Multilevel Models with multivariate mixed response types

H Goldstein, J Carpenter, M Kenward, K Levin

Research output: Contribution to journalArticle (Academic Journal)peer-review

122 Citations (Scopus)


We build upon the existing literature to formulate a class of models for multivariate mixtures of Gaussian, ordered or unordered categorical responses and continuous distributions that are not Gaussian, each of which can be defined at any level of a multilevel data hierarchy. We describe a Markov chain Monte Carlo algorithm for fitting such models. We show how this unifies a number of disparate problems, including partially observed data and missing data in generalized linear modelling. The two-level model is considered in detail with worked examples of applications to a prediction problem and to multiple imputation for missing data. We conclude with a discussion outlining possible extensions and connections in the literature. Software for estimating the models is freely available.
Translated title of the contributionMultilevel Models with multivariate mixed response types
Original languageEnglish
Pages (from-to)173 - 197
Number of pages25
JournalStatistical Modelling
Issue number3
Publication statusPublished - Oct 2009

Bibliographical note

Publisher: Sage


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