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Towards a method for synthesizing diverse evidence using hypotheses as common language

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  • F. Wesel
  • H. Boeije
  • E. Alisic

Abstract

Combining the findings obtained by different research methods in mixed-research synthesis could potentially contribute to a broader, more diverse evidence base for interventions. In this article we focus on the methodological challenges involved in synthesizing various types of research findings. We propose a method that uses hypotheses to facilitate the comparison and integration of such different findings. The method consists of four steps: (1) synthesizing findings per source of evidence, (2) formulating a mono-method hypothesis for each source, (3) integrating the monomethod hypotheses into one overall hypothesis, and (4) evaluating, using empirical data, whether the overall hypothesis better fits the data than each of the mono-method hypotheses. Using quantitative studies, qualitative studies and experts’ views in the substantive case of children and trauma, we will illustrate the proposed method. We conclude that the method provides a viable perspective for constructing an elaborate model that captures the knowledge from complementary sources. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • F. Wesel & H. Boeije & E. Alisic, 2015. "Towards a method for synthesizing diverse evidence using hypotheses as common language," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(6), pages 2237-2249, November.
    • Handle: RePEc:spr:qualqt:v:49:y:2015:i:6:p:2237-2249
    DOI: 10.1007/s11135-014-0105-9
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    References listed on IDEAS

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    1. Sandelowski, Margarete & Voils, Corrine I. & Barroso, Julie, 2007. "Comparability work and the management of difference in research synthesis studies," Social Science & Medicine, Elsevier, vol. 64(1), pages 236-247, January.
    2. Mulder, Joris & Hoijtink, Herbert & Leeuw, Christiaan de, 2012. "BIEMS: A Fortran 90 Program for Calculating Bayes Factors for Inequality and Equality Constrained Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 46(i02).
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