Course Description:
Gives an overview of "multilevel statistical models" and their application in public health and biomedical research. Multilevel models are regression models in which the predictor and outcome variables can occur at multiple levels of aggregation: for example, at the personal, family, neighborhood, community and regional levels. They are used to ask questions about the influence of factors at different levels and about their interactions. Multilevel models also account for clustering of outcomes and measurement error in the predictor variables. Students focus on the main ideas and on examples of multi-level 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.

Course 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) conduct statistical analyses of clustered data by use of multilevel models; 3) interpret parameters of multilevel statistical models; 4) fit multilevel models by use of statistical software packages.

Instructor: Sandrah (Sandy) Eckel, eckel@usc.edu website Prerequisites: Previous experience with regression analysis is required. Software: The lectures, labs, and exam use Stata. Textbook: There is no required textbook. Suggested books: Stata users: Multilevel and Longitudinal Modeling Using Stata, 2nd Edition. Rabe-Hesketh and Skrondal. SAS users: SAS for Mixed Models, 2nd Edition. Little, et al. Time: Monday-Friday, June 27-July 1 2011 1:30pm-5:00pm Place: Lecture: W2015, Lab: W3025 Student evaluation: Students taking the course for a grade will complete an exam that will be distributed on Wed and is to be submitted after the lecture on Friday.  The exam will include multiple choice and short answer questions reflecting interpretation of the key concepts discussed within the course. Administrative contact: Ayesha Khan, akhan@jhsph.edu, W6508