Elsevier

Biological Psychiatry

Volume 88, Issue 1, 1 July 2020, Pages 18-27
Biological Psychiatry

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

https://doi.org/10.1016/j.biopsych.2020.01.013Get rights and content

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.

Section snippets

Problematic Interpretation of Bifactor Models: Reliance on Global Model Fit

The major criticism of the bifactor model is its potential for overfitting (29). A common approach to evaluating structural models is to compare several possible models and then retain the model showing the best overall (global) fit statistics such as χ2, comparative fit index (CFI), Tucker–Lewis index (TLI), root mean square error of approximation (RMSEA), unbiased standardized root mean square residual (USRMR), and Akaike information criterion (AIC) (10,11). This approach is problematic

Useful Applications of the Bifactor Model

When a latent variable model is fit to psychopathology data [see (47,48) for discussions on choice of latent variable models versus alternatives such as network models], bifactor models are useful for their ability to separate indicator variance associated with a general factor from variance associated with narrower group factors or specific indicators. This separation of general variance from unique variance can inform several questions.

Comparison With Alternative Models

We describe several useful applications of bifactor models. This is not to suggest that they are a panacea or appropriate for all research questions. Below, we compare the bifactor model with several common alternatives and consider when these alternatives may be more useful.

The Bifactor Model and Biological Substrates of Psychopathology

A growing area of research examines biological substrates of psychological constructs such as neurobiological and genetic correlates of individual differences in personality, cognition, or psychopathology (98, 99, 100, 101). For example, several studies have examined or proposed correlations of psychopathology general and group factors with genetic single nucleotide polymorphisms or neurobiological variables (e.g., gray matter volume, volume or activation of amygdala/prefrontal cortex circuits,

Modeling Cannot Fix Inadequate Research Design

To close, we reiterate that statistical modeling cannot make fundamental limitations of data disappear. The questions that data can address are a function of the research design, not the model chosen to analyze them. Cross-sectional relationships among indicators cannot speak to developmental processes regardless of the type of model (bifactor or network) or indicator (behavioral symptoms or biological variables) used. The appropriate level of analysis for psychopathology (e.g., symptoms,

Acknowledgments and Disclosures

This work was supported by the National Institute on Drug Abuse (Grant No. DA032582 [to MAB]).

The authors report no biomedical financial interests or potential conflicts of interest.

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