140.656.01 | AY 2013-2014 - 4th Term | 4 Credit(s)
MW 10:30:00 AM
  • Contact Information
    Elizabeth Colantuoni
  • 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
  • Course Description
    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.
  • Intended Audience
    Masters and doctoral students in Epidemiology and health services; graduate students in Biostatistics
  • Methods of Assessment

    Student evaluation based on a lab materials (short multiple choice quiz plus graphics/model specification/fit), two homework assignments (two short answer questions and a short abstract) and a final exam which is also an analysis of a multilevel data set, presentation of the results, and a written scientific report of the analysis methods and results.

    The course grade is labs (40%), homeworks (40%) and final exam (20%).

  • Prerequisites
    140.621-24 or 140.651-4 required; 140.655 required.
  • Required Text(s)

    Additional Faculty Notes:

    Multilevel and Longitudinal Modelling Using Stata, 3rd Edition, Sophia Rabe-Hesketh and Anders Skrondal, Stata Press

    Other good references:

    Raudenbush SW, Bryk AS (2002). Hierarchical Linear Models, Applications and data analysis methods, 2nd edition. Sage Publications.

    Gelman and Hill (2007).  Data Analysis Using Regression and Multilevel/Hierarchical Models.

    Snijders and Bosker (1999). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. Sage.

    Great website with many links:

  • Course Schedule

    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.

  • Welcome Message

    We will be utilizing the CoursePlus system for the course.  All lectures, laboratory materials, etc will be posted here.

    There will be NO labs the first week of class, 3/24 and 3/26.

    Lectures 1 and 2 are posted. 

    I'm changing the lectures this year so lectures will be posted as we go.  Current materials for lectures 3 through 16 reflect last years course; please ignore for now.

  • Disability Support Services
    If you are a student with a documented disability who requires an academic accommodation, please contact the Office of Student Life Services at 410-955-3034 or via email at