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Hierarchical Models for Educational Data: An Overview

Harvard University

The use of hierarchical models in statistical applications, and for educational data, is a promising but still underutilized approach. However, because these models are more complicated than many standard methods, it is important that we, as users and developers, not rush to use them before we understand them. We emphasize here, in support of the views on hierarchical models expressed in the 3 preceding papers by Draper, by Rogosa and Saner, and by de Leeuw and Kreft, the need to not diminish hard thinking about data and iterative model checking when fitting hierarchical models, the need for more and better software, the need to test methods to assure their proper calibration, and the need to produce supporting materials to aid analysts and users of hierarchical modeling methods.

Key Words: maximum likelihood • random effects • multilevel models • multilevel model checking

Journal of Educational and Behavioral Statistics, Vol. 20, No. 2, 190-200 (1995)
DOI: 10.3102/10769986020002190


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