Sample Size Requirements of the Robust Weighted Least Squares Estimator
Sample Size Requirements of the Robust Weighted Least Squares Estimator
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DOI：
10.1027/1614-2241/a000068
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发表时间：
2014-01-01
影响因子：
3.1
通讯作者：
Musch, Jochen
中科院分区：
文献类型：
--
作者：
Moshagen, Morten;Musch, Jochen
本研究探讨了最大似然（ML）和稳健加权最小二乘（稳健WLS）估计的样本量要求的有序数据与验证性因素分析（CFA）模型，3-10个指标，每个因素，主要负载之间的.4和.9，和四个不同的分类水平（2，3，5，和7）。此外，还检查了结构可靠性的H度量（一种结合指标数量和载荷大小的指数）在预测样本量要求中的效用。结果表明，每个因子的指标数越多，因子负荷越高，正确收敛率和解的正确性越高。然而，H-措施只能部分解释的结果。此外，它被证明，强大的WLS大多是上级ML，这表明，有没有理由更喜欢ML强大的WLS时，数据是有序的。稳健WLS估计的样本量建议。
The present study investigated sample size requirements of maximum likelihood (ML) and robust weighted least squares (robust WLS) estimation for ordinal data with confirmatory factor analysis (CFA) models with 3-10 indicators per factor, primary loadings between .4 and .9, and four different levels of categorization (2, 3, 5, and 7). Additionally, the utility of the H-measure of construct reliability (an index combining the number of indicators and the magnitude of loadings) in predicting sample size requirements was examined. Results indicated that a higher number of indicators per factors and higher factor loadings increased the rates of proper convergence and solution propriety. However, the H-measure could only partly account for the results. Moreover, it was demonstrated that robust WLS was mostly superior to ML, suggesting that there is little reason to prefer ML over robust WLS when the data are ordinal. Sample size recommendations for the robust WLS estimator are provided.