Abstract
This chapter discusses the multilevel data analysis. The use of hierarchical models for analyzing data collected at several levels is not a novel idea. Procedures based on these models have been used in the context of random and mixed effects experimental design models, random coefficient regression models, and Bayesian estimation. The novelty of this approach stems partly from the ingenuity and care with which the hierarchical model must be formulated; in the experimental design and random coefficients models, the modeling process is, for all practical purposes, automatic. The exception is the Bayesian approach to estimation in the linear model where, in principle, careful thought must be given to the specification of priors. While the concept of slopes as outcomes was appealing and had great potential for modeling and explaining educational outcomes, the lack of adequate procedures for the estimation of parameters hindered its immediate acceptance and widespread use.
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