Research Methods: Applied Regressions

Course Description

This course builds upon the statistical toolkit from SOC 500 Linear Regressions and provides a foundation for conducting and evaluating regression-based works in the social sciences. The first part of the course will cover the topics in conducting transparent and reproducible research. Students will be expected to adopt these research practices throughout the semester. The second part of the course will cover generalized linear models (GLM) that examine non-linear outcome variables. The readings, lectures, and in-class discussion will address each method’s mathematical justification, execution, and interpretation using statistical software and application in published articles. The third component of the course will focus on students’ in-class presentations and discussions of their research projects.  

This course’s primary goal is for students to gain fluency in the foundational statistical methods in the social sciences. Fluency denotes the ability to 1) assess the methods’ appropriateness to address sociological questions, 2) provide thoughtful reviews to works using these methods, and 3) actively engage in collaborations that use statistical methods. This course aims to provide a broad survey of the most commonly used generalized linear models rather than expert knowledge in any particular approach; each topic is worthy of its own semester-long course.

Learning Objectives

  1. Gain fluency in the application of generalized linear models in social science research 
  2. Adopt practices for transparent and reproducible research
  3. Practice thoughtful feedback and collaboration in a working group setting
  4. Complete an extended abstract of an empirical article