Bayesian Reasoning for Qualitative Case Studies & Comparative Research (QMMR B)

Session Submission Type: Short Course Half Day

Session Description

This course introduces the Bayesian logic of qualitative case studies, building on “Social Inquiry and Bayesian Inference: Rethinking Qualitative Research” (Fairfield & Charman, CUP 2022). The material complements the morning short course on process tracing led by Bennett, Checkel, and Fairfield, but each course can be taken independently.

The first part of this course introduces the basic principles of Bayesian reasoning with the goal of leveraging common-sense understandings of inference and improving intuition when analyzing qualitative evidence. We begin with the general logic of Bayesian inference, which involves updating prior views about which explanation is more plausible when we learn new evidence. We explain the importance of working with rival hypotheses and discuss how to formulate well-constructed explanations to compare. We then elaborate practical procedures for evaluating the inferential import of evidence by “mentally inhabiting” the world of each hypothesis and asking which one makes the evidence more expected. We include examples and group exercises using real-world qualitative evidence to illustrate how this process works.

The second part of the course turns to comparative case studies. Methodological literature often treats cross-case (e.g., comparative) analysis and within-case analysis (e.g., process tracing) as distinct analytical endeavors that draw on different logics of inference. Within a Bayesian framework, however, there are no fundamental distinctions; all evidence contributes to inference in the same manner, whether we are studying a single case or multiple cases. In essence, each piece of evidence we obtain weighs in favor of one explanation over a rival to some degree, which we assess by asking which explanation makes that evidence more expected. Evidentiary weight then aggregates both within any given case, and across different cases that fall within the scope of the theories we are testing. In addition to showing how this process works with examples drawn from published comparative case studies, we will introduce a Bayesian approach to case selection and discuss how to articulate scope conditions and tentatively generalize our hypotheses.

Note: This course does not require any prior familiarity with process training, Bayesianism, probability theory, or logic. The only technical skills that will be assumed are basic arithmetic.

Instructor bios:

Andrew Bennett is Professor of Government at Georgetown University. He is the co-author, with Alexander George, of Case Studies and Theory Development in the Social Science (MIT Press, 2005), and co-author, with Jeffrey T. Checkel, of Process Tracing: From Metaphor to Analytic Tool (Cambridge University Press, 2015). Professor Bennett’s substantive research focuses on security issues in international relations, including military interventions, alliances, and decision-making. He has many years of methods teaching experience at Georgetown and at summer methods institutes around the world.

Tasha Fairfield is Associate Professor at the London School of Economics. Her methodological research examines the Bayesian logic of inference in qualitative social science. She is the co-author, with A.E. Charman, of Social Inquiry and Bayesian Inference: Rethinking Qualitative Research (Cambridge University Press, 2022). She has been teaching workshops and courses on this material since 2016 at IQMR, APSA, LSE, and other forums. Her substantive research examines the politics of policymaking, redistribution, business power, and inequality.

Sub Unit

• Pre-Conference Short Courses

Chair

• Tasha A. Fairfield London School of Economics

Presenters

• Tasha A. Fairfield London School of Economics

• Andrew Bennett Georgetown University