Multilevel Analysis in Obesity Research

  • David Rindskopf
Part of the NATO ASI Series book series (NSSA, volume 278)


In obesity research it is common to have repeated measures on subjects. Traditional statistical analyses of repeated measures data are analysis of variance (ANOVA) for random effects, and multivariate analysis of variance (MANOVA). Each assume that every subject was measured (i) the same number of times, and (ii) at the same time points. Another typical complication of many research designs is the presence of time-varying covariates. The usual ANOVA approach to repeated measures does not allow such covariates, and the MANOVA approach usually treats them as dependent variables. Hierarchical linear models deal with all of the above issues in a natural manner, making them an important tool for obesity research. This paper discusses some simple hierarchical models, and shows their application using two real data sets.


Body Mass Index Ordinary Little Square Hierarchical Linear Model Body Mass Index Measurement Obesity Research 
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Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • David Rindskopf
    • 1
  1. 1.City University of New York Graduate CenterNew YorkUSA

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