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Meaningful regression and association models for clustered ordinal data

By Jukka Jokinen, John W. McDonald and Peter W. F. Smith

Abstract

Many proposed methods for analyzing clustered ordinal data focus on the regression model and consider the association structure within a cluster as a nuisance. However, often the association structure is of equal interest, for example, temporal association in longitudinal studies and association between responses to similar questions in a survey. We discuss the use, appropriateness and interpretability of various latent variable and Markov models for the association structure and propose a new structure that exploits the ordinality of the response. The models are illustrated with a study concerning opinions regarding government spending and an analysis of stability and change in teenage marijuana use over time, where we reveal different behavioral patterns for boys and girls through a comprehensive investigation of individual response profiles

Topics: HA
Year: 2006
OAI identifier: oai:eprints.soton.ac.uk:14001
Provided by: e-Prints Soton

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