#install packages
install.packages(c("data.table","psych","car","JuliaCall","dplyr","flextable","officer","ggplot2","ggpubr","MplusAutomation","lavaan"))


#load packages
library(data.table)
library(psych)
library(car)
library(JuliaCall)
library(dplyr)
library(flextable)
library(officer)
library(ggplot2)
library(ggpubr)
library(MplusAutomation)
library(lavaan)


#set up Julia

### download and install Julia (https://julialang.org/downloads/) if not already done ###

##start Julia
julia <- julia_setup()

##save directory to the Julia folder indicated in julia_setup (e.g., "C:/AppData/Local/JULIA-~1.2/bin")
julia_wd <- "C:/AppData/Local/JULIA-~1.2/bin"

##install Julia packages
julia_install_package("MixedModels")
julia_install_package("DataFrames")
julia_install_package("StatsBase")

##load Julia packages
julia_library("MixedModels")
julia_library("DataFrames")
julia_library("StatsBase")


#save directory to the folder to which files created in R should be saved (e.g., "C:/SNP-B5/")
files_wd <- "C:/SNP-B5/"


########## S1: Pretests to Ensure the Sociocultural Norms' Credibility ##########


#read data
dat_pretest <- as.data.frame(fread("https://madata.bib.uni-mannheim.de/364/8/SNP-B5_Pretest-Data.csv", header = T, sep = ","))


#select samples
pretest1a <- subset(dat_pretest, subset = sample == 1)
pretest1b <- subset(dat_pretest, subset = sample == 2)
pretest2a <- subset(dat_pretest, subset = sample == 3)
pretest2b <- subset(dat_pretest, subset = sample == 4)


#demographics
sex_pretest1a <- as.data.frame(table(pretest1a$sex))
sex_pretest1b <- as.data.frame(table(pretest1b$sex))
sex_pretest2a <- as.data.frame(table(pretest2a$sex))
sex_pretest2b <- as.data.frame(table(pretest2b$sex))
paste0("Pretest 1a: N = ", nrow(pretest1a), ", ", round(100*(sex_pretest1a[2,2]/nrow(pretest1a)), 0), "% female, ", round(100*(sex_pretest1a[1,2]/nrow(pretest1a)), 0), "% male, ", round(100*((nrow(pretest1a)-sum(sex_pretest1a[,2]))/nrow(pretest1a)), 0), "% missing, Mage = ", round(mean(pretest1a$age, na.rm=T), 2), ", SDage = ", round(sd(pretest1a$age, na.rm=T), 2))
paste0("Pretest 1b: N = ", nrow(pretest1b), ", ", round(100*(sex_pretest1b[2,2]/nrow(pretest1b)), 0), "% female, ", round(100*(sex_pretest1b[1,2]/nrow(pretest1b)), 0), "% male, ", round(100*((nrow(pretest1b)-sum(sex_pretest1b[,2]))/nrow(pretest1b)), 0), "% missing, Mage = ", round(mean(pretest1b$age, na.rm=T), 2), ", SDage = ", round(sd(pretest1b$age, na.rm=T), 2))
paste0("Pretest 2a: N = ", nrow(pretest2a), ", ", round(100*(sex_pretest2a[2,2]/nrow(pretest2a)), 0), "% female, ", round(100*(sex_pretest2a[1,2]/nrow(pretest2a)), 0), "% male, ", round(100*((nrow(pretest2a)-sum(sex_pretest2a[,2]))/nrow(pretest2a)), 0), "% missing, Mage = ", round(mean(pretest2a$age, na.rm=T), 2), ", SDage = ", round(sd(pretest2a$age, na.rm=T), 2))
paste0("Pretest 2b: N = ", nrow(pretest2b), ", ", round(100*(sex_pretest2b[2,2]/nrow(pretest2b)), 0), "% female, ", round(100*(sex_pretest2b[1,2]/nrow(pretest2b)), 0), "% male, ", round(100*((nrow(pretest2b)-sum(sex_pretest2b[,2]))/nrow(pretest2b)), 0), "% missing, Mage = ", round(mean(pretest2b$age, na.rm=T), 2), ", SDage = ", round(sd(pretest2b$age, na.rm=T), 2))
rm(sex_pretest1a,sex_pretest1b,sex_pretest2a,sex_pretest2b)


#Table S1: Mean Preferences in Pretests 1a-1b for Pairs of Chinese Characters and Pairs of Social Values Used in the Minimal Norm Paradigm
chi_A1_error <- qt(0.975,df=length(pretest1a$chi_A1)-1)*sd(pretest1a$chi_A1)/sqrt(length(pretest1a$chi_A1))
chi_A1_mean <- mean(pretest1a$chi_A1)
chi_A1_lower_ci <- chi_A1_mean-chi_A1_error
chi_A1_upper_ci <- chi_A1_mean+chi_A1_error

chi_A2_error <- qt(0.975,df=length(pretest1a$chi_A2)-1)*sd(pretest1a$chi_A2)/sqrt(length(pretest1a$chi_A2))
chi_A2_mean <- mean(pretest1a$chi_A2)
chi_A2_lower_ci <- chi_A2_mean-chi_A2_error
chi_A2_upper_ci <- chi_A2_mean+chi_A2_error

chi_A3_error <- qt(0.975,df=length(pretest1a$chi_A3)-1)*sd(pretest1a$chi_A3)/sqrt(length(pretest1a$chi_A3))
chi_A3_mean <- mean(pretest1a$chi_A3)
chi_A3_lower_ci <- chi_A3_mean-chi_A3_error
chi_A3_upper_ci <- chi_A3_mean+chi_A3_error

chi_A4_error <- qt(0.975,df=length(pretest1a$chi_A4)-1)*sd(pretest1a$chi_A4)/sqrt(length(pretest1a$chi_A4))
chi_A4_mean <- mean(pretest1a$chi_A4)
chi_A4_lower_ci <- chi_A4_mean-chi_A4_error
chi_A4_upper_ci <- chi_A4_mean+chi_A4_error

chi_A5_error <- qt(0.975,df=length(pretest1a$chi_A5)-1)*sd(pretest1a$chi_A5)/sqrt(length(pretest1a$chi_A5))
chi_A5_mean <- mean(pretest1a$chi_A5)
chi_A5_lower_ci <- chi_A5_mean-chi_A5_error
chi_A5_upper_ci <- chi_A5_mean+chi_A5_error

chi_A6_error <- qt(0.975,df=length(pretest1a$chi_A6)-1)*sd(pretest1a$chi_A6)/sqrt(length(pretest1a$chi_A6))
chi_A6_mean <- mean(pretest1a$chi_A6)
chi_A6_lower_ci <- chi_A6_mean-chi_A6_error
chi_A6_upper_ci <- chi_A6_mean+chi_A6_error

chi_B1_error <- qt(0.975,df=length(pretest1a$chi_B1)-1)*sd(pretest1a$chi_B1)/sqrt(length(pretest1a$chi_B1))
chi_B1_mean <- mean(pretest1a$chi_B1)
chi_B1_lower_ci <- chi_B1_mean-chi_B1_error
chi_B1_upper_ci <- chi_B1_mean+chi_B1_error

chi_B2_error <- qt(0.975,df=length(pretest1a$chi_B2)-1)*sd(pretest1a$chi_B2)/sqrt(length(pretest1a$chi_B2))
chi_B2_mean <- mean(pretest1a$chi_B2)
chi_B2_lower_ci <- chi_B2_mean-chi_B2_error
chi_B2_upper_ci <- chi_B2_mean+chi_B2_error

chi_B3_error <- qt(0.975,df=length(pretest1a$chi_B3)-1)*sd(pretest1a$chi_B3)/sqrt(length(pretest1a$chi_B3))
chi_B3_mean <- mean(pretest1a$chi_B3)
chi_B3_lower_ci <- chi_B3_mean-chi_B3_error
chi_B3_upper_ci <- chi_B3_mean+chi_B3_error

chi_B4_error <- qt(0.975,df=length(pretest1a$chi_B4)-1)*sd(pretest1a$chi_B4)/sqrt(length(pretest1a$chi_B4))
chi_B4_mean <- mean(pretest1a$chi_B4)
chi_B4_lower_ci <- chi_B4_mean-chi_B4_error
chi_B4_upper_ci <- chi_B4_mean+chi_B4_error

chi_B5_error <- qt(0.975,df=length(pretest1a$chi_B5)-1)*sd(pretest1a$chi_B5)/sqrt(length(pretest1a$chi_B5))
chi_B5_mean <- mean(pretest1a$chi_B5)
chi_B5_lower_ci <- chi_B5_mean-chi_B5_error
chi_B5_upper_ci <- chi_B5_mean+chi_B5_error

chi_B6_error <- qt(0.975,df=length(pretest1a$chi_B6)-1)*sd(pretest1a$chi_B6)/sqrt(length(pretest1a$chi_B6))
chi_B6_mean <- mean(pretest1a$chi_B6)
chi_B6_lower_ci <- chi_B6_mean-chi_B6_error
chi_B6_upper_ci <- chi_B6_mean+chi_B6_error

chi_C1_error <- qt(0.975,df=length(pretest1a$chi_C1)-1)*sd(pretest1a$chi_C1)/sqrt(length(pretest1a$chi_C1))
chi_C1_mean <- mean(pretest1a$chi_C1)
chi_C1_lower_ci <- chi_C1_mean-chi_C1_error
chi_C1_upper_ci <- chi_C1_mean+chi_C1_error

chi_C2_error <- qt(0.975,df=length(pretest1a$chi_C2)-1)*sd(pretest1a$chi_C2)/sqrt(length(pretest1a$chi_C2))
chi_C2_mean <- mean(pretest1a$chi_C2)
chi_C2_lower_ci <- chi_C2_mean-chi_C2_error
chi_C2_upper_ci <- chi_C2_mean+chi_C2_error

chi_C3_error <- qt(0.975,df=length(pretest1a$chi_C3)-1)*sd(pretest1a$chi_C3)/sqrt(length(pretest1a$chi_C3))
chi_C3_mean <- mean(pretest1a$chi_C3)
chi_C3_lower_ci <- chi_C3_mean-chi_C3_error
chi_C3_upper_ci <- chi_C3_mean+chi_C3_error

chi_C4_error <- qt(0.975,df=length(pretest1a$chi_C4)-1)*sd(pretest1a$chi_C4)/sqrt(length(pretest1a$chi_C4))
chi_C4_mean <- mean(pretest1a$chi_C4)
chi_C4_lower_ci <- chi_C4_mean-chi_C4_error
chi_C4_upper_ci <- chi_C4_mean+chi_C4_error

chi_C5_error <- qt(0.975,df=length(pretest1a$chi_C5)-1)*sd(pretest1a$chi_C5)/sqrt(length(pretest1a$chi_C5))
chi_C5_mean <- mean(pretest1a$chi_C5)
chi_C5_lower_ci <- chi_C5_mean-chi_C5_error
chi_C5_upper_ci <- chi_C5_mean+chi_C5_error

chi_C6_error <- qt(0.975,df=length(pretest1a$chi_C6)-1)*sd(pretest1a$chi_C6)/sqrt(length(pretest1a$chi_C6))
chi_C6_mean <- mean(pretest1a$chi_C6)
chi_C6_lower_ci <- chi_C6_mean-chi_C6_error
chi_C6_upper_ci <- chi_C6_mean+chi_C6_error

val_A1_error <- qt(0.975,df=length(pretest1b$val_A1)-1)*sd(pretest1b$val_A1)/sqrt(length(pretest1b$val_A1))
val_A1_mean <- mean(pretest1b$val_A1)
val_A1_lower_ci <- val_A1_mean-val_A1_error
val_A1_upper_ci <- val_A1_mean+val_A1_error

val_A2_error <- qt(0.975,df=length(pretest1b$val_A2)-1)*sd(pretest1b$val_A2)/sqrt(length(pretest1b$val_A2))
val_A2_mean <- mean(pretest1b$val_A2)
val_A2_lower_ci <- val_A2_mean-val_A2_error
val_A2_upper_ci <- val_A2_mean+val_A2_error

val_A3_error <- qt(0.975,df=length(pretest1b$val_A3)-1)*sd(pretest1b$val_A3)/sqrt(length(pretest1b$val_A3))
val_A3_mean <- mean(pretest1b$val_A3)
val_A3_lower_ci <- val_A3_mean-val_A3_error
val_A3_upper_ci <- val_A3_mean+val_A3_error

val_A4_error <- qt(0.975,df=length(pretest1b$val_A4)-1)*sd(pretest1b$val_A4)/sqrt(length(pretest1b$val_A4))
val_A4_mean <- mean(pretest1b$val_A4)
val_A4_lower_ci <- val_A4_mean-val_A4_error
val_A4_upper_ci <- val_A4_mean+val_A4_error

val_A5_error <- qt(0.975,df=length(pretest1b$val_A5)-1)*sd(pretest1b$val_A5)/sqrt(length(pretest1b$val_A5))
val_A5_mean <- mean(pretest1b$val_A5)
val_A5_lower_ci <- val_A5_mean-val_A5_error
val_A5_upper_ci <- val_A5_mean+val_A5_error

val_A6_error <- qt(0.975,df=length(pretest1b$val_A6)-1)*sd(pretest1b$val_A6)/sqrt(length(pretest1b$val_A6))
val_A6_mean <- mean(pretest1b$val_A6)
val_A6_lower_ci <- val_A6_mean-val_A6_error
val_A6_upper_ci <- val_A6_mean+val_A6_error

val_B1_error <- qt(0.975,df=length(pretest1b$val_B1)-1)*sd(pretest1b$val_B1)/sqrt(length(pretest1b$val_B1))
val_B1_mean <- mean(pretest1b$val_B1)
val_B1_lower_ci <- val_B1_mean-val_B1_error
val_B1_upper_ci <- val_B1_mean+val_B1_error

val_B2_error <- qt(0.975,df=length(pretest1b$val_B2)-1)*sd(pretest1b$val_B2)/sqrt(length(pretest1b$val_B2))
val_B2_mean <- mean(pretest1b$val_B2)
val_B2_lower_ci <- val_B2_mean-val_B2_error
val_B2_upper_ci <- val_B2_mean+val_B2_error

val_B3_error <- qt(0.975,df=length(pretest1b$val_B3)-1)*sd(pretest1b$val_B3)/sqrt(length(pretest1b$val_B3))
val_B3_mean <- mean(pretest1b$val_B3)
val_B3_lower_ci <- val_B3_mean-val_B3_error
val_B3_upper_ci <- val_B3_mean+val_B3_error

val_B4_error <- qt(0.975,df=length(pretest1b$val_B4)-1)*sd(pretest1b$val_B4)/sqrt(length(pretest1b$val_B4))
val_B4_mean <- mean(pretest1b$val_B4)
val_B4_lower_ci <- val_B4_mean-val_B4_error
val_B4_upper_ci <- val_B4_mean+val_B4_error

val_B5_error <- qt(0.975,df=length(pretest1b$val_B5)-1)*sd(pretest1b$val_B5)/sqrt(length(pretest1b$val_B5))
val_B5_mean <- mean(pretest1b$val_B5)
val_B5_lower_ci <- val_B5_mean-val_B5_error
val_B5_upper_ci <- val_B5_mean+val_B5_error

val_B6_error <- qt(0.975,df=length(pretest1b$val_B6)-1)*sd(pretest1b$val_B6)/sqrt(length(pretest1b$val_B6))
val_B6_mean <- mean(pretest1b$val_B6)
val_B6_lower_ci <- val_B6_mean-val_B6_error
val_B6_upper_ci <- val_B6_mean+val_B6_error

val_C1_error <- qt(0.975,df=length(pretest1b$val_C1)-1)*sd(pretest1b$val_C1)/sqrt(length(pretest1b$val_C1))
val_C1_mean <- mean(pretest1b$val_C1)
val_C1_lower_ci <- val_C1_mean-val_C1_error
val_C1_upper_ci <- val_C1_mean+val_C1_error

val_C2_error <- qt(0.975,df=length(pretest1b$val_C2)-1)*sd(pretest1b$val_C2)/sqrt(length(pretest1b$val_C2))
val_C2_mean <- mean(pretest1b$val_C2)
val_C2_lower_ci <- val_C2_mean-val_C2_error
val_C2_upper_ci <- val_C2_mean+val_C2_error

val_C3_error <- qt(0.975,df=length(pretest1b$val_C3)-1)*sd(pretest1b$val_C3)/sqrt(length(pretest1b$val_C3))
val_C3_mean <- mean(pretest1b$val_C3)
val_C3_lower_ci <- val_C3_mean-val_C3_error
val_C3_upper_ci <- val_C3_mean+val_C3_error

val_C4_error <- qt(0.975,df=length(pretest1b$val_C4)-1)*sd(pretest1b$val_C4)/sqrt(length(pretest1b$val_C4))
val_C4_mean <- mean(pretest1b$val_C4)
val_C4_lower_ci <- val_C4_mean-val_C4_error
val_C4_upper_ci <- val_C4_mean+val_C4_error

val_C5_error <- qt(0.975,df=length(pretest1b$val_C5)-1)*sd(pretest1b$val_C5)/sqrt(length(pretest1b$val_C5))
val_C5_mean <- mean(pretest1b$val_C5)
val_C5_lower_ci <- val_C5_mean-val_C5_error
val_C5_upper_ci <- val_C5_mean+val_C5_error

val_C6_error <- qt(0.975,df=length(pretest1b$val_C6)-1)*sd(pretest1b$val_C6)/sqrt(length(pretest1b$val_C6))
val_C6_mean <- mean(pretest1b$val_C6)
val_C6_lower_ci <- val_C6_mean-val_C6_error
val_C6_upper_ci <- val_C6_mean+val_C6_error


dat_table_s1 <- data.frame(matrix(nrow=18,ncol=7))

dat_table_s1[,1] <- c("A/1","A/2","A/3","A/4","A/5","A/6","B/1","B/2","B/3","B/4","B/5","B/6","C/1","C/2","C/3","C/4","C/5","C/6")
dat_table_s1[,2] <- round(c(chi_A1_mean,chi_A2_mean,chi_A3_mean,chi_A4_mean,chi_A5_mean,chi_A6_mean,chi_B1_mean,chi_B2_mean,chi_B3_mean,chi_B4_mean,chi_B5_mean,chi_B6_mean,chi_C1_mean,chi_C2_mean,chi_C3_mean,chi_C4_mean,chi_C5_mean,chi_C6_mean), 2)
dat_table_s1[,3] <- round(c(chi_A1_lower_ci,chi_A2_lower_ci,chi_A3_lower_ci,chi_A4_lower_ci,chi_A5_lower_ci,chi_A6_lower_ci,chi_B1_lower_ci,chi_B2_lower_ci,chi_B3_lower_ci,chi_B4_lower_ci,chi_B5_lower_ci,chi_B6_lower_ci,chi_C1_lower_ci,chi_C2_lower_ci,chi_C3_lower_ci,chi_C4_lower_ci,chi_C5_lower_ci,chi_C6_lower_ci), 2)
dat_table_s1[,4] <- round(c(chi_A1_upper_ci,chi_A2_upper_ci,chi_A3_upper_ci,chi_A4_upper_ci,chi_A5_upper_ci,chi_A6_upper_ci,chi_B1_upper_ci,chi_B2_upper_ci,chi_B3_upper_ci,chi_B4_upper_ci,chi_B5_upper_ci,chi_B6_upper_ci,chi_C1_upper_ci,chi_C2_upper_ci,chi_C3_upper_ci,chi_C4_upper_ci,chi_C5_upper_ci,chi_C6_upper_ci), 2)
dat_table_s1[,5] <- round(c(val_A1_mean,val_A2_mean,val_A3_mean,val_A4_mean,val_A5_mean,val_A6_mean,val_B1_mean,val_B2_mean,val_B3_mean,val_B4_mean,val_B5_mean,val_B6_mean,val_C1_mean,val_C2_mean,val_C3_mean,val_C4_mean,val_C5_mean,val_C6_mean), 2)
dat_table_s1[,6] <- round(c(val_A1_lower_ci,val_A2_lower_ci,val_A3_lower_ci,val_A4_lower_ci,val_A5_lower_ci,val_A6_lower_ci,val_B1_lower_ci,val_B2_lower_ci,val_B3_lower_ci,val_B4_lower_ci,val_B5_lower_ci,val_B6_lower_ci,val_C1_lower_ci,val_C2_lower_ci,val_C3_lower_ci,val_C4_lower_ci,val_C5_lower_ci,val_C6_lower_ci), 2)
dat_table_s1[,7] <- round(c(val_A1_upper_ci,val_A2_upper_ci,val_A3_upper_ci,val_A4_upper_ci,val_A5_upper_ci,val_A6_upper_ci,val_B1_upper_ci,val_B2_upper_ci,val_B3_upper_ci,val_B4_upper_ci,val_B5_upper_ci,val_B6_upper_ci,val_C1_upper_ci,val_C2_upper_ci,val_C3_upper_ci,val_C4_upper_ci,val_C5_upper_ci,val_C6_upper_ci), 2)

dat_table_s1$ci_chi <- paste0("[", sprintf("%.2f",dat_table_s1[,3]), ", ", sprintf("%.2f",dat_table_s1[,4]), "]")
dat_table_s1$ci_val <- paste0("[", sprintf("%.2f",dat_table_s1[,6]), ", ", sprintf("%.2f",dat_table_s1[,7]), "]")

dat_table_s1$blank <- NA
dat_table_s1 <- dat_table_s1[,c(1:2,8,10,5,9)]

col_keys <- c("X1","X2","ci_chi","blank","X5","ci_val")
head1 <- c("Block/Pair","Chinese characters","Chinese characters","","Social values","Social values")
head2 <- c("","M","95% CI","","M","95% CI")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_s1)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:6, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:3, part="header")
tbl <- merge_at(tbl, i=1, j=5:6, part="header")
tbl <- hline(tbl, i=1, j=c(2:3,5:6), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:6, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- italic(tbl, i=2, j = c("X2","X5"), part="header")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("X1"), align="left", part="body")
tbl <- width(tbl, j =~ X1, width=.9)
tbl <- width(tbl, j =~ X2 + X5, width=.5)
tbl <- width(tbl, j =~ ci_chi + ci_val, width=1)
tbl <- width(tbl, j =~ blank, width=.1)
tbl <- colformat_lgl(tbl, j =~ blank, na_str="")
tbl <- colformat_double(tbl, j = c("X2","X5"), digits = 2)
tbl <- height_all(tbl, height=.1, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S1.docx")
rm(list=setdiff(ls(), c("julia","julia_wd","files_wd","dat_pretest","pretest1a","pretest1b","pretest2a","pretest2b")))


#Table S2: Mean Credibility Ratings in Pretests 2a-2b for Sociocultural Norms Used in the Minimal Norm Paradigm
chi_A1_l_error <- qt(0.975,df=length(pretest2a$chi_A1_l[!is.na(pretest2a$chi_A1_l)])-1)*sd(pretest2a$chi_A1_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_A1_l[!is.na(pretest2a$chi_A1_l)]))
chi_A1_l_mean <- mean(pretest2a$chi_A1_l, na.rm=TRUE)
chi_A1_l_lower_ci <- chi_A1_l_mean-chi_A1_l_error
chi_A1_l_upper_ci <- chi_A1_l_mean+chi_A1_l_error

chi_A2_l_error <- qt(0.975,df=length(pretest2a$chi_A2_l[!is.na(pretest2a$chi_A2_l)])-1)*sd(pretest2a$chi_A2_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_A2_l[!is.na(pretest2a$chi_A2_l)]))
chi_A2_l_mean <- mean(pretest2a$chi_A2_l, na.rm=TRUE)
chi_A2_l_lower_ci <- chi_A2_l_mean-chi_A2_l_error
chi_A2_l_upper_ci <- chi_A2_l_mean+chi_A2_l_error

chi_A3_l_error <- qt(0.975,df=length(pretest2a$chi_A3_l[!is.na(pretest2a$chi_A3_l)])-1)*sd(pretest2a$chi_A3_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_A3_l[!is.na(pretest2a$chi_A3_l)]))
chi_A3_l_mean <- mean(pretest2a$chi_A3_l, na.rm=TRUE)
chi_A3_l_lower_ci <- chi_A3_l_mean-chi_A3_l_error
chi_A3_l_upper_ci <- chi_A3_l_mean+chi_A3_l_error

chi_A4_l_error <- qt(0.975,df=length(pretest2a$chi_A4_l[!is.na(pretest2a$chi_A4_l)])-1)*sd(pretest2a$chi_A4_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_A4_l[!is.na(pretest2a$chi_A4_l)]))
chi_A4_l_mean <- mean(pretest2a$chi_A4_l, na.rm=TRUE)
chi_A4_l_lower_ci <- chi_A4_l_mean-chi_A4_l_error
chi_A4_l_upper_ci <- chi_A4_l_mean+chi_A4_l_error

chi_A5_l_error <- qt(0.975,df=length(pretest2a$chi_A5_l[!is.na(pretest2a$chi_A5_l)])-1)*sd(pretest2a$chi_A5_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_A5_l[!is.na(pretest2a$chi_A5_l)]))
chi_A5_l_mean <- mean(pretest2a$chi_A5_l, na.rm=TRUE)
chi_A5_l_lower_ci <- chi_A5_l_mean-chi_A5_l_error
chi_A5_l_upper_ci <- chi_A5_l_mean+chi_A5_l_error

chi_A6_l_error <- qt(0.975,df=length(pretest2a$chi_A6_l[!is.na(pretest2a$chi_A6_l)])-1)*sd(pretest2a$chi_A6_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_A6_l[!is.na(pretest2a$chi_A6_l)]))
chi_A6_l_mean <- mean(pretest2a$chi_A6_l, na.rm=TRUE)
chi_A6_l_lower_ci <- chi_A6_l_mean-chi_A6_l_error
chi_A6_l_upper_ci <- chi_A6_l_mean+chi_A6_l_error

chi_B1_l_error <- qt(0.975,df=length(pretest2a$chi_B1_l[!is.na(pretest2a$chi_B1_l)])-1)*sd(pretest2a$chi_B1_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_B1_l[!is.na(pretest2a$chi_B1_l)]))
chi_B1_l_mean <- mean(pretest2a$chi_B1_l, na.rm=TRUE)
chi_B1_l_lower_ci <- chi_B1_l_mean-chi_B1_l_error
chi_B1_l_upper_ci <- chi_B1_l_mean+chi_B1_l_error

chi_B2_l_error <- qt(0.975,df=length(pretest2a$chi_B2_l[!is.na(pretest2a$chi_B2_l)])-1)*sd(pretest2a$chi_B2_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_B2_l[!is.na(pretest2a$chi_B2_l)]))
chi_B2_l_mean <- mean(pretest2a$chi_B2_l, na.rm=TRUE)
chi_B2_l_lower_ci <- chi_B2_l_mean-chi_B2_l_error
chi_B2_l_upper_ci <- chi_B2_l_mean+chi_B2_l_error

chi_B3_l_error <- qt(0.975,df=length(pretest2a$chi_B3_l[!is.na(pretest2a$chi_B3_l)])-1)*sd(pretest2a$chi_B3_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_B3_l[!is.na(pretest2a$chi_B3_l)]))
chi_B3_l_mean <- mean(pretest2a$chi_B3_l, na.rm=TRUE)
chi_B3_l_lower_ci <- chi_B3_l_mean-chi_B3_l_error
chi_B3_l_upper_ci <- chi_B3_l_mean+chi_B3_l_error

chi_B4_l_error <- qt(0.975,df=length(pretest2a$chi_B4_l[!is.na(pretest2a$chi_B4_l)])-1)*sd(pretest2a$chi_B4_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_B4_l[!is.na(pretest2a$chi_B4_l)]))
chi_B4_l_mean <- mean(pretest2a$chi_B4_l, na.rm=TRUE)
chi_B4_l_lower_ci <- chi_B4_l_mean-chi_B4_l_error
chi_B4_l_upper_ci <- chi_B4_l_mean+chi_B4_l_error

chi_B5_l_error <- qt(0.975,df=length(pretest2a$chi_B5_l[!is.na(pretest2a$chi_B5_l)])-1)*sd(pretest2a$chi_B5_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_B5_l[!is.na(pretest2a$chi_B5_l)]))
chi_B5_l_mean <- mean(pretest2a$chi_B5_l, na.rm=TRUE)
chi_B5_l_lower_ci <- chi_B5_l_mean-chi_B5_l_error
chi_B5_l_upper_ci <- chi_B5_l_mean+chi_B5_l_error

chi_B6_l_error <- qt(0.975,df=length(pretest2a$chi_B6_l[!is.na(pretest2a$chi_B6_l)])-1)*sd(pretest2a$chi_B6_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_B6_l[!is.na(pretest2a$chi_B6_l)]))
chi_B6_l_mean <- mean(pretest2a$chi_B6_l, na.rm=TRUE)
chi_B6_l_lower_ci <- chi_B6_l_mean-chi_B6_l_error
chi_B6_l_upper_ci <- chi_B6_l_mean+chi_B6_l_error

chi_C1_l_error <- qt(0.975,df=length(pretest2a$chi_C1_l[!is.na(pretest2a$chi_C1_l)])-1)*sd(pretest2a$chi_C1_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_C1_l[!is.na(pretest2a$chi_C1_l)]))
chi_C1_l_mean <- mean(pretest2a$chi_C1_l, na.rm=TRUE)
chi_C1_l_lower_ci <- chi_C1_l_mean-chi_C1_l_error
chi_C1_l_upper_ci <- chi_C1_l_mean+chi_C1_l_error

chi_C2_l_error <- qt(0.975,df=length(pretest2a$chi_C2_l[!is.na(pretest2a$chi_C2_l)])-1)*sd(pretest2a$chi_C2_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_C2_l[!is.na(pretest2a$chi_C2_l)]))
chi_C2_l_mean <- mean(pretest2a$chi_C2_l, na.rm=TRUE)
chi_C2_l_lower_ci <- chi_C2_l_mean-chi_C2_l_error
chi_C2_l_upper_ci <- chi_C2_l_mean+chi_C2_l_error

chi_C3_l_error <- qt(0.975,df=length(pretest2a$chi_C3_l[!is.na(pretest2a$chi_C3_l)])-1)*sd(pretest2a$chi_C3_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_C3_l[!is.na(pretest2a$chi_C3_l)]))
chi_C3_l_mean <- mean(pretest2a$chi_C3_l, na.rm=TRUE)
chi_C3_l_lower_ci <- chi_C3_l_mean-chi_C3_l_error
chi_C3_l_upper_ci <- chi_C3_l_mean+chi_C3_l_error

chi_C4_l_error <- qt(0.975,df=length(pretest2a$chi_C4_l[!is.na(pretest2a$chi_C4_l)])-1)*sd(pretest2a$chi_C4_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_C4_l[!is.na(pretest2a$chi_C4_l)]))
chi_C4_l_mean <- mean(pretest2a$chi_C4_l, na.rm=TRUE)
chi_C4_l_lower_ci <- chi_C4_l_mean-chi_C4_l_error
chi_C4_l_upper_ci <- chi_C4_l_mean+chi_C4_l_error

chi_C5_l_error <- qt(0.975,df=length(pretest2a$chi_C5_l[!is.na(pretest2a$chi_C5_l)])-1)*sd(pretest2a$chi_C5_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_C5_l[!is.na(pretest2a$chi_C5_l)]))
chi_C5_l_mean <- mean(pretest2a$chi_C5_l, na.rm=TRUE)
chi_C5_l_lower_ci <- chi_C5_l_mean-chi_C5_l_error
chi_C5_l_upper_ci <- chi_C5_l_mean+chi_C5_l_error

chi_C6_l_error <- qt(0.975,df=length(pretest2a$chi_C6_l[!is.na(pretest2a$chi_C6_l)])-1)*sd(pretest2a$chi_C6_l, na.rm=TRUE)/sqrt(length(pretest2a$chi_C6_l[!is.na(pretest2a$chi_C6_l)]))
chi_C6_l_mean <- mean(pretest2a$chi_C6_l, na.rm=TRUE)
chi_C6_l_lower_ci <- chi_C6_l_mean-chi_C6_l_error
chi_C6_l_upper_ci <- chi_C6_l_mean+chi_C6_l_error

chi_A1_r_error <- qt(0.975,df=length(pretest2a$chi_A1_r[!is.na(pretest2a$chi_A1_r)])-1)*sd(pretest2a$chi_A1_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_A1_r[!is.na(pretest2a$chi_A1_r)]))
chi_A1_r_mean <- mean(pretest2a$chi_A1_r, na.rm=TRUE)
chi_A1_r_rower_ci <- chi_A1_r_mean-chi_A1_r_error
chi_A1_r_upper_ci <- chi_A1_r_mean+chi_A1_r_error

chi_A2_r_error <- qt(0.975,df=length(pretest2a$chi_A2_r[!is.na(pretest2a$chi_A2_r)])-1)*sd(pretest2a$chi_A2_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_A2_r[!is.na(pretest2a$chi_A2_r)]))
chi_A2_r_mean <- mean(pretest2a$chi_A2_r, na.rm=TRUE)
chi_A2_r_rower_ci <- chi_A2_r_mean-chi_A2_r_error
chi_A2_r_upper_ci <- chi_A2_r_mean+chi_A2_r_error

chi_A3_r_error <- qt(0.975,df=length(pretest2a$chi_A3_r[!is.na(pretest2a$chi_A3_r)])-1)*sd(pretest2a$chi_A3_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_A3_r[!is.na(pretest2a$chi_A3_r)]))
chi_A3_r_mean <- mean(pretest2a$chi_A3_r, na.rm=TRUE)
chi_A3_r_rower_ci <- chi_A3_r_mean-chi_A3_r_error
chi_A3_r_upper_ci <- chi_A3_r_mean+chi_A3_r_error

chi_A4_r_error <- qt(0.975,df=length(pretest2a$chi_A4_r[!is.na(pretest2a$chi_A4_r)])-1)*sd(pretest2a$chi_A4_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_A4_r[!is.na(pretest2a$chi_A4_r)]))
chi_A4_r_mean <- mean(pretest2a$chi_A4_r, na.rm=TRUE)
chi_A4_r_rower_ci <- chi_A4_r_mean-chi_A4_r_error
chi_A4_r_upper_ci <- chi_A4_r_mean+chi_A4_r_error

chi_A5_r_error <- qt(0.975,df=length(pretest2a$chi_A5_r[!is.na(pretest2a$chi_A5_r)])-1)*sd(pretest2a$chi_A5_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_A5_r[!is.na(pretest2a$chi_A5_r)]))
chi_A5_r_mean <- mean(pretest2a$chi_A5_r, na.rm=TRUE)
chi_A5_r_rower_ci <- chi_A5_r_mean-chi_A5_r_error
chi_A5_r_upper_ci <- chi_A5_r_mean+chi_A5_r_error

chi_A6_r_error <- qt(0.975,df=length(pretest2a$chi_A6_r[!is.na(pretest2a$chi_A6_r)])-1)*sd(pretest2a$chi_A6_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_A6_r[!is.na(pretest2a$chi_A6_r)]))
chi_A6_r_mean <- mean(pretest2a$chi_A6_r, na.rm=TRUE)
chi_A6_r_rower_ci <- chi_A6_r_mean-chi_A6_r_error
chi_A6_r_upper_ci <- chi_A6_r_mean+chi_A6_r_error

chi_B1_r_error <- qt(0.975,df=length(pretest2a$chi_B1_r[!is.na(pretest2a$chi_B1_r)])-1)*sd(pretest2a$chi_B1_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_B1_r[!is.na(pretest2a$chi_B1_r)]))
chi_B1_r_mean <- mean(pretest2a$chi_B1_r, na.rm=TRUE)
chi_B1_r_rower_ci <- chi_B1_r_mean-chi_B1_r_error
chi_B1_r_upper_ci <- chi_B1_r_mean+chi_B1_r_error

chi_B2_r_error <- qt(0.975,df=length(pretest2a$chi_B2_r[!is.na(pretest2a$chi_B2_r)])-1)*sd(pretest2a$chi_B2_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_B2_r[!is.na(pretest2a$chi_B2_r)]))
chi_B2_r_mean <- mean(pretest2a$chi_B2_r, na.rm=TRUE)
chi_B2_r_rower_ci <- chi_B2_r_mean-chi_B2_r_error
chi_B2_r_upper_ci <- chi_B2_r_mean+chi_B2_r_error

chi_B3_r_error <- qt(0.975,df=length(pretest2a$chi_B3_r[!is.na(pretest2a$chi_B3_r)])-1)*sd(pretest2a$chi_B3_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_B3_r[!is.na(pretest2a$chi_B3_r)]))
chi_B3_r_mean <- mean(pretest2a$chi_B3_r, na.rm=TRUE)
chi_B3_r_rower_ci <- chi_B3_r_mean-chi_B3_r_error
chi_B3_r_upper_ci <- chi_B3_r_mean+chi_B3_r_error

chi_B4_r_error <- qt(0.975,df=length(pretest2a$chi_B4_r[!is.na(pretest2a$chi_B4_r)])-1)*sd(pretest2a$chi_B4_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_B4_r[!is.na(pretest2a$chi_B4_r)]))
chi_B4_r_mean <- mean(pretest2a$chi_B4_r, na.rm=TRUE)
chi_B4_r_rower_ci <- chi_B4_r_mean-chi_B4_r_error
chi_B4_r_upper_ci <- chi_B4_r_mean+chi_B4_r_error

chi_B5_r_error <- qt(0.975,df=length(pretest2a$chi_B5_r[!is.na(pretest2a$chi_B5_r)])-1)*sd(pretest2a$chi_B5_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_B5_r[!is.na(pretest2a$chi_B5_r)]))
chi_B5_r_mean <- mean(pretest2a$chi_B5_r, na.rm=TRUE)
chi_B5_r_rower_ci <- chi_B5_r_mean-chi_B5_r_error
chi_B5_r_upper_ci <- chi_B5_r_mean+chi_B5_r_error

chi_B6_r_error <- qt(0.975,df=length(pretest2a$chi_B6_r[!is.na(pretest2a$chi_B6_r)])-1)*sd(pretest2a$chi_B6_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_B6_r[!is.na(pretest2a$chi_B6_r)]))
chi_B6_r_mean <- mean(pretest2a$chi_B6_r, na.rm=TRUE)
chi_B6_r_rower_ci <- chi_B6_r_mean-chi_B6_r_error
chi_B6_r_upper_ci <- chi_B6_r_mean+chi_B6_r_error

chi_C1_r_error <- qt(0.975,df=length(pretest2a$chi_C1_r[!is.na(pretest2a$chi_C1_r)])-1)*sd(pretest2a$chi_C1_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_C1_r[!is.na(pretest2a$chi_C1_r)]))
chi_C1_r_mean <- mean(pretest2a$chi_C1_r, na.rm=TRUE)
chi_C1_r_rower_ci <- chi_C1_r_mean-chi_C1_r_error
chi_C1_r_upper_ci <- chi_C1_r_mean+chi_C1_r_error

chi_C2_r_error <- qt(0.975,df=length(pretest2a$chi_C2_r[!is.na(pretest2a$chi_C2_r)])-1)*sd(pretest2a$chi_C2_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_C2_r[!is.na(pretest2a$chi_C2_r)]))
chi_C2_r_mean <- mean(pretest2a$chi_C2_r, na.rm=TRUE)
chi_C2_r_rower_ci <- chi_C2_r_mean-chi_C2_r_error
chi_C2_r_upper_ci <- chi_C2_r_mean+chi_C2_r_error

chi_C3_r_error <- qt(0.975,df=length(pretest2a$chi_C3_r[!is.na(pretest2a$chi_C3_r)])-1)*sd(pretest2a$chi_C3_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_C3_r[!is.na(pretest2a$chi_C3_r)]))
chi_C3_r_mean <- mean(pretest2a$chi_C3_r, na.rm=TRUE)
chi_C3_r_rower_ci <- chi_C3_r_mean-chi_C3_r_error
chi_C3_r_upper_ci <- chi_C3_r_mean+chi_C3_r_error

chi_C4_r_error <- qt(0.975,df=length(pretest2a$chi_C4_r[!is.na(pretest2a$chi_C4_r)])-1)*sd(pretest2a$chi_C4_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_C4_r[!is.na(pretest2a$chi_C4_r)]))
chi_C4_r_mean <- mean(pretest2a$chi_C4_r, na.rm=TRUE)
chi_C4_r_rower_ci <- chi_C4_r_mean-chi_C4_r_error
chi_C4_r_upper_ci <- chi_C4_r_mean+chi_C4_r_error

chi_C5_r_error <- qt(0.975,df=length(pretest2a$chi_C5_r[!is.na(pretest2a$chi_C5_r)])-1)*sd(pretest2a$chi_C5_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_C5_r[!is.na(pretest2a$chi_C5_r)]))
chi_C5_r_mean <- mean(pretest2a$chi_C5_r, na.rm=TRUE)
chi_C5_r_rower_ci <- chi_C5_r_mean-chi_C5_r_error
chi_C5_r_upper_ci <- chi_C5_r_mean+chi_C5_r_error

chi_C6_r_error <- qt(0.975,df=length(pretest2a$chi_C6_r[!is.na(pretest2a$chi_C6_r)])-1)*sd(pretest2a$chi_C6_r, na.rm=TRUE)/sqrt(length(pretest2a$chi_C6_r[!is.na(pretest2a$chi_C6_r)]))
chi_C6_r_mean <- mean(pretest2a$chi_C6_r, na.rm=TRUE)
chi_C6_r_rower_ci <- chi_C6_r_mean-chi_C6_r_error
chi_C6_r_upper_ci <- chi_C6_r_mean+chi_C6_r_error


val_A1_l_error <- qt(0.975,df=length(pretest2b$val_A1_l[!is.na(pretest2b$val_A1_l)])-1)*sd(pretest2b$val_A1_l, na.rm=TRUE)/sqrt(length(pretest2b$val_A1_l[!is.na(pretest2b$val_A1_l)]))
val_A1_l_mean <- mean(pretest2b$val_A1_l, na.rm=TRUE)
val_A1_l_lower_ci <- val_A1_l_mean-val_A1_l_error
val_A1_l_upper_ci <- val_A1_l_mean+val_A1_l_error

val_A2_l_error <- qt(0.975,df=length(pretest2b$val_A2_l[!is.na(pretest2b$val_A2_l)])-1)*sd(pretest2b$val_A2_l, na.rm=TRUE)/sqrt(length(pretest2b$val_A2_l[!is.na(pretest2b$val_A2_l)]))
val_A2_l_mean <- mean(pretest2b$val_A2_l, na.rm=TRUE)
val_A2_l_lower_ci <- val_A2_l_mean-val_A2_l_error
val_A2_l_upper_ci <- val_A2_l_mean+val_A2_l_error

val_A3_l_error <- qt(0.975,df=length(pretest2b$val_A3_l[!is.na(pretest2b$val_A3_l)])-1)*sd(pretest2b$val_A3_l, na.rm=TRUE)/sqrt(length(pretest2b$val_A3_l[!is.na(pretest2b$val_A3_l)]))
val_A3_l_mean <- mean(pretest2b$val_A3_l, na.rm=TRUE)
val_A3_l_lower_ci <- val_A3_l_mean-val_A3_l_error
val_A3_l_upper_ci <- val_A3_l_mean+val_A3_l_error

val_A4_l_error <- qt(0.975,df=length(pretest2b$val_A4_l[!is.na(pretest2b$val_A4_l)])-1)*sd(pretest2b$val_A4_l, na.rm=TRUE)/sqrt(length(pretest2b$val_A4_l[!is.na(pretest2b$val_A4_l)]))
val_A4_l_mean <- mean(pretest2b$val_A4_l, na.rm=TRUE)
val_A4_l_lower_ci <- val_A4_l_mean-val_A4_l_error
val_A4_l_upper_ci <- val_A4_l_mean+val_A4_l_error

val_A5_l_error <- qt(0.975,df=length(pretest2b$val_A5_l[!is.na(pretest2b$val_A5_l)])-1)*sd(pretest2b$val_A5_l, na.rm=TRUE)/sqrt(length(pretest2b$val_A5_l[!is.na(pretest2b$val_A5_l)]))
val_A5_l_mean <- mean(pretest2b$val_A5_l, na.rm=TRUE)
val_A5_l_lower_ci <- val_A5_l_mean-val_A5_l_error
val_A5_l_upper_ci <- val_A5_l_mean+val_A5_l_error

val_A6_l_error <- qt(0.975,df=length(pretest2b$val_A6_l[!is.na(pretest2b$val_A6_l)])-1)*sd(pretest2b$val_A6_l, na.rm=TRUE)/sqrt(length(pretest2b$val_A6_l[!is.na(pretest2b$val_A6_l)]))
val_A6_l_mean <- mean(pretest2b$val_A6_l, na.rm=TRUE)
val_A6_l_lower_ci <- val_A6_l_mean-val_A6_l_error
val_A6_l_upper_ci <- val_A6_l_mean+val_A6_l_error

val_B1_l_error <- qt(0.975,df=length(pretest2b$val_B1_l[!is.na(pretest2b$val_B1_l)])-1)*sd(pretest2b$val_B1_l, na.rm=TRUE)/sqrt(length(pretest2b$val_B1_l[!is.na(pretest2b$val_B1_l)]))
val_B1_l_mean <- mean(pretest2b$val_B1_l, na.rm=TRUE)
val_B1_l_lower_ci <- val_B1_l_mean-val_B1_l_error
val_B1_l_upper_ci <- val_B1_l_mean+val_B1_l_error

val_B2_l_error <- qt(0.975,df=length(pretest2b$val_B2_l[!is.na(pretest2b$val_B2_l)])-1)*sd(pretest2b$val_B2_l, na.rm=TRUE)/sqrt(length(pretest2b$val_B2_l[!is.na(pretest2b$val_B2_l)]))
val_B2_l_mean <- mean(pretest2b$val_B2_l, na.rm=TRUE)
val_B2_l_lower_ci <- val_B2_l_mean-val_B2_l_error
val_B2_l_upper_ci <- val_B2_l_mean+val_B2_l_error

val_B3_l_error <- qt(0.975,df=length(pretest2b$val_B3_l[!is.na(pretest2b$val_B3_l)])-1)*sd(pretest2b$val_B3_l, na.rm=TRUE)/sqrt(length(pretest2b$val_B3_l[!is.na(pretest2b$val_B3_l)]))
val_B3_l_mean <- mean(pretest2b$val_B3_l, na.rm=TRUE)
val_B3_l_lower_ci <- val_B3_l_mean-val_B3_l_error
val_B3_l_upper_ci <- val_B3_l_mean+val_B3_l_error

val_B4_l_error <- qt(0.975,df=length(pretest2b$val_B4_l[!is.na(pretest2b$val_B4_l)])-1)*sd(pretest2b$val_B4_l, na.rm=TRUE)/sqrt(length(pretest2b$val_B4_l[!is.na(pretest2b$val_B4_l)]))
val_B4_l_mean <- mean(pretest2b$val_B4_l, na.rm=TRUE)
val_B4_l_lower_ci <- val_B4_l_mean-val_B4_l_error
val_B4_l_upper_ci <- val_B4_l_mean+val_B4_l_error

val_B5_l_error <- qt(0.975,df=length(pretest2b$val_B5_l[!is.na(pretest2b$val_B5_l)])-1)*sd(pretest2b$val_B5_l, na.rm=TRUE)/sqrt(length(pretest2b$val_B5_l[!is.na(pretest2b$val_B5_l)]))
val_B5_l_mean <- mean(pretest2b$val_B5_l, na.rm=TRUE)
val_B5_l_lower_ci <- val_B5_l_mean-val_B5_l_error
val_B5_l_upper_ci <- val_B5_l_mean+val_B5_l_error

val_B6_l_error <- qt(0.975,df=length(pretest2b$val_B6_l[!is.na(pretest2b$val_B6_l)])-1)*sd(pretest2b$val_B6_l, na.rm=TRUE)/sqrt(length(pretest2b$val_B6_l[!is.na(pretest2b$val_B6_l)]))
val_B6_l_mean <- mean(pretest2b$val_B6_l, na.rm=TRUE)
val_B6_l_lower_ci <- val_B6_l_mean-val_B6_l_error
val_B6_l_upper_ci <- val_B6_l_mean+val_B6_l_error

val_C1_l_error <- qt(0.975,df=length(pretest2b$val_C1_l[!is.na(pretest2b$val_C1_l)])-1)*sd(pretest2b$val_C1_l, na.rm=TRUE)/sqrt(length(pretest2b$val_C1_l[!is.na(pretest2b$val_C1_l)]))
val_C1_l_mean <- mean(pretest2b$val_C1_l, na.rm=TRUE)
val_C1_l_lower_ci <- val_C1_l_mean-val_C1_l_error
val_C1_l_upper_ci <- val_C1_l_mean+val_C1_l_error

val_C2_l_error <- qt(0.975,df=length(pretest2b$val_C2_l[!is.na(pretest2b$val_C2_l)])-1)*sd(pretest2b$val_C2_l, na.rm=TRUE)/sqrt(length(pretest2b$val_C2_l[!is.na(pretest2b$val_C2_l)]))
val_C2_l_mean <- mean(pretest2b$val_C2_l, na.rm=TRUE)
val_C2_l_lower_ci <- val_C2_l_mean-val_C2_l_error
val_C2_l_upper_ci <- val_C2_l_mean+val_C2_l_error

val_C3_l_error <- qt(0.975,df=length(pretest2b$val_C3_l[!is.na(pretest2b$val_C3_l)])-1)*sd(pretest2b$val_C3_l, na.rm=TRUE)/sqrt(length(pretest2b$val_C3_l[!is.na(pretest2b$val_C3_l)]))
val_C3_l_mean <- mean(pretest2b$val_C3_l, na.rm=TRUE)
val_C3_l_lower_ci <- val_C3_l_mean-val_C3_l_error
val_C3_l_upper_ci <- val_C3_l_mean+val_C3_l_error

val_C4_l_error <- qt(0.975,df=length(pretest2b$val_C4_l[!is.na(pretest2b$val_C4_l)])-1)*sd(pretest2b$val_C4_l, na.rm=TRUE)/sqrt(length(pretest2b$val_C4_l[!is.na(pretest2b$val_C4_l)]))
val_C4_l_mean <- mean(pretest2b$val_C4_l, na.rm=TRUE)
val_C4_l_lower_ci <- val_C4_l_mean-val_C4_l_error
val_C4_l_upper_ci <- val_C4_l_mean+val_C4_l_error

val_C5_l_error <- qt(0.975,df=length(pretest2b$val_C5_l[!is.na(pretest2b$val_C5_l)])-1)*sd(pretest2b$val_C5_l, na.rm=TRUE)/sqrt(length(pretest2b$val_C5_l[!is.na(pretest2b$val_C5_l)]))
val_C5_l_mean <- mean(pretest2b$val_C5_l, na.rm=TRUE)
val_C5_l_lower_ci <- val_C5_l_mean-val_C5_l_error
val_C5_l_upper_ci <- val_C5_l_mean+val_C5_l_error

val_C6_l_error <- qt(0.975,df=length(pretest2b$val_C6_l[!is.na(pretest2b$val_C6_l)])-1)*sd(pretest2b$val_C6_l, na.rm=TRUE)/sqrt(length(pretest2b$val_C6_l[!is.na(pretest2b$val_C6_l)]))
val_C6_l_mean <- mean(pretest2b$val_C6_l, na.rm=TRUE)
val_C6_l_lower_ci <- val_C6_l_mean-val_C6_l_error
val_C6_l_upper_ci <- val_C6_l_mean+val_C6_l_error

val_A1_r_error <- qt(0.975,df=length(pretest2b$val_A1_r[!is.na(pretest2b$val_A1_r)])-1)*sd(pretest2b$val_A1_r, na.rm=TRUE)/sqrt(length(pretest2b$val_A1_r[!is.na(pretest2b$val_A1_r)]))
val_A1_r_mean <- mean(pretest2b$val_A1_r, na.rm=TRUE)
val_A1_r_rower_ci <- val_A1_r_mean-val_A1_r_error
val_A1_r_upper_ci <- val_A1_r_mean+val_A1_r_error

val_A2_r_error <- qt(0.975,df=length(pretest2b$val_A2_r[!is.na(pretest2b$val_A2_r)])-1)*sd(pretest2b$val_A2_r, na.rm=TRUE)/sqrt(length(pretest2b$val_A2_r[!is.na(pretest2b$val_A2_r)]))
val_A2_r_mean <- mean(pretest2b$val_A2_r, na.rm=TRUE)
val_A2_r_rower_ci <- val_A2_r_mean-val_A2_r_error
val_A2_r_upper_ci <- val_A2_r_mean+val_A2_r_error

val_A3_r_error <- qt(0.975,df=length(pretest2b$val_A3_r[!is.na(pretest2b$val_A3_r)])-1)*sd(pretest2b$val_A3_r, na.rm=TRUE)/sqrt(length(pretest2b$val_A3_r[!is.na(pretest2b$val_A3_r)]))
val_A3_r_mean <- mean(pretest2b$val_A3_r, na.rm=TRUE)
val_A3_r_rower_ci <- val_A3_r_mean-val_A3_r_error
val_A3_r_upper_ci <- val_A3_r_mean+val_A3_r_error

val_A4_r_error <- qt(0.975,df=length(pretest2b$val_A4_r[!is.na(pretest2b$val_A4_r)])-1)*sd(pretest2b$val_A4_r, na.rm=TRUE)/sqrt(length(pretest2b$val_A4_r[!is.na(pretest2b$val_A4_r)]))
val_A4_r_mean <- mean(pretest2b$val_A4_r, na.rm=TRUE)
val_A4_r_rower_ci <- val_A4_r_mean-val_A4_r_error
val_A4_r_upper_ci <- val_A4_r_mean+val_A4_r_error

val_A5_r_error <- qt(0.975,df=length(pretest2b$val_A5_r[!is.na(pretest2b$val_A5_r)])-1)*sd(pretest2b$val_A5_r, na.rm=TRUE)/sqrt(length(pretest2b$val_A5_r[!is.na(pretest2b$val_A5_r)]))
val_A5_r_mean <- mean(pretest2b$val_A5_r, na.rm=TRUE)
val_A5_r_rower_ci <- val_A5_r_mean-val_A5_r_error
val_A5_r_upper_ci <- val_A5_r_mean+val_A5_r_error

val_A6_r_error <- qt(0.975,df=length(pretest2b$val_A6_r[!is.na(pretest2b$val_A6_r)])-1)*sd(pretest2b$val_A6_r, na.rm=TRUE)/sqrt(length(pretest2b$val_A6_r[!is.na(pretest2b$val_A6_r)]))
val_A6_r_mean <- mean(pretest2b$val_A6_r, na.rm=TRUE)
val_A6_r_rower_ci <- val_A6_r_mean-val_A6_r_error
val_A6_r_upper_ci <- val_A6_r_mean+val_A6_r_error

val_B1_r_error <- qt(0.975,df=length(pretest2b$val_B1_r[!is.na(pretest2b$val_B1_r)])-1)*sd(pretest2b$val_B1_r, na.rm=TRUE)/sqrt(length(pretest2b$val_B1_r[!is.na(pretest2b$val_B1_r)]))
val_B1_r_mean <- mean(pretest2b$val_B1_r, na.rm=TRUE)
val_B1_r_rower_ci <- val_B1_r_mean-val_B1_r_error
val_B1_r_upper_ci <- val_B1_r_mean+val_B1_r_error

val_B2_r_error <- qt(0.975,df=length(pretest2b$val_B2_r[!is.na(pretest2b$val_B2_r)])-1)*sd(pretest2b$val_B2_r, na.rm=TRUE)/sqrt(length(pretest2b$val_B2_r[!is.na(pretest2b$val_B2_r)]))
val_B2_r_mean <- mean(pretest2b$val_B2_r, na.rm=TRUE)
val_B2_r_rower_ci <- val_B2_r_mean-val_B2_r_error
val_B2_r_upper_ci <- val_B2_r_mean+val_B2_r_error

val_B3_r_error <- qt(0.975,df=length(pretest2b$val_B3_r[!is.na(pretest2b$val_B3_r)])-1)*sd(pretest2b$val_B3_r, na.rm=TRUE)/sqrt(length(pretest2b$val_B3_r[!is.na(pretest2b$val_B3_r)]))
val_B3_r_mean <- mean(pretest2b$val_B3_r, na.rm=TRUE)
val_B3_r_rower_ci <- val_B3_r_mean-val_B3_r_error
val_B3_r_upper_ci <- val_B3_r_mean+val_B3_r_error

val_B4_r_error <- qt(0.975,df=length(pretest2b$val_B4_r[!is.na(pretest2b$val_B4_r)])-1)*sd(pretest2b$val_B4_r, na.rm=TRUE)/sqrt(length(pretest2b$val_B4_r[!is.na(pretest2b$val_B4_r)]))
val_B4_r_mean <- mean(pretest2b$val_B4_r, na.rm=TRUE)
val_B4_r_rower_ci <- val_B4_r_mean-val_B4_r_error
val_B4_r_upper_ci <- val_B4_r_mean+val_B4_r_error

val_B5_r_error <- qt(0.975,df=length(pretest2b$val_B5_r[!is.na(pretest2b$val_B5_r)])-1)*sd(pretest2b$val_B5_r, na.rm=TRUE)/sqrt(length(pretest2b$val_B5_r[!is.na(pretest2b$val_B5_r)]))
val_B5_r_mean <- mean(pretest2b$val_B5_r, na.rm=TRUE)
val_B5_r_rower_ci <- val_B5_r_mean-val_B5_r_error
val_B5_r_upper_ci <- val_B5_r_mean+val_B5_r_error

val_B6_r_error <- qt(0.975,df=length(pretest2b$val_B6_r[!is.na(pretest2b$val_B6_r)])-1)*sd(pretest2b$val_B6_r, na.rm=TRUE)/sqrt(length(pretest2b$val_B6_r[!is.na(pretest2b$val_B6_r)]))
val_B6_r_mean <- mean(pretest2b$val_B6_r, na.rm=TRUE)
val_B6_r_rower_ci <- val_B6_r_mean-val_B6_r_error
val_B6_r_upper_ci <- val_B6_r_mean+val_B6_r_error

val_C1_r_error <- qt(0.975,df=length(pretest2b$val_C1_r[!is.na(pretest2b$val_C1_r)])-1)*sd(pretest2b$val_C1_r, na.rm=TRUE)/sqrt(length(pretest2b$val_C1_r[!is.na(pretest2b$val_C1_r)]))
val_C1_r_mean <- mean(pretest2b$val_C1_r, na.rm=TRUE)
val_C1_r_rower_ci <- val_C1_r_mean-val_C1_r_error
val_C1_r_upper_ci <- val_C1_r_mean+val_C1_r_error

val_C2_r_error <- qt(0.975,df=length(pretest2b$val_C2_r[!is.na(pretest2b$val_C2_r)])-1)*sd(pretest2b$val_C2_r, na.rm=TRUE)/sqrt(length(pretest2b$val_C2_r[!is.na(pretest2b$val_C2_r)]))
val_C2_r_mean <- mean(pretest2b$val_C2_r, na.rm=TRUE)
val_C2_r_rower_ci <- val_C2_r_mean-val_C2_r_error
val_C2_r_upper_ci <- val_C2_r_mean+val_C2_r_error

val_C3_r_error <- qt(0.975,df=length(pretest2b$val_C3_r[!is.na(pretest2b$val_C3_r)])-1)*sd(pretest2b$val_C3_r, na.rm=TRUE)/sqrt(length(pretest2b$val_C3_r[!is.na(pretest2b$val_C3_r)]))
val_C3_r_mean <- mean(pretest2b$val_C3_r, na.rm=TRUE)
val_C3_r_rower_ci <- val_C3_r_mean-val_C3_r_error
val_C3_r_upper_ci <- val_C3_r_mean+val_C3_r_error

val_C4_r_error <- qt(0.975,df=length(pretest2b$val_C4_r[!is.na(pretest2b$val_C4_r)])-1)*sd(pretest2b$val_C4_r, na.rm=TRUE)/sqrt(length(pretest2b$val_C4_r[!is.na(pretest2b$val_C4_r)]))
val_C4_r_mean <- mean(pretest2b$val_C4_r, na.rm=TRUE)
val_C4_r_rower_ci <- val_C4_r_mean-val_C4_r_error
val_C4_r_upper_ci <- val_C4_r_mean+val_C4_r_error

val_C5_r_error <- qt(0.975,df=length(pretest2b$val_C5_r[!is.na(pretest2b$val_C5_r)])-1)*sd(pretest2b$val_C5_r, na.rm=TRUE)/sqrt(length(pretest2b$val_C5_r[!is.na(pretest2b$val_C5_r)]))
val_C5_r_mean <- mean(pretest2b$val_C5_r, na.rm=TRUE)
val_C5_r_rower_ci <- val_C5_r_mean-val_C5_r_error
val_C5_r_upper_ci <- val_C5_r_mean+val_C5_r_error

val_C6_r_error <- qt(0.975,df=length(pretest2b$val_C6_r[!is.na(pretest2b$val_C6_r)])-1)*sd(pretest2b$val_C6_r, na.rm=TRUE)/sqrt(length(pretest2b$val_C6_r[!is.na(pretest2b$val_C6_r)]))
val_C6_r_mean <- mean(pretest2b$val_C6_r, na.rm=TRUE)
val_C6_r_rower_ci <- val_C6_r_mean-val_C6_r_error
val_C6_r_upper_ci <- val_C6_r_mean+val_C6_r_error

dat_table_s2 <- data.frame(matrix(nrow=18,ncol=13))

dat_table_s2[,1] <- c("A/1","A/2","A/3","A/4","A/5","A/6","B/1","B/2","B/3","B/4","B/5","B/6","C/1","C/2","C/3","C/4","C/5","C/6")
dat_table_s2[,2] <- round(c(chi_A1_l_mean,chi_A2_l_mean,chi_A3_l_mean,chi_A4_l_mean,chi_A5_l_mean,chi_A6_l_mean,chi_B1_l_mean,chi_B2_l_mean,chi_B3_l_mean,chi_B4_l_mean,chi_B5_l_mean,chi_B6_l_mean,chi_C1_l_mean,chi_C2_l_mean,chi_C3_l_mean,chi_C4_l_mean,chi_C5_l_mean,chi_C6_l_mean), 2)
dat_table_s2[,3] <- round(c(chi_A1_l_lower_ci,chi_A2_l_lower_ci,chi_A3_l_lower_ci,chi_A4_l_lower_ci,chi_A5_l_lower_ci,chi_A6_l_lower_ci,chi_B1_l_lower_ci,chi_B2_l_lower_ci,chi_B3_l_lower_ci,chi_B4_l_lower_ci,chi_B5_l_lower_ci,chi_B6_l_lower_ci,chi_C1_l_lower_ci,chi_C2_l_lower_ci,chi_C3_l_lower_ci,chi_C4_l_lower_ci,chi_C5_l_lower_ci,chi_C6_l_lower_ci), 2)
dat_table_s2[,4] <- round(c(chi_A1_l_upper_ci,chi_A2_l_upper_ci,chi_A3_l_upper_ci,chi_A4_l_upper_ci,chi_A5_l_upper_ci,chi_A6_l_upper_ci,chi_B1_l_upper_ci,chi_B2_l_upper_ci,chi_B3_l_upper_ci,chi_B4_l_upper_ci,chi_B5_l_upper_ci,chi_B6_l_upper_ci,chi_C1_l_upper_ci,chi_C2_l_upper_ci,chi_C3_l_upper_ci,chi_C4_l_upper_ci,chi_C5_l_upper_ci,chi_C6_l_upper_ci), 2)
dat_table_s2[,5] <- round(c(chi_A1_r_mean,chi_A2_r_mean,chi_A3_r_mean,chi_A4_r_mean,chi_A5_r_mean,chi_A6_r_mean,chi_B1_r_mean,chi_B2_r_mean,chi_B3_r_mean,chi_B4_r_mean,chi_B5_r_mean,chi_B6_r_mean,chi_C1_r_mean,chi_C2_r_mean,chi_C3_r_mean,chi_C4_r_mean,chi_C5_r_mean,chi_C6_r_mean), 2)
dat_table_s2[,6] <- round(c(chi_A1_r_rower_ci,chi_A2_r_rower_ci,chi_A3_r_rower_ci,chi_A4_r_rower_ci,chi_A5_r_rower_ci,chi_A6_r_rower_ci,chi_B1_r_rower_ci,chi_B2_r_rower_ci,chi_B3_r_rower_ci,chi_B4_r_rower_ci,chi_B5_r_rower_ci,chi_B6_r_rower_ci,chi_C1_r_rower_ci,chi_C2_r_rower_ci,chi_C3_r_rower_ci,chi_C4_r_rower_ci,chi_C5_r_rower_ci,chi_C6_r_rower_ci), 2)
dat_table_s2[,7] <- round(c(chi_A1_r_upper_ci,chi_A2_r_upper_ci,chi_A3_r_upper_ci,chi_A4_r_upper_ci,chi_A5_r_upper_ci,chi_A6_r_upper_ci,chi_B1_r_upper_ci,chi_B2_r_upper_ci,chi_B3_r_upper_ci,chi_B4_r_upper_ci,chi_B5_r_upper_ci,chi_B6_r_upper_ci,chi_C1_r_upper_ci,chi_C2_r_upper_ci,chi_C3_r_upper_ci,chi_C4_r_upper_ci,chi_C5_r_upper_ci,chi_C6_r_upper_ci), 2)
dat_table_s2[,8] <- round(c(val_A1_l_mean,val_A2_l_mean,val_A3_l_mean,val_A4_l_mean,val_A5_l_mean,val_A6_l_mean,val_B1_l_mean,val_B2_l_mean,val_B3_l_mean,val_B4_l_mean,val_B5_l_mean,val_B6_l_mean,val_C1_l_mean,val_C2_l_mean,val_C3_l_mean,val_C4_l_mean,val_C5_l_mean,val_C6_l_mean), 2)
dat_table_s2[,9] <- round(c(val_A1_l_lower_ci,val_A2_l_lower_ci,val_A3_l_lower_ci,val_A4_l_lower_ci,val_A5_l_lower_ci,val_A6_l_lower_ci,val_B1_l_lower_ci,val_B2_l_lower_ci,val_B3_l_lower_ci,val_B4_l_lower_ci,val_B5_l_lower_ci,val_B6_l_lower_ci,val_C1_l_lower_ci,val_C2_l_lower_ci,val_C3_l_lower_ci,val_C4_l_lower_ci,val_C5_l_lower_ci,val_C6_l_lower_ci), 2)
dat_table_s2[,10] <- round(c(val_A1_l_upper_ci,val_A2_l_upper_ci,val_A3_l_upper_ci,val_A4_l_upper_ci,val_A5_l_upper_ci,val_A6_l_upper_ci,val_B1_l_upper_ci,val_B2_l_upper_ci,val_B3_l_upper_ci,val_B4_l_upper_ci,val_B5_l_upper_ci,val_B6_l_upper_ci,val_C1_l_upper_ci,val_C2_l_upper_ci,val_C3_l_upper_ci,val_C4_l_upper_ci,val_C5_l_upper_ci,val_C6_l_upper_ci), 2)
dat_table_s2[,11] <- round(c(val_A1_r_mean,val_A2_r_mean,val_A3_r_mean,val_A4_r_mean,val_A5_r_mean,val_A6_r_mean,val_B1_r_mean,val_B2_r_mean,val_B3_r_mean,val_B4_r_mean,val_B5_r_mean,val_B6_r_mean,val_C1_r_mean,val_C2_r_mean,val_C3_r_mean,val_C4_r_mean,val_C5_r_mean,val_C6_r_mean), 2)
dat_table_s2[,12] <- round(c(val_A1_r_rower_ci,val_A2_r_rower_ci,val_A3_r_rower_ci,val_A4_r_rower_ci,val_A5_r_rower_ci,val_A6_r_rower_ci,val_B1_r_rower_ci,val_B2_r_rower_ci,val_B3_r_rower_ci,val_B4_r_rower_ci,val_B5_r_rower_ci,val_B6_r_rower_ci,val_C1_r_rower_ci,val_C2_r_rower_ci,val_C3_r_rower_ci,val_C4_r_rower_ci,val_C5_r_rower_ci,val_C6_r_rower_ci), 2)
dat_table_s2[,13] <- round(c(val_A1_r_upper_ci,val_A2_r_upper_ci,val_A3_r_upper_ci,val_A4_r_upper_ci,val_A5_r_upper_ci,val_A6_r_upper_ci,val_B1_r_upper_ci,val_B2_r_upper_ci,val_B3_r_upper_ci,val_B4_r_upper_ci,val_B5_r_upper_ci,val_B6_r_upper_ci,val_C1_r_upper_ci,val_C2_r_upper_ci,val_C3_r_upper_ci,val_C4_r_upper_ci,val_C5_r_upper_ci,val_C6_r_upper_ci), 2)

dat_table_s2$ci_chi_l <- paste0("[", sprintf("%.2f",dat_table_s2[,3]), ", ", sprintf("%.2f",dat_table_s2[,4]), "]")
dat_table_s2$ci_chi_r <- paste0("[", sprintf("%.2f",dat_table_s2[,6]), ", ", sprintf("%.2f",dat_table_s2[,7]), "]")
dat_table_s2$ci_val_l <- paste0("[", sprintf("%.2f",dat_table_s2[,9]), ", ", sprintf("%.2f",dat_table_s2[,10]), "]")
dat_table_s2$ci_val_r <- paste0("[", sprintf("%.2f",dat_table_s2[,12]), ", ", sprintf("%.2f",dat_table_s2[,13]), "]")

dat_table_s2$blank <- NA
dat_table_s2 <- dat_table_s2[,c(1:2,14,5,15,18,8,16,11,17)]

col_keys <- c("X1","X2","ci_chi_l","X5","ci_chi_r","blank","X8","ci_val_l","X11","ci_val_r")
head1 <- c("Block/Pair","Chinese characters","Chinese characters","Chinese characters","Chinese characters","","Social values","Social values","Social values","Social values")
head2 <- c("","Majority left","Majority left","Majority right","Majority right","","Majority left","Majority left","Majority right","Majority right")
head3 <- c("","M","95% CI","M","95% CI","","M","95% CI","M","95% CI")
head <- data.frame(col_keys,head1,head2,head3, stringsAsFactors = FALSE)
rm(col_keys,head1,head2,head3)

tbl <- flextable(dat_table_s2)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:10, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:5, part="header")
tbl <- merge_at(tbl, i=1, j=7:10, part="header")
tbl <- merge_at(tbl, i=2, j=2:3, part="header")
tbl <- merge_at(tbl, i=2, j=4:5, part="header")
tbl <- merge_at(tbl, i=2, j=7:8, part="header")
tbl <- merge_at(tbl, i=2, j=9:10, part="header")
tbl <- hline(tbl, i=1, j=c(2:5,7:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=c(2:3,4:5,7:8,9:10), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=3, j=1:10, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- italic(tbl, i=3, j = c("X2","X5","X8","X11"), part="header")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("X1"), align="left", part="body")
tbl <- width(tbl, j =~ X1, width=.9)
tbl <- width(tbl, j =~ X2 + X5 + X8 + X11, width=.5)
tbl <- width(tbl, j =~ ci_chi_l + ci_chi_r + ci_val_l + ci_val_r, width=1)
tbl <- width(tbl, j =~ blank, width=.1)
tbl <- colformat_lgl(tbl, j =~ blank, na_str="")
tbl <- colformat_double(tbl, j = c("X2","X5","X8","X11"), digits = 2)
tbl <- height_all(tbl, height=.1, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S2.docx")


rm(list=setdiff(ls(), c("julia","julia_wd","files_wd")))


########## Data for Online Supplements S2-S13 ##########


#read data
dat <- as.data.frame(fread("https://madata.bib.uni-mannheim.de/364/2/SNP-B5_Data.csv", header = T, sep = ","))


#apply data-exclusion criteria (Footnote 7)
dat <- subset(dat, subset = bigfive_incomplete == 0)
dat <- subset(dat, subset = sample < 5 | c(sample == 5 & external_predictors_incomplete == 0) | c(sample == 6 & external_predictors_incomplete == 0) | sample > 100)
dat <- subset(dat, subset = double_participation == 0)
dat <- subset(dat, subset = instruction_check_item_part2 != 2)
dat <- subset(dat, subset = not_followed_instructions == 0)
dat <- subset(dat, subset = c(chinese_characters_known != 1 & recall_correct_total > 0) | c(sample < 100 & chinese_characters_known == 1 & recall_correct_values > 0) | c(sample > 100 & chinese_characters_known == 1 & recall_correct_other > 0))
dat <- subset(dat, subset = participated_seriously != 2)
dat <- subset(dat, subset = not_use_data != 1)


#compute variables

##compute BFI/BFI-2 domains
dat_BFI <- subset(dat, subset = sample == 1 | sample == 2 | sample == 101 | sample == 102 | sample == 103)
dat_BFI2 <- subset(dat, subset = sample == 3 | sample == 4 | sample == 5 | sample == 6)
dat_BFI$agr <- rowMeans(dat_BFI[c("agr01r","agr02","agr03r","agr04","agr05","agr06r","agr07","agr08r","agr09")], na.rm=TRUE)
dat_BFI$cns <- rowMeans(dat_BFI[c("cns01","cns02r","cns03","cns04r","cns05r","cns06","cns07","cns08","cns09r")], na.rm=TRUE)
dat_BFI$opn <- rowMeans(dat_BFI[c("opn01","opn02","opn03","opn04","opn05","opn06","opn07r","opn08","opn09r","opn10")], na.rm=TRUE)
dat_BFI$ext <- rowMeans(dat_BFI[c("ext01","ext02r","ext03","ext04","ext05r","ext06","ext07r","ext08")], na.rm=TRUE)
dat_BFI$neu <- rowMeans(dat_BFI[c("neu01","neu02r","neu03","neu04","neu05r","neu06","neu07r","neu08")], na.rm=TRUE)
dat_BFI2$agr <- rowMeans(dat_BFI2[c("agr01","agr02","agr03r","agr04r","agr05r","agr06","agr07","agr08r","agr09r","agr10r","agr11","agr12")], na.rm=TRUE)
dat_BFI2$cns <- rowMeans(dat_BFI2[c("cns01r","cns02r","cns03","cns04","cns05r","cns06r","cns07","cns08","cns09","cns10r","cns11","cns12r")], na.rm=TRUE)
dat_BFI2$opn <- rowMeans(dat_BFI2[c("opn01r","opn02","opn03","opn04","opn05r","opn06r","opn07","opn08","opn09r","opn10r","opn11r","opn12")], na.rm=TRUE)
dat_BFI2$ext <- rowMeans(dat_BFI2[c("ext01","ext02","ext03r","ext04r","ext05","ext06r","ext07r","ext08r","ext09","ext10","ext11r","ext12")], na.rm=TRUE)
dat_BFI2$neu <- rowMeans(dat_BFI2[c("neu01r","neu02r","neu03","neu04","neu05r","neu06r","neu07","neu08","neu09r","neu10r","neu11","neu12")], na.rm=TRUE)

##compute BFI-2 facets
dat_BFI2$compassion <- rowMeans(dat_BFI2[c("agr01","agr04r","agr07","agr10r")], na.rm=TRUE)
dat_BFI2$respectfulness <- rowMeans(dat_BFI2[c("agr02","agr05r","agr08r","agr11")], na.rm=TRUE)
dat_BFI2$trust <- rowMeans(dat_BFI2[c("agr03r","agr06","agr09r","agr12")], na.rm=TRUE)
dat_BFI2$organization <- rowMeans(dat_BFI2[c("cns01r","cns04","cns07","cns10r")], na.rm=TRUE)
dat_BFI2$productiveness <- rowMeans(dat_BFI2[c("cns02r","cns05r","cns08","cns11")], na.rm=TRUE)
dat_BFI2$responsibility <- rowMeans(dat_BFI2[c("cns03","cns06r","cns09","cns12r")], na.rm=TRUE)
dat_BFI2$aesthetic <- rowMeans(dat_BFI2[c("opn01r","opn04","opn07","opn10r")], na.rm=TRUE)
dat_BFI2$creative <- rowMeans(dat_BFI2[c("opn03","opn06r","opn09r","opn12")], na.rm=TRUE)
dat_BFI2$intellectual <- rowMeans(dat_BFI2[c("opn02","opn05r","opn08","opn11r")], na.rm=TRUE)
dat_BFI2$assertiveness <- rowMeans(dat_BFI2[c("ext02","ext05","ext08r","ext11r")], na.rm=TRUE)
dat_BFI2$energy <- rowMeans(dat_BFI2[c("ext03r","ext06r","ext09","ext12")], na.rm=TRUE)
dat_BFI2$sociability <- rowMeans(dat_BFI2[c("ext01","ext04r","ext07r","ext10")], na.rm=TRUE)
dat_BFI2$anxiety <- rowMeans(dat_BFI2[c("neu01r","neu04","neu07","neu10r")], na.rm=TRUE)
dat_BFI2$depression <- rowMeans(dat_BFI2[c("neu02r","neu05r","neu08","neu11")], na.rm=TRUE)
dat_BFI2$emotional <- rowMeans(dat_BFI2[c("neu03","neu06r","neu09r","neu12")], na.rm=TRUE)

##merge datasets
dat_BFI[ ,c("compassion","respectfulness","trust","organization","productiveness","responsibility","aesthetic","creative","intellectual",
            "assertiveness","energy","sociability","anxiety","depression","emotional")] <- NA
dat <- rbind(dat_BFI,dat_BFI2)

##compute external predictors in Experiment 3
dat_exp3 <- subset(dat, subset = sample == 5 | sample == 6)
dat_exp3$rational <- rowMeans(dat_exp3[c("premed01","premed02","premed03","premed04","premed05","premed06","premed07","premed08","premed09","premed10","premed11")], na.rm=TRUE)
dat_exp3$nfc <- rowMeans(dat_exp3[c("nfc01","nfc02","nfc03r","nfc04r","nfc05r","nfc06","nfc07r","nfc08r","nfc09r","nfc10","nfc11","nfc12r","nfc13","nfc14","nfc15","nfc16r","nfc17r","nfc18")], na.rm=TRUE)
dat_exp3$attention <- rowMeans(dat_exp3[c("sa01","sa02","sa03","sa04","sa05","sa06","sa07","sa08","sa09","sa10","sa11","sa12r","sa13r")], na.rm=TRUE)

##compute BFI-2 domains for indirect-effects analyses in Experiment 3
dat_exp3$agr_no_trust <- rowMeans(dat_exp3[c("agr01","agr02","agr04r","agr05r","agr07","agr08r","agr10r","agr11")], na.rm=TRUE)
dat_exp3$opn_no_intel <- rowMeans(dat_exp3[c("opn01r","opn03","opn04","opn06r","opn07","opn09r","opn10r","opn12")], na.rm=TRUE)

##merge datasets
dat_exp3 <- subset(dat_exp3, select = c(id,pair_index_within_sample,rational,nfc,attention,agr_no_trust,opn_no_intel))
dat <- merge(dat, dat_exp3, by = c("id","pair_index_within_sample"), all = TRUE)
rm(dat_BFI,dat_BFI2,dat_exp3)


#create datasets for experiments

##Experiment 1
dat_exp1 <- subset(dat, subset = sample == 1 | sample == 2)
##Experiment 2
dat_exp2 <- subset(dat, subset = sample == 3 | sample == 4)
##Experiment 3
dat_exp3 <- subset(dat, subset = sample == 5 | sample == 6)
##Experiments 1-3
dat_exp123 <- subset(dat, subset = sample == 1 | sample == 2 | sample == 3 | sample == 4 | sample == 5 | sample == 6)
##Experiments 2-3
dat_exp23 <- subset(dat, subset = sample == 3 | sample == 4 | sample == 5 | sample == 6)


#grand-mean centering of level-2 predictors in each experiment

##grand-mean centering of BFI/BFI-2 domains

###Experiment 1
dat_exp1$agr_c <- dat_exp1$agr - mean(dat_exp1$agr)
dat_exp1$cns_c <- dat_exp1$cns - mean(dat_exp1$cns)
dat_exp1$opn_c <- dat_exp1$opn - mean(dat_exp1$opn)
dat_exp1$ext_c <- dat_exp1$ext - mean(dat_exp1$ext)
dat_exp1$neu_c <- dat_exp1$neu - mean(dat_exp1$neu)
###Experiment 2
dat_exp2$agr_c <- dat_exp2$agr - mean(dat_exp2$agr)
dat_exp2$cns_c <- dat_exp2$cns - mean(dat_exp2$cns)
dat_exp2$opn_c <- dat_exp2$opn - mean(dat_exp2$opn)
dat_exp2$ext_c <- dat_exp2$ext - mean(dat_exp2$ext)
dat_exp2$neu_c <- dat_exp2$neu - mean(dat_exp2$neu)
##Experiment 3
dat_exp3$agr_c <- dat_exp3$agr - mean(dat_exp3$agr)
dat_exp3$cns_c <- dat_exp3$cns - mean(dat_exp3$cns)
dat_exp3$opn_c <- dat_exp3$opn - mean(dat_exp3$opn)
dat_exp3$ext_c <- dat_exp3$ext - mean(dat_exp3$ext)
dat_exp3$neu_c <- dat_exp3$neu - mean(dat_exp3$neu)
##Experiments 1-3
dat_exp123$agr_c <- dat_exp123$agr - mean(dat_exp123$agr)
dat_exp123$cns_c <- dat_exp123$cns - mean(dat_exp123$cns)
dat_exp123$opn_c <- dat_exp123$opn - mean(dat_exp123$opn)
dat_exp123$ext_c <- dat_exp123$ext - mean(dat_exp123$ext)
dat_exp123$neu_c <- dat_exp123$neu - mean(dat_exp123$neu)

##grand-mean centering of BFI-2 facets

###Experiment 2
dat_exp2$compassion_c <- dat_exp2$compassion - mean(dat_exp2$compassion)
dat_exp2$respectfulness_c <- dat_exp2$respectfulness - mean(dat_exp2$respectfulness)
dat_exp2$trust_c <- dat_exp2$trust - mean(dat_exp2$trust)
dat_exp2$organization_c <- dat_exp2$organization - mean(dat_exp2$organization)
dat_exp2$productiveness_c <- dat_exp2$productiveness - mean(dat_exp2$productiveness)
dat_exp2$responsibility_c <- dat_exp2$responsibility - mean(dat_exp2$responsibility)
dat_exp2$aesthetic_c <- dat_exp2$aesthetic - mean(dat_exp2$aesthetic)
dat_exp2$creative_c <- dat_exp2$creative - mean(dat_exp2$creative)
dat_exp2$intellectual_c <- dat_exp2$intellectual - mean(dat_exp2$intellectual)
dat_exp2$assertiveness_c <- dat_exp2$assertiveness - mean(dat_exp2$assertiveness)
dat_exp2$energy_c <- dat_exp2$energy - mean(dat_exp2$energy)
dat_exp2$sociability_c <- dat_exp2$sociability - mean(dat_exp2$sociability)
dat_exp2$anxiety_c <- dat_exp2$anxiety - mean(dat_exp2$anxiety)
dat_exp2$depression_c <- dat_exp2$depression - mean(dat_exp2$depression)
dat_exp2$emotional_c <- dat_exp2$emotional - mean(dat_exp2$emotional)
###Experiment 3
dat_exp3$compassion_c <- dat_exp3$compassion - mean(dat_exp3$compassion)
dat_exp3$respectfulness_c <- dat_exp3$respectfulness - mean(dat_exp3$respectfulness)
dat_exp3$trust_c <- dat_exp3$trust - mean(dat_exp3$trust)
dat_exp3$organization_c <- dat_exp3$organization - mean(dat_exp3$organization)
dat_exp3$productiveness_c <- dat_exp3$productiveness - mean(dat_exp3$productiveness)
dat_exp3$responsibility_c <- dat_exp3$responsibility - mean(dat_exp3$responsibility)
dat_exp3$aesthetic_c <- dat_exp3$aesthetic - mean(dat_exp3$aesthetic)
dat_exp3$creative_c <- dat_exp3$creative - mean(dat_exp3$creative)
dat_exp3$intellectual_c <- dat_exp3$intellectual - mean(dat_exp3$intellectual)
dat_exp3$assertiveness_c <- dat_exp3$assertiveness - mean(dat_exp3$assertiveness)
dat_exp3$energy_c <- dat_exp3$energy - mean(dat_exp3$energy)
dat_exp3$sociability_c <- dat_exp3$sociability - mean(dat_exp3$sociability)
dat_exp3$anxiety_c <- dat_exp3$anxiety - mean(dat_exp3$anxiety)
dat_exp3$depression_c <- dat_exp3$depression - mean(dat_exp3$depression)
dat_exp3$emotional_c <- dat_exp3$emotional - mean(dat_exp3$emotional)
###Experiments 2-3
dat_exp23$compassion_c <- dat_exp23$compassion - mean(dat_exp23$compassion)
dat_exp23$respectfulness_c <- dat_exp23$respectfulness - mean(dat_exp23$respectfulness)
dat_exp23$trust_c <- dat_exp23$trust - mean(dat_exp23$trust)
dat_exp23$organization_c <- dat_exp23$organization - mean(dat_exp23$organization)
dat_exp23$productiveness_c <- dat_exp23$productiveness - mean(dat_exp23$productiveness)
dat_exp23$responsibility_c <- dat_exp23$responsibility - mean(dat_exp23$responsibility)
dat_exp23$aesthetic_c <- dat_exp23$aesthetic - mean(dat_exp23$aesthetic)
dat_exp23$creative_c <- dat_exp23$creative - mean(dat_exp23$creative)
dat_exp23$intellectual_c <- dat_exp23$intellectual - mean(dat_exp23$intellectual)
dat_exp23$assertiveness_c <- dat_exp23$assertiveness - mean(dat_exp23$assertiveness)
dat_exp23$energy_c <- dat_exp23$energy - mean(dat_exp23$energy)
dat_exp23$sociability_c <- dat_exp23$sociability - mean(dat_exp23$sociability)
dat_exp23$anxiety_c <- dat_exp23$anxiety - mean(dat_exp23$anxiety)
dat_exp23$depression_c <- dat_exp23$depression - mean(dat_exp23$depression)
dat_exp23$emotional_c <- dat_exp23$emotional - mean(dat_exp23$emotional)

##grand-mean centering of external predictors in Experiment 3
dat_exp3$rational_c <- dat_exp3$rational - mean(dat_exp3$rational)
dat_exp3$nfc_c <- dat_exp3$nfc - mean(dat_exp3$nfc)
dat_exp3$attention_c <- dat_exp3$attention - mean(dat_exp3$attention)

##grand-mean centering of BFI-2 domains for indirect-effects analyses in Experiment 3
dat_exp3$agr_no_trust_c <- dat_exp3$agr_no_trust - mean(dat_exp3$agr_no_trust)
dat_exp3$opn_no_intel_c <- dat_exp3$opn_no_intel - mean(dat_exp3$opn_no_intel)


########## S2: Additional Participants Prior to Experiment 1 ##########


#demographics of additional participants
dat_s2_additional <- subset(dat, subset = sample > 100)
dat_s2_additional_sex <- aggregate(sex ~ id, dat_s2_additional, FUN = function(x) mean(x, na.rm=T))
additional_sex <- as.data.frame(table(dat_s2_additional_sex$sex))
paste0("Additional Participants: N = ", nrow(dat_s2_additional)/36, ", ", round(100*(additional_sex[2,2]/(nrow(dat_s2_additional)/36)), 1), "% female, ", round(100*(additional_sex[1,2]/(nrow(dat_s2_additional)/36)), 1), "% male, ", round(100*((nrow(dat_s2_additional)/36-sum(additional_sex[,2]))/(nrow(dat_s2_additional)/36)), 1), "% missing, Mage = ", round(mean(dat_s2_additional$age, na.rm=T), 2), ", SDage = ", round(sd(dat_s2_additional$age, na.rm=T), 2))
rm(dat_s2_additional,dat_s2_additional_sex,additional_sex)


#prepare data for mixed-effects models

##select samples
dat_s2_exp1 <- subset(dat, subset = sample == 1 | sample == 2 | sample == 101 | sample == 102 | sample == 103)
dat_s2_exp123 <- dat

##grand-mean centering of BFI/BFI-2 domains (level-2 predictors)

###Experiment 1
dat_s2_exp1$agr_c <- dat_s2_exp1$agr - mean(dat_s2_exp1$agr)
dat_s2_exp1$cns_c <- dat_s2_exp1$cns - mean(dat_s2_exp1$cns)
dat_s2_exp1$opn_c <- dat_s2_exp1$opn - mean(dat_s2_exp1$opn)
dat_s2_exp1$ext_c <- dat_s2_exp1$ext - mean(dat_s2_exp1$ext)
dat_s2_exp1$neu_c <- dat_s2_exp1$neu - mean(dat_s2_exp1$neu)
###Experiments 1-3
dat_s2_exp123$agr_c <- dat_s2_exp123$agr - mean(dat_s2_exp123$agr)
dat_s2_exp123$cns_c <- dat_s2_exp123$cns - mean(dat_s2_exp123$cns)
dat_s2_exp123$opn_c <- dat_s2_exp123$opn - mean(dat_s2_exp123$opn)
dat_s2_exp123$ext_c <- dat_s2_exp123$ext - mean(dat_s2_exp123$ext)
dat_s2_exp123$neu_c <- dat_s2_exp123$neu - mean(dat_s2_exp123$neu)

##exclude pairs of Chinese characters and social values for which the sociocultural norm was not recalled correctly

###Experiment 1
dat_s2_exp1 <- subset(dat_s2_exp1, subset = recall_correct == 1)
###Experiments 1-3
dat_s2_exp123 <- subset(dat_s2_exp123, subset = recall_correct == 1)

##exclude pairs of Chinese characters for participants who knew the meaning of at least one of the characters 

###Experiment 1
dat_s2_exp1 <- subset(dat_s2_exp1, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
###Experiments 1-3
dat_s2_exp123 <- subset(dat_s2_exp123, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))


#conduct mixed-effects models in Julia

##set working directory to Julia folder
setwd(julia_wd)

##convert grouping variable to factor and assign data to Julia

###Experiment 1
dat_s2_exp1$id <- as.factor(dat_s2_exp1$id)
julia_assign("dat_s2_exp1", dat_s2_exp1)
###Experiments 1-3
dat_s2_exp123$id <- as.factor(dat_s2_exp123$id)
julia_assign("dat_s2_exp123", dat_s2_exp123)

##conduct domains-as-predictors models

###Experiment 1
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
                + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s2_exp1)")
s2_exp1_domain_model <- julia_eval("s2_exp1_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
                :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiments 1-3
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
                + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s2_exp123)")
s2_exp123_domain_model <- julia_eval("s2_exp123_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
                :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")


#Table S3: Effects of the Big Five Domains on Personal Preferences Moderated by Sociocultural Norms 
#          With Additional Participants who Completed Some Trial Version of the Minimal Norm Paradigm

dat_table_s3 <- data.frame(s2_exp1_domain_model,s2_exp123_domain_model)

dat_table_s3[,5] <- NULL

dat_table_s3[,1] <- c("(1)   (Intercept)","(7)   Norms", "(2)   Agr", "(3)   Cns", "(4)   Opn", "(5)   Ext", "(6)   Neu", "(8)   Agr x Norms",
                      "(9)   Cns x Norms", "(10) Opn x Norms", "(11) Ext x Norms", "(12) Neu x Norms")

dat_table_s3[,2:7] <- sapply(dat_table_s3[,2:7], as.numeric)

format_numbers <- function(x){
  ifelse(abs(x) <= 0.005,  formatC(x, format = "e", digits = 0), formatC(x, format = "f", digits = 2))
}
dat_table_s3 <- dat_table_s3 %>% mutate_if(is.numeric, format_numbers)

dat_table_s3$ci <- paste0("[", dat_table_s3$lowerCI, ", ", dat_table_s3$upperCI, "]")
dat_table_s3$ci.1 <- paste0("[", dat_table_s3$lowerCI.1, ", ", dat_table_s3$upperCI.1, "]")

dat_table_s3 <- dat_table_s3[c(1,3:7,2,8:12),]

dat_table_s3$blank <- NA

dat_table_s3 <- dat_table_s3[c("predictor","Pe","ci","blank","Pe.1","ci.1")]

col_keys <- c("predictor","Pe","ci","blank","Pe.1","ci.1")
head1 <- c("Predictor","Experiment 1","Experiment 1","","Experiments 1-3","Experiments 1-3")
head2 <- c("","Estimate","  95% CI","","Estimate","  95% CI")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_s3)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:6, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:3, part="header")
tbl <- merge_at(tbl, i=1, j=5:6, part="header")
tbl <- hline(tbl, i=1, j=c(2:3,5:6), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:6, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("predictor"), align="left", part="body")
tbl <- width(tbl, j =~ predictor, width=1.75)
tbl <- width(tbl, j =~ Pe + Pe.1, width=.75)
tbl <- width(tbl, j =~ ci + ci.1, width=1.2)
tbl <- width(tbl, j =~ blank, width=.1)
tbl <- colformat_lgl(tbl, j =~ blank, na_str="")
tbl <- height_all(tbl, height=.1, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S3.docx")


rm(list=setdiff(ls(), c("julia","julia_wd","files_wd","dat","dat_exp1","dat_exp2","dat_exp3","dat_exp123","dat_exp23")))


########## S3: Recall Rates ##########


#compute percentage of correctly recalled sociocultural norms
dat_s3 <- dat
dat_s3$recall_correct_total_percent <- dat_s3$recall_correct_total / 36
dat_s3$recall_correct_values_percent <- dat_s3$recall_correct_values / 18


#select samples

##Experiment 1
dat_s3_exp1 <- subset(dat_s3, subset = sample == 1 | sample == 2)
##Experiment 2
dat_s3_exp2 <- subset(dat_s3, subset = sample == 3 | sample == 4)
##Experiment 3
dat_s3_exp3 <- subset(dat_s3, subset = sample == 5 | sample == 6)
##Experiments 1-3
dat_s3_exp123 <- subset(dat_s3, subset = sample == 1 | sample == 2 | sample == 3 | sample == 4 | sample == 5 | sample == 6)


#select participants for different recall rates

##Experiment 1
dat_s3_exp1_1 <- subset(dat_s3_exp1, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .1) | c(chinese_characters_known == 1 & recall_correct_values_percent > .1))
dat_s3_exp1_2 <- subset(dat_s3_exp1, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .2) | c(chinese_characters_known == 1 & recall_correct_values_percent > .2))
dat_s3_exp1_3 <- subset(dat_s3_exp1, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .3) | c(chinese_characters_known == 1 & recall_correct_values_percent > .3))
dat_s3_exp1_4 <- subset(dat_s3_exp1, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .4) | c(chinese_characters_known == 1 & recall_correct_values_percent > .4))
dat_s3_exp1_5 <- subset(dat_s3_exp1, subset = c(chinese_characters_known != 1 & recall_correct_total_percent >= .5) | c(chinese_characters_known == 1 & recall_correct_values_percent >= .5))
dat_s3_exp1_6 <- subset(dat_s3_exp1, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .6) | c(chinese_characters_known == 1 & recall_correct_values_percent > .6))
dat_s3_exp1_7 <- subset(dat_s3_exp1, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .7) | c(chinese_characters_known == 1 & recall_correct_values_percent > .7))
dat_s3_exp1_8 <- subset(dat_s3_exp1, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .8) | c(chinese_characters_known == 1 & recall_correct_values_percent > .8))
dat_s3_exp1_9 <- subset(dat_s3_exp1, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .9) | c(chinese_characters_known == 1 & recall_correct_values_percent > .9))
dat_s3_exp1_10 <- subset(dat_s3_exp1, subset = c(chinese_characters_known != 1 & recall_correct_total_percent == 1) | c(chinese_characters_known == 1 & recall_correct_values_percent == 1))
##Experiment 2
dat_s3_exp2_1 <- subset(dat_s3_exp2, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .1) | c(chinese_characters_known == 1 & recall_correct_values_percent > .1))
dat_s3_exp2_2 <- subset(dat_s3_exp2, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .2) | c(chinese_characters_known == 1 & recall_correct_values_percent > .2))
dat_s3_exp2_3 <- subset(dat_s3_exp2, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .3) | c(chinese_characters_known == 1 & recall_correct_values_percent > .3))
dat_s3_exp2_4 <- subset(dat_s3_exp2, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .4) | c(chinese_characters_known == 1 & recall_correct_values_percent > .4))
dat_s3_exp2_5 <- subset(dat_s3_exp2, subset = c(chinese_characters_known != 1 & recall_correct_total_percent >= .5) | c(chinese_characters_known == 1 & recall_correct_values_percent >= .5))
dat_s3_exp2_6 <- subset(dat_s3_exp2, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .6) | c(chinese_characters_known == 1 & recall_correct_values_percent > .6))
dat_s3_exp2_7 <- subset(dat_s3_exp2, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .7) | c(chinese_characters_known == 1 & recall_correct_values_percent > .7))
dat_s3_exp2_8 <- subset(dat_s3_exp2, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .8) | c(chinese_characters_known == 1 & recall_correct_values_percent > .8))
dat_s3_exp2_9 <- subset(dat_s3_exp2, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .9) | c(chinese_characters_known == 1 & recall_correct_values_percent > .9))
dat_s3_exp2_10 <- subset(dat_s3_exp2, subset = c(chinese_characters_known != 1 & recall_correct_total_percent == 1) | c(chinese_characters_known == 1 & recall_correct_values_percent == 1))
##Experiment 3
dat_s3_exp3_1 <- subset(dat_s3_exp3, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .1) | c(chinese_characters_known == 1 & recall_correct_values_percent > .1))
dat_s3_exp3_2 <- subset(dat_s3_exp3, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .2) | c(chinese_characters_known == 1 & recall_correct_values_percent > .2))
dat_s3_exp3_3 <- subset(dat_s3_exp3, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .3) | c(chinese_characters_known == 1 & recall_correct_values_percent > .3))
dat_s3_exp3_4 <- subset(dat_s3_exp3, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .4) | c(chinese_characters_known == 1 & recall_correct_values_percent > .4))
dat_s3_exp3_5 <- subset(dat_s3_exp3, subset = c(chinese_characters_known != 1 & recall_correct_total_percent >= .5) | c(chinese_characters_known == 1 & recall_correct_values_percent >= .5))
dat_s3_exp3_6 <- subset(dat_s3_exp3, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .6) | c(chinese_characters_known == 1 & recall_correct_values_percent > .6))
dat_s3_exp3_7 <- subset(dat_s3_exp3, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .7) | c(chinese_characters_known == 1 & recall_correct_values_percent > .7))
dat_s3_exp3_8 <- subset(dat_s3_exp3, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .8) | c(chinese_characters_known == 1 & recall_correct_values_percent > .8))
dat_s3_exp3_9 <- subset(dat_s3_exp3, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .9) | c(chinese_characters_known == 1 & recall_correct_values_percent > .9))
dat_s3_exp3_10 <- subset(dat_s3_exp3, subset = c(chinese_characters_known != 1 & recall_correct_total_percent == 1) | c(chinese_characters_known == 1 & recall_correct_values_percent == 1))
##Experiment 123
dat_s3_exp123_1 <- subset(dat_s3_exp123, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .1) | c(chinese_characters_known == 1 & recall_correct_values_percent > .1))
dat_s3_exp123_2 <- subset(dat_s3_exp123, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .2) | c(chinese_characters_known == 1 & recall_correct_values_percent > .2))
dat_s3_exp123_3 <- subset(dat_s3_exp123, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .3) | c(chinese_characters_known == 1 & recall_correct_values_percent > .3))
dat_s3_exp123_4 <- subset(dat_s3_exp123, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .4) | c(chinese_characters_known == 1 & recall_correct_values_percent > .4))
dat_s3_exp123_5 <- subset(dat_s3_exp123, subset = c(chinese_characters_known != 1 & recall_correct_total_percent >= .5) | c(chinese_characters_known == 1 & recall_correct_values_percent >= .5))
dat_s3_exp123_6 <- subset(dat_s3_exp123, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .6) | c(chinese_characters_known == 1 & recall_correct_values_percent > .6))
dat_s3_exp123_7 <- subset(dat_s3_exp123, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .7) | c(chinese_characters_known == 1 & recall_correct_values_percent > .7))
dat_s3_exp123_8 <- subset(dat_s3_exp123, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .8) | c(chinese_characters_known == 1 & recall_correct_values_percent > .8))
dat_s3_exp123_9 <- subset(dat_s3_exp123, subset = c(chinese_characters_known != 1 & recall_correct_total_percent > .9) | c(chinese_characters_known == 1 & recall_correct_values_percent > .9))
dat_s3_exp123_10 <- subset(dat_s3_exp123, subset = c(chinese_characters_known != 1 & recall_correct_total_percent == 1) | c(chinese_characters_known == 1 & recall_correct_values_percent == 1))


#prepare data for mixed-effects models

##grand-mean centering of BFI/BFI-2 domains (level-2 predictors)

b5_c <- c("agr_c","cns_c","opn_c","ext_c","neu_c")
dat_s3_exp1[,b5_c] <- NA
dat_s3_exp1_1[,b5_c] <- NA
dat_s3_exp1_2[,b5_c] <- NA
dat_s3_exp1_3[,b5_c] <- NA
dat_s3_exp1_4[,b5_c] <- NA
dat_s3_exp1_5[,b5_c] <- NA
dat_s3_exp1_6[,b5_c] <- NA
dat_s3_exp1_7[,b5_c] <- NA
dat_s3_exp1_8[,b5_c] <- NA
dat_s3_exp1_9[,b5_c] <- NA
dat_s3_exp1_10[,b5_c] <- NA
dat_s3_exp2[,b5_c] <- NA
dat_s3_exp2_1[,b5_c] <- NA
dat_s3_exp2_2[,b5_c] <- NA
dat_s3_exp2_3[,b5_c] <- NA
dat_s3_exp2_4[,b5_c] <- NA
dat_s3_exp2_5[,b5_c] <- NA
dat_s3_exp2_6[,b5_c] <- NA
dat_s3_exp2_7[,b5_c] <- NA
dat_s3_exp2_8[,b5_c] <- NA
dat_s3_exp2_9[,b5_c] <- NA
dat_s3_exp2_10[,b5_c] <- NA
dat_s3_exp3[,b5_c] <- NA
dat_s3_exp3_1[,b5_c] <- NA
dat_s3_exp3_2[,b5_c] <- NA
dat_s3_exp3_3[,b5_c] <- NA
dat_s3_exp3_4[,b5_c] <- NA
dat_s3_exp3_5[,b5_c] <- NA
dat_s3_exp3_6[,b5_c] <- NA
dat_s3_exp3_7[,b5_c] <- NA
dat_s3_exp3_8[,b5_c] <- NA
dat_s3_exp3_9[,b5_c] <- NA
dat_s3_exp3_10[,b5_c] <- NA
dat_s3_exp123[,b5_c] <- NA
dat_s3_exp123_1[,b5_c] <- NA
dat_s3_exp123_2[,b5_c] <- NA
dat_s3_exp123_3[,b5_c] <- NA
dat_s3_exp123_4[,b5_c] <- NA
dat_s3_exp123_5[,b5_c] <- NA
dat_s3_exp123_6[,b5_c] <- NA
dat_s3_exp123_7[,b5_c] <- NA
dat_s3_exp123_8[,b5_c] <- NA
dat_s3_exp123_9[,b5_c] <- NA
dat_s3_exp123_10[,b5_c] <- NA

i <- 1
j <- 154
k <- 181

while (i <= 5) {
  dat_s3_exp1[,k] <- dat_s3_exp1[,j] - mean(dat_s3_exp1[,j])
  dat_s3_exp1_1[,k] <- dat_s3_exp1_1[,j] - mean(dat_s3_exp1_1[,j])
  dat_s3_exp1_2[,k] <- dat_s3_exp1_2[,j] - mean(dat_s3_exp1_2[,j])
  dat_s3_exp1_3[,k] <- dat_s3_exp1_3[,j] - mean(dat_s3_exp1_3[,j])
  dat_s3_exp1_4[,k] <- dat_s3_exp1_4[,j] - mean(dat_s3_exp1_4[,j])
  dat_s3_exp1_5[,k] <- dat_s3_exp1_5[,j] - mean(dat_s3_exp1_5[,j])
  dat_s3_exp1_6[,k] <- dat_s3_exp1_6[,j] - mean(dat_s3_exp1_6[,j])
  dat_s3_exp1_7[,k] <- dat_s3_exp1_7[,j] - mean(dat_s3_exp1_7[,j])
  dat_s3_exp1_8[,k] <- dat_s3_exp1_8[,j] - mean(dat_s3_exp1_8[,j])
  dat_s3_exp1_9[,k] <- dat_s3_exp1_9[,j] - mean(dat_s3_exp1_9[,j])
  dat_s3_exp1_10[,k] <- dat_s3_exp1_10[,j] - mean(dat_s3_exp1_10[,j])
  dat_s3_exp2[,k] <- dat_s3_exp2[,j] - mean(dat_s3_exp2[,j])
  dat_s3_exp2_1[,k] <- dat_s3_exp2_1[,j] - mean(dat_s3_exp2_1[,j])
  dat_s3_exp2_2[,k] <- dat_s3_exp2_2[,j] - mean(dat_s3_exp2_2[,j])
  dat_s3_exp2_3[,k] <- dat_s3_exp2_3[,j] - mean(dat_s3_exp2_3[,j])
  dat_s3_exp2_4[,k] <- dat_s3_exp2_4[,j] - mean(dat_s3_exp2_4[,j])
  dat_s3_exp2_5[,k] <- dat_s3_exp2_5[,j] - mean(dat_s3_exp2_5[,j])
  dat_s3_exp2_6[,k] <- dat_s3_exp2_6[,j] - mean(dat_s3_exp2_6[,j])
  dat_s3_exp2_7[,k] <- dat_s3_exp2_7[,j] - mean(dat_s3_exp2_7[,j])
  dat_s3_exp2_8[,k] <- dat_s3_exp2_8[,j] - mean(dat_s3_exp2_8[,j])
  dat_s3_exp2_9[,k] <- dat_s3_exp2_9[,j] - mean(dat_s3_exp2_9[,j])
  dat_s3_exp2_10[,k] <- dat_s3_exp2_10[,j] - mean(dat_s3_exp2_10[,j])
  dat_s3_exp3[,k] <- dat_s3_exp3[,j] - mean(dat_s3_exp3[,j])
  dat_s3_exp3_1[,k] <- dat_s3_exp3_1[,j] - mean(dat_s3_exp3_1[,j])
  dat_s3_exp3_2[,k] <- dat_s3_exp3_2[,j] - mean(dat_s3_exp3_2[,j])
  dat_s3_exp3_3[,k] <- dat_s3_exp3_3[,j] - mean(dat_s3_exp3_3[,j])
  dat_s3_exp3_4[,k] <- dat_s3_exp3_4[,j] - mean(dat_s3_exp3_4[,j])
  dat_s3_exp3_5[,k] <- dat_s3_exp3_5[,j] - mean(dat_s3_exp3_5[,j])
  dat_s3_exp3_6[,k] <- dat_s3_exp3_6[,j] - mean(dat_s3_exp3_6[,j])
  dat_s3_exp3_7[,k] <- dat_s3_exp3_7[,j] - mean(dat_s3_exp3_7[,j])
  dat_s3_exp3_8[,k] <- dat_s3_exp3_8[,j] - mean(dat_s3_exp3_8[,j])
  dat_s3_exp3_9[,k] <- dat_s3_exp3_9[,j] - mean(dat_s3_exp3_9[,j])
  dat_s3_exp3_10[,k] <- dat_s3_exp3_10[,j] - mean(dat_s3_exp3_10[,j])
  dat_s3_exp123[,k] <- dat_s3_exp123[,j] - mean(dat_s3_exp123[,j])
  dat_s3_exp123_1[,k] <- dat_s3_exp123_1[,j] - mean(dat_s3_exp123_1[,j])
  dat_s3_exp123_2[,k] <- dat_s3_exp123_2[,j] - mean(dat_s3_exp123_2[,j])
  dat_s3_exp123_3[,k] <- dat_s3_exp123_3[,j] - mean(dat_s3_exp123_3[,j])
  dat_s3_exp123_4[,k] <- dat_s3_exp123_4[,j] - mean(dat_s3_exp123_4[,j])
  dat_s3_exp123_5[,k] <- dat_s3_exp123_5[,j] - mean(dat_s3_exp123_5[,j])
  dat_s3_exp123_6[,k] <- dat_s3_exp123_6[,j] - mean(dat_s3_exp123_6[,j])
  dat_s3_exp123_7[,k] <- dat_s3_exp123_7[,j] - mean(dat_s3_exp123_7[,j])
  dat_s3_exp123_8[,k] <- dat_s3_exp123_8[,j] - mean(dat_s3_exp123_8[,j])
  dat_s3_exp123_9[,k] <- dat_s3_exp123_9[,j] - mean(dat_s3_exp123_9[,j])
  dat_s3_exp123_10[,k] <- dat_s3_exp123_10[,j] - mean(dat_s3_exp123_10[,j])
  i <- i+1
  j <- j+1
  k <- k+1
}

##exclude pairs of Chinese characters and social values for which the sociocultural norm was not recalled correctly

###Experiment 1
dat_s3_exp1 <- subset(dat_s3_exp1, subset = recall_correct == 1)
dat_s3_exp1_1 <- subset(dat_s3_exp1_1, subset = recall_correct == 1)
dat_s3_exp1_2 <- subset(dat_s3_exp1_2, subset = recall_correct == 1)
dat_s3_exp1_3 <- subset(dat_s3_exp1_3, subset = recall_correct == 1)
dat_s3_exp1_4 <- subset(dat_s3_exp1_4, subset = recall_correct == 1)
dat_s3_exp1_5 <- subset(dat_s3_exp1_5, subset = recall_correct == 1)
dat_s3_exp1_6 <- subset(dat_s3_exp1_6, subset = recall_correct == 1)
dat_s3_exp1_7 <- subset(dat_s3_exp1_7, subset = recall_correct == 1)
dat_s3_exp1_8 <- subset(dat_s3_exp1_8, subset = recall_correct == 1)
dat_s3_exp1_9 <- subset(dat_s3_exp1_9, subset = recall_correct == 1)
dat_s3_exp1_10 <- subset(dat_s3_exp1_10, subset = recall_correct == 1)
###Experiment 2 
dat_s3_exp2 <- subset(dat_s3_exp2, subset = recall_correct == 1)
dat_s3_exp2_1 <- subset(dat_s3_exp2_1, subset = recall_correct == 1)
dat_s3_exp2_2 <- subset(dat_s3_exp2_2, subset = recall_correct == 1)
dat_s3_exp2_3 <- subset(dat_s3_exp2_3, subset = recall_correct == 1)
dat_s3_exp2_4 <- subset(dat_s3_exp2_4, subset = recall_correct == 1)
dat_s3_exp2_5 <- subset(dat_s3_exp2_5, subset = recall_correct == 1)
dat_s3_exp2_6 <- subset(dat_s3_exp2_6, subset = recall_correct == 1)
dat_s3_exp2_7 <- subset(dat_s3_exp2_7, subset = recall_correct == 1)
dat_s3_exp2_8 <- subset(dat_s3_exp2_8, subset = recall_correct == 1)
dat_s3_exp2_9 <- subset(dat_s3_exp2_9, subset = recall_correct == 1)
dat_s3_exp2_10 <- subset(dat_s3_exp2_10, subset = recall_correct == 1)
###Experiment 3
dat_s3_exp3 <- subset(dat_s3_exp3, subset = recall_correct == 1)
dat_s3_exp3_1 <- subset(dat_s3_exp3_1, subset = recall_correct == 1)
dat_s3_exp3_2 <- subset(dat_s3_exp3_2, subset = recall_correct == 1)
dat_s3_exp3_3 <- subset(dat_s3_exp3_3, subset = recall_correct == 1)
dat_s3_exp3_4 <- subset(dat_s3_exp3_4, subset = recall_correct == 1)
dat_s3_exp3_5 <- subset(dat_s3_exp3_5, subset = recall_correct == 1)
dat_s3_exp3_6 <- subset(dat_s3_exp3_6, subset = recall_correct == 1)
dat_s3_exp3_7 <- subset(dat_s3_exp3_7, subset = recall_correct == 1)
dat_s3_exp3_8 <- subset(dat_s3_exp3_8, subset = recall_correct == 1)
dat_s3_exp3_9 <- subset(dat_s3_exp3_9, subset = recall_correct == 1)
dat_s3_exp3_10 <- subset(dat_s3_exp3_10, subset = recall_correct == 1)
###Experiments 1-3
dat_s3_exp123 <- subset(dat_s3_exp123, subset = recall_correct == 1)
dat_s3_exp123_1 <- subset(dat_s3_exp123_1, subset = recall_correct == 1)
dat_s3_exp123_2 <- subset(dat_s3_exp123_2, subset = recall_correct == 1)
dat_s3_exp123_3 <- subset(dat_s3_exp123_3, subset = recall_correct == 1)
dat_s3_exp123_4 <- subset(dat_s3_exp123_4, subset = recall_correct == 1)
dat_s3_exp123_5 <- subset(dat_s3_exp123_5, subset = recall_correct == 1)
dat_s3_exp123_6 <- subset(dat_s3_exp123_6, subset = recall_correct == 1)
dat_s3_exp123_7 <- subset(dat_s3_exp123_7, subset = recall_correct == 1)
dat_s3_exp123_8 <- subset(dat_s3_exp123_8, subset = recall_correct == 1)
dat_s3_exp123_9 <- subset(dat_s3_exp123_9, subset = recall_correct == 1)
dat_s3_exp123_10 <- subset(dat_s3_exp123_10, subset = recall_correct == 1)

##exclude pairs of Chinese characters for participants who knew the meaning of at least one of the characters 

###Experiment 1
dat_s3_exp1 <- subset(dat_s3_exp1, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp1_1 <- subset(dat_s3_exp1_1, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp1_2 <- subset(dat_s3_exp1_2, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp1_3 <- subset(dat_s3_exp1_3, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp1_4 <- subset(dat_s3_exp1_4, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp1_5 <- subset(dat_s3_exp1_5, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp1_6 <- subset(dat_s3_exp1_6, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp1_7 <- subset(dat_s3_exp1_7, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp1_8 <- subset(dat_s3_exp1_8, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp1_9 <- subset(dat_s3_exp1_9, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp1_10 <- subset(dat_s3_exp1_10, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
###Experiment 2
dat_s3_exp2 <- subset(dat_s3_exp2, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp2_1 <- subset(dat_s3_exp2_1, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp2_2 <- subset(dat_s3_exp2_2, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp2_3 <- subset(dat_s3_exp2_3, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp2_4 <- subset(dat_s3_exp2_4, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp2_5 <- subset(dat_s3_exp2_5, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp2_6 <- subset(dat_s3_exp2_6, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp2_7 <- subset(dat_s3_exp2_7, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp2_8 <- subset(dat_s3_exp2_8, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp2_9 <- subset(dat_s3_exp2_9, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp2_10 <- subset(dat_s3_exp2_10, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
##Experiment 3
dat_s3_exp3 <- subset(dat_s3_exp3, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp3_1 <- subset(dat_s3_exp3_1, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp3_2 <- subset(dat_s3_exp3_2, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp3_3 <- subset(dat_s3_exp3_3, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp3_4 <- subset(dat_s3_exp3_4, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp3_5 <- subset(dat_s3_exp3_5, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp3_6 <- subset(dat_s3_exp3_6, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp3_7 <- subset(dat_s3_exp3_7, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp3_8 <- subset(dat_s3_exp3_8, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp3_9 <- subset(dat_s3_exp3_9, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp3_10 <- subset(dat_s3_exp3_10, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
##Experiments 1-3
dat_s3_exp123 <- subset(dat_s3_exp123, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp123_1 <- subset(dat_s3_exp123_1, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp123_2 <- subset(dat_s3_exp123_2, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp123_3 <- subset(dat_s3_exp123_3, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp123_4 <- subset(dat_s3_exp123_4, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp123_5 <- subset(dat_s3_exp123_5, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp123_6 <- subset(dat_s3_exp123_6, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp123_7 <- subset(dat_s3_exp123_7, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp123_8 <- subset(dat_s3_exp123_8, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp123_9 <- subset(dat_s3_exp123_9, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
dat_s3_exp123_10 <- subset(dat_s3_exp123_10, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))


#conduct mixed-effects models in Julia

##set working directory to Julia folder
setwd(julia_wd)

##convert grouping variable to factor and assign data to Julia

###Experiment 1
dat_s3_exp1$id <- as.factor(dat_s3_exp1$id)
julia_assign("dat_s3_exp1", dat_s3_exp1)
dat_s3_exp1_1$id <- as.factor(dat_s3_exp1_1$id)
julia_assign("dat_s3_exp1_1", dat_s3_exp1_1)
dat_s3_exp1_2$id <- as.factor(dat_s3_exp1_2$id)
julia_assign("dat_s3_exp1_2", dat_s3_exp1_2)
dat_s3_exp1_3$id <- as.factor(dat_s3_exp1_3$id)
julia_assign("dat_s3_exp1_3", dat_s3_exp1_3)
dat_s3_exp1_4$id <- as.factor(dat_s3_exp1_4$id)
julia_assign("dat_s3_exp1_4", dat_s3_exp1_4)
dat_s3_exp1_5$id <- as.factor(dat_s3_exp1_5$id)
julia_assign("dat_s3_exp1_5", dat_s3_exp1_5)
dat_s3_exp1_6$id <- as.factor(dat_s3_exp1_6$id)
julia_assign("dat_s3_exp1_6", dat_s3_exp1_6)
dat_s3_exp1_7$id <- as.factor(dat_s3_exp1_7$id)
julia_assign("dat_s3_exp1_7", dat_s3_exp1_7)
dat_s3_exp1_8$id <- as.factor(dat_s3_exp1_8$id)
julia_assign("dat_s3_exp1_8", dat_s3_exp1_8)
dat_s3_exp1_9$id <- as.factor(dat_s3_exp1_9$id)
julia_assign("dat_s3_exp1_9", dat_s3_exp1_9)
dat_s3_exp1_10$id <- as.factor(dat_s3_exp1_10$id)
julia_assign("dat_s3_exp1_10", dat_s3_exp1_10)
###Experiment 2
dat_s3_exp2$id <- as.factor(dat_s3_exp2$id)
julia_assign("dat_s3_exp2", dat_s3_exp2)
dat_s3_exp2_1$id <- as.factor(dat_s3_exp2_1$id)
julia_assign("dat_s3_exp2_1", dat_s3_exp2_1)
dat_s3_exp2_2$id <- as.factor(dat_s3_exp2_2$id)
julia_assign("dat_s3_exp2_2", dat_s3_exp2_2)
dat_s3_exp2_3$id <- as.factor(dat_s3_exp2_3$id)
julia_assign("dat_s3_exp2_3", dat_s3_exp2_3)
dat_s3_exp2_4$id <- as.factor(dat_s3_exp2_4$id)
julia_assign("dat_s3_exp2_4", dat_s3_exp2_4)
dat_s3_exp2_5$id <- as.factor(dat_s3_exp2_5$id)
julia_assign("dat_s3_exp2_5", dat_s3_exp2_5)
dat_s3_exp2_6$id <- as.factor(dat_s3_exp2_6$id)
julia_assign("dat_s3_exp2_6", dat_s3_exp2_6)
dat_s3_exp2_7$id <- as.factor(dat_s3_exp2_7$id)
julia_assign("dat_s3_exp2_7", dat_s3_exp2_7)
dat_s3_exp2_8$id <- as.factor(dat_s3_exp2_8$id)
julia_assign("dat_s3_exp2_8", dat_s3_exp2_8)
dat_s3_exp2_9$id <- as.factor(dat_s3_exp2_9$id)
julia_assign("dat_s3_exp2_9", dat_s3_exp2_9)
dat_s3_exp2_10$id <- as.factor(dat_s3_exp2_10$id)
julia_assign("dat_s3_exp2_10", dat_s3_exp2_10)
###Experiment 3
dat_s3_exp3$id <- as.factor(dat_s3_exp3$id)
julia_assign("dat_s3_exp3", dat_s3_exp3)
dat_s3_exp3_1$id <- as.factor(dat_s3_exp3_1$id)
julia_assign("dat_s3_exp3_1", dat_s3_exp3_1)
dat_s3_exp3_2$id <- as.factor(dat_s3_exp3_2$id)
julia_assign("dat_s3_exp3_2", dat_s3_exp3_2)
dat_s3_exp3_3$id <- as.factor(dat_s3_exp3_3$id)
julia_assign("dat_s3_exp3_3", dat_s3_exp3_3)
dat_s3_exp3_4$id <- as.factor(dat_s3_exp3_4$id)
julia_assign("dat_s3_exp3_4", dat_s3_exp3_4)
dat_s3_exp3_5$id <- as.factor(dat_s3_exp3_5$id)
julia_assign("dat_s3_exp3_5", dat_s3_exp3_5)
dat_s3_exp3_6$id <- as.factor(dat_s3_exp3_6$id)
julia_assign("dat_s3_exp3_6", dat_s3_exp3_6)
dat_s3_exp3_7$id <- as.factor(dat_s3_exp3_7$id)
julia_assign("dat_s3_exp3_7", dat_s3_exp3_7)
dat_s3_exp3_8$id <- as.factor(dat_s3_exp3_8$id)
julia_assign("dat_s3_exp3_8", dat_s3_exp3_8)
dat_s3_exp3_9$id <- as.factor(dat_s3_exp3_9$id)
julia_assign("dat_s3_exp3_9", dat_s3_exp3_9)
dat_s3_exp3_10$id <- as.factor(dat_s3_exp3_10$id)
julia_assign("dat_s3_exp3_10", dat_s3_exp3_10)
###Experiments 1-3
dat_s3_exp123$id <- as.factor(dat_s3_exp123$id)
julia_assign("dat_s3_exp123", dat_s3_exp123)
dat_s3_exp123_1$id <- as.factor(dat_s3_exp123_1$id)
julia_assign("dat_s3_exp123_1", dat_s3_exp123_1)
dat_s3_exp123_2$id <- as.factor(dat_s3_exp123_2$id)
julia_assign("dat_s3_exp123_2", dat_s3_exp123_2)
dat_s3_exp123_3$id <- as.factor(dat_s3_exp123_3$id)
julia_assign("dat_s3_exp123_3", dat_s3_exp123_3)
dat_s3_exp123_4$id <- as.factor(dat_s3_exp123_4$id)
julia_assign("dat_s3_exp123_4", dat_s3_exp123_4)
dat_s3_exp123_5$id <- as.factor(dat_s3_exp123_5$id)
julia_assign("dat_s3_exp123_5", dat_s3_exp123_5)
dat_s3_exp123_6$id <- as.factor(dat_s3_exp123_6$id)
julia_assign("dat_s3_exp123_6", dat_s3_exp123_6)
dat_s3_exp123_7$id <- as.factor(dat_s3_exp123_7$id)
julia_assign("dat_s3_exp123_7", dat_s3_exp123_7)
dat_s3_exp123_8$id <- as.factor(dat_s3_exp123_8$id)
julia_assign("dat_s3_exp123_8", dat_s3_exp123_8)
dat_s3_exp123_9$id <- as.factor(dat_s3_exp123_9$id)
julia_assign("dat_s3_exp123_9", dat_s3_exp123_9)
dat_s3_exp123_10$id <- as.factor(dat_s3_exp123_10$id)
julia_assign("dat_s3_exp123_10", dat_s3_exp123_10)

##conduct domains-as-predictors models

###Experiment 1
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp1)")
s3_exp1_domain_model <- julia_eval("s3_exp1_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp1_1)")
s3_exp1_1_domain_model <- julia_eval("s3_exp1_1_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp1_2)")
s3_exp1_2_domain_model <- julia_eval("s3_exp1_2_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp1_3)")
s3_exp1_3_domain_model <- julia_eval("s3_exp1_3_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp1_4)")
s3_exp1_4_domain_model <- julia_eval("s3_exp1_4_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp1_5)")
s3_exp1_5_domain_model <- julia_eval("s3_exp1_5_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp1_6)")
s3_exp1_6_domain_model <- julia_eval("s3_exp1_6_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp1_7)")
s3_exp1_7_domain_model <- julia_eval("s3_exp1_7_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp1_8)")
s3_exp1_8_domain_model <- julia_eval("s3_exp1_8_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp1_9)")
s3_exp1_9_domain_model <- julia_eval("s3_exp1_9_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp1_10)")
s3_exp1_10_domain_model <- julia_eval("s3_exp1_10_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiment 2
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp2)")
s3_exp2_domain_model <- julia_eval("s3_exp2_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp2_1)")
s3_exp2_1_domain_model <- julia_eval("s3_exp2_1_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp2_2)")
s3_exp2_2_domain_model <- julia_eval("s3_exp2_2_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp2_3)")
s3_exp2_3_domain_model <- julia_eval("s3_exp2_3_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp2_4)")
s3_exp2_4_domain_model <- julia_eval("s3_exp2_4_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp2_5)")
s3_exp2_5_domain_model <- julia_eval("s3_exp2_5_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp2_6)")
s3_exp2_6_domain_model <- julia_eval("s3_exp2_6_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp2_7)")
s3_exp2_7_domain_model <- julia_eval("s3_exp2_7_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp2_8)")
s3_exp2_8_domain_model <- julia_eval("s3_exp2_8_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp2_9)")
s3_exp2_9_domain_model <- julia_eval("s3_exp2_9_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp2_10)")
s3_exp2_10_domain_model <- julia_eval("s3_exp2_10_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiment 3
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp3)")
s3_exp3_domain_model <- julia_eval("s3_exp3_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp3_1)")
s3_exp3_1_domain_model <- julia_eval("s3_exp3_1_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp3_2)")
s3_exp3_2_domain_model <- julia_eval("s3_exp3_2_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp3_3)")
s3_exp3_3_domain_model <- julia_eval("s3_exp3_3_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp3_4)")
s3_exp3_4_domain_model <- julia_eval("s3_exp3_4_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp3_5)")
s3_exp3_5_domain_model <- julia_eval("s3_exp3_5_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp3_6)")
s3_exp3_6_domain_model <- julia_eval("s3_exp3_6_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp3_7)")
s3_exp3_7_domain_model <- julia_eval("s3_exp3_7_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp3_8)")
s3_exp3_8_domain_model <- julia_eval("s3_exp3_8_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp3_9)")
s3_exp3_9_domain_model <- julia_eval("s3_exp3_9_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp3_10)")
s3_exp3_10_domain_model <- julia_eval("s3_exp3_10_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiments 1-3
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp123)")
s3_exp123_domain_model <- julia_eval("s3_exp123_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp123_1)")
s3_exp123_1_domain_model <- julia_eval("s3_exp123_1_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp123_2)")
s3_exp123_2_domain_model <- julia_eval("s3_exp123_2_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp123_3)")
s3_exp123_3_domain_model <- julia_eval("s3_exp123_3_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp123_4)")
s3_exp123_4_domain_model <- julia_eval("s3_exp123_4_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp123_5)")
s3_exp123_5_domain_model <- julia_eval("s3_exp123_5_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp123_6)")
s3_exp123_6_domain_model <- julia_eval("s3_exp123_6_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp123_7)")
s3_exp123_7_domain_model <- julia_eval("s3_exp123_7_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp123_8)")
s3_exp123_8_domain_model <- julia_eval("s3_exp123_8_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp123_9)")
s3_exp123_9_domain_model <- julia_eval("s3_exp123_9_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s3_exp123_10)")
s3_exp123_10_domain_model <- julia_eval("s3_exp123_10_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")


#Figure S1: Estimates of the Cross-Level Interactions between the Big Five Domains and Sociocultural Norms on Personal Preferences for Different Recall Rates
cont <- c("> 0%","> 10%","> 20%","> 30%","> 40%","\u2265 50%","> 60%","> 70%","> 80%","> 90%", "100%")
BigFive <- c("Agr","Agr","Agr","Agr","Agr","Agr","Agr","Agr","Agr","Agr","Agr",
             "Cns","Cns","Cns","Cns","Cns","Cns","Cns","Cns","Cns","Cns","Cns",
             "Opn","Opn","Opn","Opn","Opn","Opn","Opn","Opn","Opn","Opn","Opn",
             "Ext","Ext","Ext","Ext","Ext","Ext","Ext","Ext","Ext","Ext","Ext",
             "Neu","Neu","Neu","Neu","Neu","Neu","Neu","Neu","Neu","Neu","Neu")

##Figure S1a. Experiment 1
Pe_exp1 <- c(s3_exp1_domain_model$Pe[8],s3_exp1_1_domain_model$Pe[8],s3_exp1_2_domain_model$Pe[8],s3_exp1_3_domain_model$Pe[8],s3_exp1_4_domain_model$Pe[8],s3_exp1_5_domain_model$Pe[8],s3_exp1_6_domain_model$Pe[8],s3_exp1_7_domain_model$Pe[8],s3_exp1_8_domain_model$Pe[8],s3_exp1_9_domain_model$Pe[8],s3_exp1_10_domain_model$Pe[8],
             s3_exp1_domain_model$Pe[9],s3_exp1_1_domain_model$Pe[9],s3_exp1_2_domain_model$Pe[9],s3_exp1_3_domain_model$Pe[9],s3_exp1_4_domain_model$Pe[9],s3_exp1_5_domain_model$Pe[9],s3_exp1_6_domain_model$Pe[9],s3_exp1_7_domain_model$Pe[9],s3_exp1_8_domain_model$Pe[9],s3_exp1_9_domain_model$Pe[9],s3_exp1_10_domain_model$Pe[9],
             s3_exp1_domain_model$Pe[10],s3_exp1_1_domain_model$Pe[10],s3_exp1_2_domain_model$Pe[10],s3_exp1_3_domain_model$Pe[10],s3_exp1_4_domain_model$Pe[10],s3_exp1_5_domain_model$Pe[10],s3_exp1_6_domain_model$Pe[10],s3_exp1_7_domain_model$Pe[10],s3_exp1_8_domain_model$Pe[10],s3_exp1_9_domain_model$Pe[10],s3_exp1_10_domain_model$Pe[10],
             s3_exp1_domain_model$Pe[11],s3_exp1_1_domain_model$Pe[11],s3_exp1_2_domain_model$Pe[11],s3_exp1_3_domain_model$Pe[11],s3_exp1_4_domain_model$Pe[11],s3_exp1_5_domain_model$Pe[11],s3_exp1_6_domain_model$Pe[11],s3_exp1_7_domain_model$Pe[11],s3_exp1_8_domain_model$Pe[11],s3_exp1_9_domain_model$Pe[11],s3_exp1_10_domain_model$Pe[11],
             s3_exp1_domain_model$Pe[12],s3_exp1_1_domain_model$Pe[12],s3_exp1_2_domain_model$Pe[12],s3_exp1_3_domain_model$Pe[12],s3_exp1_4_domain_model$Pe[12],s3_exp1_5_domain_model$Pe[12],s3_exp1_6_domain_model$Pe[12],s3_exp1_7_domain_model$Pe[12],s3_exp1_8_domain_model$Pe[12],s3_exp1_9_domain_model$Pe[12],s3_exp1_10_domain_model$Pe[12])
lowerCI_exp1 <- c(s3_exp1_domain_model$lowerCI[8],s3_exp1_1_domain_model$lowerCI[8],s3_exp1_2_domain_model$lowerCI[8],s3_exp1_3_domain_model$lowerCI[8],s3_exp1_4_domain_model$lowerCI[8],s3_exp1_5_domain_model$lowerCI[8],s3_exp1_6_domain_model$lowerCI[8],s3_exp1_7_domain_model$lowerCI[8],s3_exp1_8_domain_model$lowerCI[8],s3_exp1_9_domain_model$lowerCI[8],s3_exp1_10_domain_model$lowerCI[8],
                  s3_exp1_domain_model$lowerCI[9],s3_exp1_1_domain_model$lowerCI[9],s3_exp1_2_domain_model$lowerCI[9],s3_exp1_3_domain_model$lowerCI[9],s3_exp1_4_domain_model$lowerCI[9],s3_exp1_5_domain_model$lowerCI[9],s3_exp1_6_domain_model$lowerCI[9],s3_exp1_7_domain_model$lowerCI[9],s3_exp1_8_domain_model$lowerCI[9],s3_exp1_9_domain_model$lowerCI[9],s3_exp1_10_domain_model$lowerCI[9],
                  s3_exp1_domain_model$lowerCI[10],s3_exp1_1_domain_model$lowerCI[10],s3_exp1_2_domain_model$lowerCI[10],s3_exp1_3_domain_model$lowerCI[10],s3_exp1_4_domain_model$lowerCI[10],s3_exp1_5_domain_model$lowerCI[10],s3_exp1_6_domain_model$lowerCI[10],s3_exp1_7_domain_model$lowerCI[10],s3_exp1_8_domain_model$lowerCI[10],s3_exp1_9_domain_model$lowerCI[10],s3_exp1_10_domain_model$lowerCI[10],
                  s3_exp1_domain_model$lowerCI[11],s3_exp1_1_domain_model$lowerCI[11],s3_exp1_2_domain_model$lowerCI[11],s3_exp1_3_domain_model$lowerCI[11],s3_exp1_4_domain_model$lowerCI[11],s3_exp1_5_domain_model$lowerCI[11],s3_exp1_6_domain_model$lowerCI[11],s3_exp1_7_domain_model$lowerCI[11],s3_exp1_8_domain_model$lowerCI[11],s3_exp1_9_domain_model$lowerCI[11],s3_exp1_10_domain_model$lowerCI[11],
                  s3_exp1_domain_model$lowerCI[12],s3_exp1_1_domain_model$lowerCI[12],s3_exp1_2_domain_model$lowerCI[12],s3_exp1_3_domain_model$lowerCI[12],s3_exp1_4_domain_model$lowerCI[12],s3_exp1_5_domain_model$lowerCI[12],s3_exp1_6_domain_model$lowerCI[12],s3_exp1_7_domain_model$lowerCI[12],s3_exp1_8_domain_model$lowerCI[12],s3_exp1_9_domain_model$lowerCI[12],s3_exp1_10_domain_model$lowerCI[12])
upperCI_exp1 <- c(s3_exp1_domain_model$upperCI[8],s3_exp1_1_domain_model$upperCI[8],s3_exp1_2_domain_model$upperCI[8],s3_exp1_3_domain_model$upperCI[8],s3_exp1_4_domain_model$upperCI[8],s3_exp1_5_domain_model$upperCI[8],s3_exp1_6_domain_model$upperCI[8],s3_exp1_7_domain_model$upperCI[8],s3_exp1_8_domain_model$upperCI[8],s3_exp1_9_domain_model$upperCI[8],s3_exp1_10_domain_model$upperCI[8],
                  s3_exp1_domain_model$upperCI[9],s3_exp1_1_domain_model$upperCI[9],s3_exp1_2_domain_model$upperCI[9],s3_exp1_3_domain_model$upperCI[9],s3_exp1_4_domain_model$upperCI[9],s3_exp1_5_domain_model$upperCI[9],s3_exp1_6_domain_model$upperCI[9],s3_exp1_7_domain_model$upperCI[9],s3_exp1_8_domain_model$upperCI[9],s3_exp1_9_domain_model$upperCI[9],s3_exp1_10_domain_model$upperCI[9],
                  s3_exp1_domain_model$upperCI[10],s3_exp1_1_domain_model$upperCI[10],s3_exp1_2_domain_model$upperCI[10],s3_exp1_3_domain_model$upperCI[10],s3_exp1_4_domain_model$upperCI[10],s3_exp1_5_domain_model$upperCI[10],s3_exp1_6_domain_model$upperCI[10],s3_exp1_7_domain_model$upperCI[10],s3_exp1_8_domain_model$upperCI[10],s3_exp1_9_domain_model$upperCI[10],s3_exp1_10_domain_model$upperCI[10],
                  s3_exp1_domain_model$upperCI[11],s3_exp1_1_domain_model$upperCI[11],s3_exp1_2_domain_model$upperCI[11],s3_exp1_3_domain_model$upperCI[11],s3_exp1_4_domain_model$upperCI[11],s3_exp1_5_domain_model$upperCI[11],s3_exp1_6_domain_model$upperCI[11],s3_exp1_7_domain_model$upperCI[11],s3_exp1_8_domain_model$upperCI[11],s3_exp1_9_domain_model$upperCI[11],s3_exp1_10_domain_model$upperCI[11],
                  s3_exp1_domain_model$upperCI[12],s3_exp1_1_domain_model$upperCI[12],s3_exp1_2_domain_model$upperCI[12],s3_exp1_3_domain_model$upperCI[12],s3_exp1_4_domain_model$upperCI[12],s3_exp1_5_domain_model$upperCI[12],s3_exp1_6_domain_model$upperCI[12],s3_exp1_7_domain_model$upperCI[12],s3_exp1_8_domain_model$upperCI[12],s3_exp1_9_domain_model$upperCI[12],s3_exp1_10_domain_model$upperCI[12])

dat_s3_1 <- data.frame(cont,BigFive,unlist(Pe_exp1),unlist(lowerCI_exp1),unlist(upperCI_exp1))

dat_s3_1$cont <- factor(dat_s3_1$cont, levels=unique(as.character(dat_s3_1$cont)))
dat_s3_1$BigFive <- factor(dat_s3_1$BigFive, levels=unique(as.character(dat_s3_1$BigFive)))

Figure_S1a <- ggplot(data=dat_s3_1, aes(x=unlist(cont), y=unlist(Pe_exp1), group=BigFive, color=BigFive)) +
  geom_point(stat="identity") +
  geom_errorbar(aes(ymin=unlist(lowerCI_exp1), ymax=unlist(upperCI_exp1)), width=0.3) +
  scale_color_manual(breaks = c("Agr", "Cns", "Opn", "Ext", "Neu"),
                     values=c("#27AE60", "#0000FF", "#FF00FF", "#FF0000", "#808080")) +
  ylim(-0.6, 0.62) +
  theme(axis.title.x = element_text(size=11), legend.title = element_blank(), legend.position = "top", 
        legend.key = element_rect(colour = NA, fill = NA), legend.text = element_text(size=11),
        axis.text = element_text(colour = "black", size=11), axis.title.y = element_text(size=11),
        panel.background = element_blank(), panel.grid = element_blank(), 
        axis.line = element_line(colour = "black")) +
  geom_hline(yintercept=0) +
  ylab("Big Five x Norms")+
  xlab("Recall Rate")

Figure_S1a <- annotate_figure(Figure_S1a, fig.lab = "a. Experiment 1", fig.lab.pos = c("top.left"), fig.lab.size = 11)

##Figure S1b. Experiment 2
Pe_exp2 <- c(s3_exp2_domain_model$Pe[8],s3_exp2_1_domain_model$Pe[8],s3_exp2_2_domain_model$Pe[8],s3_exp2_3_domain_model$Pe[8],s3_exp2_4_domain_model$Pe[8],s3_exp2_5_domain_model$Pe[8],s3_exp2_6_domain_model$Pe[8],s3_exp2_7_domain_model$Pe[8],s3_exp2_8_domain_model$Pe[8],s3_exp2_9_domain_model$Pe[8],s3_exp2_10_domain_model$Pe[8],
             s3_exp2_domain_model$Pe[9],s3_exp2_1_domain_model$Pe[9],s3_exp2_2_domain_model$Pe[9],s3_exp2_3_domain_model$Pe[9],s3_exp2_4_domain_model$Pe[9],s3_exp2_5_domain_model$Pe[9],s3_exp2_6_domain_model$Pe[9],s3_exp2_7_domain_model$Pe[9],s3_exp2_8_domain_model$Pe[9],s3_exp2_9_domain_model$Pe[9],s3_exp2_10_domain_model$Pe[9],
             s3_exp2_domain_model$Pe[10],s3_exp2_1_domain_model$Pe[10],s3_exp2_2_domain_model$Pe[10],s3_exp2_3_domain_model$Pe[10],s3_exp2_4_domain_model$Pe[10],s3_exp2_5_domain_model$Pe[10],s3_exp2_6_domain_model$Pe[10],s3_exp2_7_domain_model$Pe[10],s3_exp2_8_domain_model$Pe[10],s3_exp2_9_domain_model$Pe[10],s3_exp2_10_domain_model$Pe[10],
             s3_exp2_domain_model$Pe[11],s3_exp2_1_domain_model$Pe[11],s3_exp2_2_domain_model$Pe[11],s3_exp2_3_domain_model$Pe[11],s3_exp2_4_domain_model$Pe[11],s3_exp2_5_domain_model$Pe[11],s3_exp2_6_domain_model$Pe[11],s3_exp2_7_domain_model$Pe[11],s3_exp2_8_domain_model$Pe[11],s3_exp2_9_domain_model$Pe[11],s3_exp2_10_domain_model$Pe[11],
             s3_exp2_domain_model$Pe[12],s3_exp2_1_domain_model$Pe[12],s3_exp2_2_domain_model$Pe[12],s3_exp2_3_domain_model$Pe[12],s3_exp2_4_domain_model$Pe[12],s3_exp2_5_domain_model$Pe[12],s3_exp2_6_domain_model$Pe[12],s3_exp2_7_domain_model$Pe[12],s3_exp2_8_domain_model$Pe[12],s3_exp2_9_domain_model$Pe[12],s3_exp2_10_domain_model$Pe[12])
lowerCI_exp2 <- c(s3_exp2_domain_model$lowerCI[8],s3_exp2_1_domain_model$lowerCI[8],s3_exp2_2_domain_model$lowerCI[8],s3_exp2_3_domain_model$lowerCI[8],s3_exp2_4_domain_model$lowerCI[8],s3_exp2_5_domain_model$lowerCI[8],s3_exp2_6_domain_model$lowerCI[8],s3_exp2_7_domain_model$lowerCI[8],s3_exp2_8_domain_model$lowerCI[8],s3_exp2_9_domain_model$lowerCI[8],s3_exp2_10_domain_model$lowerCI[8],
                  s3_exp2_domain_model$lowerCI[9],s3_exp2_1_domain_model$lowerCI[9],s3_exp2_2_domain_model$lowerCI[9],s3_exp2_3_domain_model$lowerCI[9],s3_exp2_4_domain_model$lowerCI[9],s3_exp2_5_domain_model$lowerCI[9],s3_exp2_6_domain_model$lowerCI[9],s3_exp2_7_domain_model$lowerCI[9],s3_exp2_8_domain_model$lowerCI[9],s3_exp2_9_domain_model$lowerCI[9],s3_exp2_10_domain_model$lowerCI[9],
                  s3_exp2_domain_model$lowerCI[10],s3_exp2_1_domain_model$lowerCI[10],s3_exp2_2_domain_model$lowerCI[10],s3_exp2_3_domain_model$lowerCI[10],s3_exp2_4_domain_model$lowerCI[10],s3_exp2_5_domain_model$lowerCI[10],s3_exp2_6_domain_model$lowerCI[10],s3_exp2_7_domain_model$lowerCI[10],s3_exp2_8_domain_model$lowerCI[10],s3_exp2_9_domain_model$lowerCI[10],s3_exp2_10_domain_model$lowerCI[10],
                  s3_exp2_domain_model$lowerCI[11],s3_exp2_1_domain_model$lowerCI[11],s3_exp2_2_domain_model$lowerCI[11],s3_exp2_3_domain_model$lowerCI[11],s3_exp2_4_domain_model$lowerCI[11],s3_exp2_5_domain_model$lowerCI[11],s3_exp2_6_domain_model$lowerCI[11],s3_exp2_7_domain_model$lowerCI[11],s3_exp2_8_domain_model$lowerCI[11],s3_exp2_9_domain_model$lowerCI[11],s3_exp2_10_domain_model$lowerCI[11],
                  s3_exp2_domain_model$lowerCI[12],s3_exp2_1_domain_model$lowerCI[12],s3_exp2_2_domain_model$lowerCI[12],s3_exp2_3_domain_model$lowerCI[12],s3_exp2_4_domain_model$lowerCI[12],s3_exp2_5_domain_model$lowerCI[12],s3_exp2_6_domain_model$lowerCI[12],s3_exp2_7_domain_model$lowerCI[12],s3_exp2_8_domain_model$lowerCI[12],s3_exp2_9_domain_model$lowerCI[12],s3_exp2_10_domain_model$lowerCI[12])
upperCI_exp2 <- c(s3_exp2_domain_model$upperCI[8],s3_exp2_1_domain_model$upperCI[8],s3_exp2_2_domain_model$upperCI[8],s3_exp2_3_domain_model$upperCI[8],s3_exp2_4_domain_model$upperCI[8],s3_exp2_5_domain_model$upperCI[8],s3_exp2_6_domain_model$upperCI[8],s3_exp2_7_domain_model$upperCI[8],s3_exp2_8_domain_model$upperCI[8],s3_exp2_9_domain_model$upperCI[8],s3_exp2_10_domain_model$upperCI[8],
                  s3_exp2_domain_model$upperCI[9],s3_exp2_1_domain_model$upperCI[9],s3_exp2_2_domain_model$upperCI[9],s3_exp2_3_domain_model$upperCI[9],s3_exp2_4_domain_model$upperCI[9],s3_exp2_5_domain_model$upperCI[9],s3_exp2_6_domain_model$upperCI[9],s3_exp2_7_domain_model$upperCI[9],s3_exp2_8_domain_model$upperCI[9],s3_exp2_9_domain_model$upperCI[9],s3_exp2_10_domain_model$upperCI[9],
                  s3_exp2_domain_model$upperCI[10],s3_exp2_1_domain_model$upperCI[10],s3_exp2_2_domain_model$upperCI[10],s3_exp2_3_domain_model$upperCI[10],s3_exp2_4_domain_model$upperCI[10],s3_exp2_5_domain_model$upperCI[10],s3_exp2_6_domain_model$upperCI[10],s3_exp2_7_domain_model$upperCI[10],s3_exp2_8_domain_model$upperCI[10],s3_exp2_9_domain_model$upperCI[10],s3_exp2_10_domain_model$upperCI[10],
                  s3_exp2_domain_model$upperCI[11],s3_exp2_1_domain_model$upperCI[11],s3_exp2_2_domain_model$upperCI[11],s3_exp2_3_domain_model$upperCI[11],s3_exp2_4_domain_model$upperCI[11],s3_exp2_5_domain_model$upperCI[11],s3_exp2_6_domain_model$upperCI[11],s3_exp2_7_domain_model$upperCI[11],s3_exp2_8_domain_model$upperCI[11],s3_exp2_9_domain_model$upperCI[11],s3_exp2_10_domain_model$upperCI[11],
                  s3_exp2_domain_model$upperCI[12],s3_exp2_1_domain_model$upperCI[12],s3_exp2_2_domain_model$upperCI[12],s3_exp2_3_domain_model$upperCI[12],s3_exp2_4_domain_model$upperCI[12],s3_exp2_5_domain_model$upperCI[12],s3_exp2_6_domain_model$upperCI[12],s3_exp2_7_domain_model$upperCI[12],s3_exp2_8_domain_model$upperCI[12],s3_exp2_9_domain_model$upperCI[12],s3_exp2_10_domain_model$upperCI[12])

dat_s3_2 <- data.frame(cont,BigFive,unlist(Pe_exp2),unlist(lowerCI_exp2),unlist(upperCI_exp2))

dat_s3_2$cont <- factor(dat_s3_2$cont, levels=unique(as.character(dat_s3_2$cont)))
dat_s3_2$BigFive <- factor(dat_s3_2$BigFive, levels=unique(as.character(dat_s3_2$BigFive)))

Figure_S1b <- ggplot(data=dat_s3_2, aes(x=unlist(cont), y=unlist(Pe_exp2), group=BigFive, color=BigFive)) +
  geom_point(stat="identity") +
  geom_errorbar(aes(ymin=unlist(lowerCI_exp2), ymax=unlist(upperCI_exp2)), width=0.3) +
  scale_color_manual(breaks = c("Agr", "Cns", "Opn", "Ext", "Neu"),
                     values=c("#27AE60", "#0000FF", "#FF00FF", "#FF0000", "#808080")) +
  ylim(-0.6, 0.62) +
  theme(axis.title.x = element_text(size=11), legend.title = element_blank(), legend.position = "top", 
        legend.key = element_rect(colour = NA, fill = NA), legend.text = element_text(size=11),
        axis.text = element_text(colour = "black", size=11), axis.title.y = element_text(size=11),
        panel.background = element_blank(), panel.grid = element_blank(), 
        axis.line = element_line(colour = "black")) +
  geom_hline(yintercept=0) +
  ylab("Big Five x Norms")+
  xlab("Recall Rate")

Figure_S1b <- annotate_figure(Figure_S1b, fig.lab = "b. Experiment 2", fig.lab.pos = c("top.left"), fig.lab.size = 11)

##Figure S1c. Experiment 3
Pe_exp3 <- c(s3_exp3_domain_model$Pe[8],s3_exp3_1_domain_model$Pe[8],s3_exp3_2_domain_model$Pe[8],s3_exp3_3_domain_model$Pe[8],s3_exp3_4_domain_model$Pe[8],s3_exp3_5_domain_model$Pe[8],s3_exp3_6_domain_model$Pe[8],s3_exp3_7_domain_model$Pe[8],s3_exp3_8_domain_model$Pe[8],s3_exp3_9_domain_model$Pe[8],s3_exp3_10_domain_model$Pe[8],
             s3_exp3_domain_model$Pe[9],s3_exp3_1_domain_model$Pe[9],s3_exp3_2_domain_model$Pe[9],s3_exp3_3_domain_model$Pe[9],s3_exp3_4_domain_model$Pe[9],s3_exp3_5_domain_model$Pe[9],s3_exp3_6_domain_model$Pe[9],s3_exp3_7_domain_model$Pe[9],s3_exp3_8_domain_model$Pe[9],s3_exp3_9_domain_model$Pe[9],s3_exp3_10_domain_model$Pe[9],
             s3_exp3_domain_model$Pe[10],s3_exp3_1_domain_model$Pe[10],s3_exp3_2_domain_model$Pe[10],s3_exp3_3_domain_model$Pe[10],s3_exp3_4_domain_model$Pe[10],s3_exp3_5_domain_model$Pe[10],s3_exp3_6_domain_model$Pe[10],s3_exp3_7_domain_model$Pe[10],s3_exp3_8_domain_model$Pe[10],s3_exp3_9_domain_model$Pe[10],s3_exp3_10_domain_model$Pe[10],
             s3_exp3_domain_model$Pe[11],s3_exp3_1_domain_model$Pe[11],s3_exp3_2_domain_model$Pe[11],s3_exp3_3_domain_model$Pe[11],s3_exp3_4_domain_model$Pe[11],s3_exp3_5_domain_model$Pe[11],s3_exp3_6_domain_model$Pe[11],s3_exp3_7_domain_model$Pe[11],s3_exp3_8_domain_model$Pe[11],s3_exp3_9_domain_model$Pe[11],s3_exp3_10_domain_model$Pe[11],
             s3_exp3_domain_model$Pe[12],s3_exp3_1_domain_model$Pe[12],s3_exp3_2_domain_model$Pe[12],s3_exp3_3_domain_model$Pe[12],s3_exp3_4_domain_model$Pe[12],s3_exp3_5_domain_model$Pe[12],s3_exp3_6_domain_model$Pe[12],s3_exp3_7_domain_model$Pe[12],s3_exp3_8_domain_model$Pe[12],s3_exp3_9_domain_model$Pe[12],s3_exp3_10_domain_model$Pe[12])
lowerCI_exp3 <- c(s3_exp3_domain_model$lowerCI[8],s3_exp3_1_domain_model$lowerCI[8],s3_exp3_2_domain_model$lowerCI[8],s3_exp3_3_domain_model$lowerCI[8],s3_exp3_4_domain_model$lowerCI[8],s3_exp3_5_domain_model$lowerCI[8],s3_exp3_6_domain_model$lowerCI[8],s3_exp3_7_domain_model$lowerCI[8],s3_exp3_8_domain_model$lowerCI[8],s3_exp3_9_domain_model$lowerCI[8],s3_exp3_10_domain_model$lowerCI[8],
                  s3_exp3_domain_model$lowerCI[9],s3_exp3_1_domain_model$lowerCI[9],s3_exp3_2_domain_model$lowerCI[9],s3_exp3_3_domain_model$lowerCI[9],s3_exp3_4_domain_model$lowerCI[9],s3_exp3_5_domain_model$lowerCI[9],s3_exp3_6_domain_model$lowerCI[9],s3_exp3_7_domain_model$lowerCI[9],s3_exp3_8_domain_model$lowerCI[9],s3_exp3_9_domain_model$lowerCI[9],s3_exp3_10_domain_model$lowerCI[9],
                  s3_exp3_domain_model$lowerCI[10],s3_exp3_1_domain_model$lowerCI[10],s3_exp3_2_domain_model$lowerCI[10],s3_exp3_3_domain_model$lowerCI[10],s3_exp3_4_domain_model$lowerCI[10],s3_exp3_5_domain_model$lowerCI[10],s3_exp3_6_domain_model$lowerCI[10],s3_exp3_7_domain_model$lowerCI[10],s3_exp3_8_domain_model$lowerCI[10],s3_exp3_9_domain_model$lowerCI[10],s3_exp3_10_domain_model$lowerCI[10],
                  s3_exp3_domain_model$lowerCI[11],s3_exp3_1_domain_model$lowerCI[11],s3_exp3_2_domain_model$lowerCI[11],s3_exp3_3_domain_model$lowerCI[11],s3_exp3_4_domain_model$lowerCI[11],s3_exp3_5_domain_model$lowerCI[11],s3_exp3_6_domain_model$lowerCI[11],s3_exp3_7_domain_model$lowerCI[11],s3_exp3_8_domain_model$lowerCI[11],s3_exp3_9_domain_model$lowerCI[11],s3_exp3_10_domain_model$lowerCI[11],
                  s3_exp3_domain_model$lowerCI[12],s3_exp3_1_domain_model$lowerCI[12],s3_exp3_2_domain_model$lowerCI[12],s3_exp3_3_domain_model$lowerCI[12],s3_exp3_4_domain_model$lowerCI[12],s3_exp3_5_domain_model$lowerCI[12],s3_exp3_6_domain_model$lowerCI[12],s3_exp3_7_domain_model$lowerCI[12],s3_exp3_8_domain_model$lowerCI[12],s3_exp3_9_domain_model$lowerCI[12],s3_exp3_10_domain_model$lowerCI[12])
upperCI_exp3 <- c(s3_exp3_domain_model$upperCI[8],s3_exp3_1_domain_model$upperCI[8],s3_exp3_2_domain_model$upperCI[8],s3_exp3_3_domain_model$upperCI[8],s3_exp3_4_domain_model$upperCI[8],s3_exp3_5_domain_model$upperCI[8],s3_exp3_6_domain_model$upperCI[8],s3_exp3_7_domain_model$upperCI[8],s3_exp3_8_domain_model$upperCI[8],s3_exp3_9_domain_model$upperCI[8],s3_exp3_10_domain_model$upperCI[8],
                  s3_exp3_domain_model$upperCI[9],s3_exp3_1_domain_model$upperCI[9],s3_exp3_2_domain_model$upperCI[9],s3_exp3_3_domain_model$upperCI[9],s3_exp3_4_domain_model$upperCI[9],s3_exp3_5_domain_model$upperCI[9],s3_exp3_6_domain_model$upperCI[9],s3_exp3_7_domain_model$upperCI[9],s3_exp3_8_domain_model$upperCI[9],s3_exp3_9_domain_model$upperCI[9],s3_exp3_10_domain_model$upperCI[9],
                  s3_exp3_domain_model$upperCI[10],s3_exp3_1_domain_model$upperCI[10],s3_exp3_2_domain_model$upperCI[10],s3_exp3_3_domain_model$upperCI[10],s3_exp3_4_domain_model$upperCI[10],s3_exp3_5_domain_model$upperCI[10],s3_exp3_6_domain_model$upperCI[10],s3_exp3_7_domain_model$upperCI[10],s3_exp3_8_domain_model$upperCI[10],s3_exp3_9_domain_model$upperCI[10],s3_exp3_10_domain_model$upperCI[10],
                  s3_exp3_domain_model$upperCI[11],s3_exp3_1_domain_model$upperCI[11],s3_exp3_2_domain_model$upperCI[11],s3_exp3_3_domain_model$upperCI[11],s3_exp3_4_domain_model$upperCI[11],s3_exp3_5_domain_model$upperCI[11],s3_exp3_6_domain_model$upperCI[11],s3_exp3_7_domain_model$upperCI[11],s3_exp3_8_domain_model$upperCI[11],s3_exp3_9_domain_model$upperCI[11],s3_exp3_10_domain_model$upperCI[11],
                  s3_exp3_domain_model$upperCI[12],s3_exp3_1_domain_model$upperCI[12],s3_exp3_2_domain_model$upperCI[12],s3_exp3_3_domain_model$upperCI[12],s3_exp3_4_domain_model$upperCI[12],s3_exp3_5_domain_model$upperCI[12],s3_exp3_6_domain_model$upperCI[12],s3_exp3_7_domain_model$upperCI[12],s3_exp3_8_domain_model$upperCI[12],s3_exp3_9_domain_model$upperCI[12],s3_exp3_10_domain_model$upperCI[12])

dat_s3_3 <- data.frame(cont,BigFive,unlist(Pe_exp3),unlist(lowerCI_exp3),unlist(upperCI_exp3))

dat_s3_3$cont <- factor(dat_s3_3$cont, levels=unique(as.character(dat_s3_3$cont)))
dat_s3_3$BigFive <- factor(dat_s3_3$BigFive, levels=unique(as.character(dat_s3_3$BigFive)))

Figure_S1c <- ggplot(data=dat_s3_3, aes(x=unlist(cont), y=unlist(Pe_exp3), group=BigFive, color=BigFive)) +
  geom_point(stat="identity") +
  geom_errorbar(aes(ymin=unlist(lowerCI_exp3), ymax=unlist(upperCI_exp3)), width=0.3) +
  scale_color_manual(breaks = c("Agr", "Cns", "Opn", "Ext", "Neu"),
                     values=c("#27AE60", "#0000FF", "#FF00FF", "#FF0000", "#808080")) +
  ylim(-0.6, 0.62) +
  theme(axis.title.x = element_text(size=11), legend.title = element_blank(), legend.position = "top", 
        legend.key = element_rect(colour = NA, fill = NA), legend.text = element_text(size=11),
        axis.text = element_text(colour = "black", size=11), axis.title.y = element_text(size=11),
        panel.background = element_blank(), panel.grid = element_blank(), 
        axis.line = element_line(colour = "black")) +
  geom_hline(yintercept=0) +
  ylab("Big Five x Norms")+
  xlab("Recall Rate")

Figure_S1c <- annotate_figure(Figure_S1c, fig.lab = "c. Experiment 3", fig.lab.pos = c("top.left"), fig.lab.size = 11)

##Figure S1d. Experiments 1-3
Pe_exp123 <- c(s3_exp123_domain_model$Pe[8],s3_exp123_1_domain_model$Pe[8],s3_exp123_2_domain_model$Pe[8],s3_exp123_3_domain_model$Pe[8],s3_exp123_4_domain_model$Pe[8],s3_exp123_5_domain_model$Pe[8],s3_exp123_6_domain_model$Pe[8],s3_exp123_7_domain_model$Pe[8],s3_exp123_8_domain_model$Pe[8],s3_exp123_9_domain_model$Pe[8],s3_exp123_10_domain_model$Pe[8],
               s3_exp123_domain_model$Pe[9],s3_exp123_1_domain_model$Pe[9],s3_exp123_2_domain_model$Pe[9],s3_exp123_3_domain_model$Pe[9],s3_exp123_4_domain_model$Pe[9],s3_exp123_5_domain_model$Pe[9],s3_exp123_6_domain_model$Pe[9],s3_exp123_7_domain_model$Pe[9],s3_exp123_8_domain_model$Pe[9],s3_exp123_9_domain_model$Pe[9],s3_exp123_10_domain_model$Pe[9],
               s3_exp123_domain_model$Pe[10],s3_exp123_1_domain_model$Pe[10],s3_exp123_2_domain_model$Pe[10],s3_exp123_3_domain_model$Pe[10],s3_exp123_4_domain_model$Pe[10],s3_exp123_5_domain_model$Pe[10],s3_exp123_6_domain_model$Pe[10],s3_exp123_7_domain_model$Pe[10],s3_exp123_8_domain_model$Pe[10],s3_exp123_9_domain_model$Pe[10],s3_exp123_10_domain_model$Pe[10],
               s3_exp123_domain_model$Pe[11],s3_exp123_1_domain_model$Pe[11],s3_exp123_2_domain_model$Pe[11],s3_exp123_3_domain_model$Pe[11],s3_exp123_4_domain_model$Pe[11],s3_exp123_5_domain_model$Pe[11],s3_exp123_6_domain_model$Pe[11],s3_exp123_7_domain_model$Pe[11],s3_exp123_8_domain_model$Pe[11],s3_exp123_9_domain_model$Pe[11],s3_exp123_10_domain_model$Pe[11],
               s3_exp123_domain_model$Pe[12],s3_exp123_1_domain_model$Pe[12],s3_exp123_2_domain_model$Pe[12],s3_exp123_3_domain_model$Pe[12],s3_exp123_4_domain_model$Pe[12],s3_exp123_5_domain_model$Pe[12],s3_exp123_6_domain_model$Pe[12],s3_exp123_7_domain_model$Pe[12],s3_exp123_8_domain_model$Pe[12],s3_exp123_9_domain_model$Pe[12],s3_exp123_10_domain_model$Pe[12])
lowerCI_exp123 <- c(s3_exp123_domain_model$lowerCI[8],s3_exp123_1_domain_model$lowerCI[8],s3_exp123_2_domain_model$lowerCI[8],s3_exp123_3_domain_model$lowerCI[8],s3_exp123_4_domain_model$lowerCI[8],s3_exp123_5_domain_model$lowerCI[8],s3_exp123_6_domain_model$lowerCI[8],s3_exp123_7_domain_model$lowerCI[8],s3_exp123_8_domain_model$lowerCI[8],s3_exp123_9_domain_model$lowerCI[8],s3_exp123_10_domain_model$lowerCI[8],
                    s3_exp123_domain_model$lowerCI[9],s3_exp123_1_domain_model$lowerCI[9],s3_exp123_2_domain_model$lowerCI[9],s3_exp123_3_domain_model$lowerCI[9],s3_exp123_4_domain_model$lowerCI[9],s3_exp123_5_domain_model$lowerCI[9],s3_exp123_6_domain_model$lowerCI[9],s3_exp123_7_domain_model$lowerCI[9],s3_exp123_8_domain_model$lowerCI[9],s3_exp123_9_domain_model$lowerCI[9],s3_exp123_10_domain_model$lowerCI[9],
                    s3_exp123_domain_model$lowerCI[10],s3_exp123_1_domain_model$lowerCI[10],s3_exp123_2_domain_model$lowerCI[10],s3_exp123_3_domain_model$lowerCI[10],s3_exp123_4_domain_model$lowerCI[10],s3_exp123_5_domain_model$lowerCI[10],s3_exp123_6_domain_model$lowerCI[10],s3_exp123_7_domain_model$lowerCI[10],s3_exp123_8_domain_model$lowerCI[10],s3_exp123_9_domain_model$lowerCI[10],s3_exp123_10_domain_model$lowerCI[10],
                    s3_exp123_domain_model$lowerCI[11],s3_exp123_1_domain_model$lowerCI[11],s3_exp123_2_domain_model$lowerCI[11],s3_exp123_3_domain_model$lowerCI[11],s3_exp123_4_domain_model$lowerCI[11],s3_exp123_5_domain_model$lowerCI[11],s3_exp123_6_domain_model$lowerCI[11],s3_exp123_7_domain_model$lowerCI[11],s3_exp123_8_domain_model$lowerCI[11],s3_exp123_9_domain_model$lowerCI[11],s3_exp123_10_domain_model$lowerCI[11],
                    s3_exp123_domain_model$lowerCI[12],s3_exp123_1_domain_model$lowerCI[12],s3_exp123_2_domain_model$lowerCI[12],s3_exp123_3_domain_model$lowerCI[12],s3_exp123_4_domain_model$lowerCI[12],s3_exp123_5_domain_model$lowerCI[12],s3_exp123_6_domain_model$lowerCI[12],s3_exp123_7_domain_model$lowerCI[12],s3_exp123_8_domain_model$lowerCI[12],s3_exp123_9_domain_model$lowerCI[12],s3_exp123_10_domain_model$lowerCI[12])
upperCI_exp123 <- c(s3_exp123_domain_model$upperCI[8],s3_exp123_1_domain_model$upperCI[8],s3_exp123_2_domain_model$upperCI[8],s3_exp123_3_domain_model$upperCI[8],s3_exp123_4_domain_model$upperCI[8],s3_exp123_5_domain_model$upperCI[8],s3_exp123_6_domain_model$upperCI[8],s3_exp123_7_domain_model$upperCI[8],s3_exp123_8_domain_model$upperCI[8],s3_exp123_9_domain_model$upperCI[8],s3_exp123_10_domain_model$upperCI[8],
                    s3_exp123_domain_model$upperCI[9],s3_exp123_1_domain_model$upperCI[9],s3_exp123_2_domain_model$upperCI[9],s3_exp123_3_domain_model$upperCI[9],s3_exp123_4_domain_model$upperCI[9],s3_exp123_5_domain_model$upperCI[9],s3_exp123_6_domain_model$upperCI[9],s3_exp123_7_domain_model$upperCI[9],s3_exp123_8_domain_model$upperCI[9],s3_exp123_9_domain_model$upperCI[9],s3_exp123_10_domain_model$upperCI[9],
                    s3_exp123_domain_model$upperCI[10],s3_exp123_1_domain_model$upperCI[10],s3_exp123_2_domain_model$upperCI[10],s3_exp123_3_domain_model$upperCI[10],s3_exp123_4_domain_model$upperCI[10],s3_exp123_5_domain_model$upperCI[10],s3_exp123_6_domain_model$upperCI[10],s3_exp123_7_domain_model$upperCI[10],s3_exp123_8_domain_model$upperCI[10],s3_exp123_9_domain_model$upperCI[10],s3_exp123_10_domain_model$upperCI[10],
                    s3_exp123_domain_model$upperCI[11],s3_exp123_1_domain_model$upperCI[11],s3_exp123_2_domain_model$upperCI[11],s3_exp123_3_domain_model$upperCI[11],s3_exp123_4_domain_model$upperCI[11],s3_exp123_5_domain_model$upperCI[11],s3_exp123_6_domain_model$upperCI[11],s3_exp123_7_domain_model$upperCI[11],s3_exp123_8_domain_model$upperCI[11],s3_exp123_9_domain_model$upperCI[11],s3_exp123_10_domain_model$upperCI[11],
                    s3_exp123_domain_model$upperCI[12],s3_exp123_1_domain_model$upperCI[12],s3_exp123_2_domain_model$upperCI[12],s3_exp123_3_domain_model$upperCI[12],s3_exp123_4_domain_model$upperCI[12],s3_exp123_5_domain_model$upperCI[12],s3_exp123_6_domain_model$upperCI[12],s3_exp123_7_domain_model$upperCI[12],s3_exp123_8_domain_model$upperCI[12],s3_exp123_9_domain_model$upperCI[12],s3_exp123_10_domain_model$upperCI[12])

dat_s3_4 <- data.frame(cont,BigFive,unlist(Pe_exp123),unlist(lowerCI_exp123),unlist(upperCI_exp123))

dat_s3_4$cont <- factor(dat_s3_4$cont, levels=unique(as.character(dat_s3_4$cont)))
dat_s3_4$BigFive <- factor(dat_s3_4$BigFive, levels=unique(as.character(dat_s3_4$BigFive)))

Figure_S1d <- ggplot(data=dat_s3_4, aes(x=unlist(cont), y=unlist(Pe_exp123), group=BigFive, color=BigFive)) +
  geom_point(stat="identity") +
  geom_errorbar(aes(ymin=unlist(lowerCI_exp123), ymax=unlist(upperCI_exp123)), width=0.3) +
  scale_color_manual(breaks = c("Agr", "Cns", "Opn", "Ext", "Neu"),
                     values=c("#27AE60", "#0000FF", "#FF00FF", "#FF0000", "#808080")) +
  ylim(-0.6, 0.62) +
  theme(axis.title.x = element_text(size=11), legend.title = element_blank(), legend.position = "top", 
        legend.key = element_rect(colour = NA, fill = NA), legend.text = element_text(size=11),
        axis.text = element_text(colour = "black", size=11), axis.title.y = element_text(size=11),
        panel.background = element_blank(), panel.grid = element_blank(), 
        axis.line = element_line(colour = "black")) +
  geom_hline(yintercept=0) +
  ylab("Big Five x Norms")+
  xlab("Recall Rate")

Figure_S1d <- annotate_figure(Figure_S1d, fig.lab = "d. Experiments 1-3", fig.lab.pos = c("top.left"), fig.lab.size = 11)

##save Figures S1a-S1d as Figure S1
Figure_S1_top <- ggarrange(Figure_S1a, Figure_S1b, Figure_S1c, nrow=3, ncol=1, heights=c(7,7,7))
ggsave("Figure_S1_top.jpg", plot=Figure_S1_top, device="jpg", path=files_wd, width=160, height=210, units="mm", dpi=720)
ggsave("Figure_S1_bottom.jpg", plot=Figure_S1d, device="jpg", path=files_wd, width=160, height=70, units="mm", dpi=720)


#Sample sizes for recall rate = 100%
exp1_ps <- aggregate(agr ~ id, dat_s3_exp1_10, mean)
exp2_ps <- aggregate(agr ~ id, dat_s3_exp2_10, mean)
exp3_ps <- aggregate(agr ~ id, dat_s3_exp3_10, mean)
exp123_ps <- aggregate(agr ~ id, dat_s3_exp123_10, mean)
paste0("Sample size of Exp. 1 for recall rate of 100% = ", nrow(exp1_ps), " participants")
paste0("Sample size of Exp. 2 for recall rate of 100% = ", nrow(exp2_ps), " participants")
paste0("Sample size of Exp. 3 for recall rate of 100% = ", nrow(exp3_ps), " participants")
paste0("Sample size of Exp. 1-3 for recall rate of 100% = ", nrow(exp123_ps), " participants")


rm(list=setdiff(ls(), c("julia","julia_wd","files_wd","dat","dat_exp1","dat_exp2","dat_exp3","dat_exp123","dat_exp23")))


########## S4: All Participants' Preferences for Chinese Characters##########


#prepare data for mixed-effects models

##exclude pairs of Chinese characters and social values for which the sociocultural norm was not recalled correctly

###Experiment 1
dat_s4_exp1 <- subset(dat_exp1, subset = recall_correct == 1)
###Experiment 2
dat_s4_exp2 <- subset(dat_exp2, subset = recall_correct == 1)
###Experiment 3
dat_s4_exp3 <- subset(dat_exp3, subset = recall_correct == 1)
###Experiments 1-3
dat_s4_exp123 <- subset(dat_exp123, subset = recall_correct == 1)


#conduct mixed-effects models in Julia

##set working directory to Julia folder
setwd(julia_wd)

##convert grouping variable to factor and assign data to Julia

###Experiment 1
dat_s4_exp1$id <- as.factor(dat_s4_exp1$id)
julia_assign("dat_s4_exp1", dat_s4_exp1)
###Experiment 2
dat_s4_exp2$id <- as.factor(dat_s4_exp2$id)
julia_assign("dat_s4_exp2", dat_s4_exp2)
###Experiment 3
dat_s4_exp3$id <- as.factor(dat_s4_exp3$id)
julia_assign("dat_s4_exp3", dat_s4_exp3)
###Experiments 1-3
dat_s4_exp123$id <- as.factor(dat_s4_exp123$id)
julia_assign("dat_s4_exp123", dat_s4_exp123)

##conduct domains-as-predictors models

###Experiment 1
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s4_exp1)")
s4_exp1_domain_model <- julia_eval("s4_exp1_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiment 2
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
                + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s4_exp2)")
s4_exp2_domain_model <- julia_eval("s4_exp2_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiment 3
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
                + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s4_exp3)")
s4_exp3_domain_model <- julia_eval("s4_exp3_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiments 1-3
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s4_exp123)")
s4_exp123_domain_model <- julia_eval("s4_exp123_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")


#Table S4: Effects of the Big Five Domains on Personal Preferences Moderated by Sociocultural Norms With all Participants' Preferences for Chinese Characters
dat_table_s4 <- data.frame(s4_exp1_domain_model,s4_exp2_domain_model,s4_exp3_domain_model,s4_exp123_domain_model)

dat_table_s4[,13] <- NULL
dat_table_s4[,9] <- NULL
dat_table_s4[,5] <- NULL

dat_table_s4[,1] <- c("(1)   (Intercept)","(7)   Norms", "(2)   Agr", "(3)   Cns", "(4)   Opn", "(5)   Ext", "(6)   Neu", "(8)   Agr x Norms",
                      "(9)   Cns x Norms", "(10) Opn x Norms", "(11) Ext x Norms", "(12) Neu x Norms")

dat_table_s4[,2:13] <- sapply(dat_table_s4[,2:13], as.numeric)

format_numbers <- function(x){
  ifelse(abs(x) <= 0.005,  formatC(x, format = "e", digits = 0), formatC(x, format = "f", digits = 2))
}
dat_table_s4 <- dat_table_s4 %>% mutate_if(is.numeric, format_numbers)

dat_table_s4$ci <- paste0("[", dat_table_s4$lowerCI, ", ", dat_table_s4$upperCI, "]")
dat_table_s4$ci.1 <- paste0("[", dat_table_s4$lowerCI.1, ", ", dat_table_s4$upperCI.1, "]")
dat_table_s4$ci.2 <- paste0("[", dat_table_s4$lowerCI.2, ", ", dat_table_s4$upperCI.2, "]")
dat_table_s4$ci.3 <- paste0("[", dat_table_s4$lowerCI.3, ", ", dat_table_s4$upperCI.3, "]")

dat_table_s4 <- dat_table_s4[c(1,3:7,2,8:12),]

dat_table_s4$blank <- NA
dat_table_s4$blank.1 <- NA
dat_table_s4$blank.2 <- NA

dat_table_s4 <- dat_table_s4[c("predictor","Pe","ci","blank","Pe.1","ci.1","blank.1","Pe.2","ci.2","blank.2","Pe.3","ci.3")]

col_keys <- c("predictor","Pe","ci","blank","Pe.1","ci.1","blank.1","Pe.2","ci.2","blank.2","Pe.3","ci.3")
head1 <- c("Predictor","Experiment 1","Experiment 1","","Experiment 2","Experiment 2","","Experiment 3","Experiment 3","","Experiments 1-3","Experiments 1-3")
head2 <- c("","Estimate","  95% CI","","Estimate","  95% CI","","Estimate","  95% CI","","Estimate","  95% CI")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_s4)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:12, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:3, part="header")
tbl <- merge_at(tbl, i=1, j=5:6, part="header")
tbl <- merge_at(tbl, i=1, j=8:9, part="header")
tbl <- merge_at(tbl, i=1, j=11:12, part="header")
tbl <- hline(tbl, i=1, j=c(2:3,5:6,8:9,11:12), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:12, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("predictor"), align="left", part="body")
tbl <- width(tbl, j =~ predictor, width=1.75)
tbl <- width(tbl, j =~ Pe + Pe.1 + Pe.2 + Pe.3, width=.75)
tbl <- width(tbl, j =~ ci + ci.1 + ci.2 + ci.3, width=1.2)
tbl <- width(tbl, j =~ blank + blank.1 + blank.2, width=.1)
tbl <- colformat_lgl(tbl, j =~ blank + blank.1 + blank.2, na_str="")
tbl <- height_all(tbl, height=.1, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S4.docx")


rm(list=setdiff(ls(), c("julia","julia_wd","files_wd","dat","dat_exp1","dat_exp2","dat_exp3","dat_exp123","dat_exp23")))


########## S5: Separate Statistical Models for Predictors ##########


#prepare data for mixed-effects models

##exclude pairs of Chinese characters and social values for which the sociocultural norm was not recalled correctly

###Experiment 1
dat_s5_exp1 <- subset(dat_exp1, subset = recall_correct == 1)
###Experiment 2
dat_s5_exp2 <- subset(dat_exp2, subset = recall_correct == 1)
###Experiment 3
dat_s5_exp3 <- subset(dat_exp3, subset = recall_correct == 1)
###Experiments 1-3
dat_s5_exp123 <- subset(dat_exp123, subset = recall_correct == 1)
###Experiments 2-3
dat_s5_exp23 <- subset(dat_exp23, subset = recall_correct == 1)

##exclude pairs of Chinese characters for participants who knew the meaning of at least one of the characters 

###Experiment 1
dat_s5_exp1 <- subset(dat_s5_exp1, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
###Experiment 2 
dat_s5_exp2 <- subset(dat_s5_exp2, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
###Experiment 3
dat_s5_exp3 <- subset(dat_s5_exp3, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
###Experiments 1-3
dat_s5_exp123 <- subset(dat_s5_exp123, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
###Experiments 2-3
dat_s5_exp23 <- subset(dat_s5_exp23, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))


#conduct mixed-effects models in Julia

##set working directory to Julia
setwd(julia_wd)

##convert grouping variable to factor and assign data to Julia

###Experiment 1
dat_s5_exp1$id <- as.factor(dat_s5_exp1$id)
julia_assign("dat_s5_exp1", dat_s5_exp1)
###Experiment 2
dat_s5_exp2$id <- as.factor(dat_s5_exp2$id)
julia_assign("dat_s5_exp2", dat_s5_exp2)
###Experiment 3
dat_s5_exp3$id <- as.factor(dat_s5_exp3$id)
julia_assign("dat_s5_exp3", dat_s5_exp3)
###Experiments 1-3
dat_s5_exp123$id <- as.factor(dat_s5_exp123$id)
julia_assign("dat_s5_exp123", dat_s5_exp123)
###Experiments 2-3
dat_s5_exp23$id <- as.factor(dat_s5_exp23$id)
julia_assign("dat_s5_exp23", dat_s5_exp23)

##conduct domains-as-predictors models

###Experiment 1
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + agr_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp1)")
s5_exp1_agr_model <- julia_eval("s5_exp1_agr_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + cns_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp1)")
s5_exp1_cns_model <- julia_eval("s5_exp1_cns_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + opn_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp1)")
s5_exp1_opn_model <- julia_eval("s5_exp1_opn_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + ext_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp1)")
s5_exp1_ext_model <- julia_eval("s5_exp1_ext_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + neu_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp1)")
s5_exp1_neu_model <- julia_eval("s5_exp1_neu_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiment 2
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + agr_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_agr_model <- julia_eval("s5_exp2_agr_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + cns_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_cns_model <- julia_eval("s5_exp2_cns_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + opn_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_opn_model <- julia_eval("s5_exp2_opn_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + ext_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_ext_model <- julia_eval("s5_exp2_ext_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + neu_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_neu_model <- julia_eval("s5_exp2_neu_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiment 3
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + agr_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_agr_model <- julia_eval("s5_exp3_agr_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + cns_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_cns_model <- julia_eval("s5_exp3_cns_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + opn_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_opn_model <- julia_eval("s5_exp3_opn_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + ext_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_ext_model <- julia_eval("s5_exp3_ext_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + neu_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_neu_model <- julia_eval("s5_exp3_neu_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiments 1-3
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + agr_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp123)")
s5_exp123_agr_model <- julia_eval("s5_exp123_agr_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + cns_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp123)")
s5_exp123_cns_model <- julia_eval("s5_exp123_cns_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + opn_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp123)")
s5_exp123_opn_model <- julia_eval("s5_exp123_opn_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + ext_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp123)")
s5_exp123_ext_model <- julia_eval("s5_exp123_ext_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + neu_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp123)")
s5_exp123_neu_model <- julia_eval("s5_exp123_neu_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")

##conduct facets-as-predictors models

###Experiment 2
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + compassion_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_comp_model <- julia_eval("s5_exp2_comp_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + respectfulness_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_respect_model <- julia_eval("s5_exp2_respect_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + trust_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_trust_model <- julia_eval("s5_exp2_trust_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + organization_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_orga_model <- julia_eval("s5_exp2_orga_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + productiveness_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_prod_model <- julia_eval("s5_exp2_prod_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + responsibility_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_respo_model <- julia_eval("s5_exp2_respo_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + aesthetic_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_aest_model <- julia_eval("s5_exp2_aest_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + creative_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_crea_model <- julia_eval("s5_exp2_crea_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + intellectual_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_intel_model <- julia_eval("s5_exp2_intel_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + assertiveness_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_assert_model <- julia_eval("s5_exp2_assert_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + energy_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_energy_model <- julia_eval("s5_exp2_energy_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociability_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_socia_model <- julia_eval("s5_exp2_socia_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + anxiety_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_anxi_model <- julia_eval("s5_exp2_anxi_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + depression_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_depr_model <- julia_eval("s5_exp2_depr_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + emotional_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp2)")
s5_exp2_emotion_model <- julia_eval("s5_exp2_emotion_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiment 3
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + compassion_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_comp_model <- julia_eval("s5_exp3_comp_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + respectfulness_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_respect_model <- julia_eval("s5_exp3_respect_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + trust_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_trust_model <- julia_eval("s5_exp3_trust_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + organization_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_orga_model <- julia_eval("s5_exp3_orga_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + productiveness_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_prod_model <- julia_eval("s5_exp3_prod_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + responsibility_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_respo_model <- julia_eval("s5_exp3_respo_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + aesthetic_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_aest_model <- julia_eval("s5_exp3_aest_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + creative_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_crea_model <- julia_eval("s5_exp3_crea_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + intellectual_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_intel_model <- julia_eval("s5_exp3_intel_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + assertiveness_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_assert_model <- julia_eval("s5_exp3_assert_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + energy_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_energy_model <- julia_eval("s5_exp3_energy_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociability_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_socia_model <- julia_eval("s5_exp3_socia_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + anxiety_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_anxi_model <- julia_eval("s5_exp3_anxi_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + depression_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_depr_model <- julia_eval("s5_exp3_depr_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + emotional_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_emotion_model <- julia_eval("s5_exp3_emotion_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiments 2-3
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + compassion_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp23)")
s5_exp23_comp_model <- julia_eval("s5_exp23_comp_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + respectfulness_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp23)")
s5_exp23_respect_model <- julia_eval("s5_exp23_respect_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + trust_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp23)")
s5_exp23_trust_model <- julia_eval("s5_exp23_trust_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + organization_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp23)")
s5_exp23_orga_model <- julia_eval("s5_exp23_orga_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + productiveness_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp23)")
s5_exp23_prod_model <- julia_eval("s5_exp23_prod_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + responsibility_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp23)")
s5_exp23_respo_model <- julia_eval("s5_exp23_respo_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + aesthetic_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp23)")
s5_exp23_aest_model <- julia_eval("s5_exp23_aest_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + creative_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp23)")
s5_exp23_crea_model <- julia_eval("s5_exp23_crea_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + intellectual_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp23)")
s5_exp23_intel_model <- julia_eval("s5_exp23_intel_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + assertiveness_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp23)")
s5_exp23_assert_model <- julia_eval("s5_exp23_assert_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + energy_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp23)")
s5_exp23_energy_model <- julia_eval("s5_exp23_energy_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociability_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp23)")
s5_exp23_socia_model <- julia_eval("s5_exp23_socia_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + anxiety_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp23)")
s5_exp23_anxi_model <- julia_eval("s5_exp23_anxi_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + depression_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp23)")
s5_exp23_depr_model <- julia_eval("s5_exp23_depr_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + emotional_c * sociocultural_norm + (1 + sociocultural_norm | id)),  dat_s5_exp23)")
s5_exp23_emotion_model <- julia_eval("s5_exp23_emotion_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")

##conduct externals-as-predictors model in Experiment 3
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + rational_c * sociocultural_norm
                + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_rational_model <- julia_eval("s5_exp3_rational_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + nfc_c * sociocultural_norm
                + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_nfc_model <- julia_eval("s5_exp3_nfc_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + attention_c * sociocultural_norm
                + (1 + sociocultural_norm | id)),  dat_s5_exp3)")
s5_exp3_attention_model <- julia_eval("s5_exp3_attention_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")


#Table S5.1: Domains-as-Predictors Model Separately for Each Big Five Domain
dat_table_s5_exp1 <- rbind(s5_exp1_agr_model,s5_exp1_cns_model,s5_exp1_opn_model,s5_exp1_ext_model,s5_exp1_neu_model)
dat_table_s5_exp2 <- rbind(s5_exp2_agr_model,s5_exp2_cns_model,s5_exp2_opn_model,s5_exp2_ext_model,s5_exp2_neu_model)
dat_table_s5_exp3 <- rbind(s5_exp3_agr_model,s5_exp3_cns_model,s5_exp3_opn_model,s5_exp3_ext_model,s5_exp3_neu_model)
dat_table_s5_exp123 <- rbind(s5_exp123_agr_model,s5_exp123_cns_model,s5_exp123_opn_model,s5_exp123_ext_model,s5_exp123_neu_model)
dat_table_s5_1 <- data.frame(dat_table_s5_exp1,dat_table_s5_exp2,dat_table_s5_exp3,dat_table_s5_exp123)

dat_table_s5_1[,13] <- NULL
dat_table_s5_1[,9] <- NULL
dat_table_s5_1[,5] <- NULL

dat_table_s5_1[,1] <- c("   (Intercept)","   Agr","   Norms","   Agr x Norms","   (Intercept)","   Cns","   Norms","   Cns x Norms","   (Intercept)","   Opn","   Norms","   Opn x Norms",
                        "   (Intercept)","   Ext","   Norms","   Ext x Norms","   (Intercept)","   Neu","   Norms","   Neu x Norms")

dat_table_s5_1[,2:13] <- sapply(dat_table_s5_1[,2:13], as.numeric)

format_numbers <- function(x){
  ifelse(abs(x) <= 0.005,  formatC(x, format = "e", digits = 0), formatC(x, format = "f", digits = 2))
}
dat_table_s5_1 <- dat_table_s5_1 %>% mutate_if(is.numeric, format_numbers)

dat_table_s5_1$ci <- paste0("[", dat_table_s5_1$lowerCI, ", ", dat_table_s5_1$upperCI, "]")
dat_table_s5_1$ci.1 <- paste0("[", dat_table_s5_1$lowerCI.1, ", ", dat_table_s5_1$upperCI.1, "]")
dat_table_s5_1$ci.2 <- paste0("[", dat_table_s5_1$lowerCI.2, ", ", dat_table_s5_1$upperCI.2, "]")
dat_table_s5_1$ci.3 <- paste0("[", dat_table_s5_1$lowerCI.3, ", ", dat_table_s5_1$upperCI.3, "]")

dat_table_s5_1$blank <- NA
dat_table_s5_1$blank.1 <- NA
dat_table_s5_1$blank.2 <- NA

dat_table_s5_1 <- dat_table_s5_1[c("predictor","Pe","ci","blank","Pe.1","ci.1","blank.1","Pe.2","ci.2","blank.2","Pe.3","ci.3")]

dat_table_s5_1[21,1] <- "Model 1"
dat_table_s5_1[22,1] <- "Model 2"
dat_table_s5_1[23,1] <- "Model 3"
dat_table_s5_1[24,1] <- "Model 4"
dat_table_s5_1[25,1] <- "Model 5"

dat_table_s5_1 <- dat_table_s5_1[c(21,1:4,22,5:8,23,9:12,24,13:16,25,17:20),]

col_keys <- c("predictor","Pe","ci","blank","Pe.1","ci.1","blank.1","Pe.2","ci.2","blank.2","Pe.3","ci.3")
head1 <- c("Predictor","Experiment 1","Experiment 1","","Experiment 2","Experiment 2","","Experiment 3","Experiment 3","","Experiments 1-3","Experiments 1-3")
head2 <- c("","Estimate","  95% CI","","Estimate","  95% CI","","Estimate","  95% CI","","Estimate","  95% CI")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_s5_1)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:12, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:3, part="header")
tbl <- merge_at(tbl, i=1, j=5:6, part="header")
tbl <- merge_at(tbl, i=1, j=8:9, part="header")
tbl <- merge_at(tbl, i=1, j=11:12, part="header")
tbl <- hline(tbl, i=1, j=c(2:3,5:6,8:9,11:12), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:12, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=11, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("predictor"), align="left", part="body")
tbl <- width(tbl, j =~ predictor, width=1.75)
tbl <- width(tbl, j =~ Pe + Pe.1 + Pe.2 + Pe.3, width=.75)
tbl <- width(tbl, j =~ ci + ci.1 + ci.2 + ci.3, width=1.2)
tbl <- width(tbl, j =~ blank + blank.1 + blank.2, width=.1)
tbl <- colformat_lgl(tbl, j =~ blank + blank.1 + blank.2, na_str="")
tbl <- height_all(tbl, height=.1, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S5_1.docx")
rm(tbl,head,doc,format_numbers)


#Table S5.2: Facets-as-Predictors Model Separately for Each Big Five Facet
dat_table_s5_2_exp2 <- rbind(s5_exp2_comp_model,s5_exp2_respect_model,s5_exp2_trust_model,s5_exp2_orga_model,s5_exp2_prod_model,
                           s5_exp2_respo_model,s5_exp2_aest_model,s5_exp2_crea_model,s5_exp2_intel_model,s5_exp2_assert_model,
                           s5_exp2_energy_model,s5_exp2_socia_model,s5_exp2_anxi_model,s5_exp2_depr_model,s5_exp2_emotion_model)
dat_table_s5_2_exp3 <- rbind(s5_exp3_comp_model,s5_exp3_respect_model,s5_exp3_trust_model,s5_exp3_orga_model,s5_exp3_prod_model,
                           s5_exp3_respo_model,s5_exp3_aest_model,s5_exp3_crea_model,s5_exp3_intel_model,s5_exp3_assert_model,
                           s5_exp3_energy_model,s5_exp3_socia_model,s5_exp3_anxi_model,s5_exp3_depr_model,s5_exp3_emotion_model)
dat_table_s5_2_exp23 <- rbind(s5_exp23_comp_model,s5_exp23_respect_model,s5_exp23_trust_model,s5_exp23_orga_model,s5_exp23_prod_model,
                            s5_exp23_respo_model,s5_exp23_aest_model,s5_exp23_crea_model,s5_exp23_intel_model,s5_exp23_assert_model,
                            s5_exp23_energy_model,s5_exp23_socia_model,s5_exp23_anxi_model,s5_exp23_depr_model,s5_exp23_emotion_model)
dat_table_s5_2 <- data.frame(dat_table_s5_2_exp2,dat_table_s5_2_exp3,dat_table_s5_2_exp23)

dat_table_s5_2[,9] <- NULL
dat_table_s5_2[,5] <- NULL

dat_table_s5_2[,1] <- c("   (Intercept)", "   A-Compassion","   Norms", "   A-Compassion x Norms",
                        "   (Intercept)", "   A-Respectfulness","   Norms", "   A-Respectfulness x Norms",
                        "   (Intercept)", "   A-Trust","   Norms", "   A-Trust x Norms",
                        "   (Intercept)", "   C-Organization","   Norms", "   C-Organization x Norms",
                        "   (Intercept)", "   C-Productiveness","   Norms", "   C-Productiveness x Norms",
                        "   (Intercept)", "   C-Responsibility","   Norms", "   C-Responsibility x Norms",
                        "   (Intercept)", "   O-Aesthetic Sensitivity","   Norms", "   O-Aesthetic Sensitivity x Norms",
                        "   (Intercept)", "   O-Creative Imagination","   Norms", "   O-Creative Imagination x Norms",
                        "   (Intercept)", "   O-Intellectual Curiosity","   Norms", "   O-Intellectual Curiosity x Norms",
                        "   (Intercept)", "   E-Assertiveness","   Norms", "   E-Assertiveness x Norms",
                        "   (Intercept)", "   E-Energy Level","   Norms", "   E-Energy Level x Norms",
                        "   (Intercept)", "   E-Sociability","   Norms", "   E-Sociability x Norms",
                        "   (Intercept)", "   N-Anxiety","   Norms", "   N-Anxiety x Norms",
                        "   (Intercept)", "   N-Depression","   Norms", "   N-Depression x Norms",
                        "   (Intercept)", "   N-Emotional Volatility","   Norms", "   N-Emotional Volatility x Norms")

dat_table_s5_2[,2:10] <- sapply(dat_table_s5_2[,2:10], as.numeric)

format_numbers <- function(x){
  ifelse(abs(x) <= 0.005,  formatC(x, format = "e", digits = 0), formatC(x, format = "f", digits = 2))
}
dat_table_s5_2 <- dat_table_s5_2 %>% mutate_if(is.numeric, format_numbers)

dat_table_s5_2$ci <- paste0("[", dat_table_s5_2$lowerCI, ", ", dat_table_s5_2$upperCI, "]")
dat_table_s5_2$ci.1 <- paste0("[", dat_table_s5_2$lowerCI.1, ", ", dat_table_s5_2$upperCI.1, "]")
dat_table_s5_2$ci.2 <- paste0("[", dat_table_s5_2$lowerCI.2, ", ", dat_table_s5_2$upperCI.2, "]")

dat_table_s5_2$blank <- NA
dat_table_s5_2$blank.1 <- NA

dat_table_s5_2 <- dat_table_s5_2[c("predictor","Pe","ci","blank","Pe.1","ci.1","blank.1","Pe.2","ci.2")]

dat_table_s5_2[61,1] <- "Model 1"
dat_table_s5_2[62,1] <- "Model 2"
dat_table_s5_2[63,1] <- "Model 3"
dat_table_s5_2[64,1] <- "Model 4"
dat_table_s5_2[65,1] <- "Model 5"
dat_table_s5_2[66,1] <- "Model 6"
dat_table_s5_2[67,1] <- "Model 7"
dat_table_s5_2[68,1] <- "Model 8"
dat_table_s5_2[69,1] <- "Model 9"
dat_table_s5_2[70,1] <- "Model 10"
dat_table_s5_2[71,1] <- "Model 11"
dat_table_s5_2[72,1] <- "Model 12"
dat_table_s5_2[73,1] <- "Model 13"
dat_table_s5_2[74,1] <- "Model 14"
dat_table_s5_2[75,1] <- "Model 15"

dat_table_s5_2 <- dat_table_s5_2[c(61,1:4,62,5:8,63,9:12,64,13:16,65,17:20,66,21:24,67,25:28,68,29:32,69,33:36,70,37:40,71,41:44,72,45:48,73,49:52,74,53:56,75,57:60),]

col_keys <- c("predictor","Pe","ci","blank","Pe.1","ci.1","blank.1","Pe.2","ci.2")
head1 <- c("Predictor","Experiment 2","Experiment 2","","Experiment 3","Experiment 3","","Experiments 2-3","Experiments 2-3")
head2 <- c("","Estimate","  95% CI","","Estimate","  95% CI","","Estimate","  95% CI")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_s5_2)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:9, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:3, part="header")
tbl <- merge_at(tbl, i=1, j=5:6, part="header")
tbl <- merge_at(tbl, i=1, j=8:9, part="header")
tbl <- hline(tbl, i=1, j=c(2:3,5:6,8:9), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:9, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=11, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("predictor"), align="left", part="body")
tbl <- width(tbl, j =~ predictor, width=2.8)
tbl <- width(tbl, j =~ Pe + Pe.1 + Pe.2, width=.75)
tbl <- width(tbl, j =~ ci + ci.1 + ci.2, width=1.2)
tbl <- width(tbl, j =~ blank + blank.1, width=.1)
tbl <- colformat_lgl(tbl, j =~ blank + blank.1, na_str="")
tbl <- height_all(tbl, height=.1, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S5_2.docx")
rm(tbl,head,doc,format_numbers)


#Table S5.3: Externals-as-Predictors Model Separately for Each External Process Variable of Experiment 3
dat_table_s5_3 <- data.frame(rbind(s5_exp3_rational_model,s5_exp3_nfc_model,s5_exp3_attention_model))

dat_table_s5_3[,1] <- c("   (Intercept)","   Rational Thought","   Norms","   Rational Thought x Norms","   (Intercept)","   Need for Cognition","   Norms","   Need for Cognition x Norms",
                        "   (Intercept)","   Social Attention","   Norms","   Social Attention x Norms")

dat_table_s5_3[,2:4] <- sapply(dat_table_s5_3[,2:4], as.numeric)

format_numbers <- function(x){
  ifelse(abs(x) <= 0.005,  formatC(x, format = "e", digits = 0), formatC(x, format = "f", digits = 2))
}
dat_table_s5_3 <- dat_table_s5_3 %>% mutate_if(is.numeric, format_numbers)

dat_table_s5_3$ci <- paste0("[", dat_table_s5_3$lowerCI, ", ", dat_table_s5_3$upperCI, "]")

dat_table_s5_3 <- dat_table_s5_3[c("predictor","Pe","ci")]

dat_table_s5_3[13,1] <- "Model 1"
dat_table_s5_3[14,1] <- "Model 2"
dat_table_s5_3[15,1] <- "Model 3"

dat_table_s5_3 <- dat_table_s5_3[c(13,1:4,14,5:8,15,9:12),]

col_keys <- c("predictor","Pe","ci")
head1 <- c("Predictor","Estimate","  95% CI")
head <- data.frame(col_keys,head1,stringsAsFactors = FALSE)
rm(col_keys,head1)

tbl <- flextable(dat_table_s5_3)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:3, border=fp_border(width=2), part="header")
tbl <- hline(tbl, i=1, j=1:3, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("predictor"), align="left", part="body")
tbl <- width(tbl, j =~ predictor, width=2.5)
tbl <- width(tbl, j =~ Pe, width=.75)
tbl <- width(tbl, j =~ ci, width=1.2)
tbl <- height_all(tbl, height=.1, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S5_3.docx")


rm(list=setdiff(ls(), c("julia","julia_wd","files_wd","dat","dat_exp1","dat_exp2","dat_exp3","dat_exp123","dat_exp23")))


########## S6: Continuous Variable for Sociocultural Norms ##########


#prepare data for mixed-effects models

##exclude pairs of Chinese characters and social values for which the sociocultural norm was not recalled correctly

###Experiment 1
dat_s6_exp1 <- subset(dat_exp1, subset = recall_correct == 1)
###Experiment 2
dat_s6_exp2 <- subset(dat_exp2, subset = recall_correct == 1)
###Experiment 3
dat_s6_exp3 <- subset(dat_exp3, subset = recall_correct == 1)
###Experiments 1-3
dat_s6_exp123 <- subset(dat_exp123, subset = recall_correct == 1)

##exclude pairs of Chinese characters for participants who knew the meaning of at least one of the characters 

###Experiment 1
dat_s6_exp1 <- subset(dat_s6_exp1, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
###Experiment 2 
dat_s6_exp2 <- subset(dat_s6_exp2, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
###Experiment 3
dat_s6_exp3 <- subset(dat_s6_exp3, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
###Experiments 1-3
dat_s6_exp123 <- subset(dat_s6_exp123, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))

##group-mean centering of continuous variable for sociocultural norms (level-1 predictor)

###Experiment 1
dat_s6_exp1$percentage_right_grp <- dat_s6_exp1$percentage_right - (ave(dat_s6_exp1$percentage_right, dat_s6_exp1$id))
###Experiment 2
dat_s6_exp2$percentage_right_grp <- dat_s6_exp2$percentage_right - (ave(dat_s6_exp2$percentage_right, dat_s6_exp2$id))
###Experiment 3
dat_s6_exp3$percentage_right_grp <- dat_s6_exp3$percentage_right - (ave(dat_s6_exp3$percentage_right, dat_s6_exp3$id))
###Experiments 1-3
dat_s6_exp123$percentage_right_grp <- dat_s6_exp123$percentage_right - (ave(dat_s6_exp123$percentage_right, dat_s6_exp123$id))


#conduct mixed-effects models in Julia

##set working directory to Julia folder
setwd(julia_wd)

##convert grouping variable to factor and assign data to Julia

###Experiment 1
dat_s6_exp1$id <- as.factor(dat_s6_exp1$id)
julia_assign("dat_s6_exp1", dat_s6_exp1)
###Experiment 2
dat_s6_exp2$id <- as.factor(dat_s6_exp2$id)
julia_assign("dat_s6_exp2", dat_s6_exp2)
###Experiment 3
dat_s6_exp3$id <- as.factor(dat_s6_exp3$id)
julia_assign("dat_s6_exp3", dat_s6_exp3)
###Experiments 1-3
dat_s6_exp123$id <- as.factor(dat_s6_exp123$id)
julia_assign("dat_s6_exp123", dat_s6_exp123)

##conduct domains-as-predictors models

###Experiment 1
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + percentage_right_grp * agr_c + percentage_right_grp * cns_c + percentage_right_grp * opn_c
                + percentage_right_grp * ext_c + percentage_right_grp * neu_c + (1 + percentage_right_grp | id)),  dat_s6_exp1)")
s6_exp1_domain_model <- julia_eval("s6_exp1_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
                :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiment 2
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + percentage_right_grp * agr_c + percentage_right_grp * cns_c + percentage_right_grp * opn_c
                + percentage_right_grp * ext_c + percentage_right_grp * neu_c + (1 + percentage_right_grp | id)),  dat_s6_exp2)")
s6_exp2_domain_model <- julia_eval("s6_exp2_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
                :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiment 3
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + percentage_right_grp * agr_c + percentage_right_grp * cns_c + percentage_right_grp * opn_c
                + percentage_right_grp * ext_c + percentage_right_grp * neu_c + (1 + percentage_right_grp | id)),  dat_s6_exp3)")
s6_exp3_domain_model <- julia_eval("s6_exp3_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
                :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiments 1-3
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + percentage_right_grp * agr_c + percentage_right_grp * cns_c + percentage_right_grp * opn_c
                + percentage_right_grp * ext_c + percentage_right_grp * neu_c + (1 + percentage_right_grp | id)),  dat_s6_exp123)")
s6_exp123_domain_model <- julia_eval("s6_exp123_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
                :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")


#Table S6: Effects of the Big Five Domains on Personal Preferences Moderated by the Continuous Variable for Sociocultural Norms
dat_table_s6 <- data.frame(s6_exp1_domain_model,s6_exp2_domain_model,s6_exp3_domain_model,s6_exp123_domain_model)

dat_table_s6[,13] <- NULL
dat_table_s6[,9] <- NULL
dat_table_s6[,5] <- NULL

dat_table_s6[,1] <- c("(1)   (Intercept)","(7)   Norms", "(2)   Agr", "(3)   Cns", "(4)   Opn", "(5)   Ext", "(6)   Neu", "(8)   Agr x Norms",
                      "(9)   Cns x Norms", "(10) Opn x Norms", "(11) Ext x Norms", "(12) Neu x Norms")

dat_table_s6[,2:13] <- sapply(dat_table_s6[,2:13], as.numeric)

format_numbers <- function(x){
  ifelse(abs(x) <= 0.005,  formatC(x, format = "e", digits = 0), formatC(x, format = "f", digits = 2))
}
dat_table_s6 <- dat_table_s6 %>% mutate_if(is.numeric, format_numbers)

dat_table_s6$ci <- paste0("[", dat_table_s6$lowerCI, ", ", dat_table_s6$upperCI, "]")
dat_table_s6$ci.1 <- paste0("[", dat_table_s6$lowerCI.1, ", ", dat_table_s6$upperCI.1, "]")
dat_table_s6$ci.2 <- paste0("[", dat_table_s6$lowerCI.2, ", ", dat_table_s6$upperCI.2, "]")
dat_table_s6$ci.3 <- paste0("[", dat_table_s6$lowerCI.3, ", ", dat_table_s6$upperCI.3, "]")

dat_table_s6 <- dat_table_s6[c(1,3:7,2,8:12),]

dat_table_s6$blank <- NA
dat_table_s6$blank.1 <- NA
dat_table_s6$blank.2 <- NA

dat_table_s6 <- dat_table_s6[c("predictor","Pe","ci","blank","Pe.1","ci.1","blank.1","Pe.2","ci.2","blank.2","Pe.3","ci.3")]

col_keys <- c("predictor","Pe","ci","blank","Pe.1","ci.1","blank.1","Pe.2","ci.2","blank.2","Pe.3","ci.3")
head1 <- c("Predictor","Experiment 1","Experiment 1","","Experiment 2","Experiment 2","","Experiment 3","Experiment 3","","Experiments 1-3","Experiments 1-3")
head2 <- c("","Estimate","  95% CI","","Estimate","  95% CI","","Estimate","  95% CI","","Estimate","  95% CI")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_s6)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:12, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:3, part="header")
tbl <- merge_at(tbl, i=1, j=5:6, part="header")
tbl <- merge_at(tbl, i=1, j=8:9, part="header")
tbl <- merge_at(tbl, i=1, j=11:12, part="header")
tbl <- hline(tbl, i=1, j=c(2:3,5:6,8:9,11:12), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:12, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("predictor"), align="left", part="body")
tbl <- width(tbl, j =~ predictor, width=1.75)
tbl <- width(tbl, j =~ Pe + Pe.1 + Pe.2 + Pe.3, width=.75)
tbl <- width(tbl, j =~ ci + ci.1 + ci.2 + ci.3, width=1.2)
tbl <- width(tbl, j =~ blank + blank.1 + blank.2, width=.1)
tbl <- colformat_lgl(tbl, j =~ blank + blank.1 + blank.2, na_str="")
tbl <- height_all(tbl, height=.1, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S6.docx")


rm(list=setdiff(ls(), c("julia","julia_wd","files_wd","dat","dat_exp1","dat_exp2","dat_exp3","dat_exp123","dat_exp23")))


########## S7: Indirect-Effects Analyses With Latent Scores for the Big Five Domains ##########


#compute latent scores for the BFI-2 domains

##latent variable models
m.agr_no_trust <- '
  agr_lat =~ a*compassion_c + a*respectfulness_c
  agr_lat ~~ 1*agr_lat'
m.cns_no_respo <- '
  cns_lat =~ c*organization_c + c*productiveness_c
  cns_lat ~~ 1*cns_lat'
m.cns <- '
  cns_lat =~ organization_c + productiveness_c + responsibility_c'
m.opn_no_intel <- '
  opn_lat =~ o*aesthetic_c + o*creative_c
  opn_lat ~~ 1*opn_lat'
m.opn <- '
  opn_lat =~ aesthetic_c + creative_c + intellectual_c'
m.ext <- '
  ext_lat =~ assertiveness_c + energy_c + sociability_c'
m.neu <- '
  neu_lat =~ anxiety_c + depression_c + emotional_c'

##fit latent variable models

###Experiment 2
fit.agr.exp2 <- sem(m.agr_no_trust, data = dat_exp2)
fscores.agr.exp2 <- lavPredict(fit.agr.exp2) 
agr.exp2 <- fscores.agr.exp2[,1]
fit.cns.exp2 <- sem(m.cns_no_respo, data = dat_exp2)
fscores.cns.exp2 <- lavPredict(fit.cns.exp2) 
cns.exp2 <- fscores.cns.exp2[,1]
fit.opn.exp2 <- sem(m.opn_no_intel, data = dat_exp2)
fscores.opn.exp2 <- lavPredict(fit.opn.exp2) 
opn.exp2 <- fscores.opn.exp2[,1]
fit.ext.exp2 <- sem(m.ext, data = dat_exp2)
fscores.ext.exp2 <- lavPredict(fit.ext.exp2) 
ext.exp2 <- fscores.ext.exp2[,1]
fit.neu.exp2 <- sem(m.neu, data = dat_exp2)
fscores.neu.exp2 <- lavPredict(fit.neu.exp2) 
neu.exp2 <- fscores.neu.exp2[,1]
###Experiment 3
fit.agr.exp3 <- sem(m.agr_no_trust, data = dat_exp3)
fscores.agr.exp3 <- lavPredict(fit.agr.exp3) 
agr.exp3 <- fscores.agr.exp3[,1]
fit.cns.exp3 <- sem(m.cns, data = dat_exp3)
fscores.cns.exp3 <- lavPredict(fit.cns.exp3) 
cns.exp3 <- fscores.cns.exp3[,1]
fit.opn.exp3 <- sem(m.opn, data = dat_exp3)
fscores.opn.exp3 <- lavPredict(fit.opn.exp3) 
opn.exp3 <- fscores.opn.exp3[,1]
fit.ext.exp3 <- sem(m.ext, data = dat_exp3)
fscores.ext.exp3 <- lavPredict(fit.ext.exp3) 
ext.exp3 <- fscores.ext.exp3[,1]
fit.neu.exp3 <- sem(m.neu, data = dat_exp3)
fscores.neu.exp3 <- lavPredict(fit.neu.exp3) 
neu.exp3 <- fscores.neu.exp3[,1]


#add latent scores to datasets
dat_s7_exp2 <- cbind(dat_exp2,agr.exp2,cns.exp2,opn.exp2,ext.exp2,neu.exp2)
dat_s7_exp3 <- cbind(dat_exp3,agr.exp3,cns.exp3,opn.exp3,ext.exp3,neu.exp3)


#create datasets for indirect-effects analyses in Mplus

##grand-mean centering of latent scores for the BFI-2 domains

###Experiment 2
dat_s7_exp2$agr_c <- dat_s7_exp2$agr.exp2 - mean(dat_s7_exp2$agr.exp2)
dat_s7_exp2$cns_c <- dat_s7_exp2$cns.exp2 - mean(dat_s7_exp2$cns.exp2)
dat_s7_exp2$opn_c <- dat_s7_exp2$opn.exp2 - mean(dat_s7_exp2$opn.exp2)
dat_s7_exp2$ext_c <- dat_s7_exp2$ext.exp2 - mean(dat_s7_exp2$ext.exp2)
dat_s7_exp2$neu_c <- dat_s7_exp2$neu.exp2 - mean(dat_s7_exp2$neu.exp2)
###Experiment 3
dat_s7_exp3$agr_c <- dat_s7_exp3$agr.exp3 - mean(dat_s7_exp3$agr.exp3)
dat_s7_exp3$cns_c <- dat_s7_exp3$cns.exp3 - mean(dat_s7_exp3$cns.exp3)
dat_s7_exp3$opn_c <- dat_s7_exp3$opn.exp3 - mean(dat_s7_exp3$opn.exp3)
dat_s7_exp3$ext_c <- dat_s7_exp3$ext.exp3 - mean(dat_s7_exp3$ext.exp3)
dat_s7_exp3$neu_c <- dat_s7_exp3$neu.exp3 - mean(dat_s7_exp3$neu.exp3)

##exclude pairs of Chinese characters and social values for which the sociocultural norm was not recalled correctly

###Experiment 2
dat_s7_exp2 <- subset(dat_s7_exp2, subset = recall_correct == 1)
###Experiment 3
dat_s7_exp3 <- subset(dat_s7_exp3, subset = recall_correct == 1)

##exclude pairs of Chinese characters for participants who knew the meaning of at least one of the characters 

###Experiment 2
dat_s7_exp2 <- subset(dat_s7_exp2, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
###Experiment 3
dat_s7_exp3 <- subset(dat_s7_exp3, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))

##select variables

###Experiment 2
dat2_mplus_latent <- subset(dat_s7_exp2, select = c(id,agr_c,cns_c,opn_c,ext_c,neu_c,personal_preference,sociocultural_norm,trust_c,responsibility_c,intellectual_c))
###Experiment 3
dat3_mplus_latent <- subset(dat_s7_exp3, select = c(id,agr_c,cns_c,opn_c,ext_c,neu_c,personal_preference,sociocultural_norm,trust_c,rational_c,nfc_c,attention_c))


#save datasets for indirect-effects analyses in Mplus
setwd(files_wd)
fwrite(dat2_mplus_latent, file = "SNP-B5_Exp2_Mplus-Data_latent.csv", col.names = FALSE)
fwrite(dat3_mplus_latent, file = "SNP-B5_Exp3_Mplus-Data_latent.csv", col.names = FALSE)


#Table S7: Indirect-Effects Analyses With Latent Scores for the Big Five Domains

##download the folder "SNP-B5_Mplus-Scripts" and set working directory to that folder (e.g., "C:/SNP-B5/SNP-B5_Mplus-Scripts/")
setwd("C:/SNP-B5/SNP-B5_Mplus-Scripts/")

##run indirect-effects analyses in Mplus
runModels("SNP-B5_Suppl7_indirect_effects_exp2.inp")
runModels("SNP-B5_Suppl7_indirect_effects_exp3.inp")

##create table
dat_table_s7 <- data.frame(matrix(nrow=10,ncol=15))
colnames(dat_table_s7) <- c("Predictor","Mediator","Direct_effect_estimate","Direct_effect_lower_ci","Direct_effect_upper_ci","Indirect_effect_path_a_estimate","Indirect_effect_path_a_lower_ci","Indirect_effect_path_a_upper_ci","Indirect_effect_path_b_estimate","Indirect_effect_path_b_lower_ci","Indirect_effect_path_b_upper_ci","Indirect_effect_estimate","Indirect_effect_lower_ci","Indirect_effect_upper_ci","Proportion_mediated")

dat_table_s7$Predictor <- c("(1)   Agreeableness","(2)   Conscientiousness","(3)   Openness","(4)   Extraversion","(5)   Neuroticism","(6)   Agreeableness","(7)   Conscientiousness","(8)   Openness","(9)   Extraversion","(10) Neuroticism")
dat_table_s7$Mediator <- c("A-Trust","C-Responsibility","O-Intellectual Curiosity","","","A-Trust","Rational Thought","Need for Cognition","Social Attention","")

exp2_output_indirect_effects <- readModels("SNP-B5_Suppl7_indirect_effects_exp2.out", what = "parameters")
exp2_output_coeff <- sapply(exp2_output_indirect_effects, "[", "ci.unstandardized")
exp2_coeff <- sapply(exp2_output_coeff, "[", c("paramHeader","param","est","low2.5","up2.5"))
exp2_dat_coeff <- data.frame(matrix(unlist(exp2_coeff), nrow=51, ncol = 5, byrow=F), stringsAsFactors=FALSE)
exp2_dat_coeff$X3 <- as.numeric(exp2_dat_coeff$X3)
exp2_dat_coeff$X4 <- as.numeric(exp2_dat_coeff$X4)
exp2_dat_coeff$X5 <- as.numeric(exp2_dat_coeff$X5)

exp3_output_indirect_effects <- readModels("SNP-B5_Suppl7_indirect_effects_exp3.out", what = "parameters")
exp3_output_coeff <- sapply(exp3_output_indirect_effects, "[", "ci.unstandardized")
exp3_coeff <- sapply(exp3_output_coeff, "[", c("paramHeader","param","est","low2.5","up2.5"))
exp3_dat_coeff <- data.frame(matrix(unlist(exp3_coeff), nrow=68, ncol = 5, byrow=F), stringsAsFactors=FALSE)
exp3_dat_coeff$X3 <- as.numeric(exp3_dat_coeff$X3)
exp3_dat_coeff$X4 <- as.numeric(exp3_dat_coeff$X4)
exp3_dat_coeff$X5 <- as.numeric(exp3_dat_coeff$X5)

dat_table_s7$Direct_effect_estimate <- c(exp2_dat_coeff$X3[2],exp2_dat_coeff$X3[3],exp2_dat_coeff$X3[4],exp2_dat_coeff$X3[5],exp2_dat_coeff$X3[6],
                                         exp3_dat_coeff$X3[2],exp3_dat_coeff$X3[3],exp3_dat_coeff$X3[4],exp3_dat_coeff$X3[5],exp3_dat_coeff$X3[6])
dat_table_s7$Direct_effect_lower_ci <- c(exp2_dat_coeff$X4[2],exp2_dat_coeff$X4[3],exp2_dat_coeff$X4[4],exp2_dat_coeff$X4[5],exp2_dat_coeff$X4[6],
                                         exp3_dat_coeff$X4[2],exp3_dat_coeff$X4[3],exp3_dat_coeff$X4[4],exp3_dat_coeff$X4[5],exp3_dat_coeff$X4[6])
dat_table_s7$Direct_effect_upper_ci <- c(exp2_dat_coeff$X5[2],exp2_dat_coeff$X5[3],exp2_dat_coeff$X5[4],exp2_dat_coeff$X5[5],exp2_dat_coeff$X5[6],
                                         exp3_dat_coeff$X5[2],exp3_dat_coeff$X5[3],exp3_dat_coeff$X5[4],exp3_dat_coeff$X5[5],exp3_dat_coeff$X5[6])
dat_table_s7$Indirect_effect_path_a_estimate <- c(exp2_dat_coeff$X3[18],exp2_dat_coeff$X3[24],exp2_dat_coeff$X3[31],"","",
                                                  exp3_dat_coeff$X3[20],exp3_dat_coeff$X3[28],exp3_dat_coeff$X3[36],exp3_dat_coeff$X3[44],"")
dat_table_s7$Indirect_effect_path_a_lower_ci <- c(exp2_dat_coeff$X4[18],exp2_dat_coeff$X4[24],exp2_dat_coeff$X4[31],"","",
                                                  exp3_dat_coeff$X4[20],exp3_dat_coeff$X4[28],exp3_dat_coeff$X4[36],exp3_dat_coeff$X4[44],"")
dat_table_s7$Indirect_effect_path_a_upper_ci <- c(exp2_dat_coeff$X5[18],exp2_dat_coeff$X5[24],exp2_dat_coeff$X5[31],"","",
                                                  exp3_dat_coeff$X5[20],exp3_dat_coeff$X5[28],exp3_dat_coeff$X5[36],exp3_dat_coeff$X5[44],"")
dat_table_s7$Indirect_effect_path_b_estimate <- c(exp2_dat_coeff$X3[7],exp2_dat_coeff$X3[8],exp2_dat_coeff$X3[9],"","",
                                                  exp3_dat_coeff$X3[7],exp3_dat_coeff$X3[8],exp3_dat_coeff$X3[9],exp3_dat_coeff$X3[10],"")
dat_table_s7$Indirect_effect_path_b_lower_ci <- c(exp2_dat_coeff$X4[7],exp2_dat_coeff$X4[8],exp2_dat_coeff$X4[9],"","",
                                                  exp3_dat_coeff$X4[7],exp3_dat_coeff$X4[8],exp3_dat_coeff$X4[9],exp3_dat_coeff$X4[10],"")
dat_table_s7$Indirect_effect_path_b_upper_ci <- c(exp2_dat_coeff$X5[7],exp2_dat_coeff$X5[8],exp2_dat_coeff$X5[9],"","",
                                                  exp3_dat_coeff$X5[7],exp3_dat_coeff$X5[8],exp3_dat_coeff$X5[9],exp3_dat_coeff$X5[10],"")
dat_table_s7$Indirect_effect_estimate <- c(exp2_dat_coeff$X3[49],exp2_dat_coeff$X3[50],exp2_dat_coeff$X3[51],"","",
                                           exp3_dat_coeff$X3[65],exp3_dat_coeff$X3[66],exp3_dat_coeff$X3[67],exp3_dat_coeff$X3[68],"")
dat_table_s7$Indirect_effect_lower_ci <- c(exp2_dat_coeff$X4[49],exp2_dat_coeff$X4[50],exp2_dat_coeff$X4[51],"","",
                                           exp3_dat_coeff$X4[65],exp3_dat_coeff$X4[66],exp3_dat_coeff$X4[67],exp3_dat_coeff$X4[68],"")
dat_table_s7$Indirect_effect_upper_ci <- c(exp2_dat_coeff$X5[49],exp2_dat_coeff$X5[50],exp2_dat_coeff$X5[51],"","",
                                           exp3_dat_coeff$X5[65],exp3_dat_coeff$X5[66],exp3_dat_coeff$X5[67],exp3_dat_coeff$X5[68],"")

dat_table_s7[,3:14] <- sapply(dat_table_s7[,3:14], as.numeric)

format_numbers <- function(x){
  ifelse(abs(x) <= 0.005,  formatC(x, format = "f", digits = 3), formatC(x, format = "f", digits = 2))
}
dat_table_s7 <- dat_table_s7 %>% mutate_if(is.numeric, format_numbers)

dat_table_s7[,3:14] <- sapply(dat_table_s7[,3:14], as.numeric)

dat_table_s7$Proportion_mediated <- round(100*(dat_table_s7$Indirect_effect_estimate / (dat_table_s7$Indirect_effect_estimate + dat_table_s7$Direct_effect_estimate)), 0)

dat_table_s7$Proportion_mediated[6] <- round(dat_table_s7$Proportion_mediated[6], -2)-100

dat_table_s7$ci <- paste0("[", sprintf("%.2f",dat_table_s7[,4]), ", ", sprintf("%.2f",dat_table_s7[,5]), "]")
dat_table_s7$ci.1 <- paste0("[", sprintf("%.2f",dat_table_s7[,7]), ", ", sprintf("%.2f",dat_table_s7[,8]), "]")
dat_table_s7$ci.2 <- paste0("[", sprintf("%.2f",dat_table_s7[,10]), ", ", sprintf("%.2f",dat_table_s7[,11]), "]")
dat_table_s7$ci.3 <- paste0("[", sprintf("%.2f",dat_table_s7[,13]), ", ", sprintf("%.2f",dat_table_s7[,14]), "]")

dat_table_s7[c(4:5,10),c(17:19)] <- NA

dat_table_s7$blank <- NA
dat_table_s7$blank.1 <- NA
dat_table_s7$blank.2 <- NA
dat_table_s7$blank.3 <- NA

dat_table_s7 <- dat_table_s7[,c("Predictor","Mediator","Direct_effect_estimate","ci","blank","Indirect_effect_path_a_estimate","ci.1","blank.1","Indirect_effect_path_b_estimate","ci.2","blank.2","Indirect_effect_estimate","ci.3","blank.3","Proportion_mediated")]

dat_table_s7[11,1] <- "Experiment 2"
dat_table_s7[12,1] <- "Experiment 3"

dat_table_s7 <- dat_table_s7[c(11,1:5,12,6:10),]

col_keys <- c("Predictor","Mediator","Direct_effect_estimate","ci","blank","Indirect_effect_path_a_estimate","ci.1","blank.1","Indirect_effect_path_b_estimate","ci.2","blank.2","Indirect_effect_estimate","ci.3","blank.3","Proportion_mediated")
head1 <- c("Predictor","Mediator","Direct effect","Direct effect","","Path a of indirect effect","Path a of indirect effect","","Path b of indirect effect","Path b of indirect effect","","Indirect effect","Indirect effect","","Proportion mediated (%)")
head2 <- c("","","Estimate","  95% CI","","Estimate","  95% CI","","Estimate","  95% CI","","Estimate","  95% CI","","")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_s7)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:15, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=3:4, part="header")
tbl <- merge_at(tbl, i=1, j=6:7, part="header")
tbl <- merge_at(tbl, i=1, j=9:10, part="header")
tbl <- merge_at(tbl, i=1, j=12:13, part="header")
tbl <- hline(tbl, i=1, j=c(3:4,6:7,9:10,12:13), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:15, border=fp_border(width=1.2), part="header")
tbl <- merge_at(tbl, i=1, j=1:15, part="body")
tbl <- merge_at(tbl, i=7, j=1:15, part="body")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=10, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- align(tbl, i=c(1,7), align="center", part="body")
tbl <- width(tbl, j =~ Predictor, width=1.45)
tbl <- width(tbl, j =~ Mediator, width=1.5)
tbl <- width(tbl, j =~ Proportion_mediated, width=.8)
tbl <- width(tbl, j =~ Direct_effect_estimate + Indirect_effect_path_a_estimate + Indirect_effect_path_b_estimate + Indirect_effect_estimate, width=.6)
tbl <- width(tbl, j =~ ci + ci.1 + ci.2 + ci.3, width=.97)
tbl <- width(tbl, j =~ blank + blank.1 + blank.2 + blank.3, width=.1)
tbl <- colformat_lgl(tbl, j =~ blank + blank.1 + blank.2 + blank.3, na_str="")
tbl <- colformat_num(tbl, j =~ Indirect_effect_path_a_estimate + Indirect_effect_path_b_estimate + Indirect_effect_estimate + Proportion_mediated, na_str="")
tbl <- colformat_num(tbl, j =~ Proportion_mediated, digits=0)
tbl <- height_all(tbl, height=.1, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S7.docx")


rm(list=setdiff(ls(), c("julia","julia_wd","files_wd","dat","dat_exp1","dat_exp2","dat_exp3","dat_exp123","dat_exp23")))


########## S8: Full-Sociocultural-Norm Model With O-Intellectual Curiosity ##########


#create dataset for indirect-effects analysis in Mplus (SNP-B5_Exp3_Mplus-Data.csv) if not already done for the main-text analyses

##exclude pairs of Chinese characters and social values for which the sociocultural norm was not recalled correctly
dat_s8_exp3 <- subset(dat_exp3, subset = recall_correct == 1)

##exclude pairs of Chinese characters for participants who knew the meaning of at least one of the characters 
dat_s8_exp3 <- subset(dat_s8_exp3, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))

##select variables
dat3_mplus <- subset(dat_s8_exp3, select = c(id,agr_no_trust_c,cns_c,opn_c,ext_c,neu_c,personal_preference,sociocultural_norm,trust_c,rational_c,nfc_c,attention_c,opn_no_intel_c,intellectual_c))

##save dataset
setwd(files_wd)
fwrite(dat3_mplus, file = "SNP-B5_Exp3_Mplus-Data.csv", col.names = FALSE)
rm(dat_s8_exp3,dat3_mplus)


#Table S8: Full-Sociocultural-Norm Model With O-Intellectual-Curiosity

##download the folder "SNP-B5_Mplus-Scripts" and set working directory to that folder (e.g., "C:/SNP-B5/SNP-B5_Mplus-Scripts/")
setwd("C:/SNP-B5/SNP-B5_Mplus-Scripts/")

##run indirect-effects analysis in Mplus
runModels("SNP-B5_Suppl8_indirect_effects.inp")

##create table
dat_table_s8 <- data.frame(matrix(nrow=5,ncol=15))
colnames(dat_table_s8) <- c("Predictor","Mediator","Direct_effect_estimate","Direct_effect_lower_ci","Direct_effect_upper_ci","Indirect_effect_path_a_estimate","Indirect_effect_path_a_lower_ci","Indirect_effect_path_a_upper_ci","Indirect_effect_path_b_estimate","Indirect_effect_path_b_lower_ci","Indirect_effect_path_b_upper_ci","Indirect_effect_estimate","Indirect_effect_lower_ci","Indirect_effect_upper_ci","Proportion_mediated")

dat_table_s8$Predictor <- c("(1)   Agreeableness","(2)   Conscientiousness","(3)   Openness","(4)   Extraversion","(5)   Neuroticism")
dat_table_s8$Mediator <- c("A-Trust","Rational Thought","O-Intellectual Curiosity","Social Attention","")

output_indirect_effects <- readModels("SNP-B5_Suppl8_indirect_effects.out", what = "parameters")
output_coeff <- sapply(output_indirect_effects, "[", "ci.unstandardized")
coeff <- sapply(output_coeff, "[", c("paramHeader","param","est","low2.5","up2.5"))
dat_coeff <- data.frame(matrix(unlist(coeff), nrow=67, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat_coeff$X3 <- as.numeric(dat_coeff$X3)
dat_coeff$X4 <- as.numeric(dat_coeff$X4)
dat_coeff$X5 <- as.numeric(dat_coeff$X5)

dat_table_s8$Direct_effect_estimate <- c(dat_coeff$X3[2],dat_coeff$X3[3],dat_coeff$X3[4],dat_coeff$X3[5],dat_coeff$X3[6])
dat_table_s8$Direct_effect_lower_ci <- c(dat_coeff$X4[2],dat_coeff$X4[3],dat_coeff$X4[4],dat_coeff$X4[5],dat_coeff$X4[6])
dat_table_s8$Direct_effect_upper_ci <- c(dat_coeff$X5[2],dat_coeff$X5[3],dat_coeff$X5[4],dat_coeff$X5[5],dat_coeff$X5[6])
dat_table_s8$Indirect_effect_path_a_estimate <- c(dat_coeff$X3[20],dat_coeff$X3[28],dat_coeff$X3[36],dat_coeff$X3[44],"")
dat_table_s8$Indirect_effect_path_a_lower_ci <- c(dat_coeff$X4[20],dat_coeff$X4[28],dat_coeff$X4[36],dat_coeff$X4[44],"")
dat_table_s8$Indirect_effect_path_a_upper_ci <- c(dat_coeff$X5[20],dat_coeff$X5[28],dat_coeff$X5[36],dat_coeff$X5[44],"")
dat_table_s8$Indirect_effect_path_b_estimate <- c(dat_coeff$X3[7],dat_coeff$X3[8],dat_coeff$X3[9],dat_coeff$X3[10],"")
dat_table_s8$Indirect_effect_path_b_lower_ci <- c(dat_coeff$X4[7],dat_coeff$X4[8],dat_coeff$X4[9],dat_coeff$X4[10],"")
dat_table_s8$Indirect_effect_path_b_upper_ci <- c(dat_coeff$X5[7],dat_coeff$X5[8],dat_coeff$X5[9],dat_coeff$X5[10],"")
dat_table_s8$Indirect_effect_estimate <- c(dat_coeff$X3[64],dat_coeff$X3[65],dat_coeff$X3[66],dat_coeff$X3[67],"")
dat_table_s8$Indirect_effect_lower_ci <- c(dat_coeff$X4[64],dat_coeff$X4[65],dat_coeff$X4[66],dat_coeff$X4[67],"")
dat_table_s8$Indirect_effect_upper_ci <- c(dat_coeff$X5[64],dat_coeff$X5[65],dat_coeff$X5[66],dat_coeff$X5[67],"")

dat_table_s8[,3:14] <- sapply(dat_table_s8[,3:14], as.numeric)

format_numbers <- function(x){
  ifelse(abs(x) <= 0.005,  formatC(x, format = "f", digits = 3), formatC(x, format = "f", digits = 2))
}
dat_table_s8 <- dat_table_s8 %>% mutate_if(is.numeric, format_numbers)

dat_table_s8[,3:14] <- sapply(dat_table_s8[,3:14], as.numeric)

dat_table_s8$Proportion_mediated <- round(100*(dat_table_s8$Indirect_effect_estimate / (dat_table_s8$Indirect_effect_estimate + dat_table_s8$Direct_effect_estimate)), 0)

dat_table_s8$Proportion_mediated[3] <- round(dat_table_s8$Proportion_mediated[3], -2)

dat_table_s8$ci <- paste0("[", sprintf("%.2f",dat_table_s8[,4]), ", ", sprintf("%.2f",dat_table_s8[,5]), "]")
dat_table_s8$ci.1 <- paste0("[", sprintf("%.2f",dat_table_s8[,7]), ", ", sprintf("%.2f",dat_table_s8[,8]), "]")
dat_table_s8$ci.2 <- paste0("[", sprintf("%.2f",dat_table_s8[,10]), ", ", sprintf("%.2f",dat_table_s8[,11]), "]")
dat_table_s8$ci.3 <- paste0("[", sprintf("%.2f",dat_table_s8[,13]), ", ", sprintf("%.2f",dat_table_s8[,14]), "]")

dat_table_s8[c(5),c(17:19)] <- NA

dat_table_s8$blank <- NA
dat_table_s8$blank.1 <- NA
dat_table_s8$blank.2 <- NA
dat_table_s8$blank.3 <- NA

dat_table_s8 <- dat_table_s8[,c("Predictor","Mediator","Direct_effect_estimate","ci","blank","Indirect_effect_path_a_estimate","ci.1","blank.1","Indirect_effect_path_b_estimate","ci.2","blank.2","Indirect_effect_estimate","ci.3","blank.3","Proportion_mediated")]

col_keys <- c("Predictor","Mediator","Direct_effect_estimate","ci","blank","Indirect_effect_path_a_estimate","ci.1","blank.1","Indirect_effect_path_b_estimate","ci.2","blank.2","Indirect_effect_estimate","ci.3","blank.3","Proportion_mediated")
head1 <- c("Predictor","Mediator","Direct effect","Direct effect","","Path a of indirect effect","Path a of indirect effect","","Path b of indirect effect","Path b of indirect effect","","Indirect effect","Indirect effect","","Proportion mediated (%)")
head2 <- c("","","Estimate","  95% CI","","Estimate","  95% CI","","Estimate","  95% CI","","Estimate","  95% CI","","")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_s8)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:15, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=3:4, part="header")
tbl <- merge_at(tbl, i=1, j=6:7, part="header")
tbl <- merge_at(tbl, i=1, j=9:10, part="header")
tbl <- merge_at(tbl, i=1, j=12:13, part="header")
tbl <- hline(tbl, i=1, j=c(3:4,6:7,9:10,12:13), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:15, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=10, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- width(tbl, j =~ Predictor, width=1.45)
tbl <- width(tbl, j =~ Mediator, width=1.5)
tbl <- width(tbl, j =~ Proportion_mediated, width=.8)
tbl <- width(tbl, j =~ Direct_effect_estimate + Indirect_effect_path_a_estimate + Indirect_effect_path_b_estimate + Indirect_effect_estimate, width=.6)
tbl <- width(tbl, j =~ ci + ci.1 + ci.2 + ci.3, width=.97)
tbl <- width(tbl, j =~ blank + blank.1 + blank.2 + blank.3, width=.1)
tbl <- colformat_lgl(tbl, j =~ blank + blank.1 + blank.2 + blank.3, na_str="")
tbl <- colformat_num(tbl, j =~ Indirect_effect_path_a_estimate + Indirect_effect_path_b_estimate + Indirect_effect_estimate + Proportion_mediated, na_str="")
tbl <- colformat_num(tbl, j =~ Proportion_mediated, digits=0)
tbl <- height_all(tbl, height=.1, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S8.docx")


rm(list=setdiff(ls(), c("julia","julia_wd","files_wd","dat","dat_exp1","dat_exp2","dat_exp3","dat_exp123","dat_exp23")))


########## S9: Indirect Effects of Each Descriptive Big Five Domain Through Each Mediator in the Full-Sociocultural-Norm Model ##########


#create dataset for indirect-effects analysis in Mplus (SNP-B5_Exp3_Mplus-Data.csv) if not already done for S8 or the main-text analyses

##exclude pairs of Chinese characters and social values for which the sociocultural norm was not recalled correctly
dat_s9_exp3 <- subset(dat_exp3, subset = recall_correct == 1)

##exclude pairs of Chinese characters for participants who knew the meaning of at least one of the characters 
dat_s9_exp3 <- subset(dat_s9_exp3, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))

##select variables
dat3_mplus <- subset(dat_s9_exp3, select = c(id,agr_no_trust_c,cns_c,opn_c,ext_c,neu_c,personal_preference,sociocultural_norm,trust_c,rational_c,nfc_c,attention_c,opn_no_intel_c,intellectual_c))

##save dataset
setwd(files_wd)
fwrite(dat3_mplus, file = "SNP-B5_Exp3_Mplus-Data.csv", col.names = FALSE)
rm(dat_s9_exp3,dat3_mplus)


#Table S9: Indirect Effects of Each Descriptive Big Five Domain Through Each Mediator in the Full-Sociocultural-Norm Model

##download the folder "SNP-B5_Mplus-Scripts" and set working directory to that folder (e.g., "C:/SNP-B5/SNP-B5_Mplus-Scripts/")
setwd("C:/SNP-B5/SNP-B5_Mplus-Scripts/")

##run indirect-effects analysis in Mplus
runModels("SNP-B5_Suppl9_indirect_effects.inp")

##create table
dat_table_s9 <- data.frame(matrix(nrow=17,ncol=15))
colnames(dat_table_s9) <- c("Predictor","Mediator","Direct_effect_estimate","Direct_effect_lower_ci","Direct_effect_upper_ci","Indirect_effect_path_a_estimate","Indirect_effect_path_a_lower_ci","Indirect_effect_path_a_upper_ci","Indirect_effect_path_b_estimate","Indirect_effect_path_b_lower_ci","Indirect_effect_path_b_upper_ci","Indirect_effect_estimate","Indirect_effect_lower_ci","Indirect_effect_upper_ci","Proportion_mediated")

dat_table_s9$Predictor <- c("(1)   Agreeableness","(2)   Agreeableness","(3)   Agreeableness","(4)   Agreeableness","(5)   Conscientiousness","(6)   Conscientiousness","(7)   Conscientiousness","(8)   Conscientiousness","(9)   Openness","(10) Openness","(11) Openness","(12) Openness",
                            "(13) Extraversion","(14) Extraversion","(15) Extraversion","(16) Extraversion","(17) Neuroticism")
dat_table_s9$Mediator <- c("A-Trust","Rational Thought","Need for Cognition","Social Attention","A-Trust","Rational Thought","Need for Cognition","Social Attention",
                           "A-Trust","Rational Thought","Need for Cognition","Social Attention","A-Trust","Rational Thought","Need for Cognition","Social Attention","")

output_indirect_effects <- readModels("SNP-B5_Suppl9_indirect_effects.out", what = "parameters")
output_coeff <- sapply(output_indirect_effects, "[", "ci.unstandardized")
coeff <- sapply(output_coeff, "[", c("paramHeader","param","est","low2.5","up2.5"))
dat_coeff <- data.frame(matrix(unlist(coeff), nrow=80, ncol = 5, byrow=F), stringsAsFactors=FALSE)
dat_coeff$X3 <- as.numeric(dat_coeff$X3)
dat_coeff$X4 <- as.numeric(dat_coeff$X4)
dat_coeff$X5 <- as.numeric(dat_coeff$X5)

dat_table_s9$Direct_effect_estimate <- c(dat_coeff$X3[2],dat_coeff$X3[2],dat_coeff$X3[2],dat_coeff$X3[2],dat_coeff$X3[3],dat_coeff$X3[3],dat_coeff$X3[3],dat_coeff$X3[3],
                                         dat_coeff$X3[4],dat_coeff$X3[4],dat_coeff$X3[4],dat_coeff$X3[4],dat_coeff$X3[5],dat_coeff$X3[5],dat_coeff$X3[5],dat_coeff$X3[5],dat_coeff$X3[6])
dat_table_s9$Direct_effect_lower_ci <- c(dat_coeff$X4[2],dat_coeff$X4[2],dat_coeff$X4[2],dat_coeff$X4[2],dat_coeff$X4[3],dat_coeff$X4[3],dat_coeff$X4[3],dat_coeff$X4[3],
                                         dat_coeff$X4[4],dat_coeff$X4[4],dat_coeff$X4[4],dat_coeff$X4[4],dat_coeff$X4[5],dat_coeff$X4[5],dat_coeff$X4[5],dat_coeff$X4[5],dat_coeff$X4[6])
dat_table_s9$Direct_effect_upper_ci <- c(dat_coeff$X5[2],dat_coeff$X5[2],dat_coeff$X5[2],dat_coeff$X5[2],dat_coeff$X5[3],dat_coeff$X5[3],dat_coeff$X5[3],dat_coeff$X5[3],
                                         dat_coeff$X5[4],dat_coeff$X5[4],dat_coeff$X5[4],dat_coeff$X5[4],dat_coeff$X5[5],dat_coeff$X5[5],dat_coeff$X5[5],dat_coeff$X5[5],dat_coeff$X5[6])
dat_table_s9$Indirect_effect_path_a_estimate <- c(dat_coeff$X3[20],dat_coeff$X3[29],dat_coeff$X3[37],dat_coeff$X3[45],dat_coeff$X3[21],dat_coeff$X3[28],dat_coeff$X3[38],dat_coeff$X3[46],
                                                  dat_coeff$X3[22],dat_coeff$X3[30],dat_coeff$X3[36],dat_coeff$X3[47],dat_coeff$X3[23],dat_coeff$X3[31],dat_coeff$X3[39],dat_coeff$X3[44],"")
dat_table_s9$Indirect_effect_path_a_lower_ci <- c(dat_coeff$X4[20],dat_coeff$X4[29],dat_coeff$X4[37],dat_coeff$X4[45],dat_coeff$X4[21],dat_coeff$X4[28],dat_coeff$X4[38],dat_coeff$X4[46],
                                                  dat_coeff$X4[22],dat_coeff$X4[30],dat_coeff$X4[36],dat_coeff$X4[47],dat_coeff$X4[23],dat_coeff$X4[31],dat_coeff$X4[39],dat_coeff$X4[44],"")
dat_table_s9$Indirect_effect_path_a_upper_ci <- c(dat_coeff$X5[20],dat_coeff$X5[29],dat_coeff$X5[37],dat_coeff$X5[45],dat_coeff$X5[21],dat_coeff$X5[28],dat_coeff$X5[38],dat_coeff$X5[46],
                                                  dat_coeff$X5[22],dat_coeff$X5[30],dat_coeff$X5[36],dat_coeff$X5[47],dat_coeff$X5[23],dat_coeff$X5[31],dat_coeff$X5[39],dat_coeff$X5[44],"")
dat_table_s9$Indirect_effect_path_b_estimate <- c(dat_coeff$X3[7],dat_coeff$X3[8],dat_coeff$X3[9],dat_coeff$X3[10],dat_coeff$X3[7],dat_coeff$X3[8],dat_coeff$X3[9],dat_coeff$X3[10],
                                                  dat_coeff$X3[7],dat_coeff$X3[8],dat_coeff$X3[9],dat_coeff$X3[10],dat_coeff$X3[7],dat_coeff$X3[8],dat_coeff$X3[9],dat_coeff$X3[10],"")
dat_table_s9$Indirect_effect_path_b_lower_ci <- c(dat_coeff$X4[7],dat_coeff$X4[8],dat_coeff$X4[9],dat_coeff$X4[10],dat_coeff$X4[7],dat_coeff$X4[8],dat_coeff$X4[9],dat_coeff$X4[10],
                                                  dat_coeff$X4[7],dat_coeff$X4[8],dat_coeff$X4[9],dat_coeff$X4[10],dat_coeff$X4[7],dat_coeff$X4[8],dat_coeff$X4[9],dat_coeff$X4[10],"")
dat_table_s9$Indirect_effect_path_b_upper_ci <- c(dat_coeff$X5[7],dat_coeff$X5[8],dat_coeff$X5[9],dat_coeff$X5[10],dat_coeff$X5[7],dat_coeff$X5[8],dat_coeff$X5[9],dat_coeff$X5[10],
                                                  dat_coeff$X5[7],dat_coeff$X5[8],dat_coeff$X5[9],dat_coeff$X5[10],dat_coeff$X5[7],dat_coeff$X5[8],dat_coeff$X5[9],dat_coeff$X5[10],"")
dat_table_s9$Indirect_effect_estimate <- c(dat_coeff$X3[65],dat_coeff$X3[72],dat_coeff$X3[75],dat_coeff$X3[78],dat_coeff$X3[69],dat_coeff$X3[66],dat_coeff$X3[76],dat_coeff$X3[79],
                                           dat_coeff$X3[70],dat_coeff$X3[73],dat_coeff$X3[67],dat_coeff$X3[80],dat_coeff$X3[71],dat_coeff$X3[74],dat_coeff$X3[77],dat_coeff$X3[68],"")
dat_table_s9$Indirect_effect_lower_ci <- c(dat_coeff$X4[65],dat_coeff$X4[72],dat_coeff$X4[75],dat_coeff$X4[78],dat_coeff$X4[69],dat_coeff$X4[66],dat_coeff$X4[76],dat_coeff$X4[79],
                                           dat_coeff$X4[70],dat_coeff$X4[73],dat_coeff$X4[67],dat_coeff$X4[80],dat_coeff$X4[71],dat_coeff$X4[74],dat_coeff$X4[77],dat_coeff$X4[68],"")
dat_table_s9$Indirect_effect_upper_ci <- c(dat_coeff$X5[65],dat_coeff$X5[72],dat_coeff$X5[75],dat_coeff$X5[78],dat_coeff$X5[69],dat_coeff$X5[66],dat_coeff$X5[76],dat_coeff$X5[79],
                                           dat_coeff$X5[70],dat_coeff$X5[73],dat_coeff$X5[67],dat_coeff$X5[80],dat_coeff$X5[71],dat_coeff$X5[74],dat_coeff$X5[77],dat_coeff$X5[68],"")

dat_table_s9[,3:14] <- sapply(dat_table_s9[,3:14], as.numeric)

format_numbers <- function(x){
  ifelse(abs(x) <= 0.005,  formatC(x, format = "f", digits = 3), formatC(x, format = "f", digits = 2))
}
dat_table_s9 <- dat_table_s9 %>% mutate_if(is.numeric, format_numbers)

dat_table_s9[,3:14] <- sapply(dat_table_s9[,3:14], as.numeric)

total_effect_agr <- dat_table_s9$Direct_effect_estimate[1] + dat_table_s9$Indirect_effect_estimate[1] + dat_table_s9$Indirect_effect_estimate[2] + dat_table_s9$Indirect_effect_estimate[3] + dat_table_s9$Indirect_effect_estimate[4]
total_effect_cns <- dat_table_s9$Direct_effect_estimate[5] + dat_table_s9$Indirect_effect_estimate[5] + dat_table_s9$Indirect_effect_estimate[6] + dat_table_s9$Indirect_effect_estimate[7] + dat_table_s9$Indirect_effect_estimate[8]
total_effect_opn <- dat_table_s9$Direct_effect_estimate[9] + dat_table_s9$Indirect_effect_estimate[9] + dat_table_s9$Indirect_effect_estimate[10] + dat_table_s9$Indirect_effect_estimate[11] + dat_table_s9$Indirect_effect_estimate[12]
total_effect_ext <- dat_table_s9$Direct_effect_estimate[13] + dat_table_s9$Indirect_effect_estimate[13] + dat_table_s9$Indirect_effect_estimate[14] + dat_table_s9$Indirect_effect_estimate[15] + dat_table_s9$Indirect_effect_estimate[16]

dat_table_s9$total_effect <- c(total_effect_agr,total_effect_agr,total_effect_agr,total_effect_agr,total_effect_cns,total_effect_cns,total_effect_cns,total_effect_cns,
                               total_effect_opn,total_effect_opn,total_effect_opn,total_effect_opn,total_effect_ext,total_effect_ext,total_effect_ext,total_effect_ext,"")

dat_table_s9$total_effect <- as.numeric(dat_table_s9$total_effect)

dat_table_s9$Proportion_mediated <- round(100*(dat_table_s9$Indirect_effect_estimate / dat_table_s9$total_effect), 0)

dat_table_s9$Proportion_mediated <- ifelse(dat_table_s9$Proportion_mediated <= 1,  "NA", dat_table_s9$Proportion_mediated)

dat_table_s9[,12] <- ifelse(abs(dat_table_s9[,12]) > .005, sprintf("%.2f",dat_table_s9[,12]), sprintf("%.3f",dat_table_s9[,12]))

dat_table_s9$ci <- paste0("[", sprintf("%.2f",dat_table_s9[,4]), ", ", sprintf("%.2f",dat_table_s9[,5]), "]")
dat_table_s9$ci.1 <- paste0("[", sprintf("%.2f",dat_table_s9[,7]), ", ", sprintf("%.2f",dat_table_s9[,8]), "]")
dat_table_s9$ci.2 <- paste0("[", sprintf("%.2f",dat_table_s9[,10]), ", ", sprintf("%.2f",dat_table_s9[,11]), "]")
dat_table_s9$ci.3 <- paste0("[", ifelse(abs(dat_table_s9[,13]) > .005, sprintf("%.2f",dat_table_s9[,13]), sprintf("%.3f",dat_table_s9[,13])), ", ", ifelse(abs(dat_table_s9[,14]) > .005, sprintf("%.2f",dat_table_s9[,14]), sprintf("%.3f",dat_table_s9[,14])), "]")

dat_table_s9[c(17),c(18:20)] <- NA

dat_table_s9$blank <- NA
dat_table_s9$blank.1 <- NA
dat_table_s9$blank.2 <- NA
dat_table_s9$blank.3 <- NA

dat_table_s9 <- dat_table_s9[,c("Predictor","Mediator","Direct_effect_estimate","ci","blank","Indirect_effect_path_a_estimate","ci.1","blank.1","Indirect_effect_path_b_estimate","ci.2","blank.2","Indirect_effect_estimate","ci.3","blank.3","Proportion_mediated")]

col_keys <- c("Predictor","Mediator","Direct_effect_estimate","ci","blank","Indirect_effect_path_a_estimate","ci.1","blank.1","Indirect_effect_path_b_estimate","ci.2","blank.2","Indirect_effect_estimate","ci.3","blank.3","Proportion_mediated")
head1 <- c("Predictor","Mediator","Direct effect","Direct effect","","Path a of indirect effect","Path a of indirect effect","","Path b of indirect effect","Path b of indirect effect","","Indirect effect","Indirect effect","","Proportion mediated (%)")
head2 <- c("","","Estimate","  95% CI","","Estimate","  95% CI","","Estimate","  95% CI","","Estimate","  95% CI","","")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_s9)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:15, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=3:4, part="header")
tbl <- merge_at(tbl, i=1, j=6:7, part="header")
tbl <- merge_at(tbl, i=1, j=9:10, part="header")
tbl <- merge_at(tbl, i=1, j=12:13, part="header")
tbl <- hline(tbl, i=1, j=c(3:4,6:7,9:10,12:13), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:15, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=10, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("Predictor"), align="left", part="body")
tbl <- width(tbl, j =~ Predictor, width=1.45)
tbl <- width(tbl, j =~ Mediator, width=1.5)
tbl <- width(tbl, j =~ Proportion_mediated, width=.8)
tbl <- width(tbl, j =~ Direct_effect_estimate + Indirect_effect_path_a_estimate + Indirect_effect_path_b_estimate + Indirect_effect_estimate, width=.6)
tbl <- width(tbl, j =~ ci + ci.1 + ci.2 + ci.3, width=.97)
tbl <- width(tbl, j =~ blank + blank.1 + blank.2 + blank.3, width=.1)
tbl <- colformat_lgl(tbl, j =~ blank + blank.1 + blank.2 + blank.3, na_str="")
tbl <- colformat_num(tbl, j =~ Indirect_effect_path_a_estimate + Indirect_effect_path_b_estimate + Indirect_effect_estimate + Proportion_mediated, na_str="")
tbl <- colformat_num(tbl, j =~ Proportion_mediated, digits=0)
tbl <- height_all(tbl, height=.1, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S9.docx")


rm(list=setdiff(ls(), c("julia","julia_wd","files_wd","dat","dat_exp1","dat_exp2","dat_exp3","dat_exp123","dat_exp23")))


########## S10: Power of Sociocultural Norms to Alter Big Five Effects ##########


#prepare data for mixed-effects models

##z-standardization of the Big Five domains
dat_s10_exp123 <- dat_exp123
dat_s10_exp123$zagr <- scale(dat_s10_exp123$agr)
dat_s10_exp123$zcns <- scale(dat_s10_exp123$cns)
dat_s10_exp123$zext <- scale(dat_s10_exp123$ext)
dat_s10_exp123$zneu <- scale(dat_s10_exp123$neu)
dat_s10_exp123$zopn <- scale(dat_s10_exp123$opn)

##exclude pairs of Chinese characters and social values for which the sociocultural norm was not recalled correctly
dat_s10_exp123 <- subset(dat_s10_exp123, subset = recall_correct == 1)

##exclude pairs of Chinese characters for participants who knew the meaning of at least one of the characters 
dat_s10_exp123 <- subset(dat_s10_exp123, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))

##z-standardization of sociocultural norms and personal preferences
dat_s10_exp123$znorm <- scale(dat_s10_exp123$sociocultural_norm)
dat_s10_exp123$zpreference <- scale(dat_s10_exp123$personal_preference)

##recentering of sociocultural norms
dat_s10_exp123$znorm_left <- dat_s10_exp123$znorm + abs(min(dat_s10_exp123$znorm))
dat_s10_exp123$znorm_right <- dat_s10_exp123$znorm - abs(max(dat_s10_exp123$znorm))


#conduct mixed-effects models in Julia

##set working directory to Julia
setwd(julia_wd)

##convert grouping variable to factor and assign data to Julia
dat_s10_exp123$id <- as.factor(dat_s10_exp123$id)
julia_assign("dat_s10_exp123", dat_s10_exp123)

##conduct domains-as-predictors models

###majority-left condition
julia_eval("fm = fit(LinearMixedModel, @formula(zpreference ~ 1 + znorm_left * zagr + znorm_left * zcns + znorm_left * zopn
                + znorm_left * zext + znorm_left * zneu + (1 + znorm_left | id)),  dat_s10_exp123)")
s10_exp123_domain_model1 <- julia_eval("s10_exp123_domain_model1 = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
                :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###majority-right condition
julia_eval("fm = fit(LinearMixedModel, @formula(zpreference ~ 1 + znorm_right * zagr + znorm_right * zcns + znorm_right * zopn
                + znorm_right * zext + znorm_right * zneu + (1 + znorm_right | id)),  dat_s10_exp123)")
s10_exp123_domain_model2 <- julia_eval("s10_exp123_domain_model2 = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
                :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")


#approximate effect size
s10_exp123_domain_model1[,2] <- as.numeric(s10_exp123_domain_model1[,2])
s10_exp123_domain_model2[,2] <- as.numeric(s10_exp123_domain_model2[,2])
effect_size <- (abs(s10_exp123_domain_model1[3,2]-s10_exp123_domain_model2[3,2]) + abs(s10_exp123_domain_model1[4,2]-s10_exp123_domain_model2[4,2])
               + abs(s10_exp123_domain_model1[5,2]-s10_exp123_domain_model2[5,2]) + abs(s10_exp123_domain_model1[6,2]-s10_exp123_domain_model2[6,2]))
paste0("Power of sociocultural norms to alter Big Five effects in our experiments: ", round(effect_size,2))


rm(list=setdiff(ls(), c("julia","julia_wd","files_wd","dat","dat_exp1","dat_exp2","dat_exp3","dat_exp123","dat_exp23")))


########## S11: Chinese Characters Versus Social Values ##########


#prepare data for mixed-effects models

##exclude pairs of Chinese characters and social values for which the sociocultural norm was not recalled correctly
dat_s11_exp123 <- subset(dat_exp123, subset = recall_correct == 1)

##exclude pairs of Chinese characters for participants who knew the meaning of at least one of the characters 
dat_s11_exp123 <- subset(dat_s11_exp123, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))

##recode outcome category
dat_s11_exp123$outcome_category_r <- car::recode(dat_s11_exp123$outcome_category, "0=1; 1=0")


#conduct mixed-effects models in Julia

##set working directory to Julia
setwd(julia_wd)

##convert grouping variable to factor and assign data to Julia
dat_s11_exp123$id <- as.factor(dat_s11_exp123$id)
julia_assign("dat_s11_exp123", dat_s11_exp123)

##conduct domains-as-predictors models

###with outcome category (0 = Chinese characters; 1 = Social values)
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c * outcome_category + sociocultural_norm * cns_c * outcome_category + sociocultural_norm * opn_c * outcome_category
                + sociocultural_norm * ext_c * outcome_category + sociocultural_norm * neu_c * outcome_category + (1 + sociocultural_norm | id)),  dat_s11_exp123)")
s11_exp123_domain_model1 <- julia_eval("s11_exp123_domain_model1 = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
                :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###with recoded outcome category (0 = Social values; 1 = Chinese characters)
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c * outcome_category_r + sociocultural_norm * cns_c * outcome_category_r + sociocultural_norm * opn_c * outcome_category_r
                + sociocultural_norm * ext_c * outcome_category_r + sociocultural_norm * neu_c * outcome_category_r + (1 + sociocultural_norm | id)),  dat_s11_exp123)")
s11_exp123_domain_model2 <- julia_eval("s11_exp123_domain_model2 = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
                :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")


#Table S10: Effects of the Big Five Domains on Personal Preferences Moderated by Sociocultural Norms and Outcome Category (Chinese Characters vs. Social Values) Across Experiments 1-3
dat_table_s10 <- data.frame(s11_exp123_domain_model1,s11_exp123_domain_model2)

dat_table_s10[,5] <- NULL

dat_table_s10[,1] <- c("(1)   (Intercept)","(7)   Norms", "(2)   Agr", "(8)   Cat", "(3)   Cns", "(4)   Opn", "(5)   Ext", "(6)   Neu", "(9)   Agr x Norms",
                       "(19) Norms x Cat", "(14) Agr x Cat", "(10) Cns x Norms", "(15) Cns x Cat", "(11) Opn x Norms", "(16) Opn x Cat", 
                       "(12) Ext x Norms", "(17) Ext x Cat", "(13) Neu x Norms", "(18) Neu x Cat", "(20) Agr x Norms x Cat", 
                       "(21) Cns x Norms x Cat", "(22) Opn x Norms x Cat", "(23) Ext x Norms x Cat", "(24) Neu x Norms x Cat")

dat_table_s10[,2:7] <- sapply(dat_table_s10[,2:7], as.numeric)

format_numbers <- function(x){
  ifelse(abs(x) <= 0.005,  formatC(x, format = "e", digits = 0), formatC(x, format = "f", digits = 2))
}
dat_table_s10 <- dat_table_s10 %>% mutate_if(is.numeric, format_numbers)

dat_table_s10$ci <- paste0("[", dat_table_s10$lowerCI, ", ", dat_table_s10$upperCI, "]")
dat_table_s10$ci.1 <- paste0("[", dat_table_s10$lowerCI.1, ", ", dat_table_s10$upperCI.1, "]")

dat_table_s10 <- dat_table_s10[c(1,3,5:8,2,4,9,12,14,16,18,11,13,15,17,19,10,20:24),]

dat_table_s10$blank <- NA

dat_table_s10 <- dat_table_s10[c("predictor","Pe","ci","blank","Pe.1","ci.1")]

col_keys <- c("predictor","Pe","ci","blank","Pe.1","ci.1")
head1 <- c("Predictor","Coding of Cat\n(0 = Chinese characters;\n1 = Social values)","Coding of Cat\n(0 = Chinese characters;\n1 = Social values)","","Coding of Cat\n(0 = Social values;\n1 = Chinese characters)","Coding of Cat\n(0 = Social values;\n1 = Chinese characters)")
head2 <- c("","Estimate","  95% CI","","Estimate","  95% CI")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_s10)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:6, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:3, part="header")
tbl <- merge_at(tbl, i=1, j=5:6, part="header")
tbl <- hline(tbl, i=1, j=c(2:3,5:6), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:6, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("predictor"), align="left", part="body")
tbl <- width(tbl, j =~ predictor, width=2.2)
tbl <- width(tbl, j =~ Pe + Pe.1, width=.75)
tbl <- width(tbl, j =~ ci + ci.1, width=1.2)
tbl <- width(tbl, j =~ blank, width=.1)
tbl <- colformat_lgl(tbl, j =~ blank, na_str="")
tbl <- height_all(tbl, height=.1, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S10.docx")


rm(list=setdiff(ls(), c("julia","julia_wd","files_wd","dat","dat_exp1","dat_exp2","dat_exp3","dat_exp123","dat_exp23")))


########## S12: Block 1 Versus Blocks 2-6 ##########


#prepare data for mixed-effects models

##exclude pairs of Chinese characters and social values for which the sociocultural norm was not recalled correctly
dat_s12_exp123 <- subset(dat_exp123, subset = recall_correct == 1)

##exclude pairs of Chinese characters for participants who knew the meaning of at least one of the characters 
dat_s12_exp123 <- subset(dat_s12_exp123, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))

##recode block_position into block (block 1 vs. blocks 2-6)
dat_s12_exp123$block <- car::recode(dat_s12_exp123$block_position, "1=0; 2=1; 3=1; 4=1; 5=1; 6=1")

##recode block
dat_s12_exp123$block_r <- car::recode(dat_s12_exp123$block, "0=1; 1=0")


#conduct mixed-effects models in Julia

##set working directory to Julia
setwd(julia_wd)

##convert grouping variable to factor and assign data to Julia
dat_s12_exp123$id <- as.factor(dat_s12_exp123$id)
julia_assign("dat_s12_exp123", dat_s12_exp123)

##conduct domains-as-predictors models

###with block (0 = Block 1; 1 = Blocks 2-6)
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c * block + sociocultural_norm * cns_c * block + sociocultural_norm * opn_c * block
                + sociocultural_norm * ext_c * block + sociocultural_norm * neu_c * block + (1 + sociocultural_norm | id)),  dat_s12_exp123)")
s12_exp123_domain_model1 <- julia_eval("s12_exp123_domain_model1 = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
                :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###with recoded block (0 = Blocks 2-6; 1 = Block 1)
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c * block_r + sociocultural_norm * cns_c * block_r + sociocultural_norm * opn_c * block_r
                + sociocultural_norm * ext_c * block_r + sociocultural_norm * neu_c * block_r + (1 + sociocultural_norm | id)),  dat_s12_exp123)")
s12_exp123_domain_model2 <- julia_eval("s12_exp123_domain_model2 = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
                :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")


#Table S11
dat_table_s11 <- data.frame(s12_exp123_domain_model1,s12_exp123_domain_model2)

dat_table_s11[,5] <- NULL

dat_table_s11[,1] <- c("(1)   (Intercept)","(7)   Norms", "(2)   Agr", "(8)   Block", "(3)   Cns", "(4)   Opn", "(5)   Ext", "(6)   Neu", "(9)   Agr x Norms",
                       "(19) Norms x Block", "(14) Agr x Block", "(10) Cns x Norms", "(15) Cns x Block", "(11) Opn x Norms", "(16) Opn x Block", 
                       "(12) Ext x Norms", "(17) Ext x Block", "(13) Neu x Norms", "(18) Neu x Block", "(20) Agr x Norms x Block", 
                       "(21) Cns x Norms x Block", "(22) Opn x Norms x Block", "(23) Ext x Norms x Block", "(24) Neu x Norms x Block")

dat_table_s11[,2:7] <- sapply(dat_table_s11[,2:7], as.numeric)

format_numbers <- function(x){
  ifelse(abs(x) <= 0.005,  formatC(x, format = "e", digits = 0), formatC(x, format = "f", digits = 2))
}
dat_table_s11 <- dat_table_s11 %>% mutate_if(is.numeric, format_numbers)

dat_table_s11$ci <- paste0("[", dat_table_s11$lowerCI, ", ", dat_table_s11$upperCI, "]")
dat_table_s11$ci.1 <- paste0("[", dat_table_s11$lowerCI.1, ", ", dat_table_s11$upperCI.1, "]")

dat_table_s11 <- dat_table_s11[c(1,3,5:8,2,4,9,12,14,16,18,11,13,15,17,19,10,20:24),]

dat_table_s11$blank <- NA

dat_table_s11 <- dat_table_s11[c("predictor","Pe","ci","blank","Pe.1","ci.1")]

col_keys <- c("predictor","Pe","ci","blank","Pe.1","ci.1")
head1 <- c("Predictor","Coding of Block\n(0 = Block 1;\n1 = Blocks 2-6)","Coding of Block\n(0 = Block 1;\n1 = Blocks 2-6)","","Coding of Block\n(0 = Blocks 2-6;\n1 = Block 1)","Coding of Block\n(0 = Blocks 2-6;\n1 = Block 1)")
head2 <- c("","Estimate","  95% CI","","Estimate","  95% CI")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_s11)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:6, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:3, part="header")
tbl <- merge_at(tbl, i=1, j=5:6, part="header")
tbl <- hline(tbl, i=1, j=c(2:3,5:6), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:6, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("predictor"), align="left", part="body")
tbl <- width(tbl, j =~ predictor, width=2.4)
tbl <- width(tbl, j =~ Pe + Pe.1, width=.75)
tbl <- width(tbl, j =~ ci + ci.1, width=1.2)
tbl <- width(tbl, j =~ blank, width=.1)
tbl <- colformat_lgl(tbl, j =~ blank, na_str="")
tbl <- height_all(tbl, height=.1, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S11.docx")


rm(list=setdiff(ls(), c("julia","julia_wd","files_wd","dat","dat_exp1","dat_exp2","dat_exp3","dat_exp123","dat_exp23")))


########## S13: Self-Insight Into the Influence of Sociocultural Norms ##########


#proportion of response options
dat_s13_exp123_influence <- subset(dat_exp123, select = c(id,selfinsight_influence))
dat_s13_exp123_influence[is.na(dat_s13_exp123_influence)] <- 999
dat_s13_exp123_influence <- aggregate(selfinsight_influence ~ id, dat_s13_exp123_influence, FUN = function(x) mean(x, na.rm=T))
influence_percent <- as.data.frame(table(dat_s13_exp123_influence$selfinsight_influence))
paste0("Percentage of participants who answered 'No': ", round(100*(influence_percent[1,2]/sum(influence_percent[,2])),1), "%")
paste0("Percentage of participants who answered 'Yes, my preferences shifted toward the preferences of the majority': ", round(100*(influence_percent[2,2]/sum(influence_percent[,2])),1), "%")
paste0("Percentage of participants who answered 'Yes, my preferences shifted away from the preferences of the majority': ", round(100*(influence_percent[3,2]/sum(influence_percent[,2])),1), "%")
paste0("Percentage of participants who did not answer the question': ", round(100*(influence_percent[4,2]/sum(influence_percent[,2])),1), "%")
rm(dat_s13_exp123_influence,influence_percent)


#prepare data for mixed-effects models

##select participants who were oblivious to the influence

###Experiment 1
dat_s13_exp1 <- subset(dat_exp1, subset = selfinsight_influence == 1)
###Experiment 2
dat_s13_exp2 <- subset(dat_exp2, subset = selfinsight_influence == 1)
###Experiment 3
dat_s13_exp3 <- subset(dat_exp3, subset = selfinsight_influence == 1)
###Experiments 1-3
dat_s13_exp123 <- subset(dat_exp123, subset = selfinsight_influence == 1)

##exclude pairs of Chinese characters and social values for which the sociocultural norm was not recalled correctly

###Experiment 1
dat_s13_exp1 <- subset(dat_s13_exp1, subset = recall_correct == 1)
###Experiment 2
dat_s13_exp2 <- subset(dat_s13_exp2, subset = recall_correct == 1)
###Experiment 3
dat_s13_exp3 <- subset(dat_s13_exp3, subset = recall_correct == 1)
###Experiments 1-3
dat_s13_exp123 <- subset(dat_s13_exp123, subset = recall_correct == 1)

##exclude pairs of Chinese characters for participants who knew the meaning of at least one of the characters 

###Experiment 1
dat_s13_exp1 <- subset(dat_s13_exp1, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
###Experiment 2 
dat_s13_exp2 <- subset(dat_s13_exp2, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
###Experiment 3
dat_s13_exp3 <- subset(dat_s13_exp3, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))
###Experiments 1-3
dat_s13_exp123 <- subset(dat_s13_exp123, subset = chinese_characters_known != 1 | c(chinese_characters_known == 1 & pair_index_across_samples > 18))


#conduct mixed-effects models in Julia

##set working directory to Julia folder
setwd(julia_wd)

##convert grouping variable to factor and assign data to Julia

###Experiment 1
dat_s13_exp1$id <- as.factor(dat_s13_exp1$id)
julia_assign("dat_s13_exp1", dat_s13_exp1)
###Experiment 2
dat_s13_exp2$id <- as.factor(dat_s13_exp2$id)
julia_assign("dat_s13_exp2", dat_s13_exp2)
###Experiment 3
dat_s13_exp3$id <- as.factor(dat_s13_exp3$id)
julia_assign("dat_s13_exp3", dat_s13_exp3)
###Experiments 1-3
dat_s13_exp123$id <- as.factor(dat_s13_exp123$id)
julia_assign("dat_s13_exp123", dat_s13_exp123)

##conduct domains-as-predictors models

###Experiment 1
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s13_exp1)")
s13_exp1_domain_model <- julia_eval("s13_exp1_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiment 2
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
                + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s13_exp2)")
s13_exp2_domain_model <- julia_eval("s13_exp2_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiment 3
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
                + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s13_exp3)")
s13_exp3_domain_model <- julia_eval("s13_exp3_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")
###Experiments 1-3
julia_eval("fm = fit(LinearMixedModel, @formula(personal_preference ~ 1 + sociocultural_norm * agr_c + sociocultural_norm * cns_c + sociocultural_norm * opn_c
               + sociocultural_norm * ext_c + sociocultural_norm * neu_c + (1 + sociocultural_norm | id)),  dat_s13_exp123)")
s13_exp123_domain_model <- julia_eval("s13_exp123_domain_model = rename!(DataFrame([(coeftable(fm).rownms) (round.(coef(fm),digits=5)) (round.(((coef(fm))-1.96*(stderror(fm))),digits=5)) (round.(((coef(fm))+1.96*(stderror(fm))),digits=5))]),
 :x1 => :predictor, :x2 => :Pe, :x3 => :lowerCI, :x4 => :upperCI)")


#Table S12: Effects of the Big Five Domains on Personal Preferences Moderated by Sociocultural Norms
#           for Participants who Were Oblivious to the Influence of Sociocultural Norms
dat_table_s12 <- data.frame(s13_exp1_domain_model,s13_exp2_domain_model,s13_exp3_domain_model,s13_exp123_domain_model)

dat_table_s12[,13] <- NULL
dat_table_s12[,9] <- NULL
dat_table_s12[,5] <- NULL

dat_table_s12[,1] <- c("(1)   (Intercept)","(7)   Norms", "(2)   Agr", "(3)   Cns", "(4)   Opn", "(5)   Ext", "(6)   Neu", "(8)   Agr x Norms",
                       "(9)   Cns x Norms", "(10) Opn x Norms", "(11) Ext x Norms", "(12) Neu x Norms")

dat_table_s12[,2:13] <- sapply(dat_table_s12[,2:13], as.numeric)

format_numbers <- function(x){
  ifelse(abs(x) <= 0.005,  formatC(x, format = "e", digits = 0), formatC(x, format = "f", digits = 2))
}
dat_table_s12 <- dat_table_s12 %>% mutate_if(is.numeric, format_numbers)

dat_table_s12$ci <- paste0("[", dat_table_s12$lowerCI, ", ", dat_table_s12$upperCI, "]")
dat_table_s12$ci.1 <- paste0("[", dat_table_s12$lowerCI.1, ", ", dat_table_s12$upperCI.1, "]")
dat_table_s12$ci.2 <- paste0("[", dat_table_s12$lowerCI.2, ", ", dat_table_s12$upperCI.2, "]")
dat_table_s12$ci.3 <- paste0("[", dat_table_s12$lowerCI.3, ", ", dat_table_s12$upperCI.3, "]")

dat_table_s12 <- dat_table_s12[c(1,3:7,2,8:12),]

dat_table_s12$blank <- NA
dat_table_s12$blank.1 <- NA
dat_table_s12$blank.2 <- NA

dat_table_s12 <- dat_table_s12[c("predictor","Pe","ci","blank","Pe.1","ci.1","blank.1","Pe.2","ci.2","blank.2","Pe.3","ci.3")]

col_keys <- c("predictor","Pe","ci","blank","Pe.1","ci.1","blank.1","Pe.2","ci.2","blank.2","Pe.3","ci.3")
head1 <- c("Predictor","Experiment 1","Experiment 1","","Experiment 2","Experiment 2","","Experiment 3","Experiment 3","","Experiments 1-3","Experiments 1-3")
head2 <- c("","Estimate","  95% CI","","Estimate","  95% CI","","Estimate","  95% CI","","Estimate","  95% CI")
head <- data.frame(col_keys,head1,head2, stringsAsFactors = FALSE)
rm(col_keys,head1,head2)

tbl <- flextable(dat_table_s12)
tbl <- set_header_df(tbl, mapping=head, key="col_keys")
tbl <- hline_top(tbl, j=1:12, border=fp_border(width=2), part="header")
tbl <- merge_at(tbl, i=1, j=2:3, part="header")
tbl <- merge_at(tbl, i=1, j=5:6, part="header")
tbl <- merge_at(tbl, i=1, j=8:9, part="header")
tbl <- merge_at(tbl, i=1, j=11:12, part="header")
tbl <- hline(tbl, i=1, j=c(2:3,5:6,8:9,11:12), border=fp_border(width=1.2), part="header")
tbl <- hline(tbl, i=2, j=1:12, border=fp_border(width=1.2), part="header")
tbl <- flextable::font(tbl, fontname="Times", part="all")
tbl <- fontsize(tbl, size=12, part="all")
tbl <- align(tbl, align="center", part="all")
tbl <- align(tbl, j = c("predictor"), align="left", part="body")
tbl <- width(tbl, j =~ predictor, width=1.75)
tbl <- width(tbl, j =~ Pe + Pe.1 + Pe.2 + Pe.3, width=.75)
tbl <- width(tbl, j =~ ci + ci.1 + ci.2 + ci.3, width=1.2)
tbl <- width(tbl, j =~ blank + blank.1 + blank.2, width=.1)
tbl <- colformat_lgl(tbl, j =~ blank + blank.1 + blank.2, na_str="")
tbl <- height_all(tbl, height=.1, part="all")
tbl

setwd(files_wd)
doc <- read_docx()
doc <- body_add_flextable(doc, value = tbl)
print(doc, target = "Table_S12.docx")


rm(list=setdiff(ls(), c("julia","julia_wd","files_wd","dat","dat_exp1","dat_exp2","dat_exp3","dat_exp123","dat_exp23")))


########## End of Supplementary Analyses ##########


#clear environment
rm(list = ls())


#quit R
q()