SOC 6020 - Linear Models

Spring. 4 credits.

K. Bischoff.

This course provides an in-depth examination of linear modeling. We begin with the basics of linear regression, including estimation, statistical inference, and model assumptions. We then review several tools for diagnosing violations of statistical assumptions and what to do when things go wrong, including dealing with outliers, missing data, omitted variables, and weights. Finally, we will explore extensions of the linear regression model, including models for categorical outcomes and hierarchical linear modeling. While statistical modeling is the focus of the course, we proceed with the assumption that models are only as good as the theoretical and substantive knowledge behind them. Thus, in covering the technical material, we will spend considerable time discussing the link between substantive knowledge and statistical practice. The course is designed primarily for graduate students in sociology.