Centre for Emotional Health, Department of Psychology, Macquarie University.
Department of Psychology, Stony Brook University.
J Abnorm Psychol. 2021 Apr;130(3):297-317. doi: 10.1037/abn0000533. Epub 2021 Feb 4.
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 ( = 43,093 and = 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 ( 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).
本研究比较了用于研究精神病理学结构的主要模型(即相关因素、高阶和双因素模型)在结构有效性(模型拟合和因子可靠性)、纵向测量不变性、与重要结果相关的同时和前瞻性预测效度以及个体因子分数特征的纵向一致性方面的差异。还检验了精神病理学一般因素的两种更简单的操作化形式——单因素模型和诊断计数。使用来自美国的两个纵向全国代表性数据波的结构化临床访谈诊断来估计模型(n = 43093 和 n = 34653)。需要包括更窄的因素(恐惧、痛苦和外化)的模型来捕捉数据的多维性。在相关因素和高阶模型中,这些更窄的因素是可靠的,在时间上基本不变,与适应功能的指标一致,并且在个体内随时间具有中等稳定性。相比之下,双因素模型中特定于恐惧和痛苦的因素在整个分析中并没有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,精神病理学一般因素(因子)在可靠性和有效性测试中的表现都相似;最简单的(单因素)模型在大多数测试中也具有可比性,除了模型拟合。考虑到分类诊断的局限性,使用维度测量重复这些分析将非常重要。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)超越模型拟合指数来选择不同的模型,(b)直接检查潜在变量的可靠性,以及(c)在隔离和解释特定精神病理学因素的独特影响时要谨慎,无论使用哪种模型。(PsycInfo 数据库记录(c)2021 APA,保留所有权利)。