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...
4/10 This implies a realist/positivist discovery orientation to research - there is a world 'out there' that exists independently of human practices & if we use the correct tools & procedures we can accurately document this pre-existing reality. These assumptions = small q TA
5/10 By small q #thematicanalysis I mean coding reliability approaches which are fundamentally different from my & @ginnybraun's reflexive approach to TA. This lecture maps out some of the differences between different styles of TA:
6/10 We (now) call our approach reflexive TA to distinguish it from other - often very different - approaches & to acknowledge the active role of the researcher in generated themes - themes are the output of analysis, they are not discovered by the researcher.
7/10 The reflexive TA researcher is like a sculptor working with a piece of marble - rather than an archaeologist digging in the dirt for buried treasures or a farmer harvesting crops. See our qual textbook for more discussion of sculptors vs. archaeologists...
9/10 The researcher as sculptor works with materials (data) that delimit what they can say & they bring to the analytic process their experiences, skills, tools & techniques - they actively generate themes to represent patterned meaning in their data... Rather than themes emerged
10/10 Themes are 'generated' or 'developed'. Identified connotes activity but also finding/discovering themes that pre-existed the analysis. Also be wary of report headings like 'Findings'. We have toyed with 'Results' but nothing beats 'Analysis' in our view!
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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.
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…
1/3 Hey Twitter with my lovely UWE colleagues @NikkiHayfield & @paulredford & others I'm producing some online resources for research methods teaching, some of which will be open access. 3 questions for you. 1) For personal study use would you prefer narrated PowerPoints or...
2/3 or person talking to camera. 2) If you were going to use these resources in teaching - narrated slides or person to camera. 3) Longer lectures - like the ones I've posted on my YouTube channel recently or lecturers divided into shorter - say 20 mins - chunks? And again...
3/3 Would you have diff prefs for teaching & personal study use? Thanks in advance!