#data8 =import("https://drive.google.com/uc?id=19uJnllP2hUtm_RwYB_ukjsSnBVvepGjN&export=download")%>%setDT()
#column_names <- names(data8)
#numeric_columns <- column_names[is.na(as.numeric(column_names))]
#HLM_ICC_rWG(data8, group="MNum", icc.var="LPERFV2")
#data8.GroC=group_mean_center(data8, names(data8),by="MNum", add.suffix=".GroC")
#export(data8.GroC,"trust esm variables 8.GroC.sav")X: Leader delegation=LDLV Level-1 Moderator [Mo1]: Leader emotional exhaustion =LEDV Level-2 Moderator [Mo2]: LMX=LMX
Mediator [M]: Follower work engagement =MWEV Y: Follower task performance(t+1)=LPERFV2
Controls: [C1] Interaction Freuencey=MIFV, [C2] Follower work engagement (t-1)=MWEV0, [C4] Leader delegation(t-1)=LDLV0, [C5] Leader emotional exhaustion (t-1)=LEDV0, [C3] Follower task performance(t-1)=LPERFV0
data8.GroC =import("https://drive.google.com/uc?id=1UflYVV6-fImExSKfGc34e58fwvsxxmZM&export=download")%>%setDT()
data8.GroC=setnames(data8.GroC,
old = c("LDLV.GroC", "LEDV", "MLMXP.GroC", "MWEV.GroC", "LPERF.GroC",
"MIFV.GroC", "MWEV0.GroC", "LDLV0.GroC", "LEDV0.GroC", "LPERFV0.GroC"),
new = c("X", "Mo1", "Mo2", "M", "Y",
"C1", "C2", "C3", "C4", "C5"))## Error: 在 'old' 中未找到如下列名：[LPERF.GroC]。请考虑设置 skip_absent=TRUE。data8.GroC =import("https://drive.google.com/uc?id=1UflYVV6-fImExSKfGc34e58fwvsxxmZM&export=download")%>%setDT()
data8.GroC$Mo2 <- scale(data8.GroC$MLMXP, center = TRUE, scale = FALSE)
data8.GroC=setnames(data8.GroC,
old = c("LDLV0.GroC", "LEEHV0.GroC", "MWEV.GroC", "LPERFV2.GroC",
"MIFV0.GroC","MIFV.GroC", "MWEV0.GroC", "LPERFV0.GroC", "LDLV.GroC", "LEDV.GroC"),
new = c("X", "Mo1", "M", "Y",
"C0","C1", "C2", "C3", "C4", "C5")) X=LDLV0; !leader delegation
C0=MIFV0;
C1=MIFV;
C2=MWEV0;
C3=LPERFV0;
C4=LDLV;
C5=LEDV;
!C6=LPERFV;
!Mo1=LEEHV0; !leader emotionale exhaustion
Mo2=MLMXP; !LSABP;
Me=MWEV;
Y=LPERFV2;
CENTER X(GROUPMEAN);
!CENTER X Mo1(GROUPMEAN);
!Inter1 = X*Mo1;
CENTER Mo2 (GRANDMEAN);data8.GroC =import("https://drive.google.com/uc?id=1UflYVV6-fImExSKfGc34e58fwvsxxmZM&export=download")%>%setDT()
data8.GroC$Mo2 <- scale(data8.GroC$MLMXP, center = TRUE, scale = FALSE)
data8.GroC=setnames(data8.GroC,
old = c("LDLV.GroC", "LEDV.GroC", "MWEV.GroC", "LPERFV2.GroC",
"MIFV.GroC", "MWEV0.GroC", "LDLV0.GroC", "LEDV0.GroC", "LPERFV0.GroC"),
new = c("X", "Mo1", "M", "Y",
"C1", "C2", "C3", "C4", "C5"))IV: LDLV Level-1 Moderator: LEDV Level-2 Moderator: LMX Mediator: MWEV DV: LPERFV2 Controls: MIFV, MWEV0, LDLV0, LEDV0, LPERFV0
# 定义变量名称列表
variables <- c("X", "Mo1", "Mo2", "M", "Y", "C1", "C2", "C3", "C4", "C5")#"C0",
# 创建一个结果列表来存储每个变量的 ICC 和 rWG 结果
results <- list()
# 循环计算每个变量的 ICC 和 rWG
for (var in variables) {
result <- HLM_ICC_rWG(data8.GroC, group = "MNum", icc.var = var)
results[[var]] <- result
}##
## ------ Sample Size Information ------
##
## Level 1: N = 1071 observations ("X")
## Level 2: K = 106 groups ("MNum")
##
## n (group sizes)
## Min. 6.0
## Median 10.0
## Mean 10.1
## Max. 16.0
##
## ------ ICC(1), ICC(2), and rWG ------
##
## ICC variable: "X"
##
## ICC(1) = 0.000 (non-independence of data)
## ICC(2) = 0.000 (reliability of group means)
##
## rWG variable: "X"
##
## rWG (within-group agreement for single-item measures)
## ─────────────────────────────────────────────
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## ─────────────────────────────────────────────
## rWG 0.103 0.782 0.866 0.833 0.920 1.000
## ─────────────────────────────────────────────
##
##
## ------ Sample Size Information ------
##
## Level 1: N = 1071 observations ("Mo1")
## Level 2: K = 106 groups ("MNum")
##
## n (group sizes)
## Min. 6.0
## Median 10.0
## Mean 10.1
## Max. 16.0
##
## ------ ICC(1), ICC(2), and rWG ------
##
## ICC variable: "Mo1"
##
## ICC(1) = 0.000 (non-independence of data)
## ICC(2) = 0.000 (reliability of group means)
##
## rWG variable: "Mo1"
##
## rWG (within-group agreement for single-item measures)
## ─────────────────────────────────────────────
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## ─────────────────────────────────────────────
## rWG 0.000 0.760 0.914 0.845 0.979 1.000
## ─────────────────────────────────────────────## Error in eval_f(x, ...): Downdated VtV is not positive definiteM1.MonX <- lmer(
M ~ X +
(X | MNum), # 随机斜率
na.action = na.exclude,
data = data8.GroC,
control = lmerControl(optimizer = "bobyqa")
)
#summary(M1.MonXC)Mo1
(Leader emotional exhaustion)M2a.MonXxMo1 <- lmer(
M ~ X * Mo1 +
(X | MNum), # 随机斜率
na.action = na.exclude,
data = data8.GroC,
control = lmerControl(optimizer = "bobyqa")
)
PROCESS(data8.GroC, y="M", x="X", mods="Mo1", cluster ="MNum", hlm.re.y = "(X | MNum)", center=FALSE)#, file="D2.doc")##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : M
## - Predictor (X) : X
## - Mediators (M) : -
## - Moderators (W) : Mo1
## - Covariates (C) : -
## - HLM Clusters : MNum
##
## Formula of Outcome:
## - M ~ X*Mo1 + (X | MNum)
##
## 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) M (2) M
## ───────────────────────────────────────────────
## (Intercept) -0.000 -0.000
## (0.026) (0.026)
## X 0.080 * 0.080 *
## (0.037) (0.037)
## Mo1 -0.005
## (0.032)
## X:Mo1 -0.003
## (0.029)
## ───────────────────────────────────────────────
## Marginal R^2 0.006 0.006
## Conditional R^2 0.031 0.031
## AIC 2760.910 2775.229
## BIC 2790.768 2815.040
## Num. obs. 1071 1071
## Num. groups: MNum 106 106
## Var: MNum (Intercept) 0.000 0.000
## Var: MNum X 0.026 0.027
## Cov: MNum (Intercept) X 0.000 0.000
## Var: Residual 0.741 0.742
## ───────────────────────────────────────────────
## 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 : 1071
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "M" (Y)
## ───────────────────────────────
## F df1 df2 p
## ───────────────────────────────
## X * Mo1 0.02 1 930 .902
## ───────────────────────────────
##
## Simple Slopes: "X" (X) ==> "M" (Y)
## ─────────────────────────────────────────────────────────────
## "Mo1" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────
## -0.855 (- SD) 0.083 (0.047) 1.754 .083 . [-0.010, 0.176]
## 0.000 (Mean) 0.080 (0.037) 2.143 .038 * [ 0.007, 0.153]
## 0.855 (+ SD) 0.077 (0.042) 1.844 .070 . [-0.005, 0.159]
## ─────────────────────────────────────────────────────────────M2b.MonXxMo1M <- lmer(
Y ~ X * Mo1 +
M +
(X | MNum), # 随机斜率
na.action = na.exclude,
data = data8.GroC,
control = lmerControl(optimizer = "bobyqa")
)
#summary(M2b.MonXC)Mo2 (LMX) on follower work engagementM3a.MonXxMo2 <- lmer(
M ~ X * Mo2 +
(X | MNum), # 随机斜率
na.action = na.exclude,
data = data8.GroC,
control = lmerControl(optimizer = "bobyqa")
)
PROCESS(data8.GroC, y="M", x="X", mods="Mo2", cluster ="MNum", hlm.re.y = "(X | MNum)", center=FALSE)#, file="D2.doc")##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : M
## - Predictor (X) : X
## - Mediators (M) : -
## - Moderators (W) : Mo2
## - Covariates (C) : -
## - HLM Clusters : MNum
##
## Formula of Outcome:
## - M ~ X*Mo2 + (X | MNum)
##
## 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) M (2) M
## ──────────────────────────────────────────────
## (Intercept) -0.000 -0.000
## (0.027) (0.027)
## X 0.070 0.073 *
## (0.036) (0.036)
## Mo2 -0.000
## (0.023)
## X:Mo2 0.045
## (0.032)
## ──────────────────────────────────────────────
## Marginal R^2 0.005 0.007
## Conditional R^2 0.027 0.027
## AIC 2704.442 2717.190
## BIC 2734.187 2756.850
## Num. obs. 1051 1051
## Num. groups: MNum 104 104
## Var: MNum (Intercept) 0.000 0.000
## Var: MNum X 0.023 0.021
## Cov: MNum (Intercept) X 0.000 0.000
## 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 : 1051 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "M" (Y)
## ───────────────────────────────
## F df1 df2 p
## ───────────────────────────────
## X * Mo2 1.96 1 45 .168
## ───────────────────────────────
##
## Simple Slopes: "X" (X) ==> "M" (Y)
## ─────────────────────────────────────────────────────────────
## "Mo2" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────
## -1.137 (- SD) 0.022 (0.050) 0.437 .664 [-0.076, 0.120]
## -0.000 (Mean) 0.073 (0.036) 2.036 .049 * [ 0.003, 0.144]
## 1.137 (+ SD) 0.125 (0.053) 2.357 .023 * [ 0.021, 0.229]
## ─────────────────────────────────────────────────────────────M3b.MonXxMo2M <- lmer(
Y ~ X * Mo2 +
M +
(X | MNum), # 随机斜率
na.action = na.exclude,
data = data8.GroC,
control = lmerControl(optimizer = "bobyqa")
)
#summary(M3b.MonXxMo2M)M4a.MonXxMo12 <- lmer(
M ~ X * Mo1 + X * Mo2 +
(X | MNum), # 随机斜率
na.action = na.exclude,
data = data8.GroC,
control = lmerControl(optimizer = "bobyqa")
)
PROCESS(data8.GroC, y="M", x="X", mods=cc("Mo1,Mo2"), cluster ="MNum", hlm.re.y = "(X | MNum)", center=FALSE)#, file="D2.doc")##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 2 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Parallel Moderation (2 mods; 2-way)
## - Outcome (Y) : M
## - Predictor (X) : X
## - Mediators (M) : -
## - Moderators (W) : Mo1, Mo2
## - Covariates (C) : -
## - HLM Clusters : MNum
##
## Formula of Outcome:
## - M ~ X*Mo1 + X*Mo2 + (X | MNum)
##
## 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) M (2) M
## ──────────────────────────────────────────────
## (Intercept) -0.000 -0.000
## (0.027) (0.027)
## X 0.070 0.074 *
## (0.036) (0.036)
## Mo1 -0.002
## (0.032)
## Mo2 0.000
## (0.023)
## X:Mo1 -0.007
## (0.029)
## X:Mo2 0.045
## (0.032)
## ──────────────────────────────────────────────
## Marginal R^2 0.005 0.007
## Conditional R^2 0.027 0.028
## AIC 2704.442 2731.467
## BIC 2734.187 2781.042
## Num. obs. 1051 1051
## Num. groups: MNum 104 104
## Var: MNum (Intercept) 0.000 0.000
## Var: MNum X 0.023 0.021
## Cov: MNum (Intercept) X 0.000 0.000
## Var: Residual 0.739 0.741
## ──────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.2.0)
## Effect Type : Parallel Moderation (2 mods; 2-way) (Model 2)
## Sample Size : 1051 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effects on "M" (Y)
## ───────────────────────────────
## F df1 df2 p
## ───────────────────────────────
## X * Mo1 0.05 1 877 .817
## X * Mo2 1.95 1 44 .170
## ───────────────────────────────
##
## Simple Slopes: "X" (X) ==> "M" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "Mo2" "Mo1" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## -1.137 (- SD) -0.851 (- SD) 0.029 (0.058) 0.489 .627 [-0.086, 0.143]
## -1.137 (- SD) 0.000 (Mean) 0.023 (0.050) 0.454 .652 [-0.076, 0.122]
## -1.137 (- SD) 0.851 (+ SD) 0.017 (0.053) 0.323 .748 [-0.087, 0.122]
## -0.000 (Mean) -0.851 (- SD) 0.080 (0.046) 1.722 .089 . [-0.011, 0.171]
## -0.000 (Mean) 0.000 (Mean) 0.074 (0.036) 2.039 .048 * [ 0.003, 0.146]
## -0.000 (Mean) 0.851 (+ SD) 0.069 (0.041) 1.677 .099 . [-0.012, 0.149]
## 1.137 (+ SD) -0.851 (- SD) 0.131 (0.060) 2.182 .032 * [ 0.013, 0.249]
## 1.137 (+ SD) 0.000 (Mean) 0.126 (0.053) 2.363 .022 * [ 0.021, 0.230]
## 1.137 (+ SD) 0.851 (+ SD) 0.120 (0.057) 2.118 .038 * [ 0.009, 0.231]
## ───────────────────────────────────────────────────────────────────────────M4b.MonXxMo12M <- lmer(
Y ~ X * Mo1 + X * Mo2 +
M +
(X | MNum), # 随机斜率
na.action = na.exclude,
data = data8.GroC,
control = lmerControl(optimizer = "bobyqa")
)
#summary(M4b.MonXC)model_summary(list(M1.MonX,M2a.MonXxMo1,M3a.MonXxMo2,M4a.MonXxMo12,M2b.MonXxMo1M,M3b.MonXxMo2M,M4b.MonXxMo12M))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) M (2) M (3) M (4) M (5) Y (6) Y (7) Y
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) -0.000 -0.000 -0.000 -0.000 0.000 0.001 0.000
## (0.026) (0.026) (0.027) (0.027) (0.024) (0.025) (0.025)
## X 0.080 * 0.080 * 0.073 * 0.074 * 0.063 0.050 0.062
## (0.037) (0.037) (0.036) (0.036) (0.039) (0.038) (0.039)
## Mo1 -0.005 -0.002 -0.018 -0.011
## (0.032) (0.032) (0.029) (0.030)
## X:Mo1 -0.003 -0.007 -0.059 * -0.059 *
## (0.029) (0.029) (0.026) (0.026)
## Mo2 -0.000 0.000 -0.000 0.001
## (0.023) (0.023) (0.022) (0.022)
## X:Mo2 0.045 0.045 0.028 0.027
## (0.032) (0.032) (0.034) (0.035)
## M 0.014 0.021 0.020
## (0.029) (0.029) (0.029)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.006 0.006 0.007 0.007 0.010 0.005 0.010
## Conditional R^2 0.031 0.031 0.027 0.028 0.067 0.059 0.067
## AIC 2760.910 2775.229 2717.190 2731.467 2269.016 2237.369 2246.696
## BIC 2790.768 2815.040 2756.850 2781.042 2312.865 2281.049 2300.082
## Num. obs. 1071 1071 1051 1051 965 947 947
## Num. groups: MNum 106 106 104 104 106 104 104
## Var: MNum (Intercept) 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## Var: MNum X 0.026 0.027 0.021 0.021 0.047 0.044 0.047
## Cov: MNum (Intercept) X 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## Var: Residual 0.741 0.742 0.740 0.741 0.565 0.572 0.569
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.M1.MonXC <- lmer(
M ~ X + C1 + C2 + C3 + C4 + C5 +
(X | MNum), # 随机斜率
na.action = na.exclude,
data = data8.GroC,
control = lmerControl(optimizer = "bobyqa")
)
#summary(M1.MonXC)Mo1
(Leader emotional exhaustion)M2a.MonXxMo1C <- lmer(
M ~ X * Mo1 +
C1 + C2 + C3 + C4 + C5 +
(X | MNum), # 随机斜率
na.action = na.exclude,
data = data8.GroC,
control = lmerControl(optimizer = "bobyqa")
)
PROCESS(data8.GroC, y="M", x="X", mods="Mo1", cluster ="MNum", hlm.re.y = "(X | MNum)", center=FALSE,
covs=cc("C1,C2,C3,C4,C5"))#, file="D2.doc")##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : M
## - Predictor (X) : X
## - Mediators (M) : -
## - Moderators (W) : Mo1
## - Covariates (C) : C1, C2, C3, C4, C5
## - HLM Clusters : MNum
##
## Formula of Outcome:
## - M ~ C1 + C2 + C3 + C4 + C5 + X*Mo1 + (X | MNum)
##
## 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) M (2) M
## ───────────────────────────────────────────────────
## (Intercept) 0.013 0.012
## (0.026) (0.026)
## C1 0.201 *** 0.202 ***
## (0.023) (0.023)
## C2 0.062 * 0.062 *
## (0.031) (0.031)
## C3 0.062 0.063
## (0.035) (0.035)
## C4 0.011 0.012
## (0.031) (0.031)
## C5 -0.046 -0.048
## (0.036) (0.036)
## X 0.051 0.051
## (0.033) (0.033)
## Mo1 -0.033
## (0.032)
## X:Mo1 -0.012
## (0.028)
## ───────────────────────────────────────────────────
## Marginal R^2 0.087 0.089
## Conditional R^2 0.087 0.089
## AIC 2408.821 2421.914
## BIC 2462.414 2485.252
## Num. obs. 965 965
## Num. groups: MNum 106 106
## Var: MNum (Intercept) 0.000 0.000
## Var: MNum X 0.000 0.000
## Cov: MNum (Intercept) X 0.000 0.000
## Var: Residual 0.674 0.674
## ───────────────────────────────────────────────────
## 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 : 965 (106 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "M" (Y)
## ───────────────────────────────
## F df1 df2 p
## ───────────────────────────────
## X * Mo1 0.18 1 956 .669
## ───────────────────────────────
##
## Simple Slopes: "X" (X) ==> "M" (Y)
## ─────────────────────────────────────────────────────────────
## "Mo1" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────
## -0.857 (- SD) 0.061 (0.043) 1.409 .159 [-0.024, 0.146]
## -0.012 (Mean) 0.051 (0.033) 1.532 .126 [-0.014, 0.117]
## 0.833 (+ SD) 0.041 (0.038) 1.084 .279 [-0.033, 0.116]
## ─────────────────────────────────────────────────────────────M2b.MonXxMo1MC <- lmer(
Y ~ X * Mo1 +
M + C1 + C2 + C3 + C4 + C5 +
(X | MNum), # 随机斜率
na.action = na.exclude,
data = data8.GroC,
control = lmerControl(optimizer = "bobyqa")
)
#summary(M2b.MonXC)Mo2 (LMX) on follower work engagementM3a.MonXxMo2C <- lmer(
M ~ X * Mo2 +
C1 + C2 + C3 + C4 + C5 +
(X | MNum), # 随机斜率
na.action = na.exclude,
data = data8.GroC,
control = lmerControl(optimizer = "bobyqa")
)
PROCESS(data8.GroC, y="M", x="X", mods="Mo2", cluster ="MNum", hlm.re.y = "(X| MNum)", center=FALSE,
covs=cc("C1,C2,C3,C4,C5"))#, file="D2.doc")##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : M
## - Predictor (X) : X
## - Mediators (M) : -
## - Moderators (W) : Mo2
## - Covariates (C) : C1, C2, C3, C4, C5
## - HLM Clusters : MNum
##
## Formula of Outcome:
## - M ~ C1 + C2 + C3 + C4 + C5 + X*Mo2 + (X| MNum)
##
## 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) M (2) M
## ───────────────────────────────────────────────────
## (Intercept) 0.009 0.008
## (0.027) (0.027)
## C1 0.201 *** 0.201 ***
## (0.024) (0.024)
## C2 0.063 * 0.061
## (0.032) (0.032)
## C3 0.063 0.059
## (0.035) (0.036)
## C4 0.013 0.011
## (0.032) (0.032)
## C5 -0.048 -0.046
## (0.037) (0.037)
## X 0.049 0.054
## (0.033) (0.034)
## Mo2 -0.014
## (0.024)
## X:Mo2 0.037
## (0.030)
## ───────────────────────────────────────────────────
## Marginal R^2 0.084 0.086
## Conditional R^2 0.084 0.086
## AIC 2380.033 2392.962
## BIC 2433.419 2456.055
## Num. obs. 947 947
## Num. groups: MNum 104 104
## Var: MNum (Intercept) 0.000 0.000
## Var: MNum X 0.000 0.000
## Cov: MNum (Intercept) X 0.000 0.000
## Var: Residual 0.685 0.685
## ───────────────────────────────────────────────────
## 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 : 947 (124 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "M" (Y)
## ───────────────────────────────
## F df1 df2 p
## ───────────────────────────────
## X * Mo2 1.51 1 938 .219
## ───────────────────────────────
##
## Simple Slopes: "X" (X) ==> "M" (Y)
## ─────────────────────────────────────────────────────────────
## "Mo2" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────
## -1.135 (- SD) 0.013 (0.045) 0.278 .781 [-0.076, 0.101]
## 0.001 (Mean) 0.055 (0.034) 1.622 .105 [-0.011, 0.120]
## 1.137 (+ SD) 0.096 (0.050) 1.916 .056 . [-0.002, 0.195]
## ─────────────────────────────────────────────────────────────M3b.MonXxMo2MC <- lmer(
Y ~ X * Mo2 +
M + C1 + C2 + C3 + C4 + C5 +
(X | MNum), # 随机斜率
na.action = na.exclude,
data = data8.GroC,
control = lmerControl(optimizer = "bobyqa")
)
#summary(M3b.MonXC)M4a.MonXxMo12C <- lmer(
M ~ X * Mo1 + X * Mo2 +
C1 + C2 + C3 + C4 + C5 +
(X | MNum), # 随机斜率
na.action = na.exclude,
data = data8.GroC,
control = lmerControl(optimizer = "bobyqa")
)
PROCESS(data8.GroC, y="M", x="X", mods=cc("Mo1,Mo2"), cluster ="MNum", hlm.re.y = "(X | MNum)", center=FALSE,
covs=cc("C1,C2,C3,C4,C5"))#, file="D2.doc")##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 2 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Parallel Moderation (2 mods; 2-way)
## - Outcome (Y) : M
## - Predictor (X) : X
## - Mediators (M) : -
## - Moderators (W) : Mo1, Mo2
## - Covariates (C) : C1, C2, C3, C4, C5
## - HLM Clusters : MNum
##
## Formula of Outcome:
## - M ~ C1 + C2 + C3 + C4 + C5 + X*Mo1 + X*Mo2 + (X | MNum)
##
## 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) M (2) M
## ───────────────────────────────────────────────────
## (Intercept) 0.009 0.007
## (0.027) (0.027)
## C1 0.201 *** 0.202 ***
## (0.024) (0.024)
## C2 0.063 * 0.060
## (0.032) (0.032)
## C3 0.063 0.060
## (0.035) (0.036)
## C4 0.013 0.012
## (0.032) (0.032)
## C5 -0.048 -0.047
## (0.037) (0.037)
## X 0.049 0.055
## (0.033) (0.034)
## Mo1 -0.031
## (0.032)
## Mo2 -0.014
## (0.024)
## X:Mo1 -0.011
## (0.028)
## X:Mo2 0.037
## (0.030)
## ───────────────────────────────────────────────────
## Marginal R^2 0.084 0.087
## Conditional R^2 0.084 0.087
## AIC 2380.033 2406.190
## BIC 2433.419 2478.990
## Num. obs. 947 947
## Num. groups: MNum 104 104
## Var: MNum (Intercept) 0.000 0.000
## Var: MNum X 0.000 0.000
## Cov: MNum (Intercept) X 0.000 0.000
## Var: Residual 0.685 0.686
## ───────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.2.0)
## Effect Type : Parallel Moderation (2 mods; 2-way) (Model 2)
## Sample Size : 947 (124 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effects on "M" (Y)
## ───────────────────────────────
## F df1 df2 p
## ───────────────────────────────
## X * Mo1 0.15 1 936 .698
## X * Mo2 1.50 1 936 .221
## ───────────────────────────────
##
## Simple Slopes: "X" (X) ==> "M" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "Mo2" "Mo1" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## -1.135 (- SD) -0.851 (- SD) 0.022 (0.054) 0.410 .682 [-0.084, 0.128]
## -1.135 (- SD) -0.011 (Mean) 0.013 (0.046) 0.285 .776 [-0.077, 0.103]
## -1.135 (- SD) 0.830 (+ SD) 0.004 (0.049) 0.080 .936 [-0.091, 0.099]
## 0.001 (Mean) -0.851 (- SD) 0.064 (0.044) 1.456 .146 [-0.022, 0.150]
## 0.001 (Mean) -0.011 (Mean) 0.055 (0.034) 1.613 .107 [-0.012, 0.121]
## 0.001 (Mean) 0.830 (+ SD) 0.046 (0.039) 1.182 .237 [-0.030, 0.121]
## 1.137 (+ SD) -0.851 (- SD) 0.106 (0.057) 1.850 .065 . [-0.006, 0.218]
## 1.137 (+ SD) -0.011 (Mean) 0.097 (0.050) 1.913 .056 . [-0.002, 0.196]
## 1.137 (+ SD) 0.830 (+ SD) 0.087 (0.054) 1.612 .107 [-0.019, 0.194]
## ───────────────────────────────────────────────────────────────────────────M4b.MonXxMo12MC <- lmer(
Y ~ X * Mo1 + X * Mo2 +
M + C1 + C2 + C3 + C4 + C5 +
(X | MNum), # 随机斜率
na.action = na.exclude,
data = data8.GroC,
control = lmerControl(optimizer = "bobyqa")
)
#summary(M4b.MonXC)model_summary(list(M1.MonXC,M2a.MonXxMo1C,M3a.MonXxMo2C,M4a.MonXxMo12C,M2b.MonXxMo1MC,M3b.MonXxMo2MC,M4b.MonXxMo12MC))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) M (2) M (3) M (4) M (5) Y (6) Y (7) Y
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 0.013 0.012 0.008 0.007 -0.006 -0.005 -0.006
## (0.026) (0.026) (0.027) (0.027) (0.025) (0.025) (0.025)
## X 0.051 0.051 0.054 0.055 0.052 0.039 0.047
## (0.033) (0.033) (0.034) (0.034) (0.045) (0.045) (0.045)
## C1 0.201 *** 0.202 *** 0.201 *** 0.202 *** -0.019 -0.010 -0.010
## (0.023) (0.023) (0.024) (0.024) (0.023) (0.024) (0.024)
## C2 0.062 * 0.062 * 0.061 0.060 0.036 0.039 0.038
## (0.031) (0.031) (0.032) (0.032) (0.030) (0.030) (0.030)
## C3 0.062 0.063 0.059 0.060 -0.037 -0.040 -0.039
## (0.035) (0.035) (0.036) (0.036) (0.033) (0.034) (0.034)
## C4 0.011 0.012 0.011 0.012 -0.031 -0.025 -0.029
## (0.031) (0.031) (0.032) (0.032) (0.029) (0.030) (0.030)
## C5 -0.046 -0.048 -0.046 -0.047 -0.110 ** -0.114 ** -0.112 **
## (0.036) (0.036) (0.037) (0.037) (0.035) (0.035) (0.035)
## Mo1 -0.033 -0.031 0.003 0.011
## (0.032) (0.032) (0.030) (0.031)
## X:Mo1 -0.012 -0.011 -0.047 -0.045
## (0.028) (0.028) (0.027) (0.027)
## Mo2 -0.014 -0.014 0.018 0.019
## (0.024) (0.024) (0.022) (0.022)
## X:Mo2 0.037 0.037 0.041 0.039
## (0.030) (0.030) (0.039) (0.039)
## M 0.034 0.034 0.033
## (0.031) (0.032) (0.032)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.087 0.089 0.086 0.087 0.031 0.030 0.034
## Conditional R^2 0.087 0.089 0.086 0.087 0.108 0.105 0.111
## AIC 2408.821 2421.914 2392.962 2406.190 1988.659 1960.618 1972.245
## BIC 2462.414 2485.252 2456.055 2478.990 2055.239 2026.936 2048.036
## Num. obs. 965 965 947 947 859 843 843
## Num. groups: MNum 106 106 104 104 106 104 104
## Var: MNum (Intercept) 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## Var: MNum X 0.000 0.000 0.000 0.000 0.067 0.065 0.067
## Cov: MNum (Intercept) X 0.000 0.000 0.000 0.000 0.003 0.002 0.002
## Var: Residual 0.674 0.674 0.685 0.686 0.517 0.523 0.521
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.