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Category: Project

Description: Symmetrical bifactor models are frequently applied to diverse symptoms of psychopathology to identify a general P factor. This factor is assumed to mark shared liability across psychopathology dimensions and mental disorders. Despite their popularity, however, symmetrical bifactor models often yield anomalous results, including but not limited to non-significant or negative specific factor variances and non-significant or negative factor loadings. To date, these anomalies have often been treated as nuisances to be explained away. In this paper, we demonstrate why these anomalies alter the substantive meaning of P such that it (1) does not reflect general liability to psychopathology and (2) differs in meaning across studies. We then describe an alternative modeling framework, the bifactor-(S − 1) approach. This approach avoids anomalous results, provides a framework for explaining unexpected findings in published symmetrical bifactor studies, and yields a general factor with well-defined meaning across studies. We present an empirical example to illustrate these points and provide concrete recommendations to help researchers decide for or against a specific variant of bifactor structures.