Description Usage Format Source References Examples
Factor solutions for the "Big 5" dimensions of personality determined using the Revised NEO Personality Inventory (NEO PI-R; Costa & McCrae, 1992). The five dimensions are measured by 240 items grouped into 30 sub-scales (“facets”), with six facets measuring each of the five dimensions.
NEO.n
is from the normative sample of
Costa & McCrae, 1992.
NEO.s
is from a cross-cultural Shona-speaking
sample from Zimbabwe
(Piedmont etal., 2002).
1 2 3 4 |
For NEO.n
:
The format is:
1 2 3 4 |
For NEO.s
:
The format is:
1 2 3 4 |
The NEO
data is the three-way array combining NEO.n
and NEO.s
:
The format is:
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Costa Jr, P. T. & McCrae, R. R. (1992). Normal personality assessment in clinical practice: The NEO Personality Inventory Psychological Assessment, 4, 5-13.
Piedmont, R. L. and Bain, E. and McCrae, R.R. and Costa Jr, P. T.(2002). "The applicability of the five-factor model in a sub-Saharan culture: The NEO PI-R in Shona", In R. R. McCrae and J. Allik (ed.) The Five-Factor Model of Personality Across Cultures, New York: Kluwer Academic/Plenum, 155-173.
Kwan, E. and Lu, I. R. R. and Friendly, M. (2009). Tableplot: A new tool for assessing precise predictions Zeitschrift für Psychologie / Journal of Psychology, 217, 38-48.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | data(NEO.n); data(NEO.s)
# Examples from Kwan et al., 2009
# Plot of Normative patter, first 12 facets:
tableplot(
values = round( 100 * t(NEO.n[1:12,])),
label.size = 1.5,
cell.specs=list(
list(0,"blue",1,1,"red",1,"white","grey90",1,1.5,"grey50",FALSE,"grey40",100)),
v.parts = c(6,6),
gap = 3,
left.space=15,
top.space=15,
assign.sets = matrix(1,5,12))
facnames <- c("N","E","O","A","C")
itmnames <- as.vector(t(outer(facnames, 1:6, paste, sep="")))
# Put the patterns together:
neopir <- array(NA, c(6,31,2))
neopir[1:5,1:30,1] <- t(NEO.n) # Normative
neopir[1:5,1:30,2] <- t(NEO.s) # Shona
# Calculate congruence coefficients for variables:
for (j in 1:30){
neopir[6,j,] <- round(congruence.coef(neopir[1:5,j,1],neopir[1:5,j,2]),2) }
# Calculate congruence coefficients for factors:
for (i in 1:5){
neopir[i,31,] <- round(congruence.coef(neopir[i,1:30,1],neopir[i,1:30,2]),2) }
# Plug in the total congruence coefficient:
neopir[6,31,] <- 0.89
# Get rid of decimals:
neopir <- round(neopir * 100)
dimnames(neopir) <- list( c(facnames, "phi"), c(itmnames, "phi"), c("Normative", "Shona"))
# Plot of Normative and Shona, superimposed and augmented:
B <- matrix(1,6,31)
B[6,] <- 2
B[,31] <- 2
tableplot(
values = neopir,
label.size = 0.8,
cell.specs=list(
list(0,"blue",1,1,"red",1,"white","grey95",2,0.6,"grey50",FALSE,"grey40",100),
list(0,"blue",1,1,"red",1,"yellow","grey60",1,0.6,"grey10",FALSE,"grey40",100)),
v.parts = c(6,6,6,6,6,1),
h.parts = c(5,1),
gap = 1,
left.space=8,
assign.sets = B)
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