Development is in the details: Investigating age differences in the Big Five domains, facets and nuances

Rene Mottus, Dmitri Rozgonjuk

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

We examined the extent to which the Big Five domains, 30 facets, and nuances (uniquely represented by individual questionnaire items) capture age differences in personality, expecting domains to contain the least and nuances the most age-related information. We used an Internet sample (N 24,000), evenly distributed between ages of 18 and 50 years and tested with a 300-item questionnaire. Separately based on domains, facets, and items, we trained models to predict age in one part of the sample and tested their predictive accuracy in another part. Big Five domains predicted age with an accuracy of r .28, whereas facets’ (r .44) and items’ (r .65) predictions were more accurate. Less than 15% of the sample was needed to train models to their optimal accuracy. Residualizing the 300 items for all facets had no impact on their predictive accuracy, suggesting that age differences in specific behaviors, thoughts, and feelings (i.e., items) were not because of domains and facets but mostly unique to nuances. These findings replicated in a multisample dataset tested with another questionnaire. We found little evidence that age differences only appeared nuanced because items referred to age-graded roles or experiences. Therefore, a substantial part of personality development may be uniquely ascribed to narrow personality characteristics, suggesting the possibility for a many-dimensional representation of personality development. Besides theoretical implications, we provide concrete illustrations of how this can open new research avenues by enabling to study systematic variations between traits.
Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalJournal of Personality and Social Psychology
Early online date2 Dec 2019
Publication statusE-pub ahead of print - 2 Dec 2019

Keywords / Materials (for Non-textual outputs)

  • personality development
  • age differences
  • nuances
  • facets
  • machine learning


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