MM8 - Integrative mixed methods, the case of thematic/clustering analysis

MM8 - Integrative mixed methods, the case of thematic/clustering analysis
MM8 - Integrative mixed methods, the case of thematic/clustering analysis

Principal speaker

Associate Professor Sama Low-Choy

This session addresses the challenges of integrating mixed methods. As noted by Maxwell et al (2015:223): "The type of design, and the paradigm views of the researchers, are less important for integration than the ability to view the results using different mental models or "lenses." Direct engagement of the researcher(s) with both types of data, and ongoing interaction between quantitative and qualitative researchers, facilitates integration, as does systematically developing and testing conclusions using both types of data." To demonstrate integration of MM, we consider a recent project where high-level experts were interviewed about the risks of data linkage, involving social science data sourced from government. The methodology integrated thematic analysis, identifying themes in the transcripts from interviews, with a cluster analysis describing how themes arise together. The workshop highlights particular challenges, either for those new to quant (here clustering), or new to qual (here thematic analysis).

Format: Online workshop, with breakout sessions. An online forum opened a week before can be used to share comments on preparatory reading or highlight questions for the presenter. During the workshop, small group exercises will help you work through ideas.

Intended audience and Connection to other workshops: Advanced beginners. You will find it easier to follow if (a) you have clarified your research question; (b) you have attended any of the other workshops in the Foundations of Mixed Methods (FMM) theme.

Recommended Readings: Maxwell J, Chmiel M, Rogers SE (2015) Designing Integration in Multimethod and Mixed Methods Research, Chapter 12 in Hesse-Biber S & Johnson N (Eds), The Oxford handbook of multimethod and mixed methods research inquiry, Oxford University Press, pp223-239. Low-Choy et al (2021) and Rose et al (2021) in Chan J & Saunders P (Eds), Big data for Australian social policy: Developments, benefits and risks, Australian Social Science Academy, available from: https://socialsciences.org.au/publications/big-data-for-australian-social-policy/

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This session is for current Griffith Univeristy staff and students only.


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RSVP on or before Sunday 26 May 2024 15.25 pm, by email red@griffith.edu.au , or via https://events.griffith.edu.au/LPgm5D

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