Spring 2016

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.

Fall 2015

Public Affairs 824:708 — Categorical and Limited Dependent Variables (cross listed as Public Administration 834:652).

824_708_2015_Categorical_Limited_DVs — Updated 8/26/2015

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. 

Spring 2015

Public Affairs 824:713 — Research Design

http://jargowsky.camden.rutgers.edu/files/2011/11/qm2_syllabus_2016.pdf

Research in the social sciences is a process of developing evidence to generate and test hypotheses.  To draw causal inferences about variables of interest, the effects of confounding variables must be controlled.  Research design refers to techniques for organizing the research process to control for confounding variables in the process of data collection and development, rather than doing so after-the-fact through the use of statistical techniques.  This course is intended to be of value both to those who will go on to conduct their own research in their chosen fields and to those who intend to use/evaluate the findings of research conducted by others.   Topics covered include: (1) experimental designs; (2) non-experimental designs; (3) sampling methodologies; (4) measurement techniques; (5) model specification in relation to research design; (5) qualitative methods; (6) quantitative methods; and (7) internal and external validity.  

Fall 2014

Public Affairs 824:702 Quantitative Methods I.  

http://jargowsky.camden.rutgers.edu/files/2011/11/qm2_syllabus_2016.pdf

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.