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

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

There’s the distinction between inductive and deductive approaches:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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Thematic Analysis: A Step by Step Guide

Saul McLeod, PhD

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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 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 coding reliability and reflexive elements.

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

Familiarization 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 or transcribe the data 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 the 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.

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• Practical thematic...

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

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

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

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

Learning objectives

By reading this article, you should be able to:

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

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

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

Examples of questions amenable to thematic analysis.

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

Alt-text: Box 1

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

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

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

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

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

Designing the data collection

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

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

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

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

Designing productive interview questions

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

How to design an interview question.

Alt-text: Box 2

Bias, positionality and reflexivity

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

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

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

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

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

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

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

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

Sampling and saturation

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

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

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

Data management

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

Data analysis

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

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

Phase 1: familiarising the researcher with the data

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

Phase 2: generating initial codes

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

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

Table 1

How to code qualitative data: an example

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

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

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

Phase 3: searching for themes

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

Phase 4: reviewing the themes

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

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

Phase 5: naming the themes

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

Phase 6: producing the report

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

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

Who should analyse the data?

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

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

Judging the quality and rigour of published studies involving thematic analysis

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

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

Ten quality appraisal criteria for qualitative literature.31

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

Alt-text: Box 3

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

Conclusions

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

Declaration of interests

The authors declare that they have no conflicts of interest.

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

Biographies

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

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

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

Matrix codes: 1A01, 2A01, 3A01

Research Writing and Analysis

• NVivo Group and Study Sessions
• SPSS This link opens in a new window
• Statistical Analysis Group sessions
• Using Qualtrics
• Dissertation and Data Analysis Group Sessions
• Defense Schedule - Commons Calendar This link opens in a new window
• Research Process Flow Chart
• Research Alignment Chapter 1 This link opens in a new window
• Step 1: Seek Out Evidence
• Step 2: Explain
• Step 3: The Big Picture
• Step 4: Own It
• Step 5: Illustrate
• Annotated Bibliography
• Seminal Authors
• Systematic Reviews & Meta-Analyses
• How to Synthesize and Analyze
• Synthesis and Analysis Practice
• Synthesis and Analysis Group Sessions
• Problem Statement
• Purpose Statement
• Conceptual Framework
• Theoretical Framework
• Locating Theoretical and Conceptual Frameworks This link opens in a new window
• Quantitative Research Questions
• Qualitative Research Questions
• Trustworthiness of Qualitative Data
• Analysis and Coding Example- Qualitative Data
• Thematic Data Analysis in Qualitative Design
• Dissertation to Journal Article This link opens in a new window
• International Journal of Online Graduate Education (IJOGE) This link opens in a new window
• Journal of Research in Innovative Teaching & Learning (JRIT&L) This link opens in a new window

Thematic Analysis

Data analysis in design and development research.

Most of the data in DDR will be qualitative in nature and best analyzed using a thematic approach such as Clarke and Braun’s 6-step process illustrated below:

Clarke and Braun’s (2013) Six Step Data Analysis Process

The 6-phase coding framework for thematic analysis will be used to identify themes and patterns in the data (Braun & Clarke, 2006). The phases are:

• Familiarization of data.
• Generation of codes.
• Combining codes into themes.
• Reviewing themes.
• Determine significance of themes.
• Reporting of findings.

For survey and other numeric data, descriptive statistics can be generated using EXCEL or SPSS.

Clarke, V. & Braun, V. (2013) Teaching thematic analysis: Overcoming challenges and developing strategies for effective learning. The Psychologist , 26(2), 120-123

Reading List

Merriam and Tysdale (2016) is considered a seminal source for qualitative methodology. Generic design is discussed on pages 23 to 25.

Merriam, S. & Tysdale, E. (2016). Qualitative research: A guide to design and implementation(4th ed). Jossey-Bass.

Elliott and Timulak (2021) provide a current summary of descriptive design.

Elliott, R. & Timulak, L. (2021). Descriptive-interpretive qualitative research; A generic approach. American Psychological Association. https://soi.org/10.1037/0000224-000  

Kalke (2014) provides overview of generic design including the criticisms. The update, in 2018, reaffirms the 2014 source.

Kalke, R. (2014). Generic qualitative approaches: Pitfalls and benefits of methodological mixology. International Journal of Qualitative Methods, 13 , 37-52. Retrieved from https://journals.sagepub.com/doi/full/10.1177/160940691401300119

Kalke, R., (2018). Reflection/commentary on a past article” Generic qualitative approaches; Pitfalls and benefits of methodological mixology. International Journal of Qualitative Methods . https://journals.sagepub.com/doi/full/10.1177/1609406918788193  

Descriptive Design has been described in the qualitative research literature since the early 2000’s. Prior to that, it was not considered a non-categorial design lacking in rigor. The following articles address those criticisms and provide insight into how to best design a study using a descriptive approach.

Caelli, K., Ray, L., & Mill, J. (2003). Clear as mud: Towards a greater clarity in generic qualitative research. International Journal of Qualitative Methods, 2( 2), 1 – 23. https://journals.sagepub.com/doi/pdf/10.1177/160940690300200201

Percy, W., Kostere, K., & Kostere, S. (2015). Generic qualitative research in psychology. The Qualitative Report, 20 (2), 76-85. https://nsuworks.nova.edu/tqr/vol20/iss2/7/

Sandelowski, M. (2000). Focus on research methods-Whatever happened to qualitative description? Research in Nursing and Health, 23 (4), 334-340. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.461.4974&rep=rep1&type=pdf

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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 &#39;how&#39; of qualitative analysis. Here we draw on Braun and Clarke&#39;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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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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Monday, May 30, 2022

Thematic Analysis with MAXQDA: Step-by-Step Guide

Confronted with an overwhelming amount of data? Thematic Analysis provides a remedy.

Qualitative data analysis may look very intimidating for novice researchers at the first glance; a notebook full of field notes, hours of conversations with many different people, and hundreds of images or documents to go through. How come it is manageable for researchers to analyze such a large amount of data and create two or three bullet points that answer research questions? Thematic Analysis is one way to make this happen. It is a systematic approach to identifying, organizing, and offering insights into patterns of meanings, in other words, themes across qualitative data (Braun & Clarke, 2012). In this blog post I will guide you through the steps of a Thematic Analysis and how you can use MAXQDA for it.

• Thematic Analysis Phase 1: Familiarize yourself with the data
• Thematic Analysis Phase 2: Generate initial codes
• Thematic Analysis Phase 3: Search for themes
• Thematic Analysis Phase 4: Review potential themes
• Thematic Analysis Phase 5: Define and name themes
• Thematic Analysis Phase 6: Produce the report

What is a Thematic Analysis?

Thematic Analysis has become one of the most commonly used analytical approaches for social sciences over the last few years. Braun et al. (2019) suggest that it has a shared history with Content Analysis and has started to appear in health and social sciences studies as a qualitative data analysis approach by the 1980s. A quick search on Google Scholar with the key term “Thematic analysis” (in-between quotation marks) brings more than 370.000 results. A key article alone, Using Thematic Analysis in Psychology authored by Braun and Clarke (2006), was cited more than 126.000 times by May 2022. These easily accessible data indicate the popularity of this approach. This popularity may be related to the modularity and flexibility it affords to researchers who analyze qualitative data. However, it is also a very systematic process that needs to be recursive. To add, Thematic Analyses require researchers to get immersed in their data set immensely. To systematize this process, Braun and Clarke (2006, 2012) proposed a six-phase procedure of Thematic Analysis to guide qualitative researchers.

The Research Project

In this blog post, I will share a recent publication of mine as an example of how I used MAXQDA to advance the six-phase Thematic Analytic approach. In this qualitative study, I intended to explain the online professional development experiences of English as a foreign language (EFL) during a massive open online course (MOOC) on teaching languages online. This study was entitled “How massive open online courses constitute digital learning spaces for EFL teachers: A netnographic case study”; and as the name suggests, I employed ethnographic methods to investigate the online experiences of two EFL teachers, as qualitative cases. A comparative case study with two participants as cases might feel that it will not yield so much data but, on the contrary, one typical characteristic of ethnographic studies is that it requires the researcher to get immersed in the culture investigated over an extended period of time.

The Research Data

To add, the multimodal nature of qualitative data provides a wide range of possibilities for qualitative researchers to collect data; accordingly, I drew on digital learner diaries (that included case participants’ reflections throughout the online learning experience), semi-structured interviews, and screenshots illustrating participants’ online forum posts and similar contributions in the online course. Intending to investigate the participants’ experiences in an explanatory way without a reconceptualized theoretical or conceptual framework in mind, I used inductive coding and Thematic Analysis. Throughout this tentative process, several features of MAXQDA informed each phase of my Thematic Analysis.

The Six Phases of Thematic Analysis

Figure1: Six-phase Thematic Analytic process (adapted from Braun & Clarke, 2006, 2012)

Phase 1: Familiarize yourself with the data

Similar to other qualitative data analysis approaches, researchers who analyze their data thematically need to start by immersing themselves in their data extensively. As shown in figure one, the six-phase process starts with that process. Braun and Clarke (2006, 2012) underscored that this process requires researchers to write down a plethora of notes and memos, annotating transcripts, underlining, highlighting, as well as (un)grouping documents. Accordingly in my research investigating EFL teachers’ MOOC experiences, I needed to listen and re-listen to participants’ audio-diary entries and interview transcripts. I also needed to review their interaction and contributions on the platform a lot and repeatedly.

Going beyond “the surface meaning of the words on the page”

According to Braun and Clarke (2006, 2012), there were certain questions that I needed to keep in mind while familiarizing myself with that set of data: How do my case participants make sense of their MOOC experiences? What assumptions and reflections do they make while interpreting their experiences? What does their interpretation reveal? Despite being the very first phase of the Thematic Analysis, this is quite an overwhelming task for all qualitative researchers; however, it is also of critical importance because this is the phase we can start “read[ing] our data as data”, which means going beyond “the surface meaning of the words on the page” (Braun & Clarke, 2012, p. 60, emphasis as original).

Grouping the data

MAXQDA was particularly helpful for me in getting familiarized with the data as it allowed me to experiment with the grouping of my various data sources, allowed getting engaged in diverse types of data, and transcribed audio files easily.

Figure 2: Document System of my research on MAXQDA 2022

First, MAXQDA’s Document System allowed me to experiment with grouping my data in various ways. As my study was a comparative case study reporting on the experiences of two case participants (Teacher 1 and Teacher 2, see Figure 2), I have decided to group data by participants.

Transcribing the Data

Secondly, I have uploaded different data types such as audio files and images. As I collected audio diaries from two different sources who had used different devices to record their diary entries, I had to deal with two different data formats. With MAXQDA, I was able to upload and play audio files in these two data formats without any difficulty. Last, I have used the transcribe audio file feature. Creating timestamps while transcribing data enabled me to go back to particular parts of the audio files and listen again and again, which is crucial for me while familiarizing myself with the dataset.

Phase 2: Generate initial codes

Coding in thematic analysis.

Once the Document System takes some shape, qualitative coding starts. According to Braun and Clarke (2012), codes are “the building blocks of analysis” (p. 61) and help researchers make sense of their data in light of the tentative research questions. According to Kuckartz and Rädiker (2019), researchers select part of the data and assign it a code , which can be done in two generic ways, the concept-driven, deductive approach and the data-driven, inductive approach. In Thematic Analysis, coding can be conducted in both ways, and the coded segments may cooccur and interconnect.

Looking out for emerging codes

Feeling immersed in my data, I started to generate my initial codes . With no pre-conceptualized theoretical framework that particularly shapes my analytical lens, I relied on data-driven, inductive coding and looked for emerging codes and code groups. I have completed coding through my entire dataset. In my initial code generation process, I have used two features of MAXQDA extensively: open coding and Memos . Open coding with MAXQDA was very a very intuitive experience, and Memos helped me to trace back my original rationalizations when I created a new code for previous coded segments. These Memos helped me if I can reuse previously created codes across 22 files that I compiled in my Document System.

Phase 3: Search for themes

Looking out for emerging themes.

After feeling saturated with coding and recoding all data sources included in the Thematic Analysis, I moved from codes to themes. According to Braun and Clarke (2006), themes are “patterned response or meaning within the data set” that somehow relates to the research questions (p. 82). Searching for themes is a very active process in which the qualitative researchers actively construct themes rather than discover them even though the name of the phase is “searching” for themes. Braun and Clarke (2012) liken researchers searching for themes to sculptors making choices that will profoundly influence the end-product of sculpturing instead of archeologists digging for some fossils (i.e. themes) that are embedded in the data regardless of the dirt to be removed around them. That resonated with me as a qualitative data analyst in my research because my entire analytical lens was data-driven and my research questions aimed to understand rather than explore. In tandem with this, I needed to be an active meaning-maker rather than a passive observer; I needed to communicate with my data and make sense of it while constructing themes.

Discovering patterns through visualizations

MAXQDA offered me a lot of choices to be a more active analyst while communicating with my data at a conceptual level. Personally, it is easier for me to synthesize information when I have visual input; accordingly, charts, images, and data visualization help me to see a relationship between codes and emerging themes as well as documents and participants. Similarly, summative workflows and pipelines help me situate myself in the long process that is required for most qualitative data analysis approaches including the thematic analysis. To this end, I have benefitted greatly from three particular features of MAXQDA: Code Maps , MAXMaps , and Questions, Themes & Theories (QTT) .

Gaining a holistic view

Figure 3: Screenshots from my QTT worksheet

In figure 3, I illustrated two screenshots from my QTT worksheet that I used in my data analysis. First of all, MAXQDA allows researchers to generate Code Maps that analyze the relationship of codes as they co-occur or occur within certain proximity across data documents. I always start with Code Maps because they take little effort to create and it helps me to take a meta-position after the long and repetitive process of immersion and coding; taking one step back and having a more holistic view of my codes and their relations with each other. However, as I explained earlier, this is one way to start the process and it should not be the last one because Thematic Analysis requires researchers to be actively involved in the meaning-making process.

Using the QTT for Thematic Analysis

This brings me to another very important visualization tool of MAXQDA: MAXMaps . MAXMaps afforded me a space for stimulated brainstorming over my codes and initial themes. In this space, I could retrieve codes and documents as icons, create and signify relations between them with links and arrows, and create code models. Last, with the latest version of MAXQDA, I could create a QTT worksheet that enabled me to import related codes and themes, coded segments, and all the visual materials that I created into one worksheet. On this worksheet, I could return constantly to my research questions and memos when I strived to move from my questions to themes, and even beyond them, my theories later.

Phase 4: Review potential themes

In this phase, the themes constructed in the previous phase are reviewed and cross-checked against the entire code system, coded segments, and documents. The themes, data, and research questions need to be relevant and in alignment. While doing so, researchers can combine some emerging themes to reach overarching themes whereas some emerging themes might get singled out and found irrelevant even though they might seem very interesting.

Guiding questions for a Thematic Analysis

I benefitted from some key questions suggested by Braun and Clarke (2012, p. 65) to review the potential themes and construct overarching themes by combining multiple emerging themes. I adapted those guiding questions to my research context as follows:

• Is this a theme that represents a pattern across my two case participants and/or data documents?
• Does this theme tell me something about my research questions and/or what EFL teachers experience in professional learning with MOOCs?
• Does this theme include or exclude many coded segments?
• Is there enough data to support that this is a strong theme?
• Is this theme coherent, which means the data to support this theme comes from similar sources (e.g., diaries)?

These guiding questions were useful for me but still, I needed to adopt smart ways to deal with them.

Detecting central themes with MAXQDA’s Visual Tools

MAXQDA afforded other Visual Tools that guided me even further. I utilized Code and Document Matrices to review and decide whether my initial themes are strong ones and whether I can construct relevant overarching themes out of them.

First, I used the Code Relations Browser to further understand the relationships between codes. One implication that I drew from this matrix, for instance, is the strong relationship between my case participants’ feeling of engagement and the MOOC platform’s facilitation. This led me to go back to my coded segments and to decide if I can construct a theme out of this relationship and if there is any other code that can be linked to this relationship (e.g., flow).

Figure 4: Code Relations Browser

Similar to the Code Relations Browser and Matrix , the Document Comparison Chart also helped me to review my initial codes by using the guiding questions of Braun and Clarke. After applying colors to the codes in my code system (see my code system on the left part of Figure 4), it can be seen that all audio-diary entries include pink blocks which indicate that the case participants revealed some kind of learning experience (e.g., new understanding, engagement, reflection, flow, or multimodal learning). Likewise, my interview data also implied participants’ reflections of their existing or developing digital literacies (red segments).

Figure 5: Document Comparison Chart

Phase 5: Define and name themes

This is also another phase of a Thematic Analysis that is closely related to the previous one. While reviewing the emerging themes and constructing the overarching themes, researchers conducting a Thematic Analysis need to make sure that these overarching themes are not repetitive, or they do not overlap (otherwise, they may need to be combined). It is also suggested by Braun and Clarke (2012) that researchers should only define and name themes when they have singular foci and address research questions.

Staying in focus with the QTT

In this phase, QTT helped me to keep my research questions constantly in mind (see Figure 3), and I reached four overarching themes: (1) the self-regulatory impact of the MOOC, (2) the provision of an online learning experience, which demystified online teaching, (3) preparing the pre-service EFL teachers for a teaching career, and (4) limitations due to the massiveness and non-situatedness in MOOC designs.

Defining themes through codes

MAXQDA’s Code System supports this process in many ways. I have specifically used the possibility of coloring the themes. Similarly, highlighters and code favorites can also help the process. Another easy thing to do is the ability to drag and drop codes across the entire Code System and create code nodes to group sub-codes. This allowed me to create code families, which helped me while defining and naming my themes.

Phase 6: Produce the report

Even though the final phase of the Thematic Analysis might seem like a happy ending for I had the overarching themes and relevant coded segments, it was actually a very tricky one. As with all qualitative research approaches, Thematic Analysis is also a very recursive process, and unlike quantitative research, it does not have a phase-gate process where the phases are initiated only when the previous phase is concluded. On the contrary, thematic analytic reporting required me to go back even to the very first phase of the data analysis because I simply needed to refamiliarize myself with the data to construct the overarching themes.

Figure 6: Integration of insights page of my QTT worksheet

Bringing everything together

This process of going back and forth really made me feel immersed in my data. This is important to report a Thematic Analysis because a thematic analytic report should include a “compelling story” [for the reader] about [my] data based on [my] analysis” (Braun & Clarke, 2012, p. 69). On the last page of my QTT worksheet (See Figure 6), I brought all my overarching themes together with my research questions and integrated them into insights, drew conclusions, and developed hypotheses. Another feature I used extensively was to retrieve coded segments to the important segments tab and linked them with overarching themes. This also helped me it was time to write down the findings and discussion sections of my research paper.

Qualitative data analysis and more specifically Thematic Analysis might look like a daunting task in which researchers need to spend a lot of time making sense of the data and synthesizing hypotheses out of them. Unlike quantitative data analysis software, which makes me feel detached from my data due to its point-and-click interface, qualitative data analysis software affords ways to really get immersed in the data set, which is paradigmatically crucial for successful (and meaningful) qualitative research.

Figure 7: The continuous cycle of Thematic Analysis (adapted from Braun & Clarke, 2006, 2012)

Creating a recursive cycle of meaning-making with MAXQDA

Thematic Analysis is an approach that requires researchers’ extensive immersion in their data. MAXQDA’s many features helped me a lot to feel immersed. With those features, I could feel like “the sculptor” (Braun & Clarke, 2012), who made choices and decisions along the way of creating something out of a block of stone. This made it possible for me as a researcher to have a voice of my own in the data analysis process. This process made the recent conversation around the Thematic Analysis that reconceptualizes the six-phase approach as a reflexive process (see Braun et al., 2019; Braun & Clarke, 2019, 2020). Another useful affordance of MAXQDA in my Thematic Analysis was that I was able to go back and forward the phases Thematic Analysis. With this affordance, my Thematic Analysis felt like a real recursive process as in Figure 7, instead of a more phase-gate process only listing the phases of Thematic Analysis in linear order as in Figure 1. This way, I was able to see how these phases communicate with one another, creating a continuous and recursive cycle of meaning-making.

NOTE: This post is based on my research experience as a user of MAXQDA. The abovementioned research project has been reported and is currently in press to be published in the June 2022 issue of The Journal of Teaching English with Technology .

• Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
• Braun, V., & Clarke, V. (2012). Thematic analysis. In H. Cooper (Ed.), APA handbook of research methods in psychology Vol 2: Research designs (Vol. 2, pp. 57–71). American Psychological Association. https://doi.org/10.1037/13620-004
• Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806
• Braun, V., & Clarke, V. (2020). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 00(00), 1–25. https://doi.org/10.1080/14780887.2020.1769238
• 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. https://doi.org/10.1007/978-981-10-5251-4_103
• Kuckartz, U., & Rädiker, S. (2019). Analyzing Qualitative Data with MAXQDA. Text, Audio, and Video. Springer. https://doi.org/10.1007/978-3-030-15671-8

About the Author

Özgehan Uştuk, Ph.D. is a professional MAXQDA trainer and and currently works at Balikesir University, Turkey as a researcher, language teacher, and teacher educator. His research interests include language teacher education, practitioner inquiry, psychology of language teaching and learning, language teacher identity, emotions, and tensions. He is the incoming chair of the Research Professional Council in the TESOL International Association

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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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How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD). Expert Reviewed By: Dr. Eunice Rautenbach | August 2021

So, you’ve collected and analysed your qualitative data, and it’s time to write up your results chapter. But where do you start? In this post, we’ll guide you through the qualitative results chapter (also called the findings chapter), step by step. 

Overview: Qualitative Results Chapter

• What (exactly) the qualitative results chapter is
• What to include in your results chapter
• How to write up your results chapter
• A few tips and tricks to help you along the way
• Free results chapter template

What exactly is the results chapter?

The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and discuss its meaning), depending on your university’s preference.  We’ll treat the two chapters as separate, as that’s the most common approach.

In contrast to a quantitative results chapter that presents numbers and statistics, a qualitative results chapter presents data primarily in the form of words . But this doesn’t mean that a qualitative study can’t have quantitative elements – you could, for example, present the number of times a theme or topic pops up in your data, depending on the analysis method(s) you adopt.

Adding a quantitative element to your study can add some rigour, which strengthens your results by providing more evidence for your claims. This is particularly common when using qualitative content analysis. Keep in mind though that qualitative research aims to achieve depth, richness and identify nuances , so don’t get tunnel vision by focusing on the numbers. They’re just cream on top in a qualitative analysis.

So, to recap, the results chapter is where you objectively present the findings of your analysis, without interpreting them (you’ll save that for the discussion chapter). With that out the way, let’s take a look at what you should include in your results chapter.

What should you include in the results chapter?

As we’ve mentioned, your qualitative results chapter should purely present and describe your results , not interpret them in relation to the existing literature or your research questions . Any speculations or discussion about the implications of your findings should be reserved for your discussion chapter.

In your results chapter, you’ll want to talk about your analysis findings and whether or not they support your hypotheses (if you have any). Naturally, the exact contents of your results chapter will depend on which qualitative analysis method (or methods) you use. For example, if you were to use thematic analysis, you’d detail the themes identified in your analysis, using extracts from the transcripts or text to support your claims.

While you do need to present your analysis findings in some detail, you should avoid dumping large amounts of raw data in this chapter. Instead, focus on presenting the key findings and using a handful of select quotes or text extracts to support each finding . The reams of data and analysis can be relegated to your appendices.

While it’s tempting to include every last detail you found in your qualitative analysis, it is important to make sure that you report only that which is relevant to your research aims, objectives and research questions .  Always keep these three components, as well as your hypotheses (if you have any) front of mind when writing the chapter and use them as a filter to decide what’s relevant and what’s not.

Need a helping hand?

How do I write the results chapter?

Now that we’ve covered the basics, it’s time to look at how to structure your chapter. Broadly speaking, the results chapter needs to contain three core components – the introduction, the body and the concluding summary. Let’s take a look at each of these.

Section 1: Introduction

The first step is to craft a brief introduction to the chapter. This intro is vital as it provides some context for your findings. In your introduction, you should begin by reiterating your problem statement and research questions and highlight the purpose of your research . Make sure that you spell this out for the reader so that the rest of your chapter is well contextualised.

The next step is to briefly outline the structure of your results chapter. In other words, explain what’s included in the chapter and what the reader can expect. In the results chapter, you want to tell a story that is coherent, flows logically, and is easy to follow , so make sure that you plan your structure out well and convey that structure (at a high level), so that your reader is well oriented.

The introduction section shouldn’t be lengthy. Two or three short paragraphs should be more than adequate. It is merely an introduction and overview, not a summary of the chapter.

Pro Tip – To help you structure your chapter, it can be useful to set up an initial draft with (sub)section headings so that you’re able to easily (re)arrange parts of your chapter. This will also help your reader to follow your results and give your chapter some coherence.  Be sure to use level-based heading styles (e.g. Heading 1, 2, 3 styles) to help the reader differentiate between levels visually. You can find these options in Word (example below).

Section 2: Body

Before we get started on what to include in the body of your chapter, it’s vital to remember that a results section should be completely objective and descriptive, not interpretive . So, be careful not to use words such as, “suggests” or “implies”, as these usually accompany some form of interpretation – that’s reserved for your discussion chapter.

The structure of your body section is very important , so make sure that you plan it out well. When planning out your qualitative results chapter, create sections and subsections so that you can maintain the flow of the story you’re trying to tell. Be sure to systematically and consistently describe each portion of results. Try to adopt a standardised structure for each portion so that you achieve a high level of consistency throughout the chapter.

For qualitative studies, results chapters tend to be structured according to themes , which makes it easier for readers to follow. However, keep in mind that not all results chapters have to be structured in this manner. For example, if you’re conducting a longitudinal study, you may want to structure your chapter chronologically. Similarly, you might structure this chapter based on your theoretical framework . The exact structure of your chapter will depend on the nature of your study , especially your research questions.

As you work through the body of your chapter, make sure that you use quotes to substantiate every one of your claims . You can present these quotes in italics to differentiate them from your own words. A general rule of thumb is to use at least two pieces of evidence per claim, and these should be linked directly to your data. Also, remember that you need to include all relevant results , not just the ones that support your assumptions or initial leanings.

In addition to including quotes, you can also link your claims to the data by using appendices , which you should reference throughout your text. When you reference, make sure that you include both the name/number of the appendix , as well as the line(s) from which you drew your data.

As referencing styles can vary greatly, be sure to look up the appendix referencing conventions of your university’s prescribed style (e.g. APA , Harvard, etc) and keep this consistent throughout your chapter.

Section 3: Concluding summary

The concluding summary is very important because it summarises your key findings and lays the foundation for the discussion chapter . Keep in mind that some readers may skip directly to this section (from the introduction section), so make sure that it can be read and understood well in isolation.

In this section, you need to remind the reader of the key findings. That is, the results that directly relate to your research questions and that you will build upon in your discussion chapter. Remember, your reader has digested a lot of information in this chapter, so you need to use this section to remind them of the most important takeaways.

Importantly, the concluding summary should not present any new information and should only describe what you’ve already presented in your chapter. Keep it concise – you’re not summarising the whole chapter, just the essentials.

Tips for writing an A-grade results chapter

Now that you’ve got a clear picture of what the qualitative results chapter is all about, here are some quick tips and reminders to help you craft a high-quality chapter:

• Your results chapter should be written in the past tense . You’ve done the work already, so you want to tell the reader what you found , not what you are currently finding .
• Make sure that you review your work multiple times and check that every claim is adequately backed up by evidence . Aim for at least two examples per claim, and make use of an appendix to reference these.
• When writing up your results, make sure that you stick to only what is relevant . Don’t waste time on data that are not relevant to your research objectives and research questions.
• Use headings and subheadings to create an intuitive, easy to follow piece of writing. Make use of Microsoft Word’s “heading styles” and be sure to use them consistently.
• When referring to numerical data, tables and figures can provide a useful visual aid. When using these, make sure that they can be read and understood independent of your body text (i.e. that they can stand-alone). To this end, use clear, concise labels for each of your tables or figures and make use of colours to code indicate differences or hierarchy.
• Similarly, when you’re writing up your chapter, it can be useful to highlight topics and themes in different colours . This can help you to differentiate between your data if you get a bit overwhelmed and will also help you to ensure that your results flow logically and coherently.

If you have any questions, leave a comment below and we’ll do our best to help. If you’d like 1-on-1 help with your results chapter (or any chapter of your dissertation or thesis), check out our private dissertation coaching service here or book a free initial consultation to discuss how we can help you.

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23 Comments

This was extremely helpful. Thanks a lot guys

Hi, thanks for the great research support platform created by the gradcoach team!

I wanted to ask- While “suggests” or “implies” are interpretive terms, what terms could we use for the results chapter? Could you share some examples of descriptive terms?

I think that instead of saying, ‘The data suggested, or The data implied,’ you can say, ‘The Data showed or revealed, or illustrated or outlined’…If interview data, you may say Jane Doe illuminated or elaborated, or Jane Doe described… or Jane Doe expressed or stated.

I found this article very useful. Thank you very much for the outstanding work you are doing.

What if i have 3 different interviewees answering the same interview questions? Should i then present the results in form of the table with the division on the 3 perspectives or rather give a results in form of the text and highlight who said what?

I think this tabular representation of results is a great idea. I am doing it too along with the text. Thanks

That was helpful was struggling to separate the discussion from the findings

this was very useful, Thank you.

Very helpful, I am confident to write my results chapter now.

It is so helpful! It is a good job. Thank you very much!

Very useful, well explained. Many thanks.

Hello, I appreciate the way you provided a supportive comments about qualitative results presenting tips

I loved this! It explains everything needed, and it has helped me better organize my thoughts. What words should I not use while writing my results section, other than subjective ones.

Thanks a lot, it is really helpful

Thank you so much dear, i really appropriate your nice explanations about this.

Thank you so much for this! I was wondering if anyone could help with how to prproperly integrate quotations (Excerpts) from interviews in the finding chapter in a qualitative research. Please GradCoach, address this issue and provide examples.

what if I’m not doing any interviews myself and all the information is coming from case studies that have already done the research.

Very helpful thank you.

This was very helpful as I was wondering how to structure this part of my dissertation, to include the quotes… Thanks for this explanation

This is very helpful, thanks! I am required to write up my results chapters with the discussion in each of them – any tips and tricks for this strategy?

For qualitative studies, can the findings be structured according to the Research questions? Thank you.

Do I need to include literature/references in my findings chapter?

This was very helpful

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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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Presenting your qualitative analysis findings: tables to include in chapter 4.

The earliest stages of developing a doctoral dissertation—most specifically the topic development  and literature review  stages—require that you immerse yourself in a ton of existing research related to your potential topic. If you have begun writing your dissertation proposal, you have undoubtedly reviewed countless results and findings sections of studies in order to help gain an understanding of what is currently known about your topic. 

In this process, we’re guessing that you observed a distinct pattern: Results sections are full of tables. Indeed, the results chapter for your own dissertation will need to be similarly packed with tables. So, if you’re preparing to write up the results of your statistical analysis or qualitative analysis, it will probably help to review your APA editing  manual to brush up on your table formatting skills. But, aside from formatting, how should you develop the tables in your results chapter?

In quantitative studies, tables are a handy way of presenting the variety of statistical analysis results in a form that readers can easily process. You’ve probably noticed that quantitative studies present descriptive results like mean, mode, range, standard deviation, etc., as well the inferential results that indicate whether significant relationships or differences were found through the statistical analysis . These are pretty standard tables that you probably learned about in your pre-dissertation statistics courses.

But, what if you are conducting qualitative analysis? What tables are appropriate for this type of study? This is a question we hear often from our dissertation assistance  clients, and with good reason. University guidelines for results chapters often contain vague instructions that guide you to include “appropriate tables” without specifying what exactly those are. To help clarify on this point, we asked our qualitative analysis experts to share their recommendations for tables to include in your Chapter 4.

Demographics Tables

As with studies using quantitative methods , presenting an overview of your sample demographics is useful in studies that use qualitative research methods. The standard demographics table in a quantitative study provides aggregate information for what are often large samples. In other words, such tables present totals and percentages for demographic categories within the sample that are relevant to the study (e.g., age, gender, job title). 

If conducting qualitative research  for your dissertation, however, you will use a smaller sample and obtain richer data from each participant than in quantitative studies. To enhance thick description—a dimension of trustworthiness—it will help to present sample demographics in a table that includes information on each participant. Remember that ethical standards of research require that all participant information be deidentified, so use participant identification numbers or pseudonyms for each participant, and do not present any personal information that would allow others to identify the participant (Blignault & Ritchie, 2009). Table 1 provides participant demographics for a hypothetical qualitative research study exploring the perspectives of persons who were formerly homeless regarding their experiences of transitioning into stable housing and obtaining employment.

Participant Demographics

Tables to Illustrate Initial Codes

Most of our dissertation consulting clients who are conducting qualitative research choose a form of thematic analysis . Qualitative analysis to identify themes in the data typically involves a progression from (a) identifying surface-level codes to (b) developing themes by combining codes based on shared similarities. As this process is inherently subjective, it is important that readers be able to evaluate the correspondence between the data and your findings (Anfara et al., 2002). This supports confirmability, another dimension of trustworthiness .

A great way to illustrate the trustworthiness of your qualitative analysis is to create a table that displays quotes from the data that exemplify each of your initial codes. Providing a sample quote for each of your codes can help the reader to assess whether your coding was faithful to the meanings in the data, and it can also help to create clarity about each code’s meaning and bring the voices of your participants into your work (Blignault & Ritchie, 2009).

Table 2 is an example of how you might present information regarding initial codes. Depending on your preference or your dissertation committee’s preference, you might also present percentages of the sample that expressed each code. Another common piece of information to include is which actual participants expressed each code. Note that if your qualitative analysis yields a high volume of codes, it may be appropriate to present the table as an appendix.

Initial Codes

Tables to Present the Groups of Codes That Form Each Theme

As noted previously, most of our dissertation assistance clients use a thematic analysis approach, which involves multiple phases of qualitative analysis  that eventually result in themes that answer the dissertation’s research questions. After initial coding is completed, the analysis process involves (a) examining what different codes have in common and then (b) grouping similar codes together in ways that are meaningful given your research questions. In other words, the common threads that you identify across multiple codes become the theme that holds them all together—and that theme answers one of your research questions.

As with initial coding, grouping codes together into themes involves your own subjective interpretations, even when aided by qualitative analysis software such as NVivo  or MAXQDA. In fact, our dissertation assistance clients are often surprised to learn that qualitative analysis software does not complete the analysis in the same ways that statistical analysis software such as SPSS does. While statistical analysis software completes the computations for you, qualitative analysis software does not have such analysis capabilities. Software such as NVivo provides a set of organizational tools that make the qualitative analysis far more convenient, but the analysis itself is still a very human process (Burnard et al., 2008).

Because of the subjective nature of qualitative analysis, it is important to show the underlying logic behind your thematic analysis in tables—such tables help readers to assess the trustworthiness of your analysis. Table 3 provides an example of how to present the codes that were grouped together to create themes, and you can modify the specifics of the table based on your preferences or your dissertation committee’s requirements. For example, this type of table might be presented to illustrate the codes associated with themes that answer each research question. 

Grouping of Initial Codes to Form Themes

Tables to Illustrate the Themes That Answer Each Research Question

Creating alignment throughout your dissertation is an important objective, and to maintain alignment in your results chapter, the themes you present must clearly answer your research questions. Conducting qualitative analysis is an in-depth process of immersion in the data, and many of our dissertation consulting  clients have shared that it’s easy to lose your direction during the process. So, it is important to stay focused on your research questions during the qualitative analysis and also to show the reader exactly which themes—and subthemes, as applicable—answered each of the research questions.

Below, Table 4 provides an example of how to display the thematic findings of your study in table form. Depending on your dissertation committee’s preference or your own, you might present all research questions and all themes and subthemes in a single table. Or, you might provide separate tables to introduce the themes for each research question as you progress through your presentation of the findings in the chapter.

Emergent Themes and Research Questions

Bonus Tip! Figures to Spice Up Your Results

Although dissertation committees most often wish to see tables such as the above in qualitative results chapters, some also like to see figures that illustrate the data. Qualitative software packages such as NVivo offer many options for visualizing your data, such as mind maps, concept maps, charts, and cluster diagrams. A common choice for this type of figure among our dissertation assistance clients is a tree diagram, which shows the connections between specified words and the words or phrases that participants shared most often in the same context. Another common choice of figure is the word cloud, as depicted in Figure 1. The word cloud simply reflects frequencies of words in the data, which may provide an indication of the importance of related concepts for the participants.

As you move forward with your qualitative analysis and development of your results chapter, we hope that this brief overview of useful tables and figures helps you to decide on an ideal presentation to showcase the trustworthiness your findings. Completing a rigorous qualitative analysis for your dissertation requires many hours of careful interpretation of your data, and your end product should be a rich and detailed results presentation that you can be proud of. Reach out if we can help  in any way, as our dissertation coaches would be thrilled to assist as you move through this exciting stage of your dissertation journey!

Anfara Jr., V. A., Brown, K. M., & Mangione, T. L. (2002). Qualitative analysis on stage: Making the research process more public.  Educational Researcher ,  31 (7), 28-38. https://doi.org/10.3102/0013189X031007028

Blignault, I., & Ritchie, J. (2009). Revealing the wood and the trees: Reporting qualitative research.  Health Promotion Journal of Australia ,  20 (2), 140-145. https://doi.org/10.1071/HE09140

Burnard, P., Gill, P., Stewart, K., Treasure, E., & Chadwick, B. (2008). Analysing and presenting qualitative data.  British Dental Journal ,  204 (8), 429-432. https://doi.org/10.1038/sj.bdj.2008.292

A Thematic Analysis of Young Adults’ Perspectives of Gambling and its Representation on Media

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Understanding the Perspectives and Experiences of Patients with Acute Severe Ulcerative Colitis in the Hospital: A Qualitative Analysis

• Original Article
• Published: 18 September 2024

Cite this article

• Dustin Romain 1 ,
• Charlotte Larson 1 ,
• Priya Kathuria 1 ,
• Daniel Aintabi 2 ,
• Nicholas Tedesco 1 , 3 ,
• Queen Saunyama 1 , 3 ,
• Melissa DeJonckheere 4 , 5 ,
• Shrinivas Bishu 1 , 3 ,
• Shirley Cohen-Mekelburg 1 , 3 , 5 , 6 ,
• Peter D. R. Higgins 1 , 3 &
• Jeffrey A. Berinstein 1 , 3 , 5  

Introduction

Acute severe ulcerative colitis (ASUC) is a life-treating presentation of ulcerative colitis (UC) that requires prompt initiation of treatment to avoid complication. Unfortunately, outcomes for ASUC are suboptimal, with as many as 20–30% of patients requiring colectomy. This can be challenging for patients and highlights the need to understand patient experiences and perspectives navigating ASUC.

A qualitative descriptive study utilizing semi-structured interviews was conducted to understand perspectives and experiences of patients navigating ASUC. Adult patients hospitalized for ASUC between January 2017 and March 2024 were eligible. Interviews were conducted both retrospectively among patients with a recent hospitalization and prospectively among patients within 24 h of hospitalization for ASUC. Interviews were analyzed using a well-established hybrid inductive-deductive approach.

Thirty-four patients (44.2% response rate) hospitalized for ASUC were interviewed. Hybrid thematic analysis uncovered five major themes: (1) the pervasive impact of UC on QoL and mental health, (2) challenges associated with navigating uncertainty, (3) prioritizing colon preservation, (4) bridging the divide between outpatient expectations and inpatient realities, and (5) balancing rapid symptom improvement with steroid safety. Our findings advocate for transparent approach to care, emphasizing the need for effective communication, education, and better alignment with patient values and expectations.

Five key themes were identified, each with significant implications for developing a more patient-centered approach to ASUC care. These themes captured meaningful insight into patient perceptions and experiences, identifying multiple areas for actionable interventions to improve care.

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Data availability

No datasets were generated or analyzed during the current study.

Abbreviations

• Acute severe ulcerative colitis

Quality of life

• Ulcerative colitis

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Dustin Romain, Charlotte Larson, Priya Kathuria, Nicholas Tedesco, Queen Saunyama, Shrinivas Bishu, Shirley Cohen-Mekelburg, Peter D. R. Higgins & Jeffrey A. Berinstein

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Study concept and design: JAB, DR, PDRH, and SB. Acquisition: JAB and DR. Analysis or interpretation of data: JAB, DR, PK, CL, QS, and MD. Drafting of the manuscript: JAB and DR. Critical revision of the manuscript: All authors. Final approval: All authors

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Romain, D., Larson, C., Kathuria, P. et al. Understanding the Perspectives and Experiences of Patients with Acute Severe Ulcerative Colitis in the Hospital: A Qualitative Analysis. Dig Dis Sci (2024). https://doi.org/10.1007/s10620-024-08633-0

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• Published: 20 September 2024

Summary of: A systematic review and thematic synthesis of the challenges associated with dental appointments, from the perspective of autistic adults

• Abigail Craven 1  

BDJ Team volume  11 ,  pages 360–363 ( 2024 ) Cite this article

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By dental hygienist and therapist, and neurodiversity advocate, Abigail Craven

©VectorMine/iStock/Getty Images Plus

Introduction

Autistic spectrum disorder (ASD) is a group of lifelong developmental differences that affect how people communicate with and experience the world. Autistic people have a higher risk of oral diseases due to poor oral hygiene, sensory sensitivities, and restrictive dietary habits. 1 Barriers to dental care for autistic patients include stress, accessibility, and social and economic inequalities. They may struggle to tolerate dental appointments due to autism-specific challenges in communication, anxiety, and sensory hypersensitivity.

Most studies around autism in dentistry are concerning children, and most of these data are secondary evidence from their parents and carers. Research which does not collect primary data from autistic individuals themselves excludes their first-hand accounts. ASD is a lifelong difference, and the lived experiences of adults are equally important.

There is a gap in research exploring how autistic adults experience challenges associated with dental appointments.

There is a gap in research exploring how autistic adults experience challenges associated with dental appointments. Self-awareness and communication skills often improve with age, bringing the ability to articulate one's subjective reality, which is imperative to research seeking to capture experiences.

To translate lived experiences in a way that can be understood by others, we need to explore the ‘how?' and ‘why?'. Positivism is tangible and does not allow us to understand the phenomenon of subjective reality. Instead, an interpretivist approach enriches the understanding of experiences through context. Without attempting to discover such nuances, we risk missing vital areas where new knowledge is needed. 2

The information provided in this review can improve practice by equipping dental practitioners (DPs) with the relative knowledge and contextual understanding of the dental experiences of autistic adults. This can enhance the provision of dental care to this patient population by providing recommendations to meet the needs explored in this review.

This review aimed to identify and synthesise qualitative research on challenges associated with dental appointments, from the perspective of autistic adults. The research question is: How do autistic adult patients experience challenges associated with dental appointments?

This review was conducted from October 2022 to April 2023 following the PRISMA Checklist 3 and ENTREQ framework. 4 Five electronic databases (MEDLINE, EMBASE, PsycInfo, EBSCO CINAHL, Web of Science) were searched using key words, Boolean Operators and search tools, and grey literature was hand-searched. Studies were screened against pre-set eligibility criteria, and included studies were critically appraised using the Mixed Methods Appraisal Tool. 5

Records identified through database and website searching (n = 207)

Reports sought for retrieval (n = 38)

Records included (Met criteria) (n = 2)

Qualitative cohort study (n = 1)

Mixed-methods case control study (n = 1).

Thematic synthesis

The data were synthesised using the process of thematic synthesis. 6 Using the software Nvivo, line-by-line coding was carried out in the study findings/results, defined as qualitative data from autistic participants' experiences (first-order constructs), and/or the researcher's interpretations (second-order constructs).

If codes representing similar experiences were present in both papers, they were grouped into broader descriptive themes/categories (third-order constructs). Categories were interpreted in view of the research question, and further developed to ‘go beyond' the primary data. This ensured distinction whilst preserving nuance, coherence and consistency. These third-order constructs represent interpretations of the author of this review. Seven analytical themes were revealed which either related to challenges for autistic patients, or recommendations for DPs.

Findings were explored considering current literature and assessed using CERQual, 7 where eight out of 31 findings were found to have a high confidence level. Core themes were represented as a model, encompassing autism-specific and external difficulties, and their interrelationships. Figure 1 represents a summary diagram of the seven analytical themes, and their descriptive themes (categories).

Summary of analytical and descriptive themes

‘Code co-occurrence', or overlapping codes, can be used to identify relationships within the data. 8 The ‘relationship' tool in Nvivo was used to show either a bi-directional or causative link between the themes, marked with ‘†'.

Anxiety was found to be a key challenge associated with dental appointments for autistic adults. Co-occurring codes were found to show the relationship † Bi-directional anxiety and communication where researchers deciphered how DPs' communication can provoke anxiety in autistic patients.

Communication difficulties are one of the core features of ASD. This analytical theme encompasses how autistic adults experienced these barriers in relation to dental appointments. Participants explained how they may need more time or alternative methods to aid this.

Autistic adults discussed the role of disclosing their diagnosis, and how DPs' lack of awareness of autism can present challenges associated with dental appointments. The interrelationship between these two categories suggests that increasing awareness and understanding of autism benefits from mutual participation from both parties.

Pain was identified as a theme where participants reported both pain hypersensitivity and hyposensitivity at the dentist.

Autistic adults commented on how sensory sensitivities present challenges at the dentist. The most common sensory category was tactile , and the relationship † Anxiety caused by sensory sensitivities was found to be causative.

Descriptive themes emerged that included recommendations from autistic participants to fellow autistic patients. This supplementary finding was not an aim of this review, and thus highlights the value of inductive research.

Finally, recommendations for DPs to support autistic adults during dental appointments were made by encompassing broad code co-occurrences from other analytical themes.

Autistic adults highlighted a variety of challenges, showcasing their diverse profiles of needs, proving that knowledge must be understood in the context of each individual.

The model in Figure 2 was created from the five ‘challenges' analytical themes and their dynamic connections, paired with a representation of external factors contributing to sensory sensitivities. It aims to go beyond previous literature by bringing barriers associated with autism and the dental environment together, demonstrating how autistic adults experience interrelating difficulties associated with dental appointments.

A proposed model to represent challenges associated with dental appointment experienced by autistic adults

This systematic review and thematic synthesis demonstrates the dynamic relationships between challenges experienced by autistic adults associated with dental appointments. Some challenges were found to be autism-specific such as sensory sensitivities and communication barriers, and fear of the unknown relating to intolerance of uncertainty. Others related to DPs' lack of awareness of ASD. Autistic adults highlighted a variety of challenges, showcasing their diverse profiles of needs, proving that knowledge must be understood in the context of each individual. A model was created to demonstrate this phenomenon. With only two included studies and one author, the findings lack robustness and cannot be generalised. Thus, the model should be continually refined to reflect emerging evidence. Further research implementing recommendations to alleviate autism-specific challenges would be intrinsic to improving the experiences of this high-need population.

Embracing neurodiversity-informed dentistry

Abigail also appears in two papers from BDJ Team 's series on Embracing neurodiversity-informed dentistry.

Part five: Diverse minds in the dental profession: https://www.nature.com/articles/s41407-023-2026-8

Part six: Neuro-inclusion within the profession: https://www.nature.com/articles/s41407-024-2561-y

Author information

Abigail qualified as a Dental Hygienist & Therapist from the University of Leeds in 2023 with a Class I Honours degree, where she attained several awards for her academic excellence. She continues to work with the University of Leeds as an Alumni representative for the School of Dentistry Academic Lead for Inclusive Pedagogies, as well as designing and implementing neuro-inclusive training and education for the dental workforce. This article is a summary of the systematic review she conducted as part of her degree. If you are interested in receiving the full version of this dissertation, please contact [email protected].

Fiske J, Dougall A, Lewis D P. A clinical guide to special care dentistry. London: BDJ Books, 2009.

Clark A M. Why qualitative research needs more and better systematic review. Int J Qualitative Methods 2016; doi: 10.1177/1609406916672741.

Page M J, McKenzie J E, Bossuyt P M et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev 2021; doi: 10.1186/s13643-021-01626-4.

Tong A, Flemming K, McInnes E, Oliver S, Craig J. Enhancing transparency in reporting the synthesis of qualitative research: ENTREQ. BMC Med Res Methodol 2012; doi: 10.1186/1471-2288-12-181.

Hong Q N, Fàbregues S, Bartlett G et al . 2018. The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Education for Information 2018; 34: 285-291.

Thomas J, Harden A. Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med Res Methodol 2008; doi: 10.1186/1471-2288-8-45.

Lewin S, Booth A, Glenton C et al . Applying GRADE-CERQual to qualitative evidence synthesis findings: introduction to the series. Implement Sci 2018; doi: 10.1186/s13012-017-0688-3.

Scharp K M. Thematic co-occurrence analysis: Advancing a theory and qualitative method to illuminate ambivalent experiences. J Commun 2021; 71: 545-571.

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Abigail Craven

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Craven, A. Summary of: A systematic review and thematic synthesis of the challenges associated with dental appointments, from the perspective of autistic adults. BDJ Team 11 , 360–363 (2024). https://doi.org/10.1038/s41407-024-2750-8

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• Published: 19 September 2024

Understanding the influences on hospital discharge decision-making from patient, carer and staff perspectives

• Kristel Ward-Stockham 1 , 2 , 4 ,
• Olumuyiwa Omonaiye 2 , 3 , 4 ,
• Peteris Darzins 1 , 4 , 5 ,
• Clinton Kitt 1 ,
• Evan Newnham 1 , 4 , 5 ,
• Nicholas F. Taylor 4 , 6 &
• Julie Considine 2 , 3 , 4  

BMC Health Services Research volume  24 , Article number:  1097 ( 2024 ) Cite this article

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Gaps in discharge planning are experienced by 41% of hospital patients in Australia. There is an established body of knowledge regarding the features of the discharge process that need to be improved to avoid subsequent hospital readmission and enhance the discharge experience. However, many of these studies have focused solely on factors related to unplanned hospital readmissions and there has been limited success in operationalising improvements to the discharge process. The aim of this study was to explore and describe the factors that influence the decision to discharge adult medical patients from hospital, from patient, carer and staff perspectives.

A qualitative descriptive study was conducted in one acute medical ward in Melbourne, Australia. The study data were collected by observations of clinical practice and semi-structured interviews with patients, carers and staff. Participants were: i) English-speaking adults identified for discharge home, ii) patient carers, and iii) staff involved in the discharge process. Observation data were analysed using content analysis and interviews data were analysed using thematic analysis.

Twenty-one discharges were observed, and 65 participants were interviewed: 21 patients, two carers, and 42 staff. Most patients (76%) were identified as being ready for discharge during morning medical rounds, and 90% of discharge decisions were made collaboratively by the medical team and the patient. Carers were observed to be notified in 15 discharges by the patient ( n  = 8), doctors ( n  = 4), or nursing staff ( n  = 3). Five themes were constructed from thematic analysis of interviews: Readiness for Home, Fragmented Collaboration, Health Literacy, Unrealistic Expectations, and Care beyond Discharge. A collaborative team and supportive carers were considered to enhance risk assessment and discharge planning, however fragmented communication between clinicians, and between clinicians and patients/carers was a barrier to discharge decision-making.

Conclusions

Our study highlights the need for a more coordinated approach to discharge decision-making that optimises communication with patients and carers and multidisciplinary workflows and reduces fragmentation. The importance of patient-centred care and a personalised approach to care are well established. However, there is a need to design systems to customise the entirety of the patient journey, including the approach to discharge decision making.

Peer Review reports

Unplanned hospital readmissions are costly, distressing and inconvenient for patients and carers, increase the risk of iatrogenic harm [ 1 ], and result in potentially avoidable resource utilisation [ 2 , 3 , 4 ]. Lack of access to healthcare outside the hospital environment is a major factor in unplanned hospital readmissions, particularly in the first few days following hospital discharge [ 1 , 5 , 6 ]. Carers, who play a vital role in safe discharge and patient support at home, are often not included in discharge planning conversations or decisions [ 1 ]. Further, carer inclusion in discharge planning often occurs by chance if they happened to be on the ward, or if they insisted on involvement, usually as a consequence of previous suboptimal experiences [ 1 ].

Gaps in discharge planning are experienced by 41% of acute hospital patients in Australia [ 7 ]. Despite an established body of knowledge regarding the features of the discharge process that need to be improved to avoid unplanned hospital readmission and enhance the discharge experience [ 1 , 6 , 8 ], there has been limited success in operationalising improvements to the discharge process in a way that meets patient, carer and staff needs [ 9 ]. Thus, it is important to understand the factors related to discharge decision-making from patient, carer and staff perspectives as a foundation for improving the discharge process.

The aim of this study was to explore and describe the factors that influence the decision to discharge adult patients from a hospital medical ward, from patient, carer, and staff perspectives. For the purpose of this study, carer refers to family members, or any other persons significant to the patient.

In this qualitative descriptive study, data were collected by observations of clinical practice and semi-structured interviews with patients, carers and staff. This study is reported according to the Consolidated Criteria for Reporting Qualitative Research [ 10 ]. The first three steps of the conceptual framework Functional Resonance Analysis Method (FRAM) [ 11 ] were used to guide the study conduct: i) deciding the purpose of the FRAM analysis (hospital discharge); ii) identifying the functions necessary for that work to be achieved (as defined by the participants involved in the activity) and describing each function in terms of six aspects (output, input, precondition, resource, control, and time); and iii) identify and describe variability in the identified functions.

Ethics approval and consent to participate

This study was undertaken according to the Declaration of Helsinki [ 12 ] and was approved by the Human Research Ethics Committees at Eastern Health (LR21-019-73462) and Deakin University (2021–237). All participants provided written informed consent.

Setting and participants

The study was conducted at Eastern Health, in Melbourne, Australia. Eastern Health provides care to 1.3 million patients per year across a large range of services and has seven hospitals. This study was conducted on a 28-bed general medical ward in a 155-bed outer metropolitan hospital. The study ward was purposively chosen for its high rate of daily discharges. The model of care on this ward is supported by daily consultant medical officer ward rounds, ward-based junior medical and allied health staff, and daily multidisciplinary team meetings. Nurse to patient ratios were 1:5 on morning, 1:6 on afternoon and 1:10 on night shift plus the nurse-in-charge. Study participants were adults (aged ≥ 18 years) who were identified for discharge and were going to their own homes. Eligible participants were identified by the nurse-in-charge, the daily multidisciplinary meeting or the electronic bed management system. Patients discharged to other facilities were excluded. Written informed consent was obtained from patients and carers for the observations of clinical practice, and from all participants who were interviewed. An opt-out consent process was used for staff who were observed. The consent process is summarised in Table  1 .

Data collection and procedure

For consenting patients (self or carer), the discharge process was observed (see Supplementary Table 1 for observation schedule). The observation schedule was informed by the FRAM [ 11 ] and the interview guide was based on a review of the literature and previous work by the research team [ 1 , 5 , 6 ]. Patients, carers, and staff who were observed were invited to participate in a 15 to30-minute follow-up interview (see Supplementary Tables 2 and 3 for interview guides). Non-participant structured observations and semi-structured interviews were completed by one of two researchers (OO, a male doctoral prepared public health researcher or KWS, a master’s prepared female nurse researcher) between November–December 2022. One researcher (KWS) was known to ward nursing staff, neither researcher had a relationship with patients or carers, or had line management or patient care responsibilities on the study ward. Patients and carers were interviewed on the ward, in the transit lounge, or by telephone. Staff were interviewed as close to the time of discharge as possible or prior to the end of their shift on the ward. The interviews, which were audio-recorded, were a maximum of 26 min duration (average = 16.7 min). Data saturation was reached when the research team determined that no new information was coming from the observations, and the interview content was repetitive.

The tenets of rigor of qualitative research are credibility, transferability, dependability, and confirmability leading to trustworthiness [ 13 ]. Credibility was established by the systematic development of the interview guides, and the sound methodological approach ensured dependability. Confirmability was established by using examples from the observations and interviews to ensure that patients’, carers’ and staff voices were represented. A reflexive approach to thematic analysis requires researchers to question their assumptions, highlights researchers’ skills as resources, and requires researchers’ reflexive engagement with the data in its interpretation [ 14 ]. The research team was diverse and comprised researchers from nursing, medical and allied health backgrounds.

Data analysis

The hand-written field notes from the structured observations were transcribed by the researchers (OO and KWS) and analysed using content analysis [ 15 ]. The semi-structured interviews were professionally transcribed verbatim and analysed using an inductive thematic analysis framework [ 14 , 16 ]: familiarisation with the data; generating initial codes; searching for themes; reviewing themes; defining and naming themes; and producing the report. Two researchers (OO and KWS) checked the transcripts for accuracy against the audio files, entered them into NVIVO software and independently performed the initial coding. Key words and phrases were utilised to extract themes that could be used to understand participant values, attitudes, and opinions. An open coding process was used, so codes were not determined a priori, but developed and modified during the coding process [ 14 , 16 ]. Two researchers (OO and KWS) individually coded the data in NVivo [ 17 ] and then came together to review the codes, and through an iterative process, identified subthemes and themes. The themes, subthemes and codes were circulated to the research team. Example interview data and their respective codes, subthemes and themes are shown in Table  2 .

Observations of clinical practice

Twenty-one discharges were observed. Patients’ average age was 67 years, 11 identified as female, and patients had an average hospital stay of 3.6 days. The majority of patients ( n  = 16) were identified as ready for discharge during morning medical rounds and 90% of discharge decisions were made collaboratively by the medical team and the patients. Confirmed discharges were discussed after medical rounds in the multidisciplinary meeting prior to actual discharge ( n  = 18). Patients were informed of their discharge by the medical team on morning rounds ( n  = 15) or by a member of the multidisciplinary team following the morning meeting ( n  = 6). One carer was observed to be consulted via telephone regarding readiness for discharge. Carers were notified of patient discharge by the patient ( n  = 8) or medical  ( n  = 4) or nursing staff ( n  = 3): in the other six discharges, carer engagement was not observed. Carers were the main method of transport home ( n  = 15), while four patients took taxi or ride-share at their own expense. The transit lounge was used in 14 discharges.

Patients were advised by the medical team to follow-up with their general practitioner ( n  = 16), and or a medical specialist or community allied health ( n  = 7), and three patients were informed they needed to book a medical procedure. In 12 of the observed discharges, patients were given the opportunity to ask questions, and nine patients were consulted to confirm they could physically get to an appointment. Sixteen patients received a nursing discharge summary from the nurse-in-charge: eleven patients were asked if they had questions or had their understanding of the paperwork clarified. Five patients were not observed to receive information. One of the patients was observed to receive a medical summary or correspondence to their general practitioner on request.

Pharmacists were observed to counsel patients about medications ( n  = 17), provide written medication information ( n  = 8), allow for questions ( n  = 16), clarify that information was understood ( n  = 14), and bring medications to the ward ( n  = 18). Observed communication about prescriptions was verbal (between pharmacists and nurses) or check marks on a whiteboard at the nurses’ station. Discharges identified on the day or previous day were observed to not have prescriptions written until the afternoon of the day of discharge. Awaiting prescriptions, and supply of medications was observed to delay discharges ( n  = 12). When there were time constraints such as arrival of available transport, pharmacists had less time to devote to patients and the patients were not able to ask questions.

Interviews with patients, carers and staff

Thematic analysis of 65 interviews from 42 staff (17 nurses, 10 doctors, 7 pharmacists, 3 ward clerks, 2 physiotherapists, 2 occupational therapists and 1 social worker), 21 patients and two carers (one adult child and one parent) resulted in five themes: Readiness for Home, Fragmented Collaboration, Health Literacy, Unrealistic Expectations, and Care beyond Discharge (Fig.  1 ).

Factors influencing discharge decision making

Theme 1: Readiness for home

The first theme “Readiness for Home” was related to ensuring patients are ready for discharge using a holistic view from the healthcare professional team. Medical stability, decided by the medical consultant, was the driving force for determining discharge: “We think about primarily the medical—their clinical status … the patient’s symptoms. As long as they have improved” (Doctor 1). Beyond medical stability, risk factors such as functional and social concerns were considered by the nursing and multi-disciplinary teams to ensure a safe discharge and avoid readmission to hospital: “ … determining if there was any risks to this patient’s safety” ( Social worker 1). Medical teams relied on allied health professionals to assist in functional assessment: “When I was going through medical training, I think talking about function is something that we mention briefly but we never focus on because our job is the medical” (Doctor 2).

Nursing and allied health staff played a significant role in risk assessment. Nursing staff were consulted regarding the patients’ progression and reliance on nursing care: “I consider how I feel about them. What I see, the changes they’ve had. … I want to make sure that they’re going home and they can look after themselves” (Nurse 1). Information about function and nursing care requirements was provided to the team by the nurse-in-charge during the multidisciplinary team meeting: “So that information would then get handed over eventually to the nurse-in-charge. She will be the one on behalf of the nurse – their assigned nurse—to relay that information to the rest of the multidiscipline team” (Nurse 2) . Nurses expressed they could advocate for patients who were not yet ready for discharge, functionally or socially, once identified as medically stable: “… if you think it’s not safe for someone to go home for a particular reason at that time, then we would advocate for the patient” (Nurse 3).

Patients described the hospital environment as disruptive and noisy, and were concerned about being unable to sleep, exercise, or have their carers visit. Patients were eager to go back to their home environment: “… not a lot I can do at home … but … least you’re in your own environment and the dog will be happy to see me” (Patient 1) . Carers were keen to have patients at home and happy to support them in their recovery, but expressed concerns about a lack of communication regarding patients’ health status: “ I think in some ways it would be good if the hospital were able to liaise with family and fill—let us know what’s going on” (Carer 1) .

Theme 2: Fragmented collaboration

The second theme “Fragmented Collaboration” describes variability in collective decision-making process, that involved to greater or lesser extents, patients, carers and the healthcare team. Medical, nursing, social work, occupational therapy, and physiotherapy staff attend daily multidisciplinary team meetings that focused on facilitating a safe discharge using a patient-centered approach. The opportunity to communicate and share decisions within the multidisciplinary team was a facilitator to assessing risks and identifying barriers to discharge. Staff expressed that the multidisciplinary team meeting was a facilitator to collaborative decision-making and communication:

“…having a chance for the nursing staff, the physios and the OTs to sit down, and the medical staff, is always good … that discussion at the 11 o’clock meeting is always a good way of talking about discharge destinations”. (Doctor 3)

Nurses felt they should have more input into discharge decisions: “ The nurses looking after the patient should definitely be involved in the discharge” (Nurse 4). However, nurses also reported being involved in decision-making only if they were in the room at the time of discussion: “… it’s when we’re in – we’re busy and we’re in a hurry and the doctors have made decisions without consulting us” (Nurse 1).

Decisions about medical stability for discharge were made by the medical team, and patients commented on a lack of involvement: “ I know that they’ve [doctors] made a decision and it’s impossible for you [doctors] to be in conference with your patients about the decisions that you’re making” (Patient 2). Patients felt they had not received enough information about their health, rationale for new medication, or management of their condition: “I’m actually going to take that script and go to my [general practitioner] GP who I trust and have a conversation with them about it” (Patient 2).

Visitor restrictions due to the COVID-19 pandemic meant that carers had little opportunity to contribute to the discharge discussion in person until discharge was confirmed. When carers were asked about being notified of patients’ discharge they replied : “…I was on the phone to the doctor this afternoon and he just notified …she [the patient] can go home. So, I said, just give me a couple of hours and he said, how about four o’clock?” (Carer 1). Nurses also noticed the lack of involvement of carers: “ We have the meeting, however, things get discussed in there that they don’t seem to come out to the patient or the families out here” (Nurse 5). Carers were contacted by the patient, the nurse-in-charge, or the medical team after the decision to discharge was made but they expressed wanting to be more involved in discharge planning: “I would like to hear from the doctors and things, what’s happening and where she is at and what the—moving forward is going to mean and look like” (Carer 2). Healthcare professionals recognised the importance of carer input in assessing risks and barriers to returning home, communicating social and functional issues, available supports at home, and if there were any concerns:

“Sometimes it’s helpful to have that person there to tell us what’s actually happening from a third-person perspective, because sometimes patients don’t always tell you the truth, or they minimise because they know that it would delay discharge if they tell you they’re struggling at home” (Doctor 2).

Communication of discharge decisions between staff was ad hoc. Clinicians’ knowledge about systems and local processes used to communicate discharge decisions was varied:

“…if we have a page, a board maybe, about what needs to be done prior to discharge, even if it’s an electronic system that we can all tick off. We just don’t have that system. W e have …[electronic bed management system] but it doesn’t give me the information of what … needs to be done” (Doctor 2).

Therefore, the nurse-in-charge assumed the critical role of coordinating the discharge process “… the nurse in charge is the one that coordinates the discharge” (Social Worker 1) and maintained workflow “… it relies a lot on the nurses to keep the flow going and tell us who to see and who to discharge first … so we rely on them (Doctor 5)”. The nurse-in-charge also facilitated communication between disciplines “…they had to update me as well as pharmacy, doctors, interns and pharmacist” (Nurse 6), completed paperwork, and kept the patient updated “ …once I know, I will let the patient know so then she can contact the next of kin…” (Nurse 2).

Theme 3: Health literacy

The third theme “Health Literacy” refers to the knowledge and skills patients need to comprehend their own health needs. Patients identified difficulties in understanding medical information such as discharge medications:

“… they gave me a sheet of all the medication I’m taking … they also had a printout like what they do, what each tablet does, because I wouldn’t have a clue. ... I can’t even pronounce the name, let alone know what I’m taking” (Patient 2).

Patients described receiving verbal information from multiple sources creating confusion: “I have had a lot … of people … but a single font of information would have been … handy just for clarity.” (Patient 2). Staff identified that information from multiple sources was problematic “It probably feels clunky to the patient” (Nurse 8) and “… the information needs to be more streamlined” (Patient 2). Both patients and staff highlighted the need for greater coordination of information: patients felt that “ … having that singular interface, an overriding liaison …one person who would come in and say this is where you’re at …” (Patient 2) was important and staff had similar comments “… allocate[ing] someone to be … that person’s discharge person who’s … responsible for that communication with the patient and their carer” (Physiotherapist 1) .

Participants identified opportunities to improve discharge documentation. For example, medical staff commented “Give them their discharge summary from this admission just so that they have a written copy of all the issues that have happened and all the investigations that have been looked at” (Doctor 1) . Doctors wanted to give patients their discharge summary before the patient left the ward: “… we try to give them their discharge summary from this admission” (Doctor 1) but reported excessive workload and time constraints resulted in “… a backlog of them [discharge summaries]” (Doctor 3) . Patients found discharge summaries from previous admissions to be useful: “When I was discharged from ED [Emergency Department] I got a printout of what was sent to the doctor. That was useful, even though it was all stuff I don’t really understand” (Patient 5). Patients found nursing documentation was less useful:

“…the paperwork needs to be more detailed because not everyone leaving hospital is in a space to receive this information and retain it” (Patient 4).

Theme 4: Unrealistic expectations

Increased patient care needs, and workforce shortages have resulted in significant workload pressures affecting staff, patients, and carers. Staff shortages affected all professions “…if the workload is not permissible, we will skip those bits [risk assessments] and let the ward team handle it” (Doctor 2) and “it’s not solely clinical, we’re juggling lots of different things …” (Pharmacy 1). Nurses described limited capacity to update patients and carers, “ … because of the workload as well. It’s usually a phone call once they’re confirmed to say yep, they’re going home” (Nurse 5) .

Staff described feeling pressured to rush discharges to create bed capacity: “I feel that it’s sometimes too rushed, especially when there’s pressure from the executives…” (Ward clerk 1) and associated risks “…things are obviously going to be missed because the pressure’s on and that’s not fair on the staff or the patient” (Ward clerk 1) . Staff commented that patients are sometimes discharged before they are well enough “ … it’s just about addressing … why they don’t feel like they’re ready and supporting them and saying, the medical team think you’re ready” (Doctor 5) . On the other hand participants also identified risk aversion influenced decision-making, resulting in longer lengths of stay with poorer patient outcomes:

“I find certain doctors are too risk averse for a discharge and we should be aiming to get more people out the door… There’s a lot of information to suggest that actually staying in hospital when you don’t need to is obviously bad for you. You become deconditioned; you need rehab.” (Doctor 3) .

Adding to the pressure of timely and safe discharges were local process barriers. Delays in discharge were often caused by waiting for prescriptions “…so we wait for a script to be prepared and that can be sometimes hours” (Nurse 8) , medications “… a lot of things happen during the whole dispensing or supply of the medication” (Pharmacist 2), or transport “… ambulances … in some cases we’ve waited for hours” (Ward clerk 2) .

Theme 5: Care beyond discharge

The final theme “Care Beyond Discharge” is about patients and carers still requiring care and support once they are at home. Nursing staff played a central role in judging home and carer supports, patients’ abilities to manage at home, and identifying when allied health staff should be involved:

“I will ask the patient themselves, what are their concerns? … then … the appropriate profession will be notified as well so then they can prioritise coming in and answer those questions for the patient.” (Nurse 2) .

Allied health assessments are critical to “ … make sure that the patient is well supported in the community when they go” (Doctor 1), and allied health professionals talked about nursing and medical staff “keep[ing] the patient until we have been able to complete our assessment, which is a good thing I guess in terms of determining risks” (Social Worker 1) . Carers expressed their willingness to support patients at home, “there’s a support network. myself and other adults … who can jump in and out to support her with her recovery” (Carer 1) . Patients described challenges in accessing follow-up care: “… the hardest part is getting into podiatry …. I was looking for like a year” (Patient 1). Staff also expressed difficulties in organising post discharge community support: “..they don’t have the staff to.. provide that service in the home… staff are sick” (Social Worker 1).

Despite all patients being advised to follow-up with their general practitioner, communication with primary care providers and medical specialists was limited and clinicians expressed wanting time to contact the general practitioner prior to discharge: “…a call to his GP [general practitioner] probably is a good idea, but … in practice, actually we don’t really call GPs that often unless it’s very complicated, just because of time demands” (Doctor 3). The acute hospital environment, high patient turnover and time constraints of general practitioners were cited as making communication with general practitioners difficult: “…the GP’s too busy. They have got their own things to do. So, I think the written summary is probably the best, still” (Doctor 7) .

In this study we explored and described the factors that influenced discharge decision-making for adult patients with medical conditions, from patient, carer and staff perspectives. Based on observations of the discharge process, and follow-up interviews with patients, carers and staff, the major influences on discharge decision-making were: i) patient factors, ii) staff capability given various work pressures, and iii) the interplay between patients, carers and staff.

Patient factors that influenced discharge decision-making were readiness for discharge, health literacy and care beyond discharge. Patient’s clinical “readiness for discharge” was largely determined by medical staff with input from other professions commonly an afterthought or the result of opportunistic presence during medical rounds. Patients and carers in this study wanted more input into discharge decision-making and patients, carers and staff all expressed a need for greater carer involvement. Other studies have shown that patients and carers often trust healthcare professionals to make discharge decisions [ 18 ].

Patient health literacy (ability to use reading, writing, verbal, and numerical skills [ 19 ] and, or language concordant care [ 20 ]) was both observed and reported in interviews with patients / carers as an influence on discharge decision-making. Patients with limited health literacy are known to have poor health outcomes, including increased risk of unplanned emergency department visits or hospital readmission following hospital discharge [ 19 ]. One of the issues that may exacerbate limitations in health literacy for patients and carers is the volume of information received during this discharge process, which was observed in our study and reinforced by patients and carers during the interviews. Patients and carers described being overwhelmed by large quantities of verbal information coming from many different people, and that written information was not always meaningful. Other studies of patients’ perception of communication of discharge decisions also allude to a need for patient-centred communication, use of understandable language, and checking that patients and carers understood the information presented to them [ 21 ].

Many participants commented that a person acting as a single point of contact would be helpful in navigating decisions during the discharge process. The complexity of care needs, and difficulties accessing primary care or community health following discharge also added weight to the notion of a discharge coordinator. ‘Navigation’ programs or discharge coordinators improve outcomes and care experiences for patients and carers in various contexts, including hospital discharge [ 22 , 23 ]. The key responsibilities of the discharge coordinator would be to address patient and family concerns, answer questions, and engage patients and families [ 23 ].

Patient care needs beyond discharge were recognised as important and also influenced discharge decision-making, but observations and interviews both showed that medical staff were less likely than nursing or allied health staff to recognise ‘non-medical’ care needs at home. The optimal model of post-discharge care is unclear for a number of reasons. First, the outcome of interest varies between studies and includes unplanned readmission avoidance [ 24 ], function and prevention of functional decline [ 25 ], and older persons experiences of adapting back to life at home after hospitalisation [ 26 ]. A meta summary of findings from 13 qualitative studies of older patients’ experience of managing at home after hospital discharge found four themes: i) experiencing an insecure and unsafe transition, ii) settling into a new situation at home, iiii) what would I do without my informal caregiver? and iv) experience of a paternalistic medical model [ 26 ]. The results of this study [ 26 ] and our study both highlight the importance of planning, information and involvement of patients and carers in decisions about discharge and follow-up care.

The staff capability factors that influenced discharge-decision-making were workflows and unrealistic expectations. Shared decision-making during discharge planning is highly valued by patients [ 21 ]. Our data showed that current workflows often precluded timely and shared discharge decision-making. Better communication between patients, carers and staff was highlighted as important in our study and has been a major finding in other studies of hospital discharge [ 26 ].

In our study, many organisational expectations were viewed by clinicians as unrealistic, of limited feasibility, or creating other pressures that hindered the decision-making process. Many organisational imperatives focus on morning discharge with the intent of improving hospital throughput and reducing emergency department overcrowding [ 27 , 28 ]. The flow-on impacts to inpatient clinician roles are often neglected in these initiatives [ 27 , 28 ]. Co-design with patients and carers of such initiatives is often lacking and can result in a less holistic approach to the experience of discharge for patients [ 28 , 29 ].

Finally, there was a complex interplay between patients, carers and staff. Carers were rarely present during discharge decision-making (despite patients, carers and staff all expressing a desire for greater carer input). Carers tended to be engaged or informed once decisions to discharge the patients had already been made, despite carers’ important roles in confirming the patients’ baseline level of function, assisting patients at home, and bridging any gaps and providing care while patients wait for services [ 23 ]. Despite multidisciplinary team meetings being highly valued by many staff and described as facilitating risk assessment, and discharge planning, observations of clinical practice showed that discharge decisions were largely operationalised around the needs of medical staff, and notably, did not actively engage patients or carers. Other studies have reported tension at multidisciplinary team meetings, largely due to differing goals of different professional groups. For example, medical staff under pressure to send patients home focused on medical stability but nursing and allied health staff focused on whether patients were physically or cognitively safe for discharge [ 23 ]. Co-design of increasing patient or carer (who wish to be involved) involvement and engagement in multidisciplinary team meetings warrants further consideration and evaluation from patient, carer and clinician perspectives.

Strengths and limitations

Use of a rigorous qualitative methodology with direct observations supplemented by interviews, and allowing the expression of experience, perspectives and opinions from patients, carers and staff was a strength of this study. The interviews were conducted directly after the discharge, thus reducing recall bias and enabling sharing of fresh experiences. There are limitations that should be considered when interpreting the study findings. Patients interviewed were English language speakers discharged to home and who were without cognitive impairment. Capturing the experiences of patients living in residential facilities, or with limited English proficiency should be a focus of further work. The focus of this work was patients discharged from hospital on the days of data collection and who had a predetermined decision that they were suitable for hospital discharge. Thus, patients (and carers) deemed not for hospital discharge were not included in this study. The factors that influence the decision not to discharge patients from hospital remains a knowledge gap that should be addressed in future studies. The study took place while visitor restrictions were in place due to COVID-19, thus limiting access to carers.

Our study highlights gaps in the approach to discharge decision-making for patients in whom a decision to discharge has been made, which has implications for workflow, communication and patient safety. Early two-way communication by staff with patients and carers, that could enhance patients’ and carers’ appreciation of their situations is lacking. Facilitation of communication by a designated staff member may improve the quality of discharge decision-making. Medical staff have a dominant role in discharge decision-making from the acute hospital setting, with multi-disciplinary team involvement having a lesser role, yet involvement of the latter is perceived to be beneficial and is desired. These insights may inform optimisation of discharge decision-making processes. Finally, the drivers of decisions not to discharge patients from hospital are still poorly understood and should be an area for future research.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Functional Resonance Analysis Method

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Acknowledgements

Thank you to the patients, carers and staff who participated in this study. The study team would like to particularly acknowledge Professor Leanne Boyd (Chief Nursing and Midwifery Officer, Eastern Health), Ms Kerrie Megee (study ward Nurse Manager), Dr David Lau (Site Director of General Medicine) and Dr Gary Yip (Director General Medicine, Eastern Health) for their support of this study.

This study was funded by the Eastern Health Foundation.

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Kristel Ward-Stockham, Peteris Darzins, Clinton Kitt & Evan Newnham

Deakin University, Geelong: School of Nursing and Midwifery and Centre for Quality and Patient Safety in the Institute for Health Transformation, 1 Gheringhap St, Geelong, VIC, 3220, Australia

Kristel Ward-Stockham, Olumuyiwa Omonaiye & Julie Considine

Centre for Quality and Patient Safety – Eastern Health Partnership, Eastern Health, 5 Arnold St, Box Hill, Victoria, 3128, Australia

Olumuyiwa Omonaiye & Julie Considine

Eastern Health Institute, Eastern Health, Box Hill, Victoria, 3128, Australia

Kristel Ward-Stockham, Olumuyiwa Omonaiye, Peteris Darzins, Evan Newnham, Nicholas F. Taylor & Julie Considine

Eastern Health Clinical School, Monash University, Clayton, VIC, 3168, Australia

Peteris Darzins & Evan Newnham

La Trobe University, Bundoora, VIC, 3086, Australia

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KWS: Formal analysis, Investigation, Project Administration, Writing—Original Draft OO: Formal analysis, Investigation, Project Administration, Writing—Original Draft CK: Conceptualization, Funding acquisition, Writing—Review & Editing EN: Conceptualization, Methodology, Funding acquisition, Writing—Review & Editing PD: Conceptualization, Methodology, Funding acquisition, Writing—Review & Editing NFT: Conceptualization, Methodology, Funding acquisition, Writing—Review & Editing JC: Conceptualization, Methodology, Formal analysis, Investigation, Writing—Original Draft, Supervision, Project administration, Funding acquisition.

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Correspondence to Julie Considine .

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This study was approved by the Human Research Ethics Committees at Eastern Health (LR21-019-73462) and Deakin University (2021–237). All participants provided written informed consent.

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Ward-Stockham, K., Omonaiye, O., Darzins, P. et al. Understanding the influences on hospital discharge decision-making from patient, carer and staff perspectives. BMC Health Serv Res 24 , 1097 (2024). https://doi.org/10.1186/s12913-024-11581-0

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DOI : https://doi.org/10.1186/s12913-024-11581-0

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