Public Affairs 824:702 Quantitative Methods I.
Draft Syllabus (756.824.702_Fall_2018_Syllabus_27Jun2018)
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 824:708 — Categorical and Limited Dependent Variables (cross listed as Public Administration 834:652).
Note: the course will meet on Thursdays, 6pm-8:50pm.
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.
Public Affairs 824:709 — Quantitative Methods II
Draft Syllabus: qm2_syllabus_2016
This is a course on empirical methods that are useful in the investigation of hypotheses in the social sciences and the analysis of 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 hypothesis. Several special topics in regression analysis are addressed as well, 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.