June 26 - June 30, 2023
1:30 p.m. – 5:00 p.m.
Course Number: 140.607.49 (online)
This is a hybrid course with both a synchronous online section (140.607.49) and an in-person section (140.607.11). You'll be able to indicate which section you want (either in-person or online) when registering in SIS.
"I really enjoyed the lectures because Elizabeth very clearly explained the concepts and was able to answer questions that came up in a very relatable/understandable way. I liked that we were able to work on the labs together in a group and then go over the solutions in lecture right after to help solidify the concepts further."—Student, 2021
"Great Content and very organized. There was more focus on concepts rather than coding which was very appreciated and realistic for a week course. Dr. Sweeney was very patient. Overall enjoyed the course"—Student, 2020
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.
Student Evaluation: Final exam
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.
Prerequisite: Previous experience with regression analysis is required.
Grading Options: Letter Grade or Pass/Fail