On the Meaning of the "P Factor" in Symmetrical Bifactor Models of Psychopathology: Recommendations for Future Research From the Bifactor-(S−1) Perspective. (April 2023)

• Record Type: Journal Article
• Authors: Heinrich, Manuel; Geiser, Christian; Zagorscak, Pavle; Burns, G. Leonard; Bohn, Johannes; Becker, Stephen P.; Eid, Michael; Beauchaine, Theodore P.; Knaevelsrud, Christine
• Is Part Of: Assessment. Volume 30:Number 3(2023)
• Page Start: 487
• Page End: 507
• Publication Date: 2023-04
• Subjects: P factor -- the general factor of psychopathology -- bifactor -- bifactor-(S−1) -- anomalous results -- collapsing factors; Psychological tests -- Periodicals; Psychodiagnostics -- Periodicals; Personality tests -- Periodicals; 150.287
• DOI: 10.1177/10731911211060298
• Languages: English
• ISSNs: 1073-1911

Abstract

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 all psychopathology dimensions and mental disorders. Despite their popularity, however, symmetrical bifactor models of P often yield anomalous results, including but not limited to nonsignificant or negative specific factor variances and nonsignificant or negative factor loadings. To date, these anomalies have often been treated as nuisances to be explained away. In this article, we demonstrate why these anomalies alter the substantive meaning of P such that it (a) does not reflect general liability to psychopathology and (b) differs in meaning across studies. We then describe an alternative modeling framework, the bifactor-( S −1) approach. This method avoids anomalous results, provides a framework for explaining unexpected findings in published symmetrical bifactor studies, and yields a well-defined general factor that can be compared across studies when researchers hypothesize what construct they consider "transdiagnostically meaningful" and measure it directly. We present an empirical example to illustrate these points and provide concrete recommendations to help researchers decide for or against specific variants of bifactor structure.