What Makes Good Thematic Analysis?

What makes good thematic analysis?

One of the criticisms of qualitative research from those outside the field is the perception that anything goes‟. For instance, this sentiment is echoed in the first sentence of Laubschagne‟s (2003) abstract: “For many scientists used to doing quantitative studies the whole concept of qualitative research is unclear, almost foreign, or ‘airy fairy’ – not ‘real’ research”. However, although

„qualitative‟ research cannot be subjected to the same criteria as „quantitative‟ approaches, it does provide methods of analysis that should be applied rigorously to the data. Furthermore, criteria for conducting good qualitative research – both data collection and analysis – do exist (e.g., Elliott, Fischer, & Rennie, 1999; Parker, 2004; Seale, 1999; Silverman, 2000; Yardley, 2000). The British Psychological Society offers relatively succinct online guidelines for assessing quality in qualitative research (see  http://www.bps.org.uk/publications/journals/joop/qualitative-guidelines.cfm).

Criteria for assessing qualitative research is a not uncontroversial topic, with concerns raised about rigid criteria limiting freedom and stifling methodological development (Elliott et al., 1999;Parker, 2004; Reicher, 2000). Reicher (2000) takes the critique further, by asking whether the incredibly diverse range of qualitative approaches can and should be subject to the same criteria.

Bracketing these critiques off, the issues raised in many general qualitative research assessment criteria can be more or less applied to thematic forms of analysis. As thematic analysis is a flexible method, you also need to be clear and explicit about what you are doing, and what you say you are doing needs to match up with what you actually do. In this sense, the theory and method need to be applied rigorously, and “rigour lies in devising a systematic method whose assumptions are congruent with the way one conceptualises the subject matter” (Reicher & Taylor, 2005: 549). A concise checklist of criteria to consider when determining whether you have generated a good thematic analysis is provided in Table 2.

So what does thematic analysis offer psychologists?

We now end this paper with some brief comments on the advantages and disadvantages of thematic analysis. As we have shown throughout this paper, thematic analysis is not a complex method.

Indeed, as you can see from Table 3, its advantages are many. However, it is not without some disadvantages, which we will now briefly consider. Many of the disadvantages depend more on poorly conducted analyses or inappropriate research question, than on the method itself. Further, the flexibility of the method – which allows for a wide range of analytic options – means that the potential range of things that can be said about your data is broad. While this is an advantage, it can also be a disadvantage in that it makes developing specific guidelines for higher-phase analysis difficult, and can be potentially paralysing to the researcher trying to decide what aspects of their data to focus on. Another issue to consider is that a thematic analysis has limited interpretative power beyond mere description if it is not used within an existing theoretical framework that anchors the analytic claims that are made.

Other disadvantages appear when you consider thematic analysis in relation to some of the other qualitative analytic methods. For instance, unlike narrative or other biographical approaches, you are unable to retain a sense of continuity and contradiction through any one individual account, and these contradictions and consistencies across individual accounts may be revealing. In contrast to methods like DA and CA, a simple thematic analysis does not allow the researcher to make claims about language use, or the fine-grained functionality of talk.

Finally, it is worth noting that thematic analysis currently has no particular kudos as an analytic method – this, we argue, stems from the very fact that it is poorly demarcated and claimed, yet widely used. This means that thematic analysis is often, or appears often to be, what is simply done by someone without the knowledge or skills to perform a supposedly more sophisticated – certainly more kudos-bearing – „branded‟ form of analysis like grounded theory, IPA or DA. We hope this paper will change this view, as, as we have argued, a rigorous thematic approach can produce an insightful analysis that answers particular research questions. What is important is choosing a method that is appropriate to your research question, rather than falling victim to „methodolatry‟, where you are committed to method rather than topic/content or research questions (Holloway & Todres, 2003). Indeed, your method of analysis should be driven by both your research question and your broader theoretical assumptions. As we have demonstrated, thematic analysis is a flexible approach that can be used across a range of epistemologies and research questions.

Table 1: Phases of Thematic Analysis

Description of the process

  1. Familiarising yourself with your data: Transcribing data (if necessary), reading and re-reading the data, noting down initial ideas.

  1. Generating initial codes: Coding interesting features of the data in a systematic fashion across the entire data set, collating data relevant to each code.

  1. Searching for themes: Collating codes into potential themes, gathering all data relevant to each potential theme.

  2. Reviewing themes: Checking in the themes work in relation to the coded extracts (Level 1) and the entire data set (Level 2), generating a thematic „map‟ of the analysis.

  1. Defining and naming themes: Ongoing analysis to refine the specifics of each theme, and the overall story the analysis tells; generating clear definitions and names for each theme.

  1. Producing the report: The final opportunity for analysis. Selection of vivid, compelling extract examples, final analysis of selected extracts, relating back of the analysis to the research question and literature, producing a scholarly report of the analysis.

Table 2: A 15-Point Checklist of Criteria for Good Thematic Analysis






The data have been transcribed to an appropriate level of detail, and the transcripts have been checked against the tapes for „accuracy‟.



Each data item has been given equal attention in the coding process.


Themes have not been generated from a few vivid examples (an anecdotal approach), but instead the coding process has been thorough, inclusive and comprehensive.


All relevant extracts for all each theme have been collated.


Themes have been checked against each other and back to the original data set.


Themes are internally coherent, consistent, and distinctive.



Data have been analysed – interpreted, made sense of – rather than just paraphrased or described.


Analysis and data match each other – the extracts illustrate the analytic claims.


Analysis tells a convincing and well-organised story about the data and topic.


A good balance between analytic narrative and illustrative extracts is provided.



Enough time has been allocated to complete all phases of the analysis adequately, without rushing a phase or giving it a once-over-lightly.

Written report


The assumptions about, and specific approach to, thematic analysis are clearly explicated.


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.


The language and concepts used in the report are consistent with the epistemological position of the analysis.


The researcher is positioned as active in the research process; themes do not

just „emerge‟.

Table 3: Advantages of Thematic Analysis


Relatively easy and quick method to learn, and do.

Accessible to researchers with little or no experience of qualitative research.

Results are generally accessible to educated general public.

Useful method for working within participatory research paradigm, with participants as collaborators.

Can usefully summarise key features of a large body of data, and/or offer a „thick description‟ of the data set.

Can highlight similarities and differences across the data set.

Can generate unanticipated insights.

Allows for social as well as psychological interpretations of data.

Can be useful for producing qualitative analyses suited to informing policy development.

Data extract

Coded for

it’s too much like hard work I mean how much paper have you got to sign to change a flippin‟ name no I I mean no I no we we have thought about it ((inaudible)) half heartedly and thought no no I jus- I can‟t be bothered, it‟s too much like hard work. (Kate F07a)

  1. Talked about with partner

  1. Too much hassle to change name

Figure 1: Data extract, with codes applied (from Clarke, Burns, & Burgoyne, 2005).