heplots (version 1.7.3)

Iwasaki_Big_Five: Personality Traits of Cultural Groups

Description

This dataset, from Grice & Iwasaki (2007), gives scores on the five personality scales of the NEO PI-r (Costa & McCrae, 1992), called the "Big Five" personality traits: Neuroticism, Extraversion, Openness-to-Experience, Agreeableness, and Conscientiousness.

Format

A data frame with 203 observations on the following 7 variables.

ID

ID number

Group

a factor with levels Eur Asian_Amer Asian_Intl

N

Neuroticism score

E

Extraversion score

O

Openness score

A

Agreeableness score

C

Conscientiousness score

Details

The groups are:

Eur

European Americans (Caucasians living in the United States their entire lives)

Asian_Amer

Asian Americans (Asians living in the United States since before the age of 6 years)

Asian_Intl

Asian Internationals (Asians who moved to the United States after their 6th birthday)

The factor Group is set up to compare E vs. Asian and the two Asian groups

References

Costa Jr, P. T., & McCrae, R. R. (1992). Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor Inventory (NEOFFI) professional manual. Psychological Assessment Resources.

Examples

data(Iwasaki_Big_Five)
# use Helmert contrasts for groups
contrasts(Iwasaki_Big_Five$Group) <- 
   matrix(c(2, -1, -1,
            0, -1,  1), ncol=2)

str(Iwasaki_Big_Five)

Big5.mod <- lm(cbind(N, E, O, A, C) ~ Group, data=Iwasaki_Big_Five)
coef(Big5.mod)

car::Anova(Big5.mod)

# test contrasts
car::linearHypothesis(Big5.mod, "Group1", title = "Eur vs Asian")
car::linearHypothesis(Big5.mod, "Group2", title = "Asian: Amer vs Inter")

# heplots
labs <- c("Neuroticism", "Extraversion", "Openness", "Agreeableness", "Conscientiousness" )

heplot(Big5.mod,
       fill = TRUE, fill.alpha = 0.2, 
       cex.lab = 1.5,
       xlab = labs[1], ylab = labs[2])

heplot(Big5.mod, variables = c(2,5),
       fill = TRUE, fill.alpha = 0.2,
       cex.lab = 1.5,
       xlab = labs[2], ylab = labs[5])

pairs(Big5.mod, 
      fill = TRUE, fill.alpha = 0.2, var.labels = labs)


# canonical discriminant analysis
if (require(candisc)) { 
library(candisc)
Big5.can <- candisc(Big5.mod)
Big5.can
heplot(Big5.can, fill = TRUE, fill.alpha = 0.1)
}