FAQs

A theme captures a common, recurring pattern across a dataset, organised around a central organising concept. A theme tends to describe the different facets of a pattern across the dataset. A subtheme exists ‘underneath’ the umbrella of a theme. It shares the same central organising concept as the theme, but focuses on one notable specific element. Subthemes generally should be used sparingly, only when there is one particular element of a theme that has a particular focus, is notable, and/or is particularly important for the research question. Through naming and analysing a specific subtheme, that aspect of the theme becomes particularly salient.

The difference between a theme and a topic or domain summary is a source of frequent confusion in much published TA research. A topic summary is a summary of an area (topic) of the data; for example, a summary of everything the participants said in relation to a particular topic or interview question. Unlike themes, there isn’t anything that unifies the description of what participants said about this topic – there is no underlying concept that ties everything together and organises the analytic observations. In our reflexive approach to TA, themes are conceptualised as patterns in the data underpinned by a central concept that organises the analytic observations; this is rather different from a topic summary, and the two should ideally not be confused when using our approach. More simply put, a theme captures an aspect of patterned meaning in the data and tells the reader something about the shared meaning within it, whereas a topic summary simply summarises participant’s responses relating to a particular topic (so shared topic but not shared meaning). To make things complicated, some approaches to TA do conceptualise themes as topic summaries (but rarely do so deliberately and knowingly – with an awareness that there are other conceptualisations of themes) – this conceptualisation of themes is evident in both coding reliability approaches (see Boyatzis, 1998; Guest et al., 2012) and codebook approaches, such as template, framework and matrix analysis. Sometimes the confusion between topic summaries and themes is simply an issue of poorly named themes – the theme itself is a conceptually founded pattern, but the theme name does not reflect this. ‘Experiences of Y’ or ‘Benefits of X’ are classic examples of topic-summary type theme names. These theme names identify that, for example, ‘benefits of X’ was an important area of the data in relation to the research question(s), but they don’t communicate the essence of this theme; they don’t tell the reader something specific about these benefits and what underlying concept underpinned what the participants had to say about the benefits of X.

Literature

To understand more about the differences between topic summaries and fully realised themes, we recommend the following three papers:

Connelly, L. M., & Peltzer, J. N. (2016). Underdeveloped themes in qualitative research: Relationships with interviews and analysis. Clinical Nurse Specialist, January/February, 51-57.

DeSantis, L., & Ugarriza, D. N. (2000). The concept of theme as used in qualitative nursing research. Western Journal of Nursing Research, 22(3), 351-372.

Sandleowski, M., & Leeman, J. (2012). Writing usable qualitative health research findings. Qualitative Health Research, 22(10), 1404-1413.

We discuss this in detail in our TA book:

Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. Sage.

We have written about this in this chapter:

Clarke, V., Braun, V., Terry, G., & Hayfield N. (2019). Thematic analysis. In Liamputtong, P. (Ed.), Handbook of research methods in health and social sciences (pp. 843-860). Springer.

We also discuss the difference in this lecture.

A central organising concept captures the essence of a theme. It is an idea or concept that captures and summarises the core point of a coherent and meaningful pattern in the data. If you can identify the central organising concept of a theme, you can capture the core of what your theme is about. If you cannot do this, your theme may lack coherence.

There is an approach to TA known as ‘thematic coding’ (e.g. Gibbs, 2007; Flick, 2014; Rivas, 2018), which most often involves the use of ground theory coding techniques, and other techniques such as memoing and constant comparison, to develop themes from data (rather than the categories and concepts associated with a grounded theory analysis). This approach to TA was particularly common before there were widely cited procedures for TA, but some qualitative researchers still advocate form the use of thematic coding (e.g. Flick, 2014; Rivas, 2018).

Gibbs’ (2007) account of thematic coding presents it as a generic technique for qualitative analysis, that informs many different analytic approaches, centred on the development of “a framework of thematic ideas” (p. 38) about data. As such there are many similarities between this approach and TA. Gibbs distinguishes between data-driven (descriptive) codes (close to the respondent’s terms) and concept-driven (analytic and theoretical) codes, a distinction that maps onto the semantic/latent coding distinction in TA (he also mentions ‘categories’ as a ‘mid-point’ between descriptive and analytic codes). Similarly, his distinction between data-driven and concept-driven coding maps on to inductive and deductive approaches to TA. He argues that these approaches to coding are not exclusive and most researchers move backwards and forwards between these different approaches. Similar to the use of codebooks in coding reliability and codebook forms of TA, he advocates for the use of coding memos – a staple of grounded theory – that record the label or name of the code, who coded it (if working in a team), the definition of the code (to ensure coding is systematic and consistent) and other notes of thinking about the code (including a record of any changes). But he also emphasises the importance of a flexible approach and warns researchers “not to become too tied to the initial codes you construct.” (p. 46) He discusses grounded theory as one of the most commonly used approaches to thematic coding and identifies line-by-line coding and constant comparison as particularly useful techniques.

Flick (2014, 2018) argued that his development of the thematic coding method in the 1990s pre-dates the development of TA, but it seems that both TA and thematic coding have been in use since at least the 1980s. Flick (2014, 2018) described his method for thematic coding as combining ideas from TA and grounded theory (specifically Strauss, 1987) to develop an approach that has both inductive and a-priori features and involves processes of simultaneous data collection and analysis and the analysis of each case (e.g. interview) in turn before developing an overall theory or thematic structure. The steps involve first producing a short description of each case, which is continuously checked and modified throughout the analysis, and then developing a thematic structure of “thematic domains and categories” (p. 425) for each case using grounded theory processes of open and selective coding. This process is repeated for each of the initial cases and then the researcher checks and compares the thematic structure developed for each case to produce an overall structure for the first few cases, which is then assessed for each further case and modified if necessary. The approach Flick describes is very similar to codebook types of TA – in that there is some delimiting of focus before the analysis, and the thematic structure, like a framework or matrix, allows for an emphasis on both cases and themes (and a comparison of cases and groups of participants).

Rivas (2018) describes thematic coding as similar to the early stages of a grounded theory, but with a focus on participants’ lived experience in and of itself rather than as a precursor to theory development. Like grounded theory, Rivas’ version of thematic coding involves memo writing, constant comparative analysis, and it can also involve some concurrent data collection and analysis, it begins with data familiarisation before moving to open coding and then the more interpretative stages of category formation (through grouping open codes and defining the categories) and finally the most interpretative stage of theme development (which can involve the use of concept or thematic mapping to review the themes). Like TA proper, this version of thematic coding can involve either or both inductive and deductive coding.

Does it matter that researchers claim to use (some) grounded theory procedures, but actually produce something more akin to a TA, than a grounded theory? Particularly now that TA is a well-developed method? Is insisting on the ‘proper’ use of methods (doing what you claim to do) simply a preoccupation with the purity of method – or proceduralism (King & Brooks, 2017: 231) – “where analysts focus on meticulously following set procedures rather than responding creatively, imaginatively (and systematically) to data”? We certainly agree with critics of proceduralism that rigid rules for good practice, insisting on one true way of applying a method, and avoiding interpretation, theory and taking reflexivity seriously, often leads to qualitative research that is rather limited – merely descriptive/summative, with little or no interpretation of the meaning of the data, implicitly peppered with positivism, and lacking depth of engagement, thoughtfulness and creativity. However, the combining of grounded theory and TA in some qualitative research doesn’t seem to result from thoughtfulness and creativity, or indeed theoretical knowingness and reflexivity, rather it seems to often stem from the researchers’ lack of knowledge, and narrow methodological range. Methodological practice is contested and debated, but not necessarily in a way that is helpful for those learning about qualitative research or developing their understanding of particular techniques or philosophical concerns; productive debate requires that researchers use techniques knowingly and are able to clearly articulate the assumptions underpinning these. On balance, we think this muddling of two distinct approaches only contributes to further confusion about qualitative research.

Literature

Flick, U. (2018). An introduction to qualitative research (6th ed.). Sage.
Gibbs, G. R. (2007). Analysing qualitative data. London: Sage.
King, N., & Brooks, J. (2018). Thematic analysis in organisational research. In C. Cassell, A. L. Cunliffe & G. Grandy (Eds.), The Sage handbook of qualitative business and management research methods (pp. 219-236). Sage.
Rivas, C. (2018). Finding themes in qualitative data. In C. Seale (Ed.), Researching society and culture, 4^th ed. (pp. 429-453). Sage.
Strauss, A. L. (1987). Qualitative analysis for social scientists. Cambridge University Press.

For a start, that depends on what is meant by grounded theory! There are seemingly endless varieties and versions of grounded theory, with different theoretical underpinnings and different procedures (for a useful overview, see Birks & Mills, 2011, 2015).

One reason for the existence of many difference approaches to grounded theory is that it represents one of the first attempts to develop a systematic method for analysing qualitative data. Grounded theory was developed in the 1960s (Glaser & Strauss, 1965, 1967) in the very early days of the (re)emergence of qualitative methods in the social sciences, and a lot has changed since then! For example, one famous recommended practice in early versions of grounded theory was not engaging with the relevant literature prior to beginning data analysis, to avoid the analysis being shaped by preconceptions from existing research, rather than being truly grounded in the data (this practice has rather assumed the status of a commandment ‘Thou shalt not…’).

More than four decades later, there’s a lot of grounded theory out there! This means that at the very least a researcher should determine what grounded theories exist relevant to their topic. Furthermore, in the 1960s, when this recommendation was developed, researchers did not routinely submit ethics applications prior to beginning their research. Today, ethics applications (and often other forms of research proposals) are mandatory, and therefore some engagement with the literature is required to produce such applications. Grounded theorists now tend to recommend approaching research with an ‘open mind but not an empty head’ (see Charmaz, 2006, 2014).

Unsurprisingly, there is now a lot of discussion and debate about this and other elements of grounded theory procedure, with lots of different (competing) recommendations for how to perform a grounded theory.

We find that it’s useful to make a distinction between a full grounded theory and an abbreviated grounded theory; the latter we have dubbed grounded theory-lite. A full grounded theory requires the implementation of the full range of grounded theory procedures, including theoretical sampling, with the aim of producing a theory grounded in data. Here’s one notable difference from TA; although TA can produce conceptually-informed interpretations of data, it does not attempt to develop a theory. A full grounded theory is something that is only achievable in a larger research project where there are the resources and time to implement practices like theoretical sampling (Pidgeon & Henwood, 1997). Charmaz and Thornberg (2020) argued that what distinguishes ground theory methods from other pattern-generating approaches is the use of theoretical sampling, more than anything else. Theoretical sampling involves recurrent processes of data collection and data analysis and the developing analysis informs the selection of further participants/data sources. A grounded theory-lite involves using grounded theory coding techniques, and other techniques like memoing and constant comparison, to develop a set of categories (and concepts), and an understanding of the relationship between the various categories (and concepts) (Pidgeon & Henwood, 1997). This is probably the most frequently used form of grounded theory. The practice of TA is similar to grounded theory-lite. Both involve coding and the generation (and interpretation) of broader patterns in data. Differences in terminology – for example, categories versus themes – can mask the underlying similarities.

Grounded theorists are also rather keen on the development of graphic or textual models (to represent their analysis and category structure or the relationship between categories), and that is not something we particularly advocate for TA (which is not to say it can’t be done – this paper provides a nice example of a tentative model developed from a TA: Anderson & Clarke, 2019). (We do recommend the use of thematic maps in the theme development process, and these can be included in your appendices, if writing a dissertation/thesis.)

A key difference between grounded theory (full or lite) and TA is that like most other analytic approaches grounded theory is a methodology (a theoretically informed and delimited framework for research), not just a method (TA is closer to a method, but particular iterations of TA are delimited by particular research values and paradigmatic assumptions). All versions of grounded theory have an inbuilt theoretical framework (and ontological/epistemological assumptions), advocate the use of particular types of research question (a focus on social processes or the factors that influence particular phenomenon is common in grounded theory), and the use of particular methods of data collection (qualitative interviews are a key method, but there are other possibilities, see Charmaz, 2006, 2014), as well as the use of a particular set of analytic procedures (coding, memoing, etc.). However, the results of a contextualist grounded theory-lite and a contextualist TA could be rather similar.

So, what does all this mean for choosing grounded theory(-lite) or TA? We’d recommend using TA:

• when you want to address research questions that are not focused on social processes or influencing factors,
• when using methods other than interviewing, and/or
• when you are new to qualitative research (we think TA is more accessible and easier to implement than grounded theory).

Literature

Anderson, S., & Clarke, V. (2019). Disgust, shame and the psychosocial impact of skin picking: Evidence from an online support forum. Journal of Health Psychology, 24(13), 1773-1784.
Birks, M., & Mills, J. (2015). Grounded theory: A practical guide (2nd ed.). Sage.
Birks, M., & Mills, J. (2011). Grounded theory: A practical guide. Sage.
Charmaz, K. (2014). Constructing grounded theory (2nd ed.). Sage.
Charmaz, K. (2006). Constructing grounded theory: A practice guide through qualitative analysis. Sage.
Charmaz, K., & Thornberg, R. (2020). The pursuit of quality in grounded theory. Qualitative Research in Psychology.
Glaser, B. G., & Strauss, A. L. (1965). Awareness of dying. Aldine.
Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Aldine.
Pidgeon, N., & Henwood, K. (1997a). Using grounded theory in psychological research. In N. Hayes (Ed.), Doing qualitative analysis in psychology (pp. 245-273). Psychology Press.

We have written about the differences between grounded theory and TA in the following paper: 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. Counselling and Psychotherapy Research, 21(1), 37-47.

There is potentially little difference between a constructionist – or ‘critical’ – TA and a thematic discourse analysis (DA) – such labels could be used to describe very similar (even identical) analytic approaches.

In general, however, we view a constructionist TA as the application of our recommended analytic procedures (including the identification of codes and themes) for TA within a constructionist theoretical framework, whereas in thematic DA, codes, themes and discourses – “underlying systems of meaning” – are identified (Taylor & Ussher, 2001, p. 297), and the analytic process might be more fluid and organic. Furthermore, as with most DA approaches, thematic DA involves attention to the constructive role of language, and with multiple and shifting meanings around social objects (Taylor & Ussher, 2001). But it retains a specific interest in patterned meaning (discourses) within the dataset.

For the novice ‘critical’ researcher, TA is a far more accessible method, as it has a clearly defined set of procedures, rather than relying on less clearly defined analytic practices such as ‘craft skills’ (Potter & Wetherell, 1987). Furthermore, although constructionist or critical TA recognises the constitutive nature of language and discourse, it does not generally involve a micro analysis of language use. Therefore, it does not require a technical knowledge of language practice which is a feature of some forms of DA (see Wiggins, 2016), and which are useful for developing the nuances of a wide range of discursive approaches including thematic discourse analysis.

Critical TA can be applied flexibly within a wide range of critical frameworks, from symbolic interactionism and psychodynamism (e.g. Nicolson & Burr, 2003) to feminist poststructuralism (e.g. Farvid & Braun, 2006). It can be used to analyse almost all forms of qualitative data (and there is no ideal form of data, unlike in some versions of DA), both small and large data-sets, and to address most of the different types of research question posed by constructionist and critical researchers. This means that critical TA potentially has a wide range of uses than thematic DA.

Literature

Farvid, P., & Braun, V. (2006). ‘Most of us guys are raring to go anytime, anyplace, anywhere’: Male and female sexuality in Cleo and Cosmo. Sex Roles, 55, 295-310.
Nicolson, P., & Burr, J. (2003). What is ‘normal’ about women’s (hetero)sexual desire and orgasm?: A report of an in-depth interview study. Social Science & Medicine, 57, 1735-1745.
Potter, J., & Wetherell, M. (1987). Discourse and social psychology: Beyond attitudes and behaviour. Sage
Taylor, G. W., & Ussher, J. M. (2001). Making sense of S&M: A discourse analytic account. Sexualities, 4(3), 293-314.
Wiggins, S. (2016). Discursive psychology: Theory, method and applications. Sage.
We have written about the differences between discourse analysis and TA in the following paper:
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. Counselling and Psychotherapy Research, 21(1), 37-47.
We have also written about the differences between thematic discourse analysis and constructionist TA here:
Clarke, V., & Braun, V. (2014). Thematic analysis. In T. Teo (Ed.), Encyclopedia of critical psychology (pp. 1947-1952). Springer.
Gareth Terry reflects on his experiences of combining thematic analysis and discourse analysis in our TA book: Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. Sage.

As a starting caveat, it’s important to recognise that TA is not one approach – there are three different broad approaches (which we call ‘coding reliability’, ‘codebook’ and ‘reflexive’); Our approach is a form of reflexive TA. So how you make sense of the differences between content analysis and TA, in part depends on how you define TA (and content analysis).

Historically the terms ‘content analysis’, ‘qualitative content analysis’ and ‘thematic analysis’ have been used interchangeably to refer to often rather similar approaches to qualitative data analysis. The terms content analysis/qualitative content analysis are less popular among qualitative social researchers, particularly in countries where qualitative approaches have flourished in the last few decades, and where ‘branded’ approaches to qualitative analysis (e.g., grounded theory; interpretative phenomenological analysis) have developed and provided researchers with systematic procedures for analysing qualitative data. The term ‘thematic analysis’ is now associated with a distinctive set of procedures – the Braun and Clarke approach is the most widely used, but the coding reliability approach advocated by US researchers such as Boyatzis (1998) and Guest et al. (2012) (and Joffe in the UK) has also grown in popularity over the last decade or so. Content analysis is less clearly branded, with lots of different versions and varieties (none of which are particularly widely used, within the qualitative social/health research fields). CA and TA can be very similar/identical, or they can be very different: it depends on how people make sense of, and use, both of these methods. In practice, CA seems to have most in common with more structured coding reliability and codebook approaches to TA. This means that by and large reflexive TA offers researchers a distinctive method. Like TA, CA is seen as a method rather than a methodology; however, the theoretical underpinnings of (particular enactments of) CA are rarely discussed. Thus, CA is often understood as an atheoretical method (at the same time as positivist assumptions are often imported into the method through the use of coding reliability measures). Our view is that qualitative analysis can never be atheoretical, we always make theoretical assumptions whether acknowledged or not. For this reason we advocate for the use of TA over CA as it is understood as a theoretically flexible method not an atheoretical one.

Literature

For a more detailed discussion, see:

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. Counselling and Psychotherapy Research, 21(1), 37-47.

Within our reflexive TA approach, we are very clear that we do not conceptualise themes as ‘emerging’ from data and that the idea that they do is problematic (e.g. Braun & Clarke, 2006, 2013). This language suggests that meaning is self-evident and somehow ‘within’ the data waiting to be revealed, and that the researcher is a neutral conduit for the revelation of said meaning. In contrast, we conceptualise analysis as a situated and interactive process, reflecting both the data, the positionality of the researcher, and the context of the research itself. Our position around this is not unique or particularly radical. Researchers within a qualitative paradigm tend to treat research as a subjective process. Given that understanding, it is disingenuous to evoke a process whereby themes simply emerge, instead of being active co-productions on the part of the researcher, the data/participants and context. 

Our objection applies specifically to people who claim that ‘themes emerged’ when using our reflexive TA approach. Although this position reflects our conceptualisation around qualitative research process, it’s not universal; other qualitative analytic approaches do use the language of emergence (e.g. IPA) in a specific way, often to express the idea that themes were generated inductively from data.

We have written more about this in our TA book: Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. Sage.

The use of multiple-coders working independently to code the same data using a codebook or coding frame and the subsequent measuring of inter-coder reliability is underpinned by the (realist/positivist) assumption that there is a reality in the data that can be accurately captured through appropriate coding techniques; that meaning is fixed within data. Our approach to TA regards data meanings as open to multiple interpretations by subjective and situated researchers. We understand data coding as an subjective and reflexive process that inevitably and inescapably bears the mark of the researcher(s). With no one ‘accurate’ way to code data, the logic behind inter-coder reliability (and multi-independent coders) disappears. We argue that inter-coder reliability scores can be understood as showing that two researchers have been trained to code data in the same way, rather than that their coding is ‘accurate’ (Yardley, 2008). This doesn’t mean you can’t collaborate with other researchers in reflexive TA – but this isn’t understood as focused on achieving an accurate interpretation of data, but rather as deepening your reflexive engagement with the data.

Literature

Yardley, L. (2008). Demonstrating validity in qualitative psychology. In J. A. Smith (Ed.), Qualitative psychology: A practical guide to research methods (pp. 235-251). Sage.
We have written more about this in our TA book: Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. Sage.

The short answer here is no, it’s not incompatible per se. But it depends on how you understand and use the codebook you can generate through NVivo. So, we need a longer answer.

Reflexive TA avoids codebooks, because coding – the process of tagging with a coding label meaning-relevant segments of your dataset – is organic and open, and can and should evolve as your analytic process progresses. Your understandings will change – deepen, get more nuanced – through the coding process. Within codebook-based approaches to TA – including what we term coding reliability approaches – a codebook (or template, or framework) is developed relatively early on in the analytic process, and used to ‘identify’ relevant data associated with the codes specified. In some cases, the codebook is relatively fixed, in others, it can evolve somewhat. One aim is to use the codebook to check for/determine consistency in coding, across a dataset or across multiple coders.

In reflexive TA, once you determine that your coding process is ‘good enough’ and you’re ready to move into theme development, you often produce a listing of all your different codes, from which you start to explore possible themes. In NVivo, a codebook is generated from your codes (in previous versions of NVivo, these were called nodes). It can be used in reflexive TA in a similar way to a manually generated listing of codes – so not as a codebook that is then systematically ‘applied’ to the dataset to determine coding, but rather as a way to start processes of clustering and meaning-mapping. If you have used a lot of code layers, an important caveat is not to treat this initial layering as your analysis – you’re still a long way off that point.

What it comes down to is being a knowing researcher – understanding what the process of TA is, and what it allows (or constrains), and so how to use the technology in a way that is compatible with the values of the approach you’re using – whether reflexive TA or another TA method.

Unfortunately, there is no precise formula for determining the number of themes in a TA, as it depends on the data, the research question, the length of the analytic report, and the focus of the analysis…

A TA can range from a detailed reporting of one theme (usually with subthemes) to a comprehensive overview of dataset, in relation to your research question. In the former, you will go into more depth; in the latter, probably less.

But regardless, you don’t want your analysis to be thin, and fall victim to common errors in TA, such as paraphrasing the data, and not actually providing an interpretative analysis. If you have a report of about 10,000 words, an overview is unlikely to be able to sufficiently cover more than six themes (including subthemes) in any depth. In general, 2-6 themes (and subthemes) is about right for a single journal article, an undergraduate project, an Honours or Masters dissertation, and a single analytic chapter in a doctoral thesis.

TA comes in lots of different shapes and forms. None are inherently better than any other, but they serve different purposes. If, in your analysis, you want to present a more (critical) realist and descriptive account of participants’ experiences of eating, for example, then semantic codes are likely better than latent ones.

If you want to present a more constructionist account of the assumptions underpinning media reports of women and weight gain, then latent codes are likely better. Latent codes allow researchers to move away from the explicit and obvious content of the data. Regardless of approach however, your analysis needs to interpret the data and make sense of them for the reader.

Duncan Babbage and Gareth Terry have made available a Macro (add on for MS Word) that allows those who have coded data in MS Word using the comments function, to export the codes (comments) and associated text into a MS Excel file for developing the thematic analysis. The Macro and instructions for use are available here.

First up, it depends on what you mean by case study – as this concept is used in different ways. It can refer to the more ‘clinical’ sense and individual person focus or orientation, to the more ethnographic or anthropological – in the sense of a specific site of focus. In the latter, multiple forms of data might be collected from multiple different sources, in potentially different formats. So if you have research questions where patterning across your dataset is your focus, then it’s likely a useful method (as long as other values and assumptions align). A few people have also used TA in the more ‘clinical’ case-oriented research – but you need to explicate why TA over another method, as this is an ‘off piste’ use. Check out our resources section for examples of papers using TA across a range of different data types.

We can understand qualitative research as addressing different types of research questions, and TA can be used to address most of these, including questions about:

• Individual experiences (e.g. What is it like to experience depression? How do people make sense of being fat?)
• People’s views and opinions (e.g. What are people’s views on living near wind farms? How do people make sense of transracial adoption?)
• People’s behaviours or practices – the things they do in the world, or their accounts or perceptions of their practices (e.g. How do married heterosexual couples manage their household finances? What role do clothing and adornment play in the development of a gay or lesbian identity?)
• The reasons why people think or feel or do particular things and the factors or processes that underpin and shape particular experiences or decisions (e.g. What factors influence the decision to become a vegetarian? How do social norms shape the experience of chronic pain?)
• Identifying and exploring the rules and norms that govern particular social practices (e.g. What are the expectations and conventions governing the behaviour of new fathers? What norms and cultural values do people navigate in using online ‘dating’ sites?)
• How particular social objects are represented in particular contexts (NB a social object can be both something concrete and something abstract) (e.g. How is ‘female sexuality’ represented in men’s magazines? How is ‘the family’ represented in Christmas advertising?)
• How social objects are constructed, interrogating the discourses surrounding a particular social object (How is the notion of a ‘healthy body weight’ constructed in focus groups with young women? How do young men construct ‘masculinity’?).

What all these questions have in common is a focus on what people say, on the content of the language used in the data. The only types of research question that do not suit a TA are ones focused on language practice – how people say things – and narrative structures. Language practice questions are typically associated with discursive psychological and conversation analytic approaches (and some forms of narrative analysis). Narrative structure questions are associated with some forms of narrative analysis.

If you follow the guidance we have provided for doing reflexive TA (e.g. Braun & Clarke 2006, 2012, 2013) and use the checklist below to avoid ‘common problems’, your will have followed a robust process, and you are well on your way to doing a good TA. However, there are no guarantees. Qualitative analysis of all types relies on interpretative critical analytic skills of the researcher. Therefore, the quality of a TA also depends on your analytic insights… So doing a good TA is a combination of following a robust process, taking a theoretically coherent analytic orientation to the data, and interpreting them light of what we already know about the issue(s) being explored.

A 15-point checklist of criteria for good thematic analysis, updated from our 2006 original checklist (reproduced from Braun & Clarke, 2022)

1. The data have been transcribed to an appropriate level of detail; all transcripts have been checked against the original recordings for ‘accuracy’.
2. Each data item has been given thorough and repeated attention in the coding process.
3. The coding process has been thorough, inclusive and comprehensive; themes have not been developed from a few vivid examples (an anecdotal approach).
4. All relevant extracts for all each theme have been collated.
5. Candidate themes have been checked against coded data and back to the original dataset.
6. Themes are internally coherent, consistent, and distinctive; each theme contains a distinct central organising concept; any subthemes share the central organising concept of the theme.
7. Data have been analysed – interpreted, made sense of – rather than just summarised, described or paraphrased.
8. Analysis and data match each other – the extracts evidence the analytic claims.
9. Analysis tells a convincing and well-organised story about the data and topic; analysis addresses the research question.
10. An appropriate balance between analytic narrative and data extracts is provided.
11. Enough time has been allocated to complete all phases of the analysis adequately, without rushing a phase, or giving it a once-over-lightly (including returning to earlier phases or redoing the analysis if need be).
12. The specific approach to thematic analysis, and the particulars of the approach, including theoretical positions and assumptions, are clearly explicated.
13. There is a good fit between what was claimed, and what was done – i.e. the described method and reported analysis are consistent.
14. The language and concepts used in the report are consistent with the ontological and epistemological positions of the analysis.
15. The researcher is positioned as active in the research process; themes do not just ‘emerge’.

In addition, our more recently developed guidelines for editors and reviewers to assess submitted manuscripts provides another guideline towards quality reflexive TA.

Literature

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.
Braun, V., & Clarke, V. (2012). Thematic analysis. In H. Cooper (Ed.), Handbook of Research Methods in Psychology (Vol 2: Research Designs, pp. 57- 71). Washington, DC: APA books.
Braun, V. & Clarke, V. (2013). Successful qualitative research: A practical guide for beginners. London: Sage.
Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. Sage

It depends on what you mean by ‘check’! If you mean check to see if it is accurate or reliable, or that the person checking ‘agrees’ with how you have coding your data, then we would say no. The assumptions underpinning that type of ‘check’ are not consistent with our reflexive TA approach, which foregrounds researcher subjectivity in the coding and theme development processes. From this perspective, coding can’t be right or wrong, but it can be weaker (superficial) or stronger (complex, nuanced, insightful). Coding is always reflective of the subjectivity of the researcher, and one’s person’s subjectivity can’t be correct and another’s incorrect.

However, if you conceptualise ‘check’ as sharing your coding with an experienced supervisor (say) and using this as an opportunity to reflect on how you have coded the data, the assumptions you’re making in coding the data, and things you might have overlooked, then this is a good idea. Working with a more experienced researcher can help us to develop our skills in coding and theme development.

Qualitative researchers often use expressions like ‘a common theme…’, ‘in a majority of the texts analysed…’, or ‘many participants commented that…’ when reporting TA. But wouldn’t it be better to use expressions like ‘25/34 participants…’ or ‘45% of the texts…’? Although occasionally reporting percentages or frequencies is useful, in general we argue no, it not better to report percentages or frequencies. As the reason for this stance isn’t necessarily obvious, and can be a big issue, especially as those outside a qualitative paradigm often think that indicating the actual number of participants/data items or proportion of the dataset reporting a theme provides more robust evidence and validity for a qualitative analysis, we will explain our position.

There are lots of reasons:

• It reflects an anxiety about the validity of qualitative research practice, to some extent suggesting that somehow our analysis might not be real (i.e., it might be ‘made up’), or our themes might be ‘anecdotal’ rather than patterned. But all research (qualitative or quantitative) relies on trust, honesty, and good research practices. Reporting actual numbers does not circumvent this issue.
• We agree with Australian health researcher Prisicilla Pyett (2003) who argued that “counting responses misses the point of qualitative research” (p. 1174), as frequency does not determine value.
• Moreover, whether something is insightful or important for answering our research questions is not necessarily determined by whether large numbers of people said it.
• Finally, because of the nature of qualitative data, we cannot assume what the absence of a certain meaning or theme in the data actually means. Consider the difference between a quantitative survey, and qualitative data collected in interview or focus group. In a quantitative survey, you ask people to select from a number of options. Reporting, and comparing, the proportions who select each of a series of response options is meaningful, because they have all been asked the same thing, and given the same response options. With an interview or focus group, the data generated from each participant can be quite different. Because, for example interviews are fluid, flexible, and interactive data collection tools, it’s not the case that every participant in an interview study discusses exactly the same issues. So if someone doing an interview study with 20 men reported that ‘12 of the men thought …’, we can’t assume that the remaining eight men didn’t think this, or thought the opposite – they may have just not discussed it. So we have no way of interpreting what is not reported in qualitative data, and this makes reporting numerical proportions somewhat deceptive and disingenuous.

Literature

Pyett, P. M. (2003). Validation of Qualitative Research in the “Real World”. Qualitative Health Research, 13(8), 1170-1179.

From a quality measure, it’s better to describe things in your own words, rather than hours. However, if you want to reproduce something and it’s clearly beyond the ‘fair use’ principles for academia, please don’t email us to ask permission. If you want to reproduce something like the table laying out phases or quality measures, you need to seek permission from the publisher. In academic publishing, the copyright sits with the publisher, rather than the author. So, find out who the publisher is, and what their permissions-seeking process is. Usually, this takes some time, and producing a synopsis in your own words will be simpler :-).

We sometimes hear from students and supervisors that TA is not perceived as sophisticated enough for doctoral level research, which is why we have included a FAQ on this issue. TA isn’t inherently sophisticated or unsophisticated. Like with all qualitative analytic approaches, what really matters, is how you implement it. We emphasise the word ‘you’ because the active role of the researcher is key to the successful implementation of any qualitative analytic approach.

We have puzzled over why people perceive TA as unsophisticated and we think this view is based on a fundamental misunderstanding of TA. TA is often assumed to be a realist/essentialist method or simply an atheoretical method for describing patterns in data. But this is not the case! TA is theoretically flexible, which means it can be used across a wide range of theoretical approaches. What is the key here is that the researcher chooses which theoretical framework will inform their use of TA. TA can be used to produce relatively straightforward descriptive overviews of the key features of the semantic content of data (within a critical realist/essentialist framework) or complex and sophisticated conceptual interrogations of the underlying meaning in data (within say a constructionist framework).

As this indicates, TA is not one thing – it can be used to produce a relatively straightforward (what some might call ‘unsophisticated’) analysis or one that is as sophisticated as an IPA, grounded theory or discourse analysis. So yes, TA definitely provides the tools to do analysis sophisticated enough for a doctoral project (assuming it’s used well!).