Webinar: Introduction to Thematic Analysis: Understanding, conceptualising, and designing (reflexive) TA for quality research

Date: 29 November 2022

Offered by NVivo

Presenters: Virginia Braun and Victoria Clarke


“The process is not the purpose” – this quote really resonated for me, as did their note of “fitting method to purpose”. They aren’t trying to say that everyone should do reflexive TA. They are saying that you should know what type of TA you are using and to chose it purposely for what you are trying to achieve. And then do the analysis in a thoughtful way, a way that aligns (your ontology/epistemology should be consistent with your methods). I quite enjoyed this webinar and I think I should check out their book!

Detailed Notes


  • thematic analysis is an approach, not a single method, more like a family of things
  • family differences:
    • paradigmatic differences – what are we (conceptually) doing here? (e.g., describing/uncovering a single reality, c0-creating knowledge, etc.)
    • what paradigm are we operating in?
      • post-positivist – “small q” (using not numbers, but still using post-positivist understanding of the world)
      • non-positivist – “big Q” or “fully qualitative”
      • view of subjectivity – a threat (as understood in postpositivism, subjective seen as leading to bias) or a resource (as understood in Big Q)?
    • research practice differences:
      • conceptual (discovery vs production)
      • practical (identifying themes vs. developing analysis; themes inputs or outputs )
        • in reflexive TA they don’t talk about “emerging themes”, since they aren’t thinking that the knowledge is being discovered, it’s being produced
      • what is a theme?
        • united by focus/topic?
        • united by shared core concept?
  • Braun & Clarke’s way of clustering these approaches:
    • coding reliability
    • codebook versions (e.g., framework analysis)
    • reflexive versions (Braun & Clarke’s version is one of the most well-known of these)
    • other versions
  • Findlay’s differentiation
    • scientifically descriptive
    • artfully interpretative
  • TA is about developing, analyzing, and interpreting patterns across a qual dataset, involves systematic processes of data coding to develop themes
    • methods, not methodology (but you do still have a worldview/paradigm you are operating in when you choose and use a method)
    • focus on patterns of meaning aka themes across a dataset (but what’s s pattern?)
    • processes of coding –> themes
    • reporting ‘themes’
  • Reflexive TA
    • conceptualizing of analysis. Research Question + Research + Data are embedded within our disciplinary training & scholarly knowledge, sociocultural meanings, and values
    • Big Q/artfully interpretative
    • research subjectivity value –> reflexivity is essential
    • coding is open and organic (codes as analytic ‘entity’)
    • themes as analytic ‘output’
    • multiple ways to do reflexive TA (theoretical alignments, etc.)
    • six phase process to do reflexive TA:
      • familiarization
      • coding
      • generating/constructing (initial) themes
      • theme development and review
      • refining, defining, and naming themes
      • writing/stopping (it’s never “complete”, so you need to pick a point to end)
      • NB. The process is not the purpose, nor a guarantor of quality.,
      • NB. It’s not a linear process. Can go back to any phase at any time. Open & recursive.
  • Take home message: there is a diversity of TA; understand what type of TA you are using!
  • Common problems in published TA:
    • misunderstanding/misrepresenting (lack of diversity)
      • e.g., saying they are doing TA when they aren’t; aren’t adequately rationalizing why TA is used; “swimming (unknowingly) into the waters of positivism”
      • e.g., saying there is no guidance for TA (when there’s lots in the literature)
      • e.g., a paper saying it’s reflexive TA but then says used reflexivity to “manage their bias”
      • inadequate description (e.g., just saying “followed the 6 phases of…” but not how you did it)
      • too many themes – thin/fragmented
      • deploying theoretically incoherent quality standards (e.g., saying intercoder reliability, which is not a coherent strategy for reflexive TA (would be appropriate for a coding relatability version of TA)
    • mismatches:
      • conceptual
      • methodology (practice-based)
      • reporting
      • quality criteria
    • Become a more knowing practitioner:
      • don’t treat TA as a single method
      • talk about what version of TA you used
      • make choices thoughtfully & appropriately and show you made choices
      • engage in conceptual and design thinking
    • conceptual thinking
      • research values (awareness)
        • ontological
        • epistemological assumptions
      • design thinking: design coherence/methodological integrity (Levit et al, 2017)
    • 10 fundamentals of reflexive TA (for conceptualization & design coherence) (Braun & Clarke, 2022 paper – go read it!)
      • coding quality doesn’t depend on a multiple coders
      • analysis can’t be accurate or objective, but can be weaker/stronger
      • good quality coding/themes come from depth of engaging and distancing (the value of time!)
      • assumptions underpinning analysis need to be acknowledged – they don’t like “saturation” (they wrote a paper on this – a lot of qualitative approaches use this concept, but their paper talks about underlying assumptions of it)
    • 5 key challenges
      • fitting method to purpose (claims and practice)
      • working in a time and using reflexive TA coherently
      • time (tensions and pressures)
      • reporting (challenges in style, length, and from reviewers & editors)
      • choosing appropriate quality criteria (e.g., in health, often COREQ is often seen as the way to go, but it has assumptions embedded in it)
    • quality and being a reflexive (TA) practitioner:
      • you are not a robot or a mechanic
      • you are an adventurer
        • values-led
        • reflexive
        • active
        • positioned
        • thoughtFULL (aka, don’t just think of this as “rules to follow”)
    • Q&A:
      • content analysis vs. TA – there are different versions of content analysis, just as there are different versions of TA. They wrote a paper comparing TA to content analysis, grounded theory, and something else.
    • Twitter: @ginnybraun @drviciclark
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