Psychological Sciences, University of California, Merced, 5200 N. Lake Road, Merced, CA, 95343, USA.
Behav Res Methods. 2024 Mar;56(3):1229-1243. doi: 10.3758/s13428-023-02088-3. Epub 2023 Mar 27.
In structural equation modeling, when multiple imputation is used for handling missing data, model fit evaluation involves pooling likelihood-ratio test statistics across imputations. Under the normality assumption, the two most popular pooling approaches were proposed by Li et al. (Statistica Sinica, 1(1), 65-92, 1991) and Meng and Rubin (Biometrika, 79(1), 103-111, 1992). When the assumption of normality is violated, it is not clear how well these pooling approaches work with the test statistics generated from various robust estimators and multiple imputation methods. Jorgensen and colleagues (2021) implemented these pooling approaches in their R package semTools; however, no systematical evaluation has been conducted. In this simulation study, we examine the performance of these approaches in working with different imputation methods and robust estimators under nonnormality. We found that the naïve pooling approach based on Meng and Rubin (Biometrika, 79(1), 103-111, 1992; D) worked the best when combining with the normal-theory-based imputation and either MLM or MLMV estimator.
在结构方程建模中，当使用多重插补处理缺失数据时，模型拟合评估涉及跨插补合并似然比检验统计量。在正态性假设下，Li 等人（《统计学报》，1（1），65-92，1991）和 Meng 和 Rubin（《生物统计学》，79（1），103-111，1992）提出了两种最流行的合并方法。当正态性假设被违反时，不清楚这些合并方法在使用各种稳健估计量和多重插补方法生成的检验统计量时效果如何。Jorgensen 及其同事（2021）在他们的 R 包 semTools 中实现了这些合并方法；然而，还没有进行系统的评估。在这项模拟研究中，我们研究了在非正态性下，这些方法与不同插补方法和稳健估计量结合使用的性能。我们发现，当与基于正态理论的插补和 MLM 或 MLMV 估计器结合使用时，基于 Meng 和 Rubin（《生物统计学》，79（1），103-111，1992；D）的简单合并方法表现最好。
