Violence Research Centre, Institute of Criminology, University of Cambridge, Cambridge, UK.
Department of Psychology, University of Edinburgh, Edinburgh, UK.
J Pers Assess. 2019 Nov-Dec;101(6):631-643. doi: 10.1080/00223891.2018.1468338. Epub 2018 May 22.
Whether or not importance should be placed on an all-encompassing general factor of psychopathology (or factor) in classifying, researching, diagnosing, and treating psychiatric disorders depends (among other issues) on the extent to which comorbidity is symptom-general rather than staying largely within the confines of narrower transdiagnostic factors such as internalizing and externalizing. In this study, we compared three methods of estimating factor strength. We compared omega hierarchical and explained common variance calculated from confirmatory factor analysis (CFA) bifactor models with maximum likelihood (ML) estimation, from exploratory structural equation modeling/exploratory factor analysis models with a bifactor rotation, and from Bayesian structural equation modeling (BSEM) bifactor models. Our simulation results suggested that BSEM with small variance priors on secondary loadings might be the preferred option. However, CFA with ML also performed well provided secondary loadings were modeled. We provide two empirical examples of applying the three methodologies using a normative sample of youth (z-proso, = 1,286) and a university counseling sample ( = 359).
是否应该在分类、研究、诊断和治疗精神障碍时重视精神病理学的全面一般因素（或因素），这取决于共病是否是症状普遍性的，而不是主要局限于内化和外化等更窄的跨诊断因素范围内。在这项研究中，我们比较了三种估计因素强度的方法。我们比较了 ω 层次和从验证性因子分析（CFA）双因素模型中计算出的解释共同方差，这些模型使用最大似然（ML）估计，从探索性结构方程建模/探索性因子分析模型中使用双因素旋转，以及从贝叶斯结构方程建模（BSEM）双因素模型中。我们的模拟结果表明，对于次要加载的小方差先验的 BSEM 可能是首选。然而，只要对次要加载进行建模，使用 ML 的 CFA 也能很好地执行。我们提供了两个使用青年常模样本（z-proso，= 1,286）和大学咨询样本（= 359）的实证示例，应用了这三种方法。
