Qualitative Bayesian Reasoning (QMMR B)
Half Day Short Course
1:30pm – 5:30pm
This half-day short course introduces participants to the Bayesian foundations of qualitative research, grounded in the framework developed in Social Inquiry and Bayesian Inference: Rethinking Qualitative Research (Fairfield & Charman, 2022). While the course complements the morning session on process tracing led by Fairfield and Checkel, it stands fully on its own and requires no prior background in Bayesian inference or formal logic.
The first portion of the course explains the basic principles of Bayesian reasoning, including how to update prior beliefs when encountering new evidence and how to compare rival hypotheses by assessing which one better accounts for observed data. Through real-world examples and group exercises, participants will learn how to “mentally inhabit” different explanatory worlds and intuitively evaluate the plausibility of competing claims based on qualitative evidence.
The second part of the course explores the iterative nature of qualitative research, emphasizing theory refinement, data collection, and continual evidence evaluation. This approach contrasts with more linear-deductive models dominant in political science and social science more broadly. The course will also address how Bayesian reasoning helps guard against common pitfalls such as confirmation bias and ad hoc theorizing, offering tools to enhance transparency and rigor in qualitative work.
Designed for scholars across subfields and disciplines, this course is ideal for anyone interested in improving their inference-making and theory-building skills using qualitative data.
Instructor Bio:
Tasha Fairfield is an Associate Professor at the London School of Economics and co-author of Social Inquiry and Bayesian Inference (CUP 2022). She has taught Bayesian qualitative methods since 2016 at APSA, IQMR, Statistical Horizons, and other academic venues. In 2024, she received the APSA-QMMR David Collier Mid-Career Achievement Award.