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It has been argued that the sex segregation of the labor market reflects personality sex differences. So far, few studies have used actual occupational titles. This study aims to tackle these limitations as well as to expand previous research by operationalizing occupations as occupational field (i.e. STEM vs. Non-STEM) and as occupational orientation (i.e. Prediger’s people-things dimension). We replicated our analyses in three independent samples (Estonia, China, and UK) and, although there was no evidence that personality sex differences mirrored personality differences between occupational fields or between occupational orientations, results suggested that personality could be more relevant when choosing occupational orientation rather than occupational field.

It has been argued that because some biological underpinnings affecting personality occur in a continuum (e.g. hormonal exposure), personality scores in a given population should also occur in a continuum. Personality sex differences have been widely studied but these do not allow to study personality as a continuum. To tackle this limitation, Lippa (2001) suggested using sexual orientation. The Shift and the Inversion Hypotheses have been proposed to predict how the personality scores of homosexual participants would differ from their heterosexual counterparts. However, these efforts have been largely ignored. This study used a large UK sample to compare these hypotheses using personality scores at the facet and domain level. The results suggested that there was evidence for the Shift and the Inversion Hypotheses at the facet level, but these patterns were obscured at domain level where groups tended to cluster according to sexual orientation. Moreover, Neuroticism was often responsible for the largest differences across groups at facet and domain level.

Research in (mostly) Western samples has demonstrated that associations between personality traits and life outcomes are replicable and often driven by facets or nuances. Using three culturally different samples (English-speaking, N = 1,257; Russian-Speaking, N = 1,616; and Mandarin-speaking, N = 1,234) we investigated within and cross-sample predictive accuracies of five domains, thirty facets and ninety nuances. Cross-sample associations were highest for domains and weakest for nuances. However, nuances best predicted outcomes both within and across samples, although cross-sample predictions were smaller than within-sample. Traits’ predictive accuracy was stronger for English-speakers than for Mandarin and Russian-speakers. These findings suggested that trait-outcome associations moderately generalise across diverse samples and nuances often contain extra information about outcomes that partly generalises across samples.