当指标残差相关时,用于测量不变性评估的确证因子分析与网络模型的比较》(A Comparison of Confirmatory Factor Analysis and Network Models for Measurement Invariance Assessment when Indicator Residuals are Correlated)。

IF 1 4区 心理学 Q4 PSYCHOLOGY, MATHEMATICAL
Applied Psychological Measurement Pub Date : 2023-03-01 Epub Date: 2023-01-14 DOI:10.1177/01466216231151700
W Holmes Finch, Brian F French, Alicia Hazelwood
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引用次数: 0

摘要

社会科学研究在很大程度上依赖于对情绪、执行功能和认知能力等各种现象的标准化评估。在使用这些工具时,一个重要的假设是它们对所有人群的表现都是相似的。如果违反了这一假设,分数的有效性证据就会受到质疑。评估测量指标在不同人群中的因子不变性的标准方法是多组确证因子分析(MGCFA)。CFA 模型通常(但并不总是)假定,一旦模型的潜在结构得到考虑,观测指标的残差项是不相关的(局部独立)。通常情况下,相关残差是在基线模型显示拟合度不足后引入的,随后会对修正指数进行检查,以弥补拟合度。当局部独立性不成立时,另一种可行的潜变量模型拟合方法是基于网络模型的。尤其是残差网络模型(RNM),它可以通过另一种搜索程序,在缺乏局部独立性的情况下拟合潜变量模型。这项模拟研究比较了 MGCFA 和 RNM 在违反局部独立性且残差协方差本身不具有不变性的情况下进行测量不变性评估的性能。结果显示,当局部不独立时,RNM 与 MGCFA 相比,具有更好的 I 类误差控制和更高的功率。本文讨论了这些结果对统计实践的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comparison of Confirmatory Factor Analysis and Network Models for Measurement Invariance Assessment When Indicator Residuals are Correlated.

Social science research is heavily dependent on the use of standardized assessments of a variety of phenomena, such as mood, executive functioning, and cognitive ability. An important assumption when using these instruments is that they perform similarly for all members of the population. When this assumption is violated, the validity evidence of the scores is called into question. The standard approach for assessing the factorial invariance of the measures across subgroups within the population involves multiple groups confirmatory factor analysis (MGCFA). CFA models typically, but not always, assume that once the latent structure of the model is accounted for, the residual terms for the observed indicators are uncorrelated (local independence). Commonly, correlated residuals are introduced after a baseline model shows inadequate fit and inspection of modification indices ensues to remedy fit. An alternative procedure for fitting latent variable models that may be useful when local independence does not hold is based on network models. In particular, the residual network model (RNM) offers promise with respect to fitting latent variable models in the absence of local independence via an alternative search procedure. This simulation study compared the performances of MGCFA and RNM for measurement invariance assessment when local independence is violated, and residual covariances are themselves not invariant. Results revealed that RNM had better Type I error control and higher power compared to MGCFA when local independence was absent. Implications of the results for statistical practice are discussed.

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来源期刊
CiteScore
2.30
自引率
8.30%
发文量
50
期刊介绍: Applied Psychological Measurement publishes empirical research on the application of techniques of psychological measurement to substantive problems in all areas of psychology and related disciplines.
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