Alternative Multiple Imputation Inference for Categorical Structural Equation Modeling

Seungwon Chung, Li Cai

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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
Issue number3
StatePublished - May 4 2019
Externally publishedYes

Bibliographical note

Funding Information:
Funding: This work was supported by Grant R305D140046 from the Institute of Education Sciences (IES).

Publisher Copyright:
© 2019, © 2019 Taylor & Francis Group, LLC.


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


Dive into the research topics of 'Alternative Multiple Imputation Inference for Categorical Structural Equation Modeling'. Together they form a unique fingerprint.

Cite this