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Modeling Joint and Marginal Distributions in the Analysis of Categorical Panel Data

Author

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  • JEROEN K. VERMUNT

    (Tilburg University)

  • MARÃ A FLORENCIA RODRIGO

    (University of Valencia)

  • MANUEL ATO-GARCIA

    (University of Murcia)

Abstract

This article presents a unifying approach to the analysis of repeated univariate categorical (ordered) responses based on the application of the generalized log-linear modeling framework proposed by Lang and Agresti. It is shown that three important research questions in longitudinal studies can be addressed simultaneously. These questions are the following: What is the overall dependence structure of the repeated responses? What is the structure of the change between consecutive time points? and What is the structure of the change in the marginal distributions? Each of these questions involves specifying log-linear models for different marginal distributions of the multiway cross classification of the responses. The proposed approach is illustrated by means of two real data examples.

Suggested Citation

  • Jeroen K. Vermunt & Marã A Florencia Rodrigo & Manuel Ato-Garcia, 2001. "Modeling Joint and Marginal Distributions in the Analysis of Categorical Panel Data," Sociological Methods & Research, , vol. 30(2), pages 170-196, November.
    • Handle: RePEc:sae:somere:v:30:y:2001:i:2:p:170-196
    DOI: 10.1177/0049124101030002002
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    References listed on IDEAS

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    1. P. Diggle & M. G. Kenward, 1994. "Informative Drop‐Out in Longitudinal Data Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 49-73, March.
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    Cited by:

    1. Z. Rezaei Ghahroodi & M. Ganjali, 2013. "A Bayesian approach for analysing longitudinal nominal outcomes using random coefficients transitional generalized logit model: an application to the labour force survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(7), pages 1425-1445, July.
    2. Hailemichael M. Worku & Mark De Rooij, 2017. "Properties of Ideal Point Classification Models for Bivariate Binary Data," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 308-328, June.

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