140.656.01
Multilevel Statistical Models in Public Health
- Location:
- East Baltimore
- Term:
- 2nd term
- Department:
- Biostatistics
- Credits:
- 4 credits
- Academic Year:
- 2022 - 2023
- Instruction Method:
- In-person
- Class Times:
-
- M W, 10:30 - 11:50am
- Lab Times:
-
-
Wednesday, 9:00 - 10:20am
-
- Auditors Allowed:
- Yes, with instructor consent
- Undergrads Allowed:
- No
- Grading Restriction:
- Letter Grade or Pass/Fail
- Course Instructor:
- Contact:
- Scott Zeger
- Resources:
- Prerequisite:
- 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:
- 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
- Instructor Consent:
No consent required
- 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.