ABSTRACT

Social science research often utilizes measurement instruments that generate ordinal data (e.g., Likert scales). Many empirical studies also face the challenge of missing data, which can be addressed by performing multiple imputation followed by analyses of the imputed datasets. However, when missing data exist on ordinal variables, there has been limited research on how to evaluate model fit of structural equation models for ordinal variables. Recent studies suggest that two multiple-imputation-based approaches show great promise: The D₂ procedure, and the Multiple Imputation Two-step (MI2S) approach, though the two have not been systematically compared. This study extends previous research by comparing the D₂ with the MI2S fit statistics in a wider range of conditions than previous studies. Our findings revealed a number of factors that can influence the performance of these test statistics.

摘要

社会科学研究通常使用生成有序数据的测量工具（例如，李克特量表）。许多实证研究还面临数据缺失的挑战，这可以通过执行多重插补然后分析插补数据集来解决。然而，当序数变量存在缺失数据时，如何评估序数变量结构方程模型的模型拟合度的研究有限。最近的研究表明，两种基于多重插补的方法显示出巨大的前景：D ₂程序和多重插补两步 (MI2S) 方法，尽管尚未对这两种方法进行系统比较。本研究通过比较D ₂与以前的研究相比，MI2S 拟合统计数据在更广泛的条件下进行。我们的发现揭示了许多可能影响这些测试统计性能的因素。