Quality & Quantity

, Volume 45, Issue 3, pp 653–669 | Cite as

Bayesian data augmentation methods for the synthesis of qualitative and quantitative research findings

  • Jamie L. CrandellEmail author
  • Corrine I. Voils
  • YunKyung Chang
  • Margarete Sandelowski


The possible utility of Bayesian methods for the synthesis of qualitative and quantitative research has been repeatedly suggested but insufficiently investigated. In this project, we developed and used a Bayesian method for synthesis, with the goal of identifying factors that influence adherence to HIV medication regimens. We investigated the effect of 10 factors on adherence. Recognizing that not all factors were examined in all studies, we considered standard methods for dealing with missing data and chose a Bayesian data augmentation method. We were able to summarize, rank, and compare the effects of each of the 10 factors on medication adherence. This is a promising methodological development in the synthesis of qualitative and quantitative research.


Meta-analysis Meta-synthesis Synthesis Cross-design synthesis Bayesian data augmentation Missing data Gibbs sampling 


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(*indicates HIV adherence report in Table 1)

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Jamie L. Crandell
    • 1
    • 2
    Email author
  • Corrine I. Voils
    • 3
  • YunKyung Chang
    • 2
  • Margarete Sandelowski
    • 2
  1. 1.Department of BiostatisticsUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.School of NursingUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Health Services Research & Development ServiceDurham Veterans Affairs Medical Center & Duke University Medical CenterDurhamUSA

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