Multilevel Models for Longitudinal Data

F.A Steele

Research output: Contribution to journalArticle (Academic Journal)peer-review

120 Citations (Scopus)

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.
Translated title of the contributionMultilevel Models for Longitudinal Data
Original languageEnglish
Pages (from-to)5 - 19
Number of pages25
JournalJournal of the Royal Statistical Society: Series A
Volume171
Issue number1
DOIs
Publication statusPublished - Jan 2008

Bibliographical note

Publisher: Blackwell Synergy

Fingerprint Dive into the research topics of 'Multilevel Models for Longitudinal Data'. Together they form a unique fingerprint.

Cite this