Statistical models for the analysis of ordered categorical data in public health and medical research.

R. D. Etzioni, S. E. Fienberg, Z. Gilula, S. J. Haberman

Research output: Contribution to journalArticle

8 Scopus citations


In the late 1970s statisticians extended the methods for analysing loglinear and logit models for cross-classified categorical data to incorporate information about the ordinal structure of the categories corresponding to some of the classification variables. In this paper we review one class of such extensions known as association models. We consider association models with and without order restrictions on the parameters and we use these models to answer research questions about several medical examples involving ordered categorical data. We emphasize the interpretation of parameters in the association models and how this relates to the research questions of interest.

Original languageEnglish (US)
Pages (from-to)179-204
Number of pages26
JournalStatistical methods in medical research
Issue number2
StatePublished - 1994


ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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