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Meaningful regression and association models for clustered ordinal data

Record type: Article

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.

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Citation

Jokinen, Jukka, McDonald, John W. and Smith, Peter W. F. (2006) Meaningful regression and association models for clustered ordinal data Sociological Methodology (doi:10.1111/j.1467-9531.2006.00173.x).

More information

Submitted date: 14 January 2005
Published date: 14 January 2006

Identifiers

Local EPrints ID: 14001
URI: http://eprints.soton.ac.uk/id/eprint/14001
ISSN: 1467-9531
PURE UUID: d737a0d6-b3b7-4b52-9f3c-c63d2628946b
ORCID for Peter W. F. Smith: ORCID iD orcid.org/0000-0003-4423-5410

Catalogue record

Date deposited: 14 Jan 2005
Last modified: 17 Jul 2017 16:59

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Contributors

Author: Jukka Jokinen
Author: John W. McDonald

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