define thematic analysis in research

What (Exactly) Is Thematic Analysis?

Plain-Language Explanation & Definition (With Examples)

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

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

Thematic Analysis 101

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

First, the lingo…

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

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

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

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

What is thematic analysis?

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

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

When should you use thematic analysis?

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

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

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

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

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

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

What are the main approaches?

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

The inductive approach

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

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

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

The deductive approach

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

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

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

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

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

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

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

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

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

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

What are the types of thematic analysis?

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

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

Let’s have a look at each of these:

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

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

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

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

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

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

Quick Recap: Thematic analysis approaches and types

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

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

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

Need a helping hand?

How to “do” thematic analysis

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

Step 1: Get familiar with the data

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

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

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

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

Step 2: Search for patterns or themes in the codes

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

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

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

Step 3: Review themes

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

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

Step 4: Finalise Themes

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

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

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

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

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

Step 5: Produce your report

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

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

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

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

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

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

Quick Recap: How to “do” thematic analysis

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

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

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

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

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

Tips & Suggestions

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

Wrapping Up

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

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

Psst... there’s more!

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

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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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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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In correlational research, a researcher measures the relationship between two or more variables or sets of scores without having control over the variables.

Quantitative research is associated with measurable numerical data. Qualitative research is where a researcher collects evidence to seek answers to a question.

A survey includes questions relevant to the research topic. The participants are selected, and the questionnaire is distributed to collect the data.

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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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Thematic Analysis

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Thematic analysis is a method of qualitative data analysis that was first described in the 1970s (Joffe, Harper and Thompson (eds), Qualitative Research Methods in Mental Health and Psychotherapy: A Guide for Students and Practitioners, Wiley-Blackwell, 2012) but became more prominent at the end of the 1990s with researchers such as Boyatzis ( 1998 ) and Hayes ( 1997 ) (as cited in Braun, V., & Clarke, V. (2012). Thematic analysis. In H. Cooper (Ed.), APA Handbook of Research Methods in Psychology (vol.2) (pp. 57–71). American Psychological Association.). As qualitfvecome more accepted across social science disciplines and now across health professions education, the need for systematic methods to analyze qualitative sets is more accentuated (Castleberry and Nolen, Currents in Pharmacy Teaching and Learning 10:807–815, 2018). ( Braun, V., & Clarke, V. (2012). Thematic analysis. In H. Cooper (Ed.), APA Handbook of Research Methods in Psychology (vol.2) (pp. 57–71). American Psychological Association.) highlighted that thematic analysis is “an accessible, flexible, and increasingly popular method of qualitative data analysis” (p. 57). Although thematic analysis shares similarities with other methodologies that have systematic processes for analyzing data such as Interpretative Phenomenological Analysis or grounded theory, it does not “require the detailed theoretical and technological knowledge” of these approaches (Braun and Clarke, Qualitative Research in Psychology 3:77–101, 2006). However, (Braun and Clarke, Qualitative Research in Psychology 3:77–101, 2006) emphasized that the theoretical position of the study needs to be made explicit, as there are inherent assumptions regarding the nature of the data that has been analyzed.

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A worked example of Braun and Clarke’s approach to reflexive thematic analysis

Qualitative Research and Content Analysis

Attempting rigour and replicability in thematic analysis of qualitative research data; a case study of codebook development

Braun, V., & Clarke, V. (2012). Thematic analysis. In H. Cooper (Ed.), APA handbook of research methods in psychology (vol.2) (pp. 57–71). American Psychological Association.

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Castleberry, A., & Nolen, A. (2018). Thematic analysis of qualitative research data: Is it as easy as it sounds? Currents in Pharmacy Teaching and Learning, 10 , 807–815. https://doi.org/10.1016/j.cptl.2018.03.019

Fereday, J., & Muir-Cochrane, E. (2006). Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods, 5 , 1–11.

Hayes, N. (1997). Theory-led thematic analysis: Social identification in small companies. In N. Hayes (Ed.), Doing qualitative analysis in psychology (pp. 93–114). Psychology Press.

Joffe, H. (2012). Thematic analysis. In D. Harper & A. Thompson (Eds.), Qualitative research methods in mental health and psychotherapy: A guide for students and practitioners (pp. 203–223). Wiley-Blackwell.

Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16 , 1–13. https://doi.org/10.1177/1609406917733847

Schwandt, T. A. (2015). The Sage dictionary of qualitative inquiry (4th ed.). Sage Publications.

Tuckett, A. G. (2005). Applying thematic analysis theory to practice: A researcher’s experience. Contemporary Nurse, 19 (1–2), 75–87. https://doi.org/10.5172/conu.19.1-2.75

Xu, W., & Zammit, K. (2020). Applying thematic analysis to education: A hybrid approach to interpreting data in practitioner research. International Journal of Qualitative Methods , 19 , 1–9. https://doi.org/10.1177/1609406920918810

Additional Resources

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11 (4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806

Maguire, M., & Delahunt, B. (2017). Doing a thematic analysis: A practical, step-by-step guide for learning and teaching scholars. All Ireland Journal of Higher Education , 9 (3), 3351–3364. http://ojs.aishe.org/index.php/aishe-j/article/view/335

Thematic analysis:-7:23

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Thematic analysis (the ‘Braun & Clarke’ way): an introduction-1:02:19

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Understanding Thematic Analysis: 6 steps to perform Thematic Analysis- 6:26

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Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & Health Sciences, 15 (3), 398–405. https://doi.org/10.1111/nhs.12048

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Squires, V. (2023). Thematic Analysis. In: Okoko, J.M., Tunison, S., Walker, K.D. (eds) Varieties of Qualitative Research Methods. Springer Texts in Education. Springer, Cham. https://doi.org/10.1007/978-3-031-04394-9_72

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

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

Content analysis

• Introduction

What is meant by thematic analysis?

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

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

Discourse analysis

Grounded theory.

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

Thematic analysis

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

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

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

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

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

A useful metaphor for thematic analysis

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

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

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

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

Turn raw data into broader insights with ATLAS.ti

Our powerful data analysis software is available with a free trial.

A simple example of thematic analysis

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

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

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

The subjectivity of themes

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

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

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

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

Applications for thematic analysis

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

• psychotherapy research
• qualitative psychology
• cultural anthropology

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

Common forms of data collection

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

• focus groups
• observations
• literature reviews

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

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

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

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

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

The process in greater detail

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

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

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

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

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

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

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

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

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

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

Narrative analysis

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

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

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

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

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

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

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

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

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

Coding qualitative data

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

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

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

ATLAS.ti tools for thematic analysis

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

Code Manager

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

Co-Occurrence Analysis

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

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

Word Frequencies

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

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

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

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

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

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

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

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

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

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

Analyze and visualize all your data in ATLAS.ti

Identify patterns and potential themes from your data with ATLAS.ti. Download a free trial today.

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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.

LEARN ABOUT: Data Analytics Projects

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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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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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.

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

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

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

Learning objectives

By reading this article, you should be able to:

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

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

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

Examples of questions amenable to thematic analysis.

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

Alt-text: Box 1

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

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

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

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

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

Designing the data collection

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

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

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

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

Designing productive interview questions

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

How to design an interview question.

Alt-text: Box 2

Bias, positionality and reflexivity

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

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

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

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

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

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

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

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

Sampling and saturation

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

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

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

Data management

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

Data analysis

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

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

Phase 1: familiarising the researcher with the data

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

Phase 2: generating initial codes

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

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

Table 1

How to code qualitative data: an example

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

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

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

Phase 3: searching for themes

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

Phase 4: reviewing the themes

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

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

Phase 5: naming the themes

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

Phase 6: producing the report

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

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

Who should analyse the data?

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

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

Judging the quality and rigour of published studies involving thematic analysis

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

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

Ten quality appraisal criteria for qualitative literature.31

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

Alt-text: Box 3

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

Conclusions

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

Declaration of interests

The authors declare that they have no conflicts of interest.

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

Biographies

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

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

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

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Content Analysis vs Thematic Analysis

Content analysis and thematic analysis are two widely used methods in qualitative research for analyzing textual data. While they share similarities, they also have distinct approaches and goals like:

• Content analysis involves analyzing content to identify recurring patterns, while thematic analysis focuses on uncovering the deeper meanings and concepts within the data.
• In content analysis, researchers use a structured approach to categorize the content, whereas thematic analysis allows for a more flexible and exploratory coding process.
• While content analysis looks at surface-level characteristics, thematic analysis goes beyond to explore the underlying significance and implications of the data.
• Content analysis is suitable for handling large and varied datasets, while thematic analysis is best suited for qualitative data, such as text or visuals.
• Content analysis is commonly employed in fields like media studies and marketing research, whereas thematic analysis finds extensive use in social sciences and psychology.

In this guide, we will explore the differences between content analysis and thematic analysis in-depth to understand their applications, and how they are used to derive meaning from qualitative data.

What is Content Analysis?

Content analysis is a method used to systematically analyze the content of textual, visual, or audio material. It involves identifying and quantifying specific elements within the data to draw inferences and conclusions. Essentially, it focuses on the manifest content, such as words, phrases, or themes that are explicitly present in the text. Researchers often use content analysis to categorize and analyze large volumes of data efficiently, making it useful for studying patterns, trends, and relationships within a body of text.

What is Thematic Analysis?

Thematic analysis, on the other hand, is a qualitative method used to identify, analyze, and interpret patterns or themes within textual data. Unlike content analysis, thematic analysis aims to uncover underlying meanings and concepts rather than focusing solely on surface-level content. It involves a process of coding and categorizing data to identify recurring themes or patterns that reflect the experiences, perspectives, or phenomena being studied. Thematic analysis is like a versatile tool that helps researchers understand different types of qualitative data. It’s great for checking complex and detailed ideas or experiences to find patterns and deeper meanings.

Content Analysis Vs Thematic Analysis : Focus and Purpose

Content analysis.

• Focus : Content analysis primarily focuses on quantifying and categorizing the content of the data. It aims to systematically analyze the text or media content to identify patterns, trends, and frequencies within the dataset.
• Purpose : The purpose of content analysis is to provide a structured and systematic overview of the data. By categorizing and quantifying the content, researchers can gain insights into the prevalence of specific themes or topics, the frequency of certain behaviors or messages, or the distribution of content across different categories or sources.

Thematic Analysis

• Focus : Thematic analysis focuses on identifying, analyzing, and reporting patterns (themes) within the data. It aims to uncover the underlying meanings, concepts, and experiences present in the dataset.
• Purpose : The purpose of thematic analysis is to provide a rich and detailed account of the data’s themes and their significance. By exploring the patterns and relationships between different themes, researchers can gain insights into the complexity and depth of the data, as well as the experiences and perspectives of the participants.

Overall, while both content analysis and thematic analysis involve analyzing patterns within data, they differ in their focus and purpose. Content analysis is more structured and quantitative, focusing on the content itself, while thematic analysis is more interpretative and qualitative, focusing on uncovering underlying meanings and concepts.

Content Analysis Vs Thematic Analysis : Coding Process

Content analysis coding process.

• Development of Coding Scheme : In content analysis, researchers begin by developing a coding scheme or framework based on predetermined categories or concepts relevant to the research question. These categories are often derived from existing theories, literature, or research objectives.
• Coding the Data : Researchers systematically code the data into these predefined categories or codes. This coding process involves assigning each unit of analysis (e.g., text segments, media content) to one or more categories based on its content or attributes.
• Quantitative Analysis : Once the data is coded, researchers conduct quantitative analysis by calculating frequencies and distributions of codes within each category. This analysis allows researchers to quantify and describe patterns, trends, or relationships in the data based on the frequency of occurrence of specific codes or categories.

Thematic Analysis Coding Process

• Open Coding : Thematic analysis begins with an open-coding approach, where researchers engage in a flexible and exploratory coding process. They immerse themselves in the data, reading and re-reading it to identify initial codes that capture meaningful concepts, ideas, or patterns.
• Identifying Themes : Codes are then grouped into themes based on similarities and patterns observed in the data. Researchers look for recurring ideas, concepts, or narratives across different data segments and organize related codes into overarching themes.
• Iterative Process : Thematic analysis involves an iterative process of coding and theme development. Researchers continuously refine and define themes as they progress through the analysis, revisiting and revising codes and themes to ensure they accurately reflect the data.
• Thematic Map : The final output of thematic analysis is often represented as a thematic map or narrative, where themes are described, supported by illustrative quotes or examples from the data, and interpreted in relation to the research question or objectives.

Comparison of Coding Processes

• Content Analysis : The coding process in content analysis is more structured and deductive, guided by predetermined categories or concepts. It focuses on quantifying and describing patterns in the data based on predefined criteria.
• Thematic Analysis : In contrast, the coding process in thematic analysis is more flexible and inductive, allowing themes to emerge organically from the data. It emphasizes the interpretation and understanding of underlying meanings and patterns, with themes evolving throughout the analysis process.

Content Analysis Vs Thematic Analysis: Level of Interpretation

In Content Analysis Interpretation tends to be more focused on surface-level characteristics and numerical or statistical summaries derived from the data. Researchers aim to objectively identify and quantify patterns, frequencies, or relationships within the content. The interpretation involves understanding the significance of these numerical findings in relation to the research objectives or hypotheses. While content analysis emphasizes objectivity in coding and analysis, interpretation still requires researchers to contextualize the numerical summaries within the broader research context and draw meaningful conclusions from the data. However, the interpretation in content analysis is generally less subjective compared to thematic analysis, as it relies more on quantifiable data points and statistical techniques.

Interpretation in thematic analysis is more nuanced and subjective, focusing on uncovering deeper meanings, patterns, and insights within the qualitative data. Researchers engage in a process of exploration and reflection to identify and interpret themes that emerge from the data. This interpretation involves understanding the context, connections, and implications of the identified themes, as well as considering the perspectives and experiences of the participants. Thematic analysis encourages researchers to delve into the underlying meanings and nuances of the data, often requiring a more reflexive and iterative approach to interpretation. Researchers may draw on their own insights, theoretical frameworks, and contextual understanding to make sense of the themes and their significance within the broader research context. While thematic analysis prioritizes depth and richness of interpretation, it also acknowledges the subjectivity inherent in the process, as interpretations may vary depending on the researcher’s perspectives and biases.

Content Analysis Vs Thematic Analysis: Data Types

• Content Analysis: Often used with large datasets, including quantitative data, text, audio, video, or images. It is suitable for analyzing a wide range of content, such as media articles, social media posts, interviews, surveys, etc.
• Thematic Analysis: Primarily used with qualitative textual or visual data, such as interview transcripts, focus group discussions, open-ended survey responses, diaries, or field notes. It focuses on in-depth analysis of the content rather than numerical quantification.

Both content analysis and thematic analysis can be applied to different types of data, they are often used with distinct types of content sources. Content analysis is suitable for large datasets with diverse content types, while thematic analysis is tailored for qualitative textual or visual data sources that require in-depth exploration and interpretation.

Content Analysis Vs Thematic Analysis: Research Context

Content analysis for research context.

Content analysis is commonly used in media studies, communication research, marketing research, and content-based analysis in various disciplines. It is particularly useful for studying media representations, content trends, and public discourse.

In media studies and communication research, content analysis allows researchers to systematically analyze and quantify media content, such as news articles, advertisements, television programs, or social media posts. It enables the study of media representations, framing effects, content trends, and changes in public discourse over time. In marketing research, content analysis can be used to analyze advertising campaigns, brand messaging, consumer reviews, or social media engagement to understand consumer perceptions, preferences, and behavior.

Thematic Analysis for Research Context

Thematic analysis is widely used in social sciences, psychology, health sciences, and other qualitative research domains. It is suitable for exploring complex phenomena, understanding participants’ perspectives, and generating rich qualitative insights.

In social sciences and psychology, thematic analysis allows researchers to explore and interpret the underlying meanings, patterns, and experiences within qualitative data sources, such as interview transcripts, focus group discussions, or open-ended survey responses. It provides a flexible and in-depth approach to understanding complex phenomena, such as human behavior, emotions, beliefs, or social interactions. In health sciences, thematic analysis is often used to explore patients’ experiences, healthcare professionals’ perspectives, or the impact of interventions on health outcomes, providing valuable insights for improving healthcare practices and policies.

Content Analysis vs Thematic Analysis: Comparison Overview

When to use content analysis.

Content analysis is a valuable research method that can be used in various contexts. Some situations where content analysis is particularly useful:

• Understanding Communication Patterns : Content analysis is beneficial when researchers aim to understand communication patterns, such as language use, themes, and trends, within textual, visual, or audio content. This method allows for systematic analysis of communication materials, such as media content, speeches, social media posts, or customer reviews, to uncover underlying messages and patterns.
• Exploring Media Representation : Content analysis is often used to examine how certain topics, groups, or events are portrayed in the media. Researchers can analyze news articles, advertisements, films, or television programs to explore themes, stereotypes, biases, or framing techniques used in media representation.
• Evaluating Public Opinion : Content analysis can be employed to assess public opinion on specific issues or topics by analyzing online discussions, social media conversations, or comments on news articles. Researchers can identify prevalent attitudes, sentiments, and opinions expressed in textual data to gain insights into public perceptions and discourse.
• Assessing Organizational Communication : Content analysis is valuable for studying organizational communication within businesses, institutions, or government agencies. Researchers can analyze internal documents, such as emails, memos, or reports, to understand communication patterns, organizational culture, leadership styles, and decision-making processes.
• Examining Historical Documents : Content analysis can be used in historical research to analyze primary sources, such as letters, diaries, newspapers, or government records. Researchers can uncover historical trends, ideologies, or societal changes by systematically analyzing textual content from different time periods.
• Monitoring Brand Perception : Content analysis is useful for businesses and marketers to monitor brand perception and sentiment by analyzing customer feedback, product reviews, or social media mentions. Researchers can identify trends, common issues, and customer preferences to inform marketing strategies and brand management efforts.

When to use Thematic Analysis?

Thematic analysis is a qualitative research method used to identify, analyze, and report patterns or themes within data. Some situations where thematic analysis is particularly appropriate:

• Exploring Complex Phenomena : Thematic analysis is suitable when researchers aim to explore complex phenomena or experiences in depth. It allows for a flexible and in-depth exploration of rich qualitative data, such as interview transcripts, focus group discussions, or open-ended survey responses, to uncover underlying meanings and patterns.
• Understanding Participant Perspectives : Thematic analysis is valuable for understanding participant perspectives, beliefs, and experiences on a particular topic. It enables researchers to identify common themes and variations in participants’ responses, providing insights into how individuals perceive and make sense of their experiences.
• Examining Social or Cultural Constructs : Thematic analysis is useful for examining social or cultural constructs, such as identity, power dynamics, or social norms. Researchers can analyze qualitative data to identify recurring themes related to these constructs, gaining insights into how they are constructed and enacted in social contexts.
• Generating Hypotheses for Further Research : Thematic analysis can be used in exploratory research to generate hypotheses or research questions for further investigation. By systematically analyzing qualitative data, researchers can identify emerging themes and patterns that warrant further exploration through quantitative or qualitative research methods.
• Evaluating Program or Intervention Outcomes : Thematic analysis is applicable for evaluating the outcomes of programs, interventions, or interventions. Researchers can analyze qualitative data, such as interviews with participants or stakeholders, to identify themes related to program effectiveness, impact, or implementation challenges.

Content analysis and thematic analysis are essential tools in qualitative research for understanding textual data. Content analysis focuses on counting and categorizing elements to study trends, while thematic analysis digs deeper to uncover meanings and patterns. The choice between these methods depends on the research goals and the level of depth required in interpreting the data. Both approaches offer valuable insights into qualitative data analysis, making them indispensable in various research contexts.

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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.

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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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What are the strengths and limitations to utilising creative methods in public and patient involvement in health and social care research? A qualitative systematic review

• Olivia R. Phillips 1 , 2   na1 ,
• Cerian Harries 2 , 3   na1 ,
• Jo Leonardi-Bee 1 , 2 , 4   na1 ,
• Holly Knight 1 , 2 ,
• Lauren B. Sherar 2 , 3 ,
• Veronica Varela-Mato 2 , 3 &
• Joanne R. Morling 1 , 2 , 5  

Research Involvement and Engagement volume  10 , Article number:  48 ( 2024 ) Cite this article

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There is increasing interest in using patient and public involvement (PPI) in research to improve the quality of healthcare. Ordinarily, traditional methods have been used such as interviews or focus groups. However, these methods tend to engage a similar demographic of people. Thus, creative methods are being developed to involve patients for whom traditional methods are inaccessible or non-engaging.

To determine the strengths and limitations to using creative PPI methods in health and social care research.

Electronic searches were conducted over five databases on 14th April 2023 (Web of Science, PubMed, ASSIA, CINAHL, Cochrane Library). Studies that involved traditional, non-creative PPI methods were excluded. Creative PPI methods were used to engage with people as research advisors, rather than study participants. Only primary data published in English from 2009 were accepted. Title, abstract and full text screening was undertaken by two independent reviewers before inductive thematic analysis was used to generate themes.

Twelve papers met the inclusion criteria. The creative methods used included songs, poems, drawings, photograph elicitation, drama performance, visualisations, social media, photography, prototype development, cultural animation, card sorting and persona development. Analysis identified four limitations and five strengths to the creative approaches. Limitations included the time and resource intensive nature of creative PPI, the lack of generalisation to wider populations and ethical issues. External factors, such as the lack of infrastructure to support creative PPI, also affected their implementation. Strengths included the disruption of power hierarchies and the creation of a safe space for people to express mundane or “taboo” topics. Creative methods are also engaging, inclusive of people who struggle to participate in traditional PPI and can also be cost and time efficient.

‘Creative PPI’ is an umbrella term encapsulating many different methods of engagement and there are strengths and limitations to each. The choice of which should be determined by the aims and requirements of the research, as well as the characteristics of the PPI group and practical limitations. Creative PPI can be advantageous over more traditional methods, however a hybrid approach could be considered to reap the benefits of both. Creative PPI methods are not widely used; however, this could change over time as PPI becomes embedded even more into research.

Plain English Summary

It is important that patients and public are included in the research process from initial brainstorming, through design to delivery. This is known as public and patient involvement (PPI). Their input means that research closely aligns with their wants and needs. Traditionally to get this input, interviews and group discussions are held, but this can exclude people who find these activities non-engaging or inaccessible, for example those with language challenges, learning disabilities or memory issues. Creative methods of PPI can overcome this. This is a broad term describing different (non-traditional) ways of engaging patients and public in research, such as through the use or art, animation or performance. This review investigated the reasons why creative approaches to PPI could be difficult (limitations) or helpful (strengths) in health and social care research. After searching 5 online databases, 12 studies were included in the review. PPI groups included adults, children and people with language and memory impairments. Creative methods included songs, poems, drawings, the use of photos and drama, visualisations, Facebook, creating prototypes, personas and card sorting. Limitations included the time, cost and effort associated with creative methods, the lack of application to other populations, ethical issues and buy-in from the wider research community. Strengths included the feeling of equality between academics and the public, creation of a safe space for people to express themselves, inclusivity, and that creative PPI can be cost and time efficient. Overall, this review suggests that creative PPI is worthwhile, however each method has its own strengths and limitations and the choice of which will depend on the research project, PPI group characteristics and other practical limitations, such as time and financial constraints.

Peer Review reports

Introduction

Patient and public involvement (PPI) is the term used to describe the partnership between patients (including caregivers, potential patients, healthcare users etc.) or the public (a community member with no known interest in the topic) with researchers. It describes research that is done “‘with’ or ‘by’ the public, rather than ‘to,’ ‘about’ or ‘for’ them” [ 1 ]. In 2009, it became a legislative requirement for certain health and social care organisations to include patients, families, carers and communities in not only the planning of health and social care services, but the commissioning, delivery and evaluation of them too [ 2 ]. For example, funding applications for the National Institute of Health and Care Research (NIHR), a UK funding body, mandates a demonstration of how researchers plan to include patients/service users, the public and carers at each stage of the project [ 3 ]. However, this should not simply be a tokenistic, tick-box exercise. PPI should help formulate initial ideas and should be an instrumental, continuous part of the research process. Input from PPI can provide unique insights not yet considered and can ensure that research and health services are closely aligned to the needs and requirements of service users PPI also generally makes research more relevant with clearer outcomes and impacts [ 4 ]. Although this review refers to both patients and the public using the umbrella term ‘PPI’, it is important to acknowledge that these are two different groups with different motivations, needs and interests when it comes to health research and service delivery [ 5 ].

Despite continuing recognition of the need of PPI to improve quality of healthcare, researchers have also recognised that there is no ‘one size fits all’ method for involving patients [ 4 ]. Traditionally, PPI methods invite people to take part in interviews or focus groups to facilitate discussion, or surveys and questionnaires. However, these can sometimes be inaccessible or non-engaging for certain populations. For example, someone with communication difficulties may find it difficult to engage in focus groups or interviews. If individuals lack the appropriate skills to interact in these types of scenarios, they cannot take advantage of the participation opportunities it can provide [ 6 ]. Creative methods, however, aim to resolve these issues. These are a relatively new concept whereby researchers use creative methods (e.g., artwork, animations, Lego), to make PPI more accessible and engaging for those whose voices would otherwise go unheard. They ensure that all populations can engage in research, regardless of their background or skills. Seminal work has previously been conducted in this area, which brought to light the use of creative methodologies in research. Leavy (2008) [ 7 ] discussed how traditional interviews had limits on what could be expressed due to their sterile, jargon-filled and formulaic structure, read by only a few specialised academics. It was this that called for more creative approaches, which included narrative enquiry, fiction-based research, poetry, music, dance, art, theatre, film and visual art. These practices, which can be used in any stage of the research cycle, supported greater empathy, self-reflection and longer-lasting learning experiences compared to interviews [ 7 ]. They also pushed traditional academic boundaries, which made the research accessible not only to researchers, but the public too. Leavy explains that there are similarities between arts-based approaches and scientific approaches: both attempts to investigate what it means to be human through exploration, and used together, these complimentary approaches can progress our understanding of the human experience [ 7 ]. Further, it is important to acknowledge the parallels and nuances between creative and inclusive methods of PPI. Although creative methods aim to be inclusive (this should underlie any PPI activity, whether creative or not), they do not incorporate all types of accessible, inclusive methodologies e.g., using sign language for people with hearing impairments or audio recordings for people who cannot read. Given that there was not enough scope to include an evaluation of all possible inclusive methodologies, this review will focus on creative methods of PPI only.

We aimed to conduct a qualitative systematic review to highlight the strengths of creative PPI in health and social care research, as well as the limitations, which might act as a barrier to their implementation. A qualitative systematic review “brings together research on a topic, systematically searching for research evidence from primary qualitative studies and drawing the findings together” [ 8 ]. This review can then advise researchers of the best practices when designing PPI.

Public involvement

The PHIRST-LIGHT Public Advisory Group (PAG) consists of a team of experienced public contributors with a diverse range of characteristics from across the UK. The PAG was involved in the initial question setting and study design for this review.

Search strategy

For the purpose of this review, the JBI approach for conducting qualitative systematic reviews was followed [ 9 ]. The search terms were (“creativ*” OR “innovat*” OR “authentic” OR “original” OR “inclu*”) AND (“public and patient involvement” OR “patient and public involvement” OR “public and patient involvement and engagement” OR “patient and public involvement and engagement” OR “PPI” OR “PPIE” OR “co-produc*” OR “co-creat*” OR “co-design*” OR “cooperat*” OR “co-operat*”). This search string was modified according to the requirements of each database. Papers were filtered by title, abstract and keywords (see Additional file 1 for search strings). The databases searched included Web of Science (WoS), PubMed, ASSIA and CINAHL. The Cochrane Library was also searched to identify relevant reviews which could lead to the identification of primary research. The search was conducted on 14/04/23. As our aim was to report on the use of creative PPI in research, rather than more generic public engagement, we used electronic databases of scholarly peer-reviewed literature, which represent a wide range of recognised databases. These identified studies published in general international journals (WoS, PubMed), those in social sciences journals (ASSIA), those in nursing and allied health journals (CINAHL), and trials of interventions (Cochrane Library).

Inclusion criteria

Only full-text, English language, primary research papers from 2009 to 2023 were included. This was the chosen timeframe as in 2009 the Health and Social Reform Act made it mandatory for certain Health and Social Care organisations to involve the public and patients in planning, delivering, and evaluating services [ 2 ]. Only creative methods of PPI were accepted, rather than traditional methods, such as interviews or focus groups. For the purposes of this paper, creative PPI included creative art or arts-based approaches (e.g., e.g. stories, songs, drama, drawing, painting, poetry, photography) to enhance engagement. Titles were related to health and social care and the creative PPI was used to engage with people as research advisors, not as study participants. Meta-analyses, conference abstracts, book chapters, commentaries and reviews were excluded. There were no limits concerning study location or the demographic characteristics of the PPI groups. Only qualitative data were accepted.

Quality appraisal

Quality appraisal using the Critical Appraisal Skills Programme (CASP) checklist [ 10 ] was conducted by the primary authors (ORP and CH). This was done independently, and discrepancies were discussed and resolved. If a consensus could not be reached, a third independent reviewer was consulted (JRM). The full list of quality appraisal questions can be found in Additional file 2 .

Data extraction

ORP extracted the study characteristics and a subset of these were checked by CH. Discrepancies were discussed and amendments made. Extracted data included author, title, location, year of publication, year study was carried out, research question/aim, creative methods used, number of participants, mean age, gender, ethnicity of participants, setting, limitations and strengths of creative PPI and main findings.

Data analysis

The included studies were analysed using inductive thematic analysis [ 11 ], where themes were determined by the data. The familiarisation stage took place during full-text reading of the included articles. Anything identified as a strength or limitation to creative PPI methods was extracted verbatim as an initial code and inputted into the data extraction Excel sheet. Similar codes were sorted into broader themes, either under ‘strengths’ or ‘limitations’ and reviewed. Themes were then assigned a name according to the codes.

The search yielded 9978 titles across the 5 databases: Web of Science (1480 results), PubMed (94 results), ASSIA (2454 results), CINAHL (5948 results) and Cochrane Library (2 results), resulting in 8553 different studies after deduplication. ORP and CH independently screened their titles and abstracts, excluding those that did not meet the criteria. After assessment, 12 studies were included (see Fig.  1 ).

PRISMA flowchart of the study selection process

Study characteristics

The included studies were published between 2018 and 2022. Seven were conducted in the UK [ 12 , 14 , 15 , 17 , 18 , 19 , 23 ], two in Canada [ 21 , 22 ], one in Australia [ 13 ], one in Norway [ 16 ] and one in Ireland [ 20 ]. The PPI activities occurred across various settings, including a school [ 12 ], social club [ 12 ], hospital [ 17 ], university [ 22 ], theatre [ 19 ], hotel [ 20 ], or online [ 15 , 21 ], however this information was omitted in 5 studies [ 13 , 14 , 16 , 18 , 23 ]. The number of people attending the PPI sessions varied, ranging from 6 to 289, however the majority (ten studies) had less than 70 participants [ 13 , 14 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. Seven studies did not provide information on the age or gender of the PPI groups. Of those that did, ages ranged from 8 to 76 and were mostly female. The ethnicities of the PPI group members were also rarely recorded (see Additional file 3 for data extraction table).

Types of creative methods

The type of creative methods used to engage the PPI groups were varied. These included songs, poems, drawings, photograph elicitation, drama performance, visualisations, Facebook, photography, prototype development, cultural animation, card sorting and creating personas (see Table  1 ). These were sometimes accompanied by traditional methods of PPI such as interviews and focus group discussions.

The 12 included studies were all deemed to be of good methodological quality, with scores ranging from 6/10 to 10/10 with the CASP critical appraisal tool [ 10 ] (Table  2 ).

Thematic analysis

Analysis identified four limitations and five strengths to creative PPI (see Fig.  2 ). Limitations included the time and resource intensity of creative PPI methods, its lack of generalisation, ethical issues and external factors. Strengths included the disruption of power hierarchies, the engaging and inclusive nature of the methods and their long-term cost and time efficiency. Creative PPI methods also allowed mundane and “taboo” topics to be discussed within a safe space.

Theme map of strengths and limitations

Limitations of creative PPI

Creative ppi methods are time and resource intensive.

The time and resource intensive nature of creative PPI methods is a limitation, most notably for the persona-scenario methodology. Valaitis et al. [ 22 ] used 14 persona-scenario workshops with 70 participants to co-design a healthcare intervention, which aimed to promote optimal aging in Canada. Using the persona method, pairs composed of patients, healthcare providers, community service providers and volunteers developed a fictional character which they believed represented an ‘end-user’ of the healthcare intervention. Due to the depth and richness of the data produced the authors reported that it was time consuming to analyse. Further, they commented that the amount of information was difficult to disseminate to scientific leads and present at team meetings. Additionally, to ensure the production of high-quality data, to probe for details and lead group discussion there was a need for highly skilled facilitators. The resource intensive nature of the creative co-production was also noted in a study using the persona scenario and creative worksheets to develop a prototype decision support tool for individuals with malignant pleural effusion [ 17 ]. With approximately 50 people, this was also likely to yield a high volume of data to consider.

To prepare materials for populations who cannot engage in traditional methods of PPI was also timely. Kearns et al. [ 18 ] developed a feedback questionnaire for people with aphasia to evaluate ICT-delivered rehabilitation. To ensure people could participate effectively, the resources used during the workshops, such as PowerPoints, online images and photographs, had to be aphasia-accessible, which was labour and time intensive. The author warned that this time commitment should not be underestimated.

There are further practical limitations to implementing creative PPI, such as the costs of materials for activities as well as hiring a space for workshops. For example, the included studies in this review utilised pens, paper, worksheets, laptops, arts and craft supplies and magazines and took place in venues such as universities, a social club, and a hotel. Further, although not limited to creative PPI methods exclusively but rather most studies involving the public, a financial incentive was often offered for participation, as well as food, parking, transport and accommodation [ 21 , 22 ].

Creative PPI lacks generalisation

Another barrier to the use of creative PPI methods in health and social care research was the individual nature of its output. Those who participate, usually small in number, produce unique creative outputs specific to their own experiences, opinions and location. Craven et al. [ 13 ], used arts-based visualisations to develop a toolbox for adults with mental health difficulties. They commented, “such an approach might still not be worthwhile”, as the visualisations were individualised and highly personal. This indicates that the output may fail to meet the needs of its end-users. Further, these creative PPI groups were based in certain geographical regions such as Stoke-on-Trent [ 19 ] Sheffield [ 23 ], South Wales [ 12 ] or Ireland [ 20 ], which limits the extent the findings can be applied to wider populations, even within the same area due to individual nuances. Further, the study by Galler et al. [ 16 ], is specific to the Norwegian context and even then, maybe only a sub-group of the Norwegian population as the sample used was of higher socioeconomic status.

However, Grindell et al. [ 17 ], who used persona scenarios, creative worksheets and prototype development, pointed out that the purpose of this type of research is to improve a certain place, rather than apply findings across other populations and locations. Individualised output may, therefore, only be a limitation to research wanting to conduct PPI on a large scale.

If, however, greater generalisation within PPI is deemed necessary, then social media may offer a resolution. Fedorowicz et al. [ 15 ], used Facebook to gain feedback from the public on the use of video-recording methodology for an upcoming project. This had the benefit of including a more diverse range of people (289 people joined the closed group), who were spread geographically around the UK, as well as seven people from overseas.

Creative PPI has ethical issues

As with other research, ethical issues must be taken into consideration. Due to the nature of creative approaches, as well as the personal effort put into them, people often want to be recognised for their work. However, this compromises principles so heavily instilled in research such as anonymity and confidentiality. With the aim of exploring issues related to health and well-being in a town in South Wales, Byrne et al. [ 12 ], asked year 4/5 and year 10 pupils to create poems, songs, drawings and photographs. Community members also created a performance, mainly of monologues, to explore how poverty and inequalities are dealt with. Byrne noted the risks of these arts-based approaches, that being the possibility of over-disclosure and consequent emotional distress, as well as people’s desire to be named for their work. On one hand, the anonymity reduces the sense of ownership of the output as it does not portray a particular individual’s lived experience anymore. On the other hand, however, it could promote a more honest account of lived experience. Supporting this, Webber et al. [ 23 ], who used the persona method to co-design a back pain educational resource prototype, claimed that the anonymity provided by this creative technique allowed individuals to externalise and anonymise their own personal experience, thus creating a more authentic and genuine resource for future users. This implies that anonymity can be both a limitation and strength here.

The use of creative PPI methods is impeded by external factors

Despite the above limitations influencing the implementation of creative PPI techniques, perhaps the most influential is that creative methodologies are simply not mainstream [ 19 ]. This could be linked to the issues above, like time and resource intensity, generalisation and ethical issues but it is also likely to involve more systemic factors within the research community. Micsinszki et al. [ 21 ], who co-designed a hub for the health and well-being of vulnerable populations, commented that there is insufficient infrastructure to conduct meaningful co-design as well as a dominant medical model. Through a more holistic lens, there are “sociopolitical environments that privilege individualism over collectivism, self-sufficiency over collaboration, and scientific expertise over other ways of knowing based on lived experience” [ 21 ]. This, it could be suggested, renders creative co-design methodologies, which are based on the foundations of collectivism, collaboration and imagination an invalid technique in the research field, which is heavily dominated by more scientific methods offering reproducibility, objectivity and reliability.

Although we acknowledge that creative PPI techniques are not always appropriate, it may be that their main limitation is the lack of awareness of these methods or lack of willingness to use them. Further, there is always the risk that PPI, despite being a mandatory part of research, is used in a tokenistic or tick-box fashion [ 20 ], without considering the contribution that meaningful PPI could make to enhancing the research. It may be that PPI, let alone creative PPI, is not at the forefront of researchers’ minds when planning research.

Strengths of creative PPI

Creative ppi disrupts power hierarchies.

One of the main strengths of creative PPI techniques, cited most frequently in the included literature, was that they disrupt traditional power hierarchies [ 12 , 13 , 17 , 19 , 23 ]. For example, the use of theatre performance blurred the lines between professional and lay roles between the community and policy makers [ 12 ]. Individuals created a monologue to portray how poverty and inequality impact daily life and presented this to representatives of the National Assembly of Wales, Welsh Government, the Local Authority, Arts Council and Westminster. Byrne et al. [ 12 ], states how this medium allowed the community to engage with the people who make decisions about their lives in an environment of respect and understanding, where the hierarchies are not as visible as in other settings, e.g., political surgeries. Creative PPI methods have also removed traditional power hierarchies between researchers and adolescents. Cook et al. [ 13 ], used arts-based approaches to explore adolescents’ ideas about the “perfect” condom. They utilised the “Life Happens” resource, where adolescents drew and then decorated a person with their thoughts about sexual relationships, not too dissimilar from the persona-scenario method. This was then combined with hypothetical scenarios about sexuality. A condom-mapping exercise was then implemented, where groups shared the characteristics that make a condom “perfect” on large pieces of paper. Cook et al. [ 13 ], noted that usually power imbalances make it difficult to elicit information from adolescents, however these power imbalances were reduced due to the use of creative co-design techniques.

The same reduction in power hierarchies was noted by Grindell et al. [ 17 ], who used the person-scenario method and creative worksheets with individuals with malignant pleural effusion. This was with the aim of developing a prototype of a decision support tool for patients to help with treatment options. Although this process involved a variety of stakeholders, such as patients, carers and healthcare professionals, creative co-design was cited as a mechanism that worked to reduce power imbalances – a limitation of more traditional methods of research. Creative co-design blurred boundaries between end-users and clinical staff and enabled the sharing of ideas from multiple, valuable perspectives, meaning the prototype was able to suit user needs whilst addressing clinical problems.

Similarly, a specific creative method named cultural animation was also cited to dissolve hierarchies and encourage equal contributions from participants. Within this arts-based approach, Keleman et al. [ 19 ], explored the concept of “good health” with individuals from Stoke-on Trent. Members of the group created art installations using ribbons, buttons, cardboard and straws to depict their idea of a “healthy community”, which was accompanied by a poem. They also created a 3D Facebook page and produced another poem or song addressing the government to communicate their version of a “picture of health”. Public participants said that they found the process empowering, honest, democratic, valuable and practical.

This dissolving of hierarchies and levelling of power is beneficial as it increases the sense of ownership experienced by the creators/producers of the output [ 12 , 17 , 23 ]. This is advantageous as it has been suggested to improve its quality [ 23 ].

Creative PPI allows the unsayable to be said

Creative PPI fosters a safe space for mundane or taboo topics to be shared, which may be difficult to communicate using traditional methods of PPI. For example, the hypothetical nature of condom mapping and persona-scenarios meant that adolescents could discuss a personal topic without fear of discrimination, judgement or personal disclosure [ 13 ]. The safe space allowed a greater volume of ideas to be generated amongst peers where they might not have otherwise. Similarly, Webber et al. [ 23 ], , who used the persona method to co-design the prototype back pain educational resource, also noted how this method creates anonymity whilst allowing people the opportunity to externalise personal experiences, thoughts and feelings. Other creative methods were also used, such as drawing, collaging, role play and creating mood boards. A cardboard cube (labelled a “magic box”) was used to symbolise a physical representation of their final prototype. These creative methods levelled the playing field and made personal experiences accessible in a safe, open environment that fostered trust, as well as understanding from the researchers.

It is not only sensitive subjects that were made easier to articulate through creative PPI. The communication of mundane everyday experiences were also facilitated, which were deemed typically ‘unsayable’. This was specifically given in the context of describing intangible aspects of everyday health and wellbeing [ 11 ]. Graphic designers can also be used to visually represent the outputs of creative PPI. These captured the movement and fluidity of people and well as the relationships between them - things that cannot be spoken but can be depicted [ 21 ].

Creative PPI methods are inclusive

Another strength of creative PPI was that it is inclusive and accessible [ 17 , 19 , 21 ]. The safe space it fosters, as well as the dismantling of hierarchies, welcomed people from a diverse range of backgrounds and provided equal opportunities [ 21 ], especially for those with communication and memory difficulties who might be otherwise excluded from PPI. Kelemen et al. [ 19 ], who used creative methods to explore health and well-being in Stoke-on-Trent, discussed how people from different backgrounds came together and connected, discussed and reached a consensus over a topic which evoked strong emotions, that they all have in common. Individuals said that the techniques used “sets people to open up as they are not overwhelmed by words”. Similarly, creative activities, such as the persona method, have been stated to allow people to express themselves in an inclusive environment using a common language. Kearns et al. [ 18 ], who used aphasia-accessible material to develop a questionnaire with aphasic individuals, described how they felt comfortable in contributing to workshops (although this material was time-consuming to make, see ‘Limitations of creative PPI’ ).

Despite the general inclusivity of creative PPI, it can also be exclusive, particularly if online mediums are used. Fedorowicz et al. [ 15 ], used Facebook to create a PPI group, and although this may rectify previous drawbacks about lack of generalisation of creative methods (as Facebook can reach a greater number of people, globally), it excluded those who are not digitally active or have limited internet access or knowledge of technology. Online methods have other issues too. Maintaining the online group was cited as challenging and the volume of responses required researchers to interact outside of their working hours. Despite this, online methods like Facebook are very accessible for people who are physically disabled.

Creative PPI methods are engaging

The process of creative PPI is typically more engaging and produces more colourful data than traditional methods [ 13 ]. Individuals are permitted and encouraged to explore a creative self [ 19 ], which can lead to the exploration of new ideas and an overall increased enjoyment of the process. This increased engagement is particularly beneficial for younger PPI groups. For example, to involve children in the development of health food products, Galler et al. [ 16 ] asked 9-12-year-olds to take photos of their food and present it to other children in a “show and tell” fashion. They then created a newspaper article describing a new healthy snack. In this creative focus group, children were given lab coats to further their identity as inventors. Galler et al. [ 16 ], notes that the methods were highly engaging and facilitated teamwork and group learning. This collaborative nature of problem-solving was also observed in adults who used personas and creative worksheets to develop the resource for lower back pain [ 23 ]. Dementia patients too have been reported to enjoy the creative and informal approach to idea generation [ 20 ].

The use of cultural animation allowed people to connect with each other in a way that traditional methods do not [ 19 , 21 ]. These connections were held in place by boundary objects, such as ribbons, buttons, fabric and picture frames, which symbolised a shared meaning between people and an exchange of knowledge and emotion. Asking groups to create an art installation using these objects further fostered teamwork and collaboration, both at an individual and collective level. The exploration of a creative self increased energy levels and encouraged productive discussions and problem-solving [ 19 ]. Objects also encouraged a solution-focused approach and permitted people to think beyond their usual everyday scope [ 17 ]. They also allowed facilitators to probe deeper about the greater meanings carried by the object, which acted as a metaphor [ 21 ].

From the researcher’s point of view, co-creative methods gave rise to ideas they might not have initially considered. Valaitis et al. [ 22 ], found that over 40% of the creative outputs were novel ideas brought to light by patients, healthcare providers/community care providers, community service providers and volunteers. One researcher commented, “It [the creative methods] took me on a journey, in a way that when we do other pieces of research it can feel disconnected” [ 23 ]. Another researcher also stated they could not return to the way they used to do research, as they have learnt so much about their own health and community and how they are perceived [ 19 ]. This demonstrates that creative processes not only benefit the project outcomes and the PPI group, but also facilitators and researchers. However, although engaging, creative methods have been criticised for not demonstrating academic rigour [ 17 ]. Moreover, creative PPI may also be exclusive to people who do not like or enjoy creative activities.

Creative PPI methods are cost and time efficient

Creative PPI workshops can often produce output that is visible and tangible. This can save time and money in the long run as the output is either ready to be implemented in a healthcare setting or a first iteration has already been developed. This may also offset the time and costs it takes to implement creative PPI. For example, the prototype of the decision support tool for people with malignant pleural effusion was developed using personas and creative worksheets. The end result was two tangible prototypes to drive the initial idea forward as something to be used in practice [ 17 ]. The use of creative co-design in this case saved clinician time as well as the time it would take to develop this product without the help of its end-users. In the development of this particular prototype, analysis was iterative and informed the next stage of development, which again saved time. The same applies for the feedback questionnaire for the assessment of ICT delivered aphasia rehabilitation. The co-created questionnaire, designed with people with aphasia, was ready to be used in practice [ 18 ]. This suggests that to overcome time and resource barriers to creative PPI, researchers should aim for it to be engaging whilst also producing output.

That useable products are generated during creative workshops signals to participating patients and public members that they have been listened to and their thoughts and opinions acted upon [ 23 ]. For example, the development of the back pain resource based on patient experiences implies that their suggestions were valid and valuable. Further, those who participated in the cultural animation workshop reported that the process visualises change, and that it already feels as though the process of change has started [ 19 ].

The most cost and time efficient method of creative PPI in this review is most likely the use of Facebook to gather feedback on project methodology [ 15 ]. Although there were drawbacks to this, researchers could involve more people from a range of geographical areas at little to no cost. Feedback was instantaneous and no training was required. From the perspective of the PPI group, they could interact however much or little they wish with no time commitment.

This systematic review identified four limitations and five strengths to the use of creative PPI in health and social care research. Creative PPI is time and resource intensive, can raise ethical issues and lacks generalisability. It is also not accepted by the mainstream. These factors may act as barriers to the implementation of creative PPI. However, creative PPI disrupts traditional power hierarchies and creates a safe space for taboo or mundane topics. It is also engaging, inclusive and can be time and cost efficient in the long term.

Something that became apparent during data analysis was that these are not blanket strengths and limitations of creative PPI as a whole. The umbrella term ‘creative PPI’ is broad and encapsulates a wide range of activities, ranging from music and poems to prototype development and persona-scenarios, to more simplistic things like the use of sticky notes and ordering cards. Many different activities can be deemed ‘creative’ and the strengths and limitations of one does not necessarily apply to another. For example, cultural animation takes greater effort to prepare than the use of sticky notes and sorting cards, and the use of Facebook is cheaper and wider reaching than persona development. Researchers should use their discretion and weigh up the benefits and drawbacks of each method to decide on a technique which suits the project. What might be a limitation to creative PPI in one project may not be in another. In some cases, creative PPI may not be suitable at all.

Furthermore, the choice of creative PPI method also depends on the needs and characteristics of the PPI group. Children, adults and people living with dementia or language difficulties all have different engagement needs and capabilities. This indicates that creative PPI is not one size fits all and that the most appropriate method will change depending on the composition of the group. The choice of method will also be determined by the constraints of the research project, namely time, money and the research aim. For example, if there are time constraints, then a method which yields a lot of data and requires a lot of preparation may not be appropriate. If generalisation is important, then an online method is more suitable. Together this indicates that the choice of creative PPI method is highly individualised and dependent on multiple factors.

Although the limitations discussed in this review apply to creative PPI, they are not exclusive to creative PPI. Ethical issues are a consideration within general PPI research, especially when working with more vulnerable populations, such as children or adults living with a disability. It can also be the case that traditional PPI methods lack generalisability, as people who volunteer to be part of such a group are more likely be older, middle class and retired [ 24 ]. Most research is vulnerable to this type of bias, however, it is worth noting that generalisation is not always a goal and research remains valid and meaningful in its absence. Although online methods may somewhat combat issues related to generalisability, these methods still exclude people who do not have access to the internet/technology or who choose not to use it, implying that online PPI methods may not be wholly representative of the general population. Saying this, however, the accessibility of creative PPI techniques differs from person to person, and for some, online mediums may be more accessible (for example for those with a physical disability), and for others, this might be face-to-face. To combat this, a range of methods should be implemented. Planning multiple focus group and interviews for traditional PPI is also time and resource intensive, however the extra resources required to make this creative may be even greater. Although, the rich data provided may be worth the preparation and analysis time, which is also likely to depend on the number of participants and workshop sessions required. PPI, not just creative PPI, often requires the provision of a financial incentive, refreshments, parking and accommodation, which increase costs. These, however, are imperative and non-negotiable, as they increase the accessibility of research, especially to minority and lower-income groups less likely to participate. Adequate funding is also important for co-design studies where repeated engagement is required. One barrier to implementation, which appears to be exclusive to creative methods, however, is that creative methods are not mainstream. This cannot be said for traditional PPI as this is often a mandatory part of research applications.

Regarding the strengths of creative PPI, it could be argued that most appear to be exclusive to creative methodologies. These are inclusive by nature as multiple approaches can be taken to evoke ideas from different populations - approaches that do not necessarily rely on verbal or written communication like interviews and focus groups do. Given the anonymity provided by some creative methods, such as personas, people may be more likely to discuss their personal experiences under the guise of a general end-user, which might be more difficult to maintain when an interviewer is asking an individual questions directly. Additionally, creative methods are by nature more engaging and interactive than traditional methods, although this is a blanket statement and there may be people who find the question-and-answer/group discussion format more engaging. Creative methods have also been cited to eliminate power imbalances which exist in traditional research [ 12 , 13 , 17 , 19 , 23 ]. These imbalances exist between researchers and policy makers and adolescents, adults and the community. Lastly, although this may occur to a greater extent in creative methods like prototype development, it could be suggested that PPI in general – regardless of whether it is creative - is more time and cost efficient in the long-term than not using any PPI to guide or refine the research process. It must be noted that these are observations based on the literature. To be certain these differences exist between creative and traditional methods of PPI, direct empirical evaluation of both should be conducted.

To the best of our knowledge, this is the first review to identify the strengths and limitations to creative PPI, however, similar literature has identified barriers and facilitators to PPI in general. In the context of clinical trials, recruitment difficulties were cited as a barrier, as well as finding public contributors who were free during work/school hours. Trial managers reported finding group dynamics difficult to manage and the academic environment also made some public contributors feel nervous and lacking confidence to speak. Facilitators, however, included the shared ownership of the research – something that has been identified in the current review too. In addition, planning and the provision of knowledge, information and communication were also identified as facilitators [ 25 ]. Other research on the barriers to meaningful PPI in trial oversight committees included trialist confusion or scepticism over the PPI role and the difficulties in finding PPI members who had a basic understanding of research [ 26 ]. However, it could be argued that this is not representative of the average patient or public member. The formality of oversight meetings and the technical language used also acted as a barrier, which may imply that the informal nature of creative methods and its lack of dependency on literacy skills could overcome this. Further, a review of 42 reviews on PPI in health and social care identified financial compensation, resources, training and general support as necessary to conduct PPI, much like in the current review where the resource intensiveness of creative PPI was identified as a limitation. However, others were identified too, such as recruitment and representativeness of public contributors [ 27 ]. Like in the current review, power imbalances were also noted, however this was included as both a barrier and facilitator. Collaboration seemed to diminish hierarchies but not always, as sometimes these imbalances remained between public contributors and healthcare staff, described as a ‘them and us’ culture [ 27 ]. Although these studies compliment the findings of the current review, a direct comparison cannot be made as they do not concern creative methods. However, it does suggest that some strengths and weaknesses are shared between creative and traditional methods of PPI.

Strengths and limitations of this review

Although a general definition of creative PPI exists, it was up to our discretion to decide exactly which activities were deemed as such for this review. For example, we included sorting cards, the use of interactive whiteboards and sticky notes. Other researchers may have a more or less stringent criteria. However, two reviewers were involved in this decision which aids the reliability of the included articles. Further, it may be that some of the strengths and limitations cannot fully be attributed to the creative nature of the PPI process, but rather their co-created nature, however this is hard to disentangle as the included papers involved both these aspects.

During screening, it was difficult to decide whether the article was utilising creative qualitative methodology or creative PPI , as it was often not explicitly labelled as such. Regardless, both approaches involved the public/patients refining a healthcare product/service. This implies that if this review were to be replicated, others may do it differently. This may call for greater standardisation in the reporting of the public’s involvement in research. For example, the NIHR outlines different approaches to PPI, namely “consultation”, “collaboration”, “co-production” and “user-controlled”, which each signify an increased level of public power and influence [ 28 ]. Papers with elements of PPI could use these labels to clarify the extent of public involvement, or even explicitly state that there was no PPI. Further, given our decision to include only scholarly peer-reviewed literature, it is possible that data were missed within the grey literature. Similarly, the literature search will not have identified all papers relating to different types of accessible inclusion. However, the intent of the review was to focus solely on those within the definition of creative.

This review fills a gap in the literature and helps circulate and promote the concept of creative PPI. Each stage of this review, namely screening and quality appraisal, was conducted by two independent reviewers. However, four full texts could not be accessed during the full text reading stage, meaning there are missing data that could have altered or contributed to the findings of this review.

Research recommendations

Given that creative PPI can require effort to prepare, perform and analyse, sufficient time and funding should be allocated in the research protocol to enable meaningful and continuous PPI. This is worthwhile as PPI can significantly change the research output so that it aligns closely with the needs of the group it is to benefit. Researchers should also consider prototype development as a creative PPI activity as this might reduce future time/resource constraints. Shifting from a top-down approach within research to a bottom-up can be advantageous to all stakeholders and can help move creative PPI towards the mainstream. This, however, is the collective responsibility of funding bodies, universities and researchers, as well as committees who approve research bids.

A few of the included studies used creative techniques alongside traditional methods, such as interviews, which could also be used as a hybrid method of PPI, perhaps by researchers who are unfamiliar with creative techniques or to those who wish to reap the benefits of both. Often the characteristics of the PPI group were not included, including age, gender and ethnicity. It would be useful to include such information to assess how representative the PPI group is of the population of interest.

Creative PPI is a relatively novel approach of engaging the public and patients in research and it has both advantages and disadvantages compared to more traditional methods. There are many approaches to implementing creative PPI and the choice of technique will be unique to each piece of research and is reliant on several factors. These include the age and ability of the PPI group as well as the resource limitations of the project. Each method has benefits and drawbacks, which should be considered at the protocol-writing stage. However, given adequate funding, time and planning, creative PPI is a worthwhile and engaging method of generating ideas with end-users of research – ideas which may not be otherwise generated using traditional methods.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Critical Appraisal Skills Programme

The Joanna Briggs Institute

National Institute of Health and Care Research

Public Advisory Group

Public and Patient Involvement

Web of Science

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Acknowledgements

With thanks to the PHIRST-LIGHT public advisory group and consortium for their thoughts and contributions to the design of this work.

The research team is supported by a National Institute for Health and Care Research grant (PHIRST-LIGHT Reference NIHR 135190).

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Olivia R. Phillips and Cerian Harries share joint first authorship.

Authors and Affiliations

Nottingham Centre for Public Health and Epidemiology, Lifespan and Population Health, School of Medicine, University of Nottingham, Clinical Sciences Building, City Hospital Campus, Hucknall Road, Nottingham, NG5 1PB, UK

Olivia R. Phillips, Jo Leonardi-Bee, Holly Knight & Joanne R. Morling

National Institute for Health and Care Research (NIHR) PHIRST-LIGHT, Nottingham, UK

Olivia R. Phillips, Cerian Harries, Jo Leonardi-Bee, Holly Knight, Lauren B. Sherar, Veronica Varela-Mato & Joanne R. Morling

School of Sport, Exercise and Health Sciences, Loughborough University, Epinal Way, Loughborough, Leicestershire, LE11 3TU, UK

Cerian Harries, Lauren B. Sherar & Veronica Varela-Mato

Nottingham Centre for Evidence Based Healthcare, School of Medicine, University of Nottingham, Nottingham, UK

Jo Leonardi-Bee

NIHR Nottingham Biomedical Research Centre (BRC), Nottingham University Hospitals NHS Trust, University of Nottingham, Nottingham, NG7 2UH, UK

Joanne R. Morling

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Contributions

Author contributions: study design: ORP, CH, JRM, JLB, HK, LBS, VVM, literature searching and screening: ORP, CH, JRM, data curation: ORP, CH, analysis: ORP, CH, JRM, manuscript draft: ORP, CH, JRM, Plain English Summary: ORP, manuscript critical review and editing: ORP, CH, JRM, JLB, HK, LBS, VVM.

Corresponding author

Correspondence to Olivia R. Phillips .

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Additional file 1: Search strings: Description of data: the search strings and filters used in each of the 5 databases in this review

Additional file 2: Quality appraisal questions: Description of data: CASP quality appraisal questions

40900_2024_580_moesm3_esm.docx.

Additional file 3: Table 1: Description of data: elements of the data extraction table that are not in the main manuscript

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Phillips, O.R., Harries, C., Leonardi-Bee, J. et al. What are the strengths and limitations to utilising creative methods in public and patient involvement in health and social care research? A qualitative systematic review. Res Involv Engagem 10 , 48 (2024). https://doi.org/10.1186/s40900-024-00580-4

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Received : 28 November 2023

Accepted : 25 April 2024

Published : 13 May 2024

DOI : https://doi.org/10.1186/s40900-024-00580-4

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