a Department of Psychology , University of British Columbia.
Multivariate Behav Res. 2018 May-Jun;53(3):419-429. doi: 10.1080/00273171.2018.1455142. Epub 2018 Apr 6.
A new type of nonnormality correction to the RMSEA has recently been developed, which has several advantages over existing corrections. In particular, the new correction adjusts the sample estimate of the RMSEA for the inflation due to nonnormality, while leaving its population value unchanged, so that established cutoff criteria can still be used to judge the degree of approximate fit. A confidence interval (CI) for the new robust RMSEA based on the mean-corrected ("Satorra-Bentler") test statistic has also been proposed. Follow up work has provided the same type of nonnormality correction for the CFI (Brosseau-Liard & Savalei, 2014). These developments have recently been implemented in lavaan. This note has three goals: a) to show how to compute the new robust RMSEA and CFI from the mean-and-variance corrected test statistic; b) to offer a new CI for the robust RMSEA based on the mean-and-variance corrected test statistic; and c) to caution that the logic of the new nonnormality corrections to RMSEA and CFI is most appropriate for the maximum likelihood (ML) estimator, and cannot easily be generalized to the most commonly used categorical data estimators.
最近开发了一种新的 RMSEA 非正态性校正方法，它具有优于现有校正方法的几个优点。特别是，新的校正方法针对非正态性导致的 RMSEA 样本估计值进行了调整，而不改变其总体值，从而仍然可以使用既定的截断标准来判断近似拟合程度。还提出了一种基于均值校正（“Satorra-Bentler”检验统计量）的新稳健 RMSEA 的置信区间（CI）。后续工作还为 CFI（Brosseau-Liard & Savalei，2014）提供了相同类型的非正态性校正。这些进展最近已在 lavaan 中实现。本说明有三个目标：a）展示如何从均值和方差校正的检验统计量计算新的稳健 RMSEA 和 CFI；b）提供基于均值和方差校正检验统计量的稳健 RMSEA 的新置信区间；c）警告说，RMSEA 和 CFI 的新非正态性校正的逻辑最适合最大似然（ML）估计量，并且不能轻易推广到最常用的分类数据估计量。
