Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Search

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

37 results

Page 1 of 2

  • Bayesian reasoning for qualitative replication analysis: Examples from climate politics

  • Tasha Fairfield, Andrew Charman
  • Journal:
    Political Science Research and Methods , First View
    Published online by Cambridge University Press:
    07 April 2025, pp. 1-16
    • Article
      • You have access
      • Open access
    • PDF
    • HTML
    • Export citation

  • This paper demonstrates how Bayesian reasoning can be used for an analog of replication analysis with qualitative research that conducts inference to best explanation. We overview the basic mechanics of Bayesian reasoning with qualitative evidence and apply our approach to recent research on climate change politics, a matter of major importance that is beginning to attract greater interest in the discipline. Our re-analysis of illustrative evidence from a prominent article on global collective-action versus distributive politics theories of climate policy largely accords with the authors’ conclusions, while illuminating the value added of Bayesian analysis. In contrast, our in-depth examination of scholarship on oil majors’ support for carbon pricing yields a Bayesian inference that diverges from the authors’ conclusions. These examples highlight the potential for Bayesian reasoning not only to improve inferences when working with qualitative evidence but also to enhance analytical transparency, facilitate communication of findings, and promote knowledge accumulation.

  • 3 - Heuristic Bayesian Reasoning

  • from Part II - Operationalizing Bayesian Reasoning in Qualitative Research
  • Tasha Fairfield, London School of Economics and Political Science, Andrew E. Charman, University of California, Berkeley
  • Book:
    Social Inquiry and Bayesian Inference
    Published online:
    28 July 2022
    Print publication:
    04 August 2022, pp 73-123

  • Summary

    This chapter explicates how to apply heuristic Bayesian reasoning in qualitative case study research. Steps include defining a set of mutually exclusive hypotheses to compare, assessing prior odds, identifying evidence, evaluating likelihood ratios for the evidence, and updating via Bayes’ rule to obtain posterior odds for the hypotheses.

  • 10 - Iterative Research

  • from Part IV - Bayesian Implications for Research Design
  • Tasha Fairfield, London School of Economics and Political Science, Andrew E. Charman, University of California, Berkeley
  • Book:
    Social Inquiry and Bayesian Inference
    Published online:
    28 July 2022
    Print publication:
    04 August 2022, pp 463-506

  • Summary

    This chapter explicates the Bayesian foundations of iterative research, where scholars move back and forth between theory revision, data collection, and data analysis. In this style of research, attention to likelihood ratios guards against common forms of confirmation bias, while Occam factors help to control ad hoc hypothesizing.

Page 1 of 2