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Multi-Level Modeling: Nested Data/Longitudinal Data

This is a course on models for multi-level nested data. These data arise in nested designs, which are quite common to education and applied social, behavioral and policy science. Traditional methods, such as OSL regression, are not appropriate in this setting, as they fail to model the complex correlational structure that is induced by these designs. Proper inference requires that we include aspects of the design in the model itself. Moreover, these more sophisticated techniques allow the researcher to learn new and important characteristics of the social and behavioral processes under study. In this module, we will develop and fit a set of models for nested designs (these are sometimes called hierarchical linear models). The course assignments will use state of the art statistical software to explore, fit and interpret the models.

Course #
Applied Statistics, Social Science, and Humanities


Marc Scott

Chair, Department of Applied Statistics, Social Science, and Humanities; Professor of Applied Statistics

Related Degree

Master of Science
Applied Statistics for Social Science Research

Learn advanced quantitative research techniques and apply them to critical policy issues across social, behavioral, and health sciences.

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