The present study compared the primary models used in research on the structure of psychopathology (i.e., correlated factor, higher-order, and bifactor models) in terms of structural validity (model fit and factor reliability), longitudinal measurement invariance, concurrent and prospective predictive validity in relation to important outcomes, and longitudinal consistency in individuals' factor score profiles. Two simpler operationalizations of a general factor of psychopathology were also examined-a single-factor model and a count of diagnoses. Models were estimated based on structured clinical interview diagnoses in two longitudinal waves of nationally representative data from the United States (n = 43,093 and n = 34,653). Models that included narrower factors (fear, distress, and externalizing) were needed to capture the observed multidimensionality of the data. In the correlated factor and higher-order models these narrower factors were reliable, largely invariant over time, had consistent associations with indicators of adaptive functioning, and had moderate stability within individuals over time. By contrast, the fear- and distress-specific factors in the bifactor model did not show good reliability or validity throughout the analyses. Notably, the general factor of psychopathology (p factor) performed similarly well across tests of reliability and validity regardless of whether the higher-order or bifactor model was used; the simplest (single factor) model was also comparable across most tests, with the exception of model fit. Given the limitations of categorical diagnoses, it will be important to repeat these analyses using dimensional measures. We conclude that when aiming to understand the structure and correlates of psychopathology it is important to (a) look beyond model fit indices to choose between different models, (b) examine the reliability of latent variables directly, and (c) be cautious when isolating and interpreting the unique effects of specific psychopathology factors, regardless of which model is used. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

中文翻译：

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三个建议基于对精神病理学经验结构研究中使用的主要模型的可靠性和有效性的比较。

本研究比较了精神病理学结构研究中使用的主要模型（即相关因子模型、高阶模型和双因子模型）的结构有效性（模型拟合和因子可靠性）、纵向测量不变性、并发和前瞻性预测。与重要结果相关的有效性，以及个人因素评分概况的纵向一致性。还检查了精神病理学一般因素的两个更简单的操作——单因素模型和诊断计数。模型是根据美国全国代表性数据的两个纵波（n = 43,093 和 n = 34,653）的结构化临床访谈诊断进行估计的。需要包含较窄因素（恐惧、痛苦和外化）的模型来捕获观察到的数据的多维性。在相关因子和高阶模型中，这些较窄的因子是可靠的，随着时间的推移基本上不变，与适应性功能指标具有一致的关联，并且随着时间的推移在个体内具有中等稳定性。相比之下，双因素模型中的恐惧和痛苦特定因素在整个分析过程中并未表现出良好的可靠性或有效性。值得注意的是，无论使用高阶模型还是双因素模型，精神病理学的一般因素（p 因素）在信度和效度测试中表现相似。最简单（单因素）模型在大多数测试中也具有可比性，但模型拟合除外。考虑到分类诊断的局限性，使用维度测量重复这些分析非常重要。我们的结论是，当旨在了解精神病理学的结构和相关性时，重要的是（a）超越模​​型拟合指数以在不同模型之间进行选择，（b）直接检查潜在变量的可靠性，以及（c）在隔离时要谨慎并解释特定精神病理学因素的独特影响，无论使用哪种模型。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）。