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Data Diffraction: Challenging Data Integration in Mixed Methods Research.

Emma Uprichard1, Leila Dawney2

  • 1University of Warwick, Coventry, UK.

Journal of Mixed Methods Research
|January 1, 2019
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Summary
This summary is machine-generated.

This study challenges assumptions in mixed methods research integration. It proposes "diffraction" as a new approach to better capture the complexity of social entities by analyzing how different data methods interact.

Keywords:
cutdiffractionintegrationmessmixed methods

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Area of Science:

  • Social Sciences
  • Research Methodology

Background:

  • Current debates in mixed methods research often assume a priori conditions for successful integration.
  • Existing approaches may not fully account for the inherent complexity and messiness of social phenomena.

Purpose of the Study:

  • To challenge conventional assumptions about integration in mixed methods research.
  • To introduce and advocate for

Main Methods:

  • Conceptual analysis drawing on feminist science and philosophy (Haraway, Barad).
  • Exploration of "diffraction" as an alternative to traditional integration methods.

Main Results:

  • Methods create "cuts" that do not always cohere, necessitating new integration frameworks.
  • Diffraction offers a way to analyze how different data sources splinter and interrupt the study's focus.

Conclusions:

  • Diffraction provides an empirical method for capturing the ontological complexity of social entities.
  • This approach enhances the ability to study the intricate and often fragmented nature of social phenomena.