Course Description
An introduction to regression models for categorical and limited dependent variables. The objective is to provide students with an understanding of (a) when particular models are appropriate, (b) the basic logic of various models, (c) how results are interpreted and evaluated, and (d) how to conduct analyses using statistical software.
Athena Title
SOCI CATEGORICAL DA
Prerequisite
SOCI 6620 and SOCI 6630
Grading System
A - F (Traditional)
Course Objectives
Students will be required to think critically in order to evaluate the logic, promise, and problems of the various approaches to categorical and limited dependent variables. They will read, evaluate, and discuss the merits and demerits of published examples of each main approach. Finally, students will demonstrate their ability to successfully choose, conduct, interpret, write up, and then present orally the results of several data analyses that draw upon the approaches discussed in the course.
Topical Outline
1. Review of Ordinary Least Squares Regression; Overview of Stata; an Introduction to the Tobit Model 2. Regression for Binary Outcomes: Purpose, Logic, Interpretation, and Estimation 3. Regression for Binary Outcomes: Examples of Published Research 4. Multinomial Logit: Purpose, Logic, Interpretation, and Estimation 5. Multinomial Logit: Examples of Published Research 6. Regression for Ordered Outcomes: Purpose, Logic, Interpretation, and Estimation 7. Regression for Ordered Outcomes: Examples of Published Research 8. Comparing Coefficients Across Equations: Conceptual Issues and the Use of Heterogeneous Choice Models 9. Models for Count Data: Purpose, Logic, Interpretation, and Estimation 10. Models for Count Data: Examples of Published Research 11. Event History or "Survival" Analysis: Discrete-Time Models 12. Discrete-Time Event History Models: Examples of Published Research 13. Event History or "Survival" Analysis: Parametric Models and Cox Regression 14. Parametric Models and Cox Regression: Examples of Published Research