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

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

There’s the distinction between inductive and deductive approaches:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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Thematic Analysis: A Step by Step Guide

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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What is Thematic Analysis?

Thematic analysis is a qualitative research method used to identify, analyze, and interpret patterns of shared meaning (themes) within a given data set, which can be in the form of interviews , focus group discussions , surveys, or other textual data.

Thematic analysis is a useful method for research seeking to understand people’s views, opinions, knowledge, experiences, or values from qualitative data.

This method is widely used in various fields, including psychology, sociology, and health sciences.

Thematic analysis minimally organizes and describes a data set in rich detail. Often, though, it goes further than this and interprets aspects of the research topic.

Key aspects of Thematic Analysis include:

• Flexibility : It can be adapted to suit the needs of various studies, providing a rich and detailed account of the data.
• Coding : The process involves assigning labels or codes to specific segments of the data that capture a single idea or concept relevant to the research question.
• Themes : Representing a broader level of analysis, encompassing multiple codes that share a common underlying meaning or pattern. They provide a more abstract and interpretive understanding of the data.
• Iterative process : Thematic analysis is a recursive process that involves constantly moving back and forth between the coded extracts, the entire data set, and the thematic analysis being produced.
• Interpretation : The researcher interprets the identified themes to make sense of the data and draw meaningful conclusions.

It’s important to note that the types of thematic analysis are not mutually exclusive, and researchers may adopt elements from different approaches depending on their research questions, goals, and epistemological stance.

The choice of approach should be guided by the research aims, the nature of the data, and the philosophical assumptions underpinning the study.

1. Coding Reliability Thematic Analysis

Coding reliability TA emphasizes using coding techniques to achieve reliable and accurate data coding, which reflects (post)positivist research values.

This approach emphasizes the reliability and replicability of the coding process. It involves multiple coders independently coding the data using a predetermined codebook.

The goal is to achieve a high level of agreement among the coders, which is often measured using inter-rater reliability metrics.

This approach often involves a coding frame or codebook determined in advance or generated after familiarization with the data.

In this type of TA, two or more researchers apply a fixed coding frame to the data, ideally working separately.

Some researchers even suggest that at least some coders should be unaware of the research question or area of study to prevent bias in the coding process.

Statistical tests are used to assess the level of agreement between coders, or the reliability of coding. Any differences in coding between researchers are resolved through consensus.

This approach is more suitable for research questions that require a more structured and reliable coding process, such as in content analysis or when comparing themes across different data sets.

2. Codebook Thematic Analysis

Codebook TA, such as template, framework, and matrix analysis, combines elements of coding reliability and reflexive.

Codebook TA, while employing structured coding methods like those used in coding reliability TA, generally prioritizes qualitative research values, such as reflexivity.

In this approach, the researcher develops a codebook based on their initial engagement with the data. The codebook contains a list of codes, their definitions, and examples from the data.

The codebook is then used to systematically code the entire data set. This approach allows for a more detailed and nuanced analysis of the data, as the codebook can be refined and expanded throughout the coding process.

It is particularly useful when the research aims to provide a comprehensive description of the data set.

Codebook TA is often chosen for pragmatic reasons in applied research, particularly when there are predetermined information needs, strict deadlines, and large teams with varying levels of qualitative research experience

The use of a codebook in this context helps to map the developing analysis, which is thought to improve teamwork, efficiency, and the speed of output delivery.

3. Reflexive Thematic Analysis

This approach emphasizes the role of the researcher in the analysis process. It acknowledges that the researcher’s subjectivity, theoretical assumptions, and interpretative framework shape the identification and interpretation of themes.

In reflexive TA, analysis starts with coding after data familiarization. Unlike other TA approaches, there is no codebook or coding frame. Instead, researchers develop codes as they work through the data.

As their understanding grows, codes can change to reflect new insights—for example, they might be renamed, combined with other codes, split into multiple codes, or have their boundaries redrawn.

If multiple researchers are involved, differences in coding are explored to enhance understanding, not to reach a consensus. The finalized coding is always open to new insights and coding.

Reflexive thematic analysis involves a more organic and iterative process of coding and theme development. The researcher continuously reflects on their role in the research process and how their own experiences and perspectives might influence the analysis.

This approach is particularly useful for exploratory research questions and when the researcher aims to provide a rich and nuanced interpretation of the data.

Six Steps Of Thematic Analysis

The process is characterized by a recursive movement between the different phases, rather than a strict linear progression.

This means that researchers might revisit earlier phases as their understanding of the data evolves, constantly refining their analysis.

For instance, during the reviewing and developing themes phase, researchers may realize that their initial codes don’t effectively capture the nuances of the data and might need to return to the coding phase. 

This back-and-forth movement continues throughout the analysis, ensuring a thorough and evolving understanding of the data

Step 1: Familiarization With the Data

Familialization is crucial, as it helps researchers figure out the type (and number) of themes that might emerge from the data.

Familiarization involves immersing yourself in the data by reading and rereading textual data items, such as interview transcripts or survey responses.

You should read through the entire data set at least once, and possibly multiple times, until you feel intimately familiar with its content.

• Read and re-read the data (e.g., interview transcripts, survey responses, or other textual data) : The researcher reads through the entire data set (e.g., interview transcripts, survey responses, or field notes) multiple times to gain a comprehensive understanding of the data’s breadth and depth. This helps the researcher develop a holistic sense of the participants’ experiences, perspectives, and the overall narrative of the data.
• Listen to the audio recordings of the interviews : This helps to pick up on tone, emphasis, and emotional responses that may not be evident in the written transcripts. For instance, they might note a participant’s hesitation or excitement when discussing a particular topic. This is an important step if you didn’t collect the data or transcribe it yourself.
• Take notes on initial ideas and observations : Note-making at this stage should be observational and casual, not systematic and inclusive, as you aren’t coding yet. Think of the notes as memory aids and triggers for later coding and analysis. They are primarily for you, although they might be shared with research team members.
• Immerse yourself in the data to gain a deep understanding of its content : It’s not about just absorbing surface meaning like you would with a novel, but about thinking about what the data  mean .

By the end of the familiarization step, the researcher should have a good grasp of the overall content of the data, the key issues and experiences discussed by the participants, and any initial patterns or themes that emerge.

This deep engagement with the data sets the stage for the subsequent steps of thematic analysis, where the researcher will systematically code and analyze the data to identify and interpret the central themes.

Step 2: Generating Initial Codes

Codes are concise labels or descriptions assigned to segments of the data that capture a specific feature or meaning relevant to the research question.

The process of qualitative coding helps the researcher organize and reduce the data into manageable chunks, making it easier to identify patterns and themes relevant to the research question.

Think of it this way:  If your analysis is a house, themes are the walls and roof, while codes are the individual bricks and tiles.

Coding is an iterative process, with researchers refining and revising their codes as their understanding of the data evolves.

The ultimate goal is to develop a coherent and meaningful coding scheme that captures the richness and complexity of the participants’ experiences and helps answer the research questions.

Coding can be done manually (paper transcription and pen or highlighter) or by means of software (e.g. by using NVivo, MAXQDA or ATLAS.ti).

Decide On Your Coding Approach

• Will you use predefined deductive codes (based on theory or prior research), or let codes emerge from the data (inductive coding)?
• Will a piece of data have one code or multiple?
• Will you code everything or selectively? Broader research questions may warrant coding more comprehensively.

If you decide not to code everything, it’s crucial to:

• Have clear criteria for what you will and won’t code
• Be transparent about your selection process in research reports
• Remain open to revisiting uncoded data later in analysis

Do A First Round Of Coding

• Go through the data and assign initial codes to chunks that stand out
• Create a code name (a word or short phrase) that captures the essence of each chunk
• Keep a codebook – a list of your codes with descriptions or definitions
• Be open to adding, revising or combining codes as you go

After generating your first code, compare each new data extract to see if an existing code applies or a new one is needed.

Coding can be done at two levels of meaning:

• Semantic:  Provides a concise summary of a portion of data, staying close to the content and the participant’s meaning. For example, “Fear/anxiety about people’s reactions to his sexuality.”
• Latent:  Goes beyond the participant’s meaning to provide a conceptual interpretation of the data. For example, “Coming out imperative” interprets the meaning behind a participant’s statement.

Most codes will be a mix of descriptive and conceptual. Novice coders tend to generate more descriptive codes initially, developing more conceptual approaches with experience.

This step ends when:

• All data is fully coded.
• Data relevant to each code has been collated.

You have enough codes to capture the data’s diversity and patterns of meaning, with most codes appearing across multiple data items.

The number of codes you generate will depend on your topic, data set, and coding precision.

Step 3: Searching for Themes

Searching for themes begins after all data has been initially coded and collated, resulting in a comprehensive list of codes identified across the data set.

This step involves shifting from the specific, granular codes to a broader, more conceptual level of analysis.

Thematic analysis is not about “discovering” themes that already exist in the data, but rather actively constructing or generating themes through a careful and iterative process of examination and interpretation.

1 . Collating codes into potential themes :

The process of collating codes into potential themes involves grouping codes that share a unifying feature or represent a coherent and meaningful pattern in the data.

The researcher looks for patterns, similarities, and connections among the codes to develop overarching themes that capture the essence of the data.

By the end of this step, the researcher will have a collection of candidate themes and sub-themes, along with their associated data extracts.

However, these themes are still provisional and will be refined in the next step of reviewing the themes.

The searching for themes step helps the researcher move from a granular, code-level analysis to a more conceptual, theme-level understanding of the data.

This process is similar to sculpting, where the researcher shapes the “raw” data into a meaningful analysis.

This involves grouping codes that share a unifying feature or represent a coherent pattern in the data:

• Review the list of initial codes and their associated data extracts
• Look for codes that seem to share a common idea or concept
• Group related codes together to form potential themes
• Some codes may form main themes, while others may be sub-themes or may not fit into any theme

Thematic maps can help visualize the relationship between codes and themes. These visual aids provide a structured representation of the emerging patterns and connections within the data, aiding in understanding the significance of each theme and its contribution to the overall research question.

Example : Studying first-generation college students, the researcher might notice that the codes “financial challenges,” “working part-time,” and “scholarships” all relate to the broader theme of “Financial Obstacles and Support.”

Shared Meaning vs. Shared Topic in Thematic Analysis

Braun and Clarke distinguish between two different conceptualizations of  themes : topic summaries and shared meaning

• Topic summary themes , which they consider to be underdeveloped, are organized around a shared topic but not a shared meaning, and often resemble “buckets” into which data is sorted.
• Shared meaning themes  are patterns of shared meaning underpinned by a central organizing concept.

When grouping codes into themes, it’s crucial to ensure they share a central organizing concept or idea, reflecting a shared meaning rather than just belonging to the same topic.

Thematic analysis aims to uncover patterns of shared meaning within the data that offer insights into the research question

For example, codes centered around the concept of “Negotiating Sexual Identity” might not form one comprehensive theme, but rather two distinct themes: one related to “coming out and being out” and another exploring “different versions of being a gay man.”

Avoid : Themes as Topic Summaries (Shared Topic)

In this approach, themes simply summarize what participants mentioned about a particular topic, without necessarily revealing a unified meaning.

These themes are often underdeveloped and lack a central organizing concept.

It’s crucial to avoid creating themes that are merely summaries of data domains or directly reflect the interview questions. 

Example : A theme titled “Incidents of homophobia” that merely describes various participant responses about homophobia without delving into deeper interpretations would be a topic summary theme.

Tip : Using interview questions as theme titles without further interpretation or relying on generic social functions (“social conflict”) or structural elements (“economics”) as themes often indicates a lack of shared meaning and thorough theme development. Such themes might lack a clear connection to the specific dataset

Ensure : Themes as Shared Meaning

Instead, themes should represent a deeper level of interpretation, capturing the essence of the data and providing meaningful insights into the research question.

These themes go beyond summarizing a topic by identifying a central concept or idea that connects the codes.

They reflect a pattern of shared meaning across different data points, even if those points come from different topics.

Example : The theme “‘There’s always that level of uncertainty’: Compulsory heterosexuality at university” effectively captures the shared experience of fear and uncertainty among LGBT students, connecting various codes related to homophobia and its impact on their lives.

2. Gathering data relevant to each potential theme

Once a potential theme is identified, all coded data extracts associated with the codes grouped under that theme are collated. This ensures a comprehensive view of the data pertaining to each theme.

This involves reviewing the collated data extracts for each code and organizing them under the relevant themes.

For example, if you have a potential theme called “Student Strategies for Test Preparation,” you would gather all data extracts that have been coded with related codes, such as “Time Management for Test Preparation” or “Study Groups for Test Preparation”.

You can then begin reviewing the data extracts for each theme to see if they form a coherent pattern. 

This step helps to ensure that your themes accurately reflect the data and are not based on your own preconceptions.

It’s important to remember that coding is an organic and ongoing process.

You may need to re-read your entire data set to see if you have missed any data that is relevant to your themes, or if you need to create any new codes or themes.

The researcher should ensure that the data extracts within each theme are coherent and meaningful.

Example : The researcher would gather all the data extracts related to “Financial Obstacles and Support,” such as quotes about struggling to pay for tuition, working long hours, or receiving scholarships.

Here’s a more detailed explanation of how to gather data relevant to each potential theme:

• Start by creating a visual representation of your potential themes, such as a thematic map or table
• List each potential theme and its associated sub-themes (if any)
• This will help you organize your data and see the relationships between themes
• Go through your coded data extracts (e.g., highlighted quotes or segments from interview transcripts)
• For each coded extract, consider which theme or sub-theme it best fits under
• If a coded extract seems to fit under multiple themes, choose the theme that it most closely aligns with in terms of shared meaning
• As you identify which theme each coded extract belongs to, copy and paste the extract under the relevant theme in your thematic map or table
• Include enough context around each extract to ensure its meaning is clear
• If using qualitative data analysis software, you can assign the coded extracts to the relevant themes within the software
• As you gather data extracts under each theme, continuously review the extracts to ensure they form a coherent pattern
• If some extracts do not fit well with the rest of the data in a theme, consider whether they might better fit under a different theme or if the theme needs to be refined

3. Considering relationships between codes, themes, and different levels of themes

Once you have gathered all the relevant data extracts under each theme, review the themes to ensure they are meaningful and distinct.

This step involves analyzing how different codes combine to form overarching themes and exploring the hierarchical relationship between themes and sub-themes.

Within a theme, there can be different levels of themes, often organized hierarchically as main themes and sub-themes.

• Main themes  represent the most overarching or significant patterns found in the data. They provide a high-level understanding of the key issues or concepts present in the data. 
• Sub-themes , as the name suggests, fall under main themes, offering a more nuanced and detailed understanding of a particular aspect of the main theme.

The process of developing these relationships is iterative and involves:

• Creating a Thematic Map : The relationship between codes, sub-themes and main themes can be visualized using a thematic map, diagram, or table. Refine the thematic map as you continue to review and analyze the data.
• Examine how the codes and themes relate to each other : Some themes may be more prominent or overarching (main themes), while others may be secondary or subsidiary (sub-themes).
• Refining Themes : This map helps researchers review and refine themes, ensuring they are internally consistent (homogeneous) and distinct from other themes (heterogeneous).
• Defining and Naming Themes : Finally, themes are given clear and concise names and definitions that accurately reflect the meaning they represent in the data.

Consider how the themes tell a coherent story about the data and address the research question.

If some themes seem to overlap or are not well-supported by the data, consider combining or refining them.

If a theme is too broad or diverse, consider splitting it into separate themes or sub-theme.

Example : The researcher might identify “Academic Challenges” and “Social Adjustment” as other main themes, with sub-themes like “Imposter Syndrome” and “Balancing Work and School” under “Academic Challenges.” They would then consider how these themes relate to each other and contribute to the overall understanding of first-generation college students’ experiences.

Step 4: Reviewing Themes

The researcher reviews, modifies, and develops the preliminary themes identified in the previous step.

This phase involves a recursive process of checking the themes against the coded data extracts and the entire data set to ensure they accurately reflect the meanings evident in the data.

The purpose is to refine the themes, ensuring they are coherent, consistent, and distinctive.

According to Braun and Clarke, a well-developed 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”.

A well-developed theme will:

• Go beyond paraphrasing the data to analyze the meaning and significance of the patterns identified.
• Provide a detailed analysis of what the theme is about.
• Be supported with a good amount of relevant data extracts.
• Be related to the research question.

Revisions at this stage might involve creating new themes, refining existing themes, or discarding themes that do not fit the data

Level One : Reviewing Themes Against Coded Data Extracts

• Researchers begin by comparing their candidate themes against the coded data extracts associated with each theme.
• This step helps to determine whether each theme is supported by the data and whether it accurately reflects the meaning found in the extracts. Determine if there is enough data to support each theme.
• Look at the relationships between themes and sub-themes in the thematic map. Consider whether the themes work together to tell a coherent story about the data. If the thematic map does not effectively represent the data, consider making adjustments to the themes or their organization.
• It’s important to ensure that each theme has a singular focus and is not trying to encompass too much. Themes should be distinct from one another, although they may build on or relate to each other.
• Discarding codes : If certain codes within a theme are not well-supported or do not fit, they can be removed.
• Relocating codes : Codes that fit better under a different theme can be moved.
• Redrawing theme boundaries : The scope of a theme can be adjusted to better capture the relevant data.
• Discarding themes : Entire themes can be abandoned if they do not work.

Level Two : Evaluating Themes Against the Entire Data Set

• Once the themes appear coherent and well-supported by the coded extracts, researchers move on to evaluate them against the entire data set.
• This involves a final review of all the data to ensure that the themes accurately capture the most important and relevant patterns across the entire dataset in relation to the research question.
• During this level, researchers may need to recode some extracts for consistency, especially if the coding process evolved significantly, and earlier data items were not recoded according to these changes.

Step 5: Defining and Naming Themes

The themes are finalized when the researcher is satisfied with the theme names and definitions.

If the analysis is carried out by a single researcher, it is recommended to seek feedback from an external expert to confirm that the themes are well-developed, clear, distinct, and capture all the relevant data.

Defining themes  means determining the exact meaning of each theme and understanding how it contributes to understanding the data.

This process involves formulating exactly what we mean by each theme. The researcher should consider what a theme says, if there are subthemes, how they interact and relate to the main theme, and how the themes relate to each other.

Themes should not be overly broad or try to encompass too much, and should have a singular focus. They should be distinct from one another and not repetitive, although they may build on one another.

In this phase the researcher specifies the essence of each theme.

• What does the theme tell us that is relevant for the research question?
• How does it fit into the ‘overall story’ the researcher wants to tell about the data?

Naming themes  involves developing a clear and concise name that effectively conveys the essence of each theme to the reader. A good name for a theme is informative, concise, and catchy.

• The researcher develops concise, punchy, and informative names for each theme that effectively communicate its essence to the reader.
• Theme names should be catchy and evocative, giving the reader an immediate sense of what the theme is about.
• Avoid using jargon or overly complex language in theme names.
• The name should go beyond simply paraphrasing the content of the data extracts and instead interpret the meaning and significance of the patterns within the theme.
• The goal is to make the themes accessible and easily understandable to the intended audience. If a theme contains sub-themes, the researcher should also develop clear and informative names for each sub-theme.
• Theme names can include direct quotations from the data, which helps convey the theme’s meaning. However, researchers should avoid using data collection questions as theme names. Using data collection questions as themes often leads to analyses that present summaries of topics rather than fully realized themes.

For example, “‘There’s always that level of uncertainty’: Compulsory heterosexuality at university” is a strong theme name because it captures the theme’s meaning. In contrast, “incidents of homophobia” is a weak theme name because it only states the topic.

For instance, a theme labeled “distrust of experts” might be renamed “distrust of authority” or “conspiracy thinking” after careful consideration of the theme’s meaning and scope.

Step 6: Producing the Report

A thematic analysis report should provide a convincing and clear, yet complex story about the data that is situated within a scholarly field.

A balance should be struck between the narrative and the data presented, ensuring that the report convincingly explains the meaning of the data, not just summarizes it.

To achieve this, the report should include vivid, compelling data extracts illustrating the themes and incorporate extracts from different data sources to demonstrate the themes’ prevalence and strengthen the analysis by representing various perspectives within the data.

The report should be written in first-person active tense, unless otherwise stated in the reporting requirements.

The analysis can be presented in two ways :

• Integrated Results and Discussion section:  This approach is suitable when the analysis has strong connections to existing research and when the analysis is more theoretical or interpretive.
• Separate Discussion section:  This approach presents the data interpretation separately from the results.

Regardless of the presentation style, researchers should aim to “show” what the data reveals and “tell” the reader what it means in order to create a convincing analysis.

• Presentation order of themes: Consider how to best structure the presentation of the themes in the report. This may involve presenting the themes in order of importance, chronologically, or in a way that tells a coherent story.
• Subheadings: Use subheadings to clearly delineate each theme and its sub-themes, making the report easy to navigate and understand.

The analysis should go beyond a simple summary of participant’s words and instead interpret the meaning of the data.

Themes should connect logically and meaningfully and, if relevant, should build on previous themes to tell a coherent story about the data.

The report should include vivid, compelling data extracts that clearly illustrate the theme being discussed and should incorporate extracts from different data sources, rather than relying on a single source.

Although it is tempting to rely on one source when it eloquently expresses a particular aspect of the theme, using multiple sources strengthens the analysis by representing a wider range of perspectives within the data.

Researchers should strive to maintain a balance between the amount of narrative and the amount of data presented.

Potential Pitfalls to Avoid

• Failing to analyze the data : Thematic analysis should involve more than simply presenting data extracts without an analytic narrative. The researcher must provide an interpretation and make sense of the data, telling the reader what it means and how it relates to the research questions.
• Using data collection questions as themes : Themes should be identified across the entire dataset, not just based on the questions asked during data collection. Reporting data collection questions as themes indicates a lack of thorough analytic work to identify patterns and meanings in the data.
• Conducting a weak or unconvincing analysis : Themes should be distinct, internally coherent, and consistent, capturing the majority of the data or providing a rich description of specific aspects. A weak analysis may have overlapping themes, fail to capture the data adequately, or lack sufficient examples to support the claims made.
• Mismatch between data and analytic claims : The researcher’s interpretations and analytic points must be consistent with the data extracts presented. Claims that are not supported by the data, contradict the data, or fail to consider alternative readings or variations in the account are problematic.
• Misalignment between theory, research questions, and analysis : The interpretations of the data should be consistent with the theoretical framework used. For example, an experiential framework would not typically make claims about the social construction of the topic. The form of thematic analysis used should also align with the research questions.
• Neglecting to clarify assumptions, purpose, and process : A good thematic analysis should spell out its theoretical assumptions, clarify how it was undertaken, and for what purpose. Without this crucial information, the analysis is lacking context and transparency, making it difficult for readers to evaluate the research.

Reducing Bias

When researchers are both reflexive and transparent in their thematic analysis, it strengthens the trustworthiness and rigor of their findings.

The explicit acknowledgement of potential biases and the detailed documentation of the analytical process provide a stronger foundation for the interpretation of the data, making it more likely that the findings reflect the perspectives of the participants rather than the biases of the researcher.

Reflexivity

Reflexivity involves critically examining one’s own assumptions and biases, is crucial in qualitative research to ensure the trustworthiness of findings.

It requires acknowledging that researcher subjectivity is inherent in the research process and can influence how data is collected, analyzed, and interpreted.

Identifying and Challenging Assumptions:

Reflexivity encourages researchers to explicitly acknowledge their preconceived notions, theoretical leanings, and potential biases.

By actively reflecting on how these factors might influence their interpretation of the data, researchers can take steps to mitigate their impact.

This might involve seeking alternative explanations, considering contradictory evidence, or discussing their interpretations with others to gain different perspectives.

Transparency

Transparency refers to clearly documenting the research process, including coding decisions, theme development, and the rationale behind behind theme development.

This openness allows others to understand how the analysis was conducted and to assess the credibility of the findings

This transparency helps ensure the trustworthiness and rigor of the findings, allowing others to understand and potentially replicate the analysis.

Documenting Decision-Making:

Transparency requires researchers to provide a clear and detailed account of their analytical choices throughout the research process.

This includes documenting the rationale behind coding decisions, the process of theme development, and any changes made to the analytical approach during the study.

By making these decisions transparent, researchers allow others to scrutinize their work and assess the potential for bias.

Practical Strategies for Reflexivity and Transparency in Thematic Analysis:

• Maintaining a reflexive journal:  Researchers can keep a journal throughout the research process to document their thoughts, assumptions, and potential biases. This journal serves as a record of the researcher’s evolving understanding of the data and can help identify potential blind spots in their analysis.
• Engaging in team-based analysis:  Collaborative analysis, involving multiple researchers, can enhance reflexivity by providing different perspectives and interpretations of the data. Discussing coding decisions and theme development as a team allows researchers to challenge each other’s assumptions and ensure a more comprehensive analysis.
• Clearly articulating the analytical process:  In reporting the findings of thematic analysis, researchers should provide a detailed account of their methods, including the rationale behind coding decisions, the process of theme development, and any challenges encountered during analysis. This transparency allows readers to understand the steps taken to ensure the rigor and trustworthiness of the analysis.
• Flexibility:  Thematic analysis is a flexible method, making it adaptable to different research questions and theoretical frameworks. It can be employed with various epistemological approaches, including realist, constructionist, and contextualist perspectives. For example, researchers can focus on analyzing meaning across the entire data set or examine a particular aspect in depth.
• Accessibility:  Thematic analysis is an accessible method, especially for novice qualitative researchers, as it doesn’t demand extensive theoretical or technical knowledge compared to methods like Discourse Analysis (DA) or Conversation Analysis (CA). It is considered a foundational qualitative analysis method.
• Rich Description:  Thematic analysis facilitates a rich and detailed description of data9. It can provide a thorough understanding of the predominant themes in a data set, offering valuable insights, particularly in under-researched areas.
• Theoretical Freedom:  Thematic analysis is not restricted to any pre-existing theoretical framework, allowing for diverse applications. This distinguishes it from methods like Grounded Theory or Interpretative Phenomenological Analysis (IPA), which are more closely tied to specific theoretical approaches

Disadvantages

• Subjectivity and Interpretation:  The flexibility of thematic analysis, while an advantage, can also be a disadvantage. The method’s openness can lead to a wide range of interpretations of the same data set, making it difficult to determine which aspects to emphasize. This potential subjectivity might raise concerns about the analysis’s reliability and consistency.
• Limited Interpretive Power:  Unlike methods like narrative analysis or biographical approaches, thematic analysis may not capture the nuances of individual experiences or contradictions within a single account. The focus on patterns across interviews could result in overlooking unique individual perspectives.
• Oversimplification:  Thematic analysis might oversimplify complex phenomena by focusing on common themes, potentially missing subtle but important variations within the data. If not carefully executed, the analysis may present a homogenous view of the data that doesn’t reflect the full range of perspectives.
• Lack of Established Theoretical Frameworks:  Thematic analysis does not inherently rely on pre-existing theoretical frameworks. While this allows for inductive exploration, it can also limit the interpretive power of the analysis if not anchored within a relevant theoretical context. The absence of a theoretical foundation might make it challenging to draw meaningful and generalizable conclusions.
• Difficulty in Higher-Phase Analysis:  While thematic analysis is relatively easy to initiate, the flexibility in its application can make it difficult to establish specific guidelines for higher-phase analysis1. Researchers may find it challenging to navigate the later stages of analysis and develop a coherent and insightful interpretation of the identified themes.
• Potential for Researcher Bias:  As with any qualitative research method, thematic analysis is susceptible to researcher bias. Researchers’ preconceived notions and assumptions can influence how they code and interpret data, potentially leading to skewed results.

Further Information

• Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology, 3 (2), 77–101.
• Braun, V., & Clarke, V. (2013). Successful qualitative research: A practical guide for beginners. Sage.
• Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysi s. Qualitative Research in Sport, Exercise and Health, 11 (4), 589–597.
• Braun, V., & Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18 (3), 328–352.
• Braun, V., & Clarke, V. (2021). To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales . Qualitative Research in Sport, Exercise and Health, 13 (2), 201–216.
• Braun, V., & Clarke, V. (2022). Conceptual and design thinking for thematic analysis .  Qualitative psychology ,  9 (1), 3.
• Braun, V., & Clarke, V. (2022b). Thematic analysis: A practical guide . Sage.
• Braun, V., Clarke, V., & Hayfield, N. (2022). ‘A starting point for your journey, not a map’: Nikki Hayfield in conversation with Virginia Braun and Victoria Clarke about thematic analysis.  Qualitative research in psychology ,  19 (2), 424-445.
• Finlay, L., & Gough, B. (Eds.). (2003). Reflexivity: A practical guide for researchers in health and social sciences. Blackwell Science.
• Gibbs, G. R. (2013). Using software in qualitative analysis. In U. Flick (ed.) The Sage handbook of qualitative data analysis (pp. 277–294). London: Sage.
• McLeod, S. (2024, May 17). Qualitative Data Coding . Simply Psychology. https://www.simplypsychology.org/qualitative-data-coding.html
• Terry, G., & Hayfield, N. (2021). Essentials of thematic analysis . American Psychological Association.

Example TA Studies

• Braun, V., Terry, G., Gavey, N., & Fenaughty, J. (2009). ‘ Risk’and sexual coercion among gay and bisexual men in Aotearoa/New Zealand–key informant accounts .  Culture, Health & Sexuality ,  11 (2), 111-124.
• Clarke, V., & Kitzinger, C. (2004). Lesbian and gay parents on talk shows: resistance or collusion in heterosexism? .  Qualitative Research in Psychology ,  1 (3), 195-217.

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 ...

23 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.

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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

I am currently conducting a research on the Economic challenges to migrant integration. Using interviews to understand the challenges by interviewing professionals working with migrants. Wouks appreciate help with how to do this using the thematic approach. Thanks

The article cleared so many issues that I was not certain of. Very informative. Thank you.

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• Knowledge Base
• Methodology
• 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:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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How to do a thematic analysis

What is a thematic analysis?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

How to do thematic analysis

Last updated

8 February 2023

Reviewed by

Miroslav Damyanov

Short on time? Get an AI generated summary of this article instead

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

• What is thematic analysis?

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

Streamline your thematic analysis

Find patterns and themes across all your qualitative data when you analyze it in Dovetail

• What are the main approaches to thematic analysis?

Inductive thematic analysis approach

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

Deductive thematic analysis approach

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

Semantic thematic analysis approach

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

Latent thematic analysis approach

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

• When should thematic analysis be used?

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

The following scenarios warrant the use of thematic analysis:

You’re new to qualitative analysis

You need to identify patterns in data

You want to involve participants in the process

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

• What are the advantages and disadvantages of thematic analysis?

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

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

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

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

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

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

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

2. Create the initial codes (open code work)

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

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

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

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

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

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

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

6. Write the report

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

A typical thematic analysis report contains the following:

An introduction

A methodology section

Results and findings

A conclusion

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

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

• How to analyze qualitative data

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

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

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

Content analysis

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

Text in various formats

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

Free AI content analysis generator

Make sense of your research by automatically summarizing key takeaways through our free content analysis tool.

Narrative analysis

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

Discourse analysis

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

Historical 

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

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

Grounded theory analysis

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

• Challenges with analyzing qualitative data

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

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

It can be hard to choose a method. 

Avoiding bias is difficult.

Human error affects accuracy and consistency.

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

Learn more about thematic analysis software

What is thematic analysis in qualitative research.

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

Can thematic analysis be done manually?

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

What are the two types of thematic analysis?

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

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

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

What makes a good thematic analysis?

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

What are examples of themes in thematic analysis?

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

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

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

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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.

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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:

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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• How It Works

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
• Introduction

What is a thematic literature review?

Advantages of a thematic literature review, structuring and writing a thematic literature review.

• Thematic Analysis vs. Phenomenology
• Thematic vs. Content Analysis
• Thematic Analysis vs. Grounded Theory
• Thematic Analysis vs. Narrative Analysis
• 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 Literature Review

A thematic literature review serves as a critical tool for synthesizing research findings within a specific subject area. By categorizing existing literature into themes, this method offers a structured approach to identify and analyze patterns and trends across studies. The primary goal is to provide a clear and concise overview that aids scholars and practitioners in understanding the key discussions and developments within a field. Unlike traditional literature reviews , which may adopt a chronological approach or focus on individual studies, a thematic literature review emphasizes the aggregation of findings through key themes and thematic connections. This introduction sets the stage for a detailed examination of what constitutes a thematic literature review, its benefits, and guidance on effectively structuring and writing one.

A thematic literature review methodically organizes and examines a body of literature by identifying, analyzing, and reporting themes found within texts such as journal articles, conference proceedings, dissertations, and other forms of academic writing. While a particular journal article may offer some specific insight, a synthesis of knowledge through a literature review can provide a comprehensive overview of theories across relevant sources in a particular field.

Unlike other review types that might organize literature chronologically or by methodology , a thematic review focuses on recurring themes or patterns across a collection of works. This approach enables researchers to draw together previous research to synthesize findings from different research contexts and methodologies, highlighting the overarching trends and insights within a field.

At its core, a thematic approach to a literature review research project involves several key steps. Initially, it requires the comprehensive collection of relevant literature that aligns with the review's research question or objectives. Following this, the process entails a meticulous analysis of the texts to identify common themes that emerge across the studies. These themes are not pre-defined but are discovered through a careful reading and synthesis of the literature.

The thematic analysis process is iterative, often involving the refinement of themes as the review progresses. It allows for the integration of a broad range of literature, facilitating a multidimensional understanding of the research topic. By organizing literature thematically, the review illuminates how various studies contribute to each theme, providing insights into the depth and breadth of research in the area.

A thematic literature review thus serves as a foundational element in research, offering a nuanced and comprehensive perspective on a topic. It not only aids in identifying gaps in the existing literature but also guides future research directions by underscoring areas that warrant further investigation. Ultimately, a thematic literature review empowers researchers to construct a coherent narrative that weaves together disparate studies into a unified analysis.

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Conducting a literature review thematically provides a comprehensive and nuanced synthesis of research findings, distinguishing it from other types of literature reviews. Its structured approach not only facilitates a deeper understanding of the subject area but also enhances the clarity and relevance of the review. Here are three significant advantages of employing a thematic analysis in literature reviews.

Enhanced understanding of the research field

Thematic literature reviews allow for a detailed exploration of the research landscape, presenting themes that capture the essence of the subject area. By identifying and analyzing these themes, reviewers can construct a narrative that reflects the complexity and multifaceted nature of the field.

This process aids in uncovering underlying patterns and relationships, offering a more profound and insightful examination of the literature. As a result, readers gain an enriched understanding of the key concepts, debates, and evolutionary trajectories within the research area.

Identification of research gaps and trends

One of the pivotal benefits of a thematic literature review is its ability to highlight gaps in the existing body of research. By systematically organizing the literature into themes, reviewers can pinpoint areas that are under-explored or warrant further investigation.

Additionally, this method can reveal emerging trends and shifts in research focus, guiding scholars toward promising areas for future study. The thematic structure thus serves as a roadmap, directing researchers toward uncharted territories and new research questions .

Facilitates comparative analysis and integration of findings

A thematic literature review excels in synthesizing findings from diverse studies, enabling a coherent and integrated overview. By concentrating on themes rather than individual studies, the review can draw comparisons and contrasts across different research contexts and methodologies . This comparative analysis enriches the review, offering a panoramic view of the field that acknowledges both consensus and divergence among researchers.

Moreover, the thematic framework supports the integration of findings, presenting a unified and comprehensive portrayal of the research area. Such integration is invaluable for scholars seeking to navigate the extensive body of literature and extract pertinent insights relevant to their own research questions or objectives.

The process of structuring and writing a thematic literature review is pivotal in presenting research in a clear, coherent, and impactful manner. This review type necessitates a methodical approach to not only unearth and categorize key themes but also to articulate them in a manner that is both accessible and informative to the reader. The following sections outline essential stages in the thematic analysis process for literature reviews , offering a structured pathway from initial planning to the final presentation of findings.

Identifying and categorizing themes

The initial phase in a thematic literature review is the identification of themes within the collected body of literature. This involves a detailed examination of texts to discern patterns, concepts, and ideas that recur across the research landscape. Effective identification hinges on a thorough and nuanced reading of the literature, where the reviewer actively engages with the content to extract and note significant thematic elements. Once identified, these themes must be meticulously categorized, often requiring the reviewer to discern between overarching themes and more nuanced sub-themes, ensuring a logical and hierarchical organization of the review content.

Analyzing and synthesizing themes

After categorizing the themes, the next step involves a deeper analysis and synthesis of the identified themes. This stage is critical for understanding the relationships between themes and for interpreting the broader implications of the thematic findings. Analysis may reveal how themes evolve over time, differ across methodologies or contexts, or converge to highlight predominant trends in the research area. Synthesis involves integrating insights from various studies to construct a comprehensive narrative that encapsulates the thematic essence of the literature, offering new interpretations or revealing gaps in existing research.

Presenting and discussing findings

The final stage of the thematic literature review is the discussion of the thematic findings in a research paper or presentation. This entails not only a descriptive account of identified themes but also a critical examination of their significance within the research field. Each theme should be discussed in detail, elucidating its relevance, the extent of research support, and its implications for future studies. The review should culminate in a coherent and compelling narrative that not only summarizes the key thematic findings but also situates them within the broader research context, offering valuable insights and directions for future inquiry.

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Chapter 22: Thematic Analysis

Darshini Ayton

Learning outcomes

Upon completion of this chapter, you should be able to:

• Describe the different approaches to thematic analysis.
• Understand how to conduct the three types of thematic analysis.
• Identify the strengths and limitations of each type of thematic analysis.

What is thematic analysis?

Thematic analysis is a common method used in the analysis of qualitative data to identify, analyse and interpret meaning through a systematic process of generating codes (see Chapter 20) that leads to the development of themes. 1 Thematic analysis requires the active engagement of the researcher with the data, in a process of sorting, categorising and interpretation. 1 Thematic analysis is exploratory analysis whereby codes are not predetermined and are data-derived, usually from primary sources of data (e,g, interviews and focus groups). This is in contrast to themes generated through directed or summative content analysis, which is considered confirmatory hypothesis-driven analysis, with predetermined codes typically generated from a hypothesis (see Chapter 21). 2 There are many forms of thematic analysis. Hence, it is important to treat thematic analysis as one of many methods of analysis, and to justify the approach on the basis of the research question and pragmatic considerations such as resources, time and audience. The three main forms of thematic analysis used in health and social care research, discussed in this chapter, are:

Applied thematic analysis

• Framework analysis
• Reflexive thematic analysis.

This involves multiple, inductive analytic techniques designed to identify and examine themes from textual data in a way that is transparent and credible, drawing from a broad range of theoretical and methodological perspectives. It focuses on presenting the stories of participants as accurately and comprehensively as possible. Applied thematic analysis mixes a bit of everything: grounded theory, positivism, interpretivism and phenomenology. 2

Applied thematic analysis borrows what we feel are the more useful techniques from each theoretical and methodological camp and adapts them to an applied research context. 2(p16)

Applied thematic analysis involves five elements:

• Text s egmentation  involves identifying a meaningful segment of text and the boundaries of the segment. Text segmentation is a useful process as a transcript from a 30-minute interview can be many pages long. Hence, segmenting the text provides a manageable section of the data for interrogation of meaning. For example, text segmentation may be a participant’s response to an interview question, a keyword or concept in context, or a complete discourse between participants. The segment of text is more than a short phrase and can be both small and large sections of text. Text segments can also overlap, and a smaller segment may be embedded within a larger segment. 3
• Creation of the codebook is a critical element of applied thematic analysis. The codebook is created when the segments of text are systematically coded into categories, types and relationships, and the codes are defined by the observed meaning in the text. The codes and their definitions are descriptive in the beginning, and then evolve into explanatory codes as the researcher examines the commonalities, differences and relationships between the codes. The codebook is an iterative document that the researcher builds and refines as they become more immersed and familiar with the data. 3 Table 22.1 outlines the key components of a codebook. 3

Table 22.1. Codebook components and an example

• Structural coding can be useful if a structured interview guide or focus group guide has been used by the researcher and the researcher stays close to the wording of the question and its prompts. The structured question is the structural code in the codebook, and the text segment should include the participant’s response and any dialogue following the question. Of course, this form of coding can be used even if the researcher does not follow a structured guide, which is often the reality of qualitative data collection. The relevant text segments are coded for the specific structure, as appropriate. 3
• Content coding is informed by the research question(s) and the questions informing the analysis. The segmented text is grouped in different ways to explore relationships, hierarchies, descriptions and explanations of events, similarities, differences and consequences. The content of the text segment should be read and re-read to identify patterns and meaning, with the generated codes added to the codebook.
• Themes vary in scope, yet at the core they are phrases or statements that explain the meaning of the text. Researchers need to be aware that themes are considered a higher conceptual level than codes, and therefore should not be comprised of single words or labels. Typically, multiple codes will lead to a theme. Revisiting the research and analysis questions will assist the researcher to identify themes. Through the coding process, the researcher actively searches the data for themes. Examples of how themes may be identified include the repetition of concepts within and across transcripts, the use of metaphors and analogies, key phrases and common phrases used in an unfamiliar way. 3

Framework a nalysis

This method originated in the 1980s in social policy research. Framework analysis is suited to research seeking to answer specific questions about a problem or issue, within a limited time frame and with homogenous data (in topics, concepts and participants); multiple researchers are usually involved in the coding process. 4-6 The process of framework analysis is methodical and suits large data sets, hence is attractive to quantitative researchers and health services researchers. Framework analysis is useful for multidisciplinary teams in which not all members are familiar with qualitative analysis. Framework analysis does not seek to generate theory and is not aligned with any particular epistemological, philosophical or theoretical approach. 5 The output of framework analysis is a matrix with rows (cases), columns (codes) and cells of summarised data that enables researchers to analyse the data case by case and code by code. The case is usually an individual interview, or it can be a defined group or organisation. 5

The process for conducting framework analysis is as follows 5 :

1. Transcription – usually verbatim transcription of the interview.

2. Familiarisation with the interview – reading the transcript and listening to the audio recording (particularly if the researcher doing the analysis did not conduct the interview) can assist in the interpretation of the data. Notes on analytical observations, thoughts and impressions are made in the margins of the transcript during this stage.

3. Coding – completed in a line-by-line method by at least two researchers from different disciplines (or with a patient or public involvement representative), where possible. Coding can be both deductive – (using a theory or specific topics relevant to the project – or inductive, whereby open coding is applied to elements such as behaviours, incidents, values, attitudes, beliefs, emotions and participant reactions. All data is coded.

4. Developing a working analytical framework – codes are collated and organised into categories, to create a structure for summarising or reducing the data.

5. Applying the analytical framework – indexing the remaining transcripts by using the categories and codes of the analytical framework.

6. Charting data into the framework matrix – summarising the data by category and from each transcript into the framework matrix, which is a spreadsheet with numbered cells in which summarised data are entered by codes (columns) and cases (rows). Charting needs to balance the reduction of data to a manageable few lines and retention of the meaning and ‘feel’ of the participant. References to illustrative quotes should be included.

7. Interpreting the data – using the framework matrix and notes taken throughout the analysis process to interpret meaning, in collaboration with team members, including lay and clinical members.

Reflexive thematic analysis

This is the thematic analysis approach developed by Braun and Clarke in 2006 and explained in the highly cited article ‘ Using thematic analysis in psychology ’ . 7 Reflexive thematic analysis recognises the subjectiveness of the analysis process, and that codes and themes are actively generated by the researcher. Hence, themes and codes are influenced by the researcher’s values, skills and experiences. 8 Reflexive thematic analysis ‘exists at the intersection of the researcher, the dataset and the various contexts of interpretation’. 9(line 5-6) In this method, the coding process is less structured and more organic than in applied thematic analysis. Braun and Clarke have been critical of the use of the term ‘emerging themes’, which many researchers use to indicate that the theme was data-driven, as opposed to a deductive approach:

This language suggests that meaning is self evident and somehow ‘within’ the data waiting to be revealed, and that the researcher is a neutral conduit for the revelation of said meaning. In contrast, we conceptualise analysis as a situated and interactive process, reflecting both the data, the positionality of the researcher, and the context of the research itself… it is disingenuous to evoke a process whereby themes simply emerge, instead of being active co-productions on the part of the researcher, the data/participants and context. 10 (p15)

Since 2006, Braun and Clarke have published extensively on reflexive thematic analysis, including a methodological paper comparing reflexive thematic analysis with other approaches to qualitative analysis, 8 and have provided resources on their website to support researchers and students. 9 There are many ways to conduct reflexive thematic analysis, but the six main steps in the method are outlined following. 9 Note that this is not a linear, prescriptive or rule-based process, but rather an approach to guide researchers in systematically and robustly exploring their data.

1.  Familiarisation with data – involves reading and re-reading transcripts so that the researcher is immersed in the data. The researcher makes notes on their initial observations, interpretations and insights for both the individual transcripts and across all the transcripts or data sources.

2.  Coding – the process of applying succinct labels (codes) to the data in a way that captures the meaning and characteristics of the data relevant to the research question. The entire data set is coded in numerous rounds; however, unlike line-by-line coding in grounded theory (Chapter 27), or data segmentation in applied thematic analysis, not all sections of data need to be coded. 8 After a few rounds of coding, the codes are collated and relevant data is extracted.

3.  Generating initial themes – using the collated codes and extracted data, the researcher identifies patterns of meaning (initial or potential themes). The researcher then revisits codes and the data to extract relevant data for the initial themes, to examine the viability of the theme.

4 .  Developing and reviewing themes – checking the initial themes against codes and the entire data set to assess whether it captures the ‘story’ of the data and addresses the research question. During this step, the themes are often reworked by combining, splitting or discarding. For reflexive thematic analysis, a theme is defined as a ‘pattern of shared meaning underpinned by a central concept or idea’. 8 (p 39 )

5.  Refining, defining and naming themes – developing the scope and boundaries of the theme, creating the story of the theme and applying an informative name for the theme.

6.  Writing up – is a key part of the analysis and involves writing the narrative of the themes, embedding the data and providing the contextual basis for the themes in the literature.

Themes versus c odes

As described above, themes are informed by codes, and themes are defined at a conceptually higher level than codes. Themes are broader categorisations that tend to describe or explain the topic or concept. Themes need to extend beyond the code and are typically statements that can stand alone to describe and/or explain the data. Fereday and Muir-Cochrane explain this development from code to theme in Table 22.2. 11

Table 22.2. Corroborating and legitimating coded themes to identify second-order themes

*Note: This table is from an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

When I [the author] first started publishing qualitative research, many of my themes were at the code level. I then got advice that when the themes are the subheadings of the results section of my paper, they should tell the story of the research. The difference in my theme naming can be seen when comparing a paper from my PhD thesis, 12 which explores the challenges of church-based health promotion, with a more recent paper that I published on antimicrobial stewardship 13 (refer to the theme tables in the publications).

Table 22.3. Examples of thematic analysis

Advantages and challenges of thematic analysis

Thematic analysis is flexible and can be used to analyse small and large data sets with homogenous and heterogenous samples. Thematic analysis can be applied to any type of data source, from interviews and focus groups to diary entries and online discussion forums. 1 Applied thematic analysis and framework analysis are accessible approaches for non-qualitative researchers or beginner researchers. However, the flexibility and accessibility of thematic analysis can lead to limitations and challenges when thematic analysis is misapplied or done poorly. Thematic analysis can be more descriptive than interpretive if not properly anchored in a theoretical framework. 1 For framework analysis, the spreadsheet matrix output can lead to quantitative researchers inappropriately quantifying the qualitative data. Therefore, training and support from a qualitative researcher with the appropriate expertise can help to ensure that the interpretation of the data is meaningful. 5

Thematic analysis is a family of analysis techniques that are flexible and inductive and involve the generation of codes and themes. There are three main types of thematic analysis: applied thematic analysis, framework analysis and reflexive thematic analysis. These approaches span from structured coding to organic and unstructured coding for theme development. The choice of approach should be guided by the research question, the research design and the available resources and skills of the researcher and team.

• Clarke V, Braun V. Thematic analysis. J Posit Psychol . 2017;12(3):297-298. doi:10.1080/17439760.2016.1262613
• Guest G, MacQueen KM, Namey EE. Introduction to applied thematic analysis. In: Guest G, MacQueen, K.M., Namey, E.E., ed. Applied thematic analysis . SAGE Publications, Inc.; 2014. Accessed September 18, 2023. https://methods.sagepub.com/book/applied-thematic-analysis
• Guest G, MacQueen, K.M., Namey, E.E.,. Themes and Codes. In: Guest G, MacQueen, K.M., Namey, E.E., ed. Applied thematic analysis . SAGE Publications, Inc.; 2014. Accessed September 18, 2023. https://methods.sagepub.com/book/applied-thematic-analysis
• Srivastava A, Thomson SB. Framework analysis: A qualitative methodology for applied policy research. Journal of Administration and Governance . 2009;72(3). Accessed September 14, 2023. https://ssrn.com/abstract=2760705
• Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol . 2013;13:117. doi:10.1186/1471-2288-13-117
• Smith J, Firth J. Qualitative data analysis: the framework approach. Nurse Res . 2011;18(2):52-62. doi:10.7748/nr2011.01.18.2.52.c8284
• Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol . 2006;3(2):77-101. doi:10.1191/1478088706qp063oa
• Braun V, Clarke V. Can I use TA? Should I use TA? Should I not use TA? Comparing reflexive thematic analysis and other pattern-based qualitative analytic approaches. Couns Psychother Res . 2021;21(1):37-47. doi:10.1002/capr.12360
• Braun V, Clarke V. Thematic analysis. University of Auckland. Accessed September 18, 2023. https://www.thematicanalysis.net/
• Braun V, Clarke V. Answers to frequently asked questions about thematic analysis. University of Auckland. Accessed September 18, 2023. https://www.thematicanalysis.net/faqs/
• Fereday J, Muir-Cochrane E. Demonstrating Rigour Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development. International Journal of Qualitative Methods . 2006;5(1):80-92. doi: 10.1177/160940690600500107
• Ayton D, Manderson L, Smith BJ. Barriers and challenges affecting the contemporary church’s engagement in health promotion. Health Promot J Austr . 2017;28(1):52-58. doi:10.1071/HE15037
• Ayton D, Watson E, Betts JM, et al. Implementation of an antimicrobial stewardship program in the Australian private hospital system: qualitative study of attitudes to antimicrobial resistance and antimicrobial stewardship. BMC Health Serv Res . 2022;22(1):1554. doi:10.1186/s12913-022-08938-8
• McKenna-Plumley PE, Graham-Wisener L, Berry E, Groarke JM. Connection, constraint, and coping: A qualitative study of experiences of loneliness during the COVID-19 lockdown in the UK. PLoS One . 2021;16(10):e0258344. doi:10.1371/journal.pone.0258344
• Dickinson BL, Gibson K, VanDerKolk K, et al. “It is this very knowledge that makes us doctors”: an applied thematic analysis of how medical students perceive the relevance of biomedical science knowledge to clinical medicine. BMC Med Educ . 2020;20(1):356. doi:10.1186/s12909-020-02251-w
• Bunzli S, O’Brien P, Ayton D, et al. Misconceptions and the acceptance of evidence-based nonsurgical interventions for knee osteoarthritis. A Qualitative Study. Clin Orthop Relat Res . 2019;477(9):1975-1983. doi:10.1097/CORR.0000000000000784

Qualitative Research – a practical guide for health and social care researchers and practitioners Copyright © 2023 by Darshini Ayton is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Student Examples of Good Practice

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

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

Suzy Anderson (Professional Doctorate)

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

An example of a description of the thematic analysis process:

Process of Coding and Developing Themes

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

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

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

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

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

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

An example of researcher reflexivity in relation to analysis process

Subjectivity as a Resource

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

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

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

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

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

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

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

Gina Broom (Research Master's)

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

A detailed description of reflexive TA analytic approach and process

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Lucie Wheeler (Professional Doctorate)

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

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

Phase 1 – Data familiarisation and writing familiarisation notes:

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

Phase 2 – Systematic data coding:

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

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

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

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

Phase 4: Developing and reviewing themes:

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

Phase 5: Refining, defining and naming themes:

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

Phase 6: Writing the report:

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

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

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

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

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

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• Practical thematic...

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

• Related content
• Peer review
• 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

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

Developing the codebook

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

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

Assigning codes to the data

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

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

Quantitative coding in context

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

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

Definition of themes

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

Themes in context

According to Braun and Clarke, a theme “captures something important about the data in relation to the research question and represents some level of patterned response or meaning within the data set.” 4 Similarly, Braun and Clarke advise against themes as domain summaries. While different approaches can draw out themes from codes, the process begins by identifying patterns. 28 35 Like Braun and Clarke and others, we recommend that researchers consider the salience of certain themes, their prevalence in the dataset, and their keyness (ie, how relevant the themes are to the overarching research questions). 4 12 34

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

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

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1 Introduction

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

2 What is reflexive thematic analysis?

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

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

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

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

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

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

3 A worked example of reflexive thematic analysis

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

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

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

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

3.1 Addressing underlying theoretical assumptions

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

3.1.1 Essentialist versus constructionist epistemologies

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

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

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

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

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

3.1.2 Experiential versus critical orientation

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

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

3.1.3 Inductive versus deductive analysis

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

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

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

3.1.4 Semantic versus latent coding

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

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

3.2 The six-phase analytical process

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

3.2.1 Phase one: familiarisation with the data

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

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

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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Children's understandings’ of obesity, a thematic analysis

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

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

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

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

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

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

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

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

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

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

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

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

Participants

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

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

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

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

Data analysis

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

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

Knowledge through education

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Role models

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Mixed messages

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

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

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

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

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

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

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

Comprehensive understanding

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

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

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

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

Comparisons with past research

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

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

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

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

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

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

Procedural issues

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

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

Implications for the future

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

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

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

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

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

Acknowledgements

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

Conflict of interest and funding

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

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