结构方程模型和确证因子分析中的拟合指数:报告指南

Sathyanarayana S, T. Mohanasundaram
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引用次数: 0

摘要

本研究探讨了结构方程建模(SEM)中报告拟合指数的基本方面,重点关注其意义、评估方法以及对模型有效性的影响。目的是全面了解拟合指数如何有助于提高 SEM 研究的严谨性和可靠性。在方法论上,本研究回顾了一些著名的拟合指数,如比较拟合指数(CFI)、塔克-刘易斯指数(TLI)、均方根近似误差(RMSEA)、标准化均方根残差(SRMR)和模型拟合的奇平方检验(χ²)。对每个指标都进行了定义,并讨论了具体的临界值,以指导研究人员有效地解释研究结果。本研究的独创性在于综合了当前的文献,强调了 SEM 透明报告实践的重要性,提高了方法的清晰度,促进了可复制的研究成果。本研究的贡献包括采用结构化方法来理解拟合指数在模型评估和验证中的作用,从而帮助研究人员推进具有强大实证支持的理论框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fit Indices in Structural Equation Modeling and Confirmatory Factor Analysis: Reporting Guidelines
This research explores the essential aspects of reporting fit indices in Structural Equation Modeling (SEM), focusing on their significance, methodologies for evaluation, and implications for model validity. The aim is to provide a comprehensive understanding of how fit indices contribute to the rigor and reliability of SEM studies. Methodologically, the study reviews prominent fit indices such as Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Residual (SRMR), and Chi-Square Test of Model Fit (χ²). Each index is defined, and specific threshold values are discussed to guide researchers in interpreting their findings effectively. Originality in this study lies in synthesizing current literature to emphasize the importance of transparent reporting practices in SEM, enhancing methodological clarity and promoting replicable research outcomes. Contributions include a structured approach to understanding fit indices’ roles in model assessment and validation, aiding researchers in advancing theoretical frameworks with robust empirical support.
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