Evaluating methods for handling missing ordinal data in structural equation modeling

Missing ordinal data are common in studies using structural equation modeling (SEM). Although several methods for dealing with missing ordinal data have been available, these methods often have not been systematically evaluated in SEM. In this study, we used Monte Carlo simulation to evaluate and co...

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Bibliographic Details
Published inBehavior research methods Vol. 51; no. 5; pp. 2337 - 2355
Main Authors Jia, Fan, Wu, Wei
Format Journal Article
LanguageEnglish
Published New York Springer US 01.10.2019
Springer Nature B.V
Subjects
Behavioral Science and Psychology
Cognitive Psychology
Computer simulation
Estimating techniques
Latent Class Analysis
Monte Carlo Method
Monte Carlo simulation
Psychology
Structural equation modeling
Missing ordinal data
Robust estimation
Structural equation modeling
Multiple imputation
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Summary:Missing ordinal data are common in studies using structural equation modeling (SEM). Although several methods for dealing with missing ordinal data have been available, these methods often have not been systematically evaluated in SEM. In this study, we used Monte Carlo simulation to evaluate and compare five existing methods, including one direct robust estimation method and four multiple imputation methods, to deal with missing ordinal data. On the basis of the simulation results, we provide practical guidance to researchers in terms of the best way to deal with missing ordinal data in SEM.
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ISSN:1554-3528
1554-3528
DOI:10.3758/s13428-018-1187-4