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Course Catalog

140.656.01 Multilevel Statistical Models in Public Health

Department:
Biostatistics
Term:
4th term
Credits:
4 credits
Academic Year:
2017 - 2018
Location:
East Baltimore
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
Contact:
Elizabeth Colantuoni
Course Instructor:
Resources:
Prerequisite:

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

Description:

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 SAS 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:

Student evaluation based on a lab materials (short multiple choice quiz plus graphics/model specification/fit), two homework assignments (three short answer questions, a short abstract and peer assessment) and a final exam which is also an analysis of a multilevel data set, presentation of the results, and a written scientific report of the analysis methods and results.

The course grade is labs (40%), homeworks (40%) and final exam (20%).

Instructor Consent:

No consent required