140.656.41
Multilevel and Longitudinal Models - Data Analysis Workshop

Discontinued

Location
Internet
Term
2nd Term
Department
Biostatistics
Credit(s)
4
Academic Year
2023 - 2024
Instruction Method
Synchronous Online
Class Time(s)
M, W, 10:30 - 11:50am
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Scott Zeger
Contact Name
Scott Zeger
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
Instructor Consent
No consent required
Special Comments

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