Estimating the Maximum Likelihood Root Mean Square Error of Approximation (RMSEA) with Non-normal Data: A Monte-Carlo Study
Estimating the Maximum Likelihood Root Mean Square Error of Approximation (RMSEA) with Non-normal Data: A Monte-Carlo Study
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DOI：
10.1080/10705511.2019.1637741
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发表时间：
2019-08-11
影响因子：
6
通讯作者：
Maydeu-Olivares, Alberto
中科院分区：
文献类型：
--
作者：
Gao, Chuanji;Shi, Dexin;Maydeu-Olivares, Alberto
最近的研究提供了公式估计的最大似然（ML）RMSEA时，平均值或平均值和方差，校正非正态性适用于似然比检验统计量。我们调查模拟的校正选择提供了最准确的点RMSEA估计，置信区间，和p值的测试下的紧密配合的正态性，并在存在非正态性。我们发现，总体而言，任何稳健的校正（选择MLM，MLMV和MLR）提供的结果都优于ML，ML假设正态性。当他们犯错时，所有的选择都倾向于表明模型比实际更不符合实际。选择MLMV（均值和方差校正）提供了最准确的RMSEA估计值和p值的紧密拟合结果的测试，但其性能下降的变量建模的数量增加。
Recent research has provided formulae for estimating the maximum likelihood (ML) RMSEA when mean or mean and variance, corrections for non-normality are applied to the likelihood ratio test statistic. We investigate by simulation which choice of corrections provides most accurate point RMSEA estimates, confidence intervals, and p-values for a test of close fit under normality, and in the presence of non-normality. We found that, overall, any robust corrections (choices MLM, MLMV, and MLR) provide better results than ML, which assumes normality. When they err, all choices tend to suggest that the model fits more poorly than it really does. Choice MLMV (mean and variance corrections) provided the most accurate RMSEA estimates and p-values for tests of close fit results but its performance decreases as the number of variables being modeled increases.