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140.656.01
Multilevel Statistical Models in Public Health

Location
East Baltimore
Term
2nd Term
Department
Biostatistics
Credit(s)
4
Academic Year
2022 - 2023
Instruction Method
In-person
Class Time(s)
M, W, 10:30 - 11:50am
Lab Times
Wednesday, 9:00 - 10:20am (01)
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
Every Year
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. Includes topics: 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. Supports STATA and R software.
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
Special Comments

Please note: This is the in-person section of a course that is also offered virtually/online. Students will need to commit to the modality for which they register.