140.607.79
Multilevel Models

Prerequisite:

Previous experience with regression analysis is required.

Description:

Gives an overview of "multilevel models" and their application in public health and biomedical research. Multilevel models are statistical regression models for data that are clustered in some way, violating the usual independence assumption. Typically, the predictor and outcome variables occur at multiple levels of aggregation (e.g., at the personal, family, neighborhood, community and/or regional levels). Multilevel models account for the clustering of the outcomes and are used to ask questions about the influence of factors at different levels and about their interactions. Students focus on the main ideas and on examples of multilevel models from public health research. Students learn to formulate their substantive questions in terms of a multilevel model, to fit multilevel models using Stata during laboratory sessions and to interpret the results.

Learning Objectives:

Upon successfully completing this course, students will be able to:

1. Prepare graphical and tabular displays of multilevel data that effectively communicate the patterns of scientific interests
2. Formulate their substantive questions in terms of a multilevel models
3. Interpret parameters of multilevel statistical models
4. Fit multilevel models using the Stata statistical software packages

Methods of Assessment:

This course is evaluated as follows:

• 40% Final Exam
• 15% Lab Assignments
• 15% Lab Assignments
• 15% Lab Assignments
• 15% Lab Assignments

Instructor Consent:

No consent required

Special Comments:

Course will be taught online via Zoom, on the dates and times the course is scheduled. For further information, please see the Institute website jhsph.edu/summerepi