Discover and read the best of Twitter Threads about #thematicanalysis

Most recents (8)

1/ @slsibbald asked for my & @ginnybraun's thoughts on theme saturation & this deserves a whole thread of its own - to unpack our gaaaahhhhh! reaction... There are several papers that set out to determine how many interviews (or focus groups) are required to achieve saturation in
2/ #thematicanalysis & make (somewhat nuanced/contextualised) claims about the number of interviews necessary to achieve theme or data saturation. In our view these papers make some rather extraordinary assumptions that speak to fundamental philosophical differences between
3/ coding reliability TA & our reflexive approach. For example, most of these papers consider a code saturated when 1 instance has been identified. For us the notion that the work of a code is done with 1 instance identified is rather puzzling. But when we look at what is being..
Read 8 tweets
1 For those of you writing dissertations right now using #thematicanalysis some thoughts on writing up a TA study. First, justifying your choice of TA. Try not to just recite general features of TA - it's flexible, it's accessible etc. - lifted from a list in one of our papers
2 These are not intended as rationales for using TA in specific studies but as general features of TA (compared to other approaches). The trick is discussing why these features mattered for *your* study. Why was it beneficial for your study that TA is theoretically flexible?
3 How did you make use of this feature? If you didn't, don't discuss. Also hold in mind a distinction between conceptual & design rationales & pragmatic & practical rationales. The former are generally seen as more important/compelling. Accessibility falls into the pragmatic...
Read 16 tweets
1/10 For those of you learning about or having a go at #thematicanalysis for the first time, & particularly the TA approach developed by me & @ginnybraun (which is quite diff from others), I want to share some thoughts on coding in our approach, & tips for learning to code well.
2/10 One thing to avoid when you're reading data is starting to think about themes straight away & use coding to identify themes in the data. Our approach involves building themes from codes, so themes happen later in the process. Make a note of your ideas & put them aside.
3/10 You want to avoid reading the data through the lens of these initial impressions - sometimes our initial thoughts are 'gold', but often they are quite superficial or obvious, & a thorough familiarisation & coding process can lead to more complex, nuanced & richer insights.
Read 10 tweets
After reading a lot of student dissertations/theses recently - some thoughts on writing discussion sections/chapters in qual reports, & particularly reports of #thematicanalysis. Often the trickiest part of a diss as we have run out of steam & have no idea what to say!
Discussions (conventional ones at least) are tricky because they are both formulaic (evaluate the study, make suggestions for future research) and also very open - there's lots of scope to choose what to focus on beyond the expected content. Some things to avoid first.
When making suggestions for future research - don't switch on the random ideas generator! The suggestions should *arise* from yr research. The limitations of your sample is often the go-to choice here but explain why it would be interesting to talk to other groups.
Read 13 tweets
1/10 For those of you teaching #thematicanalysis and #qualitativemethods or learning about these - here are some resources @ginnybraun and I have put together. First, check out our textbook Successful qualitative research:…
2/10 The companion website for SQR had lots of resources for teaching & learning - data-sets, including an audio-recording of a focus group, examples of research materials, flip card glossary, MCQs, links to readings...:…
3/10 Our latest book Collecting qualitative data with @DrDebraGray provides a practical and accessible introduction to data collection beyond the face to face interview:…
Read 11 tweets
Important process differences within #thematicanalysis family: small q (deductive, coding reliability concerns, qual data in quant thinking) vs big Q (data driven, open flexible organic coding), middle q (training with @ginnybraun @QRNHub)
But what’s a theme @ginnybraun? Domain summaries - cluster responses to a q or issue but lots of meaning variation within eg a single theme that covers ‘risks and benefits of x’ #thisisbad
Vs meaning based themes - an underlying idea or concept that holds the data together (data may look superficially different but is united by the idea) #thisisgood @ginnybraun @QRNHub
Read 18 tweets
Ten tweets on why @ginnybraun and I find the language of 'themes emerged' so problematic in #thematicanalysis and how else can you write about your themes and how they were developed, if they don't emerge from data like bubbles rising to the top of a champagne glass?
2/10 Two main reasons why we find themes emerged or emerging so problematic - 1) it implies that the themes pre-exist the analysis and are waiting in the data for the researcher to find them. We'd call this a discovery orientation to analysis - reflected in terms like 'findings'.
3/10 2) The suggestion is that the themes emerged all by themselves, that the researcher didn't play an active role in the production or generation of the themes. They just sat and waited while their themes wafted to the surface of the data, and then scooped them up...
Read 10 tweets
1/10 I knew @ginnybraun & I couldn't tackle the use of saturation in #thematicanalysis in only 10 tweets! I want to come back to this paper & explain why most papers offering concrete guidance on saturation only work for coding reliability TA:…
2/10 This & other papers use TA to analyse data & produce guidance on saturation - the problem is none define/locate their type or style of TA, explain the philosophical & procedural assumptions embedded within it, and how these diff from other types or styles of TA...
3/10 Guest et al. assume themes are like 'diamonds in the sand', entities that pre-exist the analysis, & so the analytic task is unearthing the themes that already exist in the data, we discuss this further in this commentary (on a statistical model):…
Read 10 tweets

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