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Multilevel Statistical Models in Public Health

East Baltimore
4th term
4 credits
Academic Year:
2021 - 2022
Instruction Method:
Synchronous Online
Class Times:
  • M W,  10:30 - 11:50am
Lab Times:
  • Wednesday,  9:00 - 10:20am
Auditors Allowed:
Yes, with instructor consent
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Scott Zeger

140.621-24 or 140.651-4 required; 140.655 required.


Explores conceptual and formal approaches to the design, analysis, and interpretation of studies with a “multilevel” or “hierarchical” (clustered) data structure (e.g., individuals in families in communities). Develops skills to implement and interpret random effects, variance component models that reflect the multi-level structure for both predictor and outcome variables. Topics include: building hierarchies; interpretation of population-average and level-specific summaries; estimation and inference based on variance components; shrinkage estimation; discussion of special topics including centering, use of contextual variables, ecological bias, sample size and missing data within multilevel models. STATA and R software are supported.

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Define multilevel data
  2. Implement and interpret results associated with Multi-level Statistical Models (MLMs)
  3. Identify when and why MLMs can or should be used when they are unnecessary or possibly dangerous
  4. Describe the implications of centering, contextual variables, missing data and ecological bias within MLMs
Methods of Assessment:

This course is evaluated as follows:

  • 40% Quizzes
  • 60% Assignments

Instructor Consent:

No consent required