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对称双因素模型中“因素”的意义:来自双因素-(-1)视角的未来研究建议。

On the Meaning of the " Factor" in Symmetrical Bifactor Models of Psychopathology: Recommendations for Future Research From the Bifactor-(-1) Perspective.

机构信息

Freie Universität Berlin, Germany.

Utah State University, Logan, USA.

出版信息

Assessment. 2023 Apr;30(3):487-507. doi: 10.1177/10731911211060298. Epub 2021 Dec 3.

Abstract

Symmetrical bifactor models are frequently applied to diverse symptoms of psychopathology to identify a general factor. This factor is assumed to mark shared liability across all psychopathology dimensions and mental disorders. Despite their popularity, however, symmetrical bifactor models of often yield anomalous results, including but not limited to nonsignificant or negative specific factor variances and nonsignificant or negative factor loadings. To date, these anomalies have often been treated as nuisances to be explained away. In this article, we demonstrate why these anomalies alter the substantive meaning of such that it (a) does not reflect general liability to psychopathology and (b) differs in meaning across studies. We then describe an alternative modeling framework, the bifactor-(-1) approach. This method avoids anomalous results, provides a framework for explaining unexpected findings in published symmetrical bifactor studies, and yields a well-defined general factor that can be compared across studies when researchers hypothesize what construct they consider "transdiagnostically meaningful" and measure it directly. We present an empirical example to illustrate these points and provide concrete recommendations to help researchers decide for or against specific variants of bifactor structure.

摘要

对称双因素模型常用于将精神病理学的各种症状分类为一个一般因素。该因素被认为是所有精神病理学维度和精神障碍的共同责任标志。然而,尽管这些模型很受欢迎,但它们通常会产生异常结果,包括但不限于特定因素方差和因素负荷的不显著或负性。迄今为止,这些异常情况通常被视为需要解释的干扰因素。在本文中,我们将展示为什么这些异常情况改变了 的实质性含义,使其(a)不能反映一般的精神病理学倾向,(b)在不同研究中具有不同的含义。然后,我们将描述一种替代的建模框架,即双因素-(-1)方法。这种方法避免了异常结果,为解释已发表的对称双因素研究中的意外发现提供了一个框架,并产生了一个明确定义的通用因素,当研究人员假设他们认为“跨诊断有意义”的构念并直接测量它时,可以在研究之间进行比较。我们提出了一个实证示例来说明这些要点,并提供了具体的建议,以帮助研究人员决定是否采用特定的双因素结构变体。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a0/9999288/f4db33dc615f/10.1177_10731911211060298-fig1.jpg

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