Multivariate and Multilevel Longitudinal Analysis

  • Nicholas T. LongfordEmail author


This chapter presents a review of perspectives and methods for analysis of longitudinal data on several related variables. A connection is made with multilevel analysis in which the longitudinal and multivariate dimensions of the data can naturally be subsumed. With the focus on large-scale longitudinal studies of human subjects who are in general disinterested in and not highly motivated by the agenda of the study, methods for dealing with nonresponse are an essential addendum to the analytical equipment.


Longitudinal Analysis Variance Matrix Multivariate Normal Distribution Variance Matrice Time Time 
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  1. Chatfield, C. (1995). Model uncertainty, data mining and statistical inference. Journal of the Royal Statistical Society, Ser. A, 158, 419-466.CrossRefGoogle Scholar
  2. Diggle, P., Heagerty, P., Liang, K.-Y., & Zeger, S. L. (2002). Analysis of longitudinal data (2nd ed.). Oxford: Oxford University Press.Google Scholar
  3. Draper, D. (1995). Assessment and propagation of model uncertainty. Journal of the Royal Statistical Society, Ser. B, 57, 45-97.zbMATHMathSciNetGoogle Scholar
  4. Francis, B., Green, M., & Payne, C. (1993). The GLIM system. Release 4 manual. Oxford: Oxford University Press.zbMATHGoogle Scholar
  5. Goldstein, H. (2003). Multilevel statistical models (3rd ed.). London: E. Arnold.zbMATHGoogle Scholar
  6. Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945-970.zbMATHCrossRefMathSciNetGoogle Scholar
  7. Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38, 963-974.zbMATHCrossRefGoogle Scholar
  8. Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). New York: Wiley.zbMATHGoogle Scholar
  9. Longford, N. T. (1993). Random coefficient models. Oxford: Oxford University Press.zbMATHGoogle Scholar
  10. Longford, N. T. (2001). Simulation-based diagnostics in random-coefficient models. Journal of the Royal Statistical Society, Ser. A, 64, 259-273.CrossRefMathSciNetGoogle Scholar
  11. Longford, N. T. (2007). Studying human populations. An advanced course in statistics. New York: Springer-Verlag.Google Scholar
  12. Pinheiro, J. C., & Bates, D. M. (2000). Mixed-effects models in S and Splus. New York: Springer-Verlag.zbMATHCrossRefGoogle Scholar
  13. Rasbash, J., Charlton, C., Browne, W. J., Healy, M., & Cameron, B. (2005). MLwin version 2.02. Centre for Multilevel Modelling, University of Bristol.Google Scholar
  14. Rubin, D. B. (1984). Bayesianly justifiable and relevant frequency calculations for the applied statistician. Annals of Statistics, 12, 1151-1172.zbMATHCrossRefMathSciNetGoogle Scholar
  15. Rubin, D. B. (2005). Causal inference using potential outcomes: design, modelling, decisions. 2004 Fisher Lecture. Journal of the American Statistical Association, 100, 32-331.CrossRefGoogle Scholar
  16. Verbeke, G., & Molenberghs, G. (2000). Linear mixed models for longitudinal data. New York: Springer-Verlag.zbMATHGoogle Scholar

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© Springer Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.SNTLBarcelonaSpain

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