setwd("D:/Anoop Meta-Analysis/ROUND 3 MANUSCRIPT/B5L R&R2 (May 2023)-20230820T064706Z-001/B5L R_R2 (May 2023)/SUPPLEMENTARY MATERIAL/Analysis/4. Path Analyses and Mediator Analyses")
source("../Utilities/Utilities-Main.R")
source("../Utilities/Utilities-Extra.R")
source("../Utilities/MC.R")
# cor_FFM_model <- lav_matrix_lower2full(
#   c(1,
#     0.27,	1,
#     0.25,	0.2,	1,
#     0.14,	0.16,	0.09,	1,
#     0.19,	0.21,	0.21,	0.27,	1,
#     0.21, 0.12, 0.08, 0.16, 0.20, 1,
#     -0.06, 0.03, 0.08, 0.07, 0.18, 0.17, 1,
#     0.12, 0.14, 0.11, 0.16, 0.12, 0.39, 0.52, 1))
cor_FFM_model <- lav_matrix_lower2full(
c(1,
0.35,	1,
0.43,	0.35,	1,
0.17,	0.27,	0.19,	1,
0.25,	0.26,	0.35,	0.43,	1,
0.22, 0.04, 0.12, 0.28, 0.32, 1,
0.02, 0.15, 0.04, 0.12, 0.12, 0.17, 1,
0.12, 0.13, 0.11, 0.16, 0.11, 0.39, 0.52, 1))
# ## If outlier is included
# cor_FFM_model <- lav_matrix_lower2full(
#   c(1,
#     0.47,	1,
#     0.43,	0.35,	1,
#     0.17,	0.27,	0.19,	1,
#     0.54,	0.47,	0.35,	0.43,	1,
#     0.22, 0.04, 0.12, 0.28, 0.32, 1,
#     0.02, 0.15, 0.04, 0.12, 0.12, 0.17, 1,
#     0.12, 0.13, 0.11, 0.16, 0.11, 0.39, 0.52, 1))
colnames(cor_FFM_model) <- c("C", "A", "ES", "O", "E", "IS", "CON", "Eff_J")
N_FFM_model <- c(22478,
22675, 20018,
23902, 21686, 22904,
24919, 21869, 23289, 22922,
1404, 1533, 1379, 1454, 1537,
1404, 1417, 1379, 1454, 1421, 26295,
33537, 34663, 18758, 15641, 37449, 1960, 1605)
OB_model <- 'CON ~ a1 * C + a2 * E + a3 * ES + a4 * A
IS ~ b1 * C + b2 * E + b3 * ES + b5 * O
Eff_J ~ c1 * CON + c2 * IS
CON ~~ IS'
fit_OB_model <- sem(OB_model, sample.cov = cor_FFM_model,
sample.nobs = harmonic.mean(c(N_FFM_model)))
harmonic.mean(c(N_FFM_model))
summary(fit_OB_model, standardized = TRUE, fit.measures = TRUE)
setwd("D:/Anoop Meta-Analysis/ROUND 3 MANUSCRIPT/B5L R&R2 (May 2023)-20230820T064706Z-001/B5L R_R2 (May 2023)/SUPPLEMENTARY MATERIAL/Analysis/0. Coding Procedures")
source("../Utilities/Utilities-Main.R")
library(psych)
############# This file produces the number of studies for EMG/EFF/OVERALL
data_FFM <- readxl::read_excel("../../DataSet/B5L_Analysis_MASTER_NEW1124_NEW.xlsx", sheet = "Coding")
data_FFM_J <- readxl::read_excel("../../DataSet/NEW DATASET/B5L_Analysis_MASTER_230830.xlsx", sheet = "Coding-Judge_TEMP")
## A total of 234 studies. Another study has only span of control/tenure coded. (FFM_920)
length(table(data_FFM_J %>% mutate(Article_ID = substring(`Coding ID`, 1,
last = nchar(`Coding ID`)- 1)) %>% select(Article_ID)))
library(dplyr)
data_FFM_JAll <- data_FFM_J %>% filter((`OVERALL EFFECTIVENESS INCLUSION?` == 0) | (`OVERALL EFFECTIVENESS INCLUSION?` == 1 & INCLUDE_AS_JUDGE_OP_S == 1))
## A total of 234 studies. Another study has only span of control/tenure coded. (FFM_920)
length(table(data_FFM_JAll %>% mutate(Article_ID = substring(`Coding ID`, 1,
last = nchar(`Coding ID`)- 1)) %>% select(Article_ID)))
data_FFM_emg <- data_FFM_J %>% filter(DV == 1 & `INCLUDE IN ANALYSIS?` == 1)
# data_FFM_emg2 <- data_FFM2 %>% filter(DV == 1)
data_FFM_emg %>% group_by(`Coding ID`)
length(table(data_FFM_emg %>% group_by(`Coding ID`) %>% select(`Coding ID`)))  # 108 samples
# Leadership Effectiveness
data_FFM_eff <- data_FFM_J %>% filter(`OVERALL EFFECTIVENESS INCLUSION?` == 1)
length(table(data_FFM_eff %>% group_by(`Coding ID`) %>% select(`Coding ID`)))  # 188 samples
# Leadership Effectiveness
data_FFM_eff <- data_FFM_J %>% filter(`OVERALL EFFECTIVENESS INCLUSION?` == 1  & INCLUDE_AS_JUDGE_OP_S == 1)
length(table(data_FFM_eff %>% group_by(`Coding ID`) %>% select(`Coding ID`)))  # 188 samples
intersect(data_FFM_emg$`Article ID`, data_FFM_eff$`Article ID`)
108 + 89 - 15
13767-182
### HEXACO ####
data_HEXACO <- readxl::read_excel("../../DataSet/B5L_Analysis_HEXACO_NEW_1119.xlsx", sheet = "HEXACO Coding")
length(table(data_HEXACO %>% mutate(Article_ID = substring(`Coding ID`, 1,
last = nchar(`Coding ID`)- 1)) %>% select(Article_ID)))
# Leadership Emergence
data_HEXACO_emg <- data_HEXACO %>% filter(DV == 1 & INCLUDE_TYPE == 1)
length(table(data_HEXACO_emg %>% group_by(`Coding ID`) %>% select(`Coding ID`)))  # 12 samples
sum(data_HEXACO_emg %>% group_by(`Coding ID`) %>% summarize(NN = max(N)) %>% select(NN))  # Harmonic N = 2266
data_HEXACO_eff <- data_HEXACO %>% filter(OVERALL_INCLUSION == 1 & DV_JUDGE_EFF == 1)
length(table(data_HEXACO_eff %>% group_by(`Coding ID`) %>% select(`Coding ID`)))  # 22 samples
data_HEXACO_eff <- data_HEXACO %>% filter(DV_JUDGE_EFF == 1)
length(table(data_HEXACO_eff %>% group_by(`Coding ID`) %>% select(`Coding ID`)))  # 22 samples
intersect(data_HEXACO_emg$`Article ID`, data_HEXACO_eff$`Article ID`)
length(table(data_HEXACO_emg %>% group_by(`Coding ID`) %>% select(`Coding ID`)))  # 12 samples
length(table(data_HEXACO_eff %>% group_by(`Coding ID`) %>% select(`Coding ID`)))  # 8 samples
75^1.5
35^1.5
45^1.5
3.3*50*7 + 4*35^1.5
1.2*50*7 + 4*45^1.5
5.6*50*7 + 4*75^1.5
5.6*100*7 + 4*150^1.5
3.3*100*7 + 4*70^1.5
1.2*100*7 + 4*90^1.5
5.6*150*7 + 4*(1.5*150)^1.5
3.3*150*7 + 4*(0.7*150)^1.5
1.2*150*7 + 4*(0.9*150)^1.5
