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Modelling strategies for repeated multiple response data

Journal Article


Abstract


  • This article discusses modelling strategies for repeated measurements of multiple response variables. Such data arise in the context of categorical variables where one can select more than one of the categories as the response. We consider each of the multiple responses as a binary outcome and use a marginal (or population-averaged) modelling approach to analyse its means. Generalized estimating equations are used to account for different correlation structures, both over time and between items. We also discuss an alternative approach using a generalized linear mixed model with conditional interpretations. We illustrate the methods using data from a panel study in Australia called the Household, Income, and Labour Dynamics Survey.

Publication Date


  • 2013

Citation


  • Suesse, T. & Liu, I. (2013). Modelling strategies for repeated multiple response data. International Statistical Review, 81 (2), 230-248.

Scopus Eid


  • 2-s2.0-84883136742

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2048

Number Of Pages


  • 18

Start Page


  • 230

End Page


  • 248

Volume


  • 81

Issue


  • 2

Abstract


  • This article discusses modelling strategies for repeated measurements of multiple response variables. Such data arise in the context of categorical variables where one can select more than one of the categories as the response. We consider each of the multiple responses as a binary outcome and use a marginal (or population-averaged) modelling approach to analyse its means. Generalized estimating equations are used to account for different correlation structures, both over time and between items. We also discuss an alternative approach using a generalized linear mixed model with conditional interpretations. We illustrate the methods using data from a panel study in Australia called the Household, Income, and Labour Dynamics Survey.

Publication Date


  • 2013

Citation


  • Suesse, T. & Liu, I. (2013). Modelling strategies for repeated multiple response data. International Statistical Review, 81 (2), 230-248.

Scopus Eid


  • 2-s2.0-84883136742

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2048

Number Of Pages


  • 18

Start Page


  • 230

End Page


  • 248

Volume


  • 81

Issue


  • 2