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.

中文翻译：

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在精神病理学研究中适当使用双因素分析：了解好处和局限性

精神障碍的共同发生是有据可查的。最近的定量研究已朝着对这种现象的理解迈进，一般精神病理学或 p 因子模型成为最突出的表征。在过去的十年中，双因素模型分析作为一种描述精神病理学中常见/共享和独特元素的统计方法变得越来越流行。然而，最近的工作强调了评估和解释双因子模型的常用方法的潜在问题。在这里，我们认为双因子模型如果得到适当的应用和解释，可用于回答心理学和精神病学研究中的一些重要问题。我们回顾了基于全局模型拟合统计评估双因子模型的问题。然后，我们描述了更有效的评估双因子模型的方法，并强调了双因子模型非常适合回答的 3 类研究问题。我们还讨论了双因子应用在遗传和神经生物学研究中的实用性和局限性。我们通过比较双因子模型与其他分析方法的优缺点来结束，并指出没有任何统计模型是纠正用于收集数据的研究设计的局限性的灵丹妙药。