The way we intuitively approach qualitative case research is similar to how we read detective novels. We consider various different hypotheses to explain what occurred— whether a major tax reform in Chile, or the death of Samuel Ratchett on the Orient Express—drawing on the literature we have read (e.g. theories of policy change, or other Agatha Christie mysteries) and any salient previous experiences we have had. As we gather evidence and discover new clues, we continually update our beliefs about which hypothesis provides the best explanation—or we may introduce a new alternative
that occurs to us along the way. Bayesianism provides a natural framework that is both logically rigorous and grounded in common sense, that governs how we should revise our degree of belief in the truth of a hypothesis—e.g., “the imperative of attracting globally-mobile capital motivated policymakers to reform the tax system,” or “a lone gangster sneaked onboard the train and killed Ratchett as revenge for being swindled”—given our relevant prior knowledge and new information that we obtain
during our investigation. Bayesianism is enjoying a revival across many fields, and it offers a powerful tool for improving inference and analytic transparency in qualitative process-tracing research.
This interactive course introduces the principles of Bayesian reasoning for process tracing and case study research with the goal of helping to leverage common-sense understandings of inference and hone intuition when conducting causal analysis with qualitative evidence. We will examine concrete applications to single case studies, comparative case studies, and multi-methods research. Participants will learn how to construct rival hypotheses, assess the inferential weight of qualitative evidence, and
evaluate which hypothesis provides the best explanation through Bayesian updating. The short course will also overview key aspects of research design, including iteration between theory development and data analysis. Throughout, we will conduct a wide range of exercises and group work to give participants hands-on practice at applying Bayesian techniques. Upon completing the course, participants will be able to read qualitative case studies more critically and apply Bayesian principles to their own
research.
