Statistical Analysis of Social Data: Applied Regression Methods I (401-1-20)
Topic
Linear Regression
Instructors
Lincoln G Quillian
Lincoln Quillian is Professor of Sociology and a Fellow at the Institute for Policy Research. His work focuses on inequality, race and ethnicity, urban sociology, and quantitative methods.
Meeting Info
Parkes Hall 222: Tues, Thurs 9:00AM - 10:20AM
Overview of class
This course is part of the quantitative methods sequence for graduate students in sociology. The main topic of the course is the theory and practice of linear regression analysis. We will cover multiple ordinary least squares regression, regression assumptions, regression diagnostics, basic path models, data transformations, and issues in causal inference. If time permits, we may discuss related topics such as fixed and random effects models and differences-in-differences.
Learning Objectives
By the conclusion of the course, students are expected to be able to:
1) Explain what types of questions are appropriately answered using linear regression models, the assumptions of linear regression models, and common violations of these assumptions.
2) Teach an introductory level undergraduate class session on regression models.
3) Evaluate and critique the use of linear regression models in social science research.
4) Conduct a competent analysis of secondary quantitative data using linear regression.
5) Write about the results from a linear regression analysis in a style appropriate for publication in a sociology journal with all technical terms used correctly.
Class Materials (Required)
This course will have required books/other materials.
Allison, Paul. 1999. Multiple Regression: A Primer. Pine Forge Press. Thousand Oaks, CA. ISBN-10: 0761985336
Other materials and texts may be required. Most or all of these will be available online.
Class Notes
Course is open to Sociology graduate students. Anyone who falls outside of this description should contact the instructor for consent to enroll.
Associated Classes
LAB - Locy Hall 305: Tues 1:00PM - 1:50PM