Evaluation of Model Fit in Structural Equation Models with Ordinal Missing Data: A Comparison of the D 2 and MI2S Methods

Yu Liu, Suppanut Sriutaisuk, Seungwon Chung

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)740-762
Number of pages23
JournalStructural Equation Modeling
Volume28
Issue number5
DOIs
StatePublished - May 24 2021

Bibliographical note

Funding Information:
This work was completed in part with resources provided by the Research Computing Data Core at the University of Houston.

Publisher Copyright:
© 2021 Taylor & Francis Group, LLC.

Keywords

  • missing data
  • model fit
  • multiple imputation
  • ordinal data

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