Designing for Reflexive TA
Reflexive TA can be used within the context of an already-designed project, where an already-existing qualitative dataset is analysing using reflexive TA. But often it’s the method intended for use from the start of a project – and therefore the conceptual and design thinking for the whole project, including thinking about TA, and how it fits within the scope of an overall project.
We have written a (long!) paper outlining conceptual and design thinking for reflexive TA; here we signal some of the elements to consider in designing a project where reflexive TA is your intended analytic method. Note we say intended: including reflexive TA in your design does not mean that must remain the (only) analytic method you use.
We briefly signal 10 key elements to consider/reflect on in relation to design for reflexive TA (these are expanded on in Thematic analysis: A practical guide).
- There are many routes in to design for reflexive TA, such as theoretical, or pragmatic ones. No one route is inherently better than others, as long as…
- Design should be considered and coherent. You want a design that all coheres together and makes sense. Concepts like conceptual coherence or ‘fit’ (Braun & Clarke, 2013; Willig, 2013) or methodological integrity (Levitt et al., 2017) are useful guiding tool for design.
- Your research question always matters for design. No matter what route you take into your research design, it will always be guided by your research question and purpose: your design has to be able to address what you – broadly – want to understand. And it’s useful to remember that reflexive TA can address a wide variety of research questions – not just experiential ones.
- TA has few inherent restraints around data. Reflexive TA can be used with many different types of data.
- You have a lot of flexibility around dataset composition. We encourage people to explore the wide range of data collection methods and data types within a coherent design.
- You need to be systematic in your approach to data. This flexibility doesn’t mean ‘anything goes’ – you need to be thoughtful, deliberate and systematic and what data, how, and why.
- You have lots of flexibility around dataset size and composition. Such as smaller datasets of ‘information rich’ data items, or larger datasets if ‘thinner’ individual data items; you can balance depth and breadth of data.
- Data quality matters. Not all text- or image-based information makes quality data – data that can give you access to a rich range of meanings, perspectives or experiences related to your topic, and from which you can explore, develop, and interpret patterned meaning. We recommend reflexively assessing the quality of the dataset early in the process of data collection/generation.
- There’s no easy answer to the question of dataset size. There is no simple way to take all data related elements – such as data depth, richness, complexity – and determine the right size of dataset for a particular project. We recommend avoiding claims of ‘saturation’ (Braun & Clarke, 2021); consider concepts like information power (Malterud et al., 2016) as a useful guide.
- Ethical thinking is a really important element of design. And in a broad, reflexive, political way of understanding ethics.
Braun, V., & Clarke, V. (2013). Successful qualitative research: A practical guide for beginners. Sage.
Braun, V., & Clarke, V. (2021). To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales. Qualitative Research in Sport, Exercise and Health, 13(2), 201-216.
Levitt, H. M., Motulsky, S. L., Wertz, F. J., Morrow, S. L., & Ponterotto, J. G. (2017). Recommendations for designing and reviewing qualitative research in psychology: Promoting methodological integrity. Qualitative Psychology, 4(1), 2-22.
Malterud, K., Siersma, V. D., & Guassora, A. D. (2016). Sample size in qualitative interview studies: Guided by information power. Qualitative Health Research, 26(13), 1753-1760.
Willig, C. (2013). Introducing qualitative research in psychology (3rd ed). Open University Press.