Discover and read the best of Twitter Threads about #thematicanalysis
Most recents (5)
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.
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.
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: uk.sagepub.com/en-gb/eur/succ…
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...: studysites.uk.sagepub.com/braunandclarke…
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: studysites.uk.sagepub.com/braunandclarke…
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
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...
