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.
4/10 For your 1st go at TA work in hard copy if you can - it's important to develop a robust TA sensibility & clear sense of good practice, otherwise software programmes can shape our coding process in ways that aren't always helpful - it's all to easy to generate loads of codes.
5/10 Because themes are built from codes, & the coded data, coding labels need to clearly evoke what is analytically relevant. This highlights that coding in our approach isn't just about data reduction & summary, it's also about interpretation & analysis.
6/10 For these reasons, one word coding labels are unlikely to do the job! They are too coarse - we often see people using labels like 'gender', 'benefits'... what about gender? what about benefits? What is analytically relevant here beyond that broad label?
7/10 Try the 'take away the data' test - without the data next 2 yr codes, do the codes clearly evoke what is analytically relevant for u? If not, the labels will need some refining. People are understandably anxious about the semantic/latent distinction, so let's consider that..
8/10 This distinction is there to help you reflect on how u are coding - at the surface level of meaning or looking beneath the surface to the underlying assumptions & meanings. Reflexivity is key to good coding practice in TA - try to reflect on what assumptions yr making.
9/10 This is really tricky & it can help to share yr thoughts with someone else. What I am overlooking/not seeing? How R my assumptions constraining & limiting how I'm engaging w/ the data? The S/L distinction will help you to reflect on the level at which yr engaging with data.
10/10 But u don't have to have both S & L codes, or try to force the latter. Most analyses have a mix of both - L codes R not inherently better 'cos they seem cleverer! Coding always needs to fit to yr purpose! So try not to sweat the S/L distinction & use it for reflection.

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More from @drvicclarke

Oct 4, 2018
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
Oct 1, 2018
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…
Read 11 tweets
Jul 20, 2018
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!
Read 4 tweets
Jul 5, 2018
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

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