Level: Graduate
Taught: F2026, F2023, F2022
Role: Instructor of Record
Teaching Assistants: Elle Diether (2026), Andrés Cruz (2023), Matthew Martin (2022)
Description: Social scientists seek to understand the social world, and this understanding is often critically based on the quantitative analysis of data. In this course, we introduce students to this mode of inquiry. We will explore classical approaches for bringing evidence to bear for empirical claims, examining their strengths, limitations, theoretical properties, and manner of implementation. Modeling strategies introduced will include probability models, linear regression, and some introductory causal inference approaches. Emphasis will also be placed on the effective communication of quantitative evidence. Therefore, we will emphasize throughout the course the importance of visualization methods, placebo tests, model robustness checks, and the qualitative meaning of the quantitative assumptions employed by various modeling strategies. By the end of the course, students should be able to tackle challenging empirical problems in their research with a variety of modeling strategies and to effectively communicate the implications of their analyses to their target audience.
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