Date of Award
Fall 2023
Document Type
Open Access Thesis
Department
Psychology
First Advisor
Dexin Shi
Second Advisor
Amanda Fairchild
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 (i.e., the normal theory method [Browne & Cudeck, 1993], the BSL method [Brosseau-Liard, Savalei & Li, 2012], and the Lai method [Lai, 2020]). 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 confidence intervals. The Lai method is recommended when very large models are fit under nonnormal data.
Rights
© 2024, Yunhang Yin
Recommended Citation
Yin, Y.(2023). The Effect of Model Size on the Root Mean Square Error of Approximation (RMSEA): The Nonnormal Case. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/7529