MULTILEVEL MODELS Syllabus
Course Learning Objectives
Upon successfully completing this course, students will be able to:
- Prepare graphical and tabular displays of multilevel data that effectively communicate the patterns of scientific interests
- Conduct statistical analyses of clustered data by use of multilevel models
- Interpret parameters of multilevel statistical models
- Fit multilevel models by use of statistical software packages
Course DescriptionGives 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.
Intended AudienceSummer Institute students and interested degree-seeking students within the School.
Methods of AssessmentFinal exam
Additional Faculty Notes:
Students taking the course for a grade will complete an exam that will be distributed on Wed and is to be submitted by end of day Friday. The exam will include multiple choice and short answer questions reflecting interpretation of the key concepts discussed within the course.
PrerequisitesPrevious experience with regression analysis is required.
Additional Faculty Notes:
No required textbook:
Stata Users: Multilevel and Longitudinal Modeling Using Stata, 3rd Edition. Rabe-Hesketh and Skrondal. New two volume set, can be purchased from Stata.com or amazon.
SAS Users: SAS for Mixed Models, 2nd Edition. Little, et al. http://www.sas.com/apps/pubscat/bookdetails.jsp?catid=1&pc=59882
Please see the course Session for a full list of dates and items for this course.
Academic Ethics Code
Students enrolled in the Bloomberg School of Public Health of The Johns Hopkins University assume an obligation to conduct themselves in a manner appropriate to the University's mission as an institution of higher education. A student is obligated to refrain from acts which he or she knows, or under the circumstances has reason to know, impair the academic integrity of the University. Violations of academic integrity include, but are not limited to: cheating; plagiarism; knowingly furnishing false information to any agent of the University for inclusion in the academic record; violation of the rights and welfare of animal or human subjects in research; and misconduct as a member of either School or University committees or recognized groups or organizations.
We will be in room XXX for lectures and in room XXX for lab sessions.
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