Sociology 694

Categorical Data Analysis

Richard Williams, Instructor

Spring 2005

NOTE:  This was the first time the course was taught, and students presented parts of the material; hence the handouts differ greatly in how detailed they are.  The Stata Highlights page includes links to Stata and statistical handouts from my other courses that may interest readers.  The newer version of the course is here.
 

 

NOTE: The following special types of files are used on this web page. Some materials are available only to nd.edu users.

  Pdf files. Require Adobe Acrobat.  

  SPSSWIN files.

  Stata 8 files.

Useful sites for learning about Stata and SPSS

Rich Williams' Stata Highlights Page	UCLA's Statistical Computing Resources
RW Suggestions for Using Stata at Notre Dame	UCLA's Stata Starter Kit
RW's Suggested downloads	UCLA's SPSS Starter Kit
J. Scott Long's Lab Guide for Stata	UCLA - How does Stata compare with SAS and SPSS?
Resources for learning Stata	The Stata User Support Page

Overview.  This course discusses methods and models for the analysis of categorical dependent variables and their applications in social science research. Researchers are often interested in the determinants of categorical outcomes. For example, such outcomes might be binary (lives/dies), ordinal (very likely/ somewhat likely/ not likely), nominal (taking the bus, car, or train to work) or count (the number of times something has happened, such as the number of articles written). When dependent variables are categorical rather than continuous, conventional OLS regression techniques are not appropriate. This course therefore discusses the wide array of methods that are available for examining categorical outcomes.

Syllabus

Long (1997) Stata Files

Long and Freese (2003) Stata Files

RW Stata Files

Recommended Reading

Overview of Generalized Linear Models, Maximum Likelihood Estimation

    Introduction to Generalized Linear Models

    Maximum Likelhihood Estimation

Review of Multiple Regression (Go over these as needed)

    Review of Multiple Regression

reg01.dta - Data file used in the Stata Regression handout

    Using Stata for OLS Regression (Be sure to read this if you are not familiar with Stata, as it covers several important aspects of the program)

Models for Binomial Outcomes

    Using Stata for Logistic Regression

logist.dta - Stata data file used in the Logistic Regression handout

    The following 3 handouts are "repeats" from Soc 593 and will be supplemented.

    Logistic Regression I: Problems with the Linear Probability Model (LPM)

    Logistic Regression II: The Logistic Regression Model (LRM)

    Logistic Regression III: Hypothesis Testing, Comparisons with OLS

 

    A Few Comments on the Latent Variable Model

    Prelude to Discussion of Standardized Coefficients

    Standardized Coefficients

    Pseudo R^2, AIC, BIC

    Some Comments (and Warnings) about the adjust, prvalue and prtab commands

    Marginal Effects and Discrete Changes

    Outliers and Leverage (excerpts).  Complete Soc593 handout is here.

    Alternatives to Logistic Regression (Brief Overview)

 

Models for Ordinal & Multinomial Regression

    Ordinal Regression I - Overview

    Ordinal Regression 2 - Hypothesis testing & other extensions

    Ordinal Regression 3 - Testing & dealing with violations of assumptions

 

    Multinomial Logit - Overview

    Post-Estimation Commands for mlogit

 

Models for Count Outcomes

    Count Outcomes