Bifactor model of cognition in schizophrenia: Evidence for general and specific abilities.

Overview

BACKGROUND: Despite extensive study of cognition in schizophrenia, it remains unclear as to whether cognitive deficits and their latent structure are best characterized as reflecting a generalized deficit, specific deficits, or some combination of general and specific constructs. METHOD: To clarify latent structure of cognitive abilities, confirmatory factor analysis was used to examine the latent structure of cognitive data collected for the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) for Schizophrenia study. Baseline assessment data (n = 813) were randomly divided into calibration (n = 413) and cross-validation samples (n = 400). To examine whether generalized or specific deficit models provided better explanation of the data, we estimated first-order, hierarchical, and bifactor models. RESULTS: A bifactor model with seven specific factors and one general factor provided the best fit to the data for both the calibration and cross-validation samples. CONCLUSIONS: These findings lend support for a replicable bifactor model of cognition in schizophrenia, characterized by both a general cognitive factor and specific domains. This suggests that cognitive deficits in schizophrenia might be best understood by separate general and specific contributions.