examples of research questions for thematic analysis

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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:

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:

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.

Does your Research Methodology Have the Following?

• Great Research/Sources
• Perfect Language
• Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

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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You can transcribe an interview by converting a conversation into a written format including question-answer recording sessions between two or more people.

Descriptive research is carried out to describe current issues, programs, and provides information about the issue through surveys and various fact-finding methods.

Quantitative research is associated with measurable numerical data. Qualitative research is where a researcher collects evidence to seek answers to a question.

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• How to Do Thematic Analysis | Guide & Examples

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.

Prevent plagiarism, run a free check.

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.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Caulfield, J. (2022, May 05). How to Do Thematic Analysis | Guide & Examples. Scribbr. Retrieved 25 March 2024, from https://www.scribbr.co.uk/research-methods/thematic-analysis-explained/

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

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• 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

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

• View inline

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

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.

• Ziebland S ,
• ↵ A Hybrid Approach to Thematic Analysis in Qualitative Research: Using a Practical Example. 2018. https://methods.sagepub.com/case/hybrid-approach-thematic-analysis-qualitative-research-a-practical-example .
• Maguire M ,
• Vindrola-Padros C ,
• Vindrola-Padros B
• ↵ Vindrola-Padros C. Rapid Ethnographies: A Practical Guide . Cambridge University Press 2021. https://play.google.com/store/books/details?id=n80HEAAAQBAJ
• Schroter S ,
• Merino JG ,
• Barbeau A ,
• ↵ Padgett DK. Qualitative and Mixed Methods in Public Health . SAGE Publications 2011. https://play.google.com/store/books/details?id=LcYgAQAAQBAJ
• Scharp KM ,
• Korstjens I
• Barnett-Page E ,
• ↵ Guest G, Namey EE, Mitchell ML. Collecting Qualitative Data: A Field Manual for Applied Research . SAGE 2013. https://play.google.com/store/books/details?id=-3rmWYKtloC
• Sainsbury P ,
• Emerson RM ,
• Saunders B ,
• Kingstone T ,
• Hennink MM ,
• Kaiser BN ,
• Hennink M ,
• O’Connor C ,
• ↵ Yen RW, Schubbe D, Walling L, et al. Patient engagement in the What Matters Most trial: experiences and future implications for research. Poster presented at International Shared Decision Making conference, Quebec City, Canada. July 2019.
• ↵ Got questions about Thematic Analysis? We have prepared some answers to common ones. https://www.thematicanalysis.net/faqs/ (accessed 9 Nov 2022).
• ↵ Braun V, Clarke V. Thematic Analysis. SAGE Publications. 2022. https://uk.sagepub.com/en-gb/eur/thematic-analysis/book248481 .
• Kalpokas N ,
• Radivojevic I
• Campbell KA ,
• Durepos P ,
• ↵ Understanding Thematic Analysis. https://www.thematicanalysis.net/understanding-ta/ .
• Saunders CH ,
• Stevens G ,
• CONFIDENT Study Long-Term Care Partners
• MacQueen K ,
• Vaismoradi M ,
• Turunen H ,
• Schott SL ,
• Berkowitz J ,
• Carpenter-Song EA ,
• Goldwag JL ,
• Durand MA ,
• Goldwag J ,
• Saunders C ,
• Mishra MK ,
• Rodriguez HP ,
• Shortell SM ,
• Verdinelli S ,
• Scagnoli NI
• Campbell C ,
• Sparkes AC ,
• McGannon KR
• Sandelowski M ,
• Connelly LM ,
• O’Malley AJ ,

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How to do thematic analysis

Last updated

8 February 2023

Reviewed by

Miroslav Damyanov

Uncovering themes in data requires a systematic approach. Thematic analysis organizes data so you can easily recognize the context.

• What is thematic analysis?

Thematic analysis is   a method for analyzing qualitative data that involves reading through a data set and looking for patterns to derive themes . The researcher's subjective experience plays a central role in finding meaning within the data.

Streamline your thematic analysis

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• What are the main approaches to thematic analysis?

Inductive thematic analysis approach

Inductive thematic analysis entails   deriving meaning and identifying themes from data with no preconceptions.  You analyze the data without any expected outcomes.

Deductive thematic analysis approach

In the deductive approach, you analyze data with a set of expected themes. Prior knowledge, research, or existing theory informs this approach.

Semantic thematic analysis approach

With the semantic approach, you ignore the underlying meaning of data. You take identifying themes at face value based on what is written or explicitly stated.

Latent thematic analysis approach

Unlike the semantic approach, the latent approach focuses on underlying meanings in data and looks at the reasons for semantic content. It involves an element of interpretation where you theorize meanings and don’t just take data at face value.

• When should thematic analysis be used?

Thematic analysis is beneficial when you’re working with large bodies of data. It allows you to divide and categorize huge quantities of data in a way that makes it far easier to digest.  

The following scenarios warrant the use of thematic analysis:

You’re new to qualitative analysis

You need to identify patterns in data

You want to involve participants in the process

Thematic analysis is particularly useful when you’re looking for subjective information such as experiences and opinions in surveys , interviews, conversations, or social media posts. 

• What are the advantages and disadvantages of thematic analysis?

Thematic analysis is a highly flexible approach to qualitative data analysis that you can modify to meet the needs of many studies. It enables you to generate new insights and concepts from data. 

Beginner researchers who are just learning how to analyze data will find thematic analysis very accessible. It’s easy for most people to grasp and can be relatively quick to learn.

The flexibility of thematic analysis can also be a disadvantage. It can feel intimidating to decide what’s important to emphasize, as there are many ways to interpret meaning from a data set.

• What is the step-by-step process for thematic analysis?

The basic thematic analysis process requires recognizing codes and themes within a data set. A code is a label assigned to a piece of data that you use to identify and summarize important concepts within a data set. A theme is a pattern that you identify within the data. Relevant steps may vary based on the approach and type of thematic analysis, but these are the general steps you’d take:

1. Familiarize yourself with the data(pre-coding work)

Before you can successfully work with data, you need to understand it. Get a feel for the data to see what general themes pop up. Transcribe audio files and observe any meanings and patterns across the data set. Read through the transcript, and jot down notes about potential codes to create. 

2. Create the initial codes (open code work)

Create a set of initial codes to represent the patterns and meanings in the data. Make a codebook to keep track of the codes. Read through the data again to identify interesting excerpts and apply the appropriate codes. You should use the same code to represent excerpts with the same meaning. 

3. Collate codes with supporting data (clustering of initial code)

Now it's time to group all excerpts associated with a particular code. If you’re doing this manually, cut out codes and put them together. Thematic analysis software will automatically collate them.

4. Group codes into themes (clustering of selective codes)

Once you’ve finalized the codes, you can sort them into potential themes. Themes reflect trends and patterns in data. You can combine some codes to create sub-themes.

5. Review, revise, and finalize the themes (final revision)

Now you’ve decided upon the initial themes, you can review and adjust them as needed. Each theme should be distinct, with enough data to support it. You can merge similar themes and remove those lacking sufficient supportive data. Begin formulating themes into a narrative. 

6. Write the report

The final step of telling the story of a set of data is writing the report. You should fully consider the themes to communicate the validity of your analysis.

A typical thematic analysis report contains the following:

An introduction

A methodology section

Results and findings

A conclusion

Your narrative must be coherent, and it should include vivid quotes that can back up points. It should also include an interpretive analysis and argument for your claims. In addition, consider reporting your findings in a flowchart or tree diagram, which can be independent of or part of your report.  

In conclusion, a thematic analysis is a method of analyzing qualitative data. By following the six steps, you will identify common themes from a large set of texts. This method can help you find rich and useful insights about people’s experiences, behaviors, and nuanced opinions.

• How to analyze qualitative data

Qualitative data analysis is the process of organizing, analyzing, and interpreting non-numerical and subjective data . The goal is to capture themes and patterns, answer questions, and identify the best actions to take based on that data. 

Researchers can use qualitative data to understand people’s thoughts, feelings, and attitudes. For example, qualitative researchers can help business owners draw reliable conclusions about customers’ opinions and discover areas that need improvement. 

In addition to thematic analysis, you can analyze qualitative data using the following:

Content analysis

Content analysis examines and counts the presence of certain words, subjects, and contexts in documents and communication artifacts, such as: 

Text in various formats

This method transforms qualitative input into quantitative data. You can do it manually or with electronic tools that recognize patterns to make connections between concepts.  

Narrative analysis

Narrative analysis interprets research participants' stories from testimonials, case studies, interviews, and other text or visual data. It provides valuable insights into the complexity of people's feelings, beliefs, and behaviors.

Discourse analysis

In discourse analysis , you analyze the underlying meaning of qualitative data in a particular context, including: 

Historical 

This approach allows us to study how people use language in text, audio, and video to unravel social issues, power dynamics, or inequalities. 

For example, you can look at how people communicate with their coworkers versus their bosses. Discourse analysis goes beyond the literal meaning of words to examine social reality.

Grounded theory analysis

In grounded theory analysis, you develop theories by examining real-world data. The process involves creating hypotheses and theories by systematically collecting and evaluating this data. While this approach is helpful for studying lesser-known phenomena, it might be overwhelming for a novice researcher. 

• Challenges with analyzing qualitative data

While qualitative data can answer questions that quantitative data can't, it still comes with challenges.

If done manually, qualitative data analysis is very time-consuming.

It can be hard to choose a method. 

Avoiding bias is difficult.

Human error affects accuracy and consistency.

To overcome these challenges, you should fine-tune your methods by using the appropriate tools in collaboration with teammates.

Learn more about thematic analysis software

What is thematic analysis in qualitative research.

Thematic analysis is a method of analyzing qualitative data. It is applied to texts, such as interviews or transcripts. The researcher closely examines the data to identify common patterns and themes.

Can thematic analysis be done manually?

You can do thematic analysis manually, but it is very time-consuming without the help of software.

What are the two types of thematic analysis?

The two main types of thematic analysis include codebook thematic analysis and reflexive thematic analysis.

Codebook thematic analysis uses predetermined codes and structured codebooks to analyze from a deductive perspective. You draw codes from a review of the data or an initial analysis to produce the codebooks.

Reflexive thematic analysis is more flexible and does not use a codebook. Researchers can change, remove, and add codes as they work through the data. 

What makes a good thematic analysis?

The goal of thematic analysis is more than simply summarizing data; it's about identifying important themes. Good thematic analysis interprets, makes sense of data, and explains it. It produces trustworthy and insightful findings that are easy to understand and apply. 

What are examples of themes in thematic analysis?

Grouping codes into themes summarize sections of data in a useful way to answer research questions and achieve objectives. A theme identifies an area of data and tells the reader something about it. A good theme can sit alone without requiring descriptive text beneath it.

For example, if you were analyzing data on wildlife, codes might be owls, hawks, and falcons. These codes might fall beneath the theme of birds of prey. If your data were about the latest trends for teenage girls, codes such as mini skirts, leggings, and distressed jeans would fall under fashion.  

Thematic analysis is straightforward and intuitive enough that most people have no trouble applying it.

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How to do a thematic analysis

What is a thematic analysis?

When is thematic analysis used, braun and clarke’s reflexive thematic analysis, the six steps of thematic analysis, 1. familiarizing, 2. generating initial codes, 3. generating themes, 4. reviewing themes, 5. defining and naming themes, 6. creating the report, the advantages and disadvantages of thematic analysis, disadvantages, frequently asked questions about thematic analysis, related articles.

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.

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.

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.

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.

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.

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.

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?

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.”

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.

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.

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.

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.

Thematic Analysis 101

6 Time-Saving Tips To Help You Ace Your Analysis

By: David Phair (PhD) & Amy Murdock (PhD) | September 2022

Thematic analysis is an effective method for researchers to derive meaning from large sets of qualitative data. At a basic level, it involves grouping together “codes” ( labels assigned to pieces of text ) according to common themes and relationships to reveal underlying patterns of meaning .

At Grad Coach, we’ve noticed that many students view thematic analysis as a rather daunting and drawn-out process. So, in this post, we’ll discuss 6 time-saving tips you can use for thematic analysis, along with loads of examples.

Overview: Thematic Analysis 101

• Use the golden thread to maintain consistency
• Adopt a highly iterative approach
• Expect (and embrace) the unexpected
• Keep a detailed research journal
• Make use of visualisation methods/tools
• Get input from outsiders

#1 Keep the golden thread front of mind

As we’ve covered in previous posts, it’s essential to keep your golden thread (which consists of your research aims, objectives and questions ) front of mind when you analyse your data. You should regularly refer back to your research questions , as they’ll form the foundation for the themes that you’ll identify and analyse. You’ll also make it much easier to analyse your data and maintain consistency throughout your dissertation this way.

In addition to this, you’ll want to consider the type of research questions you use: for example, are they exploratory , predictive , or interpretive ? In other words, are you approaching your data in an open-minded way to gain new insights, or are you trying to make predictions for your findings? This will help shape your thinking and guide your analysis as you work through the process.

In a similar vein, you’ll need to account for your theoretical framework , as this will help you organise and structure your analysis. For example, if you conduct research through the lens of social identity theory, then you’ll look for the underlying patterns and themes in how your participants talk about their personal relationships.

Once you’ve analysed and grouped your codes into themes, it’s good to compare them with your golden thread to ensure alignment. This saves precious time, as it prevents you from only realising at the end of the project that you’ve deviated from your research aims.

An easy way to remind yourself of your golden thread is to make your research questions highly visible as you conduct your research and write up your chapters. You could, for example, keep your research questions in your document header as you write up (and remove it once you’re done, of course).

#2 Take a highly iterative approach

The repetitive nature of thematic analysis means that it requires multiple rounds to identify core themes and sub-themes. For example, the first round may involve identifying those main themes , the second for sub-themes , and the third for spotting themes that don’t have the support from the data that you initially anticipated.

Another thing to remember is that you’ll likely have expectations with respect to what your data will reveal. To minimise this (inevitable) bias in your research , you’ll want to circle back to the raw data (such as your interview transcripts) regularly. This helps you to gain new insights and nuances that you didn’t quite capture in the initial rounds of analysis. In the end, reviewing data from multiple angles is an inherent part of qualitative research.

From a practical perspective, a great way to gain a fresh perspective for thematic analysis is to take regular breaks . When you’re so entrenched in your research, you’re bound to get tunnel vision , which is bad for avoiding bias and missing those small details that may cost you precious marks. Ultimately, you’ll need to demonstrate to your marker that your research process was rigorous, methodical, and systematic .

PS – if you get stuck, Braun and Clarke’s YouTube channel is full of helpful resources for using thematic analysis.

Need a helping hand?

#3 Expect (and embrace) the unexpected

Since qualitative research is inherently a constantly evolving process, you’ll have to accept that your findings may subvert your expectations . In these situations, we often find that younger students experience “imposter syndrome”. This needn’t be the case, as unexpected findings don’t necessarily mean that you’ve done something wrong.

For example, you may set out to study the relationship between classical music and emotional experience. You may intuitively expect that classical music improves one’s mood, but then find that it actually dampens it. If you’ve followed all the correct steps, then this is an extremely valuable contribution to existing research and warrants further investigation.

So, if your findings don’t align with the existing literature (which you explored in your literature review ), that’s okay. This is science after all, so unexpected findings supported by data are still good findings . In the end, this will enrich and contribute to the research that you’re engaged with.

At the same time, you should avoid completely ditching findings that support your expectations in lieu of those that don’t. Qualitative research is all about nuance , so it’s crucial to realise that all types of findings can be valid, provided the research process has been carried out correctly.

#4 Keep a detailed research journal

Since it’s important to convey that you were rigorous and systematic in your research, you should always be transparent about your approach and process. In other words, you’ll want each step you took (and the logic behind them) to be clear.

To help keep track of each of your design choices over extended periods of time, it’s extremely useful to keep a separate research journal. The research process is a lengthy one and you’ll forget the small (but important) details if you don’t keep a record. If you’re more digitally inclined, use Word and/or Excel and their comment features to take notes. Alternatively, hand-written notes also help to stimulate the thought process.

When conducting interviews, it’s a good idea to take “thick notes” to capture non-verbal communication. This basically means that you take exhaustive and specific notes to capture nuances in your observations. For example, a participant may give a long pause before answering a specific question. This may seem inconsequential, but write it down anyway! It could be a sign of being uncomfortable with the topic and you may even notice similar trends with other participants later on.

Remember that you don’t have to worry about how your research journal looks or what it contains. In the end, no one’s going to mark it. So just make sure that you keep it in a way that makes sense to you. Additionally, taking more notes than you may need is always better. Doing so will help turn the data into usable writing for your research project.

Lastly, a research journal is also helpful for reminding yourself of why you made certain choices. You’ll be able to remember why you’ve made certain decisions, thus building your confidence when you have to defend your research .

#5 Use visualisation methods/tools

It’s important to engage with your data through different approaches to determine the most relevant findings. To help gain a different perspective of your data and codes, it’s helpful to visualise them in different ways . This in turn will force you to think differently, which will help with identifying groups of related codes.

For example, you can use charts, pictures, graphs, spreadsheets, and/or PowerPoints – whatever makes sense to you. There’s also plenty of software for you to create mindmaps, such as Inspiration or XMind. You can use colour-coding in Excel to help cluster related pieces of data, and concurrently separate unrelated pieces of data. Word clouds also help to generate ideas and identify common phrases. At the same time, don’t underestimate the power of “offline” visualisation – sticky notes and whiteboards can also be very useful methods.

Again, just as with your research journal, don’t fixate on how these visuals look , as you won’t publish them in your final dissertation. First and foremost, they are there to help develop your themes. They could, however, form the basis for a formal visual in your final document.

#6 Get input from outsiders

The whole research process can be very overwhelming, complicated, and time-consuming, especially with the sheer amount of reading involved. Luckily, you don’t have to do everything alone. To help instil confidence in yourself, your audience, and your markers, utilise outside opinions . This will only help to refine your analysis and findings.

Within qualitative research, you can use a method called “ member checking ”. Basically, once you’ve generated your themes, you send them back to the participants you’ve interviewed to ensure that you’ve captured their thoughts accurately.

Similarly, you can employ the use of “ peer briefing ”, where someone who is not involved in your study (but with some academic background) reviews your findings. They’ll act as a fresh pair of eyes to help confirm that the study progresses logically from its aims to its conclusions. This is also crucial to identify and avoid bias.

Remember, it’s perfectly natural to feel doubt and uncertainty . Every established researcher started as a student with zero published articles and similar feelings. All that time spent systematically engaging with the research will make you an expert in that field, so have faith in yourself !

Recap: Thematic Analysis Tips

In this post, we covered 6 time-saving tips for using thematic analysis in qualitative research:

If you’re looking for a helping hand with thematic analysis, or any other aspect of your dissertation, thesis, or research project, check out our one-on-one private coaching service, where we hold your hand throughout the research process, step by step.

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 in qualitative research.

11 min read Your guide to thematic analysis, a form of qualitative research data analysis used to identify patterns in text, video and audio data.

What is thematic analysis?

Thematic analysis is used to analyze qualitative data – that is, data relating to opinions, thoughts, feelings and other descriptive information. It’s become increasingly popular in social sciences research, as it allows researchers to look at a data set containing multiple qualitative sources and pull out the broad themes running through the entire data set.

That data might consist of articles, diaries, blog posts, interview transcripts, academic research, web pages, social media and even audio and video files. They are put through data analysis as a group, with researchers seeking to identify patterns running through the corpus as a whole.

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Thematic analysis steps

Image source: https://www.nngroup.com/articles/thematic-analysis/

While there are many types of thematic analysis, the thematic analysis process can be generalized into six steps. Thematic analysis involves initial analysis, coding data, identifying themes and reporting on the findings.

• Familiarization – During the first stage of thematic analysis, the research teams or researchers become familiar with the dataset. This may involve reading and re-reading, and even transcribing the data. Researchers may note down initial thoughts about the potential themes they perceive in the data, which can be the starting point for assigning initial codes.
• Coding – Codes in thematic analysis are the method researchers use to identify the ideas and topics in their data and refer to them quickly and easily. Codes can be assigned to snippets of text data or clips from videos and audio files. Depending on the type of thematic analysis used, this can be done with a systematic and rigorous approach, or in a more intuitive manner.
• Identifying theme – Themes are the overarching ideas and subject areas within the corpus of research data. Researchers can identify themes by collating together the results of the coding process, generating themes that tie together the identified codes into groups according to their meaning or subject matter.
• Reviewing themes – Once the themes have been defined, the researchers check back to see how well the themes support the coded data extracts. At this stage they may start to organize the themes into a map, or early theoretical framework.
• Defining and naming themes – As researchers spend more time reviewing the themes, they begin to define them more precisely, giving them names. Themes are different from codes, because they capture patterns in the data rather than just topics, and they relate directly to the research question.
• Writing up – At this stage, researchers begin to develop the final report, which offers a comprehensive summary of the codes and themes, extracts from the original data that illustrate the findings, and any other data relevant to the analysis. The final report may include a literature review citing other previous research and the observations that helped frame the research question. It can also suggest areas for future research the themes support, and which have come to light during the research process.

Another step which precedes all of these is data collection. Common to almost all forms of qualitative analysis, data collection means bringing together the materials that will be part of the data set, either by finding secondary data or generating first-party data through interviews, surveys and other qualitative methods.

Types of thematic analysis

There are various thematic analysis approaches currently in use. For the most part, they can be viewed as a continuum between two different ideologies. Reflexive thematic analysis (RTA) sits at one end of the continuum of thematic analysis methods. At the other end is code reliability analysis.

Code reliability analysis emphasizes the importance of the codes given to themes in the research data being as accurate as possible. It takes a technical or pragmatic view, and places value on codes being replicable between different researchers during the coding process. Codes are based on domain summaries, which often link back to the questions in a structured research interview.

Researchers using a code reliability approach may use a codebook. A codebook is a detailed list of codes and their definitions, with exclusions and examples of how the codes should be applied.

Reflexive thematic analysis was developed by Braun & Clarke in 2006 for use in the psychology field. In contrast to code reliability analysis, it isn’t concerned with consistent codes that are agreed between researchers. Instead, it acknowledges and finds value in each researcher’s interpretation of the thematic content and how it influences the coding process. The codes they assign are specific to them and exist within a unique context that is made up of:

• The data set
• The assumptions made during the setup of the analysis process
• The researcher’s skills and resources

This doesn’t mean that reflexive thematic analysis should be unintelligible to anyone other than the researcher. It means that the researcher’s personal subjectivity and uniqueness is made part of the process, and is expected to have an influence on the findings. Reflexive thematic analysis is a flexible method, and initial codes may change during the process as the researcher’s understanding evolves.

Reflexive thematic analysis is an inductive approach to qualitative research. With an inductive approach, the final analysis is based entirely on the data set itself, rather than from any preconceived themes or structures from the research team.

Image source: https://delvetool.com/blog/thematicanalysis

Thematic analysis vs other qualitative research methods

Thematic analysis sits within a whole range of qualitative analysis methods which can be applied to social sciences, psychology and market research data.

• Thematic analysis vs comparative analysis – Comparative analysis and thematic analysis are closely related, since they both look at relationships between multiple data sources. Comparative analysis is a form of qualitative research that works with a smaller number of data sources. It focuses on causal relationships between events and outcomes in different cases, rather than on defining themes.
• Thematic analysis vs discourse analysis – Unlike discourse analysis, which is a type of qualitative research that focuses on spoken or written conversational language, thematic analysis is much more broad in scope, covering many kinds of qualitative data.
• Thematic analysis vs narrative analysis – Narrative analysis works with stories – it aims to keep information in a narrative structure, rather than allowing it to be fragmented, and often to study the stories from participants’ lives. Thematic analysis can break narratives up as it allocates codes to different parts of a data source, meaning that the narrative context might be lost and even that researchers might miss nuanced data.
• Thematic analysis vs content analysis – Both content analysis and thematic analysis use data coding and themes to find patterns in data. However, thematic analysis is always qualitative, but researchers agree there can be quantitative and qualitative content analysis, with numerical approaches to the frequency of codes in content analysis data.

Thematic analysis advantages and disadvantages

Like any kind of qualitative analysis, thematic analysis has strengths and weaknesses. Whether it’s right for you and your research project will depend on your priorities and preferences.

Thematic analysis advantages

• Easy to learn – Whether done manually or assisted by technology, the thematic analysis process is easy to understand and conduct, without the need for advanced statistical knowledge
• Flexible – Thematic analysis allows qualitative researchers flexibility throughout the process, particularly if they opt for reflexive thematic analysis
• Broadly applicable – Thematic analysis can be used to address a wide range of research questions.

Thematic analysis – the cons

As well as the benefits, there are some disadvantages thematic analysis brings up.

• Broad scope – In identifying patterns on a broad scale, researchers may become overwhelmed with the volume of potential themes, and miss outlier topics and more nuanced data that is important to the research question.
• Themes or codes? – It can be difficult for novice researchers to feel confident about the difference between themes and codes
• Language barriers – Thematic analysis relies on language-based codes that may be difficult to apply in multilingual data sets, especially if the researcher and / or research team only speaks one language.

How can you use thematic analysis for business research?

Thematic analysis, and other forms of qualitative research, are highly valuable to businesses who want to develop a deeper understanding of the people they serve, as well as the people they employ. Thematic analysis can help your business get to the ‘why’ behind the numerical information you get from quantitative research.

An easy way to think about the interplay between qualitative data and quantitative data is to consider product reviews. These typically include quantitative data in the form of scores (like ratings of up to 5 stars) plus the explanation of the score written in a customer’s own words. The word part is the qualitative data. The scores can tell you what is happening – lots of 3 star reviews indicate there’s some room for improvement for example – but you need the addition of the qualitative data, the review itself, to find out what’s going on.

Qualitative data is rich in information but hard to process manually. To do qualitative research at scale, you need methods like thematic analysis to get to the essence of what people think and feel without having to read and remember every single comment.

Qualitative analysis is one of the ways businesses are borrowing from the world of academic research, notably social sciences, statistical data analysis and psychology, to gain an advantage in their markets.

Analyzing themes across video, text, audio and more

Carrying out thematic analysis manually may be time-consuming and painstaking work, even with a large research team. Fortunately, machine learning and other technologies are now being applied to data analysis of all kinds, including thematic analysis, taking the manual work out of some of the more laborious thematic analysis steps.

The latest iterations of machine learning tools are able not only to analyze text data, but to perform efficient analysis of video and audio files, matching the qualitative coding and even helping build out the thematic map, while respecting the researcher’s theoretical commitments and research design.

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

• Open access
• 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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Avoid common mistakes on your manuscript.

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.

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.

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.

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 .

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.

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’.

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.

Boyatzis, R.E.: Transforming Qualitative Information: Thematic Analysis and Code Development. Sage Publications, Thousand Oaks (1998)

Google Scholar  

Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3 (2), 77–101 (2006). https://doi.org/10.1191/1478088706qp063oa

Article   Google Scholar  

Braun, V., Clarke, V.: Thematic analysis. In: Cooper, H., Camic, P.M., Long, D.L., Panter, A.T., Rindskopf, D., Sher, K.J. (eds.) APA Handbook of Research Methods in Psychology, Research Designs, vol. 2, pp. 57–71. American Psychological Association, Washington (2012)

Braun, V., Clarke, V.: Successful Qualitative Research: A Practical Guide for Beginners. Sage Publications, Thousand Oaks (2013)

Braun, V., Clarke, V.: Thematic analysis. In: Teo, T. (ed.) Encyclopedia of Critical Psychology, pp. 1947–1952. Springer, New York (2014)

Braun, V., Clarke, V.: Reflecting on reflexive thematic analysis. Qual. Res. Sport Exerc. Health 11 (4), 589–597 (2019). https://doi.org/10.1080/2159676X.2019.1628806

Braun, V., Clarke, V.: One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qual. Res. Psychol. (2020). https://doi.org/10.1080/14780887.2020.1769238

Braun, V., Clarke, V., Weate, P.: Using Thematic Analysis in sport and exercise research. In: Smith, B., Sparkes, A.C. (eds.) Routledge Handbook of Qualitative Research in Sport and Exercise, pp. 191–205. Routledge, London (2016)

Braun, V., Clarke, V., Terry, G., Hayfield, N.: Thematic analysis. In: Liamputtong, P. (ed.) Handbook of Research Methods in Health and Social Sciences, pp. 843–860. Springer, Singapore (2018)

Braun, V., Clarke, V., Hayfield, N., Terry, G.: Answers to frequently asked questions about thematic analysis (2019). Retrieved from https://cdn.auckland.ac.nz/assets/psych/about/our-research/documents/Answers%20to%20frequently%20asked%20questions%20about%20thematic%20analysis%20April%202019.pdf

Burr, V.: An Introduction to Social Constructionism. Routledge, London, UK (1995)

Book   Google Scholar  

Clarke, V., Braun, V.: Thematic Analysis. In: Lyons, E., Coyle, A. (eds.) Analysing Qualitative Data in Psychology, 2nd edn., pp. 84–103. Sage Publications, London (2016)

Frith, H., Gleeson, K.: Clothing and embodiment: men managing body image and appearance. Psychol. Men Mascul. 5 (1), 40–48 (2004). https://doi.org/10.1037/1524-9220.5.1.40

Joffe, H.: Thematic analysis. In: Harper, D., Thompson, A.R. (eds.) Qualitative Research Methods in Mental Health and Psychotherapy: A Guide for Students and Practitioners, pp. 209–223. Wiley, Chichester (2012)

King, N., Brooks, J.M.: Template analysis for business and management students. Sage Publications, London, UK (2017)

Patton, M.Q.: Qualitative Evaluation and Research Methods, 2nd edn. Sage Publications, Thousand Oaks (1990)

Schwandt, T.A.: Constructivist, interpretivist approaches to human inquiry. In: Denzin, N.K., Lincoln, Y.S. (eds.) The Landscape of Qualitative Research: Theories and Issues, pp. 221–259. Sage Publications, Thousand Oaks (1998)

Smith, J., Firth, J.: Qualitative data analysis: The framework approach. Nurse Res. 18 (2), 52–62 (2011). https://doi.org/10.7748/nr2011.01.18.2.52.c8284

Terry, G., Hayfield, N., Braun, V., Clarke, V.: Thematic analysis. In: Willig, C., Rogers, W.S. (eds.) The SAGE Handbook of Qualitative Research in Psychology, pp. 17–37. Sage Publications, London (2017)

Chapter   Google Scholar  

Vaismoradi, M., Turunen, H., Bondas, T.: Content analysis and thematic analysis: implications for conducting a qualitative descriptive study. Nurs. Health Sci. 15 (3), 398–405 (2013). https://doi.org/10.1111/nhs.12048

Widdicombe, S., Wooffitt, R.: The Language of Youth Subcultures: Social Identity in Action. Harvester, Hemel Hempstead (1995)

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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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A Comprehensive Guide to Thematic Analysis in Qualitative Research

What is Qualitative Data?

What do all the methods above have in common? They result in loads of qualitative data. If you're not new here, you've heard us mention qualitative data many times already. Qualitative data is non-numeric data that is collected in the form of words, images, or sound bites. Qual data is often used to understand people's experiences, perspectives, and motivations, and is often collected and sorted by UX Researchers to better understand the company's users. Qualitative data is subjective and often in response to open-ended questions, and is typically analyzed through methods such as thematic analysis, content analysis, and discourse analysis. In this resource we'll be focusing specifically on how to conduct an effective thematic analysis from scratch! Qualitative data is the sister of quantitative data, which is data that is collected in the form of numbers and can be analyzed using statistical methods. Qualitative and quantitative data are often used together in mixed methods research, which combines both types of data to gain a more comprehensive understanding of a research question.

UX Research Methods

There are many different types of UX research methods that can be used to gather insights about user behavior and attitudes. Some common UX research methods include:

• Interviews: One-on-one conversations with users to gather detailed information about their experiences, needs, and preferences.
• Surveys: Online or paper-based questionnaires that can be used to gather large amounts of data from a broad group of users.
• Focus groups: Group discussions with a moderated discussion to explore user attitudes and behaviors.
• User testing: Observing users as they interact with a product or service to identify problems and gather feedback.
• Ethnographic research: Observing and interacting with users in their natural environments to gain a deep understanding of their behaviors and motivations.
• Card sorting: A technique used to understand how users categorize and organize information.
• Tree testing: A method used to evaluate the effectiveness of a website's navigation structure.
• Heuristic evaluation: A method used to identify usability issues by having experts review a product and identify potential problems.
• Expert review: Gathering feedback from industry experts on a product or service to identify potential issues and areas for improvement.

Introduction to Thematic Analysis of Qualitative Data

Thematic analysis is a popular way of analyzing qualitative data, like transcripts or interview responses, by identifying and analyzing recurring themes (hence the name!). This method often follows a six-step process, which includes getting familiar with the data, sorting and coding the data, generating your various themes, reviewing and editing these themes, defining and naming the themes, and writing up the results to present. This process can help researchers avoid confirmation bias in their analysis. Thematic analysis was developed for psychology research, but it can be used in many different types of research and is especially prevalent in the UX research profession.

When to Use Thematic Analysis

Thematic analysis is a useful method for analyzing qualitative data when you are interested in understanding the underlying themes and patterns in the data. Some situations in which thematic analysis might be appropriate include:

• When you have a large amount of qualitative data, such as transcripts from interviews or focus groups.
• When you want to understand people's experiences, perspectives, or motivations in depth.
• When you want to identify patterns or themes that emerge from the data.
• When you want to explore complex and open-ended research questions.
• When you are interested in understanding how people make sense of their experiences and the world around them.

Some UX research specific questions that could be a good fit for thematic analysis are:

• How do users think about their experiences with a particular product, service or company?
• What are the common challenges that a user might encounter when using a product or service, and how do they overcome them?
• How do users make sense of the navigation of a website or app?
• What are the key drivers of user satisfaction or dissatisfaction with a product or service?
• How do users' experiences with a product or service compare with their expectations?

It is important to keep in mind that thematic analysis is just one of many methods for analyzing qualitative data, and it may not be the most appropriate method for every research question or situation. A key part of a UX researcher's role is being aware of the most appropriate research method to use based on the problem the company is trying to solve and the constraints of the company's research practice.

Types of Thematic Analysis

There are two primary types of thematic analysis, called inductive and deductive approaches. An inductive approach involves going into the study blind, and allowing the results of the data-capture to guide and shape the analysis and theming. Think of it like induction heating-- the data heats your results! (OK, we get it, that was a bad joke. But you won't forget now!) An example of an inductive approach would be parachuting onto a client without knowing much about their website, and discovering the checkout was difficult to use by the amount of people who brought it up. An easy theme! On the flip-side, a deductive approach involves attacking the data with some preconceived notions you expect to find in the qualitative data, based on a theory. For example, if you think your company's website navigation is hard to use because the text is too small, you may find yourself looking for themes like "small text" or "difficult navigation." We don't have a joke for this one, but we tried. To get even more nitty-gritty, there are two additional types of thematic analysis called semantic and latent thematic analysis. These are more advanced, but we'll throw them here for good measure. Semantic thematic analysis involves identifying themes in the data by analyzing the exact wording of the comments made used by participants. Latent thematic analysis involves identifying themes in the data by analyzing the underlying meanings and actions that were taken, but perhaps not necessarily stated by study participants. Both of these methods can be used in user research, though latent analysis is more popular because users often say different things than what they actually do.

Steps in Conducting a Thematic Analysis

Let's jump in! As mentioned before, there are 6 steps to completing a thematic analysis.

Step One: get familiar with your data!

This might seem obvious, but sometimes it's hard to know when to start. This might take the form of listening to the audio interviews or unmoderated studies, or reading the notes taken during a moderated interview. It's important to know the overall ideas of what you're dealing with to effectively theme your study. While you're doing this, pay attention to some big picture themes you can use in step two when you code your data. Break out key ideas from each participant. This might take the form of summarized answers for each question response, or a written review of actions taken for each task given. Just make sure to standardize it across participants.

Step Two: sort & code the data.

Now that you have your standardized notes across your participants, it's time to sort and code the collected qualitative data! Think of the themes from before when you were taking your notes. Think of these codes like metaphorical buckets, and start sorting! Every comment that fits a theme in a box, put it there. Back to our navigation example: some codes could be "small text" or "hard to use." We could put a participant action of "squinting" into the bucket for "small text," or a comment from another mentioning they had trouble finding "tents" in "hard to use."

Step Three: break the codes into themes!

Try to think of each theme as a makeup of three or more codes. For the navigation example, we could put both "small text," and "hard to use" into a theme of "Difficult Navigation."

Step Four: review and name your themes.

Now is the time to clean up the data. Are all your themes relevant to the problem you're trying to solve? Are all the themes coherent and straightforward? Are you comfortable defending your theme choices to teammates? These are all great questions to ask yourself in this stage.

Step Five: Present!!

To have a cohesive presentation of your thematic analysis, you'll need to include an introduction that explains the user problem you were trying to identify and the method you took to study it. Use the terminology from beginning of this resource to identify your research method. Usually for something like this, it will be a user survey or interview. ‍ You also need to include how you analyzed your participant data (inductive, deductive, latent or semantic) to identify your codes and themes. In the meaty section of your presentation, describe each theme and give quotations and user actions from the data to support your points.

Step Six: Insights and Recommendations

Your conclusion should not stop at your presentation of your findings. The best user researchers are valuable for both their insights and recommendations. Since UX researchers spend so much time with participants, they have indispensable knowledge about the best way to do things that make life easy for the company's users. Don't keep this information to yourself! On the final 1-3 slides of your presentation, state the "Next Steps & Recommendations" that you'd like your team and leadership to follow up on. These recommendations could include things like additional qualitative or quantitative studies, UX changes to make or test, or a copy change to make the experience clearer for readers. Your ultimate job is to create the best user experience, and you made it this far-- you got this!

And there you have it! That's everything you need to complete a thematic analysis of qualitative data to identify potential solutions or key concepts for a particular user problem. But don't stop there! We recommend using these principles in the wild to conduct research of your own. Identify a question or potential problem you'd like to analyze on one of your favorite sites. Use a service like Sprig to come up with non-bias questions to ask friends and family to try and gather your own qualitative data. Next, complete and document yourself completing the 6-step analysis process. What do you discover? Be prepared to share on interviews-- hiring managers love to see initiative! Good luck.

View the UX Research Job Guide Here

Our Sources: 

Caulfield, J. (2022, November 25). How to Do Thematic Analysis | Step-by-Step Guide & Examples . Scribbr. https://www.scribbr.com/methodology/thematic-analysis/

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What is Thematic Analysis and How to Do It Step-By-Step?

Appinio Research · 03.11.2023 · 32min read

Have you ever wondered how researchers make sense of the rich tapestry of qualitative data they gather from interviews, surveys, or textual sources? Thematic analysis serves as their guiding compass in unraveling the intricate stories within the data.

In this guide, we dive deep into thematic analysis, exploring its definition, purpose, applications, and step-by-step methodologies. Whether you're a seasoned researcher seeking to refine your qualitative analysis skills or a novice embarking on your research journey, this guide will equip you with the knowledge and tools needed to unlock the hidden meanings and patterns within your data.

What is Thematic Analysis?

Thematic analysis is a qualitative research method that involves systematically identifying, analyzing, and reporting patterns or themes within qualitative data. Its primary purpose is to uncover the underlying meanings and concepts embedded in textual, visual, or audio data.

Thematic analysis aims to provide a structured and comprehensive understanding of the content, enabling researchers to explore complex phenomena and answer research questions effectively.

Purpose of Thematic Analysis

• Data Exploration: Thematic analysis allows researchers to explore rich and unstructured qualitative data, such as interviews, focus group discussions, surveys, or written narratives. It helps reveal hidden insights that may not be apparent at first glance.
• Pattern Identification: The method is designed to identify patterns, recurring ideas, and common threads within the data. By categorizing data into themes, researchers can make sense of complex information.
• Contextual Understanding: Thematic analysis places a strong emphasis on understanding the context surrounding the data. It seeks to uncover the contextual factors that influence the emergence of specific themes.
• Interpretation and Explanation: It enables researchers to interpret and explain the meaning of the identified themes. Thematic analysis provides a deeper understanding of the phenomena under investigation.
• Theory Development: Thematic analysis can contribute to theory development by generating new concepts or refining existing theories. It helps researchers make theoretical connections based on empirical evidence.
• Practical Applications: Thematic analysis findings can have practical applications in various fields, such as healthcare, social sciences, business, and education. It informs decision-making, policy development, product improvement, and more.

In summary, the purpose of thematic analysis is to distill qualitative data into meaningful themes, providing researchers with a structured, interpretable, and contextually grounded understanding of the subject of study.

Importance of Thematic Analysis in Research

Thematic analysis holds significant importance in the field of research for several key reasons:

• Data Reduction and Organization: Qualitative data can be voluminous and unstructured. Thematic analysis acts as a powerful tool to reduce this complexity by organizing data into manageable themes and patterns. This reduction in data size makes it easier to extract meaningful insights.
• In-Depth Exploration: Thematic analysis enables researchers to conduct in-depth exploration of qualitative data. By identifying and examining themes, researchers can uncover nuances, contradictions, and intricacies within the data that may go unnoticed through other methods.
• Flexibility and Adaptability: Thematic analysis is highly flexible and adaptable to various research contexts and data types. It can be applied to textual data, visual data, audio data, and combinations thereof. Researchers can tailor the analysis to suit their specific research questions and objectives.
• Contextual Understanding: Thematic analysis places a strong emphasis on understanding the context in which data is generated. This contextual understanding is essential for accurate interpretation and meaningful insights.
• Theory Development and Testing: Thematic analysis can contribute to theory development by identifying patterns and concepts that inform or extend existing theories. It also allows researchers to test the applicability of theoretical frameworks in real-world settings.
• Practical Applications: The findings of thematic analysis have practical applications in diverse fields. They inform decision-making, guide policy development, drive product improvements, and provide valuable insights for addressing real-world challenges.
• Interdisciplinary Relevance: Thematic analysis transcends disciplinary boundaries, making it applicable in fields such as psychology, sociology, anthropology, education, healthcare , marketing, and more. Its interdisciplinary relevance enhances its utility in research.

In summary, thematic analysis plays a pivotal role in research by facilitating the systematic exploration and interpretation of qualitative data, leading to a deeper understanding of complex phenomena and informing decision-making and theory development across various domains.

How to Prepare for Thematic Analysis?

Before you embark on your thematic analysis journey, thorough preparation is vital. We'll delve into the main steps involved in getting your qualitative data ready for analysis.

1. Data Collection and Selection

Data collection is the foundation of any qualitative research project. You need to carefully plan, gather, and select your data to ensure it aligns with your research objectives.

• Research Goals: Clearly define your research questions or objectives. Your data should directly relate to what you want to explore or understand.
• Data Sources: Identify the sources of your qualitative data. Common sources include interviews , focus groups , surveys , field notes, or even existing documents and texts.
• Sampling: Decide on your sampling strategy. Will you use purposive sampling to select specific participants or content, or will you opt for more random sampling methods?
• Data Richness: Ensure your data is rich and comprehensive enough to answer your research questions. Collect enough data to reach data saturation, where new information or themes stop emerging.

2. Data Cleaning and Organization

Once you have your qualitative data in hand, the next step is data cleaning and organization. This process ensures that your data is in a usable format and is structured for efficient analysis.

• Transcription: If your data is in the form of interviews or recorded conversations, you may need to transcribe them. Accurate transcription is crucial for maintaining the integrity of the data.
• Data Format: Standardize the format of your data. This includes ensuring consistent date and time formats, naming conventions, and file organization.
• Data Validation: Check for data accuracy and consistency. Address any discrepancies or errors that may have arisen during data collection.
• Data Management: Organize your data systematically. Create a clear file structure, labeling, and version control to prevent data mix-ups or loss.

3. Choose the Right Software Tools

The choice of software tools for your thematic analysis can significantly impact the efficiency and effectiveness of your analysis process. Here's what you need to consider:

• Analysis Goals: Determine your specific analysis goals. Different software options may be better suited for certain types of projects or research questions.
• Ease of Use: Evaluate the user-friendliness of the software. Consider your team's familiarity with the tool and the learning curve involved.
• Collaboration Features: If you're working with a team, look for software that supports collaboration, allowing multiple researchers to work on the same project simultaneously.
• Data Import and Export: Ensure that the software can handle the data formats you are working with and provides robust import and export capabilities.
• Support and Training: Consider the availability of support resources, such as tutorials, user forums, and customer support, to assist you in case you encounter issues during analysis.

Some popular software options for thematic analysis include NVivo, ATLAS.ti, MAXQDA, and Dedoose. Each has its own strengths and features, so it's essential to choose the one that best fits your project's needs.

By carefully preparing your data, cleaning and organizing it effectively, and selecting the right software tools, you'll set a solid foundation for a successful thematic analysis. These steps ensure that you have high-quality data that can be analyzed efficiently and accurately, leading to meaningful insights for your research.

How to Do Thematic Analysis?

Thematic analysis involves a systematic process of identifying, analyzing, and reporting patterns or themes within qualitative data. In this section, we'll explore each step in detail, guiding you through the process of conducting thematic analysis effectively.

1. Familiarize Yourself with the Data

The initial step in thematic analysis is to become intimately acquainted with your qualitative data. This process, known as familiarization with data, allows you to gain a deep understanding of the content and context.

• Multiple Readings: Begin by reading through your data numerous times. This repeated exposure helps you become familiar with the nuances and intricacies of the material.
• Note-Taking: Take notes as you read. Document your initial thoughts, observations, and any patterns or ideas that emerge during this phase.
• Maintain an Open Mind: Avoid preconceived notions or biases. Approach the data with an open mind to allow for unbiased exploration.
• Identify Interesting Features: Look for exciting features, such as recurrent phrases, significant events, or notable trends within the data.

Familiarization sets the stage for the subsequent steps, as it enables you to approach the data with a fresh perspective and a foundation of knowledge.

2. Generate Initial Codes

Once you're familiar with the data, the next step is generating initial codes. Codes are labels or tags assigned to specific portions of text that capture the essence of what's being expressed.

• Start Small: Begin by coding smaller sections of data, such as sentences or paragraphs. Focus on breaking down the data into manageable units.
• Use In-Vivo Codes: Whenever possible, use in-vivo codes, which are codes that use the participants' own words. This helps maintain the authenticity of the data.
• Stay Close to the Data: Keep your codes closely tied to the content of the data. Avoid overly abstract or generalized labels.
• Constant Comparison: Continuously compare new data segments with existing codes to ensure consistency and relevance.
• Document Your Codebook: Create a codebook or list that outlines the codes you've generated and their definitions. This document will serve as a reference throughout your analysis.

Generating initial codes is a fundamental step that involves systematically dissecting the data into meaningful elements, setting the stage for subsequent theme development.

3. Search for Themes

With a set of initial codes in hand, it's time to move on to searching for themes. Themes are overarching patterns or recurring ideas that emerge from the coded data.

• Pattern Recognition: Look for patterns in the codes. Identify codes that appear frequently or codes that seem conceptually related.
• Grouping Codes: Start grouping codes together based on their similarities or connections. This process forms the basis for theme development.
• Stay Open to New Themes: Be open to the possibility of new themes emerging as you continue your analysis. Themes may evolve or shift as you delve deeper into the data.
• Subthemes: Recognize that themes can have subthemes, providing a hierarchical structure to your analysis.

Searching for themes is a dynamic process that involves organizing and categorizing codes to uncover the underlying patterns and meanings within the data.

4. Review and Define Themes

Once you've identified potential themes, the next step is to review and define themes more rigorously. This phase ensures that your themes accurately represent the patterns in your data.

• Refinement: Refine and clarify your themes. Review them to ensure they align with the data and accurately capture the essence of the content.
• Definition: Provide clear definitions for each theme. What does each theme represent, and how does it relate to the data?
• Validation: Seek validation from colleagues or peers. Discuss your themes with others to ensure they are robust and well-defined.
• Naming Themes: Give each theme a concise and descriptive name that encapsulates its meaning.

Reviewing and defining themes is a crucial step in the thematic analysis process, as it ensures the accuracy and validity of your findings.

5. Write and Describe Themes

With well-defined themes in hand, it's time to write and describe themes in greater detail. This step involves fleshing out the themes with supporting evidence from your data.

• Quote Integration: Include quotes or excerpts from the data that exemplify each theme. These quotes serve as concrete examples of the theme in action.
• Narrative Development: Develop a narrative around each theme. Explain its significance and relevance within the context of your research.
• Contextual Understanding: Consider the broader context in which each theme exists. How do these themes contribute to the overall understanding of your research questions?
• Illustrative Examples: Provide multiple examples within each theme to demonstrate its consistency and depth.

Writing and describing themes is where the richness of your analysis comes to life, allowing readers to grasp the significance of the patterns you've uncovered.

6. Report Results

The final step in thematic analysis is reporting results. This involves presenting your findings in a clear and structured manner.

• Structure Your Report: Organize your report according to your research objectives, themes, and supporting evidence.
• Narrative Flow: Create a narrative flow that guides the reader through your analysis process, from data familiarization to theme development.
• Visual Aids: Consider using visual aids such as tables, charts, or graphs to enhance the presentation of your themes and findings.
• Discussion: Discuss the implications of your themes in the context of your research questions or objectives. What do these themes reveal, and how do they contribute to the broader understanding of your topic?
• Conclusion: Summarize your findings and their significance. Offer suggestions for future research or practical applications if applicable.

Reporting results effectively ensures that your thematic analysis is not only comprehensive but also accessible to your target audience, whether it's fellow researchers, stakeholders, or the broader community.

Thematic Analysis Approaches

Thematic analysis is a flexible method that can be approached in different ways based on your research goals and the nature of your data. In this section, we'll explore three primary approaches to thematic analysis: inductive thematic analysis, deductive thematic analysis , and reflexive thematic analysis. Each approach has its own unique characteristics and applications.

Inductive Thematic Analysis

Inductive thematic analysis is characterized by its bottom-up, data-driven approach. In this approach, you start without predefined themes or theories. Instead, you allow themes to emerge organically from your data.

• Data Familiarization: Begin by immersing yourself in the data, reading and re-reading it multiple times to develop a deep understanding.
• Open Coding: Start coding the data without any preconceived ideas. Codes emerge directly from the data, capturing concepts and patterns as they appear.
• Code Grouping: Group similar codes together, gradually forming initial themes. These themes are derived solely from the data and may evolve as you progress.
• Theme Definition: Define and refine the emerging themes. Ensure they accurately represent the patterns and concepts within your data.
• Review and Validation: Continuously review and validate the themes with colleagues or peer researchers. This iterative process enhances the trustworthiness of the analysis.

Example: Imagine conducting interviews with employees about their experiences in the workplace. Through inductive thematic analysis, you may find that themes like "Work-Life Balance Challenges" and "Employee Empowerment" emerge from the interviews, even though you had no preconceived notions about these topics.

Deductive Thematic Analysis

Deductive thematic analysis, in contrast, begins with predefined themes or theories based on existing research or theoretical frameworks. This approach is particularly useful when you want to test specific hypotheses or apply existing concepts to your data.

• Theory or Framework Selection: Start by selecting a theoretical framework or pre-existing themes that align with your research objectives.
• Data Collection: Gather data with these predefined themes or theories in mind. Your data collection process is guided by the established concepts.
• Initial Coding: Code your data according to the predefined themes. This involves assigning data segments to specific categories based on the chosen framework.
• Theme Refinement: Refine and adapt the predefined themes as you analyze the data. You may discover nuances or subthemes that were not initially accounted for.
• Validation: Seek validation from peers or experts to ensure the adapted themes accurately represent the data.

Example: Suppose you're studying customer feedback on a new product launch. You begin with predefined themes like "Product Usability" and " Customer Satisfaction " based on established criteria for evaluating products. As you analyze the data, you refine these themes and add subthemes like "User Interface Design" and "Product Performance."

Reflexive Thematic Analysis

Reflexive thematic analysis emphasizes the researcher's active role in shaping the analysis. It is often used in interpretive and intuitive research paradigms, acknowledging that the researcher's subjectivity plays a significant role in the analysis process.

• Engage Reflexively: Acknowledge your own perspectives, biases, and preconceptions. Be aware of how your background and experiences influence the analysis.
• Data Immersion: Immerse yourself in the data while considering your own positionality. How do your personal experiences and beliefs intersect with the data?
• Coding with Reflexivity: Code the data while reflecting on your own interpretive lens. How does your perspective shape the codes and themes you identify?
• Constant Reflexivity: Continuously engage in reflexivity throughout the analysis process. Be open to adjusting your interpretations based on ongoing self-awareness.
• Interpretation: Interpret the themes within the context of both the data and your reflexive insights. Recognize the co-construction of meaning between you as the researcher and the data.

Example: In a study on cultural perceptions of healthcare, you, as the researcher, openly acknowledge your cultural background and experiences. This reflexivity prompts you to recognize nuances in the data related to cultural sensitivities that might have been overlooked otherwise. Themes related to "Cultural Health Practices" and "Healthcare Access Barriers" are informed by both the data and your reflexive insights.

These three approaches to thematic analysis offer flexibility in how you approach your data. Your choice of approach should align with your research objectives, the nature of your data, and your epistemological stance as a researcher. Whether you start with a blank slate (inductive), apply existing theories (deductive), or embrace reflexivity, thematic analysis can be tailored to suit your research needs.

Data Analysis Techniques

Thematic analysis can be conducted using various data analysis techniques, each with its advantages and considerations. In this section, we'll delve into the three primary data analysis techniques for thematic analysis: manual coding, using qualitative data analysis software, and comparison with quantitative analysis.

Manual Coding

Manual coding involves the process of reviewing your qualitative data and assigning codes to segments of text that represent specific concepts or themes. While it may be more time-consuming than using software tools, manual coding offers a deep and intimate understanding of your data.

• Data Familiarization : Begin by thoroughly immersing yourself in the data. Read through it multiple times to gain a comprehensive understanding of the content.
• Code Generation : Start identifying meaningful segments in the data and assign relevant codes to them. Codes should capture the essence of what is being expressed.
• Codebook Development: Create a codebook that documents all the codes you've generated along with their definitions. This serves as a reference throughout the analysis.
• Code Sorting and Grouping: Organize and group codes into potential themes based on similarities or connections between codes.
• Theme Development : Review and refine the themes that emerge from the grouped codes. Ensure they accurately represent the patterns in your data.
• Validation : Seek validation from colleagues or peer researchers to enhance the trustworthiness of the analysis.

Manual coding allows for a meticulous examination of the data, ensuring a deep and nuanced understanding of the content. It's especially valuable when you have a smaller dataset or want to maintain a high level of researcher involvement in the analysis.

Using Qualitative Data Analysis Software

Qualitative data analysis software provides tools and features to streamline the coding and analysis process, making it more efficient and collaborative.

Some of the top tools used for thematic analysis include:

• Appinio : A real-time market research platform that excels in providing fast access to consumer insights. With a focus on user experience and the ability to define precise target groups, Appinio helps you make data-driven decisions seamlessly and quickly, making it an exciting and intuitive choice for thematic analysis.
• NVivo: NVivo is a widely used software tool that offers a range of features for qualitative analysis, including coding, data visualization, and collaboration.
• ATLAS.ti: ATLAS.ti is known for its user-friendly interface and robust coding and analysis capabilities. It allows for the systematic organization of codes and themes.
• MAXQDA: MAXQDA provides a comprehensive suite of tools for qualitative analysis, including advanced text coding, multimedia analysis, and robust reporting options.
• Dedoose: Dedoose is a web-based application designed for qualitative and mixed-methods research. It offers real-time collaboration features and intuitive coding.

To get started with these tools, all you have to do is:

• Data Import: Import your qualitative data into the software. This can include text, audio, video, or other forms of qualitative data.
• Coding: Use the software's coding features to assign codes to segments of your data. You can create a coding structure, code hierarchy, and attach memos.
• Theme Development: Organize and analyze your codes to identify themes. Many software tools offer tools for visualizing themes and subthemes.
• Data Querying: Use the software to search for specific codes or themes within your data. This can help you identify patterns and relationships.
• Collaboration: If working with a team, collaborate in real-time within the software, making it easier to manage and validate codes and themes.

Using qualitative data analysis software can significantly speed up the coding and analysis process, especially with larger datasets. It also enhances the organization and management of your data, making it easier to revisit and revise your analysis.

Thematic Analysis vs Quantitative Analysis

Thematic analysis is a qualitative research method, but it can be valuable when used in conjunction with quantitative analysis. Here's how thematic analysis compares to quantitative analysis.

Thematic Analysis

• Qualitative method
• Focuses on exploring meanings, patterns, and themes in qualitative data.
• Involves coding, categorizing, and interpreting textual or visual data.
• Emphasizes rich, context-specific insights.
• Typically involves smaller sample sizes.
• Subjective and context-dependent.

Quantitative Analysis

• Quantitative method
• Focuses on numerical data, statistics, and generalizability.
• Involves structured surveys, experiments, or data collection instruments.
• Emphasizes statistical relationships and patterns.
• Typically involves larger sample sizes.
• Objective and aims for generalizability.

Thematic vs Quantitative Analysis Comparison

• Complementarity: Thematic analysis and quantitative analysis can complement each other. Qualitative analysis provides depth and context, while quantitative analysis offers breadth and statistical significance.
• Mixed-Methods Research: Researchers often employ mixed-methods research, combining both qualitative and quantitative approaches to gain a comprehensive understanding of a research question.
• Sequential or Concurrent: Researchers may choose to conduct thematic analysis before or after quantitative analysis, depending on the research design and objectives.

For example, in a healthcare study, qualitative thematic analysis may be used to understand patients' experiences and preferences (qualitative), while quantitative analysis can assess the effectiveness of a new treatment based on numerical outcomes (quantitative). These approaches together provide a holistic view of the research question.

How to Ensure Thematic Analysis Quality?

Ensuring the quality and rigor of your thematic analysis is essential to maintain the validity and trustworthiness of your findings. In this section, we'll explore three key aspects of quality assurance in thematic analysis: trustworthiness and credibility, inter-coder reliability , and addressing bias and reflexivity.

Trustworthiness and Credibility

Trustworthiness and credibility refer to the extent to which your thematic analysis can be considered reliable and valid. Establishing trustworthiness and credibility is crucial to ensure that your findings accurately represent the data and can withstand scrutiny.

To ensure trustworthiness and credibility:

• Member Checking: Seek feedback from participants to ensure that your analysis aligns with their perspectives and experiences.
• Peer Debriefing: Engage with colleagues or experts in the field to discuss your analysis process and findings. Their insights can help identify any potential biases or oversights.
• Audit Trail: Maintain a detailed record of your analysis process, including coding decisions, codebook development, and theme generation. This audit trail serves as a transparent documentation of your work.
• Triangulation: Use multiple sources of data or methods to validate your findings. Triangulation can involve comparing data from interviews, observations, and documents to identify converging themes.
• Peer Review: Submit your analysis and findings for peer review in academic or professional settings. Peer reviewers can provide valuable feedback and validation.
• Clear Reporting: Ensure that your research report or article clearly and transparently documents your analysis process, including the steps taken to establish trustworthiness.

By implementing these methods, you enhance the trustworthiness and credibility of your thematic analysis, increasing its validity and reliability.

Inter-Coder Reliability

Inter-coder reliability is the degree of agreement between different coders or researchers when coding the same data. It is a measure of consistency and ensures that your analysis is not overly influenced by individual subjectivity.

To establish inter-coder reliability:

• Coding Training: Train coders or researchers in the coding process, ensuring they understand the codebook and coding guidelines.
• Coding Samples: Have multiple coders independently code a sample of your data. This sample should represent the diversity of your dataset.
• Calculate Agreement: Calculate inter-coder agreement using a statistical measure such as Cohen's Kappa or percentage agreement. This measures the level of agreement between coders.
• Discuss Discrepancies: When discrepancies arise, convene coder meetings to discuss and resolve differences. This may involve refining code definitions or guidelines.
• Repeat Coding: After resolving discrepancies, have coders recode the data to assess improved inter-coder reliability.
• Ongoing Monitoring: Maintain constant communication and monitoring among coders to ensure consistency throughout the analysis process.

Establishing inter-coder reliability is crucial when working with a team of coders or researchers. It minimizes the risk of individual biases and subjectivity affecting the analysis.

Addressing Bias and Reflexivity

Bias and reflexivity acknowledgment and management are integral parts of maintaining the quality and rigor of thematic analysis.

Researchers bring their own perspectives, beliefs, and experiences to the analysis process, which can introduce bias into the interpretation of data. To address bias:

• Engage in reflexivity by regularly reflecting on your own positionality and potential biases.
• Maintain transparency by documenting your reflexive insights and how they may influence your analysis.
• Seek feedback from peers or colleagues to identify and mitigate bias in your analysis.

Reflexivity

Reflexivity involves recognizing and acknowledging the role of the researcher in shaping the analysis process and findings. Researchers should:

• Be aware of their assumptions and preconceptions and how these may impact their interpretation.
• Consider how their background, experiences, and cultural context influence their understanding of the data.
• Use reflexivity to enhance the depth and validity of their analysis by recognizing and addressing their subjectivity.

By addressing bias and embracing reflexivity, researchers can conduct a more transparent and rigorous thematic analysis, leading to more credible and valid findings.

Thematic Analysis Challenges

Thematic analysis, like any research method, comes with its own set of challenges. We'll explore three common challenges researchers may encounter during thematic analysis: data overload, maintaining consistency, and subjectivity and interpretation.

Data Overload

Data overload occurs when you have a large volume of qualitative data to analyze, making it challenging to manage and extract meaningful patterns. To address data overload:

• Chunking Data: Break the data into manageable chunks or segments for analysis. This helps prevent feeling overwhelmed.
• Prioritization: Focus on the most relevant or central data that directly relates to your research questions or objectives.
• Use of Software: Consider using qualitative data analysis software to assist with data organization and coding efficiency.

Maintaining Consistency

Maintaining consistency throughout the analysis process is crucial to ensure that codes and themes are applied consistently across the dataset. To maintain consistency:

• Develop a clear codebook with well-defined code definitions and examples.
• Regularly check in with coding team members to address any inconsistencies or questions.
• Use regular team meetings or discussions to clarify interpretations and ensure a shared understanding.

Subjectivity and Interpretation

Subjectivity and interpretation are inherent to thematic analysis, as researchers actively engage in interpreting data. To address subjectivity:

• Engage in reflexivity to acknowledge and manage your subjectivity and biases.
• Seek external validation or peer input to challenge or confirm your interpretations.
• Use transparency in reporting to clarify your interpretive stance and decision-making process.

By recognizing and addressing these common challenges, researchers can navigate the complexities of thematic analysis more effectively and produce robust, high-quality results.

Thematic Analysis Applications

Thematic analysis is a versatile qualitative research method widely applied in various fields and contexts. Its flexibility makes it suitable for exploring a wide range of research questions and topics.

Healthcare Research

Thematic analysis is frequently used in healthcare research to explore patients' experiences, healthcare provider perspectives, and healthcare policy analysis. Researchers in this field use thematic analysis to uncover themes related to patient satisfaction, healthcare disparities, the impact of treatments, and more. For example, a study might employ thematic analysis to understand the emotional challenges faced by cancer patients during their treatment journey, leading to the identification of themes like "Emotional Resilience" and "Support Systems."

Social Sciences

In the social sciences, thematic analysis helps researchers examine complex social phenomena and human behaviors. It is employed in studies related to sociology, psychology, anthropology, and education. Researchers use thematic analysis to explore themes in narratives, interviews, focus groups, and surveys. For instance, in educational research, thematic analysis can reveal themes in teacher-student interactions, leading to insights into classroom dynamics and pedagogical approaches.

Market Research

Thematic analysis is valuable in market research to extract insights from consumer feedback , product reviews, and focus group discussions. Researchers analyze themes in customer opinions to inform product development , marketing strategies, and customer experience improvements. For example, in analyzing online product reviews, thematic analysis can uncover themes like "Product Reliability" and "Customer Service Satisfaction," guiding companies in enhancing their offerings.

Psychology and Counseling

In psychology and counseling, thematic analysis is utilized to explore qualitative data from interviews, therapy sessions, or written narratives. It aids in understanding psychological processes, coping mechanisms, and therapeutic outcomes. Researchers might use thematic analysis to identify themes related to mental health stigma reduction or recovery narratives in individuals with mental health challenges.

Policy Analysis

Thematic analysis plays a critical role in policy analysis by extracting key themes from policy documents, legislative texts, or public opinion. Researchers can use thematic analysis to uncover themes related to policy effectiveness, public perception, and policy impact assessment. For instance, in analyzing environmental policies, themes like "Sustainability Goals" and "Community Engagement" may emerge, informing policymakers about areas of focus.

Examples of Thematic Analysis in Research

To gain a more comprehensive understanding of how thematic analysis is applied in research, let's explore several detailed examples across different fields and research contexts.

Example 1: Exploring Mental Health Stigma

Research Question: What are the key themes in narratives of individuals who have experienced mental health stigma?

Data: In-depth interviews with individuals who have faced mental health stigma.

Thematic Analysis Process:

• Data Familiarization: Researchers immerse themselves in interview transcripts, noting significant statements related to mental health stigma.
• Initial Coding: Initial codes are generated, including "Negative Stereotypes," "Experiences of Discrimination," and "Coping Strategies."
• Theme Development: Codes are grouped into broader themes, leading to the emergence of themes like "Internalization of Stigma" and "Empowerment through Advocacy."
• Refinement and Definition: Each theme is refined and defined with illustrative quotes to capture the nuances of participants' experiences.
• Interpretation: Researchers interpret the findings, highlighting the impact of stigma on mental health and the importance of support systems.

This thematic analysis sheds light on the multifaceted nature of mental health stigma and offers insights into the coping mechanisms individuals employ to navigate these challenges.

Example 2: Evaluating Customer Feedback for a Tech Product

Research Question: What themes emerge from an analysis of customer feedback for a new smartphone model?

Data: Analysis of online customer reviews and feedback for a recently launched smartphone.

• Data Collection: Collect customer reviews and comments from online platforms, aggregating a substantial dataset.
• Data Cleaning: Remove duplicates and irrelevant data to streamline the analysis process.
• Coding: Codes are generated for common sentiments and topics found in the reviews, such as "Camera Quality," "Battery Life," and "User-Friendly Interface."
• Theme Development: Codes are organized into overarching themes, revealing key themes like "Performance and Speed," "Durability Concerns," and "User Experience."
• Visualization: Visual aids such as word clouds and frequency distributions are used to present the prevalence of themes in customer feedback.
• Implications: The analysis highlights areas for product improvement and informs marketing strategies based on customer perceptions.

This thematic analysis of customer feedback provides valuable insights into the strengths and weaknesses of the smartphone model, guiding product development and marketing efforts.

Example 3: Analyzing Qualitative Data in Educational Research

Research Question: What themes emerge from open-ended survey responses regarding the challenges of remote learning during the COVID-19 pandemic?

• Data Organization: Survey responses are organized for systematic analysis.
• Initial Coding: Codes are generated for recurring issues, such as "Technology Challenges," "Lack of Social Interaction," and "Time Management."
• Theme Development: Codes are grouped into overarching themes, resulting in themes like "Digital Divide" and "Adaptive Teaching Strategies."
• Subtheme Identification: Subthemes may emerge within larger themes, providing a more detailed understanding of specific issues.
• Contextual Analysis: The analysis considers the broader context of the pandemic's impact on education, including policy implications and pedagogical adaptations.

This thematic analysis of survey responses offers insights into the unique challenges faced by students and educators during the pandemic, informing educational policies and strategies.

These examples showcase the adaptability and effectiveness of thematic analysis in uncovering meaningful patterns and themes across diverse research contexts. Whether exploring personal experiences, customer feedback, or educational challenges, thematic analysis serves as a versatile qualitative research method that provides valuable insights and informs decision-making.

Thematic analysis is a versatile and powerful method that helps researchers uncover patterns and themes within qualitative data. By following the steps outlined in this guide, you can embark on your journey of discovery and gain deeper insights into the world of qualitative research.

Remember, whether you're studying people's experiences, analyzing customer feedback, or exploring social phenomena, thematic analysis offers a structured approach to make sense of complex data. It's a valuable tool for researchers across diverse fields, providing a clear path to understanding, interpretation, and meaningful insights. So, as you venture into the realm of thematic analysis, embrace the richness of your data and let it tell its story. Your research journey has just begun, and the possibilities are boundless.

How to Conduct Thematic Analysis in Minutes?

In the world of thematic analysis, Appinio shines as a real-time market research platform, revolutionizing the way you gain consumer insights. Say goodbye to long, tedious research processes and embrace the exciting and intuitive world of real-time data-driven decisions.

Here's why Appinio is your go-to partner for thematic analysis:

• Lightning-Fast Insights: From formulating questions to obtaining actionable insights, Appinio delivers results in minutes. When you need answers pronto, we've got you covered.
• User-Friendly Excellence: No need for a Ph.D. in research. Our platform is designed to be so intuitive that anyone can navigate it with ease, empowering you to take control of your market research.
• Global Reach, Local Precision: With access to over 90 countries and the ability to define your target group from 1200+ characteristics, Appinio ensures that your thematic analysis is not only insightful but also globally relevant.

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Student Examples of Good Practice

Sometimes it’s good to know what ‘doing a good job’ looks like… To help those wanting to understand what describing the reflexive TA process well might look like, we offer some good examples here, from student projects. This may be particularly helpful for students doing research projects, and for people very well-trained in positivism.

As well as the example(s) we provide here, you can find a much more detailed discussion in our book Thematic Analysis: A Practical Guide (SAGE, 2022).

Suzy Anderson (Professional Doctorate)

The following sections are by Suzy Anderson, from her UWE Counselling Psychology Professional Doctorate thesis – The Problem with Picking: Permittance, Escape and Shame in Problematic Skin Picking.

An example of a description of the thematic analysis process:

Process of Coding and Developing Themes

Coding and analysis were guided by Braun and Clarke’s (2006, 2013) guidelines for using thematic analysis. Each stage of the coding and theme development process described below was clearly documented ensuring that the evolution of themes was clear and traceable. This helped to ensure research rigour and means that process and dependability may be demonstrable.

I familiarised myself with the data by reading the transcripts several times while making rough notes. As data collection took place over a protracted period of time, coding of transcribed interviews began before the full dataset was available. Transcripts were read line-by-line and initial codes were written in a column alongside the transcripts. These codes were refined and added to as interviews were revisited over time. Throughout this process I was careful to note and re-read areas of relatively sparse coding to ensure they were not neglected. My supervisor also independently coded three of the interviews for purposes of reflexivity, providing an interesting alternative standpoint. I cross-referenced our two perspectives to notice and reflect on our differences of perspective.

Once initial coding was complete, I looked for larger patterns across the dataset and grouped the codes into themes (Braun & Clarke, 2006). I found it helpful to think of the theme titles as spoken in the first person, and imagine participants saying them, to check whether they reflected the dataset and participants’ meanings. I tried not to have my coding and themes steered by ideas, categories and definitions from previous research, to allow a more inductive, data-driven approach, while recognising my role as researcher in co-creation of themes (Braun & Clarke, 2013). However, there were times when the language of previous research appeared a good fit, such as in the discussion of ‘automatic’ and ‘focussed’ picking. Given that the experience of SP is an under-researched area, particularly from a qualitative perspective, and that the aim is for this study to contribute to therapeutic developments, themes were developed with the entire dataset in mind (Braun & Clarke, 2006), such that they would more likely be relevant to someone presenting in therapy for help with SP. There was clear heterogeneity in the interviews, and in cases where I have taken a narrower perspective on an experience (such as when describing an experience only true for some of the participants), I have tried to give a loose indication of prevalence and alternative views.

I created a large ‘directory’ of themes and smaller sub-themes, with the relevant participant quotations filed under each theme or sub-theme heading. This helped me to adjust theme titles, boundaries and position, meant that I could check that themes were faithful to the data at a glance, and was of practical help when writing the analysis.

The process of coding and developing themes was intended to have both descriptive and interpretive elements (using Braun & Clarke’s definitions, 2013). The descriptive element was intended to represent what participants said, while the interpretative element drew on my subjectivity to consider less directly evident patterns, such as those that might be influenced by social context or forces such as shame. This interpretation was of particular value to the current study as participants often struggled to find words for their experience and several reported or implied that they did not understanding the mechanisms of their picking. An interpretative stance meant that I could develop ideas about what they were able to describe and consider the relationships between these experiences, making sense of them alongside previous literature (Braun & Clarke, 2006). Writing was considered an integral part of the analysis (Braun & Clarke, 2013) and it helped me to adjust the boundaries of themes, notice more latent patterns and considered how themes and their content were related.

Given the known heterogeneity of picking I was keen to make sure my analysis did not become skewed towards one type of SP experience to the detriment of another. I actively looked for participant experiences that diverged from those of the developing themes (with similar intentions to a ‘deviant case analysis’; Lincoln & Guba, 1985) so that the final analysis would represent themes in context and with balance. When adding quotations to the prose of my analysis I re-read them in their original context to ensure that my representation of their words appeared to be a credible reflection of what was said.

An example of researcher reflexivity in relation to analysis process

Subjectivity as a Resource

I considered my subjectivity to be a resource when conducting interviews and analysing data (Gough & Madill, 2012). It guided my judgement when interviewing, helping me to respond to participants’ explicit, implicit and more verbally concealed distress. I allowed aspects of my own experience to resonate with those of participants meaning that I could listen to their stories with empathy and a genuine curiosity. During analysis, themes were actively created and categorised, demanding my use of self (DeSantis & Ugarriza, 2000). I sought to interpret the data rather than simply describe it, which necessarily requires acknowledgement of both researcher and participant subjectivity. I strongly feel that we can only make sense of another’s story by relating it to our own phenomenology (Smith & Shinebourne, 2012), and that we re-construct their stories on frameworks formed by our own subjective experience. As such it is useful to be aware of my personal experiences and assumptions.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3 (2), 77-101.

Braun, V., & Clarke, V. (2013). Successful qualitative research: A practical guide for beginners. Sage.

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

Gough, B., & Madill, A. (2012). Subjectivity in psychological research: From problem to prospect. Psychological Methods, 17 (3), 374-384.

Lincoln, Y. S., & Guba, E. G. (1985). Establishing trustworthiness. Naturalistic Inquiry, 289 (331), 289-327.

Smith, J. A., & Shinebourne, P. (2012). Interpretative phenomenological analysis. In H. Cooper, P. M. Camic, D. L. Long, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.),  APA handbook of research methods in psychology, Vol. 2. Research designs: Quantitative, qualitative, neuropsychological, and biological (p. 73–82). American Psychological Association.

Gina Broom (Research Master's)

The following extract is by Gina Broom, from her University of Auckland Master’s thesis (2020): “Oh my god, this might actually be cheating”: Experiencing attractions or feelings for others in committed relationships .

A detailed description of reflexive TA analytic approach and process

I analysed data through a process of reflexive thematic analysis (reflexive TA), as outlined by Braun, Clarke, Hayfield, and Terry (2019), who describe reflexive TA as a method by which a researcher will “explore and develop an understanding of patterned meaning across the dataset” with the aim of producing “a coherent and compelling interpretation of the data, grounded in the data” (p. 848). I utilized Braun and colleagues’ reflexive approach to TA, as opposed to alternative models of TA, due to my alignment with critical qualitative research. I did not select a c oding reliability TA approach, for example, due to its foundation of (post)positivist assumptions and processes (such as predetermined hypotheses, the aim of discovering ‘accurate’ themes or “domain summaries”, and efforts to ‘remove’ researcher bias while evidencing reliability/replicability), which were not suitable for the critical realist epistemology underpinning this thesis. In contrast, Reflexive TA is a ‘Big Q’ qualitative approach, constructing patterns of meaning as an ‘output’ from the data (rather than as predetermined domain summaries) while valuing “researcher subjectivity as not just valid but a resource” (Braun et al., 2019, p. 848). As the critical realist and feminist approaches of this thesis theorize knowledge as contextual, subjective, and partial, with reflexivity valued as a crucial process, a reflexive TA was the most appropriate method for this analysis.

Braun and colleagues’ (2019) reflexive TA process involves six-phases, including familiarization with the data, generating codes, constructing themes, revising and defining themes, and producing the report of the analysis. I outline my process for each of these below:

Phase 1, familiarization: Much of my initial engagement with the data was done through my transcription of the interviews, as the process provided extended time with each interview, both listening to the audio of the participant, and in the writing of the transcript. Some qualitative researchers describe transcription as an essential process for a researcher to perform themselves, as “transcribing discourse, like photographing reality, is an interpretive practice” (Riessman, 1993, p. 13), and as a result, “analysis begins during transcription” (Bird, 2005, p. 230). Braun and Clarke (2012) suggest certain questions to consider during the process of familiarization: “How does this participant make sense of their experiences? What assumptions do they make in interpreting their experience? What kind of world is revealed through their accounts?” (p. 61). During transcription, I took notes of potential points of interest for the analysis, using these types of questions as a guide. In exploring attractions or feelings for others in committed relationships, these questions (and my notes) often related to the meaning participants applied to their feelings and relationships, particularly in terms of morality and social acceptability, while the ‘world’ of their accounts was conveyed through their discourse of the contemporary relational context.

Phase 2, generating initial codes : Following transcription, I systematically coded each interview, searching for instances of talk that produced snippets of meaning relevant to the topic of attractions or feelings for others. I coded interviews using the ‘comment’ feature in the Microsoft Word document of each transcript, highlighting the relevant text excerpt for each code comment. I used this approach, rather than working ‘on paper’, so that I would later be able to easily export my coded excerpts for use in my theme construction. The coding of thematic analysis can be either an inductive ‘bottom up’ approach, or a deductive or theoretical ‘top down’ approach, or a combination of the two, depending on the extent to which the analysis is driven by the content of the data, and the extent to which theoretical perspectives drive the analysis (Braun & Clarke, 2006, 2013). Coding can also be semantic , where codes capture “explicit meaning, close to participant language”, or latent , where codes “focus on a deeper, more implicit or conceptual level of meaning” (Braun et al., 2019, p. 853). I used an inductive approach due to the need for exploratory research on experiences attractions or feelings for others, as it is a relatively new topic without an existing theoretical foundation. The focus of my coding therefore developed throughout the process of engaging with the data, focusing on segments of participants’ meaning-making in relation to general, personal, or partner-centred experiences of: attractions or feelings for others in the contemporary relational context, implied moral and/or social acceptability (or unacceptability), related affective experiences and responses, and enacted or recommended management of attractions or feelings for others. At the beginning of the process, I mostly noted semantic codes such as ‘feels guilty about attractions or feelings for others’, particularly as my coding was exploratory and inductive, rather than guided by a knowledge of ‘deeper’ contextual meaning. As I progressed, however, I began to notice and code for more latent meanings, such as ‘love = effortless emotional exclusivity’ or ‘monogamy compulsory/unspoken relationship default’. When all interviews had been systematically and thoroughly coded (and when highly similar codes had been condensed into single codes), I had a final list of roughly 200 codes to take into the next phase of analysis.

Phase 3, constructing themes : When developing my initial candidate themes, I utilized the approach described by Braun and colleagues (2019) as “using codes as building blocks”, sorting my codes into topic areas or “clusters of meaning” (p. 855) with bullet-point lists in Microsoft Word. From this grouping of codes, I produced and refined a set of candidate themes through visual mapping and continuous engagement with the data. These candidate themes were grouped into two overarching themes: the first encompassed 2 themes and 6 sub-themes evidencing pervasive ‘traditional’ conceptions of committed relationships (as monogamous by default with an assumption of emotionally exclusivity), and the way attractions or feelings for others were positioned as an unexpected threat within this context; the second encompassed four themes and eight sub-themes exploring modern contradictions (which problematized the quality of the relationship or the ‘maturity’ of those within it, rather than the attractions or feelings), and the way attractions or feelings for others were positioned as ‘only natural’ or even positive agents of change. This process of candidate theme development was still explorative and inductive, as I worked closely with the coded data and had only brief engagement with potentially relevant theoretical literature at this stage. Further engagement with contextually relevant literature, and a deductive integration of it into the analysis, was developed in the next phases.

Phases 4 and 5, revising and defining themes : My process of revising and defining themes started by using a macro (that was developed for this project) to export all of my initial codes and their associated excerpts into a single master sheet in Microsoft Excel, with columns indicating the source interview for each excerpt, as well as relevant participant demographic information (e.g. age, gender, relationship as monogamous or non-monogamous). This master sheet contained 6006 coded excerpts. In two new columns (one for themes and one for sub-themes), I ‘tagged’ excerpts relevant to my candidate analysis by writing the themes and/or sub-themes that they fit into. I was then able to export these excerpts, using the macro designed for this project, sorting the relevant data for each theme and sub-theme into separate tabs. I then reviewed all the excerpts for each individual theme and sub-theme, which allowed me to revise and define my candidate themes into my first full thematic analysis for the writing phase.

The thematic analysis at this stage included 13 themes and seven sub-themes, and these differed from the original candidate themes in a number of ways. In reviewing the collated data, I noted that some sub-themes were nuanced and prominent enough to be promoted to themes; the sub-theme ‘stay or go? (partner or other)’, for example, became the theme ‘you have to choose’. Similarly, I found other themes or sub-themes to be ‘thin’, and either removed them, or integrated them into other parts of the analysis; the sub-theme roughly titled ‘families at stake (marriage, children)’, for example, became a smaller part of the ‘safety in exclusivity’ theme. I also noted that the first overarching theme in the candidate analysis was ‘messy’, and in an effort to improve focus and clarity, I split this first overarching theme into three new ones, each with its own “central organizing concept” (Braun et al., 2019, p. 48): the first evidenced the contemporary relational context as one of default monogamy with an idealization of exclusivity; the second evidenced infidelity as an unforgivable offence, while associating attractions or feelings for others with this threat of infidelity; the third evidenced discourses in which someone must be to blame (either the person with the feelings or their partner). The second half of the candidate analysis became a fourth and final overarching theme, which encompassed a revised list of themes evidencing favourable talk of attractions or feelings for others.

Phase 6, writing the report : In writing my first draft of my analysis, I developed an even deeper sense of which themes and sub-themes were ‘falling into place’, and which did not fit so well with the overall analysis. At this point I was also engaging in a deeper exploration of relevant literature, and writing my chapter on the context of sexuality and relationships, which provided a foundation of theoretical knowledge that I could deductively integrate into my analysis. Through a process of supervisor feedback on my initial draft, engagement with literature, and revision of the data, I developed the analysis into the final thematic structure. My initial research question of ‘how do people make sense of attractions or feelings for others in committed relationships?’ also developed into three final research questions, each of which is explored across the three overarching themes of the final analysis:

Upon revision, both of the first two overarching themes from the second (revised) thematic map (‘the safety of default monogamy’ and ‘the danger of infidelity’) involved themes and sub-themes which situated attractions or feelings for others within the dominant contemporary relational context. I combined relevant parts of these into one overarching theme in the final analysis, which explored the research question: What is the contemporary relational context, and how are attractions or feelings for others made sense of within that context? Two themes and five sub-themes together evidenced attractions or feelings for others as a threat (by association with infidelity) within the mononormative sociocultural context.

The third overarching theme from the second (revised) thematic map (‘there’s gotta be someone to blame’) did not require much revision to fit with the final analysis. I refined information that was too similar or redundant in the original analysis, such as the sub-themes ‘partner is flawed’ and ‘deficit in partner’ which were combined into one sub-theme. I also added a third theme, ‘the relationship was wrong’, from a later part of the original analysis, as this also fit with the central organizing concept of wrongness and accountability. Together, these three themes and two sub-themes formed the second overarching theme of the final analysis, exploring the question: What accountabilities are at stake with attractions or feelings for others in committed relationships? This chapter also explores the affective consequences of these attributed accountabilities, as described by participants and interpreted by myself as researcher.

I revised and developed the final overarching theme most, in contrast to the analysis previously done, as my process of writing, feedback, and revision demonstrated that this section was the least coherent, and the central organizing concept required development. There were various themes and sub-themes across the initial analysis that explored imperatives or choices that were either made or recommended by participants. These parts of the original analysis were combined to produce the third overarching theme of the final analysis, including four (contradictory) themes and four sub-themes exploring the research question: How do people navigate, or recommend navigating, attractions or feelings for others?.

Combined, these three final overarching themes tell a story of (dominant or ‘normative’) initial sense making of attractions or feelings for others, subsequent attributions of accountability, and various (often contradictory and moralized) ways these feelings are navigated. Braun and Clarke (2006) describe thematic analysis as an active production of knowledge by the researcher, as themes aren’t ‘discovered’ or a pre-existing form of knowledge that will ‘emerge’, but rather patterns that a researcher identifies through their perspective of the data. My thematic analysis was influenced by my own social context, experiences, and theoretical positioning. In the context of critical research, ethical considerations are often complex, and researcher reflexivity is a crucial part of the process (Bott, 2010; L. Finlay, 2002; Lafrance & Wigginton, 2019; Mauthner & Doucet, 2003; Price, 1996; Teo, 2019; Weatherall et al., 2002). As the theoretical foundation of this thematic analysis was a combination of critical realism and critical feminist psychology, I engaged in an ongoing consideration of ethics and reflexivity throughout my data collection and analysis, which I discuss in the following section.

Bird, C. M. (2005). How I stopped dreading and learned to love transcription. Qualitative Inquiry , 11 (2), 226–248.

Bott, E. (2010). Favourites and others: Reflexivity and the shaping of subjectivities and data in qualitative research. Qualitative Research , 10 (2), 159–173.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology , 3 (2), 77–101.

Braun, V., & Clarke, V. (2012). Thematic analysis. In H. Cooper, P. M. Camic, D. L. Long, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA Handbook of Research Methods in Psychology (Vol. 2: Research Designs: Quantitative, qualitative, neuropsychological, and biological, pp. 57-71). APA books.

Braun, V., & Clarke, V. (2013). Successful qualitative research: A practical guide for beginners . Sage.

Braun, V., Clarke, V., Hayfield, N., & Terry, G. (2019). Thematic analysis. In P. Liamputtong (Ed.), Handbook of Research Methods in Health Social Sciences (pp. 843-860). Springer.

Finlay, L. (2002). “Outing” the researcher: The provenance, process, and practice of reflexivity. Qualitative Health Research , 12 (4), 531–545.

Lafrance, M. N., & Wigginton, B. (2019). Doing critical feminist research: A Feminism & Psychology reader. Feminism & Psychology , 29 (4), 534–552.

Mauthner, N. S., & Doucet, A. (2003). Reflexive accounts and accounts of reflexivity in qualitative data analysis. Sociology , 37 (3), 413–431.

Price, J. (1996). Snakes in the swamp: Ethical issues in qualitative research. In R. Josselson (Ed.), Ethics and Process in the Narrative Study of Lives (pp. 207–215). Sage.

Riessman, C. K. (1993). Narrative analysis . Sage.

Teo, T. (2019). Beyond reflexivity in theoretical psychology: From philosophy to the psychological humanities. In T. Teo (Ed.), Re-envisioning Theoretical Psychology (pp. 273–288). Palgrave Macmillan.

Weatherall, A., Gavey, N., & Potts, A. (2002). So whose words are they anyway? Feminism & Psychology , 12 (4), 531–539.

Lucie Wheeler (Professional Doctorate)

The following sections are by Lucie Wheeler, from her UWE Counselling Psychology Professional Doctorate thesis – “It’s such a hard and lonely journey”: Women’s experiences of perinatal loss and the subsequent pregnancy .

Data from the qualitative surveys and interviews were analysed using reflexive thematic analysis within a contextualist approach, as this allows the flexibility of combining multiple sources of data (Braun & Clarke, 2006; 2020). Both forms of data provided accounts of perinatal experiences, and therefore were considered as one whole data set throughout analysis, rather than analysed separately. The inclusion of data from different perspectives, by not limiting the type of perinatal loss experienced, and offering multiple ways to engage with the research, allowed a rich understanding of the experiences being studied (Polkinghorne, 2005). However, despite the data providing a rich and complex picture of the participants’ experiences, I acknowledge that any understanding that has developed though this analysis can only ever be partial, and therefore does not aim to completely capture the phenomenon under scrutiny (Tracy, 2010). An inductive approach was taken to analysis, working with the data from the bottom-up (Braun & Clarke, 2013), exploring the perspectives of the participants, whilst also examining the contexts from which the data were produced. Through the analysis I sought to identify patterns across the data in order to tell a story about the journey through loss and the next pregnancy. The six phases of Braun and Clarke’s (2006; 2020) reflexive thematic analysis were used through an iterative process, in the following ways:

Phase 1 – Data familiarisation and writing familiarisation notes:

By conducting every aspect of the data collection myself, from developing the interview schedule and survey questions, to carrying out the face-to-face interviews, and then transcribing them, I was immersed in the data from the outset. Particularly for the interviews, the experience allowed me to engage with participants, build rapport, explore their stories with them, and then listen to each interview multiple times through the transcription process. I therefore felt familiar with the interview data before actively engaging with analysis. I found the process of transcribing the interviews a particularly useful way to engage with the data, as it slowed the interview process down, with a need to take in every word, and therefore led me to notice things that hadn’t been apparent when carrying out the interviews. The surveys, as well as the interview transcripts, were read through several times. I used a reflective journal throughout this process to makes notes about anything that came to mind during data collection and transcription. This included personal reflections, what the data had reminded me of, led me to think about, as well as what I noticed about the participant and the way in which they framed their experiences.

Phase 2 – Systematic data coding:

Coding of the data was done initially for the interviews, and then for the survey responses. I began by going line by line through each transcript, paying equal attention to each part of the data, and applying codes to anything identified as meaningful. The majority of coding was semantic, sticking closely to the participants’ understanding of their own experiences, however, as the process developed, and each transcript was re-visited, some latent coding was applied, that sought to look below the surface level meaning of what participants had said. Again, throughout this process, a reflective journal was used in order to make notes about my own experience of the data, to capture anything I felt may be drawing on my own experience, and to reflect on what I was being drawn to in the data.

Due to the quantity of data (over 70,000 words in the transcripts, and over 23,000 words of survey responses), this was a slow process, and required repeatedly stepping away from the data and coming back to it in a different frame of mind, reviewing data items in a different order, and discussions with peers and supervisors in the process. I noticed that my coding tended to be longer phrases, rather than one-to-two words, as it felt important to maintain some element of context for the codes, particularly as the stories being told had a sense of chronology to them, that seemed related to the way in which experiences were understood. The codes were then collated into a Word document. Writing up the codes in this way separately to the data, it was important to ensure that the codes captured meaning in a way that could be understood in isolation. Therefore, the wording of some of the codes was developed further at this stage. During the coding process I began to notice a number of patterns in the data, so alongside coding, I also developed some rough diagrams of ideas that could later be used in the development of thematic maps.

Phase 3: Generating initial themes from coded and collated data:

The process of generating themes from the data was initially a process of collating the codes from both the interviews and the surveys, and organising them in a way that reflected some of the commonality in what participants had expressed. Despite each of the participants having a unique story to tell, with details specific to their personal context, there was also commonality found in these experiences. Through reflecting on the codes themselves, going back to the data, and using notes and diagrams that had been made throughout the process in my reflective journal, I began to further develop ideas about the patterns that I had developed from the data. Related codes were collated, and developed into potential theme and sub theme ideas. I used thematic maps to develop my thinking, and changed these as my understanding of the data developed. I was conscious that in the development of codes and theme ideas, I wanted to ensure that my analysis was firmly grounded in the data, and therefore, repeatedly returned to the raw data during this process. The use of my reflective notes was also vital at this stage, to ensure that I did not become too fixated on limited ways of seeing the data, but was able to remain open and willing to let initial ideas go.

Phase 4: Developing and reviewing themes:

Theme development was an iterative process of going back and fore between the codes, and the way that patterns had been identified, and the data, collating quotes to illustrate ideas. A number of thematic maps were created that aimed to illustrate the way in which participants made sense of their experiences across the data set, including identifying areas of contradiction and overlap. The use of thematic maps was particularly useful as a visual tool of the way in which different ideas and patterns were connected and related.

Phase 5: Refining, defining and naming themes:

Through the process of developing thematic maps, areas of overlap became evident, which led to further refinement of ideas. There were many possible ways in which the data could be described, and therefore defining and articulating ideas to colleagues and supervisors brought helpful clarity about what could be defined as a theme, where related ideas fitted together into sub themes, and also where separation of ideas was necessary. The theme names were developed once there were clear differences between ideas, and with the use of participants’ quotes where appropriate, in order to keep close links between the themes and the data itself.

Phase 6: Writing the report:

Writing up each theme required further clarity as I sought to articulate ideas, and illustrate these through multiple participant quotes. The process of writing a theme report required further refinement of ideas, and rather than just a final part of the process, still required the iterative process of revisiting earlier phases to ensure that the ideas being presented closely represented the data whilst meeting the research aims. At this stage links were also made to existing literature in order to expand upon patterns identified in the data. Referring to relevant existing literature also helped me to further question my interpretation of the data, and to expand upon my understanding of the participants’ experiences.

Braun, V., & Clarke, V. (2013). Successful qualitative research: A practical guide for beginners . London: SAGE.

Braun, V., & Clarke, V. (2020). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology , 1-25. [online first]

Polkinghorne, D. E. (2005). Language and meaning: Data collection in qualitative research. Journal of Counseling Psychology, 52 (2), 137-145.

Tracy, S. J. (2010). Qualitative quality: Eight “big tent” criteria for excellent qualitative research. Qualitative Inquiry, 16 (10), 837.

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Children's understandings’ of obesity, a thematic analysis

Childhood obesity is a major concern in today's society. Research suggests the inclusion of the views and understandings of a target group facilitates strategies that have better efficacy. The objective of this study was to explore the concepts and themes that make up children's understandings of the causes and consequences of obesity. Participants were selected from Reception (4–5 years old) and Year 6 (10–11 years old), and attended a school in an area of Sunderland, in North East England. Participants were separated according to age and gender, resulting in four focus groups, run across two sessions. A thematic analysis (Braun & Clarke, 2006) identified overarching themes evident across all groups, suggesting the key concepts that contribute to children's understandings of obesity are “Knowledge through Education,” “Role Models,” “Fat is Bad,” and “Mixed Messages.” The implications of these findings and considerations of the methodology are discussed in full.

The Health Survey for England 2009 illustrated that 65.9% of men and 56.9% of women have a body mass index (BMI) higher than 25 kg/m 2 , classing them as overweight, obese (>30 kg/m 2 ), or morbidly obese (>40 kg/m 2 ). Obesity is linked to many chronic illnesses, including type II diabetes, heart disease, and some cancers—specifically bowel and others within the digestive system (Renehan, Tyson, Egger, Heller, & Zwahlen, 2008 ). As a result, the direct cost to the National Health Service (NHS) of treating obesity was estimated to be between £991 and £1,124 million, for the 2001/2002 financial year (McCormick & Stone, 2007 ).

Childhood obesity is of particular concern because obese children are far more likely than children of a normal weight to become obese adults (Alexander & Sherman, 1991 ). The Health Survey for England 2009 showed that between 1995 and 2008, the percentage of overweight and obese girls rose from 25.5 to 29.2% and from 24.5 to 31.4% for boys. This is despite the fact that during the same period reported total energy intake in the United Kingdom (UK) fell by around 20% (Statistics on Obesity, Physical Activity and Diet England, 2006 ). These contradictory figures highlight the complexity of factors contributing to obesity, pointing to issues such as levels of physical activity, which have significantly fallen over the past two decades (Prentice & Jebb, 1995 ).

Many other factors influence incidences of obesity. The negative impact of childhood obesity causes the greatest concern and needs to be further understood. Obese children are more likely to become obese adults and experience increased health problems. Knowler, Pettitt, and Saad ( 1991 ), highlighted the links between childhood obesity and a poor immune system, risk of raised blood pressure, and cardiovascular problems. Studies have also identified that overweight and obese children are more likely to suffer psychological problems associated with low self-esteem, bullying, and social exclusion (Breat, Mervielde, & Vandereycken, 1997 ).

On an international scale, obesity can be seen as a problem of the developed world, a result of economic wealth, high food availability, and low levels of manual labour leading to lower levels of physical activity. This is in conjunction with high levels of car ownership and wide ranging public transport systems adding to the problem. In short, at the heart of obesity lies a homeostatic biological system that works constantly to maintain energy balance to keep the body at a constant weight. This system has not yet adapted to the world in which we currently live because the pace of technological progress has surpassed evolution resulting in a more sedentary lifestyle (Department of Innovation Universities and Skills, 2007). One surprising feature of the geographical distribution of obesity is its increased prevalence in economically and socially deprived areas in the western world, including the focus of this current piece of research, the United Kingdom. This phenomenon is very much a recent development, because historically deprived areas tended to see higher levels of under-nutrition. Brunt, Lester, Davies, and Williams ( 2008 ) illustrate how this situation has now reversed. They found between 1995 and 2005 the gap between obesity levels in the most deprived areas compared to the least (the latter typically having the higher levels) was steadily closing, and that by 2005 obesity levels in the most deprived areas had overtaken those in the least deprived areas, a phenomena that persists today.

The Childhood Measurement Programme (Department of Health and Department for Children, Schools and Families, 2008 ) demonstrated Sunderland in the north-east of England has some of the highest levels of overweight and obese children in the United Kingdom. This same publication also points out the strong positive correlation between areas considered as deprived and levels of obesity in children in Reception (4–5 year olds) and Year 6 (10–11 year olds). Areas of Sunderland are considered to be economically and socially deprived meaning the children who live there can be considered high risk. The statistics relating to Sunderland, where this study took place, demonstrate that 27.8% of Reception-aged children are either overweight or obese and for Year 6 pupils this rises to 38.4%.

The Foresight Report (Department of Innovation Universities and Skills, 2007), tackling obesity, points out that current policies are failing because they do not provide the depth and range of interventions needed. This might lead to positive interventions being ineffective if they are undermined by other areas in society such as social factors and the power of media advertising. The government launched its Healthy Schools Initiative in 2005; however, there has been no substantial reduction in obesity levels since 2005 (Department of Health and Department for Children, Schools and Families, 2008). With this in mind it would seem timely to approach the problem from a different perspective. Effective policies to tackle obesity need to consider all parties involved. However, current policies have been formed using a top down approach i.e., from government, health and education professionals, and even celebrity chefs! Even though these groups are likely to have a broad understanding of the problem from its roots to the long-term consequences, there has been a notable failure to take into consideration the understandings of the individuals at highest risk of obesity, the children themselves. There is growing evidence that interventions incorporating the views of the target population have a greater level of success (Hesketh, Water, Green, Salmon, & Williams, 2005). In the United Kingdom there has been a strong movement to ensure the inclusion of children in decision making particularly in relation to issues that directly affect them such as education, social care, and health (Department of Health, 2002 ; Department of Health and Department for Education and Skills, 2004 ). The collection and dissemination of the understandings of children relating to obesity could provide an insight into why so many strategies are failing. This in turn could lead to the development of policies that can be delivered to provide more successful outcomes.

There is a clear shortage of research examining children's understandings’ of obesity, the studies that have attempted to explore this domain have focused on exploring parent and care giver perceptions (Young-Hyman, Herman, Scott, & Schlundt, 1999), and the understandings of health professionals (Chamberlin, Sherman, Jain, Powers, & Whitaker, 2002 ). More recently studies have considered the understandings of care givers, health professionals, and teachers alongside those of the children themselves (Borra, Kelly, Shirreffs, Neville, & Geiger, 2003; Hesketh et al., 2005). Studies that have examined children's understanding have been focused on body image, overweight versus underweight (Hill & Silver, 1995 ), and peer perceptions of overweight and eating behaviour (Bell & Morgan, 2000 ; Oliver & Thelen, 1996 ), but not on the understandings’ of the children themselves with regards to the causes and consequences of obesity.

Focus groups have proved to be a particularly useful method for collecting data from children, they are most effective with groups of three children and in situations where the children know and like each other. Groups must be carefully selected to ensure the children are comfortable with each other. Talking together in small groups is familiar territory for children because it simulates class work. This method allows the researcher to structure the discussion around themes or topics rather than direct questions. This in turn enables the children to take control of the discussion (Mauthner, 1997 ) with the researcher present to keep things on track. Conducting group discussions in single sex groups can also prove to be more successful because boys are often louder and more willing to talk and this can mean they direct the topic of conversation. It has also been noted the use of some sort of structured activity such as drawing, reading, or sorting cards, can help focus discussion in particular with young children. When discussing diet with children, nutritionists and dieticians regularly use replica food items to help visualise the topic under discussion and photos depicting scenes of physical activity have proved effective in qualitative studies (Hesketh et al., 2005 ).

In summary the objective of this research is to investigate the understandings of a high risk group of children (high risk because of their socio-economic status so determined by their locality), of some of the causes and consequences of obesity, and its links to diet and physical activity. The concepts and themes generated by this research should be used to provide an insight that may inform local policies and interventions that need to be developed to provide a broader and deeper range of options to address this multi-faceted issue.

In order to address the gaps in current literature it was decided this research should focus on identifying themes within the participants understanding. This would provide the researcher with scope for further investigation of the subject in question. It was therefore decided that the most appropriate method of analysis would be a thematic analysis. However, there have been criticisms of this approach in the past due to the lack of clear guidelines for researchers employing such methods. This has subsequently contributed to some researchers omitting “how” they actually analysed their results (Attride-Stirling, 2001 ). It was of upmost importance to the authors in this current study to employ a clear, replicable, and transparent methodology.

Braun and Clarke ( 2006 ) outline a series of phases through which researchers must pass in order to produce a thematic analysis. This procedure allows a clear demarcation of thematic analysis, providing researchers with a well-defined explanation of what it is and how it is carried out whilst maintaining the “flexibility” tied to its epistemological position. The authors in this paper take a position that acknowledges our desire to incorporate the individual experiences of the participants and the meanings they attach to them. However, we also wish to consider the impact of the wider social context on these meanings. Braun and Clarke describe such a position as “contextualist,” sitting firmly between essentialism or realism and constuctionism. Not all theorists describe these two poles of epistemological outlook in the same way; Madill et al. ( 2000 ) refers to them as “naive realist” and “radical relativist.” Methodologies that go hand in hand with this mid-ground position are typically phenomenological in nature, but the flexibility of thematic analysis means that it can also be underpinned by an “in-between” epistemological position. Willig ( 2008 , p. 13) summarises this by explaining a position that argues “while experience is always the product of interpretation and, therefore, constructed (and flexible) … it is nevertheless ‘real’ to the person who is having the experience.” We wish to consider the reality of obesity to the participants, through an exploration of their experiences and the meanings they attach to them, whilst incorporating the broader role society plays in contributing to and shaping the participants meaning making and subsequent understandings.

Participants

Twelve participants were selected through liaising with the school and class teachers, this was particularly important considering the sensitive nature of the research topic and the fact that the participants taking part in this study were children—a vulnerable group. Measures were taken to prevent any of the participants feeling stigmatised. Therefore, under the guidance of the class teachers, the participants approached to take part in the study were carefully selected to ensure no children who may have been made to feel uncomfortable by the discussion were included, and to make sure that the children selected to be in the same focus groups were comfortable with each other. Six (three boys and three girls) were selected from two school years; Reception, aged between 4 and 5 years and Year 6 aged between 10 and 11. The motivation for selecting these age groups was that government statistics relating to childhood obesity are published for these two age brackets. These age groups are viewed as critical points in measuring children's BMI and in monitoring their changing health status. Through looking at these age groups, it may help us to gain an insight into what understandings children arrive at school with (primarily shaped by their experiences set within a home environment) and those that they have later on in their school life when further social influence (school and peers) may play a role in shaping their understandings. Efforts were made to make the sample representative of ethnicities attending the school so a proportionate number of children of Bangladeshi and Afro-Caribbean heritage took part. Participants were not recruited on account of their BMI or weight status. The parents of the children were provided with a study information letter and, in addition, received a phone call from the school's community liaison officer to ensure that parents fully understood the nature of the study because the researcher was aware that for some parents English was not their first language. The phone calls were made in their mother tongue thus allowing the parents to sign the parental assent form with all their queries being answered. Participants were also asked for their verbal consent on the day prior to the study taking place.

The study had received ethical approval from Northumbria University's School of Psychology and Sports Science Ethics Board prior to commencing. The researcher had also been approved by means of an enhanced criminal records background check clearing her to work with children; this approval was required by both the school and the university.

The focus groups all took place in the same quiet room at the school and were conducted by the principal investigator (referred to herein as the researcher). On arrival, the researcher introduced herself and provided name badges for the participants. The researcher briefly explained to the participants that she was there to talk to them about food and exercise. The researcher also explained to the participants that she wanted them to assume that she knew nothing, they were not being tested, and she was only interested in hearing what they had to say—not whether they were right or wrong. Verbal instructions were provided to the participants and they provided verbal assent prior to the recording commencing. A series of questions were developed by the research team, these were designed to keep the focus group sessions on track whilst exploring issues relevant to the research question. The sessions started initially with a discussion centred on the replica food items laid out on the table. Participants were asked to use the replica food and pick out healthy foods and make what they thought would be a healthy lunch. They were asked to explain why it was healthy and what made it healthy. Participants were then asked about foods they liked and why they liked them. In addition, they were asked about the sorts of things they normally ate at home and in school and things they liked to eat. Once conversation had dwindled concerning the replica food the researcher introduced the laminated picture cards, and the discussion moved to physical activity with the researcher encouraging the participants to explore the relationship between diet and exercise. Questions focussed on what activities they thought were healthy (as the images depicted activities that were both physical and sedentary; that is, one image of somebody running another of somebody playing computer games). The participants were asked about what sorts of activities they liked doing and what made those activities good for them. They were asked what activities they regularly engaged with, the sorts of sports their parents and siblings took part in, and the activities they did as families. The themes of discussion were encouraged around the two elements pertinent to any strategy looking to reduce obesity: healthy eating and physical activity. Furthermore, questions also probed at what the participants thought the benefits were of following a healthy lifestyle and what the consequences were of not following one. They were also asked what advice they would give somebody who wanted to be healthier and how important it was to them to be healthy. The focus group guide was intended to provide a structure but not rigidly dictate the line of questioning. The researcher included prompts and encouraged participants to expand on their initial responses and followed up on notions that the participants raised themselves. The sessions on the first day lasted between 20 and 30 min, ending when the participants input was insufficient to continue. At the end of each session the researcher read out the participant debrief and provided each participant with a parental debrief information sheet to take home.

In order to strengthen the analysis process and gather the most appropriate data, the researchers reviewed the recording made on the first day and reflected on the procedures employed in the focus groups. Similar approaches of reviewing data to informing further data collection are used in methods such as grounded theory and it was felt that doing so would strengthen the current study. The decision was made not to use the props (replica food and cards) used on the first day in the second round of focus groups, as at times they had proved to be a distraction to the participants. As an alternative, Reception children were given colouring pens and paper to focus their attention. Year 6 focus groups were run again allowing for free discussion, following on from issues and understandings they had raised in the initial session. The second round of focus groups, other than the changes already detailed above, followed the same sequence as they had on day one and lasted around 30 min. The recordings were transcribed combining the recordings from both days creating four transcripts, one for each group.

Data analysis

The data collected from all the focus groups was transcribed by the principal investigator, during this process the initial thoughts and ideas were noted down as this is considered an essential stage in analysis (Riessman, 1993 ). The transcribed data was then read and re-read several times and, in addition, the recordings were listened to several times to ensure the accuracy of the transcription. This process of “repeated reading” (Braun & Clarke, 2006 ) and the use of the recordings to listen to the data, results in data immersion and refers to the researcher's closeness with the data. Following on from this initial stage and building on the notes and ideas generated through transcription and data immersion is the coding phase. These codes identified features of the data that the researcher considered pertinent to the research question. Furthermore, as is intrinsic to the method, the whole data set was given equal attention so that full consideration could be given to repeated patterns within the data. The third stage involved searching for themes; these explained larger sections of the data by combining different codes that may have been very similar or may have been considered the same aspect within the data. All initial codes relevant to the research question were incorporated into a theme. Braun and Clarke (2006) also suggest the development of thematic maps to aid the generation of themes. These helped the researchers to visualise and consider the links and relationships between themes. At this point any themes that did not have enough data to support them or were too diverse were discarded. This refinement of the themes took place on two levels, primarily with the coded data ensuring they formed a coherent pattern, secondly once a coherent pattern was formed the themes were considered in relation to the data set as a whole. This ensured the themes accurately reflected what was evident in the data set as a whole (Braun & Clarke, 2006 ). Further coding also took place at this stage to ensure no codes had been missed in the earlier stages. Once a clear idea of the various themes and how they fitted together emerged, analysis moved to phase five. This involves defining and naming the themes, each theme needs to be clearly defined and accompanied by a detailed analysis. Considerations were made not only of the story told within individual themes but how these related to the overall story that was evident within the data. In addition, it was highly important to develop short but punchy names that conveyed an immediate indication of the essence of the theme. The final stage or the report production involved choosing examples of transcript to illustrate elements of the themes. These extracts clearly identified issues within the theme and presented a lucid example of the point being made.

The thematic analysis process that was applied to the transcripts elicited key concepts that were evident in the data. These themes are viewed as essential in determining the understandings of all the participants. These categories have been labelled as “Knowledge through Education,” “Role Models,” “Fat is Bad,” and “Mixed Messages.” There are of course aspects of the participants’ understandings that overlap across these categories. This, however, should be viewed as a good interpretation of understandings and attitudes in general, which are never made up of isolated concepts but are all relative to each other.

Knowledge through education

This theme is defined by the ability of all the participants to understand the roles of diet and physical activity. This is, in part, likely to be defined by different levels of education that the two age groups represented have, but nothing conclusive can be drawn given the relatively small sample size. The impact of their education on their knowledge will be demonstrated through evidence from the transcript.

All participants in the reception age group expressed the ability to name and identify different food items from the replica food. When they were asked to prepare a healthy lunch from the food items, they were able to point out food that would typically be classified as healthy.

I: No none of it is real! So what have you put in your healthy lunches girls? You tell me what you have got. *: Apple, I've got pasta, egg, cracker, grapes, bun and cheese. Girls reception Open in a separate window

However, despite displaying that they “know” what healthy means there is evidence of confusion, and it would seem the concept of something being “good” for them is interpreted to be things they like to eat. This suggests that they don't yet fully understand the concept of “healthy” food.

I: And why's rice healthy? *: Because it's nice. I: What healthy food do you eat? *: Chips Boys reception Open in a separate window

Their definition of healthy is centred on food they believe will make them grow for which fruit is highlighted as being particularly important. However, they also attribute this property to the food that makes up their personal diets. This understanding might result from being told to eat so they grow up to be big and strong. It is important to consider younger children's understandings are likely to be primarily shaped by their home environment, where the emphasis is often on how much children are eating as opposed to what they are eating.

I: Why is a banana important? *: Because it makes you strong so you can grow you have to have fruit so you can grow. I: Can you tell me then girls, we have found all these things that are good, as an example can you tell me, sausage, why is sausage good? *: Because it makes you feel strong. Girls reception Open in a separate window

This understanding of the reception-aged girls represented in this study of eating so they can grow up to be strong is also evident with the boys in the same age group. However, the reception boys also place great importance on the necessity of exercise to develop strength, this demonstrates another aspect in their knowledge.

I: What about this one here, swimming, who likes swimming? *: Me *: Me *: Me I: And why is swimming good for you? *: Cos it makes you strong. Boys reception Open in a separate window

It is fair to say Year 6 groups relished the opportunity to express their knowledge. They were able to identify and name different food groups and discuss different types of physical activity; what's more they understand the link between the two in relation to obesity. It seems other influences have impacted on the children's understandings’ such as school and extracurricular groups.

*: This is a banana. I: Ok why's a banana healthy? *: Because it's got seeds inside, because it's a fruit. Girls year 6 Open in a separate window

The ability to identify a particular fruit by one of its universal characteristics shows a deeper level of understanding and suggests that a higher degree of learning. In fact it is explicitly stated that this nutritional knowledge has been gained at school.

I: So do you know the different groups of food like carbohydrates, I heard you say protein and dairy before? *: Done it in science. Girls year 6 Open in a separate window

Moreover, it isn't just a nutritional knowledge they have developed through education. They appear well versed in the concept of a balanced diet and also understand the importance of a balanced lifestyle in relation to physical activity. They are able to articulate the notion of a balanced, healthy lifestyle through a consideration of the consequences of over eating and not exercising.

I: So what happens to you if all you do is you do watch TV and play the computer, eat the food that you told me was the bad food, what would happen to you? *: You would have a miserable life. *: Get fat, teeth will fall out. Girls year 6 Open in a separate window

In the case of the Year 6 boys who took part in this study, it is apparent that although a great deal of their knowledge has come through education at school, other avenues have helped them develop different aspects of their understandings. In this case it seems to be through taking part in activities, typically sport outside of school, or and more uniquely to this group through the influence of their fathers.

*: I would say my dad likes fish so I eat fish loads. *: My dad likes chicken, so he gives me chicken cos after school I do sport, like boxing, he gives me a sandwich with loads of different toppings in cos meats a muscle maker and vegetables is like an energy maker, so if you eat those you will get fitter and healthier. Boys year 6 Open in a separate window

It is evident where the ability exists, or is encouraged, to apply knowledge they have in a context relevant to their own lives, the knowledge becomes embedded in their understandings; it is applicable to them and, therefore, moves from being written on the board in school to being important to their own existence. This is exhibited by those participants, in particular the boys who participated, who have an involvement in sport. Having a motivation to understand nutrition and exercise leads to a desire to apply it because they comprehend the potential benefits. This aspect within the initial theme of knowledge through education leads directly on to the next theme of role models. The key difference between these two themes is the first relates to information that is directly and intentionally meant to inform the children about healthy lifestyles in an institutional setting, while the second theme is typified by understandings that are formed through interactions with other people.

Role models

The application of knowledge gained through education is often facilitated by role models such as family members who reiterate this information through example. Role models play an important role in the concepts described by all the groups, for example, the older boys reported that their fathers helped encourage healthy behaviours, above and beyond the nutritional knowledge in the previous theme.

*: Like sometimes on an afternoon my dad goes to the gym, then there is these tracks outside, and I practice every day on my 100 meter sprint and I can do it in 12 seconds, and when I started doing it I was 21 second, so I keep practicing. Boys year 6 Open in a separate window

This demonstrates some of the participants’ understandings have developed by examples set for them by significant individuals in their lives. This is evident in the younger children's understandings in a less explicit manner; the example below demonstrates good health behaviours can be established through everyday behaviour exhibited by role models.

I: What about this one, walking to school? … Why is it good for you? *: Because me and my mam walk to school and its good. Girls reception Open in a separate window

There is some evidence that examples set to the girls who took part in this study, at home and by other role models, can encourage behaviours or ideals that are not beneficial to the girls health. Girls appear to look up to older female family members who aspire to be skinny.

*: I like to be skinny, my nana does as well, and she wants to be skinny because she's fat now but I still love her. Girls reception Open in a separate window

They also appear to have developed unrealistic ideas about weight loss and the consequences in terms of treatment. Viewing hospital treatment as a solution to obesity, demonstrates a lack of understanding about the role of lifestyle behaviours in the condition. This may also suggest that these participants don't appreciate the importance of lifestyle behaviours in the onset of obesity.

*: Guess what, I seen this film right the boy was fat right, his legs was right down to the bottom, he had a fat tummy, I was hiding cos I hated him, he was horrible, he will have to go to hospital, he was fat. Girls reception I: So what would you tell somebody if you pretend that I was really, really fat, what would you tell me to do. *: Go to the doctors … hospital, operation. Girls reception Open in a separate window

There was some evidence that the older girls in this study had a more balanced outlook on what sort of body shape was healthiest, because they were aware of the negative health consequences associated with being underweight. It is interesting, however, that they are aware that maintaining a healthy lifestyle may be a challenge and this may result in a barrier to adopting healthier practices.

I: What about the other end of the scale, you know if you've got overweight being fat on this side what about being underweight at this end? *: It's bad cos you're all bony and you can't do anything cos you're not strong enough, you're weak. *: So you need to be in the middle. I: Is it easy to stay in the middle? *: No, because sometimes you can't be bothered to eat well and exercise. Girls year 6 Open in a separate window

Within the theme of role models, there was some evidence of a difference between the genders in terms of available role models. The participating boys often cited football heroes as people whom they looked up to and aspired to be like. This highlights the role of the celebrity in providing a role model for today's children; the evidence from the participants in this study may suggest that typically boys look to footballers and other sporting heroes. It can be argued that such individuals do not always provide a strong moral code; they are seen as following a healthy lifestyle in terms of diet and exercise. It would seem that the female participants in this study often looked up to celebrities who weren't so explicitly seen to be following healthy lifestyles, or a sense of caution was attached to following healthier behaviours.

*: Yeah like Wayne Rooney. I: And why is he fit? *: Cos he's good at footballing. I: Do you think that they have to eat special food? *: Yes I: And what special food do they have to eat? *: Bananas and apples. Boys reception *: Actually you can put weight on running cos muscle weighs more than fat so you can put weight on—like Katie Price she put on 10 pounds cos she started running. Girls year 6 Open in a separate window

Another interesting aspect of the notion of role models’ is that the girls were more concerned with how they appeared in a physical sense; it was particularly striking that the Year 6 boys identified unhealthy behaviour in their female peers attributing this to a desire to be like models.

*: Yes, she wants to be a model so she starves herself, her mam gives her a big packed lunch and she puts most of it in the bin, she's like that skinny then she walks out of the dinner hall. Boys year 6 Open in a separate window

There were many aspects of the transcript that highlighted participants were aware that being underweight was as worrying as being overweight. However, across the board they were far more critical of individuals who were overweight and discussed wide ranging consequences for these individuals, this leads on to the next theme evident in the analysis.

There was a united consensus that being fat was something to avoid, that it was a bad thing, and had typically negative consequences. Elements of this theme have been demonstrated throughout the discussion of the previous two themes; however, this illustrates how their understanding impacts on their attitudes toward obesity.

*: Like all the fat goes through your blood and stuff. *: Like sugar, like all the sugar goes through your blood if you eat too much of it would clog up your arteries and you might die. Boys year 6 I: Like how? What would happen to you? Is something going to happen straight away or is it something that's going to happen to. *: You would get rotten teeth and you would not be as strong as you would be if you ate healthy and stuff. *: You could die. Girls year 6 *: Because fat would be horrible. *: Because it's bad for you, because it looks bad. *: Because people call you big fat. Girls reception Open in a separate window

In addition to the health issues and those relating to physical attractiveness were the issues of bullying and social exclusion, which seemed to play a big role in the children's understandings of what it would be like to be overweight. The stigma attached to being overweight is evident as participants often started giggling when talking about people being overweight.

I: Is it important to eat things that are good for you? *: Laughter I: What do you think happens to you if you eat lots of these biscuits? *: Fat I: And what good would stop you from getting fat, or would help you not be fat? *: Giggling Boys reception Open in a separate window

Inability to have a successful career and even death were understood to be the results of obesity. Participants felt people who were overweight were in some way bad or an embarrassment. There was even a sense of fear toward people who they considered overweight, indicating that they would avoid being seen with somebody who was obese.

I: So … so what do you think about being fat, like if you see somebody in the street who looks like they are not very healthy do you think? *: They can't do much, like most of the things you want to do in life, like swimming, jogging. *: Jobs when you grow older. Girls year 6 *: Like if my parents were proper massive and I went to the town with them I would just say they took me to the town and I don't know them. Boys year 6 Open in a separate window

It is clear that the participants’ understanding is that obesity is a very negative issue. However, there is also evidence that they understand the complexity of the condition and are also aware being underweight maybe as much of a problem. The older children in this study seemed to understand that it is a complex issue and fully grasped the concept of moderation. They often refer to the fact that you can have a small amount of things that maybe classified as unhealthy, as long as you don't eat them all the time or balance them out with exercise.

I: And what sort of things for eating well? *: Like fruit and vegetables. *: Some Sugar. *: If you eat vegetables and fruit and you might get back to underweight. *: And you want to be in the middle. *: You need a bit of fat on you. Girls year 6 Open in a separate window

This category of Fat is Bad highlights an issue that clouds all the children's understandings of issues surrounding obesity and that is of conflicting messages. This notion of mixed messages forms the final theme evident in the data.

Mixed messages

The evidence presented here would suggest the information intended to educate and inform children is often met with equal amounts of contradictory or confusing messages and behaviours. The result of this is easily demonstrated by comparing what the children know they should be doing with what they actually talk about doing. For the majority of the participants their knowledge did not always match with their described behaviour, their food preferences often overriding their knowledge. This was perhaps not so surprising; knowledge does not by any means dictate behaviour.

I: Do you have breakfast most mornings? Do you normally have some breakfast, what do you normally have for breakfast? *: Miss I have chocolate cookies. I: What did you have for your tea last night? *: I just had for my supper. I: What did you have last night for your supper? *: Err sandwiches, cake and I: What about what did you have last night for your tea? *: Pizza Girls reception I: You eat two, two pieces of fruit? *: Yes, cos my mam chops it into two halves. Boys reception Open in a separate window

Conflict existed in a number of forms in the understandings expressed by the participants. It is worth reiterating that the younger girls who participated believed treatment for obesity was to go to the hospital and have an operation—something they have picked up from a TV documentary—this conflicts with diet and exercise education they receive at school. Other participants gave more specific and direct examples of receiving contradictory information. This ranged from conflicts in direct health messages to conflicting information and action between school and home. They felt that at times it was difficult to know which information was the right information, not only was it conflicting but it was forever changing.

*: And people say if you make fruit smoothies its healthy for you but it said in the news something about being obese again it said that if you drink a smoothie one a day you'll put on 13 pounds, that's nearly a stone in a year. Boys year 6 I: What about at home? You know if you're taught all this stuff at school what happens when you go home? Do Mum and Dad teach you the same things or is it different? *: Different I: And why is it different? *: I eat more sweets. Girls year 6 Open in a separate window

In addition to this, older children also pointed out they felt that healthy lifestyle information wasn't always delivered in the correct manner, there was a belief that stigmatising people who were overweight was negative. There was an awareness that there is a psychological aspect to overeating, and in some individuals it is this that needs to be addressed. Moreover, there was a feeling again demonstrated solely by the older participants that being overweight/obese could be difficult to rectify and maintaining a healthy weight could be a challenge.

*: So you need to be in the middle. I: Is it easy to stay in the middle? *: No, because sometimes you can't be bothered to eat well and exercise. Girls year 6 I: Do you think it's quite easy to lose weight? *: Yes *: Well for some people. *: If you put your mind to it, it is. I: No go on cos everyone's got different ideas. *: You can't just lose weight quickly. *: Cos my dad when he was young he was obese so he told me, but he's sort of addicted really. *: Addicted to what. *: Addicted he cannot stop but he's trying. *: He cannot stop what. *: Eating when he was young, he like learnt now he's saying to me about being fit cos he tells me about what happened when he was young so I try it. Boys year 6 Open in a separate window

This understanding of the complex nature of the obesity problem, coupled with the confusion and conflict in both the information and behaviours the participants are exposed to, can help explain some of the barriers to individuals adopting a healthier lifestyle.

Comprehensive understanding

The results detailed above highlight some important findings as to how children understand obesity in terms of some of its causes and consequences. It was particularly clear that knowledge, often imparted in a school setting, is getting through to the children who participated in this study. However, it appears equally evident that this knowledge in many cases does not transfer to behaviour. Further examination of the results allows us to explore the potential reasons behind the knowledge-behaviour gap.

Role models by their nature provide examples for both the children's beliefs and their behaviour. There are a wide variety of potential role models for children from parents, teachers, peers, and celebrities. What seems particularly important, in terms of being a positive role model with regards to healthy lifestyles, is that children have an opportunity to view the process of being healthy. In this study, this was typified by the examples of the Year 6 boys who participated in sport with their fathers. It appears this close and active relationship allows the knowledge that has been started at school to grow. Allowing children the opportunity to apply their knowledge and see the steps taken by a role model to get or stay fit help translate this knowledge into behaviour. What is interesting, however, is that it seems passive behaviours by role models can have the same impact. It was the case with these participants that the effect of passive knowledge transfer seemed to be more negative, but that is by no means to say that passive behaviours by role models will not also encourage positive lifestyle behaviours in other cases. The most obvious example of this within this data set was the seemingly implicit messages that the girls received about being skinny. There was not an overtly explicit attempt on the behalf of the role models described here to encourage a “skinny” ideal; however, messages seemed to reach the participants that would indicate this is the case. The key difference between these active and passive role models appears to come from whether the role models place focus on the process; taking part in sport (in the example of the older boys) or outcome being skinny (in the example of the girls). Focus on the action of being physically active or enjoying a healthy diet in the case of these participants produces a healthier outlook on maintaining a healthy body weight. When that focus is on the outcome—the weight loss or the weight gain—there seems to be less concern for actually “being healthy” in terms of body weight and lifestyle. This notion about process and outcome is intrinsically linked to the theme of Fat is Bad.

It is interesting to note that whilst the children expressed an understanding of fat as a component of diet and were able to identify high fat foods and their link to obesity, the focus was on fat as an outcome and not so much about it as input. It is a well-documented fact that fat is a requirement of a balanced diet. The participants were able to recite in great detail the consequences of becoming fat but were not so forthright about the processes involved in becoming fat. It can be suggested that by focussing on the process of becoming fat and understanding the need for fat in moderation and being physically active it may help to discourage fat becoming the output. This may also help to draw away the focus from physical appearance that is so closely tied to the stigma attached to being overweight and place it on living a healthy lifestyle and being healthy.

The key finding of this study is that it is evident that children receive contradictory messages when it comes to following a healthy diet and taking part in exercise. The research presented here highlights children's understandings of some of the causes of obesity and the consequences of becoming overweight. However, it is equally evident that this information has reached them on a knowledge level but has not or cannot be fully translated into behaviour. It appears that central to this problem are the multiple discourses that exist around diet and exercise. Whilst government campaigns may impart facts and figures and provide advice on changes that can be made, there are a whole host of other sources to contend with. There is an undoubted role played by the media both in terms of active advertising campaigns for junk food or sedentary games and the passive portrayal of unattainable body shapes and sizes in magazines and by celebrity culture. However, more than this, health messages are competing against a variety of cultural values, social, and personal norms that may well go against messages that encourage certain behaviours. What is more is that ultimately individuals have the power and autonomy to make their own choices about diet and exercise. Stakeholders need to ensure that people are in a position to make an informed decision and not one where their judgement is clouded by an array of contradicting messages. There is also a responsibility to ensure that individuals are able to act on advice given and to provide advice that is relevant and tailored to individual circumstances. It is easy to understand why parents on a low income may struggle to incorporate “5 a day” into their families diets when they perhaps don't have access to a car and the nearest shop selling fresh fruit and vegetables is several miles away. Ensuring people know that frozen fruits and vegetables are just as good and, in some cases better, is a far more useful and usable message.

Comparisons with past research

The objective of this study was to explore children's understandings of obesity in terms of diet and physical activity; the children included were considered high risk because of their socio-economic status. To meet this objective, focus group data was analysed using thematic analysis. This analysis produced key themes pertaining to the understandings of the participants. There is not a wealth of prior research in this domain and it was for this reason thematic analysis was chosen to analyse the data. The method proved to be particularly useful in generating these exploratory data that are discussed here in relation to previous findings.

The theme of knowledge has previously been identified by Hesketh et al. ( 2005 ) in terms of information and awareness that is pertinent to children's perceptions of healthy eating, activity, and preventing obesity. Increasing knowledge relating to diet and physical activity cannot prevent obesity but it can encourage children to make informed choices.

This study, as have others (Hesketh et al., 2005 ; Borra et al., 2003 ; Musaiger, Mater, Alekri, & Mahdi, 1991 ), identified misunderstandings in children's knowledge as barriers to healthful behaviour. It might be useful to address this issue, particularly with younger children who are developing their knowledge. Previous literature has identified young children often consume their recommended daily intake of fruit but fall well short when it comes to vegetables (Dennison, Rockwell, & Baker, 1998 ). Government campaigns encourage people to eat five portions of fruit and vegetables a day ( www.5aday.nhs.co.uk ); however, nutritionists would encourage three portions of vegetables and two of fruit—fruit having high sugar content. There was no evidence in the transcripts that any of the children were aware of or understood this distinction. This needs further investigation; however, education should encourage an understanding of fruit and vegetables as separate entities to help increase the consumption of vegetables (Gibson, Wardle, & Watts, 1998 ).

The evidence in this study suggests children grasp the causes of obesity, overeating, and low levels of physical activity; however, there was a general lack of understanding of the underlying physiological processes. There was a limited understanding of the concept of energy balance or that there might also be medical reasons for the obesity. Bell and Morgan ( 2000 ) demonstrated providing medical explanations for obesity can have a positive effect on children's attitudes to obese individuals. Overweight individuals were generally stigmatised by the participants in this study, so providing better medical information could help to alleviate these negative attitudes. It is fair to say those children who did have more in-depth knowledge of obesity were more sympathetic in their considerations of overweight individuals acknowledging the difficulty in making lifestyle changes.

The influence of parents concerning diet and exercise behaviours is well documented (Prout, 1996 ). Hesketh et al. (2005), Borra et al. (2003), and Young-Hyman et al. ( 2000 ) consider parental influence to be a determining factor in children's attitudes and understandings of obesity. It is clear this influence can be as detrimental as it can be beneficial. Previous research (Borra et al., 2003 ) argues interventions need to be developed that consider the role of the parent. Children cannot be expected to apply the information they receive at school to themselves if it is not reiterated at home. Nutritional education and physical education have not formed a core or extensive part of school curriculums in the United Kingdom in previous years, and there is now a generation of young parents who do not have the skills to attractively present appropriate foods (Tuttle & Truswell, 2002 ) or who regularly take part in sport themselves. The impact of this on their children's behaviour is that they don't always have examples of healthy behaviour to model their own on.

Of particular importance was the finding that children feel that they often receive mixed and contradicting messages. This is of great relevance when considering the development of policies and strategies that can be more effective. More over this backs up the findings of Dorey and McCool ( 2009 ) who conclude that nutritional messages evident in health promotion and advertising were often perceived by child audiences to be ambiguous. The authors warn that these contradictory messages could potentially serve to weaken the trustworthiness viewers have in health promotion initiatives. This really points to a key area in which health professionals can target efforts to tackle obesity. Clarity and consistency in healthy messages and recommendations are central to helping people take on board and act on the information they receive. Contradiction allows room for people to question the advice given and when effort is required to make a change in behaviour that change is less likely to be made if there is reason to doubt the accuracy of information. Furthermore, coherent messages need to consider person specific factors that may inhibit behaviour change; when individuals are encouraged to behave in a certain way but the constraints of day-to-day life lead to another, the results are confusion and hostility to the initial message (Owens & Driffill, 2008 ).

Procedural issues

The main methodological issue arising was participants from Reception struggled to engage fully in conversation, and the sessions followed a structure more a kin to an interview (i.e., question and answer). It was difficult to encourage responses that were longer than a few words; often one word responses were given. There is the potential to gain some very useful information from children in this age group; however, it can be a long and time-consuming process to elicit enough information to make the analysis process worthwhile. The length of the sessions also must be kept relatively short because attention spans are not long lasting; this was a finding similar to that of Miller ( 2000 ). The replica food items selected to help provide structure to the focus groups were useful and did provide a catalyst for discussion; however, for very young children (i.e., those in Reception) they resemble toys too closely, this then leads to them becoming more of a distraction, hindering the discussion. The use of the picture cards and pens and paper as suggested by Backett and Alexander ( 1991 ) provided a more a suitable means of structuring focus groups for young children.

There were at times issues with certain members of the groups making themselves heard more than others, thus the researcher had to encourage those happier to sit back and let others take the lead (Kirk, 2007 ). However, through a little encouragement all participants appeared comfortable talking with each other and participated equally, a result of the careful selection process. It also appeared to be beneficial speaking to boys and girls separately, with the boys often more excitable in their discussion style in comparison to the girls. It also facilitated the identification of some important issues, for example, the Year 6 boys identified eating behaviours present in the Year 6 girls that the girls themselves did not discuss.

Implications for the future

The Foresight Report (Department of Innovation Universities and Skills, 2007 ), in tackling obesity, points out that current policies are failing because they do not provide the depth and range of interventions needed. This present study has determined that central to children's understandings of the causes and consequences of obesity are the concepts of knowledge, the opportunity to apply this knowledge to their own lives, and the existence of role models to set an example. There exist certain myths and misconceptions that need to be addressed and children need to believe they can trust the health messages they receive because they are aware some messages are misleading or forever changing.

The key to this issue seems to be children learn by example, they can have all the knowledge in the world provided to them through an institution such as a school but this information needs to be supported by life at home. This provides evidence that campaigns need to target parents to tackle childhood obesity; this is an issue that policy makers are already aware of ( National Institute for Health and Clinical Excellence, 2006 ). However, this means health messages delivered to the general public need to be clearer and avoid ambiguity. There needs to be careful considerations of the context in which health messages are received, taking into account the understandings of the target population (Hesketh et al., 2005).

There were some issues raised in the focus group that were beyond the scope of this particular study. There was a representation of different ethnic minorities in the groups, and slight differences in the understandings of these different groups were identified. Further research should investigate the understandings of different minority groups to see if ethnicity influences or results in divergent concepts. Future study also needs to look at strategies that enable children to apply healthy lifestyle information to their own lives.

Children spend, on average, a quarter of their waking lives in schools; therefore, schools can be seen as an effective environment and source to help encourage healthy lifestyles. However, that leaves three quarters of a child's time in which they are out of the control of the school environment. Strategies must be developed to unite the teaching at school with practices in the home. This supports the conclusions of Hughes, Sherman, and Whitaker ( 2010 ) who write that strategies need to be framed in a manner that makes low income mothers feel more supported in addressing issues their children may have with their weight. Ensuring that approaches to encourage healthy lives take on a holistic format will also help to provide consistent and realistic role models. There needs to be a concerted effort from within society to develop role models who have a healthy relationship with food and exercise. These seem to already exist for young boys in the form of sporting heroes but seem in short supply for young girls who already consider that being healthy is the ideal but then look to surgery as a form of weight loss. Lieberman, Gauvin, Bukowski, and White ( 2001 ) highlight the importance of role models and peer influence in the onset of disordered eating in young girls and this needs to be seriously taken into account when sending out messages that being overweight is bad, girls need to be aware that being underweight also has severe health consequences.

In conclusion, the time children spend eating and taking part in physical activity out of school is likely to be the biggest challenge to preventing the continuing obesity problems in the United Kingdom, and this is where current strategies appear to be failing. Children understand obesity and its contributing factors in terms set out to them by those people they consider role models. It is only by helping these role models to provide consistent and reliable information by setting suitable active examples and by being aware of the impact of their passive actions that we can begin to address the problem of obesity.

Acknowledgements

The authors would like to thank Sunderland Children's Centres and Back on the Map for their support in facilitating this research.

Conflict of interest and funding

The author have not received any funding or benefits from industry or elsewhere to conduct this study

• Alexander M. A., Sherman J. B. Factors associated with obesity in school children. Journal of School Nursing. 1991; 7 :6–10. [ PubMed ] [ Google Scholar ]
• Attride-Stirling J. Thematic networks: An analytical tool for qualitative research. Qualitative Research. 2001; 1 :385–405. [ Google Scholar ]
• Backett K., Alexander H. Talking to young children about health: Methods and findings. Health Education Journal. 1991; 50 (1):34–38. [ Google Scholar ]
• Bell S. K., Morgan S. M. Children's attitudes and behavioural intentions towards a peer presented as obese: Does a medical explanation for the obesity make a difference. Journal of Paediatric Psychology. 2000; 25 (3):137–145. [ PubMed ] [ Google Scholar ]
• Borra S. T., Kelly L., Shirreffs M. B., Neville K., Geiger C. J. Developing health messages: Qualitative studies with children, parents, and teachers help identify communications opportunities for healthful lifestyles and the prevention of obesity. Journal of the American Dietetic Association. 2003; 103 (6):721–728. [ PubMed ] [ Google Scholar ]
• Braun V., Clarke V. Using thematic analysis in psychology. Qualitative Research in Psychology. 2006; 3 (2):77–101. [ Google Scholar ]
• Breat C., Mervielde I., Vandereycken W. Psychological aspects of childhood obesity: A controlled study in a clinical and non clinical sample. Journal of Paediatric Psychiatry. 1997; 22 (1):59–71. [ PubMed ] [ Google Scholar ]
• Brunt H., Lester N., Davies G., Williams R. Childhood overweight and obesity: Is the gap closing the wrong way? Journal of Public Health. 2008; 30 (2):145–152. [ PubMed ] [ Google Scholar ]
• Chamberlin L. A., Sherman S. N., Jain A., Powers S. W., Whitaker R. C. The challenge of preventing and treating obesity in low-income, preschool children: Perceptions of WIC Health Care Professionals. Archives of Pediatrics & Adolescent Medicine. 2002; 156 :662–668. [ PubMed ] [ Google Scholar ]
• Dennison B. A., Rockwell H. L., Baker S. L. Fruit and vegetable intake in young children. Journal of the American College of Nutrition. 1998; 17 (4):371–378. [ PubMed ] [ Google Scholar ]
• Department of Health. Listening, hearing and responding: Department of Health Action Plan—Core principles for the involvement of young people. London: Author; 2002. [ Google Scholar ]
• Department of Health and Department for Children, Schools and Families. National Childhood Measurement Programme: results from the 2006/07 school year. The Information Centre. 2008. Retrived April 10, 2008, from http://www.ic.nhs.uk/pubs/ncmp0607 .
• Department of Health and Department for Education and Skills. The national service framework for children, young people and maternity services (executive summary) London: Author; 2004. [ Google Scholar ]
• Department of Innovation Universities and Skills. Foresight-tackling obesities: Future choices and project report. London: Government Office for Science; 2007. [ Google Scholar ]
• Dorey E., McCool J. The role of the media in influencing children's nutritional perceptions. Qualitative Health Research. 2009; 19 (5):645–654. [ PubMed ] [ Google Scholar ]
• Gibson E. L., Wardle J., Watts C. J. Fruit and vegetable consumption, nutritional knowledge and beliefs in mothers and children. Appetite. 1998; 31 :205–228. [ PubMed ] [ Google Scholar ]
• Hesketh K., Water E., Green J., Salmon L., Williams J. Healthy eating, activity and obesity prevention: A qualitative study of parent and child perceptions in Australia. Health Promotion International. 2005; 20 (1):19–26. [ PubMed ] [ Google Scholar ]
• Hill A. J., Silver E. K. Fat, friendless and unhealthy: 9-year old children's perception of bodyshape stereotypes. International Journal of Obesity and Related Metabolic Disorders. 1995; 19 :423–430. [ PubMed ] [ Google Scholar ]
• Hughes C. C., Sherman S., Whitaker R. How low-income mothers with overweight preschool children make sense of obesity. Qualitative Health Research. 2010; 20 :465–478. [ PubMed ] [ Google Scholar ]
• Kirk S. Methodological and ethical issues in conducting qualitative research with children and young people: A literature review. International Journal of Nursing Studies. 2007; 44 :1250–1260. [ PubMed ] [ Google Scholar ]
• Knowler W. C., Pettitt D. J., Saad M. F. Obesity in Pime Indians: Its magnitude and relationship with diabetes. American Journal of Clinical Nutrition. 1991; 53 :15435–15515. [ PubMed ] [ Google Scholar ]
• Lieberman M., Gauvin L., Bukowski W., White D. Interpersonal influence and disordered eating behaviours in adolescent girls. The role of peer modelling, social reinforcement, and body-related teasing. Eating Behaviour. 2001; 2 :215–236. [ PubMed ] [ Google Scholar ]
• Madill A., Jordan A., Shiley C. Objectivity and reliability in qualitative analysis: realist, contextualist and radical constructionist epistemologies. British Journal of Psychology. 2000; 91 :1–20. [ PubMed ] [ Google Scholar ]
• Mauthner M. Methodological aspects of collecting data from children: Lessons from three research projects. Children and Society. 1997; 11 :16–28. [ Google Scholar ]
• McCormick B., Stone I. Economic costs of obesity and the case for government intervention. Obesity Reviews. 2007; 8 (1):161–164. [ PubMed ] [ Google Scholar ]
• Miller S. Researching children: Issues arising from a phenomenological study with children who have diabetes mellitus. Journal of Advanced Nursing. 2000; 31 (5):1228–1234. [ PubMed ] [ Google Scholar ]
• Musaiger A. O., Mater A. M., Alekri S. A., Mahdi A. E. Knowledge and attitudes of Bahraini adolescents towards obesity. Journal of Consumer Studies and Home Economics. 1991; 15 :321–325. [ Google Scholar ]
• National Institute for Health and Clinical Excellence. Obesity guidance on the prevention, identification, assessment and management of overweight and obesity in adults and children. 2006. Clinical Guidelines. [ PubMed ] [ Google Scholar ]
• Oliver K. K., Thelen M. H. Children's perceptions of peer influence on eating concerns. Behavior Therapy. 1996; 27 :25–39. [ Google Scholar ]
• Owens S., Driffill L. How to change attitudes and behaviour in the context of energy. Energy Policy. 2008; 36 :4412–4418. [ Google Scholar ]
• Prentice A. M., Jebb S. A. Obesity in Britain: Gluttony or sloth? British Medical Journal. 1995; 11 :437–439. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Prout A. Families, cultural bias and health promotion. London: Health Education Authority; 1996. [ Google Scholar ]
• Renehan A. G., Tyson M., Egger M., Heller R. F., Zwahlen M. Body-mass index and incidence of cancer: A systematic review and meta-analysis of prospective observational studies. Lancet. 2008; 371 :569–578. [ PubMed ] [ Google Scholar ]
• Riessman C. K. Narrative analysis. London: Sage; 1993. [ Google Scholar ]
• Statistics on Obesity, Physical Activity and Diet England. The NHS Information Centre. 2006. Retrieved October 2010 from http://www.ic.nhs.uk/statistics-and-data-collections/health-and-lifestyles/obesity/statistics-on-obesity-physical-activity-and-diet-england-2006 .
• The Health Survey for England. Body mass index (BMI) by gender, updated tables including 2008 data. The NHS Information Centre. 2009. Retrieved March 2010 from http://www.ic.nhs.uk/statistics-and-data-collections/health-and-lifestyles-r elated-surveys/health-survey-for-england .
• Tuttle C., Truswell S. Childhood and adolescence. In: Mann J., Truswell S., editors. Essentials of human nutrition. Oxford: Oxford University Press; 2002. [ Google Scholar ]
• Willig C. Introducing qualitative research in psychology. 2nd ed. England: OUP; 2008. [ Google Scholar ]
• Young-Hyman D., Herman L. J., Scott D. L., Schlundt D. G. Care giver perception of children's obesity-related health risk: A study of African American families. Obesity Research. 2000; 8 :241–248. [ PubMed ] [ Google Scholar ]

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• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

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• Developing a Research Question

by acburton | Mar 22, 2024 | Resources for Students , Writing Resources

Selecting your research question and creating a clear goal and structure for your writing can be challenging – whether you are doing it for the first time or if you’ve done it many times before. It can be especially difficult when your research question starts to look and feel a little different somewhere between your first and final draft. Don’t panic! It’s normal for your research question to change a little (or even quite a bit) as you move through and engage with the writing process. Anticipating this can remind you to stay on track while you work and that it’ll be okay even if the literature takes you in a different direction.

What Makes an Effective Research Question?

The most effective research question will usually be a critical thinking question and should use “how” or “why” to ensure it can move beyond a yes/no or one-word type of answer. Consider how your research question can aim to reveal something new, fill in a gap, even if small, and contribute to the field in a meaningful way; How might the proposed project move knowledge forward about a particular place or process? This should be specific and achievable!

The CEWC’s Grad Writing Consultant Tariq says, “I definitely concentrated on those aspects of what I saw in the field where I believed there was an opportunity to move the discipline forward.”

General Tips

Do your research.

Utilize the librarians at your university and take the time to research your topic first. Try looking at very general sources to get an idea of what could be interesting to you before you move to more academic articles that support your rough idea of the topic. It is important that research is grounded in what you see or experience regarding the topic you have chosen and what is already known in the literature. Spend time researching articles, books, etc. that supports your thesis. Once you have a number of sources that you know support what you want to write about, formulate a research question that serves as the interrogative form of your thesis statement.

Grad Writing Consultant Deni advises, “Delineate your intervention in the literature (i.e., be strategic about the literature you discuss and clear about your contributions to it).”

Start Broadly…. then Narrow Your Topic Down to Something Manageable

When brainstorming your research question, let your mind veer toward connections or associations that you might have already considered or that seem to make sense and consider if new research terms, language or concepts come to mind that may be interesting or exciting for you as a researcher. Sometimes testing out a research question while doing some preliminary researching is also useful to see if the language you are using or the direction you are heading toward is fruitful when trying to search strategically in academic databases. Be prepared to focus on a specific area of a broad topic.

Writing Consultant Jessie recommends outlining: “I think some rough outlining with a research question in mind can be helpful for me. I’ll have a research question and maybe a working thesis that I feel may be my claim to the research question based on some preliminary materials, brainstorming, etc.” — Jessie, CEWC Writing Consultant

Try an Exercise

In the earliest phase of brainstorming, try an exercise suggested by CEWC Writing Specialist, Percival! While it is normally used in classroom or workshop settings, this exercise can easily be modified for someone working alone. The flow of the activity, if done within a group setting, is 1) someone starts with an idea, 2) three other people share their idea, and 3) the starting person picks two of these new ideas they like best and combines their original idea with those. The activity then begins again with the idea that was not chosen. The solo version of this exercise substitutes a ‘word bank,’ created using words, topics, or ideas similar to your broad, overarching theme. Pick two words or phrases from your word bank, combine it with your original idea or topic, and ‘start again’ with two different words. This serves as a replacement for different people’s suggestions. Ideas for your ‘word bank’ can range from vague prompts about mapping or webbing (e.g., where your topic falls within the discipline and others like it), to more specific concepts that come from tracing the history of an idea (its past, present, future) or mapping the idea’s related ideas, influences, etc. Care for a physics analogy? There is a particle (your topic) that you can describe, a wave that the particle traces, and a field that the particle is mapped on.

Get Feedback and Affirm Your Confidence!

Creating a few different versions of your research question (they may be the same topic/issue/theme or differ slightly) can be useful during this process. Sharing these with trusted friends, colleagues, mentors, (or tutors!) and having conversations about your questions and ideas with other people can help you decide which version you may feel most confident or interested in. Ask colleagues and mentors to share their research questions with you to get a lot of examples. Once you have done the work of developing an effective research question, do not forget to affirm your confidence! Based on your working thesis, think about how you might organize your chapters or paragraphs and what resources you have for supporting this structure and organization. This can help boost your confidence that the research question you have created is effective and fruitful.

Be Open to Change

Remember, your research question may change from your first to final draft. For questions along the way, make an appointment with the Writing Center. We are here to help you develop an effective and engaging research question and build the foundation for a solid research paper!

Example 1: In my field developing a research question involves navigating the relationship between 1) what one sees/experiences at their field site and 2) what is already known in the literature. During my preliminary research, I found that the financial value of land was often a matter of precisely these cultural factors. So, my research question ended up being: How do the social and material qualities of land entangle with processes of financialization in the city of Lahore. Regarding point #1, this question was absolutely informed by what I saw in the field. But regarding point #2, the question was also heavily shaped by the literature. – Tariq

Example 2: A research question should not be a yes/no question like “Is pollution bad?”; but an open-ended question where the answer has to be supported with reasons and explanation. The question also has to be narrowed down to a specific topic—using the same example as before—”Is pollution bad?” can be revised to “How does pollution affect people?” I would encourage students to be more specific then; e.g., what area of pollution do you want to talk about: water, air, plastic, climate change… what type of people or demographic can we focus on? …how does this affect marginalized communities, minorities, or specific areas in California? After researching and deciding on a focus, your question might sound something like: How does government policy affect water pollution and how does it affect the marginalized communities in the state of California? -Janella

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This paper is in the following e-collection/theme issue:

Published on 27.3.2024 in Vol 26 (2024)

Outpatient Video Visits During the COVID-19 Pandemic: Cross-Sectional Survey Study of Patients’ Experiences and Characteristics

Authors of this article:

Original Paper

• Stefanie C van den Bosch 1 * , MD, DDS   ; 
• Demi Van Dalen 2 * , MD   ; 
• Marjan Meinders 3 , PhD   ; 
• Harry van Goor 2 , MD, PhD   ; 
• Stefaan Bergé 1 , MD, PhD, DDS   ; 
• Martijn Stommel 2 * , MD, PhD   ; 
• Sandra van Dulmen 4, 5, 6 * , PhD  

1 Department Oral and Maxillofacial Surgery, Radboud University Medical Center, Nijmegen, Netherlands

2 Department of Surgery, Radboud University Medical Center, Nijmegen, Netherlands

3 IQ Healthcare, Radboud University Medical Center, Nijmegen, Netherlands

4 Department of Primary and Community Care, Radboud University Medical Center, Nijmegen, Netherlands

5 Faculty of Caring Science, Work Life and Social Welfare, University of Boras, Boras, Sweden

6 Nivel, Netherlands Institute for Health Services Research, Utrecht, Netherlands

*these authors contributed equally

Corresponding Author:

Demi Van Dalen, MD

Department of Surgery

Radboud University Medical Center

Geert Grooteplein Zuid 10

Nijmegen, 6525 GA

Netherlands

Phone: 31 243611111

Email: [email protected]

Background: During the first lockdown of the COVID-19 pandemic, an exponential increase in video consultations replacing in-person outpatient visits was observed in hospitals. Insight into patients’ experiences with this type of consultation is helpful for a broad, sustainable, and patient-centered implementation of video consultation.

Objective: This study aims to examine patients’ experiences with video consultation during the COVID-19 pandemic and identify discriminative patient and consultation characteristics to determine when video consultation is most feasible.

Methods: A cross-sectional survey study was conducted. Patients aged ≥18 years and scheduled for a video consultation at the outpatient clinic of a Dutch university medical center from August 2020 to December 2020 for all medical specialties were eligible. Patients’ experiences were explored through a study-specific survey using descriptive quantitative statistics. Open-ended questions were qualitatively analyzed and thematically categorized into appreciated aspects and aspects for improvement. Discriminative patient and consultation characteristics were identified using 3 distinctive survey items. Characteristics of patients who scored and those who did not score all 3 items positively were analyzed using binary logistic regression.

Results: A total of 1054 patients were included in the analysis. Most patients (964/1054, 91.46%) were satisfied with their video consultation, with a mean overall grade of 8.6 (SD 1.3) of 10. In the qualitative analyses, 70.02% (738/1054) of the patients cited aspects they appreciated and 44.97% (474/1054) mentioned aspects for improvement during their consultation. Patients with better self-rated health reported a positive evaluation significantly more often ( P= .001), which also held true for other medical specialties (vs surgical and nonsurgical specialties; P <.001).

Conclusions: Video consultation was perceived as highly satisfactory by patients during the COVID-19 pandemic, with the best experience reported by healthy participants and those undergoing their first consultation. Appreciated aspects are mainly at the individual professional level, organizational level, and innovation level itself. The aspects that were mentioned for improvement can be changed for the better.

Introduction

In the Netherlands, the COVID-19 pandemic led to the first lockdown from March 2020 to June 2020 and the second lockdown from mid-December 2020 to April 2021. Throughout these periods, in-person visits were minimized to reduce the risk of potential virus transmission. As a substitute for in-person visits, the use of video visits significantly increased across many medical specialties.

Video visits were already in practice on a limited scale in a wide range of contexts: for speech evaluation in patients with cleft palate [ 1 ], genetic counseling [ 2 ], follow-up after facial plastic surgery [ 3 ], and postoperative wound assessment [ 4 ]. Video visits were found to be satisfactory for patients [ 5 ] and enabled empathetic patient-professional relationships remotely [ 1 , 6 , 7 ]. However, large-scale implementation in daily practice was found to be challenging owing to the multilevel complexity of implementation, where people, organizations, and technology continuously interconnect and develop [ 8 - 11 ]. For example, attitudes and beliefs of individual professionals have been shown to act as both facilitators and barriers in the implementation of eHealth applications [ 8 ]. Furthermore, video visits appeared to be particularly successful in follow-up appointments, when a preexisting relationship of trust is established between the patient and clinician [ 10 ]. For instance, video visits were more easily adopted in follow-up care after cancer surgery compared with a multidisciplinary context of antenatal diabetes care [ 10 ].

Owing to the pandemic-driven, accelerated application of video visits, many clinicians gained experience with this mode of health care delivery. This way of providing care offers several advantages, including saving travel time and costs for both patients and their companions and the efficient use of health care resources, such as outpatient clinic space and support [ 12 - 14 ]. As we move into the post–COVID-19 era, video visits are expected to persist as a routine practice, but large-scale use has seemed to stagnate, presumably owing to a lack of guidance, vision, and attention to patients’ needs, as observed in the United States [ 15 , 16 ]. The use and implementation of video visits are expected to be most successful when tailored to the needs of patients, clinicians, and health care organizations [ 17 , 18 ]. Therefore, it is crucial to understand patients’ perspectives and experiences with video visits and identify specific patient groups that show a greater or lesser degree of appreciation for and suitability to video visits [ 19 ]. Large studies with diverse patient populations covering all medical specialties need to be conducted to learn more about patients’ perspectives [ 16 , 18 ]. However, we are concerned that only a limited number of studies have been published that evaluated video visits for patients within large, diverse populations. Consequently, this study contributes significantly to the existing body of knowledge in this area [ 20 - 22 ].

The primary aim of this study was to examine patients’ evaluations of video visits in the context of the COVID-19 pandemic. The secondary aim was to identify patient groups for whom video visits are relatively more suitable, given their positive experiences. With these results, the first step toward patient-tailored choices for type of visit can be made.

Study Design and Population

A cross-sectional study was conducted from August 2020 to December 2020 at the Radboud University Medical Center (Radboudumc). Zaurus was used as the video visit app, which is compatible with all smartphones and tablets. Patients were invited via email to register and download the app.

Patients scheduled for a video visit were automatically selected based on the registered mode of visit. Links to the questionnaire were sent by an independent research firm (Expoints) on behalf of the Radboudumc. Selected patients received the survey within 8 days after their visit via email to evaluate the visit and collect their sociodemographic details. The survey had to be completed within 2 weeks, and a reminder was sent 1 week after the initial invitation. An incomplete survey could be saved to be completed later (within 2 weeks). No reminder for completion was sent.

All patients aged ≥18 years who received a video visit at an outpatient clinic at Radboudumc were eligible for inclusion. When a patient had multiple video visits in the selected period, the most recent video visit was selected.

Patients were excluded if they had cognitive problems; had difficulties with reading and understanding Dutch owing to a hindering language barrier; were deceased at the time of selection; completed a survey regarding video visits in the 180 days before the start of our study; completed a survey regarding their admission experience or experience with an in-person visit 30 days before the start of our study; or were admitted to the hospital, as priority was given to the patient experience survey regarding admission. In addition, when the video visit was a follow-up visit after giving birth or when the video visit was replaced with another visit modality, the patient was excluded.

Ethical Considerations

All patients participated voluntarily and anonymously in the survey and gave informed consent to use their data in accordance with the General Data Protection Regulation. Ethics approval was requested and waived by the local Medical Research Ethics Committee of Radboudumc (CMO [committee on research involving human subjects] Oost-Nederland; registration number 2021-8415).

A combined survey was used, which consisted of the Patient Experience Monitor (PEM) for adult outpatient experience [ 23 ], developed by the Dutch Federation of University Medical Centers, and the patient satisfaction survey for video visits created by Hanna et al [ 7 ]. This combined survey was constructed after extensive deliberation by an expert panel. In this process, a literature review of surveys specifically about video visits was performed. The experts found the survey by Hanna et al [ 7 ] to be the most suitable for the aim of our study.

The PEM survey was constructed by adapting a validated Picker Institute survey following a comprehensive theory-driven approach of item selection by an expert panel, cognitive interviews with patients, analysis of psychometric properties, and member checking. This survey of 14 items represents eight key domains of person-centered care: (1) fast access to reliable health care advice; (2) effective treatment delivered by trusted professionals; (3) continuity of care and smooth transitions; (4) involvement and support for family and caregivers; (5) clear information, communication, and support for self-care; (6) involvement in making decisions and respect for preferences; (7) emotional support, empathy, and respect; and (8) attention to physical and environmental needs [ 24 ]. The Picker Institute surveys are measures for evaluating patients’ experiences in outpatient and inpatient clinical care and have been validated and extensively used in university medical centers in the Netherlands since 2019 [ 25 ]. The PEM survey was adapted to the videoconferencing setting by rephrasing the questions. Overall, 2 items were open-ended questions ( Multimedia Appendix 1 ). Quantitative analyses of the PEM survey were based on individual survey items, whereas open-ended questions were analyzed using qualitative methods.

The 13-item survey by Hanna et al [ 7 ] is designed according to the principles of survey development for telemedicine to evaluate patients’ experiences with video visits in pain clinics [ 26 ]. This survey was translated into Dutch, and 1 item was removed, as it was already covered by the PEM survey ( Multimedia Appendix 1 , items 16-27). In total, 4 items were rephrased based on the advice of the patient communication experts. Analysis of the survey by Hanna et al [ 7 ] is based on an overall sum score, where a higher overall sum score represents greater satisfaction. For correct analysis and to calculate an overall sum score, the 3 negatively phrased questions (items 17, 21, and 23) were reversed (eg, “No, definitely not” was converted to “Yes, definitely”), according to protocol.

Finally, a question was added to assess the visit by assigning a score on a scale ranging from 1 to 10 (with 10 being most positive). The survey was conducted according to CHERRIES (Checklist for Reporting Results of Internet-Based e-Surveys; Multimedia Appendix 2 ) [ 27 ].

Statistical Analysis

Quantitative analysis.

Descriptive statistics were used for the closed-ended items ( Multimedia Appendix 1 , items 1-11 and 16-27). To identify patient and visit characteristics associated with positive evaluation of video visits, 4 authors found consensus upon the 3 key items from the survey by Hanna et al [ 7 ] that stood out the most (item 18: “the care I received by a video visit was just as good as with an in-person appointment”; item 22: “I was comfortable talking by video to the healthcare professional”; item 27: “I would recommend the video visit option to other patients”). Throughout the paper, these 3 items have been referred to as “crucial” components of the survey by Hanna et al [ 7 ], as they best displayed a positive experience.

Patient and visit characteristics were determined for the group answering the 3 crucial items positively. In this analysis, the following characteristics were included: sex, age category, level of education, self-rated health, type of visit (first vs follow-up), and medical specialty (surgical, nonsurgical, or other). High self-rated health was defined as a score that indicates “very well” or “excellent.”

Statistical analysis was performed using SPSS Statistics (version 25; IBM Corp). Binary logistic regression analysis was used to calculate differences in patient and visit characteristics between the patient subgroup that scored positively on all crucial items and the patient subgroup that did not score positively, as the dependent variable was not normally distributed.

Qualitative Analysis

Qualitative analysis was performed on the open-ended questions (items 13 and 14) to identify appreciated aspects and aspects for improvement for video visits. Overall, 2 authors (SCvdB and DD) independently categorized all the answers into six categories of factors that influence the implementation of innovations: (1) the innovation itself, (2) the individual professional, (3) the patient, (4) social context, (5) organizational context, and (6) economic and political context [ 28 ]. In case of conflicts in the categorization, consensus was reached through discussion between the authors. Responses including multiple levels within a single response were counted as individual items. Examples of answers for both aspects in each category have been cited in the Results section.

Quantitative Results

From August 2020 to December 2020, a total of 1244 surveys were completed, with a response rate of 28.32% (1244/4392). After excluding 15.27% (190/1244) of the patients who reported that the visit was either a telephone consultation or replaced by telephone after technical difficulties, 84.73% (1054/1244) of the surveys were used in the analysis.

Table 1 shows the patients’ demographics. An equal distribution across age categories was observed. Clinical genetics, neurology, and medical oncology accounted for 65.84% (694/1054) of the total number of evaluated video visits, whereas the distribution across the other medical specialties varied widely. The numbers of first and follow-up visits were equal, with most follow-up visits (480/1054, 45.54%) performed by a known clinician. After a video visit, 36.91% (389/1054) of the patients had to make an appointment for an additional in-person visit or medical examination. A follow-up visit via video was planned in 40.32% (425/1054) of the evaluated video visits. Clinicians from medical oncology and neurology more frequently scheduled an in-person follow-up visit for their patients—18.6% (40/215) and 23.3% (50/215), respectively. Moreover, in 39.7% (69/174) and 14.4% (25/174) of cases, the visits provided by clinical genetics and neurology respectively, were followed by a consecutive visit for additional (diagnostic) testing.

The overall grading for the video visit had a mean of 8.6 (SD 1.3; median 9) of 10. For 5 PEM items, >80% of the patients answered positively, that is, patients waited no longer than 5 minutes, clinicians had read their medical records well, patients received understandable answers, patients trusted the clinician, and patients had enough time to discuss their problems with the clinician. Refer to Table 2 for details about the responses of patients.

For the remaining 6 items, more than 21% stated that the item was either not applicable or answered positively. For instance, 80.25% (829/1033) indicated not receiving any new medication for the question about whether the professional explained the adverse effects of new medication. Analysis of the items in the survey by Hanna et al [ 7 ] showed that 91.46% (964/1054) of the patients was satisfied with their video visit, 66.98% (706/1054) found it to be just as good as an in-person visit, and 68.69% (724/1054) would recommend video visits to other patients, as shown in Table 3 .

Of 1054 patients, 574 (54.46%) answered all 3 crucial items on the survey by Hanna et al [ 7 ] positively, 234 (22.2%) answered 2 of 3 positively, 138 (13.09%) answered only 1 item positively, 72 (6.83%) patients answered “not applicable” or responded negatively, and 36 (3.42%) responses were missing. Patient and visit characteristics of patients who positively answered all 3 crucial Hanna [ 7 ] items versus the group who did not are shown in Multimedia Appendix 3 .

Results of the binary logistic regression are shown in Table 4 . Negative association was found between the positive evaluation of a visit and the surgical and “other” medical specialties (B=−0.64; P <.001). Positive evaluation was also associated with the patient category who described their health as “very well” (B=1.12; P =.01). Sex, age, and educational status had no influence on whether a patient rated the visit positively. In addition, the reason for the visit was not found to have any influence on the positive evaluation of a video visit.

a ENT: ear, nose, and throat.

b Includes trauma surgery, visceral surgery, surgical oncology, and vascular surgery.

c OMF: oral and maxillofacial surgery.

a The total in some sections is not 100% owing to missing responses.

a Nagelkerke R 2 =0.08.

b N/A: not applicable.

Qualitative Results

Table 5 displays the frequencies of appreciated aspects and aspects for improvement. Most patients (738/1054, 70.02%) cited appreciated aspects of the use of video visits in the open-ended questions. The most frequently cited appreciated aspects were expressed at the individual professional level, followed by the organizational context level and the innovation level. Few aspects were mentioned at the patient level, economic and political context level, and social context level. Approximately half of the patients (474/1054, 44.97%) cited aspects for improvement. Most were cited at the innovation, organizational context, and patient levels. In contrast, no improvable aspects were reported at the economic and political context level.

a Overall, 70.01% (738/1054) of the patients cited appreciated aspects.

b Responses including multiple levels within a single response were counted as individual items.

c Overall, 44.97% (474/1054) of the patients cited aspects for improvement.

The perceived ease of use and audio-visual quality were frequently mentioned as appreciated aspects. The intuitive character of the app was seen as valuable, as not all patients were familiar with using web-based apps. Patients appreciated the audio-visual quality, allowing the video visit to be a good alternative for an in-person visit. However, not all the patients experienced the same ease of use, as the most reported aspect for improvement was poor audio and video quality, sometimes clearly caused by an unstable internet connection. Although Zaurus is compatible with all electronic devices, users have reported issues with video size specifically on smartphones ( Textbox 1 ).

Appreciated aspect

“The application is straightforward and easy to understand. Conversation went well, the doctor even asked me if I could hear her well.” [Female; aged 18-34 y; clinical genetics]

Aspect for improvement

“The video connection was really bad. Almost immediately the app crashed, and the audio stuttered, so I could not understand what the doctor was saying. After two attempts, we continued the visit by telephone.” [Female; aged 65-79 y; excluded for further analysis; clinical genetics]

Individual Professional

Patients often mentioned what they valued in the clinician’s professional behavior, such as their attitude, and communicative style. In contrast, a lack of adequate or visible body language and lack of knowledge about someone’s medical history were mentioned as aspects for improvement ( Textbox 2 ).

“There is still a kind of personal touch in the contact, which is nice for the perception as well. The doctor radiated tranquility and was understanding, and she had read my personal record well. That gives me confidence.” [Female; aged 55-64 y; clinical genetics]

“The doctor did not look at us during the video visit. Both my daughter, who was also present, and I had noticed. That felt a little awkward. He was mainly looking down (I guess at a file or something like that, which was in front of him).” [Female; aged 80-99 y; clinical genetics]

The possibility to have face-to-face interactions remotely was often mentioned as valuable, as patients were able to watch the clinicians’ nonverbal reactions. It made video visits a safe alternative for patients with a weak immune system, for example, during the pandemic. Personal lack of experience with video visits was a hindering factor, as not all patients were familiar with the use of videoconferencing apps. Some of them preferred an in-person visit, as they felt uncomfortable owing to inexperience ( Textbox 3 ).

“It is nice to see the doctor, but for a first meeting, it is something I need to get used to. However, this feels safer regarding the coronavirus and a vulnerable immune status.” [Female; aged 35-54 y; neurology]

“I’d rather have the first visit in person. Maybe I’m old fashioned, but I prefer physical contact, even during this COVID pandemic. Feelings and emotions might be more difficult to pick up on screen.” [Male; aged 55-64 y; neurology]

Social Context

The possibility of the involvement of others, such as next of kin or other family members, was one of the mentioned appreciated aspects. Creating a culture in which a patient can share their preference or opt for a certain visit modality could stimulate the use of video visits. Patients expressed that they would like to have a say in choosing which visit modality they like, especially when the nature of the visit is sensitive ( Textbox 4 ).

“On time, pleasant conversation, space for questions, clear explanation. It was nice that my partner could join with his phone.” [Female; aged 18-34 y; reproductive medicine]

“It was a shame they communicated the results by a video visit. I was shocked and found they acted a bit indignant about my reaction. I was not capable anymore to follow the conversation.” [Female; aged 18-34 y; clinical genetics]

Organizational Context

Internet-based assistance, clear instructions, and time management by the clinician during the visit were often mentioned as appreciated aspects at this level. Many patients were called in advance of the visit to check for technical problems. However, patients were not always informed correctly if the visit would start later than scheduled, and in some cases, patients received the link for the video visit just before the visit started, which was an aspect for improvement ( Textbox 5 ).

“The support was really good, as I am not so technical and there was enough explanation. Great.” [Female; aged 55-64 y; clinical genetics]

“I would like to receive a notification when the doctor is held up, especially when it’s a first visit. Also, I would like to receive a heads up when I get another doctor than the one the appointment was originally scheduled with.” [Male; aged 55-64 y; medical oncology]

Economic and Political Context

Time and financial savings were identified as valuable aspects at the economic and political context level. Patients often cited less travel time and costs as beneficial. There were no improvable aspects reported at the economic and political context level ( Textbox 6 ).

“It is pleasant that there is no need for traveling to the hospital (regarding travel time and travel distance) and still have ‘personal’ contact with the doctor through a video connection.” [Male; aged 35-54 y; neurology]

Principal Findings

In this study, we comprehensively analyzed evaluations of visits via video to a tertiary clinic made by a large, diverse patient population, including appreciated aspects and aspects for improvement. Most patients (964/1054, 91.46%) evaluated the video visits positively, with significantly more positive evaluations when the visits were provided by a clinician from “other” medical specialties, as compared to surgical and nonsurgical specialties, or when the patient rated their health status as “very well.” The appreciated aspects were mostly at the individual professional level, whereas aspects for improvement were reported at the innovation level itself.

Comparison With Previous Studies

Our finding that high self-rated health of patients is an influencing patient characteristic for suitability of video visits echoes the finding that patients with less complex, more straightforward clinical needs are more suitable candidates for video visits than those with complex, high-risk diseases [ 10 , 29 ]. Similar findings were identified in an oncological study wherein telemedicine was received favorably for low-acuity cancer care [ 16 ]. In contrast to findings that video visits appear to be more appropriate when the clinician knows the patient beforehand and when it is a follow-up visit [ 10 ], we found that the reason for the visit did not have any influence on whether patients rated the video visit positively. Remarkably, the medical specialty providing the visit was found to be a significant associated factor. Nonsurgical visits were found to be most suitable for telemedicine. One can imagine that these visits are less dependent on physical examination, such as internal medicine or dermatology, for instance, as these specialties can easily review laboratory abnormalities or skin disorders on screen [ 30 ]. In addition to specific aspects of the visit that may depend on the medical specialty, other dimensions or elements during the visit could affect the patient experience, such as the communication strategy used by the clinician [ 31 ]. Nonetheless, more studies are needed to get a clear overview about whether medical specialty is a truly discriminative characteristic or whether it is more dependent on the attitude of certain clinicians and patient groups.

Qualitative analysis of the open-ended questions revealed both facilitating and hindering factors for broad implementation and upscaling of video visits. Following Grol and Wensing [ 28 ], these factors were categorized into innovation, professional, patient, social context, organizational context, and economic and political context levels. Appreciation was mostly centered on the professionals’ skill in adapting communication to the video setup; however, there was scope for improvement among some individuals, as they might benefit from investing additional effort in making visual contact. The way in which the video visits were organized was also highly valued, especially for the provision of technical support to patients as and when needed.

The attitude of the clinician during the video visit was one of the most frequently mentioned aspects for improvement. The bedside manner, which may be better described as the “webside” manner, of a clinician refers to how the clinician behaves, approaches the patient, and communicates during the visit. Clinicians sometimes seem to lack awareness of how their nonverbal behavior looks on screen, as was also shown by a study that analyzed a large data set of >5000 patients [ 32 ]. Patients prefer increased expression of nonverbal empathy from clinicians when they show signs of distress. Inadequate nonverbal communication and body language are often reported as barriers for telemedicine adoption [ 3 , 33 , 34 ]. Despite expert recommendations dating back several years to raise awareness for nonverbal and paraverbal communication, our study also indicates that there is still considerable scope for improvement and training at the clinician level [ 31 , 35 ].

In accordance with several survey studies conducted during the pandemic, our response rate was low. This might be explained by the questionnaire fatigue that was frequently observed during the COVID-19 pandemic, as patients received multiple questionnaires and messages from the outpatient clinic, apart from research [ 36 ].

Regarding future perspectives, clinicians should seek guidelines to assess the suitability of a video visit, and the following recommendations might be useful. The Dutch Center of Expertise on Health Disparities recommends checking the patients’ digital skills beforehand, providing digital support, and evaluating whether the information is correctly understood through techniques such as “teach back” at the end of the visit [ 37 ]. Video consulting guidelines advise considering several factors while deciding whether video visits may be suitable, such as whether there is an established relationship with the patient, whether it entails nonurgent care, whether there is a need for physical examination, and whether there are factors in favor of the patient staying at home [ 38 ].

Limitations

The findings of this study must be considered in the light of some limitations. First, our study might have been exposed to selection and sampling biases for several reasons. It was an “open” survey, where patients could decide voluntarily whether they would participate in the survey, which might have led to a sample of patients that is not representative of the entire population of the hospital. In addition, owing to the exclusion criteria, not all video visits were evaluated.

Second, the validity of the combined survey was not tested. As the analysis of our data was reported at the item level, calculating the internal consistency using Cronbach α was not applicable. PEM is known to be a validated survey; however, the psychometric properties of the survey by Hanna et al [ 7 ] are not known and should be determined. As the PEM items were rephrased to the videoconferencing setting, reliability of this new PEM survey will have to be reassessed.

The educational status of patients attending a university medical center is, in general, often higher than the mean educational status of the general population. In our study population, 44.4% (468/1054) of the patients were highly educated, compared with 30% in the Dutch population in 2018 [ 39 ]. As teaching hospitals and referring hospitals might serve different populations, the generalizability of our results might be limited, and further studies including different types of hospitals are recommended.

A total of 139 patients reported a failed video visit and noted that the visit was replaced by telephone. However, the exact number of times this occurred is not known, as not all patients might have reported this failure, which also may have resulted in selection bias.

In this evaluation, we deliberately focused on the patient evaluation of video visits. As it is known that patients and clinicians have different views about quality of information and visits [ 40 - 42 ], the clinicians’ point of view should also be explored for a comprehensive evaluation of the use of video visits. Health care providers, such as clinicians, might experience different barriers and facilitators compared with patients, thus influencing the successful implementation of video visits. Key barriers to successful implementation such as the lack of training and motivation to offer video visits need to be addressed [ 3 , 8 , 43 , 44 ].

Conclusions

Video visits were perceived as highly satisfactory by patients during the COVID-19 pandemic, with the best experiences reported by healthy participants and participants who scheduled a visit with a clinician outside the realms of surgical and nonsurgical medical specialties, such as clinical genetics or radiotherapy. Appreciated aspects were mainly at the individual professional level, organizational level, and at the level of the innovation itself. The mentioned aspects for improvement can be changed for the better.

The findings cannot be directly generalized as they were collected in a university medical center with a specific patient population, but they provide additional results for understanding the suitability of video visits in a broad patient population. To be able to truly tailor the use of video visits to patients’ needs, a patient-centered perspective involving both patients and health care professionals is needed.

Data Availability

The data used in this study will be made available by the authors upon reasonable request.

Authors' Contributions

SCvdB, MM, MS, and SvD contributed to the study’s conception and design. SCvdB and DD performed the statistical analyses. SCvdB managed the project administration and data curation. SCvdB, DD, MS, and SvD drafted the manuscript. HvG, SB, MS, and SvD supervised the research project. SCvdB and DD contributed equally to this work and share first authorship. MS and SvD contributed equally to this work and share senior authorship. All authors interpreted the data, critically revised the paper, and approved the final version of the paper.

Conflicts of Interest

None declared.

Survey on video visits during the COVID-19 outbreak (August 2020 to December 2020).

CHERRIES (Checklist for Reporting Results of Internet-Based e-Surveys) checklist.

Patient and visit characteristics of those who positively answered all 3 crucial items in the checklist by Hanna et al [ 7 ] versus the group that did not answer positively.

• Whitehead E, Dorfman V, Tremper G, Kramer A, Sigler A, Gosman A. Telemedicine as a means of effective speech evaluation for patients with cleft palate. Ann Plast Surg. Apr 2012;68(4):415-417. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hilgart JS, Hayward JA, Coles B, Iredale R. Telegenetics: a systematic review of telemedicine in genetics services. Genet Med. Sep 2012;14(9):765-776. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Westra I, Niessen FB. Implementing real-time video consultation in plastic surgery. Aesthetic Plast Surg. Oct 2015;39(5):783-790. [ CrossRef ] [ Medline ]
• Stommel WJ, van Goor H, Stommel MW. The impact of video-mediated communication on closed wound assessments in postoperative consultations: conversation analytical study. J Med Internet Res. May 05, 2020;22(5):e17791. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Aneja J, Goyal T, Verma M, Kaur G, Mirza M, Gupta S. Client satisfaction with telemedicine services during COVID-19 pandemic: a cross-sectional survey from a teaching institute of North India. J Family Med Prim Care. Sep 2022;11(9):5187-5193. [ FREE Full text ] [ CrossRef ] [ Medline ]
• van Gurp J, van Selm M, Vissers K, van Leeuwen E, Hasselaar J. How outpatient palliative care teleconsultation facilitates empathic patient-professional relationships: a qualitative study. PLoS One. 2015;10(4):e0124387. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hanna GM, Fishman I, Edwards DA, Shen S, Kram C, Liu X, et al. Development and patient satisfaction of a new telemedicine service for pain management at Massachusetts general hospital to the island of Martha’s vineyard. Pain Med. Sep 2016;17(9):1658-1663. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ross J, Stevenson F, Lau R, Murray E. Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update). Implement Sci. Oct 26, 2016;11(1):146. [ FREE Full text ] [ CrossRef ] [ Medline ]
• van Gemert-Pijnen JE, Nijland N, van Limburg M, Ossebaard HC, Kelders SM, Eysenbach G, et al. A holistic framework to improve the uptake and impact of eHealth technologies. J Med Internet Res. Dec 05, 2011;13(4):e111. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Greenhalgh T, Shaw S, Wherton J, Vijayaraghavan S, Morris J, Bhattacharya S, et al. Real-world implementation of video outpatient consultations at macro, meso, and micro levels: mixed-method study. J Med Internet Res. Apr 17, 2018;20(4):e150. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ossebaard HC, Van Gemert-Pijnen L. eHealth and quality in health care: implementation time. Int J Qual Health Care. Jun 2016;28(3):415-419. [ CrossRef ] [ Medline ]
• Bradford NK, Armfield NR, Young J, Smith AC. Paediatric palliative care by video consultation at home: a cost minimisation analysis. BMC Health Serv Res. Jul 28, 2014;14:328. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Versleijen M, Martin-Khan MG, Whitty JA, Smith AC, Gray LC. A telegeriatric service in a small rural hospital: a case study and cost analysis. J Telemed Telecare. Dec 2015;21(8):459-468. [ CrossRef ] [ Medline ]
• Andino J, Zhu A, Chopra Z, Daignault-Newton S, Ellimoottil C, Dupree JM. Video visits are practical for the follow-up and management of established male infertility patients. Urology. Aug 2021;154:158-163. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Assing Hvidt E, Atherton H, Keuper J, Kristiansen E, Lüchau EC, Lønnebakke Norberg B, et al. Low adoption of video consultations in post-COVID-19 general practice in Northern Europe: barriers to use and potential action points. J Med Internet Res. May 22, 2023;25:e47173. [ CrossRef ] [ Medline ]
• Mackwood M, Butcher R, Vaclavik D, Alford-Teaster JA, Curtis KM, Lowry M, et al. Adoption of telemedicine in a rural US cancer center amid the COVID-19 pandemic: qualitative study. JMIR Cancer. Aug 16, 2022;8(3):e33768. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Communication on enabling the digital transformation of health and care in the digital single market; empowering citizens and building a healthier society. European Commission. 2018. URL: https:/​/digital-strategy.​ec.europa.eu/​en/​library/​communication-enabling-digital-transformation-health-and-care-digital-single-market-empowering [accessed 2024-01-29]
• Wherton J, Greenhalgh T, Hughes G, Shaw SE. The role of information infrastructures in scaling up video consultations during COVID-19: mixed methods case study into opportunity, disruption, and exposure. J Med Internet Res. Nov 10, 2022;24(11):e42431. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Esber A, Teufel M, Jahre L, In der Schmitten J, Skoda EM, Bäuerle A. Predictors of patients' acceptance of video consultation in general practice during the coronavirus disease 2019 pandemic applying the unified theory of acceptance and use of technology model. Digit Health. 2023;9:20552076221149317. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Rasmussen B, Perry R, Hickey M, Hua X, Wong ZW, Guy L, et al. Patient preferences using telehealth during the COVID-19 pandemic in four Victorian tertiary hospital services. Intern Med J. May 2022;52(5):763-769. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Splinter MJ, Ikram MK, Helsper CW, Bindels PJ, de Schepper EI, Licher S. Patient perspectives on telemedicine during the COVID-19 pandemic: a mixed-methods community-based study. BMC Health Serv Res. Jul 27, 2023;23(1):803. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Farrer LM, Batterham PJ, Gulliver A, Morse A, Calear AL, McCallum S, et al. The factors associated with telehealth use and avoidance during the COVID-19 pandemic: longitudinal survey. J Med Internet Res. Feb 08, 2023;25:e43798. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Nieuwe patiëntervaringsmonitor helpt zorg te verbeteren. Netherlands Federation of University Medical Centres. URL: https://www.nfu.nl/themas/kwaliteit-van-zorg/patientervaringen-en-uitkomsten-van-zorg [accessed 2024-01-29]
• Principles of person-centered care. Picker Institute Europe. URL: https://picker.org/who-we-are/the-picker-principles-of-person-centred-care/ [accessed 2024-01-29]
• Bastemeijer CM, Boosman H, Zandbelt L, Timman R, de Boer D, Hazelzet JA. Patient experience monitor (PEM): the development of new short-form picker experience questionnaires for hospital patients with a wide range of literacy levels. Patient Relat Outcome Meas. 2020;11:221-230. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Demiris G. Principles of survey development for telemedicine applications. J Telemed Telecare. 2006;12(3):111-115. [ CrossRef ] [ Medline ]
• Eysenbach G. Improving the quality of web surveys: the checklist for reporting results of internet E-surveys (CHERRIES). J Med Internet Res. Sep 29, 2004;6(3):e34. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Grol R, Wensing M. What drives change? Barriers to and incentives for achieving evidence-based practice. Med J Aust. Mar 15, 2004;180(S6):S57-S60. [ CrossRef ] [ Medline ]
• James HM, Papoutsi C, Wherton J, Greenhalgh T, Shaw SE. Spread, scale-up, and sustainability of video consulting in health care: systematic review and synthesis guided by the NASSS framework. J Med Internet Res. Jan 26, 2021;23(1):e23775. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Jaklitsch E, Shah VK, Smith B, Agarwal A, Chen J, Sweeney A, et al. Melanoma detected through teledermatology versus in-person visits. Telemed J E Health (Forthcoming). Oct 17, 2023. [ CrossRef ] [ Medline ]
• Mazouri-Karker S, Braillard O, Lüchinger R, Bajwa N, Achab S, Hudelson P, et al. Patients preferences for communication during video consultations. Patient Educ Couns. Oct 2023;115:107894. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chandrasekaran R, Bapat P, Venkata PJ, Moustakas E. Face time with physicians: how do patients assess providers in video-visits? Heliyon. Jun 2023;9(6):e16883. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Demiris G, Speedie SM, Finkelstein S. Change of patients' perceptions of TeleHomeCare. Telemed J E Health. 2001;7(3):241-248. [ CrossRef ] [ Medline ]
• Armfield N, Bradford N, White MM, Spitzer P, Smith AC. Humour sans frontieres: the feasibility of providing clown care at a distance. Telemed J E Health. May 2011;17(4):316-318. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Jiménez-Rodríguez D, Ruiz-Salvador D, Rodríguez Salvador MD, Pérez-Heredia M, Muñoz Ronda FJ, Arrogante O. Consensus on criteria for good practices in video consultation: a Delphi study. Int J Environ Res Public Health. Jul 27, 2020;17(15):5396. [ FREE Full text ] [ CrossRef ] [ Medline ]
• de Koning R, Egiz A, Kotecha J, Ciuculete AC, Ooi SZ, Bankole ND, et al. Survey fatigue during the COVID-19 pandemic: an analysis of neurosurgery survey response rates. Front Surg. Aug 12, 2021;8:690680. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Checklist beeldbellen. Pharos. URL: https://www.pharos.nl/kennisbank/stappenplan-laagdrempelig-toegankelijk-beeldbellen/ [accessed 2024-01-29]
• Gilbert AW, Billany JC, Adam R, Martin L, Tobin R, Bagdai S, et al. Rapid implementation of virtual clinics due to COVID-19: report and early evaluation of a quality improvement initiative. BMJ Open Qual. May 2020;9(2):e000985. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Imlach F, McKinlay E, Middleton L, Kennedy J, Pledger M, Russell L, et al. Telehealth consultations in general practice during a pandemic lockdown: survey and interviews on patient experiences and preferences. BMC Fam Pract. Dec 13, 2020;21(1):269. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Barsom EZ, Meijer HA, Blom J, Schuuring MJ, Bemelman WA, Schijven MP. Emergency upscaling of video consultation during the COVID-19 pandemic: contrasting user experience with data insights from the electronic health record in a large academic hospital. Int J Med Inform. Jun 2021;150:104463. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kim J, Kim S. Physicians' perception of the effects of Internet health information on the doctor-patient relationship. Inform Health Soc Care. Sep 2009;34(3):136-148. [ CrossRef ] [ Medline ]
• Murray E, Lo B, Pollack L, Donelan K, Catania J, Lee K, et al. The impact of health information on the internet on health care and the physician-patient relationship: national U.S. survey among 1.050 U.S. physicians. J Med Internet Res. 2003;5(3):e17. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Smith WR, Atala AJ, Terlecki RP, Kelly EE, Matthews CA. Implementation guide for rapid integration of an outpatient telemedicine program during the COVID-19 pandemic. J Am Coll Surg. Aug 2020;231(2):216-2.e2. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Jiménez-Rodríguez D, Santillán García A, Montoro Robles J, Rodríguez Salvador MD, Muñoz Ronda FJ, Arrogante O. Increase in video consultations during the COVID-19 pandemic: healthcare professionals' perceptions about their implementation and adequate management. Int J Environ Res Public Health. Jul 15, 2020;17(14):5112. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by T Leung; submitted 16.05.23; peer-reviewed by AW Zahoor, J Hayden; comments to author 25.10.23; revised version received 08.12.23; accepted 31.01.24; published 27.03.24.

©Stefanie C van den Bosch, Demi Van Dalen, Marjan Meinders, Harry van Goor, Stefaan Bergé, Martijn Stommel, Sandra van Dulmen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

All countries' agri-environmental policies at a glance

Dataset of over 6,000 policies from all over the world.

There can be no analysis without data. In this spirit, researchers from the University of Bonn and the Swiss Federal Institution of Technology (ETH) Zurich have published a database containing over 6,000 agri-environmental policies, thus enabling their peers as well as policymakers and businesses to seek answers to all manner of different questions. The researchers have used two examples to demonstrate how this can be done: how a country's economic development is linked to its adoption of agri-environmental policies and how such policies impact soil erosion. Their study has now been published in Nature Food .

Although agriculture is vital for our survival and well-being, it is also responsible for significant greenhouse gas emissions, biodiversity loss and soil degradation. Countries are therefore adopting all manner of different policies to make agriculture sustainable, from regulations to paying for agri-environmental services. Every year, new laws, programs, and schemes are introduced all over the world while others are abolished, making it hard to keep track of developments. This is a problem for researchers and policy decision-makers alike: how are they to go about making comparisons? How can they tell which measures work in which circumstances? Together with colleagues at ETH Zurich, Professor David Wuepper from the Institute for Food and Resource Economics at the University of Bonn has now put together an extensive, easy-to-use database containing 6,124 policies from over 200 countries that were adopted between 1960 and 2022.

In their work, the team focused on measures that meet certain criteria: "First and foremost, the measure has to be relevant in some way to agriculture, such as land use, nitrogen fertilizers or pesticides. But forest conservation is included too, because it's linked to agriculture in many countries," explains Wuepper, who is also a member of the PhenoRob Cluster of Excellence at the University of Bonn. The measures also have to have national significance, meaning that they cannot be focused too strongly on the local level, for example. New thematic areas can be added to the database at any time. "We deliberately gave it a modular structure so we can keep on expanding it."

Old question meets new data

Wuepper and his co-authors were quick to use their database to shine new light on an old but contentious question: how does a country's economic development tie in with its adoption of agri-environmental policies? "You might expect higher-income countries to implement a larger number of eco-friendly measures because the environment is becoming increasingly important in relative terms on the policy front," Wuepper explains. And, thanks to his database, he has now been able to confirm that this is indeed the case. "We've shown that richer countries actually do introduce more measures, generally speaking." Here too, however, it is the exceptions that prove the rule. "This trend doesn't apply across the board. For instance, the Middle Eastern have relatively few agri-environmental policies in place given their income level. This demonstrates that countries need to make an active effort to implement sustainable policies and that it won't happen by itself." What these policies then actually achieve, however, is another question entirely. "But this is also something that can -- and should -- be investigated with the help of the database," Wuepper says, and in fact an initial analysis of this kind is included in the article that has been published.

National policies help fight the problem of soil erosion

The database helped Wuepper answer a question that had been on his mind for some time: in a previous research project at ETH Zürich, he had studied what impact countries have on soil erosion. "Comparing levels of soil erosion along national borders showed that countries exert significant influence," Wuepper reveals. "At the time, we were able to demonstrate a link to agriculture, and we also thought that national policies might be an influencing factor. However, we couldn't look into it because we didn't have the data on the countries' relevant policies to compare on a global scale." Armed with their new policy database, the researchers have now been able to investigate the extent to which this significant influence that countries exert on global erosion can be explained by their policies. They have found that national soil management policies account for at least 43 percent of a country's impact on soil erosion.

The database is accessible to the general public at https://zenodo.org/records/10842614

• Environmental Policy
• Sustainability
• Environmental Issues
• Land Management
• Ocean Policy
• Educational Policy
• Environmental Policies
• Environmental impact assessment
• Funding policies for science
• Consumerism
• Agroecology
• Environmental engineering
• Climate change mitigation

Story Source:

Materials provided by University of Bonn . Note: Content may be edited for style and length.

Related Multimedia :

• Charts and maps showing policies around the world

Journal Reference :

• David Wuepper, Ilsabe Wiebecke, Lara Meier, Sarah Vogelsanger, Selina Bramato, Andrea Fürholz, Robert Finger. Agri-environmental policies from 1960 to 2022 . Nature Food , 2024; DOI: 10.1038/s43016-024-00945-8

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Listen to your gut: Research discusses using microbiota analysis for precision health care

by Sarah Small, Pennsylvania State University

The human body harbors approximately 30 trillion microbes, known collectively as the microbiota. These microorganisms influence various bodily functions, including digestion and metabolism to immune response, according to Pak Kin Wong, Penn State professor of biomedical engineering and of mechanical engineering. Analysis of microbiota holds potential for informing disease diagnosis, prognosis predictions and treatment, Wong said, but has yet to be adopted into clinical decision-making.

Penn State News spoke to Wong about his recent paper , published in Nature Reviews Bioengineering , that discusses the methods available for incorporating microbiota analysis into clinical decision-making, the challenges of doing so and the need for new technologies to capitalize on the potential of microbiota's role in medicine.

What is microbiota analysis? Why is it important? What medically useful information does it provide?

Microbiota analysis involves examining the composition, diversity, abundance, distribution, evolution and functions of microorganisms within specific environments, employing advanced genomic and bioinformatic tools. In the human body, these microbes are pivotal to health and disease management .

For instance, the gut microbiota is essential for intestinal health through the production of vital fermentation products and metabolites. This microbial community is also linked to various bodily functions and diseases, highlighting its significance beyond just the gut.

By analyzing the microbiota, we can gain invaluable insights for disease diagnosis , predict disease progression and tailor treatments to individual needs, paving the way for personalized medicine. For example, microbiota analysis has been crucial in understanding conditions like inflammatory bowel disease and obesity, offering new avenues for intervention.

Why isn't microbiota analysis currently incorporated into medical decision-making? What needs to be done in order to change that?

High-throughput sequencing, which allows rapid evaluation of the DNA content in a sample, has been a game-changer for studying human microbiomes, helping us dive deep into research and discovery. Yet, turning these insights into something we can use in clinics isn't straightforward. These methods can be expensive, slow, complex and labor intensive.

For microbiota analysis to fit into medical settings, it needs to be affordable, quick and user-friendly. And there's more—there are standard sequencing struggles with things like telling apart living from dead microbes, analyzing different kinds of biological molecules together and mapping out where microbes are located. In addition, since the mix of microbes varies so much from person to person, and because there's no one-size-fits-all for what a "healthy" microbiome looks like, it's tricky to adopt microbiota analysis into medical decision-making.

To really get how diseases progress, we need to keep an eye on how someone's microbiota changes over time. This tracking is key not just for understanding diseases but also for making sure treatments that affect the microbiome are working as they should. All these challenges are significant hurdles for making the most of our knowledge about the microbiome in real-world health care.

What are some examples of medical conditions that could benefit from microbiota analysis? How would it work?

Analyzing the differences in microbial composition between healthy individuals and those with specific diseases offers medical professionals crucial insights into patient health, disease risk and potential treatment outcomes. The diversity and types of microbes in our intestines, for example, are linked to a range of health issues, such as C. difficile infections, inflammatory bowel diseases and neurodegenerative conditions.

Beyond diagnosis and prognosis, there are groundbreaking approaches to altering these microbial communities to fight diseases more effectively. Strategies like the use of prebiotics, probiotics and fecal microbiota transplants are being employed to tackle persistent infections and improve the efficacy of treatments. Specifically, fecal transplants have emerged as a pivotal therapy for refractory C. difficile infections and show promise in enhancing cancer treatment outcomes.

Moreover, the direct bladder administration of the BCG, a form of bacterial immunotherapy approved by FDA, has been an effective bladder cancer treatment for decades. These developments highlight the critical importance of microbiota analysis and the innovative potential of leveraging our microbiota to combat diseases.

What next steps do you plan on taking in your research related to this topic?

We have been collaborating with experts from academia, industry and the clinical field, each bringing extensive knowledge in technology, biology and health care. Currently, we are developing innovative microbiota analysis platforms that employ single-cell analysis and artificial intelligence.

A significant challenge we face is determining the most effective methods to unlock the medical potential of the human body's microbiota. Our primary focus is on identifying a viable application that not only underscores the intrinsic value of microbiota analysis but also accelerates its integration into clinical practice.

As the health care landscape evolves, the full utilization of microbiota analysis stands to revolutionize the field. This journey we're on is all about innovation and collaboration. We believe it's going to take us to a future where precision medicine makes better health and well-being achievable for a wider array of people.

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