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Complexity and mixed methods

Bibliographic Data

ID23695316
AuthorsDavid Byrne (0000-0003-3885-6915), Gillian Callaghan
Year2022
Pages144-158
Publication date2022-07-21
Open AccessNo
TypeCHAPTER
VenueComplexity Theory and the Social Sciences (SOURCE_BOOK)
PublisherRoutledge (PUBLISHER • GB)
DOI10.4324/9781003213574-11
OpenAlexW4286499673
ISBN9781003213574
LanguageEN

This chapter begins with a review of how a mixed methods approach to social research can be understood in relation to the complexity frame of reference. It notes that the turn to mixed methods demonstrates that the ‘paradigm wars’ between quantitative and qualitative approaches are over and done with in the social sciences. The chapter takes up the idea of synthesis as proposed by Abbott (1991) who wanted to see the insights of historical sociology informing social research practice. We argue that good mixed methods work is precisely work which generated synthesized understanding and review the literature on the approach which supports this position. As is the case throughout this book we emphasize the significance of applied social research, not only as a domain of much good mixed methods work but also by a consideration of the role of the ‘organic intellectuals’ (to use Gramsci’s term) who do this work in relation to the praxis implications of their work – praxis: action which must be taken on the basis of knowledge. Here we pay particular attention to ‘action research’ – research done as part of a process of achieving substantive change and to the role of participation/co-production in the research process. The chapter reviews case studies of three examples of mixed methods co-production research: the North Tyneside Community Development project of the 1970s; the Rural Economy and Land Use Programme, which is a particularly interesting example of mixed methods research at the interface of the social and natural sciences; and the CECAN collaboration with the UK Environment Agency in using QCA to find what works in dealing with environmental crime. Finally, we use the idea of scenario to develop an example of the kind of research engagement with planning for the future which is necessary to confront the issues facing us towards mid 21st century.

Chaos, Complexity, and Education · Computer Science · Markov Chains and Monte Carlo Methods · SARS-CoV-2 detection and testing

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