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12 - Case Selection

from Part IV - Bayesian Implications for Research Design

Published online by Cambridge University Press:  28 July 2022

Tasha Fairfield  and
Andrew E. Charman
Tasha Fairfield
Affiliation:
London School of Economics and Political Science
Andrew E. Charman
Affiliation:
University of California, Berkeley
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Summary

This chapter elaborates Bayesian information-theoretic principles for optimal case selection and practical rules of thumb for real-world contexts.

Keywords

Case selectionBayesian analysisinformation theorycritical casescrucial casesmost-likely caseleast-likely caseVan Evera testshard testsstrong tests
Type
Chapter
Information
Social Inquiry and Bayesian Inference
Rethinking Qualitative Research
 , pp. 551 - 622
Publisher: Cambridge University Press
Print publication year: 2022

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  • Case Selection
  • Tasha Fairfield, London School of Economics and Political Science, Andrew E. Charman, University of California, Berkeley
  • Book: Social Inquiry and Bayesian Inference
  • Online publication: 28 July 2022
  • Chapter DOI: https://doi.org/10.1017/9781108377522.018
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  • Case Selection
  • Tasha Fairfield, London School of Economics and Political Science, Andrew E. Charman, University of California, Berkeley
  • Book: Social Inquiry and Bayesian Inference
  • Online publication: 28 July 2022
  • Chapter DOI: https://doi.org/10.1017/9781108377522.018
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Case Selection
  • Tasha Fairfield, London School of Economics and Political Science, Andrew E. Charman, University of California, Berkeley
  • Book: Social Inquiry and Bayesian Inference
  • Online publication: 28 July 2022
  • Chapter DOI: https://doi.org/10.1017/9781108377522.018
Available formats
×