Course description

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This is a course in advanced statistical techniques that covers generalized linear models and extensions that are commonly used in health and policy research. Assuming a strong foundation in the general linear model (linear regression and ANOVA) and exposure to the linear mixed model (a.k.a. multilevel models), this course focuses on data analysis that utilizes models for categorical, discrete or limited outcomes, some of which may come with hierarchical structures. Examples are drawn from broad areas of health and policy research including determinants of self-reported health status or factors influencing number of clinic visits. In this course students will also learn the principles of likelihood-based inference, which will assist them in some of the more advanced statistics courses.

Course Prerequisites:

  • REQUIRED: RESCH-GE 2003 Intermediate Statistical Methods or an equivalent course that covers linear regression analysis at intermediate level.
  • PREFERRED: a course in multilevel data analysis or longitudinal data analysis
  • Students are assumed to be able to perform basic data management and basic statistical analysis such as linear regression in R.

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  • Read this syllabus closely before the first class.