Multilevel studies and multilevel analysis have been increasingly used in the public health field. This course will discuss the rationale for multilevel studies and multilevel analysis in public health as well as differences with other study designs and other analytical approaches. Although the course will not be heavily mathematical, the basics of fitting multilevel models for different types of outcomes as well as the interpretation of estimates obtained from multilevel models will be reviewed and practiced. Emphasis will be on conceptual understanding, application and interpretation of multilevel analysis in the context of urban health research. The course will also review and critique empirical applications in urban health research and discuss conceptual and methodological challenges in using multilevel analysis.

After completing this course, participants will be able to:

• Understand the fundamentals of multilevel studies and multilevel analysis and their differences with other study designs and analytical approaches.
• Fit multilevel models and interpret estimates derived from them.
• Be familiar with applications of multilevel analysis in urban health research.
• Understand the strengths and limitations of multilevel analysis for urban health research.

Prerequisite knowledge: Knowledge of regression analysis (linear, logistic, Poisson) is required for this course.

Technical requirements: Participants will need access to SAS, Stata, or R software for the course. R free software is available for participants to install on their computers.