Testing the Factor Structure and Measurement Invariance Across Gender of the Big Five Inventory Through Exploratory Structural Equation Modeling

abstract

Confirmatory factor analyses (CFAs) typically fail to support the a priori 5-factor structure of Big Five self-report instruments, due in part to the overly restrictive CFA assumptions. We show that exploratory structural equation modeling (ESEM), an integration of CFA and exploratory factor analysis, overcomes these problems in relation to responses to the 44-item Big Five Inventory (BFI) administered to a large Italian community sample. ESEM fitted the data better and resulted in less correlated factors than CFA, although ESEM and CFA factor scores correlated at near unity with observed raw scores. Tests of gender invariance with a 13-model taxonomy of full measurement invariance showed that the factor structure of the BFI is gender-invariant and that women score higher on Neuroticism, Agreeableness, Extraversion, and Conscientiousness. Through ESEM one could address substantively important issues about BFI psychometric properties that could not be appropriately addressed through traditional approaches.