data analysis techniques for qualitative research

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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Great to hear that. Good luck with your qualitative data analysis, Pramod!

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Precise explanation of method.

Hi, may we use 2 data analysis methods in our qualitative research?

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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Thank nicely explained can I ask is Qualitative content analysis the same as thematic analysis?

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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choosing a right method for a paper is always a hard job for a student, this is a useful information, but it would be more useful personally for me, if the author provide me with a little bit more information about the data analysis techniques in type of explanatory research. Can we use qualitative content analysis technique for explanatory research ? or what is the suitable data analysis method for explanatory research in social studies?

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so educative…. but Ijust want to know which method is coding of the qualitative or tallying done?

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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

What a powerful explanation of the QDA methods. Thank you.

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I certainly hope to hear from you

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Home Market Research

Qualitative Data Analysis: What is it, Methods + Examples

In a world rich with information and narrative, understanding the deeper layers of human experiences requires a unique vision that goes beyond numbers and figures. This is where the power of qualitative data analysis comes to light.

In this blog, we’ll learn about qualitative data analysis, explore its methods, and provide real-life examples showcasing its power in uncovering insights.

What is Qualitative Data Analysis?

Qualitative data analysis is a systematic process of examining non-numerical data to extract meaning, patterns, and insights.

In contrast to quantitative analysis, which focuses on numbers and statistical metrics, the qualitative study focuses on the qualitative aspects of data, such as text, images, audio, and videos. It seeks to understand every aspect of human experiences, perceptions, and behaviors by examining the data’s richness.

Companies frequently conduct this analysis on customer feedback. You can collect qualitative data from reviews, complaints, chat messages, interactions with support centers, customer interviews, case notes, or even social media comments. This kind of data holds the key to understanding customer sentiments and preferences in a way that goes beyond mere numbers.

Importance of Qualitative Data Analysis

Qualitative data analysis plays a crucial role in your research and decision-making process across various disciplines. Let’s explore some key reasons that underline the significance of this analysis:

In-Depth Understanding

It enables you to explore complex and nuanced aspects of a phenomenon, delving into the ‘how’ and ‘why’ questions. This method provides you with a deeper understanding of human behavior, experiences, and contexts that quantitative approaches might not capture fully.

Contextual Insight

You can use this analysis to give context to numerical data. It will help you understand the circumstances and conditions that influence participants’ thoughts, feelings, and actions. This contextual insight becomes essential for generating comprehensive explanations.

Theory Development

You can generate or refine hypotheses via qualitative data analysis. As you analyze the data attentively, you can form hypotheses, concepts, and frameworks that will drive your future research and contribute to theoretical advances.

Participant Perspectives

When performing qualitative research, you can highlight participant voices and opinions. This approach is especially useful for understanding marginalized or underrepresented people, as it allows them to communicate their experiences and points of view.

Exploratory Research

The analysis is frequently used at the exploratory stage of your project. It assists you in identifying important variables, developing research questions, and designing quantitative studies that will follow.

Types of Qualitative Data

When conducting qualitative research, you can use several qualitative data collection methods , and here you will come across many sorts of qualitative data that can provide you with unique insights into your study topic. These data kinds add new views and angles to your understanding and analysis.

Interviews and Focus Groups

Interviews and focus groups will be among your key methods for gathering qualitative data. Interviews are one-on-one talks in which participants can freely share their thoughts, experiences, and opinions.

Focus groups, on the other hand, are discussions in which members interact with one another, resulting in dynamic exchanges of ideas. Both methods provide rich qualitative data and direct access to participant perspectives.

Observations and Field Notes

Observations and field notes are another useful sort of qualitative data. You can immerse yourself in the research environment through direct observation, carefully documenting behaviors, interactions, and contextual factors.

These observations will be recorded in your field notes, providing a complete picture of the environment and the behaviors you’re researching. This data type is especially important for comprehending behavior in their natural setting.

Textual and Visual Data

Textual and visual data include a wide range of resources that can be qualitatively analyzed. Documents, written narratives, and transcripts from various sources, such as interviews or speeches, are examples of textual data.

Photographs, films, and even artwork provide a visual layer to your research. These forms of data allow you to investigate what is spoken and the underlying emotions, details, and symbols expressed by language or pictures.

When to Choose Qualitative Data Analysis over Quantitative Data Analysis

As you begin your research journey, understanding why the analysis of qualitative data is important will guide your approach to understanding complex events. If you analyze qualitative data, it will provide new insights that complement quantitative methodologies, which will give you a broader understanding of your study topic.

It is critical to know when to use qualitative analysis over quantitative procedures. You can prefer qualitative data analysis when:

• Complexity Reigns: When your research questions involve deep human experiences, motivations, or emotions, qualitative research excels at revealing these complexities.
• Exploration is Key: Qualitative analysis is ideal for exploratory research. It will assist you in understanding a new or poorly understood topic before formulating quantitative hypotheses.
• Context Matters: If you want to understand how context affects behaviors or results, qualitative data analysis provides the depth needed to grasp these relationships.
• Unanticipated Findings: When your study provides surprising new viewpoints or ideas, qualitative analysis helps you to delve deeply into these emerging themes.
• Subjective Interpretation is Vital: When it comes to understanding people’s subjective experiences and interpretations, qualitative data analysis is the way to go.

You can make informed decisions regarding the right approach for your research objectives if you understand the importance of qualitative analysis and recognize the situations where it shines.

Qualitative Data Analysis Methods and Examples

Exploring various qualitative data analysis methods will provide you with a wide collection for making sense of your research findings. Once the data has been collected, you can choose from several analysis methods based on your research objectives and the data type you’ve collected.

There are five main methods for analyzing qualitative data. Each method takes a distinct approach to identifying patterns, themes, and insights within your qualitative data. They are:

Method 1: Content Analysis

Content analysis is a methodical technique for analyzing textual or visual data in a structured manner. In this method, you will categorize qualitative data by splitting it into manageable pieces and assigning the manual coding process to these units.

As you go, you’ll notice ongoing codes and designs that will allow you to conclude the content. This method is very beneficial for detecting common ideas, concepts, or themes in your data without losing the context.

Steps to Do Content Analysis

Follow these steps when conducting content analysis:

• Collect and Immerse: Begin by collecting the necessary textual or visual data. Immerse yourself in this data to fully understand its content, context, and complexities.
• Assign Codes and Categories: Assign codes to relevant data sections that systematically represent major ideas or themes. Arrange comparable codes into groups that cover the major themes.
• Analyze and Interpret: Develop a structured framework from the categories and codes. Then, evaluate the data in the context of your research question, investigate relationships between categories, discover patterns, and draw meaning from these connections.

Benefits & Challenges

There are various advantages to using content analysis:

• Structured Approach: It offers a systematic approach to dealing with large data sets and ensures consistency throughout the research.
• Objective Insights: This method promotes objectivity, which helps to reduce potential biases in your study.
• Pattern Discovery: Content analysis can help uncover hidden trends, themes, and patterns that are not always obvious.
• Versatility: You can apply content analysis to various data formats, including text, internet content, images, etc.

However, keep in mind the challenges that arise:

• Subjectivity: Even with the best attempts, a certain bias may remain in coding and interpretation.
• Complexity: Analyzing huge data sets requires time and great attention to detail.
• Contextual Nuances: Content analysis may not capture all of the contextual richness that qualitative data analysis highlights.

Example of Content Analysis

Suppose you’re conducting market research and looking at customer feedback on a product. As you collect relevant data and analyze feedback, you’ll see repeating codes like “price,” “quality,” “customer service,” and “features.” These codes are organized into categories such as “positive reviews,” “negative reviews,” and “suggestions for improvement.”

According to your findings, themes such as “price” and “customer service” stand out and show that pricing and customer service greatly impact customer satisfaction. This example highlights the power of content analysis for obtaining significant insights from large textual data collections.

Method 2: Thematic Analysis

Thematic analysis is a well-structured procedure for identifying and analyzing recurring themes in your data. As you become more engaged in the data, you’ll generate codes or short labels representing key concepts. These codes are then organized into themes, providing a consistent framework for organizing and comprehending the substance of the data.

The analysis allows you to organize complex narratives and perspectives into meaningful categories, which will allow you to identify connections and patterns that may not be visible at first.

Steps to Do Thematic Analysis

Follow these steps when conducting a thematic analysis:

• Code and Group: Start by thoroughly examining the data and giving initial codes that identify the segments. To create initial themes, combine relevant codes.
• Code and Group: Begin by engaging yourself in the data, assigning first codes to notable segments. To construct basic themes, group comparable codes together.
• Analyze and Report: Analyze the data within each theme to derive relevant insights. Organize the topics into a consistent structure and explain your findings, along with data extracts that represent each theme.

Thematic analysis has various benefits:

• Structured Exploration: It is a method for identifying patterns and themes in complex qualitative data.
• Comprehensive knowledge: Thematic analysis promotes an in-depth understanding of the complications and meanings of the data.
• Application Flexibility: This method can be customized to various research situations and data kinds.

However, challenges may arise, such as:

• Interpretive Nature: Interpreting qualitative data in thematic analysis is vital, and it is critical to manage researcher bias.
• Time-consuming: The study can be time-consuming, especially with large data sets.
• Subjectivity: The selection of codes and topics might be subjective.

Example of Thematic Analysis

Assume you’re conducting a thematic analysis on job satisfaction interviews. Following your immersion in the data, you assign initial codes such as “work-life balance,” “career growth,” and “colleague relationships.” As you organize these codes, you’ll notice themes develop, such as “Factors Influencing Job Satisfaction” and “Impact on Work Engagement.”

Further investigation reveals the tales and experiences included within these themes and provides insights into how various elements influence job satisfaction. This example demonstrates how thematic analysis can reveal meaningful patterns and insights in qualitative data.

Method 3: Narrative Analysis

The narrative analysis involves the narratives that people share. You’ll investigate the histories in your data, looking at how stories are created and the meanings they express. This method is excellent for learning how people make sense of their experiences through narrative.

Steps to Do Narrative Analysis

The following steps are involved in narrative analysis:

• Gather and Analyze: Start by collecting narratives, such as first-person tales, interviews, or written accounts. Analyze the stories, focusing on the plot, feelings, and characters.
• Find Themes: Look for recurring themes or patterns in various narratives. Think about the similarities and differences between these topics and personal experiences.
• Interpret and Extract Insights: Contextualize the narratives within their larger context. Accept the subjective nature of each narrative and analyze the narrator’s voice and style. Extract insights from the tales by diving into the emotions, motivations, and implications communicated by the stories.

There are various advantages to narrative analysis:

• Deep Exploration: It lets you look deeply into people’s personal experiences and perspectives.
• Human-Centered: This method prioritizes the human perspective, allowing individuals to express themselves.

However, difficulties may arise, such as:

• Interpretive Complexity: Analyzing narratives requires dealing with the complexities of meaning and interpretation.
• Time-consuming: Because of the richness and complexities of tales, working with them can be time-consuming.

Example of Narrative Analysis

Assume you’re conducting narrative analysis on refugee interviews. As you read the stories, you’ll notice common themes of toughness, loss, and hope. The narratives provide insight into the obstacles that refugees face, their strengths, and the dreams that guide them.

The analysis can provide a deeper insight into the refugees’ experiences and the broader social context they navigate by examining the narratives’ emotional subtleties and underlying meanings. This example highlights how narrative analysis can reveal important insights into human stories.

Method 4: Grounded Theory Analysis

Grounded theory analysis is an iterative and systematic approach that allows you to create theories directly from data without being limited by pre-existing hypotheses. With an open mind, you collect data and generate early codes and labels that capture essential ideas or concepts within the data.

As you progress, you refine these codes and increasingly connect them, eventually developing a theory based on the data. Grounded theory analysis is a dynamic process for developing new insights and hypotheses based on details in your data.

Steps to Do Grounded Theory Analysis

Grounded theory analysis requires the following steps:

• Initial Coding: First, immerse yourself in the data, producing initial codes that represent major concepts or patterns.
• Categorize and Connect: Using axial coding, organize the initial codes, which establish relationships and connections between topics.
• Build the Theory: Focus on creating a core category that connects the codes and themes. Regularly refine the theory by comparing and integrating new data, ensuring that it evolves organically from the data.

Grounded theory analysis has various benefits:

• Theory Generation: It provides a one-of-a-kind opportunity to generate hypotheses straight from data and promotes new insights.
• In-depth Understanding: The analysis allows you to deeply analyze the data and reveal complex relationships and patterns.
• Flexible Process: This method is customizable and ongoing, which allows you to enhance your research as you collect additional data.

However, challenges might arise with:

• Time and Resources: Because grounded theory analysis is a continuous process, it requires a large commitment of time and resources.
• Theoretical Development: Creating a grounded theory involves a thorough understanding of qualitative data analysis software and theoretical concepts.
• Interpretation of Complexity: Interpreting and incorporating a newly developed theory into existing literature can be intellectually hard.

Example of Grounded Theory Analysis

Assume you’re performing a grounded theory analysis on workplace collaboration interviews. As you open code the data, you will discover notions such as “communication barriers,” “team dynamics,” and “leadership roles.” Axial coding demonstrates links between these notions, emphasizing the significance of efficient communication in developing collaboration.

You create the core “Integrated Communication Strategies” category through selective coding, which unifies new topics.

This theory-driven category serves as the framework for understanding how numerous aspects contribute to effective team collaboration. This example shows how grounded theory analysis allows you to generate a theory directly from the inherent nature of the data.

Method 5: Discourse Analysis

Discourse analysis focuses on language and communication. You’ll look at how language produces meaning and how it reflects power relations, identities, and cultural influences. This strategy examines what is said and how it is said; the words, phrasing, and larger context of communication.

The analysis is precious when investigating power dynamics, identities, and cultural influences encoded in language. By evaluating the language used in your data, you can identify underlying assumptions, cultural standards, and how individuals negotiate meaning through communication.

Steps to Do Discourse Analysis

Conducting discourse analysis entails the following steps:

• Select Discourse: For analysis, choose language-based data such as texts, speeches, or media content.
• Analyze Language: Immerse yourself in the conversation, examining language choices, metaphors, and underlying assumptions.
• Discover Patterns: Recognize the dialogue’s reoccurring themes, ideologies, and power dynamics. To fully understand the effects of these patterns, put them in their larger context.

There are various advantages of using discourse analysis:

• Understanding Language: It provides an extensive understanding of how language builds meaning and influences perceptions.
• Uncovering Power Dynamics: The analysis reveals how power dynamics appear via language.
• Cultural Insights: This method identifies cultural norms, beliefs, and ideologies stored in communication.

However, the following challenges may arise:

• Complexity of Interpretation: Language analysis involves navigating multiple levels of nuance and interpretation.
• Subjectivity: Interpretation can be subjective, so controlling researcher bias is important.
• Time-Intensive: Discourse analysis can take a lot of time because careful linguistic study is required in this analysis.

Example of Discourse Analysis

Consider doing discourse analysis on media coverage of a political event. You notice repeating linguistic patterns in news articles that depict the event as a conflict between opposing parties. Through deconstruction, you can expose how this framing supports particular ideologies and power relations.

You can illustrate how language choices influence public perceptions and contribute to building the narrative around the event by analyzing the speech within the broader political and social context. This example shows how discourse analysis can reveal hidden power dynamics and cultural influences on communication.

How to do Qualitative Data Analysis with the QuestionPro Research suite?

QuestionPro is a popular survey and research platform that offers tools for collecting and analyzing qualitative and quantitative data. Follow these general steps for conducting qualitative data analysis using the QuestionPro Research Suite:

• Collect Qualitative Data: Set up your survey to capture qualitative responses. It might involve open-ended questions, text boxes, or comment sections where participants can provide detailed responses.
• Export Qualitative Responses: Export the responses once you’ve collected qualitative data through your survey. QuestionPro typically allows you to export survey data in various formats, such as Excel or CSV.
• Prepare Data for Analysis: Review the exported data and clean it if necessary. Remove irrelevant or duplicate entries to ensure your data is ready for analysis.
• Code and Categorize Responses: Segment and label data, letting new patterns emerge naturally, then develop categories through axial coding to structure the analysis.
• Identify Themes: Analyze the coded responses to identify recurring themes, patterns, and insights. Look for similarities and differences in participants’ responses.
• Generate Reports and Visualizations: Utilize the reporting features of QuestionPro to create visualizations, charts, and graphs that help communicate the themes and findings from your qualitative research.
• Interpret and Draw Conclusions: Interpret the themes and patterns you’ve identified in the qualitative data. Consider how these findings answer your research questions or provide insights into your study topic.
• Integrate with Quantitative Data (if applicable): If you’re also conducting quantitative research using QuestionPro, consider integrating your qualitative findings with quantitative results to provide a more comprehensive understanding.

Qualitative data analysis is vital in uncovering various human experiences, views, and stories. If you’re ready to transform your research journey and apply the power of qualitative analysis, now is the moment to do it. Book a demo with QuestionPro today and begin your journey of exploration.

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Home > Blog >

Data analysis in qualitative research, theertha raj, august 30, 2024.

While numbers tell us "what" and "how much," qualitative data reveals the crucial "why" and "how." But let's face it - turning mountains of text, images, and observations into meaningful insights can be daunting.

This guide dives deep into the art and science of how to analyze qualitative data. We'll explore cutting-edge techniques, free qualitative data analysis software, and strategies to make your analysis more rigorous and insightful. Expect practical, actionable advice on qualitative data analysis methods, whether you're a seasoned researcher looking to refine your skills or a team leader aiming to extract more value from your qualitative data.

What is qualitative data?

Qualitative data is non-numerical information that describes qualities or characteristics. It includes text, images, audio, and video. 

This data type captures complex human experiences, behaviors, and opinions that numbers alone can't express.

A qualitative data example can include interview transcripts, open-ended survey responses, field notes from observations, social media posts and customer reviews

Importance of qualitative data

Qualitative data is vital for several reasons:

• It provides a deep, nuanced understanding of complex phenomena.
• It captures the 'why' behind behaviors and opinions.
• It allows for unexpected discoveries and new research directions.
• It puts people's experiences and perspectives at the forefront.
• It enhances quantitative findings with depth and detail.

What is data analysis in qualitative research?

Data analysis in qualitative research is the process of examining and interpreting non-numerical data to uncover patterns, themes, and insights. It aims to make sense of rich, detailed information gathered through methods like interviews, focus groups, or observations.

This analysis moves beyond simple description. It seeks to understand the underlying meanings, contexts, and relationships within the data. The goal is to create a coherent narrative that answers research questions and generates new knowledge.

How is qualitative data analysis different from quantitative data analysis?

Qualitative and quantitative data analyses differ in several key ways:

• Data type: Qualitative analysis uses non-numerical data (text, images), while quantitative analysis uses numerical data.
• Approach: Qualitative analysis is inductive and exploratory. Quantitative analysis is deductive and confirmatory.
• Sample size: Qualitative studies often use smaller samples. Quantitative studies typically need larger samples for statistical validity.
• Depth vs. breadth: Qualitative analysis provides in-depth insights about a few cases. Quantitative analysis offers broader insights across many cases.
• Subjectivity: Qualitative analysis involves more subjective interpretation. Quantitative analysis aims for objective, statistical measures.

What are the 3 main components of qualitative data analysis?

The three main components of qualitative data analysis are:

• Data reduction: Simplifying and focusing the raw data through coding and categorization.
• Data display: Organizing the reduced data into visual formats like matrices, charts, or networks.
• Conclusion drawing/verification: Interpreting the displayed data and verifying the conclusions.

These components aren't linear steps. Instead, they form an iterative process where researchers move back and forth between them throughout the analysis.

How do you write a qualitative analysis?

Step 1: organize your data.

Start with bringing all your qualitative research data in one place. A repository can be of immense help here. Transcribe interviews , compile field notes, and gather all relevant materials.

Immerse yourself in the data. Read through everything multiple times.

Step 2: Code & identify themes

Identify and label key concepts, themes, or patterns. Group related codes into broader themes or categories. Try to connect themes to tell a coherent story that answers your research questions.

Pick out direct quotes from your data to illustrate key points.

Step 3: Interpret and reflect

Explain what your results mean in the context of your research and existing literature.

Als discuss, identify and try to eliminate potential biases or limitations in your analysis. 

Summarize main insights and their implications.

What are the 5 qualitative data analysis methods?

Thematic Analysis Identifying, analyzing, and reporting patterns (themes) within data.

Content Analysis Systematically categorizing and counting the occurrence of specific elements in text.

Grounded Theory Developing theory from data through iterative coding and analysis.

Discourse Analysis Examining language use and meaning in social contexts.

Narrative Analysis Interpreting stories and personal accounts to understand experiences and meanings.

Each method suits different research goals and data types. Researchers often combine methods for comprehensive analysis.

What are the 4 data collection methods in qualitative research?

When it comes to collecting qualitative data, researchers primarily rely on four methods.

• Interviews : One-on-one conversations to gather in-depth information.
• Focus Groups : Group discussions to explore collective opinions and experiences.
• Observations : Watching and recording behaviors in natural settings.
• Document Analysis : Examining existing texts, images, or artifacts.

Researchers often use multiple methods to gain a comprehensive understanding of their topic.

How is qualitative data analysis measured?

Unlike quantitative data, qualitative data analysis isn't measured in traditional numerical terms. Instead, its quality is evaluated based on several criteria. 

Trustworthiness is key, encompassing the credibility, transferability, dependability, and confirmability of the findings. The rigor of the analysis - the thoroughness and care taken in data collection and analysis - is another crucial factor. 

Transparency in documenting the analysis process and decision-making is essential, as is reflexivity - acknowledging and examining the researcher's own biases and influences. 

Employing techniques like member checking and triangulation all contribute to the strength of qualitative analysis.

Benefits of qualitative data analysis

The benefits of qualitative data analysis are numerous. It uncovers rich, nuanced understanding of complex phenomena and allows for unexpected discoveries and new research directions. 

By capturing the 'why' behind behaviors and opinions, qualitative data analysis methods provide crucial context. 

Qualitative analysis can also lead to new theoretical frameworks or hypotheses and enhances quantitative findings with depth and detail. It's particularly adept at capturing cultural nuances that might be missed in quantitative studies.

Challenges of Qualitative Data Analysis

Researchers face several challenges when conducting qualitative data analysis. 

Managing and making sense of large volumes of rich, complex data can lead to data overload. Maintaining consistent coding across large datasets or between multiple coders can be difficult. 

There's a delicate balance to strike between providing enough context and maintaining focus on analysis. Recognizing and mitigating researcher biases in data interpretation is an ongoing challenge. 

The learning curve for qualitative data analysis software can be steep and time-consuming. Ethical considerations, particularly around protecting participant anonymity while presenting rich, detailed data, require careful navigation. Integrating different types of data from various sources can be complex. Time management is crucial, as researchers must balance the depth of analysis with project timelines and resources. Finally, communicating complex qualitative insights in clear, compelling ways can be challenging.

Best Software to Analyze Qualitative Data

G2 rating: 4.6/5

Pricing: Starts at $30 monthly.

Looppanel is an AI-powered research assistant and repository platform that can make it 5x faster to get to insights, by automating all the manual, tedious parts of your job. 

Here’s how Looppanel’s features can help with qualitative data analysis:

• Automatic Transcription: Quickly turn speech into accurate text; it works across 8 languages and even heavy accents, with over 90% accuracy.
• AI Note-Taking: The research assistant can join you on calls and take notes, as well as automatically sort your notes based on your interview questions.
• Automatic Tagging: Easily tag and organize your data with free AI tools.
• Insight Generation: Create shareable insights that fit right into your other tools.
• Repository Search: Run Google-like searches within your projects and calls to find a data snippet/quote in seconds
• Smart Summary: Ask the AI a question on your research, and it will give you an answer, using extracts from your data as citations.

Looppanel’s focus on automating research tasks makes it perfect for researchers who want to save time and work smarter.

G2 rating: 4.7/5

Pricing: Free version available, with the Plus version costing $20 monthly.

ChatGPT, developed by OpenAI, offers a range of capabilities for qualitative data analysis including:

• Document analysis : It can easily extract and analyze text from various file formats.
• Summarization : GPT can condense lengthy documents into concise summaries.
• Advanced Data Analysis (ADA) : For paid users, Chat-GPT offers quantitative analysis of data documents.
• Sentiment analysis: Although not Chat-GPT’s specialty, it can still perform basic sentiment analysis on text data.

ChatGPT's versatility makes it valuable for researchers who need quick insights from diverse text sources.

How to use ChatGPT for qualitative data analysis

ChatGPT can be a handy sidekick in your qualitative analysis, if you do the following:

• Use it to summarize long documents or transcripts
• Ask it to identify key themes in your data
• Use it for basic sentiment analysis
• Have it generate potential codes based on your research questions
• Use it to brainstorm interpretations of your findings

G2 rating: 4.7/5 Pricing: Custom

Atlas.ti is a powerful platform built for detailed qualitative and mixed-methods research, offering a lot of capabilities for running both quantitative and qualitative research.

It’s key data analysis features include:

• Multi-format Support: Analyze text, PDFs, images, audio, video, and geo data all within one platform.
• AI-Powered Coding: Uses AI to suggest codes and summarize documents.
• Collaboration Tools: Ideal for teams working on complex research projects.
• Data Visualization: Create network views and other visualizations to showcase relationships in your data.

G2 rating: 4.1/5 Pricing: Custom

NVivo is another powerful platform for qualitative and mixed-methods research. It’s analysis features include:

• Data Import and Organization: Easily manage different data types, including text, audio, and video.
• AI-Powered Coding: Speeds up the coding process with machine learning.
• Visualization Tools: Create charts, graphs, and diagrams to represent your findings.
• Collaboration Features: Suitable for team-based research projects.

NVivo combines AI capabilities with traditional qualitative analysis tools, making it versatile for various research needs.

Can Excel do qualitative data analysis?

Excel can be a handy tool for qualitative data analysis, especially if you're just starting out or working on a smaller project. While it's not specialized qualitative data analysis software, you can use it to organize your data, maybe putting different themes in different columns. It's good for basic coding, where you label bits of text with keywords. You can use its filter feature to focus on specific themes. Excel can also create simple charts to visualize your findings. But for bigger or more complex projects, you might want to look into software designed specifically for qualitative data analysis. These tools often have more advanced features that can save you time and help you dig deeper into your data.

How do you show qualitative analysis?

Showing qualitative data analysis is about telling the story of your data. In qualitative data analysis methods, we use quotes from interviews or documents to back up our points. Create charts or mind maps to show how different ideas connect, which is a common practice in data analysis in qualitative research. Group your findings into themes that make sense. Then, write it all up in a way that flows, explaining what you found and why it matters.

What is the best way to analyze qualitative data?

There's no one-size-fits-all approach to how to analyze qualitative data, but there are some tried-and-true steps. 

Start by getting your data in order. Then, read through it a few times to get familiar with it. As you go, start marking important bits with codes - this is a fundamental qualitative data analysis method. Group similar codes into bigger themes. Look for patterns in these themes - how do they connect? 

Finally, think about what it all means in the bigger picture of your research. Remember, it's okay to go back and forth between these steps as you dig deeper into your data. Qualitative data analysis software can be a big help in this process, especially for managing large amounts of data.

In qualitative methods of test analysis, what do test developers do to generate data?

Test developers in qualitative research might sit down with people for in-depth chats or run group discussions, which are key qualitative data analysis methods. They often use surveys with open-ended questions that let people express themselves freely. Sometimes, they'll observe people in their natural environment, taking notes on what they see. They might also dig into existing documents or artifacts that relate to their topic. The goal is to gather rich, detailed information that helps them understand the full picture, which is crucial in data analysis in qualitative research.

Which is not a purpose of reflexivity during qualitative data analysis?

Reflexivity in qualitative data analysis isn't about proving you're completely objective. That's not the goal. Instead, it's about being honest about who you are as a researcher. It's recognizing that your own experiences and views might influence how you see the data. By being upfront about this, you actually make your research more trustworthy. It's also a way to dig deeper into your data, seeing things you might have missed at first glance. This self-awareness is a crucial part of qualitative data analysis methods.

What is a qualitative data analysis example?

A simple example is analyzing customer feedback for a new product. You might collect feedback, read through responses, create codes like "ease of use" or "design," and group similar codes into themes. You'd then identify patterns and support findings with specific quotes. This process helps transform raw feedback into actionable insights.

How to analyze qualitative data from a survey?

First, gather all your responses in one place. Read through them to get a feel for what people are saying. Then, start labeling responses with codes - short descriptions of what each bit is about. This coding process is a fundamental qualitative data analysis method. Group similar codes into bigger themes. Look for patterns in these themes. Are certain ideas coming up a lot? Do different groups of people have different views? Use actual quotes from your survey to back up what you're seeing. Think about how your findings relate to your original research questions. 

Which one is better, NVivo or Atlas.ti?

NVivo is known for being user-friendly and great for team projects. Atlas.ti shines when it comes to visual mapping of concepts and handling geographic data. Both can handle a variety of data types and have powerful tools for qualitative data analysis. The best way to decide is to try out both if you can. 

While these are powerful tools, the core of qualitative data analysis still relies on your analytical skills and understanding of qualitative data analysis methods.

Do I need to use NVivo for qualitative data analysis?

You don't necessarily need NVivo for qualitative data analysis, but it can definitely make your life easier, especially for bigger projects. Think of it like using a power tool versus a hand tool - you can get the job done either way, but the power tool might save you time and effort. For smaller projects or if you're just starting out, you might be fine with simpler tools or even free qualitative data analysis software. But if you're dealing with lots of data, or if you need to collaborate with a team, or if you want to do more complex analysis, then specialized qualitative data analysis software like NVivo can be a big help. It's all about finding the right tool for your specific research needs and the qualitative data analysis methods you're using.

Here’s a guide that can help you decide.

How to use NVivo for qualitative data analysis

First, you import all your data - interviews, documents, videos, whatever you've got. Then you start creating "nodes," which are like folders for different themes or ideas in your data. As you read through your material, you highlight bits that relate to these themes and file them under the right nodes. NVivo lets you easily search through all this organized data, find connections between different themes, and even create visual maps of how everything relates.

How much does NVivo cost?

NVivo's pricing isn't one-size-fits-all. They offer different plans for individuals, teams, and large organizations, but they don't publish their prices openly. Contact the team here for a custom quote.

What are the four steps of qualitative data analysis?

While qualitative data analysis is often iterative, it generally follows these four main steps:

1. Data Collection: Gathering raw data through interviews, observations, or documents.

2. Data Preparation: Organizing and transcribing the collected data.

3. Data Coding: Identifying and labeling important concepts or themes in the data.

4. Interpretation: Drawing meaning from the coded data and developing insights.

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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 data analysis 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 data analysis 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 data 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.

Whatever your data analysis approach, start with ATLAS.ti

Qualitative data analysis done quickly and intuitively with ATLAS.ti. Download a free trial today.

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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Data Analysis in Qualitative Research

Ravindran, Vinitha 1,

1 College of Nursing, CMC, Vellore, Tamil Nadu, India

Address for correspondence: Dr. Vinitha Ravindran, College of Nursing, CMC, Vellore, Tamil Nadu, India. E-Mail: [email protected]

This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

Data analysis in qualitative research is an iterative and complex process. The focus of analysis is to bring out tacit meanings that people attach to their actions and responses related to a phenomenon. Although qualitative data analysis softwares are available, the researcher is the primary instrument who attempts to bring out these meanings by a deep engagement with the data and the individuals who share their stories. Although different approaches are suggested in different qualitative methods, the basic steps of content analysis that includes preparing the data, reading and reflection, coding, categorising and developing themes are integral to all approaches. The analysis process moves the researcher from describing the phenomenon to conceptualisation and abstraction of themes without losing the voice of the participants which are represented by the findings.

INTRODUCTION

Qualitative data analysis appears simple to those who have limited knowledge of qualitative research approach, but for the seasoned qualitative researcher, it is one of the most difficult tasks. According to Thorn,[ 1 ] it is the complex and elusive part of the qualitative research process. Many challenges that are inherent in the research approach makes the analysis process demanding. The first challenge is to convert the data from visual or auditory recording to textual data. As qualitative approach includes data generation through sharing of experiences, it becomes fundamentally necessary to record data rather than writing down accounts as the stories are shared. Essential data which may become apparent or uncovered when reflecting on audiotaped interviews may be missed or overlooked if interviews are not recorded.[ 2 ] Although field notes are written they often augment the experiences conveyed by participants rather than being the primary data source. Therefore, the researcher needs to spend effort and time to record as well as transcribe data to texts which can be analysed.

The second challenge is managing the quantum of textual data. One hour of interview may produce 20–40 pages of text. Even with fewer participants as generally is in qualitative research, the researcher may have many pages of data which need coding and analysing. Although software packages such as NVivo and Atlas-ti are available, they only help to organise, sort and categorise data and will not give meaning to the text.[ 3 ] The researcher has to read, reflect, compare and analyse data. The categories and themes have to be brought forth by the researcher. The third challenge is doing data generation and data analysis at the same time. Concurrent data generation and analysis is a predominant feature in qualitative research. An iterative or cyclic method of data collection and analysis is emphasised in qualitative approach. What it means is that as the researcher collects data, the analysis process is also initiated. The researcher does not wait to complete collecting data and then do analysis.[ 2 ] Iterative process enhances the researcher to focus on emerging concepts and categories in subsequent interviews and observations. It enables the researcher to address the gaps in the data and get information to saturate the gaps in subsequent contacts with earlier or new research participants. Sufficient time and resources are needed for sustaining the iterative process throughout the research process.

The above challenges are mentioned at the beginning of this article not to discourage the researchers but to emphasise the complexity of data analysis which has to be seriously considered by all researchers who are interested in doing qualitative research. In addition to the general challenges, data analysis in qualitative research also varies between different approaches and designs. There is also the possibility of flexibility and fluidity that enhances the researcher to choose different approaches to analysis, either one specific approach or a combination of approaches.[ 4 ] The framework for the analysis should, however, be made explicit at the beginning of the analysis. In qualitative research, the researcher is a bricoleur (weaver of stories) who is creating a bricolage.[ 5 ]

CHARACTERISTICS OF DATA ANALYSIS

In qualitative data analysis.

• Researcher attempts to understand the meaning behind actions and behaviours of participants
• Researcher becomes the instrument to generate data and ask analytical questions
• Emphasis is given to quality and depth of narration about a phenomenon rather than the number of study participants
• The context and a holistic view of the participants' experience are stressed
• The research is sensitive to what the influence he/she has on the interpretation of data
• Analytical themes are projected as findings rather than quantified variables.

Process of data analysis

Qualitative data analysis can be both deductive and inductive. The deductive process, in which there is an attempt to establish causal relationships, is although associated with quantitative research, can be applied also in qualitative research as a deductive explanatory process or deductive category application.[ 6 ] When the researcher's interest is on specific aspects of the phenomenon, and the research question is focused and not general, a deductive approach to analysis may be used. For example, in a study done by Manoranjitham et al .,[ 7 ] focus group discussions were conducted to identify perceptions of suicide in terms of causes, methods of attempting suicide, impact of suicide and availability of support as perceived by family and community members and health-care professionals. Focused questions were asked to elicit information on what people thought about the above aspects of suicide. The answers from participants in focus groups were coded under each question, which was considered as categories and the number of responders and the responses were elaborated under the said questions as perceived by the participants. Deductive process in qualitative data analysis allows the researcher to be at a descriptive level where the results are closer to participants accounts, rather than moving to a more interpretive or conceptual level. This process is often used when qualitative research is used as a part of the mixed methods approach or as a part of an elaborate research study.

In contrast, the inductive process which is the hallmark of qualitative data analysis involves asking questions of the in-depth and vast data that have been generated from different sources regarding a phenomenon.[ 2 , 4 ] The inductive process is applicable to all qualitative research in which the research question has been more explorative and overarching in terms of understanding the phenomenon in peoples' lives. For example, in Rempel et al .'s[ 8 ] study on parenting children with life-threatening congenital heart disease, the researchers explored the process of parenting children with a lethal heart condition. Volumes of data generated through individual interviews with parents and grandparents were inductively analysed to understand the 'facets of parenting' children with heart disease. Inductive analysis motivates and enhances researchers to rise above describing what the participants say about their experience to interpretive conceptualisation and abstraction. The process of deduction and induction in qualitative data analysis is depicted in Figure 1 .

GENERAL STEPS IN DATA ANALYSIS

Although different analytical processes are proposed by different researchers, there are generally four basic steps to qualitative data analysis. These steps are similar to what is generally known as qualitative content analysis.[ 4 , 9 ] In any qualitative approach, the analysis starts with the steps of content analysis. The content analysis ends generally at an interpretive descriptive level. Further analysis to raise data to abstraction may be needed in some approaches such as grounded theory.

Preparation of data

Reading and reflecting, coding, categorising and memoing.

• Developing themes/conceptual models or theory.

As already discussed, the inductive process in qualitative research begins when data collection starts. Each recorded data set from individual interviews, focus groups or conversations should be first transcribed and edited. The researcher may decide on units of data that can be analysed to further help in organising.[ 10 ] The units can be the whole interview from one individual or interview transcripts from one family or data from different individuals connected with in a case (as in case study). On some occasions, the unit may consist of all answers to one question or one aspect of the phenomenon. Many researchers may not form any such units at the beginning of the analysis which is also accepted. The essential aspect of the preparation is to ensure that participants' accounts are truly represented in transcribing. Researchers who have a large amount of content will need assistance in transcription. One hour of interview may take 4–6 h to transcribe.[ 2 ] An official transcriber will do a good job than a researcher who may spend a long time in transcribing volumes of data. However, the researcher has to edit the transcription by listening to the audiotaped version and include words and connotations that are missed to maintain accuracy in transcription.[ 11 ] Another important point to note is to transcribe and prepare the data as soon as interviews are completed. This facilitates the iterative process of data collection and analysis. All data, including field notes, should be organised with date, time and identification number or pseudonym for easy retrieval.[ 2 ] Assigning numbers or pseudonyms help to maintain the confidentiality of the participants.

Reading the data as a whole, and reflecting on what the participants are sharing gives an initial understanding of the narrative. The reflection may start at the time of the interview itself. However, reading and rereading the transcribed text from an interview gives an understanding of context, situations, events and actions related to the phenomenon of interest before the data can be analysed for concepts and themes.[ 12 ] Reading and reflection help the researcher to get immersed in the data, understand the perspectives of participants and decide on an analytical framework for further data analysis.[ 13 ] As texts are read, the researcher may jot down points or questions that are striking or unusual or does or does not support assumptions. Such reflective notes assist the researcher to decide on questions to be asked in further interviews or look for similarities or differences in interview texts from other participants. These initial reflections do not complete analysis; rather, it provides a platform for the analysis to develop. An example of initial reflections when analysing interviews from a study on home care of children with chronic illness is given below.

Reflections-family 1 interview

'This family has a lot of issues related to home care. Their conversation is a list of complaints about the system and the personnel. Even though it appears that help is being rendered for support of child at home, nothing seems to satisfy the parents. The conversation revolves around how they have not been given their due in terms of material and personnel support rather than about their sick child or the siblings.

After a while, it became tedious for me to read this transcript as I resent the complaints (which I should not do I suppose). I wonder how other families perceive home care.'

The initial reflections also help to understand our position as a researcher and the assumptions the researcher brings to the study. It helps us to be aware of one's own professional and personal prejudices which may influence the interpretation of data.

For analysis to progress further the researcher has to decide on an organised way of sorting and categorising data to come to an understanding about the phenomenon or the concepts embedded in the phenomenon. Researchers may choose to analyse only the manifest content in a descriptive qualitative study or may move further to look for latent content in an analytical-qualitative study.[ 4 ] The manifest content analysis includes looking for specific words or phrases used by the participants and accounting for how many have expressed the same or similar words/phrases in the data. It looks at what is obvious. Latent content analysis, on the other hand, involves coding and categorising to identify patterns and themes that are implicit in the data.

Coding is an essential first step in sorting and organising data.[ 4 ] Codes are labels given to phrases, expressions, behaviours, images and sentences as the researcher goes through the data.[ 13 ] It can be 'in vivo' codes or 'interpretive codes'. When participants' exact expressions itself are used as codes it is called ' in vivo ' codes.[ 14 ] If the researcher interprets the expression or behaviour of the participant depicted in the text, then it is called interpretive codes. In the grounded theory method, different levels of coding are suggested. The first level is called the 'open coding' that involves sifting through the initial data line by line and creating in vivo or interpretive codes. Questions such as what are this person saying or doing or what is happening here? will help in the initial coding of data. Initial coding may reveal gaps in the data or raise questions.[ 15 , 16 ] These gaps and questions will help the researcher to locate the sources from where further data are to be collected. The second level is known as 'focus or selective coding' will be used in subsequent interviews. Focused coding involves using the most frequent or most significant earlier codes to sift through large amounts of data. Focused codes are more directed, selective and conceptual and are employed to raise the sorting of data to an analytical level.[ 17 ] The first level of coding can be done manually or can be done using qualitative software packages. In other types of content analysis, the different levels of coding may not be followed instead the researcher engages in interpretive coding as the text is read. In a grounded theory study on parenting children with burn injury open codes such as scolded, accused, unwanted, guilt, nonsupport, difficult to care, terrible pain, blaming oneself and tired came up as the data were coded [ Table 1 ]. These codes gave the researcher an initial insight into the traumatic experiences that the parents undergo when caring for their burn-injured children. As texts were coded, the researcher attempted to understand further the struggles of parents in the successive interviews with other families.

Categorising

Categorising involves grouping similar codes together and formulating an understandable set within which related data can be clubbed. A category is 'a collection of similar data sorted into the same place' – the 'what', developed using content analysis and developing trajectories and relationships over time.[ 18 ] It is a group of content that shares commonality. Data can be categorised generally when the researcher realises that the same codes or codes that are relatively similar are emerging from the data. When categories are developed based on codes, they can be still at descriptive level or can be at an abstract level.[ 10 ] By developing categories a conceptual coding structure can be formulated. At this level, there is no need to continue line by line coding. Instead, the researcher uses the coding structure to sort data. In other words, parts of data that best fit the categories, and the codes are grouped appropriately from across the data sets. The grouping of data into categories is enabled by comparing and contrasting data from different sources or individuals.[ 19 ] As constant comparison continues[ 15 ] questions such as 'What is different between the accounts of two families? What are similar? Will help in grouping data into categories. As the researcher compares data, questions such as 'what if' may come up which will propel the researcher to return to participants to know more or even purposively include participants who will answer the question. The data under each category should be read again to ensure that they appropriately represent the category.[ 4 ] Qualitative software packages are very useful in sorting and organising data from this level. Any part of data which is not fitting into any category needs to be coded newly, and the new codes should be added. The emerging new codes may later fit into a category or form new categories. All data are thus accounted for during this phase of analysis.

As analysis and grouping of further data continue, the researcher may rearrange data within categories or come up with subcategories.[ 4 ] The researcher may also go from data to codes, to sub-categories which then can be abstracted into categories.[ 10 ] In the burn study, similar codes that were repeated in many transcripts were grouped together. Grouping these codes helped in developing subcategories such as physical trauma, emotional trauma, self-blame and shame. The sub-categories were then grouped to develop meaningful categories such as facing blame and enduring the burn [ Table 2 ]. Creating categories thus assists the researcher to move from describing phenomenon to interpretation and abstraction.

Memoing is 'the researcher's record of analysis, thoughts, interpretations, questions and directions for further data collection' (pp 110).[ 20 ] Memos are elaborations of thoughts on data, codes and categories that are written down.[ 17 ] Simply put, memoing is writing down the reflections and ideas that arise from the data as data analysis progresses. As data are coded, the researcher writes down his/her thoughts on the codes and their relationships as they occur. Memo-writing is an on-going process, and memos lead to abstraction and theorising for the write-up of ideas.[ 15 ] Initial or early memos help in exploring and filling out the initial qualitative codes. It helps the researcher to understand what the participants are saying or doing and what is happening. Advanced memos help in the emergence of categories and identify the beliefs and assumptions that support the categories. Memos also help in looking at the categories and the data from different vantage points.[ 21 ] One of the early memos from burn study is given as an example.

Extensive wound

24 June, 2010, 10 pm – After coding interview texts from three families.

'I am struck by the enormity of a burn injury. I realize that family members cannot do many things for the child at home after discharge of a severely burned child because the injury is so big that even some clinics and doctors who are not familiar with burn care cannot manage care. These children need continuous attention of the health care professionals. They need professional assistance with dressing. They need professional assistance with splints and gadgets, and therapies. The injury is extensive that it is difficult for family members to do many things on their own. It is very hard, very hard for the parents to take up a role of the caregiver for children with burns because it involves large wound which has not healed or is in the process of early healing and the child suffers severe pain. The post burn care is very different from caring for other children with chronic illness or congenital defects which most often does not involve pain. The child's suffering makes it easy for the parents to view them as vulnerable. Yet the parents do their best. They try to follow the Health Care Professionals advice, they try to go for follow-up, but it seems simply not enough. I think the parents are doing all that they can within the context of severe injury, lack of finances, lack of resources in home town, or blame and ridicule from neighbors and others…'

Stopping to memo helps the researcher to reflect on data, move towards developing themes and models and lay the ground for discussion of findings later. Memos need to include the time, date, place and context at which they were written.

Developing themes, conceptual models and theory

Developing themes involves the 'threading together of the underlying meaning' that run through all the categories. It is the interpretation of the latent content in the texts.[ 10 ] Theming involves integrating all the categories and explicating the relationship in the categories.[ 4 ] In coding and categorising the researcher is involved in deconstructing or dividing the data to understand the feelings, behaviours and actions. In the phase of theming, the researcher is trying to connect the deconstructed part by understanding the implicit meaning that connects the behaviour, actions and reactions related to a phenomenon. To identify theme, the grounded theorist asks: What is the core issue which the participants are dealing with? The phenomenologist will ask about the central essence or structure of the lived experience related to the phenomenon of interest. The ethnographer may look at the cultural themes that link the categories. The researcher generally comes up with one to three themes.[ 4 ] Too many categories or themes may indicate that the analysis is prematurely closed and implies the need for the researcher to further interpret and conceptualise the data.[ 4 ] In the study on parenting children with burn injury, the researcher came up with the theme of 'Double Trauma' which explicated the experiences of parents living the burn with their children and also enduring the blame within the context of both the hospital and home [ Table 2 ].

In phenomenology and ethnography, the analysis may end with identifying themes. In other approaches, such as grounded theory and interpretive description, the analysis may progress further to developing theory or conceptual models. Identifying the core category/variable from the coding activity, memos and constant comparisons are the first step in moving towards theory development in grounded theory.[ 15 ] The core category is the main theme that the researcher identifies in the data. The next step in grounded theory is to identify the basic social process (BSP). The BSP evolves from understanding how participants are dealing with the core issue. In real-world situations, individuals develop their own strategies and process to deal with the core issue in any situation. Identifying this process is the stepping stone to theory development in grounded theory. In the example of burn study, the theme 'Double Trauma' was the core category and parenting in the burn study involved a dual process of 'embracing the survival' and 'enduring the blame'.[ 22 , 23 ] A conceptual model was developed based on these processes.

PITFALLS IN QUALITATIVE ANALYSIS

Large data sets for analysis.

As already explained, the amount of data text or field notes from observations and other sources in qualitative research can become overwhelming if data analysis is not initiated concurrently with data collection/generation. Coding large data text is tedious and takes much of the researcher's time. Postponing analysis to the end of data collection also prevents the researcher from becoming focused in subsequent interviews and filling gaps in data in further data collection. Therefore, deferring data analysis should be avoided.

Premature closure

Researcher should not hasten to conclude analysis with developing categories or themes. This may lead to 'premature closure' of the research and the danger that the participants' experiences are misunderstood or incompletely understood.[ 15 ] Qualitative data analysis involves in-depth interaction with the data and understanding the nuances in the experiences and the meanings behind actions. The researcher continues to generate data until all the categories are saturated, which means that the categories are mutually exclusive and can be explained from all aspects or angle.[ 21 ] In the burn study, although the table in this article appears simple, the codes and categories were developed from larger data sets representing multiple participant interviews and field notes. The category 'facing blame' was brought forth with parents' accounts of experiencing blame in almost all the families in one or multiple ways: from family members, health-care professionals, strangers and the child itself. The researcher needs to be reflexive and iteratively do data generation and analysis until there is no new information forthcoming in the data. Inferring conclusions too soon which is otherwise known as 'inferential leaps', will prevent the researcher from getting the whole picture of the phenomenon.[ 2 ]

Interpretation of meanings

During the analysis process as the researcher interprets and conceptualises the participants' experiences, he/she delves into the tacit meanings of actions and feelings expressed by participants or observed in various situations. The researcher endeavours to keep the interpretations as close to the participants' accounts as possible. However, it should be understood that the meanings are co-constructed by both the participant and researcher by collaborative effort which is also a hallmark of qualitative research.[ 2 ] In the process of co-construction, researcher should be cautious to not lose the voice of the participants. Discussion with peer at all steps of analysis or checks on codes and categories by others in the research team may help to avoid this problem.

Qualitative data analysis is a complex process that demands much of reading, thinking and reflection on the part of researcher. It is time-consuming as the researcher has to be constantly engaged with the texts to tease out the hidden meanings. Beyond the differences in data analysis in different qualitative methods, coding, categorising and developing themes are the essential phases of data analysis in most methods. Researchers should avoid premature conclusions and ensure that the findings are comprehensively represented by participants' accounts. Qualitative data analysis is an iterative process.

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Qualitative Data Analysis

Qualitative data refers to non-numeric information such as interview transcripts, notes, video and audio recordings, images and text documents. Qualitative data analysis can be divided into the following five categories:

1. Content analysis . This refers to the process of categorizing verbal or behavioural data to classify, summarize and tabulate the data.

2. Narrative analysis . This method involves the reformulation of stories presented by respondents taking into account context of each case and different experiences of each respondent. In other words, narrative analysis is the revision of primary qualitative data by researcher.

3. Discourse analysis . A method of analysis of naturally occurring talk and all types of written text.

4. Framework analysis . This is more advanced method that consists of several stages such as familiarization, identifying a thematic framework, coding, charting, mapping and interpretation.

5. Grounded theory . This method of qualitative data analysis starts with an analysis of a single case to formulate a theory. Then, additional cases are examined to see if they contribute to the theory.

Qualitative data analysis can be conducted through the following three steps:

Step 1: Developing and Applying Codes . Coding can be explained as categorization of data. A ‘code’ can be a word or a short phrase that represents a theme or an idea. All codes need to be assigned meaningful titles. A wide range of non-quantifiable elements such as events, behaviours, activities, meanings etc. can be coded.

There are three types of coding:

• Open coding . The initial organization of raw data to try to make sense of it.
• Axial coding . Interconnecting and linking the categories of codes.
• Selective coding . Formulating the story through connecting the categories.

Coding can be done manually or using qualitative data analysis software such as

 NVivo,  Atlas ti 6.0,  HyperRESEARCH 2.8,  Max QDA and others.

When using manual coding you can use folders, filing cabinets, wallets etc. to gather together materials that are examples of similar themes or analytic ideas. Manual method of coding in qualitative data analysis is rightly considered as labour-intensive, time-consuming and outdated.

In computer-based coding, on the other hand, physical files and cabinets are replaced with computer based directories and files. When choosing software for qualitative data analysis you need to consider a wide range of factors such as the type and amount of data you need to analyse, time required to master the software and cost considerations.

Moreover, it is important to get confirmation from your dissertation supervisor prior to application of any specific qualitative data analysis software.

The following table contains examples of research titles, elements to be coded and identification of relevant codes:

 Qualitative data coding

Step 2: Identifying themes, patterns and relationships . Unlike quantitative methods , in qualitative data analysis there are no universally applicable techniques that can be applied to generate findings. Analytical and critical thinking skills of researcher plays significant role in data analysis in qualitative studies. Therefore, no qualitative study can be repeated to generate the same results.

Nevertheless, there is a set of techniques that you can use to identify common themes, patterns and relationships within responses of sample group members in relation to codes that have been specified in the previous stage.

Specifically, the most popular and effective methods of qualitative data interpretation include the following:

• Word and phrase repetitions – scanning primary data for words and phrases most commonly used by respondents, as well as, words and phrases used with unusual emotions;
• Primary and secondary data comparisons – comparing the findings of interview/focus group/observation/any other qualitative data collection method with the findings of literature review and discussing differences between them;
• Search for missing information – discussions about which aspects of the issue was not mentioned by respondents, although you expected them to be mentioned;
• Metaphors and analogues – comparing primary research findings to phenomena from a different area and discussing similarities and differences.

Step 3: Summarizing the data . At this last stage you need to link research findings to hypotheses or research aim and objectives. When writing data analysis chapter, you can use noteworthy quotations from the transcript in order to highlight major themes within findings and possible contradictions.

It is important to note that the process of qualitative data analysis described above is general and different types of qualitative studies may require slightly different methods of data analysis.

My  e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach  contains a detailed, yet simple explanation of qualitative data analysis methods . The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in simple words. John Dudovskiy

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The Primary Methods of Qualitative Data Analysis

In academic research as well as in the business landscape, qualitative data analysis plays a crucial role in understanding and interpreting non-numerical data.

Qualitative data analysis helps us make sense of the stories and personal narratives. In the business context, qualitative data analysis turns customer feedback into an in-depth understanding of what matters to customers. Sharing the insights from this analysis with decision-makers helps them drive initiatives that improve customer experiences.

While quantitative data analysis focuses on numerical measurement and statistical analysis, qualitative data analysis delves into the rich and complex nature of human experiences and perceptions.  When analyzed effectively, customer feedback can be transformed into actionable insights for every team across the company.

This guide will provide an in-depth exploration of the different methods employed in qualitative data analysis, as well as the steps involved and challenges encountered. We’ll also have a look at what QDA means in the business context and how to turn it into a high-powered tool for CX and product teams.

Understanding Qualitative Data Analysis Methods

Definition and importance of qualitative data analysis.

Qualitative data analysis refers to the systematic process of examining and interpreting non-numerical data to gain meaningful insights and generate new knowledge. It’s what happens when you put a year’s worth of Amazon reviews into a thematic analysis engine, and end up with a thorough understanding of how users interact with your product (and half a dozen actionable insights to boot).

It involves dissecting text, images, videos, and other forms of qualitative data to identify patterns, themes, and relationships.

By capturing the nuances and depth of human experiences, the qualitative data analysis approach allows researchers to explore complex social phenomena that quantitative approaches cannot fully capture. It provides a rich and detailed understanding of social contexts, individual perspectives, and subjective experiences.

Qualitative data analysis methods offer an in-depth exploration of the hows and whys behind social phenomena, enabling researchers to gain a comprehensive understanding of complex social issues.  It is incredibly valuable in fields such as sociology, anthropology, psychology, and education, where human behavior and social interactions are studied.

In these fields, researchers often seek to understand the intricacies of human experiences, and qualitative data analysis allows them to capture the complexity of these phenomena.

In the world of business & product development, qualitative data analysis methods can work to improve user experiences. Suddenly, you’ve got the opportunity to reach a comprehensive understanding of just what your products mean on the social landscape.

User feedback gets transformed into big-picture knowledge that offers a 360-degree view of how a product performs in the real world.  Product teams get a solid, reliable basis on which to make decisions , and guesswork becomes a thing of the past.

Key Principles of Qualitative Data Analysis

Before delving into the various methods of qualitative data analysis, let’s look at the key principles that underpin these analysis techniques. Qualitative data analysis is guided by the following principles:

• Inductive Reasoning: Qualitative research focuses on specific observations and gradually develops broader interpretations and theories. It allows for the discovery of new patterns and relationships through an iterative process of data investigation.
• Contextual Understanding: Qualitative data analysis emphasizes the importance of understanding the research context and the social, cultural, and historical factors that shape it. Context provides meaning and helps researchers identify themes as well as interpret and make sense of the data.
• Subjectivity and Reflexivity: When research is human-led, the researchers acknowledge and critically reflect upon their own beliefs, biases, and experiences throughout the qualitative data analysis process. Where research is AI-driven, humans get a chance to view the actual data each insight is based on and check to see if it makes objective sense.
• Active Engagement: A qualitative data analysis method is an active and dynamic process that involves constant engagement with the data. Thematic analysis works most effectively as an ongoing process,  thoroughly examining and interpreting all available data, while continually questioning and refining the research questions and analysis as new data points are added.

Inductive reasoning is a fundamental principle of qualitative data analysis. It allows researchers to start with specific observations and gradually develop broader interpretations and theories. Through this iterative process of data investigation, new patterns and relationships can be discovered. When you’ve got AI-driven data analysis software, this inductive reasoning is going on under the hood.

Contextual understanding is another key principle of the qualitative analysis process. It emphasizes the importance of understanding the research context and the social, cultural, and historical factors that shape it.

By considering the context when analyzing qualitative data, researchers can gain a deeper understanding of the data and interpret it more accurately. Well-designed thematic analysis software has this built in.

Subjectivity and reflexivity are essential principles in qualitative data analysis. Qualitative data analysis research must be repeatable if it is to be relied on, and there should always be ways to check just what qualitative feedback particular trends and insights come from. When qualitative data analysis is done right, transparency and rigor can be maintained throughout the process, from the initial selection of research questions and gathering of raw data to final analysis techniques.

Active engagement is a crucial aspect of qualitative feedback interpretation. It involves constant engagement with the data, as researchers thoroughly examine and interpret it. This active and dynamic process allows researchers to continually question and refine their qualitative analysis, ensuring a comprehensive understanding of the data.

Different Qualitative Data Analysis Methods

Just how does qualitative analysis work out in practice? In this article, we will explore five commonly used qualitative analysis methods: content analysis, narrative analysis, discourse analysis,  grounded theory, and thematic analysis.

• Content Analysis

Content analysis is a systematic and objective approach to analyzing data by categorizing, coding, and quantifying specific words, themes, or concepts within a text. It involves identifying patterns, frequencies, and relationships in the content, which can be textual, visual, or auditory.

Researchers can employ content analysis techniques to examine interviews, focus group discussions, newspaper articles, social media posts, and other forms of textual data. By assigning codes to different segments of the text, researchers can identify recurring themes, sentiments, or messages.

This same qualitative data analysis approach can be used by CX and product teams to analyze customer feedback or support tickets.

For example, in an analysis of public response to a new product, a PX team might use content analysis to analyze social media posts discussing the topic.

By categorizing the posts based on their stance (e.g., positive, negative, neutral) and identifying recurring themes (e.g., user experience, look and feel), a company could gain insights into the dominant narratives and public perceptions surrounding the product launch.

• Narrative Analysis

Narrative analysis focuses on interpreting and understanding the stories and personal narratives shared by individuals. Researchers analyze the structure, content, and meaning of these narratives to gain insights into how individuals make sense of their experiences, construct identities, and communicate their perspectives.

Through narrative analysis techniques, qualitative researchers explore the plot, characters, setting, and themes within a narrative. They examine how the narrator constructs meaning, conveys emotions, and positions themselves within the story.

This same narrative analysis method is often used in psychology, sociology, and anthropology to understand identity formation, life histories, and personal narratives. It can be used in a business setting to analyze long-form responses and user interviews or descriptions of user behavior.

For instance, in a study on the experiences of cancer survivors, researchers may conduct narrative analysis on interviews with survivors. By examining the narratives, researchers can identify common themes such as coping strategies, support systems, and personal growth.

This qualitative analysis process can provide valuable insights into the lived experiences of cancer survivors and inform interventions and support programs.

• Discourse Analysis

Discourse analysis examines the social, cultural, and power relations that shape language use in different contexts. It focuses on the ways in which language constructs and reflects social reality, identities, and ideologies.

Researchers employing discourse analysis analyze data that includes spoken or written language, including interviews, speeches, media articles, and conversations.

They examine linguistic features such as metaphors, power dynamics, framing, and silences to uncover underlying social structures and processes.

For example, in a study on gender representation in media, researchers may use discourse analysis to analyze television advertisements. By examining the language, visual cues, and narratives used in the advertisements, researchers can identify how gender roles and stereotypes are constructed and reinforced.

It can shed light on the ways in which media perpetuates or challenges societal norms and expectations.

Another example might be using discourse analysis to analyze Tik Tok and YouTube videos to understand the societal responses to a rebranding; for instance, that from Twitter to X. Customer interviews are another good source for this analysis method.

• Grounded Theory

Grounded theory is an approach to qualitative analysis that aims to develop theories and concepts grounded in data. It involves iterative data collection and analysis to develop an inductive theory that emerges from the unstructured data itself.

Researchers using grounded theory analyze interviews, observations, and textual data to generate concepts and categories.

These concepts are continually refined and developed through theoretical sampling and constant comparison. Grounded theory analysis is particularly useful when exploring complex social phenomena where existing theories may be limited.

For instance, in a study on the experiences of individuals living with chronic pain, researchers may use grounded theory to analyze interviews with participants. Through iterative analysis, researchers can identify key concepts such as pain management strategies, social support networks, and psychological coping mechanisms.

These concepts can then be used to develop a theoretical framework within grounded theory that captures the multidimensional nature of living with chronic pain.

Although historically grounded theory analysis has been primarily used in the social sciences, grounded theory has also been used successfully for business inquiry.

• Thematic Analysis

Thematic analysis is a widely used method in qualitative data analysis that involves identifying, analyzing, and reporting patterns or themes within data. It is a flexible approach that can be applied across a variety of qualitative data, such as interview transcripts, survey responses, and observational notes.

When thematic analysis is done manually, researchers initially familiarize themselves with the raw data, reading through the material multiple times to gain a deep understanding.

Following this, they begin manual coding. The first step is to generate initial codes, which are tags or labels that identify important features of the data relevant to the research question.

These codes are then collated into potential themes, which are broader patterns that emerge across the data set.

Each theme is then reviewed and refined to ensure it accurately represents the coded data and the overall data set. The final step involves defining and naming the themes, during which researchers provide detailed analysis, including how themes relate to each other and to the research question.

Sound complicated? The great news is that advances in artificial intelligence mean we no longer have to do all that by hand.

Thematic analysis software can process thousands of pieces of consumer feedback in a matter of minutes, providing a user-friendly view of the themes and trends in the customer data pool.

What’s more, this type of software can be programmed to do content analysis, discourse analysis, and narrative analysis at the same time.   The best comprehensive business solution for thematic analysis today is Thematic; a comprehensive feedback analysis that is designed for customer-centric businesses. It makes qualitative user analysis accessible to anyone, and is able to process feedback at scale.

Across disciplines, thematic analysis is particularly valued for its ability to provide a rich and detailed, yet complex account of data. It's a method that is accessible to researchers across different levels of qualitative research experience and can be applied to a variety of theoretical and epistemological approaches, making it a versatile tool in qualitative work.

Steps in Qualitative Data Analysis

Data collection.

Data collection is the initial phase of qualitative research and data analysis. It involves selecting appropriate methods to gather data such as interviews, observations, focus groups, or archival research.

Researchers may employ various techniques to collect data. These can include developing interview protocols, conducting observations, or collecting data using audio-visual recording devices.

They may need to consider ethical considerations, ensure informed consent, and establish rapport with participants to obtain rich and reliable data. The goal is to gather qualitative data that is relevant, comprehensive, and representative of the research topic.

Qualitative research questions can be more open-ended than those used for gathering quantitative data, and the research findings have the potential to be far more extensive.

In a business context, much of the work is done for you by customers who provide feedback in reviews, on support tickets, and on social media. Customer interviews are another possible source of rich data.

Data Coding

Data coding is the process of categorizing and organizing qualitative data into meaningful segments. When this is done manually, researchers assign codes to different parts of the data based on the emerging patterns, themes, or concepts identified during analysis. This coding process helps researchers manage and make sense of large amounts of qualitative data.

There are different types of codes used in analyzing raw data, including descriptive codes, interpretive codes, and conceptual codes.

Descriptive codes capture the content and surface-level meaning of all the data, while interpretive codes delve deeper into the underlying meanings and interpretations. A conceptual coding system further abstracts the research data by identifying broader concepts or theories.

Data Interpretation

Data interpretation involves making sense of the coded data and exploring the relationships, themes, and patterns that emerge from the analysis. Researchers critically examine the data, compare different codes, and then identify themes and connections between categories and concepts.

During data interpretation, researchers may engage in constant comparison, where they continually compare new data to existing codes and categories. This iterative process helps refine the analysis and identify theoretical insights.

It involves synthesizing the findings of qualitative and quantitative data and crafting a narrative that presents a comprehensive understanding of the research phenomenon.

Both data coding and data interpretation can be done by your qualitative analytics software, either in a research or business setting. In a corporate setting, CX /PX teams and customer service can then use information gained through the data interpretation step to drive favourable outcomes.

Performing Qualitative Data Analysis with Generative AI and LLM

Running manual grounded theory analysis or content analysis on a large amount of consumer feedback has never been a practical option. But that doesn’t mean qualitative research doesn’t make sense in a business context.

Generative AI, based on large language models (LLMs) can work with qualitative data at scale, analyze it, and derive the themes, connections and insights that can inform business decisions.

An LLM is a powerful machine learning model, based on deep learning and neural networks. It’s able to process and identify the complex relationships in natural language, and it can also understand user questions and  moods and even generate text.

A natural languague processing LLM, trained on huge amounts of text data, could do all the work of a QDA researcher with the added benefits of easily verifiable, repeatable results.

Companies with extensive  in-house talent  may be able to build an in-house AI engine to analyze customer feedback and make sense of it— on a small scale. Those who are serious about getting real insights, though, will want to go with professional tools that have been trained on massive amounts of data and give reliable, dependable results.

Thematic is probably the best example of such a tool. Built to make sense of any amount of feedback data,  it works in a highly transparent way that will leave you confident in every insight you derive.

It’s also incredibly user-friendly, with helpful visualizations and an easy-to-use dashboard that enables you to keep constant tabs on exactly what your users feel about the company. It’s never been easier to transform your user experience.

Modern Methods of Qualitative Data Analysis in Action: A Case Study

Instacart is one example of a company that discovered the power of qualitative data analysis. This company has 10 million end users, 500,000 personal shoppers, and more than 40,000 retailers. Processing all this qualitative data the traditional way would have been impossible, but Ant Marty,  product operations team manager, found a method that worked.

Plugging data from the app into Thematic, she got real time information on everything happening among those millions of users: trends, themes, and deep understanding of what mattered to the people who made the company run.

Data collection is easy when you have an app with numerous feedback collection options.  Data coding is automated by Thematic. And Thematic makes the first move in interpretation as well, providing insights that can be transformed by product teams into action plans and even a long-term vision.

Challenges Facing Qualitative Data Analysis Methods

Ensuring data validity and reliability.

One of the main challenges to a qualitative approach is ensuring the validity and reliability of the findings. Validity refers to the accuracy, truthfulness, and credibility of the data collected and analysis, while reliability refers to the consistency and replicability of the research process and findings.

Researchers address these challenges by employing rigorous data collection methods, ensuring data saturation, conducting member checks, and establishing inter-rater reliability. They also maintain reflexivity by critically reflecting on their assumptions, biases, and interpretations throughout the analysis process.

If you are a business using software to conduct qualitative research, your data validation check may be somewhat different, but it’s just as important.  Some software, like Thematic, has validation built in, and the whole process is so transparent you can easily check and double-check where each insight comes from .

With other software options, you may have to run manual checks to ensure every piece of information provided has a firm basis.

Dealing with Subjectivity and Bias

Subjectivity and bias used to be considered inherent to qualitative research methods due to the interpretive nature of the process. Researchers bring their own perspectives, beliefs, and experiences, which can influence the analysis and interpretations.

To mitigate subjectivity and bias, researchers maintain transparency in their analytical processes by documenting their decision-making, providing detailed justifications for their interpretations, and engaging in peer debriefing and member checking. Using multiple researchers or an expert panel can also increase the credibility and reliability of the analysis.

Another way to decrease subjectivity is through thematic analysis software, which produces results that are repeatable and verifiable.

When it is all said and done, qualitative analysis offers a powerful and nuanced examination of human experiences and social phenomena. By employing diverse methods, adhering to key principles, and addressing potential limitations, researchers can harness the full potential of qualitative data to uncover rich insights and contribute to the advancement of knowledge.

Benefits of Qualitative Data Analysis Methods

Rich, in-depth insights.

A primary benefit of qualitative research techniques is their ability to provide rich, in-depth insights into complex phenomena. These methods delve deeply into human experiences, emotions, beliefs, and behaviors, offering a comprehensive understanding that is often unattainable through quantitative methods.

By exploring the nuances and subtleties of social interactions and personal experiences, qualitative analysis can uncover the layers of meaning that underpin human behavior. This depth of understanding is particularly valuable in fields like psychology, sociology, and anthropology, where the intricacies of human experience are central to the research question.

It is even more important for customer-focused businesses and enables them to create a product and a CX that meets their customer’s needs and desires. Quantitative analysis can provide a one-dimensional understanding of user behavior based on quantitative data, but when analysing qualitative data you get the why to every what.

Flexibility and Contextual Understanding

Another significant advantage of these analysis techniques is their inherent flexibility and capacity to provide contextual understanding. Unlike quantitative research, which relies on rigid structures and predefined hypotheses, qualitative research is adaptable to the evolving nature of the study.

This flexibility allows researchers to explore unexpected themes and patterns that emerge during the data collection process.  Qualitative analysis is how businesses like Atlassian have created infinite customer feedback loops and powered their own infinitely evolving products.

Additionally, qualitative methods are sensitive to the context in which the data is collected, acknowledging and incorporating the environmental, cultural, and social factors that influence the data. The context-rich approach used to collect qualitative data ensures a more holistic understanding of the subject matter, making it particularly useful in cross-cultural studies, community research, and exploratory investigations.

Your product may have global reach, and users in different areas may interact with it in different ways– but qualitative techniques can take all that into account.

This considered, it should be no surprise that qualitative analysis techniques have become powerful tools for researchers seeking to understand the complexities of human behavior and social phenomena. Their ability to provide depth, context, and rich narrative data makes them indispensable tools in the arsenal of social science research, and there’s no better way to gain solid information to guide your business decisions.

Whether you’re a researcher keen on analyzing and interpreting qualitative data or an entrepreneur keen on making your business more customer-centric, this research method is likely to become your next best friend.

If you’re in academia, you may want to do it all manually, and that’s totally okay. But if it’s business intelligence you’re after— try out Thematic. Your future self will thank you, as will everyone else who views the end-of-year reports.

What are the five methods to analyze qualitative data?

The five chief methods of qualitative data analysis are:

The right analysis method for your use case will depend on what context, your research questions, and the form of data available to you.

What are good sources of data for qualitative data analysis?

In a business context user reviews, support tickets, customer surveys and social media posts are all great sources of data for qualitative analysis. In a research project, gathering qualitative data may mean conducting interviews, surveys, or focus groups.

What are the benefits of qualitative data analysis?

Two big benefits of qualitative data analysis include:

• Rich, in-depth insights
• Flexibility and contextual understanding

In a business context, this translates into a loyal, well-satisfied user base, a successful product, and an upwards-ticking revenue curve. Research objectives for social sciences may include a better understanding of social dynamics or human relations.

What are the challenges of qualitative data analysis?

The two prime challenges of qualitative data analysis techniques are:

• Ensuring data validity and reliability
• Dealing with subjectivity and bias

What is the best tool for qualitative data analysis?

While a number of other options do exist, the best comprehensive software for qualitative data analysis in a business context today is Thematic.

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In This Article Expand or collapse the "in this article" section Qualitative Data Analysis Techniques

Introduction, general overviews.

• Semiotic Analysis
• Text Mining
• Discourse Analysis
• Classical Content Analysis
• Schema Analysis
• Latent Content Analysis
• Manifest Content Analysis
• Keywords-in-context
• Constant Comparison Analysis
• Membership Categorization Analysis
• Narrative Analysis
• Conversation Analysis
• Ethnographic Decision Model Analysis
• Critical Discourse Analysis
• Frame or Framing Analysis
• Social Semiotic Analysis
• Domain Analysis
• Taxonomic Analysis
• Componential Analysis
• Theme Analysis
• Dialogical Narrative Analysis
• Qualitative Comparative Analysis
• Multimodal Discourse Analysis (MDA)
• Dimensional Analysis
• Framework Analysis
• Secondary Data Analysis
• Interpretative Phenomenological Analysis (IPA)
• Consensual Qualitative Research
• Situational Analysis
• Micro-Interlocutor Analysis
• Rhetorical Analysis
• Systematic Data Integration
• Nonverbal Communication Analysis

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Qualitative Data Analysis Techniques by Anthony J. Onwuegbuzie , Magdalena Denham LAST REVIEWED: 30 June 2014 LAST MODIFIED: 30 June 2014 DOI: 10.1093/obo/9780199756810-0078

Qualitative research can be traced back to ancient times; however, the use of qualitative methods began to be formalized in certain disciplines (e.g., sociology, anthropology) only in the 19th century. Broadly speaking, qualitative research involves an in-depth examination of human experiences and human behavior, with the goal of obtaining insights into everyday experiences and meaning attached to these experiences of individuals (via qualitative methodologies such as biography, autobiography, life history, oral history, autoethnography, case study) and groups (via qualitative methodologies such as phenomenology, ethnography, grounded theory), which, optimally, can lead to understanding the meaning of behaviors from the study participant’s/group’s perspective. Qualitative researchers tend to investigate not just what, where , and when , but more importantly the why and how of events, experiences, and behaviors. Thus, qualitative researchers are much more likely to study smaller but focused samples than large samples. In general, qualitative research studies primarily involve the collection, analysis, and interpretation of data (i.e., information) that naturally occur. Of these steps, the analysis of data arguably represents one of the most difficult steps—if not the most difficult step—of the qualitative research process because it involves a systematic exploration of meaning and the achievement of verstehen (i.e., understanding). More specifically, qualitative data analysis is a process that comprises multiple phases, and from which findings are extracted or emerge. These phases include examining, cleaning, organizing, reducing, exploring, describing, explaining, displaying, interrogating, categorizing, pattern finding, transforming, correlating, consolidating, comparing, integrating, synthesizing, and interpreting data, in ways that allow researchers to see patterns, to identify categories and themes, to develop typologies, to discover relationships, to cultivate explanations, to extract interpretations, to develop critiques, to generate or to advance theories, and/or the like, with the goal of meaning making. A criticism of qualitative data analysis is that because it typically involves examination of data extracted from small, nonrandom samples, findings stemming from any qualitative analysis usually are not generalizable beyond the local research participants. However, what is a limitation for one purpose (i.e., generalization of findings to the population the sample was drawn from), is a strength for another purpose. Specifically, the examination of relatively small samples allows qualitative researchers to collect (maximally) rich data (e.g., via in-depth interviews, focus groups, observations, images, nonverbal communication). This, in turn, makes it more likely that as a result of the qualitative data analysis, verstehen will be achieved.

The analysis of data represents the most important and difficult step in the qualitative research process. Therefore, the purpose of this entry is to document the history and development of qualitative analytical approaches. In particular, described here are thirty-four formal qualitative data-analysis approaches that were identified from an exhaustive search of the literature. This OBO entry not only extends the work of Onwuegbuzie, et al. 2011 —which identified twenty-three analysis approaches—but by adding numerous other qualitative data analysis approaches, it also extends these works by documenting the origin of each analysis approach, mapping it onto the nine moments described in Denzin and Lincoln 2011 and outlining the sources of qualitative data that it can analyze. With respect to the latter, see Leech and Onwuegbuzie 2008 with typology wherein the following four major sources of qualitative data prevail: talk, observations, images, and documents. Specifically, talk represents data that are extracted directly from the voices of the participants using data collection techniques such as individual interviews and focus groups. Observations involve the collection of data by systematically watching or perceiving one or more events, interactions, or nonverbal communication to address or to inform the research question(s). Images represent still (e.g., drawings, photographs) or moving (e.g., videos) visual data that are observed or perceived. Documents represent the collection of text that exists either in printed or digital form. As Miles and Huberman 1994 declared: “The strengths of qualitative data rest on the competence with which their analysis is carried out” (p. 10). By only being aware of a few qualitative data-analysis approaches, a qualitative researcher might make the data fit the analysis rather than select the most appropriate data-analysis approach given the underlying research elements such as the research question, researcher’s lens, and sampling and design characteristics. In contrast, by being aware of the array of qualitative data-analysis approaches, as well as how and when to conduct them, a qualitative researcher is in a better position not only to conduct analyses that have integrity but also to conduct analyses that emerge as findings emerge. Thus, qualitative researchers likely would put themselves in a better position for making meaning if they adopt a constructivist approach to qualitative data analysis. However, this can only occur if they have an awareness of multiple ways of analyzing qualitative data. This goal helps to establish the significance of the current work in this field.

Denzin, Norman K., and Yvonna S. Lincoln. 2011. Introduction: The discipline and practice of qualitative research. In Sage handbook of qualitative research . 4th ed. Edited by Norman K. Denzin and Yvonna S. Lincoln, 1–25. Thousand Oaks, CA: SAGE.

The authors document the history of qualitative research. This history spans nine moments, starting with they call the “traditional” moment and continuing through to the ninth moment, which they call the “fractured future,” which is the present moment.

Leech, Nancy L., and Anthony J. Onwuegbuzie. 2008. Qualitative data analysis: A compendium of techniques and a framework for selection for school psychology research and beyond. School Psychology Quarterly 23:587–604.

DOI: 10.1037/1045-3830.23.4.587

The authors describe the following eighteen qualitative analysis techniques: method of constant comparison analysis, keywords-in-context, word count, classical content analysis, domain analysis, taxonomic analysis, componential analysis, conversation analysis, discourse analysis, secondary analysis, membership categorization analysis, narrative analysis, qualitative comparative analysis, semiotics, manifest content analysis, latent content analysis, text mining, and micro-interlocutor analysis.

Miles, Matthew B., and A. Michael Huberman. 1994. Qualitative data analysis: An expanded sourcebook . 2d ed. Thousand Oaks, CA: SAGE.

In this groundbreaking book, the authors conceptualize and describe nineteen within-case analyses (i.e., partially ordered display, time-ordered display, role-ordered display, and conceptually ordered display) and eighteen cross-case analyses (i.e., partially ordered display, case-ordered display, time-ordered display, and conceptually ordered display. Thus, this work is the most comprehensive guidebook to qualitative analysis to date.

Onwuegbuzie, Anthony J., Nancy L. Leech, and Kathleen M. T. Collins. 2011. Toward a new era for conducting mixed analyses: The role of quantitative dominant and qualitative dominant crossover mixed analyses. In The Sage handbook of innovation in social research methods . Edited by Malcolm Williams and Paul W. Vogt, 353–384. Thousand Oaks, CA: SAGE.

In this book chapter, the authors introduce a unified framework for combining qualitative analysis and quantitative analysis—which they call a mixed analysis—regardless of whether the researcher is oriented toward quantitative research or mixed research.

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5 qualitative data analysis methods

Qualitative data uncovers valuable insights that help you improve the user and customer experience. But how exactly do you measure and analyze data that isn't quantifiable?

There are different qualitative data analysis methods to help you make sense of qualitative feedback and customer insights, depending on your business goals and the type of data you've collected.

Before you choose a qualitative data analysis method for your team, you need to consider the available techniques and explore their use cases to understand how each process might help you better understand your users. 

This guide covers five qualitative analysis methods to choose from, and will help you pick the right one(s) based on your goals. 

Content analysis

Thematic analysis

Narrative analysis

Grounded theory analysis

Discourse analysis

5 qualitative data analysis methods explained

Qualitative data analysis ( QDA ) is the process of organizing, analyzing, and interpreting qualitative research data—non-numeric, conceptual information, and user feedback—to capture themes and patterns, answer research questions, and identify actions to improve your product or website.

Step 1 in the research process (after planning ) is qualitative data collection. You can use behavior analytics software—like Hotjar —to capture qualitative data with context, and learn the real motivation behind user behavior, by collecting written customer feedback with Surveys or scheduling an in-depth user interview with Engage .

Use Hotjar’s tools to collect feedback, uncover behavior trends, and understand the ‘why’ behind user actions.

1. Content analysis

Content analysis is a qualitative research method that examines and quantifies the presence of certain words, subjects, and concepts in text, image, video, or audio messages. The method transforms qualitative input into quantitative data to help you make reliable conclusions about what customers think of your brand, and how you can improve their experience and opinion.

Conduct content analysis manually (which can be time-consuming) or use analysis tools like Lexalytics to reveal communication patterns, uncover differences in individual or group communication trends, and make broader connections between concepts.

How content analysis can help your team

Content analysis is often used by marketers and customer service specialists, helping them understand customer behavior and measure brand reputation.

For example, you may run a customer survey with open-ended questions to discover users’ concerns—in their own words—about their experience with your product. Instead of having to process hundreds of answers manually, a content analysis tool helps you analyze and group results based on the emotion expressed in texts.

Some other examples of content analysis include:

Analyzing brand mentions on social media to understand your brand's reputation

Reviewing customer feedback to evaluate (and then improve) the customer and user experience (UX)

Researching competitors’ website pages to identify their competitive advantages and value propositions

Interpreting customer interviews and survey results to determine user preferences, and setting the direction for new product or feature developments

Content analysis was a major part of our growth during my time at Hypercontext.

[It gave us] a better understanding of the [blog] topics that performed best for signing new users up. We were also able to go deeper within those blog posts to better understand the formats [that worked].

2. Thematic analysis

Thematic analysis helps you identify, categorize, analyze, and interpret patterns in qualitative study data , and can be done with tools like Dovetail and Thematic .

While content analysis and thematic analysis seem similar, they're different in concept: 

Content analysis can be applied to both qualitative and quantitative data , and focuses on identifying frequencies and recurring words and subjects

Thematic analysis can only be applied to qualitative data, and focuses on identifying patterns and themes

How thematic analysis can help your team

Thematic analysis can be used by pretty much anyone: from product marketers, to customer relationship managers, to UX researchers.

For example, product teams use thematic analysis to better understand user behaviors and needs and improve UX . Analyzing customer feedback lets you identify themes (e.g. poor navigation or a buggy mobile interface) highlighted by users and get actionable insight into what they really expect from the product. 

💡 Pro tip: looking for a way to expedite the data analysis process for large amounts of data you collected with a survey? Try Hotjar’s AI for Surveys : along with generating a survey based on your goal in seconds, our AI will analyze the raw data and prepare an automated summary report that presents key thematic findings, respondent quotes, and actionable steps to take, making the analysis of qualitative data a breeze.

3. Narrative analysis

Narrative analysis is a method used to interpret research participants’ stories —things like testimonials , case studies, focus groups, interviews, and other text or visual data—with tools like Delve and AI-powered ATLAS.ti .

Some formats don’t work well with narrative analysis, including heavily structured interviews and written surveys, which don’t give participants as much opportunity to tell their stories in their own words.

How narrative analysis can help your team

Narrative analysis provides product teams with valuable insight into the complexity of customers’ lives, feelings, and behaviors.

In a marketing research context, narrative analysis involves capturing and reviewing customer stories—on social media, for example—to get in-depth insight into their lives, priorities, and challenges. 

This might look like analyzing daily content shared by your audiences’ favorite influencers on Instagram, or analyzing customer reviews on sites like G2 or Capterra to gain a deep understanding of individual customer experiences. The results of this analysis also contribute to developing corresponding customer personas .

💡 Pro tip: conducting user interviews is an excellent way to collect data for narrative analysis. Though interviews can be time-intensive, there are tools out there that streamline the workload. 

Hotjar Engage automates the entire process, from recruiting to scheduling to generating the all-important interview transcripts you’ll need for the analysis phase of your research project.

4. Grounded theory analysis

Grounded theory analysis is a method of conducting qualitative research to develop theories by examining real-world data. This technique involves the creation of hypotheses and theories through qualitative data collection and evaluation, and can be performed with qualitative data analysis software tools like MAXQDA and NVivo .

Unlike other qualitative data analysis techniques, this method is inductive rather than deductive: it develops theories from data, not the other way around.

How grounded theory analysis can help your team

Grounded theory analysis is used by software engineers, product marketers, managers, and other specialists who deal with data sets to make informed business decisions. 

For example, product marketing teams may turn to customer surveys to understand the reasons behind high churn rates , then use grounded theory to analyze responses and develop hypotheses about why users churn, and how you can get them to stay. 

Grounded theory can also be helpful in the talent management process. For example, HR representatives may use it to develop theories about low employee engagement, and come up with solutions based on their research findings.

5. Discourse analysis

Discourse analysis is the act of researching the underlying meaning of qualitative data. It involves the observation of texts, audio, and videos to study the relationships between information and its social context.

In contrast to content analysis, this method focuses on the contextual meaning of language: discourse analysis sheds light on what audiences think of a topic, and why they feel the way they do about it.

How discourse analysis can help your team

In a business context, this method is primarily used by marketing teams. Discourse analysis helps marketers understand the norms and ideas in their market , and reveals why they play such a significant role for their customers. 

Once the origins of trends are uncovered, it’s easier to develop a company mission, create a unique tone of voice, and craft effective marketing messages.

Which qualitative data analysis method should you choose?

While the five qualitative data analysis methods we list above are all aimed at processing data and answering research questions, these techniques differ in their intent and the approaches applied.  

Choosing the right analysis method for your team isn't a matter of preference—selecting a method that fits is only possible once you define your research goals and have a clear intention. When you know what you need (and why you need it), you can identify an analysis method that aligns with your research objectives.

Gather qualitative data with Hotjar

Use Hotjar’s product experience insights in your qualitative research. Collect feedback, uncover behavior trends, and understand the ‘why’ behind user actions.

FAQs about qualitative data analysis methods

What is the qualitative data analysis approach.

The qualitative data analysis approach refers to the process of systematizing descriptive data collected through interviews, focus groups, surveys, and observations and then interpreting it. The methodology aims to identify patterns and themes behind textual data, and other unquantifiable data, as opposed to numerical data.

What are qualitative data analysis methods?

Five popular qualitative data analysis methods are:

What is the process of qualitative data analysis?

The process of qualitative data analysis includes six steps:

Define your research question

Prepare the data

Choose the method of qualitative analysis

Code the data

Identify themes, patterns, and relationships

Make hypotheses and act

Qualitative data analysis guide

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Quantitative Data Analysis Guide: Methods, Examples & Uses

This guide will introduce the types of data analysis used in quantitative research, then discuss relevant examples and applications in the finance industry.

Table of Contents

An Overview of Quantitative Data Analysis

What is quantitative data analysis and what is it for .

Quantitative data analysis is the process of interpreting meaning and extracting insights from numerical data , which involves mathematical calculations and statistical reviews to uncover patterns, trends, and relationships between variables.

Beyond academic and statistical research, this approach is particularly useful in the finance industry. Financial data, such as stock prices, interest rates, and economic indicators, can all be quantified with statistics and metrics to offer crucial insights for informed investment decisions. To illustrate this, here are some examples of what quantitative data is usually used for:

• Measuring Differences between Groups: For instance, analyzing historical stock prices of different companies or asset classes can reveal which companies consistently outperform the market average.
• Assessing Relationships between Variables: An investor could analyze the relationship between a company’s price-to-earnings ratio (P/E ratio) and relevant factors, like industry performance, inflation rates, interests, etc, allowing them to predict future stock price growth.
• Testing Hypotheses: For example, an investor might hypothesize that companies with strong ESG (Environment, Social, and Governance) practices outperform those without. By categorizing these companies into two groups (strong ESG vs. weak ESG practices), they can compare the average return on investment (ROI) between the groups while assessing relevant factors to find evidence for the hypothesis. 

Ultimately, quantitative data analysis helps investors navigate the complex financial landscape and pursue profitable opportunities.

Quantitative Data Analysis VS. Qualitative Data Analysis

Although quantitative data analysis is a powerful tool, it cannot be used to provide context for your research, so this is where qualitative analysis comes in. Qualitative analysis is another common research method that focuses on collecting and analyzing non-numerical data , like text, images, or audio recordings to gain a deeper understanding of experiences, opinions, and motivations. Here’s a table summarizing its key differences between quantitative data analysis:

Due to their characteristics, quantitative analysis allows you to measure and compare large datasets; while qualitative analysis helps you understand the context behind the data. In some cases, researchers might even use both methods together for a more comprehensive understanding, but we’ll mainly focus on quantitative analysis for this article.

The 2 Main Quantitative Data Analysis Methods

Once you have your data collected, you have to use descriptive statistics or inferential statistics analysis to draw summaries and conclusions from your raw numbers. 

As its name suggests, the purpose of descriptive statistics is to describe your sample . It provides the groundwork for understanding your data by focusing on the details and characteristics of the specific group you’ve collected data from. 

On the other hand, inferential statistics act as bridges that connect your sample data to the broader population you’re truly interested in, helping you to draw conclusions in your research. Moreover, choosing the right inferential technique for your specific data and research questions is dependent on the initial insights from descriptive statistics, so both of these methods usually go hand-in-hand.

Descriptive Statistics Analysis

With sophisticated descriptive statistics, you can detect potential errors in your data by highlighting inconsistencies and outliers that might otherwise go unnoticed. Additionally, the characteristics revealed by descriptive statistics will help determine which inferential techniques are suitable for further analysis.

Measures in Descriptive Statistics

One of the key statistical tests used for descriptive statistics is central tendency . It consists of mean, median, and mode, telling you where most of your data points cluster:

• Mean: It refers to the “average” and is calculated by adding all the values in your data set and dividing by the number of values.
• Median: The middle value when your data is arranged in ascending or descending order. If you have an odd number of data points, the median is the exact middle value; with even numbers, it’s the average of the two middle values. 
• Mode: This refers to the most frequently occurring value in your data set, indicating the most common response or observation. Some data can have multiple modes (bimodal) or no mode at all.

Another statistic to test in descriptive analysis is the measures of dispersion , which involves range and standard deviation, revealing how spread out your data is relative to the central tendency measures:

• Range: It refers to the difference between the highest and lowest values in your data set. 
• Standard Deviation (SD): This tells you how the data is distributed within the range, revealing how much, on average, each data point deviates from the mean. Lower standard deviations indicate data points clustered closer to the mean, while higher standard deviations suggest a wider spread.

The shape of the distribution will then be measured through skewness. 

• Skewness: A statistic that indicates whether your data leans to one side (positive or negative) or is symmetrical (normal distribution). A positive skew suggests more data points concentrated on the lower end, while a negative skew indicates more data points on the higher end.

While the core measures mentioned above are fundamental, there are additional descriptive statistics used in specific contexts, including percentiles and interquartile range.

• Percentiles: This divides your data into 100 equal parts, revealing what percentage of data falls below a specific value. The 25th percentile (Q1) is the first quartile, the 50th percentile (Q2) is the median, and the 75th percentile (Q3) is the third quartile. Knowing these quartiles can help visualize the spread of your data.
• Interquartile Range (IQR): This measures the difference between Q3 and Q1, representing the middle 50% of your data.

Example of Descriptive Quantitative Data Analysis 

Let’s illustrate these concepts with a real-world example. Imagine a financial advisor analyzing a client’s portfolio. They have data on the client’s various holdings, including stock prices over the past year. With descriptive statistics they can obtain the following information:

• Central Tendency: The mean price for each stock reveals its average price over the year. The median price can further highlight if there were any significant price spikes or dips that skewed the mean.
• Measures of Dispersion: The standard deviation for each stock indicates its price volatility. A high standard deviation suggests the stock’s price fluctuated considerably, while a low standard deviation implies a more stable price history. This helps the advisor assess each stock’s risk profile.
• Shape of the Distribution: If data allows, analyzing skewness can be informative. A positive skew for a stock might suggest more frequent price drops, while a negative skew might indicate more frequent price increases.

By calculating these descriptive statistics, the advisor gains a quick understanding of the client’s portfolio performance and risk distribution. For instance, they could use correlation analysis to see if certain stock prices tend to move together, helping them identify expansion opportunities within the portfolio.

While descriptive statistics provide a foundational understanding, they should be followed by inferential analysis to uncover deeper insights that are crucial for making investment decisions.

Inferential Statistics Analysis

Inferential statistics analysis is particularly useful for hypothesis testing , as you can formulate predictions about group differences or potential relationships between variables , then use statistical tests to see if your sample data supports those hypotheses.

However, the power of inferential statistics hinges on one crucial factor: sample representativeness . If your sample doesn’t accurately reflect the population, your predictions won’t be very reliable. 

Statistical Tests for Inferential Statistics

Here are some of the commonly used tests for inferential statistics in commerce and finance, which can also be integrated to most analysis software:

• T-Tests: This compares the means, standard deviation, or skewness of two groups to assess if they’re statistically different, helping you determine if the observed difference is just a quirk within the sample or a significant reflection of the population.
• ANOVA (Analysis of Variance): While T-Tests handle comparisons between two groups, ANOVA focuses on comparisons across multiple groups, allowing you to identify potential variations and trends within the population.
• Correlation Analysis: This technique tests the relationship between two variables, assessing if one variable increases or decreases with the other. However, it’s important to note that just because two financial variables are correlated and move together, doesn’t necessarily mean one directly influences the other.
• Regression Analysis: Building on correlation, regression analysis goes a step further to verify the cause-and-effect relationships between the tested variables, allowing you to investigate if one variable actually influences the other.
• Cross-Tabulation: This breaks down the relationship between two categorical variables by displaying the frequency counts in a table format, helping you to understand how different groups within your data set might behave. The data in cross-tabulation can be mutually exclusive or have several connections with each other. 
• Trend Analysis: This examines how a variable in quantitative data changes over time, revealing upward or downward trends, as well as seasonal fluctuations. This can help you forecast future trends, and also lets you assess the effectiveness of the interventions in your marketing or investment strategy.
• MaxDiff Analysis: This is also known as the “best-worst” method. It evaluates customer preferences by asking respondents to choose the most and least preferred options from a set of products or services, allowing stakeholders to optimize product development or marketing strategies.
• Conjoint Analysis: Similar to MaxDiff, conjoint analysis gauges customer preferences, but it goes a step further by allowing researchers to see how changes in different product features (price, size, brand) influence overall preference.
• TURF Analysis (Total Unduplicated Reach and Frequency Analysis): This assesses a marketing campaign’s reach and frequency of exposure in different channels, helping businesses identify the most efficient channels to reach target audiences.
• Gap Analysis: This compares current performance metrics against established goals or benchmarks, using numerical data to represent the factors involved. This helps identify areas where performance falls short of expectations, serving as a springboard for developing strategies to bridge the gap and achieve those desired outcomes.
• SWOT Analysis (Strengths, Weaknesses, Opportunities, and Threats): This uses ratings or rankings to represent an organization’s internal strengths and weaknesses, along with external opportunities and threats. Based on this analysis, organizations can create strategic plans to capitalize on opportunities while minimizing risks.
• Text Analysis: This is an advanced method that uses specialized software to categorize and quantify themes, sentiment (positive, negative, neutral), and topics within textual data, allowing companies to obtain structured quantitative data from surveys, social media posts, or customer reviews.

Example of Inferential Quantitative Data Analysis

If you’re a financial analyst studying the historical performance of a particular stock, here are some predictions you can make with inferential statistics:

• The Differences between Groups: You can conduct T-Tests to compare the average returns of stocks in the technology sector with those in the healthcare sector. It can help assess if the observed difference in returns between these two sectors is simply due to random chance or if it’s statistically significant due to a significant difference in their performance.
• The Relationships between Variables: If you’re curious about the connection between a company’s price-to-earnings ratio (P/E ratios) and its future stock price movements, conducting correlation analysis can let you measure the strength and direction of this relationship. Is there a negative correlation, suggesting that higher P/E ratios might be associated with lower future stock prices? Or is there no significant correlation at all?

Understanding these inferential analysis techniques can help you uncover potential relationships and group differences that might not be readily apparent from descriptive statistics alone. Nonetheless, it’s important to remember that each technique has its own set of assumptions and limitations . Some methods are designed for parametric data with a normal distribution, while others are suitable for non-parametric data. 

Guide to Conduct Data Analysis in Quantitative Research

Now that we have discussed the types of data analysis techniques used in quantitative research, here’s a quick guide to help you choose the right method and grasp the essential steps of quantitative data analysis.

How to Choose the Right Quantitative Analysis Method?

Choosing between all these quantitative analysis methods may seem like a complicated task, but if you consider the 2 following factors, you can definitely choose the right technique:

Factor 1: Data Type

The data used in quantitative analysis can be categorized into two types, discrete data and continuous data, based on how they’re measured. They can also be further differentiated by their measurement scale. The four main types of measurement scales include: nominal, ordinal, interval or ratio. Understanding the distinctions between them is essential for choosing the appropriate statistical methods to interpret the results of your quantitative data analysis accurately.

Discrete data , which is also known as attribute data, represents whole numbers that can be easily counted and separated into distinct categories. It is often visualized using bar charts or pie charts, making it easy to see the frequency of each value. In the financial world, examples of discrete quantitative data include:

• The number of shares owned by an investor in a particular company
• The number of customer transactions processed by a bank per day
• Bond ratings (AAA, BBB, etc.) that represent discrete categories indicating the creditworthiness of a bond issuer
• The number of customers with different account types (checking, savings, investment) as seen in the pie chart below:

Discrete data usually use nominal or ordinal measurement scales, which can be then quantified to calculate their mode or median. Here are some examples:

• Nominal: This scale categorizes data into distinct groups with no inherent order. For instance, data on bank account types can be considered nominal data as it classifies customers in distinct categories which are independent of each other, either checking, savings, or investment accounts. and no inherent order or ranking implied by these account types.
• Ordinal: Ordinal data establishes a rank or order among categories. For example, investment risk ratings (low, medium, high) are ordered based on their perceived risk of loss, making it a type or ordinal data.

Conversely, continuous data can take on any value and fluctuate over time. It is usually visualized using line graphs, effectively showcasing how the values can change within a specific time frame. Examples of continuous data in the financial industry include:

• Interest rates set by central banks or offered by banks on loans and deposits
• Currency exchange rates which also fluctuate constantly throughout the day
• Daily trading volume of a particular stock on a specific day
• Stock prices that fluctuate throughout the day, as seen in the line graph below:

Source: Freepik

The measurement scale for continuous data is usually interval or ratio . Here is breakdown of their differences:

• Interval: This builds upon ordinal data by having consistent intervals between each unit, and its zero point doesn’t represent a complete absence of the variable. Let’s use credit score as an example. While the scale ranges from 300 to 850, the interval between each score rating is consistent (50 points), and a score of zero wouldn’t indicate an absence of credit history, but rather no credit score available. 
• Ratio: This scale has all the same characteristics of interval data but also has a true zero point, indicating a complete absence of the variable. Interest rates expressed as percentages are a classic example of ratio data. A 0% interest rate signifies the complete absence of any interest charged or earned, making it a true zero point.

Factor 2: Research Question

You also need to make sure that the analysis method aligns with your specific research questions. If you merely want to focus on understanding the characteristics of your data set, descriptive statistics might be all you need; if you need to analyze the connection between variables, then you have to include inferential statistics as well.

How to Analyze Quantitative Data 

Step 1: data collection  .

Depending on your research question, you might choose to conduct surveys or interviews. Distributing online or paper surveys can reach a broad audience, while interviews allow for deeper exploration of specific topics. You can also choose to source existing datasets from government agencies or industry reports.

Step 2: Data Cleaning

Raw data might contain errors, inconsistencies, or missing values, so data cleaning has to be done meticulously to ensure accuracy and consistency. This might involve removing duplicates, correcting typos, and handling missing information.

Furthermore, you should also identify the nature of your variables and assign them appropriate measurement scales , it could be nominal, ordinal, interval or ratio. This is important because it determines the types of descriptive statistics and analysis methods you can employ later. Once you categorize your data based on these measurement scales, you can arrange the data of each category in a proper order and organize it in a format that is convenient for you.

Step 3: Data Analysis

Based on the measurement scales of your variables, calculate relevant descriptive statistics to summarize your data. This might include measures of central tendency (mean, median, mode) and dispersion (range, standard deviation, variance). With these statistics, you can identify the pattern within your raw data. 

Then, these patterns can be analyzed further with inferential methods to test out the hypotheses you have developed. You may choose any of the statistical tests mentioned above, as long as they are compatible with the characteristics of your data.

Step 4. Data Interpretation and Communication 

Now that you have the results from your statistical analysis, you may draw conclusions based on the findings and incorporate them into your business strategies. Additionally, you should also transform your findings into clear and shareable information to facilitate discussion among stakeholders. Visualization techniques like tables, charts, or graphs can make complex data more digestible so that you can communicate your findings efficiently. 

Useful Quantitative Data Analysis Tools and Software 

We’ve compiled some commonly used quantitative data analysis tools and software. Choosing the right one depends on your experience level, project needs, and budget. Here’s a brief comparison: 

Quantitative Data in Finance and Investment

So how does this all affect the finance industry? Quantitative finance (or quant finance) has become a growing trend, with the quant fund market valued at $16,008.69 billion in 2023. This value is expected to increase at the compound annual growth rate of 10.09% and reach $31,365.94 billion by 2031, signifying its expanding role in the industry.

What is Quant Finance?

Quant finance is the process of using massive financial data and mathematical models to identify market behavior, financial trends, movements, and economic indicators, so that they can predict future trends.These calculated probabilities can be leveraged to find potential investment opportunities and maximize returns while minimizing risks.

Common Quantitative Investment Strategies

There are several common quantitative strategies, each offering unique approaches to help stakeholders navigate the market:

1. Statistical Arbitrage

This strategy aims for high returns with low volatility. It employs sophisticated algorithms to identify minuscule price discrepancies across the market, then capitalize on them at lightning speed, often generating short-term profits. However, its reliance on market efficiency makes it vulnerable to sudden market shifts, posing a risk of disrupting the calculations.

2. Factor Investing 

This strategy identifies and invests in assets based on factors like value, momentum, or quality. By analyzing these factors in quantitative databases , investors can construct portfolios designed to outperform the broader market. Overall, this method offers diversification and potentially higher returns than passive investing, but its success relies on the historical validity of these factors, which can evolve over time.

3. Risk Parity

This approach prioritizes portfolio balance above all else. Instead of allocating assets based on their market value, risk parity distributes them based on their risk contribution to achieve a desired level of overall portfolio risk, regardless of individual asset volatility. Although it is efficient in managing risks while potentially offering positive returns, it is important to note that this strategy’s complex calculations can be sensitive to unexpected market events.

4. Machine Learning & Artificial Intelligence (AI)

Quant analysts are beginning to incorporate these cutting-edge technologies into their strategies. Machine learning algorithms can act as data sifters, identifying complex patterns within massive datasets; whereas AI goes a step further, leveraging these insights to make investment decisions, essentially mimicking human-like decision-making with added adaptability. Despite the hefty development and implementation costs, its superior risk-adjusted returns and uncovering hidden patterns make this strategy a valuable asset.

Pros and Cons of Quantitative Data Analysis

Advantages of quantitative data analysis, minimum bias for reliable results.

Quantitative data analysis relies on objective, numerical data. This minimizes bias and human error, allowing stakeholders to make investment decisions without emotional intuitions that can cloud judgment. In turn, this offers reliable and consistent results for investment strategies.

Precise Calculations for Data-Driven Decisions

Quantitative analysis generates precise numerical results through statistical methods. This allows accurate comparisons between investment options and even predictions of future market behavior, helping investors make informed decisions about where to allocate their capital while managing potential risks.

Generalizability for Broader Insights 

By analyzing large datasets and identifying patterns, stakeholders can generalize the findings from quantitative analysis into broader populations, applying them to a wider range of investments for better portfolio construction and risk management

Efficiency for Extensive Research

Quantitative research is more suited to analyze large datasets efficiently, letting companies save valuable time and resources. The softwares used for quantitative analysis can automate the process of sifting through extensive financial data, facilitating quicker decision-making in the fast-paced financial environment.

Disadvantages of Quantitative Data Analysis

Limited scope .

By focusing on numerical data, quantitative analysis may provide a limited scope, as it can’t capture qualitative context such as emotions, motivations, or cultural factors. Although quantitative analysis provides a strong starting point, neglecting qualitative factors can lead to incomplete insights in the financial industry, impacting areas like customer relationship management and targeted marketing strategies.

Oversimplification 

Breaking down complex phenomena into numerical data could cause analysts to overlook the richness of the data, leading to the issue of oversimplification. Stakeholders who fail to understand the complexity of economic factors or market trends could face flawed investment decisions and missed opportunities.

Reliable Quantitative Data Solution 

In conclusion, quantitative data analysis offers a deeper insight into market trends and patterns, empowering you to make well-informed financial decisions. However, collecting comprehensive data and analyzing them can be a complex task that may divert resources from core investment activity. 

As a reliable provider, TEJ understands these concerns. Our TEJ Quantitative Investment Database offers high-quality financial and economic data for rigorous quantitative analysis. This data captures the true market conditions at specific points in time, enabling accurate backtesting of investment strategies.

Furthermore, TEJ offers diverse data sets that go beyond basic stock prices, encompassing various financial metrics, company risk attributes, and even broker trading information, all designed to empower your analysis and strategy development. Save resources and unlock the full potential of quantitative finance with TEJ’s data solutions today!

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Peer-reviewed

Research Article

A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study

Roles Data curation, Formal analysis, Methodology, Validation, Writing – original draft, Writing – review & editing

Affiliations Academic Unit for Ageing & Stroke Research, Bradford Teaching Hospitals NHS Foundation Trust, University of Leeds, Bradford, United Kingdom, Faculty of Medicine and Health, School of Medicine, University of Leeds, Leeds, United Kingdom

Roles Data curation, Formal analysis, Validation, Writing – original draft, Writing – review & editing

Affiliation Institute of Population Health, University of Liverpool, Liverpool, United Kingdom

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

Affiliations Institute of Population Health, University of Liverpool, Liverpool, United Kingdom, Directorate of Mental Health and Learning Disabilities, Powys Teaching Health Board, Bronllys, United Kingdom

Roles Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing – review & editing

Affiliation General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing

Roles Conceptualization, Supervision, Writing – review & editing

Roles Conceptualization, Formal analysis, Investigation, Methodology, Validation, Writing – review & editing

Roles Writing – review & editing

Affiliations Faculty of Medicine and Health, School of Medicine, University of Leeds, Leeds, United Kingdom, Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom

Roles Conceptualization, Writing – review & editing

Affiliation Department of Computer Science, University of Liverpool, Liverpool, United Kingdom

Affiliation Division of Informatics, Imaging & Data Science, University of Manchester, Manchester, United Kingdom

Affiliation NIHR Applied Research Collaboration North West Coast, United Kingdom

Affiliations Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom, School of Computing, University of Leeds, Leeds, United Kingdom

Roles Project administration, Resources

Affiliation Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, United Kingdom

Affiliation Merseycare NHS Foundation Trust, Liverpool, United Kingdom

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Writing – review & editing

•  [ ... ],

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Centre for Experimental Therapeutics, University of Liverpool, Liverpool, United Kingdom, Liverpool University Hospitals NHS Foundation Trust, Liverpool, United Kingdom

• [ view all ]
• [ view less ]
• Aseel S. Abuzour, 
• Samantha A. Wilson, 
• Alan A. Woodall, 
• Frances S. Mair, 
• Andrew Clegg, 
• Eduard Shantsila, 
• Mark Gabbay, 
• Michael Abaho, 
• Asra Aslam, 

• Published: August 30, 2024
• https://doi.org/10.1371/journal.pone.0299770
• Reader Comments

Introduction

Structured medication reviews (SMRs), introduced in the United Kingdom (UK) in 2020, aim to enhance shared decision-making in medication optimisation, particularly for patients with multimorbidity and polypharmacy. Despite its potential, there is limited empirical evidence on the implementation of SMRs, and the challenges faced in the process. This study is part of a larger DynAIRx (Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity) project which aims to introduce Artificial Intelligence (AI) to SMRs and develop machine learning models and visualisation tools for patients with multimorbidity. Here, we explore how SMRs are currently undertaken and what barriers are experienced by those involved in them.

Qualitative focus groups and semi-structured interviews took place between 2022–2023. Six focus groups were conducted with doctors, pharmacists and clinical pharmacologists (n = 21), and three patient focus groups with patients with multimorbidity (n = 13). Five semi-structured interviews were held with 2 pharmacists, 1 trainee doctor, 1 policy-maker and 1 psychiatrist. Transcripts were analysed using thematic analysis.

Two key themes limiting the effectiveness of SMRs in clinical practice were identified: ‘Medication Reviews in Practice’ and ‘Medication-related Challenges’. Participants noted limitations to the efficient and effectiveness of SMRs in practice including the scarcity of digital tools for identifying and prioritising patients for SMRs; organisational and patient-related challenges in inviting patients for SMRs and ensuring they attend; the time-intensive nature of SMRs, the need for multiple appointments and shared decision-making; the impact of the healthcare context on SMR delivery; poor communication and data sharing issues between primary and secondary care; difficulties in managing mental health medications and specific challenges associated with anticholinergic medication.

SMRs are complex, time consuming and medication optimisation may require multiple follow-up appointments to enable a comprehensive review. There is a need for a prescribing support system to identify, prioritise and reduce the time needed to understand the patient journey when dealing with large volumes of disparate clinical information in electronic health records. However, monitoring the effects of medication optimisation changes with a feedback loop can be challenging to establish and maintain using current electronic health record systems.

Citation: Abuzour AS, Wilson SA, Woodall AA, Mair FS, Clegg A, Shantsila E, et al. (2024) A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study. PLoS ONE 19(8): e0299770. https://doi.org/10.1371/journal.pone.0299770

Editor: Kathleen Bennett, Royal College of Surgeons in Ireland, IRELAND

Received: February 16, 2024; Accepted: June 24, 2024; Published: August 30, 2024

Copyright: © 2024 Abuzour et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: DynAIRx has been funded by the National Institute for Health and Care Research (NIHR) Artificial Intelligence for Multiple Long-Term Conditions (AIM) call (NIHR 203986). MG is partly funded by the NIHR Applied Research Collaboration North West Coast (ARC NWC). AW is partly funded by a Health and Care Research Wales Research Time Award (NHS-RTA-21-02). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This research is supported by the NIHR ARC NWC. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

Competing interests: No competing interests

Structured medication reviews (SMRs) were introduced in the United Kingdom (UK) in October 2020 and incorporated into the NHS England Directed Enhanced Service (DES) contract for 2021 [ 1 ]. SMRs represent a National Institute for Health and Care Excellence (NICE)-approved clinical intervention facilitating shared-decision making between clinicians and patients, to inform treatment decisions. The objective is to reduce medication-related harm in patients with complex or problematic polypharmacy [ 1 , 2 ]. While General Practitioners (GPs), pharmacists and advanced nurse practitioners (ANPs) who meet training criteria can conduct SMRs, the prevailing expectation is for clinical pharmacists within Primary Care Networks (PCNs) to assume primary responsibility as a commissioned service [ 3 ]. The varied methods employed by PCNs to proactively identify patients suitable for SMRs, and conduct these reviews, is contingent on available resources and capacity. Anecdotal evidence suggests that PCNs currently use limited digital tools, such as searching electronic health records (EHR) based on the total number of drugs prescribed or disease codes, to identify patients at risk of medication-related harm.

There is sparse empirical evidence reporting on the implementation of SMRs, their impact on patient outcomes, and the challenges faced by healthcare professionals (HCPs) and patients during SMRs [ 4 , 5 ]. This scarcity of evidence is unsurprising given that SMRs were introduced in 2020 amidst the COVID-19 pandemic [ 1 ]. Nonetheless, estimates suggest a percentage reduction in per-patient medicines following an SMR ranging from 2.7% to 9.9%, with up to 19.5% reduction in use for the highest-risk group in care homes [ 6 ].

Patients with complex multimorbidity and polypharmacy, whose medicines have not been optimised are at risk of adverse outcomes and medication-related harm [ 7 ]. The definition of complex multimorbidity is contentious [ 8 ] but here we are referring to patients living with four or more conditions, which is strongly associated with polypharmacy [ 8 , 9 ]. The use of data from EHRs to develop evidence-based digital health tools can be a promising resource to assist HCPs in conducting targeted, efficient and effective SMRs.

The NIHR-funded DynAIRx study (Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity) aims to develop AI-driven tools that integrate information from electronic health and social care records, clinical guidelines and risk-prediction models in order to support the delivery of SMRs [ 10 ]. The DynAIRx project will produce machine learning models, dashboards, and different tools including Causal Inferencing to provide clinicians and patients with evidence-based information to prioritise patients at most risk of harm and/or patients most likely to benefit from SMRs. Aligned with the NICE multimorbidity guidelines, [ 7 , 11 ] DynAIRx will focus on three patient groups at high-risk of rapidly worsening health from multimorbidity: (a) individuals with mental and physical health co-morbidity, [ 12 – 14 ] in whom the prescribing for mental health improvement can lead to adverse physical health consequences; (b) those with complex multimorbidity (four or more long-term health conditions taking ten or more drugs); [ 9 , 15 ] and (c) older people with frailty who are at high risk of adverse outcomes [ 16 ].

The initial step towards introducing AI-driven prescribing support tools into clinical practice involves understanding the current scope of work, how SMRs are presently undertaken and by whom, the time required in real-world clinical practice to undertake them, and crucially, investigate what determinants act as potential barriers to efficient and effective SMR implementation. The aim of this study was to explore how SMRs are undertaken and what barriers those undertaking them (and receiving them) experience.

Participants and recruitment

This study sought to recruit health care or management professionals working in health care settings (primary care in the community or secondary care in hospital services) across the UK where review of prescription medications is a regular part of the clinical workload. This included those working in General Practice, secondary care hospital services (geriatric medicine, clinical pharmacology, falls clinics, mental health practitioners), clinical commissioning of services or management of clinical services (practice managers), and pharmacists, including PCN pharmacists (those involved in conducting SMRs across several neighbouring GP practices). Patient participants included those with (a) multiple and physical co-morbidities; (b) complex multimorbidity; (c) older people with frailty. Patient and carer representatives of the three key multimorbidity groups outlined above were also invited. This included recruiting adult individuals (over the age of 18) with/or caring for someone with multiple (4 or more) long-term health conditions, co-existing mental and physical health problems, prescribed ≥10 regular medications, frailty.

Purposive sampling identified potential HCP participants that were known to be involved in medicines optimisation services through the researchers own clinical and professional networks. Snowballing (wherein research participants were asked to assist the recruitment by attempting to identify other potential participants) was employed to identify contacts through existing service providers along with advertisement in GP forums and at national events for individuals participating in clinical polypharmacy research [ 17 ]. Purposive sampling of potential patient representatives were identified through advertisement across the NIHR Applied Research Collaboration public advisor networks and through research databases at the researchers host institutions. Potential participants were provided with study information and an invitation to participate. Participants received comprehensive briefings from researchers about the study, and written consent was obtained prior to the focus group or interview participation. Withdrawal of consent was permitted at any stage, even after the focus group or interview.

Ethical approval

The Newcastle North Tyneside Research Ethics Committee (REC reference:22/NE/0088) granted ethical approval for the DynAIRx study.

Data collection

Data collection occurred from November 2022 to November 2023. Focus groups and semi-structured interviews were conducted to gather participant views. Focus groups were utilised for patient participants in order to stimulate discussion of common and shared experiences. Individual interviews supplemented the HCP focus groups in order to ensure as many HCPs could be involved as possible owing to time constraints for some HCPs. Focus group topic guides and interview schedules were developed and refined by the clinical members of the research team (LW, AA, AW, FM, AG) and tailored to HCP and patient groups. The topic guides (see S1 Appendix ) included questions exploring the experience of conducting or receiving SMRs, barriers to undertaking them and opinions on key medication challenges in multimorbidity groups from both the clinician and patient perspective. Sessions occurred in person and online (via Microsoft Teams), lasting from 49 to 109 minutes. Audio recordings underwent verbatim transcription and anonymisation to remove any potentially identifiable information. Each participant was assigned a code, and recordings were subsequently deleted. Data collection and analysis occurred concurrently. The Consolidated Criteria for Reporting Qualitative Research checklist was used to ensure comprehensive reporting of our qualitative study (see S1 Appendix ). No participant withdrew consent for their data to be used in the study.

Data analysis

Transcripts were imported into QSR NVivo 12® and analysed using thematic analysis [ 18 ]. Transcripts were read to familiarise researchers with the data. Inductive reasoning guided the initial coding by AA and SW, who collated and examined codes to identify themes. The multidisciplinary coding team (AA, SW, LW, AW, FM) consisted of clinicians and researchers, and engaged in regular reflexive practices to ensure a rigorous and transparent qualitative study. Recognising the diverse expertise and perspectives within the team, we held regular coding clinics where codes and initial themes were reviewed and discussed. By openly sharing our perspectives and challenging each other’s viewpoints, we aimed to balance our interpretations and avoid overemphasis on any single disciplinary lens. This allowed us to critically examine how our professional backgrounds and assumptions might shape the interpretation of findings. These reflexive sessions were instrumental in identifying and mitigating biases, ensuring that our analysis remained grounded in the participants’ narratives rather than our preconceptions. Themes were defined and supported by quotes. Detailed notes of discussions and developments were kept to ensure analytical rigour and plausibility [ 19 ]. The remaining dataset underwent hybrid inductive and deductive thematic analysis using the inductively defined themes, with codes and themes iteratively revised. Once all team members agreed no new codes or meaning to influence thematic analysis were emerging, we assumed saturation was achieved [ 20 ]. At this point, further recruitment was stopped.

Six focus groups with HCPs (n = 21) and 3 patient focus groups (n = 13) were conducted. A further five semi-structured interviews with HCPs took place (see Table 1 for details).

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https://doi.org/10.1371/journal.pone.0299770.t001

Two overarching themes developed from analysis of the HCP and patient interviews and focus groups, within which a number of sub-themes emerged:

• Limited availability of digital tools to assist in identifying and prioritising patients for a SMR
• Organisational challenges and patient factors affecting patient engagement for a SMR
• Time consuming “detective work”
• SMRs require multiple appointments
• Influence of healthcare context on delivering SMR
• Factors influencing deprescribing discussions
• Poor communication and data sharing between primary and secondary care
• Difficulties managing mental health medication for prescriber and patient
• Challenges around anticholinergic medication optimisation for prescriber

Fig 1 displays each key theme from this study and a detailed list of the barriers to each stage of the SMR process. Supplementary quotes for each theme can be found in S2 Appendix .

https://doi.org/10.1371/journal.pone.0299770.g001

• a. Limited availability of digital tools to assist in identifying and prioritising patients for a SMR

PCN pharmacists reported proactively identifying and prioritising patients to conduct SMRs. Patient identification was based on the criteria outlined by the DESGP contract, which includes patients in care homes (both nursing and residential facilities), individuals with complex multimorbidity and polypharmacy, urgently referred patients, older individuals encountering medication-related harms, and patients recently discharged from hospital. HCPs also referenced using available healthcare management automated search tools within the EHR, including ‘Ardens search’ [ 21 ] and ‘ProActive Register Management (PARM) diabetes’ [ 22 ], to identify pre-determined conditions, prescribing alerts and other variables that form part of the practice workload. They also used targets from the Investment and Impact Fund (IIF) for patient identification (IIF is an incentive scheme where PCNs can improve health and the quality of care for patients with multimorbidity), which participants described as beneficial but a waste of resources in the context of SMRs which should focus on patients with complex multimorbidity and polypharmacy [ 23 ]. Despite this, HCPs noted a limited number of digital tools to assist in identifying and prioritising patients for a SMR.

“I’ll be honest that we’ve not really had any tools that have been developed specific for supporting SMR.” (Pharmacist 2, Interview)

Since the introduction of EHRs in the NHS, HCPs are expected to assign ‘SNOMED codes’ to document patients with specific diagnostic, symptom or treatment codes in a logical hierarchical manner to specify clinical information [ 24 ]. These codes facilitate searches for specific medical conditions, symptoms and treatments within the GP EHR, facilitating the identification of individuals requiring an SMR. Pharmacists noted that EHR searches do not adequately consider the complexity of patients, making it challenging to stratify those that would benefit most from an SMR. Additionally, participants highlighted a lack of sensitivity and specificity in the current search mechanisms (meaning the searches either do not identify all the intended patients or identify too many).

“ The actual indicator that my team has been focusing on is supposed to be the ones where patients are prone to medication errors … but when I actually look at the patients, I haven’t got a clue why the actual computer system has decided that most of the time.” (Participant 1, Pharmacist FG1)

HCPs were concerned that the identification of patients who would benefit from an SMR could exceed the clinical capacity of the staff available to meet the need. They felt that any digital tool used to prioritise patients’ needs should match the clinical capacity of the practice.

“ The tools have to be a bit cleverer … We can generate a list of patients today … PCNs at the moment essentially do that, but what you have to do is almost the list that’s generated to the capacity … People would not switch it on if they felt that it could generate lots of patients you would not then see.” (Policy-maker, Interview)

• b. Organisational challenges and patient factors affecting patient engagement for a SMR

GPs or secondary care clinicians (excluding clinical pharmacologists) often conducted opportunistic medication reviews, compared to the proactive SMRs conducted by PCN pharmacists. In alignment with the DES requirements, HCPs described how the task of conducting an SMR was contingent upon organisational contracts, practice size, and staff availability. The presence of a PCN pharmacist for SMRs facilitated streamlined tasks, enabling GPs to focus on patients with more complex medical profiles. GPs voiced concerns about burnout in areas where demand for SMR exceeded the clinical capacity to undertake them. This challenge was particularly pronounced in regions of lower socioeconomic status where patients often presented with complex multimorbidity and polypharmacy at a younger age, especially those with co-morbid mental and physical health problems. Moreover, respondents felt that patients residing in deprived areas were less likely to attend scheduled SMRs, compelling GPs to resort to opportunistic reviews. This highlights potential inequity in access to SMRs and overall health surveillance.

“In the poorer area of the practice there’s no clinical pharmacist, that’s all done opportunistically, if done at all, by the GP partners there. I think that there’s a couple that are approaching burn out, if not complete burn out and the practice is almost run by locums. So, when I’m going in there, it’s quite tough and I will often see medications that are inappropriately prescribed, polypharmacy, several of the same drugs, and I will opportunistically undertake a structured medical review.” (Participant 3, GP FG1)

• c. Time consuming “detective work”

Whether HCPs identified patients proactively or opportunistically, the preparation time for a medication review ranged from 10 minutes to 1 hour. Several factors influenced this preparation time, including the availability of information, case complexity, barriers to accessing information, information density, and time constraints. The challenge in finding and collating information within the patient’s clinical records constituted a significant portion of the preparation time. For instance, discharge letters from hospitals are often located as attachments within the patient record, requiring HCPs to locate and read the letter. These necessary preparatory activities take away from the face-to-face time available with the patient.

“ Probably double the amount of prep time than it was actually with the patient. I mean, granted we did spend a while with the patient because we both like to talk, and the patient certainly did, but I think, and that’s the problem, isn’t it? You get the best information out of your patient when you let them talk and you let them tell you lots of things that you wouldn’t normally ask, but you haven’t got the time to do that so it’s tricky isn’t it to find the balance. But the biggest thing with the prep time was getting the information.” (Participant 3, Pharmacist FG1)

HCPs also conveyed frustration regarding the substantial time required to determine the original indication for a particular prescription and the ongoing necessity for it, even during major transition periods such as a patient’s admission to a care home.

“ We don’t get enough actual structured reviews, so they’ll be getting put on medication, people in care homes, and then left on those medicines. There’s no recognition of the changes. As you move in a care home, you’re generally more frailer, your renal function, haematic function might not be as great and, you know, you’re not moving as much, so your need for some medicines might not be as great as it was when the medicine was first started. ” (Policy-maker, Interview) “So along with what you said about deprescribing Selective Serotonin Reuptake Inhibitors (SSRIs) , especially , you know they’ve been on them for 4–5 years and they are adamant they don’t want to reduce them or stop them or have any sort of conversation about it , yeah , they’re quite challenging I think . Also , I think sometimes you can’t quite work out what medications people have been on . I mean if we talk about SSRIs they may have tried multiple different ones in the past and sometimes it’s difficult to work out what they’ve been on without having to go through the long , long list searching all the different medications that are SSRIs that they’ve tried . It would be so helpful if you know it could just bring up yeah been used before , and then know which one you could try…knowing that you want to try a different one . ” (Participant 6, GP FG1)

Although it is possible within the EHR system to link the prescription of an individual drug to its clinical indication, anecdotal evidence suggests this is time consuming and therefore may not be done in clinical practice.. As such, indications for prescribed medicines are recorded in the free text for the consultation which can easily become obscured over time within the extensive information contained in the clinical record. Examining the clinical free text for this information was emphasised as a challenge in efficiently conducting SMRs.

“ Although in my letters I would clearly state to the GP why I am prescribing the second line antipsychotic just so that people know, but over time that tends to get lost, the rationale for that prescribing tends to get lost and before you know you leave post, somebody else comes and begins to increase that second antipsychotic you know, so that becomes a problem. ” (Participant 1, Psychiatrist FG)

Moreover, existing EHRs are not adept at presenting patient histories in a manner conducive to HCPs pinpointing areas for potential deprescribing. This deficiency in the system leads to a cumulative high pill burden for patients, as illustrated in the quote below.

“ At the age of [ 18 – 20 ], I was diagnosed with bipolar. I am now [71–74] and I have lived for that period of time on medication, a lot of medication actually … I counted the number of tablets and my boxes on my bedside the other day and there was 13 different tablets, so that is what I am being prescribed by my GP. ” (Patient 1, Mental and physical co-morbidities FG)

Patients also expressed uncertainty about the initial reasons for starting medications. Patients reported receiving medications for many years and being unsure whether the medication was still necessary.

“ She is also on a daily injection of adult growth hormone which another consultant put her on at the time and she has been having them for probably 10 to 15 years, and no-one seems to know now who initially prescribed it and who is in charge of that. I am concerned, does she really need them? She is having them every day … Initially it was an asset to go with the immunodeficiency but now I don’t really know. ” (Patient 5, Mental and physical co-morbidities FG)

• d. SMRs require multiple appointments

SMRs typically lasted a minimum of 30 minutes, often extending beyond this duration. The variability in duration was contingent upon the patient’s complexity and the focused nature of the review. Allowing adequate time to address broader health concerns was deemed crucial, enabling the identification of potential issues requiring deeper exploration by the clinician.

HCPs acknowledged that SMRs were not a singular event, and patients might necessitate multiple appointments for a comprehensive review. Consequently, EHR systems were recognised as needing functionality to alert HCPs to schedule additional appointments after the initial SMR, emphasising the iterative and ongoing nature of medication reviews.

“ The first time I see patients, you want almost a bit of a holistic conversation, but actually when you start making interventions you go with what matters most to the patient or where the biggest risk is and you then table the others … You can imagine that being 2 or 3 hours in 4 different appointments before you get to the bottom of where you want to be … I think we had to contact on average about 2 to 3 times per patient, but there were more complex patients as well … I don’t think you can stop medicines or optimise medicines without seeing that patient again as least once. ” (Policy-maker interview)

Patients expressed a desire to be involved in the decision-making during reviews and valued the opportunity to discuss issues such as how medications fit into their routines and other resources that may be available to them.

"I’ve got a series of chronic things, take a load of pills and they’re each for separate things, and I have been concerned for years whether there’s any interaction with them, between them. And also they make me feel tired all the time and perhaps there are some of them where I could actually get rid of them." (Patient 1, Older people with frailty FG)

• e. Influence of healthcare context on delivering SMR

The duration of conducting a SMR was also contingent on the specific healthcare setting in which it took place. One pharmacist highlighted that SMRs conducted in care homes lacked a strict time limit and were oriented towards achieving specific outcomes, such as the number of medicines deprescribed in particular patient groups. This reflected the contextual variability in the conduct and objectives of SMRs, emphasising the need for flexibility in the approach based on the healthcare environment and patient population.

“ We were just told take whatever time you need but because we were not measured on the quantity, we were measured on the quality, and we were recorded the number of medicines basically stopped and in particular groups actually so, and then that would have gone on the report because that was the way of showing what we were doing and the basic value for money I guess .” (Participant 1, Pharmacist FG1)

The emphasis placed on a medication list varied depending on the reviewer and the healthcare setting. A pharmacist working in secondary care articulated a tendency to allocate less attention to certain medicines in a hospital setting, prioritising focus on medications more likely to cause harm. This perspective highlights the nuanced approach that different HCPs may adopt based on their expertise and the specific context in which they operate.

“ At the moment, the bisphosphonate would be something that I’m less concerned about it a very high acuity environment, that’s the thing that I’m probably going to, maybe if I get a chance, write in the discharge summary for the GP to check up on whether or not that’s still appropriate. Whereas I’m chasing those big harms .” (Participant 2, Pharmacist FG1)

• f. Factors influencing deprescribing discussions

Discussions around deprescribing between HCPs and patients were reported to be influenced by several factors. These included the specific type of medicine to be deprescribed, the patient’s willingness to discontinue the medication, sociodemographic location, availability of additional health services in the area, whether the medication was initially prescribed in primary or secondary care and the existence of pre-established stopping criteria for certain medications (e.g., bisphosphonates for more than 5 years). Additionally, HCPs and patients acknowledged a degree of reluctance to engage in deprescribing due to perceived potential risks associated with the cessation of certain medications. These multifaceted factors contribute to the complexity and individualised nature of deprescribing discussions within the healthcare context.

“I find it really, really difficult because all of the guidelines will say, oh, you should have this patient on statins, etc., and you think I really probably shouldn’t they’re 95, but having stopped them in the past then a patient unfortunately ends up with a stroke, they go to hospital, the hospital tells them it’s because their GP stopped their statin and puts them back on .” (Participant 2, GP FG2) “ The antidepressant one is interesting . So , I did work for 9 years in a really deprived area . . . SSRIs for a long time and they were pretty reluctant to come off it but usually willing to accept if it didn’t work out just put them back on it . It just meant in a much more middle-class area there’s the opposite where they’re all desperate to come off it and probably coming off it far too soon . So , I don’t think it needs to be doctor-led , it seems to be more about their external pressures and there’s been a lot of areas done in deprived communities with link workers and social prescribers and I think if you’re going to look at polypharmacy in these sort of groups then that’s probably the way to do it stopping all their meds .” (Participant 4, GP FG1)

In one case, a pharmacist highlighted the challenges associated with decision-making when optimising medicines for complex, younger patients, emphasising the impact of side-effects on their quality of life. Equally, the importance of considering quality of life in frail older adults with polypharmacy was acknowledged, although perceived as less complicated than in younger adults.

"I know well that’s it isn’t it, it’s not necessarily that they’re a complex medicine it’s that there is evidence to say that this can prolong your life but it’s causing them that much upset, so it’s, for me it’s not necessarily the, I can’t think of any particular group of drug its more the younger you get, you know 50 is very young and that you have got a lot of life left to live so that’s when it becomes more of a clinical decision for me that as a pharmacist I don’t feel like I would be able to make" (Participant 1, Pharmacist FG1)

• 2. Medication-related Challenges

Potential for medication-related harm identified by our key stakeholder groups included issues with specific medicines, conditions, and risky medication combinations; mental health medications; prescriptions from specialists; anticholinergic medicines; difficulties in determining prescription timelines to assist in decision-making; challenges with younger complex patients; and siloed care.

• a. Poor communication & data sharing between primary and secondary care

HCPs identified the challenge of extracting information from hospital discharge letters as a key source of frustration. Patients, in particular, assumed that EHRs seamlessly connected primary and secondary care, and in some cases, their community pharmacy. This assumption left patients bewildered and, in certain cases, reliant on the HCPs knowing the complete narrative behind their health records. The disjointed communication and misconceptions surrounding record integration emphasised the need for improved interoperability to enhance the continuum of care.

“ We will be waiting a week for an outpatient letter to come through and it’s really confusing, stop this, change that, increase the dose here, and you’re kind of stuck in the middle. So sometimes the patient will have left that meeting there and it will be ‘like go and see your GP and they will do this bit’, well [that’s] not happening until I’ve got that letter. There is that real mismatch of communication. ” (Participant 1, GP FG2) “ When the repeat prescription came through , despite the fact that the surgery had received the discharge letter , everything was all wrong , and this is just one of those things that happens . So , you get a review and it is whether that data from that review and the story behind it and who it goes back to and whether it is acted on , I think that is important .” (Patient 3, Complex multimorbidity FG)

Communication gaps between GPs, specialist clinicians, and patients were evident due to varying expectations. GPs expressed challenges in managing specialist medications with patients, including concerns about patients’ ability to self-manage their medicines. These challenges highlighted aspects of fragmented care between primary care and specialist clinicians. HCPs also cited difficulties and reluctance in communicating and potentially engaging in conflict with specialist doctors. Participants described specialist doctors as lacking a holistic view when prescribing for patients, favouring certain medicines, and having limited knowledge in drug interactions.

“I think multidisciplinary is key, however when I notice that I liaise with specialists, depending on who I speak to, their drug is the most important and trying to get a consensus about what is best for the patient is obviously one of the challenges like that’s how we got in this situation to begin with you know, so I do find that’s one of the challenges. so, I think you need someone who is you know, who is a specialist but has also got a sort of holistic view of patient care as well which doesn’t always happen in secondary care, but sometimes does.” (Participant 3, Polypharmacy FG)

Participants noted that central nervous system medicines had complicated medication regimens and hence required more coordinated care and responsibility between the specialist prescriber and GP. Addressing these challenges calls for enhanced collaboration, knowledge exchange, and a holistic approach to patient care between primary care and specialist clinicians.

“ I find it, with the pain management clinic, they stop medication, give you a list of all these other tablets you need to start to see how things go and then sort of leave you to it, discharge the patient in your hands and expect you to sort of manage it all. And the same thing is with migraine and headaches from neurology. That’s just a minefield … I think when you’re in specialty, you feel that you can give any sort of long protracted complicated regime and the patient is just going to manage it because that’s the only medication that you think that they’re on. So yeah, they can be quite difficult .” (Participant 5, GP FG1) “ He [GP] says that we can’t actually change any medication to do with your bipolar , that has got to be done by your psychiatrists … I don’t think they would change anything to do with psychiatry .” (Patient 1, Mental and physical co-morbidities FG)

• b. Difficulties managing mental health medication

Mental health medication and management emerged as a consistent sub-theme across key HCP stakeholder groups, irrespective of their professional background. Both doctors and pharmacists described difficulties in monitoring and adjusting psychiatric medicines, including uncertainties about how to address specific issues related to psychiatric medicines. Participants expressed a sense of being ‘out of their depth’, particularly concerning antipsychotic medicines. They conveyed a lack of confidence in assessing the risks and benefits of antipsychotic prescribing, feeling deskilled in this specific area of medication management, and finding it challenging to safely challenge prescribers. This sense of unease prompted participants to seek ways of contacting the mental health team, only to encounter additional hurdles, such as difficulties in locating relevant information within patient records to facilitate multidisciplinary coordinated patient care.

“ But the other one is someone with very complex psychiatric problems, still maybe under the mental health team, and I haven’t got really access to the details apart from maybe I’ve got, you know, some of the other diagnoses. But if I think maybe one of those drugs is potentially causing more harm than could then I’m not clear how then to action that and who to speak to and who were they actually seeing ” (Participant 1, Polypharmacy FG)

For example, a clinical pharmacologist explained that evaluating the success of managing antipsychotics is not as straightforward as assessing physical health conditions. This complexity may contribute to the observed lack of confidence among HCPs when it comes to deprescribing psychiatric medicines. The nuanced nature of mental health outcomes, compared to more tangible markers of success in physical health, adds an additional layer of intricacy to the decision-making process in psychiatry. This includes the complexity of managing mental health medication.

“ The biggest challenge group that I think we face in a deprived area is the patients who are on long term opioid medication, long term neuropathic meds, they’ve probably got a diagnosis of fibromyalgia, they’ve probably got personality disorder plus / minus mental health problems. And the issues that we have is that they’ve almost been sequentially added medication on because GPs don’t really often know what to do with them unless you have a special interest in that field like I do. And when they go and see pharmacists, they are very challenging to pharmacists and pharmacists don’t have the clinical knowledge to be able to sift through what can often be quite dramatic presentations. ” (Participant 1, GP FG1)

• c. Challenges around anticholinergic medicines

Anticholinergic medicines, which inhibit the neurotransmitter acetylcholine involved in numerous physiological functions, has been associated with adverse outcomes such as cognitive decline and falls, particularly when multiple anticholinergic medicines are used concurrently (termed anticholinergic burden) [ 25 , 26 ]. GPs, clinical pharmacologists, and pharmacists described the importance of reviewing and deprescribing anticholinergic medicines where possible. However, the process of calculating anticholinergic burden (ACB) in frail, older adults is time consuming, primarily due to the absence of automated calculators embedded within the EHR system.

Doctors and pharmacists expressed frustrations around the re-prescribing of anticholinergics after deprescribing them. They attributed the persistence of high ACB to limitations in prescribing guidelines and a scarcity of alternative options to replace anticholinergic drugs. These challenges highlighted the need for tools within EHR systems to facilitate efficient assessment of ACB, alongside a broader exploration of prescribing guidelines and alternatives to enhance deprescribing practices.

“ One of the things that I often see in general practice is that there’s lots of anticholinergics, usually amitriptyline because it’s kind of given out for other reasons for what it’s licensed for. So, sleep is probably the most common thing that I see it used for, or avoiding long term opioids in chronic arthritic pain, and often that’s because we have other options for them but we’re not allowed to prescribe them. So, melatonin is probably the most common thing that we could put them on which has a lot better safety profile, but we are just completely discouraged from prescribing it. And likewise access to other interventions that would help arthritic pain rather than putting them on NSAIDS which obviously carry risk or opioids which aren’t overly effective outside the acute pain window. It’s often the lack of other stuff that raises all of the anticholinergic burden. ” (Participant 1, GP FG1)

Participants welcomed any digital tool that could streamline routine work processes, including information retrieval, automated dose calculations, and assessing the risk of developing diseases to optimise medicines during a SMR in a patient-centred manner, with the goal of enhancing efficiency in the medication optimisation process.

Medication reviews by HCPs can take significant preparation, and are time consuming, primarily due to the need to gather and understand patient information and to develop an understanding of a patient’s medical history and social circumstances. In addition, currently, there is no easy way to identify from the EHR which patients are at greatest risk of medication-related harm and those most likely to benefit from an SMR. The EHR systems used in primary care contain enormous volumes of information which becomes particularly challenging and time-consuming to navigate for complex individuals living with multiple long-term conditions and taking many medications. The way that information is organised in the system leads to a large proportion of time spent linking medications to their original indication and examining the patient journey. This time could be better spent discussing shared decisions with the patient. The EHR has not evolved in line with increasing patient complexity. The findings of this report emphasise the need for enhanced functionalities in EHRs to support effective medication management in the context of deprescribing discussions where a nuanced understanding of a patient’s medication history is crucial.

Our study has highlighted the challenges facing those undertaking SMRs in more socioeconomically disadvantaged areas, where people experience multimorbidity (and co-existent polypharmacy) 10–15 years earlier than their affluent peers [ 27 , 28 ]. These populations have complex healthcare needs at a younger age, the care of which falls to the already over-stretched GPs. Areas with greater socioeconomic disadvantage often have lower health literacy, resulting from a combination of lower educational attainment, economic barriers like the need to prioritise food and heating over health seeking, and psychosocial stressors affecting decision-making relating to health [ 29 , 30 ]. Health literacy applies not only to the patient but to the clinician who may also be unaware of the psychosocioeconomic situation of the patient, leading to a communication gap when discussing the risk and benefit of medicines to reach a shared-decision [ 31 – 33 ]. Accordingly, complex conversations involving numeracy calculations of risk may take longer and require repetition, but may also be of less priority for the patient and/or carer than other more immediate life concerns.

As preparation time is repeatedly cited as a barrier to effective SMR, a potential solution that would support SMRs in those with lower health literacy should include any digital intervention that saves on preparation time. This would enable more time for the clinician to engage with the patient and discuss complexities around risk and benefit, which would go some way to addressing the existing health disparity that affects those experiencing socioeconomic disadvantage. For HCPs working in areas of socioeconomic deprivation, lack of HCP capacity alongside patients declining SMR invitations were cited as barriers to undertaking SMRs. Moreover, HCPs described the usefulness of a system to identify availability of different health services in surrounding areas (e.g. weight management service) [ 27 , 28 ]. A recent study co-produced SMR resources to empower patients in their healthcare and support them in making the most out of their SMR. This included producing resources in a number of different languages including audio recorded resources for patients with visual impairment [ 34 , 35 ]. Embedded links to resources for HCPs to provide to patients before and/or after an SMR can be one potential way to utilise digital health and empower patients to reduce inequity in access to healthcare.

Our study also highlighted medication-related challenges such as difficulties managing mental health, specialist and anticholinergic medications. HCPs reported that a lack of alternatives to medication for symptom management hampered their ability to optimise some of the more potentially harmful medication classes such as opioids, anti-depressants, anticholinergics and gabapentinoids. Non-pharmacological alternatives, where appropriate, such as counselling need to be readily and equitably accessible for this approach to be considered a reliable option [ 36 ]. Mental health medication management stood out as a consistent challenge. HCPs in our study, regardless of their professional background, expressed difficulty in monitoring and adjusting psychiatric drugs. There was also a general lack of confidence and skill when it came to monitoring and adjusting antipsychotic medications, with the measurement of success in managing these medications being ambiguous. This is consistent with previous studies that note GPs lack of confidence in managing patients with serious mental health illness [ 12 , 37 ]. One recent study reported that less than half of GP trainees in England and Wales have trained in a mental health setting between 2013 and 2015 [ 38 ]. In addition to the need for HCPs in primary care to become trained to address issues related to psychiatric medicines, EHRs must include basic information about the indication for the prescribed psychiatric medicine and the appropriate mental health team contact details for GPs to be able to address these issues. This would have the potential to enable multidisciplinary coordination of care with mental health patients.

Another challenging group of medicines was those with anticholinergic effects. This drug class was also a recurring issue among GPs, clinical pharmacologists, and pharmacists. HCPs found it time consuming to calculate the ACB in frail, older adults. Although there are several ACB scales available that have been developed and validated, participants stated that automated calculators to calculate ACB are not easily accessible or embedded into EHRs. In addition, there is considerable variability between anticholinergic scales making it difficult to ascertain which scale to use to calculate ACB [ 39 ]. As such, taking the time out to include every medicine a patient is taking to calculate their ACB is time consuming, reducing opportunities for potential deprescribing discussions with patients.

Limitations

This study was conducted in the UK, which provides universal access to healthcare. However, findings from our study may also be applicable internationally to other health systems which operate a universal social insurance model where there is a primary care and secondary care gatekeeping model, including the need for HCP coordination [ 40 , 41 ] or deprescribing challenges [ 42 , 43 ]. This study is part of a larger qualitative study examining both barriers to SMRs and potential digital solutions, including AI-assisted approaches. As such, the HCP participants likely included a number of clinicians with a particular interest in digital-driven solutions in healthcare. We sought to include a wide variety of HCPs from different practice backgrounds in order to mitigate this. In addition, some of the focus groups contained unexpected small numbers of clinicians at one time, owing to the competing demands on clinician time. However, the data collected were rich and contributed significantly to achieving thematic saturation. The insights gained from these discussions were consistent with those from larger groups, reinforcing the validity of our findings.

Conclusions

There are few useful digital tools that can identify patients that would benefit most from an SMR or monitor the effects of medication optimisation when medicines are altered. Our findings showed that significant time is needed to prepare and conduct a SMR, with complex patients sometimes needing multiple appointments to enable a comprehensive review. The DynAIRx project will use findings from this study to address the barriers of conducting an SMR by producing dashboards and visualisations to summarise the patient’s medical journey; develop digital tools to prioritise patients that would benefit most from an SMR; and identify optimal interventions for specific multimorbidity and polypharmacy patient groups.

Supporting information

S1 appendix. topic guides for interviews and focus groups and coreq checklist..

https://doi.org/10.1371/journal.pone.0299770.s001

S2 Appendix. Table containing additional explanatory quotes relating to subthemes.

https://doi.org/10.1371/journal.pone.0299770.s002

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Qualitative Research: Data Collection, Analysis, and Management

Introduction.

In an earlier paper, 1 we presented an introduction to using qualitative research methods in pharmacy practice. In this article, we review some principles of the collection, analysis, and management of qualitative data to help pharmacists interested in doing research in their practice to continue their learning in this area. Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. Whereas quantitative research methods can be used to determine how many people undertake particular behaviours, qualitative methods can help researchers to understand how and why such behaviours take place. Within the context of pharmacy practice research, qualitative approaches have been used to examine a diverse array of topics, including the perceptions of key stakeholders regarding prescribing by pharmacists and the postgraduation employment experiences of young pharmacists (see “Further Reading” section at the end of this article).

In the previous paper, 1 we outlined 3 commonly used methodologies: ethnography 2 , grounded theory 3 , and phenomenology. 4 Briefly, ethnography involves researchers using direct observation to study participants in their “real life” environment, sometimes over extended periods. Grounded theory and its later modified versions (e.g., Strauss and Corbin 5 ) use face-to-face interviews and interactions such as focus groups to explore a particular research phenomenon and may help in clarifying a less-well-understood problem, situation, or context. Phenomenology shares some features with grounded theory (such as an exploration of participants’ behaviour) and uses similar techniques to collect data, but it focuses on understanding how human beings experience their world. It gives researchers the opportunity to put themselves in another person’s shoes and to understand the subjective experiences of participants. 6 Some researchers use qualitative methodologies but adopt a different standpoint, and an example of this appears in the work of Thurston and others, 7 discussed later in this paper.

Qualitative work requires reflection on the part of researchers, both before and during the research process, as a way of providing context and understanding for readers. When being reflexive, researchers should not try to simply ignore or avoid their own biases (as this would likely be impossible); instead, reflexivity requires researchers to reflect upon and clearly articulate their position and subjectivities (world view, perspectives, biases), so that readers can better understand the filters through which questions were asked, data were gathered and analyzed, and findings were reported. From this perspective, bias and subjectivity are not inherently negative but they are unavoidable; as a result, it is best that they be articulated up-front in a manner that is clear and coherent for readers.

THE PARTICIPANT’S VIEWPOINT

What qualitative study seeks to convey is why people have thoughts and feelings that might affect the way they behave. Such study may occur in any number of contexts, but here, we focus on pharmacy practice and the way people behave with regard to medicines use (e.g., to understand patients’ reasons for nonadherence with medication therapy or to explore physicians’ resistance to pharmacists’ clinical suggestions). As we suggested in our earlier article, 1 an important point about qualitative research is that there is no attempt to generalize the findings to a wider population. Qualitative research is used to gain insights into people’s feelings and thoughts, which may provide the basis for a future stand-alone qualitative study or may help researchers to map out survey instruments for use in a quantitative study. It is also possible to use different types of research in the same study, an approach known as “mixed methods” research, and further reading on this topic may be found at the end of this paper.

The role of the researcher in qualitative research is to attempt to access the thoughts and feelings of study participants. This is not an easy task, as it involves asking people to talk about things that may be very personal to them. Sometimes the experiences being explored are fresh in the participant’s mind, whereas on other occasions reliving past experiences may be difficult. However the data are being collected, a primary responsibility of the researcher is to safeguard participants and their data. Mechanisms for such safeguarding must be clearly articulated to participants and must be approved by a relevant research ethics review board before the research begins. Researchers and practitioners new to qualitative research should seek advice from an experienced qualitative researcher before embarking on their project.

DATA COLLECTION

Whatever philosophical standpoint the researcher is taking and whatever the data collection method (e.g., focus group, one-to-one interviews), the process will involve the generation of large amounts of data. In addition to the variety of study methodologies available, there are also different ways of making a record of what is said and done during an interview or focus group, such as taking handwritten notes or video-recording. If the researcher is audio- or video-recording data collection, then the recordings must be transcribed verbatim before data analysis can begin. As a rough guide, it can take an experienced researcher/transcriber 8 hours to transcribe one 45-minute audio-recorded interview, a process than will generate 20–30 pages of written dialogue.

Many researchers will also maintain a folder of “field notes” to complement audio-taped interviews. Field notes allow the researcher to maintain and comment upon impressions, environmental contexts, behaviours, and nonverbal cues that may not be adequately captured through the audio-recording; they are typically handwritten in a small notebook at the same time the interview takes place. Field notes can provide important context to the interpretation of audio-taped data and can help remind the researcher of situational factors that may be important during data analysis. Such notes need not be formal, but they should be maintained and secured in a similar manner to audio tapes and transcripts, as they contain sensitive information and are relevant to the research. For more information about collecting qualitative data, please see the “Further Reading” section at the end of this paper.

DATA ANALYSIS AND MANAGEMENT

If, as suggested earlier, doing qualitative research is about putting oneself in another person’s shoes and seeing the world from that person’s perspective, the most important part of data analysis and management is to be true to the participants. It is their voices that the researcher is trying to hear, so that they can be interpreted and reported on for others to read and learn from. To illustrate this point, consider the anonymized transcript excerpt presented in Appendix 1 , which is taken from a research interview conducted by one of the authors (J.S.). We refer to this excerpt throughout the remainder of this paper to illustrate how data can be managed, analyzed, and presented.

Interpretation of Data

Interpretation of the data will depend on the theoretical standpoint taken by researchers. For example, the title of the research report by Thurston and others, 7 “Discordant indigenous and provider frames explain challenges in improving access to arthritis care: a qualitative study using constructivist grounded theory,” indicates at least 2 theoretical standpoints. The first is the culture of the indigenous population of Canada and the place of this population in society, and the second is the social constructivist theory used in the constructivist grounded theory method. With regard to the first standpoint, it can be surmised that, to have decided to conduct the research, the researchers must have felt that there was anecdotal evidence of differences in access to arthritis care for patients from indigenous and non-indigenous backgrounds. With regard to the second standpoint, it can be surmised that the researchers used social constructivist theory because it assumes that behaviour is socially constructed; in other words, people do things because of the expectations of those in their personal world or in the wider society in which they live. (Please see the “Further Reading” section for resources providing more information about social constructivist theory and reflexivity.) Thus, these 2 standpoints (and there may have been others relevant to the research of Thurston and others 7 ) will have affected the way in which these researchers interpreted the experiences of the indigenous population participants and those providing their care. Another standpoint is feminist standpoint theory which, among other things, focuses on marginalized groups in society. Such theories are helpful to researchers, as they enable us to think about things from a different perspective. Being aware of the standpoints you are taking in your own research is one of the foundations of qualitative work. Without such awareness, it is easy to slip into interpreting other people’s narratives from your own viewpoint, rather than that of the participants.

To analyze the example in Appendix 1 , we will adopt a phenomenological approach because we want to understand how the participant experienced the illness and we want to try to see the experience from that person’s perspective. It is important for the researcher to reflect upon and articulate his or her starting point for such analysis; for example, in the example, the coder could reflect upon her own experience as a female of a majority ethnocultural group who has lived within middle class and upper middle class settings. This personal history therefore forms the filter through which the data will be examined. This filter does not diminish the quality or significance of the analysis, since every researcher has his or her own filters; however, by explicitly stating and acknowledging what these filters are, the researcher makes it easer for readers to contextualize the work.

Transcribing and Checking

For the purposes of this paper it is assumed that interviews or focus groups have been audio-recorded. As mentioned above, transcribing is an arduous process, even for the most experienced transcribers, but it must be done to convert the spoken word to the written word to facilitate analysis. For anyone new to conducting qualitative research, it is beneficial to transcribe at least one interview and one focus group. It is only by doing this that researchers realize how difficult the task is, and this realization affects their expectations when asking others to transcribe. If the research project has sufficient funding, then a professional transcriber can be hired to do the work. If this is the case, then it is a good idea to sit down with the transcriber, if possible, and talk through the research and what the participants were talking about. This background knowledge for the transcriber is especially important in research in which people are using jargon or medical terms (as in pharmacy practice). Involving your transcriber in this way makes the work both easier and more rewarding, as he or she will feel part of the team. Transcription editing software is also available, but it is expensive. For example, ELAN (more formally known as EUDICO Linguistic Annotator, developed at the Technical University of Berlin) 8 is a tool that can help keep data organized by linking media and data files (particularly valuable if, for example, video-taping of interviews is complemented by transcriptions). It can also be helpful in searching complex data sets. Products such as ELAN do not actually automatically transcribe interviews or complete analyses, and they do require some time and effort to learn; nonetheless, for some research applications, it may be a valuable to consider such software tools.

All audio recordings should be transcribed verbatim, regardless of how intelligible the transcript may be when it is read back. Lines of text should be numbered. Once the transcription is complete, the researcher should read it while listening to the recording and do the following: correct any spelling or other errors; anonymize the transcript so that the participant cannot be identified from anything that is said (e.g., names, places, significant events); insert notations for pauses, laughter, looks of discomfort; insert any punctuation, such as commas and full stops (periods) (see Appendix 1 for examples of inserted punctuation), and include any other contextual information that might have affected the participant (e.g., temperature or comfort of the room).

Dealing with the transcription of a focus group is slightly more difficult, as multiple voices are involved. One way of transcribing such data is to “tag” each voice (e.g., Voice A, Voice B). In addition, the focus group will usually have 2 facilitators, whose respective roles will help in making sense of the data. While one facilitator guides participants through the topic, the other can make notes about context and group dynamics. More information about group dynamics and focus groups can be found in resources listed in the “Further Reading” section.

Reading between the Lines

During the process outlined above, the researcher can begin to get a feel for the participant’s experience of the phenomenon in question and can start to think about things that could be pursued in subsequent interviews or focus groups (if appropriate). In this way, one participant’s narrative informs the next, and the researcher can continue to interview until nothing new is being heard or, as it says in the text books, “saturation is reached”. While continuing with the processes of coding and theming (described in the next 2 sections), it is important to consider not just what the person is saying but also what they are not saying. For example, is a lengthy pause an indication that the participant is finding the subject difficult, or is the person simply deciding what to say? The aim of the whole process from data collection to presentation is to tell the participants’ stories using exemplars from their own narratives, thus grounding the research findings in the participants’ lived experiences.

Smith 9 suggested a qualitative research method known as interpretative phenomenological analysis, which has 2 basic tenets: first, that it is rooted in phenomenology, attempting to understand the meaning that individuals ascribe to their lived experiences, and second, that the researcher must attempt to interpret this meaning in the context of the research. That the researcher has some knowledge and expertise in the subject of the research means that he or she can have considerable scope in interpreting the participant’s experiences. Larkin and others 10 discussed the importance of not just providing a description of what participants say. Rather, interpretative phenomenological analysis is about getting underneath what a person is saying to try to truly understand the world from his or her perspective.

Once all of the research interviews have been transcribed and checked, it is time to begin coding. Field notes compiled during an interview can be a useful complementary source of information to facilitate this process, as the gap in time between an interview, transcribing, and coding can result in memory bias regarding nonverbal or environmental context issues that may affect interpretation of data.

Coding refers to the identification of topics, issues, similarities, and differences that are revealed through the participants’ narratives and interpreted by the researcher. This process enables the researcher to begin to understand the world from each participant’s perspective. Coding can be done by hand on a hard copy of the transcript, by making notes in the margin or by highlighting and naming sections of text. More commonly, researchers use qualitative research software (e.g., NVivo, QSR International Pty Ltd; www.qsrinternational.com/products_nvivo.aspx ) to help manage their transcriptions. It is advised that researchers undertake a formal course in the use of such software or seek supervision from a researcher experienced in these tools.

Returning to Appendix 1 and reading from lines 8–11, a code for this section might be “diagnosis of mental health condition”, but this would just be a description of what the participant is talking about at that point. If we read a little more deeply, we can ask ourselves how the participant might have come to feel that the doctor assumed he or she was aware of the diagnosis or indeed that they had only just been told the diagnosis. There are a number of pauses in the narrative that might suggest the participant is finding it difficult to recall that experience. Later in the text, the participant says “nobody asked me any questions about my life” (line 19). This could be coded simply as “health care professionals’ consultation skills”, but that would not reflect how the participant must have felt never to be asked anything about his or her personal life, about the participant as a human being. At the end of this excerpt, the participant just trails off, recalling that no-one showed any interest, which makes for very moving reading. For practitioners in pharmacy, it might also be pertinent to explore the participant’s experience of akathisia and why this was left untreated for 20 years.

One of the questions that arises about qualitative research relates to the reliability of the interpretation and representation of the participants’ narratives. There are no statistical tests that can be used to check reliability and validity as there are in quantitative research. However, work by Lincoln and Guba 11 suggests that there are other ways to “establish confidence in the ‘truth’ of the findings” (p. 218). They call this confidence “trustworthiness” and suggest that there are 4 criteria of trustworthiness: credibility (confidence in the “truth” of the findings), transferability (showing that the findings have applicability in other contexts), dependability (showing that the findings are consistent and could be repeated), and confirmability (the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest).

One way of establishing the “credibility” of the coding is to ask another researcher to code the same transcript and then to discuss any similarities and differences in the 2 resulting sets of codes. This simple act can result in revisions to the codes and can help to clarify and confirm the research findings.

Theming refers to the drawing together of codes from one or more transcripts to present the findings of qualitative research in a coherent and meaningful way. For example, there may be examples across participants’ narratives of the way in which they were treated in hospital, such as “not being listened to” or “lack of interest in personal experiences” (see Appendix 1 ). These may be drawn together as a theme running through the narratives that could be named “the patient’s experience of hospital care”. The importance of going through this process is that at its conclusion, it will be possible to present the data from the interviews using quotations from the individual transcripts to illustrate the source of the researchers’ interpretations. Thus, when the findings are organized for presentation, each theme can become the heading of a section in the report or presentation. Underneath each theme will be the codes, examples from the transcripts, and the researcher’s own interpretation of what the themes mean. Implications for real life (e.g., the treatment of people with chronic mental health problems) should also be given.

DATA SYNTHESIS

In this final section of this paper, we describe some ways of drawing together or “synthesizing” research findings to represent, as faithfully as possible, the meaning that participants ascribe to their life experiences. This synthesis is the aim of the final stage of qualitative research. For most readers, the synthesis of data presented by the researcher is of crucial significance—this is usually where “the story” of the participants can be distilled, summarized, and told in a manner that is both respectful to those participants and meaningful to readers. There are a number of ways in which researchers can synthesize and present their findings, but any conclusions drawn by the researchers must be supported by direct quotations from the participants. In this way, it is made clear to the reader that the themes under discussion have emerged from the participants’ interviews and not the mind of the researcher. The work of Latif and others 12 gives an example of how qualitative research findings might be presented.

Planning and Writing the Report

As has been suggested above, if researchers code and theme their material appropriately, they will naturally find the headings for sections of their report. Qualitative researchers tend to report “findings” rather than “results”, as the latter term typically implies that the data have come from a quantitative source. The final presentation of the research will usually be in the form of a report or a paper and so should follow accepted academic guidelines. In particular, the article should begin with an introduction, including a literature review and rationale for the research. There should be a section on the chosen methodology and a brief discussion about why qualitative methodology was most appropriate for the study question and why one particular methodology (e.g., interpretative phenomenological analysis rather than grounded theory) was selected to guide the research. The method itself should then be described, including ethics approval, choice of participants, mode of recruitment, and method of data collection (e.g., semistructured interviews or focus groups), followed by the research findings, which will be the main body of the report or paper. The findings should be written as if a story is being told; as such, it is not necessary to have a lengthy discussion section at the end. This is because much of the discussion will take place around the participants’ quotes, such that all that is needed to close the report or paper is a summary, limitations of the research, and the implications that the research has for practice. As stated earlier, it is not the intention of qualitative research to allow the findings to be generalized, and therefore this is not, in itself, a limitation.

Planning out the way that findings are to be presented is helpful. It is useful to insert the headings of the sections (the themes) and then make a note of the codes that exemplify the thoughts and feelings of your participants. It is generally advisable to put in the quotations that you want to use for each theme, using each quotation only once. After all this is done, the telling of the story can begin as you give your voice to the experiences of the participants, writing around their quotations. Do not be afraid to draw assumptions from the participants’ narratives, as this is necessary to give an in-depth account of the phenomena in question. Discuss these assumptions, drawing on your participants’ words to support you as you move from one code to another and from one theme to the next. Finally, as appropriate, it is possible to include examples from literature or policy documents that add support for your findings. As an exercise, you may wish to code and theme the sample excerpt in Appendix 1 and tell the participant’s story in your own way. Further reading about “doing” qualitative research can be found at the end of this paper.

CONCLUSIONS

Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. It can be used in pharmacy practice research to explore how patients feel about their health and their treatment. Qualitative research has been used by pharmacists to explore a variety of questions and problems (see the “Further Reading” section for examples). An understanding of these issues can help pharmacists and other health care professionals to tailor health care to match the individual needs of patients and to develop a concordant relationship. Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management. Further reading around the subject will be essential to truly understand this method of accessing peoples’ thoughts and feelings to enable researchers to tell participants’ stories.

Appendix 1. Excerpt from a sample transcript

The participant (age late 50s) had suffered from a chronic mental health illness for 30 years. The participant had become a “revolving door patient,” someone who is frequently in and out of hospital. As the participant talked about past experiences, the researcher asked:

• What was treatment like 30 years ago?
• Umm—well it was pretty much they could do what they wanted with you because I was put into the er, the er kind of system er, I was just on
• endless section threes.
• Really…
• But what I didn’t realize until later was that if you haven’t actually posed a threat to someone or yourself they can’t really do that but I didn’t know
• that. So wh-when I first went into hospital they put me on the forensic ward ’cause they said, “We don’t think you’ll stay here we think you’ll just
• run-run away.” So they put me then onto the acute admissions ward and – er – I can remember one of the first things I recall when I got onto that
• ward was sitting down with a er a Dr XXX. He had a book this thick [gestures] and on each page it was like three questions and he went through
• all these questions and I answered all these questions. So we’re there for I don’t maybe two hours doing all that and he asked me he said “well
• when did somebody tell you then that you have schizophrenia” I said “well nobody’s told me that” so he seemed very surprised but nobody had
• actually [pause] whe-when I first went up there under police escort erm the senior kind of consultants people I’d been to where I was staying and
• ermm so er [pause] I . . . the, I can remember the very first night that I was there and given this injection in this muscle here [gestures] and just
• having dreadful side effects the next day I woke up [pause]
• . . . and I suffered that akathesia I swear to you, every minute of every day for about 20 years.
• Oh how awful.
• And that side of it just makes life impossible so the care on the wards [pause] umm I don’t know it’s kind of, it’s kind of hard to put into words
• [pause]. Because I’m not saying they were sort of like not friendly or interested but then nobody ever seemed to want to talk about your life [pause]
• nobody asked me any questions about my life. The only questions that came into was they asked me if I’d be a volunteer for these student exams
• and things and I said “yeah” so all the questions were like “oh what jobs have you done,” er about your relationships and things and er but
• nobody actually sat down and had a talk and showed some interest in you as a person you were just there basically [pause] um labelled and you
• know there was there was [pause] but umm [pause] yeah . . .

This article is the 10th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.

Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.

Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.

Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.

Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.

Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.

Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.

Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.

Competing interests: None declared.

Further Reading

Examples of qualitative research in pharmacy practice.

• Farrell B, Pottie K, Woodend K, Yao V, Dolovich L, Kennie N, et al. Shifts in expectations: evaluating physicians’ perceptions as pharmacists integrated into family practice. J Interprof Care. 2010; 24 (1):80–9. [ PubMed ] [ Google Scholar ]
• Gregory P, Austin Z. Postgraduation employment experiences of new pharmacists in Ontario in 2012–2013. Can Pharm J. 2014; 147 (5):290–9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Marks PZ, Jennnings B, Farrell B, Kennie-Kaulbach N, Jorgenson D, Pearson-Sharpe J, et al. “I gained a skill and a change in attitude”: a case study describing how an online continuing professional education course for pharmacists supported achievement of its transfer to practice outcomes. Can J Univ Contin Educ. 2014; 40 (2):1–18. [ Google Scholar ]
• Nair KM, Dolovich L, Brazil K, Raina P. It’s all about relationships: a qualitative study of health researchers’ perspectives on interdisciplinary research. BMC Health Serv Res. 2008; 8 :110. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pojskic N, MacKeigan L, Boon H, Austin Z. Initial perceptions of key stakeholders in Ontario regarding independent prescriptive authority for pharmacists. Res Soc Adm Pharm. 2014; 10 (2):341–54. [ PubMed ] [ Google Scholar ]

Qualitative Research in General

• Breakwell GM, Hammond S, Fife-Schaw C. Research methods in psychology. Thousand Oaks (CA): Sage Publications; 1995. [ Google Scholar ]
• Given LM. 100 questions (and answers) about qualitative research. Thousand Oaks (CA): Sage Publications; 2015. [ Google Scholar ]
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• Patton M. Qualitative research and evaluation methods. Thousand Oaks (CA): Sage Publications; 2002. [ Google Scholar ]
• Willig C. Introducing qualitative research in psychology. Buckingham (UK): Open University Press; 2001. [ Google Scholar ]

Group Dynamics in Focus Groups

• Farnsworth J, Boon B. Analysing group dynamics within the focus group. Qual Res. 2010; 10 (5):605–24. [ Google Scholar ]

Social Constructivism

• Social constructivism. Berkeley (CA): University of California, Berkeley, Berkeley Graduate Division, Graduate Student Instruction Teaching & Resource Center; [cited 2015 June 4]. Available from: http://gsi.berkeley.edu/gsi-guide-contents/learning-theory-research/social-constructivism/ [ Google Scholar ]

Mixed Methods

• Creswell J. Research design: qualitative, quantitative, and mixed methods approaches. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]

Collecting Qualitative Data

• Arksey H, Knight P. Interviewing for social scientists: an introductory resource with examples. Thousand Oaks (CA): Sage Publications; 1999. [ Google Scholar ]
• Guest G, Namey EE, Mitchel ML. Collecting qualitative data: a field manual for applied research. Thousand Oaks (CA): Sage Publications; 2013. [ Google Scholar ]

Constructivist Grounded Theory

• Charmaz K. Grounded theory: objectivist and constructivist methods. In: Denzin N, Lincoln Y, editors. Handbook of qualitative research. 2nd ed. Thousand Oaks (CA): Sage Publications; 2000. pp. 509–35. [ Google Scholar ]

• Open access
• Published: 02 September 2024

Clinical supervisor’s experiences of peer group clinical supervision during COVID-19: a mixed methods study

• Owen Doody   ORCID: orcid.org/0000-0002-3708-1647 1 ,
• Kathleen Markey   ORCID: orcid.org/0000-0002-3024-0828 1 ,
• James Turner   ORCID: orcid.org/0000-0002-8360-1420 2 ,
• Claire O. Donnell   ORCID: orcid.org/0000-0003-2386-7048 1 &
• Louise Murphy   ORCID: orcid.org/0000-0003-2381-3963 1  

BMC Nursing volume  23 , Article number:  612 ( 2024 ) Cite this article

Metrics details

Providing positive and supportive environments for nurses and midwives working in ever-changing and complex healthcare services is paramount. Clinical supervision is one approach that nurtures and supports professional guidance, ethical practice, and personal development, which impacts positively on staff morale and standards of care delivery. In the context of this study, peer group clinical supervision provides allocated time to reflect and discuss care provided and facilitated by clinical supervisors who are at the same grade/level as the supervisees.

To explore the clinical supervisor’s experiences of peer group clinical supervision a mixed methods study design was utilised within Irish health services (midwifery, intellectual disability, general, mental health). The Manchester Clinical Supervision Scale was used to survey clinical supervisors ( n  = 36) and semi-structured interviews ( n  = 10) with clinical supervisors were conducted. Survey data were analysed through SPSS and interview data were analysed utilising content analysis. The qualitative and quantitative data’s reporting rigour was guided by the CROSS and SRQR guidelines.

Participants generally had a positive encounter when providing clinical supervision. They highly appreciated the value of clinical supervision and expressed a considerable degree of contentment with the supervision they provided to supervisees. The advantages of peer group clinical supervision encompass aspects related to self (such as confidence, leadership, personal development, and resilience), service and organisation (including a positive working environment, employee retention, and safety), and patient care (involving critical thinking and evaluation, patient safety, adherence to quality standards, and elevated levels of care).

There are many benefits of peer group clinical supervision at an individual, service, organisation, and patient level. Nevertheless, there is a need to address a lack of awareness and misconceptions surrounding clinical supervision to create an environment and culture conducive to realising its full potential. It is crucial that clinical supervision be accessible to nurses and midwives of all grades across all healthcare services, with national planning to address capacity and sustainability.

Peer Review reports

Within a dynamic healthcare system, nurses and midwives face growing demands, underscoring the necessity for ongoing personal and professional development. This is essential to improve the effectiveness and efficiency of care delivery for patients, families, and societies. Despite the increased emphasis on increasing the quality and safety of healthcare services and delivery, there is evidence highlighting declining standards of nursing and midwifery care [ 1 ]. The recent focus on re-affirming and re-committing to core values guiding nursing and midwifery practice is encouraging such as compassion, care and commitment [ 2 ], competence, communication, and courage [ 3 ]. However, imposing value statements in isolation is unlikely to change behaviours and greater consideration needs to be given to ways in which compassion, care, and commitment are nurtured and ultimately applied in daily practice. Furthermore, concerns have been raised about global staff shortages [ 4 ], the evidence suggesting several contributing factors such as poor workforce planning [ 5 ], job dissatisfaction [ 6 ], and healthcare migration [ 7 ]. Without adequate resources and staffing, compromising standards of care and threats to patient safety will be imminent therefore the importance of developing effective strategies for retaining competent registered nurses and midwives is paramount in today’s climate of increased staff shortages [ 4 ]. Clinical supervision serves as a means to facilitate these advancements and has been linked to heightened job satisfaction, enhanced staff retention, improved staff effectiveness, and effective clinical governance, by aiding in quality improvements, risk management, and heightened accountability [ 8 ].

Clinical supervision is a key component of professional practice and while the aim is largely known, there is no universally accepted definition of clinical supervision [ 8 ]. Clinical supervision is a structured process where clinicians are allowed protected time to reflect on their practice within a supportive environment and with the purpose of developing high-quality clinical care [ 9 ]. Recent literature published on clinical supervision [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ] highlights the advantages and merits of clinical supervision. However, there are challenges also identified such as a lack of consensus regarding the meaning and goal, implementation issues, variations in approaches in its operationalisation, and an absence of research evidence on its effectiveness. Duration and experience in clinical supervision link to positive benefits [ 8 ], but there is little evidence of how clinical supervision altered individual behaviours and practices. This is reinforced by Kuhne et al., [ 15 ] who emphasise that satisfaction rather than effectiveness is more commonly examined. It is crucial to emphasise that reviews have pinpointed that clinical supervision lowers the risks of adverse patient outcomes [ 9 ] and demonstrates enhancements in the execution of certain care processes. Peer group clinical supervision is a form of clinical supervision whereby two or more practitioners engage in a supervision or consultation process to improve their professional practice [ 17 ]. There is limited evidence regarding peer group clinical supervision and research on the experiences of peer clinical supervision and stakeholders is needed [ 13 ]. In Ireland, peer group clinical supervision has been recommended and guidelines have been developed [ 18 ]. In the Irish context, peer clinical supervision is where both clinical supervisees and clinical supervisors are peers at the same level/grade. However, greater evidence is required to inform future decisions on the implementation of peer group clinical supervision and the purpose of this study is to explore clinical supervisors’ experiences of peer group clinical supervision. As the focus is on peer group supervisors and utilising mixed methods the experiences of the other stakeholders were investigated and reported separately.

A mixed methods approach was used (survey and semi-structured interviews) to capture clinical supervisor’s experiences of clinical supervision. The study adhered to the Consensus-Based Checklist for Reporting of Survey Studies guidelines [ 19 ] (Supplementary File S1 ) and Standards for Reporting Qualitative Research guidelines [ 20 ] (Supplementary File S2 ).

Participants

This study was conducted with participants who successfully completed a professionally credited award: clinical supervision module run by a university in Ireland (74 clinical supervisors across 5 programmes over 3 years). The specific selection criteria for participants were that they were registered nurses/midwives delivering peer group clinical supervision within the West region of Ireland. The specific exclusion criteria were as follows: (1) nurses and midwives who haven’t finished the clinical supervision module at the University, (2) newly appointed peer group clinical supervisors who have yet to establish their groups and initiate the delivery of peer group clinical supervision.

Measures and procedures

The Manchester Clinical Supervision Scale-26 was used to survey participants in February/March 2022 and measure the peer group clinical supervisors’ overall experiences of facilitating peer group clinical supervision. The Manchester Clinical Supervision Scale-26 is a validated 26-item self-report questionnaire with a Likert-type (1–5) scale ranging from strongly disagree (1) to strongly agree (5) [ 21 ]. The Manchester Clinical Supervision Scale-26 measures the efficiency of and satisfaction with supervision, to investigate the skills acquisition aspect of clinical supervision and its effect on the quality of clinical care [ 21 ]. The instrument consists of two main sections to measure three (normative, restorative, and formative) dimensions of clinical supervision utilising six sub-scales: (1) trust and rapport, (2) supervisor advice/support, (3) improved care/skills, (4) importance/value of clinical supervision, (5) finding time, (6) personal issues/reflections and a total score for the Manchester Clinical Supervision Scale-26 is also calculated. Section two consisted of the demographic section of the questionnaire and was tailored to include eight demographic questions concerning the supervisor’s demographics, supervisee characteristics, and characteristics of clinical supervision sessions. There were also two open field questions on the Manchester Clinical Supervision Scale-26 (model of clinical supervision used and any other comments about experience of peer group clinical supervision). The main question about participants’ experiences with peer clinical supervision was “What was your experience of peer clinical supervision?” This was gathered through individual semi-structured interviews lasting between 20 and 45 min, in March/April 2022 (Supplementary file 3 ).

Ethical considerations

Health service institutional review boards of two University hospitals approved this study (Ref: 091/19 and Ref: C.A. 2199). Participants were recruited after receiving a full explanation of the study’s purpose and procedure and all relevant information. Participants were aware of potential risks and benefits and could withdraw from the study, or the survey could be stopped at any time. Informed consent was recorded, and participant identities were protected by using a pseudonym to protect anonymity.

Data analysis method

Survey data was analysed using the data analysis software package Statistical Package for the Social Sciences, version 26 (SPSS Inc., Chicago, Il, USA). Descriptive analysis was undertaken to summarise responses to all items and categorical variables (nominal and ordinal) were analysed using frequencies to detail the number and percentage of responses to each question. Scores on the Manchester Clinical Supervision Scale-26 were reverse scored for 9 items (Q1-Q6, Q8, Q20,21) and total scores for each of the six sub-scales were calculated by adding the scores for each item. Raw scores for the individual sub-scales varied in range from 0 to 20 and these raw scores were then converted to percentages which were used in addition to the raw scores for each sub-scale to describe and summarise the results of the Manchester Clinical Supervision Scale-26. Cronbach’s alpha coefficient was undertaken with the 26 questions included within the Manchester Clinical Supervision Scale-26 and more importantly with each of the dimensions in the Manchester Clinical Supervision Scale-26. The open-ended questions on the Manchester Clinical Supervision Scale-26 and interviews were analysed using content analysis guided by Colorafi and Evans [ 22 ] and categories were generated using their eight steps, (1) creating a coding framework, (2) adding codes and memos, (3) applying the first level of coding, (4) categorising codes and applying the second level of coding, (5) revising and redefining the codes, (6) adding memos, (7) visualising data and (8) representing the data.

Research rigour

To ensure the validity and rigour of this study the researchers utilised the Manchester Clinical Supervision Scale-26 a recognised clinical supervision tool with good reliability and wide usage. Interviews were recorded, transcribed, and verified by four participants, data were collected until no new components appeared, data collection methods and analysis procedures were described, and the authors’ biases were minimised throughout the research process. The Manchester Clinical Supervision Scale-26 instrument internal consistency reliability was assessed which was overall good (α = 0.878) with individual subscale also good e.g., normative domain 0.765, restorative domain 0.864, and formative domain 0.900. Reporting rigour was demonstrated using the Consensus-Based Checklist for Reporting of Survey Studies guidelines [ 19 ] and Standards for Reporting Qualitative Research guidelines [ 20 ].

Quantitative data

Participant and clinical supervision characteristics.

Thirty-six of the fifty-two (69.2%) peer group clinical supervisors working across a particular region of Ireland responded to the Manchester Clinical Supervision Scale-26 survey online via Qualtrics. Table 1 identifies the demographics of the sample who were predominantly female (94.4%) with a mean age of 44.7 years (SD. 7.63).

Peer group clinical supervision session characteristics (Table  2 ) highlight over half of peer group clinical supervisors ( n  = 20, 55.6%) had been delivering peer group clinical supervision for less than one year and were mainly delivered to female supervisees ( n  = 28, 77.8%). Most peer group clinical supervision sessions took place monthly ( n  = 32, 88.9%) for 31–60 min ( n  = 27, 75%).

Manchester Clinical Supervision Scale-26 results

Participants generally viewed peer group clinical supervision as effective (Table  3 ), the total mean Manchester Clinical Supervision Scale-26 score among all peer group clinical supervisors was 76.47 (SD. 12.801) out of 104, Surpassing the clinical supervision threshold score of 73, which was established by the developers of the Manchester Clinical Supervision Scale-26 as the benchmark indicating proficient clinical supervision provision [ 21 ]. Of the three domains; normative, formative, and restorative, the restorative domain scored the highest (mean 28.56, SD. 6.67). The mean scores compare favourably to that of the Manchester Clinical Supervision Scale-26 benchmark data and suggest that the peer group clinical supervisors were satisfied with both the level of support, encouragement, and guidance they provided and the level of trust/rapport they had developed during the peer group clinical supervision sessions. 83.3% ( n  = 30) of peer group clinical supervisors reported being either very satisfied ( n  = 12, 33.3%) or moderately satisfied ( n  = 18, 50%) with the peer group clinical supervision they currently delivered. Within the peer group clinical supervisor’s supervisee related issues ( n  = 17, 47.2%), work environment-related issues ( n  = 16, 44.4%), staff-related issues ( n  = 15, 41.7%) were reported as the most frequent issues, with patient/client related issues being less frequent ( n  = 8, 22.2%). The most identified model used to facilitate peer group clinical supervision was the Proctors model ( n  = 8, 22.22%), which was followed by group ( n  = 2, 5.55%), peer ( n  = 2, 5.55%), and a combination of the seven-eyed model of clinical supervision and Proctors model ( n  = 1, 2.77%) with some not sure what model they used ( n  = 2, 5.553%) and 58.33% ( n  = 21) did not report what model they used.

Survey open-ended question

‘Please enter any additional comments , which are related to your current experience of delivering Peer Group Clinical Supervision.’ There were 22 response comments to this question, which represented 61.1% of the 36 survey respondents, which were analysed using content analysis guided by Colorafi & Evans [ 22 ]. Three categories were generated. These included: personal value/benefit of peer group clinical supervision, challenges with facilitating peer group clinical supervision, and new to peer group clinical supervision.

The first category ‘personal value/benefit of peer group clinical supervision’ highlighted positive experiences of both receiving and providing peer group clinical supervision. Peer group clinical supervisors reported that they enjoyed the sessions and found them both worthwhile and beneficial for both the group and them as peer group clinical supervisors in terms of creating a trusted supportive group environment and motivation to develop. Peer group clinical supervision was highlighted as very important for the peer group clinical supervisors working lives and they hoped that there would be more uptake from all staff. One peer group clinical supervisor expressed that external clinical supervision was a ‘lifeline’ to shaping their supervisory journey to date.

The second category ‘challenges with facilitating peer group clinical supervision’, identified time constraints, lack of buy-in/support from management, staff shortages, lack of commitment by supervisees, and COVID-19 pandemic restrictions and related sick leave, as potential barriers to facilitating peer group clinical supervision. COVID-19 was perceived to have a negative impact on peer group clinical supervision sessions due to staff shortages, which resulted in difficulties for supervisees attending the sessions during work time. Peer group clinical supervisors felt that peer group clinical supervision was not supported by management and there was limited ‘buy-in’ at times. There was also a feeling expressed that peer group clinical supervision was in its infancy, as COVID-19 and its related restrictions impacted on this by either slowing down the process of commencing peer group clinical supervision in certain areas or having to move online. However, more recently improvements in managerial support and supervisee engagement with the peer group clinical supervision process are noted.

The final category ‘new to peer group clinical supervision’ highlighted that some peer group clinical supervisors were new to the process of providing peer group clinical supervision and some felt that this survey was not a true reflection of their experience of delivering peer group clinical supervision, as they were not fully established yet as clinical supervisors due to the impact of COVID-19. Peer group clinical supervisors identified that while they were new to providing peer group clinical supervision, they were enjoying it and that it was a learning curve for them.

Qualitative data

The qualitative phase explored peer group clinical supervisors’ ( n  = 10) own experiences of preparation received and experiences of being a peer group clinical supervisor. Three themes were identified through data analysis, building the foundations, enacting engagement and actions, and realities (Table  4 ).

Building the foundations

This theme highlights the importance of prior knowledge, awareness, and training but also the recruitment process and education in preparing peer group clinical supervisors.

Knowledge and awareness

Participant’s prior knowledge and awareness of peer group clinical supervision was mixed with some reporting having little or no knowledge of clinical supervision.

I’m 20 years plus trained as a nurse , and I had no awareness of clinical supervision beforehand , I really hadn’t got a clue what all of this was about , so it was a very new concept to me (Bernie) .

Others were excited about peer group clinical supervision and while they could see the need they were aware that there may be limited awareness of the value and process of clinical supervision among peers.

I find that there’s great enthusiasm and passion for clinical supervision as it’s a great support mechanism for staff in practice , however , there’s a lack of awareness of clinical supervision (Jane) .

Recruitment

Some participants highlighted that the recruitment process to become a peer group clinical supervisor was vague in some organisations with an unclear and non-transparent process evident where people were chosen by the organisation’s management rather than self-selecting interested parties.

It was just the way the training was put to the people , they were kind of nominated and told they were going and there was a lot of upset over that , so they ended up in some not going at all (Ailbhe) .

In addition, the recruitment process was seen as top loaded where senior grades of staff were chosen, and this limited staff nurse grade opportunities where there was a clear need for peer group clinical supervisors and support.

We haven’t got down to the ground level like you know we’ve done the directors , we’ve done the CNM3s the CNM2s we are at the CNM1s , so we need to get down to the staff nurse level so the nurses at the direct frontline are left out and aren’t receiving supervision because we don’t have them trained (Bernie) .

Training and education

Participants valued the training and education provided but there was a clear sense of ‘imposter syndrome’ for some peer group clinical supervisors starting out. Participants questioned their qualifications, training duration, and confidence to undertake the role of peer group clinical supervisor.

Because it is group supervision and I know that you know they say that we are qualified to do supervision and you know we’re now qualified clinical supervisors but I’m not sure that a three-month module qualifies you to be at the top of your game (Maria) .

Participants when engaged in the peer group clinical supervisor educational programme did find it beneficial and the true benefit was the actual re-engagement in education and published evidence along with the mix of nursing and midwifery practice areas.

I found it very beneficial , I mean I hadn’t been engaged in education here in a while , so it was great to be back in that field and you know with the literature that’s big (Claire) .

Enacting engagement and actions

This theme highlights the importance of forming the groups, getting a clear message out, setting the scene, and grounding the group.

Forming the groups

Recruitment for the group was of key importance to the peer group clinical supervisor and they all sent out a general invitation to form their group. Some supervisors used invitation letters or posters in addition to a general email and this was effective in recruiting supervisees.

You’re reaching out to people , I linked in with the ADoN and I put together a poster and circulated that I wasn’t ‘cherry picking , and I set up a meeting through Webex so people could get a sense of what it was if they were on the fence about it or unsure if it was for them (Karen) .

In forming the peer clinical supervision groups consideration needs to be given to the actual number of supervisees and participants reported four to six supervisees as ideal but that number can alter due to attendance.

The ideal is having five or six consistent people and that they all come on board and that you get the dynamics of the group and everything working (Claire) .

Getting a clear message out

Within the recruitment process, it was evident that there was a limited and often misguided understanding or perception of peer group clinical supervision.

Greater awareness of what actually clinical supervision is , people misjudge it as a supervision where someone is appraising you , when in fact it is more of a support mechanism , I think peer support is the key element that needs to be brought out (Jane) .

Given the lack of clarity and understanding regarding peer group clinical supervision, the participants felt strongly that further clarity is needed and that the focus needs to be on the support it offers to self, practice, and the profession.

Clinical supervision to me is clinical leadership (Jane) .

Setting the scene and grounding the group

In the initial phase of the group coming together the aspect of setting the scene and grounding the group was seen as important. A key aspect of this process was establishing the ground rules which not only set the boundaries and gave structure but also ensured the adoption of principles of trust, confidentiality, and safety.

We start with the ground rules , they give us structure it’s our contract setting out the commitment the expectation for us all , and the confidentiality as that’s so important to the trust and safety and building the relationships (Brid) .

Awareness of group dynamics is important in this process along with awareness of the group members (supervisees) as to their role and expectations.

I reiterate the role of each person in relation to confidentiality and the relationship that they would have with each other within the group and the group is very much aware that it is based on respect for each person’s point of view people may have a fear of contributing to the group and setting the ground rules is important (Jane) .

To ground the group, peer group clinical supervisors saw the importance of being present and allowing oneself to be in the room. This was evident in the time allocated at the start of each session to allow ‘grounding’ to occur in the form of techniques such as a short meditation, relaxation, or deep breathing.

At the start , I do a bit of relaxation and deep breathing , and I saw that with our own external supervisor how she settled us into place so very much about connecting with your body and you’ve arrived , then always come in with the contract in my first sentence , remember today you know we’re in a confidential space , of course , you can take away information , but the only information you will take from today is your own information and then the respect aspect (Mary Rose) .

This settling in and grounding was seen as necessary for people to feel comfortable and engage in the peer group clinical supervision process where they could focus, be open, converse, and be aware of their role and the role of peer group clinical supervision.

People have to be open, open about their practice and be willing to learn and this can only occur by sharing, clinical supervision gives us the space to do it in a space where we know we will be respected, and we can trust (Claire) .

This theme highlights the importance of the peer group clinical supervisors’ past experiences, delivering peer group clinical supervision sessions, responding to COVID-19, personal and professional development, and future opportunities.

Past experiences

Past experiences of peer group clinical supervisors were not always positive and for one participant this related to the lack of ground rules or focus of the sessions and the fact it was facilitated by a non-nurse.

In the past , I suppose I would have found it very frustrating as a participant because I just found that it was going round in circles , people moaning and you know it wasn’t very solution focused so I came from my situation where I was very frustrated with clinical supervision , it was facilitated by somebody that was non-nursing then it wasn’t very , there wasn’t the ground rules , it was very loose (Caroline) .

However, many did not have prior experience of peer group clinical supervision. Nonetheless, through the education and preparation received, there was a sense of commitment to embrace the concept, practice, and philosophy.

I did not really have any exposure or really much information on clinical supervision , but it has opened my eyes , and as one might say I am now a believer (Brid) .

Delivering peer group clinical supervision

In delivering peer group clinical supervision, participants felt supervisees were wary, as they did not know what peer group clinical supervision was, and they had focused more on the word supervision which was misleading to them. Nonetheless, the process was challenging, and buy-in was questioned at an individual and managerial level.

Buy-in wasn’t great I think now of course people will blame the pandemic , but this all happened before the pandemic , there didn’t seem to be you know , the same support from management that I would have expected so I kind of understood it in a way because then there wasn’t the same real respect from the practitioners either (Mary Rose) .

From the peer group clinical supervisor’s perspective, they were all novices in delivering/facilitating peer group clinical supervision sessions, and the support of the external clinical supervisors, and their own peer group clinical supervision sessions were invaluable along with a clinical supervision model.

Having supervision myself was key and something that is vital and needed , we all need to look at our practice and how we work it’s no good just facilitating others without being part of the process yourself but for me I would say the three principles of clinical supervision , you know the normative , formative and restorative , I keep hammering that home and bring that in regularly and revisit the contract and I have to do that often you know (Claire) .

All peer group clinical supervisors commented on the preparation for their peer group clinical supervision sessions and the importance of them having the right frame of mind and that often they needed to read over their course work and published evidence.

I want everybody to have a shared voice and you know that if one person , there is something that somebody feels very strongly and wants to talk about it that they e-mail in advance like we don’t have a set agenda but that’s agreed from the participant at the start (Caroline) .

To assist this, the peer group clinical supervisors noted the importance of their own peer group clinical supervision, the support of their peers, and external clinical supervisors. This preparation in an unpredictable situation can be difficult but drawing on one’s experience and the experience within the group can assist in navigating beyond unexpected situations.

I utilise the models of clinical supervision and this helps guide me , I am more of a facilitator of the group we are experts in our own area and our own role but you can only be an expert if you take the time to examine your practice and how you operate in your role (Brid) .

All clinical supervisors noted that the early sessions can be superficial, and the focus can be on other practice or management issues, but as time moves on and people become more engaged and involved it becomes easier as their understanding of supervision becomes clearer. In addition, there may be hesitancy and people may have difficulty opening up with certain people in the group and this is a reality that can put people off.

Initially there was so much managerial bashing and I think through supervision , I began to kind of think , I need the pillars of supervision , the governance , bringing more knowledge and it shifted everything in the room , trying to marry it with all the tensions that people have (Mary Rose) .

For some clinical supervisors, there were expected and unexpected challenges for them as clinical supervisors in terms of the discussions veering off course and expectations of their own ability.

The other big challenge is when they go off , how do you bring him back , you know when they veer off and you’re expected to be a peer , but you have to try and recoil that you have to get the balance with that right (Mary Rose) .

While peer group clinical supervision is accepted and seen as a valuable process by the peer group clinical supervisors, facilitating peer group supervision with people known to you can be difficult and may affect the process.

I’d love to supervise a group where I actually don’t know the people , I don’t know the dynamics within the group , and I’d love to see what it would be like in a group (Bernie) .

Of concern to clinical supervisors was the aspect of non-attendance and while there may be valid reasons such as COVID-19 the absence of a supervisee for several sessions can affect the group dynamics, especially if the supervisee has only engaged with early group sessions.

One of the ones that couldn’t attend because of COVID and whatever , but she’s coming to the next one and I just feel there’s a lot of issues in her area and I suppose I’m mindful that I don’t want that sort of thing to seep in , so I suppose it’s just for me just to keep reiterating the ground rules and the boundaries , that’s something I just have to manage as a facilitator , but what if they don’t attend how far will the group have progressed before she attends (Caroline) .

Responding to COVID-19

The advent of COVID-19 forced peer group clinical supervisors to find alternative means of providing peer group clinical supervision sessions which saw the move from face-to-face to online sessions. The online transition was seen as seamless for many established groups while others struggled to deliver sessions.

With COVID we did online for us it was fine because we were already formed (Corina) .

While the transition may have been positive many clinical supervisors came across issues because they were using an online format that would not be present in the face-to-face session.

We did have a session where somebody was in the main office and they have a really loud booming voice and they were saying stuff that was not appropriate to say outside of clinical supervision and I was like are you in the office can you lower it down a bit can you put your headphones on (Maria) .

However, two peer group clinical supervisors ceased or hasted the progress of rolling out peer group clinical supervision sessions mainly due to redeployment and staff availability.

With COVID it just had to be canceled here , it’s just the whole thing was canceled so it was very , very difficult for people (Mary Rose) .

It was clear from clinical supervisors that online sessions were appropriate but that they felt they were only appropriate for existing established groups that have had the opportunity to build relationships, develop trust, embed the ground rules, and create the space for open communication and once established a combined approach would be appropriate.

Since we weren’t as established as a group , not everybody knew each other it would be difficult to establish that so we would hold off/reschedule , obviously COVID is a major one but also I suppose if you have an established group now , and again , you could go to a remote one , but I felt like since we weren’t established as a group it would be difficult to develop it in that way (Karen) .

Within practice COVID-19 took priority and other aspects such as peer group clinical supervision moved lower down on the priority list for managers but not for the clinical supervisors even where redeployment occurred.

With COVID all the practical side , if one of the managers is dealing with an outbreak , they won’t be attending clinical supervision , because that has to be prioritised , whereas we’ve prioritised clinical supervision (Maria) .

The valuing of peer group clinical supervision was seen as important by clinical supervisors, and they saw it as particularly needed during COVID-19 as staff were dealing with many personal and professional issues.

During the height of COVID , we had to take a bit of a break for four months as things were so demanding at work for people but then I realised that clinical supervision was needed and started back up and they all wanted to come back (Brid) .

Having peer group clinical supervision during COVID-19 supported staff and enabled the group to form supportive relationships.

COVID has impacted over the last two years in every shape and they needed the supervision and the opportunity to have a safe supportive space and it gelled the group I think as we all were there for each other (Claire) .

While COVID-19 posed many challenges it also afforded clinical supervisors and supervisees the opportunity for change and to consider alternative means of running peer group clinical supervision sessions. This change resulted in online delivery and in reflecting on both forms of delivery (face-to-face and online) clinical supervisors saw the benefit in both. Face-to-face was seen as being needed to form the group and then the group could move online once the group was established with an occasional periodic face-to-face session to maintain motivation commitment and reinforce relationships and support.

Online formats can be effective if the group is already established or the group has gone through the storming and forming phase and the ground rules have been set and trust built , then I don’t see any problem with a blended online version of clinical supervision , and I think it will be effective (Jane) .

Personal and professional development

Growth and development were evident from peer group clinical supervisors’ experiences and this growth and development occurred at a personal, professional, and patient/client level. This development also produced an awakening and valuing of one’s passion for self and their profession.

I suppose clinical supervision is about development I can see a lot of development for me and my supervisees , you know personally and professionally , it’s the support really , clinical supervision can reinvigorate it’s very exciting and a great opportunity for nursing to support each other and in care provision (Claire) .

A key to the peer group clinical supervisor’s development was the aspect of transferable skills and the confidence they gained in fulfilling their role.

All of these skills that you learn are transferable and I am a better manager because of clinical supervision (Maria) .

The confidence and skills gained translated into the clinical supervisor’s own practice as a clinical practitioner and clinical supervisor but they were also realistic in predicting the impact on others.

I have empowered my staff , I empower them to use their voice and I give my supervisees a voice and hope they take that with them (Corina) .

Fundamental to the development process was the impact on care itself and while this cannot always be measured or identified, the clinical supervisors could see that care and support of the individual practitioner (supervisee) translated into better care for the patient/client.

Care is only as good as the person delivering it and what they know , how they function and what energy and passion they have , and clinical supervision gives the person support to begin to understand their practice and how and why they do things in a certain way and when they do that they can begin to question and even change their way of doing something (Brid) .

Future opportunities

Based on the clinical supervisor’s experiences there was a clear need identified regarding valuing and embedded peer group clinical supervision within nursing/midwifery practice.

There has to be an emphasis placed on supervision it needs to be part of the fabric of a service and valued by all in that service , we should be asking why is it not available if it’s not there but there is some work first on promoting it and people knowing what it actually is and address the misconceptions (Claire) .

While such valuing and buy-in are important, it is not to say that all staff need to have peer group clinical supervision so as to allow for personal choice. In addition, to value peer group clinical supervision it needs to be evident across all staffing grades and one could question where the best starting point is.

While we should not mandate that all staff do clinical supervision it should become embedded within practice more and I suppose really to become part of our custom and practice and be across all levels of staff (Brid) .

When peer group clinical supervision is embedded within practice then it should be custom and practice, where it is included in all staff orientations and is nationally driven.

I suppose we need to be driving it forward at the coal face at induction , at orientation and any development for the future will have to be driven by the NMPDUs or nationally (Ailbhe) .

A formalised process needs to address the release of peer group clinical supervisors but also the necessity to consider the number of peer group clinical supervisors at a particular grade.

The issue is release and the timeframe as they have a group but they also have their external supervision so you have to really work out how much time you’re talking about (Maria) .

Vital within the process of peer group clinical supervision is receiving peer group clinical supervision and peer support and this needs to underpin good peer group clinical supervision practice.

Receiving peer group supervision helps me , there are times where I would doubt myself , it’s good to have the other group that I can go to and put it out there to my own group and say , look at this , this is what we did , or this is what came up and this is how (Bernie) .

For future roll out to staff nurse/midwife grade resourcing needs to be considered as peer group clinical supervisors who were managers could see the impact of having several peer group clinical supervisors in their practice area may have on care delivery.

Facilitating groups is an issue and needs to be looked at in terms of the bigger picture because while I might be able to do a second group the question is how I would be supported and released to do so (Maria) .

While there was ambiguity regarding peer group clinical supervision there was an awareness of other disciplines availing of peer group clinical supervision, raising questions about the equality of supports available for all disciplines.

I always heard other disciplines like social workers would always have been very good saying I can’t meet you I have supervision that day and I used to think my God what’s this fabulous hour that these disciplines are getting and as a nursing staff it just wasn’t there and available (Bernie) .

To address this equity issue and the aspect of low numbers of certain grades an interdisciplinary approach within nursing and midwifery could be used or a broader interdisciplinary approach across all healthcare professionals. An interdisciplinary or across-services approach was seen as potentially fruitful.

I think the value of interprofessional or interdisciplinary learning is key it addresses problem-solving from different perspectives that mix within the group is important for cross-fertilisation and embedding the learning and developing the experience for each participant within the group (Jane) .

As we move beyond COVID-19 and into the future there is a need to actively promote peer group clinical supervision and this would clarify what peer group clinical supervision actually is, its uptake and stimulate interest.

I’d say it’s like promoting vaccinations if you could do a roadshow with people , I think that would be very beneficial , and to launch it , like you have a launch an official launch behind it (Mary Rose) .

The advantages of peer group clinical supervision highlighted in this study pertain to self-enhancement (confidence, leadership, personal development, resilience), organisational and service-related aspects (positive work environment, staff retention, safety), and professional patient care (critical thinking and evaluation, patient safety, adherence to quality standards, elevated care standards). These findings align with broader literature that acknowledges various areas, including self-confidence and facilitation [ 23 ], leadership [ 24 ], personal development [ 25 ], resilience [ 26 ], positive/supportive working environment [ 27 ], staff retention [ 28 ], sense of safety [ 29 ], critical thinking and evaluation [ 30 ], patient safety [ 31 ], quality standards [ 32 ] and increased standards of care [ 33 ].

In this study, peer group clinical supervision appeared to contribute to the alleviation of stress and anxiety. Participants recognised the significance of these sessions, where they could openly discuss and reflect on professional situations both emotionally and rationally. Central to these discussions was the creation of a safe, trustworthy, and collegial environment, aligning with evidence in the literature [ 34 ]. Clinical supervision provided a platform to share resources (information, knowledge, and skills) and address issues while offering mutual support [ 35 ]. The emergence of COVID-19 has stressed the significance of peer group clinical supervision and support for the nursing/midwifery workforce [ 36 ], highlighting the need to help nurses/midwifes preserve their well-being and participate in collaborative problem-solving. COVID-19 impacted and disrupted clinical supervision frequency, duration and access [ 37 ]. What was evident during COVID-19 was the stress and need for support for staff and given the restorative or supportive functions of clinical supervision it is a mechanism of support. However, clinical supervisors need support themselves to be able to better meet the supervisee’s needs [ 38 ].

The value of peer group clinical supervision in nurturing a conducive working environment cannot be overstated, as it indorses the understanding and adherence to workplace policies by empowering supervisees to understand the importance and rationale behind these policies [ 39 ]. This becomes vital in a continuously changing healthcare landscape, where guidelines and policies may be subject to change, especially in response to situations such as COVID-19. In an era characterised by international workforce mobility and a shortage of healthcare professionals, a supportive and positive working environment through the provision of peer group clinical supervision can positively influence staff retention [ 40 ], enhance job satisfaction [ 41 ], and mitigate burnout [ 42 ]. A critical aspect of the peer group clinical supervision process concerns providing staff the opportunity to reflect, step back, problem-solve and generate solutions. This, in turn, ensures critical thinking and evaluation within clinical supervision, focusing on understanding the issues and context, and problem-solving to draw constructive lessons for the future [ 30 ]. Research has determined a link between clinical supervision and improvements in the quality and standards of care [ 31 ]. Therefore, peer group clinical supervision plays a critical role in enhancing patient safety by nurturing improved communication among staff, facilitating reflection, promoting greater self-awareness, promoting the exchange of ideas, problem-solving, and facilitating collective learning from shared experiences.

Starting a group arose as a foundational aspect emphasised in this study. The creation of the environment through establishing ground rules, building relationships, fostering trust, displaying respect, and upholding confidentiality was evident. Vital to this process is the recruitment of clinical supervisees and deciding the suitable group size, with a specific emphasis on addressing individuals’ inclination to engage, their knowledge and understanding of peer group clinical supervision, and dissipating any lack of awareness or misconceptions regarding peer group supervision. Furthermore, the educational training of peer group clinical supervisors and the support from external clinical supervisors played a vital role in the rollout and formation of peer group clinical supervision. The evidence stresses the significance of an open and safe environment, wherein supervisees feel secure and trust their supervisor. In such an environment, they can effectively reflect on practice and related issues [ 41 ]. This study emphasises that the effectiveness of peer group supervision is more influenced by the process than the content. Clinical supervisors utilised the process to structure their sessions, fostering energy and interest to support their peers and cultivate new insights. For peer group clinical supervision to be effective, regularity is essential. Meetings should be scheduled in advance, allocate protected time, and take place in a private space [ 35 ]. While it is widely acknowledged that clinical supervisors need to be experts in their professional field to be credible, this study highlights that the crucial aspects of supervision lie in the quality of the relationship with the supervisor. The clinical supervisor should be supportive, caring, open, collaborative, sensitive, flexible, helpful, non-judgmental, and focused on tacit knowledge, experiential learning, and providing real-time feedback.

Critical to the success of peer group clinical supervision is the endorsement and support from management, considering the organisational culture and attitudes towards the practice of clinical supervision as an essential factor [ 43 ]. This support and buy-in are necessary at both the management and individual levels [ 28 ]. The primary obstacles to effective supervision often revolve around a lack of time and heavy workloads [ 44 ]. Clinical supervisors frequently struggle to find time amidst busy environments, impacting the flexibility and quality of the sessions [ 45 ]. Time constraints also limit the opportunity for reflection within clinical supervision sessions, leaving supervisees feeling compelled to resolve issues on their own without adequate support [ 45 ]. Nevertheless, time-related challenges are not unexpected, prompting a crucial question about the value placed on clinical supervision and its integration into the culture and fabric of the organisation or profession to make it a customary practice. Learning from experiences like those during the COVID-19 pandemic has introduced alternative ways of working, and the use of technology (such as Zoom, Microsoft Teams, Skype) may serve as a means to address time, resource, and travel issues associated with clinical supervision.

Despite clinical supervision having a long international history, persistent misconceptions require attention. Some of these include not considering clinical supervision a priority [ 46 ], perceiving it as a luxury [ 41 ], deeming it self-indulgent [ 47 ], or viewing it as mere casual conversation during work hours [ 48 ]. A significant challenge lies in the lack of a shared understanding regarding the role and purpose of clinical supervision, with past perceptions associating it with surveillance and being monitored [ 48 ]. These negative connotations often result in a lack of engagement [ 41 ]. Without encouragement and recognition of the importance of clinical supervision from management or the organisation, it is unlikely to become embedded in the organisational culture, impeding its normalisation [ 39 ].

In this study, some peer group clinical supervisors expressed feelings of being impostors and believed they lacked the knowledge, skills, and training to effectively fulfil their roles. While a deficiency in skills and competence are possible obstacles to providing effective clinical supervision [ 49 ], the peer group clinical supervisors in this study did not report such issues. Instead, their concerns were more about questioning their ability to function in the role of a peer group clinical supervisor, especially after a brief training program. The literature acknowledges a lack of training where clinical supervisors may feel unprepared and ill-equipped for their role [ 41 ]. To address these challenges, clinical supervisors need to be well-versed in professional guidelines and ethical standards, have clear roles, and understand the scope of practice and responsibilities associated with being a clinical supervisor [ 41 ].

The support provided by external clinical supervisors and the peer group clinical supervision sessions played a pivotal role in helping peer group clinical supervisors ease into their roles, gain experiential learning, and enhance their facilitation skills within a supportive structure. Educating clinical supervisors is an investment, but it should not be a one-time occurrence. Ongoing external clinical supervision for clinical supervisors [ 50 ] and continuous professional development [ 51 ] are crucial, as they contribute to the likelihood of clinical supervisors remaining in their roles. However, it is important to interpret the results of this study with caution due to the small sample size in the survey. Generalising the study results should be approached with care, particularly as the study was limited to two regions in Ireland. However, the addition of qualitative data in this mixed-methods study may have helped offset this limitation.

This study highlights the numerous advantages of peer group clinical supervision at individual, service, organisational, and patient/client levels. Success hinges on addressing the initial lack of awareness and misconceptions about peer group clinical supervision by creating the right environment and establishing ground rules. To unlock the full potential of peer group clinical supervision, it is imperative to secure management and organisational support for staff release. More crucially, there is a need for valuing and integrating peer group clinical supervision into nursing and midwifery education and practice. Making peer group clinical supervision accessible to all grades of nurses and midwives across various healthcare services is essential, necessitating strategic planning to tackle capacity and sustainability challenges.

Data availability

Data are available from the corresponding author upon request owing to privacy or ethical restrictions.

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Acknowledgements

The research team would like to thank all participants for their collaboration, the HSE steering group members and Carmel Hoey, NMPDU Director, HSE West Mid West, Dr Patrick Glackin, NMPD Area Director, HSE West, Annette Cuddy, Director, Centre of Nurse and Midwifery Education Mayo/Roscommon; Ms Ruth Hoban, Assistant Director of Nursing and Midwifery (Prescribing), HSE West; Ms Annette Connolly, NMPD Officer, NMPDU HSE West Mid West.

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Owen Doody, Kathleen Markey, Claire O. Donnell & Louise Murphy

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Doody, O., Markey, K., Turner, J. et al. Clinical supervisor’s experiences of peer group clinical supervision during COVID-19: a mixed methods study. BMC Nurs 23 , 612 (2024). https://doi.org/10.1186/s12912-024-02283-3

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Developing community-based physical activity interventions and recreational programming for children in rural and smaller urban centres: a qualitative exploration of service provider and parent experiences

• Emma Ostermeier 1 ,
• Jason Gilliland 2 , 3 , 4 , 5 , 6 , 7 ,
• Jennifer D. Irwin 7 ,
• Jamie A. Seabrook 3 , 4 , 5 , 6 , 8 &
• Patricia Tucker 5 , 6 , 9  

BMC Health Services Research volume  24 , Article number:  1017 ( 2024 ) Cite this article

Metrics details

Children’s physical inactivity is a persisting international public health concern. While there is a large body of literature examining physical activity interventions for children, the unique physical activity context of low-density communities in rural areas and smaller urban centres remains largely underexplored. With an influx of families migrating to rural communities and small towns, evaluations of health promotion efforts that support physical activity are needed to ensure they are meeting the needs of the growing populations in these settings. The aim of this community-based research was to explore service providers’ and parents’ perspectives on physical activity opportunities available in their community and recommendations toward the development and implementation of efficacious physical activity programming for children in rural communities and smaller urban centres.

Three in-person community forums with recreation service providers ( n  = 37 participants) and 1 online community forum with the parents of school-aged children ( n  = 9 participants) were hosted. An online survey and Mentimeter activity were conducted prior to the community forums to gather participants’ views on the barriers and facilitators to physical activities and suggestions for activity-promoting programs. The service provider and parent discussions were audio-recorded, transcribed verbatim, and analyzed following a deductive approach guided by Hseih and Shannon’s (2005) procedure for direct content analysis. A code list developed from the responses to the pre-forum survey and Mentimeter activity was used to guide the analysis and category development.

Seven distinct categories related to the existing physical activity opportunities and recommendations for programs in rural communities and smaller urban centres were identified during the analysis: (1) Recovery from Pandemic-Related Measures, (2) Knowledge and Access to Programs, (3) Availability, (4) Personnel Support, (5) Quality of Programs and Facilities, (6) Expenses and Subsidies, and (7) Inclusivity and Preferences.

To improve the health and well-being of children who reside in low-density areas, the results of this study highlight service provider and parent recommendations when developing and implementing community-based physical activity programs and interventions in rural and smaller urban settings, including skill development programs, non-competitive activity options, maximizing existing spaces for activities, and financial support.

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Physical activity is an important behaviour for children’s development, health, and well-being [ 1 ]. The World Health Organization’s guidelines for physical activity and sedentary behaviour recommend that children 5–17 years of age accumulate an average of 60 min of daily moderate-to-vigorous physical activity to attain physical, mental, and cognitive health benefits, including improved quality of life [ 2 ]; however, most children are not meeting the recommendations [ 3 , 4 ]. The high rates of physical inactivity have been further exacerbated by the COVID-19 pandemic, with the literature reporting considerable declines in children’s physical activity during stay-at-home orders [ 5 ] and activity levels remaining low following the reopening of recreational facilities [ 6 ]. In Canada, only 28% of children aged 5 to 17 years met the recommended amount of physical activity during the early years of the pandemic [ 7 ], an 11% decrease from the reported activity levels prior to the pandemic [ 8 ]. This is particularly troubling as sedentary lifestyles during childhood can cultivate unhealthy habits that will continue as they transition into adolescence [ 9 ] and persist into adulthood [ 10 ]. To help engage children in more physical activity as the public health precautions were lifted, parents highlighted the need for a variety of accessible, affordable programs that offered children the opportunity to be active outside of school [ 11 , 12 ]. Therefore, tailored and feasible health promotion interventions and initiatives are essential in preventing the persistent rise in physical inactivity.

Although there has been increasing support for interventions to promote physical activity in children, low-density areas – including dispersed rural communities (i.e., rural areas with a low population density and low population size), villages (i.e., small, semi-dense, rural settlements with a small population size), and smaller urban centres (i.e., semi-dense areas with a moderate population size) – have been underexplored [ 13 , 14 , 15 ], even though thinly populated communities have higher rates of obesity, chronic conditions (e.g., asthma and developmental delays) and mortality among children [ 16 , 17 ]. Due to the lower densities of development in rural and smaller urban settings, children in these areas commonly experience issues related to limited local resources and program options, reduced access to health-related services, and greater need for vehicular transportation to activities [ 18 , 19 ]. With the recent rise in migration of Canadians to rural areas [ 20 ], finding ways to help children from smaller communities overcome the barriers to physical activity participation is valuable. As Canada has the fastest-growing rural communities of the G7 countries [ 20 ], it can serve as an ideal location for additional research on children’s physical activity in less densely populated settings.

The Grade 5 ACT-i-Pass Program is a community-based physical activity intervention originally developed for London, Ontario, Canada that offers children in grade 5 free organized and drop-in activities at participating recreational facilities for the school year [ 21 ]. As previous evaluations of the program have indicated that the pass improved children’s physical activity [ 22 ], expanding the program to additional communities may be a promising approach to address children’s low physical activity levels; therefore, plans for offering the program in the neighbouring rural and smaller urban areas are underway.

Despite community-based interventions having the potential to foster much-needed population-level changes in physical activity [ 23 ], the effective implementation and intended outputs of these programs are vulnerable to the context and can be hindered by a variety of complex individual, social, and environmental conditions [ 24 ]. Durlak and Dupre [ 25 ] suggest that understanding the factors that influence program uptake and adoption by a specific community can help close the gap between an evidence-based intervention plan and its effectiveness in a real-world context. Thus, prior to investing the funds necessary to scale-up this program to rural and smaller urban settings, the extent to which community members would find programs like the ACT-i-Pass suitable needs to be determined to ensure a tailored version of the program that is most likely to be used by the target population is offered.

As an initial step of the program development phase, a needs assessment provides context into the factors associated with children’s engagement in physical activity and service providers’ capacity to offer recreation programs [ 26 ]. Specifically, a multisector approach to physical activity promotion can improve the quality and implementation of interventions in real-world settings by allowing families and community organizations to advise on the development and design of interventions based on their experiences and knowledge of the area [ 27 ]. Gaining input from the target audience during the planning stages of interventions can be used to highlight strategies to address the various social and environmental factors that influence physical activity participation, help align components of interventions with the needs and preferences of the target audience, generate buy-in from the community, and incentivize organizations to promote and adopt programs [ 28 , 29 ]. Notably, studies have shown that multi-disciplinary collaborations that integrate partners during the design stage of interventions can lead to more effective and sustainable health promotion initiatives [ 29 , 30 , 31 ].

The aim of this study was to host discussions with service providers and parents in Oxford, Elgin and Middlesex Counties to understand their experiences with the physical activity opportunities available in rural communities and smaller urban centres and gather their recommendations toward the development and implementation of efficacious physical activity programming for children in dispersed, resource-limited areas. To achieve this aim, this study explored factors which positively or negatively influence children’s physical activity participation in rural communities and smaller urban centres. Moreover, this study gathered parents’ and service providers’ perspectives about the design and/or implementation of health promotion initiatives in their community, specifically, the ACT-i-Pass Program and physical activity interventions targeting children.

Study design

This naturally-unfolding experiment is part of a larger study exploring the adaptation, implementation, and evaluation of the Grade 5 ACT-i-Pass Program expansion. As a case study, this research focuses on a predominantly rural region in Southwestern Ontario, Canada. Oxford, Elgin, and Middlesex counties are made up of farmland, outdoor attractions including conservation areas and beaches, and a variety of smaller urban centres (i.e., towns and small cities) and rural settlements (i.e., villages and dispersed communities) with populations of 22,015, 17,030, and 83,160 children ages 0 to 14 years, respectively [ 32 , 33 , 34 ]. To achieve the aim of this study, we hosted community forums, a group information collection technique that empowers members of the target area to use their knowledge and lived experiences to identify community-level impacts of interventions and provide locally derived strategies that can support beneficial behaviour changes while minimizing potential harms [ 35 ]. This study protocol was approved by Western University’s Non-Medical Research Ethics Board (REB #103954).

Participants and recruitment

Service providers and parents were recruited to participate in this study. Service providers were identified through an online search of recreational facilities, which was reviewed for missing organizations with program partners at the two health units and the municipal governments that attend to the residents of Oxford, Elgin, and Middlesex Counties in an effort to produce a comprehensive list of potential participants. Identified service providers were contacted via email and phone and provided details about the community forum, including an overview of the study. Potential parent participants were identified via the ACT-i-Pass registration form. For year 1 of the expanded program, information was distributed earlier than previous program years, including early access to the registration form, as part of a promotional effort to inform families that the program was now available to children in the counties. An extended pre-program promotion timeline also offered the project team time to recruit parents for the community forums and integrate their feedback into the program design for the upcoming year. Of those who consented to be contacted about research activities, parents were emailed an invitation to participate in the community forum, which included a brief overview of the study and the pre-forum survey.

Service providers were defined as any business, organization or community group that works with children and their families in the counties. To be eligible to participate in this study, service providers had to: (1) offer programs related to physical activity or have mandates that aimed to improve the health and well-being of children (i.e., physical activity program providers, municipal recreation representatives, small business owners who offered activities for children, government employees from family service branches, health unit representatives, and not-for-profit organizations); (2) provide services for families in Oxford County, Elgin County (including the City of St. Thomas), or Middlesex County; (3) speak and understand English; and (4) provided written and oral consent to participate in the study and to be audio-recorded.

Parents were eligible to participate in a community forum if they were the parent or guardian of a grade 5 child(ren) in Oxford, Elgin or Middlesex County who enrolled their child in the ACT-i-Pass during the early registration stage and consented to participate in the research study.

Data collection

Pre-forum survey.

As part of the invitation email for the community forum, service providers and parents were asked to complete an online (via Qualtrics) pre-forum survey. The service provider survey gathered details about their organization, key barriers and facilitators to physical activity opportunities, and the extent to which community members would find the ACT-i-Pass program appropriate for children in their area. Parents were posed similar survey questions as service providers except the parent survey asked to provide socio-demographic information instead of organization details.

Mentimeter activity

Before the start of the community forum conversations, service providers and parents were asked to engage in a brainwriting activity using Mentimeter interactive presentation software ( https://www.mentimeter.com/ ). Brainwriting is a form of idea generation where participants silently and independently record their ideas [ 36 ]. As an alternative to collaborative group-sharing sessions, brainwriting can be an effective way to gain a greater variety of unique ideas by engaging more participants in an activity while minimizing group conflicts, social pressure to conform to the group, and dominance of a few participants’ perspectives [ 37 , 38 ]. Participants could provide an unlimited number of responses to two questions: (1) What are the factors that influence children’s physical activity participation?; and (2) What program components or strategies can lead to successful physical activity programs and interventions in your community? Service provider and parent responses to the Mentimeter activity and the pre-forum survey, including their frequency counts, were amalgamated into a single list.

Community forum discussions

In total, 4 community forums were hosted for service providers ( n  = 3 forums) and parents ( n  = 1 forum) in Spring 2023. Community forums were organized and hosted separately for parents and service providers to acquire the perspective of those trying to access the activities as well as those trying to develop and run programs. In-person community forums with service providers were hosted at local community centres and libraries. Separate community forums were offered in Oxford, Elgin, and Middlesex Counties to improve geographic accessibility. The agenda of the community forums was organized in two parts. The first hour of the forum served as a promotional event for the health units to educate and recruit organizations to the ACT-i-Pass Program. Following a short break, the second hour was a research effort conducted by the research team to gather perspectives from community stakeholders about the physical activity opportunities that exist in the area.

Parent community forums were planned to be in-person, but the research team experienced issues with geographic accessibility, scheduling conflicts, and commitments impacting attendance; consequently, parent community forums were hosted online via Microsoft Teams. Differing from the service provider agenda, the first half hour consisted of an overview of the ACT-i-Pass and a question and answer session, following an hour of discussion guided by the research team about the physical activity opportunities for children in their community. The perspectives of children were not collected for this study as their input will be most valuable after completing a year of the program. By collecting children’s perspectives once they have used the pass, they can offer the research team insight into their experiences and propose adaptations to the ACT-i-Pass design that can improve the quality of the program.

The discussions lasted between 50 and 75 min ( \(\bar x\) = 61 min). Two members of the research team attended each community forum. One member acted as the moderator for all community forum discussions to ensure consistency. The second member took notes to capture all key ideas and thoughts from the participants. Prior to the questions, participants were provided an overview of the topics being discussed and asked if they still consented to be recorded.

The community forum conversations followed a semi-structured interview guide (Additional Files 1 & 2) developed by the research team. The guides for service providers and parents consisted of 7 and 6 questions respectively and a series of prompts. The questions were related to the recreational spaces and activity options available in their community (i.e., What organizations in your community provide physical activity programming for children?), the characteristics of the community that positively or negatively influence physical activity participation (i.e., What characteristics of Oxford/Elgin/Middlesex would you describe as factors that positively or negatively influence children’s physical activity participation?), and the adoption of the community-based programs into their communities (i.e., Do you have any recommendations for the ACT-i-Pass as we begin offering activities in Oxford/Elgin/Middlesex?). Conversations with service providers and parents were audio-recorded and transcribed verbatim via Microsoft Streams. A member of the research team de-identified and reviewed the transcripts for accuracy.

Data analysis

All transcripts were imported into QSR NVivo 12 and analyzed following the steps outlined in Hseih and Shannon’s [ 39 ] procedure for direct content analysis. A deductive approach to the content analysis was deemed appropriate for this study as the responses generated during the pre-forum survey and Mentimeter activity offered a participant-directed list of codes related to children’s physical activity participation, recreation programs, and health promotion interventions in the 3 counties [ 40 ].

The analysis started with the preparation of the coding list by developing the initial coding categories. A list of 119 codes was derived from the service provider- and parent-generated responses in the pre-forum survey and Mentimeter activity. As similar words and terms were used to describe the same phenomena, the responses were refined into a universal term, resulting in 102 unique codes. Subsequently, the codes were grouped into initial categories based on key concepts and a definition for each category was generated. The initial categories were developed by members of the research team who attended the community forums as they had more in-depth knowledge of the data and the nuances associated with statements made by the participants [ 41 ]. An audit trail with a detailed record of the research process was developed to add trustworthiness to the findings [ 42 , 43 ]. The list of pre-determined categories and their definitions were reviewed by an auditor to increase their accuracy and relevance to the responses provided by community forum participants [ 39 ].

Two reviewers analyzed the transcripts independently and collaborated to identify the final categories. Using multiple reviewers during coding can add reliability to the findings and improve the quality of the analysis by introducing various perspectives and lived experiences that can produce a deep, thorough exploration of the data [ 44 ]. The researchers first reviewed the transcripts to familiarize themselves with the data and note any initial patterns or thoughts on the discussions. To isolate the nuances in the topics discussed during the service provider and parent discussions, the data were organized by adding attribute codes to each transcript to identify the study population (i.e., parents or service providers) and location (i.e., Oxford, Elgin, or Middlesex) [ 45 ]. The reviewers then went through the transcript a second time and coded categories using the pre-determined code list. As some factors could be perceived as beneficial or a hindrance in different circumstances, reviewers included a second code, when applicable, to identify if the quote referred to a positive or negative experience. Statements that did not fit into one of the pre-determined codes were highlighted and reviewed to see if a new data-driven code was required.

Recommendations presented by Elo et al. [ 46 ] and Smith et al. [ 47 ] were integrated into the methodology of the study to add trustworthiness (i.e., credibility, transferability, dependability, and confirmability [ 48 ]) and rigour to the findings [ 49 ]. Transferability was introduced to the study by gathering direct testimony from service providers and parents in the counties and providing descriptions of the community and participant characteristics, which allows the reader to make a judgement if the findings are applicable to their settings [ 49 , 50 ]. To establish dependability to the analysis, reviewers engaged in memoing throughout the analysis process, which involved recording thoughts of the transcripts or possible answers to the research question to improve the transparency of the findings [ 45 ]. This process included a critical analysis of the transcripts to identify the potential influence of the focus group facilitators on participants’ responses and to identify potential leading or vague questions [ 46 ]. The reviewers met at various points throughout the analysis to discuss coding and to share notes. Following the categories being finalized by the two reviewers, the research team engaged in the process of “critical friends” to add credibility and conformability to the findings [ 47 ]. As an alternative to inter-rater reliability where the aim is to reach a consensus, this is a reflexive activity that encourages in-depth discussions amongst the research team, where the reviewers offer their interpretations of the data and others present critical feedback that can challenge the reviewers’ biases, pre-conceived ideas and knowledge of the subject matter that may have influenced the findings [ 47 ].

Participants

In total, 94 physical activity service providers and community organizations from across the counties of Oxford, Elgin and Middlesex were contacted. From the invited organizations, 42 representatives from 38 organizations attended one of the community forums, with 37 representatives (39.36%) consenting to participate in the research study (with time constraints noted as the primary reason for not staying for the community forum group discussion). Additionally, 79 parents consented to be contacted about ACT-i-Pass research projects. Of those who consented, 9 parents participated in the community forum (11.39%). Participants were dispersed across the counties, with most parents characterizing themselves as white ( n  = 8; 88.89%) and female ( n  = 9; 100%). See participant characteristics for both the service provider and parent community forums in Table  1 .

Category development

The positive and negative factors related to children’s physical activity participation and physical activity programs identified by service providers and parents during the Mentimeter activity and the pre-forum survey are visually represented in Fig.  1 A and B respectively.

Positive and negative factors related to children’s physical activity in rural and smaller urban centres. Positive factors are represented in blue ( A ) and negative factors are represented in red ( B ). The words represent service provider and parent responses to the pre-forum survey and Mentimeter questions related to children’s barriers and facilitators to physical activity participation, the design and implementation of physical activity programs, and recommendations for physical activity programs in their community

The synthesis of the service provider and parent responses to the Mentimeter activity and pre-forum survey resulted in 10 initial coding categories. Following the analysis of the transcripts and discussion amongst the research team, 1 new category was added and 4 categories were integrated into other existing categories due to similarities in content. This resulted in 7 unique categories. Further details on the categories and their definitions can be found in Fig.  2 .

Categories developed and adapted from the pre-forum survey, Mentimeter activity and community forum discussions. Yellow codes represent ideas discussed during service provider community forums, blue codes represent the ideas from the parent community forum, and green codes represent the ideas discussed by both groups

Recovery from pandemic-related measures

Conversations in all the community forums highlighted the long-term impacts of the COVID-19 pandemic on children’s physical activity. Specifically, service providers and parents believed the public health protections introduced to reduce transmission of the virus were associated with lower physical activity levels that have yet to return to pre-pandemic levels.

Despite both groups describing the barriers and challenges created by the pandemic, the focus of the discussions differed between parents and service providers. The community forum discussions with parents were directed toward their child’s quality of life. During the early stages of the COVID-19 pandemic in 2020 and 2021, all the parents agreed that children lacked access to activities, resulting in, “two years or so of limited access to everything and they didn’t even do it for an entire summer”. Without their regular opportunities during the closure of recreational facilities and gyms, some parents expressed concerns about the physical activity-related skills their children may lack, with one parent explaining, “they [gyms] had to modify a lot longer than other places due to the fact that they were known as potential super spreader locations”. As a result, some parents felt that “it’s unfortunate for our kids now who didn’t get that opportunity that you didn’t realize at the time was such a big developmental stage that they were in”. Without the opportunity for children to try different activities and develop their physical activity-related skills, parents worried about the long-term influence the early years of the pandemic may have had on their children’s physical activity participation.

Alternatively, service providers were focused on the influence of the pandemic-related protocols on program attendance and the consequential changes to the current program offerings and schedules. Following the re-opening of gyms and recreational spaces after the removal of COVID-19 protocols, many service providers felt that enrollment rates had not returned to pre-pandemic numbers. As one service provider mentioned, “getting kids to sign up for anything is difficult. Getting them to register for anything is impossible”. Another service provider expanded on this topic, discussing their experience recruiting children after they re-opened: “Pre-pandemic, all our programs were full. We were bursting at the seams March 2020. We are just slowly trying to figure out what people want right now. Our membership base is really changed and we’re not seeing the kids in the drop-in programs like we used to”. As a result, service providers had to adapt their programming options and scheduling. This includes “I would say at 6 out of our 10 branches we’ve changed our hours” and “trying to figure out what works and we’re hoping in the next session [Summer] to add a few more programs”.

Knowledge and access to programs

Both service providers and parents noted the concept of accessibility of activities for children in their communities; specifically, discussions were focused on the knowledge of and ability to partake in physical activity programs. One of the primary topics explored during the community forums was the unique aspects of the rural environment that influence children’s ability to get to the recreational facilities or small businesses offering activities. In addition to physical accessibility, service providers and parents discussed families’ awareness of the local physical activity opportunities.

Rural environments were described as low-density and dispersed spaces that, “if you live in a rural community, there’s no option if you don’t have a car” (Service Provider). The dispersed organization of these communities limits children’s ability to get to activities by themselves. Service providers and parents both described safety concerns with children travelling to activities by themselves, referring to “they’re [recreation facilities] a distance away and it’s the time of the year that’s dark” (Parent), and “there’s no bike paths leading to here [our facility], so those are barriers for that age” (Service Provider). Public transportation is non-existent in rural areas, placing pressure on parents to get their children to activities. As described by one parent, “I think it’s just access is a really big one, so like physically getting into the program and getting to London isn’t going to work for a lot of the community because there’s no public transportation between here and there.” This is a particularly large issue in small rural communities that lack resource availability and require families to travel to other municipalities or towns to access services, as mentioned by one parent: “I live in a town where we piggyback off the other town, so I have to travel only because my town doesn’t offer sports”. One challenge service providers can encounter is families’ unwillingness to travel to activities. Rural communities can cover a large area and it can be difficult to come up with programs that are accessible to all families within the region. As one service provider explained, “when we do county-wide scavenger hunts or something like that, if they live in the Far East they’re not going to [go]. Absolutely not. They might go to St. Thomas, but they’re not going from one end [of the county] to the other”.

In addition, many parents highlighted having difficulties finding programs for children, describing that it requires time and research on multiple platforms: “I think there’s programs all over the place. Some are private. Some are public. Some are invite only. Some of them are on Facebook and some of them are word of mouth.” As a result, one parent believed that they needed to be self-reliant to find their child after school activities and “sometimes we have to seek the questions and ask ourselves and not wait for the information to come to us”. One parent noted that access to information also differs among different socio-demographic groups in their region, with those from “the lower income side … [they] don’t have a lot of access to the information that gets sent out and be educated on things so there’s certainly a barrier of almost classism.”

One of the obstacles for service providers is figuring out how to best promote programs. While deliberating about effective ways to get information to parents, service providers indicated that the ultimate difficulty is that “there’s so much information out there that everything just gets bogged down, right? Gets lost in Facebook walls or Instagram or whatever”. Some service providers attributed promotion challenges to the popularity of different media platforms, specifically highlighting previously used modes of promotion now have limited effectiveness. Some examples provided by service providers included, “a newsletter every quarter of what’s going on and the newsprint in our area, people don’t read it anymore”, “FM radio is there and that’s supposed to be our local news for all that and most people don’t listen”, and “internet out in the rural areas is not always easy”.

Recommendations

To alleviate the issues associated with the physical accessibility of programs, parents and service providers recommended that interventions take the environment into greater consideration when developing programs for rural and smaller urban centres. Service providers encouraged more efforts to be focused on smaller communities that lack local recreational facilities and programs, including boosting the community’s use of outdoor spaces.

To better support parents’ understanding of the recreational opportunities available to their children, several parents spoke of the need for an online repository where the information for all physical activity programs can be found in one location, as emphasized by one who said, “it would be nice if there was a central spot where all of that [recreation programs] could be held and not necessarily relying on Facebook to find all that… ”.

Availability

A large portion of the community forum conversations centred around the availability of physical activity opportunities related to the programs, facilities, and resources in the community that can be used by children. Primarily, service providers and parents focused on the variety of activity options available to children.

In the counties, the activity options offered by municipalities can vary between communities, with some places not having programs, services and/or spaces for children to play. As one parent described:

They have the space, but they don’t have necessarily the programs. I’ll give you an example. We have a tennis court, but there’s nobody to run a tennis program. We don’t have the trained athlete or adult to run the programs. There’s badminton areas and volleyball areas, but there’s no one to run the program in our area again.

When trying to enrol in programs, some parents mentioned having difficulties getting a space for their child, with one parent highlighting, “show up two minutes late [to register] and now they can’t get in [the program]. Yeah, it really feels like if you already know then you’re good, but if it’s something new you’re trying to try out, good luck”. By not being able to enrol their child in local physical activity opportunities, parents struggle to get their children active outside of school.

In response to parents’ concerns about activities not being available or programs having insufficient spaces, service providers explained that limited activity offerings may be a consequence of previous attendance rates. As one service provider explained, “it gives you that justification to run the program that the numbers [participants] are there and it[s] driving revenue into your pocket, then you could say yeah let’s drive it forward”. Attendance is especially important in smaller, rural communities that have limited recreation budgets as underscored by one service provider who said, “[our municipality] does have a community center, but I know that they have been struggling to get people, so that’s affecting their offerings”. Consequently, local private organizations and small businesses are critical resources for physical activity in non-urban areas.

In addition to the activities, service providers referred to the available spaces for physical activity in rural and smaller urban centres. Predominantly, service providers focused on dispersed rural communities as they do not have local indoor recreational facilities. One service provider detailed, “again, it comes down to amenities and facilities. There aren’t really any there. It’s the rural part. There’s no facilities so there’s no programs”. While there may be a lack of indoor facilities for physical activity, a variety of outdoor spaces do exist in the counties; however, children can encounter challenges when trying to use these spaces. For instance, the definitions linked to specific places can limit children’s use of outdoor recreational facilities. One service provider referred to the definition of a space in terms of the associated activity: “Yeah, so if you have a big open park that is a soccer field, you can’t do anything else there but soccer. You can’t go and run around or do stuff because then they think you get kicked off”. In addition, service providers believed demographics, particularly age, influenced the places children felt they were allowed to use to play. For example, one service provider discussed older children’s experiences playing on the local playgrounds:

The facilities seem to be claimed by another group. It’s like your sense of belonging, like ‘well, I can’t go there’, and I hear it quite regularly by youth that are in that transitional age that they don’t feel like they could even go to the playground facility because it’s for younger kids and they’re deemed troublemakers if they’re there… so the facility might be there, but they’re not welcomed there.

Parents requested additional spaces in organized recreation programs to help alleviate their current frustrations. Conversely, based on the conversations with service providers, capacity can vary across community types and resource availability, as one service provider described, “if you look at what the capacity of the City of London compared to the capacity of the county and the capacity of each municipality is very different”. Service providers suggested that the development of seasonal programming should be influenced by the available spaces in the community, prioritizing activities that they can offer consistently and sustainably.

For service providers, particularly municipal recreation departments, to maximize the available spaces in the community and increase their capacity for additional programming, non-traditional locations for physical activity programs were suggested. This includes offering activities in any large, open room that is available such as a church, school, or library. The discussions also highlighted the large number of outdoor spaces in their communities. However, some parents noted that outdoor spaces were being underutilized, “you’re not just going to meet a bunch of kids at the park for a few hours. It’s rare that we just find random kids on the street that they can go play with… Yeah, my kids don’t have the internal appetite to just go outside and play”. Thus, parents believed additional outdoor organized activities, particularly during the summer, would be an advantageous way to increase the number of physical activity options and encourage more children to be active. Service providers did note that children may perceive certain outdoor locations as unwelcoming and unavailable and emphasized the importance of educating and redefining the way children view the spaces in their community.

Personnel support

There are multiple levels of support required for children to engage in physical activity. Service providers and parents highlighted four groups: friends and peers, parents/guardians, schools, and governments and municipalities.

Both service providers and parents discussed the difficulties parents/guardians face when trying to engage their children in physical activity. The discussions with service providers indicated that many families in rural communities “have to travel… My town is close enough to bigger centers, but, and as I hate to say, behind the times so there’s nothing”. Consequently, it can be difficult for parents who live in rural communities who drive longer distances to work. As one parent mentioned, “parents that work outside of their community have to drive all the way home at the end of the workday to pick up their child, and then to drive an hour back into [the city] is a lot of hours in a car. That is a lot of time consumed that is difficult for families and gas”. An additional issue service providers mentioned about parents’ ability to support active lifestyles was their knowledge of physical activity expectations for children. Some service providers felt, “the parents that I talked to in training have very little idea of physical activity guidelines, but they have an idea of what their child looks like. There are a lot of barriers and to kind of make sense of what’s out there and how it applies to raising a child”. As a result, service providers believed that low registration rates were potentially attributed to inadequate physical activity literacy.

While peers were primarily described as a positive influence on children’s physical activity, peer pressure was recognized by parents. If friends exhibit dislike for, or remove themselves from, an activity, this may discourage a child from participating. As one parent noted, “depending on who’s in their class, my daughter would definitely choose to sit on the sideline with her friend than try dodgeball”.

Governments and municipality officials were also highlighted by service providers as a group that has hindered children’s ability to be physically active. As one service provider describes, “a lot of policies in these small towns… I know that’s an issue in a lot of small communities, the liability issues”. Specifically, the safety protocols that need to be enforced at their facilities have led to inequities in activity access. As one service provider mentioned, “A lot of street hockey going on right now and the powers that be shutting it down… Hard getting their kids out to let them do anything because there’s always somebody watching saying ‘no, no, no you can’t’”. Similarly, another service provider talked about their skating programs and the new helmet regulations:

It was felt really hard this year with the new board policy for skating at the arenas. The school board implemented a policy of CSA-approved helmets, so children that only had a bicycle helmet could no longer participate in the school field trip for skating unless their families could pay to get them a hockey helmet or ice hockey helmet. Very limiting policy for those children to be able to participate.

While the government’s efforts aim to create a safety measure that protects children, they have also led to greater inequities in physical activity participation.

Facilitators

Peers were characterized as key influencers in children’s lives, with parents and service providers describing how they can encourage each other to be active. For example, parents highlighted, “if you can bring a friend with you they’re more than likely to go with a buddy or two or a couple people instead of by themselves”, and “you both can kind of support each other on the [basketball] court and it’ll be great and they had a great time, but it was only because her friend was joining that she joined”. Some service providers have also seen the benefits of peers encouraging participation in recreation programs, explaining, “our badminton program almost didn’t run this past season because we had one kid signed up for the first month and then within probably a week or so of us cancelling the program, we had 15 kids sign up because one kid told his friends”. Overall, peers were viewed as an important driver of physical activity for children by acting as a key support system during activities.

Besides peers, parents and guardians have a pivotal role in their children’s health and are “key to their child’s physical activity” (Parent). Many parents felt that it was their responsibility to encourage their children to be active: “I guess it also at the grade 5 level, it’s really the parent that needs to push it [physical activity]. The parent is the one that has to drive them. The parent has to free time up in the afternoon, not to be cooking or cleaning or picking up from the week, but let’s pause and do physical activity”. Some of the service providers believed parents demonstrated they recognized the relationship between physical activity and their children’s health and well-being: “I have parents emailing me every day right now about stuff, so I think parents are starting to see what we are seeing, that their kids aren’t active enough”. Many parents described being happy to take their children to activities, stating, “it’s a choice, but you also see the joy in the kid, your kid’s eyes and you wanna keep going because they just love it so much”.

In addition, schools were described as key settings for physical activity, with staff playing an important role in physical activity promotion. Parents believed schools, specifically physical education classes, are responsible for introducing children to activities:

The other thing with sports is that you have to sign up for a period of time and we were just saying, if they’re not introduced to it in school, how would they know if they like it? And then why would a parent pay $300 for them to try something that they might absolutely hate? So, something like school can help introduce sports.

Similarly, many service providers viewed schools as advantageous places for physical activity, specifically for afterschool programs as “schools can provide space after hours and the kids are already there”. Schools were also labelled as a central location for program promotion, with one service provider stating, “schools are actually sending their papers home. They send their newsletter home once a week, electronically”. In terms of staff, teachers can be ambassadors and advocates for children’s participation in physical activity. As one parent explains, “if you get it to the right teachers, they interact with parents all the time. I know that they will send like a video or something”.

Based on the conversations with service providers and parents, creating partnerships is important for community-based interventions and recreation programs. Some service providers believed that talking with “established organizations that have the audience has been a driver of success for programs especially”. Teachers and administrative staff at schools were key collaborators identified during the community forums as they are constantly in contact with parents and can easily share information about recreation programs with their classes. Service providers have talked about the benefits of teacher advocates for physical activity interventions like the ACT-i-Pass Program, with one recommending, “put it in some of the teachers’ brains that ‘hey, guess what? We got this ACT-i-Pass thing’. They can physically talk to a parent instead of just a paper or something that gets missed”. Additionally, service providers recommended that parents be provided more education about the national movement guidelines to reinforce the amount of physical activity children should be acquiring.

Quality of programs and facilities

The quality of the physical activity offerings and facilities was discussed during the service provider community forums. By quality, service providers referred to the facilities being in good condition and programs being led by trained personnel who are skilled in the activity.

A few service providers noted changes to the composition of the counties over the last few years, including the growing population, changing demographics and redevelopment, as one of the underlying reasons for lower program quality. This has been particularly difficult in rural and smaller urban centres, with one service provider explaining, “everyone’s moving out of the city into the smaller towns so it makes sense to expand them now, establish them now, but [my community] hasn’t done anything”. As a result, service providers stressed that the internal migration “changes the dynamic of how you look at programming too because you could have a group you catered to for a while and then you have a line of families that are coming in from other places. They are expecting a lot of different standards of smaller areas which forces us to grow too”.

To offer a quality program, many service providers emphasized the demand for qualified staff that are knowledgeable about the activity and “skilled enough to be able to actually provide the program”. As mentioned by one of the service providers, “finding that instructor is definitely the hardest part when you’re trying to either start or restart a program, because if you don’t have that person to lead it or you don’t have the right person to lead it, your program doesn’t work no matter whether you had 1500 kids interested in that program if you don’t have someone excited and skilled to run it”. Due to the low population size of rural and smaller urban centres, finding community members who are proficient in an activity and willing to teach the skills to children is one of the service providers’ key obstacles in offering recreation programs.

When offering new programs, service providers stressed the time needed to gain community buy-in, as recreation programs are a “community service, it’s a service that you’re offering the community, so their interest is important”. The challenge highlighted by service providers is the time and effort required to gain awareness and secure regular enrollment in programs, which is necessary for their longevity:

It doesn’t happen overnight that people will come … It’s building the consistency, so families know that’s what’s gonna happen, whether they have 3 people show up for open basketball or whether there’s 20 people show up. If you don’t have the consistency, I think it’s really hard to be able to keep programming and families close within that area to participate in it.

To encourage community engagement, service providers have found that partnerships can help provide useful insight into the program models that work and the different approaches that have been unsuccessful. For instance, some service providers believed that sharing their experiences with other organizations can improve the quality of physical activity offerings across the community. One service provider referred to their experience meeting with the recreation programmers across their county:

I mentioned earlier how the municipalities who are in recreation are more than willing to talk to each other and share information with each other about what works and what doesn’t work. We started to try to open a membership option with some of our recreation programs and we reached out to a couple [of organizations], like, ‘hey, have you seen that this is a good thing or not?’

Consulting families was also viewed as vital for higher-quality programs. One service provider found that “a big piece, if you wanted to utilize those spaces, would be to engage with the youth to understand, like, if we open the gym or do we have a structured basketball tournament or badminton tournament or whatever that be”. By talking with potential users, this provides “validation that if they are going to pay staffing to be there and that people are going to show up”.

To account for the rising population, a service provider suggested that municipalities need to account for physical activity-related facilities and staffing during the development of rural communities and smaller urban centres: “we need to be able to provide the programs and amenities that come with that [the county growing], but until other things grow, whether it’s facilities or staffing or availability or whatever it is, you won’t grow with the population”. Service providers from rural communities also noted that it takes time to gain awareness among families when they introduce new program offerings, recommending that fellow program coordinators “… keep in mind with timing, it’ll take time. The population is lower, but we find things take longer and you have to build over time. Be patient”.

Expenses and subsidies

The expenses related to physical activity programming were a predominant topic among all community forums; however, the focus of expenses for parents was related to the cost of attending activities, while service providers were associated with the cost of managing programs.

For parents, the topic of expenses was related to the cost of their child attending and participating in activities. Ultimately, many parents felt that the price of organized physical activity is too high, with some describing sports as unfeasible opportunities for their children. As one parent described her son’s hockey season, “we’ll be in at $5000 by the time the season’s done and that’s just local league. That is cheap hockey. Now, if he wants to go competitive, some of my friends are saying they’re spending $7,000 to $10,000 for them to play competitive”. Families attributed the challenges associated with expenses to the cost of living “getting worse. We had a conversation at our dinner table about the cost of living. Everyone’s talking about it increasing”. Due to the high prices, parents felt that it can be difficult for children to try a variety of activities and find what they enjoy as one parent reported, “we’d be more than willing to sign our kids up for a bunch of programs if they had them, if we could… I can maybe pick one and then that’s all you can get this year because it’s all financially I can do”, meaning that “the cost of certain programs are just not attainable for some people… there’s a much larger cost to getting into the programs, so that negates it for some people”.

In addition to the registration fee, parents attributed transportation and unplanned expenses as challenging supplemental costs. Parents described the cost of gas accumulating quickly throughout the season, “now I’m driving him every day, not every day, but to his practices and his games. Well, that’s gas money, that’s another thousand dollars”. There are also team events that can lead to activities being more expensive than planned. For instance, one parent discussed the extra costs they noticed as their child engaged in more team sports:

It’s not only just the cost of equipment, but people go out for dinner after or they go out for ice cream. It’s all those things that if you can’t afford to bring your child, pay for it, the child might just decide ‘I don’t wanna be the one who’s going and I can’t go out for a meal after or get that ice cream cone with the group because I don’t have the $4’, so it’s a lot.

In contrast, service providers were focused on the expenses of managing physical activity programs. Service providers described having to limit the types of activities they can offer due to their available funds. Service providers supporting rural communities believed that it “might be easier for cities and towns to run them [recreation programs] because maybe they have that built into their budget that they can have money to give a program. We don’t, unfortunately”. Also, due to limited funds, they may not be able to offer some free and low-cost programs, with one service provider explaining, “there’s pickleball nets and they get so many people out of that but it’s free and that’s not something that I can do with our programs”.

Service providers also discussed the available resources in their communities. Due to budgets, service providers reported issues getting access to the necessary equipment and the need to borrow supplies from partners or schools. For those who have the equipment, service providers experienced time and cost challenges of transporting their equipment to facilities: “We have our equipment because we have our own space… we can bring it there [to the school] but we can’t store it there, which means there’s an extra amount of time and money that goes into that transportation every week for each day”.

Finally, a lack of funds influences the type of staff working at service providers. As one service provider expressed, “getting actual programmers for us, ‘cause we don’t have the Y budget that would provide a programmer to us, so that is a challenge”. In order to recruit the necessary staff, many service providers have to counter the extra costs by increasing the price of their activities: “So then you start paying that that main instructor that price needs to go up in order for us to continue”. Either the price goes up or you don’t run the program”.

To improve access to resources, one suggestion offered by service providers involved partnering with other publicly-funded organizations, such as community centres or libraries, to supply children with equipment that they can borrow and bring home: “Through the Y[MCA] or a program like that where you could come and get sports equipment or things so they can try a sport whether it be a hockey stick or a baseball glove or a soccer ball or a basketball. To have a sports lending library there”.

To help fund activities, a few service providers found that gaining sponsorships from organizations was a beneficial way to acquire additional funds. As described by one service provider, “maybe there would be another business that might be willing to provide funding so if a child wanted to sign up or to be able to help out businesses that are keen to help but maybe just can’t afford it financially”. External funding partners can also subsidize activity fees for children by acting as a “sponsor a dance class or a Taekwondo class or a something like that”. As offering free programming was deemed difficult or impractical for service providers, it was suggested that grants and subsidy programs be used to help improve families’ access to recreation programs. Funding support offerings can provide opportunities related to “their income level and if they were under a certain level then they received 50% funding for all the registration fees”, or “a necessity program so money is just for low-income families to help cover the cost of activities”.

Inclusion and preferences

Offering a variety of activity types and levels to make service providers more welcoming to all children was another frequently discussed topic during the community forums. As stated by one service provider, “inclusivity is crucial to youth right now, right? So, if you’re not inclusive you’re not being positive and allowing everyone to participate and then you’re not gonna be successful and kids aren’t gonna participate”. The discussions concentrated on service providers having a diverse number of activity types and levels within each activity to consider children’s abilities and preferences.

Some service providers and parents credited children’s low engagement in physical activity to the confidence or skillset to participate in a specific activity. As one service provider discussed, “I have noticed a huge confidence issue. Not picking things up that they aren’t fantastic at right off the bat… ‘I’m not good, I’m outta here, everyone’s better than me’”. A few parents reported seeing confidence issues in their children, with one parent describing, “it’s so tricky, especially when you think about that confidence. The ability to do sport, especially hitting that grade 7, that 13-year-old where you’re very self-conscious.” An explanation for confidence issues is the pressure they feel from their peers when they “size themselves up. It’s a natural thing people do. The ‘am I better than you? Are you better than me?’ mentality” (Parent). To help grow children’s confidence, children are looking for “proper skills and drills, it’s very popular” (Service Provider).

The appropriateness of the available activities may also be lacking with the current program options. Specifically, children have different needs and a greater variety of activities will help offer programs suitable to the different skill sets and ages of children. One characteristic highlighted throughout the conversations was the competitive spirit of children. Some of the parents attributed the lack of participation in organized programs to the absence of non-competitive options for sports. As one parent mentioned, “I find that there’s kind of a gap between like rec hockey players and just base recreation players… They don’t like high levels.” Parents felt that many activities were “the team sport atmosphere. My child’s not competitive, so knowing that she wants to learn, she wants to be better, but she has her own internal competitiveness, not external”. A problem many parents encountered was trying to find programs for their children to try and learn activities, as underscored by one parent while discussing an introductory hockey program in their community:

Now, one thing I don’t know is having those same kids on the ice at the same time as those who have been playing the sport for years because if that’s the case, that’s gonna fail immediately. They almost need their own ice time or their own space that they’re learning at their level.

In addition to the activity options for non-competitive children, the activities need to be age-appropriate. For instance, children can be embarrassed when “my child who is 10 is doing say beginner hockey, but then there’s also 5-year-olds in that group. Even if she’s at the same level as them, she is not going back. She’s like ‘I’m at the same level as a 5-year-old. No, thank you’” (Parent). The financial and personnel constraints service providers experience have also affected the program offerings by prompting more co-ed activities that combine both boys and girls; however, one parent said this has negatively impacted her daughter’s participation in team sports as, “at her age, they’re often both male and female combined, so co-ed. What I’m seeing as a parent is that the boys are becoming bigger and more aggressive as in they’re competitive and she is not, so therefore, she gets intimidated”.

Offering children activities they want to participate in and are passionate about was described as critical for continued physical activity participation. Ultimately, parents cannot force their child to want to take part in an activity. As one service provider highlighted, “you know we have parents bringing kids 3 or 4 years old to take martial arts. The parents are making them do something that doesn’t really draw [their] interest, but after 11 years old they seem to make their own choices”. As one parent noted, providing children with the opportunity to try various activities can be beneficial “if you want them to stay active in the long run, they need to find something they enjoy”.

Moving forward, it will be important to offer activities for various skill levels. As noted by one service provider, “building people’s confidence up, giving them an opportunity—a safe space to try a sport or try an activity with people with the same skill level as them”. In order to develop children’s self-efficacy and increase program uptake, there needs to be a variety of program offerings to account for “the diversity in who the kids are, the ages of the kids and interests” (Service Provider). This can also be done by offering flexible activities where the programs are “something more that evolves and keeps them interested” and they can be adapted by “asking them if they feel good and you’re teaching them to help structure play” (Service Provider). In addition, offering non-competitive and entry-level programs can encourage children to join activities where “everybody that joined it was just kind of trying it. Nothing serious and it made it easier to attend those things as opposed to going with a group of kids who have been playing that sport for 7 years and you’re trying it for the first time” (Parent).

One strategy to alleviate the issue of activity options for all children is offering non-traditional activities. For instance, service providers reported, “people get bogged down with the traditional programming like soccer and basketball. There’s so many other programs that are out there” and “dodgeball’s huge right now. Just those off the cuff programs that aren’t traditional… just doing something that they don’t have the opportunity to do and just being creative with that”. Similarly, service providers suggested that program offerings should integrate trending activities among youth: Working on some trends in certain sports. Like, who would’ve thought pickleball? Cornholes replaced horseshoes. You know what I mean? You gotta kind of recognize it’s replacing something in a more modernizing way.

Through a series of community forums with service providers and parents, this study aimed to explore the physical activity opportunities in rural communities and smaller urban centres and to understand how to develop and implement community-based physical activity programs for children in areas with low resource availability. The discussions with service providers and parents highlighted a variety of barriers and facilitators to physical activity participation. Some examples of barriers included the distance to activities, the expenses related to physical activity programs, and limited resources to meet the population growth. In contrast, flexible activities, promoting programs through schools, and outdoor spaces were described as facilitators. In addition, recommendations for the development and implementation of physical activity programs for children in low-density and minimally resourced areas were noted. Recommendations covered a range of topics such as developing physical activity-related skills, utilizing non-traditional physical activity spaces, and centring program offerings around equipment and personnel capacities.

When asked about the factors that influence children’s physical activity, service providers and parents believed that the loss of organized programs and the closure of recreational facilities due to the government-regulated COVID-19 public health protections had a negative effect on their child(ren)’s physical activity. Children’s preference for organized recreational opportunities and limited involvement in active play is consistent with the evaluations of Canadian children’s physical activity participation [ 7 , 8 ], For instance, Sharp et al. [ 52 ] found that most rural children were looking for structured after school or weekend activities and would enrol in a wide variety of organized programs, such as physical activities, music, clubs, and tutoring. However, children’s desire to engage in organized activities conflicts with the body of literature asserting that there is a lack of resources in non-urban communities [ 53 , 54 ]. In a comparison of rural and urban Canadians, participants from rural communities are more likely to report barriers to accessing recreational facilities [ 55 ]. Due to the interest in more structured activities, implementing community-wide programs and finding strategies to improve recreation offerings can be a beneficial way to promote physical activity participation in resource-limited communities.

Accessibility was noted as a common barrier throughout the community forums, consistent with the literature on rural physical activity [ 56 ]. Poor accessibility was associated with the community structure and resources varying between communities. For instance, Gilbert et al. [ 19 ] found smaller rural communities with a population size of less than 6,000 residents had fewer resources and less infrastructure than larger communities, which may require a tailored intervention plan. Due to the longer distances between home and program offerings, transportation is one of the main barriers to physical activity in rural and smaller urban centres. In non-urban communities, public transportation is non-existent or unreliable, and active transportation is not available to children as parents may be concerned about the lack of bicycle lanes and sidewalks, their children travelling on underutilized routes, and wild animals [ 57 ]. Consequently, children cannot attend programs without a parent or family member acting as a driver. As a result, researchers and program coordinators need to understand the unique characteristics of the different communities in their jurisdiction when developing community-based programs and create an implementation plan that best meets the needs of the whole target population.

Outdoor spaces were also identified as a beneficial method for improving children’s physical activity. Both parents and service providers highlighted the variety of outdoor spaces that are unused by children without organized activities. In addition to engaging children in more physical activity, outdoor spaces have been found to provide various other health-related benefits, including increased self-esteem, problem-solving abilities, social behaviours, and motor skills [ 58 ]. While outdoor spaces can provide additional recreational opportunities when programs and facilities are limited, they may target those who are sufficiently active. For instance, children from rural and remote communities who reported being involved in a higher number of organized activities also reported greater involvement in unstructured leisure activities; this refutes the ‘over-scheduling hypothesis’ that proposes those who participate in more organized activities face time constraints that inhibit participation in unstructured forms of physical activity such as outdoor play [ 52 ]. As the outdoors can provide an open space for imagination and creative activities, offering non-traditional activities in these settings can help engage children who are not interested in sport-focused activity offerings.

In addition, parents and service providers described select individual-level factors as barriers to physical activity participation. Consistent across evaluations of urban and non-urban communities, children are potentially not participating in any programs due to their lack of interest in physical activity options [ 59 ]. Parents and service providers presented conflicting accounts for why there are issues with the current program offerings. Consequently, it is difficult to conclude if service providers’ limited capacity or families’ low uptake has led to a reduced variety of activity options, but they both likely play a role in children’s physical activity opportunities. With the rising internal migration to rural communities on account of the transition to virtual and hybrid work options available during the COVID-19 pandemic [ 20 ], there is an increasing demand for resources and services in these areas. As there are difficulties associated with recruiting staff and the capacity for communities to build more recreational facilities, program offerings should prioritize the resources that currently exist in the community, including integrating the land use and development plans for the municipality to account for the growing population [ 60 ].

One finding highlighted in the current study by both service providers and parents was the cost of recreation programs. Due to the high cost of extracurricular activities, family income is an important factor in physical activity participation for children [ 61 ]. For example, Kellstedt and colleagues [ 62 ] found that children’s chances of partaking in sports were 4 times more likely when they lived in a higher-income household. This aligns with the idea that socioeconomic-based health inequalities increase across the life course because of the cumulative advantage or disadvantage associated with differential access to health-promoting resources, much of which is rooted in early life exposures [ 63 ]. While many recommendations for reducing the economic accessibility of physical activity surround affordable programs, one frequently reported barrier among rural populations is the shortage of free and low-cost physical activity opportunities [ 55 ]. The high cost of activities was also noted as a challenge for service providers. Local governments in smaller communities tend to face financial challenges with limited revenue, minimal financial capacity, and a high cost of living [ 15 ]. As a result, service providers have difficulties maintaining their facilities and creating environments that better support physical activity, which means regular free activity offerings are not a viable solution in many communities.

Recommendations for physical activity interventions and recreation programs

In response to the identified facilitators and barriers related to recreation programs, service providers and parents offered recommendations to integrate into the expansion of the ACT-i-Pass Program and future physical activity interventions. Recognizing that the number of physical activity providers declines as the ACT-i-Pass shifts from a densely-populated city to more dispersed, resource-limited settings, the recommendations provide valuable adaptations to the intervention’s design and implementation that can offer physical activity opportunities tailored to the needs of families in rural and smaller urban communities. For instance, due to the range of conditions that exist in non-urban areas (e.g., population size, resources), the unique characteristics of the different communities and available resources need to be incorporated into community-based programs to ensure activities are accessible to all children, particularly those in low-density rural areas [ 64 ]. For example, the transportation options in dispersed communities differ from urban environments; therefore, additional attention needs to be placed on creating more programs in a variety of neighbourhoods or reducing transportation barriers by offering busing from schools to service providers or encouraging carpooling with other families.

Primarily, creating additional structured activity options for children was deemed a beneficial strategy for engaging children in greater amounts of physical activity. One suggestion included utilizing the abundance of outdoor spaces available in the area. Encouraging outdoor play and creating more outdoor programs in a variety of communities can help children be more active [ 65 ]. In addition, increasing the program offerings to service a greater variety of activity preferences and skill levels can allow programs and interventions to have a greater impact on the health behaviours of children. Traditional activity offerings are not reaching all children, particularly those not interested in sports or competitive environments; therefore, providing unique and fluid programs may help gain their interest in activities and engage them in more physical activity. Program coordinators were encouraged to integrate trending activities (e.g., pickleball) and flexible programs into their offerings. Flexible programs, alternatively termed scaffold play, are child-directed activities that are guided by an adult [ 66 ]. The objective of these activities is to foster children’s development and creativity as they work towards a specified objective outlined by the adult [ 67 ]. While this strategy is primarily used in a preschool context [ 68 ], it may continue to have benefits among older children.

Additionally, partnerships were a key recommendation from service providers, reinforcing the importance of collaborations in successful community-based interventions [ 69 ]. Specifically, it was stressed that community organizations and families are valuable sources of information and support when creating programs for children. Community organizations, such as government agencies and businesses, can assist in the administration of programs and interventions by offering financial support via subsidies or grants that reduce the financial strain of registration fees for families or facility management costs for service providers [ 70 ]. Other partners, such as schools, can also improve awareness of programs and interventions by acting as promoters [ 71 ]. Alternatively, engaging with families can give greater context to the community and help set priorities for interventions based on the interests and the supports needed by the target population [ 72 ].

As COVID-19 continues to influence the physical activity context, there are additional recommendations that need to be integrated into health promotion efforts. For instance, children missed pivotal years of physical education due to the closure of schools and recreational facilities. Perceptions of athletic ability, self-efficacy, and motivation to be active are all factors that can have a significant influence on physical activity behaviours [ 73 ]. Thus, interventions should integrate programs with a greater focus directed toward building children’s physical activity confidence by teaching skill sets and movement competence [ 74 ]. In addition, with many small businesses closing during the pandemic, redefining what qualifies as a setting for physical activity is important. In rural communities, children do take advantage of existing afterschool program opportunities (e.g., church youth groups) when school athletics programs, sports leagues, and recreation activities are limited or unavailable [ 52 ]. As the findings indicate that children are hesitant to use spaces without the guidance of an adult, creating structured programs will make non-conventional physical activity spaces more accessible for children. A full list of the recommendations provided by service provider and parent community forum participants is provided in Fig.  3 .

Service provider and parent-derived recommendations for physical activity programs and interventions in rural and smaller urban centres

Limitations

While this study provides valuable insights into rural and smaller urban centres and physical activity programs, there are limitations that must be considered. The parent community forums exclusively involved responses from mothers. While it is common that parental perspectives on their children’s health behaviours tend to come from mothers [ 75 ], we are missing the paternal perspective that may offer different experiences with their child(ren)’s physical activity. Additionally, our study consisted of families and service providers from Elgin (including the City of St. Thomas), Oxford, and Middlesex Counties. Based on responses to the Census Profile, the populations of these three communities consist primarily of English speakers and non-immigrants and have a lack of racial and ethnic diversity [ 32 , 33 , 34 ]. Due to the similarities between participants, we are unable to make conclusions about the influence of demographic characteristics on the experiences of families from our study area. While efforts were made to produce a thorough list of service providers, the perspectives of some organizations may have been missed if they did not have an online presence or if our community partners were unaware of their existence. Finally, rural communities and smaller urban centres are contextually diverse based on population size and physical activity-specific resources [ 19 ]. There are multiple definitions used to differentiate between urban, suburban, and rural areas that vary based on one or more community characteristic(s), such as population density, population size, distance from an urban area or distance to an essential service [ 76 ]. As a result, the applicability of findings to other non-urban spaces can be challenging and may only relate to the experiences of service providers and families who reside in rural communities, villages and small urban centres that are within an hour’s drive of a large urban centre.

To counter the rise in physical inactivity associated with the COVID-19 pandemic, developing and implementing interventions that can encourage children to live more active lifestyles are critical. To improve the quality and effectiveness of community-based interventions, researchers and program developers should collaborate with community members and organizations to adapt interventions to meet the needs of their target community. This is particularly important for small, dispersed communities that have unique characteristics based on their population size, number of recreational facilities, and activity options. Service providers and parents emphasized the need for interventions and programs that offer accessible, diverse, high-quality program options that are inclusive and meet the needs of all children in the community. To account for the impacts of the COVID-19 pandemic, interventions need to integrate additional opportunities for children to develop their confidence and physical activity-related skills and find resources that can reduce the economic strain associated with recreation programs. While a variety of suggestions from parents and strategies used by service providers were noted, further studies are needed to evaluate the impact of the recommendations on the effectiveness of interventions and recreation programs in rural and smaller urban centres with a focus on fidelity, uptake, use and changes to physical activity levels.

Data availability

The datasets generated and analyzed during the current study are not publicly available due to research ethics board requirements but are available from the corresponding author on reasonable request.

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Acknowledgements

We would like to thank Southwestern Public Health for their support in administering and organizing the ACT-i-Pass community forums. We also thank program service providers and local school boards (the London District Catholic School Board, Thames Valley District School Board, Conseil Scolaire Viamonde and Conseil Scolaire Catholique Providence) for their continued support of the ACT-i-Pass Program. We also thank the parents and organization representatives who took the time to attend the community forum and participate in a community forum discussion. Finally, we thank our research assistant, Samantha Lotzkar, who reviewed the transcripts for accuracy and acted as a secondary analyst.

This research was funded by the Lawson Foundation Miggsie Fund’s Community Grants (GRT 2022-49).

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E.O., J.G., J.I., J.S. and P.T. conceptualized the study. E.O. and P.T. developed the community forum guides. E.O. recruited study participants, moderated the community forums, conducted the analysis, and wrote the original manuscript draft. J.G., J.I., J.S. and P.T. reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

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The study was approved by Western University’s Non-Medical Research Ethics Board (REB #103954). Written and oral informed consent was obtained from all service providers and parents who participated in this study. All methods were carried out in accordance with relevant guidelines and regulations.

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Ostermeier, E., Gilliland, J., Irwin, J.D. et al. Developing community-based physical activity interventions and recreational programming for children in rural and smaller urban centres: a qualitative exploration of service provider and parent experiences. BMC Health Serv Res 24 , 1017 (2024). https://doi.org/10.1186/s12913-024-11418-w

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