0.094 [-0.010,  0.195]  .077 .  
## WP.SB-WA.PR             0.006 [-0.097,  0.110]  .904    
## WP.SB-WA.ER             0.109 [ 0.006,  0.210]  .039 *  
## WP.SB-WA.SlpQnV.        0.159 [ 0.052,  0.261]  .004 ** 
## WP.SP-WP.SN             0.208 [ 0.110,  0.303] <.001 ***
## WP.SP-WP.BV             0.042 [-0.059,  0.142]  .415    
## WP.SP-WP.JC             0.087 [-0.014,  0.186]  .091 .  
## WP.SP-WP.CP             0.112 [ 0.011,  0.210]  .030 *  
## WP.SP-WP.PrbIV.        -0.032 [-0.132,  0.069]  .531    
## WP.SP-WP.IS             0.057 [-0.044,  0.157]  .267    
## WP.SP-WP.IG             0.188 [ 0.089,  0.283] <.001 ***
## WP.SP-WP.SL            -0.036 [-0.136,  0.065]  .482    
## WP.SP-WP.OL            -0.064 [-0.163,  0.037]  .217    
## WP.SP-WP.AS             0.017 [-0.084,  0.117]  .743    
## WP.SP-WP.PrfIV.         0.028 [-0.073,  0.128]  .585    
## WP.SP-WP.TC             0.120 [ 0.019,  0.218]  .020 *  
## WP.SP-WA.WRV            0.121 [ 0.019,  0.220]  .021 *  
## WP.SP-WA.PW             0.081 [-0.022,  0.181]  .122    
## WP.SP-WA.NW             0.100 [-0.002,  0.200]  .054 .  
## WP.SP-WA.RmV.           0.147 [ 0.045,  0.245]  .005 ** 
## WP.SP-WA.PA            -0.027 [-0.128,  0.076]  .610    
## WP.SP-WA.NA             0.035 [-0.068,  0.136]  .508    
## WP.SP-WA.ImV.           0.150 [ 0.048,  0.248]  .004 ** 
## WP.SP-WA.WA             0.004 [-0.099,  0.106]  .947    
## WP.SP-WA.TA             0.134 [ 0.032,  0.232]  .010 *  
## WP.SP-WA.WRF            0.124 [ 0.022,  0.223]  .018 *  
## WP.SP-WA.InV.           0.012 [-0.090,  0.114]  .816    
## WP.SP-WA.EV             0.065 [-0.038,  0.166]  .216    
## WP.SP-WA.SlpQlV.        0.005 [-0.097,  0.107]  .918    
## WP.SP-WA.RdV.          -0.043 [-0.145,  0.061]  .421    
## WP.SP-WA.PR            -0.060 [-0.163,  0.043]  .255    
## WP.SP-WA.ER            -0.016 [-0.119,  0.087]  .756    
## WP.SP-WA.SlpQnV.        0.137 [ 0.030,  0.240]  .012 *  
## WP.SN-WP.BV             0.086 [-0.015,  0.185]  .096 .  
## WP.SN-WP.JC            -0.000 [-0.101,  0.100]  .997    
## WP.SN-WP.CP             0.129 [ 0.029,  0.227]  .012 *  
## WP.SN-WP.PrbIV.         0.027 [-0.073,  0.128]  .594    
## WP.SN-WP.IS             0.092 [-0.008,  0.191]  .073 .  
## WP.SN-WP.IG             0.146 [ 0.046,  0.243]  .005 ** 
## WP.SN-WP.SL            -0.025 [-0.125,  0.076]  .627    
## WP.SN-WP.OL            -0.097 [-0.196,  0.004]  .059 .  
## WP.SN-WP.AS             0.082 [-0.019,  0.181]  .113    
## WP.SN-WP.PrfIV.         0.055 [-0.046,  0.154]  .290    
## WP.SN-WP.TC             0.146 [ 0.046,  0.243]  .005 ** 
## WP.SN-WA.WRV            0.098 [-0.004,  0.198]  .060 .  
## WP.SN-WA.PW             0.135 [ 0.034,  0.234]  .009 ** 
## WP.SN-WA.NW             0.019 [-0.083,  0.121]  .717    
## WP.SN-WA.RmV.           0.079 [-0.024,  0.179]  .132    
## WP.SN-WA.PA             0.049 [-0.054,  0.150]  .353    
## WP.SN-WA.NA            -0.021 [-0.123,  0.081]  .686    
## WP.SN-WA.ImV.           0.135 [ 0.034,  0.234]  .009 ** 
## WP.SN-WA.WA             0.052 [-0.050,  0.154]  .317    
## WP.SN-WA.TA             0.058 [-0.044,  0.159]  .264    
## WP.SN-WA.WRF            0.128 [ 0.026,  0.227]  .014 *  
## WP.SN-WA.InV.           0.075 [-0.028,  0.176]  .152    
## WP.SN-WA.EV             0.137 [ 0.035,  0.235]  .009 ** 
## WP.SN-WA.SlpQlV.        0.087 [-0.016,  0.187]  .097 .  
## WP.SN-WA.RdV.           0.171 [ 0.069,  0.270]  .001 ** 
## WP.SN-WA.PR             0.060 [-0.044,  0.162]  .256    
## WP.SN-WA.ER             0.171 [ 0.069,  0.270]  .001 ** 
## WP.SN-WA.SlpQnV.        0.112 [ 0.005,  0.217]  .040 *  
## WP.BV-WP.JC             0.132 [ 0.032,  0.230]  .010 *  
## WP.BV-WP.CP             0.172 [ 0.073,  0.268] <.001 ***
## WP.BV-WP.PrbIV.         0.195 [ 0.097,  0.290] <.001 ***
## WP.BV-WP.IS             0.097 [-0.003,  0.196]  .058 .  
## WP.BV-WP.IG             0.084 [-0.016,  0.183]  .101    
## WP.BV-WP.SL             0.150 [ 0.050,  0.247]  .004 ** 
## WP.BV-WP.OL             0.121 [ 0.020,  0.218]  .019 *  
## WP.BV-WP.AS             0.118 [ 0.017,  0.216]  .022 *  
## WP.BV-WP.PrfIV.         0.093 [-0.008,  0.192]  .072 .  
## WP.BV-WP.TC             0.124 [ 0.024,  0.222]  .016 *  
## WP.BV-WA.WRV           -0.038 [-0.139,  0.065]  .470    
## WP.BV-WA.PW             0.066 [-0.036,  0.167]  .206    
## WP.BV-WA.NW            -0.114 [-0.214, -0.012]  .029 *  
## WP.BV-WA.RmV.           0.094 [-0.008,  0.194]  .073 .  
## WP.BV-WA.PA             0.065 [-0.037,  0.166]  .211    
## WP.BV-WA.NA             0.034 [-0.069,  0.135]  .521    
## WP.BV-WA.ImV.           0.113 [ 0.011,  0.212]  .031 *  
## WP.BV-WA.WA             0.112 [ 0.010,  0.212]  .031 *  
## WP.BV-WA.TA             0.213 [ 0.113,  0.308] <.001 ***
## WP.BV-WA.WRF            0.061 [-0.042,  0.162]  .245    
## WP.BV-WA.InV.           0.182 [ 0.081,  0.279] <.001 ***
## WP.BV-WA.EV            -0.068 [-0.169,  0.035]  .195    
## WP.BV-WA.SlpQlV.        0.021 [-0.081,  0.123]  .686    
## WP.BV-WA.RdV.           0.001 [-0.103,  0.104]  .992    
## WP.BV-WA.PR             0.012 [-0.091,  0.115]  .819    
## WP.BV-WA.ER             0.000 [-0.103,  0.104]  .996    
## WP.BV-WA.SlpQnV.       -0.004 [-0.111,  0.103]  .939    
## WP.JC-WP.CP             0.207 [ 0.109,  0.302] <.001 ***
## WP.JC-WP.PrbIV.         0.201 [ 0.102,  0.296] <.001 ***
## WP.JC-WP.IS             0.187 [ 0.088,  0.282] <.001 ***
## WP.JC-WP.IG             0.079 [-0.022,  0.178]  .125    
## WP.JC-WP.SL             0.209 [ 0.111,  0.304] <.001 ***
## WP.JC-WP.OL             0.199 [ 0.101,  0.294] <.001 ***
## WP.JC-WP.AS             0.124 [ 0.023,  0.221]  .016 *  
## WP.JC-WP.PrfIV.         0.238 [ 0.141,  0.331] <.001 ***
## WP.JC-WP.TC             0.146 [ 0.046,  0.243]  .004 ** 
## WP.JC-WA.WRV            0.279 [ 0.182,  0.370] <.001 ***
## WP.JC-WA.PW             0.272 [ 0.175,  0.364] <.001 ***
## WP.JC-WA.NW             0.155 [ 0.053,  0.253]  .003 ** 
## WP.JC-WA.RmV.           0.159 [ 0.057,  0.256]  .002 ** 
## WP.JC-WA.PA             0.060 [-0.042,  0.162]  .247    
## WP.JC-WA.NA             0.044 [-0.059,  0.145]  .402    
## WP.JC-WA.ImV.           0.081 [-0.021,  0.181]  .122    
## WP.JC-WA.WA             0.042 [-0.060,  0.144]  .421    
## WP.JC-WA.TA             0.011 [-0.091,  0.113]  .831    
## WP.JC-WA.WRF            0.042 [-0.060,  0.144]  .416    
## WP.JC-WA.InV.          -0.015 [-0.116,  0.088]  .781    
## WP.JC-WA.EV            -0.023 [-0.124,  0.080]  .665    
## WP.JC-WA.SlpQlV.        0.101 [-0.001,  0.201]  .053 .  
## WP.JC-WA.RdV.           0.101 [-0.002,  0.203]  .055 .  
## WP.JC-WA.PR             0.028 [-0.076,  0.131]  .597    
## WP.JC-WA.ER             0.104 [ 0.001,  0.205]  .049 *  
## WP.JC-WA.SlpQnV.       -0.133 [-0.236, -0.026]  .015 *  
## WP.CP-WP.PrbIV.         0.722 [ 0.670,  0.767] <.001 ***
## WP.CP-WP.IS             0.662 [ 0.601,  0.715] <.001 ***
## WP.CP-WP.IG             0.754 [ 0.707,  0.794] <.001 ***
## WP.CP-WP.SL             0.228 [ 0.131,  0.321] <.001 ***
## WP.CP-WP.OL             0.235 [ 0.138,  0.328] <.001 ***
## WP.CP-WP.AS             0.111 [ 0.011,  0.210]  .030 *  
## WP.CP-WP.PrfIV.         0.222 [ 0.125,  0.316] <.001 ***
## WP.CP-WP.TC             0.289 [ 0.194,  0.378] <.001 ***
## WP.CP-WA.WRV            0.104 [ 0.002,  0.204]  .047 *  
## WP.CP-WA.PW             0.133 [ 0.032,  0.232]  .011 *  
## WP.CP-WA.NW             0.029 [-0.074,  0.130]  .585    
## WP.CP-WA.RmV.           0.109 [ 0.007,  0.209]  .036 *  
## WP.CP-WA.PA             0.065 [-0.038,  0.166]  .217    
## WP.CP-WA.NA            -0.004 [-0.106,  0.099]  .946    
## WP.CP-WA.ImV.           0.050 [-0.053,  0.151]  .342    
## WP.CP-WA.WA             0.076 [-0.026,  0.177]  .146    
## WP.CP-WA.TA             0.094 [-0.008,  0.195]  .070 .  
## WP.CP-WA.WRF            0.012 [-0.090,  0.114]  .820    
## WP.CP-WA.InV.          -0.025 [-0.127,  0.077]  .629    
## WP.CP-WA.EV            -0.074 [-0.175,  0.028]  .158    
## WP.CP-WA.SlpQlV.        0.120 [ 0.018,  0.220]  .021 *  
## WP.CP-WA.RdV.          -0.031 [-0.134,  0.073]  .561    
## WP.CP-WA.PR            -0.046 [-0.148,  0.058]  .388    
## WP.CP-WA.ER            -0.011 [-0.114,  0.093]  .840    
## WP.CP-WA.SlpQnV.       -0.054 [-0.161,  0.053]  .320    
## WP.PrbIV.-WP.IS         0.325 [ 0.232,  0.412] <.001 ***
## WP.PrbIV.-WP.IG         0.268 [ 0.172,  0.359] <.001 ***
## WP.PrbIV.-WP.SL         0.158 [ 0.058,  0.254]  .002 ** 
## WP.PrbIV.-WP.OL         0.220 [ 0.122,  0.314] <.001 ***
## WP.PrbIV.-WP.AS         0.001 [-0.100,  0.101]  .989    
## WP.PrbIV.-WP.PrfIV.     0.138 [ 0.038,  0.235]  .007 ** 
## WP.PrbIV.-WP.TC         0.189 [ 0.090,  0.284] <.001 ***
## WP.PrbIV.-WA.WRV        0.062 [-0.040,  0.163]  .236    
## WP.PrbIV.-WA.PW         0.112 [ 0.010,  0.212]  .032 *  
## WP.PrbIV.-WA.NW        -0.012 [-0.114,  0.090]  .814    
## WP.PrbIV.-WA.RmV.       0.123 [ 0.021,  0.222]  .019 *  
## WP.PrbIV.-WA.PA         0.092 [-0.010,  0.192]  .078 .  
## WP.PrbIV.-WA.NA        -0.001 [-0.103,  0.101]  .991    
## WP.PrbIV.-WA.ImV.       0.121 [ 0.020,  0.221]  .020 *  
## WP.PrbIV.-WA.WA         0.042 [-0.060,  0.144]  .417    
## WP.PrbIV.-WA.TA         0.139 [ 0.037,  0.237]  .008 ** 
## WP.PrbIV.-WA.WRF       -0.070 [-0.171,  0.032]  .179    
## WP.PrbIV.-WA.InV.       0.025 [-0.077,  0.127]  .632    
## WP.PrbIV.-WA.EV        -0.124 [-0.223, -0.022]  .017 *  
## WP.PrbIV.-WA.SlpQlV.    0.155 [ 0.054,  0.253]  .003 ** 
## WP.PrbIV.-WA.RdV.      -0.095 [-0.197,  0.008]  .072 .  
## WP.PrbIV.-WA.PR        -0.043 [-0.145,  0.061]  .420    
## WP.PrbIV.-WA.ER        -0.081 [-0.183,  0.022]  .125    
## WP.PrbIV.-WA.SlpQnV.   -0.139 [-0.243, -0.033]  .011 *  
## WP.IS-WP.IG             0.213 [ 0.115,  0.307] <.001 ***
## WP.IS-WP.SL             0.074 [-0.027,  0.173]  .149    
## WP.IS-WP.OL             0.099 [-0.002,  0.197]  .055 .  
## WP.IS-WP.AS             0.007 [-0.094,  0.107]  .893    
## WP.IS-WP.PrfIV.         0.175 [ 0.076,  0.271] <.001 ***
## WP.IS-WP.TC             0.228 [ 0.131,  0.321] <.001 ***
## WP.IS-WA.WRV            0.004 [-0.099,  0.106]  .946    
## WP.IS-WA.PW             0.049 [-0.053,  0.150]  .347    
## WP.IS-WA.NW            -0.039 [-0.141,  0.063]  .451    
## WP.IS-WA.RmV.          -0.015 [-0.117,  0.087]  .775    
## WP.IS-WA.PA             0.037 [-0.065,  0.138]  .480    
## WP.IS-WA.NA            -0.023 [-0.124,  0.080]  .664    
## WP.IS-WA.ImV.          -0.014 [-0.116,  0.088]  .782    
## WP.IS-WA.WA             0.043 [-0.060,  0.144]  .415    
## WP.IS-WA.TA             0.017 [-0.085,  0.119]  .741    
## WP.IS-WA.WRF            0.008 [-0.094,  0.110]  .871    
## WP.IS-WA.InV.           0.069 [-0.033,  0.170]  .183    
## WP.IS-WA.EV            -0.058 [-0.159,  0.044]  .266    
## WP.IS-WA.SlpQlV.        0.079 [-0.023,  0.180]  .129    
## WP.IS-WA.RdV.          -0.064 [-0.166,  0.040]  .228    
## WP.IS-WA.PR            -0.034 [-0.137,  0.070]  .520    
## WP.IS-WA.ER            -0.062 [-0.164,  0.042]  .242    
## WP.IS-WA.SlpQnV.        0.010 [-0.098,  0.116]  .862    
## WP.IG-WP.SL             0.234 [ 0.136,  0.327] <.001 ***
## WP.IG-WP.OL             0.178 [ 0.078,  0.273] <.001 ***
## WP.IG-WP.AS             0.197 [ 0.099,  0.292] <.001 ***
## WP.IG-WP.PrfIV.         0.166 [ 0.066,  0.262]  .001 ** 
## WP.IG-WP.TC             0.206 [ 0.108,  0.301] <.001 ***
## WP.IG-WA.WRV            0.134 [ 0.032,  0.233]  .010 *  
## WP.IG-WA.PW             0.113 [ 0.011,  0.213]  .030 *  
## WP.IG-WA.NW             0.090 [-0.012,  0.191]  .084 .  
## WP.IG-WA.RmV.           0.107 [ 0.005,  0.207]  .040 *  
## WP.IG-WA.PA             0.014 [-0.088,  0.116]  .784    
## WP.IG-WA.NA             0.011 [-0.092,  0.113]  .838    
## WP.IG-WA.ImV.           0.001 [-0.102,  0.103]  .991    
## WP.IG-WA.WA             0.071 [-0.031,  0.172]  .173    
## WP.IG-WA.TA             0.044 [-0.058,  0.146]  .396    
## WP.IG-WA.WRF            0.073 [-0.030,  0.174]  .163    
## WP.IG-WA.InV.          -0.117 [-0.216, -0.015]  .025 *  
## WP.IG-WA.EV             0.011 [-0.091,  0.113]  .835    
## WP.IG-WA.SlpQlV.        0.032 [-0.070,  0.134]  .535    
## WP.IG-WA.RdV.           0.069 [-0.034,  0.172]  .190    
## WP.IG-WA.PR            -0.023 [-0.126,  0.081]  .670    
## WP.IG-WA.ER             0.093 [-0.011,  0.194]  .079 .  
## WP.IG-WA.SlpQnV.        0.011 [-0.096,  0.118]  .845    
## WP.SL-WP.OL             0.851 [ 0.821,  0.877] <.001 ***
## WP.SL-WP.AS             0.723 [ 0.672,  0.768] <.001 ***
## WP.SL-WP.PrfIV.         0.122 [ 0.022,  0.220]  .017 *  
## WP.SL-WP.TC             0.040 [-0.061,  0.140]  .437    
## WP.SL-WA.WRV            0.113 [ 0.011,  0.212]  .031 *  
## WP.SL-WA.PW             0.110 [ 0.008,  0.210]  .035 *  
## WP.SL-WA.NW             0.062 [-0.040,  0.163]  .232    
## WP.SL-WA.RmV.           0.194 [ 0.094,  0.290] <.001 ***
## WP.SL-WA.PA             0.087 [-0.016,  0.187]  .097 .  
## WP.SL-WA.NA             0.044 [-0.059,  0.145]  .401    
## WP.SL-WA.ImV.          -0.007 [-0.109,  0.095]  .897    
## WP.SL-WA.WA            -0.071 [-0.171,  0.032]  .177    
## WP.SL-WA.TA            -0.008 [-0.110,  0.094]  .874    
## WP.SL-WA.WRF            0.021 [-0.081,  0.123]  .687    
## WP.SL-WA.InV.          -0.040 [-0.141,  0.063]  .448    
## WP.SL-WA.EV            -0.057 [-0.159,  0.045]  .272    
## WP.SL-WA.SlpQlV.       -0.028 [-0.129,  0.075]  .597    
## WP.SL-WA.RdV.           0.042 [-0.061,  0.145]  .425    
## WP.SL-WA.PR             0.080 [-0.023,  0.182]  .128    
## WP.SL-WA.ER            -0.003 [-0.107,  0.100]  .950    
## WP.SL-WA.SlpQnV.       -0.152 [-0.255, -0.045]  .005 ** 
## WP.OL-WP.AS             0.254 [ 0.157,  0.346] <.001 ***
## WP.OL-WP.PrfIV.         0.172 [ 0.072,  0.268] <.001 ***
## WP.OL-WP.TC             0.037 [-0.063,  0.137]  .468    
## WP.OL-WA.WRV            0.046 [-0.056,  0.148]  .377    
## WP.OL-WA.PW             0.078 [-0.024,  0.179]  .136    
## WP.OL-WA.NW            -0.004 [-0.106,  0.098]  .939    
## WP.OL-WA.RmV.           0.143 [ 0.042,  0.242]  .006 ** 
## WP.OL-WA.PA             0.012 [-0.090,  0.114]  .816    
## WP.OL-WA.NA             0.036 [-0.066,  0.137]  .493    
## WP.OL-WA.ImV.          -0.044 [-0.146,  0.058]  .396    
## WP.OL-WA.WA            -0.094 [-0.195,  0.008]  .070 .  
## WP.OL-WA.TA             0.011 [-0.091,  0.113]  .835    
## WP.OL-WA.WRF           -0.007 [-0.109,  0.095]  .899    
## WP.OL-WA.InV.          -0.020 [-0.122,  0.082]  .695    
## WP.OL-WA.EV            -0.077 [-0.177,  0.026]  .141    
## WP.OL-WA.SlpQlV.        0.047 [-0.056,  0.148]  .371    
## WP.OL-WA.RdV.           0.017 [-0.087,  0.120]  .755    
## WP.OL-WA.PR             0.091 [-0.013,  0.193]  .085 .  
## WP.OL-WA.ER            -0.039 [-0.142,  0.064]  .459    
## WP.OL-WA.SlpQnV.       -0.194 [-0.295, -0.089] <.001 ***
## WP.AS-WP.PrfIV.        -0.001 [-0.101,  0.100]  .987    
## WP.AS-WP.TC             0.025 [-0.076,  0.125]  .633    
## WP.AS-WA.WRV            0.146 [ 0.045,  0.245]  .005 ** 
## WP.AS-WA.PW             0.101 [-0.001,  0.201]  .054 .  
## WP.AS-WA.NW             0.120 [ 0.018,  0.219]  .022 *  
## WP.AS-WA.RmV.           0.169 [ 0.068,  0.266]  .001 ** 
## WP.AS-WA.PA             0.143 [ 0.042,  0.242]  .006 ** 
## WP.AS-WA.NA             0.034 [-0.069,  0.135]  .517    
## WP.AS-WA.ImV.           0.045 [-0.057,  0.147]  .387    
## WP.AS-WA.WA            -0.007 [-0.109,  0.096]  .900    
## WP.AS-WA.TA            -0.029 [-0.131,  0.073]  .574    
## WP.AS-WA.WRF            0.047 [-0.055,  0.149]  .366    
## WP.AS-WA.InV.          -0.046 [-0.148,  0.056]  .377    
## WP.AS-WA.EV            -0.005 [-0.107,  0.097]  .919    
## WP.AS-WA.SlpQlV.       -0.111 [-0.211, -0.009]  .033 *  
## WP.AS-WA.RdV.           0.056 [-0.047,  0.159]  .286    
## WP.AS-WA.PR             0.030 [-0.073,  0.133]  .568    
## WP.AS-WA.ER             0.045 [-0.059,  0.147]  .397    
## WP.AS-WA.SlpQnV.       -0.030 [-0.136,  0.078]  .588    
## WP.PrfIV.-WP.TC         0.237 [ 0.140,  0.330] <.001 ***
## WP.PrfIV.-WA.WRV        0.073 [-0.030,  0.173]  .165    
## WP.PrfIV.-WA.PW         0.159 [ 0.058,  0.257]  .002 ** 
## WP.PrfIV.-WA.NW        -0.040 [-0.141,  0.063]  .447    
## WP.PrfIV.-WA.RmV.       0.035 [-0.068,  0.136]  .505    
## WP.PrfIV.-WA.PA        -0.084 [-0.185,  0.018]  .107    
## WP.PrfIV.-WA.NA         0.031 [-0.072,  0.132]  .559    
## WP.PrfIV.-WA.ImV.      -0.057 [-0.158,  0.046]  .279    
## WP.PrfIV.-WA.WA         0.001 [-0.101,  0.103]  .988    
## WP.PrfIV.-WA.TA        -0.054 [-0.155,  0.048]  .301    
## WP.PrfIV.-WA.WRF        0.094 [-0.008,  0.195]  .071 .  
## WP.PrfIV.-WA.InV.       0.048 [-0.055,  0.149]  .362    
## WP.PrfIV.-WA.EV         0.077 [-0.025,  0.178]  .138    
## WP.PrfIV.-WA.SlpQlV.    0.136 [ 0.035,  0.235]  .009 ** 
## WP.PrfIV.-WA.RdV.       0.028 [-0.075,  0.131]  .594    
## WP.PrfIV.-WA.PR         0.040 [-0.064,  0.142]  .453    
## WP.PrfIV.-WA.ER         0.011 [-0.092,  0.114]  .835    
## WP.PrfIV.-WA.SlpQnV.   -0.134 [-0.238, -0.027]  .014 *  
## WP.TC-WA.WRV            0.109 [ 0.007,  0.209]  .037 *  
## WP.TC-WA.PW             0.129 [ 0.027,  0.228]  .013 *  
## WP.TC-WA.NW             0.040 [-0.062,  0.141]  .445    
## WP.TC-WA.RmV.           0.051 [-0.051,  0.152]  .328    
## WP.TC-WA.PA             0.045 [-0.057,  0.147]  .386    
## WP.TC-WA.NA            -0.031 [-0.133,  0.071]  .552    
## WP.TC-WA.ImV.           0.117 [ 0.015,  0.217]  .025 *  
## WP.TC-WA.WA             0.046 [-0.057,  0.147]  .382    
## WP.TC-WA.TA             0.095 [-0.007,  0.195]  .069 .  
## WP.TC-WA.WRF            0.220 [ 0.121,  0.315] <.001 ***
## WP.TC-WA.InV.           0.009 [-0.093,  0.111]  .864    
## WP.TC-WA.EV            -0.014 [-0.116,  0.088]  .784    
## WP.TC-WA.SlpQlV.        0.047 [-0.055,  0.149]  .367    
## WP.TC-WA.RdV.          -0.031 [-0.134,  0.072]  .555    
## WP.TC-WA.PR             0.007 [-0.096,  0.111]  .890    
## WP.TC-WA.ER            -0.049 [-0.152,  0.054]  .349    
## WP.TC-WA.SlpQnV.       -0.030 [-0.137,  0.077]  .582    
## WA.WRV-WA.PW            0.729 [ 0.680,  0.772] <.001 ***
## WA.WRV-WA.NW            0.782 [ 0.740,  0.817] <.001 ***
## WA.WRV-WA.RmV.          0.467 [ 0.386,  0.540] <.001 ***
## WA.WRV-WA.PA            0.105 [ 0.007,  0.202]  .036 *  
## WA.WRV-WA.NA            0.071 [-0.027,  0.168]  .156    
## WA.WRV-WA.ImV.          0.219 [ 0.123,  0.310] <.001 ***
## WA.WRV-WA.WA            0.038 [-0.060,  0.136]  .445    
## WA.WRV-WA.TA            0.123 [ 0.025,  0.219]  .014 *  
## WA.WRV-WA.WRF           0.142 [ 0.044,  0.237]  .005 ** 
## WA.WRV-WA.InV.          0.100 [ 0.002,  0.196]  .046 *  
## WA.WRV-WA.EV            0.026 [-0.073,  0.124]  .608    
## WA.WRV-WA.SlpQlV.       0.046 [-0.052,  0.144]  .356    
## WA.WRV-WA.RdV.          0.137 [ 0.038,  0.233]  .007 ** 
## WA.WRV-WA.PR            0.035 [-0.064,  0.134]  .488    
## WA.WRV-WA.ER            0.135 [ 0.036,  0.231]  .008 ** 
## WA.WRV-WA.SlpQnV.      -0.018 [-0.121,  0.086]  .739    
## WA.PW-WA.NW             0.143 [ 0.046,  0.238]  .004 ** 
## WA.PW-WA.RmV.           0.376 [ 0.289,  0.458] <.001 ***
## WA.PW-WA.PA             0.107 [ 0.009,  0.203]  .033 *  
## WA.PW-WA.NA             0.001 [-0.097,  0.099]  .985    
## WA.PW-WA.ImV.           0.146 [ 0.048,  0.241]  .004 ** 
## WA.PW-WA.WA             0.098 [-0.000,  0.194]  .051 .  
## WA.PW-WA.TA             0.142 [ 0.045,  0.237]  .005 ** 
## WA.PW-WA.WRF            0.196 [ 0.100,  0.289] <.001 ***
## WA.PW-WA.InV.           0.089 [-0.009,  0.186]  .075 .  
## WA.PW-WA.EV             0.070 [-0.028,  0.167]  .162    
## WA.PW-WA.SlpQlV.        0.041 [-0.057,  0.139]  .409    
## WA.PW-WA.RdV.           0.120 [ 0.021,  0.217]  .018 *  
## WA.PW-WA.PR             0.091 [-0.008,  0.189]  .073 .  
## WA.PW-WA.ER             0.086 [-0.014,  0.183]  .092 .  
## WA.PW-WA.SlpQnV.       -0.013 [-0.116,  0.090]  .805    
## WA.NW-WA.RmV.           0.332 [ 0.241,  0.417] <.001 ***
## WA.NW-WA.PA             0.055 [-0.044,  0.152]  .277    
## WA.NW-WA.NA             0.102 [ 0.004,  0.199]  .042 *  
## WA.NW-WA.ImV.           0.183 [ 0.087,  0.277] <.001 ***
## WA.NW-WA.WA            -0.034 [-0.132,  0.065]  .502    
## WA.NW-WA.TA             0.048 [-0.050,  0.146]  .337    
## WA.NW-WA.WRF            0.026 [-0.072,  0.124]  .603    
## WA.NW-WA.InV.           0.063 [-0.035,  0.161]  .208    
## WA.NW-WA.EV            -0.027 [-0.125,  0.072]  .595    
## WA.NW-WA.SlpQlV.        0.029 [-0.069,  0.127]  .560    
## WA.NW-WA.RdV.           0.089 [-0.011,  0.187]  .080 .  
## WA.NW-WA.PR            -0.032 [-0.131,  0.068]  .531    
## WA.NW-WA.ER             0.119 [ 0.019,  0.215]  .020 *  
## WA.NW-WA.SlpQnV.       -0.013 [-0.116,  0.090]  .803    
## WA.RmV.-WA.PA           0.152 [ 0.054,  0.246]  .003 ** 
## WA.RmV.-WA.NA           0.058 [-0.040,  0.155]  .248    
## WA.RmV.-WA.ImV.         0.154 [ 0.057,  0.249]  .002 ** 
## WA.RmV.-WA.WA           0.078 [-0.020,  0.175]  .119    
## WA.RmV.-WA.TA           0.123 [ 0.025,  0.219]  .014 *  
## WA.RmV.-WA.WRF          0.170 [ 0.073,  0.264] <.001 ***
## WA.RmV.-WA.InV.         0.176 [ 0.079,  0.269] <.001 ***
## WA.RmV.-WA.EV           0.009 [-0.089,  0.107]  .860    
## WA.RmV.-WA.SlpQlV.      0.059 [-0.040,  0.156]  .241    
## WA.RmV.-WA.RdV.         0.091 [-0.008,  0.189]  .073 .  
## WA.RmV.-WA.PR           0.063 [-0.037,  0.162]  .215    
## WA.RmV.-WA.ER           0.077 [-0.023,  0.175]  .130    
## WA.RmV.-WA.SlpQnV.     -0.114 [-0.215, -0.011]  .030 *  
## WA.PA-WA.NA            -0.062 [-0.160,  0.036]  .215    
## WA.PA-WA.ImV.           0.244 [ 0.149,  0.334] <.001 ***
## WA.PA-WA.WA             0.204 [ 0.108,  0.296] <.001 ***
## WA.PA-WA.TA             0.235 [ 0.140,  0.326] <.001 ***
## WA.PA-WA.WRF            0.198 [ 0.102,  0.291] <.001 ***
## WA.PA-WA.InV.           0.140 [ 0.042,  0.235]  .005 ** 
## WA.PA-WA.EV             0.136 [ 0.038,  0.231]  .007 ** 
## WA.PA-WA.SlpQlV.       -0.039 [-0.136,  0.060]  .442    
## WA.PA-WA.RdV.           0.042 [-0.058,  0.140]  .414    
## WA.PA-WA.PR             0.016 [-0.084,  0.115]  .758    
## WA.PA-WA.ER             0.041 [-0.058,  0.140]  .416    
## WA.PA-WA.SlpQnV.       -0.144 [-0.244, -0.042]  .006 ** 
## WA.NA-WA.ImV.           0.095 [-0.003,  0.192]  .058 .  
## WA.NA-WA.WA            -0.018 [-0.116,  0.080]  .716    
## WA.NA-WA.TA            -0.046 [-0.143,  0.053]  .365    
## WA.NA-WA.WRF           -0.037 [-0.135,  0.062]  .466    
## WA.NA-WA.InV.           0.010 [-0.088,  0.108]  .843    
## WA.NA-WA.EV             0.048 [-0.050,  0.146]  .338    
## WA.NA-WA.SlpQlV.       -0.035 [-0.133,  0.064]  .489    
## WA.NA-WA.RdV.          -0.012 [-0.111,  0.088]  .821    
## WA.NA-WA.PR             0.034 [-0.065,  0.133]  .498    
## WA.NA-WA.ER            -0.035 [-0.133,  0.065]  .497    
## WA.NA-WA.SlpQnV.        0.146 [ 0.044,  0.246]  .006 ** 
## WA.ImV.-WA.WA           0.107 [ 0.009,  0.203]  .033 *  
## WA.ImV.-WA.TA           0.315 [ 0.223,  0.401] <.001 ***
## WA.ImV.-WA.WRF          0.123 [ 0.025,  0.219]  .014 *  
## WA.ImV.-WA.InV.         0.318 [ 0.226,  0.403] <.001 ***
## WA.ImV.-WA.EV          -0.047 [-0.144,  0.052]  .354    
## WA.ImV.-WA.SlpQlV.      0.138 [ 0.041,  0.233]  .006 ** 
## WA.ImV.-WA.RdV.        -0.038 [-0.137,  0.062]  .456    
## WA.ImV.-WA.PR          -0.074 [-0.173,  0.025]  .143    
## WA.ImV.-WA.ER          -0.007 [-0.106,  0.093]  .898    
## WA.ImV.-WA.SlpQnV.     -0.083 [-0.185,  0.020]  .115    
## WA.WA-WA.TA             0.221 [ 0.126,  0.313] <.001 ***
## WA.WA-WA.WRF            0.424 [ 0.340,  0.501] <.001 ***
## WA.WA-WA.InV.           0.247 [ 0.153,  0.338] <.001 ***
## WA.WA-WA.EV             0.112 [ 0.014,  0.208]  .025 *  
## WA.WA-WA.SlpQlV.        0.041 [-0.057,  0.139]  .412    
## WA.WA-WA.RdV.           0.077 [-0.023,  0.175]  .130    
## WA.WA-WA.PR             0.031 [-0.069,  0.130]  .549    
## WA.WA-WA.ER             0.073 [-0.027,  0.171]  .151    
## WA.WA-WA.SlpQnV.        0.083 [-0.020,  0.185]  .115    
## WA.TA-WA.WRF            0.348 [ 0.259,  0.432] <.001 ***
## WA.TA-WA.InV.           0.424 [ 0.340,  0.501] <.001 ***
## WA.TA-WA.EV             0.049 [-0.050,  0.146]  .334    
## WA.TA-WA.SlpQlV.        0.117 [ 0.019,  0.213]  .020 *  
## WA.TA-WA.RdV.           0.103 [ 0.003,  0.200]  .043 *  
## WA.TA-WA.PR             0.050 [-0.049,  0.149]  .324    
## WA.TA-WA.ER             0.089 [-0.010,  0.187]  .080 .  
## WA.TA-WA.SlpQnV.       -0.059 [-0.161,  0.044]  .263    
## WA.WRF-WA.InV.          0.222 [ 0.126,  0.313] <.001 ***
## WA.WRF-WA.EV            0.130 [ 0.032,  0.225]  .010 ** 
## WA.WRF-WA.SlpQlV.      -0.020 [-0.118,  0.079]  .694    
## WA.WRF-WA.RdV.          0.096 [-0.003,  0.194]  .058 .  
## WA.WRF-WA.PR           -0.016 [-0.115,  0.083]  .752    
## WA.WRF-WA.ER            0.122 [ 0.023,  0.219]  .016 *  
## WA.WRF-WA.SlpQnV.       0.035 [-0.068,  0.138]  .504    
## WA.InV.-WA.EV          -0.036 [-0.134,  0.062]  .473    
## WA.InV.-WA.SlpQlV.      0.153 [ 0.056,  0.248]  .002 ** 
## WA.InV.-WA.RdV.         0.132 [ 0.033,  0.229]  .009 ** 
## WA.InV.-WA.PR           0.070 [-0.029,  0.169]  .166    
## WA.InV.-WA.ER           0.120 [ 0.021,  0.216]  .018 *  
## WA.InV.-WA.SlpQnV.     -0.111 [-0.212, -0.008]  .035 *  
## WA.EV-WA.SlpQlV.       -0.026 [-0.124,  0.073]  .611    
## WA.EV-WA.RdV.           0.115 [ 0.016,  0.212]  .024 *  
## WA.EV-WA.PR             0.115 [ 0.016,  0.212]  .023 *  
## WA.EV-WA.ER             0.073 [-0.027,  0.171]  .152    
## WA.EV-WA.SlpQnV.        0.054 [-0.050,  0.156]  .307    
## WA.SlpQlV.-WA.RdV.      0.098 [-0.001,  0.196]  .054 .  
## WA.SlpQlV.-WA.PR        0.048 [-0.052,  0.146]  .350    
## WA.SlpQlV.-WA.ER        0.092 [-0.008,  0.189]  .071 .  
## WA.SlpQlV.-WA.SlpQnV.  -0.262 [-0.356, -0.163] <.001 ***
## WA.RdV.-WA.PR           0.585 [ 0.515,  0.647] <.001 ***
## WA.RdV.-WA.ER           0.878 [ 0.853,  0.899] <.001 ***
## WA.RdV.-WA.SlpQnV.     -0.058 [-0.162,  0.046]  .275    
## WA.PR-WA.ER             0.125 [ 0.026,  0.222]  .014 *  
## WA.PR-WA.SlpQnV.       -0.018 [-0.122,  0.087]  .743    
## WA.ER-WA.SlpQnV.       -0.064 [-0.167,  0.041]  .231    
## ────────────────────────────────────────────────────────
## 
## Between-Level Correlation [95% CI]:
## ────────────────────────────────────────────────────────
##                             r         [95% CI]     p    
## ────────────────────────────────────────────────────────
## WA.GV-WP.SB             0.506 [ 0.373,  0.618] <.001 ***
## WA.GV-WP.SP             0.467 [ 0.329,  0.586] <.001 ***
## WA.GV-WP.SN             0.483 [ 0.347,  0.600] <.001 ***
## WA.GV-WP.BV             0.478 [ 0.341,  0.595] <.001 ***
## WA.GV-WP.JC             0.321 [ 0.166,  0.460] <.001 ***
## WA.GV-WP.CP             0.469 [ 0.331,  0.588] <.001 ***
## WA.GV-WP.PrbIV.         0.356 [ 0.204,  0.491] <.001 ***
## WA.GV-WP.IS             0.481 [ 0.344,  0.597] <.001 ***
## WA.GV-WP.IG             0.424 [ 0.280,  0.549] <.001 ***
## WA.GV-WP.SL             0.401 [ 0.254,  0.530] <.001 ***
## WA.GV-WP.OL             0.349 [ 0.196,  0.485] <.001 ***
## WA.GV-WP.AS             0.393 [ 0.246,  0.523] <.001 ***
## WA.GV-WP.PrfIV.         0.325 [ 0.171,  0.464] <.001 ***
## WA.GV-WP.TC             0.320 [ 0.165,  0.460] <.001 ***
## WA.GV-WA.WRV            0.504 [ 0.374,  0.614] <.001 ***
## WA.GV-WA.PW             0.490 [ 0.358,  0.602] <.001 ***
## WA.GV-WA.NW             0.346 [ 0.197,  0.479] <.001 ***
## WA.GV-WA.RmV.           0.537 [ 0.413,  0.642] <.001 ***
## WA.GV-WA.PA             0.430 [ 0.290,  0.552] <.001 ***
## WA.GV-WA.NA            -0.100 [-0.255,  0.061]  .224    
## WA.GV-WA.ImV.           0.408 [ 0.266,  0.533] <.001 ***
## WA.GV-WA.WA             0.416 [ 0.275,  0.540] <.001 ***
## WA.GV-WA.TA             0.477 [ 0.344,  0.592] <.001 ***
## WA.GV-WA.WRF            0.396 [ 0.252,  0.523] <.001 ***
## WA.GV-WA.InV.           0.445 [ 0.307,  0.565] <.001 ***
## WA.GV-WA.EV             0.095 [-0.066,  0.251]  .245    
## WA.GV-WA.SlpQlV.        0.065 [-0.096,  0.222]  .430    
## WA.GV-WA.RdV.           0.136 [-0.025,  0.290]  .098 .  
## WA.GV-WA.PR             0.076 [-0.086,  0.233]  .357    
## WA.GV-WA.ER             0.131 [-0.030,  0.285]  .110    
## WA.GV-WA.SlpQnV.        0.065 [-0.101,  0.227]  .442    
## WP.SB-WP.SP             0.929 [ 0.902,  0.948] <.001 ***
## WP.SB-WP.SN             0.952 [ 0.934,  0.966] <.001 ***
## WP.SB-WP.BV             0.533 [ 0.405,  0.641] <.001 ***
## WP.SB-WP.JC             0.500 [ 0.366,  0.613] <.001 ***
## WP.SB-WP.CP             0.562 [ 0.438,  0.664] <.001 ***
## WP.SB-WP.PrbIV.         0.498 [ 0.364,  0.612] <.001 ***
## WP.SB-WP.IS             0.367 [ 0.217,  0.501] <.001 ***
## WP.SB-WP.IG             0.578 [ 0.458,  0.678] <.001 ***
## WP.SB-WP.SL             0.395 [ 0.248,  0.525] <.001 ***
## WP.SB-WP.OL             0.394 [ 0.246,  0.524] <.001 ***
## WP.SB-WP.AS             0.313 [ 0.158,  0.454] <.001 ***
## WP.SB-WP.PrfIV.         0.531 [ 0.403,  0.639] <.001 ***
## WP.SB-WP.TC             0.481 [ 0.345,  0.598] <.001 ***
## WP.SB-WA.WRV            0.633 [ 0.523,  0.722] <.001 ***
## WP.SB-WA.PW             0.646 [ 0.539,  0.732] <.001 ***
## WP.SB-WA.NW             0.404 [ 0.258,  0.533] <.001 ***
## WP.SB-WA.RmV.           0.649 [ 0.543,  0.734] <.001 ***
## WP.SB-WA.PA             0.498 [ 0.364,  0.611] <.001 ***
## WP.SB-WA.NA            -0.039 [-0.201,  0.125]  .633    
## WP.SB-WA.ImV.           0.597 [ 0.480,  0.693] <.001 ***
## WP.SB-WA.WA             0.567 [ 0.445,  0.669] <.001 ***
## WP.SB-WA.TA             0.619 [ 0.507,  0.711] <.001 ***
## WP.SB-WA.WRF            0.546 [ 0.420,  0.652] <.001 ***
## WP.SB-WA.InV.           0.602 [ 0.486,  0.697] <.001 ***
## WP.SB-WA.EV             0.154 [-0.010,  0.310]  .059 .  
## WP.SB-WA.SlpQlV.       -0.013 [-0.177,  0.151]  .871    
## WP.SB-WA.RdV.           0.168 [ 0.004,  0.323]  .040 *  
## WP.SB-WA.PR             0.074 [-0.091,  0.236]  .366    
## WP.SB-WA.ER             0.170 [ 0.006,  0.325]  .037 *  
## WP.SB-WA.SlpQnV.        0.015 [-0.154,  0.183]  .858    
## WP.SP-WP.SN             0.771 [ 0.695,  0.830] <.001 ***
## WP.SP-WP.BV             0.583 [ 0.463,  0.681] <.001 ***
## WP.SP-WP.JC             0.508 [ 0.376,  0.620] <.001 ***
## WP.SP-WP.CP             0.529 [ 0.400,  0.637] <.001 ***
## WP.SP-WP.PrbIV.         0.484 [ 0.348,  0.600] <.001 ***
## WP.SP-WP.IS             0.315 [ 0.159,  0.455] <.001 ***
## WP.SP-WP.IG             0.552 [ 0.427,  0.656] <.001 ***
## WP.SP-WP.SL             0.348 [ 0.196,  0.484] <.001 ***
## WP.SP-WP.OL             0.344 [ 0.191,  0.481] <.001 ***
## WP.SP-WP.AS             0.281 [ 0.123,  0.425] <.001 ***
## WP.SP-WP.PrfIV.         0.526 [ 0.396,  0.635] <.001 ***
## WP.SP-WP.TC             0.472 [ 0.334,  0.590] <.001 ***
## WP.SP-WA.WRV            0.586 [ 0.467,  0.684] <.001 ***
## WP.SP-WA.PW             0.653 [ 0.548,  0.738] <.001 ***
## WP.SP-WA.NW             0.319 [ 0.164,  0.459] <.001 ***
## WP.SP-WA.RmV.           0.641 [ 0.533,  0.728] <.001 ***
## WP.SP-WA.PA             0.534 [ 0.406,  0.641] <.001 ***
## WP.SP-WA.NA            -0.078 [-0.239,  0.087]  .341    
## WP.SP-WA.ImV.           0.646 [ 0.540,  0.732] <.001 ***
## WP.SP-WA.WA             0.578 [ 0.457,  0.677] <.001 ***
## WP.SP-WA.TA             0.628 [ 0.518,  0.718] <.001 ***
## WP.SP-WA.WRF            0.564 [ 0.442,  0.666] <.001 ***
## WP.SP-WA.InV.           0.585 [ 0.467,  0.684] <.001 ***
## WP.SP-WA.EV             0.151 [-0.013,  0.307]  .064 .  
## WP.SP-WA.SlpQlV.       -0.023 [-0.186,  0.141]  .776    
## WP.SP-WA.RdV.           0.132 [-0.033,  0.290]  .108    
## WP.SP-WA.PR             0.065 [-0.101,  0.226]  .432    
## WP.SP-WA.ER             0.130 [-0.035,  0.288]  .114    
## WP.SP-WA.SlpQnV.       -0.019 [-0.187,  0.150]  .823    
## WP.SN-WP.BV             0.436 [ 0.293,  0.560] <.001 ***
## WP.SN-WP.JC             0.440 [ 0.297,  0.563] <.001 ***
## WP.SN-WP.CP             0.529 [ 0.400,  0.638] <.001 ***
## WP.SN-WP.PrbIV.         0.456 [ 0.316,  0.577] <.001 ***
## WP.SN-WP.IS             0.372 [ 0.221,  0.504] <.001 ***
## WP.SN-WP.IG             0.538 [ 0.411,  0.645] <.001 ***
## WP.SN-WP.SL             0.392 [ 0.244,  0.522] <.001 ***
## WP.SN-WP.OL             0.393 [ 0.246,  0.523] <.001 ***
## WP.SN-WP.AS             0.307 [ 0.151,  0.448] <.001 ***
## WP.SN-WP.PrfIV.         0.479 [ 0.342,  0.596] <.001 ***
## WP.SN-WP.TC             0.438 [ 0.296,  0.562] <.001 ***
## WP.SN-WA.WRV            0.604 [ 0.489,  0.699] <.001 ***
## WP.SN-WA.PW             0.571 [ 0.450,  0.672] <.001 ***
## WP.SN-WA.NW             0.431 [ 0.288,  0.555] <.001 ***
## WP.SN-WA.RmV.           0.586 [ 0.467,  0.684] <.001 ***
## WP.SN-WA.PA             0.415 [ 0.270,  0.542] <.001 ***
## WP.SN-WA.NA            -0.003 [-0.166,  0.161]  .971    
## WP.SN-WA.ImV.           0.493 [ 0.358,  0.607] <.001 ***
## WP.SN-WA.WA             0.499 [ 0.365,  0.612] <.001 ***
## WP.SN-WA.TA             0.546 [ 0.420,  0.652] <.001 ***
## WP.SN-WA.WRF            0.473 [ 0.336,  0.591] <.001 ***
## WP.SN-WA.InV.           0.551 [ 0.426,  0.656] <.001 ***
## WP.SN-WA.EV             0.140 [-0.024,  0.297]  .086 .  
## WP.SN-WA.SlpQlV.       -0.004 [-0.167,  0.160]  .963    
## WP.SN-WA.RdV.           0.180 [ 0.016,  0.334]  .027 *  
## WP.SN-WA.PR             0.074 [-0.091,  0.236]  .366    
## WP.SN-WA.ER             0.186 [ 0.022,  0.340]  .023 *  
## WP.SN-WA.SlpQnV.        0.042 [-0.128,  0.209]  .622    
## WP.BV-WP.JC             0.697 [ 0.603,  0.773] <.001 ***
## WP.BV-WP.CP             0.693 [ 0.597,  0.769] <.001 ***
## WP.BV-WP.PrbIV.         0.648 [ 0.542,  0.734] <.001 ***
## WP.BV-WP.IS             0.496 [ 0.362,  0.610] <.001 ***
## WP.BV-WP.IG             0.672 [ 0.571,  0.753] <.001 ***
## WP.BV-WP.SL             0.545 [ 0.419,  0.651] <.001 ***
## WP.BV-WP.OL             0.548 [ 0.422,  0.653] <.001 ***
## WP.BV-WP.AS             0.426 [ 0.282,  0.551] <.001 ***
## WP.BV-WP.PrfIV.         0.681 [ 0.582,  0.760] <.001 ***
## WP.BV-WP.TC             0.702 [ 0.608,  0.776] <.001 ***
## WP.BV-WA.WRV            0.675 [ 0.575,  0.755] <.001 ***
## WP.BV-WA.PW             0.698 [ 0.603,  0.773] <.001 ***
## WP.BV-WA.NW             0.422 [ 0.278,  0.548] <.001 ***
## WP.BV-WA.RmV.           0.719 [ 0.629,  0.790] <.001 ***
## WP.BV-WA.PA             0.502 [ 0.369,  0.615] <.001 ***
## WP.BV-WA.NA            -0.008 [-0.171,  0.156]  .924    
## WP.BV-WA.ImV.           0.703 [ 0.609,  0.777] <.001 ***
## WP.BV-WA.WA             0.610 [ 0.495,  0.703] <.001 ***
## WP.BV-WA.TA             0.754 [ 0.674,  0.817] <.001 ***
## WP.BV-WA.WRF            0.640 [ 0.532,  0.728] <.001 ***
## WP.BV-WA.InV.           0.740 [ 0.655,  0.806] <.001 ***
## WP.BV-WA.EV             0.219 [ 0.058,  0.370]  .007 ** 
## WP.BV-WA.SlpQlV.        0.124 [-0.040,  0.282]  .129    
## WP.BV-WA.RdV.           0.154 [-0.011,  0.310]  .060 .  
## WP.BV-WA.PR             0.053 [-0.112,  0.215]  .522    
## WP.BV-WA.ER             0.164 [-0.000,  0.320]  .045 *  
## WP.BV-WA.SlpQnV.       -0.065 [-0.231,  0.105]  .444    
## WP.JC-WP.CP             0.684 [ 0.585,  0.762] <.001 ***
## WP.JC-WP.PrbIV.         0.640 [ 0.532,  0.727] <.001 ***
## WP.JC-WP.IS             0.458 [ 0.318,  0.578] <.001 ***
## WP.JC-WP.IG             0.679 [ 0.580,  0.759] <.001 ***
## WP.JC-WP.SL             0.566 [ 0.443,  0.667] <.001 ***
## WP.JC-WP.OL             0.589 [ 0.471,  0.687] <.001 ***
## WP.JC-WP.AS             0.411 [ 0.265,  0.538] <.001 ***
## WP.JC-WP.PrfIV.         0.699 [ 0.605,  0.774] <.001 ***
## WP.JC-WP.TC             0.756 [ 0.677,  0.819] <.001 ***
## WP.JC-WA.WRV            0.686 [ 0.589,  0.764] <.001 ***
## WP.JC-WA.PW             0.605 [ 0.490,  0.700] <.001 ***
## WP.JC-WA.NW             0.533 [ 0.405,  0.641] <.001 ***
## WP.JC-WA.RmV.           0.623 [ 0.511,  0.713] <.001 ***
## WP.JC-WA.PA             0.536 [ 0.409,  0.643] <.001 ***
## WP.JC-WA.NA            -0.041 [-0.203,  0.124]  .620    
## WP.JC-WA.ImV.           0.680 [ 0.581,  0.759] <.001 ***
## WP.JC-WA.WA             0.591 [ 0.473,  0.688] <.001 ***
## WP.JC-WA.TA             0.636 [ 0.527,  0.724] <.001 ***
## WP.JC-WA.WRF            0.616 [ 0.503,  0.708] <.001 ***
## WP.JC-WA.InV.           0.622 [ 0.510,  0.713] <.001 ***
## WP.JC-WA.EV             0.308 [ 0.152,  0.449] <.001 ***
## WP.JC-WA.SlpQlV.        0.042 [-0.123,  0.204]  .611    
## WP.JC-WA.RdV.           0.242 [ 0.082,  0.391]  .003 ** 
## WP.JC-WA.PR             0.191 [ 0.027,  0.344]  .019 *  
## WP.JC-WA.ER             0.203 [ 0.040,  0.356]  .013 *  
## WP.JC-WA.SlpQnV.        0.065 [-0.104,  0.231]  .440    
## WP.CP-WP.PrbIV.         0.868 [ 0.822,  0.904] <.001 ***
## WP.CP-WP.IS             0.829 [ 0.770,  0.874] <.001 ***
## WP.CP-WP.IG             0.946 [ 0.926,  0.961] <.001 ***
## WP.CP-WP.SL             0.602 [ 0.486,  0.697] <.001 ***
## WP.CP-WP.OL             0.593 [ 0.476,  0.690] <.001 ***
## WP.CP-WP.AS             0.487 [ 0.351,  0.602] <.001 ***
## WP.CP-WP.PrfIV.         0.713 [ 0.622,  0.785] <.001 ***
## WP.CP-WP.TC             0.711 [ 0.619,  0.783] <.001 ***
## WP.CP-WA.WRV            0.686 [ 0.588,  0.763] <.001 ***
## WP.CP-WA.PW             0.619 [ 0.507,  0.711] <.001 ***
## WP.CP-WA.NW             0.518 [ 0.388,  0.629] <.001 ***
## WP.CP-WA.RmV.           0.727 [ 0.639,  0.796] <.001 ***
## WP.CP-WA.PA             0.578 [ 0.457,  0.677] <.001 ***
## WP.CP-WA.NA            -0.023 [-0.186,  0.141]  .781    
## WP.CP-WA.ImV.           0.749 [ 0.667,  0.813] <.001 ***
## WP.CP-WA.WA             0.653 [ 0.548,  0.738] <.001 ***
## WP.CP-WA.TA             0.685 [ 0.587,  0.763] <.001 ***
## WP.CP-WA.WRF            0.609 [ 0.495,  0.703] <.001 ***
## WP.CP-WA.InV.           0.638 [ 0.530,  0.726] <.001 ***
## WP.CP-WA.EV             0.272 [ 0.114,  0.417] <.001 ***
## WP.CP-WA.SlpQlV.        0.138 [-0.026,  0.295]  .091 .  
## WP.CP-WA.RdV.           0.232 [ 0.071,  0.382]  .004 ** 
## WP.CP-WA.PR             0.141 [-0.024,  0.298]  .086 .  
## WP.CP-WA.ER             0.217 [ 0.055,  0.368]  .008 ** 
## WP.CP-WA.SlpQnV.       -0.011 [-0.179,  0.158]  .897    
## WP.PrbIV.-WP.IS         0.617 [ 0.505,  0.709] <.001 ***
## WP.PrbIV.-WP.IG         0.739 [ 0.654,  0.805] <.001 ***
## WP.PrbIV.-WP.SL         0.538 [ 0.410,  0.645] <.001 ***
## WP.PrbIV.-WP.OL         0.529 [ 0.400,  0.638] <.001 ***
## WP.PrbIV.-WP.AS         0.437 [ 0.294,  0.560] <.001 ***
## WP.PrbIV.-WP.PrfIV.     0.617 [ 0.504,  0.709] <.001 ***
## WP.PrbIV.-WP.TC         0.640 [ 0.532,  0.727] <.001 ***
## WP.PrbIV.-WA.WRV        0.610 [ 0.496,  0.704] <.001 ***
## WP.PrbIV.-WA.PW         0.581 [ 0.461,  0.680] <.001 ***
## WP.PrbIV.-WA.NW         0.432 [ 0.289,  0.556] <.001 ***
## WP.PrbIV.-WA.RmV.       0.667 [ 0.566,  0.749] <.001 ***
## WP.PrbIV.-WA.PA         0.497 [ 0.363,  0.611] <.001 ***
## WP.PrbIV.-WA.NA         0.000 [-0.163,  0.164]  .999    
## WP.PrbIV.-WA.ImV.       0.671 [ 0.570,  0.752] <.001 ***
## WP.PrbIV.-WA.WA         0.575 [ 0.454,  0.675] <.001 ***
## WP.PrbIV.-WA.TA         0.615 [ 0.502,  0.707] <.001 ***
## WP.PrbIV.-WA.WRF        0.555 [ 0.431,  0.659] <.001 ***
## WP.PrbIV.-WA.InV.       0.564 [ 0.441,  0.666] <.001 ***
## WP.PrbIV.-WA.EV         0.284 [ 0.126,  0.428] <.001 ***
## WP.PrbIV.-WA.SlpQlV.    0.135 [-0.030,  0.292]  .099 .  
## WP.PrbIV.-WA.RdV.       0.165 [ 0.001,  0.321]  .043 *  
## WP.PrbIV.-WA.PR         0.098 [-0.067,  0.258]  .232    
## WP.PrbIV.-WA.ER         0.155 [-0.010,  0.311]  .059 .  
## WP.PrbIV.-WA.SlpQnV.   -0.045 [-0.212,  0.124]  .592    
## WP.IS-WP.IG             0.675 [ 0.575,  0.755] <.001 ***
## WP.IS-WP.SL             0.534 [ 0.406,  0.642] <.001 ***
## WP.IS-WP.OL             0.467 [ 0.329,  0.586] <.001 ***
## WP.IS-WP.AS             0.521 [ 0.391,  0.631] <.001 ***
## WP.IS-WP.PrfIV.         0.463 [ 0.324,  0.582] <.001 ***
## WP.IS-WP.TC             0.470 [ 0.332,  0.588] <.001 ***
## WP.IS-WA.WRV            0.470 [ 0.332,  0.588] <.001 ***
## WP.IS-WA.PW             0.397 [ 0.249,  0.526] <.001 ***
## WP.IS-WA.NW             0.383 [ 0.234,  0.514] <.001 ***
## WP.IS-WA.RmV.           0.524 [ 0.394,  0.633] <.001 ***
## WP.IS-WA.PA             0.446 [ 0.304,  0.568] <.001 ***
## WP.IS-WA.NA            -0.025 [-0.188,  0.139]  .760    
## WP.IS-WA.ImV.           0.524 [ 0.395,  0.634] <.001 ***
## WP.IS-WA.WA             0.475 [ 0.337,  0.592] <.001 ***
## WP.IS-WA.TA             0.467 [ 0.328,  0.586] <.001 ***
## WP.IS-WA.WRF            0.377 [ 0.227,  0.509] <.001 ***
## WP.IS-WA.InV.           0.413 [ 0.268,  0.540] <.001 ***
## WP.IS-WA.EV             0.209 [ 0.047,  0.360]  .010 ** 
## WP.IS-WA.SlpQlV.        0.219 [ 0.057,  0.369]  .007 ** 
## WP.IS-WA.RdV.           0.260 [ 0.100,  0.407]  .001 ** 
## WP.IS-WA.PR             0.166 [ 0.001,  0.321]  .043 *  
## WP.IS-WA.ER             0.241 [ 0.080,  0.390]  .003 ** 
## WP.IS-WA.SlpQnV.       -0.050 [-0.216,  0.120]  .558    
## WP.IG-WP.SL             0.542 [ 0.415,  0.648] <.001 ***
## WP.IG-WP.OL             0.566 [ 0.444,  0.668] <.001 ***
## WP.IG-WP.AS             0.390 [ 0.242,  0.520] <.001 ***
## WP.IG-WP.PrfIV.         0.744 [ 0.661,  0.809] <.001 ***
## WP.IG-WP.TC             0.724 [ 0.635,  0.793] <.001 ***
## WP.IG-WA.WRV            0.693 [ 0.597,  0.769] <.001 ***
## WP.IG-WA.PW             0.624 [ 0.513,  0.714] <.001 ***
## WP.IG-WA.NW             0.526 [ 0.396,  0.635] <.001 ***
## WP.IG-WA.RmV.           0.709 [ 0.617,  0.782] <.001 ***
## WP.IG-WA.PA             0.567 [ 0.445,  0.669] <.001 ***
## WP.IG-WA.NA            -0.030 [-0.192,  0.135]  .719    
## WP.IG-WA.ImV.           0.749 [ 0.667,  0.813] <.001 ***
## WP.IG-WA.WA             0.648 [ 0.542,  0.734] <.001 ***
## WP.IG-WA.TA             0.690 [ 0.594,  0.767] <.001 ***
## WP.IG-WA.WRF            0.630 [ 0.520,  0.719] <.001 ***
## WP.IG-WA.InV.           0.660 [ 0.556,  0.743] <.001 ***
## WP.IG-WA.EV             0.239 [ 0.079,  0.388]  .003 ** 
## WP.IG-WA.SlpQlV.        0.067 [-0.098,  0.228]  .416    
## WP.IG-WA.RdV.           0.204 [ 0.041,  0.356]  .012 *  
## WP.IG-WA.PR             0.121 [-0.044,  0.279]  .141    
## WP.IG-WA.ER             0.192 [ 0.029,  0.345]  .019 *  
## WP.IG-WA.SlpQnV.        0.031 [-0.138,  0.198]  .714    
## WP.SL-WP.OL             0.944 [ 0.924,  0.960] <.001 ***
## WP.SL-WP.AS             0.871 [ 0.825,  0.906] <.001 ***
## WP.SL-WP.PrfIV.         0.717 [ 0.627,  0.788] <.001 ***
## WP.SL-WP.TC             0.651 [ 0.546,  0.737] <.001 ***
## WP.SL-WA.WRV            0.484 [ 0.348,  0.600] <.001 ***
## WP.SL-WA.PW             0.440 [ 0.298,  0.563] <.001 ***
## WP.SL-WA.NW             0.363 [ 0.212,  0.497] <.001 ***
## WP.SL-WA.RmV.           0.470 [ 0.332,  0.589] <.001 ***
## WP.SL-WA.PA             0.338 [ 0.184,  0.475] <.001 ***
## WP.SL-WA.NA             0.061 [-0.104,  0.222]  .457    
## WP.SL-WA.ImV.           0.440 [ 0.298,  0.563] <.001 ***
## WP.SL-WA.WA             0.373 [ 0.223,  0.506] <.001 ***
## WP.SL-WA.TA             0.536 [ 0.409,  0.643] <.001 ***
## WP.SL-WA.WRF            0.360 [ 0.209,  0.494] <.001 ***
## WP.SL-WA.InV.           0.576 [ 0.456,  0.676] <.001 ***
## WP.SL-WA.EV             0.257 [ 0.097,  0.403]  .001 ** 
## WP.SL-WA.SlpQlV.        0.195 [ 0.033,  0.348]  .016 *  
## WP.SL-WA.RdV.           0.290 [ 0.132,  0.434] <.001 ***
## WP.SL-WA.PR             0.108 [-0.057,  0.267]  .190    
## WP.SL-WA.ER             0.308 [ 0.151,  0.449] <.001 ***
## WP.SL-WA.SlpQnV.        0.057 [-0.112,  0.224]  .497    
## WP.OL-WP.AS             0.662 [ 0.558,  0.745] <.001 ***
## WP.OL-WP.PrfIV.         0.711 [ 0.620,  0.784] <.001 ***
## WP.OL-WP.TC             0.682 [ 0.584,  0.761] <.001 ***
## WP.OL-WA.WRV            0.520 [ 0.389,  0.630] <.001 ***
## WP.OL-WA.PW             0.474 [ 0.337,  0.592] <.001 ***
## WP.OL-WA.NW             0.388 [ 0.240,  0.519] <.001 ***
## WP.OL-WA.RmV.           0.480 [ 0.344,  0.597] <.001 ***
## WP.OL-WA.PA             0.380 [ 0.231,  0.512] <.001 ***
## WP.OL-WA.NA             0.128 [-0.036,  0.285]  .118    
## WP.OL-WA.ImV.           0.482 [ 0.345,  0.598] <.001 ***
## WP.OL-WA.WA             0.415 [ 0.270,  0.542] <.001 ***
## WP.OL-WA.TA             0.570 [ 0.448,  0.671] <.001 ***
## WP.OL-WA.WRF            0.424 [ 0.279,  0.549] <.001 ***
## WP.OL-WA.InV.           0.608 [ 0.493,  0.702] <.001 ***
## WP.OL-WA.EV             0.241 [ 0.080,  0.389]  .003 ** 
## WP.OL-WA.SlpQlV.        0.137 [-0.027,  0.294]  .094 .  
## WP.OL-WA.RdV.           0.285 [ 0.127,  0.429] <.001 ***
## WP.OL-WA.PR             0.126 [-0.039,  0.284]  .126    
## WP.OL-WA.ER             0.292 [ 0.134,  0.435] <.001 ***
## WP.OL-WA.SlpQnV.        0.049 [-0.121,  0.215]  .566    
## WP.AS-WP.PrfIV.         0.574 [ 0.453,  0.674] <.001 ***
## WP.AS-WP.TC             0.467 [ 0.329,  0.586] <.001 ***
## WP.AS-WA.WRV            0.328 [ 0.174,  0.467] <.001 ***
## WP.AS-WA.PW             0.295 [ 0.138,  0.438] <.001 ***
## WP.AS-WA.NW             0.249 [ 0.089,  0.397]  .002 ** 
## WP.AS-WA.RmV.           0.356 [ 0.204,  0.491] <.001 ***
## WP.AS-WA.PA             0.203 [ 0.040,  0.355]  .013 *  
## WP.AS-WA.NA            -0.052 [-0.214,  0.113]  .527    
## WP.AS-WA.ImV.           0.285 [ 0.127,  0.428] <.001 ***
## WP.AS-WA.WA             0.232 [ 0.071,  0.381]  .004 ** 
## WP.AS-WA.TA             0.373 [ 0.223,  0.505] <.001 ***
## WP.AS-WA.WRF            0.188 [ 0.026,  0.341]  .021 *  
## WP.AS-WA.InV.           0.408 [ 0.261,  0.535] <.001 ***
## WP.AS-WA.EV             0.227 [ 0.066,  0.376]  .005 ** 
## WP.AS-WA.SlpQlV.        0.242 [ 0.081,  0.390]  .003 ** 
## WP.AS-WA.RdV.           0.237 [ 0.075,  0.386]  .004 ** 
## WP.AS-WA.PR             0.058 [-0.107,  0.220]  .480    
## WP.AS-WA.ER             0.266 [ 0.107,  0.412] <.001 ***
## WP.AS-WA.SlpQnV.        0.059 [-0.111,  0.225]  .487    
## WP.PrfIV.-WP.TC         0.766 [ 0.689,  0.826] <.001 ***
## WP.PrfIV.-WA.WRV        0.576 [ 0.455,  0.676] <.001 ***
## WP.PrfIV.-WA.PW         0.540 [ 0.413,  0.647] <.001 ***
## WP.PrfIV.-WA.NW         0.415 [ 0.270,  0.542] <.001 ***
## WP.PrfIV.-WA.RmV.       0.577 [ 0.456,  0.676] <.001 ***
## WP.PrfIV.-WA.PA         0.458 [ 0.318,  0.578] <.001 ***
## WP.PrfIV.-WA.NA         0.013 [-0.151,  0.176]  .875    
## WP.PrfIV.-WA.ImV.       0.629 [ 0.519,  0.719] <.001 ***
## WP.PrfIV.-WA.WA         0.530 [ 0.401,  0.638] <.001 ***
## WP.PrfIV.-WA.TA         0.650 [ 0.544,  0.735] <.001 ***
## WP.PrfIV.-WA.WRF        0.524 [ 0.395,  0.634] <.001 ***
## WP.PrfIV.-WA.InV.       0.727 [ 0.639,  0.796] <.001 ***
## WP.PrfIV.-WA.EV         0.253 [ 0.093,  0.400]  .002 ** 
## WP.PrfIV.-WA.SlpQlV.    0.121 [-0.043,  0.279]  .139    
## WP.PrfIV.-WA.RdV.       0.281 [ 0.123,  0.426] <.001 ***
## WP.PrfIV.-WA.PR         0.186 [ 0.022,  0.339]  .023 *  
## WP.PrfIV.-WA.ER         0.255 [ 0.095,  0.402]  .002 ** 
## WP.PrfIV.-WA.SlpQnV.    0.065 [-0.104,  0.231]  .441    
## WP.TC-WA.WRV            0.608 [ 0.494,  0.702] <.001 ***
## WP.TC-WA.PW             0.599 [ 0.482,  0.694] <.001 ***
## WP.TC-WA.NW             0.410 [ 0.264,  0.538] <.001 ***
## WP.TC-WA.RmV.           0.635 [ 0.526,  0.723] <.001 ***
## WP.TC-WA.PA             0.498 [ 0.364,  0.612] <.001 ***
## WP.TC-WA.NA             0.031 [-0.133,  0.194]  .704    
## WP.TC-WA.ImV.           0.640 [ 0.532,  0.727] <.001 ***
## WP.TC-WA.WA             0.526 [ 0.396,  0.635] <.001 ***
## WP.TC-WA.TA             0.650 [ 0.544,  0.735] <.001 ***
## WP.TC-WA.WRF            0.577 [ 0.457,  0.677] <.001 ***
## WP.TC-WA.InV.           0.711 [ 0.620,  0.784] <.001 ***
## WP.TC-WA.EV             0.285 [ 0.127,  0.428] <.001 ***
## WP.TC-WA.SlpQlV.        0.050 [-0.114,  0.212]  .538    
## WP.TC-WA.RdV.           0.287 [ 0.129,  0.431] <.001 ***
## WP.TC-WA.PR             0.174 [ 0.010,  0.329]  .033 *  
## WP.TC-WA.ER             0.269 [ 0.110,  0.415] <.001 ***
## WP.TC-WA.SlpQnV.        0.021 [-0.148,  0.189]  .803    
## WA.WRV-WA.PW            0.830 [ 0.772,  0.874] <.001 ***
## WA.WRV-WA.NW            0.829 [ 0.772,  0.873] <.001 ***
## WA.WRV-WA.RmV.          0.742 [ 0.661,  0.806] <.001 ***
## WA.WRV-WA.PA            0.523 [ 0.397,  0.630] <.001 ***
## WA.WRV-WA.NA            0.004 [-0.155,  0.164]  .958    
## WA.WRV-WA.ImV.          0.644 [ 0.539,  0.729] <.001 ***
## WA.WRV-WA.WA            0.652 [ 0.549,  0.735] <.001 ***
## WA.WRV-WA.TA            0.702 [ 0.611,  0.775] <.001 ***
## WA.WRV-WA.WRF           0.678 [ 0.581,  0.756] <.001 ***
## WA.WRV-WA.InV.          0.610 [ 0.498,  0.701] <.001 ***
## WA.WRV-WA.EV            0.317 [ 0.165,  0.453] <.001 ***
## WA.WRV-WA.SlpQlV.       0.017 [-0.143,  0.176]  .840    
## WA.WRV-WA.RdV.          0.146 [-0.014,  0.300]  .074 .  
## WA.WRV-WA.PR            0.113 [-0.048,  0.268]  .168    
## WA.WRV-WA.ER            0.124 [-0.037,  0.279]  .130    
## WA.WRV-WA.SlpQnV.       0.092 [-0.074,  0.253]  .277    
## WA.PW-WA.NW             0.376 [ 0.230,  0.505] <.001 ***
## WA.PW-WA.RmV.           0.785 [ 0.715,  0.840] <.001 ***
## WA.PW-WA.PA             0.663 [ 0.563,  0.744] <.001 ***
## WA.PW-WA.NA            -0.096 [-0.252,  0.065]  .242    
## WA.PW-WA.ImV.           0.645 [ 0.542,  0.730] <.001 ***
## WA.PW-WA.WA             0.768 [ 0.693,  0.826] <.001 ***
## WA.PW-WA.TA             0.702 [ 0.610,  0.775] <.001 ***
## WA.PW-WA.WRF            0.779 [ 0.707,  0.835] <.001 ***
## WA.PW-WA.InV.           0.674 [ 0.576,  0.752] <.001 ***
## WA.PW-WA.EV             0.246 [ 0.090,  0.390]  .002 ** 
## WA.PW-WA.SlpQlV.        0.023 [-0.138,  0.182]  .782    
## WA.PW-WA.RdV.           0.166 [ 0.006,  0.318]  .042 *  
## WA.PW-WA.PR             0.126 [-0.035,  0.281]  .124    
## WA.PW-WA.ER             0.143 [-0.018,  0.296]  .082 .  
## WA.PW-WA.SlpQnV.        0.072 [-0.094,  0.234]  .396    
## WA.NW-WA.RmV.           0.446 [ 0.308,  0.565] <.001 ***
## WA.NW-WA.PA             0.205 [ 0.047,  0.353]  .012 *  
## WA.NW-WA.NA             0.103 [-0.058,  0.259]  .208    
## WA.NW-WA.ImV.           0.422 [ 0.282,  0.545] <.001 ***
## WA.NW-WA.WA             0.313 [ 0.162,  0.451] <.001 ***
## WA.NW-WA.TA             0.463 [ 0.328,  0.580] <.001 ***
## WA.NW-WA.WRF            0.346 [ 0.197,  0.479] <.001 ***
## WA.NW-WA.InV.           0.338 [ 0.188,  0.472] <.001 ***
## WA.NW-WA.EV             0.279 [ 0.125,  0.420] <.001 ***
## WA.NW-WA.SlpQlV.        0.005 [-0.155,  0.164]  .953    
## WA.NW-WA.RdV.           0.077 [-0.084,  0.234]  .348    
## WA.NW-WA.PR             0.062 [-0.099,  0.220]  .450    
## WA.NW-WA.ER             0.064 [-0.097,  0.222]  .434    
## WA.NW-WA.SlpQnV.        0.080 [-0.085,  0.242]  .341    
## WA.RmV.-WA.PA           0.654 [ 0.552,  0.737] <.001 ***
## WA.RmV.-WA.NA          -0.152 [-0.304,  0.008]  .062 .  
## WA.RmV.-WA.ImV.         0.697 [ 0.605,  0.771] <.001 ***
## WA.RmV.-WA.WA           0.714 [ 0.625,  0.784] <.001 ***
## WA.RmV.-WA.TA           0.687 [ 0.592,  0.763] <.001 ***
## WA.RmV.-WA.WRF          0.711 [ 0.622,  0.782] <.001 ***
## WA.RmV.-WA.InV.         0.661 [ 0.561,  0.742] <.001 ***
## WA.RmV.-WA.EV           0.249 [ 0.093,  0.393]  .002 ** 
## WA.RmV.-WA.SlpQlV.      0.055 [-0.105,  0.213]  .499    
## WA.RmV.-WA.RdV.         0.205 [ 0.046,  0.353]  .012 *  
## WA.RmV.-WA.PR           0.120 [-0.041,  0.275]  .145    
## WA.RmV.-WA.ER           0.196 [ 0.037,  0.345]  .016 *  
## WA.RmV.-WA.SlpQnV.      0.041 [-0.124,  0.205]  .624    
## WA.PA-WA.NA            -0.260 [-0.403, -0.104]  .001 ** 
## WA.PA-WA.ImV.           0.738 [ 0.655,  0.803] <.001 ***
## WA.PA-WA.WA             0.743 [ 0.662,  0.807] <.001 ***
## WA.PA-WA.TA             0.537 [ 0.413,  0.642] <.001 ***
## WA.PA-WA.WRF            0.702 [ 0.611,  0.775] <.001 ***
## WA.PA-WA.InV.           0.567 [ 0.448,  0.667] <.001 ***
## WA.PA-WA.EV             0.198 [ 0.039,  0.346]  .015 *  
## WA.PA-WA.SlpQlV.       -0.096 [-0.252,  0.065]  .241    
## WA.PA-WA.RdV.           0.125 [-0.036,  0.279]  .128    
## WA.PA-WA.PR             0.112 [-0.049,  0.267]  .173    
## WA.PA-WA.ER             0.100 [-0.062,  0.256]  .225    
## WA.PA-WA.SlpQnV.        0.012 [-0.153,  0.176]  .888    
## WA.NA-WA.ImV.          -0.106 [-0.261,  0.055]  .196    
## WA.NA-WA.WA            -0.082 [-0.239,  0.079]  .317    
## WA.NA-WA.TA             0.031 [-0.129,  0.190]  .703    
## WA.NA-WA.WRF           -0.041 [-0.199,  0.120]  .621    
## WA.NA-WA.InV.          -0.035 [-0.194,  0.125]  .666    
## WA.NA-WA.EV            -0.134 [-0.287,  0.026]  .101    
## WA.NA-WA.SlpQlV.        0.141 [-0.019,  0.294]  .085 .  
## WA.NA-WA.RdV.           0.089 [-0.073,  0.246]  .280    
## WA.NA-WA.PR             0.092 [-0.069,  0.249]  .262    
## WA.NA-WA.ER             0.063 [-0.098,  0.221]  .443    
## WA.NA-WA.SlpQnV.       -0.004 [-0.168,  0.161]  .967    
## WA.ImV.-WA.WA           0.694 [ 0.601,  0.769] <.001 ***
## WA.ImV.-WA.TA           0.731 [ 0.647,  0.797] <.001 ***
## WA.ImV.-WA.WRF          0.683 [ 0.587,  0.760] <.001 ***
## WA.ImV.-WA.InV.         0.748 [ 0.668,  0.811] <.001 ***
## WA.ImV.-WA.EV           0.210 [ 0.052,  0.358]  .010 ** 
## WA.ImV.-WA.SlpQlV.     -0.008 [-0.167,  0.152]  .924    
## WA.ImV.-WA.RdV.         0.190 [ 0.031,  0.340]  .020 *  
## WA.ImV.-WA.PR           0.173 [ 0.013,  0.324]  .035 *  
## WA.ImV.-WA.ER           0.148 [-0.013,  0.301]  .071 .  
## WA.ImV.-WA.SlpQnV.     -0.149 [-0.306,  0.016]  .076 .  
## WA.WA-WA.TA             0.632 [ 0.525,  0.719] <.001 ***
## WA.WA-WA.WRF            0.846 [ 0.793,  0.886] <.001 ***
## WA.WA-WA.InV.           0.592 [ 0.477,  0.687] <.001 ***
## WA.WA-WA.EV             0.192 [ 0.033,  0.341]  .018 *  
## WA.WA-WA.SlpQlV.        0.088 [-0.072,  0.245]  .280    
## WA.WA-WA.RdV.           0.162 [ 0.002,  0.314]  .048 *  
## WA.WA-WA.PR             0.113 [-0.048,  0.268]  .168    
## WA.WA-WA.ER             0.146 [-0.015,  0.299]  .075 .  
## WA.WA-WA.SlpQnV.        0.009 [-0.156,  0.174]  .915    
## WA.TA-WA.WRF            0.650 [ 0.547,  0.734] <.001 ***
## WA.TA-WA.InV.           0.818 [ 0.757,  0.865] <.001 ***
## WA.TA-WA.EV             0.208 [ 0.050,  0.356]  .011 *  
## WA.TA-WA.SlpQlV.        0.072 [-0.089,  0.229]  .381    
## WA.TA-WA.RdV.           0.214 [ 0.056,  0.362]  .009 ** 
## WA.TA-WA.PR             0.162 [ 0.001,  0.314]  .048 *  
## WA.TA-WA.ER             0.184 [ 0.024,  0.334]  .025 *  
## WA.TA-WA.SlpQnV.        0.086 [-0.080,  0.247]  .310    
## WA.WRF-WA.InV.          0.620 [ 0.511,  0.710] <.001 ***
## WA.WRF-WA.EV            0.269 [ 0.114,  0.411] <.001 ***
## WA.WRF-WA.SlpQlV.      -0.088 [-0.244,  0.073]  .285    
## WA.WRF-WA.RdV.          0.155 [-0.005,  0.308]  .058 .  
## WA.WRF-WA.PR            0.134 [-0.027,  0.288]  .102    
## WA.WRF-WA.ER            0.127 [-0.034,  0.281]  .122    
## WA.WRF-WA.SlpQnV.       0.079 [-0.087,  0.241]  .348    
## WA.InV.-WA.EV           0.192 [ 0.033,  0.341]  .018 *  
## WA.InV.-WA.SlpQlV.      0.022 [-0.138,  0.181]  .786    
## WA.InV.-WA.RdV.         0.271 [ 0.116,  0.413] <.001 ***
## WA.InV.-WA.PR           0.240 [ 0.083,  0.385]  .003 ** 
## WA.InV.-WA.ER           0.213 [ 0.055,  0.361]  .009 ** 
## WA.InV.-WA.SlpQnV.      0.075 [-0.091,  0.237]  .377    
## WA.EV-WA.SlpQlV.        0.028 [-0.132,  0.187]  .731    
## WA.EV-WA.RdV.           0.164 [ 0.004,  0.316]  .045 *  
## WA.EV-WA.PR             0.288 [ 0.134,  0.429] <.001 ***
## WA.EV-WA.ER             0.056 [-0.106,  0.214]  .500    
## WA.EV-WA.SlpQnV.       -0.070 [-0.232,  0.096]  .411    
## WA.SlpQlV.-WA.RdV.      0.149 [-0.011,  0.302]  .068 .  
## WA.SlpQlV.-WA.PR        0.146 [-0.015,  0.299]  .075 .  
## WA.SlpQlV.-WA.ER        0.111 [-0.050,  0.266]  .177    
## WA.SlpQlV.-WA.SlpQnV.  -0.249 [-0.397, -0.088]  .003 ** 
## WA.RdV.-WA.PR           0.652 [ 0.549,  0.735] <.001 ***
## WA.RdV.-WA.ER           0.918 [ 0.888,  0.940] <.001 ***
## WA.RdV.-WA.SlpQnV.     -0.071 [-0.234,  0.096]  .402    
## WA.PR-WA.ER             0.297 [ 0.144,  0.437] <.001 ***
## WA.PR-WA.SlpQnV.       -0.104 [-0.265,  0.062]  .217    
## WA.ER-WA.SlpQnV.       -0.033 [-0.197,  0.133]  .696    
## ────────────────────────────────────────────────────────
## 
## 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      0.682                             0.846                                     0.782                                     0.788                0.597           0.756                         0.778                     0.585                 0.613              0.766              0.746                     0.707             0.643                      0.705            0.750              0.725                      0.705                      0.656          0.629              0.666              0.577           0.697              0.689                      0.590               0.688           0.663        0.796            0.625       0.771         0.698     0.761             0.597
## ICC2      0.849                             0.935                                     0.904                                     0.908                0.796           0.891                         0.902                     0.788                 0.807              0.896              0.886                     0.864             0.826                      0.863            0.888              0.874                      0.863                      0.834          0.817              0.840              0.782           0.858              0.854                      0.791               0.853           0.838        0.912            0.815       0.897         0.857     0.892             0.790
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

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)                           3.523 ***                  3.272 ***                          3.523 ***              
##                                      (0.034)                    (0.014)                            (0.034)                 
## WP.CreativeProcessEngagementV_mean    0.864 ***                  1.000 ***                         -0.107                  
##                                      (0.051)                    (0.022)                            (0.087)                 
## Manipulation                          0.053                      0.101 **                          -0.045                  
##                                      (0.055)                    (0.031)                            (0.042)                 
## WP.CreativeProcessEngagementV                                                                       0.973 ***              
##                                                                                                    (0.071)                 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.509                      0.849                              0.622                  
## Conditional R^2                       0.622                      0.849                              0.786                  
## AIC                                 643.960                    135.718                            508.827                  
## BIC                                 663.661                    155.419                            532.468                  
## Num. obs.                           380                        380                                380                      
## Num. groups: B.ID                   144                        144                                144                      
## Var: B.ID (Intercept)                 0.073                      0.000                              0.106                  
## Var: Residual                         0.243                      0.078                              0.138                  
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 380 (29 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WP.InformationSearchV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)   0.098 (0.033)  2.998  .003 **  [ 0.041, 0.164]
## Direct (c')    -0.046 (0.038) -1.218  .223     [-0.113, 0.030]
## Total (c)       0.051 (0.052)  0.989  .322     [-0.043, 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.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)                  1.914 ***                 -0.013                     1.916 ***              
##                             (0.234)                    (0.110)                   (0.234)                 
## WP.learningBehaviorV_mean    0.489 ***                  0.996 ***                 0.409 ***              
##                             (0.070)                    (0.032)                   (0.091)                 
## Manipulation                 0.061                      0.034                     0.058                  
##                             (0.056)                    (0.050)                   (0.056)                 
## WP.learningBehaviorV                                                              0.081                  
##                                                                                  (0.059)                 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                 0.196                      0.718                     0.198                  
## Conditional R^2              0.620                      0.718                     0.622                  
## AIC                        760.987                    499.058                   764.930                  
## BIC                        780.688                    518.759                   788.571                  
## Num. obs.                  380                        380                       380                      
## Num. groups: B.ID          144                        144                       144                      
## Var: B.ID (Intercept)        0.276                      0.000                     0.277                  
## Var: Residual                0.248                      0.206                     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 : 380 (29 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)   0.003 (0.006) 0.462  .644     [-0.007, 0.016]
## Direct (c')     0.071 (0.060) 1.186  .235     [-0.045, 0.179]
## Total (c)       0.073 (0.060) 1.227  .220     [-0.043, 0.181]
## ─────────────────────────────────────────────────────────────
## 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)                           3.364 ***                              3.272 ***                          3.364 ***                          
##                                      (0.053)                                (0.014)                            (0.053)                             
## WP.CreativeProcessEngagementV_mean    0.641 ***                              1.000 ***                          0.491 ***                          
##                                      (0.078)                                (0.022)                            (0.099)                             
## Manipulation                         -0.021                                  0.101 **                          -0.037                              
##                                      (0.036)                                (0.031)                            (0.036)                             
## WP.CreativeProcessEngagementV                                                                                   0.151 *                            
##                                                                                                                (0.060)                             
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.282                                  0.849                              0.284                              
## Conditional R^2                       0.844                                  0.849                              0.848                              
## AIC                                 554.666                                135.718                            554.295                              
## BIC                                 574.367                                155.419                            577.936                              
## Num. obs.                           380                                    380                                380                                  
## Num. groups: B.ID                   144                                    144                                144                                  
## Var: B.ID (Intercept)                 0.359                                  0.000                              0.361                              
## Var: Residual                         0.100                                  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 : 380 (29 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)   0.016 (0.007)  2.113  .035 *   [ 0.004, 0.032]
## Direct (c')    -0.038 (0.032) -1.182  .237     [-0.093, 0.025]
## Total (c)      -0.023 (0.032) -0.700  .484     [-0.079, 0.040]
## ──────────────────────────────────────────────────────────────
## 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 ***                                      3.272 ***                          3.327 ***                                  
##                                      (0.058)                                        (0.014)                            (0.058)                                     
## WP.CreativeProcessEngagementV_mean    0.662 ***                                      1.000 ***                          0.549 ***                                  
##                                      (0.086)                                        (0.022)                            (0.118)                                     
## Manipulation                          0.037                                          0.101 **                           0.025                                      
##                                      (0.047)                                        (0.031)                            (0.048)                                     
## WP.CreativeProcessEngagementV                                                                                           0.113                                      
##                                                                                                                        (0.080)                                     
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.246                                          0.849                              0.246                                      
## Conditional R^2                       0.779                                          0.849                              0.781                                      
## AIC                                 711.874                                        135.718                            715.103                                      
## BIC                                 731.575                                        155.419                            738.744                                      
## Num. obs.                           380                                            380                                380                                          
## Num. groups: B.ID                   144                                            144                                144                                          
## Var: B.ID (Intercept)                 0.416                                          0.000                              0.419                                      
## Var: Residual                         0.172                                          0.078                              0.171                                      
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 380 (29 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)   0.012 (0.008) 1.437  .151     [ 0.001, 0.029]
## Direct (c')     0.023 (0.043) 0.526  .599     [-0.051, 0.107]
## Total (c)       0.035 (0.042) 0.820  .412     [-0.040, 0.116]
## ─────────────────────────────────────────────────────────────
## 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)                           3.392 ***                                      3.272 ***                          3.392 ***                                  
##                                      (0.057)                                        (0.014)                            (0.057)                                     
## WP.CreativeProcessEngagementV_mean    0.626 ***                                      1.000 ***                          0.449 ***                                  
##                                      (0.085)                                        (0.022)                            (0.113)                                     
## Manipulation                         -0.068                                          0.101 **                          -0.087                                      
##                                      (0.045)                                        (0.031)                            (0.045)                                     
## WP.CreativeProcessEngagementV                                                                                           0.177 *                                    
##                                                                                                                        (0.076)                                     
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.234                                          0.849                              0.238                                      
## Conditional R^2                       0.788                                          0.849                              0.792                                      
## AIC                                 683.096                                        135.718                            682.993                                      
## BIC                                 702.797                                        155.419                            706.634                                      
## Num. obs.                           380                                            380                                380                                          
## Num. groups: B.ID                   144                                            144                                144                                          
## Var: B.ID (Intercept)                 0.407                                          0.000                              0.407                                      
## Var: Residual                         0.156                                          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 : 380 (29 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 (0.009)  2.039  .041 *   [ 0.004,  0.038]
## Direct (c')    -0.088 (0.041) -2.179  .029 *   [-0.158, -0.009]
## Total (c)      -0.070 (0.041) -1.728  .084 .   [-0.140,  0.009]
## ───────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="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)                           3.203 ***                 3.272 ***                          3.203 ***             
##                                      (0.045)                   (0.014)                            (0.045)                
## WP.CreativeProcessEngagementV_mean    0.775 ***                 1.000 ***                          0.518 ***             
##                                      (0.067)                   (0.022)                            (0.122)                
## Manipulation                          0.043                     0.101 **                           0.016                 
##                                      (0.061)                   (0.031)                            (0.061)                
## WP.CreativeProcessEngagementV                                                                      0.257 *               
##                                                                                                   (0.102)                
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.363                     0.849                              0.369                 
## Conditional R^2                       0.595                     0.849                              0.606                 
## AIC                                 762.495                   135.718                            760.941                 
## BIC                                 782.196                   155.419                            784.582                 
## Num. obs.                           380                       380                                380                     
## Num. groups: B.ID                   144                       144                                144                     
## Var: B.ID (Intercept)                 0.169                     0.000                              0.172                 
## Var: Residual                         0.294                     0.078                              0.287                 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 380 (29 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)   0.027 (0.013) 2.134  .033 *   [ 0.006, 0.054]
## Direct (c')     0.014 (0.055) 0.259  .796     [-0.082, 0.124]
## Total (c)       0.041 (0.055) 0.745  .456     [-0.056, 0.147]
## ─────────────────────────────────────────────────────────────
## 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)                           3.034 ***            3.272 ***                          3.034 ***        
##                                      (0.040)              (0.014)                            (0.040)           
## WP.CreativeProcessEngagementV_mean    0.689 ***            1.000 ***                          0.472 ***        
##                                      (0.060)              (0.022)                            (0.090)           
## Manipulation                          0.020                0.101 **                          -0.003            
##                                      (0.040)              (0.031)                            (0.040)           
## WP.CreativeProcessEngagementV                                                                 0.217 **         
##                                                                                              (0.067)           
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.401                0.849                              0.407            
## Conditional R^2                       0.754                0.849                              0.763            
## AIC                                 537.470              135.718                            532.856            
## BIC                                 557.171              155.419                            556.497            
## Num. obs.                           380                  380                                380                
## Num. groups: B.ID                   144                  144                                144                
## Var: B.ID (Intercept)                 0.183                0.000                              0.184            
## Var: Residual                         0.128                0.078                              0.123            
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 380 (29 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)   0.022 (0.009)  2.399  .016 *   [ 0.007, 0.043]
## Direct (c')    -0.005 (0.036) -0.142  .887     [-0.067, 0.067]
## Total (c)       0.017 (0.037)  0.470  .638     [-0.048, 0.089]
## ──────────────────────────────────────────────────────────────
## 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)                           3.278 ***                      3.272 ***                          3.279 ***                  
##                                      (0.033)                        (0.014)                            (0.031)                     
## WP.CreativeProcessEngagementV_mean    0.952 ***                      1.000 ***                         -0.209 *                    
##                                      (0.050)                        (0.022)                            (0.086)                     
## Manipulation                          0.211 ***                      0.101 **                           0.090 *                    
##                                      (0.059)                        (0.031)                            (0.043)                     
## WP.CreativeProcessEngagementV                                                                           1.160 ***                  
##                                                                                                        (0.072)                     
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.550                          0.849                              0.695                      
## Conditional R^2                       0.613                          0.849                              0.804                      
## AIC                                 673.714                        135.718                            495.347                      
## BIC                                 693.415                        155.419                            518.988                      
## Num. obs.                           380                            380                                380                          
## Num. groups: B.ID                   144                            144                                144                          
## Var: B.ID (Intercept)                 0.046                          0.000                              0.080                      
## Var: Residual                         0.286                          0.078                              0.144                      
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 380 (29 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)   0.116 (0.039) 3.002  .003 **  [0.048, 0.196]
## Direct (c')     0.088 (0.039) 2.282  .023 *   [0.020, 0.166]
## Total (c)       0.204 (0.057) 3.580 <.001 *** [0.097, 0.317]
## ────────────────────────────────────────────────────────────
## 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)                           3.523 ***                  3.272 ***                          3.523 ***              
##                                      (0.034)                    (0.014)                            (0.034)                 
## WP.CreativeProcessEngagementV_mean    0.864 ***                  1.000 ***                         -0.107                  
##                                      (0.051)                    (0.022)                            (0.087)                 
## Manipulation                          0.053                      0.101 **                          -0.045                  
##                                      (0.055)                    (0.031)                            (0.042)                 
## WP.CreativeProcessEngagementV                                                                       0.973 ***              
##                                                                                                    (0.071)                 
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.509                      0.849                              0.622                  
## Conditional R^2                       0.622                      0.849                              0.786                  
## AIC                                 643.960                    135.718                            508.827                  
## BIC                                 663.661                    155.419                            532.468                  
## Num. obs.                           380                        380                                380                      
## Num. groups: B.ID                   144                        144                                144                      
## Var: B.ID (Intercept)                 0.073                      0.000                              0.106                  
## Var: Residual                         0.243                      0.078                              0.138                  
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 380 (29 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Running 100 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.CreativeProcessEngagementV" (M) ==> "WP.InformationSearchV" (Y)
## ──────────────────────────────────────────────────────────────
##                Effect    S.E.      z     p       [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────
## Indirect (ab)   0.098 (0.033)  2.998  .003 **  [ 0.041, 0.164]
## Direct (c')    -0.046 (0.038) -1.218  .223     [-0.113, 0.030]
## Total (c)       0.051 (0.052)  0.989  .322     [-0.043, 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.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)                           3.119 ***               3.272 ***                          3.117 ***           
##                                      (0.024)                 (0.014)                            (0.022)              
## WP.CreativeProcessEngagementV_mean    1.110 ***               1.000 ***                          0.188 **            
##                                      (0.036)                 (0.022)                            (0.061)              
## Manipulation                          0.067                   0.101 **                          -0.027               
##                                      (0.044)                 (0.031)                            (0.030)              
## WP.CreativeProcessEngagementV                                                                    0.922 ***           
##                                                                                                 (0.051)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.749                   0.849                              0.843               
## Conditional R^2                       0.775                   0.849                              0.900               
## AIC                                 446.721                 135.718                            235.942               
## BIC                                 466.422                 155.419                            259.583               
## Num. obs.                           380                     380                                380                   
## Num. groups: B.ID                   144                     144                                144                   
## Var: B.ID (Intercept)                 0.019                   0.000                              0.041               
## Var: Residual                         0.162                   0.078                              0.072               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 380 (29 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)   0.092 (0.031)  3.003  .003 **  [ 0.038, 0.157]
## Direct (c')    -0.028 (0.027) -1.018  .309     [-0.076, 0.027]
## Total (c)       0.065 (0.043)  1.506  .132     [-0.018, 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.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)                           3.202 ***               3.272 ***                          3.202 ***           
##                                      (0.052)                 (0.014)                            (0.052)              
## WP.CreativeProcessEngagementV_mean    0.717 ***               1.000 ***                          0.437 ***           
##                                      (0.077)                 (0.022)                            (0.112)              
## Manipulation                          0.025                   0.101 **                          -0.005               
##                                      (0.048)                 (0.031)                            (0.048)              
## WP.CreativeProcessEngagementV                                                                    0.281 ***           
##                                                                                                 (0.081)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.310                   0.849                              0.318               
## Conditional R^2                       0.747                   0.849                              0.760               
## AIC                                 695.033                 135.718                            688.364               
## BIC                                 714.734                 155.419                            712.005               
## Num. obs.                           380                     380                                380                   
## Num. groups: B.ID                   144                     144                                144                   
## Var: B.ID (Intercept)                 0.317                   0.000                              0.321               
## Var: Residual                         0.183                   0.078                              0.175               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 380 (29 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)   0.029 (0.012)  2.476  .013 *   [ 0.010, 0.054]
## Direct (c')    -0.007 (0.043) -0.156  .876     [-0.081, 0.079]
## Total (c)       0.022 (0.044)  0.502  .616     [-0.057, 0.108]
## ──────────────────────────────────────────────────────────────
## 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)                           3.027 ***                      3.272 ***                          3.027 ***                  
##                                      (0.058)                        (0.014)                            (0.058)                     
## WP.CreativeProcessEngagementV_mean    0.767 ***                      1.000 ***                          0.424 ***                  
##                                      (0.086)                        (0.022)                            (0.128)                     
## Manipulation                          0.048                          0.101 **                           0.011                      
##                                      (0.057)                        (0.031)                            (0.057)                     
## WP.CreativeProcessEngagementV                                                                           0.343 ***                  
##                                                                                                        (0.096)                     
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.289                          0.849                              0.299                      
## Conditional R^2                       0.709                          0.849                              0.723                      
## AIC                                 805.708                        135.718                            797.970                      
## BIC                                 825.409                        155.419                            821.611                      
## Num. obs.                           380                            380                                380                          
## Num. groups: B.ID                   144                            144                                144                          
## Var: B.ID (Intercept)                 0.374                          0.000                              0.379                      
## Var: Residual                         0.259                          0.078                              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 : 380 (29 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)   0.035 (0.014) 2.504  .012 *   [ 0.012, 0.066]
## Direct (c')     0.009 (0.051) 0.174  .862     [-0.079, 0.111]
## Total (c)       0.044 (0.052) 0.843  .399     [-0.050, 0.146]
## ─────────────────────────────────────────────────────────────
## 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)                           3.465 ***              3.272 ***                          3.464 ***          
##                                      (0.063)                (0.014)                            (0.063)             
## WP.CreativeProcessEngagementV_mean    0.641 ***              1.000 ***                          0.458 **           
##                                      (0.093)                (0.022)                            (0.145)             
## Manipulation                         -0.011                  0.101 **                          -0.030              
##                                      (0.065)                (0.031)                            (0.066)             
## WP.CreativeProcessEngagementV                                                                   0.183              
##                                                                                                (0.111)             
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.190                  0.849                              0.192              
## Conditional R^2                       0.645                  0.849                              0.649              
## AIC                                 890.396                135.718                            892.246              
## BIC                                 910.097                155.419                            915.887              
## Num. obs.                           380                    380                                380                  
## Num. groups: B.ID                   144                    144                                144                  
## Var: B.ID (Intercept)                 0.431                  0.000                              0.433              
## Var: Residual                         0.336                  0.078                              0.333              
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 380 (29 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)   0.019 (0.012)  1.619  .105     [ 0.003, 0.044]
## Direct (c')    -0.033 (0.059) -0.555  .579     [-0.136, 0.085]
## Total (c)      -0.014 (0.059) -0.230  .818     [-0.118, 0.100]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WP.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)                           2.993 ***                       3.272 ***                          2.993 ***                   
##                                      (0.049)                         (0.014)                            (0.049)                      
## WP.CreativeProcessEngagementV_mean    0.884 ***                       1.000 ***                          0.540 ***                   
##                                      (0.073)                         (0.022)                            (0.119)                      
## Manipulation                         -0.034                           0.101 **                          -0.069                       
##                                      (0.056)                         (0.031)                            (0.056)                      
## WP.CreativeProcessEngagementV                                                                            0.344 ***                   
##                                                                                                         (0.094)                      
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.406                           0.849                              0.416                       
## Conditional R^2                       0.700                           0.849                              0.716                       
## AIC                                 754.917                         135.718                            746.701                       
## BIC                                 774.618                         155.419                            770.342                       
## Num. obs.                           380                             380                                380                           
## Num. groups: B.ID                   144                             144                                144                           
## Var: B.ID (Intercept)                 0.248                           0.000                              0.253                       
## Var: Residual                         0.252                           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 : 380 (29 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)   0.035 (0.014)  2.524  .012 *   [ 0.013, 0.066]
## Direct (c')    -0.072 (0.050) -1.426  .154     [-0.159, 0.029]
## Total (c)      -0.036 (0.051) -0.707  .480     [-0.129, 0.063]
## ──────────────────────────────────────────────────────────────
## 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)                           2.878 ***             3.272 ***                          2.878 ***         
##                                      (0.050)               (0.014)                            (0.050)            
## WP.CreativeProcessEngagementV_mean    0.908 ***             1.000 ***                          0.527 ***         
##                                      (0.074)               (0.022)                            (0.111)            
## Manipulation                          0.058                 0.101 **                           0.017             
##                                      (0.051)               (0.031)                            (0.050)            
## WP.CreativeProcessEngagementV                                                                  0.382 ***         
##                                                                                               (0.084)            
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.431                 0.849                              0.445             
## Conditional R^2                       0.755                 0.849                              0.774             
## AIC                                 707.920               135.718                            692.988             
## BIC                                 727.621               155.419                            716.629             
## Num. obs.                           380                   380                                380                 
## Num. groups: B.ID                   144                   144                                144                 
## Var: B.ID (Intercept)                 0.272                 0.000                              0.276             
## Var: Residual                         0.205                 0.078                              0.190             
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 380 (29 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)   0.039 (0.014) 2.695  .007 **  [ 0.016, 0.069]
## Direct (c')     0.015 (0.045) 0.339  .735     [-0.062, 0.104]
## Total (c)       0.054 (0.047) 1.157  .247     [-0.032, 0.145]
## ─────────────────────────────────────────────────────────────
## 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)                           3.126 ***               3.269 ***                          3.126 ***           
##                                      (0.043)                 (0.014)                            (0.043)              
## WP.CreativeProcessEngagementV_mean    0.665 ***               0.999 ***                          0.502 ***           
##                                      (0.064)                 (0.022)                            (0.100)              
## Manipulation                         -0.047                   0.093 **                          -0.062               
##                                      (0.044)                 (0.031)                            (0.044)              
## WP.CreativeProcessEngagementV                                                                    0.162 *             
##                                                                                                 (0.077)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.354                   0.850                              0.358               
## Conditional R^2                       0.728                   0.850                              0.731               
## AIC                                 573.653                 125.727                            574.504               
## BIC                                 593.207                 145.281                            597.968               
## Num. obs.                           369                     369                                369                   
## Num. groups: B.ID                   142                     142                                142                   
## Var: B.ID (Intercept)                 0.203                   0.000                              0.202               
## Var: Residual                         0.148                   0.077                              0.146               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 369 (40 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)   0.016 (0.008)  1.848  .065 .   [ 0.003, 0.034]
## Direct (c')    -0.064 (0.040) -1.603  .109     [-0.132, 0.015]
## Total (c)      -0.048 (0.040) -1.209  .227     [-0.118, 0.029]
## ──────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="WA.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)                           3.225 ***                       3.269 ***                          3.225 ***                   
##                                      (0.057)                         (0.014)                            (0.057)                      
## WP.CreativeProcessEngagementV_mean    0.704 ***                       0.999 ***                          0.460 ***                   
##                                      (0.084)                         (0.022)                            (0.127)                      
## Manipulation                         -0.117 *                         0.093 **                          -0.140 *                     
##                                      (0.055)                         (0.031)                            (0.055)                      
## WP.CreativeProcessEngagementV                                                                            0.244 *                     
##                                                                                                         (0.095)                      
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.270                           0.850                              0.275                       
## Conditional R^2                       0.716                           0.850                              0.723                       
## AIC                                 748.642                         125.727                            747.056                       
## BIC                                 768.196                         145.281                            770.520                       
## Num. obs.                           369                             369                                369                           
## Num. groups: B.ID                   142                             142                                142                           
## Var: B.ID (Intercept)                 0.359                           0.000                              0.363                       
## Var: Residual                         0.229                           0.077                              0.224                       
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 369 (40 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)   0.023 (0.011)  2.051  .040 *   [ 0.005,  0.048]
## Direct (c')    -0.142 (0.049) -2.886  .004 **  [-0.227, -0.045]
## Total (c)      -0.119 (0.050) -2.397  .017 *   [-0.206, -0.022]
## ───────────────────────────────────────────────────────────────
## 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 ***                       3.269 ***                          3.028 ***                   
##                                      (0.060)                         (0.014)                            (0.060)                      
## WP.CreativeProcessEngagementV_mean    0.623 ***                       0.999 ***                          0.541 ***                   
##                                      (0.089)                         (0.022)                            (0.139)                      
## Manipulation                          0.024                           0.093 **                           0.016                       
##                                      (0.061)                         (0.031)                            (0.062)                      
## WP.CreativeProcessEngagementV                                                                            0.082                       
##                                                                                                         (0.107)                      
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.200                           0.850                              0.201                       
## Conditional R^2                       0.663                           0.850                              0.662                       
## AIC                                 813.385                         125.727                            817.421                       
## BIC                                 832.939                         145.281                            840.886                       
## Num. obs.                           369                             369                                369                           
## Num. groups: B.ID                   142                             142                                142                           
## Var: B.ID (Intercept)                 0.391                           0.000                              0.389                       
## Var: Residual                         0.285                           0.077                              0.286                       
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 369 (40 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)   0.009 (0.010) 0.859  .390     [-0.008, 0.029]
## Direct (c')     0.013 (0.056) 0.242  .809     [-0.082, 0.124]
## Total (c)       0.022 (0.055) 0.404  .686     [-0.076, 0.128]
## ─────────────────────────────────────────────────────────────
## 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)                           3.371 ***           3.269 ***                          3.370 ***       
##                                      (0.039)             (0.014)                            (0.039)          
## WP.CreativeProcessEngagementV_mean    0.721 ***           0.999 ***                          0.506 ***       
##                                      (0.058)             (0.022)                            (0.105)          
## Manipulation                         -0.103 *             0.093 **                          -0.123 *         
##                                      (0.051)             (0.031)                            (0.051)          
## WP.CreativeProcessEngagementV                                                                0.216 *         
##                                                                                             (0.088)          
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.406               0.850                              0.413           
## Conditional R^2                       0.642               0.850                              0.649           
## AIC                                 614.333             125.727                            613.388           
## BIC                                 633.887             145.281                            636.852           
## Num. obs.                           369                 369                                369               
## Num. groups: B.ID                   142                 142                                142               
## Var: B.ID (Intercept)                 0.133               0.000                              0.133           
## Var: Residual                         0.201               0.077                              0.197           
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 369 (40 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)   0.021 (0.010)  2.012  .044 *   [ 0.004,  0.043]
## Direct (c')    -0.125 (0.046) -2.736  .006 **  [-0.205, -0.033]
## Total (c)      -0.105 (0.046) -2.267  .023 *   [-0.186, -0.015]
## ───────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

PROCESS(data, y="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)                           3.526 ***               3.269 ***                          3.526 ***           
##                                      (0.049)                 (0.014)                            (0.049)              
## WP.CreativeProcessEngagementV_mean    0.593 ***               0.999 ***                          0.540 ***           
##                                      (0.072)                 (0.022)                            (0.117)              
## Manipulation                          0.066                   0.093 **                           0.061               
##                                      (0.052)                 (0.031)                            (0.053)              
## WP.CreativeProcessEngagementV                                                                    0.053               
##                                                                                                 (0.092)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.253                   0.850                              0.252               
## Conditional R^2                       0.658                   0.850                              0.658               
## AIC                                 684.605                 125.727                            689.215               
## BIC                                 704.159                 145.281                            712.680               
## Num. obs.                           369                     369                                369                   
## Num. groups: B.ID                   142                     142                                142                   
## Var: B.ID (Intercept)                 0.248                   0.000                              0.248               
## Var: Residual                         0.209                   0.077                              0.209               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 369 (40 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)   0.006 (0.008) 0.675  .500     [-0.009, 0.023]
## Direct (c')     0.059 (0.048) 1.240  .215     [-0.023, 0.153]
## Total (c)       0.065 (0.047) 1.387  .165     [-0.019, 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="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)                           2.008 ***               3.269 ***                          2.008 ***           
##                                      (0.051)                 (0.014)                            (0.051)              
## WP.CreativeProcessEngagementV_mean   -0.012                   0.999 ***                          0.010               
##                                      (0.075)                 (0.022)                            (0.122)              
## Manipulation                          0.046                   0.093 **                           0.048               
##                                      (0.055)                 (0.031)                            (0.056)              
## WP.CreativeProcessEngagementV                                                                   -0.021               
##                                                                                                 (0.097)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.001                   0.850                              0.001               
## Conditional R^2                       0.536                   0.850                              0.535               
## AIC                                 719.080                 125.727                            723.870               
## BIC                                 738.634                 145.281                            747.334               
## Num. obs.                           369                     369                                369                   
## Num. groups: B.ID                   142                     142                                142                   
## Var: B.ID (Intercept)                 0.267                   0.000                              0.267               
## Var: Residual                         0.231                   0.077                              0.232               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 369 (40 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)  -0.001 (0.009) -0.115  .908     [-0.020, 0.016]
## Direct (c')     0.046 (0.050)  0.917  .359     [-0.041, 0.145]
## Total (c)       0.045 (0.049)  0.919  .358     [-0.041, 0.141]
## ──────────────────────────────────────────────────────────────
## 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)                           3.428 ***            3.269 ***                          3.428 ***        
##                                      (0.032)              (0.014)                            (0.032)           
## WP.CreativeProcessEngagementV_mean    0.669 ***            0.999 ***                          0.672 ***        
##                                      (0.048)              (0.022)                            (0.086)           
## Manipulation                          0.070                0.093 **                           0.071            
##                                      (0.041)              (0.031)                            (0.041)           
## WP.CreativeProcessEngagementV                                                                -0.003            
##                                                                                              (0.072)           
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.471                0.850                              0.471            
## Conditional R^2                       0.688                0.850                              0.687            
## AIC                                 459.503              125.727                            464.937            
## BIC                                 479.057              145.281                            488.402            
## Num. obs.                           369                  369                                369                
## Num. groups: B.ID                   142                  142                                142                
## Var: B.ID (Intercept)                 0.090                0.000                              0.090            
## Var: Residual                         0.130                0.077                              0.131            
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 369 (40 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)   0.000 (0.007) 0.056  .955     [-0.013, 0.013]
## Direct (c')     0.069 (0.037) 1.855  .064 .   [ 0.004, 0.144]
## Total (c)       0.069 (0.036) 1.908  .056 .   [ 0.005, 0.142]
## ─────────────────────────────────────────────────────────────
## 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)                           3.278 ***               3.269 ***                          3.278 ***           
##                                      (0.050)                 (0.014)                            (0.050)              
## WP.CreativeProcessEngagementV_mean    0.726 ***               0.999 ***                          0.615 ***           
##                                      (0.075)                 (0.022)                            (0.119)              
## Manipulation                         -0.020                   0.093 **                          -0.030               
##                                      (0.053)                 (0.031)                            (0.053)              
## WP.CreativeProcessEngagementV                                                                    0.111               
##                                                                                                 (0.093)              
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.322                   0.850                              0.323               
## Conditional R^2                       0.700                   0.850                              0.701               
## AIC                                 698.841                 125.727                            702.323               
## BIC                                 718.395                 145.281                            725.788               
## Num. obs.                           369                     369                                369                   
## Num. groups: B.ID                   142                     142                                142                   
## Var: B.ID (Intercept)                 0.268                   0.000                              0.269               
## Var: Residual                         0.214                   0.077                              0.213               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 369 (40 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)   0.011 (0.009)  1.239  .215     [-0.001, 0.029]
## Direct (c')    -0.032 (0.048) -0.671  .502     [-0.115, 0.063]
## Total (c)      -0.021 (0.047) -0.445  .656     [-0.106, 0.071]
## ──────────────────────────────────────────────────────────────
## 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)                           3.348 ***                       3.269 ***                          3.348 ***                   
##                                      (0.043)                         (0.014)                            (0.043)                      
## WP.CreativeProcessEngagementV_mean    0.691 ***                       0.999 ***                          0.578 ***                   
##                                      (0.064)                         (0.022)                            (0.119)                      
## Manipulation                          0.100                           0.093 **                           0.089                       
##                                      (0.058)                         (0.031)                            (0.059)                      
## WP.CreativeProcessEngagementV                                                                            0.112                       
##                                                                                                         (0.101)                      
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.336                           0.850                              0.337                       
## Conditional R^2                       0.582                           0.850                              0.584                       
## AIC                                 700.793                         125.727                            704.305                       
## BIC                                 720.347                         145.281                            727.770                       
## Num. obs.                           369                             369                                369                           
## Num. groups: B.ID                   142                             142                                142                           
## Var: B.ID (Intercept)                 0.154                           0.000                              0.155                       
## Var: Residual                         0.261                           0.077                              0.261                       
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 369 (40 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)   0.011 (0.010) 1.168  .243     [-0.002, 0.031]
## Direct (c')     0.087 (0.052) 1.658  .097 .   [-0.005, 0.193]
## Total (c)       0.098 (0.052) 1.894  .058 .   [ 0.004, 0.199]
## ─────────────────────────────────────────────────────────────
## 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)                           3.034 ***                3.269 ***                          3.034 ***            
##                                      (0.055)                  (0.014)                            (0.056)               
## WP.CreativeProcessEngagementV_mean    0.693 ***                0.999 ***                          0.668 ***            
##                                      (0.082)                  (0.022)                            (0.129)               
## Manipulation                         -0.081                    0.093 **                          -0.083                
##                                      (0.056)                  (0.031)                            (0.057)               
## WP.CreativeProcessEngagementV                                                                     0.025                
##                                                                                                  (0.100)               
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.266                    0.850                              0.265                
## Conditional R^2                       0.690                    0.850                              0.689                
## AIC                                 758.317                  125.727                            763.028                
## BIC                                 777.871                  145.281                            786.493                
## Num. obs.                           369                      369                                369                    
## Num. groups: B.ID                   142                      142                                142                    
## Var: B.ID (Intercept)                 0.335                    0.000                              0.335                
## Var: Residual                         0.245                    0.077                              0.246                
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 369 (40 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)   0.003 (0.009)  0.353  .724     [-0.014, 0.021]
## Direct (c')    -0.086 (0.052) -1.660  .097 .   [-0.175, 0.017]
## Total (c)      -0.083 (0.051) -1.633  .102     [-0.172, 0.016]
## ──────────────────────────────────────────────────────────────
## 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)                           3.252 ***            3.269 ***                          3.252 ***        
##                                      (0.053)              (0.014)                            (0.053)           
## WP.CreativeProcessEngagementV_mean    0.752 ***            0.999 ***                          0.789 ***        
##                                      (0.079)              (0.022)                            (0.132)           
## Manipulation                         -0.007                0.093 **                          -0.003            
##                                      (0.060)              (0.031)                            (0.061)           
## WP.CreativeProcessEngagementV                                                                -0.037            
##                                                                                              (0.106)           
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.304                0.850                              0.304            
## Conditional R^2                       0.654                0.850                              0.653            
## AIC                                 776.055              125.727                            780.584            
## BIC                                 795.609              145.281                            804.048            
## Num. obs.                           369                  369                                369                
## Num. groups: B.ID                   142                  142                                142                
## Var: B.ID (Intercept)                 0.283                0.000                              0.283            
## Var: Residual                         0.281                0.077                              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 : 369 (40 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)  -0.002 (0.010) -0.239  .811     [-0.023, 0.017]
## Direct (c')    -0.006 (0.055) -0.100  .920     [-0.101, 0.104]
## Total (c)      -0.008 (0.054) -0.148  .883     [-0.102, 0.098]
## ──────────────────────────────────────────────────────────────
## 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)                           3.824 ***      3.269 ***                          3.824 ***  
##                                      (0.052)        (0.014)                            (0.052)     
## WP.CreativeProcessEngagementV_mean    0.481 ***      0.999 ***                          0.350 **   
##                                      (0.078)        (0.022)                            (0.119)     
## Manipulation                          0.021          0.093 **                           0.009      
##                                      (0.051)        (0.031)                            (0.052)     
## WP.CreativeProcessEngagementV                                                           0.130      
##                                                                                        (0.090)     
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.166          0.850                              0.168      
## Conditional R^2                       0.671          0.850                              0.673      
## AIC                                 696.088        125.727                            698.965      
## BIC                                 715.642        145.281                            722.430      
## Num. obs.                           369            369                                369          
## Num. groups: B.ID                   142            142                                142          
## Var: B.ID (Intercept)                 0.307          0.000                              0.307      
## Var: Residual                         0.200          0.077                              0.199      
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 369 (40 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 (0.009) 1.433  .152     [ 0.001, 0.031]
## Direct (c')     0.007 (0.047) 0.152  .879     [-0.073, 0.099]
## Total (c)       0.020 (0.046) 0.432  .666     [-0.062, 0.109]
## ─────────────────────────────────────────────────────────────
## 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)                           1.247 ***         3.269 ***                          1.247 ***     
##                                      (0.054)           (0.014)                            (0.054)        
## WP.CreativeProcessEngagementV_mean    0.298 ***         0.999 ***                          0.324 ***     
##                                      (0.080)           (0.022)                            (0.097)        
## Manipulation                         -0.124 ***         0.093 **                          -0.121 ***     
##                                      (0.031)           (0.031)                            (0.031)        
## WP.CreativeProcessEngagementV                                                             -0.027         
##                                                                                           (0.055)        
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.083             0.850                              0.084         
## Conditional R^2                       0.858             0.850                              0.858         
## AIC                                 469.988           125.727                            475.730         
## BIC                                 489.542           145.281                            499.195         
## Num. obs.                           369               369                                369             
## Num. groups: B.ID                   142               142                                142             
## Var: B.ID (Intercept)                 0.387             0.000                              0.387         
## Var: Residual                         0.071             0.077                              0.071         
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 369 (40 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)  -0.002 (0.005) -0.364  .716     [-0.013,  0.008]
## Direct (c')    -0.123 (0.028) -4.360 <.001 *** [-0.170, -0.068]
## Total (c)      -0.125 (0.027) -4.562 <.001 *** [-0.170, -0.072]
## ───────────────────────────────────────────────────────────────
## 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)                           2.484 ***             3.269 ***                          2.483 ***         
##                                      (0.075)               (0.014)                            (0.076)            
## WP.CreativeProcessEngagementV_mean    0.224 *               0.999 ***                         -0.038             
##                                      (0.112)               (0.022)                            (0.168)            
## Manipulation                         -0.078                 0.093 **                          -0.103             
##                                      (0.072)               (0.031)                            (0.072)            
## WP.CreativeProcessEngagementV                                                                  0.263 *           
##                                                                                               (0.126)            
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.022                 0.850                              0.027             
## Conditional R^2                       0.627                 0.850                              0.634             
## AIC                                 952.518               125.727                            952.512             
## BIC                                 972.072               145.281                            975.977             
## Num. obs.                           369                   369                                369                 
## Num. groups: B.ID                   142                   142                                142                 
## Var: B.ID (Intercept)                 0.642                 0.000                              0.648             
## Var: Residual                         0.396                 0.077                              0.390             
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 369 (40 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)   0.025 (0.014)  1.833  .067 .   [ 0.004, 0.055]
## Direct (c')    -0.106 (0.065) -1.632  .103     [-0.218, 0.023]
## Total (c)      -0.081 (0.065) -1.243  .214     [-0.194, 0.045]
## ──────────────────────────────────────────────────────────────
## 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)                            1.372 ***       3.260 ***                           1.372 ***   
##                                       (0.112)         (0.015)                             (0.112)      
## WP.CreativeProcessEngagementV_mean     0.402 *         0.995 ***                           0.429 *     
##                                       (0.165)         (0.022)                             (0.214)      
## Manipulation                          -0.203 **        0.088 **                           -0.200 **    
##                                       (0.077)         (0.031)                             (0.078)      
## WP.CreativeProcessEngagementV                                                             -0.027       
##                                                                                           (0.137)      
## ───────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                           0.038           0.850                               0.038       
## Conditional R^2                        0.797           0.850                               0.796       
## AIC                                 1055.716         123.964                            1059.818       
## BIC                                 1075.146         143.394                            1083.135       
## Num. obs.                            360             360                                 360           
## Num. groups: B.ID                    141             141                                 141           
## Var: B.ID (Intercept)                  1.591           0.000                               1.590       
## Var: Residual                          0.426           0.077                               0.428       
## ───────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 360 (49 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)  -0.001 (0.012) -0.094  .925     [-0.026,  0.022]
## Direct (c')    -0.204 (0.070) -2.901  .004 **  [-0.322, -0.067]
## Total (c)      -0.205 (0.069) -2.987  .003 **  [-0.321, -0.072]
## ───────────────────────────────────────────────────────────────
## 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)                           0.324 ***          3.260 ***                          0.324 ***      
##                                      (0.048)            (0.015)                            (0.048)         
## WP.CreativeProcessEngagementV_mean    0.101              0.995 ***                          0.144          
##                                      (0.071)            (0.022)                            (0.096)         
## Manipulation                         -0.044              0.088 **                          -0.041          
##                                      (0.037)            (0.031)                            (0.037)         
## WP.CreativeProcessEngagementV                                                              -0.043          
##                                                                                            (0.065)         
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.013              0.850                              0.013          
## Conditional R^2                       0.748              0.850                              0.747          
## AIC                                 498.980            123.964                            504.177          
## BIC                                 518.410            143.394                            527.494          
## Num. obs.                           360                360                                360              
## Num. groups: B.ID                   141                141                                141              
## Var: B.ID (Intercept)                 0.285              0.000                              0.284          
## Var: Residual                         0.098              0.077                              0.098          
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 360 (49 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 (0.006) -0.513  .608     [-0.016, 0.008]
## Direct (c')    -0.042 (0.033) -1.267  .205     [-0.099, 0.023]
## Total (c)      -0.046 (0.033) -1.396  .163     [-0.101, 0.018]
## ──────────────────────────────────────────────────────────────
## 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)                           1.048 ***      3.259 ***                          1.048 ***  
##                                      (0.088)        (0.015)                            (0.088)     
## WP.CreativeProcessEngagementV_mean    0.298 *        0.995 ***                          0.286      
##                                      (0.130)        (0.022)                            (0.173)     
## Manipulation                         -0.156 *        0.088 **                          -0.157 *    
##                                      (0.065)        (0.031)                            (0.066)     
## WP.CreativeProcessEngagementV                                                           0.012      
##                                                                                        (0.116)     
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                          0.033          0.850                              0.033      
## Conditional R^2                       0.766          0.850                              0.765      
## AIC                                 917.287        123.277                            921.757      
## BIC                                 936.732        142.721                            945.090      
## Num. obs.                           361            361                                361          
## Num. groups: B.ID                   141            141                                141          
## Var: B.ID (Intercept)                 0.958          0.000                              0.957      
## Var: Residual                         0.306          0.077                              0.307      
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 361 (48 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)   0.002 (0.010)  0.207  .836     [-0.018,  0.022]
## Direct (c')    -0.160 (0.059) -2.700  .007 **  [-0.260, -0.044]
## Total (c)      -0.158 (0.058) -2.721  .007 **  [-0.258, -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.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)                            6.788 ***             3.291 ***                           6.789 ***         
##                                       (0.097)               (0.015)                             (0.097)            
## WP.CreativeProcessEngagementV_mean    -0.187                 1.003 ***                          -0.026             
##                                       (0.144)               (0.022)                             (0.233)            
## Manipulation                           0.069                 0.088 **                            0.083             
##                                       (0.101)               (0.031)                             (0.102)            
## WP.CreativeProcessEngagementV                                                                   -0.161             
##                                                                                                 (0.183)            
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                           0.010                 0.863                               0.011             
## Conditional R^2                        0.576                 0.863                               0.577             
## AIC                                 1045.460                97.472                            1048.242             
## BIC                                 1064.545               116.558                            1071.145             
## Num. obs.                            336                   336                                 336                 
## Num. groups: B.ID                    133                   133                                 133                 
## Var: B.ID (Intercept)                  0.949                 0.000                               0.950             
## Var: Residual                          0.711                 0.073                               0.711             
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 336 (73 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)  -0.012 (0.018) -0.707  .480     [-0.050, 0.020]
## Direct (c')     0.079 (0.092)  0.856  .392     [-0.080, 0.261]
## Total (c)       0.066 (0.090)  0.740  .459     [-0.088, 0.242]
## ──────────────────────────────────────────────────────────────
## 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. :)