Data Diffraction: Challenging Data Integration in Mixed Methods Research

Abstract

This article extends the debates relating to integration in mixed methods research. We challenge the a priori assumptions on which integration is assumed to be possible in the first place. More specifically, following Haraway and Barad, we argue that methods produce 'cuts' which may or may not cohere and that 'diffraction', as an expanded approach to integration, has much to offer mixed methods research. Diffraction pays attention to the ways in which data produced through different methods can both splinter and interrupt the object of study. As such, it provides an explicit way of empirically capturing the mess and complexity intrinsic to the ontology of the social entity being studied.

Conceptual models to guide integration during analysis in convergent mixed methods studies

“…The scope of general procedures for linking quantitative and qualitative data at the methods level has been classified as connecting (linking through sampling), building (findings from one strand inform development of data collection tools or procedures for the other strand), hypothesis generating and testing (using one type of data to generate hypothesis and another type of data to test that hypothesis), matching (reflecting the intent to have themes/constructs match on a domain by domain basis), diffracting (using cuts of data to understand a phenomenon), embedding (the addition of qualitative data into a multistage study at multiple points), and merging (the two databases are brought together for analysis and for comparison) (Fetters et al, 2013;Uprichard and Dawney, 2016). Five of these approaches occur during data collection, while merging remains the most obtuse as it refers to the analytical process, and diffraction may refer to multiple levels.…”

“…3 by domain basis), diffracting (using cuts of data to understand a phenomenon), embedding (the addition of qualitative data into a multistage study at multiple points), and merging (the two databases are brought together for analysis and for comparison) (Fetters et al, 2013;Uprichard and Dawney, 2016). Five of these approaches occur during data collection, while merging remains the most obtuse as it refers to the analytical process, and diffraction may refer to multiple levels.…”

“…The third pattern, the "data diffraction" approach, alludes to the design (and often inevitability) of the quantitative and qualitative data collection instruments to obtain different aspects of a central phenomenon. The intent is to obtain different "cuts of data" that will reveal information about different aspects of a phenomenon of interest (Uprichard and Dawney, 2016). …”

Women’s Well-Being and Rural Development in Depopulated Spain

“…A mixed methodology, combining quantitative and qualitative data collection techniques, was used to inquire into the object of study [32,33]. This method is particularly useful when we want to gain in-depth knowledge of a multidimensional phenomenon that is difficult to analyse using a single research approach [34].…”

New Requirements to Include the Methodological Contribution in Articles Published in the Journal of Mixed Methods Research

“…Alatinga and Williams (2019) ''The purpose of this article is to provide an overview of health services research to the multidisciplinary audience of the Journal of Mixed Methods Research, to examine the potential for novel innovations in mixed methods research procedures, and to illustrate these points through a large-scale health services research investigation on care management implementation in primary care'' (p. 86). Holtrop et al (2019) using the language of ''contribution to mixed methods research,'' Uprichard and Dawney (2019) devoted four paragraphs in their discussion about the concept of data diffraction and its contribution to the field. The intellectual content of this section should be robust and referenced using existing literature.…”