ABSTRACT

The root mean square error of approximation (RMSEA) with various corrections for non-normality is a common fit index in structural equation modeling (SEM). The present study analyzed the performance of the uncorrected, the “sample corrected”, and the “population corrected” RMSEA in misspecified models for both complete and incomplete data sets under multivariate normality and multivariate non-normality. Additionally, the effect of the multivariate distribution under non-normality was investigated by comparing two different data generation approaches. The results show that missingness is associated with a downward bias, whereas non-normality tends to incur an upward bias. Because the corrections reduce the RMSEA estimate under non-normality, corrected values exhibited a stronger bias compared to uncorrected values when non-normality and missing data were simultaneously present. However, the extent of bias also depended on properties of the multivariate distribution.

摘要

具有各种非正态性校正的近似均方根误差 (RMSEA) 是结构方程建模 (SEM) 中的常见拟合指数。本研究分析了未校正、“样本校正”和“总体校正”RMSEA 在多元正态性和多元非正态性下的完整和不完整数据集的错误指定模型中的性能。此外，通过比较两种不同的数据生成方法，研究了非正态下多变量分布的影响。结果表明，缺失与向下偏差相关，而非正态性往往会导致向上偏差。因为修正减少了非正态下的 RMSEA 估计，当非正态性和缺失数据同时存在时，校正值与未校正值相比表现出更强的偏差。然而，偏差的程度也取决于多元分布的特性。