Equivalence Testing Based Fit Index: Standardized Root Mean Squared Residual.

IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Nataly Beribisky, Robert A Cribbie
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引用次数: 0

Abstract

A popular measure of model fit in structural equation modeling (SEM) is the standardized root mean squared residual (SRMR) fit index. Equivalence testing has been used to evaluate model fit in structural equation modeling (SEM) but has yet to be applied to SRMR. Accordingly, the present study proposed equivalence-testing based fit tests for the SRMR (ESRMR). Several variations of ESRMR were introduced, incorporating different equivalence bounds and methods of computing confidence intervals. A Monte Carlo simulation study compared these novel tests with traditional methods for evaluating model fit. The results demonstrated that certain ESRMR tests based on an analytic computation of the confidence interval correctly reject poor-fitting models and are well-powered for detecting good-fitting models. We also present an illustrative example with real data to demonstrate how ESRMR may be incorporated into model fit evaluation and reporting. Our recommendation is that ESRMR tests be presented in addition to descriptive fit indices for model fit reporting in SEM.

基于等效检验的拟合指数:标准化均方根残差。
在结构方程建模(SEM)中,衡量模型拟合度的常用指标是标准化均方根残差(SRMR)拟合指数。等效检验已被用于评估结构方程建模(SEM)中的模型拟合度,但尚未应用于 SRMR。因此,本研究提出了基于等效检验的 SRMR(ESRMR)拟合检验。本研究引入了 ESRMR 的几种变体,结合了不同的等效边界和计算置信区间的方法。蒙特卡罗模拟研究将这些新型检验与传统的模型拟合度评估方法进行了比较。结果表明,某些基于置信区间分析计算的 ESRMR 检验能正确拒绝拟合度较差的模型,并能很好地检测拟合度较好的模型。我们还用真实数据举例说明了如何将 ESRMR 纳入模型拟合度评估和报告中。我们的建议是,在 SEM 的模型拟合报告中,除了描述性拟合指数外,还应提供 ESRMR 检验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Multivariate Behavioral Research
Multivariate Behavioral Research 数学-数学跨学科应用
CiteScore
7.60
自引率
2.60%
发文量
49
审稿时长
>12 weeks
期刊介绍: Multivariate Behavioral Research (MBR) publishes a variety of substantive, methodological, and theoretical articles in all areas of the social and behavioral sciences. Most MBR articles fall into one of two categories. Substantive articles report on applications of sophisticated multivariate research methods to study topics of substantive interest in personality, health, intelligence, industrial/organizational, and other behavioral science areas. Methodological articles present and/or evaluate new developments in multivariate methods, or address methodological issues in current research. We also encourage submission of integrative articles related to pedagogy involving multivariate research methods, and to historical treatments of interest and relevance to multivariate research methods.
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