research design thematic analysis

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 coding. If this is a new concept to you, be sure to check out our detailed post about qualitative coding .

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

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

What is thematic analysis?

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

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

When should you use thematic analysis?

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

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

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

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

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

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

What are the main approaches?

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

The inductive approach

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

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

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

The deductive approach

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

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

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

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

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

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

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

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

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

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

What are the types of thematic analysis?

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

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

Let’s have a look at each of these:

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

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

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

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

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

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

Quick Recap: Thematic analysis approaches and types

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

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

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

Need a helping hand?

How to “do” thematic analysis

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

Step 1: Get familiar with the data

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

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

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

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

Step 2: Search for patterns or themes in the codes

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

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

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

Step 3: Review themes

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

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

Step 4: Finalise Themes

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

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

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

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

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

Step 5: Produce your report

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

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

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

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

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

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

Quick Recap: How to “do” thematic analysis

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

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

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

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

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

Tips & Suggestions

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

Wrapping Up

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

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

Psst... there’s more!

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

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21 Comments

I really appreciate the help

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

I’ve found the article very educative and useful

Excellent. Very helpful and easy to understand.

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

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

It is a great help. Thanks.

Best advice. Worth reading. Thank you.

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

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

Hello, Sir/Maam

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

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

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

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

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

lovely and helpful. thanks

very informative information.

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

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

Thanks a lot, really enlightening

excellent! very helpful thank a lot for your great efforts

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• How to Do Thematic Analysis | Guide & Examples

How to Do Thematic Analysis | Guide & Examples

Published on 5 May 2022 by Jack Caulfield .

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

There are various approaches to conducting thematic analysis, but the most common form follows a six-step process:

• Familiarisation
• Generating themes
• Reviewing themes
• Defining and naming themes

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

Table of contents

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

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

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

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

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

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

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

Prevent plagiarism, run a free check.

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

There’s the distinction between inductive and deductive approaches:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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How to do thematic analysis

Last updated

8 February 2023

Reviewed by

Miroslav Damyanov

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.  

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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• 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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You can transcribe an interview by converting a conversation into a written format including question-answer recording sessions between two or more people.

A case study is a detailed analysis of a situation concerning organizations, industries, and markets. The case study generally aims at identifying the weak areas.

Discourse analysis is an essential aspect of studying a language. It is used in various disciplines of social science and humanities such as linguistic, sociolinguistics, and psycholinguistic.

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• Practical thematic...

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

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

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

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

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

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

Summary points

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

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

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

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

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

Key features and applications of practical thematic analysis

Step 1: reading.

All manuscript authors read the data

All manuscript authors write summary memos

Step 2: Coding

Coders perform both data management and early data analysis

Codes are complete thoughts or sentences, not categories

Step 3: Theming

Researchers host a thematic analysis session and share different perspectives

Themes are complete thoughts or sentences, not categories

Applications

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

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

When time and resources are limited

Steps in practical thematic analysis

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We present linear steps, but as qualitative research is usually iterative, so too is thematic analysis. 15 Qualitative researchers circle back to earlier work to check whether their interpretations still make sense in the light of additional insights, adapting as necessary. While we focus here on the practical application of thematic analysis in health services research, we recognise our approach exists in the context of the broader literature on thematic analysis and the theoretical underpinnings of qualitative methods as a whole. For a more detailed discussion of these theoretical points, as well as other methods widely used in health services research, we recommend reviewing the sources outlined in supplemental material 1. A strong and nuanced understanding of the context and underlying principles of thematic analysis will allow for higher quality research. 16

Practical thematic analysis is a highly flexible approach that can draw out valuable findings and generate new hypotheses, including in cases with a lack of previous research to build on. The approach can also be used with a variety of data, such as transcripts from interviews or focus groups, patient encounter transcripts, professional publications, observational field notes, and online activity logs. Importantly, successful practical thematic analysis is predicated on having high quality data collected with rigorous methods. We do not describe qualitative research design or data collection here. 11 17

In supplemental material 1, we summarise the foundational methods, concepts, and terminology in qualitative research. Along with our guide below, we include a companion slide presentation for teaching practical thematic analysis to research teams in supplemental material 2. We provide a theme coproduction template for teams to use during thematic analysis sessions in supplemental material 3. Our method aligns with the major qualitative reporting frameworks, including the Consolidated Criteria for Reporting Qualitative Research (COREQ). 18 We indicate the corresponding step in practical thematic analysis for each COREQ item in supplemental material 4.

Familiarisation and memoing

We encourage all manuscript authors to review the full dataset (eg, interview transcripts) to familiarise themselves with it. This task is most critical for those who will later be engaged in the coding and theming steps. Although time consuming, it is the best way to involve team members in the intellectual work of data interpretation, so that they can contribute to the analysis and contextualise the results. If this task is not feasible given time limitations or large quantities of data, the data can be divided across team members. In this case, each piece of data should be read by at least two individuals who ideally represent different professional roles or perspectives.

We recommend that researchers reflect on the data and independently write memos, defined as brief notes on thoughts and questions that arise during reading, and a summary of their impressions of the dataset. 2 19 Memoing is an opportunity to gain insights from varying perspectives, particularly from patients, care partners, clinicians, and others. It also gives researchers the opportunity to begin to scope which elements of and concepts in the dataset are relevant to the research question.

Data saturation

The concept of data saturation ( box 2 ) is a foundation of qualitative research. It is defined as the point in analysis at which new data tend to be redundant of data already collected. 21 Qualitative researchers are expected to report their approach to data saturation. 18 Because thematic analysis is iterative, the team should discuss saturation throughout the entire process, beginning with data collection and continuing through all steps of the analysis. 22 During step 1 (reading), team members might discuss data saturation in the context of summary memos. Conversations about saturation continue during step 2 (coding), with confirmation that saturation has been achieved during step 3 (theming). As a rule of thumb, researchers can often achieve saturation in 9-17 interviews or 4-8 focus groups, but this will vary depending on the specific characteristics of the study. 23

Data saturation in context

Braun and Clarke discourage the use of data saturation to determine sample size (eg, number of interviews), because it assumes that there is an objective truth to be captured in the data (sometimes known as a positivist perspective). 20 Qualitative researchers often try to avoid positivist approaches, arguing that there is no one true way of seeing the world, and will instead aim to gather multiple perspectives. 5 Although this theoretical debate with qualitative methods is important, we recognise that a priori estimates of saturation are often needed, particularly for investigators newer to qualitative research who might want a more pragmatic and applied approach. In addition, saturation based, sample size estimation can be particularly helpful in grant proposals. However, researchers should still follow a priori sample size estimation with a discussion to confirm saturation has been achieved.

Definition of coding

We describe codes as labels for concepts in the data that are directly relevant to the study objective. Historically, the purpose of coding was to distil the large amount of data collected into conceptually similar buckets so that researchers could review it in aggregate and identify key themes. 5 24 We advocate for a more analytical approach than is typical with thematic analysis. With our method, coding is both the foundation for and the beginning of thematic analysis—that is, early data analysis, management, and reduction occur simultaneously rather than as different steps. This approach moves the team more efficiently towards being able to describe themes.

Building the coding team

Coders are the research team members who directly assign codes to the data, reading all material and systematically labelling relevant data with appropriate codes. Ideally, at least two researchers would code every discrete data document, such as one interview transcript. 25 If this task is not possible, individual coders can each code a subset of the data that is carefully selected for key characteristics (sometimes known as purposive selection). 26 When using this approach, we recommend that at least 10% of data be coded by two or more coders to ensure consistency in codebook application. We also recommend coding teams of no more than four to five people, for practical reasons concerning maintaining consistency.

Clinicians, patients, and care partners bring unique perspectives to coding and enrich the analytical process. 27 Therefore, we recommend choosing coders with a mix of relevant experiences so that they can challenge and contextualise each other’s interpretations based on their own perspectives and opinions ( box 3 ). We recommend including both coders who collected the data and those who are naive to it, if possible, given their different perspectives. We also recommend all coders review the summary memos from the reading step so that key concepts identified by those not involved in coding can be integrated into the analytical process. In practice, this review means coding the memos themselves and discussing them during the code development process. This approach ensures that the team considers a diversity of perspectives.

Coding teams in context

The recommendation to use multiple coders is a departure from Braun and Clarke. 28 29 When the views, experiences, and training of each coder (sometimes known as positionality) 30 are carefully considered, having multiple coders can enhance interpretation and enrich findings. When these perspectives are combined in a team setting, researchers can create shared meaning from the data. Along with the practical consideration of distributing the workload, 31 inclusion of these multiple perspectives increases the overall quality of the analysis by mitigating the impact of any one coder’s perspective. 30

Coding tools

Qualitative analysis software facilitates coding and managing large datasets but does not perform the analytical work. The researchers must perform the analysis themselves. Most programs support queries and collaborative coding by multiple users. 32 Important factors to consider when choosing software can include accessibility, cost, interoperability, the look and feel of code reports, and the ease of colour coding and merging codes. Coders can also use low tech solutions, including highlighters, word processors, or spreadsheets.

Drafting effective codes

To draft effective codes, we recommend that the coders review each document line by line. 33 As they progress, they can assign codes to segments of data representing passages of interest. 34 Coders can also assign multiple codes to the same passage. Consensus among coders on what constitutes a minimum or maximum amount of text for assigning a code is helpful. As a general rule, meaningful segments of text for coding are shorter than one paragraph, but longer than a few words. Coders should keep the study objective in mind when determining which data are relevant ( box 4 ).

Code types in context

Similar to Braun and Clarke’s approach, practical thematic analysis does not specify whether codes are based on what is evident from the data (sometimes known as semantic) or whether they are based on what can be inferred at a deeper level from the data (sometimes known as latent). 4 12 35 It also does not specify whether they are derived from the data (sometimes known as inductive) or determined ahead of time (sometimes known as deductive). 11 35 Instead, it should be noted that health services researchers conducting qualitative studies often adopt all these approaches to coding (sometimes known as hybrid analysis). 3

In practical thematic analysis, codes should be more descriptive than general categorical labels that simply group data with shared characteristics. At a minimum, codes should form a complete (or full) thought. An easy way to conceptualise full thought codes is as complete sentences with subjects and verbs ( table 1 ), although full sentence coding is not always necessary. With full thought codes, researchers think about the data more deeply and capture this insight in the codes. This coding facilitates the entire analytical process and is especially valuable when moving from codes to broader themes. Experienced qualitative researchers often intuitively use full thought or sentence codes, but this practice has not been explicitly articulated as a path to higher quality coding elsewhere in the literature. 6

Example transcript with codes used in practical thematic analysis 36

• View inline

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

Developing the codebook

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

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

Assigning codes to the data

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

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

Quantitative coding in context

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

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

Definition of themes

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

Themes in context

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

Use of themes in practical thematic analysis

Constructing meaningful themes

After coding all the data, each coder should independently reflect on the team’s summary memos (step 1), the codebook (step 2), and the coded data itself to develop draft themes (step 3). It can be illuminating for coders to review all excerpts associated with each code, so that they derive themes directly from the data. Researchers should remain focused on the research question during this step, so that themes have a clear relation with the overall project aim. Use of qualitative analysis software will make it easy to view each segment of data tagged with each code. Themes might neatly correspond to groups of codes. Or—more likely—they will unite codes and data in unexpected ways. A whiteboard or presentation slides might be helpful to organise, craft, and revise themes. We also provide a template for coproducing themes (supplemental material 3). As with codebook justification, team members will ideally produce individual drafts of the themes that they have identified in the data. They can then discuss these with the group and reach alignment or consensus on the final themes.

The team should ensure that all themes are salient, meaning that they are: supported by the data, relevant to the study objectives, and important. Similar to codes, themes are framed as complete thoughts or sentences, not categories. While codes and themes might appear to be similar to each other, the key distinction is that the themes represent a broader concept. Table 2 shows examples of codes and their corresponding themes from a previously published project that used practical thematic analysis. 36 Identifying three to four key themes that comprise a broader overarching theme is a useful approach. Themes can also have subthemes, if appropriate. 40 41 42 43 44

Example codes with themes in practical thematic analysis 36

Thematic analysis session

After each coder has independently produced draft themes, a carefully selected subset of the manuscript team meets for a thematic analysis session ( table 3 ). The purpose of this session is to discuss and reach alignment or consensus on the final themes. We recommend a session of three to five hours, either in-person or virtually.

Example agenda of thematic analysis session

The composition of the thematic analysis session team is important, as each person’s perspectives will shape the results. This group is usually a small subset of the broader research team, with three to seven individuals. We recommend that primary and senior authors work together to include people with diverse experiences related to the research topic. They should aim for a range of personalities and professional identities, particularly those of clinicians, trainees, patients, and care partners. At a minimum, all coders and primary and senior authors should participate in the thematic analysis session.

The session begins with each coder presenting their draft themes with supporting quotes from the data. 5 Through respectful and collaborative deliberation, the group will develop a shared set of final themes.

One team member facilitates the session. A firm, confident, and consistent facilitation style with good listening skills is critical. For practical reasons, this person is not usually one of the primary coders. Hierarchies in teams cannot be entirely flattened, but acknowledging them and appointing an external facilitator can reduce their impact. The facilitator can ensure that all voices are heard. For example, they might ask for perspectives from patient partners or more junior researchers, and follow up on comments from senior researchers to say, “We have heard your perspective and it is important; we want to make sure all perspectives in the room are equally considered.” Or, “I hear [senior person] is offering [x] idea, I’d like to hear other perspectives in the room.” The role of the facilitator is critical in the thematic analysis session. The facilitator might also privately discuss with more senior researchers, such as principal investigators and senior authors, the importance of being aware of their influence over others and respecting and eliciting the perspectives of more junior researchers, such as patients, care partners, and students.

To our knowledge, this discrete thematic analysis session is a novel contribution of practical thematic analysis. It helps efficiently incorporate diverse perspectives using the session agenda and theme coproduction template (supplemental material 3) and makes the process of constructing themes transparent to the entire research team.

Writing the report

We recommend beginning the results narrative with a summary of all relevant themes emerging from the analysis, followed by a subheading for each theme. Each subsection begins with a brief description of the theme and is illustrated with relevant quotes, which are contextualised and explained. The write-up should not simply be a list, but should contain meaningful analysis and insight from the researchers, including descriptions of how different stakeholders might have experienced a particular situation differently or unexpectedly.

In addition to weaving quotes into the results narrative, quotes can be presented in a table. This strategy is a particularly helpful when submitting to clinical journals with tight word count limitations. Quote tables might also be effective in illustrating areas of agreement and disagreement across stakeholder groups, with columns representing different groups and rows representing each theme or subtheme. Quotes should include an anonymous label for each participant and any relevant characteristics, such as role or gender. The aim is to produce rich descriptions. 5 We recommend against repeating quotations across multiple themes in the report, so as to avoid confusion. The template for coproducing themes (supplemental material 3) allows documentation of quotes supporting each theme, which might also be useful during report writing.

Visual illustrations such as a thematic map or figure of the findings can help communicate themes efficiently. 4 36 42 44 If a figure is not possible, a simple list can suffice. 36 Both must clearly present the main themes with subthemes. Thematic figures can facilitate confirmation that the researchers’ interpretations reflect the study populations’ perspectives (sometimes known as member checking), because authors can invite discussions about the figure and descriptions of findings and supporting quotes. 46 This process can enhance the validity of the results. 46

In supplemental material 4, we provide additional guidance on reporting thematic analysis consistent with COREQ. 18 Commonly used in health services research, COREQ outlines a standardised list of items to be included in qualitative research reports ( box 7 ).

Reporting in context

We note that use of COREQ or any other reporting guidelines does not in itself produce high quality work and should not be used as a substitute for general methodological rigor. Rather, researchers must consider rigor throughout the entire research process. As the issue of how to conceptualise and achieve rigorous qualitative research continues to be debated, 47 48 we encourage researchers to explicitly discuss how they have looked at methodological rigor in their reports. Specifically, we point researchers to Braun and Clarke’s 2021 tool for evaluating thematic analysis manuscripts for publication (“Twenty questions to guide assessment of TA [thematic analysis] research quality”). 16

Avoiding common pitfalls

Awareness of common mistakes can help researchers avoid improper use of qualitative methods. Improper use can, for example, prevent researchers from developing meaningful themes and can risk drawing inappropriate conclusions from the data. Braun and Clarke also warn of poor quality in qualitative research, noting that “coherence and integrity of published research does not always hold.” 16

Weak themes

An important distinction between high and low quality themes is that high quality themes are descriptive and complete thoughts. As such, they often contain subjects and verbs, and can be expressed as full sentences ( table 2 ). Themes that are simply descriptive categories or topics could fail to impart meaningful knowledge beyond categorisation. 16 49 50

Researchers will often move from coding directly to writing up themes, without performing the work of theming or hosting a thematic analysis session. Skipping concerted theming often results in themes that look more like categories than unifying threads across the data.

Unfocused analysis

Because data collection for qualitative research is often semi-structured (eg, interviews, focus groups), not all data will be directly relevant to the research question at hand. To avoid unfocused analysis and a correspondingly unfocused manuscript, we recommend that all team members keep the research objective in front of them at every stage, from reading to coding to theming. During the thematic analysis session, we recommend that the research question be written on a whiteboard so that all team members can refer back to it, and so that the facilitator can ensure that conversations about themes occur in the context of this question. Consistently focusing on the research question can help to ensure that the final report directly answers it, as opposed to the many other interesting insights that might emerge during the qualitative research process. Such insights can be picked up in a secondary analysis if desired.

Inappropriate quantification

Presenting findings quantitatively (eg, “We found 18 instances of participants mentioning safety concerns about the vaccines”) is generally undesirable in practical thematic analysis reporting. 51 Descriptive terms are more appropriate (eg, “participants had substantial concerns about the vaccines,” or “several participants were concerned about this”). This descriptive presentation is critical because qualitative data might not be consistently elicited across participants, meaning that some individuals might share certain information while others do not, simply based on how conversations evolve. Additionally, qualitative research does not aim to draw inferences outside its specific sample. Emphasising numbers in thematic analysis can lead to readers incorrectly generalising the findings. Although peer reviewers unfamiliar with thematic analysis often request this type of quantification, practitioners of practical thematic analysis can confidently defend their decision to avoid it. If quantification is methodologically important, we recommend simultaneously conducting a survey or incorporating standardised interview techniques into the interview guide. 11

Neglecting group dynamics

Researchers should concertedly consider group dynamics in the research team. Particular attention should be paid to power relations and the personality of team members, which can include aspects such as who most often speaks, who defines concepts, and who resolves disagreements that might arise within the group. 52

The perspectives of patient and care partners are particularly important to cultivate. Ideally, patient partners are meaningfully embedded in studies from start to finish, not just for practical thematic analysis. 53 Meaningful engagement can build trust, which makes it easier for patient partners to ask questions, request clarification, and share their perspectives. Professional team members should actively encourage patient partners by emphasising that their expertise is critically important and valued. Noting when a patient partner might be best positioned to offer their perspective can be particularly powerful.

Insufficient time allocation

Researchers must allocate enough time to complete thematic analysis. Working with qualitative data takes time, especially because it is often not a linear process. As the strength of thematic analysis lies in its ability to make use of the rich details and complexities of the data, we recommend careful planning for the time required to read and code each document.

Estimating the necessary time can be challenging. For step 1 (reading), researchers can roughly calculate the time required based on the time needed to read and reflect on one piece of data. For step 2 (coding), the total amount of time needed can be extrapolated from the time needed to code one document during codebook development. We also recommend three to five hours for the thematic analysis session itself, although coders will need to independently develop their draft themes beforehand. Although the time required for practical thematic analysis is variable, teams should be able to estimate their own required effort with these guidelines.

Practical thematic analysis builds on the foundational work of Braun and Clarke. 4 16 We have reframed their six phase process into three condensed steps of reading, coding, and theming. While we have maintained important elements of Braun and Clarke’s reflexive thematic analysis, we believe that practical thematic analysis is conceptually simpler and easier to teach to less experienced researchers and non-researcher stakeholders. For teams with different levels of familiarity with qualitative methods, this approach presents a clear roadmap to the reading, coding, and theming of qualitative data. Our practical thematic analysis approach promotes efficient learning by doing—experiential learning. 12 29 Practical thematic analysis avoids the risk of relying on complex descriptions of methods and theory and places more emphasis on obtaining meaningful insights from those close to real world clinical environments. Although practical thematic analysis can be used to perform intensive theory based analyses, it lends itself more readily to accelerated, pragmatic approaches.

Strengths and limitations

Our approach is designed to smooth the qualitative analysis process and yield high quality themes. Yet, researchers should note that poorly performed analyses will still produce low quality results. Practical thematic analysis is a qualitative analytical approach; it does not look at study design, data collection, or other important elements of qualitative research. It also might not be the right choice for every qualitative research project. We recommend it for applied health services research questions, where diverse perspectives and simplicity might be valuable.

We also urge researchers to improve internal validity through triangulation methods, such as member checking (supplemental material 1). 46 Member checking could include soliciting input on high level themes, theme definitions, and quotations from participants. This approach might increase rigor.

Implications

We hope that by providing clear and simple instructions for practical thematic analysis, a broader range of researchers will be more inclined to use these methods. Increased transparency and familiarity with qualitative approaches can enhance researchers’ ability to both interpret qualitative studies and offer up new findings themselves. In addition, it can have usefulness in training and reporting. A major strength of this approach is to facilitate meaningful inclusion of patient and care partner perspectives, because their lived experiences can be particularly valuable in data interpretation and the resulting findings. 11 30 As clinicians are especially pressed for time, they might also appreciate a practical set of instructions that can be immediately used to leverage their insights and access to patients and clinical settings, and increase the impact of qualitative research through timely results. 8

Practical thematic analysis is a simplified approach to performing thematic analysis in health services research, a field where the experiences of patients, care partners, and clinicians are of inherent interest. We hope that it will be accessible to those individuals new to qualitative methods, including patients, care partners, clinicians, and other health services researchers. We intend to empower multidisciplinary research teams to explore unanswered questions and make new, important, and rigorous contributions to our understanding of important clinical and health systems research.

Acknowledgments

All members of the Coproduction Laboratory provided input that shaped this manuscript during laboratory meetings. We acknowledge advice from Elizabeth Carpenter-Song, an expert in qualitative methods.

Coproduction Laboratory group contributors: Stephanie C Acquilano ( http://orcid.org/0000-0002-1215-5531 ), Julie Doherty ( http://orcid.org/0000-0002-5279-6536 ), Rachel C Forcino ( http://orcid.org/0000-0001-9938-4830 ), Tina Foster ( http://orcid.org/0000-0001-6239-4031 ), Megan Holthoff, Christopher R Jacobs ( http://orcid.org/0000-0001-5324-8657 ), Lisa C Johnson ( http://orcid.org/0000-0001-7448-4931 ), Elaine T Kiriakopoulos, Kathryn Kirkland ( http://orcid.org/0000-0002-9851-926X ), Meredith A MacMartin ( http://orcid.org/0000-0002-6614-6091 ), Emily A Morgan, Eugene Nelson, Elizabeth O’Donnell, Brant Oliver ( http://orcid.org/0000-0002-7399-622X ), Danielle Schubbe ( http://orcid.org/0000-0002-9858-1805 ), Gabrielle Stevens ( http://orcid.org/0000-0001-9001-178X ), Rachael P Thomeer ( http://orcid.org/0000-0002-5974-3840 ).

Contributors: Practical thematic analysis, an approach designed for multidisciplinary health services teams new to qualitative research, was based on CHS’s experiences teaching thematic analysis to clinical teams and students. We have drawn heavily from qualitative methods literature. CHS is the guarantor of the article. CHS, AS, CvP, AMK, JRK, and JAP contributed to drafting the manuscript. AS, JG, CMM, JAP, and RWY provided feedback on their experiences using practical thematic analysis. CvP, LCL, SLB, AVC, GE, and JKL advised on qualitative methods in health services research, given extensive experience. All authors meaningfully edited the manuscript content, including AVC and RKS. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: This manuscript did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Provenance and peer review: Not commissioned; externally peer reviewed.

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Handbook of Research Methods in Health Social Sciences pp 843–860 Cite as

Thematic Analysis

• Virginia Braun 2 ,
• Victoria Clarke 3 ,
• Nikki Hayfield 3 &
• Gareth Terry 4  
• Reference work entry
• First Online: 13 January 2019

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This chapter maps the terrain of thematic analysis (TA), a method for capturing patterns (“themes”) across qualitative datasets. We identify key concepts and different orientations and practices, illustrating why TA is often better understood as an umbrella term, used for sometimes quite different approaches, than a single qualitative analytic approach. Under the umbrella, three broad approaches can be identified: a “coding reliability” approach, a “codebook” approach, and a “reflexive” approach. These are often characterized by distinctive – sometimes radically different – conceptualizations of what a theme is, as well as methods for theme identification and development, and indeed coding. We then provide practical guidance on completing TA within our popular (reflexive) approach to TA, discussing each phase of the six-phase approach we have developed in relation to a project on men, rehabilitation, and embodiment. We conclude with a discussion of key concerns related to ensuring the TA you do – within whatever approach – is of the highest quality.

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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?

In the world of thematic analysis, Appinio shines as a real-time market research platform, revolutionizing the way you gain consumer insights. Say goodbye to long, tedious research processes and embrace the exciting and intuitive world of real-time data-driven decisions.

Here's why Appinio is your go-to partner for thematic analysis:

• Lightning-Fast Insights: From formulating questions to obtaining actionable insights, Appinio delivers results in minutes. When you need answers pronto, we've got you covered.
• User-Friendly Excellence: No need for a Ph.D. in research. Our platform is designed to be so intuitive that anyone can navigate it with ease, empowering you to take control of your market research.
• Global Reach, Local Precision: With access to over 90 countries and the ability to define your target group from 1200+ characteristics, Appinio ensures that your thematic analysis is not only insightful but also globally relevant.

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How to analyze qualitative data from ux research: thematic analysis.

August 17, 2022 2022-08-17

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Uncovering themes in qualitative data can be daunting and difficult. Summarizing a quantitative study is relatively clear: you scored 25% better than the competition, let’s say. But how do you summarize a collection of qualitative observations?

In the discovery phase , exploratory research is often carried out. This research often produces a lot of qualitative data, which can include:

Qualitative attitudinal data, such as people’s thoughts, beliefs and self-reported needs obtained from user interviews, focus groups and even diary studies

Qualitative behavioral data, such as observations about people’s behavior collected through contextual inquiry and other ethnographic approaches

Thematic analysis, which anyone can do, renders important aspects of qualitative data visible and makes uncovering themes easier.

In This Article:

What is a thematic analysis, challenges with analyzing qualitative data, tools and methods for conducting thematic analysis, steps to conduct a thematic analysis.

Definition: Thematic analysis is a systematic method of breaking down and organizing rich data from qualitative research by tagging individual observations and quotations with appropriate codes, to facilitate the discovery of significant themes.

As the name implies, a thematic analysis involves finding themes .

Definition: A theme :

• is a description of a belief, practice, need, or another phenomenon that is discovered from the data
• emerges when related findings appear multiple times across participants or data sources

Many researchers feel overwhelmed by qualitative data from exploratory research conducted in the early stages of a project. The table below highlights some common challenges and resulting issues.

Without some form of systematic process, the problems outlined easily arise when analyzing qualitative data. Thematic analysis keeps researchers organized and focused and gives them a general process to follow when analyzing qualitative data.

A thematic analysis can be done in many different ways. The best tool or method for this process is determined based on the:

• context and constraints of the data-analysis phase
• the researcher’s personal style of work

3 common methods include:

• Using software
• Using affinity diagramming techniques

Using Software

Researchers often use data-analysis software for analyzing large amounts of qualitative data . Researchers upload their raw data (such as transcripts or field notes) into the software and then use the software’s features to code the data. Some tools even support transcription of the video or audio recordings. Examples of data-analysis software include:

• The analysis is very thorough.
• The analysis can be done collaboratively.
• The raw data and the results of the analysis are always accessible in the software and can be revisited when needed.
• The analysis can be time-consuming, as it results in many codes which need to be condensed into a small, manageable list.
• Subscriptions or licenses can be expensive
• Some learning of the software is required.

Writing thought processes and ideas you have about a text is common among researchers practicing grounded-theory methodology. Journaling as a form of thematic analysis is based on this methodology and involves manual annotation and highlighting of the data, followed by writing down the researchers’ ideas and thought processes. The notes are known as memos ( not to be confused with the office memo delivering news to employees).

• The process encourages reflection through the writing of detailed notes.
• Researchers have a record of how they arrived at their themes.
• The analysis is cheap and flexible.
• Hard to do collaboratively

Affinity-Diagramming Techniques

The data is highlighted, cut out physically or digitally, and reassembled into meaningful groups until themes emerge on a physical or digital board. (See a video demonstrating affinity-diagramming .)

• Can be done collaboratively
• Quick arriving at themes
• Cheap and flexible
• Visual, and supports an iterative-analysis process
• Not as thorough as other methods as often segments of text aren’t coded multiple times
• Hard to do when data is very varied, or there is a lot of data

Codes and Coding

All methods of thematic analysis assume some amount of coding (not to be confused with writing a program in a programming language).

Definition: A code is a word or phrase that acts as a label for a segment of text.

A code describes what the text is about and is a shorthand for more complicated information. (A good analogy is that a code describes data like a keyword describes an article or like a hashtag describes a tweet.) Often, qualitative researchers will not only have a name for each code but will also have a description of what the code means and examples of text that fit or don’t fit the code. These descriptions and examples are especially useful if more than one person is responsible for coding the data or if coding is done over a longer period of time.

Definition: Coding refers to the process of labeling segments of text with the appropriate codes.

Once codes are assigned, it’s easy to identify and compare segments of text that are about the same thing. The codes allow us to sort information easily and to analyze data to uncover similarities, differences, and relationships among segments. We can then arrive at an understanding of the essential themes.

Code Types: Descriptive and Interpretive

Codes can be:

• Descriptive: They describe what the data is about
• Interpretive: They are an analytical reading of the data, adding the researcher’s interpretive lens to it.

To see examples of descriptive and interpretive codes, let’s look at a quote from an interview I performed with a UX practitioner earlier this year (as part of our UX Careers research, to be published in our UX Careers report ).

“I was petrified about facilitating a meeting and my company offered a day-and-a-half– long course. So, I went in there and the instructor did something that I felt was horrible at the time, but I've since really come to appreciate it. The first thing that we did was we filled out a sheet of paper with our name and wrote down our worst fear of moderating or facilitating and we turned it in and then he said, okay, tomorrow you're going to act out this situation (…) the next day we came back and I would leave the room while the rest of the team read, they read my worst fear, figured out how they'd act it out, and then I'd walk in and facilitate for 10 minutes with that. And that really helped me realize that there isn't anything to be afraid of, that our fears are really in our head most of the time and facing that made me realize I can handle these situations.”

Here are possible descriptive and interpretive codes for the text above:

Descriptive code: how skills are acquired Rationale behind the code label: Participants were asked to describe how they came to possess certain skills.

Interpretive code: self-reflection Rationale behind the code label: The participant describes how this experience changed her beliefs about facilitation and how she reflected on her fear.

Regardless of which tool you use (software, journaling, or affinity diagraming), the act of conducting a thematic analysis can be broken down into 6 steps.

Step 1: Gather All Your Data

S tart with the raw data , such as interview or focus-group transcripts, field notes, or diary study entries. I recommended transcribing audio recordings from interviews and using the transcriptions for analysis instead of relying on patchy memory or notes.

Step 2: Read All Your Data from Beginning to End

Familiarize yourself with the data before you begin the analysis, even if you were the one to perform the research. Read all your transcripts, field notes, and other data sources before analyzing them. At this step, you can involve your team in the project. Involving your team instills knowledge of users and empathy for them and their needs .

Run a workshop (or a series of workshops if your team is very large or you have a lot of data). Follow these steps:

• Before your team members engage with the data, write your research questions on a whiteboard or piece of flipchart paper to make the questions easy to refer to while working.
• Give each member a transcript or one field- or diary-study entry. Tell people to highlight anything they think is important.
• Once team members have completed reading their entries, they can pass their transcript or entry to someone else and receive a new one from another team member. This step is repeated until all team members have engaged with all the data.
• Discuss as a group what you noticed or found surprising.

While it’s best if your team observes all your research sessions, that may not be possible if you have a lot of sessions or a big team. When individual team members observe only a handful of sessions, they sometimes walk away with an incomplete understanding of the findings. The workshop can solve that problem, since everyone will read all the session transcripts.

Step 3: Code the Text Based on What It’s About

In the coding step, highlighted sections need to be categorized so that the highlighted sections can be easily compared.

At this stage, remind yourself of your research objectives. Print your research questions out. Stick them up on a wall or on a whiteboard in the room where you’re conducting the analysis.

If you have adequate time, you can involve your team in this initial coding step. If time is limited and there is a lot of data to work through, then do this step by yourself and invite your team later to review your codes and help flesh out the themes.

As you are coding, review each segment of text and ask yourself “ What is this about?” Give the fragment a name that describes the data (a descriptive code). You can also add interpretive codes to the text at this stage. However, these will typically become easier to assign later.

The code can be created before or after you have grouped the data . The next two sections of this step describe how and when you may add the codes.

Traditional Method: Create Codes Before Grouping

In the traditional approach, as you highlight segments of the data, like sentences, paragraphs, phrases, you code them. It’s helpful to keep a record of all the codes used and outline what they are, so you can refer to this list when coding further sections of the text (especially if multiple people are coding the text). This approach avoids creating multiple codes (that will later need to be consolidated) for the same type of issue.

Once all the text has been coded, you can group all the data that has the same code.

If you’re using software for this process, then it will automatically log the codes you assign while coding, so you can use them again. It will then provide a way for you to view all text coded with the same code.

Quick Method: Group Segments of Text, Then Assign a Code

Rather than coming up with a code when you highlight text, you cut up (physically or digitally) and cluster all the similar highlighted segments (similarly to how different stickies may be grouped in an affinity map ). The groupings are then given a code. If you’re doing the clustering digitally, you might pull coded sections into a new document or a visual collaboration platform.

In the pictures below, the grouping was done manually. Transcripts were cut up, fixed to stickies, and moved around the board until they fell into natural topic groups. The researcher then assigned a pink sticky with a descriptive code to the grouping.

At the end of this step, you should have data grouped by topics and codes for each topic.

Let’s look at an example. I interviewed 3 people about their experience of cooking at home. In these interviews, participants talked about how they chose to cook certain things and not others. They talked about specific challenges they faced while cooking (e.g., dietary requirements, tight budgets, lack of time and physical space) and about solutions for some of these challenges.

After grouping the highlighted clippings from my interviews by topic, I ended up with 3 broad descriptive codes and corresponding groupings:

• Cooking experiences : memorable positive and negative experiences related to cooking
• Pain points : anything that stops someone from cooking or makes cooking difficult (including navigating dietary restrictions, limited budgets, etc.)
• Things that help: what helps (or is believed to possibly help) someone overcome specific challenges or pain points

Step 4: Create New Codes that Encapsulate Potential Themes

Look across all the codes and explore any causal relationships, similarities, differences, or contradictions to see if you can uncover underlying themes. While doing so, some of the codes will be set aside (either archived or deleted) and new interpretive codes will be created. If you’re using a physical-mapping approach like that discussed in step 3, then some of these initial groupings may collapse or expand as you look for themes.

Ask yourself the following questions:

• What’s going on in each group?
• How are these codes related?
• How do these relate to my research questions?

Returning to our cooking topic, when analyzing the text within each grouping and looking for relationships between the data, I noticed that two participants said that they liked ingredients that can be prepared in different ways and go well with other different ingredients. A third participant talked about wishing she could have a set of ingredients that can be used for many different meals throughout the week, rather than having to buy separate ingredients for each meal plan. Thus, a new theme about the flexibility of ingredients emerged. For this theme, I came up with the code one ingredient fits all, for which I then wrote a detailed description.

Step 5: Take a Break for a Day, then Return to the Data

It almost always is a good idea to take a break and come back and look at the data with a fresh pair of eyes. Doing so sometimes helps you to see significant patterns in the data clearly and derive breakthrough insights.

Step 6: Evaluate Your Themes for Good Fit

In this step, it can be useful to have others involved to help you review your codes and emerging themes. Not only are new insights drawn out, but your conclusions can be challenged and critiqued by fresh eyes and brains. This practice reduces the potential for your interpretation to be colored by personal biases.

Put your themes under scrutiny. Ask yourself these questions:

• Is the theme well supported by the data? Or could you find data that don’t support your theme?
• Is the theme saturated with lots of instances?
• Do others agree with the themes you have found in the data after analyzing the data separately?

If the answer to these questions is no , it might mean that you need to return to the analysis board. Assuming you collected sound data, there is almost always something to be learned, so spending more time with your team repeating steps 4–6 will be worthwhile.

A thematic analysis can uncover the major themes from your research. There’s no one way to do a thematic analysis. Choose a method of analysis that suits the kind and volume of data you’ve collected. When possible, invite others into the analysis process to both increases the accuracy of the analysis and your team’s knowledge of your users’ behaviors, motivations, and needs. Analysis can be a lengthy process, so a good rule of thumb is to budget at least as much time as you had for the data collection to complete the analysis.

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Chapter 22: Thematic Analysis

Darshini Ayton

Learning outcomes

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

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

What is thematic analysis?

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

Applied thematic analysis

• Framework analysis
• Reflexive thematic analysis.

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

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

Applied thematic analysis involves five elements:

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

Table 22.1. Codebook components and an example

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

Framework a nalysis

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

The process for conducting framework analysis is as follows 5 :

1. Transcription – usually verbatim transcription of the interview.

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

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

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

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

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

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

Reflexive thematic analysis

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

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

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

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

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

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

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

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

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

Themes versus c odes

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

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

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

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

Table 22.3. Examples of thematic analysis

Advantages and challenges of thematic analysis.

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

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

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

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

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Thematic analysis in qualitative research.

11 min read Your guide to thematic analysis, a form of qualitative research data analysis used to identify patterns in text, video and audio data.

What is thematic analysis?

Thematic analysis is used to analyze qualitative data – that is, data relating to opinions, thoughts, feelings and other descriptive information. It’s become increasingly popular in social sciences research, as it allows researchers to look at a data set containing multiple qualitative sources and pull out the broad themes running through the entire data set.

That data might consist of articles, diaries, blog posts, interview transcripts, academic research, web pages, social media and even audio and video files. They are put through data analysis as a group, with researchers seeking to identify patterns running through the corpus as a whole.

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Thematic analysis steps

Image source: https://www.nngroup.com/articles/thematic-analysis/

While there are many types of thematic analysis, the thematic analysis process can be generalized into six steps. Thematic analysis involves initial analysis, coding data, identifying themes and reporting on the findings.

• Familiarization – During the first stage of thematic analysis, the research teams or researchers become familiar with the dataset. This may involve reading and re-reading, and even transcribing the data. Researchers may note down initial thoughts about the potential themes they perceive in the data, which can be the starting point for assigning initial codes.
• Coding – Codes in thematic analysis are the method researchers use to identify the ideas and topics in their data and refer to them quickly and easily. Codes can be assigned to snippets of text data or clips from videos and audio files. Depending on the type of thematic analysis used, this can be done with a systematic and rigorous approach, or in a more intuitive manner.
• Identifying theme – Themes are the overarching ideas and subject areas within the corpus of research data. Researchers can identify themes by collating together the results of the coding process, generating themes that tie together the identified codes into groups according to their meaning or subject matter.
• Reviewing themes – Once the themes have been defined, the researchers check back to see how well the themes support the coded data extracts. At this stage they may start to organize the themes into a map, or early theoretical framework.
• Defining and naming themes – As researchers spend more time reviewing the themes, they begin to define them more precisely, giving them names. Themes are different from codes, because they capture patterns in the data rather than just topics, and they relate directly to the research question.
• Writing up – At this stage, researchers begin to develop the final report, which offers a comprehensive summary of the codes and themes, extracts from the original data that illustrate the findings, and any other data relevant to the analysis. The final report may include a literature review citing other previous research and the observations that helped frame the research question. It can also suggest areas for future research the themes support, and which have come to light during the research process.

Another step which precedes all of these is data collection. Common to almost all forms of qualitative analysis, data collection means bringing together the materials that will be part of the data set, either by finding secondary data or generating first-party data through interviews, surveys and other qualitative methods.

Types of thematic analysis

There are various thematic analysis approaches currently in use. For the most part, they can be viewed as a continuum between two different ideologies. Reflexive thematic analysis (RTA) sits at one end of the continuum of thematic analysis methods. At the other end is code reliability analysis.

Code reliability analysis emphasizes the importance of the codes given to themes in the research data being as accurate as possible. It takes a technical or pragmatic view, and places value on codes being replicable between different researchers during the coding process. Codes are based on domain summaries, which often link back to the questions in a structured research interview.

Researchers using a code reliability approach may use a codebook. A codebook is a detailed list of codes and their definitions, with exclusions and examples of how the codes should be applied.

Reflexive thematic analysis was developed by Braun & Clarke in 2006 for use in the psychology field. In contrast to code reliability analysis, it isn’t concerned with consistent codes that are agreed between researchers. Instead, it acknowledges and finds value in each researcher’s interpretation of the thematic content and how it influences the coding process. The codes they assign are specific to them and exist within a unique context that is made up of:

• The data set
• The assumptions made during the setup of the analysis process
• The researcher’s skills and resources

This doesn’t mean that reflexive thematic analysis should be unintelligible to anyone other than the researcher. It means that the researcher’s personal subjectivity and uniqueness is made part of the process, and is expected to have an influence on the findings. Reflexive thematic analysis is a flexible method, and initial codes may change during the process as the researcher’s understanding evolves.

Reflexive thematic analysis is an inductive approach to qualitative research. With an inductive approach, the final analysis is based entirely on the data set itself, rather than from any preconceived themes or structures from the research team.

Image source: https://delvetool.com/blog/thematicanalysis

Thematic analysis vs other qualitative research methods

Thematic analysis sits within a whole range of qualitative analysis methods which can be applied to social sciences, psychology and market research data.

• Thematic analysis vs comparative analysis – Comparative analysis and thematic analysis are closely related, since they both look at relationships between multiple data sources. Comparative analysis is a form of qualitative research that works with a smaller number of data sources. It focuses on causal relationships between events and outcomes in different cases, rather than on defining themes.
• Thematic analysis vs discourse analysis – Unlike discourse analysis, which is a type of qualitative research that focuses on spoken or written conversational language, thematic analysis is much more broad in scope, covering many kinds of qualitative data.
• Thematic analysis vs narrative analysis – Narrative analysis works with stories – it aims to keep information in a narrative structure, rather than allowing it to be fragmented, and often to study the stories from participants’ lives. Thematic analysis can break narratives up as it allocates codes to different parts of a data source, meaning that the narrative context might be lost and even that researchers might miss nuanced data.
• Thematic analysis vs content analysis – Both content analysis and thematic analysis use data coding and themes to find patterns in data. However, thematic analysis is always qualitative, but researchers agree there can be quantitative and qualitative content analysis, with numerical approaches to the frequency of codes in content analysis data.

Thematic analysis advantages and disadvantages

Like any kind of qualitative analysis, thematic analysis has strengths and weaknesses. Whether it’s right for you and your research project will depend on your priorities and preferences.

Thematic analysis advantages

• Easy to learn – Whether done manually or assisted by technology, the thematic analysis process is easy to understand and conduct, without the need for advanced statistical knowledge
• Flexible – Thematic analysis allows qualitative researchers flexibility throughout the process, particularly if they opt for reflexive thematic analysis
• Broadly applicable – Thematic analysis can be used to address a wide range of research questions.

Thematic analysis – the cons

As well as the benefits, there are some disadvantages thematic analysis brings up.

• Broad scope – In identifying patterns on a broad scale, researchers may become overwhelmed with the volume of potential themes, and miss outlier topics and more nuanced data that is important to the research question.
• Themes or codes? – It can be difficult for novice researchers to feel confident about the difference between themes and codes
• Language barriers – Thematic analysis relies on language-based codes that may be difficult to apply in multilingual data sets, especially if the researcher and / or research team only speaks one language.

How can you use thematic analysis for business research?

Thematic analysis, and other forms of qualitative research, are highly valuable to businesses who want to develop a deeper understanding of the people they serve, as well as the people they employ. Thematic analysis can help your business get to the ‘why’ behind the numerical information you get from quantitative research.

An easy way to think about the interplay between qualitative data and quantitative data is to consider product reviews. These typically include quantitative data in the form of scores (like ratings of up to 5 stars) plus the explanation of the score written in a customer’s own words. The word part is the qualitative data. The scores can tell you what is happening – lots of 3 star reviews indicate there’s some room for improvement for example – but you need the addition of the qualitative data, the review itself, to find out what’s going on.

Qualitative data is rich in information but hard to process manually. To do qualitative research at scale, you need methods like thematic analysis to get to the essence of what people think and feel without having to read and remember every single comment.

Qualitative analysis is one of the ways businesses are borrowing from the world of academic research, notably social sciences, statistical data analysis and psychology, to gain an advantage in their markets.

Analyzing themes across video, text, audio and more

Carrying out thematic analysis manually may be time-consuming and painstaking work, even with a large research team. Fortunately, machine learning and other technologies are now being applied to data analysis of all kinds, including thematic analysis, taking the manual work out of some of the more laborious thematic analysis steps.

The latest iterations of machine learning tools are able not only to analyze text data, but to perform efficient analysis of video and audio files, matching the qualitative coding and even helping build out the thematic map, while respecting the researcher’s theoretical commitments and research design.

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Thematic Analysis

Data analysis in design and development research.

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

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

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

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

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

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

Reading List

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

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

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

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

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

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

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

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

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

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

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

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A Comprehensive Guide to Thematic Analysis in Qualitative Research

What is Qualitative Data?

What do all the methods above have in common? They result in loads of qualitative data. If you're not new here, you've heard us mention qualitative data many times already. Qualitative data is non-numeric data that is collected in the form of words, images, or sound bites. Qual data is often used to understand people's experiences, perspectives, and motivations, and is often collected and sorted by UX Researchers to better understand the company's users. Qualitative data is subjective and often in response to open-ended questions, and is typically analyzed through methods such as thematic analysis, content analysis, and discourse analysis. In this resource we'll be focusing specifically on how to conduct an effective thematic analysis from scratch! Qualitative data is the sister of quantitative data, which is data that is collected in the form of numbers and can be analyzed using statistical methods. Qualitative and quantitative data are often used together in mixed methods research, which combines both types of data to gain a more comprehensive understanding of a research question.

UX Research Methods

There are many different types of UX research methods that can be used to gather insights about user behavior and attitudes. Some common UX research methods include:

• Interviews: One-on-one conversations with users to gather detailed information about their experiences, needs, and preferences.
• Surveys: Online or paper-based questionnaires that can be used to gather large amounts of data from a broad group of users.
• Focus groups: Group discussions with a moderated discussion to explore user attitudes and behaviors.
• User testing: Observing users as they interact with a product or service to identify problems and gather feedback.
• Ethnographic research: Observing and interacting with users in their natural environments to gain a deep understanding of their behaviors and motivations.
• Card sorting: A technique used to understand how users categorize and organize information.
• Tree testing: A method used to evaluate the effectiveness of a website's navigation structure.
• Heuristic evaluation: A method used to identify usability issues by having experts review a product and identify potential problems.
• Expert review: Gathering feedback from industry experts on a product or service to identify potential issues and areas for improvement.

Introduction to Thematic Analysis of Qualitative Data

Thematic analysis is a popular way of analyzing qualitative data, like transcripts or interview responses, by identifying and analyzing recurring themes (hence the name!). This method often follows a six-step process, which includes getting familiar with the data, sorting and coding the data, generating your various themes, reviewing and editing these themes, defining and naming the themes, and writing up the results to present. This process can help researchers avoid confirmation bias in their analysis. Thematic analysis was developed for psychology research, but it can be used in many different types of research and is especially prevalent in the UX research profession.

When to Use Thematic Analysis

Thematic analysis is a useful method for analyzing qualitative data when you are interested in understanding the underlying themes and patterns in the data. Some situations in which thematic analysis might be appropriate include:

• When you have a large amount of qualitative data, such as transcripts from interviews or focus groups.
• When you want to understand people's experiences, perspectives, or motivations in depth.
• When you want to identify patterns or themes that emerge from the data.
• When you want to explore complex and open-ended research questions.
• When you are interested in understanding how people make sense of their experiences and the world around them.

Some UX research specific questions that could be a good fit for thematic analysis are:

• How do users think about their experiences with a particular product, service or company?
• What are the common challenges that a user might encounter when using a product or service, and how do they overcome them?
• How do users make sense of the navigation of a website or app?
• What are the key drivers of user satisfaction or dissatisfaction with a product or service?
• How do users' experiences with a product or service compare with their expectations?

It is important to keep in mind that thematic analysis is just one of many methods for analyzing qualitative data, and it may not be the most appropriate method for every research question or situation. A key part of a UX researcher's role is being aware of the most appropriate research method to use based on the problem the company is trying to solve and the constraints of the company's research practice.

Types of Thematic Analysis

There are two primary types of thematic analysis, called inductive and deductive approaches. An inductive approach involves going into the study blind, and allowing the results of the data-capture to guide and shape the analysis and theming. Think of it like induction heating-- the data heats your results! (OK, we get it, that was a bad joke. But you won't forget now!) An example of an inductive approach would be parachuting onto a client without knowing much about their website, and discovering the checkout was difficult to use by the amount of people who brought it up. An easy theme! On the flip-side, a deductive approach involves attacking the data with some preconceived notions you expect to find in the qualitative data, based on a theory. For example, if you think your company's website navigation is hard to use because the text is too small, you may find yourself looking for themes like "small text" or "difficult navigation." We don't have a joke for this one, but we tried. To get even more nitty-gritty, there are two additional types of thematic analysis called semantic and latent thematic analysis. These are more advanced, but we'll throw them here for good measure. Semantic thematic analysis involves identifying themes in the data by analyzing the exact wording of the comments made used by participants. Latent thematic analysis involves identifying themes in the data by analyzing the underlying meanings and actions that were taken, but perhaps not necessarily stated by study participants. Both of these methods can be used in user research, though latent analysis is more popular because users often say different things than what they actually do.

Steps in Conducting a Thematic Analysis

Let's jump in! As mentioned before, there are 6 steps to completing a thematic analysis.

Step One: get familiar with your data!

This might seem obvious, but sometimes it's hard to know when to start. This might take the form of listening to the audio interviews or unmoderated studies, or reading the notes taken during a moderated interview. It's important to know the overall ideas of what you're dealing with to effectively theme your study. While you're doing this, pay attention to some big picture themes you can use in step two when you code your data. Break out key ideas from each participant. This might take the form of summarized answers for each question response, or a written review of actions taken for each task given. Just make sure to standardize it across participants.

Step Two: sort & code the data.

Now that you have your standardized notes across your participants, it's time to sort and code the collected qualitative data! Think of the themes from before when you were taking your notes. Think of these codes like metaphorical buckets, and start sorting! Every comment that fits a theme in a box, put it there. Back to our navigation example: some codes could be "small text" or "hard to use." We could put a participant action of "squinting" into the bucket for "small text," or a comment from another mentioning they had trouble finding "tents" in "hard to use."

Step Three: break the codes into themes!

Try to think of each theme as a makeup of three or more codes. For the navigation example, we could put both "small text," and "hard to use" into a theme of "Difficult Navigation."

Step Four: review and name your themes.

Now is the time to clean up the data. Are all your themes relevant to the problem you're trying to solve? Are all the themes coherent and straightforward? Are you comfortable defending your theme choices to teammates? These are all great questions to ask yourself in this stage.

Step Five: Present!!

To have a cohesive presentation of your thematic analysis, you'll need to include an introduction that explains the user problem you were trying to identify and the method you took to study it. Use the terminology from beginning of this resource to identify your research method. Usually for something like this, it will be a user survey or interview. ‍ You also need to include how you analyzed your participant data (inductive, deductive, latent or semantic) to identify your codes and themes. In the meaty section of your presentation, describe each theme and give quotations and user actions from the data to support your points.

Step Six: Insights and Recommendations

Your conclusion should not stop at your presentation of your findings. The best user researchers are valuable for both their insights and recommendations. Since UX researchers spend so much time with participants, they have indispensable knowledge about the best way to do things that make life easy for the company's users. Don't keep this information to yourself! On the final 1-3 slides of your presentation, state the "Next Steps & Recommendations" that you'd like your team and leadership to follow up on. These recommendations could include things like additional qualitative or quantitative studies, UX changes to make or test, or a copy change to make the experience clearer for readers. Your ultimate job is to create the best user experience, and you made it this far-- you got this!

And there you have it! That's everything you need to complete a thematic analysis of qualitative data to identify potential solutions or key concepts for a particular user problem. But don't stop there! We recommend using these principles in the wild to conduct research of your own. Identify a question or potential problem you'd like to analyze on one of your favorite sites. Use a service like Sprig to come up with non-bias questions to ask friends and family to try and gather your own qualitative data. Next, complete and document yourself completing the 6-step analysis process. What do you discover? Be prepared to share on interviews-- hiring managers love to see initiative! Good luck.

View the UX Research Job Guide Here

Our Sources: 

Caulfield, J. (2022, November 25). How to Do Thematic Analysis | Step-by-Step Guide & Examples . Scribbr. https://www.scribbr.com/methodology/thematic-analysis/

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Thematic Analysis: What it is and How to Do It

Qualitative analysis may be a highly effective analytical approach when done correctly. Thematic analysis is one of the most frequently used qualitative analysis approaches.

One advantage of this analysis is that it is a versatile technique that can be utilized for both exploratory research (where you don’t know what patterns to look for) and more deductive studies (where you see what you’re searching for).

LEARN ABOUT:  Research Process Steps

This article will break it down and show you how to do the thematic analysis correctly.

What is thematic analysis?

Thematic analysis is a method for analyzing qualitative data that involves reading through a set of data and looking for patterns in the meaning of the data to find themes. It is an active process of reflexivity in which the researcher’s subjective experience is at the center of making sense of the data.

LEARN ABOUT: Qualitative Interview

Thematic analysis is typical in qualitative research. It emphasizes identifying, analyzing, and interpreting qualitative data patterns.

With this analysis, you can look at qualitative data in a certain way. It is usually used to describe a group of texts, like an interview or a set of transcripts. The researcher looks closely at the data to find common themes: repeated ideas, topics, or ways of putting things.

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Thematic Analysis Advantages and Disadvantages

A technical or pragmatic view of research design focuses on researchers conducting qualitative analyzes using the method most appropriate to the research question. However, there is seldom a single ideal or suitable method, so other criteria are often used to select methods of analysis: the researcher’s theoretical commitments and familiarity with particular techniques.

The thematic analysis provides a flexible method of data analysis and allows researchers with diverse methodological backgrounds to participate in this type of analysis. Data analytics and data analysis are closely related processes that involve extracting insights from data to make informed decisions.

For positivists, ‘reliability’ is a concern because of the many possible interpretations of the data and the potential for researcher subjectivity to ‘bias’ or distort the analysis. For those committed to the values ​​of steps in qualitative research , researcher subjectivity is seen as a resource (rather than a threat to credibility), so concerns about reliability do not remain.

There is no correct or precise interpretation of the data. The interpretations are inevitably subjective and reflect the position of the researcher. Quality is achieved through a systematic and rigorous approach and the researcher’s continual reflection on how they shape the developing analysis.

Thematic analysis has several advantages and disadvantages. It is up to the researchers to decide if this analysis method is suitable for their research design.

• The flexibility of theoretical and research design allows researchers multiple theories that can be applied to this process in various epistemologies.
• Very suitable for large data sets.
• The coding and codebook reliability approaches are designed for use with research teams.
• Interpretation of themes supported by data.
• Applicable to research questions that go beyond the experience of an individual.
• It allows the inductive development of codes and themes from data.

Disadvantages

• Thematic analysis can miss nuanced data if the researcher is not careful and uses thematic analysis in a theoretical vacuum.
• The flexibility can make it difficult for novice researchers to decide which aspects of the data to focus on.
• Limited interpretive power if the analysis is not based on a theoretical framework.
• It is challenging to maintain a sense of data continuity across individual accounts due to the focus on identifying themes across all data elements.
• Unlike discourse analysis and narrative analysis, it does not allow researchers to make technical claims about language use.

LEARN ABOUT: Level of Analysis

Thematic Analysis Steps

Let’s jump right into the process of thematic analysis. Remember that what we’ll talk about here is a general process, and the steps you need to take will depend on your approach and the research design .

1. Familiarization

The first stage in thematic analysis is examining your data for broad themes. This is where you transcribe audio data to text.

At this stage, you’ll need to decide what to code, what to employ, and which codes best represent your content. Now consider your topic’s emphasis and goals.

Keep a reflexivity diary. You’ll explain how you coded the data, why, and the results here. You may reflect on the coding process and examine if your codes and themes support your results. Using a reflective notebook from the start can help you in the later phases of your analysis.

A reflexivity journal increases dependability by allowing systematic, consistent data analysis . If using a reflexivity journal, specify your starting codes to see what your data reflects. Later on, the coded data may be analyzed more extensively or may find separate codes.

2. Look for themes in the codes.

At this stage, search for coding patterns or themes. From codes to themes is not a smooth or straightforward process. You may need to assign alternative codes or themes to learn more about the data.

As you analyze the data, you may uncover subthemes and subdivisions of themes that concentrate on a significant or relevant component. At this point, your reflexivity diary entries should indicate how codes were understood and integrated to produce themes.

3. Review themes

Now that you know your codes, themes, and subthemes. Evaluate your topics. At this stage, you’ll verify that everything you’ve classified as a theme matches the data and whether it exists in the data. If any themes are missing, you can continue to the next step, knowing you’ve coded all your themes properly and thoroughly.

If your topics are too broad and there’s too much material under each one, you may want to separate them so you can be more particular with your research .

In your reflexivity journal, please explain how you comprehended the themes, how they’re backed by evidence, and how they connect with your codes. You should also evaluate your research questions to ensure the facts and topics you’ve uncovered are relevant.

4. Finalize Themes

Your analysis will take shape now after reviewing and refining your themes, labeling, and finishing them. Just because you’ve moved on doesn’t mean you can’t edit or rethink your topics. Finalizing your themes requires explaining them in-depth, unlike the previous phase. Whether you have trouble, check your data and code to see if they reflect the themes and whenever you need to split them into multiple pieces.

Make sure your theme name appropriately describes its features.

Ensure your themes match your research questions at this point. When refining, you’re reaching the end of your analysis. You must remember that your final report (covered in the following phase) must meet your research’s goals and objectives.

In your reflexivity journal, explain how you choose your topics. Mention how the theme will affect your research results and what it implies for your research questions and emphasis.

By the conclusion of this stage, you’ll have finished your topics and be able to write a report.

5. Report writing

At this stage, you are nearly done! Now that you’ve examined your data write a report. A thematic analysis report includes:

• An approach
• The results

When drafting your report, provide enough details for a client to assess your findings. In other words, the viewer wants to know how you analyzed the data and why. “What”, “how”, “why”, “who”, and “when” are helpful here.

So, what did you find? What did you do? How did you choose this method? Who are your research’s focus and participants? When were your studies, data collection , and data production? Your reflexivity notebook will help you name, explain, and support your topics.

While writing up your results, you must identify every single one. The reader needs to be able to verify your findings. Make sure to relate your results to your research questions when reporting them. Practical business intelligence relies on the synergy between analytics and reporting , where analytics uncovers valuable insights, and reporting communicates these findings to stakeholders. You don’t want your client to wonder about your results, so make sure they’re related to your subject and queries.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Because it is easy to apply, thematic analysis suits beginner researchers unfamiliar with more complicated qualitative research . It permits the researcher to choose a theoretical framework with freedom.

The versatility of thematic analysis enables you to describe your data in a rich, intricate, and sophisticated way. This technique may be utilized with whatever theory the researcher chooses, unlike other methods of analysis that are firmly bound to specific approaches. These steps can be followed to master proper thematic analysis for research.

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• v.6(3); 2019 Jul

Qualitative thematic analysis based on descriptive phenomenology

Annelie j. sundler.

1 Faculty of Caring Science, Work Life and Social Welfare, University of Borås, Borås, Sweden

Elisabeth Lindberg

Christina nilsson, lina palmér.

The aim of this paper was to discuss how to understand and undertake thematic analysis based on descriptive phenomenology. Methodological principles to guide the process of analysis are offered grounded on phenomenological philosophy. This is further discussed in relation to how scientific rigour and validity can be achieved.

This is a discursive article on thematic analysis based on descriptive phenomenology.

This paper takes thematic analysis based on a descriptive phenomenological tradition forward and provides a useful description on how to undertake the analysis. Ontological and epistemological foundations of descriptive phenomenology are outlined. Methodological principles are explained to guide the process of analysis, as well as help to understand validity and rigour. Researchers and students in nursing and midwifery conducting qualitative research need comprehensible and valid methods to analyse the meaning of lived experiences and organize data in meaningful ways.

1. INTRODUCTION

Qualitative research in health care is an increasingly complex research field, particularly when doing phenomenology. In nursing and midwifery, qualitative approaches dealing with the lived experiences of patients, families and professionals are necessary. Today, there are number of diverse research approaches. Still, the clarity regarding approaches for thematic analysis is not yet fully described in the literature and only a few papers describe thematic analysis (Ho, Chiang, & Leung, 2017 ; Vaismoradi, Turunen, & Bondas, 2013 ). It may be difficult to find a single paper that can guide researchers and students in doing thematic analysis in phenomenology.

From our research experiences, it may be complex to read and understand phenomenological approaches. Similarly, the process of analysis can be challenging to comprehend. This makes methodological issues related to the clarity of ontological and epistemological underpinnings and discussions of validity and rigour complex. Norlyk and Harder ( 2010 ) points to difficulties finding a guide for phenomenological research. There is a need for understandable guidelines to take thematic analysis forward. Useful approaches are required to provide researchers and students guidance in the process of thematic analysis. With this paper, we hope to clarify some important methodological stances related to the thematic analysis of meaning from lived experiences that are grounded in descriptive phenomenology and useful to teachers and researchers in nursing and midwifery.

1.1. Background

Phenomenology has been widely used to understand human phenomena in nursing and midwifery practices (Matua, 2015 ). Today, there are several phenomenological approaches available. When using phenomenology, the researcher needs an awareness of basic assumptions to make important methodological decisions. Thus, it is important to understand the underpinnings of the approach used (Dowling & Cooney, 2012 ). Phenomenological underpinnings may, however, be difficult to understand and apply in the research process.

Thematizing meaning has been emphasized as one of a few shared aspects across different qualitative approaches (Holloway & Todres, 2003 ), suggesting that some qualitative research strategies are more generic than others. Although different approaches sometimes overlap, they have different ontological and epistemological foundations. A range of approaches are used to thematize meaning, but some of them would benefit from clarifying ontological and epistemological assumptions. In hermeneutic phenomenological traditions, thematizing meaning can be understood as related to the interpretation of data, illuminating the underlying or unspoken meanings embodied or hidden in lived experiences (Ho et al., 2017 ; van Manen, 2016 ). Another commonly used approach to thematic analysis is the method presented in the psychology literature by Braun and Clarke ( 2006 ). The method is frequently used to find repeated patterns of meaning in the data. However, there is a lack of thematic analysis approaches based on the traditions of descriptive phenomenology.

Researchers must make methodological considerations. In phenomenology, an awareness of the philosophical underpinning of the approach is needed when it is used in depth (Dowling & Cooney, 2012 ; Holloway & Todres, 2003 ). This places demands on methods to be comprehensible and flexible yet consistent and coherent. Questions remain regarding how thematic analysis can be further clarified and used based on descriptive phenomenology.

In this discursive paper, we provide guidance for thematic analysis based on descriptive phenomenology, which, to our knowledge, has not been made explicit in this way previously. This can be used as a guiding framework to analyse lived experiences in nursing and midwifery research. The aim of this paper was to discuss how to understand and undertake thematic analysis based on descriptive phenomenology. Methodological principles to guide the process of analysis are offered grounded on phenomenological philosophy. This is further discussed in relation to how scientific rigour and validity can be achieved.

2. ONTOLOGICAL AND EPISTEMOLOGICAL FOUNDATIONS OF DESCRIPTIVE PHENOMENOLOGY

Phenomenology consists of a complex philosophical tradition in human science, containing different concepts interpreted in various ways. One main theme among phenomenological methods is the diversity between descriptive versus interpretive phenomenology (Norlyk & Harder, 2010 ). Both traditions are commonly used in nursing and midwifery research. Several phenomenological methods have been recognized in the descriptive or interpretative approaches (Dowling, 2007 ; Dowling & Cooney, 2012 ; Norlyk & Harder, 2010 ). The descriptive tradition of phenomenology originated from the writings of Husserl was further developed by Merleau‐Ponty, while the interpretive approach was developed mainly from Heidegger and Gadamer.

The thematic analysis in this paper uses a descriptive approach with focus on lived experience, which refers to our experiences of the world. The philosophy of phenomenology is the study of a phenomenon, for example something as it is experienced (or lived) by a human being that means how things appear in our experiences. Consequently, there is a strong emphasis on lived experiences in phenomenological research (Dowling & Cooney, 2012 ; Norlyk & Harder, 2010 ). In this paper, lived experience is understood from a lifeworld approach originating from the writing of Husserl (Dahlberg, Dahlberg, & Nyström, 2008 ). The lifeworld is crucial and becomes the starting point for understanding lived experiences. Hence, the lifeworld forms the ontological and epistemological foundation for our understanding of lived experiences. In the lifeworld, our experiences must be regarded in the light of the body and the lifeworld of a person (i.e., our subjectivity). Consequently, humans cannot be reduced to a biological or psychological being (Merleau‐Ponty, 2002 /1945). When understanding the meaning of lived experiences, we need to be aware of the lifeworld, our bodily being in the world and how we interact with others.

The understanding of lived experiences is closely linked to the idea of the intentionality of consciousness, or how meaning is experienced. Intentionality encompasses the idea that our consciousness is always directed towards something, which means that when we experience something, the “thing” is experienced as “something” that has meaning for us. For example, a birthing woman's experience of pain or caregiving as it is experienced by a nurse. In a descriptive phenomenological approach, based on the writing of Husserl (Dahlberg et al., 2008 ) such meanings can be described. From this point of view, there are no needs for interpretations of these meanings, although this may be argued differently in interpretive phenomenology. Intentionality is also linked to our natural attitude. In our ordinary life, we take ourselves and our life for granted, which is our natural attitude and how we approach our experiences. We usually take for granted that the world around us is as we perceive it and that others perceive it as we do. We also take for granted that the world exists independently of us. Within our natural attitude, we normally do not constantly analyse our experiences. In phenomenology, an awareness of the natural attitude is important.

3. METHODOLOGICAL PRINCIPLES

In the ontological and epistemological foundations of descriptive phenomenology, some methodological principles can be recognized and how these are managed throughout the research process. Phenomenological studies have been criticized for lacking in clarity on philosophical underpinnings (Dowling & Cooney, 2012 ; Norlyk & Harder, 2010 ). Thus, philosophical stances must be understood and clarified for the reader of a study. Our suggestion is to let the entire research process, from data gathering to data analysis and reporting the findings, be guided by the methodological principles of emphasizing openness , questioning pre‐understanding and adopting a reflective attitude . We will acknowledge that the principles presented here may not be totally distinct from, or do follow, a particular phenomenological research approach. However, the outlined approach has some commonalities with the approaches of, for example, Dahlberg et al. ( 2008 ) and van Manen ( 2016 ).

When researching lived experiences, openness to the lifeworld and the phenomenon focused on must be emphasized (i.e., having curiosity and maintaining an open mind when searching for meaning). The researcher must adopt an open stance with sensitivity to the meaning of the lived experiences currently in focus. Openness involves being observant, attentive and sensitive to the expression of experiences (Dahlberg et al., 2008 ). It also includes questioning the understanding of data (Dahlberg & Dahlberg, 2003 ). Thus, researchers must strive to maintain an attitude that includes the assumption that hitherto the researcher does not know the participants experience and the researcher wants to understand the studied phenomenon in a new light to make invisible aspects of the experience become visible.

When striving for openness, researchers need to question their pre‐understanding , which means identifying and becoming aware of preconceptions that might influence the analysis. Throughout the research process and particularly the analysis, researchers must deal with the natural attitude and previous assumptions, when analysing and understanding the data. Questioning involves attempting to set aside one's experiences and assumptions as much as possible and means maintaining a critical stance and reflecting on the understanding of data and the phenomenon. This is similar to bracketing, a commonly used term in descriptive phenomenology based on Husserl, but it has been criticized (Dowling & Cooney, 2012 ). Some would argue that bracketing means to put aside such assumptions, which may not be possible. Instead, Gadamer ( 2004 ) deals with this in a different way, arguing that such assumptions are part of our understanding. Instead of using bracketing, our intention is to build on questioning as a representative way to describe what something means. Accordingly, researchers need to recognize personal beliefs, theories or other assumptions that can restrict the researcher's openness. Otherwise, the researcher risks describing his or her own pre‐understanding instead of the participants' experiences. Our pre‐understanding, described as “prejudice” in interpretive phenomenology by Gadamer ( 2004 ), is what we already know or think we know about a phenomena. As humans, we always have such a pre‐understanding or prejudice and Gadamer ( 2004 ) posits this is the tradition of our lived context and emphasizes that our tradition has a powerful influence on us. This means that it might be more difficult to see something new in the data than describe something already known by the researcher. Therefore, an open and sensitive stance is needed towards oneself, one's pre‐understanding and the understanding of data. However, one must be reflective and critical towards the data, as well as how to understand meanings from the data. Questioning can help researchers become aware of their pre‐understanding and set aside previous assumptions about the phenomenon (Dahlberg et al., 2008 ).

Questioning one's pre‐understanding is closely linked to having a reflective attitude . With a reflective attitude, the researcher needs to shift from the ordinary natural understanding of everyday life to a more self‐reflective and open stance towards the data (Dahlberg et al., 2008 ). An inquiring approach throughout the research process helps researchers become more aware of one's assumptions and reflect regarding the context of the actual research. For instance, researchers may need to reflect on why some meanings occur, how meanings are described and if meanings are grounded in the data. In striving for an awareness of the natural attitude, a reflective attitude becomes imperative. By having such an awareness, some of the pitfalls related to our natural attitude can be handled in favour of an open and reflective mind.

To summarize, methodological principles have been described in terms of emphasizing openness, questioning pre‐understanding and adopting a reflective attitude, which are three related concepts. To emphasis openness, one needs to reflect on preconceptions and judgements concerning the world and our experiences with a reflective approach to become aware of the natural attitude and process of understanding. Engaging in critical reflection throughout the research process may facilitate an awareness of how the researcher influences the research process. These methodological principles, related to ontological and epistemological foundations of phenomenology, are suggested to guide the research process, particularly the analysis.

4. THEMATIC ANALYSIS OF LIVED EXPERIENCES

The thematic analysis approach described in this paper is inductive. A prerequisite for the analysis is that it includes data on lived experiences, such as interviews or narratives. Themes derived from the analysis are data driven (i.e., grounded in data and the experience of the participants). The analysis begins with a search for meaning and goes on with different meanings being identified and related to each other. The analysis is aimed to try to understand the complexity of meanings in the data rather than measure their frequency. It involves researcher engaging in the data and the analysis. The analysis contains a search for patterns of meanings being further explored and determining how such patterns can be organized into themes. Moreover, the analysis must be guided by openness. Thus, the analysis involves a reflective process designed to illuminate meaning. Although the process of analysis is similar to descriptive phenomenological approaches focusing on the understanding and description of meaning‐oriented themes (Dahlberg et al., 2008 ; van Manen, 2016 ), there are important differences. While the thematic analysis in this paper focuses on how to organize patterns of meaning into themes, some would argue that an essential, general structure of meaning, rather than fragmented themes, is preferred (van Wijngaarden, Meide, & Dahlberg, 2017 ) and that such an essential meaning structure is a strength. We argue that meaning‐oriented themes can contribute to robust qualitative research findings. Still, it is important that the findings move between concrete expressions and descriptive text on meanings of lived experiences.

4.1. The process of analysis

The goal of the thematic analysis is to achieve an understanding of patterns of meanings from data on lived experiences (i.e., informants' descriptions of experiences related to the research question in, e.g., interviews or narratives). The analysis begins with data that needs to be textual and aims to organize meanings found in the data into patterns and, finally, themes. While conducting the analysis, the researcher strives to understand meanings embedded in experiences and describe these meanings textually. Through the analysis, details and aspects of meaning are explored, requiring reading and a reflective writing. Parts of the text need to be understood in terms of the whole and the whole in terms of its parts. However, the researcher also needs to move between being close to and distant from the data. Overall, the process of analysis can be complex and the researcher needs to be flexible. This process is summarized in Figure ​ Figure1 1 and detailed in the description below.

Summary of thematic analysis

To begin the analysis, the researcher needs to achieve familiarity with the data through open‐minded reading. The text must be read several times in its entirety. This is an open‐ended reading that puts the principle of openness into practice with the intention of opening one's mind to the text and its meanings. When reading, the researcher starts to explore experiences expressed in the data, such as determining how these are narrated and how meanings can be understood. The goal is to illuminate novel information rather than confirm what is already known while keeping the study aim in mind.

Thereafter, the parts of the data are further illuminated and the search for meanings and themes deepens. By moving back and forth between the whole and its parts, a sensitive dialogue with the text may be facilitated. While reading, meanings corresponding to the study's aim are marked. Notes and short descriptive words can be used to give meanings a preliminary name. As the analysis progresses, meanings related to each other are compared to identify differences and similarities. Meanings need to be related to each other to get a sense of patterns. Patterns of meanings are further examined. It is important to not make meanings definite too rapidly, slow down the understanding of data and its meanings. This demands the researcher's openness to let meanings emerge.

Lastly, the researcher needs to organize themes into a meaningful wholeness. Methodological principles must remind the researcher to maintain a reflective mind, while meanings are further developed into themes. Meanings are organized into patterns and, finally, themes. While deriving meaning from text, it is helpful to compare meanings and themes derived from the original data. Nothing is taken for granted, and the researcher must be careful and thoughtful during this part of the process. It can be valuable to discuss and reflect on tentative themes emerging from the data. Findings need to be meaningful, and the naming and wording of themes becomes important. The writing up of the themes is aimed to outline meanings inherent in the described experiences. At this point, findings are written and rewritten. Faithful descriptions of meanings usually need more than a single word, and the writing is important.

To conclude, the process of thematic analysis, based in a descriptive phenomenological approach, goes from the original data to the identification of meanings, organizing these into patterns and writing the results of themes related to the study aim and the actual context. When the findings are reported, these are described conversely (i.e., starting with the themes and the descriptive text, illustrated with quotes). Thus, meanings found from participants experiences are described in a meaningful text organized in themes.

4.2. Validity and Rigour

Hereby follows our discussion on scientific quality in terms of validity and rigour in the thematic analysis process. There is no consensus on which concepts should be used regarding validity in qualitative and phenomenological research. The term validity is typically used in relation to quantitative methods; however, qualitative researchers claim that the term is suitable in all paradigms as a generic term implying whether the research conclusions are sound, just and well‐founded (Morse, 2015 ; Whittemore, Chase, & Mandle, 2001 ). Rolfe ( 2006 ) states that scientific rigour can be judged based on how the research is presented for the reader and appraising research lies with both the reader and the writer of the research. Thus, clarity regarding methodological principles used becomes necessary. Porter ( 2007 ) argues that a more realistic approach is needed and that scientific rigour needs to be taken seriously in qualitative research (Porter, 2007 ). It has been stressed that strategies are needed to ensure rigour and validity; such strategies must be built into the research process and not solely evaluated afterwards (Cypress, 2017 ). Therefore, we further discuss scientific rigour and phenomenological validity in relation to reflexivity , credibility and transferability .

Reflexivity is strictly connected to previously described methodological principles of a reflective attitude and questioning one's pre‐understanding. Reflexivity must be maintained during the entire process, and the researcher needs to sustain a reflective attitude. Particularly, reflexivity must involve questioning the understanding of data and themes derived. Qualitative researchers are closely engaged in this process and must reflect on what the data actually state that may be different from the researcher's understanding. This means the researcher should question the findings instead of taking them for granted. Malterud ( 2001 ) claims that multiple researchers might strengthen the study since they can give supplementary views and question each other's statements, while an independent researcher must find other strategies. Another way to maintain reflexivity is comparing the original data with the descriptive text of themes derived. Moreover, findings need to be illustrated with original data to demonstrate how the derived descriptions are grounded in the data rather than in the researcher's understanding. Furthermore, information is needed on the setting so the reader can understand the context of the findings.

Credibility refers to the meaningfulness of the findings and whether these are well presented (Kitto, Chesters, & Grbich, 2008 ). Credibility and reflexivity are not totally distinct but are correlated with each other. Credibility stresses that nothing can be taken for granted and is associated with the methodological principles described above. The researcher needs to emphasize how the analysis and findings are presented for the reader. The analysis needs to be transparent, which means that the researcher should present it as thoroughly as possible to strive for credibility. The reader needs information concerning the methodology used and methodological decisions and considerations made. This includes, for instance, how the thematic analysis was performed, descriptions of how meanings were derived from the data and how themes were identified. Descriptions need to be clear and consistent. However, it must be possible to agree with and understand the logic of the findings and themes. Credibility lies in both the methodology and in the presentation of findings. Thus, in striving for credibility, the procedures and methods need to be presented as thoroughly and transparently as possible. Themes described must be illustrated with quotes to ensure the content and described meanings are consistent.

Transferability refers to the usefulness and relevance of the findings. However, the method used does not guarantee transferability in itself. Transferability is not explicitly related to any of the methodological principles, but it may be a result of them. Transferability is a measure of whether the findings are sound and if the study adds new knowledge to what is already known. The clarity of findings is also important. Thus, findings must be understandable and transferable to other research (i.e., findings need to be recognizable and relevant to a specific or broader context other than the original study). Specifically, the relevance, usefulness and meaningfulness of research findings to other contexts are important components of the study's transferability.

To conclude, reflexivity, credibility and transferability are concepts important to acknowledge and consider throughout the research process to engender validity and rigour. We maintain that meaning‐oriented themes can contribute to robust findings, if reported in a text describing patterns of meanings illustrated with examples of expressions from lived experiences. Questions researchers need to ask themselves in relation to validity when conducting a thematic analysis are presented in Figure ​ Figure2. 2 . Since the method in itself is no guarantee of validity and rigour, discussions related to these areas are needed.

Overview of questions useful to the uphold reflexivity, credibility and transferability of the research process in the thematic analysis of meanings

5. IMPLICATIONS FOR NURSING AND MIDWIFERY

In this paper, a method for thematic analysis based on phenomenology has been outlined. Doing phenomenological research is challenging. Therefore, we hope this paper contributes to the understanding of phenomenological underpinnings and methodological principles of thematic analysis based on descriptive phenomenology. This approach can be useful for teachers and researchers in nursing and midwifery. The thematic analysis presented can offer guidance on how to understand meaning and analyse lived experiences. Methodological stances of descriptive phenomenology are clarified, linking the process of analysis with theoretical underpinnings. Methodological principles are explained to give guidance to the analysis and help understand validity and rigour. Thus, this paper has the potential to provide researchers and students who have an interest in research on lived experiences with a comprehensive and useful method to thematic analysis in phenomenology. Nurses and midwives conducting qualitative research on lived experiences need robust methods to ensure high quality in health care to benefit patients, childbearing women and their families.

6. CONCLUSION

We provide researchers in nursing and midwifery with some clarity regarding thematic analysis grounded in the tradition of descriptive phenomenology. We argue that researchers need to comprehend phenomenological underpinnings and be guided by these in the research process. In thematic analysis, descriptive phenomenology is a useful framework when analysing lived experiences with clarified applicable ontological and epistemological underpinnings. Emphasizing openness, questioning pre‐understanding and adopting a reflective attitude were identified as important methodological principles that can guide researchers throughout the analysis and help uphold scientific rigour and validity. For novice researchers, the present paper may serve as an introduction to phenomenological approaches.

CONFLICT OF INTEREST

No conflict of interest has been declared by the authors.

AUTHOR CONTRIBUTIONS

AS, EL, CN, LP: Made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; involved in drafting the manuscript or revising it critically for important intellectual content; given final approval of the version to be published and each author should have participated sufficiently in the work to take public responsibility for appropriate portions of the content; and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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