Spring 2026

Quantitative Methods II —  Monday 6-8:50pm, BSB 134
Public Affairs 56:824:709
Cross-listed as Data Science 56:219:593

This is a course on empirical methods that are useful for investigating hypotheses in the social sciences and analyzing public policies and programs. The course is a detailed examination of the bivariate and multiple regression models estimated using Ordinary Least Squares (OLS), with an emphasis on constructing regression models to test social and economic hypotheses. Several special topics in regression analysis are also addressed, including violations of OLS assumptions, the use of dummy variables, fixed effects models, and the analysis of program effects using experimental and quasi-experimental data. Throughout, examples are drawn from the research literature in several fields so students can see the models and methods in action.

Categorical and Limited Dependent Variables — Tuesday 6-8:50pm, BSB 108
Public Affairs 56:824:708
Cross-listed as Data Science 56:219:592

This course examines several types of advanced regression models for dependent variables that violate one or more of the assumptions of the OLS regression model. For example, some dependent variables may be categorical, such as pregnant or not, employed or not, etc. Other dependent variables may be truncated or censored, such as contributions to an individual retirement account that are limited by law to certain dollar amounts. The principal models examined in the course are binary logit and probit, multinomial logit, ordinal logit and probit, tobit, Poisson regression models, and event history analysis. The course focuses primarily on the application and interpretation of the models, rather than statistical theory. 

 

Fall 2025

Public Affairs 56:824:702 — Quantitative Methods I.

This course is designed to prepare students for advanced quantitative methodology courses required of doctoral students. The course begins by reviewing descriptive statistics and data presentation techniques. In preparation for the study of inferential statistics, the next section of the course covers the basics of probability. A solid grounding in probability is necessary to understand how and why statistical techniques work. Building on that foundation, the heart of the course is a rigorous introduction to statistical inference: sampling theory, confidence intervals, and hypothesis tests. The final section of the course is an introduction to regression analysis, with an emphasis on interpretation of regression results, using examples from recent research. This course is part of a two semester sequence; the second semester is Quantitative Methods II, which is a more advanced and detailed treatment of regression analysis and related topics.

Public Affairs 56:824:620:40 — Inequality and Segregation
Cross-listed as Public Administration  56:834:620:40

This course examines the dimensions of inequality, including economic inequality and poverty, residential segregation by race and class, and the concentration of poverty. The focus is primarily on the US, but comparisons with other industrialized nations will also be discussed. The course will address questions of definition and measurement, historical and current trends, causes and consequences, and policy responses. Students will be expected to work with official data to calculate measures of poverty, inequality, and segregation; to understand the main theoretical and empirical debates; and to understand the role of public policy in addressing or exacerbating these problems. 

While there is no formal prerequisite, a familiarity with descriptive statistics and experience working with data are recommended. Alternatively, the course may be taken concurrently with Quantitative Methods I.