Y637: Categorical Data Analysis
This course will introduce students to the theory and practice of categorical data analysis including measures of association, logistic regression, Poisson regression, loglinear modeling and other selected topics. Students will learn to develop, implement, interpret, and report research involving categorical analyses. Further, students are expected to gain proficiency in SAS as it pertains to categorical data analysis.
Prerequisites: Two semesters of graduate-level statistics
Algebra Required?: For notation, some proofs
Calculus Required?: Used for some concepts and derivations
Day(s) per week offered: 2 days per week, usually Tue/Thu
Recommended follow-up classes: Depends on research interests.
Substantive Orientation: Social sciences; however, links to the natural/physical sciences are plentiful
Statistical Orientation: Frequentist
Books used: Agresti, A. (2007). An introduction to categorical data analysis (2nd ed.). Hoboken, NJ: Wiley.
Applied/Theoretical: A mix of theory and application
Formal Computing Lab?: Yes
Software Used: SAS
How the software is used: Software will be SAS. Used for computation, some programming, and data analysis
Problem Sets: Yes, with a mix of theoretical/conceptual/analytic problems and applied, data analysis.
Data Analysis: Yes, for problem sets and final project
Presentations: Yes, students will present their final projects.
Exams: A final project is required for the course. There is no formal exam at the moment; however, that could change.
Keywords: Categorical data, Poisson regression, loglinear modeling, logistic regression