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

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

There’s the distinction between inductive and deductive approaches:

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

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

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

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

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

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

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

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

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

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

Coding qualitative data

Interview extract	Codes
Personally, I’m not sure. I think the climate is changing, sure, but I don’t know why or how. People say you should trust the experts, but who’s to say they don’t have their own reasons for pushing this narrative? I’m not saying they’re wrong, I’m just saying there’s reasons not to 100% trust them. The facts keep changing – it used to be called global warming.

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

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

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

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

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

Turning codes into themes

Codes	Theme
	Uncertainty
	Distrust of experts
	Misinformation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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How to Write a Thematic Literature Review: A Beginner’s Guide

Literature reviews provide a comprehensive understanding of existing knowledge in a particular field, offer insights into gaps and trends, and ultimately lay the foundation for innovative research. However, when tackling complex topics spanning multiple issues, the conventional approach of a standard literature review might not suffice. Many researchers present a literature review without giving any thought to its organization or structure, but this is where a thematic literature review comes into play. In this article, we will explore the significance of thematic reviews, delve into how and when to undertake them, and offer invaluable guidance on structuring and crafting a compelling thematic literature review.

Table of Contents

What is a thematic literature review?

A thematic literature review, also known as a thematic review, involves organizing and synthesizing the existing literature based on recurring themes or topics rather than a chronological or methodological sequence. Typically, when a student or researcher works intensively on their research there are many sub-domains or associated spheres of knowledge that one encounters. While these may not have a direct bearing on the main idea being explored, they provide a much-needed background or context to the discussion. This is where a thematic literature review is useful when dealing with complex research questions that involve multiple facets, as it allows for a more in-depth exploration of specific themes within the broader context.

When to opt for thematic literature review?

It is common practice for early career researchers and students to collate all the literature reviews they have undertaken under one single broad umbrella. However, when working on a literature review that involves multiple themes, lack of organization and structure can slow you down and create confusion. Deciding to embark on a thematic literature review is a strategic choice that should align with your research objectives. Here are some scenarios where opting for a thematic review is advantageous:

• Broad Research Questions: When your research question spans across various dimensions and cannot be adequately addressed through a traditional literature review.
• Interdisciplinary Research: In cases where your research draws from multiple disciplines, a thematic review helps in synthesizing diverse literature cohesively.
• Emerging Research Areas: When exploring emerging fields or topics with limited existing literature, a thematic review can provide valuable insights by focusing on available themes.
• Complex Issues: Thematic reviews are ideal for dissecting complex issues with multiple contributing factors or dimensions.

Advantages of a Thematic Literature Review

With better comprehension and broad insights, thematic literature reviews can help in identifying possible research gaps across themes. A thematic literature review has several advantages over a general or broad-based approach, especially for those working on multiple related themes.

• It provides a comprehensive understanding of specific themes within a broader context, allowing for a deep exploration of relevant literature.
• Thematic reviews offer a structured approach to organizing and synthesizing diverse sources, making it easier to identify trends, patterns, and gaps.
• Researchers can focus on key themes, enabling a more detailed analysis of specific aspects of the research question.
• Thematic reviews facilitate the integration of literature from various disciplines, offering a holistic view of the topic.
• Researchers can provide targeted recommendations or insights related to specific themes, aiding in the formulation of research hypotheses.

Now that we know the benefits of a thematic literature review, what is the best way to arrange reviewed literature in a thematic format?

How to write a thematic literature review

To effectively structure and write a thematic literature review, follow these key steps:

• Define Your Research Question: Clearly define the overarching research question or topic you aim to explore thematically. When writing a thematic literature review, go through different literature review sections of published research work and understand the subtle nuances associated with this approach.
• Identify Themes: Analyze the literature to identify recurring themes or topics relevant to your research question. Categorize the bibliography by dividing them into relevant clusters or units, each dealing with a specific issue. For example, you can divide a topic based on a theoretical approach, methodology, discipline or by epistemology. A theoretical review of related literature for example, may also look to break down geography or issues pertaining to a single country into its different parts or along rural and urban divides.
• Organize the Literature: Group the literature into thematic clusters based on the identified themes. Each cluster represents a different aspect of your research question. It is up to you to define the different narratives of thematic literature reviews depending on the project being undertaken; there is no one formal way of doing this. You can weigh how specific areas stack up against others in terms of existing literature or studies and how many more aspects may need to be added or further looked into.
• Review and Synthesize: Within each thematic cluster, review and synthesize the relevant literature, highlighting key findings and insights. It is recommended to identify any theme-related strengths or weaknesses using an analytical lens.
• Integrate Themes: Analyze how the themes interact with each other, draw linkages between earlier studies and see how they contribute to your own research. A thematic literature review presents readers with a comprehensive overview of the literature available on and around the research topic.
• Provide a Framework: Develop a framework or conceptual model that illustrates the relationships between the themes. Present the most relevant part of the thematic review toward the end and study it in greater detail as it reflects the literature most relevant and directly related to the main research topic.
• Conclusion: Conclude your thematic literature review by summarizing the key findings and their implications for your research question. Be sure to highlight any gaps or areas requiring further investigation in this section.
• Cite and Reference: It is important to remember that a thematic review of literature for a PhD thesis or research paper lends greater credibility to the student or researcher. So ensure that you properly cite and reference all sources according to your chosen citation style.
• Edit and Proofread: Take some time to review your work, ensure proper structure and flow and eliminate any language, grammar, or spelling errors that could deviate reader attention. This will help you deliver a well-structured and elegantly written thematic literature review.

Thematic literature review example

In essence, a thematic literature review allows researchers to dissect complex topics into smaller manageable themes, providing a more focused and structured approach to literature synthesis. This method empowers researchers to gain deeper insights, identify gaps, and generate new knowledge within the context of their research.

To illustrate the process mentioned above, let’s consider an example of a thematic literature review in the context of sustainable development. Imagine the overarching research question is: “What are the key factors influencing sustainable urban planning?” Potential themes could include environmental sustainability, social equity, economic viability, and governance. Each theme would have a dedicated section in the review, summarizing relevant literature and discussing how these factors intersect and impact sustainable urban planning. Close with a strong conclusion that highlights research gaps or areas of investigation. Finally, review and refine the thematic literature review, adding citations and references as required.

In conclusion, when tackling multifaceted research questions, a thematic literature review proves to be an indispensable tool for researchers and students alike. By adopting this approach, scholars can navigate the intricate web of existing literature, unearth meaningful patterns, and contribute to the advancement of knowledge in their respective fields. We hope the information in this article helps you create thematic reviews that illuminate your path to new discoveries and innovative insights.

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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.

Interview snippet	Codes
It’s hard to get a handle on it. It’s so different from how things used to be done, when networking was about handshakes and business cards.	Confusion Comparison with old networking methods
It makes me feel like a dinosaur.	Sense of being left behind
Plus, I've been burned a few times. I'll spend time making what I think are professional connections with male peers, only for the conversation to unexpectedly turn romantic on me. It seems like a lot of men use these sites as a way to meet women, not to develop their careers. It's stressful, to be honest.	Discomfort and unease Unexpected experience with other users

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:

Codes	Theme
Confusion, Discomfort and unease, Unexpected experience with other users	Negative experience
Comparison with old networking methods, Sense of being left behind	Perceived lack of skills

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.

What (Exactly) Is Thematic Analysis?

Plain-Language Explanation & Definition (With Examples)

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

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

Thematic Analysis 101

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

First, the lingo…

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

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

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

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

What is thematic analysis?

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

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

When should you use thematic analysis?

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

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

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

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

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

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

What are the main approaches?

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

The inductive approach

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

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

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

The deductive approach

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

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

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

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

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

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

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

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

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

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

What are the types of thematic analysis?

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

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

Let’s have a look at each of these:

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

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

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

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

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

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

Quick Recap: Thematic analysis approaches and types

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

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

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

Need a helping hand?

How to “do” thematic analysis

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

Step 1: Get familiar with the data

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

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

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

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

Step 2: Search for patterns or themes in the codes

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

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

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

Step 3: Review themes

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

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

Step 4: Finalise Themes

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

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

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

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

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

Step 5: Produce your report

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

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

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

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

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

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

Quick Recap: How to “do” thematic analysis

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

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

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

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

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

Tips & Suggestions

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

Wrapping Up

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

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

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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21 Comments

I really appreciate the help

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

I’ve found the article very educative and useful

Excellent. Very helpful and easy to understand.

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

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

It is a great help. Thanks.

Best advice. Worth reading. Thank you.

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

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

Hello, Sir/Maam

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

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

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

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

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

lovely and helpful. thanks

very informative information.

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

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

Thanks a lot, really enlightening

excellent! very helpful thank a lot for your great efforts

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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 . Revised on 7 June 2024.

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:

Coding qualitative data

Interview extract	Codes
Personally, I’m not sure. I think the climate is changing, sure, but I don’t know why or how. People say you should trust the experts, but who’s to say they don’t have their own reasons for pushing this narrative? I’m not saying they’re wrong, I’m just saying there’s reasons not to 100% trust them. The facts keep changing – it used to be called global warming.

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:

Turning codes into themes

Codes	Theme
	Uncertainty
	Distrust of experts
	Misinformation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Thematic analysis part 1: introduction to the topic and an explanation of ‘themes’

Posted on 21st February 2020 by Dolly Sud

This is the first of a three-part blog which will provide an introduction to Thematic analysis and discussion of what a theme is (part 1), a description of the three schools of TA and some study design recommendations (part 2), and an outline of the six phases of reflexive TA (part 3). A list of key reference sources is also provided.

Introduction

There is an array of methods available to researchers that can be used to identify patterned meaning across a dataset. Thematic analysis (TA) is one of these and is a widely embraced method for analysing qualitative data to inform many different research questions across a wide range of disciplines. It can be used for a variety of types of datasets and applied in a variety of different ways, thus, demonstrating its flexibility. Importantly, it is a very accessible method for novice researchers.

TA is an umbrella term that describes approaches which are aimed at identifying patterns (“themes”) across qualitative datasets [1,2]. It should not be considered to be a single qualitative analytic approach [1] and neither should it be considered a methodology – it is a method .

Victoria Clarke and Virginia Braun are authors of the most widely cited resources on TA – the content of this blog is based on information available on their website and published papers [1,2,3].

Take-home messages:

• thematic analysis is a method not a methodology
• thematic analysis should not be considered to be a single qualitative analytic approach

What is a theme?

There are two conceptualizations of themes which are articulated in the literature [2]:

1. Shared meaning based patterns

Shared meaning based patterns are organised around a central organising concept (core concept). In one of the online lectures [4] available for TA this is likened to a dandelion spherical seed head containing many single-seeded fruits. The seed head being the central organising concept, and the fruits being the themes.

Themes are built from smaller units known as codes.

Shared meaning based patterns [2]:

• capture the essence and spread of meaning;
• unite data that might otherwise appear disparate, or meaning that occurs in multiple and varied contexts;
• they (often) explain large portions of a dataset;
• are often abstract entities or ideas, capturing implicit ideas “beneath the surface” of the data, but can also capture more explicit and concrete meaning.

Braun & Clarke view themes as being shared meaning based patterns.

A good way of understanding the idea of themes is to look at published [2] examples of good TA (a full reference list is available on the website [5]).

Examples of themes as shared meaning based patterns taken from a paper which sought to explore anorexia nervosa clients’ perceptions of their therapists’ body [6]:

• “Wearing eating disorder glasses,”
• “You’re making all sorts of assumptions as a client,”
• “Appearance matters.”

2. Domain summary [2]

This conceptualisation is in contrast to that of a theme as shared meaning based patterns. It summarizes what participants said in relation to a topic or issue, typically at the semantic or surface level of meaning, and usually reports multiple or even contradictory meaning content. The issues are often based around data collection tools, such as responses to a particular interview question.

Example of themes as domain summary from a paper on Muslim views on mental health and psychotherapy [7], the seven themes were outlined as follows:

• “problem management,”
• “relevance of services,”
• “barriers,”
• “service delivery,”
• “therapy content,”
• “therapist characteristics”

You can see that domain summaries don’t appear to consider shared meaning or differences.

A useful pointer here is to consider domain summaries as collecting data under headings which are often composed of single words. Whereas shared meaning based patterns seek to unite data.

Take-home message:

• domain summaries and shared meaning-based patterns, although both articulated as being themes in published literature, are not the same thing.

References (pdf)

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No Comments on Thematic analysis part 1: introduction to the topic and an explanation of ‘themes’

Hi Keith, I think you would need to cite the website using the method dictated by whichever citation method you have chosen to use. I hope that helps!

Hi, I’m drafting a paper on story completion using some of your stuff, but don’t know how to reference you. Keith

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PY2106: Human Development Across the Lifespan Guide: Writing a Thematic Analysis

• Subject Basics
• Finding Resources
• Writing a Thematic Analysis
• Formatting & Referencing
• Psychology Guide
• Literature review guide This link opens in a new window

What is a thematic analysis?

A thematic analysis is used in qualitative research to focus on examining themes within a topic by identifying, analysing and reporting patterns (themes) within the research topic. It is similar to a literature review, which is a critical survey and assessment of the existing research on your particular topic.

The following links provide more information about the thematic analysis process.

• About Thematic Analysis
• Using thematic analysis in psychology

Thematic Analysis Process

Writing a literature review

You may also find these resources on writing literature reviews useful to help you write your analysis.

• Writing a Literature Review
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Writing a literature review (part 1)

Literature review books

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The Literature Review is a concise step-by-step guide to conducting a literature search and writing up the literature review chapter in graduate dissertations and in professional doctorate theses. 

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An interactive approach to writing essays and research reports in psychology

This writing guide offers students an engaging, accessible introduction to the conventions of writing in the psychology discipline.

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Doing a thematic analysis: A practical, step-by-step guide for learning and teaching scholars

Data analysis is central to credible qualitative research. Indeed the qualitative researcher is often described as the research instrument insofar as his or her ability to understand, describe and interpret experiences and perceptions is key to uncovering meaning in particular circumstances and contexts. While much has been written about qualitative analysis from a theoretical perspective we noticed that often novice, and even more experienced researchers, grapple with the ‘how’ of qualitative analysis. Here we draw on Braun and Clarke’s (2006) framework and apply it in a systematic manner to describe and explain the process of analysis within the context of learning and teaching research. We illustrate the process using a worked example based on (with permission) a short extract from a focus group interview, conducted with undergraduate students.

Related Papers

A Brief Introduction to Thematic Analysis

Abisha Kampira

This paper discusses thematic analysis, a popular yet often misunderstood qualitative data analysis method. It is written with postgraduate and institutional researchers who already have an appreciation of qualitative data analysis in mind. The paper takes a practical rather than a theoretical approach to thematic analysis focusing on methods and processes that are applied by our researchers as they go about the analysis. The approaches taken may, therefore, slightly differ from theory although they are highly relatable.

Katina Zammit

Thematic analysis (TA), as a qualitative analytic method, is widely used in health care, psychology, and beyond. However, scant details are often given to demonstrate the process of data analysis, especially in the field of education. This article describes how a hybrid approach of TA was applied to interpret multiple data sources in a practitioner inquiry. Particular attention is given to the inductive and deductive coding and theme development process of TA. Underpinned by the constructivist epistemology, codes were driven by both data per se and theories, through a &quot;bottom-up&quot; and &quot;top-down&quot; approach to identify themes. A detailed example of six steps of data analysis is presented, which evidences the systematic analysis of raw data from observation and research journals, students&#39; focus groups, and a classroom teacher&#39;s semistructured interviews. This example demonstrates how classroom practice was unpacked and how insiders&#39; insights were interpre...

carly smith

The Qualitative Report

Elih Sutisna Yanto

Gareth Terry and Nikki Hayfield's book, Essentials of Thematic Analysis, introduces readers to reflexive thematic analysis, a method for analyzing interview and focus group transcripts, qualitative survey responses, and other qualitative data. This method is based on the understanding that we all exist in a context from which we can see and speak. In this way, researchers produce knowledge that represents situated truths and allow them to understand others' perspectives on a given topic. The book shows how to construct a "positioned reality of the situation" from qualitative data. According to the authors, this method is not a methodology but rather a method; that is, a theoretical framework. They emphasize adaptability and subjectivity and go beyond data summaries to understand underlying structures. This method requires frequent data exploration and re-evaluation. It can be studied by both novices and experts. The method is illustrated with notes, illustration, and examples. This book provides a straightforward, concise, and comprehensive description of the authors' approach, including its methodological rigor, advantages, and limitations.

Qualitative techniques for workplace data analysis

Anindita Majumdar

The popularity of qualitative methods in social science research is a well-noted and most welcomed fact. Thematic analysis, the often-used methods of qualitative research, provides concise description and interpretation in terms of themes and patterns from a data set. The application of thematic analysis requires trained expertise and should not be used in a prescriptive, linear, and inflexible manner while analyzing data. It should rather be implemented in relation to research question and data availability. To ensure its proper usage, Braun and Clarke have propounded the simplest yet effective six-step method to conduct thematic analysis. In spite of its systematic step-driven process, thematic analysis provides core skills to conduct different other forms of qualitative analysis. Thematic analysis, through its theoretical freedom, flexibility, rich and detailed yet complex analytical account has emerged as the widely used and most effective qualitative research tool in social and organizational context.

Prokopis A Christou

Thematic analysis has received increased attention from the research academic community, echoing Braun and Clarke's (2006) influential argument of its theoretical accessibility and flexibility. Along with its current status, dilemmas have arisen in regard to its practice as a result of escalated demand for analytical software programs. Synchysis, the rhetorical practice of creating bewilderment by scattering words, endures in critical reviews and in the analysis of data derived from social media platforms. This paper departs from a simple replication of existing studies by addressing current issues as a result of the evolution of thematic analysis. Furthermore, it outlines specific implications (step-by-step guidance) while incorporating the somewhat overlooked phase of the creation of conceptual diagrams and theory-development during the stages of conducting a rigorous thematic analysis.

Demola V Akinyoade

Qualitative data analysis is a distinctive form of analysis in the social research enterprise. It is an approach that is less understood than its counterpart—quantitative analysis. Diversity and flexibility are main features of qualitative data analysis. These features also expose it to the danger of doing it anyhow—a slapdash analysis unbecoming of scientific endeavor. Despite its diversity there are common features to the analysis of qualitative data that beginning researchers or trainee-social scientists, such as undergraduates, should be familiar with. This is the focus of this chapter. It focuses on necessary areas in data analysis to help this category of students to make sense of their qualitative data. It covers sources and types of qualitative data, basic issues and procedures in qualitative data analysis. It presents a systematic, disciplined, transparent and describable process to the analysis of qualitative data in consonance with the nature of the science and its method.

Nurse Researcher

Sharon Rallis

Dr King Costa

The C.O.S.T.A. Research Framework is a tool that was developed to assist students and novice researchers to navigate postgraduate studies with clarity and understanding. The emphasis of the method is on a five step approach comprising of (1) Concepts in research that are foundational to research language; (2) Objective of research which is being undertaken-likened to development of protocol or proposal; (3) Situation, emphasizing pre-comprehension of the current debate, likened to literature review; (4) Tact, which deals with methodological approaches to formulation of conclusions; (5) Assessment of output-ability to make judgments on trajectory followed and study results. This current study focused on the use of thematic analysis by students and novice researchers in completion of their research reports and scientific writings, which essentially forms part of Stage 4 of the C.O.S.T.A Framework. The author used a systematic mapping review to select units of analysis for the sole purpose of demonstrating gaps and inconsistencies on the application of the concept of thematic analysis. In most research reports, students demonstrate clear articulation of methods in coding and themes generation, however, evidence of these codes and how themes were generated was not reflected on most of reports reviewed. This situation seems to be a prevalent in most research reports, which detailed explication of interpretation of participant's comments during interviews taking the prominent feature of presentation of results/findings section. It is for this reason that the C.O.S.T.A. Research Framework (Costak, 2019) is proposed as one of the methods for teaching students about different methods of qualitative data analysis in general and thematic analysis in particular.

Victoria Clarke

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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:

Profile No.	Data Item	Initial Codes
1	I am a tall, single(widowed), easy-going, honest, good listener with a good sense of humour. Being a handyperson, I keep busy working around the house; I also like to follow my favourite hockey team on TV or spoiling my two granddaughters when I get the chance!! I enjoy most music except for 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.	Physical description Widowed Positive qualities Humour Keep busy Hobbies Family Music Active Travel Plans Partner qualities Plans

Profile No.	Data Item	Initial Codes
2	I enjoy photography, lapidary & seeking collectables 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.	HobbiesFuture plans Travel Unique Values Humour Music

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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• Practical thematic...

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

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

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

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

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

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

Summary points

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

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

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

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

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

Key features and applications of practical thematic analysis

Step 1: reading.

All manuscript authors read the data

All manuscript authors write summary memos

Step 2: Coding

Coders perform both data management and early data analysis

Codes are complete thoughts or sentences, not categories

Step 3: Theming

Researchers host a thematic analysis session and share different perspectives

Themes are complete thoughts or sentences, not categories

Applications

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

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

When time and resources are limited

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.

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

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

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

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1 Introduction

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

2 What is reflexive thematic analysis?

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

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

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

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

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

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

3 A worked example of reflexive thematic analysis

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

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

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

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

3.1 Addressing underlying theoretical assumptions

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

3.1.1 Essentialist versus constructionist epistemologies

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

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

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

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

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

3.1.2 Experiential versus critical orientation

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

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

3.1.3 Inductive versus deductive analysis

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

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

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

3.1.4 Semantic versus latent coding

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

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

3.2 The six-phase analytical process

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

3.2.1 Phase one: familiarisation with the data

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

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

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.

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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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General-purpose thematic analysis: a useful qualitative method for anaesthesia research

1 Centre for Medical and Health Sciences Education, School of Medicine, University of Auckland, Auckland, New Zealand

2 Department of Anaesthesia, Auckland City Hospital, Auckland, New Zealand

Learning objectives

By reading this article, you should be able to:

• • Explain when to use thematic analysis.
• • Describe the steps in thematic analysis of interview data.
• • Critique the quality of a study that uses the method of thematic analysis.
• • Thematic analysis is a popular method for systematically analysing qualitative data, such as interview and focus group transcripts.
• • It is one of a cluster of methods that focus on identifying patterns of meaning, or themes, across a data set.
• • It is relevant to many questions in perioperative medicine and a good starting point for those new to qualitative research.
• • Systematic approaches to thematically analysing data exist, with key components to demonstrate rigour, accountability, confirmability and reliability.
• • In one study, a useful six-step approach to analysing data is offered.

Anaesthesia research commonly uses quantitative methods, such as surveys, RCTs or observational studies. Such methods are often concerned with answering what questions and how many questions. Qualitative research is more concerned with why questions that enable us to understand social complexities. ‘Qualitative studies in the anaesthetic setting’, write Shelton and colleagues, ‘have been used to define excellence in anaesthesia, explore the reasons behind drug errors, investigate the acquisition of expertise and examine incentives for hand hygiene in the operating theatre’. 1

General-purpose thematic analysis (termed thematic analysis hereafter) is a qualitative research method commonly used with interview and focus group data to understand people's experiences, ideas and perceptions about a given topic. Thematic analysis is a good starting point for those new to qualitative research and is relevant to many questions in the perioperative context. It can be used to understand the experiences of healthcare professionals and patients and their families. Box 1 gives examples of questions amenable to thematic analysis in anaesthesia research.

Examples of questions amenable to thematic analysis.

• (i) How do operating theatre staff feel about speaking up with their concerns?
• (ii) What are trainee's conceptions of the balance between service and learning?
• (iii) What are patients' experiences of preoperative neurocognitive screening?

Alt-text: Box 1

Thematic analysis involves a process of assigning data to a number of codes, grouping codes into themes and then identifying patterns and interconnections between these themes. 2 Thematic analysis allows for a nuanced understanding of what people say and do within their particular social contexts. Of note, thematic analysis can be used with interviews and focus groups and other sources of data, such as documents or images.

Thematic analysis is not the same as content analysis. Content analysis involves counting the frequency with which words or phrases appear in data. Content analysis is a method used to code and categorise textual information systematically to determine trends, frequency and patterns of words used. 3 Conversely, thematic analysis focuses on the relative importance of ideas and how ideas connect and govern practices. Thematic analysis does not rely on frequency counts to indicate the importance of coded data. Content analysis can be coupled with thematic analysis, where both themes and frequencies of particular statements or words are reported.

Thematic analysis is a research method, not a methodology. A methodology is a method with a philosophical underpinning. If researchers report only on what they did, this is the method. If, in addition, they report on the philosophy that governed what they did, this is methodology. Common methodologies in qualitative research include phenomenology, grounded theory, hermeneutics, narrative enquiry and ethnography. 4 Each of these methodologies has associated methods for data analysis. Thematic analysis can be combined with many different qualitative methodologies.

There are also different types of thematic analysis, such as inductive (including general purpose), applied, deductive or semantic thematic analysis. Inductive analysis involves approaching the data with an open mind, inductively looking for patterns and themes and interpreting these for meaning. 2 , 4 Of note, researchers can never have a truly open mind on their topic of interest, so the process will be influenced by their particular perspectives, which need to be declared. In applied and deductive thematic analysis, the researcher will have a pre-existing framework (which may be informed by theory or philosophy) against which they will attempt to categorise the data. 4 , 5 , 6 For semantic thematic analysis, the data are coded on explicit content, and tend to be descriptive rather than interpretative. 6

In this review, we outline what thematic analysis entails and when to use it. We also list some markers to look for to appraise the quality of a published study.

Designing the data collection

Before embarking on qualitative research, as with quantitative research, it is important to seek ethical review of the proposed study. Ethical considerations include such issues as consent, data security and confidentiality, permission to use quotes, potential for identifying individuals or institutions, risk of psychological harm to participants with studies on sensitive issues (e.g. suicide or sexual harassment), power relationships between interviewer and interviewee or intrusion on other activities (such as teaching time or work commitments). 7

Qualitative research often involves asking people questions during interviews or focus groups. Merriam and Tisdell stated that, ‘The most common form of interview is the person-to-person encounter in which one person elicits information from the other’. 8 Information is elicited through careful and purposeful questioning and listening. 9 Research interviews in anaesthesia are generally purposeful conversations with a structure that allows the researcher to gather information about a participant's ideas, perceptions and experiences concerning a given topic.

A structured interview is when the researcher has already decided on a set of questions to ask. 9 If the researcher will ask a set of questions, but has flexibility to follow up responses with further questions, this is called a semi-structured interview. Semi-structured interviews are commonly used in research involving thematic analysis. The researcher can also use other forms of questioning, such as single-question interview. Semi-structured interviews are commonly used in anaesthesia, such as the studies from our own research group. 10 , 11 , 12

Interviews are usually recorded in audio form and then transcribed. For each interview or focus group, a single transcript is created. The transcripts become the written form of data and the collection of transcripts from the research participants becomes the data set.

Designing productive interview questions

The design of interview questions significantly shapes a participant's response. Interview questions should be designed using ‘sensitising concepts’ to encourage participants to share information that will increase a researcher's understanding of the participants' experiences, views, beliefs and behaviours. 13 ‘Sensitising concepts’ describe words in questions that bring the participants' attention to a concept of research interest. Examples of sensitising concepts include speaking up, teamwork and theoretical concepts (such as Kolb's experiential learning cycle or Foucauldian power theory in relation to trainee learning and operating theatre culture). 14 , 15 Specifically, the questions should be framed in such a way as to encourage participants to make sense of their own experience and in their own words. The researcher should try to minimise the influences of their own biases when they design questions. Using open-ended questions will increase the richness of data. Box 2 gives examples of question design.

How to design an interview question.

Alt-text: Box 2

Bias, positionality and reflexivity

Bias is an inclination or prejudice for or against someone or something, whereas positionality is a person's position in society or their stance towards someone or something. For example, Tanisha once had an inexperienced anaesthetist accidentally rupture one of her veins whilst they were siting an i.v. cannula in an emergency situation. Now, Tanisha has a bias against inexperienced anaesthetists. Tanisha's positionality —a medical anthropologist with no anaesthesia training, but working with many anaesthesia colleagues, including her director—may also inform that bias or the way that Tanisha interacts with anaesthetists. Reflexivity is a process whereby people/researchers proactively reflect on their biases and positionality. Biases shape positionality (i.e. the stance of the researcher in relation to the social, historical and political contexts of the study). In practical research terms, biases and positionality inform the way researchers design and undertake research, and the way they interpret data. It is important in qualitative research to both identify biases and positionality, and to take steps to minimise the impact of these on the research.

Some ways to minimise the influence of bias and positionality on findings include:

(i) Raise awareness amongst the research team of bias and positionality.

(ii) Design research/interview questions that minimise potential for these to distort which data are collected or how they are collected.

(iii) Researchers ask reflexive questions during data analysis, such as, ‘Is my bias about xxx informing my view of these data?’

(iv) Two or more researchers are involved in the analysis process.

(v) Data analysis member check (e.g. checking back with participants if the interpretation of their data is consistent with their experience and with what they said).

Before embarking on the study, researchers should consider their own experiences, knowledge and views; how this influences their own position in relation to the study question; and how this position could potentially introduce bias in how they collect and analyse the data. Taking time to reflect on the impact of the researchers' position is an important step towards being reflective and transparent throughout the research process. When writing up the study, researchers should include statements on bias and positionality. In quantitative research, we aim to eliminate bias. In qualitative research, we acknowledge that bias is inevitable (and sometimes even unconscious), and we take steps to make it explicit and to minimise its effect on study design and data interpretation.

Sampling and saturation

Qualitative research typically uses systematic, non-probability sampling. Unlike quantitative research, the goal of sampling is not to randomly select a representative sample from a population. Instead, researchers identify and select individuals or groups relevant to the research question. Commonly used sampling techniques in anaesthesia qualitative research are homogeneous (group) sampling and maximum variation sampling. In the former, researchers may be concerned with the experiences of participants from a distinct group or who share a certain characteristic (e.g. female anaesthesia trainees), so they recruit selectively from within the group with this shared characteristic to gain a rich, in-depth understanding of their experiences. Conversely, the aim with maximum variation sampling is to recruit participants with diverse characteristics to obtain a broad understanding of the question being studied (e.g. members of different professional groups within operating theatre teams, who have diverse ages, gender and ethnicities).

As with quantitative research, the purpose of sampling is to recruit sufficient numbers of participants to enable identification of patterns or richness in what they say or do to understand or explain the phenomenon of interest, and where collecting more data is unlikely to change this understanding.

In qualitative research, data collection and analysis often occur concurrently. This is because data collection is an iterative process both in recruitment and in questioning. The researchers may identify that more data are needed from a particular demographic group or on a particular theme to reach data saturation, so the next participants may be selected from a particular demographic, or be asked slightly different questions or probes to draw out that theme. Sample size is considered adequate when little or no new information emerges from interviews or focus groups; this is generally termed ‘data saturation’, although some qualitative researchers use the term ‘data sufficiency’. This could also be explained in terms of data reliability (i.e. the researcher is satisfied that collecting more data will not substantially change the results). Data saturation typically occurs with between 12 and 17 participants in a relatively homogeneous sampling, but larger numbers may be required, where the interviewees are from distinct groups or cultures. 16 , 17

Data management

For data sets that involve 10 or more transcripts or lengthy interviews (e.g. 90 min or more), researchers often use software to help them collate and manage the data. The most commonly used qualitative software packages are QSR NVivo, Atlas and Dedoose. 18 , 19 , 20 Many researchers use Microsoft Excel instead, or for small data sets the analysis can be done by hand, with pen, paper and scissors (i.e. researchers cut up printed transcripts and reorder the information according to code and theme). 21 NVivo and Atlas are simply repositories, in which you can input the transcripts and, using your coding scheme, sort the text into codes. They facilitate the task of analysis, rather than doing the analysis for you. Some advantages over coding by hand are that text can be allocated to more than one code, and you can easily identify the source of the segment of text you have coded.

Data analysis

Qualitative data analysis is ‘the classification and interpretation of linguistic (or visual) material to make statements about implicit and explicit dimensions and structures of meaning-making in the material and what is represented in it’. 22

Several social scientists have described this analytical process in depth. 2 , 6 , 22 , 23 , 24 , 25 For inductive studies, we recommend researchers follow Braun and Clarke's practical six-phase approach to thematic analysis. 26 The phases are (i) familiarising the researcher with the data, (ii) generating initial codes, (iii) searching for themes, (iv) reviewing themes, (v) defining and naming themes and (vi) producing the report. These six phases are described next.

Phase 1: familiarising the researcher with the data

In this step, the researchers read the transcripts to become familiar with them and take notes on potential recurring ideas or potential themes. They share and discuss their ideas and, in conjunction with any sensitising concepts, they start thinking about possible codes or themes.

Phase 2: generating initial codes

The first step in Phase 2 is ‘assigning some sort of short-hand designation to various aspects of your data so that you can easily retrieve specific pieces of the data’. 2 The designation might be a word or a short phrase that summarises or captures the essence of a particular piece of text. Coding makes it easier to summarise and compare, which is important because qualitative research is primarily about synthesis and comparison of data. 2 , 25 As the researcher reads through the data, they assign codes. If they are coding a transcript, they might highlight some words, for example, and attach to them a single word that summarises their meaning.

Researchers undertaking thematic analysis should iteratively develop a ‘coding scheme’, which is essentially a list of the codes they create as they read the data, and definitions for each code. 25 , 26 Code definitions are important, as they help the researcher make decisions on whether to assign this code or another one to a segment of data. In Table 1 , we have provided an example of text data in Column 1. TJ analysed these data. To do so, she asked, ‘What are these data about? How does it answer the research question? What is the essence of this statement?’ She underlined keywords and created codes and definitions (Columns 2 and 3). Then, TJ searched the remaining data to see if any more data met each code definition, and if so, coded that (see Table 1 ). As demonstrated in Table 1 , data can be coded to multiple codes.

Table 1

How to code qualitative data: an example

Research question To what extent do you think the surgical safety checklist (SSC) has changed teamwork culture in New Zealand operating theatres?
Data (The following quotes are excerpts from written responses to the above question that the authors CD and JW independently wrote, and TJ coded)	Potential code	Code definition
‘In New Zealand, we have spent a lot of time trying to build whole of with the SSC through a change in the way it is delivered and by introducing local auditors who observe SSC delivery and score it against a marking scale. I think this has had a big effect on the way the SSC is delivered’. (JW)  ‘We changed it so it was to lead different parts of the checklist, and got rid of the paper. This really helped’. (JW)  ‘SSC has significantly by encouraging all disciplines of the operating theatre team to speak up and of safety in the operating theatre’. (CD)	Team responsibility	Participant describes processes or behaviour that demonstrates the SSC promotes teamwork or is managed by the team (rather than by one person). This includes behavioural change.
‘It started out being a paper checklist that a nurse was tasked with signing off to certify that the SCC had been done. We changed it so it was , and got rid of the paper. This really helped’. (JW)	Embedding the checklist	Participant describes processes that have made use of the SSC routine.
‘I think that the SSC, along with our own approach to implementing it in New Zealand, and possibly , such as NetworkZ and OWR, is changing the culture in New Zealand operating theatres. I think it's a that's influencing the culture in the operating theatres to be more team oriented, more inclusive and less hierarchical’. (JW)	Other influences on cultural change	Participant describes influences other than the SSC on teamwork.
‘SSC has significantly improved teamwork culture by encouraging all disciplines of the operating theatre team to and take ownership of safety in the operating theatre’. (CD)  ‘In particular, nursing staff say that because of the in the SSC and because they are , they feel more part of the team’. (JW)  ‘The overall management of the patient also feels more like teamwork as from each discipline are so that one aspect of a patient care is from another’. (CD)	Communication	Participant describes how communication (as an element of teamwork) is influenced by SSC

In thematic analysis of interview data, we recommend that code definitions begin with something objective, such as ‘participant describes’. This keeps the researcher's focus on what participants said rather than what the researcher thought or said.

There is no set rule for how many codes to create. 25 However, in our experience, effective manageable coding schemes tend to have between 15 and 50 codes. The coding scheme is iterative. This means that the coding scheme is developed over time, with new codes being created as more data are coded. For example, after a close reading of the first transcript, the researcher might create, say, 10 codes that convey the key points. Then, the researcher reads and codes the next transcript and may, for instance, create additional four codes. As additional transcripts are read and coded, more codes may be created. Not all codes are relevant to all transcripts. The researcher will notice patterns as they code more transcripts. Some codes may be too broad and will need to be refined into two or three smaller codes (and vice versa ). Once the coding scheme is deemed complete and all transcripts have been coded, the researcher should go back to the beginning and recode the first few transcripts to ensure coding rigour.

The second step in Phase 2, once the coding is complete, is to collate all the data relevant to each of these codes.

Phase 3: searching for themes

In this phase, the researchers look across the codes to identify connections between them, with the intention of collating the codes into possible themes. Once these possible themes have been identified, all the data relevant to each possible theme are pulled together under that theme.

Phase 4: reviewing the themes

After the initial collation of the data into themes, the researchers undertake a rigorous process of checking the integrity of these themes, through reading and re-reading their data. This process includes checking to see if the themes ‘fit’ in relation to the coded excerpts (i.e. Do all the data collected under that theme fit within that theme?). Next is checking if the themes fit in relation to the whole data set (i.e. Do the themes adequately reflect the data?) This step may result in the search for additional themes. As a final step in this phase, the researchers create a thematic ‘map’ of the analysis.

When viewed together, the themes should answer the research question and should summarise participant experiences, views or behaviours.

Phase 5: naming the themes

Once researchers have checked the themes and included any additional emerging themes they name the final set of themes identified. Each theme and any subthemes should be listed in turn.

Phase 6: producing the report

The report should summarise the themes and illustrate them by choosing vivid or persuasive extracts from the data. For data arising from interviews, extracts will be quotes from participants. In some studies, researchers also report strong associations between themes, or divide a theme into sub-themes.

Tight word limits on many academic journals can make it difficult to include multiple quotes in the text. 27 One way around a word limit is to provide quotes in a table or a supplementary file, although quotes within the text tend to make for more interesting and compelling reading.

Who should analyse the data?

Ideally, each researcher in the team should be involved in the data analysis. Contrasting researcher viewpoints on the same study subject enhance data quality and validity, and minimise research bias. Independent analysis is time and resource intensive. In clinical research, close independent analysis by each member of the research team may be impractical, and one or two members may undertake the analysis while the rest of the research team read sections of data (e.g. reading two or three transcripts rather than closely analysing the whole data set), thus contributing to Phase 1 and Phase 2 of Braun and Clarke's method. 2

The research team should regularly meet to discuss the analytical process, as described earlier, to workshop and reach agreement on the coding and emergent themes (Phase 4 and Phase 5). The research team members compare their perspectives on the data, analyse divergences and coincidences and reach agreement on codes and emerging themes. Contrasting researcher viewpoints on the same study subject enhance data quality and validity, and minimise research bias.

Judging the quality and rigour of published studies involving thematic analysis

There are a number of indicators of quality when reading and appraising studies. 28 , 29 , 30 , 31 In essence, the authors should clearly state their method of analysis (e.g. thematic analysis) and should reference the literature relevant to their qualitative method, for example Braun and Clarke. 2 This is to indicate that they are following established steps in thematic analysis. The authors should include in the methods a description of the research team, their biases and experience and the efforts made to ensure analytical rigour. Verbatim quotes should be included in the findings to provide evidence to support the themes.

A number of guides have been published to assist readers, researchers and reviewers to evaluate the quality of a qualitative study. 30 , 31 The Joanna Briggs Institute guide to critical appraisal of qualitative studies is a good start. 30 This guide includes a set of 10 criteria, which can be used to rate the study. The criteria are summarised in Box 3 . Within these criteria lie rigorous methodological approaches to how data are collected, analysed and interpreted.

Ten quality appraisal criteria for qualitative literature.31

• (i) Alignment between the stated philosophical perspective and the research methodology
• (ii) Alignment between the research methodology and the research question or objectives
• (iii) Alignment between the research methodology and the methods used to collect data
• (iv) Alignment between the research methodology and the representation and analysis of data
• (v) Alignment between the research methodology and the interpretation of results
• (vi) A statement locating the researcher culturally or theoretically (positionality and bias)
• (vii) The influence of the researcher on the research, and vice versa
• (viii) Adequate representation of participants and their voices
• (ix) Ethical research conduct and evidence of ethical approval by an appropriate body
• (x) Conclusions flow from the analysis, or interpretation, of the data

Alt-text: Box 3

Another approach to quality appraisal comes from Lincoln and Guba, who have published widely on the topic of judging qualitative quality. 28 They look for quality in terms of credibility, transferability, dependability, confirmability and authenticity. There are many qualitative checklists readily accessible online, such as the Standards for Reporting Qualitative Research checklist or the Consolidated Criteria for Reporting Qualitative Research checklist, which researchers can include in their work to demonstrate quality in these areas.

Conclusions

As with quantitative research, qualitative research has requirements for rigour and trustworthiness. Thematic analysis is an accessible qualitative method that can offer researchers insight into the shared experiences, views and behaviours of research participants.

Declaration of interests

The authors declare that they have no conflicts of interest.

The associated MCQs (to support CME/CPD activity) will be accessible at www.bjaed.org/cme/home by subscribers to BJA Education .

Biographies

Tanisha Jowsey PhD BA (Hons) MA PhD is a senior lecturer in the Centre for Medical and Health Sciences Education, School of Medicine, University of Auckland. She has a background in medical anthropology and has expertise as a qualitative researcher.

Carolyn Deng MPH FANZCA is a specialist anaesthetist at Auckland City Hospital. She has a Master of Public Health degree. She is embarking on qualitative research in perioperative medicine and hopes to use it as a tool to complement quantitative research findings in the future.

Jennifer Weller MD MClinEd FANZCA FRCA is head of the Centre for Medical and Health Sciences Education at the University of Auckland. Professor Weller is a specialist anaesthetist at Auckland City Hospital and often uses qualitative methods in her research in clinical education, teamwork and patients' safety.

Matrix codes: 1A01, 2A01, 3A01

The Guide to Thematic Analysis

• What is Thematic Analysis?
• Advantages of Thematic Analysis
• Disadvantages of Thematic Analysis
• Thematic Analysis Examples
• How to Do Thematic Analysis
• Thematic Coding
• Collaborative Thematic Analysis
• Thematic Analysis Software
• Thematic Analysis in Mixed Methods Approach
• Abductive Thematic Analysis
• Deductive Thematic Analysis
• Inductive Thematic Analysis
• Reflexive Thematic Analysis
• Thematic Analysis in Observations
• Thematic Analysis in Surveys
• Thematic Analysis for Interviews
• Thematic Analysis for Focus Groups
• Thematic Analysis for Case Studies
• Thematic Analysis of Secondary Data
• Thematic Analysis Literature Review
• Thematic Analysis vs. Phenomenology
• Thematic vs. Content Analysis
• Thematic Analysis vs. Grounded Theory
• Introduction

What is the difference between thematic and narrative analysis?

Conducting a thematic analysis on narratives.

• Thematic Analysis vs. Discourse Analysis
• Thematic Analysis vs. Framework Analysis
• Thematic Analysis in Social Work
• Thematic Analysis in Psychology
• Thematic Analysis in Educational Research
• Thematic Analysis in UX Research
• How to Present Thematic Analysis Results
• Increasing Rigor in Thematic Analysis
• Peer Review in Thematic Analysis

Thematic Analysis vs. Narrative Analysis

Understanding the nuances of qualitative analysis is crucial for researchers who aim to extract meaningful insights from narrative data. Thematic analysis and narrative analysis are two distinct methodologies in qualitative research that serve different research purposes. While thematic analysis focuses on identifying and interpreting patterns within data, narrative analysis provides a framework for understanding individual experiences and stories. This article aims to clarify the distinctions between these methods and guide researchers on how to effectively apply thematic analysis to narrative materials. By emphasizing a clear, practical approach, we aim to assist researchers in choosing the appropriate analysis technique for their data and research objectives, ensuring that their findings are robust and insightful.

Understanding the distinctions between thematic analysis, narrative analysis , and other analytical approaches is pivotal for researchers deciding on the most appropriate method for their qualitative study. While both methodologies are employed to interpret data from textual materials, they focus on different aspects of the data and serve varied research objectives. Below, we look into the specifics of each approach to highlight their unique features and applications.

Thematic analysis

Thematic analysis is a versatile method for identifying, analyzing, and reporting patterns (themes) within data. It is not tied to any specific philosophical framework, making it applicable across a range of qualitative research approaches. The process involves six key stages: familiarization with the data, generating initial codes , searching for themes, reviewing themes, defining and naming themes, and producing the report. This method is particularly effective when the research objective is to elucidate the broader patterns across a data set, allowing for a deep dive into the shared experiences or perceptions of research participants. It emphasizes the occurrence of and connections between concepts, rather than the context or form of data presentation. Researchers can use thematic analysis to aggregate data from various narratives, highlighting overarching insights that transcend individual stories.

Narrative analysis

Narrative analysis places a premium on understanding stories or accounts, focusing on how individuals construct and interpret their experiences. This approach is inherently interpretative, aiming to comprehend how a narrative is structured and the meanings it conveys, often within a particular cultural, social, or personal context. Narrative analysis examines the elements of storytelling, including plot, characters, and narrative arc, to understand how individuals make sense of their experiences and identities. It is particularly suited to research questions that are concerned with subjective experiences and the ways people use stories to organize and communicate their experiences. While thematic analysis might dissect a narrative to extract themes, narrative analysis retains the story's integrity, exploring its composition, sequence, and the interplay of different narrative elements. This approach provides a nuanced understanding of how narratives function as a medium for individual and collective meaning-making.

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Conducting a thematic analysis on narratives involves a systematic process of reading, coding, and interpreting data to identify recurring themes that provide insight into the underlying meaning of the narratives. This method allows researchers to organize and describe their dataset in rich detail and interpret various aspects of the research topic.

The first step in conducting a thematic analysis on narratives is to familiarize yourself with the data. This means thoroughly reading the narrative materials, perhaps multiple times, to gain a deep understanding of the content. It is crucial to immerse yourself in the data to appreciate the context and complexities of the narratives.

Next, generating initial codes from the narratives is a foundational step. This involves annotating the text with labels that summarize key points or ideas. These codes should be as close to the data as possible. At this stage, it's beneficial to be descriptive rather than interpretative, ensuring that the codes are rooted in the narratives themselves.

After coding, the next step is to search for themes. This involves collating all the codes and relevant data extracts and organizing them into potential themes. Themes are broader patterns of meaning that emerge from the coded data and represent something significant about the narratives in relation to the research question . It is essential to review these themes in the context of the coded extracts and the entire data set to ensure they form a coherent pattern.

The next phase is reviewing and refining the themes. This step may involve merging, subdividing, or discarding themes that do not have a clear narrative basis or do not contribute to the understanding of the data. The aim is to end up with a set of distinct, coherent, and meaningful themes.

Defining and naming themes is the subsequent step, which requires a detailed analysis of each theme and the overall narrative. Researchers should aim to define what each theme captures about the data and consider how it helps to answer the research question. This step often involves a detailed write-up that elaborates on each theme, supported by data extracts that provide examples of how the theme is manifested in the narratives.

The final step in conducting thematic analysis on narratives is to contextualize the themes within the broader research findings, integrating them into existing literature and theoretical frameworks . This involves interpreting the significance of the themes, considering how they contribute to understanding the research topic, and discussing their implications for theory, policy, or practice.

Throughout the thematic analysis process, it is crucial to maintain a reflexive stance, continually questioning your assumptions and interpretations and considering how they may be influenced by your own preconceptions and perspectives. This ensures the rigor and credibility of the analysis, providing valuable insights into the narratives being studied.

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Tracking Abortion Bans Across the Country

By The New York Times Updated May 1, 4:40 P.M. ET

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Twenty-one states ban abortion or restrict the procedure earlier in pregnancy than the standard set by Roe v. Wade, which governed reproductive rights for nearly half a century until the Supreme Court overturned the decision in 2022.

In some states, the fight over abortion access is still taking place in courtrooms, where advocates have sued to block bans and restrictions. Other states have moved to expand access to abortion by adding legal protections.

Latest updates

• The Arizona state legislature voted to repeal an 1864 ban on nearly all abortions. Officials warned that the near-total ban may be briefly enforceable this summer until the repeal takes effect in the fall. A 15-week ban remains in effect.
• A ban on abortion after about six weeks of pregnancy took effect in Florida , following a ruling by the Florida Supreme Court that the privacy protections of the state’s Constitution do not extend to abortion.

The New York Times is tracking abortion laws in each state after the Supreme Court’s decision in Dobbs v. Jackson Women’s Health Organization , which ended the constitutional right to an abortion.

Where abortion is legal

In a few states that have enacted bans or restrictions, abortion remains legal for now as courts determine whether these laws can take effect. Abortion is legal in the rest of the country, and many states have added new protections since Dobbs.

Ban in effect

Note: TK note here.

Legal for now

State details.

More details on the current status of abortion in each state are below.

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An earlier version of this article misstated the legal status of abortion in Utah. As of 4 p.m. on June 24, the state attorney general had issued a statement saying the state’s abortion ban had been triggered, but it had not yet been authorized by the legislature’s general counsel. By 8:30 p.m., the counsel authorized the ban and it went into effect.

A table in an earlier version of this article misstated which abortion ban is being challenged in Texas state court. Abortion rights supporters are challenging a pre-Roe ban, not the state’s trigger ban.

An earlier version of this article referred incorrectly to the legal status of abortion in Indiana. While Indiana abortion providers stopped offering abortion services in anticipation of an abortion ban taking effect on Aug. 1, the law did not take effect.