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Mixing methods in a qualitatively driven way

Open access
Mason, Jennifer·2006·Qualitative Research

Abstract

This article makes an argument for a ‘qualitatively driven’ approach to mixing methods. It focuses on the value of mixed-methods approaches for researching questions about social experience and lived realities. It suggests that ‘qualitative thinking’ is a useful starting point for mixing methods, but that it is ultimately more helpful to think in terms of multi-dimensional research strategies that transcend or even subvert the so-called qualitative-quantitative divide. Mixing methods helps us to think creatively and ‘outside the box’, to theorize beyond the micro-macro divide, and to enhance and extend the logic of qualitative explanation. Mixed-methods approaches raise challenges in reconciling different epistemologies and ontologies, and in integrating different forms of data and knowledge. The article argues that we should think more in terms of ‘meshing’ or ‘linking’ than ‘integrating’ data and method. It goes on to argue for the development of ‘multi-nodal’ dialogic explanations that allow the distinctiveness of different methods and approaches to be held in creative tension. The article concludes with a discussion of qualitatively derived principles for mixing methods.

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Metadata

Authors
Mason, J.
Venue
Qualitative Research
Published
2006-02
Volume
6
Issue
1
Pages
9-25
ISSN
  • 1468-7941 (ISSN-L)
  • 1741-3109
OpenAlex
W2153429133
Cited by
828
References
26
FWCI
64.04
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Mixing methods in a qualitatively driven way. https://app.cochat.ai/papers/10.1177/1468794106058866
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Mason, J. (2006). Mixing methods in a qualitatively driven way. Qualitative Research. https://doi.org/10.1177/1468794106058866

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.