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10 - Iterative Research

from Part IV - Bayesian Implications for Research Design

Published online by Cambridge University Press:  28 July 2022

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 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.

Type
Chapter
Information
Social Inquiry and Bayesian Inference
Rethinking Qualitative Research
, pp. 463 - 506
Publisher: Cambridge University Press
Print publication year: 2022

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  • Iterative Research
  • 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/10.1017/9781108377522.016
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  • Iterative Research
  • 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/10.1017/9781108377522.016
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.

  • Iterative Research
  • 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/10.1017/9781108377522.016
Available formats
×