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Questions about thematic analysis

What’s the difference between a code and a theme?

A theme captures a common, recurring pattern across a dataset, clustered around a central organising concept. A theme tends to describe the different facets of that singular idea, demonstrating the theme’s patterning in the dataset.

Codes tend to be more specific than themes. They capture a single idea associated with a segment of data, and consist of pithy labels identifying what is of interest in the data (in relation to the research question). Codes can be conceptualised as the building-blocks that combine to create themes – so multiple codes typically are combined to create themes, during the process of TA.


What’s the difference between a subtheme and a theme?

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.


What’s the difference between a domain summary and a theme?

The difference between a theme and a domain summary is a source of frequent confusion in much published TA research. A domain summary is a summary of an area (domain) 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 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 domain summary, and the two should ideally not be confused when using our approach. More simply put, a theme identifies an area of the data and tells the reader something about it. To make things complicated, some approaches to TA do conceptualise themes as domain summaries – 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 domain 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 domain-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.

To understand more about the differences between domain 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.[VB1]



What is a central organising concept and why is it important in thematic analysis?

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.


How many themes should I have?

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 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.


I’m not sure whether to use IPA or thematic analysis – what’s the difference?

There are some differences between interpretative phenomenological analysis (IPA) and TA; however, the end result of an IPA and a TA analysis can be very similar.

Let’s consider the divergences between IPA and TA first, which result mainly from:

  1. The fact that IPA is better thought of as a methodology (a theoretically informed framework for how you do research) rather than a   method (a technique for collecting/analysing data), whereas TA is just a method.
  2. Differences of procedure between IPA and TA.

We can think of IPA as like a piece of ready-made furniture – all the design choices (colour-scheme, dimensions, materials etc.) have been made for you. As well as outlining a range of analytic procedures, IPA specifies:

  • what the ontological and epistemological underpinnings of your research are (critical realism and contextualism; see Larkin, Watts & Clifton, 2006),
  • what theoretical framework should inform your research (phenomenology),
  • what types of research questions you can ask (about people’s experiences and perspectives),
  • what sampling strategy you should use (homogenous, small N), and
  • how (ideally) you should collect data (qualitative interviews) (see Smith, Flowers & Larkin, 2009).

Obviously, we are simplifying the characteristics of IPA a bit, but the main point to take away is that IPA provides an entire framework for conducting research.

By contrast, TA is like the piece of furniture you build yourself, where you choose your colour scheme, the dimensions of the piece, the materials, etc. Because TA is just a method and the hallmark of TA is its flexibility:

  • It can be used across the epistemological and ontological spectrum (TA can be realist or constructionist),
  • It can be underpinned by phenomenology, as well as by any number of other theories,
  • It can be used to address a wide range of research questions (including questions about people’s experiences and perspectives)
  • There are no specific requirements for sampling in TA (we would generally recommend a larger N than for  PA studies because TA does not share IPA’s ideographic focus – more on this below – but some degree of homogeneity in sampling is helpful in smaller studies), and
  • It can be used to analyse most types of qualitative data (interviews, focus groups, diaries, qualitative surveys, secondary sources, vignettes, story completion tasks etc.)

IPA has a dual focus on the unique characteristics of individual participants (the idiographic focus mentioned above) and on patterning of meaning across participants. In contrast, TA focuses mainly on patterning of meaning across participants (this is not to say it can’t capture difference and divergence in data).

In terms of analytic procedures, both IPA and TA involve coding and theme development, but these processes are somewhat different for each method. Coding in TA begins after a process of data familiarisation, in which the researcher notes any initial analytic observations about each data item and the entire data-set. The researcher then codes across all of the data items. The researcher either collates the data relevant to each code as they code, or they collate all the relevant data at the end of the coding process. By contrast, coding in IPA consists of a process of ‘initial commenting’ or ‘initial noting,’ in which the researcher writes their initial analytic observations about the data on the data item (if working with interview transcripts, initial notes are usually recorded in a wide margin on the right hand side of the transcript). These initial notes are brief commentaries on the data (rather than succinct codes). This means initial noting in IPA lies somewhere between data familiarisation and coding in TA.

Another difference is that in IPA, the researcher codes their first data item then progresses to developing themes for that data item, rather than coding across the entire dataset, and then progressing to theme development. So IPA focuses on developing each stage of the analysis for each data item, before moving to the next; whereas TA involves developing each stage of analysis across the whole dataset.

With regard to types of code, IPA refers to both ‘descriptive’ and ‘conceptual’ comments and these are very similar to ‘semantic’ and ‘latent’ codes in TA .

In terms of procedures for theme development, there are two levels of theme development in IPA and one level in TA. In IPA, these are referred to as ‘emergent’ and ‘superordinate’ themes. Emergent themes are noted on the data item (if working with interview transcripts, emergent themes are usually recorded in a wide margin on the left hand side of the transcript). Superordinate themes are developed from emergent themes. Once coding and theme development is complete for each data item, the researcher develops superordinate themes across the dataset. In TA, themes are developed from the codes (and collated data), across all data items.

As a general rule, there will be a lot more emergent themes generated from an IPA compared to the number of themes generated from a TA; this means that emergent themes in IPA lie somewhere between codes and themes in a TA (somewhat akin tosubthemes in a TA). An IPA will generate roughly the same number of superordinate themes as the number of themes generated from a TA. However, another difference is that superordinate themes in an IPA simply provide an organising framework for the analysis, it is the emergent themes that are discussed in detail in the write-up.

Overall, IPA procedures help the researcher to stay close to the data, because you develop codes and themes on the actual data item, and to focus on the unique characteristics of each individual participant, because you code and develop themes for each data item in turn. By contrast, the procedures of TA help the researcher to identify patterns across the entire data-set.

Despite these procedural differences, the end result of an IPA and a phenomenologically-informed TA of interview data would potentially be quite similar – analytic procedures play some role in shaping analysis, but, in general, our conceptual lenses play a much greater role.

So what does this all mean for choosing whether to use IPA or TA? We’d recommend using TA when you want to address research questions that are not about people’s experiences or perspectives, when you want to use data that do not capture first-person accounts of personal experiences (such as focus groups or story completion tasks), and when working with larger samples. If you are asking an experiential-type question within a phenomenological framework based on the analysis of interview data – how do you choose between IPA and TA? There’s not a lot in it, but we’d recommend using IPA if you are working with a smaller sample and want to maintain a more idiographic focus, and TA if you are working with a larger sample and want to focus more on patterned meaning across the data-set.



I’m not sure whether to use grounded theory or thematic analysis – what’s the difference?

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).

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 grounded theory is 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! So 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.

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 what we call 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 then is something that is only achievable in a large research project (Pidgeon & Henwood, 1997), and is rarely used, even when a grounded theory method is claimed. A grounded theory-lite involves using the techniques of grounded theory for the development 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 – e.g, categories vs. themes – can mask the underlying similarities.

Grounded theorists are also rather keen on the development of graphic or textual models (to represent their analytic findings), and that is not something we particularly advocate for TA. (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, not just a method (which TA is). 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), 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).


  • Birks, M., & Mills, J. (2011). Grounded theory: A practical guide. London: Sage.
  • Charmaz, K. (2006). Constructing grounded theory: A practice guide through qualitative analysis. Thousand Oaks, CA: Sage.
  • Glaser, B. G., & Strauss, A. L. (1965). Awareness of dying. Chicago: Aldine.
  • Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Chicago: Aldine.
  • Pidgeon, N., & Henwood, K. (1997a). Using grounded theory in psychological research. In N. Hayes (Ed.), Doing qualitative analysis in psychology (pp. 245-273). Hove, UK: Psychology Press.



What’s the difference between constructionist thematic analysis and discourse analysis?

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: 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, 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.




What’s the difference between thematic analysis and (qualitative) content analysis?

As a starting caveat, it’s important to recognise that TA is not one approach - these are at least two very different approaches; the Braun and Clarke approach is one of these. So how you make sense of the differences between content analysis and TA, in part depends on how you define TA.

 Historically the terms 'content analysis', 'qualitative content analysis' and 'thematic analysis' have been used interchangeably to refer to very 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 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). So 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.


Are latent codes/themes better than semantic ones?

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 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.


What’s the difference between the Braun and Clarke approach to thematic analysis and other approaches?

While we would agree that it can be helpful to code data with another researcher (to bounce around ideas), this doesn’t necessarily result in ‘better’ coding (particularly in the sense of ‘more accurate’ coding), just different coding. This is why we don’t advocate the use of a coding frame, or the calculation of inter-rater reliability scores. Other approaches to TA recommend the use of a coding frame precisely because this enables the calculation of inter-reliability scores. The use of inter-rater reliability scores is underpinned by the (realist) assumption that there is an accurate reality in the data that can be captured through coding. Our approach to coding is flexible and organic, and coding should evolve throughout the coding process – to some extent our approach to coding is similar to initial coding in grounded theory (Charmaz, 2006). We understand coding as an active 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-rater reliability (and multi-independent coders) disappears. We argue that inter-rater 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’.



Why don’t "we" advocate multiple-coders and inter-rater reliability for TA?

The use of inter-rater reliability is underpinned by the (realist) assumption that there is an accurate reality in the data that can be captured through coding. Our approach to TA sees coding as flexible and organic, and coding should evolve throughout the coding process. We understand coding as an active 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-rater reliability (and multi-independent coders) disappears. We argue that inter-rater 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’.


Should I use numbers when reporting themes?

Qualitative researchers often uses 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.

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


What types of data is thematic analysis suitable for?

There is no ideal type of data for a TA analysis. TA can be used to analyse most types of qualitative data including interviews, focus groups, qualitative surveys, story completion tasks, diaries, vignettes, and a wide range of secondary sources including printed materials, online and electronic materials, and broadcast media and film.


What types of research questions is thematic analysis suitable for?

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’s 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 inter-racial adoption?)
  • People’s practices – the things they do in the world (e.g., How do married heterosexual couples manage their household finances? What role does clothing 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?)
  • 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/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 type of research question that does not suit a TA is one focused on language practice – how people say things. Language practice questions are typically associated with discursive psychological and conversation analytic approaches (and some forms of narrative analysis).

What are some of the common problems I should avoid?

There is now a lot of published TA. In looking at your own, and evaluating the TA of others, the following questions will help in identifying whether common problems in TA have been avoided:

  • Have the data actually been analysed, or have they just been reported and paraphrased? Does the analysis go beyond the content of the data to tell me something about what the data might mean, and the implications of the patterns identified? Data need to actually be analysed.
  • Does the analysis go beyond data collection questions? Does the analysis report patterns that don’t simply translate from the questions participants were asked? In general, patterns should not map directly from data collection questions
  • Does the researcher claim the themes ‘emerged’? This is a key problem, as it suggests themes ‘exist’ in the data waiting to be discovered, when analysis is really a more active process of developing themes through our interaction with the data.
  • Are there a lot of data extracts and very little analytic narrative? Or are analytic claims made without supporting evidence from the data? Aim for a good balance of data and analytic narrative.
  • Are themes weak and poorly realised? Are they thin and sketchy or overly complex and unwieldy? Do they lack coherence? Aim for themes that are coherent and have a clear and identifiable central organising concept ?
  • Is there lots of overlap between the themes? Is the relationship between the themes unclear? Do the themes fail to work together to tell a story about the data? Aim for a set of themes in which each theme distinctive from the other themes, but there is also a clear relationship between the different themes.
  • Overall, do the themes provide a rich and coherent analysis that answers the research questions? The TA needs to provide a compelling and plausible answer to the research question
  • Is the analysis plausible? Do you see what the researcher(s) claim they see in the data? You analytic claims need to be able to be ‘seen’ in the data.
  • Are the assumptions around TA – and the form of TA used – clearly spelled out? The approach you use needs to be explained.
  • Is the analysis theoretically coherent? Do the claims made about the approach to TA used (inductive vs. deductive; latent vs. semantic; essentialist vs. constructionist) and how the data are actually analysed, fit? Your use of TA needs to be theoretically coherent and consistent (e.g., if you say your analysis is deductive and constructionist, it needs to be that). Simply put, you need to do what you say you do!

How do I know if I have done a good thematic analysis?

If you follow the guidance provided for doing 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, applying an analytic eye to the data, and interpreting it light of what we already know about the issue(s) being explored.

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

  1. Transcription: The data have been transcribed to an appropriate level of detail, and the transcripts have been checked against the tapes for ‘accuracy’
  2. Coding: Each data item has been given equal attention in the coding process
  3. Themes have not been generated from a few vivid examples (an anecdotal approach), but instead the coding process has been thorough, inclusive and comprehensive
  4. All relevant extracts for all each theme have been collated
  5. Themes have been checked against each other and back to the original data set
  6. Themes are internally coherent, consistent, and distinctive
  7. Analysis: Data have been analysed – interpreted, made sense of - rather than just paraphrased or described
  8. Analysis and data match each other – the extracts illustrate the analytic claims
  9. Analysis tells a convincing and well-organised story about the data and topic
  10. A good balance between analytic narrative and illustrative extracts is provided
  11. Overall: Enough time has been allocated to complete all phases of the analysis adequately, without rushing a phase or giving it a once-over-lightly
  12. Written report: The assumptions about, and specific approach to, thematic analysis are clearly explicated
  13. There is a good fit between what you claim you do, and what you show you have done – i.e., described method and reported analysis are consistent
  14. The language and concepts used in the report are consistent with the epistemological position of the analysis
  15. The researcher is positioned as active in the research process; themes do not just ‘emerge’



Should I teach my students thematic analysis?

Yes! We teach TA to both our undergraduate and postgraduate students, and our interest in TA as a method developed from our experiences of teaching TA to our students.

In many ways TA is an ideal ‘starter’ analytic method for those new to qualitative research because it is accessible, flexible, and involves analytic processes common to most forms of qualitative research. The theoretical flexibility of TA means it can be learned without some of the potentially bewildering (for new students) theoretical knowledge essential to many other qualitative analytic approaches. In our experience, understanding the practice of qualitative (thematic) analysis first seems to allow space for the theory of qualitative research to make sense for students new to qualitative methods. Students can progress from learning TA to learning other analytic approaches such as grounded theory, IPA and discourse analysis, or progress from producing largely descriptive TA to producing rich and complex, conceptually informed TA.

We provide some guidance on teaching TA in the followingpaper: Clarke, V. & Braun, V. (2013) Teaching thematic analysis: Overcoming challenges and developing strategies for effective learning. The Psychologist, 26(2), 120-123.


Is thematic analysis sophisticated enough for a doctoral project?

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 methods, 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 identifying 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 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!).


I’ve collected 5 interviews – is that enough for a TA?

The size of your sample does depend on the size and scope of your project, but generally 5 interviews (however long and detailed) is too small a sample size for a TA. This is because TA focuses on the identification of patterns across data. We provide detailed information about sample size in our book Successful qualitative research – but in brief, we recommend a sample size of:

  • 6-10 interviews for a small TA project (e.g., UK undergraduate; NZ Honours)
  • 10-20 for a medium TA project(e.g., UK or NZ Masters; UK Professional Doctorate)
  • 30+ for a large TA project (e.g., NZ or UK PhD; NZ Professional Doctorate).

You also need to keep in mind that if you are interested in publishing your research, a smaller sample size may limit your choice of publication outlets to less prestigious journals, and journals that specialise in publishing qualitative research. More prestigious journals, North American journals and journals that don’t specialise in publishing qualitative research are likely to expect larger samples. We’ve had experiences where TA sample sizes of around 20 participants have been rejected outright from journals as being ‘too small a sample’.