Multilevel modelling of longitudinal data is an important new statistical technique. In this article some of the basic concepts and ideas of multilevel modelling are explained. The model is introduced by showing how individual and average growth can be modelled. The intercept, linear and quadratic coefficient, between and within variance, fixed and random part, and other concepts of multilevel modelling are explained. Attention is also given to the reading of statistical tables of the results of multilevel analysis. In the conclusion some of the advantages of multilevel modelling of cephalometric data are mentioned.
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Hoeksma et al. (1991) studied this question.
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