A multiple imputation method for incomplete correlated ordinal data using multivariate probit models

Xiao Zhang, Quanlin Li, Karen Cropsey, Xiaowei Yang, Kui Zhang, Thomas Belin

Research output: Contribution to journalArticle

1 Scopus citations


The multiple imputation technique has proven to be a useful tool in missing data analysis. We propose a Markov chain Monte Carlo method to conduct multiple imputation for incomplete correlated ordinal data using the multivariate probit model. We conduct a thorough simulation study to compare the performance of our proposed method with two available imputation methods – multivariate normal-based and chain equation methods for various missing data scenarios. For illustration, we present an application using the data from the smoking cessation treatment study for low-income community corrections smokers.

Original languageEnglish (US)
Pages (from-to)1-16
Number of pages16
JournalCommunications in Statistics: Simulation and Computation
Publication statusAccepted/In press - Nov 29 2016
Externally publishedYes



  • Chain equation
  • Correlated ordinal
  • Data, Multivariate probit model
  • MCMC
  • Multiple imputation

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation

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