Education
Are you just starting out with qualitative research? Or perhaps you have experience in other forms of qualitative research but want to learn a bit more about Thematic Analysis specifically? You’ve come to the right place. In this podcast we (three early career researchers) talk about our understanding and experiences of conducting Thematic Analysis (TA) with the help of NVivo Software. We delve under the umbrella term of TA to ask, what is TA? Why did it appeal to our different research projects? And, of course, no research project is complete without a few stumbling blocks along the way, so we talk about those as well.
To polish off and add a little extra shine to the podcast we include a short interview with Dr. Katherine Ashbullby, Lecturer in Psychology at the University of Exeter, who shares her knowledge and experience of TA with the benefit of her experience in the field.
Resources
NVivo QSR International (2021)
For more information about NVivo and a range of training resources visit the NVivo website:
https://www.qsrinternational.com/nvivo-qualitative-data-analysis-software/home/
Sandelowski M, Barroso J. (2003) Classifying the findings in qualitative studies. Qual Health Res. 13(7):905–923.
Braun V, Clarke, V (2019) Reflecting on reflexive thematic analysis, Qualitative Research in Sport, Exercise and Health, 11:4, 589-597, DOI 10.1080/2159676X.2019.1628806 [this paper was referred to as ‘the 2016 one’ by Emily in the podcast]
Braun V, Clarke V. (2021) Can I use TA? Should I use TA? Should I not use TA? Comparing reflexive thematic analysis and other pattern-based qualitative analytic
approaches. Couns Psychother Res.;21:37–47. https://doi.org/10.1002/capr.12360
Victoria Clarke has tweeted a useful twitter thread on the Big Q/small q qualitative distinction, which be accessed through the following link: https://twitter.com/drvicclarke/status/1444258228439764993?s=20
YouTube videos by Victoria Clarke on Thematic Analysis: https://www.youtube.com/channel/UCLBw6Qig8KBId9YuIMzAg7w
Kiger M.E., Varpio L. (2020) Thematic analysis of qualitative data: AMEE Guide No. 131, Medical Teacher, 42:8, 846-854, DOI: 10.1080/0142159X.2020.1755030
Contact and Feedback
This podcast is supported by the GW4 institutions – Bristol, Bath, Cardiff, and Exeter – as part of their NVivo Resource Development project, a pool of resources for researchers wishing to get started with NVivo software.
We hope that you enjoyed our podcast. We’d love to hear how you found it. Share your feedback with any of the GW4 doctoral college Twitter accounts:
@ExeterDoctoral @DoctoralBath @bristoldc
Thank you for listening!
A big thank you from us, Ailsa Naismith, Merve Mollaahmetoglu and Emily Taylor, for listening and we wish you all the best in your research endeavours.
Podcast transcript:
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Hello and welcome to R, D and the In Betweens, a fortnightly podcast where we talk to guests about research, development and everything in between.
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This week is a special episode with three guest hosts, Ailsa Merve and Emily from the University of Bristol and Exeter.
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You're listening to a podcast on thematic analysis and how to tease meaning from qualitative data.
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If you're interested about thematic analysis,
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keep listening for some insights from three researchers from the University of Exeter and Bristol who have been through the process.
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We're also going to hear a little bit from an expert on thematic analysis who shares their key tips on the process.
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I'm Ailsa and I work at Earth Sciences at the University of Bristol.
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I'm here with Merve working in psychology and Emily, who works in the College of Medicine and Health, and both are at the University of Exeter.
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Hi there. Hi. Great.
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So lovely to chat today. And let's make some introductions.
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I myself am a volcanologist, and I started using thematic analysis to study how people remember past volcanic eruptions.
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How did both of you get into the topic from what backgrounds? Yes, my name is Merve and I'm in the psychology department.
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So I started using thematic analysis to understand experiences of people who were being ketamine for the treatment,
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who were being given ketamine for the treatment of alcohol use disorders.
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Yeah. How about you? I'm Emily and I use thematic analysis for my project looking at independent and older people.
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And this was a mixed method analysis. So I was using quantitative and qualitative data.
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So I found thematic analysis with some of its flexibility was really quite helpful for that.
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That's really interesting. It sounds like we're coming from very different backgrounds and using thematic analysis in different ways,
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but for those people who for those listeners who are not so familiar with thematic analysis, how would we define that message to them?
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That's a really good question.
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And I think one thing to understand is that thematic analysis is not a single method, but it's used as an umbrella term for a family of methods.
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And as Emily mentioned, it can be flexible in both theoretically, but also in the way that it can be used with inductive.
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So data driven and deductive, so theory driven approaches and approaches to coding.
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And it can also capture both semantics, explicit or latent implicit meanings and data.
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So what is actually thematic analysis?
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So it is a pattern based qualitative method and it's considered to belong to the phenomenological or experiential qualitative research tradition.
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So it tries to understand exploration of participants subjective experiences and making sense of their.
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I think the only thing I can think to add is some people would say it's sort of in the middle in terms of descriptive vs. interpretive.
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Some people would argue it can go any place on the scale depending on how you use it.
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But I think it can you sort of sit in the middle? Yeah, and I definitely agree with that.
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And I think that ties in with what Merve says about it could be an inductive or deductive
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approach that you kind of start with a you start with a theory of what you're expecting to see.
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And you might find that in your research you confirm that, or conversely,
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you might start with almost kind of no expectations of what you're going to find in your research.
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And then you build up your themes as you as you go along.
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And I think that that is one of the really good things about thematic analysis,
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the flexibility that you mention, Emily and Merve, you use this term of pattern based methods.
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I'm kind of interested in that. How could you elaborate on that pattern based, similar pattern based?
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I'm referring to qualitative analysis methods that focus on analysing patterns
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of meaning across data items or cases and a qualitative qualitative data set.
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So what I mean by data items are cases. I'm referring to participants.
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So call it a thematic analysis is one approach, one pattern based approach that others, such as qualitative content analysis,
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IPA, grounded theory, reflexive thematic analysis, the one I just mentioned, and also a pattern based discourse analysis.
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I guess pattern based methods are different than other qualitative methods that examine,
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for example, the more fine grained or interactional work of speech,
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such as conversation, analysis, or it's also different from methods that focus on biographies or stories such as narrative analysis.
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So that's how we can distinguish thematic analysis from other types of qualitative analysis approaches.
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Emily, did you have anything to add? No. Again, I think you've put it really well.
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I think one of the things about it being pattern based, so it also lends to it being a useful foundational tool for for other qualitative methods.
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So grounded theory and an IPA, I think both kind of expand on and of some of the concepts of thematic analysis,
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although thematic analysis is definitelu argued as a standalone method in itself.
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I just realised we haven't quite defined what it is, and for me, I initially forgot,
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well, not forgot, but it's quite a long road, so we should probably specify that.
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I think it's interpretative phenomenological analysis, just as a note to the listener.
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Yeah, good point. Very nicely pronounced. I'm always like shying away from saying it because it's such a long one.
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But yet when we say IPA, that's what we're referring to. Got you got you, not the IPA beer
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That would be a great type of uh. I'd be very interested. Yeah.
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Emily, I really liked what you picked up on in that thematic analysis can be kind of standalone,
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but it also is the foundation for a lot of different other types of analysis.
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I think that's really key and that for me in my research was something I instinctively felt.
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So I haven't done any other types of qualitative analysis than the analysis, but it kind of feels when you're doing it that it's so,
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so powerful and so flexible that you could really use it for and other other methods.
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And yeah, I wondered I mean, like I've said, I haven't done anything else apart from thematic analysis.
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But I wondered if you had both worked on some of these other methods that that you mentioned Merve and whether you wanted to kind of
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briefly elaborate on on how perhaps whether you liked them and whether thematic analysis itself really informed those other methods.
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So I will I am I have only really used thematic analysis, although I didn't really realise that it was counted as thematic analysis,
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because going back to the comment you made earlier is an umbrella term.
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So I actually use framework analysis, which if you go by and Clarke's definition,
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that would be counted as sort of a code book type of thematic analysis.
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And so that's just it's not as rigid as another form, which is coding reliability,
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which is very keen on having accurate codes that are repeatable and have different researchers.
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So that's kind of the key quality of coding reliability. And then you've got the bottom part version of reflexive analysis,
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which is much more recognising the generation and and sending of the researcher and the impact to the researcher on things.
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So a code book, which is where mine sits this framework is sort of in between those two,
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because it does have a framework which has some sort of deductive codes coming in to start with.
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And for me that was useful because that related to the mixed methods sort of side of my project that I,
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I did want to explore and sort of the more abstract and deeper kind of meanings within my studies.
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But I also needed to relate it to the quantitative work as well. So then use the deductive side for that.
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Mm hmm. That's so interesting, Emily. And I think that kind of brings us to a point that I wanted to mention about this,
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because we defined we said that thematic analysis is an umbrella term, but we haven't really quite defined what sits under that.
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And you refer to these sort of three main approaches within themantic analysis that Braun and Clark mentioned.
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So, you know, you said the coding reliability approaches,
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the reflexive approaches and the codebook approaches with that continuum from coding reliability to reflexive themantic analysis.
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And, yeah, I think that's an important distinction to make.
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And I think what I would add to that is that Braun and Clark refer to coding reliability.
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Thematic analysis is what's called a small q qualitative research.
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So when you use qualitative tools and techniques with a post positivist research values
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so sort of the research values that underpin quantitative research and emphasise sort of the objective and replicable knowledge as ideal,
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whereas the reflexive thematic analysis sits more within the big Q qualitative research
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which where qualitative research is not simply conceptualised as tools and techniques,
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what that means is qualitative, both in terms of techniques but also values.
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So I think that's a really interesting discussion. Yeah, that is an interesting discussion, rather.
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And I wanted to ask you a bit more about that, because I still find some of these terms a bit confusing.
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So you kind of said that the small q qualitative research is use qualitative tools,
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but you have values of, I'm guessing, understanding that there's maybe a objective truth out.
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There are things to learn,
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whereas the big Q qualitative would be both that you use the qualitative tools but also have a qualitative approach in that you say,
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well, the truth is subjective and this is my interpretation of what you said,
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but perhaps you can elaborate because it's always it's good to hear in your own words.
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I've just got a note here that the big Q is around encompassing the philosophy and procedure.
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And so sort of what you were saying. Yeah, I guess the point to make here is that there's the what is referred to as small q qualitative research,
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which uses maybe the quantitative research values within a qualitative method.
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And then there's the big Q qualitative research which where the methods and the values are aligned in qualitative research.
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Yeah, that's a really good way of putting it actually.
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And I guess you can see where you sit within this continuum of thematic analysis or qualitative research more generally,
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depending on what the needs of the research that you're conducting are.
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And I think the reference for that is from Sandelowski and Barroso in 2003,
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just from reading this morning that we might be able to put that in the notes.
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And you've also both mentioned Braun and Clarke.
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So I think this would be this is a really key article to it, kind of in reference for people to be able to look back on.
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It seems that I think all of us have found that a really useful resource from our very different backgrounds.
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I think one of the really interesting things about Braun and Clark is that they do they have the original paper in 2006,
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but they have done lots of papers since and encourage you to read those papers because they.
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You reflect on what how they've learnt to learn from teaching about as well, and I think that makes and is really helpful,
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but also quite informative for a new researcher to realise actually there was all this reflection and all of this has gone before.
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Yeah, definitely, if you're just starting with qualitative research, don't just go and read their paper from 2006, that was 15 years ago.
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And there they have so many more papers come out since then that are really informative.
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So I think that's one of the most referenced papers in the whole world.
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I'm not entirely sure it's about hundred thousand times.
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But, you know, I think they also emphasise that things have moved on from the their understanding at that time.
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So I would definitely recommend reading some of their most recent papers, which we can link in the show notes as well.
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This is a mad numbers of references. Yeah, it's crazy, but it's also, I think,
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confidence building that these people who have written such a seminal resource have also shown that in their subsequent papers,
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they've been pretty reflexive.
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The because this is kind of a theme or a common feature of thematic analysis itself that's kind of going over and and refining looking back on.
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So to have some of the most prominent practitioners of it do it in their own work and in their own understanding,
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that's pretty, pretty great, I think.
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I just want to say one other aspect perhaps that we haven't discussed in terms of thematic analysis is, is the issue of method versus methodology.
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And I think before I started doing qualitative research, before I started being involved with qualitative research,
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I kind of assumed method and methodology were the same thing.
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So I kind of used interchangeably. But they actually refer to different things and I think it would be really useful for people to know.
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And so the way methodology is defined is that methodology refers to theoretically informed frameworks for research.
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So this include things like IPA discourse, analysis, and on the other hand,
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method refers to technically it's sort of not technically, theoretically independent tools and techniques such as thematic analysis.
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So, you know, from the examples that we've given earlier about pattern based methods from pattern based methods and methodologies,
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thematic analysis and qualitative content analysis are are considered pattern based methods.
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So these offer people, researchers, tools and techniques that are either a theoretical or theoretically flexible in the case of thematic analysis,
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for example, and things like IPA, grounded theory, discourse, analysis, these are considered methodology.
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So these have theoretically informed framework's research. That's an important distinction to clarify for people.
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Yeah, Merve I think you nailed it. I mean, I, I still struggle with method versus methodology, but I think that's that's quite clear.
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And for me, it's kind of useful, you know, like what's in an ology
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Like, what's the difference that I think I think I mean, one one one thing that's just occurred to me as as you describe that Merve is that,
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you know, the set method, as I understand it, is theory.
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So you said it's the theoretically independent. So I could approach that with different research philosophies.
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Yes. And the methodology is is informed by a particular research philosophy.
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I think in a way like what Emily said was really helpful in understanding that themantic analysis is theoretically flexible because, you know,
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she said how she adapted it to suit the needs of her research project in the
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sense that she still needed things to be reliable and replicable in a sense.
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So she didn't use perhaps the reflexive thematic analysis, which doesn't necessarily concern itself with reliability.
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And it understands that themes are quite subjective. So it doesn't try to reduce that research researcher bias.
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So, you know, she's adopted the thematic analysis to her research values and philosophy.
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Yeah.
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Yeah, yeah, I really I keep coming back to that that that thing you said the start, I believe, how you liked the flexibility of thematic analysis.
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And I also in my research, that was a really big pool for me because I had this this.
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Yeah, I just I just wanted to have a powerful tool that could do what I wanted it to do.
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So, yeah. And I wanted to ask if there were other other appeals of thematic analysis that really led you to choose it to to analyse your research.
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That's a good question, I think. It sort of led me on to think of something else, which may not be quite answering the question,
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but I think it's sort of relevant and I don't want to ask again, I think it's a 2016 paper.
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They talk about and using it as a tool to be used flexibly, but also with knowingness.
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So and thinking about although it can be flexible with the very thinking about
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what I still think about what's underpinning it and how you're using that.
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And for me, this it just worked.
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And I think the conversation it was having going on in my research is looking
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at the quantitative and qualitative and how they speak to each other or not,
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and the fact that I could use a guess sort of deductive and inductive within that analysis.
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And also the fact is looking at patterns so I can only see other patterns
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between the two types of data and what a contrast and just works well for me,
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I think. Mm hmm. I think what I wanted to also say is something that Emily said is that it can do both.
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It sort of sits between descriptive and analytical approaches. And again, that fits within more descriptive, more themantic approach,
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a systematic analysis versus more light and versus approaches that try to on the cover more detail and implicit meanings.
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So I think that's some other benefit of thematic analysis that you can sort of do both of those things with it.
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Yeah, yeah, I like that.
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So I imagine that if you're under covering a theme, a theme could be something that someone's kind of one of your, let's say, an interview.
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He says something that you say, well, this can't this text can be taken as read a descriptive theme or it's kind of
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the meaning behind the words is the kind of latent thing that you pick up.
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And yeah.
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Emily, from your your what you described, it sounds like you like the flexibility, but there was also some kind of structure underpinning it.
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So you didn't kind of just jump in and say, oh, I'm going to do whatever,
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but that you use thematci analysis to kind of marry that quantitative and qualitative analysis.
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And I really like that. I think that's. Yeah, a really, really positive thing of thematic analysis.
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So one thing I was going to go on to after that was that I think that we all use the software NVivo, for for thematic analysis.
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And I wondered if you felt that it was easy to kind of marry the analysis of the different qualitative and quantitative data in NVivo
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And that's also a good question. It certainly works well, I think can be very for me, it works how I think.
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So if I had a word my interview transcripts in paper form, I would probably be highlighting and then putting little notes in the margin.
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And actually, NVivo allows me to do that because I can highlight it and then make annotations.
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Or if I'm actually thinking about organising it, I can highlight to encode it.
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And that works. I believe it's a quantitative code or a qualitative code.
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Yeah. So it just works for me. And the benefit of and we believe we're doing that on paper is that I can then
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take those bits that I've coded and move them around and look at them together.
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Hmm. I mean, it's a great tool, isn't it, because, you know, before computers and NVivo, I imagine people had to do this by hand.
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And I think they would print out the interviews and they would highlight cut and paste, move around, you know, the whole floor being covered by paper.
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And, you know, I guess in a way you might become more involved with your data,
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but it also is very difficult to manage and share with other people and also very prone to getting lost.
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So and we were kind of does all of that in a computer system.
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And I think it's really helpful in terms of collaborating with people, because we know that, you know, in most qualitative research,
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interviews are coded by more than one people one person, one researcher,
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or even if it is just coded by you, you still probably want to share it with other people.
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So it's a great tool for facilitating facilitating that.
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Yeah. So there's a lot of tools around how to work with other people.
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And this is one of the tools that we've created for the for the enviable resources as part of the GW4 network.
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So if you are in one of those institutions,
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you will be able to access access some information about how to facilitate collaboration on NVivo as well, which we will link to at the end.
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Yeah, I love that.
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My personal experience I remember the first my very first getting into thematic analysis and having only three interviews to analyse,
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but the transcripts werfe each like 20 pages long. And before I got to use NVivo, I was just like, you know, writing down texts and stuff.
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And I had I think I had interesting themes, but it was like impossible to organise that or to get a sense of,
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you know, what was significant or what was just, you know, a kind of small idea, what could be descriptive.
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And I think in particular,
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the kind of latent themes for me were much harder to to to tease out and to understand when I just had big stacks of paper coming.
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And for me, uploading these transcripts into and being able to organise themes through notes and kind of linked them was like really a game changer.
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Yeah. Was it the same for you? Every. Yeah, yeah, there's a couple of things you said that it made me think I mean,
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I find it really helpful that you can sort of have everything in one place.
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You can have you can use memos to be to maybe reflexive memos or so you can have a project log, as almost, maybe your diary.
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And because I don't know if you're anything like me,
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but we have bits of paper everywhere that have little notes that you can have it all on and NVivo, which is quite handy.
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And also, um, I'm a very visual thinker. So some of the visualisation tools, that computer has had been really helpful, I think.
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Mm hmm. Yeah, I was just about to mention that.
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And I think another really cool tool is if you're using thematic analysis with a more quantitative approach,
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let's say you can run coding comparison a query.
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So if you have multiple people coding on the same project, you can automatically compare how much do they agree in terms of their coding?
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And you can highlight differences and you can highlight areas where they disagree.
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But it can be really useful tool to enable comparisons of integrated reliability and things like that.
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That's really useful to know because I have only ever coded as a I've only ever
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coded so low that going forward it could be a really useful thing to be able to,
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again, kind of reflect on whether these systems are robust, if other researchers involved are kind of seeing those who are picking them out.
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And if not, then there's an interesting dialogue to be had there with other researchers.
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And yeah, but I have I have also used the visualisation tools.
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I don't know if both of you use, but I'm a particular fan of the word clouds.
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I mean, talking about, you know, we've talked a lot about all the benefits of thematic analysis,
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and I think listeners will be able to tell that we're all fans. But I know that with everything there comes some challenges.
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And for instance, I found initially that it was quite difficult to know how much significance to ascribe to a theme that was emerging in my data.
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And I wanted to ask you both, you know, any particular challenges that you've come across while doing thematic analysis?
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Yeah, I think that's a good point about describing how much weight to ascribe to the different bits of coding,
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and especially where we've talked about coming from Quantitative maybe a more quantitative background where
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you may be looking at Frequency's and things like that and actually realising that in thematic analysis,
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actually some of the very important and possibly the richest themes can be ones that don't appear all that often.
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But they they're really potent when they do.
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And they might also encourage you to explore a bit more into the other of the transcripts as well to see whether it does actually come up.
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It might just have been a bit more subtle than some of the others. That's really interesting.
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I guess for me, one of the challenges was getting my head around sort of this distinction between what's referred to as themes and domain summaries,
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especially within reflexive thematic analysis. So now now I do understand what domain summaries are.
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So domain summaries are basically a summary of what's been said, everything that's been said about a particular topic.
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So, for example, if I asked the participants a question, I might have asked something like,
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what are some of the negative experiences you've had with this treatment?
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And if I just summarise everything that said, that would be a domain summary,
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but it doesn't actually uncover the latents meanings behind what they've said.
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So the themes now I understand within reflect systematic analysis.
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The themes are sort of uniting the more implicit and or latent meanings behind what people have said, not just summarising what everyone has said.
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So, for example, a list of people have reported these as negative effects of the treatment sort of thing.
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So initially that was quite a challenge for me. But again, there are some useful resources around this as well, which we can link to.
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We're going to have so many links in the show. Great.
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Yeah, I think one other challenge I had starting off with is that I had some research questions that I think were led by my my certain approach,
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feeling that I feeling that I when I was coding my data, I wasn't actually getting answers that matched particularly well to the questions.
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And so initially that that felt quite worrisome.
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And then I think that what was helpful was understanding that the the themes that were emerging could then inform the questions.
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And in my case, I was able to do more interviews to then kind of revise the question.
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So, again, it was that thing that, you know,
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just because the things didn't necessarily answer exactly the questions that I had posed, that didn't mean that they're wrong.
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It was a case of of kind of recasting things, you know, re re.
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Yeah, recreating things and reflecting to understand that things could change.
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So I'd say moving from a kind of fixed mindset of, you know, the my hypothesis is wrong,
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which as a as a natural scientist, that is kind of that is the approach that we take.
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And it's like a very ingrained thing that we don't really reflect on research philosophy at
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all to meeting something that was like a lot more reflective and a lot more understanding
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of the subjectivity of meaning and of experience that I think is really key to thematic
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analysis and for me and maybe for you guys too really attractive to this kind of research.
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And I think in a way, what you're saying is that your research questions were informed by your data as well,
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rather than the other way around, which usually is the case with quantitative research.
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You have a theory which informs the research questions and then you get the data to support or not supported,
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whereas here you got some data and that led you to revise your research questions.
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Yes, exactly. Nail on the head. And that is a really exciting for me everything exciting new ways to do research.
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Yeah. I think one one interesting thing about qualitative research generally is that it can generate a lot of hypotheses.
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Right. So I think that's one of the things that I've enjoyed so much about being involved in qualitative research is that you get such a deep insight
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into a topic and it can sort of generate more questions for research that either you answer with qualitative or with quantitative research.
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Yeah, I think, you know, so your example was sort of just thinking about deductive and inductive that the deductive is it can be very useful
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sometimes to kind of if you really need to pinpoint a particular aspect and you've got that in your question.
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But actually the inductive has that place to explore a bit further and may deviate from actually what that initial question was.
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But as you say, it's just that much more informative.
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And it's one of the I think one of the as it if it was one of the joys of qualitative research and how it can be really informative.
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You're so right. And it's it's cool to think of OK to think of it as an ongoing process.
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I think that that it's not kind of done and dusted it can kind of continually we can continually learn more and ascribe more meaning.
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Absolutely. I think there's several cases where it's been, you know, actually, although there might be steps,
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I think one of the papers we're looking at gets six steps to to or I think it's the reflectivity.
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But actually, although it might be presented as six steps, though, things are very much you kind of go cyclical,
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you might get to step two and then have to go back to that one and you might just kind of keep reinforcing or learning more so it develops as you go,
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which I think is very important as well. And that is part of the adding depth and richness to to your data as well.
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Definitely, yeah. I think before we wrap up, I just wanted to add something that might be reassuring to people.
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You know, if you're sort of thinking, is thematic analysis the right choice for me or, you know, how do I choose a type of analysis?
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I think what I found really interesting reading in one of Braun and Clark's paper,
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they're basically that they have a wealth of knowledge in this area.
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So we refer to them a lot. But I think they say that considering and choosing an analytical approach is sort of more like
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deciding between which type of fruit you will choose to eat rather than deciding whether
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to have fruit a slice of cake or a burger.
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So they kind of emphasise that a lot of different pattern based methods for examples, for example, can have very similar outputs.
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So it is an important decision, but it's not choosing between an apple and a burger,
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but it's more choosing between the types of fruits, which I find quite a reassuring analogy.
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Yeah, I like that one. Yeah, great. For someone is indecisive as me.
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That's very helpful. Yeah. And I guess yeah.
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There's a lot of resources around how to choose between different types of different types of pattern based methodology,
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methods or methodologies, and there are similarities and differences.
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So I think one of their papers was comparing thematic analysis to different types of other types of pattern based methods or methodology,
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which can be quite useful for some people to read. So we will link that as well.
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Definitely, we'll we'll put that in the show notes, and so I think we'll wrap up there because it's been a really lovely and informative
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discussion and we've talked around various aspects of thematic analysis,
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how we first came to you to join it or how we first came to use it in our research and the the
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benefits and some of its challenges and also some of the definitions of thematic analysis.
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And for me, it's been a real pleasure to to host this and to share with you guys a really great discussion.
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So I'd like to thank both of you. Oh, thank you. Yeah, it's been really interesting talking to you both about this.
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I really enjoyed it. Thank you. Oh, it's lovely.
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And, yeah, we've we've learnt a huge well, I personally learnt a huge amount and hope the listeners have to.
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But as we've said at various points through the podcast we have,
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we will include a link in links in the show, notes to all of the resources that we've mentioned.
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So, again, a huge thanks to Merve and Emily for our conversation.
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I have Dr. Kat Ashbullby with me right now. She's a lecturer in psychology at the University of Exeter.
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Kat, would you like to tell us a little bit about yourself? Hi, thank you so much for having me.
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So, yeah, so I'm a lecturer in psychology at the university and I did all my training at Exeter as well.
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And I'm really interested in qualitative methods.
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A lot of my research has involved qualitative work and my background is in something called economic psychology,
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which is how people make decisions about everyday financial life.
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So things like spending behaviour, saving behaviour, money and relationships.
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And then after my PhD, I worked in outside academia in a charity as well, doing research about health and wellbeing at work.
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So I've had an opportunity to work in different areas using qualitative research.
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Yeah, great. And the way we know each other is obviously you've been really helpful in our qualitative
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project and you have a lot more expertise in this topic than I do or any of us do.
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And so we have this we're having this podcast to give a bit of our resource to postgraduate researchers who want to get into qualitative research,
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specifically thematic analysis. And so we have had some definitions of thematic analysis.
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But I wonder if you could give us like a brief definition in your own words?
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Yeah, of course. A thematic analysis is perhaps best understood as like an umbrella term for different approaches to making sense of qualitative data.
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So there's some really nice resources that you can find online, actually, through Victoria Clarke, like on YouTube, for example,
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where she talks about the different types of thematic analysis that might be helpful for some of your sort of listeners to go to.
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But really, it's just the idea that you're making sense of qualitative data through identifying themes is the very sort of base level.
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But then when you go into it, that's kind of different ways of doing that,
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whether you're doing it in terms of like what you might have heard of a code book,
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thematic analysis, where you've got kind of the more a description already,
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even before you've looked at your data of what you might want to find or like what is this more reflexive organic approach where
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you're much more open to the data when you're going through is on a line by line basis looking at what the people are saying.
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So you've got no idea before you start what your what your findings will be.
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And that's quite different to the kind of code book approach where you might already have an idea of what your themes would look like.
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So there are these kind of differences within it. But yes, it's all about making sense of qualitative data.
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So whether that be from interviews or focus groups or an online source, yeah, that's reassuring that it matches up with what we discussed.
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Yeah, that's great. Thank you. And I guess our perspective in this podcast has been from three researchers have mainly trained in
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quantitative research methods and coming into qualitative research methods later on in our research journeys.
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So I wondered, in your experience,
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what are some of the common mistakes people might make when they're using thematic analysis, for example, in our position?
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Yeah. So I guess like from a positive starting point that is accessible,
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the masterclasses people from different backgrounds, I suppose there are like common, I guess,
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mistakes people make in the it's getting used to like working in a completely different way,
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isn't it, with the different kinds of language of research.
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So you're moving away from talking about kind of variables and control to talking about people's lived experiences.
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So I guess that's something that just people not aren't necessarily always used to, you know,
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moving away from the research tradition that they've been in to kind of open their eyes to a new way of doing research in terms of make mistakes.
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I guess maybe, you know, like we've just talked about, that definition of thematic analysis,
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I guess sometimes is some lack of understanding that it can actually be this umbrella term,
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that there are quite different things that you can do as kind of one thing.
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So I guess familiarise yourself with the different approaches to try and doing a bit more reading around.
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It's really helpful, I guess, as well. Also, sometimes people maybe underestimate the amount of work involved.
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So and I guess you know yourself from having done it, some people think it's just quite, very quick that you just,
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you know, suddenly have these themes, whereas in reality, it's actually quite a lot of work, isn't it?
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First we'll get the transcription and then code the data and then this kind of intrusive nature that
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you're going back between the data and your codes and developing it and the work that goes into that.
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People might underestimate Definitely And I think especially with the reflexive analysis, there's a lot of interpretative work that's involved.
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And yeah, and perhaps I might have made the same mistake in that thinking.
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It was a lot more descriptive than. Yeah, it really is.
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Yeah, yeah. Yeah. So definitely. So I guess that's another one isn't it, that that kind of take.
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So people get to the stage where they kind of got this descriptive sort of piece about their.
366
00:40:42,680 --> 00:40:47,380
That it's taking at the next level of them, putting those things together to say, first of all, my key findings,
367
00:40:47,380 --> 00:40:55,220
what does this mean in relation to my research question and Braun and Braun and Clark talk about the like, storybook theme.
368
00:40:55,220 --> 00:41:00,700
So that idea that you're really telling a story with your research first is kind of the bucket themes,
369
00:41:00,700 --> 00:41:04,270
which is more like just shoving everything in there that, you know.
370
00:41:04,270 --> 00:41:11,470
So it's kind of a storybook thing where you're trying to say, you know, what's really going on here with my with my findings.
371
00:41:11,470 --> 00:41:16,390
That's really interesting. It reminds me of something that we discussed when we were doing the qualitative
372
00:41:16,390 --> 00:41:21,490
analysis together about the difference between the domain summaries and the themes
373
00:41:21,490 --> 00:41:27,220
And I did mention this as one of the difficulties that I initially found with thematic in the podcast.
374
00:41:27,220 --> 00:41:33,370
But I wondered maybe if you can sort of give a more elaborate description of what that means.
375
00:41:33,370 --> 00:41:37,080
Yeah, I can try. Now, you did a really good job, though, with your paper, didn't you?
376
00:41:37,080 --> 00:41:45,520
And so I think it was more like, you know, say with the Ketamine paper, you had, like, for example, all the different things that people experienced.
377
00:41:45,520 --> 00:41:51,670
And and that's kind of if you're just writing that all down, that's kind of like what some people call like a domain summary.
378
00:41:51,670 --> 00:41:53,830
It's like all different things that happened.
379
00:41:53,830 --> 00:42:00,040
But then taking that next level was then looking at, OK, so maybe these were really contradictory things.
380
00:42:00,040 --> 00:42:05,380
These are about transformation. So it's like then those labels of like contradiction or transformation,
381
00:42:05,380 --> 00:42:09,300
which then become your themes in themselves rather than the list of experiences.
382
00:42:09,300 --> 00:42:12,550
It's like taking in the next level. That makes sense. Yeah, yeah.
383
00:42:12,550 --> 00:42:16,360
That's a really good description. And so what would you advise?
384
00:42:16,360 --> 00:42:23,560
I think you sort of answered this, but what would you advise quantitatively, researchers who are new to qualitative methods or thematic analysis?
385
00:42:23,560 --> 00:42:29,680
Yeah, what I think doing some like, you know, more study or more reading, like I said, there's some really good online resources.
386
00:42:29,680 --> 00:42:38,090
So Victoria Clarke has been really influential in, like, kind of defining and delineating what thematic analysis is.
387
00:42:38,090 --> 00:42:41,140
And she's got some really nice YouTube videos that are quite straightforward
388
00:42:41,140 --> 00:42:45,020
just to watch to introduce you to some of these things about thematic analysis.
389
00:42:45,020 --> 00:42:48,670
And there's also a lot of like papers around that as well that they've done recently,
390
00:42:48,670 --> 00:42:53,090
just talking about different stages of their analysis, I guess, as well.
391
00:42:53,090 --> 00:42:57,700
It's just about being open to a new way of working and a new kind of language
392
00:42:57,700 --> 00:43:03,100
of research where you're more interested in different people's viewpoints, different people's lived experiences.
393
00:43:03,100 --> 00:43:10,120
And it's not necessarily about the number of times somebody says something and trying to get out of that purely quantitative mindset.
394
00:43:10,120 --> 00:43:15,460
It's as well as about, you know, the different range of experiences people are having and whether that's something that is
395
00:43:15,460 --> 00:43:19,810
interesting and meaningful to your research and could be taken forward to explore more.
396
00:43:19,810 --> 00:43:25,540
Certainly. I was just going to say it's hard to get out of the quantitative mindset initially because, you know,
397
00:43:25,540 --> 00:43:31,540
when we were first approaching it, we were trying to define how many times or how many participants have said a certain thing.
398
00:43:31,540 --> 00:43:39,220
But then you've explained to us, you know, actually that's not very useful way of approaching things in qualitative research,
399
00:43:39,220 --> 00:43:44,950
because just because half of the people in this interview said this doesn't mean that half of the
400
00:43:44,950 --> 00:43:51,670
people in the general public would say this or we're not approaching generalisability in the same way.
401
00:43:51,670 --> 00:43:56,530
Yeah, exactly. And the other thing that's really tricky, because obviously, if you use and say an in-depth interview,
402
00:43:56,530 --> 00:44:02,950
it might be that because obviously with a certain of certainly structured interviews, you don't always follow exactly the same interview questions.
403
00:44:02,950 --> 00:44:09,760
So it might be that some people had the opportunity because they were asked or it just went down the avenue to talk about their views on something.
404
00:44:09,760 --> 00:44:14,500
So they expressed it, whereas the other people in the other half of interviews might have had the opportunity, say,
405
00:44:14,500 --> 00:44:17,950
rather than them not necessarily agreeing or bringing up as meaningful,
406
00:44:17,950 --> 00:44:21,850
it might not have just been part of the questions, whereas it was a questionnaire.
407
00:44:21,850 --> 00:44:25,780
Everybody's getting exactly the same things that you can kind of compare it.
408
00:44:25,780 --> 00:44:29,050
So I it's just getting used to that different way of thinking about things.
409
00:44:29,050 --> 00:44:36,250
But it is tricky because, you know, it can sometimes be interesting that every single person thinks something versus nobody.
410
00:44:36,250 --> 00:44:40,150
But, yeah, it's just getting that balance, isn't it, and thinking about it in a new way.
411
00:44:40,150 --> 00:44:41,950
Yeah, yeah, definitely.
412
00:44:41,950 --> 00:44:51,910
So if we were to think a little bit about our philosophical position before approaching a qualitative research or more specifically thematic analysis,
413
00:44:51,910 --> 00:44:56,440
do you think it's important to define this before starting with analysis?
414
00:44:56,440 --> 00:45:02,350
And what how would you define your philosophical position? That's really difficult question to ask.
415
00:45:02,350 --> 00:45:05,860
That's a very good yeah. So I think in terms of yeah, there's all these different words,
416
00:45:05,860 --> 00:45:11,560
people can get quite confused about the symbology and ontology and philosophy, philosophical positions.
417
00:45:11,560 --> 00:45:17,800
But I think a lot of it's about thinking about, OK, so what am I trying to find, am I like inductive?
418
00:45:17,800 --> 00:45:21,550
So am I really driven by my data and what people are saying?
419
00:45:21,550 --> 00:45:26,750
The participants are saying and I'm quite open or am I more deductive and more theory based?
420
00:45:26,750 --> 00:45:34,420
So, for example, if I was doing a search, this is a nice paper that looks at social identity approach to food banks and social psychology.
421
00:45:34,420 --> 00:45:42,490
And so that would be very much like a theoretical theoretical basis where you you're very much looking for social identity that would help explain it.
422
00:45:42,490 --> 00:45:49,660
So I think they're having this different theoretical position, whether you're very much data driven or theory driven,
423
00:45:49,660 --> 00:45:53,350
can influence as well the questions that you ask people in your interview.
424
00:45:53,350 --> 00:46:00,880
So in some cases, you know, defining that in advance can be important, but it kind of depends on the stage that you get the data,
425
00:46:00,880 --> 00:46:06,330
if you see what I mean, and other people, you know, use different kind of methods.
426
00:46:06,330 --> 00:46:12,640
So if you're using like this, we're talking about thematic analysis, for example, discourse analysis.
427
00:46:12,640 --> 00:46:16,480
If you're looking at the way things are constructed in language versus you've got
428
00:46:16,480 --> 00:46:20,980
like a more straightforward view of what the language is and what people say.
429
00:46:20,980 --> 00:46:28,240
And that's a more like essentialist position. I guess in the past that I've had more essentialist realist one and more inductive approach.
430
00:46:28,240 --> 00:46:31,660
So it's kind of you're just open to what the people are saying.
431
00:46:31,660 --> 00:46:37,520
And that's kind of a straightforward relationship between what they say and what you're writing.
432
00:46:37,520 --> 00:46:43,750
But, yeah, I think just being aware that it's more complex than the being one type of thematic analysis of them,
433
00:46:43,750 --> 00:46:50,140
all these different positions that people take that can lead to quite different analyses and quite different results,
434
00:46:50,140 --> 00:46:53,570
I think is is beneficial really when you're doing the work.
435
00:46:53,570 --> 00:47:01,600
So and we talk specifically about small q and big Q, which feeds into these kind of debates as well.
436
00:47:01,600 --> 00:47:05,800
So yeah, I was about to ask that. So yeah, that was something that we discussed.
437
00:47:05,800 --> 00:47:12,500
And some are reading this idea between the big Q qualitative research versus small qq ualitative research.
438
00:47:12,500 --> 00:47:16,540
So I wondered, yeah. If you can tell us a little bit more about that.
439
00:47:16,540 --> 00:47:24,280
So that I think was Killoran Fine. And that comes into the idea that you're doing like a project from a so if you're doing a big key,
440
00:47:24,280 --> 00:47:29,590
one is from like a qualitative background, a qualitative like philosophy.
441
00:47:29,590 --> 00:47:36,460
And your it's what broaden out talk about the organic reflexive one is like a big key one because you're just very
442
00:47:36,460 --> 00:47:42,190
open to all the participants are saying you don't think that you have to count the number of times things happen.
443
00:47:42,190 --> 00:47:51,370
It's very iterative. Your you know, you're recognising that the researcher as an analyst is very involved in interpreting the data,
444
00:47:51,370 --> 00:47:59,980
whereas like a small q one is much more in line with, like quantitative thinking, thinking that you'd have to maybe, you know,
445
00:47:59,980 --> 00:48:05,110
like a kind of more like a kind of qualitative content analysis where you were counting the number of times something
446
00:48:05,110 --> 00:48:11,440
happened that you had like an idea beforehand of what exactly you were going to count before you even saw the data.
447
00:48:11,440 --> 00:48:14,290
You'd know what you were going to count or not, and then you'd count that thing.
448
00:48:14,290 --> 00:48:21,130
And that would be a much more small, cute sample because you're not really doing the research from a very qualitative philosophy in the sense that,
449
00:48:21,130 --> 00:48:26,440
you know, it's not so much about the participants lived experiences or being open to interpreting the findings.
450
00:48:26,440 --> 00:48:28,180
It's much more like closed off,
451
00:48:28,180 --> 00:48:33,760
like a questionnaire would be something that is much it's like a much more quantitative way to do qualitative research.
452
00:48:33,760 --> 00:48:43,420
So that's kind of part of the divide, I think within and it's not necessarily bad to do small q that could be exactly what you need in a study,
453
00:48:43,420 --> 00:48:52,240
but it is recognising that it is a very different approach from having much more open questions in your interviews and be much more
454
00:48:52,240 --> 00:49:00,850
open to following kind of lines of enquiry from the participant versus is this much more kind of closed off way of of doing it?
455
00:49:00,850 --> 00:49:05,890
And I guess this kind of shows in terms of thematic analysis, different approach,
456
00:49:05,890 --> 00:49:12,190
a thematic analysis kind of set along different ends of this continuum from big Q to small q research, is that right?
457
00:49:12,190 --> 00:49:14,200
Yeah, yeah, that's right. That's what they talk about.
458
00:49:14,200 --> 00:49:22,820
Some of the papers, this kind of codebook one or the more kind of content analysis or their reflexive organic one, which is like the big Q So it does.
459
00:49:22,820 --> 00:49:29,710
And that kind of middle that big ish q in the middle where you are some maybe predefined ideas in mind,
460
00:49:29,710 --> 00:49:36,400
but also you're open to what the participants are saying as well, which is kind of where I think the keramine paper sits in the middle.
461
00:49:36,400 --> 00:49:45,580
Yeah, I guess. Before we wrap up, do you have any other final thoughts or tips that you'd have for me, such as approaching qualitative research?
462
00:49:45,580 --> 00:49:48,220
Yeah, I guess just to be open to qualitative research,
463
00:49:48,220 --> 00:49:52,880
if you haven't done it before as a it's just I think most people that even if they haven't done it before,
464
00:49:52,880 --> 00:49:58,150
they're going to say to do find it intrinsically really interesting finding out more about their experiences,
465
00:49:58,150 --> 00:50:02,680
because it you know, compared to the questionnaire studies where you just really can't get much information
466
00:50:02,680 --> 00:50:05,890
from people about how they finding out how they're thinking about things.
467
00:50:05,890 --> 00:50:11,110
It does provide this other perspective, which I think is really valuable in so many areas of research.
468
00:50:11,110 --> 00:50:18,430
Yeah, definitely. I agree. I mean, I found that there was such a it sounds like quite a bit like a cliche, but it's such a deep insight.
469
00:50:18,430 --> 00:50:23,950
You're getting into people's experiences. And it was really interesting and informative study.
470
00:50:23,950 --> 00:50:32,920
Yeah. Thank you so much. Thanks for your advice. And yeah, I was really helpful for me and I'm sure it'll be helpful for the as well.
471
00:50:32,920 --> 00:50:37,270
Thank you. And that's it for this episode.
472
00:50:37,270 --> 00:50:42,600
Don't forget to like rare and subscribe and join us next time when we'll be talking to somebody else
473
00:50:42,600 --> 00:51:06,930
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