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.

Keywords: Bifactor; Construct validity; Criterion validity; General psychopathology factor; Nomological net; Structural equations modeling; Taxonomy.

Figure 1.

Bifactor models fit to measures of cognitive ability (A) and social attitudes (B). Standardized factor loadings and residual variances are shown. (A) N = 213, χ²₁₈ = 24.331, Tucker–Lewis index (TLI) = .988, root mean square error of approximation (RMSEA) [90% confidence interval (CI)] = .041 [.000, .078], unbiased standardized root mean square residual (USRMR) [90% CI] = .029 [.015, .044]. This model shows clear bifactor structure, with good global model fit as well as strong general and group factor loadings. (B) N = 463, χ²₄₃ = 67.984, TLI = .978, RMSEA [90% CI] = .035 [.018, .051], USRMR [90% CI] = .042 [.024, .059]. Despite good global model fit, this model does not show clear bifactor structure. Factor loadings ≥ .40 are in bold. Models were fit using the lavaan package (version 0.6–3) (113) in R (version 3.5.3) (114). A–C, indicators (subtests or item parcels); F, group factors; G, general factor.