Thematic Analysis in Psychology Notes
Thematic Analysis Overview
Thematic analysis is a qualitative analytic method that is used widely in psychology.
It is an approachable and adaptable method for analysing qualitative data.
Thematic analysis is a method for finding themes or patterns within data and relating them to different epistemological and ontological positions.
This paper provides guidelines for conducting thematic analysis and considers potential pitfalls, advantages, and disadvantages.
Thematic analysis is a flexible method for qualitative research.
It is a poorly demarcated and rarely acknowledged, yet widely used qualitative analytic method.
This paper aims to fill the current gap by outlining the theory, application, and evaluation of thematic analysis.
The paper is intended to be a useful teaching and research tool in qualitative psychology.
Qualitative Approaches and Thematic Analysis
Qualitative approaches are diverse, complex, and nuanced.
Thematic analysis is a foundational method for qualitative analysis.
It is the first qualitative method of analysis that researchers should learn because it provides core skills that will be useful for conducting many other forms of qualitative analysis.
'Thematizing meanings' is identified as one of a few shared generic skills across qualitative analysis.
Thematic analysis is a tool to use across different methods, not as a specific method.
Thematic coding is a process performed within ‘major’ analytic traditions (such as grounded theory), rather than a specific approach.
Thematic analysis should be considered a method in its own right.
One of the benefits of thematic analysis is its flexibility.
Qualitative analytic methods can be roughly divided into two camps:
Those tied to a particular theoretical or epistemological position.
Methods that are essentially independent of theory and epistemology.
Thematic analysis is compatible with both essentialist and constructionist paradigms within psychology.
Thematic analysis provides a flexible and useful research tool, which can potentially provide a rich and detailed, yet complex, account of data.
It is important to have clear and concise guidelines around thematic analysis so that the ‘anything goes’ critique of qualitative research may not apply.
A clear demarcation of this method will be useful to ensure that those who use thematic analysis can make active choices about the particular form of analysis they are engaged in.
This paper seeks to celebrate the flexibility of the method and provide a vocabulary and ‘recipe’ for people to undertake thematic analysis in a way that is theoretically and methodologically sound.
Researchers need to make their (epistemological and other) assumptions explicit.
Key Elements of Thematic Analysis
Qualitative psychologists need to be clear about what they are doing and why, and to include the often-omitted ‘how’ they did their analysis in their reports.
This paper outlines:
What thematic analysis is.
A 6-phase guide to performing thematic analysis.
Potential pitfalls to avoid when doing thematic analysis.
What makes good thematic analysis.
Advantages and disadvantages of thematic analysis.
Examples from the research literature and our own research are provided.
Examples show the types of research questions and topics that thematic analysis can be used to study.
Data corpus: all data collected for a particular research project.
Data set: all the data from the corpus that are being used for a particular analysis.
The data set may consist of many, or all, individual data items within your data corpus.
The data set might be identified by a particular analytic interest in some topic in the data, and the data set then becomes all instances in the corpus where that topic is referred.
Data item: each individual piece of data collected, which together make up the data set or corpus.
Data extract: an individual coded chunk of data, which has been identified within, and extracted from, a data item.
Thematic analysis is a method for identifying, analysing, and reporting patterns (themes) within data.
It minimally organizes and describes your data set in detail.
It interprets various aspects of the research topic.
There is no clear agreement about what thematic analysis is and how you go about doing it.
It can be seen as a very poorly ‘branded’ method, in that it does not appear to exist as a ‘named’ analysis in the same way that other methods do.
It is often not explicitly claimed as the method of analysis, when, in actuality, a lot of analysis is essentially thematic.
If we do not know how people went about analysing their data, or what assumptions informed their analysis, it is difficult to evaluate their research, and to compare and/or synthesize it with other studies on that topic, and it can impede other researchers carrying out related projects in the future.
Insufficient detail is often given to reporting the process and detail of analysis.
It is not uncommon to read of themes ‘emerging’ from the data.
An account of themes ‘emerging’ or being ‘discovered’ is a passive account of the process of analysis, and it denies the active role the researcher always plays in identifying patterns/themes, selecting which are of interest, and reporting them to the readers.
The language of ‘themes emerging’ can be misinterpreted to mean that themes ‘reside’ in the data, and if we just look hard enough they will ‘emerge’ like Venus on the half shell.
Themes reside in our heads from our thinking about our data and creating links as we understand them.
We do not subscribe to a naïve realist view of qualitative research, where the researcher can simply ‘give voice’ to their participants.
Even a ‘giving voice’ approach ‘involves carving out unacknowledged pieces of narrative evidence that we select, edit, and deploy to border our arguments’.
What is important is that the theoretical framework and methods match what the researcher wants to know, and that they acknowledge these decisions, and recognize them as decisions.
Comparison with Other Analytic Methods
Thematic analysis differs from other analytic methods that seek to describe patterns across qualitative data.
IPA and grounded theory seek patterns in the data, but are theoretically bounded.
IPA is attached to a phenomenological epistemology, which gives experience primacy, and is about understanding people’s everyday experience of reality, in great detail, in order to gain an understanding of the phenomenon in question.
The goal of a grounded theory analysis is to generate a plausible and useful theory of the phenomena that is grounded in the data.
Grounded theory seems increasingly to be used in a way that is essentially grounded theory ‘lite’ as a set of procedures for coding data very much akin to thematic analysis.
A ‘named and claimed’ thematic analysis means researchers need not subscribe to the implicit theoretical commitments of grounded theory if they do not wish to produce a fully worked-up grounded-theory analysis.
The term ‘thematic DA’ is used to refer to a wide range of pattern-type analysis of data, ranging from thematic analysis within a social constructionist epistemology, to forms of analysis very much akin to the interpretative repertoire form of DA.
Thematic decomposition analysis is a specifically named form of ‘thematic’ DA, which identifies patterns (themes, stories) within data, and theorizes language as constitutive of meaning and meaning as social.
These different methods share a search for certain themes or patterns across an (entire) data set, rather than within a data item.
Thematic analysis does not require the detailed theoretical and technological knowledge of approaches, such as grounded theory and DA, it can offer a more accessible form of analysis, particularly for those early in a qualitative research career.
Thematic analysis is not wedded to any pre-existing theoretical framework, and therefore it can be used within different theoretical frameworks.
Thematic analysis can be an essentialist or realist method, which reports experiences, meanings and the reality of participants, or it can be a constructionist method, which examines the ways in which events, realities, meanings, experiences and so on are the effects of a range of discourses operating within society.
It can also be a ‘contextualist’ method, sitting between the two poles of essentialism and constructionism, and characterized by theories, such as critical realism, which acknowledge the ways individuals make meaning of their experience, and, in turn, the ways the broader social context impinges on those meanings, while retaining focus on the material and other limits of ‘reality’.
Thematic analysis can be a method that works both to reflect reality and to unpick or unravel the surface of ‘reality’.
The theoretical position of a thematic analysis is made clear, as this is all too often left unspoken (and is then typically a realist account).
Any theoretical framework carries with it a number of assumptions about the nature of the data, what they represent in terms of the ‘the world’, ‘reality’, and so forth.
A good thematic analysis will make this transparent.
Core Decisions in Thematic Analysis
Thematic analysis involves a number of choices which are often not made explicit, but which need explicitly to be considered and discussed.
In practice, these questions should be considered before analysis (and sometimes even collection) of the data begins, and there needs to be an ongoing reflexive dialogue on the part of the researcher or researchers with regards to these issues, throughout the analytic process.
What counts as a theme?
A theme captures something important about the data in relation to the research question, and represents some level of patterned response or meaning within the data set.
An important question to address in terms of coding is: what counts as a pattern/theme, or what ‘size’ does a theme need to be?
This is a question of prevalence, in terms both of space within each data item and of prevalence across the entire data set.
Ideally, there will be a number of instances of the theme across the data set, but more instances do not necessarily mean the theme itself is more crucial.
There is no hard-and-fast answer to the question of what proportion of your data set needs to display evidence of the theme for it to be considered a theme.
Researcher judgement is necessary to determine what a theme is.
You need to retain some flexibility, and rigid rules really do not work.
The ‘keyness’ of a theme is not necessarily dependent on quantifiable measures but rather on whether it captures something important in relation to the overall research question.
Prevalence can be determined in a number of different ways.
There is no right or wrong method for determining prevalence.
It is important that you are consistent in how you do this within any particular analysis.
There are various ‘conventions’ for representing prevalence in thematic analysis that does not provide a quantified measure.
Types of Analysis
It is important to determine the type of analysis you want to do, and the claims you want to make, in relation to your data set.
You might wish to provide a rich thematic description of your entire data set, so that the reader gets a sense of the predominant or important themes.
In such an analysis, some depth and complexity is necessarily lost, but a rich overall description is maintained.
This might be a particularly useful method when you are investigating an under-researched area, or you are working with participants whose views on the topic are not known.
An alternative use of thematic analysis is to provide a more detailed and nuanced account of one particular theme, or group of themes, within the data.
This might relate to a specific question or area of interest within the data, or to a particular ‘latent’ theme across the whole or majority of the data set.
Inductive versus theoretical thematic analysis
Themes or patterns within data can be identified in one of two primary ways in thematic analysis: in an inductive or ‘bottom up’ way, or in a theoretical or deductive or ‘top down’ way.
An inductive approach means the themes identified are strongly linked to the data themselves.
In this approach, if the data have been collected specifically for the research, the themes identified may bear little relation to the specific questions that were asked of the participants.
They would also not be driven by the researcher’s theoretical interest in the area or topic.
Inductive analysis is therefore a process of coding the data without trying to fit it into a pre-existing coding frame, or the researcher’s analytic preconceptions.
This form of thematic analysis is data-driven.
Researchers cannot free themselves of their theoretical and epistemological commitments, and data are not coded in an epistemological vacuum.
A ‘theoretical’ thematic analysis would tend to be driven by the researcher’s theoretical or analytic interest in the area, and is thus more explicitly analyst-driven.
This form of thematic analysis tends to provide less a rich description of the data overall, and more a detailed analysis of some aspect of the data.
You can either code for a quite specific research question (which maps onto the more theoretical approach) or the specific research question can evolve through the coding process (which maps onto the inductive approach).
Semantic or latent themes
Another decision revolves around the ‘level’ at which themes are to be identified: at a semantic or explicit level, or at a latent or interpretative level.
A thematic analysis typically focuses exclusively or primarily on one level.
With a semantic approach, the themes are identified within the explicit or surface meanings of the data, and the analyst is not looking for anything beyond what a participant has said or what has been written.
The analytic process involves a progression from description, where the data have simply been organized to show patterns in semantic content, and summarized, to interpretation, where there is an attempt to theorize the significance of the patterns and their broader meanings and implications, often in relation to previous literature.
A thematic analysis at the latent level goes beyond the semantic content of the data, and starts to identify or examine the underlying ideas, assumptions, and conceptualizations and ideologies that are theorized as shaping or informing the semantic content of the data.
For latent thematic analysis, the development of the themes themselves involves interpretative work, and the analysis that is produced is not just description, but is already theorized.
Analysis within this latter tradition tends to come from a constructionist paradigm, and in this form, thematic analysis overlaps with some forms of ‘DA’ where broader assumptions, structures and/or meanings are theorized as underpinning what is actually articulated in the data.
Epistemology: essentialist/realist versus constructionist thematic analysis
Thematic analysis can be conducted within both realist/essentialist and constructionist paradigms, although the outcome and focus will be different for each.
The question of epistemology is usually determined when a research project is being conceptualized, although epistemology may also raise its head again during analysis, when the research focus may shift to an interest in different aspects of the data.
The research epistemology guides what you can say about your data, and informs how you theorize meaning.
With an essentialist/realist approach, you can theorize motivations, experience, and meaning in a straightforward way, because a simple, largely unidirectional relationship is assumed between meaning and experience and language (language reflects and enables us to articulate meaning and experience).
From a constructionist perspective, meaning and experience are socially produced and reproduced, rather than inhering within individuals.
Therefore, thematic analysis conducted within a constructionist framework cannot and does not seek to focus on motivation or individual psychologies, but instead seeks to theorize the sociocultural contexts, and structural conditions, that enable the individual accounts that are provided.
Thematic analysis that focuses on ‘latent’ themes tends to be more constructionist, and it also tends to start to overlap with thematic DA at this point.
However, not all ‘latent’ thematic analysis is constructionist.
Qualitative Research Questions
Qualitative research involves a series of questions, and there is a need to be clear about the relationship between these different questions.
First, there is the overall research question or questions that drive the project.
Second, if data from interviews or focus groups have been collected, there are the questions that participants have responded to.
Finally, there are the questions that guide the coding and analysis of the data.
There is no necessary relationship between these three, and indeed, it is often desirable that there is a disjuncture between them.
Some of the worst examples of ‘thematic’ analysis we have read have simply used the questions put to participants as the ‘themes’ identified in the ‘analysis’, although in such instances, no analysis has really been done at all!
Thematic analysis involves the searching across a data set to find repeated patterns of meaning.
The exact form and product of thematic analysis varies, as indicated above, and so it is important that the questions outlined above are considered before and during thematic analyses.
Those approaches which consider specific aspects, latent themes and are constructionist tend to often cluster together, while those that consider meanings across the whole data set, semantic themes, and are realist, often cluster together.
There are no hard-and-fast rules in relation to this, and different combinations are possible.
What is important is that the finished product contains an account of what was done, and why.
Step-by-Step Guide to Thematic Analysis
Some of the phases of thematic analysis are similar to the phases of other qualitative research, so these stages are not necessarily all unique to thematic analysis.
The process starts when the analyst begins to notice, and look for, patterns of meaning and issues of potential interest in the data as well as during data collection.
The endpoint is the reporting of the content and meaning of patterns (themes) in the data, where ‘themes are abstract (and often fuzzy) constructs the investigators identify before, during, and after analysis’ (Ryan \ and \ Bernard, 2000: 780).
Analysis involves a constant moving back and forth between the entire data set, the coded extracts of data that you are analysing, and the analysis of the data that you are producing.
Writing is an integral part of analysis, not something that takes place at the end, as it does with statistical analyses.
Writing should begin in phase one, with the jotting down of ideas and potential coding schemes, and continue right through the entire coding/analysis process.
There are different positions regarding when you should engage with the literature relevant to your analysis, with some arguing that early reading can narrow your analytic field of vision, leading you to focus on some aspects of the data at the expense of other potentially crucial aspects.
Others argue that engagement with the literature can enhance your analysis by sensitizing you to more subtle features of the data (Tuckett, 2005).
A more inductive approach would be enhanced by not engaging with literature in the early stages of analysis, whereas a theoretical approach requires engagement with the literature prior to analysis.
Qualitative analysis guidelines are exactly that they are not rules, and, following the basic precepts, will need to be applied flexibly to fit the research questions and data (Patton, 1990).
Analysis is not a linear process of simply moving from one phase to the next.
Instead, it is more recursive process, where movement is back and forth as needed, throughout the phases.
It is also a process that develops over time (Ely \ et \ al., 1997), and should not be rushed.
Phase 1: Familiarizing Yourself with Your Data
When you engage in analysis, you may have collected the data yourself, or they may have been given to you.
If you collected them through interactive means, you will come to the analysis with some prior knowledge of the data, and possibly some initial analytic interests or thoughts.
It is vital that you immerse yourself in the data to the extent that you are familiar with the depth and breadth of the content.
Immersion usually involves ‘repeated reading’ of the data, and reading the data in an active way searching for meanings, patterns and so on.
It is ideal to read through the entire data set at least once before you begin your coding, as ideas and identification of possible patterns will be shaped as you read through.
It is important to be familiar with all aspects of your data.
At this phase, one of the reasons why qualitative research tends to use far smaller samples than, for example, questionnaire research will become apparent the reading and re-reading of data is time-consuming.
During this phase, it is a good idea to start taking notes or marking ideas for coding that you will then go back to in subsequent phases.
Once you have done this, you are ready to begin, the more formal coding process.
In essence, coding continues to be developed and defined throughout the entire analysis.
Transcription of verbal data
If you are working with verbal data, such as interviews, television programmes or political speeches, the data will need to be transcribed into written form in order to conduct a thematic analysis.
The process of transcription, while it may seen time-consuming, frustrating, and at times boring, can be an excellent way to start familiarizing yourself with the data (Riessman, 1993).
Some researchers even argue it should be seen as ‘a key phase of data analysis within interpretative qualitative methodology’ (Bird, 2005: 227), and recognized as an interpretative act, where meanings are created, rather than simply a mechanical act of putting spoken sounds on paper (Lapadat \ and \ Lindsay, 1999).
Various conventions exist for transforming spoken texts into written texts (see \ Edwards \ and \ Lampert, 1993; \ Lapadat \ and \ Lindsay, 1999).
Some systems of transcription have been developed for specific forms of analysis such as the ‘Jefferson’ system for CA (see \ Atkinson \ and \ Heritage, 1984; \ Hutchby \ and \ Wooffitt, 1998).
Thematic analysis, even constructionist thematic analysis, does not require the same level of detail in the transcript as conversation, discourse or even narrative analysis.
At a minimum it requires a rigorous and thorough ‘orthographic’ transcript a ‘verbatim’ account of all verbal \left( and \ sometimes \ nonverbal \right)
What is important is that the transcript retains the information you need, from the verbal account, and in a way which is ‘true’ to its original nature.
Punctuation added can alter the meaning of data.
The transcription convention is practically suited to the purpose of analysis (Edwards, 1993).
The time spent in transcription is not wasted, as it informs the early stages of analysis, and you will develop a far more thorough understanding of your data through having transcribed it.
The close attention needed to transcribe data may facilitate the close reading and interpretative skills needed to analyse the data (Lapadat \ and \ Lindsay, 1999).
If your data have already been, or will be, transcribed for you, it is important that you spend more time familiarising yourself with the data, and also check the transcripts back against the original audio recordings for ‘accuracy’.
Phase 2: Generating Initial Codes
Phase 2 begins when you have read and familiarized yourself with the data, and have generated an initial list of ideas about what is in the data and what is interesting about them.
This phase then involves the production of initial codes from the data.
Codes identify a feature of the data \left( semantic \ content \ or \ latent \right)
That appears interesting to the analyst, and refer to ‘the most basic segment, or element, of the raw data or information that can be assessed in a meaningful way regarding the phenomenon’ (Boyatzis, 1998: 63).
The process of coding is part of analysis (Miles \ and \ Huberman, 1994), as you are organising your data into meaningful groups (Tuckett, 2005).
Your coded data differ from the units of analysis \left( your \ themes \right), which are \left( often\right)
broader.
Your themes, which you start to develop in the next phase, are where the interpretative analysis of the data occurs, and in relation to which arguments about the phenomenon being examined are made (Boyatzis, 1998).
Coding will, to some extent, depend on whether the themes are more ‘data-driven’ or ‘theory-driven.’
Coding can be performed either manually or through a software programme (see, eg, Kelle, 2004; Seale, 2000, for \ discussion \ of \ software \ programmes).
Work systematically through the entire data set, giving full and equal attention to each data item, and identify interesting aspects in the data items that may form the basis of repeated patterns (themes) across the data set.
If coding manually, you can code your data by writing notes on the texts you are analysing, by using highlighters or coloured pens to indicate potential patterns, or by using ‘post-it’ notes to identify segments of data.
You may initially identify the codes, and then match them with data extracts that demonstrate that code, but it is important in this phase to ensure that all actual data extracts are coded, and then collated together within each code.
If using computer software, you code by tagging and naming selections of text within each data item.
Key advice for this phase:
Code for as many potential themes/patterns as possible.
Code extracts of data inclusively.
Remember that you can code individual extracts of data in as many different ‘themes’ as they fit into.
No data set is without contradiction, and a satisfactory thematic ‘map’ that you will eventually produce does not have to smooth out or ignore the tensions and inconsistencies within and across data items.
It is important to retain accounts that depart from the dominant story in the analysis, so do not ignore these in your coding.
Phase 3: Searching for Themes
Phase 3 begins when all data have been initially coded and collated, and you have a long list of the different codes that you have identified across the data set.
This phase, which re-focuses the analysis at the broader level of themes, rather than codes, involves sorting the different codes into potential themes, and collating all the relevant coded data extracts within the identified themes.
Essentially, you are starting to analyse your codes and consider how different codes may combine to form an overarching theme.
It may be helpful at this phase to use visual representations to help you sort the different codes into themes.
This is when you start thinking about the relationship between codes, between themes, and between different levels of themes.
Some initial codes may go on to form main themes, whereas others may form sub-themes, and others still may be discarded.
At this stage, you may also have a set of codes that do not seem to belong anywhere, and it is perfectly acceptable to create a ‘theme’ called ‘miscellaneous’ to house the codes possibly temporarily that do not seem to fit into your main themes.
You end this phase with a collection of candidate themes, and sub-themes, and all extracts of data that have been coded in relation to them.
At this point, you will start to have a sense of the significance of individual themes.
However, do not abandon anything at this stage, as without looking at all the extracts in detail the next phase it is uncertain whether the themes hold as they are, or whether some need to be combined, refined and separated, or discarded.
Phase 4: Reviewing Themes
Phase 4 begins when you have devised a set of candidate themes, and it involves the refinement of those themes.
During this phase, it will become evident that some candidate themes are not really themes.
Others might collapse into each other.
Other themes might need to be broken down into separate themes.
Data within themes should cohere together meaningfully, while there should be clear and identifiable distinctions between themes.
This phase involves two levels of reviewing and refining your themes.
Level one involves reviewing at the level of the coded data extracts.
This means you need to read all the collated extracts for each theme, and consider whether they appear to form a coherent pattern.
If your candidate themes do appear to form a coherent pattern, you then move on to the second level of this phase.
If your candidate themes do not fit, you will need to consider whether the theme itself is problematic, or whether some of the data extracts within it simply do not fit there in which case, you would rework your theme, creating a new theme, finding a home for those extracts that do not currently work in an already-existing theme, or discarding them from the analysis.
Level two involves a similar process, but in relation to the entire data set.
At this level, you consider the validity of individual themes in relation to the data set, but also whether your candidate thematic map ‘accurately’ reflects the meanings evident in the data set as a whole.
What counts as ‘accurate representation’ depends on your theoretical and analytic approach.
In this phase you re-read your entire data set for two purposes:
To ascertain whether the themes ‘work’ in relation to the data set.
To code any additional data within themes that has been missed in earlier coding stages.
If the thematic map works, then you moves on to the next phase.
However, if the map does not fit the data set, you need to return to further reviewing and refining of your coding until you have devised a satisfactory thematic map.
In so doing, it is possible that you will identify potential new themes, and you will need to start coding for these as well, if they are of interest and relevant.
A word of warning: as coding data and generating themes could go on ad infinitum, it is important not to get over-enthusiastic with endless re-coding.
It is impossible to provide clear guidelines on when to stop, but when your refinements are not adding anything substantial, stop!
If the process of recoding is only fine-tuning and making more nuanced a coding frame that already works ie, it fits the data well recognize this and stop.
Stop additional work as similar to editing written work if you believe any further work is a unnecessary refinement.
At the end of this phase, you should have a fairly good idea of what your different themes are, how they fit together, and the overall story they tell about the data.
Phase 5: Defining and Naming Themes
Phase 5 begins when you have a satisfactory thematic map of your data.
At this point, you then define and further refine the themes you will present for your analysis, and analyse the data within them.
By ‘define and refine’, we mean identifying the ‘essence’ of what each theme is about (as well as the themes overall), and determining what aspect of the data each theme captures.
It is important not to try and get a theme to do too much, or to be too diverse and complex.
You do this by going back to collated data extracts for each theme, and organizing them into a coherent and internally consistent account, with accompanying narrative.
It is vital that you do not just paraphrase the content of the data extracts presented, but identify what is of interest about them and why.
For each individual theme, you need to conduct and write a detailed analysis.
As well as identifying the ‘story’ that each theme tells, it is important to consider how it fits into the broader overall ‘story’ that you are telling about your data, in relation to the research question or questions, to ensure there is not too much overlap between themes.
It is necessary to consider the themes themselves, and each theme in relation to the others.
As part of the refinement, you will need to identify whether or not a theme contains any sub-themes.
Sub-themes are essentially themes-within-a-theme.
They can be useful for giving structure to a particularly large and complex theme, and also for demonstrating the hierarchy of meaning within the data.
It is important that by the end of this phase you can clearly define what your themes are and what they are not.
One test for this is to see whether you can describe the scope and content of each theme in a couple of sentences.
If not, further refinement of that theme may be needed.
This is also the point to start thinking about the names you will give them in the final analysis names need to be concise, punchy, and immediately give the reader a sense of what the theme is about.
Phase 6: Producing the Report
Phase 6 begins when you have a set of fully worked-out themes, and involves the final analysis and write-up of the report.
The task of the write-up of a thematic analysis, whether it is for publication or for a research assignment or dissertation, is to tell the complicated story of your data in a way which convinces the reader of the merit and validity of your analysis.
It is important that the analysis provides a concise, coherent, logical, non-repetitive and interesting account of the story the data tell within and across themes.
Your write-up must provide sufficient evidence of the themes within the data ie, enough data extracts to demonstrate the prevalence of the theme.
Choose particularly vivid examples, or extracts which capture the essence of the point you are demonstrating, without unnecessary complexity.
The extract should be easily identifiable as an example of the issue.
Your write-up needs to do more than just provide data extracts need to be embedded within an analytic narrative that compellingly illustrates the story you are telling about your data, and your analytic narrative needs to go beyond description of the data, and make an argument in relation to your research question.
Interpretative Analysis Details
It is difficult to specify exactly what interpretative analysis actually entails, particularly as the specifics of it will vary from study to study.
Analysts need to take a dual position: as both cultural members and cultural commentators.
Your analytic claims need to be grounded in, but go beyond, the ‘surface’ of the data, even for a ‘semantic’ level analysis.
The sorts of questions you need to be asking, towards the end phases of your analysis, include: ‘What does this theme mean?’ ‘What are the assumptions underpinning it?’ ‘What are the implications of this theme?’ ‘What conditions are likely to have given rise to it?’ ‘Why do people talk about this thing in this particular way (as opposed to other ways)?’ and ‘What is the overall story the different themes reveal about the topic?’.
These sorts of questions should guide the analysis once you have a clear sense of your thematic map.
Potential Pitfalls to Avoid
Thematic analysis is a relatively straightforward form of qualitative analysis, which does not require the same detailed theoretical and technical knowledge that approaches such as DA or CA do.
It is relatively easy to conduct a good thematic analysis on qualitative data, even when you are still learning qualitative techniques.
There are a number of things that can result in a poor analysis. In this section we identify these potential pitfalls, in the hope that they can be avoided.
The first of these is a failure to actually analyse the data at all!
Thematic analysis is not just a collection of extracts strung together with little or no analytic narrative.
Nor is it a selection of extracts with analytic comment that simply or primarily paraphrases their content.
The extracts in thematic analysis are illustrative of the analytic points the researcher makes about the data, and should be used to illustrate/support an analysis that goes beyond their specific content, to make sense of the data, and tell the reader what it does or might mean as discussed above.
A second, associated pitfall is the using of the data collection questions as the ‘themes’ that are reported.
In such a case, no analytic work has been carried out to identify themes across the entire data set, or make sense of the patterning of responses.
The third is a weak or unconvincing analysis, where the themes do not appear to work,