X
-> Y: Three groups comparisions
Treating W.X as moderator
##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : BA.AIOnlineCommunicationSkillsV
## - Mediators (M) : -
## - Moderators (W) : W.X
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ BA.AIOnlineCommunicationSkillsV*W.X + (W.X|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.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 2.260 *** 1.730 ***
## (0.346) (0.377)
## BA.AIOnlineCommunicationSkillsV 0.263 *** 0.387 ***
## (0.079) (0.086)
## W.X1 0.986 ***
## (0.254)
## W.X2 0.593 *
## (0.253)
## BA.AIOnlineCommunicationSkillsV:W.X1 -0.232 ***
## (0.059)
## BA.AIOnlineCommunicationSkillsV:W.X2 -0.137 *
## (0.058)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.045 0.051
## Conditional R^2 0.671 0.678
## AIC 2726.715 2733.715
## BIC 2770.328 2796.711
## Num. obs. 940 940
## Num. groups: B.ID 161 161
## Var: B.ID (Intercept) 1.266 1.271
## Var: B.ID W.X1 0.038 0.032
## Var: B.ID W.X2 0.026 0.036
## Cov: B.ID (Intercept) W.X1 -0.006 -0.005
## Cov: B.ID (Intercept) W.X2 -0.034 -0.037
## Cov: B.ID W.X1 W.X2 -0.031 -0.033
## Var: Residual 0.663 0.650
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.2.0)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 940 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## BA.AIOnlineCommunicationSkillsV * W.X 8.18 2 259 <.001 ***
## ─────────────────────────────────────────────────────────────
##
## Simple Slopes: "BA.AIOnlineCommunicationSkillsV" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ─────────────────────────────────────────────────────
## "W.X" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────
## 0 0.387 (0.086) 4.495 <.001 *** [ 0.218, 0.555]
## 1 0.155 (0.088) 1.764 .080 . [-0.017, 0.326]
## 2 0.250 (0.085) 2.947 .004 ** [ 0.084, 0.416]
## ─────────────────────────────────────────────────────
Treating W.X as X
##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X01, W.X01BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X01 + W.X01BA.AIOnlineCommunicationSkillsV + W.X10*BA.AIOnlineCommunicationSkillsV + (W.X10+W.X01|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.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.365 *** 1.730 ***
## (0.105) (0.377)
## W.X01 0.008 0.593 *
## (0.224) (0.253)
## W.X01BA.AIOnlineCommunicationSkillsV 0.003 -0.137 *
## (0.051) (0.058)
## W.X10 0.012 0.986 ***
## (0.067) (0.254)
## BA.AIOnlineCommunicationSkillsV 0.387 ***
## (0.086)
## W.X10:BA.AIOnlineCommunicationSkillsV -0.232 ***
## (0.059)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.051
## Conditional R^2 0.671 0.678
## AIC 2748.753 2733.715
## BIC 2802.057 2796.711
## Num. obs. 940 940
## Num. groups: B.ID 161 161
## Var: B.ID (Intercept) 1.436 1.271
## Var: B.ID W.X10 0.045 0.032
## Var: B.ID W.X01 0.033 0.036
## Cov: B.ID (Intercept) W.X10 -0.088 -0.005
## Cov: B.ID (Intercept) W.X01 -0.082 -0.037
## Cov: B.ID W.X10 W.X01 -0.028 -0.033
## Var: Residual 0.663 0.650
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.2.0)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 940 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ────────────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────────────
## W.X10 * BA.AIOnlineCommunicationSkillsV 15.74 1 437 <.001 ***
## ────────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────────────
## 3.062 (- SD) 0.276 (0.093) 2.957 .003 ** [ 0.093, 0.458]
## 4.201 (Mean) 0.011 (0.066) 0.167 .868 [-0.119, 0.141]
## 5.341 (+ SD) -0.254 (0.095) -2.676 .008 ** [-0.439, -0.068]
## ───────────────────────────────────────────────────────────────────────────────────
##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X10, W.X10BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X10 + W.X10BA.AIOnlineCommunicationSkillsV + W.X01*BA.AIOnlineCommunicationSkillsV + (W.X10+W.X01|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.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.365 *** 1.730 ***
## (0.106) (0.377)
## W.X10 0.579 * 0.986 ***
## (0.228) (0.254)
## W.X10BA.AIOnlineCommunicationSkillsV -0.135 ** -0.232 ***
## (0.052) (0.059)
## W.X01 0.018 0.593 *
## (0.067) (0.253)
## BA.AIOnlineCommunicationSkillsV 0.387 ***
## (0.086)
## W.X01:BA.AIOnlineCommunicationSkillsV -0.137 *
## (0.058)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.004 0.051
## Conditional R^2 0.680 0.678
## AIC 2742.494 2733.715
## BIC 2795.799 2796.711
## Num. obs. 940 940
## Num. groups: B.ID 161 161
## Var: B.ID (Intercept) 1.449 1.271
## Var: B.ID W.X10 0.031 0.032
## Var: B.ID W.X01 0.040 0.036
## Cov: B.ID (Intercept) W.X10 -0.043 -0.005
## Cov: B.ID (Intercept) W.X01 -0.093 -0.037
## Cov: B.ID W.X10 W.X01 -0.029 -0.033
## Var: Residual 0.655 0.650
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.2.0)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 940 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X01 * BA.AIOnlineCommunicationSkillsV 5.59 1 427 .019 *
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.062 (- SD) 0.173 (0.094) 1.838 .067 . [-0.011, 0.358]
## 4.201 (Mean) 0.017 (0.066) 0.258 .796 [-0.113, 0.148]
## 5.341 (+ SD) -0.139 (0.094) -1.482 .140 [-0.322, 0.045]
## ──────────────────────────────────────────────────────────────────────────────────
X
-> M: Three groups comparisions
WP.TakingChargeBehaviorsForSystemImprovementV
##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X01, W.X01BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X01 + W.X01BA.AIOnlineCommunicationSkillsV + W.X10*BA.AIOnlineCommunicationSkillsV + (W.X10+W.X01|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.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.420 *** 2.102 ***
## (0.114) (0.414)
## W.X01 0.215 0.643 **
## (0.217) (0.245)
## W.X01BA.AIOnlineCommunicationSkillsV -0.033 -0.135 *
## (0.049) (0.056)
## W.X10 0.069 0.594 *
## (0.064) (0.242)
## BA.AIOnlineCommunicationSkillsV 0.312 ***
## (0.094)
## W.X10:BA.AIOnlineCommunicationSkillsV -0.125 *
## (0.056)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.032
## Conditional R^2 0.729 0.724
## AIC 2709.815 2709.732
## BIC 2763.119 2772.728
## Num. obs. 940 940
## Num. groups: B.ID 161 161
## Var: B.ID (Intercept) 1.752 1.618
## Var: B.ID W.X10 0.026 0.000
## Var: B.ID W.X01 0.102 0.021
## Cov: B.ID (Intercept) W.X10 -0.086 -0.025
## Cov: B.ID (Intercept) W.X01 -0.146 -0.073
## Cov: B.ID W.X10 W.X01 0.051 0.001
## Var: Residual 0.612 0.623
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.2.0)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 940 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X10 * BA.AIOnlineCommunicationSkillsV 5.00 1 507 .026 *
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.062 (- SD) 0.211 (0.090) 2.362 .019 * [ 0.036, 0.387]
## 4.201 (Mean) 0.068 (0.064) 1.072 .284 [-0.056, 0.193]
## 5.341 (+ SD) -0.075 (0.091) -0.825 .410 [-0.253, 0.103]
## ──────────────────────────────────────────────────────────────────────────────────
##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X10, W.X10BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X10 + W.X10BA.AIOnlineCommunicationSkillsV + W.X01*BA.AIOnlineCommunicationSkillsV + (W.X10+W.X01|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.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.420 *** 2.102 ***
## (0.114) (0.414)
## W.X10 0.236 0.594 *
## (0.209) (0.242)
## W.X10BA.AIOnlineCommunicationSkillsV -0.040 -0.125 *
## (0.048) (0.056)
## W.X01 0.074 0.643 **
## (0.068) (0.245)
## BA.AIOnlineCommunicationSkillsV 0.312 ***
## (0.094)
## W.X01:BA.AIOnlineCommunicationSkillsV -0.135 *
## (0.056)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.032
## Conditional R^2 0.729 0.724
## AIC 2709.624 2709.732
## BIC 2762.929 2772.728
## Num. obs. 940 940
## Num. groups: B.ID 161 161
## Var: B.ID (Intercept) 1.753 1.618
## Var: B.ID W.X10 0.026 0.000
## Var: B.ID W.X01 0.112 0.021
## Cov: B.ID (Intercept) W.X10 -0.075 -0.025
## Cov: B.ID (Intercept) W.X01 -0.162 -0.073
## Cov: B.ID W.X10 W.X01 0.054 0.001
## Var: Residual 0.610 0.623
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.2.0)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 940 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X01 * BA.AIOnlineCommunicationSkillsV 5.80 1 115 .018 *
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.062 (- SD) 0.229 (0.091) 2.521 .013 * [ 0.051, 0.407]
## 4.201 (Mean) 0.075 (0.064) 1.168 .246 [-0.051, 0.201]
## 5.341 (+ SD) -0.079 (0.090) -0.876 .383 [-0.256, 0.098]
## ──────────────────────────────────────────────────────────────────────────────────
Moderated mediation
##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 8 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : WP.TakingChargeBehaviorsForSystemImprovementV.GroC
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X01, W.X01BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Mediator:
## - WP.TakingChargeBehaviorsForSystemImprovementV.GroC ~ W.X01 + W.X01BA.AIOnlineCommunicationSkillsV + W.X10*BA.AIOnlineCommunicationSkillsV + (1 | B.ID)
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X01 + W.X01BA.AIOnlineCommunicationSkillsV + W.X10*BA.AIOnlineCommunicationSkillsV + WP.TakingChargeBehaviorsForSystemImprovementV.GroC + (W.X10+W.X01|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.SystemPerformanceImprovementBehaviorV (2) WP.TakingChargeBehaviorsForSystemImprovementV.GroC (3) WP.SystemPerformanceImprovementBehaviorV
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.365 *** -0.291 1.786 ***
## (0.105) (0.162) (0.370)
## W.X01 0.008 0.776 *** 0.345
## (0.224) (0.230) (0.240)
## W.X01BA.AIOnlineCommunicationSkillsV 0.003 -0.174 *** -0.083
## (0.051) (0.053) (0.055)
## W.X10 0.012 0.525 * 0.776 **
## (0.067) (0.232) (0.238)
## BA.AIOnlineCommunicationSkillsV 0.052 0.383 ***
## (0.037) (0.084)
## W.X10:BA.AIOnlineCommunicationSkillsV -0.106 * -0.188 ***
## (0.053) (0.055)
## WP.TakingChargeBehaviorsForSystemImprovementV.GroC 0.363 ***
## (0.034)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.017 0.091
## Conditional R^2 0.671 0.017 0.732
## AIC 2748.753 2181.541 2636.853
## BIC 2802.057 2220.308 2704.696
## Num. obs. 940 940 940
## Num. groups: B.ID 161 161 161
## Var: B.ID (Intercept) 1.436 0.000 1.262
## Var: B.ID W.X10 0.045 0.047
## Var: B.ID W.X01 0.033 0.062
## Cov: B.ID (Intercept) W.X10 -0.088 0.018
## Cov: B.ID (Intercept) W.X01 -0.082 0.015
## Cov: B.ID W.X10 W.X01 -0.028 -0.054
## Var: Residual 0.663 0.573 0.551
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.2.0)
## Effect Type : Moderated Mediation (Model 8)
## Sample Size : 940 (6 missing observations deleted)
## Random Seed : set.seed(1)
## Simulations : 1000 (Bootstrap)
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ────────────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────────────
## W.X10 * BA.AIOnlineCommunicationSkillsV 11.74 1 362 <.001 ***
## ────────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## (Conditional Direct Effects [c'] of X on Y)
## ───────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────────────
## 3.062 (- SD) 0.200 (0.088) 2.282 .023 * [ 0.028, 0.371]
## 4.201 (Mean) -0.015 (0.062) -0.240 .811 [-0.136, 0.107]
## 5.341 (+ SD) -0.229 (0.089) -2.586 .010 * [-0.403, -0.056]
## ───────────────────────────────────────────────────────────────────────────────────
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV.GroC" (M)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X10 * BA.AIOnlineCommunicationSkillsV 3.97 1 934 .047 *
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV.GroC" (M)
## (Conditional Effects [a] of X on M)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.062 (- SD) 0.199 (0.085) 2.336 .020 * [ 0.032, 0.367]
## 4.201 (Mean) 0.078 (0.061) 1.289 .198 [-0.041, 0.197]
## 5.341 (+ SD) -0.043 (0.086) -0.498 .619 [-0.212, 0.126]
## ──────────────────────────────────────────────────────────────────────────────────
##
## Running 1000 * 3 simulations...
## Indirect Path: "W.X10" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV.GroC" (M) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ───────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. z p [MCMC 95% CI]
## ───────────────────────────────────────────────────────────────────────────────────
## 3.062 (- SD) 0.074 (0.032) 2.311 .021 * [0.017, 0.139]
## 4.201 (Mean) 0.029 (0.022) 1.283 .200 [-0.013, 0.071]
## 5.341 (+ SD) -0.016 (0.033) -0.501 .616 [-0.083, 0.049]
## ───────────────────────────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 1000 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. :)
##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 8 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Moderated Mediation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : WP.TakingChargeBehaviorsForSystemImprovementV.GroC
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X10, W.X10BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Mediator:
## - WP.TakingChargeBehaviorsForSystemImprovementV.GroC ~ W.X10 + W.X10BA.AIOnlineCommunicationSkillsV + W.X01*BA.AIOnlineCommunicationSkillsV + (1 | B.ID)
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X10 + W.X10BA.AIOnlineCommunicationSkillsV + W.X01*BA.AIOnlineCommunicationSkillsV + WP.TakingChargeBehaviorsForSystemImprovementV.GroC + (W.X10+W.X01|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.SystemPerformanceImprovementBehaviorV (2) WP.TakingChargeBehaviorsForSystemImprovementV.GroC (3) WP.SystemPerformanceImprovementBehaviorV
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.365 *** -0.291 1.786 ***
## (0.106) (0.162) (0.370)
## W.X10 0.579 * 0.525 * 0.776 **
## (0.228) (0.232) (0.238)
## W.X10BA.AIOnlineCommunicationSkillsV -0.135 ** -0.106 * -0.188 ***
## (0.052) (0.053) (0.055)
## W.X01 0.018 0.776 *** 0.345
## (0.067) (0.230) (0.240)
## BA.AIOnlineCommunicationSkillsV 0.052 0.383 ***
## (0.037) (0.084)
## W.X01:BA.AIOnlineCommunicationSkillsV -0.174 *** -0.083
## (0.053) (0.055)
## WP.TakingChargeBehaviorsForSystemImprovementV.GroC 0.363 ***
## (0.034)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.004 0.017 0.091
## Conditional R^2 0.680 0.017 0.732
## AIC 2742.494 2181.541 2636.853
## BIC 2795.799 2220.308 2704.696
## Num. obs. 940 940 940
## Num. groups: B.ID 161 161 161
## Var: B.ID (Intercept) 1.449 0.000 1.262
## Var: B.ID W.X10 0.031 0.047
## Var: B.ID W.X01 0.040 0.062
## Cov: B.ID (Intercept) W.X10 -0.043 0.018
## Cov: B.ID (Intercept) W.X01 -0.093 0.015
## Cov: B.ID W.X10 W.X01 -0.029 -0.054
## Var: Residual 0.655 0.573 0.551
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.2.0)
## Effect Type : Moderated Mediation (Model 8)
## Sample Size : 940 (6 missing observations deleted)
## Random Seed : set.seed(1)
## Simulations : 1000 (Bootstrap)
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X01 * BA.AIOnlineCommunicationSkillsV 2.30 1 327 .130
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## (Conditional Direct Effects [c'] of X on Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.062 (- SD) 0.090 (0.089) 1.006 .315 [-0.085, 0.264]
## 4.201 (Mean) -0.005 (0.063) -0.087 .930 [-0.128, 0.117]
## 5.341 (+ SD) -0.100 (0.088) -1.140 .255 [-0.273, 0.072]
## ──────────────────────────────────────────────────────────────────────────────────
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV.GroC" (M)
## ────────────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────────────
## W.X01 * BA.AIOnlineCommunicationSkillsV 10.92 1 934 <.001 ***
## ────────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV.GroC" (M)
## (Conditional Effects [a] of X on M)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.062 (- SD) 0.244 (0.086) 2.851 .004 ** [ 0.076, 0.412]
## 4.201 (Mean) 0.046 (0.060) 0.762 .446 [-0.072, 0.164]
## 5.341 (+ SD) -0.152 (0.084) -1.799 .072 . [-0.318, 0.014]
## ──────────────────────────────────────────────────────────────────────────────────
##
## Running 1000 * 3 simulations...
## Indirect Path: "W.X01" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV.GroC" (M) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## (Conditional Indirect Effects [ab] of X through M on Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. z p [MCMC 95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.062 (- SD) 0.090 (0.032) 2.786 .005 ** [ 0.032, 0.155]
## 4.201 (Mean) 0.017 (0.022) 0.764 .445 [-0.026, 0.059]
## 5.341 (+ SD) -0.056 (0.032) -1.726 .084 . [-0.124, 0.006]
## ──────────────────────────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 1000 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. :)
