1 FUNCTIONS

2 DATA PREPARATION

2.1 Load all sheets of an Excel

#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

2.2 Same day: All group mean centered

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。

2.3 Reproduce HRs (f - mod lmx.out): All group mean centered

data8.GroC =import("https://drive.google.com/uc?id=1UflYVV6-fImExSKfGc34e58fwvsxxmZM&export=download")%>%setDT()

data8.GroC=setnames(data8.GroC, 
         old = c("LDLV0.GroC", "LEEHV0.GroC", "MLMXP", "MWEV.GroC", "LPERFV2.GroC", 
                 "MIFV0.GroC","MIFV.GroC", "MWEV0.GroC", "LPERFV0.GroC", "LDLV.GroC", "LEDV.GroC"),
         new = c("X", "Mo1", "Mo2", "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);

# 定义变量名称列表
variables <- c("X", "Mo1", "Mo2", "M", "Y", "C0", "C1", "C2", "C3", "C4", "C5")

# 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 = 965 observations ("X")
## Level 2: K = 106 groups ("MNum")
## 
##        n (group sizes)
## Min.             5.000
## Median           9.000
## Mean             9.104
## Max.            15.000
## 
## ------ 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  0.000   0.766  0.868 0.825   0.938 1.000
## ─────────────────────────────────────────────
## 
## 
## ------ Sample Size Information ------
## 
## Level 1: N = 965 observations ("Mo1")
## Level 2: K = 106 groups ("MNum")
## 
##        n (group sizes)
## Min.             5.000
## Median           9.000
## Mean             9.104
## Max.            15.000
## 
## ------ 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   0.616  0.853 0.768   0.971 1.000
## ─────────────────────────────────────────────
## 
## 
## ------ Sample Size Information ------
## 
## Level 1: N = 1051 observations ("Mo2")
## Level 2: K = 104 groups ("MNum")
## 
##        n (group sizes)
## Min.              6.00
## Median           10.00
## Mean             10.11
## Max.             16.00
## 
## ------ ICC(1), ICC(2), and rWG ------
## 
## ICC variable: "Mo2"
## 
## ICC(1) = 1.000 (non-independence of data)
## ICC(2) = 1.000 (reliability of group means)
## 
## rWG variable: "Mo2"
## 
## rWG (within-group agreement for single-item measures)
## ─────────────────────────────────────────────
##       Min. 1st Qu. Median  Mean 3rd Qu.  Max.
## ─────────────────────────────────────────────
## rWG  1.000   1.000  1.000 1.000   1.000 1.000
## ─────────────────────────────────────────────
## 
## 
## ------ Sample Size Information ------
## 
## Level 1: N = 1071 observations ("M")
## 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: "M"
## 
## ICC(1) = 0.000 (non-independence of data)
## ICC(2) = 0.000 (reliability of group means)
## 
## rWG variable: "M"
## 
## rWG (within-group agreement for single-item measures)
## ─────────────────────────────────────────────
##       Min. 1st Qu. Median  Mean 3rd Qu.  Max.
## ─────────────────────────────────────────────
## rWG  0.169   0.819  0.925 0.865   0.972 1.000
## ─────────────────────────────────────────────
## 
## 
## ------ Sample Size Information ------
## 
## Level 1: N = 965 observations ("Y")
## Level 2: K = 106 groups ("MNum")
## 
##        n (group sizes)
## Min.             5.000
## Median           9.000
## Mean             9.104
## Max.            15.000
## 
## ------ ICC(1), ICC(2), and rWG ------
## 
## ICC variable: "Y"
## 
## ICC(1) = 0.000 (non-independence of data)
## ICC(2) = 0.000 (reliability of group means)
## 
## rWG variable: "Y"
## 
## rWG (within-group agreement for single-item measures)
## ─────────────────────────────────────────────
##       Min. 1st Qu. Median  Mean 3rd Qu.  Max.
## ─────────────────────────────────────────────
## rWG  0.240   0.842  0.933 0.881   0.980 1.000
## ─────────────────────────────────────────────
## 
## 
## ------ Sample Size Information ------
## 
## Level 1: N = 965 observations ("C0")
## Level 2: K = 106 groups ("MNum")
## 
##        n (group sizes)
## Min.             5.000
## Median           9.000
## Mean             9.104
## Max.            15.000
## 
## ------ ICC(1), ICC(2), and rWG ------
## 
## ICC variable: "C0"
## 
## ICC(1) = 0.000 (non-independence of data)
## ICC(2) = 0.000 (reliability of group means)
## 
## rWG variable: "C0"
## 
## rWG (within-group agreement for single-item measures)
## ─────────────────────────────────────────────
##       Min. 1st Qu. Median  Mean 3rd Qu.  Max.
## ─────────────────────────────────────────────
## rWG  0.000   0.705  0.868 0.781   0.950 1.000
## ─────────────────────────────────────────────
## 
## 
## ------ Sample Size Information ------
## 
## Level 1: N = 1071 observations ("C1")
## 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: "C1"
## 
## ICC(1) = 0.000 (non-independence of data)
## ICC(2) = 0.000 (reliability of group means)
## 
## rWG variable: "C1"
## 
## rWG (within-group agreement for single-item measures)
## ─────────────────────────────────────────────
##       Min. 1st Qu. Median  Mean 3rd Qu.  Max.
## ─────────────────────────────────────────────
## rWG  0.000   0.690  0.844 0.772   0.942 1.000
## ─────────────────────────────────────────────
## 
## 
## ------ Sample Size Information ------
## 
## Level 1: N = 965 observations ("C2")
## Level 2: K = 106 groups ("MNum")
## 
##        n (group sizes)
## Min.             5.000
## Median           9.000
## Mean             9.104
## Max.            15.000
## 
## ------ ICC(1), ICC(2), and rWG ------
## 
## ICC variable: "C2"
## 
## ICC(1) = 0.000 (non-independence of data)
## ICC(2) = 0.000 (reliability of group means)
## 
## rWG variable: "C2"
## 
## rWG (within-group agreement for single-item measures)
## ─────────────────────────────────────────────
##       Min. 1st Qu. Median  Mean 3rd Qu.  Max.
## ─────────────────────────────────────────────
## rWG  0.034   0.810  0.926 0.859   0.975 1.000
## ─────────────────────────────────────────────
## 
## 
## ------ Sample Size Information ------
## 
## Level 1: N = 965 observations ("C3")
## Level 2: K = 106 groups ("MNum")
## 
##        n (group sizes)
## Min.             5.000
## Median           9.000
## Mean             9.104
## Max.            15.000
## 
## ------ ICC(1), ICC(2), and rWG ------
## 
## ICC variable: "C3"
## 
## ICC(1) = 0.000 (non-independence of data)
## ICC(2) = 0.000 (reliability of group means)
## 
## rWG variable: "C3"
## 
## rWG (within-group agreement for single-item measures)
## ─────────────────────────────────────────────
##       Min. 1st Qu. Median  Mean 3rd Qu.  Max.
## ─────────────────────────────────────────────
## rWG  0.096   0.857  0.939 0.873   0.980 1.000
## ─────────────────────────────────────────────
## 
## 
## ------ Sample Size Information ------
## 
## Level 1: N = 1071 observations ("C4")
## 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: "C4"
## 
## ICC(1) = 0.000 (non-independence of data)
## ICC(2) = 0.000 (reliability of group means)
## 
## rWG variable: "C4"
## 
## 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 ("C5")
## 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: "C5"
## 
## ICC(1) = 0.000 (non-independence of data)
## ICC(2) = 0.000 (reliability of group means)
## 
## rWG variable: "C5"
## 
## 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
## ─────────────────────────────────────────────

#Describe(data8.GroC[,.variables])

3 ANALYSIS WITHOUT CONTROL

3.1 Model 1: Testing the main effect

M1.MonX <- lmer(
  M ~ X + 
    (X | MNum),  # 随机斜率
  na.action = na.exclude, 
  data = data8.GroC, 
  control = lmerControl(optimizer = "bobyqa")
)
#summary(M1.MonXC)

3.2 Model 2a: Adding the moderation effect of `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")
)
#summary(M2a.MonXC)

3.3 Model 2b: Testing the moderation effect on follower task performance (Y)

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)

3.4 Model 3a: Adding the cross-level moderation effect of `Mo2` (LMX) on follower work engagement

M3a.MonXxMo2 <- lmer(
  M ~ X * Mo2 + 
    
    (X | MNum),  # 随机斜率
  na.action = na.exclude, 
  data = data8.GroC, 
  control = lmerControl(optimizer = "bobyqa")
)
#summary(M3a.MonXC)

3.5 Model 3b: Testing the cross-level moderation effect on follower task performance

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)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Y ~ X * Mo2 + M + (X | MNum)
##    Data: data8.GroC
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 1950
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
## -5.010 -0.402  0.025  0.396  5.717 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr 
##  MNum     (Intercept) 3.09e-05 0.00555       
##           X           8.55e-03 0.09245  -1.00
##  Residual             5.71e-01 0.75568       
## Number of obs: 843, groups:  MNum, 104
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)
## (Intercept)  -0.1095     0.1221 814.0225   -0.90     0.37
## X            -0.0854     0.1567 149.7884   -0.54     0.59
## Mo2           0.0196     0.0230 813.8790    0.85     0.39
## M             0.0376     0.0313 837.8634    1.20     0.23
## X:Mo2         0.0113     0.0301 167.4704    0.38     0.71
## 
## Correlation of Fixed Effects:
##       (Intr) X      Mo2    M     
## X     -0.027                     
## Mo2   -0.977  0.026              
## M     -0.016  0.123  0.016       
## X:Mo2  0.026 -0.978 -0.026 -0.138
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

3.6 Model 4a: Testing both moderators (Mo1 and Mo2) on follower work engagement

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")
)
#summary(M4a.MonXC)

3.7 Model 4b: Testing both moderators (Mo1 and Mo2) on follower task performance

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)                 0.007      0.006      0.085         0.083        -0.009      -0.110     -0.108  
##                            (0.027)    (0.027)    (0.129)       (0.130)       (0.026)     (0.122)    (0.122) 
## X                           0.052      0.059     -0.606 ***    -0.585 ***    -0.015      -0.085     -0.062  
##                            (0.036)    (0.036)    (0.164)       (0.163)       (0.033)     (0.157)    (0.157) 
## Mo1                                    0.002                    0.002        -0.048                 -0.046  
##                                       (0.027)                  (0.027)       (0.025)                (0.025) 
## X:Mo1                                 -0.030                   -0.026        -0.047 *               -0.047 *
##                                       (0.024)                  (0.024)       (0.022)                (0.023) 
## Mo2                                              -0.016        -0.016                     0.020      0.019  
##                                                  (0.024)       (0.024)                   (0.023)    (0.023) 
## X:Mo2                                             0.128 ***     0.126 ***                 0.011      0.009  
##                                                  (0.031)       (0.031)                   (0.030)    (0.030) 
## M                                                                             0.035       0.038      0.036  
##                                                                              (0.031)     (0.031)    (0.031) 
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                0.003      0.004      0.022         0.023         0.012       0.004      0.013  
## Conditional R^2             0.018      0.016      0.031         0.030         0.023       0.015      0.024  
## AIC                      2458.597   2472.100   2423.185      2436.960      1991.007    1968.254   1976.025  
## BIC                      2487.830   2511.077   2462.012      2485.493      2033.809    2010.887   2028.132  
## 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                 0.016      0.012      0.009         0.007         0.009       0.009      0.009  
## Cov: MNum (Intercept) X     0.000      0.000      0.000         0.000         0.000      -0.001      0.000  
## Var: Residual               0.722      0.725      0.724         0.726         0.561       0.571      0.567  
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.

4 ANALYSIS WITH CONTROL

4.1 Model 1: Testing the main effect

M1.MonXC <- lmer(
  M ~ X + C0+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)

4.2 Model 2a: Adding the moderation effect of `Mo1` (Leader emotional exhaustion)

M2a.MonXxMo1C <- lmer(
  M ~ X * Mo1 + 
    C0+C1 + C2 + C3 + C4 + C5 + 
    (X | MNum),  # 随机斜率
  na.action = na.exclude, 
  data = data8.GroC, 
  control = lmerControl(optimizer = "bobyqa")
)
#summary(M2a.MonXC)

4.3 Model 2b: Testing the moderation effect on follower task performance (Y)

M2b.MonXxMo1MC <- lmer(
  Y ~ X * Mo1 + 
    M + C0+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)

4.4 Model 3a: Adding the cross-level moderation effect of `Mo2` (LMX) on follower work engagement

M3a.MonXxMo2C <- lmer(
  M ~ X * Mo2 + 
    C0+C1 + C2 + C3 + C4 + C5 + 
    (X | MNum),  # 随机斜率
  na.action = na.exclude, 
  data = data8.GroC, 
  control = lmerControl(optimizer = "bobyqa")
)
#summary(M3a.MonXC)

4.5 Model 3b: Testing the cross-level moderation effect on follower task performance

M3b.MonXxMo2MC <- lmer(
  Y ~ X * Mo2 + 
    M + C0+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)

4.6 Model 4a: Testing both moderators (Mo1 and Mo2) on follower work engagement

M4a.MonXxMo12C <- lmer(
  M ~ X * Mo1 + X * Mo2 + 
    C0+C1 + C2 + C3 + C4 + C5 + 
    (X | MNum),  # 随机斜率
  na.action = na.exclude, 
  data = data8.GroC, 
  control = lmerControl(optimizer = "bobyqa")
)
#summary(M4a.MonXC)

4.7 Model 4b: Testing both moderators (Mo1 and Mo2) on follower task performance

M4b.MonXxMo12MC <- lmer(
  Y ~ X * Mo1 + X * Mo2 + 
    M + C0+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         0.011         0.085         0.082        -0.009      -0.105     -0.103  
##                            (0.026)       (0.026)       (0.125)       (0.126)       (0.026)     (0.122)    (0.122) 
## X                           0.063         0.071 *      -0.390 *      -0.373 *       0.014      -0.012      0.019  
##                            (0.035)       (0.036)       (0.157)       (0.157)       (0.036)     (0.161)    (0.161) 
## C0                          0.007         0.006         0.010         0.010        -0.025      -0.024     -0.024  
##                            (0.024)       (0.024)       (0.024)       (0.024)       (0.023)     (0.024)    (0.024) 
## C1                          0.201 ***     0.203 ***     0.193 ***     0.195 ***    -0.028      -0.020     -0.021  
##                            (0.023)       (0.023)       (0.024)       (0.024)       (0.024)     (0.024)    (0.024) 
## C2                          0.060         0.060         0.054         0.054         0.060       0.059      0.062  
##                            (0.033)       (0.033)       (0.033)       (0.033)       (0.032)     (0.032)    (0.032) 
## C3                         -0.050        -0.049        -0.032        -0.032        -0.077 *    -0.069     -0.078 *
##                            (0.036)       (0.036)       (0.037)       (0.037)       (0.036)     (0.036)    (0.036) 
## C4                          0.049         0.046         0.042         0.039         0.004      -0.001     -0.001  
##                            (0.033)       (0.033)       (0.033)       (0.034)       (0.032)     (0.033)    (0.033) 
## C5                         -0.032        -0.031        -0.030        -0.029         0.005       0.002      0.012  
##                            (0.031)       (0.032)       (0.032)       (0.032)       (0.031)     (0.031)    (0.031) 
## Mo1                                       0.010                       0.011        -0.058 *               -0.056 *
##                                          (0.026)                     (0.027)       (0.025)                (0.026) 
## X:Mo1                                    -0.033                      -0.030        -0.047 *               -0.047 *
##                                          (0.023)                     (0.023)       (0.023)                (0.023) 
## Mo2                                                    -0.015        -0.014                     0.019      0.018  
##                                                        (0.024)       (0.024)                   (0.023)    (0.023) 
## X:Mo2                                                   0.088 **      0.086 **                  0.002     -0.001  
##                                                        (0.029)       (0.029)                   (0.030)    (0.030) 
## M                                                                                   0.044       0.045      0.045  
##                                                                                    (0.032)     (0.033)    (0.033) 
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2                0.088         0.090         0.094         0.095         0.023       0.014      0.024  
## Conditional R^2             0.088         0.090         0.094         0.095         0.033       0.024      0.034  
## AIC                      2415.416      2428.343      2392.466      2405.716      2024.569    2003.462   2009.627  
## BIC                      2473.882      2496.553      2460.412      2483.369      2095.906    2074.516   2090.155  
## 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         0.008       0.008      0.008  
## Cov: MNum (Intercept) X     0.000         0.000         0.000         0.000        -0.000      -0.001      0.000  
## Var: Residual               0.674         0.674         0.680         0.680         0.559       0.570      0.565  
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.

LEADER DAILY DELEGATION AND PERFORMANCE

Prof. Yucheng Zhang

2024-11-24

1 FUNCTIONS

2 DATA PREPARATION

2.1 Load all sheets of an Excel

2.2 Same day: All group mean centered

2.3 Reproduce HRs (f - mod lmx.out): All group mean centered

3 ANALYSIS WITHOUT CONTROL

3.1 Model 1: Testing the main effect

3.2 Model 2a: Adding the moderation effect of `Mo1` (Leader emotional exhaustion)

3.3 Model 2b: Testing the moderation effect on follower task performance (Y)

3.4 Model 3a: Adding the cross-level moderation effect of `Mo2` (LMX) on follower work engagement

3.5 Model 3b: Testing the cross-level moderation effect on follower task performance

3.6 Model 4a: Testing both moderators (Mo1 and Mo2) on follower work engagement

3.7 Model 4b: Testing both moderators (Mo1 and Mo2) on follower task performance

4 ANALYSIS WITH CONTROL

4.1 Model 1: Testing the main effect

4.2 Model 2a: Adding the moderation effect of `Mo1` (Leader emotional exhaustion)

4.3 Model 2b: Testing the moderation effect on follower task performance (Y)

4.4 Model 3a: Adding the cross-level moderation effect of `Mo2` (LMX) on follower work engagement

4.5 Model 3b: Testing the cross-level moderation effect on follower task performance

4.6 Model 4a: Testing both moderators (Mo1 and Mo2) on follower work engagement

4.7 Model 4b: Testing both moderators (Mo1 and Mo2) on follower task performance

LEADER DAILY DELEGATION AND PERFORMANCE

Prof. Yucheng Zhang

2024-11-24

1 FUNCTIONS

2 DATA PREPARATION

2.1 Load all sheets of an Excel

2.2 Same day: All group mean centered

2.3 Reproduce HRs (f - mod lmx.out): All group mean centered

3 ANALYSIS WITHOUT CONTROL

3.1 Model 1: Testing the main effect

3.2 Model 2a: Adding the moderation effect of Mo1 (Leader emotional exhaustion)

3.3 Model 2b: Testing the moderation effect on follower task performance (Y)

3.4 Model 3a: Adding the cross-level moderation effect of Mo2 (LMX) on follower work engagement

3.5 Model 3b: Testing the cross-level moderation effect on follower task performance

3.6 Model 4a: Testing both moderators (Mo1 and Mo2) on follower work engagement

3.7 Model 4b: Testing both moderators (Mo1 and Mo2) on follower task performance

4 ANALYSIS WITH CONTROL

4.1 Model 1: Testing the main effect

4.2 Model 2a: Adding the moderation effect of Mo1 (Leader emotional exhaustion)

4.3 Model 2b: Testing the moderation effect on follower task performance (Y)

4.4 Model 3a: Adding the cross-level moderation effect of Mo2 (LMX) on follower work engagement

4.5 Model 3b: Testing the cross-level moderation effect on follower task performance

4.6 Model 4a: Testing both moderators (Mo1 and Mo2) on follower work engagement

4.7 Model 4b: Testing both moderators (Mo1 and Mo2) on follower task performance

3.2 Model 2a: Adding the moderation effect of `Mo1` (Leader emotional exhaustion)

3.4 Model 3a: Adding the cross-level moderation effect of `Mo2` (LMX) on follower work engagement

4.2 Model 2a: Adding the moderation effect of `Mo1` (Leader emotional exhaustion)

4.4 Model 3a: Adding the cross-level moderation effect of `Mo2` (LMX) on follower work engagement