Modeling the hierarchy of teacher personality: The predictive power of domains and incremental facets for teacher job-related outcomes

Teacher personality has been recognized as an important factor in predicting job-related outcomes such as burnout, job performance, or motivation. Although personality facets can better predict various outcomes than domains, research on teacher personality at the facet level is still scarce. Thus, the aim of this study was to examine the associations between teacher personality domains and incremental facets and indicators of teachers' job-related outcomes (i.e., job satisfaction, emotional exhaustion, turnover intentions, self-efficacy). Data from 1137 secondary school teachers was collected at two time points. To decompose the variance in personality items into unique domain and facets parts while simultaneously controlling for the response style method factor and the measurement error, the domain-incremental facet-acquiescence bifactor (DIFAB) model was tested. The results showed that incremental facets explained variance in analyzed job-related outcomes beyond the domains, but the amount of the explained variance differed across the criteria.

Automated summary

Teacher personality is an important predictor of job-related outcomes, and personality facets are more accurate in predicting outcomes than domains. This study found associations between teacher personality domains and incremental facets and indicators of job-related outcomes, with incremental facets playing a significant role in explaining the variance in these outcomes.