Evaluation of Model Fit in Structural Equation Models with Ordinal Missing Data: A Comparison of the D2 and MI2S Methods. Issue 5 (3rd September 2021)

• Record Type: Journal Article
• Authors: Liu, Yu; Sriutaisuk, Suppanut; Chung, Seungwon
• Is Part Of: Structural equation modeling. Volume 28:Issue 5(2021)
• Page Start: 740
• Page End: 762
• Publication Date: 2021-09-03
• Subjects: missing data -- model fit -- multiple imputation -- ordinal data; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2021.1919118
• Languages: English
• ISSNs: 1070-5511

Abstract

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 D 2 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 D 2 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.