Alternative Multiple Imputation Inference for Categorical Structural Equation Modeling

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

The use of item responses from questionnaire data is ubiquitous in social science research. One side effect of using such data is that researchers must often account for item level missingness. Multiple imputation is one of the most widely used missing data handling techniques. The traditional multiple imputation approach in structural equation modeling has a number of limitations. Motivated by Lee and Cai’s approach, we propose an alternative method for conducting statistical inference from multiple imputation in categorical structural equation modeling. We examine the performance of our proposed method via a simulation study and illustrate it with one empirical data set.

Original languageEnglish (US)
Pages (from-to)323-337
Number of pages15
JournalMultivariate Behavioral Research
Volume54
Issue number3
DOIs
StatePublished - May 4 2019
Externally publishedYes

Keywords

  • Categorical variables
  • goodness-of-fit test
  • multiple imputation
  • structural equation modeling

PubMed: MeSH publication types

  • Journal Article

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