Zhang Xijuan, Savalei Victoria
Department of Psychology, York University.
Department of Psychology, University of British Columbia.
Psychol Methods. 2023 Apr;28(2):263-283. doi: 10.1037/met0000445. Epub 2022 Jan 10.
The full-information maximum likelihood (FIML) is a popular estimation method for missing data in structural equation modeling (SEM). However, previous research has shown that SEM approximate fit indices (AFIs) such as the root mean square error of approximation (RMSEA) and the comparative fit index (CFI) can be distorted relative to their complete data counterparts when they are computed following the FIML estimation. The main goal of the current paper is to propose and examine an alternative approach for computing AFIs following the FIML estimation, which we refer to as the FIML-corrected or FIML-C approach. The secondary goal of the article is to examine another existing estimation method, the two-stage (TS) approach, for computing AFIs in the presence of missing data. Both FIML-C and TS approaches remove the bias due to missing data, so that the resulting incomplete data AFIs estimate the same population values as their complete data counterparts. For both approaches, we also propose a series of small sample corrections to improve the estimates of AFIs. In two simulation studies, we found that the FIML-C and TS approaches, when implemented with small sample corrections, estimated the population-complete-data AFIs with little bias across a variety of conditions, although the FIML-C approach can fail in a small number of conditions with a high percentage of missing data and a high degree of model misspecification. In contrast, the FIML AFIs as currently computed often performed poorly. We recommend FIML-C and TS approaches for computing AFIs in SEM. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
完全信息最大似然法（FIML）是结构方程模型（SEM）中处理缺失数据的一种常用估计方法。然而，先前的研究表明，在FIML估计之后计算结构方程模型的近似拟合指数（AFIs），如近似误差均方根（RMSEA）和比较拟合指数（CFI）时，相对于其完整数据对应的指数可能会产生偏差。本文的主要目标是提出并检验一种在FIML估计之后计算AFIs的替代方法，我们将其称为FIML校正法或FIML-C方法。本文的次要目标是检验另一种现有的估计方法，即两阶段（TS）法，用于在存在缺失数据的情况下计算AFIs。FIML-C法和TS法都消除了由于缺失数据导致的偏差，因此由此产生的不完整数据AFIs估计的总体值与其完整数据对应的指数相同。对于这两种方法，我们还提出了一系列小样本校正方法，以改进AFIs的估计。在两项模拟研究中，我们发现，当采用小样本校正时，FIML-C法和TS法在各种条件下对总体完整数据AFIs的估计偏差很小，尽管FIML-C法在少数缺失数据百分比高且模型错误设定程度高的条件下可能会失效。相比之下，目前计算的FIML AFIs通常表现不佳。我们推荐使用FIML-C法和TS法在结构方程模型中计算AFIs。（PsycInfo数据库记录（c）2023美国心理学会，保留所有权利）
