在使用序数数据的结构方程建模中评估基于估算的拟合统计量:MI2S 方法

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-07-27 DOI:10.1177/00131644241261271
Suppanut Sriutaisuk, Yu Liu, Seungwon Chung, Hanjoe Kim, Fei Gu
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引用次数: 0

摘要

两阶段多重估算(MI2S)方法有望评估具有多重估算数据的序变量结构方程模型的拟合度。然而,以前的研究只考察了基于 MI2S 的残差检验统计量的性能。本研究扩展了之前的研究,检验了两种可选检验统计量的性能:均值调整检验统计量(T M)和均值与方差调整检验统计量(T MV)。我们的结果表明,在各种条件下,基于 MI2S 的 T MV 总体上优于其他测试统计量。基于 MI2S 的均方根近似误差也表现出良好的性能。本文用一组经验数据演示了 MI2S 方法,并提供了实现该方法的 Mplus 和 R 代码。
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Evaluating Imputation-Based Fit Statistics in Structural Equation Modeling With Ordinal Data: The MI2S Approach
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 ( T M) and the mean- and variance-adjusted test statistic ( T MV). Our results showed that the MI2S-based T MV 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.
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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