Are Fit Indices Used to Test Psychopathology Structure Biased? A Simulation Study
Are Fit Indices Used to Test Psychopathology Structure Biased? A Simulation Study
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DOI：
10.1037/abn0000434
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发表时间：
2019-10-01
影响因子：
4.6
通讯作者：
Kotov, Roman
中科院分区：
文献类型：
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作者：
Greene, Ashley L.;Eaton, Nicholas R.;Kotov, Roman
精神病理学的结构模型为传统的分类系统提供了维度选择。竞争模型，如双因子和相关因子模型，通常通过统计指标进行比较，以评估每个模型对相同数据的拟合程度。然而，模拟研究发现，在几个心理学研究领域的probifactor拟合指数偏差的证据。本研究试图将这项研究扩展到精神病理学模型，其中双因素模型受到了广泛关注，但其对偏见的敏感性并没有得到很好的表征。我们使用Monte Carlo模拟来研究各种模型误设如何产生2个常用估计量WLSMV和MLR的拟合指数偏差。我们模拟了二元指标来代表精神病诊断，并模拟了正偏态连续指标来代表症状计数。在估计量、指标分布和错误指定的组合中，出现了复杂的偏差模式，拟合指数往往无法正确识别相关因子模型作为数据生成模型。没有拟合指数出现可靠的无偏在所有的错误设定的情况下。虽然，模型等效性测试表明，在一个实例中，拟合指数没有偏见，他们有利于双因素模型，虽然不是不公平的。总体而言，结果表明，双因子模型的替代品使用拟合指数的比较可能会产生误导，并质疑以往的研究，确定双因子模型作为上级的基础上适合的证据意义。我们强调了基于实质性可解释性及其实用性来比较模型的重要性，以解决研究目标，模型等效性的方法学意义，以及实施评估模型质量的统计指标的必要性。
Structural models of psychopathology provide dimensional alternatives to traditional categorical classification systems. Competing models, such as the bifactor and correlated factors models, are typically compared via statistical indices to assess how well each model fits the same data. However, simulation studies have found evidence for probifactor fit index bias in several psychological research domains. The present study sought to extend this research to models of psychopathology, wherein the bifactor model has received much attention, but its susceptibility to bias is not well characterized. We used Monte Carlo simulations to examine how various model misspecifications produced fit index bias for 2 commonly used estimators, WLSMV and MLR. We simulated binary indicators to represent psychiatric diagnoses and positively skewed continuous indicators to represent symptom counts. Across combinations of estimators, indicator distributions, and misspecifications, complex patterns of bias emerged, with fit indices more often than not failing to correctly identify the correlated factors model as the data-generating model. No fit index emerged as reliably unbiased across all misspecification scenarios. Although, tests of model equivalence indicated that in one instance fit indices were not biased-they favored the bifactor model, albeit not unfairly. Overall, results suggest that comparisons of bifactor models to alternatives using fit indices may be misleading and call into question the evidentiary meaning of previous studies that identified the bifactor model as superior based on fit. We highlight the importance of comparing models based on substantive interpretability and their utility for addressing study aims, the methodological significance of model equivalence, as well as the need for implementation of statistical metrics that evaluate model quality.