Explicit Bayesian Analysis for Process Tracing

Political Analysis, Vol 25 (3):363-380, 2017

Posted: 5 Aug 2019

See all articles by Tasha Fairfield

Tasha Fairfield

London School of Economics & Political Science (LSE)

Andrew Charman

University of California, Berkeley

Date Written: 2017

Abstract

Bayesian probability holds the potential to serve as an important bridge between qualitative and quantitative methodology. Yet whereas Bayesian statistical techniques have been successfully elaborated for quantitative research, applying Bayesian probability to qualitative research remains an open frontier. This paper advances the burgeoning literature on Bayesian process tracing by drawing on expositions of Bayesian “probability as extended logic” from the physical sciences, where probabilities represent rational degrees of belief in propositions given the inevitably limited information we possess. We provide step-by-step guidelines for explicit Bayesian process tracing, calling attention to technical points that have been overlooked or inadequately addressed, and we illustrate how to apply this approach with the first systematic application to a case study that draws on multiple pieces of detailed evidence. While we caution that efforts to explicitly apply Bayesian learning in qualitative social science will inevitably run up against the difficulty that probabilities cannot be unambiguously specified, we nevertheless envision important roles for explicit Bayesian analysis in pinpointing the locus of contention when scholars disagree on inferences, and in training intuition to follow Bayesian probability more systematically.

Suggested Citation

Fairfield, Tasha and Charman, Andrew, Explicit Bayesian Analysis for Process Tracing (2017). Political Analysis, Vol 25 (3):363-380, 2017, Available at SSRN: https://ssrn.com/abstract=3430104

Tasha Fairfield (Contact Author)

London School of Economics & Political Science (LSE) ( email )

Houghton Street
London, WC2A 2AE
United Kingdom

Andrew Charman

University of California, Berkeley ( email )

310 Barrows Hall
Berkeley, CA 94720
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
295
PlumX Metrics