DATA
PREPARATION
## √ Successfully imported: 308 obs. of 185 variables
## [1] "BMDM" "creativityV" "coachingV" "pychcapitalV" "trustV"
## [6] "YG1_a" "YG2_a" "LD3"
## [1] "BMDM" "creativityV" "coachingV"
## [4] "pychcapitalV" "trustV" "YG1_a"
## [7] "YG2_a" "LD3" "creativityV_mean"
## [10] "creativityV.GroC" "coachingV_mean" "coachingV.GroC"
## [13] "pychcapitalV_mean" "pychcapitalV.GroC" "trustV_mean"
## [16] "trustV.GroC" "YG1_a_mean" "YG1_a.GroC"
## [19] "YG2_a_mean" "YG2_a.GroC" "LD3_mean"
## [22] "LD3.GroC"
##
## ------ Sample Size Information ------
##
## Level 1: N = 303 observations ("creativityV")
## Level 2: K = 65 groups ("BMDM")
##
## n (group sizes)
## Min. 1.000
## Median 5.000
## Mean 4.662
## Max. 6.000
##
## ------ ICC(1), ICC(2), and rWG ------
##
## ICC variable: "creativityV"
##
## ICC(1) = 0.528 (non-independence of data)
## ICC(2) = 0.821 (reliability of group means)
##
## rWG variable: "creativityV"
##
## rWG (within-group agreement for single-item measures)
## ───────────────────────────────────────────────────
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## ───────────────────────────────────────────────────
## rWG 0.000 0.763 0.907 0.826 0.965 1.000 1.000
## ───────────────────────────────────────────────────
##
## ------ Sample Size Information ------
##
## Level 1: N = 308 observations ("pychcapitalV")
## Level 2: K = 65 groups ("BMDM")
##
## n (group sizes)
## Min. 2.000
## Median 5.000
## Mean 4.738
## Max. 7.000
##
## ------ ICC(1), ICC(2), and rWG ------
##
## ICC variable: "pychcapitalV"
##
## ICC(1) = 0.131 (non-independence of data)
## ICC(2) = 0.406 (reliability of group means)
##
## rWG variable: "pychcapitalV"
##
## rWG (within-group agreement for single-item measures)
## ─────────────────────────────────────────────
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## ─────────────────────────────────────────────
## rWG 0.000 0.732 0.831 0.758 0.918 0.996
## ─────────────────────────────────────────────
##
## ------ Sample Size Information ------
##
## Level 1: N = 308 observations ("coachingV")
## Level 2: K = 65 groups ("BMDM")
##
## n (group sizes)
## Min. 2.000
## Median 5.000
## Mean 4.738
## Max. 7.000
##
## ------ ICC(1), ICC(2), and rWG ------
##
## ICC variable: "coachingV"
##
## ICC(1) = 0.252 (non-independence of data)
## ICC(2) = 0.598 (reliability of group means)
##
## rWG variable: "coachingV"
##
## rWG (within-group agreement for single-item measures)
## ─────────────────────────────────────────────
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## ─────────────────────────────────────────────
## rWG 0.000 0.561 0.788 0.695 0.939 0.998
## ─────────────────────────────────────────────
##
## ------ Sample Size Information ------
##
## Level 1: N = 305 observations ("trustV")
## Level 2: K = 65 groups ("BMDM")
##
## n (group sizes)
## Min. 2.000
## Median 5.000
## Mean 4.692
## Max. 7.000
##
## ------ ICC(1), ICC(2), and rWG ------
##
## ICC variable: "trustV"
##
## ICC(1) = 0.240 (non-independence of data)
## ICC(2) = 0.578 (reliability of group means)
##
## rWG variable: "trustV"
##
## rWG (within-group agreement for single-item measures)
## ─────────────────────────────────────────────
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## ─────────────────────────────────────────────
## rWG 0.000 0.588 0.772 0.707 0.898 0.996
## ─────────────────────────────────────────────
SINGLE-LVEL ANALYSIS
Model 58
##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 58 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## - Outcome (Y) : creativityV
## - Predictor (X) : coachingV
## - Mediators (M) : pychcapitalV
## - Moderators (W) : trustV
## - Covariates (C) : YG1_a, YG2_a, LD3
## - HLM Clusters : -
##
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
##
## Formula of Mediator:
## - pychcapitalV ~ YG1_a + YG2_a + LD3 + coachingV*trustV
## Formula of Outcome:
## - creativityV ~ YG1_a + YG2_a + LD3 + coachingV + pychcapitalV*trustV
##
## 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) creativityV (2) pychcapitalV (3) creativityV
## ───────────────────────────────────────────────────────────────────────
## (Intercept) 4.790 *** 5.023 *** 4.760 ***
## (0.050) (0.035) (0.052)
## YG1_a -0.341 ** -0.049 -0.320 *
## (0.128) (0.074) (0.126)
## YG2_a -0.009 0.008 -0.012
## (0.008) (0.005) (0.008)
## LD3 -0.154 -0.015 -0.132
## (0.084) (0.049) (0.084)
## coachingV 0.202 *** 0.360 *** 0.289 **
## (0.060) (0.057) (0.103)
## trustV -0.024 -0.168
## (0.057) (0.094)
## coachingV:trustV 0.077 *
## (0.037)
## pychcapitalV 0.229
## (0.121)
## pychcapitalV:trustV 0.211
## (0.121)
## ───────────────────────────────────────────────────────────────────────
## R^2 0.100 0.283 0.139
## Adj. R^2 0.082 0.262 0.109
## Num. obs. 209 209 209
## ───────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.1.5)
## Effect Type : Moderated Mediation (Model 58)
## Sample Size : 209 (99 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 2000 (Bootstrap)
##
## Direct Effect: "coachingV" (X) ==> "creativityV" (Y)
## ──────────────────────────────────────────────────────────
## Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────
## Direct (c') 0.289 (0.103) 2.792 .006 ** [0.085, 0.493]
## ──────────────────────────────────────────────────────────
##
## Interaction Effect on "pychcapitalV" (M)
## ──────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────
## coachingV * trustV 4.34 1 202 .039 *
## ──────────────────────────────────────────
##
## Simple Slopes: "coachingV" (X) ==> "pychcapitalV" (M)
## (Conditional Effects [a] of X on M)
## ───────────────────────────────────────────────────────────
## "trustV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────
## 4.116 (- SD) 0.295 (0.060) 4.943 <.001 *** [0.177, 0.412]
## 4.966 (Mean) 0.360 (0.057) 6.279 <.001 *** [0.247, 0.473]
## 5.816 (+ SD) 0.425 (0.071) 6.025 <.001 *** [0.286, 0.564]
## ───────────────────────────────────────────────────────────
##
## Interaction Effect on "creativityV" (Y)
## ─────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────
## pychcapitalV * trustV 3.03 1 201 .083 .
## ─────────────────────────────────────────────
##
## Simple Slopes: "pychcapitalV" (M) ==> "creativityV" (Y)
## (Conditional Effects [b] of M on Y)
## ────────────────────────────────────────────────────────────
## "trustV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────
## 4.116 (- SD) 0.049 (0.142) 0.348 .728 [-0.231, 0.330]
## 4.966 (Mean) 0.229 (0.121) 1.896 .059 . [-0.009, 0.467]
## 5.816 (+ SD) 0.408 (0.174) 2.347 .020 * [ 0.065, 0.752]
## ────────────────────────────────────────────────────────────
##
## Running 2000 * 3 simulations...
## Indirect Path: "coachingV" (X) ==> "pychcapitalV" (M) ==> "creativityV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ────────────────────────────────────────────────────────────
## "trustV" Effect S.E. z p [Boot 95% CI]
## ────────────────────────────────────────────────────────────
## 4.116 (- SD) 0.015 (0.042) 0.351 .726 [-0.058, 0.112]
## 4.966 (Mean) 0.082 (0.060) 1.368 .171 [0.002, 0.236]
## 5.816 (+ SD) 0.174 (0.101) 1.719 .086 . [0.032, 0.424]
## ────────────────────────────────────────────────────────────
## Percentile Bootstrap Confidence Interval
## (SE and CI are estimated based on 2000 Bootstrap 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. :)
Model 4
##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## - Outcome (Y) : creativityV
## - Predictor (X) : coachingV
## - Mediators (M) : pychcapitalV
## - Moderators (W) : -
## - Covariates (C) : YG1_a, YG2_a, LD3
## - HLM Clusters : -
##
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
##
## Formula of Mediator:
## - pychcapitalV ~ YG1_a + YG2_a + LD3 + coachingV
## Formula of Outcome:
## - creativityV ~ YG1_a + YG2_a + LD3 + coachingV + pychcapitalV
##
## 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) creativityV (2) pychcapitalV (3) creativityV
## ────────────────────────────────────────────────────────────────
## (Intercept) 4.790 *** 5.065 *** 4.790 ***
## (0.050) (0.029) (0.049)
## YG1_a -0.341 ** -0.045 -0.332 **
## (0.128) (0.075) (0.127)
## YG2_a -0.009 0.009 -0.011
## (0.008) (0.005) (0.008)
## LD3 -0.154 -0.025 -0.149
## (0.084) (0.049) (0.084)
## coachingV 0.202 *** 0.292 *** 0.143 *
## (0.060) (0.035) (0.069)
## pychcapitalV 0.201
## (0.119)
## ────────────────────────────────────────────────────────────────
## R^2 0.100 0.264 0.112
## Adj. R^2 0.082 0.250 0.090
## Num. obs. 209 209 209
## ────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 209 (99 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 2000 (Bootstrap)
##
## Running 2000 simulations...
## Indirect Path: "coachingV" (X) ==> "pychcapitalV" (M) ==> "creativityV" (Y)
## ─────────────────────────────────────────────────────────────
## Effect S.E. z p [Boot 95% CI]
## ─────────────────────────────────────────────────────────────
## Indirect (ab) 0.059 (0.041) 1.423 .155 [-0.003, 0.158]
## Direct (c') 0.143 (0.076) 1.870 .061 . [-0.025, 0.267]
## Total (c) 0.202 (0.058) 3.492 <.001 *** [ 0.083, 0.310]
## ─────────────────────────────────────────────────────────────
## Percentile Bootstrap Confidence Interval
## (SE and CI are estimated based on 2000 Bootstrap 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. :)
Model 1
##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : creativityV
## - Predictor (X) : coachingV
## - Mediators (M) : -
## - Moderators (W) : trustV
## - Covariates (C) : YG1_a, YG2_a, LD3
## - HLM Clusters : -
##
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
##
## Formula of Outcome:
## - creativityV ~ YG1_a + YG2_a + LD3 + coachingV*trustV
##
## 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) creativityV (2) creativityV
## ──────────────────────────────────────────────────
## (Intercept) 4.790 *** 4.835 ***
## (0.050) (0.060)
## YG1_a -0.341 ** -0.338 **
## (0.128) (0.127)
## YG2_a -0.009 -0.008
## (0.008) (0.008)
## LD3 -0.154 -0.166 *
## (0.084) (0.084)
## coachingV 0.202 *** 0.322 **
## (0.060) (0.098)
## trustV -0.214 *
## (0.097)
## coachingV:trustV -0.081
## (0.063)
## ──────────────────────────────────────────────────
## R^2 0.100 0.123
## Adj. R^2 0.082 0.097
## Num. obs. 209 209
## ──────────────────────────────────────────────────
## 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 : 209 (99 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "creativityV" (Y)
## ──────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────
## coachingV * trustV 1.66 1 202 .199
## ──────────────────────────────────────────
##
## Simple Slopes: "coachingV" (X) ==> "creativityV" (Y)
## ───────────────────────────────────────────────────────────
## "trustV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────
## 4.116 (- SD) 0.391 (0.102) 3.838 <.001 *** [0.190, 0.592]
## 4.966 (Mean) 0.322 (0.098) 3.288 .001 ** [0.129, 0.516]
## 5.816 (+ SD) 0.253 (0.121) 2.098 .037 * [0.015, 0.491]
## ───────────────────────────────────────────────────────────
Model 7
##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 7 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## - Outcome (Y) : creativityV
## - Predictor (X) : coachingV
## - Mediators (M) : pychcapitalV
## - Moderators (W) : trustV
## - Covariates (C) : YG1_a, YG2_a, LD3
## - HLM Clusters : -
##
## All numeric predictors have been grand-mean centered.
## (For details, please see the help page of PROCESS.)
##
## Formula of Mediator:
## - pychcapitalV ~ YG1_a + YG2_a + LD3 + coachingV*trustV
## Formula of Outcome:
## - creativityV ~ YG1_a + YG2_a + LD3 + coachingV + trustV + pychcapitalV
##
## 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) creativityV (2) pychcapitalV (3) creativityV
## ────────────────────────────────────────────────────────────────────
## (Intercept) 4.790 *** 5.023 *** 4.790 ***
## (0.050) (0.035) (0.049)
## YG1_a -0.341 ** -0.049 -0.334 **
## (0.128) (0.074) (0.127)
## YG2_a -0.009 0.008 -0.011
## (0.008) (0.005) (0.008)
## LD3 -0.154 -0.015 -0.150
## (0.084) (0.049) (0.083)
## coachingV 0.202 *** 0.360 *** 0.285 **
## (0.060) (0.057) (0.104)
## trustV -0.024 -0.171
## (0.057) (0.094)
## coachingV:trustV 0.077 *
## (0.037)
## pychcapitalV 0.186
## (0.119)
## ────────────────────────────────────────────────────────────────────
## R^2 0.100 0.283 0.126
## Adj. R^2 0.082 0.262 0.100
## Num. obs. 209 209 209
## ────────────────────────────────────────────────────────────────────
## 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 : 209 (99 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 2000 (Bootstrap)
##
## Direct Effect: "coachingV" (X) ==> "creativityV" (Y)
## ──────────────────────────────────────────────────────────
## Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────
## Direct (c') 0.285 (0.104) 2.741 .007 ** [0.080, 0.490]
## ──────────────────────────────────────────────────────────
##
## Interaction Effect on "pychcapitalV" (M)
## ──────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────
## coachingV * trustV 4.34 1 202 .039 *
## ──────────────────────────────────────────
##
## Simple Slopes: "coachingV" (X) ==> "pychcapitalV" (M)
## (Conditional Effects [a] of X on M)
## ───────────────────────────────────────────────────────────
## "trustV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────
## 4.116 (- SD) 0.295 (0.060) 4.943 <.001 *** [0.177, 0.412]
## 4.966 (Mean) 0.360 (0.057) 6.279 <.001 *** [0.247, 0.473]
## 5.816 (+ SD) 0.425 (0.071) 6.025 <.001 *** [0.286, 0.564]
## ───────────────────────────────────────────────────────────
##
## Running 2000 * 3 simulations...
## Indirect Path: "coachingV" (X) ==> "pychcapitalV" (M) ==> "creativityV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ────────────────────────────────────────────────────────────
## "trustV" Effect S.E. z p [Boot 95% CI]
## ────────────────────────────────────────────────────────────
## 4.116 (- SD) 0.055 (0.044) 1.241 .214 [-0.008, 0.166]
## 4.966 (Mean) 0.067 (0.051) 1.324 .186 [-0.009, 0.190]
## 5.816 (+ SD) 0.079 (0.059) 1.342 .180 [-0.012, 0.217]
## ────────────────────────────────────────────────────────────
## Percentile Bootstrap Confidence Interval
## (SE and CI are estimated based on 2000 Bootstrap 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. :)
## [1] "BMDM" "creativityV" "coachingV"
## [4] "pychcapitalV" "trustV" "YG1_a"
## [7] "YG2_a" "LD3" "creativityV_mean"
## [10] "creativityV.GroC" "coachingV_mean" "coachingV.GroC"
## [13] "pychcapitalV_mean" "pychcapitalV.GroC" "trustV_mean"
## [16] "trustV.GroC" "YG1_a_mean" "YG1_a.GroC"
## [19] "YG2_a_mean" "YG2_a.GroC" "LD3_mean"
## [22] "LD3.GroC"
MULTILEVEL ANALYSIS
Model 58
##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 58 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## - Outcome (Y) : creativityV
## - Predictor (X) : coachingV.GroC
## - Mediators (M) : pychcapitalV.GroC
## - Moderators (W) : trustV.GroC
## - Covariates (C) : YG1_a, YG2_a, LD3
## - HLM Clusters : BMDM
##
## Formula of Mediator:
## - pychcapitalV.GroC ~ YG1_a + YG2_a + LD3 + coachingV.GroC*trustV.GroC + (1|BMDM)
## Formula of Outcome:
## - creativityV ~ YG1_a + YG2_a + LD3 + coachingV.GroC + pychcapitalV.GroC*trustV.GroC + (1|BMDM)
##
## 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) creativityV (2) pychcapitalV.GroC (3) creativityV
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.933 *** -0.150 4.936 ***
## (0.353) (0.165) (0.355)
## YG1_a -0.214 * -0.010 -0.212 *
## (0.100) (0.060) (0.100)
## YG2_a 0.005 0.006 0.003
## (0.007) (0.004) (0.007)
## LD3 -0.039 -0.015 -0.025
## (0.152) (0.039) (0.151)
## coachingV.GroC 0.093 0.290 *** 0.050
## (0.052) (0.052) (0.082)
## trustV.GroC 0.037 -0.055
## (0.052) (0.077)
## coachingV.GroC:trustV.GroC 0.013
## (0.042)
## pychcapitalV.GroC 0.280 *
## (0.112)
## pychcapitalV.GroC:trustV.GroC 0.168
## (0.137)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.022 0.279 0.038
## Conditional R^2 0.645 0.279 0.652
## AIC 416.402 180.353 423.722
## BIC 439.798 210.434 457.145
## Num. obs. 209 209 209
## Num. groups: BMDM 59 59 59
## Var: BMDM (Intercept) 0.386 0.000 0.380
## Var: Residual 0.220 0.110 0.215
## ──────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.1.5)
## Effect Type : Moderated Mediation (Model 58)
## Sample Size : 209 (99 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 2000 (Monte Carlo)
##
## Direct Effect: "coachingV.GroC" (X) ==> "creativityV" (Y)
## ────────────────────────────────────────────────────────────
## Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────
## Direct (c') 0.050 (0.082) 0.604 .547 [-0.110, 0.209]
## ────────────────────────────────────────────────────────────
##
## Interaction Effect on "pychcapitalV.GroC" (M)
## ────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────
## coachingV.GroC * trustV.GroC 0.10 1 202 .757
## ────────────────────────────────────────────────────
##
## Simple Slopes: "coachingV.GroC" (X) ==> "pychcapitalV.GroC" (M)
## (Conditional Effects [a] of X on M)
## ────────────────────────────────────────────────────────────
## "trustV.GroC" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────
## -0.625 (- SD) 0.282 (0.054) 5.186 <.001 *** [0.175, 0.389]
## 0.015 (Mean) 0.290 (0.052) 5.552 <.001 *** [0.188, 0.393]
## 0.656 (+ SD) 0.299 (0.063) 4.739 <.001 *** [0.175, 0.422]
## ────────────────────────────────────────────────────────────
##
## Interaction Effect on "creativityV" (Y)
## ───────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────
## pychcapitalV.GroC * trustV.GroC 1.51 1 165 .221
## ───────────────────────────────────────────────────────
##
## Simple Slopes: "pychcapitalV.GroC" (M) ==> "creativityV" (Y)
## (Conditional Effects [b] of M on Y)
## ─────────────────────────────────────────────────────────────
## "trustV.GroC" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────
## -0.625 (- SD) 0.175 (0.117) 1.499 .136 [-0.054, 0.404]
## 0.015 (Mean) 0.283 (0.113) 2.510 .013 * [ 0.062, 0.504]
## 0.656 (+ SD) 0.391 (0.165) 2.370 .019 * [ 0.068, 0.714]
## ─────────────────────────────────────────────────────────────
##
## Running 2000 * 3 simulations...
## Indirect Path: "coachingV.GroC" (X) ==> "pychcapitalV.GroC" (M) ==> "creativityV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ─────────────────────────────────────────────────────────────
## "trustV.GroC" Effect S.E. z p [MCMC 95% CI]
## ─────────────────────────────────────────────────────────────
## -0.625 (- SD) 0.048 (0.035) 1.365 .172 [-0.016, 0.125]
## 0.015 (Mean) 0.081 (0.036) 2.276 .023 * [ 0.018, 0.155]
## 0.656 (+ SD) 0.117 (0.054) 2.144 .032 * [ 0.017, 0.233]
## ─────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 2000 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. :)
Model 4
##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 4 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Mediation
## - Outcome (Y) : creativityV
## - Predictor (X) : coachingV.GroC
## - Mediators (M) : pychcapitalV.GroC
## - Moderators (W) : -
## - Covariates (C) : YG1_a, YG2_a, LD3
## - HLM Clusters : BMDM
##
## Formula of Mediator:
## - pychcapitalV.GroC ~ YG1_a + YG2_a + LD3 + coachingV.GroC + (1 | BMDM)
## Formula of Outcome:
## - creativityV ~ YG1_a + YG2_a + LD3 + coachingV.GroC + pychcapitalV.GroC + (1|BMDM)
##
## 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) creativityV (2) pychcapitalV.GroC (3) creativityV
## ──────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.933 *** -0.146 5.000 ***
## (0.353) (0.164) (0.352)
## YG1_a -0.214 * -0.015 -0.207 *
## (0.100) (0.059) (0.099)
## YG2_a 0.005 0.006 0.002
## (0.007) (0.004) (0.007)
## LD3 -0.039 -0.015 -0.040
## (0.152) (0.039) (0.151)
## coachingV.GroC 0.093 0.314 *** 0.017
## (0.052) (0.036) (0.062)
## pychcapitalV.GroC 0.235 *
## (0.106)
## ──────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.022 0.279 0.031
## Conditional R^2 0.645 0.279 0.653
## AIC 416.402 168.374 416.186
## BIC 439.798 191.770 442.925
## Num. obs. 209 209 209
## Num. groups: BMDM 59 59 59
## Var: BMDM (Intercept) 0.386 0.000 0.384
## Var: Residual 0.220 0.110 0.215
## ──────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘mediation’ (v4.5.0)
## Effect Type : Simple Mediation (Model 4)
## Sample Size : 209 (99 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 2000 (Monte Carlo)
##
## Running 2000 simulations...
## Indirect Path: "coachingV.GroC" (X) ==> "pychcapitalV.GroC" (M) ==> "creativityV" (Y)
## ─────────────────────────────────────────────────────────────
## Effect S.E. z p [MCMC 95% CI]
## ─────────────────────────────────────────────────────────────
## Indirect (ab) 0.073 (0.034) 2.167 .030 * [ 0.010, 0.140]
## Direct (c') 0.016 (0.060) 0.265 .791 [-0.102, 0.134]
## Total (c) 0.089 (0.051) 1.738 .082 . [-0.009, 0.190]
## ─────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 2000 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. :)
Model 1
##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : creativityV
## - Predictor (X) : coachingV.GroC
## - Mediators (M) : -
## - Moderators (W) : trustV.GroC
## - Covariates (C) : YG1_a, YG2_a, LD3
## - HLM Clusters : BMDM
##
## Formula of Outcome:
## - creativityV ~ YG1_a + YG2_a + LD3 + coachingV.GroC*trustV.GroC + (1|BMDM)
##
## 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) creativityV (2) creativityV
## ────────────────────────────────────────────────────────────
## (Intercept) 4.933 *** 4.948 ***
## (0.353) (0.353)
## YG1_a -0.214 * -0.222 *
## (0.100) (0.102)
## YG2_a 0.005 0.005
## (0.007) (0.007)
## LD3 -0.039 -0.042
## (0.152) (0.150)
## coachingV.GroC 0.093 0.106
## (0.052) (0.078)
## trustV.GroC -0.048
## (0.077)
## coachingV.GroC:trustV.GroC -0.081
## (0.073)
## ────────────────────────────────────────────────────────────
## Marginal R^2 0.022 0.025
## Conditional R^2 0.645 0.639
## AIC 416.402 425.504
## BIC 439.798 455.585
## Num. obs. 209 209
## Num. groups: BMDM 59 59
## Var: BMDM (Intercept) 0.386 0.377
## Var: Residual 0.220 0.222
## ────────────────────────────────────────────────────────────
## 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 : 209 (99 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "creativityV" (Y)
## ────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────
## coachingV.GroC * trustV.GroC 1.23 1 165 .270
## ────────────────────────────────────────────────────
##
## Simple Slopes: "coachingV.GroC" (X) ==> "creativityV" (Y)
## ─────────────────────────────────────────────────────────────
## "trustV.GroC" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────
## -0.625 (- SD) 0.156 (0.081) 1.922 .056 . [-0.003, 0.316]
## 0.015 (Mean) 0.104 (0.079) 1.327 .187 [-0.050, 0.259]
## 0.656 (+ SD) 0.052 (0.101) 0.520 .604 [-0.145, 0.250]
## ─────────────────────────────────────────────────────────────
## Descriptive Statistics:
## ───────────────────────────────────────────────────────────────────────────────
## N (NA) Mean SD | Median Min Max Skewness Kurtosis
## ───────────────────────────────────────────────────────────────────────────────
## BMDM* 308 34.28 18.91 | 35.50 1.00 65.00 -0.11 -1.21
## creativityV 303 5 4.79 0.74 | 4.92 1.85 6.00 -0.74 0.52
## coachingV 308 5.01 0.86 | 5.20 1.96 6.00 -1.09 1.00
## pychcapitalV 308 5.01 0.49 | 5.08 2.83 6.00 -1.04 2.31
## trustV 305 3 4.96 0.84 | 5.11 1.67 6.00 -1.05 1.06
## YG1_a 239 69 1.21 0.40 | 1.00 1.00 2.00 1.45 0.11
## YG2_a 224 84 32.12 6.42 | 31.00 20.00 48.00 0.45 -0.83
## LD3 296 12 1.41 0.57 | 1.00 1.00 3.00 1.02 0.03
## creativityV_mean 308 4.77 0.59 | 4.82 3.18 5.92 -0.62 0.02
## creativityV.GroC 303 5 -0.00 0.45 | 0.00 -1.87 1.67 -0.11 1.57
## coachingV_mean 308 5.01 0.55 | 5.14 3.02 5.96 -0.55 0.63
## coachingV.GroC 308 0.00 0.66 | 0.05 -3.11 1.52 -0.92 2.47
## pychcapitalV_mean 308 5.01 0.27 | 5.03 3.75 5.52 -0.75 2.03
## pychcapitalV.GroC 308 -0.00 0.40 | 0.02 -1.68 1.24 -0.49 1.48
## trustV_mean 308 4.96 0.53 | 4.96 2.39 5.94 -0.92 2.54
## trustV.GroC 305 3 0.00 0.65 | 0.09 -2.31 1.78 -0.78 1.14
## YG1_a_mean 302 6 1.20 0.26 | 1.00 1.00 2.00 1.25 0.85
## YG1_a.GroC 239 69 0.00 0.32 | 0.00 -0.67 0.86 0.65 0.69
## YG2_a_mean 302 6 32.74 4.99 | 31.25 22.00 43.33 0.58 -0.61
## YG2_a.GroC 224 84 0.00 4.44 | -0.08 -10.60 15.75 0.50 0.29
## LD3_mean 296 12 1.41 0.57 | 1.00 1.00 3.00 1.02 0.03
## LD3.GroC 296 12 0.00 0.00 | 0.00 0.00 0.00 NaN NaN
## ───────────────────────────────────────────────────────────────────────────────
##
## NOTE: `BMDM` transformed to numeric.
## Error: Confidence intervals could not be computed.
Model 7
##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 7 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## - Outcome (Y) : creativityV
## - Predictor (X) : coachingV.GroC
## - Mediators (M) : pychcapitalV.GroC
## - Moderators (W) : trustV.GroC
## - Covariates (C) : YG1_a, YG2_a, LD3
## - HLM Clusters : BMDM
##
## Formula of Mediator:
## - pychcapitalV.GroC ~ YG1_a + YG2_a + LD3 + coachingV.GroC*trustV.GroC + (1 | BMDM)
## Formula of Outcome:
## - creativityV ~ YG1_a + YG2_a + LD3 + coachingV.GroC + trustV.GroC + pychcapitalV.GroC + (1|BMDM)
##
## 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) creativityV (2) pychcapitalV.GroC (3) creativityV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.933 *** -0.150 4.995 ***
## (0.353) (0.165) (0.352)
## YG1_a -0.214 * -0.010 -0.217 *
## (0.100) (0.060) (0.100)
## YG2_a 0.005 0.006 0.003
## (0.007) (0.004) (0.007)
## LD3 -0.039 -0.015 -0.039
## (0.152) (0.039) (0.151)
## coachingV.GroC 0.093 0.290 *** 0.051
## (0.052) (0.052) (0.083)
## trustV.GroC 0.037 -0.049
## (0.052) (0.076)
## coachingV.GroC:trustV.GroC 0.013
## (0.042)
## pychcapitalV.GroC 0.236 *
## (0.106)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.022 0.279 0.032
## Conditional R^2 0.645 0.279 0.650
## AIC 416.402 180.353 421.092
## BIC 439.798 210.434 451.173
## Num. obs. 209 209 209
## Num. groups: BMDM 59 59 59
## Var: BMDM (Intercept) 0.386 0.000 0.382
## Var: Residual 0.220 0.110 0.216
## ───────────────────────────────────────────────────────────────────────────────────
## 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 : 209 (99 missing observations deleted)
## Random Seed : set.seed(1223)
## Simulations : 2000 (Monte Carlo)
##
## Direct Effect: "coachingV.GroC" (X) ==> "creativityV" (Y)
## ────────────────────────────────────────────────────────────
## Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────
## Direct (c') 0.051 (0.083) 0.618 .537 [-0.109, 0.211]
## ────────────────────────────────────────────────────────────
##
## Interaction Effect on "pychcapitalV.GroC" (M)
## ────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────
## coachingV.GroC * trustV.GroC 0.10 1 202 .757
## ────────────────────────────────────────────────────
##
## Simple Slopes: "coachingV.GroC" (X) ==> "pychcapitalV.GroC" (M)
## (Conditional Effects [a] of X on M)
## ────────────────────────────────────────────────────────────
## "trustV.GroC" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────
## -0.625 (- SD) 0.282 (0.054) 5.186 <.001 *** [0.175, 0.389]
## 0.015 (Mean) 0.290 (0.052) 5.552 <.001 *** [0.188, 0.393]
## 0.656 (+ SD) 0.299 (0.063) 4.739 <.001 *** [0.175, 0.422]
## ────────────────────────────────────────────────────────────
##
## Running 2000 * 3 simulations...
## Indirect Path: "coachingV.GroC" (X) ==> "pychcapitalV.GroC" (M) ==> "creativityV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ─────────────────────────────────────────────────────────────
## "trustV.GroC" Effect S.E. z p [MCMC 95% CI]
## ─────────────────────────────────────────────────────────────
## -0.625 (- SD) 0.065 (0.032) 2.034 .042 * [ 0.008, 0.133]
## 0.015 (Mean) 0.067 (0.033) 2.068 .039 * [ 0.008, 0.135]
## 0.656 (+ SD) 0.069 (0.034) 2.018 .044 * [ 0.007, 0.142]
## ─────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 2000 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. :)