dissertation qualitative data analysis example

Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

By: Kerryn Warren (PhD) | Reviewed By: Eunice Rautenbach (D.Tech) | May 2020 (Updated April 2023)

Qualitative data analysis methods. Wow, that’s a mouthful. 

If you’re new to the world of research, qualitative data analysis can look rather intimidating. So much bulky terminology and so many abstract, fluffy concepts. It certainly can be a minefield!

Don’t worry – in this post, we’ll unpack the most popular analysis methods , one at a time, so that you can approach your analysis with confidence and competence – whether that’s for a dissertation, thesis or really any kind of research project.

What (exactly) is qualitative data analysis?

To understand qualitative data analysis, we need to first understand qualitative data – so let’s step back and ask the question, “what exactly is qualitative data?”.

Qualitative data refers to pretty much any data that’s “not numbers” . In other words, it’s not the stuff you measure using a fixed scale or complex equipment, nor do you analyse it using complex statistics or mathematics.

So, if it’s not numbers, what is it?

Words, you guessed? Well… sometimes , yes. Qualitative data can, and often does, take the form of interview transcripts, documents and open-ended survey responses – but it can also involve the interpretation of images and videos. In other words, qualitative isn’t just limited to text-based data.

So, how’s that different from quantitative data, you ask?

Simply put, qualitative research focuses on words, descriptions, concepts or ideas – while quantitative research focuses on numbers and statistics . Qualitative research investigates the “softer side” of things to explore and describe , while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them. If you’re keen to learn more about the differences between qual and quant, we’ve got a detailed post over here .

So, qualitative analysis is easier than quantitative, right?

Not quite. In many ways, qualitative data can be challenging and time-consuming to analyse and interpret. At the end of your data collection phase (which itself takes a lot of time), you’ll likely have many pages of text-based data or hours upon hours of audio to work through. You might also have subtle nuances of interactions or discussions that have danced around in your mind, or that you scribbled down in messy field notes. All of this needs to work its way into your analysis.

Making sense of all of this is no small task and you shouldn’t underestimate it. Long story short – qualitative analysis can be a lot of work! Of course, quantitative analysis is no piece of cake either, but it’s important to recognise that qualitative analysis still requires a significant investment in terms of time and effort.

Need a helping hand?

In this post, we’ll explore qualitative data analysis by looking at some of the most common analysis methods we encounter. We’re not going to cover every possible qualitative method and we’re not going to go into heavy detail – we’re just going to give you the big picture. That said, we will of course includes links to loads of extra resources so that you can learn more about whichever analysis method interests you.

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

There are many different types of qualitative data analysis, all of which serve different purposes and have unique strengths and weaknesses . We’ll start by outlining the analysis methods and then we’ll dive into the details for each.

The 6 most popular methods (or at least the ones we see at Grad Coach) are:

• Content analysis
• Narrative analysis
• Discourse analysis
• Thematic analysis
• Grounded theory (GT)
• Interpretive phenomenological analysis (IPA)

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches.

With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about – like the number of times a Kardashian is mentioned on Twitter. Or you could identify patterns of deeper underlying interpretations – for instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country.

Because content analysis can be used in such a wide variety of ways, it’s important to go into your analysis with a very specific question and goal, or you’ll get lost in the fog. With content analysis, you’ll group large amounts of text into codes , summarise these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method.

Naturally, while content analysis is widely useful, it’s not without its drawbacks . One of the main issues with content analysis is that it can be very time-consuming , as it requires lots of reading and re-reading of the texts. Also, because of its multidimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication.

Content analysis also tends to concentrate on a very specific timeline and doesn’t take into account what happened before or after that timeline. This isn’t necessarily a bad thing though – just something to be aware of. So, keep these factors in mind if you’re considering content analysis. Every analysis method has its limitations , so don’t be put off by these – just be aware of them ! If you’re interested in learning more about content analysis, the video below provides a good starting point.

QDA Method #2: Narrative Analysis 

As the name suggests, narrative analysis is all about listening to people telling stories and analysing what that means . Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analysing their stories and the ways they’re told.

You could, for example, use narrative analysis to explore whether how something is being said is important. For instance, the narrative of a prisoner trying to justify their crime could provide insight into their view of the world and the justice system. Similarly, analysing the ways entrepreneurs talk about the struggles in their careers or cancer patients telling stories of hope could provide powerful insights into their mindsets and perspectives . Simply put, narrative analysis is about paying attention to the stories that people tell – and more importantly, the way they tell them.

Of course, the narrative approach has its weaknesses , too. Sample sizes are generally quite small due to the time-consuming process of capturing narratives. Because of this, along with the multitude of social and lifestyle factors which can influence a subject, narrative analysis can be quite difficult to reproduce in subsequent research. This means that it’s difficult to test the findings of some of this research.

Similarly, researcher bias can have a strong influence on the results here, so you need to be particularly careful about the potential biases you can bring into your analysis when using this method. Nevertheless, narrative analysis is still a very useful qualitative analysis method – just keep these limitations in mind and be careful not to draw broad conclusions . If you’re keen to learn more about narrative analysis, the video below provides a great introduction to this qualitative analysis method.

QDA Method #3: Discourse Analysis 

Discourse is simply a fancy word for written or spoken language or debate . So, discourse analysis is all about analysing language within its social context. In other words, analysing language – such as a conversation, a speech, etc – within the culture and society it takes place. For example, you could analyse how a janitor speaks to a CEO, or how politicians speak about terrorism.

To truly understand these conversations or speeches, the culture and history of those involved in the communication are important factors to consider. For example, a janitor might speak more casually with a CEO in a company that emphasises equality among workers. Similarly, a politician might speak more about terrorism if there was a recent terrorist incident in the country.

So, as you can see, by using discourse analysis, you can identify how culture , history or power dynamics (to name a few) have an effect on the way concepts are spoken about. So, if your research aims and objectives involve understanding culture or power dynamics, discourse analysis can be a powerful method.

Because there are many social influences in terms of how we speak to each other, the potential use of discourse analysis is vast . Of course, this also means it’s important to have a very specific research question (or questions) in mind when analysing your data and looking for patterns and themes, or you might land up going down a winding rabbit hole.

Discourse analysis can also be very time-consuming  as you need to sample the data to the point of saturation – in other words, until no new information and insights emerge. But this is, of course, part of what makes discourse analysis such a powerful technique. So, keep these factors in mind when considering this QDA method. Again, if you’re keen to learn more, the video below presents a good starting point.

QDA Method #4: Thematic Analysis

Thematic analysis looks at patterns of meaning in a data set – for example, a set of interviews or focus group transcripts. But what exactly does that… mean? Well, a thematic analysis takes bodies of data (which are often quite large) and groups them according to similarities – in other words, themes . These themes help us make sense of the content and derive meaning from it.

Let’s take a look at an example.

With thematic analysis, you could analyse 100 online reviews of a popular sushi restaurant to find out what patrons think about the place. By reviewing the data, you would then identify the themes that crop up repeatedly within the data – for example, “fresh ingredients” or “friendly wait staff”.

So, as you can see, thematic analysis can be pretty useful for finding out about people’s experiences , views, and opinions . Therefore, if your research aims and objectives involve understanding people’s experience or view of something, thematic analysis can be a great choice.

Since thematic analysis is a bit of an exploratory process, it’s not unusual for your research questions to develop , or even change as you progress through the analysis. While this is somewhat natural in exploratory research, it can also be seen as a disadvantage as it means that data needs to be re-reviewed each time a research question is adjusted. In other words, thematic analysis can be quite time-consuming – but for a good reason. So, keep this in mind if you choose to use thematic analysis for your project and budget extra time for unexpected adjustments.

QDA Method #5: Grounded theory (GT) 

Grounded theory is a powerful qualitative analysis method where the intention is to create a new theory (or theories) using the data at hand, through a series of “ tests ” and “ revisions ”. Strictly speaking, GT is more a research design type than an analysis method, but we’ve included it here as it’s often referred to as a method.

What’s most important with grounded theory is that you go into the analysis with an open mind and let the data speak for itself – rather than dragging existing hypotheses or theories into your analysis. In other words, your analysis must develop from the ground up (hence the name). 

Let’s look at an example of GT in action.

Assume you’re interested in developing a theory about what factors influence students to watch a YouTube video about qualitative analysis. Using Grounded theory , you’d start with this general overarching question about the given population (i.e., graduate students). First, you’d approach a small sample – for example, five graduate students in a department at a university. Ideally, this sample would be reasonably representative of the broader population. You’d interview these students to identify what factors lead them to watch the video.

After analysing the interview data, a general pattern could emerge. For example, you might notice that graduate students are more likely to read a post about qualitative methods if they are just starting on their dissertation journey, or if they have an upcoming test about research methods.

From here, you’ll look for another small sample – for example, five more graduate students in a different department – and see whether this pattern holds true for them. If not, you’ll look for commonalities and adapt your theory accordingly. As this process continues, the theory would develop . As we mentioned earlier, what’s important with grounded theory is that the theory develops from the data – not from some preconceived idea.

So, what are the drawbacks of grounded theory? Well, some argue that there’s a tricky circularity to grounded theory. For it to work, in principle, you should know as little as possible regarding the research question and population, so that you reduce the bias in your interpretation. However, in many circumstances, it’s also thought to be unwise to approach a research question without knowledge of the current literature . In other words, it’s a bit of a “chicken or the egg” situation.

Regardless, grounded theory remains a popular (and powerful) option. Naturally, it’s a very useful method when you’re researching a topic that is completely new or has very little existing research about it, as it allows you to start from scratch and work your way from the ground up .

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

Interpretive. Phenomenological. Analysis. IPA . Try saying that three times fast…

Let’s just stick with IPA, okay?

IPA is designed to help you understand the personal experiences of a subject (for example, a person or group of people) concerning a major life event, an experience or a situation . This event or experience is the “phenomenon” that makes up the “P” in IPA. Such phenomena may range from relatively common events – such as motherhood, or being involved in a car accident – to those which are extremely rare – for example, someone’s personal experience in a refugee camp. So, IPA is a great choice if your research involves analysing people’s personal experiences of something that happened to them.

It’s important to remember that IPA is subject – centred . In other words, it’s focused on the experiencer . This means that, while you’ll likely use a coding system to identify commonalities, it’s important not to lose the depth of experience or meaning by trying to reduce everything to codes. Also, keep in mind that since your sample size will generally be very small with IPA, you often won’t be able to draw broad conclusions about the generalisability of your findings. But that’s okay as long as it aligns with your research aims and objectives.

Another thing to be aware of with IPA is personal bias . While researcher bias can creep into all forms of research, self-awareness is critically important with IPA, as it can have a major impact on the results. For example, a researcher who was a victim of a crime himself could insert his own feelings of frustration and anger into the way he interprets the experience of someone who was kidnapped. So, if you’re going to undertake IPA, you need to be very self-aware or you could muddy the analysis.

How to choose the right analysis method

In light of all of the qualitative analysis methods we’ve covered so far, you’re probably asking yourself the question, “ How do I choose the right one? ”

Much like all the other methodological decisions you’ll need to make, selecting the right qualitative analysis method largely depends on your research aims, objectives and questions . In other words, the best tool for the job depends on what you’re trying to build. For example:

• Perhaps your research aims to analyse the use of words and what they reveal about the intention of the storyteller and the cultural context of the time.
• Perhaps your research aims to develop an understanding of the unique personal experiences of people that have experienced a certain event, or
• Perhaps your research aims to develop insight regarding the influence of a certain culture on its members.

As you can probably see, each of these research aims are distinctly different , and therefore different analysis methods would be suitable for each one. For example, narrative analysis would likely be a good option for the first aim, while grounded theory wouldn’t be as relevant. 

It’s also important to remember that each method has its own set of strengths, weaknesses and general limitations. No single analysis method is perfect . So, depending on the nature of your research, it may make sense to adopt more than one method (this is called triangulation ). Keep in mind though that this will of course be quite time-consuming.

As we’ve seen, all of the qualitative analysis methods we’ve discussed make use of coding and theme-generating techniques, but the intent and approach of each analysis method differ quite substantially. So, it’s very important to come into your research with a clear intention before you decide which analysis method (or methods) to use.

Start by reviewing your research aims , objectives and research questions to assess what exactly you’re trying to find out – then select a qualitative analysis method that fits. Never pick a method just because you like it or have experience using it – your analysis method (or methods) must align with your broader research aims and objectives.

Let’s recap on QDA methods…

In this post, we looked at six popular qualitative data analysis methods:

• First, we looked at content analysis , a straightforward method that blends a little bit of quant into a primarily qualitative analysis.
• Then we looked at narrative analysis , which is about analysing how stories are told.
• Next up was discourse analysis – which is about analysing conversations and interactions.
• Then we moved on to thematic analysis – which is about identifying themes and patterns.
• From there, we went south with grounded theory – which is about starting from scratch with a specific question and using the data alone to build a theory in response to that question.
• And finally, we looked at IPA – which is about understanding people’s unique experiences of a phenomenon.

Of course, these aren’t the only options when it comes to qualitative data analysis, but they’re a great starting point if you’re dipping your toes into qualitative research for the first time.

If you’re still feeling a bit confused, consider our private coaching service , where we hold your hand through the research process to help you develop your best work.

Psst... there’s more!

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

87 Comments

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Thanks for your comment. Most commonly, one would use one type of analysis method, but it depends on your research aims and objectives.

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Thanks for your comment. No, QCA and thematic are two different types of analysis. This article might help clarify – https://onlinelibrary.wiley.com/doi/10.1111/nhs.12048

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Is there a video the captures the practical process of coding using automated applications?

Thanks for the comment. We don’t recommend using automated applications for coding, as they are not sufficiently accurate in our experience.

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do you have any material on Data collection

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

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

What is qualitative data analysis?

Qualitative data analysis methods, how do you analyze qualitative data, content analysis, thematic analysis.

• Thematic analysis vs. content analysis
• Narrative research

Phenomenological research

Discourse analysis, grounded theory.

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

Qualitative data analysis

Analyzing qualitative data is the next step after you have completed the use of qualitative data collection methods . The qualitative analysis process aims to identify themes and patterns that emerge across the data.

In simplified terms, qualitative research methods involve non-numerical data collection followed by an explanation based on the attributes of the data . For example, if you are asked to explain in qualitative terms a thermal image displayed in multiple colors, then you would explain the color differences rather than the heat's numerical value. If you have a large amount of data (e.g., of group discussions or observations of real-life situations), the next step is to transcribe and prepare the raw data for subsequent analysis.

Researchers can conduct studies fully based on qualitative methodology, or researchers can preface a quantitative research study with a qualitative study to identify issues that were not originally envisioned but are important to the study. Quantitative researchers may also collect and analyze qualitative data following their quantitative analyses to better understand the meanings behind their statistical results.

Conducting qualitative research can especially help build an understanding of how and why certain outcomes were achieved (in addition to what was achieved). For example, qualitative data analysis is often used for policy and program evaluation research since it can answer certain important questions more efficiently and effectively than quantitative approaches.

Qualitative data analysis can also answer important questions about the relevance, unintended effects, and impact of programs, such as:

• Were expectations reasonable?
• Did processes operate as expected?
• Were key players able to carry out their duties?
• Were there any unintended effects of the program?

The importance of qualitative data analysis

Qualitative approaches have the advantage of allowing for more diversity in responses and the capacity to adapt to new developments or issues during the research process itself. While qualitative analysis of data can be demanding and time-consuming to conduct, many fields of research utilize qualitative software tools that have been specifically developed to provide more succinct, cost-efficient, and timely results.

Qualitative data analysis is an important part of research and building greater understanding across fields for a number of reasons. First, cases for qualitative data analysis can be selected purposefully according to whether they typify certain characteristics or contextual locations. In other words, qualitative data permits deep immersion into a topic, phenomenon, or area of interest. Rather than seeking generalizability to the population the sample of participants represent, qualitative research aims to construct an in-depth and nuanced understanding of the research topic.

Secondly, the role or position of the researcher in qualitative analysis of data is given greater critical attention. This is because, in qualitative data analysis, the possibility of the researcher taking a ‘neutral' or transcendent position is seen as more problematic in practical and/or philosophical terms. Hence, qualitative researchers are often exhorted to reflect on their role in the research process and make this clear in the analysis.

Thirdly, while qualitative data analysis can take a wide variety of forms, it largely differs from quantitative research in the focus on language, signs, experiences, and meaning. In addition, qualitative approaches to analysis are often holistic and contextual rather than analyzing the data in a piecemeal fashion or removing the data from its context. Qualitative approaches thus allow researchers to explore inquiries from directions that could not be accessed with only numerical quantitative data.

Establishing research rigor

Systematic and transparent approaches to the analysis of qualitative data are essential for rigor . For example, many qualitative research methods require researchers to carefully code data and discern and document themes in a consistent and credible way.

Perhaps the most traditional division in the way qualitative and quantitative research have been used in the social sciences is for qualitative methods to be used for exploratory purposes (e.g., to generate new theory or propositions) or to explain puzzling quantitative results, while quantitative methods are used to test hypotheses .

After you’ve collected relevant data , what is the best way to look at your data ? As always, it will depend on your research question . For instance, if you employed an observational research method to learn about a group’s shared practices, an ethnographic approach could be appropriate to explain the various dimensions of culture. If you collected textual data to understand how people talk about something, then a discourse analysis approach might help you generate key insights about language and communication.

The qualitative data coding process involves iterative categorization and recategorization, ensuring the evolution of the analysis to best represent the data. The procedure typically concludes with the interpretation of patterns and trends identified through the coding process.

To start off, let’s look at two broad approaches to data analysis.

Deductive analysis

Deductive analysis is guided by pre-existing theories or ideas. It starts with a theoretical framework , which is then used to code the data. The researcher can thus use this theoretical framework to interpret their data and answer their research question .

The key steps include coding the data based on the predetermined concepts or categories and using the theory to guide the interpretation of patterns among the codings. Deductive analysis is particularly useful when researchers aim to verify or extend an existing theory within a new context.

Inductive analysis

Inductive analysis involves the generation of new theories or ideas based on the data. The process starts without any preconceived theories or codes, and patterns, themes, and categories emerge out of the data.

The researcher codes the data to capture any concepts or patterns that seem interesting or important to the research question . These codes are then compared and linked, leading to the formation of broader categories or themes. The main goal of inductive analysis is to allow the data to 'speak for itself' rather than imposing pre-existing expectations or ideas onto the data.

Deductive and inductive approaches can be seen as sitting on opposite poles, and all research falls somewhere within that spectrum. Most often, qualitative analysis approaches blend both deductive and inductive elements to contribute to the existing conversation around a topic while remaining open to potential unexpected findings. To help you make informed decisions about which qualitative data analysis approach fits with your research objectives, let's look at some of the common approaches for qualitative data analysis.

Content analysis is a research method used to identify patterns and themes within qualitative data. This approach involves systematically coding and categorizing specific aspects of the content in the data to uncover trends and patterns. An often important part of content analysis is quantifying frequencies and patterns of words or characteristics present in the data .

It is a highly flexible technique that can be adapted to various data types , including text, images, and audiovisual content . While content analysis can be exploratory in nature, it is also common to use pre-established theories and follow a more deductive approach to categorizing and quantifying the qualitative data.

Thematic analysis is a method used to identify, analyze, and report patterns or themes within the data. This approach moves beyond counting explicit words or phrases and focuses on also identifying implicit concepts and themes within the data.

Researchers conduct detailed coding of the data to ascertain repeated themes or patterns of meaning. Codes can be categorized into themes, and the researcher can analyze how the themes relate to one another. Thematic analysis is flexible in terms of the research framework, allowing for both inductive (data-driven) and deductive (theory-driven) approaches. The outcome is a rich, detailed, and complex account of the data.

Grounded theory is a systematic qualitative research methodology that is used to inductively generate theory that is 'grounded' in the data itself. Analysis takes place simultaneously with data collection , and researchers iterate between data collection and analysis until a comprehensive theory is developed.

Grounded theory is characterized by simultaneous data collection and analysis, the development of theoretical codes from the data, purposeful sampling of participants, and the constant comparison of data with emerging categories and concepts. The ultimate goal is to create a theoretical explanation that fits the data and answers the research question .

Discourse analysis is a qualitative research approach that emphasizes the role of language in social contexts. It involves examining communication and language use beyond the level of the sentence, considering larger units of language such as texts or conversations.

Discourse analysts typically investigate how social meanings and understandings are constructed in different contexts, emphasizing the connection between language and power. It can be applied to texts of all kinds, including interviews , documents, case studies , and social media posts.

Phenomenological research focuses on exploring how human beings make sense of an experience and delves into the essence of this experience. It strives to understand people's perceptions, perspectives, and understandings of a particular situation or phenomenon.

It involves in-depth engagement with participants, often through interviews or conversations, to explore their lived experiences. The goal is to derive detailed descriptions of the essence of the experience and to interpret what insights or implications this may bear on our understanding of this phenomenon.

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Now that we've summarized the major approaches to data analysis, let's look at the broader process of research and data analysis. Suppose you need to do some research to find answers to any kind of research question, be it an academic inquiry, business problem, or policy decision. In that case, you need to collect some data. There are many methods of collecting data: you can collect primary data yourself by conducting interviews, focus groups , or a survey , for instance. Another option is to use secondary data sources. These are data previously collected for other projects, historical records, reports, statistics – basically everything that exists already and can be relevant to your research.

The data you collect should always be a good fit for your research question . For example, if you are interested in how many people in your target population like your brand compared to others, it is no use to conduct interviews or a few focus groups . The sample will be too small to get a representative picture of the population. If your questions are about "how many….", "what is the spread…" etc., you need to conduct quantitative research . If you are interested in why people like different brands, their motives, and their experiences, then conducting qualitative research can provide you with the answers you are looking for.

Let's describe the important steps involved in conducting research.

Step 1: Planning the research

As the saying goes: "Garbage in, garbage out." Suppose you find out after you have collected data that

• you talked to the wrong people
• asked the wrong questions
• a couple of focus groups sessions would have yielded better results because of the group interaction, or
• a survey including a few open-ended questions sent to a larger group of people would have been sufficient and required less effort.

Think thoroughly about sampling, the questions you will be asking, and in which form. If you conduct a focus group or an interview, you are the research instrument, and your data collection will only be as good as you are. If you have never done it before, seek some training and practice. If you have other people do it, make sure they have the skills.

Step 2: Preparing the data

When you conduct focus groups or interviews, think about how to transcribe them. Do you want to run them online or offline? If online, check out which tools can serve your needs, both in terms of functionality and cost. For any audio or video recordings , you can consider using automatic transcription software or services. Automatically generated transcripts can save you time and money, but they still need to be checked. If you don't do this yourself, make sure that you instruct the person doing it on how to prepare the data.

• How should the final transcript be formatted for later analysis?
• Which names and locations should be anonymized?
• What kind of speaker IDs to use?

What about survey data ? Some survey data programs will immediately provide basic descriptive-level analysis of the responses. ATLAS.ti will support you with the analysis of the open-ended questions. For this, you need to export your data as an Excel file. ATLAS.ti's survey import wizard will guide you through the process.

Other kinds of data such as images, videos, audio recordings, text, and more can be imported to ATLAS.ti. You can organize all your data into groups and write comments on each source of data to maintain a systematic organization and documentation of your data.

Step 3: Exploratory data analysis

You can run a few simple exploratory analyses to get to know your data. For instance, you can create a word list or word cloud of all your text data or compare and contrast the words in different documents. You can also let ATLAS.ti find relevant concepts for you. There are many tools available that can automatically code your text data, so you can also use these codings to explore your data and refine your coding.

For instance, you can get a feeling for the sentiments expressed in the data. Who is more optimistic, pessimistic, or neutral in their responses? ATLAS.ti can auto-code the positive, negative, and neutral sentiments in your data. Naturally, you can also simply browse through your data and highlight relevant segments that catch your attention or attach codes to begin condensing the data.

Step 4: Build a code system

Whether you start with auto-coding or manual coding, after having generated some first codes, you need to get some order in your code system to develop a cohesive understanding. You can build your code system by sorting codes into groups and creating categories and subcodes. As this process requires reading and re-reading your data, you will become very familiar with your data. Counting on a tool like ATLAS.ti qualitative data analysis software will support you in the process and make it easier to review your data, modify codings if necessary, change code labels, and write operational definitions to explain what each code means.

Step 5: Query your coded data and write up the analysis

Once you have coded your data, it is time to take the analysis a step further. When using software for qualitative data analysis , it is easy to compare and contrast subsets in your data, such as groups of participants or sets of themes.

For instance, you can query the various opinions of female vs. male respondents. Is there a difference between consumers from rural or urban areas or among different age groups or educational levels? Which codes occur together throughout the data set? Are there relationships between various concepts, and if so, why?

Step 6: Data visualization

Data visualization brings your data to life. It is a powerful way of seeing patterns and relationships in your data. For instance, diagrams allow you to see how your codes are distributed across documents or specific subpopulations in your data.

Exploring coded data on a canvas, moving around code labels in a virtual space, linking codes and other elements of your data set, and thinking about how they are related and why – all of these will advance your analysis and spur further insights. Visuals are also great for communicating results to others.

Step 7: Data presentation

The final step is to summarize the analysis in a written report . You can now put together the memos you have written about the various topics, select some salient quotes that illustrate your writing, and add visuals such as tables and diagrams. If you follow the steps above, you will already have all the building blocks, and you just have to put them together in a report or presentation.

When preparing a report or a presentation, keep your audience in mind. Does your audience better understand numbers than long sections of detailed interpretations? If so, add more tables, charts, and short supportive data quotes to your report or presentation. If your audience loves a good interpretation, add your full-length memos and walk your audience through your conceptual networks and illustrative data quotes.

Qualitative data analysis begins with ATLAS.ti

For tools that can make the most out of your data, check out ATLAS.ti with a free trial.

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Review our examples before placing an order, learn how to draft academic papers, a step-by-step guide to dissertation data analysis.

How to Write a Dissertation Conclusion? | Tips & Examples

What is PhD Thesis Writing? | Beginner’s Guide

A data analysis dissertation is a complex and challenging project requiring significant time, effort, and expertise. Fortunately, it is possible to successfully complete a data analysis dissertation with careful planning and execution.

As a student, you must know how important it is to have a strong and well-written dissertation, especially regarding data analysis. Proper data analysis is crucial to the success of your research and can often make or break your dissertation.

To get a better understanding, you may review the data analysis dissertation examples listed below;

• Impact of Leadership Style on the Job Satisfaction of Nurses
• Effect of Brand Love on Consumer Buying Behaviour in Dietary Supplement Sector
• An Insight Into Alternative Dispute Resolution
• An Investigation of Cyberbullying and its Impact on Adolescent Mental Health in UK

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Types of data analysis for dissertation.

The various types of data Analysis in a Dissertation are as follows;

1.   Qualitative Data Analysis

Qualitative data analysis is a type of data analysis that involves analyzing data that cannot be measured numerically. This data type includes interviews, focus groups, and open-ended surveys. Qualitative data analysis can be used to identify patterns and themes in the data.

2.   Quantitative Data Analysis

Quantitative data analysis is a type of data analysis that involves analyzing data that can be measured numerically. This data type includes test scores, income levels, and crime rates. Quantitative data analysis can be used to test hypotheses and to look for relationships between variables.

3.   Descriptive Data Analysis

Descriptive data analysis is a type of data analysis that involves describing the characteristics of a dataset. This type of data analysis summarizes the main features of a dataset.

4.   Inferential Data Analysis

Inferential data analysis is a type of data analysis that involves making predictions based on a dataset. This type of data analysis can be used to test hypotheses and make predictions about future events.

5.   Exploratory Data Analysis

Exploratory data analysis is a type of data analysis that involves exploring a data set to understand it better. This type of data analysis can identify patterns and relationships in the data.

Time Period to Plan and Complete a Data Analysis Dissertation?

When planning dissertation data analysis, it is important to consider the dissertation methodology structure and time series analysis as they will give you an understanding of how long each stage will take. For example, using a qualitative research method, your data analysis will involve coding and categorizing your data.

This can be time-consuming, so allowing enough time in your schedule is important. Once you have coded and categorized your data, you will need to write up your findings. Again, this can take some time, so factor this into your schedule.

Finally, you will need to proofread and edit your dissertation before submitting it. All told, a data analysis dissertation can take anywhere from several weeks to several months to complete, depending on the project’s complexity. Therefore, starting planning early and allowing enough time in your schedule to complete the task is important.

Essential Strategies for Data Analysis Dissertation

A.   Planning

The first step in any dissertation is planning. You must decide what you want to write about and how you want to structure your argument. This planning will involve deciding what data you want to analyze and what methods you will use for a data analysis dissertation.

B.   Prototyping

Once you have a plan for your dissertation, it’s time to start writing. However, creating a prototype is important before diving head-first into writing your dissertation. A prototype is a rough draft of your argument that allows you to get feedback from your advisor and committee members. This feedback will help you fine-tune your argument before you start writing the final version of your dissertation.

C.   Executing

After you have created a plan and prototype for your data analysis dissertation, it’s time to start writing the final version. This process will involve collecting and analyzing data and writing up your results. You will also need to create a conclusion section that ties everything together.

D.   Presenting

The final step in acing your data analysis dissertation is presenting it to your committee. This presentation should be well-organized and professionally presented. During the presentation, you’ll also need to be ready to respond to questions concerning your dissertation.

Data Analysis Tools

Numerous suggestive tools are employed to assess the data and deduce pertinent findings for the discussion section. The tools used to analyze data and get a scientific conclusion are as follows:

a.     Excel

Excel is a spreadsheet program part of the Microsoft Office productivity software suite. Excel is a powerful tool that can be used for various data analysis tasks, such as creating charts and graphs, performing mathematical calculations, and sorting and filtering data.

b.     Google Sheets

Google Sheets is a free online spreadsheet application that is part of the Google Drive suite of productivity software. Google Sheets is similar to Excel in terms of functionality, but it also has some unique features, such as the ability to collaborate with other users in real-time.

c.     SPSS

SPSS is a statistical analysis software program commonly used in the social sciences. SPSS can be used for various data analysis tasks, such as hypothesis testing, factor analysis, and regression analysis.

d.     STATA

STATA is a statistical analysis software program commonly used in the sciences and economics. STATA can be used for data management, statistical modelling, descriptive statistics analysis, and data visualization tasks.

SAS is a commercial statistical analysis software program used by businesses and organizations worldwide. SAS can be used for predictive modelling, market research, and fraud detection.

R is a free, open-source statistical programming language popular among statisticians and data scientists. R can be used for tasks such as data wrangling, machine learning, and creating complex visualizations.

g.     Python

A variety of applications may be used using the distinctive programming language Python, including web development, scientific computing, and artificial intelligence. Python also has a number of modules and libraries that can be used for data analysis tasks, such as numerical computing, statistical modelling, and data visualization.

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Tips to Compose a Successful Data Analysis Dissertation

a.   Choose a Topic You’re Passionate About

The first step to writing a successful data analysis dissertation is to choose a topic you’re passionate about. Not only will this make the research and writing process more enjoyable, but it will also ensure that you produce a high-quality paper.

Choose a topic that is particular enough to be covered in your paper’s scope but not so specific that it will be challenging to obtain enough evidence to substantiate your arguments.

b.   Do Your Research

data analysis in research is an important part of academic writing. Once you’ve selected a topic, it’s time to begin your research. Be sure to consult with your advisor or supervisor frequently during this stage to ensure that you are on the right track. In addition to secondary sources such as books, journal articles, and reports, you should also consider conducting primary research through surveys or interviews. This will give you first-hand insights into your topic that can be invaluable when writing your paper.

c.   Develop a Strong Thesis Statement

After you’ve done your research, it’s time to start developing your thesis statement. It is arguably the most crucial part of your entire paper, so take care to craft a clear and concise statement that encapsulates the main argument of your paper.

Remember that your thesis statement should be arguable—that is, it should be capable of being disputed by someone who disagrees with your point of view. If your thesis statement is not arguable, it will be difficult to write a convincing paper.

d.   Write a Detailed Outline

Once you have developed a strong thesis statement, the next step is to write a detailed outline of your paper. This will offer you a direction to write in and guarantee that your paper makes sense from beginning to end.

Your outline should include an introduction, in which you state your thesis statement; several body paragraphs, each devoted to a different aspect of your argument; and a conclusion, in which you restate your thesis and summarize the main points of your paper.

e.   Write Your First Draft

With your outline in hand, it’s finally time to start writing your first draft. At this stage, don’t worry about perfecting your grammar or making sure every sentence is exactly right—focus on getting all of your ideas down on paper (or onto the screen). Once you have completed your first draft, you can revise it for style and clarity.

And there you have it! Following these simple tips can increase your chances of success when writing your data analysis dissertation. Just remember to start early, give yourself plenty of time to research and revise, and consult with your supervisor frequently throughout the process.

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Studying the above examples gives you valuable insight into the structure and content that should be included in your own data analysis dissertation. You can also learn how to effectively analyze and present your data and make a lasting impact on your readers.

In addition to being a useful resource for completing your dissertation, these examples can also serve as a valuable reference for future academic writing projects. By following these examples and understanding their principles, you can improve your data analysis skills and increase your chances of success in your academic career.

You may also contact Premier Dissertations to develop your data analysis dissertation.

For further assistance, some other resources in the dissertation writing section are shared below;

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Analysis and Coding Example: Qualitative Data

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The following is an example of how to engage in a three step analytic process of coding, categorizing, and identifying themes within the data presented. Note that different researchers would come up with different results based on their specific research questions, literature review findings, and theoretical perspective.

There are many ways cited in the literature to analyze qualitative data. The specific analytic plan in this exercise involved a constant comparative (Glaser & Strauss, 1967) approach that included a three-step process of open coding, categorizing, and synthesizing themes. The constant comparative process involved thinking about how these comments were interrelated. Intertwined within this three step process, this example engages in content analysis techniques as described by Patton (1987) through which coherent and salient themes and patterns are identified throughout the data. This is reflected in the congruencies and incongruencies reflected in the memos and relational matrix.

Step 1: Open Coding

Codes for the qualitative data are created through a line by line analysis of the comments. Codes would be based on the research questions, literature review, and theoretical perspective articulated. Numbering the lines is helpful so that the researcher can make notes regarding which comments they might like to quote in their report.

It is also useful to include memos to remind yourself of what you were thinking and allow you to reflect on the initial interpretations as you engage in the next two analytic steps. In addition, memos will be a reminder of issues that need to be addressed if there is an opportunity for follow up data collection. This technique allows the researcher time to reflect on how his/her biases might affect the analysis. Using different colored text for memos makes it easy to differentiate thoughts from the data.

Many novice researchers forgo this step.  Rather, they move right into arranging the entire statements into the various categories that have been pre-identified. There are two problems with the process. First, since the categories have been listed open coding, it is unclear from where the categories have been derived. Rather, when a researcher uses the open coding process, he/she look at each line of text individually and without consideration for the others. This process of breaking the pieces down and then putting them back together through analysis ensures that the researcher consider all for the data equally and limits the bias that might introduced. In addition, if a researcher is coding interviews or other significant amounts of qualitative data it will likely become overwhelming as the researcher tries to organize and remember from which context each piece of data came.

Step 2: Categorizing

To categorize the codes developed in Step 1 , list the codes and group them by similarity.  Then, identify an appropriate label for each group. The following table reflects the result of this activity.

Step 3: Identification of Themes

In this step, review the categories as well as the memos to determine the themes that emerge.   In the discussion below, three themes emerged from the synthesis of the categories. Relevant quotes from the data are included that exemplify the essence of the themes.These can be used in the discussion of findings. The relational matrix demonstrates the pattern of thinking of the researcher as they engaged in this step in the analysis. This is similar to an axial coding strategy.

Note that this set of data is limited and leaves some questions in mind. In a well-developed study, this would just be a part of the data collected and there would be other data sets and/or opportunities to clarify/verify some of the interpretations made below.  In addition, since there is no literature review or theoretical statement, there are no reference points from which to draw interferences in the data. Some assumptions were made for the purposes of this demonstration in these areas.

T h eme 1:  Professional Standing

Individual participants have articulated issues related to their own professional position. They are concerned about what and when they will teach, their performance, and the respect/prestige that they have within the school. For example, they are concerned about both their physical environment and the steps that they have to take to ensure that they have the up to date tools that they need. They are also concerned that their efforts are being acknowledged, sometimes in relation to their peers and their beliefs that they are more effective.

Selected quotes:

• Some teachers are carrying the weight for other teachers. (demonstrates that they think that some of their peers are not qualified.)
• We need objective observations and feedback from the principal (demonstrates that they are looking for acknowledgement for their efforts.  Or this could be interpreted as a belief that their peers who are less qualified should be acknowledged).
• There is a lack of support for individual teachers

Theme 2:  Group Dynamics and Collegiality

Rationale: There are groups or clicks that have formed. This seems to be the basis for some of the conflict.  This conflict is closely related to the status and professional standing themes. This theme however, has more to do with the group issues while the first theme is an individual perspective. Some teachers and/or subjects are seen as more prestigious than others.  Some of this is related to longevity. This creates jealously and inhibits collegiality. This affects peer-interaction, instruction, and communication.

• Grade level teams work against each other rather than together.
• Each team of teachers has stereotypes about the other teams.
• There is a division between the old and new teachers

Theme 3:  Leadership Issues

Rationale: There seems to be a lack of leadership and shared understanding of the general direction in which the school will go. This is also reflected in a lack of two way communications.  There doesn’t seem to be information being offered by the leadership of the school, nor does there seem to be an opportunity for individuals to share their thoughts, let alone decision making. There seems to be a lack of intervention in the conflict from leadership.

• Decisions are made on inaccurate information.
• We need consistent decisions about school rules

Glaser, B.G., & Strauss, A.  (1967).   The discovery of grounded theory:  Strategies for qualitative research . Chicago, IL: Aldine.

Patton, M. Q.  (1987).   How to use qualitative methods in evaluation .  Newbury Park, CA:  Sage Publications.

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

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

There’s the distinction between inductive and deductive approaches:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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Tips for a qualitative dissertation

Veronika Williams

17 October 2017

Tips for students

This blog is part of a series for Evidence-Based Health Care MSc students undertaking their dissertations.

Undertaking an MSc dissertation in Evidence-Based Health Care (EBHC) may be your first hands-on experience of doing qualitative research. I chatted to Dr. Veronika Williams, an experienced qualitative researcher, and tutor on the EBHC programme, to find out her top tips for producing a high-quality qualitative EBHC thesis.

1) Make the switch from a quantitative to a qualitative mindset

It’s not just about replacing numbers with words. Doing qualitative research requires you to adopt a different way of seeing and interpreting the world around you. Veronika asks her students to reflect on positivist and interpretivist approaches: If you come from a scientific or medical background, positivism is often the unacknowledged status quo. Be open to considering there are alternative ways to generate and understand knowledge.

2) Reflect on your role

Quantitative research strives to produce “clean” data unbiased by the context in which it was generated.  With qualitative methods, this is neither possible nor desirable.  Students should reflect on how their background and personal views shape the way they collect and analyse their data. This will not only add to the transparency of your work but will also help you interpret your findings.

3)  Don’t forget the theory

Qualitative researchers use theories as a lens through which they understand the world around them. Veronika suggests that students consider the theoretical underpinning to their own research at the earliest stages. You can read an article about why theories are useful in qualitative research  here.

4) Think about depth rather than breadth

Qualitative research is all about developing a deep and insightful understanding of the phenomenon/ concept you are studying. Be realistic about what you can achieve given the time constraints of an MSc.  Veronika suggests that collecting and analysing a smaller dataset well is preferable to producing a superficial, rushed analysis of a larger dataset.

5) Blur the boundaries between data collection, analysis and writing up

Veronika strongly recommends keeping a research diary or using memos to jot down your ideas as your research progresses. Not only do these add to your audit trail, these entries will help contribute to your first draft and the process of moving towards theoretical thinking. Qualitative researchers move back and forward between their dataset and manuscript as their ideas develop. This enriches their understanding and allows emerging theories to be explored.

6) Move beyond the descriptive

When analysing interviews, for example, it can be tempting to think that having coded your transcripts you are nearly there. This is not the case!  You need to move beyond the descriptive codes to conceptual themes and theoretical thinking in order to produce a high-quality thesis.  Veronika warns against falling into the pitfall of thinking writing up is, “Two interviews said this whilst three interviewees said that”.

7) It’s not just about the average experience

When analysing your data, consider the outliers or negative cases, for example, those that found the intervention unacceptable.  Although in the minority, these respondents will often provide more meaningful insight into the phenomenon or concept you are trying to study.

8) Bounce ideas

Veronika recommends sharing your emerging ideas and findings with someone else, maybe with a different background or perspective. This isn’t about getting to the “right answer” rather it offers you the chance to refine your thinking.  Be sure, though, to fully acknowledge their contribution in your thesis.

9) Be selective

In can be a challenge to meet the dissertation word limit.  It won’t be possible to present all the themes generated by your dataset so focus! Use quotes from across your dataset that best encapsulate the themes you are presenting.  Display additional data in the appendix.  For example, Veronika suggests illustrating how you moved from your coding framework to your themes.

10) Don’t panic!

There will be a stage during analysis and write up when it seems undoable.  Unlike quantitative researchers who begin analysis with a clear plan, qualitative research is more of a journey. Everything will fall into place by the end.  Be sure, though, to allow yourself enough time to make sense of the rich data qualitative research generates.

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Top 4 Steps of Qualitative Data Analysis for Dissertation

Welcome to our blog on qualitative data analysis for dissertation. We’re here to guide you through the essential steps, demystifying the methods of qualitative data analysis in dissertation. In simple terms, we’ll break down the importance of qualitative data analysis in a dissertation. Think of it as a roadmap to help you navigate and understand each step easily. 

Qualitative data analysis is the process of examining non-numerical information, such as text, images, or audio, to uncover patterns, themes, and insights. In the world of dissertation research, qualitative analysis plays a crucial role in exploring the depth and nuances of human experiences, opinions, and behaviors. 

Unlike quantitative methods that deal with measurable data, qualitative analysis offers a rich and contextual understanding, making it particularly valuable in social sciences, humanities, and other fields where the focus is on the quality rather than quantity of information. It allows researchers to delve into the intricacies of their subjects, providing a holistic view that goes beyond statistical figures. 

We’ll also provide an example of qualitative data analysis to make things clearer. By the end, you’ll see how these steps are like puzzle pieces that come together to tell the story of your dissertation. Let’s dive in and make qualitative data analysis less daunting and more understandable for your academic journey.

Methods of Qualitative Data Analysis in Dissertation

1. thematic analysis:.

– Identify and analyze recurring themes or patterns within the data.

– Organize these themes to capture the essence of your findings.

2. Content Analysis:

– Examine the content of your data for specific words, phrases, or themes.

– Categorize and quantify these elements to draw meaningful insights.

3. Grounded Theory:

– Develop theories from the data rather than applying pre-existing ones.

– Constantly compare data, allowing emerging theories to guide the analysis.

4. Narrative Analysis:

– Focus on the stories within the data.

– Explore the structure and content of these narratives to extract meaningful information.

5. Case Study Analysis:

– In-depth examination of a specific case or cases.

– Explore the details and context to understand the broader implications.

Step 1: Pre-Analysis Meditation

Importance of this step: .

Understanding the Significance: Pre-analysis meditation serves as a mental reset, helping researchers clear preconceived notions and biases. By fostering a reflective mindset, researchers approach the data with openness, encouraging unbiased exploration.

How PhD Researchers Can Implement Pre-Analysis Meditation:

Schedule Mindful Moments: Dedicate a few minutes before each analysis session for mindfulness. Incorporate deep-breathing exercises to ease into a focused state.

Step 2: Metaphorical Mapping

Unlocking creative insights:.

– Visual Representation: Metaphorical mapping allows for a visual interpretation of complex data, aiding in comprehension.

– Stimulates Creativity: Translating findings into metaphors encourages creative thinking, unveiling nuanced insights not immediately apparent in the raw data.

Enhancing Communication:

– Facilitates Explanation: Metaphors serve as powerful tools for conveying complex ideas, making it easier to communicate findings to a broader audience.

– Captures Essence: Mapping metaphors captures the essence of data, providing a holistic perspective that goes beyond literal interpretation.

How PhD Researchers Can Implement Metaphorical Mapping:

– Pinpoint central themes or concepts within the data.

– Select metaphors that resonate with the essence of these concepts.

– Create a visual map connecting metaphors to corresponding data points.

– Explore the relationships and interplay between different metaphors.

– Refine metaphors iteratively as analysis progresses.

– Allow the metaphorical map to evolve, reflecting deeper insights gained during analysis.

Step 3: Parallel Universe Probing

Importance of this step:, encouraging diverse perspectives:.

Promotes Critical Thinking: Parallel universe probing encourages researchers to explore diverse analytical angles, fostering critical thinking.

Challenges Assumptions: By envisioning alternative interpretations, researchers challenge preconceived notions, ensuring a more robust analysis.

Enhancing Analytical Rigor:

Strengthens Validity: Considering multiple perspectives strengthens the validity of findings, ensuring a comprehensive and well-rounded analysis.

Minimizes Bias: Actively engaging in parallel universe probing minimizes confirmation bias, promoting a more objective evaluation of the data.

How PhD Researchers Can Implement Parallel Universe Probing:

1. imagine alternative scenarios:.

Envision different ways the data could be interpreted.

Challenge assumptions by exploring contrasting analytical pathways.

2. Seek Diverse Opinions:

Collaborate with peers or mentors to gain diverse perspectives.

Discuss alternative interpretations to broaden the analytical scope.

3. Document Comparative Analysis:

Record and compare the outcomes of parallel universe probing.

Consider the advantages and disadvantages of every viewpoint.

Step 4: Multisensory Synthesis

– Expands Interpretation Horizons: Multisensory synthesis encourages researchers to go beyond reading, engaging various senses for a more holistic understanding of the data.

– Taps into Intuition: Incorporating multiple senses, including visual, auditory, and tactile, taps into intuitive insights, enriching the analytical experience.

– Enhances Data Connection: Multisensory exploration strengthens the connection with data, allowing for a more immersive and empathetic analysis.

– Facilitates Creative Expression: Engaging different senses facilitates creative expression, providing alternative avenues for expressing complex findings.

How PhD Researchers Can Implement Multisensory Synthesis:

Explore visual representations, auditory descriptions, or tactile expressions of data. Experiment with alternative formats to capture the essence of the findings. Discuss multisensory interpretations with peers or mentors. Gain insights from diverse perspectives on how different senses can contribute to a richer analysis. Recognize individual sensory strengths and preferences. Tailor the multisensory approach to align with personal modes of perception.

Final Thoughts

In summing up our journey through the top 4 steps of analyzing qualitative data for dissertations, we’ve explored important methods of qualitative data analysis in dissertation. Qualitative data analysis for dissertations, done through steps like thematic analysis and grounded theory, is like crafting a narrative that adds depth to the data. It’s not just about going through the motions; it’s about revealing valuable insights. 

Understanding the importance of qualitative data analysis in a dissertation is like realizing how these steps build the strong foundation of your research. They act like a guide, ensuring your dissertation not only makes sense but also captures the essence of what you’ve studied. To put it simply, these example of qualitative data analysis empower researchers, showing them how to unravel the complexities and tell a compelling academic story in their dissertations.

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The company doesn’t merely advise but gets involved in your dissertation through collaborative pedagogy of their support service. They offer services such as dissertation editing, data analysis, methodology, literature review, and concept paper assistance. Their comprehensive package for dissertation help is value for money. They have in-depth knowledge and provide 24X7 services.  

FAQ Questions

1. what are the three important steps in qualitative data analysis.

Data coding, theme identification, and constant comparison.

2. What is the aim of qualitative research?

To investigate and comprehend underlying viewpoints, experiences, and meanings.

3. What is the main advantage of using Qualitative data analysis in a dissertation?

Provides in-depth insights and context, enriching the research narrative.

4. How do you ensure credibility in qualitative research?

Through methods like member checking, peer debriefing, and triangulation.

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What other aspects of the experience could be improved?

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What other comments do you have for the owner of the website?

I like that you can sort by what you are looking for and i like the idea of collections

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• Please begin by downloading the app to your device.
• Choose Italian and get started with the first lesson (stop once you reach the first question).
• Now go all the way through the rest of the first lesson, describing your thoughts as you go.
• Get your profile set up, then view your account page. What information and options are there? Do you feel that these are useful? Why or why not?
• After a week in Italy, you're going to spend a few days in Austria. How would you take German lessons on Duolingo?
• What other languages does the app offer? Do any of them interest you?

I felt like there could have been a little more of an instructional component to the lesson.

It would be cool if there were some feature that could allow two learners studying the same language to take lessons together. I imagine that their screens would be synced and they could go through lessons together and chat along the way.

Overall, the app was very intuitive to use and visually appealing. I also liked the option to connect with others.

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What is Qualitative Data? Definition, Types, Examples and Analysis

What is Qualitative Data?

Qualitative data is defined as a type of data that is non-numerical in nature and often consists of descriptive, subjective, and context-rich details. This form of data is commonly associated with qualitative research methods, which aim to explore and understand the underlying meanings, patterns, and nuances of social phenomena.

Unlike quantitative data, which involves numerical measurements and statistical analysis, qualitative data is characterized by its focus on the depth and complexity of human experiences, behaviors, and perspectives.

In qualitative research, data is typically gathered through methods such as interviews, focus groups, observations, or content analysis of texts. These methods generate a diverse range of data, including textual information, narratives, images, and audio recordings. The richness of qualitative data allows researchers to delve into the intricacies of social phenomena, exploring the context, emotions, and subjective interpretations that quantitative data may not capture.

Qualitative data can take various forms, including direct quotes from participants, field notes, transcripts, or visual materials. The goal is to uncover the depth and diversity of responses, opinions, and experiences related to the research question. Researchers engage in a process of coding and thematic analysis to identify patterns, themes, and insights within the qualitative data, contributing to a holistic and nuanced understanding of the studied phenomenon.

Ultimately, qualitative data provides a more comprehensive and contextually rich perspective, allowing researchers to explore the complexity of social interactions, cultural contexts, and individual experiences. Its non-numeric nature allows for a deeper exploration of the “how” and “why” behind human behaviors and perceptions, making it a valuable approach for researchers seeking to uncover the depth and meaning inherent in social phenomena.

Key Characteristics of Qualitative Data

Qualitative data possesses several key characteristics that distinguish it from quantitative data. These characteristics highlight the nature of the information gathered through qualitative research methods. Here are some key features of qualitative data:

• Non-Numerical : Qualitative data is non-numerical, representing information in the form of words, narratives, images, or observations. Unlike quantitative data, which involves measurable quantities, qualitative data focuses on the quality and context of the information.
• Subjective and Descriptive:  Qualitative data often reflects subjective interpretations and descriptions. It captures the richness of human experiences, emotions, and perspectives. Researchers seek to understand the depth and meaning of phenomena through detailed descriptions rather than numerical measurements.
• Contextual and Rich: Qualitative data is contextual and rich in detail. It provides a holistic view of the studied phenomenon within its real-world context. This richness allows researchers to explore the intricacies, nuances, and cultural influences that shape individuals’ behaviors and experiences.
• In-Depth Exploration: The primary purpose of qualitative data is to facilitate in-depth exploration and understanding. Researchers use methods such as interviews, focus groups, or participant observation to gather detailed information, uncovering layers of meaning and complexity associated with the research question.
• Flexible and Emergent:  Qualitative data collection and analysis are often flexible and emergent. Research designs can adapt based on emerging insights, allowing researchers to explore unexpected themes or adjust their approach as the study progresses. This flexibility accommodates the evolving nature of qualitative inquiry.
• Open-Ended Responses: Qualitative data is frequently derived from open-ended questions or prompts. This approach encourages participants to express themselves freely and allows for a diverse range of responses, contributing to the exploration of multiple perspectives and viewpoints.
• Varied Data Types: Qualitative data can take various forms, including textual data (interview transcripts, field notes), visual data (photographs, videos), or auditory data (audio recordings). The diversity of data types enables researchers to capture different aspects of the phenomenon being studied.
• Interpretive Analysis: The analysis of qualitative data is interpretive. Researchers engage in coding, thematic analysis, or narrative analysis to interpret patterns, themes, and meanings within the data. The goal is to uncover the underlying insights and contribute to a more nuanced understanding of the research question.
• Holistic Understanding:  Qualitative data provides a holistic understanding of a phenomenon. By considering the context, social dynamics, and individual perspectives, researchers can gain a comprehensive view that goes beyond statistical trends, contributing to a deeper comprehension of complex social phenomena.

These characteristics collectively define the nature of qualitative data, emphasizing its subjective, context-rich, and exploratory qualities. Qualitative research methods leverage these characteristics to uncover the depth and complexity of human experiences, behaviors, and social phenomena.

Key Components of Qualitative Data

Qualitative data encompasses a range of components that capture the richness and complexity of human experiences, behaviors, and perceptions. Understanding these key components is essential for researchers engaged in qualitative inquiry. Here are the key components of qualitative data:

1. Textual Data:

Textual data is a fundamental component of qualitative research, representing written or spoken language. This includes interview transcripts, field notes, written responses, and any form of text that conveys participants’ experiences and perspectives.

2. Narratives:

Narratives are accounts or stories shared by participants, providing a chronological or thematic depiction of their experiences. Narratives contribute to a contextual understanding of the research topic, emphasizing the subjective and personal dimensions of participants’ lives.

3. Quotes and Verbatim Responses:

Direct quotes and verbatim responses from participants are often used to capture their exact words. These quotes add authenticity and vividness to the data, allowing researchers to convey participants’ voices and expressions accurately.

4. Field Notes:

Field notes are written observations made by researchers during participant observation or other fieldwork. These notes capture details about the research setting, interactions, and the researcher’s reflections, providing contextual insights.

5. Visual Data: 

Visual data includes photographs, videos, drawings, or other visual representations that participants create or that researchers capture during the study. Visual data add a layer of richness and can convey aspects of the experience that may be challenging to express verbally.

6. Audio Recordings:

Audio recordings capture participants’ spoken words, tone, and intonation during interviews or focus group discussions. They serve as valuable resources for preserving the nuances of communication, contributing to the authenticity of qualitative data.

7. Thematic Codes:

Thematic codes are labels or keywords assigned to segments of qualitative data during the analysis process. These codes represent recurring patterns, themes, or concepts within the data, allowing researchers to organize and interpret the information systematically.

8. Categories:

Categories are broader groupings of related codes that help organize and structure the data. They provide a higher-level conceptual framework for understanding patterns and relationships within the qualitative dataset.

9. Contextual Information:

Contextual information includes details about the social, cultural, or environmental factors that influence participants’ experiences. Understanding context is crucial for interpreting qualitative data accurately and situating findings within a broader framework.

10. Participant Characteristics:

Information about participant characteristics, such as demographics or relevant background details, adds depth to the data. Understanding who the participants are helps researchers contextualize their experiences and identify potential patterns related to specific groups.

11. Researcher Reflections:

Researcher reflections involve the researcher’s own thoughts, feelings, and interpretations documented during and after data collection. These reflections contribute to reflexivity and transparency in qualitative research, acknowledging the role of the researcher in shaping the study.

12. Emergent Insights:

Emergent insights are unexpected or unanticipated findings that arise during the research process. Qualitative data often yield insights that go beyond initial expectations, highlighting the exploratory and dynamic nature of qualitative inquiry.

Understanding these key components is essential for researchers engaged in qualitative data collection and analysis. These components collectively contribute to the depth, authenticity, and interpretive richness of qualitative research findings.

Types of Qualitative Data with Examples

Qualitative data can take various forms, each offering unique insights into human experiences, behaviors, and perceptions.

Here are types of qualitative data along with examples:

• Textual Data:

Textual data in qualitative research consists of written or spoken words, providing a detailed account of participants’ responses and experiences. This type of data is often transcribed from interviews, focus group discussions, or open-ended survey responses.

Example: An interview transcript capturing a participant’s detailed description of their experiences with a particular product or service.

• Narrative Data:

Narrative data involve the presentation of stories or accounts that describe events, experiences, or personal journeys. Narratives are often used to convey the richness and depth of participants’ perspectives.

Example: A participant sharing a personal narrative about their journey to overcoming a specific challenge, providing a detailed account of the events and emotions involved.

• Visual Data:

Visual data include images, photographs, videos, or any visual representation that captures aspects of the research context. Visual data can enhance the understanding of participants’ environments and experiences.

Example: Photographs taken during ethnographic fieldwork depicting community gatherings, cultural practices, or participants’ living spaces.

• Audio Data:

Audio data capture spoken words, tone, and intonation, preserving the auditory aspects of participants’ expressions. This type of data is valuable for capturing the nuances of communication.

Example: An audio recording of a focus group discussion where participants express their opinions, emotions, and reactions to a specific topic.

• Field Notes:

Field notes are written observations made by researchers during participant observation or other fieldwork activities. These notes provide context, details, and the researcher’s reflections.

Example: Field notes documenting the researcher’s observations of participant interactions, physical surroundings, and any unexpected events during fieldwork.

• Thematic Codes:

Thematic codes are labels assigned to segments of data that represent recurring patterns, themes, or concepts. Coding helps organize and analyze qualitative data systematically.

Example: Coding interview transcripts with thematic labels such as “barriers to access,” “communication challenges,” or “positive experiences” to identify common themes.

• Categories:

Categories are broader groupings that organize and structure coded segments of data. They provide a higher-level conceptual framework for understanding patterns and relationships within the data.

Example: Grouping thematic codes related to “community engagement” and “sustainability” into broader categories that represent overarching themes in the dataset.

Qualitative Data Analysis Best Practices

Qualitative data analysis is a crucial phase in the research process, involving the systematic examination and interpretation of qualitative data to derive meaningful insights. Here are some best practices for qualitative data analysis:

1. Immerse Yourself in the Data:

Take the time to thoroughly immerse yourself in the qualitative data. By repeatedly reading and revisiting transcripts, field notes, and other sources, you’ll develop a nuanced understanding of the material, allowing for more insightful analysis.

2. Adopt a Systematic Approach:

Develop a comprehensive plan for qualitative data analysis. Clearly outline your research questions, establish well-defined coding procedures, and select appropriate analysis techniques. A systematic approach ensures that your analysis is organized, focused, and aligned with the study’s objectives.

3. Ensure Coding Consistency:

Uphold consistency in coding practices by explicitly defining coding categories. Provide clear guidelines for the application of codes across the dataset, and periodically review and refine codes to maintain accuracy and reliability in your analysis.

4. Triangulate Coders:

When multiple researchers are involved, conduct inter-coder reliability checks. Triangulating the analysis through different perspectives enhances the credibility of your findings and helps ensure that coding decisions are aligned among the research team. This collaborative approach strengthens the rigor of your qualitative data analysis.

5. Thematic Analysis:

Utilize thematic analysis as a flexible and comprehensive method for identifying, analyzing, and reporting patterns or themes within the qualitative data. This approach allows for a systematic exploration of recurrent ideas, enhancing the depth of understanding in your analysis.

6. Constant Comparative Method :

Apply the constant comparative method throughout the analysis process. Continuously compare new data with existing codes and categories, allowing for the refinement and development of themes as the analysis progresses. This iterative process contributes to the richness and accuracy of your findings.

7. Contextualize Findings:

Contextualize your findings by considering the broader social, cultural, or environmental context in which the data was collected. This practice ensures that your interpretations are grounded in the real-world settings of participants, enhancing the relevance and applicability of your results.

8. Member Checking:

Implement member checking as a validation technique. Share preliminary findings with participants to obtain their feedback, confirming the accuracy and resonance of your interpretations. Member checking enhances the credibility and trustworthiness of your qualitative data analysis.

9. Maintain Reflexivity:

Maintain reflexivity throughout the analysis process. Reflect on your own biases, assumptions, and perspectives that may influence the interpretation of data. By acknowledging and documenting your positionality, you enhance transparency and credibility in your qualitative analysis.

10. Saturation Awareness:

Be mindful of data saturation. Regularly assess whether new data are contributing novel insights or if saturation has been reached. Ceasing data collection when saturation is achieved ensures that your analysis is thorough, and further data collection is unlikely to reveal additional significant information.

11. Create an Audit Trail:

Establish an audit trail by documenting the decisions and steps taken during the analysis process. This documentation serves as a transparent record of your analytical choices, facilitating transparency, and allowing others to follow and evaluate your analytic process.

12. Use Software Tools Judiciously:

If employing qualitative data analysis software, use it judiciously. While these tools can aid in managing and organizing data, ensure they align with the chosen analytical approach. Strive for a balance between technological assistance and maintaining a deep engagement with the data.

13. Team Collaboration and Debriefing:

Foster collaboration within your research team by regularly engaging in debriefing sessions. Discuss your interpretations, insights, and challenges to benefit from diverse perspectives, refine analytical strategies, and enhance the overall rigor of the qualitative analysis.

14. Peer Review:

Seek peer review of your qualitative data analysis. Having colleagues critically review your coding, interpretations, and findings helps identify potential biases, offers alternative perspectives, and ensures the robustness and trustworthiness of your analytical process.

15. Useful Quotes and Rich Examples:

Incorporate meaningful quotes and rich examples from participants in your analysis reports. These excerpts serve as illustrative evidence, allowing readers to connect with participants’ voices and experiences, thereby enhancing the credibility and authenticity of your findings.

16. Ethical Considerations:

Adhere to ethical considerations throughout the analysis process. Protect participant confidentiality, ensure informed consent, and handle sensitive information with care. Ethical practices contribute to the integrity of the research and uphold the rights of the participants.

Applying these additional best practices in qualitative data analysis ensures a robust, transparent, and ethically sound analytical process, ultimately enhancing the validity and reliability of your research findings.

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Qualitative vs. quantitative data analysis: How do they differ?

Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction.

What is qualitative data?

Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1

What is quantitative data?

Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2

Key difference between qualitative and quantitative data

It's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing.

Data Types and Nature

Examples of qualitative data types in learning analytics:

• Observational data of human behavior from classroom settings such as student engagement, teacher-student interactions, and classroom dynamics
• Textual data from open-ended survey responses, reflective journals, and written assignments
• Feedback and discussions from focus groups or interviews
• Content analysis from various media

Examples of quantitative data types:

• Standardized test, assessment, and quiz scores
• Grades and grade point averages
• Attendance records
• Time spent on learning tasks
• Data gathered from learning management systems (LMS), including login frequency, online participation, and completion rates of assignments

Methods of Collection

Qualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data.

Qualitative research methods

Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data:

• Conduct interviews to learn about subjective experiences
• Host focus groups to gather feedback and personal accounts
• Observe in-person or use audio or video recordings to record nuances of human behavior in a natural setting
• Distribute surveys with open-ended questions

Quantitative research methods

Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as:

• Surveys with close-ended questions that gather numerical data like birthdates or preferences
• Observational research and record measurable information like the number of students in a classroom
• Automated numerical data collection like information collected on the backend of a computer system like button clicks and page views

Analysis techniques

Qualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis.

Qualitative data analysis methods

Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3

Quantitative analysis techniques

The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4

Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4

Qualitative and quantitative research tools

From the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types.

Qualitative research software:

NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5

ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6

SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7

R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8

Applications in Educational Research

Both quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions.

Master Data Analysis with an M.S. in Learning Sciences From SMU

Whether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals.

For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today.

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Qualitative Research Questionnaire – Types & Examples

Published by Alvin Nicolas at August 19th, 2024 , Revised On August 20, 2024

Before you start your research, the first thing you need to identify is the research method . Depending on different factors, you will either choose a quantitative or qualitative study.

Qualitative research is a great tool that helps understand the depth and richness of human opinions and experiences. Unlike quantitative research, which focuses on numerical data , qualitative research allows exploring and interpreting the experiences of the subject. Questionnaires, although mostly associated with quantitative research, can also be a valuable instrument in qualitative studies. Let’s explore what qualitative research questionnaires are and how you can create one.

What Is A Qualitative Research Questionnaire

Qualitative research questionnaires are a structured or semi-structured set of questions designed to gather detailed, open-ended participant responses. It allows you to uncover underlying reasons and opinions and provides insights into a particular phenomenon.

While quantitative questionnaires often have closed-ended questions and numerical responses, a qualitative questionnaire encourages participants to express themselves freely. Before you design your questionnaire, you should know exactly what you need so you can keep your questions specific enough for the participants to understand.

For example:

• Describe your experience using our product.
• How has technology impacted your work-life balance?

Types of Qualitative Research Questions With Examples

Now that you are familiar with what qualitative research questions are, let’s look at the different types of questions you can use in your survey .

Descriptive Questions

These are used to explore and describe a phenomenon in detail. It helps answer the “what” part of the research, and the questions are mostly foundational.

Example: How do students experience online learning?

Comparative Questions

This type allows you to compare and contrast different groups or situations. You can explore the differences and similarities to highlight the impact of specific variables.

Example: How do the study habits of first-year and fourth-year university students differ?

Interpretive Questions

These questions help you understand the meanings people attach to experiences or phenomena by answering the “how” and “why”.

Example: What does “success” mean to entrepreneurs?

Evaluative Questions

You can use these to assess the quality or value of something. These allow you to understand the outcomes of various situations.

Example: How effective is the new customer service training program?

Process-Oriented Questions

To understand how something happens or develops over time, researchers often use process-oriented questions.

Example: How do individuals develop their career goals?

Exploratory Questions

These allow you to discover new perspectives on a topic. However, you have to be careful that there must be no preconceived notions or research biases to it.

Example: What are the emerging trends in the mobile gaming industry?

How To Write Qualitative Research Questions?

For your study to be successful, it is important to consider designing a questionnaire for qualitative research critically, as it will shape your research and data collection. Here is an easy guide to writing your qualitative research questions perfectly.

Tip 1: Understand Your Research Goals

Many students start their research without clear goals, and they have to make substantial changes to their study in the middle of the research. This wastes time and resources.

Before you start crafting your questions, it is important to know your research objectives. You should know what you aim to discover through your research, or what specific knowledge gaps you are going to fill. With the help of a well-defined research focus, you can develop relevant and meaningful information.

Tip 2: Choose The Structure For Research Questions

There are mostly open-ended questionnaires in qualitative research. They begin with words like “how,” “what,” and “why.” However, the structure of your research questions depends on your research design . You have to consider using broad, overarching questions to explore the main research focus, and then add some specific probes to further research the particular aspects of the topic.

Tip 3: Use Clear Language

The more clear and concise your research questions are, the more effective and free from ambiguity they will be. Do not use complex terminology that might confuse participants. Try using simple and direct language that accurately conveys your intended meaning.

Here is a table to explain the wrong and right ways of writing your qualitative research questions.

Tip 4: Check Relevance With Research Goals

Once you have developed some questions, check if they align with your research objectives. You must ensure that each question contributes to your overall research questions. After this, you can eliminate any questions that do not serve a clear purpose in your study.

Tip 5: Concentrate On A Single Theme

While it is tempting to cover multiple aspects of a topic in one question, it is best to focus on a single theme per question. This helps to elicit focused responses from participants. Moreover, you have to avoid combining unrelated concepts into a single question.

If your main research question is complicated, you can create sub-questions with a “ladder structure”. These allow you to understand the attributes, consequences, and core values of your research. For example, let’s say your main broad research question is:

• How do you feel about your overall experience with our company?

The intermediate questions may be:

• What aspects of your experience were positive?
• What aspects of your experience were negative?
• How likely are you to recommend our company to a friend or colleague?

Types Of Survey Questionnaires In Qualitative Research

It is important to consider your research objectives, target population, resources and needed depth of research when selecting a survey method. The main types of qualitative surveys are discussed below.

Face To Face Surveys

Face-to-face surveys involve direct interaction between the researcher and the participant. This method allows observers to capture non-verbal cues, body language, and facial expressions, and helps adapt questions based on participant responses. They also let you clarify any misunderstandings. Moreover, there is a higher response rate because of personal interaction.

Example: A researcher conducting a study on consumer experiences with a new product might visit participants’ homes to conduct a detailed interview.

Telephone Surveys

These type of qualitative research survey questionnaires provide a less intrusive method for collecting qualitative data. The benefits of telephone surveys include, that it allows you to collect data from a wider population. Moreover, it is generally less expensive than face-to-face interviews and interviews can be conducted efficiently.

Example: A market research firm might conduct telephone surveys to understand customer satisfaction with a telecommunication service.

Online Surveys

Online survey questionnaires are a convenient and cost-effective way to gather qualitative data. You can reach a wide audience quickly, and participants may feel more comfortable sharing sensitive information because of anonymity. Additionally, there are no travel or printing expenses.

Example: A university might use online surveys to explore students’ perceptions of online learning experiences.

Strengths & Limitations Of Questionnaires In Qualitative Research

Questionnaires are undoubtedly a great data collection tool. However, it comes with its fair share of advantages and disadvantages. Let’s discuss the benefits of questionnaires in qualitative research and their cons as well.

Qualitative Research Questionnaire Example

Here is a concise qualitative research questionnaire sample for research papers to give you a better idea of its format and how it is presented.

Thank you for participating in our survey. We value your feedback on our new mobile app. Your responses will help us improve the applications and better meet your needs.

Demographic Information

• Occupation:
• How long have you been using smartphones:
• How would you describe your overall experience with the new mobile app?
• What do you like most about the app?
• What do you dislike most about the app?
• Are there any specific features you find particularly useful or helpful? Please explain.
• Are there any features you think are missing or could be improved? Please elaborate.
• How easy is the app to navigate? Please explain any difficulties you encountered.
• How does this app compare to other similar apps you have used?
• What are your expectations for future updates or improvements to the app?
• Is there anything else you would like to share about your experience with the app?

Are questionnaires quantitative or qualitative research?

A survey research questionnaire can have both qualitative and quantitative questions. The qualitative questions are mostly open-ended, and quantitative questions take the form of yes/no, or Likert scale rating. 

Can we use questionnaires in qualitative research?

Yes, survey questionnaires can be used in qualitative research for data collection. However, instead of a Likert scale or rating, you can post open-ended questions to your respondents. The participants can provide detailed responses to the questions asked.

Why are questionnaires good for qualitative research?

In qualitative research, questionnaires allow you to collect qualitative data. The open-ended and unstructured questions help respondents present their ideas freely and provide insights. 

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Qualitative data analysis example for beginners.

Home » Qualitative data analysis example for beginners

Beginner Qualitative Analysis is an essential skill for emerging researchers and practitioners. Imagine embarking on a journey where the stories of people guide your insights, shaping your understanding of complex social dynamics. This method emphasizes the subtle nuances found in human experiences, making it invaluable for those starting in research fields.

As you delve into qualitative analysis, it's important to grasp its fundamental principles. You'll learn how to identify themes, extract meanings, and interpret data with a fresh perspective. By focusing on open-ended questions and participant narratives, you can uncover deeper insights that quantitative data often overlooks. This section will provide you with a foundational understanding and practical examples to kick-start your journey into qualitative research.

Understanding Qualitative Data Analysis

Qualitative data analysis involves examining non-numerical data to understand patterns, themes, and insights. In beginner qualitative analysis, it’s essential to gather rich descriptions and perspectives. This process often begins with data collection through methods such as interviews, focus groups, or open-ended surveys. The goal is to capture the participants' feelings, experiences, and viewpoints, which provide valuable context to the research inquiry.

Once data is collected, coding techniques can be utilized to identify significant themes and categories. This enables beginners to streamline large amounts of qualitative data into manageable sections. Organizing insights helps draw connections and understand the underlying meaning of the data. Additionally, reflecting on these themes aids in answering research questions and guiding future actions based on the findings. This structured approach to beginner qualitative analysis facilitates deeper understanding and clearer communication of results.

What is Qualitative Data Analysis?

Qualitative data analysis is a systematic approach for examining non-numerical information. It helps researchers identify patterns, themes, and insights from text, audio, or video sources. This form of analysis is particularly valuable for understanding human behaviors, experiences, and opinions in various contexts.

The process often begins with data collection and transcription. Researchers then organize and categorize the information to draw meaningful connections. Key steps in a beginner qualitative analysis include coding, which involves labeling segments of data with tags or themes. Following this, researchers interpret the data to uncover deeper insights. Ultimately, qualitative data analysis allows for a rich understanding of complex phenomena that quantitative methods may miss, making it an essential skill for aspiring researchers.

Why is Qualitative Analysis Important for Beginners?

Qualitative analysis holds significant importance for beginners seeking to understand complex data. It provides a rich narrative that goes beyond mere numbers and statistics, revealing the underlying motivations, feelings, and behaviors of individuals. By engaging with qualitative data, newcomers can develop a nuanced understanding of their subjects, facilitating more informed decision-making.

Beginning with qualitative analysis fosters critical thinking and enhances observational skills. Beginners learn to identify patterns, themes, and insights, which are crucial for drawing meaningful conclusions. Additionally, this method encourages creativity and adaptability, as researchers can tailor their approaches based on evolving data. These foundational skills provide a solid groundwork for future research endeavors, proving invaluable in various fields such as marketing, social sciences, and user experience design. Embracing beginner qualitative analysis now ensures that newcomers are equipped to tackle more complex analyses later.

Beginner Qualitative Analysis Example: Step-by-Step Guide

Beginner qualitative analysis involves a structured approach to understanding complex data derived from interviews or open-ended surveys. It is essential to start by gathering relevant qualitative data, as this is the foundation of your analysis. Ensure that your data is organized and accurately categorized, making it easier to identify key themes and patterns throughout your research.

Next, focus on coding your data. Assign labels or codes to similar concepts within your data set. This step helps you break down the information into manageable segments for detailed examination. Additionally, reviewing and refining these codes can enhance the depth of your analysis. Once coding is complete, look for overarching themes that emerge, drawing connections between different data points and offering insights into the subject matter. By following these steps, you can confidently navigate beginner qualitative analysis, gaining valuable insights into your research topic.

Step 1: Collecting Qualitative Data

Collecting qualitative data lays the groundwork for insightful analysis. Start by determining your data sources, such as interviews, focus groups, or surveys. Each source offers unique perspectives that contribute significantly to your understanding. For beginners in qualitative analysis, diversifying your data collection methods helps capture a broader range of viewpoints. A mix of audio files and text-based reports can enhance the depth of your findings.

Next, focus on crafting open-ended questions that encourage participants to share their thoughts freely. This open dialogue is crucial for gathering rich, descriptive data. Additionally, maintain a systematic approach in recording and organizing the information you collect. Consistently categorizing your data will streamline the analysis phase, ensuring you can easily reference insights later. By establishing these foundational steps, you set the stage for conducting effective qualitative data analysis.

Step 2: Organizing and Coding Data

In Step 2: Organizing and Coding Data, you start transforming your raw qualitative data into a structured format. For beginners in qualitative analysis, this organization is crucial as it makes the data manageable and accessible. Gather all relevant materials, such as interview transcriptions or survey responses, and group them into folders based on themes or questions. This initial step allows you to see patterns and connections within your data, facilitating deeper understanding.

Once your data is organized, coding becomes the next pivotal action. This involves labeling segments of text with codes that represent key themes or ideas. Think of codes as tags that help categorize your data effectively. For example, if multiple participants mention "customer service," you would assign a code to those segments. Using a consistent coding scheme allows for systematic analysis. Ultimately, by following these steps, you build a robust foundation for insightful qualitative analysis that will guide your findings and conclusions.

Conclusion: Mastering Beginner Qualitative Analysis

Mastering beginner qualitative analysis is a journey that empowers individuals to extract meaningful insights from qualitative data. As you practice, remember that effective analysis hinges on understanding participants' perspectives, identifying patterns, and synthesizing information thoughtfully. It's essential to approach this process with an open mind and a willingness to learn.

By embracing beginner qualitative analysis, you enhance your ability to comprehend complex human experiences. The skills you develop in this area not only enrich your research capabilities but also contribute to making informed decisions based on rich, qualitative insights. Ultimately, as you progress, you'll find that the art of qualitative analysis opens doors to deeper understanding and better outcomes in your projects.

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• Published: 20 August 2024

“Because people don’t know what it is, they don’t really know it exists” : a qualitative study of postgraduate medical educators’ perceptions of dyscalculia

• Laura Josephine Cheetham 1  

BMC Medical Education volume  24 , Article number:  896 ( 2024 ) Cite this article

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Dyscalculia is defined as a specific learning difference or neurodiversity. Despite a move within postgraduate medical education (PGME) towards promoting inclusivity and addressing differential attainment, dyscalculia remains an unexplored area.

Using an interpretivist, constructivist, qualitative methodology, this scoping study explores PGME educators’ attitudes, understanding and perceived challenges of supporting doctors in training (DiT) with dyscalculia. Through purposive sampling, semi-structured interviews and reflexive thematic analysis, the stories of ten Wales-based PGME educators were explored.

Multiple themes emerged relating to lack of educator knowledge, experience and identification of learners with dyscalculia. Participants’ roles as educators and clinicians were inextricably linked, with PGME seen as deeply embedded in social interactions. Overall, a positive attitude towards doctors with dyscalculia underpinned the strongly DiT-centred approach to supporting learning, tempered by uncertainty over potential patient safety-related risks. Perceiving themselves as learners, educators saw the educator-learner relationship as a major learning route given the lack of dyscalculia training available, with experience leading to confidence.

Conclusions

Overall, educators perceived a need for greater dyscalculia awareness, understanding and knowledge, pre-emptive training and evidence-based, feasible guidance introduction. Although methodological limitations are inherent, this study constructs novel, detailed understanding from educators relating to dyscalculia in PGME, providing a basis for future research.

Peer Review reports

Dyscalculia is categorised as a specific learning difference or part of neurodiversity in the UK and a learning disability in North America. Learners with dyscalculia are said to have significant difficulties in numerical processing [ 1 ]. It is increasingly acknowledged that these relate to arithmetic, statistics, ordinance, number and code memorisation and recall, with other individual variance [ 2 , 3 ]. Here, I chose to use “specific learning difference” (SpLD) to acknowledge that some feel SpLDs relate to a difference in learning needs but may not always result in learners identifying as disabled [ 4 , 5 ]. Most contemporary definitions state that these challenges are out of keeping with learner age, intelligence level and educational background [ 1 ], evolve over time but persist during adulthood.

Dyscalculia is a comparatively recently recognised SpLD with a relatively low ‘diagnosed’ population prevalence, with estimates ranging between 3% and 7% [ 2 ]. Awareness of dyscalculia is lower than more highly ‘diagnosed’ SpLDs such as dyslexia, dyspraxia and Attention Deficit and Hyperactivity Disorder (ADHD) [ 3 ], with a paucity of research-based evidence, especially relating to adult learners [ 2 ]. Of the two studies exploring dyscalculia in Higher Education Institutions (HEI), from the perspective of learners, both Drew [ 3 ] and Lynn [ 6 , 7 ] outlined poor understanding within adult learning environments and a lack of recognition of dyscalculia and of HEI learning support provision. Additionally, learner challenges were different to those described in dyslexia and dyspraxia studies, with understanding and perception of time, distance, finances, non-integer numbers, memorisation and recall of numerical codes and values being frequent issues. Potential complexity arose through possible coexistence of dyslexia or mathematical anxiety, varying learner-developed coping strategies effectiveness and learner coping mechanisms becoming ineffective during undergraduate or postgraduate education [ 3 ]. Drew’s [ 3 ] three healthcare learner participants had also experienced potential fitness to practice concerns either from themselves or educators.

Context for medical education

The number of DiT in postgraduate medical education (PGME) with dyscalculia remains unknown. Similarly, awareness levels of PGME educators, or what their experiences might be, of facilitating the learning of DiT with dyscalculia is unexplored. Indeed, there has been no published research to date relating to dyscalculia in PGME or undergraduate medical education.

This paucity of knowledge is set in the context of a presumed increasing proportion of UK PGME DiT learners with a disability resulting from increasing numbers of medical students in the UK reporting a disability [ 8 , 9 ] and in other countries such as Australia [ 10 ]. Data collection via the statutory education bodies, and the medical regulator, the General Medical Council (GMC), is challenging given the voluntary nature of SpLD declaration and persisting concerns regarding discrimination and stigma [ 11 ]. My Freedom of Information request to the GMC in February 2022 revealed that 1.25% of registered doctors have declared a ‘learning disability’ (including SpLDs) such as dyslexia.

The impact of dyscalculia on DiT and their educators is unknown. The GMC defines differential attainment as the gap in assessment outcomes between learners grouped by protected characteristic [ 12 ]. It recently commissioned research into recommending education providers create more inclusive learning environments for disabled learners [ 13 ]. Other recent research indicates that differential attainment may persist from school-based examinations through to medical school exit ranking scores and onto PGME examinations [ 14 ].

Currently, there is no publicly available information addressing the support of PGME DiT with dyscalculia within the UK, and no known prospective screening in place. Support, including reasonable adjustments for PGME DiT with additional learning needs is accessed through, and coordinated by, education bodies’ Professional Support Units (PSU), including Health Educator and Improvement Wales’ (HEIW) PSU in Wales. More widely, HEIW, the education body in Wales, is responsible for delivery and quality management of PGME in accordance with UK-level standards set by the GMC and medical speciality Royal Colleges and Faculties. Reasonable adjustments are changes, additions, or the removal of learning environment elements to provide learners with additional support and remediate disadvantage [ 15 ]. They are frequently purported to enable learners with SpLDs to learn and perform to their potential, although evidence for this is variable [ 16 , 17 ], with a marked lack of research relating to adult learners with dyscalculia.

Despite recent shifts from more teacher-centred to more student-centred learning approaches, with a range of andrological learning theories emphasising the learner being at the centre of learning [ 18 ], the educationalist remains a key element of many learning theories and PGME. Many PGME educators are practising doctors and, alongside this, must maintain a contemporaneous understanding of learning theory, training delivery, teaching, supervision and wider educational policies. However, how they approach, or would plan to approach, supporting learning for DiT with dyscalculia is unknown. Therefore, exploring the attitudes and perspectives of PGME DiT or educators regarding dyscalculia, both unresearched previously, through this paradigm could be valuable [ 19 ].

Educational challenges, learning needs and local context

For educators, a pivotal part of facilitating learning is understanding the learning needs of learners, felt to be a cornerstone of adult pedagogy [ 19 , 20 ]. Davis et al. [ 20 ] define learning needs as ‘’any gap between what is and what should be”. These can be established subjectively, objectively or a combination approach. However, Grant [ 19 ] cautions against conducting limiting, formulaic learning need assessments.

Identifying attitudes and understanding

Furthermore, attitudes are said to frame educator approaches and thus the learning experiences learners will have [ 21 ]. Attitudes are defined as “a feeling or opinion about something or someone, or a way of behaving that is caused by this” [ 22 ]. Interpretivism offers a route to exploring such attitudes by outlining that there is no one universal truth or fact, but instead many equally valid realities constructed by different individuals, their meaning-making and their experiences.

Again, research is absent within medical education relating to educators’ attitudes and understanding of learners with dyscalculia and how these might influence their approach. Current research indicates attitudes of HEI educators are often formed through their past - or absent past - experiences, lack of legal obligations knowledge and, for healthcare educators, the patient-centred role of clinical learners [ 23 ]. These appeared to help form their approach to facilitating teaching [ 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Therefore, understanding PGME educationalist attitudes towards DiT with dyscalculia would be important in helping understand how learning is facilitated.

Thus, there exists a clear lack of published knowledge and understanding regarding dyscalculia set in a context of increasing awareness of the importance of inclusivity and addressing differential attainment within medical education. The importance of educators in facilitating learning of such PGME DiT suggests that exploring their perspectives and understanding could provide valuable insights into this understudied area. Such knowledge could provide benefit to learners and those designing and delivering programmes of learning for DiT and programmes of support for educators. This includes potentially exploring the attitudes and understanding of educators who have no direct experience of dyscalculia, given that this could be the context in which a DiT with dyscalculia finds themselves in a postgraduate learning environment. Assumptions, or perceptions generated without experience or knowledge of dyscalculia, are equally important to understand in a learning context when the awareness level and prevalence of dyscalculia within DiT is unknown. This allows understanding of how learning for DiT with dyscalculia may be facilitated in a knowledge and understanding-poor context, and furthermore, what educator needs exist and what further research is needed.

Consequently, the research question and aims below were constructed.

Research question:

What are the attitudes towards , understanding and perceived challenges of dyscalculia within postgraduate medical training by postgraduate medical educators?

Research aims:

To explore the awareness and understanding of dyscalculia that postgraduate medical educators may or may not have.

To determine the attitudes that postgraduate educators have towards dyscalculia and DiT with dyscalculia and how these might be formed.

To establish the challenges that postgraduate educators perceive they encounter or might encounter when facilitating the learning of a DiT who has dyscalculia.

To provide the basis for future research studies exploring how to facilitate the learning of DiT with dyscalculia during postgraduate training.

This scoping study was designed using an interpretivist, constructivist qualitative methodology to understand the phenomenon, in detail [ 30 ] as part of a Masters in Medical Education programme.

A literature review was undertaken to enable research question and aim construction. Firstly, a focused literature search ascertained the level, and lack, of evidence existing for the study phenomenon followed by four, progressively broader, searches to understand the wider context, between October 2021 and May 2022, revealing the lack of, or limited, literature existing.

The literature search was then performed by me using guidance [ 31 , 32 ] and twenty-seven research search engines. Additionally, a spectrum of journals was searched directly. Literature was also identified through snowballing.

Keyword search terms were developed and refined during the literature search, with limits on further broadening the search based on relevance to the areas of interest: postgraduate learners, educators and SpLDs using different term combinations exploring dyscalculia and postgraduate education, SpLDs and postgraduate healthcare learners, postgraduate educators and attitudes or knowledge or experiences of facilitating learning (appendix 1, supplementary material). Broadening of search terms allowed for exploration of analogous phenomena (other SpLDs), in other postgraduate healthcare and learning contexts, and for further research question development, returning 2,638 items. Papers were initially screened using their titles and the inclusion/exclusion criteria (below) generating 182 articles, papers and theses, with abstracts and reference lists reviewed. 174 papers and eight PhD theses were appraised using guidance [ 32 , 33 , 34 ].

Inclusion criteria were:

Primary research or review.

International or UK-based research reported in English.

Postgraduate higher education (university-level, post Bachelor or equivalent degree) setting.

Relating to postgraduate or higher educationalists’ views from any discipline and knowledge of SpLDs.

Exclusion criteria were:

Literature published in non-English languages.

Opinion and commentary articles.

Undergraduate setting, unless mixed cohort/study with postgraduate learners.

Ultimately, 17 papers and one doctoral thesis were included. Whilst grey literature, this thesis [ 3 ] was included due to the dyscalculia-focused insights provided and limited adult-based dyscalculia research elsewhere. After literature appraisal, research aims and a research question were formed.

Semi-structured interviews were chosen to enable data collection and interpretation through a constructivist lens, via open enquiry rather than hypothesis testing [ 30 , 35 , 36 ]. Study participants were PGME educators, actively involved in DiT learning within any PGME programme within Wales whilst holding a Medical Trainer agreement with HEIW. Participants held a range of educationalist roles, from education supervisor to local speciality-specific Royal College tutor (local speciality training lead) to training programme director (responsible for delivery of speciality-specific training across a region).

Interview question and guide design (appendix 2, supplementary material) drew on the six qualitative and six quantitative research-based, validated published tools used to explore similar phenomena, particularly those of O’Hara [ 37 ], Ryder [ 38 ], L’Ecuyer [ 23 ] and Schabmann et al. [ 39 ]. Design also drew upon Cohen et al’s [ 40 ] recommendations of composing open, neutral questioning.

Interview format was piloted using a PGME educator from England (thus ineligible for study recruitment) with modifications resulting from participant feedback and through adopting reflexivity; as per Cohen et al. [ 41 ] and Malmqvist et al. [ 42 ]. Participant interviews took place between May and June 2022 and were recorded via the University-hosted Microsoft Teams platform, due to the pandemic-based situation and large geographical area involved, whilst maintaining interviewer-interviewee visibility during the dialogue [ 35 ]. Recruitment occurred via purposive sampling, through two HEIW gatekeepers, the national Directors of Postgraduate Secondary (hospital-based) and Primary (General Practice-based) Medical Training in Wales. An email-based invitation with project information was distributed to all postgraduate medical educators with a current HEIW Medical Trainer agreement, regularly engaging in the support of learners within PGME training, in Wales. In this case, the gatekeepers in HEIW were individuals who could grant permission and make contact with all potential eligible participants on behalf of myself, through their email databases, whilst adhering to UK data protection regulations [ 43 , 44 ].

Ethical considerations

Formal ethics approval was gained from the Cardiff University School of Medicine Research Ethics Committee. Health Research Authority ethics approval was considered but deemed unnecessary. Informed written and verbal participant consent was obtained prior to, and at the point of, interview respectively. Additionally, verbal consent for video recording was sought, offering audio recording or notetaking alternatives; however, participant discomfort was not reported. Mitigation options to avoid selection bias included selecting alternative volunteers if significant relationships between the researcher and participant had existed.

Invitations to participate were circulated to approximately 2,400 to 2,500 postgraduate secondary care trainers and 600 primary care trainers. 18 individuals indicated interest in participating, one cancelled and seven did not respond to follow-up within the two-month timeframe the MSc project schedule allowed for. Subsequent reasons given for two out of seven who subsequently responded out of timeframe included clinical demands and unexpected personal matters. 10 postgraduate educators were interviewed and all allowed video-based interview recording. Interviews lasted between 40 and 60 min. Interviews were transcribed verbatim by me and checked twice for accuracy, with participants assigned pseudonyms. Data analysis was conducted using reflexive thematic analysis (RTA) and undertaken by me, the author, as the single coder and Masters student, with transcripts analysed three times.

RTA followed the six-step approach of Braun et al. [ 45 ], Braun and Clarke [ 46 ] and Braun and Clarke [ 47 ], with a primarily inductive approach [ 47 , 48 ] through an iterative process. Both latent and semantic coding approaches were used, guided by meaning interpretation [ 49 ].

RTA allowed exploration through an interpretivist lens. Discussions persist regarding how RTA sample size sufficiency and ‘data saturation’ are determined, with RTA placing more emphasis on the analyst-based individualism of meaning-making. Therefore, mechanisms for determining thematic saturation are purportedly inconsistent and unreliable [ 50 ]. Consequently, sample size was based on the maximum number of participants recruited within the set project time limits.

Reflexivity

I strove to adopt reflexivity throughout, using a research diary and personal reflections, referring to Finlay [ 51 ] who stated that such subjectivity can evolve into an opportunity. My interest in the studied phenomenon resulted partially from my experiences as a DiT with SpLDs and from being a DiT representative. Acknowledging this was important given my perspective, as an intrinsic part of this research, could have affected data gathering, interpretation, and, ultimately, study findings through introducing insider status.

Additionally, holding an influential role within the research, with potential for ‘interviewer bias’ [ 52 ], I adopted Cohen et al.’s [ 53 ] recommendations, committing to conscious neutrality during interviews and use of an interview prompt list, whilst striving to maintain a reflexive approach. Alongside this, the impact on credibility of this study being part of a Masters project, limiting scale and timeframes were considered and mitigated by exploring these within the discussion and referring to this research as a scoping study.

Educators with limited to no direct experience of learners with dyscalculia knew little to nothing about dyscalculia (Fig.  1 ).

Summary of themes and subthemes generated

Furthermore, of the participants who did, these educators cited close second-hand experiences with family members or past learners with dyscalculia which helped shape their understanding of dyscalculia. Those that had no direct experience drew on empathy and generalisation, extrapolating from the greater knowledge and confidence they had in their understanding regarding dyslexia or other SpLDs or even analysis of the term ‘dyscalculia’ to form definitions and perceptions.

“Absolutely nothing… I saw it , [dyscalculia in the study invitation] didn’t know what it was and Googled it so very , very little really. I suppose in my simplistic surgical sieve head , I would just sort of apply the bits and pieces I know around dyslexia.” P10 .

All suggested dyscalculia represented a specific set of challenges and associated learning needs relating to numbers, numeracy or quantity where overall intelligence was preserved. Educators saw each learner as being an individual, therefore felt dyscalculia would present as a spectrum, with varying challenges and needs existing. Dyscalculia was seen as persisting lifelong, with the challenges and needs evolving with age and experiences. Common challenges suggested related to calculations, statistics, critical appraisal, awareness of time, organisation and recall of number-based information (such as job lists, blood results), spatial dimension quantification, prescribing, fast-paced tasks and emergencies, exams and learning-based fatigue or high cognitive load. Wellbeing issues relating to dyscalculia were also frequently perceived, with this potentially negatively affecting self-confidence and anxiety levels. All educators saw a key aspect of their role to be provision of pastoral support, in enabling effective learning.

Past educator experiences of dyscalculia were linked to perceived confidence in ability to support future DiT with dyscalculia. Educators felt their limited knowledge, with the primary source of information regarding dyscalculia being DiT with dyscalculia themselves, to be reflective of low levels of awareness, knowledge and identification within PGME, education systems and wider society. Some felt the proportion of PGME DiT with dyscalculia would be lower than for the general population, following challenging assessments during secondary school and undergraduate studies, but might be changing given widening participation initiatives within medicine. Others saw a potential hidden iceberg of later career stage doctors with unidentified dyscalculia who had completed training when speciality assessments relied less on numeracy.

“[It] was only because of my own experiences and my [relative] that I was able to kind of wheedle around and , you know , make them recognise that there was an issue and that , you know. But I - I think had I not had an awareness of it , I probably wouldn’t have recognised it , I think.” P7 .

Educators frequently used empathy when attempting to understand dyscalculia. Educators had mixed feelings about ‘labelling’ DiT as having dyscalculia although all felt identification of additional learning needs was key. Some felt labels were necessary to enable and better support DiT with dyscalculia in the absence of effective, feasible, inclusive education approaches, others noted the potential for stigma or generalisations.

None of the participants had received dyscalculia training. Some felt widespread societal normalisation of mathematics challenges adversely impacted upon if, and at what educational stage, dyscalculia identification occurred and needs were recognised. Many felt assumptions might occur regarding dyscalculia through others making generalisations from better known SpLDs, including dyslexia and dyspraxia, in the absence of other knowledge sources but that these extrapolations could be inaccurate and unhelpful.

“And I think there’s a lot of ‘oh you’re just bad with numbers’ or ‘ohh , you just can’t do , you know people are just , I , I suspect there’s a lot of people who have just been told they’re not very good at maths , aren’t there? And it’s just , you know they can’t , can’t do it , which you know is not really very fair , is it?” P7 .

Many felt PGME might represent a critical juncture for DiT with dyscalculia, where effective coping mechanisms developed in the past become ineffective. A variety of such coping mechanisms were suggested or hypothesised, often outlined as depending on the dyscalculia-based experience level of the educator, including checking work with others, calculator use and avoidance of numeracy-dense work or specialities.

Mechanisms were generally viewed positively except where perceived to reduce the likelihood of a DiT recognising dyscalculia themselves and seeking support.

Most felt positively towards learners with dyscalculia and their learning facilitation, especially those with greater experience of dyscalculia. Many balanced this positivity with potential concerns regarding patient safety. Concerns focused especially on heavily numeracy-based tasks, fast-paced situations, or when working independently in surgical or emergency prescription-based situations. Overall, concerns were heightened due to the clinical patient-based context to PGME learning. Two participants felt that not all DiT with dyscalculia should be supported to continue training in particular specialities where numeracy skills were seen as critical, such as ophthalmology.

“I am , and it just seemed really unfair that this one small thing could potentially have such a big impact and could potentially prevent [them] progressing and succeeding in the way that I think you know , [they , they] had the potential to.” P6 .

Educators outlined a dependence on the bidirectionality of learner-educator relationships to best facilitate DiT learning per se, and it was felt all DiT had a responsibility to be honest with educators. Some cited potential barriers to this collaboration, including past negative learner experiences, felt stigma, limited educator time and frequent DiT rotations.

“It’s a wonderful opportunity for learning which I really enjoy , because I think that this is a two-way process. You know , I think the DiT gives you things that you reflect on and you should be giving the DiT things that they reflect on” P5 .

Most felt they would take a one-to-one learning approach for DiT with dyscalculia. Group-based, fast-paced or numeracy-rich, higher risk clinical activity-based teaching would be more challenging to cater for.

For some, patient safety uncertainties abutted with the duality of being a clinician and educator, with perceived difficulty in quantifying clinical risks associated with learning and educators’ clinical workload demands limiting available time and resources. Thus, many felt that their educator roles always needed to be tempered with their duties as a doctor, prioritising patient safety and quality of care above all else.

“So , it’s not so much the learning , uh , issue that worries me. I think even if someone had dyscalculia the , uh , concepts of medicine could be understood and the basic outline of what we’re doing , but actually you’ve got to be quite precise in the vocational aspect of , of , of the training , and if you get it wrong , it’s a potential major clinical risk and obviously patient safety has to come first in everything that , that we do.” P4 .

Educators wished strongly for pre-emptive support in facilitating the learning of DiT with dyscalculia, feeling great responsibility both for DiT learning but also for upholding clinical standards and safety. Many felt they would approach HEIW’s PSU for reactive support, including seeking learner ‘diagnosis’, although some predicted this support, and their knowledge, might be limited. However, two participants outlined positive experiences after seeking PSU support.

Most educator participants supported reasonable adjustment use if patient safety and quality of care remained prioritised and preserved. Other conditions for supporting reasonable adjustments included if they enabled without giving undue advantage and if educator-related workload was not overly burdensome. Those with experience of dyscalculia more confidently volunteered reasonable adjustments suggestions, ranging from calculation-table or App access to additional time for numeracy-rich activities. Some perceived a challenging divide between clinical educators and SpLD education experts who could make potentially unfeasible reasonable adjustment recommendations, with participants suggesting the importance of greater involvement of clinical educators in developing support processes.

“If I’m honest , I don’t think we do it very well…They’re [reasonable adjustments offered] very simplistic , … you know , they’re very much based on a sort of global ability rather than realising that processing and other things might be impacted… We’re , we’re probably behind the curve and not really doing what could be done” P8 .

Further example quotes for each theme and subtheme can be found within appendix 3, supplementary material.

Experience shapes educator knowledge, understanding and attitudes

This study reveals novel findings regarding dyscalculia in PGME within a vacuum of prior research. Notably, participants’ views towards PGME learners with dyscalculia, including DiT potential to learn, practise and develop effective coping strategies, were substantially more positive and empathetic than in the closest comparable healthcare studies of other SpLDs [ 23 , 24 , 27 , 29 , 54 ]. Furthermore, the potential impact of societal normalisation of numeracy challenges on awareness of, and attitudes towards, dyscalculia explored by some participants has only previously been noted by Drew [ 3 ].

Educators’ expressions of a sense of personal or healthcare-wide lack of awareness and understanding of dyscalculia aligns with the current UK position [ 2 ]. But they also built on this, outlining how generalisation from other SpLDs or disabilities was frequently used to bridge the dyscalculia knowledge gap with some not recognising this as potentially problematic. This suggests a need for enhanced awareness and understanding within the healthcare education community of the potential fallibility of using generalisation to support learners with poorly understood additional needs.

Moreover, no other studies have revealed that healthcare educators with personal experience of a learner relative with a SpLD displayed universally positive attitudes towards DiT with the same SpLD. Whilst this could reflect inter-study methodological differences, inter-professional differences or the increasing emphasis on compassionate clinical practice [ 55 ], it also suggests influence of educator experience in attitude formation.

In addition to their attitudes, the impact of prior experience of learners with dyscalculia on educators’ knowledge, understanding and confidence was often acknowledged as important by participants. This was seen to an extent in the closest comparable SpLD studies, [ 24 , 54 ] and further shows the diverse influence of past educationalist experiences, particularly the establishment of deep, longitudinal relative-based relationships, aligning with social constructivism [ 56 ].

Unlike HEI lecturers in dyslexia studies [ 24 , 54 ], who frequently questioned the needs of learners, educators saw DiT with dyscalculia as intelligent and high-functioning, having credible additional learning needs. Needs were seen as variable unlike elsewhere. Additionally, the level of detail constructed regarding educators’ perceptions of the needs, strengths and challenges of each DiT with dyscalculia, evolving over time and experience, is not seen in non-dyscalculia SpLD studies and only alluded to for dyscalculia [ 3 ]. These differences, which may be partially explained by varying methodologies or cultural norms regarding how different SpLDs are regarded, are important to better understand.

Furthermore, the preferred educator approach of individualising learning for DiT with dyscalculia is not seen elsewhere in the literature, although this aligns with supporting learning within their zone of proximal development (ZPD). Rather, Ryder and Norwich found HEI educators actually expressed negative attitudes towards individualising learning [ 24 ]. Methodological and SpLD-specific factors may contribute to these differences, with this study’s findings aligning more closely with Swanwick’s proposal that PGME often emulates apprenticeship-type learning [ 57 ]. It would be valuable to establish the efficacy of individualised PGME-based approaches to facilitating learning with dyscalculia from DiT and educator perspectives.

Greater educator support and training regarding dyscalculia is needed

Educators’ perceived need for wider awareness of dyscalculia, alongside greater pre-emptive training and guidance tailored towards dyscalculia within PGME learning environments has also been described for other SpLDs [ 23 , 58 , 59 ]. Greater research is needed to develop such awareness and evidence-based training, with similar needs identified more widely in HEI for dyscalculia [ 3 ] and for other SpLDs [ 23 , 24 , 27 ]. Akin to some participants, Swanwick and Morris [ 60 ] discuss the increasing expectations on clinical educationalists to deliver professional-level education and Sandhu [ 61 ] explores participants’ expressed need for greater faculty development whilst rectifying the deficit of evidence-base for PGME educators to use.

The crucial importance of the bidirectionality of the educator-learner relationship, with educators perceiving themselves as learners too, is only subtly alluded to elsewhere [ 3 ]. Given the bidirectional learning relationship was reportedly undermined by frequent DiT placement rotations, fast-paced clinical environments and shift-based training patterns, further exploration of the appropriateness of current UK PGME training design for DiT with dyscalculia could be important.

Coping strategies are important to better understand

As with this study, Drew’s research suggested coping strategies for learners with dyscalculia to be potentially important, effective and helpful but could have limitations [ 3 ]. However, this study provides the first examples of coping strategies, potential or already used, by DiT with dyscalculia. It is crucial that research to develop better understanding of both positive and negative dyscalculia-based coping mechanisms occurs in the future given the broad participant concerns.

Identification is key but not fully enabling

Educators perceived early identification of dyscalculia to be key, showing commonality with dyscalculia, dyslexia and dyspraxia-based studies [ 3 , 25 , 28 ]. That identification was not seen as an absolute solution reinforces the need for further research exploring other disabling factors. However, the witnessed or potential negatives of being ‘labelled’ following dyscalculia ‘diagnosis/identification’, outlined by some participants, have been found only minimally elsewhere within learner-based dyslexia and dyscalculia HEI studies [ 3 , 25 , 28 ]. Negative consequences to labelling included the attitudes learners encountered within the clinical community, suggesting a need to understand cultural norm-related impacts. In contrast, the far greater positives to identification, and the necessity of labelling perceived by educators, were also seen in other SpLD studies [ 3 , 25 , 28 ], enabling self-understanding and access to support. Certainly, the need for improved dyscalculia identification approaches and training is highlighted by the lack of educator confidence in identifying dyscalculia where they had no relative-based experience.

Within the UK, voluntary dyslexia ‘screening’ processes are now offered to some medical students and DiT and similar opportunities could be offered for dyscalculia in the future. Moreover, accumulating evidence indicates an ever-greater importance of establishing equity of learning opportunity and that identification has a positive performance effect for DiT with dyslexia [ 16 , 62 , 63 ].

The PGME clinical context may limit support

Whilst educators clearly adopted a strongly student-centred approach to supporting learning with dyscalculia, addressing the influence of the duality of clinical educator roles on this approach is important. Educator supportive intent was twinned with tension between balancing effective DiT learning with guaranteeing patient safety within diverse, predominantly clinical learning PGME environments, sharing commonalty with L’Ecuyer’s nursing study [ 23 ]. Swanwick and Morris [ 60 ] note this influence on delivering training, with Sandhu [ 61 ] exploring general concerns regarding risk and clinical learning.

Even more pronounced perceived patient safety concerns were expressed in other nursing SpLD studies [ 23 , 29 , 54 , 64 ], and further post-qualification independent working concerns emerged [ 23 , 65 , 66 ], which limited educators’ willingness to support learning. Together, these tensions appear to set learning facilitation for those with dyscalculia within healthcare apart from non-healthcare settings. Therefore, healthcare-specific education research and training is needed to address this, especially given thus far, analogous concerns regarding dyslexia and clinical risk remain unproven.

The influence of educator-reported increasing clinical workload and resource limitations on approach towards supporting DiT with dyscalculia was similarly seen within nursing studies [ 23 , 29 ]. Whilst the impact of clinical demands on UK-based educators are broadly known [ 67 ], greater recognition of the potentially disproportionately negative impact on DiT with dyscalculia needs to be made by those overseeing training delivery.

Uncertainty regarding reasonable adjustments need addressing

Additionally, whilst educators were generally supportive of RAs for DiT with dyscalculia, most intending these to be enabling, caveats to RA introduction were substantial for some. Concerns regarding RA implementation for DiT with dyscalculia were similar to nursing and wider HEI SpLD studies [ 24 , 66 ], but less common or absolute, most relating to feasibility, fairness and adverse impact on educators. These are important to explore if inclusivity in PGME is to be further embraced. Furthermore, and similarly to HEI findings [ 24 ], participant concerns about externally-mandated RAs derived from distant SpLD experts suggest that harnessing coproduction, with greater involvement of clinical educators in RA design, could be important for future endorsement. Additionally, whilst the scale of potential RA suggestions for dyscalculia made in this study is novel, it is important that the experiences of DiT with dyscalculia themselves are captured and used to ensure adjustments are truly enabling.

Therefore, whilst this study reveals important and novel discoveries relating to educators, PGME and dyscalculia, establishing DiT experiences of dyscalculia and PGME is the most crucial avenue of future research to next undertake to better understand and enable both DiT and educators to fulfil their roles effectively and inclusively.

Limitations

As a small, qualitative scoping study undertaken in Wales, study findings cannot and should not be generalisable. Seemingly the first study in this area, transferability should also be considered carefully. Due to purposive sampling, those volunteering may have been more interested in this topic; therefore, findings may not reflect the range of knowledge, attitudes, and experiences of all PGME educators.

Furthermore, use of interviews for data collection and the resultant lack of anonymity may have altered participant contributions. Moreover, despite adopting reflexivity, as a relatively inexperienced, sole researcher, I will have engaged in interviews and analysed data with intrinsic unconscious biases, introducing variability and affecting finding credibility. Despite methodological limitations within this small scoping study, my intention was to construct detailed understanding, providing a basis for future research.

This study reveals, seemingly for the first time, the attitudes, understanding and perceptions of PGME educators relating to DiT with dyscalculia. It highlights that lack of awareness and understanding of dyscalculia exists within the PGME educator community, especially in the absence of relatives with dyscalculia, and that widely accessible, evidence-based approaches to identification, support, teaching approaches and RA provisions are needed and wanted by PGME educators.

The rich stories of participants illuminate the emphasis educators place on experiential learning in informing their perceptions and training approaches, especially in the absence of prospective dyscalculia training or evidence base to draw upon. Given this, including the impact of limited or complete lack of dyscalculia experience and the substitution of generalisation to fill knowledge gaps found in this study, there is a real need for greater PGME-focused research to pre-emptively inform and support all educators.

Furthermore, greater acknowledgement and understanding of the seminal influence that clinical context has on educators, their attitudes towards supporting DiT with dyscalculia and the highly prized bidirectional learning relationships, as revealed in this study, are needed. It highlights the need for greater research to better understand the impact that specific nuances of PGME might have on educators’ support of DiT with dyscalculia and further characterise unmet needs. Future research must begin to address educator uncertainties revealed in this study around potential concerns relating to patient safety and care and differential approaches for dyscalculia and unfairness to other learners to move PGME forward in an effective, inclusive and enabling way.

Notable in this study is the lack of the learner voice, and future research needs to begin to better understand the perceptions and experiences of DiT with dyscalculia of PGME across a wide range of aspects. These could involve those suggested by participants, including DiT PGME learning and assessment experiences, coping strategies, reasonable adjustments and cultural norm impact. Furthermore, clarifying the wider awareness and knowledge levels of PGME educators regarding dyscalculia via more quantitative approaches could help build breadth to the understanding of this poorly understood phenomenon alongside the depth provided by this study.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Attention Deficit and Hyperactivity Disorder

Doctors in Training

General Medical Council

Higher Education Institution

Health Education and Improvement Wales

Postgraduate Medical Education

Professional Support Unit

Reasonable Adjustment

Reflexive Thematic Analysis

Specific Learning Difference

United Kingdom

Zone of Proximal Development

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Riddick R, English E. Meeting the standards? Dyslexic students and the selection process for initial teacher training. Eur J Teach Educ. 2006;29(2):203–22. https://doi.org/10.1080/02619760600617383 .

Morris D, Turnbull P. Clinical experiences of students with dyslexia. J Adv Nurs. 2006;54(2):238–47. https://doi.org/10.1111/j.1365-2648.2006.03806.x .

General Medical Council. National Training Survey 2024 results. [Internet]. 2024 p. 4–5, 24–25, 28–32. https://www.gmc-uk.org/-/media/documents/national-training-survey-summary-report-2024_pdf-107834344.pdf . Accessed 26/7/2024.

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Acknowledgements

LJC would like to thank her academic supervisor Ms Helen Pugsley, Centre for Medical Education at Cardiff University, for her guidance and encouragement during LJC’s Masters project. LJC would also like to thank all the interview participants who took an active part in shaping this project. LJC is extremely grateful for their time, honesty and for providing such vivid and illuminating windows into their roles as educators. LJC would also like to thank Dr Colette McNulty, Dr Helen Baker and wider staff members at HEIW for their support in circulating her study invitation to trainers across Wales.

LJC did not receive any funding for, or as part of, the research project described in this paper.

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LJC designed and undertook the entirety of the research project described in this paper. She also wrote this paper in entirety.

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This study received ethical approval from Cardiff University’s Medical Ethics Committee. After discussions, it was felt that NHS Research Ethics Committee approval was not needed. Written and verbally informed consent to participate was obtained, with prospective participants being provided with information regarding the study and their rights at least three weeks before interviews took place.

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LJC is currently a final year GP registrar working in Wales with keen interests in differential attainment, inclusivity within education and civil learning environments. This paper is borne from a project she designed and undertook as part of her Masters in Medical Education at Cardiff University.

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Cheetham, L.J. “Because people don’t know what it is, they don’t really know it exists” : a qualitative study of postgraduate medical educators’ perceptions of dyscalculia. BMC Med Educ 24 , 896 (2024). https://doi.org/10.1186/s12909-024-05912-2

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• Volume 33, Issue 9
• Patient safety in remote primary care encounters: multimethod qualitative study combining Safety I and Safety II analysis
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• Rebecca Payne 1 ,
• Aileen Clarke 1 ,
• Nadia Swann 1 ,
• Jackie van Dael 1 ,
• Natassia Brenman 1 ,
• Rebecca Rosen 2 ,
• Adam Mackridge 3 ,
• Lucy Moore 1 ,
• Asli Kalin 1 ,
• Emma Ladds 1 ,
• Nina Hemmings 2 ,
• Sarah Rybczynska-Bunt 4 ,
• Stuart Faulkner 1 ,
• Isabel Hanson 1 ,
• Sophie Spitters 5 ,
• http://orcid.org/0000-0002-7758-8493 Sietse Wieringa 1 , 6 ,
• Francesca H Dakin 1 ,
• Sara E Shaw 1 ,
• Joseph Wherton 1 ,
• Richard Byng 4 ,
• Laiba Husain 1 ,
• http://orcid.org/0000-0003-2369-8088 Trisha Greenhalgh 1
• 1 Nuffield Department of Primary Care Health Sciences , University of Oxford , Oxford , UK
• 2 Nuffield Trust , London , UK
• 3 Betsi Cadwaladr University Health Board , Bangor , UK
• 4 Peninsula Schools of Medicine and Dentistry , University of Plymouth , Plymouth , UK
• 5 Wolfson Institute of Population Health , Queen Mary University of London , London , UK
• 6 Sustainable Health Unit , University of Oslo , Oslo , Norway
• Correspondence to Professor Trisha Greenhalgh; trish.greenhalgh{at}phc.ox.ac.uk

Background Triage and clinical consultations increasingly occur remotely. We aimed to learn why safety incidents occur in remote encounters and how to prevent them.

Setting and sample UK primary care. 95 safety incidents (complaints, settled indemnity claims and reports) involving remote interactions. Separately, 12 general practices followed 2021–2023.

Methods Multimethod qualitative study. We explored causes of real safety incidents retrospectively (‘Safety I’ analysis). In a prospective longitudinal study, we used interviews and ethnographic observation to produce individual, organisational and system-level explanations for why safety and near-miss incidents (rarely) occurred and why they did not occur more often (‘Safety II’ analysis). Data were analysed thematically. An interpretive synthesis of why safety incidents occur, and why they do not occur more often, was refined following member checking with safety experts and lived experience experts.

Results Safety incidents were characterised by inappropriate modality, poor rapport building, inadequate information gathering, limited clinical assessment, inappropriate pathway (eg, wrong algorithm) and inadequate attention to social circumstances. These resulted in missed, inaccurate or delayed diagnoses, underestimation of severity or urgency, delayed referral, incorrect or delayed treatment, poor safety netting and inadequate follow-up. Patients with complex pre-existing conditions, cardiac or abdominal emergencies, vague or generalised symptoms, safeguarding issues, failure to respond to previous treatment or difficulty communicating seemed especially vulnerable. General practices were facing resource constraints, understaffing and high demand. Triage and care pathways were complex, hard to navigate and involved multiple staff. In this context, patient safety often depended on individual staff taking initiative, speaking up or personalising solutions.

Conclusion While safety incidents are extremely rare in remote primary care, deaths and serious harms have resulted. We offer suggestions for patient, staff and system-level mitigations.

• Primary care
• Diagnostic errors
• Safety culture
• Qualitative research
• Prehospital care

Data availability statement

Data are available upon reasonable request. Details of real safety incidents are not available for patient confidentiality reasons. Requests for data on other aspects of the study from other researchers will be considered.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/bmjqs-2023-016674

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Safety incidents are extremely rare in primary care but they do happen. Concerns have been raised about the safety of remote triage and remote consultations.

WHAT THIS STUDY ADDS

Rare safety incidents (involving death or serious harm) in remote encounters can be traced back to various clinical, communicative, technical and logistical causes. Telephone and video encounters in general practice are occurring in a high-risk (extremely busy and sometimes understaffed) context in which remote workflows may not be optimised. Front-line staff use creativity and judgement to help make care safer.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

As remote modalities become mainstreamed in primary care, staff should be trained in the upstream causes of safety incidents and how they can be mitigated. The subtle and creative ways in which front-line staff already contribute to safety culture should be recognised and supported.

Introduction

In early 2020, remote triage and remote consultations (together, ‘remote encounters’), in which the patient is in a different physical location from the clinician or support staff member, were rapidly expanded as a safety measure in many countries because they eliminated the risk of transmitting COVID-19. 1–4 But by mid-2021, remote encounters had begun to be depicted as potentially unsafe because they had come to be associated with stories of patient harm, including avoidable deaths and missed cancers. 5–8

Providing triage and clinical care remotely is sometimes depicted as a partial solution to the system pressures facing primary healthcare in many countries, 9–11 including rising levels of need or demand, the ongoing impact of the COVID-19 pandemic and workforce challenges (especially short-term or longer-term understaffing). In this context, remote encounters may be an important component of a mixed-modality health service when used appropriately alongside in-person contacts. 12 13 But this begs the question of what ‘appropriate’ and ‘safe’ use of remote modalities in a primary care context is. Safety incidents (defined as ‘any unintended or unexpected incident which could have, or did, lead to harm for one or more patients receiving healthcare 14 ’) are extremely rare in primary healthcare consultations generally, 15 16 in-hours general practice telephone triage 17 and out-of-hours primary care. 18 But the recent widespread expansion of remote triage and remote consulting in primary care means that a wider range of patients and conditions are managed remotely, making it imperative to re-examine where the risks lie.

Theoretical approaches to safety in healthcare fall broadly into two traditions. 19 ‘Safety I’ studies focus on what went wrong. Incident reports are analysed to identify ‘root causes’ and ‘safety gaps’, and recommendations are made to reduce the chance that further similar incidents will happen in the future. 20 Such studies, undertaken in isolation, tend to lead to a tightening of rules, procedures and protocols. ‘Safety II’ studies focus on why, most of the time, things do not go wrong. Ethnography and other qualitative methods are employed to study how humans respond creatively to unique and unforeseen situations, thereby preventing safety incidents most of the time. 19 Such studies tend to show that actions which achieve safety are highly context specific, may entail judiciously breaking the rules and require human qualities such as courage, initiative and adaptability. 21 Few previous studies have combined both approaches.

In this study, we aimed to use Safety I methods to learn why safety incidents occur (although rarely) in remote primary care encounters and also apply Safety II methods to examine the kinds of creative actions taken by front-line staff that contribute to a safety culture and thereby prevent such incidents.

Study design and origins

Multimethod qualitative study across UK, including incident analysis, longitudinal ethnography and national stakeholder interviews.

The idea for this safety study began during a longitudinal ethnographic study of 12 general practices across England, Scotland and Wales as they introduced (and, in some cases, subsequently withdrew) various remote and digital modalities. Practices were selected for maximum diversity in geographical location, population served and digital maturity and followed from mid-2021 to end 2023 using staff and patient interviews and in-person ethnographic visits. The study protocol, 22 baseline findings 23 and a training needs analysis 24 have been published. To provide context for our ethnography, we interviewed a sample of national stakeholders in remote and digital primary care, including out-of-hours providers running telephone-led services, and held four online multistakeholder workshops, one of which was on the theme of safety, for policymakers, clinicians, patients and other parties. Early data from this detailed qualitative work revealed staff and patient concerns about the safety of remote encounters but no actual examples of harm.

To explore the safety theme further, we decided to take a dual approach. First, following Safety I methodology for the study of rare harms, 20 we set out to identify and analyse a sample of safety incidents involving remote encounters. These were sourced from arm’s-length bodies (NHS England, NHS Resolution, Healthcare Safety Investigation Branch) and providers of healthcare at scale (health boards, integrated care systems and telephone advice services), since our own small sample had not identified any of these rare occurrences. Second, we extended our longitudinal ethnographic design to more explicitly incorporate Safety II methodology, 19 allowing us to examine safety culture and safety practices in our 12 participating general practices, especially the adaptive work done by staff to avert potential safety incidents.

Data sources and management

Table 1 summarises the data sources.

• View inline

Summary of data sources

The Safety I dataset (rows 2-5) consisted of 95 specific incident reports, including complaints submitted to the main arm’s-length NHS body in England, NHS England, between 2020 and 2023 (n=69), closed indemnity claims that had been submitted to a national indemnity body, NHS Resolution, between 2015 and 2023 (n=16), reports from an urgent care telephone service in Wales (NHS 111 Wales) between 2020 and 2023 (n=6) and a report on an investigation of telephone advice during the COVID-19 crisis between 2020 and 2022 7 (n=4). These 95 incidents were organised using Microsoft Excel spreadsheets.

The Safety II dataset (rows 6-10) consisted of extracts from fieldnotes, workshop transcripts and interviews collected over 2 years, stored and coded on NVivo qualitative software. These were identified by searching for text words and codes (e.g. ‘risk’, ‘safety’, ‘incident’) and by asking researchers-in-residence, who were closely familiar with practices, to highlight safety incidents involving harm and examples of safety-conscious work practices. This dataset included over 100 formal interviews and numerous on-the-job interviews with practice staff, plus interviews with a sample of 10 GP (general practitioner) trainers and 10 GP trainees (penultimate row of table 1 ) and with six clinical safety experts identified through purposive sampling from government, arm’s-length bodies and health boards (bottom row of table 1 ).

Data analysis

We analysed incident reports, interview data and ethnographic fieldnotes using thematic analysis as described by Braun and Clarke. 25 These authors define a theme as an important, broad pattern in a set of qualitative data, which can (where necessary) be further refined using coding.

Themes in the incident dataset were identified by five steps. First, two researchers (both medically qualified) read each source repeatedly to gain familiarity. Second, those researchers worked independently using Braun and Clarke’s criterion (‘whether it captures something important in relation to the overall research question’—p 82 25 ) to identify themes. Third, they discussed their initial interpretations with each other and resolved differences through discussion. Fourth, they extracted evidence from the data sources to illustrate and refine each theme. Finally, they presented their list of themes along with illustrative examples to the wider team. Cases used to illustrate themes were systematically fictionalised by changing age, randomly allocating gender and altering clinical details. 26 For example, an acute appendicitis could be changed to acute diverticulitis if the issue was a missed acute abdomen.

These safety themes were then used to sensitise us to seek relevant (confirming and disconfirming) material from our ethnographic and interview datasets. For example, the theme ‘poor communication’ (and subthemes such as ‘failure to seek further clarification’ within this) promoted us to look for examples in our stakeholder interviews of poor communication offered as a cause of safety incidents and examples in our ethnographic notes of good communication (including someone seeking clarification). We used these wider data to add nuance to the initial list of themes.

As a final sense-checking step, the draft findings from this study were shown to each of the six safety experts in our sample and refined in the light of their comments (in some cases, for example, they considered the case to have been overfictionalised, thereby losing key clinical messages; they also gave additional examples to illustrate some of the themes we had identified, which underlined the importance of those themes).

Overview of dataset

The dataset ( table 1 ) consisted of 95 incident reports (see fictionalised examples in box 1 ), plus approximately 400 pages of extracts from interviews, ethnographic fieldnotes and workshop discussions, including situated safety practices (see examples in box 2 ), plus strategic insights relating to policy, organisation and planning of services. Notably, almost all incidents related to telephone calls.

Examples of safety incidents involving death or serious harm in remote encounters

All these cases have been systematically fictionalised as explained in the text.

Case 1 (death)

A woman in her 70s experiencing sudden breathlessness called her GP (general practitioner) surgery. The receptionist answered the phone and informed her that she would place her on the doctor’s list for an emergency call-back. The receptionist was distracted by a patient in the waiting room and did not do so. The patient deteriorated and died at home that afternoon.—NHS Resolution case, pre-2020

Case 2 (death)

An elderly woman contacted her GP after a telephone contact with the out-of-hours service, where constipation had been diagnosed. The GP prescribed laxatives without seeing the patient. The patient self-presented to the emergency department (ED) the following day in obstruction secondary to an incarcerated hernia and died in the operating theatre.—NHS Resolution case, pre-2020

Case 3 (risk to vulnerable patients)

A daughter complained that her elderly father was unable to access his GP surgery as he could not navigate the online triage system. When he phoned the surgery directly, he was directed back to the online system and told to get a relative to complete the form for him.—Complaint to NHS England, 2021

Case 4 (harm)

A woman in her first pregnancy at 28 weeks’ gestation experiencing urinary incontinence called NHS 111. She was taken down by a ‘urinary problems’ algorithm. Both the call handler and the subsequent clinician failed to recognise that she had experienced premature rupture of membranes. She later presented to the maternity department in active labour, and the opportunity to give early steroids to the premature infant was missed.—NHS Resolution case, pre-2020

Case 5 (death)

A doctor called about a 16-year-old girl with lethargy, shaking, fever and poor oral intake who had been unwell for 5 days. The doctor spoke to her older sister and advised that the child had likely glandular fever and should rest. When the parents arrived home, they called an ambulance but the child died of sepsis in the ED.—NHS Resolution case, pre-2020

Case 6 (death)

A 40-year-old woman, 6 weeks after caesarean section, contacted her GP due to shortness of breath, increased heart rate and dry cough. She was advised to get a COVID test and to dial 111 if she developed a productive cough, fever or pain. The following day she collapsed and died at home. The postmortem revealed a large pulmonary embolus. On reviewing the case, her GP surgery felt that had she been seen face to face, her oxygen saturations would have been measured and may have led to suspicion of the diagnosis.—NHS Resolution case, 2020

Case 7 (death)

A son complained that his father with diabetes and chronic kidney disease did not receive any in-person appointments over a period of 1 year. His father went on to die following a leg amputation arising from a complication of his diabetes.—Complaint to NHS England, 2021

Case 8 (death)

A 73-year-old diabetic woman with throat pain and fatigue called the surgery. She was diagnosed with a viral illness and given self-care advice. Over the next few days, she developed worsening breathlessness and was advised to do a COVID test and was given a pulse oximeter. She was found dead at home 4 days later. Postmortem found a blocked coronary artery and a large amount of pulmonary oedema. The cause of death was myocardial infarction and heart failure.—NHS Resolution case, pre-2020

Case 9 (harm)

A patient with a history of successfully treated cervical cancer developed vaginal bleeding. A diagnosis of fibroids was made and the patient received routine care by telephone over the next few months until a scan revealed a local recurrence of the original cancer.—Complaint to NHS England, 2020

Case 10 (death)

A 65-year-old female smoker with chronic cough and breathlessness presented to her GP. She was diagnosed with chronic obstructive pulmonary disease (COPD) and monitored via telephone. She did not respond to inhalers or antibiotics but continued to receive telephone monitoring without further investigation. Her symptoms continued to worsen and she called an ambulance. In the ED, she was diagnosed with heart failure and died soon after.—Complaint to NHS England, 2021

Case 11 (harm)

A 30-year-old woman presented with intermittent episodes of severe dysuria over a period of 2 years. She was given repeated courses of antibiotics but no urine was sent for culture and she was not examined. After 4 months of symptoms, she saw a private GP and was diagnosed with genital herpes.—Complaint to NHS England, 2021

Case 12 (harm)

There were repeated telephone consultations about a baby whose parents were concerned that the child was having a funny colour when feeding or crying. The 6-week check was done by telephone and at no stage was the child seen in person. Photos were sent in, but the child’s dark skin colour meant that cyanosis was not easily apparent to the reviewing clinician. The child was subsequently admitted by emergency ambulance where a significant congenital cardiac abnormality was found.—Complaint to NHS England, 2020 1

Case 13 (harm)

A 35-year-old woman in her third trimester of pregnancy had a telephone appointment with her GP about a breast lump. She was informed that this was likely due to antenatal breast changes and was not offered an in-person appointment. She attended after delivery and was referred to a breast clinic where a cancer was diagnosed.—Complaint to NHS England, 2020

Case 14 (harm)

A 63-year-old woman with a variety of physical symptoms including diarrhoea, hip girdle pain, palpitations, light-headedness and insomnia called her surgery on multiple occasions. She was told her symptoms were likely due to anxiety, but was diagnosed with stage 4 ovarian cancer and died soon after.—Complaint to NHS England, 2021

Case 15 (death)

A man with COPD with worsening shortness of breath called his GP surgery. The staff asked him if it was an emergency, and when the patient said no, scheduled him for 2 weeks later. The patient died before the appointment.—Complaint to NHS England, 2021

Examples of safety practices

Case 16 (safety incident averted by switching to video call for a sick child)

‘I’ve remembered one father that called up. Really didn’t seem to be too concerned. And was very much under-playing it and then when I did a video call, you know this child… had intercostal recession… looked really, really poorly. And it was quite scary actually that, you know, you’d had the conversation and if you’d just listened to what Dad was saying, actually, you probably wouldn’t be concerned.’—GP (general practitioner) interview 2022

Case 17 (‘red flag’ spotted by support staff member)

A receptionist was processing routine ‘administrative’ encounters sent in by patients using AccuRx (text messaging software). She became concerned about a sick note renewal request from a patient with a mental health condition. The free text included a reference to feeling suicidal, so the receptionist moved the request to the ‘red’ (urgent call-back) list. In interviews with staff, it became apparent that there had recently been heated discussion in the practice about whether support staff were adding ‘too many’ patients to the red list. After discussing cases, the doctors concluded that it should be them, not the support staff, who should absorb the risk in uncertain cases. The receptionist said that they had been told: ‘if in doubt, put it down as urgent and then the duty doctor can make a decision.’—Ethnographic fieldnotes from general practice 2023

Case 18 (‘check-in’ phone call added on busy day)

A duty doctor was working through a very busy Monday morning ‘urgent’ list. One patient had acute abdominal pain, which would normally have triggered an in-person appointment, but there were no slots and hard decisions were being made. This patient had had the pain already for a week, so the doctor judged that the general rule of in-person examination could probably be over-ridden. But instead of simply allocating to a call-back, the doctor asked a support staff member to phone the patient, ask ‘are you OK to wait until tomorrow?’ and offer basic safety-netting advice.—Ethnographic fieldnotes from general practice 2023

Case 19 (receptionist advocating on behalf of ‘angry’ walk-in patient)

A young Afghan man with limited English walked into a GP surgery on a very busy day, ignoring the prevailing policy of ‘total triage’ (make contact by phone or online in the first instance). He indicated that he wanted a same-day in-person appointment for a problem he perceived as urgent. A heated exchange occurred with the first receptionist, and the patient accused her of ‘racism’. A second receptionist of non-white ethnicity herself noted the man’s distress and suspected that there may indeed be an urgent problem. She asked the first receptionist to leave the scene, saying she wanted to ‘have a chat’ with the patient (‘the colour of my skin probably calmed him down more than anything’). Through talking to the patient and looking through his record, she ascertained that he had an acute infection that likely needed prompt attention. She tried to ‘bend the rules’ and persuade the duty doctor to see the patient, conveying the clinical information but deliberately omitting the altercation. But the first receptionist complained to the doctor (‘he called us racists’) and the doctor decided that the patient would not therefore be offered a same-day appointment. The second receptionist challenged the doctor (‘that’s not a reason to block him from getting care’). At this point, the patient cried and the second receptionist also became upset (‘this must be serious, you know’). On this occasion, despite her advocacy the patient was not given an immediate appointment.—Ethnographic fieldnotes from general practice 2022

Case 20 (long-term condition nurse visits ‘unengaged’ patients at home)

An advanced nurse practitioner talks of two older patients, each with a long-term condition, who are ‘unengaged’ and lacking a telephone. In this practice, all long-term condition reviews are routinely done by phone. She reflects that some people ‘choose not to have avenues of communication’ (ie, are deliberately not contactable), and that there may be reasons for this (‘maybe health anxiety or just old’). She has, on occasion, ‘turned up’ unannounced at the patient’s home and asked to come in and do the review, including bloods and other tests. She reflects that while most patients engage well with the service, ‘half my job is these patients who don’t engage very well.’—Ethnographic fieldnotes from digitally advanced general practice 2022

Case 21 (doctor over-riding patient’s request for telephone prescribing)

A GP trainee described a case of a 53-year-old first-generation immigrant from Pakistan, a known smoker with hypertension and diabetes. He had booked a telephone call for vomiting and sinus pain. There was no interpreter available but the man spoke some English. He said he had awoken in the night with pain in his sinuses and vomiting. All he wanted was painkillers for his sinuses. The story did not quite make sense, and the man ‘sounded unwell’. The GP told him he needed to come in and be examined. The patient initially resisted but was persuaded to come in. When the GP went to call him in, the man was visibly unwell and lying down in the waiting room. When seen in person, he admitted to shoulder pain. The GP sent him to accident and emergency (A&E) where a myocardial infarction was diagnosed.—Trainee interview 2023

Below, we describe the main themes that were evident in the safety incidents: a challenging organisational and system context, poor communication compounded by remote modalities, limited clinical information, patient and carer burden and inadequate training. Many safety incidents illustrated multiple themes—for example, poor communication and failures of clinical assessment or judgement and patient complexity and system pressures. In the detailed findings below, we illustrate why safety incidents occasionally occur and why they are usually avoided.

The context for remote consultations: system and operational challenges

Introduction of remote triage and expansion of remote consultations in UK primary care occurred at a time of unprecedented system stress (an understaffed and chronically under-resourced primary care sector, attempting to cope with a pandemic). 23 Many organisations had insufficient telephone lines or call handlers, so patients struggled to access services (eg, half of all calls to the emergency COVID-19 telephone service in March 2020 were never answered 7 ). Most remote consultations were by telephone. 27

Our safety incident dataset included examples of technically complex access routes which patients found difficult or impossible to navigate (case 3 in box 1 ) and which required non-clinical staff to make clinical or clinically related judgements (cases 4 and 15). Our ethnographic dataset contained examples of inflexible application of triage rules (eg, no face-to-face consultation unless the patient had already had a telephone call), though in other practices these rules could be over-ridden by staff using their judgement or asking colleagues. Some practices had a high rate of failed telephone call-backs (patient unobtainable).

High demand, staff shortages and high turnover of clinical and support staff made the context for remote encounters inherently risky. Several incidents were linked to a busy staff member becoming distracted (case 1). Telephone consultations, which tend to be shorter, were sometimes used in the hope of improving efficiency. Some safety incidents suggested perfunctory and transactional telephone consultations, with flawed decisions made on the basis of incomplete information (eg, case 2).

Many practices had shifted—at least to some extent—from a demand-driven system (in which every request for an appointment was met) to a capacity-driven one (in which, if a set capacity was exceeded, patients were advised to seek care elsewhere), though the latter was often used flexibly rather than rigidly with an expectation that some patients would be ‘squeezed in’. In some practices, capacity limits had been introduced to respond to escalation of demand linked to overuse of triage templates (eg, to inquire about minor symptoms).

As a result of task redistribution and new staff roles, a single episode of care for one problem often involved multiple encounters or tasks distributed among clinical and non-clinical staff (often in different locations and sometimes also across in-hours and out-of-hours providers). Capacity constraints in onward services placed pressure on primary care to manage risk in the community, leading in some cases to failure to escalate care appropriately (case 6).

Some safety incidents were linked to organisational routines that had not adapted sufficiently to remote—for example, a prescription might be issued but (for various reasons) it could not be transmitted electronically to the pharmacy. Certain urgent referrals were delayed if the consultation occurred remotely (a referral for suspected colon cancer, for example, would not be accepted without a faecal immunochemical test).

Training, supervising and inducting staff was more difficult when many were working remotely. If teams saw each other less frequently, relationship-building encounters and ‘corridor’ conversations were reduced, with knock-on impacts for individual and team learning and patient care. Those supervising trainees or allied professionals reported loss of non-verbal cues (eg, more difficult to assess how confident or distressed the trainee was).

Clinical and support staff regularly used initiative and situated judgement to compensate for an overall lack of system resilience ( box 1 ). Many practices had introduced additional safety measures such as lists of patients who, while not obviously urgent, needed timely review by a clinician. Case 17 illustrates how a rule of thumb ‘if in doubt, put it down as urgent’ was introduced and then applied to avert a potentially serious mental health outcome. Case 18 illustrates how, in the context of insufficient in-person slots to accommodate all high-risk cases, a unique safety-netting measure was customised for a patient.

Poor communication is compounded by remote modalities

Because sense data (eg, sight, touch, smell) are missing, 28 remote consultations rely heavily on the history. Many safety incidents were characterised by insufficient or inaccurate information for various reasons. Sometimes (cases 2, 5, 6, 8, 9, 10 and 11), the telephone consultation was too short to do justice to the problem; the clinician asked few or no questions to build rapport, obtain a full history, probe the patient’s answers for additional detail, confirm or exclude associated symptoms and inquire about comorbidities and medication. Video provided some visual cues but these were often limited to head and shoulders, and photographs were sometimes of poor quality.

Cases 2, 4, 5 and 9 illustrate the dangers of relying on information provided by a third party (another staff member or a relative). A key omission (eg, in case 5) was failing to ask why the patient was unable to come to the phone or answer questions directly.

Some remote triage conversations were conducted using an inappropriate algorithm. In case 4, for example, the call handler accepted a pregnant patient’s assumption that leaking fluid was urine when the problem was actually ruptured membranes. The wrong pathway was selected; vital questions remained unasked; and a skewed history was passed to (and accepted by) the clinician. In case 8, the patient’s complaint of ‘throat’ pain was taken literally and led to ‘viral illness’ advice, overlooking a myocardial infarction.

The cases in box 2 illustrate how staff compensated for communication challenges. In case 16, a GP plays a hunch that a father’s account of his child’s asthma may be inaccurate and converts a phone encounter to video, revealing the child’s respiratory distress. In case 19 (an in-person encounter but relevant because the altercation occurs partly because remote triage is the default modality), one receptionist correctly surmises that the patient’s angry demeanour may indicate urgency and uses her initiative and interpersonal skills to obtain additional clinical information. In case 20, a long-term condition nurse develops a labour-intensive workaround to overcome her elderly patients’ ‘lack of engagement’. More generally, we observed numerous examples of staff using both formal tools (eg, see ‘red list’ in case 17) and informal measures (eg, corridor chats) to pass on what they believed to be crucial information.

Remote consulting can provide limited clinical information

Cases 2 and 4–14 all describe serious conditions including congenital cyanotic heart disease, pulmonary oedema, sepsis, cancer and diabetic foot which would likely have been readily diagnosed with an in-person examination. While patients often uploaded still images of skin lesions, these were not always of sufficient quality to make a confident diagnosis.

Several safety incidents involved clinicians assuming that a diagnosis made on a remote consultation was definitive rather than provisional. Especially when subsequent consultations were remote, such errors could become ingrained, leading to diagnostic overshadowing and missed or delayed diagnosis (cases 2, 8, 9, 10, 11 and 13). Patients with pre-existing conditions (especially if multiple or progressive), the very young and the elderly were particularly difficult to assess by telephone (cases 1, 2, 8, 10, 12 and 16). Clinical conditions difficult to assess remotely included possible cardiac pain (case 8), acute abdomen (case 2), breathing difficulties (cases 1, 6 and 10), vague and generalised symptoms (cases 5 and 14) and symptoms which progressed despite treatment (cases 9, 10 and 11). All these categories came up repeatedly in interviews and workshops as clinically risky.

Subtle aspects of the consultation which may have contributed to safety incidents in a telephone consultation included the inability to fully appraise the patient’s overall health and well-being (including indicators relevant to mental health such as affect, eye contact, personal hygiene and evidence of self-harm), general demeanour, level of agitation and concern, and clues such as walking speed and gait (cases 2, 5, 6, 7, 8, 10, 12 and 14). Our interviews included stories of missed cases of new-onset frailty and dementia in elderly patients assessed by telephone.

In most practices we studied, most long-term condition management was undertaken by telephone. This may be appropriate (and indeed welcome) when the patient is well and confident and a physical examination is not needed. But diabetes reviews, for example, require foot examination. Case 7 describes the deterioration and death of a patient with diabetes whose routine check-ups had been entirely by telephone. We also heard stories of delayed diagnosis of new diabetes in children when an initial telephone assessment failed to pick up lethargy, weight loss and smell of ketones, and point-of-care tests of blood or urine were not possible.

Nurses observed that remote consultations limit opportunities for demonstrating or checking the patient’s technique in using a device for monitoring or treating their condition such as an inhaler, oximeter or blood pressure machine.

Safety netting was inadequate in many remote safety incidents, even when provided by a clinician (cases 2, 5, 6, 8, 10, 12 and 13) but especially when conveyed by a non-clinician (case 15). Expert interviewees identified that making life-changing diagnoses remotely and starting patients on long-term medication without an in-person appointment was also risky.

Our ethnographic data showed that various measures were used to compensate for limited clinical information, including converting a phone consultation to video (case 16), asking the patient if they felt they could wait until an in-person slot was available (case 18), visiting the patient at home (case 20) and enacting a ‘if the history doesn’t make sense, bring the patient in for an in-person assessment’ rule of thumb (case 21). Out-of-hours providers added examples of rules of thumb that their services had developed over years of providing remote services, including ‘see a child face-to-face if the parent rings back’, ‘be cautious about third-party histories’, ‘visit a palliative care patient before starting a syringe driver’ and ‘do not assess abdominal pain remotely’.

Remote modalities place additional burdens on patients and carers

Given the greater importance of the history in remote consultations, patients who lacked the ability to communicate and respond in line with clinicians’ expectations were at a significant disadvantage. Several safety incidents were linked to patients’ limited fluency in the language and culture of the clinician or to specific vulnerabilities such as learning disability, cognitive impairment, hearing impairment or neurodiversity. Those with complex medical histories and comorbidities, and those with inadequate technical set-up and skills (case 3), faced additional challenges.

In many practices, in-person appointments were strictly limited according to more or less rigid triage criteria. Some patients were unable to answer the question ‘is this an emergency?’ correctly, leading to their condition being deprioritised (case 15). Some had learnt to ‘game’ the triage system (eg, online templates 29 ) by adapting their story to obtain the in-person appointment they felt they needed. This could create distrust and lead to inaccurate information on the patient record.

Our ethnographic dataset contained many examples of clinical and support staff using initiative to compensate for vulnerable patients’ inability or unwillingness to take on the additional burden of remote modalities (cases 19 and 20 in Box 2 30 31 ).

Training for remote encounters is often inadequate

Safety incidents highlighted various training needs for support staff members (eg, customer care skills, risks of making clinical judgements) and clinicians (eg, limitations of different modalities, risks of diagnostic overshadowing). Whereas out-of-hours providers gave thorough training to novice GPs (covering such things as attentiveness, rapport building, history taking, probing, attending to contextual cues and safety netting) in telephone consultations, 32–34 many in-hours clinicians had never been formally taught to consult by telephone. Case 17 illustrates how on-the-job training based on acknowledgement of contextual pressures and judicious use of rules of thumb may be very effective in averting safety incidents.

Statement of principal findings

An important overall finding from this study is that examples of deaths or serious harms associated with remote encounters in primary care were extremely rare, amounting to fewer than 100 despite an extensive search going back several years.

Analysis of these 95 safety incidents, drawn from multiple complementary sources, along with rich qualitative data from ethnography, interviews and workshops has clarified where the key risks lie in remote primary care. Remote triage and consultations expanded rapidly in the context of the COVID-19 crisis; they were occurring in the context of resource constraints, understaffing and high demand. Triage and care pathways were complex, multilayered and hard to navigate; some involved distributed work among multiple clinical and non-clinical staff. In some cases, multiple remote encounters preceded (and delayed) a needed in-person assessment.

In this high-risk context, safety incidents involving death or serious harm were rare, but those that occurred were characterised by a combination of inappropriate choice of modality, poor rapport building, inadequate information gathering, limited clinical assessment, inappropriate clinical pathway (eg, wrong algorithm) and failure to take account of social circumstances. These led to missed, inaccurate or delayed diagnoses, underestimation of severity or urgency, delayed referral, incorrect or delayed treatment, poor safety netting and inadequate follow-up. Patients with complex or multiple pre-existing conditions, cardiac or abdominal emergencies, vague or generalised symptoms, safeguarding issues and failure to respond to previous treatment, and those who (for any reason) had difficulty communicating, seemed particularly at risk.

Strengths and limitations of the study

The main strength of this study was that it combined the largest Safety I study undertaken to date of safety incidents in remote primary care (using datasets which have not previously been tapped for research), with a large, UK-wide ethnographic Safety II analysis of general practice as well as stakeholder interviews and workshops. Limitations of the safety incident sample (see final column in table 1 ) include that it was skewed towards very rare cases of death and serious harm, with relatively few opportunities for learning that did not result in serious harm. Most sources were retrospective and may have suffered from biases in documentation and recall. We also failed to obtain examples of safeguarding incidents (which would likely turn up in social care audits). While all cases involved a remote modality (or a patient who would not or could not use one), it is impossible to definitively attribute the harm to that modality.

Comparison with existing literature

This study has affirmed previous findings that processes, workflows and training in in-hours general practice have not adapted adequately to the booking, delivery and follow-up of remote consultations. 24 35 36 Safety issues can arise, for example, from how the remote consultation interfaces with other key practice routines (eg, for making urgent referrals for possible cancer). The sheer complexity and fragmentation of much remote and digital work underscores the findings from a systematic review of the importance of relational coordination (defined as ‘a mutually reinforcing process of communicating and relating for the purpose of task integration ’ (p 3) 37 ) and psychological safety (defined as ‘people’s perceptions of the consequences of taking interpersonal risks in a particular context such as a workplace ’ (p 23) 38 ) in building organisational resilience and assuring safety.

The additional workload and complexity associated with running remote appointments alongside in-person ones is cognitively demanding for staff and requires additional skills for which not all are adequately trained. 24 39 40 We have written separately about the loss of traditional continuity of care as primary care services become digitised, 41–43 and about the unmet training needs of both clinical and support staff for managing remote and digital encounters. 24

Our findings also resonate with research showing that remote modalities can interfere with communicative tasks such as rapport building, establishing a therapeutic relationship and identifying non-verbal cues such as tearfulness 35 36 44 ; that remote consultations tend to be shorter and feature less discussion, information gathering and safety netting 45–48 ; and that clinical assessment in remote encounters may be challenging, 27 49 50 especially when physical examination is needed. 35 36 51 These factors may rarely contribute to incorrect or delayed diagnoses, underestimation of the seriousness or urgency of a case, and failure to identify a deteriorating trajectory. 35 36 52–54

Even when systems seem adequate, patients may struggle to navigate them. 23 30 31 This finding aligns with an important recent review of cognitive load theory in the context of remote and digital health services: because such services are more cognitively demanding for patients, they may widen inequities of access. 55 Some patients lack navigating and negotiating skills, access to key technologies 13 36 or confidence in using them. 30 35 The remote encounter may require the patient to have a sophisticated understanding of access and cross-referral pathways, interpret their own symptoms (including making judgements about severity and urgency), obtain and use self-monitoring technologies (such as a blood pressure machine or oximeter) and convey these data in medically meaningful ways (eg, by completing algorithmic triage forms or via a telephone conversation). 30 56 Furthermore, the remote environment may afford fewer opportunities for holistically evaluating, supporting or safeguarding the vulnerable patient, leading to widening inequities. 13 35 57 Previous work has also shown that patients with pre-existing illness, complex comorbidities or high-risk states, 58 59 language non-concordance, 13 35 inability to describe their symptoms (eg, due to autism 60 ), extremes of age 61 and those with low health or system literacy 30 are more difficult to assess remotely.

Lessons for safer care

Many of the contributory factors to safety incidents in remote encounters have been suggested previously, 35 36 and align broadly with factors that explain safety incidents more generally. 53 62 63 This new study has systematically traced how upstream factors may, very rarely, combine to contribute to avoidable human tragedies—and also how primary care teams develop local safety practices and cultures to help avoid them. Our study provides some important messages for practices and policymakers.

First, remote encounters in general practice are mostly occurring in a system designed for in-person encounters, so processes and workflows may work less well.

Second, because the remote encounter depends more on history taking and dialogue, verbal communication is even more mission critical. Working remotely under system pressures and optimising verbal communication should both be priorities for staff training.

Third, the remote environment may increase existing inequities as patients’ various vulnerabilities (eg, extremes of age, poverty, language and literacy barriers, comorbidities) make remote communication and assessment more difficult. Our study has revealed impressive efforts from staff to overcome these inequities on an individual basis; some of these workarounds may become normalised and increase efficiency, but others are labour intensive and not scalable.

A final message from this study is that clinical assessment provides less information when a physical examination (and even a basic visual overview) is not possible. Hence, the remote consultation has a higher degree of inherent uncertainty. Even when processes have been optimised (eg, using high-quality triage to allocate modality), but especially when they have not, diagnoses and assessments of severity or urgency should be treated as more provisional and revisited accordingly. We have given examples in the Results section of how local adaptation and rule breaking bring flexibility into the system and may become normalised over time, leading to the creation of locally understood ‘rules of thumb’ which increase safety.

Overall, these findings underscore the need to share learning and develop guidance about the drivers of risk, how these play out in different kinds of remote encounters and how to develop and strengthen Safety II approaches to mitigate those risks. Table 2 shows proposed mitigations at staff, process and system levels, as well as a preliminary list of suggestions for patients, which could be refined with patient input using codesign methods. 64

Reducing safety incidents in remote primary care

Unanswered questions and future research

This study has helped explain where the key risks lie in remote primary care encounters, which in our dataset were almost all by telephone. It has revealed examples of how front-line staff create and maintain a safety culture, thereby helping to prevent such incidents. We suggest four key avenues for further research. First, additional ethnographic studies in general practice might extend these findings and focus on specific subquestions (eg, how practices identify, capture and learn from near-miss incidents). Second, ethnographic studies of out-of-hours services, which are mostly telephone by default, may reveal additional elements of safety culture from which in-hours general practice could learn. Third, the rise in asynchronous e-consultations (in which patients complete an online template and receive a response by email) raises questions about the safety of this new modality which could be explored in mixed-methods studies including quantitative analysis of what kinds of conditions these consultations cover and qualitative analysis of the content and dynamics of the interaction. Finally, our findings suggest that the safety of new clinically related ‘assistant’ roles in general practice should be urgently evaluated, especially when such staff are undertaking remote assessment or remote triage.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

Ethical approval was granted by the East Midlands—Leicester South Research Ethics Committee and UK Health Research Authority (September 2021, 21/EM/0170 and subsequent amendments). Access to the NHS Resolution dataset was obtained by secondment of the RP via honorary employment contract, where she worked with staff to de-identify and fictionalise relevant cases. The Remote by Default 2 study (referenced in main text) was co-designed by patients and lay people; it includes a diverse patient panel. Oversight was provided by an independent external advisory group with a lay chair and patient representation. A person with lived experience of a healthcare safety incident (NS) is a co-author on this paper and provided input to data analysis and writing up, especially the recommendations for patients in table 2 .

Acknowledgments

We thank the participating organisations for cooperating with this study and giving permission to use fictionalised safety incidents. We thank the participants in the ethnographic study (patients, practice staff, policymakers, other informants) who gave generously of their time and members of the study advisory group.

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X @dakinfrancesca, @trishgreenhalgh

Contributors RP led the Safety I analysis with support from AC. The Safety II analysis was part of a wider ethnographic study led by TG and SS, on which all other authors undertook fieldwork and contributed data. TG and RP wrote the paper, with all other authors contributing refinements. All authors checked and approved the final manuscript. RP is guarantor.

Funding Funding was from NIHR HS&DR (grant number 132807) (Remote by Default 2 study) and NIHR School for Primary Care Research (grant number 594) (ModCons study), plus an NIHR In-Practice Fellowship for RP.

Competing interests RP was National Professional Advisor, Care Quality Commission 2017–2022, where her role included investigation of safety issues.

Provenance and peer review Not commissioned; externally peer reviewed.

Linked Articles

• Editorial Examining telehealth through the Institute of Medicine quality domains: unanswered questions and research agenda Timothy C Guetterman Lorraine R Buis BMJ Quality & Safety 2024; 33 552-555 Published Online First: 09 May 2024. doi: 10.1136/bmjqs-2023-016872

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