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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

Source

Grice, J., & Iwasaki, M. (2007). A truly multivariate approach to MANOVA. Applied Multivariate Research, 12, 199-226. https://doi.org/10.22329/amr.v12i3.660.

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)
#> tibble [203 × 7] (S3: tbl_df/tbl/data.frame)
#>  $ ID   : num [1:203] 1 2 3 4 5 6 7 8 9 10 ...
#>  $ Group: Factor w/ 3 levels "Eur","Asian_Amer",..: 3 3 3 3 3 3 3 3 3 3 ...
#>   ..- attr(*, "contrasts")= num [1:3, 1:2] 2 -1 -1 0 -1 1
#>   .. ..- attr(*, "dimnames")=List of 2
#>   .. .. ..$ : chr [1:3] "Eur" "Asian_Amer" "Asian_Intl"
#>   .. .. ..$ : NULL
#>  $ N    : num [1:203] 87 101 63 104 70 63 77 123 76 59 ...
#>  $ E    : num [1:203] 117 134 135 118 114 131 140 122 141 133 ...
#>  $ O    : num [1:203] 130 123 111 108 110 108 118 115 109 104 ...
#>  $ A    : num [1:203] 124 115 71 107 119 125 131 99 134 130 ...
#>  $ C    : num [1:203] 127 124 134 111 120 138 103 107 142 126 ...

Big5.mod <- lm(cbind(N, E, O, A, C) ~ Group, data=Iwasaki_Big_Five)
coef(Big5.mod)
#>                      N           E          O          A           C
#> (Intercept) 96.0172751 116.4677513 117.247751 114.311746 111.8603175
#> Group1       0.5092196  -0.7428042   4.867196   2.683413   0.7394841
#> Group2      -1.5613889   9.0161111   1.019444   5.211667   2.7458333

car::Anova(Big5.mod)
#> 
#> Type II MANOVA Tests: Pillai test statistic
#>       Df test stat approx F num Df den Df    Pr(>F)    
#> Group  2   0.41862    10.43     10    394 1.106e-15 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# test contrasts
car::linearHypothesis(Big5.mod, "Group1", title = "Eur vs Asian")
#> 
#> Sum of squares and products for the hypothesis:
#>            N          E          O         A         C
#> N   94.62635  -138.0325   904.4526  498.6485  137.4155
#> E -138.03251   201.3495 -1319.3351 -727.3840 -200.4496
#> O  904.45262 -1319.3351  8644.8915 4766.1554 1313.4380
#> A  498.64846  -727.3840  4766.1554 2627.7065  724.1328
#> C  137.41555  -200.4496  1313.4380  724.1328  199.5536
#> 
#> Sum of squares and products for error:
#>           N          E         O         A          C
#> N  90650.37 -19544.030 -2139.170 -6905.080 -31111.087
#> E -19544.03  68087.407 25963.127 -5340.435  24283.356
#> O  -2139.17  25963.127 58283.593 10117.645   6850.589
#> A  -6905.08  -5340.435 10117.645 61033.794   3838.257
#> C -31111.09  24283.356  6850.589  3838.257  68134.095
#> 
#> Multivariate Tests: Eur vs Asian
#>                  Df test stat approx F num Df den Df     Pr(>F)    
#> Pillai            1 0.1821649 8.731426      5    196 1.7084e-07 ***
#> Wilks             1 0.8178351 8.731426      5    196 1.7084e-07 ***
#> Hotelling-Lawley  1 0.2227405 8.731426      5    196 1.7084e-07 ***
#> Roy               1 0.2227405 8.731426      5    196 1.7084e-07 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::linearHypothesis(Big5.mod, "Group2", title = "Asian: Amer vs Inter")
#> 
#> Sum of squares and products for the hypothesis:
#>            N         E         O          A         C
#> N   358.2272 -2068.553 -233.8897 -1195.7052 -629.9726
#> E -2068.5535 11944.691 1350.5766  6904.5011 3637.7248
#> O  -233.8897  1350.577  152.7086   780.6864  411.3146
#> A -1195.7052  6904.501  780.6864  3991.0731 2102.7480
#> C  -629.9726  3637.725  411.3146  2102.7480 1107.8597
#> 
#> Sum of squares and products for error:
#>           N          E         O         A          C
#> N  90650.37 -19544.030 -2139.170 -6905.080 -31111.087
#> E -19544.03  68087.407 25963.127 -5340.435  24283.356
#> O  -2139.17  25963.127 58283.593 10117.645   6850.589
#> A  -6905.08  -5340.435 10117.645 61033.794   3838.257
#> C -31111.09  24283.356  6850.589  3838.257  68134.095
#> 
#> Multivariate Tests: Asian: Amer vs Inter
#>                  Df test stat approx F num Df den Df     Pr(>F)    
#> Pillai            1 0.2385785 12.28265      5    196 2.2803e-10 ***
#> Wilks             1 0.7614215 12.28265      5    196 2.2803e-10 ***
#> Hotelling-Lawley  1 0.3133330 12.28265      5    196 2.2803e-10 ***
#> Roy               1 0.3133330 12.28265      5    196 2.2803e-10 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# 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)
}

#> Vector scale factor set to  5.44373