Shi Dexin, Zhang Bo, Liu Ren, Jiang Zhehan
University of South Carolina, Columbia, USA.
University of Illinois Urbana-Champaign, USA.
Educ Psychol Meas. 2024 Feb;84(1):171-189. doi: 10.1177/00131644231158854. Epub 2023 Mar 27.
Multiple imputation (MI) is one of the recommended techniques for handling missing data in ordinal factor analysis models. However, methods for computing MI-based fit indices under ordinal factor analysis models have yet to be developed. In this short note, we introduced the methods of using the standardized root mean squared residual (SRMR) and the root mean square error of approximation (RMSEA) to assess the fit of ordinal factor analysis models with multiply imputed data. Specifically, we described the procedure for computing the MI-based sample estimates and constructing the confidence intervals. Simulation results showed that the proposed methods could yield sufficiently accurate point and interval estimates for both SRMR and RMSEA, especially in conditions with larger sample sizes, less missing data, more response categories, and higher degrees of misfit. Based on the findings, implications and recommendations were discussed.
多重填补（MI）是在有序因子分析模型中处理缺失数据的推荐技术之一。然而，在有序因子分析模型下计算基于MI的拟合指数的方法尚未得到发展。在本简短报告中，我们介绍了使用标准化均方根残差（SRMR）和近似均方根误差（RMSEA）来评估具有多重填补数据的有序因子分析模型拟合度的方法。具体而言，我们描述了计算基于MI的样本估计值和构建置信区间的过程。模拟结果表明，所提出的方法能够为SRMR和RMSEA产生足够准确的点估计和区间估计，特别是在样本量较大、缺失数据较少、响应类别较多以及拟合不佳程度较高的情况下。基于这些发现，我们讨论了其影响和建议。
