## S503: Statistical Methods II: 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

**School/Department: **Statistics

**Semester(s): **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: ** Soc 554: Statistical Methods I - course on linear regression

**Prerequisites: ** 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 per week

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

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

**Books used: ** Lecture notes; Long & Freese. 2014. Regression Models for Categorical Dependent Variables Using Stata, 3rd ddition. Recommended: Long. 1997. Regression Models for Categorical and Limited Dependent Variables.

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

**Formal Computing Lab?: ** Yes

**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; count models

**Comments: ** This is equivalent to Soc 650