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.
Is there any evidence to support the speculation that other groups might have different experiences? Avoid the 'more is better' trap 2 - do the same research with a larger sample so we can generalise. You R telling the reader qual is only a precursor 2 or poor substitute 4 quant.
Don't evaluate qual through the lens of quant values - researcher bias, small samples, lack of (statistical) generalisability... Use this great paper by @BrettSmithProf to discuss generalisability in a way that reflects qual values:…
Perhaps use qualitative quality criteria to guide your study evaluation. I like Lucy Yardley's widely cited open ended, flexible principles for guiding qualitative quality:…
Sarah Tracy's 8 'big tent' criteria for excellence in qual research are also worth a read:… Be wary of criteria and quality strategies/standards that are not theoretically open and flexible and have unacknowledged theoretical limits.
When contextualising your analysis in relation to existing literature - the theme-by-theme discussion is best avoided. It's not the most interesting approach & your themes R not yr analytic conclusions - you need to draw out the story & conclusions across the themes.
Writing your discussion is another good reason to keep a research journal - make a note as you go along of as things that might be interesting to reflect on in the discussion. Discussions are 4 contextualisation, highlighting contributions and reflection.
Your contributions might not just be your findings - they might be methodological too. Used a novel method - reflect on that. Used a method rarely/never used in your field - reflect on that, make an argument about its exciting potential for your field of research.
It's something went wrong, or there were challenges - and these might have wider relevance for researchers in your field, reflect on these. What did you learn? What do other researchers need to know? What needs to change? This is all part of looking forward to future research.
To help decide what 2 focus on - who do you want to speak to, influence? Who is your audience? You have lots of scope and flexibility - try to craft a discussion that highlights the contributions of your research and showcases your capacity as a reflective/reflexive-researcher.
Don't forget to end with a conclusion - one or a few paras depending on diss/thesis length. This is like the #thematicanalysis of your whole report - what are the key 'themes' or stories of your research? What have you contributed? What are the potential implications?

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More from @drvicclarke

Oct 5, 2018
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
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:…
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
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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