examples of research questions for thematic analysis

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Thematic Analysis – A Guide with Examples

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

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

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

Importance of Thematic Analysis

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

Thematic analysis is used because:

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

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

How to Conduct a Thematic Analysis?

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

Understand the Data

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

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

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

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

Development of Initial Coding:

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

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

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

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

Make Themes

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

Extracted Data Review

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

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

Reviewing all the Themes Again

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

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

Corpus Data

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

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

Define all the Themes here

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

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

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

Make a Report

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

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

Frequently Asked Questions

What is meant by thematic analysis.

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

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Thematic Analysis: A Step by Step Guide

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

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Associate Editor for Simply 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 data segments 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 recursive, not linear. Researchers move back and forth between phases, refining codes and themes as their understanding of the data evolves.
• Interpretation : The researcher interprets the identified themes to tell a compelling and insightful story about the data.

Many researchers mistakenly treat thematic analysis (TA) as a single, homogenous method. However, as Braun and Clarke emphasize, TA is more accurately described as an “umbrella term” encompassing a diverse family of approaches.

These approaches differ significantly in terms of their procedure and underlying philosophies regarding the nature of knowledge and the role of the researcher.

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, frequently employed in the US, leans towards a positivist philosophy . It prioritizes objectivity and replicability, often using predetermined themes or codes.

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. Reflexive Thematic Analysis

Braun and Clarke’s reflexive thematic analysis is an approach to qualitative data analysis that emphasizes researchers’ active role in knowledge construction.

It involves identifying patterns across data, acknowledging how researchers’ perspectives shape theme development, and critically reflecting on the analysis process throughout the study.

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.

3. 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.

Why coding reliability doesn’t fit with reflexive TA:

• Using coding reliability measures in reflexive TA represents an attempt to quantify and control for subjectivity in a research approach that explicitly values the researcher’s unique contribution to knowledge construction.
• Braun and Clarke argue that such attempts to bridge the “divide” between positivist and qualitative research ultimately undermine the integrity and richness of the reflexive TA approach.
• The emphasis on coding consistency can stifle the very reflexivity that reflexive TA encourages.

Six Phases Of Reflective Thematic Analysis

Reflexive thematic analysis was developed by Virginia Braun and Victoria Clarke, two prominent qualitative researchers.

The process of thematic analysis is characterized by an iterative 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.

Here’s a breakdown of the six phases:

• This initial phase involves immersing oneself in the data.
• It includes transcribing audio or video data (if necessary) and engaging in repeated readings of the transcripts.
• The goal is to gain a thorough understanding of the content and begin to notice initial patterns or interesting features.
• This phase involves systematically identifying and labeling segments of data that are relevant to the research question.
• Codes are like labels attached to meaningful chunks of data, helping to organize and categorize information.
• This phase marks the shift from individual codes to broader patterns of meaning.
• The researcher starts grouping codes that seem to cluster together, indicating potential themes.
• It’s crucial to recognize that themes do not simply “emerge” from the data; rather, the researcher actively constructs them based on their interpretation of the coded data.
• This phase involves critically evaluating the initial themes against the coded data and the entire data set.
• It’s a process of quality checking and ensuring that the themes accurately and comprehensively reflect the data.
• Researchers may need to refine, discard, or even generate new themes based on this review process.
• This phase involves developing clear and concise definitions for each theme, capturing their scope and boundaries.
• The researcher aims to identify the “essence” of each theme and ensure that each theme has a distinct and meaningful contribution to the overall analysis.
• This stage also involves developing succinct and evocative names for the themes, conveying their central meaning to the reader.
• The final phase involves weaving the themes together to present a coherent and compelling narrative of the data.
• The write-up should not merely describe the data but should offer insightful interpretations, relate the findings back to the research question, and connect them to existing literature.

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 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.

Research question(s) and coding

• Braun and Clarke argue that the research question should be at the forefront of the researcher’s mind as they engage with the data, helping them focus their attention on what is relevant and meaningful.
• The research question is not set in stone; it can, and often should, evolve throughout the analysis.
• Braun and Clarke encourage a flexible and iterative dance between the research question and the coding process in reflexive thematic analysis.
• They advocate for a dynamic interplay where the research question guides the analysis while remaining open to refinement and even transformation based on the insights gleaned from deep engagement with the data.
• The coding process, with its close engagement with the data, can reveal new insights, nuances, and avenues for exploration, potentially leading to a reframing or narrowing of the initial 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 question(s).

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).

Qualitative data analysis software, such as NVivo can streamline the coding process, help you organize your data, and facilitate searching for patterns.

Example: Instead of manually writing codes on note cards or in separate documents, you can use software to directly tag and categorize segments of text within your data. This allows for easy retrieval and comparison of coded extracts later in the analysis

However, while software can assist with tasks like organizing codes and visually representing relationships, the researcher maintains responsibility for interpreting the data, defining themes, and making analytical decisions.

Decide On Your Coding Approach

• Will you use a predefined deductive coding framework with examples (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.

Instead of chasing data saturation , Clarke advocates for aiming for “ theoretical sufficiency “. This means coding data until you have enough evidence to confidently and convincingly support your interpretations and answer your research question.

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 the research report write-up.
• Remain open to revisiting uncoded data later in analysis.

Do A First Round Of Coding

• You are not required to code every single line or sentence. The size of the data segment you code can vary depending on what is meaningful and relevant to your research question.
• Go through the data and assign initial codes to chunks that could contribute to answering your research question, even if the connection seems tenuous at first.
• Instead of aiming for absolute certainty, Braun and Clarke suggest researchers consider whether a data segment is “potentially relevant” to the research question.
• 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.
• Recognize that your understanding of the data, and therefore your codes, will likely evolve as you work through the data

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

Avoid getting bogged down in trying to create the “perfect” set of codes from the outset. Embrace the iterative nature of coding, refining, and adjusting as needed

When grappling with the decision of whether to code a particular data segment, Braun and Clarke advocate for an inclusive approach, particularly in the initial stages of analysis.

They emphasize that it’s easier to discard codes later than to revisit the entire dataset for recording.

Coding can be done at two levels of meaning:

Semantic codes provide a descriptive snapshot of the data, while latent codes offer a more interpretive and deeper understanding of the underlying meanings and assumptions present.

• Semantic:  These codes capture the surface meaning or explicit content of the data. They stay close to the participants’ intended meaning, mirroring their language and concepts. Think of semantic codes as a direct representation of what the participant says, with minimal interpretation by the researcher. They provide a concise summary of a portion of data, staying close to the content and the participant’s meaning.
• Latent:  Goes beyond the participant’s meaning to provide a conceptual interpretation of the data. They often draw on existing theories or concepts to interpret the data, providing a more conceptual “take” on what the participants are saying. Latent codes require the researcher to dig beneath the surface and make inferences based on their expertise and knowledge.

The decision of whether to use semantic or latent codes, or a mix of both, depends on the research question, the specific data, and the theoretical orientation of the researcher.

Latent coding requires more experience and theoretical knowledge than semantic coding.

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.

Both types of codes are valuable in thematic analysis and contribute to a more comprehensive and insightful analysis of qualitative data.

Evolution of codes:

Coding in reflexive TA is not a linear, pre-determined process; instead, it’s an iterative process characterized by constant development, refinement, and transformation.

Braun and Clarke underscore that in reflexive TA, codes are not static categories but rather evolving tools that the researcher actively shapes and reshapes in response to the emerging insights from the data.

Don’t be afraid to revisit and adjust your codes —this is a sign of thoughtful engagement, not failure.

Braun and Clark highlight how codes might be:

• Renamed: As the researcher’s understanding of the data deepens, they might find that a code’s initial label no longer accurately reflects the nuances of the meaning it captures. Renaming allows for a more precise and insightful representation of the data.
• Combined: Codes that initially seemed distinct might reveal overlaps or shared connections as the analysis progresses, leading to their merging into a broader, more encompassing code.
• Split: Conversely, a code that initially seemed cohesive might later reveal subtle distinctions within it, prompting the researcher to split it into two or more more focused codes, reflecting a more nuanced understanding of the data.
• Redrawn boundaries: The scope and focus of a code can also shift throughout the analysis, leading to a redrawing of its boundaries to better encapsulate the emerging patterns and insights.

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: Generating Initial Themes

Generating initial provisional (candidate) 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.

What is the difference between a theme and a code?

• A code is attached to a segment of data (your “coding chunk”) that is potentially relevant to your research question
• Themes are built from codes, meaning they’re more abstract and interpretive.
• Codes capture a single idea or observation, while a theme pulls together multiple codes to create a broader, more nuanced understanding of the data.
• Think of codes as the building blocks, and themes as the structure you create using those blocks.

Themes are higher-level units of analysis that organize and interpret the codes, revealing the overarching stories and key insights within the data. The focus is on making sense of the coded data by identifying connections, similarities, and overarching patterns that address the research question.

Phase 3 of thematic analysis is about actively “generating initial themes” rather than passively “searching for themes.” The distinction highlights that researchers don’t just uncover pre-existing themes hidden within the data.

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.

Themes involve a higher level of abstraction and interpretation. They go beyond merely summarizing the data (what participants said) and require the researcher to synthesize codes into meaningful clusters that offer insights into the underlying meaning and significance of the findings in relation to the research question.

Collating codes into potential themes :

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

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.

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 relevant to your themes, or if you need to create any new codes or themes.

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.

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

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

By the end of this step, the researcher will have a collection of candidate themes (and maybe 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.

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 (e.g., highlighted quotes or segments from interview transcripts).
• Look for codes that seem to share a common idea or concept.
• Group related codes together to form potential themes.
• If using qualitative data analysis software, you can assign the coded extracts to the relevant themes within the software.
• Some codes may form main themes, while others may be sub-themes or may not fit into any theme.
• If a coded extract seems to fit under multiple themes, choose the theme that it most closely aligns with in terms of shared meaning.

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.

Thematic maps

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.

• 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.

Thematic maps often use visual elements like boxes, circles, arrows, and lines to represent different codes and themes and to illustrate how they connect to one another.

Thematic maps typically display themes and subthemes in a hierarchical structure, moving from broader, overarching themes to more specific, nuanced subthemes.

Maps can help researchers visualize the connections and tensions between different themes, revealing how they intersect or diverge to create a more nuanced understanding of the data.

Similar to the iterative nature of thematic analysis itself, thematic maps are fluid and adaptable, changing as the researcher gains a deeper understanding of the data.

Maps can highlight overlaps between themes or areas where a theme might be too broad or too narrow, prompting the researcher to adjust their analysis accordingly.

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.”

Two main conceptualizations of a theme exist:

• Bucket theme (domain summary) : This approach identifies a pre-defined area of interest (often from interview questions) and summarizes all data relevant to that area.
• Storybook theme (shared meaning) : This approach focuses on identifying broader patterns of meaning that tell a story about the data. These themes go beyond simply summarizing and involve a greater degree of interpretation from the researcher.

Avoid : Themes as Domain Summaries (Shared Topic or “Bucket Themes”)

Domain summary themes are organized around a shared topic but not a shared meaning, and often resemble “buckets” into which data is sorted.

A domain summary organizes data around a shared topic but not a shared meaning.

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

Domain summaries group data extracts around a common topic or area of inquiry, often reflecting the interview questions or predetermined categories.

The emphasis is on collating all relevant data points related to that topic, regardless of whether they share a unifying meaning or concept.

While potentially useful for organizing data, domain summaries often remain at a descriptive level, failing to offer deeper insights into the data’s underlying meanings and implications.

These themes are often underdeveloped and lack a central organizing concept that ties all the different observations together.

A strong theme has a “central organizing concept” that connects all the observations and interpretations within that theme and goes beyond surface-level observations to uncover implicit meanings and assumptions.

A theme should not just be a collection of unrelated observations of a topic. This means going beyond just describing the “surface” of the data and identifying the assumptions, conceptualizations, and ideologies that inform the data’s meaning.

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

Example 1 : 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.

Example 2 : A theme titled “Benefits of Being Single” that lists all the positive aspects of singlehood mentioned by participants would be a domain summary. A more insightful theme might explore the underlying reasons behind these benefits, such as “Redefining Independence in Singlehood.”

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 (or “Storybook Themes”)

Braun and Clarke stress that a theme should offer more than a mere description of the data; it should tell a story about the data.

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

Shared meaning themes are patterns of shared meaning underpinned by a central organizing concept.

In contrast to domain summaries, shared meaning themes go beyond merely identifying a topic. They are organized around a “ central organizing concept ” that ties together all the observations and interpretations within that theme.

This central organizing concept represents the researcher’s interpretation of the shared meaning that connects seemingly disparate data points.

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

• Emphasis on interpretation and insight: Shared meaning themes require the researcher to move beyond surface-level descriptions and engage in a more interpretive and nuanced analysis. This involves identifying the underlying assumptions, conceptualizations, and ideologies that shape participants’ experiences and perspectives.
• Themes as interpretive stories: Braun and Clarke use the metaphor of a “storybook” to capture the essence of shared meaning themes. These themes aim to tell a compelling and insightful story about the data, going beyond a mere restatement of what participants said.

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.

Key considerations for developing shared meaning themes:

• Identifying the “Essence”: Developing a strong shared meaning theme involves identifying the “essence” or “core idea” that underpins a cluster of codes and data extracts. This requires asking questions like: What is the common thread that connects these observations? What underlying assumptions or beliefs are being expressed? What is the larger story that these data points tell about the phenomenon being studied?
• Moving beyond the literal: Shared meaning themes often involve uncovering the implicit or latent meanings embedded within the data. This requires the researcher to look beyond the literal interpretations of participants’ words and consider the broader social and cultural contexts that shape their perspectives.

Step 4: Reviewing Themes

The researcher reviews, modifies, and develops the preliminary themes identified in the previous step, transforming them into final, well-developed themes.

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. For example, you might realize that two provisional themes actually overlap significantly and decide to merge them into a single, more nuanced theme.

Level One : Reviewing Themes Against Coded Data Extracts

• Researchers begin by comparing their initial candidate themes against the coded data extracts associated with each theme to ensure they form a coherent pattern.
• 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.
• 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.
• 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.

Level Three : Considering relationships between codes, themes, and different levels of themes (sub-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.

Some themes may be more prominent or overarching (main themes), while others may be secondary or subsidiary (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 are essentially themes within a theme. They represent a further level of nuance and complexity within a broader theme, highlighting specific and important aspects of the central organizing concept of that theme.

Sub-themes provide a way to add depth and richness to your thematic analysis, but they should be used thoughtfully and strategically. A well-structured analysis might rely primarily on clearly defined main themes, using sub-themes selectively to highlight particularly important nuances within those themes.

Too many sub-themes can create a thin, fragmented analysis and suggest that the analysis hasn’t been developed sufficiently to identify the overarching concepts that tie the data together.

It’s important to note that sub-themes are not a necessary feature of a reflexive TA. You can have a robust analysis with just two to six main themes, especially if you are working with a limited word count

The relationship between codes, sub-themes and main themes can be visualized using a thematic map, diagram, or table.

This map helps researchers review and refine themes, ensuring they are internally consistent (homogeneous) and distinct from other themes (heterogeneous).

Refine the thematic map as you continue to review and analyze 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.

Final Questions:

• Does this provisional theme capture something meaningful? Is it coherent, with a central idea that meshes the data and codes together? Does it have clear boundaries?”
• “Can I identify the boundaries of this theme?”
• “Are there enough meaningful data to evidence this theme?”
• “Are there multiple articulations around the core idea, and are they nuanced, complex, and diverse?”
• “Does the theme feel rich?”
• “Are the data contained within each theme too diverse and wide-ranging?”
• “Does the theme convey something important?”

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.

• A well-crafted theme name should immediately convey the theme’s central organizing concept and give the reader a sense of the story the theme will tell.
• 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 one-word theme names or names that simply identify the topic, as this often signifies a domain summary rather than a well-developed theme.
• 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 domain 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

Braun and Clarke differentiate between two distinct approaches to presenting the analysis in qualitative research: the “establishing the gap model” and the “making the argument model” (p.120).

Establishing the Gap Model:

This model operates on the premise that knowledge gaps exist due to limited research in specific areas or shortcomings in current research.

This approach frames the research’s purpose as filling these identified gaps. Braun and Clarke critique this model as echoing a positivist-empiricist view of research as a quest for definitive truth, which they argue is incongruent with the nature of qualitative research.

They suggest this approach aligns more with a quantitative perspective that seeks to uncover objective truths.

Making the Argument Model:

Braun and Clarke advocate for the “making the argument model,” particularly in the context of qualitative research.

This model situates the research’s rationale within existing knowledge and theoretical frameworks.

Rather than striving to unearth a singular truth, this approach aims to contribute to a comprehensive and nuanced understanding of the subject matter by offering a well-supported, contextually grounded, and persuasive perspective on the issue at hand.

This approach might negate the need for a literature review before data analysis, allowing the research findings to guide the exploration of relevant literature.

Method Section of Thematic Analysis

A well-crafted method section goes beyond a superficial summary of the six phases.

It provides a clear and comprehensive account of the analytical journey, allowing readers to trace the researchers’ thought process, assess the trustworthiness of the findings, and understand the rationale behind the methodological choices made.

This transparency is essential for ensuring the rigor and validity of thematic analysis as a qualitative research method.

1. Description of the thematic approach:

The method section should explicitly state the type of thematic analysis undertaken and the specific version used (e.g., reflexive thematic analysis, codebook thematic analysis).

It should also explain the rationale for selecting this specific approach in relation to the research questions.

For instance, if a study focuses on exploring participants’ lived experiences, an inductive (reflexive) approach might be more suitable.

If the research question is driven by a particular theoretical framework, a deductive (codebook) approach may be chosen.

2. Data collection method and data set:

Clearly describe the method used to collect data (e.g., interviews, focus groups , surveys, documents).

Specify the size of the data set (e.g., number of interviews, focus groups, or documents) and the characteristics of the participants or texts included.

3. Reflexivity and transparency:

Braun and Clarke caution against merely listing the six phases of thematic analysis because presenting the phases as a series of steps implies that thematic analysis is a linear and objective process that can be separated from the researcher’s influence.

It should demonstrate an understanding of the principles of reflexivity and transparency.

By embracing reflexivity and transparency, researchers using thematic analysis can move away from a simplistic “recipe” approach and acknowledge the iterative and interpretive nature of qualitative research.

Reflexivity involves acknowledging and critically examining how the researcher’s own subjectivity might be shaping the research process.

It requires reflecting on how personal experiences, beliefs, and assumptions could influence the interpretation of data and the development of themes.

For example, a researcher studying experiences of discrimination might reflect on how their own social identities and experiences with prejudice could impact their understanding of the data.

Transparency involves clearly documenting the decisions made throughout the research process.

This includes explaining the rationale behind coding choices, theme development, and the selection of data extracts to illustrate themes.

For example, the researcher(s) might discuss the process of selecting particular data extracts or how their initial interpretations evolved over time.

Transparency allows readers to understand how the findings were generated and to assess the trustworthiness of the research.

The researcher(s) could provide a detailed account of how they moved from initial codes to broader themes, including examples of how they resolved discrepancies between codes or combined them into overarching categories.

While transparency requires detail and rigor, it should not come at the expense of clarity and accessibility.

Braun and Clarke encourage researchers to write in a clear, engaging style that makes the research process and findings accessible to a wide audience, including those who might not be familiar with qualitative research methods.

Writing About Themes

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. The order in which themes are presented should be logical and meaningful, creating a clear storyline for the reader.
• Subheadings: Use subheadings to clearly delineate each theme and its sub-themes, making the report easy to navigate and understand.

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

Avoid using phrases like “themes emerged” as it suggests that the themes were pre-existing entities in the data, waiting to be discovered. This undermines the active role of the researcher in interpreting and constructing themes from the data.

Themes should be supported with compelling data extracts that illustrate the identified patterns.

Data extracts serve as evidence for the themes identified in TA. Without them, the analysis becomes unsubstantiated and potentially unconvincing to the reader.

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.

Not all data extracts are equally effective. Choose extracts that vividly and concisely illustrate the theme’s central organizing concept.

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.

Having too few data extracts for a theme weakens the analysis and makes it appear “thin and sketchy”. This may leave the reader unconvinced about the theme’s validity and prevalence within the data.

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

Data extracts should not be presented without being integrated into the analytic narrative. They should be used to illustrate and support the interpretation of the data, not just reiterate what the participants said.

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

A good thematic analysis strikes a balance between presenting data extracts and providing analytic commentary. A common rule of thumb is to aim for a 50/50 ratio.

The importance of examining contradictory data

A robust thematic analysis acknowledges and explores the full range of data, including those that challenge the dominant patterns.

Ignoring data that doesn’t neatly fit into identified themes is a significant pitfall in thematic analysis.

Failing to acknowledge and explore contradictory data can lead to an incomplete or misleading analysis, potentially obscuring valuable insights.

• Data sets are rarely completely uniform : Human experiences and perspectives are complex and often contradictory. It’s unrealistic to expect that every piece of data will perfectly align with the identified themes.
• Contradictory data can challenge assumptions : Data that contradicts the emerging themes can challenge the researcher’s assumptions and interpretations, leading to a more nuanced and insightful understanding of the data.
• Ignoring contradictions can create an overly simplistic analysis : An analysis that smooths over contradictions or presents a completely unified picture of the data might lack depth and fail to capture the complexities of the phenomenon being studied.
• Alternative interpretations : Contradictory data might suggest alternative interpretations or explanations that need to be considered and addressed in the analysis.
• Value of outliers : Instead of dismissing data that doesn’t fit, view it as potentially valuable. These outliers might reveal limitations in the analysis, highlight the influence of contextual factors, or uncover new avenues for inquiry.

Embracing contradictions and exploring their potential meanings leads to a more comprehensive and insightful analysis.

Discussion Section

The discussion section should engage critically with the findings, connect them to existing knowledge, and contribute to a deeper understanding of the phenomenon under investigation.

Braun and Clarke emphasize that the discussion section should not merely summarize the themes but rather weave a compelling and insightful narrative that connects the analysis back to the research question, existing literature, and broader theoretical discussions.

While each theme should have a distinct focus, the discussion should also draw connections between themes, creating a cohesive and interconnected narrative.

They advocate for a style that engages the reader, convinces them of the validity of the findings, and leaves them with a sense of “ so what? ” – a clear understanding of the significance and implications of the research.

• Connecting themes and building a narrative: The discussion section should move beyond simply describing individual themes to explore the relationships and connections between them. The goal is to present a coherent and nuanced narrative that addresses the research questions.
• Interpreting the findings: The discussion section should interpret the significance of the findings about the research questions and existing literature. It should go beyond merely summarizing the data to offer insights into what the themes mean, why they might have emerged, and what their implications are. Asking questions like “So what?” and “What is relevant or useful here to addressing my question?” can help you guide the interpretation of the data.
• Integrating literature: The discussion section should connect the findings to relevant scholarly literature. This could involve comparing and contrasting the findings with previous research, exploring how the study supports or challenges existing theories, or discussing the implications of the findings in light of existing knowledge.
• Theoretical insights: For analyses that go beyond the semantic level, the discussion section should explore the theoretical insights that emerge from the data. This could involve identifying underlying assumptions, ideologies, or power dynamics that shape the experiences or perspectives of the participants.
• Critical reflection on the method: Reflect on the methodological choices made during the analysis and their potential implications for the findings. This could involve discussing the benefits and limitations of the chosen thematic analysis approach, acknowledging any potential biases, and suggesting areas for future research.

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.
• Confusing themes with summaries : Themes are not merely summaries of what participants said about a topic. Instead, they represent rich and multifaceted patterns of shared meaning organized around a central concept and are generated by the researcher through intense analytic engagement with the data. Good themes often uncover the implicit or latent meanings behind the data rather than just summarizing what’s explicitly stated.
• 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.
• Ignoring contradictory data : An analysis that smooths over contradictions or presents a completely unified picture of the data might lack depth and fail to capture the complexities of the phenomenon being studied. Acknowledging and exploring data that does not fit neatly into identified themes can lead to more nuanced findings.
• 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

Braun and Clarke’s approach to thematic analysis, which they term “reflexive TA,” places the researcher’s subjectivity and reflexivity at the forefront of the research process.

Rather than striving for an illusory objectivity, reflexive TA recognizes and values the researcher’s active role in shaping the research, from data interpretation to theme construction.

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:

Braun and Clarke argue that the researcher’s background, experiences, theoretical commitments, and social position inevitably shape how they approach and make sense of the data.

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

Reflexivity involves critically examining how these personal and professional experiences influence the research process, particularly during data interpretation and theme development.

Researchers are encouraged to make these influences transparent in their methodology and throughout their analysis, fostering a more honest and nuanced account of the research.

Memos offer a space for researchers to step back from the data and ask themselves probing questions about their own perspectives and potential biases.

Researchers can ask: How might my background or beliefs be shaping my interpretation of this data? Am I overlooking alternative explanations? Am I imposing my own values or expectations on the participants?

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.

Reflexivity as an Ongoing Process

Reflexivity is not a one-time activity but an ongoing process that should permeate all stages of the research, from the initial design to the final write-up.

This involves constantly questioning one’s assumptions, interpretations, and reactions to the data, considering alternative perspectives, and remaining open to revising initial understandings.

Braun and Clarke provide a series of probing questions that researchers can ask themselves throughout the analytic process to encourage this reflexivity.

• “Why might I be reacting to the data in this way?”
• “What does my interpretation rely on?”
• “How would I feel if I was in that situation? (Is this different from or similar to how the person feels, and why might that be?)”

Transparency

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

Transparency is not merely about documenting what was done but also about clearly articulating why and how specific analytic choices were made throughout the research process, from study design to data interpretation.

This transparency allows readers to understand the researchers’ perspectives, the rationale behind their decisions, and the potential influences on the findings, ultimately strengthening the credibility and trustworthiness of the research

This transparency helps ensure the trustworthiness and rigor of the findings, allowing other researchers to assess the credibility of the findings and potentially replicate the analysis.

Transparency in Braun and Clarke’s approach to thematic analysis is not merely about adhering to a set of reporting guidelines; it’s about embracing an ethos of openness, reflexivity, and accountability throughout the research process.

By illuminating the “messiness” of qualitative research and clearly articulating the researchers’ perspectives and decisions, reflexive TA promotes a more honest, trustworthy, and ultimately, more insightful form of qualitative inquiry.

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.

• Data selection and sampling: Why were particular data sources chosen? How were participants selected, and what were the inclusion/exclusion criteria?
• Coding strategies: How were codes developed? Was the coding primarily inductive, deductive, or a combination of both? Did the coding process evolve, and if so, how? Were any coding tools or software used?
• Theme development: How were themes identified, refined, and named? What was the process of moving from codes to themes? How was the final thematic structure decided upon?

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.

Reading List

• 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.
• Trainor, L. R., & Bundon, A. (2021). Developing the craft: Reflexive accounts of doing reflexive thematic analysis .  Qualitative research in sport, exercise and health ,  13 (5), 705-726.

Examples of Good Practice

• Anderson, S., Clarke, V., & Thomas, Z. (2023). The problem with picking: Permittance, escape and shame in problematic skin picking .  Psychology and Psychotherapy: Theory, Research and Practice ,  96 (1), 83-100.
• 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.
• Hayfield, N., Jones, B., Carter, J., & Jowett, A. (2024). Exploring civil partnership from the perspective of those in mixed-sex relationships: Embracing a clean slate of equality .  Journal of Family Issues ,  45 (8), 1925-1948.
• Hayfield, N., Moore, H., & Terry, G. (2024). “Friends? Supported. Partner? Not so much…”: Women’s experiences of friendships, family, and relationships during perimenopause and menopause .  Feminism & Psychology , 09593535241242563.
• Lovell, D., Hayfield, N., & Thomas, Z. (2023). “No one has ever asked me and I’m grateful that you have” men’s experiences of their partner’s female sexual pain .  Sexual and Relationship Therapy , 1-24.
• Wheeler, L., Fragkiadaki, E., Clarke, V., & DiCaccavo, A. (2022). ‘Sunshine’,‘angels’ and ‘rainbows’: language developed by mothers bereaved by perinatal loss.   British Journal of Midwifery ,  30 (7), 368-374.
• Answers to frequently asked questions about thematic analysis
• Thematic analysis – data for coding exercise
• University of Auckland – Thematic Analysis Resources

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.

How to Do Thematic Analysis: 6 Steps & Examples

Unlock qualitative insights with our step-by-step guide on thematic analysis. Identify patterns, and generate meaningful insights in six simple steps.

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Thematic analysis is a game-changer for qualitative researchers. It's the key to unlocking the hidden patterns and meanings buried deep within your data.

In this step-by-step guide, you'll discover how to master thematic analysis and transform your raw data into powerful insights. From familiarizing yourself with the data to generating codes and themes, you'll learn the essential techniques to conduct a rigorous and systematic analysis.

Whether you're a seasoned researcher or just starting out, this guide will demystify the process and provide you with a clear roadmap to success. So get ready to dive into the world of thematic analysis!

Table of contents

What is thematic analysis

6 Steps for doing thematic analysis

Thematic Analysis in Action: A Real-World Example

Method Pros and Cons

Applications in Qualitative Research

What is thematic analysis.

Thematic analysis is a qualitative research method that focuses on identifying, analyzing, and reporting patterns or themes within a dataset. Thematic analysis involves reading through a data set, identifying patterns in meaning, and deriving themes, providing a systematic and flexible way to interpret various aspects of the research topic.

The primary purpose of thematic analysis is to uncover and make sense of the collective or shared meanings and experiences within a dataset. By identifying common threads that extend across the data, researchers can gain a deeper understanding of the phenomenon under study and draw meaningful conclusions.

Key Characteristics

One of the key characteristics of thematic analysis is its flexibility. The approach is adaptable to a wide range of research questions and data types. Researchers can use thematic analysis inductively, allowing themes to emerge from the data itself, or, deductively, using existing theories or frameworks to guide the analysis process.

Another important aspect of thematic analysis is its focus on identifying and describing both implicit and explicit ideas within the data. Themes are not always directly observable but can be uncovered through a careful and systematic analysis of the dataset. This process involves looking beyond the surface-level content and examining the underlying meanings, assumptions, and ideas that shape participants' responses.

Inductive vs. Deductive Approaches

When conducting thematic analysis, researchers can choose between inductive (data-driven) or deductive (theory-driven) analysis approach. Inductive data analysis involves allowing themes to emerge from the data without any preconceived notions or theoretical frameworks guiding the analysis. This approach is particularly useful when exploring a new or under-researched topic, as it allows for the discovery of unexpected insights and patterns.

On the other hand, the deductive approach involves using existing theories or frameworks to guide the analysis process. In this case, researchers start with a set of pre-determined themes or categories and look for evidence within the data that supports or refutes these ideas. This approach is useful when testing or extending existing theories or when comparing findings across different studies or populations.

Thematic Analysis Simplified: A 6 Step-by-Step Process for Qualitative Data Analysis

This step-by-step guide breaks down the process into six manageable stages.

By following these steps, you can effectively analyze and interpret qualitative data to gain valuable insights .

Step 1: Familiarize Yourself with the Data

The first step in thematic analysis is to immerse yourself in the data. Read and re-read the transcripts, field notes, or other qualitative data sources to gain a deep understanding of the content. As you read, take notes on initial ideas and observations that come to mind. This process helps you become familiar with the depth and breadth of the data.

Pay attention to patterns, recurring ideas, and potential themes that emerge during this initial review. It's important to approach the data with an open mind, allowing the content to guide your understanding rather than imposing preconceived notions or expectations.

Tips for Familiarizing Yourself with the Data

Set aside dedicated time to read through the data without distractions.

Use colors or and notes to mark interesting or significant passages.

Create a summary or overview of each data source to help you remember key points.

Step 2: Generate Initial Codes

Once you've familiarized yourself with the data, the next step is to generate initial codes. Coding involves systematically labeling and organizing the data into meaningful groups. Go through the entire dataset and assign codes to interesting features or segments that are relevant to your research question.

Codes can be descriptive, interpretive, or pattern-based. Descriptive codes summarize the content, interpretive codes reflect the researcher's understanding, and pattern codes identify emerging themes or explanations. As you code, collate the data relevant to each code.

Tips for Generating Initial Codes

Use a qualitative data analysis software or a spreadsheet to organize your codes.

Be open to creating new codes as you progress through the data.

Regularly review and refine your codes to ensure consistency and relevance.

Step 3: Search for Themes

After coding the data, the next step is to search for themes. Themes are broader patterns or categories that capture significant aspects of the data in relation to the research question. Review your codes and consider how they can be grouped or combined to form overarching themes.

Collate all the data relevant to each potential theme. This may involve creating thematic maps or diagrams to visualize the relationships between codes and themes. Consider the different levels of themes, such as main themes and sub-themes , and how they connect to one another.

Tips for Searching for Themes

Look for recurring ideas, concepts, or patterns across the coded data.

Consider the relationships and connections between different codes.

Use visual aids like mind maps or sticky notes to organize and explore potential themes.

Step 4: Review Themes

Once you've identified potential themes, it's crucial to review and refine them. Check if the themes work in relation to the coded extracts and the entire dataset. This involves a two-level review process.

First, read through the collated extracts for each theme to ensure they form a coherent pattern. If some extracts don't fit, consider reworking the theme, creating a new theme, or discarding the extracts. Second, re-read the entire dataset to assess whether the themes accurately represent the data and capture the most important and relevant aspects.

Tips for Reviewing Themes

Ensure each theme is distinct and coherent.

Look for any data that contradicts or challenges your themes.

Create a thematic map to visually represent the relationships between themes.

Step 5: Define and Name Themes

After refining your themes, the next step is to define and name them. Conduct ongoing analysis to identify the essence and scope of each theme. Develop a clear and concise name for each theme that captures its central concept and significance.

Write a detailed analysis for each theme, explaining its meaning, relevance, and how it relates to the research question. Consider the story that each theme tells and how it contributes to the overall understanding of the data.

Tips for Defining and Naming Themes

Choose names that are concise, informative, and engaging.

Ensure the theme names and definitions are easily understandable to others.

Use quotes or examples from the data to illustrate and support each theme.

Step 6: Write Up

The final step in thematic analysis is to write up your findings in a clear and structured report. Your report should include an introduction that outlines the research question and methodology, followed by a detailed presentation of your themes and their significance.

Use examples and quotes from the data to support and illustrate each theme. Discuss how the themes relate to one another and to the overall research question. Consider the implications of your findings and how they contribute to existing knowledge or practice.

Tips for Writing Up

Use a clear and logical structure to guide the reader through your analysis.

Provide sufficient evidence and examples to support your themes.

Discuss the limitations of your study and suggest areas for future research.

Let's consider a real-world example to illustrate thematic analysis in action. Suppose an online retailer was looking to conduct semi-structured interviews with 20 customers who recently purchased products in their new footwear line. The researcher will likely want to understand the customers' experiences with the product, including its performance, design, and overall impact on their quality of life.

Step 1: Familiarizing Yourself with the Data

The first step in thematic analysis is to become familiar with the data. In this case, the researcher would transcribe the audio recordings of the interviews and read through the transcripts multiple times to get a sense of the overall content.

Immersing Yourself in the Data

During this familiarization process, the researcher should take notes on initial impressions, ideas, and potential patterns. This step is crucial for gaining a deep understanding of the data and laying the foundation for the subsequent analysis.

Step 2: Generating Initial Codes

Once familiar with the data, the researcher begins the coding process . Coding involves identifying and labeling segments of the text that are relevant to the research question.

In this example, the researcher might create codes such as "side effects," "quality of life," "treatment effectiveness," and "patient satisfaction." These codes help organize the data and make it easier to identify patterns and themes.

Using Coding Software

To streamline the coding process, researchers can use qualitative data analysis software like Kapiche . The platform allows uers to highlight and label segments of text , organize codes into categories, and visualize the relationships between the data.

Step 3: Searching for Themes

After coding the data, the researcher looks for broader patterns of meaning, known as themes. Themes capture something important about the data in relation to the research question and represent a level of patterned response or meaning within the dataset.

In this example, the researcher might identify themes such as "patients experienced significant improvement in symptoms," "side effects were manageable and tolerable," and "treatment enhanced overall quality of life."

Step 4: Reviewing and Refining Themes

The researcher then reviews and refines the themes to ensure they accurately represent the data. This process involves checking that the themes work in relation to the coded extracts and the entire dataset.

Ensuring Theme Coherence

The researcher should also consider whether the themes are internally coherent, consistent, and distinctive. If necessary, themes may be combined, split, or discarded to better capture the essence of the data.

Step 5: Defining and Naming Themes

The researcher defines and names the themes, capturing the essence of what each theme is about. Clear and concise theme names help convey the key findings of the analysis to readers.

In this example, the researcher might define and name the themes as "Treatment Effectiveness," "Manageable Side Effects," and "Improved Quality of Life."

By following these steps, the researcher can use thematic analysis to make sense of the patient interview data and gain valuable insights into their experiences with the new treatment. This real-world example demonstrates the power of thematic analysis in identifying patterns of meaning and providing a rich, detailed account of qualitative data.

Step 6: Report write-up

Finally, the researcher can package the findings in a clear report to communicate to other key stakeholders. The report would ideally include a summary themes, methodology, as well as detailed examples that bring the overarching trends to life.

Thematic Analysis: Weighing the Pros and Cons

Having explored the steps in doing thematic analysis, it's important to consider the advantages and disadvantages of the research method.

Thematic analysis has gained popularity due to its flexibility and accessibility, but it also has some limitations that researchers should be aware of.

Advantages of Thematic Analysis

Thematic analysis offers several benefits, making it a popular choice for qualitative analysis. One of its main advantages is its flexibility in application across a range of theoretical approaches. This means that researchers can use thematic analysis in various fields, from psychology and sociology to healthcare and education.

Another advantage is that thematic analysis is accessible to researchers with little or no experience in qualitative research methods. The process is relatively straightforward and does not require advanced technical skills or specialized software. This makes it an attractive option for novice researchers or those working with limited resources.

Thematic analysis also produces results that are generally accessible to an educated general public. The themes generated from the data are often easy to understand and can be presented in a clear and concise manner. This is particularly useful when communicating research findings to stakeholders or policymakers who may not have a background in the specific field of study.

Disadvantages of Thematic Analysis

Despite its advantages, thematic analysis also has some limitations that researchers should consider. One of the main disadvantages is the lack of substantial rigour on thematic analysis methodology compared to other qualitative approaches. This can make it challenging for researchers to find guidance or examples of best practices when conducting thematic analysis.

The flexibility of thematic analysis can also be a double-edged sword. While it allows for adaptability across different research contexts, it can also lead to inconsistency and lack of coherence in developing themes. Researchers may struggle to maintain a consistent approach throughout the analysis process, resulting in themes that are not well-defined or integrated.

Another limitation of thematic analysis is its limited interpretive power if not used within an existing theoretical framework. Without a guiding theory or conceptual framework, the analysis may remain descriptive rather than interpretive, failing to provide the deeper insights you're after.

Ensuring Rigorous Thematic Analysis

To overcome the limitations of thematic analysis process and ensure rigorous results, researchers should:

Familiarize themselves with the existing literature on thematic analysis and seek guidance from experienced researchers in the field.

Develop a clear and consistent approach to coding and theme development, documenting each step of the process to ensure transparency and reproducibility.

Consider using thematic analysis in conjunction with other qualitative methods or within an existing theoretical framework to enhance its interpretive power.

Be flexible throughout the research process, acknowledging biases and assumptions and how these may influence the analysis.

By weighing the pros and cons of thematic analysis and taking steps to ensure rigour, researchers can harness the benefits of this method while minimizing its limitations, producing valuable insights from qualitative data.

Thematic analysis is widely used in various fields, including psychology, social sciences, and health research. This approach is particularly suitable for anyone doing qualitative content analysis of interviews, focus groups, and open-ended survey responses.

In psychology, thematic analysis has been used to explore a range of topics, such as experiences of mental health issues, identity formation, and interpersonal relationships. A key paper by Braun and Clarke (2006) demonstrated how thematic analysis can be used in psychology studies, providing guidelines on how to approach generating themes and leveraging a systematic coding process.

Combining Thematic Analysis with Other Methods

Thematic analysis can be used as a standalone method or in combination with other qualitative or quantitative approaches. When used in conjunction with other methods, thematic analysis can provide a more comprehensive understanding of the research topic and can enhance the credibility of the findings.

For example, researchers can use thematic analysis to analyze raw interview data, and then use the identified themes to inform the development of a quantitative survey to probe deeper. This approach allows for effective exploration of a topic, providing a more complete picture of the research themes.

Thematic Analysis: Your Key to Unlocking Qualitative Insights

Thematic analysis is a powerful tool for making sense of research data. By familiarizing yourself with data, generating initial codes, searching for themes, reviewing and refining them, and finally writing up your findings, you can uncover rich insights that might otherwise remain hidden.

Ready to put thematic analysis into practice? Start by gathering your qualitative data, whether it's interview transcripts, open-ended survey responses, or focus group discussions.

Then, leverage a tool like Kapiche as you follow the step-by-step process outlined in this guide. From pre-coding to post-coding, this guide should help arrive at the themes that best capture the essence of your data.

Want to see how Kapiche can support your thematic research goals? Watch a demo here today to get a tour of the platform.

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The Guide to Thematic Analysis

• What is Thematic Analysis?
• Advantages of Thematic Analysis
• Disadvantages of Thematic Analysis
• Introduction

Thematic analysis example list

Takeaways for qualitative research and data analysis.

• How to Do Thematic Analysis
• Thematic Coding
• Collaborative Thematic Analysis
• Thematic Analysis Software
• Thematic Analysis in Mixed Methods Approach
• Abductive Thematic Analysis
• Deductive Thematic Analysis
• Inductive Thematic Analysis
• Reflexive Thematic Analysis
• Thematic Analysis in Observations
• Thematic Analysis in Surveys
• Thematic Analysis for Interviews
• Thematic Analysis for Focus Groups
• Thematic Analysis for Case Studies
• Thematic Analysis of Secondary Data
• Thematic Analysis Literature Review
• Thematic Analysis vs. Phenomenology
• Thematic vs. Content Analysis
• Thematic Analysis vs. Grounded Theory
• Thematic Analysis vs. Narrative Analysis
• Thematic Analysis vs. Discourse Analysis
• Thematic Analysis vs. Framework Analysis
• Thematic Analysis in Social Work
• Thematic Analysis in Psychology
• Thematic Analysis in Educational Research
• Thematic Analysis in UX Research
• How to Present Thematic Analysis Results
• Increasing Rigor in Thematic Analysis
• Peer Review in Thematic Analysis

Thematic Analysis Examples

Thematic analysis in qualitative research is a widely utilized qualitative research method that provides a systematic approach to identifying, analyzing, and reporting potential themes and patterns within data. Whereas quantitative data often relies on statistical analysis to make judgments about insights, thematic analysis involves researchers conducting qualitative data analysis to interpret various aspects (or themes) of the research topic they are exploring, offering rich, detailed, and complex accounts of the underlying meanings within the data. Thematic analysis is flexible and applicable across a diverse range of disciplines, underscoring its utility in providing insightful interpretations of nuanced datasets.

By focusing on examples of thematic analysis, this article aims to illustrate the practical steps involved in this method and showcase how it can be effectively applied to identify patterns and draw meaningful conclusions from qualitative data . Through this approach, readers will gain an understanding of the systematic nature of thematic analysis and its contribution to a deeper comprehension of research findings.

A typical thematic analysis report conveys researchers' identification of patterns or themes across various domains that answers their research questions . This robust analytical method is particularly valuable in the social sciences, where understanding human behavior, experiences, and societal structures is key. The following sections illustrate how various types of thematic analysis can be applied in different social science fields, each through a hypothetical qualitative research process.

Education: Understanding student motivation

In an educational context, a study was designed to explore the motivational drivers among high school students. Researchers conducted semi-structured interviews with a diverse group of 30 students, probing into their academic experiences, aspirations, and challenges. The thematic analysis of interview transcripts revealed distinct but interconnected themes.

Firstly, 'Teacher Influence' emerged as a critical theme, illustrating how educators' attitudes, feedback, and engagement levels affected student motivation. Positive reinforcement, constructive criticism, and personal attention were highlighted as aspects that fueled students' drive to learn and succeed. Another prominent theme was 'Peer Dynamics,' reflecting the impact of classmates and friends on students' motivation. This theme encompassed both positive influences, such as camaraderie and academic collaboration, and negative aspects like peer pressure and competition.

'Personal Aspirations' was identified as a third theme, indicating how students' goals and perceived future opportunities shaped their current academic engagement. Ambitions related to higher education, career prospects, and personal fulfillment were common motivators. Lastly, 'Learning Environment' emerged, encompassing aspects of the school setting that influenced motivation, including extracurricular activities, school facilities, and the overall educational atmosphere.

Identifying themes such as these underscores the complexity of student motivation, suggesting that multifaceted strategies are needed to enhance educational engagement and achievement.

Healthcare: Patient experiences in chronic disease management

In the healthcare sector, a qualitative study focused on patients with chronic conditions to understand their daily management challenges and support needs. Through interviews and diary entries from patients dealing with diseases like diabetes and hypertension, researchers conducted a thematic analysis to distill the patient experience into core themes.

The 'Healthcare Interaction' theme underscored the importance of patient-provider relationships, highlighting how empathy, communication, and responsiveness from healthcare professionals can significantly impact patient satisfaction and engagement in disease management. Another critical theme was 'Lifestyle Adaptation,' reflecting the ongoing adjustments patients make in diet, exercise, and medication routines. This theme highlighted the emotional and practical challenges of integrating disease management into daily life, as well as the strategies patients employed to cope with these changes.

'Social Support Networks' emerged as a vital theme, illustrating the role of family, friends, and peer support groups in providing emotional encouragement, practical assistance, and motivation. The contrast between strong and lacking support networks provided insights into how social dynamics can influence disease management outcomes. 'Psychological Resilience' was identified as a theme capturing patients' mental and emotional responses to living with a chronic condition. This included coping mechanisms, attitudes toward illness, and the impact on personal identity and life perspective.

These themes offer a comprehensive view of the patient experience in chronic disease management, suggesting areas for improvement in healthcare practices and support systems.

Organizational behavior: Workplace culture and employee satisfaction

A study within the realm of organizational behavior examined how workplace culture influences employee satisfaction and retention. Through thematic analysis of focus group discussions with employees from various sectors, researchers identified key themes that shaped workplace experiences.

The 'Leadership Influence' theme highlighted the critical role of management styles, communication, and decision-making processes in shaping employees' perceptions of their workplace. Leadership approaches that fostered transparency, involvement, and recognition were associated with higher satisfaction levels. 'Work Environment and Resources' was another significant theme, emphasizing the importance of physical workspace, tools, and resources in employee productivity and contentment. Factors such as workspace design, technology access, and resource availability were pivotal.

'Interpersonal Relationships and Team Dynamics' emerged as a theme reflecting the impact of collegial relationships and team cohesion on job satisfaction. Positive interactions, collaborative teamwork, and a supportive atmosphere were key drivers of employee engagement. 'Personal Growth and Development' captured employees' desire for opportunities to learn, advance, and take on new challenges. The availability of training programs, career advancement paths, and feedback mechanisms were crucial to employee fulfillment.

These findings underscore the multifaceted nature of workplace satisfaction, providing valuable insights for organizational development and employee engagement strategies.

Sociology: Social media's role in shaping public opinion

In a sociological study, researchers explored the influence of social media on public opinion regarding environmental issues. Content analysis and narrative analysis of discussions, posts, and comments across various platforms provided a semantic approach for thematic analysis, revealing how online narratives shaped perceptions and discourse. Meanwhile, the resulting themes support discussion for potential applications within social media and other forms of online discourse.

The 'Information Dissemination' theme illustrated the rapid spread of environmental information and misinformation, highlighting the dual role of social media as a tool for awareness and a source of confusion. 'Influencer Impact' emerged as a theme, underscoring the role of prominent figures and opinion leaders in shaping environmental discourse. The credibility and reach of these influencers significantly affected public engagement and perspective.

'Community Engagement' was also identified, showing how online communities mobilized around environmental causes, sharing experiences, organizing actions, and providing mutual support. This theme reflected the potential of social media to foster collective action and advocacy. Lastly, 'Emotional Engagement' captured the affective responses elicited by environmental content, including hope, anger, anxiety, and inspiration. These emotional reactions were pivotal in driving awareness, concern, and action among the public.

Through these themes, the study illustrates the complex dynamics through which social media influences public opinion on critical issues, offering insights into the power of digital platforms in shaping societal discourse.

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While these thematic analysis examples span various fields within the social sciences, they share common methodological threads that highlight best practices in conducting effective thematic analysis. These commonalities can serve as valuable pointers for researchers aiming to employ thematic analysis in their work, regardless of their specific domain of study.

Systematic data engagement : Across all examples, a systematic approach to qualitative analysis is fundamental. Researchers personally engage in data collection and immerse themselves in the resulting data through repeated readings, enabling a deep familiarity to identify themes relevant to the data and research question . This immersion facilitates the initial coding process, where researchers employ data coding to capture data features relevant to the research questions. Researchers should approach their data systematically, ensuring thorough and consistent engagement to capture the depth and breadth of themes.

Iterative theme development : The examples illustrate that identifying themes is an iterative process. Preliminary themes are generated from initial codes that cluster similar data segments. These themes are then reviewed and refined, ensuring they accurately represent the data set. Researchers should be prepared to revisit their data and themes multiple times, refining their themes to ensure they are coherent, distinct, and meaningful. This iterative process is central to the rigor of thematic analysis.

Richness and complexity of themes : Thematic analysis, as demonstrated in these examples, excels in capturing the richness and complexity of qualitative data. Themes are not just surface-level categories; they encapsulate intricate patterns that provide deep insights into the data. Researchers should strive for generating themes that capture the complexity of their data, offering rich, detailed, and nuanced interpretations.

Contextual understanding : Each example underscores the importance of understanding the context from which the data are derived. Context shapes the data and the themes that emerge from them, influencing how researchers interpret and understand the identified patterns. Effective thematic analysis requires researchers to be acutely aware of their data's contextual backdrop, integrating this understanding into their analysis and interpretation.

Transparency and rigor : These examples demonstrate the need for rigor throughout the thematic analysis process. This includes maintaining detailed records of the process of coding data, providing clear definitions for each theme, and offering comprehensive explanations of how themes were derived from the data. Researchers should document their analytic decisions meticulously, ensuring their analysis is transparent and credible.

Triangulation and validation : These hypothetical studies exemplify the value of triangulating thematic analysis findings with other data sources, theories, or methods to enhance credibility. Researchers should consider using additional data sources or analytical methods to validate and enrich their thematic analysis findings, ensuring robust and trustworthy conclusions.

Reflective practice : Finally, these examples highlight the importance of a reflexive thematic analysis. Researchers must continually reflect on their own contexts, assumptions, and perspectives, considering how these might influence their analysis. By engaging in reflective practice, researchers can mitigate potential biases, enhance analytic rigor, and ensure their findings are a credible representation of the data.

These common threads across different thematic analysis examples provide a foundation for conducting robust and insightful thematic analysis. By adhering to these best practices, researchers can leverage thematic analysis to yield meaningful, impactful insights from their qualitative data.

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How to do thematic analysis

Last updated

8 February 2023

Reviewed by

Miroslav Damyanov

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

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

• What is thematic analysis?

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

Streamline your thematic analysis

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

• What are the main approaches to thematic analysis?

Inductive thematic analysis approach

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

Deductive thematic analysis approach

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

Semantic thematic analysis approach

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

Latent thematic analysis approach

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

• When should thematic analysis be used?

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

The following scenarios warrant the use of thematic analysis:

You’re new to qualitative analysis

You need to identify patterns in data

You want to involve participants in the process

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

• What are the advantages and disadvantages of thematic analysis?

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

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

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

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

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

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

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

2. Create the initial codes (open code work)

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

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

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

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

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

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

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

6. Write the report

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

A typical thematic analysis report contains the following:

An introduction

A methodology section

Results and findings

A conclusion

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

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

• How to analyze qualitative data

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

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

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

Content analysis

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

Text in various formats

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

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Narrative analysis

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

Discourse analysis

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

Historical 

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

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

Grounded theory analysis

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

• Challenges with analyzing qualitative data

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

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

It can be hard to choose a method. 

Avoiding bias is difficult.

Human error affects accuracy and consistency.

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

Learn more about thematic analysis software

What is thematic analysis in qualitative research.

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

Can thematic analysis be done manually?

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

What are the two types of thematic analysis?

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

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

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

What makes a good thematic analysis?

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

What are examples of themes in thematic analysis?

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

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

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

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The Art of Interpretation: A Journey through Thematic Analysis

Uncover the intricacies of thematic analysis with this comprehensive guide. Get useful step-by-step instructions and best practices.

Thematic analysis is a widely used qualitative research method that involves identifying patterns or themes in qualitative data. It is a flexible and versatile method that can be applied to a wide range of research questions and data types. It is commonly used in fields such as psychology, sociology, education, and healthcare to analyze data collected through methods such as interviews, focus groups, and open-ended surveys. In this article, we will provide an overview of thematic analysis, including its definition, main steps, and different approaches. We will also discuss the advantages and disadvantages of this method, as well as provide practical tips for conducting thematic analysis in research.

What is Thematic Analysis?

The thematic analysis involves systematically identifying, analyzing, and reporting patterns (or themes) within data that capture its essential meaning. The process of this method typically involves several stages, including data familiarization, generating initial codes, searching for themes, reviewing and refining themes, and defining and naming themes. During the analysis, the researcher aims to identify meaningful patterns within the data that help to answer the research question or explore a phenomenon of interest. 

Thematic analysis is a flexible and highly interpretive method that allows researchers to capture the complexity and richness of qualitative data. It can be used to generate new insights, identify patterns and trends, and provide a detailed and nuanced understanding of social phenomena.

When Should I Use Thematic Analysis?

Thematic analysis can be used when you want to gain an in-depth understanding of qualitative data and identify patterns and themes within it. Here are some situations where you might consider using thematic analysis:

Exploratory Research

By identifying themes within the data, researchers can generate new insights and hypotheses for further investigation. Thematic analysis is particularly useful in exploratory research, as it allows for a general understanding of a phenomenon or exploration of a topic that has not been extensively studied before.

Data-rich Research

When dealing with large amounts of qualitative data, such as from focus groups, interviews, or surveys, systematic analysis and organization of data becomes crucial. Thematic analysis can be applied to identify key themes and patterns that emerge across the data set, making it a particularly useful method.

Interpretive Research

Thematic analysis is a highly interpretive method that allows researchers to capture the complexity and nuance of qualitative data. It is well-suited to interpretive research, where the aim is to explore subjective experiences, meanings, and perspectives.

Cross-cultural Research

By identifying themes that are common across cultures, researchers can use thematic analysis to generate insights into cultural patterns and differences across different groups or contexts.

What Are The Advantages and Disadvantages of Thematic Analysis?

Thematic analysis has several advantages and disadvantages that researchers should consider when deciding whether to use this method. While it has advantages, such as flexibility and depth, it also has some disadvantages, such as subjectivity and time-consuming nature. Therefore, it is essential to weigh the pros and cons of thematic analysis carefully and consider whether this method is appropriate for the research question and data type. Here are some of the main advantages and disadvantages of thematic analysis:

Flexibility

It is possible to apply the flexible and adaptable method of thematic analysis to a variety of qualitative data types, such as interviews, focus groups, surveys, and other forms of qualitative data.

Through the use of thematic analysis, researchers are able to gain a deeper understanding of the data they are analyzing and uncover patterns and themes that may not be readily apparent using other methods.

The rigor and systematic approach of thematic analysis involves multiple stages of analysis, which can improve the reliability and validity of the findings, making it a valuable method in qualitative research.

Interpretive

The interpretive nature of thematic analysis enables researchers to capture the complex and nuanced aspects of qualitative data, leading to rich and detailed insights into various social phenomena, making it a valuable tool in qualitative research.

Disadvantages

Time-consuming.

A significant disadvantage of thematic analysis is its time-consuming nature when dealing with substantial amounts of data, which requires researchers to allocate adequate time and resources to conduct a comprehensive analysis.

Subjectivity

The subjectivity of thematic analysis can be a potential limitation, as it relies heavily on the researcher’s interpretations and may be influenced by their biases, preconceptions, and perspectives. This can affect the reliability and validity of the findings, and researchers need to acknowledge and address potential biases in their analysis.

Lack of Transparency

The lack of transparency in thematic analysis can be a potential disadvantage, as researchers may not always provide clear and detailed explanations of how themes were identified. This can limit the ability of others to replicate the study or assess the credibility of the findings.

Oversimplification

The reductionist nature of thematic analysis can be a potential drawback, as it may oversimplify the data and lead to the loss of important nuances and complexities that may be present in the data.

Step-by-Step Process of How To Do a Thematic Analysis

The thematic analysis involves familiarizing yourself with the data, generating initial codes, searching for themes, reviewing and refining themes, defining and naming themes, and finally analyzing and reporting the findings. Here is a step-by-step process for conducting a thematic analysis:

Step 1: Familiarization with the data

Start by thoroughly reading and reviewing the data to gain a general understanding of the content. This involves listening to or reading the data multiple times to identify important concepts, ideas, or recurring patterns. It is essential to take detailed notes throughout this stage to aid in the identification of themes.

Step 2: Generating initial codes

Begin coding the data by marking the text with relevant words or phrases that capture the essence of the content. The codes should be short, descriptive, and closely related to the content of the data. At this stage, it is essential to code all aspects of the data that relate to the research question.

Step 3: Searching for themes

After generating initial codes, start grouping them into potential themes that reflect the patterns and relationships in the data. It is essential to organize the codes into groups that make sense, even if some codes do not fit neatly into any category.

Step 4: Reviewing and refining themes

After identifying potential themes, review them to determine if they accurately capture the content of the data. Themes should be refined and clarified to make sure they reflect the essence of the data. Ensuring that the themes are relevant to the research question is also crucial.

Step 5: Defining and naming themes

Once themes have been reviewed and refined, define and name them. Themes should be named using a descriptive and meaningful label that accurately reflects the content of the data. It is essential to define each theme and outline the data supporting it.

Step 6: Analyzing and reporting

Finally, analyze the data by synthesizing the themes to provide a comprehensive account of the data. This involves interpreting the findings, drawing conclusions, and making recommendations based on the research question. It is important to report the findings in a clear, concise, and organized manner, using relevant examples from the data to illustrate each theme.

Different Approaches to Thematic Analysis

There are different approaches to thematic analysis, but the two main ones are Inductive Thematic and Deductive Thematic. Other approaches include Critical Thematic Analysis, Latent Thematic Analysis, and Semantic Analysis, among others. However, the Inductive and Deductive Thematic approaches are the most commonly used in research.

Inductive Thematic Analysis

In this approach, themes emerge from the data itself, without any preconceived ideas or theories. The researcher codes the data and identifies patterns and relationships, which are then grouped into themes. This approach is useful when there is no clear theoretical framework or when the aim is to generate new insights. It is particularly useful when the topic has not been extensively studied before, and the researcher wants to gain a broad understanding of the data without imposing preconceived categories or themes. 

Deductive Thematic Analysis

This approach begins with a pre-existing theory or framework that guides the analysis. The researcher begins by identifying the concepts and themes that are relevant to the research question and then searches for evidence of these in the data. This approach is useful when there is an existing theory that needs to be tested or when the aim is to confirm or refute hypotheses. A deductive approach is best suited to research when the researcher has a specific research question or hypothesis that they want to test using existing theory or previous research findings.

Semantic Thematic Analysis

In semantic thematic analysis, the focus is on the literal meaning of the words and phrases used in the data. Themes are identified by analyzing the explicit content of the data.

Latent Thematic Analysis

This approach goes beyond the surface level of the data to uncover underlying meanings and assumptions. The researcher identifies implicit or hidden meanings in the data, which are then grouped into themes.

Critical Thematic Analysis

This approach emphasizes the power dynamics in society and how they influence the data. The researcher analyzes the data to identify themes related to social justice, power, and oppression.

Reflexive Thematic Analysis

In this approach, the researcher is aware of their own biases and assumptions and actively reflects on how these might be influencing the analysis. The researcher may use a diary or other means of recording their thoughts and feelings during the analysis process.

These approaches are not mutually exclusive and can be used in combination to gain a more nuanced understanding of the data. The choice of approach depends on the research question, the data, and the researcher’s goals and perspective.

Tips for Thematic Analysis

Here are some tips for conducting thematic analysis in your qualitative research:

Familiarize yourself with the data: To conduct an effective thematic analysis, it’s crucial to familiarize yourself with the data. This means spending time reading and re-reading the data to get a sense of the content and themes that may emerge. This step helps researchers develop a good understanding of the data they are working with, which can lead to the identification of themes and patterns that may be missed otherwise.

Code systematically: Coding the data systematically and thoroughly ensures that all themes are captured. It involves systematically labeling or tagging data segments with relevant codes, which can be used to identify emerging themes. This step helps to keep the analysis organized and to identify emerging themes.

Engage in reflexivity: Reflexivity involves reflecting on your own biases and assumptions throughout the analysis process. This step is essential to minimize the impact of the researcher’s own beliefs and values on the analysis process. Researchers need to be aware of their biases and actively work to overcome them.

Create a clear coding scheme: Developing a clear and comprehensive coding scheme that captures all relevant themes is essential for effective thematic analysis. This step involves identifying all the relevant themes and creating a set of codes to label data segments related to each theme. A clear coding scheme helps researchers maintain consistency in their analysis and makes it easier to identify emerging themes.

Maintain transparency: Documenting the analysis process and providing clear explanations for how themes were identified and coded is crucial for maintaining transparency. It allows other researchers to follow the analysis process and assess the validity of the findings.

Validate findings: Using member checking or other methods to validate the findings and ensure accuracy is essential for ensuring the credibility of the analysis. Member checking involves sharing the analysis with the participants to validate whether the findings accurately represent their experiences or perspectives.

Examples of Thematic Analysis

Research Question: How do young adults perceive the impact of social media on their mental health?

Data Collection: In-depth interviews with 20 young adults (aged 18-25) who use social media regularly.

Data Analysis: The interviews were transcribed and analyzed using a thematic analysis approach. The following themes emerged:

• Negative self-comparison: Many participants discussed feeling inadequate or inferior when comparing themselves to others on social media. They described feeling pressure to present a certain image and the impact this had on their self-esteem.
• Fear of missing out (FOMO): Participants talked about feeling anxious or stressed when they saw posts from friends or acquaintances engaging in activities they were not part of. They described feeling pressure to stay connected and up-to-date on social media to avoid missing out.
• Cyberbullying: Some participants discussed experiences of being bullied or harassed on social media. They talked about feeling helpless and isolated when this happened and the impact it had on their mental health.
• Positive social connections: Despite the negative aspects, many participants also described how social media helped them stay connected with friends and family, especially during times of social distancing.
• Strategies for managing social media use: Participants discussed various strategies for managing the negative impact of social media on their mental health, such as setting limits on their use, unfollowing accounts that made them feel bad, and focusing on positive aspects of social media.

Conclusion: This thematic analysis suggests that social media use can have both positive and negative effects on young adults’ mental health. Negative self-comparison, FOMO, and cyberbullying emerged as significant negative themes, while positive social connections and strategies for managing social media use emerged as positive themes. These findings can inform interventions aimed at promoting healthy social media use among young adults.

Research Question: What are the key themes in teachers’ perceptions of the challenges and benefits of remote teaching during the COVID-19 pandemic?

Data Collection: Online survey of 100 K-12 teachers in the United States who were teaching remotely during the COVID-19 pandemic.

Data Analysis: The survey responses were analyzed using a thematic analysis approach. The following themes emerged:

• Technological challenges: Many teachers reported struggling with the technological aspects of remote teaching, such as unreliable internet connections and difficulties with online platforms.
• Student engagement: Participants discussed challenges related to engaging students in remote learning, such as difficulties with monitoring student progress and maintaining student motivation.
• Work-life balance: Several participants described struggling to balance their work and personal lives while teaching remotely, particularly due to the blurring of boundaries between work and home.
• Benefits of remote teaching: Despite the challenges, many participants also discussed the benefits of remote teaching, such as increased flexibility and opportunities for personalized learning.
• Support from colleagues and administrators: Some participants talked about the importance of support from colleagues and administrators in navigating the challenges of remote teaching.

Conclusion: This thematic analysis suggests that remote teaching during the COVID-19 pandemic presented a variety of challenges for teachers, particularly related to technology, student engagement, and work-life balance. However, participants also identified the benefits of remote teaching and the importance of support from colleagues and administrators. These findings can inform efforts to improve remote teaching practices and support teachers in navigating the challenges of remote teaching.

These are hypothetical examples created for the purpose of understanding thematic analysis. For more examples, access this website .

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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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