Multilevel Models for Longitudinal Data

Authors
Steele, F.
Year
2008
Journal
Journal of the Royal Statistical Society, Series A, 171(1), 5-19
DOI
10.1111/j.1467-985X.2007.00509.x
Abstract

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.

Number of levels
2
Model data structure
Response types
Multivariate response model?
Yes
Longitudinal data?
Yes
Further model keywords
Substantive discipline
Paper submitted by
Fiona Steele, Graduate School of Education, University of Bristol
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