Reflection

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"), 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 : 7 (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 + Stability.GraC + WP.InformationSearchV + (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                       0.086                
##                                           (0.253)                  (0.116)                     (0.260)               
## WP.SupervisoryBehavioralFeedbackV_mean     0.474 ***                0.002                       0.410 ***            
##                                           (0.079)                  (0.037)                     (0.084)               
## WP.InformationSearchV_mean                 0.495 ***                1.001 ***                   0.281 **             
##                                           (0.076)                  (0.034)                     (0.093)               
## Manipulation                              -0.097 *                  0.005                      -0.100 *              
##                                           (0.049)                  (0.037)                     (0.048)               
## Stability.GraC                                                     -0.158                       0.329 *              
##                                                                    (0.094)                     (0.154)               
## Manipulation:Stability.GraC                                         0.310 *                                          
##                                                                    (0.127)                                           
## WP.InformationSearchV                                                                           0.234 ***            
##                                                                                                (0.054)               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.397                    0.708                       0.416                
## Conditional R^2                            0.612                    0.708                       0.630                
## AIC                                     1152.966                  695.252                    1140.114                
## BIC                                     1178.901                  729.833                    1174.695                
## Num. obs.                                557                      557                         557                    
## Num. groups: B.ID                        158                      158                         158                    
## Var: B.ID (Intercept)                      0.183                    0.000                       0.181                
## Var: Residual                              0.328                    0.191                       0.314                
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 7)
## 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')  -0.100 (0.048) -2.080  .038 *   [-0.194, -0.006]
## ─────────────────────────────────────────────────────────────
## 
## 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]
## ─────────────────────────────────────────────────────────────────
## 
## 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)    -0.022 (0.014) -1.517  .129     [-0.048,  0.001]
##  -0.008 (Mean)     0.001 (0.009)  0.153  .878     [-0.017,  0.016]
##  0.284 (+ SD)      0.024 (0.014)  1.686  .092 .   [-0.003,  0.052]
## ──────────────────────────────────────────────────────────────────
## 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"), 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 : 7 (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 + Stability.GraC + WP.InformationSearchV + (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                     -0.135            
##                                          (0.219)              (0.116)                    (0.227)           
## WP.SupervisoryBehavioralFeedbackV_mean    0.422 ***            0.002                      0.395 ***        
##                                          (0.069)              (0.037)                    (0.074)           
## WP.InformationSearchV_mean                0.533 ***            1.001 ***                  0.351 ***        
##                                          (0.065)              (0.034)                    (0.077)           
## Manipulation                             -0.016                0.005                     -0.017            
##                                          (0.037)              (0.037)                    (0.036)           
## Stability.GraC                                                -0.158                      0.139            
##                                                               (0.094)                    (0.135)           
## Manipulation:Stability.GraC                                    0.310 *                                     
##                                                               (0.127)                                      
## WP.InformationSearchV                                                                     0.191 ***        
##                                                                                          (0.041)           
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                              0.489                0.708                      0.500            
## Conditional R^2                           0.721                0.708                      0.735            
## AIC                                     882.970              695.252                    871.196            
## BIC                                     908.905              729.833                    905.777            
## Num. obs.                               557                  557                        557                
## Num. groups: B.ID                       158                  158                        158                
## Var: B.ID (Intercept)                     0.154                0.000                      0.157            
## 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 7)
## 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')  -0.017 (0.036) -0.465  .642     [-0.088,  0.054]
## ─────────────────────────────────────────────────────────────
## 
## 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]
## ─────────────────────────────────────────────────────────────────
## 
## 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)    -0.018 (0.011) -1.538  .124     [-0.039, 0.001]
##  -0.008 (Mean)     0.001 (0.007)  0.154  .877     [-0.014, 0.013]
##  0.284 (+ SD)      0.020 (0.012)  1.709  .087 .   [-0.002, 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. :)

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"), 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 : 7 (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 + Stability.GraC + WP.InformationSearchV + (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                      -0.225            
##                                           (0.287)              (0.116)                     (0.297)           
## WP.SupervisoryBehavioralFeedbackV_mean     0.423 ***            0.002                       0.386 ***        
##                                           (0.090)              (0.037)                     (0.096)           
## WP.InformationSearchV_mean                 0.534 ***            1.001 ***                   0.316 **         
##                                           (0.085)              (0.034)                     (0.101)           
## Manipulation                              -0.011                0.005                      -0.013            
##                                           (0.048)              (0.037)                     (0.047)           
## Stability.GraC                                                 -0.158                       0.188            
##                                                                (0.094)                     (0.177)           
## Manipulation:Stability.GraC                                     0.310 *                                      
##                                                                (0.127)                                       
## WP.InformationSearchV                                                                       0.230 ***        
##                                                                                            (0.053)           
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.361                0.708                       0.374            
## Conditional R^2                            0.655                0.708                       0.670            
## AIC                                     1172.799              695.252                    1162.997            
## BIC                                     1198.734              729.833                    1197.577            
## Num. obs.                                557                  557                         557                
## Num. groups: B.ID                        158                  158                         158                
## Var: B.ID (Intercept)                      0.266                0.000                       0.270            
## Var: Residual                              0.313                0.191                       0.299            
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 7)
## 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')  -0.013 (0.047) -0.276  .783     [-0.105,  0.079]
## ─────────────────────────────────────────────────────────────
## 
## 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]
## ─────────────────────────────────────────────────────────────────
## 
## 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)    -0.021 (0.014) -1.520  .129     [-0.047, 0.001]
##  -0.008 (Mean)     0.001 (0.008)  0.153  .878     [-0.017, 0.015]
##  0.284 (+ SD)      0.024 (0.014)  1.686  .092 .   [-0.003, 0.051]
## ─────────────────────────────────────────────────────────────────
## 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"), 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 : 7 (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 + Stability.GraC + WP.InformationSearchV + (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                      -0.220                      
##                                           (0.268)                        (0.116)                     (0.278)                     
## WP.SupervisoryBehavioralFeedbackV_mean     0.488 ***                      0.002                       0.485 ***                  
##                                           (0.084)                        (0.037)                     (0.090)                     
## WP.InformationSearchV_mean                 0.482 ***                      1.001 ***                   0.307 **                   
##                                           (0.080)                        (0.034)                     (0.104)                     
## Manipulation                               0.022                          0.005                       0.021                      
##                                           (0.059)                        (0.037)                     (0.058)                     
## Stability.GraC                                                           -0.158                       0.018                      
##                                                                          (0.094)                     (0.165)                     
## Manipulation:Stability.GraC                                               0.310 *                                                
##                                                                          (0.127)                                                 
## WP.InformationSearchV                                                                                 0.175 **                   
##                                                                                                      (0.066)                     
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.342                          0.708                       0.347                      
## Conditional R^2                            0.521                          0.708                       0.529                      
## AIC                                     1312.031                        695.252                    1314.345                      
## BIC                                     1337.966                        729.833                    1348.925                      
## Num. obs.                                557                            557                         557                          
## Num. groups: B.ID                        158                            158                         158                          
## Var: B.ID (Intercept)                      0.175                          0.000                       0.179                      
## 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 7)
## 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')   0.021 (0.058) 0.360  .719     [-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]
## ─────────────────────────────────────────────────────────────────
## 
## 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)    -0.017 (0.012) -1.346  .178     [-0.042, 0.000]
##  -0.008 (Mean)     0.001 (0.007)  0.141  .888     [-0.013, 0.013]
##  0.284 (+ SD)      0.018 (0.012)  1.483  .138     [-0.002, 0.046]
## ─────────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

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"), 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 : 7 (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 + Stability.GraC + WP.InformationSearchV + (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                       0.433                     
##                                           (0.297)                       (0.116)                     (0.309)                    
## WP.SupervisoryBehavioralFeedbackV_mean     0.398 ***                     0.002                       0.410 ***                 
##                                           (0.093)                       (0.037)                     (0.100)                    
## WP.InformationSearchV_mean                 0.388 ***                     1.001 ***                   0.200                     
##                                           (0.089)                       (0.034)                     (0.111)                    
## Manipulation                              -0.029                         0.005                      -0.030                     
##                                           (0.058)                       (0.037)                     (0.057)                    
## Stability.GraC                                                          -0.158                      -0.063                     
##                                                                         (0.094)                     (0.184)                    
## Manipulation:Stability.GraC                                              0.310 *                                               
##                                                                         (0.127)                                                
## WP.InformationSearchV                                                                                0.185 **                  
##                                                                                                     (0.065)                    
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.237                         0.708                       0.244                     
## Conditional R^2                            0.510                         0.708                       0.521                     
## AIC                                     1330.961                       695.252                    1331.952                     
## BIC                                     1356.896                       729.833                    1366.532                     
## 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                       0.444                     
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 7)
## 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')  -0.030 (0.057) -0.516  .606     [-0.142,  0.083]
## ─────────────────────────────────────────────────────────────
## 
## 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]
## ─────────────────────────────────────────────────────────────────
## 
## 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)    -0.017 (0.013) -1.380  .168     [-0.043, 0.001]
##  -0.008 (Mean)     0.001 (0.007)  0.143  .886     [-0.014, 0.013]
##  0.284 (+ SD)      0.019 (0.013)  1.519  .129     [-0.003, 0.047]
## ─────────────────────────────────────────────────────────────────
## 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"), 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 : 7 (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 + Stability.GraC + WP.InformationSearchV + (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                       1.288 ***         
##                                           (0.241)               (0.116)                     (0.252)            
## WP.SupervisoryBehavioralFeedbackV_mean     0.326 ***             0.002                       0.337 ***         
##                                           (0.075)               (0.037)                     (0.081)            
## WP.InformationSearchV_mean                 0.362 ***             1.001 ***                  -0.073             
##                                           (0.072)               (0.034)                     (0.096)            
## Manipulation                              -0.100                 0.005                      -0.103             
##                                           (0.058)               (0.037)                     (0.055)            
## Stability.GraC                                                  -0.158                      -0.038             
##                                                                 (0.094)                     (0.149)            
## Manipulation:Stability.GraC                                      0.310 *                                       
##                                                                 (0.127)                                        
## WP.InformationSearchV                                                                        0.429 ***         
##                                                                                             (0.062)            
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.228                 0.708                       0.274             
## Conditional R^2                            0.388                 0.708                       0.455             
## AIC                                     1271.584               695.252                    1236.028             
## BIC                                     1297.519               729.833                    1270.609             
## Num. obs.                                557                   557                         557                 
## Num. groups: B.ID                        158                   158                         158                 
## Var: B.ID (Intercept)                      0.120                 0.000                       0.136             
## Var: Residual                              0.458                 0.191                       0.409             
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 7)
## 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')  -0.103 (0.055) -1.874  .062 .   [-0.210,  0.005]
## ─────────────────────────────────────────────────────────────
## 
## 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]
## ─────────────────────────────────────────────────────────────────
## 
## 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)    -0.039 (0.024) -1.590  .112     [-0.084,  0.002]
##  -0.008 (Mean)     0.002 (0.015)  0.160  .873     [-0.031, 0.029] 
##  0.284 (+ SD)      0.044 (0.025)  1.785  .074 .   [-0.005, 0.085] 
## ──────────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

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"), 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 : 7 (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 + Stability.GraC + WP.InformationSearchV + (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 ***                 -0.171                
##                                           (0.186)                   (0.213)                    (0.182)               
## WP.SupervisoryBehavioralFeedbackV_mean     0.281 ***                 0.228 **                   0.215 **             
##                                           (0.072)                   (0.082)                    (0.068)               
## WA.WorkReflectionV_mean                    0.587 ***                 0.381 ***                  0.471 ***            
##                                           (0.093)                   (0.100)                    (0.089)               
## WA.WorkReflectionV                         0.096                     0.124 *                    0.075                
##                                           (0.049)                   (0.050)                    (0.049)               
## Stability.GraC                                                      -0.727 *                    0.008                
##                                                                     (0.309)                    (0.116)               
## WA.WorkReflectionV:Stability.GraC                                    0.220 *                                         
##                                                                     (0.092)                                          
## WP.InformationSearchV                                                                           0.269 ***            
##                                                                                                (0.039)               
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.510                     0.375                      0.553                
## Conditional R^2                            0.696                     0.688                      0.706                
## AIC                                     1182.414                  1131.216                   1147.543                
## BIC                                     1209.422                  1167.226                   1183.553                
## Num. obs.                                666                       666                        666                    
## Num. groups: B.ID                        150                       150                        150                    
## Var: B.ID (Intercept)                      0.152                     0.210                      0.124                
## Var: Residual                              0.249                     0.209                      0.239                
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 7)
## 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')   0.075 (0.049) 1.554  .121     [-0.020,  0.171]
## ────────────────────────────────────────────────────────────
## 
## 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]
## ────────────────────────────────────────────────────────────────
## 
## 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)     0.015 (0.013) 1.211  .226     [-0.010, 0.040]
##  -0.000 (Mean)     0.034 (0.014) 2.530  .011 *   [ 0.011, 0.062]
##  0.311 (+ SD)      0.053 (0.019) 2.883  .004 **  [ 0.020, 0.087]
## ────────────────────────────────────────────────────────────────
## 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"), 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 : 7 (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 + Stability.GraC + WP.InformationSearchV + (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                     -0.112            
##                                          (0.190)              (0.089)                    (0.194)           
## WP.SupervisoryBehavioralFeedbackV_mean    0.195 **            -0.009                      0.204 **         
##                                          (0.070)              (0.032)                    (0.071)           
## WP.InformationSearchV_mean                0.294 ***            0.997 ***                  0.083            
##                                          (0.069)              (0.031)                    (0.077)           
## WA.WorkReflectionV_mean                   0.521 ***           -0.088                      0.539 ***        
##                                          (0.089)              (0.055)                    (0.088)           
## WA.WorkReflectionV                       -0.050                0.093 *                   -0.066            
##                                          (0.037)              (0.042)                    (0.036)           
## Stability.GraC                                                -0.221                     -0.084            
##                                                               (0.187)                    (0.117)           
## WA.WorkReflectionV:Stability.GraC                              0.073                                       
##                                                               (0.059)                                      
## WP.InformationSearchV                                                                     0.208 ***        
##                                                                                          (0.035)           
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                              0.564                0.757                      0.575            
## Conditional R^2                           0.793                0.757                      0.806            
## AIC                                     862.652              732.113                    838.176            
## BIC                                     894.161              772.625                    878.687            
## Num. obs.                               666                  666                        666                
## Num. groups: B.ID                       150                  150                        150                
## Var: B.ID (Intercept)                     0.152                0.000                      0.154            
## Var: Residual                             0.138                0.164                      0.129            
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 7)
## 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')  -0.066 (0.036) -1.859  .064 .   [-0.136,  0.004]
## ─────────────────────────────────────────────────────────────
## 
## 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]
## ────────────────────────────────────────────────────────────────
## 
## 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)     0.014 (0.009) 1.666  .096 .   [-0.000,  0.034]
##  -0.000 (Mean)     0.019 (0.009) 2.042  .041 *   [ 0.004,  0.038]
##  0.311 (+ SD)      0.023 (0.012) 1.989  .047 *   [ 0.003,  0.047]
## ─────────────────────────────────────────────────────────────────
## 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"), 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 : 7 (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 + Stability.GraC + WP.InformationSearchV + (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                      -0.439            
##                                           (0.248)              (0.089)                     (0.252)           
## WP.SupervisoryBehavioralFeedbackV_mean     0.328 ***           -0.009                       0.307 ***        
##                                           (0.091)              (0.032)                     (0.092)           
## WP.InformationSearchV_mean                 0.273 **             0.997 ***                   0.090            
##                                           (0.090)              (0.031)                     (0.100)           
## WA.WorkReflectionV_mean                    0.374 **            -0.088                       0.382 ***        
##                                           (0.115)              (0.055)                     (0.115)           
## WA.WorkReflectionV                         0.075                0.093 *                     0.060            
##                                           (0.047)              (0.042)                     (0.046)           
## Stability.GraC                                                 -0.221                       0.227            
##                                                                (0.187)                     (0.153)           
## WA.WorkReflectionV:Stability.GraC                               0.073                                        
##                                                                (0.059)                                       
## WP.InformationSearchV                                                                       0.188 ***        
##                                                                                            (0.044)           
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.484                0.757                       0.493            
## Conditional R^2                            0.763                0.757                       0.771            
## AIC                                     1184.903              732.113                    1175.338            
## BIC                                     1216.412              772.625                    1215.849            
## Num. obs.                                666                  666                         666                
## Num. groups: B.ID                        150                  150                         150                
## Var: B.ID (Intercept)                      0.261                0.000                       0.261            
## Var: Residual                              0.221                0.164                       0.214            
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 7)
## 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')   0.060 (0.046) 1.302  .193     [-0.030,  0.151]
## ────────────────────────────────────────────────────────────
## 
## 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]
## ────────────────────────────────────────────────────────────────
## 
## 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)     0.013 (0.008) 1.567  .117     [-0.000, 0.033]
##  -0.000 (Mean)     0.017 (0.009) 1.888  .059 .   [ 0.004, 0.036]
##  0.311 (+ SD)      0.021 (0.011) 1.849  .065 .   [ 0.003, 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. :)

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"), 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 : 7 (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 + Stability.GraC + WP.InformationSearchV + (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                      -0.321                      
##                                           (0.232)                        (0.089)                     (0.237)                     
## WP.SupervisoryBehavioralFeedbackV_mean     0.330 ***                     -0.009                       0.326 ***                  
##                                           (0.085)                        (0.032)                     (0.087)                     
## WP.InformationSearchV_mean                 0.346 ***                      0.997 ***                   0.142                      
##                                           (0.084)                        (0.031)                     (0.100)                     
## WA.WorkReflectionV_mean                    0.283 *                       -0.088                       0.297 **                   
##                                           (0.113)                        (0.055)                     (0.113)                     
## WA.WorkReflectionV                         0.064                          0.093 *                     0.047                      
##                                           (0.055)                        (0.042)                     (0.055)                     
## Stability.GraC                                                           -0.221                       0.050                      
##                                                                          (0.187)                     (0.144)                     
## WA.WorkReflectionV:Stability.GraC                                         0.073                                                  
##                                                                          (0.059)                                                 
## WP.InformationSearchV                                                                                 0.205 ***                  
##                                                                                                      (0.053)                     
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.455                          0.757                       0.461                      
## Conditional R^2                            0.671                          0.757                       0.680                      
## AIC                                     1336.313                        732.113                    1331.338                      
## BIC                                     1367.822                        772.625                    1371.849                      
## Num. obs.                                666                            666                         666                          
## Num. groups: B.ID                        150                            150                         150                          
## Var: B.ID (Intercept)                      0.202                          0.000                       0.206                      
## Var: Residual                              0.308                          0.164                       0.300                      
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 7)
## 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')   0.047 (0.055) 0.867  .387     [-0.059,  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]
## ────────────────────────────────────────────────────────────────
## 
## 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) 1.539  .124     [-0.000, 0.036]
##  -0.000 (Mean)     0.018 (0.010) 1.849  .065 .   [ 0.004, 0.039]
##  0.311 (+ SD)      0.023 (0.013) 1.813  .070 .   [ 0.003, 0.047]
## ────────────────────────────────────────────────────────────────
## 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"), 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 : 7 (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 + Stability.GraC + WP.InformationSearchV + (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 ***                  0.178                     
##                                           (0.251)                        (0.213)                    (0.258)                    
## WP.SupervisoryBehavioralFeedbackV_mean     0.198 *                        0.228 **                   0.138                     
##                                           (0.098)                        (0.082)                    (0.098)                    
## WA.WorkReflectionV_mean                    0.635 ***                      0.381 ***                  0.544 ***                 
##                                           (0.118)                        (0.100)                    (0.117)                    
## WA.WorkReflectionV                         0.018                          0.124 *                    0.001                     
##                                           (0.052)                        (0.050)                    (0.051)                    
## Stability.GraC                                                           -0.727 *                    0.086                     
##                                                                          (0.309)                    (0.166)                    
## WA.WorkReflectionV:Stability.GraC                                         0.220 *                                              
##                                                                          (0.092)                                               
## WP.InformationSearchV                                                                                0.214 ***                 
##                                                                                                     (0.044)                    
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.355                          0.375                      0.378                     
## Conditional R^2                            0.702                          0.688                      0.712                     
## AIC                                     1318.209                       1131.216                   1304.952                     
## BIC                                     1345.216                       1167.226                   1340.962                     
## Num. obs.                                666                            666                        666                         
## Num. groups: B.ID                        150                            150                        150                         
## Var: B.ID (Intercept)                      0.319                          0.210                      0.307                     
## 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 7)
## 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')   0.001 (0.051) 0.020  .984     [-0.099,  0.102]
## ────────────────────────────────────────────────────────────
## 
## 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]
## ────────────────────────────────────────────────────────────────
## 
## 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) 1.176  .240     [-0.008, 0.032]
##  -0.000 (Mean)     0.027 (0.012) 2.341  .019 *   [ 0.007, 0.052]
##  0.311 (+ SD)      0.042 (0.016) 2.629  .009 **  [ 0.014, 0.072]
## ────────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

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"), 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 : 7 (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 + Stability.GraC + WP.InformationSearchV + (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 ***                  0.859 ***         
##                                           (0.241)                (0.213)                    (0.236)            
## WP.SupervisoryBehavioralFeedbackV_mean     0.100                  0.228 **                   0.024             
##                                           (0.094)                (0.082)                    (0.089)            
## WA.WorkReflectionV_mean                    0.572 ***              0.381 ***                  0.442 ***         
##                                           (0.118)                (0.100)                    (0.113)            
## WA.WorkReflectionV                         0.027                  0.124 *                    0.005             
##                                           (0.060)                (0.050)                    (0.060)            
## Stability.GraC                                                   -0.727 *                    0.025             
##                                                                  (0.309)                    (0.151)            
## WA.WorkReflectionV:Stability.GraC                                 0.220 *                                      
##                                                                  (0.092)                                       
## WP.InformationSearchV                                                                        0.302 ***         
##                                                                                             (0.048)            
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                               0.261                  0.375                      0.312             
## Conditional R^2                            0.571                  0.688                      0.574             
## AIC                                     1461.291               1131.216                   1434.468             
## BIC                                     1488.299               1167.226                   1470.478             
## Num. obs.                                666                    666                        666                 
## Num. groups: B.ID                        150                    150                        150                 
## Var: B.ID (Intercept)                      0.267                  0.210                      0.220             
## Var: Residual                              0.369                  0.209                      0.359             
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 7)
## 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')   0.005 (0.060) 0.082  .934     [-0.112,  0.122]
## ────────────────────────────────────────────────────────────
## 
## 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]
## ────────────────────────────────────────────────────────────────
## 
## 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)     0.017 (0.014) 1.203  .229     [-0.011, 0.045]
##  -0.000 (Mean)     0.038 (0.015) 2.482  .013 *   [ 0.012, 0.070]
##  0.311 (+ SD)      0.060 (0.021) 2.818  .005 **  [ 0.021, 0.098]
## ────────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
## 
## Note. The results based on bootstrapping or other random processes
## are unlikely identical to other statistical software (e.g., SPSS).
## To make results reproducible, you need to set a seed (any number).
## Please see the help page for details: help(PROCESS)
## Ignore this note if you have already set a seed. :)

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)",}