Skip to main content
Article thumbnail
Location of Repository

Multilevel models for longitudinal data

By Fiona Steele


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

Topics: HA Statistics
Publisher: Wiley on behalf of the Royal Statistical Society
Year: 2008
DOI identifier: 10.1111/j.1467-985X.2007.00509.x
OAI identifier:
Provided by: LSE Research Online

Suggested articles


  1. (2004). A general multistate competing risks model for event history data, with an application to a study of contraceptive use dynamics. doi
  2. (1993). A joint model of marital childbearing and marital disruption. doi
  3. (2006). A multilevel factor model for mixed binary and ordinal indicators of women's status. doi
  4. (2004). A User's Guide to MLwiN, v2.0.
  5. (1997). Analysis of Incomplete Multivariate Data. doi
  6. (2002). Analysis of Longitudinal Data. 2nd edn. doi
  7. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. doi
  8. (2004). Archive doi
  9. (1989). Balanced Versus Unbalanced Designs for Linear Structural Relations in 2-Level Data. doi
  10. (2002). Computational strategies for multivariate linear mixedeffects models with missing values. doi
  11. (1982). Discrete-time methods for the analysis of event histories. In: Sociological Methodology doi
  12. (2004). for Social and Economic Research, [original data producer(s)].
  13. (2004). Generalized Latent Variable Modelling: Multilevel, Longitudinal, and Structural Equation Models. Chapman & Hall/CRC, doi
  14. (2004). Generalized Linear Structural Equation Modelling. doi
  15. (2002). Hierarchical Linear Models. Sage, doi
  16. (2006). Latent Curve Models: A Structural Equation Perspective. doi
  17. (1997). Latent variable modeling of longitudinal and multilevel data. In: Sociological Methodology doi
  18. (1994). Lawrence Erlbaum, doi
  19. (1994). Multilevel Covariance Structure-Analysis. doi
  20. (2003). Multilevel Statistical Models. 3rd edn. doi
  21. (2007). Multilevel structural equation models for the analysis of comparative data on educational performance. doi
  22. (2004). Multiple imputation in MLwiN. Multilevel Modelling Newsletter, 16 Curran P.J.
  23. (1982). Random-Effects Models for Longitudinal Data. doi
  24. (2005). Stata 9.0 Base Reference Manual.
  25. (1996). The determinants of the duration of contraceptive use in China: A multilevel multinomial discrete-hazards modeling approach. doi
  26. (1997). The National Child Development Study: An Introduction to the Origins of the Study and the Methods of Data Collection.
  27. (2005). The relationship between childbearing and transitions from marriage and cohabitation in Great Britain. doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.