Quantitative Methods in Social Science Research

Catalog description

This course is designed for students interested in the social sciences who wish to understand the methodology and techniques involved in the estimation of structural relationships between variables (i.e., regression analysis). The course is intended for students who wish to be able to carry out empirical work in their particular field, both at Sarah Lawrence College and beyond, and critically engage THE CURRICULUM 52 with empirical work done by academic or professional social scientists. In fall, the course will cover the theoretical and applied statistical principles that underlie Ordinary Least Squares (OLS) regression techniques. The course will begin with a review of basic statistical and probability theory, as well as relevant mathematical techniques. We will then study the assumptions needed to obtain the Best Linear Unbiased Estimates (BLUE) conditions of a regression equation. Particular emphasis will be placed on the assumptions regarding the distribution of a model’s error term and other BLUE conditions. The course will cover hypothesis testing, sample selection, and the critical role of the t- and F- statistic in determining the statistical significance of an econometric model and its associated slope or “β” parameters. Further, we will address three main problems associated with the violation of a particular BLUE assumption: multicollinearity, serial correlation, and heteroscedasticity. We will learn how to identify, address, and remedy each of these problems. In addition, the course will take a similar approach to understanding and correcting model specification errors. In spring, the course will build on fall learning by introducing advanced econometrics topics. We will study difference-in- difference estimators, autoregressive dependent lag (ARDL) models, co-integration, and error correction models involving nonstationary time series. We will…