路径模型拟合评估:有效性证据和RMSEA-P使用建议

IF 8.9 2区 管理学 Q1 MANAGEMENT Organizational Research Methods Pub Date : 2022-10-17 DOI:10.1177/10944281221124946
L. J. Williams, Aaron R. Williams, Ernest H. O’Boyle
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引用次数: 0

摘要

我们回顾了潜在变量模型的路径模型拟合措施的发展,并强调了它们与全局拟合措施的不同之处。接下来,我们考虑了两篇发表的模拟文章的发现,这些文章对单路径模型拟合度量(RMSEA-P)的有效性得出了不同的结论。然后,我们报告了一项新的模拟研究的结果,该研究旨在解决组织研究人员是否应该以及如何使用RMSEA-P的问题。这些结果表明,RMSEA-P及其置信区间在识别大样本和小样本的错误规范方面对多指标模型非常有效,并且在识别中等到大样本的真实模型方面非常有效。最后,我们对如何将RMSEA-P与其他信息一起纳入模型评估提出了建议。
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Assessment of Path Model Fit: Evidence of Effectiveness and Recommendations for use of the RMSEA-P
We review the development of path model fit measures for latent variable models and highlight how they are different from global fit measures. Next, we consider findings from two published simulation articles that reach different conclusions about the effectiveness of one path model fit measure (RMSEA-P). We then report the results of a new simulation study aimed at resolving the questions of whether and how the RMSEA-P should be used by organizational researchers. These results show that the RMSEA-P and its confidence interval is very effective with multiple indicator models at identifying misspecifications across large and small sample sizes and is effective at identifying true models at moderate to large sample sizes. We conclude with recommendations for how the RMSEA-P can be incorporated along with other information into model evaluation.
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来源期刊
CiteScore
23.20
自引率
3.20%
发文量
17
期刊介绍: Organizational Research Methods (ORM) was founded with the aim of introducing pertinent methodological advancements to researchers in organizational sciences. The objective of ORM is to promote the application of current and emerging methodologies to advance both theory and research practices. Articles are expected to be comprehensible to readers with a background consistent with the methodological and statistical training provided in contemporary organizational sciences doctoral programs. The text should be presented in a manner that facilitates accessibility. For instance, highly technical content should be placed in appendices, and authors are encouraged to include example data and computer code when relevant. Additionally, authors should explicitly outline how their contribution has the potential to advance organizational theory and research practice.
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