![]() ![]() Given its prime place of importance in this area of methodological research, it is surprising, therefore, that integration has not received more attention. Integration is really the heart of the whole mixed methods exercise because the purpose of mixing methods is to get information from multiple sources and so the issues in bringing together the information are crucial (p. In effect, data integration is seen as fundamental to what we do as mixed methods researchers. Overall, the possibility of data integration lies in the extent to which data from different methods can be interpreted together in a meaningful way. As Fielding (2012) suggests, mixed methods allow for greater “analytic density.” The goal of data integration, then, can be seen to produce a (more) comprehensible object. Instead of “adding up” data, where findings of one method are considered alongside findings of another, integrating data goes beyond any individual method and considers the interaction-or synthesis-of data. There has been a growing debate about how best to integrate quantitative and qualitative data, but the basic idea behind it is that, within a single study, the resulting analysis is done across or through the different data. In doing so, we provoke a discussion around the orthodoxy of integration as a goal of mixed methods research. Rather, we emphasize the need for an approach that explicitly supports instances where data do not integrate or “cohere” and argue that this may be due to the messy nature of the object of study. To be clear, in arguing for diffraction as an alternative to integration, we not wish to negate or undo the efforts colleagues have made regarding data integration. Finally, we reflect on the rich opportunities diffraction offers to mixed methods work in contemporary contexts where data are increasingly mixed. Step 7 reiterates that diffraction and integration can work together, but that data diffraction offers mixed methods researchers a means of empirically capturing some of the messiness of social objects, letting messy objects be messy in a way that data integration cannot. We show that mixed methods have the capacity to produce sets of messy empirical “cuts” of the object being studied that do not always “cohere” ( Barad, 2007) and that these cuts can be addressed through data “diffraction” ( Haraway & Randolph, 1997). Step 6 elaborates on this by drawing on Donna Haraway’s and Karen Barad’s work. Steps 4 and 5 together introduce the notion of research as “cuts” and argue for an approach that acknowledges that data can “mess up” the object. ![]() Third, we outline a fundamental paradox at the heart of mixed methods research, namely, that mixed methods are assumed to be useful because of the complexity of the social world, yet in spite of this, it is also assumed to be both possible and desirable to integrate data relating to the study of complex, messy social objects. In doing so, we question the assumption that integration is necessarily possible and suggest that, sometimes, integration may be problematic because of the object of research itself, which is complex, ontologically unstable, and may not be clearly bounded. Second, we suggest that authors typically respond to the difficulties of integration by reexamining the impact of different epistemologies relating to different methods, or by proposing that the research is conducted differently. Step 1 introduces integration in mixed methods work, highlighting the many different approaches and arguing that, while integration is a desired goal, it is not always successfully achieved in practice. This challenge is made in eight methodical steps. We do this by presenting an alternative to data integration based on the concept of diffraction, defined as a process of paying attention to the ways in which process produces “cuts” that can interrupt and splinter the object of study. Although data integration is a sensible goal, we challenge the presupposition that it is necessarily the optimal outcome of mixed methods research. There is also a general consensus across that body of research that, whichever methods are used, integrating the mixed data is the desirable outcome. This field has developed rapidly and there is now a proliferation of work that combines quantitative and qualitative methods to explore social phenomena. The merits of mixed methods 1 are now well recognized ( Brannen, 1992 Bryman, 1984, 2006, 2007 Caracelli & Greene, 1993 Creswell, 2003 Fielding, 2012 Greene, 2007 Greene, Caracelli, & Graham, 1989 Greenhalgh et al., 2010 Huberman & Miles, 1983 Johnson & Onwuegbuzie, 2004 Johnson, Onwuegbuzie, & Turner, 2007 Pluye, Grad, Levine, & Nicolau, 2009 Tashakkori & Creswell, 2007 Tashakkori & Teddlie, 1998 Teddlie & Yu, 2007).
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