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Credits: 3.0

The primary focus of this course is single predictor and multiple regression analysis for continuous and binary outcomes. The pedagogical strategy will be to learn statistical analysis by doing statistical analysis. We will rely primarily on the stata statistical software package. Over the course of the semester, we will examine a variety of data sets, each of which can be used to address substantive research questions by fitting increasingly sophisticated regression models. As we build understanding about how to use these methods in practice, we will discuss the regression model's purpose, mathematical representation, assumptions, implementation, interpretation, presentation, relationship to other statistical methods, implications for research design, and limitations. Additional principles of research design will be incorporated throughout the semester. The course will also include introductory coverage of more advanced topics, such as: multi-level modeling, structural equation modeling, regression discontinuity and propensity score matching. Introduction to these topics is meant primarily to inform students about upper-level courses that they might consider and not necessarily to prepare students to execute these methods on their own.

Recent and Upcoming Instructors

Lindsay Page

  • Spring 2020 (2204) - Syllabus
  • Spring 2021 (2214)

Francis A. Pearman

  • Spring 2019 (2194)