Multilevel Models for Longitudinal Data

Repeated measures and repeated events data have a hierarchical structure which can be analysed using multilevel models. A growth curve model is an example of a multilevel random coefficients model, while 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 autocorrelated 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.