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Multi-Level Modeling: Nested Data/Longitudinal Data

This course teaches models for multilevel nested data that arise in nested designs. Traditional methods, such as OLS regression, are not appropriate in this setting as they fail to model the complex correlational structure that is induced by these designs. This class explores how to develop and fit a set of models for nested designs through multilevel regression, also known as mixed effects models or hierarchical linear models.

Course #
APSTA-GE 2042
Credits
2
Department
Applied Statistics, Social Science, and Humanities

Professors

Marc Scott

Co-Department Chair, Professor of Applied Statistics; Co-Director of PRIISM

marc.scott@nyu.edu

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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