VISN 2 Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center.
Department of Psychology, Stony Brook University.
J Psychopathol Clin Sci. 2022 Aug;131(6):696-703. doi: 10.1037/abn0000770.
As evidenced by our exchange with Bader and Moshagen (2022), the degree to which model fit indices can and should be used for the purpose of model selection remains a contentious topic. Here, we make three core points. First, we discuss the common misconception about fit statistics' abilities to identify the "best model," arguing that mechanical application of model fit indices contributes to faulty inferences in the field of quantitative psychopathology. We illustrate the consequences of this practice through examples in the literature. Second, we highlight the parsimony-adjacent concept of fitting propensity, which is not accounted for by commonly used fit statistics. Finally, we present specific strategies to overcome interpretative bias and increase generalizability of study results and stress the importance of carefully balancing substantive and statistical criteria in model selection scenarios. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
正如我们与 Bader 和 Moshagen（2022 年）的交流所表明的那样，模型拟合指数在多大程度上可以并且应该用于模型选择仍然是一个有争议的话题。在这里，我们提出三个核心观点。首先，我们讨论了关于拟合统计数据识别“最佳模型”能力的常见误解，认为机械应用模型拟合指数会导致定量精神病理学领域的错误推断。我们通过文献中的例子说明了这种做法的后果。其次，我们强调了拟合倾向的简约邻近概念，这是常用拟合统计数据无法解释的。最后，我们提出了克服解释偏差和提高研究结果的可推广性的具体策略，并强调在模型选择场景中仔细平衡实质性和统计性标准的重要性。（PsycInfo 数据库记录（c）2022 APA，保留所有权利）。
