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Explicit Bayesian analysis for process tracing: guidelines, opportunities, and caveats

Fairfield, Tasha and Charman, Andrew (2017) Explicit Bayesian analysis for process tracing: guidelines, opportunities, and caveats. Political Analysis, 25 (3). pp. 363-380. ISSN 1047-1987

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Identification Number: 10.1017/pan.2017.14

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.

Item Type: Article
Official URL: https://www.cambridge.org/core/journals/political-...
Additional Information: © 2017 The Authors
Subjects: H Social Sciences > HA Statistics
Sets: Departments > International Development
Date Deposited: 06 Feb 2017 17:19
Last Modified: 13 Sep 2017 09:31
URI: http://eprints.lse.ac.uk/id/eprint/69203

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