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

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

There’s the distinction between inductive and deductive approaches:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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Thematic Analysis: A Step by Step Guide

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Editor-in-Chief 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 segments of the data that capture a single idea or concept relevant to the research question.
• Themes : Representing a broader level of analysis, encompassing multiple codes that share a common underlying meaning or pattern. They provide a more abstract and interpretive understanding of the data.
• Iterative process : Thematic analysis is a recursive process that involves constantly moving back and forth between the coded extracts, the entire data set, and the thematic analysis being produced.
• Interpretation : The researcher interprets the identified themes to make sense of the data and draw meaningful conclusions.

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

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

1. Coding Reliability Thematic Analysis

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

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

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

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

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

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

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

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

2. Codebook Thematic Analysis

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

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

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

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

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

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

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

3. Reflexive Thematic Analysis

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

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

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

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

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

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

Six Steps Of Thematic Analysis

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

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

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

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

Step 1: Familiarization With the Data

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

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

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

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

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

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

Step 2: Generating Initial Codes

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

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

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

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

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

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

Decide On Your Coding Approach

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

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

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

Do A First Round Of Coding

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

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

Coding can be done at two levels of meaning:

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

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

This step ends when:

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

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

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

Step 3: Searching for Themes

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

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

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

1 . Collating codes into potential themes :

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

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

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

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

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

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

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

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

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

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

Shared Meaning vs. Shared Topic in Thematic Analysis

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

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

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

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

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

Avoid : Themes as Topic Summaries (Shared Topic)

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

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

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

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

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

Ensure : Themes as Shared Meaning

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

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

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

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

2. Gathering data relevant to each potential theme

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The process of developing these relationships is iterative and involves:

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

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

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

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

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

Step 4: Reviewing Themes

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

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

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

According to Braun and Clarke, a well-developed theme “captures something important about the data in relation to the research question and represents some level of patterned response or meaning within the data set”.

A well-developed theme will:

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

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

Level One : Reviewing Themes Against Coded Data Extracts

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

Level Two : Evaluating Themes Against the Entire Data Set

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

Step 5: Defining and Naming Themes

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

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

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

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

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

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

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

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

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

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

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

Step 6: Producing the Report

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

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

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

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

The analysis can be presented in two ways :

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

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

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

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

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

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

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

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

Potential Pitfalls to Avoid

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

Reducing Bias

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

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

Reflexivity

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

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

Identifying and Challenging Assumptions:

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

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

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

Transparency

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

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

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

Documenting Decision-Making:

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

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

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

Practical Strategies for Reflexivity and Transparency in Thematic Analysis:

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

Disadvantages

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

Further Information

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

Example TA Studies

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

• How it works

Thematic Analysis – A Guide with Examples

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

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

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

Importance of Thematic Analysis

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

Thematic analysis is used because:

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

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

How to Conduct a Thematic Analysis?

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

Understand the Data

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

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

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

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

Development of Initial Coding:

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

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

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

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

Make Themes

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

Extracted Data Review

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

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

Reviewing all the Themes Again

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

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

Corpus Data

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

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

Define all the Themes here

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

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

Does your Research Methodology Have the Following?

• Great Research/Sources
• Perfect Language
• Accurate Sources

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

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

Make a Report

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

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

Frequently Asked Questions

What is meant by thematic analysis.

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

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

Experimental research refers to the experiments conducted in the laboratory or under observation in controlled conditions. Here is all you need to know about experimental research.

This post provides the key disadvantages of secondary research so you know the limitations of secondary research before making a decision.

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• How It Works

How to Write a Thematic Essay

A thematic essay explores a central message or theme that runs through a piece of literature, a historical event, or even a societal trend. It analyzes evidence like characters' actions, plot development, or real-world examples to explain how this matter is revealed and unpack its significance, showing a deeper understanding of the subject at hand.

Here’s a quick breakdown of what is a thematic essay :

• Analyzes a central message (theme) in a text.
• Explains how the text explores that theme.
• Uses evidence (quotes, details) to support your analysis.
• Shows how the evidence connects to the overall theme.

If you’re having trouble with this type of assignment, feel free to use our paper writing service and streamline the process.

Dissecting a text's central message and how it unfolds can be a rewarding challenge. Here's a step-by-step breakdown to conquer your next thematic essay:

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Thematic Essay Checklist

• State a focused main argument about the theme.
• Hook the reader and introduce the theme.
• Begin each with a clear topic sentence related to the theme.
• Use specific examples, quotes, or facts.
• Explain how the evidence supports the thesis.
• Link analysis back to the central theme throughout.
• Ensure paragraphs and ideas progress logically.
• Summarize key points and restate the thesis.
• Check for clarity, coherence, and grammar.
• Properly cite sources used.

How to Pick a Thematic Topic

A crucial aspect of writing a good thematic essay is choosing a theme. Follow the hints listed below to help you create a thematic topic:

Thematic Essay Topics

• Star-Crossed Fate: Destiny in "Romeo and Juliet"
• Gatsby's Illusion: The Mirage of the American Dream
• Thoreau's Call to Action: Civil Disobedience and Its Echoes
• Grit and Grind: Industrial Strife in "Hard Times"
• Monster or Man? Isolation in "Frankenstein"
• Voices of Change: The Civil Rights Movement Unveiled
• Big Brother's Watch: Propaganda in "1984"
• Silent Scars: The Aftermath of War in "All Quiet on the Western Front"
• Pride, Prejudice, and Power: Women in Austen's World
• Cultural Cracks: Colonialism in "Things Fall Apart"
• Echoes of Justice: Moral Struggles in "To Kill a Mockingbird"
• Surviving Hard Times: Life During the Great Depression
• Invisible Chains: Identity in "Invisible Man"
• Worlds Apart: Control and Conformity in "Brave New World"
• Chains of Oppression: Freedom in "Uncle Tom's Cabin"
• Hester's Burden: Sin and Redemption in "The Scarlet Letter"
• Wired Society: The Tech Revolution's Impact
• Vengeance and Virtue: The Journey in "The Count of Monte Cristo"
• Island Power Struggles: Leadership in "Lord of the Flies"
• Dreams of Freedom: Martin Luther King Jr.'s Enduring Impact

How to Start a Thematic Essay

Every strong essay starts with a captivating introduction. For a thematic essay, this introduction should:

• Hook the reader: Grab their attention with a thought-provoking question, a relevant quote, or an interesting anecdote related to the theme.
• Introduce the topic: Briefly mention the literary work you'll be analyzing.
• State the theme: Clearly identify the central theme you'll be exploring.
• Preview the analysis: Briefly hint at how the theme is developed in the work.

Here's an example of a thematic essay introduction:

“Have you ever wondered why some stories keep coming back to the idea of forgiveness? In Harper Lee's classic novel To Kill a Mockingbird , the seemingly simple town of Maycomb grapples with racial injustice. However, beneath the surface lies a powerful exploration of the theme of forgiveness, where characters must confront their own prejudices and learn to let go of resentment. This essay will analyze how Lee uses character interactions, symbolism, and the trial of Tom Robinson to demonstrate the transformative power of forgiveness.”

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Thematic Essay Outline

A thematic essay structure has several key components. Primarily, it should be five paragraphs or more, depending on the depth of the theme. Next, it should have a concrete thesis statement, which is the thematic statement that comes from the main subject.

The introduction presents the reader with the subject and the thesis statement. The body paragraphs each discuss one literary element or more to defend the validity of your thesis, all the while providing many supporting details from the text itself. 

Lastly, the thematic essay conclusion summarizes the main points presented and finishes off with a statement of significance.

Learn more: How to create a winning outline .

Introduction

The thematic essay introduction presents the main subject of discussion captivatingly. The first sentence of the intro should be a hook statement that makes some intriguing claims about the subject of discussion. If done correctly, this will grab your reader's attention. 

Then, provide any necessary background information from the literature to help the audience understand your claims later. Lastly, put together a well-thought-out thesis statement that reflects the novel's central theme.

Body Paragraphs

The body paragraphs follow a thematic essay format. Since each body paragraph’s purpose should be to present a literary device as evidence, the topic sentence should introduce the claim and gateway into the evidence. Every topic sentence must mention a literary device and its relationship to the literature.

Afterward, to validate your claim, use examples from the book that strengthen the reasoning of your statement. These can be actions from the plot or quotations parallel with the central theme. Explaining how the action/quote links back to your thesis statement is imperative, as it shows that you can support your logic.

Remember : Each claim must use a literary device. It can not just be a random moment or inference. Thematic essays are all about proving thesis statements through critical literary devices.

The thematic essay conclusion has three main objectives before wrapping up the paper. It should not present any new information or facts but summarize the information already given. First of all, restate your thesis statement in a new way. 

Then, summarize the central claims you made within the body of your paper and their influence on the thesis statement. To finish off the entire work, present an overall concluding statement with a global analysis of the subject. Leave your reader with another hook, making him/her interested in digging deeper into the topic.

Learn more: Poetry analysis essay . 

Try also read an article on poetry analysis essay , it could be useful and can give you new insights.

Thematic Essay Example

The best way to familiarize yourself with this type of writing is to learn from thematic essay examples.

Wrap Things Up

Thematic essays are a powerful tool for students. They unlock deeper meaning in texts, sharpen critical thinking and analytical skills, and build strong writing foundations.

Before submitting your thematic essay, cross off all these items from the to-do list.

Learn more: Jem Finch character traits .

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What Is a Thematic Essay?

How to write a thematic essay, what is the main point of a thematic essay.

is an expert in nursing and healthcare, with a strong background in history, law, and literature. Holding advanced degrees in nursing and public health, his analytical approach and comprehensive knowledge help students navigate complex topics. On EssayPro blog, Adam provides insightful articles on everything from historical analysis to the intricacies of healthcare policies. In his downtime, he enjoys historical documentaries and volunteering at local clinics.

• Updated writing steps.
• Added new topics.
• Added a new example.
• Added a checklist.
• https://www.wboro.org/cms/lib/NY01914047/Centricity/Domain/1006/Thematic%20Essays%20Helpful%20Hints.pdf
• Thematic Essay - Regents Exam Rubric  | New Visions - Social Studies. (n.d.). New Visions - Social Studies. https://curriculum.newvisions.org/social-studies/resources/resource/thematic-essay-regents-exam-rubric/
• How to Structure Your Essay Introduction | Essay Writing Part 2. (2023, October 31). Matrix Education. https://www.matrix.edu.au/essay-writing-guide/how-to-structure-your-essay-introduction/  

What (Exactly) Is Thematic Analysis?

Plain-Language Explanation & Definition (With Examples)

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

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

Thematic Analysis 101

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

First, the lingo…

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

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

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

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

What is thematic analysis?

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

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

When should you use thematic analysis?

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

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

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

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

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

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

What are the main approaches?

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

The inductive approach

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

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

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

The deductive approach

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

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

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

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

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

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

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

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

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

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

What are the types of thematic analysis?

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

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

Let’s have a look at each of these:

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

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

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

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

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

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

Quick Recap: Thematic analysis approaches and types

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

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

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

Need a helping hand?

How to “do” thematic analysis

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

Step 1: Get familiar with the data

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

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

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

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

Step 2: Search for patterns or themes in the codes

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

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

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

Step 3: Review themes

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

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

Step 4: Finalise Themes

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

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

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

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

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

Step 5: Produce your report

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

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

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

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

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

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

Quick Recap: How to “do” thematic analysis

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

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

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

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

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

Tips & Suggestions

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

Wrapping Up

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

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

Psst... there’s more!

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

23 Comments

I really appreciate the help

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

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

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

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

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

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

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How to do a thematic analysis

What is a thematic analysis?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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.

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

There’s the distinction between inductive and deductive approaches:

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

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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The Ultimate Guide to Qualitative Research - Part 2: Handling Qualitative Data

• Handling qualitative data
• Transcripts
• Field notes
• Survey data and responses
• Visual and audio data
• Data organization
• Data coding
• Coding frame
• Auto and smart coding
• Organizing codes
• Qualitative data analysis

Content analysis

• Introduction

What is meant by thematic analysis?

The thematic analysis process, thematic analysis in other research methods, using atlas.ti for qualitative analysis, considerations for thematic analysis.

• Thematic analysis vs. content analysis
• Narrative research
• Phenomenological research

Discourse analysis

Grounded theory.

• Deductive reasoning
• Inductive reasoning
• Inductive vs. deductive reasoning
• Qualitative data interpretation
• Qualitative data analysis software

Thematic analysis

One of the most straightforward forms of qualitative data analysis involves the identification of themes and patterns that appear in otherwise unstructured qualitative data . Thematic analysis is an integral component of qualitative research because it provides an entry point into analyzing qualitative data.

Let's look at thematic analysis, its role in qualitative research methods , and how ATLAS.ti can help you form themes from raw data to generate a theoretical framework .

The main objective of research is to order data into meaningful patterns and generate new knowledge arising from theories about that data. Quantitative data is analyzed to measure a phenomenon's quantifiable aspects (e.g., an element's melting point, the effective income tax rate in the suburbs). The advantage of quantitative research is that data is often already structured, or at least easily structured, to quickly draw insights from numerical values.

On the other hand, some phenomena cannot be easily quantified, or they require conceptual development before they can be quantified. For example, what do people mean when they think of a movie or TV show as "good"? In the everyday world, people in a casual discussion may judge the quality of entertainment as a matter of personal preference, something that cannot be defined, let alone universally understood.

As a result, researchers analyze qualitative data for identifying themes or phenomena that occur often or in telling patterns. In the case of TV shows, a collection of reviews of TV shows may frequently mention the acting, the script writing, and the production values, among other things. If these aspects are mentioned the most often, researchers can think of these as the themes determining the quality of a given TV show.

A useful metaphor for thematic analysis

Even if this is an easy concept to grasp, realizing this concept in qualitative research is a significant challenge. The biggest consideration for thematic analysis is that qualitative data is often unstructured and requires some organization to make it relevant to researchers and their audience.

Imagine that you have a bag of marbles. Each marble has one of a set of different colors. If you were to sort the marbles by color, you could determine how many colors are in the bag and which colors are the most common.

The thematic analysis process is similar to sorting different-colored marbles. Instead of sorting colors, you are sorting themes in a data set to determine which themes appear the most often or to identify patterns among these themes.

After your initial analysis, you can take this one step further and separate "dark" colors from "light" colors or "warm" colors from "cool" colors. Blue and green are distinctly different colors, but you can group them under the "cool" category of colors to form a more overarching theme.

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A simple example of thematic analysis

Imagine a simple research question : how do teachers determine if a student's essay is good? Suppose you have a set of transcripts of interviews with teachers discussing writing classes and students' essays. In this case, the objective of thematic analysis is to determine the main factors teachers use to determine the quality of a piece of writing.

As you read the transcripts, you might find that teachers share some common answers. Of course, you might have an intuition that correct grammar and spelling are important, which will likely be confirmed by the teachers in their interviews. However, other considerations might surface in the data.

The next question in this casual thematic analysis is, what considerations appear most often? A few teachers may occasionally mention the size and typeface of the text as deciding factors, but more often they might say that the flow and organization of students' writing are more important. Analyzing the occurrences and patterns among themes across your transcripts can help you develop an answer to your research question.

The subjectivity of themes

One challenge is that themes in qualitative analysis, as with determining the themes of good writing, are not as visible to the naked eye as colors on a marble. The color "red" is relatively easy to see, but the fields in which thematic analysis is often applied do not deal with concepts that can necessarily be seen "objectively." It is up to the researcher to derive themes from the data from an inductive approach. Researchers can also utilize deductive approaches if they want to analyze their data according to themes that have been previously identified in other research.

Think about the picture up above. To the naked eye, these children are holding hands. But themes that can be interpreted from this picture may include "friendship," "happiness," or even "family." The thematic analysis of pictures like this one often depends on a researcher's theoretical commitments, knowledge base, and cultural perspective.

This also means that you are responsible for explaining how you arrived at the themes arising from your data set. While colors are intuitively easy to distinguish, you are often required to explain more subjective codes and themes like "resilience" or "entitlement" so that you and your research audience have a common understanding of your data analysis .

This explanation should account for who you are as a researcher and how you see the data (since, after all, a word like "resilience" can mean different things to different people). A fully reflexive thematic analysis documents and presents where the researcher is relative to their data and to their research audience.

Applications for thematic analysis

Many disciplines within qualitative research employ thematic analysis to make sense of social phenomena. For instances, these fields might be:

• psychotherapy research
• qualitative psychology
• cultural anthropology

In a nutshell, any research discipline that relies on the understanding of social phenomena or insights that may not easily be quantifiable will attract researchers engaged in thematic analysis. Moreover, any exploratory research design lends itself easily to the identification of previously unknown themes that can later be used in a qualitative, quantitative, or confirmatory research project.

Common forms of data collection

Thematic analysis can involve any number of qualitative research methods to collect data, including:

• focus groups
• observations
• literature reviews

Any unstructured data set, particularly any data set that captures social phenomena, can benefit from thematic analysis. The main consideration in ensuring rigor in data collection for thematic analysis is ensuring that your data is representative of the population or phenomenon you are trying to capture.

Virginia Braun and Victoria Clarke are the key researchers involved in making thematic analysis a commonly utilized approach in qualitative research . A quick search for their scholarship will tell you the basic steps involved in thematic analysis:

• Become familiar with the data
• Generate codes from the data
• Generate themes based on the codes
• Review the potential themes
• Define the themes for the final reporting

In a nutshell, thematic analysis requires the researcher to look at their data, summarize their data with codes , and develop those codes to the extent that they can contribute a broader understanding of the context from which the data is collected.

While these are the key points in a robust and rigorous thematic analysis , there are understated parts of the qualitative research process that can often be taken for granted but must never be overlooked to ensure that researchers can analyze their data quickly and with as few challenges as possible.

The process in greater detail

Thematic analysis relies on research questions that are exploratory in nature, thus requiring an inductive approach to examining the data. While you might rely on an existing theoretical framework to decide your research questions and collect all the data for your project, thematic analysis primarily looks at your data inductively for what it says and what it says most often.

After data collection, you need to organize the data in some way to make the data analysis process easier (or, at minimum, possible). A data set in qualitative research is often akin to a crowd of people where individuals move in any direction without any sense of organization. This is a challenge if your research question involves understanding the crowd's age, gender, ethnicity, or style of clothing.

The role of qualitative researchers at this stage is to sort out the crowd. In this example, perhaps this means having the crowd split into different groups according to those demographic identifiers to see which groups are the largest. Reorganizing the crowd from what was previously a group of wandering individuals can offer a better sense of who is in the room.

Qualitative data is often similarly unstructured and in need of reorganization. When dealing with thematic analysis, you need to reorganize the information so that the themes become more apparent to you and your research audience. In most cases, this means reducing the entire data set, as large as it might be, into a more concise form that allows for a more feasible analysis .

Codes and themes are forms of data reduction that address this need. In a thematic analysis involving qualitative data analysis software , researchers code their data by applying short but descriptive phrases to larger data segments to summarize them for later analysis. Later stages of thematic analysis reorganize these codes into larger categories and then themes, where ultimately the themes support contribution to meaningful insights and existing theory.

As you progress in the coding process, you should start to notice that distinct codes may be related to each other. In a sense, codes provide researchers with visual data that they can examine to generate useful themes. ATLAS.ti, for example, lets you examine your codes in the margin to give you a sense of which codes and themes frequently appear in your data. As you code your data, you can apply colors to your codes. This is a flexible method that allows you to create preliminary categories that you can examine visually for their abundance and patterns.

Later on, your codes can be organized into more formal categories or nested in hierarchies to contribute to a more robust thematic analysis.

Especially in qualitative research , discrete analytical approaches overlap with each other, meaning that a sufficiently thorough analysis of your data can eventually yield themes useful to your research. Let's examine a few of the more prominent approaches in qualitative research and their relation to thematic analysis.

Using grounded theory involves developing analysis iteratively through an inductive approach . While there is a great deal of overlap with thematic analysis approaches, grounded theory relies on incorporating more data to support the analysis in previous iterations of the research.

Nonetheless, the analytic process is largely the same for both approaches as they rely on seeking out phenomena that occur in abundance or distinct patterns. As you analyze qualitative data in either orientation, your main consideration is to observe which patterns emerge that can help contribute to a more universal understanding of the population or phenomenon under observation.

Narrative analysis

Understanding narratives is often less about taking large samples of data and more about unpacking the meaning that is produced in the data that is collected. In narrative research analysis , the data set is merely the narrative to be examined for its meaning, intent, and effect on its audience.

Searching for abundant or patterned themes is still a common objective when examining narratives. However, specific questions guide a narrative analysis , such as what the narrator is trying to say, how they say it, and how their audience receives the narrator's message.

Analyzing discourse is similar to analyzing narratives in that there is an examination of the subtext informing the use of words in communication. Research questions under both of these approaches focus specifically on language and communication, while thematic analysis can apply to all forms of data.

The scope of analysis is also different among approaches. Thematic analysis seeks to identify patterns in abundance. In contrast, discourse analysis can look at individual instances in discursive practices to more fully understand why people use language in a particular way.

However, the data resulting from an analysis of discursive practices can also be examined thematically. Discursive patterns within culturally-defined groups and cultural practices can be determined with a thematic analysis when utterances or interactional turns and patterns among them can be identified.

Among all the approaches in this section, content analysis is arguably the most quantitative. Strictly speaking, the words or phrases that appear most often in a body of textual data can tell something useful about the data as a whole. For example, imagine how we feel when a public speaker says "um" or "uh" an excessive number of times compared to another speaker who doesn't use these utterances at all. In another case, what can we say about the confidence of a person who frequently writes, "I don't know, but..."?

Content analysis seeks to determine the frequencies of aspects of language to understand a body of data. Unlike discourse analysis, however, content analysis looks strictly at what is said or written, with analysis primarily stemming from a statistical understanding of the data.

Oftentimes, content analysis is deductive in that it might apply previous theory to new data, unlike thematic analysis, which is primarily inductive in nature. That said, the findings from a content analysis can be used to determine themes, particularly if your research question can be addressed by directly looking at the textual data.

For thematic analysis, software is especially useful for identifying themes within large data sets. After all, thematically analyzing data by hand can be time-consuming, and a researcher might miss nuanced data without software to help them look at all the data thoroughly.

Coding qualitative data

For qualitative researchers, the coding process is one of the key tools for structuring qualitative data to facilitate any data analysis . In ATLAS.ti, data is broken down into quotations or segments of data that can be reduced to a set of codes that can be analyzed later.

The codes and quotations appear in the margin next to a document in ATLAS.ti. This visualization is useful in showing how much of your data is coded and what concise meaning can be inferred from the data. In terms of thematic analysis, however, the codes can be assigned different colors based on what the researcher perceives as categories emerging from their project, as seen in the example above.

As you code the data iteratively, reviewing themes as they emerge, you can organize discrete codes within larger categories. ATLAS.ti provides spaces in your project called code groups and code categories where sets of codes in tandem represent broader, more theoretically developed themes. This approach to data organization , rather than merging codes together as broader units, allows for a more particular analysis of individual codes as your research questions evolve and develop over the course of your project.

ATLAS.ti tools for thematic analysis

As discussed above, analyzing qualitative data for themes can often be a matter of determining which codes and which categories of codes appear across the data and patterns among them. Indeed, any analysis software can assist you with this coding process for thematic analysis. The tools in ATLAS.ti, however, can help to make the process easier and more insightful. Let's look at a few of the many important features that are invaluable to conducting thematic analysis.

Code Manager

The Code Manager is ATLAS.ti's central space where researchers can organize and analyze their codes independent of the raw data . Researchers can perform numerous tasks in the Code Manager depending on their research questions and objectives, including looking just at the data that is associated with a particular code, organizing codes into hierarchies through code categories and nested sub-codes, and determining the frequencies and level of theoretical development for each code.

Co-Occurrence Analysis

Combinations of codes that overlap with each other can also illuminate themes in your data, perhaps more ably than discrete codes. This is different from understanding codes as groups, as an analysis for codes that frequently occur together in the data can give a sense of the relationships between different aspects of a phenomenon.

The Co-Occurrence Analysis tool helps researchers determine co-occurrence between different codes by placing them in a table, a bar chart, a Sankey diagram, or a force-directed graph. These visualizations can illustrate the strength of relationships between codes to you and your research audience. The relationships themselves can also be useful in generating themes useful for your analysis.

Word Frequencies

Qualitative content analysis depends on the frequencies of words, phrases, and other important aspects found in textual data. These frequencies can also help you in generating themes, particularly if your research questions are focused on the textual data itself.

The Word Frequencies tool in ATLAS.ti can facilitate a content analysis leading to a thematic analysis by giving you statistical data about what words appear most often in your project. Suppose these words can contribute to the development of themes. In that case, you can click on these words to find relevant quotations that you can code for thematic analysis. In addition, you can use ATLAS.ti’s Text Search tool to search for data segments that contain your word(s) of interest and automatically code them .

You can also use themes to refine the scope of the Word Frequencies tool. By default, Word Frequencies looks at documents, but the tool also allows researchers to filter the data by selecting the codes relevant to their query. That way, you can look at the most relevant data quotations that match your desired codes for a richer thematic analysis.

Patterns and themes may also emerge from combinations of codes, in which case the Query Tool can help you construct smart codes. Smart codes are more versatile than nested sub-codes or code groups as they allow you to set multiple criteria based on true/false conditions as well as proximity. For example, while a code group simply aggregates distinct codes together to show you quotations with any of the included codes, you can define a set of rules to filter the data and find the most relevant quotations for your thematic analysis.

A systematic and rigorous approach to thematic analysis involves showing your research audience how you arrived at your codes and themes. In qualitative research , visualizations offer clarity about the data in your project, which is a critical skill when explaining the broader meaning derived from otherwise unstructured data .

A TreeMap of codes is a representation of the application of codes relative to each other. In other words, codes that have been applied the most often in your data occupy the largest portions of the TreeMap, while less frequently used codes appear smaller in your visualization. This can give you a sense of the prevalence of certain codes over other codes. Moreover, when you assign colors to codes along the lines of themes and categories, you can quickly get a visual understanding of the themes that appear most often in your project.

As a result, the TreeMap for codes can help provide a visual, thematic map that you can export as an image for use in explaining key themes in your research reports .

In qualitative research , thematic analysis is a useful means for generating a theoretical framework for qualitative concepts and phenomena. As always, though, theoretical development is best supported by thorough research. A theory that emerges from thematic analysis can be affirmed by additional inquiries, whether through a qualitative, quantitative , or mixed methods study .

Further research is always recommended for qualitative research, such as those that employ a thematic analysis, for the very reason that themes in qualitative concepts are socially constructed by the researcher. In turn, future research building on thematic analysis depends on a research design that is transparent and clearly defined so that other researchers can understand how the themes were generated in the first place. This requires a detailed accounting of the data and the analysis through comprehensive detail and visualizations in the final report.

To that end, ATLAS.ti's various tools are specifically designed to allow researchers to share and report their data to their research audiences through data reports and visualizations. Especially where qualitative research and thematic analysis are involved, researchers can benefit from transparently showing their analysis through data excerpts, visualizations , and descriptions of their methodology.

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

Narrative analysis

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

Discourse analysis

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

Historical 

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

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

Grounded theory analysis

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

• Challenges with analyzing qualitative data

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

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

It can be hard to choose a method. 

Avoiding bias is difficult.

Human error affects accuracy and consistency.

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

Learn more about thematic analysis software

What is thematic analysis in qualitative research.

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

Can thematic analysis be done manually?

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

What are the two types of thematic analysis?

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

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

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

What makes a good thematic analysis?

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

What are examples of themes in thematic analysis?

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

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

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

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

Appinio Research · 03.11.2023 · 33min read

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

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

What is Thematic Analysis?

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

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

Purpose of Thematic Analysis

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

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

Importance of Thematic Analysis in Research

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

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

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

How to Prepare for Thematic Analysis?

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

1. Data Collection and Selection

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

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

2. Data Cleaning and Organization

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

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

3. Choose the Right Software Tools

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

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

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

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

How to Do Thematic Analysis?

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

1. Familiarize Yourself with the Data

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

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

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

2. Generate Initial Codes

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

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

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

3. Search for Themes

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

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

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

4. Review and Define Themes

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

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

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

5. Write and Describe Themes

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

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

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

6. Report Results

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

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

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

Thematic Analysis Approaches

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

Inductive Thematic Analysis

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

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

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

Deductive Thematic Analysis

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

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

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

Reflexive Thematic Analysis

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

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

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

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

Data Analysis Techniques

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

Manual Coding

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

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

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

Using Qualitative Data Analysis Software

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

Some of the top tools used for thematic analysis include:

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

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

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

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

Thematic Analysis vs Quantitative Analysis

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

Thematic Analysis

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

Quantitative Analysis

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

Thematic vs Quantitative Analysis Comparison

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

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

How to Ensure Thematic Analysis Quality?

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

Trustworthiness and Credibility

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

To ensure trustworthiness and credibility:

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

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

Inter-Coder Reliability

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

To establish inter-coder reliability:

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

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

Addressing Bias and Reflexivity

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

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

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

Reflexivity

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

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

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

Thematic Analysis Challenges

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

Data Overload

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

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

Maintaining Consistency

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

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

Subjectivity and Interpretation

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

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

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

Thematic Analysis Applications

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

Healthcare Research

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

Social Sciences

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

Market Research

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

Psychology and Counseling

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

Policy Analysis

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

Examples of Thematic Analysis in Research

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

Example 1: Exploring Mental Health Stigma

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

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

Thematic Analysis Process:

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

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

Example 2: Evaluating Customer Feedback for a Tech Product

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

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

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

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

Example 3: Analyzing Qualitative Data in Educational Research

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

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

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

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

Conclusion for Thematic Analysis

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

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

How to Conduct Thematic Analysis in Minutes?

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Student Examples of Good Practice

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

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

Suzy Anderson (Professional Doctorate)

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

An example of a description of the thematic analysis process:

Process of Coding and Developing Themes

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

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

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

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

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

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

An example of researcher reflexivity in relation to analysis process

Subjectivity as a Resource

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

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

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

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

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

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

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

Gina Broom (Research Master's)

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

A detailed description of reflexive TA analytic approach and process

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Lucie Wheeler (Professional Doctorate)

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

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

Phase 1 – Data familiarisation and writing familiarisation notes:

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

Phase 2 – Systematic data coding:

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

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

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

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

Phase 4: Developing and reviewing themes:

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

Phase 5: Refining, defining and naming themes:

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

Phase 6: Writing the report:

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

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

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

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

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

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How to Write a Thematic Analysis

Thematic analysis is a method of analyzing qualitative data—such as survey responses, interview transcripts, or social media profiles—to identify common themes that come up repeatedly. The data is "coded," or labeled so that similar responses can be grouped together to facilitate further analysis (sticky notes are a must). For example, survey responses about online learning during a pandemic might be coded for "slow internet connection", "frequent interruptions", and "lack of peer interactions."

Thematic analysis is commonly used in psychology research, and is appreciated for its flexibility and ease of use. Despite its widespread use, the steps involved in thematic analysis were not formally described until 2006 . Those published guidelines by Braun and Clarke have been widely adopted, and have over 90,000 estimated citations as of January 2021.

The published guidelines for thematic analysis include six steps:

• Familiarizing yourself with the data
• Generating initial codes
• Searching for themes
• Reviewing themes
• Defining and naming themes
• Producing the report

Braun and Clarke emphasize that researchers must make active choices about their methods of analysis, and must clearly describe their choices and methods in their manuscripts. Otherwise, it is difficult to evaluate the research, and to compare it with other studies. Braun and Clarke come out staunchly against the idea that themes "emerge" from data. Rather, they feel that researchers must actively identify patterns and themes, select those of interest, and report them to readers. By doing so, researchers can analyze qualitative data in a deliberate and rigorous way. Minimally, thematic analysis allows you to organize your data set and describe it in rich detail. Thematic analysis can also allow you to better interpret your research topic.

Things to know before getting started

Some definitions.

• Data corpus: All data collected for a particular research project
• Data set: All data from the corpus that are being used for a particular analysis
• Data item: An individual piece of data collected (e.g. an individual interview)
• Data extract: An individual coded chunk of data (e.g. a quote from an interview)

Questions that should be considered before data is analyzed (or even collected)

• Breadth or depth of coverage? If you aim to provide a description of your entire data set (breadth), some of the nuances within the data will necessarily be lost. However, this can be an effective approach when the topic is now well understood. Alternatively, you can aim to gain a detailed understanding of one or a few specific aspects of your data (depth).
• Inductive or deductive approach to thematic analysis? Adopting an inductive approach means that you will allow your research question to evolve as you analyze and code the data. A deductive approach (also called a theoretical thematic analysis) involves coding the data with a specific research question in mind .
• Semantic or latent approach? A semantic approach involves identifying themes based entirely on the words used by the participants (e.g. in a survey, interview, website, etc.). Once the data is coded, organized, and described, the researcher can provide further interpretation. Alternatively, a latent approach allows the researcher to code data items based on what the item reveals about a participant's underlying ideas, patterns, and assumptions.

Let's now consider the six steps of thematic analysis.

Step 1: Familiarizing yourself with your data

Before you can effectively code your data, you must become intimately familiar with it. Immerse yourself in the entire data set by actively reading through it at least once, searching for meanings, possible patterns, etc. Importantly, you should read with an open mind, and not just search for data that support your hypothesis or assumptions. This process of actively reading and re-reading the data is time consuming, but is the foundation for any meaningful interpretation of it.

Verbal data (e.g., interviews, recorded presentations, etc.) need to be transcribed in order to be used for thematic analysis. Transcribing data is an excellent way to become familiar with it. The act of transcription should be considered an interpretive act, especially for spontaneous responses (e.g., an interview compared to a recorded presentation). The simple placement of punctuation marks can dramatically alter the meaning of words (e.g., "Let's eat Bob" is very different from "Let's eat, Bob"). If you are provided with data that has already been transcribed, you should check the transcription against the original recording to ensure its accuracy. Listening to the original recording will also improve your understanding of the data.

As you familiarize yourself with your data, you can start to better focus your research question, and jot down ideas for coding. The process of coding, and improving your codes to better address your research question, will continue through the entire analysis.

Step 2: Generating initial codes

Codes identify a portion of the data of potential interest to the researcher. The relevant text is highlighted and labeled with a "code" that is short and descriptive. For example, survey responses about online learning during a pandemic might be coded for "frequent interruptions" when the text includes a mention of being interrupted by family members, the doorbell, internet lapses, etc.

Coding can be done manually (with highlighters, colored pens, sticky notes, etc.), or on a computer. You need to work systematically through your entire data set, giving equal attention to each data item, whether or not it supports your hypothesis. Add new codes as necessary (e.g. "slow internet connection" and "lack of peer interactions"). It is better to add a code that might not be helpful, than to go back to your data to add it later. An individual section of text might remain uncoded (e.g. if unrelated to your research question about online learning), coded once (e.g. for "frequent interruptions" from family members), or coded multiple times (e.g. for "frequent interruptions" due to "slow internet connection"). As you work through your data, you may find you need to combine or subdivide your codes (e.g. add a new code for "interruption by sibling").

You are summarizing your data with these codes, so be thorough in your work.

Step 3: Searching for themes

Once you have completed the initial coding of your data, you can organize your data into groups. Include the relevant text, not just the code, to facilitate searching for themes. Consider how different codes may contribute to an overarching theme. For example, you may find that the codes "slow internet connection", "financial stress", and "no help with schoolwork" commonly occur together. You might then choose to place these codes within a larger theme of "low-income students face additional challenges with online learning." You might consider adding "financial stress" as a sub-theme. The code "lack of peer interactions" might fit within a larger theme of "mental health stressors." Some codes may occur too infrequently to be included, may be too vague, or may not be relevant to your evolving research question.

This step provides candidate themes that will need to be refined, so don't exclude any data or possibilities just yet. In the next step, reviewing all the data extracts will help you determine how the themes might need to be combined, separated, or discarded.

Step 4: Reviewing themes

Now that you have a set of candidate themes, you need to revise them to accurately reflect your data and your research question. This may involve eliminating themes that are not well supported, creating new themes, combining closely related themes, and dividing overly broad themes into multiple themes.

You will need to review your data at two levels. First, you should review the coded data extracts for each theme, and determine whether they are sufficiently coherent. Problems may occur if the theme itself is problematic, or if some of the data extracts do not fit within their theme. You may need to rework the theme, re-code some data to create finer divisions, or remove a code from a theme.

Next, you need to evaluate whether your themes accurately reflect your entire data set. You should re-read your entire data set to determine whether your themes fit the data set, and to code any relevant data that may have been missed in earlier rounds of coding.

Step 5: Defining and naming themes

Formulate exactly what you mean by each theme, and examine how it helps one understand your data. Devise a concise and informative name for each theme. Take the data extracts for each theme, organize them so that they are easy to follow, and add an accompanying narrative to explain why the data are interesting and how they support the theme.

Step 6: Producing the report

At this point, you should have produced quite a bit of writing as you have moved through steps 1 through 5. You should have your coded data extracts, your themes, and some narrative explaining how your data support your themes. For your final report, you should organize this information according to the guidelines of your graduate program or target journal, and write a compelling story that convinces the reader of the validity of your analysis. Choose examples from your data that clearly and memorably illustrate your main points. Go beyond presenting your data, and make an argument that could influence policy, or serve as the basis for future studies.

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A thematic essay is a type of writing assignment that focuses on a specific theme or topic. It requires you to identify a central theme, discuss it in detail, and make connections between various facts. Your main goal is to demonstrate understanding and interpretation of the given subject matter. This type of essay is commonly used in literature classes or history exams.

If you’ve got an assignment to write a theme essay, you might wonder where you should even start from. No worries, we’ve got you covered here! The first thing you must know about this specific type of paper is that it aims to analyze a certain well-known theme and make an interesting statement about it. Here, you must explain meaning and relevance or complexity of your topic. You should summarize details that support your conclusion. In this article, we will conduct a detailed review of theme essay concept. We will also provide you a step by step guide on how to write a proper one. Let's dive right into it!  

Thematic Essay Definition

Let’s start with defining what is a thematic essay and its purpose. In this type, one should select a thesis and form unique statement related to its aspects. You should write about it, explaining or elaborating to your audience the following:

• How is your statement related to your topic?
• Which important or interesting aspects does it highlight?
• What approaches and literary devices are you using for analysis ? How do you explain your general theme? This can be comparison, metaphor, personification etc.

When composing such an essay, you must formulate and defend your statement. Here, you will demonstrate abilities of analysis and literary devices usage. At least several paragraphs would be needed to display such skills properly.

Thematic Essay Outline: What's Inside

The best way to begin is creating a theme essay outline for your topic. An outline should contain all key parts, concepts and ideas of your paper. You should put it in a sketchy but logical manner. This way you'll quickly prepare a shortened version of your assignment. It will also help you in reviewing it. Adding missing points and correcting significant mistakes would be easier at this early stage. Outline should include all main essay parts:  

• Introduction
• Thesis statement
• Body section
• Conclusion.

Keeping it brief, you should not provide complete sentences to describe your statements, ideas and arguments. A few words would suffice for each important point. Purpose is to make it readable for yourself! You should review it quickly and spot any inconsistencies.

How to Write a Thematic Essay Step-By-Step

Now it is time to focus on how to write a theme analysis essay – the complete text from scratch. Is your goal to impress readers and achieve a good grade? Then it is important that you create a proper essay structure template and don't lose any of your key questions! Stay methodical and keep it logical! Make sure your audience is engaged and don’t disappoint them in the end. Below we’ll provide a general idea for each step of this process.

Step 1. Define the Topic for Your Thematic Essay

When it comes to choosing among thematic essay topics, it is important that you pick an interesting and maybe even a controversial one. At the same time, make sure you can actually provide some meaningful input about it. Your assignment should impress readers with detailed analysis and its author’s writing skills. That's why your chosen topic must provide enough material for that.  There is a diverse choice of topics. Choose the one you are really interested in whether it is  Bullying essay  or  Happiness essay . If you need some ideas for great essay topics, feel free to check out our other articles.  

Step 2. Create a Thematic Essay Outline

We've already covered the main points of theme essay outline concept. When writing it, include all the main parts of your future work. Keep it as short as possible, one paragraph per each key point will be enough. It isn’t even necessary to describe everything with complete sentences! A few words would suffice. Once done, review it first and make necessary corrections. It is advised to review an outline several times. That's how any noticeable gaps or mistakes would be spotted early.

Step 3. Start a Thematic Essay with a Hook

A good thematic essay introduction ought to captivate readers right from the start. That’s why it is always advised to add some ‘hook’ into it. You can begin with an unexpected statement, use wordplay or a plot twist. Then you can explain this in the main body part. This way your audience would be interested to hear those explanations. As a result, your paper will have better chances of success. Apart from that, introduction should contain the main statement and some information about its content.  

Step 4. Write Body Paragraphs for Your Theme Essay

Goal of thematic essay body is to answer all the questions stated in an introduction. You must elaborate the meaning of each key idea. Finally, display your usage of literary devices, as we’ve specified earlier. Common practice is to use at least one paragraph per a literary device disclosure. Besides, the main body is the right place to use all relevant sources that can support your analysis or provide you with helpful analogies. Keep the main body logical, so that every paragraph is somehow connected to the previous and the next ones.  

Step 5. Create a Thematic Essay Conclusion

A strong thematic essay conclusion should highlight all important points from tyourhe essay while avoiding adding new facts or evidence. Just restate your thesis, answer all questions and summarize your arguments. It might be also useful to leave some final note for readers with some deeper analysis of your topic. You can also highlight the need for further exploration of the chosen theme and thus to prepare readers for your future works on this topic.  

Step 6. Proofread Your Thematic Analysis Essay

After completing theme essay, it is highly recommended to review it thoroughly, even several times if possible. The goal is to find mistakes and to spot logical gaps or missing details. Even best essays typically have inconsistencies left at the early stage. Taking a fresh look at your text often reveals some issues. If possible, ask your friends or colleagues to review your text. They might notice something you could not.  

How to Format a Thematic Essay

When it comes to thematic essay format, you need to find out what are the requirements in your assignment or which format is common in the institution you will be presenting your essay for. In case no special requirements were made for you, just choose one of the most popular formats for scholarly papers:  

• APA paper format : typically used in natural sciences, education and psychology fields
• MLA: typically used for works in humanities
• Chicago: typically used in business, history, and fine arts fields.

Thematic Essay Example

Let’s illustrate the explanations above with a few theme essay examples. We’ll provide some real ones here so that your every question would be answered. Hopefully you’ll find some inspiration in these examples for your own winning paper! The examples can be found below. Please scroll down to find them.  

Thematic Essay: Final Thoughts

In this article we have explored the theme essay concept in detail. Its central purpose and main definition were examined and a step by step guide for writing a strong one was suggested. We’ve also provided a few working examples for your convenience. Hopefully, all this information will be useful for your scholarly endeavors!

Feel free to check out our  paper writing services ! We’ve got a team of skilled writers with expertise in different literary areas, ready to help you. They deliver high quality content, always on time.  

Frequently Asked Questions About Theme Essay

1. what is the thematic statement.

A thematic statement typically takes the place of a thesis in a thematic essay. It consists of 1-2 complete sentences that express a theme which you have chosen for your work. This statement must convey the main message and also show what analysis will be done. It should be brief however as most of the details are to be provided in the main body.

2. What is the goal of thematic essay?

The thematic essay goal is to express an idea or some insights about the surrounding world and to change readers' minds about certain issues. As an author, you are expected to illustrate the team, provide all necessary explanations and conduct an analysis if needed. Besides, you typically should demonstrate familiarity with some literary interpretations and methods which are used to examine your theme.

3. How long should a theme essay be?

The minimum length of a theme essay is five paragraphs. One is for introduction, one for conclusion and remaining three for the main body. Of course, it can be more than that, depending on the depth of the theme that was chosen. The main rule is to keep your essay logical and concise, avoiding adding too many details. Otherwise your audience might get tired and the effect produced by your writing would be damaged.

4. What is a thematic essay history?

Thematic essay (history class) should be written to analyze some historical facts or significance of specific literary pieces. A typical case is examining different aspects of a controversial leader from the past or a political event that has produced a number of various important consequences. Or you might argue about a specific role of a certain book during a certain period or its influence on different nations or cultural groups.

Daniel Howard is an Essay Writing guru. He helps students create essays that will strike a chord with the readers.

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Home ➔ How to Write an Essay ➔ Thematic Essay

Thematic Essay Guide

Thematic writing is a staple of high school English and college writing courses. The idea behind thematic writing is to create a piece that uses a theme to tie together different ideas or topics. Thematic writing can be used for essays, short stories, novels, and even non-fiction pieces. In academic writing, thematic essays often center on a specific issue or theme and develop that theme throughout the essay.

Thematic essays are often assigned in high school English classes and college writing courses. They are also found on standardized tests such as the SAT and ACT.

If you need to refresh your memory regarding general essay writing, check our detailed guide: How to Write an Essay

There are a few different literary devices that are often used in thematic writing. These devices can help create a more cohesive essay or story and can also help emphasize the theme.

One literary device that is often used in thematic writing is symbolism. Symbolism is when an object, person, or place represents something else. For example, in the novel The Great Gatsby, the character Daisy Buchanan symbolizes wealth and shallowness. In the story “The Lottery” by Shirley Jackson, the black box that is used for the lottery represents tradition and the blind following of rules.

Definition and Purpose

A thematic essay is an essay that requires you to write about a particular question or theme. Thematic essays are often written in response to prompts that ask you to discuss a specific aspect of a larger topic.

The main point of a thematic essay is to show the development of a theme throughout a work of literature or to compare the way different authors or works deal with similar themes.

Essentials of a thematic essay:

• must be focused on a central theme
• must develop that theme with specific examples from the text(s)
• must synthesize several elements of the text(s), including plot, character, setting, style, tone, etc.
• must not be a mere plot summary
• must use evidence from the text(s) to support your argument
• must be well organized, with a clear introduction, body paragraphs, and conclusion

If you want to learn more about essays in general, we suggest you read this guide: Essay Definition

Pre-writing stage

Before you write a thematic essay, it is important first to understand the prompt and Rubric . The prompt will ask you to write about a specific theme, such as “Justice in Othello.” The Rubric will outline the specific requirements for the essay, including things like length, formatting, and the inclusion of outside sources.

So, before writing a thematic essay, you should carefully read the prompt and think about what you want to write about. Make sure that you understand what the prompt is asking you to do.

Some tips for pre-writing:

• Brainstorm possible themes or ideas that could be related to the prompt.
• Choose a central theme or idea you are interested in and think you can write about persuasively.
• Come up with specific examples from the text(s) that you can use to support your argument.
• Make a list of the different ways that the overall significance of the theme or idea can be developed.
• Decide on the main point you want to make about the theme or idea.
• Organize your thoughts and develop a thesis statement.

Analyzing the prompt and developing a thesis statement

To write a thematic essay, you need to analyze the prompt first. You will need to identify the task that the prompt is asking you to do, and you will need to identify the main idea of the passage.

For example, a prompt might ask you to write about the use of symbols in a work of literature. To answer this prompt, you would need to identify the different ways that symbols are used in the text. You also need to determine what the author is trying to communicate using symbols.

Literary theme analysis prompt example ( TKAM ):

How does Harper Lee use the symbol of the mockingbird to explore the theme of innocence in To Kill a Mockingbird?

When analyzing this prompt, consider what the mockingbird symbolizes in the novel and how this relates to the larger theme of innocence. You might discuss specific characters who embody innocence, such as Scout or Atticus, and how the events of the novel impact them. You might also discuss the impact of innocence on the town of Maycomb as a whole.

One example of how Harper Lee uses the symbol of the mockingbird to explore the theme of innocence is through the character of Scout. Scout is a young girl who is innocent and naive in many ways. She does not understand the prejudice and hatred that exists in her community. However, she can also see the good in people, even when they are not perfect. The mockingbird symbolizes this innocence.

Your thesis statement can look something like this:

The symbol of the mockingbird in To Kill a Mockingbird by Harper Lee is used to represent the innocence of characters like Scout and Atticus, as well as the innocence of the town of Maycomb as a whole.

Other examples of thematic essay topics:

• The Catcher in the Rye: How does J.D. Salinger use the character of Holden Caulfield to explore the theme of teenage angst and rebellion?
• Heart of Darkness: How does Joseph Conrad use the character of Kurtz to explore the theme of colonialism and its effects on the human psyche?
• All Summer in a Day: How does Ray Bradbury use the character of Margot to explore the theme of bullying and its effects on victims?
• The Great Gatsby: How does F. Scott Fitzgerald use the character of Daisy Buchanan to explore the theme of the American Dream?
• Metamorphosis: How does Franz Kafka use the character of Gregor Samsa to explore the theme of alienation?

Outlining your thematic essay

After you have analyzed the prompt and developed a thesis statement, you can begin to outline your essay .

Thematic essays usually have a standard structure and consist of five paragraphs. That typically requires you to include an introduction, three body paragraphs, and a conclusion.

Each body paragraph should discuss a different aspect of your thesis. For example, if you need to write a thematic essay about the use of symbols in a work of literature, you might discuss how symbols represent different themes in the novel.

Your thematic essay outline might look something like this:

Introduction:

• Provide a brief overview of the work of literature you will be discussing
• Introduce the main idea or central theme that you will be discussing
• Thesis: The symbol of the mockingbird in To Kill a Mockingbird by Harper Lee is used to represent the innocence of characters like Scout and Atticus, as well as the innocence of the town of Maycomb as a whole.

Body Paragraph 1: Symbols representing innocence

• The mockingbird symbolizes Scout’s innocence
• The mockingbird symbolizes Atticus’ innocence
• The mockingbird symbolizes the innocence of Maycomb

Body Paragraph 2: The loss of innocence

• Scout loses her innocence when she witnesses the trial
• Atticus loses his innocence when Mr. Ewell attacks him
• Maycomb loses its innocence when Tom Robinson is convicted

Body Paragraph 3: The importance of innocence

• Innocence is important because it allows people to see the good in others
• Innocence is important because it allows people to hope for a better future
• Innocence is important because it allows people to see the world in a more positive light

Conclusion:

• Restate your thesis statement
• Discuss the larger implications of your thesis statement
• Leave the reader with something to think about

You can also dedicate each body paragraph to one specific character or one specific event in the novel.

For example, you might discuss how Scout’s innocence is represented by the mockingbird symbol and how this innocence is lost when she witnesses the trial. In your second body paragraph, you could discuss how Atticus’ innocence is represented by the mockingbird symbol and how this innocence is lost when he is attacked by Mr. Ewell. In your third body paragraph, you might discuss how the innocence of Maycomb is represented by the mockingbird symbol and how this innocence is lost when Tom Robinson is convicted.

Writing the thematic essay

Once you have developed a clear thesis statement and created a thematic essay outline, you can begin writing. We complement each section with a thematic essay example part to better illustrate how it can look.

Introduction

The introduction of your thematic essay should briefly state what you will be discussing in your paper and provide background information. The introduction should also include your thesis statement, which is the main argument of your paper, at the end.

At the very start, you can also use a hook to grab the reader’s attention . A hook is usually a sentence or two that draws the reader in and piques their interest.

Introduction example of a thematic essay:

“Mockingbirds don’t do one thing but make music for us to enjoy. They don’t eat up people’s gardens, don’t nest in corncribs, they don’t do one thing but sing their hearts out for us. That’s why it’s a sin to kill a mockingbird.” This quote, spoken by Atticus Finch, perfectly encapsulates the symbol of the mockingbird in Harper Lee’s To Kill a Mockingbird. To Kill a Mockingbird by Harper Lee is a novel about the loss of innocence. The symbol of the mockingbird is used to represent the innocence of characters like Scout and Atticus, as well as the innocence of the town of Maycomb as a whole. The loss of innocence is an important theme in the novel, and it is represented in the characters of Scout, Atticus, and Maycomb.

Body paragraphs

Your body paragraphs should each be dedicated to one specific character or event in the novel and start with a topic sentence . After the topic sentence, you should use evidence to support it. You must analyze the evidence, not just merely state it. Lastly, end your body paragraph with a conclusion and transition to the next section.

A thematic essay body paragraph example:

One example of the loss of innocence is Scout. When the novel begins, Scout is an innocent child who does not understand the ways of the world. “‘Don’t you ever do that again,’ I said. ‘Don’t you ever do that again, it’s bad enough having Jem tell on me without you adding to it.’” (Lee 9). Scout is chastised by her father, Atticus, for fighting with her cousin, Francis. In this quote, Scout does not understand why fighting is bad and must be scolded by Atticus. However, by the end of the novel, Scout has learned the true evil that exists in the world and has lost her innocence. “It was then that I finally understood Mrs. Dubose’s courage, not because she had won, but because she had had the courage to fight and the strength to lose.” (Lee 281). Scout has come to understand that even though Mrs. Dubose lost her battle with addiction, she was still brave for fighting it. Mrs. Dubose’s courage has taught Scout that even in the face of defeat, there is still hope. Scout has lost her innocence because she has learned that the world is not a perfect place and that people are not always good.

Check our citation guide to learn more about using quotes in essay: How to Introduce a Quote

Your concluding statement should sum up the main points of your thematic essay and rephrase your thesis (restate it in different words). You can also discuss broader implications or give your opinion on the topic, but don’t add any new information.

A conclusion example of a thematic essay:

To Kill a Mockingbird is a novel about innocence—the loss of it, the destruction of it, and the fight to keep it. The characters of Scout, Atticus, and Maycomb all represent different aspects of innocence, and the novel explores how they each deal with the loss of their innocence. Scout loses innocence when she learns that the world is not perfect and people are not always good. Atticus loses his innocence when he realizes that sometimes the justice system does not work as it should. Maycomb loses its innocence when it is revealed that racism and prejudice are alive and well in the town. The loss of innocence is a sad but inevitable part of life, and To Kill a Mockingbird shows us that this loss can sometimes be for the better.

Final tips on thematic essay writing

Now that you know the main steps involved in the writing process of a decent thematic essay, here are some of the final tips and key takeaways that you should keep in mind when doing this task:

• Make sure to brainstorm first and develop a good thesis statement that will guide the rest of your essay.
• Create a thematic essay outline before starting to write the essay itself. This will help you stay on track and not miss any important points that you want to include.
• Start each body paragraph with a topic sentence that introduces the paragraph’s main point.
• Use concrete and specific examples to support your points.
• Pay attention to your grammar and spelling.
• Make sure to conclude your thematic essay in a strong way that ties everything together.
• Analyze the work, not just summarize it.
• Take your time, and don’t rush the essay.
• And lastly, proofread your work before submitting it.

By following these tips, you should be able to write a thematic essay that will impress your teacher or professor.

• Solano Community College – World Literature
• Jefferson State Community College – Literary Theme Analysis

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How to Write a Thematic Analysis Essay Correctly: Expert Advice

So, a thematic analysis essay. This kind of essay might be not the most complicated one. However, it doesn’t mean that you have to be confident and peaceful. You aren’t a professional writer and being worried is a normal human condition. And this is normal. People are different.

There are people that don’t like writing, you know. There are people that like writing but don’t have much experience in doing that. As well, some people want to learn how to write. That’s why, we have selected the most useful tips, which will be extremely helpful if you decide to write your essay on your own. So, just check how to do it, how to write a thematic analysis essay, and do it!

What Is a Thematic Analysis Essay: Clear Definition

But first of all, let us check exactly what kind of paper you should write. So, the definition you can guess already from the title: a thematic essay is an essay about some themes or concepts. When you are working on this kind of essay, you should consider the following moments:

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• All your attention should be focused on ideas and themes that you should analyze.
• Provide not only your analyses but the reference to external sources, because the reader might want to check them.
• Every aspect of the task should be taken into consideration, if you are writing an analysis, just consider it.
• Provide enough details, but be specific; deviations aren’t good at this kind of paper, but being too short is not helpful either.

Thematic Analysis Essay Structure Follows the Standard

The thematic analysis essay structure is the same as for any other kind of essay. Make sure you follow the standard and don’t invent anything new. So, the general structure of a thematic analysis essay is the following:

• An introduction, where you introduce the theme and explain its importance.
• The main part, where you provide detailed information and all the opinions that you consider relevant.
• A conclusion, where you actually make a conclusion for your analysis, for all your work.

How to Start a Thematic Analysis Essay: Strong Introduction

Here, introduce your theme or concept. Make it as strong as possible — the impression should be unforgettable! Only then, your reader might consider reading it till the end just out of interest and give you a good grade. So, if you are thinking about how to start a thematic analysis essay efficiently, start it with a question or a personal story. Then, the introduction will rock! The thematic analysis essay introduction is one of the most important parts of your paper.

The Main Part

In the main part, analyze the given themes. Pay attention to all aspects of your theme or concept. Provide not only your opinions but the ideas and opinions of experts or even just some other people who have been working with the same topic.

How to End a Thematic Analysis Essay Impressively

Now, it is time to check how to end a thematic analysis essay. The rules are like everywhere, like any other conclusion. You should make the final statement, the conclusions based on your analysis. Well, your conclusion should be impressive, as well. You know, a weak conclusion of a thematic analysis essay can spoil the impression from the entire work, and that is definitely not what you are looking for.

Thematic Analysis Essay Outline Processing

The thematic analysis essay outline doesn’t differ much from a standard essay outline. The same parts, the same structure. Like in the case with any other essay, you should be precise, logical, and try to make all parts of your essay as strong and impressive as you can.

However, there are some writing tips that you shouldn’t ignore. For this kind of essay, the preparation process is essential. You cannot analyze a theme if you don’t know what experts think about it. How can you compare ideas and views, if you don’t know them?

So prepare yourself to the writing process very carefully. Select the most reliable sources. If they are recommended for schools and universities, it is perfect. But even if not, just make sure they provide reliable information. In most cases, they should be published in reputable journals or websites.

When you are writing a theme analysis essay, it is important to be very attentive to the smallest detail. That’s why it is an analysis essay, you know. Other than that, the essay is just like any different kind of paper. You give your opinions, discuss them, compare them with the opinions of other people and so on.

How to Write a Thematic Essay With Explanations and Examples

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Thematic essays are common essay assignments in college across all disciplines. Basically, this guide begins with a definition of a thematic essay and provides sample topics for illustration. Next, the manual deconstructs the process of writing a thematic essay. Moreover, the guide covers three core stages of thematic essay writing: preliminary actions, establishing the paper’s foundation, and writing. Finally, the manual presents a sample outline and an example of a thematic essay to demonstrate a real writing situation where a student implements these guidelines of thematic paper writing correctly. Hence, students need to learn how to write a thematic essay.

Definition of a Thematic Essay

A thematic essay is a form of academic writing that requires an author to react to a particular question or theme. In this case, instructors expect students to develop a written reaction to a question or theme by connecting various pieces of information to reach a reasonable conclusion. Moreover, writing a thematic essay has a high demand for research and a critical examination of subtle relationships that exist between sources. Then, the research process yields a significant amount of information, which learners may use to generate numerous logical relationships that lead to rational inferences. Consequently, students may select any set of evidence with a clear, logical association provided that their central claim centers on a theme of interest.

Sample Topics for Writing Thematic Essays

• A pure democracy.
• Privacy rights in the big data age.
• Life in prison and the ex-convict experience.

2. Sociology

• Major parenting issues of the 21 st century.
• Thematic family bonds in immigrant families.
• Escaping the cycle of poverty.

3. Literature

• The thematic significance of a muse.
• Corruption in 18th-century short stories.
• Female authors who left a mark on classical literature.
• Memories of the holocaust.
• Landmines on the modern political landscape.
• The cause of the collapse of Middle East alliances in the 20 th century.

5. Psychology

• Teenage confidence after the thematic emergence of social media.
• The impact of abuse on the formation of relationships.
• The efficacy of proactive counseling.

How Do You Know if Your Paper Is Thematic?

A student can readily identify a thematic essay topic because it boldly pronounces a writing theme but does not hint at any specific point of view. In particular, the primary goal of a topic of a thematic essay is to inform readers of a theme rather than the particular approach of an author, which becomes more apparent in the introductory paragraph. Specifically, a thematic essay topic does not allow students to develop a particular supposition concerning a theme because authors realize there may be multiple points of view concerning the idea of a thematic essay.

3 Steps For Writing an Effective Thematic Essay

Step 1: preliminary actions, a. define a thematic topic.

The ability of students to define a topic is dependent on the extent to which they understand essay rubric and instructions. Basically, once learners receive thematic essay instructions, they should critically read their prompts to ensure they comprehend all demands of writing requirements. Then, writers should use keywords from their instructions to write one or more questions, which represent the expectations of instructors. Based on developed questions, students can create a topic that adequately captures the content of possible responses holistically. Also, authors must consider the information in the instructions, which establishes the leeway they have in the selection of a topic, for example, choose a theme not covered in-class readings.

B. Identify a Purpose for Writing a Thematic Essay

The procedure of identifying a purpose occurs in two distinct stages: the selection of a general goal and defining a specific purpose. Basically, authors may write a thematic essay to achieve two general purposes: explanation and persuasion. In this case, expectations of instructors influence the choice of the general purpose of a thematic essay to a large extent. After learners pick the general purpose for writing a paper, they should create a specific purpose that shows the particular effect their papers must have on readers. Mostly, writers generate a specific purpose from questions that represent thematic essay instructions. In turn, the early determination of the purpose is crucial because it affects the students’ approach to research and word choice during drafting.

C. Analyze an Audience

Before writing a thematic essay, students need to determine the characteristics and expectations of readers. Basically, knowledge concerning the characteristics and expectations of the audience is valuable because writing allows authors to understand the interaction between the characteristics and attitudes toward a topic, the readers’ level of expertise, and the significance of misconceptions, which aid in selecting the appropriate presentation approach. Specifically, learners can determine the most effective organization patterns, identify the best evidence, and employ an accepted documentation style. Moreover, students ensure a suitable level of explanation accompanies specialized writing terms that appear in a thematic essay.

D. Generate and Write Ideas

After learners define the purpose and comprehend the needs and traits of the audience, they begin to develop ideas for the content of a thematic essay. Mostly, thematic essay assignments for writing a particular subject focus on topics lecturers discuss in classrooms and other course readings. Consequently, students may generate ideas through brainstorming based on the relevant information from the unit and other related units they encounter during their schooling. During brainstorming, writers engage in idea mapping and clustering, which enables them to keep track of relationships between ideas.

Step 2: Establishing a Foundation for Writing a Thematic Essay

A. search for sources.

The author’s initial ideas regarding a topic act as the starting point for acquiring credible sources that support and refine those ideas. Basically, contemporary learners engage in electronic searches to find useful and reliable sources for thematic essays. In this case, students should begin their search on the library’s website, which provides them with material that is reliable for academic writing. Also, library search engines have complex filter functionalities, which make the process of searching for academic sources quite simple. Then, authors turn to open-web searches using Google Scholar or other public search engines that generate a significantly larger number of sources. However, some articles may not be accessible to students. Moreover, the burden of determining the reliability of sources falls on authors when they use open-web search engines. In turn, students rely entirely on keywords or keyword combinations to generate working bibliographies.

B. Evaluate Sources Before Writing

Working bibliographies undergo an intensive evaluation process to establish whether they meet the necessary quality standards for inclusion in writing a college-level thematic essay. In this case, the evaluation process involves two primary stages: relevance determination and reliability test. During relevance determination, authors should examine each source by using three criteria:

• The level of attention that the source gives to the topic.
• The suitability of the sources’ sophistication to the purpose and audience’s needs.
• Impact of publication date on the relevance of its information.

Next, the reliability test investigates five critical writing aspects:

• The origin of the thematic source.
• The level of expertise of authors.
• Biases of a source in the context of existing literature.
• Availability and quality of the evidence supporting the source’s claims.
• Objectivity in the presentation of the author’s claims and handling of evidence.

C. Write a Thematic Annotated Bibliography

At this point, the revised working bibliography now contains fewer sources that are relevant and reliable. In particular, students should engage in critical reading of all sources in the working bibliography to identify useful pieces of information they may incorporate into a thematic essay. After reading each source, learners write an annotation that contains a summary of a source, ideas for using this source, and an assessment of this source. Besides the three main elements of an annotated bibliography entry, writers may choose to mention specific pieces of evidence, which are the most significant contributions of a source to a thematic essay. Typically, writers develop an annotated bibliography from notes they make as they read through a text.

D. Develop an Outline

Based on an annotated bibliography, students create an essay outline. Basically, the content of an annotated bibliography entry allows learners to develop relationships between sources, which is essential because it begins to shape an essay structure. In this case, writers identify and group sources that support a general point, which splits a body of a thematic essay into discernible sections. Then, authors break down each general point into specific points that can exist as a body paragraph and assign appropriate sources to individual body paragraphs. Furthermore, scholars logically organize particular points and establish some form of flow within each section of a body paragraph. In turn, writers document the organization of general and specific ideas and the distribution of evidence in a list, like a format that allows for easy identification of hierarchy.

Step 3: Writing a Thematic Essay

A. design a working thesis.

A student develops a working thesis statement, which presents his or her central claim. Basically, questions that writers derive from assignment instructions and specific minor arguments listed in a thematic essay outline are the main pieces of information they use to generate a thesis statement. Initially, writing a working thesis statement may appear as a simple combination of individual responses to assignment questions in the context of the information that forms an outline. However, authors must unite individual answers under a specific inference that demonstrates the significance of writing a thematic essay. Also, a working thesis statement undergoes multiple revisions, which occur randomly during the writing process.

B. Review an Outline

Once an author writes a working thesis statement, a person subjects an informal outline to a revision that results in the creation of a formal outline for a thematic essay. Basically, body paragraphs of a thematic essay are building blocks for a central claim. Consequently, learners must review an informal outline to ensure there is an apparent logical build-up to the inference, which they announce in a written thesis statement. During this review, students focus on the organization of minor arguments to ensure that body paragraphs contain a single minor idea while maintaining a rational relationship with other body paragraphs. Moreover, writing a formal outline contains a systematic arrangement where information with the same level of significance or roles has identical indentation or numbering.

C. Select Sources for Writing

Based on writing a formal outline, students make a final assessment of sources for each body paragraph. In particular, a formal outline contains some changes in its organization and the framing of minor arguments of a thematic essay. Also, these changes may affect the relevance of sources to each body paragraph’s argument. Then, the subdivision or merging of minor arguments may cause some sources to become inadequate because they do not extensively cover new minor ideas. Therefore, writers should check the suitability of each source to arguments it supports to ensure each source provides strong, relevant, and accurate evidence before commencing the drafting process.

D. Draft a Thematic Paper

During the drafting stage, authors expand a thematic outline into a complete paper by changing statements and brief notes into coherent paragraphs. Basically, there is no fixed approach to the drafting of a thematic essay because students may start writing at any point in a thematic essay with the aid of a formal outline. Nonetheless, it is an excellent practice for learners to begin drafting from a paragraph they understand the best because it ensures writers waste very little time trying to overcome the fear of creating the first draft. In turn, scholars should allocate adequate time for drafting.

How to Perfect a Thematic Essay

1. revision, a. self-critic.

After completing the first draft, students undertake a self-conducted revision process, which involves rethinking and rewriting. Basically, the process of revision focuses on the evaluation of the evidence and organization of body paragraphs to ensure they support a working thesis statement entirely. Further, learners revisit a working thesis statement to refine its wording and the claim it presents. Before starting the revision process, writers should take a break, which allows a human brain to reset and attain a higher level of objectivity while revising. Moreover, scholars should use a checklist to reduce the risk of overlooking various crucial thematic essay dynamics during individual revision.

B. Peer Review

The individual revision process identifies the apparent flaws in content presentation, but numerous flaws may go unnoticed due to the authors’ subconscious biases concerning their writing styles. As a result, students should subject their thematic essays to a peer review by a classmate, tutor, parent, or writing center staff. In this case, learners should select a peer reviewer that best represents a member of the target audience. Moreover, authors may provide peer reviewers with a checklist to guide them through the revision process, especially if a person is not an expert editor. Then, students should assign adequate time to the peer review process to allow reviewers to carry out the revision task comfortably. In turn, once writers receive feedback from peer reviewers, they consider comments when making the final revision of a paper.

A. Clarity and Effectiveness

The first consideration in the editing process is the clarity and effectiveness of sentences in a thematic essay. Basically, authors should edit each sentence to ensure statements convey the intended meaning to readers. In this case, it is advisable to focus on six clarity issues, which are the most common in writing thematic papers: lack of parallelism, dangling modifiers, vague references to pronouns, incomplete sentences, and incorrect separation of sentences. Besides clarity, learners should evaluate the efficacy of each statement separately and as part of a paragraph. In turn, the effectiveness of writing statements revolves around the smoothness of transitions, conciseness, variability in sentence structure and length, the distinctiveness of the author’s voice, and emphasis on core ideas.

B. Surface Errors

The subsequent stage of the editing process involves editing for thematic surface and documentation errors. In particular, students should strive to eliminate all surface errors because they divert the readers’ attention to meaning, although some writing errors do not necessarily change the meaning of sentences. For example, learners can edit surface errors by using six-item criteria: spelling errors, comma splices, sentence fragments, verb errors, punctuation errors, and pronoun errors. Moreover, students should not attempt to conduct clarity and effectiveness editing simultaneously with surface error editing, which may result in poor writing because of the extensive nature of rules governing the English language. In turn, the final step in editing a thematic essay is the correction of any documentation errors while referring to the appropriate style manual.

Sample Outline for Writing a Thematic Essay

I. introduction.

A. Hook sentence. B. Background information. C. Thematic thesis statement.

A. First paragraph

1. The idea for writing the first thematic paragraph. 2. Evidence supporting this paragraph’s claim. 3. Interpretation and analysis of evidence.

• First specific deduction from evidence.
• Second specific deduction from evidence.

4. A concluding statement that demonstrates the link between the first paragraph’s claim and thesis statement.

B. Second body paragraph

1. The idea for writing the second thematic paragraph. 2. Evidence supporting this paragraph’s claim. 3. Interpretation and analysis of evidence.

4. A concluding statement that demonstrates the link between the second paragraph’s claim and thesis statement.

C. Third body paragraph

1. The idea for writing the third thematic paragraph. 2. Evidence supporting this paragraph’s claim. 3. Interpretation and analysis of evidence.

4. A concluding statement that demonstrates the link between the third paragraph’s claim and thesis statement.

III. Conclusion

A. Restatement of the thesis statement written in a thematic essay. B. Summary of the three minor arguments in the body paragraphs. C. Closing remarks emphasizing the significance of the central claim in the context of the three minor arguments.

Commentary on a Thematic Essay Outline

1. identify a central theme.

The audience can determine the central theme of a thematic essay from a thesis statement or an overview of topic statements. Basically, writing a well-composed thesis statement must explicitly mention the central theme or implicitly hint at the central theme. Alternatively, the audience can read through topic sentences and correctly speculate the central theme of a thematic essay because minor arguments in individual body paragraphs are building blocks of a thesis statement. However, the readers’ ability to identify the central theme from a formal outline is dependent on their pre-existing knowledge concerning a topic because an outline uses statements and annotations with writing little explanation.

2. Uniqueness

A thematic essay stands out from other types of essays because of the high level of freedom that writers enjoy during authorship. During the writing of a thematic essay, authors can choose any purpose or a combination of purposes to use in different sections of 5 parts of an essay, which is a luxury that argumentative essays and expository essays do not extend to writers. Also, essay instructions for writing a thematic essay tend to define a broad scope for research, which implies that authors may develop a wide variety of arguments. In turn, the expansive nature of the subject of a paper is not present for argumentative essays, which forces students to choose one of the two sides of a controversial issue.

Outlining a Thematic Essay

The introduction section is the first part of a thematic essay, which consists of three main elements: hook, background information, and thesis statement. Firstly, a hook is the first statement of any paper that plays the role of capturing the audience’s attention through creative wording, which gives them a reason to read the entire paper. Since students know how to write a hook, they provide the essential background information that readers require to understand a thesis statement. Moreover, the background information element does not have a fixed length. Instead, it is dependent on the complexity of a thesis statement and the overall length of a thematic essay. Also, writing a thesis statement is the final item of the introductory paragraph. In turn, the length of introductions is approximately 10% of the essay’s word count.

II. Body Paragraphs

A. topic sentence.

A topic sentence contains a minor claim that an author discusses within a paragraph. For example, its primary role is to establish content boundaries, which ensures students focus on a particular idea in each paragraph. Moreover, a topic statement should present a minor argument and mention a relation that it has to the central idea of writing a thematic essay or make a tacit suggestion of its link to a thesis statement. Therefore, writers should avoid the use of in-text citations in a topic statement because it implies that an idea is not original.

B. Evidence

After a topic sentence, students unveil the evidence that supports their claims. In college writing, an in-text citation should accompany any evidence that learners introduce into a thematic essay to direct readers to its origin. Also, learners should rely heavily on summary and paraphrasing in their writing, as opposed to direct quotations from sources. Nevertheless, some thematic essay instructions may specify a particular technique writers must use when integrating evidence into a paper.

C. Evaluation

This element of a paragraph structure allows authors to explain the significance of the evidence to the paragraph’s argument. In this section of a thematic essay, students provide an interpretation of the evidence, which informs the audience of the meaning of the evidence in the context of a source text. Then, writers explain the value of the evidence in developing a reasonable justification for the idea proposed in topic sentences. In turn, learners should avoid the inclusion of lengthy pieces of evidence because it creates a situation where the voice of sources is more dominant than the author’s voice.

D. Concluding Statement

A concluding statement emphasizes the logical relationship that exists between the topic sentence, evidence, evaluation, and thesis statement. In some cases, it may establish the relationship of a paragraph with the preceding paragraph. Also, students should ensure a concluding sentence of a paragraph does not contain a meaningless summary of the key pieces of evidence. Then, a concluding statement of a thematic essay must not contain any new evidence because there is no opportunity to explain the contribution of the evidence in supporting the paragraph’s argument.

A concluding paragraph has three critical features: a restatement of the main claim, a summary of minor arguments, and closing remarks. Basically, the opening statement of a thematic essay reminds students of the central argument by using new words and syntax. After the opening statement, learners summarize minor claims that appear in individual body paragraphs while maintaining a logical organization, which is identical to the arrangement of ideas in the body. Finally, authors write a strong closing statement that knits together the introduction, thesis statement, and minor claims to create a lasting impression on the audience. Moreover, students should ensure they do not introduce new evidence or arguments in a concluding paragraph. In turn, authors must not apologize for a lack of expertise on a topic or make absolute claims because it diminishes the efficacy of writing a good conclusion.

Example of Writing a Thematic Essay

Topic: Recruitment of Terrorists

I. Sample Introduction of a Thematic Essay

Terrorism is a global problem, which appears to be spreading despite an increment in the efforts to suppress its growth. Basically, the prevention of recruitment is a crucial counterterrorism strategy. Moreover, its efficacy is dependent on the understanding of the terrorists’ recruitment techniques. In turn, the terrorist groups’ recruitment methods focus on the target’s identity crisis, which puts a potential member at risk of falling for the ‘appeal’ of terrorism.

II. Examples of Body Paragraphs in a Thematic Essay

A. motivation.

An individual’s desire to be part of a movement that is effecting a radical change in society is a significant motivator for participation in terrorism. For example, recent studies show that terrorist groups begin conversations with most young recruits on social media platforms, which discuss topics concerning social, political, and economic oppression (Jacks, 2020). Basically, this finding suggests that young people in contemporary society have a desire to correct the ‘wrongs’ in society as a means of identity. Consequently, terrorists use the increased sensitivity to social injustices as a common ground to initiate and build a relationship with a prospective recruit. In turn, the youth’s strong desire to do something to stop social injustices that their respective governments ignore leaves them vulnerable to radicalization by terrorist groups.

B. Religious Beliefs

Fanatical religious belief may drive an individual to support or participate in terrorism for the sake of being part of a group. According to Mohammed (2020), the constant pressure from religious parents causes the blind indoctrination of adolescents and young adults, which enables recruiters from terrorist groups to present religious concepts as justifications for terrorism, for example, the holy war. In this case, Mohammed concedes that the mosque is an ideal site for the recruitment of terrorism because of the presence of youth with highly impressionable minds. Moreover, youths depend on teachings at places of worship for knowledge that defines their perspective of the world. As a result, terrorist recruiters disguised as spiritual leaders can easily nurture fanatical beliefs that endorse terrorist activities. Eventually, a feeling of separation from the conservative believers pushes them to pursue groups that share their fanatical religious beliefs.

C. Recruiting

The loss of a family member to counterterrorism activities may act as a motivation factor for grievers who are trying to re-establish their identities because they no longer fit into the traditional social structures. For instance, Tobias (2020) argues that recruiters prey on the pain of grieving family members by offering them retribution as a solution to the overwhelming feeling of incompleteness. After the death of a family member, the emotional turmoil increases the susceptibility of individuals to the idea that terrorist acts are an appropriate response to the ‘killers’ of their loved ones. Often, recruitment occurs during this unstable state and encourages the individual to relive the pain each day, which results in the permanent erosion of their former identity. Accordingly, grievers may find themselves as sympathizers of terrorism, which leads to active or passive participation.

III. Sample Conclusion of a Thematic Essay

Most members of terrorist organizations experience an identity crisis at the time of recruitment. Basically, the need to gain membership to a group that fights against social injustices tolerates fanatical religious beliefs or seeks revenge for the death of loved ones is a sign that identity crisis is a common characteristic in recruits. In turn, current counterterrorism initiatives should seek to break the cycle of recruitment, which will weaken terrorist groups.

Takeaway on How to Write a Good Thematic Essay

• Students should define a narrow writing topic to guide them in generating ideas in response to a thematic paper prompt.
• The characteristics and expectations of the audience are vital in determining an appropriate presentation approach.
• Before drafting a thematic essay, authors must write a formal outline and annotated bibliography, which are critical for organization and evidence selection.
• The maintenance of a high level of fluidity during drafting is critical during drafting because it allows writers to experiment with different styles of expression.
• Revision and editing are aspects of the writing process that a student should not take lightly.
• All body paragraphs must adhere to the four-element paragraph structure.
• The conclusion of a thematic essay should not contain any new evidence or arguments.
• Learners must write a thesis statement that captures a theme that instructors highlight in essay prompts.

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Thematic Analysis

Ai generator.

Thematic Analysis is a method for identifying, analyzing, and reporting patterns (themes) within data. It minimally organizes and describes your data set in rich detail. A thematic statement captures the main ideas and insights derived from the data, providing a clear narrative for the study. Significance of the Study Qualitative Research lies in its flexibility and ability to uncover complex phenomena from detailed textual data. Qualitative data , including interviews, focus groups, and open-ended survey responses, are ideal for Thematic Analysis as they provide in-depth insights into participants’ perspectives and experiences.

What is Thematic Analysis?

Thematic Analysis is a qualitative research method for identifying, analyzing, and reporting patterns or themes within data. It organizes and interprets various aspects of the research topic to reveal meaningful insights.

Examples of Thematic Analysis

• Customer Feedback Analysis : Identifying common themes in customer reviews to improve service.
• Employee Satisfaction Surveys : Analyzing responses to understand workplace morale.
• Patient Experience in Healthcare : Extracting themes from patient interviews to enhance care quality.
• Student Feedback on Courses : Summarizing themes from course evaluations to improve teaching methods.
• Media Content Analysis : Identifying themes in news articles to understand media bias.
• Literature Review : Categorizing themes in academic papers on a specific topic.
• Social Media Trends : Analyzing hashtags and posts to identify popular trends and sentiments.
• Community Needs Assessment : Gathering themes from focus group discussions to address community issues.
• Marketing Research : Identifying themes in consumer preferences to develop new products.
• Political Speech Analysis : Extracting themes from political speeches to understand campaign strategies.
• Cultural Studies : Analyzing themes in cultural texts to understand societal values.
• Educational Research : Identifying themes in teacher interviews to improve educational practices.
• Environmental Studies : Analyzing themes in public opinion on environmental issues.
• Psychological Research : Identifying themes in therapy session transcripts to understand patient issues.
• Workplace Diversity Studies : Extracting themes from employee interviews to improve inclusion policies.
• Gender Studies : Analyzing themes in narratives to understand gender roles and experiences.
• Urban Planning : Identifying themes in community feedback on city development projects.
• Historical Research : Extracting themes from historical documents to understand past events.
• Consumer Behavior Studies : Analyzing shopping habits to identify purchasing themes.
• Public Health Research : Identifying themes in health behavior studies to design better interventions.

When should you Use Thematic Analysis?

Exploratory Research : When you need to explore unknown aspects of a phenomenon or gather insights without a predetermined hypothesis.

Qualitative Data : When your research involves qualitative data such as interviews, focus groups, open-ended survey responses, or textual content.

Complex or Multifaceted Data : When you aim to understand and interpret complex, detailed, or multifaceted data to identify underlying patterns and themes.

Comparative Analysis : When you want to compare themes across different groups or contexts to understand variations and commonalities.

Theory Development : When you are aiming to develop new theories or refine existing ones based on in-depth qualitative insights.

Rich Descriptions : When your goal is to provide a rich, detailed description of a particular phenomenon, experience, or process.

Policy and Practice Insights : When you seek to derive practical recommendations or policy implications from qualitative data.

Participant Perspectives : When understanding participants’ perspectives, experiences, and meanings is central to your research objectives.

Types of Thematic Analysis

Inductive approach.

• Data-Driven : Themes are identified from the data without a pre-existing framework or theory.
• Bottom-Up : Themes emerge from the raw data through coding and analysis.
• Flexibility : Allows for the discovery of unexpected insights.

Deductive Approach

• Theory-Driven : Themes are identified based on existing theories or prior research.
• Top-Down : Uses a predetermined coding framework to guide the analysis.
• Focused : Ensures the analysis addresses specific research questions or hypotheses.

Semantic Approach

• Explicit Meanings : Focuses on the explicit content and surface meanings of the data.
• Descriptive : Describes what participants have said without inferring deeper meanings.
• Clarity : Provides clear and straightforward themes that are easily understood.

Latent Approach

• Underlying Ideas : Looks beyond explicit content to identify underlying ideas, assumptions, and conceptualizations.
• Interpretive : Requires interpretation to uncover deeper meanings and insights.
• Depth : Provides a deeper understanding of the data by exploring the root causes and implications of the themes.

Contextualist Approach

• Context-Sensitive : Considers the context in which the data was collected and the influence of the researcher.
• Balanced : Balances between participants’ subjective experiences and the broader social context.
• Reflexive : Emphasizes the importance of reflexivity and the researcher’s influence on the analysis.

Hybrid Approach

• Combination : Integrates elements of both inductive and deductive approaches.
• Flexible : Allows for the use of a theoretical framework while remaining open to new themes emerging from the data.
• Comprehensive : Provides a thorough analysis by combining the strengths of different approaches.

How to do Thematic Analysis

1. familiarization with the data.

Description: This initial step involves getting to know your data deeply. It includes transcribing a data, reading through the text multiple times, and taking initial notes.

• Transcribe data: If you have a recordings, transcribe them verbatim.
• Read and re-read: Go through the text several times to become deeply familiar with the content.
• Initial notes: Jot down initial impressions and ideas.

2. Generating Initial Codes

Description: Coding involves organizing your data into meaningful groups. This process helps to summarize and condense the data into key points.

• Identify interesting features: Highlight significant pieces of data that seem relevant to your research question.
• Label segments: Assign codes to different parts of the text. Codes are concise phrases or words that capture the essence of the segment.
• Collate data: Group all data extracts that are related to each code.

3. Searching for Themes

Description: In this step, you begin to identify broader patterns of meaning (themes) within your coded data.

• Review codes: Examine your codes and look for patterns or overarching themes.
• Organize codes: Group similar codes together into potential themes.
• Create themes: Develop themes that capture the main ideas and narratives present in your data.

4. Reviewing Themes

Description: This stage involves refining your themes to ensure they accurately represent the data.

• Check against data: Ensure that each theme accurately reflects the coded data and the overall dataset.
• Refine themes: Modify, combine, or discard themes as necessary.
• Create a thematic map: Visualize the relationship between themes and sub-themes to get a clearer picture.

5. Defining and Naming Themes

Description: At this point, you clearly define what each theme is about and give them concise, descriptive names.

• Detailed analysis: Write a detailed analysis for each theme, specifying what it captures and how it relates to the research question.
• Theme names: Assign clear, descriptive names to each theme that encapsulate their essence.

6. Producing the Report

Description: The final step involves writing up your thematic analysis. This should provide a coherent and persuasive account of your data, highlighting the key themes and supporting them with evidence.

• Introduction: Outline your research question, the importance of the study, and the methodology.
• Theme descriptions: Present each theme with detailed explanations and illustrative quotes from the data.
• Discussion: Relate your findings to the broader context, existing literature, and theoretical frameworks.
• Conclusion: Summarize the key findings and their implications.

Qualitative Research of Thematic Analysis

1. data collection.

Collect qualitative data through various methods such as interviews, focus groups, observations, or document analysis.

• Interviews: Conduct semi-structured or unstructured interviews to gather detailed, personal accounts.
• Focus Groups: Facilitate group discussions to explore collective perspectives.
• Observations: Record behaviors and interactions in natural settings.
• Document Analysis: Analyze texts, reports, and media to extract relevant information.

2. Data Familiarization

Immerse yourself in the data by reading and re-reading it to become deeply familiar with its content and context.

Activities:

• Transcription: Transcribe a recordings verbatim.
• Reading: Go through the transcripts and notes multiple times.
• Initial Notes: Jot down initial thoughts and impressions.

3. Initial Coding

Break down the data into meaningful units by assigning codes to different segments.

• Highlighting: Identify and highlight significant pieces of data.
• Coding: Label segments with concise codes that capture their essence.
• Collating: Group all data extracts related to each code.

4. Theme Development

Identify patterns among the codes to develop broader themes that represent the data’s main ideas.

• Review Codes: Examine the codes to find commonalities.
• Group Codes: Organize similar codes into potential themes.
• Create Themes: Formulate themes that encapsulate the core ideas.

5. Theme Review

Refine the themes to ensure they accurately reflect the data and provide a coherent narrative.

• Validation: Compare themes against the original data to verify accuracy.
• Refinement: Modify, combine, or discard themes as necessary.
• Thematic Map: Visualize the relationships between themes and sub-themes.

6. Theme Definition and Naming

Clearly define and name each theme to convey its essence and relevance.

• Detailed Analysis: Write comprehensive descriptions for each theme.
• Naming: Assign descriptive names that succinctly capture the themes’ meanings.

7. Reporting

Write up the findings in a coherent and persuasive report that presents the themes and supports them with data extracts.

• Introduction: Outline the research question, importance, and methodology.
• Theme Presentation: Describe each theme in detail, supported by quotes from the data.
• Discussion: Relate findings to existing literature and theoretical frameworks.
• Conclusion: Summarize key insights and discuss implications.

Tips & Suggestions

• Stay Close to the Data: Ensure themes accurately represent participants’ perspectives.
• Be Flexible: Adapt your approach as new insights emerge.
• Maintain Rigor: Keep a detailed audit trail to ensure transparency and credibility.
• Use Software Tools: Consider using qualitative data analysis software like NVivo or ATLAS.ti.
• Reflect on Biases: Continuously reflect on and address your biases throughout the analysis process.

What is thematic analysis?

Thematic analysis identifies and interprets patterns (themes) within qualitative data.

Why use thematic analysis?

It offers flexibility and provides rich, detailed, and complex data interpretation.

How do you start thematic analysis?

Begin with data familiarization through thorough reading and note-taking.

What is coding in thematic analysis?

Coding involves labeling significant data segments to identify patterns.

What are themes in thematic analysis?

Themes are broader patterns or ideas that emerge from coded data.

How are themes validated?

Themes are validated by comparing them against the original data.

What software aids thematic analysis?

Tools like NVivo and ATLAS.ti help organize and analyze qualitative data.

Can thematic analysis be both inductive and deductive?

Yes, themes can emerge from data (inductive) or pre-existing theories (deductive).

How to ensure thematic analysis reliability?

Maintain a detailed audit trail and regularly review data consistency.

What is a thematic map?

A visual representation showing the relationships between identified themes.

Text prompt

• Instructive
• Professional

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