Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-27T21:59:11.899Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  15 May 2017

Tasha Fairfield  and
Andrew E. Charman
Tasha Fairfield*
Affiliation:
Department of International Development, London School of Economics, WC2A 2AE, UK. Email: [email protected]
Andrew E. Charman
Affiliation:
Department of Physics, University of California, Berkeley, 94720, USA
*
*Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

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.

Type
Articles
Information
Political Analysis , Volume 25 , Issue 3 , July 2017 , pp. 363 - 380
Copyright
Copyright © The Author(s) 2017. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Authors’ note: The authors thank Andrew Bennett, David Collier, Macartan Humphreys, Alan Jacobs, and James Mahoney, as well as four anonymous reviewers and Editor Jonathan Katz, for valuable detailed comments on previous versions of this paper. We are also grateful to Devin Caughey, Gustavo Flores-Macías, Peter Kingstone, Richard Nielsen, Tom Pepinsky, Kenneth Roberts, Andrew Schrank, Ken Shadlen, and participants at the 2015 and 2016 APSA Annual Meetings, LSE’s ID and CP/CPE seminars, MIT’s Political Methodology Research Series, and the 2016 Southwest Mixed-Methods Research Workshop. We dedicate this paper to the memory of Kenneth Fairfield.

Contributing Editor: Jonathan Katz

References

Abell, Peter. 2009. A case for cases: Comparative narratives in sociological research. Sociological Methods and Research 38:38–70.Google Scholar
Barrenechea, Rodrigo, and Mahoney, James. 2016. A set-theoretic approach to Bayesian process tracing. Syracuse Institute for Qualitative and Multi-Method Research.Google Scholar
Beach, Derek, and Pedersen, Rasmus. 2013. Process-tracing methods . University of Michigan Press.Google Scholar
Bennett, Andrew. 2008. Process tracing: A Bayesian perspective. In The Oxford handbook of political methodology , ed. Box-Steffensmeier, Janet, Brady, Henry, and Collier, David. Oxford University Press, pp. 702–721.Google Scholar
Bennett, Andrew. 2015. Disciplining our conjectures: Systematizing process tracing with Bayesian analysis. In Process tracing in the social sciences: From metaphor to analytic tool , ed. Bennett, Andrew and Checkel, Jeffrey. Cambridge University Press, pp. 276–298.Google Scholar
Bennett, Andrew, and Checkel, Jeffrey, eds. 2015. Process tracing in the social sciences: From metaphor to analytic tool , Cambridge University Press.Google Scholar
Büthe, Tim, and Jacobs Alan. eds. 2015. Symposium: Transparency in Qualitative and Multi-Method Research. Newsletter of the American Political Science Association Organized Section for Qualitative and Multi-Method Research 13 (1).Google Scholar
Collier, David. 2011. Understanding process tracing. PS: Political Science and Politics 44(4):823–830.Google Scholar
Cox, Richard. 1961. The algebra of probable inference . Johns Hopkins University Press.Google Scholar
Fairfield, Tasha. 2013. Going where the money is: Strategies for taxing economic elites in unequal democracies. World Development 47:42–57.Google Scholar
Fairfield, Tasha. 2015. Private wealth and public revenue in Latin America: Business power and tax politics . Cambridge University Press.Google Scholar
Gelman, Andrew, Carlin, John, Stern, Hal, Dunson, David, Vehtari, Aki, and Rubin, Donald. 2013. Bayesian data analysis , 3rd edn. CRC Press.Google Scholar
Gill, Jeff. 2008. Bayesian methods: A social and behavioral sciences approach . Taylor & Francis.Google Scholar
Good, I. J. 1985. Weight of evidence: A brief survey. In Bayesian statistics 2 , ed. Bernardo, J. M., de Groot, M. H., Lindley, D. V., and Smith, A. F. M.. New York: Elsevier.Google Scholar
Gregory, Phil. 2005. Bayesian logical data analysis for the physical sciences . Cambridge University Press.Google Scholar
Howson, Colin, and Urbach, Peter. 2006. Scientific reasoning: The Bayesian approach . Caris Publishing Company.Google Scholar
Humphreys, Macartan, and Jacobs, Alan. 2015. Mixing methods: A Bayesian approach. American Political Science Review 109(4):653–673.Google Scholar
Hunter, Douglas. 1984. Political/military applications of Bayesian analysis . Boulder: Westview.Google Scholar
Iversen, Gudmund. 1984. Bayesian statistical inference . Sage Publications.Google Scholar
Jaynes, E. T. 2003. Probability theory: The logic of science . Cambridge University Press.Google Scholar
Jackman, Simon. 2009. Bayesian analysis for the social sciences . Wiley.Google Scholar
Jeffrey, Richard. 1983. The logic of decision . University of Chicago Press.Google Scholar
Lupia, Arthur, and Elman, Colin. 2014. Openness in Political Science: Data Access and Research Transparency. PS: Political Science and Politics 47(1):19–42.Google Scholar
Mahoney, James. 2012. The logic of process tracing tests in the social sciences. Sociological Methods and Research 41:570–597.Google Scholar
McKeown, Timothy. 1999. Case studies and the statistical worldview. International Organization 53(1):161–190.Google Scholar
Rohlfing, Ingo. 2013. Case studies and causal inference . Palgrave Macmillan.Google Scholar
Savage, Leonard. 2003. The foundations of statistics . New York: Wiley.Google Scholar
Sivia, D. S. 2006. Data analysis—A dialogue with the data. In Advanced mathematical and computational tools in metrology VII , ed. Ciarlini, P., Filipe, E., Forbes, A. B., Pavese, F., Perruchet, C., and Siebert, B.. World Scientific Publishing Co., pp. 108–118.Google Scholar
Stokes, Susan. 2001. Mandates and democracy: Neoliberalism by surprise in Latin America . Cambridge University Press.Google Scholar
Tannenwald, Nina. 2007. The nuclear taboo . Cambridge University Press.Google Scholar
Van Evera, Stephen. 1997. Guide to methods for students of political science . Cornell University Press.Google Scholar
Supplementary material: PDF

Fairfield and Charman supplementary material

Appendices A and B

Download Fairfield and Charman supplementary material(PDF)
PDF 14.9 MB