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  • How to Do Thematic Analysis | Step-by-Step Guide & Examples

How to Do Thematic Analysis | Step-by-Step Guide & Examples

Published on September 6, 2019 by Jack Caulfield . Revised on June 22, 2023.

Thematic analysis is a method of analyzing qualitative data . It is usually applied to a set of texts, such as an interview or transcripts . The researcher closely examines the data to identify common themes – topics, ideas and patterns of meaning that come up repeatedly.

There are various approaches to conducting thematic analysis, but the most common form follows a six-step process: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. Following this process can also help you avoid confirmation bias when formulating your analysis.

This process was originally developed for psychology research by Virginia Braun and Victoria Clarke . However, thematic analysis is a flexible method that can be adapted to many different kinds of research.

Table of contents

When to use thematic analysis, different approaches to thematic analysis, step 1: familiarization, step 2: coding, step 3: generating themes, step 4: reviewing themes, step 5: defining and naming themes, step 6: writing up, other interesting articles.

Thematic analysis is a good approach to research where you’re trying to find out something about people’s views, opinions, knowledge, experiences or values from a set of qualitative data – for example, interview transcripts , social media profiles, or survey responses .

Some types of research questions you might use thematic analysis to answer:

  • How do patients perceive doctors in a hospital setting?
  • What are young women’s experiences on dating sites?
  • What are non-experts’ ideas and opinions about climate change?
  • How is gender constructed in high school history teaching?

To answer any of these questions, you would collect data from a group of relevant participants and then analyze it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large data sets more easily by sorting them into broad themes.

However, it also involves the risk of missing nuances in the data. Thematic analysis is often quite subjective and relies on the researcher’s judgement, so you have to reflect carefully on your own choices and interpretations.

Pay close attention to the data to ensure that you’re not picking up on things that are not there – or obscuring things that are.

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Once you’ve decided to use thematic analysis, there are different approaches to consider.

There’s the distinction between inductive and deductive approaches:

  • An inductive approach involves allowing the data to determine your themes.
  • A deductive approach involves coming to the data with some preconceived themes you expect to find reflected there, based on theory or existing knowledge.

Ask yourself: Does my theoretical framework give me a strong idea of what kind of themes I expect to find in the data (deductive), or am I planning to develop my own framework based on what I find (inductive)?

There’s also the distinction between a semantic and a latent approach:

  • A semantic approach involves analyzing the explicit content of the data.
  • A latent approach involves reading into the subtext and assumptions underlying the data.

Ask yourself: Am I interested in people’s stated opinions (semantic) or in what their statements reveal about their assumptions and social context (latent)?

After you’ve decided thematic analysis is the right method for analyzing your data, and you’ve thought about the approach you’re going to take, you can follow the six steps developed by Braun and Clarke .

The first step is to get to know our data. It’s important to get a thorough overview of all the data we collected before we start analyzing individual items.

This might involve transcribing audio , reading through the text and taking initial notes, and generally looking through the data to get familiar with it.

Next up, we need to code the data. Coding means highlighting sections of our text – usually phrases or sentences – and coming up with shorthand labels or “codes” to describe their content.

Let’s take a short example text. Say we’re researching perceptions of climate change among conservative voters aged 50 and up, and we have collected data through a series of interviews. An extract from one interview looks like this:

In this extract, we’ve highlighted various phrases in different colors corresponding to different codes. Each code describes the idea or feeling expressed in that part of the text.

At this stage, we want to be thorough: we go through the transcript of every interview and highlight everything that jumps out as relevant or potentially interesting. As well as highlighting all the phrases and sentences that match these codes, we can keep adding new codes as we go through the text.

After we’ve been through the text, we collate together all the data into groups identified by code. These codes allow us to gain a a condensed overview of the main points and common meanings that recur throughout the data.

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Next, we look over the codes we’ve created, identify patterns among them, and start coming up with themes.

Themes are generally broader than codes. Most of the time, you’ll combine several codes into a single theme. In our example, we might start combining codes into themes like this:

At this stage, we might decide that some of our codes are too vague or not relevant enough (for example, because they don’t appear very often in the data), so they can be discarded.

Other codes might become themes in their own right. In our example, we decided that the code “uncertainty” made sense as a theme, with some other codes incorporated into it.

Again, what we decide will vary according to what we’re trying to find out. We want to create potential themes that tell us something helpful about the data for our purposes.

Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the data set and compare our themes against it. Are we missing anything? Are these themes really present in the data? What can we change to make our themes work better?

If we encounter problems with our themes, we might split them up, combine them, discard them or create new ones: whatever makes them more useful and accurate.

For example, we might decide upon looking through the data that “changing terminology” fits better under the “uncertainty” theme than under “distrust of experts,” since the data labelled with this code involves confusion, not necessarily distrust.

Now that you have a final list of themes, it’s time to name and define each of them.

Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data.

Naming themes involves coming up with a succinct and easily understandable name for each theme.

For example, we might look at “distrust of experts” and determine exactly who we mean by “experts” in this theme. We might decide that a better name for the theme is “distrust of authority” or “conspiracy thinking”.

Finally, we’ll write up our analysis of the data. Like all academic texts, writing up a thematic analysis requires an introduction to establish our research question, aims and approach.

We should also include a methodology section, describing how we collected the data (e.g. through semi-structured interviews or open-ended survey questions ) and explaining how we conducted the thematic analysis itself.

The results or findings section usually addresses each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question.

In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents’ perceptions.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Normal distribution
  • Measures of central tendency
  • Chi square tests
  • Confidence interval
  • Quartiles & Quantiles
  • Cluster sampling
  • Stratified sampling
  • Discourse analysis
  • Cohort study
  • Peer review
  • Ethnography

Research bias

  • Implicit bias
  • Cognitive bias
  • Conformity bias
  • Hawthorne effect
  • Availability heuristic
  • Attrition bias
  • Social desirability bias

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Thematic Analysis – A Guide with Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On August 29, 2023

Thematic analysis is one of the most important types of analysis used for qualitative data . When researchers have to analyse audio or video transcripts, they give preference to thematic analysis. A researcher needs to look keenly at the content to identify the context and the message conveyed by the speaker.

Moreover, with the help of this analysis, data can be simplified.  

Importance of Thematic Analysis

Thematic analysis has so many unique and dynamic features, some of which are given below:

Thematic analysis is used because:

  • It is flexible.
  • It is best for complex data sets.
  • It is applied to qualitative data sets.
  • It takes less complexity compared to other theories of analysis.

Intellectuals and researchers give preference to thematic analysis due to its effectiveness in the research.

How to Conduct a Thematic Analysis?

While doing any research , if your data and procedure are clear, it will be easier for your reader to understand how you concluded the results . This will add much clarity to your research.

Understand the Data

This is the first step of your thematic analysis. At this stage, you have to understand the data set. You need to read the entire data instead of reading the small portion. If you do not have the data in the textual form, you have to transcribe it.

Example: If you are visiting an adult dating website, you have to make a data corpus. You should read and re-read the data and consider several profiles. It will give you an idea of how adults represent themselves on dating sites. You may get the following results:

I am a tall, single(widowed), easy-going, honest, good listener with a good sense of humor. Being a handyperson, I keep busy working around the house, and I also like to follow my favourite hockey team on TV or spoil my two granddaughters when I get the chance!! Enjoy most music except Rap! I keep fit by jogging, walking, and bicycling (at least three times a week). I have travelled to many places and RVD the South-West U.S., but I would now like to find that special travel partner to do more travel to warm and interesting countries. I now feel it’s time to meet a nice, kind, honest woman who has some of the same interests as I do; to share the happy times, quiet times, and adventures together

I enjoy photography, lapidary & seeking collectibles in the form of classic movies & 33 1/3, 45 & 78 RPM recordings from the 1920s, ’30s & ’40s. I am retired & looking forward to travelling to Canada, the USA, the UK & Europe, China. I am unique since I do not judge a book by its cover. I accept people for who they are. I will not demand or request perfection from anyone until I am perfect, so I guess that means everyone is safe. My musical tastes range from Classical, big band era, early jazz, classic ’50s & 60’s rock & roll & country since its inception.

Development of Initial Coding:

At this stage, you have to do coding. It’s the essential step of your research . Here you have two options for coding. Either you can do the coding manually or take the help of any tool. A software named the NOVIC is considered the best tool for doing automatic coding.

For manual coding, you can follow the steps given below:

  • Please write down the data in a proper format so that it can be easier to proceed.
  • Use a highlighter to highlight all the essential points from data.
  • Make as many points as possible.
  • Take notes very carefully at this stage.
  • Apply themes as much possible.
  • Now check out the themes of the same pattern or concept.
  • Turn all the same themes into the single one.

Example: For better understanding, the previously explained example of Step 1 is continued here. You can observe the coded profiles below:

Make Themes

At this stage, you have to make the themes. These themes should be categorised based on the codes. All the codes which have previously been generated should be turned into themes. Moreover, with the help of the codes, some themes and sub-themes can also be created. This process is usually done with the help of visuals so that a reader can take an in-depth look at first glance itself.

Extracted Data Review

Now you have to take an in-depth look at all the awarded themes again. You have to check whether all the given themes are organised properly or not. It would help if you were careful and focused because you have to note down the symmetry here. If you find that all the themes are not coherent, you can revise them. You can also reshape the data so that there will be symmetry between the themes and dataset here.

For better understanding, a mind-mapping example is given here:

Extracted Data

Reviewing all the Themes Again

You need to review the themes after coding them. At this stage, you are allowed to play with your themes in a more detailed manner. You have to convert the bigger themes into smaller themes here. If you want to combine some similar themes into a single theme, then you can do it. This step involves two steps for better fragmentation. 

You need to observe the coded data separately so that you can have a precise view. If you find that the themes which are given are following the dataset, it’s okay. Otherwise, you may have to rearrange the data again to coherence in the coded data.

Corpus Data

Here you have to take into consideration all the corpus data again. It would help if you found how themes are arranged here. It would help if you used the visuals to check out the relationship between them. Suppose all the things are not done accordingly, so you should check out the previous steps for a refined process. Otherwise, you can move to the next step. However, make sure that all the themes are satisfactory and you are not confused.

When all the two steps are completed, you need to make a more précised mind map. An example following the previous cases has been given below:

Corpus Data

Define all the Themes here

Now you have to define all the themes which you have given to your data set. You can recheck them carefully if you feel that some of them can fit into one concept, you can keep them, and eliminate the other irrelevant themes. Because it should be precise and clear, there should not be any ambiguity. Now you have to think about the main idea and check out that all the given themes are parallel to your main idea or not. This can change the concept for you.

The given names should be so that it can give any reader a clear idea about your findings. However, it should not oppose your thematic analysis; rather, everything should be organised accurately.

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Also, read about discourse analysis , content analysis and survey conducting . we have provided comprehensive guides.

Make a Report

You need to make the final report of all the findings you have done at this stage. You should include the dataset, findings, and every aspect of your analysis in it.

While making the final report , do not forget to consider your audience. For instance, you are writing for the Newsletter, Journal, Public awareness, etc., your report should be according to your audience. It should be concise and have some logic; it should not be repetitive. You can use the references of other relevant sources as evidence to support your discussion.  

Frequently Asked Questions

What is meant by thematic analysis.

Thematic Analysis is a qualitative research method that involves identifying, analyzing, and interpreting recurring themes or patterns in data. It aims to uncover underlying meanings, ideas, and concepts within the dataset, providing insights into participants’ perspectives and experiences.

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Practical thematic analysis: a guide for multidisciplinary health services research teams engaging in qualitative analysis

  • Related content
  • Peer review
  • Catherine H Saunders , scientist and assistant professor 1 2 ,
  • Ailyn Sierpe , research project coordinator 2 ,
  • Christian von Plessen , senior physician 3 ,
  • Alice M Kennedy , research project manager 2 4 ,
  • Laura C Leviton , senior adviser 5 ,
  • Steven L Bernstein , chief research officer 1 ,
  • Jenaya Goldwag , resident physician 1 ,
  • Joel R King , research assistant 2 ,
  • Christine M Marx , patient associate 6 ,
  • Jacqueline A Pogue , research project manager 2 ,
  • Richard K Saunders , staff physician 1 ,
  • Aricca Van Citters , senior research scientist 2 ,
  • Renata W Yen , doctoral student 2 ,
  • Glyn Elwyn , professor 2 ,
  • JoAnna K Leyenaar , associate professor 1 2
  • on behalf of the Coproduction Laboratory
  • 1 Dartmouth Health, Lebanon, NH, USA
  • 2 Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
  • 3 Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
  • 4 Jönköping Academy for Improvement of Health and Welfare, School of Health and Welfare, Jönköping University, Jönköping, Sweden
  • 5 Highland Park, NJ, USA
  • 6 Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, MO, USA
  • Correspondence to: C H Saunders catherine.hylas.saunders{at}dartmouth.edu
  • Accepted 26 April 2023

Qualitative research methods explore and provide deep contextual understanding of real world issues, including people’s beliefs, perspectives, and experiences. Whether through analysis of interviews, focus groups, structured observation, or multimedia data, qualitative methods offer unique insights in applied health services research that other approaches cannot deliver. However, many clinicians and researchers hesitate to use these methods, or might not use them effectively, which can leave relevant areas of inquiry inadequately explored. Thematic analysis is one of the most common and flexible methods to examine qualitative data collected in health services research. This article offers practical thematic analysis as a step-by-step approach to qualitative analysis for health services researchers, with a focus on accessibility for patients, care partners, clinicians, and others new to thematic analysis. Along with detailed instructions covering three steps of reading, coding, and theming, the article includes additional novel and practical guidance on how to draft effective codes, conduct a thematic analysis session, and develop meaningful themes. This approach aims to improve consistency and rigor in thematic analysis, while also making this method more accessible for multidisciplinary research teams.

Through qualitative methods, researchers can provide deep contextual understanding of real world issues, and generate new knowledge to inform hypotheses, theories, research, and clinical care. Approaches to data collection are varied, including interviews, focus groups, structured observation, and analysis of multimedia data, with qualitative research questions aimed at understanding the how and why of human experience. 1 2 Qualitative methods produce unique insights in applied health services research that other approaches cannot deliver. In particular, researchers acknowledge that thematic analysis is a flexible and powerful method of systematically generating robust qualitative research findings by identifying, analysing, and reporting patterns (themes) within data. 3 4 5 6 Although qualitative methods are increasingly valued for answering clinical research questions, many researchers are unsure how to apply them or consider them too time consuming to be useful in responding to practical challenges 7 or pressing situations such as public health emergencies. 8 Consequently, researchers might hesitate to use them, or use them improperly. 9 10 11

Although much has been written about how to perform thematic analysis, practical guidance for non-specialists is sparse. 3 5 6 12 13 In the multidisciplinary field of health services research, qualitative data analysis can confound experienced researchers and novices alike, which can stoke concerns about rigor, particularly for those more familiar with quantitative approaches. 14 Since qualitative methods are an area of specialisation, support from experts is beneficial. However, because non-specialist perspectives can enhance data interpretation and enrich findings, there is a case for making thematic analysis easier, more rapid, and more efficient, 8 particularly for patients, care partners, clinicians, and other stakeholders. A practical guide to thematic analysis might encourage those on the ground to use these methods in their work, unearthing insights that would otherwise remain undiscovered.

Given the need for more accessible qualitative analysis approaches, we present a simple, rigorous, and efficient three step guide for practical thematic analysis. We include new guidance on the mechanics of thematic analysis, including developing codes, constructing meaningful themes, and hosting a thematic analysis session. We also discuss common pitfalls in thematic analysis and how to avoid them.

Summary points

Qualitative methods are increasingly valued in applied health services research, but multidisciplinary research teams often lack accessible step-by-step guidance and might struggle to use these approaches

A newly developed approach, practical thematic analysis, uses three simple steps: reading, coding, and theming

Based on Braun and Clarke’s reflexive thematic analysis, our streamlined yet rigorous approach is designed for multidisciplinary health services research teams, including patients, care partners, and clinicians

This article also provides companion materials including a slide presentation for teaching practical thematic analysis to research teams, a sample thematic analysis session agenda, a theme coproduction template for use during the session, and guidance on using standardised reporting criteria for qualitative research

In their seminal work, Braun and Clarke developed a six phase approach to reflexive thematic analysis. 4 12 We built on their method to develop practical thematic analysis ( box 1 , fig 1 ), which is a simplified and instructive approach that retains the substantive elements of their six phases. Braun and Clarke’s phase 1 (familiarising yourself with the dataset) is represented in our first step of reading. Phase 2 (coding) remains as our second step of coding. Phases 3 (generating initial themes), 4 (developing and reviewing themes), and 5 (refining, defining, and naming themes) are represented in our third step of theming. Phase 6 (writing up) also occurs during this third step of theming, but after a thematic analysis session. 4 12

Key features and applications of practical thematic analysis

Step 1: reading.

All manuscript authors read the data

All manuscript authors write summary memos

Step 2: Coding

Coders perform both data management and early data analysis

Codes are complete thoughts or sentences, not categories

Step 3: Theming

Researchers host a thematic analysis session and share different perspectives

Themes are complete thoughts or sentences, not categories

Applications

For use by practicing clinicians, patients and care partners, students, interdisciplinary teams, and those new to qualitative research

When important insights from healthcare professionals are inaccessible because they do not have qualitative methods training

When time and resources are limited

Fig 1

Steps in practical thematic analysis

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We present linear steps, but as qualitative research is usually iterative, so too is thematic analysis. 15 Qualitative researchers circle back to earlier work to check whether their interpretations still make sense in the light of additional insights, adapting as necessary. While we focus here on the practical application of thematic analysis in health services research, we recognise our approach exists in the context of the broader literature on thematic analysis and the theoretical underpinnings of qualitative methods as a whole. For a more detailed discussion of these theoretical points, as well as other methods widely used in health services research, we recommend reviewing the sources outlined in supplemental material 1. A strong and nuanced understanding of the context and underlying principles of thematic analysis will allow for higher quality research. 16

Practical thematic analysis is a highly flexible approach that can draw out valuable findings and generate new hypotheses, including in cases with a lack of previous research to build on. The approach can also be used with a variety of data, such as transcripts from interviews or focus groups, patient encounter transcripts, professional publications, observational field notes, and online activity logs. Importantly, successful practical thematic analysis is predicated on having high quality data collected with rigorous methods. We do not describe qualitative research design or data collection here. 11 17

In supplemental material 1, we summarise the foundational methods, concepts, and terminology in qualitative research. Along with our guide below, we include a companion slide presentation for teaching practical thematic analysis to research teams in supplemental material 2. We provide a theme coproduction template for teams to use during thematic analysis sessions in supplemental material 3. Our method aligns with the major qualitative reporting frameworks, including the Consolidated Criteria for Reporting Qualitative Research (COREQ). 18 We indicate the corresponding step in practical thematic analysis for each COREQ item in supplemental material 4.

Familiarisation and memoing

We encourage all manuscript authors to review the full dataset (eg, interview transcripts) to familiarise themselves with it. This task is most critical for those who will later be engaged in the coding and theming steps. Although time consuming, it is the best way to involve team members in the intellectual work of data interpretation, so that they can contribute to the analysis and contextualise the results. If this task is not feasible given time limitations or large quantities of data, the data can be divided across team members. In this case, each piece of data should be read by at least two individuals who ideally represent different professional roles or perspectives.

We recommend that researchers reflect on the data and independently write memos, defined as brief notes on thoughts and questions that arise during reading, and a summary of their impressions of the dataset. 2 19 Memoing is an opportunity to gain insights from varying perspectives, particularly from patients, care partners, clinicians, and others. It also gives researchers the opportunity to begin to scope which elements of and concepts in the dataset are relevant to the research question.

Data saturation

The concept of data saturation ( box 2 ) is a foundation of qualitative research. It is defined as the point in analysis at which new data tend to be redundant of data already collected. 21 Qualitative researchers are expected to report their approach to data saturation. 18 Because thematic analysis is iterative, the team should discuss saturation throughout the entire process, beginning with data collection and continuing through all steps of the analysis. 22 During step 1 (reading), team members might discuss data saturation in the context of summary memos. Conversations about saturation continue during step 2 (coding), with confirmation that saturation has been achieved during step 3 (theming). As a rule of thumb, researchers can often achieve saturation in 9-17 interviews or 4-8 focus groups, but this will vary depending on the specific characteristics of the study. 23

Data saturation in context

Braun and Clarke discourage the use of data saturation to determine sample size (eg, number of interviews), because it assumes that there is an objective truth to be captured in the data (sometimes known as a positivist perspective). 20 Qualitative researchers often try to avoid positivist approaches, arguing that there is no one true way of seeing the world, and will instead aim to gather multiple perspectives. 5 Although this theoretical debate with qualitative methods is important, we recognise that a priori estimates of saturation are often needed, particularly for investigators newer to qualitative research who might want a more pragmatic and applied approach. In addition, saturation based, sample size estimation can be particularly helpful in grant proposals. However, researchers should still follow a priori sample size estimation with a discussion to confirm saturation has been achieved.

Definition of coding

We describe codes as labels for concepts in the data that are directly relevant to the study objective. Historically, the purpose of coding was to distil the large amount of data collected into conceptually similar buckets so that researchers could review it in aggregate and identify key themes. 5 24 We advocate for a more analytical approach than is typical with thematic analysis. With our method, coding is both the foundation for and the beginning of thematic analysis—that is, early data analysis, management, and reduction occur simultaneously rather than as different steps. This approach moves the team more efficiently towards being able to describe themes.

Building the coding team

Coders are the research team members who directly assign codes to the data, reading all material and systematically labelling relevant data with appropriate codes. Ideally, at least two researchers would code every discrete data document, such as one interview transcript. 25 If this task is not possible, individual coders can each code a subset of the data that is carefully selected for key characteristics (sometimes known as purposive selection). 26 When using this approach, we recommend that at least 10% of data be coded by two or more coders to ensure consistency in codebook application. We also recommend coding teams of no more than four to five people, for practical reasons concerning maintaining consistency.

Clinicians, patients, and care partners bring unique perspectives to coding and enrich the analytical process. 27 Therefore, we recommend choosing coders with a mix of relevant experiences so that they can challenge and contextualise each other’s interpretations based on their own perspectives and opinions ( box 3 ). We recommend including both coders who collected the data and those who are naive to it, if possible, given their different perspectives. We also recommend all coders review the summary memos from the reading step so that key concepts identified by those not involved in coding can be integrated into the analytical process. In practice, this review means coding the memos themselves and discussing them during the code development process. This approach ensures that the team considers a diversity of perspectives.

Coding teams in context

The recommendation to use multiple coders is a departure from Braun and Clarke. 28 29 When the views, experiences, and training of each coder (sometimes known as positionality) 30 are carefully considered, having multiple coders can enhance interpretation and enrich findings. When these perspectives are combined in a team setting, researchers can create shared meaning from the data. Along with the practical consideration of distributing the workload, 31 inclusion of these multiple perspectives increases the overall quality of the analysis by mitigating the impact of any one coder’s perspective. 30

Coding tools

Qualitative analysis software facilitates coding and managing large datasets but does not perform the analytical work. The researchers must perform the analysis themselves. Most programs support queries and collaborative coding by multiple users. 32 Important factors to consider when choosing software can include accessibility, cost, interoperability, the look and feel of code reports, and the ease of colour coding and merging codes. Coders can also use low tech solutions, including highlighters, word processors, or spreadsheets.

Drafting effective codes

To draft effective codes, we recommend that the coders review each document line by line. 33 As they progress, they can assign codes to segments of data representing passages of interest. 34 Coders can also assign multiple codes to the same passage. Consensus among coders on what constitutes a minimum or maximum amount of text for assigning a code is helpful. As a general rule, meaningful segments of text for coding are shorter than one paragraph, but longer than a few words. Coders should keep the study objective in mind when determining which data are relevant ( box 4 ).

Code types in context

Similar to Braun and Clarke’s approach, practical thematic analysis does not specify whether codes are based on what is evident from the data (sometimes known as semantic) or whether they are based on what can be inferred at a deeper level from the data (sometimes known as latent). 4 12 35 It also does not specify whether they are derived from the data (sometimes known as inductive) or determined ahead of time (sometimes known as deductive). 11 35 Instead, it should be noted that health services researchers conducting qualitative studies often adopt all these approaches to coding (sometimes known as hybrid analysis). 3

In practical thematic analysis, codes should be more descriptive than general categorical labels that simply group data with shared characteristics. At a minimum, codes should form a complete (or full) thought. An easy way to conceptualise full thought codes is as complete sentences with subjects and verbs ( table 1 ), although full sentence coding is not always necessary. With full thought codes, researchers think about the data more deeply and capture this insight in the codes. This coding facilitates the entire analytical process and is especially valuable when moving from codes to broader themes. Experienced qualitative researchers often intuitively use full thought or sentence codes, but this practice has not been explicitly articulated as a path to higher quality coding elsewhere in the literature. 6

Example transcript with codes used in practical thematic analysis 36

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Depending on the nature of the data, codes might either fall into flat categories or be arranged hierarchically. Flat categories are most common when the data deal with topics on the same conceptual level. In other words, one topic is not a subset of another topic. By contrast, hierarchical codes are more appropriate for concepts that naturally fall above or below each other. Hierarchical coding can also be a useful form of data management and might be necessary when working with a large or complex dataset. 5 Codes grouped into these categories can also make it easier to naturally transition into generating themes from the initial codes. 5 These decisions between flat versus hierarchical coding are part of the work of the coding team. In both cases, coders should ensure that their code structures are guided by their research questions.

Developing the codebook

A codebook is a shared document that lists code labels and comprehensive descriptions for each code, as well as examples observed within the data. Good code descriptions are precise and specific so that coders can consistently assign the same codes to relevant data or articulate why another coder would do so. Codebook development is iterative and involves input from the entire coding team. However, as those closest to the data, coders must resist undue influence, real or perceived, from other team members with conflicting opinions—it is important to mitigate the risk that more senior researchers, like principal investigators, exert undue influence on the coders’ perspectives.

In practical thematic analysis, coders begin codebook development by independently coding a small portion of the data, such as two to three transcripts or other units of analysis. Coders then individually produce their initial codebooks. This task will require them to reflect on, organise, and clarify codes. The coders then meet to reconcile the draft codebooks, which can often be difficult, as some coders tend to lump several concepts together while others will split them into more specific codes. Discussing disagreements and negotiating consensus are necessary parts of early data analysis. Once the codebook is relatively stable, we recommend soliciting input on the codes from all manuscript authors. Yet, coders must ultimately be empowered to finalise the details so that they are comfortable working with the codebook across a large quantity of data.

Assigning codes to the data

After developing the codebook, coders will use it to assign codes to the remaining data. While the codebook’s overall structure should remain constant, coders might continue to add codes corresponding to any new concepts observed in the data. If new codes are added, coders should review the data they have already coded and determine whether the new codes apply. Qualitative data analysis software can be useful for editing or merging codes.

We recommend that coders periodically compare their code occurrences ( box 5 ), with more frequent check-ins if substantial disagreements occur. In the event of large discrepancies in the codes assigned, coders should revise the codebook to ensure that code descriptions are sufficiently clear and comprehensive to support coding alignment going forward. Because coding is an iterative process, the team can adjust the codebook as needed. 5 28 29

Quantitative coding in context

Researchers should generally avoid reporting code counts in thematic analysis. However, counts can be a useful proxy in maintaining alignment between coders on key concepts. 26 In practice, therefore, researchers should make sure that all coders working on the same piece of data assign the same codes with a similar pattern and that their memoing and overall assessment of the data are aligned. 37 However, the frequency of a code alone is not an indicator of its importance. It is more important that coders agree on the most salient points in the data; reviewing and discussing summary memos can be helpful here. 5

Researchers might disagree on whether or not to calculate and report inter-rater reliability. We note that quantitative tests for agreement, such as kappa statistics or intraclass correlation coefficients, can be distracting and might not provide meaningful results in qualitative analyses. Similarly, Braun and Clarke argue that expecting perfect alignment on coding is inconsistent with the goal of co-constructing meaning. 28 29 Overall consensus on codes’ salience and contributions to themes is the most important factor.

Definition of themes

Themes are meta-constructs that rise above codes and unite the dataset ( box 6 , fig 2 ). They should be clearly evident, repeated throughout the dataset, and relevant to the research questions. 38 While codes are often explicit descriptions of the content in the dataset, themes are usually more conceptual and knit the codes together. 39 Some researchers hypothesise that theme development is loosely described in the literature because qualitative researchers simply intuit themes during the analytical process. 39 In practical thematic analysis, we offer a concrete process that should make developing meaningful themes straightforward.

Themes in context

According to Braun and Clarke, 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.” 4 Similarly, Braun and Clarke advise against themes as domain summaries. While different approaches can draw out themes from codes, the process begins by identifying patterns. 28 35 Like Braun and Clarke and others, we recommend that researchers consider the salience of certain themes, their prevalence in the dataset, and their keyness (ie, how relevant the themes are to the overarching research questions). 4 12 34

Fig 2

Use of themes in practical thematic analysis

Constructing meaningful themes

After coding all the data, each coder should independently reflect on the team’s summary memos (step 1), the codebook (step 2), and the coded data itself to develop draft themes (step 3). It can be illuminating for coders to review all excerpts associated with each code, so that they derive themes directly from the data. Researchers should remain focused on the research question during this step, so that themes have a clear relation with the overall project aim. Use of qualitative analysis software will make it easy to view each segment of data tagged with each code. Themes might neatly correspond to groups of codes. Or—more likely—they will unite codes and data in unexpected ways. A whiteboard or presentation slides might be helpful to organise, craft, and revise themes. We also provide a template for coproducing themes (supplemental material 3). As with codebook justification, team members will ideally produce individual drafts of the themes that they have identified in the data. They can then discuss these with the group and reach alignment or consensus on the final themes.

The team should ensure that all themes are salient, meaning that they are: supported by the data, relevant to the study objectives, and important. Similar to codes, themes are framed as complete thoughts or sentences, not categories. While codes and themes might appear to be similar to each other, the key distinction is that the themes represent a broader concept. Table 2 shows examples of codes and their corresponding themes from a previously published project that used practical thematic analysis. 36 Identifying three to four key themes that comprise a broader overarching theme is a useful approach. Themes can also have subthemes, if appropriate. 40 41 42 43 44

Example codes with themes in practical thematic analysis 36

Thematic analysis session

After each coder has independently produced draft themes, a carefully selected subset of the manuscript team meets for a thematic analysis session ( table 3 ). The purpose of this session is to discuss and reach alignment or consensus on the final themes. We recommend a session of three to five hours, either in-person or virtually.

Example agenda of thematic analysis session

The composition of the thematic analysis session team is important, as each person’s perspectives will shape the results. This group is usually a small subset of the broader research team, with three to seven individuals. We recommend that primary and senior authors work together to include people with diverse experiences related to the research topic. They should aim for a range of personalities and professional identities, particularly those of clinicians, trainees, patients, and care partners. At a minimum, all coders and primary and senior authors should participate in the thematic analysis session.

The session begins with each coder presenting their draft themes with supporting quotes from the data. 5 Through respectful and collaborative deliberation, the group will develop a shared set of final themes.

One team member facilitates the session. A firm, confident, and consistent facilitation style with good listening skills is critical. For practical reasons, this person is not usually one of the primary coders. Hierarchies in teams cannot be entirely flattened, but acknowledging them and appointing an external facilitator can reduce their impact. The facilitator can ensure that all voices are heard. For example, they might ask for perspectives from patient partners or more junior researchers, and follow up on comments from senior researchers to say, “We have heard your perspective and it is important; we want to make sure all perspectives in the room are equally considered.” Or, “I hear [senior person] is offering [x] idea, I’d like to hear other perspectives in the room.” The role of the facilitator is critical in the thematic analysis session. The facilitator might also privately discuss with more senior researchers, such as principal investigators and senior authors, the importance of being aware of their influence over others and respecting and eliciting the perspectives of more junior researchers, such as patients, care partners, and students.

To our knowledge, this discrete thematic analysis session is a novel contribution of practical thematic analysis. It helps efficiently incorporate diverse perspectives using the session agenda and theme coproduction template (supplemental material 3) and makes the process of constructing themes transparent to the entire research team.

Writing the report

We recommend beginning the results narrative with a summary of all relevant themes emerging from the analysis, followed by a subheading for each theme. Each subsection begins with a brief description of the theme and is illustrated with relevant quotes, which are contextualised and explained. The write-up should not simply be a list, but should contain meaningful analysis and insight from the researchers, including descriptions of how different stakeholders might have experienced a particular situation differently or unexpectedly.

In addition to weaving quotes into the results narrative, quotes can be presented in a table. This strategy is a particularly helpful when submitting to clinical journals with tight word count limitations. Quote tables might also be effective in illustrating areas of agreement and disagreement across stakeholder groups, with columns representing different groups and rows representing each theme or subtheme. Quotes should include an anonymous label for each participant and any relevant characteristics, such as role or gender. The aim is to produce rich descriptions. 5 We recommend against repeating quotations across multiple themes in the report, so as to avoid confusion. The template for coproducing themes (supplemental material 3) allows documentation of quotes supporting each theme, which might also be useful during report writing.

Visual illustrations such as a thematic map or figure of the findings can help communicate themes efficiently. 4 36 42 44 If a figure is not possible, a simple list can suffice. 36 Both must clearly present the main themes with subthemes. Thematic figures can facilitate confirmation that the researchers’ interpretations reflect the study populations’ perspectives (sometimes known as member checking), because authors can invite discussions about the figure and descriptions of findings and supporting quotes. 46 This process can enhance the validity of the results. 46

In supplemental material 4, we provide additional guidance on reporting thematic analysis consistent with COREQ. 18 Commonly used in health services research, COREQ outlines a standardised list of items to be included in qualitative research reports ( box 7 ).

Reporting in context

We note that use of COREQ or any other reporting guidelines does not in itself produce high quality work and should not be used as a substitute for general methodological rigor. Rather, researchers must consider rigor throughout the entire research process. As the issue of how to conceptualise and achieve rigorous qualitative research continues to be debated, 47 48 we encourage researchers to explicitly discuss how they have looked at methodological rigor in their reports. Specifically, we point researchers to Braun and Clarke’s 2021 tool for evaluating thematic analysis manuscripts for publication (“Twenty questions to guide assessment of TA [thematic analysis] research quality”). 16

Avoiding common pitfalls

Awareness of common mistakes can help researchers avoid improper use of qualitative methods. Improper use can, for example, prevent researchers from developing meaningful themes and can risk drawing inappropriate conclusions from the data. Braun and Clarke also warn of poor quality in qualitative research, noting that “coherence and integrity of published research does not always hold.” 16

Weak themes

An important distinction between high and low quality themes is that high quality themes are descriptive and complete thoughts. As such, they often contain subjects and verbs, and can be expressed as full sentences ( table 2 ). Themes that are simply descriptive categories or topics could fail to impart meaningful knowledge beyond categorisation. 16 49 50

Researchers will often move from coding directly to writing up themes, without performing the work of theming or hosting a thematic analysis session. Skipping concerted theming often results in themes that look more like categories than unifying threads across the data.

Unfocused analysis

Because data collection for qualitative research is often semi-structured (eg, interviews, focus groups), not all data will be directly relevant to the research question at hand. To avoid unfocused analysis and a correspondingly unfocused manuscript, we recommend that all team members keep the research objective in front of them at every stage, from reading to coding to theming. During the thematic analysis session, we recommend that the research question be written on a whiteboard so that all team members can refer back to it, and so that the facilitator can ensure that conversations about themes occur in the context of this question. Consistently focusing on the research question can help to ensure that the final report directly answers it, as opposed to the many other interesting insights that might emerge during the qualitative research process. Such insights can be picked up in a secondary analysis if desired.

Inappropriate quantification

Presenting findings quantitatively (eg, “We found 18 instances of participants mentioning safety concerns about the vaccines”) is generally undesirable in practical thematic analysis reporting. 51 Descriptive terms are more appropriate (eg, “participants had substantial concerns about the vaccines,” or “several participants were concerned about this”). This descriptive presentation is critical because qualitative data might not be consistently elicited across participants, meaning that some individuals might share certain information while others do not, simply based on how conversations evolve. Additionally, qualitative research does not aim to draw inferences outside its specific sample. Emphasising numbers in thematic analysis can lead to readers incorrectly generalising the findings. Although peer reviewers unfamiliar with thematic analysis often request this type of quantification, practitioners of practical thematic analysis can confidently defend their decision to avoid it. If quantification is methodologically important, we recommend simultaneously conducting a survey or incorporating standardised interview techniques into the interview guide. 11

Neglecting group dynamics

Researchers should concertedly consider group dynamics in the research team. Particular attention should be paid to power relations and the personality of team members, which can include aspects such as who most often speaks, who defines concepts, and who resolves disagreements that might arise within the group. 52

The perspectives of patient and care partners are particularly important to cultivate. Ideally, patient partners are meaningfully embedded in studies from start to finish, not just for practical thematic analysis. 53 Meaningful engagement can build trust, which makes it easier for patient partners to ask questions, request clarification, and share their perspectives. Professional team members should actively encourage patient partners by emphasising that their expertise is critically important and valued. Noting when a patient partner might be best positioned to offer their perspective can be particularly powerful.

Insufficient time allocation

Researchers must allocate enough time to complete thematic analysis. Working with qualitative data takes time, especially because it is often not a linear process. As the strength of thematic analysis lies in its ability to make use of the rich details and complexities of the data, we recommend careful planning for the time required to read and code each document.

Estimating the necessary time can be challenging. For step 1 (reading), researchers can roughly calculate the time required based on the time needed to read and reflect on one piece of data. For step 2 (coding), the total amount of time needed can be extrapolated from the time needed to code one document during codebook development. We also recommend three to five hours for the thematic analysis session itself, although coders will need to independently develop their draft themes beforehand. Although the time required for practical thematic analysis is variable, teams should be able to estimate their own required effort with these guidelines.

Practical thematic analysis builds on the foundational work of Braun and Clarke. 4 16 We have reframed their six phase process into three condensed steps of reading, coding, and theming. While we have maintained important elements of Braun and Clarke’s reflexive thematic analysis, we believe that practical thematic analysis is conceptually simpler and easier to teach to less experienced researchers and non-researcher stakeholders. For teams with different levels of familiarity with qualitative methods, this approach presents a clear roadmap to the reading, coding, and theming of qualitative data. Our practical thematic analysis approach promotes efficient learning by doing—experiential learning. 12 29 Practical thematic analysis avoids the risk of relying on complex descriptions of methods and theory and places more emphasis on obtaining meaningful insights from those close to real world clinical environments. Although practical thematic analysis can be used to perform intensive theory based analyses, it lends itself more readily to accelerated, pragmatic approaches.

Strengths and limitations

Our approach is designed to smooth the qualitative analysis process and yield high quality themes. Yet, researchers should note that poorly performed analyses will still produce low quality results. Practical thematic analysis is a qualitative analytical approach; it does not look at study design, data collection, or other important elements of qualitative research. It also might not be the right choice for every qualitative research project. We recommend it for applied health services research questions, where diverse perspectives and simplicity might be valuable.

We also urge researchers to improve internal validity through triangulation methods, such as member checking (supplemental material 1). 46 Member checking could include soliciting input on high level themes, theme definitions, and quotations from participants. This approach might increase rigor.

Implications

We hope that by providing clear and simple instructions for practical thematic analysis, a broader range of researchers will be more inclined to use these methods. Increased transparency and familiarity with qualitative approaches can enhance researchers’ ability to both interpret qualitative studies and offer up new findings themselves. In addition, it can have usefulness in training and reporting. A major strength of this approach is to facilitate meaningful inclusion of patient and care partner perspectives, because their lived experiences can be particularly valuable in data interpretation and the resulting findings. 11 30 As clinicians are especially pressed for time, they might also appreciate a practical set of instructions that can be immediately used to leverage their insights and access to patients and clinical settings, and increase the impact of qualitative research through timely results. 8

Practical thematic analysis is a simplified approach to performing thematic analysis in health services research, a field where the experiences of patients, care partners, and clinicians are of inherent interest. We hope that it will be accessible to those individuals new to qualitative methods, including patients, care partners, clinicians, and other health services researchers. We intend to empower multidisciplinary research teams to explore unanswered questions and make new, important, and rigorous contributions to our understanding of important clinical and health systems research.

Acknowledgments

All members of the Coproduction Laboratory provided input that shaped this manuscript during laboratory meetings. We acknowledge advice from Elizabeth Carpenter-Song, an expert in qualitative methods.

Coproduction Laboratory group contributors: Stephanie C Acquilano ( http://orcid.org/0000-0002-1215-5531 ), Julie Doherty ( http://orcid.org/0000-0002-5279-6536 ), Rachel C Forcino ( http://orcid.org/0000-0001-9938-4830 ), Tina Foster ( http://orcid.org/0000-0001-6239-4031 ), Megan Holthoff, Christopher R Jacobs ( http://orcid.org/0000-0001-5324-8657 ), Lisa C Johnson ( http://orcid.org/0000-0001-7448-4931 ), Elaine T Kiriakopoulos, Kathryn Kirkland ( http://orcid.org/0000-0002-9851-926X ), Meredith A MacMartin ( http://orcid.org/0000-0002-6614-6091 ), Emily A Morgan, Eugene Nelson, Elizabeth O’Donnell, Brant Oliver ( http://orcid.org/0000-0002-7399-622X ), Danielle Schubbe ( http://orcid.org/0000-0002-9858-1805 ), Gabrielle Stevens ( http://orcid.org/0000-0001-9001-178X ), Rachael P Thomeer ( http://orcid.org/0000-0002-5974-3840 ).

Contributors: Practical thematic analysis, an approach designed for multidisciplinary health services teams new to qualitative research, was based on CHS’s experiences teaching thematic analysis to clinical teams and students. We have drawn heavily from qualitative methods literature. CHS is the guarantor of the article. CHS, AS, CvP, AMK, JRK, and JAP contributed to drafting the manuscript. AS, JG, CMM, JAP, and RWY provided feedback on their experiences using practical thematic analysis. CvP, LCL, SLB, AVC, GE, and JKL advised on qualitative methods in health services research, given extensive experience. All authors meaningfully edited the manuscript content, including AVC and RKS. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: This manuscript did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Provenance and peer review: Not commissioned; externally peer reviewed.

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thematic analysis research paper example

Reference management. Clean and simple.

How to do a thematic analysis [6 steps]

thematic analysis research paper example

  • What is a thematic analysis?

Thematic analysis is a broad term that describes an approach to analyzing qualitative data . This approach can encompass diverse methods and is usually applied to a collection of texts, such as survey responses and transcriptions of interviews or focus group discussions. Learn more about different research methods.

A researcher performing a thematic analysis will study a set of data to pinpoint repeating patterns, or themes, in the topics and ideas that are expressed in the texts.

In analyzing qualitative data, thematic analysis focuses on concepts, opinions, and experiences, as opposed to pure statistics. This requires an approach to data that is complex and exploratory and can be anchored by different philosophical and conceptual foundations.

A six-step system was developed to help establish clarity and rigor around this process, and it is this system that is most commonly used when conducting a thematic analysis. The six steps are:

  • Familiarization
  • Generating codes
  • Generating themes
  • Reviewing themes
  • Defining and naming themes
  • Creating the report

It is important to note that even though the six steps are listed in sequence, thematic analysis is not necessarily a linear process that advances forward in a one-way, predictable fashion from step one through step six. Rather, it involves a more fluid shifting back and forth between the phases, adjusting to accommodate new insights when they arise.

And arriving at insight is a key goal of this approach. A good thematic analysis doesn’t just seek to present or summarize data. It interprets and makes a statement about it; it extracts meaning from the data.

  • When is thematic analysis used?

Since thematic analysis is used to study qualitative data, it works best in cases where you’re looking to gather information about people’s views, values, opinions, experiences, and knowledge.

Some examples of research questions that thematic analysis can be used to answer are:

  • What are senior citizens’ experiences of long-term care homes?
  • How do women view social media sites as a tool for professional networking?
  • How do non-religious people perceive the role of the church in a society?
  • What are financial analysts’ ideas and opinions about cryptocurrency?

To begin answering these questions, you would need to gather data from participants who can provide relevant responses. Once you have the data, you would then analyze and interpret it.

Because you’re dealing with personal views and opinions, there is a lot of room for flexibility in terms of how you interpret the data. In this way, thematic analysis is systematic but not purely scientific.

  • Braun and Clarke’s Reflexive Thematic Analysis

A landmark 2006 paper by Victoria Braun and Victoria Clarke (“ Using thematic analysis in psychology ”) established parameters around thematic analysis—what it is and how to go about it in a systematic way—which had until then been widely used but poorly defined.

Since then, their work has been updated, with the name being revised, notably, to “reflexive thematic analysis.”

One common misconception that Braun and Clarke have taken pains to clarify about their work is that they do not believe that themes “emerge” from the data. To think otherwise is problematic since this suggests that meaning is somehow inherent to the data and that a researcher is merely an objective medium who identifies that meaning.

Conversely, Braun and Clarke view analysis as an interactive process in which the researcher is an active participant in constructing meaning, rather than simply identifying it.

The six stages they presented in their paper are still the benchmark for conducting a thematic analysis. They are presented below.

  • The six steps of thematic analysis

1. Familiarizing

This step is where you take a broad, high-level view of your data, looking at it as a whole and taking note of your first impressions.

This typically involves reading through written survey responses and other texts, transcribing audio, and recording any patterns that you notice. It’s important to read through and revisit the data in its entirety several times during this stage so that you develop a thorough grasp of all your data.

2. Generating Initial Codes

After familiarizing yourself with your data, the next step is coding notable features of the data in a methodical way. This often means highlighting portions of the text and applying labels, aka codes, to them that describe the nature of their content.

In our example scenario, we’re researching the experiences of women over the age of 50 on professional networking social media sites. Interviews were conducted to gather data, with the following excerpt from one interview.

In the example interview snippet, portions have been highlighted and coded. The codes describe the idea or perception described in the text.

It pays to be exhaustive and thorough at this stage. Good practice involves scrutinizing the data several times, since new information and insight may become apparent upon further review that didn’t jump out at first glance. Multiple rounds of analysis also allow for the generation of more new codes.

Once the text is thoroughly reviewed, it’s time to collate the data into groups according to their code.

3. Generating themes

Now that we’ve created our codes, we can examine them, identify patterns within them, and begin generating themes.

Keep in mind that themes are more encompassing than codes. In general, you’ll be bundling multiple codes into a single theme.

To draw on the example we used above about women and networking through social media, codes could be combined into themes in the following way:

You’ll also be curating your codes and may elect to discard some on the basis that they are too broad or not directly relevant. You may also choose to redefine some of your codes as themes and integrate other codes into them. It all depends on the purpose and goal of your research.

4. Reviewing themes

This is the stage where we check that the themes we’ve generated accurately and relevantly represent the data they are based on. Once again, it’s beneficial to take a thorough, back-and-forth approach that includes review, assessment, comparison, and inquiry. The following questions can support the review:

  • Has anything been overlooked?
  • Are the themes definitively supported by the data?
  • Is there any room for improvement?

5. Defining and naming themes

With your final list of themes in hand, the next step is to name and define them.

In defining them, we want to nail down the meaning of each theme and, importantly, how it allows us to make sense of the data.

Once you have your themes defined, you’ll need to apply a concise and straightforward name to each one.

In our example, our “perceived lack of skills” may be adjusted to reflect that the texts expressed uncertainty about skills rather than the definitive absence of them. In this case, a more apt name for the theme might be “questions about competence.”

6. Creating the report

To finish the process, we put our findings down in writing. As with all scholarly writing, a thematic analysis should open with an introduction section that explains the research question and approach.

This is followed by a statement about the methodology that includes how data was collected and how the thematic analysis was performed.

Each theme is addressed in detail in the results section, with attention paid to the frequency and presence of the themes in the data, as well as what they mean, and with examples from the data included as supporting evidence.

The conclusion section describes how the analysis answers the research question and summarizes the key points.

In our example, the conclusion may assert that it is common for women over the age of 50 to have negative experiences on professional networking sites, and that these are often tied to interactions with other users and a sense that using these sites requires specialized skills.

  • The advantages and disadvantages of thematic analysis

Thematic analysis is useful for analyzing large data sets, and it allows a lot of flexibility in terms of designing theoretical and research frameworks. Moreover, it supports the generation and interpretation of themes that are backed by data.

Disadvantages

There are times when thematic analysis is not the best approach to take because it can be highly subjective, and, in seeking to identify broad patterns, it can overlook nuance in the data.

What’s more, researchers must be judicious about reflecting on how their own position and perspective bears on their interpretations of the data and if they are imposing meaning that is not there or failing to pick up on meaning that is.

Thematic analysis offers a flexible and recursive way to approach qualitative data that has the potential to yield valuable insights about people’s opinions, views, and lived experience. It must be applied, however, in a conscientious fashion so as not to allow subjectivity to taint or obscure the results.

  • Frequently Asked Questions about thematic analysis

The purpose of thematic analysis is to find repeating patterns, or themes, in qualitative data. Thematic analysis can encompass diverse methods and is usually applied to a collection of texts, such as survey responses and transcriptions of interviews or focus group discussions. In analyzing qualitative data, thematic analysis focuses on concepts, opinions, and experiences, as opposed to pure statistics.

A big advantage of thematic analysis is that it allows a lot of flexibility in terms of designing theoretical and research frameworks. It also supports the generation and interpretation of themes that are backed by data.

A disadvantage of thematic analysis is that it can be highly subjective and can overlook nuance in the data. Also, researchers must be aware of how their own position and perspective influences their interpretations of the data and if they are imposing meaning that is not there or failing to pick up on meaning that is.

How many themes make sense in your thematic analysis of course depends on your topic and the material you are working with. In general, it makes sense to have no more than 6-10 broader themes, instead of having many really detailed ones. You can then identify further nuances and differences under each theme when you are diving deeper into the topic.

Since thematic analysis is used to study qualitative data, it works best in cases where you’re looking to gather information about people’s views, values, opinions, experiences, and knowledge. Therefore, it makes sense to use thematic analysis for interviews.

After familiarizing yourself with your data, the first step of a thematic analysis is coding notable features of the data in a methodical way. This often means highlighting portions of the text and applying labels, aka codes, to them that describe the nature of their content.

  • Related Articles

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  • v.6(3); 2019 Jul

Qualitative thematic analysis based on descriptive phenomenology

Annelie j. sundler.

1 Faculty of Caring Science, Work Life and Social Welfare, University of Borås, Borås, Sweden

Elisabeth Lindberg

Christina nilsson, lina palmér.

The aim of this paper was to discuss how to understand and undertake thematic analysis based on descriptive phenomenology. Methodological principles to guide the process of analysis are offered grounded on phenomenological philosophy. This is further discussed in relation to how scientific rigour and validity can be achieved.

This is a discursive article on thematic analysis based on descriptive phenomenology.

This paper takes thematic analysis based on a descriptive phenomenological tradition forward and provides a useful description on how to undertake the analysis. Ontological and epistemological foundations of descriptive phenomenology are outlined. Methodological principles are explained to guide the process of analysis, as well as help to understand validity and rigour. Researchers and students in nursing and midwifery conducting qualitative research need comprehensible and valid methods to analyse the meaning of lived experiences and organize data in meaningful ways.

1. INTRODUCTION

Qualitative research in health care is an increasingly complex research field, particularly when doing phenomenology. In nursing and midwifery, qualitative approaches dealing with the lived experiences of patients, families and professionals are necessary. Today, there are number of diverse research approaches. Still, the clarity regarding approaches for thematic analysis is not yet fully described in the literature and only a few papers describe thematic analysis (Ho, Chiang, & Leung, 2017 ; Vaismoradi, Turunen, & Bondas, 2013 ). It may be difficult to find a single paper that can guide researchers and students in doing thematic analysis in phenomenology.

From our research experiences, it may be complex to read and understand phenomenological approaches. Similarly, the process of analysis can be challenging to comprehend. This makes methodological issues related to the clarity of ontological and epistemological underpinnings and discussions of validity and rigour complex. Norlyk and Harder ( 2010 ) points to difficulties finding a guide for phenomenological research. There is a need for understandable guidelines to take thematic analysis forward. Useful approaches are required to provide researchers and students guidance in the process of thematic analysis. With this paper, we hope to clarify some important methodological stances related to the thematic analysis of meaning from lived experiences that are grounded in descriptive phenomenology and useful to teachers and researchers in nursing and midwifery.

1.1. Background

Phenomenology has been widely used to understand human phenomena in nursing and midwifery practices (Matua, 2015 ). Today, there are several phenomenological approaches available. When using phenomenology, the researcher needs an awareness of basic assumptions to make important methodological decisions. Thus, it is important to understand the underpinnings of the approach used (Dowling & Cooney, 2012 ). Phenomenological underpinnings may, however, be difficult to understand and apply in the research process.

Thematizing meaning has been emphasized as one of a few shared aspects across different qualitative approaches (Holloway & Todres, 2003 ), suggesting that some qualitative research strategies are more generic than others. Although different approaches sometimes overlap, they have different ontological and epistemological foundations. A range of approaches are used to thematize meaning, but some of them would benefit from clarifying ontological and epistemological assumptions. In hermeneutic phenomenological traditions, thematizing meaning can be understood as related to the interpretation of data, illuminating the underlying or unspoken meanings embodied or hidden in lived experiences (Ho et al., 2017 ; van Manen, 2016 ). Another commonly used approach to thematic analysis is the method presented in the psychology literature by Braun and Clarke ( 2006 ). The method is frequently used to find repeated patterns of meaning in the data. However, there is a lack of thematic analysis approaches based on the traditions of descriptive phenomenology.

Researchers must make methodological considerations. In phenomenology, an awareness of the philosophical underpinning of the approach is needed when it is used in depth (Dowling & Cooney, 2012 ; Holloway & Todres, 2003 ). This places demands on methods to be comprehensible and flexible yet consistent and coherent. Questions remain regarding how thematic analysis can be further clarified and used based on descriptive phenomenology.

In this discursive paper, we provide guidance for thematic analysis based on descriptive phenomenology, which, to our knowledge, has not been made explicit in this way previously. This can be used as a guiding framework to analyse lived experiences in nursing and midwifery research. The aim of this paper was to discuss how to understand and undertake thematic analysis based on descriptive phenomenology. Methodological principles to guide the process of analysis are offered grounded on phenomenological philosophy. This is further discussed in relation to how scientific rigour and validity can be achieved.

2. ONTOLOGICAL AND EPISTEMOLOGICAL FOUNDATIONS OF DESCRIPTIVE PHENOMENOLOGY

Phenomenology consists of a complex philosophical tradition in human science, containing different concepts interpreted in various ways. One main theme among phenomenological methods is the diversity between descriptive versus interpretive phenomenology (Norlyk & Harder, 2010 ). Both traditions are commonly used in nursing and midwifery research. Several phenomenological methods have been recognized in the descriptive or interpretative approaches (Dowling, 2007 ; Dowling & Cooney, 2012 ; Norlyk & Harder, 2010 ). The descriptive tradition of phenomenology originated from the writings of Husserl was further developed by Merleau‐Ponty, while the interpretive approach was developed mainly from Heidegger and Gadamer.

The thematic analysis in this paper uses a descriptive approach with focus on lived experience, which refers to our experiences of the world. The philosophy of phenomenology is the study of a phenomenon, for example something as it is experienced (or lived) by a human being that means how things appear in our experiences. Consequently, there is a strong emphasis on lived experiences in phenomenological research (Dowling & Cooney, 2012 ; Norlyk & Harder, 2010 ). In this paper, lived experience is understood from a lifeworld approach originating from the writing of Husserl (Dahlberg, Dahlberg, & Nyström, 2008 ). The lifeworld is crucial and becomes the starting point for understanding lived experiences. Hence, the lifeworld forms the ontological and epistemological foundation for our understanding of lived experiences. In the lifeworld, our experiences must be regarded in the light of the body and the lifeworld of a person (i.e., our subjectivity). Consequently, humans cannot be reduced to a biological or psychological being (Merleau‐Ponty, 2002 /1945). When understanding the meaning of lived experiences, we need to be aware of the lifeworld, our bodily being in the world and how we interact with others.

The understanding of lived experiences is closely linked to the idea of the intentionality of consciousness, or how meaning is experienced. Intentionality encompasses the idea that our consciousness is always directed towards something, which means that when we experience something, the “thing” is experienced as “something” that has meaning for us. For example, a birthing woman's experience of pain or caregiving as it is experienced by a nurse. In a descriptive phenomenological approach, based on the writing of Husserl (Dahlberg et al., 2008 ) such meanings can be described. From this point of view, there are no needs for interpretations of these meanings, although this may be argued differently in interpretive phenomenology. Intentionality is also linked to our natural attitude. In our ordinary life, we take ourselves and our life for granted, which is our natural attitude and how we approach our experiences. We usually take for granted that the world around us is as we perceive it and that others perceive it as we do. We also take for granted that the world exists independently of us. Within our natural attitude, we normally do not constantly analyse our experiences. In phenomenology, an awareness of the natural attitude is important.

3. METHODOLOGICAL PRINCIPLES

In the ontological and epistemological foundations of descriptive phenomenology, some methodological principles can be recognized and how these are managed throughout the research process. Phenomenological studies have been criticized for lacking in clarity on philosophical underpinnings (Dowling & Cooney, 2012 ; Norlyk & Harder, 2010 ). Thus, philosophical stances must be understood and clarified for the reader of a study. Our suggestion is to let the entire research process, from data gathering to data analysis and reporting the findings, be guided by the methodological principles of emphasizing openness , questioning pre‐understanding and adopting a reflective attitude . We will acknowledge that the principles presented here may not be totally distinct from, or do follow, a particular phenomenological research approach. However, the outlined approach has some commonalities with the approaches of, for example, Dahlberg et al. ( 2008 ) and van Manen ( 2016 ).

When researching lived experiences, openness to the lifeworld and the phenomenon focused on must be emphasized (i.e., having curiosity and maintaining an open mind when searching for meaning). The researcher must adopt an open stance with sensitivity to the meaning of the lived experiences currently in focus. Openness involves being observant, attentive and sensitive to the expression of experiences (Dahlberg et al., 2008 ). It also includes questioning the understanding of data (Dahlberg & Dahlberg, 2003 ). Thus, researchers must strive to maintain an attitude that includes the assumption that hitherto the researcher does not know the participants experience and the researcher wants to understand the studied phenomenon in a new light to make invisible aspects of the experience become visible.

When striving for openness, researchers need to question their pre‐understanding , which means identifying and becoming aware of preconceptions that might influence the analysis. Throughout the research process and particularly the analysis, researchers must deal with the natural attitude and previous assumptions, when analysing and understanding the data. Questioning involves attempting to set aside one's experiences and assumptions as much as possible and means maintaining a critical stance and reflecting on the understanding of data and the phenomenon. This is similar to bracketing, a commonly used term in descriptive phenomenology based on Husserl, but it has been criticized (Dowling & Cooney, 2012 ). Some would argue that bracketing means to put aside such assumptions, which may not be possible. Instead, Gadamer ( 2004 ) deals with this in a different way, arguing that such assumptions are part of our understanding. Instead of using bracketing, our intention is to build on questioning as a representative way to describe what something means. Accordingly, researchers need to recognize personal beliefs, theories or other assumptions that can restrict the researcher's openness. Otherwise, the researcher risks describing his or her own pre‐understanding instead of the participants' experiences. Our pre‐understanding, described as “prejudice” in interpretive phenomenology by Gadamer ( 2004 ), is what we already know or think we know about a phenomena. As humans, we always have such a pre‐understanding or prejudice and Gadamer ( 2004 ) posits this is the tradition of our lived context and emphasizes that our tradition has a powerful influence on us. This means that it might be more difficult to see something new in the data than describe something already known by the researcher. Therefore, an open and sensitive stance is needed towards oneself, one's pre‐understanding and the understanding of data. However, one must be reflective and critical towards the data, as well as how to understand meanings from the data. Questioning can help researchers become aware of their pre‐understanding and set aside previous assumptions about the phenomenon (Dahlberg et al., 2008 ).

Questioning one's pre‐understanding is closely linked to having a reflective attitude . With a reflective attitude, the researcher needs to shift from the ordinary natural understanding of everyday life to a more self‐reflective and open stance towards the data (Dahlberg et al., 2008 ). An inquiring approach throughout the research process helps researchers become more aware of one's assumptions and reflect regarding the context of the actual research. For instance, researchers may need to reflect on why some meanings occur, how meanings are described and if meanings are grounded in the data. In striving for an awareness of the natural attitude, a reflective attitude becomes imperative. By having such an awareness, some of the pitfalls related to our natural attitude can be handled in favour of an open and reflective mind.

To summarize, methodological principles have been described in terms of emphasizing openness, questioning pre‐understanding and adopting a reflective attitude, which are three related concepts. To emphasis openness, one needs to reflect on preconceptions and judgements concerning the world and our experiences with a reflective approach to become aware of the natural attitude and process of understanding. Engaging in critical reflection throughout the research process may facilitate an awareness of how the researcher influences the research process. These methodological principles, related to ontological and epistemological foundations of phenomenology, are suggested to guide the research process, particularly the analysis.

4. THEMATIC ANALYSIS OF LIVED EXPERIENCES

The thematic analysis approach described in this paper is inductive. A prerequisite for the analysis is that it includes data on lived experiences, such as interviews or narratives. Themes derived from the analysis are data driven (i.e., grounded in data and the experience of the participants). The analysis begins with a search for meaning and goes on with different meanings being identified and related to each other. The analysis is aimed to try to understand the complexity of meanings in the data rather than measure their frequency. It involves researcher engaging in the data and the analysis. The analysis contains a search for patterns of meanings being further explored and determining how such patterns can be organized into themes. Moreover, the analysis must be guided by openness. Thus, the analysis involves a reflective process designed to illuminate meaning. Although the process of analysis is similar to descriptive phenomenological approaches focusing on the understanding and description of meaning‐oriented themes (Dahlberg et al., 2008 ; van Manen, 2016 ), there are important differences. While the thematic analysis in this paper focuses on how to organize patterns of meaning into themes, some would argue that an essential, general structure of meaning, rather than fragmented themes, is preferred (van Wijngaarden, Meide, & Dahlberg, 2017 ) and that such an essential meaning structure is a strength. We argue that meaning‐oriented themes can contribute to robust qualitative research findings. Still, it is important that the findings move between concrete expressions and descriptive text on meanings of lived experiences.

4.1. The process of analysis

The goal of the thematic analysis is to achieve an understanding of patterns of meanings from data on lived experiences (i.e., informants' descriptions of experiences related to the research question in, e.g., interviews or narratives). The analysis begins with data that needs to be textual and aims to organize meanings found in the data into patterns and, finally, themes. While conducting the analysis, the researcher strives to understand meanings embedded in experiences and describe these meanings textually. Through the analysis, details and aspects of meaning are explored, requiring reading and a reflective writing. Parts of the text need to be understood in terms of the whole and the whole in terms of its parts. However, the researcher also needs to move between being close to and distant from the data. Overall, the process of analysis can be complex and the researcher needs to be flexible. This process is summarized in Figure ​ Figure1 1 and detailed in the description below.

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Summary of thematic analysis

To begin the analysis, the researcher needs to achieve familiarity with the data through open‐minded reading. The text must be read several times in its entirety. This is an open‐ended reading that puts the principle of openness into practice with the intention of opening one's mind to the text and its meanings. When reading, the researcher starts to explore experiences expressed in the data, such as determining how these are narrated and how meanings can be understood. The goal is to illuminate novel information rather than confirm what is already known while keeping the study aim in mind.

Thereafter, the parts of the data are further illuminated and the search for meanings and themes deepens. By moving back and forth between the whole and its parts, a sensitive dialogue with the text may be facilitated. While reading, meanings corresponding to the study's aim are marked. Notes and short descriptive words can be used to give meanings a preliminary name. As the analysis progresses, meanings related to each other are compared to identify differences and similarities. Meanings need to be related to each other to get a sense of patterns. Patterns of meanings are further examined. It is important to not make meanings definite too rapidly, slow down the understanding of data and its meanings. This demands the researcher's openness to let meanings emerge.

Lastly, the researcher needs to organize themes into a meaningful wholeness. Methodological principles must remind the researcher to maintain a reflective mind, while meanings are further developed into themes. Meanings are organized into patterns and, finally, themes. While deriving meaning from text, it is helpful to compare meanings and themes derived from the original data. Nothing is taken for granted, and the researcher must be careful and thoughtful during this part of the process. It can be valuable to discuss and reflect on tentative themes emerging from the data. Findings need to be meaningful, and the naming and wording of themes becomes important. The writing up of the themes is aimed to outline meanings inherent in the described experiences. At this point, findings are written and rewritten. Faithful descriptions of meanings usually need more than a single word, and the writing is important.

To conclude, the process of thematic analysis, based in a descriptive phenomenological approach, goes from the original data to the identification of meanings, organizing these into patterns and writing the results of themes related to the study aim and the actual context. When the findings are reported, these are described conversely (i.e., starting with the themes and the descriptive text, illustrated with quotes). Thus, meanings found from participants experiences are described in a meaningful text organized in themes.

4.2. Validity and Rigour

Hereby follows our discussion on scientific quality in terms of validity and rigour in the thematic analysis process. There is no consensus on which concepts should be used regarding validity in qualitative and phenomenological research. The term validity is typically used in relation to quantitative methods; however, qualitative researchers claim that the term is suitable in all paradigms as a generic term implying whether the research conclusions are sound, just and well‐founded (Morse, 2015 ; Whittemore, Chase, & Mandle, 2001 ). Rolfe ( 2006 ) states that scientific rigour can be judged based on how the research is presented for the reader and appraising research lies with both the reader and the writer of the research. Thus, clarity regarding methodological principles used becomes necessary. Porter ( 2007 ) argues that a more realistic approach is needed and that scientific rigour needs to be taken seriously in qualitative research (Porter, 2007 ). It has been stressed that strategies are needed to ensure rigour and validity; such strategies must be built into the research process and not solely evaluated afterwards (Cypress, 2017 ). Therefore, we further discuss scientific rigour and phenomenological validity in relation to reflexivity , credibility and transferability .

Reflexivity is strictly connected to previously described methodological principles of a reflective attitude and questioning one's pre‐understanding. Reflexivity must be maintained during the entire process, and the researcher needs to sustain a reflective attitude. Particularly, reflexivity must involve questioning the understanding of data and themes derived. Qualitative researchers are closely engaged in this process and must reflect on what the data actually state that may be different from the researcher's understanding. This means the researcher should question the findings instead of taking them for granted. Malterud ( 2001 ) claims that multiple researchers might strengthen the study since they can give supplementary views and question each other's statements, while an independent researcher must find other strategies. Another way to maintain reflexivity is comparing the original data with the descriptive text of themes derived. Moreover, findings need to be illustrated with original data to demonstrate how the derived descriptions are grounded in the data rather than in the researcher's understanding. Furthermore, information is needed on the setting so the reader can understand the context of the findings.

Credibility refers to the meaningfulness of the findings and whether these are well presented (Kitto, Chesters, & Grbich, 2008 ). Credibility and reflexivity are not totally distinct but are correlated with each other. Credibility stresses that nothing can be taken for granted and is associated with the methodological principles described above. The researcher needs to emphasize how the analysis and findings are presented for the reader. The analysis needs to be transparent, which means that the researcher should present it as thoroughly as possible to strive for credibility. The reader needs information concerning the methodology used and methodological decisions and considerations made. This includes, for instance, how the thematic analysis was performed, descriptions of how meanings were derived from the data and how themes were identified. Descriptions need to be clear and consistent. However, it must be possible to agree with and understand the logic of the findings and themes. Credibility lies in both the methodology and in the presentation of findings. Thus, in striving for credibility, the procedures and methods need to be presented as thoroughly and transparently as possible. Themes described must be illustrated with quotes to ensure the content and described meanings are consistent.

Transferability refers to the usefulness and relevance of the findings. However, the method used does not guarantee transferability in itself. Transferability is not explicitly related to any of the methodological principles, but it may be a result of them. Transferability is a measure of whether the findings are sound and if the study adds new knowledge to what is already known. The clarity of findings is also important. Thus, findings must be understandable and transferable to other research (i.e., findings need to be recognizable and relevant to a specific or broader context other than the original study). Specifically, the relevance, usefulness and meaningfulness of research findings to other contexts are important components of the study's transferability.

To conclude, reflexivity, credibility and transferability are concepts important to acknowledge and consider throughout the research process to engender validity and rigour. We maintain that meaning‐oriented themes can contribute to robust findings, if reported in a text describing patterns of meanings illustrated with examples of expressions from lived experiences. Questions researchers need to ask themselves in relation to validity when conducting a thematic analysis are presented in Figure ​ Figure2. 2 . Since the method in itself is no guarantee of validity and rigour, discussions related to these areas are needed.

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Overview of questions useful to the uphold reflexivity, credibility and transferability of the research process in the thematic analysis of meanings

5. IMPLICATIONS FOR NURSING AND MIDWIFERY

In this paper, a method for thematic analysis based on phenomenology has been outlined. Doing phenomenological research is challenging. Therefore, we hope this paper contributes to the understanding of phenomenological underpinnings and methodological principles of thematic analysis based on descriptive phenomenology. This approach can be useful for teachers and researchers in nursing and midwifery. The thematic analysis presented can offer guidance on how to understand meaning and analyse lived experiences. Methodological stances of descriptive phenomenology are clarified, linking the process of analysis with theoretical underpinnings. Methodological principles are explained to give guidance to the analysis and help understand validity and rigour. Thus, this paper has the potential to provide researchers and students who have an interest in research on lived experiences with a comprehensive and useful method to thematic analysis in phenomenology. Nurses and midwives conducting qualitative research on lived experiences need robust methods to ensure high quality in health care to benefit patients, childbearing women and their families.

6. CONCLUSION

We provide researchers in nursing and midwifery with some clarity regarding thematic analysis grounded in the tradition of descriptive phenomenology. We argue that researchers need to comprehend phenomenological underpinnings and be guided by these in the research process. In thematic analysis, descriptive phenomenology is a useful framework when analysing lived experiences with clarified applicable ontological and epistemological underpinnings. Emphasizing openness, questioning pre‐understanding and adopting a reflective attitude were identified as important methodological principles that can guide researchers throughout the analysis and help uphold scientific rigour and validity. For novice researchers, the present paper may serve as an introduction to phenomenological approaches.

CONFLICT OF INTEREST

No conflict of interest has been declared by the authors.

AUTHOR CONTRIBUTIONS

AS, EL, CN, LP: Made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; involved in drafting the manuscript or revising it critically for important intellectual content; given final approval of the version to be published and each author should have participated sufficiently in the work to take public responsibility for appropriate portions of the content; and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Original research article, a thematic analysis investigating the impact of positive behavioral support training on the lives of service providers: “it makes you think differently”.

thematic analysis research paper example

  • 1 Department of Psychology, Manchester Metropolitan University, Manchester, United Kingdom
  • 2 Acquired Brain Injury Ireland, Co., Offaly, Ireland
  • 3 Future Directions CIC, Greater Manchester, United Kingdom

Positive behavioral support (PBS) employs applied behavioral analysis to enhance the quality of life of people who behave in challenging ways. PBS builds on the straightforward and intuitively appealing notion that if people know how to control their environments, they will have less need to behave in challenging ways. Accordingly, PBS focuses on the perspective of those who have behavioral issues, and assesses success via reduction in incidences of challenging behaviors. The qualitative research presented in this report approaches PBS from a different viewpoint and, using thematic analysis, considers the impact of PBS training on the lived experience of staff who deliver services. Thirteen support staff who work for a company supplying social care and supported living services for people with learning disabilities and complex needs in the northwest of England took part. Analysis of interviews identified five major themes. These were: (1) training: enjoyable and useful; (2) widening of perspective: different ways of thinking; (3) increased competence: better outcomes; (4) spill over into private lives: increased tolerance in relationships; and (5) reflecting on practice and moving to a holistic view: “I am aware that people…are not just being naughty.” These themes evidenced personal growth on the part of service providers receiving training. Explicitly, they demonstrated that greater awareness of PBS equipped recipients with an appropriate set of values, and the technical knowledge required to realize them.

Introduction

Positive behavioral support (PBS) is the application of applied behavioral analysis (ABA) ( Baer et al., 1968 ; Allen et al., 2005 ). Hence, researchers define PBS as “the scientific study of behavior change, using the principles of behavior, to evoke or elicit a targeted behavioral change” ( Furman and Lepper, 2018 , p. 104) in people with challenging behaviors. Its primary goal is to enhance the quality of life of people who behave in challenging ways ( LaVigna and Willis, 2005 ). Hence, a key focus is individual environments. These can be adapted so that challenging behavior is less necessary. Particularly, through the acquisition of more socially effective alternative behaviors, where people are motivated to replace inappropriate, stigmatizing, or destructive ways of responding ( LaVigna and Willis, 2005 ).

The core idea that PBS builds on is straightforward and intuitively appealing: if people know how to control their environments, they will have less need to behave in challenging ways ( Hassiotis et al., 2014 ). PBS imparts this knowledge via instruction, and considers the efficacy of training from the point of view of quality of life of the person behaving in challenging ways, and in terms of reduction in incidences of challenging behaviors (e.g., McClean et al., 2005 ; Walsh et al., 2018 ). In this context, behaving in challenging ways refers to “Culturally abnormal behavior of such intensity, frequency and duration that may put the person or others physical safety in jeopardy or seriously limit the use of community activities” ( Emerson, 2001 , p. 7).

PBS is an important treatment framework in the field of learning disability ( Hassiotis et al., 2014 ; Gore et al., 2019 ). PBS is also a useful approach for those working with teenagers and young adolescents, groups for whom challenging behaviors can have a serious impact on the services that they receive ( Bohanon et al., 2006 ). An important pathway through which challenging behaviors can negatively affect service delivery is via the staff who deliver the services. As such, staff welfare is of fundamental importance ( Williams and Glisson, 2013 ).

Traditionally, expert opinion rather than user-perceptions has driven behavioral interventions ( LaVigna and Willis, 2005 ). In contrast, some theorists switch the focus of PBS to person-focused training of stakeholders (i.e., McClean et al., 2005 ; Grey and McClean, 2007 ), for example, the person presenting with challenging behavior, their families, and service providers. From this perspective, those people impacted by the behavior are paramount – rather than passive recipients of instruction from an “expert.” Stakeholders are active participants in assessment, determining intervention strategies, evaluation of these strategies, and thinking about what outcomes might influence service user’s quality of life ( World Health Organization, 2006 ). In working environments where resources are scarce, even when the benefits of staff training appear evident, justifying incumbent costs can prove difficult ( Dench, 2005 ). In this context, acknowledging the central role of stakeholders with regard to the implementation of PBS ( Dench, 2005 ), it is vital that researchers consider the impact of PBS training on those who deliver the support.

The present study explored how training in PBS affected the lived experience of those receiving training. Thus, it adopted the view that training is a “collaborative project” to which people commit themselves and meaning making is understood as residing between people rather than within individuals. Previous research in PBS has tended to focus on the impact of PBS on incidents of challenging behavior ( Walsh et al., 2018 ). An important, and yet unanswered, question was whether those trained to deliver PBS, with a view to improving the lives of others, experienced any benefit from such training in their own lives.

Materials and Methods

Approach to data collection.

This study, consistent with Braun and Clarke (2006) , used thematic analysis in an open-ended way, to investigate how participants experienced the impact of PBS training in both their professional and private lives. The researchers employed a purposive sampling strategy whereby they engaged with a service provider who delivers PBS training to staff as part of their on-going professional development.

Ethical Protocol

The study received full ethical approval from the Manchester Metropolitan University (MMU) ethics committee. All participants provided written informed consent. The study brief informed them that they were free to withdraw at any time, should they wish to do so. Participants consented to the recording of interviews, which were subsequently anonymized and transcribed. Interviews were stored on a password-protected (encrypted) computer, which housed all data.

Interview Process

Participant interviews occurred in their place of work on a prearranged and mutually agreed day. Interviews were semi-structured; a guide provided a loose structure within which to explore the topics of interest. The central question was “what impact has PBS training in the lives of those who receive it?” Where appropriate, the interviewer prompted participants to expand on relevant and interesting responses.

Participants

Purposeful sampling is a widely used technique in qualitative research whereby those cases most likely to be information-rich on the point of interest are selected in order to effectively use limited resources ( Patton, 2002 ). To this end, only staff who had received PBS training were recruited. All staff approached for participation were over the age of 18 and all were permanent employees. The researchers sent an email to potential staff participants requesting volunteers to take part in interviews with regard to their experience of PBS training. A similar advertisement appeared also notice boards in common areas. Respondents participated without incentives. Thirteen participants were interviewed for the purpose of this study. As the goal of the study was to gain a depth of understanding on the point of interest (i.e., participants’ experience of PBS training), through the recruitment of a homogenous 1 sample, data such as mean age etc. are not reported as it might convey the unwarranted impression of generalizability and quantitative robustness.

The service provider delivering the training in PBS was a Community Interest Company providing social care and supported living services for people with learning disabilities and complex needs in the northwest of England. The company is a value-based, high-quality social care provider whose goal is to enable meaningful living among clients. The service provider works with people across a range of environments to provide a continuum of support ranging from a few hours of home care to 24/7 supported living services, and higher levels of support in residential services. All clients are over the age of 16. Supported individuals may have a mental health diagnosis, autism, complex health, profound multiple disabilities, be young people in transition, or have a learning disability, forensic history, acquired brain injury, or dementia.

The company has embedded PBS within service provision. In addition to training staff in PBS, the company has a PBS lead who ensures that staff training remains current. The company also has trained active support champions who facilitate the application of learning to practice.

All staff receive 3-day induction training, which includes consideration of autism, communication, and positive behavioral support. All managers have a level 2 training day covering PBS key components, values, theory, and process. Managers learn also how to develop individual PBS plans, which include functional assessment. PBS plans are evidence based, with 80% of the plan being proactive in order to ensure the achievement of good client outcomes. Training emphasizes that all behavior is for a reason.

All staff receive active support training. This outlines that participation and engagement represent meaningful activities that anybody can engage. The service provider has PBS champions that support staff practically in their job, ensuring that active support is embedded as part of the culture. This role ensures the people supported are empowered in their environment regardless of their ability and actively participate and engage in every part of their life.

Additionally, the company distributes Monthly Newsletters to staff as an additional teaching aid. These share good news stories including information on telecare and other technology designed to give people more choice and control over their lives. Alongside this, training facilitators provide further specialist training. Finally, teams use a training DVD produced by service users to embed staff training.

Data Analysis

This study used thematic analysis ( Braun and Clarke, 2006 ). This required the transcription of interview recordings and followed coding stages. Initially, the authors read and re-read transcripts in order to identify potential themes, which they then forwarded to the lead author. The second level of analysis involved both the first and last authors reviewing these initial codes. They considered particularly how to retain the diversity of the initial codes, while producing overarching elements, higher level sub-themes. The research question, the impact of PBS training in the lives of participants, informed this process. At the third stage, analysis conducted by the first and last authors identified quotes that were congruent with the overarching themes. Next, the authors reviewed themes prior to defining and naming them. Finally, once themes were finalized, by the first and last authors, the write-up of the report began.

The analysis produced five themes.

Training: Enjoyable and Useful

Almost all participants reported that the training that they received was both enjoyable and useful. Illustrative examples appear below.

One participant stated:

MOLLY: I really enjoyed the course and everything and it did make me understand a little bit more.

Participants highlight the enjoyment that they derive from their PBS training course and they explicitly tie this enjoyment to their capacity to internalize it.

ANN: Just listen. Enjoy it. You’ll take something from it even if you do not realize that you do. You think back to what it actually was and you realize that you did take a lot from it.

Widening of Perspective: “Different Ways of Thinking”

In their accounts, most participants highlighted how their perspectives broadened following PBS training.

CATH: “ This is a different way of thinking and getting staff to think differently.”

Participants gave examples of changes in their thinking such as exploring why a person might be upset (Molly), looking for triggers (Maria) being more aware of the possibilities for support in a given moment (p1, Ann) and many participants noted a widening of perspective:

ZOE: “ I see it differently now when somebody is getting anxious. We only see people for a short time. Not that we would leave anybody anxious but it makes you think differently. Thinking outside the box.”

“Increased Competence: Better Outcomes”

A third theme is perceptions of increased competence, and the role of increased competence in promoting better client outcomes.

HEATHER: “ I know what to do in a certain situation whereas some people who hadn’t had it wouldn’t know what to do.”

Participants noted more detailed understanding of triggers (Freya) and better ability to read the communicative intent of clients (Molly). One participant reported that clients “ don’t get to that agitated point like you can prevent it from happening because you know that the reason they are, like, representing the challenging behavior is that they want something or something’s annoyed them.” (Ann).

Other participants reported better outcomes for clients as a result:

ROBIN: “ One person I support has been with [service provider] for 11 years and has always been supported 1:1. I would say roughly he was having 3 incidents a week. (Since PBS was introduced) he has been going out on his own now for 2 months on local walks, walking 2 miles. I don’t think we have had incidents in 2 months. PBS has made it easier, the paperwork side, trying to show staff they wouldn’t be at fault. The staff were scared. I was 5, 6 years ago, but when you come to think about it, it’s better for the person.”

Spill Over Into Private Lives: Increased Tolerance in Relationships

Many participants describe the impact PBS training made in their lives beyond the workplace. Participants say they can apply the principles directly with their family members.

MARIA: “ At home, my children … will come to talk to me, they need attention, and I say ‘I’m talking on the phone, you have to wait’, but now, instead of shouting at them I give them attention but not stop, ask them to write it and come to me, then I will tell you what to do.... To know that there is a reason for any behavior, and how to handle it.”

MAUREEN: “ It (PBS training) has impacted on outside as my partner has high anxiety levels. I have looked at triggers, I have tried to reduce his anxieties using PBS and the techniques.”

ANN: My sister, my middle sister, she’s got learning disabilities. So, like, cos it is simple things like you have got to recognize that most of the challenging behavior is because they are trying to communicate something. Even that feel good factor, or they are just doing it to release some stimulation. But you just need to realize that there is a reason behind all of it, is not there? It’s not just the naughty child, or whatever people use as an excuse.

In terms of indirect impact on participants’ private lives, they spoke about being less judgmental and more effective in their close personal relationships after PBS training:

MARIA: I apply it in my everyday life, especially to be non-judgemental.

ZOE: Yes, because when I had the training we talked about when you have a bad day how you would react, and how your partner would (react) to you…(as a result) I tried something different.

Deeper Understanding of People

Participants reflected on a theme that their philosophy of people had changed. For example, they noted a different attitude to behaviors outside work.

HEATHER: “ I’m aware of people when I’m out (outside of work) that they have got behavioral problems and they’re not just being naughty.”

Commensurate with this change in philosophy is a different or deeper understanding of how people need to be treated.

ANN: It’s encouragement, rather than punishment. That’s what I have taken from it, less telling off and more understanding and encouragement .

Traditionally, expert opinion rather than user-perceptions has driven behavioral interventions ( LaVigna and Willis, 2005 ). Training in PBS is important because it switches focus to the training of stakeholders. The impact of PBS training on staff has been under researched ( Dench, 2005 ). Dench (2005) argues that organizational best practice means that personal development should link to institutional goals and that training evaluation should include qualitative perspectives. It was therefore vital to consider the impact training in PBS has on staff from a qualitative point of view. Staff are key stakeholders and active participants in assessment, determining intervention strategies, evaluation of these strategies, and thinking about what outcomes might affect service user’s quality of life. Vygotsky regarded learning as the ingrowing of lived experience into personal meaning, an outside-in approach ( Frawley, 1997 ). This outside-in perspective lends itself readily to a consideration of how being trained in PBS influences the lived experience of those receiving training. Our results show, within the cohort sampled, that the impact on individuals was overwhelmingly positive.

Specifically, the participants in our research reported that PBS training was enjoyable. This was the case at both emotional and cognitive levels, where training represented both participant’s experience as well as its environmental context ( Wankel, 1993 ). Thus, consistent with Dench (2005) , enjoyment constituted a framework for further embedding training content. Participants also described a widening of perspective – this experience is consistent with the person-based focus advocated by PBS. Moreover, a widening perspective is congruent with the approach advocated by educationalists who build on Vygotsky’s legacy to move education and training away from a focus on test performance to addressing individual capabilities in a grounded and creative manner (e.g., Craft et al., 2008 ).

PBS training is perhaps best conceived as a “collaborative project”, an aggregate of actions that are directed toward an aim. However, at the same time, a project is not equated with its aim, “a unit of educative work in which the most prominent feature was some form of positive and concrete achievement”. Participant 8 spoke about a client who had shifted from three incidents of challenging behavior per week pre PBS training to a position where the client is now going out, unaccompanied, for 2-mile walks.

When a project manages to achieve relatively permanent changes in the social practices of a community, it evolves from being a social movement into an institution. This fits well with Dench (2005) , who argues that best practice in training leads to an integration between human resource development and management policies and processes. There was evidence that this was indeed the case with our participants. For example, one participant expressed a desire to have all staff undergo PBS training at induction, and for the implementation of annual refresher training. Participants described also how the benefits of PBS training have “spilled over” into their private lives. Specifically, people who received training described how their marital, sibling, and parental relationships improved. Increased self-efficacy ( Bandura, 1997 ) is a key factor in how individuals’ personal development opportunities link to specified organizational goals.

Our final theme pertained to the deeper understanding of people whom participants describe because of their training. Several of those interviewed made reference to moving beyond considerations based around ideas of people “being naughty” and reflected on a move to a more holistic approach, where their attitude was significantly less judgmental, efficacious, and increasingly tolerant [e.g., “I apply it in my everyday life, especially to be non-judgemental. To know that there is a reason for any behavior and how to handle it” ( Gore et al., 2013 )].

These themes all appear to link with the concept of perceived control, and perceptions of personal control are key to managing both work and home environments in positive ways. According to Bandura (1997) , knowing how to develop and exercise efficacy is a useful basis for well-being enhancement. Social norms convey standards of conduct, when participants adopt these, as they clearly did in the present study, a self-regulatory system consistent with these standards emerges ( Bandura, 1986 ). From an organizational perspective, this last point is key. Training staff in PBS produced elevated perceptions of increased control. Such perceived control is of benefit to both the individuals and their organization ( Bandura, 1997 ). In the current climate, where resources are scarce, all expenditure, including that on training, must, and should, be fully justified. The results of the current study clearly suggest that training staff in PBS offers benefits at the level of service provision as well as at both personal and corporate levels.

Limitations and Future Research

The present paper identified the importance of perceived control. This is an important finding, which requires cautious interpretation because researchers define perceived control in different ways ( Chipperfield et al., 2012 ). Some employ the classic definition, which refers to beliefs about influence. Other theorists prefer a liberal interpretation that denotes perceived control as a psychological state of control. The emphasis with this delineation is whether individuals feels “in or out of” control. The former conceptualization focuses on specific outcomes, whereas the latter is broad and general. This distinction is one which might usefully be considered in future studies. Of importance will be investigating the extent to which vocational training increases perceived control across life domains. The implicit assumption within the present paper was that the benefits were broad (extended beyond practice to family and relationships generally). However, this is difficult to establish without further consideration of different contexts/situations.

In addition, other factors limit the generalizability of findings presented in this report. Specifically, conclusions derive from a small-scale qualitative study centering on a single service provider. Consequently, it is unclear whether the observed benefits extend across service providers and organizations. This is something that subsequent studies should investigate. This could include evaluation of similar service providers, service providers generally, and extend eventually to consider the benefits of occupational training. Clearly, if research evidences that training benefits both clients and practitioners, this from a vocational and practical perspective indicates that it necessitates resourcing.

Noting the limited scope of the current study, further work could examine the outcomes using larger samples and relevant objective psychometric measures, for example, scales assessing perceived control, self-efficacy, and well-being. Longitudinal analysis might establish causal relationships and reveal whether benefits sustained over time. Furthermore, larger samples allow the testing of predictive relationships and the development of models.

Acknowledging these limitations, readers should best consider the study findings in terms of transferability rather than in terms of generalizability. It is also necessary to put on the record the specific interests of the author and research team, which may have inadvertently influenced both the content and findings presented in this report. In particular, their interest and in the service provider and the accompanying community psychology project.

PBS training equips those who receive it with a set of values, as well as the technical knowledge required to realize those values ( Walsh et al., 2018 ). An important goal of PBS training, in common with training in all fields, is that the training is internalized by those who receive it in order to widen their perspectives and contribute positively to wider institutional and societal well-being. There is evidence that the training has been internalized, in Vygotsky’s sense of the inter-psychological becoming the intra-psychological. Staff understand themselves as having benefitted from PBS training and they believe this benefit extending beyond their professional lives. This perceived gain speaks to the importance of training. Particularly, it evidences the positive impact it has on both the lives of those who receive it as well as on the lives of those around them. As such, PBS training fits with a holistic approach to service provision that is mindful of the importance of caregiver well-being in addition to client well-being (see MacDonald and McGill, 2013 ). In sum, what the themes identified in this research evidence, and share, is growth on the part of those who received training in PBS.

Ethics Statement

The study received full ethical approval from MMU ethics committee. Participants were advised that they were free to withdraw at any time, should they wish to do so. All interviews were recorded with the permission of participants and they were later anonymized and transcribed. Anonymized interviews were stored on a password-protected computer for later analysis.

Author Contributions

NDa, S-JS-L, and SR collected data. All authors participated in thematic analysis. RW, BM, and NDa wrote up the final report with feedback and contribution from all authors.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

1. Homogenous on the point of interest – PBS training.

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Keywords: positive behavioral support (PBS), training, thematic analysis, staff experience, challenging behavior

Citation: Walsh RS, McClean B, Doyle N, Ryan S, Scarborough-Lang S-J, Rishton A and Dagnall N (2019) A Thematic Analysis Investigating the Impact of Positive Behavioral Support Training on the Lives of Service Providers: “It Makes You Think Differently”. Front. Psychol . 10:2408. doi: 10.3389/fpsyg.2019.02408

Received: 11 January 2019; Accepted: 09 October 2019; Published: 29 October 2019.

Reviewed by:

Copyright © 2019 Walsh, McClean, Doyle, Ryan, Scarborough-Lang, Rishton and Dagnall. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: R. Stephen Walsh, [email protected]

Grad Coach

What (Exactly) Is Thematic Analysis?

Plain-Language Explanation & Definition (With Examples)

By: Jenna Crosley (PhD). Expert Reviewed By: Dr Eunice Rautenbach | April 2021

Thematic analysis is one of the most popular qualitative analysis techniques we see students opting for at Grad Coach – and for good reason. Despite its relative simplicity, thematic analysis can be a very powerful analysis technique when used correctly. In this post, we’ll unpack thematic analysis using plain language (and loads of examples) so that you can conquer your analysis with confidence.

Thematic Analysis 101

  • Basic terminology relating to thematic analysis
  • What is thematic analysis
  • When to use thematic analysis
  • The main approaches to thematic analysis
  • The three types of thematic analysis
  • How to “do” thematic analysis (the process)
  • Tips and suggestions

First, the lingo…

Before we begin, let’s first lay down some terminology. When undertaking thematic analysis, you’ll make use of codes . A code is a label assigned to a piece of text, and the aim of using a code is to identify and summarise important concepts within a set of data, such as an interview transcript.

For example, if you had the sentence, “My rabbit ate my shoes”, you could use the codes “rabbit” or “shoes” to highlight these two concepts. The process of assigning codes is called coding. If this is a new concept to you, be sure to check out our detailed post about qualitative coding .

Codes are vital as they lay a foundation for themes . But what exactly is a theme? Simply put, a theme is a pattern that can be identified within a data set. In other words, it’s a topic or concept that pops up repeatedly throughout your data. Grouping your codes into themes serves as a way of summarising sections of your data in a useful way that helps you answer your research question(s) and achieve your research aim(s).

Alright – with that out of the way, let’s jump into the wonderful world of thematic analysis…

Thematic analysis 101

What is thematic analysis?

Thematic analysis is the study of patterns to uncover meaning . In other words, it’s about analysing the patterns and themes within your data set to identify the underlying meaning. Importantly, this process is driven by your research aims and questions , so it’s not necessary to identify every possible theme in the data, but rather to focus on the key aspects that relate to your research questions .

Although the research questions are a driving force in thematic analysis (and pretty much all analysis methods), it’s important to remember that these questions are not necessarily fixed . As thematic analysis tends to be a bit of an exploratory process, research questions can evolve as you progress with your coding and theme identification.

Thematic analysis is about analysing the themes within your data set to identify meaning, based on your research questions.

When should you use thematic analysis?

There are many potential qualitative analysis methods that you can use to analyse a dataset. For example, content analysis , discourse analysis , and narrative analysis are popular choices. So why use thematic analysis?

Thematic analysis is highly beneficial when working with large bodies of data ,  as it allows you to divide and categorise large amounts of data in a way that makes it easier to digest. Thematic analysis is particularly useful when looking for subjective information , such as a participant’s experiences, views, and opinions. For this reason, thematic analysis is often conducted on data derived from interviews , conversations, open-ended survey responses , and social media posts.

Your research questions can also give you an idea of whether you should use thematic analysis or not. For example, if your research questions were to be along the lines of:

  • How do dog walkers perceive rules and regulations on dog-friendly beaches?
  • What are students’ experiences with the shift to online learning?
  • What opinions do health professionals hold about the Hippocratic code?
  • How is gender constructed in a high school classroom setting?

These examples are all research questions centering on the subjective experiences of participants and aim to assess experiences, views, and opinions. Therefore, thematic analysis presents a possible approach.

In short, thematic analysis is a good choice when you are wanting to categorise large bodies of data (although the data doesn’t necessarily have to be large), particularly when you are interested in subjective experiences .

Thematic analysis allows you to divide and categorise large amounts of data in a way that makes it far easier to digest.

What are the main approaches?

Broadly speaking, there are two overarching approaches to thematic analysis: inductive and deductive . The approach you take will depend on what is most suitable in light of your research aims and questions. Let’s have a look at the options.

The inductive approach

The inductive approach involves deriving meaning and creating themes from data without any preconceptions . In other words, you’d dive into your analysis without any idea of what codes and themes will emerge, and thus allow these to emerge from the data.

For example, if you’re investigating typical lunchtime conversational topics in a university faculty, you’d enter the research without any preconceived codes, themes or expected outcomes. Of course, you may have thoughts about what might be discussed (e.g., academic matters because it’s an academic setting), but the objective is to not let these preconceptions inform your analysis.

The inductive approach is best suited to research aims and questions that are exploratory in nature , and cases where there is little existing research on the topic of interest.

The deductive approach

In contrast to the inductive approach, a deductive approach involves jumping into your analysis with a pre-determined set of codes . Usually, this approach is informed by prior knowledge and/or existing theory or empirical research (which you’d cover in your literature review ).

For example, a researcher examining the impact of a specific psychological intervention on mental health outcomes may draw on an existing theoretical framework that includes concepts such as coping strategies, social support, and self-efficacy, using these as a basis for a set of pre-determined codes.

The deductive approach is best suited to research aims and questions that are confirmatory in nature , and cases where there is a lot of existing research on the topic of interest.

Regardless of whether you take the inductive or deductive approach, you’ll also need to decide what level of content your analysis will focus on – specifically, the semantic level or the latent level.

A semantic-level focus ignores the underlying meaning of data , and identifies themes based only on what is explicitly or overtly stated or written – in other words, things are taken at face value.

In contrast, a latent-level focus concentrates on the underlying meanings and looks at the reasons for semantic content. Furthermore, in contrast to the semantic approach, a latent approach involves an element of interpretation , where data is not just taken at face value, but meanings are also theorised.

“But how do I know when to use what approach?”, I hear you ask.

Well, this all depends on the type of data you’re analysing and what you’re trying to achieve with your analysis. For example, if you’re aiming to analyse explicit opinions expressed in interviews and you know what you’re looking for ahead of time (based on a collection of prior studies), you may choose to take a deductive approach with a semantic-level focus.

On the other hand, if you’re looking to explore the underlying meaning expressed by participants in a focus group, and you don’t have any preconceptions about what to expect, you’ll likely opt for an inductive approach with a latent-level focus.

Simply put, the nature and focus of your research, especially your research aims , objectives and questions will  inform the approach you take to thematic analysis.

The four main approaches to thematic analysis are inductive, deductive, semantic and latent. The choice of approach depends on the type of data and what you're trying to achieve

What are the types of thematic analysis?

Now that you’ve got an understanding of the overarching approaches to thematic analysis, it’s time to have a look at the different types of thematic analysis you can conduct. Broadly speaking, there are three “types” of thematic analysis:

  • Reflexive thematic analysis
  • Codebook thematic analysis
  • Coding reliability thematic analysis

Let’s have a look at each of these:

Reflexive thematic analysis takes an inductive approach, letting the codes and themes emerge from that data. This type of thematic analysis is very flexible, as it allows researchers to change, remove, and add codes as they work through the data. As the name suggests, reflexive thematic analysis emphasizes the active engagement of the researcher in critically reflecting on their assumptions, biases, and interpretations, and how these may shape the analysis.

Reflexive thematic analysis typically involves iterative and reflexive cycles of coding, interpreting, and reflecting on data, with the aim of producing nuanced and contextually sensitive insights into the research topic, while at the same time recognising and addressing the subjective nature of the research process.

Codebook thematic analysis , on the other hand, lays on the opposite end of the spectrum. Taking a deductive approach, this type of thematic analysis makes use of structured codebooks containing clearly defined, predetermined codes. These codes are typically drawn from a combination of existing theoretical theories, empirical studies and prior knowledge of the situation.

Codebook thematic analysis aims to produce reliable and consistent findings. Therefore, it’s often used in studies where a clear and predefined coding framework is desired to ensure rigour and consistency in data analysis.

Coding reliability thematic analysis necessitates the work of multiple coders, and the design is specifically intended for research teams. With this type of analysis, codebooks are typically fixed and are rarely altered.

The benefit of this form of analysis is that it brings an element of intercoder reliability where coders need to agree upon the codes used, which means that the outcome is more rigorous as the element of subjectivity is reduced. In other words, multiple coders discuss which codes should be used and which shouldn’t, and this consensus reduces the bias of having one individual coder decide upon themes.

Quick Recap: Thematic analysis approaches and types

To recap, the two main approaches to thematic analysis are inductive , and deductive . Then we have the three types of thematic analysis: reflexive, codebook and coding reliability . Which type of thematic analysis you opt for will need to be informed by factors such as:

  • The approach you are taking. For example, if you opt for an inductive approach, you’ll likely utilise reflexive thematic analysis.
  • Whether you’re working alone or in a group . It’s likely that, if you’re doing research as part of your postgraduate studies, you’ll be working alone. This means that you’ll need to choose between reflexive and codebook thematic analysis.

Now that we’ve covered the “what” in terms of thematic analysis approaches and types, it’s time to look at the “how” of thematic analysis.

Need a helping hand?

thematic analysis research paper example

How to “do” thematic analysis

At this point, you’re ready to get going with your analysis, so let’s dive right into the thematic analysis process. Keep in mind that what we’ll cover here is a generic process, and the relevant steps will vary depending on the approach and type of thematic analysis you opt for.

Step 1: Get familiar with the data

The first step in your thematic analysis involves getting a feel for your data and seeing what general themes pop up. If you’re working with audio data, this is where you’ll do the transcription , converting audio to text.

At this stage, you’ll want to come up with preliminary thoughts about what you’ll code , what codes you’ll use for them, and what codes will accurately describe your content. It’s a good idea to revisit your research topic , and your aims and objectives at this stage. For example, if you’re looking at what people feel about different types of dogs, you can code according to when different breeds are mentioned (e.g., border collie, Labrador, corgi) and when certain feelings/emotions are brought up.

As a general tip, it’s a good idea to keep a reflexivity journal . This is where you’ll write down how you coded your data, why you coded your data in that particular way, and what the outcomes of this data coding are. Using a reflexive journal from the start will benefit you greatly in the final stages of your analysis because you can reflect on the coding process and assess whether you have coded in a manner that is reliable and whether your codes and themes support your findings.

As you can imagine, a reflexivity journal helps to increase reliability as it allows you to analyse your data systematically and consistently. If you choose to make use of a reflexivity journal, this is the stage where you’ll want to take notes about your initial codes and list them in your journal so that you’ll have an idea of what exactly is being reflected in your data. At a later stage in the analysis, this data can be more thoroughly coded, or the identified codes can be divided into more specific ones.

Keep a research journal for thematic analysis

Step 2: Search for patterns or themes in the codes

Step 2! You’re going strong. In this step, you’ll want to look out for patterns or themes in your codes. Moving from codes to themes is not necessarily a smooth or linear process. As you become more and more familiar with the data, you may find that you need to assign different codes or themes according to new elements you find. For example, if you were analysing a text talking about wildlife, you may come across the codes, “pigeon”, “canary” and “budgerigar” which can fall under the theme of birds.

As you work through the data, you may start to identify subthemes , which are subdivisions of themes that focus specifically on an aspect within the theme that is significant or relevant to your research question. For example, if your theme is a university, your subthemes could be faculties or departments at that university.

In this stage of the analysis, your reflexivity journal entries need to reflect how codes were interpreted and combined to form themes.

Step 3: Review themes

By now you’ll have a good idea of your codes, themes, and potentially subthemes. Now it’s time to review all the themes you’ve identified . In this step, you’ll want to check that everything you’ve categorised as a theme actually fits the data, whether the themes do indeed exist in the data, whether there are any themes missing , and whether you can move on to the next step knowing that you’ve coded all your themes accurately and comprehensively . If you find that your themes have become too broad and there is far too much information under one theme, it may be useful to split this into more themes so that you’re able to be more specific with your analysis.

In your reflexivity journal, you’ll want to write about how you understood the themes and how they are supported by evidence, as well as how the themes fit in with your codes. At this point, you’ll also want to revisit your research questions and make sure that the data and themes you’ve identified are directly relevant to these questions .

If you find that your themes have become too broad and there is too much information under one theme, you can split them up into more themes, so that you can be more specific with your analysis.

Step 4: Finalise Themes

By this point, your analysis will really start to take shape. In the previous step, you reviewed and refined your themes, and now it’s time to label and finalise them . It’s important to note here that, just because you’ve moved onto the next step, it doesn’t mean that you can’t go back and revise or rework your themes. In contrast to the previous step, finalising your themes means spelling out what exactly the themes consist of, and describe them in detail . If you struggle with this, you may want to return to your data to make sure that your data and coding do represent the themes, and if you need to divide your themes into more themes (i.e., return to step 3).

When you name your themes, make sure that you select labels that accurately encapsulate the properties of the theme . For example, a theme name such as “enthusiasm in professionals” leaves the question of “who are the professionals?”, so you’d want to be more specific and label the theme as something along the lines of “enthusiasm in healthcare professionals”.

It is very important at this stage that you make sure that your themes align with your research aims and questions . When you’re finalising your themes, you’re also nearing the end of your analysis and need to keep in mind that your final report (discussed in the next step) will need to fit in with the aims and objectives of your research.

In your reflexivity journal, you’ll want to write down a few sentences describing your themes and how you decided on these. Here, you’ll also want to mention how the theme will contribute to the outcomes of your research, and also what it means in relation to your research questions and focus of your research.

By the end of this stage, you’ll be done with your themes – meaning it’s time to write up your findings and produce a report.

It is very important at the theme finalisation stage to make sure that your themes align with your research questions.

Step 5: Produce your report

You’re nearly done! Now that you’ve analysed your data, it’s time to report on your findings. A typical thematic analysis report consists of:

  • An introduction
  • A methodology section
  • Your results and findings
  • A conclusion

When writing your report, make sure that you provide enough information for a reader to be able to evaluate the rigour of your analysis. In other words, the reader needs to know the exact process you followed when analysing your data and why. The questions of “what”, “how”, “why”, “who”, and “when” may be useful in this section.

So, what did you investigate? How did you investigate it? Why did you choose this particular method? Who does your research focus on, and who are your participants? When did you conduct your research, when did you collect your data, and when was the data produced? Your reflexivity journal will come in handy here as within it you’ve already labelled, described, and supported your themes.

If you’re undertaking a thematic analysis as part of a dissertation or thesis, this discussion will be split across your methodology, results and discussion chapters . For more information about those chapters, check out our detailed post about dissertation structure .

It’s absolutely vital that, when writing up your results, you back up every single one of your findings with quotations . The reader needs to be able to see that what you’re reporting actually exists within the results. Also make sure that, when reporting your findings, you tie them back to your research questions . You don’t want your reader to be looking through your findings and asking, “So what?”, so make sure that every finding you represent is relevant to your research topic and questions.

Quick Recap: How to “do” thematic analysis

Getting familiar with your data: Here you’ll read through your data and get a general overview of what you’re working with. At this stage, you may identify a few general codes and themes that you’ll make use of in the next step.

Search for patterns or themes in your codes : Here you’ll dive into your data and pick out the themes and codes relevant to your research question(s).

Review themes : In this step, you’ll revisit your codes and themes to make sure that they are all truly representative of the data, and that you can use them in your final report.

Finalise themes : Here’s where you “solidify” your analysis and make it report-ready by describing and defining your themes.

Produce your report : This is the final step of your thematic analysis process, where you put everything you’ve found together and report on your findings.

Tips & Suggestions

In the video below, we share 6 time-saving tips and tricks to help you approach your thematic analysis as effectively and efficiently as possible.

Wrapping Up

In this article, we’ve covered the basics of thematic analysis – what it is, when to use it, the different approaches and types of thematic analysis, and how to perform a thematic analysis.

If you have any questions about thematic analysis, drop a comment below and we’ll do our best to assist. If you’d like 1-on-1 support with your thematic analysis, be sure to check out our research coaching services here .

thematic analysis research paper example

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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Thematic analysis explainer

20 Comments

Ollie

I really appreciate the help

Oliv

Hello Sir, how many levels of coding can be done in thematic analysis? We generate codes from the transcripts, then subthemes from the codes and themes from subthemes, isn’t it? Should these themes be again grouped together? how many themes can be derived?can you please share an example of coding through thematic analysis in a tabular format?

Abdullahi Maude

I’ve found the article very educative and useful

TOMMY BIN SEMBEH

Excellent. Very helpful and easy to understand.

SK

This article so far has been most helpful in understanding how to write an analysis chapter. Thank you.

Ruwini

My research topic is the challenges face by the school principal on the process of procurement . Thematic analysis is it sutable fir data analysis ?

M. Anwar

It is a great help. Thanks.

Pari

Best advice. Worth reading. Thank you.

Yvonne Worrell

Where can I find an example of a template analysis table ?

aishch

Finally I got the best article . I wish they also have every psychology topics.

Rosa Ophelia Velarde

Hello, Sir/Maam

I am actually finding difficulty in doing qualitative analysis of my data and how to triangulate this with quantitative data. I encountered your web by accident in the process of searching for a much simplified way of explaining about thematic analysis such as coding, thematic analysis, write up. When your query if I need help popped up, I was hesitant to answer. Because I think this is for fee and I cannot afford. So May I just ask permission to copy for me to read and guide me to study so I can apply it myself for my gathered qualitative data for my graduate study.

Thank you very much! this is very helpful to me in my Graduate research qualitative data analysis.

SAMSON ROTTICH

Thank you very much. I find your guidance here helpful. Kindly let help me understand how to write findings and discussions.

arshad ahmad

i am having troubles with the concept of framework analysis which i did not find here and i have been an assignment on framework analysis

tayron gee

I was discouraged and felt insecure because after more than a year of writing my thesis, my work seemed lost its direction after being checked. But, I am truly grateful because through the comments, corrections, and guidance of the wisdom of my director, I can already see the bright light because of thematic analysis. I am working with Biblical Texts. And thematic analysis will be my method. Thank you.

OLADIPO TOSIN KABIR

lovely and helpful. thanks

Imdad Hussain

very informative information.

Ricky Fordan

thank you very much!, this is very helpful in my report, God bless……..

Akosua Andrews

Thank you for the insight. I am really relieved as you have provided a super guide for my thesis.

Christelle M.

Thanks a lot, really enlightening

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thematic analysis research paper example

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A worked example of Braun and Clarke’s approach to reflexive thematic analysis

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  • Published: 26 June 2021
  • volume  56 ,  pages 1391–1412 ( 2022 )

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  • David Byrne   ORCID: orcid.org/0000-0002-0587-4677 1  

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Since the publication of their inaugural paper on the topic in 2006, Braun and Clarke’s approach has arguably become one of the most thoroughly delineated methods of conducting thematic analysis (TA). However, confusion persists as to how to implement this specific approach to TA appropriately. The authors themselves have identified that many researchers who purport to adhere to this approach—and who reference their work as such—fail to adhere fully to the principles of ‘reflexive thematic analysis’ (RTA). Over the course of numerous publications, Braun and Clarke have elaborated significantly upon the constitution of RTA and attempted to clarify numerous misconceptions that they have found in the literature. This paper will offer a worked example of Braun and Clarke’s contemporary approach to reflexive thematic analysis with the aim of helping to dispel some of the confusion regarding the position of RTA among the numerous existing typologies of TA. While the data used in the worked example has been garnered from health and wellbeing education research and was examined to ascertain educators’ attitudes regarding such, the example offered of how to implement the RTA would be easily transferable to many other contexts and research topics.

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1 Introduction

Although the lineage of thematic analysis (TA) can be traced back as far as the early twentieth century (Joffe 2012 ), it has up until recently been a relatively poorly demarcated and poorly understood method of qualitative analysis. Much of the credit for the recent enlightenment and subsequent increase in interest in TA can arguably be afforded to Braun and Clarke’s ( 2006 ) inaugural publication on the topic of thematic analysis in the field of psychology. These authors have since published several articles and book chapters, as well as their own book, all of which make considerable contributions to further delineating their approach to TA (see, for example, Braun and Clarke 2012 , 2013 , 2014 , 2019 , 2020 ; Braun et al. 2016 ; Terry et al. 2017 ). However, on numerous occasions Braun and Clarke have identified a tendency for scholars to cite their 2006 article, but fail to fully adhere to their contemporary approach to RTA (see Braun and Clarke 2013 , 2019 , 2020 ). Commendably, they have acknowledged that their 2006 paper left several aspect of their approach incompletely defined and open to interpretation. Indeed, the term ‘reflexive thematic analysis’ only recently came about in response to these misconceptions (Braun and Clarke 2019 ). Much of their subsequent body of literature in this area addresses these issues and attempts to correct some of the misconceptions in the wider literature regarding their approach. Braun and Clarke have repeatedly iterated that researchers who chose to adopt their approach should interrogate their relevant publications beyond their 2006 article and adhere to their contemporary approach (Braun and Clarke 2019 , 2020 ). The purpose of this paper is to contribute to dispelling some of the confusion and misconceptions regarding Braun and Clarke’s approach by providing a worked example of their contemporary approach to reflexive thematic analysis. The worked example will be presented in relation to the author’s own research, which examined the attitudes of post-primary educators’ regarding the promotion of student wellbeing. This paper is intended to be a supplementary resource for any prospective proponents of RTA, but may be of particular interest to scholars conducting attitudinal studies in an educational context. While this paper is aimed at all scholars regardless of research experience, it may be most useful to research students and their supervisors. Ultimately, the provided example of how to implement the six-phase analysis is easily transferable to many contexts and research topics.

2 What is reflexive thematic analysis?

Reflexive thematic analysis is an easily accessible and theoretically flexible interpretative approach to qualitative data analysis that facilitates the identification and analysis of patterns or themes in a given data set (Braun and Clarke 2012 ). RTA sits among a number of varied approaches to conducting thematic analysis. Braun and Clarke have noted that very often, researchers who purport to have adopted RTA have failed to fully delineate their implementation of RTA, of have confused RTA with other approaches to thematic analysis. The over-riding tendency in this regard is for scholars to mislabel their analysis as RTA, or to draw from a number of different approaches to TA, some of which may not be compatible with each other (Braun and Clarke 2012 , 2013 , 2019 ; Terry et al. 2017 ). In an attempt to resolve this confusion, Braun and Clarke have demarcated the position of RTA among the other forms of thematic analysis by differentiating between three principal approaches to TA: (1) coding reliability TA; (2) codebook approaches to TA, and; (3) the reflexive approach to TA (Braun et al. 2019 ).

Coding reliability approaches, such as those espoused by Boyatzis ( 1998 ) and Joffe ( 2012 ), accentuate the measurement of accuracy or reliability when coding data, often involving the use of a structured codebook. The researcher would also seek a degree of consensus among multiple coders, which can be measured using Cohen’s Kappa (Braun and Clarke 2013 ). When adopting a coding reliability approach, themes tend to be developed very early in the analytical process. Themes can be hypothesised based on theory prior to data collection, with evidence to support these hypotheses then gathered from the data in the form of codes. Alternatively, themes can be hypothesised following a degree of familiarisation with the data (Terry et al. 2017 ). Themes are typically understood to constitute ‘domain summaries’, or “summaries of what participants said in relation to a particular topic or data collection question” (Braun et al. 2019 , p. 5), and are likely to be discussed as residing within the data in a positivistic sense.

Codebook approaches, such as framework analysis (Smith and Firth 2011 ) or template analysis (King and Brooks 2017 ), can be understood to be something of a mid-point between coding reliability approaches and the reflexive approach. Like coding reliability approaches, codebook approaches adopt the use of a structured codebook and share the conceptualisation of themes as domain summaries. However, codebook approaches are more akin to the reflexive approach in terms of the prioritisation of a qualitative philosophy with regard to coding. Proponents of codebook approaches would typically forgo positivistic conceptions of coding reliability, instead recognising the interpretive nature of data coding (Braun et al. 2019 ).

The reflexive approach to TA highlights the researcher’s active role in knowledge production (Braun and Clarke 2019 ). Codes are understood to represent the researcher’s interpretations of patterns of meaning across the dataset. Reflexive thematic analysis is considered a reflection of the researcher’s interpretive analysis of the data conducted at the intersection of: (1) the dataset; (2) the theoretical assumptions of the analysis, and; (3) the analytical skills/resources of the researcher (Braun and Clarke 2019 ). It is fully appreciated—even expected—that no two researchers will intersect this tripartite of criteria in the same way. As such, there should be no expectation that codes or themes interpreted by one researcher may be reproduced by another (although, this is of course possible). Prospective proponents of RTA are discouraged from attempting to provide accounts of ‘accurate’ or ‘reliable’ coding, or pursuing consensus among multiple coders or using Cohen’s Kappa values. Rather, RTA is about “the researcher’s reflective and thoughtful engagement with their data and their reflexive and thoughtful engagement with the analytic process” (Braun and Clarke 2019 , p. 594). Multiple coders may, however, be beneficial in a reflexive manner (e.g. to sense-check ideas, or to explore multiple assumptions or interpretations of the data). If analysis does involve more than one researcher, the approach should be collaborative and reflexive, aiming to achieve richer interpretations of meaning, rather than attempting to achieve consensus of meaning. Indeed, in this sense it would be beneficial for proponents of RTA to remain cognisant that qualitative analysis as a whole does not contend to provide a single or ‘correct’ answer (Braun and Clarke 2013 ).

The process of coding (and theme development) is flexible and organic, and very often will evolve throughout the analytical process (Braun et al. 2019 ). Progression through the analysis will tend to facilitate further familiarity with the data, which may in turn result in the interpretation of new patterns of meaning. This is converse to the use of codebooks, which can often predefine themes before coding. Through the reflexive approach, themes are not predefined in order to ‘find’ codes. Rather, themes are produced by organising codes around a relative core commonality, or ‘central organising concept’, that the researcher interprets from the data (Braun and Clarke 2019 ).

In their 2006 paper, Braun and Clarke ( 2006 ) originally conceptualised RTA as a paradigmatically flexible analytical method, suitable for use within a wide range of ontological and epistemological considerations. In recent publications, the authors have moved away from this view, instead defining RTA as a purely qualitative approach. This pushes the use RTA into exclusivity under appropriate qualitative paradigms (e.g. constructionism) (Braun and Clarke 2019 , 2020 ). As opposed to other forms of qualitative analysis such as content analysis (Vaismoradi et al. 2013 ), and even other forms of TA such as Boyatzis’ ( 1998 ) approach, RTA eschews any positivistic notions of data interpretation. Braun and Clarke ( 2019 ) encourage the researcher to embrace reflexivity, subjectivity and creativity as assets in knowledge production, where they argue some scholars, such as Boyatzis ( 1998 ), may otherwise construe these assets as threats.

3 A worked example of reflexive thematic analysis

The data used in the following example is taken from the qualitative phase of a mixed methods study I conducted, which examined mental health in an educational context. This study set out to understand the attitudes and opinions of Irish post-primary educators with regard to the promotion of students’ social and emotional wellbeing, with the intention to feed this information back to key governmental and non-governmental stakeholders such as the National Council for Curriculum and Assessment and the Department of Education. The research questions for this study aimed to examine educators’ general attitudes toward the promotion of student wellbeing and towards a set of ‘wellbeing guidelines’ that had recently been introduced in Irish post-primary schools. I also wanted to identify any potential barriers to wellbeing promotion and to solicit educators’ opinions as to what might constitute apposite remedial measures in this regard.

The qualitative phase of this study, from which the data for this example is garnered, involved eleven semi-structured interviews, which lasted approximately 25–30 min each. Participants consisted of core-curriculum teachers, wellbeing curriculum teachers, pastoral care team-members and senior management members. Participants were questioned on their attitudes regarding the promotion of student wellbeing, the wellbeing curriculum, the wellbeing guidelines and their perceptions of their own wellbeing. When conducting these interviews, I loosely adhered to an interview agenda to ensure each of these four key topics were addressed. However, discussions were typically guided by what I interpreted to be meaningful to the interviewee, and would often weave in and out of these different topics.

The research questions for this study were addressed within a paradigmatic framework of interpretivism and constructivism. A key principle I adopted for this study was to reflect educators’ own accounts of their attitudes, opinions and experiences as faithfully as was possible, while also accounting for the reflexive influence of my own interpretations as the researcher. I felt RTA was highly appropriate in the context of the underlying theoretical and paradigmatic assumptions of my study and would allow me to ensure qualitative data was collected and analysed in a manner that respected and expressed the subjectivity of participants’ accounts of their attitudes, while also acknowledging and embracing the reflexive influence of my interpretations as the researcher.

In the next section, I will outline the theoretical assumptions of the RTA conducted in my original study in more detail. It should be noted that outlining these theoretical assumptions is not a task specific to reflexive thematic analysis. Rather, these assumptions should be addressed prior to implementing any form of thematic analysis (Braun and Clarke 2012 , 2019 , 2020 ; Braun et al. 2016 ). The six-phase process for conducting reflexive thematic analysis will then be appropriately detailed and punctuated with examples from my study.

3.1 Addressing underlying theoretical assumptions

Across several publications, Braun and Clarke ( 2012 , 2014 , 2020 ) have identified a number of theoretical assumptions that should be addressed when conducting RTA, or indeed any form of thematic analysis. These assumptions are conceptualised as a series of continua as follows: essentialist versus constructionist epistemologies; experiential versus critical orientation to data; inductive versus deductive analyses, and; semantic versus latent coding of data. The aim is not just for the researcher to identify where their analysis is situated on each of these continua, but why the analysis is situated as it is and why this conceptualisation is appropriate to answering the research question(s).

3.1.1 Essentialist versus constructionist epistemologies

Ontological and epistemological considerations would usually be determined when a study is first being conceptualised. However, these considerations may become salient again when data analysis becomes the research focus, particularly with regard to mixed methods. The purpose of addressing this continuum is to conceptualise theoretically how the researcher understands their data and the way in which the reader should interpret the findings (Braun and Clarke 2013 , 2014 ). By adhering to essentialism, the researcher adopts a unidirectional understanding of the relationship between language and communicated experience, in that it is assumed that language is a simple reflection of our articulated meanings and experiences (Widdicombe and Wooffiitt 1995 ). The meanings and systems inherent in constructing these meanings are largely uninterrogated, with the interpretive potential of TA largely unutilised (Braun et al. 2016 ).

Conversely, researchers of a constructionist persuasion would tend to adopt a bidirectional understanding of the language/experience relationship, viewing language as implicit in the social production and reproduction of both meaning and experience (Burr 1995 ; Schwandt 1998 ). A constructionist epistemology has particular implications with regard to thematic analysis, namely that in addition to the recurrence of perceptibly important information, meaningfulness is highly influential in the development and interpretation of codes and themes. The criteria for a theme to be considered noteworthy via recurrence is simply that the theme should present repeatedly within the data. However, what is common is not necessarily meaningful or important to the analysis. Braun and Clarke ( 2012 , p. 37) offer this example:

…in researching white-collar workers’ experiences of sociality at work, a researcher might interview people about their work environment and start with questions about their typical workday. If most or all reported that they started work at around 9:00 a.m., this would be a pattern in the data, but it would not necessarily be a meaningful or important one.

Furthermore, there may be varying degrees of conviction in respondents’ expression when addressing different issues that may facilitate in identifying the salience of a prospective theme. Therefore, meaningfulness can be conceptualised, firstly on the part of the researcher, with regard to the necessity to identify themes that are relevant to answering the research questions, and secondly on the part of the respondent, as the expression of varying degrees of importance with regard to the issues being addressed. By adopting a constructionist epistemology, the researcher acknowledges the importance of recurrence, but appreciates meaning and meaningfulness as the central criteria in the coding process.

In keeping with the qualitative philosophy of RTA, epistemological consideration regarding the example data were constructionist. As such, meaning and experience was interpreted to be socially produced and reproduced via an interplay of subjective and inter-subjective construction. Footnote 1

3.1.2 Experiential versus critical orientation

An experiential orientation to understanding data typically prioritises the examination of how a given phenomenon may be experienced by the participant. This involves investigating the meaning ascribed to the phenomenon by the respondent, as well as the meaningfulness of the phenomenon to the respondent. However, although these thoughts, feelings and experiences are subjectively and inter-subjectively (re)produced, the researcher would cede to the meaning and meaningfulness ascribed by the participant (Braun and Clarke 2014 ). Adopting an experiential orientation requires an appreciation that the thoughts, feelings and experiences of participants are a reflection of personal states held internally by the participant. Conversely, a critical orientation appreciates and analyses discourse as if it were constitutive, rather than reflective, of respondents’ personal states (Braun and Clarke 2014 ). As such, a critical perspective seeks to interrogate patterns and themes of meaning with a theoretical understanding that language can create, rather than merely reflect, a given social reality (Terry et al. 2017 ). A critical perspective can examine the mechanisms that inform the construction of systems of meaning, and therefore offer interpretations of meaning further to those explicitly communicated by participants. It is then also possible to examine how the wider social context may facilitate or impugn these systems of meaning (Braun and Clarke 2012 ). In short, the researcher uses this continuum to clarify their intention to reflect the experience of a social reality (experiential orientation) or examine the constitution of a social reality (critical orientation).

In the present example, an experiential orientation to data interpretation was adopted in order to emphasise meaning and meaningfulness as ascribed by participants. Adopting this approach meant that this analysis did not seek to make claims about the social construction of the research topic (which would more so necessitate a critical perspective), but rather acknowledged the socially constructed nature of the research topic when examining the subjective ‘personal states’ of participants. An experiential orientation was most appropriate as the aim of the study was to prioritise educators’ own accounts of their attitudes, opinions. More importantly, the research questions aimed to examine educators’ attitudes regarding their experience of promoting student wellbeing—or the ‘meanings made’—and not, for example, the socio-cultural factors that may underlie the development of these attitudes—or the ‘meaning making’.

3.1.3 Inductive versus deductive analysis

A researcher who adopts a deductive or ‘theory-driven’ approach may wish to produce codes relative to a pre-specified conceptual framework or codebook. In this case, the analysis would tend to be ‘analyst-driven’, predicated on the theoretically informed interpretation of the researcher. Conversely, a researcher who adopts an inductive or ‘data-driven’ approach may wish to produce codes that are solely reflective of the content of the data, free from any pre-conceived theory or conceptual framework. In this case, data are not coded to fit a pre-existing coding frame, but instead ‘open-coded’ in order to best represent meaning as communicated by the participants (Braun and Clarke 2013 ). Data analysed and coded deductively can often provide a less rich description of the overall dataset, instead focusing on providing a detailed analysis of a particular aspect of the dataset interpreted through a particular theoretical lens (Braun and Clarke 2020 ). Deductive analysis has typically been associated with positivistic/essentialist approaches (e.g. Boyatzis 1998 ), while inductive analysis tends to be aligned with constructivist approaches (e.g. Frith and Gleeson 2004 ). That being said, inductive/deductive approaches to analysis are by no means exclusively or intrinsically linked to a particular epistemology.

Coding and analysis rarely fall cleanly into one of these approaches and, more often than not, use a combination of both (Braun and Clarke 2013 , 2019 , 2020 ). It is arguably not possible to conduct an exclusively deductive analysis, as an appreciation for the relationship between different items of information in the data set is necessary in order to identify recurring commonalities with regard to a pre-specified theory or conceptual framework. Equally, it is arguably not possible to conduct an exclusively inductive analysis, as the researcher would require some form of criteria to identify whether or not a piece of information may be conducive to addressing the research question(s), and therefore worth coding. When addressing this issue, Braun and Clarke ( 2012 ) clarify that one approach does tend to predominate over the other, and that the predominance of the deductive or inductive approach can indicate an overall orientation towards prioritising either researcher/theory-based meaning or respondent/data-based meaning, respectively.

A predominantly inductive approach was adopted in this example, meaning data was open-coded and respondent/data-based meanings were emphasised. A degree of deductive analysis was, however, employed to ensure that the open-coding contributed to producing themes that were meaningful to the research questions, and to ensure that the respondent/data-based meanings that were emphasised were relevant to the research questions.

3.1.4 Semantic versus latent coding

Semantic codes are identified through the explicit or surface meanings of the data. The researcher does not examine beyond what a respondent has said or written. The production of semantic codes can be described as a descriptive analysis of the data, aimed solely at presenting the content of the data as communicated by the respondent. Latent coding goes beyond the descriptive level of the data and attempts to identify hidden meanings or underlying assumptions, ideas, or ideologies that may shape or inform the descriptive or semantic content of the data. When coding is latent, the analysis becomes much more interpretive, requiring a more creative and active role on the part of the researcher. Indeed, Braun and Clarke ( 2012 , 2013 , 2020 ) have repeatedly presented the argument that codes and themes do not ‘emerge’ from the data or that they may be residing in the data, waiting to be found. Rather, the researcher plays an active role in interpreting codes and themes, and identifying which are relevant to the research question(s). Analyses that use latent coding can often overlap with aspects of thematic discourse analysis in that the language used by the respondent can be used to interpret deeper levels of meaning and meaningfulness (Braun and Clarke 2006 ).

In this example, both semantic and latent coding were utilised. No attempt was made to prioritise semantic coding over latent coding or vice-versa. Rather, semantic codes were produced when meaningful semantic information was interpreted, and latent codes were produced when meaningful latent information was interpreted. As such, any item of information could be double-coded in accordance with the semantic meaning communicated by the respondent, and the latent meaning interpreted by the researcher (Patton 1990 ). This was reflective of the underlying theoretical assumptions of the analysis, as the constructive and interpretive epistemology and ontology were addressed by affording due consideration to both the meaning constructed and communicated by the participant and my interpretation of this meaning as the researcher.

3.2 The six-phase analytical process

Braun and Clarke ( 2012 , 2013 , 2014 , 2020 ) have proposed a six-phase process, which can facilitate the analysis and help the researcher identify and attend to the important aspects of a thematic analysis. In this sense, Braun and Clarke ( 2012 ) have identified the six-phase process as an approach to doing TA, as well as learning how to do TA. While the six phases are organised in a logical sequential order, the researcher should be cognisant that the analysis is not a linear process of moving forward through the phases. Rather, the analysis is recursive and iterative, requiring the researcher to move back and forth through the phases as necessary (Braun and Clarke 2020 ). TA is a time consuming process that evolves as the researcher navigates the different phases. This can lead to new interpretations of the data, which may in turn require further iterations of earlier phases. As such, it is important to appreciate the six-phase process as a set of guidelines, rather than rules, that should be applied in a flexible manner to fit the data and the research question(s) (Braun and Clarke 2013 , 2020 ).

3.2.1 Phase one: familiarisation with the data

The ‘familiarisation’ phase is prevalent in many forms of qualitative analysis. Familiarisation entails the reading and re-reading of the entire dataset in order to become intimately familiar with the data. This is necessary to be able to identify appropriate information that may be relevant to the research question(s). Manual transcription of data can be a very useful activity for the researcher in this regard, and can greatly facilitate a deep immersion into the data. Data should be transcribed orthographically, noting inflections, breaks, pauses, tones, etc. on the part of both the interviewer and the participant (Braun and Clarke 2013 ). Often times, data may not have been gathered or transcribed by the researcher, in which case, it would be beneficial for the researcher to watch/listen to video or audio recordings to achieve a greater contextual understanding of the data. This phase can be quite time consuming and requires a degree of patience. However, it is important to afford equal consideration across the entire depth and breadth of the dataset, and to avoid the temptation of being selective of what to read, or even ‘skipping over’ this phase completely (Braun and Clarke 2006 ).

At this phase, I set about familiarising myself with the data by firstly listening to each interview recording once before transcribing that particular recording. This first playback of each interview recording required ‘active listening’ and, as such, I did not take any notes at this point. I performed this active-listen in order to develop an understanding of the primary areas addressed in each interview prior to transcription. This also provided me an opportunity, unburdened by tasks such as note taking, to recall gestures and mannerisms that may or may not have been documented in interview notes. I manually transcribed each interview immediately after the active-listen playback. When transcription of all interviews was complete, I read each transcripts numerous times. At this point, I took note of casual observations of initial trends in the data and potentially interesting passages in the transcripts. I also documented my thoughts and feelings regarding both the data and the analytical process (in terms of transparency, it would be beneficial to adhere to this practice throughout the entire analysis). Some preliminary notes made during the early iterations of familiarisation with the data can be seen in Box 1. It will be seen later that some of these notes would go on to inform the interpretation of the finalised thematic framework.

figure a

Example of preliminary notes taken during phase one

3.2.2 Phase two: generating initial codes

Codes are the fundamental building blocks of what will later become themes. The process of coding is undertaken to produce succinct, shorthand descriptive or interpretive labels for pieces of information that may be of relevance to the research question(s). It is recommended that the researcher work systematically through the entire dataset, attending to each data item with equal consideration, and identifying aspects of data items that are interesting and may be informative in developing themes. Codes should be brief, but offer sufficient detail to be able to stand alone and inform of the underlying commonality among constituent data items in relation to the subject of the research (Braun and Clarke 2012 ; Braun et al. 2016 ).

A brief excerpt of the preliminary coding process of one participant’s interview transcript is presented in Box 2. The preliminary iteration of coding was conducted using the ‘comments’ function in Microsoft Word (2016). This allowed codes to be noted in the side margin, while also highlighting the area of text assigned to each respective code. This is a relatively straightforward example with no double-codes or overlap in data informing different codes, as new codes begin where previous codes end. The code C5 offers an exemplar of the provision of sufficient detail to explain what I interpreted from the related data item. A poor example of this code would be to say “the wellbeing guidelines are not relatable” or “not relatable for students”. Each of these examples lack context. Understanding codes written in this way would be contingent upon knowledge of the underlying data extract. The code C8 exemplifies this issue. It is unclear if the positivity mentioned relates to the particular participant, their colleagues, or their students. This code was subsequently redefined in later iterations of coding. It can also be seen in this short example that the same code has been produced for both C4 and C9. This code was prevalent throughout the entire dataset and would subsequently be informative in the development of a theme.

figure b

Extract of preliminary coding

Any item of data that might be useful in addressing the research question(s) should be coded. Through repeated iterations of coding and further familiarisation, the researcher can identify which codes are conducive to interpreting themes and which can be discarded. I would recommend that the researcher document their progression through iterations of coding to track the evolution of codes and indeed prospective themes. RTA is a recursive process and it is rare that a researcher would follow a linear path through the six phases (Braun and Clarke 2014 ). It is very common for the researcher to follow a particular train of thought when coding, only to encounter an impasse where several different interpretations of the data come to light. It may be necessary to explore each of these prospective options to identify the most appropriate path to follow. Tracking the evolution of codes will not only aid transparency, but will afford the researcher signposts and waypoints to which they may return should a particular approach to coding prove unfruitful. I tracked the evolution of my coding process in a spreadsheet, with data items documented in the first column and iterations of codes in each successive column. I found it useful to highlight which codes were changed in each successive iteration. Table 1 provides an excerpt of a Microsoft Excel (2016) spreadsheet that was established to track iterations of coding and document the overall analytical process. All codes developed during the first iteration of coding were transferred into this spreadsheet along with a label identifying the respective participant. Subsequent iterations of coding were documented in this spreadsheet. The original transcripts were still regularly consulted to assess existing codes and examine for the interpretation of new codes as further familiarity with the data developed. Column one presents a reference number for the data item that was coded, while column two indicates the participant who provided each data item. Column three presents the data item that was coded. Columns four and five indicate the iteration of the coding process to be the third and fourth iteration, respectively. Codes revised between iterations three and four are highlighted.

With regard to data item one, I initially considered that a narrative might develop exploring a potential discrepancy in levels of training received by wellbeing educators and non-wellbeing educators. In early iterations of coding, I adopted a convention of coding training-related information with reference to the wellbeing or non-wellbeing status of the participant. While this discrepancy in levels of training remained evident throughout the dataset, I eventually deemed it unnecessary to pursue interpretation of the data in this way. This coding convention was abandoned at iteration four in favour of the pre-existing generalised code “insufficient training in wellbeing curriculum”. With data item three, I realised that the code was descriptive at a semantic level, but not very informative. Upon re-evaluating this data item, I found the pre-existing code “lack of clarity in assessing student wellbeing” to be much more appropriate and representative of what the participant seemed to be communicating. Finally, I realised that the code for data item five was too specific to this particular data item. No other data item shared this code, which would preclude this code (and data item) from consideration when construction themes. I decided that this item would be subsumed under the pre-existing code “more training is needed for wellbeing promotion”.

The process of generating codes is non-prescriptive regarding how data is segmented and itemised for coding, and how many codes or what type of codes (semantic or latent) are interpreted from an item of data. The same data item can be coded both semantically and latently if deemed necessary. For example, when discussing how able they felt to attend to their students’ wellbeing needs, one participant stated “…if someone’s struggling a bit with their schoolwork and it’s getting them down a bit, it’s common sense that determines what we say to them or how we approach them. And it might help to talk, but I don’t know that it has a lasting effect” [2B]. Here, I understood that the participant was explicitly sharing the way in which they address their students’ wellbeing concerns, but also that the participant was implying that this commonsense approach might not be sufficient. As such, this data item was coded both semantically as “educators rely on common sense when attending to wellbeing issues”, and latently as “common sense inadequate for wellbeing promotion”. Both codes were revised later in the analysis. However, this example illustrates the way in which any data item can be coded in multiple ways and for multiple meanings. There is also no upper or lower limit regarding how many codes should be interpreted. What is important is that, when the dataset is fully coded and codes are collated, sufficient depth exists to examine the patterns within the data and the diversity of the positions held by participants. It is, however, necessary to ensure that codes pertain to more than one data item (Braun and Clarke 2012 ).

3.2.3 Phase three: generating themes

This phase begins when all relevant data items have been coded. The focus shifts from the interpretation of individual data items within the dataset, to the interpretation of aggregated meaning and meaningfulness across the dataset. The coded data is reviewed and analysed as to how different codes may be combined according to shared meanings so that they may form themes or sub-themes. This will often involve collapsing multiple codes that share a similar underlying concept or feature of the data into one single code. Equally, one particular code may turn out to be representative of an over-arching narrative within the data and be promoted as a sub-theme or even a theme (Braun and Clarke 2012 ). It is important to re-emphasise that themes do not reside in the data waiting to be found. Rather, the researcher must actively construe the relationship among the different codes and examine how this relationship may inform the narrative of a given theme. Construing the importance or salience of a theme is not contingent upon the number of codes or data items that inform a particular theme. What is important is that the pattern of codes and data items communicates something meaningful that helps answer the research question(s) (Braun and Clarke 2013 ).

Themes should be distinctive and may even be contradictory to other themes, but should tie together to produce a coherent and lucid picture of the dataset. The researcher must be able and willing to let go of codes or prospective themes that may not fit within the overall analysis. It may be beneficial to construct a miscellaneous theme (or category) to contain all the codes that do not appear to fit in among any prospective themes. This miscellaneous theme may end up becoming a theme in its own right, or may simple be removed from the analysis during a later phase (Braun and Clarke 2012 ). Much the same as with codes, there is no correct amount of themes. However, with too many themes the analysis may become unwieldy and incoherent, whereas too few themes can result in the analysis failing to explore fully the depth and breadth of the data. At the end of this stage, the researcher should be able to produce a thematic map (e.g. a mind map or affinity map) or table that collates codes and data items relative to their respective themes (Braun and Clarke 2012 , 2020 ).

At this point in the analysis, I assembled codes into initial candidate themes. A thematic map of the initial candidate themes can be seen in Fig.  1 . The theme “best practice in wellbeing promotion” was clearly definable, with constituent coded data presenting two concurrent narratives. These narratives were constructed as two separate sub-themes, which emphasised the involvement of the entire school staff and the active pursuit of practical measures in promoting student wellbeing, respectively. The theme “recognising student wellbeing” was similarly clear. Again, I interpreted a dichotomy of narratives. However, in this case, the two narratives seemed to be even more synergetic. The two sub-themes for “best practice…” highlighted two independently informative factors in best practice. Here, the sub-themes are much more closely related, with one sub-theme identifying factors that may inhibit the development of student wellbeing, while the second sub-theme discusses factors that may improve student wellbeing. At this early stage in the analysis, I was considering that this sub-theme structure might also be used to delineate the theme “recognising educator wellbeing”. Finally, the theme “factors influencing wellbeing promotion” collated coded data items that addressed inhibitive factors with regard to wellbeing promotion. These factors were conceptualised as four separate sub-themes reflecting a lack of training, a lack of time, a lack of appropriate value for wellbeing promotion, and a lack of knowledge of supporting wellbeing-related documents. While it was useful to bring all of this information together under one theme, even at this early stage it was evident that this particular theme was very dense and unwieldy, and would likely require further revision.

figure 1

Initial thematic map indicating four candidate themes

3.2.4 Phase four: reviewing potential themes

This phase requires the researcher to conduct a recursive review of the candidate themes in relation to the coded data items and the entire dataset (Braun and Clarke 2012 , 2020 ). At this phase, it is not uncommon to find that some candidate themes may not function well as meaningful interpretations of the data, or may not provide information that addresses the research question(s). It may also come to light that some of the constituent codes and/or data items that inform these themes may be incongruent and require revision. Braun and Clarke ( 2012 , p. 65) proposed a series of key questions that the researcher should address when reviewing potential themes. They are:

Is this a theme (it could be just a code)?

If it is a theme, what is the quality of this theme (does it tell me something useful about the data set and my research question)?

What are the boundaries of this theme (what does it include and exclude)?

Are there enough (meaningful) data to support this theme (is the theme thin or thick)?

Are the data too diverse and wide ranging (does the theme lack coherence)?

The analysis conducted at this phase involves two levels of review. Level one is a review of the relationships among the data items and codes that inform each theme and sub-theme. If the items/codes form a coherent pattern, it can be assumed that the candidate theme/sub-theme makes a logical argument and may contribute to the overall narrative of the data. At level two, the candidate themes are reviewed in relation to the data set. Themes are assessed as to how well they provide the most apt interpretation of the data in relation to the research question(s). Braun and Clarke have proposed that, when addressing these key questions, it may be useful to observe Patton’s ( 1990 ) ‘dual criteria for judging categories’ (i.e. internal homogeneity and external heterogeneity). The aim of Patton’s dual criteria would be to observe internal homogeneity within themes at the level one review, while observing external heterogeneity among themes at the level two review. Essentially, these two levels of review function to demonstrate that items and codes are appropriate to inform a theme, and that a theme is appropriate to inform the interpretation of the dataset (Braun and Clarke 2006 ). The outcome of this dual-level review is often that some sub-themes or themes may need to be restructured by adding or removing codes, or indeed adding or removing themes/sub-themes. The finalised thematic framework that resulted from the review of the candidate themes can be seen in Fig.  2 .

figure 2

Finalised thematic map demonstrating five themes

During the level one review, inspection of the prospective sub-theme “sources of negative affect” in relation to the theme “recognising educator wellbeing” resulted in a new interpretation of the constituent coded data items. Participants communicated numerous pre-existing work-related factors that they felt had a negative impact upon their wellbeing. However, it was also evident that participants felt the introduction of the new wellbeing curriculum and the newly mandated task of formally attending to student wellbeing had compounded these pre-existing issues. While pre-existing issues and wellbeing-related issues were both informative of educators’ negative affect, the new interpretation of this data informed the realisation of two concurrent narratives, with wellbeing-related issues being a compounding factor in relation to pre-existing issues. This resulted in the “sources of negative affect” sub-theme being split into two new sub-themes; “work-related negative affect” and “the influence of wellbeing promotion”. The “actions to improve educator wellbeing” sub-theme was folded into these sub-themes, with remedial measures for each issue being discussed in respective sub-themes.

During the level two review, my concerns regarding the theme “factors inhibiting wellbeing promotion” were addressed. With regard to Braun and Clarke’s key questions, it was quite difficult to identify the boundaries of this theme. It was also particularly dense (or too thick) and somewhat incoherent. At this point, I concluded that this theme did not constitute an appropriate representation of the data. Earlier phases of the analysis were reiterated and new interpretations of the data were developed. This candidate theme was subsequently broken down into three separate themes. While the sub-themes of this candidate theme were, to a degree, informative in the development of the new themes, the way in which the constituent data was understood was fundamentally reconceptualised. The new theme, entitled “the influence of time”, moves past merely describing time constraints as an inhibitive factor in wellbeing promotion. A more thorough account of the bi-directional nature of time constraints was realised, which acknowledged that previously existing time constraints affected wellbeing promotion, while wellbeing promotion compounded previously existing time constraints. This added an analysis of the way in which the introduction of wellbeing promotion also produced time constraints in relation to core curricular activities.

The candidate sub-themes “lack of training” and “knowledge of necessary documents” were re-evaluated and considered to be topical rather than thematic aspects of the data. Upon further inspection, I felt that the constituent coded data items of these two sub-themes were informative of a single narrative of participants attending to their students’ wellbeing in an atheoretical manner. As such, these two candidate sub-themes were folded into each other to produce the theme “incompletely theorised agreements”. Finally, the level two review led me to the conclusion that the full potential of the data that informed the candidate sub-theme “lack of value of wellbeing promotion” was not realised. I found that a much richer understanding of this data was possible, which was obscured by the initial, relatively simplistic, descriptive account offered. An important distinction was made, in that participants held differing perceptions of the value attributed to wellbeing promotion by educators and by students. Further, I realised that educators’ perceptions of wellbeing promotion were not necessarily negative and should not be exclusively presented as an inhibitive factor in wellbeing promotion. A new theme, named “the axiology of wellbeing” and informed by the sub-themes “students’ valuation of wellbeing promotion” and “educators’ valuation of wellbeing promotion”, was developed to delineate this multifaceted understanding of participants’ accounts of the value of wellbeing promotion.

It is quite typical at this phase that codes, as well as themes, may be revised or removed to facilitate the most meaningful interpretation of the data. As such, it may be necessary to reiterate some of the activities undertaken during phases two and three of the analysis. It may be necessary to recode some data items, collapse some codes into one, remove some codes, or promote some codes as sub-themes or themes. For example, when re-examining the data items that informed the narrative of the value ascribed to wellbeing promotion, I observed that participants offered very different perceptions of the value ascribed by educators and by students. To pursue this line of analysis, numerous codes were reconceptualised to reflect the two different perspectives. Codes such as “positivity regarding the wellbeing curriculum” were split into the more specified codes “student positivity regarding the wellbeing curriculum” and “educator positivity regarding the wellbeing curriculum”. Amending codes in this way ultimately contributed to the reinterpretation of the data and the development of the finalised thematic map.

As with all other phases, it is very important to track and document all of these changes. With regard to some of the more significant changes (removing a theme, for example), I would recommend making notes on why it might be necessary to take this action. The aim of this phase is to produce a revised thematic map or table that captures the most important elements of the data in relation to the research question(s).

3.2.5 Phase five: defining and naming theme

At this phase, the researcher is tasked with presenting a detailed analysis of the thematic framework. Each individual theme and sub-theme is to be expressed in relation to both the dataset and the research question(s). As per Patton’s ( 1990 ) dual criteria, each theme should provide a coherent and internally consistent account of the data that cannot be told by the other themes. However, all themes should come together to create a lucid narrative that is consistent with the content of the dataset and informative in relation to the research question(s). The names of the themes are also subject to a final revision (if necessary) at this point.

Defining themes requires a deep analysis of the underlying data items. There will likely be many data items underlying each theme. It is at this point that the researcher is required to identify which data items to use as extracts when writing up the results of the analysis. The chosen extracts should provide a vivid and compelling account of the arguments being made by a respective theme. Multiple extracts should be used from the entire pool of data items that inform a theme in order to convey the diversity of expressions of meaning across these data items, and to demonstrate the cohesion of the theme’s constituent data items. Furthermore, each of the reported data extracts should be subject to a deep analysis, going beyond merely reporting what a participant may have said. Each extract should be interpreted in relation to its constitutive theme, as well as the broader context of the research question(s), creating an analytic narrative that informs the reader what is interesting about this extract and why (Braun and Clarke 2012 ).

Data extracts can be presented either illustratively, providing a surface-level description of what participants said, or analytically, interrogating what has been interpreted to be important about what participants said and contextualising this interpretation in relation to the available literature. If the researcher were aiming to produce a more illustrative write-up of the analysis, relating the results to the available literature would tend to be held until the ‘discussion’ section of the report. If the researcher were aiming to produce an analytical write-up, extracts would tend to be contextualised in relation to the literature as and when they are reported in the ‘results’ section (Braun and Clarke 2013 ; Terry et al. 2017 ). While an illustrative write-up of RTA results is completely acceptable, the researcher should remain cognisant that the narrative of the write-up should communicate the complexities of the data, while remaining “embedded in the scholarly field” (Braun and Clarke 2012 , p. 69). RTA is an interpretive approach to analysis and, as such, the overall report should go beyond describing the data, providing theoretically informed arguments as to how the data addresses the research question(s). To this end, a relatively straightforward test can reveal a researcher’s potential proclivity towards one particular reporting convention: If an extract can be removed and the write-up still makes sense, the reporting style is illustrative; if an extract is removed and the write-up no longer makes sense, the reporting style is analytical (Terry et al. 2017 ).

The example in Box 3 contains a brief excerpt from the sub-theme “the whole-school approach”, which demonstrates the way in which a data extract may be reported in an illustrative manner. Here, the narrative discussed the necessity of having an ‘appropriate educator’ deliver the different aspects of the wellbeing curriculum. One participant provided a particularly useful real-world example of the potential negative implications of having ‘the wrong person’ for this job in relation to physical education (one of the aspects of the wellbeing curriculum). This data extract very much informed the narrative and illustrated participants’ arguments regarding the importance of choosing an appropriate educator for the job.

figure c

Example of data extract reported illustratively

In Box 4, an example is offered of how a data extract may be reported in an analytical manner. This excerpt is also taken from the sub-theme “the whole-school approach”, and also informs the ‘appropriate educator for the job’ narrative. Here, however, sufficient evidence has already been established to illustrate the perspectives of the participants. The report turns to a deeper analysis of what has been said and how it has been said. Specifically, the way in which participants seemed to construe an ‘appropriate educator’ was examined and related to existing literature. The analytical interpretation of this data extract (and others) proposes interesting implications regarding the way in which participants constructed their schema of an ‘appropriate educator’.

figure d

Example of data extract reported analytically

The names of themes are also subject to a final review (if necessary) at this point. Naming themes may seem trivial and might subsequently receive less attention than it actually requires. However, naming themes is a very important task. Theme names are the first indication to the reader of what has been captured from the data. Names should be concise, informative, and memorable. The overriding tendency may be to create names that are descriptors of the theme. Braun and Clarke ( 2013 , 2014 , 2020 ) encourage creativity and advocate the use of catchy names that may more immediately capture the attention of the reader, while also communicating an important aspect of the theme. To this end, they suggest that it may be useful to examine data items for a short extract that could be used to punctuate the theme name.

3.2.6 Phase six: producing the report

The separation between phases five and six can often be blurry. Further, this ‘final’ phase would rarely only occur at the end of the analysis. As opposed to practices typical of quantitative research that would see the researcher conduct and then write up the analysis, the write-up of qualitative research is very much interwoven into the entire process of the analysis (Braun and Clarke 2012 ). Again, as with previous phases, this will likely require a recursive approach to report writing. As codes and themes change and evolve over the course of the analysis, so too can the write-up. Changes should be well documented by this phase and reflected in informal notes and memos, as well as a research journal that should be kept over the entire course of the research. Phase six then, can be seen as the completion and final inspection of the report that the researcher would most likely have begun writing before even undertaking their thematic analysis (e.g. a journal article or thesis/dissertation).

A useful task to address at this point would be to establish the order in which themes are reported. Themes should connect in a logical and meaningful manner, building a cogent narrative of the data. Where relevant, themes should build upon previously reported themes, while remaining internally consistent and capable of communicating their own individual narrative if isolated from other themes (Braun and Clarke 2012 ). I reported the theme “best practice in wellbeing promotion” first, as I felt it established the positivity that seemed to underlie the accounts provided by all of my participants. This theme was also strongly influence by semantic codes, with participants being very capable of describing what they felt would constitute ‘best practice’. I saw this as an easily digestible first theme to ease the reader into the wider analysis. It made sense to report “the axiology of wellbeing promotion” next. This theme introduced the reality that, despite an underlying degree of positivity, participants did indeed have numerous concerns regarding wellbeing promotion, and that participants’ attitudes were generally positive with a significant ‘but’. This theme provided good sign-posting for the next two themes that would be reported, which were “the influence of time” and “incompletely theorised agreements”, respectively. I reported “the influence of time” first, as this theme established how time constraints could negatively affect educator training, contributing to a context in which educators were inadvertently pushed towards adopting incompletely theorised agreements when promoting student wellbeing. The last theme to be reported was “recognising educator wellbeing”. As the purpose of the analysis was to ascertain the attitudes of educators regarding wellbeing promotion, it felt appropriate to offer the closing commentary of the analysis to educators’ accounts of their own wellbeing. This became particularly pertinent when the sub-themes were revised to reflect the influence of pre-existing work-related issues and the subsequent influence of wellbeing promotion.

An issue proponents of RTA may realise when writing up their analysis is the potential for incongruence between traditional conventions for report writing and the appropriate style for reporting RTA—particularly when adopting an analytical approach to reporting on data. The document structure for academic journal articles and Masters or PhD theses typically subscribe to the convention of reporting results of analyses in a ‘results’ section and then synthesising and contextualising the results of analyses in a ‘discussion’ section. Conversely, Braun and Clarke recommend synthesising and contextualising data as and when they are reported in the ‘results’ section (Braun and Clarke 2013 ; Terry et al. 2017 ). This is a significant departure from the traditional reporting convention, which researchers—particularly post-graduate students—may find difficult to reconcile. While Braun and Clarke do not explicitly address this potential issue, it is implicitly evident that they would advocate that researchers prioritise the appropriate reporting style for RTA and not cede to the traditional reporting convention.

4 Conclusion

Although Braun and Clarke are widely published on the topic of reflexive thematic analysis, confusion persists in the wider literature regarding the appropriate implementation of this approach. The aim of this paper has been to contribute to dispelling some of this confusion by provide a worked example of Braun and Clarke’s contemporary approach to reflexive thematic analysis. To this end, this paper provided instruction in how to address the theoretical underpinnings of RTA by operationalising the theoretical assumptions of the example data in relation to the study from which the data was taken. Clear instruction was also provided in how to conduct a reflexive thematic analysis. This was achieved by providing a detailed step-by-step guide to Braun and Clarke’s six-phase process, and by providing numerous examples of the implementation of each phase based on my own research. Braun and Clarke have made (and continue to make) an extremely valuable contribution to the discourse regarding qualitative analysis. I strongly recommended that any prospective proponents of RTA who may read this paper thoroughly examine Braun and Clarke’s full body of literature in this area, and aim to achieve an understanding of RTA’s nuanced position among the numerous different approaches to thematic analysis.

While the reconceptualisation of RTA as falling within the remit of a purely qualitative paradigm precipitates that the research fall on the constructionist end of this continuum, it is nevertheless good practice to explicate this theoretical position.

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Byrne, D. A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Qual Quant 56 , 1391–1412 (2022). https://doi.org/10.1007/s11135-021-01182-y

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Accepted : 06 June 2021

Published : 26 June 2021

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DOI : https://doi.org/10.1007/s11135-021-01182-y

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Inductive thematic analysis, qualitative and quantitative data research paper to use for practical writing help.

An interview of five students was conducted for qualitative data. A set of structured interview question was used to gather information for the qualitative data. For quantitative data, a news article named “Sydney Swans' Adam Goodes celebrates goal with Indigenous war dance, ruffles feathers” was selected.Step 2

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Thematic analysis is one of the most commonly used methods in analyzing qualitative data. It is an approach that has been tried and tested as a rigorous means of analyzing qualitative data in disciplines such as psychology, management and media studies. Boyatzis describes it as a method that “allows for the translation of qualitative information into quantitative data” (4). In this assignment, the first section is the diagrammatic representation of the hierarchical model of the themes developed from the survey responses. The second section describes these themes in detail with quotes to support them.

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Benchmarking: An International Journal

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Article publication date: 24 November 2023

The purpose of this paper is to provide an organizing lens for viewing the distinct contributions to knowledge production from those research communities addressing the impact of competitive strategy on company performance in general, and the influence of cost leadership and differentiation strategy on organizational performance in detail.

Design/methodology/approach

The research methodology was based on the PRISMA review, and thematic analysis based on an iterative process of open coding was analyzed and then the sample was analyzed by illustrating the research title, objectives, method, data analysis, sample size, variables and country.

The main factor that influenced the competitive strategy is strategic growth; strategic growth has a significant influence on competitive strategy. Furthermore, competitive strategy will boost firm network, performance measurement and organization behavior. In the same way, the internal goal factor will enhance organizational effectiveness. Also, a differentiation strategy will support management practice factors, strategic positions, product price, product characteristics and company performance.

Originality/value

This study contributes to the literature by identifying a framework of competitive strategy factors, company performance factors, cost leadership strategy factors, differentiation strategy factors and competitive strategy with global market factors. This study provides a complete picture and description of the resulting body knowledge in competitive strategy and organizational performance.

  • Competitive strategy
  • Cost leadership strategy
  • Differentiation strategy
  • Enterprise performance
  • Competitive advantage

Zairbani, A. and Jaya Prakash, S.K. (2023), "Competitive strategy and organizational performance: a systematic literature review", Benchmarking: An International Journal , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/BIJ-04-2023-0225

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How to Do Thematic Analysis | Guide & Examples

Published on 5 May 2022 by Jack Caulfield .

Thematic analysis is a method of analysing qualitative data . It is usually applied to a set of texts, such as an interview or transcripts . The researcher closely examines the data to identify common themes, topics, ideas and patterns of meaning that come up repeatedly.

There are various approaches to conducting thematic analysis, but the most common form follows a six-step process:

  • Familiarisation
  • Generating themes
  • Reviewing themes
  • Defining and naming themes

This process was originally developed for psychology research by Virginia Braun and Victoria Clarke . However, thematic analysis is a flexible method that can be adapted to many different kinds of research.

Table of contents

When to use thematic analysis, different approaches to thematic analysis, step 1: familiarisation, step 2: coding, step 3: generating themes, step 4: reviewing themes, step 5: defining and naming themes, step 6: writing up.

Thematic analysis is a good approach to research where you’re trying to find out something about people’s views, opinions, knowledge, experiences, or values from a set of qualitative data – for example, interview transcripts , social media profiles, or survey responses .

Some types of research questions you might use thematic analysis to answer:

  • How do patients perceive doctors in a hospital setting?
  • What are young women’s experiences on dating sites?
  • What are non-experts’ ideas and opinions about climate change?
  • How is gender constructed in secondary school history teaching?

To answer any of these questions, you would collect data from a group of relevant participants and then analyse it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large datasets more easily by sorting them into broad themes.

However, it also involves the risk of missing nuances in the data. Thematic analysis is often quite subjective and relies on the researcher’s judgement, so you have to reflect carefully on your own choices and interpretations.

Pay close attention to the data to ensure that you’re not picking up on things that are not there – or obscuring things that are.

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Once you’ve decided to use thematic analysis, there are different approaches to consider.

There’s the distinction between inductive and deductive approaches:

  • An inductive approach involves allowing the data to determine your themes.
  • A deductive approach involves coming to the data with some preconceived themes you expect to find reflected there, based on theory or existing knowledge.

There’s also the distinction between a semantic and a latent approach:

  • A semantic approach involves analysing the explicit content of the data.
  • A latent approach involves reading into the subtext and assumptions underlying the data.

After you’ve decided thematic analysis is the right method for analysing your data, and you’ve thought about the approach you’re going to take, you can follow the six steps developed by Braun and Clarke .

The first step is to get to know our data. It’s important to get a thorough overview of all the data we collected before we start analysing individual items.

This might involve transcribing audio , reading through the text and taking initial notes, and generally looking through the data to get familiar with it.

Next up, we need to code the data. Coding means highlighting sections of our text – usually phrases or sentences – and coming up with shorthand labels or ‘codes’ to describe their content.

Let’s take a short example text. Say we’re researching perceptions of climate change among conservative voters aged 50 and up, and we have collected data through a series of interviews. An extract from one interview looks like this:

In this extract, we’ve highlighted various phrases in different colours corresponding to different codes. Each code describes the idea or feeling expressed in that part of the text.

At this stage, we want to be thorough: we go through the transcript of every interview and highlight everything that jumps out as relevant or potentially interesting. As well as highlighting all the phrases and sentences that match these codes, we can keep adding new codes as we go through the text.

After we’ve been through the text, we collate together all the data into groups identified by code. These codes allow us to gain a condensed overview of the main points and common meanings that recur throughout the data.

Next, we look over the codes we’ve created, identify patterns among them, and start coming up with themes.

Themes are generally broader than codes. Most of the time, you’ll combine several codes into a single theme. In our example, we might start combining codes into themes like this:

At this stage, we might decide that some of our codes are too vague or not relevant enough (for example, because they don’t appear very often in the data), so they can be discarded.

Other codes might become themes in their own right. In our example, we decided that the code ‘uncertainty’ made sense as a theme, with some other codes incorporated into it.

Again, what we decide will vary according to what we’re trying to find out. We want to create potential themes that tell us something helpful about the data for our purposes.

Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the dataset and compare our themes against it. Are we missing anything? Are these themes really present in the data? What can we change to make our themes work better?

If we encounter problems with our themes, we might split them up, combine them, discard them, or create new ones: whatever makes them more useful and accurate.

For example, we might decide upon looking through the data that ‘changing terminology’ fits better under the ‘uncertainty’ theme than under ‘distrust of experts’, since the data labelled with this code involves confusion, not necessarily distrust.

Now that you have a final list of themes, it’s time to name and define each of them.

Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data.

Naming themes involves coming up with a succinct and easily understandable name for each theme.

For example, we might look at ‘distrust of experts’ and determine exactly who we mean by ‘experts’ in this theme. We might decide that a better name for the theme is ‘distrust of authority’ or ‘conspiracy thinking’.

Finally, we’ll write up our analysis of the data. Like all academic texts, writing up a thematic analysis requires an introduction to establish our research question, aims, and approach.

We should also include a methodology section, describing how we collected the data (e.g., through semi-structured interviews or open-ended survey questions ) and explaining how we conducted the thematic analysis itself.

The results or findings section usually addresses each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question.

In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents’ perceptions.

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