Sriutaisuk Suppanut, Liu Yu, Chung Seungwon, Kim Hanjoe, Gu Fei
Faculty of Psychology, Chulalongkorn University, Bangkok, Thailand.
University of Houston, Houston, TX, USA.
Educ Psychol Meas. 2024 Jul 27:00131644241261271. doi: 10.1177/00131644241261271.
The multiple imputation two-stage (MI2S) approach holds promise for evaluating the model fit of structural equation models for ordinal variables with multiply imputed data. However, previous studies only examined the performance of MI2S-based residual-based test statistics. This study extends previous research by examining the performance of two alternative test statistics: the mean-adjusted test statistic ( ) and the mean- and variance-adjusted test statistic ( ). Our results showed that the MI2S-based generally outperformed other test statistics examined in a wide range of conditions. The MI2S-based root mean square error of approximation also exhibited good performance. This article demonstrates the MI2S approach with an empirical data set and provides Mplus and R code for its implementation.
多重填补两阶段（MI2S）方法有望用于评估具有多重填补数据的有序变量结构方程模型的模型拟合度。然而，以往的研究仅考察了基于MI2S的基于残差的检验统计量的性能。本研究通过考察两种替代检验统计量的性能扩展了先前的研究：均值调整检验统计量（ ）和均值与方差调整检验统计量（ ）。我们的结果表明，基于MI2S的 通常在广泛的条件下优于其他检验统计量。基于MI2S的近似均方根误差也表现出良好的性能。本文用一个实证数据集展示了MI2S方法，并提供了用于实现它的Mplus和R代码。
