AI REFLECTION AND LEARNING
Prof. Yucheng Zhang
1 PREPARATION
data1=import("2_AIReflectionBW.NoMissingS1.sav")%>%data.table#AIReflectionBW.NoMissingS1
data2=import("AIReflectionBW.NoMissingS2.sav")%>%data.table #%>%
data2$W.dX=as.factor(data2$W.X)
data2 <- data2 %>%
mutate(
W.X10 = ifelse(W.X == 1, 1, 0),
W.X01 = ifelse(W.X == 2, 1, 0)
)
data2=data2[, W.CheckDummyX := paste(W.Intervention_new,W.X,W.X10,W.X01, sep = "_")]
Freq(data2$W.CheckDummyX)## Frequency Statistics:
## ───────────────────────────
## N %
## ───────────────────────────
## AI_0_0_0 328 33.3
## No_1_1_0 328 33.3
## Traditional_2_0_1 328 33.3
## ───────────────────────────
## Total N = 984#data2=import("AIReflectionBW.NoMissingS2.sav")%>%data.table #%>%
# mutate(
# W.X1 = ifelse(W.X == 1, 1, 0),
# W.X2 = ifelse(W.X == 2, 1, 0)
# )
#head(data2[,.(W.X,W.X1,W.X2)],10)%>%print_table()
data2=added(data2,{
W.X10BA.AIOnlineCommunicationSkillsV=W.X10*BA.AIOnlineCommunicationSkillsV
W.X01BA.AIOnlineCommunicationSkillsV=W.X01*BA.AIOnlineCommunicationSkillsV})
variables <- c(
"BA.AIOnlineCommunicationSkillsV", "BA.StructureV", "BA.WayOfQuestioningV",
"BA.ClarityOfInformationV", "BA.AIInteractionQualityV", "BA.ProblemSolvingConfidenceV",
"BA.NeedForPersonalizationDueToAIV", "BA.ReflectionOnAIUseV", "BA.CapabilityV",
"BA.PositiveReflectionOnAIUseV", "BA.NegativeReflectionOnAIUseV", "BB.AIUsageV",
"BB.AITechnologyAnxietyV", "BB.TrustInAIV", "BA.EffectivenessV",
"BA.QualityV", "BA.PersonalControlV", "BA.AIServiceFailureV",
"BA.AnthropomorphismV"
)
# 使用 lapply 和 := 动态创建交乘项变量
data2=data2[, paste0("W.X10", variables) := lapply(.SD, function(x) W.X10 * x), .SDcols = variables]
data2=data2[, paste0("W.X01", variables) := lapply(.SD, function(x) W.X01 * x), .SDcols = variables]2 ILLUSTRATION-FULL MODEL: M ON X
2.2 Study 1
基于原始值的调节分析
\[Y=i_Y+b_1X+b_2W+b_3XW+e_Y\]
\[Y=i_y+(b_1+b_3W)X+b_2W+e_Y\]
判断标准：同号同向加强（++更正/–更负）；异号反向削弱（+-不那么正/-+不那么负）
可以写成：
\[Y=i_y+f(W)X+b_2W+e_Y\]
PROCESS(data1, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X", mods="BA.ReflectionOnAIUseV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** 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.X
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X*BA.ReflectionOnAIUseV + (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.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.707 *** 2.143 ***
## (0.107) (0.333)
## W.X -0.154 0.357
## (0.082) (0.271)
## BA.ReflectionOnAIUseV 0.390 ***
## (0.079)
## W.X:BA.ReflectionOnAIUseV -0.127 *
## (0.064)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.077
## Conditional R^2 0.532 0.536
## AIC 2256.283 2244.539
## BIC 2283.273 2280.525
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.352 1.124
## Var: B.ID W.X 0.006 0.001
## Cov: B.ID (Intercept) W.X -0.093 -0.027
## Var: Residual 1.115 1.110
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X * BA.ReflectionOnAIUseV 3.92 1 495 .048 *
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────────
## 2.741 (- SD) 0.008 (0.116) 0.072 .942 [-0.218, 0.235]
## 4.013 (Mean) -0.154 (0.082) -1.878 .061 . [-0.314, 0.007]
## 5.286 (+ SD) -0.316 (0.116) -2.727 .007 ** [-0.542, -0.089]
## ─────────────────────────────────────────────────────────────────────────interact_plot(S1$model.y, W.X, BA.ReflectionOnAIUseV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))## Error in eval(expr, envir, enclos): 找不到对象'S1'2.3 Study 2
表10.1包含了两个模型的同归系数并总了统计量。前四行构建了指示符代码和两个乘积，以及剩余的行估计出：
\[\hat{Y}=i_Y+b_1 D_1+b_2 D_2+b_3\] \[\hat{Y}=i_Y+b_1 D_1+b_2 D_2+b_3 W+b_4 D_1 W+b_5 D_2 W\] 公式可改写为：
\[\hat{Y}=i_Y+(b_1 +b_4 W)D_1+(b_2+b_5 W)D_2 +b_3 W\]
2.3.1 Treating W.X as moderator
data2$W.X=as.factor(data2$W.X)
S2=PROCESS(data2, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="BA.ReflectionOnAIUseV", mods="W.X", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** 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) : BA.ReflectionOnAIUseV
## - Mediators (M) : -
## - Moderators (W) : W.X
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ BA.ReflectionOnAIUseV*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.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.597 *** 1.170 **
## (0.335) (0.369)
## BA.ReflectionOnAIUseV 0.494 *** 0.590 ***
## (0.079) (0.087)
## W.X1 0.479 *
## (0.219)
## W.X2 0.640 *
## (0.250)
## BA.ReflectionOnAIUseV:W.X1 -0.093
## (0.052)
## BA.ReflectionOnAIUseV:W.X2 -0.149 *
## (0.059)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.145 0.155
## Conditional R^2 0.738 0.742
## AIC 2903.012 2917.085
## BIC 2946.982 2980.596
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.589 1.583
## Var: B.ID W.X1 0.005 0.003
## Var: B.ID W.X2 0.223 0.208
## Cov: B.ID (Intercept) W.X1 0.009 0.015
## Cov: B.ID (Intercept) W.X2 -0.200 -0.190
## Cov: B.ID W.X1 W.X2 0.031 0.022
## Var: Residual 0.679 0.677
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## BA.ReflectionOnAIUseV * W.X 3.39 2 278 .035 *
## ───────────────────────────────────────────────────
##
## Simple Slopes: "BA.ReflectionOnAIUseV" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ────────────────────────────────────────────────────
## "W.X" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────
## 0 0.590 (0.087) 6.741 <.001 *** [0.418, 0.761]
## 1 0.496 (0.088) 5.623 <.001 *** [0.323, 0.670]
## 2 0.441 (0.083) 5.283 <.001 *** [0.277, 0.605]
## ────────────────────────────────────────────────────2.3.2 Treating W.X as X
S2.i=PROCESS(data2, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X10", mods="BA.ReflectionOnAIUseV",covs=c("W.X01","W.X01BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** 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.ReflectionOnAIUseV
## - Covariates (C) : W.X01, W.X01BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X01 + W.X01BA.ReflectionOnAIUseV + W.X10*BA.ReflectionOnAIUseV + (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) 3.546 *** 1.170 **
## (0.122) (0.369)
## W.X01 -0.001 0.640 *
## (0.214) (0.250)
## W.X01BA.ReflectionOnAIUseV 0.010 -0.149 *
## (0.050) (0.059)
## W.X10 0.103 0.479 *
## (0.065) (0.219)
## BA.ReflectionOnAIUseV 0.590 ***
## (0.087)
## W.X10:BA.ReflectionOnAIUseV -0.093
## (0.052)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.155
## Conditional R^2 0.740 0.742
## AIC 2946.196 2917.085
## BIC 2999.937 2980.596
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.106 1.583
## Var: B.ID W.X10 0.005 0.003
## Var: B.ID W.X01 0.240 0.208
## Cov: B.ID (Intercept) W.X10 -0.062 0.015
## Cov: B.ID (Intercept) W.X01 -0.327 -0.190
## Cov: B.ID W.X10 W.X01 0.036 0.022
## Var: Residual 0.678 0.677
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X10 * BA.ReflectionOnAIUseV 3.20 1 625 .074 .
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 2.788 (- SD) 0.219 (0.091) 2.397 .017 * [ 0.040, 0.398]
## 4.030 (Mean) 0.103 (0.065) 1.599 .110 [-0.023, 0.230]
## 5.272 (+ SD) -0.012 (0.091) -0.136 .892 [-0.191, 0.167]
## ────────────────────────────────────────────────────────────────────────S2.ii=PROCESS(data2, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X01", mods="BA.ReflectionOnAIUseV",covs=c("W.X10","W.X10BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** 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.ReflectionOnAIUseV
## - Covariates (C) : W.X10, W.X10BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X10 + W.X10BA.ReflectionOnAIUseV + W.X01*BA.ReflectionOnAIUseV + (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) 3.546 *** 1.170 **
## (0.122) (0.369)
## W.X10 0.102 0.479 *
## (0.197) (0.219)
## W.X10BA.ReflectionOnAIUseV 0.000 -0.093
## (0.046) (0.052)
## W.X01 0.041 0.640 *
## (0.074) (0.250)
## BA.ReflectionOnAIUseV 0.590 ***
## (0.087)
## W.X01:BA.ReflectionOnAIUseV -0.149 *
## (0.059)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.155
## Conditional R^2 0.739 0.742
## AIC 2946.787 2917.085
## BIC 3000.527 2980.596
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.100 1.583
## Var: B.ID W.X10 0.001 0.003
## Var: B.ID W.X01 0.202 0.208
## Cov: B.ID (Intercept) W.X10 -0.055 0.015
## Cov: B.ID (Intercept) W.X01 -0.302 -0.190
## Cov: B.ID W.X10 W.X01 0.008 0.022
## Var: Residual 0.681 0.677
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X01 * BA.ReflectionOnAIUseV 6.28 1 182 .013 *
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 2.788 (- SD) 0.226 (0.104) 2.165 .032 * [ 0.021, 0.430]
## 4.030 (Mean) 0.041 (0.074) 0.555 .579 [-0.103, 0.185]
## 5.272 (+ SD) -0.144 (0.104) -1.380 .169 [-0.348, 0.060]
## ────────────────────────────────────────────────────────────────────────## ──────────────────────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────────────────────
## (Intercept) 1.170 (0.369) 162.865 3.173 .002 **
## W.X10 0.479 (0.219) 625.412 2.181 .030 *
## W.X10BA.ReflectionOnAIUseV -0.093 (0.052) 625.412 -1.790 .074 .
## W.X01 0.640 (0.250) 181.662 2.558 .011 *
## BA.ReflectionOnAIUseV 0.590 (0.087) 162.865 6.741 <.001 ***
## W.X01:BA.ReflectionOnAIUseV -0.149 (0.059) 181.662 -2.505 .013 *
## ──────────────────────────────────────────────────────────────────────interact_plot(S2.i$model.y, W.X10, BA.ReflectionOnAIUseV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.ReflectionOnAIUseV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S2.ii$model.y, W.X01, BA.ReflectionOnAIUseV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.ReflectionOnAIUseV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
## Error in eval(expr, envir, enclos): 找不到对象'S1'\(\theta_{D_1\rightarrow Y}=b_1+b_4W=-.604+.162W\)：相对于不干预的条件，即那些被AI干预与没有干预的人M的差异。
总体解读：一个正向的估计值反映了在那些被AI干预的人M更高；而负向的估计意味着与被AI干预的人相比，那些没有干预的人M更高。
\(D_1\) 的系数 (\(b_1\))：在性别歧视得分为零时（W=0），相对于不干预，AI干预对M的影响。负向估计值-.604意味着AI干预M更低。
交互项 \(D_1 \times W\) 的系数 (\(b_4\)):
意义：它量化了这个相对条件效应随着W变化一个单位而变化的程度。AI干预与W的交互效应。
b4=.162：说明W每增加一个单位，AI干预对M的影响增加.162个单位。此交互项显著，表明W调节了AI干预的影响。
判断标准：同号同向加强（++更正/–更负）；异号反向削弱（+-不那么正/-+不那么负）
\(\theta_{D_2\rightarrow Y}=b_2+b_5 W\)有类似的解释
3 BA.ReflectionOnAIUseV
3.1 Study 1
WA.LearningFromOperationalFailure.S1=PROCESS(data1, y="WA.LearningFromOperationalFailureV", x="W.X", mods="BA.ReflectionOnAIUseV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromOperationalFailureV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromOperationalFailureV ~ W.X*BA.ReflectionOnAIUseV + (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) WA.LearningFromOperationalFailureV (2) WA.LearningFromOperationalFailureV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.779 *** 3.636 ***
## (0.085) (0.266)
## W.X 0.139 0.181
## (0.073) (0.241)
## BA.ReflectionOnAIUseV 0.285 ***
## (0.063)
## W.X:BA.ReflectionOnAIUseV -0.010
## (0.057)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.081
## Conditional R^2 0.458 0.459
## AIC 2057.038 2045.263
## BIC 2084.028 2081.250
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 0.758 0.631
## Var: B.ID W.X 0.001 0.001
## Cov: B.ID (Intercept) W.X -0.023 -0.018
## Var: Residual 0.875 0.877
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromOperationalFailureV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X * BA.ReflectionOnAIUseV 0.03 1 495 .856
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.LearningFromOperationalFailureV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 2.741 (- SD) 0.152 (0.103) 1.483 .139 [-0.049, 0.354]
## 4.013 (Mean) 0.139 (0.073) 1.916 .056 . [-0.003, 0.282]
## 5.286 (+ SD) 0.126 (0.103) 1.226 .221 [-0.075, 0.328]
## ───────────────────────────────────────────────────────────────────────WA.LearningFromErrors.S1=PROCESS(data1, y="WA.LearningFromErrorsV", x="W.X", mods="BA.ReflectionOnAIUseV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X*BA.ReflectionOnAIUseV + (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) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.291 *** 2.812 ***
## (0.104) (0.325)
## W.X 0.031 0.325
## (0.075) (0.247)
## BA.ReflectionOnAIUseV 0.368 ***
## (0.077)
## W.X:BA.ReflectionOnAIUseV -0.073
## (0.059)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.084
## Conditional R^2 0.612 0.613
## AIC 2122.701 2111.978
## BIC 2149.690 2147.965
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.395 1.184
## Var: B.ID W.X 0.094 0.091
## Cov: B.ID (Intercept) W.X -0.131 -0.090
## Var: Residual 0.832 0.832
## ─────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X * BA.ReflectionOnAIUseV 1.56 1 164 .213
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.LearningFromErrorsV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 2.741 (- SD) 0.124 (0.105) 1.177 .241 [-0.083, 0.331]
## 4.013 (Mean) 0.031 (0.075) 0.414 .679 [-0.115, 0.177]
## 5.286 (+ SD) -0.062 (0.105) -0.592 .555 [-0.269, 0.144]
## ────────────────────────────────────────────────────────────────────────WA.ThrivingInLearning.S1=PROCESS(data1, y="WA.ThrivingInLearningV", x="W.X", mods="BA.ReflectionOnAIUseV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)####
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ThrivingInLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ThrivingInLearningV ~ W.X*BA.ReflectionOnAIUseV + (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) WA.ThrivingInLearningV (2) WA.ThrivingInLearningV
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.702 *** 3.019 ***
## (0.093) (0.277)
## W.X -0.058 0.354
## (0.062) (0.204)
## BA.ReflectionOnAIUseV 0.420 ***
## (0.066)
## W.X:BA.ReflectionOnAIUseV -0.103 *
## (0.048)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.138
## Conditional R^2 0.616 0.618
## AIC 1920.298 1895.302
## BIC 1947.288 1931.288
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.132 0.853
## Var: B.ID W.X 0.018 0.007
## Cov: B.ID (Intercept) W.X -0.142 -0.075
## Var: Residual 0.624 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ThrivingInLearningV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X * BA.ReflectionOnAIUseV 4.50 1 472 .034 *
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.ThrivingInLearningV" (Y)
## ─────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────────
## 2.741 (- SD) 0.073 (0.087) 0.837 .403 [-0.098, 0.244]
## 4.013 (Mean) -0.058 (0.062) -0.939 .348 [-0.179, 0.063]
## 5.286 (+ SD) -0.189 (0.087) -2.164 .031 * [-0.359, -0.018]
## ─────────────────────────────────────────────────────────────────────────WP.LearningBehavior.S1=PROCESS(data1, y="WP.LearningBehaviorV", x="W.X", mods="BA.ReflectionOnAIUseV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X*BA.ReflectionOnAIUseV + (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.LearningBehaviorV (2) WP.LearningBehaviorV
## ─────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.648 *** 1.789 ***
## (0.111) (0.337)
## W.X -0.056 0.039
## (0.093) (0.308)
## BA.ReflectionOnAIUseV 0.463 ***
## (0.080)
## W.X:BA.ReflectionOnAIUseV -0.024
## (0.073)
## ─────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.124
## Conditional R^2 0.466 0.467
## AIC 2385.192 2357.735
## BIC 2412.181 2393.721
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.343 1.001
## Var: B.ID W.X 0.008 0.007
## Cov: B.ID (Intercept) W.X -0.103 -0.085
## Var: Residual 1.426 1.429
## ─────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X * BA.ReflectionOnAIUseV 0.10 1 484 .747
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.LearningBehaviorV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 2.741 (- SD) -0.026 (0.132) -0.195 .845 [-0.284, 0.232]
## 4.013 (Mean) -0.056 (0.093) -0.599 .549 [-0.238, 0.127]
## 5.286 (+ SD) -0.086 (0.132) -0.652 .515 [-0.344, 0.172]
## ────────────────────────────────────────────────────────────────────────WP.SocialLearning.S1=PROCESS(data1, y="WP.SocialLearningV", x="W.X", mods="BA.ReflectionOnAIUseV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)###
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X*BA.ReflectionOnAIUseV + (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.SocialLearningV (2) WP.SocialLearningV
## ─────────────────────────────────────────────────────────────────────────
## (Intercept) 3.759 *** 2.112 ***
## (0.104) (0.319)
## W.X -0.178 * 0.207
## (0.079) (0.262)
## BA.ReflectionOnAIUseV 0.410 ***
## (0.076)
## W.X:BA.ReflectionOnAIUseV -0.096
## (0.062)
## ─────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.004 0.100
## Conditional R^2 0.535 0.537
## AIC 2211.535 2194.844
## BIC 2238.525 2230.830
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.282 1.021
## Var: B.ID W.X 0.007 0.002
## Cov: B.ID (Intercept) W.X -0.097 -0.040
## Var: Residual 1.040 1.039
## ─────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X * BA.ReflectionOnAIUseV 2.38 1 493 .124
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.SocialLearningV" (Y)
## ─────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────────
## 2.741 (- SD) -0.056 (0.112) -0.502 .616 [-0.276, 0.163]
## 4.013 (Mean) -0.178 (0.079) -2.254 .025 * [-0.334, -0.023]
## 5.286 (+ SD) -0.301 (0.112) -2.685 .008 ** [-0.520, -0.081]
## ─────────────────────────────────────────────────────────────────────────WP.IndependentObservationBasedSocialLearning.S1=PROCESS(data1, y="WP.IndependentObservationBasedSocialLearningV", x="W.X", mods="BA.ReflectionOnAIUseV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.IndependentObservationBasedSocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.IndependentObservationBasedSocialLearningV ~ W.X*BA.ReflectionOnAIUseV + (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.IndependentObservationBasedSocialLearningV (2) WP.IndependentObservationBasedSocialLearningV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.736 *** 2.390 ***
## (0.114) (0.363)
## W.X -0.170 -0.150
## (0.094) (0.313)
## BA.ReflectionOnAIUseV 0.335 ***
## (0.086)
## W.X:BA.ReflectionOnAIUseV -0.005
## (0.074)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.066
## Conditional R^2 0.478 0.479
## AIC 2410.168 2402.378
## BIC 2437.158 2438.364
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.438 1.264
## Var: B.ID W.X 0.007 0.008
## Cov: B.ID (Intercept) W.X -0.104 -0.101
## Var: Residual 1.469 1.471
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.IndependentObservationBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X * BA.ReflectionOnAIUseV 0.00 1 483 .945
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.IndependentObservationBasedSocialLearningV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 2.741 (- SD) -0.164 (0.134) -1.225 .221 [-0.425, 0.098]
## 4.013 (Mean) -0.170 (0.094) -1.803 .072 . [-0.355, 0.015]
## 5.286 (+ SD) -0.177 (0.134) -1.323 .186 [-0.439, 0.085]
## ────────────────────────────────────────────────────────────────────────WP.AdviceThinkingBasedSocialLearning.S1=PROCESS(data1, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X", mods="BA.ReflectionOnAIUseV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)####
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X*BA.ReflectionOnAIUseV + (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.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.782 *** 1.833 ***
## (0.112) (0.339)
## W.X -0.187 * 0.564
## (0.091) (0.299)
## BA.ReflectionOnAIUseV 0.486 ***
## (0.080)
## W.X:BA.ReflectionOnAIUseV -0.187 **
## (0.071)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.103
## Conditional R^2 0.483 0.489
## AIC 2364.000 2341.727
## BIC 2390.990 2377.713
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.418 1.062
## Var: B.ID W.X 0.018 0.001
## Cov: B.ID (Intercept) W.X -0.159 -0.038
## Var: Residual 1.365 1.354
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X * BA.ReflectionOnAIUseV 6.92 1 494 .009 **
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ─────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────────
## 2.741 (- SD) 0.051 (0.128) 0.400 .689 [-0.199, 0.302]
## 4.013 (Mean) -0.187 (0.090) -2.067 .039 * [-0.364, -0.010]
## 5.286 (+ SD) -0.425 (0.128) -3.322 <.001 *** [-0.675, -0.174]
## ─────────────────────────────────────────────────────────────────────────3.2 Study 2
3.2.1 AI VS Control
WA.LearningFromOperationalFailure.Sb10=PROCESS(data2, y="WA.LearningFromOperationalFailureV", x="W.X10", mods="BA.ReflectionOnAIUseV",covs=c("W.X01","W.X01BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)###
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromOperationalFailureV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : W.X01, W.X01BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromOperationalFailureV ~ W.X01 + W.X01BA.ReflectionOnAIUseV + W.X10*BA.ReflectionOnAIUseV + (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) WA.LearningFromOperationalFailureV (2) WA.LearningFromOperationalFailureV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.363 *** 2.819 ***
## (0.109) (0.347)
## W.X01 -0.508 * -0.084
## (0.206) (0.245)
## W.X01BA.ReflectionOnAIUseV 0.162 *** 0.057
## (0.048) (0.058)
## W.X10 0.113 0.442
## (0.072) (0.244)
## BA.ReflectionOnAIUseV 0.383 ***
## (0.082)
## W.X10:BA.ReflectionOnAIUseV -0.082
## (0.058)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.008 0.102
## Conditional R^2 0.614 0.624
## AIC 3016.547 3006.983
## BIC 3070.288 3070.494
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.501 1.286
## Var: B.ID W.X10 0.011 0.006
## Var: B.ID W.X01 0.017 0.009
## Cov: B.ID (Intercept) W.X10 -0.129 -0.085
## Cov: B.ID (Intercept) W.X01 -0.162 -0.106
## Cov: B.ID W.X10 W.X01 0.014 0.007
## Var: Residual 0.838 0.837
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromOperationalFailureV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X10 * BA.ReflectionOnAIUseV 1.99 1 766 .159
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.LearningFromOperationalFailureV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 2.788 (- SD) 0.214 (0.102) 2.105 .036 * [ 0.015, 0.413]
## 4.030 (Mean) 0.113 (0.072) 1.568 .117 [-0.028, 0.254]
## 5.272 (+ SD) 0.011 (0.102) 0.112 .911 [-0.188, 0.211]
## ───────────────────────────────────────────────────────────────────────WA.LearningFromErrors.Sb10=PROCESS(data2, y="WA.LearningFromErrorsV", x="W.X10", mods="BA.ReflectionOnAIUseV",covs=c("W.X01","W.X01BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : W.X01, W.X01BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X01 + W.X01BA.ReflectionOnAIUseV + W.X10*BA.ReflectionOnAIUseV + (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) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.163 *** 2.680 ***
## (0.118) (0.384)
## W.X01 -0.742 *** -0.460 *
## (0.203) (0.234)
## W.X01BA.ReflectionOnAIUseV 0.190 *** 0.120 *
## (0.047) (0.056)
## W.X10 0.038 0.248
## (0.068) (0.231)
## BA.ReflectionOnAIUseV 0.368 ***
## (0.091)
## W.X10:BA.ReflectionOnAIUseV -0.052
## (0.055)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.007 0.092
## Conditional R^2 0.710 0.717
## AIC 2982.557 2977.818
## BIC 3036.298 3041.329
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.904 1.708
## Var: B.ID W.X10 0.006 0.006
## Var: B.ID W.X01 0.030 0.025
## Cov: B.ID (Intercept) W.X10 -0.052 -0.025
## Cov: B.ID (Intercept) W.X01 -0.101 -0.065
## Cov: B.ID W.X10 W.X01 -0.008 -0.010
## Var: Residual 0.749 0.749
## ───────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X10 * BA.ReflectionOnAIUseV 0.90 1 536 .342
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.LearningFromErrorsV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 2.788 (- SD) 0.102 (0.096) 1.065 .287 [-0.086, 0.291]
## 4.030 (Mean) 0.038 (0.068) 0.553 .580 [-0.096, 0.171]
## 5.272 (+ SD) -0.027 (0.096) -0.283 .777 [-0.216, 0.161]
## ────────────────────────────────────────────────────────────────────────WA.ThrivingInLearning.Sb10=PROCESS(data2, y="WA.ThrivingInLearningV", x="W.X10", mods="BA.ReflectionOnAIUseV",covs=c("W.X01","W.X01BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ThrivingInLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : W.X01, W.X01BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ThrivingInLearningV ~ W.X01 + W.X01BA.ReflectionOnAIUseV + W.X10*BA.ReflectionOnAIUseV + (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) WA.ThrivingInLearningV (2) WA.ThrivingInLearningV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.291 *** 2.431 ***
## (0.109) (0.340)
## W.X01 -0.464 * 0.065
## (0.202) (0.237)
## W.X01BA.ReflectionOnAIUseV 0.117 * -0.014
## (0.047) (0.056)
## W.X10 -0.026 0.238
## (0.061) (0.208)
## BA.ReflectionOnAIUseV 0.461 ***
## (0.081)
## W.X10:BA.ReflectionOnAIUseV -0.066
## (0.049)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.135
## Conditional R^2 0.711 0.718
## AIC 2823.632 2804.873
## BIC 2877.373 2868.384
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.642 1.326
## Var: B.ID W.X10 0.001 0.001
## Var: B.ID W.X01 0.192 0.180
## Cov: B.ID (Intercept) W.X10 -0.035 0.006
## Cov: B.ID (Intercept) W.X01 -0.273 -0.190
## Cov: B.ID W.X10 W.X01 0.006 0.008
## Var: Residual 0.613 0.612
## ───────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ThrivingInLearningV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X10 * BA.ReflectionOnAIUseV 1.77 1 645 .184
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.ThrivingInLearningV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 2.788 (- SD) 0.055 (0.087) 0.636 .525 [-0.115, 0.225]
## 4.030 (Mean) -0.026 (0.061) -0.430 .667 [-0.147, 0.094]
## 5.272 (+ SD) -0.108 (0.087) -1.244 .214 [-0.278, 0.062]
## ────────────────────────────────────────────────────────────────────────WP.LearningBehavior.Sb10=PROCESS(data2, y="WP.LearningBehaviorV", x="W.X10", mods="BA.ReflectionOnAIUseV",covs=c("W.X01","W.X01BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : W.X01, W.X01BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X01 + W.X01BA.ReflectionOnAIUseV + W.X10*BA.ReflectionOnAIUseV + (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.LearningBehaviorV (2) WP.LearningBehaviorV
## ───────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.549 *** 1.242 ***
## (0.123) (0.372)
## W.X01 -0.419 * -0.101
## (0.205) (0.237)
## W.X01BA.ReflectionOnAIUseV 0.108 * 0.029
## (0.048) (0.056)
## W.X10 0.124 0.012
## (0.068) (0.232)
## BA.ReflectionOnAIUseV 0.573 ***
## (0.088)
## W.X10:BA.ReflectionOnAIUseV 0.028
## (0.055)
## ───────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.194
## Conditional R^2 0.724 0.730
## AIC 3006.970 2974.026
## BIC 3060.710 3037.538
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.076 1.578
## Var: B.ID W.X10 0.003 0.003
## Var: B.ID W.X01 0.049 0.038
## Cov: B.ID (Intercept) W.X10 -0.008 -0.033
## Cov: B.ID (Intercept) W.X01 -0.166 -0.096
## Cov: B.ID W.X10 W.X01 -0.009 -0.006
## Var: Residual 0.757 0.758
## ───────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X10 * BA.ReflectionOnAIUseV 0.26 1 641 .613
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.LearningBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 2.788 (- SD) 0.090 (0.097) 0.927 .354 [-0.100, 0.279]
## 4.030 (Mean) 0.124 (0.068) 1.818 .069 . [-0.010, 0.258]
## 5.272 (+ SD) 0.159 (0.097) 1.644 .101 [-0.031, 0.348]
## ───────────────────────────────────────────────────────────────────────WP.SocialLearning.Sb10=PROCESS(data2, y="WP.SocialLearningV", x="W.X10", mods="BA.ReflectionOnAIUseV",covs=c("W.X01","W.X01BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : W.X01, W.X01BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X01 + W.X01BA.ReflectionOnAIUseV + W.X10*BA.ReflectionOnAIUseV + (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.SocialLearningV (2) WP.SocialLearningV
## ───────────────────────────────────────────────────────────────────────────
## (Intercept) 3.656 *** 2.030 ***
## (0.109) (0.346)
## W.X01 0.151 0.104
## (0.205) (0.231)
## W.X01BA.ReflectionOnAIUseV -0.020 -0.008
## (0.048) (0.055)
## W.X10 0.115 -0.410
## (0.069) (0.232)
## BA.ReflectionOnAIUseV 0.404 ***
## (0.082)
## W.X10:BA.ReflectionOnAIUseV 0.130 *
## (0.055)
## ───────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.132
## Conditional R^2 0.688 0.688
## AIC 2965.306 2937.362
## BIC 3019.046 3000.873
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.566 1.325
## Var: B.ID W.X10 0.030 0.019
## Var: B.ID W.X01 0.015 0.013
## Cov: B.ID (Intercept) W.X10 0.045 -0.043
## Cov: B.ID (Intercept) W.X01 0.036 0.027
## Cov: B.ID W.X10 W.X01 -0.019 -0.016
## Var: Residual 0.742 0.741
## ───────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X10 * BA.ReflectionOnAIUseV 5.60 1 482 .018 *
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.SocialLearningV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 2.788 (- SD) -0.047 (0.097) -0.484 .629 [-0.236, 0.143]
## 4.030 (Mean) 0.115 (0.068) 1.684 .093 . [-0.019, 0.249]
## 5.272 (+ SD) 0.277 (0.097) 2.865 .004 ** [ 0.087, 0.466]
## ────────────────────────────────────────────────────────────────────────WP.IndependentObservationBasedSocialLearning.Sb10=PROCESS(data2, y="WP.IndependentObservationBasedSocialLearningV", x="W.X10", mods="BA.ReflectionOnAIUseV",covs=c("W.X01","W.X01BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.IndependentObservationBasedSocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : W.X01, W.X01BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.IndependentObservationBasedSocialLearningV ~ W.X01 + W.X01BA.ReflectionOnAIUseV + W.X10*BA.ReflectionOnAIUseV + (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.IndependentObservationBasedSocialLearningV (2) WP.IndependentObservationBasedSocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.695 *** 2.103 ***
## (0.121) (0.392)
## W.X01 0.111 -0.012
## (0.236) (0.271)
## W.X01BA.ReflectionOnAIUseV -0.008 0.022
## (0.055) (0.064)
## W.X10 0.172 * -0.548 *
## (0.081) (0.272)
## BA.ReflectionOnAIUseV 0.395 ***
## (0.093)
## W.X10:BA.ReflectionOnAIUseV 0.179 **
## (0.064)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.115
## Conditional R^2 0.652 0.653
## AIC 3258.907 3232.342
## BIC 3312.648 3295.854
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.878 1.652
## Var: B.ID W.X10 0.036 0.004
## Var: B.ID W.X01 0.001 0.000
## Cov: B.ID (Intercept) W.X10 0.042 -0.077
## Cov: B.ID (Intercept) W.X01 0.034 0.011
## Cov: B.ID W.X10 W.X01 -0.002 -0.001
## Var: Residual 1.039 1.038
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.IndependentObservationBasedSocialLearningV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X10 * BA.ReflectionOnAIUseV 7.70 1 787 .006 **
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.IndependentObservationBasedSocialLearningV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 2.788 (- SD) -0.050 (0.113) -0.443 .658 [-0.272, 0.172]
## 4.030 (Mean) 0.172 (0.080) 2.149 .032 * [ 0.015, 0.328]
## 5.272 (+ SD) 0.394 (0.113) 3.481 <.001 *** [ 0.172, 0.615]
## ────────────────────────────────────────────────────────────────────────WP.AdviceThinkingBasedSocialLearning.Sb10=PROCESS(data2, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X10", mods="BA.ReflectionOnAIUseV",covs=c("W.X01","W.X01BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : W.X01, W.X01BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X01 + W.X01BA.ReflectionOnAIUseV + W.X10*BA.ReflectionOnAIUseV + (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.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.617 *** 1.956 ***
## (0.111) (0.354)
## W.X01 0.139 0.220
## (0.250) (0.286)
## W.X01BA.ReflectionOnAIUseV -0.018 -0.038
## (0.058) (0.068)
## W.X10 0.058 -0.271
## (0.082) (0.275)
## BA.ReflectionOnAIUseV 0.412 ***
## (0.084)
## W.X10:BA.ReflectionOnAIUseV 0.082
## (0.065)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.105
## Conditional R^2 0.609 0.606
## AIC 3264.231 3244.414
## BIC 3317.971 3307.926
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.489 1.240
## Var: B.ID W.X10 0.037 0.002
## Var: B.ID W.X01 0.055 0.087
## Cov: B.ID (Intercept) W.X10 0.100 0.047
## Cov: B.ID (Intercept) W.X01 0.096 0.087
## Cov: B.ID W.X10 W.X01 -0.032 0.003
## Var: Residual 1.060 1.068
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X10 * BA.ReflectionOnAIUseV 1.57 1 626 .210
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 2.788 (- SD) -0.043 (0.116) -0.375 .708 [-0.270, 0.183]
## 4.030 (Mean) 0.058 (0.082) 0.713 .477 [-0.102, 0.219]
## 5.272 (+ SD) 0.160 (0.116) 1.382 .168 [-0.067, 0.387]
## ────────────────────────────────────────────────────────────────────────3.2.2 AI VS Self
WA.LearningFromOperationalFailure.Sb01=PROCESS(data2, y="WA.LearningFromOperationalFailureV", x="W.X01", mods="BA.ReflectionOnAIUseV",covs=c("W.X10","W.X10BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromOperationalFailureV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : W.X10, W.X10BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromOperationalFailureV ~ W.X10 + W.X10BA.ReflectionOnAIUseV + W.X01*BA.ReflectionOnAIUseV + (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) WA.LearningFromOperationalFailureV (2) WA.LearningFromOperationalFailureV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.363 *** 2.819 ***
## (0.109) (0.347)
## W.X10 0.269 0.442
## (0.207) (0.244)
## W.X10BA.ReflectionOnAIUseV -0.039 -0.082
## (0.048) (0.058)
## W.X01 0.146 * -0.084
## (0.072) (0.245)
## BA.ReflectionOnAIUseV 0.383 ***
## (0.082)
## W.X01:BA.ReflectionOnAIUseV 0.057
## (0.058)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.102
## Conditional R^2 0.624 0.624
## AIC 3025.907 3006.983
## BIC 3079.648 3070.494
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.502 1.286
## Var: B.ID W.X10 0.008 0.006
## Var: B.ID W.X01 0.003 0.009
## Cov: B.ID (Intercept) W.X10 -0.108 -0.085
## Cov: B.ID (Intercept) W.X01 -0.069 -0.106
## Cov: B.ID W.X10 W.X01 0.005 0.007
## Var: Residual 0.839 0.837
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromOperationalFailureV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X01 * BA.ReflectionOnAIUseV 0.96 1 742 .327
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.LearningFromOperationalFailureV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 2.788 (- SD) 0.075 (0.102) 0.736 .462 [-0.125, 0.275]
## 4.030 (Mean) 0.146 (0.072) 2.023 .043 * [ 0.005, 0.287]
## 5.272 (+ SD) 0.216 (0.102) 2.124 .034 * [ 0.017, 0.416]
## ───────────────────────────────────────────────────────────────────────WA.LearningFromErrors.Sb01=PROCESS(data2, y="WA.LearningFromErrorsV", x="W.X01", mods="BA.ReflectionOnAIUseV",covs=c("W.X10","W.X10BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : W.X10, W.X10BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X10 + W.X10BA.ReflectionOnAIUseV + W.X01*BA.ReflectionOnAIUseV + (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) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.163 *** 2.680 ***
## (0.118) (0.384)
## W.X10 0.288 0.248
## (0.203) (0.231)
## W.X10BA.ReflectionOnAIUseV -0.062 -0.052
## (0.047) (0.055)
## W.X01 0.022 -0.460 *
## (0.069) (0.234)
## BA.ReflectionOnAIUseV 0.368 ***
## (0.091)
## W.X01:BA.ReflectionOnAIUseV 0.120 *
## (0.056)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.092
## Conditional R^2 0.719 0.717
## AIC 2995.068 2977.818
## BIC 3048.809 3041.329
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.904 1.708
## Var: B.ID W.X10 0.005 0.006
## Var: B.ID W.X01 0.038 0.025
## Cov: B.ID (Intercept) W.X10 -0.016 -0.025
## Cov: B.ID (Intercept) W.X01 0.009 -0.065
## Cov: B.ID W.X10 W.X01 -0.014 -0.010
## Var: Residual 0.749 0.749
## ───────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X01 * BA.ReflectionOnAIUseV 4.65 1 315 .032 *
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.LearningFromErrorsV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 2.788 (- SD) -0.127 (0.098) -1.290 .198 [-0.319, 0.066]
## 4.030 (Mean) 0.022 (0.069) 0.321 .749 [-0.114, 0.158]
## 5.272 (+ SD) 0.171 (0.098) 1.744 .082 . [-0.021, 0.363]
## ────────────────────────────────────────────────────────────────────────WA.ThrivingInLearning.Sb01=PROCESS(data2, y="WA.ThrivingInLearningV", x="W.X01", mods="BA.ReflectionOnAIUseV",covs=c("W.X10","W.X10BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ThrivingInLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : W.X10, W.X10BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ThrivingInLearningV ~ W.X10 + W.X10BA.ReflectionOnAIUseV + W.X01*BA.ReflectionOnAIUseV + (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) WA.ThrivingInLearningV (2) WA.ThrivingInLearningV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.291 *** 2.431 ***
## (0.109) (0.340)
## W.X10 0.072 0.238
## (0.187) (0.208)
## W.X10BA.ReflectionOnAIUseV -0.024 -0.066
## (0.044) (0.049)
## W.X01 0.009 0.065
## (0.069) (0.237)
## BA.ReflectionOnAIUseV 0.461 ***
## (0.081)
## W.X01:BA.ReflectionOnAIUseV -0.014
## (0.056)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.135
## Conditional R^2 0.718 0.718
## AIC 2828.713 2804.873
## BIC 2882.454 2868.384
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.645 1.326
## Var: B.ID W.X10 0.000 0.000
## Var: B.ID W.X01 0.171 0.180
## Cov: B.ID (Intercept) W.X10 -0.020 0.006
## Cov: B.ID (Intercept) W.X01 -0.194 -0.190
## Cov: B.ID W.X10 W.X01 0.002 0.008
## Var: Residual 0.613 0.612
## ───────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ThrivingInLearningV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X01 * BA.ReflectionOnAIUseV 0.06 1 188 .803
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.ThrivingInLearningV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 2.788 (- SD) 0.026 (0.099) 0.264 .792 [-0.167, 0.219]
## 4.030 (Mean) 0.009 (0.070) 0.123 .902 [-0.128, 0.145]
## 5.272 (+ SD) -0.009 (0.099) -0.090 .929 [-0.202, 0.184]
## ────────────────────────────────────────────────────────────────────────WP.LearningBehavior.Sb01=PROCESS(data2, y="WP.LearningBehaviorV", x="W.X01", mods="BA.ReflectionOnAIUseV",covs=c("W.X10","W.X10BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : W.X10, W.X10BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X10 + W.X10BA.ReflectionOnAIUseV + W.X01*BA.ReflectionOnAIUseV + (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.LearningBehaviorV (2) WP.LearningBehaviorV
## ───────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.549 *** 1.242 ***
## (0.123) (0.372)
## W.X10 -0.193 0.012
## (0.201) (0.232)
## W.X10BA.ReflectionOnAIUseV 0.079 0.028
## (0.047) (0.055)
## W.X01 0.017 -0.101
## (0.070) (0.237)
## BA.ReflectionOnAIUseV 0.573 ***
## (0.088)
## W.X01:BA.ReflectionOnAIUseV 0.029
## (0.056)
## ───────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.194
## Conditional R^2 0.724 0.730
## AIC 3008.860 2974.026
## BIC 3062.601 3037.538
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.076 1.578
## Var: B.ID W.X10 0.003 0.003
## Var: B.ID W.X01 0.042 0.038
## Cov: B.ID (Intercept) W.X10 -0.078 -0.033
## Cov: B.ID (Intercept) W.X01 -0.074 -0.096
## Cov: B.ID W.X10 W.X01 0.003 -0.006
## Var: Residual 0.761 0.758
## ───────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X01 * BA.ReflectionOnAIUseV 0.27 1 310 .604
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.LearningBehaviorV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 2.788 (- SD) -0.019 (0.099) -0.196 .845 [-0.214, 0.175]
## 4.030 (Mean) 0.017 (0.070) 0.241 .810 [-0.120, 0.154]
## 5.272 (+ SD) 0.053 (0.099) 0.536 .592 [-0.141, 0.247]
## ────────────────────────────────────────────────────────────────────────WP.SocialLearning.Sb01=PROCESS(data2, y="WP.SocialLearningV", x="W.X01", mods="BA.ReflectionOnAIUseV",covs=c("W.X10","W.X10BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : W.X10, W.X10BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X10 + W.X10BA.ReflectionOnAIUseV + W.X01*BA.ReflectionOnAIUseV + (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.SocialLearningV (2) WP.SocialLearningV
## ───────────────────────────────────────────────────────────────────────────
## (Intercept) 3.656 *** 2.030 ***
## (0.109) (0.346)
## W.X10 -0.688 *** -0.410
## (0.201) (0.232)
## W.X10BA.ReflectionOnAIUseV 0.199 *** 0.130 *
## (0.047) (0.055)
## W.X01 0.073 0.104
## (0.068) (0.231)
## BA.ReflectionOnAIUseV 0.404 ***
## (0.082)
## W.X01:BA.ReflectionOnAIUseV -0.008
## (0.055)
## ───────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.010 0.132
## Conditional R^2 0.678 0.688
## AIC 2950.498 2937.362
## BIC 3004.239 3000.873
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.567 1.325
## Var: B.ID W.X10 0.024 0.019
## Var: B.ID W.X01 0.012 0.013
## Cov: B.ID (Intercept) W.X10 -0.083 -0.043
## Cov: B.ID (Intercept) W.X01 0.023 0.027
## Cov: B.ID W.X10 W.X01 -0.016 -0.016
## Var: Residual 0.741 0.741
## ───────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X01 * BA.ReflectionOnAIUseV 0.02 1 542 .888
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.SocialLearningV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 2.788 (- SD) 0.082 (0.096) 0.856 .392 [-0.106, 0.271]
## 4.030 (Mean) 0.073 (0.068) 1.071 .285 [-0.061, 0.206]
## 5.272 (+ SD) 0.063 (0.096) 0.657 .511 [-0.125, 0.252]
## ───────────────────────────────────────────────────────────────────────WP.IndependentObservationBasedSocialLearning.Sb01=PROCESS(data2, y="WP.IndependentObservationBasedSocialLearningV", x="W.X01", mods="BA.ReflectionOnAIUseV",covs=c("W.X10","W.X10BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.IndependentObservationBasedSocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : W.X10, W.X10BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.IndependentObservationBasedSocialLearningV ~ W.X10 + W.X10BA.ReflectionOnAIUseV + W.X01*BA.ReflectionOnAIUseV + (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.IndependentObservationBasedSocialLearningV (2) WP.IndependentObservationBasedSocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.695 *** 2.103 ***
## (0.121) (0.392)
## W.X10 -0.794 *** -0.548 *
## (0.230) (0.272)
## W.X10BA.ReflectionOnAIUseV 0.240 *** 0.179 **
## (0.054) (0.064)
## W.X01 0.078 -0.012
## (0.080) (0.271)
## BA.ReflectionOnAIUseV 0.395 ***
## (0.093)
## W.X01:BA.ReflectionOnAIUseV 0.022
## (0.064)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.012 0.115
## Conditional R^2 0.642 0.653
## AIC 3242.107 3232.342
## BIC 3295.848 3295.854
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.882 1.652
## Var: B.ID W.X10 0.009 0.004
## Var: B.ID W.X01 0.000 0.000
## Cov: B.ID (Intercept) W.X10 -0.113 -0.077
## Cov: B.ID (Intercept) W.X01 0.024 0.011
## Cov: B.ID W.X10 W.X01 -0.002 -0.001
## Var: Residual 1.037 1.038
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.IndependentObservationBasedSocialLearningV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X01 * BA.ReflectionOnAIUseV 0.12 1 810 .728
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.IndependentObservationBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 2.788 (- SD) 0.050 (0.113) 0.447 .655 [-0.171, 0.272]
## 4.030 (Mean) 0.078 (0.080) 0.980 .327 [-0.078, 0.235]
## 5.272 (+ SD) 0.106 (0.113) 0.939 .348 [-0.115, 0.327]
## ───────────────────────────────────────────────────────────────────────WP.AdviceThinkingBasedSocialLearning.Sb01=PROCESS(data2, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X01", mods="BA.ReflectionOnAIUseV",covs=c("W.X10","W.X10BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : W.X10, W.X10BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X10 + W.X10BA.ReflectionOnAIUseV + W.X01*BA.ReflectionOnAIUseV + (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.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.617 *** 1.956 ***
## (0.111) (0.354)
## W.X10 -0.632 ** -0.271
## (0.243) (0.275)
## W.X10BA.ReflectionOnAIUseV 0.171 ** 0.082
## (0.057) (0.065)
## W.X01 0.067 0.220
## (0.082) (0.286)
## BA.ReflectionOnAIUseV 0.412 ***
## (0.084)
## W.X01:BA.ReflectionOnAIUseV -0.038
## (0.068)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.006 0.105
## Conditional R^2 0.598 0.606
## AIC 3256.619 3244.414
## BIC 3310.359 3307.926
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.483 1.240
## Var: B.ID W.X10 0.032 0.002
## Var: B.ID W.X01 0.041 0.087
## Cov: B.ID (Intercept) W.X10 -0.002 0.047
## Cov: B.ID (Intercept) W.X01 0.092 0.087
## Cov: B.ID W.X10 W.X01 -0.034 0.003
## Var: Residual 1.063 1.068
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X01 * BA.ReflectionOnAIUseV 0.31 1 239 .578
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 2.788 (- SD) 0.114 (0.117) 0.979 .329 [-0.115, 0.344]
## 4.030 (Mean) 0.067 (0.083) 0.817 .415 [-0.094, 0.229]
## 5.272 (+ SD) 0.021 (0.117) 0.176 .861 [-0.209, 0.250]
## ───────────────────────────────────────────────────────────────────────4 BA.CapabilityV
4.1 Study 1
WA.LearningFromOperationalFailure.S1=PROCESS(data1, y="WA.LearningFromOperationalFailureV", x="W.X", mods="BA.CapabilityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromOperationalFailureV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromOperationalFailureV ~ W.X*BA.CapabilityV + (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) WA.LearningFromOperationalFailureV (2) WA.LearningFromOperationalFailureV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.779 *** 3.800 ***
## (0.085) (0.252)
## W.X 0.139 0.058
## (0.073) (0.226)
## BA.CapabilityV 0.233 ***
## (0.057)
## W.X:BA.CapabilityV 0.019
## (0.051)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.077
## Conditional R^2 0.458 0.460
## AIC 2057.038 2046.683
## BIC 2084.028 2082.669
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 0.758 0.651
## Var: B.ID W.X 0.001 0.002
## Cov: B.ID (Intercept) W.X -0.023 -0.032
## Var: Residual 0.875 0.876
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromOperationalFailureV" (Y)
## ────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────
## W.X * BA.CapabilityV 0.14 1 492 .705
## ────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.LearningFromOperationalFailureV" (Y)
## ────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────
## 2.768 (- SD) 0.112 (0.103) 1.086 .278 [-0.090, 0.313]
## 4.197 (Mean) 0.139 (0.073) 1.916 .056 . [-0.003, 0.282]
## 5.625 (+ SD) 0.167 (0.103) 1.622 .105 [-0.035, 0.369]
## ────────────────────────────────────────────────────────────────WA.LearningFromErrors.S1=PROCESS(data1, y="WA.LearningFromErrorsV", x="W.X", mods="BA.CapabilityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X*BA.CapabilityV + (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) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.291 *** 2.992 ***
## (0.104) (0.307)
## W.X 0.031 -0.010
## (0.075) (0.233)
## BA.CapabilityV 0.309 ***
## (0.069)
## W.X:BA.CapabilityV 0.010
## (0.052)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.094
## Conditional R^2 0.612 0.613
## AIC 2122.701 2110.174
## BIC 2149.690 2146.161
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.395 1.209
## Var: B.ID W.X 0.094 0.099
## Cov: B.ID (Intercept) W.X -0.131 -0.140
## Var: Residual 0.832 0.832
## ─────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────
## W.X * BA.CapabilityV 0.04 1 164 .852
## ────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.LearningFromErrorsV" (Y)
## ────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────
## 2.768 (- SD) 0.017 (0.106) 0.159 .874 [-0.191, 0.225]
## 4.197 (Mean) 0.031 (0.075) 0.412 .681 [-0.116, 0.178]
## 5.625 (+ SD) 0.045 (0.106) 0.424 .672 [-0.163, 0.253]
## ────────────────────────────────────────────────────────────────WA.ThrivingInLearning.S1=PROCESS(data1, y="WA.ThrivingInLearningV", x="W.X", mods="BA.CapabilityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ThrivingInLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ThrivingInLearningV ~ W.X*BA.CapabilityV + (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) WA.ThrivingInLearningV (2) WA.ThrivingInLearningV
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.702 *** 3.566 ***
## (0.093) (0.275)
## W.X -0.058 0.137
## (0.062) (0.193)
## BA.CapabilityV 0.271 ***
## (0.062)
## W.X:BA.CapabilityV -0.046
## (0.043)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.078
## Conditional R^2 0.616 0.617
## AIC 1920.298 1913.420
## BIC 1947.288 1949.406
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.132 0.988
## Var: B.ID W.X 0.018 0.014
## Cov: B.ID (Intercept) W.X -0.142 -0.117
## Var: Residual 0.624 0.625
## ─────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ThrivingInLearningV" (Y)
## ────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────
## W.X * BA.CapabilityV 1.14 1 450 .286
## ────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.ThrivingInLearningV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.768 (- SD) 0.009 (0.088) 0.097 .923 [-0.163, 0.181]
## 4.197 (Mean) -0.058 (0.062) -0.932 .352 [-0.179, 0.064]
## 5.625 (+ SD) -0.124 (0.088) -1.415 .158 [-0.296, 0.048]
## ─────────────────────────────────────────────────────────────────WP.LearningBehavior.S1=PROCESS(data1, y="WP.LearningBehaviorV", x="W.X", mods="BA.CapabilityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X*BA.CapabilityV + (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.LearningBehaviorV (2) WP.LearningBehaviorV
## ─────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.648 *** 2.502 ***
## (0.111) (0.333)
## W.X -0.056 -0.153
## (0.093) (0.289)
## BA.CapabilityV 0.273 ***
## (0.075)
## W.X:BA.CapabilityV 0.023
## (0.065)
## ─────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.062
## Conditional R^2 0.466 0.468
## AIC 2385.192 2377.903
## BIC 2412.181 2413.889
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.343 1.199
## Var: B.ID W.X 0.008 0.011
## Cov: B.ID (Intercept) W.X -0.103 -0.117
## Var: Residual 1.426 1.428
## ─────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────
## W.X * BA.CapabilityV 0.13 1 478 .722
## ────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.LearningBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.768 (- SD) -0.089 (0.132) -0.675 .500 [-0.347, 0.169]
## 4.197 (Mean) -0.056 (0.093) -0.598 .550 [-0.238, 0.127]
## 5.625 (+ SD) -0.023 (0.132) -0.171 .864 [-0.281, 0.236]
## ─────────────────────────────────────────────────────────────────WP.SocialLearning.S1=PROCESS(data1, y="WP.SocialLearningV", x="W.X", mods="BA.CapabilityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)###
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X*BA.CapabilityV + (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.SocialLearningV (2) WP.SocialLearningV
## ─────────────────────────────────────────────────────────────────────────
## (Intercept) 3.759 *** 2.106 ***
## (0.104) (0.295)
## W.X -0.178 * 0.197
## (0.079) (0.246)
## BA.CapabilityV 0.394 ***
## (0.067)
## W.X:BA.CapabilityV -0.090
## (0.055)
## ─────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.004 0.116
## Conditional R^2 0.535 0.537
## AIC 2211.535 2190.279
## BIC 2238.525 2226.265
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.282 0.978
## Var: B.ID W.X 0.007 0.001
## Cov: B.ID (Intercept) W.X -0.097 -0.033
## Var: Residual 1.040 1.038
## ─────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────
## W.X * BA.CapabilityV 2.61 1 494 .107
## ────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.SocialLearningV" (Y)
## ──────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────
## 2.768 (- SD) -0.051 (0.112) -0.451 .652 [-0.270, 0.169]
## 4.197 (Mean) -0.178 (0.079) -2.255 .025 * [-0.334, -0.023]
## 5.625 (+ SD) -0.306 (0.112) -2.737 .006 ** [-0.526, -0.087]
## ──────────────────────────────────────────────────────────────────WP.IndependentObservationBasedSocialLearning.S1=PROCESS(data1, y="WP.IndependentObservationBasedSocialLearningV", x="W.X", mods="BA.CapabilityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.IndependentObservationBasedSocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.IndependentObservationBasedSocialLearningV ~ W.X*BA.CapabilityV + (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.IndependentObservationBasedSocialLearningV (2) WP.IndependentObservationBasedSocialLearningV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.736 *** 2.322 ***
## (0.114) (0.337)
## W.X -0.170 -0.053
## (0.094) (0.293)
## BA.CapabilityV 0.337 ***
## (0.076)
## W.X:BA.CapabilityV -0.028
## (0.066)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.078
## Conditional R^2 0.478 0.479
## AIC 2410.168 2399.176
## BIC 2437.158 2435.163
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.438 1.215
## Var: B.ID W.X 0.007 0.006
## Cov: B.ID (Intercept) W.X -0.104 -0.085
## Var: Residual 1.469 1.472
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.IndependentObservationBasedSocialLearningV" (Y)
## ────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────
## W.X * BA.CapabilityV 0.18 1 487 .673
## ────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.IndependentObservationBasedSocialLearningV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.768 (- SD) -0.130 (0.133) -0.976 .329 [-0.392, 0.131]
## 4.197 (Mean) -0.170 (0.094) -1.804 .072 . [-0.355, 0.015]
## 5.625 (+ SD) -0.210 (0.133) -1.574 .116 [-0.472, 0.052]
## ─────────────────────────────────────────────────────────────────WP.AdviceThinkingBasedSocialLearning.S1=PROCESS(data1, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X", mods="BA.CapabilityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)####
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X*BA.CapabilityV + (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.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.782 *** 1.891 ***
## (0.112) (0.315)
## W.X -0.187 * 0.448
## (0.091) (0.281)
## BA.CapabilityV 0.451 ***
## (0.071)
## W.X:BA.CapabilityV -0.151 *
## (0.063)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.116
## Conditional R^2 0.483 0.488
## AIC 2364.000 2338.694
## BIC 2390.990 2374.680
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.418 1.026
## Var: B.ID W.X 0.018 0.002
## Cov: B.ID (Intercept) W.X -0.159 -0.041
## Var: Residual 1.365 1.357
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────
## W.X * BA.CapabilityV 5.69 1 493 .017 *
## ────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ──────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────
## 2.768 (- SD) 0.029 (0.128) 0.229 .819 [-0.222, 0.280]
## 4.197 (Mean) -0.187 (0.090) -2.064 .040 * [-0.364, -0.009]
## 5.625 (+ SD) -0.403 (0.128) -3.146 .002 ** [-0.654, -0.152]
## ──────────────────────────────────────────────────────────────────4.2 Study 2
4.2.1 AI VS Control
WA.LearningFromOperationalFailure.Sb10=PROCESS(data2, y="WA.LearningFromOperationalFailureV", x="W.X10", mods="BA.CapabilityV",covs=c("W.X01","W.X01BA.CapabilityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)###
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromOperationalFailureV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : W.X01, W.X01BA.CapabilityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromOperationalFailureV ~ W.X01 + W.X01BA.CapabilityV + W.X10*BA.CapabilityV + (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) WA.LearningFromOperationalFailureV (2) WA.LearningFromOperationalFailureV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.363 *** 2.652 ***
## (0.109) (0.315)
## W.X01 -0.117 0.309
## (0.195) (0.228)
## W.X01BA.CapabilityV 0.063 -0.039
## (0.043) (0.051)
## W.X10 0.113 0.480 *
## (0.072) (0.228)
## BA.CapabilityV 0.408 ***
## (0.071)
## W.X10:BA.CapabilityV -0.087
## (0.052)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.120
## Conditional R^2 0.616 0.622
## AIC 3024.903 3006.021
## BIC 3078.644 3069.532
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.499 1.187
## Var: B.ID W.X10 0.011 0.004
## Var: B.ID W.X01 0.009 0.001
## Cov: B.ID (Intercept) W.X10 -0.130 -0.067
## Cov: B.ID (Intercept) W.X01 -0.113 -0.041
## Cov: B.ID W.X10 W.X01 0.010 0.002
## Var: Residual 0.843 0.841
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromOperationalFailureV" (Y)
## ──────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────
## W.X10 * BA.CapabilityV 2.87 1 780 .090 .
## ──────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.LearningFromOperationalFailureV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.799 (- SD) 0.235 (0.102) 2.306 .021 * [ 0.035, 0.434]
## 4.196 (Mean) 0.113 (0.072) 1.566 .118 [-0.028, 0.254]
## 5.593 (+ SD) -0.009 (0.102) -0.092 .927 [-0.209, 0.190]
## ─────────────────────────────────────────────────────────────────WA.LearningFromErrors.Sb10=PROCESS(data2, y="WA.LearningFromErrorsV", x="W.X10", mods="BA.CapabilityV",covs=c("W.X01","W.X01BA.CapabilityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : W.X01, W.X01BA.CapabilityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X01 + W.X01BA.CapabilityV + W.X10*BA.CapabilityV + (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) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.163 *** 2.710 ***
## (0.118) (0.356)
## W.X01 -0.436 * -0.217
## (0.194) (0.220)
## W.X01BA.CapabilityV 0.109 * 0.057
## (0.043) (0.050)
## W.X10 0.038 0.153
## (0.068) (0.216)
## BA.CapabilityV 0.346 ***
## (0.080)
## W.X10:BA.CapabilityV -0.028
## (0.049)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.094
## Conditional R^2 0.711 0.717
## AIC 2991.400 2984.245
## BIC 3045.140 3047.756
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.908 1.682
## Var: B.ID W.X10 0.002 0.009
## Var: B.ID W.X01 0.058 0.040
## Cov: B.ID (Intercept) W.X10 -0.056 -0.036
## Cov: B.ID (Intercept) W.X01 -0.078 -0.035
## Cov: B.ID W.X10 W.X01 0.002 -0.018
## Var: Residual 0.752 0.749
## ───────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ──────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────
## W.X10 * BA.CapabilityV 0.32 1 501 .573
## ──────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.LearningFromErrorsV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.799 (- SD) 0.076 (0.096) 0.791 .430 [-0.112, 0.265]
## 4.196 (Mean) 0.038 (0.068) 0.553 .581 [-0.096, 0.171]
## 5.593 (+ SD) -0.001 (0.096) -0.009 .993 [-0.189, 0.188]
## ─────────────────────────────────────────────────────────────────WA.ThrivingInLearning.Sb10=PROCESS(data2, y="WA.ThrivingInLearningV", x="W.X10", mods="BA.CapabilityV",covs=c("W.X01","W.X01BA.CapabilityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ThrivingInLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : W.X01, W.X01BA.CapabilityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ThrivingInLearningV ~ W.X01 + W.X01BA.CapabilityV + W.X10*BA.CapabilityV + (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) WA.ThrivingInLearningV (2) WA.ThrivingInLearningV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.291 *** 2.611 ***
## (0.109) (0.319)
## W.X01 -0.300 0.158
## (0.190) (0.221)
## W.X01BA.CapabilityV 0.074 -0.036
## (0.042) (0.050)
## W.X10 -0.026 0.210
## (0.061) (0.194)
## BA.CapabilityV 0.400 ***
## (0.072)
## W.X10:BA.CapabilityV -0.056
## (0.044)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.124
## Conditional R^2 0.713 0.718
## AIC 2826.465 2809.814
## BIC 2880.205 2873.326
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.644 1.342
## Var: B.ID W.X10 0.001 0.000
## Var: B.ID W.X01 0.199 0.179
## Cov: B.ID (Intercept) W.X10 -0.037 0.003
## Cov: B.ID (Intercept) W.X01 -0.254 -0.173
## Cov: B.ID W.X10 W.X01 0.014 0.006
## Var: Residual 0.613 0.613
## ───────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ThrivingInLearningV" (Y)
## ──────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────
## W.X10 * BA.CapabilityV 1.64 1 647 .200
## ──────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.ThrivingInLearningV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.799 (- SD) 0.052 (0.087) 0.603 .547 [-0.118, 0.222]
## 4.196 (Mean) -0.026 (0.061) -0.430 .667 [-0.147, 0.094]
## 5.593 (+ SD) -0.105 (0.087) -1.211 .226 [-0.275, 0.065]
## ─────────────────────────────────────────────────────────────────WP.LearningBehavior.Sb10=PROCESS(data2, y="WP.LearningBehaviorV", x="W.X10", mods="BA.CapabilityV",covs=c("W.X01","W.X01BA.CapabilityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : W.X01, W.X01BA.CapabilityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X01 + W.X01BA.CapabilityV + W.X10*BA.CapabilityV + (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.LearningBehaviorV (2) WP.LearningBehaviorV
## ───────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.549 *** 1.602 ***
## (0.123) (0.355)
## W.X01 -0.285 -0.110
## (0.192) (0.221)
## W.X01BA.CapabilityV 0.072 0.030
## (0.043) (0.050)
## W.X10 0.124 -0.140
## (0.068) (0.216)
## BA.CapabilityV 0.464 ***
## (0.080)
## W.X10:BA.CapabilityV 0.063
## (0.049)
## ───────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.172
## Conditional R^2 0.726 0.731
## AIC 3008.883 2979.391
## BIC 3062.624 3042.902
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.077 1.667
## Var: B.ID W.X10 0.003 0.005
## Var: B.ID W.X01 0.047 0.041
## Cov: B.ID (Intercept) W.X10 -0.010 -0.070
## Cov: B.ID (Intercept) W.X01 -0.137 -0.100
## Cov: B.ID W.X10 W.X01 -0.010 -0.005
## Var: Residual 0.757 0.756
## ───────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ──────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────
## W.X10 * BA.CapabilityV 1.65 1 629 .199
## ──────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.LearningBehaviorV" (Y)
## ────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────
## 2.799 (- SD) 0.036 (0.097) 0.376 .707 [-0.153, 0.226]
## 4.196 (Mean) 0.124 (0.068) 1.818 .070 . [-0.010, 0.258]
## 5.593 (+ SD) 0.212 (0.097) 2.194 .029 * [ 0.023, 0.402]
## ────────────────────────────────────────────────────────────────WP.SocialLearning.Sb10=PROCESS(data2, y="WP.SocialLearningV", x="W.X10", mods="BA.CapabilityV",covs=c("W.X01","W.X01BA.CapabilityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : W.X01, W.X01BA.CapabilityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X01 + W.X01BA.CapabilityV + W.X10*BA.CapabilityV + (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.SocialLearningV (2) WP.SocialLearningV
## ───────────────────────────────────────────────────────────────────────────
## (Intercept) 3.656 *** 1.896 ***
## (0.109) (0.314)
## W.X01 0.298 0.190
## (0.191) (0.215)
## W.X01BA.CapabilityV -0.054 -0.028
## (0.043) (0.049)
## W.X10 0.115 -0.475 *
## (0.069) (0.215)
## BA.CapabilityV 0.419 ***
## (0.071)
## W.X10:BA.CapabilityV 0.141 **
## (0.049)
## ───────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.177
## Conditional R^2 0.691 0.688
## AIC 2964.547 2918.689
## BIC 3018.287 2982.201
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.564 1.232
## Var: B.ID W.X10 0.024 0.011
## Var: B.ID W.X01 0.013 0.008
## Cov: B.ID (Intercept) W.X10 0.048 -0.079
## Cov: B.ID (Intercept) W.X01 0.070 0.047
## Cov: B.ID W.X10 W.X01 -0.013 -0.009
## Var: Residual 0.744 0.741
## ───────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ──────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────
## W.X10 * BA.CapabilityV 8.34 1 601 .004 **
## ──────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.SocialLearningV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.799 (- SD) -0.081 (0.096) -0.846 .398 [-0.270, 0.107]
## 4.196 (Mean) 0.115 (0.068) 1.693 .091 . [-0.018, 0.248]
## 5.593 (+ SD) 0.311 (0.096) 3.239 .001 ** [ 0.123, 0.500]
## ─────────────────────────────────────────────────────────────────WP.IndependentObservationBasedSocialLearning.Sb10=PROCESS(data2, y="WP.IndependentObservationBasedSocialLearningV", x="W.X10", mods="BA.CapabilityV",covs=c("W.X01","W.X01BA.CapabilityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.IndependentObservationBasedSocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : W.X01, W.X01BA.CapabilityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.IndependentObservationBasedSocialLearningV ~ W.X01 + W.X01BA.CapabilityV + W.X10*BA.CapabilityV + (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.IndependentObservationBasedSocialLearningV (2) WP.IndependentObservationBasedSocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.695 *** 1.937 ***
## (0.121) (0.357)
## W.X01 0.119 -0.027
## (0.220) (0.252)
## W.X01BA.CapabilityV -0.010 0.025
## (0.049) (0.057)
## W.X10 0.172 * -0.639 *
## (0.081) (0.253)
## BA.CapabilityV 0.419 ***
## (0.081)
## W.X10:BA.CapabilityV 0.193 ***
## (0.057)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.164
## Conditional R^2 0.652 0.655
## AIC 3259.130 3213.541
## BIC 3312.871 3277.053
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.878 1.554
## Var: B.ID W.X10 0.034 0.012
## Var: B.ID W.X01 0.001 0.000
## Cov: B.ID (Intercept) W.X10 0.042 -0.138
## Cov: B.ID (Intercept) W.X01 0.037 0.003
## Cov: B.ID W.X10 W.X01 -0.002 -0.000
## Var: Residual 1.039 1.030
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.IndependentObservationBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────
## W.X10 * BA.CapabilityV 11.39 1 735 <.001 ***
## ───────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.IndependentObservationBasedSocialLearningV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.799 (- SD) -0.098 (0.113) -0.868 .385 [-0.320, 0.123]
## 4.196 (Mean) 0.172 (0.080) 2.148 .032 * [ 0.015, 0.329]
## 5.593 (+ SD) 0.442 (0.113) 3.905 <.001 *** [ 0.220, 0.664]
## ─────────────────────────────────────────────────────────────────WP.AdviceThinkingBasedSocialLearning.Sb10=PROCESS(data2, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X10", mods="BA.CapabilityV",covs=c("W.X01","W.X01BA.CapabilityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : W.X01, W.X01BA.CapabilityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X01 + W.X01BA.CapabilityV + W.X10*BA.CapabilityV + (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.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.617 *** 1.854 ***
## (0.111) (0.322)
## W.X01 0.390 0.407
## (0.232) (0.264)
## W.X01BA.CapabilityV -0.077 -0.081
## (0.052) (0.060)
## W.X10 0.058 -0.310
## (0.082) (0.256)
## BA.CapabilityV 0.420 ***
## (0.073)
## W.X10:BA.CapabilityV 0.088
## (0.058)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.132
## Conditional R^2 0.614 0.606
## AIC 3262.851 3231.618
## BIC 3316.591 3295.130
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.489 1.155
## Var: B.ID W.X10 0.037 0.001
## Var: B.ID W.X01 0.045 0.066
## Cov: B.ID (Intercept) W.X10 0.100 0.027
## Cov: B.ID (Intercept) W.X01 0.148 0.136
## Cov: B.ID W.X10 W.X01 -0.021 0.003
## Var: Residual 1.059 1.067
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ──────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────
## W.X10 * BA.CapabilityV 2.29 1 633 .131
## ──────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.799 (- SD) -0.064 (0.115) -0.557 .578 [-0.291, 0.162]
## 4.196 (Mean) 0.058 (0.082) 0.714 .476 [-0.102, 0.218]
## 5.593 (+ SD) 0.181 (0.115) 1.567 .118 [-0.045, 0.407]
## ─────────────────────────────────────────────────────────────────4.2.2 AI VS Self
WA.LearningFromOperationalFailure.Sb01=PROCESS(data2, y="WA.LearningFromOperationalFailureV", x="W.X01", mods="BA.CapabilityV",covs=c("W.X10","W.X10BA.CapabilityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromOperationalFailureV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : W.X10, W.X10BA.CapabilityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromOperationalFailureV ~ W.X10 + W.X10BA.CapabilityV + W.X01*BA.CapabilityV + (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) WA.LearningFromOperationalFailureV (2) WA.LearningFromOperationalFailureV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.363 *** 2.652 ***
## (0.109) (0.315)
## W.X10 0.095 0.480 *
## (0.193) (0.228)
## W.X10BA.CapabilityV 0.004 -0.087
## (0.043) (0.052)
## W.X01 0.146 * 0.309
## (0.072) (0.228)
## BA.CapabilityV 0.408 ***
## (0.071)
## W.X01:BA.CapabilityV -0.039
## (0.051)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.120
## Conditional R^2 0.620 0.622
## AIC 3026.690 3006.021
## BIC 3080.430 3069.532
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.502 1.187
## Var: B.ID W.X10 0.012 0.004
## Var: B.ID W.X01 0.003 0.001
## Cov: B.ID (Intercept) W.X10 -0.134 -0.067
## Cov: B.ID (Intercept) W.X01 -0.071 -0.041
## Cov: B.ID W.X10 W.X01 0.006 0.002
## Var: Residual 0.842 0.841
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromOperationalFailureV" (Y)
## ──────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────
## W.X01 * BA.CapabilityV 0.57 1 799 .450
## ──────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.LearningFromOperationalFailureV" (Y)
## ────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────
## 2.799 (- SD) 0.200 (0.102) 1.968 .049 * [ 0.001, 0.399]
## 4.196 (Mean) 0.146 (0.072) 2.027 .043 * [ 0.005, 0.287]
## 5.593 (+ SD) 0.091 (0.102) 0.898 .369 [-0.108, 0.291]
## ────────────────────────────────────────────────────────────────WA.LearningFromErrors.Sb01=PROCESS(data2, y="WA.LearningFromErrorsV", x="W.X01", mods="BA.CapabilityV",covs=c("W.X10","W.X10BA.CapabilityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : W.X10, W.X10BA.CapabilityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X10 + W.X10BA.CapabilityV + W.X01*BA.CapabilityV + (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) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.163 *** 2.710 ***
## (0.118) (0.356)
## W.X10 0.068 0.153
## (0.190) (0.216)
## W.X10BA.CapabilityV -0.007 -0.028
## (0.042) (0.049)
## W.X01 0.022 -0.217
## (0.070) (0.220)
## BA.CapabilityV 0.346 ***
## (0.080)
## W.X01:BA.CapabilityV 0.057
## (0.050)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.094
## Conditional R^2 0.717 0.717
## AIC 2996.724 2984.245
## BIC 3050.465 3047.756
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.904 1.682
## Var: B.ID W.X10 0.011 0.009
## Var: B.ID W.X01 0.043 0.040
## Cov: B.ID (Intercept) W.X10 -0.049 -0.036
## Cov: B.ID (Intercept) W.X01 0.006 -0.035
## Cov: B.ID W.X10 W.X01 -0.021 -0.018
## Var: Residual 0.748 0.749
## ───────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ──────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────
## W.X01 * BA.CapabilityV 1.31 1 273 .253
## ──────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.LearningFromErrorsV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.799 (- SD) -0.057 (0.098) -0.583 .560 [-0.250, 0.135]
## 4.196 (Mean) 0.022 (0.070) 0.320 .750 [-0.114, 0.159]
## 5.593 (+ SD) 0.102 (0.098) 1.035 .301 [-0.091, 0.295]
## ─────────────────────────────────────────────────────────────────WA.ThrivingInLearning.Sb01=PROCESS(data2, y="WA.ThrivingInLearningV", x="W.X01", mods="BA.CapabilityV",covs=c("W.X10","W.X10BA.CapabilityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ThrivingInLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : W.X10, W.X10BA.CapabilityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ThrivingInLearningV ~ W.X10 + W.X10BA.CapabilityV + W.X01*BA.CapabilityV + (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) WA.ThrivingInLearningV (2) WA.ThrivingInLearningV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.291 *** 2.611 ***
## (0.109) (0.319)
## W.X10 0.018 0.210
## (0.174) (0.194)
## W.X10BA.CapabilityV -0.010 -0.056
## (0.039) (0.044)
## W.X01 0.009 0.158
## (0.069) (0.221)
## BA.CapabilityV 0.400 ***
## (0.072)
## W.X01:BA.CapabilityV -0.036
## (0.050)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.124
## Conditional R^2 0.717 0.718
## AIC 2829.143 2809.814
## BIC 2882.884 2873.326
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.644 1.342
## Var: B.ID W.X10 0.000 0.000
## Var: B.ID W.X01 0.172 0.179
## Cov: B.ID (Intercept) W.X10 -0.028 0.003
## Cov: B.ID (Intercept) W.X01 -0.195 -0.173
## Cov: B.ID W.X10 W.X01 0.003 0.006
## Var: Residual 0.613 0.613
## ───────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ThrivingInLearningV" (Y)
## ──────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────
## W.X01 * BA.CapabilityV 0.51 1 189 .475
## ──────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.ThrivingInLearningV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.799 (- SD) 0.058 (0.099) 0.593 .554 [-0.135, 0.252]
## 4.196 (Mean) 0.009 (0.070) 0.123 .902 [-0.128, 0.145]
## 5.593 (+ SD) -0.041 (0.099) -0.419 .676 [-0.234, 0.152]
## ─────────────────────────────────────────────────────────────────WP.LearningBehavior.Sb01=PROCESS(data2, y="WP.LearningBehaviorV", x="W.X01", mods="BA.CapabilityV",covs=c("W.X10","W.X10BA.CapabilityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : W.X10, W.X10BA.CapabilityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X10 + W.X10BA.CapabilityV + W.X01*BA.CapabilityV + (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.LearningBehaviorV (2) WP.LearningBehaviorV
## ───────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.549 *** 1.602 ***
## (0.123) (0.355)
## W.X10 -0.337 -0.140
## (0.188) (0.216)
## W.X10BA.CapabilityV 0.110 ** 0.063
## (0.042) (0.049)
## W.X01 0.017 -0.110
## (0.070) (0.221)
## BA.CapabilityV 0.464 ***
## (0.080)
## W.X01:BA.CapabilityV 0.030
## (0.050)
## ───────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.004 0.172
## Conditional R^2 0.724 0.731
## AIC 3005.723 2979.391
## BIC 3059.463 3042.902
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.078 1.667
## Var: B.ID W.X10 0.009 0.005
## Var: B.ID W.X01 0.041 0.041
## Cov: B.ID (Intercept) W.X10 -0.111 -0.070
## Cov: B.ID (Intercept) W.X01 -0.072 -0.100
## Cov: B.ID W.X10 W.X01 -0.007 -0.005
## Var: Residual 0.756 0.756
## ───────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ──────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────
## W.X01 * BA.CapabilityV 0.37 1 303 .546
## ──────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.LearningBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.799 (- SD) -0.025 (0.099) -0.257 .797 [-0.219, 0.168]
## 4.196 (Mean) 0.017 (0.070) 0.241 .809 [-0.120, 0.154]
## 5.593 (+ SD) 0.059 (0.099) 0.599 .550 [-0.135, 0.253]
## ─────────────────────────────────────────────────────────────────WP.SocialLearning.Sb01=PROCESS(data2, y="WP.SocialLearningV", x="W.X01", mods="BA.CapabilityV",covs=c("W.X10","W.X10BA.CapabilityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : W.X10, W.X10BA.CapabilityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X10 + W.X10BA.CapabilityV + W.X01*BA.CapabilityV + (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.SocialLearningV (2) WP.SocialLearningV
## ───────────────────────────────────────────────────────────────────────────
## (Intercept) 3.656 *** 1.896 ***
## (0.109) (0.314)
## W.X10 -0.872 *** -0.475 *
## (0.183) (0.215)
## W.X10BA.CapabilityV 0.235 *** 0.141 **
## (0.040) (0.049)
## W.X01 0.073 0.190
## (0.068) (0.215)
## BA.CapabilityV 0.419 ***
## (0.071)
## W.X01:BA.CapabilityV -0.028
## (0.049)
## ───────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.017 0.177
## Conditional R^2 0.673 0.688
## AIC 2939.950 2918.689
## BIC 2993.691 2982.201
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.565 1.232
## Var: B.ID W.X10 0.021 0.011
## Var: B.ID W.X01 0.006 0.008
## Cov: B.ID (Intercept) W.X10 -0.151 -0.079
## Cov: B.ID (Intercept) W.X01 0.027 0.047
## Cov: B.ID W.X10 W.X01 -0.009 -0.009
## Var: Residual 0.742 0.741
## ───────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ──────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────
## W.X01 * BA.CapabilityV 0.33 1 632 .565
## ──────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.SocialLearningV" (Y)
## ────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────
## 2.799 (- SD) 0.112 (0.096) 1.167 .244 [-0.076, 0.300]
## 4.196 (Mean) 0.073 (0.068) 1.075 .283 [-0.060, 0.206]
## 5.593 (+ SD) 0.034 (0.096) 0.353 .724 [-0.154, 0.222]
## ────────────────────────────────────────────────────────────────WP.IndependentObservationBasedSocialLearning.Sb01=PROCESS(data2, y="WP.IndependentObservationBasedSocialLearningV", x="W.X01", mods="BA.CapabilityV",covs=c("W.X10","W.X10BA.CapabilityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.IndependentObservationBasedSocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : W.X10, W.X10BA.CapabilityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.IndependentObservationBasedSocialLearningV ~ W.X10 + W.X10BA.CapabilityV + W.X01*BA.CapabilityV + (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.IndependentObservationBasedSocialLearningV (2) WP.IndependentObservationBasedSocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.695 *** 1.937 ***
## (0.121) (0.357)
## W.X10 -0.981 *** -0.639 *
## (0.212) (0.253)
## W.X10BA.CapabilityV 0.275 *** 0.193 ***
## (0.047) (0.057)
## W.X01 0.078 -0.027
## (0.080) (0.252)
## BA.CapabilityV 0.419 ***
## (0.081)
## W.X01:BA.CapabilityV 0.025
## (0.057)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.019 0.164
## Conditional R^2 0.640 0.655
## AIC 3232.354 3213.541
## BIC 3286.095 3277.053
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.887 1.554
## Var: B.ID W.X10 0.022 0.012
## Var: B.ID W.X01 0.000 0.000
## Cov: B.ID (Intercept) W.X10 -0.202 -0.138
## Cov: B.ID (Intercept) W.X01 0.023 0.003
## Cov: B.ID W.X10 W.X01 -0.002 -0.000
## Var: Residual 1.031 1.030
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.IndependentObservationBasedSocialLearningV" (Y)
## ──────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────
## W.X01 * BA.CapabilityV 0.20 1 811 .659
## ──────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.IndependentObservationBasedSocialLearningV" (Y)
## ────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────
## 2.799 (- SD) 0.043 (0.112) 0.383 .702 [-0.177, 0.264]
## 4.196 (Mean) 0.078 (0.080) 0.984 .326 [-0.078, 0.234]
## 5.593 (+ SD) 0.113 (0.112) 1.008 .314 [-0.107, 0.334]
## ────────────────────────────────────────────────────────────────WP.AdviceThinkingBasedSocialLearning.Sb01=PROCESS(data2, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X01", mods="BA.CapabilityV",covs=c("W.X10","W.X10BA.CapabilityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : W.X10, W.X10BA.CapabilityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X10 + W.X10BA.CapabilityV + W.X01*BA.CapabilityV + (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.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.617 *** 1.854 ***
## (0.111) (0.322)
## W.X10 -0.813 *** -0.310
## (0.223) (0.256)
## W.X10BA.CapabilityV 0.208 *** 0.088
## (0.049) (0.058)
## W.X01 0.067 0.407
## (0.082) (0.264)
## BA.CapabilityV 0.420 ***
## (0.073)
## W.X01:BA.CapabilityV -0.081
## (0.060)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.011 0.132
## Conditional R^2 0.594 0.606
## AIC 3250.589 3231.618
## BIC 3304.330 3295.130
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.481 1.155
## Var: B.ID W.X10 0.039 0.001
## Var: B.ID W.X01 0.037 0.066
## Cov: B.ID (Intercept) W.X10 -0.061 0.027
## Cov: B.ID (Intercept) W.X01 0.094 0.136
## Cov: B.ID W.X10 W.X01 -0.015 0.003
## Var: Residual 1.064 1.067
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ──────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────
## W.X01 * BA.CapabilityV 1.84 1 261 .176
## ──────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.799 (- SD) 0.181 (0.116) 1.552 .122 [-0.047, 0.409]
## 4.196 (Mean) 0.067 (0.082) 0.820 .413 [-0.094, 0.229]
## 5.593 (+ SD) -0.046 (0.116) -0.393 .695 [-0.274, 0.182]
## ─────────────────────────────────────────────────────────────────5 BA.AIInteractionQualityV
5.1 Study 1
WA.LearningFromOperationalFailure.S1=PROCESS(data1, y="WA.LearningFromOperationalFailureV", x="W.X", mods="BA.AIInteractionQualityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromOperationalFailureV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromOperationalFailureV ~ W.X*BA.AIInteractionQualityV + (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) WA.LearningFromOperationalFailureV (2) WA.LearningFromOperationalFailureV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.779 *** 3.826 ***
## (0.085) (0.247)
## W.X 0.139 0.039
## (0.073) (0.222)
## BA.AIInteractionQualityV 0.239 ***
## (0.059)
## W.X:BA.AIInteractionQualityV 0.025
## (0.052)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.078
## Conditional R^2 0.458 0.460
## AIC 2057.038 2046.203
## BIC 2084.028 2082.189
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 0.758 0.653
## Var: B.ID W.X 0.001 0.002
## Cov: B.ID (Intercept) W.X -0.023 -0.035
## Var: Residual 0.875 0.876
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromOperationalFailureV" (Y)
## ──────────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────────
## W.X * BA.AIInteractionQualityV 0.23 1 491 .632
## ──────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.LearningFromOperationalFailureV" (Y)
## ──────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────
## 2.600 (- SD) 0.104 (0.103) 1.015 .310 [-0.097, 0.306]
## 3.986 (Mean) 0.139 (0.073) 1.916 .056 . [-0.003, 0.282]
## 5.372 (+ SD) 0.174 (0.103) 1.693 .091 . [-0.028, 0.376]
## ──────────────────────────────────────────────────────────────────────────WA.LearningFromErrors.S1=PROCESS(data1, y="WA.LearningFromErrorsV", x="W.X", mods="BA.AIInteractionQualityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)####
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X*BA.AIInteractionQualityV + (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) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.291 *** 2.688 ***
## (0.104) (0.290)
## W.X 0.031 0.410
## (0.075) (0.226)
## BA.AIInteractionQualityV 0.402 ***
## (0.069)
## W.X:BA.AIInteractionQualityV -0.095
## (0.054)
## ────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.114
## Conditional R^2 0.612 0.613
## AIC 2122.701 2102.870
## BIC 2149.690 2138.856
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.395 1.092
## Var: B.ID W.X 0.094 0.082
## Cov: B.ID (Intercept) W.X -0.131 -0.060
## Var: Residual 0.832 0.832
## ────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ──────────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────────
## W.X * BA.AIInteractionQualityV 3.16 1 164 .077 .
## ──────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.LearningFromErrorsV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 2.600 (- SD) 0.163 (0.105) 1.552 .123 [-0.043, 0.369]
## 3.986 (Mean) 0.031 (0.074) 0.416 .678 [-0.115, 0.176]
## 5.372 (+ SD) -0.101 (0.105) -0.963 .337 [-0.307, 0.105]
## ───────────────────────────────────────────────────────────────────────────WA.ThrivingInLearning.S1=PROCESS(data1, y="WA.ThrivingInLearningV", x="W.X", mods="BA.AIInteractionQualityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)####
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ThrivingInLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ThrivingInLearningV ~ W.X*BA.AIInteractionQualityV + (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) WA.ThrivingInLearningV (2) WA.ThrivingInLearningV
## ────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.702 *** 3.575 ***
## (0.093) (0.269)
## W.X -0.058 0.327
## (0.062) (0.188)
## BA.AIInteractionQualityV 0.283 ***
## (0.064)
## W.X:BA.AIInteractionQualityV -0.096 *
## (0.045)
## ────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.068
## Conditional R^2 0.616 0.618
## AIC 1920.298 1913.872
## BIC 1947.288 1949.858
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.132 0.987
## Var: B.ID W.X 0.018 0.009
## Cov: B.ID (Intercept) W.X -0.142 -0.094
## Var: Residual 0.624 0.622
## ────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ThrivingInLearningV" (Y)
## ──────────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────────
## W.X * BA.AIInteractionQualityV 4.70 1 465 .031 *
## ──────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.ThrivingInLearningV" (Y)
## ────────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────────
## 2.600 (- SD) 0.076 (0.087) 0.870 .385 [-0.095, 0.247]
## 3.986 (Mean) -0.058 (0.062) -0.938 .349 [-0.179, 0.063]
## 5.372 (+ SD) -0.192 (0.087) -2.196 .029 * [-0.363, -0.021]
## ────────────────────────────────────────────────────────────────────────────WP.LearningBehavior.S1=PROCESS(data1, y="WP.LearningBehaviorV", x="W.X", mods="BA.AIInteractionQualityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X*BA.AIInteractionQualityV + (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.LearningBehaviorV (2) WP.LearningBehaviorV
## ────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.648 *** 2.126 ***
## (0.111) (0.316)
## W.X -0.056 0.288
## (0.093) (0.283)
## BA.AIInteractionQualityV 0.382 ***
## (0.075)
## W.X:BA.AIInteractionQualityV -0.086
## (0.067)
## ────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.084
## Conditional R^2 0.466 0.468
## AIC 2385.192 2370.655
## BIC 2412.181 2406.641
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.343 1.075
## Var: B.ID W.X 0.008 0.002
## Cov: B.ID (Intercept) W.X -0.103 -0.046
## Var: Residual 1.426 1.427
## ────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ──────────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────────
## W.X * BA.AIInteractionQualityV 1.66 1 493 .198
## ──────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.LearningBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 2.600 (- SD) 0.064 (0.131) 0.487 .626 [-0.193, 0.321]
## 3.986 (Mean) -0.056 (0.093) -0.601 .548 [-0.238, 0.126]
## 5.372 (+ SD) -0.175 (0.131) -1.337 .182 [-0.433, 0.082]
## ───────────────────────────────────────────────────────────────────────────WP.SocialLearning.S1=PROCESS(data1, y="WP.SocialLearningV", x="W.X", mods="BA.AIInteractionQualityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X*BA.AIInteractionQualityV + (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.SocialLearningV (2) WP.SocialLearningV
## ────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.759 *** 2.024 ***
## (0.104) (0.284)
## W.X -0.178 * 0.110
## (0.079) (0.241)
## BA.AIInteractionQualityV 0.435 ***
## (0.067)
## W.X:BA.AIInteractionQualityV -0.072
## (0.057)
## ────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.004 0.141
## Conditional R^2 0.535 0.536
## AIC 2211.535 2182.954
## BIC 2238.525 2218.940
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.282 0.927
## Var: B.ID W.X 0.007 0.002
## Cov: B.ID (Intercept) W.X -0.097 -0.041
## Var: Residual 1.040 1.040
## ────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ──────────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────────
## W.X * BA.AIInteractionQualityV 1.60 1 492 .206
## ──────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.SocialLearningV" (Y)
## ────────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────────
## 2.600 (- SD) -0.078 (0.112) -0.697 .486 [-0.298, 0.142]
## 3.986 (Mean) -0.178 (0.079) -2.252 .025 * [-0.334, -0.023]
## 5.372 (+ SD) -0.279 (0.112) -2.487 .013 * [-0.498, -0.059]
## ────────────────────────────────────────────────────────────────────────────WP.IndependentObservationBasedSocialLearning.S1=PROCESS(data1, y="WP.IndependentObservationBasedSocialLearningV", x="W.X", mods="BA.AIInteractionQualityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.IndependentObservationBasedSocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.IndependentObservationBasedSocialLearningV ~ W.X*BA.AIInteractionQualityV + (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.IndependentObservationBasedSocialLearningV (2) WP.IndependentObservationBasedSocialLearningV
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.736 *** 2.260 ***
## (0.114) (0.327)
## W.X -0.170 -0.112
## (0.094) (0.288)
## BA.AIInteractionQualityV 0.370 ***
## (0.078)
## W.X:BA.AIInteractionQualityV -0.014
## (0.068)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.092
## Conditional R^2 0.478 0.479
## AIC 2410.168 2394.545
## BIC 2437.158 2430.532
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.438 1.182
## Var: B.ID W.X 0.007 0.007
## Cov: B.ID (Intercept) W.X -0.104 -0.093
## Var: Residual 1.469 1.472
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.IndependentObservationBasedSocialLearningV" (Y)
## ──────────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────────
## W.X * BA.AIInteractionQualityV 0.05 1 484 .832
## ──────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.IndependentObservationBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 2.600 (- SD) -0.150 (0.134) -1.124 .262 [-0.412, 0.112]
## 3.986 (Mean) -0.170 (0.094) -1.803 .072 . [-0.355, 0.015]
## 5.372 (+ SD) -0.190 (0.134) -1.425 .155 [-0.452, 0.071]
## ───────────────────────────────────────────────────────────────────────────WP.AdviceThinkingBasedSocialLearning.S1=PROCESS(data1, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X", mods="BA.AIInteractionQualityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)####
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X*BA.AIInteractionQualityV + (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.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.782 *** 1.788 ***
## (0.112) (0.302)
## W.X -0.187 * 0.332
## (0.091) (0.276)
## BA.AIInteractionQualityV 0.500 ***
## (0.072)
## W.X:BA.AIInteractionQualityV -0.130 *
## (0.065)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.144
## Conditional R^2 0.483 0.486
## AIC 2364.000 2330.273
## BIC 2390.990 2366.259
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.418 0.953
## Var: B.ID W.X 0.018 0.002
## Cov: B.ID (Intercept) W.X -0.159 -0.048
## Var: Residual 1.365 1.362
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ──────────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────────
## W.X * BA.AIInteractionQualityV 3.96 1 492 .047 *
## ──────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ────────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────────
## 2.600 (- SD) -0.006 (0.128) -0.048 .962 [-0.258, 0.245]
## 3.986 (Mean) -0.187 (0.091) -2.060 .040 * [-0.364, -0.009]
## 5.372 (+ SD) -0.367 (0.128) -2.864 .004 ** [-0.619, -0.116]
## ────────────────────────────────────────────────────────────────────────────5.2 Study 2
5.2.1 AI VS Control
WA.LearningFromOperationalFailure.Sb10=PROCESS(data2, y="WA.LearningFromOperationalFailureV", x="W.X10", mods="BA.AIInteractionQualityV",covs=c("W.X01","W.X01BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromOperationalFailureV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X01, W.X01BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromOperationalFailureV ~ W.X01 + W.X01BA.AIInteractionQualityV + W.X10*BA.AIInteractionQualityV + (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) WA.LearningFromOperationalFailureV (2) WA.LearningFromOperationalFailureV
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.363 *** 3.019 ***
## (0.109) (0.319)
## W.X01 -0.284 0.043
## (0.190) (0.223)
## W.X01BA.AIInteractionQualityV 0.107 * 0.026
## (0.044) (0.053)
## W.X10 0.113 0.343
## (0.072) (0.224)
## BA.AIInteractionQualityV 0.336 ***
## (0.075)
## W.X10:BA.AIInteractionQualityV -0.058
## (0.053)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.005 0.091
## Conditional R^2 0.615 0.622
## AIC 3021.388 3013.371
## BIC 3075.129 3076.883
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.501 1.301
## Var: B.ID W.X10 0.011 0.007
## Var: B.ID W.X01 0.012 0.006
## Cov: B.ID (Intercept) W.X10 -0.131 -0.097
## Cov: B.ID (Intercept) W.X01 -0.134 -0.088
## Cov: B.ID W.X10 W.X01 0.012 0.007
## Var: Residual 0.840 0.840
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromOperationalFailureV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X10 * BA.AIInteractionQualityV 1.19 1 755 .276
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.LearningFromOperationalFailureV" (Y)
## ──────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) 0.191 (0.102) 1.876 .061 . [-0.009, 0.391]
## 4.006 (Mean) 0.113 (0.072) 1.563 .118 [-0.029, 0.254]
## 5.372 (+ SD) 0.034 (0.102) 0.334 .738 [-0.166, 0.234]
## ──────────────────────────────────────────────────────────────────────────WA.LearningFromErrors.Sb10=PROCESS(data2, y="WA.LearningFromErrorsV", x="W.X10", mods="BA.AIInteractionQualityV",covs=c("W.X01","W.X01BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X01, W.X01BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X01 + W.X01BA.AIInteractionQualityV + W.X10*BA.AIInteractionQualityV + (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) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.163 *** 3.006 ***
## (0.118) (0.355)
## W.X01 -0.606 ** -0.425 *
## (0.188) (0.214)
## W.X01BA.AIInteractionQualityV 0.157 *** 0.112 *
## (0.044) (0.051)
## W.X10 0.038 0.132
## (0.068) (0.211)
## BA.AIInteractionQualityV 0.289 ***
## (0.084)
## W.X10:BA.AIInteractionQualityV -0.024
## (0.050)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.006 0.074
## Conditional R^2 0.712 0.718
## AIC 2985.004 2984.423
## BIC 3038.744 3047.935
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.906 1.762
## Var: B.ID W.X10 0.009 0.009
## Var: B.ID W.X01 0.031 0.028
## Cov: B.ID (Intercept) W.X10 -0.055 -0.043
## Cov: B.ID (Intercept) W.X01 -0.081 -0.059
## Cov: B.ID W.X10 W.X01 -0.012 -0.013
## Var: Residual 0.748 0.748
## ──────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X10 * BA.AIInteractionQualityV 0.22 1 495 .638
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.LearningFromErrorsV" (Y)
## ──────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) 0.070 (0.096) 0.723 .470 [-0.119, 0.259]
## 4.006 (Mean) 0.038 (0.068) 0.551 .582 [-0.096, 0.171]
## 5.372 (+ SD) 0.005 (0.096) 0.056 .955 [-0.184, 0.194]
## ──────────────────────────────────────────────────────────────────────────WA.ThrivingInLearning.Sb10=PROCESS(data2, y="WA.ThrivingInLearningV", x="W.X10", mods="BA.AIInteractionQualityV",covs=c("W.X01","W.X01BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ThrivingInLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X01, W.X01BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ThrivingInLearningV ~ W.X01 + W.X01BA.AIInteractionQualityV + W.X10*BA.AIInteractionQualityV + (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) WA.ThrivingInLearningV (2) WA.ThrivingInLearningV
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.291 *** 2.791 ***
## (0.109) (0.317)
## W.X01 -0.363 0.023
## (0.185) (0.216)
## W.X01BA.AIInteractionQualityV 0.093 * -0.004
## (0.043) (0.051)
## W.X10 -0.026 0.110
## (0.061) (0.190)
## BA.AIInteractionQualityV 0.374 ***
## (0.075)
## W.X10:BA.AIInteractionQualityV -0.034
## (0.045)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.113
## Conditional R^2 0.712 0.717
## AIC 2824.991 2812.686
## BIC 2878.732 2876.198
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.644 1.392
## Var: B.ID W.X10 0.001 0.000
## Var: B.ID W.X01 0.189 0.180
## Cov: B.ID (Intercept) W.X10 -0.036 -0.015
## Cov: B.ID (Intercept) W.X01 -0.257 -0.197
## Cov: B.ID W.X10 W.X01 0.006 0.008
## Var: Residual 0.613 0.614
## ──────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ThrivingInLearningV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X10 * BA.AIInteractionQualityV 0.58 1 646 .448
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.ThrivingInLearningV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) 0.020 (0.087) 0.233 .816 [-0.150, 0.190]
## 4.006 (Mean) -0.026 (0.061) -0.430 .667 [-0.147, 0.094]
## 5.372 (+ SD) -0.073 (0.087) -0.841 .401 [-0.243, 0.097]
## ───────────────────────────────────────────────────────────────────────────WP.LearningBehavior.Sb10=PROCESS(data2, y="WP.LearningBehaviorV", x="W.X10", mods="BA.AIInteractionQualityV",covs=c("W.X01","W.X01BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X01, W.X01BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X01 + W.X01BA.AIInteractionQualityV + W.X10*BA.AIInteractionQualityV + (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.LearningBehaviorV (2) WP.LearningBehaviorV
## ──────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.549 *** 1.676 ***
## (0.123) (0.348)
## W.X01 -0.320 -0.075
## (0.188) (0.217)
## W.X01BA.AIInteractionQualityV 0.084 0.023
## (0.043) (0.051)
## W.X10 0.124 0.050
## (0.068) (0.212)
## BA.AIInteractionQualityV 0.467 ***
## (0.082)
## W.X10:BA.AIInteractionQualityV 0.018
## (0.050)
## ──────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.155
## Conditional R^2 0.725 0.730
## AIC 3008.031 2985.372
## BIC 3061.772 3048.884
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.076 1.677
## Var: B.ID W.X10 0.002 0.002
## Var: B.ID W.X01 0.045 0.037
## Cov: B.ID (Intercept) W.X10 -0.008 -0.024
## Cov: B.ID (Intercept) W.X01 -0.143 -0.090
## Cov: B.ID W.X10 W.X01 -0.009 -0.007
## Var: Residual 0.757 0.758
## ──────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X10 * BA.AIInteractionQualityV 0.14 1 643 .712
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.LearningBehaviorV" (Y)
## ──────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) 0.099 (0.097) 1.024 .306 [-0.090, 0.288]
## 4.006 (Mean) 0.124 (0.068) 1.818 .069 . [-0.010, 0.258]
## 5.372 (+ SD) 0.149 (0.097) 1.547 .122 [-0.040, 0.339]
## ──────────────────────────────────────────────────────────────────────────WP.SocialLearning.Sb10=PROCESS(data2, y="WP.SocialLearningV", x="W.X10", mods="BA.AIInteractionQualityV",covs=c("W.X01","W.X01BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X01, W.X01BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X01 + W.X01BA.AIInteractionQualityV + W.X10*BA.AIInteractionQualityV + (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.SocialLearningV (2) WP.SocialLearningV
## ──────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.656 *** 1.986 ***
## (0.109) (0.309)
## W.X01 0.137 0.065
## (0.188) (0.211)
## W.X01BA.AIInteractionQualityV -0.016 0.002
## (0.044) (0.050)
## W.X10 0.115 -0.477 *
## (0.069) (0.211)
## BA.AIInteractionQualityV 0.417 ***
## (0.073)
## W.X10:BA.AIInteractionQualityV 0.148 **
## (0.050)
## ──────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.175
## Conditional R^2 0.688 0.689
## AIC 2965.530 2921.419
## BIC 3019.270 2984.931
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.566 1.253
## Var: B.ID W.X10 0.030 0.017
## Var: B.ID W.X01 0.015 0.012
## Cov: B.ID (Intercept) W.X10 0.045 -0.083
## Cov: B.ID (Intercept) W.X01 0.037 0.020
## Cov: B.ID W.X10 W.X01 -0.019 -0.013
## Var: Residual 0.742 0.740
## ──────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X10 * BA.AIInteractionQualityV 8.78 1 539 .003 **
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.SocialLearningV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) -0.087 (0.096) -0.902 .368 [-0.276, 0.102]
## 4.006 (Mean) 0.115 (0.068) 1.688 .092 . [-0.018, 0.249]
## 5.372 (+ SD) 0.317 (0.096) 3.289 .001 ** [ 0.128, 0.506]
## ───────────────────────────────────────────────────────────────────────────WP.IndependentObservationBasedSocialLearning.Sb10=PROCESS(data2, y="WP.IndependentObservationBasedSocialLearningV", x="W.X10", mods="BA.AIInteractionQualityV",covs=c("W.X01","W.X01BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.IndependentObservationBasedSocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X01, W.X01BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.IndependentObservationBasedSocialLearningV ~ W.X01 + W.X01BA.AIInteractionQualityV + W.X10*BA.AIInteractionQualityV + (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.IndependentObservationBasedSocialLearningV (2) WP.IndependentObservationBasedSocialLearningV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.695 *** 2.096 ***
## (0.121) (0.353)
## W.X01 0.144 0.054
## (0.216) (0.247)
## W.X01BA.AIInteractionQualityV -0.016 0.006
## (0.050) (0.058)
## W.X10 0.172 * -0.454
## (0.081) (0.248)
## BA.AIInteractionQualityV 0.399 ***
## (0.083)
## W.X10:BA.AIInteractionQualityV 0.156 **
## (0.059)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.133
## Conditional R^2 0.652 0.653
## AIC 3259.030 3227.634
## BIC 3312.771 3291.146
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.878 1.593
## Var: B.ID W.X10 0.034 0.004
## Var: B.ID W.X01 0.001 0.000
## Cov: B.ID (Intercept) W.X10 0.043 -0.082
## Cov: B.ID (Intercept) W.X01 0.042 0.022
## Cov: B.ID W.X10 W.X01 -0.001 -0.001
## Var: Residual 1.039 1.038
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.IndependentObservationBasedSocialLearningV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X10 * BA.AIInteractionQualityV 7.12 1 783 .008 **
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.IndependentObservationBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) -0.042 (0.113) -0.368 .713 [-0.263, 0.180]
## 4.006 (Mean) 0.172 (0.080) 2.148 .032 * [ 0.015, 0.328]
## 5.372 (+ SD) 0.385 (0.113) 3.406 <.001 *** [ 0.164, 0.607]
## ───────────────────────────────────────────────────────────────────────────WP.AdviceThinkingBasedSocialLearning.Sb10=PROCESS(data2, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X10", mods="BA.AIInteractionQualityV",covs=c("W.X01","W.X01BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X01, W.X01BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X01 + W.X01BA.AIInteractionQualityV + W.X10*BA.AIInteractionQualityV + (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.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.617 *** 1.877 ***
## (0.111) (0.315)
## W.X01 0.062 0.076
## (0.229) (0.260)
## W.X01BA.AIInteractionQualityV 0.001 -0.002
## (0.053) (0.061)
## W.X10 0.058 -0.500 *
## (0.082) (0.250)
## BA.AIInteractionQualityV 0.434 ***
## (0.074)
## W.X10:BA.AIInteractionQualityV 0.139 *
## (0.059)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.162
## Conditional R^2 0.608 0.608
## AIC 3264.496 3223.892
## BIC 3318.236 3287.404
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.489 1.153
## Var: B.ID W.X10 0.038 0.000
## Var: B.ID W.X01 0.057 0.085
## Cov: B.ID (Intercept) W.X10 0.100 -0.024
## Cov: B.ID (Intercept) W.X01 0.084 0.066
## Cov: B.ID W.X10 W.X01 -0.035 -0.001
## Var: Residual 1.060 1.062
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X10 * BA.AIInteractionQualityV 5.56 1 647 .019 *
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) -0.132 (0.115) -1.152 .250 [-0.357, 0.093]
## 4.006 (Mean) 0.058 (0.081) 0.719 .473 [-0.101, 0.217]
## 5.372 (+ SD) 0.249 (0.115) 2.169 .031 * [ 0.024, 0.474]
## ───────────────────────────────────────────────────────────────────────────5.2.2 AI VS Self
WA.LearningFromOperationalFailure.Sb01=PROCESS(data2, y="WA.LearningFromOperationalFailureV", x="W.X01", mods="BA.AIInteractionQualityV",covs=c("W.X10","W.X10BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromOperationalFailureV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X10, W.X10BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromOperationalFailureV ~ W.X10 + W.X10BA.AIInteractionQualityV + W.X01*BA.AIInteractionQualityV + (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) WA.LearningFromOperationalFailureV (2) WA.LearningFromOperationalFailureV
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.363 *** 3.019 ***
## (0.109) (0.319)
## W.X10 0.138 0.343
## (0.190) (0.224)
## W.X10BA.AIInteractionQualityV -0.006 -0.058
## (0.044) (0.053)
## W.X01 0.146 * 0.043
## (0.072) (0.223)
## BA.AIInteractionQualityV 0.336 ***
## (0.075)
## W.X01:BA.AIInteractionQualityV 0.026
## (0.053)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.091
## Conditional R^2 0.621 0.622
## AIC 3026.634 3013.371
## BIC 3080.375 3076.883
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.502 1.301
## Var: B.ID W.X10 0.011 0.007
## Var: B.ID W.X01 0.003 0.006
## Cov: B.ID (Intercept) W.X10 -0.127 -0.097
## Cov: B.ID (Intercept) W.X01 -0.070 -0.088
## Cov: B.ID W.X10 W.X01 0.006 0.007
## Var: Residual 0.841 0.840
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromOperationalFailureV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X01 * BA.AIInteractionQualityV 0.24 1 763 .626
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.LearningFromOperationalFailureV" (Y)
## ──────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) 0.111 (0.102) 1.085 .278 [-0.089, 0.310]
## 4.006 (Mean) 0.146 (0.072) 2.022 .044 * [ 0.004, 0.287]
## 5.372 (+ SD) 0.181 (0.102) 1.774 .076 . [-0.019, 0.381]
## ──────────────────────────────────────────────────────────────────────────WA.LearningFromErrors.Sb01=PROCESS(data2, y="WA.LearningFromErrorsV", x="W.X01", mods="BA.AIInteractionQualityV",covs=c("W.X10","W.X10BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X10, W.X10BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X10 + W.X10BA.AIInteractionQualityV + W.X01*BA.AIInteractionQualityV + (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) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.163 *** 3.006 ***
## (0.118) (0.355)
## W.X10 0.178 0.132
## (0.186) (0.211)
## W.X10BA.AIInteractionQualityV -0.035 -0.024
## (0.043) (0.050)
## W.X01 0.022 -0.425 *
## (0.070) (0.214)
## BA.AIInteractionQualityV 0.289 ***
## (0.084)
## W.X01:BA.AIInteractionQualityV 0.112 *
## (0.051)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.074
## Conditional R^2 0.718 0.718
## AIC 2996.129 2984.423
## BIC 3049.870 3047.935
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.904 1.762
## Var: B.ID W.X10 0.008 0.009
## Var: B.ID W.X01 0.040 0.028
## Cov: B.ID (Intercept) W.X10 -0.033 -0.043
## Cov: B.ID (Intercept) W.X01 0.008 -0.059
## Cov: B.ID W.X10 W.X01 -0.017 -0.013
## Var: Residual 0.749 0.748
## ──────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X01 * BA.AIInteractionQualityV 4.88 1 316 .028 *
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.LearningFromErrorsV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) -0.130 (0.098) -1.334 .183 [-0.322, 0.061]
## 4.006 (Mean) 0.022 (0.069) 0.322 .747 [-0.113, 0.157]
## 5.372 (+ SD) 0.175 (0.098) 1.790 .074 . [-0.017, 0.366]
## ───────────────────────────────────────────────────────────────────────────WA.ThrivingInLearning.Sb01=PROCESS(data2, y="WA.ThrivingInLearningV", x="W.X01", mods="BA.AIInteractionQualityV",covs=c("W.X10","W.X10BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ThrivingInLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X10, W.X10BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ThrivingInLearningV ~ W.X10 + W.X10BA.AIInteractionQualityV + W.X01*BA.AIInteractionQualityV + (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) WA.ThrivingInLearningV (2) WA.ThrivingInLearningV
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.291 *** 2.791 ***
## (0.109) (0.317)
## W.X10 -0.027 0.110
## (0.170) (0.190)
## W.X10BA.AIInteractionQualityV 0.000 -0.034
## (0.040) (0.045)
## W.X01 0.009 0.023
## (0.069) (0.216)
## BA.AIInteractionQualityV 0.374 ***
## (0.075)
## W.X01:BA.AIInteractionQualityV -0.004
## (0.051)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.113
## Conditional R^2 0.716 0.717
## AIC 2829.159 2812.686
## BIC 2882.900 2876.198
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.644 1.392
## Var: B.ID W.X10 0.001 0.000
## Var: B.ID W.X01 0.173 0.180
## Cov: B.ID (Intercept) W.X10 -0.036 -0.015
## Cov: B.ID (Intercept) W.X01 -0.195 -0.197
## Cov: B.ID W.X10 W.X01 0.004 0.008
## Var: Residual 0.614 0.614
## ──────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ThrivingInLearningV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X01 * BA.AIInteractionQualityV 0.00 1 189 .945
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.ThrivingInLearningV" (Y)
## ──────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) 0.013 (0.099) 0.136 .892 [-0.180, 0.207]
## 4.006 (Mean) 0.009 (0.070) 0.123 .902 [-0.128, 0.145]
## 5.372 (+ SD) 0.004 (0.099) 0.039 .969 [-0.190, 0.197]
## ──────────────────────────────────────────────────────────────────────────WP.LearningBehavior.Sb01=PROCESS(data2, y="WP.LearningBehaviorV", x="W.X01", mods="BA.AIInteractionQualityV",covs=c("W.X10","W.X10BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X10, W.X10BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X10 + W.X10BA.AIInteractionQualityV + W.X01*BA.AIInteractionQualityV + (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.LearningBehaviorV (2) WP.LearningBehaviorV
## ──────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.549 *** 1.676 ***
## (0.123) (0.348)
## W.X10 -0.105 0.050
## (0.186) (0.212)
## W.X10BA.AIInteractionQualityV 0.057 0.018
## (0.043) (0.050)
## W.X01 0.017 -0.075
## (0.070) (0.217)
## BA.AIInteractionQualityV 0.467 ***
## (0.082)
## W.X01:BA.AIInteractionQualityV 0.023
## (0.051)
## ──────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.155
## Conditional R^2 0.726 0.730
## AIC 3009.655 2985.372
## BIC 3063.395 3048.884
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.075 1.677
## Var: B.ID W.X10 0.004 0.002
## Var: B.ID W.X01 0.037 0.037
## Cov: B.ID (Intercept) W.X10 -0.057 -0.024
## Cov: B.ID (Intercept) W.X01 -0.069 -0.090
## Cov: B.ID W.X10 W.X01 -0.008 -0.007
## Var: Residual 0.758 0.758
## ──────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X01 * BA.AIInteractionQualityV 0.20 1 309 .656
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.LearningBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) -0.014 (0.099) -0.145 .885 [-0.209, 0.180]
## 4.006 (Mean) 0.017 (0.070) 0.241 .810 [-0.120, 0.154]
## 5.372 (+ SD) 0.048 (0.099) 0.485 .628 [-0.146, 0.242]
## ───────────────────────────────────────────────────────────────────────────WP.SocialLearning.Sb01=PROCESS(data2, y="WP.SocialLearningV", x="W.X01", mods="BA.AIInteractionQualityV",covs=c("W.X10","W.X10BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X10, W.X10BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X10 + W.X10BA.AIInteractionQualityV + W.X01*BA.AIInteractionQualityV + (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.SocialLearningV (2) WP.SocialLearningV
## ──────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.656 *** 1.986 ***
## (0.109) (0.309)
## W.X10 -0.801 *** -0.477 *
## (0.181) (0.211)
## W.X10BA.AIInteractionQualityV 0.229 *** 0.148 **
## (0.042) (0.050)
## W.X01 0.073 0.065
## (0.068) (0.211)
## BA.AIInteractionQualityV 0.417 ***
## (0.073)
## W.X01:BA.AIInteractionQualityV 0.002
## (0.050)
## ──────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.015 0.175
## Conditional R^2 0.675 0.689
## AIC 2942.661 2921.419
## BIC 2996.402 2984.931
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.569 1.253
## Var: B.ID W.X10 0.026 0.017
## Var: B.ID W.X01 0.012 0.012
## Cov: B.ID (Intercept) W.X10 -0.143 -0.083
## Cov: B.ID (Intercept) W.X01 0.022 0.020
## Cov: B.ID W.X10 W.X01 -0.014 -0.013
## Var: Residual 0.739 0.740
## ──────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X01 * BA.AIInteractionQualityV 0.00 1 553 .970
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.SocialLearningV" (Y)
## ──────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) 0.070 (0.096) 0.732 .465 [-0.118, 0.259]
## 4.006 (Mean) 0.073 (0.068) 1.073 .284 [-0.060, 0.206]
## 5.372 (+ SD) 0.075 (0.096) 0.785 .433 [-0.113, 0.264]
## ──────────────────────────────────────────────────────────────────────────WP.IndependentObservationBasedSocialLearning.Sb01=PROCESS(data2, y="WP.IndependentObservationBasedSocialLearningV", x="W.X01", mods="BA.AIInteractionQualityV",covs=c("W.X10","W.X10BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.IndependentObservationBasedSocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X10, W.X10BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.IndependentObservationBasedSocialLearningV ~ W.X10 + W.X10BA.AIInteractionQualityV + W.X01*BA.AIInteractionQualityV + (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.IndependentObservationBasedSocialLearningV (2) WP.IndependentObservationBasedSocialLearningV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.695 *** 2.096 ***
## (0.121) (0.353)
## W.X10 -0.744 *** -0.454
## (0.210) (0.248)
## W.X10BA.AIInteractionQualityV 0.229 *** 0.156 **
## (0.048) (0.059)
## W.X01 0.078 0.054
## (0.080) (0.247)
## BA.AIInteractionQualityV 0.399 ***
## (0.083)
## W.X01:BA.AIInteractionQualityV 0.006
## (0.058)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.013 0.133
## Conditional R^2 0.641 0.653
## AIC 3240.974 3227.634
## BIC 3294.714 3291.146
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.880 1.593
## Var: B.ID W.X10 0.009 0.004
## Var: B.ID W.X01 0.000 0.000
## Cov: B.ID (Intercept) W.X10 -0.133 -0.082
## Cov: B.ID (Intercept) W.X01 0.026 0.022
## Cov: B.ID W.X10 W.X01 -0.002 -0.001
## Var: Residual 1.038 1.038
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.IndependentObservationBasedSocialLearningV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X01 * BA.AIInteractionQualityV 0.01 1 809 .919
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.IndependentObservationBasedSocialLearningV" (Y)
## ──────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) 0.070 (0.113) 0.621 .535 [-0.151, 0.291]
## 4.006 (Mean) 0.078 (0.080) 0.980 .327 [-0.078, 0.235]
## 5.372 (+ SD) 0.086 (0.113) 0.765 .444 [-0.135, 0.308]
## ──────────────────────────────────────────────────────────────────────────WP.AdviceThinkingBasedSocialLearning.Sb01=PROCESS(data2, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X01", mods="BA.AIInteractionQualityV",covs=c("W.X10","W.X10BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X10, W.X10BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X10 + W.X10BA.AIInteractionQualityV + W.X01*BA.AIInteractionQualityV + (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.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.617 *** 1.877 ***
## (0.111) (0.315)
## W.X10 -0.908 *** -0.500 *
## (0.217) (0.250)
## W.X10BA.AIInteractionQualityV 0.241 *** 0.139 *
## (0.050) (0.059)
## W.X01 0.067 0.076
## (0.083) (0.260)
## BA.AIInteractionQualityV 0.434 ***
## (0.074)
## W.X01:BA.AIInteractionQualityV -0.002
## (0.061)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.014 0.162
## Conditional R^2 0.593 0.608
## AIC 3246.811 3223.892
## BIC 3300.552 3287.404
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.488 1.153
## Var: B.ID W.X10 0.025 0.000
## Var: B.ID W.X01 0.050 0.085
## Cov: B.ID (Intercept) W.X10 -0.096 -0.024
## Cov: B.ID (Intercept) W.X01 0.086 0.066
## Cov: B.ID W.X10 W.X01 -0.034 -0.001
## Var: Residual 1.060 1.062
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X01 * BA.AIInteractionQualityV 0.00 1 262 .971
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ──────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) 0.071 (0.119) 0.594 .553 [-0.162, 0.303]
## 4.006 (Mean) 0.067 (0.084) 0.804 .422 [-0.097, 0.232]
## 5.372 (+ SD) 0.064 (0.119) 0.543 .588 [-0.168, 0.297]
## ──────────────────────────────────────────────────────────────────────────6 BA.EffectivenessV
6.1 Study 1
WA.LearningFromOperationalFailure.S1=PROCESS(data1, y="WA.LearningFromOperationalFailureV", x="W.X", mods="BA.EffectivenessV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromOperationalFailureV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromOperationalFailureV ~ W.X*BA.EffectivenessV + (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) WA.LearningFromOperationalFailureV (2) WA.LearningFromOperationalFailureV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.779 *** 4.082 ***
## (0.085) (0.251)
## W.X 0.139 -0.189
## (0.073) (0.219)
## BA.EffectivenessV 0.178 **
## (0.060)
## W.X:BA.EffectivenessV 0.084
## (0.053)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.061
## Conditional R^2 0.458 0.463
## AIC 2057.038 2049.337
## BIC 2084.028 2085.323
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 0.758 0.707
## Var: B.ID W.X 0.001 0.005
## Cov: B.ID (Intercept) W.X -0.023 -0.058
## Var: Residual 0.875 0.871
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromOperationalFailureV" (Y)
## ───────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────
## W.X * BA.EffectivenessV 2.52 1 483 .113
## ───────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.LearningFromOperationalFailureV" (Y)
## ───────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────
## 2.544 (- SD) 0.024 (0.103) 0.233 .816 [-0.177, 0.225]
## 3.920 (Mean) 0.139 (0.073) 1.918 .056 . [-0.003, 0.282]
## 5.296 (+ SD) 0.255 (0.103) 2.479 .014 * [ 0.053, 0.456]
## ───────────────────────────────────────────────────────────────────WA.LearningFromErrors.S1=PROCESS(data1, y="WA.LearningFromErrorsV", x="W.X", mods="BA.EffectivenessV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X*BA.EffectivenessV + (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) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.291 *** 2.984 ***
## (0.104) (0.297)
## W.X 0.031 0.132
## (0.075) (0.226)
## BA.EffectivenessV 0.333 ***
## (0.072)
## W.X:BA.EffectivenessV -0.026
## (0.054)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.091
## Conditional R^2 0.612 0.613
## AIC 2122.701 2111.213
## BIC 2149.690 2147.199
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.395 1.193
## Var: B.ID W.X 0.094 0.098
## Cov: B.ID (Intercept) W.X -0.131 -0.118
## Var: Residual 0.832 0.832
## ─────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ───────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────
## W.X * BA.EffectivenessV 0.22 1 164 .637
## ───────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.LearningFromErrorsV" (Y)
## ────────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────
## 2.544 (- SD) 0.066 (0.106) 0.626 .532 [-0.141, 0.274]
## 3.920 (Mean) 0.031 (0.075) 0.412 .681 [-0.116, 0.178]
## 5.296 (+ SD) -0.005 (0.106) -0.043 .965 [-0.212, 0.203]
## ────────────────────────────────────────────────────────────────────WA.ThrivingInLearning.S1=PROCESS(data1, y="WA.ThrivingInLearningV", x="W.X", mods="BA.EffectivenessV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)####
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ThrivingInLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ThrivingInLearningV ~ W.X*BA.EffectivenessV + (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) WA.ThrivingInLearningV (2) WA.ThrivingInLearningV
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.702 *** 3.235 ***
## (0.093) (0.255)
## W.X -0.058 0.464 *
## (0.062) (0.185)
## BA.EffectivenessV 0.374 ***
## (0.061)
## W.X:BA.EffectivenessV -0.133 **
## (0.045)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.116
## Conditional R^2 0.616 0.620
## AIC 1920.298 1898.828
## BIC 1947.288 1934.814
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.132 0.878
## Var: B.ID W.X 0.018 0.004
## Cov: B.ID (Intercept) W.X -0.142 -0.057
## Var: Residual 0.624 0.619
## ─────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ThrivingInLearningV" (Y)
## ───────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────
## W.X * BA.EffectivenessV 8.93 1 482 .003 **
## ───────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.ThrivingInLearningV" (Y)
## ─────────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────
## 2.544 (- SD) 0.125 (0.087) 1.447 .149 [-0.044, 0.295]
## 3.920 (Mean) -0.058 (0.061) -0.945 .345 [-0.178, 0.062]
## 5.296 (+ SD) -0.241 (0.087) -2.782 .006 ** [-0.411, -0.071]
## ─────────────────────────────────────────────────────────────────────WP.LearningBehavior.S1=PROCESS(data1, y="WP.LearningBehaviorV", x="W.X", mods="BA.EffectivenessV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X*BA.EffectivenessV + (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.LearningBehaviorV (2) WP.LearningBehaviorV
## ─────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.648 *** 2.188 ***
## (0.111) (0.315)
## W.X -0.056 0.070
## (0.093) (0.281)
## BA.EffectivenessV 0.372 ***
## (0.076)
## W.X:BA.EffectivenessV -0.032
## (0.068)
## ─────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.090
## Conditional R^2 0.466 0.467
## AIC 2385.192 2369.285
## BIC 2412.181 2405.272
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.343 1.087
## Var: B.ID W.X 0.008 0.006
## Cov: B.ID (Intercept) W.X -0.103 -0.081
## Var: Residual 1.426 1.429
## ─────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ───────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────
## W.X * BA.EffectivenessV 0.23 1 486 .635
## ───────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.LearningBehaviorV" (Y)
## ────────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────
## 2.544 (- SD) -0.012 (0.132) -0.088 .930 [-0.269, 0.246]
## 3.920 (Mean) -0.056 (0.093) -0.599 .549 [-0.238, 0.127]
## 5.296 (+ SD) -0.100 (0.132) -0.759 .448 [-0.358, 0.158]
## ────────────────────────────────────────────────────────────────────WP.SocialLearning.S1=PROCESS(data1, y="WP.SocialLearningV", x="W.X", mods="BA.EffectivenessV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)###
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X*BA.EffectivenessV + (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.SocialLearningV (2) WP.SocialLearningV
## ─────────────────────────────────────────────────────────────────────────
## (Intercept) 3.759 *** 1.951 ***
## (0.104) (0.278)
## W.X -0.178 * -0.061
## (0.079) (0.240)
## BA.EffectivenessV 0.461 ***
## (0.067)
## W.X:BA.EffectivenessV -0.030
## (0.058)
## ─────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.004 0.172
## Conditional R^2 0.535 0.536
## AIC 2211.535 2173.141
## BIC 2238.525 2209.128
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.282 0.884
## Var: B.ID W.X 0.007 0.006
## Cov: B.ID (Intercept) W.X -0.097 -0.071
## Var: Residual 1.040 1.042
## ─────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ───────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────
## W.X * BA.EffectivenessV 0.27 1 484 .605
## ───────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.SocialLearningV" (Y)
## ─────────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────
## 2.544 (- SD) -0.137 (0.112) -1.222 .222 [-0.358, 0.083]
## 3.920 (Mean) -0.178 (0.079) -2.247 .025 * [-0.334, -0.023]
## 5.296 (+ SD) -0.220 (0.112) -1.954 .051 . [-0.440, 0.001]
## ─────────────────────────────────────────────────────────────────────WP.IndependentObservationBasedSocialLearning.S1=PROCESS(data1, y="WP.IndependentObservationBasedSocialLearningV", x="W.X", mods="BA.EffectivenessV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.IndependentObservationBasedSocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.IndependentObservationBasedSocialLearningV ~ W.X*BA.EffectivenessV + (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.IndependentObservationBasedSocialLearningV (2) WP.IndependentObservationBasedSocialLearningV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.736 *** 2.073 ***
## (0.114) (0.318)
## W.X -0.170 -0.282
## (0.094) (0.286)
## BA.EffectivenessV 0.424 ***
## (0.077)
## W.X:BA.EffectivenessV 0.029
## (0.069)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.132
## Conditional R^2 0.478 0.480
## AIC 2410.168 2380.639
## BIC 2437.158 2416.625
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.438 1.103
## Var: B.ID W.X 0.007 0.015
## Cov: B.ID (Intercept) W.X -0.104 -0.128
## Var: Residual 1.469 1.468
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.IndependentObservationBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────
## W.X * BA.EffectivenessV 0.17 1 473 .677
## ───────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.IndependentObservationBasedSocialLearningV" (Y)
## ────────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────
## 2.544 (- SD) -0.210 (0.134) -1.567 .118 [-0.472, 0.053]
## 3.920 (Mean) -0.170 (0.095) -1.800 .072 . [-0.355, 0.015]
## 5.296 (+ SD) -0.131 (0.134) -0.978 .329 [-0.393, 0.131]
## ────────────────────────────────────────────────────────────────────WP.AdviceThinkingBasedSocialLearning.S1=PROCESS(data1, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X", mods="BA.EffectivenessV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)####
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X*BA.EffectivenessV + (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.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.782 *** 1.828 ***
## (0.112) (0.300)
## W.X -0.187 * 0.160
## (0.091) (0.275)
## BA.EffectivenessV 0.498 ***
## (0.072)
## W.X:BA.EffectivenessV -0.088
## (0.066)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.152
## Conditional R^2 0.483 0.484
## AIC 2364.000 2328.403
## BIC 2390.990 2364.389
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.418 0.956
## Var: B.ID W.X 0.018 0.007
## Cov: B.ID (Intercept) W.X -0.159 -0.080
## Var: Residual 1.365 1.366
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────
## W.X * BA.EffectivenessV 1.79 1 484 .182
## ───────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ─────────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────
## 2.544 (- SD) -0.065 (0.129) -0.506 .613 [-0.317, 0.187]
## 3.920 (Mean) -0.187 (0.091) -2.053 .041 * [-0.365, -0.008]
## 5.296 (+ SD) -0.308 (0.129) -2.397 .017 * [-0.561, -0.056]
## ─────────────────────────────────────────────────────────────────────6.2 Study 2
6.2.1 AI VS Control
WA.LearningFromOperationalFailure.Sb10=PROCESS(data2, y="WA.LearningFromOperationalFailureV", x="W.X10", mods="BA.EffectivenessV",covs=c("W.X01","W.X01BA.EffectivenessV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromOperationalFailureV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : W.X01, W.X01BA.EffectivenessV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromOperationalFailureV ~ W.X01 + W.X01BA.EffectivenessV + W.X10*BA.EffectivenessV + (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) WA.LearningFromOperationalFailureV (2) WA.LearningFromOperationalFailureV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.363 *** 3.189 ***
## (0.109) (0.326)
## W.X01 -0.119 0.143
## (0.192) (0.225)
## W.X01BA.EffectivenessV 0.067 0.001
## (0.045) (0.053)
## W.X10 0.113 0.307
## (0.072) (0.225)
## BA.EffectivenessV 0.295 ***
## (0.077)
## W.X10:BA.EffectivenessV -0.049
## (0.054)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.066
## Conditional R^2 0.617 0.622
## AIC 3024.583 3021.510
## BIC 3078.323 3085.021
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.501 1.353
## Var: B.ID W.X10 0.011 0.008
## Var: B.ID W.X01 0.007 0.004
## Cov: B.ID (Intercept) W.X10 -0.131 -0.107
## Cov: B.ID (Intercept) W.X01 -0.103 -0.071
## Cov: B.ID W.X10 W.X01 0.009 0.006
## Var: Residual 0.842 0.842
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromOperationalFailureV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X10 * BA.EffectivenessV 0.83 1 747 .362
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.LearningFromOperationalFailureV" (Y)
## ───────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────
## 2.634 (- SD) 0.179 (0.102) 1.748 .081 . [-0.022, 0.379]
## 3.982 (Mean) 0.113 (0.072) 1.561 .119 [-0.029, 0.254]
## 5.329 (+ SD) 0.047 (0.102) 0.459 .647 [-0.153, 0.247]
## ───────────────────────────────────────────────────────────────────WA.LearningFromErrors.Sb10=PROCESS(data2, y="WA.LearningFromErrorsV", x="W.X10", mods="BA.EffectivenessV",covs=c("W.X01","W.X01BA.EffectivenessV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : W.X01, W.X01BA.EffectivenessV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X01 + W.X01BA.EffectivenessV + W.X10*BA.EffectivenessV + (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) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.163 *** 2.845 ***
## (0.118) (0.354)
## W.X01 -0.515 ** -0.273
## (0.190) (0.218)
## W.X01BA.EffectivenessV 0.135 ** 0.074
## (0.044) (0.052)
## W.X10 0.038 0.244
## (0.068) (0.212)
## BA.EffectivenessV 0.331 ***
## (0.084)
## W.X10:BA.EffectivenessV -0.052
## (0.050)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.004 0.080
## Conditional R^2 0.711 0.716
## AIC 2988.414 2985.032
## BIC 3042.155 3048.543
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.904 1.720
## Var: B.ID W.X10 0.007 0.000
## Var: B.ID W.X01 0.039 0.046
## Cov: B.ID (Intercept) W.X10 -0.052 -0.026
## Cov: B.ID (Intercept) W.X01 -0.073 -0.048
## Cov: B.ID W.X10 W.X01 -0.012 0.001
## Var: Residual 0.749 0.752
## ───────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X10 * BA.EffectivenessV 1.06 1 647 .304
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.LearningFromErrorsV" (Y)
## ────────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────
## 2.634 (- SD) 0.107 (0.096) 1.119 .264 [-0.081, 0.296]
## 3.982 (Mean) 0.038 (0.068) 0.553 .580 [-0.096, 0.171]
## 5.329 (+ SD) -0.032 (0.096) -0.337 .737 [-0.221, 0.156]
## ────────────────────────────────────────────────────────────────────WA.ThrivingInLearning.Sb10=PROCESS(data2, y="WA.ThrivingInLearningV", x="W.X10", mods="BA.EffectivenessV",covs=c("W.X01","W.X01BA.EffectivenessV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ThrivingInLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : W.X01, W.X01BA.EffectivenessV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ThrivingInLearningV ~ W.X01 + W.X01BA.EffectivenessV + W.X10*BA.EffectivenessV + (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) WA.ThrivingInLearningV (2) WA.ThrivingInLearningV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.291 *** 2.705 ***
## (0.109) (0.316)
## W.X01 -0.242 0.098
## (0.188) (0.218)
## W.X01BA.EffectivenessV 0.063 -0.022
## (0.044) (0.052)
## W.X10 -0.026 -0.019
## (0.061) (0.192)
## BA.EffectivenessV 0.398 ***
## (0.075)
## W.X10:BA.EffectivenessV -0.002
## (0.046)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.128
## Conditional R^2 0.713 0.717
## AIC 2827.224 2810.045
## BIC 2880.964 2873.557
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.645 1.365
## Var: B.ID W.X10 0.001 0.001
## Var: B.ID W.X01 0.193 0.179
## Cov: B.ID (Intercept) W.X10 -0.038 -0.037
## Cov: B.ID (Intercept) W.X01 -0.244 -0.183
## Cov: B.ID W.X10 W.X01 0.009 0.008
## Var: Residual 0.613 0.614
## ───────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ThrivingInLearningV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X10 * BA.EffectivenessV 0.00 1 642 .968
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.ThrivingInLearningV" (Y)
## ────────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────
## 2.634 (- SD) -0.024 (0.087) -0.275 .783 [-0.194, 0.146]
## 3.982 (Mean) -0.026 (0.061) -0.429 .668 [-0.147, 0.094]
## 5.329 (+ SD) -0.029 (0.087) -0.332 .740 [-0.199, 0.141]
## ────────────────────────────────────────────────────────────────────WP.LearningBehavior.Sb10=PROCESS(data2, y="WP.LearningBehaviorV", x="W.X10", mods="BA.EffectivenessV",covs=c("W.X01","W.X01BA.EffectivenessV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : W.X01, W.X01BA.EffectivenessV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X01 + W.X01BA.EffectivenessV + W.X10*BA.EffectivenessV + (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.LearningBehaviorV (2) WP.LearningBehaviorV
## ───────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.549 *** 1.594 ***
## (0.123) (0.348)
## W.X01 -0.004 0.122
## (0.190) (0.218)
## W.X01BA.EffectivenessV 0.005 -0.026
## (0.044) (0.052)
## W.X10 0.124 -0.112
## (0.068) (0.213)
## BA.EffectivenessV 0.491 ***
## (0.083)
## W.X10:BA.EffectivenessV 0.059
## (0.051)
## ───────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.165
## Conditional R^2 0.729 0.730
## AIC 3011.009 2980.557
## BIC 3064.749 3044.069
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.075 1.647
## Var: B.ID W.X10 0.002 0.002
## Var: B.ID W.X01 0.038 0.035
## Cov: B.ID (Intercept) W.X10 -0.007 -0.064
## Cov: B.ID (Intercept) W.X01 -0.072 -0.046
## Cov: B.ID W.X10 W.X01 -0.008 0.002
## Var: Residual 0.758 0.759
## ───────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X10 * BA.EffectivenessV 1.37 1 633 .242
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.LearningBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────
## 2.634 (- SD) 0.044 (0.097) 0.457 .648 [-0.145, 0.234]
## 3.982 (Mean) 0.124 (0.068) 1.818 .069 . [-0.010, 0.258]
## 5.329 (+ SD) 0.204 (0.097) 2.114 .035 * [ 0.015, 0.394]
## ───────────────────────────────────────────────────────────────────WP.SocialLearning.Sb10=PROCESS(data2, y="WP.SocialLearningV", x="W.X10", mods="BA.EffectivenessV",covs=c("W.X01","W.X01BA.EffectivenessV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : W.X01, W.X01BA.EffectivenessV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X01 + W.X01BA.EffectivenessV + W.X10*BA.EffectivenessV + (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.SocialLearningV (2) WP.SocialLearningV
## ───────────────────────────────────────────────────────────────────────────
## (Intercept) 3.656 *** 1.804 ***
## (0.109) (0.304)
## W.X01 0.142 0.124
## (0.189) (0.212)
## W.X01BA.EffectivenessV -0.017 -0.013
## (0.044) (0.050)
## W.X10 0.115 -0.389
## (0.069) (0.213)
## BA.EffectivenessV 0.465 ***
## (0.072)
## W.X10:BA.EffectivenessV 0.127 *
## (0.051)
## ───────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.197
## Conditional R^2 0.688 0.688
## AIC 2965.492 2916.999
## BIC 3019.233 2980.511
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.566 1.181
## Var: B.ID W.X10 0.030 0.018
## Var: B.ID W.X01 0.015 0.012
## Cov: B.ID (Intercept) W.X10 0.045 -0.070
## Cov: B.ID (Intercept) W.X01 0.039 0.034
## Cov: B.ID W.X10 W.X01 -0.019 -0.014
## Var: Residual 0.742 0.741
## ───────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X10 * BA.EffectivenessV 6.24 1 509 .013 *
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.SocialLearningV" (Y)
## ────────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────
## 2.634 (- SD) -0.056 (0.097) -0.575 .566 [-0.245, 0.134]
## 3.982 (Mean) 0.115 (0.068) 1.686 .092 . [-0.019, 0.249]
## 5.329 (+ SD) 0.286 (0.097) 2.958 .003 ** [ 0.096, 0.475]
## ────────────────────────────────────────────────────────────────────WP.IndependentObservationBasedSocialLearning.Sb10=PROCESS(data2, y="WP.IndependentObservationBasedSocialLearningV", x="W.X10", mods="BA.EffectivenessV",covs=c("W.X01","W.X01BA.EffectivenessV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.IndependentObservationBasedSocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : W.X01, W.X01BA.EffectivenessV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.IndependentObservationBasedSocialLearningV ~ W.X01 + W.X01BA.EffectivenessV + W.X10*BA.EffectivenessV + (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.IndependentObservationBasedSocialLearningV (2) WP.IndependentObservationBasedSocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.695 *** 1.822 ***
## (0.121) (0.346)
## W.X01 0.145 0.034
## (0.217) (0.248)
## W.X01BA.EffectivenessV -0.017 0.011
## (0.051) (0.059)
## W.X10 0.172 * -0.571 *
## (0.081) (0.250)
## BA.EffectivenessV 0.470 ***
## (0.082)
## W.X10:BA.EffectivenessV 0.187 **
## (0.059)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.181
## Conditional R^2 0.653 0.655
## AIC 3259.009 3209.045
## BIC 3312.750 3272.557
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.878 1.491
## Var: B.ID W.X10 0.033 0.012
## Var: B.ID W.X01 0.001 0.000
## Cov: B.ID (Intercept) W.X10 0.043 -0.134
## Cov: B.ID (Intercept) W.X01 0.044 0.016
## Cov: B.ID W.X10 W.X01 -0.001 -0.001
## Var: Residual 1.039 1.031
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.IndependentObservationBasedSocialLearningV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X10 * BA.EffectivenessV 9.86 1 736 .002 **
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.IndependentObservationBasedSocialLearningV" (Y)
## ────────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────
## 2.634 (- SD) -0.080 (0.113) -0.704 .482 [-0.301, 0.142]
## 3.982 (Mean) 0.172 (0.080) 2.147 .032 * [ 0.015, 0.329]
## 5.329 (+ SD) 0.423 (0.113) 3.739 <.001 *** [ 0.201, 0.645]
## ────────────────────────────────────────────────────────────────────WP.AdviceThinkingBasedSocialLearning.Sb10=PROCESS(data2, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X10", mods="BA.EffectivenessV",covs=c("W.X01","W.X01BA.EffectivenessV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : W.X01, W.X01BA.EffectivenessV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X01 + W.X01BA.EffectivenessV + W.X10*BA.EffectivenessV + (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.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.617 *** 1.786 ***
## (0.111) (0.314)
## W.X01 0.073 0.214
## (0.230) (0.263)
## W.X01BA.EffectivenessV -0.001 -0.037
## (0.054) (0.063)
## W.X10 0.058 -0.207
## (0.082) (0.253)
## BA.EffectivenessV 0.460 ***
## (0.075)
## W.X10:BA.EffectivenessV 0.067
## (0.060)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.149
## Conditional R^2 0.608 0.606
## AIC 3264.469 3231.672
## BIC 3318.210 3295.184
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.489 1.117
## Var: B.ID W.X10 0.038 0.002
## Var: B.ID W.X01 0.057 0.087
## Cov: B.ID (Intercept) W.X10 0.100 0.043
## Cov: B.ID (Intercept) W.X01 0.086 0.093
## Cov: B.ID W.X10 W.X01 -0.034 0.004
## Var: Residual 1.060 1.069
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X10 * BA.EffectivenessV 1.22 1 639 .269
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ────────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────
## 2.634 (- SD) -0.031 (0.116) -0.271 .787 [-0.258, 0.196]
## 3.982 (Mean) 0.058 (0.082) 0.712 .477 [-0.102, 0.219]
## 5.329 (+ SD) 0.148 (0.116) 1.277 .202 [-0.079, 0.375]
## ────────────────────────────────────────────────────────────────────6.2.2 AI VS Self
WA.LearningFromOperationalFailure.Sb01=PROCESS(data2, y="WA.LearningFromOperationalFailureV", x="W.X01", mods="BA.EffectivenessV",covs=c("W.X10","W.X10BA.EffectivenessV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)###
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromOperationalFailureV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : W.X10, W.X10BA.EffectivenessV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromOperationalFailureV ~ W.X10 + W.X10BA.EffectivenessV + W.X01*BA.EffectivenessV + (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) WA.LearningFromOperationalFailureV (2) WA.LearningFromOperationalFailureV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.363 *** 3.189 ***
## (0.109) (0.326)
## W.X10 0.089 0.307
## (0.191) (0.225)
## W.X10BA.EffectivenessV 0.006 -0.049
## (0.044) (0.054)
## W.X01 0.146 * 0.143
## (0.072) (0.225)
## BA.EffectivenessV 0.295 ***
## (0.077)
## W.X01:BA.EffectivenessV 0.001
## (0.053)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.066
## Conditional R^2 0.620 0.622
## AIC 3026.610 3021.510
## BIC 3080.350 3085.021
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.502 1.353
## Var: B.ID W.X10 0.012 0.008
## Var: B.ID W.X01 0.003 0.004
## Cov: B.ID (Intercept) W.X10 -0.134 -0.107
## Cov: B.ID (Intercept) W.X01 -0.071 -0.071
## Cov: B.ID W.X10 W.X01 0.006 0.006
## Var: Residual 0.842 0.842
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromOperationalFailureV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X01 * BA.EffectivenessV 0.00 1 780 .991
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.LearningFromOperationalFailureV" (Y)
## ───────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────
## 2.634 (- SD) 0.145 (0.102) 1.422 .156 [-0.055, 0.345]
## 3.982 (Mean) 0.146 (0.072) 2.023 .043 * [ 0.005, 0.287]
## 5.329 (+ SD) 0.147 (0.102) 1.438 .151 [-0.053, 0.346]
## ───────────────────────────────────────────────────────────────────WA.LearningFromErrors.Sb01=PROCESS(data2, y="WA.LearningFromErrorsV", x="W.X01", mods="BA.EffectivenessV",covs=c("W.X10","W.X10BA.EffectivenessV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : W.X10, W.X10BA.EffectivenessV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X10 + W.X10BA.EffectivenessV + W.X01*BA.EffectivenessV + (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) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.163 *** 2.845 ***
## (0.118) (0.354)
## W.X10 0.216 0.244
## (0.187) (0.212)
## W.X10BA.EffectivenessV -0.045 -0.052
## (0.044) (0.050)
## W.X01 0.022 -0.273
## (0.070) (0.218)
## BA.EffectivenessV 0.331 ***
## (0.084)
## W.X01:BA.EffectivenessV 0.074
## (0.052)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.080
## Conditional R^2 0.718 0.716
## AIC 2995.766 2985.032
## BIC 3049.506 3048.543
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.905 1.720
## Var: B.ID W.X10 0.007 0.000
## Var: B.ID W.X01 0.041 0.046
## Cov: B.ID (Intercept) W.X10 -0.026 -0.026
## Cov: B.ID (Intercept) W.X01 0.007 -0.048
## Cov: B.ID W.X10 W.X01 -0.017 0.001
## Var: Residual 0.749 0.752
## ───────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X01 * BA.EffectivenessV 2.03 1 267 .155
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.LearningFromErrorsV" (Y)
## ────────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────
## 2.634 (- SD) -0.078 (0.098) -0.790 .430 [-0.270, 0.115]
## 3.982 (Mean) 0.022 (0.069) 0.321 .749 [-0.114, 0.158]
## 5.329 (+ SD) 0.122 (0.098) 1.243 .215 [-0.070, 0.314]
## ────────────────────────────────────────────────────────────────────WA.ThrivingInLearning.Sb01=PROCESS(data2, y="WA.ThrivingInLearningV", x="W.X01", mods="BA.EffectivenessV",covs=c("W.X10","W.X10BA.EffectivenessV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ThrivingInLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : W.X10, W.X10BA.EffectivenessV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ThrivingInLearningV ~ W.X10 + W.X10BA.EffectivenessV + W.X01*BA.EffectivenessV + (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) WA.ThrivingInLearningV (2) WA.ThrivingInLearningV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.291 *** 2.705 ***
## (0.109) (0.316)
## W.X10 -0.220 -0.019
## (0.171) (0.192)
## W.X10BA.EffectivenessV 0.049 -0.002
## (0.040) (0.046)
## W.X01 0.009 0.098
## (0.070) (0.218)
## BA.EffectivenessV 0.398 ***
## (0.075)
## W.X01:BA.EffectivenessV -0.022
## (0.052)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.128
## Conditional R^2 0.713 0.717
## AIC 2827.896 2810.045
## BIC 2881.637 2873.557
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.643 1.365
## Var: B.ID W.X10 0.003 0.001
## Var: B.ID W.X01 0.175 0.179
## Cov: B.ID (Intercept) W.X10 -0.070 -0.037
## Cov: B.ID (Intercept) W.X01 -0.196 -0.183
## Cov: B.ID W.X10 W.X01 0.008 0.008
## Var: Residual 0.614 0.614
## ───────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ThrivingInLearningV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X01 * BA.EffectivenessV 0.19 1 175 .667
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.ThrivingInLearningV" (Y)
## ────────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────
## 2.634 (- SD) 0.039 (0.099) 0.392 .695 [-0.155, 0.232]
## 3.982 (Mean) 0.009 (0.070) 0.123 .902 [-0.128, 0.145]
## 5.329 (+ SD) -0.022 (0.099) -0.218 .828 [-0.215, 0.172]
## ────────────────────────────────────────────────────────────────────WP.LearningBehavior.Sb01=PROCESS(data2, y="WP.LearningBehaviorV", x="W.X01", mods="BA.EffectivenessV",covs=c("W.X10","W.X10BA.EffectivenessV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : W.X10, W.X10BA.EffectivenessV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X10 + W.X10BA.EffectivenessV + W.X01*BA.EffectivenessV + (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.LearningBehaviorV (2) WP.LearningBehaviorV
## ───────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.549 *** 1.594 ***
## (0.123) (0.348)
## W.X10 -0.413 * -0.112
## (0.184) (0.213)
## W.X10BA.EffectivenessV 0.135 ** 0.059
## (0.043) (0.051)
## W.X01 0.017 0.122
## (0.070) (0.218)
## BA.EffectivenessV 0.491 ***
## (0.083)
## W.X01:BA.EffectivenessV -0.026
## (0.052)
## ───────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.005 0.165
## Conditional R^2 0.722 0.730
## AIC 3003.266 2980.557
## BIC 3057.007 3044.069
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.074 1.647
## Var: B.ID W.X10 0.008 0.002
## Var: B.ID W.X01 0.032 0.035
## Cov: B.ID (Intercept) W.X10 -0.128 -0.064
## Cov: B.ID (Intercept) W.X01 -0.068 -0.046
## Cov: B.ID W.X10 W.X01 0.004 0.002
## Var: Residual 0.760 0.759
## ───────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X01 * BA.EffectivenessV 0.26 1 264 .612
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.LearningBehaviorV" (Y)
## ────────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────
## 2.634 (- SD) 0.052 (0.099) 0.530 .596 [-0.141, 0.246]
## 3.982 (Mean) 0.017 (0.070) 0.242 .809 [-0.120, 0.154]
## 5.329 (+ SD) -0.019 (0.099) -0.188 .851 [-0.212, 0.175]
## ────────────────────────────────────────────────────────────────────WP.SocialLearning.Sb01=PROCESS(data2, y="WP.SocialLearningV", x="W.X01", mods="BA.EffectivenessV",covs=c("W.X10","W.X10BA.EffectivenessV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : W.X10, W.X10BA.EffectivenessV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X10 + W.X10BA.EffectivenessV + W.X01*BA.EffectivenessV + (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.SocialLearningV (2) WP.SocialLearningV
## ───────────────────────────────────────────────────────────────────────────
## (Intercept) 3.656 *** 1.804 ***
## (0.109) (0.304)
## W.X10 -0.778 *** -0.389
## (0.182) (0.213)
## W.X10BA.EffectivenessV 0.224 *** 0.127 *
## (0.042) (0.051)
## W.X01 0.073 0.124
## (0.068) (0.212)
## BA.EffectivenessV 0.465 ***
## (0.072)
## W.X01:BA.EffectivenessV -0.013
## (0.050)
## ───────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.014 0.197
## Conditional R^2 0.674 0.688
## AIC 2944.980 2916.999
## BIC 2998.721 2980.511
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.566 1.181
## Var: B.ID W.X10 0.029 0.018
## Var: B.ID W.X01 0.010 0.012
## Cov: B.ID (Intercept) W.X10 -0.148 -0.070
## Cov: B.ID (Intercept) W.X01 0.025 0.034
## Cov: B.ID W.X10 W.X01 -0.015 -0.014
## Var: Residual 0.741 0.741
## ───────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X01 * BA.EffectivenessV 0.06 1 563 .800
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.SocialLearningV" (Y)
## ───────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────
## 2.634 (- SD) 0.090 (0.096) 0.937 .349 [-0.098, 0.279]
## 3.982 (Mean) 0.073 (0.068) 1.072 .284 [-0.060, 0.206]
## 5.329 (+ SD) 0.056 (0.096) 0.579 .563 [-0.133, 0.244]
## ───────────────────────────────────────────────────────────────────WP.IndependentObservationBasedSocialLearning.Sb01=PROCESS(data2, y="WP.IndependentObservationBasedSocialLearningV", x="W.X01", mods="BA.EffectivenessV",covs=c("W.X10","W.X10BA.EffectivenessV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.IndependentObservationBasedSocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : W.X10, W.X10BA.EffectivenessV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.IndependentObservationBasedSocialLearningV ~ W.X10 + W.X10BA.EffectivenessV + W.X01*BA.EffectivenessV + (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.IndependentObservationBasedSocialLearningV (2) WP.IndependentObservationBasedSocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.695 *** 1.822 ***
## (0.121) (0.346)
## W.X10 -0.977 *** -0.571 *
## (0.209) (0.250)
## W.X10BA.EffectivenessV 0.288 *** 0.187 **
## (0.048) (0.059)
## W.X01 0.078 0.034
## (0.080) (0.248)
## BA.EffectivenessV 0.470 ***
## (0.082)
## W.X01:BA.EffectivenessV 0.011
## (0.059)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.019 0.181
## Conditional R^2 0.638 0.655
## AIC 3232.266 3209.045
## BIC 3286.007 3272.557
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.884 1.491
## Var: B.ID W.X10 0.025 0.012
## Var: B.ID W.X01 0.000 0.000
## Cov: B.ID (Intercept) W.X10 -0.218 -0.134
## Cov: B.ID (Intercept) W.X01 0.026 0.016
## Cov: B.ID W.X10 W.X01 -0.003 -0.001
## Var: Residual 1.032 1.031
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.IndependentObservationBasedSocialLearningV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X01 * BA.EffectivenessV 0.04 1 810 .849
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.IndependentObservationBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────
## 2.634 (- SD) 0.063 (0.113) 0.561 .575 [-0.157, 0.284]
## 3.982 (Mean) 0.078 (0.080) 0.983 .326 [-0.078, 0.234]
## 5.329 (+ SD) 0.093 (0.113) 0.829 .407 [-0.127, 0.314]
## ───────────────────────────────────────────────────────────────────WP.AdviceThinkingBasedSocialLearning.Sb01=PROCESS(data2, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X01", mods="BA.EffectivenessV",covs=c("W.X10","W.X10BA.EffectivenessV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : W.X10, W.X10BA.EffectivenessV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X10 + W.X10BA.EffectivenessV + W.X01*BA.EffectivenessV + (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.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.617 *** 1.786 ***
## (0.111) (0.314)
## W.X10 -0.629 ** -0.207
## (0.223) (0.253)
## W.X10BA.EffectivenessV 0.173 *** 0.067
## (0.052) (0.060)
## W.X01 0.067 0.214
## (0.082) (0.263)
## BA.EffectivenessV 0.460 ***
## (0.075)
## W.X01:BA.EffectivenessV -0.037
## (0.063)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.007 0.149
## Conditional R^2 0.595 0.606
## AIC 3255.912 3231.672
## BIC 3309.653 3295.184
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.481 1.117
## Var: B.ID W.X10 0.038 0.002
## Var: B.ID W.X01 0.037 0.087
## Cov: B.ID (Intercept) W.X10 -0.035 0.043
## Cov: B.ID (Intercept) W.X01 0.094 0.093
## Cov: B.ID W.X10 W.X01 -0.036 0.004
## Var: Residual 1.064 1.069
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X01 * BA.EffectivenessV 0.35 1 264 .557
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────
## 2.634 (- SD) 0.117 (0.117) 1.001 .318 [-0.112, 0.346]
## 3.982 (Mean) 0.067 (0.083) 0.817 .415 [-0.094, 0.229]
## 5.329 (+ SD) 0.018 (0.117) 0.153 .878 [-0.211, 0.247]
## ───────────────────────────────────────────────────────────────────7 BA.PersonalControlV
7.1 Study 1
WA.LearningFromOperationalFailure.S1=PROCESS(data1, y="WA.LearningFromOperationalFailureV", x="W.X", mods="BA.PersonalControlV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromOperationalFailureV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromOperationalFailureV ~ W.X*BA.PersonalControlV + (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) WA.LearningFromOperationalFailureV (2) WA.LearningFromOperationalFailureV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.779 *** 5.840 ***
## (0.085) (0.271)
## W.X 0.139 -0.005
## (0.073) (0.243)
## BA.PersonalControlV -0.261 ***
## (0.064)
## W.X:BA.PersonalControlV 0.035
## (0.057)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.063
## Conditional R^2 0.458 0.460
## AIC 2057.038 2050.903
## BIC 2084.028 2086.889
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 0.758 0.653
## Var: B.ID W.X 0.001 0.000
## Cov: B.ID (Intercept) W.X -0.023 -0.009
## Var: Residual 0.875 0.876
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromOperationalFailureV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X * BA.PersonalControlV 0.39 1 496 .534
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.LearningFromOperationalFailureV" (Y)
## ─────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────
## 2.794 (- SD) 0.094 (0.103) 0.915 .361 [-0.107, 0.296]
## 4.072 (Mean) 0.139 (0.073) 1.917 .056 . [-0.003, 0.282]
## 5.350 (+ SD) 0.185 (0.103) 1.795 .073 . [-0.017, 0.386]
## ─────────────────────────────────────────────────────────────────────WA.LearningFromErrors.S1=PROCESS(data1, y="WA.LearningFromErrorsV", x="W.X", mods="BA.PersonalControlV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X*BA.PersonalControlV + (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) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.291 *** 5.583 ***
## (0.104) (0.334)
## W.X 0.031 -0.340
## (0.075) (0.249)
## BA.PersonalControlV -0.317 ***
## (0.078)
## W.X:BA.PersonalControlV 0.091
## (0.058)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.058
## Conditional R^2 0.612 0.614
## AIC 2122.701 2118.213
## BIC 2149.690 2154.199
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.395 1.240
## Var: B.ID W.X 0.094 0.086
## Cov: B.ID (Intercept) W.X -0.131 -0.086
## Var: Residual 0.832 0.832
## ─────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X * BA.PersonalControlV 2.45 1 164 .120
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.LearningFromErrorsV" (Y)
## ──────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────
## 2.794 (- SD) -0.086 (0.105) -0.813 .417 [-0.292, 0.121]
## 4.072 (Mean) 0.031 (0.074) 0.415 .678 [-0.115, 0.177]
## 5.350 (+ SD) 0.147 (0.105) 1.401 .163 [-0.059, 0.353]
## ──────────────────────────────────────────────────────────────────────WA.ThrivingInLearning.S1=PROCESS(data1, y="WA.ThrivingInLearningV", x="W.X", mods="BA.PersonalControlV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ThrivingInLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ThrivingInLearningV ~ W.X*BA.PersonalControlV + (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) WA.ThrivingInLearningV (2) WA.ThrivingInLearningV
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.702 *** 5.760 ***
## (0.093) (0.300)
## W.X -0.058 -0.295
## (0.062) (0.207)
## BA.PersonalControlV -0.260 ***
## (0.070)
## W.X:BA.PersonalControlV 0.058
## (0.049)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.055
## Conditional R^2 0.616 0.617
## AIC 1920.298 1918.763
## BIC 1947.288 1954.749
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.132 1.028
## Var: B.ID W.X 0.018 0.014
## Cov: B.ID (Intercept) W.X -0.142 -0.118
## Var: Residual 0.624 0.625
## ─────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ThrivingInLearningV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X * BA.PersonalControlV 1.43 1 451 .232
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.ThrivingInLearningV" (Y)
## ──────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────
## 2.794 (- SD) -0.132 (0.088) -1.507 .133 [-0.304, 0.040]
## 4.072 (Mean) -0.058 (0.062) -0.933 .352 [-0.179, 0.064]
## 5.350 (+ SD) 0.017 (0.088) 0.188 .851 [-0.155, 0.188]
## ──────────────────────────────────────────────────────────────────────WP.LearningBehavior.S1=PROCESS(data1, y="WP.LearningBehaviorV", x="W.X", mods="BA.PersonalControlV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X*BA.PersonalControlV + (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.LearningBehaviorV (2) WP.LearningBehaviorV
## ─────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.648 *** 4.809 ***
## (0.111) (0.361)
## W.X -0.056 -0.236
## (0.093) (0.311)
## BA.PersonalControlV -0.285 ***
## (0.085)
## W.X:BA.PersonalControlV 0.044
## (0.073)
## ─────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.043
## Conditional R^2 0.466 0.467
## AIC 2385.192 2383.672
## BIC 2412.181 2419.659
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.343 1.219
## Var: B.ID W.X 0.008 0.006
## Cov: B.ID (Intercept) W.X -0.103 -0.084
## Var: Residual 1.426 1.429
## ─────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X * BA.PersonalControlV 0.37 1 487 .543
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.LearningBehaviorV" (Y)
## ──────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────
## 2.794 (- SD) -0.112 (0.132) -0.854 .394 [-0.370, 0.145]
## 4.072 (Mean) -0.056 (0.093) -0.599 .549 [-0.238, 0.126]
## 5.350 (+ SD) 0.001 (0.132) 0.006 .995 [-0.257, 0.259]
## ──────────────────────────────────────────────────────────────────────WP.SocialLearning.S1=PROCESS(data1, y="WP.SocialLearningV", x="W.X", mods="BA.PersonalControlV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X*BA.PersonalControlV + (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.SocialLearningV (2) WP.SocialLearningV
## ─────────────────────────────────────────────────────────────────────────
## (Intercept) 3.759 *** 5.678 ***
## (0.104) (0.312)
## W.X -0.178 * -0.561 *
## (0.079) (0.265)
## BA.PersonalControlV -0.471 ***
## (0.073)
## W.X:BA.PersonalControlV 0.094
## (0.062)
## ─────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.004 0.136
## Conditional R^2 0.535 0.537
## AIC 2211.535 2183.811
## BIC 2238.525 2219.797
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.282 0.930
## Var: B.ID W.X 0.007 0.001
## Cov: B.ID (Intercept) W.X -0.097 -0.031
## Var: Residual 1.040 1.039
## ─────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X * BA.PersonalControlV 2.30 1 494 .130
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.SocialLearningV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 2.794 (- SD) -0.298 (0.112) -2.665 .008 ** [-0.518, -0.079]
## 4.072 (Mean) -0.178 (0.079) -2.255 .025 * [-0.334, -0.023]
## 5.350 (+ SD) -0.058 (0.112) -0.522 .602 [-0.278, 0.161]
## ───────────────────────────────────────────────────────────────────────WP.IndependentObservationBasedSocialLearning.S1=PROCESS(data1, y="WP.IndependentObservationBasedSocialLearningV", x="W.X", mods="BA.PersonalControlV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.IndependentObservationBasedSocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.IndependentObservationBasedSocialLearningV ~ W.X*BA.PersonalControlV + (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.IndependentObservationBasedSocialLearningV (2) WP.IndependentObservationBasedSocialLearningV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.736 *** 5.381 ***
## (0.114) (0.359)
## W.X -0.170 -0.284
## (0.094) (0.315)
## BA.PersonalControlV -0.404 ***
## (0.084)
## W.X:BA.PersonalControlV 0.028
## (0.074)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.091
## Conditional R^2 0.478 0.479
## AIC 2410.168 2394.826
## BIC 2437.158 2430.812
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.438 1.180
## Var: B.ID W.X 0.007 0.006
## Cov: B.ID (Intercept) W.X -0.104 -0.086
## Var: Residual 1.469 1.472
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.IndependentObservationBasedSocialLearningV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X * BA.PersonalControlV 0.14 1 486 .706
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.IndependentObservationBasedSocialLearningV" (Y)
## ──────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────
## 2.794 (- SD) -0.206 (0.133) -1.542 .124 [-0.467, 0.056]
## 4.072 (Mean) -0.170 (0.094) -1.804 .072 . [-0.355, 0.015]
## 5.350 (+ SD) -0.135 (0.133) -1.008 .314 [-0.396, 0.127]
## ──────────────────────────────────────────────────────────────────────WP.AdviceThinkingBasedSocialLearning.S1=PROCESS(data1, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X", mods="BA.PersonalControlV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X*BA.PersonalControlV + (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.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.782 *** 5.975 ***
## (0.112) (0.333)
## W.X -0.187 * -0.838 **
## (0.091) (0.303)
## BA.PersonalControlV -0.539 ***
## (0.078)
## W.X:BA.PersonalControlV 0.160 *
## (0.071)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.137
## Conditional R^2 0.483 0.487
## AIC 2364.000 2331.634
## BIC 2390.990 2367.620
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.418 0.964
## Var: B.ID W.X 0.018 0.001
## Cov: B.ID (Intercept) W.X -0.159 -0.037
## Var: Residual 1.365 1.359
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X * BA.PersonalControlV 5.09 1 493 .024 *
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 2.794 (- SD) -0.391 (0.128) -3.054 .002 ** [-0.642, -0.140]
## 4.072 (Mean) -0.187 (0.091) -2.063 .040 * [-0.364, -0.009]
## 5.350 (+ SD) 0.018 (0.128) 0.138 .890 [-0.233, 0.269]
## ───────────────────────────────────────────────────────────────────────7.2 Study 2
7.2.1 AI VS Control
WA.LearningFromOperationalFailure.Sb10=PROCESS(data2, y="WA.LearningFromOperationalFailureV", x="W.X10", mods="BA.PersonalControlV",covs=c("W.X01","W.X01BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromOperationalFailureV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X01, W.X01BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromOperationalFailureV ~ W.X01 + W.X01BA.PersonalControlV + W.X10*BA.PersonalControlV + (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) WA.LearningFromOperationalFailureV (2) WA.LearningFromOperationalFailureV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.363 *** 5.783 ***
## (0.109) (0.335)
## W.X01 0.343 0.157
## (0.201) (0.235)
## W.X01BA.PersonalControlV -0.050 -0.003
## (0.047) (0.056)
## W.X10 0.113 0.197
## (0.072) (0.236)
## BA.PersonalControlV -0.359 ***
## (0.081)
## W.X10:BA.PersonalControlV -0.021
## (0.057)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.100
## Conditional R^2 0.617 0.622
## AIC 3025.529 3011.245
## BIC 3079.269 3074.756
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.503 1.301
## Var: B.ID W.X10 0.012 0.017
## Var: B.ID W.X01 0.007 0.004
## Cov: B.ID (Intercept) W.X10 -0.134 -0.147
## Cov: B.ID (Intercept) W.X01 -0.100 -0.074
## Cov: B.ID W.X10 W.X01 0.009 0.008
## Var: Residual 0.843 0.841
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromOperationalFailureV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X10 * BA.PersonalControlV 0.14 1 694 .708
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.LearningFromOperationalFailureV" (Y)
## ─────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────
## 2.678 (- SD) 0.140 (0.103) 1.364 .173 [-0.061, 0.341]
## 3.954 (Mean) 0.113 (0.073) 1.554 .121 [-0.029, 0.255]
## 5.230 (+ SD) 0.086 (0.103) 0.833 .405 [-0.116, 0.287]
## ─────────────────────────────────────────────────────────────────────WA.LearningFromErrors.Sb10=PROCESS(data2, y="WA.LearningFromErrorsV", x="W.X10", mods="BA.PersonalControlV",covs=c("W.X01","W.X01BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X01, W.X01BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X01 + W.X01BA.PersonalControlV + W.X10*BA.PersonalControlV + (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) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.163 *** 5.433 ***
## (0.118) (0.372)
## W.X01 0.078 -0.007
## (0.203) (0.228)
## W.X01BA.PersonalControlV -0.014 0.007
## (0.048) (0.055)
## W.X10 0.038 0.098
## (0.068) (0.222)
## BA.PersonalControlV -0.321 ***
## (0.090)
## W.X10:BA.PersonalControlV -0.015
## (0.054)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.064
## Conditional R^2 0.716 0.718
## AIC 2996.412 2992.672
## BIC 3050.153 3056.184
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.906 1.749
## Var: B.ID W.X10 0.011 0.011
## Var: B.ID W.X01 0.048 0.046
## Cov: B.ID (Intercept) W.X10 -0.055 -0.063
## Cov: B.ID (Intercept) W.X01 -0.002 0.009
## Cov: B.ID W.X10 W.X01 -0.021 -0.021
## Var: Residual 0.748 0.749
## ───────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X10 * BA.PersonalControlV 0.08 1 497 .777
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.LearningFromErrorsV" (Y)
## ─────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────
## 2.678 (- SD) 0.057 (0.097) 0.590 .556 [-0.132, 0.246]
## 3.954 (Mean) 0.038 (0.068) 0.550 .582 [-0.096, 0.171]
## 5.230 (+ SD) 0.018 (0.097) 0.188 .851 [-0.171, 0.207]
## ─────────────────────────────────────────────────────────────────────WA.ThrivingInLearning.Sb10=PROCESS(data2, y="WA.ThrivingInLearningV", x="W.X10", mods="BA.PersonalControlV",covs=c("W.X01","W.X01BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ThrivingInLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X01, W.X01BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ThrivingInLearningV ~ W.X01 + W.X01BA.PersonalControlV + W.X10*BA.PersonalControlV + (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) WA.ThrivingInLearningV (2) WA.ThrivingInLearningV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.291 *** 5.627 ***
## (0.109) (0.340)
## W.X01 -0.054 -0.324
## (0.196) (0.226)
## W.X01BA.PersonalControlV 0.016 0.084
## (0.046) (0.054)
## W.X10 -0.026 -0.095
## (0.061) (0.200)
## BA.PersonalControlV -0.338 ***
## (0.082)
## W.X10:BA.PersonalControlV 0.017
## (0.048)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.070
## Conditional R^2 0.717 0.717
## AIC 2828.746 2822.996
## BIC 2882.486 2886.508
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.645 1.468
## Var: B.ID W.X10 0.001 0.001
## Var: B.ID W.X01 0.173 0.169
## Cov: B.ID (Intercept) W.X10 -0.037 -0.029
## Cov: B.ID (Intercept) W.X01 -0.187 -0.153
## Cov: B.ID W.X10 W.X01 0.004 0.007
## Var: Residual 0.613 0.614
## ───────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ThrivingInLearningV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X10 * BA.PersonalControlV 0.13 1 645 .717
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.ThrivingInLearningV" (Y)
## ──────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────
## 2.678 (- SD) -0.049 (0.087) -0.560 .576 [-0.219, 0.122]
## 3.954 (Mean) -0.026 (0.061) -0.430 .668 [-0.147, 0.094]
## 5.230 (+ SD) -0.004 (0.087) -0.047 .962 [-0.174, 0.166]
## ──────────────────────────────────────────────────────────────────────WP.LearningBehavior.Sb10=PROCESS(data2, y="WP.LearningBehaviorV", x="W.X10", mods="BA.PersonalControlV",covs=c("W.X01","W.X01BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X01, W.X01BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X01 + W.X01BA.PersonalControlV + W.X10*BA.PersonalControlV + (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.LearningBehaviorV (2) WP.LearningBehaviorV
## ───────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.549 *** 5.127 ***
## (0.123) (0.379)
## W.X01 0.137 -0.047
## (0.198) (0.227)
## W.X01BA.PersonalControlV -0.030 0.016
## (0.047) (0.055)
## W.X10 0.124 0.145
## (0.068) (0.223)
## BA.PersonalControlV -0.399 ***
## (0.091)
## W.X10:BA.PersonalControlV -0.005
## (0.054)
## ───────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.092
## Conditional R^2 0.728 0.730
## AIC 3010.642 3001.437
## BIC 3064.382 3064.949
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.076 1.827
## Var: B.ID W.X10 0.000 0.002
## Var: B.ID W.X01 0.043 0.035
## Cov: B.ID (Intercept) W.X10 -0.009 -0.011
## Cov: B.ID (Intercept) W.X01 -0.091 -0.058
## Cov: B.ID W.X10 W.X01 0.000 -0.008
## Var: Residual 0.759 0.759
## ───────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X10 * BA.PersonalControlV 0.01 1 645 .921
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.LearningBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────
## 2.678 (- SD) 0.131 (0.097) 1.356 .176 [-0.058, 0.320]
## 3.954 (Mean) 0.124 (0.068) 1.819 .069 . [-0.010, 0.258]
## 5.230 (+ SD) 0.117 (0.097) 1.215 .225 [-0.072, 0.307]
## ─────────────────────────────────────────────────────────────────────WP.SocialLearning.Sb10=PROCESS(data2, y="WP.SocialLearningV", x="W.X10", mods="BA.PersonalControlV",covs=c("W.X01","W.X01BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)####
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X01, W.X01BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X01 + W.X01BA.PersonalControlV + W.X10*BA.PersonalControlV + (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.SocialLearningV (2) WP.SocialLearningV
## ───────────────────────────────────────────────────────────────────────────
## (Intercept) 3.656 *** 5.289 ***
## (0.109) (0.330)
## W.X01 0.540 ** 0.451 *
## (0.195) (0.222)
## W.X01BA.PersonalControlV -0.118 * -0.096
## (0.046) (0.053)
## W.X10 0.115 0.603 **
## (0.069) (0.224)
## BA.PersonalControlV -0.413 ***
## (0.079)
## W.X10:BA.PersonalControlV -0.123 *
## (0.054)
## ───────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.004 0.165
## Conditional R^2 0.683 0.691
## AIC 2960.470 2929.806
## BIC 3014.211 2993.317
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.573 1.305
## Var: B.ID W.X10 0.046 0.033
## Var: B.ID W.X01 0.023 0.021
## Cov: B.ID (Intercept) W.X10 0.036 -0.053
## Cov: B.ID (Intercept) W.X01 -0.067 -0.054
## Cov: B.ID W.X10 W.X01 -0.032 -0.022
## Var: Residual 0.736 0.736
## ───────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X10 * BA.PersonalControlV 5.24 1 410 .023 *
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.SocialLearningV" (Y)
## ──────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────
## 2.678 (- SD) 0.272 (0.097) 2.804 .005 ** [ 0.082, 0.463]
## 3.954 (Mean) 0.115 (0.069) 1.674 .095 . [-0.020, 0.250]
## 5.230 (+ SD) -0.042 (0.097) -0.436 .663 [-0.233, 0.148]
## ──────────────────────────────────────────────────────────────────────WP.IndependentObservationBasedSocialLearning.Sb10=PROCESS(data2, y="WP.IndependentObservationBasedSocialLearningV", x="W.X10", mods="BA.PersonalControlV",covs=c("W.X01","W.X01BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)###
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.IndependentObservationBasedSocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X01, W.X01BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.IndependentObservationBasedSocialLearningV ~ W.X01 + W.X01BA.PersonalControlV + W.X10*BA.PersonalControlV + (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.IndependentObservationBasedSocialLearningV (2) WP.IndependentObservationBasedSocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.695 *** 5.423 ***
## (0.121) (0.370)
## W.X01 0.244 0.286
## (0.226) (0.260)
## W.X01BA.PersonalControlV -0.042 -0.052
## (0.053) (0.063)
## W.X10 0.172 * 0.840 **
## (0.082) (0.262)
## BA.PersonalControlV -0.437 ***
## (0.089)
## W.X10:BA.PersonalControlV -0.169 **
## (0.063)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.146
## Conditional R^2 0.650 0.653
## AIC 3258.500 3224.999
## BIC 3312.241 3288.511
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.882 1.583
## Var: B.ID W.X10 0.047 0.014
## Var: B.ID W.X01 0.001 0.000
## Cov: B.ID (Intercept) W.X10 0.035 -0.093
## Cov: B.ID (Intercept) W.X01 -0.005 -0.016
## Cov: B.ID W.X10 W.X01 -0.006 -0.000
## Var: Residual 1.037 1.037
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.IndependentObservationBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X10 * BA.PersonalControlV 7.20 1 350 .008 **
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.IndependentObservationBasedSocialLearningV" (Y)
## ──────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────
## 2.678 (- SD) 0.387 (0.114) 3.410 <.001 *** [ 0.165, 0.610]
## 3.954 (Mean) 0.172 (0.080) 2.139 .033 * [ 0.014, 0.329]
## 5.230 (+ SD) -0.044 (0.114) -0.385 .700 [-0.266, 0.179]
## ──────────────────────────────────────────────────────────────────────WP.AdviceThinkingBasedSocialLearning.Sb10=PROCESS(data2, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X10", mods="BA.PersonalControlV",covs=c("W.X01","W.X01BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)####
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X01, W.X01BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X01 + W.X01BA.PersonalControlV + W.X10*BA.PersonalControlV + (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.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.617 *** 5.154 ***
## (0.111) (0.341)
## W.X01 0.828 *** 0.617 *
## (0.234) (0.267)
## W.X01BA.PersonalControlV -0.192 *** -0.139 *
## (0.055) (0.064)
## W.X10 0.058 0.366
## (0.082) (0.268)
## BA.PersonalControlV -0.389 ***
## (0.082)
## W.X10:BA.PersonalControlV -0.078
## (0.064)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.008 0.130
## Conditional R^2 0.599 0.610
## AIC 3254.563 3236.374
## BIC 3308.304 3299.886
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.495 1.257
## Var: B.ID W.X10 0.051 0.043
## Var: B.ID W.X01 0.045 0.038
## Cov: B.ID (Intercept) W.X10 0.092 0.041
## Cov: B.ID (Intercept) W.X01 -0.046 -0.013
## Cov: B.ID W.X10 W.X01 -0.047 -0.040
## Var: Residual 1.055 1.056
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X10 * BA.PersonalControlV 1.46 1 283 .228
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ──────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────
## 2.678 (- SD) 0.158 (0.116) 1.356 .176 [-0.070, 0.385]
## 3.954 (Mean) 0.058 (0.082) 0.710 .479 [-0.103, 0.219]
## 5.230 (+ SD) -0.041 (0.116) -0.353 .724 [-0.269, 0.187]
## ──────────────────────────────────────────────────────────────────────7.2.2 AI VS Self
WA.LearningFromOperationalFailure.Sb01=PROCESS(data2, y="WA.LearningFromOperationalFailureV", x="W.X01", mods="BA.PersonalControlV",covs=c("W.X10","W.X10BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromOperationalFailureV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X10, W.X10BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromOperationalFailureV ~ W.X10 + W.X10BA.PersonalControlV + W.X01*BA.PersonalControlV + (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) WA.LearningFromOperationalFailureV (2) WA.LearningFromOperationalFailureV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.363 *** 5.783 ***
## (0.109) (0.335)
## W.X10 0.505 * 0.197
## (0.197) (0.236)
## W.X10BA.PersonalControlV -0.099 * -0.021
## (0.046) (0.057)
## W.X01 0.146 * 0.157
## (0.072) (0.235)
## BA.PersonalControlV -0.359 ***
## (0.081)
## W.X01:BA.PersonalControlV -0.003
## (0.056)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.004 0.100
## Conditional R^2 0.615 0.622
## AIC 3022.547 3011.245
## BIC 3076.288 3074.756
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.502 1.301
## Var: B.ID W.X10 0.024 0.017
## Var: B.ID W.X01 0.003 0.004
## Cov: B.ID (Intercept) W.X10 -0.190 -0.147
## Cov: B.ID (Intercept) W.X01 -0.072 -0.074
## Cov: B.ID W.X10 W.X01 0.009 0.008
## Var: Residual 0.841 0.841
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromOperationalFailureV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X01 * BA.PersonalControlV 0.00 1 777 .958
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.LearningFromOperationalFailureV" (Y)
## ─────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────
## 2.678 (- SD) 0.149 (0.102) 1.467 .143 [-0.050, 0.349]
## 3.954 (Mean) 0.146 (0.072) 2.023 .043 * [ 0.005, 0.287]
## 5.230 (+ SD) 0.142 (0.102) 1.393 .164 [-0.058, 0.342]
## ─────────────────────────────────────────────────────────────────────WA.LearningFromErrors.Sb01=PROCESS(data2, y="WA.LearningFromErrorsV", x="W.X01", mods="BA.PersonalControlV",covs=c("W.X10","W.X10BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X10, W.X10BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X10 + W.X10BA.PersonalControlV + W.X01*BA.PersonalControlV + (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) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.163 *** 5.433 ***
## (0.118) (0.372)
## W.X10 0.283 0.098
## (0.191) (0.222)
## W.X10BA.PersonalControlV -0.062 -0.015
## (0.045) (0.054)
## W.X01 0.022 -0.007
## (0.071) (0.228)
## BA.PersonalControlV -0.321 ***
## (0.090)
## W.X01:BA.PersonalControlV 0.007
## (0.055)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.064
## Conditional R^2 0.713 0.718
## AIC 2995.234 2992.672
## BIC 3048.974 3056.184
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.909 1.749
## Var: B.ID W.X10 0.004 0.011
## Var: B.ID W.X01 0.058 0.046
## Cov: B.ID (Intercept) W.X10 -0.088 -0.063
## Cov: B.ID (Intercept) W.X01 -0.005 0.008
## Cov: B.ID W.X10 W.X01 0.000 -0.021
## Var: Residual 0.753 0.749
## ───────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X01 * BA.PersonalControlV 0.02 1 262 .893
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.LearningFromErrorsV" (Y)
## ─────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────
## 2.678 (- SD) 0.013 (0.099) 0.130 .897 [-0.181, 0.206]
## 3.954 (Mean) 0.022 (0.070) 0.318 .750 [-0.115, 0.159]
## 5.230 (+ SD) 0.032 (0.099) 0.320 .749 [-0.162, 0.225]
## ─────────────────────────────────────────────────────────────────────WA.ThrivingInLearning.Sb01=PROCESS(data2, y="WA.ThrivingInLearningV", x="W.X01", mods="BA.PersonalControlV",covs=c("W.X10","W.X10BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ThrivingInLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X10, W.X10BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ThrivingInLearningV ~ W.X10 + W.X10BA.PersonalControlV + W.X01*BA.PersonalControlV + (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) WA.ThrivingInLearningV (2) WA.ThrivingInLearningV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.291 *** 5.627 ***
## (0.109) (0.340)
## W.X10 0.151 -0.095
## (0.178) (0.200)
## W.X10BA.PersonalControlV -0.045 0.017
## (0.042) (0.048)
## W.X01 0.009 -0.324
## (0.070) (0.226)
## BA.PersonalControlV -0.338 ***
## (0.082)
## W.X01:BA.PersonalControlV 0.084
## (0.054)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.070
## Conditional R^2 0.714 0.717
## AIC 2827.986 2822.996
## BIC 2881.727 2886.508
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.642 1.468
## Var: B.ID W.X10 0.002 0.001
## Var: B.ID W.X01 0.173 0.169
## Cov: B.ID (Intercept) W.X10 -0.058 -0.029
## Cov: B.ID (Intercept) W.X01 -0.195 -0.153
## Cov: B.ID W.X10 W.X01 0.011 0.007
## Var: Residual 0.615 0.614
## ───────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ThrivingInLearningV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X01 * BA.PersonalControlV 2.40 1 191 .123
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.ThrivingInLearningV" (Y)
## ──────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────
## 2.678 (- SD) -0.099 (0.098) -1.008 .315 [-0.291, 0.093]
## 3.954 (Mean) 0.009 (0.069) 0.124 .902 [-0.127, 0.144]
## 5.230 (+ SD) 0.116 (0.098) 1.183 .238 [-0.076, 0.308]
## ──────────────────────────────────────────────────────────────────────WP.LearningBehavior.Sb01=PROCESS(data2, y="WP.LearningBehaviorV", x="W.X01", mods="BA.PersonalControlV",covs=c("W.X10","W.X10BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X10, W.X10BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X10 + W.X10BA.PersonalControlV + W.X01*BA.PersonalControlV + (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.LearningBehaviorV (2) WP.LearningBehaviorV
## ───────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.549 *** 5.127 ***
## (0.123) (0.379)
## W.X10 0.318 0.145
## (0.194) (0.223)
## W.X10BA.PersonalControlV -0.049 -0.005
## (0.046) (0.054)
## W.X01 0.017 -0.047
## (0.070) (0.227)
## BA.PersonalControlV -0.399 ***
## (0.091)
## W.X01:BA.PersonalControlV 0.016
## (0.055)
## ───────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.092
## Conditional R^2 0.726 0.730
## AIC 3010.030 3001.437
## BIC 3063.771 3064.949
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.075 1.827
## Var: B.ID W.X10 0.001 0.002
## Var: B.ID W.X01 0.036 0.035
## Cov: B.ID (Intercept) W.X10 -0.038 -0.011
## Cov: B.ID (Intercept) W.X01 -0.070 -0.058
## Cov: B.ID W.X10 W.X01 0.001 -0.008
## Var: Residual 0.761 0.759
## ───────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X01 * BA.PersonalControlV 0.09 1 306 .768
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.LearningBehaviorV" (Y)
## ──────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────
## 2.678 (- SD) -0.004 (0.099) -0.038 .970 [-0.198, 0.190]
## 3.954 (Mean) 0.017 (0.070) 0.241 .810 [-0.120, 0.154]
## 5.230 (+ SD) 0.037 (0.099) 0.378 .705 [-0.157, 0.232]
## ──────────────────────────────────────────────────────────────────────WP.SocialLearning.Sb01=PROCESS(data2, y="WP.SocialLearningV", x="W.X01", mods="BA.PersonalControlV",covs=c("W.X10","W.X10BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X10, W.X10BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X10 + W.X10BA.PersonalControlV + W.X01*BA.PersonalControlV + (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.SocialLearningV (2) WP.SocialLearningV
## ───────────────────────────────────────────────────────────────────────────
## (Intercept) 3.656 *** 5.289 ***
## (0.109) (0.330)
## W.X10 0.724 *** 0.603 **
## (0.197) (0.224)
## W.X10BA.PersonalControlV -0.154 *** -0.123 *
## (0.047) (0.054)
## W.X01 0.073 0.451 *
## (0.068) (0.222)
## BA.PersonalControlV -0.413 ***
## (0.079)
## W.X01:BA.PersonalControlV -0.096
## (0.053)
## ───────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.007 0.165
## Conditional R^2 0.681 0.691
## AIC 2957.005 2929.806
## BIC 3010.746 2993.317
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.574 1.305
## Var: B.ID W.X10 0.037 0.033
## Var: B.ID W.X01 0.025 0.021
## Cov: B.ID (Intercept) W.X10 -0.073 -0.053
## Cov: B.ID (Intercept) W.X01 0.015 -0.054
## Cov: B.ID W.X10 W.X01 -0.030 -0.022
## Var: Residual 0.736 0.736
## ───────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X01 * BA.PersonalControlV 3.21 1 499 .074 .
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.SocialLearningV" (Y)
## ──────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────
## 2.678 (- SD) 0.195 (0.096) 2.022 .044 * [ 0.006, 0.384]
## 3.954 (Mean) 0.073 (0.068) 1.069 .286 [-0.061, 0.206]
## 5.230 (+ SD) -0.049 (0.096) -0.511 .610 [-0.238, 0.140]
## ──────────────────────────────────────────────────────────────────────WP.IndependentObservationBasedSocialLearning.Sb01=PROCESS(data2, y="WP.IndependentObservationBasedSocialLearningV", x="W.X01", mods="BA.PersonalControlV",covs=c("W.X10","W.X10BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.IndependentObservationBasedSocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X10, W.X10BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.IndependentObservationBasedSocialLearningV ~ W.X10 + W.X10BA.PersonalControlV + W.X01*BA.PersonalControlV + (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.IndependentObservationBasedSocialLearningV (2) WP.IndependentObservationBasedSocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.695 *** 5.423 ***
## (0.121) (0.370)
## W.X10 1.082 *** 0.840 **
## (0.222) (0.262)
## W.X10BA.PersonalControlV -0.230 *** -0.169 **
## (0.052) (0.063)
## W.X01 0.078 0.286
## (0.080) (0.260)
## BA.PersonalControlV -0.437 ***
## (0.089)
## W.X01:BA.PersonalControlV -0.052
## (0.063)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.012 0.146
## Conditional R^2 0.641 0.653
## AIC 3243.509 3224.999
## BIC 3297.250 3288.511
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.885 1.583
## Var: B.ID W.X10 0.024 0.014
## Var: B.ID W.X01 0.001 0.000
## Cov: B.ID (Intercept) W.X10 -0.137 -0.093
## Cov: B.ID (Intercept) W.X01 0.021 -0.016
## Cov: B.ID W.X10 W.X01 -0.004 -0.000
## Var: Residual 1.037 1.037
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.IndependentObservationBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X01 * BA.PersonalControlV 0.70 1 667 .402
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.IndependentObservationBasedSocialLearningV" (Y)
## ─────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────
## 2.678 (- SD) 0.145 (0.113) 1.286 .199 [-0.076, 0.366]
## 3.954 (Mean) 0.078 (0.080) 0.980 .327 [-0.078, 0.235]
## 5.230 (+ SD) 0.011 (0.113) 0.100 .921 [-0.210, 0.232]
## ─────────────────────────────────────────────────────────────────────WP.AdviceThinkingBasedSocialLearning.Sb01=PROCESS(data2, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X01", mods="BA.PersonalControlV",covs=c("W.X10","W.X10BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X10, W.X10BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X10 + W.X10BA.PersonalControlV + W.X01*BA.PersonalControlV + (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.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.617 *** 5.154 ***
## (0.111) (0.341)
## W.X10 0.414 0.366
## (0.238) (0.268)
## W.X10BA.PersonalControlV -0.090 -0.078
## (0.056) (0.064)
## W.X01 0.067 0.617 *
## (0.083) (0.267)
## BA.PersonalControlV -0.389 ***
## (0.082)
## W.X01:BA.PersonalControlV -0.139 *
## (0.064)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.130
## Conditional R^2 0.604 0.610
## AIC 3262.344 3236.374
## BIC 3316.085 3299.886
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.493 1.257
## Var: B.ID W.X10 0.043 0.043
## Var: B.ID W.X01 0.061 0.038
## Cov: B.ID (Intercept) W.X10 0.037 0.041
## Cov: B.ID (Intercept) W.X01 0.079 -0.013
## Cov: B.ID W.X10 W.X01 -0.047 -0.040
## Var: Residual 1.057 1.056
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X01 * BA.PersonalControlV 4.67 1 297 .031 *
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ──────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────
## 2.678 (- SD) 0.245 (0.116) 2.111 .036 * [ 0.017, 0.472]
## 3.954 (Mean) 0.067 (0.082) 0.824 .411 [-0.093, 0.228]
## 5.230 (+ SD) -0.110 (0.116) -0.946 .345 [-0.337, 0.118]
## ──────────────────────────────────────────────────────────────────────8 ONE COMPLETE STUDY
8.1 Study 1
S1=PROCESS(data1, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X", mods="BA.AIOnlineCommunicationSkillsV", cluster ="B.ID", hlm.re.y ="(W.X|B.ID)", center=FALSE)##
## ****************** 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.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X*BA.AIOnlineCommunicationSkillsV + (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) 3.449 *** 2.447 ***
## (0.100) (0.390)
## W.X -0.121 0.530
## (0.082) (0.321)
## BA.AIOnlineCommunicationSkillsV 0.235 **
## (0.089)
## W.X:BA.AIOnlineCommunicationSkillsV -0.153 *
## (0.073)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.019
## Conditional R^2 0.496 0.502
## AIC 2229.463 2231.960
## BIC 2256.453 2267.947
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.119 1.068
## Var: B.ID W.X 0.001 0.000
## Cov: B.ID (Intercept) W.X -0.037 -0.008
## Var: Residual 1.101 1.094
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## W.X * BA.AIOnlineCommunicationSkillsV 4.39 1 496 .037 *
## ─────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────────────
## 3.146 (- SD) 0.049 (0.115) 0.428 .669 [-0.176, 0.274]
## 4.260 (Mean) -0.121 (0.081) -1.491 .137 [-0.280, 0.038]
## 5.374 (+ SD) -0.291 (0.115) -2.536 .012 * [-0.516, -0.066]
## ───────────────────────────────────────────────────────────────────────────────────interact_plot(S1$model.y, W.X, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
8.2 Study 2
8.2.1 X -> Y: Three groups comparisions for
8.2.1.1 Treating W.X as moderator
data2$W.X=as.factor(data2$W.X)
S2=PROCESS(data2, y="WP.SystemPerformanceImprovementBehaviorV", x="BA.AIOnlineCommunicationSkillsV", mods="W.X", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** 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) 1.956 *** 1.511 ***
## (0.349) (0.375)
## BA.AIOnlineCommunicationSkillsV 0.348 *** 0.447 ***
## (0.079) (0.085)
## W.X1 0.692 **
## (0.259)
## W.X2 0.699 **
## (0.260)
## BA.AIOnlineCommunicationSkillsV:W.X1 -0.154 **
## (0.059)
## BA.AIOnlineCommunicationSkillsV:W.X2 -0.155 **
## (0.059)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.076 0.077
## Conditional R^2 0.683 0.687
## AIC 2931.920 2944.105
## BIC 2975.890 3007.617
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.291 1.294
## Var: B.ID W.X1 0.078 0.084
## Var: B.ID W.X2 0.084 0.090
## Cov: B.ID (Intercept) W.X1 0.039 0.038
## Cov: B.ID (Intercept) W.X2 -0.011 -0.012
## Cov: B.ID W.X1 W.X2 -0.081 -0.087
## Var: Residual 0.712 0.702
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## BA.AIOnlineCommunicationSkillsV * W.X 5.15 2 257 .006 **
## ─────────────────────────────────────────────────────────────
##
## Simple Slopes: "BA.AIOnlineCommunicationSkillsV" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ────────────────────────────────────────────────────
## "W.X" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────
## 0 0.447 (0.085) 5.263 <.001 *** [0.280, 0.613]
## 1 0.293 (0.089) 3.297 .001 ** [0.119, 0.467]
## 2 0.292 (0.087) 3.370 <.001 *** [0.122, 0.461]
## ────────────────────────────────────────────────────8.2.1.2 Treating W.X as X
S2.i=PROCESS(data2, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X10", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X01","W.X01BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** 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) 3.413 *** 1.511 ***
## (0.108) (0.375)
## W.X01 0.212 0.699 **
## (0.239) (0.260)
## W.X01BA.AIOnlineCommunicationSkillsV -0.040 -0.155 **
## (0.054) (0.059)
## W.X10 0.038 0.692 **
## (0.070) (0.259)
## BA.AIOnlineCommunicationSkillsV 0.447 ***
## (0.085)
## W.X10:BA.AIOnlineCommunicationSkillsV -0.154 **
## (0.059)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.077
## Conditional R^2 0.685 0.687
## AIC 2960.196 2944.105
## BIC 3013.937 3007.617
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.552 1.294
## Var: B.ID W.X10 0.087 0.084
## Var: B.ID W.X01 0.087 0.090
## Cov: B.ID (Intercept) W.X10 -0.040 0.038
## Cov: B.ID (Intercept) W.X01 -0.066 -0.012
## Cov: B.ID W.X10 W.X01 -0.083 -0.087
## Var: Residual 0.710 0.702
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X10 * BA.AIOnlineCommunicationSkillsV 6.84 1 340 .009 **
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.220 (0.098) 2.237 .026 * [ 0.027, 0.412]
## 4.260 (Mean) 0.038 (0.069) 0.547 .584 [-0.098, 0.174]
## 5.444 (+ SD) -0.144 (0.098) -1.463 .144 [-0.336, 0.049]
## ──────────────────────────────────────────────────────────────────────────────────S2.ii=PROCESS(data2, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X01", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X10","W.X10BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** 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) 3.413 *** 1.511 ***
## (0.108) (0.375)
## W.X10 0.274 0.692 **
## (0.241) (0.259)
## W.X10BA.AIOnlineCommunicationSkillsV -0.055 -0.154 **
## (0.054) (0.059)
## W.X01 0.039 0.699 **
## (0.070) (0.260)
## BA.AIOnlineCommunicationSkillsV 0.447 ***
## (0.085)
## W.X01:BA.AIOnlineCommunicationSkillsV -0.155 **
## (0.059)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.077
## Conditional R^2 0.686 0.687
## AIC 2959.761 2944.105
## BIC 3013.501 3007.617
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.553 1.294
## Var: B.ID W.X10 0.079 0.084
## Var: B.ID W.X01 0.096 0.090
## Cov: B.ID (Intercept) W.X10 -0.008 0.038
## Cov: B.ID (Intercept) W.X01 -0.092 -0.012
## Cov: B.ID W.X10 W.X01 -0.084 -0.087
## Var: Residual 0.709 0.702
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X01 * BA.AIOnlineCommunicationSkillsV 6.92 1 329 .009 **
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.223 (0.099) 2.258 .025 * [ 0.029, 0.416]
## 4.260 (Mean) 0.039 (0.070) 0.563 .574 [-0.097, 0.176]
## 5.444 (+ SD) -0.144 (0.099) -1.462 .145 [-0.337, 0.049]
## ──────────────────────────────────────────────────────────────────────────────────## ────────────────────────────────────────────────────────────────────────────────
## Estimate S.E. df t p
## ────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.511 (0.375) 164.766 4.027 <.001 ***
## W.X10 0.692 (0.259) 339.510 2.667 .008 **
## W.X10BA.AIOnlineCommunicationSkillsV -0.154 (0.059) 339.510 -2.615 .009 **
## W.X01 0.699 (0.260) 329.378 2.685 .008 **
## BA.AIOnlineCommunicationSkillsV 0.447 (0.085) 164.765 5.263 <.001 ***
## W.X01:BA.AIOnlineCommunicationSkillsV -0.155 (0.059) 329.378 -2.630 .009 **
## ────────────────────────────────────────────────────────────────────────────────interact_plot(S2.i$model.y, W.X10, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S2.ii$model.y, W.X01, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 2.447 *** 1.511 ***
## (0.390) (0.375)
## W.X 0.530
## (0.321)
## BA.AIOnlineCommunicationSkillsV 0.235 ** 0.447 ***
## (0.089) (0.085)
## W.X:BA.AIOnlineCommunicationSkillsV -0.153 *
## (0.073)
## W.X1 0.692 **
## (0.259)
## W.X2 0.699 **
## (0.260)
## BA.AIOnlineCommunicationSkillsV:W.X1 -0.154 **
## (0.059)
## BA.AIOnlineCommunicationSkillsV:W.X2 -0.155 **
## (0.059)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.019 0.077
## Conditional R^2 0.502 0.687
## AIC 2231.960 2944.105
## BIC 2267.947 3007.617
## Num. obs. 664 978
## Num. groups: B.ID 166 163
## Var: B.ID (Intercept) 1.068 1.294
## Var: B.ID W.X 0.000
## Cov: B.ID (Intercept) W.X -0.008
## Var: Residual 1.094 0.702
## Var: B.ID W.X1 0.084
## Var: B.ID W.X2 0.090
## Cov: B.ID (Intercept) W.X1 0.038
## Cov: B.ID (Intercept) W.X2 -0.012
## Cov: B.ID W.X1 W.X2 -0.087
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.8.2.2 X -> M: Three groups comparisions
8.2.2.1 WA.AffectiveRuminationV
S2mA.i=PROCESS(data2, y="WA.AffectiveRuminationV", x="W.X10", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X01","W.X01BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.AffectiveRuminationV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X01, W.X01BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.AffectiveRuminationV ~ 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) WA.AffectiveRuminationV (2) WA.AffectiveRuminationV
## ───────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.731 *** 2.810 ***
## (0.114) (0.421)
## W.X01 0.237 0.537 *
## (0.241) (0.269)
## W.X01BA.AIOnlineCommunicationSkillsV -0.043 -0.113
## (0.054) (0.061)
## W.X10 0.093 0.632 *
## (0.071) (0.266)
## BA.AIOnlineCommunicationSkillsV 0.216 *
## (0.095)
## W.X10:BA.AIOnlineCommunicationSkillsV -0.127 *
## (0.060)
## ───────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.013
## Conditional R^2 0.681 0.683
## AIC 3044.495 3048.114
## BIC 3098.236 3111.626
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.714 1.666
## Var: B.ID W.X10 0.030 0.027
## Var: B.ID W.X01 0.042 0.045
## Cov: B.ID (Intercept) W.X10 -0.053 -0.026
## Cov: B.ID (Intercept) W.X01 0.015 0.027
## Cov: B.ID W.X10 W.X01 -0.035 -0.035
## Var: Residual 0.803 0.800
## ───────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.AffectiveRuminationV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X10 * BA.AIOnlineCommunicationSkillsV 4.42 1 452 .036 *
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.AffectiveRuminationV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.243 (0.101) 2.413 .016 * [ 0.046, 0.441]
## 4.260 (Mean) 0.093 (0.071) 1.309 .191 [-0.046, 0.233]
## 5.444 (+ SD) -0.057 (0.101) -0.562 .575 [-0.254, 0.141]
## ──────────────────────────────────────────────────────────────────────────────────S2mA.ii=PROCESS(data2, y="WA.AffectiveRuminationV", x="W.X01", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X10","W.X10BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.AffectiveRuminationV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X10, W.X10BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.AffectiveRuminationV ~ 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) WA.AffectiveRuminationV (2) WA.AffectiveRuminationV
## ───────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.731 *** 2.810 ***
## (0.114) (0.421)
## W.X10 0.327 0.632 *
## (0.235) (0.266)
## W.X10BA.AIOnlineCommunicationSkillsV -0.055 -0.127 *
## (0.053) (0.060)
## W.X01 0.055 0.537 *
## (0.072) (0.269)
## BA.AIOnlineCommunicationSkillsV 0.216 *
## (0.095)
## W.X01:BA.AIOnlineCommunicationSkillsV -0.113
## (0.061)
## ───────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.013
## Conditional R^2 0.681 0.683
## AIC 3044.095 3048.114
## BIC 3097.835 3111.626
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.715 1.666
## Var: B.ID W.X10 0.026 0.027
## Var: B.ID W.X01 0.046 0.045
## Cov: B.ID (Intercept) W.X10 -0.039 -0.026
## Cov: B.ID (Intercept) W.X01 0.002 0.027
## Cov: B.ID W.X10 W.X01 -0.034 -0.035
## Var: Residual 0.802 0.800
## ───────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.AffectiveRuminationV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X01 * BA.AIOnlineCommunicationSkillsV 3.47 1 367 .063 .
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.AffectiveRuminationV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.189 (0.102) 1.853 .065 . [-0.011, 0.388]
## 4.260 (Mean) 0.055 (0.072) 0.759 .449 [-0.086, 0.196]
## 5.444 (+ SD) -0.080 (0.102) -0.781 .435 [-0.279, 0.120]
## ──────────────────────────────────────────────────────────────────────────────────## ────────────────────────────────────────────────────────────────────────────────
## Estimate S.E. df t p
## ────────────────────────────────────────────────────────────────────────────────
## (Intercept) 2.810 (0.421) 163.646 6.683 <.001 ***
## W.X10 0.632 (0.266) 452.018 2.377 .018 *
## W.X10BA.AIOnlineCommunicationSkillsV -0.127 (0.060) 452.018 -2.103 .036 *
## W.X01 0.537 (0.269) 367.087 1.997 .047 *
## BA.AIOnlineCommunicationSkillsV 0.216 (0.095) 163.646 2.272 .024 *
## W.X01:BA.AIOnlineCommunicationSkillsV -0.113 (0.061) 367.087 -1.862 .063 .
## ────────────────────────────────────────────────────────────────────────────────interact_plot(S2mA.i$model.y, W.X10, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S2mA.ii$model.y, W.X01, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
8.2.2.2 WP.TakingChargeBehaviorsForSystemImprovementV
S2mB.i=PROCESS(data2, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X10", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X01","W.X01BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** 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) 3.546 *** 1.773 ***
## (0.122) (0.435)
## W.X01 0.372 0.785 **
## (0.233) (0.270)
## W.X01BA.AIOnlineCommunicationSkillsV -0.078 -0.175 **
## (0.052) (0.061)
## W.X10 0.103 0.322
## (0.065) (0.241)
## BA.AIOnlineCommunicationSkillsV 0.416 ***
## (0.098)
## W.X10:BA.AIOnlineCommunicationSkillsV -0.051
## (0.055)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.066
## Conditional R^2 0.743 0.741
## AIC 2944.121 2938.140
## BIC 2997.862 3001.651
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.106 1.871
## Var: B.ID W.X10 0.005 0.000
## Var: B.ID W.X01 0.206 0.170
## Cov: B.ID (Intercept) W.X10 -0.062 -0.029
## Cov: B.ID (Intercept) W.X01 -0.273 -0.206
## Cov: B.ID W.X10 W.X01 0.030 0.003
## Var: Residual 0.678 0.680
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X10 * BA.AIOnlineCommunicationSkillsV 0.89 1 648 .347
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.164 (0.091) 1.795 .073 . [-0.015, 0.343]
## 4.260 (Mean) 0.103 (0.065) 1.598 .110 [-0.023, 0.230]
## 5.444 (+ SD) 0.042 (0.091) 0.464 .643 [-0.137, 0.222]
## ─────────────────────────────────────────────────────────────────────────────────S2mB.ii=PROCESS(data2, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X01", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X10","W.X10BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** 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) 3.546 *** 1.773 ***
## (0.122) (0.435)
## W.X10 -0.082 0.322
## (0.213) (0.241)
## W.X10BA.AIOnlineCommunicationSkillsV 0.043 -0.051
## (0.048) (0.055)
## W.X01 0.041 0.785 **
## (0.075) (0.270)
## BA.AIOnlineCommunicationSkillsV 0.416 ***
## (0.098)
## W.X01:BA.AIOnlineCommunicationSkillsV -0.175 **
## (0.061)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.066
## Conditional R^2 0.738 0.741
## AIC 2945.554 2938.140
## BIC 2999.294 3001.651
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.102 1.871
## Var: B.ID W.X10 0.007 0.000
## Var: B.ID W.X01 0.226 0.170
## Cov: B.ID (Intercept) W.X10 -0.082 -0.029
## Cov: B.ID (Intercept) W.X01 -0.314 -0.206
## Cov: B.ID W.X10 W.X01 0.038 0.003
## Var: Residual 0.681 0.680
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X01 * BA.AIOnlineCommunicationSkillsV 8.19 1 237 .005 **
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.248 (0.104) 2.392 .018 * [ 0.045, 0.451]
## 4.260 (Mean) 0.041 (0.073) 0.559 .577 [-0.103, 0.184]
## 5.444 (+ SD) -0.166 (0.104) -1.602 .111 [-0.369, 0.037]
## ──────────────────────────────────────────────────────────────────────────────────## ────────────────────────────────────────────────────────────────────────────────
## Estimate S.E. df t p
## ────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.773 (0.435) 162.496 4.076 <.001 ***
## W.X10 0.322 (0.241) 647.717 1.335 .182
## W.X10BA.AIOnlineCommunicationSkillsV -0.051 (0.055) 647.717 -0.941 .347
## W.X01 0.785 (0.270) 236.714 2.908 .004 **
## BA.AIOnlineCommunicationSkillsV 0.416 (0.098) 162.496 4.230 <.001 ***
## W.X01:BA.AIOnlineCommunicationSkillsV -0.175 (0.061) 236.714 -2.861 .005 **
## ────────────────────────────────────────────────────────────────────────────────interact_plot(S2mB.i$model.y, W.X10, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S2mB.ii$model.y, W.X01, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
8.2.3 M -> Y
PROCESS(data2, y="WP.SystemPerformanceImprovementBehaviorV", x="BA.AIOnlineCommunicationSkillsV", mods="W.X", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE,
,covs=cc("WA.ErrorStrainV.GroC,WA.ErrorStrainV_mean,WA.AffectiveRuminationV.GroC,WA.AffectiveRuminationV_mean"))##
## ****************** 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) : WA.ErrorStrainV.GroC, WA.ErrorStrainV_mean, WA.AffectiveRuminationV.GroC, WA.AffectiveRuminationV_mean
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ WA.ErrorStrainV.GroC + WA.ErrorStrainV_mean + WA.AffectiveRuminationV.GroC + WA.AffectiveRuminationV_mean + 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) 0.569 0.175
## (0.379) (0.403)
## WA.ErrorStrainV.GroC 0.000 -0.006
## (0.035) (0.035)
## WA.ErrorStrainV_mean 0.269 ** 0.269 **
## (0.104) (0.104)
## WA.AffectiveRuminationV.GroC 0.161 *** 0.156 ***
## (0.038) (0.038)
## WA.AffectiveRuminationV_mean 0.170 0.170
## (0.101) (0.101)
## BA.AIOnlineCommunicationSkillsV 0.304 *** 0.392 ***
## (0.071) (0.078)
## W.X1 0.597 *
## (0.257)
## W.X2 0.619 *
## (0.260)
## BA.AIOnlineCommunicationSkillsV:W.X1 -0.135 *
## (0.058)
## BA.AIOnlineCommunicationSkillsV:W.X2 -0.138 *
## (0.059)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.214 0.216
## Conditional R^2 0.695 0.698
## AIC 2894.917 2909.420
## BIC 2958.429 2992.473
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.014 1.016
## Var: B.ID W.X1 0.078 0.084
## Var: B.ID W.X2 0.097 0.101
## Cov: B.ID (Intercept) W.X1 0.027 0.026
## Cov: B.ID (Intercept) W.X2 -0.007 -0.008
## Cov: B.ID W.X1 W.X2 -0.087 -0.092
## Var: Residual 0.689 0.682
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## BA.AIOnlineCommunicationSkillsV * W.X 4.11 2 257 .018 *
## ─────────────────────────────────────────────────────────────
##
## Simple Slopes: "BA.AIOnlineCommunicationSkillsV" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ────────────────────────────────────────────────────
## "W.X" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────
## 0 0.392 (0.078) 5.046 <.001 *** [0.240, 0.544]
## 1 0.257 (0.081) 3.163 .002 ** [0.098, 0.417]
## 2 0.254 (0.080) 3.170 .002 ** [0.097, 0.410]
## ────────────────────────────────────────────────────8.2.4 Moderated mediation
S2mB.i=PROCESS(data2, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X10",
meds=cc("WP.TakingChargeBehaviorsForSystemImprovementV.GroC,WA.AffectiveRuminationV.GroC"),
mods="BA.AIOnlineCommunicationSkillsV",mod.path=c("x-m", "x-y"),
covs=c("W.X01","W.X01BA.AIOnlineCommunicationSkillsV"),
cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE,
ci="boot", nsim=1000, seed=1)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 8 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Parallel Multiple Moderated Mediation (2 meds)
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : WP.TakingChargeBehaviorsForSystemImprovementV.GroC, WA.AffectiveRuminationV.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)
## - WA.AffectiveRuminationV.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 + WA.AffectiveRuminationV.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) WA.AffectiveRuminationV.GroC (4) WP.SystemPerformanceImprovementBehaviorV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.413 *** -0.369 * -0.390 * 1.690 ***
## (0.108) (0.161) (0.172) (0.364)
## W.X01 0.212 0.785 *** 0.537 * 0.352
## (0.239) (0.227) (0.243) (0.246)
## W.X01BA.AIOnlineCommunicationSkillsV -0.040 -0.175 *** -0.113 * -0.078
## (0.054) (0.051) (0.055) (0.056)
## W.X10 0.038 0.322 0.632 ** 0.502 *
## (0.070) (0.227) (0.243) (0.240)
## BA.AIOnlineCommunicationSkillsV 0.075 * 0.080 * 0.410 ***
## (0.036) (0.039) (0.082)
## W.X10:BA.AIOnlineCommunicationSkillsV -0.051 -0.127 * -0.120 *
## (0.051) (0.055) (0.054)
## WP.TakingChargeBehaviorsForSystemImprovementV.GroC 0.363 ***
## (0.033)
## WA.AffectiveRuminationV.GroC 0.116 ***
## (0.031)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.015 0.009 0.119
## Conditional R^2 0.685 0.015 0.009 0.737
## AIC 2960.196 2320.820 2450.643 2826.787
## BIC 3013.937 2359.905 2489.727 2900.070
## Num. obs. 978 978 978 978
## Num. groups: B.ID 163 163 163 163
## Var: B.ID (Intercept) 1.552 0.000 0.000 1.253
## Var: B.ID W.X10 0.087 0.077
## Var: B.ID W.X01 0.087 0.106
## Cov: B.ID (Intercept) W.X10 -0.040 0.062
## Cov: B.ID (Intercept) W.X01 -0.066 0.051
## Cov: B.ID W.X10 W.X01 -0.083 -0.085
## Var: Residual 0.710 0.604 0.691 0.591
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : Parallel Multiple Moderated Mediation (2 meds) (Model 8)
## Sample Size : 978 (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 4.92 1 314 .027 *
## ───────────────────────────────────────────────────────────────
##
## 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]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.132 (0.091) 1.453 .147 [-0.046, 0.310]
## 4.260 (Mean) -0.010 (0.064) -0.159 .874 [-0.136, 0.115]
## 5.444 (+ SD) -0.153 (0.091) -1.685 .093 . [-0.330, 0.025]
## ──────────────────────────────────────────────────────────────────────────────────
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV.GroC" (M)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X10 * BA.AIOnlineCommunicationSkillsV 1.00 1 972 .318
## ───────────────────────────────────────────────────────────────
##
## 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]
## ─────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.164 (0.086) 1.905 .057 . [-0.005, 0.333]
## 4.260 (Mean) 0.103 (0.061) 1.696 .090 . [-0.016, 0.223]
## 5.444 (+ SD) 0.042 (0.086) 0.493 .622 [-0.126, 0.211]
## ─────────────────────────────────────────────────────────────────────────────────
##
## 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]
## ─────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.060 (0.032) 1.896 .058 . [ 0.003, 0.122]
## 4.260 (Mean) 0.038 (0.023) 1.672 .094 . [-0.005, 0.084]
## 5.444 (+ SD) 0.015 (0.032) 0.464 .643 [-0.051, 0.079]
## ─────────────────────────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 1000 Monte Carlo samples.)
##
## Interaction Effect on "WA.AffectiveRuminationV.GroC" (M)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X10 * BA.AIOnlineCommunicationSkillsV 5.29 1 972 .022 *
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.AffectiveRuminationV.GroC" (M)
## (Conditional Effects [a] of X on M)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.243 (0.092) 2.640 .008 ** [ 0.063, 0.424]
## 4.260 (Mean) 0.093 (0.065) 1.432 .152 [-0.034, 0.221]
## 5.444 (+ SD) -0.057 (0.092) -0.615 .539 [-0.237, 0.124]
## ──────────────────────────────────────────────────────────────────────────────────
##
## Running 1000 * 3 simulations...
## Indirect Path: "W.X10" (X) ==> "WA.AffectiveRuminationV.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]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.029 (0.014) 2.115 .034 * [ 0.007, 0.058]
## 4.260 (Mean) 0.011 (0.008) 1.290 .197 [-0.004, 0.029]
## 5.444 (+ SD) -0.007 (0.012) -0.581 .561 [-0.032, 0.015]
## ──────────────────────────────────────────────────────────────────────────────────
## 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. :)S2mB.ii=PROCESS(data2, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X01",
meds=cc("WP.TakingChargeBehaviorsForSystemImprovementV.GroC,WA.AffectiveRuminationV.GroC"),
mods="BA.AIOnlineCommunicationSkillsV",mod.path=c("x-m", "x-y"),
covs=c("W.X10","W.X10BA.AIOnlineCommunicationSkillsV"),
cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE,
ci="boot", nsim=1000, seed=1)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 8 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Parallel Multiple Moderated Mediation (2 meds)
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : WP.TakingChargeBehaviorsForSystemImprovementV.GroC, WA.AffectiveRuminationV.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)
## - WA.AffectiveRuminationV.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 + WA.AffectiveRuminationV.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) WA.AffectiveRuminationV.GroC (4) WP.SystemPerformanceImprovementBehaviorV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.413 *** -0.369 * -0.390 * 1.690 ***
## (0.108) (0.161) (0.172) (0.364)
## W.X10 0.274 0.322 0.632 ** 0.502 *
## (0.241) (0.227) (0.243) (0.240)
## W.X10BA.AIOnlineCommunicationSkillsV -0.055 -0.051 -0.127 * -0.120 *
## (0.054) (0.051) (0.055) (0.054)
## W.X01 0.039 0.785 *** 0.537 * 0.352
## (0.070) (0.227) (0.243) (0.246)
## BA.AIOnlineCommunicationSkillsV 0.075 * 0.080 * 0.410 ***
## (0.036) (0.039) (0.082)
## W.X01:BA.AIOnlineCommunicationSkillsV -0.175 *** -0.113 * -0.078
## (0.051) (0.055) (0.056)
## WP.TakingChargeBehaviorsForSystemImprovementV.GroC 0.363 ***
## (0.033)
## WA.AffectiveRuminationV.GroC 0.116 ***
## (0.031)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.015 0.009 0.119
## Conditional R^2 0.686 0.015 0.009 0.737
## AIC 2959.761 2320.820 2450.643 2826.787
## BIC 3013.501 2359.905 2489.727 2900.070
## Num. obs. 978 978 978 978
## Num. groups: B.ID 163 163 163 163
## Var: B.ID (Intercept) 1.553 0.000 0.000 1.253
## Var: B.ID W.X10 0.079 0.077
## Var: B.ID W.X01 0.096 0.106
## Cov: B.ID (Intercept) W.X10 -0.008 0.062
## Cov: B.ID (Intercept) W.X01 -0.092 0.051
## Cov: B.ID W.X10 W.X01 -0.084 -0.085
## Var: Residual 0.709 0.604 0.691 0.591
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 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 : Parallel Multiple Moderated Mediation (2 meds) (Model 8)
## Sample Size : 978 (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 1.99 1 274 .159
## ───────────────────────────────────────────────────────────────
##
## 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]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.111 (0.093) 1.194 .233 [-0.071, 0.293]
## 4.260 (Mean) 0.018 (0.065) 0.277 .782 [-0.110, 0.146]
## 5.444 (+ SD) -0.075 (0.093) -0.808 .420 [-0.257, 0.107]
## ──────────────────────────────────────────────────────────────────────────────────
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV.GroC" (M)
## ────────────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────────────
## W.X01 * BA.AIOnlineCommunicationSkillsV 11.51 1 972 <.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]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.248 (0.086) 2.875 .004 ** [ 0.079, 0.416]
## 4.260 (Mean) 0.041 (0.061) 0.672 .502 [-0.078, 0.160]
## 5.444 (+ SD) -0.166 (0.086) -1.925 .054 . [-0.335, 0.003]
## ──────────────────────────────────────────────────────────────────────────────────
##
## 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]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.091 (0.032) 2.795 .005 ** [ 0.031, 0.156]
## 4.260 (Mean) 0.015 (0.022) 0.677 .498 [-0.028, 0.060]
## 5.444 (+ SD) -0.061 (0.032) -1.868 .062 . [-0.125, 0.003]
## ──────────────────────────────────────────────────────────────────────────────────
## Monte Carlo (Quasi-Bayesian) Confidence Interval
## (Effect, SE, and CI are estimated based on 1000 Monte Carlo samples.)
##
## Interaction Effect on "WA.AffectiveRuminationV.GroC" (M)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X01 * BA.AIOnlineCommunicationSkillsV 4.24 1 972 .040 *
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.AffectiveRuminationV.GroC" (M)
## (Conditional Effects [a] of X on M)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.189 (0.092) 2.049 .041 * [ 0.008, 0.369]
## 4.260 (Mean) 0.055 (0.065) 0.839 .402 [-0.073, 0.182]
## 5.444 (+ SD) -0.080 (0.092) -0.863 .388 [-0.260, 0.101]
## ──────────────────────────────────────────────────────────────────────────────────
##
## Running 1000 * 3 simulations...
## Indirect Path: "W.X01" (X) ==> "WA.AffectiveRuminationV.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]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.022 (0.013) 1.759 .079 . [ 0.002, 0.050]
## 4.260 (Mean) 0.006 (0.008) 0.781 .435 [-0.010, 0.024]
## 5.444 (+ SD) -0.010 (0.012) -0.796 .426 [-0.035, 0.013]
## ──────────────────────────────────────────────────────────────────────────────────
## 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. :)