0.26, 1,
0.25, 0.35,	1,
0.35, 0.35,	0.43,	1,
0.43, 0.27,	0.17,	0.19,	1,
-0.09, 0.54, 0.23,	0.13,	0.07,	1,
0.22, 0.11, 0.15, 0.16, 0.16, 0.01, 1))
colnames(cor_HH_FFM_emg) <- c("E", "A", "C", "ES", "O", "HH", "Emg")
N_HH_FFM_eff <- c(24919,
23902, 22478,
23289, 21686, 22675,
22922, 22904, 20018, 21869,
12636, 15692, 14445, 14341, 10768,
37449, 34663, 33537, 19868, 15641, 1045)
N_HH_FFM_emg <- c(24919,
23902, 22478,
23289, 21686, 22675,
22922, 22904, 20018, 21869,
12636, 15692, 14445, 14341, 10768,
37449, 34663, 33537, 19868, 15641, 2186)
#### Effectiveness for FFM ------------------
eff_FFM_model <- 'Eff ~ E + A + C + ES + O'
eff_FFM_fit <- sem(eff_FFM_model, sample.cov = cor_HH_FFM_eff,
sample.nobs = psych::harmonic.mean(N_HH_FFM_eff[-c(11:15)]))  # 10005.31
summary(eff_FFM_fit, fit.measures = TRUE, standardized = TRUE)
summary(eff_FFM_fit, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)[[6]]
sqrt(0.03978918)
N_HH_FFM_eff <- c(24919,
23902, 22478,
23289, 21686, 22675,
22922, 22904, 20018, 21869,
12636, 15692, 14445, 14341, 10768,
37449, 34663, 33537, 19868, 15641, 1045)
N_HH_FFM_emg <- c(24919,
23902, 22478,
23289, 21686, 22675,
22922, 22904, 20018, 21869,
12636, 15692, 14445, 14341, 10768,
19870, 13921, 25288, 15358, 13769, 2186)
emg_HH_FFM_model <- 'Emg ~ E + A + C + ES + O + HH'
emg_HH_FFM_fit <- sem(emg_HH_FFM_model, sample.cov = cor_HH_FFM_emg,
sample.nobs = psych::harmonic.mean(N_HH_FFM_emg))  # 14243.45
summary(emg_HH_FFM_fit, fit.measures = TRUE, standardized = TRUE)
summary(emg_HH_FFM_fit, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)[[6]]
psych::harmonic.mean(N_HH_FFM_emg)
emg_HH_FFM_fit <- sem(emg_HH_FFM_model, sample.cov = cor_HH_FFM_emg,
sample.nobs = harmonic.mean(N_HH_FFM_emg))  # 14243.45
summary(emg_HH_FFM_fit, fit.measures = TRUE, standardized = TRUE)
summary(emg_HH_FFM_fit, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)[[6]]
sqrt(0.06542970)
eff_HH_FFM_model <- 'Eff ~ E + A + C + ES + O + HH'
eff_HH_FFM_fit <- sem(eff_HH_FFM_model, sample.cov = cor_HH_FFM_eff,
sample.nobs = harmonic.mean(N_HH_FFM_eff))
harmonic.mean(N_HH_FFM_eff)
eff_HH_FFM_model <- 'Eff ~ E + A + C + ES + O + HH'
eff_HH_FFM_fit <- sem(eff_HH_FFM_model, sample.cov = cor_HH_FFM_eff,
sample.nobs = harmonic.mean(N_HH_FFM_eff))  # 10639.14
summary(eff_HH_FFM_fit, fit.measures = TRUE, standardized = TRUE)
summary(eff_HH_FFM_fit, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)[[6]]
sqrt(0.09716575)
eff_FFM_model <- 'Eff ~ E + A + C + ES + O'
eff_FFM_fit <- sem(eff_FFM_model, sample.cov = cor_HH_FFM_eff,
sample.nobs = psych::harmonic.mean(N_HH_FFM_eff[-c(11:15)]))  # 10005.31
summary(eff_FFM_fit, fit.measures = TRUE, standardized = TRUE)
summary(eff_FFM_fit, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)[[6]]
sqrt(0.03978918)
setwd("D:/Anoop Meta-Analysis/ROUND 3 MANUSCRIPT/B5L R&R2 (May 2023)-20230820T064706Z-001/B5L R_R2 (May 2023)/ROUND 4 MANUSCRIPT/SUPPLEMENTARY MATERIAL_1101/Analysis/3. Moderating Effect of Collectivism")
### Effectiveness
data_FFM_eff <- data_FFM %>% filter(INCLUDE_AS_JUDGE_OP == 1)
results <- BF_analysis(data_FFM_eff, IV_name = "IV",
var_names = list(rxx = "rxx", ryy = "ryy", rxy = "rxy", N = "N"))
results$overall[c("E", "A", "C", "N", "O")]
eff_E <- data_FFM_eff %>% filter(IV == 3)
eff_A <- data_FFM_eff %>% filter(IV == 4)
eff_C <- data_FFM_eff %>% filter(IV == 2)
eff_N <- data_FFM_eff %>% filter(IV == 5)
eff_O <- data_FFM_eff %>% filter(IV == 1)
# p = .0266, k = 67
# p = .0347, k = 67 (Using Z)
calculation_mod(eff_E, mods = eff_E$Collectivism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_E, mods = eff_E$Collectivism)$detail_CI
lm.beta(calculation_mod(eff_E, mods = eff_E$Collectivism)$linear_estimate)
# p = .0266, k = 67
# p = .0347, k = 67 (Using Z)
calculation_mod(eff_E, mods = eff_E$Collectivism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod
calculation_mod(eff_E, mods = eff_E$Collectivism, method = "HS")$result
calculation_mod(eff_E, mods = eff_E$Collectivism)$detail_CI
# p = .0356, k = 52
calculation_mod(eff_A, mods = eff_A$Collectivism, method = "HS")$result
calculation_mod(eff_A, mods = eff_A$Collectivism)$detail_CI
# p = .0266, k = 67
calculation_mod(eff_E, mods = eff_E$Collectivism, method = "HS")$result
# p = .0356, k = 53
calculation_mod(eff_A, mods = eff_A$Collectivism, method = "HS")$result
calculation_mod(eff_C, mods = eff_C$Collectivism)$detail_CI
calculation_mod(eff_C, mods = eff_C$Collectivism)$result
calculation_mod(eff_C, mods = eff_C$Collectivism, method = "HS")$result
calculation_mod(eff_O, mods = eff_O$Collectivism, method = "HS")$result
calculation_mod(eff_N, mods = eff_N$Collectivism, method = "HS")$result
data_HEXACO_eff <- data_HEXACO %>% filter(INCLUDE_AS_JUDGE_OP == 1)
eff_HH <- data_HEXACO_eff %>% filter(IV == 6)
eff_EE <- data_HEXACO_eff %>% filter(IV == 5)
eff_XX <- data_HEXACO_eff %>% filter(IV == 3)
eff_AA <- data_HEXACO_eff %>% filter(IV == 4)
eff_CC <- data_HEXACO_eff %>% filter(IV == 2)
eff_OO <- data_HEXACO_eff %>% filter(IV == 1)
calculation_mod(eff_HH, mods = eff_HH$Collectivism)$result
calculation_mod(eff_HH, mods = eff_HH$Collectivism, method = "HS")$result
calculation_mod(eff_HH, mods = eff_HH$Collectivism, method = "HS")$result
calculation_mod(eff_HH, mods = eff_HH$Collectivism, method = "HS")$detail_CI
# p = .0266, k = 68
calculation_mod(eff_E, mods = eff_E$Collectivism, method = "HS")$result
# p = .0356, k = 53
calculation_mod(eff_A, mods = eff_A$Collectivism, method = "HS")$result
setwd("D:/Anoop Meta-Analysis/ROUND 3 MANUSCRIPT/B5L R&R2 (May 2023)-20230820T064706Z-001/B5L R_R2 (May 2023)/ROUND 4 MANUSCRIPT/SUPPLEMENTARY MATERIAL_1101/Analysis/3. Moderating Effect of Collectivism")
##### Effectiveness
data_FFM_eff <- data_FFM %>% filter(INCLUDE_AS_JUDGE_OP == 1)
eff_E <- data_FFM_eff %>% filter(IV == 3)
eff_A <- data_FFM_eff %>% filter(IV == 4)
eff_C <- data_FFM_eff %>% filter(IV == 2)
eff_N <- data_FFM_eff %>% filter(IV == 5)
eff_O <- data_FFM_eff %>% filter(IV == 1)
### Collectivism
calculation_mod(eff_E, mods = eff_E$Collectivism, method = "HS")$result
calculation_mod(eff_A, mods = eff_A$Collectivism, method = "HS")$result
calculation_mod(eff_C, mods = eff_C$Collectivism, method = "HS")$result
calculation_mod(eff_N, mods = eff_N$Collectivism, method = "HS")$result
calculation_mod(eff_O, mods = eff_O$Collectivism, method = "HS")$result
### Power Distance
calculation_mod(eff_E, mods = eff_E$Power_Distance, method = "HS")$result
calculation_mod(eff_A, mods = eff_A$Power_Distance, method = "HS")$result
calculation_mod(eff_C, mods = eff_C$Power_Distance, method = "HS")$result
calculation_mod(eff_N, mods = eff_N$Power_Distance, method = "HS")$result
calculation_mod(eff_O, mods = eff_O$Power_Distance, method = "HS")$result
### Uncertainty Avoidance
calculation_mod(eff_E, mods = eff_E$Uncertainty_Avoidance, method = "HS")$result
calculation_mod(eff_A, mods = eff_A$Uncertainty_Avoidance, method = "HS")$result
calculation_mod(eff_C, mods = eff_C$Uncertainty_Avoidance, method = "HS")$result
calculation_mod(eff_N, mods = eff_N$Uncertainty_Avoidance, method = "HS")$result
calculation_mod(eff_O, mods = eff_O$Uncertainty_Avoidance, method = "HS")$result
### Uncertainty Avoidance
calculation_mod(eff_E, mods = eff_E$Uncertainty_Avoidance, method = "HS")$result   #
calculation_mod(eff_A, mods = eff_A$Uncertainty_Avoidance, method = "HS")$result
calculation_mod(eff_C, mods = eff_C$Uncertainty_Avoidance, method = "HS")$result
calculation_mod(eff_N, mods = eff_N$Uncertainty_Avoidance, method = "HS")$result
calculation_mod(eff_O, mods = eff_O$Uncertainty_Avoidance, method = "HS")$result
### Masculinity-Femininity (Assertiveness)
calculation_mod(eff_E, mods = eff_E$Assertiveness, method = "HS")$result
calculation_mod(eff_A, mods = eff_A$Assertiveness, method = "HS")$result
calculation_mod(eff_C, mods = eff_C$Assertiveness, method = "HS")$result
calculation_mod(eff_N, mods = eff_N$Assertiveness, method = "HS")$result
calculation_mod(eff_O, mods = eff_O$Assertiveness, method = "HS")$result
### Tightness-Looseness
calculation_mod(eff_E, mods = eff_E$TL, method = "HS")$result
### Tightness-Looseness
calculation_mod(eff_E, mods = eff_E$Tightness_Looseness, method = "HS")$result
calculation_mod(eff_A, mods = eff_A$Tightness_Looseness, method = "HS")$result
calculation_mod(eff_C, mods = eff_C$Tightness_Looseness, method = "HS")$result
calculation_mod(eff_N, mods = eff_N$Tightness_Looseness, method = "HS")$result
calculation_mod(eff_O, mods = eff_O$Tightness_Looseness, method = "HS")$result
##### Emergence
data_FFM_emg <- data_FFM %>% filter(DV == 1 & INCLUDE_IN_ANALYSIS == 1)
emg_E <- data_FFM_emg %>% filter(IV == 3)
emg_A <- data_FFM_emg %>% filter(IV == 4)
emg_C <- data_FFM_emg %>% filter(IV == 2)
emg_N <- data_FFM_emg %>% filter(IV == 5)
emg_O <- data_FFM_emg %>% filter(IV == 1)
### Collectivism
calculation_mod(emg_E, mods = emg_E$Collectivism, method = "HS")$result
calculation_mod(emg_A, mods = emg_A$Collectivism, method = "HS")$result
calculation_mod(emg_C, mods = emg_C$Collectivism, method = "HS")$result
calculation_mod(emg_N, mods = emg_N$Collectivism, method = "HS")$result
calculation_mod(emg_O, mods = emg_O$Collectivism, method = "HS")$result
### Power Distance
calculation_mod(emg_E, mods = emg_E$Power_Distance, method = "HS")$result
calculation_mod(emg_A, mods = emg_A$Power_Distance, method = "HS")$result
calculation_mod(emg_C, mods = emg_C$Power_Distance, method = "HS")$result
calculation_mod(emg_N, mods = emg_N$Power_Distance, method = "HS")$result
calculation_mod(emg_O, mods = emg_O$Power_Distance, method = "HS")$result
calculation_mod(emg_A, mods = emg_A$Power_Distance, method = "HS")$result   # p = .0472
### Uncertainty Avoidance
calculation_mod(emg_E, mods = emg_E$Uncertainty_Avoidance, method = "HS")$result
calculation_mod(emg_A, mods = emg_A$Uncertainty_Avoidance, method = "HS")$result
calculation_mod(emg_C, mods = emg_C$Uncertainty_Avoidance, method = "HS")$result
calculation_mod(emg_N, mods = emg_N$Uncertainty_Avoidance, method = "HS")$result
calculation_mod(emg_O, mods = emg_O$Uncertainty_Avoidance, method = "HS")$result
### Masculinity-Femininity (Assertiveness)
calculation_mod(emg_E, mods = emg_E$Assertiveness, method = "HS")$result
calculation_mod(emg_A, mods = emg_A$Assertiveness, method = "HS")$result
calculation_mod(emg_C, mods = emg_C$Assertiveness, method = "HS")$result
calculation_mod(emg_N, mods = emg_N$Assertiveness, method = "HS")$result
calculation_mod(emg_O, mods = emg_O$Assertiveness, method = "HS")$result
### Tightness-Looseness
calculation_mod(emg_E, mods = emg_E$Tightness_Looseness, method = "HS")$result
calculation_mod(emg_A, mods = emg_A$Tightness_Looseness, method = "HS")$result
calculation_mod(emg_C, mods = emg_C$Tightness_Looseness, method = "HS")$result
calculation_mod(emg_N, mods = emg_N$Tightness_Looseness, method = "HS")$result
calculation_mod(emg_O, mods = emg_O$Tightness_Looseness, method = "HS")$result
setwd("D:/Anoop Meta-Analysis/ROUND 3 MANUSCRIPT/B5L R&R2 (May 2023)-20230820T064706Z-001/B5L R_R2 (May 2023)/ROUND 4 MANUSCRIPT/SUPPLEMENTARY MATERIAL_1101/Analysis/4. Path Analyses and Mediator Analyses")
### Correlation for IS and CON
data_IS <- data_FFM %>% filter(DV == 2 & INCLUDE_IN_ANALYSIS == 2 & DV_TYPE == 1)
format_file(BF_analysis(data_IS, IV_name = "IV")$overall)[c("E","A","C","N","O"),]
data_CON <- data_FFM %>% filter(DV == 2 & INCLUDE_IN_ANALYSIS == 2 & DV_TYPE == 5)
format_file(BF_analysis(data_CON, IV_name = "IV")$overall)[c("E","A","C","N","O"),]
### FFM Intercorrelations
intercorrelation_data <- as.data.frame(readxl::read_excel("../../DataSet/FFM_INTERCORRELATIONS.xlsx", sheet = "FFM_Corr"))
intercorrelation_data <- intercorrelation_data %>% filter(is.na(`Exclude Reason`)) %>% filter(N < 10000)
table(intercorrelation_data$Article_ID)
EA_data <- intercorrelation_data %>% filter(x %in% c("E", "A") & y %in% c("E", "A"))
EC_data <- intercorrelation_data %>% filter(x %in% c("E", "C") & y %in% c("E", "C"))
EN_data <- intercorrelation_data %>% filter(x %in% c("E", "ES") & y %in% c("E", "ES"))
EO_data <- intercorrelation_data %>% filter(x %in% c("E", "O") & y %in% c("E", "O"))
AC_data <- intercorrelation_data %>% filter(x %in% c("A", "C") & y %in% c("A", "C"))
AN_data <- intercorrelation_data %>% filter(x %in% c("A", "ES") & y %in% c("A", "ES"))
AO_data <- intercorrelation_data %>% filter(x %in% c("A", "O") & y %in% c("A", "O"))
CN_data <- intercorrelation_data %>% filter(x %in% c("C", "ES") & y %in% c("C", "ES"))
CO_data <- intercorrelation_data %>% filter(x %in% c("C", "O") & y %in% c("C", "O"))
NO_data <- intercorrelation_data %>% filter(x %in% c("ES", "O") & y %in% c("ES", "O"))
## Here we get the intercorrelations and the corresponding SD_rho
calculation(EA_data)$details
calculation(EC_data)$details
calculation(EN_data)$details
calculation(EO_data)$details
calculation(AC_data)$details
calculation(AN_data)$details
calculation(CN_data)$details
calculation(CN_data)$details
calculation(CN_data)$details
calculation(AO_data)$details
calculation(CO_data)$details
calculation(NO_data)$details
setwd("D:/Anoop Meta-Analysis/ROUND 3 MANUSCRIPT/B5L R&R2 (May 2023)-20230820T064706Z-001/B5L R_R2 (May 2023)/ROUND 4 MANUSCRIPT/SUPPLEMENTARY MATERIAL_1101/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,
0.26, 1,
0.25, 0.35, 1,
0.35, 0.35, 0.43, 1,
0.43, 0.27, 0.17, 0.19, 1,
0.12, 0.15, 0.02, 0.04, 0.12, 1,
0.32, 0.04, 0.22, 0.12, 0.28, 0.17, 1,
0.11, 0.13, 0.12, 0.11, 0.16, 0.52, 0.39, 1))
colnames(cor_FFM_model) <- c("E", "A", "C", "ES", "O", "CON", "IS", "Eff")
N_FFM_model <- c(22478,
22675, 20018,
23902, 21686, 22904,
24919, 21869, 23289, 22922,
1058, 1054, 1041, 1069, 1091,
1174, 1170, 1041, 1016, 1091, 26295,
37449, 34663, 33537, 19868, 15641, 1960, 1605)
OB_model <- 'CON ~ a1 * C + a2 * E + a3 * ES + a4 * A
IS ~ b1 * C + b2 * E + b3 * ES + b5 * O
Eff ~ 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)
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)
modificationindices(fit_OB_model, sort = TRUE)
set.seed(2023)
### Mediation for Extraversion
MCMed("a2 * c1", object = fit_OB_model)  # CON
MCMed("b2 * c2", object = fit_OB_model)  # IS
### Mediation for Extraversion
MCMed("a2 * c1", object = fit_OB_model)  # CON
MCMed("b2 * c2", object = fit_OB_model)  # IS
### Mediation for Agreeableness
MCMed("a4 * c1", object = fit_OB_model)  # CON
### Mediation for Conscientiousness
MCMed("a1 * c1", object = fit_OB_model)  # CON
MCMed("b1 * c2", object = fit_OB_model)  # IS
### Mediation for Emotional Stability
MCMed("a3 * c1", object = fit_OB_model)  # CON
MCMed("b3 * c2", object = fit_OB_model)  # IS
### Mediation for Openness
MCMed("b5 * c2", object = fit_OB_model)  # IS
### Mediation for Extraversion
MCMed("a2 * c1", object = fit_OB_model)  # CON
MCMed("b2 * c2", object = fit_OB_model)  # IS
### Mediation for Agreeableness
MCMed("a4 * c1", object = fit_OB_model)  # CON
### Mediation for Conscientiousness
MCMed("a1 * c1", object = fit_OB_model)  # CON
MCMed("b1 * c2", object = fit_OB_model)  # IS
### Mediation for Emotional Stability
MCMed("a3 * c1", object = fit_OB_model)  # CON
MCMed("b3 * c2", object = fit_OB_model)  # IS
### Mediation for Openness
MCMed("b5 * c2", object = fit_OB_model)  # IS
MCMed("b3 * c2", object = fit_OB_model)  # IS
### Mediation for Emotional Stability
MCMed("a3 * c1", object = fit_OB_model)  # CON
OB_model_x <- 'CON ~ a1 * C + a2 * E + a3 * ES + a4 * A
IS ~ b1 * C + b2 * E + b3 * ES + b5 * O
Eff ~ c1 * CON + c2 * IS + c3 * ES
CON ~~ IS'
fit_OB_model_x <- sem(OB_model_x, sample.cov = cor_FFM_model,
sample.nobs = harmonic.mean(c(N_FFM_model)))
summary(fit_OB_model_x, 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)/ROUND 4 MANUSCRIPT/SUPPLEMENTARY MATERIAL_1101/Analysis/5. R Square for High & Low Collectivism")
source("../Utilities/Utilities-Main.R")
library(lavaan)
library(psych)
## Conditional Effect for Collectivism - Effectiveness -----------------------
## Conditional Effect for Collectivism - Effectiveness -----------------------
data_FFM <- data_FFM %>% group_by(IV) %>% mutate(S = median(Collectivism, na.rm = TRUE), split_on = Collectivism > S) %>% ungroup()
data_FFM_eff <- data_FFM %>% filter(INCLUDE_AS_JUDGE_OP == 1)
eff_J_O <- data_FFM_eff %>% filter(IV == 1)
eff_J_C <- data_FFM_eff %>% filter(IV == 2)
eff_J_E <- data_FFM_eff %>% filter(IV == 3)
eff_J_A <- data_FFM_eff %>% filter(IV == 4)
eff_J_N <- data_FFM_eff %>% filter(IV == 5)
data_FFM_eff %>% filter(!is.na(Collectivism)) %>% group_by(IV) %>% summarize(N_count = sum(N), k_count = length(`Article ID`))  # 12732 - 18935
### What if we treat collectivism as a categorical moderator?
eff_mod_coll <- BF_analysis(data_FFM_eff, mod = "split_on", IV_name = "IV")$mod_result
## Low: E/A/C/N/O - .14, .12, .12, .08, .12
## High: E/A/C/N/O - .24, .19, .20, .20, .20
format_file_mods(eff_mod_coll, order = c("E","A","C","N","O"), write = FALSE)
cor_FFM_eff_lowcol <- lav_matrix_lower2full(
c(1,
0.26, 1,
0.25, 0.35,	1,
0.35, 0.35,	0.43,	1,
0.43, 0.27,	0.17,	0.19,	1,
0.14, 0.12, 0.12, 0.08, 0.12, 1))
cor_FFM_eff_highcol <- lav_matrix_lower2full(
c(1,
0.26, 1,
0.25, 0.35,	1,
0.35, 0.35,	0.43,	1,
0.43, 0.27,	0.17,	0.19,	1,
0.24, 0.19, 0.20, 0.20, 0.20, 1))
N_FFM_eff_lowcol  <- c(24919,
23902, 22478,
23289, 21686, 22675,
22922, 22904, 20018, 21869,
15785, 14626, 13406, 15115, 10422)
N_FFM_eff_highcol  <- c(24919,
23902, 22478,
23289, 21686, 22675,
22922, 22904, 20018, 21869,
3150, 2408, 3542, 2771, 2310)
colnames(cor_FFM_eff_lowcol) <- c("E", "A", "C", "ES", "O", "Eff")
colnames(cor_FFM_eff_highcol) <- c("E", "A", "C", "ES", "O", "Eff")
eff_model_lowcol <- 'Eff ~ E + A + C + ES + O'
eff_fit_lowcol <- sem(eff_model_lowcol, sample.cov = cor_FFM_eff_lowcol,
sample.nobs = harmonic.mean(N_FFM_eff_lowcol))
summary(eff_fit_lowcol, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)
summary(eff_fit_lowcol, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)[[6]]
# sqrt(0.02337987) ## If we use conditional rho
sqrt(0.03392037)  ## If we use categorical mod
eff_model_highcol <- 'Eff ~ E + A + C + ES + O'
eff_fit_highcol <- sem(eff_model_highcol, sample.cov = cor_FFM_eff_highcol,
sample.nobs = harmonic.mean(N_FFM_eff_highcol))
summary(eff_fit_highcol, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)
summary(eff_fit_highcol, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)[[6]]
sqrt(0.09737494)  ## If we use categorical mod
setwd("D:/Anoop Meta-Analysis/ROUND 3 MANUSCRIPT/B5L R&R2 (May 2023)-20230820T064706Z-001/B5L R_R2 (May 2023)/ROUND 4 MANUSCRIPT/SUPPLEMENTARY MATERIAL_1101/Analysis/6. Robustness_Check")
source("../Utilities/Utilities-Main.R")
source("../Utilities/Utilities-Extra.R")
data_FFM_eff <- data_FFM %>% filter(INCLUDE_AS_JUDGE_OP == 1)
eff_E <- data_FFM_eff %>% filter(IV == 3)
eff_A <- data_FFM_eff %>% filter(IV == 4)
eff_E_control <- get_data(eff_E)
eff_A_control <- get_data(eff_A)
eff_E_control$mods <- eff_E$Collectivism
eff_E_control$Occupation <- as.factor(eff_E$`Occupation Code`)
eff_E_control$Percent_Male <- eff_E$mod_menamongleaders
control_model_E <- rma(ri.m, var.i2, weights = 1/var.i2, mods = ~ mods + Occupation + Percent_Male,
method = "HS", test = "knha", data = eff_E_control)
summary(control_model_E)   # p < .05, k = 75
eff_A_control$mods <- eff_A$Collectivism
eff_A_control$Occupation <- as.factor(eff_A$`Occupation Code`)
eff_A_control$Percent_Male <- eff_A$mod_menamongleaders
control_model_A <- rma(ri.m, var.i2, weights = 1/var.i2, mods = ~ mods + Occupation + Percent_Male, method = "HS", test = "knha", data = eff_A_control)
summary(control_model_A)   # p < .05, k = 75
data_FFM_emg <- data_FFM %>% filter(DV == 1 & INCLUDE_IN_ANALYSIS == 1)
data_FFM_eff <- data_FFM %>% filter(INCLUDE_AS_JUDGE_OP == 1)
pub_bias_emg <- TF_analysis(data_FFM_emg, IV_name = "IV")
source("../Utilities/Utilities-Main.R")
source("../Utilities/Utilities-Publication_Bias.R")
pub_bias_emg <- TF_analysis(data_FFM_emg, IV_name = "IV")
# Egger's test showed that nothing is significant, except for agreeableness.
pub_bias_emg$regs
# For agreeableness, the corrected T&F rho = .15
pub_bias_emg$tfs
pub_bias_eff <- TF_analysis(data_FFM_eff, IV_name = "IV")
# Egger's test showed that nothing is significant.
pub_bias_eff$regs
### HEXACO Publication bias check
data_HEXACO_emg <- data_HEXACO %>% filter(DV == 1 & INCLUDE_TYPE == 1)
data_HEXACO_eff <- data_HEXACO %>% filter(INCLUDE_AS_JUDGE_OP == 1)
pub_bias_HEXACO_emg <- TF_analysis_HEX(data_HEXACO_emg, IV_name = "IV")
# Egger's test showed that nothing is significant.
pub_bias_HEXACO_emg$regs
# But we can check for T&F results anyway.
pub_bias_HEXACO_emg$tfs
pub_bias_HEXACO_eff <- TF_analysis_HEX(data_HEXACO_eff, IV_name = "IV")
# Egger's test showed that nothing is significant, except for conscientiousness.
pub_bias_HEXACO_eff$regs
# Egger's test showed that nothing is significant, except for conscientiousness.
pub_bias_HEXACO_eff$regs
# For conscientiousness, the corrected T&F rho = .48
pub_bias_HEXACO_eff$tfs$C
setwd("D:/Anoop Meta-Analysis/ROUND 3 MANUSCRIPT/B5L R&R2 (May 2023)-20230820T064706Z-001/B5L R_R2 (May 2023)/ROUND 4 MANUSCRIPT/SUPPLEMENTARY MATERIAL_1101/Analysis/6. Robustness_Check")
source("../Utilities/Utilities-Main.R")
source("../Utilities/Utilities-Extra.R")
#### FFM - Second Order meta-analysis
data_FFM$Country_New <- data_FFM$Country
data_FFM$Country_New[which(data_FFM$Country_New == "mixed")] <- "Mixed"
data_FFM$Country_New[which(data_FFM$Country %in% c("international sample", "Mixed (Swiss, Swedish, and USA)",
"USA and Canada","U.S.A & Canada",
"usa, switzerland, sweden",
"Mixed (6 Different Countries)"))] <- "Mixed"
data_FFM_emg <- data_FFM %>% filter(DV == 1 & INCLUDE_IN_ANALYSIS == 1)
data_FFM_emg <- data_FFM %>% filter(DV == 1 & INCLUDE_IN_ANALYSIS == 1)
data_FFM_eff <- data_FFM %>% filter(INCLUDE_AS_JUDGE_OP == 1)
country_result_emg <- BF_analysis(data_FFM_emg, mod = "Country_New", IV_name = "IV")
calculation_secondary(country_result_emg$mod_result$C$results)   # 15.20%
calculation_secondary(country_result_emg$mod_result$A$results)   # 4.33%
calculation_secondary(country_result_emg$mod_result$N$results)   # 33.96%
calculation_secondary(country_result_emg$mod_result$O$results)   # 17.30%
calculation_secondary(country_result_emg$mod_result$E$results)   # 33.13%
country_result_eff <- BF_analysis(data_FFM_eff, mod = "Country_New", IV_name = "IV")
calculation_secondary(country_result_eff$mod_result$C$results)   # 3.48%
calculation_secondary(country_result_eff$mod_result$A$results)   # 5.79%
calculation_secondary(country_result_eff$mod_result$N$results)   # 1.61%
calculation_secondary(country_result_eff$mod_result$O$results)   # 15.73%
calculation_secondary(country_result_eff$mod_result$E$results)   # 2.66%
#### HEXACO - Second Order meta-analysis
data_HEXACO$Country_New <- data_HEXACO$Country
#### HEXACO - Second Order meta-analysis
data_HEXACO$Country_New <- data_HEXACO$Country
data_HEXACO$Country_New[which(data_HEXACO$Country_New == "mixed (USA & Canada)")] <- "mixed"
data_HEXACO$Country_New[which(data_HEXACO$Country_New == "Canada + US + UK")] <- "mixed"
data_HEXACO_eff <- data_HEXACO %>% filter(INCLUDE_AS_JUDGE_OP == 1)
country_result_emg_HEXACO <- HEX_analysis(data_HEXACO_emg, mod = "Country_New", IV_name = "IV")
data_HEXACO$Country_New <- data_HEXACO$Country
data_HEXACO$Country_New[which(data_HEXACO$Country_New == "mixed (USA & Canada)")] <- "mixed"
data_HEXACO$Country_New[which(data_HEXACO$Country_New == "Canada + US + UK")] <- "mixed"
data_HEXACO_emg <- data_HEXACO %>% filter(DV == 1 & INCLUDE_TYPE == 1)
data_HEXACO_eff <- data_HEXACO %>% filter(INCLUDE_AS_JUDGE_OP == 1)
country_result_emg_HEXACO <- HEX_analysis(data_HEXACO_emg, mod = "Country_New", IV_name = "IV")
calculation_secondary(country_result_emg_HEXACO$mod_result$H$results)   # US Only
calculation_secondary(country_result_emg_HEXACO$mod_result$E$results)   # 85.50%
calculation_secondary(country_result_emg_HEXACO$mod_result$X$results)   # US Only
calculation_secondary(country_result_emg_HEXACO$mod_result$A$results)   # US Only
calculation_secondary(country_result_emg_HEXACO$mod_result$C$results)   # US Only
calculation_secondary(country_result_emg_HEXACO$mod_result$O$results)   # US Only
country_result_eff_HEXACO <- HEX_analysis(data_HEXACO_eff, mod = "Country_New", IV_name = "IV")
calculation_secondary(country_result_eff_HEXACO$mod_result$H$results)   # 3.22%
calculation_secondary(country_result_eff_HEXACO$mod_result$E$results)   # US Only
calculation_secondary(country_result_eff_HEXACO$mod_result$X$results)   # US Only
calculation_secondary(country_result_eff_HEXACO$mod_result$A$results)   # US Only
calculation_secondary(country_result_eff_HEXACO$mod_result$C$results)   # US Only
calculation_secondary(country_result_eff_HEXACO$mod_result$O$results)   # US Only
HLM_emg_null <- BF_analysis_HLM(data_FFM_emg, mods = NULL,
cluster_var = "Country_New",
ind_var = "Coding ID", IV_name = "IV")
# Proportion of level 3 variance
var.comp(HLM_emg_null$O$result)$results    # 0
source("../Utilities/varcomp.R")
##### HLM Analysis #####
#### FFM
####### Emergence #######
HLM_emg_null <- BF_analysis_HLM(data_FFM_emg, mods = NULL,
cluster_var = "Country_New",
ind_var = "Coding ID", IV_name = "IV")
# Proportion of level 3 variance
var.comp(HLM_emg_null$O$result)$results    # 0
var.comp(HLM_emg_null$C$result)$results    # 68.15%
var.comp(HLM_emg_null$E$result)$results    # 0
var.comp(HLM_emg_null$A$result)$results    # 35.65%
var.comp(HLM_emg_null$N$result)$results    # 0
#### FFM
####### Emergence #######
HLM_emg_null <- BF_analysis_HLM(data_FFM_emg, mods = NULL,
cluster_var = "Country_New",
ind_var = "Coding ID", IV_name = "IV")
# Proportion of level 3 variance
var.comp(HLM_emg_null$O$result)$results    # 0
var.comp(HLM_emg_null$C$result)$results    # 68.15%
var.comp(HLM_emg_null$E$result)$results    # 0
var.comp(HLM_emg_null$A$result)$results    # 35.65%
var.comp(HLM_emg_null$N$result)$results    # 0
####### Effectiveness #######
HLM_eff_null <- BF_analysis_HLM(data_FFM_eff, mods = NULL,
cluster_var = "Country_New",
ind_var = "Coding ID", IV_name = "IV")
var.comp(HLM_eff_null$O$result)$results    # 60.62%
var.comp(HLM_eff_null$C$result)$results    # 20.74%
var.comp(HLM_eff_null$E$result)$results    # 59.53%
var.comp(HLM_eff_null$A$result)$results    # 89.24%
var.comp(HLM_eff_null$N$result)$results    # 54.28%
HLM_eff_HEXACO_null <- HEX_analysis_HLM(data_HEXACO_eff, mods = NULL,
cluster_var = "Country_New",
ind_var = "Coding ID", IV_name = "IV")
var.comp(HLM_eff_HEXACO_null$H$result)$results    # 51.98%
HLM_emg_HEXACO_null <- HEX_analysis_HLM(data_HEXACO_emg, mods = NULL,
cluster_var = "Country_New",
ind_var = "Coding ID", IV_name = "IV")
var.comp(HLM_emg_HEXACO_null$H$result)$results    # 51.98%
# write.csv(studies_eff_HEX, "EFF_HEX_STUDIES.csv", fileEncoding = "UTF-8")
intersect(data_HEXACO_emg$`Article ID`, data_HEXACO_eff$`Article ID`)
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
