Id: 042593 Credits Min: 3 Credits Max: 3

Description

This is a graduate-level course on multilevel modeling, a popular statistical approach in social, behavioral, and health sciences research. Multilevel modeling, also known as hierarchical linear models and mixed models, analyzes nested data structures. Nesting can occur from hierarchical data structures (e.g., siblings nested within family; patients nested within therapist), or both. Topics of this course will include two- and three-level multilevel models, random intercepts and slopes, longitudinal models, model assumptions and evaluations, as well as some recent developments in multilevel modeling. Students will have hands-on experience performing analyses using their own data or data the instructor provides.

Prerequisites

CRIM.6900 Advanced Regression Analysis, or PUBH.6890 Advanced Regression Modeling, or PSYC.6500 Advanced Quantitative Methods, or Permission of Instructor.

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