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Explicit Bayesian Analysis for Process Tracing: Guidelines, Opportunities, and Caveats

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  • Fairfield, Tasha
  • Charman, Andrew E.

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 & Charman, Andrew E., 2017. "Explicit Bayesian Analysis for Process Tracing: Guidelines, Opportunities, and Caveats," Political Analysis, Cambridge University Press, vol. 25(3), pages 363-380, July.
  • Handle: RePEc:cup:polals:v:25:y:2017:i:03:p:363-380_00
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    Cited by:

    1. David A. Bateman & Dawn Langan Teele, 2020. "A developmental approach to historical causal inference," Public Choice, Springer, vol. 185(3), pages 253-279, December.
    2. Amengual, Matthew, 2018. "Buying stability: The distributive outcomes of private politics in the Bolivian mining industry," World Development, Elsevier, vol. 104(C), pages 31-45.
    3. Martin Rabbia, 2023. "Why did Argentina and Uruguay decide to pursue a carbon tax? Fiscal reforms and explicit carbon prices," Review of Policy Research, Policy Studies Organization, vol. 40(2), pages 230-259, March.
    4. Alvarado, Miriam & Penney, Tarra L. & Unwin, Nigel & Murphy, Madhuvanti M. & Adams, Jean, 2021. "Evidence of a health risk ‘signalling effect’ following the introduction of a sugar-sweetened beverage tax," Food Policy, Elsevier, vol. 102(C).
    5. Fernández Milmanda, Belén & Garay, Candelaria, 2019. "Subnational variation in forest protection in the Argentine Chaco," World Development, Elsevier, vol. 118(C), pages 79-90.
    6. Blair, Graeme & Cooper, Jasper & Coppock, Alexander & Humphreys, Macartan, 2019. "Declaring and Diagnosing Research Designs," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 113(3), pages 838-859.
    7. Alejandro Avenburg & John Gerring & Jason Seawright, 2023. "How do social scientists reach causal inferences? A study of reception," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 257-275, February.
    8. Brandão, Frederico & Befani, Barbara & Soares-Filho, Jaílson & Rajão, Raoni & Garcia, Edenise, 2023. "How to halt deforestation in the Amazon? A Bayesian process-tracing approach," Land Use Policy, Elsevier, vol. 133(C).
    9. Fanelli, Daniele, 2022. "The "Tau" of Science - How to Measure, Study, and Integrate Quantitative and Qualitative Knowledge," MetaArXiv 67sak, Center for Open Science.
    10. Rodrigo Barrenechea & James Mahoney, 2019. "A Set-Theoretic Approach to Bayesian Process Tracing," Sociological Methods & Research, , vol. 48(3), pages 451-484, August.
    11. Ezequiel Gonzalez-Ocantos & Jody LaPorte, 2021. "Process Tracing and the Problem of Missing Data," Sociological Methods & Research, , vol. 50(3), pages 1407-1435, August.
    12. Fairfield, Tasha & Charman, Andrew, 2019. "A Dialogue with the Data: the Bayesian foundations of iterative research in qualitative social science," LSE Research Online Documents on Economics 89261, London School of Economics and Political Science, LSE Library.

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