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Personality can be described at different levels of abstraction. Whereas the Big Five domains are the dominant level of analysis, several researchers have called for more fine-grained approaches, such as facet-level analysis. Personality facets allow more comprehensive descriptions, more accurate predictions of outcomes, and a better understanding of the mechanisms underlying trait–outcome relationships. However, several methodological issues plague existing evidence on the added value of facet-level descriptions: Manifest facet scale scores differ with respect to their reliability, domain-level variance (variance that is due to the domain factor) and incremental facet-level variance (variance that is specific to a facet and not shared with the other facets). Moreover, manifest scale scores overlap substantially, which affects associations with criterion variables. We suggest a structural equation modelling approach that allows domain-level variance to be separated from incremental facet-level variance. We analysed data from a heterogeneous sample of adults in the USA (N = 1193) who completed the 60-item Big Five Inventory-2. The results illustrate how the variance of manifest personality items and scale scores can be decomposed into domain-level and incremental facet-level variance. The association with criterion variables (educational attainment, income, health, and life satisfaction) further demonstrates the incremental predictive power of personality facets.

Modelling the incremental value of personality facets: the domains-incremental facets-acquiescence bifactor showmodel