## S650 : Categorical Data Analysis

### Class Description

Categorical Data Analysis deals with regression models in which the dependent variable is categorical: binary, nominal, ordinal, and count. Models that are discussed include probit and logit for binary outcomes, ordered logit and ordered probit for ordinal outcomes, multinomial logit for nominal outcomes, and Poisson regression and related models for count outcomes.

### Class Information

**Semester(s): **Fall

**Semester(s) Offered: ** Fall

**Website: **http://www.indiana.edu/~jslsoc/teaching_CDAiu.htm

**Capacity: ** 30

### Contact Information

**Instructor: **Scott Long - jslong@indiana.edu

**Other Contact(s): ** Scott Long - jslong@indiana.edu

### Other Details

**Sequence: ** Stat 501/Soc 554: Statistical Methods I: Introduction to Statistics {Note: this title is misleading and should be changed since course is primarily linear regression.}

**Prerequisites: ** Stat 501/Soc 554 - linear regression or similar class.

**Algebra Required?: ** Notation only

**Calculus Required?: ** Concepts

**Day(s) per week offered: ** Two lectures a week; two computer labs

**Substantive Orientation: ** Social sciences; non-experimental

**Statistical Orientation: ** non-experimental; maximum likelihood

**Books used: ** Lecture notes; Long & Freese. 2005. Regression Models for Categorical Dependent Variables Using Stata, 2nd Edition. Long. 1997. Regression Models for Categorical and Limited Dependent Variables.

**Applied/Theoretical: ** applied with explanations(not derivations) of statistical foundations.

**Software Used: **
Stata

**How the software is used: ** data analysis, some programming

**Problem Sets: ** Yes, with most involving analysis and interpretation of data.

**Data Analysis: ** Yes as part of problem sets.

**Presentations: ** No

**Exams: ** No

**Keywords: ** regression models; categorical outcomes; logit; probit; maximum likelihood;

**Comments: ** The course is equivalent to Stat 503