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Bayesian Inference for Qualitative Evidence (SPS-RE-FAI-BA-25)

SPS-RE-FAI-BA-25

Purpose

The way we intuitively approach qualitative research is similar to how we read detective novels.  We consider different hypotheses to explain what happened—whether democratization in South Africa, or the death of Samuel Ratchett on the Orient Express—drawing on the literature we have read (e.g. theories of regime change; earlier Agatha Christie mysteries) and any salient additional knowledge we possess.  As we gather evidence and discover clues, we update our views about which hypothesis provides the best explanation—or we may introduce new alternatives that we think up along the way.  Bayesianism provides a logically rigorous and intuitive framework that governs how we should revise our views about which hypothesis is more plausible, given our relevant prior knowledge and the evidence we find 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 research. 

This interactive course introduces the principles of Bayesian probability, with applications to single case studies (within-case analysis), comparative case studies (cross-case analysis), and multi-method research that draws on both qualitative evidence and quantitative data. Participants will learn how to construct well-articulated rival hypotheses to compare, systematically assess the inferential weight of qualitative evidence, avoid common cognitive biases that can lead to sloppy reasoning, and evaluate which hypothesis provides the best explanation through Bayesian updating. The course will also address key aspects of research design. Throughout, we will work will examples and exercises drawn from a wide range of published social science research to give participants hands-on practice applying Bayesian techniques. Upon completing the course, participants will be able to read qualitative research more critically, evaluate whether and to what extent the evidence presented supports the authors’ conclusions, and apply Bayesian principles in their own research.

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