Corrected Goodness-of-Fit Test in Covariance Structure Analysis
Corrected Goodness-of-Fit Test in Covariance Structure Analysis
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DOI：
10.2139/ssrn.2965271
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Kazuhiko Hayakawa
中科院分区：
文献类型：
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作者：
Kazuhiko Hayakawa
以往许多研究报告的模拟证据表明，协方差结构分析或结构方程建模中的拟合优度检验在显变量数量大于样本量时存在过拒绝问题。在这项研究中，我们证明了Browne（1974）中考虑的一种测试可以解决这个长期存在的问题。我们还提出了对非正态数据的Satorra和Bentler的均值和方差调整检验的简单修改。进行蒙特卡罗模拟，以研究在验证性因素模型、面板自回归模型和交叉滞后面板（面板向量自回归）模型背景下校正测试的性能。仿真结果表明，修正后的测试克服了过抑制问题，在大多数情况下优于现有测试。（PsycINFO数据库记录(c) 2019 APA，版权所有）。
Many previous studies report simulation evidence that the goodness-of-fit test in covariance structure analysis or structural equation modeling suffers from the overrejection problem when the number of manifest variables is large compared with the sample size. In this study, we demonstrate that one of the tests considered in Browne (1974) can address this long-standing problem. We also propose a simple modification of Satorra and Bentler's mean and variance adjusted test for non-normal data. A Monte Carlo simulation is carried out to investigate the performance of the corrected tests in the context of a confirmatory factor model, a panel autoregressive model, and a cross-lagged panel (panel vector autoregressive) model. The simulation results reveal that the corrected tests overcome the overrejection problem and outperform existing tests in most cases. (PsycINFO Database Record (c) 2019 APA, all rights reserved).