Methods: Teaching thematic analysis
With qualitative research methods an integral part of the psychology curriculum, questions arise of what approaches to teach, and how to teach them. We think thematic analysis (TA) offers a useful – and a relatively easy to teach and learn – basic introduction to qualitative analysis (see Braun & Clarke, 2006, 2012, 2013; Clarke & Braun, 2013); yet even teaching a fairly accessible approach like TA presents challenges in the classroom. Drawing on our experiences, and 38 responses from psychology students to a short qualitative survey on students’ experiences of qualitative and TA teaching, we explore some of the challenges of teaching TA to students new to qualitative research, and suggest strategies for overcoming these. Many of these are not specific to TA; they apply to teaching qualitative research more broadly.
What is thematic analysis?
Despite widespread use, TA has only recently started to achieve the ‘brand recognition’ held by methodologies such as grounded theory and interpretative phenomenological analysis (IPA). TA is essentially a method for identifying and analysing patterns in qualitative data. Since first being named as an approach in the 1970s (Merton, 1975), a number of different versions of thematic analysis have been proposed within psychology (e.g. Aronson, 1994; Attride-Stirling, 2001; Boyatzis, 1998; Joffe & Yardley, 2004; Tuckett, 2005), including our own (Braun & Clarke, 2006). Some authors demarcate TA as a phenomenological method (e.g. Guest et al., 2012, Joffe, 2011); we, in contrast, emphasise the theoretical flexibility of TA, and identify
it as just an analytic method, rather than a methodology, which most other qualitative approaches are. We view TA as theoretically flexible because the search for, and examination of, patterning across language does not require adherence to any particular theory of language, or explanatory meaning framework for human beings, experiences or practices. This means TA can be applied within a range of theoretical frameworks, from essentialist to constructionist; thematic discourse analysis (see Taylor & Ussher, 2001) is even possible. This theoretical independence means TA can be learned without some of the potentially bewildering (for new students) theoretical knowledge essential to many other qualitative approaches.
TA (see box) is suited to a wide range of research interests and theoretical perspectives, and is useful as a ‘basic’ method because: (a) it works with a wide range of research questions, from those about people’s experiences or understandings to those about the representation and construction of particular phenomena in particular contexts; (b) it can be used to analyse different types of data, from secondary sources such as media to transcripts of focus groups or interviews; (c) it works with large or small data-sets; and (d) it can be applied to produce data-driven or theory-driven analyses.
Challenges in teaching TA and other qualitative methods
What challenges do we face in teaching TA to novice qualitative researchers? A primary one – definitely not unique to TA – stems from the frequent domination of quantitative methods and ‘scientific’ psychology in the psychology curriculum (Gough et al., 2002). Qualitative methods are often allocated very little time on the curriculum and taught after quantitative methods, meaning students are already thoroughly inculcated into the assumptions and values of ‘scientific’ psychology, and believe that this is the ‘right’ way to do psychological research. When we introduce students to the assumptions and values of qualitative research, we can start off back-footed; we also have to teach them that there is more than one way to do research within psychology, and that qualities such as subjectivity do not produce bias that undermines the research, but are essential to good qualitative research practice.
When we surveyed students about their perceptions of qualitative research prior to teaching, roughly equal proportions had what we characterised as (a) negative perceptions (qualitative research is ‘long-winded and tedious’, not valid, ‘airy-fairy’), (b) positive perceptions (qualitative research ‘makes more sense in real-world terms’, and provides ‘rich, detailed data’ and ‘deeper insight’) or (c) mixed/neutral perceptions (some reflecting a lack of knowledge). Despite some anticipatory enthusiasm, many students bring to the qualitative classroom a lot of scepticism about the validity of qualitative research. Many (even those enthusiastic) also experience high levels of anxiety about the ‘lack of rules’ they perceive to be associated with qualitative research, and the need to rely on their own (subjective) judgement, something which they have frequently been taught is flawed.
Prior to learning about TA, most students did not know what TA was; some thought it would be ‘hard and complicated’ to understand (and do), and ‘long and laborious’; some questioned how ‘you could present results from data that was so subjective so that it holds validity’ (this last quotation illustrates well our earlier point about the challenges posed by students’ inculcation into scientific psychology). Only a handful of students reported positive (but inaccurate!) perceptions of TA (‘rich quality data could be gathered from it’) or reported that they were ‘interested to find out what it was all about!’
So,we teach qualitative research and TA in a classroom environment of often very mixed views – from sceptical, or even hostile, to enthusiastic and embracing Students learning TA not only have to learn how to do TA (the principles and procedures of TA: see box), they also have to develop an understanding of the values and assumptions of different research paradigms, as well as their own subjective positionings. They also need to engage with the topic of the dataset. Within an often short teaching time-frame, this can be a lot to expect of students; when compounded with high levels of anxiety students often feel about doing qualitative analysis, this is a tall order.
Our general principles for teaching qualitative research
Over the years we have learnt to simplify how we teach TA and other qualitative methods, and our maxim has become ‘learning by doing’. We have found approaching TA (and qualitative research) teaching in a ‘theory first, methods second’ way – the way we were taught it – can exacerbate student anxiety, and undermine students’ sense of competence and thus their engagement and learning. Understanding the practice first seems to allow space for the theory to make sense, so we now (largely) bracket questions of theory until after students have some understanding and experience of doing TA. Obviously this is viable as TA is a theoretically flexible method (Braun & Clarke, 2006); it would not work with all qualitative analytic approaches. We also make it clear that theory is an essential rather than a marginal concern.
As well as learning theory through practice, the core principles guiding how we teach qualitative research include:
I Emphasising the messy reality. We always describe and explore the practicalities of qualitative research and its often messy reality, something usually hidden in published research or textbook guides.
I Teaching from our own standpoint. We emphasise that there is no one way to map the landscape of qualitative research, and that any attempt to map this often tricky terrain always reflects a standpoint. We teach students our own map of where and how different approaches and theories relate to each other; we encourage them to develop their own map.
I Emphasising fundamental principles. We aim to teach ‘basic’ and ‘generic’ research skills and knowledge, that can be translated across different ‘named’ qualitative analysis methods. We also discuss the ways in which the varied (and often idiosyncratic) terminology employed by different methods can mask underlying similarities.
I Prioritising the practical. We prioritise giving students the chance to practically do TA or conduct an interview, rather than just giving them information they could gain from a textbook.
Strategies for effectively teaching TA
As we rarely teach TA in ideal circumstances, how can we best engage students in learning TA with limited classroom time, guided by these core principles? There are a number of key strategies, discussed below, that can be employed (and that may be of interest to those teaching other analytic methods).
Use real, primary data
Made-up or secondary (e.g. media) data are generally far less engaging than data generated from people. They also undersell qualitative research by not providing students with access to one of its most the exciting aspects – an intimate window onto the life worlds of people.
What can you do if you don’t have any data? On occasion, we have used a colleague’s data, or coordinated students generating their own data-set. The latter involves us determining a research topic and question, designing a data collection tool and all relevant research materials, and gaining ethical approval. Students are then invited to recruit 1–2 participants, and collect data from them. Certain methods of data collection – e.g. qualitative surveys, story completion tasks, vignettes – work well for this: they don’t require a particular skill set and so are not particularly demanding for the student-researcher to collect data; a participant can complete them quickly and with some anonymity; they ensure some standardisation across the data collected from multiple student-researchers; and avoid potential ethical concerns associated with asking student-researchers to interact with participants (see Braun & Clarke, 2013). The use of online survey software (e.g. Qualtrics, SurveyMonkey) makes compiling a data-set from multiple student-researchers easy and efficient. When we’ve used this approach, about two thirds of students have recruited participants, and feedback on this task is generally very positive, with many valuing the opportunity to experience different elements of the research process in a safe and structured way before undertaking their own research.
Illustrate concepts and steps of TA using worked examples
Show students what, for example, a coded data extract, codes, collated data extracts, and thematic maps look like (see Braun & Clarke, 2012, 2013), so that they get a concrete sense of what they are working towards.
Use examples of published TA studies
Provide students with examples of excellent published TA studies (sadly, many are not), and explain why they are excellent. An example we often use is Frith and Gleeson’s (2004) TA of men’s accounts of how their feelings about their bodies shape their clothing practices, because: the paper is clear and accessible; the topic has inherent interest and is not particularly threatening (important to consider as our experiences suggest that strong negative feelings about a topic can be a barrier to effective learning); the analysis both reflects and goes beyond the data collection questions, and captures contradictions and complexities in the data (two different stories about men and clothing); it locates the data in the wider social context; it includes an excellent ‘overview table’ (an alternative to a thematic map); and the length of the analysis section is roughly comparable to the length of a TA practical/lab report.
Use a reflexivity exercise
(See Braun & Clarke, 20123, Hesse-Biber, 2007.) We ask our students to spend a few minutes reflecting and making notes on two things prior to beginning analysis: (1) the assumptions, if any, they hold about the research topic; (2) their values and life experiences, and how all this might shape how they read and interpret the data. Many students struggle with this exercise, especially the second part, but come to appreciate its value after completing their analysis, as it can help them be reflective and reflexive, and to gain ‘deeper’ analytic insight into the data. To keep the task as safe and unthreatening as possible, we don’t require students to share their insights with the whole group; however, they do often discuss their observations in small group work.
Demonstrate the importance of coding
Students are often tempted to skip data familiarisation and coding and straight away identify themes in the data. But these are essential for systematic and deep engagement, to develop a rich and complex account beyond the obvious meanings in the data. Students’ initial attempts at coding often produce rather vague codes that do not ‘work’ independently of the data (e.g., ‘gender’ or ‘responding’). Failure to construct codes that successfully evoke (relevant features of) the data is problematic because themes are developed from codes, rather than directly from the data. We use a ‘remove the data’ test to demonstrate these elements, and to provide a litmus test of whether students’ coding is any ‘good’: we take away their copy of the data leaving them with just their codes.
If the codes, alone, successfully evoke the data then they work; if not, coding labels need to be refined.
Do a structured coding exercise
Getting students to code the same data extract in small groups helps consolidate their understanding of coding, and builds analytic confidence. After group coding, we compile the analytic insights and observations (codes) from each small group, identify similarities and differences in the observations made, and refine and develop the codes. Looking at and discussing others’ coding is also useful
for getting students to reflect on the assumptions underpinning different analytic observations and developing personal reflexivity.
Use out-of-classroom exercises to expedite classroom learning
For example, we use a guided study where students code a small data-set and generate initial themes, in their own time (we suggest this as a group exercise). We ask them to then bring to class: a list of their codes and relevant data extracts; a list of potential themes; and a thematic map outlining their potential themes, and the relationships between them. We then use class time in three ways: (1) reflecting on the task and addressing questions; (2) helping students to review and refine their analyses through sharing analytic insights, discussing divergences, and identifying the assumptions they have made in their analysis;
(3) discussing strategies for moving beyond summarising or describing the data to provide interpretative analysis. We find that by using the limited classroom time to extend their out-of-class theme development is useful, both for building analytic confidence and for demonstrating the potential of TA in a way that is grounded in their analytic experience.
Demonstrate ‘bad’ practice as it arises
We aim to teach the pitfalls and things you can do wrong with TA (see Braun & Clarke, 2006) with reference to practical examples (such as examples of vague codes, incoherent themes, and thin and sketchy theme definitions). The same goes for ‘good’ practices.
End the teaching with a discussion of theory
We discuss the importance of engaging with the ‘many questions of thematic analysis’ (Braun & Clarke, 2006) – such as whether we are working bottom-up from the data or reading the data through the lens of existing theory or whether we are working within an essentialist or constructionist framework.
Student views after teaching
Many students valued highly these elements of teaching: practical activities; working with real data (the most fun element of the teaching for many); group work (and learning with and from other students); the opportunity to ask questions; and seeing clear examples of
a TA and the different elements of the six phase process were all noted as helpful learning tools. Many emphasised the positive aspects of ‘being able to let us have a go ourselves at using thematic analysis rather than talking at us about what you do’. These responses suggest
the value of practice-based teaching for learning about TA and other aspects of qualitative research. Indeed, many students reported they felt more confident in their analytic abilities after teaching
(‘I feel 100 per cent more confident’), although some still lacked confidence in their analytic judgements (‘I’m a little unsure on what labels would be the best to use’). Many understood the importance of practising and doing to develop analytic skills (‘I can see that it obviously gets easier to use with practice’). However, even though we try to ‘keep it very simple’, and even worry we have ‘dumbed it down’ too much sometimes, some students still felt that there was
‘a lot of information to digest’ and TA is ‘overly complicated’. A few felt the teaching staff were too enthusiastic about TA and qualitative methods; it’s unclear what impact this might have in the classroom, as enthusiasm is usually seen positively in the teaching environment (Frenzel et al., 2009).
Although students still express doubts and concerns, a practice-based approach appears to offer a useful way to teach TA as an ideal ‘starter’ method for students (and psychology lecturers) new to qualitative research – TA is accessible, flexible, and involves analytic processes common to most forms of qualitative research. Students can progress from TA to grounded theory, IPA and discourse analysis, or progress from producing largely descriptive TA to producing rich and complex, conceptually informed TA. We encourage further discussion of strategies for effectively teaching TA and other qualitative methods and greater dialogue between those who teach quantitative and qualitative methods, with the aim of teaching research methods in ways that allow students to fully appreciate the benefits of both quantitative and qualitative approaches.
Box text - Six phases of thematic analysis
These stages (Braun & Clarke, 2006) should not be viewed as a linear model, where one cannot proceed to the next phase without completing the prior phase (correctly); rather analysis is a recursive process.
1. Familiarisation with the data: Common to all forms of qualitative analysis, the researcher must immerse themselves in, and become intimately familiar with, their data; reading and re-reading the data (and listening to audio-recorded data at least once, if relevant) and noting any initial analytic observations.
2. Coding: Also a common element of many approaches to qualitative analysis (see Braun & Clarke, 2013, for thorough comparison), this involves generating pithy labels for important features of the data of relevance to the (broad) research question guiding the analysis. Coding is not simply a method of data reduction, it is also an analytic process, so codes capture both a semantic and conceptual reading of the data. The researcher codes every data item and ends this phase by collating all their codes and relevant data extracts.
3. Searching for themes: A theme is a coherent and meaningful pattern in the data relevant to the research question. If codes are the bricks and tiles in a brick-and-tile house, then themes are the walls and roof panels. Searching for themes is a bit like coding your codes to identify similarity in the data. This ‘searching’ is an active process; themes are not hidden in the data waiting to be discovered by the intrepid researcher, rather the researcher constructs themes. The researcher ends this phase by collating all the coded data relevant to each theme.
4. Reviewing themes: Involves checking that the themes ‘work’ in relation to both the coded extracts and the full data-set. The researcher should reflect on whether the themes tell a convincing and compelling story about the data, and begin to define the nature of each individual theme, and the relationship between the themes. It may be necessary to collapse two themes together or to split a theme into two or more themes, or to discard the candidate themes altogether and begin again the process of theme development.
5. Defining and naming themes: Requires the researcher to conduct and write a detailed analysis of each theme (the researcher should ask ‘What story does this theme tell?’ and ‘How does this theme fit into the overall story about the data?’), identifying the ‘essence’ of each theme and constructing a concise, punchy and informative name for each theme.
6. Writing-up: Writing is an integral element of the analytic process in TA (and most qualitative research). Writing-up involves weaving together the analytic narrative and (vivid) data extracts to tell the reader a coherent and persuasive story about the data, and contextualising it in relation to existing literature.
Interpretative TA in action
TA is often assumed to be a method for identifying semantic patterns in data, but it can also produce sophisticated, interpretative analyses that go beyond the obvious content. Frith and Gleeson’s (2004) analysis of 75 men’s views on their bodies and clothing provides an excellent example of this. They used a qualitative survey to ask men three main questions:
1. How does the way you feel about your body influence the kinds of clothing you buy or wear?
2. Do you dress in a way that hides aspects of your body?
3. Do you dress in a way that emphasises aspects of your body?
Their analysis looked across the survey questions to identify four themes in the data. Two of the themes – ‘Men value practicality’ and ‘Men should not care about how they look’ – captured a conventional story of (heterosexual) men and clothing. However, the other two themes – ‘Clothes are used to conceal or reveal’ and ‘Clothes are used to fit a cultural ideal’ – told a more nuanced and unexpected story. Based on less obvious meanings in the data, they identified that men actively use clothing to ‘manipulate their appearance to meet cultural ideals of masculinity’ (p.45), an approach to dress more traditionally associated with women. Going beyond the semantic surface meaning in the data, their analysis captured multiple and competing stories about men and clothing. The analysis also raised questions about the social-cognitive conceptualisation of ‘body image’: ‘our data suggest that it is fluid, contradictory, and constantly renegotiated’ (p.45).
In a subsequent paper, Gleeson and Frith (2006) reconceptualised body image as ‘body imaging’, a process and activity, consistent with the principles of social constructionism. Frith and Gleeson’s work provides an excellent example of how TA can be used to provide nuanced and complex interpretations of data, to rework theoretical concepts, and to make arguments.
Victoria Clarke is Associate Professor in Sexuality Studies in the
Department of Psychology, University of the West of England,
Virginia Braun is Associate Professor in
the School of Psychology, University of Auckland, New Zealand
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