MULTILEVEL STATISTICAL MODELS IN PUBLIC HEALTH Syllabus
Course Learning Objectives
Upon successfully completing this course, students will be able to:
- Define multilevel data
- Implement and interpret results associated with Multi-level Statistical Models (MLMs),
- identify when and why MLMs can or should be used when they are unnecessary or possibly dangerous
- describe the implications of centering, contextual variables, missing data and ecological bias within MLMs
Explores conceptual and formal approaches to the design, analysis, and interpretation of studies with a “multilevel” or “hierarchical” (clustered) data structure (e.g., individuals in families in communities). Develops skills to implement and interpret random effects, variance component models that reflect the multi-level structure for both predictor and outcome variables. Topics include: building hierarchies; interpretation of population-average and level-specific summaries; estimation and inference based on variance components; shrinkage estimation; discussion of special topics including centering, use of contextual variables, ecological bias, sample size and missing data within multilevel models. STATA and SAS software are supported.
Masters and doctoral students in Epidemiology and health services; graduate students in Biostatistics
Methods of Assessment
Grading Policy: Student evaluation based on a multiple choice exam and an analysis of a multilevel data set, presentation of the results, and a written scientific report of the analysis methods and results.
Grading Restrictions: Letter grade
140.621-24 or 140.651-4 required; 140.655 required.
Academic Ethics Code
The code, discussed in the Policy and Procedure Memorandum for Students, March 31, 2002, will be adhered to in this class:
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.
Disability Support Services
If you are a student with a documented disability who requires an academic accommodation, please contact Betty H. Addison in the Office of Student Life Services: firstname.lastname@example.org, 410-955-3034, or 2017 E. Monument Street.