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Reconciling the Quantitative and Qualitative Traditions—The Bayesian Approach

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  • Richard J. Lilford
  • David Braunholtz

Abstract

Qualitative research is often ‘hypothesis generating’ and so determines the agenda for quantitative research, as well as often helping to provide tools (for example instruments for measuring outcomes). The results of qualitative research can also influence decisions directly. This article explains how the results of qualitative research can be taken into account when making policy decisions. The authors argue that this should be done explicitly: the decision-maker should ‘convert’ the qualitative data into a quantitative ‘prior’ belief about the true value of the key parameter(s) on which the decision turns and then use Bayes' law to combine the ‘prior’ with (any) comparative quantitative results to produce a ‘posterior’ quantitative belief about the key parameter. This produces transparency—it allows proper assessment of the impact of qualitative data on the analysis and assumptions behind this impact.

Suggested Citation

  • Richard J. Lilford & David Braunholtz, 2003. "Reconciling the Quantitative and Qualitative Traditions—The Bayesian Approach," Public Money & Management, Taylor & Francis Journals, vol. 23(3), pages 203-208, July.
    • Handle: RePEc:taf:pubmmg:v:23:y:2003:i:3:p:203-208
    DOI: 10.1111/1467-9302.00369
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    Cited by:

    1. Karla Hemming & Peter J Chilton & Richard J Lilford & Anthony Avery & Aziz Sheikh, 2012. "Bayesian Cohort and Cross-Sectional Analyses of the PINCER Trial: A Pharmacist-Led Intervention to Reduce Medication Errors in Primary Care," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-8, June.

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