Abstract

This study aimed to understand the effect of model size on the root mean square error of approximation (RMSEA) under nonnormal data. We considered three methods for computing the sample RMSEA and the associated confidence intervals (CIs; i.e., the normal theory method, the BSL method, and the Lai method). The performance of the three methods was compared across various model sizes, sample sizes, levels of misspecification, and levels of nonnormality. Results indicated that the normal theory RMSEA should not be used under nonnormal data unless the model size is very small. In the presence of nonnormal data, researchers should consider using either the BSL or the Lai method to estimate RMSEA and its CIs. The Lai method is recommended when very large models are fit under nonnormal data.

摘要

本研究旨在了解模型大小对非正态数据下的均方根近似误差 (RMSEA) 的影响。我们考虑了三种计算样本 RMSEA 和相关置信区间（CI；即正态理论方法、BSL 方法和 Lai 方法）的方法。三种方法的性能在各种模型大小、样本大小、错误指定水平和非正态性水平上进行了比较。结果表明，除非模型尺寸非常小，否则不应在非正态数据下使用正态理论 RMSEA。在存在非正态数据时，研究人员应考虑使用 BSL 或 Lai 方法来估计 RMSEA 及其 CI。当在非正态数据下拟合非常大的模型时，建议使用 Lai 方法。