library(pacman); p_load(psych, lavaan, dplyr, sjmisc, sandwich, lmtest, ggplot2, effsize, GenAlgo)
Variables to scroll through. Data from https://osf.io/t4rms/.
psych::describe(IPIPA)
## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
Countries represented in the dataset and the total number of them.
with(droplevels(IPIPA), list(levels = levels(as.factor(COUNTRY)), nlevels = nlevels(as.factor(COUNTRY))))
## $levels
## [1] "" " Taiwan" "Afghanist" "Albania" "Algeria" "Andorra"
## [7] "Angola" "Anguilla" "Antarctic" "Antigua" "Arabian G" "Argentina"
## [13] "Armenia" "Aruba" "Australia" "Austria" "Azerbaija" "Bahamas"
## [19] "Bahrain" "Banglades" "Barbados" "Belarus" "Belgium" "Belize"
## [25] "Benin" "Bermuda" "Bhutan" "Bolivia" "Borneo" "Bosnia He"
## [31] "Botswana" "Bouvet Is" "Brazil" "British I" "British V" "Brunei"
## [37] "Bulgaria" "Burkina F" "Burma" "Burma(Mya" "Burundi" "Cambodia"
## [43] "Cameroon" "Canada" "Cape Verd" "Cayman Is" "Central A" "Chad"
## [49] "Chile" "China" "Christmas" "Cocos (Ke" "Columbia" "Comoros"
## [55] "Congo" "Cook Isla" "Costa Ric" "Croatia" "Cuba" "Cyprus"
## [61] "Czech Rep" "Denmark" "Djibouti" "Dominica" "Dominican" "East Timo"
## [67] "Ecuador" "Egypt" "El Salvad" "Equatoria" "Eritrea" "Estonia"
## [73] "Ethiopia" "Faeroe Is" "Falkland" "Fiji" "Finland" "France"
## [79] "French Gu" "French Po" "Gabon" "Gambia" "Georgia" "Germany"
## [85] "Ghana" "Gibraltar" "Greece" "Greenland" "Grenada" "Guadeloup"
## [91] "Guam" "Guatemala" "Guinea" "Guinea-Bi" "Guyana" "Haiti"
## [97] "Honduras" "Hong Kong" "Hungary" "Iceland" "India" "Indonesia"
## [103] "Iran" "Iraq" "Ireland" "Israel" "Italy" "Ivory Coa"
## [109] "Jamaica" "Japan" "Johnston" "Jordan" "Kazakhsta" "Kenya"
## [115] "Kiribati" "Kuwait" "Kyrgystan" "Lao P.Dem" "Latvia" "Lebanon"
## [121] "Lesotho" "Liberia" "Libyan Ar" "Liechtens" "Lithuania" "Luxembour"
## [127] "Macau" "Macedonia" "Madagasca" "Malawi" "Malaysia" "Maldives"
## [133] "Mali" "Malta" "Marshall" "Martiniqu" "Mauritani" "Mauritius"
## [139] "Mexico" "Micronesi" "Midway Is" "Moldova" "Monaco" "Mongolia"
## [145] "Montserra" "Morocco" "Mozambiqu" "Namibia" "Nauru" "Nepal"
## [151] "Netherlan" "New Caled" "New Zeala" "Nicaragua" "Niger" "Nigeria"
## [157] "Niue" "Norfolk I" "North Kor" "Northern" "Norway" "Oman"
## [163] "Pakistan" "Palau" "Panama" "Papua New" "Paraguay" "Peru"
## [169] "Philippin" "Pitcairn" "Poland" "Portugal" "Puerto Ri" "Qatar"
## [175] "Republic" "Reunion" "Romania" "Russian F" "Rwanda" "Saint Hel"
## [181] "Saint Kit" "Samoa" "San Marin" "Sao Tome" "Saudi Ara" "Senegal"
## [187] "Serbia" "Seychelle" "Sierra Le" "Singapore" "Slovakia" "Slovenia"
## [193] "Solomon I" "Somalia" "South Afr" "South Kor" "Spain" "Sri Lanka"
## [199] "St Lucia" "St Vincen" "Sudan" "Suriname" "Svalbard" "Swaziland"
## [205] "Sweden" "Switzerla" "Syria" "Taiwan" "Tajikista" "Tanzania"
## [211] "Thailand" "Togo" "Tokelau" "Tonga" "Trinidad" "Tunisia"
## [217] "Turkey" "Turkmenis" "Turks and" "Tuvalu" "Uganda" "UK"
## [223] "Ukraine" "United Ar" "Uruguay" "USA" "Uzbekista" "Vanuatu"
## [229] "Vatican" "Vatican C" "Venezuela" "Vietnam" "Virgin Is" "W. Samoa"
## [235] "Wake Isla" "Western S" "Yemen" "Yugoslavi" "Zaire" "Zambia"
## [241] "Zimbabwe"
##
## $nlevels
## [1] 241
Countries, split by sex, where 1 is male and 2 is female.
IPIPA %>% group_by(COUNTRY, SEX) %>% summarise(Participants = length(COUNTRY), .groups = "keep")
I used the 2015 Gender Gap Index (henceforth GEI) values for available countries with sufficient numbers of participants from both sexes (around 100 and similar group sizes at the lower end, with a GEI entry). Those values are available here: https://www3.weforum.org/docs/GGGR2015/cover.pdf or https://reports.weforum.org/global-gender-gap-report-2015/rankings/. I did not use a more recent year because I wanted one near the year the data was gathered and the data went online in 2015.
#Select countries
IPIPA <- IPIPA[IPIPA$COUNTRY %in% c("Albania", "Algeria", "Angola", "Argentina", "Australia", "Austria", "Belgium", "Brazil", "Canada", "China", "Columbia", "Croatia", "Denmark", "Egypt", "Finland", "France", "Germany", "Greece", "India", "Indonesia", "Iran", "Ireland", "Israel", "Italy", "Jamaica", "Japan", "Kenya", "Lebanon", "Malaysia", "Mexico", "Netherlan", "New Zeala", "Nigeria", "Norway", "Pakistan", "Peru", "Philippin", "Poland", "Portugal", "Romania", "Russian F", "Singapore", "Slovakia", "Slovenia", "South Afr", "South Kor", "Spain", "Sweden", "Switzerla", "Thailand", "Trinidad", "Turkey", "Uganda", "UK", "Ukraine", "United Ar", "USA", "Venezuela", "Vietnam"), ]
with(droplevels(IPIPA), list(levels = levels(as.factor(COUNTRY)), nlevels = nlevels(as.factor(COUNTRY))))
## $levels
## [1] "Albania" "Algeria" "Angola" "Argentina" "Australia" "Austria"
## [7] "Belgium" "Brazil" "Canada" "China" "Columbia" "Croatia"
## [13] "Denmark" "Egypt" "Finland" "France" "Germany" "Greece"
## [19] "India" "Indonesia" "Iran" "Ireland" "Israel" "Italy"
## [25] "Jamaica" "Japan" "Kenya" "Lebanon" "Malaysia" "Mexico"
## [31] "Netherlan" "New Zeala" "Nigeria" "Norway" "Pakistan" "Peru"
## [37] "Philippin" "Poland" "Portugal" "Romania" "Russian F" "Singapore"
## [43] "Slovakia" "Slovenia" "South Afr" "South Kor" "Spain" "Sweden"
## [49] "Switzerla" "Thailand" "Trinidad" "Turkey" "Uganda" "UK"
## [55] "Ukraine" "United Ar" "USA" "Venezuela" "Vietnam"
##
## $nlevels
## [1] 59
Computing facet scores.
#Compute Variables
IPIPA <- mutate(IPIPA,
AchievementStriving = I20 + I50 - I80 - I110,
ActivityLevel = I17 + I47 + I77 - I107,
Adventurousness = I18 - I48 - I78 - I108,
Altruism = I14 + I44 - I74 - I104,
Anger = I6 + I36 + I66 - I96,
Anxiety = I1 + I31 + I61 + I91,
ArtisticInterests = I8 + I38 - I68 - I98,
Assertiveness = I12 + I42 + I72 - I102,
Cautiousness = -I30 - I60 - I90 - I120,
Cheerfulness = I27 + I57 + I87 + I117,
Cooperation = -I19 - I49 - I79 - I109,
Depression = I11 + I41 + I71 - I101,
Dutifulness = I15 + I45 - I75 - I105,
Emotionality = I13 + I43 - I73 - I103,
ExcitementSeeking = I22 + I52 + I82 + I112,
Friendliness = I2 + I32 - I62 - I92,
Gregariousness = I7 + I37 - I67 - I97,
Imagination = I3 + I33 + I63 + I93,
Immoderation = I21 - I51 - I81 - I111,
Intellect = I23 - I53 - I83 - I113,
Liberalism = I28 + I58 - I88 - I118,
Modesty = -I24 - I54 - I84 - I114,
Morality = -I9 - I39 - I69 - I99,
Orderliness = I10 - I40 - I70 - I100,
SelfConsciousness = I16 + I46 + I76 - I106,
SelfDiscipline = I25 + I55 - I85 - I115,
SelfEfficacy = I5 + I35 + I65 + I95,
Sympathy = I29 + I59 - I89 - I119,
Trust = I4 + I34 + I64 - I94,
Vulnerability = I26 + I56 + I86 - I116,
Openness = (1/6)*(scale(Liberalism) + scale(Intellect) + scale(Adventurousness) + scale(Emotionality) + scale(ArtisticInterests) + scale(Imagination)),
Neuroticism = (1/6)*(scale(Vulnerability) + scale(Immoderation) + scale(SelfConsciousness) + scale(Depression) + scale(Anger) + scale(Anxiety)),
Extraversion = (1/6)*(scale(Cheerfulness) + scale(ExcitementSeeking) + scale(ActivityLevel) + scale(Assertiveness) + scale(Gregariousness) + scale(Friendliness)),
Conscientiousness = (1/6)*(scale(Cautiousness) + scale(SelfDiscipline) + scale(AchievementStriving) + scale(Dutifulness) + scale(Orderliness) + scale(SelfEfficacy)),
Agreeableness = (1/6)*(-1*(scale(Sympathy) + scale(Modesty) + scale(Cooperation) + scale(Altruism) + scale(Morality) + scale(Trust))),
GFP = (1/5)*(scale(Openness) + scale(Conscientiousness) + scale(Extraversion) + scale(Agreeableness) - scale(Neuroticism))) #Musek (2007) GFP directions
IPIPA$SEXO <- rec(IPIPA$SEX, rec = "1 = MALE; 2 = FEMALE")
IPIPA$GEI <- rec(IPIPA$COUNTRY, rec = "Albania = 0.701; Algeria = 0.632; Angola = 0.637; Argentina = 0.734; Australia, Austria = 0.733; Belgium = 0.753; Brazil = 0.686; Canada, USA = 0.740; China = 0.682; Columbia = 0.725; Croatia, Uganda = 0.708; Denmark = 0.767; Egypt = 0.599; Finland, Norway = 0.850; France = 0.761; Germany = 0.779; Greece = 0.685; India = 0.664; Indonesia = 0.681; Iran = 0.580; Ireland = 0.807; Israel = 0.712; Italy = 0.726; Jamaica = 0.703; Japan = 0.670; Kenya = 0.719; Lebanon = 0.598; Malaysia = 0.655; Mexico = 0.699; Netherlan = 0.776; New Zeala = 0.782; Nigeria = 0.638; Pakistan = 0.559; Peru = 0.683; Philippin = 0.790; Poland = 0.715; Portugal = 0.731; Romania = 0.693; Russian F = 0.694; Singapore = 0.711; Slovakia = 0.675; Slovenia = 0.784; South Afr = 0.759; South Kor = 0.651; Spain = 0.742; Sweden = 0.823; Switzerla = 0.785; Thailand = 0.706; Trinidad = 0.720; Turkey = 0.624; UK = 0.758; Ukraine = 0.702; United Ar = 0.646; Venezuela = 0.691; Vietnam = 0.687")
And country-specific dataframes.
#Create Country-specific DFs
ALBIP <- subset(IPIPA, COUNTRY == "Albania")
ALGIP <- subset(IPIPA, COUNTRY == "Algeria")
ANGIP <- subset(IPIPA, COUNTRY == "Angola")
ARGIP <- subset(IPIPA, COUNTRY == "Argentina")
AUSIP <- subset(IPIPA, COUNTRY == "Australia")
HABIP <- subset(IPIPA, COUNTRY == "Austria")
BELIP <- subset(IPIPA, COUNTRY == "Belgium")
BRAIP <- subset(IPIPA, COUNTRY == "Brazil")
CANIP <- subset(IPIPA, COUNTRY == "Canada")
CHIIP <- subset(IPIPA, COUNTRY == "China")
COLIP <- subset(IPIPA, COUNTRY == "Columbia")
CROIP <- subset(IPIPA, COUNTRY == "Croatia")
DENIP <- subset(IPIPA, COUNTRY == "Denmark")
EGYIP <- subset(IPIPA, COUNTRY == "Egypt")
FINIP <- subset(IPIPA, COUNTRY == "Finland")
FRAIP <- subset(IPIPA, COUNTRY == "France")
GERIP <- subset(IPIPA, COUNTRY == "Germany")
GREIP <- subset(IPIPA, COUNTRY == "Greece")
INDIP <- subset(IPIPA, COUNTRY == "India")
INOIP <- subset(IPIPA, COUNTRY == "Indonesia")
IRAIP <- subset(IPIPA, COUNTRY == "Iran")
IREIP <- subset(IPIPA, COUNTRY == "Ireland")
ISRIP <- subset(IPIPA, COUNTRY == "Israel")
ITAIP <- subset(IPIPA, COUNTRY == "Italy")
JAMIP <- subset(IPIPA, COUNTRY == "Jamaica")
JAPIP <- subset(IPIPA, COUNTRY == "Japan")
KENIP <- subset(IPIPA, COUNTRY == "Kenya")
LEBIP <- subset(IPIPA, COUNTRY == "Lebanon")
MALIP <- subset(IPIPA, COUNTRY == "Malaysia")
MEXIP <- subset(IPIPA, COUNTRY == "Mexico")
NETIP <- subset(IPIPA, COUNTRY == "Netherlan")
NEWIP <- subset(IPIPA, COUNTRY == "New Zeala")
NIRIP <- subset(IPIPA, COUNTRY == "Nigeria")
NORIP <- subset(IPIPA, COUNTRY == "Norway")
PERIP <- subset(IPIPA, COUNTRY == "Peru")
PAKIP <- subset(IPIPA, COUNTRY == "Pakistan")
PHIIP <- subset(IPIPA, COUNTRY == "Philippin")
POLIP <- subset(IPIPA, COUNTRY == "Poland")
PORIP <- subset(IPIPA, COUNTRY == "Portugal")
ROMIP <- subset(IPIPA, COUNTRY == "Romania")
RUSIP <- subset(IPIPA, COUNTRY == "Russian F")
SINIP <- subset(IPIPA, COUNTRY == "Singapore")
SLOIP <- subset(IPIPA, COUNTRY == "Slovakia")
SLVIP <- subset(IPIPA, COUNTRY == "Slovenia")
SOUIP <- subset(IPIPA, COUNTRY == "South Afr")
SKOIP <- subset(IPIPA, COUNTRY == "South Kor")
SPAIP <- subset(IPIPA, COUNTRY == "Spain")
SWEIP <- subset(IPIPA, COUNTRY == "Sweden")
SWIIP <- subset(IPIPA, COUNTRY == "Switzerla")
THAIP <- subset(IPIPA, COUNTRY == "Thailand")
TRIIP <- subset(IPIPA, COUNTRY == "Trinidad")
TURIP <- subset(IPIPA, COUNTRY == "Turkey")
UGAIP <- subset(IPIPA, COUNTRY == "Uganda")
UKIP <- subset(IPIPA, COUNTRY == "UK")
UKRIP <- subset(IPIPA, COUNTRY == "Ukraine")
UAEIP <- subset(IPIPA, COUNTRY == "United Ar")
USAIP <- subset(IPIPA, COUNTRY == "USA")
VENIP <- subset(IPIPA, COUNTRY == "Venezuela")
VIEIP <- subset(IPIPA, COUNTRY == "Vietnam")
I initially used the function for dMACS_Signed, with which positive scores indicated bias increased scores for the second group and vice-versa. I swapped this to dMACS_True, which has the same interpretation but proper magnitudes.
dMACS_True <- function(fit.cfa, group1, group2) {
nitems <- lavaan::lavInspect(fit.cfa, what = "rsquare") %>%
.[[1]] %>%
names(.) %>%
length(.)
cfa_minmax <- function(fit.cfa) {
dt <- lavaan::inspect(fit.cfa, what = "data")
latentMin <- min(dt[[1]]) - 1
latentMax <- max(dt[[1]]) + 1
out <- cbind(as.numeric(latentMin), as.numeric(latentMax))
return(out)}
reference_load <- lavaan::inspect(fit.cfa, what = "est") %>%
.[[1]] %>%
.$lambda
focal_load <- lavaan::inspect(fit.cfa, what = "est") %>%
.[[2]] %>%
.$lambda
reference_intrcp <- lavaan::inspect(fit.cfa, what = "est") %>%
.[[1]] %>%
.$nu
focal_intrcp <- lavaan::inspect(fit.cfa, what = "est") %>%
.[[2]] %>%
.$nu
pool.sd <- function(fit.cfa) {
cfa.se <- lavaan::lavInspect(fit.cfa, what = "se")
cfa.n <- lavaan::lavInspect(fit.cfa, what = "nobs")
l <- list()
test <- lavaan::lavInspect(fit.cfa, what = "rsquare") %>%
.[[1]] %>%
names(.) %>%
length(.)
for (i in 1:test) {
grp1 <- cfa.se[[group1]]$nu[i] * sqrt(cfa.n[1])
grp2 <- cfa.se[[group2]]$nu[i] * sqrt(cfa.n[2])
numerator <- ((cfa.n[1] - 1) * grp1 + (cfa.n[2] - 1) * grp2)
denominator <- (cfa.n[1] - 1) + (cfa.n[2] - 1)
pooled.sd <- numerator / denominator
l[[paste("item", i)]] <- pooled.sd}
result <- matrix(unlist(l), nrow = test, byrow = TRUE)
return(result)}
pld_sd <- pool.sd(fit.cfa)
fcl_lt_vrnc <- lavaan::inspect(fit.cfa, what = "est") %>%
.[[2]] %>%
.$psi
l <- list()
rowlab <- c()
for (i in c(1:nitems)) {
focal.fn <- function(x) {
mpr <- focal_intrcp[i] + focal_load[i] * x
return(mpr)}
reference.fn <- function(x) {
mpr <- reference_intrcp[i] + reference_load[i] * x
return(mpr)}
diff.fn <- function(x, i = i) {
d <- ((reference.fn(x) - focal.fn(x))^2) * dnorm(x, mean = 0, sd = sqrt(fcl_lt_vrnc))
return(d)}
dMACS <- round((1 / pld_sd[i]) * sqrt(integrate(diff.fn,
lower = cfa_minmax(fit.cfa)[, 1],
upper = cfa_minmax(fit.cfa)[, 2]
)$value), 3)
l[[length(l) + 1]] <- dMACS
rowlab[[length(rowlab) + 1]] <- paste("Item", i)}
m <- matrix(unlist(l), nrow = nitems, dimnames = list(rowlab, "dMACS")) #Magnitude
l <- list()
rowlab <- c()
for (i in c(1:nitems)) {
focal.fn <- function(x) {
mpr <- focal_intrcp[i] + focal_load[i] * x
return(mpr)}
reference.fn <- function(x) {
mpr <- reference_intrcp[i] + reference_load[i] * x
return(mpr)}
diff.fn <- function(x, i = i) {
d <- ((reference.fn(x) - focal.fn(x))) * dnorm(x, mean = 0, sd = sqrt(fcl_lt_vrnc))
return(d)}
dMACS <- round((1 / pld_sd[i]) * integrate(diff.fn,
lower = cfa_minmax(fit.cfa)[, 1],
upper = cfa_minmax(fit.cfa)[, 2])$value, 3)
l[[length(l) + 1]] <- dMACS
rowlab[[length(rowlab) + 1]] <- paste("Item", i)}
d <- matrix(unlist(l), nrow = nitems, dimnames = list(rowlab, "dMACS_True")) #Direction
D <- ifelse(d < 0, -1, 1) #Recode d to a matrix of signs
H = D * m #Multiply magnitudes by signs
return(H)}
IPIPA_Means <- IPIPA %>% group_by(GEI, SEX) %>% summarise(Openness = mean(Openness),
Conscientiousness = mean(Conscientiousness),
Extraversion = mean(Extraversion),
Agreeableness = mean(Agreeableness),
Neuroticism = mean(Neuroticism),
GFP = mean(GFP),
N = n(), .groups = "keep")
ggplot(IPIPA_Means, aes(x = GEI, y = scale(Openness), color = as.factor(SEX), group = as.factor(SEX))) +
geom_point() +
geom_smooth(method = "lm", formula = "y ~ x") +
labs(title = "", x = "Gender Equality Index", y = "Openness Mean") +
theme_bw() +
scale_color_manual(values = c("steelblue", "magenta"), name = "Sex", labels = c("Male", "Female")) +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5),
legend.background = element_blank(), legend.key = element_blank(), legend.position = c(0.1, 0.1)) +
guides(color=guide_legend(override.aes=list(fill=NA)))
ggplot(IPIPA, aes(x = GEI, y = scale(Openness), color = as.factor(SEX), group = as.factor(SEX))) +
geom_point() +
geom_smooth(method = "lm", formula = "y ~ x") +
labs(title = "", x = "Gender Equality Index", y = "Openness Score") + theme_bw() +
scale_color_manual(values = c("steelblue", "magenta"), name = "Sex", labels = c("Male", "Female")) +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5),
legend.position = c(0.86, 0.92), legend.background = element_blank(), legend.key = element_blank()) +
guides(color=guide_legend(override.aes=list(fill=NA))) + geom_jitter()
G1 <- lm(scale(Openness) ~ scale(GEI), IPIPA_Means); summary(G1)
##
## Call:
## lm(formula = scale(Openness) ~ scale(GEI), data = IPIPA_Means)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.68126 -0.59948 -0.04886 0.51048 2.35923
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.137e-16 8.325e-02 0.000 1
## scale(GEI) -4.947e-01 8.363e-02 -5.915 3.95e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8731 on 108 degrees of freedom
## Multiple R-squared: 0.2447, Adjusted R-squared: 0.2377
## F-statistic: 34.99 on 1 and 108 DF, p-value: 3.955e-08
G2 <- lm(scale(Openness) ~ scale(GEI) * SEX, IPIPA_Means); summary(G2)
##
## Call:
## lm(formula = scale(Openness) ~ scale(GEI) * SEX, data = IPIPA_Means)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.79948 -0.53096 -0.04878 0.44934 2.33446
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.48985 0.26092 -1.877 0.0632 .
## scale(GEI) -0.52734 0.26212 -2.012 0.0468 *
## SEX 0.32656 0.16502 1.979 0.0504 .
## scale(GEI):SEX 0.02178 0.16578 0.131 0.8957
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8654 on 106 degrees of freedom
## Multiple R-squared: 0.2717, Adjusted R-squared: 0.2511
## F-statistic: 13.18 on 3 and 106 DF, p-value: 2.223e-07
VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1373e-16 8.3120e-02 0.0000 1
## scale(GEI) -4.9467e-01 9.0586e-02 -5.4607 3.058e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.489846 0.263028 -1.8623 0.06533 .
## scale(GEI) -0.527339 0.281400 -1.8740 0.06369 .
## SEX 0.326564 0.164606 1.9839 0.04985 *
## scale(GEI):SEX 0.021782 0.182975 0.1190 0.90547
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G1 <- lm(scale(Openness) ~ scale(GEI), IPIPA); summary(G1)
##
## Call:
## lm(formula = scale(Openness) ~ scale(GEI), data = IPIPA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.7007 -0.6471 -0.0159 0.6330 7.6498
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.873e-15 1.284e-03 0.00 1
## scale(GEI) -5.631e-02 1.284e-03 -43.85 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9984 on 604559 degrees of freedom
## Multiple R-squared: 0.003171, Adjusted R-squared: 0.003169
## F-statistic: 1923 on 1 and 604559 DF, p-value: < 2.2e-16
G2 <- lm(scale(Openness) ~ scale(GEI) * SEX, IPIPA); summary(G2)
##
## Call:
## lm(formula = scale(Openness) ~ scale(GEI) * SEX, data = IPIPA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.7249 -0.6474 -0.0163 0.6321 7.7131
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.096515 0.004386 -22.005 < 2e-16 ***
## scale(GEI) -0.076357 0.004099 -18.628 < 2e-16 ***
## SEX 0.060262 0.002621 22.996 < 2e-16 ***
## scale(GEI):SEX 0.013017 0.002568 5.068 4.02e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.998 on 604557 degrees of freedom
## Multiple R-squared: 0.004083, Adjusted R-squared: 0.004078
## F-statistic: 826.2 on 3 and 604557 DF, p-value: < 2.2e-16
VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.8730e-15 1.2841e-03 0.000 1
## scale(GEI) -5.6309e-02 1.3632e-03 -41.307 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0965147 0.0044543 -21.6678 < 2.2e-16 ***
## scale(GEI) -0.0763568 0.0044316 -17.2302 < 2.2e-16 ***
## SEX 0.0602618 0.0026384 22.8404 < 2.2e-16 ***
## scale(GEI):SEX 0.0130171 0.0027322 4.7643 1.895e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ggplot(IPIPA_Means, aes(x = GEI, y = scale(Conscientiousness), color = as.factor(SEX), group = as.factor(SEX))) +
geom_point() +
geom_smooth(method = "lm", formula = "y ~ x") +
labs(title = "", x = "Gender Equality Index", y = "Conscientiousness Mean") +
theme_bw() +
scale_color_manual(values = c("steelblue", "magenta"), name = "Sex", labels = c("Male", "Female")) +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5),
legend.background = element_blank(), legend.key = element_blank(), legend.position = c(0.1, 0.1)) +
guides(color=guide_legend(override.aes=list(fill=NA)))
ggplot(IPIPA, aes(x = GEI, y = scale(Conscientiousness), color = as.factor(SEX), group = as.factor(SEX))) +
geom_point() +
geom_smooth(method = "lm", formula = "y ~ x") +
labs(title = "", x = "Gender Equality Index", y = "Conscientiousness Score") + theme_bw() +
scale_color_manual(values = c("steelblue", "magenta"), name = "Sex", labels = c("Male", "Female")) +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5),
legend.position = c(0.86, 0.92), legend.background = element_blank(), legend.key = element_blank()) +
guides(color=guide_legend(override.aes=list(fill=NA))) + geom_jitter()
G1 <- lm(scale(Conscientiousness) ~ scale(GEI), IPIPA_Means); summary(G1)
##
## Call:
## lm(formula = scale(Conscientiousness) ~ scale(GEI), data = IPIPA_Means)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.78464 -0.65290 -0.00871 0.74483 1.93611
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.315e-17 9.228e-02 0.000 1.00000
## scale(GEI) -2.681e-01 9.270e-02 -2.892 0.00463 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9678 on 108 degrees of freedom
## Multiple R-squared: 0.07188, Adjusted R-squared: 0.06328
## F-statistic: 8.364 on 1 and 108 DF, p-value: 0.00463
G2 <- lm(scale(Conscientiousness) ~ scale(GEI) * SEX, IPIPA_Means); summary(G2)
##
## Call:
## lm(formula = scale(Conscientiousness) ~ scale(GEI) * SEX, data = IPIPA_Means)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.54191 -0.64968 -0.02466 0.72292 2.13802
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.67820 0.28623 2.369 0.0196 *
## scale(GEI) -0.22859 0.28754 -0.795 0.4284
## SEX -0.45214 0.18103 -2.498 0.0140 *
## scale(GEI):SEX -0.02634 0.18185 -0.145 0.8851
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9493 on 106 degrees of freedom
## Multiple R-squared: 0.1236, Adjusted R-squared: 0.09882
## F-statistic: 4.984 on 3 and 106 DF, p-value: 0.002834
VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.3148e-17 9.2113e-02 0.0000 1.000000
## scale(GEI) -2.6810e-01 9.7447e-02 -2.7512 0.006965 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.678203 0.287125 2.3620 0.02000 *
## scale(GEI) -0.228592 0.281496 -0.8121 0.41858
## SEX -0.452135 0.180378 -2.5066 0.01371 *
## scale(GEI):SEX -0.026335 0.192046 -0.1371 0.89119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G1 <- lm(scale(Conscientiousness) ~ scale(GEI), IPIPA); summary(G1)
##
## Call:
## lm(formula = scale(Conscientiousness) ~ scale(GEI), data = IPIPA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.2126 -0.6822 -0.0480 0.6386 7.3885
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.304e-16 1.286e-03 0.000 1.00000
## scale(GEI) 4.156e-03 1.286e-03 3.232 0.00123 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1 on 604559 degrees of freedom
## Multiple R-squared: 1.728e-05, Adjusted R-squared: 1.562e-05
## F-statistic: 10.44 on 1 and 604559 DF, p-value: 0.00123
G2 <- lm(scale(Conscientiousness) ~ scale(GEI) * SEX, IPIPA); summary(G2)
##
## Call:
## lm(formula = scale(Conscientiousness) ~ scale(GEI) * SEX, data = IPIPA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.3045 -0.6800 -0.0475 0.6378 7.3009
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2450600 0.0043827 55.916 <2e-16 ***
## scale(GEI) -0.0007359 0.0040957 -0.180 0.857
## SEX -0.1531323 0.0026185 -58.481 <2e-16 ***
## scale(GEI):SEX 0.0037626 0.0025664 1.466 0.143
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9972 on 604557 degrees of freedom
## Multiple R-squared: 0.005647, Adjusted R-squared: 0.005642
## F-statistic: 1144 on 3 and 604557 DF, p-value: < 2.2e-16
VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.3039e-16 1.2861e-03 0.0000 1.000000
## scale(GEI) 4.1564e-03 1.3566e-03 3.0639 0.002185 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.24506003 0.00443615 55.2416 <2e-16 ***
## scale(GEI) -0.00073588 0.00438423 -0.1678 0.8667
## SEX -0.15313231 0.00263253 -58.1693 <2e-16 ***
## scale(GEI):SEX 0.00376257 0.00271993 1.3833 0.1666
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ggplot(IPIPA_Means, aes(x = GEI, y = scale(Extraversion), color = as.factor(SEX), group = as.factor(SEX))) +
geom_point() +
geom_smooth(method = "lm", formula = "y ~ x") +
labs(title = "", x = "Gender Equality Index", y = "Extraversion Mean") +
theme_bw() +
scale_color_manual(values = c("steelblue", "magenta"), name = "Sex", labels = c("Male", "Female")) +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5),
legend.background = element_blank(), legend.key = element_blank(), legend.position = c(0.1, 0.1)) +
guides(color=guide_legend(override.aes=list(fill=NA)))
ggplot(IPIPA, aes(x = GEI, y = scale(Extraversion), color = as.factor(SEX), group = as.factor(SEX))) +
geom_point() +
geom_smooth(method = "lm", formula = "y ~ x") +
labs(title = "", x = "Gender Equality Index", y = "Extraversion Score") + theme_bw() +
scale_color_manual(values = c("steelblue", "magenta"), name = "Sex", labels = c("Male", "Female")) +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5),
legend.position = c(0.86, 0.92), legend.background = element_blank(), legend.key = element_blank()) +
guides(color=guide_legend(override.aes=list(fill=NA))) + geom_jitter()
G1 <- lm(scale(Extraversion) ~ scale(GEI), IPIPA_Means); summary(G1)
##
## Call:
## lm(formula = scale(Extraversion) ~ scale(GEI), data = IPIPA_Means)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4751 -0.7593 0.1310 0.7441 1.9948
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.157e-16 9.166e-02 0.000 1.0000
## scale(GEI) -2.903e-01 9.208e-02 -3.152 0.0021 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9614 on 108 degrees of freedom
## Multiple R-squared: 0.08425, Adjusted R-squared: 0.07577
## F-statistic: 9.936 on 1 and 108 DF, p-value: 0.002099
G2 <- lm(scale(Extraversion) ~ scale(GEI) * SEX, IPIPA_Means); summary(G2)
##
## Call:
## lm(formula = scale(Extraversion) ~ scale(GEI) * SEX, data = IPIPA_Means)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.36532 -0.66596 0.07521 0.70402 2.30129
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5719 0.2855 -2.003 0.0477 *
## scale(GEI) -0.5450 0.2868 -1.900 0.0601 .
## SEX 0.3813 0.1806 2.112 0.0371 *
## scale(GEI):SEX 0.1698 0.1814 0.936 0.3513
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9469 on 106 degrees of freedom
## Multiple R-squared: 0.1281, Adjusted R-squared: 0.1035
## F-statistic: 5.193 on 3 and 106 DF, p-value: 0.002191
VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1572e-16 9.1532e-02 0.0000 1.000000
## scale(GEI) -2.9025e-01 9.5230e-02 -3.0479 0.002898 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.57190 0.30027 -1.9046 0.05954 .
## scale(GEI) -0.54497 0.31760 -1.7159 0.08911 .
## SEX 0.38127 0.18009 2.1171 0.03659 *
## scale(GEI):SEX 0.16981 0.18993 0.8941 0.37330
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G1 <- lm(scale(Extraversion) ~ scale(GEI), IPIPA); summary(G1)
##
## Call:
## lm(formula = scale(Extraversion) ~ scale(GEI), data = IPIPA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.8012 -0.6459 0.0446 0.6908 5.2497
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.593e-15 1.286e-03 0.00 1
## scale(GEI) -1.755e-02 1.286e-03 -13.65 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9998 on 604559 degrees of freedom
## Multiple R-squared: 0.0003081, Adjusted R-squared: 0.0003064
## F-statistic: 186.3 on 1 and 604559 DF, p-value: < 2.2e-16
G2 <- lm(scale(Extraversion) ~ scale(GEI) * SEX, IPIPA); summary(G2)
##
## Call:
## lm(formula = scale(Extraversion) ~ scale(GEI) * SEX, data = IPIPA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.8037 -0.6458 0.0446 0.6910 5.2856
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.009583 0.004394 -2.181 0.0292 *
## scale(GEI) -0.042098 0.004107 -10.251 < 2e-16 ***
## SEX 0.005934 0.002625 2.260 0.0238 *
## scale(GEI):SEX 0.016174 0.002573 6.286 3.27e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9998 on 604557 degrees of freedom
## Multiple R-squared: 0.0003818, Adjusted R-squared: 0.0003768
## F-statistic: 76.96 on 3 and 604557 DF, p-value: < 2.2e-16
VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.5930e-15 1.2859e-03 0.000 1
## scale(GEI) -1.7553e-02 1.3227e-03 -13.271 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0095833 0.0044479 -2.1545 0.03120 *
## scale(GEI) -0.0420976 0.0042862 -9.8217 < 2.2e-16 ***
## SEX 0.0059344 0.0026396 2.2482 0.02456 *
## scale(GEI):SEX 0.0161737 0.0026496 6.1042 1.034e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ggplot(IPIPA_Means, aes(x = GEI, y = scale(Agreeableness), color = as.factor(SEX), group = as.factor(SEX))) +
geom_point() +
geom_smooth(method = "lm", formula = "y ~ x") +
labs(title = "", x = "Gender Equality Index", y = "Agreeableness Mean") +
theme_bw() +
scale_color_manual(values = c("steelblue", "magenta"), name = "Sex", labels = c("Male", "Female")) +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5),
legend.background = element_blank(), legend.key = element_blank(), legend.position = c(0.1, 0.9)) +
guides(color=guide_legend(override.aes=list(fill=NA)))
ggplot(IPIPA, aes(x = GEI, y = scale(Agreeableness), color = as.factor(SEX), group = as.factor(SEX))) +
geom_point() +
geom_smooth(method = "lm", formula = "y ~ x") +
labs(title = "", x = "Gender Equality Index", y = "Agreeableness Score") + theme_bw() +
scale_color_manual(values = c("steelblue", "magenta"), name = "Sex", labels = c("Male", "Female")) +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5),
legend.position = c(0.86, 0.92), legend.background = element_blank(), legend.key = element_blank()) +
guides(color=guide_legend(override.aes=list(fill=NA))) + geom_jitter()
G1 <- lm(scale(Agreeableness) ~ scale(GEI), IPIPA_Means); summary(G1)
##
## Call:
## lm(formula = scale(Agreeableness) ~ scale(GEI), data = IPIPA_Means)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.30117 -0.55049 0.02476 0.62062 1.76785
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.632e-16 8.679e-02 0.000 1
## scale(GEI) 4.231e-01 8.719e-02 4.852 4.14e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9103 on 108 degrees of freedom
## Multiple R-squared: 0.179, Adjusted R-squared: 0.1714
## F-statistic: 23.54 on 1 and 108 DF, p-value: 4.136e-06
G2 <- lm(scale(Agreeableness) ~ scale(GEI) * SEX, IPIPA_Means); summary(G2)
##
## Call:
## lm(formula = scale(Agreeableness) ~ scale(GEI) * SEX, data = IPIPA_Means)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.37899 -0.28494 0.07555 0.51534 1.31929
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.51351 0.22946 -6.596 1.70e-09 ***
## scale(GEI) 0.33017 0.23051 1.432 0.155
## SEX 1.00901 0.14512 6.953 3.04e-10 ***
## scale(GEI):SEX 0.06192 0.14579 0.425 0.672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.761 on 106 degrees of freedom
## Multiple R-squared: 0.4368, Adjusted R-squared: 0.4208
## F-statistic: 27.4 on 3 and 106 DF, p-value: 3.377e-13
VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.6315e-16 8.6493e-02 0.000 1
## scale(GEI) 4.2305e-01 8.1246e-02 5.207 9.239e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.51351 0.22150 -6.8330 5.447e-10 ***
## scale(GEI) 0.33017 0.18689 1.7667 0.08016 .
## SEX 1.00900 0.14387 7.0131 2.269e-10 ***
## scale(GEI):SEX 0.06192 0.12492 0.4957 0.62116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G1 <- lm(scale(Agreeableness) ~ scale(GEI), IPIPA); summary(G1)
##
## Call:
## lm(formula = scale(Agreeableness) ~ scale(GEI), data = IPIPA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.1645 -0.6262 0.0545 0.6827 5.1689
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.094e-15 1.285e-03 0.00 1
## scale(GEI) 4.520e-02 1.285e-03 35.18 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.999 on 604559 degrees of freedom
## Multiple R-squared: 0.002043, Adjusted R-squared: 0.002041
## F-statistic: 1238 on 1 and 604559 DF, p-value: < 2.2e-16
G2 <- lm(scale(Agreeableness) ~ scale(GEI) * SEX, IPIPA); summary(G2)
##
## Call:
## lm(formula = scale(Agreeableness) ~ scale(GEI) * SEX, data = IPIPA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.9492 -0.6165 0.0515 0.6698 5.3678
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.529732 0.004332 -122.272 <2e-16 ***
## scale(GEI) 0.035977 0.004049 8.886 <2e-16 ***
## SEX 0.330974 0.002588 127.865 <2e-16 ***
## scale(GEI):SEX 0.004928 0.002537 1.943 0.0521 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9857 on 604557 degrees of freedom
## Multiple R-squared: 0.02832, Adjusted R-squared: 0.02832
## F-statistic: 5874 on 3 and 604557 DF, p-value: < 2.2e-16
VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.0942e-15 1.2848e-03 0.000 1
## scale(GEI) 4.5198e-02 1.3370e-03 33.806 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5297319 0.0044181 -119.8998 <2e-16 ***
## scale(GEI) 0.0359771 0.0042515 8.4623 <2e-16 ***
## SEX 0.3309739 0.0026111 126.7589 <2e-16 ***
## scale(GEI):SEX 0.0049285 0.0026539 1.8571 0.0633 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ggplot(IPIPA_Means, aes(x = GEI, y = scale(Neuroticism), color = as.factor(SEX), group = as.factor(SEX))) +
geom_point() +
geom_smooth(method = "lm", formula = "y ~ x") +
labs(title = "", x = "Gender Equality Index", y = "Neuroticism Mean") +
theme_bw() +
scale_color_manual(values = c("steelblue", "magenta"), name = "Sex", labels = c("Male", "Female")) +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5),
legend.background = element_blank(), legend.key = element_blank(), legend.position = c(0.1, 0.1)) +
guides(color=guide_legend(override.aes=list(fill=NA)))
ggplot(IPIPA, aes(x = GEI, y = scale(Neuroticism), color = as.factor(SEX), group = as.factor(SEX))) +
geom_point() +
geom_smooth(method = "lm", formula = "y ~ x") +
labs(title = "", x = "Gender Equality Index", y = "Neuroticism Score") + theme_bw() +
scale_color_manual(values = c("steelblue", "magenta"), name = "Sex", labels = c("Male", "Female")) +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5),
legend.position = c(0.86, 0.92), legend.background = element_blank(), legend.key = element_blank()) +
guides(color=guide_legend(override.aes=list(fill=NA))) + geom_jitter()
G1 <- lm(scale(Neuroticism) ~ scale(GEI), IPIPA_Means); summary(G1)
##
## Call:
## lm(formula = scale(Neuroticism) ~ scale(GEI), data = IPIPA_Means)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.82181 -0.56687 0.03765 0.58733 2.35406
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.105e-16 8.889e-02 0.000 1
## scale(GEI) -3.727e-01 8.929e-02 -4.174 6.08e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9322 on 108 degrees of freedom
## Multiple R-squared: 0.1389, Adjusted R-squared: 0.1309
## F-statistic: 17.42 on 1 and 108 DF, p-value: 6.078e-05
G2 <- lm(scale(Neuroticism) ~ scale(GEI) * SEX, IPIPA_Means); summary(G2)
##
## Call:
## lm(formula = scale(Neuroticism) ~ scale(GEI) * SEX, data = IPIPA_Means)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3150 -0.5666 -0.0401 0.6039 1.8639
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.51311 0.23767 -6.366 5.06e-09 ***
## scale(GEI) -0.34151 0.23876 -1.430 0.156
## SEX 1.00874 0.15032 6.711 9.82e-10 ***
## scale(GEI):SEX -0.02079 0.15100 -0.138 0.891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7883 on 106 degrees of freedom
## Multiple R-squared: 0.3957, Adjusted R-squared: 0.3786
## F-statistic: 23.14 on 3 and 106 DF, p-value: 1.34e-11
VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.1054e-16 8.8661e-02 0.0000 1
## scale(GEI) -3.7270e-01 9.0192e-02 -4.1324 7.11e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.513114 0.240732 -6.2855 7.404e-09 ***
## scale(GEI) -0.341514 0.241806 -1.4123 0.1608
## SEX 1.008743 0.149565 6.7445 8.349e-10 ***
## scale(GEI):SEX -0.020793 0.153380 -0.1356 0.8924
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G1 <- lm(scale(Neuroticism) ~ scale(GEI), IPIPA); summary(G1)
##
## Call:
## lm(formula = scale(Neuroticism) ~ scale(GEI), data = IPIPA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0556 -0.7213 -0.0287 0.6980 4.5025
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.744e-15 1.286e-03 0.00 1
## scale(GEI) -1.708e-02 1.286e-03 -13.28 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9999 on 604559 degrees of freedom
## Multiple R-squared: 0.0002917, Adjusted R-squared: 0.00029
## F-statistic: 176.4 on 1 and 604559 DF, p-value: < 2.2e-16
G2 <- lm(scale(Neuroticism) ~ scale(GEI) * SEX, IPIPA); summary(G2)
##
## Call:
## lm(formula = scale(Neuroticism) ~ scale(GEI) * SEX, data = IPIPA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9282 -0.7127 -0.0288 0.6904 4.6529
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.400529 0.004361 -91.838 < 2e-16 ***
## scale(GEI) -0.032862 0.004076 -8.063 7.47e-16 ***
## SEX 0.250229 0.002606 96.031 < 2e-16 ***
## scale(GEI):SEX 0.009540 0.002554 3.736 0.000187 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9923 on 604557 degrees of freedom
## Multiple R-squared: 0.01533, Adjusted R-squared: 0.01533
## F-statistic: 3138 on 3 and 604557 DF, p-value: < 2.2e-16
VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.7437e-15 1.2859e-03 0.000 1
## scale(GEI) -1.7078e-02 1.2642e-03 -13.508 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4005288 0.0043371 -92.3485 < 2.2e-16 ***
## scale(GEI) -0.0328618 0.0040125 -8.1899 2.619e-16 ***
## SEX 0.2502293 0.0025994 96.2648 < 2.2e-16 ***
## scale(GEI):SEX 0.0095401 0.0025156 3.7923 0.0001493 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ggplot(IPIPA_Means, aes(x = GEI, y = scale(GFP), color = as.factor(SEX), group = as.factor(SEX))) +
geom_point() +
geom_smooth(method = "lm", formula = "y ~ x") +
labs(title = "", x = "Gender Equality Index", y = "GFP Mean") +
theme_bw() +
scale_color_manual(values = c("steelblue", "magenta"), name = "Sex", labels = c("Male", "Female")) +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5),
legend.background = element_blank(), legend.key = element_blank(), legend.position = c(0.1, 0.1)) +
guides(color=guide_legend(override.aes=list(fill=NA)))
ggplot(IPIPA, aes(x = GEI, y = scale(GFP), color = as.factor(SEX), group = as.factor(SEX))) +
geom_point() +
geom_smooth(method = "lm", formula = "y ~ x") +
labs(title = "", x = "Gender Equality Index", y = "GFP Score") + theme_bw() +
scale_color_manual(values = c("steelblue", "magenta"), name = "Sex", labels = c("Male", "Female")) +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5),
legend.position = c(0.86, 0.92), legend.background = element_blank(), legend.key = element_blank()) +
guides(color=guide_legend(override.aes=list(fill=NA))) + geom_jitter()
G1 <- lm(scale(GFP) ~ scale(GEI), IPIPA_Means); summary(G1)
##
## Call:
## lm(formula = scale(GFP) ~ scale(GEI), data = IPIPA_Means)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0645 -0.7551 -0.0079 0.8552 1.9958
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.955e-17 9.576e-02 0.000 1.000
## scale(GEI) -2.228e-02 9.620e-02 -0.232 0.817
##
## Residual standard error: 1.004 on 108 degrees of freedom
## Multiple R-squared: 0.0004963, Adjusted R-squared: -0.008758
## F-statistic: 0.05362 on 1 and 108 DF, p-value: 0.8173
G2 <- lm(scale(GFP) ~ scale(GEI) * SEX, IPIPA_Means); summary(G2)
##
## Call:
## lm(formula = scale(GFP) ~ scale(GEI) * SEX, data = IPIPA_Means)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.05601 -0.67474 -0.00041 0.84228 1.83604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5466 0.2994 -1.826 0.0707 .
## scale(GEI) -0.2695 0.3008 -0.896 0.3724
## SEX 0.3644 0.1894 1.924 0.0570 .
## scale(GEI):SEX 0.1648 0.1903 0.866 0.3884
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9932 on 106 degrees of freedom
## Multiple R-squared: 0.04079, Adjusted R-squared: 0.01364
## F-statistic: 1.503 on 3 and 106 DF, p-value: 0.2181
VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.9548e-17 9.5812e-02 0.0000 1.0000
## scale(GEI) -2.2277e-02 1.1072e-01 -0.2012 0.8409
VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.54665 0.29766 -1.8365 0.06909 .
## scale(GEI) -0.26945 0.35764 -0.7534 0.45288
## SEX 0.36443 0.18980 1.9201 0.05753 .
## scale(GEI):SEX 0.16478 0.22557 0.7305 0.46669
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G1 <- lm(scale(GFP) ~ scale(GEI), IPIPA); summary(G1)
##
## Call:
## lm(formula = scale(GFP) ~ scale(GEI), data = IPIPA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.4491 -0.6540 0.0045 0.6605 6.3242
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.712e-16 1.286e-03 0.000 1.00000
## scale(GEI) -3.612e-03 1.286e-03 -2.808 0.00498 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1 on 604559 degrees of freedom
## Multiple R-squared: 1.305e-05, Adjusted R-squared: 1.139e-05
## F-statistic: 7.887 on 1 and 604559 DF, p-value: 0.004979
G2 <- lm(scale(GFP) ~ scale(GEI) * SEX, IPIPA); summary(G2)
##
## Call:
## lm(formula = scale(GFP) ~ scale(GEI) * SEX, data = IPIPA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.4506 -0.6540 0.0045 0.6605 6.3050
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.004745 0.004395 1.080 0.280
## scale(GEI) -0.024483 0.004107 -5.961 2.51e-09 ***
## SEX -0.003011 0.002626 -1.147 0.252
## scale(GEI):SEX 0.013781 0.002574 5.355 8.57e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1 on 604557 degrees of freedom
## Multiple R-squared: 6.27e-05, Adjusted R-squared: 5.774e-05
## F-statistic: 12.64 on 3 and 604557 DF, p-value: 2.959e-08
VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.7117e-16 1.2861e-03 0.000 1.000000
## scale(GEI) -3.6119e-03 1.3226e-03 -2.731 0.006314 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0047453 0.0043982 1.0789 0.2806
## scale(GEI) -0.0244831 0.0042507 -5.7598 8.426e-09 ***
## SEX -0.0030105 0.0026267 -1.1461 0.2517
## scale(GEI):SEX 0.0137810 0.0026486 5.2030 1.961e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Specify the models.
OpennessModel <- '
LatentOpenness =~ Liberalism + Intellect + Adventurousness + Emotionality + ArtisticInterests + Imagination'
ConscientiousModel <- '
LatentConscientiousness =~ Cautiousness + SelfDiscipline + AchievementStriving + Dutifulness + Orderliness + SelfEfficacy'
ExtraversionModel <- '
LatentExtraversion =~ Cheerfulness + ExcitementSeeking + ActivityLevel + Assertiveness + Gregariousness + Friendliness'
AgreeablenessModel <- '
LatentAgreeableness =~ Sympathy + Modesty + Cooperation + Altruism + Morality + Trust'
NeuroticismModel <- '
LatentNeuroticism =~ Vulnerability + Immoderation + SelfConsciousness + Depression + Anger + Anxiety'
GFPModel <- '
LatentGFP =~ Openness + Conscientiousness + Extraversion + Agreeableness + Neuroticism'
Averaging is done for comparability to the unbiased d value (i.e., the one based on part-wise averaging to circumvent issues with their correlations; see below). In cases where models yielded negative variances, the datapoint was omitted because resulting values of dMACS would not be trustworthy due to the resulting misestimation of variances. If items with negative variances are included or adjusted to a variance of 0 or 1, the models will be invalid, fit poorly, and can produce extreme and inaccurate values for dMACS. In a first attempt at this, I discovered that failing to omit these invalid models lead to a point with d = 12 in one case, as an example of just how bad this can be. Attempting to get around this and have more datapoints is why there is remnant code not using mean() and instead summing and dividing. Unfortunately, some of these negative variances might be minor and attributable to multigroup sampling error that would be corrected in a model with a higher level of invariance, but that would require tailoring to assess. The choice to omit rather than tailor means some of the regressions using this data have different numbers of datapoints. The Agreeableness value for Austria was removed because the model simply did not fit at all and the resulting dMACS was an astonishing -11.733.
#Openness
OpenAlb <- cfa(OpennessModel, data = ALBIP, group = "SEXO"); dAlbO <- dMACS_True(OpenAlb, group1 = "MALE", group2 = "FEMALE")/6; sum(dAlbO)
OpenAlg <- cfa(OpennessModel, data = ALGIP, group = "SEXO", control=list(rel.tol=1e-4)); dAlgO <- dMACS_True(OpenAlg, group1 = "MALE", group2 = "FEMALE")/6; sum(dAlgO)
#OpenAng <- cfa(OpennessModel, data = ANGIP, group = "SEXO", control=list(rel.tol=1e-4)); dAngO <- dMACS_True(OpenAng, group1 = "MALE", group2 = "FEMALE")/6; sum(dAngO)
#OpenArg <- cfa(OpennessModel, data = ARGIP, group = "SEXO"); dArgO <- dMACS_True(OpenArg, group1 = "MALE", group2 = "FEMALE")/6; sum(dArgO)
OpenAus <- cfa(OpennessModel, data = AUSIP, group = "SEXO"); dAusO <- dMACS_True(OpenAus, group1 = "MALE", group2 = "FEMALE")/6; sum(dAusO)
#OpenHab <- cfa(OpennessModel, data = HABIP, group = "SEXO", control=list(rel.tol=1e-4)); dHabO <- dMACS_True(OpenHab, group1 = "MALE", group2 = "FEMALE")/6; sum(dHabO)
#OpenBel <- cfa(OpennessModel, data = BELIP, group = "SEXO", check.gradient = F, std.lv = T, control=list(rel.tol=1e-4)); dBelO <- dMACS_True(OpenBel, group1 = "MALE", group2 = "FEMALE")/6; sum(dBelO)
#OpenBra <- cfa(OpennessModel, data = BRAIP, group = "SEXO", std.lv = T, control=list(rel.tol=1e-4)); dBraO <- dMACS_True(OpenAus, group1 = "MALE", group2 = "FEMALE")/6; sum(dBraO)
#OpenCan <- cfa(OpennessModel, data = CANIP, group = "SEXO"); dCanO <- dMACS_True(OpenCan, group1 = "MALE", group2 = "FEMALE")/6; sum(dCanO)
OpenChi <- cfa(OpennessModel, data = CHIIP, group = "SEXO"); dChiO <- dMACS_True(OpenChi, group1 = "MALE", group2 = "FEMALE")/6; sum(dChiO)
OpenCol <- cfa(OpennessModel, data = COLIP, group = "SEXO"); dColO <- dMACS_True(OpenCol, group1 = "MALE", group2 = "FEMALE")/6; sum(dColO)
#OpenCro <- cfa(OpennessModel, data = CROIP, group = "SEXO"); dCroO <- dMACS_True(OpenCro, group1 = "MALE", group2 = "FEMALE")/6; sum(dCroO)
OpenDen <- cfa(OpennessModel, data = DENIP, group = "SEXO", control=list(rel.tol=1e-4)); dDenO <- dMACS_True(OpenDen, group1 = "MALE", group2 = "FEMALE")/6; sum(dDenO)
OpenEgy <- cfa(OpennessModel, data = EGYIP, group = "SEXO", std.lv = T, control=list(rel.tol=1e-4)); dEgyO <- dMACS_True(OpenEgy, group1 = "MALE", group2 = "FEMALE")/6; sum(dEgyO)
#OpenFin <- cfa(OpennessModel, data = FINIP, group = "SEXO", check.gradient = F); dFinO <- dMACS_True(OpenFin, group1 = "MALE", group2 = "FEMALE")/6; sum(dFinO)
#OpenFra <- cfa(OpennessModel, data = FRAIP, group = "SEXO"); dFraO <- dMACS_True(OpenFra, group1 = "MALE", group2 = "FEMALE")/6; sum(dFraO)
#OpenGer <- cfa(OpennessModel, data = GERIP, group = "SEXO"); dGerO <- dMACS_True(OpenGer, group1 = "MALE", group2 = "FEMALE")/6; sum(dGerO)
OpenGre <- cfa(OpennessModel, data = GREIP, group = "SEXO"); dGreO <- dMACS_True(OpenGre, group1 = "MALE", group2 = "FEMALE")/6; sum(dGreO)
OpenInd <- cfa(OpennessModel, data = INDIP, group = "SEXO"); dIndO <- dMACS_True(OpenInd, group1 = "MALE", group2 = "FEMALE")/6; sum(dIndO)
OpenIno <- cfa(OpennessModel, data = INOIP, group = "SEXO"); dInoO <- dMACS_True(OpenIno, group1 = "MALE", group2 = "FEMALE")/6; sum(dInoO)
OpenIra <- cfa(OpennessModel, data = IRAIP, group = "SEXO", control=list(rel.tol=1e-4)); dIraO <- dMACS_True(OpenIra, group1 = "MALE", group2 = "FEMALE")/6; sum(dIraO)
OpenIre <- cfa(OpennessModel, data = IREIP, group = "SEXO", control=list(rel.tol=1e-4)); dIreO <- dMACS_True(OpenIre, group1 = "MALE", group2 = "FEMALE")/6; sum(dIreO)
OpenIsr <- cfa(OpennessModel, data = ISRIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F); dIsrO <- dMACS_True(OpenIsr, group1 = "MALE", group2 = "FEMALE")/6; sum(dIsrO)
OpenIta <- cfa(OpennessModel, data = ITAIP, group = "SEXO"); dItaO <- dMACS_True(OpenIta, group1 = "MALE", group2 = "FEMALE")/6; sum(dItaO)
OpenJam <- cfa(OpennessModel, data = JAMIP, group = "SEXO", control=list(rel.tol=1e-4)); dJamO <- dMACS_True(OpenJam, group1 = "MALE", group2 = "FEMALE")/6; sum(dJamO)
OpenJap <- cfa(OpennessModel, data = JAPIP, group = "SEXO", control=list(rel.tol=1e-4)); dJapO <- dMACS_True(OpenJap, group1 = "MALE", group2 = "FEMALE")/6; sum(dJapO)
#OpenKen <- cfa(OpennessModel, data = KENIP, group = "SEXO", std.lv = T, control=list(rel.tol=1e-4), std.ov = T, check.gradient = F); dKenO <- dMACS_True(OpenKen, group1 = "MALE", group2 = "FEMALE")/6; sum(dKenO)
OpenLeb <- cfa(OpennessModel, data = LEBIP, group = "SEXO", control=list(rel.tol=1e-4)); dLebO <- dMACS_True(OpenLeb, group1 = "MALE", group2 = "FEMALE")/6; sum(dLebO)
OpenMal <- cfa(OpennessModel, data = MALIP, group = "SEXO"); dMalO <- dMACS_True(OpenMal, group1 = "MALE", group2 = "FEMALE")/6; sum(dMalO)
OpenMex <- cfa(OpennessModel, data = MEXIP, group = "SEXO", control=list(rel.tol=1e-4)); dMexO <- dMACS_True(OpenMex, group1 = "MALE", group2 = "FEMALE")/6; sum(dMexO)
OpenNet <- cfa(OpennessModel, data = NETIP, group = "SEXO", control=list(rel.tol=1e-4)); dNetO <- dMACS_True(OpenNet, group1 = "MALE", group2 = "FEMALE")/6; sum(dNetO)
#OpenNew <- cfa(OpennessModel, data = NEWIP, group = "SEXO", check.gradient = F); dNewO <- dMACS_True(OpenNew, group1 = "MALE", group2 = "FEMALE")/6; sum(dNewO)
OpenNir <- cfa(OpennessModel, data = NIRIP, group = "SEXO"); dNirO <- dMACS_True(OpenNir, group1 = "MALE", group2 = "FEMALE")/6; sum(dNirO)
#OpenNor <- cfa(OpennessModel, data = NORIP, group = "SEXO", check.gradient = F, control=list(rel.tol=1e-4), std.lv = T); dNorO <- dMACS_True(OpenNor, group1 = "MALE", group2 = "FEMALE")/6; sum(dNorO)
#OpenPer <- cfa(OpennessModel, data = PERIP, group = "SEXO", std.lv = T); dPerO <- dMACS_True(OpenPer, group1 = "MALE", group2 = "FEMALE")/6; sum(dPerO)
#OpenPak <- cfa(OpennessModel, data = PAKIP, group = "SEXO", std.lv = T, control=list(rel.tol=1e-4)); dPakO <- dMACS_True(OpenPak, group1 = "MALE", group2 = "FEMALE")/6; sum(dPakO)
OpenPhi <- cfa(OpennessModel, data = PHIIP, group = "SEXO"); dPhiO <- dMACS_True(OpenPhi, group1 = "MALE", group2 = "FEMALE")/6; sum(dPhiO)
OpenPol <- cfa(OpennessModel, data = POLIP, group = "SEXO", control=list(rel.tol=1e-4)); dPolO <- dMACS_True(OpenPol, group1 = "MALE", group2 = "FEMALE")/6; sum(dPolO)
#OpenPor <- cfa(OpennessModel, data = PORIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F, std.lv = T); dPorO <- dMACS_True(OpenPor, group1 = "MALE", group2 = "FEMALE")/6; sum(dPorO)
OpenRom <- cfa(OpennessModel, data = ROMIP, group = "SEXO"); dRomO <- dMACS_True(OpenRom, group1 = "MALE", group2 = "FEMALE")/6; sum(dRomO)
#OpenRus <- cfa(OpennessModel, data = RUSIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F); dRusO <- dMACS_True(OpenRus, group1 = "MALE", group2 = "FEMALE")/6; sum(dRusO)
#OpenSin <- cfa(OpennessModel, data = SINIP, group = "SEXO"); dSinO <- dMACS_True(OpenSin, group1 = "MALE", group2 = "FEMALE")/6; sum(dSinO)
#OpenSlo <- cfa(OpennessModel, data = SLOIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F, std.lv = T); dSloO <- dMACS_True(OpenSlo, group1 = "MALE", group2 = "FEMALE")/6; sum(dSloO)
OpenSlv <- cfa(OpennessModel, data = SLVIP, group = "SEXO"); dSlvO <- dMACS_True(OpenSlv, group1 = "MALE", group2 = "FEMALE")/6; sum(dSlvO)
OpenSou <- cfa(OpennessModel, data = SOUIP, group = "SEXO", check.gradient = F, control=list(rel.tol=1e-4)); dSouO <- dMACS_True(OpenSou, group1 = "MALE", group2 = "FEMALE")/6; sum(dSouO)
#OpenSKo <- cfa(OpennessModel, data = SKOIP, group = "SEXO"); dSKoO <- dMACS_True(OpenSKo, group1 = "MALE", group2 = "FEMALE")/6; sum(dSKoO)
#OpenSpa <- cfa(OpennessModel, data = SPAIP, group = "SEXO", control=list(rel.tol=1e-4)); dSpaO <- dMACS_True(OpenSpa, group1 = "MALE", group2 = "FEMALE")/6; sum(dSpaO)
OpenSwe <- cfa(OpennessModel, data = SWEIP, group = "SEXO", control=list(rel.tol=1e-4)); dSweO <- dMACS_True(OpenSwe, group1 = "MALE", group2 = "FEMALE")/6; sum(dSweO)
OpenSwi <- cfa(OpennessModel, data = SWIIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F); dSwiO <- dMACS_True(OpenSwi, group1 = "MALE", group2 = "FEMALE")/6; sum(dSwiO)
#OpenTha <- cfa(OpennessModel, data = THAIP, group = "SEXO"); dThaO <- dMACS_True(OpenTha, group1 = "MALE", group2 = "FEMALE")/6; sum(dThaO)
#OpenTri <- cfa(OpennessModel, data = TRIIP, group = "SEXO"); dTriO <- dMACS_True(OpenTri, group1 = "MALE", group2 = "FEMALE")/6; sum(dTriO)
OpenTur <- cfa(OpennessModel, data = TURIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F, std.lv = T); dTurO <- dMACS_True(OpenTur, group1 = "MALE", group2 = "FEMALE")/6; sum(dTurO)
OpenUga <- cfa(OpennessModel, data = UGAIP, group = "SEXO", check.gradient = F, control=list(rel.tol=1e-4)); dUgaO <- dMACS_True(OpenUga, group1 = "MALE", group2 = "FEMALE")/6; sum(dUgaO)
OpenUK <- cfa(OpennessModel, data = UKIP, group = "SEXO", check.gradient = F, control=list(rel.tol=1e-4)); dUKO <- dMACS_True(OpenUK, group1 = "MALE", group2 = "FEMALE")/6; sum(dUKO)
#OpenUkr <- cfa(OpennessModel, data = UKRIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F, std.lv = T); dUkrO <- dMACS_True(OpenUkr, group1 = "MALE", group2 = "FEMALE")/6; sum(dUkrO)
#OpenUAE <- cfa(OpennessModel, data = UAEIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F, std.lv = T); dUAEO <- dMACS_True(OpenUAE, group1 = "MALE", group2 = "FEMALE")/6; sum(dUAEO)
#OpenUSA <- cfa(OpennessModel, data = USAIP, group = "SEXO"); dUSAO <- dMACS_True(OpenUSA, group1 = "MALE", group2 = "FEMALE")/6; sum(dUSAO)
OpenVen <- cfa(OpennessModel, data = VENIP, group = "SEXO", control=list(rel.tol=1e-4)); dVenO <- dMACS_True(OpenVen, group1 = "MALE", group2 = "FEMALE")/6; sum(dVenO)
#OpenVie <- cfa(OpennessModel, data = VIEIP, group = "SEXO", std.lv = T, control=list(rel.tol=1e-4)); dVieO <- dMACS_True(OpenVie, group1 = "MALE", group2 = "FEMALE")/6; sum(dVieO)
#Conscientiousness
ConsAlb <- cfa(ConscientiousModel, data = ALBIP, group = "SEXO"); dAlbC <- dMACS_True(ConsAlb, group1 = "MALE", group2 = "FEMALE")/6; sum(dAlbC)
ConsAlg <- cfa(ConscientiousModel, data = ALGIP, group = "SEXO"); dAlgC <- dMACS_True(ConsAlg, group1 = "MALE", group2 = "FEMALE")/6; sum(dAlgC)
#ConsAng <- cfa(ConscientiousModel, data = ANGIP, group = "SEXO"); dAngC <- dMACS_True(ConsAng, group1 = "MALE", group2 = "FEMALE")/6; sum(dAngC) #Will not run
ConsArg <- cfa(ConscientiousModel, data = ARGIP, group = "SEXO"); dArgC <- dMACS_True(ConsArg, group1 = "MALE", group2 = "FEMALE")/6; sum(dArgC)
ConsAus <- cfa(ConscientiousModel, data = AUSIP, group = "SEXO"); dAusC <- dMACS_True(ConsAus, group1 = "MALE", group2 = "FEMALE")/6; sum(dAusC)
#ConsHab <- cfa(ConscientiousModel, data = HABIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F, std.lv = T); dHabC <- dMACS_True(ConsHab, group1 = "MALE", group2 = "FEMALE")/6; sum(dHabC)
ConsBel <- cfa(ConscientiousModel, data = BELIP, group = "SEXO"); dBelC <- dMACS_True(ConsBel, group1 = "MALE", group2 = "FEMALE")/6; sum(dBelC)
ConsBra <- cfa(ConscientiousModel, data = BRAIP, group = "SEXO"); dBraC <- dMACS_True(ConsAus, group1 = "MALE", group2 = "FEMALE")/6; sum(dBraC)
ConsCan <- cfa(ConscientiousModel, data = CANIP, group = "SEXO"); dCanC <- dMACS_True(ConsCan, group1 = "MALE", group2 = "FEMALE")/6; sum(dCanC)
ConsChi <- cfa(ConscientiousModel, data = CHIIP, group = "SEXO"); dChiC <- dMACS_True(ConsChi, group1 = "MALE", group2 = "FEMALE")/6; sum(dChiC)
#ConsCol <- cfa(ConscientiousModel, data = COLIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F); dColC <- dMACS_True(ConsCol, group1 = "MALE", group2 = "FEMALE")/6; sum(dColC)
ConsCro <- cfa(ConscientiousModel, data = CROIP, group = "SEXO"); dCroC <- dMACS_True(ConsCro, group1 = "MALE", group2 = "FEMALE")/6; sum(dCroC)
ConsDen <- cfa(ConscientiousModel, data = DENIP, group = "SEXO"); dDenC <- dMACS_True(ConsDen, group1 = "MALE", group2 = "FEMALE")/6; sum(dDenC)
ConsEgy <- cfa(ConscientiousModel, data = EGYIP, group = "SEXO"); dEgyC <- dMACS_True(ConsEgy, group1 = "MALE", group2 = "FEMALE")/6; sum(dEgyC)
ConsFin <- cfa(ConscientiousModel, data = FINIP, group = "SEXO"); dFinC <- dMACS_True(ConsFin, group1 = "MALE", group2 = "FEMALE")/6; sum(dFinC)
ConsFra <- cfa(ConscientiousModel, data = FRAIP, group = "SEXO"); dFraC <- dMACS_True(ConsFra, group1 = "MALE", group2 = "FEMALE")/6; sum(dFraC)
ConsGer <- cfa(ConscientiousModel, data = GERIP, group = "SEXO"); dGerC <- dMACS_True(ConsGer, group1 = "MALE", group2 = "FEMALE")/6; sum(dGerC)
ConsGre <- cfa(ConscientiousModel, data = GREIP, group = "SEXO"); dGreC <- dMACS_True(ConsGre, group1 = "MALE", group2 = "FEMALE")/6; sum(dGreC)
ConsInd <- cfa(ConscientiousModel, data = INDIP, group = "SEXO"); dIndC <- dMACS_True(ConsInd, group1 = "MALE", group2 = "FEMALE")/6; sum(dIndC)
ConsIno <- cfa(ConscientiousModel, data = INOIP, group = "SEXO"); dInoC <- dMACS_True(ConsIno, group1 = "MALE", group2 = "FEMALE")/6; sum(dInoC)
ConsIra <- cfa(ConscientiousModel, data = IRAIP, group = "SEXO"); dIraC <- dMACS_True(ConsIra, group1 = "MALE", group2 = "FEMALE")/6; sum(dIraC)
ConsIre <- cfa(ConscientiousModel, data = IREIP, group = "SEXO"); dIreC <- dMACS_True(ConsIre, group1 = "MALE", group2 = "FEMALE")/6; sum(dIreC)
ConsIsr <- cfa(ConscientiousModel, data = ISRIP, group = "SEXO"); dIsrC <- dMACS_True(ConsIsr, group1 = "MALE", group2 = "FEMALE")/6; sum(dIsrC)
ConsIta <- cfa(ConscientiousModel, data = ITAIP, group = "SEXO"); dItaC <- dMACS_True(ConsIta, group1 = "MALE", group2 = "FEMALE")/6; sum(dItaC)
ConsJam <- cfa(ConscientiousModel, data = JAMIP, group = "SEXO"); dJamC <- dMACS_True(ConsJam, group1 = "MALE", group2 = "FEMALE")/6; sum(dJamC)
ConsJap <- cfa(ConscientiousModel, data = JAPIP, group = "SEXO"); dJapC <- dMACS_True(ConsJap, group1 = "MALE", group2 = "FEMALE")/6; sum(dJapC)
ConsKen <- cfa(ConscientiousModel, data = KENIP, group = "SEXO"); dKenC <- dMACS_True(ConsKen, group1 = "MALE", group2 = "FEMALE")/6; sum(dKenC)
ConsLeb <- cfa(ConscientiousModel, data = LEBIP, group = "SEXO"); dLebC <- dMACS_True(ConsLeb, group1 = "MALE", group2 = "FEMALE")/6; sum(dLebC)
ConsMal <- cfa(ConscientiousModel, data = MALIP, group = "SEXO"); dMalC <- dMACS_True(ConsMal, group1 = "MALE", group2 = "FEMALE")/6; sum(dMalC)
ConsMex <- cfa(ConscientiousModel, data = MEXIP, group = "SEXO"); dMexC <- dMACS_True(ConsMex, group1 = "MALE", group2 = "FEMALE")/6; sum(dMexC)
ConsNet <- cfa(ConscientiousModel, data = NETIP, group = "SEXO"); dNetC <- dMACS_True(ConsNet, group1 = "MALE", group2 = "FEMALE")/6; sum(dNetC)
ConsNew <- cfa(ConscientiousModel, data = NEWIP, group = "SEXO"); dNewC <- dMACS_True(ConsNew, group1 = "MALE", group2 = "FEMALE")/6; sum(dNewC)
ConsNir <- cfa(ConscientiousModel, data = NIRIP, group = "SEXO"); dNirC <- dMACS_True(ConsNir, group1 = "MALE", group2 = "FEMALE")/6; sum(dNirC)
ConsNor <- cfa(ConscientiousModel, data = NORIP, group = "SEXO"); dNorC <- dMACS_True(ConsNor, group1 = "MALE", group2 = "FEMALE")/6; sum(dNorC)
#ConsPer <- cfa(ConscientiousModel, data = PERIP, group = "SEXO"); dPerC <- dMACS_True(ConsPer, group1 = "MALE", group2 = "FEMALE")/6; sum(dPerC) #Will not run
ConsPak <- cfa(ConscientiousModel, data = PAKIP, group = "SEXO"); dPakC <- dMACS_True(ConsPak, group1 = "MALE", group2 = "FEMALE")/6; sum(dPakC)
ConsPhi <- cfa(ConscientiousModel, data = PHIIP, group = "SEXO"); dPhiC <- dMACS_True(ConsPhi, group1 = "MALE", group2 = "FEMALE")/6; sum(dPhiC)
#ConsPol <- cfa(ConscientiousModel, data = POLIP, group = "SEXO"); dPolC <- dMACS_True(ConsPol, group1 = "MALE", group2 = "FEMALE")/6; sum(dPolC)
ConsPor <- cfa(ConscientiousModel, data = PORIP, group = "SEXO"); dPorC <- dMACS_True(ConsPor, group1 = "MALE", group2 = "FEMALE")/6; sum(dPorC)
ConsRom <- cfa(ConscientiousModel, data = ROMIP, group = "SEXO"); dRomC <- dMACS_True(ConsRom, group1 = "MALE", group2 = "FEMALE")/6; sum(dRomC)
ConsRus <- cfa(ConscientiousModel, data = RUSIP, group = "SEXO"); dRusC <- dMACS_True(ConsRus, group1 = "MALE", group2 = "FEMALE")/6; sum(dRusC)
ConsSin <- cfa(ConscientiousModel, data = SINIP, group = "SEXO"); dSinC <- dMACS_True(ConsSin, group1 = "MALE", group2 = "FEMALE")/6; sum(dSinC)
#ConsSlo <- cfa(ConscientiousModel, data = SLOIP, group = "SEXO"); dSloC <- dMACS_True(ConsSlo, group1 = "MALE", group2 = "FEMALE")/6; sum(dSloC)
ConsSlv <- cfa(ConscientiousModel, data = SLVIP, group = "SEXO"); dSlvC <- dMACS_True(ConsSlv, group1 = "MALE", group2 = "FEMALE")/6; sum(dSlvC)
ConsSou <- cfa(ConscientiousModel, data = SOUIP, group = "SEXO"); dSouC <- dMACS_True(ConsSou, group1 = "MALE", group2 = "FEMALE")/6; sum(dSouC)
ConsSKo <- cfa(ConscientiousModel, data = SKOIP, group = "SEXO"); dSKoC <- dMACS_True(ConsSKo, group1 = "MALE", group2 = "FEMALE")/6; sum(dSKoC)
#ConsSpa <- cfa(ConscientiousModel, data = SPAIP, group = "SEXO"); dSpaC <- dMACS_True(ConsSpa, group1 = "MALE", group2 = "FEMALE")/6; sum(dSpaC)
ConsSwe <- cfa(ConscientiousModel, data = SWEIP, group = "SEXO"); dSweC <- dMACS_True(ConsSwe, group1 = "MALE", group2 = "FEMALE")/6; sum(dSweC)
ConsSwi <- cfa(ConscientiousModel, data = SWIIP, group = "SEXO"); dSwiC <- dMACS_True(ConsSwi, group1 = "MALE", group2 = "FEMALE")/6; sum(dSwiC)
#ConsTha <- cfa(ConscientiousModel, data = THAIP, group = "SEXO"); dThaC <- dMACS_True(ConsTha, group1 = "MALE", group2 = "FEMALE")/6; sum(dThaC)
ConsTri <- cfa(ConscientiousModel, data = TRIIP, group = "SEXO"); dTriC <- dMACS_True(ConsTri, group1 = "MALE", group2 = "FEMALE")/6; sum(dTriC)
ConsTur <- cfa(ConscientiousModel, data = TURIP, group = "SEXO"); dTurC <- dMACS_True(ConsTur, group1 = "MALE", group2 = "FEMALE")/6; sum(dTurC)
#ConsUga <- cfa(ConscientiousModel, data = UGAIP, group = "SEXO", control=list(rel.tol=1e-4)); dUgaC <- dMACS_True(ConsUga, group1 = "MALE", group2 = "FEMALE")/6; sum(dUgaC)
ConsUK <- cfa(ConscientiousModel, data = UKIP, group = "SEXO"); dUKC <- dMACS_True(ConsUK, group1 = "MALE", group2 = "FEMALE")/6; sum(dUKC)
ConsUkr <- cfa(ConscientiousModel, data = UKRIP, group = "SEXO"); dUkrC <- dMACS_True(ConsUkr, group1 = "MALE", group2 = "FEMALE")/6; sum(dUkrC)
ConsUAE <- cfa(ConscientiousModel, data = UAEIP, group = "SEXO"); dUAEC <- dMACS_True(ConsUAE, group1 = "MALE", group2 = "FEMALE")/6; sum(dUAEC)
ConsUSA <- cfa(ConscientiousModel, data = USAIP, group = "SEXO"); dUSAC <- dMACS_True(ConsUSA, group1 = "MALE", group2 = "FEMALE")/6; sum(dUSAC)
ConsVen <- cfa(ConscientiousModel, data = VENIP, group = "SEXO"); dVenC <- dMACS_True(ConsVen, group1 = "MALE", group2 = "FEMALE")/6; sum(dVenC)
ConsVie <- cfa(ConscientiousModel, data = VIEIP, group = "SEXO"); dVieC <- dMACS_True(ConsVie, group1 = "MALE", group2 = "FEMALE")/6; sum(dVieC)
#Extraversion
ExtrAlb <- cfa(ExtraversionModel, data = ALBIP, group = "SEXO"); dAlbE <- dMACS_True(ExtrAlb, group1 = "MALE", group2 = "FEMALE")/6; sum(dAlbE)
ExtrAlg <- cfa(ExtraversionModel, data = ALGIP, group = "SEXO"); dAlgE <- dMACS_True(ExtrAlg, group1 = "MALE", group2 = "FEMALE")/6; sum(dAlgE)
ExtrAng <- cfa(ExtraversionModel, data = ANGIP, group = "SEXO"); dAngE <- dMACS_True(ExtrAng, group1 = "MALE", group2 = "FEMALE")/6; sum(dAngE)
ExtrArg <- cfa(ExtraversionModel, data = ARGIP, group = "SEXO"); dArgE <- dMACS_True(ExtrArg, group1 = "MALE", group2 = "FEMALE")/6; sum(dArgE)
ExtrAus <- cfa(ExtraversionModel, data = AUSIP, group = "SEXO"); dAusE <- dMACS_True(ExtrAus, group1 = "MALE", group2 = "FEMALE")/6; sum(dAusE)
ExtrHab <- cfa(ExtraversionModel, data = HABIP, group = "SEXO"); dHabE <- dMACS_True(ExtrHab, group1 = "MALE", group2 = "FEMALE")/6; sum(dHabE)
ExtrBel <- cfa(ExtraversionModel, data = BELIP, group = "SEXO"); dBelE <- dMACS_True(ExtrBel, group1 = "MALE", group2 = "FEMALE")/6; sum(dBelE)
ExtrBra <- cfa(ExtraversionModel, data = BRAIP, group = "SEXO"); dBraE <- dMACS_True(ExtrAus, group1 = "MALE", group2 = "FEMALE")/6; sum(dBraE)
ExtrCan <- cfa(ExtraversionModel, data = CANIP, group = "SEXO"); dCanE <- dMACS_True(ExtrCan, group1 = "MALE", group2 = "FEMALE")/6; sum(dCanE)
ExtrChi <- cfa(ExtraversionModel, data = CHIIP, group = "SEXO"); dChiE <- dMACS_True(ExtrChi, group1 = "MALE", group2 = "FEMALE")/6; sum(dChiE)
ExtrCol <- cfa(ExtraversionModel, data = COLIP, group = "SEXO"); dColE <- dMACS_True(ExtrCol, group1 = "MALE", group2 = "FEMALE")/6; sum(dColE)
ExtrCro <- cfa(ExtraversionModel, data = CROIP, group = "SEXO"); dCroE <- dMACS_True(ExtrCro, group1 = "MALE", group2 = "FEMALE")/6; sum(dCroE)
ExtrDen <- cfa(ExtraversionModel, data = DENIP, group = "SEXO"); dDenE <- dMACS_True(ExtrDen, group1 = "MALE", group2 = "FEMALE")/6; sum(dDenE)
ExtrEgy <- cfa(ExtraversionModel, data = EGYIP, group = "SEXO"); dEgyE <- dMACS_True(ExtrEgy, group1 = "MALE", group2 = "FEMALE")/6; sum(dEgyE)
ExtrFin <- cfa(ExtraversionModel, data = FINIP, group = "SEXO"); dFinE <- dMACS_True(ExtrFin, group1 = "MALE", group2 = "FEMALE")/6; sum(dFinE)
ExtrFra <- cfa(ExtraversionModel, data = FRAIP, group = "SEXO"); dFraE <- dMACS_True(ExtrFra, group1 = "MALE", group2 = "FEMALE")/6; sum(dFraE)
ExtrGer <- cfa(ExtraversionModel, data = GERIP, group = "SEXO"); dGerE <- dMACS_True(ExtrGer, group1 = "MALE", group2 = "FEMALE")/6; sum(dGerE)
ExtrGre <- cfa(ExtraversionModel, data = GREIP, group = "SEXO"); dGreE <- dMACS_True(ExtrGre, group1 = "MALE", group2 = "FEMALE")/6; sum(dGreE)
ExtrInd <- cfa(ExtraversionModel, data = INDIP, group = "SEXO"); dIndE <- dMACS_True(ExtrInd, group1 = "MALE", group2 = "FEMALE")/6; sum(dIndE)
ExtrIno <- cfa(ExtraversionModel, data = INOIP, group = "SEXO"); dInoE <- dMACS_True(ExtrIno, group1 = "MALE", group2 = "FEMALE")/6; sum(dInoE)
ExtrIra <- cfa(ExtraversionModel, data = IRAIP, group = "SEXO"); dIraE <- dMACS_True(ExtrIra, group1 = "MALE", group2 = "FEMALE")/6; sum(dIraE)
ExtrIre <- cfa(ExtraversionModel, data = IREIP, group = "SEXO"); dIreE <- dMACS_True(ExtrIre, group1 = "MALE", group2 = "FEMALE")/6; sum(dIreE)
ExtrIsr <- cfa(ExtraversionModel, data = ISRIP, group = "SEXO"); dIsrE <- dMACS_True(ExtrIsr, group1 = "MALE", group2 = "FEMALE")/6; sum(dIsrE)
ExtrIta <- cfa(ExtraversionModel, data = ITAIP, group = "SEXO"); dItaE <- dMACS_True(ExtrIta, group1 = "MALE", group2 = "FEMALE")/6; sum(dItaE)
ExtrJam <- cfa(ExtraversionModel, data = JAMIP, group = "SEXO"); dJamE <- dMACS_True(ExtrJam, group1 = "MALE", group2 = "FEMALE")/6; sum(dJamE)
ExtrJap <- cfa(ExtraversionModel, data = JAPIP, group = "SEXO"); dJapE <- dMACS_True(ExtrJap, group1 = "MALE", group2 = "FEMALE")/6; sum(dJapE)
ExtrKen <- cfa(ExtraversionModel, data = KENIP, group = "SEXO"); dKenE <- dMACS_True(ExtrKen, group1 = "MALE", group2 = "FEMALE")/6; sum(dKenE)
ExtrLeb <- cfa(ExtraversionModel, data = LEBIP, group = "SEXO"); dLebE <- dMACS_True(ExtrLeb, group1 = "MALE", group2 = "FEMALE")/6; sum(dLebE)
ExtrMal <- cfa(ExtraversionModel, data = MALIP, group = "SEXO"); dMalE <- dMACS_True(ExtrMal, group1 = "MALE", group2 = "FEMALE")/6; sum(dMalE)
ExtrMex <- cfa(ExtraversionModel, data = MEXIP, group = "SEXO"); dMexE <- dMACS_True(ExtrMex, group1 = "MALE", group2 = "FEMALE")/6; sum(dMexE)
ExtrNet <- cfa(ExtraversionModel, data = NETIP, group = "SEXO"); dNetE <- dMACS_True(ExtrNet, group1 = "MALE", group2 = "FEMALE")/6; sum(dNetE)
ExtrNew <- cfa(ExtraversionModel, data = NEWIP, group = "SEXO"); dNewE <- dMACS_True(ExtrNew, group1 = "MALE", group2 = "FEMALE")/6; sum(dNewE)
ExtrNir <- cfa(ExtraversionModel, data = NIRIP, group = "SEXO"); dNirE <- dMACS_True(ExtrNir, group1 = "MALE", group2 = "FEMALE")/6; sum(dNirE)
ExtrNor <- cfa(ExtraversionModel, data = NORIP, group = "SEXO"); dNorE <- dMACS_True(ExtrNor, group1 = "MALE", group2 = "FEMALE")/6; sum(dNorE)
ExtrPer <- cfa(ExtraversionModel, data = PERIP, group = "SEXO"); dPerE <- dMACS_True(ExtrPer, group1 = "MALE", group2 = "FEMALE")/6; sum(dPerE)
ExtrPak <- cfa(ExtraversionModel, data = PAKIP, group = "SEXO"); dPakE <- dMACS_True(ExtrPak, group1 = "MALE", group2 = "FEMALE")/6; sum(dPakE)
ExtrPhi <- cfa(ExtraversionModel, data = PHIIP, group = "SEXO"); dPhiE <- dMACS_True(ExtrPhi, group1 = "MALE", group2 = "FEMALE")/6; sum(dPhiE)
ExtrPol <- cfa(ExtraversionModel, data = POLIP, group = "SEXO"); dPolE <- dMACS_True(ExtrPol, group1 = "MALE", group2 = "FEMALE")/6; sum(dPolE)
ExtrPor <- cfa(ExtraversionModel, data = PORIP, group = "SEXO"); dPorE <- dMACS_True(ExtrPor, group1 = "MALE", group2 = "FEMALE")/6; sum(dPorE)
ExtrRom <- cfa(ExtraversionModel, data = ROMIP, group = "SEXO"); dRomE <- dMACS_True(ExtrRom, group1 = "MALE", group2 = "FEMALE")/6; sum(dRomE)
ExtrRus <- cfa(ExtraversionModel, data = RUSIP, group = "SEXO"); dRusE <- dMACS_True(ExtrRus, group1 = "MALE", group2 = "FEMALE")/6; sum(dRusE)
ExtrSin <- cfa(ExtraversionModel, data = SINIP, group = "SEXO"); dSinE <- dMACS_True(ExtrSin, group1 = "MALE", group2 = "FEMALE")/6; sum(dSinE)
ExtrSlo <- cfa(ExtraversionModel, data = SLOIP, group = "SEXO"); dSloE <- dMACS_True(ExtrSlo, group1 = "MALE", group2 = "FEMALE")/6; sum(dSloE)
ExtrSlv <- cfa(ExtraversionModel, data = SLVIP, group = "SEXO"); dSlvE <- dMACS_True(ExtrSlv, group1 = "MALE", group2 = "FEMALE")/6; sum(dSlvE)
ExtrSou <- cfa(ExtraversionModel, data = SOUIP, group = "SEXO"); dSouE <- dMACS_True(ExtrSou, group1 = "MALE", group2 = "FEMALE")/6; sum(dSouE)
ExtrSKo <- cfa(ExtraversionModel, data = SKOIP, group = "SEXO"); dSKoE <- dMACS_True(ExtrSKo, group1 = "MALE", group2 = "FEMALE")/6; sum(dSKoE)
ExtrSpa <- cfa(ExtraversionModel, data = SPAIP, group = "SEXO"); dSpaE <- dMACS_True(ExtrSpa, group1 = "MALE", group2 = "FEMALE")/6; sum(dSpaE)
ExtrSwe <- cfa(ExtraversionModel, data = SWEIP, group = "SEXO"); dSweE <- dMACS_True(ExtrSwe, group1 = "MALE", group2 = "FEMALE")/6; sum(dSweE)
ExtrSwi <- cfa(ExtraversionModel, data = SWIIP, group = "SEXO"); dSwiE <- dMACS_True(ExtrSwi, group1 = "MALE", group2 = "FEMALE")/6; sum(dSwiE)
ExtrTha <- cfa(ExtraversionModel, data = THAIP, group = "SEXO"); dThaE <- dMACS_True(ExtrTha, group1 = "MALE", group2 = "FEMALE")/6; sum(dThaE)
ExtrTri <- cfa(ExtraversionModel, data = TRIIP, group = "SEXO"); dTriE <- dMACS_True(ExtrTri, group1 = "MALE", group2 = "FEMALE")/6; sum(dTriE)
ExtrTur <- cfa(ExtraversionModel, data = TURIP, group = "SEXO"); dTurE <- dMACS_True(ExtrTur, group1 = "MALE", group2 = "FEMALE")/6; sum(dTurE)
ExtrUga <- cfa(ExtraversionModel, data = UGAIP, group = "SEXO"); dUgaE <- dMACS_True(ExtrUga, group1 = "MALE", group2 = "FEMALE")/6; sum(dUgaE)
ExtrUK <- cfa(ExtraversionModel, data = UKIP, group = "SEXO"); dUKE <- dMACS_True(ExtrUK, group1 = "MALE", group2 = "FEMALE")/6; sum(dUKE)
ExtrUkr <- cfa(ExtraversionModel, data = UKRIP, group = "SEXO"); dUkrE <- dMACS_True(ExtrUkr, group1 = "MALE", group2 = "FEMALE")/6; sum(dUkrE)
ExtrUAE <- cfa(ExtraversionModel, data = UAEIP, group = "SEXO"); dUAEE <- dMACS_True(ExtrUAE, group1 = "MALE", group2 = "FEMALE")/6; sum(dUAEE)
ExtrUSA <- cfa(ExtraversionModel, data = USAIP, group = "SEXO"); dUSAE <- dMACS_True(ExtrUSA, group1 = "MALE", group2 = "FEMALE")/6; sum(dUSAE)
ExtrVen <- cfa(ExtraversionModel, data = VENIP, group = "SEXO"); dVenE <- dMACS_True(ExtrVen, group1 = "MALE", group2 = "FEMALE")/6; sum(dVenE)
ExtrVie <- cfa(ExtraversionModel, data = VIEIP, group = "SEXO"); dVieE <- dMACS_True(ExtrVie, group1 = "MALE", group2 = "FEMALE")/6; sum(dVieE)
#Agreeableness
#AgreAlb <- cfa(AgreeablenessModel, data = ALBIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F, std.lv = T); dAlbA <- dMACS_True(AgreAlb, group1 = "MALE", group2 = "FEMALE")/6; sum(dAlbA)
AgreAlg <- cfa(AgreeablenessModel, data = ALGIP, group = "SEXO"); dAlgA <- dMACS_True(AgreAlg, group1 = "MALE", group2 = "FEMALE")/6; sum(dAlgA)
#AgreAng <- cfa(AgreeablenessModel, data = ANGIP, group = "SEXO", control=list(rel.tol=1e-4)); dAngA <- dMACS_True(AgreAng, group1 = "MALE", group2 = "FEMALE")/6; sum(dAngA)
AgreArg <- cfa(AgreeablenessModel, data = ARGIP, group = "SEXO"); dArgA <- dMACS_True(AgreArg, group1 = "MALE", group2 = "FEMALE")/6; sum(dArgA)
AgreAus <- cfa(AgreeablenessModel, data = AUSIP, group = "SEXO"); dAusA <- dMACS_True(AgreAus, group1 = "MALE", group2 = "FEMALE")/6; sum(dAusA)
#AgreHab <- cfa(AgreeablenessModel, data = HABIP, group = "SEXO"); dHabA <- dMACS_True(AgreHab, group1 = "MALE", group2 = "FEMALE")/6; sum(dHabA)
AgreBel <- cfa(AgreeablenessModel, data = BELIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F); dBelA <- dMACS_True(AgreBel, group1 = "MALE", group2 = "FEMALE")/6; sum(dBelA)
AgreBra <- cfa(AgreeablenessModel, data = BRAIP, group = "SEXO"); dBraA <- dMACS_True(AgreAus, group1 = "MALE", group2 = "FEMALE")/6; sum(dBraA)
AgreCan <- cfa(AgreeablenessModel, data = CANIP, group = "SEXO"); dCanA <- dMACS_True(AgreCan, group1 = "MALE", group2 = "FEMALE")/6; sum(dCanA)
AgreChi <- cfa(AgreeablenessModel, data = CHIIP, group = "SEXO"); dChiA <- dMACS_True(AgreChi, group1 = "MALE", group2 = "FEMALE")/6; sum(dChiA)
AgreCol <- cfa(AgreeablenessModel, data = COLIP, group = "SEXO"); dColA <- dMACS_True(AgreCol, group1 = "MALE", group2 = "FEMALE")/6; sum(dColA)
AgreCro <- cfa(AgreeablenessModel, data = CROIP, group = "SEXO"); dCroA <- dMACS_True(AgreCro, group1 = "MALE", group2 = "FEMALE")/6; sum(dCroA)
AgreDen <- cfa(AgreeablenessModel, data = DENIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F, std.lv = T); dDenA <- dMACS_True(AgreDen, group1 = "MALE", group2 = "FEMALE")/6; sum(dDenA)
AgreEgy <- cfa(AgreeablenessModel, data = EGYIP, group = "SEXO"); dEgyA <- dMACS_True(AgreEgy, group1 = "MALE", group2 = "FEMALE")/6; sum(dEgyA)
AgreFin <- cfa(AgreeablenessModel, data = FINIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F, std.lv = T); dFinA <- dMACS_True(AgreFin, group1 = "MALE", group2 = "FEMALE")/6; sum(dFinA)
AgreFra <- cfa(AgreeablenessModel, data = FRAIP, group = "SEXO", control=list(rel.tol=1e-3), check.gradient = F); dFraA <- dMACS_True(AgreFra, group1 = "MALE", group2 = "FEMALE")/6; sum(dFraA)
AgreGer <- cfa(AgreeablenessModel, data = GERIP, group = "SEXO"); dGerA <- dMACS_True(AgreGer, group1 = "MALE", group2 = "FEMALE")/6; sum(dGerA)
AgreGre <- cfa(AgreeablenessModel, data = GREIP, group = "SEXO"); dGreA <- dMACS_True(AgreGre, group1 = "MALE", group2 = "FEMALE")/6; sum(dGreA)
AgreInd <- cfa(AgreeablenessModel, data = INDIP, group = "SEXO"); dIndA <- dMACS_True(AgreInd, group1 = "MALE", group2 = "FEMALE")/6; sum(dIndA)
AgreIno <- cfa(AgreeablenessModel, data = INOIP, group = "SEXO"); dInoA <- dMACS_True(AgreIno, group1 = "MALE", group2 = "FEMALE")/6; sum(dInoA)
AgreIra <- cfa(AgreeablenessModel, data = IRAIP, group = "SEXO"); dIraA <- dMACS_True(AgreIra, group1 = "MALE", group2 = "FEMALE")/6; sum(dIraA)
AgreIre <- cfa(AgreeablenessModel, data = IREIP, group = "SEXO"); dIreA <- dMACS_True(AgreIre, group1 = "MALE", group2 = "FEMALE")/6; sum(dIreA)
AgreIsr <- cfa(AgreeablenessModel, data = ISRIP, group = "SEXO"); dIsrA <- dMACS_True(AgreIsr, group1 = "MALE", group2 = "FEMALE")/6; sum(dIsrA)
AgreIta <- cfa(AgreeablenessModel, data = ITAIP, group = "SEXO"); dItaA <- dMACS_True(AgreIta, group1 = "MALE", group2 = "FEMALE")/6; sum(dItaA)
AgreJam <- cfa(AgreeablenessModel, data = JAMIP, group = "SEXO"); dJamA <- dMACS_True(AgreJam, group1 = "MALE", group2 = "FEMALE")/6; sum(dJamA)
AgreJap <- cfa(AgreeablenessModel, data = JAPIP, group = "SEXO"); dJapA <- dMACS_True(AgreJap, group1 = "MALE", group2 = "FEMALE")/6; sum(dJapA)
AgreKen <- cfa(AgreeablenessModel, data = KENIP, group = "SEXO"); dKenA <- dMACS_True(AgreKen, group1 = "MALE", group2 = "FEMALE")/6; sum(dKenA)
AgreLeb <- cfa(AgreeablenessModel, data = LEBIP, group = "SEXO"); dLebA <- dMACS_True(AgreLeb, group1 = "MALE", group2 = "FEMALE")/6; sum(dLebA)
AgreMal <- cfa(AgreeablenessModel, data = MALIP, group = "SEXO"); dMalA <- dMACS_True(AgreMal, group1 = "MALE", group2 = "FEMALE")/6; sum(dMalA)
AgreMex <- cfa(AgreeablenessModel, data = MEXIP, group = "SEXO"); dMexA <- dMACS_True(AgreMex, group1 = "MALE", group2 = "FEMALE")/6; sum(dMexA)
AgreNet <- cfa(AgreeablenessModel, data = NETIP, group = "SEXO"); dNetA <- dMACS_True(AgreNet, group1 = "MALE", group2 = "FEMALE")/6; sum(dNetA)
AgreNew <- cfa(AgreeablenessModel, data = NEWIP, group = "SEXO"); dNewA <- dMACS_True(AgreNew, group1 = "MALE", group2 = "FEMALE")/6; sum(dNewA)
AgreNor <- cfa(AgreeablenessModel, data = NORIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F); dNorA <- dMACS_True(AgreNor, group1 = "MALE", group2 = "FEMALE")/6; sum(dNorA)
#AgrePer <- cfa(AgreeablenessModel, data = PERIP, group = "SEXO"); dPerA <- dMACS_True(AgrePer, group1 = "MALE", group2 = "FEMALE")/6; sum(dPerA) #Does not run
AgrePak <- cfa(AgreeablenessModel, data = PAKIP, group = "SEXO"); dPakA <- dMACS_True(AgrePak, group1 = "MALE", group2 = "FEMALE")/6; sum(dPakA)
AgrePhi <- cfa(AgreeablenessModel, data = PHIIP, group = "SEXO"); dPhiA <- dMACS_True(AgrePhi, group1 = "MALE", group2 = "FEMALE")/6; sum(dPhiA)
AgrePol <- cfa(AgreeablenessModel, data = POLIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F, std.lv = T); dPolA <- dMACS_True(AgrePol, group1 = "MALE", group2 = "FEMALE")/6; sum(dPolA)
AgrePor <- cfa(AgreeablenessModel, data = PORIP, group = "SEXO"); dPorA <- dMACS_True(AgrePor, group1 = "MALE", group2 = "FEMALE")/6; sum(dPorA)
#AgreRom <- cfa(AgreeablenessModel, data = ROMIP, group = "SEXO"); dRomA <- dMACS_True(AgreRom, group1 = "MALE", group2 = "FEMALE")/6; sum(dRomA)
#AgreRus <- cfa(AgreeablenessModel, data = RUSIP, group = "SEXO"); dRusA <- dMACS_True(AgreRus, group1 = "MALE", group2 = "FEMALE")/6; sum(dRusA)
AgreSin <- cfa(AgreeablenessModel, data = SINIP, group = "SEXO"); dSinA <- dMACS_True(AgreSin, group1 = "MALE", group2 = "FEMALE")/6; sum(dSinA)
AgreSlo <- cfa(AgreeablenessModel, data = SLOIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F, std.lv = T); dSloA <- dMACS_True(AgreSlo, group1 = "MALE", group2 = "FEMALE")/6; sum(dSloA)
AgreSlv <- cfa(AgreeablenessModel, data = SLVIP, group = "SEXO"); dSlvA <- dMACS_True(AgreSlv, group1 = "MALE", group2 = "FEMALE")/6; sum(dSlvA)
AgreSou <- cfa(AgreeablenessModel, data = SOUIP, group = "SEXO"); dSouA <- dMACS_True(AgreSou, group1 = "MALE", group2 = "FEMALE")/6; sum(dSouA)
AgreSKo <- cfa(AgreeablenessModel, data = SKOIP, group = "SEXO", control=list(rel.tol=1e-3), check.gradient = F); dSKoA <- dMACS_True(AgreSKo, group1 = "MALE", group2 = "FEMALE")/6; sum(dSKoA)
AgreSpa <- cfa(AgreeablenessModel, data = SPAIP, group = "SEXO"); dSpaA <- dMACS_True(AgreSpa, group1 = "MALE", group2 = "FEMALE")/6; sum(dSpaA)
AgreSwe <- cfa(AgreeablenessModel, data = SWEIP, group = "SEXO"); dSweA <- dMACS_True(AgreSwe, group1 = "MALE", group2 = "FEMALE")/6; sum(dSweA)
#AgreSwi <- cfa(AgreeablenessModel, data = SWIIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F); dSwiA <- dMACS_True(AgreSwi, group1 = "MALE", group2 = "FEMALE")/6; sum(dSwiA)
AgreTha <- cfa(AgreeablenessModel, data = THAIP, group = "SEXO"); dThaA <- dMACS_True(AgreTha, group1 = "MALE", group2 = "FEMALE")/6; sum(dThaA)
#AgreTri <- cfa(AgreeablenessModel, data = TRIIP, group = "SEXO"); dTriA <- dMACS_True(AgreTri, group1 = "MALE", group2 = "FEMALE")/6; sum(dTriA) #Will not run
AgreNir <- cfa(AgreeablenessModel, data = NIRIP, group = "SEXO"); dNirA <- dMACS_True(AgreNir, group1 = "MALE", group2 = "FEMALE")/6; sum(dNirA)
AgreTur <- cfa(AgreeablenessModel, data = TURIP, group = "SEXO"); dTurA <- dMACS_True(AgreTur, group1 = "MALE", group2 = "FEMALE")/6; sum(dTurA)
#AgreUga <- cfa(AgreeablenessModel, data = UGAIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F, std.lv = T); dUgaA <- dMACS_True(AgreUga, group1 = "MALE", group2 = "FEMALE")/6; sum(dUgaA)
AgreUK <- cfa(AgreeablenessModel, data = UKIP, group = "SEXO"); dUKA <- dMACS_True(AgreUK, group1 = "MALE", group2 = "FEMALE")/6; sum(dUKA)
AgreUkr <- cfa(AgreeablenessModel, data = UKRIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F, std.lv = T); dUkrA <- dMACS_True(AgreUkr, group1 = "MALE", group2 = "FEMALE")/6; sum(dUkrA)
AgreUAE <- cfa(AgreeablenessModel, data = UAEIP, group = "SEXO"); dUAEA <- dMACS_True(AgreUAE, group1 = "MALE", group2 = "FEMALE")/6; sum(dUAEA)
AgreUSA <- cfa(AgreeablenessModel, data = USAIP, group = "SEXO"); dUSAA <- dMACS_True(AgreUSA, group1 = "MALE", group2 = "FEMALE")/6; sum(dUSAA)
AgreVen <- cfa(AgreeablenessModel, data = VENIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F, std.lv = T); dVenA <- dMACS_True(AgreVen, group1 = "MALE", group2 = "FEMALE")/6; sum(dVenA)
AgreVie <- cfa(AgreeablenessModel, data = VIEIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F); dVieA <- dMACS_True(AgreVie, group1 = "MALE", group2 = "FEMALE")/6; sum(dVieA)
#Neuroticism
NeurAlb <- cfa(NeuroticismModel, data = ALBIP, group = "SEXO"); dAlbN <- dMACS_True(NeurAlb, group1 = "MALE", group2 = "FEMALE")/6; sum(dAlbN)
NeurAlg <- cfa(NeuroticismModel, data = ALGIP, group = "SEXO"); dAlgN <- dMACS_True(NeurAlg, group1 = "MALE", group2 = "FEMALE")/6; sum(dAlgN)
NeurAng <- cfa(NeuroticismModel, data = ANGIP, group = "SEXO"); dAngN <- dMACS_True(NeurAng, group1 = "MALE", group2 = "FEMALE")/6; sum(dAngN)
NeurArg <- cfa(NeuroticismModel, data = ARGIP, group = "SEXO"); dArgN <- dMACS_True(NeurArg, group1 = "MALE", group2 = "FEMALE")/6; sum(dArgN)
NeurAus <- cfa(NeuroticismModel, data = AUSIP, group = "SEXO"); dAusN <- dMACS_True(NeurAus, group1 = "MALE", group2 = "FEMALE")/6; sum(dAusN)
NeurHab <- cfa(NeuroticismModel, data = HABIP, group = "SEXO"); dHabN <- dMACS_True(NeurHab, group1 = "MALE", group2 = "FEMALE")/6; sum(dHabN)
NeurBel <- cfa(NeuroticismModel, data = BELIP, group = "SEXO"); dBelN <- dMACS_True(NeurBel, group1 = "MALE", group2 = "FEMALE")/6; sum(dBelN)
NeurBra <- cfa(NeuroticismModel, data = BRAIP, group = "SEXO"); dBraN <- dMACS_True(NeurAus, group1 = "MALE", group2 = "FEMALE")/6; sum(dBraN)
NeurCan <- cfa(NeuroticismModel, data = CANIP, group = "SEXO"); dCanN <- dMACS_True(NeurCan, group1 = "MALE", group2 = "FEMALE")/6; sum(dCanN)
NeurChi <- cfa(NeuroticismModel, data = CHIIP, group = "SEXO"); dChiN <- dMACS_True(NeurChi, group1 = "MALE", group2 = "FEMALE")/6; sum(dChiN)
NeurCol <- cfa(NeuroticismModel, data = COLIP, group = "SEXO"); dColN <- dMACS_True(NeurCol, group1 = "MALE", group2 = "FEMALE")/6; sum(dColN)
NeurCro <- cfa(NeuroticismModel, data = CROIP, group = "SEXO"); dCroN <- dMACS_True(NeurCro, group1 = "MALE", group2 = "FEMALE")/6; sum(dCroN)
NeurDen <- cfa(NeuroticismModel, data = DENIP, group = "SEXO"); dDenN <- dMACS_True(NeurDen, group1 = "MALE", group2 = "FEMALE")/6; sum(dDenN)
NeurEgy <- cfa(NeuroticismModel, data = EGYIP, group = "SEXO"); dEgyN <- dMACS_True(NeurEgy, group1 = "MALE", group2 = "FEMALE")/6; sum(dEgyN)
NeurFin <- cfa(NeuroticismModel, data = FINIP, group = "SEXO"); dFinN <- dMACS_True(NeurFin, group1 = "MALE", group2 = "FEMALE")/6; sum(dFinN)
NeurFra <- cfa(NeuroticismModel, data = FRAIP, group = "SEXO"); dFraN <- dMACS_True(NeurFra, group1 = "MALE", group2 = "FEMALE")/6; sum(dFraN)
NeurGer <- cfa(NeuroticismModel, data = GERIP, group = "SEXO"); dGerN <- dMACS_True(NeurGer, group1 = "MALE", group2 = "FEMALE")/6; sum(dGerN)
NeurGre <- cfa(NeuroticismModel, data = GREIP, group = "SEXO"); dGreN <- dMACS_True(NeurGre, group1 = "MALE", group2 = "FEMALE")/6; sum(dGreN)
NeurInd <- cfa(NeuroticismModel, data = INDIP, group = "SEXO"); dIndN <- dMACS_True(NeurInd, group1 = "MALE", group2 = "FEMALE")/6; sum(dIndN)
NeurIno <- cfa(NeuroticismModel, data = INOIP, group = "SEXO"); dInoN <- dMACS_True(NeurIno, group1 = "MALE", group2 = "FEMALE")/6; sum(dInoN)
NeurIra <- cfa(NeuroticismModel, data = IRAIP, group = "SEXO"); dIraN <- dMACS_True(NeurIra, group1 = "MALE", group2 = "FEMALE")/6; sum(dIraN)
NeurIre <- cfa(NeuroticismModel, data = IREIP, group = "SEXO"); dIreN <- dMACS_True(NeurIre, group1 = "MALE", group2 = "FEMALE")/6; sum(dIreN)
NeurIsr <- cfa(NeuroticismModel, data = ISRIP, group = "SEXO"); dIsrN <- dMACS_True(NeurIsr, group1 = "MALE", group2 = "FEMALE")/6; sum(dIsrN)
NeurIta <- cfa(NeuroticismModel, data = ITAIP, group = "SEXO"); dItaN <- dMACS_True(NeurIta, group1 = "MALE", group2 = "FEMALE")/6; sum(dItaN)
NeurJam <- cfa(NeuroticismModel, data = JAMIP, group = "SEXO"); dJamN <- dMACS_True(NeurJam, group1 = "MALE", group2 = "FEMALE")/6; sum(dJamN)
NeurJap <- cfa(NeuroticismModel, data = JAPIP, group = "SEXO"); dJapN <- dMACS_True(NeurJap, group1 = "MALE", group2 = "FEMALE")/6; sum(dJapN)
NeurKen <- cfa(NeuroticismModel, data = KENIP, group = "SEXO"); dKenN <- dMACS_True(NeurKen, group1 = "MALE", group2 = "FEMALE")/6; sum(dKenN)
NeurLeb <- cfa(NeuroticismModel, data = LEBIP, group = "SEXO"); dLebN <- dMACS_True(NeurLeb, group1 = "MALE", group2 = "FEMALE")/6; sum(dLebN)
NeurMal <- cfa(NeuroticismModel, data = MALIP, group = "SEXO"); dMalN <- dMACS_True(NeurMal, group1 = "MALE", group2 = "FEMALE")/6; sum(dMalN)
NeurMex <- cfa(NeuroticismModel, data = MEXIP, group = "SEXO"); dMexN <- dMACS_True(NeurMex, group1 = "MALE", group2 = "FEMALE")/6; sum(dMexN)
NeurNet <- cfa(NeuroticismModel, data = NETIP, group = "SEXO"); dNetN <- dMACS_True(NeurNet, group1 = "MALE", group2 = "FEMALE")/6; sum(dNetN)
NeurNew <- cfa(NeuroticismModel, data = NEWIP, group = "SEXO"); dNewN <- dMACS_True(NeurNew, group1 = "MALE", group2 = "FEMALE")/6; sum(dNewN)
NeurNir <- cfa(NeuroticismModel, data = NIRIP, group = "SEXO"); dNirN <- dMACS_True(NeurNir, group1 = "MALE", group2 = "FEMALE")/6; sum(dNirN)
NeurNor <- cfa(NeuroticismModel, data = NORIP, group = "SEXO"); dNorN <- dMACS_True(NeurNor, group1 = "MALE", group2 = "FEMALE")/6; sum(dNorN)
NeurPer <- cfa(NeuroticismModel, data = PERIP, group = "SEXO"); dPerN <- dMACS_True(NeurPer, group1 = "MALE", group2 = "FEMALE")/6; sum(dPerN)
NeurPak <- cfa(NeuroticismModel, data = PAKIP, group = "SEXO"); dPakN <- dMACS_True(NeurPak, group1 = "MALE", group2 = "FEMALE")/6; sum(dPakN)
NeurPhi <- cfa(NeuroticismModel, data = PHIIP, group = "SEXO"); dPhiN <- dMACS_True(NeurPhi, group1 = "MALE", group2 = "FEMALE")/6; sum(dPhiN)
NeurPol <- cfa(NeuroticismModel, data = POLIP, group = "SEXO"); dPolN <- dMACS_True(NeurPol, group1 = "MALE", group2 = "FEMALE")/6; sum(dPolN)
NeurPor <- cfa(NeuroticismModel, data = PORIP, group = "SEXO"); dPorN <- dMACS_True(NeurPor, group1 = "MALE", group2 = "FEMALE")/6; sum(dPorN)
NeurRom <- cfa(NeuroticismModel, data = ROMIP, group = "SEXO"); dRomN <- dMACS_True(NeurRom, group1 = "MALE", group2 = "FEMALE")/6; sum(dRomN)
NeurRus <- cfa(NeuroticismModel, data = RUSIP, group = "SEXO"); dRusN <- dMACS_True(NeurRus, group1 = "MALE", group2 = "FEMALE")/6; sum(dRusN)
NeurSin <- cfa(NeuroticismModel, data = SINIP, group = "SEXO"); dSinN <- dMACS_True(NeurSin, group1 = "MALE", group2 = "FEMALE")/6; sum(dSinN)
NeurSlo <- cfa(NeuroticismModel, data = SLOIP, group = "SEXO"); dSloN <- dMACS_True(NeurSlo, group1 = "MALE", group2 = "FEMALE")/6; sum(dSloN)
NeurSlv <- cfa(NeuroticismModel, data = SLVIP, group = "SEXO"); dSlvN <- dMACS_True(NeurSlv, group1 = "MALE", group2 = "FEMALE")/6; sum(dSlvN)
NeurSou <- cfa(NeuroticismModel, data = SOUIP, group = "SEXO"); dSouN <- dMACS_True(NeurSou, group1 = "MALE", group2 = "FEMALE")/6; sum(dSouN)
NeurSKo <- cfa(NeuroticismModel, data = SKOIP, group = "SEXO"); dSKoN <- dMACS_True(NeurSKo, group1 = "MALE", group2 = "FEMALE")/6; sum(dSKoN)
NeurSpa <- cfa(NeuroticismModel, data = SPAIP, group = "SEXO"); dSpaN <- dMACS_True(NeurSpa, group1 = "MALE", group2 = "FEMALE")/6; sum(dSpaN)
NeurSwe <- cfa(NeuroticismModel, data = SWEIP, group = "SEXO"); dSweN <- dMACS_True(NeurSwe, group1 = "MALE", group2 = "FEMALE")/6; sum(dSweN)
NeurSwi <- cfa(NeuroticismModel, data = SWIIP, group = "SEXO"); dSwiN <- dMACS_True(NeurSwi, group1 = "MALE", group2 = "FEMALE")/6; sum(dSwiN)
NeurTha <- cfa(NeuroticismModel, data = THAIP, group = "SEXO"); dThaN <- dMACS_True(NeurTha, group1 = "MALE", group2 = "FEMALE")/6; sum(dThaN)
NeurTri <- cfa(NeuroticismModel, data = TRIIP, group = "SEXO"); dTriN <- dMACS_True(NeurTri, group1 = "MALE", group2 = "FEMALE")/6; sum(dTriN)
NeurTur <- cfa(NeuroticismModel, data = TURIP, group = "SEXO"); dTurN <- dMACS_True(NeurTur, group1 = "MALE", group2 = "FEMALE")/6; sum(dTurN)
NeurUga <- cfa(NeuroticismModel, data = UGAIP, group = "SEXO"); dUgaN <- dMACS_True(NeurUga, group1 = "MALE", group2 = "FEMALE")/6; sum(dUgaN)
NeurUK <- cfa(NeuroticismModel, data = UKIP, group = "SEXO"); dUKN <- dMACS_True(NeurUK, group1 = "MALE", group2 = "FEMALE")/6; sum(dUKN)
NeurUkr <- cfa(NeuroticismModel, data = UKRIP, group = "SEXO"); dUkrN <- dMACS_True(NeurUkr, group1 = "MALE", group2 = "FEMALE")/6; sum(dUkrN)
NeurUAE <- cfa(NeuroticismModel, data = UAEIP, group = "SEXO"); dUAEN <- dMACS_True(NeurUAE, group1 = "MALE", group2 = "FEMALE")/6; sum(dUAEN)
NeurUSA <- cfa(NeuroticismModel, data = USAIP, group = "SEXO"); dUSAN <- dMACS_True(NeurUSA, group1 = "MALE", group2 = "FEMALE")/6; sum(dUSAN)
NeurVen <- cfa(NeuroticismModel, data = VENIP, group = "SEXO"); dVenN <- dMACS_True(NeurVen, group1 = "MALE", group2 = "FEMALE")/6; sum(dVenN)
NeurVie <- cfa(NeuroticismModel, data = VIEIP, group = "SEXO"); dVieN <- dMACS_True(NeurVie, group1 = "MALE", group2 = "FEMALE")/6; sum(dVieN)
#GFP
GFPAlb <- cfa(GFPModel, data = ALBIP, group = "SEXO"); dAlbG <- dMACS_True(GFPAlb, group1 = "MALE", group2 = "FEMALE")/5; sum(dAlbG)
GFPAlg <- cfa(GFPModel, data = ALGIP, group = "SEXO"); dAlgG <- dMACS_True(GFPAlg, group1 = "MALE", group2 = "FEMALE")/5; sum(dAlgG)
#GFPAng <- cfa(GFPModel, data = ANGIP, group = "SEXO", check.gradient = F); dAngG <- dMACS_True(GFPAng, group1 = "MALE", group2 = "FEMALE")/5; sum(dAngG)
GFPArg <- cfa(GFPModel, data = ARGIP, group = "SEXO"); dArgG <- dMACS_True(GFPArg, group1 = "MALE", group2 = "FEMALE")/5; sum(dArgG)
GFPAus <- cfa(GFPModel, data = AUSIP, group = "SEXO"); dAusG <- dMACS_True(GFPAus, group1 = "MALE", group2 = "FEMALE")/5; sum(dAusG)
#GFPHab <- cfa(GFPModel, data = HABIP, group = "SEXO"); dHabG <- dMACS_True(GFPHab, group1 = "MALE", group2 = "FEMALE")/5; sum(dHabG) #Will not run
#GFPBel <- cfa(GFPModel, data = BELIP, group = "SEXO"); dBelG <- dMACS_True(GFPBel, group1 = "MALE", group2 = "FEMALE")/5; sum(dBelG) #Will not run
GFPBra <- cfa(GFPModel, data = BRAIP, group = "SEXO"); dBraG <- dMACS_True(GFPAus, group1 = "MALE", group2 = "FEMALE")/5; sum(dBraG)
GFPCan <- cfa(GFPModel, data = CANIP, group = "SEXO"); dCanG <- dMACS_True(GFPCan, group1 = "MALE", group2 = "FEMALE")/5; sum(dCanG)
GFPChi <- cfa(GFPModel, data = CHIIP, group = "SEXO"); dChiG <- dMACS_True(GFPChi, group1 = "MALE", group2 = "FEMALE")/5; sum(dChiG)
#GFPCol <- cfa(GFPModel, data = COLIP, group = "SEXO"); dColG <- dMACS_True(GFPCol, group1 = "MALE", group2 = "FEMALE")/5; sum(dColG) #Will not run
#GFPCro <- cfa(GFPModel, data = CROIP, group = "SEXO", check.gradient = F); dCroG <- dMACS_True(GFPCro, group1 = "MALE", group2 = "FEMALE")/5; sum(dCroG)
#GFPDen <- cfa(GFPModel, data = DENIP, group = "SEXO", control=list(rel.tol=1e-4), check.gradient = F); dDenG <- dMACS_True(GFPDen, group1 = "MALE", group2 = "FEMALE")/5; sum(dDenG)
GFPEgy <- cfa(GFPModel, data = EGYIP, group = "SEXO"); dEgyG <- dMACS_True(GFPEgy, group1 = "MALE", group2 = "FEMALE")/5; sum(dEgyG)
#GFPFin <- cfa(GFPModel, data = FINIP, group = "SEXO", std.lv = T, std.ov = T); dFinG <- dMACS_True(GFPFin, group1 = "MALE", group2 = "FEMALE")/5; sum(dFinG) #Will not run
GFPFra <- cfa(GFPModel, data = FRAIP, group = "SEXO"); dFraG <- dMACS_True(GFPFra, group1 = "MALE", group2 = "FEMALE")/5; sum(dFraG)
GFPGer <- cfa(GFPModel, data = GERIP, group = "SEXO"); dGerG <- dMACS_True(GFPGer, group1 = "MALE", group2 = "FEMALE")/5; sum(dGerG)
GFPGre <- cfa(GFPModel, data = GREIP, group = "SEXO"); dGreG <- dMACS_True(GFPGre, group1 = "MALE", group2 = "FEMALE")/5; sum(dGreG)
GFPInd <- cfa(GFPModel, data = INDIP, group = "SEXO"); dIndG <- dMACS_True(GFPInd, group1 = "MALE", group2 = "FEMALE")/5; sum(dIndG)
GFPIno <- cfa(GFPModel, data = INOIP, group = "SEXO"); dInoG <- dMACS_True(GFPIno, group1 = "MALE", group2 = "FEMALE")/5; sum(dInoG)
GFPIra <- cfa(GFPModel, data = IRAIP, group = "SEXO"); dIraG <- dMACS_True(GFPIra, group1 = "MALE", group2 = "FEMALE")/5; sum(dIraG)
GFPIre <- cfa(GFPModel, data = IREIP, group = "SEXO"); dIreG <- dMACS_True(GFPIre, group1 = "MALE", group2 = "FEMALE")/5; sum(dIreG)
#GFPIsr <- cfa(GFPModel, data = ISRIP, group = "SEXO"); dIsrG <- dMACS_True(GFPIsr, group1 = "MALE", group2 = "FEMALE")/5; sum(dIsrG)
#GFPIta <- cfa(GFPModel, data = ITAIP, group = "SEXO", check.gradient = F); dItaG <- dMACS_True(GFPIta, group1 = "MALE", group2 = "FEMALE")/5; sum(dItaG)
GFPJam <- cfa(GFPModel, data = JAMIP, group = "SEXO"); dJamG <- dMACS_True(GFPJam, group1 = "MALE", group2 = "FEMALE")/5; sum(dJamG)
GFPJap <- cfa(GFPModel, data = JAPIP, group = "SEXO"); dJapG <- dMACS_True(GFPJap, group1 = "MALE", group2 = "FEMALE")/5; sum(dJapG)
GFPKen <- cfa(GFPModel, data = KENIP, group = "SEXO"); dKenG <- dMACS_True(GFPKen, group1 = "MALE", group2 = "FEMALE")/5; sum(dKenG)
GFPLeb <- cfa(GFPModel, data = LEBIP, group = "SEXO"); dLebG <- dMACS_True(GFPLeb, group1 = "MALE", group2 = "FEMALE")/5; sum(dLebG)
GFPMal <- cfa(GFPModel, data = MALIP, group = "SEXO"); dMalG <- dMACS_True(GFPMal, group1 = "MALE", group2 = "FEMALE")/5; sum(dMalG)
GFPMex <- cfa(GFPModel, data = MEXIP, group = "SEXO"); dMexG <- dMACS_True(GFPMex, group1 = "MALE", group2 = "FEMALE")/5; sum(dMexG)
#GFPNet <- cfa(GFPModel, data = NETIP, group = "SEXO"); dNetG <- dMACS_True(GFPNet, group1 = "MALE", group2 = "FEMALE")/5; sum(dNetG)
GFPNew <- cfa(GFPModel, data = NEWIP, group = "SEXO"); dNewG <- dMACS_True(GFPNew, group1 = "MALE", group2 = "FEMALE")/5; sum(dNewG)
GFPNir <- cfa(GFPModel, data = NIRIP, group = "SEXO"); dNirG <- dMACS_True(GFPNir, group1 = "MALE", group2 = "FEMALE")/5; sum(dNirG)
#GFPNor <- cfa(GFPModel, data = NORIP, group = "SEXO"); dNorG <- dMACS_True(GFPNor, group1 = "MALE", group2 = "FEMALE")/5; sum(dNorG)
#GFPPer <- cfa(GFPModel, data = PERIP, group = "SEXO", std.lv = T); dPerG <- dMACS_True(GFPPer, group1 = "MALE", group2 = "FEMALE")/5; sum(dPerG)
GFPPak <- cfa(GFPModel, data = PAKIP, group = "SEXO"); dPakG <- dMACS_True(GFPPak, group1 = "MALE", group2 = "FEMALE")/5; sum(dPakG)
GFPPhi <- cfa(GFPModel, data = PHIIP, group = "SEXO"); dPhiG <- dMACS_True(GFPPhi, group1 = "MALE", group2 = "FEMALE")/5; sum(dPhiG)
#GFPPol <- cfa(GFPModel, data = POLIP, group = "SEXO"); dPolG <- dMACS_True(GFPPol, group1 = "MALE", group2 = "FEMALE")/5; sum(dPolG)
#GFPPor <- cfa(GFPModel, data = PORIP, group = "SEXO"); dPorG <- dMACS_True(GFPPor, group1 = "MALE", group2 = "FEMALE")/5; sum(dPorG)
GFPRom <- cfa(GFPModel, data = ROMIP, group = "SEXO"); dRomG <- dMACS_True(GFPRom, group1 = "MALE", group2 = "FEMALE")/5; sum(dRomG)
GFPRus <- cfa(GFPModel, data = RUSIP, group = "SEXO"); dRusG <- dMACS_True(GFPRus, group1 = "MALE", group2 = "FEMALE")/5; sum(dRusG)
GFPSin <- cfa(GFPModel, data = SINIP, group = "SEXO"); dSinG <- dMACS_True(GFPSin, group1 = "MALE", group2 = "FEMALE")/5; sum(dSinG)
#GFPSlo <- cfa(GFPModel, data = SLOIP, group = "SEXO"); dSloG <- dMACS_True(GFPSlo, group1 = "MALE", group2 = "FEMALE")/5; sum(dSloG)
#GFPSlv <- cfa(GFPModel, data = SLVIP, group = "SEXO"); dSlvG <- dMACS_True(GFPSlv, group1 = "MALE", group2 = "FEMALE")/5; sum(dSlvG)
GFPSou <- cfa(GFPModel, data = SOUIP, group = "SEXO"); dSouG <- dMACS_True(GFPSou, group1 = "MALE", group2 = "FEMALE")/5; sum(dSouG)
GFPSKo <- cfa(GFPModel, data = SKOIP, group = "SEXO"); dSKoG <- dMACS_True(GFPSKo, group1 = "MALE", group2 = "FEMALE")/5; sum(dSKoG)
#GFPSpa <- cfa(GFPModel, data = SPAIP, group = "SEXO"); dSpaG <- dMACS_True(GFPSpa, group1 = "MALE", group2 = "FEMALE")/5; sum(dSpaG)
#GFPSwe <- cfa(GFPModel, data = SWEIP, group = "SEXO"); dSweG <- dMACS_True(GFPSwe, group1 = "MALE", group2 = "FEMALE")/5; sum(dSweG)
GFPSwi <- cfa(GFPModel, data = SWIIP, group = "SEXO"); dSwiG <- dMACS_True(GFPSwi, group1 = "MALE", group2 = "FEMALE")/5; sum(dSwiG)
GFPTha <- cfa(GFPModel, data = THAIP, group = "SEXO"); dThaG <- dMACS_True(GFPTha, group1 = "MALE", group2 = "FEMALE")/5; sum(dThaG)
#GFPTri <- cfa(GFPModel, data = TRIIP, group = "SEXO"); dTriG <- dMACS_True(GFPTri, group1 = "MALE", group2 = "FEMALE")/5; sum(dTriG)
GFPTur <- cfa(GFPModel, data = TURIP, group = "SEXO"); dTurG <- dMACS_True(GFPTur, group1 = "MALE", group2 = "FEMALE")/5; sum(dTurG)
#GFPUga <- cfa(GFPModel, data = UGAIP, group = "SEXO"); dUgaG <- dMACS_True(GFPUga, group1 = "MALE", group2 = "FEMALE")/5; sum(dUgaG)
GFPUK <- cfa(GFPModel, data = UKIP, group = "SEXO"); dUKG <- dMACS_True(GFPUK, group1 = "MALE", group2 = "FEMALE")/5; sum(dUKG)
GFPUkr <- cfa(GFPModel, data = UKRIP, group = "SEXO"); dUkrG <- dMACS_True(GFPUkr, group1 = "MALE", group2 = "FEMALE")/5; sum(dUkrG)
GFPUAE <- cfa(GFPModel, data = UAEIP, group = "SEXO"); dUAEG <- dMACS_True(GFPUAE, group1 = "MALE", group2 = "FEMALE")/5; sum(dUAEG)
GFPUSA <- cfa(GFPModel, data = USAIP, group = "SEXO"); dUSAG <- dMACS_True(GFPUSA, group1 = "MALE", group2 = "FEMALE")/5; sum(dUSAG)
GFPVen <- cfa(GFPModel, data = VENIP, group = "SEXO"); dVenG <- dMACS_True(GFPVen, group1 = "MALE", group2 = "FEMALE")/5; sum(dVenG)
GFPVie <- cfa(GFPModel, data = VIEIP, group = "SEXO"); dVieG <- dMACS_True(GFPVie, group1 = "MALE", group2 = "FEMALE")/5; sum(dVieG)
As should be done due to the distortion in sumscores as a result of the imperfect correlations of their parts (i.e., the composite score extremity effect), we can compute and compare the sum of d’s for facets and the d for the sumscore. The former is the desired quantity until we have an actual aggregated dMACS. We could figure it out with the loadings, but it’s needlessly time-consuming.
#d for Sumscore
## Openness
dOAlbO = cohen.d(ALBIP$Openness, ALBIP$SEX); dOAlbO$estimate
dOAlgO = cohen.d(ALGIP$Openness, ALGIP$SEX); dOAlgO$estimate
dOAngO = cohen.d(ANGIP$Openness, ANGIP$SEX); dOAngO$estimate
dOArgO = cohen.d(ARGIP$Openness, ARGIP$SEX); dOArgO$estimate
dOAusO = cohen.d(AUSIP$Openness, AUSIP$SEX); dOAusO$estimate
dOHabO = cohen.d(HABIP$Openness, HABIP$SEX); dOHabO$estimate
dOBelO = cohen.d(BELIP$Openness, BELIP$SEX); dOBelO$estimate
dOBraO = cohen.d(BRAIP$Openness, BRAIP$SEX); dOBraO$estimate
dOCanO = cohen.d(CANIP$Openness, CANIP$SEX); dOCanO$estimate
dOChiO = cohen.d(CHIIP$Openness, CHIIP$SEX); dOChiO$estimate
dOColO = cohen.d(COLIP$Openness, COLIP$SEX); dOColO$estimate
dOCroO = cohen.d(CROIP$Openness, CROIP$SEX); dOCroO$estimate
dODenO = cohen.d(DENIP$Openness, DENIP$SEX); dODenO$estimate
dOEgyO = cohen.d(EGYIP$Openness, EGYIP$SEX); dOEgyO$estimate
dOFinO = cohen.d(FINIP$Openness, FINIP$SEX); dOFinO$estimate
dOFraO = cohen.d(FRAIP$Openness, FRAIP$SEX); dOFraO$estimate
dOGerO = cohen.d(GERIP$Openness, GERIP$SEX); dOGerO$estimate
dOGreO = cohen.d(GREIP$Openness, GREIP$SEX); dOGreO$estimate
dOIndO = cohen.d(INDIP$Openness, INDIP$SEX); dOIndO$estimate
dOInoO = cohen.d(INOIP$Openness, INOIP$SEX); dOInoO$estimate
dOIraO = cohen.d(IRAIP$Openness, IRAIP$SEX); dOIraO$estimate
dOIreO = cohen.d(IREIP$Openness, IREIP$SEX); dOIreO$estimate
dOIsrO = cohen.d(ISRIP$Openness, ISRIP$SEX); dOIsrO$estimate
dOItaO = cohen.d(ITAIP$Openness, ITAIP$SEX); dOItaO$estimate
dOJamO = cohen.d(JAMIP$Openness, JAMIP$SEX); dOJamO$estimate
dOJapO = cohen.d(JAPIP$Openness, JAPIP$SEX); dOJapO$estimate
dOKenO = cohen.d(KENIP$Openness, KENIP$SEX); dOKenO$estimate
dOLebO = cohen.d(LEBIP$Openness, LEBIP$SEX); dOLebO$estimate
dOMalO = cohen.d(MALIP$Openness, MALIP$SEX); dOMalO$estimate
dOMexO = cohen.d(MEXIP$Openness, MEXIP$SEX); dOMexO$estimate
dONetO = cohen.d(NETIP$Openness, NETIP$SEX); dONetO$estimate
dONewO = cohen.d(NEWIP$Openness, NEWIP$SEX); dONewO$estimate
dONirO = cohen.d(NIRIP$Openness, NIRIP$SEX); dONirO$estimate
dONorO = cohen.d(NORIP$Openness, NORIP$SEX); dONorO$estimate
dOPerO = cohen.d(PERIP$Openness, PERIP$SEX); dOPerO$estimate
dOPakO = cohen.d(PAKIP$Openness, PAKIP$SEX); dOPakO$estimate
dOPhiO = cohen.d(PHIIP$Openness, PHIIP$SEX); dOPhiO$estimate
dOPolO = cohen.d(POLIP$Openness, POLIP$SEX); dOPolO$estimate
dOPorO = cohen.d(PORIP$Openness, PORIP$SEX); dOPorO$estimate
dORomO = cohen.d(ROMIP$Openness, ROMIP$SEX); dORomO$estimate
dORusO = cohen.d(RUSIP$Openness, RUSIP$SEX); dORusO$estimate
dOSinO = cohen.d(SINIP$Openness, SINIP$SEX); dOSinO$estimate
dOSloO = cohen.d(SLOIP$Openness, SLOIP$SEX); dOSloO$estimate
dOSlvO = cohen.d(SLVIP$Openness, SLVIP$SEX); dOSlvO$estimate
dOSouO = cohen.d(SOUIP$Openness, SOUIP$SEX); dOSouO$estimate
dOSKoO = cohen.d(SKOIP$Openness, SKOIP$SEX); dOSKoO$estimate
dOSpaO = cohen.d(SPAIP$Openness, SPAIP$SEX); dOSpaO$estimate
dOSweO = cohen.d(SWEIP$Openness, SWEIP$SEX); dOSweO$estimate
dOSwiO = cohen.d(SWIIP$Openness, SWIIP$SEX); dOSwiO$estimate
dOThaO = cohen.d(THAIP$Openness, THAIP$SEX); dOThaO$estimate
dOTriO = cohen.d(TRIIP$Openness, TRIIP$SEX); dOTriO$estimate
dOTurO = cohen.d(TURIP$Openness, TURIP$SEX); dOTurO$estimate
dOUgaO = cohen.d(UGAIP$Openness, UGAIP$SEX); dOUgaO$estimate
dOUKO = cohen.d(UKIP$Openness, UKIP$SEX); dOUKO$estimate
dOUkrO = cohen.d(UKRIP$Openness, UKRIP$SEX); dOUkrO$estimate
dOUAEO = cohen.d(UAEIP$Openness, UAEIP$SEX); dOUAEO$estimate
dOUSAO = cohen.d(USAIP$Openness, USAIP$SEX); dOUSAO$estimate
dOVenO = cohen.d(VENIP$Openness, VENIP$SEX); dOVenO$estimate
dOVieO = cohen.d(VIEIP$Openness, VIEIP$SEX); dOVieO$estimate
## Conscientiousness
dOAlbC = cohen.d(ALBIP$Conscientiousness, ALBIP$SEX); dOAlbC$estimate
dOAlgC = cohen.d(ALGIP$Conscientiousness, ALGIP$SEX); dOAlgC$estimate
dOAngC = cohen.d(ANGIP$Conscientiousness, ANGIP$SEX); dOAngC$estimate
dOArgC = cohen.d(ARGIP$Conscientiousness, ARGIP$SEX); dOArgC$estimate
dOAusC = cohen.d(AUSIP$Conscientiousness, AUSIP$SEX); dOAusC$estimate
dOHabC = cohen.d(HABIP$Conscientiousness, HABIP$SEX); dOHabC$estimate
dOBelC = cohen.d(BELIP$Conscientiousness, BELIP$SEX); dOBelC$estimate
dOBraC = cohen.d(BRAIP$Conscientiousness, BRAIP$SEX); dOBraC$estimate
dOCanC = cohen.d(CANIP$Conscientiousness, CANIP$SEX); dOCanC$estimate
dOChiC = cohen.d(CHIIP$Conscientiousness, CHIIP$SEX); dOChiC$estimate
dOColC = cohen.d(COLIP$Conscientiousness, COLIP$SEX); dOColC$estimate
dOCroC = cohen.d(CROIP$Conscientiousness, CROIP$SEX); dOCroC$estimate
dODenC = cohen.d(DENIP$Conscientiousness, DENIP$SEX); dODenC$estimate
dOEgyC = cohen.d(EGYIP$Conscientiousness, EGYIP$SEX); dOEgyC$estimate
dOFinC = cohen.d(FINIP$Conscientiousness, FINIP$SEX); dOFinC$estimate
dOFraC = cohen.d(FRAIP$Conscientiousness, FRAIP$SEX); dOFraC$estimate
dOGerC = cohen.d(GERIP$Conscientiousness, GERIP$SEX); dOGerC$estimate
dOGreC = cohen.d(GREIP$Conscientiousness, GREIP$SEX); dOGreC$estimate
dOIndC = cohen.d(INDIP$Conscientiousness, INDIP$SEX); dOIndC$estimate
dOInoC = cohen.d(INOIP$Conscientiousness, INOIP$SEX); dOInoC$estimate
dOIraC = cohen.d(IRAIP$Conscientiousness, IRAIP$SEX); dOIraC$estimate
dOIreC = cohen.d(IREIP$Conscientiousness, IREIP$SEX); dOIreC$estimate
dOIsrC = cohen.d(ISRIP$Conscientiousness, ISRIP$SEX); dOIsrC$estimate
dOItaC = cohen.d(ITAIP$Conscientiousness, ITAIP$SEX); dOItaC$estimate
dOJamC = cohen.d(JAMIP$Conscientiousness, JAMIP$SEX); dOJamC$estimate
dOJapC = cohen.d(JAPIP$Conscientiousness, JAPIP$SEX); dOJapC$estimate
dOKenC = cohen.d(KENIP$Conscientiousness, KENIP$SEX); dOKenC$estimate
dOLebC = cohen.d(LEBIP$Conscientiousness, LEBIP$SEX); dOLebC$estimate
dOMalC = cohen.d(MALIP$Conscientiousness, MALIP$SEX); dOMalC$estimate
dOMexC = cohen.d(MEXIP$Conscientiousness, MEXIP$SEX); dOMexC$estimate
dONetC = cohen.d(NETIP$Conscientiousness, NETIP$SEX); dONetC$estimate
dONewC = cohen.d(NEWIP$Conscientiousness, NEWIP$SEX); dONewC$estimate
dONirC = cohen.d(NIRIP$Conscientiousness, NIRIP$SEX); dONirC$estimate
dONorC = cohen.d(NORIP$Conscientiousness, NORIP$SEX); dONorC$estimate
dOPerC = cohen.d(PERIP$Conscientiousness, PERIP$SEX); dOPerC$estimate
dOPakC = cohen.d(PAKIP$Conscientiousness, PAKIP$SEX); dOPakC$estimate
dOPhiC = cohen.d(PHIIP$Conscientiousness, PHIIP$SEX); dOPhiC$estimate
dOPolC = cohen.d(POLIP$Conscientiousness, POLIP$SEX); dOPolC$estimate
dOPorC = cohen.d(PORIP$Conscientiousness, PORIP$SEX); dOPorC$estimate
dORomC = cohen.d(ROMIP$Conscientiousness, ROMIP$SEX); dORomC$estimate
dORusC = cohen.d(RUSIP$Conscientiousness, RUSIP$SEX); dORusC$estimate
dOSinC = cohen.d(SINIP$Conscientiousness, SINIP$SEX); dOSinC$estimate
dOSloC = cohen.d(SLOIP$Conscientiousness, SLOIP$SEX); dOSloC$estimate
dOSlvC = cohen.d(SLVIP$Conscientiousness, SLVIP$SEX); dOSlvC$estimate
dOSouC = cohen.d(SOUIP$Conscientiousness, SOUIP$SEX); dOSouC$estimate
dOSKoC = cohen.d(SKOIP$Conscientiousness, SKOIP$SEX); dOSKoC$estimate
dOSpaC = cohen.d(SPAIP$Conscientiousness, SPAIP$SEX); dOSpaC$estimate
dOSweC = cohen.d(SWEIP$Conscientiousness, SWEIP$SEX); dOSweC$estimate
dOSwiC = cohen.d(SWIIP$Conscientiousness, SWIIP$SEX); dOSwiC$estimate
dOThaC = cohen.d(THAIP$Conscientiousness, THAIP$SEX); dOThaC$estimate
dOTriC = cohen.d(TRIIP$Conscientiousness, TRIIP$SEX); dOTriC$estimate
dOTurC = cohen.d(TURIP$Conscientiousness, TURIP$SEX); dOTurC$estimate
dOUgaC = cohen.d(UGAIP$Conscientiousness, UGAIP$SEX); dOUgaC$estimate
dOUKC = cohen.d(UKIP$Conscientiousness, UKIP$SEX); dOUKC$estimate
dOUkrC = cohen.d(UKRIP$Conscientiousness, UKRIP$SEX); dOUkrC$estimate
dOUAEC = cohen.d(UAEIP$Conscientiousness, UAEIP$SEX); dOUAEC$estimate
dOUSAC = cohen.d(USAIP$Conscientiousness, USAIP$SEX); dOUSAC$estimate
dOVenC = cohen.d(VENIP$Conscientiousness, VENIP$SEX); dOVenC$estimate
dOVieC = cohen.d(VIEIP$Conscientiousness, VIEIP$SEX); dOVieC$estimate
## Extraversion
dOAlbE = cohen.d(ALBIP$Extraversion, ALBIP$SEX); dOAlbE$estimate
dOAlgE = cohen.d(ALGIP$Extraversion, ALGIP$SEX); dOAlgE$estimate
dOAngE = cohen.d(ANGIP$Extraversion, ANGIP$SEX); dOAngE$estimate
dOArgE = cohen.d(ARGIP$Extraversion, ARGIP$SEX); dOArgE$estimate
dOAusE = cohen.d(AUSIP$Extraversion, AUSIP$SEX); dOAusE$estimate
dOHabE = cohen.d(HABIP$Extraversion, HABIP$SEX); dOHabE$estimate
dOBelE = cohen.d(BELIP$Extraversion, BELIP$SEX); dOBelE$estimate
dOBraE = cohen.d(BRAIP$Extraversion, BRAIP$SEX); dOBraE$estimate
dOCanE = cohen.d(CANIP$Extraversion, CANIP$SEX); dOCanE$estimate
dOChiE = cohen.d(CHIIP$Extraversion, CHIIP$SEX); dOChiE$estimate
dOColE = cohen.d(COLIP$Extraversion, COLIP$SEX); dOColE$estimate
dOCroE = cohen.d(CROIP$Extraversion, CROIP$SEX); dOCroE$estimate
dODenE = cohen.d(DENIP$Extraversion, DENIP$SEX); dODenE$estimate
dOEgyE = cohen.d(EGYIP$Extraversion, EGYIP$SEX); dOEgyE$estimate
dOFinE = cohen.d(FINIP$Extraversion, FINIP$SEX); dOFinE$estimate
dOFraE = cohen.d(FRAIP$Extraversion, FRAIP$SEX); dOFraE$estimate
dOGerE = cohen.d(GERIP$Extraversion, GERIP$SEX); dOGerE$estimate
dOGreE = cohen.d(GREIP$Extraversion, GREIP$SEX); dOGreE$estimate
dOIndE = cohen.d(INDIP$Extraversion, INDIP$SEX); dOIndE$estimate
dOInoE = cohen.d(INOIP$Extraversion, INOIP$SEX); dOInoE$estimate
dOIraE = cohen.d(IRAIP$Extraversion, IRAIP$SEX); dOIraE$estimate
dOIreE = cohen.d(IREIP$Extraversion, IREIP$SEX); dOIreE$estimate
dOIsrE = cohen.d(ISRIP$Extraversion, ISRIP$SEX); dOIsrE$estimate
dOItaE = cohen.d(ITAIP$Extraversion, ITAIP$SEX); dOItaE$estimate
dOJamE = cohen.d(JAMIP$Extraversion, JAMIP$SEX); dOJamE$estimate
dOJapE = cohen.d(JAPIP$Extraversion, JAPIP$SEX); dOJapE$estimate
dOKenE = cohen.d(KENIP$Extraversion, KENIP$SEX); dOKenE$estimate
dOLebE = cohen.d(LEBIP$Extraversion, LEBIP$SEX); dOLebE$estimate
dOMalE = cohen.d(MALIP$Extraversion, MALIP$SEX); dOMalE$estimate
dOMexE = cohen.d(MEXIP$Extraversion, MEXIP$SEX); dOMexE$estimate
dONetE = cohen.d(NETIP$Extraversion, NETIP$SEX); dONetE$estimate
dONewE = cohen.d(NEWIP$Extraversion, NEWIP$SEX); dONewE$estimate
dONirE = cohen.d(NIRIP$Extraversion, NIRIP$SEX); dONirE$estimate
dONorE = cohen.d(NORIP$Extraversion, NORIP$SEX); dONorE$estimate
dOPerE = cohen.d(PERIP$Extraversion, PERIP$SEX); dOPerE$estimate
dOPakE = cohen.d(PAKIP$Extraversion, PAKIP$SEX); dOPakE$estimate
dOPhiE = cohen.d(PHIIP$Extraversion, PHIIP$SEX); dOPhiE$estimate
dOPolE = cohen.d(POLIP$Extraversion, POLIP$SEX); dOPolE$estimate
dOPorE = cohen.d(PORIP$Extraversion, PORIP$SEX); dOPorE$estimate
dORomE = cohen.d(ROMIP$Extraversion, ROMIP$SEX); dORomE$estimate
dORusE = cohen.d(RUSIP$Extraversion, RUSIP$SEX); dORusE$estimate
dOSinE = cohen.d(SINIP$Extraversion, SINIP$SEX); dOSinE$estimate
dOSloE = cohen.d(SLOIP$Extraversion, SLOIP$SEX); dOSloE$estimate
dOSlvE = cohen.d(SLVIP$Extraversion, SLVIP$SEX); dOSlvE$estimate
dOSouE = cohen.d(SOUIP$Extraversion, SOUIP$SEX); dOSouE$estimate
dOSKoE = cohen.d(SKOIP$Extraversion, SKOIP$SEX); dOSKoE$estimate
dOSpaE = cohen.d(SPAIP$Extraversion, SPAIP$SEX); dOSpaE$estimate
dOSweE = cohen.d(SWEIP$Extraversion, SWEIP$SEX); dOSweE$estimate
dOSwiE = cohen.d(SWIIP$Extraversion, SWIIP$SEX); dOSwiE$estimate
dOThaE = cohen.d(THAIP$Extraversion, THAIP$SEX); dOThaE$estimate
dOTriE = cohen.d(TRIIP$Extraversion, TRIIP$SEX); dOTriE$estimate
dOTurE = cohen.d(TURIP$Extraversion, TURIP$SEX); dOTurE$estimate
dOUgaE = cohen.d(UGAIP$Extraversion, UGAIP$SEX); dOUgaE$estimate
dOUKE = cohen.d(UKIP$Extraversion, UKIP$SEX); dOUKE$estimate
dOUkrE = cohen.d(UKRIP$Extraversion, UKRIP$SEX); dOUkrE$estimate
dOUAEE = cohen.d(UAEIP$Extraversion, UAEIP$SEX); dOUAEE$estimate
dOUSAE = cohen.d(USAIP$Extraversion, USAIP$SEX); dOUSAE$estimate
dOVenE = cohen.d(VENIP$Extraversion, VENIP$SEX); dOVenE$estimate
dOVieE = cohen.d(VIEIP$Extraversion, VIEIP$SEX); dOVieE$estimate
## Agreeableness
dOAlbA = cohen.d(ALBIP$Agreeableness, ALBIP$SEX); dOAlbA$estimate
dOAlgA = cohen.d(ALGIP$Agreeableness, ALGIP$SEX); dOAlgA$estimate
dOAngA = cohen.d(ANGIP$Agreeableness, ANGIP$SEX); dOAngA$estimate
dOArgA = cohen.d(ARGIP$Agreeableness, ARGIP$SEX); dOArgA$estimate
dOAusA = cohen.d(AUSIP$Agreeableness, AUSIP$SEX); dOAusA$estimate
dOHabA = cohen.d(HABIP$Agreeableness, HABIP$SEX); dOHabA$estimate
dOBelA = cohen.d(BELIP$Agreeableness, BELIP$SEX); dOBelA$estimate
dOBraA = cohen.d(BRAIP$Agreeableness, BRAIP$SEX); dOBraA$estimate
dOCanA = cohen.d(CANIP$Agreeableness, CANIP$SEX); dOCanA$estimate
dOChiA = cohen.d(CHIIP$Agreeableness, CHIIP$SEX); dOChiA$estimate
dOColA = cohen.d(COLIP$Agreeableness, COLIP$SEX); dOColA$estimate
dOCroA = cohen.d(CROIP$Agreeableness, CROIP$SEX); dOCroA$estimate
dODenA = cohen.d(DENIP$Agreeableness, DENIP$SEX); dODenA$estimate
dOEgyA = cohen.d(EGYIP$Agreeableness, EGYIP$SEX); dOEgyA$estimate
dOFinA = cohen.d(FINIP$Agreeableness, FINIP$SEX); dOFinA$estimate
dOFraA = cohen.d(FRAIP$Agreeableness, FRAIP$SEX); dOFraA$estimate
dOGerA = cohen.d(GERIP$Agreeableness, GERIP$SEX); dOGerA$estimate
dOGreA = cohen.d(GREIP$Agreeableness, GREIP$SEX); dOGreA$estimate
dOIndA = cohen.d(INDIP$Agreeableness, INDIP$SEX); dOIndA$estimate
dOInoA = cohen.d(INOIP$Agreeableness, INOIP$SEX); dOInoA$estimate
dOIraA = cohen.d(IRAIP$Agreeableness, IRAIP$SEX); dOIraA$estimate
dOIreA = cohen.d(IREIP$Agreeableness, IREIP$SEX); dOIreA$estimate
dOIsrA = cohen.d(ISRIP$Agreeableness, ISRIP$SEX); dOIsrA$estimate
dOItaA = cohen.d(ITAIP$Agreeableness, ITAIP$SEX); dOItaA$estimate
dOJamA = cohen.d(JAMIP$Agreeableness, JAMIP$SEX); dOJamA$estimate
dOJapA = cohen.d(JAPIP$Agreeableness, JAPIP$SEX); dOJapA$estimate
dOKenA = cohen.d(KENIP$Agreeableness, KENIP$SEX); dOKenA$estimate
dOLebA = cohen.d(LEBIP$Agreeableness, LEBIP$SEX); dOLebA$estimate
dOMalA = cohen.d(MALIP$Agreeableness, MALIP$SEX); dOMalA$estimate
dOMexA = cohen.d(MEXIP$Agreeableness, MEXIP$SEX); dOMexA$estimate
dONetA = cohen.d(NETIP$Agreeableness, NETIP$SEX); dONetA$estimate
dONewA = cohen.d(NEWIP$Agreeableness, NEWIP$SEX); dONewA$estimate
dONirA = cohen.d(NIRIP$Agreeableness, NIRIP$SEX); dONirA$estimate
dONorA = cohen.d(NORIP$Agreeableness, NORIP$SEX); dONorA$estimate
dOPerA = cohen.d(PERIP$Agreeableness, PERIP$SEX); dOPerA$estimate
dOPakA = cohen.d(PAKIP$Agreeableness, PAKIP$SEX); dOPakA$estimate
dOPhiA = cohen.d(PHIIP$Agreeableness, PHIIP$SEX); dOPhiA$estimate
dOPolA = cohen.d(POLIP$Agreeableness, POLIP$SEX); dOPolA$estimate
dOPorA = cohen.d(PORIP$Agreeableness, PORIP$SEX); dOPorA$estimate
dORomA = cohen.d(ROMIP$Agreeableness, ROMIP$SEX); dORomA$estimate
dORusA = cohen.d(RUSIP$Agreeableness, RUSIP$SEX); dORusA$estimate
dOSinA = cohen.d(SINIP$Agreeableness, SINIP$SEX); dOSinA$estimate
dOSloA = cohen.d(SLOIP$Agreeableness, SLOIP$SEX); dOSloA$estimate
dOSlvA = cohen.d(SLVIP$Agreeableness, SLVIP$SEX); dOSlvA$estimate
dOSouA = cohen.d(SOUIP$Agreeableness, SOUIP$SEX); dOSouA$estimate
dOSKoA = cohen.d(SKOIP$Agreeableness, SKOIP$SEX); dOSKoA$estimate
dOSpaA = cohen.d(SPAIP$Agreeableness, SPAIP$SEX); dOSpaA$estimate
dOSweA = cohen.d(SWEIP$Agreeableness, SWEIP$SEX); dOSweA$estimate
dOSwiA = cohen.d(SWIIP$Agreeableness, SWIIP$SEX); dOSwiA$estimate
dOThaA = cohen.d(THAIP$Agreeableness, THAIP$SEX); dOThaA$estimate
dOTriA = cohen.d(TRIIP$Agreeableness, TRIIP$SEX); dOTriA$estimate
dOTurA = cohen.d(TURIP$Agreeableness, TURIP$SEX); dOTurA$estimate
dOUgaA = cohen.d(UGAIP$Agreeableness, UGAIP$SEX); dOUgaA$estimate
dOUKA = cohen.d(UKIP$Agreeableness, UKIP$SEX); dOUKA$estimate
dOUkrA = cohen.d(UKRIP$Agreeableness, UKRIP$SEX); dOUkrA$estimate
dOUAEA = cohen.d(UAEIP$Agreeableness, UAEIP$SEX); dOUAEA$estimate
dOUSAA = cohen.d(USAIP$Agreeableness, USAIP$SEX); dOUSAA$estimate
dOVenA = cohen.d(VENIP$Agreeableness, VENIP$SEX); dOVenA$estimate
dOVieA = cohen.d(VIEIP$Agreeableness, VIEIP$SEX); dOVieA$estimate
## Neuroticism
dOAlbN = cohen.d(ALBIP$Neuroticism, ALBIP$SEX); dOAlbN$estimate
dOAlgN = cohen.d(ALGIP$Neuroticism, ALGIP$SEX); dOAlgN$estimate
dOAngN = cohen.d(ANGIP$Neuroticism, ANGIP$SEX); dOAngN$estimate
dOArgN = cohen.d(ARGIP$Neuroticism, ARGIP$SEX); dOArgN$estimate
dOAusN = cohen.d(AUSIP$Neuroticism, AUSIP$SEX); dOAusN$estimate
dOHabN = cohen.d(HABIP$Neuroticism, HABIP$SEX); dOHabN$estimate
dOBelN = cohen.d(BELIP$Neuroticism, BELIP$SEX); dOBelN$estimate
dOBraN = cohen.d(BRAIP$Neuroticism, BRAIP$SEX); dOBraN$estimate
dOCanN = cohen.d(CANIP$Neuroticism, CANIP$SEX); dOCanN$estimate
dOChiN = cohen.d(CHIIP$Neuroticism, CHIIP$SEX); dOChiN$estimate
dOColN = cohen.d(COLIP$Neuroticism, COLIP$SEX); dOColN$estimate
dOCroN = cohen.d(CROIP$Neuroticism, CROIP$SEX); dOCroN$estimate
dODenN = cohen.d(DENIP$Neuroticism, DENIP$SEX); dODenN$estimate
dOEgyN = cohen.d(EGYIP$Neuroticism, EGYIP$SEX); dOEgyN$estimate
dOFinN = cohen.d(FINIP$Neuroticism, FINIP$SEX); dOFinN$estimate
dOFraN = cohen.d(FRAIP$Neuroticism, FRAIP$SEX); dOFraN$estimate
dOGerN = cohen.d(GERIP$Neuroticism, GERIP$SEX); dOGerN$estimate
dOGreN = cohen.d(GREIP$Neuroticism, GREIP$SEX); dOGreN$estimate
dOIndN = cohen.d(INDIP$Neuroticism, INDIP$SEX); dOIndN$estimate
dOInoN = cohen.d(INOIP$Neuroticism, INOIP$SEX); dOInoN$estimate
dOIraN = cohen.d(IRAIP$Neuroticism, IRAIP$SEX); dOIraN$estimate
dOIreN = cohen.d(IREIP$Neuroticism, IREIP$SEX); dOIreN$estimate
dOIsrN = cohen.d(ISRIP$Neuroticism, ISRIP$SEX); dOIsrN$estimate
dOItaN = cohen.d(ITAIP$Neuroticism, ITAIP$SEX); dOItaN$estimate
dOJamN = cohen.d(JAMIP$Neuroticism, JAMIP$SEX); dOJamN$estimate
dOJapN = cohen.d(JAPIP$Neuroticism, JAPIP$SEX); dOJapN$estimate
dOKenN = cohen.d(KENIP$Neuroticism, KENIP$SEX); dOKenN$estimate
dOLebN = cohen.d(LEBIP$Neuroticism, LEBIP$SEX); dOLebN$estimate
dOMalN = cohen.d(MALIP$Neuroticism, MALIP$SEX); dOMalN$estimate
dOMexN = cohen.d(MEXIP$Neuroticism, MEXIP$SEX); dOMexN$estimate
dONetN = cohen.d(NETIP$Neuroticism, NETIP$SEX); dONetN$estimate
dONewN = cohen.d(NEWIP$Neuroticism, NEWIP$SEX); dONewN$estimate
dONirN = cohen.d(NIRIP$Neuroticism, NIRIP$SEX); dONirN$estimate
dONorN = cohen.d(NORIP$Neuroticism, NORIP$SEX); dONorN$estimate
dOPerN = cohen.d(PERIP$Neuroticism, PERIP$SEX); dOPerN$estimate
dOPakN = cohen.d(PAKIP$Neuroticism, PAKIP$SEX); dOPakN$estimate
dOPhiN = cohen.d(PHIIP$Neuroticism, PHIIP$SEX); dOPhiN$estimate
dOPolN = cohen.d(POLIP$Neuroticism, POLIP$SEX); dOPolN$estimate
dOPorN = cohen.d(PORIP$Neuroticism, PORIP$SEX); dOPorN$estimate
dORomN = cohen.d(ROMIP$Neuroticism, ROMIP$SEX); dORomN$estimate
dORusN = cohen.d(RUSIP$Neuroticism, RUSIP$SEX); dORusN$estimate
dOSinN = cohen.d(SINIP$Neuroticism, SINIP$SEX); dOSinN$estimate
dOSloN = cohen.d(SLOIP$Neuroticism, SLOIP$SEX); dOSloN$estimate
dOSlvN = cohen.d(SLVIP$Neuroticism, SLVIP$SEX); dOSlvN$estimate
dOSouN = cohen.d(SOUIP$Neuroticism, SOUIP$SEX); dOSouN$estimate
dOSKoN = cohen.d(SKOIP$Neuroticism, SKOIP$SEX); dOSKoN$estimate
dOSpaN = cohen.d(SPAIP$Neuroticism, SPAIP$SEX); dOSpaN$estimate
dOSweN = cohen.d(SWEIP$Neuroticism, SWEIP$SEX); dOSweN$estimate
dOSwiN = cohen.d(SWIIP$Neuroticism, SWIIP$SEX); dOSwiN$estimate
dOThaN = cohen.d(THAIP$Neuroticism, THAIP$SEX); dOThaN$estimate
dOTriN = cohen.d(TRIIP$Neuroticism, TRIIP$SEX); dOTriN$estimate
dOTurN = cohen.d(TURIP$Neuroticism, TURIP$SEX); dOTurN$estimate
dOUgaN = cohen.d(UGAIP$Neuroticism, UGAIP$SEX); dOUgaN$estimate
dOUKN = cohen.d(UKIP$Neuroticism, UKIP$SEX); dOUKN$estimate
dOUkrN = cohen.d(UKRIP$Neuroticism, UKRIP$SEX); dOUkrN$estimate
dOUAEN = cohen.d(UAEIP$Neuroticism, UAEIP$SEX); dOUAEN$estimate
dOUSAN = cohen.d(USAIP$Neuroticism, USAIP$SEX); dOUSAN$estimate
dOVenN = cohen.d(VENIP$Neuroticism, VENIP$SEX); dOVenN$estimate
dOVieN = cohen.d(VIEIP$Neuroticism, VIEIP$SEX); dOVieN$estimate
## GFP
dOAlbG = cohen.d(ALBIP$GFP, ALBIP$SEX); dOAlbG$estimate
dOAlgG = cohen.d(ALGIP$GFP, ALGIP$SEX); dOAlgG$estimate
dOAngG = cohen.d(ANGIP$GFP, ANGIP$SEX); dOAngG$estimate
dOArgG = cohen.d(ARGIP$GFP, ARGIP$SEX); dOArgG$estimate
dOAusG = cohen.d(AUSIP$GFP, AUSIP$SEX); dOAusG$estimate
dOHabG = cohen.d(HABIP$GFP, HABIP$SEX); dOHabG$estimate
dOBelG = cohen.d(BELIP$GFP, BELIP$SEX); dOBelG$estimate
dOBraG = cohen.d(BRAIP$GFP, BRAIP$SEX); dOBraG$estimate
dOCanG = cohen.d(CANIP$GFP, CANIP$SEX); dOCanG$estimate
dOChiG = cohen.d(CHIIP$GFP, CHIIP$SEX); dOChiG$estimate
dOColG = cohen.d(COLIP$GFP, COLIP$SEX); dOColG$estimate
dOCroG = cohen.d(CROIP$GFP, CROIP$SEX); dOCroG$estimate
dODenG = cohen.d(DENIP$GFP, DENIP$SEX); dODenG$estimate
dOEgyG = cohen.d(EGYIP$GFP, EGYIP$SEX); dOEgyG$estimate
dOFinG = cohen.d(FINIP$GFP, FINIP$SEX); dOFinG$estimate
dOFraG = cohen.d(FRAIP$GFP, FRAIP$SEX); dOFraG$estimate
dOGerG = cohen.d(GERIP$GFP, GERIP$SEX); dOGerG$estimate
dOGreG = cohen.d(GREIP$GFP, GREIP$SEX); dOGreG$estimate
dOIndG = cohen.d(INDIP$GFP, INDIP$SEX); dOIndG$estimate
dOInoG = cohen.d(INOIP$GFP, INOIP$SEX); dOInoG$estimate
dOIraG = cohen.d(IRAIP$GFP, IRAIP$SEX); dOIraG$estimate
dOIreG = cohen.d(IREIP$GFP, IREIP$SEX); dOIreG$estimate
dOIsrG = cohen.d(ISRIP$GFP, ISRIP$SEX); dOIsrG$estimate
dOItaG = cohen.d(ITAIP$GFP, ITAIP$SEX); dOItaG$estimate
dOJamG = cohen.d(JAMIP$GFP, JAMIP$SEX); dOJamG$estimate
dOJapG = cohen.d(JAPIP$GFP, JAPIP$SEX); dOJapG$estimate
dOKenG = cohen.d(KENIP$GFP, KENIP$SEX); dOKenG$estimate
dOLebG = cohen.d(LEBIP$GFP, LEBIP$SEX); dOLebG$estimate
dOMalG = cohen.d(MALIP$GFP, MALIP$SEX); dOMalG$estimate
dOMexG = cohen.d(MEXIP$GFP, MEXIP$SEX); dOMexG$estimate
dONetG = cohen.d(NETIP$GFP, NETIP$SEX); dONetG$estimate
dONewG = cohen.d(NEWIP$GFP, NEWIP$SEX); dONewG$estimate
dONirG = cohen.d(NIRIP$GFP, NIRIP$SEX); dONirG$estimate
dONorG = cohen.d(NORIP$GFP, NORIP$SEX); dONorG$estimate
dOPerG = cohen.d(PERIP$GFP, PERIP$SEX); dOPerG$estimate
dOPakG = cohen.d(PAKIP$GFP, PAKIP$SEX); dOPakG$estimate
dOPhiG = cohen.d(PHIIP$GFP, PHIIP$SEX); dOPhiG$estimate
dOPolG = cohen.d(POLIP$GFP, POLIP$SEX); dOPolG$estimate
dOPorG = cohen.d(PORIP$GFP, PORIP$SEX); dOPorG$estimate
dORomG = cohen.d(ROMIP$GFP, ROMIP$SEX); dORomG$estimate
dORusG = cohen.d(RUSIP$GFP, RUSIP$SEX); dORusG$estimate
dOSinG = cohen.d(SINIP$GFP, SINIP$SEX); dOSinG$estimate
dOSloG = cohen.d(SLOIP$GFP, SLOIP$SEX); dOSloG$estimate
dOSlvG = cohen.d(SLVIP$GFP, SLVIP$SEX); dOSlvG$estimate
dOSouG = cohen.d(SOUIP$GFP, SOUIP$SEX); dOSouG$estimate
dOSKoG = cohen.d(SKOIP$GFP, SKOIP$SEX); dOSKoG$estimate
dOSpaG = cohen.d(SPAIP$GFP, SPAIP$SEX); dOSpaG$estimate
dOSweG = cohen.d(SWEIP$GFP, SWEIP$SEX); dOSweG$estimate
dOSwiG = cohen.d(SWIIP$GFP, SWIIP$SEX); dOSwiG$estimate
dOThaG = cohen.d(THAIP$GFP, THAIP$SEX); dOThaG$estimate
dOTriG = cohen.d(TRIIP$GFP, TRIIP$SEX); dOTriG$estimate
dOTurG = cohen.d(TURIP$GFP, TURIP$SEX); dOTurG$estimate
dOUgaG = cohen.d(UGAIP$GFP, UGAIP$SEX); dOUgaG$estimate
dOUKG = cohen.d(UKIP$GFP, UKIP$SEX); dOUKG$estimate
dOUkrG = cohen.d(UKRIP$GFP, UKRIP$SEX); dOUkrG$estimate
dOUAEG = cohen.d(UAEIP$GFP, UAEIP$SEX); dOUAEG$estimate
dOUSAG = cohen.d(USAIP$GFP, USAIP$SEX); dOUSAG$estimate
dOVenG = cohen.d(VENIP$GFP, VENIP$SEX); dOVenG$estimate
dOVieG = cohen.d(VIEIP$GFP, VIEIP$SEX); dOVieG$estimate
#d for Parts of Sumscores
## Openness
dOPAlbO = mean(c(cohen.d(ALBIP$Liberalism, ALBIP$SEX)$estimate + cohen.d(ALBIP$Intellect, ALBIP$SEX)$estimate + cohen.d(ALBIP$Adventurousness, ALBIP$SEX)$estimate + cohen.d(ALBIP$Emotionality, ALBIP$SEX)$estimate + cohen.d(ALBIP$ArtisticInterests, ALBIP$SEX)$estimate + cohen.d(ALBIP$Imagination, ALBIP$SEX)$estimate)); dOPAlbO
dOPAlgO = mean(c(cohen.d(ALGIP$Liberalism, ALGIP$SEX)$estimate + cohen.d(ALGIP$Intellect, ALGIP$SEX)$estimate + cohen.d(ALGIP$Adventurousness, ALGIP$SEX)$estimate + cohen.d(ALGIP$Emotionality, ALGIP$SEX)$estimate + cohen.d(ALGIP$ArtisticInterests, ALGIP$SEX)$estimate + cohen.d(ALGIP$Imagination, ALGIP$SEX)$estimate)); dOPAlgO
dOPAngO = mean(c(cohen.d(ANGIP$Intellect, ANGIP$SEX)$estimate + cohen.d(ANGIP$Adventurousness, ANGIP$SEX)$estimate + cohen.d(ANGIP$Emotionality, ANGIP$SEX)$estimate + cohen.d(ANGIP$ArtisticInterests, ANGIP$SEX)$estimate + cohen.d(ANGIP$Imagination, ANGIP$SEX)$estimate)); dOPAngO
dOPArgO = mean(c(cohen.d(ARGIP$Liberalism, ARGIP$SEX)$estimate + cohen.d(ARGIP$Intellect, ARGIP$SEX)$estimate + cohen.d(ARGIP$Adventurousness, ARGIP$SEX)$estimate + cohen.d(ARGIP$Emotionality, ARGIP$SEX)$estimate + cohen.d(ARGIP$ArtisticInterests, ARGIP$SEX)$estimate)); dOPArgO
dOPAusO = mean(c(cohen.d(AUSIP$Liberalism, AUSIP$SEX)$estimate + cohen.d(AUSIP$Intellect, AUSIP$SEX)$estimate + cohen.d(AUSIP$Adventurousness, AUSIP$SEX)$estimate + cohen.d(AUSIP$Emotionality, AUSIP$SEX)$estimate + cohen.d(AUSIP$ArtisticInterests, AUSIP$SEX)$estimate + cohen.d(AUSIP$Imagination, AUSIP$SEX)$estimate)); dOPAusO
dOPHabO = mean(c(cohen.d(HABIP$Liberalism, HABIP$SEX)$estimate + cohen.d(HABIP$Intellect, HABIP$SEX)$estimate + cohen.d(HABIP$Emotionality, HABIP$SEX)$estimate + cohen.d(HABIP$ArtisticInterests, HABIP$SEX)$estimate + cohen.d(HABIP$Imagination, HABIP$SEX)$estimate)); dOPHabO
dOPBelO = mean(c(cohen.d(BELIP$Liberalism, BELIP$SEX)$estimate + cohen.d(BELIP$Adventurousness, BELIP$SEX)$estimate + cohen.d(BELIP$Emotionality, BELIP$SEX)$estimate + cohen.d(BELIP$ArtisticInterests, BELIP$SEX)$estimate + cohen.d(BELIP$Imagination, BELIP$SEX)$estimate)); dOPBelO
dOPBraO = mean(c(cohen.d(BRAIP$Liberalism, BRAIP$SEX)$estimate + cohen.d(BRAIP$Intellect, BRAIP$SEX)$estimate + cohen.d(BRAIP$Adventurousness, BRAIP$SEX)$estimate + cohen.d(BRAIP$Emotionality, BRAIP$SEX)$estimate + cohen.d(BRAIP$ArtisticInterests, BRAIP$SEX)$estimate)); dOPBraO
dOPCanO = mean(c(cohen.d(CANIP$Liberalism, CANIP$SEX)$estimate + cohen.d(CANIP$Adventurousness, CANIP$SEX)$estimate + cohen.d(CANIP$Emotionality, CANIP$SEX)$estimate + cohen.d(CANIP$ArtisticInterests, CANIP$SEX)$estimate + cohen.d(CANIP$Imagination, CANIP$SEX)$estimate)); dOPCanO
dOPChiO = mean(c(cohen.d(CHIIP$Liberalism, CHIIP$SEX)$estimate + cohen.d(CHIIP$Intellect, CHIIP$SEX)$estimate + cohen.d(CHIIP$Adventurousness, CHIIP$SEX)$estimate + cohen.d(CHIIP$Emotionality, CHIIP$SEX)$estimate + cohen.d(CHIIP$ArtisticInterests, CHIIP$SEX)$estimate + cohen.d(CHIIP$Imagination, CHIIP$SEX)$estimate)); dOPChiO
dOPColO = mean(c(cohen.d(COLIP$Liberalism, COLIP$SEX)$estimate + cohen.d(COLIP$Intellect, COLIP$SEX)$estimate + cohen.d(COLIP$Adventurousness, COLIP$SEX)$estimate + cohen.d(COLIP$Emotionality, COLIP$SEX)$estimate + cohen.d(COLIP$ArtisticInterests, COLIP$SEX)$estimate + cohen.d(COLIP$Imagination, COLIP$SEX)$estimate)); dOPColO
dOPCroO = mean(c(cohen.d(CROIP$Liberalism, CROIP$SEX)$estimate + cohen.d(CROIP$Intellect, CROIP$SEX)$estimate + cohen.d(CROIP$Adventurousness, CROIP$SEX)$estimate + cohen.d(CROIP$Emotionality, CROIP$SEX)$estimate + cohen.d(CROIP$ArtisticInterests, CROIP$SEX)$estimate)); dOPCroO
dOPDenO = mean(c(cohen.d(DENIP$Liberalism, DENIP$SEX)$estimate + cohen.d(DENIP$Intellect, DENIP$SEX)$estimate + cohen.d(DENIP$Adventurousness, DENIP$SEX)$estimate + cohen.d(DENIP$Emotionality, DENIP$SEX)$estimate + cohen.d(DENIP$ArtisticInterests, DENIP$SEX)$estimate + cohen.d(DENIP$Imagination, DENIP$SEX)$estimate)); dOPDenO
dOPEgyO = mean(c(cohen.d(EGYIP$Liberalism, EGYIP$SEX)$estimate + cohen.d(EGYIP$Intellect, EGYIP$SEX)$estimate + cohen.d(EGYIP$Adventurousness, EGYIP$SEX)$estimate + cohen.d(EGYIP$Emotionality, EGYIP$SEX)$estimate + cohen.d(EGYIP$ArtisticInterests, EGYIP$SEX)$estimate + cohen.d(EGYIP$Imagination, EGYIP$SEX)$estimate)); dOPEgyO
dOPFinO = mean(c(cohen.d(FINIP$Liberalism, FINIP$SEX)$estimate + cohen.d(FINIP$Adventurousness, FINIP$SEX)$estimate + cohen.d(FINIP$Emotionality, FINIP$SEX)$estimate + cohen.d(FINIP$ArtisticInterests, FINIP$SEX)$estimate + cohen.d(FINIP$Imagination, FINIP$SEX)$estimate)); dOPFinO
dOPFraO = mean(c(cohen.d(FRAIP$Liberalism, FRAIP$SEX)$estimate + cohen.d(FRAIP$Adventurousness, FRAIP$SEX)$estimate + cohen.d(FRAIP$Emotionality, FRAIP$SEX)$estimate + cohen.d(FRAIP$ArtisticInterests, FRAIP$SEX)$estimate + cohen.d(FRAIP$Imagination, FRAIP$SEX)$estimate)); dOPFraO
dOPGerO = mean(c(cohen.d(GERIP$Liberalism, GERIP$SEX)$estimate + cohen.d(GERIP$Intellect, GERIP$SEX)$estimate + cohen.d(GERIP$Adventurousness, GERIP$SEX)$estimate + cohen.d(GERIP$Emotionality, GERIP$SEX)$estimate + cohen.d(GERIP$ArtisticInterests, GERIP$SEX)$estimate)); dOPGerO
dOPGreO = mean(c(cohen.d(GREIP$Liberalism, GREIP$SEX)$estimate + cohen.d(GREIP$Intellect, GREIP$SEX)$estimate + cohen.d(GREIP$Adventurousness, GREIP$SEX)$estimate + cohen.d(GREIP$Emotionality, GREIP$SEX)$estimate + cohen.d(GREIP$ArtisticInterests, GREIP$SEX)$estimate + cohen.d(GREIP$Imagination, GREIP$SEX)$estimate)); dOPGreO
dOPIndO = mean(c(cohen.d(INDIP$Liberalism, INDIP$SEX)$estimate + cohen.d(INDIP$Intellect, INDIP$SEX)$estimate + cohen.d(INDIP$Adventurousness, INDIP$SEX)$estimate + cohen.d(INDIP$Emotionality, INDIP$SEX)$estimate + cohen.d(INDIP$ArtisticInterests, INDIP$SEX)$estimate + cohen.d(INDIP$Imagination, INDIP$SEX)$estimate)); dOPIndO
dOPInoO = mean(c(cohen.d(INOIP$Liberalism, INOIP$SEX)$estimate + cohen.d(INOIP$Intellect, INOIP$SEX)$estimate + cohen.d(INOIP$Adventurousness, INOIP$SEX)$estimate + cohen.d(INOIP$Emotionality, INOIP$SEX)$estimate + cohen.d(INOIP$ArtisticInterests, INOIP$SEX)$estimate + cohen.d(INOIP$Imagination, INOIP$SEX)$estimate)); dOPInoO
dOPIraO = mean(c(cohen.d(IRAIP$Liberalism, IRAIP$SEX)$estimate + cohen.d(IRAIP$Intellect, IRAIP$SEX)$estimate + cohen.d(IRAIP$Adventurousness, IRAIP$SEX)$estimate + cohen.d(IRAIP$Emotionality, IRAIP$SEX)$estimate + cohen.d(IRAIP$ArtisticInterests, IRAIP$SEX)$estimate + cohen.d(IRAIP$Imagination, IRAIP$SEX)$estimate)); dOPIraO
dOPIreO = mean(c(cohen.d(IREIP$Liberalism, IREIP$SEX)$estimate + cohen.d(IREIP$Intellect, IREIP$SEX)$estimate + cohen.d(IREIP$Adventurousness, IREIP$SEX)$estimate + cohen.d(IREIP$Emotionality, IREIP$SEX)$estimate + cohen.d(IREIP$ArtisticInterests, IREIP$SEX)$estimate + cohen.d(IREIP$Imagination, IREIP$SEX)$estimate)); dOPIreO
dOPIsrO = mean(c(cohen.d(ISRIP$Liberalism, ISRIP$SEX)$estimate + cohen.d(ISRIP$Intellect, ISRIP$SEX)$estimate + cohen.d(ISRIP$Adventurousness, ISRIP$SEX)$estimate + cohen.d(ISRIP$Emotionality, ISRIP$SEX)$estimate + cohen.d(ISRIP$ArtisticInterests, ISRIP$SEX)$estimate + cohen.d(ISRIP$Imagination, ISRIP$SEX)$estimate)); dOPIsrO
dOPItaO = mean(c(cohen.d(ITAIP$Liberalism, ITAIP$SEX)$estimate + cohen.d(ITAIP$Intellect, ITAIP$SEX)$estimate + cohen.d(ITAIP$Adventurousness, ITAIP$SEX)$estimate + cohen.d(ITAIP$Emotionality, ITAIP$SEX)$estimate + cohen.d(ITAIP$ArtisticInterests, ITAIP$SEX)$estimate + cohen.d(ITAIP$Imagination, ITAIP$SEX)$estimate)); dOPItaO
dOPJamO = mean(c(cohen.d(JAMIP$Liberalism, JAMIP$SEX)$estimate + cohen.d(JAMIP$Intellect, JAMIP$SEX)$estimate + cohen.d(JAMIP$Adventurousness, JAMIP$SEX)$estimate + cohen.d(JAMIP$Emotionality, JAMIP$SEX)$estimate + cohen.d(JAMIP$ArtisticInterests, JAMIP$SEX)$estimate + cohen.d(JAMIP$Imagination, JAMIP$SEX)$estimate)); dOPJamO
dOPJapO = mean(c(cohen.d(JAPIP$Liberalism, JAPIP$SEX)$estimate + cohen.d(JAPIP$Intellect, JAPIP$SEX)$estimate + cohen.d(JAPIP$Adventurousness, JAPIP$SEX)$estimate + cohen.d(JAPIP$Emotionality, JAPIP$SEX)$estimate + cohen.d(JAPIP$ArtisticInterests, JAPIP$SEX)$estimate + cohen.d(JAPIP$Imagination, JAPIP$SEX)$estimate)); dOPJapO
dOPKenO = mean(c(cohen.d(KENIP$Liberalism, KENIP$SEX)$estimate + cohen.d(KENIP$Emotionality, KENIP$SEX)$estimate + cohen.d(KENIP$ArtisticInterests, KENIP$SEX)$estimate + cohen.d(KENIP$Imagination, KENIP$SEX)$estimate)); dOPKenO
dOPLebO = mean(c(cohen.d(LEBIP$Liberalism, LEBIP$SEX)$estimate + cohen.d(LEBIP$Intellect, LEBIP$SEX)$estimate + cohen.d(LEBIP$Adventurousness, LEBIP$SEX)$estimate + cohen.d(LEBIP$Emotionality, LEBIP$SEX)$estimate + cohen.d(LEBIP$ArtisticInterests, LEBIP$SEX)$estimate + cohen.d(LEBIP$Imagination, LEBIP$SEX)$estimate)); dOPLebO
dOPMalO = mean(c(cohen.d(MALIP$Liberalism, MALIP$SEX)$estimate + cohen.d(MALIP$Intellect, MALIP$SEX)$estimate + cohen.d(MALIP$Adventurousness, MALIP$SEX)$estimate + cohen.d(MALIP$Emotionality, MALIP$SEX)$estimate + cohen.d(MALIP$ArtisticInterests, MALIP$SEX)$estimate + cohen.d(MALIP$Imagination, MALIP$SEX)$estimate)); dOPMalO
dOPMexO = mean(c(cohen.d(MEXIP$Liberalism, MEXIP$SEX)$estimate + cohen.d(MEXIP$Intellect, MEXIP$SEX)$estimate + cohen.d(MEXIP$Adventurousness, MEXIP$SEX)$estimate + cohen.d(MEXIP$Emotionality, MEXIP$SEX)$estimate + cohen.d(MEXIP$ArtisticInterests, MEXIP$SEX)$estimate + cohen.d(MEXIP$Imagination, MEXIP$SEX)$estimate)); dOPMexO
dOPNetO = mean(c(cohen.d(NETIP$Liberalism, NETIP$SEX)$estimate + cohen.d(NETIP$Adventurousness, NETIP$SEX)$estimate + cohen.d(NETIP$Emotionality, NETIP$SEX)$estimate + cohen.d(NETIP$ArtisticInterests, NETIP$SEX)$estimate + cohen.d(NETIP$Imagination, NETIP$SEX)$estimate)); dOPNetO
dOPNewO = mean(c(cohen.d(NEWIP$Liberalism, NEWIP$SEX)$estimate + cohen.d(NEWIP$Intellect, NEWIP$SEX)$estimate + cohen.d(NEWIP$Adventurousness, NEWIP$SEX)$estimate + cohen.d(NEWIP$Emotionality, NEWIP$SEX)$estimate + cohen.d(NEWIP$ArtisticInterests, NEWIP$SEX)$estimate + cohen.d(NEWIP$Imagination, NEWIP$SEX)$estimate)); dOPNewO
dOPNirO = mean(c(cohen.d(NIRIP$Liberalism, NIRIP$SEX)$estimate + cohen.d(NIRIP$Intellect, NIRIP$SEX)$estimate + cohen.d(NIRIP$Adventurousness, NIRIP$SEX)$estimate + cohen.d(NIRIP$Emotionality, NIRIP$SEX)$estimate + cohen.d(NIRIP$ArtisticInterests, NIRIP$SEX)$estimate + cohen.d(NIRIP$Imagination, NIRIP$SEX)$estimate)); dOPNirO
dOPNorO = mean(c(cohen.d(NORIP$Liberalism, NORIP$SEX)$estimate + cohen.d(NORIP$Adventurousness, NORIP$SEX)$estimate + cohen.d(NORIP$Emotionality, NORIP$SEX)$estimate + cohen.d(NORIP$ArtisticInterests, NORIP$SEX)$estimate + cohen.d(NORIP$Imagination, NORIP$SEX)$estimate)); dOPNorO
dOPPerO = mean(c(cohen.d(PERIP$Liberalism, PERIP$SEX)$estimate + cohen.d(PERIP$Intellect, PERIP$SEX)$estimate + cohen.d(PERIP$Adventurousness, PERIP$SEX)$estimate + cohen.d(PERIP$Emotionality, PERIP$SEX)$estimate + cohen.d(PERIP$ArtisticInterests, PERIP$SEX)$estimate + cohen.d(PERIP$Imagination, PERIP$SEX)$estimate)); dOPPerO
dOPPakO = mean(c(cohen.d(PAKIP$Liberalism, PAKIP$SEX)$estimate + cohen.d(PAKIP$Adventurousness, PAKIP$SEX)$estimate + cohen.d(PAKIP$Emotionality, PAKIP$SEX)$estimate + cohen.d(PAKIP$ArtisticInterests, PAKIP$SEX)$estimate + cohen.d(PAKIP$Imagination, PAKIP$SEX)$estimate)); dOPPakO
dOPPhiO = mean(c(cohen.d(PHIIP$Liberalism, PHIIP$SEX)$estimate + cohen.d(PHIIP$Intellect, PHIIP$SEX)$estimate + cohen.d(PHIIP$Adventurousness, PHIIP$SEX)$estimate + cohen.d(PHIIP$Emotionality, PHIIP$SEX)$estimate + cohen.d(PHIIP$ArtisticInterests, PHIIP$SEX)$estimate + cohen.d(PHIIP$Imagination, PHIIP$SEX)$estimate)); dOPPhiO
dOPPolO = mean(c(cohen.d(POLIP$Liberalism, POLIP$SEX)$estimate + cohen.d(POLIP$Intellect, POLIP$SEX)$estimate + cohen.d(POLIP$Adventurousness, POLIP$SEX)$estimate + cohen.d(POLIP$Emotionality, POLIP$SEX)$estimate + cohen.d(POLIP$ArtisticInterests, POLIP$SEX)$estimate + cohen.d(POLIP$Imagination, POLIP$SEX)$estimate)); dOPPolO
dOPPorO = mean(c(cohen.d(PORIP$Liberalism, PORIP$SEX)$estimate + cohen.d(PORIP$Adventurousness, PORIP$SEX)$estimate + cohen.d(PORIP$Emotionality, PORIP$SEX)$estimate + cohen.d(PORIP$ArtisticInterests, PORIP$SEX)$estimate + cohen.d(PORIP$Imagination, PORIP$SEX)$estimate)); dOPPorO
dOPRomO = mean(c(cohen.d(ROMIP$Liberalism, ROMIP$SEX)$estimate + cohen.d(ROMIP$Intellect, ROMIP$SEX)$estimate + cohen.d(ROMIP$Adventurousness, ROMIP$SEX)$estimate + cohen.d(ROMIP$Emotionality, ROMIP$SEX)$estimate + cohen.d(ROMIP$ArtisticInterests, ROMIP$SEX)$estimate + cohen.d(ROMIP$Imagination, ROMIP$SEX)$estimate)); dOPRomO
dOPRusO = mean(c(cohen.d(RUSIP$Liberalism, RUSIP$SEX)$estimate + cohen.d(RUSIP$Adventurousness, RUSIP$SEX)$estimate + cohen.d(RUSIP$Emotionality, RUSIP$SEX)$estimate + cohen.d(RUSIP$ArtisticInterests, RUSIP$SEX)$estimate + cohen.d(RUSIP$Imagination, RUSIP$SEX)$estimate)); dOPRusO
dOPSinO = mean(c(cohen.d(SINIP$Liberalism, SINIP$SEX)$estimate + cohen.d(SINIP$Adventurousness, SINIP$SEX)$estimate + cohen.d(SINIP$Emotionality, SINIP$SEX)$estimate + cohen.d(SINIP$ArtisticInterests, SINIP$SEX)$estimate + cohen.d(SINIP$Imagination, SINIP$SEX)$estimate)); dOPSinO
dOPSloO = mean(c(cohen.d(SLOIP$Liberalism, SLOIP$SEX)$estimate + cohen.d(SLOIP$Adventurousness, SLOIP$SEX)$estimate + cohen.d(SLOIP$Emotionality, SLOIP$SEX)$estimate + cohen.d(SLOIP$ArtisticInterests, SLOIP$SEX)$estimate + cohen.d(SLOIP$Imagination, SLOIP$SEX)$estimate)); dOPSloO
dOPSlvO = mean(c(cohen.d(SLVIP$Liberalism, SLVIP$SEX)$estimate + cohen.d(SLVIP$Intellect, SLVIP$SEX)$estimate + cohen.d(SLVIP$Adventurousness, SLVIP$SEX)$estimate + cohen.d(SLVIP$Emotionality, SLVIP$SEX)$estimate + cohen.d(SLVIP$ArtisticInterests, SLVIP$SEX)$estimate + cohen.d(SLVIP$Imagination, SLVIP$SEX)$estimate)); dOPSlvO
dOPSouO = mean(c(cohen.d(SOUIP$Liberalism, SOUIP$SEX)$estimate + cohen.d(SOUIP$Intellect, SOUIP$SEX)$estimate + cohen.d(SOUIP$Adventurousness, SOUIP$SEX)$estimate + cohen.d(SOUIP$Emotionality, SOUIP$SEX)$estimate + cohen.d(SOUIP$ArtisticInterests, SOUIP$SEX)$estimate + cohen.d(SOUIP$Imagination, SOUIP$SEX)$estimate)); dOPSouO
dOPSKoO = mean(c(cohen.d(SKOIP$Liberalism, SKOIP$SEX)$estimate + cohen.d(SKOIP$Intellect, SKOIP$SEX)$estimate + cohen.d(SKOIP$Adventurousness, SKOIP$SEX)$estimate + cohen.d(SKOIP$Emotionality, SKOIP$SEX)$estimate + cohen.d(SKOIP$ArtisticInterests, SKOIP$SEX)$estimate)); dOPSKoO
dOPSpaO = mean(c(cohen.d(SPAIP$Liberalism, SPAIP$SEX)$estimate + cohen.d(SPAIP$Intellect, SPAIP$SEX)$estimate + cohen.d(SPAIP$Adventurousness, SPAIP$SEX)$estimate + cohen.d(SPAIP$Emotionality, SPAIP$SEX)$estimate + cohen.d(SPAIP$ArtisticInterests, SPAIP$SEX)$estimate)); dOPSpaO
dOPSweO = mean(c(cohen.d(SWEIP$Liberalism, SWEIP$SEX)$estimate + cohen.d(SWEIP$Intellect, SWEIP$SEX)$estimate + cohen.d(SWEIP$Adventurousness, SWEIP$SEX)$estimate + cohen.d(SWEIP$Emotionality, SWEIP$SEX)$estimate + cohen.d(SWEIP$ArtisticInterests, SWEIP$SEX)$estimate + cohen.d(SWEIP$Imagination, SWEIP$SEX)$estimate)); dOPSweO
dOPSwiO = mean(c(cohen.d(SWIIP$Liberalism, SWIIP$SEX)$estimate + cohen.d(SWIIP$Intellect, SWIIP$SEX)$estimate + cohen.d(SWIIP$Adventurousness, SWIIP$SEX)$estimate + cohen.d(SWIIP$Emotionality, SWIIP$SEX)$estimate + cohen.d(SWIIP$ArtisticInterests, SWIIP$SEX)$estimate + cohen.d(SWIIP$Imagination, SWIIP$SEX)$estimate)); dOPSwiO
dOPThaO = mean(c(cohen.d(THAIP$Liberalism, THAIP$SEX)$estimate + cohen.d(THAIP$Intellect, THAIP$SEX)$estimate + cohen.d(THAIP$Emotionality, THAIP$SEX)$estimate + cohen.d(THAIP$ArtisticInterests, THAIP$SEX)$estimate + cohen.d(THAIP$Imagination, THAIP$SEX)$estimate)); dOPThaO
dOPTriO = mean(c(cohen.d(TRIIP$Liberalism, TRIIP$SEX)$estimate + cohen.d(TRIIP$Intellect, TRIIP$SEX)$estimate + cohen.d(TRIIP$Adventurousness, TRIIP$SEX)$estimate + cohen.d(TRIIP$ArtisticInterests, TRIIP$SEX)$estimate + cohen.d(TRIIP$Imagination, TRIIP$SEX)$estimate)); dOPTriO
dOPTurO = mean(c(cohen.d(TURIP$Liberalism, TURIP$SEX)$estimate + cohen.d(TURIP$Intellect, TURIP$SEX)$estimate + cohen.d(TURIP$Adventurousness, TURIP$SEX)$estimate + cohen.d(TURIP$Emotionality, TURIP$SEX)$estimate + cohen.d(TURIP$ArtisticInterests, TURIP$SEX)$estimate + cohen.d(TURIP$Imagination, TURIP$SEX)$estimate)); dOPTurO
dOPUgaO = mean(c(cohen.d(UGAIP$Liberalism, UGAIP$SEX)$estimate + cohen.d(UGAIP$Intellect, UGAIP$SEX)$estimate + cohen.d(UGAIP$Adventurousness, UGAIP$SEX)$estimate + cohen.d(UGAIP$Emotionality, UGAIP$SEX)$estimate + cohen.d(UGAIP$ArtisticInterests, UGAIP$SEX)$estimate + cohen.d(UGAIP$Imagination, UGAIP$SEX)$estimate)); dOPUgaO
dOPUKO = mean(c(cohen.d(UKIP$Liberalism, UKIP$SEX)$estimate + cohen.d(UKIP$Intellect, UKIP$SEX)$estimate + cohen.d(UKIP$Adventurousness, UKIP$SEX)$estimate + cohen.d(UKIP$Emotionality, UKIP$SEX)$estimate + cohen.d(UKIP$ArtisticInterests, UKIP$SEX)$estimate + cohen.d(UKIP$Imagination, UKIP$SEX)$estimate)); dOPUKO
dOPUkrO = mean(c(cohen.d(UKRIP$Liberalism, UKRIP$SEX)$estimate + cohen.d(UKRIP$Intellect, UKRIP$SEX)$estimate + cohen.d(UKRIP$Adventurousness, UKRIP$SEX)$estimate + cohen.d(UKRIP$Emotionality, UKRIP$SEX)$estimate + cohen.d(UKRIP$ArtisticInterests, UKRIP$SEX)$estimate + cohen.d(UKRIP$Imagination, UKRIP$SEX)$estimate)); dOPUkrO
dOPUAEO = mean(c(cohen.d(UAEIP$Liberalism, UAEIP$SEX)$estimate + cohen.d(UAEIP$Intellect, UAEIP$SEX)$estimate + cohen.d(UAEIP$Adventurousness, UAEIP$SEX)$estimate + cohen.d(UAEIP$Emotionality, UAEIP$SEX)$estimate + cohen.d(UAEIP$ArtisticInterests, UAEIP$SEX)$estimate)); dOPUAEO
dOPUSAO = mean(c(cohen.d(USAIP$Liberalism, USAIP$SEX)$estimate + cohen.d(USAIP$Adventurousness, USAIP$SEX)$estimate + cohen.d(USAIP$Emotionality, USAIP$SEX)$estimate + cohen.d(USAIP$ArtisticInterests, USAIP$SEX)$estimate + cohen.d(USAIP$Imagination, USAIP$SEX)$estimate)); dOPUSAO
dOPVenO = mean(c(cohen.d(VENIP$Liberalism, VENIP$SEX)$estimate + cohen.d(VENIP$Intellect, VENIP$SEX)$estimate + cohen.d(VENIP$Adventurousness, VENIP$SEX)$estimate + cohen.d(VENIP$Emotionality, VENIP$SEX)$estimate + cohen.d(VENIP$ArtisticInterests, VENIP$SEX)$estimate + cohen.d(VENIP$Imagination, VENIP$SEX)$estimate)); dOPVenO
dOPVieO = mean(c(cohen.d(VIEIP$Liberalism, VIEIP$SEX)$estimate + cohen.d(VIEIP$Adventurousness, VIEIP$SEX)$estimate + cohen.d(VIEIP$Emotionality, VIEIP$SEX)$estimate + cohen.d(VIEIP$ArtisticInterests, VIEIP$SEX)$estimate + cohen.d(VIEIP$Imagination, VIEIP$SEX)$estimate)); dOPVieO
## Conscientiousness
dOPAlbC = mean(c(cohen.d(ALBIP$Cautiousness, ALBIP$SEX)$estimate + cohen.d(ALBIP$SelfDiscipline, ALBIP$SEX)$estimate + cohen.d(ALBIP$AchievementStriving, ALBIP$SEX)$estimate + cohen.d(ALBIP$Dutifulness, ALBIP$SEX)$estimate + cohen.d(ALBIP$Orderliness, ALBIP$SEX)$estimate + cohen.d(ALBIP$SelfEfficacy, ALBIP$SEX)$estimate)); dOPAlbC
dOPAlgC = mean(c(cohen.d(ALGIP$Cautiousness, ALGIP$SEX)$estimate + cohen.d(ALGIP$SelfDiscipline, ALGIP$SEX)$estimate + cohen.d(ALGIP$AchievementStriving, ALGIP$SEX)$estimate + cohen.d(ALGIP$Dutifulness, ALGIP$SEX)$estimate + cohen.d(ALGIP$Orderliness, ALGIP$SEX)$estimate + cohen.d(ALGIP$SelfEfficacy, ALGIP$SEX)$estimate)); dOPAlgC
#dOPAngC = mean(c(cohen.d(ANGIP$Cautiousness, ANGIP$SEX)$estimate + cohen.d(ANGIP$SelfDiscipline, ANGIP$SEX)$estimate + cohen.d(ANGIP$AchievementStriving, ANGIP$SEX)$estimate + cohen.d(ANGIP$Dutifulness, ANGIP$SEX)$estimate + cohen.d(ANGIP$Orderliness, ANGIP$SEX)$estimate + cohen.d(ANGIP$SelfEfficacy, ANGIP$SEX)$estimate)); dOPAngC
dOPArgC = mean(c(cohen.d(ARGIP$Cautiousness, ARGIP$SEX)$estimate + cohen.d(ARGIP$SelfDiscipline, ARGIP$SEX)$estimate + cohen.d(ARGIP$AchievementStriving, ARGIP$SEX)$estimate + cohen.d(ARGIP$Dutifulness, ARGIP$SEX)$estimate + cohen.d(ARGIP$Orderliness, ARGIP$SEX)$estimate + cohen.d(ARGIP$SelfEfficacy, ARGIP$SEX)$estimate)); dOPArgC
dOPAusC = mean(c(cohen.d(AUSIP$Cautiousness, AUSIP$SEX)$estimate + cohen.d(AUSIP$SelfDiscipline, AUSIP$SEX)$estimate + cohen.d(AUSIP$AchievementStriving, AUSIP$SEX)$estimate + cohen.d(AUSIP$Dutifulness, AUSIP$SEX)$estimate + cohen.d(AUSIP$Orderliness, AUSIP$SEX)$estimate + cohen.d(AUSIP$SelfEfficacy, AUSIP$SEX)$estimate)); dOPAusC
dOPHabC = mean(c(cohen.d(HABIP$SelfDiscipline, HABIP$SEX)$estimate + cohen.d(HABIP$AchievementStriving, HABIP$SEX)$estimate + cohen.d(HABIP$Dutifulness, HABIP$SEX)$estimate + cohen.d(HABIP$Orderliness, HABIP$SEX)$estimate)); dOPHabC
dOPBelC = mean(c(cohen.d(BELIP$Cautiousness, BELIP$SEX)$estimate + cohen.d(BELIP$SelfDiscipline, BELIP$SEX)$estimate + cohen.d(BELIP$AchievementStriving, BELIP$SEX)$estimate + cohen.d(BELIP$Dutifulness, BELIP$SEX)$estimate + cohen.d(BELIP$Orderliness, BELIP$SEX)$estimate + cohen.d(BELIP$SelfEfficacy, BELIP$SEX)$estimate)); dOPBelC
dOPBraC = mean(c(cohen.d(BRAIP$Cautiousness, BRAIP$SEX)$estimate + cohen.d(BRAIP$SelfDiscipline, BRAIP$SEX)$estimate + cohen.d(BRAIP$AchievementStriving, BRAIP$SEX)$estimate + cohen.d(BRAIP$Dutifulness, BRAIP$SEX)$estimate + cohen.d(BRAIP$Orderliness, BRAIP$SEX)$estimate + cohen.d(BRAIP$SelfEfficacy, BRAIP$SEX)$estimate)); dOPBraC
dOPCanC = mean(c(cohen.d(CANIP$Cautiousness, CANIP$SEX)$estimate + cohen.d(CANIP$SelfDiscipline, CANIP$SEX)$estimate + cohen.d(CANIP$AchievementStriving, CANIP$SEX)$estimate + cohen.d(CANIP$Dutifulness, CANIP$SEX)$estimate + cohen.d(CANIP$Orderliness, CANIP$SEX)$estimate + cohen.d(CANIP$SelfEfficacy, CANIP$SEX)$estimate)); dOPCanC
dOPChiC = mean(c(cohen.d(CHIIP$Cautiousness, CHIIP$SEX)$estimate + cohen.d(CHIIP$SelfDiscipline, CHIIP$SEX)$estimate + cohen.d(CHIIP$AchievementStriving, CHIIP$SEX)$estimate + cohen.d(CHIIP$Dutifulness, CHIIP$SEX)$estimate + cohen.d(CHIIP$Orderliness, CHIIP$SEX)$estimate + cohen.d(CHIIP$SelfEfficacy, CHIIP$SEX)$estimate)); dOPChiC
dOPColC = mean(c(cohen.d(COLIP$Cautiousness, COLIP$SEX)$estimate + cohen.d(COLIP$SelfDiscipline, COLIP$SEX)$estimate + cohen.d(COLIP$AchievementStriving, COLIP$SEX)$estimate + cohen.d(COLIP$Dutifulness, COLIP$SEX)$estimate + cohen.d(COLIP$Orderliness, COLIP$SEX)$estimate)); dOPColC
dOPCroC = mean(c(cohen.d(CROIP$Cautiousness, CROIP$SEX)$estimate + cohen.d(CROIP$SelfDiscipline, CROIP$SEX)$estimate + cohen.d(CROIP$AchievementStriving, CROIP$SEX)$estimate + cohen.d(CROIP$Dutifulness, CROIP$SEX)$estimate + cohen.d(CROIP$Orderliness, CROIP$SEX)$estimate + cohen.d(CROIP$SelfEfficacy, CROIP$SEX)$estimate)); dOPCroC
dOPDenC = mean(c(cohen.d(DENIP$Cautiousness, DENIP$SEX)$estimate + cohen.d(DENIP$SelfDiscipline, DENIP$SEX)$estimate + cohen.d(DENIP$AchievementStriving, DENIP$SEX)$estimate + cohen.d(DENIP$Dutifulness, DENIP$SEX)$estimate + cohen.d(DENIP$Orderliness, DENIP$SEX)$estimate + cohen.d(DENIP$SelfEfficacy, DENIP$SEX)$estimate)); dOPDenC
dOPEgyC = mean(c(cohen.d(EGYIP$Cautiousness, EGYIP$SEX)$estimate + cohen.d(EGYIP$SelfDiscipline, EGYIP$SEX)$estimate + cohen.d(EGYIP$AchievementStriving, EGYIP$SEX)$estimate + cohen.d(EGYIP$Dutifulness, EGYIP$SEX)$estimate + cohen.d(EGYIP$Orderliness, EGYIP$SEX)$estimate + cohen.d(EGYIP$SelfEfficacy, EGYIP$SEX)$estimate)); dOPEgyC
dOPFinC = mean(c(cohen.d(FINIP$Cautiousness, FINIP$SEX)$estimate + cohen.d(FINIP$SelfDiscipline, FINIP$SEX)$estimate + cohen.d(FINIP$AchievementStriving, FINIP$SEX)$estimate + cohen.d(FINIP$Dutifulness, FINIP$SEX)$estimate + cohen.d(FINIP$Orderliness, FINIP$SEX)$estimate + cohen.d(FINIP$SelfEfficacy, FINIP$SEX)$estimate)); dOPFinC
dOPFraC = mean(c(cohen.d(FRAIP$Cautiousness, FRAIP$SEX)$estimate + cohen.d(FRAIP$SelfDiscipline, FRAIP$SEX)$estimate + cohen.d(FRAIP$AchievementStriving, FRAIP$SEX)$estimate + cohen.d(FRAIP$Dutifulness, FRAIP$SEX)$estimate + cohen.d(FRAIP$Orderliness, FRAIP$SEX)$estimate + cohen.d(FRAIP$SelfEfficacy, FRAIP$SEX)$estimate)); dOPFraC
dOPGerC = mean(c(cohen.d(GERIP$Cautiousness, GERIP$SEX)$estimate + cohen.d(GERIP$SelfDiscipline, GERIP$SEX)$estimate + cohen.d(GERIP$AchievementStriving, GERIP$SEX)$estimate + cohen.d(GERIP$Dutifulness, GERIP$SEX)$estimate + cohen.d(GERIP$Orderliness, GERIP$SEX)$estimate + cohen.d(GERIP$SelfEfficacy, GERIP$SEX)$estimate)); dOPGerC
dOPGreC = mean(c(cohen.d(GREIP$Cautiousness, GREIP$SEX)$estimate + cohen.d(GREIP$SelfDiscipline, GREIP$SEX)$estimate + cohen.d(GREIP$AchievementStriving, GREIP$SEX)$estimate + cohen.d(GREIP$Dutifulness, GREIP$SEX)$estimate + cohen.d(GREIP$Orderliness, GREIP$SEX)$estimate + cohen.d(GREIP$SelfEfficacy, GREIP$SEX)$estimate)); dOPGreC
dOPIndC = mean(c(cohen.d(INDIP$Cautiousness, INDIP$SEX)$estimate + cohen.d(INDIP$SelfDiscipline, INDIP$SEX)$estimate + cohen.d(INDIP$AchievementStriving, INDIP$SEX)$estimate + cohen.d(INDIP$Dutifulness, INDIP$SEX)$estimate + cohen.d(INDIP$Orderliness, INDIP$SEX)$estimate + cohen.d(INDIP$SelfEfficacy, INDIP$SEX)$estimate)); dOPIndC
dOPInoC = mean(c(cohen.d(INOIP$Cautiousness, INOIP$SEX)$estimate + cohen.d(INOIP$SelfDiscipline, INOIP$SEX)$estimate + cohen.d(INOIP$AchievementStriving, INOIP$SEX)$estimate + cohen.d(INOIP$Dutifulness, INOIP$SEX)$estimate + cohen.d(INOIP$Orderliness, INOIP$SEX)$estimate + cohen.d(INOIP$SelfEfficacy, INOIP$SEX)$estimate)); dOPInoC
dOPIraC = mean(c(cohen.d(IRAIP$Cautiousness, IRAIP$SEX)$estimate + cohen.d(IRAIP$SelfDiscipline, IRAIP$SEX)$estimate + cohen.d(IRAIP$AchievementStriving, IRAIP$SEX)$estimate + cohen.d(IRAIP$Dutifulness, IRAIP$SEX)$estimate + cohen.d(IRAIP$Orderliness, IRAIP$SEX)$estimate + cohen.d(IRAIP$SelfEfficacy, IRAIP$SEX)$estimate)); dOPIraC
dOPIreC = mean(c(cohen.d(IREIP$Cautiousness, IREIP$SEX)$estimate + cohen.d(IREIP$SelfDiscipline, IREIP$SEX)$estimate + cohen.d(IREIP$AchievementStriving, IREIP$SEX)$estimate + cohen.d(IREIP$Dutifulness, IREIP$SEX)$estimate + cohen.d(IREIP$Orderliness, IREIP$SEX)$estimate + cohen.d(IREIP$SelfEfficacy, IREIP$SEX)$estimate)); dOPIreC
dOPIsrC = mean(c(cohen.d(ISRIP$Cautiousness, ISRIP$SEX)$estimate + cohen.d(ISRIP$SelfDiscipline, ISRIP$SEX)$estimate + cohen.d(ISRIP$AchievementStriving, ISRIP$SEX)$estimate + cohen.d(ISRIP$Dutifulness, ISRIP$SEX)$estimate + cohen.d(ISRIP$Orderliness, ISRIP$SEX)$estimate + cohen.d(ISRIP$SelfEfficacy, ISRIP$SEX)$estimate)); dOPIsrC
dOPItaC = mean(c(cohen.d(ITAIP$Cautiousness, ITAIP$SEX)$estimate + cohen.d(ITAIP$SelfDiscipline, ITAIP$SEX)$estimate + cohen.d(ITAIP$AchievementStriving, ITAIP$SEX)$estimate + cohen.d(ITAIP$Dutifulness, ITAIP$SEX)$estimate + cohen.d(ITAIP$Orderliness, ITAIP$SEX)$estimate + cohen.d(ITAIP$SelfEfficacy, ITAIP$SEX)$estimate)); dOPItaC
dOPJamC = mean(c(cohen.d(JAMIP$Cautiousness, JAMIP$SEX)$estimate + cohen.d(JAMIP$SelfDiscipline, JAMIP$SEX)$estimate + cohen.d(JAMIP$AchievementStriving, JAMIP$SEX)$estimate + cohen.d(JAMIP$Dutifulness, JAMIP$SEX)$estimate + cohen.d(JAMIP$Orderliness, JAMIP$SEX)$estimate + cohen.d(JAMIP$SelfEfficacy, JAMIP$SEX)$estimate)); dOPJamC
dOPJapC = mean(c(cohen.d(JAPIP$Cautiousness, JAPIP$SEX)$estimate + cohen.d(JAPIP$SelfDiscipline, JAPIP$SEX)$estimate + cohen.d(JAPIP$AchievementStriving, JAPIP$SEX)$estimate + cohen.d(JAPIP$Dutifulness, JAPIP$SEX)$estimate + cohen.d(JAPIP$Orderliness, JAPIP$SEX)$estimate + cohen.d(JAPIP$SelfEfficacy, JAPIP$SEX)$estimate)); dOPJapC
dOPKenC = mean(c(cohen.d(KENIP$Cautiousness, KENIP$SEX)$estimate + cohen.d(KENIP$SelfDiscipline, KENIP$SEX)$estimate + cohen.d(KENIP$AchievementStriving, KENIP$SEX)$estimate + cohen.d(KENIP$Dutifulness, KENIP$SEX)$estimate + cohen.d(KENIP$Orderliness, KENIP$SEX)$estimate + cohen.d(KENIP$SelfEfficacy, KENIP$SEX)$estimate)); dOPKenC
dOPLebC = mean(c(cohen.d(LEBIP$Cautiousness, LEBIP$SEX)$estimate + cohen.d(LEBIP$SelfDiscipline, LEBIP$SEX)$estimate + cohen.d(LEBIP$AchievementStriving, LEBIP$SEX)$estimate + cohen.d(LEBIP$Dutifulness, LEBIP$SEX)$estimate + cohen.d(LEBIP$Orderliness, LEBIP$SEX)$estimate + cohen.d(LEBIP$SelfEfficacy, LEBIP$SEX)$estimate)); dOPLebC
dOPMalC = mean(c(cohen.d(MALIP$Cautiousness, MALIP$SEX)$estimate + cohen.d(MALIP$SelfDiscipline, MALIP$SEX)$estimate + cohen.d(MALIP$AchievementStriving, MALIP$SEX)$estimate + cohen.d(MALIP$Dutifulness, MALIP$SEX)$estimate + cohen.d(MALIP$Orderliness, MALIP$SEX)$estimate + cohen.d(MALIP$SelfEfficacy, MALIP$SEX)$estimate)); dOPMalC
dOPMexC = mean(c(cohen.d(MEXIP$Cautiousness, MEXIP$SEX)$estimate + cohen.d(MEXIP$SelfDiscipline, MEXIP$SEX)$estimate + cohen.d(MEXIP$AchievementStriving, MEXIP$SEX)$estimate + cohen.d(MEXIP$Dutifulness, MEXIP$SEX)$estimate + cohen.d(MEXIP$Orderliness, MEXIP$SEX)$estimate + cohen.d(MEXIP$SelfEfficacy, MEXIP$SEX)$estimate)); dOPMexC
dOPNetC = mean(c(cohen.d(NETIP$Cautiousness, NETIP$SEX)$estimate + cohen.d(NETIP$SelfDiscipline, NETIP$SEX)$estimate + cohen.d(NETIP$AchievementStriving, NETIP$SEX)$estimate + cohen.d(NETIP$Dutifulness, NETIP$SEX)$estimate + cohen.d(NETIP$Orderliness, NETIP$SEX)$estimate + cohen.d(NETIP$SelfEfficacy, NETIP$SEX)$estimate)); dOPNetC
dOPNewC = mean(c(cohen.d(NEWIP$Cautiousness, NEWIP$SEX)$estimate + cohen.d(NEWIP$SelfDiscipline, NEWIP$SEX)$estimate + cohen.d(NEWIP$AchievementStriving, NEWIP$SEX)$estimate + cohen.d(NEWIP$Dutifulness, NEWIP$SEX)$estimate + cohen.d(NEWIP$Orderliness, NEWIP$SEX)$estimate + cohen.d(NEWIP$SelfEfficacy, NEWIP$SEX)$estimate)); dOPNewC
dOPNirC = mean(c(cohen.d(NIRIP$Cautiousness, NIRIP$SEX)$estimate + cohen.d(NIRIP$SelfDiscipline, NIRIP$SEX)$estimate + cohen.d(NIRIP$AchievementStriving, NIRIP$SEX)$estimate + cohen.d(NIRIP$Dutifulness, NIRIP$SEX)$estimate + cohen.d(NIRIP$Orderliness, NIRIP$SEX)$estimate + cohen.d(NIRIP$SelfEfficacy, NIRIP$SEX)$estimate)); dOPNirC
dOPNorC = mean(c(cohen.d(NORIP$Cautiousness, NORIP$SEX)$estimate + cohen.d(NORIP$SelfDiscipline, NORIP$SEX)$estimate + cohen.d(NORIP$AchievementStriving, NORIP$SEX)$estimate + cohen.d(NORIP$Dutifulness, NORIP$SEX)$estimate + cohen.d(NORIP$Orderliness, NORIP$SEX)$estimate + cohen.d(NORIP$SelfEfficacy, NORIP$SEX)$estimate)); dOPNorC
#dOPPerC = mean(c(cohen.d(PERIP$Cautiousness, PERIP$SEX)$estimate + cohen.d(PERIP$SelfDiscipline, PERIP$SEX)$estimate + cohen.d(PERIP$AchievementStriving, PERIP$SEX)$estimate + cohen.d(PERIP$Dutifulness, PERIP$SEX)$estimate + cohen.d(PERIP$Orderliness, PERIP$SEX)$estimate + cohen.d(PERIP$SelfEfficacy, PERIP$SEX)$estimate)); dOPPerC
dOPPakC = mean(c(cohen.d(PAKIP$Cautiousness, PAKIP$SEX)$estimate + cohen.d(PAKIP$SelfDiscipline, PAKIP$SEX)$estimate + cohen.d(PAKIP$AchievementStriving, PAKIP$SEX)$estimate + cohen.d(PAKIP$Dutifulness, PAKIP$SEX)$estimate + cohen.d(PAKIP$Orderliness, PAKIP$SEX)$estimate + cohen.d(PAKIP$SelfEfficacy, PAKIP$SEX)$estimate)); dOPPakC
dOPPhiC = mean(c(cohen.d(PHIIP$Cautiousness, PHIIP$SEX)$estimate + cohen.d(PHIIP$SelfDiscipline, PHIIP$SEX)$estimate + cohen.d(PHIIP$AchievementStriving, PHIIP$SEX)$estimate + cohen.d(PHIIP$Dutifulness, PHIIP$SEX)$estimate + cohen.d(PHIIP$Orderliness, PHIIP$SEX)$estimate + cohen.d(PHIIP$SelfEfficacy, PHIIP$SEX)$estimate)); dOPPhiC
dOPPolC = mean(c(cohen.d(POLIP$SelfDiscipline, POLIP$SEX)$estimate + cohen.d(POLIP$AchievementStriving, POLIP$SEX)$estimate + cohen.d(POLIP$Dutifulness, POLIP$SEX)$estimate + cohen.d(POLIP$Orderliness, POLIP$SEX)$estimate + cohen.d(POLIP$SelfEfficacy, POLIP$SEX)$estimate)); dOPPolC
dOPPorC = mean(c(cohen.d(PORIP$Cautiousness, PORIP$SEX)$estimate + cohen.d(PORIP$SelfDiscipline, PORIP$SEX)$estimate + cohen.d(PORIP$AchievementStriving, PORIP$SEX)$estimate + cohen.d(PORIP$Dutifulness, PORIP$SEX)$estimate + cohen.d(PORIP$Orderliness, PORIP$SEX)$estimate + cohen.d(PORIP$SelfEfficacy, PORIP$SEX)$estimate)); dOPPorC
dOPRomC = mean(c(cohen.d(ROMIP$Cautiousness, ROMIP$SEX)$estimate + cohen.d(ROMIP$SelfDiscipline, ROMIP$SEX)$estimate + cohen.d(ROMIP$AchievementStriving, ROMIP$SEX)$estimate + cohen.d(ROMIP$Dutifulness, ROMIP$SEX)$estimate + cohen.d(ROMIP$Orderliness, ROMIP$SEX)$estimate + cohen.d(ROMIP$SelfEfficacy, ROMIP$SEX)$estimate)); dOPRomC
dOPRusC = mean(c(cohen.d(RUSIP$Cautiousness, RUSIP$SEX)$estimate + cohen.d(RUSIP$SelfDiscipline, RUSIP$SEX)$estimate + cohen.d(RUSIP$AchievementStriving, RUSIP$SEX)$estimate + cohen.d(RUSIP$Dutifulness, RUSIP$SEX)$estimate + cohen.d(RUSIP$Orderliness, RUSIP$SEX)$estimate + cohen.d(RUSIP$SelfEfficacy, RUSIP$SEX)$estimate)); dOPRusC
dOPSinC = mean(c(cohen.d(SINIP$Cautiousness, SINIP$SEX)$estimate + cohen.d(SINIP$SelfDiscipline, SINIP$SEX)$estimate + cohen.d(SINIP$AchievementStriving, SINIP$SEX)$estimate + cohen.d(SINIP$Dutifulness, SINIP$SEX)$estimate + cohen.d(SINIP$Orderliness, SINIP$SEX)$estimate + cohen.d(SINIP$SelfEfficacy, SINIP$SEX)$estimate)); dOPSinC
dOPSloC = mean(c(cohen.d(SLOIP$SelfDiscipline, SLOIP$SEX)$estimate + cohen.d(SLOIP$AchievementStriving, SLOIP$SEX)$estimate + cohen.d(SLOIP$Orderliness, SLOIP$SEX)$estimate + cohen.d(SLOIP$SelfEfficacy, SLOIP$SEX)$estimate)); dOPSloC
dOPSlvC = mean(c(cohen.d(SLVIP$Cautiousness, SLVIP$SEX)$estimate + cohen.d(SLVIP$SelfDiscipline, SLVIP$SEX)$estimate + cohen.d(SLVIP$AchievementStriving, SLVIP$SEX)$estimate + cohen.d(SLVIP$Dutifulness, SLVIP$SEX)$estimate + cohen.d(SLVIP$Orderliness, SLVIP$SEX)$estimate + cohen.d(SLVIP$SelfEfficacy, SLVIP$SEX)$estimate)); dOPSlvC
dOPSouC = mean(c(cohen.d(SOUIP$Cautiousness, SOUIP$SEX)$estimate + cohen.d(SOUIP$SelfDiscipline, SOUIP$SEX)$estimate + cohen.d(SOUIP$AchievementStriving, SOUIP$SEX)$estimate + cohen.d(SOUIP$Dutifulness, SOUIP$SEX)$estimate + cohen.d(SOUIP$Orderliness, SOUIP$SEX)$estimate + cohen.d(SOUIP$SelfEfficacy, SOUIP$SEX)$estimate)); dOPSouC
dOPSKoC = mean(c(cohen.d(SKOIP$Cautiousness, SKOIP$SEX)$estimate + cohen.d(SKOIP$SelfDiscipline, SKOIP$SEX)$estimate + cohen.d(SKOIP$AchievementStriving, SKOIP$SEX)$estimate + cohen.d(SKOIP$Dutifulness, SKOIP$SEX)$estimate + cohen.d(SKOIP$Orderliness, SKOIP$SEX)$estimate + cohen.d(SKOIP$SelfEfficacy, SKOIP$SEX)$estimate)); dOPSKoC
dOPSpaC = mean(c(cohen.d(SPAIP$Cautiousness, SPAIP$SEX)$estimate + cohen.d(SPAIP$SelfDiscipline, SPAIP$SEX)$estimate + cohen.d(SPAIP$AchievementStriving, SPAIP$SEX)$estimate + cohen.d(SPAIP$Dutifulness, SPAIP$SEX)$estimate + cohen.d(SPAIP$Orderliness, SPAIP$SEX)$estimate)); dOPSpaC
dOPSweC = mean(c(cohen.d(SWEIP$Cautiousness, SWEIP$SEX)$estimate + cohen.d(SWEIP$SelfDiscipline, SWEIP$SEX)$estimate + cohen.d(SWEIP$AchievementStriving, SWEIP$SEX)$estimate + cohen.d(SWEIP$Dutifulness, SWEIP$SEX)$estimate + cohen.d(SWEIP$Orderliness, SWEIP$SEX)$estimate + cohen.d(SWEIP$SelfEfficacy, SWEIP$SEX)$estimate)); dOPSweC
dOPSwiC = mean(c(cohen.d(SWIIP$Cautiousness, SWIIP$SEX)$estimate + cohen.d(SWIIP$SelfDiscipline, SWIIP$SEX)$estimate + cohen.d(SWIIP$AchievementStriving, SWIIP$SEX)$estimate + cohen.d(SWIIP$Dutifulness, SWIIP$SEX)$estimate + cohen.d(SWIIP$Orderliness, SWIIP$SEX)$estimate + cohen.d(SWIIP$SelfEfficacy, SWIIP$SEX)$estimate)); dOPSwiC
dOPThaC = mean(c(cohen.d(THAIP$Cautiousness, THAIP$SEX)$estimate + cohen.d(THAIP$SelfDiscipline, THAIP$SEX)$estimate + cohen.d(THAIP$AchievementStriving, THAIP$SEX)$estimate + cohen.d(THAIP$Dutifulness, THAIP$SEX)$estimate + cohen.d(THAIP$Orderliness, THAIP$SEX)$estimate)); dOPThaC
dOPTriC = mean(c(cohen.d(TRIIP$Cautiousness, TRIIP$SEX)$estimate + cohen.d(TRIIP$SelfDiscipline, TRIIP$SEX)$estimate + cohen.d(TRIIP$AchievementStriving, TRIIP$SEX)$estimate + cohen.d(TRIIP$Dutifulness, TRIIP$SEX)$estimate + cohen.d(TRIIP$Orderliness, TRIIP$SEX)$estimate + cohen.d(TRIIP$SelfEfficacy, TRIIP$SEX)$estimate)); dOPTriC
dOPTurC = mean(c(cohen.d(TURIP$Cautiousness, TURIP$SEX)$estimate + cohen.d(TURIP$SelfDiscipline, TURIP$SEX)$estimate + cohen.d(TURIP$AchievementStriving, TURIP$SEX)$estimate + cohen.d(TURIP$Dutifulness, TURIP$SEX)$estimate + cohen.d(TURIP$Orderliness, TURIP$SEX)$estimate + cohen.d(TURIP$SelfEfficacy, TURIP$SEX)$estimate)); dOPTurC
dOPUgaC = mean(c(cohen.d(UGAIP$Cautiousness, UGAIP$SEX)$estimate + cohen.d(UGAIP$SelfDiscipline, UGAIP$SEX)$estimate + cohen.d(UGAIP$AchievementStriving, UGAIP$SEX)$estimate + cohen.d(UGAIP$Dutifulness, UGAIP$SEX)$estimate + cohen.d(UGAIP$Orderliness, UGAIP$SEX)$estimate)); dOPUgaC
dOPUKC = mean(c(cohen.d(UKIP$Cautiousness, UKIP$SEX)$estimate + cohen.d(UKIP$SelfDiscipline, UKIP$SEX)$estimate + cohen.d(UKIP$AchievementStriving, UKIP$SEX)$estimate + cohen.d(UKIP$Dutifulness, UKIP$SEX)$estimate + cohen.d(UKIP$Orderliness, UKIP$SEX)$estimate + cohen.d(UKIP$SelfEfficacy, UKIP$SEX)$estimate)); dOPUKC
dOPUkrC = mean(c(cohen.d(UKRIP$Cautiousness, UKRIP$SEX)$estimate + cohen.d(UKRIP$SelfDiscipline, UKRIP$SEX)$estimate + cohen.d(UKRIP$AchievementStriving, UKRIP$SEX)$estimate + cohen.d(UKRIP$Dutifulness, UKRIP$SEX)$estimate + cohen.d(UKRIP$Orderliness, UKRIP$SEX)$estimate + cohen.d(UKRIP$SelfEfficacy, UKRIP$SEX)$estimate)); dOPUkrC
dOPUAEC = mean(c(cohen.d(UAEIP$Cautiousness, UAEIP$SEX)$estimate + cohen.d(UAEIP$SelfDiscipline, UAEIP$SEX)$estimate + cohen.d(UAEIP$AchievementStriving, UAEIP$SEX)$estimate + cohen.d(UAEIP$Dutifulness, UAEIP$SEX)$estimate + cohen.d(UAEIP$Orderliness, UAEIP$SEX)$estimate + cohen.d(UAEIP$SelfEfficacy, UAEIP$SEX)$estimate)); dOPUAEC
dOPUSAC = mean(c(cohen.d(USAIP$Cautiousness, USAIP$SEX)$estimate + cohen.d(USAIP$SelfDiscipline, USAIP$SEX)$estimate + cohen.d(USAIP$AchievementStriving, USAIP$SEX)$estimate + cohen.d(USAIP$Dutifulness, USAIP$SEX)$estimate + cohen.d(USAIP$Orderliness, USAIP$SEX)$estimate + cohen.d(USAIP$SelfEfficacy, USAIP$SEX)$estimate)); dOPUSAC
dOPVenC = mean(c(cohen.d(VENIP$Cautiousness, VENIP$SEX)$estimate + cohen.d(VENIP$SelfDiscipline, VENIP$SEX)$estimate + cohen.d(VENIP$AchievementStriving, VENIP$SEX)$estimate + cohen.d(VENIP$Dutifulness, VENIP$SEX)$estimate + cohen.d(VENIP$Orderliness, VENIP$SEX)$estimate + cohen.d(VENIP$SelfEfficacy, VENIP$SEX)$estimate)); dOPVenC
dOPVieC = mean(c(cohen.d(VIEIP$Cautiousness, VIEIP$SEX)$estimate + cohen.d(VIEIP$SelfDiscipline, VIEIP$SEX)$estimate + cohen.d(VIEIP$AchievementStriving, VIEIP$SEX)$estimate + cohen.d(VIEIP$Dutifulness, VIEIP$SEX)$estimate + cohen.d(VIEIP$Orderliness, VIEIP$SEX)$estimate + cohen.d(VIEIP$SelfEfficacy, VIEIP$SEX)$estimate)); dOPVieC
## Extraversion
dOPAlbE = mean(c(cohen.d(ALBIP$Cheerfulness, ALBIP$SEX)$estimate + cohen.d(ALBIP$ExcitementSeeking, ALBIP$SEX)$estimate + cohen.d(ALBIP$ActivityLevel, ALBIP$SEX)$estimate + cohen.d(ALBIP$Assertiveness, ALBIP$SEX)$estimate + cohen.d(ALBIP$Gregariousness, ALBIP$SEX)$estimate + cohen.d(ALBIP$Friendliness, ALBIP$SEX)$estimate)); dOPAlbE
dOPAlgE = mean(c(cohen.d(ALGIP$Cheerfulness, ALGIP$SEX)$estimate + cohen.d(ALGIP$ExcitementSeeking, ALGIP$SEX)$estimate + cohen.d(ALGIP$ActivityLevel, ALGIP$SEX)$estimate + cohen.d(ALGIP$Assertiveness, ALGIP$SEX)$estimate + cohen.d(ALGIP$Gregariousness, ALGIP$SEX)$estimate + cohen.d(ALGIP$Friendliness, ALGIP$SEX)$estimate)); dOPAlgE
dOPAngE = mean(c(cohen.d(ANGIP$Cheerfulness, ANGIP$SEX)$estimate + cohen.d(ANGIP$ExcitementSeeking, ANGIP$SEX)$estimate + cohen.d(ANGIP$ActivityLevel, ANGIP$SEX)$estimate + cohen.d(ANGIP$Assertiveness, ANGIP$SEX)$estimate + cohen.d(ANGIP$Gregariousness, ANGIP$SEX)$estimate + cohen.d(ANGIP$Friendliness, ANGIP$SEX)$estimate)); dOPAngE
dOPArgE = mean(c(cohen.d(ARGIP$Cheerfulness, ARGIP$SEX)$estimate + cohen.d(ARGIP$ExcitementSeeking, ARGIP$SEX)$estimate + cohen.d(ARGIP$ActivityLevel, ARGIP$SEX)$estimate + cohen.d(ARGIP$Assertiveness, ARGIP$SEX)$estimate + cohen.d(ARGIP$Gregariousness, ARGIP$SEX)$estimate + cohen.d(ARGIP$Friendliness, ARGIP$SEX)$estimate)); dOPArgE
dOPAusE = mean(c(cohen.d(AUSIP$Cheerfulness, AUSIP$SEX)$estimate + cohen.d(AUSIP$ExcitementSeeking, AUSIP$SEX)$estimate + cohen.d(AUSIP$ActivityLevel, AUSIP$SEX)$estimate + cohen.d(AUSIP$Assertiveness, AUSIP$SEX)$estimate + cohen.d(AUSIP$Gregariousness, AUSIP$SEX)$estimate + cohen.d(AUSIP$Friendliness, AUSIP$SEX)$estimate)); dOPAusE
dOPHabE = mean(c(cohen.d(HABIP$Cheerfulness, HABIP$SEX)$estimate + cohen.d(HABIP$ExcitementSeeking, HABIP$SEX)$estimate + cohen.d(HABIP$ActivityLevel, HABIP$SEX)$estimate + cohen.d(HABIP$Assertiveness, HABIP$SEX)$estimate + cohen.d(HABIP$Gregariousness, HABIP$SEX)$estimate + cohen.d(HABIP$Friendliness, HABIP$SEX)$estimate)); dOPHabE
dOPBelE = mean(c(cohen.d(BELIP$Cheerfulness, BELIP$SEX)$estimate + cohen.d(BELIP$ExcitementSeeking, BELIP$SEX)$estimate + cohen.d(BELIP$ActivityLevel, BELIP$SEX)$estimate + cohen.d(BELIP$Assertiveness, BELIP$SEX)$estimate + cohen.d(BELIP$Gregariousness, BELIP$SEX)$estimate + cohen.d(BELIP$Friendliness, BELIP$SEX)$estimate)); dOPBelE
dOPBraE = mean(c(cohen.d(BRAIP$Cheerfulness, BRAIP$SEX)$estimate + cohen.d(BRAIP$ExcitementSeeking, BRAIP$SEX)$estimate + cohen.d(BRAIP$ActivityLevel, BRAIP$SEX)$estimate + cohen.d(BRAIP$Assertiveness, BRAIP$SEX)$estimate + cohen.d(BRAIP$Gregariousness, BRAIP$SEX)$estimate + cohen.d(BRAIP$Friendliness, BRAIP$SEX)$estimate)); dOPBraE
dOPCanE = mean(c(cohen.d(CANIP$Cheerfulness, CANIP$SEX)$estimate + cohen.d(CANIP$ExcitementSeeking, CANIP$SEX)$estimate + cohen.d(CANIP$ActivityLevel, CANIP$SEX)$estimate + cohen.d(CANIP$Assertiveness, CANIP$SEX)$estimate + cohen.d(CANIP$Gregariousness, CANIP$SEX)$estimate + cohen.d(CANIP$Friendliness, CANIP$SEX)$estimate)); dOPCanE
dOPChiE = mean(c(cohen.d(CHIIP$Cheerfulness, CHIIP$SEX)$estimate + cohen.d(CHIIP$ExcitementSeeking, CHIIP$SEX)$estimate + cohen.d(CHIIP$ActivityLevel, CHIIP$SEX)$estimate + cohen.d(CHIIP$Assertiveness, CHIIP$SEX)$estimate + cohen.d(CHIIP$Gregariousness, CHIIP$SEX)$estimate + cohen.d(CHIIP$Friendliness, CHIIP$SEX)$estimate)); dOPChiE
dOPColE = mean(c(cohen.d(COLIP$Cheerfulness, COLIP$SEX)$estimate + cohen.d(COLIP$ExcitementSeeking, COLIP$SEX)$estimate + cohen.d(COLIP$ActivityLevel, COLIP$SEX)$estimate + cohen.d(COLIP$Assertiveness, COLIP$SEX)$estimate + cohen.d(COLIP$Gregariousness, COLIP$SEX)$estimate + cohen.d(COLIP$Friendliness, COLIP$SEX)$estimate)); dOPColE
dOPCroE = mean(c(cohen.d(CROIP$Cheerfulness, CROIP$SEX)$estimate + cohen.d(CROIP$ExcitementSeeking, CROIP$SEX)$estimate + cohen.d(CROIP$ActivityLevel, CROIP$SEX)$estimate + cohen.d(CROIP$Assertiveness, CROIP$SEX)$estimate + cohen.d(CROIP$Gregariousness, CROIP$SEX)$estimate + cohen.d(CROIP$Friendliness, CROIP$SEX)$estimate)); dOPCroE
dOPDenE = mean(c(cohen.d(DENIP$Cheerfulness, DENIP$SEX)$estimate + cohen.d(DENIP$ExcitementSeeking, DENIP$SEX)$estimate + cohen.d(DENIP$ActivityLevel, DENIP$SEX)$estimate + cohen.d(DENIP$Assertiveness, DENIP$SEX)$estimate + cohen.d(DENIP$Gregariousness, DENIP$SEX)$estimate + cohen.d(DENIP$Friendliness, DENIP$SEX)$estimate)); dOPDenE
dOPEgyE = mean(c(cohen.d(EGYIP$Cheerfulness, EGYIP$SEX)$estimate + cohen.d(EGYIP$ExcitementSeeking, EGYIP$SEX)$estimate + cohen.d(EGYIP$ActivityLevel, EGYIP$SEX)$estimate + cohen.d(EGYIP$Assertiveness, EGYIP$SEX)$estimate + cohen.d(EGYIP$Gregariousness, EGYIP$SEX)$estimate + cohen.d(EGYIP$Friendliness, EGYIP$SEX)$estimate)); dOPEgyE
dOPFinE = mean(c(cohen.d(FINIP$Cheerfulness, FINIP$SEX)$estimate + cohen.d(FINIP$ExcitementSeeking, FINIP$SEX)$estimate + cohen.d(FINIP$ActivityLevel, FINIP$SEX)$estimate + cohen.d(FINIP$Assertiveness, FINIP$SEX)$estimate + cohen.d(FINIP$Gregariousness, FINIP$SEX)$estimate + cohen.d(FINIP$Friendliness, FINIP$SEX)$estimate)); dOPFinE
dOPFraE = mean(c(cohen.d(FRAIP$Cheerfulness, FRAIP$SEX)$estimate + cohen.d(FRAIP$ExcitementSeeking, FRAIP$SEX)$estimate + cohen.d(FRAIP$ActivityLevel, FRAIP$SEX)$estimate + cohen.d(FRAIP$Assertiveness, FRAIP$SEX)$estimate + cohen.d(FRAIP$Gregariousness, FRAIP$SEX)$estimate + cohen.d(FRAIP$Friendliness, FRAIP$SEX)$estimate)); dOPFraE
dOPGerE = mean(c(cohen.d(GERIP$Cheerfulness, GERIP$SEX)$estimate + cohen.d(GERIP$ExcitementSeeking, GERIP$SEX)$estimate + cohen.d(GERIP$ActivityLevel, GERIP$SEX)$estimate + cohen.d(GERIP$Assertiveness, GERIP$SEX)$estimate + cohen.d(GERIP$Gregariousness, GERIP$SEX)$estimate + cohen.d(GERIP$Friendliness, GERIP$SEX)$estimate)); dOPGerE
dOPGreE = mean(c(cohen.d(GREIP$Cheerfulness, GREIP$SEX)$estimate + cohen.d(GREIP$ExcitementSeeking, GREIP$SEX)$estimate + cohen.d(GREIP$ActivityLevel, GREIP$SEX)$estimate + cohen.d(GREIP$Assertiveness, GREIP$SEX)$estimate + cohen.d(GREIP$Gregariousness, GREIP$SEX)$estimate + cohen.d(GREIP$Friendliness, GREIP$SEX)$estimate)); dOPGreE
dOPIndE = mean(c(cohen.d(INDIP$Cheerfulness, INDIP$SEX)$estimate + cohen.d(INDIP$ExcitementSeeking, INDIP$SEX)$estimate + cohen.d(INDIP$ActivityLevel, INDIP$SEX)$estimate + cohen.d(INDIP$Assertiveness, INDIP$SEX)$estimate + cohen.d(INDIP$Gregariousness, INDIP$SEX)$estimate + cohen.d(INDIP$Friendliness, INDIP$SEX)$estimate)); dOPIndE
dOPInoE = mean(c(cohen.d(INOIP$Cheerfulness, INOIP$SEX)$estimate + cohen.d(INOIP$ExcitementSeeking, INOIP$SEX)$estimate + cohen.d(INOIP$ActivityLevel, INOIP$SEX)$estimate + cohen.d(INOIP$Assertiveness, INOIP$SEX)$estimate + cohen.d(INOIP$Gregariousness, INOIP$SEX)$estimate + cohen.d(INOIP$Friendliness, INOIP$SEX)$estimate)); dOPInoE
dOPIraE = mean(c(cohen.d(IRAIP$Cheerfulness, IRAIP$SEX)$estimate + cohen.d(IRAIP$ExcitementSeeking, IRAIP$SEX)$estimate + cohen.d(IRAIP$ActivityLevel, IRAIP$SEX)$estimate + cohen.d(IRAIP$Assertiveness, IRAIP$SEX)$estimate + cohen.d(IRAIP$Gregariousness, IRAIP$SEX)$estimate + cohen.d(IRAIP$Friendliness, IRAIP$SEX)$estimate)); dOPIraE
dOPIreE = mean(c(cohen.d(IREIP$Cheerfulness, IREIP$SEX)$estimate + cohen.d(IREIP$ExcitementSeeking, IREIP$SEX)$estimate + cohen.d(IREIP$ActivityLevel, IREIP$SEX)$estimate + cohen.d(IREIP$Assertiveness, IREIP$SEX)$estimate + cohen.d(IREIP$Gregariousness, IREIP$SEX)$estimate + cohen.d(IREIP$Friendliness, IREIP$SEX)$estimate)); dOPIreE
dOPIsrE = mean(c(cohen.d(ISRIP$Cheerfulness, ISRIP$SEX)$estimate + cohen.d(ISRIP$ExcitementSeeking, ISRIP$SEX)$estimate + cohen.d(ISRIP$ActivityLevel, ISRIP$SEX)$estimate + cohen.d(ISRIP$Assertiveness, ISRIP$SEX)$estimate + cohen.d(ISRIP$Gregariousness, ISRIP$SEX)$estimate + cohen.d(ISRIP$Friendliness, ISRIP$SEX)$estimate)); dOPIsrE
dOPItaE = mean(c(cohen.d(ITAIP$Cheerfulness, ITAIP$SEX)$estimate + cohen.d(ITAIP$ExcitementSeeking, ITAIP$SEX)$estimate + cohen.d(ITAIP$ActivityLevel, ITAIP$SEX)$estimate + cohen.d(ITAIP$Assertiveness, ITAIP$SEX)$estimate + cohen.d(ITAIP$Gregariousness, ITAIP$SEX)$estimate + cohen.d(ITAIP$Friendliness, ITAIP$SEX)$estimate)); dOPItaE
dOPJamE = mean(c(cohen.d(JAMIP$Cheerfulness, JAMIP$SEX)$estimate + cohen.d(JAMIP$ExcitementSeeking, JAMIP$SEX)$estimate + cohen.d(JAMIP$ActivityLevel, JAMIP$SEX)$estimate + cohen.d(JAMIP$Assertiveness, JAMIP$SEX)$estimate + cohen.d(JAMIP$Gregariousness, JAMIP$SEX)$estimate + cohen.d(JAMIP$Friendliness, JAMIP$SEX)$estimate)); dOPJamE
dOPJapE = mean(c(cohen.d(JAPIP$Cheerfulness, JAPIP$SEX)$estimate + cohen.d(JAPIP$ExcitementSeeking, JAPIP$SEX)$estimate + cohen.d(JAPIP$ActivityLevel, JAPIP$SEX)$estimate + cohen.d(JAPIP$Assertiveness, JAPIP$SEX)$estimate + cohen.d(JAPIP$Gregariousness, JAPIP$SEX)$estimate + cohen.d(JAPIP$Friendliness, JAPIP$SEX)$estimate)); dOPJapE
dOPKenE = mean(c(cohen.d(KENIP$Cheerfulness, KENIP$SEX)$estimate + cohen.d(KENIP$ExcitementSeeking, KENIP$SEX)$estimate + cohen.d(KENIP$ActivityLevel, KENIP$SEX)$estimate + cohen.d(KENIP$Assertiveness, KENIP$SEX)$estimate + cohen.d(KENIP$Gregariousness, KENIP$SEX)$estimate + cohen.d(KENIP$Friendliness, KENIP$SEX)$estimate)); dOPKenE
dOPLebE = mean(c(cohen.d(LEBIP$Cheerfulness, LEBIP$SEX)$estimate + cohen.d(LEBIP$ExcitementSeeking, LEBIP$SEX)$estimate + cohen.d(LEBIP$ActivityLevel, LEBIP$SEX)$estimate + cohen.d(LEBIP$Assertiveness, LEBIP$SEX)$estimate + cohen.d(LEBIP$Gregariousness, LEBIP$SEX)$estimate + cohen.d(LEBIP$Friendliness, LEBIP$SEX)$estimate)); dOPLebE
dOPMalE = mean(c(cohen.d(MALIP$Cheerfulness, MALIP$SEX)$estimate + cohen.d(MALIP$ExcitementSeeking, MALIP$SEX)$estimate + cohen.d(MALIP$ActivityLevel, MALIP$SEX)$estimate + cohen.d(MALIP$Assertiveness, MALIP$SEX)$estimate + cohen.d(MALIP$Gregariousness, MALIP$SEX)$estimate + cohen.d(MALIP$Friendliness, MALIP$SEX)$estimate)); dOPMalE
dOPMexE = mean(c(cohen.d(MEXIP$Cheerfulness, MEXIP$SEX)$estimate + cohen.d(MEXIP$ExcitementSeeking, MEXIP$SEX)$estimate + cohen.d(MEXIP$ActivityLevel, MEXIP$SEX)$estimate + cohen.d(MEXIP$Assertiveness, MEXIP$SEX)$estimate + cohen.d(MEXIP$Gregariousness, MEXIP$SEX)$estimate + cohen.d(MEXIP$Friendliness, MEXIP$SEX)$estimate)); dOPMexE
dOPNetE = mean(c(cohen.d(NETIP$Cheerfulness, NETIP$SEX)$estimate + cohen.d(NETIP$ExcitementSeeking, NETIP$SEX)$estimate + cohen.d(NETIP$ActivityLevel, NETIP$SEX)$estimate + cohen.d(NETIP$Assertiveness, NETIP$SEX)$estimate + cohen.d(NETIP$Gregariousness, NETIP$SEX)$estimate + cohen.d(NETIP$Friendliness, NETIP$SEX)$estimate)); dOPNetE
dOPNewE = mean(c(cohen.d(NEWIP$Cheerfulness, NEWIP$SEX)$estimate + cohen.d(NEWIP$ExcitementSeeking, NEWIP$SEX)$estimate + cohen.d(NEWIP$ActivityLevel, NEWIP$SEX)$estimate + cohen.d(NEWIP$Assertiveness, NEWIP$SEX)$estimate + cohen.d(NEWIP$Gregariousness, NEWIP$SEX)$estimate + cohen.d(NEWIP$Friendliness, NEWIP$SEX)$estimate)); dOPNewE
dOPNirE = mean(c(cohen.d(NIRIP$Cheerfulness, NIRIP$SEX)$estimate + cohen.d(NIRIP$ExcitementSeeking, NIRIP$SEX)$estimate + cohen.d(NIRIP$ActivityLevel, NIRIP$SEX)$estimate + cohen.d(NIRIP$Assertiveness, NIRIP$SEX)$estimate + cohen.d(NIRIP$Gregariousness, NIRIP$SEX)$estimate + cohen.d(NIRIP$Friendliness, NIRIP$SEX)$estimate)); dOPNirE
dOPNorE = mean(c(cohen.d(NORIP$Cheerfulness, NORIP$SEX)$estimate + cohen.d(NORIP$ExcitementSeeking, NORIP$SEX)$estimate + cohen.d(NORIP$ActivityLevel, NORIP$SEX)$estimate + cohen.d(NORIP$Assertiveness, NORIP$SEX)$estimate + cohen.d(NORIP$Gregariousness, NORIP$SEX)$estimate + cohen.d(NORIP$Friendliness, NORIP$SEX)$estimate)); dOPNorE
dOPPerE = mean(c(cohen.d(PERIP$Cheerfulness, PERIP$SEX)$estimate + cohen.d(PERIP$ExcitementSeeking, PERIP$SEX)$estimate + cohen.d(PERIP$ActivityLevel, PERIP$SEX)$estimate + cohen.d(PERIP$Assertiveness, PERIP$SEX)$estimate + cohen.d(PERIP$Gregariousness, PERIP$SEX)$estimate + cohen.d(PERIP$Friendliness, PERIP$SEX)$estimate)); dOPPerE
dOPPakE = mean(c(cohen.d(PAKIP$Cheerfulness, PAKIP$SEX)$estimate + cohen.d(PAKIP$ExcitementSeeking, PAKIP$SEX)$estimate + cohen.d(PAKIP$ActivityLevel, PAKIP$SEX)$estimate + cohen.d(PAKIP$Assertiveness, PAKIP$SEX)$estimate + cohen.d(PAKIP$Gregariousness, PAKIP$SEX)$estimate + cohen.d(PAKIP$Friendliness, PAKIP$SEX)$estimate)); dOPPakE
dOPPhiE = mean(c(cohen.d(PHIIP$Cheerfulness, PHIIP$SEX)$estimate + cohen.d(PHIIP$ExcitementSeeking, PHIIP$SEX)$estimate + cohen.d(PHIIP$ActivityLevel, PHIIP$SEX)$estimate + cohen.d(PHIIP$Assertiveness, PHIIP$SEX)$estimate + cohen.d(PHIIP$Gregariousness, PHIIP$SEX)$estimate + cohen.d(PHIIP$Friendliness, PHIIP$SEX)$estimate)); dOPPhiE
dOPPolE = mean(c(cohen.d(POLIP$Cheerfulness, POLIP$SEX)$estimate + cohen.d(POLIP$ExcitementSeeking, POLIP$SEX)$estimate + cohen.d(POLIP$ActivityLevel, POLIP$SEX)$estimate + cohen.d(POLIP$Assertiveness, POLIP$SEX)$estimate + cohen.d(POLIP$Gregariousness, POLIP$SEX)$estimate + cohen.d(POLIP$Friendliness, POLIP$SEX)$estimate)); dOPPolE
dOPPorE = mean(c(cohen.d(PORIP$Cheerfulness, PORIP$SEX)$estimate + cohen.d(PORIP$ExcitementSeeking, PORIP$SEX)$estimate + cohen.d(PORIP$ActivityLevel, PORIP$SEX)$estimate + cohen.d(PORIP$Assertiveness, PORIP$SEX)$estimate + cohen.d(PORIP$Gregariousness, PORIP$SEX)$estimate + cohen.d(PORIP$Friendliness, PORIP$SEX)$estimate)); dOPPorE
dOPRomE = mean(c(cohen.d(ROMIP$Cheerfulness, ROMIP$SEX)$estimate + cohen.d(ROMIP$ExcitementSeeking, ROMIP$SEX)$estimate + cohen.d(ROMIP$ActivityLevel, ROMIP$SEX)$estimate + cohen.d(ROMIP$Assertiveness, ROMIP$SEX)$estimate + cohen.d(ROMIP$Gregariousness, ROMIP$SEX)$estimate + cohen.d(ROMIP$Friendliness, ROMIP$SEX)$estimate)); dOPRomE
dOPRusE = mean(c(cohen.d(RUSIP$Cheerfulness, RUSIP$SEX)$estimate + cohen.d(RUSIP$ExcitementSeeking, RUSIP$SEX)$estimate + cohen.d(RUSIP$ActivityLevel, RUSIP$SEX)$estimate + cohen.d(RUSIP$Assertiveness, RUSIP$SEX)$estimate + cohen.d(RUSIP$Gregariousness, RUSIP$SEX)$estimate + cohen.d(RUSIP$Friendliness, RUSIP$SEX)$estimate)); dOPRusE
dOPSinE = mean(c(cohen.d(SINIP$Cheerfulness, SINIP$SEX)$estimate + cohen.d(SINIP$ExcitementSeeking, SINIP$SEX)$estimate + cohen.d(SINIP$ActivityLevel, SINIP$SEX)$estimate + cohen.d(SINIP$Assertiveness, SINIP$SEX)$estimate + cohen.d(SINIP$Gregariousness, SINIP$SEX)$estimate + cohen.d(SINIP$Friendliness, SINIP$SEX)$estimate)); dOPSinE
dOPSloE = mean(c(cohen.d(SLOIP$Cheerfulness, SLOIP$SEX)$estimate + cohen.d(SLOIP$ExcitementSeeking, SLOIP$SEX)$estimate + cohen.d(SLOIP$ActivityLevel, SLOIP$SEX)$estimate + cohen.d(SLOIP$Assertiveness, SLOIP$SEX)$estimate + cohen.d(SLOIP$Gregariousness, SLOIP$SEX)$estimate + cohen.d(SLOIP$Friendliness, SLOIP$SEX)$estimate)); dOPSloE
dOPSlvE = mean(c(cohen.d(SLVIP$Cheerfulness, SLVIP$SEX)$estimate + cohen.d(SLVIP$ExcitementSeeking, SLVIP$SEX)$estimate + cohen.d(SLVIP$ActivityLevel, SLVIP$SEX)$estimate + cohen.d(SLVIP$Assertiveness, SLVIP$SEX)$estimate + cohen.d(SLVIP$Gregariousness, SLVIP$SEX)$estimate + cohen.d(SLVIP$Friendliness, SLVIP$SEX)$estimate)); dOPSlvE
dOPSouE = mean(c(cohen.d(SOUIP$Cheerfulness, SOUIP$SEX)$estimate + cohen.d(SOUIP$ExcitementSeeking, SOUIP$SEX)$estimate + cohen.d(SOUIP$ActivityLevel, SOUIP$SEX)$estimate + cohen.d(SOUIP$Assertiveness, SOUIP$SEX)$estimate + cohen.d(SOUIP$Gregariousness, SOUIP$SEX)$estimate + cohen.d(SOUIP$Friendliness, SOUIP$SEX)$estimate)); dOPSouE
dOPSKoE = mean(c(cohen.d(SKOIP$Cheerfulness, SKOIP$SEX)$estimate + cohen.d(SKOIP$ExcitementSeeking, SKOIP$SEX)$estimate + cohen.d(SKOIP$ActivityLevel, SKOIP$SEX)$estimate + cohen.d(SKOIP$Assertiveness, SKOIP$SEX)$estimate + cohen.d(SKOIP$Gregariousness, SKOIP$SEX)$estimate + cohen.d(SKOIP$Friendliness, SKOIP$SEX)$estimate)); dOPSKoE
dOPSpaE = mean(c(cohen.d(SPAIP$Cheerfulness, SPAIP$SEX)$estimate + cohen.d(SPAIP$ExcitementSeeking, SPAIP$SEX)$estimate + cohen.d(SPAIP$ActivityLevel, SPAIP$SEX)$estimate + cohen.d(SPAIP$Assertiveness, SPAIP$SEX)$estimate + cohen.d(SPAIP$Gregariousness, SPAIP$SEX)$estimate + cohen.d(SPAIP$Friendliness, SPAIP$SEX)$estimate)); dOPSpaE
dOPSweE = mean(c(cohen.d(SWEIP$Cheerfulness, SWEIP$SEX)$estimate + cohen.d(SWEIP$ExcitementSeeking, SWEIP$SEX)$estimate + cohen.d(SWEIP$ActivityLevel, SWEIP$SEX)$estimate + cohen.d(SWEIP$Assertiveness, SWEIP$SEX)$estimate + cohen.d(SWEIP$Gregariousness, SWEIP$SEX)$estimate + cohen.d(SWEIP$Friendliness, SWEIP$SEX)$estimate)); dOPSweE
dOPSwiE = mean(c(cohen.d(SWIIP$Cheerfulness, SWIIP$SEX)$estimate + cohen.d(SWIIP$ExcitementSeeking, SWIIP$SEX)$estimate + cohen.d(SWIIP$ActivityLevel, SWIIP$SEX)$estimate + cohen.d(SWIIP$Assertiveness, SWIIP$SEX)$estimate + cohen.d(SWIIP$Gregariousness, SWIIP$SEX)$estimate + cohen.d(SWIIP$Friendliness, SWIIP$SEX)$estimate)); dOPSwiE
dOPThaE = mean(c(cohen.d(THAIP$Cheerfulness, THAIP$SEX)$estimate + cohen.d(THAIP$ExcitementSeeking, THAIP$SEX)$estimate + cohen.d(THAIP$ActivityLevel, THAIP$SEX)$estimate + cohen.d(THAIP$Assertiveness, THAIP$SEX)$estimate + cohen.d(THAIP$Gregariousness, THAIP$SEX)$estimate + cohen.d(THAIP$Friendliness, THAIP$SEX)$estimate)); dOPThaE
dOPTriE = mean(c(cohen.d(TRIIP$Cheerfulness, TRIIP$SEX)$estimate + cohen.d(TRIIP$ExcitementSeeking, TRIIP$SEX)$estimate + cohen.d(TRIIP$ActivityLevel, TRIIP$SEX)$estimate + cohen.d(TRIIP$Assertiveness, TRIIP$SEX)$estimate + cohen.d(TRIIP$Gregariousness, TRIIP$SEX)$estimate + cohen.d(TRIIP$Friendliness, TRIIP$SEX)$estimate)); dOPTriE
dOPTurE = mean(c(cohen.d(TURIP$Cheerfulness, TURIP$SEX)$estimate + cohen.d(TURIP$ExcitementSeeking, TURIP$SEX)$estimate + cohen.d(TURIP$ActivityLevel, TURIP$SEX)$estimate + cohen.d(TURIP$Assertiveness, TURIP$SEX)$estimate + cohen.d(TURIP$Gregariousness, TURIP$SEX)$estimate + cohen.d(TURIP$Friendliness, TURIP$SEX)$estimate)); dOPTurE
dOPUgaE = mean(c(cohen.d(UGAIP$Cheerfulness, UGAIP$SEX)$estimate + cohen.d(UGAIP$ExcitementSeeking, UGAIP$SEX)$estimate + cohen.d(UGAIP$ActivityLevel, UGAIP$SEX)$estimate + cohen.d(UGAIP$Assertiveness, UGAIP$SEX)$estimate + cohen.d(UGAIP$Gregariousness, UGAIP$SEX)$estimate + cohen.d(UGAIP$Friendliness, UGAIP$SEX)$estimate)); dOPUgaE
dOPUKE = mean(c(cohen.d(UKIP$Cheerfulness, UKIP$SEX)$estimate + cohen.d(UKIP$ExcitementSeeking, UKIP$SEX)$estimate + cohen.d(UKIP$ActivityLevel, UKIP$SEX)$estimate + cohen.d(UKIP$Assertiveness, UKIP$SEX)$estimate + cohen.d(UKIP$Gregariousness, UKIP$SEX)$estimate + cohen.d(UKIP$Friendliness, UKIP$SEX)$estimate)); dOPUKE
dOPUkrE = mean(c(cohen.d(UKRIP$Cheerfulness, UKRIP$SEX)$estimate + cohen.d(UKRIP$ExcitementSeeking, UKRIP$SEX)$estimate + cohen.d(UKRIP$ActivityLevel, UKRIP$SEX)$estimate + cohen.d(UKRIP$Assertiveness, UKRIP$SEX)$estimate + cohen.d(UKRIP$Gregariousness, UKRIP$SEX)$estimate + cohen.d(UKRIP$Friendliness, UKRIP$SEX)$estimate)); dOPUkrE
dOPUAEE = mean(c(cohen.d(UAEIP$Cheerfulness, UAEIP$SEX)$estimate + cohen.d(UAEIP$ExcitementSeeking, UAEIP$SEX)$estimate + cohen.d(UAEIP$ActivityLevel, UAEIP$SEX)$estimate + cohen.d(UAEIP$Assertiveness, UAEIP$SEX)$estimate + cohen.d(UAEIP$Gregariousness, UAEIP$SEX)$estimate + cohen.d(UAEIP$Friendliness, UAEIP$SEX)$estimate)); dOPUAEE
dOPUSAE = mean(c(cohen.d(USAIP$Cheerfulness, USAIP$SEX)$estimate + cohen.d(USAIP$ExcitementSeeking, USAIP$SEX)$estimate + cohen.d(USAIP$ActivityLevel, USAIP$SEX)$estimate + cohen.d(USAIP$Assertiveness, USAIP$SEX)$estimate + cohen.d(USAIP$Gregariousness, USAIP$SEX)$estimate + cohen.d(USAIP$Friendliness, USAIP$SEX)$estimate)); dOPUSAE
dOPVenE = mean(c(cohen.d(VENIP$Cheerfulness, VENIP$SEX)$estimate + cohen.d(VENIP$ExcitementSeeking, VENIP$SEX)$estimate + cohen.d(VENIP$ActivityLevel, VENIP$SEX)$estimate + cohen.d(VENIP$Assertiveness, VENIP$SEX)$estimate + cohen.d(VENIP$Gregariousness, VENIP$SEX)$estimate + cohen.d(VENIP$Friendliness, VENIP$SEX)$estimate)); dOPVenE
dOPVieE = mean(c(cohen.d(VIEIP$Cheerfulness, VIEIP$SEX)$estimate + cohen.d(VIEIP$ExcitementSeeking, VIEIP$SEX)$estimate + cohen.d(VIEIP$ActivityLevel, VIEIP$SEX)$estimate + cohen.d(VIEIP$Assertiveness, VIEIP$SEX)$estimate + cohen.d(VIEIP$Gregariousness, VIEIP$SEX)$estimate + cohen.d(VIEIP$Friendliness, VIEIP$SEX)$estimate)); dOPVieE
## Agreeableness
dOPAlbA = mean(c(abs(cohen.d(ALBIP$Sympathy, ALBIP$SEX)$estimate + cohen.d(ALBIP$Modesty, ALBIP$SEX)$estimate + cohen.d(ALBIP$Altruism, ALBIP$SEX)$estimate + cohen.d(ALBIP$Morality, ALBIP$SEX)$estimate))); dOPAlbA
dOPAlgA = mean(c(abs(cohen.d(ALGIP$Sympathy, ALGIP$SEX)$estimate + cohen.d(ALGIP$Modesty, ALGIP$SEX)$estimate + cohen.d(ALGIP$Cooperation, ALGIP$SEX)$estimate + cohen.d(ALGIP$Altruism, ALGIP$SEX)$estimate + cohen.d(ALGIP$Morality, ALGIP$SEX)$estimate + cohen.d(ALGIP$Trust, ALGIP$SEX)$estimate))); dOPAlgA
dOPAngA = mean(c(abs(cohen.d(ANGIP$Sympathy, ANGIP$SEX)$estimate + cohen.d(ANGIP$Modesty, ANGIP$SEX)$estimate + cohen.d(ANGIP$Cooperation, ANGIP$SEX)$estimate + cohen.d(ANGIP$Altruism, ANGIP$SEX)$estimate))); dOPAngA
dOPArgA = mean(c(abs(cohen.d(ARGIP$Sympathy, ARGIP$SEX)$estimate + cohen.d(ARGIP$Modesty, ARGIP$SEX)$estimate + cohen.d(ARGIP$Cooperation, ARGIP$SEX)$estimate + cohen.d(ARGIP$Altruism, ARGIP$SEX)$estimate + cohen.d(ARGIP$Morality, ARGIP$SEX)$estimate + cohen.d(ARGIP$Trust, ARGIP$SEX)$estimate))); dOPArgA
dOPAusA = mean(c(abs(cohen.d(AUSIP$Sympathy, AUSIP$SEX)$estimate + cohen.d(AUSIP$Modesty, AUSIP$SEX)$estimate + cohen.d(AUSIP$Cooperation, AUSIP$SEX)$estimate + cohen.d(AUSIP$Altruism, AUSIP$SEX)$estimate + cohen.d(AUSIP$Morality, AUSIP$SEX)$estimate + cohen.d(AUSIP$Trust, AUSIP$SEX)$estimate))); dOPAusA
dOPHabA = mean(c(abs(cohen.d(HABIP$Sympathy, HABIP$SEX)$estimate + cohen.d(HABIP$Modesty, HABIP$SEX)$estimate + cohen.d(HABIP$Cooperation, HABIP$SEX)$estimate + cohen.d(HABIP$Altruism, HABIP$SEX)$estimate + cohen.d(HABIP$Morality, HABIP$SEX)$estimate + cohen.d(HABIP$Trust, HABIP$SEX)$estimate))); dOPHabA
dOPBelA = mean(c(abs(cohen.d(BELIP$Sympathy, BELIP$SEX)$estimate + cohen.d(BELIP$Modesty, BELIP$SEX)$estimate + cohen.d(BELIP$Cooperation, BELIP$SEX)$estimate + cohen.d(BELIP$Altruism, BELIP$SEX)$estimate + cohen.d(BELIP$Morality, BELIP$SEX)$estimate + cohen.d(BELIP$Trust, BELIP$SEX)$estimate))); dOPBelA
dOPBraA = mean(c(abs(cohen.d(BRAIP$Sympathy, BRAIP$SEX)$estimate + cohen.d(BRAIP$Modesty, BRAIP$SEX)$estimate + cohen.d(BRAIP$Cooperation, BRAIP$SEX)$estimate + cohen.d(BRAIP$Altruism, BRAIP$SEX)$estimate + cohen.d(BRAIP$Morality, BRAIP$SEX)$estimate + cohen.d(BRAIP$Trust, BRAIP$SEX)$estimate))); dOPBraA
dOPCanA = mean(c(abs(cohen.d(CANIP$Sympathy, CANIP$SEX)$estimate + cohen.d(CANIP$Modesty, CANIP$SEX)$estimate + cohen.d(CANIP$Cooperation, CANIP$SEX)$estimate + cohen.d(CANIP$Altruism, CANIP$SEX)$estimate + cohen.d(CANIP$Morality, CANIP$SEX)$estimate + cohen.d(CANIP$Trust, CANIP$SEX)$estimate))); dOPCanA
dOPChiA = mean(c(abs(cohen.d(CHIIP$Sympathy, CHIIP$SEX)$estimate + cohen.d(CHIIP$Modesty, CHIIP$SEX)$estimate + cohen.d(CHIIP$Cooperation, CHIIP$SEX)$estimate + cohen.d(CHIIP$Altruism, CHIIP$SEX)$estimate + cohen.d(CHIIP$Morality, CHIIP$SEX)$estimate + cohen.d(CHIIP$Trust, CHIIP$SEX)$estimate))); dOPChiA
dOPColA = mean(c(abs(cohen.d(COLIP$Sympathy, COLIP$SEX)$estimate + cohen.d(COLIP$Modesty, COLIP$SEX)$estimate + cohen.d(COLIP$Cooperation, COLIP$SEX)$estimate + cohen.d(COLIP$Altruism, COLIP$SEX)$estimate + cohen.d(COLIP$Morality, COLIP$SEX)$estimate + cohen.d(COLIP$Trust, COLIP$SEX)$estimate))); dOPColA
dOPCroA = mean(c(abs(cohen.d(CROIP$Sympathy, CROIP$SEX)$estimate + cohen.d(CROIP$Modesty, CROIP$SEX)$estimate + cohen.d(CROIP$Cooperation, CROIP$SEX)$estimate + cohen.d(CROIP$Altruism, CROIP$SEX)$estimate + cohen.d(CROIP$Morality, CROIP$SEX)$estimate + cohen.d(CROIP$Trust, CROIP$SEX)$estimate))); dOPCroA
dOPDenA = mean(c(abs(cohen.d(DENIP$Sympathy, DENIP$SEX)$estimate + cohen.d(DENIP$Modesty, DENIP$SEX)$estimate + cohen.d(DENIP$Cooperation, DENIP$SEX)$estimate + cohen.d(DENIP$Altruism, DENIP$SEX)$estimate + cohen.d(DENIP$Morality, DENIP$SEX)$estimate + cohen.d(DENIP$Trust, DENIP$SEX)$estimate))); dOPDenA
dOPEgyA = mean(c(abs(cohen.d(EGYIP$Sympathy, EGYIP$SEX)$estimate + cohen.d(EGYIP$Modesty, EGYIP$SEX)$estimate + cohen.d(EGYIP$Cooperation, EGYIP$SEX)$estimate + cohen.d(EGYIP$Altruism, EGYIP$SEX)$estimate + cohen.d(EGYIP$Morality, EGYIP$SEX)$estimate + cohen.d(EGYIP$Trust, EGYIP$SEX)$estimate))); dOPEgyA
dOPFinA = mean(c(abs(cohen.d(FINIP$Sympathy, FINIP$SEX)$estimate + cohen.d(FINIP$Modesty, FINIP$SEX)$estimate + cohen.d(FINIP$Cooperation, FINIP$SEX)$estimate + cohen.d(FINIP$Altruism, FINIP$SEX)$estimate + cohen.d(FINIP$Morality, FINIP$SEX)$estimate + cohen.d(FINIP$Trust, FINIP$SEX)$estimate))); dOPFinA
dOPFraA = mean(c(abs(cohen.d(FRAIP$Sympathy, FRAIP$SEX)$estimate + cohen.d(FRAIP$Modesty, FRAIP$SEX)$estimate + cohen.d(FRAIP$Cooperation, FRAIP$SEX)$estimate + cohen.d(FRAIP$Altruism, FRAIP$SEX)$estimate + cohen.d(FRAIP$Morality, FRAIP$SEX)$estimate + cohen.d(FRAIP$Trust, FRAIP$SEX)$estimate))); dOPFraA
dOPGerA = mean(c(abs(cohen.d(GERIP$Sympathy, GERIP$SEX)$estimate + cohen.d(GERIP$Modesty, GERIP$SEX)$estimate + cohen.d(GERIP$Cooperation, GERIP$SEX)$estimate + cohen.d(GERIP$Altruism, GERIP$SEX)$estimate + cohen.d(GERIP$Morality, GERIP$SEX)$estimate + cohen.d(GERIP$Trust, GERIP$SEX)$estimate))); dOPGerA
dOPGreA = mean(c(abs(cohen.d(GREIP$Sympathy, GREIP$SEX)$estimate + cohen.d(GREIP$Modesty, GREIP$SEX)$estimate + cohen.d(GREIP$Cooperation, GREIP$SEX)$estimate + cohen.d(GREIP$Altruism, GREIP$SEX)$estimate + cohen.d(GREIP$Morality, GREIP$SEX)$estimate + cohen.d(GREIP$Trust, GREIP$SEX)$estimate))); dOPGreA
dOPIndA = mean(c(abs(cohen.d(INDIP$Sympathy, INDIP$SEX)$estimate + cohen.d(INDIP$Modesty, INDIP$SEX)$estimate + cohen.d(INDIP$Cooperation, INDIP$SEX)$estimate + cohen.d(INDIP$Altruism, INDIP$SEX)$estimate + cohen.d(INDIP$Morality, INDIP$SEX)$estimate + cohen.d(INDIP$Trust, INDIP$SEX)$estimate))); dOPIndA
dOPInoA = mean(c(abs(cohen.d(INOIP$Sympathy, INOIP$SEX)$estimate + cohen.d(INOIP$Modesty, INOIP$SEX)$estimate + cohen.d(INOIP$Cooperation, INOIP$SEX)$estimate + cohen.d(INOIP$Altruism, INOIP$SEX)$estimate + cohen.d(INOIP$Morality, INOIP$SEX)$estimate + cohen.d(INOIP$Trust, INOIP$SEX)$estimate))); dOPInoA
dOPIraA = mean(c(abs(cohen.d(IRAIP$Sympathy, IRAIP$SEX)$estimate + cohen.d(IRAIP$Modesty, IRAIP$SEX)$estimate + cohen.d(IRAIP$Cooperation, IRAIP$SEX)$estimate + cohen.d(IRAIP$Altruism, IRAIP$SEX)$estimate + cohen.d(IRAIP$Morality, IRAIP$SEX)$estimate + cohen.d(IRAIP$Trust, IRAIP$SEX)$estimate))); dOPIraA
dOPIreA = mean(c(abs(cohen.d(IREIP$Sympathy, IREIP$SEX)$estimate + cohen.d(IREIP$Modesty, IREIP$SEX)$estimate + cohen.d(IREIP$Cooperation, IREIP$SEX)$estimate + cohen.d(IREIP$Altruism, IREIP$SEX)$estimate + cohen.d(IREIP$Morality, IREIP$SEX)$estimate + cohen.d(IREIP$Trust, IREIP$SEX)$estimate))); dOPIreA
dOPIsrA = mean(c(abs(cohen.d(ISRIP$Sympathy, ISRIP$SEX)$estimate + cohen.d(ISRIP$Modesty, ISRIP$SEX)$estimate + cohen.d(ISRIP$Cooperation, ISRIP$SEX)$estimate + cohen.d(ISRIP$Altruism, ISRIP$SEX)$estimate + cohen.d(ISRIP$Morality, ISRIP$SEX)$estimate + cohen.d(ISRIP$Trust, ISRIP$SEX)$estimate))); dOPIsrA
dOPItaA = mean(c(abs(cohen.d(ITAIP$Sympathy, ITAIP$SEX)$estimate + cohen.d(ITAIP$Modesty, ITAIP$SEX)$estimate + cohen.d(ITAIP$Cooperation, ITAIP$SEX)$estimate + cohen.d(ITAIP$Altruism, ITAIP$SEX)$estimate + cohen.d(ITAIP$Morality, ITAIP$SEX)$estimate + cohen.d(ITAIP$Trust, ITAIP$SEX)$estimate))); dOPItaA
dOPJamA = mean(c(abs(cohen.d(JAMIP$Sympathy, JAMIP$SEX)$estimate + cohen.d(JAMIP$Modesty, JAMIP$SEX)$estimate + cohen.d(JAMIP$Cooperation, JAMIP$SEX)$estimate + cohen.d(JAMIP$Altruism, JAMIP$SEX)$estimate + cohen.d(JAMIP$Morality, JAMIP$SEX)$estimate + cohen.d(JAMIP$Trust, JAMIP$SEX)$estimate))); dOPJamA
dOPJapA = mean(c(abs(cohen.d(JAPIP$Sympathy, JAPIP$SEX)$estimate + cohen.d(JAPIP$Modesty, JAPIP$SEX)$estimate + cohen.d(JAPIP$Cooperation, JAPIP$SEX)$estimate + cohen.d(JAPIP$Altruism, JAPIP$SEX)$estimate + cohen.d(JAPIP$Morality, JAPIP$SEX)$estimate + cohen.d(JAPIP$Trust, JAPIP$SEX)$estimate))); dOPJapA
dOPKenA = mean(c(abs(cohen.d(KENIP$Sympathy, KENIP$SEX)$estimate + cohen.d(KENIP$Modesty, KENIP$SEX)$estimate + cohen.d(KENIP$Cooperation, KENIP$SEX)$estimate + cohen.d(KENIP$Altruism, KENIP$SEX)$estimate + cohen.d(KENIP$Morality, KENIP$SEX)$estimate + cohen.d(KENIP$Trust, KENIP$SEX)$estimate))); dOPKenA
dOPLebA = mean(c(abs(cohen.d(LEBIP$Sympathy, LEBIP$SEX)$estimate + cohen.d(LEBIP$Modesty, LEBIP$SEX)$estimate + cohen.d(LEBIP$Cooperation, LEBIP$SEX)$estimate + cohen.d(LEBIP$Altruism, LEBIP$SEX)$estimate + cohen.d(LEBIP$Morality, LEBIP$SEX)$estimate + cohen.d(LEBIP$Trust, LEBIP$SEX)$estimate))); dOPLebA
dOPMalA = mean(c(abs(cohen.d(MALIP$Sympathy, MALIP$SEX)$estimate + cohen.d(MALIP$Modesty, MALIP$SEX)$estimate + cohen.d(MALIP$Cooperation, MALIP$SEX)$estimate + cohen.d(MALIP$Altruism, MALIP$SEX)$estimate + cohen.d(MALIP$Morality, MALIP$SEX)$estimate + cohen.d(MALIP$Trust, MALIP$SEX)$estimate))); dOPMalA
dOPMexA = mean(c(abs(cohen.d(MEXIP$Sympathy, MEXIP$SEX)$estimate + cohen.d(MEXIP$Modesty, MEXIP$SEX)$estimate + cohen.d(MEXIP$Cooperation, MEXIP$SEX)$estimate + cohen.d(MEXIP$Altruism, MEXIP$SEX)$estimate + cohen.d(MEXIP$Morality, MEXIP$SEX)$estimate + cohen.d(MEXIP$Trust, MEXIP$SEX)$estimate))); dOPMexA
dOPNetA = mean(c(abs(cohen.d(NETIP$Sympathy, NETIP$SEX)$estimate + cohen.d(NETIP$Modesty, NETIP$SEX)$estimate + cohen.d(NETIP$Cooperation, NETIP$SEX)$estimate + cohen.d(NETIP$Altruism, NETIP$SEX)$estimate + cohen.d(NETIP$Morality, NETIP$SEX)$estimate + cohen.d(NETIP$Trust, NETIP$SEX)$estimate))); dOPNetA
dOPNewA = mean(c(abs(cohen.d(NEWIP$Sympathy, NEWIP$SEX)$estimate + cohen.d(NEWIP$Modesty, NEWIP$SEX)$estimate + cohen.d(NEWIP$Cooperation, NEWIP$SEX)$estimate + cohen.d(NEWIP$Altruism, NEWIP$SEX)$estimate + cohen.d(NEWIP$Morality, NEWIP$SEX)$estimate + cohen.d(NEWIP$Trust, NEWIP$SEX)$estimate))); dOPNewA
dOPNirA = mean(c(abs(cohen.d(NIRIP$Sympathy, NIRIP$SEX)$estimate + cohen.d(NIRIP$Modesty, NIRIP$SEX)$estimate + cohen.d(NIRIP$Cooperation, NIRIP$SEX)$estimate + cohen.d(NIRIP$Altruism, NIRIP$SEX)$estimate + cohen.d(NIRIP$Morality, NIRIP$SEX)$estimate + cohen.d(NIRIP$Trust, NIRIP$SEX)$estimate))); dOPNirA
dOPNorA = mean(c(abs(cohen.d(NORIP$Sympathy, NORIP$SEX)$estimate + cohen.d(NORIP$Modesty, NORIP$SEX)$estimate + cohen.d(NORIP$Cooperation, NORIP$SEX)$estimate + cohen.d(NORIP$Altruism, NORIP$SEX)$estimate + cohen.d(NORIP$Morality, NORIP$SEX)$estimate + cohen.d(NORIP$Trust, NORIP$SEX)$estimate))); dOPNorA
#dOPPerA = mean(c(abs(cohen.d(PERIP$Sympathy, PERIP$SEX)$estimate + cohen.d(PERIP$Modesty, PERIP$SEX)$estimate + cohen.d(PERIP$Cooperation, PERIP$SEX)$estimate + cohen.d(PERIP$Altruism, PERIP$SEX)$estimate + cohen.d(PERIP$Morality, PERIP$SEX)$estimate + cohen.d(PERIP$Trust, PERIP$SEX)$estimate))); dOPPerA
dOPPakA = mean(c(abs(cohen.d(PAKIP$Sympathy, PAKIP$SEX)$estimate + cohen.d(PAKIP$Modesty, PAKIP$SEX)$estimate + cohen.d(PAKIP$Cooperation, PAKIP$SEX)$estimate + cohen.d(PAKIP$Altruism, PAKIP$SEX)$estimate + cohen.d(PAKIP$Morality, PAKIP$SEX)$estimate + cohen.d(PAKIP$Trust, PAKIP$SEX)$estimate))); dOPPakA
dOPPhiA = mean(c(abs(cohen.d(PHIIP$Sympathy, PHIIP$SEX)$estimate + cohen.d(PHIIP$Modesty, PHIIP$SEX)$estimate + cohen.d(PHIIP$Cooperation, PHIIP$SEX)$estimate + cohen.d(PHIIP$Altruism, PHIIP$SEX)$estimate + cohen.d(PHIIP$Morality, PHIIP$SEX)$estimate + cohen.d(PHIIP$Trust, PHIIP$SEX)$estimate))); dOPPhiA
dOPPolA = mean(c(abs(cohen.d(POLIP$Sympathy, POLIP$SEX)$estimate + cohen.d(POLIP$Modesty, POLIP$SEX)$estimate + cohen.d(POLIP$Cooperation, POLIP$SEX)$estimate + cohen.d(POLIP$Altruism, POLIP$SEX)$estimate + cohen.d(POLIP$Morality, POLIP$SEX)$estimate + cohen.d(POLIP$Trust, POLIP$SEX)$estimate))); dOPPolA
dOPPorA = mean(c(abs(cohen.d(PORIP$Sympathy, PORIP$SEX)$estimate + cohen.d(PORIP$Modesty, PORIP$SEX)$estimate + cohen.d(PORIP$Cooperation, PORIP$SEX)$estimate + cohen.d(PORIP$Altruism, PORIP$SEX)$estimate + cohen.d(PORIP$Morality, PORIP$SEX)$estimate + cohen.d(PORIP$Trust, PORIP$SEX)$estimate))); dOPPorA
dOPRomA = mean(c(abs(cohen.d(ROMIP$Sympathy, ROMIP$SEX)$estimate + cohen.d(ROMIP$Modesty, ROMIP$SEX)$estimate + cohen.d(ROMIP$Cooperation, ROMIP$SEX)$estimate + cohen.d(ROMIP$Altruism, ROMIP$SEX)$estimate))); dOPRomA
dOPRusA = mean(c(abs(cohen.d(RUSIP$Sympathy, RUSIP$SEX)$estimate + cohen.d(RUSIP$Modesty, RUSIP$SEX)$estimate + cohen.d(RUSIP$Cooperation, RUSIP$SEX)$estimate + cohen.d(RUSIP$Altruism, RUSIP$SEX)$estimate + cohen.d(RUSIP$Trust, RUSIP$SEX)$estimate))); dOPRusA
dOPSinA = mean(c(abs(cohen.d(SINIP$Sympathy, SINIP$SEX)$estimate + cohen.d(SINIP$Modesty, SINIP$SEX)$estimate + cohen.d(SINIP$Cooperation, SINIP$SEX)$estimate + cohen.d(SINIP$Altruism, SINIP$SEX)$estimate + cohen.d(SINIP$Morality, SINIP$SEX)$estimate + cohen.d(SINIP$Trust, SINIP$SEX)$estimate))); dOPSinA
dOPSloA = mean(c(abs(cohen.d(SLOIP$Sympathy, SLOIP$SEX)$estimate + cohen.d(SLOIP$Modesty, SLOIP$SEX)$estimate + cohen.d(SLOIP$Cooperation, SLOIP$SEX)$estimate + cohen.d(SLOIP$Altruism, SLOIP$SEX)$estimate + cohen.d(SLOIP$Morality, SLOIP$SEX)$estimate + cohen.d(SLOIP$Trust, SLOIP$SEX)$estimate))); dOPSloA
dOPSlvA = mean(c(abs(cohen.d(SLVIP$Sympathy, SLVIP$SEX)$estimate + cohen.d(SLVIP$Modesty, SLVIP$SEX)$estimate + cohen.d(SLVIP$Cooperation, SLVIP$SEX)$estimate + cohen.d(SLVIP$Altruism, SLVIP$SEX)$estimate + cohen.d(SLVIP$Morality, SLVIP$SEX)$estimate + cohen.d(SLVIP$Trust, SLVIP$SEX)$estimate))); dOPSlvA
dOPSouA = mean(c(abs(cohen.d(SOUIP$Sympathy, SOUIP$SEX)$estimate + cohen.d(SOUIP$Modesty, SOUIP$SEX)$estimate + cohen.d(SOUIP$Cooperation, SOUIP$SEX)$estimate + cohen.d(SOUIP$Altruism, SOUIP$SEX)$estimate + cohen.d(SOUIP$Morality, SOUIP$SEX)$estimate + cohen.d(SOUIP$Trust, SOUIP$SEX)$estimate))); dOPSouA
dOPSKoA = mean(c(abs(cohen.d(SKOIP$Sympathy, SKOIP$SEX)$estimate + cohen.d(SKOIP$Modesty, SKOIP$SEX)$estimate + cohen.d(SKOIP$Cooperation, SKOIP$SEX)$estimate + cohen.d(SKOIP$Altruism, SKOIP$SEX)$estimate + cohen.d(SKOIP$Morality, SKOIP$SEX)$estimate + cohen.d(SKOIP$Trust, SKOIP$SEX)$estimate))); dOPSKoA
dOPSpaA = mean(c(abs(cohen.d(SPAIP$Sympathy, SPAIP$SEX)$estimate + cohen.d(SPAIP$Modesty, SPAIP$SEX)$estimate + cohen.d(SPAIP$Cooperation, SPAIP$SEX)$estimate + cohen.d(SPAIP$Altruism, SPAIP$SEX)$estimate + cohen.d(SPAIP$Morality, SPAIP$SEX)$estimate + cohen.d(SPAIP$Trust, SPAIP$SEX)$estimate))); dOPSpaA
dOPSweA = mean(c(abs(cohen.d(SWEIP$Sympathy, SWEIP$SEX)$estimate + cohen.d(SWEIP$Modesty, SWEIP$SEX)$estimate + cohen.d(SWEIP$Cooperation, SWEIP$SEX)$estimate + cohen.d(SWEIP$Altruism, SWEIP$SEX)$estimate + cohen.d(SWEIP$Trust, SWEIP$SEX)$estimate))); dOPSweA
dOPSwiA = mean(c(abs(cohen.d(SWIIP$Sympathy, SWIIP$SEX)$estimate + cohen.d(SWIIP$Modesty, SWIIP$SEX)$estimate + cohen.d(SWIIP$Cooperation, SWIIP$SEX)$estimate + cohen.d(SWIIP$Altruism, SWIIP$SEX)$estimate + cohen.d(SWIIP$Morality, SWIIP$SEX)$estimate + cohen.d(SWIIP$Trust, SWIIP$SEX)$estimate))); dOPSwiA
dOPThaA = mean(c(abs(cohen.d(THAIP$Sympathy, THAIP$SEX)$estimate + cohen.d(THAIP$Modesty, THAIP$SEX)$estimate + cohen.d(THAIP$Cooperation, THAIP$SEX)$estimate + cohen.d(THAIP$Altruism, THAIP$SEX)$estimate + cohen.d(THAIP$Morality, THAIP$SEX)$estimate + cohen.d(THAIP$Trust, THAIP$SEX)$estimate))); dOPThaA
#dOPTriA = mean(c(abs(cohen.d(TRIIP$Sympathy, TRIIP$SEX)$estimate + cohen.d(TRIIP$Modesty, TRIIP$SEX)$estimate + cohen.d(TRIIP$Cooperation, TRIIP$SEX)$estimate + cohen.d(TRIIP$Altruism, TRIIP$SEX)$estimate + cohen.d(TRIIP$Morality, TRIIP$SEX)$estimate + cohen.d(TRIIP$Trust, TRIIP$SEX)$estimate))); dOPTriA
dOPTurA = mean(c(abs(cohen.d(TURIP$Sympathy, TURIP$SEX)$estimate + cohen.d(TURIP$Modesty, TURIP$SEX)$estimate + cohen.d(TURIP$Cooperation, TURIP$SEX)$estimate + cohen.d(TURIP$Altruism, TURIP$SEX)$estimate + cohen.d(TURIP$Morality, TURIP$SEX)$estimate + cohen.d(TURIP$Trust, TURIP$SEX)$estimate))); dOPTurA
dOPUgaA = mean(c(abs(cohen.d(UGAIP$Sympathy, UGAIP$SEX)$estimate + cohen.d(UGAIP$Modesty, UGAIP$SEX)$estimate + cohen.d(UGAIP$Cooperation, UGAIP$SEX)$estimate + cohen.d(UGAIP$Morality, UGAIP$SEX)$estimate + cohen.d(UGAIP$Trust, UGAIP$SEX)$estimate))); dOPUgaA
dOPUKA = mean(c(abs(cohen.d(UKIP$Sympathy, UKIP$SEX)$estimate + cohen.d(UKIP$Modesty, UKIP$SEX)$estimate + cohen.d(UKIP$Cooperation, UKIP$SEX)$estimate + cohen.d(UKIP$Altruism, UKIP$SEX)$estimate + cohen.d(UKIP$Morality, UKIP$SEX)$estimate + cohen.d(UKIP$Trust, UKIP$SEX)$estimate))); dOPUKA
dOPUkrA = mean(c(abs(cohen.d(UKRIP$Sympathy, UKRIP$SEX)$estimate + cohen.d(UKRIP$Modesty, UKRIP$SEX)$estimate + cohen.d(UKRIP$Cooperation, UKRIP$SEX)$estimate + cohen.d(UKRIP$Altruism, UKRIP$SEX)$estimate + cohen.d(UKRIP$Morality, UKRIP$SEX)$estimate + cohen.d(UKRIP$Trust, UKRIP$SEX)$estimate))); dOPUkrA
dOPUAEA = mean(c(abs(cohen.d(UAEIP$Sympathy, UAEIP$SEX)$estimate + cohen.d(UAEIP$Modesty, UAEIP$SEX)$estimate + cohen.d(UAEIP$Cooperation, UAEIP$SEX)$estimate + cohen.d(UAEIP$Altruism, UAEIP$SEX)$estimate + cohen.d(UAEIP$Morality, UAEIP$SEX)$estimate + cohen.d(UAEIP$Trust, UAEIP$SEX)$estimate))); dOPUAEA
dOPUSAA = mean(c(abs(cohen.d(USAIP$Sympathy, USAIP$SEX)$estimate + cohen.d(USAIP$Modesty, USAIP$SEX)$estimate + cohen.d(USAIP$Cooperation, USAIP$SEX)$estimate + cohen.d(USAIP$Altruism, USAIP$SEX)$estimate + cohen.d(USAIP$Morality, USAIP$SEX)$estimate + cohen.d(USAIP$Trust, USAIP$SEX)$estimate))); dOPUSAA
dOPVenA = mean(c(abs(cohen.d(VENIP$Sympathy, VENIP$SEX)$estimate + cohen.d(VENIP$Modesty, VENIP$SEX)$estimate + cohen.d(VENIP$Cooperation, VENIP$SEX)$estimate + cohen.d(VENIP$Altruism, VENIP$SEX)$estimate + cohen.d(VENIP$Morality, VENIP$SEX)$estimate + cohen.d(VENIP$Trust, VENIP$SEX)$estimate))); dOPVenA
dOPVieA = mean(c(abs(cohen.d(VIEIP$Sympathy, VIEIP$SEX)$estimate + cohen.d(VIEIP$Modesty, VIEIP$SEX)$estimate + cohen.d(VIEIP$Cooperation, VIEIP$SEX)$estimate + cohen.d(VIEIP$Altruism, VIEIP$SEX)$estimate + cohen.d(VIEIP$Morality, VIEIP$SEX)$estimate + cohen.d(VIEIP$Trust, VIEIP$SEX)$estimate))); dOPVieA
## Neuroticism
dOPAlbN = mean(c(cohen.d(ALBIP$Vulnerability, ALBIP$SEX)$estimate + cohen.d(ALBIP$Immoderation, ALBIP$SEX)$estimate + cohen.d(ALBIP$SelfConsciousness, ALBIP$SEX)$estimate + cohen.d(ALBIP$Depression, ALBIP$SEX)$estimate + cohen.d(ALBIP$Anger, ALBIP$SEX)$estimate + cohen.d(ALBIP$Anxiety, ALBIP$SEX)$estimate)); dOPAlbN
dOPAlgN = mean(c(cohen.d(ALGIP$Vulnerability, ALGIP$SEX)$estimate + cohen.d(ALGIP$Immoderation, ALGIP$SEX)$estimate + cohen.d(ALGIP$SelfConsciousness, ALGIP$SEX)$estimate + cohen.d(ALGIP$Depression, ALGIP$SEX)$estimate + cohen.d(ALGIP$Anger, ALGIP$SEX)$estimate + cohen.d(ALGIP$Anxiety, ALGIP$SEX)$estimate)); dOPAlgN
dOPAngN = mean(c(cohen.d(ANGIP$Vulnerability, ANGIP$SEX)$estimate + cohen.d(ANGIP$Immoderation, ANGIP$SEX)$estimate + cohen.d(ANGIP$SelfConsciousness, ANGIP$SEX)$estimate + cohen.d(ANGIP$Depression, ANGIP$SEX)$estimate + cohen.d(ANGIP$Anger, ANGIP$SEX)$estimate + cohen.d(ANGIP$Anxiety, ANGIP$SEX)$estimate)); dOPAngN
dOPArgN = mean(c(cohen.d(ARGIP$Vulnerability, ARGIP$SEX)$estimate + cohen.d(ARGIP$Immoderation, ARGIP$SEX)$estimate + cohen.d(ARGIP$SelfConsciousness, ARGIP$SEX)$estimate + cohen.d(ARGIP$Depression, ARGIP$SEX)$estimate + cohen.d(ARGIP$Anger, ARGIP$SEX)$estimate + cohen.d(ARGIP$Anxiety, ARGIP$SEX)$estimate)); dOPArgN
dOPAusN = mean(c(cohen.d(AUSIP$Vulnerability, AUSIP$SEX)$estimate + cohen.d(AUSIP$Immoderation, AUSIP$SEX)$estimate + cohen.d(AUSIP$SelfConsciousness, AUSIP$SEX)$estimate + cohen.d(AUSIP$Depression, AUSIP$SEX)$estimate + cohen.d(AUSIP$Anger, AUSIP$SEX)$estimate + cohen.d(AUSIP$Anxiety, AUSIP$SEX)$estimate)); dOPAusN
dOPHabN = mean(c(cohen.d(HABIP$Vulnerability, HABIP$SEX)$estimate + cohen.d(HABIP$Immoderation, HABIP$SEX)$estimate + cohen.d(HABIP$SelfConsciousness, HABIP$SEX)$estimate + cohen.d(HABIP$Depression, HABIP$SEX)$estimate + cohen.d(HABIP$Anger, HABIP$SEX)$estimate + cohen.d(HABIP$Anxiety, HABIP$SEX)$estimate)); dOPHabN
dOPBelN = mean(c(cohen.d(BELIP$Vulnerability, BELIP$SEX)$estimate + cohen.d(BELIP$Immoderation, BELIP$SEX)$estimate + cohen.d(BELIP$SelfConsciousness, BELIP$SEX)$estimate + cohen.d(BELIP$Depression, BELIP$SEX)$estimate + cohen.d(BELIP$Anger, BELIP$SEX)$estimate + cohen.d(BELIP$Anxiety, BELIP$SEX)$estimate)); dOPBelN
dOPBraN = mean(c(cohen.d(BRAIP$Vulnerability, BRAIP$SEX)$estimate + cohen.d(BRAIP$Immoderation, BRAIP$SEX)$estimate + cohen.d(BRAIP$SelfConsciousness, BRAIP$SEX)$estimate + cohen.d(BRAIP$Depression, BRAIP$SEX)$estimate + cohen.d(BRAIP$Anger, BRAIP$SEX)$estimate + cohen.d(BRAIP$Anxiety, BRAIP$SEX)$estimate)); dOPBraN
dOPCanN = mean(c(cohen.d(CANIP$Vulnerability, CANIP$SEX)$estimate + cohen.d(CANIP$Immoderation, CANIP$SEX)$estimate + cohen.d(CANIP$SelfConsciousness, CANIP$SEX)$estimate + cohen.d(CANIP$Depression, CANIP$SEX)$estimate + cohen.d(CANIP$Anger, CANIP$SEX)$estimate + cohen.d(CANIP$Anxiety, CANIP$SEX)$estimate)); dOPCanN
dOPChiN = mean(c(cohen.d(CHIIP$Vulnerability, CHIIP$SEX)$estimate + cohen.d(CHIIP$Immoderation, CHIIP$SEX)$estimate + cohen.d(CHIIP$SelfConsciousness, CHIIP$SEX)$estimate + cohen.d(CHIIP$Depression, CHIIP$SEX)$estimate + cohen.d(CHIIP$Anger, CHIIP$SEX)$estimate + cohen.d(CHIIP$Anxiety, CHIIP$SEX)$estimate)); dOPChiN
dOPColN = mean(c(cohen.d(COLIP$Vulnerability, COLIP$SEX)$estimate + cohen.d(COLIP$Immoderation, COLIP$SEX)$estimate + cohen.d(COLIP$SelfConsciousness, COLIP$SEX)$estimate + cohen.d(COLIP$Depression, COLIP$SEX)$estimate + cohen.d(COLIP$Anger, COLIP$SEX)$estimate + cohen.d(COLIP$Anxiety, COLIP$SEX)$estimate)); dOPColN
dOPCroN = mean(c(cohen.d(CROIP$Vulnerability, CROIP$SEX)$estimate + cohen.d(CROIP$Immoderation, CROIP$SEX)$estimate + cohen.d(CROIP$SelfConsciousness, CROIP$SEX)$estimate + cohen.d(CROIP$Depression, CROIP$SEX)$estimate + cohen.d(CROIP$Anger, CROIP$SEX)$estimate + cohen.d(CROIP$Anxiety, CROIP$SEX)$estimate)); dOPCroN
dOPDenN = mean(c(cohen.d(DENIP$Vulnerability, DENIP$SEX)$estimate + cohen.d(DENIP$Immoderation, DENIP$SEX)$estimate + cohen.d(DENIP$SelfConsciousness, DENIP$SEX)$estimate + cohen.d(DENIP$Depression, DENIP$SEX)$estimate + cohen.d(DENIP$Anger, DENIP$SEX)$estimate + cohen.d(DENIP$Anxiety, DENIP$SEX)$estimate)); dOPDenN
dOPEgyN = mean(c(cohen.d(EGYIP$Vulnerability, EGYIP$SEX)$estimate + cohen.d(EGYIP$Immoderation, EGYIP$SEX)$estimate + cohen.d(EGYIP$SelfConsciousness, EGYIP$SEX)$estimate + cohen.d(EGYIP$Depression, EGYIP$SEX)$estimate + cohen.d(EGYIP$Anger, EGYIP$SEX)$estimate + cohen.d(EGYIP$Anxiety, EGYIP$SEX)$estimate)); dOPEgyN
dOPFinN = mean(c(cohen.d(FINIP$Vulnerability, FINIP$SEX)$estimate + cohen.d(FINIP$Immoderation, FINIP$SEX)$estimate + cohen.d(FINIP$SelfConsciousness, FINIP$SEX)$estimate + cohen.d(FINIP$Depression, FINIP$SEX)$estimate + cohen.d(FINIP$Anger, FINIP$SEX)$estimate + cohen.d(FINIP$Anxiety, FINIP$SEX)$estimate)); dOPFinN
dOPFraN = mean(c(cohen.d(FRAIP$Vulnerability, FRAIP$SEX)$estimate + cohen.d(FRAIP$Immoderation, FRAIP$SEX)$estimate + cohen.d(FRAIP$SelfConsciousness, FRAIP$SEX)$estimate + cohen.d(FRAIP$Depression, FRAIP$SEX)$estimate + cohen.d(FRAIP$Anger, FRAIP$SEX)$estimate + cohen.d(FRAIP$Anxiety, FRAIP$SEX)$estimate)); dOPFraN
dOPGerN = mean(c(cohen.d(GERIP$Vulnerability, GERIP$SEX)$estimate + cohen.d(GERIP$Immoderation, GERIP$SEX)$estimate + cohen.d(GERIP$SelfConsciousness, GERIP$SEX)$estimate + cohen.d(GERIP$Depression, GERIP$SEX)$estimate + cohen.d(GERIP$Anger, GERIP$SEX)$estimate + cohen.d(GERIP$Anxiety, GERIP$SEX)$estimate)); dOPGerN
dOPGreN = mean(c(cohen.d(GREIP$Vulnerability, GREIP$SEX)$estimate + cohen.d(GREIP$Immoderation, GREIP$SEX)$estimate + cohen.d(GREIP$SelfConsciousness, GREIP$SEX)$estimate + cohen.d(GREIP$Depression, GREIP$SEX)$estimate + cohen.d(GREIP$Anger, GREIP$SEX)$estimate + cohen.d(GREIP$Anxiety, GREIP$SEX)$estimate)); dOPGreN
dOPIndN = mean(c(cohen.d(INDIP$Vulnerability, INDIP$SEX)$estimate + cohen.d(INDIP$Immoderation, INDIP$SEX)$estimate + cohen.d(INDIP$SelfConsciousness, INDIP$SEX)$estimate + cohen.d(INDIP$Depression, INDIP$SEX)$estimate + cohen.d(INDIP$Anger, INDIP$SEX)$estimate + cohen.d(INDIP$Anxiety, INDIP$SEX)$estimate)); dOPIndN
dOPInoN = mean(c(cohen.d(INOIP$Vulnerability, INOIP$SEX)$estimate + cohen.d(INOIP$Immoderation, INOIP$SEX)$estimate + cohen.d(INOIP$SelfConsciousness, INOIP$SEX)$estimate + cohen.d(INOIP$Depression, INOIP$SEX)$estimate + cohen.d(INOIP$Anger, INOIP$SEX)$estimate + cohen.d(INOIP$Anxiety, INOIP$SEX)$estimate)); dOPInoN
dOPIraN = mean(c(cohen.d(IRAIP$Vulnerability, IRAIP$SEX)$estimate + cohen.d(IRAIP$Immoderation, IRAIP$SEX)$estimate + cohen.d(IRAIP$SelfConsciousness, IRAIP$SEX)$estimate + cohen.d(IRAIP$Depression, IRAIP$SEX)$estimate + cohen.d(IRAIP$Anger, IRAIP$SEX)$estimate + cohen.d(IRAIP$Anxiety, IRAIP$SEX)$estimate)); dOPIraN
dOPIreN = mean(c(cohen.d(IREIP$Vulnerability, IREIP$SEX)$estimate + cohen.d(IREIP$Immoderation, IREIP$SEX)$estimate + cohen.d(IREIP$SelfConsciousness, IREIP$SEX)$estimate + cohen.d(IREIP$Depression, IREIP$SEX)$estimate + cohen.d(IREIP$Anger, IREIP$SEX)$estimate + cohen.d(IREIP$Anxiety, IREIP$SEX)$estimate)); dOPIreN
dOPIsrN = mean(c(cohen.d(ISRIP$Vulnerability, ISRIP$SEX)$estimate + cohen.d(ISRIP$Immoderation, ISRIP$SEX)$estimate + cohen.d(ISRIP$SelfConsciousness, ISRIP$SEX)$estimate + cohen.d(ISRIP$Depression, ISRIP$SEX)$estimate + cohen.d(ISRIP$Anger, ISRIP$SEX)$estimate + cohen.d(ISRIP$Anxiety, ISRIP$SEX)$estimate)); dOPIsrN
dOPItaN = mean(c(cohen.d(ITAIP$Vulnerability, ITAIP$SEX)$estimate + cohen.d(ITAIP$Immoderation, ITAIP$SEX)$estimate + cohen.d(ITAIP$SelfConsciousness, ITAIP$SEX)$estimate + cohen.d(ITAIP$Depression, ITAIP$SEX)$estimate + cohen.d(ITAIP$Anger, ITAIP$SEX)$estimate + cohen.d(ITAIP$Anxiety, ITAIP$SEX)$estimate)); dOPItaN
dOPJamN = mean(c(cohen.d(JAMIP$Vulnerability, JAMIP$SEX)$estimate + cohen.d(JAMIP$Immoderation, JAMIP$SEX)$estimate + cohen.d(JAMIP$SelfConsciousness, JAMIP$SEX)$estimate + cohen.d(JAMIP$Depression, JAMIP$SEX)$estimate + cohen.d(JAMIP$Anger, JAMIP$SEX)$estimate + cohen.d(JAMIP$Anxiety, JAMIP$SEX)$estimate)); dOPJamN
dOPJapN = mean(c(cohen.d(JAPIP$Vulnerability, JAPIP$SEX)$estimate + cohen.d(JAPIP$Immoderation, JAPIP$SEX)$estimate + cohen.d(JAPIP$SelfConsciousness, JAPIP$SEX)$estimate + cohen.d(JAPIP$Depression, JAPIP$SEX)$estimate + cohen.d(JAPIP$Anger, JAPIP$SEX)$estimate + cohen.d(JAPIP$Anxiety, JAPIP$SEX)$estimate)); dOPJapN
dOPKenN = mean(c(cohen.d(KENIP$Vulnerability, KENIP$SEX)$estimate + cohen.d(KENIP$Immoderation, KENIP$SEX)$estimate + cohen.d(KENIP$SelfConsciousness, KENIP$SEX)$estimate + cohen.d(KENIP$Depression, KENIP$SEX)$estimate + cohen.d(KENIP$Anger, KENIP$SEX)$estimate + cohen.d(KENIP$Anxiety, KENIP$SEX)$estimate)); dOPKenN
dOPLebN = mean(c(cohen.d(LEBIP$Vulnerability, LEBIP$SEX)$estimate + cohen.d(LEBIP$Immoderation, LEBIP$SEX)$estimate + cohen.d(LEBIP$SelfConsciousness, LEBIP$SEX)$estimate + cohen.d(LEBIP$Depression, LEBIP$SEX)$estimate + cohen.d(LEBIP$Anger, LEBIP$SEX)$estimate + cohen.d(LEBIP$Anxiety, LEBIP$SEX)$estimate)); dOPLebN
dOPMalN = mean(c(cohen.d(MALIP$Vulnerability, MALIP$SEX)$estimate + cohen.d(MALIP$Immoderation, MALIP$SEX)$estimate + cohen.d(MALIP$SelfConsciousness, MALIP$SEX)$estimate + cohen.d(MALIP$Depression, MALIP$SEX)$estimate + cohen.d(MALIP$Anger, MALIP$SEX)$estimate + cohen.d(MALIP$Anxiety, MALIP$SEX)$estimate)); dOPMalN
dOPMexN = mean(c(cohen.d(MEXIP$Vulnerability, MEXIP$SEX)$estimate + cohen.d(MEXIP$Immoderation, MEXIP$SEX)$estimate + cohen.d(MEXIP$SelfConsciousness, MEXIP$SEX)$estimate + cohen.d(MEXIP$Depression, MEXIP$SEX)$estimate + cohen.d(MEXIP$Anger, MEXIP$SEX)$estimate + cohen.d(MEXIP$Anxiety, MEXIP$SEX)$estimate)); dOPMexN
dOPNetN = mean(c(cohen.d(NETIP$Vulnerability, NETIP$SEX)$estimate + cohen.d(NETIP$Immoderation, NETIP$SEX)$estimate + cohen.d(NETIP$SelfConsciousness, NETIP$SEX)$estimate + cohen.d(NETIP$Depression, NETIP$SEX)$estimate + cohen.d(NETIP$Anger, NETIP$SEX)$estimate + cohen.d(NETIP$Anxiety, NETIP$SEX)$estimate)); dOPNetN
dOPNewN = mean(c(cohen.d(NEWIP$Vulnerability, NEWIP$SEX)$estimate + cohen.d(NEWIP$Immoderation, NEWIP$SEX)$estimate + cohen.d(NEWIP$SelfConsciousness, NEWIP$SEX)$estimate + cohen.d(NEWIP$Depression, NEWIP$SEX)$estimate + cohen.d(NEWIP$Anger, NEWIP$SEX)$estimate + cohen.d(NEWIP$Anxiety, NEWIP$SEX)$estimate)); dOPNewN
dOPNirN = mean(c(cohen.d(NIRIP$Vulnerability, NIRIP$SEX)$estimate + cohen.d(NIRIP$Immoderation, NIRIP$SEX)$estimate + cohen.d(NIRIP$SelfConsciousness, NIRIP$SEX)$estimate + cohen.d(NIRIP$Depression, NIRIP$SEX)$estimate + cohen.d(NIRIP$Anger, NIRIP$SEX)$estimate + cohen.d(NIRIP$Anxiety, NIRIP$SEX)$estimate)); dOPNirN
dOPNorN = mean(c(cohen.d(NORIP$Vulnerability, NORIP$SEX)$estimate + cohen.d(NORIP$Immoderation, NORIP$SEX)$estimate + cohen.d(NORIP$SelfConsciousness, NORIP$SEX)$estimate + cohen.d(NORIP$Depression, NORIP$SEX)$estimate + cohen.d(NORIP$Anger, NORIP$SEX)$estimate + cohen.d(NORIP$Anxiety, NORIP$SEX)$estimate)); dOPNorN
dOPPerN = mean(c(cohen.d(PERIP$Vulnerability, PERIP$SEX)$estimate + cohen.d(PERIP$Immoderation, PERIP$SEX)$estimate + cohen.d(PERIP$SelfConsciousness, PERIP$SEX)$estimate + cohen.d(PERIP$Depression, PERIP$SEX)$estimate + cohen.d(PERIP$Anger, PERIP$SEX)$estimate + cohen.d(PERIP$Anxiety, PERIP$SEX)$estimate)); dOPPerN
dOPPakN = mean(c(cohen.d(PAKIP$Vulnerability, PAKIP$SEX)$estimate + cohen.d(PAKIP$Immoderation, PAKIP$SEX)$estimate + cohen.d(PAKIP$SelfConsciousness, PAKIP$SEX)$estimate + cohen.d(PAKIP$Depression, PAKIP$SEX)$estimate + cohen.d(PAKIP$Anger, PAKIP$SEX)$estimate + cohen.d(PAKIP$Anxiety, PAKIP$SEX)$estimate)); dOPPakN
dOPPhiN = mean(c(cohen.d(PHIIP$Vulnerability, PHIIP$SEX)$estimate + cohen.d(PHIIP$Immoderation, PHIIP$SEX)$estimate + cohen.d(PHIIP$SelfConsciousness, PHIIP$SEX)$estimate + cohen.d(PHIIP$Depression, PHIIP$SEX)$estimate + cohen.d(PHIIP$Anger, PHIIP$SEX)$estimate + cohen.d(PHIIP$Anxiety, PHIIP$SEX)$estimate)); dOPPhiN
dOPPolN = mean(c(cohen.d(POLIP$Vulnerability, POLIP$SEX)$estimate + cohen.d(POLIP$Immoderation, POLIP$SEX)$estimate + cohen.d(POLIP$SelfConsciousness, POLIP$SEX)$estimate + cohen.d(POLIP$Depression, POLIP$SEX)$estimate + cohen.d(POLIP$Anger, POLIP$SEX)$estimate + cohen.d(POLIP$Anxiety, POLIP$SEX)$estimate)); dOPPolN
dOPPorN = mean(c(cohen.d(PORIP$Vulnerability, PORIP$SEX)$estimate + cohen.d(PORIP$Immoderation, PORIP$SEX)$estimate + cohen.d(PORIP$SelfConsciousness, PORIP$SEX)$estimate + cohen.d(PORIP$Depression, PORIP$SEX)$estimate + cohen.d(PORIP$Anger, PORIP$SEX)$estimate + cohen.d(PORIP$Anxiety, PORIP$SEX)$estimate)); dOPPorN
dOPRomN = mean(c(cohen.d(ROMIP$Vulnerability, ROMIP$SEX)$estimate + cohen.d(ROMIP$Immoderation, ROMIP$SEX)$estimate + cohen.d(ROMIP$SelfConsciousness, ROMIP$SEX)$estimate + cohen.d(ROMIP$Depression, ROMIP$SEX)$estimate + cohen.d(ROMIP$Anger, ROMIP$SEX)$estimate + cohen.d(ROMIP$Anxiety, ROMIP$SEX)$estimate)); dOPRomN
dOPRusN = mean(c(cohen.d(RUSIP$Vulnerability, RUSIP$SEX)$estimate + cohen.d(RUSIP$Immoderation, RUSIP$SEX)$estimate + cohen.d(RUSIP$SelfConsciousness, RUSIP$SEX)$estimate + cohen.d(RUSIP$Depression, RUSIP$SEX)$estimate + cohen.d(RUSIP$Anger, RUSIP$SEX)$estimate + cohen.d(RUSIP$Anxiety, RUSIP$SEX)$estimate)); dOPRusN
dOPSinN = mean(c(cohen.d(SINIP$Vulnerability, SINIP$SEX)$estimate + cohen.d(SINIP$Immoderation, SINIP$SEX)$estimate + cohen.d(SINIP$SelfConsciousness, SINIP$SEX)$estimate + cohen.d(SINIP$Depression, SINIP$SEX)$estimate + cohen.d(SINIP$Anger, SINIP$SEX)$estimate + cohen.d(SINIP$Anxiety, SINIP$SEX)$estimate)); dOPSinN
dOPSloN = mean(c(cohen.d(SLOIP$Vulnerability, SLOIP$SEX)$estimate + cohen.d(SLOIP$Immoderation, SLOIP$SEX)$estimate + cohen.d(SLOIP$SelfConsciousness, SLOIP$SEX)$estimate + cohen.d(SLOIP$Depression, SLOIP$SEX)$estimate + cohen.d(SLOIP$Anger, SLOIP$SEX)$estimate + cohen.d(SLOIP$Anxiety, SLOIP$SEX)$estimate)); dOPSloN
dOPSlvN = mean(c(cohen.d(SLVIP$Vulnerability, SLVIP$SEX)$estimate + cohen.d(SLVIP$Immoderation, SLVIP$SEX)$estimate + cohen.d(SLVIP$SelfConsciousness, SLVIP$SEX)$estimate + cohen.d(SLVIP$Depression, SLVIP$SEX)$estimate + cohen.d(SLVIP$Anger, SLVIP$SEX)$estimate + cohen.d(SLVIP$Anxiety, SLVIP$SEX)$estimate)); dOPSlvN
dOPSouN = mean(c(cohen.d(SOUIP$Vulnerability, SOUIP$SEX)$estimate + cohen.d(SOUIP$Immoderation, SOUIP$SEX)$estimate + cohen.d(SOUIP$SelfConsciousness, SOUIP$SEX)$estimate + cohen.d(SOUIP$Depression, SOUIP$SEX)$estimate + cohen.d(SOUIP$Anger, SOUIP$SEX)$estimate + cohen.d(SOUIP$Anxiety, SOUIP$SEX)$estimate)); dOPSouN
dOPSKoN = mean(c(cohen.d(SKOIP$Vulnerability, SKOIP$SEX)$estimate + cohen.d(SKOIP$Immoderation, SKOIP$SEX)$estimate + cohen.d(SKOIP$SelfConsciousness, SKOIP$SEX)$estimate + cohen.d(SKOIP$Depression, SKOIP$SEX)$estimate + cohen.d(SKOIP$Anger, SKOIP$SEX)$estimate + cohen.d(SKOIP$Anxiety, SKOIP$SEX)$estimate)); dOPSKoN
dOPSpaN = mean(c(cohen.d(SPAIP$Vulnerability, SPAIP$SEX)$estimate + cohen.d(SPAIP$Immoderation, SPAIP$SEX)$estimate + cohen.d(SPAIP$SelfConsciousness, SPAIP$SEX)$estimate + cohen.d(SPAIP$Depression, SPAIP$SEX)$estimate + cohen.d(SPAIP$Anger, SPAIP$SEX)$estimate + cohen.d(SPAIP$Anxiety, SPAIP$SEX)$estimate)); dOPSpaN
dOPSweN = mean(c(cohen.d(SWEIP$Vulnerability, SWEIP$SEX)$estimate + cohen.d(SWEIP$Immoderation, SWEIP$SEX)$estimate + cohen.d(SWEIP$SelfConsciousness, SWEIP$SEX)$estimate + cohen.d(SWEIP$Depression, SWEIP$SEX)$estimate + cohen.d(SWEIP$Anger, SWEIP$SEX)$estimate + cohen.d(SWEIP$Anxiety, SWEIP$SEX)$estimate)); dOPSweN
dOPSwiN = mean(c(cohen.d(SWIIP$Vulnerability, SWIIP$SEX)$estimate + cohen.d(SWIIP$Immoderation, SWIIP$SEX)$estimate + cohen.d(SWIIP$SelfConsciousness, SWIIP$SEX)$estimate + cohen.d(SWIIP$Depression, SWIIP$SEX)$estimate + cohen.d(SWIIP$Anger, SWIIP$SEX)$estimate + cohen.d(SWIIP$Anxiety, SWIIP$SEX)$estimate)); dOPSwiN
dOPThaN = mean(c(cohen.d(THAIP$Vulnerability, THAIP$SEX)$estimate + cohen.d(THAIP$Immoderation, THAIP$SEX)$estimate + cohen.d(THAIP$SelfConsciousness, THAIP$SEX)$estimate + cohen.d(THAIP$Depression, THAIP$SEX)$estimate + cohen.d(THAIP$Anger, THAIP$SEX)$estimate + cohen.d(THAIP$Anxiety, THAIP$SEX)$estimate)); dOPThaN
dOPTriN = mean(c(cohen.d(TRIIP$Vulnerability, TRIIP$SEX)$estimate + cohen.d(TRIIP$Immoderation, TRIIP$SEX)$estimate + cohen.d(TRIIP$SelfConsciousness, TRIIP$SEX)$estimate + cohen.d(TRIIP$Depression, TRIIP$SEX)$estimate + cohen.d(TRIIP$Anger, TRIIP$SEX)$estimate + cohen.d(TRIIP$Anxiety, TRIIP$SEX)$estimate)); dOPTriN
dOPTurN = mean(c(cohen.d(TURIP$Vulnerability, TURIP$SEX)$estimate + cohen.d(TURIP$Immoderation, TURIP$SEX)$estimate + cohen.d(TURIP$SelfConsciousness, TURIP$SEX)$estimate + cohen.d(TURIP$Depression, TURIP$SEX)$estimate + cohen.d(TURIP$Anger, TURIP$SEX)$estimate + cohen.d(TURIP$Anxiety, TURIP$SEX)$estimate)); dOPTurN
dOPUgaN = mean(c(cohen.d(UGAIP$Vulnerability, UGAIP$SEX)$estimate + cohen.d(UGAIP$Immoderation, UGAIP$SEX)$estimate + cohen.d(UGAIP$SelfConsciousness, UGAIP$SEX)$estimate + cohen.d(UGAIP$Depression, UGAIP$SEX)$estimate + cohen.d(UGAIP$Anger, UGAIP$SEX)$estimate + cohen.d(UGAIP$Anxiety, UGAIP$SEX)$estimate)); dOPUgaN
dOPUKN = mean(c(cohen.d(UKIP$Vulnerability, UKIP$SEX)$estimate + cohen.d(UKIP$Immoderation, UKIP$SEX)$estimate + cohen.d(UKIP$SelfConsciousness, UKIP$SEX)$estimate + cohen.d(UKIP$Depression, UKIP$SEX)$estimate + cohen.d(UKIP$Anger, UKIP$SEX)$estimate + cohen.d(UKIP$Anxiety, UKIP$SEX)$estimate)); dOPUKN
dOPUkrN = mean(c(cohen.d(UKRIP$Vulnerability, UKRIP$SEX)$estimate + cohen.d(UKRIP$Immoderation, UKRIP$SEX)$estimate + cohen.d(UKRIP$SelfConsciousness, UKRIP$SEX)$estimate + cohen.d(UKRIP$Depression, UKRIP$SEX)$estimate + cohen.d(UKRIP$Anger, UKRIP$SEX)$estimate + cohen.d(UKRIP$Anxiety, UKRIP$SEX)$estimate)); dOPUkrN
dOPUAEN = mean(c(cohen.d(UAEIP$Vulnerability, UAEIP$SEX)$estimate + cohen.d(UAEIP$Immoderation, UAEIP$SEX)$estimate + cohen.d(UAEIP$SelfConsciousness, UAEIP$SEX)$estimate + cohen.d(UAEIP$Depression, UAEIP$SEX)$estimate + cohen.d(UAEIP$Anger, UAEIP$SEX)$estimate + cohen.d(UAEIP$Anxiety, UAEIP$SEX)$estimate)); dOPUAEN
dOPUSAN = mean(c(cohen.d(USAIP$Vulnerability, USAIP$SEX)$estimate + cohen.d(USAIP$Immoderation, USAIP$SEX)$estimate + cohen.d(USAIP$SelfConsciousness, USAIP$SEX)$estimate + cohen.d(USAIP$Depression, USAIP$SEX)$estimate + cohen.d(USAIP$Anger, USAIP$SEX)$estimate + cohen.d(USAIP$Anxiety, USAIP$SEX)$estimate)); dOPUSAN
dOPVenN = mean(c(cohen.d(VENIP$Vulnerability, VENIP$SEX)$estimate + cohen.d(VENIP$Immoderation, VENIP$SEX)$estimate + cohen.d(VENIP$SelfConsciousness, VENIP$SEX)$estimate + cohen.d(VENIP$Depression, VENIP$SEX)$estimate + cohen.d(VENIP$Anger, VENIP$SEX)$estimate + cohen.d(VENIP$Anxiety, VENIP$SEX)$estimate)); dOPVenN
dOPVieN = mean(c(cohen.d(VIEIP$Vulnerability, VIEIP$SEX)$estimate + cohen.d(VIEIP$Immoderation, VIEIP$SEX)$estimate + cohen.d(VIEIP$SelfConsciousness, VIEIP$SEX)$estimate + cohen.d(VIEIP$Depression, VIEIP$SEX)$estimate + cohen.d(VIEIP$Anger, VIEIP$SEX)$estimate + cohen.d(VIEIP$Anxiety, VIEIP$SEX)$estimate)); dOPVieN
## GFP
dOPAlbG = mean(c(cohen.d(ALBIP$Openness, ALBIP$SEX)$estimate + cohen.d(ALBIP$Conscientiousness, ALBIP$SEX)$estimate + cohen.d(ALBIP$Extraversion, ALBIP$SEX)$estimate + cohen.d(ALBIP$Agreeableness, ALBIP$SEX)$estimate - cohen.d(ALBIP$Neuroticism, ALBIP$SEX)$estimate)); dOPAlbG
dOPAlgG = mean(c(cohen.d(ALGIP$Openness, ALGIP$SEX)$estimate + cohen.d(ALGIP$Conscientiousness, ALGIP$SEX)$estimate + cohen.d(ALGIP$Extraversion, ALGIP$SEX)$estimate + cohen.d(ALGIP$Agreeableness, ALGIP$SEX)$estimate - cohen.d(ALGIP$Neuroticism, ALGIP$SEX)$estimate)); dOPAlgG
dOPAngG = mean(c(cohen.d(ANGIP$Openness, ANGIP$SEX)$estimate + cohen.d(ANGIP$Conscientiousness, ANGIP$SEX)$estimate + cohen.d(ANGIP$Extraversion, ANGIP$SEX)$estimate + cohen.d(ANGIP$Agreeableness, ANGIP$SEX)$estimate - cohen.d(ANGIP$Neuroticism, ANGIP$SEX)$estimate)); dOPAngG
dOPArgG = mean(c(cohen.d(ARGIP$Openness, ARGIP$SEX)$estimate + cohen.d(ARGIP$Conscientiousness, ARGIP$SEX)$estimate + cohen.d(ARGIP$Extraversion, ARGIP$SEX)$estimate + cohen.d(ARGIP$Agreeableness, ARGIP$SEX)$estimate - cohen.d(ARGIP$Neuroticism, ARGIP$SEX)$estimate)); dOPArgG
dOPAusG = mean(c(cohen.d(AUSIP$Openness, AUSIP$SEX)$estimate + cohen.d(AUSIP$Conscientiousness, AUSIP$SEX)$estimate + cohen.d(AUSIP$Extraversion, AUSIP$SEX)$estimate + cohen.d(AUSIP$Agreeableness, AUSIP$SEX)$estimate - cohen.d(AUSIP$Neuroticism, AUSIP$SEX)$estimate)); dOPAusG
#dOPHabG = mean(c(cohen.d(HABIP$Openness, HABIP$SEX)$estimate + cohen.d(HABIP$Conscientiousness, HABIP$SEX)$estimate + cohen.d(HABIP$Extraversion, HABIP$SEX)$estimate + cohen.d(HABIP$Agreeableness, HABIP$SEX)$estimate - cohen.d(HABIP$Neuroticism, HABIP$SEX)$estimate)); dOPHabG
#dOPBelG = mean(c(cohen.d(BELIP$Openness, BELIP$SEX)$estimate + cohen.d(BELIP$Conscientiousness, BELIP$SEX)$estimate + cohen.d(BELIP$Extraversion, BELIP$SEX)$estimate + cohen.d(BELIP$Agreeableness, BELIP$SEX)$estimate - cohen.d(BELIP$Neuroticism, BELIP$SEX)$estimate)); dOPBelG
dOPBraG = mean(c(cohen.d(BRAIP$Openness, BRAIP$SEX)$estimate + cohen.d(BRAIP$Conscientiousness, BRAIP$SEX)$estimate + cohen.d(BRAIP$Extraversion, BRAIP$SEX)$estimate + cohen.d(BRAIP$Agreeableness, BRAIP$SEX)$estimate - cohen.d(BRAIP$Neuroticism, BRAIP$SEX)$estimate)); dOPBraG
dOPCanG = mean(c(cohen.d(CANIP$Openness, CANIP$SEX)$estimate + cohen.d(CANIP$Conscientiousness, CANIP$SEX)$estimate + cohen.d(CANIP$Extraversion, CANIP$SEX)$estimate + cohen.d(CANIP$Agreeableness, CANIP$SEX)$estimate - cohen.d(CANIP$Neuroticism, CANIP$SEX)$estimate)); dOPCanG
dOPChiG = mean(c(cohen.d(CHIIP$Openness, CHIIP$SEX)$estimate + cohen.d(CHIIP$Conscientiousness, CHIIP$SEX)$estimate + cohen.d(CHIIP$Extraversion, CHIIP$SEX)$estimate + cohen.d(CHIIP$Agreeableness, CHIIP$SEX)$estimate - cohen.d(CHIIP$Neuroticism, CHIIP$SEX)$estimate)); dOPChiG
#dOPColG = mean(c(cohen.d(COLIP$Openness, COLIP$SEX)$estimate + cohen.d(COLIP$Conscientiousness, COLIP$SEX)$estimate + cohen.d(COLIP$Extraversion, COLIP$SEX)$estimate + cohen.d(COLIP$Agreeableness, COLIP$SEX)$estimate - cohen.d(COLIP$Neuroticism, COLIP$SEX)$estimate)); dOPColG
dOPCroG = mean(c(cohen.d(CROIP$Openness, CROIP$SEX)$estimate + cohen.d(CROIP$Conscientiousness, CROIP$SEX)$estimate + cohen.d(CROIP$Extraversion, CROIP$SEX)$estimate + cohen.d(CROIP$Agreeableness, CROIP$SEX)$estimate - cohen.d(CROIP$Neuroticism, CROIP$SEX)$estimate)); dOPCroG
dOPDenG = mean(c(cohen.d(DENIP$Openness, DENIP$SEX)$estimate + cohen.d(DENIP$Conscientiousness, DENIP$SEX)$estimate + cohen.d(DENIP$Extraversion, DENIP$SEX)$estimate + cohen.d(DENIP$Agreeableness, DENIP$SEX)$estimate)); dOPDenG #Biased up due to exclusion
dOPEgyG = mean(c(cohen.d(EGYIP$Openness, EGYIP$SEX)$estimate + cohen.d(EGYIP$Conscientiousness, EGYIP$SEX)$estimate + cohen.d(EGYIP$Extraversion, EGYIP$SEX)$estimate + cohen.d(EGYIP$Agreeableness, EGYIP$SEX)$estimate - cohen.d(EGYIP$Neuroticism, EGYIP$SEX)$estimate)); dOPEgyG
dOPFinG = mean(c(cohen.d(FINIP$Openness, FINIP$SEX)$estimate + cohen.d(FINIP$Conscientiousness, FINIP$SEX)$estimate + cohen.d(FINIP$Extraversion, FINIP$SEX)$estimate + cohen.d(FINIP$Agreeableness, FINIP$SEX)$estimate - cohen.d(FINIP$Neuroticism, FINIP$SEX)$estimate)); dOPFinG
dOPFraG = mean(c(cohen.d(FRAIP$Openness, FRAIP$SEX)$estimate + cohen.d(FRAIP$Conscientiousness, FRAIP$SEX)$estimate + cohen.d(FRAIP$Extraversion, FRAIP$SEX)$estimate + cohen.d(FRAIP$Agreeableness, FRAIP$SEX)$estimate - cohen.d(FRAIP$Neuroticism, FRAIP$SEX)$estimate)); dOPFraG
dOPGerG = mean(c(cohen.d(GERIP$Openness, GERIP$SEX)$estimate + cohen.d(GERIP$Conscientiousness, GERIP$SEX)$estimate + cohen.d(GERIP$Extraversion, GERIP$SEX)$estimate + cohen.d(GERIP$Agreeableness, GERIP$SEX)$estimate - cohen.d(GERIP$Neuroticism, GERIP$SEX)$estimate)); dOPGerG
dOPGreG = mean(c(cohen.d(GREIP$Openness, GREIP$SEX)$estimate + cohen.d(GREIP$Conscientiousness, GREIP$SEX)$estimate + cohen.d(GREIP$Extraversion, GREIP$SEX)$estimate + cohen.d(GREIP$Agreeableness, GREIP$SEX)$estimate - cohen.d(GREIP$Neuroticism, GREIP$SEX)$estimate)); dOPGreG
dOPIndG = mean(c(cohen.d(INDIP$Openness, INDIP$SEX)$estimate + cohen.d(INDIP$Conscientiousness, INDIP$SEX)$estimate + cohen.d(INDIP$Extraversion, INDIP$SEX)$estimate + cohen.d(INDIP$Agreeableness, INDIP$SEX)$estimate - cohen.d(INDIP$Neuroticism, INDIP$SEX)$estimate)); dOPIndG
dOPInoG = mean(c(cohen.d(INOIP$Openness, INOIP$SEX)$estimate + cohen.d(INOIP$Conscientiousness, INOIP$SEX)$estimate + cohen.d(INOIP$Extraversion, INOIP$SEX)$estimate + cohen.d(INOIP$Agreeableness, INOIP$SEX)$estimate - cohen.d(INOIP$Neuroticism, INOIP$SEX)$estimate)); dOPInoG
dOPIraG = mean(c(cohen.d(IRAIP$Openness, IRAIP$SEX)$estimate + cohen.d(IRAIP$Conscientiousness, IRAIP$SEX)$estimate + cohen.d(IRAIP$Extraversion, IRAIP$SEX)$estimate + cohen.d(IRAIP$Agreeableness, IRAIP$SEX)$estimate - cohen.d(IRAIP$Neuroticism, IRAIP$SEX)$estimate)); dOPIraG
dOPIreG = mean(c(cohen.d(IREIP$Openness, IREIP$SEX)$estimate + cohen.d(IREIP$Conscientiousness, IREIP$SEX)$estimate + cohen.d(IREIP$Extraversion, IREIP$SEX)$estimate + cohen.d(IREIP$Agreeableness, IREIP$SEX)$estimate - cohen.d(IREIP$Neuroticism, IREIP$SEX)$estimate)); dOPIreG
dOPIsrG = mean(c(cohen.d(ISRIP$Openness, ISRIP$SEX)$estimate + cohen.d(ISRIP$Conscientiousness, ISRIP$SEX)$estimate + cohen.d(ISRIP$Agreeableness, ISRIP$SEX)$estimate - cohen.d(ISRIP$Neuroticism, ISRIP$SEX)$estimate)); dOPIsrG #Biased down due to exclusion
dOPItaG = mean(c(cohen.d(ITAIP$Openness, ITAIP$SEX)$estimate + cohen.d(ITAIP$Conscientiousness, ITAIP$SEX)$estimate + cohen.d(ITAIP$Extraversion, ITAIP$SEX)$estimate + cohen.d(ITAIP$Agreeableness, ITAIP$SEX)$estimate - cohen.d(ITAIP$Neuroticism, ITAIP$SEX)$estimate)); dOPItaG
dOPJamG = mean(c(cohen.d(JAMIP$Openness, JAMIP$SEX)$estimate + cohen.d(JAMIP$Conscientiousness, JAMIP$SEX)$estimate + cohen.d(JAMIP$Extraversion, JAMIP$SEX)$estimate + cohen.d(JAMIP$Agreeableness, JAMIP$SEX)$estimate - cohen.d(JAMIP$Neuroticism, JAMIP$SEX)$estimate)); dOPJamG
dOPJapG = mean(c(cohen.d(JAPIP$Openness, JAPIP$SEX)$estimate + cohen.d(JAPIP$Conscientiousness, JAPIP$SEX)$estimate + cohen.d(JAPIP$Extraversion, JAPIP$SEX)$estimate + cohen.d(JAPIP$Agreeableness, JAPIP$SEX)$estimate - cohen.d(JAPIP$Neuroticism, JAPIP$SEX)$estimate)); dOPJapG
dOPKenG = mean(c(cohen.d(KENIP$Openness, KENIP$SEX)$estimate + cohen.d(KENIP$Conscientiousness, KENIP$SEX)$estimate + cohen.d(KENIP$Extraversion, KENIP$SEX)$estimate + cohen.d(KENIP$Agreeableness, KENIP$SEX)$estimate - cohen.d(KENIP$Neuroticism, KENIP$SEX)$estimate)); dOPKenG
dOPLebG = mean(c(cohen.d(LEBIP$Openness, LEBIP$SEX)$estimate + cohen.d(LEBIP$Conscientiousness, LEBIP$SEX)$estimate + cohen.d(LEBIP$Extraversion, LEBIP$SEX)$estimate + cohen.d(LEBIP$Agreeableness, LEBIP$SEX)$estimate - cohen.d(LEBIP$Neuroticism, LEBIP$SEX)$estimate)); dOPLebG
dOPMalG = mean(c(cohen.d(MALIP$Openness, MALIP$SEX)$estimate + cohen.d(MALIP$Conscientiousness, MALIP$SEX)$estimate + cohen.d(MALIP$Extraversion, MALIP$SEX)$estimate + cohen.d(MALIP$Agreeableness, MALIP$SEX)$estimate - cohen.d(MALIP$Neuroticism, MALIP$SEX)$estimate)); dOPMalG
dOPMexG = mean(c(cohen.d(MEXIP$Openness, MEXIP$SEX)$estimate + cohen.d(MEXIP$Conscientiousness, MEXIP$SEX)$estimate + cohen.d(MEXIP$Extraversion, MEXIP$SEX)$estimate + cohen.d(MEXIP$Agreeableness, MEXIP$SEX)$estimate - cohen.d(MEXIP$Neuroticism, MEXIP$SEX)$estimate)); dOPMexG
dOPNetG = mean(c(cohen.d(NETIP$Openness, NETIP$SEX)$estimate + cohen.d(NETIP$Conscientiousness, NETIP$SEX)$estimate + cohen.d(NETIP$Extraversion, NETIP$SEX)$estimate + cohen.d(NETIP$Agreeableness, NETIP$SEX)$estimate - cohen.d(NETIP$Neuroticism, NETIP$SEX)$estimate)); dOPNetG
dOPNewG = mean(c(cohen.d(NEWIP$Openness, NEWIP$SEX)$estimate + cohen.d(NEWIP$Conscientiousness, NEWIP$SEX)$estimate + cohen.d(NEWIP$Extraversion, NEWIP$SEX)$estimate + cohen.d(NEWIP$Agreeableness, NEWIP$SEX)$estimate - cohen.d(NEWIP$Neuroticism, NEWIP$SEX)$estimate)); dOPNewG
dOPNirG = mean(c(cohen.d(NIRIP$Openness, NIRIP$SEX)$estimate + cohen.d(NIRIP$Conscientiousness, NIRIP$SEX)$estimate + cohen.d(NIRIP$Extraversion, NIRIP$SEX)$estimate + cohen.d(NIRIP$Agreeableness, NIRIP$SEX)$estimate - cohen.d(NIRIP$Neuroticism, NIRIP$SEX)$estimate)); dOPNirG
dOPNorG = mean(c(cohen.d(NORIP$Openness, NORIP$SEX)$estimate + cohen.d(NORIP$Conscientiousness, NORIP$SEX)$estimate + cohen.d(NORIP$Extraversion, NORIP$SEX)$estimate + cohen.d(NORIP$Agreeableness, NORIP$SEX)$estimate - cohen.d(NORIP$Neuroticism, NORIP$SEX)$estimate)); dOPNorG
dOPPerG = mean(c(cohen.d(PERIP$Openness, PERIP$SEX)$estimate + cohen.d(PERIP$Conscientiousness, PERIP$SEX)$estimate + cohen.d(PERIP$Extraversion, PERIP$SEX)$estimate + cohen.d(PERIP$Agreeableness, PERIP$SEX)$estimate - cohen.d(PERIP$Neuroticism, PERIP$SEX)$estimate)); dOPPerG
dOPPakG = mean(c(cohen.d(PAKIP$Openness, PAKIP$SEX)$estimate + cohen.d(PAKIP$Conscientiousness, PAKIP$SEX)$estimate + cohen.d(PAKIP$Extraversion, PAKIP$SEX)$estimate + cohen.d(PAKIP$Agreeableness, PAKIP$SEX)$estimate - cohen.d(PAKIP$Neuroticism, PAKIP$SEX)$estimate)); dOPPakG
dOPPhiG = mean(c(cohen.d(PHIIP$Openness, PHIIP$SEX)$estimate + cohen.d(PHIIP$Conscientiousness, PHIIP$SEX)$estimate + cohen.d(PHIIP$Extraversion, PHIIP$SEX)$estimate + cohen.d(PHIIP$Agreeableness, PHIIP$SEX)$estimate - cohen.d(PHIIP$Neuroticism, PHIIP$SEX)$estimate)); dOPPhiG
dOPPolG = mean(c(cohen.d(POLIP$Openness, POLIP$SEX)$estimate + cohen.d(POLIP$Conscientiousness, POLIP$SEX)$estimate + cohen.d(POLIP$Extraversion, POLIP$SEX)$estimate + cohen.d(POLIP$Agreeableness, POLIP$SEX)$estimate - cohen.d(POLIP$Neuroticism, POLIP$SEX)$estimate)); dOPPolG
dOPPorG = mean(c(cohen.d(PORIP$Openness, PORIP$SEX)$estimate + cohen.d(PORIP$Conscientiousness, PORIP$SEX)$estimate + cohen.d(PORIP$Extraversion, PORIP$SEX)$estimate + cohen.d(PORIP$Agreeableness, PORIP$SEX)$estimate - cohen.d(PORIP$Neuroticism, PORIP$SEX)$estimate)); dOPPorG
dOPRomG = mean(c(cohen.d(ROMIP$Openness, ROMIP$SEX)$estimate + cohen.d(ROMIP$Conscientiousness, ROMIP$SEX)$estimate + cohen.d(ROMIP$Extraversion, ROMIP$SEX)$estimate + cohen.d(ROMIP$Agreeableness, ROMIP$SEX)$estimate - cohen.d(ROMIP$Neuroticism, ROMIP$SEX)$estimate)); dOPRomG
dOPRusG = mean(c(cohen.d(RUSIP$Openness, RUSIP$SEX)$estimate + cohen.d(RUSIP$Conscientiousness, RUSIP$SEX)$estimate + cohen.d(RUSIP$Extraversion, RUSIP$SEX)$estimate + cohen.d(RUSIP$Agreeableness, RUSIP$SEX)$estimate - cohen.d(RUSIP$Neuroticism, RUSIP$SEX)$estimate)); dOPRusG
dOPSinG = mean(c(cohen.d(SINIP$Openness, SINIP$SEX)$estimate + cohen.d(SINIP$Conscientiousness, SINIP$SEX)$estimate + cohen.d(SINIP$Extraversion, SINIP$SEX)$estimate + cohen.d(SINIP$Agreeableness, SINIP$SEX)$estimate - cohen.d(SINIP$Neuroticism, SINIP$SEX)$estimate)); dOPSinG
dOPSloG = mean(c(cohen.d(SLOIP$Openness, SLOIP$SEX)$estimate + cohen.d(SLOIP$Conscientiousness, SLOIP$SEX)$estimate + cohen.d(SLOIP$Extraversion, SLOIP$SEX)$estimate + cohen.d(SLOIP$Agreeableness, SLOIP$SEX)$estimate - cohen.d(SLOIP$Neuroticism, SLOIP$SEX)$estimate)); dOPSloG
dOPSlvG = mean(c(cohen.d(SLVIP$Openness, SLVIP$SEX)$estimate + cohen.d(SLVIP$Conscientiousness, SLVIP$SEX)$estimate + cohen.d(SLVIP$Extraversion, SLVIP$SEX)$estimate + cohen.d(SLVIP$Agreeableness, SLVIP$SEX)$estimate - cohen.d(SLVIP$Neuroticism, SLVIP$SEX)$estimate)); dOPSlvG
dOPSouG = mean(c(cohen.d(SOUIP$Openness, SOUIP$SEX)$estimate + cohen.d(SOUIP$Conscientiousness, SOUIP$SEX)$estimate + cohen.d(SOUIP$Extraversion, SOUIP$SEX)$estimate + cohen.d(SOUIP$Agreeableness, SOUIP$SEX)$estimate - cohen.d(SOUIP$Neuroticism, SOUIP$SEX)$estimate)); dOPSouG
dOPSKoG = mean(c(cohen.d(SKOIP$Openness, SKOIP$SEX)$estimate + cohen.d(SKOIP$Conscientiousness, SKOIP$SEX)$estimate + cohen.d(SKOIP$Extraversion, SKOIP$SEX)$estimate + cohen.d(SKOIP$Agreeableness, SKOIP$SEX)$estimate - cohen.d(SKOIP$Neuroticism, SKOIP$SEX)$estimate)); dOPSKoG
dOPSpaG = mean(c(cohen.d(SPAIP$Openness, SPAIP$SEX)$estimate + cohen.d(SPAIP$Conscientiousness, SPAIP$SEX)$estimate + cohen.d(SPAIP$Extraversion, SPAIP$SEX)$estimate + cohen.d(SPAIP$Agreeableness, SPAIP$SEX)$estimate - cohen.d(SPAIP$Neuroticism, SPAIP$SEX)$estimate)); dOPSpaG
dOPSweG = mean(c(cohen.d(SWEIP$Openness, SWEIP$SEX)$estimate + cohen.d(SWEIP$Conscientiousness, SWEIP$SEX)$estimate + cohen.d(SWEIP$Extraversion, SWEIP$SEX)$estimate + cohen.d(SWEIP$Agreeableness, SWEIP$SEX)$estimate - cohen.d(SWEIP$Neuroticism, SWEIP$SEX)$estimate)); dOPSweG
dOPSwiG = mean(c(cohen.d(SWIIP$Openness, SWIIP$SEX)$estimate + cohen.d(SWIIP$Conscientiousness, SWIIP$SEX)$estimate + cohen.d(SWIIP$Extraversion, SWIIP$SEX)$estimate + cohen.d(SWIIP$Agreeableness, SWIIP$SEX)$estimate - cohen.d(SWIIP$Neuroticism, SWIIP$SEX)$estimate)); dOPSwiG
dOPThaG = mean(c(cohen.d(THAIP$Openness, THAIP$SEX)$estimate + cohen.d(THAIP$Conscientiousness, THAIP$SEX)$estimate + cohen.d(THAIP$Extraversion, THAIP$SEX)$estimate + cohen.d(THAIP$Agreeableness, THAIP$SEX)$estimate - cohen.d(THAIP$Neuroticism, THAIP$SEX)$estimate)); dOPThaG
dOPTriG = mean(c(cohen.d(TRIIP$Openness, TRIIP$SEX)$estimate + cohen.d(TRIIP$Conscientiousness, TRIIP$SEX)$estimate + cohen.d(TRIIP$Extraversion, TRIIP$SEX)$estimate + cohen.d(TRIIP$Agreeableness, TRIIP$SEX)$estimate - cohen.d(TRIIP$Neuroticism, TRIIP$SEX)$estimate)); dOPTriG
dOPTurG = mean(c(cohen.d(TURIP$Openness, TURIP$SEX)$estimate + cohen.d(TURIP$Conscientiousness, TURIP$SEX)$estimate + cohen.d(TURIP$Extraversion, TURIP$SEX)$estimate + cohen.d(TURIP$Agreeableness, TURIP$SEX)$estimate - cohen.d(TURIP$Neuroticism, TURIP$SEX)$estimate)); dOPTurG
dOPUgaG = mean(c(cohen.d(UGAIP$Openness, UGAIP$SEX)$estimate + cohen.d(UGAIP$Conscientiousness, UGAIP$SEX)$estimate + cohen.d(UGAIP$Extraversion, UGAIP$SEX)$estimate + cohen.d(UGAIP$Agreeableness, UGAIP$SEX)$estimate - cohen.d(UGAIP$Neuroticism, UGAIP$SEX)$estimate)); dOPUgaG
dOPUKG = mean(c(cohen.d(UKIP$Openness, UKIP$SEX)$estimate + cohen.d(UKIP$Conscientiousness, UKIP$SEX)$estimate + cohen.d(UKIP$Extraversion, UKIP$SEX)$estimate + cohen.d(UKIP$Agreeableness, UKIP$SEX)$estimate - cohen.d(UKIP$Neuroticism, UKIP$SEX)$estimate)); dOPUKG
dOPUkrG = mean(c(cohen.d(UKRIP$Openness, UKRIP$SEX)$estimate + cohen.d(UKRIP$Conscientiousness, UKRIP$SEX)$estimate + cohen.d(UKRIP$Extraversion, UKRIP$SEX)$estimate + cohen.d(UKRIP$Agreeableness, UKRIP$SEX)$estimate - cohen.d(UKRIP$Neuroticism, UKRIP$SEX)$estimate)); dOPUkrG
dOPUAEG = mean(c(cohen.d(UAEIP$Openness, UAEIP$SEX)$estimate + cohen.d(UAEIP$Conscientiousness, UAEIP$SEX)$estimate + cohen.d(UAEIP$Extraversion, UAEIP$SEX)$estimate + cohen.d(UAEIP$Agreeableness, UAEIP$SEX)$estimate - cohen.d(UAEIP$Neuroticism, UAEIP$SEX)$estimate)); dOPUAEG
dOPUSAG = mean(c(cohen.d(USAIP$Openness, USAIP$SEX)$estimate + cohen.d(USAIP$Conscientiousness, USAIP$SEX)$estimate + cohen.d(USAIP$Extraversion, USAIP$SEX)$estimate + cohen.d(USAIP$Agreeableness, USAIP$SEX)$estimate - cohen.d(USAIP$Neuroticism, USAIP$SEX)$estimate)); dOPUSAG
dOPVenG = mean(c(cohen.d(VENIP$Openness, VENIP$SEX)$estimate + cohen.d(VENIP$Conscientiousness, VENIP$SEX)$estimate + cohen.d(VENIP$Extraversion, VENIP$SEX)$estimate + cohen.d(VENIP$Agreeableness, VENIP$SEX)$estimate - cohen.d(VENIP$Neuroticism, VENIP$SEX)$estimate)); dOPVenG
dOPVieG = mean(c(cohen.d(VIEIP$Openness, VIEIP$SEX)$estimate + cohen.d(VIEIP$Conscientiousness, VIEIP$SEX)$estimate + cohen.d(VIEIP$Extraversion, VIEIP$SEX)$estimate + cohen.d(VIEIP$Agreeableness, VIEIP$SEX)$estimate - cohen.d(VIEIP$Neuroticism, VIEIP$SEX)$estimate)); dOPVieG
#Mahalanobis' D
ALBMA <- maha(ALBIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], ALBIP$SEXO); ALBMA[1]
ALGMA <- maha(ALGIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], ALGIP$SEXO); ALGMA[1]
ANGMA <- maha(ANGIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], ANGIP$SEXO); ANGMA[1]
ARGMA <- maha(ARGIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], ARGIP$SEXO); ARGMA[1]
AUSMA <- maha(AUSIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], AUSIP$SEXO); AUSMA[1]
HABMA <- maha(HABIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], HABIP$SEXO); HABMA[1]
BELMA <- maha(BELIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], BELIP$SEXO); BELMA[1]
BRAMA <- maha(BRAIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], BRAIP$SEXO); BRAMA[1]
CANMA <- maha(CANIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], CANIP$SEXO); CANMA[1]
CHIMA <- maha(CHIIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], CHIIP$SEXO); CHIMA[1]
COLMA <- maha(COLIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], COLIP$SEXO); COLMA[1]
CROMA <- maha(CROIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], CROIP$SEXO); CROMA[1]
DENMA <- maha(DENIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], DENIP$SEXO); DENMA[1]
EGYMA <- maha(EGYIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], EGYIP$SEXO); EGYMA[1]
FINMA <- maha(FINIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], FINIP$SEXO); FINMA[1]
FRAMA <- maha(FRAIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], FRAIP$SEXO); FRAMA[1]
GERMA <- maha(GERIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], GERIP$SEXO); GERMA[1]
GREMA <- maha(GREIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], GREIP$SEXO); GREMA[1]
INDMA <- maha(INDIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], INDIP$SEXO); INDMA[1]
INOMA <- maha(INOIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], INOIP$SEXO); INOMA[1]
IRAMA <- maha(IRAIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], IRAIP$SEXO); IRAMA[1]
IREMA <- maha(IREIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], IREIP$SEXO); IREMA[1]
ISRMA <- maha(ISRIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], ISRIP$SEXO); ISRMA[1]
ITAMA <- maha(ITAIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], ITAIP$SEXO); ITAMA[1]
JAMMA <- maha(JAMIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], JAMIP$SEXO); JAMMA[1]
JAPMA <- maha(JAPIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], JAPIP$SEXO); JAPMA[1]
KENMA <- maha(KENIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], KENIP$SEXO); KENMA[1]
LEBMA <- maha(LEBIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], LEBIP$SEXO); LEBMA[1]
MALMA <- maha(MALIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], MALIP$SEXO); MALMA[1]
MEXMA <- maha(MEXIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], MEXIP$SEXO); MEXMA[1]
NETMA <- maha(NETIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], NETIP$SEXO); NETMA[1]
NEWMA <- maha(NEWIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], NEWIP$SEXO); NEWMA[1]
NIRMA <- maha(NIRIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], NIRIP$SEXO); NIRMA[1]
NORMA <- maha(NORIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], NORIP$SEXO); NORMA[1]
PERMA <- maha(PERIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], PERIP$SEXO); PERMA[1]
PAKMA <- maha(PAKIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], PAKIP$SEXO); PAKMA[1]
PHIMA <- maha(PHIIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], PHIIP$SEXO); PHIMA[1]
POLMA <- maha(POLIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], POLIP$SEXO); POLMA[1]
PORMA <- maha(PORIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], PORIP$SEXO); PORMA[1]
ROMMA <- maha(ROMIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], ROMIP$SEXO); ROMMA[1]
RUSMA <- maha(RUSIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], RUSIP$SEXO); RUSMA[1]
SINMA <- maha(SINIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], SINIP$SEXO); SINMA[1]
SLOMA <- maha(SLOIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], SLOIP$SEXO); SLOMA[1]
SLVMA <- maha(SLVIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], SLVIP$SEXO); SLVMA[1]
SOUMA <- maha(SOUIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], SOUIP$SEXO); SOUMA[1]
SKOMA <- maha(SKOIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], SKOIP$SEXO); SKOMA[1]
SPAMA <- maha(SPAIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], SPAIP$SEXO); SPAMA[1]
SWEMA <- maha(SWEIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], SWEIP$SEXO); SWEMA[1]
SWIMA <- maha(SWIIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], SWIIP$SEXO); SWIMA[1]
THAMA <- maha(THAIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], THAIP$SEXO); THAMA[1]
TRIMA <- maha(TRIIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], TRIIP$SEXO); TRIMA[1]
TURMA <- maha(TURIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], TURIP$SEXO); TURMA[1]
UGAMA <- maha(UGAIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], UGAIP$SEXO); UGAMA[1]
UKMA <- maha(UKIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], UKIP$SEXO); UKMA[1]
UKRMA <- maha(UKRIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], UKRIP$SEXO); UKRMA[1]
UAEMA <- maha(UAEIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], UAEIP$SEXO); UAEMA[1]
USAMA <- maha(USAIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], USAIP$SEXO); USAMA[1]
VENMA <- maha(VENIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], VENIP$SEXO); VENMA[1]
VIEMA <- maha(VIEIP[c("Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism")], VIEIP$SEXO); VIEMA[1]
Create a dataframe of these scores.
NeuCou <- data.frame("Country" = c("Albania", "Algeria", "Angola", "Argentina", "Australia", "Austria", "Belgium", "Brazil", "Canada", "China", "Colombia", "Croatia", "Denmark", "Egypt", "Finland", "France", "Germany", "Greece", "India", "Indonesia", "Iran", "Ireland", "Israel", "Italy", "Jamaica", "Japan", "Kenya", "Lebanon", "Malaysia", "Mexico", "Netherlan", "New Zeala", "Nigeria", "Norway", "Pakistan", "Peru", "Philippin", "Poland", "Portugal", "Romania", "Russian F", "Singapore", "Slovakia", "Slovenia", "South Afr", "South Kor", "Spain", "Sweden", "Switzerla", "Thailand", "Trinidad", "Turkey", "Uganda", "UK", "Ukraine", "United Ar", "USA", "Venezuela", "Vietnam"),
"GEI" = c(0.701, 0.632, 0.637, 0.734, 0.733, 0.733, 0.753, 0.686, 0.740, 0.682, 0.725, 0.708, 0.767, 0.599, 0.850, 0.761, 0.779, 0.685, 0.664, 0.681, 0.580, 0.807, 0.712, 0.726, 0.703, 0.670, 0.719, 0.598, 0.655, 0.699, 0.776, 0.782, 0.638, 0.850, 0.559, 0.683, 0.790, 0.715, 0.731, 0.693, 0.694, 0.711, 0.675, 0.784, 0.759, 0.651, 0.742, 0.823, 0.785, 0.706, 0.720, 0.624, 0.708, 0.758, 0.702, 0.646, 0.740, 0.691, 0.687),
"dBiasO" = c(sum(dAlbO), sum(dAlgO), NA, NA, sum(dAusO), NA, NA, NA, NA, sum(dChiO), sum(dColO), NA, sum(dDenO), sum(dEgyO), NA, NA, NA, sum(dGreO), sum(dIndO), sum(dInoO), sum(dIraO), sum(dIreO), sum(dIsrO), sum(dItaO), sum(dJamO), sum(dJapO), NA, sum(dLebO), sum(dMalO), sum(dMexO), sum(dNetO), NA, sum(dNirO), NA, NA, NA, sum(dPhiO), sum(dPolO), NA, sum(dRomO), NA, NA, NA, sum(dSlvO), sum(dSouO), NA, NA, sum(dSweO), sum(dSwiO), NA, NA, sum(dTurO), sum(dUgaO), sum(dUKO), NA, NA, NA, sum(dVenO), NA),
"dBiasC" = c(sum(dAlbC), sum(dAlgC), NA, sum(dArgC), sum(dAusC), NA, sum(dBelC), sum(dBraC), sum(dCanC), sum(dChiC), NA, sum(dCroC), sum(dDenC), sum(dEgyC), sum(dFinC), sum(dFraC), sum(dGerC), sum(dGreC), sum(dIndC), sum(dInoC), sum(dIraC), sum(dIreC), sum(dIsrC), sum(dItaC), sum(dJamC), sum(dJapC), sum(dKenC), sum(dLebC), sum(dMalC), sum(dMexC), sum(dNetC), sum(dNewC), sum(dNirC), sum(dNorC), NA, sum(dPakC), sum(dPhiC), NA, sum(dPorC), sum(dRomC), sum(dRusC), sum(dSinC), NA, sum(dSlvC), sum(dSouC), sum(dSKoC), NA, sum(dSweC), sum(dSwiC), NA, sum(dTriC), sum(dTurC), NA, sum(dUKC), sum(dUkrC), sum(dUAEC), sum(dUSAC), sum(dVenC), sum(dVieC)),
"dBiasE" = c(sum(dAlbE), sum(dAlgE), sum(dAngE), sum(dArgE), sum(dAusE), sum(dHabE), sum(dBelE), sum(dBraE), sum(dCanE), sum(dChiE), sum(dColE), sum(dCroE), sum(dDenE), sum(dEgyE), sum(dFinE), sum(dFraE), sum(dGerE), sum(dGreE), sum(dIndE), sum(dInoE), sum(dIraE), sum(dIreE), sum(dIsrE), sum(dItaE), sum(dJamE), sum(dJapE), sum(dKenE), sum(dLebE), sum(dMalE), sum(dMexE), sum(dNetE), sum(dNewE), sum(dNirE), sum(dNorE), sum(dPerE), sum(dPakE), sum(dPhiE), sum(dPolE), sum(dPorE), sum(dRomE), sum(dRusE), sum(dSinE), sum(dSloE), sum(dSlvE), sum(dSouE), sum(dSKoE), sum(dSpaE), sum(dSweE), sum(dSwiE), sum(dThaE), sum(dTriE), sum(dTurE), sum(dUgaE), sum(dUKE), sum(dUkrE), sum(dUAEE), sum(dUSAE), sum(dVenE), sum(dVieE)),
"dBiasA" = c(NA, sum(dAlgA), NA, sum(dArgA), sum(dAusA), NA, sum(dBelA), sum(dBraA), sum(dCanA), sum(dChiA), sum(dColA), sum(dCroA), sum(dDenA), sum(dEgyA), sum(dFinA), sum(dFraA), sum(dGerA), sum(dGreA), sum(dIndA), sum(dInoA), sum(dIraA), sum(dIreA), sum(dIsrA), sum(dItaA), sum(dJamA), sum(dJapA), sum(dKenA), sum(dLebA), sum(dMalA), sum(dMexA), sum(dNetA), sum(dNewA), sum(dNirA), sum(dNorA), NA, sum(dPakA), sum(dPhiA), sum(dPolA), sum(dPorA), NA, NA, sum(dSinA), sum(dSloA), sum(dSlvA), sum(dSouA), sum(dSKoA), sum(dSpaA), sum(dSweA), NA, sum(dThaA), NA, sum(dTurA), NA, sum(dUKA), sum(dUkrA), sum(dUAEA), sum(dUSAA), sum(dVenA), sum(dVieA)),
"dBiasN" = c(sum(dAlbN), sum(dAlgN), sum(dAngN), sum(dArgN), sum(dAusN), sum(dHabN), sum(dBelN), sum(dBraN), sum(dCanN), sum(dChiN), sum(dColN), sum(dCroN), sum(dDenN), sum(dEgyN), sum(dFinN), sum(dFraN), sum(dGerN), sum(dGreN), sum(dIndN), sum(dInoN), sum(dIraN), sum(dIreN), sum(dIsrN), sum(dItaN), sum(dJamN), sum(dJapN), sum(dKenN), sum(dLebN), sum(dMalN), sum(dMexN), sum(dNetN), sum(dNewN), sum(dNirN), sum(dNorN), sum(dPerN), sum(dPakN), sum(dPhiN), sum(dPolN), sum(dPorN), sum(dRomN), sum(dRusN), sum(dSinN), sum(dSloN), sum(dSlvN), sum(dSouN), sum(dSKoN), sum(dSpaN), sum(dSweN), sum(dSwiN), sum(dThaN), sum(dTriN), sum(dTurN), sum(dUgaN), sum(dUKN), sum(dUkrN), sum(dUAEN), sum(dUSAN), sum(dVenN), sum(dVieN)),
"dBiasG" = c(sum(dAlbG), sum(dAlgG), NA, sum(dArgG), sum(dAusG), NA, NA, sum(dBraG), sum(dCanG), sum(dChiG), NA, NA, NA, sum(dEgyG), NA, sum(dFraG), sum(dGerG), sum(dGreG), sum(dIndG), sum(dInoG), sum(dIraG), sum(dIreG), NA, NA, sum(dJamG), sum(dJapG), sum(dKenG), sum(dLebG), sum(dMalG), sum(dMexG), NA, sum(dNewG), sum(dNirG), NA, NA, sum(dPakG), sum(dPhiG), NA, NA, sum(dRomG), sum(dRusG), sum(dSinG), NA, NA, sum(dSouG), sum(dSKoG), NA, NA, sum(dSwiG), sum(dThaG), NA, sum(dTurG), NA, sum(dUKG), sum(dUkrG), sum(dUAEG), sum(dUSAG), sum(dVenG), sum(dVieG)),
"dObsO" = c(dOAlbO$estimate, dOAlgO$estimate, dOAngO$estimate, dOArgO$estimate, dOAusO$estimate, dOHabO$estimate, dOBelO$estimate, dOBraO$estimate, dOCanO$estimate, dOChiO$estimate, dOColO$estimate, dOCroO$estimate, dODenO$estimate, dOEgyO$estimate, dOFinO$estimate, dOFraO$estimate, dOGerO$estimate, dOGreO$estimate, dOIndO$estimate, dOInoO$estimate, dOIraO$estimate, dOIreO$estimate, dOIsrO$estimate, dOItaO$estimate, dOJamO$estimate, dOJapO$estimate, dOKenO$estimate, dOLebO$estimate, dOMalO$estimate, dOMexO$estimate, dONetO$estimate, dONewO$estimate, dONirO$estimate, dONorO$estimate, dOPerO$estimate, dOPakO$estimate, dOPhiO$estimate, dOPolO$estimate, dOPorO$estimate, dORomO$estimate, dORusO$estimate, dOSinO$estimate, dOSloO$estimate, dOSlvO$estimate, dOSouO$estimate, dOSKoO$estimate, dOSpaO$estimate, dOSweO$estimate, dOSwiO$estimate, dOThaO$estimate, dOTriO$estimate, dOTurO$estimate, dOUgaO$estimate, dOUKO$estimate, dOUkrO$estimate, dOUAEO$estimate, dOUSAO$estimate, dOVenO$estimate, dOVieO$estimate),
"dObsC" = c(dOAlbC$estimate, dOAlgC$estimate, dOAngC$estimate, dOArgC$estimate, dOAusC$estimate, dOHabC$estimate, dOBelC$estimate, dOBraC$estimate, dOCanC$estimate, dOChiC$estimate, dOColC$estimate, dOCroC$estimate, dODenC$estimate, dOEgyC$estimate, dOFinC$estimate, dOFraC$estimate, dOGerC$estimate, dOGreC$estimate, dOIndC$estimate, dOInoC$estimate, dOIraC$estimate, dOIreC$estimate, dOIsrC$estimate, dOItaC$estimate, dOJamC$estimate, dOJapC$estimate, dOKenC$estimate, dOLebC$estimate, dOMalC$estimate, dOMexC$estimate, dONetC$estimate, dONewC$estimate, dONirC$estimate, dONorC$estimate, dOPerC$estimate, dOPakC$estimate, dOPhiC$estimate, dOPolC$estimate, dOPorC$estimate, dORomC$estimate, dORusC$estimate, dOSinC$estimate, dOSloC$estimate, dOSlvC$estimate, dOSouC$estimate, dOSKoC$estimate, dOSpaC$estimate, dOSweC$estimate, dOSwiC$estimate, dOThaC$estimate, dOTriC$estimate, dOTurC$estimate, dOUgaC$estimate, dOUKC$estimate, dOUkrC$estimate, dOUAEC$estimate, dOUSAC$estimate, dOVenC$estimate, dOVieC$estimate),
"dObsE" = c(dOAlbE$estimate, dOAlgE$estimate, dOAngE$estimate, dOArgE$estimate, dOAusE$estimate, dOHabE$estimate, dOBelE$estimate, dOBraE$estimate, dOCanE$estimate, dOChiE$estimate, dOColE$estimate, dOCroE$estimate, dODenE$estimate, dOEgyE$estimate, dOFinE$estimate, dOFraE$estimate, dOGerE$estimate, dOGreE$estimate, dOIndE$estimate, dOInoE$estimate, dOIraE$estimate, dOIreE$estimate, dOIsrE$estimate, dOItaE$estimate, dOJamE$estimate, dOJapE$estimate, dOKenE$estimate, dOLebE$estimate, dOMalE$estimate, dOMexE$estimate, dONetE$estimate, dONewE$estimate, dONirE$estimate, dONorE$estimate, dOPerE$estimate, dOPakE$estimate, dOPhiE$estimate, dOPolE$estimate, dOPorE$estimate, dORomE$estimate, dORusE$estimate, dOSinE$estimate, dOSloE$estimate, dOSlvE$estimate, dOSouE$estimate, dOSKoE$estimate, dOSpaE$estimate, dOSweE$estimate, dOSwiE$estimate, dOThaE$estimate, dOTriE$estimate, dOTurE$estimate, dOUgaE$estimate, dOUKE$estimate, dOUkrE$estimate, dOUAEE$estimate, dOUSAE$estimate, dOVenE$estimate, dOVieE$estimate),
"dObsA" = c(dOAlbA$estimate, dOAlgA$estimate, dOAngA$estimate, dOArgA$estimate, dOAusA$estimate, dOHabA$estimate, dOBelA$estimate, dOBraA$estimate, dOCanA$estimate, dOChiA$estimate, dOColA$estimate, dOCroA$estimate, dODenA$estimate, dOEgyA$estimate, dOFinA$estimate, dOFraA$estimate, dOGerA$estimate, dOGreA$estimate, dOIndA$estimate, dOInoA$estimate, dOIraA$estimate, dOIreA$estimate, dOIsrA$estimate, dOItaA$estimate, dOJamA$estimate, dOJapA$estimate, dOKenA$estimate, dOLebA$estimate, dOMalA$estimate, dOMexA$estimate, dONetA$estimate, dONewA$estimate, dONirA$estimate, dONorA$estimate, dOPerA$estimate, dOPakA$estimate, dOPhiA$estimate, dOPolA$estimate, dOPorA$estimate, dORomA$estimate, dORusA$estimate, dOSinA$estimate, dOSloA$estimate, dOSlvA$estimate, dOSouA$estimate, dOSKoA$estimate, dOSpaA$estimate, dOSweA$estimate, dOSwiA$estimate, dOThaA$estimate, dOTriA$estimate, dOTurA$estimate, dOUgaA$estimate, dOUKA$estimate, dOUkrA$estimate, dOUAEA$estimate, dOUSAA$estimate, dOVenA$estimate, dOVieA$estimate),
"dObsN" = c(dOAlbN$estimate, dOAlgN$estimate, dOAngN$estimate, dOArgN$estimate, dOAusN$estimate, dOHabN$estimate, dOBelN$estimate, dOBraN$estimate, dOCanN$estimate, dOChiN$estimate, dOColN$estimate, dOCroN$estimate, dODenN$estimate, dOEgyN$estimate, dOFinN$estimate, dOFraN$estimate, dOGerN$estimate, dOGreN$estimate, dOIndN$estimate, dOInoN$estimate, dOIraN$estimate, dOIreN$estimate, dOIsrN$estimate, dOItaN$estimate, dOJamN$estimate, dOJapN$estimate, dOKenN$estimate, dOLebN$estimate, dOMalN$estimate, dOMexN$estimate, dONetN$estimate, dONewN$estimate, dONirN$estimate, dONorN$estimate, dOPerN$estimate, dOPakN$estimate, dOPhiN$estimate, dOPolN$estimate, dOPorN$estimate, dORomN$estimate, dORusN$estimate, dOSinN$estimate, dOSloN$estimate, dOSlvN$estimate, dOSouN$estimate, dOSKoN$estimate, dOSpaN$estimate, dOSweN$estimate, dOSwiN$estimate, dOThaN$estimate, dOTriN$estimate, dOTurN$estimate, dOUgaN$estimate, dOUKN$estimate, dOUkrN$estimate, dOUAEN$estimate, dOUSAN$estimate, dOVenN$estimate, dOVieN$estimate),
"dObsG" = c(dOAlbG$estimate, dOAlgG$estimate, dOAngG$estimate, dOArgG$estimate, dOAusG$estimate, dOHabG$estimate, dOBelG$estimate, dOBraG$estimate, dOCanG$estimate, dOChiG$estimate, dOColG$estimate, dOCroG$estimate, dODenG$estimate, dOEgyG$estimate, dOFinG$estimate, dOFraG$estimate, dOGerG$estimate, dOGreG$estimate, dOIndG$estimate, dOInoG$estimate, dOIraG$estimate, dOIreG$estimate, dOIsrG$estimate, dOItaG$estimate, dOJamG$estimate, dOJapG$estimate, dOKenG$estimate, dOLebG$estimate, dOMalG$estimate, dOMexG$estimate, dONetG$estimate, dONewG$estimate, dONirG$estimate, dONorG$estimate, dOPerG$estimate, dOPakG$estimate, dOPhiG$estimate, dOPolG$estimate, dOPorG$estimate, dORomG$estimate, dORusG$estimate, dOSinG$estimate, dOSloG$estimate, dOSlvG$estimate, dOSouG$estimate, dOSKoG$estimate, dOSpaG$estimate, dOSweG$estimate, dOSwiG$estimate, dOThaG$estimate, dOTriG$estimate, dOTurG$estimate, dOUgaG$estimate, dOUKG$estimate, dOUkrG$estimate, dOUAEG$estimate, dOUSAG$estimate, dOVenG$estimate, dOVieG$estimate),
"dObsPO" = c(dOPAlbO, dOPAlgO, dOPAngO, dOPArgO, dOPAusO, dOPHabO, dOPBelO, dOPBraO, dOPCanO, dOPChiO, dOPColO, dOPCroO, dOPDenO, dOPEgyO, dOPFinO, dOPFraO, dOPGerO, dOPGreO, dOPIndO, dOPInoO, dOPIraO, dOPIreO, dOPIsrO, dOPItaO, dOPJamO, dOPJapO, dOPKenO, dOPLebO, dOPMalO, dOPMexO, dOPNetO, dOPNewO, dOPNirO, dOPNorO, dOPPerO, dOPPakO, dOPPhiO, dOPPolO, dOPPorO, dOPRomO, dOPRusO, dOPSinO, dOPSloO, dOPSlvO, dOPSouO, dOPSKoO, dOPSpaO, dOPSweO, dOPSwiO, dOPThaO, dOPTriO, dOPTurO, dOPUgaO, dOPUKO, dOPUkrO, dOPUAEO, dOPUSAO, dOPVenO, dOPVieO),
"dObsPC" = c(dOPAlbC, dOPAlgC, NA, dOPArgC, dOPAusC, dOPHabC, dOPBelC, dOPBraC, dOPCanC, dOPChiC, dOPColC, dOPCroC, dOPDenC, dOPEgyC, dOPFinC, dOPFraC, dOPGerC, dOPGreC, dOPIndC, dOPInoC, dOPIraC, dOPIreC, dOPIsrC, dOPItaC, dOPJamC, dOPJapC, dOPKenC, dOPLebC, dOPMalC, dOPMexC, dOPNetC, dOPNewC, dOPNirC, dOPNorC, NA, dOPPakC, dOPPhiC, dOPPolC, dOPPorC, dOPRomC, dOPRusC, dOPSinC, dOPSloC, dOPSlvC, dOPSouC, dOPSKoC, dOPSpaC, dOPSweC, dOPSwiC, dOPThaC, dOPTriC, dOPTurC, dOPUgaC, dOPUKC, dOPUkrC, dOPUAEC, dOPUSAC, dOPVenC, dOPVieC),
"dObsPE" = c(dOPAlbE, dOPAlgE, dOPAngE, dOPArgE, dOPAusE, dOPHabE, dOPBelE, dOPBraE, dOPCanE, dOPChiE, dOPColE, dOPCroE, dOPDenE, dOPEgyE, dOPFinE, dOPFraE, dOPGerE, dOPGreE, dOPIndE, dOPInoE, dOPIraE, dOPIreE, dOPIsrE, dOPItaE, dOPJamE, dOPJapE, dOPKenE, dOPLebE, dOPMalE, dOPMexE, dOPNetE, dOPNewE, dOPNirE, dOPNorE, dOPPerE, dOPPakE, dOPPhiE, dOPPolE, dOPPorE, dOPRomE, dOPRusE, dOPSinE, dOPSloE, dOPSlvE, dOPSouE, dOPSKoE, dOPSpaE, dOPSweE, dOPSwiE, dOPThaE, dOPTriE, dOPTurE, dOPUgaE, dOPUKE, dOPUkrE, dOPUAEE, dOPUSAE, dOPVenE, dOPVieE),
"dObsPA" = c(dOPAlbA, dOPAlgA, dOPAngA, dOPArgA, dOPAusA, dOPHabA, dOPBelA, dOPBraA, dOPCanA, dOPChiA, dOPColA, dOPCroA, dOPDenA, dOPEgyA, dOPFinA, dOPFraA, dOPGerA, dOPGreA, dOPIndA, dOPInoA, dOPIraA, dOPIreA, dOPIsrA, dOPItaA, dOPJamA, dOPJapA, dOPKenA, dOPLebA, dOPMalA, dOPMexA, dOPNetA, dOPNewA, dOPNirA, dOPNorA, NA, dOPPakA, dOPPhiA, dOPPolA, dOPPorA, dOPRomA, dOPRusA, dOPSinA, dOPSloA, dOPSlvA, dOPSouA, dOPSKoA, dOPSpaA, dOPSweA, dOPSwiA, dOPThaA, NA, dOPTurA, dOPUgaA, dOPUKA, dOPUkrA, dOPUAEA, dOPUSAA, dOPVenA, dOPVieA),
"dObsPN" = c(dOPAlbN, dOPAlgN, dOPAngN, dOPArgN, dOPAusN, dOPHabN, dOPBelN, dOPBraN, dOPCanN, dOPChiN, dOPColN, dOPCroN, dOPDenN, dOPEgyN, dOPFinN, dOPFraN, dOPGerN, dOPGreN, dOPIndN, dOPInoN, dOPIraN, dOPIreN, dOPIsrN, dOPItaN, dOPJamN, dOPJapN, dOPKenN, dOPLebN, dOPMalN, dOPMexN, dOPNetN, dOPNewN, dOPNirN, dOPNorN, dOPPerN, dOPPakN, dOPPhiN, dOPPolN, dOPPorN, dOPRomN, dOPRusN, dOPSinN, dOPSloN, dOPSlvN, dOPSouN, dOPSKoN, dOPSpaN, dOPSweN, dOPSwiN, dOPThaN, dOPTriN, dOPTurN, dOPUgaN, dOPUKN, dOPUkrN, dOPUAEN, dOPUSAN, dOPVenN, dOPVieN),
"dObsPG" = c(dOPAlbG, dOPAlgG, dOPAngG, dOPArgG, dOPAusG, NA, NA, dOPBraG, dOPCanG, dOPChiG, NA, dOPCroG, dOPDenG, dOPEgyG, dOPFinG, dOPFraG, dOPGerG, dOPGreG, dOPIndG, dOPInoG, dOPIraG, dOPIreG, dOPIsrG, dOPItaG, dOPJamG, dOPJapG, dOPKenG, dOPLebG, dOPMalG, dOPMexG, dOPNetG, dOPNewG, dOPNirG, dOPNorG, dOPPerG, dOPPakG, dOPPhiG, dOPPolG, dOPPorG, dOPRomG, dOPRusG, dOPSinG, dOPSloG, dOPSlvG, dOPSouG, dOPSKoG, dOPSpaG, dOPSweG, dOPSwiG, dOPThaG, dOPTriG, dOPTurG, dOPUgaG, dOPUKG, dOPUkrG, dOPUAEG, dOPUSAG, dOPVenG, dOPVieG),
"BigD" = c(ALBMA[1], ALGMA[1], ANGMA[1], ARGMA[1], AUSMA[1], HABMA[1], BELMA[1], BRAMA[1], CANMA[1], CHIMA[1], COLMA[1], CROMA[1], DENMA[1], EGYMA[1], FINMA[1], FRAMA[1], GERMA[1], GREMA[1], INDMA[1], INOMA[1], IRAMA[1], IREMA[1], ISRMA[1], ITAMA[1], JAMMA[1], JAPMA[1], KENMA[1], LEBMA[1], MALMA[1], MEXMA[1], NETMA[1], NEWMA[1], NIRMA[1], NORMA[1], PERMA[1], PAKMA[1], PHIMA[1], POLMA[1], PORMA[1], ROMMA[1], RUSMA[1], SINMA[1], SLOMA[1], SLVMA[1], SOUMA[1], SKOMA[1], SPAMA[1], SWEMA[1], SWIMA[1], THAMA[1], TRIMA[1], TURMA[1], UGAMA[1], UKMA[1], UKRMA[1], UAEMA[1], USAMA[1], VENMA[1], VIEMA[1]),
"N" = c(nrow(ALBIP), nrow(ALGIP), nrow(ANGIP), nrow(ARGIP), nrow(AUSIP), nrow(HABIP), nrow(BELIP), nrow(BRAIP), nrow(CANIP), nrow(CHIIP), nrow(COLIP), nrow(CROIP), nrow(DENIP), nrow(EGYIP), nrow(FINIP), nrow(FRAIP), nrow(GERIP), nrow(GREIP), nrow(INDIP), nrow(INOIP), nrow(IRAIP), nrow(IREIP), nrow(ISRIP), nrow(ITAIP), nrow(JAMIP), nrow(JAPIP), nrow(KENIP), nrow(LEBIP), nrow(MALIP), nrow(MEXIP), nrow(NETIP), nrow(NEWIP), nrow(NIRIP), nrow(NORIP), nrow(PERIP), nrow(PAKIP), nrow(PHIIP), nrow(POLIP), nrow(PORIP), nrow(ROMIP), nrow(RUSIP), nrow(SINIP), nrow(SLOIP), nrow(SLVIP), nrow(SOUIP), nrow(SKOIP), nrow(SPAIP), nrow(SWEIP), nrow(SWIIP), nrow(THAIP), nrow(TRIIP), nrow(TURIP), nrow(UGAIP), nrow(UKIP), nrow(UKRIP), nrow(UAEIP), nrow(USAIP), nrow(VENIP), nrow(VIEIP)))
NeuCou$dDiffO = NeuCou$dObsO - NeuCou$dBiasO #Ignore these
NeuCou$dDiffC = NeuCou$dObsC - NeuCou$dBiasC
NeuCou$dDiffE = NeuCou$dObsE - NeuCou$dBiasE
NeuCou$dDiffA = NeuCou$dObsA - NeuCou$dBiasA
NeuCou$dDiffN = NeuCou$dObsN - NeuCou$dBiasN
NeuCou$dDiffG = NeuCou$dObsG - NeuCou$dBiasG
NeuCou$dDiffOP = NeuCou$dObsPO - NeuCou$dBiasO #But not these
NeuCou$dDiffCP = NeuCou$dObsPC - NeuCou$dBiasC
NeuCou$dDiffEP = NeuCou$dObsPE - NeuCou$dBiasE
NeuCou$dDiffAP = NeuCou$dObsPA - NeuCou$dBiasA
NeuCou$dDiffNP = NeuCou$dObsPN - NeuCou$dBiasN
NeuCou$dDiffGP = NeuCou$dObsPG - NeuCou$dBiasG
#NOT RUN - This was for an interpretation robustness check
#To aid in interpretability, we will add the absolute version of the lowest value in each column with negative values to each value so that a value of 0 will correspond to the lowest value in the data and higher values will represent greater mean differences. Must be run without scaling in regressions.
describe(NeuCou) # Notice the number of negative values
#Bias Variables with negative values
NeuCou$dBiasO <- NeuCou$dBiasO + abs(min(NeuCou$dBiasO))
NeuCou$dBiasC <- NeuCou$dBiasC + abs(min(NeuCou$dBiasC, na.rm = T))
NeuCou$dBiasE <- NeuCou$dBiasE + abs(min(NeuCou$dBiasE))
NeuCou$dBiasA <- NeuCou$dBiasA + abs(min(NeuCou$dBiasA, na.rm = T))
NeuCou$dBiasN <- NeuCou$dBiasN + abs(min(NeuCou$dBiasN))
NeuCou$dBiasG <- NeuCou$dBiasG + abs(min(NeuCou$dBiasG, na.rm = T))
#Observed Differences with negative values
NeuCou$dObsO <- NeuCou$dObsO + abs(min(NeuCou$dObsO))
NeuCou$dObsC <- NeuCou$dObsC + abs(min(NeuCou$dObsC))
describe(NeuCou)
mean(NeuCou$dBiasO, na.rm = T); weighted.mean(NeuCou$dBiasO, sqrt(NeuCou$N), na.rm = T)
## [1] 0.07505208
## [1] 0.038158
mean(NeuCou$dBiasC, na.rm = T); weighted.mean(NeuCou$dBiasC, sqrt(NeuCou$N), na.rm = T)
## [1] -0.002146667
## [1] -0.02835197
mean(NeuCou$dBiasE); weighted.mean(NeuCou$dBiasE, sqrt(NeuCou$N))
## [1] 0.007107345
## [1] -0.009673159
mean(NeuCou$dBiasA, na.rm = T); weighted.mean(NeuCou$dBiasA, sqrt(NeuCou$N), na.rm = T)
## [1] 0.02184
## [1] -0.006542113
mean(NeuCou$dBiasN); weighted.mean(NeuCou$dBiasN, sqrt(NeuCou$N))
## [1] 0.01676836
## [1] 0.04391294
mean(NeuCou$dBiasG, na.rm = T); weighted.mean(NeuCou$dBiasG, sqrt(NeuCou$N), na.rm = T)
## [1] 0.02282564
## [1] 0.03344029
mean(NeuCou$BigD); weighted.mean(NeuCou$BigD, sqrt(NeuCou$N))
## [1] 0.3074815
## [1] 0.3013816
The first set in each grouping of plots is the plot of the Cohen’s d values versus GEI. The second set is the test bias versus GEI. The third involves the debiased means for each country, which are the means with their bias subtracted. In some cases, it may have been more appropriate to add, so I may come back to this later.
ggplot(NeuCou, aes(x = GEI, y = scale(dObsPO))) +
geom_point() +
geom_smooth(method = lm, color = "steelblue", formula = "y ~ x") +
labs(title = "Openness Differences vs Gender Equality", x = "Gender Equality Index", y = "Openness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
ggplot(NeuCou, aes(x = GEI, y = scale(dObsPO))) +
geom_point() +
geom_smooth(method = lm, color = "steelblue", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Openness Differences vs Gender Equality (Square Root-N-Weighted)", x = "Gender Equality Index", y = "Openness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
ggplot(NeuCou, aes(x = dBiasO, y = scale(dObsPO))) +
geom_point() +
geom_smooth(method = lm, color = "gold", formula = "y ~ x") +
labs(title = "Sex Differences vs Test Bias", x = "Mean Openness Bias (dMACS)", y = "Mean Openness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 27 rows containing non-finite values (stat_smooth).
## Warning: Removed 27 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = dBiasO, y = scale(dObsPO))) +
geom_point() +
geom_smooth(method = lm, color = "gold", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Sex Differences vs Test Bias", x = "Mean Openness Bias (dMACS)", y = "Mean Openness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 27 rows containing non-finite values (stat_smooth).
## Warning: Removed 27 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = GEI, y = scale(dDiffOP))) +
geom_point() +
geom_smooth(method = lm, color = "orangered", formula = "y ~ x") +
labs(title = "Sex Differences in Openness sans Bias vs the Gender Equality Index", x = "Gender Equality Index", y = "Unbiased Openness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 27 rows containing non-finite values (stat_smooth).
## Warning: Removed 27 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = GEI, y = scale(dDiffOP))) +
geom_point() +
geom_smooth(method = lm, color = "orangered", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Sex Differences in Openness sans Bias vs the Gender Equality Index (Square Root-N-Weighted)", x = "Gender Equality Index", y = "Unbiased Openness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 27 rows containing non-finite values (stat_smooth).
## Warning: Removed 27 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = GEI, y = scale(dObsPC))) +
geom_point() +
geom_smooth(method = lm, color = "steelblue", formula = "y ~ x") +
labs(title = "Conscientiousness Differences vs Gender Equality", x = "Gender Equality Index", y = "Conscientiousness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = GEI, y = scale(dObsPC))) +
geom_point() +
geom_smooth(method = lm, color = "steelblue", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Conscientiousness Differences vs Gender Equality (Square Root-N-Weighted)", x = "Gender Equality Index", y = "Conscientiousness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = dBiasC, y = scale(dObsPC))) +
geom_point() +
geom_smooth(method = lm, color = "gold", formula = "y ~ x") +
labs(title = "Sex Differences vs Test Bias", x = "Mean Conscientiousness Bias (dMACS)", y = "Mean Conscientiousness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = dBiasC, y = scale(dObsPC))) +
geom_point() +
geom_smooth(method = lm, color = "gold", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Sex Differences vs Test Bias", x = "Mean Conscientiousness Bias (dMACS)", y = "Mean Conscientiousness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = GEI, y = scale(dDiffCP))) +
geom_point() +
geom_smooth(method = lm, color = "orangered", formula = "y ~ x") +
labs(title = "Sex Differences in Conscientiousness sans Bias vs the Gender Equality Index", x = "Gender Equality Index", y = "Unbiased Conscientiousness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = GEI, y = scale(dDiffCP))) +
geom_point() +
geom_smooth(method = lm, color = "orangered", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Sex Differences in Conscientiousness sans Bias vs the Gender Equality Index (Square Root-N-Weighted)", x = "Gender Equality Index", y = "Unbiased Conscientiousness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = GEI, y = scale(dObsPE))) +
geom_point() +
geom_smooth(method = lm, color = "steelblue", formula = "y ~ x") +
labs(title = "Extraversion Differences vs Gender Equality", x = "Gender Equality Index", y = "Extraversion Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
ggplot(NeuCou, aes(x = GEI, y = scale(dObsPE))) +
geom_point() +
geom_smooth(method = lm, color = "steelblue", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Extraversion Differences vs Gender Equality (Square Root-N-Weighted)", x = "Gender Equality Index", y = "Extraversion Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
ggplot(NeuCou, aes(x = dBiasE, y = scale(dObsPE))) +
geom_point() +
geom_smooth(method = lm, color = "gold", formula = "y ~ x") +
labs(title = "Sex Differences vs Test Bias", x = "Mean Extraversion Bias (dMACS)", y = "Mean Extraversion Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
ggplot(NeuCou, aes(x = dBiasE, y = scale(dObsPE))) +
geom_point() +
geom_smooth(method = lm, color = "gold", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Sex Differences vs Test Bias", x = "Mean Extraversion Bias (dMACS)", y = "Mean Extraversion Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
ggplot(NeuCou, aes(x = GEI, y = scale(dDiffEP))) +
geom_point() +
geom_smooth(method = lm, color = "orangered", formula = "y ~ x") +
labs(title = "Sex Differences in Extraversion sans Bias vs the Gender Equality Index", x = "Gender Equality Index", y = "Unbiased Extraversion Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
ggplot(NeuCou, aes(x = GEI, y = scale(dDiffEP))) +
geom_point() +
geom_smooth(method = lm, color = "orangered", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Sex Differences in Extraversion sans Bias vs the Gender Equality Index (Square Root-N-Weighted)", x = "Gender Equality Index", y = "Unbiased Extraversion Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
ggplot(NeuCou, aes(x = GEI, y = scale(dObsPA))) +
geom_point() +
geom_smooth(method = lm, color = "steelblue", formula = "y ~ x") +
labs(title = "Agreeableness Differences vs Gender Equality", x = "Gender Equality Index", y = "Agreeableness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = GEI, y = scale(dObsPA))) +
geom_point() +
geom_smooth(method = lm, color = "steelblue", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Agreeableness Differences vs Gender Equality (Square Root-N-Weighted)", x = "Gender Equality Index", y = "Agreeableness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = dBiasA, y = scale(dObsPA))) +
geom_point() +
geom_smooth(method = lm, color = "gold", formula = "y ~ x") +
labs(title = "Sex Differences vs Test Bias", x = "Mean Agreeableness Bias (dMACS)", y = "Mean Agreeableness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = dBiasA, y = scale(dObsPA))) +
geom_point() +
geom_smooth(method = lm, color = "gold", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Sex Differences vs Test Bias", x = "Mean Agreeableness Bias (dMACS)", y = "Mean Agreeableness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = GEI, y = scale(dDiffAP))) +
geom_point() +
geom_smooth(method = lm, color = "orangered", formula = "y ~ x") +
labs(title = "Sex Differences in Agreeableness sans Bias vs the Gender Equality Index", x = "Gender Equality Index", y = "Unbiased Agreeableness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = GEI, y = scale(dDiffAP))) +
geom_point() +
geom_smooth(method = lm, color = "orangered", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Sex Differences in Agreeableness sans Bias vs the Gender Equality Index (Square Root-N-Weighted)", x = "Gender Equality Index", y = "Unbiased Agreeableness Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = GEI, y = scale(dObsPN))) +
geom_point() +
geom_smooth(method = lm, color = "steelblue", formula = "y ~ x") +
labs(title = "Neuroticism Differences vs Gender Equality", x = "Gender Equality Index", y = "Neuroticism Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
ggplot(NeuCou, aes(x = GEI, y = scale(dObsPN))) +
geom_point() +
geom_smooth(method = lm, color = "steelblue", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Neuroticism Differences vs Gender Equality (Square Root-N-Weighted)", x = "Gender Equality Index", y = "Neuroticism Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
ggplot(NeuCou, aes(x = dBiasN, y = scale(dObsPN))) +
geom_point() +
geom_smooth(method = lm, color = "gold", formula = "y ~ x") +
labs(title = "Sex Differences vs Test Bias", x = "Mean Neuroticism Bias (dMACS)", y = "Mean Neuroticism Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
ggplot(NeuCou, aes(x = dBiasN, y = scale(dObsPN))) +
geom_point() +
geom_smooth(method = lm, color = "gold", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Sex Differences vs Test Bias", x = "Mean Neuroticism Bias (dMACS)", y = "Mean Neuroticism Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
ggplot(NeuCou, aes(x = GEI, y = scale(dDiffNP))) +
geom_point() +
geom_smooth(method = lm, color = "orangered", formula = "y ~ x") +
labs(title = "Sex Differences in Neuroticism sans Bias vs the Gender Equality Index", x = "Gender Equality Index", y = "Unbiased Neuroticism Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
ggplot(NeuCou, aes(x = GEI, y = scale(dDiffNP))) +
geom_point() +
geom_smooth(method = lm, color = "orangered", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Sex Differences in Neuroticism sans Bias vs the Gender Equality Index (Square Root-N-Weighted)", x = "Gender Equality Index", y = "Unbiased Neuroticism Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
ggplot(NeuCou, aes(x = GEI, y = scale(dObsPG))) +
geom_point() +
geom_smooth(method = lm, color = "steelblue", formula = "y ~ x") +
labs(title = "Personality Differences vs Gender Equality", x = "Gender Equality Index", y = "Personality Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Warning: Removed 3 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = GEI, y = scale(dObsPG))) +
geom_point() +
geom_smooth(method = lm, color = "steelblue", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Personality Differences vs Gender Equality (Square Root-N-Weighted)", x = "Gender Equality Index", y = "Personality Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Warning: Removed 3 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = dBiasG, y = scale(dObsG))) +
geom_point() +
geom_smooth(method = lm, color = "gold", formula = "y ~ x") +
labs(title = "Sex Differences vs Test Bias", x = "Mean Personality Bias (dMACS)", y = "Mean Personality Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 20 rows containing non-finite values (stat_smooth).
## Warning: Removed 20 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = dBiasG, y = scale(dObsG))) +
geom_point() +
geom_smooth(method = lm, color = "gold", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Sex Differences vs Test Bias", x = "Mean Personality Bias (dMACS)", y = "Mean Personality Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 20 rows containing non-finite values (stat_smooth).
## Warning: Removed 20 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = GEI, y = scale(dDiffGP))) +
geom_point() +
geom_smooth(method = lm, color = "orangered", formula = "y ~ x") +
labs(title = "Sex Differences in Personality sans Bias vs the Gender Equality Index", x = "Gender Equality Index", y = "Unbiased Personality Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 20 rows containing non-finite values (stat_smooth).
## Warning: Removed 20 rows containing missing values (geom_point).
ggplot(NeuCou, aes(x = GEI, y = scale(dDiffGP))) +
geom_point() +
geom_smooth(method = lm, color = "orangered", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Sex Differences in Personality sans Bias vs the Gender Equality Index (Square Root-N-Weighted)", x = "Gender Equality Index", y = "Unbiased Personality Difference (Cohen's d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
## Warning: Removed 20 rows containing non-finite values (stat_smooth).
## Warning: Removed 20 rows containing missing values (geom_point).
Note: I did not assess the comparability of correlation matrices, since they were assumed to be different.
ggplot(NeuCou, aes(x = GEI, y = scale(BigD))) +
geom_point() +
geom_smooth(method = lm, color = "steelblue", formula = "y ~ x") +
labs(title = "Personality Differences vs Gender Equality", x = "Gender Equality Index", y = "Personality Difference (Mahalanobis' d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
ggplot(NeuCou, aes(x = GEI, y = scale(BigD))) +
geom_point() +
geom_smooth(method = lm, color = "steelblue", formula = "y ~ x", mapping = aes(weight = sqrt(N))) +
labs(title = "Personality Differences vs Gender Equality (Square Root-N-Weighted)", x = "Gender Equality Index", y = "Personality Difference (Mahalanobis' d)") +
theme_bw() +
theme(text = element_text(size = 12, family = "serif"), plot.title = element_text(hjust = 0.5))
I used HC5 robust regressions because this heteroskedasticity-consistent standard error beats at least the lmrob() MM and collapses to HC4, which beats HC3, which beats HC2 and 1, especially at small sample sizes. The second set of results is \(\sqrt{N}\) weighted results.
G1 <- lm(scale(dObsPO) ~ scale(GEI), NeuCou); VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.9677e-17 1.3001e-01 0.00 1.0000
## scale(GEI) 4.2633e-02 1.0399e-01 0.41 0.6834
G2 <- lm(scale(dBiasO) ~ scale(GEI), NeuCou); VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.023169 0.161003 -0.1439 0.8865
## scale(GEI) -0.246869 0.175001 -1.4107 0.1686
G3 <- lm(scale(dDiffOP) ~ scale(GEI), NeuCou); VG3 <- vcovHC(G3, type = "HC5"); VM3 <- coeftest(G3, vcov = VG3); VM3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.025753 0.180084 -0.1430 0.8872
## scale(GEI) -0.274399 0.198379 -1.3832 0.1768
N1 <- lm(N ~ GEI, NeuCou); VN1 <- vcovHC(N1, type = "HC5"); VN1 <- coeftest(N1, vcov = VN1); VN1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -45732 37994 -1.2036 0.2337
## GEI 78745 63348 1.2431 0.2189
N2 <- lm(N ~ dObsPO, NeuCou); VN2 <- vcovHC(N2, type = "HC5"); VN2 <- coeftest(N2, vcov = VN2); VN2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24242.1 20606.7 1.1764 0.2443
## dObsPO 2723.4 2569.3 1.0600 0.2936
N3 <- lm(N ~ dBiasO, NeuCou); VN3 <- vcovHC(N3, type = "HC5"); VN3 <- coeftest(N3, vcov = VN3); VN3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3059.2 1213.9 2.5202 0.01728 *
## dBiasO -1954.3 1667.5 -1.1720 0.25043
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
N4 <- lm(N ~ dDiffOP, NeuCou); VN4 <- vcovHC(N4, type = "HC5"); VN4 <- coeftest(N4, vcov = VN4); VN4
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2433.313 1475.164 1.6495 0.1095
## dDiffOP -82.067 335.663 -0.2445 0.8085
G1 <- lm(scale(dObsPO) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.269402 0.276059 0.9759 0.3332
## scale(GEI) 0.097173 0.147315 0.6596 0.5122
G2 <- lm(scale(dBiasO) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.067887 0.137319 -0.4944 0.6246
## scale(GEI) -0.255579 0.129555 -1.9728 0.0578 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G3 <- lm(scale(dDiffOP) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG3 <- vcovHC(G3, type = "HC5"); VM3 <- coeftest(G3, vcov = VG3); VM3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.077093 0.184950 0.4168 0.6798
## scale(GEI) -0.282666 0.234235 -1.2068 0.2370
GD <- lm(scale(dBiasO) ~ scale(dObsPO), NeuCou); VGD <- vcovHC(GD, type = "HC5"); VMD <- coeftest(GD, vcov = VGD); VMD
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05951 0.21770 0.2734 0.7865
## scale(dObsPO) 0.32803 0.38633 0.8491 0.4026
GD <- lm(scale(dBiasO) ~ scale(dObsPO), NeuCou, weight = sqrt(N)); VGD <- vcovHC(GD, type = "HC5"); VMD <- coeftest(GD, vcov = VGD); VMD
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.051466 0.181797 -0.2831 0.7790
## scale(dObsPO) 0.341715 0.421466 0.8108 0.4239
G1 <- lm(scale(dObsPC) ~ scale(GEI), NeuCou); VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0075475 0.1312209 0.0575 0.95434
## scale(GEI) -0.1175107 0.0671953 -1.7488 0.08591 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G2 <- lm(scale(dBiasC) ~ scale(GEI), NeuCou); VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0051433 0.1444186 -0.0356 0.9717
## scale(GEI) 0.0801215 0.1489138 0.5380 0.5930
G3 <- lm(scale(dDiffCP) ~ scale(GEI), NeuCou); VG3 <- vcovHC(G3, type = "HC5"); VM3 <- coeftest(G3, vcov = VG3); VM3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.012917 0.141129 0.0915 0.92745
## scale(GEI) -0.201222 0.114687 -1.7545 0.08572 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
N1 <- lm(N ~ dObsPC, NeuCou); VN1 <- vcovHC(N1, type = "HC5"); VN1 <- coeftest(N1, vcov = VN1); VN1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16831.5 13168.8 1.2781 0.2066
## dObsPC 1512.4 1355.2 1.1160 0.2693
N2 <- lm(N ~ dBiasC, NeuCou); VN2 <- vcovHC(N2, type = "HC5"); VN2 <- coeftest(N2, vcov = VN2); VN2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11843.4 8766.6 1.3510 0.1830
## dBiasC -81617.9 88971.2 -0.9174 0.3635
N3 <- lm(N ~ dDiffCP, NeuCou); VN3 <- vcovHC(N3, type = "HC5"); VN3 <- coeftest(N3, vcov = VN3); VN3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 49109.3 42014.7 1.1689 0.2482
## dDiffCP 9995.6 8989.1 1.1120 0.2717
G1 <- lm(scale(dObsPC) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.160782 0.101818 1.5791 0.1200
## scale(GEI) -0.012415 0.057848 -0.2146 0.8309
G2 <- lm(scale(dBiasC) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.266536 0.201395 -1.3234 0.192
## scale(GEI) 0.022907 0.122939 0.1863 0.853
G3 <- lm(scale(dDiffCP) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG3 <- vcovHC(G3, type = "HC5"); VM3 <- coeftest(G3, vcov = VG3); VM3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.267190 0.212963 1.2546 0.2157
## scale(GEI) -0.041333 0.148700 -0.2780 0.7822
GD <- lm(scale(dBiasC) ~ scale(dObsPC), NeuCou); VGD <- vcovHC(GD, type = "HC5"); VMD <- coeftest(GD, vcov = VGD); VMD
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.032491 0.201318 0.1614 0.8725
## scale(dObsPC) -0.193861 0.565147 -0.3430 0.7331
GD <- lm(scale(dBiasC) ~ scale(dObsPC), NeuCou, weight = sqrt(N)); VGD <- vcovHC(GD, type = "HC5"); VMD <- coeftest(GD, vcov = VGD); VMD
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.081355 0.173203 -0.4697 0.6407
## scale(dObsPC) -0.740979 0.567185 -1.3064 0.1976
G1 <- lm(scale(dObsPE) ~ scale(GEI), NeuCou); VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.6486e-16 1.1940e-01 0.000 1.000000
## scale(GEI) -4.0859e-01 1.2007e-01 -3.403 0.001225 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G2 <- lm(scale(dBiasE) ~ scale(GEI), NeuCou); VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.3621e-16 1.1871e-01 0.0000 1
## scale(GEI) -4.0437e-01 7.7822e-02 -5.1961 2.849e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G3 <- lm(scale(dDiffEP) ~ scale(GEI), NeuCou); VG3 <- vcovHC(G3, type = "HC5"); VM3 <- coeftest(G3, vcov = VG3); VM3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.9404e-16 1.2147e-01 0.0000 1.000000
## scale(GEI) -3.7031e-01 1.2102e-01 -3.0599 0.003372 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
N1 <- lm(N ~ dObsPE, NeuCou); VN1 <- vcovHC(N1, type = "HC5"); VN1 <- coeftest(N1, vcov = VN1); VN1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1627.23 19144.09 -0.0850 0.9326
## dObsPE 778.03 1571.36 0.4951 0.6224
N2 <- lm(N ~ dBiasE, NeuCou); VN2 <- vcovHC(N2, type = "HC5"); VN2 <- coeftest(N2, vcov = VN2); VN2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10325.7 7600.4 1.3586 0.1796
## dBiasE -11102.3 12860.8 -0.8633 0.3916
N3 <- lm(N ~ dDiffEP, NeuCou); VN3 <- vcovHC(N3, type = "HC5"); VN3 <- coeftest(N3, vcov = VN3); VN3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3815.54 20499.83 -0.1861 0.8530
## dDiffEP 921.84 1679.79 0.5488 0.5853
G1 <- lm(scale(dObsPE) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.15820 0.12959 1.2208 0.227183
## scale(GEI) -0.49409 0.15969 -3.0941 0.003057 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G2 <- lm(scale(dBiasE) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.028612 0.082230 -0.3479 0.7292
## scale(GEI) -0.350167 0.079878 -4.3838 5.076e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G3 <- lm(scale(dDiffEP) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG3 <- vcovHC(G3, type = "HC5"); VM3 <- coeftest(G3, vcov = VG3); VM3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.16309 0.13202 1.2353 0.221793
## scale(GEI) -0.46258 0.16101 -2.8729 0.005704 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
GD <- lm(scale(dBiasE) ~ scale(dObsPE), NeuCou); VGD <- vcovHC(GD, type = "HC5"); VMD <- coeftest(GD, vcov = VGD); VMD
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.2527e-16 1.2888e-01 0.0000 1.0000
## scale(dObsPE) 1.6114e-01 1.2614e-01 1.2775 0.2066
GD <- lm(scale(dBiasE) ~ scale(dObsPE), NeuCou, weight = sqrt(N)); VGD <- vcovHC(GD, type = "HC5"); VMD <- coeftest(GD, vcov = VGD); VMD
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.13469 0.07881 -1.7091 0.09288 .
## scale(dObsPE) 0.10728 0.10605 1.0116 0.31598
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G1 <- lm(scale(dObsPA) ~ scale(GEI), NeuCou); VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0081824 0.1292452 -0.0633 0.9497
## scale(GEI) 0.2014612 0.1599238 1.2597 0.2131
G2 <- lm(scale(dBiasA) ~ scale(GEI), NeuCou); VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.016629 0.134794 -0.1234 0.90233
## scale(GEI) 0.305316 0.117016 2.6092 0.01207 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G3 <- lm(scale(dDiffAP) ~ scale(GEI), NeuCou); VG3 <- vcovHC(G3, type = "HC5"); VM3 <- coeftest(G3, vcov = VG3); VM3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0016581 0.1369248 -0.0121 0.9904
## scale(GEI) 0.0304435 0.2013069 0.1512 0.8804
N1 <- lm(N ~ dObsPA, NeuCou); VN1 <- vcovHC(N1, type = "HC5"); VN1 <- coeftest(N1, vcov = VN1); VN1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -13874.1 19367.3 -0.7164 0.4768
## dObsPA 1323.9 1430.1 0.9257 0.3587
N2 <- lm(N ~ dBiasA, NeuCou); VN2 <- vcovHC(N2, type = "HC5"); VN2 <- coeftest(N2, vcov = VN2); VN2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12200.4 9097.6 1.3411 0.1862
## dBiasA -9494.6 10742.7 -0.8838 0.3812
N3 <- lm(N ~ dDiffAP, NeuCou); VN3 <- vcovHC(N3, type = "HC5"); VN3 <- coeftest(N3, vcov = VN3); VN3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -17804.6 33147.5 -0.5371 0.5937
## dDiffAP 1585.4 2179.1 0.7276 0.4704
G1 <- lm(scale(dObsPA) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04189 0.10535 0.3976 0.6924
## scale(GEI) 0.10734 0.17051 0.6295 0.5316
G2 <- lm(scale(dBiasA) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.13830 0.18738 -0.7381 0.46407
## scale(GEI) 0.22959 0.12854 1.7862 0.08039 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G3 <- lm(scale(dDiffAP) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG3 <- vcovHC(G3, type = "HC5"); VM3 <- coeftest(G3, vcov = VG3); VM3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.021353 0.133596 0.1598 0.8737
## scale(GEI) -0.059440 0.214509 -0.2771 0.7829
GD <- lm(scale(dBiasA) ~ scale(dObsPA), NeuCou); VGD <- vcovHC(GD, type = "HC5"); VMD <- coeftest(GD, vcov = VGD); VMD
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.03951 0.20822 -0.1898 0.8503
## scale(dObsPA) 0.22814 0.46806 0.4874 0.6282
GD <- lm(scale(dBiasA) ~ scale(dObsPA), NeuCou, weight = sqrt(N)); VGD <- vcovHC(GD, type = "HC5"); VMD <- coeftest(GD, vcov = VGD); VMD
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.085284 0.326356 -0.2613 0.795
## scale(dObsPA) 0.153858 0.886544 0.1735 0.863
G1 <- lm(scale(dObsPN) ~ scale(GEI), NeuCou); VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.9270e-17 1.2330e-01 0.0000 1.00000
## scale(GEI) -3.3951e-01 1.3456e-01 -2.5232 0.01444 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G2 <- lm(scale(dBiasN) ~ scale(GEI), NeuCou); VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.9695e-17 1.2267e-01 0.0000 1.000000
## scale(GEI) -3.3410e-01 1.0267e-01 -3.2541 0.001915 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G3 <- lm(scale(dDiffNP) ~ scale(GEI), NeuCou); VG3 <- vcovHC(G3, type = "HC5"); VM3 <- coeftest(G3, vcov = VG3); VM3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.9946e-16 1.2513e-01 0.0000 1.00000
## scale(GEI) -2.9316e-01 1.2938e-01 -2.2659 0.02727 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
N1 <- lm(N ~ dObsPN, NeuCou); VN1 <- vcovHC(N1, type = "HC5"); VN1 <- coeftest(N1, vcov = VN1); VN1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37475.4 39731.6 0.9432 0.3496
## dObsPN -3510.6 4179.2 -0.8400 0.4044
N2 <- lm(N ~ dBiasN, NeuCou); VN2 <- vcovHC(N2, type = "HC5"); VN2 <- coeftest(N2, vcov = VN2); VN2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9478.1 6805.0 1.3928 0.1691
## dBiasN 45844.3 43572.8 1.0521 0.2972
N3 <- lm(N ~ dDiffNP, NeuCou); VN3 <- vcovHC(N3, type = "HC5"); VN3 <- coeftest(N3, vcov = VN3); VN3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46043.8 47806.6 0.9631 0.3396
## dDiffNP -4625.4 5224.8 -0.8853 0.3797
G1 <- lm(scale(dObsPN) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.076794 0.177442 0.4328 0.6668
## scale(GEI) -0.366571 0.151439 -2.4206 0.0187 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G2 <- lm(scale(dBiasN) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.25131 0.24239 1.0368 0.3042
## scale(GEI) -0.23124 0.13831 -1.6720 0.1000
G3 <- lm(scale(dDiffNP) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG3 <- vcovHC(G3, type = "HC5"); VM3 <- coeftest(G3, vcov = VG3); VM3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.039907 0.201565 0.1980 0.84376
## scale(GEI) -0.335980 0.151694 -2.2148 0.03078 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
GD <- lm(scale(dBiasN) ~ scale(dObsPN), NeuCou); VGD <- vcovHC(GD, type = "HC5"); VMD <- coeftest(GD, vcov = VGD); VMD
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.8685e-17 1.2972e-01 0.0000 1.0000
## scale(dObsPN) 1.4922e-01 1.4639e-01 1.0193 0.3124
GD <- lm(scale(dBiasN) ~ scale(dObsPN), NeuCou, weight = sqrt(N)); VGD <- vcovHC(GD, type = "HC5"); VMD <- coeftest(GD, vcov = VGD); VMD
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.18668 0.31145 0.5994 0.5513
## scale(dObsPN) 0.14223 0.22513 0.6318 0.5301
G1 <- lm(scale(dObsPG) ~ scale(GEI), NeuCou); VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0036585 0.1321063 -0.0277 0.9780
## scale(GEI) -0.1612396 0.1001508 -1.6100 0.1132
G2 <- lm(scale(dBiasG) ~ scale(GEI), NeuCou); VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.088382 0.152209 -0.5807 0.564988
## scale(GEI) -0.446200 0.163635 -2.7268 0.009718 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G3 <- lm(scale(dDiffGP) ~ scale(GEI), NeuCou); VG3 <- vcovHC(G3, type = "HC5"); VM3 <- coeftest(G3, vcov = VG3); VM3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0025537 0.1639716 0.0156 0.9877
## scale(GEI) 0.0128925 0.1514597 0.0851 0.9326
G4 <- lm(scale(dDiffGP) ~ scale(dBiasG), NeuCou); VG4 <- vcovHC(G4, type = "HC5"); VM4 <- coeftest(G4, vcov = VG4); VM4
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.3252e-16 1.5872e-01 0.0000 1.0000
## scale(dBiasG) -1.7076e-01 1.6527e-01 -1.0332 0.3082
N1 <- lm(N ~ dObsPG, NeuCou); VN1 <- vcovHC(N1, type = "HC5"); VN1 <- coeftest(N1, vcov = VN1); VN1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -30775.1 33073.7 -0.9305 0.3563
## dObsPG -4182.6 3970.2 -1.0535 0.2968
N2 <- lm(N ~ dBiasG, NeuCou); VN2 <- vcovHC(N2, type = "HC5"); VN2 <- coeftest(N2, vcov = VN2); VN2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14083 10355 1.3600 0.1821
## dBiasG 43735 47000 0.9305 0.3581
N3 <- lm(N ~ dDiffGP, NeuCou); VN3 <- vcovHC(N3, type = "HC5"); VN3 <- coeftest(N3, vcov = VN3); VN3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -98709.6 84008.8 -1.1750 0.2475
## dDiffGP -11451.0 9546.9 -1.1994 0.2380
G1 <- lm(scale(dObsPG) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.10667 0.11666 -0.9144 0.36456
## scale(GEI) -0.21465 0.11732 -1.8296 0.07283 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G2 <- lm(scale(dBiasG) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.12281 0.21221 0.5787 0.5663
## scale(GEI) -0.19652 0.20207 -0.9725 0.3371
G3 <- lm(scale(dDiffGP) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG3 <- vcovHC(G3, type = "HC5"); VM3 <- coeftest(G3, vcov = VG3); VM3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.22941 0.15675 -1.4636 0.1518
## scale(GEI) -0.20249 0.19024 -1.0644 0.2940
G4 <- lm(scale(dDiffGP) ~ scale(dBiasG), NeuCou, weight = sqrt(N)); VG4 <- vcovHC(G4, type = "HC5"); VM4 <- coeftest(G4, vcov = VG4); VM4
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.25557 0.14797 -1.7271 0.09248 .
## scale(dBiasG) -0.19364 0.15886 -1.2189 0.23058
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
GD <- lm(scale(dBiasG) ~ scale(dObsPG), NeuCou); VGD <- vcovHC(GD, type = "HC5"); VMD <- coeftest(GD, vcov = VGD); VMD
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00062324 0.15901397 -0.0039 0.9969
## scale(dObsPG) 0.02934638 0.20948873 0.1401 0.8894
GD <- lm(scale(dBiasG) ~ scale(dObsPG), NeuCou, weight = sqrt(N)); VGD <- vcovHC(GD, type = "HC5"); VMD <- coeftest(GD, vcov = VGD); VMD
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08203582 0.19972890 0.4107 0.6836
## scale(dObsPG) 0.00014695 0.20522258 0.0007 0.9994
G1 <- lm(scale(BigD) ~ scale(GEI), NeuCou); VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.6006e-16 1.2911e-01 0.0000 1.0000
## scale(GEI) 1.7448e-01 1.4345e-01 1.2163 0.2289
G2 <- lm(scale(BigD) ~ scale(dBiasG), NeuCou); VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.18487 0.14251 -1.2972 0.20258
## scale(dBiasG) 0.30914 0.14075 2.1963 0.03441 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
N1 <- lm(N ~ BigD, NeuCou); VN1 <- vcovHC(N1, type = "HC5"); VN1 <- coeftest(N1, vcov = VN1); VN1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13257.8 11241.0 1.1794 0.2431
## BigD -9792.6 14764.2 -0.6633 0.5098
G1 <- lm(scale(BigD) ~ scale(GEI), NeuCou, weight = sqrt(N)); VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.094942 0.099029 -0.9587 0.3417
## scale(GEI) 0.155901 0.121716 1.2809 0.2054
G2 <- lm(scale(BigD) ~ scale(dBiasG), NeuCou, weight = sqrt(N)); VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.15699 0.10331 -1.5196 0.13711
## scale(dBiasG) 0.22384 0.13101 1.7085 0.09592 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Firstly, Giolla & Kajonius (2018) found a number of interesting results. Among them were
My results differed somewhat, most likely - because our samples overlapped - because I did not subset by age.
Openness was higher in more gender-unequal countries. Using the means, there was a significant negative relationship between openness and GEI and when sex and an interaction were thrown in, this remained but was marginal and the interaction was not significant. At the individual level, the relationship was significant, went in the same direction, and the interaction was also highly significant, but with sample sizes this large, it is hard to say how meaningful that is.
Conscientiousness was higher in more gender-unequal countries. Using the means, there was a significant relationship between GEI and conscientiousness. At the level of the means, there was no interaction with sex. At the individual level, the relationship between GEI and conscientiousness was significant and negative and with an interaction thrown in, it was no longer significant, which, at this (\(N \approx 600000\)) sample size, is excellent evidence for the interaction’s absence. There was a significant effect of sex, such that women had a slightly lower mean.
Extraversion findings largely mirrored those observed by Giolla & Kajonius, with extraversion higher in less gender-equal countries and the gap between the sexes growing with gender equality. Moreover, there did appear to be some minor evidence for a cross-over at the lowest levels of gender equality. Interestingly, though the graph appeared like it would showcase the greatest evidence for an interaction of those reviewed so far, there was no significant interaction at the level of the means. There was, however, evidence for an interaction at the individual level and crossover was evident. Women appeared more extraverted than men.
The agreeableness results were like Giolla & Kajonius’ as well, in that agreeableness was higher in more gender-equal countries. The lines for men and women are basically parallel with a slight appearance that they might have been diverging in more gender-equal countries, but there was still no significant interaction. There was a significant interaction at the individual level but, again, remember the sample sizes involved here. The interaction was so marginal for this sample size that I have my doubts about its veracity. Apparently, if there is an interaction of this sort, it must be so small that it is hard to observe as an interaction. Women averaged considerably more agreeable than men.
The neuroticism results were almost identical to those for agreeableness except the relationship between neuroticism and gender equality ran in the opposite direction.
The “GFP” results were some of the most interesting. At the mean level, the relationship between gender equality and the GFP scores was positive and not significant, with no significant interaction even though it definitely appeared to be there. The individual-level results supported such an interaction and higher male means in GFP scores at lower levels of gender equality as compared to higher levels for the female group in more equal countries.
Overall, findings were only variously consistent with Giolla & Kajonius’ conclusions. To overcome the exceptionally limited power to detect an interaction of a meaningful magnitude, it would clearly be better to just use a standardized effect size for the differences between groups, like Cohen’s d, Hedge’s g, Glass’ Delta, or Mahalanobis’ D.
Next, I investigated bias across countries, since a social comparison-based explanation for the gender equality paradox predicts greater mean bias in more gender-equal countries - and thus greater variance in general -, whereas a choice-based explanation predicated on the idea that in more gender-equal countries women are more able to express their ‘innate’ personalities would lead to additional bias (and thus relatively greater female variability) in more gender-unequal countries. However, if this effect was expected to act on both men and women homogeneously, it would just shift the distributions and not evince bias. This is, however, extremely unlikely as its specification would make it incredibly unique, but it would generate cross-cultural bias. Unfortunately, we could not ascertain whether it or some other form of bias was driving cross-cultural measurement non-invariance since much more than that surely impinges on cross-cultural bias. Interestingly, theories that rely on the assumption that women will be affected and men not (since, e.g., men have ample opportunity to develop across countries while women do not, as but one example of a possibility) predict larger female variances, though that is not observed. One could posit that men vary more for other reasons and the effect is counterbalanced as such, but we would nonetheless predict greater female variances in the populations expected to showcase bias, compared to men in their own population and women elsewhere, and as far as I know, this is not the case (and the opposite may be true), but it nonetheless deserves attention. I may investigate this in the future. There are many other theories that could explain these findings, but these are what I am investigating right now: whether the predictions of a social comparison explanation (i.e., greater bias in more gender-equal countries) or a naive equal/enhanced opportunity and environmental self-selection explanation pan out (i.e., greater bias in more gender-unequal countries).
Bias was not significantly related to GEI for openness or conscientiousness with unweighted or weighted (by \(\sqrt{N}\)) regressions. Insignificant relationships remained with imputation and using adjusted models to increase sample size. Bias was related to GEI for extraversion with and without weights and this relationship was negative in direction (i.e., more gender-equal countries showcased less bias). Bias was positively related to GEI for agreeableness but when the results were weighted, this relationship disappeared. Bias was related to GEI for neuroticism when unweighted but not when weighted; the relationship was negative in direction. For a “GFP” - which is basically just ‘personality’ in general, GEI was related to bias but this did not survive weighting.
Using standardized differences to assess the Gender Equality Paradox requires much smaller samples to get sufficient power compared to testing for an interaction. Despite this, most variables analyzed were not significantly related to GEI. Openness, conscientiousness, agreeableness, and the GFP d values and Mahalanobis’ D were not related to GEI, while extraversion and neuroticism differences were significantly negatively related to GEI across all specifications, though in each case, this was marginal.
A robustness check was conducted (code for it is presented but not run here) because a colleague did not intuit that negative points would not affect the results. What this entailed was setting the lowest value for observed differences to 0 and adjusting everything up by the same amount so that higher positive values of d would indicate greater differences, because without this adjustment, a negative value might be present that would also be the largest difference in the data. This could obviously have been assessed visually so it is hardly a real robustness check, but it makes it possible to check the consistency of the results without confusion. All results were consistent with and without this check and much of this should be unsurprising as it did not apply to all tests.
With unstandardized data, the relationship between means and GEI for openness, conscientiousness, extraversion, agreeableness, neuroticism, the GFP, and Mahalanobis’ D were, respectively, positive and nonsignificant, negative and nonsignificant, negative and significant, positive and nonsignificant, negative and significant, positive and nonsignificant, and positive and nonsignificant; for bias, these relationships were negative and nonsignificant, positive and nonsignificant, negative and significant, positive and significant, negative and significant, and negative and significant. Out of the significant relationships, the bias-GEI relationship for agreeableness and the same relationship for neuroticism became nonsignificant when weighting by N and the agreeableness relationship was not significant after removing 0 values, while, for GFP, they were not significant at all.
Overall, there was limited support for the gender equality paradox, but since my goal was not to test it as much as to test if there was a systematic pattern to sex-related bias across countries, that may matter little, unless only those datasets in which it is present ought to showcase measurement bias. Ignoring that possibility that I do not think people could plausibly argue for, the mean (unweighted and then weighted) amounts of bias across countries were very small. As shown above, the averages were never greater than |0.1| d. It is hard to argue that bias explains anything about sex differences across countries as a result. Moreover, relationships were typically not meaningful, although mean values did vary considerably with bias in some cases. This is misleading though, as the absolute change with those relationships is nearly zero. The differences by the degree of test bias may have coefficients like r = 0.40, but that represents only, say, 0.01 d of bias and is thus hardly meaningful.
Provided these results generalize, there are considerable implications for theorizing not only about the gender equality paradox, but about sex differences across countries in general, regardless of their functional form. Assuming generalization, explanations cannot plausibly take the form of sex-specific explanations, as there is too little bias to explain the typically much greater scale of sex differences. Explanations to the effect of “in country Y, where the sexes differ a lot, the greater difference compared to country W is due to the greater/lesser freedom of women to differentiate” cannot be argued if the extent of bias is the same or altogether too small in both countries. Those explanations are not consistent with (near-) measurement invariance. To argue around this requires positing variables that affect one or another sex or both completely homogeneously, which, with the differences in things like sex and gender identification within sex or any other number of background variables, does not seem like it would be easy to argue for without delving into the realm of not only speculation, but total nonsense. Perhaps someone wants to argue for a genital by environment interaction and could test their theory with certain intersex individuals. That sounds like nonsense, but that it is nonsense is what I want to convey: large effect homogeneously-acting variables that no one can explain? Good luck. We have very large samples, so it is not as if heterogeneity of effect for such a variable would be hidden unless selection bias can militate against it.
In fewer words, explanations for sex differences across countries cannot plausibly be those which entail bias with respect to sex, as so many existing theories do. Even theories like attributional ones which imply matching, counterbalanced effects in men and women if they are to be thrown around as explanations for the gender equality paradox (which may not exist) are hard to believe since, really, when are there exactly matching in magnitude, homogeneously-acting effects in different, at least vastly discretely defined (as has to be the case if we have large samples and proposed large effects, unless differences in definition internally have no effect) populations? There is, at present, very little evidence for theories of sex differences in personality that suggest considerable psychometric bias. Most theories imply considerable bias and none I am aware of even mention this.
Levels of bias may be understated due to ipsativity as a result of not setting equal parameters that are equal or at least not significantly different to be equal. This problem is more commonly observed with item analyses, where it is has also been dubbed circularity bias. This is a problem that may have plagued an earlier cross-country alignment analysis done as part of an investigation of the socioecological complexity hypothesis (further harmed by the fact that alignment does not fully equate parameters and bias will thus be represented in the resulting, e.g., factor scores). To assess this possibility would require fitting partial metric and scalar models for each country and then calculating the amount of bias. I have serious doubts that this would change the aggregate results since it would suggest larger ipsativity bias than I have ever seen, but I welcome someone checking (I did not, since it would be very time-consuming).
The gender equality index may be altogether too irrelevant to be useful. Many of the things included in it like infant mortality sex differentials should hardly be expected to have relationships with personality at all, except insofar as they represent grounds for theories like that reduced poverty allows differentiation. This may explain why earlier analyses that have investigated personality change with respect to gender equality changes returned null results: the changes in the indices do not have to be changes that have any meaning with respect to social influences on personality. Those tests, thus, do not represent meaningful challenges to the gender equality paradox.
My results come from the same dataset Giolla & Kajonius used but my results differed, perhaps because I used more countries, did not use their GEI values and instead used the ones for the year that was closest to the sampling period for the personality data, and moreover, I did not subset in the ways they did. I cannot think of a theoretically-informed reason why I should do that though, since I have no reason to think the effects they argued for should be strongly age-varying beyond the range 16-69 unless we think there is a large early-life social effect that persists in older people and that these were a large part of the dataset. Because this would entail a lot of bias not actually observed, I doubt it! Importantly, the dataset I ended up using was around 4x as large as the one they used. I did use a smaller inclusion sample size (100 per sex vs 1000 total) because factor loadings ought to be stable enough at n = 100/k = 2, but this does not explain my results when I subset to their sample. I did not correct for missingness by imputing the item mean, but doing this also does not undo my result. With regards to Mahalanobis’ D, perhaps Tucker’s congruence coefficient would not have worked well here but, again, subsetting to their countries didn’t change the results. In any case, it does not matter. The point about bias remains and if I have made an error in my code - which is all there -, I can just update it and reassess the results. I welcome any emails to this effect!
The selected nature of the sample may impact my results. There are more invalid responses in internet than in pen-and-paper assessments in the typical assessment. Individuals who are online may be, as a colleague remarked to me recently, “basically WEIRD [but] in non-WEIRD places” as well, but this would also threaten the evidence for the effect in general and would make it so that only pen-and-paper testing should be acknowledged as theory-relevant without additional assurances about data quality when using online samples that are obtained through volunteering rather than seeking participants.
GK <- data.frame("Country" = c("Australia", "Canada", "China", "Finland", "France", "Germany", "India", "Ireland", "Japan", "Malaysia", "Mexico", "Netherlands", "New Zealand", "Norway", "Philippines", "Romania", "Singapore", "South Africa", "South Korea", "Sweden", "UK", "USA"),
GEI = c(0.72, 0.72, 0.68, 0.82, 0.70, 0.75, 0.61, 0.76, 0.65, 0.65, 0.65, 0.74, 0.78, 0.82, 0.76, 0.68, 0.67, 0.74, 0.62, 0.81, 0.74, 0.72),
dO = c(-0.19, -0.14, -0.10, -0.25, -0.24, -0.16, -0.17, -0.13, -0.17, -0.01, -0.14, -0.23, -0.16, -0.28, -0.04, -0.35, -0.02, -0.22, -0.18, -0.27, -0.14, -0.17),
dE = c(-0.07, -0.07, 0.03, -0.23, -0.2, -0.2, -0.02, -0.04, -0.10, -0.01, 0.04, 0.12, -0.21, -0.27, 0.10, -0.02, 0.12, -0.04, -0.01, -0.14, -0.14, -0.02),
dA = c(-0.55, -0.59, -0.32, -0.35, -0.54, -0.52, -0.37, -0.52, -0.35, -0.23, -0.39, -0.68, -0.47, -0.52, -0.36, -0.40, -0.34, -0.47, -0.36, -0.56, -0.60, -0.58),
dC = c(-0.08, -0.16, 0.01, -0.07, 0.01, -0.2, -0.01, 0.07, -0.02, -0.05, 0.16, -0.15, -0.13, -0.14, 0.03, 0.02, 0.23, -0.12, -0.02, -0.11, -0.14, -0.14),
dN = c(-0.40, -0.39, -0.10, -0.40, -0.40, -0.35, -0.30, -0.41, -0.25, -0.27, -0.46, -0.42, -0.29, -0.41, -0.37, -0.37, -0.54, -0.27, -0.21, -0.43, -0.35, -0.38),
dAvg = c(0.26, 0.27, 0.11, 0.26, 0.28, 0.29, 0.17, 0.23, 0.18, 0.11, 0.24, 0.32, 0.25, 0.32, 0.18, 0.23, 0.25, 0.22, 0.16, 0.30, 0.28, 0.26),
D = c(0.90, 0.94, 0.39, 0.85, 0.88, 0.91, 0.61, 0.85, 0.52, 0.49, 0.74, 1.02, 0.81, 0.98, 0.68, 0.78, 0.76, 0.71, 0.51, 0.98, 0.93, 0.91),
DC = c(1.01, 1.07, 0.47, 0.97, 1, 1.04, 0.70, 0.96, 0.59, 0.58, 0.86, 1.17, 0.94, 1.13, 0.79, 0.88, 0.84, 0.79, 0.59, 1.11, 1.06, 1.03))
G1 <- lm(scale(dO) ~ scale(GEI), GK); VG1 <- vcovHC(G1, type = "HC5"); VM1 <- coeftest(G1, vcov = VG1); VM1
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.5114e-17 2.0108e-01 0.0000 1.00000
## scale(GEI) -3.3216e-01 1.8645e-01 -1.7814 0.09003 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G2 <- lm(scale(dC) ~ scale(GEI), GK); VG2 <- vcovHC(G2, type = "HC5"); VM2 <- coeftest(G2, vcov = VG2); VM2
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.0639e-16 1.8605e-01 0.0000 1.000000
## scale(GEI) -4.8277e-01 1.5881e-01 -3.0399 0.006468 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G3 <- lm(scale(dE) ~ scale(GEI), GK); VG3 <- vcovHC(G3, type = "HC5"); VM3 <- coeftest(G3, vcov = VG3); VM3
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.6552e-16 1.8080e-01 0.0000 1.000000
## scale(GEI) -5.2932e-01 1.5998e-01 -3.3086 0.003506 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G4 <- lm(scale(dA) ~ scale(GEI), GK); VG4 <- vcovHC(G4, type = "HC5"); VM4 <- coeftest(G4, vcov = VG4); VM4
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.2205e-16 1.8739e-01 0.0000 1.00000
## scale(GEI) -4.8691e-01 1.8602e-01 -2.6175 0.01649 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G5 <- lm(scale(dN) ~ scale(GEI), GK); VG5 <- vcovHC(G5, type = "HC5"); VM5 <- coeftest(G5, vcov = VG5); VM5
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.5127e-17 2.0039e-01 0.0000 1.00000
## scale(GEI) -3.3337e-01 1.7728e-01 -1.8805 0.07468 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G6 <- lm(scale(dAvg) ~ scale(GEI), GK); VG6 <- vcovHC(G6, type = "HC5"); VM6 <- coeftest(G6, vcov = VG6); VM6
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.2596e-16 1.6387e-01 0.0000 1.0000000
## scale(GEI) 6.3575e-01 1.3694e-01 4.6425 0.0001571 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G7 <- lm(scale(D) ~ scale(GEI), GK); VG7 <- vcovHC(G7, type = "HC5"); VM7 <- coeftest(G7, vcov = VG7); VM7
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.0101e-16 1.5613e-01 0.0000 1
## scale(GEI) 6.7753e-01 1.2605e-01 5.3749 2.921e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
G8 <- lm(scale(DC) ~ scale(GEI), GK); VG8 <- vcovHC(G8, type = "HC5"); VM8 <- coeftest(G8, vcov = VG8); VM8
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4237e-16 1.5395e-01 0.0000 1
## scale(GEI) 6.8865e-01 1.2447e-01 5.5324 2.047e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Redo with IPIP-NEO 300. Show the analyses with the outliers removed (results do not change) and explain how they are due to the realities of trying to fit the largest possible model to groups where they do not fit. Present results with factor scores and latent path models, the results of which correlate at just over r = 0.98. Use Giolla & Kajonius’ years’ values of the GEI, despite them being the wrong values for the years their data came from.
As a note, scaled plots/results were presented for a correlation interpretation, as in 1 SD of x = Z SD of Y.
Musek, J. (2007). A general factor of personality: Evidence for the Big One in the five-factor model. Journal of Research in Personality, 41(6), 1213–1233. https://doi.org/10.1016/j.jrp.2007.02.003
Giolla, E. M., & Kajonius, P. J. (2018). Sex differences in personality are larger in gender equal countries: Replicating and extending a surprising finding. International Journal of Psychology, 54(6), 705–711. https://doi.org/10.1002/ijop.12529