IPIP-50 / OCEAN Validation
Description
The IPIP-50 (International Personality Item Pool; Goldberg, 1992, 1999) is a 50-item public-domain measure of the Big Five personality dimensions. Respondents rated each item on a 1–5 scale (Very Inaccurate to Very Accurate); 0 encodes missing data. The dataset contains responses from a large online sample.
Items are grouped into five dimensions: Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness to Experience. Items marked R are reverse-keyed and are recoded (5 − x) before analysis.
Goldberg, L. R. (1992). The development of markers for the Big-Five factor structure. Psychological Assessment, 4(1), 26–42.
Goldberg, L. R. (1999). A broad-bandwidth, public domain, personality inventory measuring the lower-level facets of several Five-Factor models. In I. Mervielde, I. Deary, F. De Fruyt, & F. Ostendorf (Eds.), Personality Psychology in Europe (Vol. 7, pp. 7–28). Tilburg University Press.
Item List
|Label |Dimension |Reverse-scored |Item wording | |:-----|:-----------------|:--------------|:------------------------------------------------------| |E1 |Extraversion |FALSE |Am the life of the party. | |E2R |Extraversion |TRUE |Don't talk a lot. | |E3 |Extraversion |FALSE |Feel comfortable around people. | |E4R |Extraversion |TRUE |Keep in the background. | |E5 |Extraversion |FALSE |Start conversations. | |E6R |Extraversion |TRUE |Have little to say. | |E7 |Extraversion |FALSE |Talk to a lot of different people at parties. | |E8R |Extraversion |TRUE |Don't like to draw attention to myself. | |E9 |Extraversion |FALSE |Don't mind being the center of attention. | |E10R |Extraversion |TRUE |Am quiet around strangers. | |N1 |Neuroticism |FALSE |Get stressed out easily. | |N2R |Neuroticism |TRUE |Am relaxed most of the time. | |N3 |Neuroticism |FALSE |Worry about things. | |N4R |Neuroticism |TRUE |Seldom feel blue. | |N5 |Neuroticism |FALSE |Am easily disturbed. | |N6 |Neuroticism |FALSE |Get upset easily. | |N7 |Neuroticism |FALSE |Change my mood a lot. | |N8 |Neuroticism |FALSE |Have frequent mood swings. | |N9 |Neuroticism |FALSE |Get irritated easily. | |N10 |Neuroticism |FALSE |Often feel blue. | |A1R |Agreeableness |TRUE |Feel little concern for others. | |A2 |Agreeableness |FALSE |Am interested in people. | |A3R |Agreeableness |TRUE |Insult people. | |A4 |Agreeableness |FALSE |Sympathize with others' feelings. | |A5R |Agreeableness |TRUE |Am not interested in other people's problems. | |A6 |Agreeableness |FALSE |Have a soft heart. | |A7R |Agreeableness |TRUE |Am not really interested in others. | |A8 |Agreeableness |FALSE |Take time out for others. | |A9 |Agreeableness |FALSE |Feel others' emotions. | |A10 |Agreeableness |FALSE |Make people feel at ease. | |C1 |Conscientiousness |FALSE |Am always prepared. | |C2R |Conscientiousness |TRUE |Leave my belongings around. | |C3 |Conscientiousness |FALSE |Pay attention to details. | |C4R |Conscientiousness |TRUE |Make a mess of things. | |C5 |Conscientiousness |FALSE |Get chores done right away. | |C6R |Conscientiousness |TRUE |Often forget to put things back in their proper place. | |C7 |Conscientiousness |FALSE |Like order. | |C8R |Conscientiousness |TRUE |Shirk my duties. | |C9 |Conscientiousness |FALSE |Follow a schedule. | |C10 |Conscientiousness |FALSE |Am exacting in my work. | |O1 |Openness |FALSE |Have a rich vocabulary. | |O2R |Openness |TRUE |Have difficulty understanding abstract ideas. | |O3 |Openness |FALSE |Have a vivid imagination. | |O4R |Openness |TRUE |Am not interested in abstract ideas. | |O5 |Openness |FALSE |Have excellent ideas. | |O6R |Openness |TRUE |Do not have a good imagination. | |O7 |Openness |FALSE |Am quick to understand things. | |O8 |Openness |FALSE |Use difficult words. | |O9 |Openness |FALSE |Spend time reflecting on things. | |O10 |Openness |FALSE |Am full of ideas. |
Inter-Item Correlation Matrix
cor_mat <- cor(df[, factor_order], use="pairwise.complete.obs")
cor_long <- melt(cor_mat)
cor_long$Var1 <- factor(cor_long$Var1, levels=factor_order)
cor_long$Var2 <- factor(cor_long$Var2, levels=factor_order)
ggplot(cor_long, aes(x=Var1, y=Var2, fill=value)) +
geom_tile(color="white", linewidth=0.2) +
scale_fill_gradient2(low="#2166ac", mid="white", high="#d6604d",
midpoint=0, limits=c(-1,1), name="r") +
theme_bw(base_size=9) +
theme(axis.text.x=element_text(angle=90, hjust=1, size=7),
axis.text.y=element_text(size=7),
axis.title=element_blank(),
panel.grid=element_blank()) +
ggtitle("IPIP-50 inter-item correlation matrix\nItems ordered by dimension; R = reverse-scored (already recoded)")
Subscale Correlations
subscale_scores <- as.data.frame(sapply(names(factors), function(f) {
rowMeans(df[, factors[[f]]], na.rm=TRUE)
}))
cat("Subscale descriptives\n")## Subscale descriptives
print(psych::describe(subscale_scores))## vars n mean sd median trimmed mad min max range skew kurtosis se ## Extraversion 1 19719 2.51 0.92 2.5 2.52 1.04 0.5 4.5 4 -0.04 -0.71 0.01 ## Neuroticism 2 19719 2.90 0.86 2.9 2.91 0.89 0.8 4.8 4 -0.08 -0.60 0.01 ## Agreeableness 3 19719 3.44 0.71 3.5 3.50 0.74 0.6 4.6 4 -0.76 0.53 0.01 ## Conscientiousness 4 19719 2.95 0.73 3.0 2.95 0.74 0.6 4.6 4 -0.09 -0.38 0.01 ## Openness 5 19719 3.61 0.63 3.7 3.64 0.59 0.7 4.7 4 -0.47 -0.03 0.00
cat("\nSubscale intercorrelations\n")## ## Subscale intercorrelations
sub_cor <- round(cor(subscale_scores, use="pairwise.complete.obs"), 2)
print(sub_cor)## Extraversion Neuroticism Agreeableness Conscientiousness Openness ## Extraversion 1.00 -0.26 0.33 0.11 0.17 ## Neuroticism -0.26 1.00 -0.11 -0.26 -0.10 ## Agreeableness 0.33 -0.11 1.00 0.18 0.12 ## Conscientiousness 0.11 -0.26 0.18 1.00 0.09 ## Openness 0.17 -0.10 0.12 0.09 1.00
sub_long <- melt(sub_cor)
ggplot(sub_long, aes(x=Var1, y=Var2, fill=value)) +
geom_tile(color="white", linewidth=0.5) +
geom_text(aes(label=value), size=3.5) +
scale_fill_gradient2(low="#2166ac", mid="white", high="#d6604d",
midpoint=0, limits=c(-1,1), name="r") +
theme_bw(base_size=10) +
theme(axis.title=element_blank(), panel.grid=element_blank()) +
ggtitle("IPIP-50 subscale intercorrelations")
Exploratory Factor Analysis (5 Factors)
set.seed(42)
efa <- fa(df, nfactors=5, rotate="oblimin", fm="ml", scores="regression")
print(efa$loadings, cutoff=0.30, sort=TRUE)## ## Loadings: ## ML4 ML1 ML3 ML2 ML5 ## E1 0.690 ## E2R 0.697 ## E3 0.640 ## E4R 0.717 ## E5 0.732 ## E6R 0.546 ## E7 0.749 ## E8R 0.582 ## E9 0.623 ## E10R 0.662 ## N1 0.671 ## N3 0.592 ## N5 0.537 ## N6 0.746 ## N7 0.734 ## N8 0.760 ## N9 0.732 ## N10 0.576 ## A4 0.808 ## A5R 0.651 ## A6 0.615 ## A7R 0.582 ## A8 0.571 ## A9 0.713 ## C1 0.599 ## C2R 0.543 ## C4R 0.530 ## C5 0.633 ## C6R 0.586 ## C7 0.555 ## C9 0.641 ## O1 0.583 ## O2R 0.536 ## O3 0.538 ## O5 0.608 ## O6R 0.501 ## O8 0.552 ## O10 0.680 ## N2R 0.496 ## N4R ## A1R 0.432 ## A2 0.496 ## A3R 0.404 ## A10 0.335 ## C3 0.395 ## C8R 0.453 ## C10 0.466 ## O4R 0.461 ## O7 0.493 ## O9 0.347 ## ## ML4 ML1 ML3 ML2 ML5 ## SS loadings 4.911 4.411 3.600 3.172 3.164 ## Proportion Var 0.098 0.088 0.072 0.063 0.063 ## Cumulative Var 0.098 0.186 0.258 0.322 0.385
loadings_mat <- as.data.frame(unclass(efa$loadings))
colnames(loadings_mat) <- paste0("F", 1:5)
loadings_mat$item <- rownames(loadings_mat)
loadings_mat$expected <- item_labels[loadings_mat$item]
load_long <- melt(loadings_mat, id.vars=c("item","expected"),
variable.name="factor", value.name="loading")
load_long$item <- factor(load_long$item, levels=factor_order)
ggplot(load_long, aes(x=factor, y=item, fill=loading)) +
geom_tile(color="white", linewidth=0.3) +
geom_text(aes(label=ifelse(abs(loading)>=0.30, round(loading,2), "")), size=2.5) +
scale_fill_gradient2(low="#2166ac", mid="white", high="#d6604d",
midpoint=0, limits=c(-1,1), name="loading") +
facet_grid(expected ~ ., scales="free_y", space="free_y") +
theme_bw(base_size=9) +
theme(strip.text.y=element_text(angle=0, size=8),
axis.text.y=element_text(size=7),
axis.title=element_blank(),
panel.grid=element_blank()) +
ggtitle("EFA factor loadings (oblimin rotation, 5 factors)\nValues >= |0.30| shown; R = reverse-scored item")
Parallel Analysis
fa.parallel(df, fm="ml", fa="fa",
main="Parallel analysis scree plot (IPIP-50)")
## Parallel analysis suggests that the number of factors = 10 and the number of components = NA