1 PREPARATION

1.1 Loading Data

#Week 1
data.M=rio::import("BWReflect2.ABBAw1.sav")%>%as.data.table()
data.M$Stability=(max(data.M$WP.SupervisoryBehavioralFeedbackV_sd, na.rm = T)-data.M$WP.SupervisoryBehavioralFeedbackV_sd)
data.M$Stability.GraC=scale(data.M$Stability, center = TRUE, scale = FALSE)
#Describe(data.M$Stability.GraC)

#Week 2
data.D=rio::import("BWReflect2.ESMw2.sav")%>%as.data.table()
data.D$Stability=(max(data.D$WP.SupervisoryBehavioralFeedbackV_sd, na.rm = T)-data.D$WP.SupervisoryBehavioralFeedbackV_sd)
data.D$Stability.GraC=scale(data.D$Stability, center = TRUE, scale = FALSE)
##-Choose X
#data.D=data[,!"Manipulation"]
#data <- rename(data, c(WA.WorkReflectionV = "Manipulation"))
cor_multilevel(data.D[,.(B.ID, WP.ObservationalLearningV,WP.AdviceSeekingV)],"B.ID")
## Correlations below and above the diagonal represent
## within-level and between-level correlations, respectively:
## ──────────────────────────────────────────────────────────────────────
##                            WP.ObservationalLearningV WP.AdviceSeekingV
## ──────────────────────────────────────────────────────────────────────
## WP.ObservationalLearningV                      1.000             0.778
## WP.AdviceSeekingV                              0.440             1.000
## ──────────────────────────────────────────────────────────────────────
## 
## Within-Level Correlation [95% CI]:
## ───────────────────────────────────────────
##                  r       [95% CI]     p    
## ───────────────────────────────────────────
## WP.OL-WP.AS  0.440 [0.378, 0.498] <.001 ***
## ───────────────────────────────────────────
## 
## Between-Level Correlation [95% CI]:
## ───────────────────────────────────────────
##                  r       [95% CI]     p    
## ───────────────────────────────────────────
## WP.OL-WP.AS  0.778 [0.709, 0.832] <.001 ***
## ───────────────────────────────────────────
## 
## Intraclass Correlation:
## ─────────────────────────────────────────────────
##       WP.ObservationalLearningV WP.AdviceSeekingV
## ─────────────────────────────────────────────────
## ICC1                      0.706             0.572
## ICC2                      0.911             0.850
## ─────────────────────────────────────────────────
cor_multilevel(data.M[,.(B.ID, WP.ObservationalLearningV,WP.AdviceSeekingV)],"B.ID")
## Correlations below and above the diagonal represent
## within-level and between-level correlations, respectively:
## ──────────────────────────────────────────────────────────────────────
##                            WP.ObservationalLearningV WP.AdviceSeekingV
## ──────────────────────────────────────────────────────────────────────
## WP.ObservationalLearningV                      1.000             0.584
## WP.AdviceSeekingV                              0.409             1.000
## ──────────────────────────────────────────────────────────────────────
## 
## Within-Level Correlation [95% CI]:
## ───────────────────────────────────────────
##                  r       [95% CI]     p    
## ───────────────────────────────────────────
## WP.OL-WP.AS  0.409 [0.337, 0.475] <.001 ***
## ───────────────────────────────────────────
## 
## Between-Level Correlation [95% CI]:
## ───────────────────────────────────────────
##                  r       [95% CI]     p    
## ───────────────────────────────────────────
## WP.OL-WP.AS  0.584 [0.473, 0.677] <.001 ***
## ───────────────────────────────────────────
## 
## Intraclass Correlation:
## ─────────────────────────────────────────────────
##       WP.ObservationalLearningV WP.AdviceSeekingV
## ─────────────────────────────────────────────────
## ICC1                      0.506             0.386
## ICC2                      0.778             0.683
## ─────────────────────────────────────────────────

1.2 Theoretical model

#covar=list(names=c("C"),site=list(c("M","Y")))
pmacroModel(7,labels=list(X="Reflection on FSI", M="Information Search", Y="Outcomes", W="Instability of SBF"))#covar=covar,

2 SUPPLEMENTARY ANALYSIS

3 LONGITUDINAL FIELD EXPERIMENT STUDY

3.1 Primary analysis

3.1.1 Frequency analysis

Freq(data.M$Manipulation)
## Frequency Statistics:
## ───────────
##      N    %
## ───────────
## 0  309 50.2
## 1  307 49.8
## ───────────
## Total N = 616
Freq(data.M$W.Day)
## Frequency Statistics:
## ───────────
##      N    %
## ───────────
## 1  156 25.3
## 2  159 25.8
## 4  148 24.0
## 5  153 24.8
## ───────────
## Total N = 616

3.1.2 ICC and RWG

HLM_ICC_rWG(data.M, group="B.ID", icc.var="Manipulation")
## 
## ------ Sample Size Information ------
## 
## Level 1: N = 616 observations ("Manipulation")
## Level 2: K = 165 groups ("B.ID")
## 
##        n (group sizes)
## Min.             1.000
## Median           4.000
## Mean             3.733
## Max.             4.000
## 
## ------ ICC(1), ICC(2), and rWG ------
## 
## ICC variable: "Manipulation"
## 
## ICC(1) = 0.000 (non-independence of data)
## ICC(2) = 0.000 (reliability of group means)
## 
## rWG variable: "Manipulation"
## 
## rWG (within-group agreement for single-item measures)
## ───────────────────────────────────────────────────
##       Min. 1st Qu. Median  Mean 3rd Qu.  Max.  NA's
## ───────────────────────────────────────────────────
## rWG  0.000   0.000  0.000 0.000   0.000 0.000 4.000
## ───────────────────────────────────────────────────
HLM_ICC_rWG(data.M, group="B.ID", icc.var="WP.InformationSearchV")
## 
## ------ Sample Size Information ------
## 
## Level 1: N = 563 observations ("WP.InformationSearchV")
## Level 2: K = 164 groups ("B.ID")
## 
##        n (group sizes)
## Min.             1.000
## Median           4.000
## Mean             3.433
## Max.             4.000
## 
## ------ ICC(1), ICC(2), and rWG ------
## 
## ICC variable: "WP.InformationSearchV"
## 
## ICC(1) = 0.594 (non-independence of data)
## ICC(2) = 0.821 (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   0.767  0.917 0.810   1.000 1.000 9.000
## ───────────────────────────────────────────────────

3.1.3 Manipulation check

3.1.3.1 MLM

Manipulation.MLM= lmer(WA.WorkReflectionV~Manipulation + (1|B.ID), na.action = na.exclude, data = data.M, control=lmerControl(optimizer="bobyqa"))
HLM_summary(Manipulation.MLM)
## 
## 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 = 602
## Level-2 Groups/Clusters: B.ID, 165
## 
## Model Fit:
## AIC = 1144.250
## BIC = 1161.851
## R_(m)² = 0.00850  (Marginal R²: fixed effects)
## R_(c)² = 0.51629  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ─────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF     F     p    
## ─────────────────────────────────────────────────────────
## Manipulation    2.59    2.59  1.00 442.71 10.38  .001 ** 
## ─────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WA.WorkReflectionV
## ─────────────────────────────────────────────────────────────────
##                 b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ─────────────────────────────────────────────────────────────────
## (Intercept)   3.278 (0.049) 66.39 233.7 <.001 ***  [3.180, 3.375]
## Manipulation  0.132 (0.041)  3.22 442.7  .001 **   [0.052, 0.213]
## ─────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WA.WorkReflectionV
## ───────────────────────────────────────────────────────────────
##                   β    S.E.    t    df     p      [95% CI of β]
## ───────────────────────────────────────────────────────────────
## Manipulation  0.092 (0.029) 3.22 442.7  .001 **  [0.036, 0.149]
## ───────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     165 (Intercept)  0.26167 0.51214
##  Residual                  0.24927        
## ──────────────────────────────────────────

3.1.4 T-test

Manipulation.T=MANOVA(data=data.M, subID="B.ID", dv="WA.WorkReflectionV", within=c("Manipulation"))
## 
## ====== ANOVA (Within-Subjects Design) ======
## 
## Descriptives:
## ─────────────────────────────────
##  "Manipulation"  Mean    S.D.   n
## ─────────────────────────────────
##   Manipulation0 3.255 (0.589) 147
##   Manipulation1 3.403 (0.651) 147
## ─────────────────────────────────
## Total sample size: N = 165
## 
## ANOVA Table:
## Dependent variable(s):      WA.WorkReflectionV
## Between-subjects factor(s): –
## Within-subjects factor(s):  Manipulation
## Covariate(s):               –
## ───────────────────────────────────────────────────────────────────────────
##                  MS   MSE df1 df2      F     p     η²p [90% CI of η²p]  η²G
## ───────────────────────────────────────────────────────────────────────────
## Manipulation  1.621 0.142   1 146 11.380 <.001 ***   .072 [.019, .148] .014
## ───────────────────────────────────────────────────────────────────────────
## MSE = mean square error (the residual variance of the linear model)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## ω²p = partial omega-squared = (F - 1) * df1 / (F * df1 + df2 + 1)
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## Cohen’s f² = η²p / (1 - η²p)
## 
## Levene’s Test for Homogeneity of Variance:
## No between-subjects factors. No need to do the Levene’s test.
## 
## Mauchly’s Test of Sphericity:
## The repeated measures have only two levels. The assumption of sphericity is always met.
emmip(Manipulation.T, ~Manipulation, CIs=TRUE, style = "factor", linearg = list(), CIarg = list( col = "grey",size = 20), dotarg = list(size = 2)) +
    ggplot2::theme_bw()

3.1.5 Main effect

Main= lmer(WP.InformationSearchV~Manipulation + (1|B.ID), na.action = na.exclude, data = data.M, control=lmerControl(optimizer="bobyqa")) 
HLM_summary(Main)
## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WP.InformationSearchV ~ Manipulation + (1 | B.ID)
## Level-1 Observations: N = 563
## Level-2 Groups/Clusters: B.ID, 164
## 
## Model Fit:
## AIC = 1159.640
## BIC = 1176.973
## R_(m)² = 0.00000  (Marginal R²: fixed effects)
## R_(c)² = 0.59311  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────
##               Sum Sq Mean Sq NumDF  DenDF    F     p    
## ────────────────────────────────────────────────────────
## Manipulation    0.00    0.00  1.00 408.36 0.00  .997    
## ────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WP.InformationSearchV
## ──────────────────────────────────────────────────────────────────
##                  b/γ    S.E.     t    df     p     [95% CI of b/γ]
## ──────────────────────────────────────────────────────────────────
## (Intercept)    3.600 (0.058) 61.71 217.4 <.001 *** [ 3.485, 3.715]
## Manipulation  -0.000 (0.045) -0.00 408.4  .997     [-0.088, 0.088]
## ──────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WP.InformationSearchV
## ──────────────────────────────────────────────────────────────────
##                    β    S.E.     t    df     p       [95% CI of β]
## ──────────────────────────────────────────────────────────────────
## Manipulation  -0.000 (0.028) -0.00 408.4  .997     [-0.054, 0.054]
## ──────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     164 (Intercept)  0.39062 0.59311
##  Residual                  0.26797        
## ──────────────────────────────────────────

3.2 Moderation effect

3.2.1 Model

Mo=PROCESS(data.M, y="WP.InformationSearchV", x="Manipulation", mods="Stability.GraC", 
           covs=c("WP.SupervisoryBehavioralFeedbackV_mean"),
           cluster ="B.ID", center=FALSE)#, file="D2.doc")hlm.re.y = "(1|B.ID)", 
## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## -    Outcome (Y) : WP.InformationSearchV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : -
## - Moderators (W) : Stability.GraC
## - Covariates (C) : WP.SupervisoryBehavioralFeedbackV_mean
## -   HLM Clusters : B.ID
## 
## Formula of Outcome:
## -    WP.InformationSearchV ~ WP.SupervisoryBehavioralFeedbackV_mean + Manipulation*Stability.GraC + (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.InformationSearchV
## ────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                                1.521 ***                  1.367 ***             
##                                           (0.238)                    (0.253)                
## WP.SupervisoryBehavioralFeedbackV_mean     0.610 ***                  0.654 ***             
##                                           (0.068)                    (0.073)                
## Manipulation                               0.001                      0.006                 
##                                           (0.045)                    (0.044)                
## Stability.GraC                                                       -0.415 *               
##                                                                      (0.180)                
## Manipulation:Stability.GraC                                           0.305 *               
##                                                                      (0.152)                
## ────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.245                      0.255                 
## Conditional R^2                            0.594                      0.600                 
## AIC                                     1085.620                   1086.810                 
## BIC                                     1107.233                   1117.068                 
## Num. obs.                                557                        557                     
## Num. groups: B.ID                        158                        158                     
## Var: B.ID (Intercept)                      0.231                      0.229                 
## Var: Residual                              0.269                      0.266                 
## ────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 557 (59 missing observations deleted)
## Random Seed : -
## Simulations : -
## 
## Interaction Effect on "WP.InformationSearchV" (Y)
## ─────────────────────────────────────────────────────
##                                   F df1 df2     p    
## ─────────────────────────────────────────────────────
## Manipulation * Stability.GraC  4.02   1 405  .046 *  
## ─────────────────────────────────────────────────────
## 
## Simple Slopes: "Manipulation" (X) ==> "WP.InformationSearchV" (Y)
## ─────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.      t     p            [95% CI]
## ─────────────────────────────────────────────────────────────────
##  -0.301 (- SD)    -0.086 (0.062) -1.377  .169     [-0.208, 0.036]
##  -0.008 (Mean)     0.003 (0.044)  0.070  .944     [-0.084, 0.090]
##  0.284 (+ SD)      0.092 (0.063)  1.457  .146     [-0.032, 0.216]
## ─────────────────────────────────────────────────────────────────

3.2.2 Plot

interact_plot(Mo$model.y, pred = Manipulation, modx = Stability.GraC,#Basic setup
              modx.values = "plus-minus", modx.labels= c("Low group", "High group"),legend.main="Stability of SBF",)+#Set moderators in plot
  ylab("Information Search")+xlab("Reflection on FSI")#+#Set labels of X and Y

  #scale_y_continuous(limits = c(20, 50))+#limit X and Y
  #scale_x_continuous(limits = c(1, 4),breaks = c(1, 2, 3, 4),labels=c("2010年", "2011年", "2012年","2013年"))#set label of X

3.3 Multilevel moderated mediation effect

3.3.1 Self-improvement

3.3.1.1 Learning Behavior

MoMe.S=PROCESS(data.M, y="WP.learningBehaviorV", x="Manipulation", 
           mods="Stability.GraC",meds="WP.InformationSearchV",
           covs=cc("WP.SupervisoryBehavioralFeedbackV_mean,WP.InformationSearchV_mean"),
           cluster ="B.ID", mod.path=c("x-m", "m-y"), center=FALSE,
           ci="mcmc", nsim=100, seed=1223)#, file="D2.doc")hlm.re.y = "(1|B.ID)",
## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 58 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## -    Outcome (Y) : WP.learningBehaviorV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.InformationSearchV
## - Moderators (W) : Stability.GraC
## - Covariates (C) : WP.SupervisoryBehavioralFeedbackV_mean, WP.InformationSearchV_mean
## -   HLM Clusters : B.ID
## 
## Formula of Mediator:
## -    WP.InformationSearchV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + Manipulation*Stability.GraC + (1 | B.ID)
## Formula of Outcome:
## -    WP.learningBehaviorV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + Manipulation + WP.InformationSearchV*Stability.GraC + (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.InformationSearchV  (3) WP.learningBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                               -0.062                   -0.012                       0.134                
##                                           (0.253)                  (0.116)                     (0.261)               
## WP.SupervisoryBehavioralFeedbackV_mean     0.474 ***                0.002                       0.402 ***            
##                                           (0.079)                  (0.037)                     (0.084)               
## WP.InformationSearchV_mean                 0.495 ***                1.001 ***                   0.293 **             
##                                           (0.076)                  (0.034)                     (0.093)               
## Manipulation                              -0.097 *                  0.005                      -0.096 *              
##                                           (0.049)                  (0.037)                     (0.048)               
## Stability.GraC                                                     -0.158                       0.913                
##                                                                    (0.094)                     (0.502)               
## Manipulation:Stability.GraC                                         0.310 *                                          
##                                                                    (0.127)                                           
## WP.InformationSearchV                                                                           0.217 ***            
##                                                                                                (0.056)               
## WP.InformationSearchV:Stability.GraC                                                           -0.159                
##                                                                                                (0.130)               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.397                    0.708                       0.419                
## Conditional R^2                            0.612                    0.708                       0.628                
## AIC                                     1152.966                  695.252                    1142.886                
## BIC                                     1178.901                  729.833                    1181.789                
## Num. obs.                                557                      557                         557                    
## Num. groups: B.ID                        158                      158                         158                    
## Var: B.ID (Intercept)                      0.183                    0.000                       0.177                
## Var: Residual                              0.328                    0.191                       0.315                
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.1.5)
## Effect Type : Moderated Mediation (Model 58)
## Sample Size : 557 (59 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Direct Effect: "Manipulation" (X) ==> "WP.learningBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────
##              Effect    S.E.      t     p             [95% CI]
## ─────────────────────────────────────────────────────────────
## Direct (c')  -0.096 (0.048) -1.996  .047 *   [-0.191, -0.002]
## ─────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.InformationSearchV" (M)
## ─────────────────────────────────────────────────────
##                                   F df1 df2     p    
## ─────────────────────────────────────────────────────
## Manipulation * Stability.GraC  5.97   1 551  .015 *  
## ─────────────────────────────────────────────────────
## 
## Simple Slopes: "Manipulation" (X) ==> "WP.InformationSearchV" (M)
## (Conditional Effects [a] of X on M)
## ─────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.      t     p            [95% CI]
## ─────────────────────────────────────────────────────────────────
##  -0.301 (- SD)    -0.088 (0.052) -1.683  .093 .   [-0.191, 0.015]
##  -0.008 (Mean)     0.002 (0.037)  0.066  .947     [-0.070, 0.075]
##  0.284 (+ SD)      0.093 (0.052)  1.776  .076 .   [-0.010, 0.196]
## ─────────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.learningBehaviorV" (Y)
## ──────────────────────────────────────────────────────────────
##                                            F df1 df2     p    
## ──────────────────────────────────────────────────────────────
## WP.InformationSearchV * Stability.GraC  1.50   1 501  .222    
## ──────────────────────────────────────────────────────────────
## 
## Simple Slopes: "WP.InformationSearchV" (M) ==> "WP.learningBehaviorV" (Y)
## (Conditional Effects [b] of M on Y)
## ───────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p           [95% CI]
## ───────────────────────────────────────────────────────────────
##  -0.301 (- SD)     0.264 (0.060) 4.429 <.001 *** [0.147, 0.381]
##  -0.008 (Mean)     0.218 (0.056) 3.885 <.001 *** [0.108, 0.328]
##  0.284 (+ SD)      0.172 (0.075) 2.291  .022 *   [0.025, 0.318]
## ───────────────────────────────────────────────────────────────
## 
## Running 100 * 3 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.InformationSearchV" (M) ==> "WP.learningBehaviorV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ──────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.      z     p        [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────────
##  -0.301 (- SD)    -0.024 (0.016) -1.558  .119     [-0.056,  0.002]
##  -0.008 (Mean)     0.001 (0.008)  0.160  .873     [-0.018,  0.016]
##  0.284 (+ SD)      0.017 (0.012)  1.370  .171     [-0.002,  0.043]
## ──────────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

3.3.1.2 Job Crafting

MoMe.S=PROCESS(data.M, y="WP.JobCraftingV", x="Manipulation", 
           mods="Stability.GraC",meds="WP.InformationSearchV",
           covs=cc("WP.SupervisoryBehavioralFeedbackV_mean,WP.InformationSearchV_mean"),
           cluster ="B.ID", mod.path=c("x-m", "m-y"), center=FALSE,
           ci="mcmc", nsim=100, seed=1223)#, file="D2.doc")hlm.re.y = "(1|B.ID)",
## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 58 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## -    Outcome (Y) : WP.JobCraftingV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.InformationSearchV
## - Moderators (W) : Stability.GraC
## - Covariates (C) : WP.SupervisoryBehavioralFeedbackV_mean, WP.InformationSearchV_mean
## -   HLM Clusters : B.ID
## 
## Formula of Mediator:
## -    WP.InformationSearchV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + Manipulation*Stability.GraC + (1 | B.ID)
## Formula of Outcome:
## -    WP.JobCraftingV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + Manipulation + WP.InformationSearchV*Stability.GraC + (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.InformationSearchV  (3) WP.JobCraftingV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                              -0.197               -0.012                     -0.155            
##                                          (0.219)              (0.116)                    (0.230)           
## WP.SupervisoryBehavioralFeedbackV_mean    0.422 ***            0.002                      0.398 ***        
##                                          (0.069)              (0.037)                    (0.074)           
## WP.InformationSearchV_mean                0.533 ***            1.001 ***                  0.345 ***        
##                                          (0.065)              (0.034)                    (0.078)           
## Manipulation                             -0.016                0.005                     -0.018            
##                                          (0.037)              (0.037)                    (0.036)           
## Stability.GraC                                                -0.158                     -0.105            
##                                                               (0.094)                    (0.404)           
## Manipulation:Stability.GraC                                    0.310 *                                     
##                                                               (0.127)                                      
## WP.InformationSearchV                                                                     0.199 ***        
##                                                                                          (0.042)           
## WP.InformationSearchV:Stability.GraC                                                      0.066            
##                                                                                          (0.103)           
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                              0.489                0.708                      0.499            
## Conditional R^2                           0.721                0.708                      0.736            
## AIC                                     882.970              695.252                    875.491            
## BIC                                     908.905              729.833                    914.394            
## Num. obs.                               557                  557                        557                
## Num. groups: B.ID                       158                  158                        158                
## Var: B.ID (Intercept)                     0.154                0.000                      0.158            
## Var: Residual                             0.186                0.191                      0.177            
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.1.5)
## Effect Type : Moderated Mediation (Model 58)
## Sample Size : 557 (59 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Direct Effect: "Manipulation" (X) ==> "WP.JobCraftingV" (Y)
## ─────────────────────────────────────────────────────────────
##              Effect    S.E.      t     p             [95% CI]
## ─────────────────────────────────────────────────────────────
## Direct (c')  -0.018 (0.036) -0.507  .612     [-0.090,  0.053]
## ─────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.InformationSearchV" (M)
## ─────────────────────────────────────────────────────
##                                   F df1 df2     p    
## ─────────────────────────────────────────────────────
## Manipulation * Stability.GraC  5.97   1 551  .015 *  
## ─────────────────────────────────────────────────────
## 
## Simple Slopes: "Manipulation" (X) ==> "WP.InformationSearchV" (M)
## (Conditional Effects [a] of X on M)
## ─────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.      t     p            [95% CI]
## ─────────────────────────────────────────────────────────────────
##  -0.301 (- SD)    -0.088 (0.052) -1.683  .093 .   [-0.191, 0.015]
##  -0.008 (Mean)     0.002 (0.037)  0.066  .947     [-0.070, 0.075]
##  0.284 (+ SD)      0.093 (0.052)  1.776  .076 .   [-0.010, 0.196]
## ─────────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.JobCraftingV" (Y)
## ──────────────────────────────────────────────────────────────
##                                            F df1 df2     p    
## ──────────────────────────────────────────────────────────────
## WP.InformationSearchV * Stability.GraC  0.41   1 540  .523    
## ──────────────────────────────────────────────────────────────
## 
## Simple Slopes: "WP.InformationSearchV" (M) ==> "WP.JobCraftingV" (Y)
## (Conditional Effects [b] of M on Y)
## ───────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p           [95% CI]
## ───────────────────────────────────────────────────────────────
##  -0.301 (- SD)     0.179 (0.045) 3.957 <.001 *** [0.090, 0.267]
##  -0.008 (Mean)     0.198 (0.042) 4.698 <.001 *** [0.115, 0.281]
##  0.284 (+ SD)      0.217 (0.058) 3.761 <.001 *** [0.104, 0.331]
## ───────────────────────────────────────────────────────────────
## 
## Running 100 * 3 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.InformationSearchV" (M) ==> "WP.JobCraftingV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ─────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.      z     p       [MCMC 95% CI]
## ─────────────────────────────────────────────────────────────────
##  -0.301 (- SD)    -0.017 (0.011) -1.541  .123     [-0.038, 0.001]
##  -0.008 (Mean)     0.001 (0.007)  0.162  .871     [-0.016, 0.014]
##  0.284 (+ SD)      0.022 (0.013)  1.619  .105     [-0.002, 0.048]
## ─────────────────────────────────────────────────────────────────
## 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. :)

3.3.2 System-improvement

3.3.2.1 Taking Charge

MoMe.S=PROCESS(data.M, y="WP.TakingChargeV", x="Manipulation", 
           mods="Stability.GraC",meds="WP.InformationSearchV",
           covs=cc("WP.SupervisoryBehavioralFeedbackV_mean,WP.InformationSearchV_mean"),
           cluster ="B.ID", mod.path=c("x-m", "m-y"), center=FALSE,
           ci="mcmc", nsim=100, seed=1223)#, file="D2.doc")hlm.re.y = "(1|B.ID)",
## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 58 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## -    Outcome (Y) : WP.TakingChargeV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.InformationSearchV
## - Moderators (W) : Stability.GraC
## - Covariates (C) : WP.SupervisoryBehavioralFeedbackV_mean, WP.InformationSearchV_mean
## -   HLM Clusters : B.ID
## 
## Formula of Mediator:
## -    WP.InformationSearchV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + Manipulation*Stability.GraC + (1 | B.ID)
## Formula of Outcome:
## -    WP.TakingChargeV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + Manipulation + WP.InformationSearchV*Stability.GraC + (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.InformationSearchV  (3) WP.TakingChargeV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                               -0.309               -0.012                      -0.280            
##                                           (0.287)              (0.116)                     (0.301)           
## WP.SupervisoryBehavioralFeedbackV_mean     0.423 ***            0.002                       0.395 ***        
##                                           (0.090)              (0.037)                     (0.097)           
## WP.InformationSearchV_mean                 0.534 ***            1.001 ***                   0.302 **         
##                                           (0.085)              (0.034)                     (0.102)           
## Manipulation                              -0.011                0.005                      -0.017            
##                                           (0.048)              (0.037)                     (0.047)           
## Stability.GraC                                                 -0.158                      -0.470            
##                                                                (0.094)                     (0.527)           
## Manipulation:Stability.GraC                                     0.310 *                                      
##                                                                (0.127)                                       
## WP.InformationSearchV                                                                       0.250 ***        
##                                                                                            (0.055)           
## WP.InformationSearchV:Stability.GraC                                                        0.179            
##                                                                                            (0.135)           
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.361                0.708                       0.374            
## Conditional R^2                            0.655                0.708                       0.673            
## AIC                                     1172.799              695.252                    1165.419            
## BIC                                     1198.734              729.833                    1204.322            
## Num. obs.                                557                  557                         557                
## Num. groups: B.ID                        158                  158                         158                
## Var: B.ID (Intercept)                      0.266                0.000                       0.273            
## Var: Residual                              0.313                0.191                       0.298            
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.1.5)
## Effect Type : Moderated Mediation (Model 58)
## Sample Size : 557 (59 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Direct Effect: "Manipulation" (X) ==> "WP.TakingChargeV" (Y)
## ─────────────────────────────────────────────────────────────
##              Effect    S.E.      t     p             [95% CI]
## ─────────────────────────────────────────────────────────────
## Direct (c')  -0.017 (0.047) -0.367  .714     [-0.109,  0.075]
## ─────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.InformationSearchV" (M)
## ─────────────────────────────────────────────────────
##                                   F df1 df2     p    
## ─────────────────────────────────────────────────────
## Manipulation * Stability.GraC  5.97   1 551  .015 *  
## ─────────────────────────────────────────────────────
## 
## Simple Slopes: "Manipulation" (X) ==> "WP.InformationSearchV" (M)
## (Conditional Effects [a] of X on M)
## ─────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.      t     p            [95% CI]
## ─────────────────────────────────────────────────────────────────
##  -0.301 (- SD)    -0.088 (0.052) -1.683  .093 .   [-0.191, 0.015]
##  -0.008 (Mean)     0.002 (0.037)  0.066  .947     [-0.070, 0.075]
##  0.284 (+ SD)      0.093 (0.052)  1.776  .076 .   [-0.010, 0.196]
## ─────────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.TakingChargeV" (Y)
## ──────────────────────────────────────────────────────────────
##                                            F df1 df2     p    
## ──────────────────────────────────────────────────────────────
## WP.InformationSearchV * Stability.GraC  1.76   1 541  .185    
## ──────────────────────────────────────────────────────────────
## 
## Simple Slopes: "WP.InformationSearchV" (M) ==> "WP.TakingChargeV" (Y)
## (Conditional Effects [b] of M on Y)
## ───────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p           [95% CI]
## ───────────────────────────────────────────────────────────────
##  -0.301 (- SD)     0.196 (0.059) 3.339 <.001 *** [0.081, 0.311]
##  -0.008 (Mean)     0.248 (0.055) 4.534 <.001 *** [0.141, 0.356]
##  0.284 (+ SD)      0.301 (0.075) 3.998 <.001 *** [0.153, 0.448]
## ───────────────────────────────────────────────────────────────
## 
## Running 100 * 3 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.InformationSearchV" (M) ==> "WP.TakingChargeV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ─────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.      z     p       [MCMC 95% CI]
## ─────────────────────────────────────────────────────────────────
##  -0.301 (- SD)    -0.018 (0.012) -1.507  .132     [-0.044, 0.002]
##  -0.008 (Mean)     0.001 (0.009)  0.162  .872     [-0.020, 0.017]
##  0.284 (+ SD)      0.030 (0.018)  1.641  .101     [-0.003, 0.065]
## ─────────────────────────────────────────────────────────────────
## 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. :)

3.3.2.2 Performance Improvement

MoMe.S=PROCESS(data.M, y="WP.PerformanceImprovementV", x="Manipulation", 
           mods="Stability.GraC",meds="WP.InformationSearchV",
           covs=cc("WP.SupervisoryBehavioralFeedbackV_mean,WP.InformationSearchV_mean"),
           cluster ="B.ID", mod.path=c("x-m", "m-y"), center=FALSE,
           ci="mcmc", nsim=100, seed=1223)#, file="D2.doc")hlm.re.y = "(1|B.ID)",
## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 58 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## -    Outcome (Y) : WP.PerformanceImprovementV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.InformationSearchV
## - Moderators (W) : Stability.GraC
## - Covariates (C) : WP.SupervisoryBehavioralFeedbackV_mean, WP.InformationSearchV_mean
## -   HLM Clusters : B.ID
## 
## Formula of Mediator:
## -    WP.InformationSearchV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + Manipulation*Stability.GraC + (1 | B.ID)
## Formula of Outcome:
## -    WP.PerformanceImprovementV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + Manipulation + WP.InformationSearchV*Stability.GraC + (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.InformationSearchV  (3) WP.PerformanceImprovementV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                               -0.229                         -0.012                      -0.226                      
##                                           (0.268)                        (0.116)                     (0.283)                     
## WP.SupervisoryBehavioralFeedbackV_mean     0.488 ***                      0.002                       0.486 ***                  
##                                           (0.084)                        (0.037)                     (0.090)                     
## WP.InformationSearchV_mean                 0.482 ***                      1.001 ***                   0.305 **                   
##                                           (0.080)                        (0.034)                     (0.105)                     
## Manipulation                               0.022                          0.005                       0.020                      
##                                           (0.059)                        (0.037)                     (0.058)                     
## Stability.GraC                                                           -0.158                      -0.056                      
##                                                                          (0.094)                     (0.574)                     
## Manipulation:Stability.GraC                                               0.310 *                                                
##                                                                          (0.127)                                                 
## WP.InformationSearchV                                                                                 0.178 **                   
##                                                                                                      (0.068)                     
## WP.InformationSearchV:Stability.GraC                                                                  0.020                      
##                                                                                                      (0.149)                     
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.342                          0.708                       0.346                      
## Conditional R^2                            0.521                          0.708                       0.530                      
## AIC                                     1312.031                        695.252                    1318.295                      
## BIC                                     1337.966                        729.833                    1357.198                      
## Num. obs.                                557                            557                         557                          
## Num. groups: B.ID                        158                            158                         158                          
## Var: B.ID (Intercept)                      0.175                          0.000                       0.180                      
## Var: Residual                              0.468                          0.191                       0.461                      
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.1.5)
## Effect Type : Moderated Mediation (Model 58)
## Sample Size : 557 (59 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Direct Effect: "Manipulation" (X) ==> "WP.PerformanceImprovementV" (Y)
## ────────────────────────────────────────────────────────────
##              Effect    S.E.     t     p             [95% CI]
## ────────────────────────────────────────────────────────────
## Direct (c')   0.020 (0.058) 0.351  .726     [-0.093,  0.135]
## ────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.InformationSearchV" (M)
## ─────────────────────────────────────────────────────
##                                   F df1 df2     p    
## ─────────────────────────────────────────────────────
## Manipulation * Stability.GraC  5.97   1 551  .015 *  
## ─────────────────────────────────────────────────────
## 
## Simple Slopes: "Manipulation" (X) ==> "WP.InformationSearchV" (M)
## (Conditional Effects [a] of X on M)
## ─────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.      t     p            [95% CI]
## ─────────────────────────────────────────────────────────────────
##  -0.301 (- SD)    -0.088 (0.052) -1.683  .093 .   [-0.191, 0.015]
##  -0.008 (Mean)     0.002 (0.037)  0.066  .947     [-0.070, 0.075]
##  0.284 (+ SD)      0.093 (0.052)  1.776  .076 .   [-0.010, 0.196]
## ─────────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.PerformanceImprovementV" (Y)
## ──────────────────────────────────────────────────────────────
##                                            F df1 df2     p    
## ──────────────────────────────────────────────────────────────
## WP.InformationSearchV * Stability.GraC  0.02   1 460  .893    
## ──────────────────────────────────────────────────────────────
## 
## Simple Slopes: "WP.InformationSearchV" (M) ==> "WP.PerformanceImprovementV" (Y)
## (Conditional Effects [b] of M on Y)
## ───────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p           [95% CI]
## ───────────────────────────────────────────────────────────────
##  -0.301 (- SD)     0.172 (0.072) 2.395  .017 *   [0.031, 0.312]
##  -0.008 (Mean)     0.177 (0.068) 2.622  .009 **  [0.045, 0.310]
##  0.284 (+ SD)      0.183 (0.089) 2.070  .039 *   [0.010, 0.357]
## ───────────────────────────────────────────────────────────────
## 
## Running 100 * 3 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.InformationSearchV" (M) ==> "WP.PerformanceImprovementV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ─────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.      z     p       [MCMC 95% CI]
## ─────────────────────────────────────────────────────────────────
##  -0.301 (- SD)    -0.016 (0.012) -1.401  .161     [-0.043, 0.001]
##  -0.008 (Mean)     0.001 (0.007)  0.153  .879     [-0.014, 0.014]
##  0.284 (+ SD)      0.018 (0.014)  1.306  .192     [-0.004, 0.048]
## ─────────────────────────────────────────────────────────────────
## 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. :)

3.3.3 Learning

3.3.3.1 Learning through independant observation

MoMe.S=PROCESS(data.M, y="WP.ObservationalLearningV", x="Manipulation",
               mods="Stability.GraC",meds="WP.InformationSearchV",
               covs=cc("WP.SupervisoryBehavioralFeedbackV_mean,WP.InformationSearchV_mean"),
               cluster ="B.ID", mod.path=c("x-m", "m-y"), center=FALSE,
               ci="mcmc", nsim=100, seed=1223)#, file="D2.doc")hlm.re.y = "(1|B.ID)",
## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 58 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## -    Outcome (Y) : WP.ObservationalLearningV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.InformationSearchV
## - Moderators (W) : Stability.GraC
## - Covariates (C) : WP.SupervisoryBehavioralFeedbackV_mean, WP.InformationSearchV_mean
## -   HLM Clusters : B.ID
## 
## Formula of Mediator:
## -    WP.InformationSearchV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + Manipulation*Stability.GraC + (1 | B.ID)
## Formula of Outcome:
## -    WP.ObservationalLearningV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + Manipulation + WP.InformationSearchV*Stability.GraC + (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.InformationSearchV  (3) WP.ObservationalLearningV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                                0.462                        -0.012                       0.474                     
##                                           (0.297)                       (0.116)                     (0.313)                    
## WP.SupervisoryBehavioralFeedbackV_mean     0.398 ***                     0.002                       0.404 ***                 
##                                           (0.093)                       (0.037)                     (0.100)                    
## WP.InformationSearchV_mean                 0.388 ***                     1.001 ***                   0.210                     
##                                           (0.089)                       (0.034)                     (0.111)                    
## Manipulation                              -0.029                         0.005                      -0.027                     
##                                           (0.058)                       (0.037)                     (0.057)                    
## Stability.GraC                                                          -0.158                       0.430                     
##                                                                         (0.094)                     (0.598)                    
## Manipulation:Stability.GraC                                              0.310 *                                               
##                                                                         (0.127)                                                
## WP.InformationSearchV                                                                                0.169 *                   
##                                                                                                     (0.067)                    
## WP.InformationSearchV:Stability.GraC                                                                -0.134                     
##                                                                                                     (0.155)                    
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.237                         0.708                       0.245                     
## Conditional R^2                            0.510                         0.708                       0.521                     
## AIC                                     1330.961                       695.252                    1335.098                     
## BIC                                     1356.896                       729.833                    1374.001                     
## Num. obs.                                557                           557                         557                         
## Num. groups: B.ID                        158                           158                         158                         
## Var: B.ID (Intercept)                      0.252                         0.000                       0.256                     
## Var: Residual                              0.452                         0.191                       0.445                     
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.1.5)
## Effect Type : Moderated Mediation (Model 58)
## Sample Size : 557 (59 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Direct Effect: "Manipulation" (X) ==> "WP.ObservationalLearningV" (Y)
## ─────────────────────────────────────────────────────────────
##              Effect    S.E.      t     p             [95% CI]
## ─────────────────────────────────────────────────────────────
## Direct (c')  -0.027 (0.057) -0.462  .644     [-0.139,  0.086]
## ─────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.InformationSearchV" (M)
## ─────────────────────────────────────────────────────
##                                   F df1 df2     p    
## ─────────────────────────────────────────────────────
## Manipulation * Stability.GraC  5.97   1 551  .015 *  
## ─────────────────────────────────────────────────────
## 
## Simple Slopes: "Manipulation" (X) ==> "WP.InformationSearchV" (M)
## (Conditional Effects [a] of X on M)
## ─────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.      t     p            [95% CI]
## ─────────────────────────────────────────────────────────────────
##  -0.301 (- SD)    -0.088 (0.052) -1.683  .093 .   [-0.191, 0.015]
##  -0.008 (Mean)     0.002 (0.037)  0.066  .947     [-0.070, 0.075]
##  0.284 (+ SD)      0.093 (0.052)  1.776  .076 .   [-0.010, 0.196]
## ─────────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.ObservationalLearningV" (Y)
## ──────────────────────────────────────────────────────────────
##                                            F df1 df2     p    
## ──────────────────────────────────────────────────────────────
## WP.InformationSearchV * Stability.GraC  0.75   1 505  .387    
## ──────────────────────────────────────────────────────────────
## 
## Simple Slopes: "WP.InformationSearchV" (M) ==> "WP.ObservationalLearningV" (Y)
## (Conditional Effects [b] of M on Y)
## ────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p            [95% CI]
## ────────────────────────────────────────────────────────────────
##  -0.301 (- SD)     0.210 (0.071) 2.957  .003 **  [ 0.071, 0.349]
##  -0.008 (Mean)     0.171 (0.067) 2.560  .011 *   [ 0.040, 0.301]
##  0.284 (+ SD)      0.131 (0.089) 1.475  .141     [-0.043, 0.306]
## ────────────────────────────────────────────────────────────────
## 
## Running 100 * 3 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.InformationSearchV" (M) ==> "WP.ObservationalLearningV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ─────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.      z     p       [MCMC 95% CI]
## ─────────────────────────────────────────────────────────────────
##  -0.301 (- SD)    -0.020 (0.013) -1.473  .141     [-0.049, 0.002]
##  -0.008 (Mean)     0.001 (0.006)  0.152  .879     [-0.014, 0.013]
##  0.284 (+ SD)      0.013 (0.012)  1.064  .287     [-0.005, 0.038]
## ─────────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

3.3.3.2 Learning through social interaction

MoMe.S=PROCESS(data.M, y="WP.AdviceSeekingV", x="Manipulation",
               mods="Stability.GraC",meds="WP.InformationSearchV",
               covs=cc("WP.SupervisoryBehavioralFeedbackV_mean,WP.InformationSearchV_mean"),
               cluster ="B.ID", mod.path=c("x-m", "m-y"), center=FALSE,
               ci="mcmc", nsim=100, seed=1223)#, file="D2.doc")hlm.re.y = "(1|B.ID)",
## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 58 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## -    Outcome (Y) : WP.AdviceSeekingV
## -  Predictor (X) : Manipulation
## -  Mediators (M) : WP.InformationSearchV
## - Moderators (W) : Stability.GraC
## - Covariates (C) : WP.SupervisoryBehavioralFeedbackV_mean, WP.InformationSearchV_mean
## -   HLM Clusters : B.ID
## 
## Formula of Mediator:
## -    WP.InformationSearchV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + Manipulation*Stability.GraC + (1 | B.ID)
## Formula of Outcome:
## -    WP.AdviceSeekingV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + Manipulation + WP.InformationSearchV*Stability.GraC + (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.InformationSearchV  (3) WP.AdviceSeekingV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                                1.304 ***            -0.012                       1.359 ***         
##                                           (0.241)               (0.116)                     (0.255)            
## WP.SupervisoryBehavioralFeedbackV_mean     0.326 ***             0.002                       0.325 ***         
##                                           (0.075)               (0.037)                     (0.081)            
## WP.InformationSearchV_mean                 0.362 ***             1.001 ***                  -0.055             
##                                           (0.072)               (0.034)                     (0.097)            
## Manipulation                              -0.100                 0.005                      -0.098             
##                                           (0.058)               (0.037)                     (0.055)            
## Stability.GraC                                                  -0.158                       0.796             
##                                                                 (0.094)                     (0.528)            
## Manipulation:Stability.GraC                                      0.310 *                                       
##                                                                 (0.127)                                        
## WP.InformationSearchV                                                                        0.403 ***         
##                                                                                             (0.064)            
## WP.InformationSearchV:Stability.GraC                                                        -0.227             
##                                                                                             (0.137)            
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.228                 0.708                       0.278             
## Conditional R^2                            0.388                 0.708                       0.459             
## AIC                                     1271.584               695.252                    1237.447             
## BIC                                     1297.519               729.833                    1276.350             
## Num. obs.                                557                   557                         557                 
## Num. groups: B.ID                        158                   158                         158                 
## Var: B.ID (Intercept)                      0.120                 0.000                       0.137             
## Var: Residual                              0.458                 0.191                       0.408             
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.1.5)
## Effect Type : Moderated Mediation (Model 58)
## Sample Size : 557 (59 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Direct Effect: "Manipulation" (X) ==> "WP.AdviceSeekingV" (Y)
## ─────────────────────────────────────────────────────────────
##              Effect    S.E.      t     p             [95% CI]
## ─────────────────────────────────────────────────────────────
## Direct (c')  -0.098 (0.055) -1.789  .074 .   [-0.205,  0.010]
## ─────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.InformationSearchV" (M)
## ─────────────────────────────────────────────────────
##                                   F df1 df2     p    
## ─────────────────────────────────────────────────────
## Manipulation * Stability.GraC  5.97   1 551  .015 *  
## ─────────────────────────────────────────────────────
## 
## Simple Slopes: "Manipulation" (X) ==> "WP.InformationSearchV" (M)
## (Conditional Effects [a] of X on M)
## ─────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.      t     p            [95% CI]
## ─────────────────────────────────────────────────────────────────
##  -0.301 (- SD)    -0.088 (0.052) -1.683  .093 .   [-0.191, 0.015]
##  -0.008 (Mean)     0.002 (0.037)  0.066  .947     [-0.070, 0.075]
##  0.284 (+ SD)      0.093 (0.052)  1.776  .076 .   [-0.010, 0.196]
## ─────────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.AdviceSeekingV" (Y)
## ──────────────────────────────────────────────────────────────
##                                            F df1 df2     p    
## ──────────────────────────────────────────────────────────────
## WP.InformationSearchV * Stability.GraC  2.72   1 440  .100    
## ──────────────────────────────────────────────────────────────
## 
## Simple Slopes: "WP.InformationSearchV" (M) ==> "WP.AdviceSeekingV" (Y)
## (Conditional Effects [b] of M on Y)
## ───────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p           [95% CI]
## ───────────────────────────────────────────────────────────────
##  -0.301 (- SD)     0.471 (0.067) 7.025 <.001 *** [0.340, 0.603]
##  -0.008 (Mean)     0.405 (0.064) 6.377 <.001 *** [0.281, 0.530]
##  0.284 (+ SD)      0.339 (0.082) 4.110 <.001 *** [0.177, 0.501]
## ───────────────────────────────────────────────────────────────
## 
## Running 100 * 3 simulations...
## Indirect Path: "Manipulation" (X) ==> "WP.InformationSearchV" (M) ==> "WP.AdviceSeekingV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ──────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.      z     p        [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────────
##  -0.301 (- SD)    -0.043 (0.027) -1.601  .109     [-0.098,  0.004]
##  -0.008 (Mean)     0.002 (0.014)  0.164  .869     [-0.032,  0.027]
##  0.284 (+ SD)      0.034 (0.020)  1.652  .099 .   [-0.003, 0.073] 
## ──────────────────────────────────────────────────────────────────
## 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. :)

4 ESM STUDY

4.1 Primary analysis

4.1.1 Frequency analysis

Freq(data.D$WA.WorkReflectionV)
## Frequency Statistics:
## ──────────────────────────
##                     N    %
## ──────────────────────────
## 1                   5  0.7
## 1.5                 1  0.1
## 1.66666666666667    4  0.5
## 2                  87 11.8
## 2.16666666666667    8  1.1
## 2.33333333333333   31  4.2
## 2.5                14  1.9
## 2.66666666666667   42  5.7
## 2.83333333333333   17  2.3
## 3                 124 16.9
## 3.16666666666667   22  3.0
## 3.33333333333333   41  5.6
## 3.5                35  4.8
## 3.66666666666667   48  6.5
## 3.83333333333333   29  3.9
## 4                 164 22.3
## 4.16666666666667    2  0.3
## 4.33333333333333   10  1.4
## 4.5                 5  0.7
## 4.66666666666667    4  0.5
## 5                  26  3.5
## (NA)               16  2.2
## ──────────────────────────
## Total N = 735
## Valid N = 719
Freq(data.D$W.Day)
## Frequency Statistics:
## ────────────
##       N    %
## ────────────
## 6   148 20.1
## 7   152 20.7
## 8   144 19.6
## 9   148 20.1
## 10  143 19.5
## ────────────
## Total N = 735

4.1.2 ICC and RWG

HLM_ICC_rWG(data.D, group="B.ID", icc.var="WA.WorkReflectionV")
## 
## ------ Sample Size Information ------
## 
## Level 1: N = 719 observations ("WA.WorkReflectionV")
## Level 2: K = 165 groups ("B.ID")
## 
##        n (group sizes)
## Min.             1.000
## Median           5.000
## Mean             4.358
## Max.             5.000
## 
## ------ ICC(1), ICC(2), and rWG ------
## 
## ICC variable: "WA.WorkReflectionV"
## 
## ICC(1) = 0.702 (non-independence of data)
## ICC(2) = 0.900 (reliability of group means)
## 
## rWG variable: "WA.WorkReflectionV"
## 
## rWG (within-group agreement for single-item measures)
## ───────────────────────────────────────────────────
##       Min. 1st Qu. Median  Mean 3rd Qu.  Max.  NA's
## ───────────────────────────────────────────────────
## rWG  0.000   0.816  0.942 0.858   0.993 1.000 9.000
## ───────────────────────────────────────────────────
HLM_ICC_rWG(data.D, group="B.ID", icc.var="WP.InformationSearchV")
## 
## ------ Sample Size Information ------
## 
## Level 1: N = 689 observations ("WP.InformationSearchV")
## Level 2: K = 162 groups ("B.ID")
## 
##        n (group sizes)
## Min.             1.000
## Median           5.000
## Mean             4.253
## Max.             5.000
## 
## ------ ICC(1), ICC(2), and rWG ------
## 
## ICC variable: "WP.InformationSearchV"
## 
## ICC(1) = 0.691 (non-independence of data)
## ICC(2) = 0.890 (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   0.808  0.933 0.848   1.000 1.000 12.000
## ────────────────────────────────────────────────────

4.1.3 Main effect

Main= lmer(WP.InformationSearchV~WA.WorkReflectionV+ WA.WorkReflectionV_mean + (1|B.ID), na.action = na.exclude, data = data.D, control=lmerControl(optimizer="bobyqa")) 
HLM_summary(Main)
## 
## Hierarchical Linear Model (HLM)
## (also known as) Linear Mixed Model (LMM)
## (also known as) Multilevel Linear Model (MLM)
## 
## Model Information:
## Formula: WP.InformationSearchV ~ WA.WorkReflectionV + WA.WorkReflectionV_mean + (1 | B.ID)
## Level-1 Observations: N = 675
## Level-2 Groups/Clusters: B.ID, 159
## 
## Model Fit:
## AIC = 1149.812
## BIC = 1172.385
## R_(m)² = 0.34124  (Marginal R²: fixed effects)
## R_(c)² = 0.68153  (Conditional R²: fixed + random effects)
## Omega² = NA  (= 1 - proportion of unexplained variance)
## 
## ANOVA Table:
## ────────────────────────────────────────────────────────────────────
##                          Sum Sq Mean Sq NumDF  DenDF     F     p    
## ────────────────────────────────────────────────────────────────────
## WA.WorkReflectionV         0.60    0.60  1.00 520.65  2.84  .092 .  
## WA.WorkReflectionV_mean   13.73   13.73  1.00 365.40 65.20 <.001 ***
## ────────────────────────────────────────────────────────────────────
## 
## Fixed Effects:
## Unstandardized Coefficients (b or γ):
## Outcome Variable: WP.InformationSearchV
## ───────────────────────────────────────────────────────────────────────────
##                            b/γ    S.E.    t    df     p     [95% CI of b/γ]
## ───────────────────────────────────────────────────────────────────────────
## (Intercept)              1.337 (0.196) 6.83 161.4 <.001 *** [ 0.950, 1.723]
## WA.WorkReflectionV       0.077 (0.046) 1.69 520.7  .092 .   [-0.013, 0.166]
## WA.WorkReflectionV_mean  0.603 (0.075) 8.07 365.4 <.001 *** [ 0.456, 0.750]
## ───────────────────────────────────────────────────────────────────────────
## 'df' is estimated by Satterthwaite approximation.
## 
## Standardized Coefficients (β):
## Outcome Variable: WP.InformationSearchV
## ───────────────────────────────────────────────────────────────────────────
##                              β    S.E.    t    df     p       [95% CI of β]
## ───────────────────────────────────────────────────────────────────────────
## WA.WorkReflectionV       0.075 (0.044) 1.69 520.7  .092 .   [-0.012, 0.162]
## WA.WorkReflectionV_mean  0.513 (0.064) 8.07 365.4 <.001 *** [ 0.388, 0.638]
## ───────────────────────────────────────────────────────────────────────────
## 
## Random Effects:
## ──────────────────────────────────────────
##  Cluster  K   Parameter   Variance     ICC
## ──────────────────────────────────────────
##  B.ID     159 (Intercept)  0.22507 0.51656
##  Residual                  0.21064        
## ──────────────────────────────────────────

4.2 Moderation effect

4.2.1 Model

Mo=PROCESS(data.D, y="WP.InformationSearchV", x="WA.WorkReflectionV", mods="Stability.GraC", 
           covs=cc("WP.SupervisoryBehavioralFeedbackV_mean,WA.WorkReflectionV_mean"),
           cluster ="B.ID", center=FALSE)#, file="D2.doc")hlm.re.y = "(1|B.ID)", 
## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## -    Outcome (Y) : WP.InformationSearchV
## -  Predictor (X) : WA.WorkReflectionV
## -  Mediators (M) : -
## - Moderators (W) : Stability.GraC
## - Covariates (C) : WP.SupervisoryBehavioralFeedbackV_mean, WA.WorkReflectionV_mean
## -   HLM Clusters : B.ID
## 
## Formula of Outcome:
## -    WP.InformationSearchV ~ WP.SupervisoryBehavioralFeedbackV_mean + WA.WorkReflectionV_mean + WA.WorkReflectionV*Stability.GraC + (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.InformationSearchV
## ────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                                1.104 ***                  1.124 ***             
##                                           (0.207)                    (0.213)                
## WP.SupervisoryBehavioralFeedbackV_mean     0.245 **                   0.228 **              
##                                           (0.081)                    (0.082)                
## WA.WorkReflectionV_mean                    0.419 ***                  0.381 ***             
##                                           (0.098)                    (0.100)                
## WA.WorkReflectionV                         0.077                      0.124 *               
##                                           (0.046)                    (0.050)                
## Stability.GraC                                                       -0.727 *               
##                                                                      (0.309)                
## WA.WorkReflectionV:Stability.GraC                                     0.220 *               
##                                                                      (0.092)                
## ────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.369                      0.375                 
## Conditional R^2                            0.685                      0.688                 
## AIC                                     1128.083                   1131.216                 
## BIC                                     1155.091                   1167.226                 
## Num. obs.                                666                        666                     
## Num. groups: B.ID                        150                        150                     
## Var: B.ID (Intercept)                      0.211                      0.210                 
## Var: Residual                              0.211                      0.209                 
## ────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 666 (69 missing observations deleted)
## Random Seed : -
## Simulations : -
## 
## Interaction Effect on "WP.InformationSearchV" (Y)
## ───────────────────────────────────────────────────────────
##                                         F df1 df2     p    
## ───────────────────────────────────────────────────────────
## WA.WorkReflectionV * Stability.GraC  5.67   1 660  .018 *  
## ───────────────────────────────────────────────────────────
## 
## Simple Slopes: "WA.WorkReflectionV" (X) ==> "WP.InformationSearchV" (Y)
## ────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p            [95% CI]
## ────────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.056 (0.046) 1.204  .229     [-0.035, 0.147]
##  -0.000 (Mean)     0.124 (0.050) 2.505  .013 *   [ 0.027, 0.221]
##  0.311 (+ SD)      0.192 (0.066) 2.899  .004 **  [ 0.062, 0.322]
## ────────────────────────────────────────────────────────────────

4.2.2 Plot

interact_plot(Mo$model.y, pred = WA.WorkReflectionV, modx = Stability.GraC,#Basic setup
              modx.values = "plus-minus", modx.labels= c("Low group", "High group"),legend.main="Stability of SBF",)+#Set moderators in plot
  ylab("Information Search")+xlab("Reflection on FSI")#+#Set labels of X and Y

  #scale_y_continuous(limits = c(20, 50))+#limit X and Y
  #scale_x_continuous(limits = c(1, 4),breaks = c(1, 2, 3, 4),labels=c("2010年", "2011年", "2012年","2013年"))#set label of X

4.3 Multilevel moderated mediation effect

4.3.1 Self-improvement

4.3.1.1 Learning Behavior

MoMe.S=PROCESS(data.D, y="WP.learningBehaviorV", x="WA.WorkReflectionV", 
           mods="Stability.GraC",meds="WP.InformationSearchV",
           covs=cc("WP.SupervisoryBehavioralFeedbackV_mean,WA.WorkReflectionV_mean"),
           cluster ="B.ID", mod.path=c("x-m", "m-y"), center=FALSE,
           ci="mcmc", nsim=100, seed=1223)#, file="D2.doc")hlm.re.y = "(1|B.ID)",
## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 58 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## -    Outcome (Y) : WP.learningBehaviorV
## -  Predictor (X) : WA.WorkReflectionV
## -  Mediators (M) : WP.InformationSearchV
## - Moderators (W) : Stability.GraC
## - Covariates (C) : WP.SupervisoryBehavioralFeedbackV_mean, WA.WorkReflectionV_mean
## -   HLM Clusters : B.ID
## 
## Formula of Mediator:
## -    WP.InformationSearchV ~ WP.SupervisoryBehavioralFeedbackV_mean + WA.WorkReflectionV_mean + WA.WorkReflectionV*Stability.GraC + (1 | B.ID)
## Formula of Outcome:
## -    WP.learningBehaviorV ~ WP.SupervisoryBehavioralFeedbackV_mean + WA.WorkReflectionV_mean + WA.WorkReflectionV + WP.InformationSearchV*Stability.GraC + (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.InformationSearchV  (3) WP.learningBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                                0.118                     1.124 ***                 -0.199                
##                                           (0.186)                   (0.213)                    (0.183)               
## WP.SupervisoryBehavioralFeedbackV_mean     0.281 ***                 0.228 **                   0.233 ***            
##                                           (0.072)                   (0.082)                    (0.069)               
## WA.WorkReflectionV_mean                    0.587 ***                 0.381 ***                  0.491 ***            
##                                           (0.093)                   (0.100)                    (0.089)               
## WA.WorkReflectionV                         0.096                     0.124 *                    0.083                
##                                           (0.049)                   (0.050)                    (0.048)               
## Stability.GraC                                                      -0.727 *                    0.880 **             
##                                                                     (0.309)                    (0.336)               
## WA.WorkReflectionV:Stability.GraC                                    0.220 *                                         
##                                                                     (0.092)                                          
## WP.InformationSearchV                                                                           0.237 ***            
##                                                                                                (0.040)               
## WP.InformationSearchV:Stability.GraC                                                           -0.257 **             
##                                                                                                (0.093)               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.510                     0.375                      0.555                
## Conditional R^2                            0.696                     0.688                      0.711                
## AIC                                     1182.414                  1131.216                   1144.889                
## BIC                                     1209.422                  1167.226                   1185.400                
## Num. obs.                                666                       666                        666                    
## Num. groups: B.ID                        150                       150                        150                    
## Var: B.ID (Intercept)                      0.152                     0.210                      0.127                
## Var: Residual                              0.249                     0.209                      0.235                
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.1.5)
## Effect Type : Moderated Mediation (Model 58)
## Sample Size : 666 (69 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Direct Effect: "WA.WorkReflectionV" (X) ==> "WP.learningBehaviorV" (Y)
## ────────────────────────────────────────────────────────────
##              Effect    S.E.     t     p             [95% CI]
## ────────────────────────────────────────────────────────────
## Direct (c')   0.083 (0.048) 1.722  .086 .   [-0.011,  0.178]
## ────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.InformationSearchV" (M)
## ───────────────────────────────────────────────────────────
##                                         F df1 df2     p    
## ───────────────────────────────────────────────────────────
## WA.WorkReflectionV * Stability.GraC  5.67   1 660  .018 *  
## ───────────────────────────────────────────────────────────
## 
## Simple Slopes: "WA.WorkReflectionV" (X) ==> "WP.InformationSearchV" (M)
## (Conditional Effects [a] of X on M)
## ────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p            [95% CI]
## ────────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.056 (0.046) 1.204  .229     [-0.035, 0.147]
##  -0.000 (Mean)     0.124 (0.050) 2.505  .013 *   [ 0.027, 0.221]
##  0.311 (+ SD)      0.192 (0.066) 2.899  .004 **  [ 0.062, 0.322]
## ────────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.learningBehaviorV" (Y)
## ──────────────────────────────────────────────────────────────
##                                            F df1 df2     p    
## ──────────────────────────────────────────────────────────────
## WP.InformationSearchV * Stability.GraC  7.64   1 616  .006 ** 
## ──────────────────────────────────────────────────────────────
## 
## Simple Slopes: "WP.InformationSearchV" (M) ==> "WP.learningBehaviorV" (Y)
## (Conditional Effects [b] of M on Y)
## ───────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p           [95% CI]
## ───────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.317 (0.042) 7.477 <.001 *** [0.234, 0.400]
##  -0.000 (Mean)     0.237 (0.040) 5.898 <.001 *** [0.158, 0.316]
##  0.311 (+ SD)      0.157 (0.056) 2.825  .005 **  [0.048, 0.267]
## ───────────────────────────────────────────────────────────────
## 
## Running 100 * 3 simulations...
## Indirect Path: "WA.WorkReflectionV" (X) ==> "WP.InformationSearchV" (M) ==> "WP.learningBehaviorV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     z     p       [MCMC 95% CI]
## ────────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.018 (0.014) 1.222  .222     [-0.009, 0.045]
##  -0.000 (Mean)     0.030 (0.011) 2.646  .008 **  [0.009, 0.050] 
##  0.311 (+ SD)      0.031 (0.014) 2.301  .021 *   [0.007, 0.059] 
## ────────────────────────────────────────────────────────────────
## 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. :)

4.3.1.2 Job Crafting

MoMe.S=PROCESS(data.D, y="WP.JobCraftingV", x="WA.WorkReflectionV", 
           mods="Stability.GraC",meds="WP.InformationSearchV",
           covs=cc("WP.SupervisoryBehavioralFeedbackV_mean,WP.InformationSearchV_mean,WA.WorkReflectionV_mean"),
           cluster ="B.ID", mod.path=c("x-m", "m-y"), center=FALSE,
           ci="mcmc", nsim=100, seed=1223)#, file="D2.doc")hlm.re.y = "(1|B.ID)",
## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 58 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## -    Outcome (Y) : WP.JobCraftingV
## -  Predictor (X) : WA.WorkReflectionV
## -  Mediators (M) : WP.InformationSearchV
## - Moderators (W) : Stability.GraC
## - Covariates (C) : WP.SupervisoryBehavioralFeedbackV_mean, WP.InformationSearchV_mean, WA.WorkReflectionV_mean
## -   HLM Clusters : B.ID
## 
## Formula of Mediator:
## -    WP.InformationSearchV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + WA.WorkReflectionV_mean + WA.WorkReflectionV*Stability.GraC + (1 | B.ID)
## Formula of Outcome:
## -    WP.JobCraftingV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + WA.WorkReflectionV_mean + WA.WorkReflectionV + WP.InformationSearchV*Stability.GraC + (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.InformationSearchV  (3) WP.JobCraftingV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                              -0.084                0.020                     -0.152            
##                                          (0.190)              (0.089)                    (0.193)           
## WP.SupervisoryBehavioralFeedbackV_mean    0.195 **            -0.009                      0.212 **         
##                                          (0.070)              (0.032)                    (0.071)           
## WP.InformationSearchV_mean                0.294 ***            0.997 ***                  0.110            
##                                          (0.069)              (0.031)                    (0.077)           
## WA.WorkReflectionV_mean                   0.521 ***           -0.088                      0.544 ***        
##                                          (0.089)              (0.055)                    (0.088)           
## WA.WorkReflectionV                       -0.050                0.093 *                   -0.060            
##                                          (0.037)              (0.042)                    (0.036)           
## Stability.GraC                                                -0.221                      0.555 *          
##                                                               (0.187)                    (0.281)           
## WA.WorkReflectionV:Stability.GraC                              0.073                                       
##                                                               (0.059)                                      
## WP.InformationSearchV                                                                     0.177 ***        
##                                                                                          (0.037)           
## WP.InformationSearchV:Stability.GraC                                                     -0.188 *          
##                                                                                          (0.075)           
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                              0.564                0.757                      0.579            
## Conditional R^2                           0.793                0.757                      0.808            
## AIC                                     862.652              732.113                    837.270            
## BIC                                     894.161              772.625                    882.282            
## Num. obs.                               666                  666                        666                
## Num. groups: B.ID                       150                  150                        150                
## Var: B.ID (Intercept)                     0.152                0.000                      0.152            
## Var: Residual                             0.138                0.164                      0.128            
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.1.5)
## Effect Type : Moderated Mediation (Model 58)
## Sample Size : 666 (69 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Direct Effect: "WA.WorkReflectionV" (X) ==> "WP.JobCraftingV" (Y)
## ─────────────────────────────────────────────────────────────
##              Effect    S.E.      t     p             [95% CI]
## ─────────────────────────────────────────────────────────────
## Direct (c')  -0.060 (0.036) -1.686  .092 .   [-0.130,  0.010]
## ─────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.InformationSearchV" (M)
## ───────────────────────────────────────────────────────────
##                                         F df1 df2     p    
## ───────────────────────────────────────────────────────────
## WA.WorkReflectionV * Stability.GraC  1.52   1 659  .218    
## ───────────────────────────────────────────────────────────
## 
## Simple Slopes: "WA.WorkReflectionV" (X) ==> "WP.InformationSearchV" (M)
## (Conditional Effects [a] of X on M)
## ────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p            [95% CI]
## ────────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.070 (0.040) 1.736  .083 .   [-0.009, 0.150]
##  -0.000 (Mean)     0.093 (0.042) 2.219  .027 *   [ 0.011, 0.175]
##  0.311 (+ SD)      0.116 (0.051) 2.288  .022 *   [ 0.017, 0.215]
## ────────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.JobCraftingV" (Y)
## ──────────────────────────────────────────────────────────────
##                                            F df1 df2     p    
## ──────────────────────────────────────────────────────────────
## WP.InformationSearchV * Stability.GraC  6.27   1 657  .013 *  
## ──────────────────────────────────────────────────────────────
## 
## Simple Slopes: "WP.InformationSearchV" (M) ==> "WP.JobCraftingV" (Y)
## (Conditional Effects [b] of M on Y)
## ───────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p           [95% CI]
## ───────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.236 (0.036) 6.532 <.001 *** [0.165, 0.306]
##  -0.000 (Mean)     0.177 (0.037) 4.845 <.001 *** [0.105, 0.249]
##  0.311 (+ SD)      0.119 (0.050) 2.392  .017 *   [0.021, 0.216]
## ───────────────────────────────────────────────────────────────
## 
## Running 100 * 3 simulations...
## Indirect Path: "WA.WorkReflectionV" (X) ==> "WP.InformationSearchV" (M) ==> "WP.JobCraftingV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     z     p       [MCMC 95% CI]
## ────────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.017 (0.010) 1.718  .086 .   [-0.000, 0.037]
##  -0.000 (Mean)     0.017 (0.008) 2.064  .039 *   [ 0.004, 0.035]
##  0.311 (+ SD)      0.014 (0.009) 1.654  .098 .   [ 0.000, 0.034]
## ────────────────────────────────────────────────────────────────
## 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. :)

4.3.2 System-improvement

4.3.2.1 Taking Charge

MoMe.S=PROCESS(data.D, y="WP.TakingChargeV", x="WA.WorkReflectionV", 
           mods="Stability.GraC",meds="WP.InformationSearchV",
           covs=cc("WP.SupervisoryBehavioralFeedbackV_mean,WP.InformationSearchV_mean,WA.WorkReflectionV_mean"),
           cluster ="B.ID", mod.path=c("x-m", "m-y"), center=FALSE,
           ci="mcmc", nsim=100, seed=1223)#, file="D2.doc")hlm.re.y = "(1|B.ID)",
## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 58 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## -    Outcome (Y) : WP.TakingChargeV
## -  Predictor (X) : WA.WorkReflectionV
## -  Mediators (M) : WP.InformationSearchV
## - Moderators (W) : Stability.GraC
## - Covariates (C) : WP.SupervisoryBehavioralFeedbackV_mean, WP.InformationSearchV_mean, WA.WorkReflectionV_mean
## -   HLM Clusters : B.ID
## 
## Formula of Mediator:
## -    WP.InformationSearchV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + WA.WorkReflectionV_mean + WA.WorkReflectionV*Stability.GraC + (1 | B.ID)
## Formula of Outcome:
## -    WP.TakingChargeV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + WA.WorkReflectionV_mean + WA.WorkReflectionV + WP.InformationSearchV*Stability.GraC + (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.InformationSearchV  (3) WP.TakingChargeV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                               -0.513 *              0.020                      -0.463            
##                                           (0.248)              (0.089)                     (0.254)           
## WP.SupervisoryBehavioralFeedbackV_mean     0.328 ***           -0.009                       0.312 ***        
##                                           (0.091)              (0.032)                     (0.093)           
## WP.InformationSearchV_mean                 0.273 **             0.997 ***                   0.105            
##                                           (0.090)              (0.031)                     (0.101)           
## WA.WorkReflectionV_mean                    0.374 **            -0.088                       0.385 ***        
##                                           (0.115)              (0.055)                     (0.115)           
## WA.WorkReflectionV                         0.075                0.093 *                     0.064            
##                                           (0.047)              (0.042)                     (0.046)           
## Stability.GraC                                                 -0.221                       0.595            
##                                                                (0.187)                     (0.365)           
## WA.WorkReflectionV:Stability.GraC                               0.073                                        
##                                                                (0.059)                                       
## WP.InformationSearchV                                                                       0.170 ***        
##                                                                                            (0.047)           
## WP.InformationSearchV:Stability.GraC                                                       -0.108            
##                                                                                            (0.097)           
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.484                0.757                       0.492            
## Conditional R^2                            0.763                0.757                       0.773            
## AIC                                     1184.903              732.113                    1178.942            
## BIC                                     1216.412              772.625                    1223.955            
## Num. obs.                                666                  666                         666                
## Num. groups: B.ID                        150                  150                         150                
## Var: B.ID (Intercept)                      0.261                0.000                       0.265            
## Var: Residual                              0.221                0.164                       0.213            
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.1.5)
## Effect Type : Moderated Mediation (Model 58)
## Sample Size : 666 (69 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Direct Effect: "WA.WorkReflectionV" (X) ==> "WP.TakingChargeV" (Y)
## ────────────────────────────────────────────────────────────
##              Effect    S.E.     t     p             [95% CI]
## ────────────────────────────────────────────────────────────
## Direct (c')   0.064 (0.046) 1.379  .168     [-0.027,  0.154]
## ────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.InformationSearchV" (M)
## ───────────────────────────────────────────────────────────
##                                         F df1 df2     p    
## ───────────────────────────────────────────────────────────
## WA.WorkReflectionV * Stability.GraC  1.52   1 659  .218    
## ───────────────────────────────────────────────────────────
## 
## Simple Slopes: "WA.WorkReflectionV" (X) ==> "WP.InformationSearchV" (M)
## (Conditional Effects [a] of X on M)
## ────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p            [95% CI]
## ────────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.070 (0.040) 1.736  .083 .   [-0.009, 0.150]
##  -0.000 (Mean)     0.093 (0.042) 2.219  .027 *   [ 0.011, 0.175]
##  0.311 (+ SD)      0.116 (0.051) 2.288  .022 *   [ 0.017, 0.215]
## ────────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.TakingChargeV" (Y)
## ──────────────────────────────────────────────────────────────
##                                            F df1 df2     p    
## ──────────────────────────────────────────────────────────────
## WP.InformationSearchV * Stability.GraC  1.23   1 656  .267    
## ──────────────────────────────────────────────────────────────
## 
## Simple Slopes: "WP.InformationSearchV" (M) ==> "WP.TakingChargeV" (Y)
## (Conditional Effects [b] of M on Y)
## ───────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p           [95% CI]
## ───────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.204 (0.047) 4.379 <.001 *** [0.113, 0.295]
##  -0.000 (Mean)     0.170 (0.047) 3.608 <.001 *** [0.078, 0.263]
##  0.311 (+ SD)      0.137 (0.064) 2.131  .033 *   [0.011, 0.263]
## ───────────────────────────────────────────────────────────────
## 
## Running 100 * 3 simulations...
## Indirect Path: "WA.WorkReflectionV" (X) ==> "WP.InformationSearchV" (M) ==> "WP.TakingChargeV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     z     p       [MCMC 95% CI]
## ────────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.015 (0.009) 1.644  .100     [-0.000, 0.033]
##  -0.000 (Mean)     0.016 (0.008) 1.935  .053 .   [ 0.004, 0.036]
##  0.311 (+ SD)      0.017 (0.011) 1.569  .117     [ 0.000, 0.041]
## ────────────────────────────────────────────────────────────────
## 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. :)

4.3.2.2 Performance Improvement

MoMe.S=PROCESS(data.D, y="WP.PerformanceImprovementV", x="WA.WorkReflectionV", 
           mods="Stability.GraC",meds="WP.InformationSearchV",
           covs=cc("WP.SupervisoryBehavioralFeedbackV_mean,WP.InformationSearchV_mean,WA.WorkReflectionV_mean"),
           cluster ="B.ID", mod.path=c("x-m", "m-y"), center=FALSE,
           ci="mcmc", nsim=100, seed=1223)#, file="D2.doc")hlm.re.y = "(1|B.ID)",
## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 58 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## -    Outcome (Y) : WP.PerformanceImprovementV
## -  Predictor (X) : WA.WorkReflectionV
## -  Mediators (M) : WP.InformationSearchV
## - Moderators (W) : Stability.GraC
## - Covariates (C) : WP.SupervisoryBehavioralFeedbackV_mean, WP.InformationSearchV_mean, WA.WorkReflectionV_mean
## -   HLM Clusters : B.ID
## 
## Formula of Mediator:
## -    WP.InformationSearchV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + WA.WorkReflectionV_mean + WA.WorkReflectionV*Stability.GraC + (1 | B.ID)
## Formula of Outcome:
## -    WP.PerformanceImprovementV ~ WP.SupervisoryBehavioralFeedbackV_mean + WP.InformationSearchV_mean + WA.WorkReflectionV_mean + WA.WorkReflectionV + WP.InformationSearchV*Stability.GraC + (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.InformationSearchV  (3) WP.PerformanceImprovementV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                               -0.337                          0.020                      -0.314                      
##                                           (0.232)                        (0.089)                     (0.238)                     
## WP.SupervisoryBehavioralFeedbackV_mean     0.330 ***                     -0.009                       0.324 ***                  
##                                           (0.085)                        (0.032)                     (0.087)                     
## WP.InformationSearchV_mean                 0.346 ***                      0.997 ***                   0.138                      
##                                           (0.084)                        (0.031)                     (0.101)                     
## WA.WorkReflectionV_mean                    0.283 *                       -0.088                       0.296 **                   
##                                           (0.113)                        (0.055)                     (0.113)                     
## WA.WorkReflectionV                         0.064                          0.093 *                     0.046                      
##                                           (0.055)                        (0.042)                     (0.055)                     
## Stability.GraC                                                           -0.221                      -0.054                      
##                                                                          (0.187)                     (0.398)                     
## WA.WorkReflectionV:Stability.GraC                                         0.073                                                  
##                                                                          (0.059)                                                 
## WP.InformationSearchV                                                                                 0.210 ***                  
##                                                                                                      (0.056)                     
## WP.InformationSearchV:Stability.GraC                                                                  0.031                      
##                                                                                                      (0.109)                     
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.455                          0.757                       0.461                      
## Conditional R^2                            0.671                          0.757                       0.679                      
## AIC                                     1336.313                        732.113                    1335.852                      
## BIC                                     1367.822                        772.625                    1380.865                      
## Num. obs.                                666                            666                         666                          
## Num. groups: B.ID                        150                            150                         150                          
## Var: B.ID (Intercept)                      0.202                          0.000                       0.205                      
## Var: Residual                              0.308                          0.164                       0.301                      
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.1.5)
## Effect Type : Moderated Mediation (Model 58)
## Sample Size : 666 (69 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Direct Effect: "WA.WorkReflectionV" (X) ==> "WP.PerformanceImprovementV" (Y)
## ────────────────────────────────────────────────────────────
##              Effect    S.E.     t     p             [95% CI]
## ────────────────────────────────────────────────────────────
## Direct (c')   0.046 (0.055) 0.845  .398     [-0.061,  0.153]
## ────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.InformationSearchV" (M)
## ───────────────────────────────────────────────────────────
##                                         F df1 df2     p    
## ───────────────────────────────────────────────────────────
## WA.WorkReflectionV * Stability.GraC  1.52   1 659  .218    
## ───────────────────────────────────────────────────────────
## 
## Simple Slopes: "WA.WorkReflectionV" (X) ==> "WP.InformationSearchV" (M)
## (Conditional Effects [a] of X on M)
## ────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p            [95% CI]
## ────────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.070 (0.040) 1.736  .083 .   [-0.009, 0.150]
##  -0.000 (Mean)     0.093 (0.042) 2.219  .027 *   [ 0.011, 0.175]
##  0.311 (+ SD)      0.116 (0.051) 2.288  .022 *   [ 0.017, 0.215]
## ────────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.PerformanceImprovementV" (Y)
## ──────────────────────────────────────────────────────────────
##                                            F df1 df2     p    
## ──────────────────────────────────────────────────────────────
## WP.InformationSearchV * Stability.GraC  0.08   1 642  .779    
## ──────────────────────────────────────────────────────────────
## 
## Simple Slopes: "WP.InformationSearchV" (M) ==> "WP.PerformanceImprovementV" (Y)
## (Conditional Effects [b] of M on Y)
## ───────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p           [95% CI]
## ───────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.200 (0.055) 3.641 <.001 *** [0.092, 0.308]
##  -0.000 (Mean)     0.210 (0.056) 3.767 <.001 *** [0.101, 0.319]
##  0.311 (+ SD)      0.219 (0.074) 2.963  .003 **  [0.074, 0.365]
## ───────────────────────────────────────────────────────────────
## 
## Running 100 * 3 simulations...
## Indirect Path: "WA.WorkReflectionV" (X) ==> "WP.InformationSearchV" (M) ==> "WP.PerformanceImprovementV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     z     p       [MCMC 95% CI]
## ────────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.014 (0.009) 1.596  .111     [-0.000, 0.033]
##  -0.000 (Mean)     0.020 (0.010) 1.959  .050 .   [ 0.005, 0.044]
##  0.311 (+ SD)      0.026 (0.015) 1.799  .072 .   [ 0.003, 0.060]
## ────────────────────────────────────────────────────────────────
## 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. :)

4.3.3 Learning

4.3.3.1 Learning through independant observation

MoMe.S=PROCESS(data.D, y="WP.ObservationalLearningV", x="WA.WorkReflectionV", 
           mods="Stability.GraC",meds="WP.InformationSearchV",
           covs=cc("WP.SupervisoryBehavioralFeedbackV_mean,WA.WorkReflectionV_mean"),
           cluster ="B.ID", mod.path=c("x-m", "m-y"), center=FALSE,
           ci="mcmc", nsim=100, seed=1223)#, file="D2.doc")hlm.re.y = "(1|B.ID)",
## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 58 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## -    Outcome (Y) : WP.ObservationalLearningV
## -  Predictor (X) : WA.WorkReflectionV
## -  Mediators (M) : WP.InformationSearchV
## - Moderators (W) : Stability.GraC
## - Covariates (C) : WP.SupervisoryBehavioralFeedbackV_mean, WA.WorkReflectionV_mean
## -   HLM Clusters : B.ID
## 
## Formula of Mediator:
## -    WP.InformationSearchV ~ WP.SupervisoryBehavioralFeedbackV_mean + WA.WorkReflectionV_mean + WA.WorkReflectionV*Stability.GraC + (1 | B.ID)
## Formula of Outcome:
## -    WP.ObservationalLearningV ~ WP.SupervisoryBehavioralFeedbackV_mean + WA.WorkReflectionV_mean + WA.WorkReflectionV + WP.InformationSearchV*Stability.GraC + (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.InformationSearchV  (3) WP.ObservationalLearningV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                                0.385                          1.124 ***                  0.180                     
##                                           (0.251)                        (0.213)                    (0.257)                    
## WP.SupervisoryBehavioralFeedbackV_mean     0.198 *                        0.228 **                   0.136                     
##                                           (0.098)                        (0.082)                    (0.098)                    
## WA.WorkReflectionV_mean                    0.635 ***                      0.381 ***                  0.542 ***                 
##                                           (0.118)                        (0.100)                    (0.117)                    
## WA.WorkReflectionV                         0.018                          0.124 *                    0.000                     
##                                           (0.052)                        (0.050)                    (0.051)                    
## Stability.GraC                                                           -0.727 *                    0.005                     
##                                                                          (0.309)                    (0.399)                    
## WA.WorkReflectionV:Stability.GraC                                         0.220 *                                              
##                                                                          (0.092)                                               
## WP.InformationSearchV                                                                                0.218 ***                 
##                                                                                                     (0.047)                    
## WP.InformationSearchV:Stability.GraC                                                                 0.024                     
##                                                                                                     (0.107)                    
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.355                          0.375                      0.378                     
## Conditional R^2                            0.702                          0.688                      0.711                     
## AIC                                     1318.209                       1131.216                   1309.538                     
## BIC                                     1345.216                       1167.226                   1350.050                     
## Num. obs.                                666                            666                        666                         
## Num. groups: B.ID                        150                            150                        150                         
## Var: B.ID (Intercept)                      0.319                          0.210                      0.306                     
## Var: Residual                              0.273                          0.209                      0.265                     
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.1.5)
## Effect Type : Moderated Mediation (Model 58)
## Sample Size : 666 (69 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Direct Effect: "WA.WorkReflectionV" (X) ==> "WP.ObservationalLearningV" (Y)
## ────────────────────────────────────────────────────────────
##              Effect    S.E.     t     p             [95% CI]
## ────────────────────────────────────────────────────────────
## Direct (c')   0.000 (0.051) 0.006  .996     [-0.100,  0.101]
## ────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.InformationSearchV" (M)
## ───────────────────────────────────────────────────────────
##                                         F df1 df2     p    
## ───────────────────────────────────────────────────────────
## WA.WorkReflectionV * Stability.GraC  5.67   1 660  .018 *  
## ───────────────────────────────────────────────────────────
## 
## Simple Slopes: "WA.WorkReflectionV" (X) ==> "WP.InformationSearchV" (M)
## (Conditional Effects [a] of X on M)
## ────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p            [95% CI]
## ────────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.056 (0.046) 1.204  .229     [-0.035, 0.147]
##  -0.000 (Mean)     0.124 (0.050) 2.505  .013 *   [ 0.027, 0.221]
##  0.311 (+ SD)      0.192 (0.066) 2.899  .004 **  [ 0.062, 0.322]
## ────────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.ObservationalLearningV" (Y)
## ──────────────────────────────────────────────────────────────
##                                            F df1 df2     p    
## ──────────────────────────────────────────────────────────────
## WP.InformationSearchV * Stability.GraC  0.05   1 659  .825    
## ──────────────────────────────────────────────────────────────
## 
## Simple Slopes: "WP.InformationSearchV" (M) ==> "WP.ObservationalLearningV" (Y)
## (Conditional Effects [b] of M on Y)
## ───────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p           [95% CI]
## ───────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.210 (0.048) 4.389 <.001 *** [0.116, 0.304]
##  -0.000 (Mean)     0.218 (0.047) 4.676 <.001 *** [0.126, 0.309]
##  0.311 (+ SD)      0.225 (0.065) 3.453 <.001 *** [0.097, 0.353]
## ───────────────────────────────────────────────────────────────
## 
## Running 100 * 3 simulations...
## Indirect Path: "WA.WorkReflectionV" (X) ==> "WP.InformationSearchV" (M) ==> "WP.ObservationalLearningV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     z     p       [MCMC 95% CI]
## ────────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.012 (0.010) 1.171  .241     [-0.006, 0.031]
##  -0.000 (Mean)     0.028 (0.011) 2.535  .011 *   [ 0.008, 0.047]
##  0.311 (+ SD)      0.045 (0.018) 2.550  .011 *   [ 0.012, 0.079]
## ────────────────────────────────────────────────────────────────
## 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. :)

4.3.3.2 Learning through social interaction

MoMe.S=PROCESS(data.D, y="WP.AdviceSeekingV", x="WA.WorkReflectionV", 
           mods="Stability.GraC",meds="WP.InformationSearchV",
           covs=cc("WP.SupervisoryBehavioralFeedbackV_mean,WA.WorkReflectionV_mean"),
           cluster ="B.ID", mod.path=c("x-m", "m-y"), center=FALSE,
           ci="mcmc", nsim=100, seed=1223)#, file="D2.doc")hlm.re.y = "(1|B.ID)",
## 
## ****************** PART 1. Regression Model Summary ******************
## 
## PROCESS Model Code : 58 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## -    Outcome (Y) : WP.AdviceSeekingV
## -  Predictor (X) : WA.WorkReflectionV
## -  Mediators (M) : WP.InformationSearchV
## - Moderators (W) : Stability.GraC
## - Covariates (C) : WP.SupervisoryBehavioralFeedbackV_mean, WA.WorkReflectionV_mean
## -   HLM Clusters : B.ID
## 
## Formula of Mediator:
## -    WP.InformationSearchV ~ WP.SupervisoryBehavioralFeedbackV_mean + WA.WorkReflectionV_mean + WA.WorkReflectionV*Stability.GraC + (1 | B.ID)
## Formula of Outcome:
## -    WP.AdviceSeekingV ~ WP.SupervisoryBehavioralFeedbackV_mean + WA.WorkReflectionV_mean + WA.WorkReflectionV + WP.InformationSearchV*Stability.GraC + (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.InformationSearchV  (3) WP.AdviceSeekingV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                                1.176 ***              1.124 ***                  0.876 ***         
##                                           (0.241)                (0.213)                    (0.233)            
## WP.SupervisoryBehavioralFeedbackV_mean     0.100                  0.228 **                   0.012             
##                                           (0.094)                (0.082)                    (0.088)            
## WA.WorkReflectionV_mean                    0.572 ***              0.381 ***                  0.429 ***         
##                                           (0.118)                (0.100)                    (0.113)            
## WA.WorkReflectionV                         0.027                  0.124 *                    0.000             
##                                           (0.060)                (0.050)                    (0.060)            
## Stability.GraC                                                   -0.727 *                   -0.504             
##                                                                  (0.309)                    (0.422)            
## WA.WorkReflectionV:Stability.GraC                                 0.220 *                                      
##                                                                  (0.092)                                       
## WP.InformationSearchV                                                                        0.323 ***         
##                                                                                             (0.050)            
## WP.InformationSearchV:Stability.GraC                                                         0.156             
##                                                                                             (0.116)            
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.261                  0.375                      0.318             
## Conditional R^2                            0.571                  0.688                      0.569             
## AIC                                     1461.291               1131.216                   1437.218             
## BIC                                     1488.299               1167.226                   1477.729             
## Num. obs.                                666                    666                        666                 
## Num. groups: B.ID                        150                    150                        150                 
## Var: B.ID (Intercept)                      0.267                  0.210                      0.211             
## Var: Residual                              0.369                  0.209                      0.362             
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
## 
## ************ PART 2. Mediation/Moderation Effect Estimate ************
## 
## Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.1.5)
## Effect Type : Moderated Mediation (Model 58)
## Sample Size : 666 (69 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 100 (Monte Carlo)
## 
## Direct Effect: "WA.WorkReflectionV" (X) ==> "WP.AdviceSeekingV" (Y)
## ────────────────────────────────────────────────────────────
##              Effect    S.E.     t     p             [95% CI]
## ────────────────────────────────────────────────────────────
## Direct (c')   0.000 (0.060) 0.007  .994     [-0.117,  0.118]
## ────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.InformationSearchV" (M)
## ───────────────────────────────────────────────────────────
##                                         F df1 df2     p    
## ───────────────────────────────────────────────────────────
## WA.WorkReflectionV * Stability.GraC  5.67   1 660  .018 *  
## ───────────────────────────────────────────────────────────
## 
## Simple Slopes: "WA.WorkReflectionV" (X) ==> "WP.InformationSearchV" (M)
## (Conditional Effects [a] of X on M)
## ────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p            [95% CI]
## ────────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.056 (0.046) 1.204  .229     [-0.035, 0.147]
##  -0.000 (Mean)     0.124 (0.050) 2.505  .013 *   [ 0.027, 0.221]
##  0.311 (+ SD)      0.192 (0.066) 2.899  .004 **  [ 0.062, 0.322]
## ────────────────────────────────────────────────────────────────
## 
## Interaction Effect on "WP.AdviceSeekingV" (Y)
## ──────────────────────────────────────────────────────────────
##                                            F df1 df2     p    
## ──────────────────────────────────────────────────────────────
## WP.InformationSearchV * Stability.GraC  1.79   1 621  .182    
## ──────────────────────────────────────────────────────────────
## 
## Simple Slopes: "WP.InformationSearchV" (M) ==> "WP.AdviceSeekingV" (Y)
## (Conditional Effects [b] of M on Y)
## ───────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     t     p           [95% CI]
## ───────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.274 (0.053) 5.180 <.001 *** [0.171, 0.378]
##  -0.000 (Mean)     0.323 (0.050) 6.406 <.001 *** [0.224, 0.421]
##  0.311 (+ SD)      0.371 (0.070) 5.311 <.001 *** [0.234, 0.508]
## ───────────────────────────────────────────────────────────────
## 
## Running 100 * 3 simulations...
## Indirect Path: "WA.WorkReflectionV" (X) ==> "WP.InformationSearchV" (M) ==> "WP.AdviceSeekingV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ────────────────────────────────────────────────────────────────
##  "Stability.GraC" Effect    S.E.     z     p       [MCMC 95% CI]
## ────────────────────────────────────────────────────────────────
##  -0.311 (- SD)     0.015 (0.013) 1.194  .233     [-0.008, 0.040]
##  -0.000 (Mean)     0.041 (0.015) 2.675  .007 **  [ 0.013, 0.067]
##  0.311 (+ SD)      0.074 (0.025) 2.956  .003 **  [ 0.025, 0.119]
## ────────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

5

6 -----------------------------------------------

6.1 Mediation effect

6.1.1 Path a

{r} Manipulation.MLM= lmer(WP.InformationSearchV~Manipulation + (1|B.ID), na.action = na.exclude, data = data.M, control=lmerControl(optimizer="bobyqa")) HLM_summary(Manipulation.MLM)}

6.1.2 Learning Behavior

PROCESS(data.M, y=“score”, x=“SCH_free”, meds=“late”, clusters=“SCH_ID”, ci=“mcmc”, nsim=1000, seed=1)

{r} Me=PROCESS(data.M, y="WP.learningBehaviorV", x="Manipulation", meds="WP.InformationSearchV", covs=cc("WP.SupervisoryBehavioralFeedbackV_mean, WP.InformationSearchV_mean"), cluster ="B.ID", center=FALSE,ci="mcmc", nsim=100, seed=1)#, file="D2.doc")hlm.re.y = "(1|B.ID)",}