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Courses

Multilevel Models

June 28 - July 2, 2021
1:30 p.m. – 5:00 p.m.
2 credits
Course Number: 140.607.11

 

This summer this course will be taught online via Zoom, on the dates and times listed above. Registered students will attend their classes virtually via Zoom, in real time with faculty and other students.

"Really enjoyed the course and the Professor's enthusiasm for the subject. I found the labs to be extremely helpful in putting the material into context, and thought the course was very well structured."—Student, 2019

"Dr. Eckel does a fantastic job of packing a lot of information into a short course.  It is an impressive amount to cover, and she does it clearly."—Student, 2018
 

Course Instructor:

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.

Student Evaluation: Final exam

Learning Objective:

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.

Location: Baltimore

Prerequisite: Previous experience with regression analysis is required.

Grading Options: Letter Grade or Pass/Fail

Course Materials: