University of British Columbia, Vancouver, Canada.
Br J Math Stat Psychol. 2013 May;66(2):201-23. doi: 10.1111/j.2044-8317.2012.02049.x. Epub 2012 May 8.
This paper reports on a simulation study that evaluated the performance of five structural equation model test statistics appropriate for categorical data. Both Type I error rate and power were investigated. Different model sizes, sample sizes, numbers of categories, and threshold distributions were considered. Statistics associated with both the diagonally weighted least squares (cat-DWLS) estimator and with the unweighted least squares (cat-ULS) estimator were studied. Recent research suggests that cat-ULS parameter estimates and robust standard errors slightly outperform cat-DWLS estimates and robust standard errors (Forero, Maydeu-Olivares, & Gallardo-Pujol, 2009). The findings of the present research suggest that the mean- and variance-adjusted test statistic associated with the cat-ULS estimator performs best overall. A new version of this statistic now exists that does not require a degrees-of-freedom adjustment (Asparouhov & Muthén, 2010), and this statistic is recommended. Overall, the cat-ULS estimator is recommended over cat-DWLS, particularly in small to medium sample sizes.
本文报告了一项模拟研究，评估了适用于分类数据的五种结构方程模型检验统计量的性能。研究了Ⅰ类错误率和功效。考虑了不同的模型大小、样本大小、分类数量和阈值分布。研究了与对角线加权最小二乘法（cat-DWLS）估计量和未加权最小二乘法（cat-ULS）估计量相关的统计量。最近的研究表明，cat-ULS 参数估计值和稳健标准误差略优于 cat-DWLS 估计值和稳健标准误差（Forero、Maydeu-Olivares 和 Gallardo-Pujol，2009）。本研究的结果表明，与 cat-ULS 估计量相关的均值和方差调整检验统计量总体性能最佳。现在存在一个不需要自由度调整的这个统计量的新版本（Asparouhov 和 Muthén，2010），推荐使用这个统计量。总体而言，推荐使用 cat-ULS 估计量而不是 cat-DWLS，特别是在小到中等样本量的情况下。
