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
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
Resources
Prerequisite
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. 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:
- 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
This course is evaluated as follows:
- 40% Quizzes
- 60% Assignments
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.