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Multilevel models for longitudinal data

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Abstract:

Summary. 

Repeated measures and repeated events data have a hierarchical structure which can be analysed by using multilevel models. A growth curve model is an example of a multilevel random-coefficients model, whereas a discrete time event history model for recurrent events can be fitted as a multilevel logistic regression model. The paper describes extensions to the basic growth curve model to handle auto-correlated residuals, multiple-indicator latent variables and correlated growth processes, and event history models for correlated event processes. The multilevel approach to the analysis of repeated measures data is contrasted with structural equation modelling. The methods are illustrated in analyses of children's growth, changes in social and political attitudes, and the interrelationship between partnership transitions and childbearing.

Keywords: Event history analysis; Multilevel models; Repeated measures; Simultaneous equation models; Structural equation models

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/j.1467-985X.2007.00509.x

Affiliations: University of Bristol, UK

Publication date: January 1, 2008

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