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Department: Biostatistics
Term: 4th term
Credits: 4 credits
Academic Year: 2012 - 2013
Course Instructor:
  • Elizabeth Colantuoni

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 Objective(s):
Upon successfully completing this course, students will be able to:
Define multilevel data
Implement and interpret results associated with Multi-level Statistical Models (MLMs),
identify when and why MLMs can or should be used when they are unnecessary or possibly dangerous
describe the implications of centering, contextual variables, missing data and ecological bias within MLMs

Methods of Assessment: Student evaluation based on a multiple choice exam and an analysis of a multilevel data set, presentation of the results, and a written scientific report of the analysis methods and results.
Location: East Baltimore
Class Times:
  • Monday 10:30 - 11:50
  • Wednesday 10:30 - 11:50
Lab Times:
  • M W 9:00 - 10:20 (1)
Enrollment Minimum: 10
Instructor Consent: No consent required

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

Auditors Allowed: Yes, with instructor consent
Grading Restriction: Letter Grade or Pass/Fail