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Appropriate Use of Bifactor Analysis in Psychopathology Research: Appreciating Benefits and Limitations

      Abstract

      Co-occurrence of psychiatric disorders is well documented. Recent quantitative efforts have moved toward an understanding of this phenomenon, with the general psychopathology or p-factor model emerging as the most prominent characterization. Over the past decade, bifactor model analysis has become increasingly popular as a statistical approach to describe common/shared and unique elements in psychopathology. However, recent work has highlighted potential problems with common approaches to evaluating and interpreting bifactor models. Here, we argue that bifactor models, when properly applied and interpreted, can be useful for answering some important questions in psychology and psychiatry research. We review problems with evaluating bifactor models based on global model fit statistics. We then describe more valid approaches to evaluating bifactor models and highlight 3 types of research questions for which bifactor models are well suited to answer. We also discuss the utility and limits of bifactor applications in genetic and neurobiological research. We close by comparing advantages and disadvantages of bifactor models with other analytic approaches and note that no statistical model is a panacea to rectify limitations of the research design used to gather data.

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      Linked Article

      • The Indispensable Value of a Coherent Phenotypic Model of Psychopathology
        Biological PsychiatryVol. 88Issue 1
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          Psychiatric research is greatly in need of a coherent phenotypic model of psychopathology, by which we mean a model encompassing the manifest signs and symptoms that lead patients to seek psychiatric services. A model of these signs and symptoms is needed because they constitute the problematic thoughts, feelings, and behaviors that are the foci of psychiatric practice and research.
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