eff_J_A <- data_FFM_eff %>% filter(IV == 4)
eff_J_N <- data_FFM_eff %>% filter(IV == 5)
calculation_mod(eff_J_E, mods = eff_J_E$Collectivism)$detail_CI    # .11 / .20
calculation_mod(eff_J_A, mods = eff_J_A$Collectivism)$detail_CI    # .09 / .16
calculation_mod(eff_J_C, mods = eff_J_C$Collectivism)$detail_CI    # .11 / .15
calculation_mod(eff_J_N, mods = eff_J_N$Collectivism)$detail_CI    # .06 / .13
calculation_mod(eff_J_O, mods = eff_J_O$Collectivism)$detail_CI    # .10 / .17
data_FFM_eff %>% filter(!is.na(Collectivism)) %>% group_by(IV) %>% summarize(N_count = sum(N))  # 12732 - 18935
## Assume fixed FFM intercorrelations, use conditional rho
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,
0.11, 0.09, 0.11, 0.06, 0.10, 1))
## Assume fixed FFM intercorrelations, use conditional rho
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,
0.11, 0.09, 0.11, 0.06, 0.10, 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,
0.20, 0.16, 0.15, 0.13, 0.17, 1))
N_FFM_eff_lowcol  <- c(24919,
23902, 22478,
23289, 21686, 22675,
22922, 22904, 20018, 21869,
12732, 16948, 18935, 17034, 17886)
N_FFM_eff_highcol  <- N_FFM_eff_lowcol
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))
colnames(cor_FFM_eff_lowcol) <- c("E", "A", "C", "ES", "O", "Eff")
colnames(cor_FFM_eff_highcol) <- c("E", "A", "C", "ES", "O", "Eff")
N_FFM_eff_lowcol  <- c(24919,
23902, 22478,
23289, 21686, 22675,
22922, 22904, 20018, 21869,
12732, 16948, 18935, 17034, 17886)
N_FFM_eff_highcol  <- N_FFM_eff_lowcol
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)
sqrt(0.02578173)  # If FFM intercorrelations are allowed to vary for collectivism
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(0.06321359)
sqrt(0.06052519)  # If FFM intercorrelations are allowed to vary for collectivism
### 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
format_file_mods(eff_mod_coll, order = FFM_order, write = FALSE)
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))
colnames(cor_FFM_eff_lowcol) <- c("E", "A", "C", "ES", "O", "Eff")
colnames(cor_FFM_eff_highcol) <- c("E", "A", "C", "ES", "O", "Eff")
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
sqrt(0.02337987)
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)
summary(eff_fit_lowcol, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)
summary(eff_fit_highcol, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)
# Assume fixed FFM intercorrelations, use conditional rho
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,
0.11, 0.09, 0.11, 0.06, 0.10, 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,
0.20, 0.16, 0.15, 0.13, 0.17, 1))
N_FFM_eff_lowcol  <- c(24919,
23902, 22478,
23289, 21686, 22675,
22922, 22904, 20018, 21869,
12732, 16948, 18935, 17034, 17886)
N_FFM_eff_highcol  <- N_FFM_eff_lowcol
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]]
setwd("D:/Anoop Meta-Analysis/ROUND 3 MANUSCRIPT/B5L R&R2 (May 2023)-20230820T064706Z-001/B5L R_R2 (May 2023)/SUPPLEMENTARY MATERIAL_1101/Analysis/5. Predicting Leadership Effectiveness")
setwd("D:/Anoop Meta-Analysis/ROUND 3 MANUSCRIPT/B5L R&R2 (May 2023)-20230820T064706Z-001/B5L R_R2 (May 2023)/SUPPLEMENTARY MATERIAL_1101/Analysis/1. FFM Trait-Leadership Relationships")
library(lavaan)
library(psych)
## FFM intercorrelations from Ones, Viswesvaran, & Reiss (1996) / Ones (1993)
## FFM-EFF correlations re-attenuated from Judge et al., (2002).
## These correlations are calculated in the corresponding Excel file.
cor_FFM_redis <- lav_matrix_lower2full(
c(1,
0.19,	1,
0.16,	0.17,	1,
0.25,	0.17,	0.11,	1,
0.26,	0.00,	-0.06,	0.27,	1,
0.18, 0.20, 0.20, 0.17, 0.13, 1))
colnames(cor_FFM_redis) <- c("ES", "E", "O", "A", "C", "Eff")
### N from Ones, Viswesvaran, & Reiss (1996) / Ones (1993)
### As well as weighted N in Judge et al., (2002) weighted by the number of studies (EFF vs EMG)
N_FFM_redis <- c(148721,
440440, 294515,
254937, 252004, 47177,
415679, 135529, 144205, 79303,
490296, 683001, 356680, 162975, 288512,
7510*18/35, 9801*19/42, 8025*18/48, 7221*17/37, 11705*23/60)
redis_model <- 'Eff ~ E + A + C + ES + O'
redis_fit <- sem(redis_model, sample.cov = cor_FFM_redis,
sample.nobs = harmonic.mean(N_FFM_redis))  ## 14008.12
summary(redis_fit, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)
data_FFM_eff %>% filter(!is.na(Collectivism)) %>% group_by(IV) %>% summarize(N_count = sum(N))  # 12732 - 18935
data_FFM_eff %>% filter(!is.na(Collectivism)) %>% group_by(IV) %>% summarize(N_count = sum(N), k_count = unique(`Article ID`))  # 12732 - 18935
data_FFM_eff %>% filter(!is.na(Collectivism)) %>% group_by(IV) %>% summarize(N_count = sum(N), k_count = length(`Article ID`))  # 12732 - 18935
setwd("D:/Anoop Meta-Analysis/ROUND 3 MANUSCRIPT/B5L R&R2 (May 2023)-20230820T064706Z-001/B5L R_R2 (May 2023)/SUPPLEMENTARY MATERIAL_1101/Analysis/1. FFM Trait-Leadership Relationships")
source("../Utilities/Utilities-Main.R")
source("../Utilities/Utilities-Extra.R")
############# This file produces results analyzing the moderating effect of categorical moderators #############
###### Main effect for FFM #############################
data_FFM <- readxl::read_excel("../../DataSet/B5L_Analysis_MASTER_NEW1124_NEW.xlsx", sheet = "Coding")
data_FFM <- readxl::read_excel("../../DataSet/NEW DATASET/B5L_Analysis_MASTER_230830.xlsx", sheet = "Coding-Judge_TEMP")
data_FFM_emg <- data_FFM %>% filter(DV == 1 & `INCLUDE IN ANALYSIS?` == 1)
FFM_order <- c("E","A","C","N","O")
data_FFM_emg <- data_FFM_emg %>% group_by(IV) %>% mutate(S = median(Collectivism, na.rm = TRUE), split_on = Collectivism > S) %>% ungroup()
emg_mod_coll <- BF_analysis(data_FFM_emg, mod = "split_on", IV_name = "IV")$mod_result
# data_FFM_eff %>% filter(Collectivism_Code == 1) %>% select(Country)
format_file_mods(emg_mod_coll, order = FFM_order, write = FALSE)
#
# Type of organization.
# 1 - Social Service, 2 - Education, 3 - Business, 5 - Military, 6 - Mixed
emg_mod_org <- BF_analysis(data_FFM_emg, mod = "mod_organization", IV_name = "IV")$mod_result
type_of_organization <- format_file_mods(emg_mod_org, order = FFM_order, write = TRUE, filename = "emg_org.csv")
# # 1 - Lower level supervisors, 2 - Middle managers, 3 - Upper level managers, 4 - Mixed or unclear
emg_mod_hier <- BF_analysis(data_FFM_emg, mod = "mod_hierarchicallevel", IV_name = "IV")$mod_result
hierarchical_level <- format_file_mods(emg_mod_hier, order = FFM_order, write = TRUE, filename = "emg_hier.csv")
# # 1 - Laboratory, 2 - Organization, 3 - Classroom
emg_mod_setting <- BF_analysis(data_FFM_emg, mod = "mod_setting", IV_name = "IV")$mod_result
study_setting <- format_file_mods(emg_mod_setting, order = FFM_order, write = TRUE, filename = "emg_setting.csv")
emg_mod_complexity <- BF_analysis(data_FFM_emg, mod = "mod_socialcomplexityoftask", IV_name = "IV")$mod_result
social_complexity <- format_file_mods(emg_mod_complexity, order = FFM_order, write = TRUE, filename = "emg_social.csv")
emg_mod_appointleader <- BF_analysis(data_FFM_emg, mod = "mod_basisforlaboratoryleadership", IV_name = "IV")$mod_result
basis_appointment <- format_file_mods(emg_mod_appointleader, order = FFM_order, write = TRUE, filename = "emg_basis.csv")
emg_mod_length_interaction <- BF_analysis(data_FFM_emg, mod = "mod_lengthofinteraction", IV_name = "IV")$mod_result
length_interaction <- format_file_mods(emg_mod_length_interaction, order = FFM_order, write = TRUE, filename = "emg_length.csv")
emg_mod_emgmeasure <- BF_analysis(data_FFM_emg, mod = "mod_emergencemeasure", IV_name = "IV")$mod_result
emg_measure <- format_file_mods(emg_mod_emgmeasure, order = FFM_order, write = TRUE, filename = "emg_measure.csv")
emg_mod_publication_type <- BF_analysis(data_FFM_emg, mod = "Pub", IV_name = "IV")$mod_result
data_FFM_emg$VALID_IV <- ifelse(data_FFM_emg$`INCLUDE IN VALIDATED FFM ANALYSIS? (INCLUDING TYPE 4)` %in% c(1, 2, 3), "Yes", "No")
emg_mod_IV_validated <- BF_analysis(data_FFM_emg, mod = "VALID_IV", IV_name = "IV")$mod_result
validated_IV <- format_file_mods(emg_mod_IV_validated, order = FFM_order, write = TRUE, filename = "emg_validIV.csv")
data_FFM_emg$Pub <- ifelse(data_FFM_emg$`Publication code` == "J", "J", "D/T")
emg_mod_publication_type <- BF_analysis(data_FFM_emg, mod = "Pub", IV_name = "IV")$mod_result
publication_type <- format_file_mods(emg_mod_publication_type, order = FFM_order, write = TRUE, filename = "emg_pub.csv")
data_FFM_emg$Pub
# # 1 - Leader self-report, 2 - Non self-report, 3 - Mixed
emg_mod_IVRater <- BF_analysis(data_FFM_emg, mod = "mod_IV_RATER", IV_name = "IV")$mod_result
emg_mod_design <- BF_analysis(data_FFM_emg, mod = "mod_design", IV_name = "IV")$mod_result
emg_mod_IVRater <- BF_analysis(data_FFM_emg, mod = "mod_IV_RATER", IV_name = "IV")$mod_result
data_FFM <- readxl::read_excel("../../DataSet/NEW DATASET/B5L_Analysis_MASTER_230830.xlsx", sheet = "Coding-Judge_TEMP")
data_FFM_emg <- data_FFM %>% filter(DV == 1 & `INCLUDE IN ANALYSIS?` == 1)
FFM_order <- c("E","A","C","N","O")
data_FFM_emg <- data_FFM_emg %>% group_by(IV) %>% mutate(S = median(Collectivism, na.rm = TRUE), split_on = Collectivism > S) %>% ungroup()
emg_mod_coll <- BF_analysis(data_FFM_emg, mod = "split_on", IV_name = "IV")$mod_result
# data_FFM_eff %>% filter(Collectivism_Code == 1) %>% select(Country)
format_file_mods(emg_mod_coll, order = FFM_order, write = FALSE)
#
# Type of organization.
# 1 - Social Service, 2 - Education, 3 - Business, 5 - Military, 6 - Mixed
emg_mod_org <- BF_analysis(data_FFM_emg, mod = "mod_organization", IV_name = "IV")$mod_result
type_of_organization <- format_file_mods(emg_mod_org, order = FFM_order, write = TRUE, filename = "emg_org.csv")
#
# # Hierarchical level.
# # 1 - Lower level supervisors, 2 - Middle managers, 3 - Upper level managers, 4 - Mixed or unclear
# emg_mod_hier <- BF_analysis(data_FFM_emg, mod = "mod_hierarchicallevel", IV_name = "IV")$mod_result
# hierarchical_level <- format_file_mods(emg_mod_hier, order = FFM_order, write = TRUE, filename = "emg_hier.csv")
#
# # Study setting.
# # 1 - Laboratory, 2 - Organization, 3 - Classroom
emg_mod_setting <- BF_analysis(data_FFM_emg, mod = "mod_setting", IV_name = "IV")$mod_result
study_setting <- format_file_mods(emg_mod_setting, order = FFM_order, write = TRUE, filename = "emg_setting.csv")
#
# # Social complexity.
# # 1 - Low complexity, 2 - High complexity
emg_mod_complexity <- BF_analysis(data_FFM_emg, mod = "mod_socialcomplexityoftask", IV_name = "IV")$mod_result
social_complexity <- format_file_mods(emg_mod_complexity, order = FFM_order, write = TRUE, filename = "emg_social.csv")
#
# # Basis for appointing leader.
# # 1 - Random, 2 - Qualification, 3 - Emerged/Leaderless group, 4 - Mixed or unclear
emg_mod_appointleader <- BF_analysis(data_FFM_emg, mod = "mod_basisforlaboratoryleadership", IV_name = "IV")$mod_result
basis_appointment <- format_file_mods(emg_mod_appointleader, order = FFM_order, write = TRUE, filename = "emg_basis.csv")
#
# # Length of interaction.
# # 1 - < 20 mins, 2 - > 20 mins, one interaction, 3 - More than one interaction, 4 - Mixed or not applicable
# # Category 4 not presented because many emergence studies happened to be not involving interaction contexts.
emg_mod_length_interaction <- BF_analysis(data_FFM_emg, mod = "mod_lengthofinteraction", IV_name = "IV")$mod_result
length_interaction <- format_file_mods(emg_mod_length_interaction, order = FFM_order, write = TRUE, filename = "emg_length.csv")
#
# # Measure of emergence.
# # 1 - Election, 2 - Questionnaire, 3 - Rank, 4 - Mixed or unclear
emg_mod_emgmeasure <- BF_analysis(data_FFM_emg, mod = "mod_emergencemeasure", IV_name = "IV")$mod_result
emg_measure <- format_file_mods(emg_mod_emgmeasure, order = FFM_order, write = TRUE, filename = "emg_measure.csv")
#
# # Publication type. In the original data file, theses and dissertations are separated. Merged into one.
data_FFM_emg$Pub <- ifelse(data_FFM_emg$`Publication code` == "J", "J", "D/T")
emg_mod_publication_type <- BF_analysis(data_FFM_emg, mod = "Pub", IV_name = "IV")$mod_result
publication_type <- format_file_mods(emg_mod_publication_type, order = FFM_order, write = TRUE, filename = "emg_pub.csv")
#
# # Validated personality measure.
# # Yes - Validated FFM scale (See a list from Judge et al., 2013), Translation of these scales, Scales derived from these validated scales
# # No - None of the above
data_FFM_emg$VALID_IV <- ifelse(data_FFM_emg$`INCLUDE IN VALIDATED FFM ANALYSIS? (INCLUDING TYPE 4)` %in% c(1, 2, 3), "Yes", "No")
emg_mod_IV_validated <- BF_analysis(data_FFM_emg, mod = "VALID_IV", IV_name = "IV")$mod_result
validated_IV <- format_file_mods(emg_mod_IV_validated, order = FFM_order, write = TRUE, filename = "emg_validIV.csv")
#
# # Rating source of personality.
# # 1 - Leader self-report, 2 - Non self-report, 3 - Mixed
emg_mod_IVRater <- BF_analysis(data_FFM_emg, mod = "mod_IV_RATER", IV_name = "IV")$mod_result
IV_rater <- format_file_mods(emg_mod_IVRater, order = FFM_order, write = TRUE, filename = "emg_IVRater.csv")
#
# # Rating source of emergence
# # 1 - Leader self-report, 3 - Skip-level supervisor, 4 - Observer, 5 - Objective, 7 - Peer, 6 - Mixed or Unclear
emg_mod_DVRater <- BF_analysis(data_FFM_emg, mod = "mod_DV_RATER_SOURCE", IV_name = "IV")$mod_result
DV_rater <- format_file_mods(emg_mod_DVRater, order = FFM_order, write = TRUE, filename = "emg_DVRater.csv")
#
# # Common source
emg_mod_commonsource <- BF_analysis(data_FFM_emg, mod = "mod_common_source", IV_name = "IV")$mod_result
common_source <- format_file_mods(emg_mod_commonsource, order = FFM_order, write = TRUE, filename = "emg_commonsource.csv")
#
# # Research design
emg_mod_design <- BF_analysis(data_FFM_emg, mod = "mod_design", IV_name = "IV")$mod_result
research_design <- format_file_mods(emg_mod_design, order = FFM_order, write = TRUE, filename = "emg_timedesign.csv")
data_FFM_eff <- data_FFM %>% filter(INCLUDE_AS_JUDGE_OP_S == 1)
eff_mod_coll <- BF_analysis(data_FFM_eff, mod = "split_on", IV_name = "IV")$mod_result
data_FFM_eff <- data_FFM_eff %>% group_by(IV) %>% mutate(S = median(Collectivism, na.rm = TRUE), split_on = Collectivism > S) %>% ungroup()
eff_mod_coll <- BF_analysis(data_FFM_eff, mod = "split_on", IV_name = "IV")$mod_result
# data_FFM_eff %>% filter(Collectivism_Code == 1) %>% select(Country)
format_file_mods(eff_mod_coll, order = FFM_order, write = TRUE, filename = "eff_coll.csv")
format_file(BF_analysis(data_FFM_eff, IV_name = "IV",
var_names = list(rxx = "rxx", ryy = "ryy", rxy = "rxy", N = "N"))$overall[c("E", "A", "C", "N", "O")])
# Type of organization.
# 1 - Social Service, 2 - Education, 3 - Business, 5 - Military, 6 - Mixed
eff_mod_org <- BF_analysis(data_FFM_eff, mod = "mod_organization", IV_name = "IV")$mod_result
type_of_organization_eff <- format_file_mods(eff_mod_org, order = FFM_order, write = TRUE, filename = "eff_org.csv")
# Hierarchical level.
# 1 - Lower level supervisors, 2 - Middle managers, 3 - Upper level managers, 4 - Mixed or unclear
eff_mod_hier <- BF_analysis(data_FFM_eff, mod = "mod_hierarchicallevel", IV_name = "IV")$mod_result
hierarchical_level_eff <- format_file_mods(eff_mod_hier, order = FFM_order, write = TRUE, filename = "eff_hier.csv")
# Study setting.
# 1 - Laboratory, 2 - Organization, 3 - Classroom
eff_mod_setting <- BF_analysis(data_FFM_eff, mod = "mod_setting", IV_name = "IV")$mod_result
study_setting_eff <- format_file_mods(eff_mod_setting, order = FFM_order, write = TRUE, filename = "eff_setting.csv")
# Social complexity.
# 1 - Low complexity, 2 - High complexity
eff_mod_complexity <- BF_analysis(data_FFM_eff, mod = "mod_socialcomplexityoftask", IV_name = "IV")$mod_result
social_complexity_eff <- format_file_mods(eff_mod_complexity, order = FFM_order, write = TRUE, filename = "eff_comp.csv")
# Publication type. In the original data file, theses and dissertations are separated. Merged into one.
data_FFM_eff$Pub <- ifelse(data_FFM_eff$`Publication code` == "J", "J", "D/T")
eff_mod_publication_type <- BF_analysis(data_FFM_eff, mod = "Pub", IV_name = "IV")$mod_result
publication_type_eff <- format_file_mods(eff_mod_publication_type, order = FFM_order, write = TRUE, filename = "eff_pub.csv")
# Validated personality measure.
# Yes - Validated FFM scale (See a list from Judge et al., 2013), Translation of these scales, Scales derived from these validated scales
# No - None of the above
data_FFM_eff$VALID_IV <- ifelse(data_FFM_eff$`INCLUDE IN VALIDATED FFM ANALYSIS? (INCLUDING TYPE 4)` %in% c(1, 2, 3), "Yes", "No")
eff_mod_IV_validated <- BF_analysis(data_FFM_eff, mod = "VALID_IV", IV_name = "IV")$mod_result
validated_IV_eff <- format_file_mods(eff_mod_IV_validated, order = FFM_order, write = TRUE, filename = "eff_validIV.csv")
# Rating source of personality.
# 1 - Leader self-report, 2 - Non self-report, 3 - Mixed
eff_mod_IVRater <- BF_analysis(data_FFM_eff, mod = "IV RATER = SELF? (1 = Yes, 2 = No, 3 = Mixed)", IV_name = "IV")$mod_result
eff_mod_IVRater <- BF_analysis(data_FFM_eff, mod = "mod_IV_RATER", IV_name = "IV")$mod_result
IV_rater_eff <- format_file_mods(eff_mod_IVRater, order = FFM_order, write = TRUE, filename = "eff_IVRater.csv")
# Rating source of effectiveness.
# 1 - Leader self-report, 3 - Skip-level supervisor, 4 - Observer, 5 - Objective, 7 - Peer, 6 - Mixed or Unclear
eff_mod_DVRater <- BF_analysis(data_FFM_eff, mod = "mod_DV_RATER_SOURCE", IV_name = "IV")$mod_result
DV_rater_eff <- format_file_mods(eff_mod_DVRater, order = FFM_order, write = TRUE, filename = "eff_DVRater.csv")
# Common source
eff_mod_commonsource <- BF_analysis(data_FFM_eff, mod = "mod_common_source", IV_name = "IV")$mod_result
common_source_eff <- format_file_mods(eff_mod_commonsource, order = FFM_order, write = TRUE, filename = "eff_commonsource.csv")
# Research design
eff_mod_design <- BF_analysis(data_FFM_eff, mod = "mod_design", IV_name = "IV")$mod_result
research_design_eff <- format_file_mods(eff_mod_design, order = FFM_order, write = TRUE, filename = "eff_timedesign.csv")
social_complexity_eff <- format_file_mods(eff_mod_complexity, order = FFM_order, write = TRUE, filename = "eff_comp.csv")
data_HEXACO <- readxl::read_excel("../../DataSet/B5L_Analysis_HEXACO_NEW_1119.xlsx", sheet = "HEXACO Coding")
###### Main effect for HEXACO ######
data_HEXACO <- readxl::read_excel("../../DataSet/NEW DATASET/B5L_Analysis_HEXACO_NEW_1119.xlsx", sheet = "HEXACO Coding")
data_HEXACO_emg <- data_HEXACO %>% filter(DV == 1 & INCLUDE_TYPE == 1)
HEXACO_order <- c("H","E","X","A","C","O")
format_file(HEX_analysis(data_HEXACO_emg, IV_name = "IV")$overall)
setwd("D:/Anoop Meta-Analysis/ROUND 3 MANUSCRIPT/B5L R&R2 (May 2023)-20230820T064706Z-001/B5L R_R2 (May 2023)/SUPPLEMENTARY MATERIAL_1101/Analysis/3. Moderating Effect of Collectivism")
data_HEXACO <- readxl::read_excel("../../DataSet/NEW DATASET/B5L_Analysis_HEXACO_NEW_1119.xlsx", sheet = "HEXACO Coding")
data_HEXACO_eff <- data_HEXACO %>% filter(OVERALL_INCLUSION == 1)
eff_H <- data_HEXACO_eff %>% filter(IV == 6)
calculation_mod(eff_H, mods = eff_H$Collectivism)$result
source("../Utilities/Utilities-Main.R")
source("../Utilities/Utilities-Extra.R")
library(lm.beta)
### Effectiveness
# data_FFM_J <- readxl::read_excel("../../DataSet/B5L_Analysis_MASTER_NEW1124_NEW.xlsx", sheet = "Coding-Judge_Op")
data_FFM <- readxl::read_excel("../../DataSet/NEW DATASET/B5L_Analysis_MASTER_230830.xlsx", sheet = "Coding-Judge_TEMP")
data_FFM_eff <- data_FFM %>% filter(INCLUDE_AS_JUDGE_OP_S == 1)
# table(data_FFM_J_eff$`Article ID`)
results <- BF_analysis(data_FFM_eff, IV_name = "IV",
var_names = list(rxx = "rxx", ryy = "ryy", rxy = "rxy", N = "N"))
results$overall[c("E", "A", "C", "N", "O")]
eff_E_J <- data_FFM_J_eff %>% filter(IV == 3)
# p = .0266, k = 67
# p = .0347, k = 67 (Using Z)
calculation_mod(eff_E_J, mods = eff_E_J$Collectivism, method = "HS", use_Z_mod = FALSE)$result
eff_E_J <- data_FFM_eff %>% filter(IV == 3)
eff_A_J <- data_FFM_eff %>% filter(IV == 4)
eff_C_J <- data_FFM_eff %>% filter(IV == 2)
eff_N_J <- data_FFM_eff %>% filter(IV == 5)
eff_O_J <- data_FFM_eff %>% filter(IV == 1)
eff_E <- data_FFM_eff %>% filter(IV == 3)
eff_A <- data_FFM_eff %>% filter(IV == 4)
eff_C <- data_FFM_eff %>% filter(IV == 2)
eff_N <- data_FFM_eff %>% filter(IV == 5)
eff_O <- data_FFM_eff %>% filter(IV == 1)
# p = .0266, k = 67
# p = .0347, k = 67 (Using Z)
calculation_mod(eff_E, mods = eff_E$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)$result
# p = .0356, k = 52
# p = .0659, k = 52 (Using Z)
calculation_mod(eff_A, mods = eff_A$Collectivism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_C, mods = eff_C$Collectivism)$detail_CI
calculation_mod(eff_C, mods = eff_C$Collectivism)$detail_CI
calculation_mod(eff_C, mods = eff_C$Collectivism)
calculation_mod(eff_N, mods = eff_N$Collectivism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_O, mods = eff_O$Collectivism)
data_HEXACO_eff <- data_HEXACO %>% filter(DVUDGE_EFF == 1)
data_HEXACO_eff <- data_HEXACO %>% filter(DV_JUDGE_EFF == 1)
data_FFM <- readxl::read_excel("../../DataSet/NEW DATASET/B5L_Analysis_MASTER_230830.xlsx", sheet = "Coding-Judge_TEMP")
data_FFM_eff <- data_FFM %>% filter(INCLUDE_AS_JUDGE_OP_S == 1)
eff_HH <- data_HEXACO_eff %>% filter(IV == 6)
eff_EE <- data_HEXACO_eff %>% filter(IV == 5)
eff_XX <- data_HEXACO_eff %>% filter(IV == 3)
eff_AA <- data_HEXACO_eff %>% filter(IV == 4)
eff_CC <- data_HEXACO_eff %>% filter(IV == 2)
eff_OO <- data_HEXACO_eff %>% filter(IV == 1)
calculation_mod(eff_HH, mods = eff_HH$Collectivism)$detail_CI
calculation_mod(eff_EE, mods = eff_EE$Collectivism)$detail_CI
calculation_mod(eff_XX, mods = eff_XX$Collectivism)$detail_CI
calculation_mod(eff_AA, mods = eff_AA$Collectivism)$detail_CI
calculation_mod(eff_CC, mods = eff_CC$Collectivism)$detail_CI
calculation_mod(eff_OO, mods = eff_OO$Collectivism)$detail_CI
calculation_mod(eff_HH, mods = eff_HH$Collectivism)
setwd("D:/Anoop Meta-Analysis/ROUND 3 MANUSCRIPT/B5L R&R2 (May 2023)-20230820T064706Z-001/B5L R_R2 (May 2023)/SUPPLEMENTARY MATERIAL_1101/Analysis/3. Moderating Effect of Collectivism")
source("../Utilities/Utilities-Main.R")
source("../Utilities/Utilities-Extra.R")
### Effectiveness
# data_FFM <- readxl::read_excel("../../DataSet/B5L_Analysis_MASTER_NEW1124_NEW.xlsx", sheet = "Coding-Judge_Op")
data_FFM <- readxl::read_excel("../../DataSet/NEW DATASET/B5L_Analysis_MASTER_230830.xlsx", sheet = "Coding-Judge_TEMP")
data_FFM_eff <- data_FFM %>% filter(INCLUDE_AS_JUDGE_OP_S == 1)
eff_E$Power_Distance
eff_E$Power_Distance_Value
### Power Distance
calculation_mod(eff_E, mods = eff_E$Power_Distance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_A, mods = eff_E$Power_Distance, method = "HS", use_Z_mod = FALSE)$result
### Power Distance
calculation_mod(eff_E, mods = eff_E$Power_Distance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_A, mods = eff_A$Power_Distance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_C, mods = eff_C$Power_Distance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_N, mods = eff_N$Power_Distance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_N, mods = eff_N$Power_Distance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_O, mods = eff_O$Power_Distance, method = "HS", use_Z_mod = FALSE)$result
### Uncertainty Avoidance
calculation_mod(eff_E, mods = eff_E$Uncertainty_Avoidance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_E, mods = eff_E$Uncertainty_Avoidance, method = "HS", use_Z_mod = FALSE)$detail_CI
calculation_mod(eff_A, mods = eff_A$Uncertainty_Avoidance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_C, mods = eff_C$Uncertainty_Avoidance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_N, mods = eff_N$Uncertainty_Avoidance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_O, mods = eff_O$Uncertainty_Avoidance, method = "HS", use_Z_mod = FALSE)$result
### Tightness-Looseness
calculation_mod(eff_E, mods = eff_E$TL, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_A, mods = eff_A$TL, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_C, mods = eff_C$TL, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_N, mods = eff_N$TL, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_O, mods = eff_O$TL, method = "HS", use_Z_mod = FALSE)$result
### Masculinity-Femininity
calculation_mod(eff_E, mods = eff_E$Gender_Egalitarianism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_A, mods = eff_A$Gender_Egalitarianism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_C, mods = eff_C$Gender_Egalitarianism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_N, mods = eff_N$Gender_Egalitarianism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_O, mods = eff_O$Gender_Egalitarianism, method = "HS", use_Z_mod = FALSE)$result
##### Emergence
data_FFM_emg <- data_FFM %>% filter(DV == 1 & `INCLUDE IN ANALYSIS?` == 1)
##### 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)
### Power Distance
calculation_mod(emg_E, mods = emg_E$Power_Distance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_A, mods = emg_A$Power_Distance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_C, mods = emg_C$Power_Distance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_N, mods = emg_N$Power_Distance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_O, mods = emg_O$Power_Distance, method = "HS", use_Z_mod = FALSE)$result
### Uncertainty Avoidance
calculation_mod(emg_E, mods = emg_E$Uncertainty_Avoidance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_A, mods = emg_A$Uncertainty_Avoidance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_C, mods = emg_C$Uncertainty_Avoidance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_N, mods = emg_N$Uncertainty_Avoidance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_O, mods = emg_O$Uncertainty_Avoidance, method = "HS", use_Z_mod = FALSE)$result
### Masculinity-Femininity
calculation_mod(emg_E, mods = emg_E$Gender_Egalitarianism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_A, mods = emg_A$Gender_Egalitarianism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_C, mods = emg_C$Gender_Egalitarianism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_N, mods = emg_N$Gender_Egalitarianism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_O, mods = emg_O$Gender_Egalitarianism, method = "HS", use_Z_mod = FALSE)$result
### Tightness-Looseness
calculation_mod(emg_E, mods = emg_E$TL, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_A, mods = emg_A$TL, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_C, mods = emg_C$TL, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_N, mods = emg_N$TL, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_O, mods = emg_O$TL, method = "HS", use_Z_mod = FALSE)$result
### Power Distance
calculation_mod(emg_E, mods = emg_E$Power_Distance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_A, mods = emg_A$Power_Distance, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_A, mods = emg_A$Power_Distance, method = "HS", use_Z_mod = FALSE)$detail_CI
### Collectivism
calculation_mod(eff_E, mods = eff_E$Collectivism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_A, mods = eff_A$Collectivism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_C, mods = eff_C$Collectivism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_N, mods = eff_N$Collectivism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(eff_O, mods = eff_O$Collectivism, method = "HS", use_Z_mod = FALSE)$result
### Collectivism
calculation_mod(emg_E, mods = emg_E$Collectivism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_A, mods = emg_A$Collectivism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_C, mods = emg_C$Collectivism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_N, mods = emg_N$Collectivism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_O, mods = emg_O$Collectivism, method = "HS", use_Z_mod = FALSE)$result
calculation_mod(emg_O, mods = emg_O$Collectivism, method = "HS", use_Z_mod = FALSE)$detail_CI
### Uncertainty Avoidance
calculation_mod(emg_E, mods = emg_E$Uncertainty_Avoidance, method = "HS", use_Z_mod = FALSE)$result
### Masculinity-Femininity
calculation_mod(emg_E, mods = emg_E$Gender_Egalitarianism, method = "HS", use_Z_mod = FALSE)$result
### Masculinity-Femininity
calculation_mod(eff_E, mods = eff_E$Gender_Egalitarianism, method = "HS", use_Z_mod = FALSE)$result
