descriptive coding in qualitative research

Qualitative Data Coding 101

How to code qualitative data, the smart way (with examples).

By: Jenna Crosley (PhD) | Reviewed by:Dr Eunice Rautenbach | December 2020

As we’ve discussed previously , qualitative research makes use of non-numerical data – for example, words, phrases or even images and video. To analyse this kind of data, the first dragon you’ll need to slay is  qualitative data coding  (or just “coding” if you want to sound cool). But what exactly is coding and how do you do it? 

Overview: Qualitative Data Coding

In this post, we’ll explain qualitative data coding in simple terms. Specifically, we’ll dig into:

• What exactly qualitative data coding is
• What different types of coding exist
• How to code qualitative data (the process)
• Moving from coding to qualitative analysis
• Tips and tricks for quality data coding

What is qualitative data coding?

Let’s start by understanding what a code is. At the simplest level,  a code is a label that describes the content  of a piece of text. For example, in the sentence:

“Pigeons attacked me and stole my sandwich.”

You could use “pigeons” as a code. This code simply describes that the sentence involves pigeons.

So, building onto this,  qualitative data coding is the process of creating and assigning codes to categorise data extracts.   You’ll then use these codes later down the road to derive themes and patterns for your qualitative analysis (for example, thematic analysis ). Coding and analysis can take place simultaneously, but it’s important to note that coding does not necessarily involve identifying themes (depending on which textbook you’re reading, of course). Instead, it generally refers to the process of  labelling and grouping similar types of data  to make generating themes and analysing the data more manageable. 

Makes sense? Great. But why should you bother with coding at all? Why not just look for themes from the outset? Well, coding is a way of making sure your  data is valid . In other words, it helps ensure that your  analysis is undertaken systematically  and that other researchers can review it (in the world of research, we call this transparency). In other words, good coding is the foundation of high-quality analysis.

What are the different types of coding?

Now that we’ve got a plain-language definition of coding on the table, the next step is to understand what overarching types of coding exist – in other words, coding approaches . Let’s start with the two main approaches, inductive and deductive .

With deductive coding, you, as the researcher, begin with a set of  pre-established codes  and apply them to your data set (for example, a set of interview transcripts). Inductive coding on the other hand, works in reverse, as you create the set of codes based on the data itself – in other words, the codes emerge from the data. Let’s take a closer look at both.

Deductive coding 101

With deductive coding, we make use of pre-established codes, which are developed before you interact with the present data. This usually involves drawing up a set of  codes based on a research question or previous research . You could also use a code set from the codebook of a previous study.

For example, if you were studying the eating habits of college students, you might have a research question along the lines of 

“What foods do college students eat the most?”

As a result of this research question, you might develop a code set that includes codes such as “sushi”, “pizza”, and “burgers”.  

Deductive coding allows you to approach your analysis with a very tightly focused lens and quickly identify relevant data . Of course, the downside is that you could miss out on some very valuable insights as a result of this tight, predetermined focus. 

Inductive coding 101 

But what about inductive coding? As we touched on earlier, this type of coding involves jumping right into the data and then developing the codes  based on what you find  within the data. 

For example, if you were to analyse a set of open-ended interviews , you wouldn’t necessarily know which direction the conversation would flow. If a conversation begins with a discussion of cats, it may go on to include other animals too, and so you’d add these codes as you progress with your analysis. Simply put, with inductive coding, you “go with the flow” of the data.

Inductive coding is great when you’re researching something that isn’t yet well understood because the coding derived from the data helps you explore the subject. Therefore, this type of coding is usually used when researchers want to investigate new ideas or concepts , or when they want to create new theories. 

A little bit of both… hybrid coding approaches

If you’ve got a set of codes you’ve derived from a research topic, literature review or a previous study (i.e. a deductive approach), but you still don’t have a rich enough set to capture the depth of your qualitative data, you can  combine deductive and inductive  methods – this is called a  hybrid  coding approach. 

To adopt a hybrid approach, you’ll begin your analysis with a set of a priori codes (deductive) and then add new codes (inductive) as you work your way through the data. Essentially, the hybrid coding approach provides the best of both worlds, which is why it’s pretty common to see this in research.

Need a helping hand?

How to code qualitative data

Now that we’ve looked at the main approaches to coding, the next question you’re probably asking is “how do I actually do it?”. Let’s take a look at the  coding process , step by step.

Both inductive and deductive methods of coding typically occur in two stages:  initial coding  and  line by line coding . 

In the initial coding stage, the objective is to get a general overview of the data by reading through and understanding it. If you’re using an inductive approach, this is also where you’ll develop an initial set of codes. Then, in the second stage (line by line coding), you’ll delve deeper into the data and (re)organise it according to (potentially new) codes. 

Step 1 – Initial coding

The first step of the coding process is to identify  the essence  of the text and code it accordingly. While there are various qualitative analysis software packages available, you can just as easily code textual data using Microsoft Word’s “comments” feature. 

Let’s take a look at a practical example of coding. Assume you had the following interview data from two interviewees:

What pets do you have?

I have an alpaca and three dogs.

Only one alpaca? They can die of loneliness if they don’t have a friend.

I didn’t know that! I’ll just have to get five more. 

I have twenty-three bunnies. I initially only had two, I’m not sure what happened. 

In the initial stage of coding, you could assign the code of “pets” or “animals”. These are just initial,  fairly broad codes  that you can (and will) develop and refine later. In the initial stage, broad, rough codes are fine – they’re just a starting point which you will build onto in the second stage. 

How to decide which codes to use

But how exactly do you decide what codes to use when there are many ways to read and interpret any given sentence? Well, there are a few different approaches you can adopt. The  main approaches  to initial coding include:

• In vivo coding 

Process coding

• Open coding

Descriptive coding

Structural coding.

• Value coding

Let’s take a look at each of these:

In vivo coding

When you use in vivo coding , you make use of a  participants’ own words , rather than your interpretation of the data. In other words, you use direct quotes from participants as your codes. By doing this, you’ll avoid trying to infer meaning, rather staying as close to the original phrases and words as possible. 

In vivo coding is particularly useful when your data are derived from participants who speak different languages or come from different cultures. In these cases, it’s often difficult to accurately infer meaning due to linguistic or cultural differences. 

For example, English speakers typically view the future as in front of them and the past as behind them. However, this isn’t the same in all cultures. Speakers of Aymara view the past as in front of them and the future as behind them. Why? Because the future is unknown, so it must be out of sight (or behind us). They know what happened in the past, so their perspective is that it’s positioned in front of them, where they can “see” it. 

In a scenario like this one, it’s not possible to derive the reason for viewing the past as in front and the future as behind without knowing the Aymara culture’s perception of time. Therefore, in vivo coding is particularly useful, as it avoids interpretation errors.

Next up, there’s process coding , which makes use of  action-based codes . Action-based codes are codes that indicate a movement or procedure. These actions are often indicated by gerunds (words ending in “-ing”) – for example, running, jumping or singing.

Process coding is useful as it allows you to code parts of data that aren’t necessarily spoken, but that are still imperative to understanding the meaning of the texts. 

An example here would be if a participant were to say something like, “I have no idea where she is”. A sentence like this can be interpreted in many different ways depending on the context and movements of the participant. The participant could shrug their shoulders, which would indicate that they genuinely don’t know where the girl is; however, they could also wink, showing that they do actually know where the girl is. 

Simply put, process coding is useful as it allows you to, in a concise manner, identify the main occurrences in a set of data and provide a dynamic account of events. For example, you may have action codes such as, “describing a panda”, “singing a song about bananas”, or “arguing with a relative”.

Descriptive coding aims to summarise extracts by using a  single word or noun  that encapsulates the general idea of the data. These words will typically describe the data in a highly condensed manner, which allows the researcher to quickly refer to the content. 

Descriptive coding is very useful when dealing with data that appear in forms other than traditional text – i.e. video clips, sound recordings or images. For example, a descriptive code could be “food” when coding a video clip that involves a group of people discussing what they ate throughout the day, or “cooking” when coding an image showing the steps of a recipe. 

Structural coding involves labelling and describing  specific structural attributes  of the data. Generally, it includes coding according to answers to the questions of “ who ”, “ what ”, “ where ”, and “ how ”, rather than the actual topics expressed in the data. This type of coding is useful when you want to access segments of data quickly, and it can help tremendously when you’re dealing with large data sets. 

For example, if you were coding a collection of theses or dissertations (which would be quite a large data set), structural coding could be useful as you could code according to different sections within each of these documents – i.e. according to the standard  dissertation structure . What-centric labels such as “hypothesis”, “literature review”, and “methodology” would help you to efficiently refer to sections and navigate without having to work through sections of data all over again. 

Structural coding is also useful for data from open-ended surveys. This data may initially be difficult to code as they lack the set structure of other forms of data (such as an interview with a strict set of questions to be answered). In this case, it would useful to code sections of data that answer certain questions such as “who?”, “what?”, “where?” and “how?”.

Let’s take a look at a practical example. If we were to send out a survey asking people about their dogs, we may end up with a (highly condensed) response such as the following: 

Bella is my best friend. When I’m at home I like to sit on the floor with her and roll her ball across the carpet for her to fetch and bring back to me. I love my dog.

In this set, we could code  Bella  as “who”,  dog  as “what”,  home  and  floor  as “where”, and  roll her ball  as “how”. 

Values coding

Finally, values coding involves coding that relates to the  participant’s worldviews . Typically, this type of coding focuses on excerpts that reflect the values, attitudes, and beliefs of the participants. Values coding is therefore very useful for research exploring cultural values and intrapersonal and experiences and actions.   

To recap, the aim of initial coding is to understand and  familiarise yourself with your data , to  develop an initial code set  (if you’re taking an inductive approach) and to take the first shot at  coding your data . The coding approaches above allow you to arrange your data so that it’s easier to navigate during the next stage, line by line coding (we’ll get to this soon). 

While these approaches can all be used individually, it’s important to remember that it’s possible, and potentially beneficial, to  combine them . For example, when conducting initial coding with interviews, you could begin by using structural coding to indicate who speaks when. Then, as a next step, you could apply descriptive coding so that you can navigate to, and between, conversation topics easily. You can check out some examples of various techniques here .

Step 2 – Line by line coding

Once you’ve got an overall idea of our data, are comfortable navigating it and have applied some initial codes, you can move on to line by line coding. Line by line coding is pretty much exactly what it sounds like – reviewing your data, line by line,  digging deeper  and assigning additional codes to each line. 

With line-by-line coding, the objective is to pay close attention to your data to  add detail  to your codes. For example, if you have a discussion of beverages and you previously just coded this as “beverages”, you could now go deeper and code more specifically, such as “coffee”, “tea”, and “orange juice”. The aim here is to scratch below the surface. This is the time to get detailed and specific so as to capture as much richness from the data as possible. 

In the line-by-line coding process, it’s useful to  code everything  in your data, even if you don’t think you’re going to use it (you may just end up needing it!). As you go through this process, your coding will become more thorough and detailed, and you’ll have a much better understanding of your data as a result of this, which will be incredibly valuable in the analysis phase.

Moving from coding to analysis

Once you’ve completed your initial coding and line by line coding, the next step is to  start your analysis . Of course, the coding process itself will get you in “analysis mode” and you’ll probably already have some insights and ideas as a result of it, so you should always keep notes of your thoughts as you work through the coding.  

When it comes to qualitative data analysis, there are  many different types of analyses  (we discuss some of the  most popular ones here ) and the type of analysis you adopt will depend heavily on your research aims, objectives and questions . Therefore, we’re not going to go down that rabbit hole here, but we’ll cover the important first steps that build the bridge from qualitative data coding to qualitative analysis.

When starting to think about your analysis, it’s useful to  ask yourself  the following questions to get the wheels turning:

• What actions are shown in the data? 
• What are the aims of these interactions and excerpts? What are the participants potentially trying to achieve?
• How do participants interpret what is happening, and how do they speak about it? What does their language reveal?
• What are the assumptions made by the participants? 
• What are the participants doing? What is going on? 
• Why do I want to learn about this? What am I trying to find out? 
• Why did I include this particular excerpt? What does it represent and how?

Code categorisation

Categorisation is simply the process of reviewing everything you’ve coded and then  creating code categories  that can be used to guide your future analysis. In other words, it’s about creating categories for your code set. Let’s take a look at a practical example.

If you were discussing different types of animals, your initial codes may be “dogs”, “llamas”, and “lions”. In the process of categorisation, you could label (categorise) these three animals as “mammals”, whereas you could categorise “flies”, “crickets”, and “beetles” as “insects”. By creating these code categories, you will be making your data more organised, as well as enriching it so that you can see new connections between different groups of codes. 

Theme identification

From the coding and categorisation processes, you’ll naturally start noticing themes. Therefore, the logical next step is to  identify and clearly articulate the themes  in your data set. When you determine themes, you’ll take what you’ve learned from the coding and categorisation and group it all together to develop themes. This is the part of the coding process where you’ll try to draw meaning from your data, and start to  produce a narrative . The nature of this narrative depends on your research aims and objectives, as well as your research questions (sounds familiar?) and the  qualitative data analysis method  you’ve chosen, so keep these factors front of mind as you scan for themes. 

Tips & tricks for quality coding

Before we wrap up, let’s quickly look at some general advice, tips and suggestions to ensure your qualitative data coding is top-notch.

• Before you begin coding,  plan out the steps  you will take and the coding approach and technique(s) you will follow to avoid inconsistencies. 
• When adopting deductive coding, it’s useful to  use a codebook  from the start of the coding process. This will keep your work organised and will ensure that you don’t forget any of your codes. 
• Whether you’re adopting an inductive or deductive approach,  keep track of the meanings  of your codes and remember to revisit these as you go along.
• Avoid using synonyms  for codes that are similar, if not the same. This will allow you to have a more uniform and accurate coded dataset and will also help you to not get overwhelmed by your data.
• While coding, make sure that you  remind yourself of your aims  and coding method. This will help you to  avoid  directional drift , which happens when coding is not kept consistent. 
• If you are working in a team, make sure that everyone has  been trained and understands  how codes need to be assigned. 

32 Comments

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Qualitative Data Analysis: Coding

• Atlas.ti web
• R for text analysis
• Microsoft Excel & spreadsheets
• Other options
• Planning Qual Data Analysis
• Free Tools for QDA
• QDA with NVivo
• QDA with Atlas.ti
• QDA with MAXQDA
• PKM for QDA
• QDA with Quirkos
• Working Collaboratively
• Qualitative Methods Texts
• Transcription
• Data organization
• Example Publications

Coding Qualitative Data

Planning your coding strategy.

Coding is a qualitative data analysis strategy in which some aspect of the data is assigned a descriptive label that allows the researcher to identify related content across the data. How you decide to code - or whether to code- your data should be driven by your methodology. But there are rarely step-by-step descriptions, and you'll have to make many decisions about how to code for your own project.

Some questions to consider as you decide how to code your data:

What will you code? 

What aspects of your data will you code? If you are not coding all of your available data, how will you decide which elements need to be coded? If you have recordings interviews or focus groups, or other types of multimedia data, will you create transcripts to analyze and code? Or will you code the media itself (see Farley, Duppong & Aitken, 2020 on direct coding of audio recordings rather than transcripts). 

Where will your codes come from? 

Depending on your methodology, your coding scheme may come from previous research and be applied to your data (deductive). Or you my try to develop codes entirely from the data, ignoring as much as possible, previous knowledge of the topic under study, to develop a scheme grounded in your data (inductive). In practice, however, many practices will fall between these two approaches. 

How will you apply your codes to your data? 

You may decide to use software to code your qualitative data, to re-purpose other software tools (e.g. Word or spreadsheet software) or work primarily with physical versions of your data. Qualitative software is not strictly necessary, though it does offer some advantages, like: 

• Codes can be easily re-labeled, merged, or split. You can also choose to apply multiple coding schemes to the same data, which means you can explore multiple ways of understanding the same data. Your analysis, then, is not limited by how often you are able to work with physical data, such as paper transcripts. 
• Most software programs for QDA include the ability to export and import coding schemes. This means you can create a re-use a coding scheme from a previous study, or that was developed in outside of the software, without having to manually create each code. 
• Some software for QDA includes the ability to directly code image, video, and audio files. This may mean saving time over creating transcripts. Or, your coding may be enhanced by access to the richness of mediated content, compared to transcripts.
• Using QDA software may also allow you the ability to use auto-coding functions. You may be able to automatically code all of the statements by speaker in a focus group transcript, for example, or identify and code all of the paragraphs that include a specific phrase. 

What will be coded? 

Will you deploy a line-by-line coding approach, with smaller codes eventually condensed into larger categories or concepts? Or will you start with codes applied to larger segments of the text, perhaps later reviewing the examples to explore and re-code for differences between the segments? 

How will you explain the coding process? 

• Regardless of how you approach coding, the process should be clearly communicated when you report your research, though this is not always the case (Deterding & Waters, 2021).
• Carefully consider the use of phrases like "themes emerged." This phrasing implies that the themes lay passively in the data, waiting for the researcher to pluck them out. This description leaves little room for describing how the researcher "saw" the themes and decided which were relevant to the study. Ryan and Bernard (2003) offer a terrific guide to ways that you might identify themes in the data, using both your own observations as well as manipulations of the data. 

How will you report the results of your coding process? 

How you report your coding process should align with the methodology you've chosen. Your methodology may call for careful and consistent application of a coding scheme, with reports of inter-rater reliability and counts of how often a code appears within the data. Or you may use the codes to help develop a rich description of an experience, without needing to indicate precisely how often the code was applied. 

How will you code collaboratively?

If you are working with another researcher or a team, your coding process requires careful planning and implementation. You will likely need to have regular conversations about your process, particularly if your goal is to develop and consistently apply a coding scheme across your data. 

Coding Features in QDA Software Programs

• Atlas.ti (Mac)
• Atlas.ti (Windows)
• NVivo (Windows)
• NVivo (Mac)
• Coding data See how to create and manage codes and apply codes to segments of the data (known as quotations in Atlas.ti).

• Search and Code Using the search and code feature lets you locate and automatically code data through text search, regular expressions, Named Entity Recognition, and Sentiment Analysis.
• Focus Group Coding Properly prepared focus group documents can be automatically coded by speaker.
• Inter-Coder Agreement Coded text, audio, and video documents can be tested for inter-coder agreement. ICA is not available for images or PDF documents.
• Quotation Reader Once you've coded data, you can view just the data that has been assigned that code.

• Find Redundant Codings (Mac) This tool identifies "overlapping or embedded" quotations that have the same code, that are the result of manual coding or errors when merging project files.
• Coding Data in Atlas.ti (Windows) Demonstrates how to create new codes, manage codes and applying codes to segments of the data (known as quotations in Atlas.ti)
• Search and Code in Atlas.ti (Windows) You can use a text search, regular expressions, Named Entity Recognition, and Sentiment Analysis to identify and automatically code data in Atlas.ti.
• Focus Group Coding in Atlas.ti (Windows) Properly prepared focus group transcripts can be automatically coded by speaker.
• Inter-coder Agreement in Atlas.ti (Windows) Coded text, audio, and video documents can be tested for inter-coder agreement. ICA is not available for images or PDF documents.
• Quotation Reader in Atlas.ti (Windows) Once you've coded data, you can view and export the quotations that have been assigned that code.
• Find Redundant Codings in Atlas.ti (Windows) This tool identifies "overlapping or embedded" quotations that have the same code, that are the result of manual coding or errors when merging project files.
• Coding in NVivo (Windows) This page includes an overview of the coding features in NVivo.
• Automatic Coding in Documents in NVivo (Windows) You can use paragraph formatting styles or speaker names to automatically format documents.
• Coding Comparison Query in NVivo (Windows) You can use the coding comparison feature to compare how different users have coded data in NVivo.
• Review the References in a Node in NVivo (Windows) References are the term that NVivo uses for coded segments of the data. This shows you how to view references related to a code (or any node)
• Text Search Queries in NVivo (Windows) Text queries let you search for specific text in your data. The results of your query can be saved as a node (a form of auto coding).
• Coding Query in NVivo (Windows) Use a coding query to display references from your data for a single code or multiples of codes.
• Code Files and Manage Codes in NVivo (Mac) This page offers an overview of coding features in NVivo. Note that NVivo uses the concept of a node to refer to any structure around which you organize your data. Codes are a type of node, but you may see these terms used interchangeably.
• Automatic Coding in Datasets in NVivo (Mac) A dataset in NVivo is data that is in rows and columns, as in a spreadsheet. If a column is set to be codable, you can also automatically code the data. This approach could be used for coding open-ended survey data.
• Text Search Query in NVivo (Mac) Use the text search query to identify relevant text in your data and automatically code references by saving as a node.
• Review the References in a Node in NVivo (Mac) NVivo uses the term references to refer to data that has been assigned to a code or any node. You can use the reference view to see the data linked to a specific node or combination of nodes.
• Coding Comparison Query in NVivo (Mac) Use the coding comparison query to calculate a measure of inter-rater reliability when you've worked with multiple coders.

The MAXQDA interface is the same across Mac and Windows devices. 

• The "Code System" in MAXQDA This section of the manual shows how to create and manage codes in MAXQDA's code system.
• How to Code with MAXQDA

• Display Coded Segments in the Document Browser Once you've coded a document within MAXQDA, you can choose which of those codings will appear on the document, as well as choose whether or not the text is highlighted in the color linked to the code.
• Creative Coding in MAXQDA Use the creative coding feature to explore the relationships between codes in your system. If you develop a new structure to you codes that you like, you can apply the changes to your overall code scheme.
• Text Search in MAXQDA Use a Text Search to identify data that matches your search terms and automatically code the results. You can choose whether to code only the matching results, the sentence the results are in, or the paragraph the results appear in.
• Segment Retrieval in MAXQDA Data that has been coded is considered a segment. Segment retrieval is how you display the segments that match a code or combination of codes. You can use the activation feature to show only the segments from a document group, or that match a document variable.
• Intercorder Agreement in MAXQDA MAXQDA includes the ability to compare coding between two coders on a single project.
• Create Tags in Taguette Taguette uses the term tag to refer to codes. You can create single tags as well as a tag hierarchy using punctuation marks.
• Highlighting in Taguette Select text with a document (a highlight) and apply tags to code data in Taguette.

Useful Resources on Coding

Deterding, N. M., & Waters, M. C. (2021). Flexible coding of in-depth interviews: A twenty-first-century approach. Sociological Methods & Research , 50 (2), 708–739. https://doi.org/10.1177/0049124118799377

Farley, J., Duppong Hurley, K., & Aitken, A. A. (2020). Monitoring implementation in program evaluation with direct audio coding. Evaluation and Program Planning , 83 , 101854. https://doi.org/10.1016/j.evalprogplan.2020.101854

Ryan, G. W., & Bernard, H. R. (2003). Techniques to identify themes. Field Methods , 15 (1), 85–109. https://doi.org/10.1177/1525822X02239569. 

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• v.25(5); 2020 Aug

An overview of the qualitative descriptive design within nursing research

Louise doyle.

Associate Professor in Mental Health Nursing, School of Nursing and Midwifery, Trinity College Dublin, Ireland

Catherine McCabe

Associate Professor in General Nursing, School of Nursing and Midwifery, Trinity College Dublin, Ireland

Brian Keogh

Assistant Professor in Mental Health Nursing, School of Nursing and Midwifery, Trinity College Dublin, Ireland

Annemarie Brady

Chair of Nursing and Chronic Illness, School of Nursing and Midwifery, Trinity College Dublin, Ireland

Qualitative descriptive designs are common in nursing and healthcare research due to their inherent simplicity, flexibility and utility in diverse healthcare contexts. However, the application of descriptive research is sometimes critiqued in terms of scientific rigor. Inconsistency in decision making within the research process coupled with a lack of transparency has created issues of credibility for this type of approach. It can be difficult to clearly differentiate what constitutes a descriptive research design from the range of other methodologies at the disposal of qualitative researchers.

This paper provides an overview of qualitative descriptive research, orientates to the underlying philosophical perspectives and key characteristics that define this approach and identifies the implications for healthcare practice and policy.

Methods and results

Using real-world examples from healthcare research, the paper provides insight to the practical application of descriptive research at all stages of the design process and identifies the critical elements that should be explicit when applying this approach.

Conclusions

By adding to the existing knowledge base, this paper enhances the information available to researchers who wish to use the qualitative descriptive approach, influencing the standard of how this approach is employed in healthcare research.

Introduction

Qualitative descriptive approaches to nursing and healthcare research provide a broad insight into particular phenomena and can be used in a variety of ways including as a standalone research design, as a precursor to larger qualitative studies and commonly as the qualitative component in mixed-methods studies. Despite the widespread use of descriptive approaches within nursing research, there is limited methodological guidance about this type of design in research texts or papers. The lack of adequate representation in research texts has at times resulted in novice researchers using other more complex qualitative designs including grounded theory or phenomenology without meeting the requirements of these approaches ( Lambert and Lambert, 2012 ), or having an appropriate rationale for use of these approaches. This suggests there is a need to have more discussion about how and why descriptive approaches to qualitative research are used. This serves to not only provide information and guidance for researchers, but to ensure acceptable standards in how this approach is applied in healthcare research.

Rationale for qualitative descriptive research

The selection of an appropriate approach to answer research questions is one of the most important stages of the research process; consequently, there is a requirement that researchers can clearly articulate and defend their selection. Those who wish to undertake qualitative research have a range of approaches available to them including grounded theory, phenomenology and ethnography. However, these designs may not be the most suitable for studies that do not require a deeply theoretical context and aim to stay close to and describe participants’ experiences. The most frequently proposed rationale for the use of a descriptive approach to is to provide straightforward descriptions of experiences and perceptions ( Sandelowski, 2010 ), particularly in areas where little is known about the topic under investigation. A qualitative descriptive design may be deemed most appropriate as it recognises the subjective nature of the problem, the different experiences participants have and will present the findings in a way that directly reflects or closely resembles the terminology used in the initial research question ( Bradshaw et al., 2017 ). This is particularly relevant in nursing and healthcare research, which is commonly concerned with how patients experience illness and associated healthcare interventions. The utilisation of a qualitative descriptive approach is often encouraged in Master’s level nurse education programmes as it enables novice clinical nurse researchers explore important healthcare questions that have direct implications and impact for their specific healthcare setting (Colorafi and Evans, 2016). As a Master’s level project is often the first piece of primary research undertaken by nurses, the use of a qualitative descriptive design provides an excellent method to address important clinical issues where the focus is not on increasing theoretical or conceptual understanding, but rather contributing to change and quality improvement in the practice setting ( Chafe, 2017 ).

This design is also frequently used within mixed-methods studies where qualitative data can explain quantitative findings in explanatory studies, be used for questionnaire development in exploratory studies and validate and corroborate findings in convergent studies ( Doyle et al., 2016 ). There has also been an increase in the use of qualitative descriptive research embedded in large-scale healthcare intervention studies, which can serve a number of purposes including identifying participants’ perceptions of why an intervention worked or, just as importantly, did not work and how the intervention might be improved ( Doyle et al., 2016 ). Using qualitative descriptive research in this manner can help to make the findings of intervention studies more clinically meaningful.

Philosophical and theoretical influences

Qualitative descriptive research generates data that describe the ‘who, what, and where of events or experiences’ from a subjective perspective ( Kim et al., 2017 , p. 23). From a philosophical perspective, this approach to research is best aligned with constructionism and critical theories that use interpretative and naturalistic methods ( Lincoln et al., 2017 ). These philosophical perspectives represent the view that reality exists within various contexts that are dynamic and perceived differently depending on the subject, therefore, reality is multiple and subjective ( Lincoln et al., 2017 ). In qualitative descriptive research, this translates into researchers being concerned with understanding the individual human experience in its unique context. This type of inquiry requires flexible research processes that are inductive and dynamic but do not transform the data beyond recognition from the phenomenon being studied ( Ormston et al., 2014 ; Sandelwoski 2010). Descriptive qualitative research has also been aligned with pragmatism ( Neergaard et al., 2009 ) where decisions are made about how the research should be conducted based on the aims or objectives and context of the study ( Ormston et al., 2014 ). The pragmatist researcher is not aligned to one particular view of knowledge generation or one particular methodology. Instead they look to the concepts or phenomena being studied to guide decision making in the research process, facilitating the selection of the most appropriate methods to answer the research question ( Bishop, 2015 ).

Perhaps linked to the practical application of pragmatism to research, that is, applying the best methods to answer the research question, is the classification of qualitative descriptive research by Sandelowski ( 2010 , p. 82) into a ‘distributed residual category’. This recognises and incorporates uncertainty about the phenomena being studied and the research methods used to study them. For researchers, it permits the use of one or more different types of inquiry, which is essential when acknowledging and exploring different realities and subjective experiences in relation to phenomena ( Long et al., 2018 ). Clarity, in terms of the rationale for the phenomenon being studied and the methods used by the researcher, emerges from the qualitative descriptive approach because the data gathered continue to remain close to the phenomenon throughout the study ( Sandelowski, 2010 ). For this to happen a flexible approach is required and this is evident in the practice of ‘borrowing’ elements of other qualitative methodologies such as grounded theory, phenomenology and ethnography ( Vaismoradi et al., 2013 ).

Regarded as a positive aspect by many researchers who are interested in studying human nature and phenomenon, others believe this flexibility leads to inconsistency across studies and in some cases complacency by researchers. This can result in vague or unexplained decision making around the research process and subsequent lack of credibility. Accordingly, nurse researchers need to be reflexive, that is, clear about their role and position in terms of the phenomena being studied, the context, the theoretical framework and all decision-making processes used in a qualitative descriptive study. This adds credibility to both the study and qualitative descriptive research.

Methods in qualitative descriptive research

As with any research study, the application of descriptive methods will emerge in response to the aims and objectives, which will influence the sampling, data collection and analysis phases of the study.

Most qualitative research aligns itself with non-probability sampling and descriptive research is no different. Descriptive research generally uses purposive sampling and a range of purposive sampling techniques have been described ( Palinkas et al., 2015 ). Many researchers use a combination of approaches such as convenience, opportunistic or snowball sampling as part of the sampling framework, which is determined by the desired sample and the phenomena being studied.

Purposive sampling refers to selecting research participants that can speak to the research aims and who have knowledge and experience of the phenomenon under scrutiny ( Ritchie et al., 2014 ). When purposive sampling is used in a study it delimits and narrows the study population; however, researchers need to remember that other characteristics of the sample will also affect the population, such as the location of the researcher and their flexibility to recruit participants from beyond their base. In addition, the heterogeneity of the population will need to be considered and how this might influence sampling and subsequent data collection and analysis ( Palinkas et al ., 2015 ). Take, for example, conducting research on the experience of caring for people with Alzheimer’s disease (AD). For the most part AD is a condition that affects older people and experiences of participants caring for older people will ultimately dominate the sample. However, AD also affects younger people and how this will impact on sampling needs to be considered before recruitment as both groups will have very different experiences, although there will be overlap. Teddlie and Fu (2007) suggest that although some purposive sampling techniques generate representative cases, most result in describing contrasting cases, which they argue are at the heart of qualitative analysis. To achieve this, Sandelowski (2010) suggests that maximum variation sampling is particularly useful in qualitative descriptive research, which may acknowledge the range of experiences that exist especially in healthcare research. Palinkas et al . (2015) describe maximum variation sampling as identifying shared patterns that emerge from heterogeneity. In other words, researchers attempt to include a wide range of participants and experiences when collecting data. This may be more difficult to achieve in areas where little is known about the substantive area and may depend on the researcher’s knowledge and immersion within the subject area.

Sample size will also need to be considered and although small sample sizes are common in qualitative descriptive research, researchers need to be careful they have enough data collected to meet the study aims ( Ritchie et al., 2014 ). Pre-determining the sample size prior to data collection may stifle the analytic process, resulting in too much or too little data. Traditionally, the gold standard for sample size in qualitative research is data saturation, which differs depending on the research design and the size of the population ( Fusch and Ness, 2015 ). Data saturation is reached ‘when there is enough information to replicate the study, when the ability to obtain additional new information has been attained, and when further coding is no longer feasible’ ( Fusch and Ness, 2015 , p. 1408). However, some argue that although saturation is often reported, it is rarely demonstrated in qualitative descriptive research reports ( Caelli et al., 2003 ; Malterud et al., 2016 ). If data saturation is used to determine sample size, it is suggested that greater emphasis be placed on demonstrating how saturation was reached and at what level to provide more credibility to sample sizes ( Caelli et al., 2003 ). Sample size calculation should be an estimate until saturation has been achieved through the concurrent processes of data collection and analysis. Where saturation has not been achieved, or where sample size has been predetermined for resource reasons, this should be clearly acknowledged. However, there is also a movement away from the reliance on data saturation as a measure of sample size in qualitative research ( Malterud et al., 2016 ). O’Reilly and Parker (2012) question the appropriateness of the rigid application of saturation as a sample size measure arguing that outside of Grounded Theory, its use is inconsistent and at times questionable. Malterud et al. (2016) focus instead on the concept of ‘information power’ to determine sample size. Here, they suggest sample size is determined by the amount of information the sample holds relevant to the actual study rather than the number of participants ( Malterud et al., 2016 ). Some guidance on specific sample size depending on research design has been provided in the literature; however, these are sometimes conflicting and in some cases lack evidence to support their claims ( Guest et al., 2006 ). This is further complicated by the range of qualitative designs and data collection approaches available.

Data collection

Data collection methods in qualitative descriptive research are diverse and aim to discover the who, what and where of phenomena ( Sandelowski, 2000 ). Although semi-structured individual face-to-face interviews are the most commonly used data collection approaches ( Kim et al ., 2017 ), focus groups, telephone interviews and online approaches are also used.

Focus groups involve people with similar characteristics coming together in a relaxed and permissive environment to share their thoughts, experiences and insights ( Krueger and Casey, 2009 ). Participants share their own views and experiences, but also listen to and reflect on the experiences of other group members. It is this synergistic process of interacting with other group members that refines individuals’ viewpoints to a deeper and more considered level and produces data and insights that would not be accessible without the interaction found in a group (Finch et al., 2014). Telephone interviews and online approaches are gaining more traction as they offer greater flexibility and reduced costs for researchers and ease of access for participants. In addition, they may help to achieve maximum variation sampling or examine experiences from a national or international perspective. Face-to-face interviews are often perceived as more appropriate than telephone interviews; however, this assumption has been challenged as evidence to support the use of telephone interviews emerges ( Ward et al., 2015 ). Online data collection also offers the opportunity to collect synchronous and asynchronous data using instant messaging and other online media ( Hooley et al., 2011 ). Online interviews or focus groups conducted via Skype or other media may overcome some of the limitations of telephone interviews, although observation of non-verbal communication may be more difficult to achieve ( Janghorban et al., 2014 ). Open-ended free-text responses in surveys have also been identified as useful data sources in qualitative descriptive studies ( Kim et al . , 2017 ) and in particular the use of online open-ended questions, which can have a large geographical reach ( Seixas et al., 2018 ). Observation is also cited as an approach to data collection in qualitative descriptive research ( Sandelowski, 2000 ; Lambert and Lambert, 2012 ); however, in a systematic review examining the characteristics of qualitative research studies, observation was cited as an additional source of data and was not used as a primary source of data collection ( Kim et al. , 2017 ).

Data analysis and interpretation

According to Lambert and Lambert (2012) , data analysis in qualitative descriptive research is data driven and does not use an approach that has emerged from a pre-existing philosophical or epistemological perspective. Within qualitative descriptive research, it is important analysis is kept at a level at which those to whom the research pertains are easily able to understand and so can use the findings in healthcare practice ( Chafe, 2017 ). The approach to analysis is dictated by the aims of the research and as qualitative descriptive research is generally explorative, inductive approaches will commonly need to be applied although deductive approaches can also be used ( Kim et al . , 2017 ).

Content and thematic analyses are the most commonly used data analysis techniques in qualitative descriptive research. Vaismoradi et al . (2013) argue that content and thematic analysis, although poorly understood and unevenly applied, offer legitimate ways of a lower level of interpretation that is often required in qualitative descriptive research. Sandelowski (2000) indicated that qualitative content analysis is the approach of choice in descriptive research; however, confusion exists between content and thematic analysis, which sometimes means researchers use a combination of the two. Vaismoradi et al. (2013) argue there are differences between the two and that content analysis allows the researchers to analyse the data qualitatively as well as being able to quantify the data whereas thematic analysis provides a purely qualitative account of the data that is richer and more detailed. Decisions to use one over the other will depend on the aims of the study, which will dictate the depth of analysis required. Although there is a range of analysis guidelines available, they share some characteristics and an overview of these, derived from some key texts ( Sandleowski, 2010 ; Braun and Clark, 2006 ; Newell and Burnard, 2006), is presented in Table 1 . Central to these guidelines is an attempt by the researcher to immerse themselves in the data and the ability to demonstrate a consistent and systematic approach to the analysis.

Common characteristics of descriptive qualitative analysis.

Coding in qualitative descriptive research can be inductive and emerge from the data, or a priori where they are based on a pre-determined template as in template analysis. Inductive codes can be ‘in vivo’ where the researcher uses the words or concepts as stated by the participants ( Howitt, 2019 ), or can be named by the researcher and grouped together to form emerging themes or categories through an iterative systematic process until the final themes emerge. Template analysis involves designing a coding template, which is designed inductively from a subset of the data and then applied to all the data and refined as appropriate ( King, 2012 ). It offers a standardised approach that may be useful when several researchers are involved in the analysis process.

Within qualitative research studies generally, the analysis of data and subsequent presentation of research findings can range from studies with a relatively minimal amount of interpretation to those with high levels of interpretation ( Sandelowski and Barroso, 2003 ). The degree of interpretation required in qualitative descriptive research is contentious. Sandelowski (2010) argues that although descriptive research produces findings that are ‘data-near’, they are nevertheless interpretative. Sandelowski (2010) reports that a common misconception in qualitative descriptive designs is that researchers do not need to include any level of analysis and interpretation and can rely solely on indiscriminately selecting direct quotations from participants to answer the research question(s). Although it is important to ensure those familiar with the topic under investigation can recognise their experiences in the description of it ( Kim et al . , 2017 ), this is not to say that there should be no transformation of data. Researchers using a qualitative descriptive design need to, through data analysis, move from un-interpreted participant quotations to interpreted research findings, which can still remain ‘data-near’ ( Sandeklwoski, 2010 ). Willis et al. (2016) suggest that researchers using the qualitative descriptive method might report a comprehensive thematic summary as findings, which moves beyond individual participant reports by developing an interpretation of a common theme. The extent of description and/or interpretation in a qualitative descriptive study is ultimately determined by the focus of the study (Neergard et al ., 2009).

As with any research design, ensuring the rigor or trustworthiness of findings from a qualitative descriptive study is crucial. For a more detailed consideration of the quality criteria in qualitative studies, readers are referred to the seminal work of Lincoln and Guba (1985) in which the four key criteria of credibility, dependability, confirmability and transferability are discussed. At the very least, researchers need to be clear about the methodological decisions taken during the study so readers can judge the trustworthiness of the study and ultimately the findings ( Hallberg, 2013 ). Being aware of personal assumptions and the role they play in the research process is also an important quality criterion (Colorafi and Evans, 2016) and these assumptions can be made explicit through the use of researcher reflexivity in the study ( Bradshaw et al., 2017 ).

Challenges in using a qualitative descriptive design

One of the challenges of utilising a qualitative descriptive design is responding to the charge that many qualitative designs have historically encountered, which is that qualitative designs lack the scientific rigor associated with quantitative approaches ( Vaismoradi et al . , 2013 ). The descriptive design faces further critique in this regard as, unlike other qualitative approaches such as phenomenology or grounded theory, it is not theory driven or oriented ( Neergaard et al ., 2009 ). However, it is suggested that this perceived limitation of qualitative descriptive research only holds true if it is used for the wrong purposes and not primarily for describing the phenomenon ( Neergaard et al ., 2009 ). Kahlke (2014) argues that rather than being atheoretical, qualitative descriptive approaches require researchers to consider to what extent theory will inform the study and are sufficiently flexible to leave space for researchers to utilise theoretical frameworks that are relevant and inform individual research studies. Kim et al. (2017) reported that most descriptive studies reviewed did not identify a theoretical or philosophical framework, but those that did used it to inform the development of either the interview guide or the data analysis framework, thereby identifying the potential use of theory in descriptive designs.

Another challenge around the use of qualitative descriptive research is that it can erroneously be seen as a ‘quick fix’ for researchers who want to employ qualitative methods, but perhaps lack the expertise or familiarity with qualitative research ( Sandelowski, 2010 ). Kim et al. (2017) report how in their review fewer than half of qualitative descriptive papers explicitly identified a rationale for choosing this design, suggesting that in some cases the rationale behind its use was ill considered. Providing a justification for choosing a particular research design is an important part of the research process and, in the case of qualitative descriptive research, a clear justification can offset concerns that a descriptive design was an expedient rather than a measured choice. For studies exploring participants’ experiences, which could be addressed using other qualitative designs, it also helps to clearly make a distinction as to why a descriptive design was the best choice for the research study ( Kim et al ., 2017 ). Similarly, there is a perception that the data analysis techniques most commonly associated with descriptive research – thematic and content analysis are the ‘easiest’ approaches to qualitative analysis; however, as Vaismoradi et al . (2013) suggest, this does not mean they produce low-quality research findings.

As previously identified, a further challenge with the use of qualitative descriptive methods is that as a research design it has limited visibility in research texts and methodological papers ( Kim et al ., 2017 ). This means that novice qualitative researchers have little guidance on how to design and implement a descriptive study as there is a lack of a ‘methodological rulebook’ to guide researchers ( Kahlke, 2014 ). It is also suggested that this lack of strict boundaries and rules around qualitative descriptive research also offers researchers flexibility to design a study using a variety of data collection and analysis approaches that best answer the research question ( Kahlke, 2014 ; Kim et al . , 2017 ). However, should researchers choose to integrate methods ‘borrowed’ from other qualitative designs such as phenomenology or grounded theory, they should do so with the caveat that they do not claim they are using designs they are not actually using ( Neergaard et al . , 2009 ).

Examples of the use of qualitative descriptive research in healthcare

Findings from qualitative descriptive studies within healthcare have the potential to describe the experiences of patients, families and health providers, inform the development of health interventions and policy and promote health and quality of life ( Neergaard et al ., 2009 ; Willis et al ., 2016 ). The examples provided here demonstrate different ways qualitative descriptive methods can be used in a range of healthcare settings.

Simon et al. (2015) used a qualitative descriptive design to identify the perspectives of seriously ill, older patients and their families on the barriers and facilitators to advance care planning. The authors provided a rationale for using a descriptive design, which was to gain a deeper understanding of the phenomenon under investigation. Data were gathered through nine open-ended questions on a researcher-administered questionnaire. Responses to all questions were recorded verbatim and transcribed. Using descriptive, interpretative and explanatory coding that transformed raw data recorded from 278 patients and 225 family members to more abstract ideas and concepts ( Simon et al. , 2015 ), a deeper understanding of the barriers and facilitators to advance care planning was developed. Three categories were developed that identified personal beliefs, access to doctors and interaction with doctors as the central barriers and facilitators to advance care planning. The use of a qualitative descriptive design facilitated the development of a schematic based on these three themes, which provides a framework for use by clinicians to guide improvement in advance care planning.

Focus group interviews are a common data collection method in qualitative descriptive studies and were the method of choice in a study by Pelentsov et al. (2015), which sought to identify the supportive care needs of parents whose child has a rare disease. The rationale provided for using a qualitative descriptive design was to obtain a ‘straight description of the phenomena’ and to provide analysis and interpretation of the findings that remained data-near and representative of the responses of participants. In this study, four semi-structured focus group interviews were conducted with 23 parents. The data from these focus groups were then subjected to a form of thematic analysis during which emerging theories and inferences were identified and organised into a series of thematic networks and ultimately into three global themes. These themes identified that a number of factors including social isolation and lack of knowledge on behalf of healthcare professionals significantly affected how supported parents felt. Identifying key areas of the supportive needs of parents using qualitative description provides direction to health professionals on how best to respond to and support parents of children with a rare disease.

The potential for findings from a qualitative descriptive study to impact on policy was identified in a study by Syme et al. (2016) , who noted a lack of guidance and policies around sexual expression management of residents in long-term care settings. In this study, 20 directors of nursing from long-term care settings were interviewed with a view to identifying challenges in addressing sexual expression in these settings and elicit their recommendations for addressing these challenges in practice and policy. Following thematic analysis, findings relating to what directors of nursing believed to be important components of policy to address sexual expression were identified. These included providing educational resources, having a person-centred care delivery model when responding to sexual expression and providing guidance when working with families. Findings from this qualitative descriptive study provide recommendations that can then feed in to a broader policy on sexual expression in long-term care settings.

The final example of the use of a qualitative descriptive study comes from a mixed-methods study comprising a randomised control trial and a qualitative process evaluation. He et al. (2015) sought to determine the effects of a play intervention for children on parental perioperative anxiety and to explore parents’ perceptions of the intervention. Parents who had children going for surgery were assigned to a control group or an intervention group. The intervention group took part in a 1-hour play therapy session with their child whereas the control group received usual care. Quantitative findings identified there was no difference in parents’ anxiety levels between the intervention and control group. However, qualitative findings identified that parents found the intervention helpful in preparing both themselves and their child for surgery and perceived a reduction in their anxiety about the procedure thereby capturing findings that were not captured by the quantitative measures. In addition, in the qualitative interviews, parents made suggestions about how the play group could be improved, which provides important data for the further development of the intervention.

These examples across a range of healthcare settings provide evidence of the way findings from qualitative descriptive research can be directly used to more fully understand the experiences and perspectives of patients, their families and healthcare providers in addition to guiding future healthcare practice and informing further research.

Qualitative research designs have made significant contributions to the development of nursing and healthcare practices and policy. The utilisation of qualitative descriptive research is common within nursing research and is gaining popularity with other healthcare professions. This paper has identified that the utilisation of this design can be particularly relevant to nursing and healthcare professionals undertaking a primary piece of research and provides an excellent method to address issues that are of real clinical significance to them and their practice setting. However, the conundrum facing researchers who wish to use this approach is its lack of visibility and transparency within methodological papers and texts, resulting in a deficit of available information to researchers when designing such studies. By adding to the existing knowledge base, this paper enhances the information available to researchers who wish to use the qualitative descriptive approach, thus influencing the standard in how this approach is employed in healthcare research. We highlight the need for researchers using this research approach to clearly outline the context, theoretical framework and concepts underpinning it and the decision-making process that informed the design of their qualitative descriptive study including chosen research methods, and how these contribute to the achievement of the study’s aims and objectives. Failure to describe these issues may have a negative impact on study credibility. As seen in our paper, qualitative descriptive studies have a role in healthcare research providing insight into service users and providers’ perceptions and experiences of a particular phenomenon, which can inform healthcare service provision.

Key points for policy, practice and/or research

• Despite its widespread use, there is little methodological guidance to orientate novice nurse researchers when using the qualitative descriptive design. This paper provides this guidance and champions the qualitative descriptive design as appropriate to explore research questions that require accessible and understandable findings directly relevant to healthcare practice and policy.
• This paper identifies how the use of a qualitative descriptive design gives direct voice to participants including patients and healthcare staff, allowing exploration of issues of real and immediate importance in the practice area.
• This paper reports how within qualitative descriptive research, the analysis of data and presentation of findings in a way that is easily understood and recognised is important to contribute to the utilisation of research findings in nursing practice.
• As this design is often overlooked in research texts despite its suitability to exploring many healthcare questions, this paper adds to the limited methodological guidance and has utility for researchers who wish to defend their rationale for the use of the qualitative descriptive design in nursing and healthcare research.

Louise Doyle (PhD, MSc, BNS, RNT, RPN) is an Associate Professor in Mental Health Nursing at the School of Nursing and Midwifery, Trinity College Dublin. Her research interests are in the area of self-harm and suicide and she has a particular interest and expertise in mixed-methods and qualitative research designs.

Catherine McCabe (PhD, MSc, BNS, RNT, RGN) is an Associate Professor in General Nursing at the School of Nursing and Midwifery, Trinity College Dublin. Her research interests and expertise are in the areas of digital health (chronic disease self-management and social/cultural wellbeing), cancer, dementia, arts and health and systematic reviews.

Brian Keogh (PhD, MSc, BNS, RNT, RPN) is an Assistant Professor in Mental Health Nursing at the School of Nursing and Midwifery, Trinity College Dublin. His main area of research interest is mental health recovery and he specialises in qualitative research approaches with a particular emphasis on grounded theory.

Annemarie Brady (PhD, MSc, BNS, RNT, RPN) is Chair of Nursing and Chronic Illness and Head of School of Nursing and Midwifery at Trinity College Dublin. Her research work has focused on the development of healthcare systems and workforce solutions to respond to increased chronic illness demands within healthcare. She has conducted a range of mixed-method research studies in collaboration with health service providers to examine issues around patient-related outcomes measures, workload measurement, work conditions, practice development, patient safety and competency among healthcare workers.

Margaret McCann (PhD, MSc, BNS, RNT, RGN) is an Assistant Professor in General Nursing at the School of Nursing and Midwifery, Trinity College Dublin. Research interests are focused on chronic illness management, the use of digital health and smart technology in supporting patient/client education, self-management and independence. Other research interests include conducting systematic reviews, infection prevention and control and exploring patient outcomes linked to chronic kidney disease.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Ethical approval was not required for this paper as it is a methodological paper and does not report on participant data.

The author(s) received no financial support for the research, authorship and/or publication of this article.

Louise Doyle https://orcid.org/0000-0002-0153-8326

Margaret McCann https://orcid.org/0000-0002-7925-6396

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The Coding Manual for Qualitative Researchers

Student resources.

Welcome to the companion website for The Coding Manual for Qualitative Research , third edition, by Johnny Saldaña.  This website offers a wealth of additional resources to support students and lecturers including:

CAQDAS links giving guidance and links to a variety of qualitative data analysis software.

Code lists including data extracted from the author’s study, “Lifelong Learning Impact: Adult Perceptions of Their High School Speech and/or Theatre Participation” (McCammon, Saldaña, Hines, & Omasta, 2012), which you can download and make your own practice manipulations to the data.

Coding examples from SAGE journals providing actual examples of coding at work, giving you insight into coding procedures.

Three sample interview transcripts that allow you to test your coding skills.

Group exercises for small and large groups encourage you to get to grips with basic principles of coding, partner development, categorization and qualitative data analysis

Flashcard glossary of terms enables you to test your knowledge of the terminology commonly used in qualitative research and coding.

About the book

Johnny Saldaña’s unique and invaluable manual demystifies the qualitative coding process with a comprehensive assessment of different coding types, examples and exercises. The ideal reference for students, teachers, and practitioners of qualitative inquiry, it is essential reading across the social sciences and neatly guides you through the multiple approaches available for coding qualitative data.

Its wide array of strategies, from the more straightforward to the more complex, is skilfully explained and carefully exemplified, providing a complete toolkit of codes and skills that can be applied to any research project. For each code Saldaña provides information about the method's origin, gives a detailed description of the method, demonstrates its practical applications, and sets out a clearly illustrated example with analytic follow up. 

This international bestseller is an extremely usable, robust manual and is a must-have resource for qualitative researchers at all levels.

This website may contain links to both internal and external websites. All links included were active at the time the website was launched. SAGE does not operate these external websites and does not necessarily endorse the views expressed within them. SAGE cannot take responsibility for the changing content or nature of linked sites, as these sites are outside of our control and subject to change without our knowledge. If you do find an inactive link to an external website, please try to locate that website by using a search engine. SAGE will endeavour to update inactive or broken links when possible. 

A guide to coding qualitative research data

Last updated

12 February 2023

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Each time you ask open-ended and free-text questions, you'll end up with numerous free-text responses. When your qualitative data piles up, how do you sift through it to determine what customers value? And how do you turn all the gathered texts into quantifiable and actionable information related to your user's expectations and needs?

Qualitative data can offer significant insights into respondents’ attitudes and behavior. But to distill large volumes of text / conversational data into clear and insightful results can be daunting. One way to resolve this is through qualitative research coding.

Streamline data coding

Use global data tagging systems in Dovetail so everyone analyzing research is speaking the same language

• What is coding in qualitative research?

This is the system of classifying and arranging qualitative data . Coding in qualitative research involves separating a phrase or word and tagging it with a code. The code describes a data group and separates the information into defined categories or themes. Using this system, researchers can find and sort related content.

They can also combine categorized data with other coded data sets for analysis, or analyze it separately. The primary goal of coding qualitative data is to change data into a consistent format in support of research and reporting.

A code can be a phrase or a word that depicts an idea or recurring theme in the data. The code’s label must be intuitive and encapsulate the essence of the researcher's observations or participants' responses. You can generate these codes using two approaches to coding qualitative data: manual coding and automated coding.

• Why is it important to code qualitative data?

By coding qualitative data, it's easier to identify consistency and scale within a set of individual responses. Assigning codes to phrases and words within feedback helps capture what the feedback entails. That way, you can better analyze and   understand the outcome of the entire survey.

Researchers use coding and other qualitative data analysis procedures to make data-driven decisions according to customer responses. Coding in customer feedback will help you assess natural themes in the customers’ language. With this, it's easy to interpret and analyze customer satisfaction .

• How do inductive and deductive approaches to qualitative coding work?

Before you start qualitative research coding, you must decide whether you're starting with some predefined code frames, within which the data will be sorted (deductive approach). Or, you may plan to develop and evolve the codes while reviewing the qualitative data generated by the research (inductive approach). A combination of both approaches is also possible.

In most instances, a combined approach will be best. For example, researchers will have some predefined codes/themes they expect to find in the data, but will allow for a degree of discovery in the data where new themes and codes come to light.

Inductive coding

This is an exploratory method in which new data codes and themes are generated by the review of qualitative data. It initiates and generates code according to the source of the data itself. It's ideal for investigative research, in which you devise a new idea, theory, or concept. 

Inductive coding is otherwise called open coding. There's no predefined code-frame within inductive coding, as all codes are generated by reviewing the raw qualitative data.

If you're adding a new code, changing a code descriptor, or dividing an existing code in half, ensure you review the wider code frame to determine whether this alteration will impact other feedback codes.  Failure to do this may lead to similar responses at various points in the qualitative data,  generating different codes while containing similar themes or insights.

Inductive coding is more thorough and takes longer than deductive coding, but offers a more unbiased and comprehensive overview of the themes within your data.

Deductive coding

This is a hierarchical approach to coding. In this method, you develop a codebook using your initial code frames. These frames may depend on an ongoing research theory or questions. Go over the data once again and filter data to different codes. 

After generating your qualitative data, your codes must be a match for the code frame you began with. Program evaluation research could use this coding approach.

Inductive and deductive approaches

Research studies usually blend both inductive and deductive coding approaches. For instance, you may use a deductive approach for your initial set of code sets, and later use an inductive approach to generate fresh codes and recalibrate them while you review and analyze your data.

• What are the practical steps for coding qualitative data?

You can code qualitative data in the following ways:

1. Conduct your first-round pass at coding qualitative data

You need to review your data and assign codes to different pieces in this step. You don't have to generate the right codes since you will iterate and evolve them ahead of the second-round coding review.

Let's look at examples of the coding methods you may use in this step.

Open coding : This involves the distilling down of qualitative data into separate, distinct coded elements.

Descriptive coding : In this method, you create a description that encapsulates the data section’s content. Your code name must be a noun or a term that describes what the qualitative data relates to.

Values coding : This technique categorizes qualitative data that relates to the participant's attitudes, beliefs, and values.

Simultaneous coding : You can apply several codes to a single piece of qualitative data using this approach.

Structural coding : In this method, you can classify different parts of your qualitative data based on a predetermined design to perform additional analysis within the design.

In Vivo coding : Use this as the initial code to represent specific phrases or single words generated via a qualitative interview (i.e., specifically what the respondent said).

Process coding : A process of coding which captures action within data.  Usually, this will be in the form of gerunds ending in “ing” (e.g., running, searching, reviewing).

2. Arrange your qualitative codes into groups and subcodes

You can start organizing codes into groups once you've completed your initial round of qualitative data coding. There are several ways to arrange these groups. 

You can put together codes related to one another or address the same subjects or broad concepts, under each category. Continue working with these groups and rearranging the codes until you develop a framework that aligns with your analysis.

3. Conduct more rounds of qualitative coding

Conduct more iterations of qualitative data coding to review the codes and groups you've already established. You can change the names and codes, combine codes, and re-group the work you've already done during this phase. 

In contrast, the initial attempt at data coding may have been hasty and haphazard. But these coding rounds focus on re-analyzing, identifying patterns, and drawing closer to creating concepts and ideas.

Below are a few techniques for qualitative data coding that are often applied in second-round coding.

Pattern coding : To describe a pattern, you join snippets of data, similarly classified under a single umbrella code.

Thematic analysis coding : When examining qualitative data, this method helps to identify patterns or themes.

Selective coding/focused coding : You can generate finished code sets and groups using your first pass of coding.

Theoretical coding : By classifying and arranging codes, theoretical coding allows you to create a theoretical framework's hypothesis. You develop a theory using the codes and groups that have been generated from the qualitative data.

Content analysis coding : This starts with an existing theory or framework and uses qualitative data to either support or expand upon it.

Axial coding : Axial coding allows you to link different codes or groups together. You're looking for connections and linkages between the information you discovered in earlier coding iterations.

Longitudinal coding : In this method, by organizing and systematizing your existing qualitative codes and categories, it is possible to monitor and measure them over time.

Elaborative coding : This involves applying a hypothesis from past research and examining how your present codes and groups relate to it.

4. Integrate codes and groups into your concluding narrative

When you finish going through several rounds of qualitative data coding and applying different forms of coding, use the generated codes and groups to build your final conclusions. The final result of your study could be a collection of findings, theory, or a description, depending on the goal of your study.

Start outlining your hypothesis , observations , and story while citing the codes and groups that served as its foundation. Create your final study results by structuring this data.

• What are the two methods of coding qualitative data?

You can carry out data coding in two ways: automatic and manual. Manual coding involves reading over each comment and manually assigning labels. You'll need to decide if you're using inductive or deductive coding.

Automatic qualitative data analysis uses a branch of computer science known as Natural Language Processing to transform text-based data into a format that computers can comprehend and assess. It's a cutting-edge area of artificial intelligence and machine learning that has the potential to alter how research and insight is designed and delivered.

Although automatic coding is faster than human coding, manual coding still has an edge due to human oversight and limitations in terms of computer power and analysis.

• What are the advantages of qualitative research coding?

Here are the benefits of qualitative research coding:

Boosts validity : gives your data structure and organization to be more certain the conclusions you are drawing from it are valid

Reduces bias : minimizes interpretation biases by forcing the researcher to undertake a systematic review and analysis of the data 

Represents participants well : ensures your analysis reflects the views and beliefs of your participant pool and prevents you from overrepresenting the views of any individual or group

Fosters transparency : allows for a logical and systematic assessment of your study by other academics

• What are the challenges of qualitative research coding?

It would be best to consider theoretical and practical limitations while analyzing and interpreting data. Here are the challenges of qualitative research coding:

Labor-intensive: While you can use software for large-scale text management and recording, data analysis is often verified or completed manually.

Lack of reliability: Qualitative research is often criticized due to a lack of transparency and standardization in the coding and analysis process, being subject to a collection of researcher bias. 

Limited generalizability : Detailed information on specific contexts is often gathered using small samples. Drawing generalizable findings is challenging even with well-constructed analysis processes as data may need to be more widely gathered to be genuinely representative of attitudes and beliefs within larger populations.

Subjectivity : It is challenging to reproduce qualitative research due to researcher bias in data analysis and interpretation. When analyzing data, the researchers make personal value judgments about what is relevant and what is not. Thus, different people may interpret the same data differently.

• What are the tips for coding qualitative data?

Here are some suggestions for optimizing the value of your qualitative research now that you are familiar with the fundamentals of coding qualitative data.

Keep track of your codes using a codebook or code frame

It can be challenging to recall all your codes offhand as you code more and more data. Keeping track of your codes in a codebook or code frame will keep you organized as you analyze the data. An Excel spreadsheet or word processing document might be your codebook's basic format.

Ensure you track:

The label applied to each code and the time it was first coded or modified

An explanation of the idea or subject matter that the code relates to

Who the original coder is

Any notes on the relationship between the code and other codes in your analysis

Add new codes to your codebook as you code new data, and rearrange categories and themes as necessary.

• How do you create high-quality codes?

Here are four useful tips to help you create high-quality codes.

1. Cover as many survey responses as possible

The code should be generic enough to aid your analysis while remaining general enough to apply to various comments. For instance, "product" is a general code that can apply to many replies but is also ambiguous. 

Also, the specific statement, "product stops working after using it for 3 hours" is unlikely to apply to many answers. A good compromise might be "poor product quality" or "short product lifespan."

2. Avoid similarities

Having similar codes is acceptable only if they serve different objectives. While "product" and "customer service" differ from each other, "customer support" and "customer service" can be unified into a single code.

3. Take note of the positive and the negative

Establish contrasting codes to track an issue's negative and positive aspects separately. For instance, two codes to identify distinct themes would be "excellent customer service" and "poor customer service."

4. Minimize data—to a point

Try to balance having too many and too few codes in your analysis to make it as useful as possible.

What is the best way to code qualitative data?

Depending on the goal of your research, the procedure of coding qualitative data can vary. But generally, it entails: 

Reading through your data

Assigning codes to selected passages

Carrying out several rounds of coding

Grouping codes into themes

Developing interpretations that result in your final research conclusions 

You can begin by first coding snippets of text or data to summarize or characterize them and then add your interpretative perspective in the second round of coding.

A few techniques are more or less acceptable depending on your study’s goal; there is no right or incorrect way to code a data set.

What is an example of a code in qualitative research?

A code is, at its most basic level, a label specifying how you should read a text. The phrase, "Pigeons assaulted me and took my meal," is an illustration. You can use pigeons as a code word.

Is there coding in qualitative research?

An essential component of qualitative data analysis is coding. Coding aims to give structure to free-form data so one can systematically study it.

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Qualitative Data Coding

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Coding is the process of analyzing qualitative data (usually text) by assigning labels (codes) to chunks of data that capture their essence or meaning. It allows you to condense, organize and interpret your data.

A code is a word or brief phrase that captures the essence of why you think a particular bit of data may be useful. A good analogy is that a code describes data like a hashtag describes a tweet.

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

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

Step 1: Familiarize yourself with the data

• Read through your data (interview transcripts, field notes, documents, etc.) several times. This process is called immersion.
• Think and reflect on what may be important in the data before making any firm decisions about ideas, or potential patterns.

Step 2: Decide on your coding approach

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

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

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

Step 3: Do a first round of coding

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

Descriptive codes

• In vivo coding / Semantic coding : This method uses words or short phrases directly from the participant’s own language as codes. It deals with the surface-level content, labeling what participants directly say or describe. It identifies keywords, phrases, or sentences that capture the literal content. Participant : “I was just so overwhelmed with everything.” Code : “overwhelmed”
• Process coding : Uses gerunds (“-ing” words) to connote observable or conceptual action in the data. Participant : “I started by brainstorming ideas, then I narrowed them down.” Codes : “brainstorming ideas,” “narrowing down”
• Open coding : A form of initial coding where the researcher remains open to any possible theoretical directions indicated by the data. Participant : “I found the class really challenging, but I learned a lot.” Codes : “challenging class,” “learning experience”
• Descriptive coding : Summarizes the primary topic of a passage in a word or short phrase. Participant : “I usually study in the library because it’s quiet.” Code : “study environment”

Step 4: Review and refine codes

• Look over your initial codes and see if any can be combined, split up, or revised
• Ensure your code names clearly convey the meaning of the data
• Check if your codes are applied consistently across the dataset
• Get a second opinion from a peer or advisor if possible

Interpretive codes

Interpretive codes go beyond simple description and reflect the researcher’s understanding of the underlying meanings, experiences, or processes captured in the data.

These codes require the researcher to interpret the participants’ words and actions in light of the research questions and theoretical framework.

For example, latent coding is a type of interpretive coding which goes beyond surface meaning in data. It digs for underlying emotions, motivations, or unspoken ideas the participant might not explicitly state

Latent coding looks for subtext, interprets the “why” behind what’s said, and considers the context (e.g. cultural influences, or unconscious biases).

• Example: A participant might say, “Whenever I see a spider, I feel like I’m going to pass out. It takes me back to a bad experience as a kid.” A latent code here could be “Feelings of Panic Triggered by Spiders” because it goes beyond the surface fear and explores the emotional response and potential cause.

It’s useful to ask yourself the following questions:

• What are the assumptions made by the participants? 
• What emotions or feelings are expressed or implied in the data?
• How do participants relate to or interact with others in the data?
• How do the participants’ experiences or perspectives change over time?
• What is surprising, unexpected, or contradictory in the data?
• What is not being said or shown in the data? What are the silences or absences?

Theoretical codes

Theoretical codes are the most abstract and conceptual type of codes. They are used to link the data to existing theories or to develop new theoretical insights.

Theoretical codes often emerge later in the analysis process, as researchers begin to identify patterns and connections across the descriptive and interpretive codes.

• Structural coding : Applies a content-based phrase to a segment of data that relates to a specific research question. Research question : What motivates students to succeed? Participant : “I want to make my parents proud and be the first in my family to graduate college.” Interpretive Code : “family motivation” Theoretical code : “Social identity theory”
• Value coding : This method codes data according to the participants’ values, attitudes, and beliefs, representing their perspectives or worldviews. Participant : “I believe everyone deserves access to quality healthcare.” Interpretive Code : “healthcare access” (value) Theoretical code : “Distributive justice”

Pattern codes

Pattern coding is often used in the later stages of data analysis, after the researcher has thoroughly familiarized themselves with the data and identified initial descriptive and interpretive codes.

By identifying patterns and relationships across the data, pattern codes help to develop a more coherent and meaningful understanding of the phenomenon and can contribute to theory development or refinement.

For Example

Let’s say a researcher is studying the experiences of new mothers returning to work after maternity leave. They conduct interviews with several participants and initially use descriptive and interpretive codes to analyze the data. Some of these codes might include:

• “Guilt about leaving baby”
• “Struggle to balance work and family”
• “Support from colleagues”
• “Flexible work arrangements”
• “Breastfeeding challenges”

As the researcher reviews the coded data, they may notice that several of these codes relate to the broader theme of “work-family conflict.”

They might create a pattern code called “Navigating work-family conflict” that pulls together the various experiences and challenges described by the participants.

Chapter 18. Data Analysis and Coding

Introduction.

Piled before you lie hundreds of pages of fieldnotes you have taken, observations you’ve made while volunteering at city hall. You also have transcripts of interviews you have conducted with the mayor and city council members. What do you do with all this data? How can you use it to answer your original research question (e.g., “How do political polarization and party membership affect local politics?”)? Before you can make sense of your data, you will have to organize and simplify it in a way that allows you to access it more deeply and thoroughly. We call this process coding . [1] Coding is the iterative process of assigning meaning to the data you have collected in order to both simplify and identify patterns. This chapter introduces you to the process of qualitative data analysis and the basic concept of coding, while the following chapter (chapter 19) will take you further into the various kinds of codes and how to use them effectively.

To those who have not yet conducted a qualitative study, the sheer amount of collected data will be a surprise. Qualitative data can be absolutely overwhelming—it may mean hundreds if not thousands of pages of interview transcripts, or fieldnotes, or retrieved documents. How do you make sense of it? Students often want very clear guidelines here, and although I try to accommodate them as much as possible, in the end, analyzing qualitative data is a bit more of an art than a science: “The process of bringing order, structure, and interpretation to a mass of collected data is messy, ambiguous, time-consuming, creative, and fascinating. It does not proceed in a linear fashion: it is not neat. At times, the researcher may feel like an eccentric and tormented artist; not to worry, this is normal” ( Marshall and Rossman 2016:214 ).

To complicate matters further, each approach (e.g., Grounded Theory, deep ethnography, phenomenology) has its own language and bag of tricks (techniques) when it comes to analysis. Grounded Theory, for example, uses in vivo coding to generate new theoretical insights that emerge from a rigorous but open approach to data analysis. Ethnographers, in contrast, are more focused on creating a rich description of the practices, behaviors, and beliefs that operate in a particular field. They are less interested in generating theory and more interested in getting the picture right, valuing verisimilitude in the presentation. And then there are some researchers who seek to account for the qualitative data using almost quantitative methods of analysis, perhaps counting and comparing the uses of certain narrative frames in media accounts of a phenomenon. Qualitative content analysis (QCA) often includes elements of counting (see chapter 17). For these researchers, having very clear hypotheses and clearly defined “variables” before beginning analysis is standard practice, whereas the same would be expressly forbidden by those researchers, like grounded theorists, taking a more emergent approach.

All that said, there are some helpful techniques to get you started, and these will be presented in this and the following chapter. As you become more of an expert yourself, you may want to read more deeply about the tradition that speaks to your research. But know that there are many excellent qualitative researchers that use what works for any given study, who take what they can from each tradition. Most of us find this permissible (but watch out for the methodological purists that exist among us).

Qualitative Data Analysis as a Long Process!

Although most of this and the following chapter will focus on coding, it is important to understand that coding is just one (very important) aspect of the long data-analysis process. We can consider seven phases of data analysis, each of which is important for moving your voluminous data into “findings” that can be reported to others. The first phase involves data organization. This might mean creating a special password-protected Dropbox folder for storing your digital files. It might mean acquiring computer-assisted qualitative data-analysis software ( CAQDAS ) and uploading all transcripts, fieldnotes, and digital files to its storage repository for eventual coding and analysis. Finding a helpful way to store your material can take a lot of time, and you need to be smart about this from the very beginning. Losing data because of poor filing systems or mislabeling is something you want to avoid. You will also want to ensure that you have procedures in place to protect the confidentiality of your interviewees and informants. Filing signed consent forms (with names) separately from transcripts and linking them through an ID number or other code that only you have access to (and store safely) are important.

Once you have all of your material safely and conveniently stored, you will need to immerse yourself in the data. The second phase consists of reading and rereading or viewing and reviewing all of your data. As you do this, you can begin to identify themes or patterns in the data, perhaps writing short memos to yourself about what you are seeing. You are not committing to anything in this third phase but rather keeping your eyes and mind open to what you see. In an actual study, you may very well still be “in the field” or collecting interviews as you do this, and what you see might push you toward either concluding your data collection or expanding so that you can follow a particular group or factor that is emerging as important. For example, you may have interviewed twelve international college students about how they are adjusting to life in the US but realized as you read your transcripts that important gender differences may exist and you have only interviewed two women (and ten men). So you go back out and make sure you have enough female respondents to check your impression that gender matters here. The seven phases do not proceed entirely linearly! It is best to think of them as recursive; conceptually, there is a path to follow, but it meanders and flows.

Coding is the activity of the fourth phase . The second part of this chapter and all of chapter 19 will focus on coding in greater detail. For now, know that coding is the primary tool for analyzing qualitative data and that its purpose is to both simplify and highlight the important elements buried in mounds of data. Coding is a rigorous and systematic process of identifying meaning, patterns, and relationships. It is a more formal extension of what you, as a conscious human being, are trained to do every day when confronting new material and experiences. The “trick” or skill is to learn how to take what you do naturally and semiconsciously in your mind and put it down on paper so it can be documented and verified and tested and refined.

At the conclusion of the coding phase, your material will be searchable, intelligible, and ready for deeper analysis. You can begin to offer interpretations based on all the work you have done so far. This fifth phase might require you to write analytic memos, beginning with short (perhaps a paragraph or two) interpretations of various aspects of the data. You might then attempt stitching together both reflective and analytical memos into longer (up to five pages) general interpretations or theories about the relationships, activities, patterns you have noted as salient.

As you do this, you may be rereading the data, or parts of the data, and reviewing your codes. It’s possible you get to this phase and decide you need to go back to the beginning. Maybe your entire research question or focus has shifted based on what you are now thinking is important. Again, the process is recursive , not linear. The sixth phase requires you to check the interpretations you have generated. Are you really seeing this relationship, or are you ignoring something important you forgot to code? As we don’t have statistical tests to check the validity of our findings as quantitative researchers do, we need to incorporate self-checks on our interpretations. Ask yourself what evidence would exist to counter your interpretation and then actively look for that evidence. Later on, if someone asks you how you know you are correct in believing your interpretation, you will be able to explain what you did to verify this. Guard yourself against accusations of “ cherry-picking ,” selecting only the data that supports your preexisting notion or expectation about what you will find. [2]

The seventh and final phase involves writing up the results of the study. Qualitative results can be written in a variety of ways for various audiences (see chapter 20). Due to the particularities of qualitative research, findings do not exist independently of their being written down. This is different for quantitative research or experimental research, where completed analyses can somewhat speak for themselves. A box of collected qualitative data remains a box of collected qualitative data without its written interpretation. Qualitative research is often evaluated on the strength of its presentation. Some traditions of qualitative inquiry, such as deep ethnography, depend on written thick descriptions, without which the research is wholly incomplete, even nonexistent. All of that practice journaling and writing memos (reflective and analytical) help develop writing skills integral to the presentation of the findings.

Remember that these are seven conceptual phases that operate in roughly this order but with a lot of meandering and recursivity throughout the process. This is very different from quantitative data analysis, which is conducted fairly linearly and processually (first you state a falsifiable research question with hypotheses, then you collect your data or acquire your data set, then you analyze the data, etc.). Things are a bit messier when conducting qualitative research. Embrace the chaos and confusion, and sort your way through the maze. Budget a lot of time for this process. Your research question might change in the middle of data collection. Don’t worry about that. The key to being nimble and flexible in qualitative research is to start thinking and continue thinking about your data, even as it is being collected. All seven phases can be started before all the data has been gathered. Data collection does not always precede data analysis. In some ways, “qualitative data collection is qualitative data analysis.… By integrating data collection and data analysis, instead of breaking them up into two distinct steps, we both enrich our insights and stave off anxiety. We all know the anxiety that builds when we put something off—the longer we put it off, the more anxious we get. If we treat data collection as this mass of work we must do before we can get started on the even bigger mass of work that is analysis, we set ourselves up for massive anxiety” ( Rubin 2021:182–183 ; emphasis added).

The Coding Stage

A code is “a word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data” ( Saldaña 2014:5 ). Codes can be applied to particular sections of or entire transcripts, documents, or even videos. For example, one might code a video taken of a preschooler trying to solve a puzzle as “puzzle,” or one could take the transcript of that video and highlight particular sections or portions as “arranging puzzle pieces” (a descriptive code) or “frustration” (a summative emotion-based code). If the preschooler happily shouts out, “I see it!” you can denote the code “I see it!” (this is an example of an in vivo, participant-created code). As one can see from even this short example, there are many different kinds of codes and many different strategies and techniques for coding, more of which will be discussed in detail in chapter 19. The point to remember is that coding is a rigorous systematic process—to some extent, you are always coding whenever you look at a person or try to make sense of a situation or event, but you rarely do this consciously. Coding is the process of naming what you are seeing and how you are simplifying the data so that you can make sense of it in a way that is consistent with your study and in a way that others can understand and follow and replicate. Another way of saying this is that a code is “a researcher-generated interpretation that symbolizes or translates data” ( Vogt et al. 2014:13 ).

As with qualitative data analysis generally, coding is often done recursively, meaning that you do not merely take one pass through the data to create your codes. Saldaña ( 2014 ) differentiates first-cycle coding from second-cycle coding. The goal of first-cycle coding is to “tag” or identify what emerges as important codes. Note that I said emerges—you don’t always know from the beginning what will be an important aspect of the study or not, so the coding process is really the place for you to begin making the kinds of notes necessary for future analyses. In second-cycle coding, you will want to be much more focused—no longer gathering wholly new codes but synthesizing what you have into metacodes.

You might also conceive of the coding process in four parts (figure 18.1). First, identify a representative or diverse sample set of interview transcripts (or fieldnotes or other documents). This is the group you are going to use to get a sense of what might be emerging. In my own study of career obstacles to success among first-generation and working-class persons in sociology, I might select one interview from each career stage: a graduate student, a junior faculty member, a senior faculty member.

Second, code everything (“ open coding ”). See what emerges, and don’t limit yourself in any way. You will end up with a ton of codes, many more than you will end up with, but this is an excellent way to not foreclose an interesting finding too early in the analysis. Note the importance of starting with a sample of your collected data, because otherwise, open coding all your data is, frankly, impossible and counterproductive. You will just get stuck in the weeds.

Third, pare down your coding list. Where you may have begun with fifty (or more!) codes, you probably want no more than twenty remaining. Go back through the weeds and pull out everything that does not have the potential to bloom into a nicely shaped garden. Note that you should do this before tackling all of your data . Sometimes, however, you might need to rethink the sample you chose. Let’s say that the graduate student interview brought up some interesting gender issues that were pertinent to female-identifying sociologists, but both the junior and the senior faculty members identified as male. In that case, I might read through and open code at least one other interview transcript, perhaps a female-identifying senior faculty member, before paring down my list of codes.

This is also the time to create a codebook if you are using one, a master guide to the codes you are using, including examples (see Sample Codebooks 1 and 2 ). A codebook is simply a document that lists and describes the codes you are using. It is easy to forget what you meant the first time you penciled a coded notation next to a passage, so the codebook allows you to be clear and consistent with the use of your codes. There is not one correct way to create a codebook, but generally speaking, the codebook should include (1) the code (either name or identification number or both), (2) a description of what the code signifies and when and where it should be applied, and (3) an example of the code to help clarify (2). Listing all the codes down somewhere also allows you to organize and reorganize them, which can be part of the analytical process. It is possible that your twenty remaining codes can be neatly organized into five to seven master “themes.” Codebooks can and should develop as you recursively read through and code your collected material. [3]

Fourth, using the pared-down list of codes (or codebook), read through and code all the data. I know many qualitative researchers who work without a codebook, but it is still a good practice, especially for beginners. At the very least, read through your list of codes before you begin this “ closed coding ” step so that you can minimize the chance of missing a passage or section that needs to be coded. The final step is…to do it all again. Or, at least, do closed coding (step four) again. All of this takes a great deal of time, and you should plan accordingly.

Researcher Note

People often say that qualitative research takes a lot of time. Some say this because qualitative researchers often collect their own data. This part can be time consuming, but to me, it’s the analytical process that takes the most time. I usually read every transcript twice before starting to code, then it usually takes me six rounds of coding until I’m satisfied I’ve thoroughly coded everything. Even after the coding, it usually takes me a year to figure out how to put the analysis together into a coherent argument and to figure out what language to use. Just deciding what name to use for a particular group or idea can take months. Understanding this going in can be helpful so that you know to be patient with yourself.

—Jessi Streib, author of The Power of the Past and Privilege Lost 

Note that there is no magic in any of this, nor is there any single “right” way to code or any “correct” codes. What you see in the data will be prompted by your position as a researcher and your scholarly interests. Where the above codes on a preschooler solving a puzzle emerged from my own interest in puzzle solving, another researcher might focus on something wholly different. A scholar of linguistics, for example, may focus instead on the verbalizations made by the child during the discovery process, perhaps even noting particular vocalizations (incidence of grrrs and gritting of the teeth, for example). Your recording of the codes you used is the important part, as it allows other researchers to assess the reliability and validity of your analyses based on those codes. Chapter 19 will provide more details about the kinds of codes you might develop.

Saldaña ( 2014 ) lists seven “necessary personal attributes” for successful coding. To paraphrase, they are the following:

• Having (or practicing) good organizational skills
• Perseverance
• The ability and willingness to deal with ambiguity
• Flexibility
• Creativity, broadly understood, which includes “the ability to think visually, to think symbolically, to think in metaphors, and to think of as many ways as possible to approach a problem” (20)
• Commitment to being rigorously ethical
• Having an extensive vocabulary [4]

Writing Analytic Memos during/after Coding

Coding the data you have collected is only one aspect of analyzing it. Too many beginners have coded their data and then wondered what to do next. Coding is meant to help organize your data so that you can see it more clearly, but it is not itself an analysis. Thinking about the data, reviewing the coded data, and bringing in the previous literature (here is where you use your literature review and theory) to help make sense of what you have collected are all important aspects of data analysis. Analytic memos are notes you write to yourself about the data. They can be short (a single page or even a paragraph) or long (several pages). These memos can themselves be the subject of subsequent analytic memoing as part of the recursive process that is qualitative data analysis.

Short analytic memos are written about impressions you have about the data, what is emerging, and what might be of interest later on. You can write a short memo about a particular code, for example, and why this code seems important and where it might connect to previous literature. For example, I might write a paragraph about a “cultural capital” code that I use whenever a working-class sociologist says anything about “not fitting in” with their peers (e.g., not having the right accent or hairstyle or private school background). I could then write a little bit about Bourdieu, who originated the notion of cultural capital, and try to make some connections between his definition and how I am applying it here. I can also use the memo to raise questions or doubts I have about what I am seeing (e.g., Maybe the type of school belongs somewhere else? Is this really the right code?). Later on, I can incorporate some of this writing into the theory section of my final paper or article. Here are some types of things that might form the basis of a short memo: something you want to remember, something you noticed that was new or different, a reaction you had, a suspicion or hunch that you are developing, a pattern you are noticing, any inferences you are starting to draw. Rubin ( 2021 ) advises, “Always include some quotation or excerpt from your dataset…that set you off on this idea. It’s happened to me so many times—I’ll have a really strong reaction to a piece of data, write down some insight without the original quotation or context, and then [later] have no idea what I was talking about and have no way of recreating my insight because I can’t remember what piece of data made me think this way” ( 203 ).

All CAQDAS programs include spaces for writing, generating, and storing memos. You can link a memo to a particular transcript, for example. But you can just as easily keep a notebook at hand in which you write notes to yourself, if you prefer the more tactile approach. Drawing pictures that illustrate themes and patterns you are beginning to see also works. The point is to write early and write often, as these memos are the building blocks of your eventual final product (chapter 20).

In the next chapter (chapter 19), we will go a little deeper into codes and how to use them to identify patterns and themes in your data. This chapter has given you an idea of the process of data analysis, but there is much yet to learn about the elements of that process!

Qualitative Data-Analysis Samples

The following three passages are examples of how qualitative researchers describe their data-analysis practices. The first, by Harvey, is a useful example of how data analysis can shift the original research questions. The second example, by Thai, shows multiple stages of coding and how these stages build upward to conceptual themes and theorization. The third example, by Lamont, shows a masterful use of a variety of techniques to generate theory.

Example 1: “Look Someone in the Eye” by Peter Francis Harvey ( 2022 )

I entered the field intending to study gender socialization. However, through the iterative process of writing fieldnotes, rereading them, conducting further research, and writing extensive analytic memos, my focus shifted. Abductive analysis encourages the search for unexpected findings in light of existing literature. In my early data collection, fieldnotes, and memoing, classed comportment was unmistakably prominent in both schools. I was surprised by how pervasive this bodily socialization proved to be and further surprised by the discrepancies between the two schools.…I returned to the literature to compare my empirical findings.…To further clarify patterns within my data and to aid the search for disconfirming evidence, I constructed data matrices (Miles, Huberman, and Saldaña 2013). While rereading my fieldnotes, I used ATLAS.ti to code and recode key sections (Miles et al. 2013), punctuating this process with additional analytic memos. ( 2022:1420 )

Example 2:” Policing and Symbolic Control” by Mai Thai ( 2022 )

Conventional to qualitative research, my analyses iterated between theory development and testing. Analytical memos were written throughout the data collection, and my analyses using MAXQDA software helped me develop, confirm, and challenge specific themes.…My early coding scheme which included descriptive codes (e.g., uniform inspection, college trips) and verbatim codes of the common terms used by field site participants (e.g., “never quit,” “ghetto”) led me to conceptualize valorization. Later analyses developed into thematic codes (e.g., good citizens, criminality) and process codes (e.g., valorization, criminalization), which helped refine my arguments. ( 2022:1191–1192 )

Example 3: The Dignity of Working Men by Michèle Lamont ( 2000 )

To analyze the interviews, I summarized them in a 13-page document including socio-demographic information as well as information on the boundary work of the interviewees. To facilitate comparisons, I noted some of the respondents’ answers on grids and summarized these on matrix displays using techniques suggested by Miles and Huberman for standardizing and processing qualitative data. Interviews were also analyzed one by one, with a focus on the criteria that each respondent mobilized for the evaluation of status. Moreover, I located each interviewee on several five-point scales pertaining to the most significant dimensions they used to evaluate status. I also compared individual interviewees with respondents who were similar to and different from them, both within and across samples. Finally, I classified all the transcripts thematically to perform a systematic analysis of all the important themes that appear in the interviews, approaching the latter as data against which theoretical questions can be explored. ( 2000:256–257 )

Sample Codebook 1

This is an abridged version of the codebook used to analyze qualitative responses to a question about how class affects careers in sociology. Note the use of numbers to organize the flow, supplemented by highlighting techniques (e.g., bolding) and subcoding numbers.

01. CAPS: Any reference to “capitals” in the response, even if the specific words are not used

01.1: cultural capital 01.2: social capital 01.3: economic capital

(can be mixed: “0.12”= both cultural and asocial capital; “0.23”= both social and economic)

01. CAPS: a reference to “capitals” in which the specific words are used [ bold : thus, 01.23 means that both social capital and economic capital were mentioned specifically

02. DEBT: discussion of debt

02.1: mentions personal issues around debt 02.2: discusses debt but in the abstract only (e.g., “people with debt have to worry”)

03. FirstP: how the response is positioned

03.1: neutral or abstract response 03.2: discusses self (“I”) 03.3: discusses others (“they”)

Sample Coded Passage:

* Question: What other codes jump out to you here? Shouldn’t there be a code for feelings of loneliness or alienation? What about an emotions code ?

Sample Codebook 2

This is an example that uses "word" categories only, with descriptions and examples for each code

Further Readings

Elliott, Victoria. 2018. “Thinking about the Coding Process in Qualitative Analysis.” Qualitative Report 23(11):2850–2861. Address common questions those new to coding ask, including the use of “counting” and how to shore up reliability.

Friese, Susanne. 2019. Qualitative Data Analysis with ATLAS.ti. 3rd ed. A good guide to ATLAS.ti, arguably the most used CAQDAS program. Organized around a series of “skills training” to get you up to speed.

Jackson, Kristi, and Pat Bazeley. 2019. Qualitative Data Analysis with NVIVO . 3rd ed. Thousand Oaks, CA: SAGE. If you want to use the CAQDAS program NVivo, this is a good affordable guide to doing so. Includes copious examples, figures, and graphic displays.

LeCompte, Margaret D. 2000. “Analyzing Qualitative Data.” Theory into Practice 39(3):146–154. A very practical and readable guide to the entire coding process, with particular applicability to educational program evaluation/policy analysis.

Miles, Matthew B., and A. Michael Huberman. 1994. Qualitative Data Analysis: An Expanded Sourcebook . 2nd ed. Thousand Oaks, CA: SAGE. A classic reference on coding. May now be superseded by Miles, Huberman, and Saldaña (2019).

Miles, Matthew B., A. Michael Huberman, and Johnny Saldaña. 2019. Qualitative Data Analysis: A Methods Sourcebook . 4th ed. Thousand Oaks, CA; SAGE. A practical methods sourcebook for all qualitative researchers at all levels using visual displays and examples. Highly recommended.

Saldaña, Johnny. 2014. The Coding Manual for Qualitative Researchers . 2nd ed. Thousand Oaks, CA: SAGE. The most complete and comprehensive compendium of coding techniques out there. Essential reference.

Silver, Christina. 2014. Using Software in Qualitative Research: A Step-by-Step Guide. 2nd ed. Thousand Oaks, CA; SAGE. If you are unsure which CAQDAS program you are interested in using or want to compare the features and usages of each, this guidebook is quite helpful.

Vogt, W. Paul, Elaine R. Vogt, Diane C. Gardner, and Lynne M. Haeffele2014. Selecting the Right Analyses for Your Data: Quantitative, Qualitative, and Mixed Methods . New York: The Guilford Press. User-friendly reference guide to all forms of analysis; may be particularly helpful for those engaged in mixed-methods research.

• When you have collected content (historical, media, archival) that interests you because of its communicative aspect, content analysis (chapter 17) is appropriate. Whereas content analysis is both a research method and a tool of analysis, coding is a tool of analysis that can be used for all kinds of data to address any number of questions. Content analysis itself includes coding. ↵
• Scientific research, whether quantitative or qualitative, demands we keep an open mind as we conduct our research, that we are “neutral” regarding what is actually there to find. Students who are trained in non-research-based disciplines such as the arts or philosophy or who are (admirably) focused on pursuing social justice can too easily fall into the trap of thinking their job is to “demonstrate” something through the data. That is not the job of a researcher. The job of a researcher is to present (and interpret) findings—things “out there” (even if inside other people’s hearts and minds). One helpful suggestion: when formulating your research question, if you already know the answer (or think you do), scrap that research. Ask a question to which you do not yet know the answer. ↵
• Codebooks are particularly useful for collaborative research so that codes are applied and interpreted similarly. If you are working with a team of researchers, you will want to take extra care that your codebooks remain in synch and that any refinements or developments are shared with fellow coders. You will also want to conduct an “intercoder reliability” check, testing whether the codes you have developed are clearly identifiable so that multiple coders are using them similarly. Messy, unclear codes that can be interpreted differently by different coders will make it much more difficult to identify patterns across the data. ↵
• Note that this is important for creating/denoting new codes. The vocabulary does not need to be in English or any particular language. You can use whatever words or phrases capture what it is you are seeing in the data. ↵

A first-cycle coding process in which gerunds are used to identify conceptual actions, often for the purpose of tracing change and development over time.  Widely used in the Grounded Theory approach.

A first-cycle coding process in which terms or phrases used by the participants become the code applied to a particular passage.  It is also known as “verbatim coding,” “indigenous coding,” “natural coding,” “emic coding,” and “inductive coding,” depending on the tradition of inquiry of the researcher.  It is common in Grounded Theory approaches and has even given its name to one of the primary CAQDAS programs (“NVivo”).

Computer-assisted qualitative data-analysis software.  These are software packages that can serve as a repository for qualitative data and that enable coding, memoing, and other tools of data analysis.  See chapter 17 for particular recommendations.

The purposeful selection of some data to prove a preexisting expectation or desired point of the researcher where other data exists that would contradict the interpretation offered.  Note that it is not cherry picking to select a quote that typifies the main finding of a study, although it would be cherry picking to select a quote that is atypical of a body of interviews and then present it as if it is typical.

A preliminary stage of coding in which the researcher notes particular aspects of interest in the data set and begins creating codes.  Later stages of coding refine these preliminary codes.  Note: in Grounded Theory , open coding has a more specific meaning and is often called initial coding : data are broken down into substantive codes in a line-by-line manner, and incidents are compared with one another for similarities and differences until the core category is found.  See also closed coding .

A set of codes, definitions, and examples used as a guide to help analyze interview data.  Codebooks are particularly helpful and necessary when research analysis is shared among members of a research team, as codebooks allow for standardization of shared meanings and code attributions.

The final stages of coding after the refinement of codes has created a complete list or codebook in which all the data is coded using this refined list or codebook.  Compare to open coding .

A first-cycle coding process in which emotions and emotionally salient passages are tagged.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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Qualitative Data Analysis

21 Qualitative Coding

Mikaila Mariel Lemonik Arthur

Codes are words or phrases that capture a central or notable attribute of a particular segment of text or visual data (Saldaña 2016). Coding , then, is the process of applying codes to texts or visuals. It is one of the most common strategies for data reduction and analysis of qualitative data, though many qualitative projects do not require or use coding. This chapter will provide an overview of approaches based in coding, including how to develop codes and how to go through the coding process.

In order to understand coding, it is essential to think about what it means for something to be a code. To analogize to social media, codes might function a bit like tags or hashtags. They are words or phrases that convey content, ideas, perspectives, or other key elements of segments of text. Codes are not the same as themes. Themes are broader than codes—they are concepts or topics around which a discussion, analysis, or text focuses. Themes are more general and more explanatory—often, once we code, we find themes emerge as ideas to explore in our further analysis (Saldaña 2016). Codes are also different from descriptors. Descriptors are words or phrases that describe characteristics of the entire text and/or the person who created it. For example, if we note the profession of an interview respondent, whether an article is news or opinion, or the type of camera used to take a photograph, those would be descriptors. Saldaña (2016) instead calls these attributes . The term attributes more typically refers to the possible answer choices or options for a variable, so it is possible to think about descriptors as variables (or perhaps their attributes) as well.

Let’s consider an example. Imagine that you were conducting an interview-based study looking at minor-league athletes’ workplace experiences and later-life career plans. In this study, themes might be broad ideas like “aspirations” or “work experiences.” There would be a vast array of codes, but they might include things like “short-term goals,” “educational plans,” “pay,” “team bonding,” “travel,” “treatment by managers,” “family demands,” and many more. Descriptors might include the athlete’s gender and what sport they play.

Developing a Coding System

While all approaches to coding have in common the idea that codes are applied to segments of text or visuals, there are many different ways to go about coding. These approaches differ in terms of when they occur during the research process and how codes are developed. First of all, there is a distinction between first- and second-cycle coding approaches (Saldaña 2016). First-cycle coding happens early in the research process and is really a bridge from data reduction to data analysis, while second-cycle coding occurs later in the research process and is more analytical in nature. Another version of this distinction is the comparison between rough, analytic, and focused coding. Rough coding is really part of the process of data reduction. It often involves little more than putting a few words near each segment of text to make clear what is important in that segment, with the approach being further refined as coding continues. In contrast, analytic coding involves more detailed techniques designed to move towards the development of themes and findings. Finally, focused coding involves selecting ideas of interest and going back and re-coding your texts to orient your approach more specifically around these ideas (Bergin 2018).

A second set of distinctions concerns whether the data drives the development of codes or whether codes are instead developed in advance. If codes are determined in advance, or predetermined, researchers develop a set of codes based on their theory, hypothesis, or research question. This sort of coding is typically called deductive coding or closed coding . In contrast, open coding or inductive coding refers to a process in which researchers develop codes based on what they observe in their data, grounding their codes in the texts. This second approach is more common, though by no means universal, in qualitative data analysis. In both types of coding, however, researcher may rely upon ideas generated by writing theoretical memos as they work through the connections between concepts, theory, and data (Saldaña 2016).

Finally, a third set of distinctions focuses on what is coded. Manifest coding refers to the coding of surface-level and easily observable elements of texts (Berg 2009). In contrast, latent coding is a more interpretive approach based on looking deeply into texts for the meanings that are encoded within or symbolized by them (Berg 2009). For example, consider a research project focused on gender in car advertisements. A manifest approach might count the number of men versus women who appear in the ads. A latent approach would instead focus on the use of gendered language and the extent to which men and women are depicted in gender-stereotyped ways.

Researchers need to answer two more questions as they develop their coding systems. First, what to code, and second, how many codes. When thinking about what to code, researchers can look at the level of individual words, characters or actors in the text, paragraphs, entire textual items (like complete books or articles), or really any unit of text (Berg 2009), but the most useful procedure is to look for chunks of words that together express a thought or idea, here referred to as “segments of text” or “textual segments,” and then code to represent the ideas, concepts, emotions, or other relevant thoughts expressed in those chunks.

How many codes should a particular coding system have? There is no simple answer to this question. Some researchers develop complex coding systems with many codes and may have over a hundred different codes. Others may use no more than 25, perhaps fewer, even for the same size project (Saldaña 2016). Some researchers nest codes into code trees, with several related “child” codes (or subcodes) under a single “parent” code. For example, a code “negative emotions” could be the parent code for a series of codes like “anger,” “frustration,” “sadness,” and “fear.” This approach enables researcher to use a smaller or larger number of codes in their analysis as seems fit after coding is complete. While there is no formula for determining the right number of codes for a particular project, researchers should be attentive to overgrowth in the number of codes. Codes have limited analytical value if they are used only once or twice—if a coding system includes many codes that are applied only a small number of times, consider whether there are larger categories of codes that might be more useful. Occasionally, there are codes worth keeping but applying rarely, for example when there is a rare but important phenomenon that arises in the data. But for the most part, codes should be used with some degree of frequency in order for them to be useful for uncovering themes and patterns.

Types of Codes

A wide variety of different types of codes can be used in coding systems. The discussion below, which draws heavily on the work of Saldaña (2016), details a variety of different approaches to coding and code development. Researchers do not need to choose just one of these approaches—most researchers combine multiple coding approaches to create an overall system that is right for the texts they are coding and the project they are conducting. The approaches detailed here are presented roughly in order of the degree of complexity they represent.

At the most basic level is descriptive coding . Descriptive codes are nouns or phrases describing the content covered in a segment of text or the topic the segment of text focuses on. All studies can use descriptive coding, but it often is less productive of rich data for analysis than other approaches might be. Descriptive coding is often used as part of rough coding and data reduction to prepare for later iterations of coding that delve more deeply into the texts. So, for instance, that study of sexism in advertisements might involve some rough coding in which the researcher notes what type of product or service is being advertised in each advertisement.

Structural coding , in contrast, attends more closely to the research question rather than to the ideas in the text. In structural coding, codes indicate which specific research question, part of a research question, or hypothesis is being addressed by a particular segment of text. This may be most useful as part of rough coding to help researchers ensure that their data addresses the questions and foci central to their project.

In vivo coding captures short phrases derived from participants’ own language, typically action-oriented. This is particularly important when researchers are studying subcultural groups that use language in different ways than researchers are accustomed to and where this language is important for subsequent analysis (Manning 2017). In this approach, researchers choose actual portions of respondents’ words and use those as codes. In vivo coding can be used as part of both rough and analytical coding processes.

A related approach is process coding , which involves “the use of gerunds to label actual or conceptual actions relayed by participants” (Saldaña 2016:77). ( Gerunds are verb forms that end in -ing and can function grammatically as if they are nouns when used in sentences). Process coding draws researchers’ attention to actions, but in contrast to in vivo coding it uses the researcher’s vocabulary to build the coding system. So, for instance, in the study of minor league athletes discussed earlier in the chapter, process codes might include “traveling,” “planning,” “exercising,” “competing,” and “socializing.”

Concept coding involves codes consisting of words or short phrases that represent broader concepts or ideas rather than tangible objects or actions. Sticking with the minor league athletes example, concept codes might include “for the love of the game,” “youth,” and “exploitation.” A combination of concept, process, and descriptive coding may be useful if researchers want their coding system to result in an inventory of the ideas, objects, and actions discussed in the texts.

Emotion codes are codes indicating the emotions participants discuss in or that are evoked by a segment of text. A more contemporary version of emotion codes relies on “emoticodes” or the emoji that express specific kinds of emotions, as shown in Figure 2.

Values coding involves the use of codes designed to represent the “perspectives or worldview” of a respondent by conveying participants’ “values, attitudes, and beliefs” (Saldaña 2016:131). For example, a project on elementary school teachers’ workplace satisfaction might include values codes like “equity,” “learning,” “commitment,” and “the pursuit of excellence.” Do note that choices made in values coding are, even more so than in other forms of coding, likely to reflect the values and worldviews of the coder. Thus, it can be essential to use a team of multiple coders with different backgrounds and perspectives in order to ensure a values coding approach that reflects the contents of the texts rather than the ideas of the coders.

Versus coding requires the construction of a series of binary oppositions and then the application of one or the other of the items in the binary as a code to each relevant segment of text. This may be a particularly useful approach for deductive coding, as the researcher can set out a series of hypothesized binaries to use as the basis for coding. For example, the project on elementary school teachers’ workplace satisfaction might use binaries like feeling supported vs. feeling unsupported, energized vs. tired, unfulfilled needs vs. fulfilled needs, kids ready to learn vs. kids needing services, academic vs non-academic concerns, and so on.

Evaluation coding is used to signify what is and is not working in the policy, program, or endeavor that respondents are discussing or that the research focuses on. This approach is obviously especially useful in evaluation research designed to assess the merit or functioning of particular policies or programs. For example, if the project about elementary school teachers was part of a mentoring program designed to keep new teachers in the education profession, codes might include “future orientation” to flag portions of the text in which teachers discuss their longer-term plans and “mentor/mentee match” to flag portions in which they explore how they feel about their mentors, both key elements of the program and its goals.

There are a variety of other approaches more common outside of sociology, such as dramaturgical coding , which is a coding approach that treats interview transcripts or fieldnotes as if they are scripts for a play, coding such things as actors, attitudes, conflicts, and subtexts; coding approaches relying on terms and ideas from literary analysis; and those drawn from communications studies, which focus on facets of verbal exchange. Finally, some researchers have outlined very specific coding strategies and procedures such that someone else could pick up their methods and apply them exactly. This sort of approach is typically deductive, as it requires the advance specification of the decisions that will be made about coding.

Some coding strategies incorporate measures of weight or intensity, and this can be combined with many of the approaches detailed above. For example, consider a project collecting narratives of people’s experiences with losing their jobs. Respondents might include a variety of emotional content in their narratives, whether sadness, fear, stress, relief, or something else. But the emotions they discuss will vary not only in type, they will also vary in extent. A worker who is fired from a job they liked well enough but who knows they will be able to find another job soon may express sadness while a worker whose company closed after she worked there for 20 years and who has few other equivalent employment opportunities in the region may express devastation. Code weights help account for these differences.

A final question researchers must consider is whether they will apply only one code per segment of text or will permit overlapping codes. Overlapping codes make data analysis more complex but can facilitate the process of looking for relationships between different concepts or ideas in the data.

As a coding system is developed and certainly upon its completion, researchers create documents known as codebooks . As is the case with survey research, codebooks lay out the details of how the measurement instrument works to capture data and measure it. For surveys, a codebook tells researchers how to transform the multiple-choice and short-answer responses to survey questions into the numerical data used for quantitative analysis. For qualitative coding, codebooks instead explain when and how to use each of the codes included in the project. Codebooks are an important part of the coding process because they remind the researcher, and any other coders working on the project, what each code means, what types of data it is meant to apply to, and when it should and should not be used (Luker 2008). Even if a researcher is coding without others, it is easy to lose sight of what you were thinking when you initially developed your coding system, and so the codebook serves as an important reminder.

For each code, the codebook should state the name of the code, include a couple of sentences describing the code and what it should be used for, any information about when the code should not be used, examples of both typical and atypical conditions under which the code would be used, and a discussion of the role the code plays in analysis (Saldaña 2016). Codebooks thus serve as instruction manuals for when and how to apply codes. They can also help researchers think about taxonomies of codes as they organize the code book, with higher-level ideas serving as categories for groups of child, or more precise, codes.

The Process of Coding

So, what does the process of coding look like? While qualitative research can and does involve deductive approaches, the process that will be detailed here is an inductive approach, as this is more common in qualitative research. This discussion will lay out a series of steps in the coding process as well as some additional questions researchers and analysts must consider as they develop and carry out their coding.

The first step in inductive coding is to completely and thoroughly read through the data several times while taking detailed notes. To Saldaña (2016), the most important question to ask during this initial read is what is especially interesting or surprising or otherwise stands out. In addition, researchers might contemplate the actions people take, how people go about accomplishing things, how people use language or understand the world, and what people seem to be thinking. The notes should include anything and everything—objects, people, emotions, actions, theoretical ideas, questions—really anything, whether it comes up again and again in the data or only once, though it is useful to flag or highlight those concepts that seem to recur frequently in the data.

Next, researchers need to organize these notes into a coding system. This involves deciding which coding approach(es) to incorporate, whether or not to use parent and child codes, and what sort of vocabulary to use for codes. Remember that readers will not see the coding system except insofar as the researcher chooses to convey it, so vocabulary and terms should be chosen based on the extent to which they make sense to the research team. Once a coding system has been developed, the researcher must create a codebook. If paper coding will be used, a paper codebook should be created. If researchers will be using CAQDAS, or computer-aided qualitative data analysis software, to do their coding, it is often the case that the codebook can be built into the software itself.

Next, the researcher or research team should rough code, applying codes to the text while taking notes to reflect upon missing pieces in the coding system, ways to reorganize the codes or combine them to enhance meaning, and relevant theoretical ideas and insights. Upon completing the rough coding process, researchers should revise the coding system and codebook to fully reflect the data and the project’s needs.

At this point, researchers are ready to engage in coding using the revised codebook. They should always have someone else code a portion of the texts—usually a minimum of 10%—for interrater reliability checks, and if a larger research team is used, 10% of the texts should be coded in common by all coders who are part of the research team. Even in cases where researchers are working alone, it truly strengthens data analysis to be able to check for interrater reliability, so most analysts suggest having a portion of the data coded by another coder, using the codebook. If at all possible, additional coding staff should not be told what the hypothesis or research question is, as one of the strengths of this approach is that additional coding staff will be less likely to be influenced by preexisting ideas about what the data should show (Luker 2008). There are various quantitative measures, such as Chronbach’s alpha and Kappa , that researchers use to calculate interrater reliability, the measure of how closely the ratings of multiple coders correspond. All coders should keep detailed notes about their coding process and any obstacles or difficulties they encounter.

How do researchers know they are done coding? Not just because they have gone through each text once or twice! Researchers may need to continue repeating this process of revision and re-coding until additional coding does not reveal anything more. This repetition is an essential part of coding, as coding always requires refinement and rethinking (Saldaña 2016). In Berg’s (2009:354-55) words, it is essential to “code minutely,” beginning with a rough view of the entire text and then refining as you go until you are examining each detail of a text. Then, researchers think about why and how they developed their codes and what jumps out at them as important from the research as they delve into findings, making sure that nothing has been left out of the coding process before they move towards data analysis.

One interesting question is whether the identities and standpoints (as discussed in the chapter “The Qualitative Approach”) of coders matter to the coding process. Eduardo Bonila-Silva (Zuberi and Bonilla-Silva 2008:17) has described how, after a presentation discussing his research on racism, a colleague asked whether the coders were White or Black—and he responded by asking the colleague “if he asked such questions across the board or only to researchers saying race matters.” As Bonilla-Silva’s question suggests, race (like other aspects of identity and experience, such as gender, immigration status, disability status, age, and social class, just to name a few) very well might shape the way coders see and understand data, functioning as part of a particular coding filter (Saldaña 2016). But that shaping extends broadly across all issues, not just those we might assume are particularly salient in relationship to identities. Thus, it is best for research teams to be diverse so as to ensure that a variety of perspectives are brought to bear on the data and that the findings reflect more than just a narrow set of ideas about how the world works.

Coding and What Comes After

If researchers will code by hand, they will need multiple copies of their data, one for reference and one for writing on (Luker 2008). On the copy that will be written on, researchers use a note-taking system that makes sense to them—whether different-colored markers, Roman numerals in the margins, a complex series of sticky notes, or whatever—to mark the application of various codes to sections of your data. You can see an example of what hand coding might look like in Figure 3 below, which is taken from a study of the comments faculty members make on student writing. Segments of text are highlighted in different colors, with codes noted in the margins next to the text. You can see how codes are repeated but in different combinations. Once the initial coding process is complete, researchers often cut apart the pieces of paper to make chunks of text with individual codes and sort the pieces of paper by code (if multiple codes appear in individual chunks of text, additional copies might be needed). Then, each pile is organized and used as the basis for writing theoretical memos. Another option for coding by hand is to use an index sheet (Berg 2009). This approach entails developing a set of codes and categories, arranging them on paper, and entering transcript, page, and paragraph information to identify where relevant quotes can be found.

For more complex analytical processes, researchers will likely want to use software, though there are limitations to software. Luker (2008), for instance, argues that when coding manually, she tends to start with big themes and only breaks them into their constituent parts later, while coding using software leads her to start with the smallest possible codes. (One solution to this, offered by some software packages, is upcoding, where a so-called “parent” code is simultaneously applied to all of the “child” codes under it. For instance, you might have a parent code of “activism” and then child codes that you apply to different kinds of activism, whether protest, legislative advocacy, community organizing, or whatever.)

Coding does not stand on its own, and thus simply completing the coding process does not move a research project from data to analysis. While the analysis process will be discussed in more detail in a subsequent chapter, there are several steps researchers take alongside coding or immediately after completing coding that facilitate analysis and are thus useful to discuss in the context of coding. Many of these are best understood as part of the process of data reduction. One of the most important of these is categorizing codes into larger groupings, a step that helps to enable the development of themes. These larger groupings, sometimes called “parent” codes, can collapse related but not identical ideas. This is always useful, but it is especially useful in cases where researchers have used a large number of codes and each one is applied only a few times. Once parent codes have been created, researchers then go back and ensure that the appropriate parent code is assigned to all segments of text that were initially coded with the relevant “child” codes (a step that can be automated in CAQDAS). If appropriate, researchers may repeat this process to see if parent codes can be further grouped. An alternative approach to this grouping process is to wait until coding is complete, and then create more analytical categories that make sense as thematic groupings for the codes that have been utilized in the project so far (Saldaña 2016).

There are a variety of other approaches researchers may take as part of data reduction or preliminary analysis after completing coding. They may outline the codes that have occurred most frequently for specific participants or texts, or for the entire body of data, or the codes that are most likely to co-occur in the same segment of text or in the same document. They may print out or photocopy documents or segments of text and rearrange them on a surface until the arrangement is analytically meaningful. They may develop diagrams or models of the relationships between codes. In doing this, it is especially helpful to focus on the use of verbs or other action words to specify the nature of these relationships—not just stating that relationships exist, but exploring what the relationships do and how they work.

In inductive coding especially, it is often useful to write theoretical and analytical memos while coding occurs, and after coding is completed it is a good time to go back and review and refine these memos. Here, researchers both clearly articulate to themselves how the coding process occurred and what methodological choices they made as well as what preliminary ideas they have about analysis and potential findings. It can be very useful to summarize one’s thinking and any patterns that might have been observed so far as a step in moving towards analysis. However, it is extremely important to remember the data and not just the codes. Qualitative researchers always go back to the actual text and not just the summaries or categories. So a final step in the process of moving toward analysis might be to flag quotes or data excerpts that seem particularly noteworthy, meaningful, or analytically useful, as researchers need these examples to make their data come alive during analysis and when they ultimately present their results.

Becoming a Coder

This chapter has provided an overview of how to develop a coding system and apply that system to the task of conducting qualitative coding as part of a research project. Many new researchers find it easy—if sometimes time-consuming and not always fascinating—to get engaged with the coding process. But what does it take to become an effective coder? Saldaña (2016) emphasizes personality attributes and skills that can help. Some of these are attributes and skills that are important for anyone who is involved in any aspect of research and data analysis: organization, to keep track of data, ideas, and procedures; perseverance, to ensure that one keeps going even when the going is tough, as is often the case in research; and ethics, to ensure proper treatment of research participants, appropriate data security behaviors, and integrity in the use of sources. In most aspects of data analysis, creativity is also important, though there are some roles in quantitative data analysis that require more in the way of technical skills and ability to follow directions. In qualitative data analysis, creativity remains important because of the need to think deeply and differently about the data as analysis continues. Flexibility and the ability to deal with ambiguity are much more important in qualitative research, as the data itself is more variable and less concrete; quantitative research tends to place more emphasis on rules and procedures. A final strength that is particularly important for those working in qualitative coding is having a strong vocabulary, as vocabulary both helps researchers understand the data and enhances their ability to create effective and useful coding systems. The best way to develop a stronger vocabulary is to read more, especially within your discipline or field but broadly as well, so researchers should be sure to stay engaged with reading, learning, and growing.

Reading, learning, and growing, along with a lot of practice, is of course how researchers enhance their data collection, coding, and data analysis skills, so keep working at it. Qualitative research can indeed be easy to get started with, but it takes time to become an expert. Put in the time, and you, too, can become a skilled qualitative data analyst.

• Female respondent
• The relationship between poverty and social control
• The process of divorce
• Social hierarchies
• Pick a research topic you find interesting and determine which of the approaches to coding detailed in this chapter might be most appropriate for your topic, then write a paragraph about why this approach is the best.
• Sticking with the same topic you used to respond to Exercise 2, brainstorm some codes that might be useful for coding texts related to this topic. Then, write appropriate text for a codebook for each of those codes.
• Select a hashtag of interest on a particular social media site and randomly sample every other post using that hashtag until you have selected 15 tweets. Then inductively code those posts and engage in summarization or classification to determine what the most important themes they express might be.
• Create a codebook based on what you did in Exercise 4. Exchange codebooks and tweets with a classmate and code each other’s tweets according to the instructions in the codebook. Compare your results—how often did your coding decisions agree and how often did they disagree? What does this tell you about interrater reliability, codebook construction, and coder training?

Media Attributions

• codes themes descriptors © Mikaila Mariel Lemonik Arthur is licensed under a CC BY-NC (Attribution NonCommercial) license
• Emoticodes © AnnaliseArt is licensed under a CC BY (Attribution) license
• Hand Coding Example © Mikaila Mariel Lemonik Arthur is licensed under a CC BY-NC-ND (Attribution NonCommercial NoDerivatives) license

Words or phrases that capture a central or notable attribute of a particular segment of textual or visual data.

The process of assigning observations to categories.

Concepts, topics, or ideas around which a discussion, analysis, or text focuses.

A category in an information storage system; more specifically in Dedoose, a characteristic of an author or entire text. Also, the word used to indicate that category or characteristic.

The possible levels or response choices of a given variable.

Coding that occurs early in the research process as part of a bridge from data reduction to data analysis.

Analytical coding that occurs later in the data analysis process.

Coding for data reduction or as part of an initial pass through the data.

Coding designed to move analysis towards the development of themes and findings.

Selective coding designed to orient an analytical approach around certain ideas.

Coding in which the researcher developed a coding system in advance based on their theory, hypothesis, or research question.

Coding in which the researcher develops codes based on what they observe in the data they have collected.

Coding of surface-level and/or easily observable elements of texts.

Interpretive coding that focuses on meanings within texts.

Coding that relies on nouns or phrases describing the content or topic of a segment of text.

Coding that indicates which research question or hypothesis is being addressed by a given segment of text.

Coding that relies on research participants' own language.

Coding in which gerunds are applied to actions that are described in segments of text.

Verb forms that end in -ing and function grammatically in sentences as if they are nouns.

Coding using words or phrases that represent concepts or ideas.

Codes indicating emotions discussed by or present in the text, sometimes indicated by the use of emoji/emoticons.

Coding that relies on codes indicating the perspective, worldview, values, attitudes, and/or beliefs of research participants.

Coding that relies on a series of binary oppositions, one of which must be applied to each segment of text.

A coding system used to indicate what is or is not working in a program or policy.

Coding that treats texts as if they are scripts for a play.

Elements of a coding strategy that help identify the intensity or degree of presence of a code in a text.

Documents that lay out the details of measurement. Codebooks may be used in surveys to indicate the way survey questions and responses are entered into data analysis software. Codebooks may be used in coding to lay out details about how and when to use each code that has been developed.

A measure of association especially likely to be used for testing interrater reliability.

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Coding Qualitative Data: How To Guide

How many hours have you spent sitting in front of Excel spreadsheets trying to find new insights from customer feedback?

You know that asking open-ended survey questions gives you more actionable insights than asking your customers for just a numerical Net Promoter Score (NPS) . But when you ask open-ended, free-text questions, you end up with hundreds (or even thousands) of free-text responses.

How can you turn all of that text into quantifiable, applicable information about your customers’ needs and expectations? By coding qualitative data.

In this article, we will cover different coding methods for qualitative data, including both manual and automated approaches, to provide a comprehensive understanding of the techniques used in the first-round pass at coding.

Keep reading to learn:

• What coding qualitative data means (and why it’s important)
• Different methods of coding qualitative data
• How to manually code qualitative data to find significant themes in your data

What is coding in qualitative research?

Conducting qualitative research, particularly through coding, is a crucial step in ensuring the validity and reliability of the findings. Coding is the process of labeling and organizing your qualitative data to identify different themes and the relationships between them.

When coding customer feedback , you assign labels to words or phrases that represent important (and recurring) themes in each response. These labels can be words, phrases, or numbers; we recommend using words or short phrases, since they’re easier to remember, skim, and organize.

Coding qualitative research to find common themes and concepts is part of thematic analysis . Thematic analysis extracts themes from text by analyzing the word and sentence structure.

Within the context of customer feedback, it’s important to understand the many different types of qualitative feedback a business can collect, such as open-ended surveys, social media comments, reviews & more.

What is qualitative data analysis?

Qualitative data analysis , including coding and analyzing qualitative data, is essential for understanding the depth and complexity of qualitative data. It is the process of examining and interpreting qualitative data to understand what it represents.

Qualitative analysis is crucial as it involves various methods such as thematic analysis, emotion coding, inductive and deductive thematic analysis, and content analysis. These methods help in coding the data, which is vital for the validity of the analysis.

Qualitative data is defined as any non-numerical and unstructured data; when looking at customer feedback, qualitative data usually refers to any verbatim or text-based feedback such as reviews, open-ended responses in surveys , complaints, chat messages, customer interviews, case notes or social media posts.

For example, NPS metric can be strictly quantitative, but when you ask customers why they gave you a rating a score, you will need qualitative data analysis methods in place to understand the comments that customers leave alongside numerical responses.

Methods of qualitative data analysis

Thematic analysis.

This refers to the uncovering of themes, by analyzing the patterns and relationships in a set of qualitative data. A theme emerges or is built when related findings appear to be meaningful and there are multiple occurrences. Thematic analysis can be used by anyone to transform and organize open-ended responses, analyze online reviews , and other qualitative data into significant themes. Thematic analysis coding is a method that aids in categorizing data extracts and deriving themes and patterns for qualitative analysis, facilitating the identification of themes revolving around a particular concept or phenomenon in the social sciences.

Content analysis:

This refers to the categorization, tagging and thematic analysis of qualitative data. Essentially content analysis is a quantification of themes, by counting the occurrence of concepts, topics or themes. Content analysis can involve combining the categories in qualitative data with quantitative data, such as behavioral data or demographic data, for deeper insights.

Narrative analysis:

Some qualitative data, such as interviews or field notes may contain a story on how someone experienced something. For example, the process of choosing a product, using it, evaluating its quality and decision to buy or not buy this product next time. The goal of narrative analysis is to turn the individual narratives into data that can be coded. This is then analyzed to understand how events or experiences had an impact on the people involved. Process coding is particularly useful in narrative analysis for identifying specific phases, sequences, and movements within the stories, capturing actions within qualitative data by using codes that typically represent gerunds ending in 'ing', providing a dynamic account of events within the data.

Discourse analysis:

This refers to analysis of what people say in social and cultural context. The goal of discourse analysis is to understand user or customer behavior by uncovering their beliefs, interests and agendas. These are reflected in the way they express their opinions, preferences and experiences. Structural coding is a method that can be applied here, organizing data based on predetermined structures, such as the structure of discourse elements, to enhance the analysis of discourse. It’s particularly useful when your focus is on building or strengthening a brand , by examining how they use metaphors and rhetorical devices.

Framework analysis:

When performing qualitative data analysis, it is useful to have a framework to organize the buckets of meaning. A taxonomy or code frame (a hierarchical set of themes used in coding qualitative data) is an example of the result. Don't fall into the trap of starting with a framework to make it faster to organize your data.  You should look at how themes relate to each other by analyzing the data and consistently check that you can validate that themes are related to each other .

Grounded theory:

This method of analysis starts by formulating a theory around a single data case. Therefore the theory is “grounded' in actual data. Then additional cases can be examined to see if they are relevant and can add to the original theory.

Why is it important to code qualitative data?

Coding qualitative data makes it easier to interpret customer feedback. Assigning codes to words and phrases in each response helps capture what the response is about which, in turn, helps you better analyze and summarize the results of the entire survey.

Researchers use coding and other qualitative data analysis processes to help them make data-driven decisions based on customer feedback. When you use coding to analyze your customer feedback, you can quantify the common themes in customer language. This makes it easier to accurately interpret and analyze customer satisfaction.

What is thematic coding?

Thematic coding, also called thematic analysis, is a type of qualitative data analysis that finds themes in text by analyzing the meaning of words and sentence structure.

When you use thematic coding to analyze customer feedback for example, you can learn which themes are most frequent in feedback. This helps you understand what drives customer satisfaction in an accurate, actionable way.

To learn more about how Thematic analysis software helps you automate the data coding process, check out this article .

Automated vs. Manual coding of qualitative data

Methods of coding qualitative data fall into three categories: automated coding and manual coding, and a blend of the two.

You can automate the coding of your qualitative data with thematic analysis software . Thematic analysis and qualitative data analysis software use machine learning, artificial intelligence (AI) natural language processing (NLP) to code your qualitative data and break text up into themes.

Thematic analysis software is autonomous , which means…

• You don't need to set up themes or categories in advance.
• You don't need to train the algorithm — it learns on its own.
• You can easily capture the “unknown unknowns” to identify themes you may not have spotted on your own.

…all of which will save you time (and lots of unnecessary headaches) when analyzing your customer feedback.

Businesses are also seeing the benefit of using thematic analysis software. The capacity to aggregate data sources into a single source of analysis helps to break down data silos, unifying the analysis and insights across departments . This is now being referred to as Omni channel analysis or Unified Data Analytics .

Use Thematic Analysis Software

Try Thematic today to discover why leading companies rely on the platform to automate the coding of qualitative customer feedback at scale. Whether you have tons of customer reviews, support chat, customer service conversationals ( conversational analytics ) or open-ended survey responses, Thematic brings every valuable insight to the surface, while saving you thousands of hours.

Advances in natural language processing & machine learning have made it possible to automate the analysis of qualitative data, in particular content and framework analysis.  The most commonly used software for automated coding of qualitative data is text analytics software such as Thematic .

While manual human analysis is still popular due to its perceived high accuracy, automating most of the analysis is quickly becoming the preferred choice. Unlike manual analysis, which is prone to bias and doesn't scale to the amount of qualitative data that is generated today, automating analysis is not only more consistent and therefore can be more accurate, but can also save a ton of time, and therefore money.

Our Theme Editor tool ensures you take a reflexive approach, an important step in thematic analysis. The drag-and-drop tool makes it easy to refine, validate, and rename themes as you get more data. By guiding the AI, you can ensure your results are always precise, easy to understand and perfectly aligned with your objectives.

Thematic is the best software to automate code qualitative feedback at scale.

Don't just take it from us. Here's what some of our customers have to say:

I'm a fan of Thematic's ability to save time and create heroes. It does an excellent job using a single view to break down the verbatims into themes displayed by volume, sentiment and impact on our beacon metric, often but not exclusively NPS.

It does a superlative job using GenAI in summarizing a theme or sub-theme down to a single paragraph making it clear what folks are trying to say. Peter K, Snr Research Manager.

Thematic is a very intuitive tool to use. It boasts a robust level of granularity, allowing the user to see the general breadth of verbatim themes, dig into the sub-themes, and further into the sentiment of the open text itself. Artem C, Sr Manager of Research. LinkedIn.

AI-powered software to transform qualitative data at scale through a thematic and content analysis.

How to manually code qualitative data

For the rest of this post, we'll focus on manual coding. Different researchers have different processes, but manual coding usually looks something like this:

• Choose whether you'll use deductive or inductive coding.
• Read through your data to get a sense of what it looks like. Assign your first set of codes.
• Go through your data line-by-line to code as much as possible. Your codes should become more detailed at this step.
• Categorize your codes and figure out how they fit into your coding frame.
• Identify which themes come up the most — and act on them.

Let's break it down a little further…

Deductive coding vs. inductive coding

Before you start qualitative data coding, you need to decide which codes you'll use.

What is Deductive Coding?

Deductive coding means you start with a predefined set of codes, then assign those codes to the new qualitative data. These codes might come from previous research, or you might already know what themes you're interested in analyzing. Deductive coding is also called concept-driven coding.

For example, let's say you're conducting a survey on customer experience . You want to understand the problems that arise from long call wait times, so you choose to make “wait time” one of your codes before you start looking at the data.

The deductive approach can save time and help guarantee that your areas of interest are coded. But you also need to be careful of bias; when you start with predefined codes, you have a bias as to what the answers will be. Make sure you don't miss other important themes by focusing too hard on proving your own hypothesis.

What is Inductive Coding?

Inductive coding , also called open coding, starts from scratch and creates codes based on the qualitative data itself. You don't have a set codebook; all codes arise directly from the survey responses.

Here's how inductive coding works:

• Break your qualitative dataset into smaller samples.
• Read a sample of the data.
• Create codes that will cover the sample.
• Reread the sample and apply the codes.
• Read a new sample of data, applying the codes you created for the first sample.
• Note where codes don't match or where you need additional codes.
• Create new codes based on the second sample.
• Go back and recode all responses again.
• Repeat from step 5 until you've coded all of your data.

If you add a new code, split an existing code into two, or change the description of a code, make sure to review how this change will affect the coding of all responses. Otherwise, the same responses at different points in the survey could end up with different codes.

Sounds like a lot of work, right? Inductive coding is an iterative process, which means it takes longer and is more thorough than deductive coding. A major advantage is that it gives you a more complete, unbiased look at the themes throughout your data.

Combining inductive and deductive coding

In practice, most researchers use a blend of inductive and deductive approaches to coding.

For example, with Thematic, the AI inductively comes up with themes , while also framing the analysis so that it reflects how business decisions are made . At the end of the analysis, researchers use the Theme Editor to iterate or refine themes. Then, in the next wave of analysis, as new data comes in, the AI starts deductively with the theme taxonomy.

Categorize your codes with coding frames

Once you create your codes, you need to put them into a coding frame. A coding frame represents the organizational structure of the themes in your research. There are two types of coding frames: flat and hierarchical.

Flat Coding Frame

A flat coding frame assigns the same level of specificity and importance to each code. While this might feel like an easier and faster method for manual coding, it can be difficult to organize and navigate the themes and concepts as you create more and more codes. It also makes it hard to figure out which themes are most important, which can slow down decision making.

Hierarchical Coding Frame

Hierarchical frames help you organize codes based on how they relate to one another. For example, you can organize the codes based on your customers' feelings on a certain topic:

Hierarchical Coding Frame example

In this example:

• The top-level code describes the topic (customer service)
• The mid-level code specifies whether the sentiment is positive or negative
• The third level details the attribute or specific theme associated with the topic

Hierarchical framing supports a larger code frame and lets you organize codes based on organizational structure. It also allows for different levels of granularity in your coding.

Whether your code frames are hierarchical or flat, your code frames should be flexible. Manually analyzing survey data takes a lot of time and effort; make sure you can use your results in different contexts.

For example, if your survey asks customers about customer service, you might only use codes that capture answers about customer service. Then you realize that the same survey responses have a lot of comments about your company's products. To learn more about what people say about your products, you may have to code all of the responses from scratch! A flexible coding frame covers different topics and insights, which lets you reuse the results later on.

Tips for manually coding qualitative data

Now that you know the basics of coding your qualitative data, here are some tips on making the most of your qualitative research.

Use a codebook to keep track of your codes

As you code more and more data, it can be hard to remember all of your codes off the top of your head. Tracking your codes in a codebook helps keep you organized throughout the data analysis process. Your codebook can be as simple as an Excel spreadsheet or word processor document. As you code new data, add new codes to your codebook and reorganize categories and themes as needed.

Make sure to track:

• The label used for each code
• A description of the concept or theme the code refers to
• Who originally coded it
• The date that it was originally coded or updated
• Any notes on how the code relates to other codes in your analysis

How to create high-quality codes - 4 tips

1. cover as many survey responses as possible..

The code should be generic enough to apply to multiple comments, but specific enough to be useful in your analysis. For example, “Product” is a broad code that will cover a variety of responses — but it's also pretty vague. What about the product? On the other hand, “Product stops working after using it for 3 hours” is very specific and probably won't apply to many responses. “Poor product quality” or “short product lifespan” might be a happy medium.

2. Avoid commonalities.

Having similar codes is okay as long as they serve different purposes. “Customer service” and “Product” are different enough from one another, while “Customer service” and “Customer support” may have subtle differences but should likely be combined into one code.

3. Capture the positive and the negative.

Try to create codes that contrast with each other to track both the positive and negative elements of a topic separately. For example, “Useful product features” and “Unnecessary product features” would be two different codes to capture two different themes.

4. Reduce data — to a point.

Let's look at the two extremes: There are as many codes as there are responses, or each code applies to every single response. In both cases, the coding exercise is pointless; you don't learn anything new about your data or your customers. To make your analysis as useful as possible, try to find a balance between having too many and too few codes.

Group responses based on themes, not words

Make sure to group responses with the same themes under the same code, even if they don't use the same exact wording. For example, a code such as “cleanliness” could cover responses including words and phrases like:

• Looked like a dump
• Could eat off the floor

Having only a few codes and hierarchical framing makes it easier to group different words and phrases under one code. If you have too many codes, especially in a flat frame, your results can become ambiguous and themes can overlap. Manual coding also requires the coder to remember or be able to find all of the relevant codes; the more codes you have, the harder it is to find the ones you need, no matter how organized your codebook is.

Make accuracy a priority

Manually coding qualitative data means that the coder's cognitive biases can influence the coding process. For each study, make sure you have coding guidelines and training in place to keep coding reliable, consistent, and accurate .

One thing to watch out for is definitional drift, which occurs when the data at the beginning of the data set is coded differently than the material coded later. Check for definitional drift across the entire dataset and keep notes with descriptions of how the codes vary across the results.

If you have multiple coders working on one team, have them check one another's coding to help eliminate cognitive biases.

Conclusion: 6 main takeaways for coding qualitative data

Here are 6 final takeaways for manually coding your qualitative data:

• Coding is the process of labeling and organizing your qualitative data to identify themes. After you code your qualitative data, you can analyze it just like numerical data.
• Inductive coding (without a predefined code frame) is more difficult, but less prone to bias, than deductive coding.
• Code frames can be flat (easier and faster to use) or hierarchical (more powerful and organized).
• Your code frames need to be flexible enough that you can make the most of your results and use them in different contexts.
• When creating codes, make sure they cover several responses, contrast one another, and strike a balance between too much and too little information.
• Consistent coding = accuracy. Establish coding procedures and guidelines and keep an eye out for definitional drift in your qualitative data analysis.

Some more detail in our downloadable guide

If you've made it this far, you'll likely be interested in our free guide: Best practices for analyzing open-ended questions.

The guide includes some of the topics covered in this article, and goes into some more niche details.

If your company is looking to automate your qualitative coding process, try Thematic !

If you're looking to trial multiple solutions, check out our free buyer's guide . It covers what to look for when trialing different feedback analytics solutions to ensure you get the depth of insights you need.

Happy coding!

Authored by Alyona Medelyan, PhD – Natural Language Processing & Machine Learning

CEO and Co-Founder

Alyona has a PhD in NLP and Machine Learning. Her peer-reviewed articles have been cited by over 2600 academics. Her love of writing comes from years of PhD research.

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Data coding in qualitative research: a step-by-step guide.

Home » Data coding in qualitative research: A step-by-step guide

Qualitative Data Coding serves as a crucial technique in qualitative research, transforming raw data into actionable insights. Researchers often collect vast amounts of unstructured information through interviews, focus groups, or other methods. Without effective coding, this rich data can become overwhelming and difficult to analyze. Qualitative Data Coding provides a systematic approach to categorize and interpret this information, uncovering meaningful patterns and themes that emerge from respondents' experiences.

This process not only helps in organizing data but also facilitates deeper understanding and comparison across different data sets. By assigning labels or codes to various segments of data, researchers can efficiently navigate through responses and draw relevant conclusions. As we move forward in this guide, we will explore detailed steps and strategies to enhance your skills in Qualitative Data Coding, empowering you to uncover valuable insights from your research endeavors.

Understanding Qualitative Data Coding

Qualitative Data Coding is a systematic process that transforms raw qualitative information into organized, analyzable data. This approach enables researchers to identify patterns, themes, and narratives within the data, providing richer insights into human behavior and experiences. Understanding the nuances of this coding process is crucial for successfully interpreting the greater context behind participants' responses.

To effectively grasp qualitative coding, one should consider several key steps. First, familiarize yourself with the different coding techniques—such as open, axial, and selective coding. Each technique serves unique purposes and can enhance analytical depth. Second, ensure that a clear coding framework aligns with your research questions. A well-structured framework aids in maintaining consistency throughout your analysis. Finally, continuously refine your codes as new insights emerge, allowing your research to be adaptive and responsive to the data collected. This iterative approach will contribute significantly to a comprehensive understanding of qualitative data, enriching the overall research findings.

The Importance of Qualitative Data Coding in Research

Qualitative Data Coding plays a pivotal role in transforming raw information into meaningful insights. By systematically organizing this type of data, researchers can identify patterns, themes, and core concepts that inform their analyses. This process enhances the overall quality and reliability of research findings, making it an essential component of qualitative work.

Moreover, qualitative coding helps ensure consistency and accuracy in data interpretation. It allows researchers to group similar information, making it easier to analyze large volumes of qualitative data, such as interviews or open-ended survey responses. When conducted correctly, qualitative data coding not only clarifies the data but also reveals deeper insights that may not be immediately obvious. The structured approach fosters a more robust understanding of participants' perspectives, enriching the research outcomes and providing valuable guidance for decision-making.

Key Concepts in Qualitative Data Coding

Qualitative Data Coding is essential for transforming raw qualitative data into meaningful insights. This process involves identifying themes, patterns, and categories from diverse data sources such as interviews, focus groups, and open-ended survey responses. The first key concept in qualitative data coding is open coding, where researchers assign initial labels to segments of data. This enables them to recognize significant themes as they emerge in the text.

After open coding, researchers engage in axial coding, which refines and correlates these initial codes into broader categories. This helps in developing a more structured understanding of the data. Finally, selective coding focuses on integrating the categorized data to form a coherent narrative that answers the research questions. Overall, a systematic approach to these coding types enhances the accuracy and depth of qualitative analysis, leading to actionable insights. Understanding these concepts is critical for anyone involved in qualitative research.

Steps in the Qualitative Data Coding Process

The qualitative data coding process consists of several key steps that help researchers derive meaningful insights from text or interview data. Initially, researchers must familiarize themselves with the data by reading and re-reading transcripts or notes. This step aids in identifying recurring themes, concepts, or patterns that warrant further exploration. Next, coding involves assigning labels or tags to these themes, allowing researchers to categorize the information systematically.

Following the coding, it is crucial to review and refine these codes to ensure accuracy and consistency. Researchers can then group similar codes into broader categories, which facilitates the organization of data into a coherent narrative. Finally, the coded data can be analyzed and interpreted, resulting in actionable insights. Each step is essential for effective qualitative data coding, ensuring a thorough understanding of the underlying meanings within the data collected.

Preparing Your Data for Coding

Preparing your data for coding is a crucial step in the qualitative data coding process. Begin by gathering all relevant materials, which can include transcripts, notes, and articles. Ensure that these documents are organized and formatted consistently to facilitate smoother analysis. Use a project management tool to maintain clarity and structure as you import data from various sources.

Next, familiarize yourself with the content you'll be coding. Read through each document to identify key themes and concepts. Highlight or annotate significant quotes and insights that might inform your coding framework. This understanding will serve as a valuable foundation as you transition into the coding phase. Ultimately, the objective is to create an accessible and well-prepared dataset that enhances your ability to uncover patterns and themes in your research findings.

Initial Coding: Creating Categories

Initial coding is a crucial phase in qualitative data coding, where researchers begin to organize raw data into meaningful categories. This process involves reviewing transcripts, notes, or other documents to identify recurring themes or significant patterns. By breaking down the data into manageable parts, researchers can create initial codes that represent key concepts or ideas.

During initial coding, it’s helpful to follow specific steps to maintain focus and clarity. First, familiarize yourself with the data by reading it thoroughly. Second, highlight terms or phrases that stand out and resonate with your research objectives. Third, categorize these highlighted items into broader themes based on their similarities. Finally, label each category with a descriptive name that encapsulates its essence. This systematic approach not only aids in data organization but also enhances the overall analysis process, ensuring that crucial insights are not overlooked.

Axial Coding: Identifying Themes

Axial coding is a pivotal step in qualitative data coding, helping researchers refine and interconnect initial codes. At this stage, the aim is to identify central themes that emerge from gathered data, transforming raw information into coherent concepts. This process involves grouping related data excerpts and examining their relationships to ensure a robust understanding of the core issues.

To effectively conduct axial coding, follow these steps:

• Identify Core Categories : Examine the initial codes and determine central themes.
• Organize Data : Reassemble the data around these themes, creating a framework for analysis.
• Explore Relationships : Investigate how different themes interact, helping to reveal patterns and insights.
• Refine Codes : Continuously update and refine codes based on the evolving understanding of the data.

This systematic approach clarifies the narrative within the data, enhancing the depth and quality of qualitative analysis. In turn, it allows researchers to derive reliable insights that inform further studies or practical applications.

Advanced Techniques in Qualitative Data Coding

Advanced techniques in qualitative data coding enhance understanding and interpretation of qualitative research data. By applying these strategic approaches, researchers can identify patterns and themes that might initially go unnoticed. Effective data coding allows for more nuanced insights, fostering a comprehensive analysis that supports robust findings.

One key technique is thematic coding , where researchers categorize data by identifying overarching themes. Another important method is in vivo coding , which utilizes participants' own words to maintain authenticity and context. Additionally, using mixed coding methods can combine approaches for richer analysis. Lastly, collaborative coding encourages multiple researchers to analyze data collectively, enhancing reliability and integrating diverse perspectives. Each technique contributes uniquely to qualitative data coding, enriching the overall research process and outcomes. Such advanced strategies ensure that insights derived are not only detailed but also meaningful to the study's objectives.

Selective Coding: Refining Themes

Selective coding is a vital aspect of qualitative data coding that allows researchers to refine and consolidate identified themes. At this stage, you will focus on the central categories that emerged during preliminary coding, ensuring they accurately reflect the data. Engaging with this process helps illuminate connections among different themes, revealing deeper insights that may not have been apparent initially.

To effectively carry out selective coding, follow these key steps:

Identify Core Categories : Review the themes identified during earlier coding stages, focusing on those that are most relevant to your research questions.

Group Related Themes : Combine themes that share commonalities, creating broader categories to simplify and clarify the findings.

Review and Revise : Continuously revisit these categories to ensure they remain true to the data and reflect any new insights gained during analysis.

Develop a Narrative : Formulate a compelling story that integrates your core categories, helping convey the significance of your findings in a coherent manner.

This approach enhances the clarity and impact of your qualitative findings, ultimately contributing to a more robust analysis.

Ensuring Rigor in Qualitative Data Coding

Ensuring rigor in qualitative data coding is essential for the credibility of research findings. One effective approach is to establish clear coding guidelines before beginning the analysis. This includes determining code definitions and ensuring they align closely with the research questions. A second key aspect is maintaining consistency across coding efforts. Involving multiple coders can help validate the coding process, but it’s important to hold calibration sessions to align their interpretations.

Furthermore, revisiting the data and codes regularly during analysis fosters a deeper understanding of the emerging themes. This iterative process allows researchers to refine codes as new insights come forward. Lastly, using tools that support transparency in coding decisions can enhance rigor. Documenting code applications and interpretations helps ensure that qualitative data coding stands up to scrutiny and supports robust conclusions, empowering researchers to confidently communicate their findings.

Conclusion: Mastering Qualitative Data Coding in Your Research

Mastering qualitative data coding is essential for researchers aiming to extract meaningful insights from their data. This process involves categorizing, comparing, and interpreting qualitative information, allowing researchers to uncover themes and patterns that may not be immediately visible. Through effective coding, researchers can transform raw data into structured findings that can inform decisions and enhance understanding.

Embracing qualitative data coding requires practice and an awareness of potential biases. By employing systematic coding techniques, researchers can ensure a more objective analysis. Ultimately, mastering this skill will not only improve the quality of your research but also empower you to communicate your findings more clearly and convincingly to your audience.

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Young people’s views and experience of diet-related inequalities in England (UK): a qualitative study

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Vanessa Er, Mary Crowder, Eleanor Holding, Nicholas Woodrow, Naomi Griffin, Carolyn Summerbell, Matt Egan, Hannah Fairbrother, Young people’s views and experience of diet-related inequalities in England (UK): a qualitative study, Health Promotion International , Volume 39, Issue 4, August 2024, daae107, https://doi.org/10.1093/heapro/daae107

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Inequalities in diets contribute to overall inequalities in health. Economic inequality and inequalities in access to healthy food are key drivers of poor diet and ill health among young people (YP). Despite mounting evidence of structural barriers to healthy eating, less is known about how YP view and experience these inequalities where they live, and how to address them. To explore YP’s perspectives on the drivers of diet-related health inequalities, we conducted three interlinked focus groups with YP aged 13–21 years from six youth groups across three geographical areas in England. We analysed the data inductively and deductively using reflexive thematic analysis and generated themes by examining how social structure, context and agency interact and impact YP’s diet. YP were aware of how inequalities in employment conditions impact their families’ income and ability to eat a healthy diet. They cited the high availability of hot food takeaways in their local areas as a significant barrier to healthy eating but did not support closing or restricting these outlets. They held strong views on policies to tackle diet inequality and showed a nuanced understanding of the strengths and limitations of universal and targeted approaches. Our study showed that YP have an awareness and understanding of food as important in relation to health, and of diet-related inequalities. However, further efforts are needed to shape and promote policies that resonate with YP and address both their health and wider social concerns.

Our study recognizes that young people have an awareness and understanding of food as important in relation to health, and of diet-related inequalities.

Young people have a nuanced appreciation of bounded agency: that is, the way social, cultural and economic factors shape individual food choices and practices.

Young people are potential sources of support for health equity strategies that include social determinist approaches.

Further efforts are needed to shape and promote policies that resonate with young people and reflect and address both their health and wider social concerns.

Inequalities in diets contribute to overall inequalities in health ( The Parliamentary Office of Science and Technology, 2022 ). Improvements in diets can improve population-wide health and reduce wider health inequalities. Poor diet in childhood and adolescence tracks into adulthood ( Hovdenak et al. , 2019 ; Appannah et al. , 2021 ) and is associated with lower health-related quality of life ( Wu et al. , 2019 ) and higher risk of chronic diseases, such as cardiovascular disease ( Daniels et al. , 2011 ), diabetes ( Lascar et al. , 2018 ) and some cancers ( World Cancer Research Fund and American Institute for Cancer Research, 2018 ). According to the Global Burden of Disease dashboard, poor diet was the cause of 7.9 million deaths worldwide in 2019, accounting for 14% of all deaths ( Global Burden of Disease Collaborative Network, 2020 ).

One of the main drivers for poor dietary quality is economic inequality and the relatively high cost of eating a healthy diet. According to the Food and Agriculture Organization’s (FAO) report on ‘The State of Food Security and Nutrition in the World’, more than 3.1 billion people across the world could not afford a healthy diet in 2021 ( Food and Agriculture Organization et al. , 2023 ). In the UK, The Food Foundation reported an increase in the proportion of food insecure households with children, from 12.2% in 2022 to 24.4% in 2023 ( The Food Foundation, 2023 ). Additionally, healthy nutritious food was two times more expensive than unhealthy products. Young people (YP) from disadvantaged backgrounds are more likely to experience food insecurity ( O’Connell et al. , 2019 ), which has worsened since the COVID-19 pandemic and the cost-of-living crisis. Studies conducted in England have shown that YP from socioeconomically disadvantaged backgrounds are more likely to have poor diets ( Johnson et al. , 2018 ; Public Health England and Food Standards Agency, 2021 ). The 2020–21 UK National Diet and Nutrition Survey revealed higher consumption of sugar-sweetened beverages and energy-dense food, and lower consumption of fruits and vegetables among the most socioeconomically deprived YP ( Public Health England and Food Standards Agency, 2021 ). Furthermore, the latest statistics from the UK National Child Measurement Programme revealed that children aged 10–11 years living in the most deprived areas were more than twice as likely to be categorized as obese, based on body mass index, compared to those living in the least deprived areas (31.3% vs. 13.5%) ( Office for National Statistics, 2022 ).

Another driver of YP’s diet is the neighbourhood food environment. There is consistent evidence showing a high density of fast-food and takeaway outlets in socioeconomically deprived areas and areas with a high concentration of ethnic minority population ( Fleischhacker et al. , 2011 ; Molaodi et al. , 2012 ; Thornton et al. , 2016 ; Public Health England, 2018 ; Sanchez-Vaznaugh et al. , 2019 ). This means greater availability of, and access to unhealthy food, as these outlets tend to sell relatively cheap, energy-dense and nutrient-poor food ( Jaworowska et al. , 2014 ; Huang et al. , 2022 ; Rinaldi et al. , 2022 ). The inequalities also extend to unhealthy food advertising where YP from ethnic minority and socioeconomically disadvantaged backgrounds are disproportionately targeted where they live, as well as online ( Backholer et al. , 2021 ). In the UK, children living in low-income households are more likely to eat takeaway meals at home, and those who consume takeaways more frequently have poorer diets ( Adams et al. , 2015 ; Taher et al. , 2019 ). The density of takeaway outlets across England increases each year ( MRC Epidemiology Unit, University of Cambridge, 2019 ). A study of takeaway outlets in Norfolk, UK revealed that the density of takeaway outlets grew between 1990 and 2008, and the increase in the number of outlets was higher in the most deprived areas compared to the least deprived, with this widening over time (3.5 times higher in 2008 vs. 2.8 times higher in 1990) ( Maguire et al. , 2015 ). There is emerging evidence of socioeconomic patterning in online access to takeaway outlets too. A recent study by Keeble et al. (2021) found that the percentage of registered food outlets on an online food delivery service in the most deprived areas was approximately two times greater than in the least deprived areas in the UK.

Despite mounting evidence of the structural barriers to eating a healthy diet, such as economic inequalities and the food environment, less is known about how YP view and experience these inequalities where they live, or how to address them. There have been two previous systematic reviews and a scoping review of YP’s views on healthy eating, all focusing on body size (e.g. obesity, body shape and weight) rather than health or health inequalities per se . One review included 11- to 16-year-olds ( Shepherd et al. , 2006 ); another 12- to 18-year-olds ( Rees et al. , 2014 ) and the third 18- to 24-year-olds ( Munt et al. , 2017 ). None of these reviews presented detailed analysis focused on health inequalities, as the reviewers noted that included studies typically reported few details on equity dimensions. The reviews by Shepherd et al. (2006) and Rees et al. (2014) found that YP tended not to frame food as a health issue. Rather they tended to view food in terms of what they liked and disliked.

The case has been made that low agency population interventions, such as free school meals (FSMs), advertising restrictions of foods high in fat, salt and sugar (HFSS) and restrictions of takeaway outlets, are effective and equitable ways to reduce poor diet in the population ( Adams et al. , 2016 ). However, research literature is sparse on YP’s views of such interventions, or their views on alternative (high agency) education interventions or the relative merits of targeted and population-level approaches. In England, the FSM ( Department for Education, 2018 ) and holiday activities and food programmes ( Department for Education, 2022 ) provide healthy food to school-aged children from low-income backgrounds, but there is no provision for older YP (e.g. aged 18–24) from similar backgrounds. Currently, there is a restriction on broadcast advertising for HFSS foods, but only for those aimed at children ( UK Advertising Standards Authority, 2024 ). The existing planning policy only restricts the opening of new takeaway outlets ( Keeble et al. , 2019 ), which has been perceived as less useful in areas that already have a high density of takeaway outlets.

To address the gap in the literature, our study was guided by the following questions:

What is YP’s understanding and experience of diet-related health inequalities?

What are the drivers of diet-related health inequalities where YP live?

What are YP’s views on addressing diet-related health inequalities where they live?

The research in this article drew on data from a wider study exploring YP’s perceptions of what influences their opportunities to be healthy within their local area and their understanding of health inequalities ( Fairbrother et al. , 2022 ). The philosophical underpinning of our approach is critical realism. We view health inequalities as real (they exist independently of human practices and awareness) while acknowledging the role of human practices, perspectives and social context in shaping how we know about health inequalities. In other words, our knowledge of health inequalities is subjective and incomplete. Our approach was critical in orientation ( Braun and Clarke, 2024 ) in that, we sought to unpack and interrogate participants’ accounts to provide causal explanations of health inequalities and make recommendations that are relevant and beneficial to YP. We conducted a qualitative study to understand YP’s perceptions and experiences of health inequalities, with a focus on explaining the causal mechanisms of health inequalities.

Positionality

As a team of public health researchers, our work is rooted in explaining and addressing health inequalities. Based on our knowledge of the literature, we assumed that YP have a more individualistic explanation of the causes of, and solutions to health inequalities. We believe that YP have a right to good health and they should be supported to live to their fullest potential. We made a conscious effort to include disadvantaged and marginalized voices. However, we recognized the power imbalance as we are older, well-educated and ‘relatively advantaged’ (though this term masks some variation in the research team, such as variation in social backgrounds, job security, housing security and other intersecting equity dimensions). Therefore, we took a participatory approach to research in our attempt to balance power dynamics and meaningfully include YP of disadvantaged backgrounds in discussions on health inequalities. Engaging in a process of reflexivity, we acknowledge that our analysis is informed by our prior understandings of health equity, including pre-held assumptions that social determinants, as described by Marmot et al. (2020) , are particularly important for both explaining and tackling health inequalities. We acknowledge that our research findings extend from our subjective experience of the research, influenced by our social and professional backgrounds, and our particular interactions with the YP who participated.

Sampling and recruitment

We recruited YP aged 13–21 years through six youth groups across three geographical areas in England; London, South Yorkshire and North East. Our original sampling frame targeted YP living in areas with contrasting levels of deprivation and geography (e.g. rural and urban). This was hampered by the COVID-19 pandemic, so we recruited YP from youth groups with whom we had established relationships, all of which were located in areas in the most deprived quintile based on the 2019 English indices of multiple deprivation. Furthermore, while we initially aimed to work with YP aged 13–17 years, we took an inclusive approach as some of our youth groups also included YP over 18.

Data collection

We conducted a series of three interlinked focus groups with YP from each of the six youth groups between February 2021 and June 2021, resulting in a total of 18 focus group sessions. The focus groups were planned to be in-person, but we switched to an online format for all but three sessions with one youth group in the North East due to the UK’s lockdown and social distancing restrictions during the COVID-19 pandemic.

Session 1 used a participatory concept mapping activity ( Jessiman et al. , 2021 ) to explore perceptions of what influences YP’s opportunities to be healthy in their local area. Session 2 examined YP’s understanding of health inequalities through prompted discussion of selected health-related news headlines, including FSMs, fast food and advertising of less healthy food. Session 3 focused on YP’s priorities for change to improve health in their area.

Facilitators used a topic guide (see Supplementary Material ) that had been piloted with youth organizations to aid discussions. At least two researchers facilitated each focus group, accompanied by a youth worker for safeguarding purposes and to support YP if required ( Woodrow et al. , 2022 ). Focus groups lasted between 90 and 100 minutes and were audio-recorded with consent. Participants also provided information on ethnicity, age and residential postcode (used to determine area deprivation level). We gave participants £20 vouchers at the end of each focus group as a token of appreciation for their time.

The study has ethics approval from the School of Health and Related Research (SCHARR) Ethics Committee at the University of Sheffield (ref: 037145). All participants provided written consent. For participants under the age of 16 years, opt-in consent was also obtained from parents/guardians.

Data analysis

Prior to analysis, audio recordings were transcribed verbatim and anonymized by approved transcription services. The research in this article drew on data from a wider study exploring YP’s perceptions of what influences their opportunities to be healthy within their local area and their understanding of health inequalities ( Fairbrother et al. , 2022 ). Here, we only include discussions about food as data. We analysed the data using reflective thematic analysis ( Braun and Clarke, 2022 ) as it is theoretically flexible and fits with a critical realist approach. Using this approach, we examined the mechanisms and structures that give rise to diet-related inequalities by focusing on participants’ accounts and situating them within the contexts (realities) that participants live in. This requires continual reflexivity and critical engagement with the data and the analytical process.

V.E. and M.C. read the transcripts and applied a mix of semantic (surface meaning) and latent coding (underlying meaning), aided by a qualitative analysis software, NVivo 12. During the coding process, V.E. and M.C. met regularly to discuss the meaning of the data to ensure reflexivity and expand the interpretation of the data. We analysed the data deductively by using two frameworks as a lens to make sense of the data: Smith and Anderson’s (2018) framework for lay perspectives of socioeconomic health inequalities, and Pearce et al.’s (2019) conceptual model of pathways to inequalities in child health. We combined it with inductive analysis as we were open to the possibility that the data may not fit these frameworks.

Upon reflection, we decided that Giddens’s (1989) structuration theory which posits social practices as an interplay between agency and social structure, was a better fit for the data and used it to inform the conceptualization of themes. We focused on how diet-related inequalities were produced, by contextualizing YP’s eating practices and interactions with their local food environment, and connecting it with the social history and structure of the area where they live. The themes were further developed by V.E. and M.C. alongside discussions with C.S. and H.F. to ensure the themes capture the central meanings and patterns identified from the data and answer the research questions. V.E. wrote a narrative for each theme, with the scope of each theme being iteratively defined and refined with inputs from M.C., C.S., M.E. and H.F.

Our final sample consisted of 42 YP aged 13–21 living in urban and rural areas, and of different genders and ethnicity (see Table 1 ).

Participant characteristics

The YP in this study perceived eating a healthy diet as unattainable due to intersecting inequalities that manifested in their daily food practices and environment. YP’s agency to eat healthily was constrained by structural inequalities, mainly economic inequality and low availability and access to healthy food in deprived areas. We identified three key themes: (i) impact of economic inequality on family food practices, (ii) availability and access to hot food takeaways in areas of high deprivation and (iii) making healthy food more accessible to families.

(i) Perceived impacts of economic inequality on family food practices

This theme captures YP’s understanding of economic inequality. They viewed economic inequality through a ‘place’ lens, whereby some regions or neighbouring local areas in the UK were viewed as more economically disadvantaged than others. A recurring view suggested a mechanism whereby places with economic problems had poorer employment opportunities for YP and their parents. This in turn led to two types of barriers to healthy eating. Firstly, low incomes—YP believed that healthy foods tend to be more expensive than unhealthy food and that low-income families may have to choose between healthy diets and other essentials such as heating and school uniforms. Secondly, YP perceived that for many low-income families, there is less opportunity to prepare healthy meals at home due to a lack of time as a result of working long hours and/or multiple jobs.

Though YP acknowledged the importance of nutrition knowledge and cooking skills for healthy eating, they were acutely aware of how family income (or lack of it) restricted their ability to have an adequate and healthy diet.

I see it sort of like if you have a run down job, you don’t have as much pay to pay for the food. Meanwhile, if you have a high job and you have the high society, you have more pay, therefore you’re able to take on more food. Which brings in the inequality with food discussed tonight. (North East Group 2, Session 2)

There was a common perception among YP that unhealthy food is cheap while healthy food (described mainly as fresh fruits and vegetables) is expensive and thus unattainable on a low income.

things like salads they’re expensive man, like five, six quid then the opposite of that, like portion of chips is like a quid…Even the prices of fruit and veg, I don’t know why they price up a bit too much. (London Group 1, Session 1) I’d say so because like there’s a lot of income inequality where we are especially, so a lot of like poorer households find it hard to buy the more healthy stuff. They tend to be more expensive, especially in supermarkets… (South Yorkshire Group 2, Session 1)

YP also knew of low-income families having to prioritize household bills and expenses over food. A few spoke from experience about their parents having to spend less on food to pay for essentials such as school uniforms. The need to make trade-offs came to the fore while discussing the impact of the COVID-19 pandemic. YP noted that some in their community could hardly afford to pay utility bills and were thus unable to store and/or cook fresh food, resorting to the convenience of fast-food or hot food takeaways.

I think, with fast food, it doesn’t need to be maintained in a sense, so like, let’s say you can’t afford electricity bills, you can’t afford to keep your fridge running, or something. Buying healthy stuff isn’t, it’s not going to last, so just buying fast food, might be cheese and everything, is like OK, I’ve bought it, I can eat it, through it in a day, like it’s finished. (London Group 1, Session 2) …that goes back to the budgeting thing. Some people, especially people who don’t necessarily have a lot of money might want to spend less on food and more on making sure that their child has the right stuff for school. (South Yorkshire Group 1, Session 1)

While YP made direct reference to low income as a barrier to healthy eating, they also demonstrated an in-depth understanding of inequalities in employment conditions faced by those working in low-paid jobs, and how that negatively impacts one’s ability to eat a healthy diet. They pointed out that those in low-paid jobs often lack time to prepare and cook meals as they tend to work long hours or multiple jobs. The North-South (England) divide in economic opportunities was regularly brought up by YPs from the North East. According to one YP, movement restrictions during the COVID-19 pandemic further highlighted the inequalities experienced by those in low-paid jobs (e.g. bus drivers and delivery drivers) as they were less able to work from home.

We did talk a bit about how people in the North, the sort of jobs that we have, it’s less likely that you’ll be able to work from home. So if you are working from home – which predominantly, especially if you’re in the South because a lot of the economies, they’re very knowledge-based – you can afford to do that sort of thing from home…So they have certainly got more time and more time that they can dedicate to something like cooking. (North East Group 1, Session 2)

(ii) Availability and access to hot food takeaways in areas of high deprivation

This theme describes how YP felt physically surrounded by hot food takeaways in their local neighbourhood, and digitally surrounded via food delivery apps. They showed an understanding of how different elements of underserved communities intersect to encourage negative health practices.

YP cited the high density of hot food takeaways in their local area and a lack of food retail shops selling affordable and healthy options as barriers to healthy eating. As a YP related:

I wanted to talk about like fast food joints, like in [Name of location] like there’s a lot of like fried chicken shops, a lot of like and they’ve renamed themselves to grills…like to get like more consumer support. And basically like, what I was saying was like it makes it harder for me. (London Group 2, Session 1).

In terms of family food practices, the constant exposure to hot food takeaway was perceived as offering easy access to food for those whose home situations made it challenging to prepare home-cooked meals. For example, YP described how parents in low-income families worked long hours, leading YP and their families to take the ‘easier option’ of purchasing and consuming readily available hot food takeaways. Even though they were aware that hot food takeaways are unhealthy, they felt that they had to choose convenience over the nutritional quality of their meals.

Because it could be quite a busy job that involves travel where they’d be an air hostess or a conductor for a train. It just might take a lot of time away from their family, so that has to force them to do – in a way – irrational things. Such as constantly sending a fast food order instead of healthy objects. (North East Group 2, Session 2) Like if you come from a lower, like a poor area, maybe – like I knew someone who his mum would give him a quid and he’d go to the shop and just buy something and have it for his tea and he’d be out all night until like 10. His upbringings, obviously it’s not going to be the same as someone who has home-cooked meals every night that are prepared with nutritional value in mind. (South Yorkshire Group 2, Session 3)

YP also talked about how the high availability of cheap and ‘tasty’ hot food takeaways, particularly around the school vicinity, made it convenient for YP to purchase and consume them. It was clear during the discussions that hot food takeaway outlets had become part of the social fabric of local life even though YP were critical of the ubiquitousness of these outlets where they live. They were considered by some YP as the ‘place to go’ after school as there was not much to do for YP in their local area, which also highlighted the lack of services and facilities that cater to youths in areas of high deprivation. Inadvertently, YPs socialized at hot food takeaway outlets and ended up consuming food deemed to be less healthy despite their intentions.

When probed about the high density of hot food takeaways, a YP highlighted the complex interdependency of individual and structural factors that affect one’s ability to eat healthily. Specifically, they explained that demand for cheap and quick meals resulted in the opening of hot food takeaway outlets, which in turn created high visibility and high consumption. This then reinforced the need for more outlets. In contrast, the cycle could be broken or disrupted by the lower demand for ‘cheap’ hot food takeaways in more affluent neighbourhoods.

And then I guess because I think, like, yeah, if you’re in an area where demand is higher you’re going to have more takeaway so then if you’ve got more takeaway then that’s, kind of, what you see most of the time then you’re going to end up going to those takeaways more maybe. And seeing that, like, seeing it as more of an option compared to in a wealthier area where if at first you’re not, like, if there isn’t too much demand for fast food in wealthier areas then the takeaways aren’t really going to go there and then, because, like, and it is then, it is also easier to just go to the supermarket and, like, get stuff. (London Group 2, Session 3)

YP’s narratives also suggested that hot food takeaways have a prominent online presence in their everyday lives. They related their experience of being inundated with advertising of HFSS sold by hot food takeaways in their local area when using food delivery applications, and though healthier options were available, they were more expensive and deemed unaffordable.

…there’s about, I think it’s about 20 on my app just around me, because I live near a load of take aways. (South Yorkshire Group 1, Session 2) …and like delivery companies, some of them actually offer the, the opportunity to like buy healthier options. Some of them do salads and all sort of meals that are meant to be healthier. But it’s like those options are very expensive, compared to the junk food, so called junk food options. So it still leaves you with no choice than to go for the junk food rather than the healthier option. (London Group 1, Session 2)

(iii) Making healthy food more accessible to families

This theme presents YP’s views on public policies or interventions to address inequalities in accessing healthy food. YP had strong views on policies to tackle food inequality and showed a nuanced understanding of the strengths and limitations of universal and targeted approaches. Discussions about interventions, especially those targeted at low-income families were strongly tied to the stigma of poverty. YP in general were not in favour of a targeted FSM approach as they perceived it to be stigmatizing. As recounted by a participant: ‘ There’s an element of shame to it as to whether or not you will accept for yourself that you need that help, feeding your kids and feeding your family’ (North East Group 1, Session 2) .

Although YP from low-income families who were eligible for FSM appreciated the assistance from the government, their accounts of receiving FSM referred to shame and centred on who was ‘poor enough’ to receive government assistance. One YP felt guilty for receiving FSM because their parents held ‘decent’ jobs (i.e. perceived to be well-paid) with full-time employment, and did not consider themselves to be impoverished.

My mum’s a teacher and my dad works at the NHS, but a couple of years ago we were eligible for free school meals because it was something like me mum wasn’t earning enough so we therefore qualified. We felt kind of guilty, like we were robbing it from someone… because there are literally people on it who are choosing between feeding the kids at lunch or clothes for school uniform. (South Yorkshire Group 1, Session 2)

One focus group referred to food voucher schemes, of the kind administered by some schools during the COVID-19 epidemic. Vouchers were seen as a way of giving families a way to decide how best to meet dietary needs and food preferences. This came up while discussing FSM provision during the COVID-19 pandemic, whereby most schools provided food parcels to students. There were strong criticisms of the quality of food parcels as the items were not nutritious and were overpriced. In contrast, another YP who received food vouchers shared how that gave her family the freedom to purchase food according to their needs, and thus they were able to maximize the value of the vouchers:

So for instance, I get them (food vouchers) and we go shopping every month so we just save up all the vouchers and spend it on different places…We get tinned vegetables, like peas and carrots and that, but we don’t really like fresh veg or owt like that because it runs out of date really quickly. There’s no point really getting it. (South Yorkshire Group 1, Session 2)

Most YP demonstrated empathy for those who were perceived to be worst off, for example, those who had to use food banks. Alongside feelings of shame and guilt for receiving FSM, YP shared concerns about the proliferation of food banks in their area and the stigma associated with going to one, particularly the fear of parents being blamed for their inability to provide food for their children. YP’s empathy also extended to local business owners. Although they recognized the adverse impact of hot food takeaways, YP did not want these outlets to be closed or restricted by local authorities—a planning policy that can be introduced by local authorities to limit the number of hot food takeaways, especially within the school vicinity. They expressed concern about the potential loss of income for the businesses and more importantly, loss of employment for workers at the outlets. In terms of supporting customer choice, YP often framed this as wishing to see healthier food options at local outlets as this is where they felt the choice was limited. Incentivizing customers to purchase healthier meals through a loyalty scheme was one of the examples given by YP.

And, I don’t know, I was thinking incentives so, like, in a fast food chain, because you can’t shut them and you don’t really want to disturb their business, but if there was something, like, if you buy a certain number of the healthier meals and you get one of the less healthy meals for free or something, kind of, like a loyalty card…because I don’t think awareness alone necessarily helps because I think people are generally aware but it’s a case of actually, like, putting that into action and I think that can be quite tricky. (London Group 2, Session 3)

This article examines YP’s views on and experiences of inequalities in relation to their access to healthy food and diets. While previous studies have explored YP’s views of healthy eating ( Shepherd et al. , 2006 ; Rees et al. , 2014 ; Munt et al. , 2017 ), there is little evidence on YP’s views of how inequalities in healthy eating occur and how to address them.

The YP we spoke to viewed inequalities in food and health partly in terms of how different people were more or less likely to consume healthy food (with fast food often used by participants as an archetypal ‘unhealthy’ food type). YP discussed inequalities in more explicit food poverty terms: showing awareness that some individuals and families struggle to afford sufficient food. Low income, coupled with the high cost of healthier food products, was singled out as a significant barrier to eating a healthy diet by YP. This is supported by analyses of food costs in the USA, UK and Europe ( Kern et al. , 2017 ; Penne and Goedemé, 2021 ; The Food Foundation, 2023 ), which demonstrated that the cost of healthier food products was higher than less healthy products (two times more in the UK). Furthermore, one in five households in the UK would have to spend almost half of their disposable income to achieve a healthy diet, leaving little for other expenditures ( The Food Foundation, 2023 ). YP also linked low income to inequality in employment opportunities, specifically low access to, and availability of higher paying jobs where they live, and its impact on their diet. This was raised by participants in all three study sites, but more prominently in the North East of England, which has one of the highest rates of unemployment and proportion of benefit claimants ( Office for National Statistics, 2023b ), and the lowest average weekly salary in England ( Office for National Statistics, 2023a ).

We found that YP in our study had a less individualistic understanding of inequality than was suggested in the literature ( Backett-Milburn et al. , 2003 ; Vromen et al. , 2015 ; L’Hôte et al. , 2018 ; Smith and Anderson, 2018 ). While acknowledging the importance of dietary knowledge and cooking skills, our study participants showed an understanding of how structural inequalities impact their ability to acquire and consume a healthy diet. For example, YP were able to articulate the reinforcing connection between availability (supply) and purchase (demand) of hot food takeaways in their local area, which in turn made it easier for them to consume energy-dense and nutrient-poor food. The literature supports YP’s view that disparities in both physical and online food availability reinforce area inequalities. The high density of affordable but less healthy hot food takeaway outlets in deprived areas, both physically and online, has been well-documented ( Fleischhacker et al. , 2011 ; Maguire et al. , 2015 ; Keeble et al. , 2021 ). There is also evidence that it is often the same communities who experience both low income and high exposure to fast-food takeaway in the UK. Burgoine et al. (2018) , for example, demonstrating the double burden of low income and exposure to fast-food takeaway and its impact, found that the lowest income combined with the highest fast-food outlet proportion was associated with greater odds of obesity (odds ratio = 2.43, 95% confidence intervals: 2.09, 2.84). Another study in the UK found that within 4 years of Gateshead Council’s ban on planning permission for fast-food outlets, there was a 13.88% reduction in the proportion of fast-food outlets compared to five other local authorities in the North East of England which did not implement the ban ( Brown et al. , 2022 ). The ban was associated with a decrease in the prevalence of overweight and obesity among children in year 6, living in areas that have a high density of fast-food outlets ( Xiang et al. , 2022 ). This further demonstrates that obesity (as measured by body mass index) is a manifestation of social inequalities in health.

Previous reviews have found that health tends to be deprioritized in YP’s accounts of food, body shape and weight. The previous evidence suggests that YP focus instead on what they like and dislike, particularly on social factors such as social norms and peer expectations relating to body shape, and the social isolation that may result from not living up to those norms ( Shepherd et al. , 2006 ; Rees et al. , 2014 ; Munt et al. , 2017 ). The previous reviews have little to say on health inequalities, perhaps reflecting YP’s apparent lack of interest in health per se . However, some reviewers pointed out that many authors of included studies neglected to provide data on health equity dimensions. In contrast, many of the YP we spoke to were willing to discuss food in relation to health and health inequalities. Possibly, this is because we (the researchers) made our interest in health inequalities known to participants, in contrast to some previously published studies where the researcher's interest in health inequalities may not have been apparent. However, we think it reasonable to hypothesize that health and inequalities may have been more present in YP’s minds at the time of our data collection, given that it occurred during the COVID-19 pandemic. The study also took place in an era where food poverty and food charity (such as food banks) have become more prominent in UK discourse ( The Food Foundation, 2023 ).

Whilst we present YP’s interest in health inequality as a finding from our study, we are careful not to exaggerate the point. The YP we spoke to were also capable of discussing food availability and access in more social terms. For example, participants viewed fast-food chains as places where YP can socialize with friends. This is consistent with findings from a qualitative study of local (adults) perceptions and experiences of chicken shops in a deprived area in East London, which found the shops described as a part of everyday life and valued community spaces ( Thompson et al. , 2018 ). Some participants of our study even felt protective towards local businesses, not wishing to see them shut down. YP’s attitudes towards measures intended to address food poverty were also shaped by more than food considerations. The thought of using welfare or charity was associated by some YP with stigma—and also guilt that seeking help to obtain food could prevent assistance from going to someone with a greater need. These attitudes present insights into what YP regard as social responsibility: supporting local businesses (even unhealthy ones), and assuming that charity and welfare are finite resources best targeted at those in greatest need.

YP’s attitude to universalism was complex. Some appeared to support universal approaches, for example, universal FSMs as a means of reducing stigma. Some wanted further intervention from the government to extend the FSM funding to include after-school and holiday meals. This is in line with the findings of two studies with YP in the UK ( Fairbrother et al. , 2012 ; Knight et al. , 2018 ) which emphasized government and corporate responsibility for ensuring adequate nutrition and a healthy diet is affordable for families. However, other studies on the public’s attitudes towards inequalities have revealed a reticence towards government intervention and a preference for educational (information) and individual behavioural change interventions instead ( Backett-Milburn et al. , 2003 ; Vromen et al. , 2015 ; Smith and Anderson, 2018 ). A comparison study of YP’s view on inequality in the USA, UK and Australia, also found most participants focused on individualized (agency) explanations of, and solutions for inequalities, with little critical engagement of the structural causes of inequalities ( Vromen et al. , 2015 ).

Implications

Our findings show YP have a nuanced appreciation of bounded agency: that is, the way social, cultural and economic factors shape individual choice and practices. However, public health policymakers might view some of the YP’s views with mixed feelings. On the one hand, there is evidence of YP’s understanding of health inequalities, and social determinants, and a clear desire for improvement. On the other hand, this section of the public holds views on social responsibility that do not all fit neatly within the kind of universalist and regulatory approaches to health equity that many UK public health practitioners have long espoused ( Bambra et al. , 2011 ; L’Hôte et al. , 2018 ; Marmot et al. , 2020 ). We found evidence of common ground between YP and public health viewpoints, but further bridging work between the public health community and the public is still required.

We believe that our study can contribute to a re-imagining and updating of the evidence base about YP’s views about food and inequalities. In contrast with previous evidence, this re-imagining recognizes that YP have an awareness and understanding of food as important in relation to health, and of diet-related inequalities—including considerations of both individual behaviour and social determinants. While different findings between studies may reflect methodological differences, there are also plausible reasons for hypothesizing that times have changed, and that YP’s views have changed with them. This hypothesis should be explored further in future research.

Strengths and limitations

We recruited YP living in areas of high deprivation. Using a place lens coupled with participatory concept mapping was an accessible way of eliciting YP’s views on diet-related health inequalities. It made tangible the structural inequalities that manifest in everyday life and impact YP’s ability to eat a healthy diet. It also allowed us to explore YP’s diet in multiple aspects of their life and a range of settings, including school, home and community.

Though our study showed that YP’s agency of purchasing and consuming a healthy diet was constrained by individual and area-level economic inequalities, we were unable to explore how that differs by levels of autonomy and agency. A Lancet series on dietary intake among adolescents (aged 10–24 years) emphasized the need to view adolescents as unique; each with different development trajectories within diverse sociocultural contexts ( Neufeld et al. , 2022 ), rather than being defined by age only. Most of our participants still lived at home and thus conversations about food centred around school meals and hot food takeaways. However, a few participants who had more independence and agency felt that they were being forgotten and not supported by existing policies to obtain and consume a healthy diet. In two of the three study sites, all participants were white British. Although these areas have a high proportion of White population, we may have obtained more diverse views if we had included YP of different ethnicity. There were mentions of value for money as a key factor influencing food purchases in our study. A deeper exploration would enhance our understanding of what value for money means to YP, and its implications.

Individual and area-level economic inequalities constrain YP’s ability to eat a healthy diet. The YP we spoke to appeared to be aware of this. We hypothesize that this awareness may reflect changing contextual factors such as the experience of the COVID-19 pandemic and widely discussed concerns about food poverty. YP are potential sources of support for health equity strategies that include social determinist approaches. However, it would be a mistake to assume this support can be relied on without further efforts to shape and promote policies that resonate with YP and address both their health and wider social concerns.

H.F., C.S. and M.E. conceived the study. V.E., M.C., E.H., N.W. and N.G. collected and analysed the data. V.E. wrote the first draft. All interpreted the data, edited and reviewed drafts and approved the final version of the manuscript.

We would like to thank the young people and the youth organizations who took part in the study. Thanks also to Phillippa Kyle and Nicky Knights for their contribution to data collection and analysis.

This study was funded by the National Institute for Health and Care Research (NIHR) School for Public Health Research (SPHR) (Grant Reference Number PD-SPH-2015). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

We do not have any conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision to publish the results.

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• Volume 14, Issue 8
• Qualitative exploration of patients’ experiences with Intrabeam TARGeted Intraoperative radioTherapy (TARGIT-IORT) and External-Beam RadioTherapy Treatment (EBRT) for breast cancer
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• http://orcid.org/0000-0002-7573-6712 Sandeep Kumar Bagga 1 ,
• Natalie Swiderska 2 ,
• Charlotte Hooker 1 ,
• Jennifer Royle 3 ,
• Marie Ennis-O'Connor 4 ,
• Siobhan Freeney 5 ,
• Dympna Watson 4 ,
• Robin Woolcock 6 ,
• George Lodge 7 ,
• Siobhan Laws 7 ,
• http://orcid.org/0000-0003-1760-1278 Jayant S Vaidya 8
• 1 Research , MediPaCe , London , UK
• 2 Patient Engagement , MediPaCe , London , UK
• 3 Strategy , MediPaCe , London , UK
• 4 Independent Patient Advocate , Dublin , Ireland
• 5 Lobular (Breast Cancer) Ireland , Dublin , Ireland
• 6 Triple Negative Breast Cancer Foundation Inc , London , UK
• 7 Royal Hampshire County Hospital , Winchester , UK
• 8 Division of Surgery and Interventional Science , University College London , London , UK
• Correspondence to Dr Sandeep Kumar Bagga; sandeep{at}medipace.com

Objective To gather a deep qualitative understanding of the perceived benefits and impacts of External-Beam RadioTherapy (EBRT) and TARGeted Intraoperative radioTherapy (TARGIT-IORT) using Intrabeam to assess how the treatments affected patient/care partner experiences during their cancer treatment and beyond.

Design and participants A patient-led working group was established to guide study design and to help validate findings. Patients with experience of receiving EBRT or TARGIT-IORT were purposively sampled by Hampshire Hospitals NHS Foundation Trust. These patients had been offered both regimens as per their clinical features and eligibility. Semistructured interviews were conducted with 29 patients and care partners with lived experience of either EBRT (n=12, 5-day FAST-Forward regimen and n=3, 3-week regimen) or TARGIT-IORT (n=14). Thematic analysis was then carried out by two coders generating 11 themes related to EBRT or TARGIT-IORT.

Setting Semistructured interviews were conducted virtually via Zoom during February and March 2023.

Results A number of procedural grievances were noted among EBRT patients. EBRT was perceived as being disruptive to normal routines (work, home and travel) and caused discomfort from side effects. TARGIT-IORT was perceived by patients and care partners as the safer option and efficient with minimal if any disruptions to quality of life. The need for timely accessible information to reduce anxieties was noted in both cohorts.

Conclusions This qualitative study found that patients perceived EBRT as being greatly disruptive to their lives. In contrast, the one-off feature of TARGIT-IORT given while they are asleep during surgery gives them the feeling of stamping out the cancer without conscious awareness. These insights can help healthcare staff and policy-makers further justify the incorporation of the treatment favoured by these patient perceptions (TARGIT-IORT) more widely in routine practice. Further research is planned to explore TARGIT-IORT in more diverse populations and in the 35 countries where it is an established treatment option.

• breast cancer
• radiotherapy
• qualitative research
• breast surgery
• quality of life

Data availability statement

Data are available upon reasonable request. Raw data such as interview transcripts are not publicly available due to participant confidentiality and risk of compromising privacy but can be made available to researchers if appropriate confidentiality, ethics, regulatory and consent processes can be put in place.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2023-081222

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STRENGTHS AND LIMITATIONS OF THIS STUDY

This qualitative study included the two routinely offered radiotherapy treatment options (External-Beam RadioTherapy and TARgeted Intraoperative radioTherapy using Intrabeam) allowing for assessment of patients’ perceptions and experiences in each.

Methodological strengths include measures to prevent researcher bias, such as producing reflexive accounts, independent coding, and exploring both patient and care partner perspectives .

Extensive involvement of a patient-led working group ensures the study design and delivery is robust yet sensitive and respectful.

A limitation is the lack of diversity in the study population, being predominantly white and of higher socioeconomic status from a single English location because of which we are planning to explore these concepts in more diverse populations and in the 35 other countries where TARGIT-IORT is already a well-established treatment option.

The COVID-19 pandemic during which the patients were treated may have introduced some confounding factors, but they did provide useful insights into patient isolation issues.

Introduction

Conventionally, radiotherapy treatment for breast cancer has involved patients undergoing External-Beam Radiotherapy (EBRT) several weeks or months after their surgical removal (lumpectomy). EBRT is usually delivered postoperatively to the whole breast. For external beam radiotherapy, patients are required to attend 15 treatment sessions, each lasting about 15 min, 5 days a week over 3–6 weeks. 1 , 2 In 2020, the FAST-Forward protocol, administering radiotherapy over five sessions, was adopted in some parts of UK, partly as a response to the COVID-19 pandemic and even before the results of the FAST-Forward trial were published. 3 An additional 5–8 days of tumour bed boost is given in about a quarter of cases who are found to have higher-risk disease. 3

Targeted Intraoperative Radiotherapy (TARGIT-IORT) using Intrabeam offers an alternative to women with early breast cancer that is currently being used in a small number of hospitals across England. This approach, first used in 1998, delivers a single dose of radiotherapy directly to the breast tissue surrounding the tumour immediately after the tumour has been removed and the patient is still under the same anaesthetic in the operating theatre. The long-term results of the international randomised TARGIT-A trial (n=2298) in which TARGIT-IORT was compared with EBRT found TARGIT-IORT to be effective as whole breast radiotherapy, reduced non-breast cancer deaths and improved overall survival in those with grade 1 and grade 2 cancers. 4–12

To date, several studies have investigated patients’ experiences with TARGIT-IORT quantitatively. 13–18 These studies gathered information about patients’ quality of life (QoL) during and after treatment via questionnaires and have concluded that patients receiving TARGIT-IORT report high QoL scores 13 and better emotional well-being, less pain, fewer breast and arm symptoms compared with patients receiving EBRT. 14 19 The social impact of reducing the repeated journeys to the radiotherapy centre for both the patient and their care partner has been established. 20 Patient preferences have also been explored in studies based in the USA and Australia. 21–24 However, qualitative insights can give researchers and practitioners an in-depth understanding of patient perceptions that can help explain, with confidence, the reasoning behind the difference in QoL experienced by patients having these treatments.

Much of the literature on patients’ experiences of receiving radiotherapy has focused on EBRT alone where qualitative studies have used various methods such as workshops, interviews and diary entry analysis. Recurring themes include the need for adequate information provision, healthcare professionals’ knowledge of breast or arm lymphoedema (sluggish drainage of lymph fluid), perceived lack of choice, experiences of being naked and feelings of disempowerment, 25 psychological burdens of impact (and the resources required to support patients), 26 impact of side effects such as skin toxicity on patients’ QoL, life and health after radiotherapy and feeling mystified by radiotherapy and how it works. 27 28 While there are other studies investigating breast cancer patients’ lived experiences of receiving the diagnosis, treatment perceptions, experiences of survivorship and symptoms from radiotherapy, 29–34 they do not focus on lived experiences of receiving EBRT specifically.

In addition, the literature review has highlighted that no qualitative comparison of patients’ experiences of TARGIT-IORT and EBRT has been conducted although one qualitative study, exploring overdiagnosis of breast cancer, did briefly describe the experiences of patients having TARGIT-IORT and EBRT. 35 Rich descriptions of authentic experience can help to place the treatment pathway in the context of patients’ everyday world and to truly understand the perceived barriers, benefits and personal consequences of treatment. Therefore, this study is designed to gather a deep understanding of how patients define the benefits and impacts of each therapeutic regimen and how this qualitatively affects patients’ and/or care partners’ experiences. As a secondary aim, the study will also identify where there have been unnecessary treatment-related impacts on QoL and areas of potential improvement.

Methodology

Study design.

This study used a qualitative research design with semistructured interviews as the primary research instrument. Researchers adopted a phenomenological approach which encourages a bracketing off of researchers’ own preconceptions and opinions to help mitigate bias and promotes a special importance to individual human experience where multiple realities exist (based on participants’ own subjective experiences).

At the inception of the study, a patient-led working group was established to ensure the research was designed and conducted in a respectful and sensitive manner. Figure 1 outlines the research process. The authors have used the Consolidated criteria for Reporting Qualitative research to report the study 36 (see online supplemental file 1 ).

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Overview of research process. EBRT, External-Beam RadioTherapy; TARGIT-IORT, TARGeted intraoperative radiotherapy; REC, Research Ethics Committee.

Working group

Four patient advocates (three patients who had been treated for breast cancer and one care partner) with lived experiences of radiotherapy were invited to participate in a working group with the researchers. An initial meeting was held on 19 August 2022. In this meeting, the research design was discussed which included reviewing the study aims, the need for a comparison group, data collection method and participant recruitment channels. The second meeting, on 30 May 2023, focused on validating the emerging themes from the analysis. Between these meetings, the researchers shared early drafts of the core research material (eg, participant information sheet and consent form) to obtain members’ feedback and suggestions for amendments. The working group has also coauthored this paper.

Recruitment of participants and consent

A key outcome of the first working group meeting was to ensure the study design had a comparison group. This meant recruiting patients or care partners with lived experience of receiving EBRT to enable a comparison to those who had received TARGIT-IORT. The eligibility criteria are based on criteria used previously in TARGIT-IORT clinical trials ( table 1 ). All patients were, at the time of their cancer diagnosis, eligible for both TARGIT-IORT and EBRT.

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Eligibility criteria for interview participants

Participants were first identified by SL and GL at Hampshire Hospitals NHS Foundation Trust in accordance with the eligibility criteria, using purposive sampling (NHS stands for National Health Service, which is provided free for cost to patients in the United Kingdom). This Trust recruited patients to the randomised TARGIT-A trial between 2000 and 2012. Since the National Institute of Health and Care Excellence (NICE) recommendation to offer TARGIT-IORT to suitable patients, they have been offering the procedure to their patients. For this study, GL compiled a list of all patients who received either TARGIT-IORT or EBRT. Prospective participants were stratified first into rural and urban subgroups and then by age (50–60 and 60–70). A randomiser was then applied to these subgroups to ensure the final selection process was free from bias from clinicians who had treated patients. Cover letters and recruitment advertisements (approved by the ethics committee) were posted by GL to 58 eligible patients. Those interested in participating in the study contacted researchers voluntarily. Subsequently, the researchers shared a participant information sheet, consent form and provided further information during a short introductory call where participants also had the opportunity to ask further questions about the purpose and conduct of the research. In total 29 participants responded, and all were successfully recruited to the study. Participant characteristics can be found in table 2 .

Sample characteristics of interview participants

Semistructured interviews

Working group members agreed semistructured interviews should be used to gain rich descriptive accounts of experiences with EBRT and TARGIT-IORT. Members felt discussing sensitive and privileged information, namely people’s experiences of receiving the cancer diagnosis and treatment, would be more uncomfortable in, for example, a focus group environment. Therefore, two discussion guides (one for each type of radiotherapy) were developed and refined with the help of working group members (see online supplemental files 2 and 3 ). Interviews were conducted between 9 February 2023 and 2 March 2023 by researchers SKB, NS and JR who are experienced in using qualitative research methods as part of their professional roles. Each interview lasted approximately 60 min and was conducted virtually through Zoom and either digitally video recorded or audio recorded depending on participants’ wishes. The identity of the interviewer can positively or negatively affect the interviewer–interviewee relationship. Participants were, therefore, asked whether they would prefer an interviewer of the same sex, with the default position being that a female interviewer (NS or JR) will conduct the interview with patients and similarly, a male interviewer (SKB) for male care partners. During the interviews, informal member checking took place by interviewers routinely summarising what participants had said to check for accuracy and understanding.

Recordings were transcribed verbatim, with potentially identifying details anonymised and assigned a unique identifier.

In keeping with a phenomenological approach to analysis, researchers began by writing reflexive accounts. This involved reflecting on their own experiences, preconceptions and assumptions that have the potential to influence interpretations of participants’ accounts. This process helps to create the self-awareness required when attempting to consciously bracket out thoughts and opinions that could lead to bias.

Reflexive thematic analysis based on Braun and Clarke’s 37–39 six-step approach was used to analyse the qualitative data and to identify recurring themes related to patient and care partner experience ( figure 2 ). The process of data familiarisation took place during data collection, postinterview reflections, transcribing and re-reading the transcriptions and interview field notes. Initial transcripts were individually coded (identifying units of meaning) by two researchers (SKB and NS) who then reviewed the other’s codes. Through subsequent discussion and reflection and agreement that theoretical saturation was sufficiently achieved, codes were finalised and applied throughout the remaining transcripts. Through an iterative process, descriptive and interpretative codes were categorised to form 28 subthemes and 11 major themes. Microsoft Word and Excel were used to facilitate coding, grouping and text retrieval to identify illustrative quotes.

Reflexive thematic analysis (adapted from Braun and Clarke, 2020). 37

Patient and public involvement

Patients were involved in refining the research question, study design and outcome measures. Their contributions during these discussions were informed by their lived experiences, priorities and preferences during the first working group meeting (described above in the ‘Methodology, Working group’ section). Participant identification and recruitment channels were also discussed with the patient-led working group though they were not directly involved in recruitment into the study nor in the conduct of the study. The results of the study will be disseminated to the study participants once the peer-reviewed paper is published. The burden of the intervention was assessed by the patients themselves for this qualitative study.

The following section presents findings from the thematic analysis which looked at EBRT and TARGIT-IORT separately and the outputs from discussions of the second working group meeting ( table 3 ). Participants’ quotes have been labelled with identifiers (eg, P1, P2) not known to anyone other than the researchers (ie, not the hospital staff, participants themselves or anyone else).

Themes and subthemes arising from the interviews

Themes coming out of interviews with patients who had EBRT

Dissatisfaction with unalterable elements of ebrt procedure.

The majority of EBRT participants expressed discontent with many of the standard elements of the EBRT procedure. Some participants felt intimidated by the size of the room being ‘disturbing’ (P22) and the radiotherapy machine being ‘scary’ (P19):

…the room that you go into where the machine is, is cold…it could be a bit warmer. Now, some of that could be psychological because you're in a big white room with a big, huge machine… (P3) …the 2 nurses go into another side room, so, you feel so alone, and you know, and this machine sort of moving around you. It’s, it is quite scary to deal with. (P19)

Four participants also described the challenge associated with needing to hold one’s breath during sessions. This is done with the hope that the heart may receive less radiation by pushing the chest wall and the breast away from it. Participants described it as saying, ‘ that was the worst bit’ (P12), ‘ it’s going to be difficult’ (P15), ‘ I don’t want to be zapped on my heart’ (P21) and another felt it was ‘ really claustrophobic’ (P19) or causing ‘ panic’ (P19). The planning appointment required for EBRT was met with similar dissatisfaction. While there is a clear appreciation for healthcare staff and their workload, participants were unhappy with the dehumanising nature of these appointments:

You become another face… you do feel like a slab of meat while they're trying to get you in the right position and it’s not a pleasant experience. (P19)

These experiences resonated with working group members’ recollections: ‘silent’ and ‘cold, dark room’ and finding it difficult, a ‘physical challenge’ to maintain position after surgery. Another member felt that while healthcare staff were pleasant, the experience of receiving radiotherapy itself is ‘quite traumatic’ and emotional, ‘I remember lying there and tears came from nowhere…’.

As with study participants, working group members acknowledged that while healthcare staff themselves are not at fault, the ‘system’ causes the dehumanising elements described by participants referring to poor staffing levels and a high-pressure work environment within healthcare.

In contrast, one participant positively describes the EBRT sessions, ‘ there’s music on, and I didn’t find any cause to worry at all’ (P15). The relaxing effect of music was echoed by a working group member who also recalled how music helped in which she felt was ‘ brilliant’ and stated ‘ it helped my head’ .

Finally, one-third of participants expressed a strong objection to being tattooed which was required to ensure radiation is delivered to the right location.

What really did wind me up actually, I had to have the 3 dots tattooed on me and I didn’t want tattoos. (P3)

Participants were frustrated by the fact that it is permanent, the colour used and two participants felt it affected their confidence in wearing certain clothes, ‘ I can still see that now, if I wear a swimsuit or something’ (19 ). One working group member felt that patients are often uninformed about radiotherapy and that patients’ preferences are not listened to. She concluded that this was a good example of an area that required adequate information sharing at the right time.

Unanticipated disruptions cause helplessness

One-third of EBRT participants experienced either delays on the day of the EBRT session or extensions to their course owing to either machine failure or staff absences. The impacts on patients and care partners include stress, aggravation and disappointment with knock-on consequences proving to be burdensome:

…the machine broke down…but I couldn't find [the new hospital], and I got really tired and upset. I was trying to find where I was supposed to go, and nobody seemed to know, and I just managed to grab the team before they went home. I was like, ‘Give me my last radiotherapy now!’ It was down in some basement I mean. Location S is a maze. So, that was a bit stressy. (P1)

Patients who received their EBRT during the COVID-19 pandemic were unable to have their care partners (in most cases husbands) with them at key treatment stages. This isolation caused additional anxieties in the EBRT cohort as one care partner stated, ‘ it’s disappointing and it would have been nicer for me to be able to support her more…’ (P23). Similarly, a patient participant states:

…it’s anxiety level of just having that, that security blanket of having somebody there with you. (P19)

The working group discussed isolation and the emotional impact during EBRT sessions. Since EBRT continues for days and indeed weeks in some cases (5–6 weeks for all members), patients truly feel alone during this phase. They recalled overwhelming feelings of sadness during the sessions with thoughts such as, ‘ how did I get here’ . One member expressed empathy with study participants who would have had to endure further isolation during the COVID-19 pandemic period.

Straightforward sessions were met with surprise but travelling for EBRT was still a concern

Four out of the 15 participants interviewed from the EBRT cohort, stated that no side effects or other complications were experienced with one participant saying she felt like ‘ one of the lucky ones’ (P20). In the absence of side effects such as pain, burning or tiredness, patients experience a sense of surprise and relief after having received prior warnings from either clinical staff or hearing stories from friends and family.

…they said to put a couple of tubes of aloe vera, and keep in the fridge, and put it on religiously. But I was fine. I was fine. I literally had no burning. No rash, no nothing. I’d heard about friends having burns, but I just didn’t, I didn’t. (P12)

Similarly, three participants were appreciative of the fact that individual sessions of EBRT were in fact ‘ very straightforward’ (P1) and quick:

I did find the time, actually, went quite quick. It wasn’t very long; it might have been an hour. Yeah, it wasn’t too long. (P1)

Although these four participants did not experience radiation-related side effects, it should be noted that two of the four participants did, express frustrations about the burden of travelling during EBRT sessions and that the overall radiotherapy course was ‘ time-consuming’ (P2).

Disruption to normal life routines

A few participants were either employed or self-employed and described how EBRT impacted their own work performance (eg, tiredness, weakness in arm) with one person concluding, ‘ I’m an office worker but if I’d be doing a manual job, I think it might have impacted more.’ (P3). There is also the realisation that patients would likely need to adjust work sessions to accommodate for the EBRT sessions and the related side effects: ‘ I’ve worked out a part time basis to get back into work.’ (P19).

Participants shared concerns about the impact on work colleagues. One participant states: ‘ I’m the only person that does my job. So, I was acutely aware that when I’m not at work other people are picking up my job’ (P19). Similarly:

…we were a short-staffed team, I was aware that when I wasn’t there, it was putting work onto other people, and I felt I should have been there…. (P3)

The EBRT cohort also revealed the emotional toll of work-related worries and impacts, and the work guilt associated with the impact on colleagues. Similarly, the potential impact on the care partner’s work is also, clearly, a significant consideration for patients:

The first night after my first session I was in so much pain, I mean I didn’t sleep a wink the first night. It was absolute agony…it was my self-confidence, and everything was destroyed…and I didn’t dare, I didn’t want to wake my husband up. He had to go to work early, so then he could take me to radiotherapy in the afternoon. (P19)

These impacts on employment were corroborated during working group discussions. One member, commenting in particular on self-employed people, described the impact on financial standing and home life as ‘catastrophic’.

In contrast, those who had flexible hours of working were less affected, as one patient participant describes her care partner: ‘ luckily he was doing a job where could sort of pick and choose what he did and his hours, so it was all right.’ (P3).

The repetitive nature of the EBRT sessions, the travel involved and the side effects experienced all also impacted on participants’ normal daily routines. For instance, home activities such as shopping, gardening and caring responsibilities were impacted:

I think we might have cut down [caring for elderly parents] to once a week instead of two or three times… (P3)

One participant mentioned a close family member taking a week off from work to support looking after her, her husband, family pets and ‘ doing a few household chores like pushing the Hoover around’ (P22).

Severe pain brought on by EBRT was described as ‘ agony’ (P19) both during sessions and after sessions (P7). Ongoing pain months and years after the radiotherapy means patients need to settle into a new norm, now constantly having to be aware of their own ‘ limitations’ (P6):

I’m still aching like mad from [radiotherapy], that’s two years later and I’m still achy and in pain. (P6)

For working group members, these experiences were familiar. One member clarified that although there were no specific side effects from the radiotherapy the disruption to life was ‘ hugely problematic’ .

Many of the patients who received the FAST-Forward protocol included sessions on either side of a weekend resulting in a total of 7 days to complete the course. No participants complained specifically about the weekend being involved in this way however many described dedicated activities for the weekend, for example, food shopping, family commitments and short trips away which would undoubtedly be affected while in active treatment.

Conversations with participants revealed personalities play a role in patients’ cancer journey. One participant felt the emotional impact of radiotherapy treatment (and the subsequent withdrawal of routine contact with healthcare staff) more than all the others. The anxieties associated with this perceived void and the mental health impact itself are disruptive to re-engaging with normal life and routines:

…you’ve got people checking on you as in consultants or the breast care specialist nurses, your GP, the radiographer, people ringing you, checking on you, you’re seeing them all the time. After the radiotherapy, I’m suddenly though on my own now. I didn’t realise it was a real security blanket…it was so reassuring to seeing the consultant and seeing this nurses every day. That was the bit where I took a bit of a dive…I had two or three days where I couldn’t stop crying, I thought, ‘Oh I’m on my own now’… (P12)

There were also those who demonstrated a pragmatic mindset viewing any discomfort and impactful delays associated with EBRT sessions as realities to, in effect, take in their stride, ‘ any inconvenience, you just get on with it.’ (P20 )

Travel is clearly an uncomfortable reality and a demanding aspect of receiving EBRT. Many participants complained about the repeated journeys required for EBRT sessions. The burden has been described as ‘ dreadful’ (P7) with people feeling ‘ exhausted’ particularly where the effects of radiotherapy (tiredness and pain) are felt. The burden of travel also manifests as experiencing a longer day overall as well as the sheer cost of public transport used to make the trips independently:

It’s a pain having to go to Location S, there’s a hospital there where the bus doesn’t even go. So, from Location O, I have to get a bus from here to Location H, then wait half an hour, then from Location H to Location W, bus station, then a taxi to the hospital. That all costs 30 quid and is time-consuming, and when you’re sore, it’s not ideal. (P2)

In the majority of cases, there is a reliance on others (the husband but on occasion friends or children) to drive participants to the EBRT sessions. While there are no direct statements indicating a burden to care partners, it is important to note that in these cases both the patient and the care partner endure the repeated journeys:

My husband drove…I’m not a good driver…I certainly can’t park so it was good that he went with me because in case, if something went wrong or something because he’s like my rock he is. (P3)

The location of the EBRT sessions is critical to the quality of patient experience and when participants were presented with two hospitals to choose from it was clearly valued:

…it’s the same surgeons, all the same team, who were in Hospital W or B. I opted for Hospital B, it’s nearer to me and I could get to Hospital B easily, whereas Hospital W was an ordeal for me. (P21)

Two participants who were retired and for who the location of the hospital was particularly close indicated that travel was not a burden and considered themselves ‘ very lucky’ (P22), though of note, one of these participants experienced no EBRT side effects which may have contributed to relatively positive experience.

In contrast to many of the experiences described above, one working group member shared that she was relieved to have regained her independence since she was able to travel to her EBRT sessions herself. However, the working group notes that all their experiences included a difficult period of receiving chemotherapy first where radiotherapy was viewed as the ‘lesser of evils’.

Experience characterised by discomfort from side effects

A wide range of side effects, clearly attributed to EBRT, were reported by the vast majority of participants and this characterises an important part of the EBRT experience. These included varying intensities of tiredness; burning (from warm sensation to blistered and sore); skin-related conditions (dryness, itchiness, rash); pain; and breast size and density changes. The most common complaint was tiredness:

…there were 3 or 4 days the following week when I just had to go to bed, or just have to, you know, lie on the sofa in the afternoon, or you know I was just really bombed out, and I’m not someone who goes to bed and afternoon normally, I’m always busy, and but I had to. I was knackered. (P12)

EBRT sessions can be very uncomfortable. Severe pain, described as ‘ agony’ (P7, P19) was experienced by two participants. One individual, unable to withstand the pain and the tiredness experienced, made an independent decision to stop attending the sessions for a few days:

I was very tired…I think I might have missed a few days, because I couldn’t make it in between…I thought I’ve done so much now, I’m not going to go anymore because it was really, really hurting…I just wanted it to end and go away? And not think about it anymore basically. (P7)

Patients experienced some side effects such pain in the breast or weakness in the arm for a prolonged period—months and years after the EBRT sessions. There were also reports of new symptoms requiring follow-up that were attributed to EBRT. One participant (P8), who felt uninformed about the ‘long-term, lasting and late effects of radiotherapy’ had developed a new pain under her ribs—she reflected:

I would not have had radiotherapy, and I would not be beating myself up about having had it now had I been given the full information about the long-term effects. You know, it’s life-shortening, radiotherapy is life shortening in itself like chemotherapy. (P8)

During discussions at the second working group meeting, members were not surprised by the insights captured from study participants and felt strongly that there were ‘no positives’ from EBRT (particularly when compared with TARGIT-IORT). One member reflecting on their experience with EBRT stated they came away thinking ‘almost anything is better than this.’ (WG member)

Specific anxieties about receiving EBRT

Discussions with the EBRT cohort revealed three main anxieties associated with receiving radiotherapy. First, while there is evidence that more information early on (particularly from consultants) helps to reduce worry, stories of radiotherapy experiences from friends and family members can raise concerns and anxiety levels:

I only knew what I knew as a lay person, you know, various friends have had radiotherapy, unfortunately, people know a lot of people who have all these sort of things… I’d heard about friends having burns… (P12) …and actually, talking to another friend, she said she would do chemotherapy any day over radiotherapy because of how the radiotherapy, the pushing around and making you feel like a piece of meat, how it how it made her feel. (P19)

The second concern was the potential for radiation to cause harm: (a) so soon after surgery and (b) to healthy tissue and organs: ‘ You’ve got to heal a bit; you can’t go straight into radiotherapy because obviously you’re as raw as hell’ (P6). Two participants in particular experienced discomfort in their arms during their EBRT sessions—one participant had a number of lymph nodes removed from the armpit and one participant developed a seroma which is an abnormal accumulation of fluid following surgery. A working group member shared a similar experience where the development of seroma delayed the start of her radiotherapy course.

Another participant who was particularly concerned about unnecessary radiation exposure requested that only half the breast be irradiated because she wanted ‘ the absolute minimum’ (P8). Similarly, another care partner described his wife’s concerns:

One thing is that my wife was worried about was the radiotherapy because obviously there is this thing with radiotherapy, particularly on the breast, of potential damage to the lungs and she was very concerned about that. (P23)

Third, since many of the participants received their EBRT during the COVID-19 pandemic, a few expressed worries about potential delays either due to staff shortages, protocol-driven cancellations (ie, limiting patient numbers) or themselves contracting COVID-19 (since multiple visits are required with EBRT) and thereby being unable to attend hospital:

But COVID was going on and I remember being so scared that my appointments would be cancelled. (P12)

Targeted Intraoperative radiotherapy using Intrabeam

Perception that targit-iort is efficient and aggravation-free.

Of the 14 TARGIT-IORT participants interviewed, 11 indicated the one-off feature of the procedure was appealing. There are many references to how quickly the procedure was completed ‘ it’s lovely to get it all done and finished with on the day’ (P26). Similarly:

…having it done and dusted, and then then waving goodbye at the hospital gates, it was like why, why would I say, ‘No thank you’? (P16)

As a consequence of this efficiency, there is relief that the procedure permitted radiotherapy to be administered without any COVID-19-related delays, or exposure to the COVID-19 virus during travel or hospital during the multiple visits for EBRT, or complications for which three participants had expressed initial concerns:

I was just delighted that it was dealt with really, really quickly, because back at that time the news was full of things where, you know, because of COVID-19, you know, everything has been delayed, and people not getting cancer treatment and that was one of my, I remember having that conversation with the consultant and said, ‘Look are we going to be delayed’. (P25)

There is a similar relief detected in participants discussing the potential positive impact TARGIT-IORT can have on patient’s mental health as one care partner states:

…the alternative would have been [EBRT]… her symptoms of depression are she gets very, very tired… so intuitively our reaction to [TARGIT-IORT] was… actually quite a good idea. (P24)

Going through a cancer diagnosis and receiving treatment was clearly an emotional time. One participant was impressed with TARGIT-IORT precisely because the efficient delivery of radiotherapy facilitates her moving on quickly:

…the beauty of intraoperative radiotherapy is that I could say ‘OK, been there, done that, move on. (P9)

Convenience of performing TARGIT-IORT during surgery is valued

Most participants from the TARGIT-IORT cohort shared why they preferred to receive radiotherapy at the same time as the surgery. There is a recognition of the convenience that TARGIT-IORT brings as a result of not having to attend hospital on multiple occasions, for example, less travel and car parking and supporting independence (particularly for retired individuals):

It’s my choice to have [TARGIT-IORT] because I thought that it was a better option for me particularly because I live on my own and it would allow me to be more independent. (P18)

While the majority of participants were retired, those who did have young children felt TARGIT-IORT supports their caring responsibilities: ‘ I’ve got a [child] and I’ve got to look after him… This is a better way to go…’ (P14). Additionally, one retired participant who valued the independence TARGIT-IORT facilitated concluded it would also suit younger, busy, women well:

…particularly for younger women this would be an extremely good thing, if they're working, it allows them to get back to work without that constant interruption and if they've got a young family. (P18)

Many participants were able to draw from stories and experiences they had heard from friends and families. The apparent inconvenience and impact of daily radiotherapy doses discouraged patients from EBRT when TARGIT-IORT was presented as an option. One participant whose father received daily doses for prostate cancer felt she would ‘ rather get it all over in one go’ (P10). Similarly:

[TARGIT-IORT] was perfect, because it just meant I didn't have to queue up in the car park with the other poor people having radiotherapy, and I did have friends who had serious cancers who were having radiotherapy at the time, and it was just miserable. (P9)

There is also a perception that with TARGIT-IORT recovery times are likely to be faster since it would signify the end of their cancer treatment: ‘ I’m going to get [TARGIT-IORT] and it’s done’ (P16) and ‘ I can just then get on and recover’ (P4). Another participant summarises her main reasons for opting to receive TARGIT-IORT:

So, there were probably 3 reasons I went for [TARGIT-IORT]. You know, COVID, convenience, and the fact that I thought, you know, ultimately, I’d probably recover quicker. (P9)

Only one participant from the TARGIT-IORT cohort, a care partner, described a significant logistical impact due to his wife’s cancer treatment in general:

…created quite a challenge really for me, I mean, I was never going to moan about it, I wasn’t the one who just had cancer surgery! But you know, it meant the days suddenly got very challenging… (P25)

Perception TARGIT-IORT is a safer alternative to standard practice

Five participants felt that they did not experience any complications as a result of TARGIT-IORT and were able to resume their normal activities quickly. While there are a few cases of soreness and itchiness that participants specifically attributed to TARGIT-IORT, most participants did not report the range of side effects seen in the EBRT cohort. As a result, participants gave their endorsements for TARGIT-IORT, respectively:

I moved around, I got up, got changed, got dressed. It was surprising actually, this is why I’ve decided to do this, if this is what it gives you then everyone should have it. You know you don't need to feel debilitated, and you can carry on with your life. I've got a [child], and I've got to look after him. So, if you can, why not. This is a better way to go if the prognosis allows it. (P14) There were no, no after-effects, no problems. It all healed up very well, because it was quite a small incision anyway and very, very successful. (P28)

The majority of participants felt the procedure prevented healthy tissue and organs from being unnecessarily exposed to radiation because ‘ the radiotherapy is directed immediately where the lump [is]’ (P17).

I confess I heard that and thought ‘God, that’s a bloody good idea, why don’t they do that more often?’. Because obviously if you don’t have to beam through loads of flesh and muscle to get at what you're aiming for then that’s got to be better to be honest. (P24)

A few participants described side effects (soreness, tiredness), precautions (new bra needed, seatbelt cushion) and restrictions (no pressure, sport, lifting), however, they were unable to clearly attribute whether these were related to the surgery or the TARGIT-IORT procedure since both occur at the same time.

…yeah, my arm was a little bit sore…I’m sure it must have been the radiotherapy or the operation, I don’t know. (P29) …a special seat belt cushion that protects your breast from the seat belt and I had one another cushion under my breast supporting it… (P11)

Novel nature of TARGIT-IORT impresses while prompting early caution

Although it has been in use for the last 25 years since the first case was done in 1998 TARGIT-IORT is seen as novel and innovative with advantages acknowledged over EBRT. The decision to proceed with TARGIT-IORT is widely considered ‘ easy’ (P28) or ‘ intuitive’ (P24) or a ‘ no brainer’ (P4):

…well, you’re in there, so you might as well get on and do it and that would surely save the need for me having to come back, I can then just get on and recover basically…it was a no brainer for me, an absolute no brainer. (P4)

However, a few participants described their initial concerns since TARGIT-IORT was introduced by the consultants as a clinical trial and was largely unheard or ‘ unknown’ (P9). Care partners, often husbands and sometimes participants’ children wanted to carry out their own research to help making an informed decision about TARGIT-IORT. One participant had already felt she was convinced by the consultant’s explanation and the advantages over EBRT, however, her daughter, who worked in healthcare, stated ‘ …‘hold on a minute, we need to look at the statistics and the recovery times, side effects’…’ (P10). Similarly, another participant’s husband wanted an opportunity to ask the consultant more questions to help feel more reassured:

…but [care partner] just wanted to have the conversation around the intraoperative radiotherapy because it was an unknown really. (P9)

It should be noted that many of the participants were either themselves or their close family (eg, husband) highly educated, often with a science-based background and were able to explore clinical study papers and statistics: ‘ I’ve got a little statistical training…so I looked at the stats and what the mean variation was…what the levels of certitude at either end of the scale were…’ (P24).

TARGIT-IORT patients have high information needs

As mentioned above, due to the relative novelty of TARGIT-IORT and in the absence of experiences of TARGIT-IORT among participants’ friends and family, reliable information from trustworthy sources is critical. The majority of participants (in both EBRT and TARGIT-IORT cohorts) displayed high levels of trust in their consultant. Receiving adequate information from them about TARGIT-IORT, particularly due to its initial availability via a clinical trial, was appreciated:

I think what was good was the way that it was explained in the first place and what the pros and cons were, or if in fact, there weren't any cons really at all…So, you know, we were told that the treatment, doing it during the operation, is just as effective but it would mean that you would have no subsequent radiotherapy and, you know, of course I’m young and foolish, I assume that to be true, we trust the doctor… (P25) [The consultant] said’ ‘This is this, that is that…pluses and minus’…gone through pros and cons and I had made up my mind that that was a good way to go. (P14)

Working group members could relate closely to this subtheme of trust. They explained that the retrospective perception of TARGIT-IORT was always likely to be a ‘no brainer’, however, for a patient going through the highly emotionally charged process of receiving their diagnosis and treatment, at a time when they are already overwhelmed with new information, the relationship with the doctor is important:

…if it’s being offered to you, it’s important how it’s being offered to you. We put out trust in, so much, our doctors. (WG member)

Two participants described receiving explanations from radiation oncologists during their presurgery appointment, however, these discussions were not influential in helping to decide which type of radiotherapy they would receive. A few participants were wary of using the Internet to search for information related to their treatment options: ‘ I’m very cautious of what information I take in from Google’ (P4). However, the majority did conduct their own Internet searches to bolster their understanding of TARGIT-IORT:

I then went away and looked the bugger up, and then you could learn for yourself a little bit, reading between the technical stuff, what it’s all about, the success rate is there or there about the same, it’s not wonderful but for me, it was a no brainer. (P16)

The provision of information was discussed on a number of occasions by working group members. Simple and clear language is particularly important at a time when patients are already in a vulnerable, stressed and emotional state:

…you are so blindsided…the normal way you operate doesn’t necessarily apply. (WG member)

Working group members pointed to the need for information sources to be created adequately in the first place, for example, being written by patients/care partners who possess the lived experiences and so are able to elaborate on the areas that matter.

…there should never be a need for a patient to go home and want to Google, you should go home with the information in hand or go home with reputable evidence-based sources of information. (WG member)

The primary finding of this study is that the subjective experiences of patients and care partners receiving EBRT or TARGIT-IORT differ significantly. Strong recurring themes of appreciation and recognition of innovation, convenience, absence of side effects and lack of disruption to life have emerged from the TARGIT-IORT cohort while in the EBRT cohort, we have largely heard about discomfort and disruption to life. These themes—centring around (a) treatment procedure itself; (b) impact on QoL and (c) information needs—were presented to and were validated by a patient-led working group.

Patients and care partners involved in this study described numerous challenges, concerns and dissatisfaction with elements of the EBRT procedure while processing a difficult and emotional diagnosis. These findings are consistent with the existing literature on EBRT experience. 25 Probst et al 25 also identified procedural grievances, for instance, patients described the radiotherapy sessions as ‘dehumanising’, ‘emotionally draining’ and complained about the tattoos being a permanent reminder of the cancer. Previous studies exploring patient-perceived barriers to radiotherapy include patients’ fear surrounding radiation toxicity which can result in non-compliance and insufficient treatment. 13 40 In fact, research has identified that fears and anxiety regarding the EBRT experience can influence a patient’s decision to opt for a mastectomy over EBRT, despite the latter having equivalent if not non-inferior survival rates. 30 Several studies have demonstrated that as the distance from radiotherapy centre increases, the rate of mastectomy also increases. 41–45 Indeed, this was the primary patient-centric reason that the TARGIT-IORT procedure was originally conceived. 10–12 46

Our study demonstrates the need for improvements in the way EBRT is delivered and has implications for practice that extend to cases where patients are not eligible for TARGIT-IORT. In stark contrast, those receiving TARGIT-IORT have no awareness or recollection of the procedure since radiation is administered during surgery. Patients and care partners found this feature particularly appealing which contributed to their decision to opt for TARGIT-IORT. Indeed, TARGIT-IORT has been widely adopted elsewhere and treated 45 000 patients across 38 countries. 6

A high proportion of our EBRT cohort (12/15) received the FAST-Forward regimen. This regimen of highly compressed higher-dose-per-fraction radiotherapy was adopted in the UK even before the results of the FAST-Forward trial were published with the aim of reducing waiting time pressures during the early part of the COVID-19 pandemic. We recognise this 5-day regimen is indeed not adopted elsewhere in the world—it has much higher toxicity—19 times higher fibrosis and a quarter of women reporting hardened breasts 3 and this toxicity is seen even with the short follow-up of the FAST-Forward trial. 47 48 It is noteworthy that a large part of the patient’s perceived benefit came from the immediacy of TARGIT-IORT due to its administration during the same anaesthetic as their lumpectomy, the resulting convenience and the absence of additional hospital visits for radiotherapy that would be otherwise required for EBRT. In our study, this benefit of TARGIT-IORT was perceived by patients even though the majority of the comparator group received EBRT over just 5 days rather than the international standard of 3 weeks. It is, therefore, likely that the contrasting experience of patients may have even higher significance and the perceived benefit may be greater when TARGIT-IORT is compared with 3 weeks of EBRT.

Patients in the study who received TARGIT-IORT had been given the option to have it because they fulfilled the eligibility criteria ( table 1 ). Since patients made a conscious choice, it is plausible that the results of this study could be biased favouring TARGIT-IORT. However, the authors of this study submit that patients should be given a choice. Our study shows that those who choose TARGIT-IORT have a positive perception of treatment and the overall experience is better than those who opted for EBRT. Others have shown that if given a choice between no radiotherapy, mastectomy, EBRT and TARGIT-IORT, 75% of patients preferentially choose TARGIT-IORT. 24

It is evident that QoL-related benefits and impacts are a central component of radiotherapy lived experiences. Compared with TARGIT-IORT, EBRT has a prolonged impact on patients and perhaps a compounded impact on QoL where patients live alone (lack emotional or practical support), do not drive (reliance on others or public transport with additional costs and travel time) or have caring responsibilities (partners, parents, children and pets). Travel and mobility issues have been recognised as barriers already 49 as has the inconvenience of a prolonged treatment plan which can affect those living in remote areas even more. 13 Our findings demonstrate the advantages TARGIT-IORT offers to those who are eligible. All participants in our study acknowledged the efficiency of the procedure with many drawn to the option (over EBRT) because it was considered ‘straightforward’ and ‘over-and-done’ during surgery. The benefits of TARGIT-IORT to patients in terms of cost, travel time and distance have been demonstrated, in principle, elsewhere. 49 50 Furthermore, the environmental and social impact of the substantially more travel required for EBRT, and a huge reduction in carbon footprint from cancer treatment by use of TARGIT-IORT has also been well documented. 20

In our study, inconveniences and logistical complications were exacerbated by EBRT side effects which were recognised as a key characteristic of the EBRT patient experience and have implications on QoL. Stanton et al 51 investigated factors affecting QoL during and after radiotherapy and found that functional impacts of treatment, particularly breast-specific pain (eg, mobility) are important correlates of QoL. In addition, Schnur et al 27 showed that key patient concerns include the timing of side effects and the impact of side effects on self-esteem affecting patients’ perception of being attractive, good workers, patients and parents. Another study supports these findings and also shares one case of such extreme physical discomfort (pain, burning, etc) that the patient admitted she had considered ending treatment and another saying she would never choose to have radiotherapy again due to the burning sensation. 28 Our study has captured similar cases. This study also underscores the emotional toll, anxieties and stresses that disruptions to life (eg, work-related) cause and have been heard at a NICE Committee meeting. 52 There were fewer reports of side effects directly attributed to TARGIT-IORT in our findings. This is consistent with a study comparing TARGIT-IORT with EBRT (quantitatively) in which patients receiving TARGIT-IORT also reported less pain, fewer breast, and arm symptoms, and better everyday functioning when compared with patients receiving EBRT. 14 We recognise that our study has found stark differences in patient experience and perception between TARGIT-IORT and EBRT. This can seem obvious because patients with TARGIT-IORT have almost no poor experience in relation to actually receiving the treatment (mainly because they are under general anaesthetic when it is given). Our findings resonate with others who also report the negative patient experiences with EBRT and have suggested interventions to improve them. 25 The important qualitative patient benefits identified in this study are of course in addition to the quantitatively proven significant reduction in non-breast cancer deaths, and an improved overall survival in patients with grade 1 and grade 2 cancers within the randomised TARGIT-A trial. 7 However, our study does detect apparent patient obscurity between surgery-linked or TARGIT-IORT-linked side effects—this clearly needs to be addressed through appropriate education and adequate information provision.

Results from our study cohorts point to the need for improvements in communication and information provision. The role of high-quality communication by healthcare staff and access to emotional support services, particularly when radiotherapy treatment ends has been highlighted already. 26 Previous research has also identified that patients can often feel mystified by radiotherapy (EBRT), how it works and will have anxieties about life and health after radiotherapy 27 or feel disempowered and lacking the ability to make an informed choice. 25 In our study, working group members emphasised the importance of trust in connection with information provision, particularly during an emotional cancer diagnosis. Members felt a number of study findings could be addressed adequately by effectively communicating the right information at the right time. Examples include letting participants know clearly that tattoos will be permanent; what the immediate and long-term side effects of both radiotherapy types are; understanding the side effects of surgery thereby avoiding confusion with TARGIT-IORT; ensuring TARGIT-IORT explanations are always supplemented with lay language overviews of the efficacy and safety profile compared with EBRT. One study showed that more than 90% of patients felt that if they were more informed about radiotherapy, they would be less scared about it. 30 The working group advocated for any shared information, such as leaflets, to be written by patients, that is, those who have experiences of receiving radiotherapy, and therefore, have an awareness of where there are likely to be challenges in understanding treatments and their impacts clearly. Similarly, considerations also ought to be given to ensuring people with learning disabilities and communication difficulties are able to make an informed choice by developing accessible information. To satisfy ‘valid consent’, doctors in the UK are now obliged to follow the new GMC guidelines underlining the essential nature of adequate patient information, 53 about all proven treatment options, even if they are not available at their own centre. In the UK, this powerful principle is now fully enshrined in law (Montgomery vs Lancashire Health Board, 2015). 54 55 The substantially better patient perception and experience documented in this study need to be included during consultations with patients when discussing treatment options before they have their surgery for breast cancer.

This qualitative study, co-led by patients, uncovered detailed lived experiences of receiving either EBRT or TARGIT-IORT from patients treated for early breast cancer, as well as those of their care partners. The research demonstrated a patient-perceived superiority of TARGIT-IORT over EBRT—it is considered more efficient with less disruption to life routines. The paper also illustrates the importance of provision of accessible information about all radiotherapy treatment options from trusted sources, at the right time (before breast cancer surgery), to reduce initial anxieties and help patients make informed choices. These new insights need to be taken together with the established quantitative survival and QoL benefits of TARGIT-IORT over EBRT. We believe that these deep insights into the patient’s perspective will substantially improve our understanding of the lived experiences of patients with breast cancer and will help clinicians, patients and policy-makers to comprehensively consider how access to better treatments can improve patients’ lives.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by Health and Social Care Research Ethics Committee B (HSC REC B), Office for Research Ethics Committees Northern Ireland (ORECNI). IRAS Project ID number: 320976. Participants gave informed consent to participate in the study before taking part.

Acknowledgments

The authors thank the patients and care partners who volunteered to help in development of the research question, helping design the study and outcome measures. The authors thank patients and care partners (the study participants) who volunteered to participate in this study. We are grateful to them for giving up their time and sharing their treatment experiences and valuable insights during a difficult period in their lives for the benefit of science and clinical research.

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X @jsvaidya

Contributors SKB, NS, CH, ME-O'C, SF, DW and RW were responsible for the study concept and design. SL and GL identified patients who met the eligibility criteria. SL, GL and JSV contributed to the study design. GL posted cover letters and recruitment adverts to all identified patients. SKB, NS and JR collected the data. SKB and NS analysed and conducted the thematic analysis from the data. ME-O'C, SF, DW and RW approved the initial report. SKB and CH wrote the first draft of this manuscript. JSV and all other authors were involved in interpreting the data and made substantial contributions to the intellectual content of the manuscript and approved the final version. The authors took full responsibility for the manuscript. SKB is responsible for the overall content (as guarantor).

Funding The study was sponsored by MediPaCe. Unrestricted funding was provided to MediPaCe by Carl Zeiss Medtech AG. The manufacturers of the Intrabeam device (Carl Zeiss Medtech AG) did not have any part in concept, design, or management of the study, or in data analysis, data interpretation, or writing of the report. A grant/award number was not issued for the funder.

Competing interests This qualitative study was initiated by MediPaCe, a patient engagement and patient research company. The manufacturers of the TARGIT-IORT device (Carl Zeiss Medtech AG) did not have any part in concept, design, or management of the study, or in data analysis, data interpretation, or writing of the report. Authors SKB, NS, CH and JR are employed at MediPaCe. MediPaCe received payment to independently plan, coordinate and conduct this study. JSV declares Support from University College London Hospitals (UCLH)/ UCL Comprehensive Biomedical Research Centre, UCLH Charities, HTA, NIHR, National Institute for Health Research (NIHR) Health Technology Assessment (HTA) programme, Department of Health and Social Care, UK Ninewells Cancer Campaign and Cancer Research Campaign (now Cancer Research UK); Research grant from Photoelectron Corp (1996–1999) and for supporting data management at the University of Dundee (Dundee, UK, 2004–2008) and travel reimbursements and honorariums from Carl Zeiss. SL and GL declare no conflicts of interest.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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