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Buttoning up research: How to present and visualize qualitative data

15 Minute Read

There is no doubt that data visualization is an important part of the qualitative research process. Whether you're preparing a presentation or writing up a report, effective visualizations can help make your findings clear and understandable for your audience. 

In this blog post, we'll discuss some tips for creating effective visualizations of qualitative data. 

First, let's take a closer look at what exactly qualitative data is.

What is qualitative data?

Qualitative data is information gathered through observation, questionnaires, and interviews. It's often subjective, meaning that the researcher has to interpret it to draw meaningful conclusions from it. 

The difference between qualitative data and quantitative data

When researchers use the terms qualitative and quantitative, they're referring to two different types of data. Qualitative data is subjective and descriptive, while quantitative data is objective and numerical.

Qualitative data is often used in research involving psychology or sociology. This is usually where a researcher may be trying to identify patterns or concepts related to people's behavior or attitudes. It may also be used in research involving economics or finance, where the focus is on numerical values such as price points or profit margins. 

Before we delve into how best to present and visualize qualitative data, it's important that we highlight how to be gathering this data in the first place. ‍

How best to gather qualitative data

In order to create an effective visualization of qualitative data, ensure that the right kind of information has been gathered. 

Here are six ways to gather the most accurate qualitative data:

• Define your research question: What data is being set out to collect? A qualitative research question is a definite or clear statement about a condition to be improved, a project’s area of concern, a troubling question that exists, or a difficulty to be eliminated. It not only defines who the participants will be but guides the data collection methods needed to achieve the most detailed responses.
• ‍ Determine the best data collection method(s): The data collected should be appropriate to answer the research question. Some common qualitative data collection methods include interviews, focus groups, observations, or document analysis. Consider the strengths and weaknesses of each option before deciding which one is best suited to answer the research question.  ‍
• Develop a cohesive interview guide: Creating an interview guide allows researchers to ask more specific questions and encourages thoughtful responses from participants. It’s important to design questions in such a way that they are centered around the topic of discussion and elicit meaningful insight into the issue at hand. Avoid leading or biased questions that could influence participants’ answers, and be aware of cultural nuances that may affect their answers.
• ‍ Stay neutral – let participants share their stories: The goal is to obtain useful information, not to influence the participant’s answer. Allowing participants to express themselves freely will help to gather more honest and detailed responses. It’s important to maintain a neutral tone throughout interviews and avoid judgment or opinions while they are sharing their story. 
• ‍ Work with at least one additional team member when conducting qualitative research: Participants should always feel comfortable while providing feedback on a topic, so it can be helpful to have an extra team member present during the interview process – particularly if this person is familiar with the topic being discussed. This will ensure that the atmosphere of the interview remains respectful and encourages participants to speak openly and honestly.
• ‍ Analyze your findings: Once all of the data has been collected, it’s important to analyze it in order to draw meaningful conclusions. Use tools such as qualitative coding or content analysis to identify patterns or themes in the data, then compare them with prior research or other data sources. This will help to draw more accurate and useful insights from the results. 

By following these steps, you will be well-prepared to collect and analyze qualitative data for your research project. Next, let's focus on how best to present the qualitative data that you have gathered and analyzed.

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How to visually present qualitative data.

When it comes to how to present qualitative data visually, the goal is to make research findings clear and easy to understand. To do this, use visuals that are both attractive and informative. 

Presenting qualitative data visually helps to bring the user’s attention to specific items and draw them into a more in-depth analysis. Visuals provide an efficient way to communicate complex information, making it easier for the audience to comprehend. 

Additionally, visuals can help engage an audience by making a presentation more interesting and interactive.

Here are some tips for creating effective visuals from qualitative data:

• ‍ Choose the right type of visualization: Consider which type of visual would best convey the story that is being told through the research. For example, bar charts or line graphs might be appropriate for tracking changes over time, while pie charts or word clouds could help show patterns in categorical data. 
• ‍ Include contextual information: In addition to showing the actual numbers, it's helpful to include any relevant contextual information in order to provide context for the audience. This can include details such as the sample size, any anomalies that occurred during data collection, or other environmental factors.
• ‍ Make it easy to understand: Always keep visuals simple and avoid adding too much detail or complexity. This will help ensure that viewers can quickly grasp the main points without getting overwhelmed by all of the information. 
• ‍ Use color strategically: Color can be used to draw attention to certain elements in your visual and make it easier for viewers to find the most important parts of it. Just be sure not to use too many different colors, as this could create confusion instead of clarity. 
• ‍ Use charts or whiteboards: Using charts or whiteboards can help to explain the data in more detail and get viewers engaged in a discussion. This type of visual tool can also be used to create storyboards that illustrate the data over time, helping to bring your research to life. 

Visualizing qualitative data in Notably

Notably helps researchers visualize their data on a flexible canvas, charts, and evidence based insights. As an all-in-one research platform, Notably enables researchers to collect, analyze and present qualitative data effectively.

Notably provides an intuitive interface for analyzing data from a variety of sources, including interviews, surveys, desk research, and more. Its powerful analytics engine then helps you to quickly identify insights and trends in your data . Finally, the platform makes it easy to create beautiful visuals that will help to communicate research findings with confidence. 

Research Frameworks in Analysis

The canvas in Analysis is a multi-dimensional workspace to play with your data spatially to find likeness and tension. Here, you may use a grounded theory approach to drag and drop notes into themes or patterns that emerge in your research. Utilizing the canvas tools such as shapes, lines, and images, allows researchers to build out frameworks such as journey maps, empathy maps, 2x2's, etc. to help synthesize their data.

Going one step further, you may begin to apply various lenses to this data driven canvas. For example, recoloring by sentiment shows where pain points may distributed across your customer journey. Or, recoloring by participant may reveal if one of your participants may be creating a bias towards a particular theme.

Exploring Qualitative Data through a Quantitative Lens

Once you have begun your analysis, you may visualize your qualitative data in a quantitative way through charts. You may choose between a pie chart and or a stacked bar chart to visualize your data. From here, you can segment your data to break down the ‘bar’ in your bar chart and slices in your pie chart one step further.

To segment your data, you can choose between ‘Tag group’, ‘Tag’, ‘Theme’, and ‘Participant'. Each group shows up as its own bar in the bar chart or slice in the pie chart. For example, try grouping data as ‘Participant’ to see the volume of notes assigned to each person. Or, group by ‘Tag group’ to see which of your tag groups have the most notes.

Depending on how you’ve grouped or segmented your charts will affect the options available to color your chart. Charts use colors that are a mix of sentiment, tag, theme, and default colors. Consider color as a way of assigning another layer of meaning to your data. For example, choose a red color for tags or themes that are areas of friction or pain points. Use blue for tags that represent opportunities.

AI Powered Insights and Cover Images

One of the most powerful features in Analysis is the ability to generate insights with AI. Insights combine information, inspiration, and intuition to help bridge the gap between knowledge and wisdom. Even before you have any tags or themes, you may generate an AI Insight from your entire data set. You'll be able to choose one of our AI Insight templates that are inspired by trusted design thinking frameworks to stimulate generative, and divergent thinking. With just the click of a button, you'll get an insight that captures the essence and story of your research. You may experiment with a combination of tags, themes, and different templates or, create your own custom AI template. These insights are all evidence-based, and are centered on the needs of real people. You may package these insights up to present your research by embedding videos, quotes and using AI to generate unique cover image.

You can sign up to run an end to end research project for free and receive tips on how to make the most out of your data. Want to chat about how Notably can help your team do better, faster research? Book some time here for a 1:1 demo with your whole team.

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Qualitative Data Analysis: Step-by-Step Guide (Manual vs. Automatic)

When we conduct qualitative methods of research, need to explain changes in metrics or understand people's opinions, we always turn to qualitative data. Qualitative data is typically generated through:

• Interview transcripts
• Surveys with open-ended questions
• Contact center transcripts
• Texts and documents
• Audio and video recordings
• Observational notes

Compared to quantitative data, which captures structured information, qualitative data is unstructured and has more depth. It can answer our questions, can help formulate hypotheses and build understanding.

It's important to understand the differences between quantitative data & qualitative data . But unfortunately, analyzing qualitative data is difficult. While tools like Excel, Tableau and PowerBI crunch and visualize quantitative data with ease, there are a limited number of mainstream tools for analyzing qualitative data . The majority of qualitative data analysis still happens manually.

That said, there are two new trends that are changing this. First, there are advances in natural language processing (NLP) which is focused on understanding human language. Second, there is an explosion of user-friendly software designed for both researchers and businesses. Both help automate the qualitative data analysis process.

In this post we want to teach you how to conduct a successful qualitative data analysis. There are two primary qualitative data analysis methods; manual & automatic. We will teach you how to conduct the analysis manually, and also, automatically using software solutions powered by NLP. We’ll guide you through the steps to conduct a manual analysis, and look at what is involved and the role technology can play in automating this process.

More businesses are switching to fully-automated analysis of qualitative customer data because it is cheaper, faster, and just as accurate. Primarily, businesses purchase subscriptions to feedback analytics platforms so that they can understand customer pain points and sentiment.

We’ll take you through 5 steps to conduct a successful qualitative data analysis. Within each step we will highlight the key difference between the manual, and automated approach of qualitative researchers. Here's an overview of the steps:

The 5 steps to doing qualitative data analysis

• Gathering and collecting your qualitative data
• Organizing and connecting into your qualitative data
• Coding your qualitative data
• Analyzing the qualitative data for insights
• Reporting on the insights derived from your analysis

What is Qualitative Data Analysis?

Qualitative data analysis is a process of gathering, structuring and interpreting qualitative data to understand what it represents.

Qualitative data is non-numerical and unstructured. Qualitative data generally refers to text, such as open-ended responses to survey questions or user interviews, but also includes audio, photos and video.

Businesses often perform qualitative data analysis on customer feedback. And within this context, qualitative data generally refers to verbatim text data collected from sources such as reviews, complaints, chat messages, support centre interactions, customer interviews, case notes or social media comments.

How is qualitative data analysis different from quantitative data analysis?

Understanding the differences between quantitative & qualitative data is important. When it comes to analyzing data, Qualitative Data Analysis serves a very different role to Quantitative Data Analysis. But what sets them apart?

Qualitative Data Analysis dives into the stories hidden in non-numerical data such as interviews, open-ended survey answers, or notes from observations. It uncovers the ‘whys’ and ‘hows’ giving a deep understanding of people’s experiences and emotions.

Quantitative Data Analysis on the other hand deals with numerical data, using statistics to measure differences, identify preferred options, and pinpoint root causes of issues.  It steps back to address questions like "how many" or "what percentage" to offer broad insights we can apply to larger groups.

In short, Qualitative Data Analysis is like a microscope,  helping us understand specific detail. Quantitative Data Analysis is like the telescope, giving us a broader perspective. Both are important, working together to decode data for different objectives.

Qualitative Data Analysis methods

Once all the data has been captured, there are a variety of analysis techniques available and the choice is determined by your specific research objectives and the kind of data you’ve gathered.  Common qualitative data analysis methods include:

Content Analysis

This is a popular approach to qualitative data analysis. Other qualitative analysis techniques may fit within the broad scope of content analysis. Thematic analysis is a part of the content analysis.  Content analysis is used to identify the patterns that emerge from text, by grouping content into words, concepts, and themes. Content analysis is useful to quantify the relationship between all of the grouped content. The Columbia School of Public Health has a detailed breakdown of content analysis .

Narrative Analysis

Narrative analysis focuses on the stories people tell and the language they use to make sense of them.  It is particularly useful in qualitative research methods where customer stories are used to get a deep understanding of customers’ perspectives on a specific issue. A narrative analysis might enable us to summarize the outcomes of a focused case study.

Discourse Analysis

Discourse analysis is used to get a thorough understanding of the political, cultural and power dynamics that exist in specific situations.  The focus of discourse analysis here is on the way people express themselves in different social contexts. Discourse analysis is commonly used by brand strategists who hope to understand why a group of people feel the way they do about a brand or product.

Thematic Analysis

Thematic analysis is used to deduce the meaning behind the words people use. This is accomplished by discovering repeating themes in text. These meaningful themes reveal key insights into data and can be quantified, particularly when paired with sentiment analysis . Often, the outcome of thematic analysis is a code frame that captures themes in terms of codes, also called categories. So the process of thematic analysis is also referred to as “coding”. A common use-case for thematic analysis in companies is analysis of customer feedback.

Grounded Theory

Grounded theory is a useful approach when little is known about a subject. Grounded theory starts by formulating a theory around a single data case. This means that the theory is “grounded”. Grounded theory analysis is based on actual data, and not entirely speculative. Then additional cases can be examined to see if they are relevant and can add to the original grounded theory.

Challenges of Qualitative Data Analysis

While Qualitative Data Analysis offers rich insights, it comes with its challenges. Each unique QDA method has its unique hurdles. Let’s take a look at the challenges researchers and analysts might face, depending on the chosen method.

• Time and Effort (Narrative Analysis): Narrative analysis, which focuses on personal stories, demands patience. Sifting through lengthy narratives to find meaningful insights can be time-consuming, requires dedicated effort.
• Being Objective (Grounded Theory): Grounded theory, building theories from data, faces the challenges of personal biases. Staying objective while interpreting data is crucial, ensuring conclusions are rooted in the data itself.
• Complexity (Thematic Analysis): Thematic analysis involves identifying themes within data, a process that can be intricate. Categorizing and understanding themes can be complex, especially when each piece of data varies in context and structure. Thematic Analysis software can simplify this process.
• Generalizing Findings (Narrative Analysis): Narrative analysis, dealing with individual stories, makes drawing broad challenging. Extending findings from a single narrative to a broader context requires careful consideration.
• Managing Data (Thematic Analysis): Thematic analysis involves organizing and managing vast amounts of unstructured data, like interview transcripts. Managing this can be a hefty task, requiring effective data management strategies.
• Skill Level (Grounded Theory): Grounded theory demands specific skills to build theories from the ground up. Finding or training analysts with these skills poses a challenge, requiring investment in building expertise.

Benefits of qualitative data analysis

Qualitative Data Analysis (QDA) is like a versatile toolkit, offering a tailored approach to understanding your data. The benefits it offers are as diverse as the methods. Let’s explore why choosing the right method matters.

• Tailored Methods for Specific Needs: QDA isn't one-size-fits-all. Depending on your research objectives and the type of data at hand, different methods offer unique benefits. If you want emotive customer stories, narrative analysis paints a strong picture. When you want to explain a score, thematic analysis reveals insightful patterns
• Flexibility with Thematic Analysis: thematic analysis is like a chameleon in the toolkit of QDA. It adapts well to different types of data and research objectives, making it a top choice for any qualitative analysis.
• Deeper Understanding, Better Products: QDA helps you dive into people's thoughts and feelings. This deep understanding helps you build products and services that truly matches what people want, ensuring satisfied customers
• Finding the Unexpected: Qualitative data often reveals surprises that we miss in quantitative data. QDA offers us new ideas and perspectives, for insights we might otherwise miss.
• Building Effective Strategies: Insights from QDA are like strategic guides. They help businesses in crafting plans that match people’s desires.
• Creating Genuine Connections: Understanding people’s experiences lets businesses connect on a real level. This genuine connection helps build trust and loyalty, priceless for any business.

How to do Qualitative Data Analysis: 5 steps

Now we are going to show how you can do your own qualitative data analysis. We will guide you through this process step by step. As mentioned earlier, you will learn how to do qualitative data analysis manually , and also automatically using modern qualitative data and thematic analysis software.

To get best value from the analysis process and research process, it’s important to be super clear about the nature and scope of the question that’s being researched. This will help you select the research collection channels that are most likely to help you answer your question.

Depending on if you are a business looking to understand customer sentiment, or an academic surveying a school, your approach to qualitative data analysis will be unique.

Once you’re clear, there’s a sequence to follow. And, though there are differences in the manual and automatic approaches, the process steps are mostly the same.

The use case for our step-by-step guide is a company looking to collect data (customer feedback data), and analyze the customer feedback - in order to improve customer experience. By analyzing the customer feedback the company derives insights about their business and their customers. You can follow these same steps regardless of the nature of your research. Let’s get started.

Step 1: Gather your qualitative data and conduct research (Conduct qualitative research)

The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.

Classic methods of gathering qualitative data

Most companies use traditional methods for gathering qualitative data: conducting interviews with research participants, running surveys, and running focus groups. This data is typically stored in documents, CRMs, databases and knowledge bases. It’s important to examine which data is available and needs to be included in your research project, based on its scope.

Using your existing qualitative feedback

As it becomes easier for customers to engage across a range of different channels, companies are gathering increasingly large amounts of both solicited and unsolicited qualitative feedback.

Most organizations have now invested in Voice of Customer programs , support ticketing systems, chatbot and support conversations, emails and even customer Slack chats.

These new channels provide companies with new ways of getting feedback, and also allow the collection of unstructured feedback data at scale.

The great thing about this data is that it contains a wealth of valubale insights and that it’s already there! When you have a new question about user behavior or your customers, you don’t need to create a new research study or set up a focus group. You can find most answers in the data you already have.

Typically, this data is stored in third-party solutions or a central database, but there are ways to export it or connect to a feedback analysis solution through integrations or an API.

Utilize untapped qualitative data channels

There are many online qualitative data sources you may not have considered. For example, you can find useful qualitative data in social media channels like Twitter or Facebook. Online forums, review sites, and online communities such as Discourse or Reddit also contain valuable data about your customers, or research questions.

If you are considering performing a qualitative benchmark analysis against competitors - the internet is your best friend, and review analysis is a great place to start. Gathering feedback in competitor reviews on sites like Trustpilot, G2, Capterra, Better Business Bureau or on app stores is a great way to perform a competitor benchmark analysis.

Customer feedback analysis software often has integrations into social media and review sites, or you could use a solution like DataMiner to scrape the reviews.

Step 2: Connect & organize all your qualitative data

Now you all have this qualitative data but there’s a problem, the data is unstructured. Before feedback can be analyzed and assigned any value, it needs to be organized in a single place. Why is this important? Consistency!

If all data is easily accessible in one place and analyzed in a consistent manner, you will have an easier time summarizing and making decisions based on this data.

The manual approach to organizing your data

The classic method of structuring qualitative data is to plot all the raw data you’ve gathered into a spreadsheet.

Typically, research and support teams would share large Excel sheets and different business units would make sense of the qualitative feedback data on their own. Each team collects and organizes the data in a way that best suits them, which means the feedback tends to be kept in separate silos.

An alternative and a more robust solution is to store feedback in a central database, like Snowflake or Amazon Redshift .

Keep in mind that when you organize your data in this way, you are often preparing it to be imported into another software. If you go the route of a database, you would need to use an API to push the feedback into a third-party software.

Computer-assisted qualitative data analysis software (CAQDAS)

Traditionally within the manual analysis approach (but not always), qualitative data is imported into CAQDAS software for coding.

In the early 2000s, CAQDAS software was popularised by developers such as ATLAS.ti, NVivo and MAXQDA and eagerly adopted by researchers to assist with the organizing and coding of data.  

The benefits of using computer-assisted qualitative data analysis software:

• Assists in the organizing of your data
• Opens you up to exploring different interpretations of your data analysis
• Allows you to share your dataset easier and allows group collaboration (allows for secondary analysis)

However you still need to code the data, uncover the themes and do the analysis yourself. Therefore it is still a manual approach.

Organizing your qualitative data in a feedback repository

Another solution to organizing your qualitative data is to upload it into a feedback repository where it can be unified with your other data , and easily searchable and taggable. There are a number of software solutions that act as a central repository for your qualitative research data. Here are a couple solutions that you could investigate:  

• Dovetail: Dovetail is a research repository with a focus on video and audio transcriptions. You can tag your transcriptions within the platform for theme analysis. You can also upload your other qualitative data such as research reports, survey responses, support conversations ( conversational analytics ), and customer interviews. Dovetail acts as a single, searchable repository. And makes it easier to collaborate with other people around your qualitative research.
• EnjoyHQ: EnjoyHQ is another research repository with similar functionality to Dovetail. It boasts a more sophisticated search engine, but it has a higher starting subscription cost.

Organizing your qualitative data in a feedback analytics platform

If you have a lot of qualitative customer or employee feedback, from the likes of customer surveys or employee surveys, you will benefit from a feedback analytics platform. A feedback analytics platform is a software that automates the process of both sentiment analysis and thematic analysis . Companies use the integrations offered by these platforms to directly tap into their qualitative data sources (review sites, social media, survey responses, etc.). The data collected is then organized and analyzed consistently within the platform.

If you have data prepared in a spreadsheet, it can also be imported into feedback analytics platforms.

Once all this rich data has been organized within the feedback analytics platform, it is ready to be coded and themed, within the same platform. Thematic is a feedback analytics platform that offers one of the largest libraries of integrations with qualitative data sources.

Step 3: Coding your qualitative data

Your feedback data is now organized in one place. Either within your spreadsheet, CAQDAS, feedback repository or within your feedback analytics platform. The next step is to code your feedback data so we can extract meaningful insights in the next step.

Coding is the process of labelling and organizing your data in such a way that you can then identify themes in the data, and the relationships between these themes.

To simplify the coding process, you will take small samples of your customer feedback data, come up with a set of codes, or categories capturing themes, and label each piece of feedback, systematically, for patterns and meaning. Then you will take a larger sample of data, revising and refining the codes for greater accuracy and consistency as you go.

If you choose to use a feedback analytics platform, much of this process will be automated and accomplished for you.

The terms to describe different categories of meaning (‘theme’, ‘code’, ‘tag’, ‘category’ etc) can be confusing as they are often used interchangeably.  For clarity, this article will use the term ‘code’.

To code means to identify key words or phrases and assign them to a category of meaning. “I really hate the customer service of this computer software company” would be coded as “poor customer service”.

How to manually code your qualitative data

• Decide whether you will use deductive or inductive coding. Deductive coding is when you create a list of predefined codes, and then assign them to the qualitative data. Inductive coding is the opposite of this, you create codes based on the data itself. Codes arise directly from the data and you label them as you go. You need to weigh up the pros and cons of each coding method and select the most appropriate.
• Read through the feedback data to get a broad sense of what it reveals. Now it’s time to start assigning your first set of codes to statements and sections of text.
• Keep repeating step 2, adding new codes and revising the code description as often as necessary.  Once it has all been coded, go through everything again, to be sure there are no inconsistencies and that nothing has been overlooked.
• Create a code frame to group your codes. The coding frame is the organizational structure of all your codes. And there are two commonly used types of coding frames, flat, or hierarchical. A hierarchical code frame will make it easier for you to derive insights from your analysis.
• Based on the number of times a particular code occurs, you can now see the common themes in your feedback data. This is insightful! If ‘bad customer service’ is a common code, it’s time to take action.

We have a detailed guide dedicated to manually coding your qualitative data .

Using software to speed up manual coding of qualitative data

An Excel spreadsheet is still a popular method for coding. But various software solutions can help speed up this process. Here are some examples.

• CAQDAS / NVivo - CAQDAS software has built-in functionality that allows you to code text within their software. You may find the interface the software offers easier for managing codes than a spreadsheet.
• Dovetail/EnjoyHQ - You can tag transcripts and other textual data within these solutions. As they are also repositories you may find it simpler to keep the coding in one platform.
• IBM SPSS - SPSS is a statistical analysis software that may make coding easier than in a spreadsheet.
• Ascribe - Ascribe’s ‘Coder’ is a coding management system. Its user interface will make it easier for you to manage your codes.

Automating the qualitative coding process using thematic analysis software

In solutions which speed up the manual coding process, you still have to come up with valid codes and often apply codes manually to pieces of feedback. But there are also solutions that automate both the discovery and the application of codes.

Advances in machine learning have now made it possible to read, code and structure qualitative data automatically. This type of automated coding is offered by thematic analysis software .

Automation makes it far simpler and faster to code the feedback and group it into themes. By incorporating natural language processing (NLP) into the software, the AI looks across sentences and phrases to identify common themes meaningful statements. Some automated solutions detect repeating patterns and assign codes to them, others make you train the AI by providing examples. You could say that the AI learns the meaning of the feedback on its own.

Thematic automates the coding of qualitative feedback regardless of source. There’s no need to set up themes or categories in advance. Simply upload your data and wait a few minutes. You can also manually edit the codes to further refine their accuracy.  Experiments conducted indicate that Thematic’s automated coding is just as accurate as manual coding .

Paired with sentiment analysis and advanced text analytics - these automated solutions become powerful for deriving quality business or research insights.

You could also build your own , if you have the resources!

The key benefits of using an automated coding solution

Automated analysis can often be set up fast and there’s the potential to uncover things that would never have been revealed if you had given the software a prescribed list of themes to look for.

Because the model applies a consistent rule to the data, it captures phrases or statements that a human eye might have missed.

Complete and consistent analysis of customer feedback enables more meaningful findings. Leading us into step 4.

Step 4: Analyze your data: Find meaningful insights

Now we are going to analyze our data to find insights. This is where we start to answer our research questions. Keep in mind that step 4 and step 5 (tell the story) have some overlap . This is because creating visualizations is both part of analysis process and reporting.

The task of uncovering insights is to scour through the codes that emerge from the data and draw meaningful correlations from them. It is also about making sure each insight is distinct and has enough data to support it.

Part of the analysis is to establish how much each code relates to different demographics and customer profiles, and identify whether there’s any relationship between these data points.

Manually create sub-codes to improve the quality of insights

If your code frame only has one level, you may find that your codes are too broad to be able to extract meaningful insights. This is where it is valuable to create sub-codes to your primary codes. This process is sometimes referred to as meta coding.

Note: If you take an inductive coding approach, you can create sub-codes as you are reading through your feedback data and coding it.

While time-consuming, this exercise will improve the quality of your analysis. Here is an example of what sub-codes could look like.

You need to carefully read your qualitative data to create quality sub-codes. But as you can see, the depth of analysis is greatly improved. By calculating the frequency of these sub-codes you can get insight into which  customer service problems you can immediately address.

Correlate the frequency of codes to customer segments

Many businesses use customer segmentation . And you may have your own respondent segments that you can apply to your qualitative analysis. Segmentation is the practise of dividing customers or research respondents into subgroups.

Segments can be based on:

• Demographic
• And any other data type that you care to segment by

It is particularly useful to see the occurrence of codes within your segments. If one of your customer segments is considered unimportant to your business, but they are the cause of nearly all customer service complaints, it may be in your best interest to focus attention elsewhere. This is a useful insight!

Manually visualizing coded qualitative data

There are formulas you can use to visualize key insights in your data. The formulas we will suggest are imperative if you are measuring a score alongside your feedback.

If you are collecting a metric alongside your qualitative data this is a key visualization. Impact answers the question: “What’s the impact of a code on my overall score?”. Using Net Promoter Score (NPS) as an example, first you need to:

• Calculate overall NPS
• Calculate NPS in the subset of responses that do not contain that theme
• Subtract B from A

Then you can use this simple formula to calculate code impact on NPS .

You can then visualize this data using a bar chart.

You can download our CX toolkit - it includes a template to recreate this.

Trends over time

This analysis can help you answer questions like: “Which codes are linked to decreases or increases in my score over time?”

We need to compare two sequences of numbers: NPS over time and code frequency over time . Using Excel, calculate the correlation between the two sequences, which can be either positive (the more codes the higher the NPS, see picture below), or negative (the more codes the lower the NPS).

Now you need to plot code frequency against the absolute value of code correlation with NPS. Here is the formula:

The visualization could look like this:

These are two examples, but there are more. For a third manual formula, and to learn why word clouds are not an insightful form of analysis, read our visualizations article .

Using a text analytics solution to automate analysis

Automated text analytics solutions enable codes and sub-codes to be pulled out of the data automatically. This makes it far faster and easier to identify what’s driving negative or positive results. And to pick up emerging trends and find all manner of rich insights in the data.

Another benefit of AI-driven text analytics software is its built-in capability for sentiment analysis, which provides the emotive context behind your feedback and other qualitative textual data therein.

Thematic provides text analytics that goes further by allowing users to apply their expertise on business context to edit or augment the AI-generated outputs.

Since the move away from manual research is generally about reducing the human element, adding human input to the technology might sound counter-intuitive. However, this is mostly to make sure important business nuances in the feedback aren’t missed during coding. The result is a higher accuracy of analysis. This is sometimes referred to as augmented intelligence .

Step 5: Report on your data: Tell the story

The last step of analyzing your qualitative data is to report on it, to tell the story. At this point, the codes are fully developed and the focus is on communicating the narrative to the audience.

A coherent outline of the qualitative research, the findings and the insights is vital for stakeholders to discuss and debate before they can devise a meaningful course of action.

Creating graphs and reporting in Powerpoint

Typically, qualitative researchers take the tried and tested approach of distilling their report into a series of charts, tables and other visuals which are woven into a narrative for presentation in Powerpoint.

Using visualization software for reporting

With data transformation and APIs, the analyzed data can be shared with data visualisation software, such as Power BI or Tableau , Google Studio or Looker. Power BI and Tableau are among the most preferred options.

Visualizing your insights inside a feedback analytics platform

Feedback analytics platforms, like Thematic, incorporate visualisation tools that intuitively turn key data and insights into graphs.  This removes the time consuming work of constructing charts to visually identify patterns and creates more time to focus on building a compelling narrative that highlights the insights, in bite-size chunks, for executive teams to review.

Using a feedback analytics platform with visualization tools means you don’t have to use a separate product for visualizations. You can export graphs into Powerpoints straight from the platforms.

Conclusion - Manual or Automated?

There are those who remain deeply invested in the manual approach - because it’s familiar, because they’re reluctant to spend money and time learning new software, or because they’ve been burned by the overpromises of AI.  

For projects that involve small datasets, manual analysis makes sense. For example, if the objective is simply to quantify a simple question like “Do customers prefer X concepts to Y?”. If the findings are being extracted from a small set of focus groups and interviews, sometimes it’s easier to just read them

However, as new generations come into the workplace, it’s technology-driven solutions that feel more comfortable and practical. And the merits are undeniable.  Especially if the objective is to go deeper and understand the ‘why’ behind customers’ preference for X or Y. And even more especially if time and money are considerations.

The ability to collect a free flow of qualitative feedback data at the same time as the metric means AI can cost-effectively scan, crunch, score and analyze a ton of feedback from one system in one go. And time-intensive processes like focus groups, or coding, that used to take weeks, can now be completed in a matter of hours or days.

But aside from the ever-present business case to speed things up and keep costs down, there are also powerful research imperatives for automated analysis of qualitative data: namely, accuracy and consistency.

Finding insights hidden in feedback requires consistency, especially in coding.  Not to mention catching all the ‘unknown unknowns’ that can skew research findings and steering clear of cognitive bias.

Some say without manual data analysis researchers won’t get an accurate “feel” for the insights. However, the larger data sets are, the harder it is to sort through the feedback and organize feedback that has been pulled from different places.  And, the more difficult it is to stay on course, the greater the risk of drawing incorrect, or incomplete, conclusions grows.

Though the process steps for qualitative data analysis have remained pretty much unchanged since psychologist Paul Felix Lazarsfeld paved the path a hundred years ago, the impact digital technology has had on types of qualitative feedback data and the approach to the analysis are profound.  

If you want to try an automated feedback analysis solution on your own qualitative data, you can get started with Thematic .

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Qualitative Data Analysis

23 Presenting the Results of Qualitative Analysis

Mikaila Mariel Lemonik Arthur

Qualitative research is not finished just because you have determined the main findings or conclusions of your study. Indeed, disseminating the results is an essential part of the research process. By sharing your results with others, whether in written form as scholarly paper or an applied report or in some alternative format like an oral presentation, an infographic, or a video, you ensure that your findings become part of the ongoing conversation of scholarship in your field, forming part of the foundation for future researchers. This chapter provides an introduction to writing about qualitative research findings. It will outline how writing continues to contribute to the analysis process, what concerns researchers should keep in mind as they draft their presentations of findings, and how best to organize qualitative research writing

As you move through the research process, it is essential to keep yourself organized. Organizing your data, memos, and notes aids both the analytical and the writing processes. Whether you use electronic or physical, real-world filing and organizational systems, these systems help make sense of the mountains of data you have and assure you focus your attention on the themes and ideas you have determined are important (Warren and Karner 2015). Be sure that you have kept detailed notes on all of the decisions you have made and procedures you have followed in carrying out research design, data collection, and analysis, as these will guide your ultimate write-up.

First and foremost, researchers should keep in mind that writing is in fact a form of thinking. Writing is an excellent way to discover ideas and arguments and to further develop an analysis. As you write, more ideas will occur to you, things that were previously confusing will start to make sense, and arguments will take a clear shape rather than being amorphous and poorly-organized. However, writing-as-thinking cannot be the final version that you share with others. Good-quality writing does not display the workings of your thought process. It is reorganized and revised (more on that later) to present the data and arguments important in a particular piece. And revision is totally normal! No one expects the first draft of a piece of writing to be ready for prime time. So write rough drafts and memos and notes to yourself and use them to think, and then revise them until the piece is the way you want it to be for sharing.

Bergin (2018) lays out a set of key concerns for appropriate writing about research. First, present your results accurately, without exaggerating or misrepresenting. It is very easy to overstate your findings by accident if you are enthusiastic about what you have found, so it is important to take care and use appropriate cautions about the limitations of the research. You also need to work to ensure that you communicate your findings in a way people can understand, using clear and appropriate language that is adjusted to the level of those you are communicating with. And you must be clear and transparent about the methodological strategies employed in the research. Remember, the goal is, as much as possible, to describe your research in a way that would permit others to replicate the study. There are a variety of other concerns and decision points that qualitative researchers must keep in mind, including the extent to which to include quantification in their presentation of results, ethics, considerations of audience and voice, and how to bring the richness of qualitative data to life.

Quantification, as you have learned, refers to the process of turning data into numbers. It can indeed be very useful to count and tabulate quantitative data drawn from qualitative research. For instance, if you were doing a study of dual-earner households and wanted to know how many had an equal division of household labor and how many did not, you might want to count those numbers up and include them as part of the final write-up. However, researchers need to take care when they are writing about quantified qualitative data. Qualitative data is not as generalizable as quantitative data, so quantification can be very misleading. Thus, qualitative researchers should strive to use raw numbers instead of the percentages that are more appropriate for quantitative research. Writing, for instance, “15 of the 20 people I interviewed prefer pancakes to waffles” is a simple description of the data; writing “75% of people prefer pancakes” suggests a generalizable claim that is not likely supported by the data. Note that mixing numbers with qualitative data is really a type of mixed-methods approach. Mixed-methods approaches are good, but sometimes they seduce researchers into focusing on the persuasive power of numbers and tables rather than capitalizing on the inherent richness of their qualitative data.

A variety of issues of scholarly ethics and research integrity are raised by the writing process. Some of these are unique to qualitative research, while others are more universal concerns for all academic and professional writing. For example, it is essential to avoid plagiarism and misuse of sources. All quotations that appear in a text must be properly cited, whether with in-text and bibliographic citations to the source or with an attribution to the research participant (or the participant’s pseudonym or description in order to protect confidentiality) who said those words. Where writers will paraphrase a text or a participant’s words, they need to make sure that the paraphrase they develop accurately reflects the meaning of the original words. Thus, some scholars suggest that participants should have the opportunity to read (or to have read to them, if they cannot read the text themselves) all sections of the text in which they, their words, or their ideas are presented to ensure accuracy and enable participants to maintain control over their lives.

Audience and Voice

When writing, researchers must consider their audience(s) and the effects they want their writing to have on these audiences. The designated audience will dictate the voice used in the writing, or the individual style and personality of a piece of text. Keep in mind that the potential audience for qualitative research is often much more diverse than that for quantitative research because of the accessibility of the data and the extent to which the writing can be accessible and interesting. Yet individual pieces of writing are typically pitched to a more specific subset of the audience.

Let us consider one potential research study, an ethnography involving participant-observation of the same children both when they are at daycare facility and when they are at home with their families to try to understand how daycare might impact behavior and social development. The findings of this study might be of interest to a wide variety of potential audiences: academic peers, whether at your own academic institution, in your broader discipline, or multidisciplinary; people responsible for creating laws and policies; practitioners who run or teach at day care centers; and the general public, including both people who are interested in child development more generally and those who are themselves parents making decisions about child care for their own children. And the way you write for each of these audiences will be somewhat different. Take a moment and think through what some of these differences might look like.

If you are writing to academic audiences, using specialized academic language and working within the typical constraints of scholarly genres, as will be discussed below, can be an important part of convincing others that your work is legitimate and should be taken seriously. Your writing will be formal. Even if you are writing for students and faculty you already know—your classmates, for instance—you are often asked to imitate the style of academic writing that is used in publications, as this is part of learning to become part of the scholarly conversation. When speaking to academic audiences outside your discipline, you may need to be more careful about jargon and specialized language, as disciplines do not always share the same key terms. For instance, in sociology, scholars use the term diffusion to refer to the way new ideas or practices spread from organization to organization. In the field of international relations, scholars often used the term cascade to refer to the way ideas or practices spread from nation to nation. These terms are describing what is fundamentally the same concept, but they are different terms—and a scholar from one field might have no idea what a scholar from a different field is talking about! Therefore, while the formality and academic structure of the text would stay the same, a writer with a multidisciplinary audience might need to pay more attention to defining their terms in the body of the text.

It is not only other academic scholars who expect to see formal writing. Policymakers tend to expect formality when ideas are presented to them, as well. However, the content and style of the writing will be different. Much less academic jargon should be used, and the most important findings and policy implications should be emphasized right from the start rather than initially focusing on prior literature and theoretical models as you might for an academic audience. Long discussions of research methods should also be minimized. Similarly, when you write for practitioners, the findings and implications for practice should be highlighted. The reading level of the text will vary depending on the typical background of the practitioners to whom you are writing—you can make very different assumptions about the general knowledge and reading abilities of a group of hospital medical directors with MDs than you can about a group of case workers who have a post-high-school certificate. Consider the primary language of your audience as well. The fact that someone can get by in spoken English does not mean they have the vocabulary or English reading skills to digest a complex report. But the fact that someone’s vocabulary is limited says little about their intellectual abilities, so try your best to convey the important complexity of the ideas and findings from your research without dumbing them down—even if you must limit your vocabulary usage.

When writing for the general public, you will want to move even further towards emphasizing key findings and policy implications, but you also want to draw on the most interesting aspects of your data. General readers will read sociological texts that are rich with ethnographic or other kinds of detail—it is almost like reality television on a page! And this is a contrast to busy policymakers and practitioners, who probably want to learn the main findings as quickly as possible so they can go about their busy lives. But also keep in mind that there is a wide variation in reading levels. Journalists at publications pegged to the general public are often advised to write at about a tenth-grade reading level, which would leave most of the specialized terminology we develop in our research fields out of reach. If you want to be accessible to even more people, your vocabulary must be even more limited. The excellent exercise of trying to write using the 1,000 most common English words, available at the Up-Goer Five website ( https://www.splasho.com/upgoer5/ ) does a good job of illustrating this challenge (Sanderson n.d.).

Another element of voice is whether to write in the first person. While many students are instructed to avoid the use of the first person in academic writing, this advice needs to be taken with a grain of salt. There are indeed many contexts in which the first person is best avoided, at least as long as writers can find ways to build strong, comprehensible sentences without its use, including most quantitative research writing. However, if the alternative to using the first person is crafting a sentence like “it is proposed that the researcher will conduct interviews,” it is preferable to write “I propose to conduct interviews.” In qualitative research, in fact, the use of the first person is far more common. This is because the researcher is central to the research project. Qualitative researchers can themselves be understood as research instruments, and thus eliminating the use of the first person in writing is in a sense eliminating information about the conduct of the researchers themselves.

But the question really extends beyond the issue of first-person or third-person. Qualitative researchers have choices about how and whether to foreground themselves in their writing, not just in terms of using the first person, but also in terms of whether to emphasize their own subjectivity and reflexivity, their impressions and ideas, and their role in the setting. In contrast, conventional quantitative research in the positivist tradition really tries to eliminate the author from the study—which indeed is exactly why typical quantitative research avoids the use of the first person. Keep in mind that emphasizing researchers’ roles and reflexivity and using the first person does not mean crafting articles that provide overwhelming detail about the author’s thoughts and practices. Readers do not need to hear, and should not be told, which database you used to search for journal articles, how many hours you spent transcribing, or whether the research process was stressful—save these things for the memos you write to yourself. Rather, readers need to hear how you interacted with research participants, how your standpoint may have shaped the findings, and what analytical procedures you carried out.

Making Data Come Alive

One of the most important parts of writing about qualitative research is presenting the data in a way that makes its richness and value accessible to readers. As the discussion of analysis in the prior chapter suggests, there are a variety of ways to do this. Researchers may select key quotes or images to illustrate points, write up specific case studies that exemplify their argument, or develop vignettes (little stories) that illustrate ideas and themes, all drawing directly on the research data. Researchers can also write more lengthy summaries, narratives, and thick descriptions.

Nearly all qualitative work includes quotes from research participants or documents to some extent, though ethnographic work may focus more on thick description than on relaying participants’ own words. When quotes are presented, they must be explained and interpreted—they cannot stand on their own. This is one of the ways in which qualitative research can be distinguished from journalism. Journalism presents what happened, but social science needs to present the “why,” and the why is best explained by the researcher.

So how do authors go about integrating quotes into their written work? Julie Posselt (2017), a sociologist who studies graduate education, provides a set of instructions. First of all, authors need to remain focused on the core questions of their research, and avoid getting distracted by quotes that are interesting or attention-grabbing but not so relevant to the research question. Selecting the right quotes, those that illustrate the ideas and arguments of the paper, is an important part of the writing process. Second, not all quotes should be the same length (just like not all sentences or paragraphs in a paper should be the same length). Include some quotes that are just phrases, others that are a sentence or so, and others that are longer. We call longer quotes, generally those more than about three lines long, block quotes , and they are typically indented on both sides to set them off from the surrounding text. For all quotes, be sure to summarize what the quote should be telling or showing the reader, connect this quote to other quotes that are similar or different, and provide transitions in the discussion to move from quote to quote and from topic to topic. Especially for longer quotes, it is helpful to do some of this writing before the quote to preview what is coming and other writing after the quote to make clear what readers should have come to understand. Remember, it is always the author’s job to interpret the data. Presenting excerpts of the data, like quotes, in a form the reader can access does not minimize the importance of this job. Be sure that you are explaining the meaning of the data you present.

A few more notes about writing with quotes: avoid patchwriting, whether in your literature review or the section of your paper in which quotes from respondents are presented. Patchwriting is a writing practice wherein the author lightly paraphrases original texts but stays so close to those texts that there is little the author has added. Sometimes, this even takes the form of presenting a series of quotes, properly documented, with nothing much in the way of text generated by the author. A patchwriting approach does not build the scholarly conversation forward, as it does not represent any kind of new contribution on the part of the author. It is of course fine to paraphrase quotes, as long as the meaning is not changed. But if you use direct quotes, do not edit the text of the quotes unless how you edit them does not change the meaning and you have made clear through the use of ellipses (…) and brackets ([])what kinds of edits have been made. For example, consider this exchange from Matthew Desmond’s (2012:1317) research on evictions:

The thing was, I wasn’t never gonna let Crystal come and stay with me from the get go. I just told her that to throw her off. And she wasn’t fittin’ to come stay with me with no money…No. Nope. You might as well stay in that shelter.

A paraphrase of this exchange might read “She said that she was going to let Crystal stay with her if Crystal did not have any money.” Paraphrases like that are fine. What is not fine is rewording the statement but treating it like a quote, for instance writing:

The thing was, I was not going to let Crystal come and stay with me from beginning. I just told her that to throw her off. And it was not proper for her to come stay with me without any money…No. Nope. You might as well stay in that shelter.

But as you can see, the change in language and style removes some of the distinct meaning of the original quote. Instead, writers should leave as much of the original language as possible. If some text in the middle of the quote needs to be removed, as in this example, ellipses are used to show that this has occurred. And if a word needs to be added to clarify, it is placed in square brackets to show that it was not part of the original quote.

Data can also be presented through the use of data displays like tables, charts, graphs, diagrams, and infographics created for publication or presentation, as well as through the use of visual material collected during the research process. Note that if visuals are used, the author must have the legal right to use them. Photographs or diagrams created by the author themselves—or by research participants who have signed consent forms for their work to be used, are fine. But photographs, and sometimes even excerpts from archival documents, may be owned by others from whom researchers must get permission in order to use them.

A large percentage of qualitative research does not include any data displays or visualizations. Therefore, researchers should carefully consider whether the use of data displays will help the reader understand the data. One of the most common types of data displays used by qualitative researchers are simple tables. These might include tables summarizing key data about cases included in the study; tables laying out the characteristics of different taxonomic elements or types developed as part of the analysis; tables counting the incidence of various elements; and 2×2 tables (two columns and two rows) illuminating a theory. Basic network or process diagrams are also commonly included. If data displays are used, it is essential that researchers include context and analysis alongside data displays rather than letting them stand by themselves, and it is preferable to continue to present excerpts and examples from the data rather than just relying on summaries in the tables.

If you will be using graphs, infographics, or other data visualizations, it is important that you attend to making them useful and accurate (Bergin 2018). Think about the viewer or user as your audience and ensure the data visualizations will be comprehensible. You may need to include more detail or labels than you might think. Ensure that data visualizations are laid out and labeled clearly and that you make visual choices that enhance viewers’ ability to understand the points you intend to communicate using the visual in question. Finally, given the ease with which it is possible to design visuals that are deceptive or misleading, it is essential to make ethical and responsible choices in the construction of visualization so that viewers will interpret them in accurate ways.

The Genre of Research Writing

As discussed above, the style and format in which results are presented depends on the audience they are intended for. These differences in styles and format are part of the genre of writing. Genre is a term referring to the rules of a specific form of creative or productive work. Thus, the academic journal article—and student papers based on this form—is one genre. A report or policy paper is another. The discussion below will focus on the academic journal article, but note that reports and policy papers follow somewhat different formats. They might begin with an executive summary of one or a few pages, include minimal background, focus on key findings, and conclude with policy implications, shifting methods and details about the data to an appendix. But both academic journal articles and policy papers share some things in common, for instance the necessity for clear writing, a well-organized structure, and the use of headings.

So what factors make up the genre of the academic journal article in sociology? While there is some flexibility, particularly for ethnographic work, academic journal articles tend to follow a fairly standard format. They begin with a “title page” that includes the article title (often witty and involving scholarly inside jokes, but more importantly clearly describing the content of the article); the authors’ names and institutional affiliations, an abstract , and sometimes keywords designed to help others find the article in databases. An abstract is a short summary of the article that appears both at the very beginning of the article and in search databases. Abstracts are designed to aid readers by giving them the opportunity to learn enough about an article that they can determine whether it is worth their time to read the complete text. They are written about the article, and thus not in the first person, and clearly summarize the research question, methodological approach, main findings, and often the implications of the research.

After the abstract comes an “introduction” of a page or two that details the research question, why it matters, and what approach the paper will take. This is followed by a literature review of about a quarter to a third the length of the entire paper. The literature review is often divided, with headings, into topical subsections, and is designed to provide a clear, thorough overview of the prior research literature on which a paper has built—including prior literature the new paper contradicts. At the end of the literature review it should be made clear what researchers know about the research topic and question, what they do not know, and what this new paper aims to do to address what is not known.

The next major section of the paper is the section that describes research design, data collection, and data analysis, often referred to as “research methods” or “methodology.” This section is an essential part of any written or oral presentation of your research. Here, you tell your readers or listeners “how you collected and interpreted your data” (Taylor, Bogdan, and DeVault 2016:215). Taylor, Bogdan, and DeVault suggest that the discussion of your research methods include the following:

• The particular approach to data collection used in the study;
• Any theoretical perspective(s) that shaped your data collection and analytical approach;
• When the study occurred, over how long, and where (concealing identifiable details as needed);
• A description of the setting and participants, including sampling and selection criteria (if an interview-based study, the number of participants should be clearly stated);
• The researcher’s perspective in carrying out the study, including relevant elements of their identity and standpoint, as well as their role (if any) in research settings; and
• The approach to analyzing the data.

After the methods section comes a section, variously titled but often called “data,” that takes readers through the analysis. This section is where the thick description narrative; the quotes, broken up by theme or topic, with their interpretation; the discussions of case studies; most data displays (other than perhaps those outlining a theoretical model or summarizing descriptive data about cases); and other similar material appears. The idea of the data section is to give readers the ability to see the data for themselves and to understand how this data supports the ultimate conclusions. Note that all tables and figures included in formal publications should be titled and numbered.

At the end of the paper come one or two summary sections, often called “discussion” and/or “conclusion.” If there is a separate discussion section, it will focus on exploring the overall themes and findings of the paper. The conclusion clearly and succinctly summarizes the findings and conclusions of the paper, the limitations of the research and analysis, any suggestions for future research building on the paper or addressing these limitations, and implications, be they for scholarship and theory or policy and practice.

After the end of the textual material in the paper comes the bibliography, typically called “works cited” or “references.” The references should appear in a consistent citation style—in sociology, we often use the American Sociological Association format (American Sociological Association 2019), but other formats may be used depending on where the piece will eventually be published. Care should be taken to ensure that in-text citations also reflect the chosen citation style. In some papers, there may be an appendix containing supplemental information such as a list of interview questions or an additional data visualization.

Note that when researchers give presentations to scholarly audiences, the presentations typically follow a format similar to that of scholarly papers, though given time limitations they are compressed. Abstracts and works cited are often not part of the presentation, though in-text citations are still used. The literature review presented will be shortened to only focus on the most important aspects of the prior literature, and only key examples from the discussion of data will be included. For long or complex papers, sometimes only one of several findings is the focus of the presentation. Of course, presentations for other audiences may be constructed differently, with greater attention to interesting elements of the data and findings as well as implications and less to the literature review and methods.

Concluding Your Work

After you have written a complete draft of the paper, be sure you take the time to revise and edit your work. There are several important strategies for revision. First, put your work away for a little while. Even waiting a day to revise is better than nothing, but it is best, if possible, to take much more time away from the text. This helps you forget what your writing looks like and makes it easier to find errors, mistakes, and omissions. Second, show your work to others. Ask them to read your work and critique it, pointing out places where the argument is weak, where you may have overlooked alternative explanations, where the writing could be improved, and what else you need to work on. Finally, read your work out loud to yourself (or, if you really need an audience, try reading to some stuffed animals). Reading out loud helps you catch wrong words, tricky sentences, and many other issues. But as important as revision is, try to avoid perfectionism in writing (Warren and Karner 2015). Writing can always be improved, no matter how much time you spend on it. Those improvements, however, have diminishing returns, and at some point the writing process needs to conclude so the writing can be shared with the world.

Of course, the main goal of writing up the results of a research project is to share with others. Thus, researchers should be considering how they intend to disseminate their results. What conferences might be appropriate? Where can the paper be submitted? Note that if you are an undergraduate student, there are a wide variety of journals that accept and publish research conducted by undergraduates. Some publish across disciplines, while others are specific to disciplines. Other work, such as reports, may be best disseminated by publication online on relevant organizational websites.

After a project is completed, be sure to take some time to organize your research materials and archive them for longer-term storage. Some Institutional Review Board (IRB) protocols require that original data, such as interview recordings, transcripts, and field notes, be preserved for a specific number of years in a protected (locked for paper or password-protected for digital) form and then destroyed, so be sure that your plans adhere to the IRB requirements. Be sure you keep any materials that might be relevant for future related research or for answering questions people may ask later about your project.

And then what? Well, then it is time to move on to your next research project. Research is a long-term endeavor, not a one-time-only activity. We build our skills and our expertise as we continue to pursue research. So keep at it.

• Find a short article that uses qualitative methods. The sociological magazine Contexts is a good place to find such pieces. Write an abstract of the article.
• Choose a sociological journal article on a topic you are interested in that uses some form of qualitative methods and is at least 20 pages long. Rewrite the article as a five-page research summary accessible to non-scholarly audiences.
• Choose a concept or idea you have learned in this course and write an explanation of it using the Up-Goer Five Text Editor ( https://www.splasho.com/upgoer5/ ), a website that restricts your writing to the 1,000 most common English words. What was this experience like? What did it teach you about communicating with people who have a more limited English-language vocabulary—and what did it teach you about the utility of having access to complex academic language?
• Select five or more sociological journal articles that all use the same basic type of qualitative methods (interviewing, ethnography, documents, or visual sociology). Using what you have learned about coding, code the methods sections of each article, and use your coding to figure out what is common in how such articles discuss their research design, data collection, and analysis methods.
• Return to an exercise you completed earlier in this course and revise your work. What did you change? How did revising impact the final product?
• Find a quote from the transcript of an interview, a social media post, or elsewhere that has not yet been interpreted or explained. Write a paragraph that includes the quote along with an explanation of its sociological meaning or significance.

The style or personality of a piece of writing, including such elements as tone, word choice, syntax, and rhythm.

A quotation, usually one of some length, which is set off from the main text by being indented on both sides rather than being placed in quotation marks.

A classification of written or artistic work based on form, content, and style.

A short summary of a text written from the perspective of a reader rather than from the perspective of an author.

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

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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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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Qualitative Research Resources: Presenting Qualitative Research

Created by health science librarians.

• What is Qualitative Research?
• Qualitative Research Basics
• Special Topics
• Training Opportunities: UNC & Beyond
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• Qualitative Software for Coding/Analysis
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• Finding Qualitative Studies
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Presenting Qualitative Research, with a focus on posters

• Qualitative & Libraries: a few gems
• Data Repositories

Example posters

• The Meaning of Work for People with MS: a Qualitative Study A good example with quotes
• Fostering Empathy through Design Thinking Among Fourth Graders in Trinidad and Tobago Includes quotes, photos, diagrams, and other artifacts from qualitative study
• Examining the Use and Perception of Harm of JUULs by College Students: A Qualitative Study Another interesting example to consider
• NLM Informationist Supplement Grant: Daring to Dive into Documentation to Determine Impact An example from the Carolina Digital Repository discussed in a class more... less... Allegri, F., Hayes, B., & Renner, B. (2017). NLM Informationist Supplement Grant: Daring to Dive into Documentation to Determine Impact. https://doi.org/10.17615/bk34-p037
• Qualitative Posters in F1000 Research Archive (filtered on "qualitative" in title) Sample qualitative posters
• Qualitative Posters in F1000 Research Archive (filtered on "qualitative" in keywords) Sample qualitative posters

Michelle A. Krieger Blog (example, posts follow an APA convention poster experience with qualitative posters):

• Qualitative Data and Research Posters I
• Qualitative Data and Research Posters II

"Oldies but goodies":

• How to Visualize Qualitative Data: Ann K. Emery, September 25, 2014 Data Visualization / Chart Choosing, Color-Coding by Category, Diagrams, Icons, Photographs, Qualitative, Text, Timelines, Word Clouds more... less... Getting a little older, and a commercial site, but with some good ideas to get you think.
• Russell, C. K., Gregory, D. M., & Gates, M. F. (1996). Aesthetics and Substance in Qualitative Research Posters. Qualitative Health Research, 6(4), 542–552. Older article with much good information. Poster materials section less applicable.Link is for UNC-Chapel Hill affiliated users.

Additional resources

• CDC Coffee Break: Considerations for Presenting Qualitative Data (Mark D. Rivera, March 13, 2018) PDF download of slide presentation. Display formats section begins on slide 10.
• Print Book (Davis Library): Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative data analysis: A methods sourcebook, 3rd edition From Paul Mihas, Assistant Director of Education and Qualitative Research at the Odum Institute for Research in Social Science at UNC: Qualitative Data Analysis: A Methods Sourcebook (4th ed.) by Miles, Huberman, and Saldana has a section on Displaying the Data (and a chapter on Designing Matrix, Network, and Graphic Displays) that can help students consider numerous options for visually synthesizing data and findings. Many of the suggestions can be applied to designing posters (April 15, 2021).
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• Last Updated: Jul 28, 2024 4:11 PM
• URL: https://guides.lib.unc.edu/qual

Monograph Matters

Qualitative analysis: process and examples | powerpoint – 85.2.

Authors Laura Wray-Lake and Laura Abrams describe qualitative data analysis, with illustrative examples from their SRCD monograph,  Pathways to Civic Engagement Among Urban Youth of Color . This PowerPoint document includes presenter notes, making it an ideal resource for researchers learning about qualitative analysis and for instructors teaching about it in upper-level undergraduate or graduate courses.

Created by Laura Wray-Lake and Laura S. Abrams. All rights reserved.

Citation: Wray-Lake, L. & Abrams, L. S. (2020) Qualitative Analysis: Process and Examples [PowerPoint]. Retrieved from https://monographmatters.srcd.org/2020/05/12/teachingresources-qualitativeanalysis-powerpoint-85-2

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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 ]
• Miles B, Huberman AM. Qualitative data analysis. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]
• 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 ]

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• Published: 06 September 2024

Clinical reasoning amongst paramedics using nebulised β₂ agonists to treat acute asthma exacerbations: a qualitative study

• Craig Mortimer   ORCID: orcid.org/0000-0001-6989-2244 1 , 2 ,
• Dimitra Nikoletou   ORCID: orcid.org/0000-0001-8229-3190 3 ,
• Ann Ooms   ORCID: orcid.org/0000-0002-5217-1907 2 &
• Julia Williams   ORCID: orcid.org/0000-0003-0796-5465 1  

npj Primary Care Respiratory Medicine volume  34 , Article number:  24 ( 2024 ) Cite this article

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• Respiratory signs and symptoms

The heterogeneous nature of asthma results in a wide range of presentations during exacerbation. Despite UK pre-hospital management guidelines focusing on β₂ agonists, variables such as cause, severity, underlying health, comorbidities, and drug side effects can often make emergency treatment optimisation difficult. This article examines paramedics’ methods of observing, perceiving, interpreting, and treating asthma with β₂ agonists, often acting on limited information in rapidly evolving situations. We recruited paramedics from a single UK National Health Service ambulance Trust for qualitative semi-structured interviews. Responses underwent framework analysis to identify data similarities and differences. Fifteen qualitative interviews with paramedics revealed three main themes affecting patient management: clinician experience of presentation, adaptation of patient management approaches, and severity of side effects. Paramedics felt their ability to manage various asthma presentations was enhanced through guideline adaptation based on their own clinical experience and understanding of β₂ agonist side effects, allowing tailored responses based on a set of reinforcing factors. Inductive analysis revealed additional complexities within these themes, such as anxiety and diabetes, which may influence β₂ agonist administration and result in multiple care pathways being initiated during exacerbation. Paramedic care mirrors asthma’s complexity, accounting for a range of characteristics. A dynamic, critically thought approach enables patient management to be based on the presenting conditions rather than strict adherence to a single algorithm. Comprehending the complexities and variables in treatment can be crucial to how paramedics rationalise their treatment and optimise the care provided.

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Qualitative study on perceptions of use of Fractional Exhaled Nitric Oxide (FeNO) in asthma reviews

Improving primary care management of asthma: do we know what really works?

A multinational observational study identifying primary care patients at risk of overestimation of asthma control, introduction.

Globally the prevalence of asthma continues to increase, resulting in greater demand for prehospital and intrahospital services, with predicted increases in morbidity and mortality 1 . According to the National Review of Asthma Deaths 2 , optimisation of basic primary care can achieve significant reductions in these morbidity and mortality rates. Therefore, it is imperative that first-line treatment in the emergency setting and support during recovery is optimised in terms of effectiveness and timeliness 3 , 4 .

However, despite asthma’s heterogeneous nature, including different phenotypes and compounding variables such as comorbidities, the standard treatment for patients experiencing an exacerbation of asthma has remained constant for over forty years and involves the use of bronchodilator drugs (β₂ agonists) to relieve the associated bronchospasm 5 , 6 . Notwithstanding, recent studies suggest that the use of β₂ agonists may result in negative short and long-term effects, with adverse physiological effects, such as arrhythmia, potentially resulting in reciprocal changes including altered blood pressures and modified respiratory patterns, or metabolic changes including increased blood glucose levels, all which can cause greater ventilation/perfusion (V/Q) mismatch 7 , 8 , 9 , 10 , 11 , 12 .

Nonetheless, current international asthma guidelines published by the Global Initiative for Asthma (GINA) continue to state that asthma patients in exacerbation should receive ‘ repetitive administration of short-acting inhaled bronchodilators, early introduction of systemic corticosteroids, and controlled flow oxygen supplementation ’ which can be further supported with additional drugs as required, including ipratropium bromide and magnesium 13 . However, in contrast to these guidelines and those published by the British Thoracic Society 14 , UK ambulance services primarily focus on the use of β₂ agonists without the supplementation of corticosteroids, unless the exacerbation is severe or life-threatening in nature.

Given the divergence of asthma patients, the varying degrees of airway inflammation and the variety of physiological changes induced by first-line treatment during exacerbation, this study sought to explore the perceptions and experiences of ambulance paramedics managing acute exacerbations of asthma in the pre-hospital setting and how they alter their management in response to changes in physiological observations and in consideration of other variables.

This focus is important as healthcare providers may not record these observations or perceive them as problematic in the presence of an asthma exacerbation 8 , 12 , 15 , 16 . Furthermore, patients experiencing an exacerbation may be managed solely by paramedics without subsequent examination by a hospital or General Practitioner (GP) 17 , 18 .

Study design

An exploratory study employed semi-structured interviews devised using a schedule (S 1 Table ) developed applying current literature, the ‘ clinical reasoning cycle ’ framework (Fig. 1 ) 19 and based on an interpretivism philosophy with epistemology construct. By referring to the framework during schedule development, it was hoped that all aspects of clinical reasoning could be considered during interviews.

Cyclical approach used by healthcare professionals to make a clinical judgement.

The interview schedule was primarily developed by CM an experienced paramedic with extensive knowledge and experience of asthma in a variety of settings, with the support of two academics (ND, AO) and a paramedic practitioner with expertise in pre-hospital emergency and primary care. The interviews focused on five key priori categories with additional sub-themes (Table 1 ).

Health and Care Professions Council registered paramedics with experience in face-to-face management of at least three acute asthma exacerbations during the preceding 12-month time frame, were recruited from a single UK ambulance Trust using Trust-wide adverts between 1st April 2021 and 15th December 2021.

During the timeframe, 15 paramedics volunteered to participate in online interviews. A view taken by Guest et al. 20 and Vasileiou et al. 21 amongst others, suggest that data saturation, especially when considering data quality and commonality, is key to effective qualitative research. Given the study location and target population, it was expected that data saturation would be reached with this number of participants. The sample outline for participants is shown in Table 2 . A description of the specified roles can be found in the S 2 Table . Lasting 45 min on average and using Microsoft Teams, interviews were digitally recorded and transcribed verbatim.

Data analysis

Framework analysis 22 , 23 was used to analyse the interviews, allowing a descriptive paradigm to facilitate a deductive and inductive approach. The initial deductive approach used the Joint Royal Colleges Ambulance Liaison Committee (JRCALC) guidelines to develop priori coding. Developed by the committee through consultation with representatives from a wide range of healthcare experts, including paramedics, nurses and GPs, these central guidelines are used by all ambulance clinicians within National Health Service (NHS) ambulance services, and facilitated data coding of collected data into sub-themes aligned to existing literature relevant to the administration of β₂ agonists. Further analysis allowed these codes to be grouped and combined to provide targeted core themes that influence how paramedics manage asthma patients. Inductive analysis was used where appropriate to identify the complexities faced by paramedics when managing patients with asthma.

All data were reviewed by CM and DN for accuracy and consensus regarding coding 24 .

Ethical and regulatory considerations

This article observes the Standards for Reporting Qualitative Research 25 and the study was approved by the Health Research Authority (IRAS 291421). Further ethical approval (KU2756) was obtained from Kingston University, London. Local research governance was gained from the participating Trust and prior to the interview participants provided informed consent both verbally and in writing having reviewed a Participant Information Sheet.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Clinical reasoning themes

Participants’ responses provided a range of codes across the priori categories (Table 1 ). When combined and analysed for commonality the deductively analysed data provided three core themes that focused on the participants’ use of clinical reasoning:

Clinician experience of presentation

Adaptation of patient management approaches, severity of side effects.

Inductive analysis provided fewer codes that whilst associated with the priori category of comorbidity/multimorbidity, referenced variables outside of those stated within the JRCALC’s asthma guideline. These variables are noted as appropriate to support the following themes.

On average participants encountered a patient with asthma every month, with the majority of calls being for patients experiencing mild-moderate exacerbations, rather than severe or life-threatening.

‘Most of the ones who we go to at the moment are quite mild. Actually. I haven’t been to many severe asthma patients in quite some time.’ (32110)

‘I would say the majority are mild to moderate and sometimes edging to severe. Life threatening in my experience is rare.’ (32212)

No consensus could be reached on the average duration of an exacerbation. On average nine of the fifteen participants had experienced exacerbations resolving within 30 min. However, some noted that the duration is not always clear, as once patients are handed over to hospital staff, paramedics tend to move on to the next call. Participants found that mild exacerbations can resolve in the patient’s home environment if the patient’s personal inhaler takes effect before paramedics arrive and/or before the ambulance arrives and provides reassurance.

‘Sometimes they’ve called us out and their pumps have controlled it mostly before us arriving. So, we put them on a single salbutamol neb. and it controls that in the five minutes it takes to run through.’ (4729)

‘The problem is we may have a patient for forty minutes and then hand them over to hospital staff and they are still struggling to breath, so the final time is unknown.’ (1646)

‘I think there’s probably a degree of anxiety around not being able to breath that comes with a worsening asthma attack, and I think that although the salbutamol takes effect, it’s getting that person to calm down and the body just to relax back to normal.’ (1626)

All participants agreed that the patient’s general health status, underlying medical condition, environment, and initial trigger are important for the effective management of the patient. Over half of the participants suggested that the potential cause of exacerbations was influenced by environmental factors, including time of year and corresponding weather.

‘..so whenever you get a change in weather from cold to hot, hot to cold, and things like that, it tends to be a bit of an uptake.’ (32110)

‘I think there’s an environmental factor, depending on what’s set this exacerbation off and whether we’ve been able to remove them from that.’ (4729)

‘If it is initiated by an allergen then that takes time to resolve as the body is fighting on two fronts. The same with an underlying condition, the body will already be weakened so it takes longer to resolve multiple issues with multiple systems and the weaker you are the less you have to fight with.’ (3215)

Additionally, the majority of participants said that patient age should be considered, but agreed that whilst it may be a compounding variable in respiratory disease, it is not a cause in itself. It is believed that older respiratory patients often have comorbidities that may require a change in overall management.

‘The older they get the more complications there are, like COPD.’ (1617 )

All participants reported compliance with JRCALC guidelines when administering salbutamol. However, variations in approach were demonstrated by many when it came to the administration of supporting drugs including ipratropium bromide and hydrocortisone.

‘I don’t tend to step away from the algorithm as it is pretty straight forward. Salbutamol to start, then ipratropium bromide as a follow up with hydrocortisone or adrenaline as required.’ (32212)

‘If we’re seeing like a response from the salbutamol and they’re getting better, then we would stick with that.’ (1616)

Considering the patient’s condition and subsequent response to initial treatment, some participants expressed quite reactionary attitudes. Of note is the use of hydrocortisone, with participants reporting only administering when nothing else seemed to be effective, resulting in infrequent use.

‘I’ll use hydrocortisone after two nebs if the patient is still really struggling and it’s not having much effect and they are haemodynamically compromised, and still can’t speak in full sentence.’ (16111)

In contrast some participants are more regimented with their approach, suggesting that its use is more commonly part of their practice.

‘As soon as the patient hits the criteria of severe, but I know from my practice people are always really surprised when I do get it out and I think that works amazing.’ (1618)

Based on their experience, all participants agreed that patients with asthma often have complex medical conditions which primarily involve cardiorespiratory or immunoglobulin E (IgE) sensitization pathways (e.g. allergies).

The most common compounding condition noted was Chronic Obstructive Pulmonary Disease (COPD), with the majority of participants stating its presence within the elderly population.

‘I wouldn’t like to say that COPD is a comorbidity of asthma. It’s basically a mixture of conditions.’ (3215)

‘I suppose quite frequently you get the patients who potentially could be presenting as a COPD patient or have a history of COPD and they say asthma, and you have to kind of figure out’ (1619)

When a patient has both asthma and COPD, most participants emphasised the need to adjust treatment to take both into account. The process of delivering oxygen for 6 min at a time is often combined with nebulisation to prevent hypercapnic respiratory failure.

‘We change treatment around a central guideline, so COPD patients may receive the same treatment, but the timings may be changed to accommodate the six minutes.’ (1646)

The potential for mimics and confounding presentations was further elaborated by some, noting that misidentification and misdiagnosis can often occur in the absence of a complete medical history, or confirmatory test results. This may initially lead to them treating patients more generally.

‘Is it COPD? Is it asthma? Is it Congestive Heart Failure?’ (32212)

Participants expressed similar confusion when attending patients experiencing some form of IgE pathway reaction. Despite the precedented link between asthma and allergies, just over half of the participants said it was a factor they were aware of, or had experienced.

‘When looking at the lower age ranges you often get patients who have allergic conditions which could act as the trigger for their asthma in the first place…’ (3215)

‘I recently had a patient that was asthmatic and was having either anaphylaxis or just an allergic reaction that set the asthma off, but it was quite severe in terms of stridor which I think was due to the allergic reaction rather than the asthma, so instead of going down the asthma protocol, went down the anaphylaxis protocol.’ (1626)

The majority of participants focused on changes in overall treatment due to additional respiratory or cardiac conditions, while more than half also mentioned the potential for secondary conditions and common risk factors to affect the stability of the patient’s asthma, how it presents and the treatment thereof.

‘I’m just trying to remember the last chap I saw he was quite poorly. I think he was diabetic Type 2 and I think he had high blood pressure as well.’ (1616)

‘There are those patients that have asthma and other related conditions such as obesity, where one supports the other to some extent, which then could lead to additional conditions such as diabetes or cardiac conditions.’ (32212)

‘Even pregnant patients, stroke patients, diabetic patients, cardiac patients and all the others have their own requirements, so it is more about adding to the treatment.’ (1646)

Alongside the physiological changes, some participants noted the importance of recognising psychological changes and their impact on overall management. Participants discussed variables surrounding comprehension, language and a range of mental health conditions, noting that the changes needed to treat a particular patient may result in suboptimal treatment for the exacerbation at that time.

‘The only other things are the patients with learning difficulties or dementia, which is more of a challenge as you need to get them to understand what you are doing and strategies to get them to do what you need them to do, instead of something impacting your treatment strategy.’ (4729)

Overall, participants expressed a dynamic approach to treating patients with asthma. Most suggested prioritising airway treatment, but stated that the focus of treatment could change depending on other observations.

‘We tend to supplement how we treat patients so I may nebulise someone whilst assessing their heart and then I may treat that as well. It is more about stacking treatments.’ (32212)

‘In general it depends on how well controlled they are. If they don’t get much better then there might be something else happening. It isn’t always clear, and I wouldn’t necessarily know……. You change your treatment if needed as your proximity to hospital may be longer and modifying your treatment to manage that presentation, as opposed to that presenting condition.’ (1618)

‘There’s also an element of anxiety that causes problems. Are they having effects from the drug or from the way they are?’ (1618)

Of the fifteen participants, eight reported that they had not knowingly treated a patient with a complaint the patient was unaware of, although the majority of participants highlighted that it is sometimes difficult to distinguish between what is a comorbidity and what are negative side effects from the treatment.

All participants reported that the most commonly observed side effect when administering nebulised salbutamol was cardiac abnormalities. Participants further confirmed that an overall increase in heart rate is common, which may lead to ultimate tachycardia.

‘I’d say that the most obvious ones that I would find are measurable changes in heart rate and blood pressure from the B 2 stimulation. I would say with all adults that I am continuously monitoring, I’d be surprised if there wasn’t an increase in heart rate and usually blood pressure as well.’ (16211)

These cardiac changes do not always occur in isolation, with most participants stating that muscle tremors, although not present in every patient, are commonly witnessed.

‘Like I say, I’d see tremors in one in every five patients I give salbutamol to… The same with tachycardia, I see that maybe every two or three.’ (16111)

‘I would say about fifty percent of the time the patient ends up with like tremors or shaking.’ (4736)

Although increased heart rate was the main side effect, participants did not necessarily see it as a potential problem. Half of the participants would only raise concern when rates reach levels of supraventricular tachycardia (a rate of >150 bpm). To clarify this, several participants said that these effects mainly depend on the patients themselves, taking into account individual characteristics and the different presentations witnessed.

‘I think if the patient became symptomatic and if the patient decompensates with it, then I would probably stop, but I suppose I look at ABC’s and if I’m still significantly concerned about the wheezing and stuff.’ (1619)

‘If their heart rate gets too high the risk is that they may have cardiac issues as well, so it’s a balancing act.’ (1617)

‘Very much I think it’s down to the individual patient. Obviously, they need to breathe. Am I getting enough air in or are they getting enough air in, or am I causing another problem with the gaseous exchange and stuff due to the heart rate…’ (4729)

Participants did not identify other side effects that could potentially be problematic for patients and/or the care provided. Instead, they were considered incidental and included as signs, symptoms, or effects of treatment.

‘You get a lot of headaches reported and sometimes they feel the odd palpitation, but we only hear about that if the patient recognises what is happening and the obvious dry mouth as well, but that is more from the nebuliser effect opposed to the drug itself.’ (3215)

‘I’ve had a few people complaining about headaches after the nebuliser, but I’m not sure if that’s just the extra effort of breathing and the fact that they are having an asthma attack and the stress, or whether it’s just the nebuliser….’ (1626)

The findings from this study demonstrate an approach to practice that is in line with the Clinical Reasoning Cycle 19 , where paramedics instinctually collect information about their patients and use both existing guidelines, patient demographics and their own clinical experience to inform their clinical decision-making. The Clinical Reasoning Cycle 19 illustrates the cognitive processes of acquiring and processing information, from perception to reasoning and demonstrates how medical professionals can effectively treat patients with various contradictory or worsening conditions. Ritz et al. 26 discuss this challenge, suggesting that healthcare is not an exact science, and it is not always possible to label patients by their presenting medical condition 19 .

The analysis found that paramedics effectively followed the guidelines while responding to changes in the patient’s condition and relevant medical history. Participants highlighted this when referring to the older population where the presence of asthma is often complicated by other respiratory conditions such as COPD, or other compounding factors, such as those caused by a cardiac history. Additionally, participants reported that patients often experience side effects such as arrhythmias, tremors and headaches, but these are often accepted as part of the treatment process and only considered problematic when levels are reached that overshadow the presenting asthma exacerbation.

Similarly, the influence of psychological factors can also directly affect the current condition and cause its deterioration. When treating exacerbations, paramedics try to address anxiety and prevent it from becoming a contributing factor. This is discussed by Stubbs et al. 27 who note the impact that anxiety and associated depression can have on the control of asthma, often resulting in poorer health outcomes. These views and subsequent approaches demonstrate a more holistic understanding of the presenting complaint where clinicians look at common linking and underlying factors that may be present. This was reinforced with reference to diabetes as a significant comorbidity, strengthened with discussion of shared risk factors such as weight control and mobility, something Stubbs et al. 27 also note and which is commonly discussed within literature relating to the two conditions 28 . The way paramedics treat patients is based on the clear understanding that no two patients are the same, and depending on the patient’s current situation, paramedics will objectively decide on a treatment plan that may involve an adaptive application of guidelines. Despite three clinical grades of paramedics participating in this study, their roles did not appear to influence their perception or decision-making when managing asthma patients. The reason may be as one participant stated, because ‘ the algorithm ….. is pretty straightforward,’ meaning they simply follow this until treatment needs to be escalated or deescalated, or when contributory factors such as comorbidity require attention.

Despite the lack of homogeneity in asthma patients, the priority in patient management is to ensure airway patency before providing further treatment. Although all participants in this study shared this view, their deeper understanding that the respiratory system does not function independently was apparent and supported by Sarkar et al. 10 who discussed the importance of homoeostasis in maintaining a suitable V/Q balance. Participants often referenced the possibility that cardiac changes could affect the patient’s breathing and corresponding mechanisms. Further consideration showed that these effects may be due to other medical conditions or drug side effects. However, it is unclear at what level these changes become problematic.

Consistent with current literature 7 , 8 and clinical expectations, a general acceptance that cardiac-related side effects may occur when managing asthma patients with β₂ agonists is shown. However, in this instance despite a clinical reasoning approach, clinicians may overlook them as a potential problem, which would not be the case if the effects presented outside of this specific treatment. The reflective component of the Clinical Reasoning Cycle 24 can help conceptualize this, as each asthma patient cared for is exposed to a greater variety of systemic changes, thereby increasing understanding and expectations for the next patient.

Knowing that a patient has a comorbidity which may impact the presenting condition is important. However, surreptitiously replicating those same effects through the use of treatment brings an added risk 7 , 8 , 9 , 10 . The participants demonstrated an awareness of contributing factors present when asthma patients experience an exacerbation of their condition, a point considered by Boulet 29 who outlines that underlying conditions can impact the severity of asthma in several ways, from changing the phenotype to altering presentation.

Patient-centred care heralds the view that patients are individuals, a concept Entwistle and Watt 30 supported when discussing communication and patient empowerment, suggesting that clinicians should also be mindful not to manage patients according to a set list of expectations. Chouchane et al. 31 further advocate this view when citing human factors as key to effectively managing patients, explaining that sometimes the most effective drug may not be suitable for all patients.

Participants clearly demonstrated that they consider the complexity of patient’s individual needs and individual treatment needs, especially when conditions such as stroke, pregnancy, diabetes, and even anxiety are identified. This level of awareness suggests that paramedics and other clinicians may consider varied physiological, psychological and behavioural changes, but as Rosendal 32 Pizzorno 33 and Richens et al. 34 suggest, if we are only treating the presence of symptoms without identifying the causes, these may present themselves as larger problems in the patient’s future. An example drawn from these data is: If administration of salbutamol causes reciprocal changes, including an increase in blood glucose levels, how do clinicians know if their patient is also hyperglycaemic at the time of treatment if they do not routinely assess Capillary Blood Glucose levels for asthma patients 35 .

The findings suggest that the level of understanding demonstrated by all paramedics regarding causation, treatment and compounding factors shows a preference to manage their patients on a ladder of escalation, as opposed to a scripted ‘one-size-fits-all’ approach. The data suggests varying degrees of acceptance that other medical conditions may be present, or that treatment may result in physiological changes that affect the patient’s health status. However, a pre-emptive approach to compensate for this is not always demonstrated. Future research could investigate the specific comorbidities and/or side effects experienced by asthma patients and ascertain the point at which clinicians perceive non-therapeutic effects to be clinically significant for patients, both in the short and long term.

The study recruited at a time when the UK was starting to recover from the COVID-19 pandemic, meaning there were still a number of effects impacting how patients were managed. This was both from the patients’ and clinicians’ perspective and included not only the clinical complications associated with COVID-19, but also a reduction in calls to milder and more moderate asthma exacerbations 36 , 37 . Paramedics may have seen fewer asthma patients during this time compared to the previous two years, and when they did their considerations with these patients would have been complicated by consideration of the COVID-19 infection.

The COVID-19 pandemic further meant we undertook all interviews online. Although this approach may have had benefits, we accept that in-person interviews are preferred to build a rapport between interviewers and interviewees. However, this is not thought to have significantly impacted the data collected for this study.

Although 15 interviews may seem like a small number on the surface, the literature suggests that the number of participants in such studies should not be considered in isolation, but rather as part of the larger element of data adequacy which looks at the quality, richness and level of commonality or differences between data 20 , 21 . We therefore believe that the data collected and presented is suitably representative of the views/actions of paramedics across the participating Trust.

In conclusion, asthma is an inflammatory disease with diverse phenotype characteristics. Recognising the factors present during an exacerbation allows paramedics to respond effectively to individual complexities through consideration of changing phenotypes, compounding comorbidities and response to pharmacological treatment. Consideration of the various physiological (e.g. blood glucose level), psychological (e.g. anxiety) and behavioural changes (e.g. sedentary time) experienced by patients allows paramedics to improve their level of clinical reasoning and dynamically adapt their management to provide optimised care in the short-term and potentially reduce any long-term health implications. These underlying considerations are often based on experience and developmental reflection, influencing drug selection and interpretation of the relevance/impact of any observed side effects or comorbidities.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The researchers wish to acknowledge Peter Eaton-Williams for his support and contribution to the development of the interview schedule and draft manuscript review. We would also like to thank all paramedics from the South East Coast Ambulance Service NHS Foundation Trust who participated in the study. This study received no funding.

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St George’s School of Health and Medical Sciences, City St George’s, University of London, Tooting, London, UK

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Contributions

All authors contributed to the study’s conception and design. Material preparation, data collection and analysis were performed by C.M. and D.N. The first draft of the manuscript was written by C.M. All authors commented on previous versions of the manuscript and approved the final manuscript.

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Correspondence to Craig Mortimer .

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Mortimer, C., Nikoletou, D., Ooms, A. et al. Clinical reasoning amongst paramedics using nebulised β₂ agonists to treat acute asthma exacerbations: a qualitative study. npj Prim. Care Respir. Med. 34 , 24 (2024). https://doi.org/10.1038/s41533-024-00383-w

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Promoting higher education students’ self-regulated learning through learning analytics: A qualitative study

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• Published: 07 September 2024

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• Riina Kleimola   ORCID: orcid.org/0000-0003-2091-2798 1 ,
• Laura Hirsto   ORCID: orcid.org/0000-0002-8963-3036 2 &
• Heli Ruokamo   ORCID: orcid.org/0000-0002-8679-781X 1  

Learning analytics provides a novel means to support the development and growth of students into self-regulated learners, but little is known about student perspectives on its utilization. To address this gap, the present study proposed the following research question: what are the perceptions of higher education students on the utilization of a learning analytics dashboard to promote self-regulated learning? More specifically, this can be expressed via the following threefold sub-question: how do higher education students perceive the use of a learning analytics dashboard and its development as promoting the (1) forethought, (2) performance, and (3) reflection phase processes of self-regulated learning? Data for the study were collected from students ( N  = 16) through semi-structured interviews and analyzed using a qualitative content analysis. Results indicated that the students perceived the use of the learning analytics dashboard as an opportunity for versatile learning support, providing them with a means to control and observe their studies and learning, while facilitating various performance phase processes. Insights from the analytics data could also be used in targeting the students’ development areas as well as in reflecting on their studies and learning, both individually and jointly with their educators, thus contributing to the activities of forethought and reflection phases. However, in order for the learning analytics dashboard to serve students more profoundly across myriad studies, its further development was deemed necessary. The findings of this investigation emphasize the need to integrate the use and development of learning analytics into versatile learning processes and mechanisms of comprehensive support and guidance.

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1 Introduction

Promoting students to become autonomous, self-regulated learners is a fundamental goal of education (Lodge et al., 2019 ; Puustinen & Pulkkinen, 2001 ). The importance of doing so is particularly highlighted in higher education (HE) contexts that strive to prepare its students for highly demanding and autonomous expert tasks (Virtanen, 2019 ). In order to perform successfully in diverse educational and professional settings, students need to take an active, self-initiated role in managing their learning processes, thereby assuming primary responsibility for their educational pursuits. Self-regulated learning (SRL) invites students to actively monitor, control, and regulate their cognition, motivation, and behavior in relation to their learning goals and contextual conditions (Pintrich, 2000 ). In an effort to create a favorable foundation for the development of SRL, many HE institutions have begun to explore and exploit the potential of emerging educational technologies, such as learning analytics (LA).

Despite the growing interest in adopting LA for educational purposes (Van Leeuwen et al., 2022 ), little is known about students’ perspectives on its utilization (Jivet et al., 2020 ; Wise et al., 2016 ). Additionally, there is only limited evidence on using LA to support SRL (Heikkinen et al., 2022 ; Jivet et al., 2018 ; Matcha et al., 2020 ; Viberg et al., 2020 ). Thus, more research is inevitably needed to better understand how students themselves consider the potential of analytics applications from the perspective of SRL. Involving students in the development of LA is particularly important, as they represent primary stakeholders targeted to benefit from its utilization (Dollinger & Lodge, 2018 ; West et al., 2020 ). LA should not only be developed for users but also with them in order to adapt its potential to their needs and expectations (Dollinger & Lodge, 2018 ; Klein et al., 2019 ).

LA is thought to provide a promising means to enhance student SRL by harnessing the massive amount of data stored in educational systems and facilitating appropriate means of support (Lodge et al., 2019 ). It is generally defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs” (Conole et al., 2011 , para. 4). The reporting of such data is typically conducted through learning analytics dashboards (LADs) that aggregate diverse types of indicators about learners and learning processes in a visualized form (Corrin & De Barba, 2014 ; Park & Jo, 2015 ; Schwendimann et al., 2017 ). Recently, there has been a rapid movement into LADs that present analytics data directly to students themselves (Schwendimann et al., 2017 ; Teasley, 2017 ; Van Leeuwen et al., 2022 ). Such analytics applications generally aim to provide students with insights into their study progress as well as support for optimizing learning outcomes (Molenaar et al., 2019 ; Sclater et al., 2016 ; Susnjak et al., 2022 ).

The purpose of this qualitative study is to examine how HE students perceive the use and development of an LAD to promote the different phases and processes of SRL. Instead of taking a course-level approach, this study addresses a less-examined study path perspective that covers the entirety of studies, from the start of an HE degree to its completion. A specific emphasis is placed on such an LAD that students could use both independently across studies and together with their tutor teachers as a component of educational guidance. As analytics applications are largely still under development (Sclater et al., 2016 ), and mainly in the exploratory phase (Schwendimann et al., 2017 ; Susnjak et al., 2022 ), it is essential to gain an understanding of how students perceive the use of these applications as a form of learning support. Preparing students to become efficient self-regulated learners is increasingly—and simultaneously—a matter of helping them develop into efficient users of analytics data.

2 Theoretical framework

2.1 enhancing srl in he.

SRL, which has been the subject of wide research interest over the last two decades (Panadero, 2017 ), is referred to as “self-generated thoughts, feelings, and actions that are planned and cyclically adapted to the attainment of personal goals” (Zimmerman, 1999 , p. 14). Self-regulated students are proactive in their endeavors to learn, and they engage in diverse, personally initiated metacognitive, motivational, and behavioral processes to achieve their goals (Zimmerman, 1999 ). They master their learning through covert, cognitive means but also through behavioral, social, and environmental approaches that are reciprocally interdependent and interrelated (Zimmerman, 1999 , 2015 ), thus emphasizing the sociocognitive views on SRL (Bandura, 1986 ).

When describing and modelling SRL, researchers have widely agreed on its cyclical nature and its organization into several distinct phases and processes (Panadero, 2017 ; Puustinen & Pulkkinen, 2001 ). In the well-established model by Zimmerman and Moylan ( 2009 ), SRL occurs in the cyclic phases of forethought, performance, and self-reflection that take place before, during, and after students’ efforts to learn. In the forethought phase, students prepare themselves for learning and approach the learning tasks through the processes of planning and goal setting, and the activation of self-motivation beliefs, such as self-efficacy perceptions, outcome expectations, and personal interests. Next, in the performance phase, they carry out the actual learning tasks and make use of self-control processes and strategies, such as self-instruction, time management, help-seeking, and interest enhancement. Moreover, they keep records of their performance and monitor their learning, while promoting the achievement of desired goals. In the final self-reflection phase, students participate in the processes of evaluating their learning and reflecting on the perceived causes of their successes and failures, which typically results in different types of cognitive and affective self-reactions as responses to such activity. This phase also forms the basis for the approaches to be adjusted for and applied in the subsequent forethought phase, thereby completing the SRL cycle. The model suggests that the processes in each phase influence the following ones in a cyclical and interactive manner and provide feedback for subsequent learning efforts (Zimmerman & Moylan, 2009 ; Zimmerman, 2011 ). Participation in these processes allows students to become self-aware, competent, and decisive in their learning approaches (Kramarski & Michalsky, 2009 ).

Although several other prevalent SRL models with specific emphases also exist (e.g., Pintrich, 2000 ; Winne & Hadwin, 1998 ; for a review, see Panadero, 2017 ), the one presented above provides a comprehensive yet straightforward framework for identifying and examining the key phases and processes related to SRL (Panadero & Alonso-Tapia, 2014 ). Developing thorough insights into the student SRL is especially needed in an HE context, where the increase in digitized educational settings and tools requires students to manage their learning in a way that is autonomous and self-initiated. When pursuing an HE degree, students are expected to engage in the cyclical phases and processes of SRL as a continuous effort throughout their studies. Involvement in SRL is needed not only to successfully perform a single study module, course, or task but also to actively promote the entirety of studies throughout semesters and academic years. It therefore plays a central role in the successful completion of HE studies.

From the study path perspective, the forethought phase requires HE students to be active in the directing and planning of their studies and learning—that is, setting achievable goals, making detailed plans, finding personal interests, and trusting in their abilities to complete the degree. The performance phase, in turn, invites students to participate in the control and observation of their studies and learning. While completing their studies, they must regularly track study performance, visualize relevant study information, create functional study environments, maintain motivation and interest, and seek and receive productive guidance. The reflection phase, on the other hand, involves students in evaluating and reflecting on their studies and learning—that is, analyzing their learning achievements and processing resulting responses. These activities typically occur as overlapping, cyclic, and connected processes and as a continuum across studies. Additionally, the phases may appear simultaneously, as students strive to learn and receive feedback from different processes (Pintrich, 2000 ). The processes may also emerge in more than one phase (Panadero & Alonso-Tapia, 2014 ), and boundaries between the phases are not always that precise.

SRL is shown to benefit HE students in various ways. Research has evidenced, for instance, that online students who use their time efficiently, are aware of their learning behavior, think critically, and show efforts to learn despite challenges are likely to achieve academic success when studying in online settings (Broadbent & Poon, 2015 ). SRL is also shown to contribute to many non-academic outcomes in HE blended environments (for a review, see Anthonysamy et al., 2020 ). Despite this importance, research (e.g., Azevedo et al., 2004 ; Barnard-Brak et al., 2010 ) has indicated that students differ in their ways to self-regulate, and not all are competent self-regulated learners by default. As such, many students would require and benefit from support to develop their SRL (Moos, 2018 ; Wong et al., 2019 ).

Supporting student SRL is generally considered the responsibility of a teaching staff (Callan et al., 2022 ; Kramarski, 2018 ). It can also be a specific task given to tutor teachers assigned to each student or to a group of students for particular academic years. Sometimes referred to as advisors, they are often teachers of study programs who aim to help students in decision-making, study planning, and career reflection (De Laet et al., 2020 ), while offering them guidance and support for the better management of learning. In recent years, efforts have also been made to promote student SRL with educational technologies such as LA (e.g., Marzouk et al., 2016 ; Wise et al., 2016 ). LA is used to deliver insights for students themselves to better self-regulate their learning (e.g., Jivet et al., 2021 ; Molenaar et al., 2019 ), and also to facilitate the interaction between students and guidance personnel (e.g., Charleer et al., 2018 ). It is generally thought to promote the development of future competences needed by students in education and working life (Kleimola & Leppisaari, 2022 ), and to offer novel insights into their motivational drivers (Kleimola et al., 2023 ).

2.2 LA as a potential tool to promote SRL

Much of the recent development in the field of LA has focused on the design and implementation of LADs. In general, their purpose is to support sensemaking and encourage students and teachers to make informed decisions about learning and teaching processes (Jivet et al., 2020 ; Verbert et al., 2020 ). Schwendimann and colleagues ( 2017 ) refer to an LAD as a “display that aggregates different indicators about learner(s), learning process(es) and/or learning context(s) into one or multiple visualizations” (p. 37). Such indicators may provide information, for instance, about student actions and use of learning contents on a learning platform, or the results of one’s learning performance, such as grades (Schwendimann et al., 2017 ). Data can also be extracted from educational institutions’ student information systems to provide students with snapshots of their study progress and access to learning support (Elouazizi, 2014 ). While visualizations enable intuitive and quick interpretations of educational data (Papamitsiou & Economides, 2015 ), they additionally require careful preparation, as not all users may necessarily interpret them uniformly (Aguilar, 2018 ).

LADs can target various stakeholders, and recently there has been a growing interest in their development for students’ personal use (Van Leeuwen et al., 2022 ). Such displays, also known as student-facing dashboards, are thought to increase students’ knowledge of themselves and to assist them in achieving educational goals (Eickholt et al., 2022 ). They are also believed to promote student autonomy by encouraging students to take control of their learning and by supporting their intrinsic motivation to succeed (Bodily & Verbert, 2017 ). However, simply making analytics applications available to students does not guarantee that they will be used productively in terms of learning (Wise, 2014 ; Wise et al., 2016 ). Moreover, they may not necessarily cover or address the relevant aspects of learning (Clow, 2013 ). Thus, to promote the widespread acceptance and adoption of LADs, it is crucial to consider students’ perspectives on their use as a means of learning support (Divjak et al., 2023 ; Schumacher & Ifenthaler, 2018 ). If students’ needs are not adequately examined and met, such analytics applications may fail to encourage or even hinder the process of SRL (Schumacher & Ifenthaler, 2018 ).

Although previous research on students’ perceptions of LA to enhance their SRL appears to be limited, some studies have addressed such perspectives. Schumacher and Ifenthaler ( 2018 ) found that HE students appreciated LADs that help them plan and initiate their learning activities with supporting elements such as reminders, to-do lists, motivational prompts, learning objectives, and adaptive recommendations, thus promoting the forethought phase of SRL. The students in their study also expected such analytics applications to support the performance phase by providing analyses of their current situation and progress towards goals, materials to meet their individual learning needs, and opportunities for learning exploration and social interaction. To promote the self-reflection phase, the students anticipated LADs to allow for self-assessment, real-time feedback, and future recommendations but were divided as to whether they should receive comparative information about their own or their peers’ performance (Schumacher & Ifenthaler, 2018 ). Additionally, the students desired analytics applications to be holistic and advanced, as well as adaptable to individual needs (Schumacher & Ifenthaler, 2018 ).

Somewhat similar notions were made by Divjak and colleagues ( 2023 ), who discovered that students welcomed LADs that promote short-term planning and organization of learning but were wary of making comparisons or competing with peers, as they might demotivate learners. Correspondingly, De Barba et al. ( 2022 ) noted that students perceived goal setting and monitoring of progress from a multiple-goals approach as key features in LADs, but they were hesitant to view peer comparisons, as they could promote unproductive competition between students and challenge data privacy. In a similar vein, Rets et al. ( 2021 ) reported that students favored LADs that provide them with study recommendations but did not favor peer comparison unless additional information was included. Roberts et al. ( 2017 ), in turn, stressed that LADs should be customizable by students and offer them some level of control to support their SRL. Silvola et al. ( 2023 ) found that students perceived LADs as supportive for their study planning and monitoring at a study path level but also associated some challenges with them in terms of SRL. Further, Bennett ( 2018 ) found that students’ responses to receiving analytics data varied and were highly individual. There were different views, for instance, on the potential of analytics to motivate students: although it seemed to inspire most students, not all students felt the same way (Bennett, 2018 ; see also Corrin & De Barba, 2014 ; Schumacher & Ifenthaler, 2018 ). Moreover, LADs were reported to evoke varying affective responses in students (Bennett, 2018 ; Lim et al., 2021 ).

To promote student SRL, it is imperative that LADs comprehensively address and support all phases of SRL (Schumacher & Ifenthaler, 2018 ). However, a systematic literature review conducted by Jivet et al. ( 2017 ) indicated that students were often offered only limited support for goal setting and planning, and comprehensive self-monitoring, as very few of the LADs included in their study enabled the management of self-set learning goals or the tracking of study progress over time. According to Jivet et al. ( 2017 ), this might indicate that most LADs were mainly harnessed to support the reflection and self-evaluation phase of SRL, as the other phases were mostly ignored. Somewhat contradictory results were obtained by Viberg et al. ( 2020 ), whose literature review revealed that most studies aiming to measure or support SRL with LA were primarily focused on the forethought and performance phases and less on the reflection phase. Heikkinen et al. ( 2022 ) discovered that not many of the studies combining analytics-based interventions and SRL processes covered all phases of SRL.

It appears that further development is inevitably required for LADs to better promote student SRL as a whole. Similarly, there is a demand for their tight integration into pedagogical practices and learning processes to encourage their productive use (Wise, 2014 ; Wise et al., 2016 ). One such strategy is to use these analytics applications as a part of guidance activity and as a joint tool for both students and guidance personnel. In the study by Charleer et al. ( 2018 ), the LAD was shown to trigger conversations and to facilitate dialogue between students and study advisors, improve the personalization of guidance, and provide insights into factual data for further interpretation and reflection. However, offering students access to an LAD only during the guidance meeting may not be sufficient to meet their requirements for the entire duration of their studies. For instance, Charleer and colleagues ( 2018 ) found that the students were also interested in using the LAD independently, outside of the guidance context. Also, it seems that encouraging students to actively advance their studies with such analytics applications necessitates a student-centered approach and holistic development through research. According to Rets et al. ( 2021 ), there is a particular call for qualitative insights, as many previous LAD studies that included students have primarily used quantitative approaches (e.g., Beheshitha et al., 2016 ; Divjak et al., 2023 ; Kim et al., 2016 ).

2.3 Research questions

The purpose of this qualitative study is to examine how HE students perceive the utilization of an LAD in SRL. A specific emphasis was placed on its utilization as part of the forethought, performance, and reflection phase processes, considered central to student SRL. The main research question (RQ) and the threefold sub-question are as follows:

RQ: What are the perceptions of HE students on the utilization of an LAD to promote SRL?

How do HE students perceive the use of an LAD and its development as promoting the (1) forethought, (2) performance, and (3) reflection phase processes of SRL?

3.1 Context

The study was conducted in a university of applied sciences (UAS) in Finland that had launched an initial version of an LAD to be piloted together with its students and tutor teachers as a part of the guidance process. The LAD was descriptive in nature and consisted of commonly available analytics data and simple analytics indicators showing an individual student’s study progress and success in a study path. As is typical for descriptive analytics, it offered insights to better understand the past and present (Costas-Jauregui et al., 2021 ) while informing the future action (Van Leeuwen et al., 2022 ). The data were extracted from the UAS’ student information system and presented using Microsoft Power BI tools. No predictive or comparative information was included. The main display of the LAD consisted of three data visualizations and an information bar (see Fig.  1 , a–d), all presented originally in Finnish. Each visualization could also be expanded into a single display for more accurate viewing.

An example of the main display of the piloted LAD with data visualizations ( a – c ) and an information bar ( d )

First, the LAD included a data visualization that illustrated a student’s study progress and success per semester using a line chart (Fig.  1 , a). It displayed the scales for total number of credit points (left) and grade point averages (right) for courses completed on a semester timeline. Data points on the chart displayed an individual student’s study performance with respect to these indicators in each semester and were connected to each other with a line. Pointing to one of these data points also opened a data box that indicated the student name and information about courses (course name, scope, grade, assessment date) from which the credit points and grade point averages were obtained.

Second, the LAD contained another type of line chart that indicated a student’s individual study progress over time in more detail (Fig.  1 , b). The chart displayed a timeline with three-month periods and illustrated a scale for the accumulated credit points. Data points on the chart indicated the accumulated number of credit points obtained from the courses and appeared in blue if the student had passed the course(s) and in red if the student had failed the course(s) at that time. As with the line chart above it, the data points in this chart also provided more detailed information about the courses behind the credit points and were intertwined with a line.

Third, the LAD offered information related to a student’s study success through a radar chart (Fig.  1 , c). The chart represented the courses taken by the student and displayed a scale for the grades received from them. The lowest grade was placed in the center of the chart and the highest one on its outer circle. The grades in between were scaled on the chart accordingly, and the courses performed with a similar grade were displayed close to each other. Data points on the chart represented the grades obtained from numerically evaluated courses and were merged with a line. Each data point also had a data box with the course name and the grade obtained.

Fourth, the LAD included an information bar (Fig.  1 , d) that displayed the student number and the student name (removed from the figure), the total number of accumulated credit points, the grade point average for passed courses, and the amount of credit points obtained from practical training.

The LAD was piloted in authentic guidance meetings in which a tutor teacher and a student discussed topical issues related to the completion of studies. Such meetings were a part of the UAS’ standard guidance discussions that were typically held 1–2 times during the academic year, or more often if needed. In the studied meetings, the students and tutor teachers collectively reviewed the LAD to support the discussion. Only the tutor teachers were commonly able to access the LAD, as it was still under development and in the pilot phase. However, the students could examine its use as presented by the tutor teacher. In addition to the LAD, the meeting focused on reviewing the student’s personal study plan, which contained information about their studies to be completed and could be viewed through the student information system. Most of the meetings were organized online, and their duration varied according to an individual student’s needs. A researcher (first author) attended the meetings as an observer.

3.2 Participants and procedures

Participants were HE students ( N  = 16) pursuing a bachelor’s degree at the Finnish University of Applied Sciences (UAS), ranging from 21 to 49 years of age (mean = 30.38, median = 29.5); 11 (68.75%) were female, and 5 (31.25%) were male. HE studies commenced between 2016 and 2020, and comprised different academic fields, including business administration, culture, engineering, humanities and education, and social services and health care. Depending on the degree, study scope ranged from 210 to 240 ECTS credit points, which take approximately three and a half to four years to complete. However, the students could also proceed at a faster or slower pace under certain conditions. The students were selected to represent different study fields and study stages, and to have studied for more than one academic year. Informed consent to participate in the study was obtained from all students, and their participation was voluntary. The research design was approved by the respective UAS.

Data for this qualitative study was collected through semi-structured, individual student interviews conducted in April–September 2022. To address certain topics in each interview, an interview guide was used. The interview questions incorporated into the guide were tested in two student test interviews to simulate a real interview situation and to assure intelligibility, as also suggested by Chenail ( 2011 ). Findings indicated that the questions were largely usable, functional, and understandable, but some had to be slightly refined to ensure their conciseness and to improve clarity and familiarity of expressions vis-à-vis the target group. Also, the order of questions was partly reshaped to support the flow of discussion.

In the interviews, the students were asked to provide information about their demographic and educational backgrounds as well as their overall opinions of educational practices and the use of LA. In particular, they were invited to share their views on the use of the piloted LAD and its development as promoting different phases and processes of SRL. Students’ perceptions were generally based on the assumption that they could use the LAD both independently during their studies and collectively with their tutor teachers as a component of the guidance process.

Interviews were conducted immediately or shortly after the guidance meeting. Interview duration ranged from 42 to 70 min. The graphical presentation of the LAD was commonly shown to the students to provide stimuli and evoke discussion, as suggested by Kwasnicka et al. ( 2015 ). The interviews were conducted by the same researcher (first author) who observed the guidance meetings. They were primarily held online, and only one was organized face-to-face. All interviews were video recorded for subsequent analysis.

3.3 Data analysis

Interview recordings were transcribed verbatim, accumulating a total of 187 pages of textual material for analysis (Times New Roman, 12-point font, line spacing 1). A qualitative content analysis method was used to analyze the data (see Mayring, 2000 ; Schreier, 2014 ) to enhance in-depth understanding of the research phenomenon and to inform practical actions (Krippendorf, 2019 ). Also, data were approached both deductively and inductively (see Elo & Kyngäs, 2008 ; Merriam & Tisdell, 2016 ), and the analysis was supported using the ATLAS.ti program.

Analysis began with a thorough familiarization with the data in order to develop a general understanding of the students’ perspectives. First, the data were deductively coded using Zimmerman and Moylan’s ( 2009 ) SRL model as a theoretical guide for analysis and as applied to the study path perspective. All relevant units of analysis—such as paragraphs, sentences, or phrases that addressed the use of the LAD or its development in relation to the processes of SRL presented in the model—were initially identified from the data, and then sorted into meaningful units with specific codes. The focus was placed on instances in the data that were applicable and similar to the processes represented in the model, but the analysis was not limited to those that fully corresponded to them. The preliminary analysis involved several rounds of coding that ultimately led to the formation of main categories, grouped into the phases of SRL. The forethought phase consisted of processes that emphasized the planning and directing of studies and learning with the LAD. The performance phase, in turn, involved processes that addressed the control and observation of studies and learning through the LAD. Finally, the reflection phase included processes that focused on evaluating and reflecting on studies and learning with the LAD.

Second, the data were approached inductively by examining the use of the LAD and its development as distinct aspects within each phase and process of SRL (i.e., the main categories). The aim was, on the one hand, to identify how the use of the LAD was considered to serve the students in the phases and processes of SRL in its current form, and on the other hand, how it should be improved to better support them. The analysis not only focused on the characteristics of the LAD but also on the practices that surrounded its use and development. The units of analysis were first condensed from the data and then organized into subcategories for similar units. As suggested by Schreier ( 2014 ), the process was continued until a saturation point was reached—that is, no additional categories could be found. As a result, subcategories for all of the main categories were identified.

Following Schreier’s ( 2014 ) recommendation, the categories were named and described with specific data examples. Additionally, some guidelines were added to highlight differences between categories and to avoid overlap. Using parts of this categorization framework as a coding scheme, a portion of the data (120 text segments) was independently coded into the main categories by the first and second authors. The results were then compared, and all disagreements were resolved through negotiation until a shared consensus was reached. After minor changes were made to the coding scheme, the first author recoded all data. The number of students who had provided responses to each subcategory was counted and added to provide details on the study. For study integrity, the results are supported by data examples with the students’ aliases and the study fields they represented. The quotations were translated from Finnish to English.

The results are reported by first answering the threefold sub-question, that is, how do HE students perceive the use of an LAD and its development as promoting the (1) forethought, (2) performance, and (3) reflection phase processes of SRL. The subsequent results are then summarized to address the main RQ, that is, what are the main findings on HE students’ perceptions on the utilization of an LAD to promote SRL.

4.1 LAD as a part of the forethought phase processes

The students perceived the use of the LAD and its development as related to the forethought phase processes of SRL through the categorization presented in Table  1 below.

Regarding the process of goal setting , almost all students ( n  = 15) emphasized that the use of the LAD promoted the targeting of goal-oriented study completion and competence development. Analytics indicators—such as grades, grade point averages, and accumulated credit points—adequately informed the students of areas they should aim for, further improve, or put more effort into. Only one student ( n  = 1) considered the analytics data too general for establishing goals. However, some students ( n  = 7) specifically mentioned their desire to set and enter individual goals in the LAD. The students were considered to have individual intentions, which should also be made visible in the LAD:

For example, someone might complete [their studies] in four years, someone might do [them] even faster, so maybe in a way that there is the possibility…to set…that, well, I want or my goal is to graduate in this time, and then it would kind of show in it. (Sophia, Humanities and Education student)

Moreover, some students ( n  = 6) wanted to obtain information on the degree program’s overall target times, study requirements, or pace recommendations through the LAD.

In relation to the process of study planning , the use of the LAD provided many students ( n  = 8) grounds to plan and structure the promotion and completion of their studies, such as which courses and types of studies to choose, and what kind of study pace and schedule to follow. However, an even greater set of students ( n  = 12) hoped that the LAD could provide them with more sophisticated tools for planning. For instance, it could inform them about studies to be completed, analyze their study performance in detail, or make predictions for the future. Moreover, it should offer them opportunities to choose courses, make enrollments, set schedules, get reminders, and take notes. One example of such an advanced analytics application was described as follows: ‟It would be a bit like a conversational tool with the student as well, that you would first put…your studies in the program, so it would [then] remind you regularly that hey, do this” (James, Humanities and Education student).

When discussing the use of the LAD, most students ( n  = 12) emphasized the critical role of personal interests and preferences , which was found to not only guide studying and learning in general but to also drive and shape the utilization of the LAD. According to the students, using such an analytics application could particularly benefit those students who, for instance, prefer monitoring of study performance, perceive information in a visualized form, are interested in analytics or themselves, or find it relevant for their studies. Prior familiarization was also considered useful: ‟Of course, there are those who use this kind of thing more and those who use this kind of thing in daily life, so they could especially benefit from this, probably more than I do” (Olivia, Social Services and Health Care student). Even though the LAD was considered to offer pertinent insights for many types of learners, it might not be suitable for all. For instance, it could be challenging for some students to comprehend analytics data or to make effective use of them in their studies. In the development of the LAD, such personal aspects should be noted. The students ( n  = 7) believed the LAD might better adapt to students’ individual needs if it allows them to customize its features and displays or to use it voluntarily based on one’s personal interests and needs.

When describing the use of LAD, half of the students ( n  = 8) discussed its connections with self-efficacy . Making use of analytics data appeared to strengthen the students’ beliefs in their abilities to study and learn in a targeted manner, even if their own feelings suggested otherwise. As one of the students stated:

It’s nice to see that progress, that it has happened although it feels that it hasn’t. So, you can probably set goals based on [an idea] that you’re likely to progress, you could set [them] that you could graduate sometime. (Emma, Engineering student)

On the other hand, the use of the LAD also seemed to require students to have sufficient self-efficacy. It was perceived as vital especially when the analytics data showed unfavorable study performance, such as failed or incomplete courses, or gaps in the study performance with respect to peers. One student ( n  = 1) suggested that the LAD could include praises as evidence of and support for appropriate study performance. Such incentives may help improve the students’ self-confidence as learners. Apart from this, however, the students had no other recommendations for developing the use of the LAD to support self-efficacy.

4.2 LAD as a part of the performance phase processes

The students discussed the use of the LAD and its development in relation to the performance phase processes of SRL according to the categories described in Table  2 below.

The students ( n  = 16) widely agreed that using the LAD benefited them in the process of metacognitive monitoring. By indicating the progress and success of study performance, the LAD was thought to be well suited for observing the course of studies and the development of competences. Moreover, it helped the students to gain awareness of their individual strengths and weaknesses, as well as successes and failures, in a study path. Tracking individual study performance was also found to contribute to purposeful study completion, as the following data example demonstrates:

It’s important especially when there is a target time to graduate, so of course you must follow and stay on track in many ways as there are many such pitfalls to easily fall into, [and] as I’ve fallen quite many times, it’s good [to monitor]. (Sarah, Culture student)

Additionally, the insights of monitoring could be used in future job searches to provide information about acquired competences to potential employers. The successful promotion of studies was generally perceived to require regular monitoring by both students and their educators. However, one of the students considered it a particular responsibility of the students themselves, as the studies were completed at an HE level and were thus voluntary for them. To provide more in-depth insights, many students ( n  = 12) recommended the incorporation of a course-level monitoring opportunity in the LAD. More detailed information was needed, for instance, about course descriptions, assignments completed, and grades received. The rest of the students ( n  = 4), however, wanted to keep the course-level monitoring within the learning management system. One of them stated that it could also be a place through which the students could use the LAD. Some students ( n  = 6) emphasized the need to reconsider current assessment practices to enable better tracking of study performance. Specifically, assessments could be made in greater detail and grades given immediately after course completion. The variation in scales and time points of assessments between the courses and degree programs posed potential challenges for monitoring, thus prompting the need to unify educational practices at the organizational level.

As an activity closely related to metacognitive monitoring, the process of imaging and visualizing was emphasized by the students as helping them to advance in their educational pursuits. Most students ( n  = 15) mentioned that using the LAD allowed them to easily image their study path and clarify their study situation. As one of them stated, ‟This is quite clear, this like, that you can see the overall situation with a quick glance” (Anna, Business Administration student). The visualizations were perceived as informative, tangible, and understandable. However, they were also thought to carry the risk of students neglecting some other relevant aspects of studying and learning in the course of attracting such focused attention. Although the visualizations were generally considered clear, some students ( n  = 11) noted that they could be further improved to better organize the analytics data. For instance, the students suggested the attractive use of colors and the categorization of different types of courses. Visual symbols, in turn, may be particularly effective in course-level data. Technical aspects should also be carefully considered to avoid false visualizations.

Regarding the process of environmental structuring , the LAD appeared to be a welcome addition to the study toolkit and overall study environment. A few students ( n  = 4) considered it appropriate to utilize the LAD as a separate PowerBI application alongside other (Microsoft O365) study tools, but they also felt that it could be utilized through other systems if necessary. However, one student ( n  = 1) raised the need for overall system integrations and some students ( n  = 8) expressed a specific wish to use the LAD as an integrated part of the student information system that was thought to improve its accessibility. A few students ( n  = 6) also wanted to receive some additional analytics data as related to the information stored in such a system. For instance, the students could be informed about their study progress or offered feedback on their overall performance in relation to the personal study plan. Other students ( n  = 10), in turn, did not consider the need for this or did not mention it. It was generally emphasized that the LAD should remain sufficiently clear and simple, as too much information can make its use ineffective:

I think there is just enough information in this. Of course, if you would want to add something small, you could, but I don’t know how much, because I feel that when there is too much information, so it’s a bit like you can’t get as much out of it as you could get. (Olivia, Social Services and Health Care student)

Moreover, the analytics data must be kept private and protected. The students generally desired personal access to the LAD; if given such an opportunity, almost all ( n  = 15) believed they would utilize it in the future, and only one ( n  = 1) was unsure about this prospect. The analytics data were believed to be of particular use when studies were actively promoted. Hence they should be made available to the students from the start of their studies.

Regarding the process of interest and motivation enhancement , all students ( n  = 16) mentioned that using the LAD stimulated their interest or enhanced their motivation, although to varying degrees. For some students, a general tracking of studies was enough to encourage them to continue their pursuits, while others were particularly inspired by seeing either high or low study performance. The development of motivation and interest was generally thought to be a hindrance if the students perceived the analytics data as unfavorable or lacking essential information. As one of students mentioned, ‟If your [chart] line was downward, and if there were only ones and zeros or something like that, it could in a way decrease the motivation” (Helen, Humanities and Education student). It appeared that enhancing interest and motivation was mainly dependent on the students’ own efforts to succeed in their course of study and thus to generate favorable analytics data. However, some students ( n  = 7) felt that it could be additionally enhanced by diversifying and improving the analytics tools in the LAD. For example, the opportunities for more detailed analyses and future study planning or comparisons of study performance with that of peers might further increase these students’ motivation and interest in their studies. Even so, it was also considered possible that especially comparisons between students might have the opposite, demotivating and discouraging effect.

All students ( n  = 16) mentioned that using the LAD facilitated the process of seeking and accessing help . It enabled the identification of potential support needs—for instance, if several courses were failed or left unfinished. As noted, they were perceived as alarming signals for the students themselves to seek help and for the guidance personnel to provide targeted support. As one of the students emphasized, it was important that not only ‟a teacher [tutor] gets interested in looking at what the situation is but also that a student would understand to communicate regarding the promotion of studies and situations” (Emily, Social Services and Health Care student). Some students ( n  = 9) suggested that the students, tutor teachers, or both could receive automated alerts if concerns were to arise. On the other hand, the impact of such automated notifications on changing the course of study was considered somewhat questionable. Above all, the students ( n  = 16) preferred human contact and personal support by the guidance personnel, who would use a sensitive approach to address possibly delicate issues. Support would be important to include in existing practices, as the tutor teachers should not be overburdened. One of the students also stated that the automated alerts could be sufficient if they just worked effectively.

4.3 LAD as a part of the reflection phase and processes

The students addressed the use of the LAD and its development as a part of the reflection phase processes of SRL through categories outlined in Table  3 .

The students widely appreciated the support provided by the use of the LAD for the process of evaluation and reflection. The majority ( n  = 15) mentioned that it allowed them to individually reflect on the underlying aspects of their study performance, such as what kind of learners they are, what type of teaching or learning methods suit them, and what factors impact their learning. Similarly, the students ( n  = 16) valued the possibility of examining the analytics data together with the guidance personnel, such as tutor teachers, and commonly expressed a desire to revisit the LAD in future guidance meetings. It was thought to promote the interpretation of analytics data and to facilitate collective reflection on the reasons behind one’s study success or failure. However, this might require a certain orientation from the guidance personnel, as the student describes below:

I feel that it’s possible to address such themes that what may perhaps cause this. Of course, a lot depends on how amenable the teacher [tutor] is, like are we focusing on how the studies are going but in a way, not so much on what may cause it. (Sophia, Humanities and Education student)

Some students ( n  = 8) proposed incorporating familiarization with analytics insights into course implementations of the degree programs. Additionally, many students ( n  = 11) expressed a desire to examine the student group’s general progress in tutoring classes together with the tutor teacher and peers, particularly if the results were properly targeted and anonymized, and presented in a discreet manner. However, some students ( n  = 5) found this irrelevant. The students were generally wary to evaluate and compare an individual student’s study performance in relation to the peer average through the LAD. While some students ( n  = 4) welcomed such an opportunity, others ( n  = 6) considered it unnecessary. A few students ( n  = 5) emphasized that such comparisons between students should be optional and visible if desired, and one student ( n  = 1) did not have a definite view about it. Rather than competing with others, the students stressed the importance of challenging themselves and evaluating study performance against their own goals or previous achievements.

According to the students ( n  = 16), the use of the LAD was associated with a wide range of affective reactions . Positive responses such as joy, relief, and satisfaction were considered to emerge if the analytics data displayed by the LAD was perceived as favorable and expected, and supportive of future learning. Similarly, negative responses such as anxiety, pressure, or stress were likely to occur if such data indicated poor performance, thus challenging the learning process. On the other hand, such self-reactions could also appear as neutral or indifferent, depending on the student and the situation. Individual responses were related not only to the current version of the LAD but also to its further development targets. Some students ( n  = 3) pointed out the importance of guidance and support, through which the affective reactions could be processed together with professionals. As one of the students underlined, it is important “that there is also that support for the studies, that it isn’t just like you have this chart, and it looks bad, that try to manage. Perhaps there is that support, support in a significant role as well” (Sophia, Humanities and Education student). It seemed critical that the students were not left alone with the LAD but rather were given assistance to deal with the various responses its use may elicit.

4.4 Summary of findings on LAD utilization to promote SRL among HE students

In summary, HE students’ perceptions on the utilization of an LAD to promote SRL phases and processes were largely congruent, but nonetheless partly varied. In particular, the students agreed on the support provided by the LAD during the performance phase and for the purpose of metacognitive monitoring. Such activity was thought to not only enable the students to observe their studies and learning, but to also create the basis for the emergence of all other processes, which were facilitated by the monitoring. That is, while the students familiarized themselves with the course of their studies via the analytics data, they could further apply these insights—for instance, to visualize study situations, enhance motivation, and identify possible support needs. Monitoring with the LAD was also perceived to partly promote the students to the forethought and reflection phases and processes by giving them grounds to target their development areas as well as to reflect on their studies and learning individually and jointly with their tutor teachers. However, it was clear that less emphasis was placed on using the LAD for study planning, addressing individual interests, activating self-efficacy, and supporting environmental structuring, thus giving incentives for their further investigation and future improvement.

Although the LAD used in this study seemed to serve many functions as such, its holistic development was deemed necessary for more thorough SRL support. In particular, the students agreed on the need to improve such an analytics application to further strengthen the performance phase processes—particularly monitoring—by, for instance, developing it for the students’ independent use, and by integrating it with instructional and guidance practices provided by their educators. Moreover, the students commonly wished for more advanced analytics tools that could more directly contribute to the planning of studies and joint reflection of group-level analytics data. To better support the various processes of SRL, new features were generally welcomed into the LAD, although the students’ views and emphases on them also varied. Mixed perspectives were related, for instance, to the need to enrich data or compare students within the LAD. Thus, it seemed important to develop the LAD to conform to the preferences of its users. Along with improving the LAD, students also paid attention to the development of pedagogical practices and guidance processes that together could create appropriate conditions for the emergence of SRL.

5 Discussion

The purpose of this study was to gain insights into HE students’ perceptions on the utilization of an LAD to promote their SRL. The investigation extended the previous research by offering in-depth descriptions of the specific phases and processes of SRL associated with the use of an LAD and its development targets. By applying a study path perspective, it also provided novel insights into how to promote students to become self-regulated learners and effective users of analytics data as an integral part of their studies in HE.

The students’ perspectives on the use of LAD and its development were initially explored as a part of the forethought phase processes of SRL, with a particular focus on the planning and directing of studies and learning. In line with previous research (e.g., Divjak et al., 2023 ; Schumacher & Ifenthaler, 2018 ; Silvola et al., 2023 ), the students in this study appreciated an analytics application that helped them prepare for their future learning endeavors—that is, the initial phase of the SRL cycle (see Zimmerman & Moylan, 2009 ). Using the LAD specifically allowed the students to recognize their development areas and offered a basis to organize their future coursework. However, improvements to allow students to set individual goals and make plans directly within the LAD, as well as to increase awareness of general degree goals, were also desired. These seem to be pertinent avenues for development, as goals may inspire the students not only to invest greater efforts in learning but also to track their achievements against these goals (Wise, 2014 ; Wise et al., 2016 ). While education is typically entered with individual starting points, it is important to allow the students to set personal targets and routes for their learning (Wise, 2014 ; Wise et al., 2016 ).

The results of this study indicate that the use of LADs is primarily driven and shaped by students’ personal interests and preferences, which commonly play a crucial role in the development of SRL (see Zimmerman & Moylan, 2009 ; Panadero & Alonso-Tapia, 2014 ). It might particularly benefit those students for whom analytics-related activities are characteristic and of interest, and who consider them personally meaningful for their studies. It has been argued that if students consider analytics applications serve their learning, they are also willing to use them (Schumacher & Ifenthaler, 2018 ; Wise et al., 2016 ). On the other hand, it has also been stated that not all students are necessarily able to maximize its possible benefits on their own and might need support in understanding its purpose (Wise, 2014 ) and in finding personal relevance for its use. The findings of this study suggest that a more individual fit of LADs could be promoted by allowing students to customize its functionalities and displays. Comparable results have also been obtained from other studies (e.g., Bennett, 2018 ; Rets et al., 2021 ; Roberts et al., 2017 ; Schumacher & Ifenthaler, 2018 ), thus highlighting the need to develop customized LADs that better meet the needs of diverse students and that empower them to control their analytics data. More attention may also be needed to promote the use and development of LADs to support self-efficacy, as it appeared to be an unrecognized potential still for many students in this study. According to Rets et al. ( 2021 ), using LADs for such a purpose might particularly benefit online learners and part-time students, who often face various requirements and thus may forget the efforts put into learning and giving themselves enough credit. By facilitating students’ self-confidence, it could also promote the necessary changes in study behavior, at least for those students with low self-efficacy (Rets et al., 2021 ).

Second, the students’ views on the use of the LAD and its development were investigated in terms of the performance phase processes of SRL, with an emphasis on the control and observation of studies and learning. In line with the results of other studies (De Barba et al., 2022 ; Rets et al., 2021 ; Schumacher & Ifenthaler, 2018 ; Silvola et al., 2023 ), the students preferred using the LAD to monitor their study performance—they wanted to follow their progress and success over time and keep themselves and their educators up to date. According to Jivet et al. ( 2017 ), such functionality directly promotes the performance phase of SRL. Moreover, it seemed to serve as a basis for other activities under SRL, all of which were heavily dependent and built on the monitoring. The results of this study, however, imply that monitoring opportunities should be further expanded to provide even more detailed insights. Moreover, they indicate the need to develop and refine pedagogical practices at the organizational level in order to better serve student monitoring. As monitoring plays a crucial role in SRL (Zimmerman & Moylan, 2009 ), it is essential to examine how it is related to other SRL processes and how it can be effectively promoted with analytics applications (Viberg et al., 2020 ).

In this study, the students used the LAD not only to monitor but also to image and visualize their learning. In accordance with the views of Papamitsiou and Economides ( 2015 ), the visualizations transformed the analytics data into an easily interpretable visual form. The visualizations were not considered to generate information overload, although such a concern has sometimes been associated with the use of LADs (e.g., Susnjak et al., 2022 ). However, the students widely preferred even more descriptive and nuanced illustrations to clarify and structure the analytics data. At the same time, care must be taken to ensure that the visualizations do not divert too much attention from other relevant aspects of learning, as was also found important in prior research (e.g., Charleer et al., 2018 ; Wise, 2014 ). It seems critical that an LAD inform but not overwhelm its users (Susnjak et al., 2022 ). As argued by Klein et al. ( 2019 ), confusing visualizations may not only generate mistrust but also lead to their complete nonuse.

Although the LAD piloted in the study was considered to be a relatively functional application, it could be even more accessible and usable if it was incorporated into the student information system and enriched with the data from it. Even then, however, the LAD should remain simple to use and its data privacy ensured. It has been argued that more information is not always better (Aguilar, 2018 ), and the analytics indicators must be carefully considered to truly optimize learning (Clow, 2013 ). While developing their SRL, students would particularly benefit from a well-structured environment with fewer distractions and more facilitators for learning (Panadero & Alonso-Tapia, 2014 ). The smooth promotion of studies also seems to require personal access to the analytics data. Similar to the learners in Charleer and colleagues’ ( 2018 ) study, the students in this study desired to take advantage of the LAD autonomously, beyond the guidance context. It was believed to be especially used when they were actively promoting their studies. This is seen as a somewhat expected finding given the significant role of study performance indicators in the LAD. However, the question is also raised as to whether such an analytics application would be used mainly by those students who progress diligently but would be ignored by those who advance only a little or not at all. Ideally, the LAD would serve students in different situations and at various stages of studies.

Using the LAD offered the students a promising means to enhance motivation and interest in their studies through the monitoring of analytics data. However, not all students were inspired in the same manner or similar analytics data displayed by the LAD. Although the LAD was seen as inspiring and interesting in many ways, it also had the potential to demotivate or even discourage. This finding corroborates the results of other studies reporting mixed results on the power of LADs to motivate students (e.g., Bennett, 2018 ; Corrin & de Barba, 2014 ; Schumacher & Ifenthaler, 2018 ). As such, it would be essential that the analytics applications consider and address students with different performance levels and motivational factors (Jivet et al., 2017 ). Based on the results of this study, diversifying the tools included in the LAD might also be necessary. On the other hand, the enhancement of motivation was also found to be the responsibility of the students themselves—that is, if the students wish the analytics application to display favorable analytics data and thus motivate them, they must first display concomitant effort in their studies.

The use of the LAD provided a convenient way to intervene if the students’ study performance did not meet expectations. With the LAD, both the students and their tutor teachers could detect signs of possible support needs and address them with guidance. In the future, such needs could also be reported through automated alerts. Overall, however, the students in this study preferred human contact and personal support over automated interventions, contrary to the findings obtained by Roberts and colleagues ( 2017 ). Being identified to their educators did not seem to be a particular concern for them, although it has been found to worry students in other contexts (e.g., Roberts et al., 2017 ). Rather, the students felt they would benefit more from personal support that was specifically targeted to them and sensitive in its approach. The students generally demanded delicate, ethical consideration when acting upon analytics data and in the provision of support, which was also found to be important in prior research (e.g., Kleimola & Leppisaari, 2022 ). Additionally, Wise and colleagues ( 2016 ) underlined the need to foster student agency and to prevent students from becoming overly reliant on analytics-based interventions: if all of the students’ mistakes are pointed out to them, they may no longer learn to recognize mistakes on their own. Therefore, to support SRL, it is essential to know when to intervene and when to let students solve challenges independently (Kramarski & Michalsky, 2009 ).

Lastly, the students’ perceptions on the use and development of the LAD were examined from the perspective of the reflection phase processes of SRL, with particular attention given to evaluation and reflection on studies and learning. The use of the LAD provided the students with a basis to individually reflect on the potential causes behind their study performance, for better or worse. Moreover, they could address such issues together with guidance personnel and thus make better sense of the analytics data. Corresponding to the results of Charleer et al.’s ( 2018 ) study, collective reflection on analytical data provided the students with new insights and supported their understanding. Engaging in such reflective practices offered the students the opportunity to complete the SRL cycle and draw the necessary conclusions regarding their performance for subsequent actions (see Zimmerman & Moylan, 2009 ). In the future, analytics-based reflection could also be implemented in joint tutoring classes and courses included in the degree programs. This would likely promote the integration of LADs into the activity flow of educational environments, as recommended by Wise and colleagues ( 2016 ). In sum, using LADs should be a regular part of pedagogical practices and learning processes (Wise et al., 2016 ).

When evaluating and reflecting on their studies and learning, the students preferred to focus on themselves and their own development as learners. Similar to earlier findings (e.g., Divjak et al., 2023 ; Rets et al., 2021 ; Roberts et al., 2017 ; Schumacher & Ifenthaler, 2018 ), the students felt differently about the need to develop LADs to compare their study performance with that of other students. Although this function could help some of the students to position themselves in relation to their peers, others thought it should be optional or completely avoided. In agreement with the findings of Divjak et al. ( 2023 ), it seemed that the students wanted to avoid mutual competition comparisons; however, it might not be harmful for everyone and in every case. Consequently, care is required when considering the kind of features in the LAD that offer real value to students in a particular context (Divjak et al., 2023 ). Rather than limiting the point of reference only to peers, it might be useful to also offer students other targets for comparative activity, such as individual students’ previous progress or goals set for the activity (Wise, 2014 ; Wise et al., 2016 ; see also Bandura, 1986 ). In addition, it is important that students not be left alone to face and cope with the various reactions that may be elicited by such evaluation and reflection with analytics data (Kleimola & Leppisaari, 2022 ). As the results of this study and those of others (e.g., Bennett, 2018 ; Lim et al., 2021 ) generally indicate, affective responses evoked by LADs may vary and are not always exclusively positive. Providing a safe environment for students to reflect on successes and failures and to process the resulting responses might not only encourage necessary changes in future studies but also promote the use of an LAD as a learning support.

In summary, the results of this study imply that making an effective use of an analytics application—even with a limited amount of analytics data and functionality available—may facilitate the growth of students into self-regulated learners. That is, even if the LAD principally addresses some particular phase or process of SRL, it can act as a catalyst to encourage students in the development of SRL on a wider scale. This finding also emphasizes the interdependent and interactive nature of SRL (see Zimmerman, 2011 ; Zimmerman & Moylan, 2009 ) that similarly seems to characterize the use of an LAD. However, the potential of LADs to promote SRL may be lost unless students themselves are (pro)active in initiating and engaging with such activity or receive appropriate pedagogical support for it. There appears to be a specific need for guidance that is sensitive to the students’ affective reactions and would help students learn and develop with analytics data. Providing the students with adequate support is particularly critical if their studies have not progressed favorably or as planned. It seems important that the LAD would not only target those students who are already self-regulated learners but, with appropriate support and guidance, would also serve those students who are gradually growing in that direction.

5.1 Limitations and further research

This study has some limitations. First, it involved a relatively small number of HE students who were examined in a pilot setting. Although the sample was sufficient to provide in-depth insights and the saturation point was reached, it might be useful in further research to use quantitative approaches and diverse groups of students to improve the generalizability of results to a larger student population. Also, addressing the perspectives of guidance personnel, specifically tutor teachers, could provide additional insights into the use and development of LADs to promote SRL.

Second, the LAD piloted and investigated in this study was not yet widely in use or accessible by the students. Moreover, it was examined for a relatively brief time, so the students’ perceptions were shaped not only by their experiences but also by their expectations of its potential. Future research on students and tutor teachers with more extensive user experience could build an even more profound picture of the possibilities and limitations of the LAD from a study path perspective. Such investigation might also benefit from trace data collected from the students’ and tutor teachers’ interactions with the LAD. It would be valuable to examine how the students and tutor teachers make use of the LAD in the long term and how it is integrated into learning activities and pedagogical practices.

Third, due to the emphasis on an HE institution and the analytics application used in this specific context, the transferability of results may be limited. However, the results of this study offer many important and applicable perspectives to consider in various educational environments where LADs are implemented and aimed at supporting students across their studies.

6 Conclusions

The results of this study offer useful insights for the creation of LADs that are closely related to the theoretical aspects of learning and that meet the particular needs of their users. In particular, the study increases the understanding of how such analytics applications should be connected to the entirety of studies—that is, what kind of learning processes and pedagogical support are needed alongside them to best serve students in their learning. Consequently, it encourages a comprehensive consideration and promotion of pedagogy, educational technology, and related practices in HE. The role of LA in supporting learning and guidance seems significant, so investments must be made in its appropriate use and development. In particular, the voice of the students must be listened to, as it promotes their commitment to the joint development process and fosters the productive use of analytics applications in learning. At its best, LA becomes an integral part of HE settings, one that helps students to complete their studies and contributes to their development into self-regulated learners.

Data availability

Not applicable.

Abbreviations

Higher education

• Learning analytics
• Learning analytics dashboard

Research question

• Self-regulated learning

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Language process: In the preparation process of the manuscript, the Quillbot Paraphraser tool was used to improve language clarity in some parts of the text (e.g., word choice). The manuscript was also proofread by a professional. After using this tool and service, the authors reviewed and revised the text as necessary, taking full responsibility for the content of this manuscript.

The authors also thank the communications and information technology specialists of the UAS under study for their support in editing Fig. 1 for publication.

This research was partly funded by Business Finland through the European Regional Development Fund (ERDF) project “Utilization of learning analytics in the various educational levels for supporting self-regulated learning (OAHOT)” (Grant no. 5145/31/2019). The article was completed with grants from the Finnish Cultural Foundation’s Central Ostrobothnia Regional Fund (Grant no. 25221232) and The Emil Aaltonen Foundation (Grant no. 230078), which were awarded to the first author.

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Kleimola, R., Hirsto, L. & Ruokamo, H. Promoting higher education students’ self-regulated learning through learning analytics: A qualitative study. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12978-4

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Exploring transitions in care among patients with head and neck CANCER: a multimethod study

• Jaling Kersen 1 ,
• Pamela Roach 1 , 4 , 5 ,
• Shamir Chandarana 2 , 3 ,
• Paul Ronksley 1 , 4 &
• Khara Sauro 1 , 2 , 3 , 4  

BMC Cancer volume  24 , Article number:  1108 ( 2024 ) Cite this article

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Patients with head and neck cancers (HNC) experience many transitions in care (TiC), occurring when patients are transferred between healthcare providers and/or settings. TiC can compromise patient safety, decrease patient satisfaction, and increase healthcare costs. The evidence around TiC among patients with HNC is sparse. The objective of this study was to improve our understanding of TiC among patients with HNC to identify ways to improve care.

This multimethod study consisted of two phases: Phase I (retrospective population-based cohort study) characterized the number and type of TiC that patients with HNC experienced using deterministically linked, population-based administrative health data in Alberta, Canada (January 1, 2012, to September 1, 2020), and Phase II (qualitative descriptive study) used semi-structured interviews to explore the lived experiences of patients with HNC and their healthcare providers during TiC.

There were 3,752 patients with HNC; most were male (70.8%) with a mean age at diagnosis of 63.3 years (SD 13.1). Patients underwent an average of 1.6 (SD 0.7) treatments, commonly transitioning from surgery to radiotherapy (21.2%). Many patients with HNC were admitted to the hospital during the study period, averaging 3.3 (SD 3.0) hospital admissions and 7.8 (SD 12.6) emergency department visits per patient over the study period. Visits to healthcare providers were also frequent, with the highest number of physician visits being to general practitioners (average = 70.51 per patient). Analysis of sixteen semi-structured interviews (ten patients with HNC and six healthcare providers) revealed three themes: (1) Navigating the healthcare system including challenges with the complexity of HNC care amongst healthcare system pressures, (2) Relational head and neck cancer care which encompasses patient expectations and relationships, and (3) System and individual impact of transitions in care.

Conclusions

This study identified challenges faced by both patients with HNC and their healthcare providers amidst the frequent TiC within cancer care, which was perceived to have an impact on quality of care. These findings provide crucial insights that can inform and guide future research or the development of health interventions aiming to improve the quality of TiC within this patient population.

Peer Review reports

Head and Neck Cancers (HNC) are a group of cancers that affect diverse anatomical structures including the pharynx, larynx, naso-, oro-, hypopharynx, nasal cavity, oral cavity, middle ear, and salivary glands [ 1 ]. HNC are the sixth most common cancer in the world [ 2 ] accounting for approximately 931,931 new diagnoses worldwide each year, with a projected 30% annual increase by the year 2030 [ 1 , 3 ]. There are several risk factors for HNC which include alcohol and tobacco use, malnutrition, lower socioeconomic status, age, sex, exposure to carcinogens and contracting the Human Papilloma Virus (HPV) [ 4 , 5 ]. The presence of these risk factors results in differences in disease presentation, progression, and patient outcomes [ 6 , 7 ]. Although the 5-year age-standardized survival rates for HNC vary between countries and the site of the HNC, they remain relatively low, with age standardized estimates ranging from 2.5 to 8.3 per 100,000 [ 8 , 9 , 10 , 11 ].

Patients with HNC are cared for by multidisciplinary healthcare teams which collaboratively develop treatment plans, manage treatment sequelae, and provide comprehensive supportive care and rehabilitation [ 12 , 13 ]. These treatment plans often include a combination of surgery, radiation, chemotherapy, and immunotherapy [ 12 , 14 ]. As a result of their complex treatment plans, and vulnerability to physical and psychological effects from HNC and its associated treatments, patients with HNC are some of the highest users of healthcare resources [ 15 , 16 , 17 ]. As this patient population requires care from many healthcare providers across different healthcare settings, they consequently experience many transitions in care (TiC) [ 12 , 18 , 19 ]. TiC occur when the responsibility for a patient’s care is transferred between healthcare providers, institutions, or settings [ 20 ]. TiC can also occur when patients move from one level of care to another (e.g., from the intensive care unit to the hospital ward) [ 20 ]. TiC represent a challenging period in the delivery of care as they interrupt continuity of care, opening opportunities for inadequate transfer of information and potential breakdown in communication [ 21 ]. In other patient populations, poorly executed TiC have been associated with compromised patient safety, increased medical errors, high distress levels in patients and their families, excessive healthcare cost and resource use [ 22 , 23 , 24 , 25 , 26 , 27 ]. As such, organizations such as the Joint Commission and the National Academy of Medicine have identified a critical need to effectively bridge the TiC that patients experience to help mitigate adverse effects on patient health outcomes [ 28 , 29 ]. Despite the complex multidisciplinary care of patients with HNC and the established risks of poor care during TiC, there is a gap in our understanding of TiC for patients with HNC.

The objectives of this study are to (1) estimate the number and type of TiC patients with HNC experience, (2) understand TiC from the perspective of patients with HNC and healthcare providers, (3) explore the quality of care during TiC, from the perspective of patients with HNC and healthcare providers.

This multimethod study was approved by the University of Calgary Health Research Ethics Board of Alberta (HREABA.CC-20-0474) and consisted of two distinct phases: a retrospective population-based cohort study and a qualitative descriptive study.

Phase 1: retrospective cohort study

This study was conducted in Alberta, Canada where healthcare services are provided by a provincially integrated single-payer healthcare system (Alberta Health Services (AHS)) [ 30 , 31 ]. AHS is the largest integrated provincial healthcare system in Canada [ 31 ] composed of 106 acute care hospitals, five psychiatric facilities, and partners with 40 primary care networks [ 30 ]. Cancer care for patients with HNC is delivered by AHS in two main cancer care centres in Alberta: Tom Baker Cancer Centre in Calgary and the Holy Cross Cancer Centre in Edmonton [ 30 , 31 ] .

Population and data sources

This retrospective population-based cohort study quantitatively characterized the number and type of TiC that patients with HNC experience throughout their cancer journey, using routinely collected, population-based administrative health data. The cohort included adult patients ( \(\:\ge\:\:\) 18 years old) diagnosed with HNC in Alberta between January 1, 2012, to September 1, 2020, as indicated by a record in the population-based Alberta Cancer Registry (ACR).

Data from four administrative health data sources were deterministically linked using a unique personal healthcare number that is assigned to each person in the province at birth or immigration to the province and follows them throughout their life. The ACR, a population-based registry, records patient demographic information (name, date of birth, sex, postal code) and cancer-specific variables (diagnosis with dates, pathology, treatments with dates, and staging) [ 32 ]. The Discharge Abstract Database (DAD) includes information on acute care hospital admissions including demographics, diagnoses (using the International Classification of Disease version 10 (ICD-10) codes), procedures, and admission and discharge information [ 33 ]. Physician billing claims include diagnostic codes (ICD-9) assigned by physicians used to bill the Government of Alberta for services provided to patients. The National Ambulatory Care Reporting System (NACRS) collects demographic, administrative, clinical, and service-specific data from both hospital-based and community-based ambulatory care visits [ 34 , 35 ].

Patient demographic variables (age, sex, and socioeconomic status), cancer-related variables (site, stage, and presence of multiple tumours) and treatment-related variables (treatment modalities) were extracted from ACR. The Charlson Comorbidity Index (categorized as 0,1,2+) was calculated using established coding algorithms [ 36 ].

The primary outcome variables were the number and type of TiC. The TiC examined included the transitions between treatments, healthcare institutions (hospital visits and associated discharges, emergency department visits), and different healthcare providers. The type of TiC variables were dichotomously coded, with a 0 indicating that a patient did not experience a TiC and a 1 that a patient experienced a TiC (Supplementary Table 1 ). TiC rates were computed individually for each patient across four distinct periods: six months before diagnosis, from diagnosis to initial treatment, one-year post-first treatment, and three years post-first treatment.

The cohort characteristics were described using descriptive statistics; means with standard deviations (SD) or medians and interquartile ranges (IQR) where appropriate, and frequencies (proportions). The TiC rates were calculated as number per month across the 4 periods described above. All analyses were conducted using STATA 16 SE [ 37 ]. This phase is reported according to the STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) and the REporting of studies Conducted using Observational Routinely-collected Data (RECORD) (Appendix 1 ) [ 38 , 39 ].

Phase 2: qualitative description

The qualitative descriptive study followed the general tenets of naturalistic inquiry to provide a richer understanding of TiC and care delivery among patients with HNC and their healthcare providers [ 40 , 41 ]. In-depth, semi-structured interviews were conducted with two participant groups: (1) adult patients with HNC, who received care in Alberta, at any stage of their cancer journey and (2) multi-professional healthcare providers who care for patients with HNC in Alberta. This phase was reported in line with the Consolidated Criteria for Reporting Qualitative Research (COREQ) (Appendix 2 ) [ 42 ].

Participants and recruitment

Recruitment occurred between September 1, 2022, to July 15th, 2023. Different sampling strategies were employed for the two participant groups. Patients with HNC were recruited through purposive sampling and employed media-based approaches to ensure maximum variation in patient characteristics [ 43 ]. Healthcare providers were also recruited through purposive sampling, using the researchers’ professional networks. All potential participants were provided with the contact information for the principal investigator.

Data collection

Interviews were completed using semi-structured interview guides uniquely developed for each participant group, informed by our methodological framework. These guides included open-ended questions relevant to the objectives of the study and probing questions to elicit data [ 44 ]. The interview guides were pilot-tested within the research team and iteratively refined by the authors (J.K and K.M.S).

Semi-structured interviews were conducted via telephone ( n  = 2) or secure video conferencing platform (Zoom) ( n  = 14) [ 45 ] with no other participants present. The interviews were conducted by the first author (J.K) who has formal graduate training and practical experience in qualitative methods and interview facilitation. The interviewer did not have any previous relationships with patients with HNC, however, due to the sampling method for healthcare professionals, the interviewer had a professional relationship with one of the included healthcare providers. The interviews were audio recorded and transcribed verbatim using transcription services (rev.com), with a unique study identifier replacing the participant’s name. Participants were recruited and data was collected until saturation was reached. Saturation was achieved when no novel themes were generated from the data [ 46 ].

Written, informed consent was obtained from all participants before participating in the semi-structured interviews. Additional consent was received from all participants to audio record the interviews and to use anonymized quotations in research publications and dissemination.

Data analysis

The data from the semi-structured interviews was analyzed using an inductive approach, whereby two independent reviewers (J.K and K.M.S) followed Braun and Clarke’s approach to thematic analysis [ 46 ]. Thematic analysis was facilitated by the qualitative software Nvivo [ 47 ]. The themes from the patients with HNC and the healthcare providers were also compared and contrasted to ensure alignment to an overarching central concept [ 48 ]. Qualitative analysis and generated themes were discussed with the study team to facilitate peer debriefing [ 41 ] .

Reflexivity

The interview facilitator and first analyst (J.K) was a graduate student (MSc), familiar with evidence suggesting that TiC are vulnerable periods for patients. The second analyst (K.M.S) was an Assistant Professor whose broad research area encompassed TiC among patients with HNC. Due to their previous experiences and knowledge, both researchers acknowledged that their perception of TiC may be inherently negative and this was frequently debriefed with the team in order to consciously reduce any impact on their findings. Throughout the research process, the reviewers also critically examined their assumptions and challenged them to mitigate any potential bias.

Retrospective cohort study

The cohort included 3,752 patients with HNC. Demographic characteristics of the cohort are presented in Table  1 . Briefly, most patients were male (70.8%) and had a mean age at diagnosis of 63.3 years (SD = 13.1).

The overall, all-cause mortality of patients was 44.86% ( n  = 1,683) within the study period. The mean number of TiC per patient was 210.3 (SD = 164.6). The mean TiC rate per patient was 7.7 (SD = 10.2) during the six months preceding diagnosis. Following diagnosis and before the first treatment, the mean TiC rate per patient increased to 23.1 (SD = 34.9) per patient. Subsequently, the mean TiC rate per patient declined to 4.2 (SD = 5.3) one year post-first treatment with a further decrease to 2.0 (SD = 2.2) per patient three years post-first treatment. Patients with HNC had frequent transitions between different treatments. A total of 47.9% of patients underwent two or more treatments during the study period, with an average of 1.6 (SD = 0.7) treatments per patient. The most common treatment was radiotherapy, followed closely by surgery; the most common treatment transition was the transition from surgery to radiotherapy (21.2%) (Table  2 ).

Many patients with HNC were admitted to the hospital during the study period (Table  3 ), with an average of 3.3 (SD = 3.0) hospital admissions per patient. When patients were admitted to hospital, frequently they were admitted from home (82.6%) and discharged to home with no support (as indicated in their discharge report; 82.8%) (Table  3 ). Visits to an emergency department were common ( n  = 3,475 patients) with an average of 7.8 (SD = 12.6) visits per patient during the study period. The number of emergency department visits increased after diagnosis and again after the first treatment (Table  4 ). The highest number of physician visits were to general practitioners (family medicine) (average = 70.6 per patient) followed by other specialists (average = 65.2 per patient; Table  5 ). The number of visits to most healthcare providers increased after diagnosis (Table  5 ).

Qualitative description

Sixteen interviews (ten patients and six healthcare providers) were conducted between July 17 th, 2022, to June 7 th, 2023. The interviews ranged between 30 and 90 min (median = 46 min). The mean age of patients with HNC at the time of the interview was 66 years old (range = 47–77 years), most were female ( n  = 7, 70%) and experienced many TiC. Healthcare providers included surgeons ( n  = 3), a medical oncologist ( n  = 1), a radiation oncologist ( n  = 1), and a licensed practical nurse ( n  = 1). The healthcare providers were mostly male ( n  = 4), with the average years of practice being 21 years (range = 7.5–31 years).

The qualitative analysis generated three interconnected themes: (1) Navigating the Healthcare System (subthemes: head and neck cancer care complexities, disrupted continuity of head and neck cancer care, and healthcare system pressures), (2) Relational Head and Neck Cancer Care (subthemes: patient expectations during Transitions in Care, feeling valued as a Head and Neck Cancer patient and healthcare provider roles and responsibilities) and (3) System and Individual Impact of Transitions in Care (subthemes: impact of resource-intensive nature of TiC and the Impact of Transitions in Care on Quality of Care). Additional exemplar quotations for each theme are presented in Table  6 .

Navigating the healthcare system

Despite the healthcare system’s overarching goal of providing high-quality care, participants expressed struggles with navigating complex healthcare systems, leading to poor quality of care.

Head and neck cancer care complexities

While both patients with HNC and healthcare providers reported that navigating the complexities of HNC care is challenging, healthcare providers noted the necessity for multidisciplinary care, while patients described confusion by the care pathways used during their treatment. Patients perceived their care to be overly complex, involving many healthcare disciplines and many of the processes were unfamiliar to them. Although healthcare providers recognized the need for care pathways, guidelines, and protocols to ensure standardized care, they also recognized that patients did not intuitively know how to navigate these complex pathways and the health system.

Disrupted continuity of head and neck cancer care

Patients and healthcare providers described a siloed healthcare system which affected all aspects of patient care, hindering communication, resulting in prolonged waits, and disrupting the continuity of care. There were many instances where healthcare silos were identified as the cause of communication breakdowns among patients with HNC and their healthcare providers, with patients struggling to contact the appropriate healthcare providers and the communication between the multidisciplinary healthcare teams becoming strained. However, only patients with HNC associated the siloed healthcare system structure with prolonged waits and fragmented care, reporting wait times of five to six weeks between different healthcare departments (surgery, dermatology, and psychiatry) and lapses in continuous care. Healthcare silos were physical and geographical, requiring patients to travel to different healthcare institutions for their care.

Patients with HNC and healthcare providers proposed potential solutions which they felt could address these issues, including consolidating healthcare teams and departments into a centralized facility and the direct communication via telephone between patients, oncologists, and other healthcare providers.

Healthcare system pressures

Patients with HNC and healthcare providers recognized that a prominent constraint facing the healthcare system is the shortage of trained healthcare professionals. Patients with HNC described inadequate access to healthcare providers, specifically primary care providers and psychologists who they felt were critical to their care. Healthcare providers commonly noted that constrained human resources necessitated taking on additional responsibilities beyond their designated roles, often without appropriate support or adjustment to their workload. Healthcare providers, most notably specialists, were sometimes placed into uncomfortable situations where patients expected them to manage their daily care which is beyond their scope of practice.

Relational head and neck cancer care

Patients with HNC and healthcare providers identified interconnected factors including patients’ expectations of their care, their perception of being valued within the healthcare system and their understanding of healthcare provider roles and responsibilities that influenced how patients experienced their healthcare journey and TiC.

Patient expectations during transitions in care

Patient expectations of the healthcare system and their care were pivotal in shaping their experiences with cancer care. Some patients faced difficulties aligning what they anticipated their care to look like with the actual care they received. Patients struggled to establish expectations for their TiC that were aligned with their actual care due to their unfamiliarity with the complex healthcare system and the complexity of HNC care. These divergent expectations were especially evident during the transition from active treatment to survivorship when a patient’s interactions with the healthcare team dramatically decreased. Expectations of care not being met also led to heightened frustration and stress among patients with HNC. However, patients hesitated to voice these feelings with their healthcare providers for fear of being labelled as “high maintenance” when advocating for their needs.

Feeling valued as a head and neck cancer patient

Emotional support during the cancer continuum was perceived to be crucial by patients with HNC and healthcare providers. The healthcare providers noted that professional psychological care, although in short supply, is a key source of support. However, patients perceived the existing patient-provider relationship as an equally crucial source of support. Strong patient-provider relationships were rooted in mutual knowledge, respect, and trust and thrived when healthcare providers exhibited compassion and empathy. Patients valued long-lasting patient-provider relationships, which extended beyond a specific episode of care, creating an enduring continuum of support. While an abundance of patients experienced strong patient-provider relationships, some patients had weak patient-provider relationships, where the relationship lacked trust, communication and collaboration leaving patients feeling unsupported and abandoned. These weak patient-provider relationships triggered patients to transition to other healthcare providers more frequently. Patients with HNC proposed that further involvement in the decision-making process would help make them feel valued. They expressed a desire to have their opinions heard, particularly when it came to decisions that governed their care during TiC.

Healthcare provider roles and responsibilities

Patients with HNC and healthcare providers acknowledged that there is a lack of clarity about the specific roles and responsibilities of each healthcare provider caring for patients with HNC. This lack of clarity was especially endorsed by patients. Patients found understanding who was responsible for specific aspects of their cancer care (usually by oncologists) and non-cancer related care (usually by general practitioners) confusing which led to challenges in deciding who to consult about their concerns, with the general practitioners and oncologists often redirecting the patient to each other. To add to this confusion, the addition of medical residents and fellows to the patients with HNC’s care team was described as confusing and disruptive.

System and individual impact of transitions in care

Patients and providers experienced TiC differently during the cancer continuum. Healthcare providers perceived TiC as contributing to increased workloads and emotional distress, while patients with HNC associated TiC with decreased quality of care due to ineffective care and lack of patient-centeredness.

Impact of resource-intensive nature of transitions in care

Healthcare providers perceived the volume of tasks associated with TiC contributed to the burden on healthcare providers and patients with HNC. Many healthcare providers noted that high-quality TiC demanded significant time and effort to do well (transfer information between providers). Additional challenges related to ensuring a high-quality TiC included, adapting to evolving electronic health record systems and collaborating with patients and colleagues. The extensive documentation required during TiC also created time constraints for healthcare providers, preventing them from spending adequate time with each patient. The introduction of a new electronic medical records system in Alberta further compounded this issue, with surgeons and oncologists describing difficulties in understanding and utilizing the system when transferring a patient’s care, potentially resulting in insufficient information for the healthcare provider the patient is transitioning to. Additionally, healthcare providers noted that the additional responsibilities associated with TiC led to heightened stress levels and emotional repercussions due to witnessing their patients deteriorate after experiencing poor TiC. Although the resource-intensive nature of TiC was commonly discussed by healthcare providers, patients with HNC also described instances where they had to dedicate significant time and effort to organize their care, facilitate communication between healthcare providers and access information during TiC. A widely endorsed solution by both patients with HNC and their healthcare providers to improve TiC was the introduction of a care coordinator or patient navigator envisioning it as a strategy to improve TiC.

Impact of transitions in care on quality of care

The opinions of patients with HNC and their healthcare providers diverged when discussing the factors that influenced the quality of care throughout the cancer continuum. Patients highlighted areas that needed improvement such as effectiveness and patient-centeredness. Ineffective care occurred when healthcare providers (especially primary care providers and dentists) failed to provide the appropriate care or identify symptoms as potential cancer resulting in prolonged diagnosis. A lack of patient-centeredness was noted by patients when healthcare providers did not take the time to understand them holistically, resulting in healthcare that was not customized around a patient’s needs, characteristics and preferences. In contrast, healthcare providers focussed on the broader healthcare system barriers including budgetary constraints and a lack of awareness about TiC. Healthcare providers believed that the allocated operating budgets were constrained, which prevented the optimization of HNC care within the healthcare system. Additionally, healthcare providers emphasized the lack of awareness about TiC and their consequences for patient outcomes and healthcare expenditure.

This multimethod study quantified the large number and variety of types of TiC patients with HNC encounter during their care; patients with HNC had an especially high number of emergency department visits, hospital admissions and visits to general practitioners. The high number of TiC was described as a source of distress by patients and providers.

The findings of this study are consistent with evidence showing that patients with cancer use a high amount of healthcare resources. Studies have found that patients with cancer account for 10.5% of hospital admissions and patients with lung and colorectal cancer have, on average, 3.3 hospital admissions (Sauro K: Transitions in care among patients with lung, bladder, colorectal and head and neck cancer: A retrospective cohort study, In preparation). Similarly, in the present study patients had, on average, 7.8 emergency department visits during the study period, which may be higher compared to other oncological patient populations with a median number of emergency department visits of 2.0 over 4 years [ 49 ]. The differences in estimates between the present study and previous work may be partly explained by differences in patient populations and the objectives of each study. Similarly, consistent with our findings that patients with HNC consult many physicians, previous work found that patients with cancer see a median of 32 different physicians during their cancer journey [ 50 ]. Unfortunately, each TiC represents an opportunity for the occurrence of medical errors, adverse events, increased mortality risk, low patient satisfaction, elevated emergency department visits and poor quality of care [ 51 , 52 , 53 , 54 , 55 , 56 ]. Poor quality of care can stem from challenges reported in this study such as fragmented care, resource limitations and communication breakdowns between patients and healthcare providers. Patients with HNC in this study also emphasized the importance of patient-centeredness which are pillars of quality of care, and mirror findings in other oncology patient populations [ 57 , 58 , 59 , 60 ], where it has been reported that preferences, values, and needs are not adequately considered in their care [ 57 , 58 , 61 ].

As healthcare providers play a pivotal role in delivering care, factors such as physician wellness and burnout have been associated with poor quality of care [ 62 , 63 ]. Oncology ranks as a highly stressful specialty compared to other healthcare specialties (internal medicine, neurology, and cardiology), leading to higher healthcare provider burnout rates [ 64 , 65 ]. This elevated burnout can also be attributed to electronic health records usage, high workloads, and emotional distress resulting from long hours, high work intensity, time constraints and under-resourced work environments [ 63 , 66 , 67 , 68 , 69 ]. Our findings illustrate a need to develop and implement tailored interventions prioritizing quality of care and alleviating healthcare provider burnout. Addressing the challenges identified by patients with HNC and their healthcare providers can help bridge the quality-of-care gaps within HNC care.

While the present study found a high number of TiC among patients with HNC, some of these transitions are appropriate and necessary to provide high-quality, evidence-based care. While minimizing the TiC can decrease the risk associated with these vulnerable points in cancer care, another approach, which acknowledges the importance and necessity of TiC is to develop interventions to improve TiC. While some of the patient suggested strategies are innovative and understudied (consolidating HNC care), others have been shown to be effective at improving the quality of care during TiC [ 70 , 71 ]. Shared decision-making (patients and their healthcare providers jointly making informed treatment decisions) [ 72 ], reduces decision conflict, sets accurate expectations, and aligns with patient values [ 73 , 74 ]. Telehealth (the process of facilitating or delivering health services through any form of digital or telecommunication) [ 75 ], can improve TiC (reduce preventable hospital admissions) by fostering better multidisciplinary communication, strengthening the patient-provider relationship, and mitigating the impact of healthcare-provider shortages [ 76 , 77 , 78 , 79 ]. However, telehealth may not be feasible among a patient population whose ability to hear and speak can be significantly compromised. The implementation of patient navigators within HNC care was the most widely endorsed strategy. Patient navigators are dedicated members of the multidisciplinary team who are liaisons and advocates internally and externally within the healthcare system [ 80 ]. This strategy can alleviate some of the concerns expressed by patients with HNC in our study: enhancing continuity of care, providing more information, connecting patients with the most appropriate healthcare provider, and ultimately mitigating the difficulties they face when navigating the healthcare system [ 81 , 82 ]. Additionally, this resource can ease the strain on healthcare providers by providing patients additional support, therefore, reducing burnout rates, minimizing the occurrence of medical errors and preventing turnover among healthcare providers [ 83 ]. In oncology, patient navigators have decreased hospital admissions, optimized resource consumption, increased quality of life and improved patient outcomes, among other benefits [ 84 , 85 ]. Implementing a feasible and effective intervention such as patient navigators within HNC care can address challenges faced by patients and their healthcare providers during TiC. Future research should investigate the effectiveness of these strategies within HNC care, especially impacts on patient outcomes, provider well-being and the overall healthcare system.

Limitations

This multimethod study integrated both quantitative and qualitative methodology, which generated a rich understanding of TiC among patients with HNC, enhancing the strengths of each method and mitigating their respective weaknesses. In the retrospective cohort study, we were limited to the recorded information and existing variables in the databases used in this study, thus limiting the TiC we could explore. There were also limitations to the qualitative component of this work. Our included patient population is mostly female which does not reflect the general demographic of patients with HNC who is mostly male. This may be attributed to women being more inclined to seek medical attention, visit healthcare facilities and participate in research. Therefore, the perspectives of patients with HNC in our study may be less transferable to other populations of patients with HNC. Potential mitigation strategies for recruitment difficulties could be to partner with additional patient advocacy groups and offer tailored participation incentives for patients.

In conclusion, this study identified a myriad of challenges faced by both patients with HNC and healthcare providers amidst frequent TiC. Our findings suggest that TiC can impact the quality of care and provide crucial insights that can inform and guide future research or the development of health interventions aiming to improve TiC within this patient population. These findings also identified potentially feasible interventions for further exploration, such as shared decision-making, telehealth, or a patient navigator within HNC care.

Availability of data and materials

Data will be made available upon reasonable request to the corresponding author.

Abbreviations

Head and Neck Cancers

Transitions in Care

Health Research Ethics Board of Alberta

Alberta Health Services

STrengthening the Reporting of Observational Studies in Epidemiology

REporting of studies Conducted using Observational Routinely-collected Data

Alberta Cancer Registry

Discharge Abstract Database

National Ambulatory Care Reporting System

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This study was supported by an Alberta Graduate Excellence Scholarship and the Cumming School of Medicine Graduate Program Scholarship awarded to Jaling Kersen, and a Canadian Cancer Society Challenge Grant awarded to Khara Sauro. The funders of the study had no role in study design, data collection, data analysis, data interpretation or writing of the manuscript.

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J.K made substantial contributions to the conceptualization of the study, data collection, interpretation of the data, writing the original draft, and reviewing and editing the original draft. K.M.S made substantial contributions to funding acquisition, conceptualization of the study, data collection, interpretation of the data, drafting, revising the original draft, and reviewing and editing the original draft. S.C, P.R, P.R and J.D made substantial contributions to reviewing and editing the original draft. All authors reviewed and critically revised the article and approved the version to be published and agree to be accountable for all aspects of the work.

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Kersen, J., Roach, P., Chandarana, S. et al. Exploring transitions in care among patients with head and neck CANCER: a multimethod study. BMC Cancer 24 , 1108 (2024). https://doi.org/10.1186/s12885-024-12862-x

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Iranian experts’ perspectives on facilitators and barriers influencing the infectious disease knowledge network:  A qualitative study

• Mina Mahami-Oskouei   ORCID: orcid.org/0000-0001-8561-8042 1 ,
• Leila Nemati-Anaraki   ORCID: orcid.org/0000-0002-9436-2533 2 ,
• Sirous Panahi   ORCID: orcid.org/0000-0001-7610-906X 3 &
• Shadi Asadzandi   ORCID: orcid.org/0000-0002-1350-9629 3  

BMC Health Services Research volume  24 , Article number:  1040 ( 2024 ) Cite this article

Metrics details

Knowledge networks, such as Communities of Practice (CoP), are essential elements of knowledge management. They play a crucial role in assimilating various knowledge domains and converting individual knowledge into collective knowledge. This study aimed to assess the concept of knowledge networks and identify facilitators and barriers influencing knowledge sharing in infectious diseases, according to Iranian experts.

This qualitative study employed content analysis and used purposive and snowball sampling. The data were collected via online or face-to-face interviews with 25 participants with diverse expertise in infectious diseases (both clinical and non-clinical), epidemiology, knowledge management, and knowledge-based business management in Iran. The thematic analysis technique was used to code the interviews, and the collected data were analyzed using MAXQDA 20 software.

Thematic analysis of the interviews led to 437 codes. These codes were categorized into two groups: facilitators and barriers. The facilitators shaping the knowledge network for infectious diseases were classified into three main categories: individual factors, organizational factors, and communication mechanisms. Individual factors involved two themes: strengthening knowledge exchange between experts in infectious diseases and personal characteristics such as the criteria for network membership. Organizational factors comprised three themes: organizational and trans-organizational factors, management strategies, and interactions with non-governmental sectors. Communication mechanisms included two themes: the use of information technology and knowledge brokers. In addition, three important challenges were identified as barriers influencing the knowledge network: administration and policy-making, organizational and trans-organizational, and personal challenges.

Conclusions

Several facilitators and barriers influence the formation of an infectious disease knowledge network, which must be addressed to ensure its effectiveness, development, and long-term sustainability. Addressing these factors will enable the network to effectively integrate diverse knowledge and contribute to advancing infectious disease management.

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Introduction

Knowledge networks are networks that appear in various forms, such as Communities of Practice (CoP). These networks bring together scientists from different disciplines, fostering tacit knowledge sharing and exchange between specialists in scientific fields. These networks can be created at the organizational and inter-organizational levels and even beyond organizational boundaries. They also offer opportunities to break down professional barriers, address complex systemic challenges, and support professionals. These networks are of utmost importance in the synthesis of diverse knowledge streams from various contributors in knowledge management [ 1 , 2 , 3 , 4 ]. These structures facilitate the fluid exchange of knowledge and expertise among individuals and groups, surpassing organizational boundaries to convert individual insights into collective comprehension. These interactive environments cultivate the generation of innovative information, enhancement of pre-existing knowledge, and efficient resolution of intricate challenges [ 5 , 6 ].

Reliance on knowledge networks is significant in the constantly evolving field of healthcare [ 7 ]. Health organizations worldwide prioritize enhancing the caliber, efficacy, and productivity of their services [ 8 ]. A crucial element of this commitment involves embracing research-based and evidence-driven decision-making processes [ 9 ]. However, efficient and timely dissemination of diverse knowledge into health practice and policy-making requires a new framework for organizing and disseminating the evidence. Central to this framework is the promotion of active participation among physicians and healthcare practitioners in creating and exchanging knowledge, as well as attaining consensus on its practical application [ 10 , 11 ]. For instance, Chan et al. showed that improving the quality of drug prescriptions in the geriatrics department requires effective communication and collaboration between various specialties in multidisciplinary knowledge networks [ 12 ]. Moreover, during the COVID-19 crisis in Canada, inter-organizational collaboration among experts through knowledge exchanges proved to be an effective strategy for collectively shouldering public health responsibilities [ 13 ].

However, infectious diseases still pose a threat to people’s health. This risk is exacerbated by the emergence of new pathogens, re-emergence of infections, and environmental and lifestyle-related challenges to disease control [ 14 , 15 ]. The COVID-19 pandemic has been unprecedented in recent years, with the World Health Organization (WHO) reporting more than 153 million cases and 2.3 million deaths by mid-2021 [ 16 ].

Effectively combating infectious diseases requires strategic collaboration and a broad knowledge base. The scientific community, government bodies, non-governmental organizations, and health policymakers must engage in extensive cooperation. However, the lack of coordination and cooperation among all stakeholders and authorities in the control of infectious diseases is one of the most important weaknesses of the care systems in developing countries, such as Iran [ 15 , 17 ] According to a study conducted in southern Nigeria, managing endemic infectious diseases is significantly hindered by the inadequate application of research findings in policy-making and practice. The creation of knowledge networks and strengthening collaboration between research producers and evidence users have been proposed as possible solutions to this problem. This finding highlights the crucial role of knowledge networks in achieving evidence-based healthcare delivery in these diseases [ 18 ].

Although in the field of infectious diseases, there are knowledge networks based on technological tools such as the Global Public Health Information Network (GPHIN), Global Outbreak Alert and Response Network (GOARN), Google Flu Trends, BioCaster, GET WE, and MedISys. These networks use a variety of technologies to collect, analyze, and disseminate data on infectious diseases. They focus on facilitating the exchange of information and expertise among a wide range of researchers, healthcare professionals, policymakers, and the public. However, these networks are transactional, and their members share information and resources based on need [ 17 ]. In contrast, networks such as Communities of Practice (CoPs) emphasize the strengthening of cooperation between specialists with common interests and expertise in a particular field. This leads to more stable and collaborative relationships and creates a space for members to engage in meaningful discussions, share their experiences, and develop collective knowledge and skills [ 1 ]. In other words, while researchers and other experts play a pivotal role in these efforts, a lack of communication between research findings and health policy can hinder disease control efforts despite the existence of prevention and treatment protocols. Therefore, enhanced coordination and collaboration among healthcare practitioners through the creation and implementation of interactive knowledge networks such as Communities of Practice (CoPs) are essential for overcoming these obstacles in infectious diseases [ 19 ].

In this regard, McKellar et al. also emphasized the importance of networked knowledge exchange and capacity building. They discussed the potential costs and benefits of these activities, as well as ways to maximize the benefits [ 20 ]. Communication can be improved within and across national borders by strengthening knowledge exchange and forging stronger partnerships between research institutions and healthcare organizations. In turn, this will enhance initiatives aimed at tackling infectious diseases [ 21 ].

The purpose of this research was to evaluate the factors that enable or hinder the need and significance of establishing interactive knowledge exchange networks and communities of practice (CoPs) among infectious disease specialists. Given the nature and rapid spread of infectious diseases, the sharing of information, knowledge, and experiences among experts in this field is highly valuable. As a result, the findings of this research are particularly important for infectious disease managers and policymakers, as well as for promoting a community of specialists and enhancing healthcare providers’ grasp of collective knowledge and practices.

Participants

This qualitative study employing content analysis was carried out in Iran. Semi-structured interviews were conducted with 25 experts, including infectious disease specialists (clinical and non-clinical), epidemiologists, knowledge management experts, and executives of knowledge-based firms) Like companies that produce new services such as medicine, vaccine, medical equipment, etc.). The participants were selected based on purposive and snowball sampling. They met the following criteria: willingness to engage with the research team, holding a degree in fields such as infectious diseases (e.g., a Ph.D. In Medical Virology, Medical Bacteriology, Medical Microbiology, or Epidemiology) or health knowledge management (e.g., a Ph.D. In the Medical Library of Information Sciences), and being actively involved in their respective field. The interviews were continued until data saturation. We conducted a total of 23 interviews, with one interview conducted per participant. After we reached data saturation, two more interviews were conducted to ensure that no new piece of data pertaining to the research topic would be obtained. The interviews were conducted either in person (face to face) at the specialists’ workplaces or online through social networks such as Skype, WhatsApp, and Google Meet.

Data collection

The study used a semi-structured interview guide with close-ended questions to gather information about the study’s goals and limitations (see Appendix 1). The interview guide consisted of two parts: demographic data and the knowledge network and factors affecting it. The interviews were conducted from October to December 2022 and took an average of 30 to 45 min. The participants provided informed consent before interviews after being ensured of the confidentiality of the information. They were also asked to recommend other individuals whom they believed were pertinent to the investigation. In addition, they offered consent to have their voices recorded and were assured of their ability to withdraw from the study at any time.

Data analysis

The data collected from the interviews was analyzed based on thematic analysis. At the end of each interview, we meticulously transcribed the interview in Microsoft Word 2019. Upon reviewing the transcripts multiple times, the initial themes and sub-themes were identified using MAXQDA 20. The coding into major and minor themes was done independently by MO, M, and NA, L. Discrepancies were resolved through discussion with a third reviewer (A, SH). The member check strategy [ 22 ] was used to ensure the validity and reliability of the data. Initial codes were shared with some participants to compare and verify the consistency of the ideas generated from the data with their content. Inter-coder agreement was checked to control the validity of the data. After the initial analysis, the researchers re-analyzed the interview texts and combined similar codes and subsets. In the next step, the relationships between subsets were identified and categorized into larger themes. Finally, the main themes of this study were extracted.

The study involved interviews with 25 participants, 20 of whom were male. All the participants were subject specialists. They were actively working as professors and researchers at Iranian universities of medical sciences, and two were managers of knowledge-based companies in the field of health systems. Table  1 lists the participants’ demographics. Initially, 871 codes were examined and then refined to 564 unique codes by eliminating repetitions and consolidating similar codes. Subsequently, through expert assessment, these codes were further narrowed to 437 definitive codes. These codes were categorized into two groups: influential facilitators and barriers (Table  2 ).

Based on the data gathered from interviews, the facilitators shaping the knowledge network for infectious diseases were classified into three main categories: individual factors, organizational factors, and communication mechanisms. In total, these categories encompassed seven key themes.

Individual factors involved two themes: “strengthening knowledge exchange between experts in infectious diseases” and “personal characteristics” such as the criteria for network membership.

Organizational factors encompassed factors related to the organization and involve three themes: “organizational and trans-organizational factors”, “management strategies”, and “interactions with non-governmental sectors”. Communication mechanisms involved two themes: the use of “information technology” and “knowledge brokers”. In addition, three important challenges were identified as barriers to the knowledge network: “administration and policy-making challenges”, “organizational and trans-organizational challenges”, and “individual challenges”. Detailed descriptions of these influential facilitators and barriers are provided to offer a comprehensive understanding of their role in shaping the knowledge network for infectious diseases.

Facilitators related to the knowledge network of infectious diseases

Individual factors, theme 1: strengthening knowledge exchange between experts in infectious diseases.

A primary concern impacting all types of knowledge networks the knowledge network of infectious diseases is the need to improve connections between specialists. According to many interviewees, building and maintaining a knowledge network in this field depends on establishing a robust scientific framework strong scientific platform for specialists to communicate and share knowledge while recognizing different viewpoints, areas of expertise, and skills. By promoting collaboration and interdisciplinary communication, knowledge networks can benefit from diverse perspectives and expertise. This approach allows for a more comprehensive understanding of infectious diseases as specialists can share unique insights and contribute to developing innovative solutions. One participant highlighted the significance of taking immediate action based on the exchange of knowledge among specialists:

“A scientific platform that caters to the elite is highly beneficial. It allows for the scientific communication and acceptance of diverse tastes and specialties , such as microbiology , laboratory sciences , and academic professors of traditional medicine from prestigious universities. This applies to all areas and activities within the platform , ensuring a specific and valid focus.” (P5) .

Theme 2: Personal characteristics (Criteria for membership)

The second theme of individual factors among facilitators of the knowledge network of infectious diseases is personal characteristics, e.g., personality, beliefs, behaviors, and attitudes shaping interactions and knowledge sharing within infectious diseases knowledge networks. By recognizing and utilizing these factors, we can enhance knowledge flow, support collaboration, and effectively combat the complexities of infectious diseases.

“It is important to acknowledge the importance of individual agents in every discipline. Despite our perceived abilities and knowledge , it’s crucial to acknowledge the boundaries of our understanding. The desire for group interaction and involvement can significantly affect motivation. Thus , when choosing members , it is crucial to carefully consider individual factors.” (P5) .

Organizational factors

Theme 1: organizational and trans-organizational factors.

One of the main facilitators of establishing knowledge networks for infectious diseases is the influence of organizational and trans-organizational factors. These factors encompass the processes, structures, and mechanisms directly impacting the formation and enhancement of knowledge networks. These factors are critical to developing successful knowledge networks that can effectively respond to outbreaks, conduct research, and inform public health policy. As stated by the contributors, these factors are essential for shaping successful knowledge networks. Infectious disease stakeholders can collaborate more effectively to address current challenges and prepare for future threats by recognizing and addressing these factors.

“…Organizational factors are the same as hierarchy and definition of processes , which are very much needed in such teamwork…” (P3) . “To ensure the legal approval of these networks , it is necessary to establish clear organizational frameworks. These frameworks should outline permissible exchanges within each area , ensuring the usefulness of the network for all parties involved. It is crucial to identify and clarify the specific points relevant to individuals and their associated institutions.” (P14) .

Theme 2: Management strategies

Effective management strategies can direct the network path, determine the input and output of processes, and regulate them. A key aspect of effective management strategies is clearly defining the tasks and roles of individuals within an organization. This can significantly impact the outcomes of knowledge networks. By assigning specific responsibilities to individuals based on their skills, expertise, and knowledge, managers can ensure that each member effectively contributes to the network’s goals. This can foster a sense of ownership, accountability, and collaboration among team members, thereby improving coordination and communication within the network. Participants P2 and P5 stated the following:

“Effective management policies are crucial for success. Without targeted guidance , leadership , and excellent management , nothing would come to fruition. Conflicts are inevitable when different views and experiences collide.” (P2) . “The management must know how to manage , divide the tasks , hold multiple meetings… interact with them more… If the network management is strong and capable , it can lead the affairs , and , most importantly , the support of top managers from Take these processes “. (P11)

Theme 3: Strengthening interactions with non-governmental sectors

An important factor for the successful formation of knowledge networks is establishing a stronger relationship with non-governmental sectors. Input from those interviewed shows that the non-profit sector can provide several advantages: More efficient and effective research in healthcare can be achieved by working collaboratively with the non-governmental sector, thus facilitating the creation of new products and the provision of high-quality healthcare. This collaboration allows for the transformation of research knowledge into tangible actions and policies that serve to benefit society at large. Moreover, the partnership between knowledge institutions and non-governmental organizations encourages the development of innovative and comprehensive approaches to problem-solving. This partnership empowers societies by driving positive change and promoting inclusiveness. Participant P12 specifically emphasized these advantages:

“The exchange of knowledge between non-governmental companies and academic professionals is currently limited. While there is some exchange on face-to-face platforms , it is mostly through trial and error in the production of products. However , external interactions with companies can greatly impact the effectiveness of research results and the achievement of the knowledge network’s main goals , ultimately promoting network performance.” (P12) .

Communication mechanisms

Theme 1: use of information technology.

The use of information technology significantly influences knowledge networks in infectious diseases. The participants emphasized that information technologies help the knowledge network by facilitating the acquisition, assimilation, sharing, and dissemination of knowledge, particularly in generating new knowledge. Information technology has revolutionized research by facil.itating collaboration and knowledge sharing among researchers globally. Online platforms and databases allow researchers to access and contribute to a vast repository of data on infectious diseases, accelerating the spread of fresh research and treatment advancements. They also stated that technological tools supporting a knowledge network should offer a robust infrastructure for managing a comprehensive knowledge base and creating collaborative workspaces. This viewpoint was shared by P10 and P22.

“Technological infrastructure is a significant factor shaping the growth of networks. It is essential for the formation of networks and is widely used in today’s world.” (P 10) . “Specialists rely on this infrastructure to effectively apply technology and build a comprehensive knowledge base through collaborative efforts.” (P 22) .

Theme 2: Use of knowledge brokers

Knowledge brokers facilitate and enhance the transmission and communication of knowledge among individuals. Their presence in networks can lead to better communication between members and teams, resulting in successful cooperation. Librarians, as experts in connecting people and information, have a vital role in the knowledge ecosystem of infectious diseases. They facilitate communication and understanding among researchers, healthcare practitioners, policymakers, and the wider community. As intermediaries, librarians facilitate communication and knowledge exchange, ensuring that diverse perspectives and expertise are considered in understanding and addressing infectious disease challenges. For instance, Participants P8 and P1 stated the following:

“The profession of librarianship plays a crucial role in using technology to effectively mediate networks. Librarians can use these resources to form networks and play a significant role in their development.” (P8) . “Unfortunately , our librarians were introduced mostly as book repositories… while as a good source of knowledge , they can be facilitators for these scientific networks… Informants can help in informing and publishing.” (P17) .

Barriers to the knowledge network for infectious diseases

Theme 1: administration and policy-making challenges.

According to the participants, a significant barrier to developing a successful infectious knowledge network is the limited understanding of infectious diseases, management difficulties, and policy-making. They emphasized that without a solid understanding of infectious diseases, it becomes challenging to effectively manage and make informed policy decisions. They noted that health organizations struggle to manage and create policies in the field of infectious diseases because managers and policymakers often overlook the shortcomings of these areas. Considering these challenges, the participants suggested that focusing on elements, such as policy and management frameworks, can lead to successful outcomes. For instance, two participants said,

“We have identified a weakness in knowledge transfer within our organization. Therefore , organizations must prioritize both organizational and managerial policies in addressing this issue” (P 15). “The role of management is important here , and it is better to choose managers who have a lot of general expertise , are impartial , and can invite different people from different disciplines to interactions. The result is empathy and effectiveness of interactions to control and find solutions to challenges for patients and the health system… , something that we do not see in our health system at the moment…” (P10) .

Theme 2: Organizational and trans-organizational challenges

Organizational and trans-organizational challenges are the second obstacle to the knowledge network of infectious diseases. Many participants identified structural problems, weak organizational culture, and inadequate infrastructure as major obstacles to forming societies and knowledge exchange networks in infectious diseases. A comprehensive strategy is needed to improve collaboration, communication, and efficiency in knowledge sharing within an infectious disease knowledge network. This includes promoting a culture that values collaboration across different organizations, establishing better ways to communicate and share information, simplifying administrative procedures to facilitate knowledge sharing, and providing enough funding and resources to support the network’s activities. By overcoming these challenges, the network can improve its knowledge-sharing capabilities, resulting in more effective prevention and management of infectious diseases. For example, two participants stated the following:

“We have structural problems and no culture of information exchange and sharing within our organization… Important individuals with knowledge and experience are not given proper attention.” (P 21) . “When we talk about the knowledge network , the infrastructure must be provided first because it strengthens the knowledge in the system. Lack of infrastructure , culture , process , and support from organizational managers for these processes are challenges. If these issues are resolved , it can lead to the flourishing of networks and increased interactions , and we can witness its positive effects on society in the field of infectious disease control and treatment…” (P9) .

Theme 3: Individual challenges

One of the main barriers to developing knowledge networks in infectious diseases is individual challenges. The participants identified several problems related to these challenges that hinder the formation of knowledge networks, such as the heavy workload, a lack of a clear definition of knowledge networks for individuals, and social factors. Overcoming these obstacles will foster a collaborative and innovative atmosphere. This environment will enhance our abilities to prevent, identify, and manage infectious diseases effectively.

“Most people don’t want to participate in activities like knowledge networks due to a lack of time… Time limitations can explain why people don’t participate and prefer to do things that are their main responsibility. …“(P4) . “… In my view , this matter remains incompletely comprehended , and what I have failed to grasp is that while many individuals may possess familiarity with the discourse from a scientific perspective , they may lack any practical insights on the subject matter…. They have no background regarding the issue. Now , how can this network be implemented?” (P8) . “Social factors are also important… People’s expectations can affect the quality and amount of knowledge exchange.” (P19) .

This qualitative study evaluated knowledge networks and identified facilitators and barriers influencing knowledge sharing in infectious diseases. Since infectious diseases are important not only in Iran, but also worldwide, it is crucial to strengthen the scientific foundation of specialists and promote their participation in sharing information and knowledge. The results showed that facilitating collaboration among individuals with diverse expertise promotes knowledge exchange, learning, and innovation. This, in turn, enhances therapeutic approaches and facilitates forming a professional knowledge network and innovation to enhance therapeutic approaches.

The findings demonstrated a pressing problem in healthcare regarding the consistent and effective application of research findings in practice, a dilemma that has persisted for many years [ 23 ]. A deficiency in translating research into clinical practice has detrimental impacts, compromising clinical outcomes and the overall quality of healthcare services. Additionally, it leads to the squandering of resources and perpetuates an inefficient and unproductive health system. Deploying effective strategies is crucial to bridging this gap in knowledge applications. Facilitating the exchange of knowledge by creating wide networks with different goals, such as control, prevention, and the development of new treatment methods that include different organizations, professions, and people, is vital to addressing these challenges in infectious diseases [ 24 , 25 , 26 ].

Personal attributes are another critical factor influencing the development of knowledge networks in infectious diseases. Individual characteristics that vary and influence engagement in a knowledge network have been evaluated in several studies for their impact on knowledge sharing and exchange ( 27 – 28 ).

The findings revealed that organizational and trans-organizational factors are among the most important aspects influencing the development of knowledge networks in infectious diseases. Findings from earlier studies align with this, showcasing that characteristics within organizations significantly influence the distribution and exchange of knowledge between organizations [ 29 , 30 , 31 ]. This study demonstrates that management strategies and practices play a crucial role in fostering the development and enhancement of interconnected knowledge-sharing networks within an organization. The effective implementation of these processes can significantly enhance the flow of knowledge within the network, making it a crucial factor for the successful launch of knowledge networks [ 32 ]. This study highlights the significant role of management practices in driving the effectiveness of knowledge networks. Similar findings have been observed in multiple previous studies [ 33 , 34 , 35 ].

A recent study suggests that the network of information in infectious diseases can aid a reflective and inclusive approach to improving primary healthcare. It can also provide a platform for discussions, support the transmission process, and facilitate knowledge exchange [ 13 ]. The network should include representatives from public and non-public sectors, requiring a mixed resource allocation model involving all stakeholders. Partnering with the non-public sector can provide valuable features, such as skill effectiveness, innovation, technology, motivation, financing, and risk sharing. Public-private partnership is a sustainable financial mechanism for health development initiatives and improves the quality of public services [ 36 ]. Hernandez and Zaragoza’s investigation corroborates the desirability of public and non-public collaboration in mitigating healthcare challenges and advancing service provision [ 37 ].

The analysis revealed that using information technology, specifically tools that enable streamlined communication and information sharing, holds considerable sway over the knowledge exchange network in the field of infectious diseases. These technologies are pivotal for promoting knowledge sharing, forging connections between individuals and various organizational departments, and mediating knowledge transfer across disparate entities. Therefore, access to these technologies is imperative for the efficiency of knowledge networks, providing the necessary infrastructure to expedite knowledge sharing and transfer within a network [ 38 , 39 ]. Although information technologies can present risks or threats, such as diminishing in-person interactions and potentially undermining trust and collaboration, their benefits are much more significant [ 40 ]. Goh’s research emphasizes the substantial role that information systems and informatics play in enabling expertise sharing and dissemination among professionals [ 41 ].

This study suggests that using brokers such as librarians and medical informants can enhance the effectiveness of a knowledge network. These intermediaries act as bridges between scientists, leaders, and policymakers, making communication and comprehension easier. As a result, there is more teamwork, and knowledge is shared more widely. The study recommends that knowledge networks use these mediators more frequently to enhance their overall effectiveness [ 42 ]. Several studies have indicated that knowledge brokers facilitate social interactions and participatory processes by focusing on tasks such as identifying, appraising, and translating evidence. These professionals identify emerging problems that can be resolved using the knowledge contained in evidence and documentation [ 43 , 44 , 45 ].

Regarding the barriers to the formation of the infectious disease knowledge network, the findings from this study suggest that the health system must implement management policies and open scientific frameworks to enhance knowledge sharing and exchange among specialists. Misaligned management strategies or a lack of coherence with organizational goals can be primary factors in the failure of networks and knowledge communities to fulfill their knowledge-sharing and professional expertise goals. Elements such as the provision of organizational facilities, the overarching environment, and the prevailing culture exert significant influence on the efficacy of the knowledge network’s operational capabilities [ 46 , 47 , 48 ]. Furthermore, this study suggests that specific personal traits can make it harder to build knowledge networks and share knowledge with others. This decreased knowledge sharing can harm collaborative efforts. This presents a significant challenge, as has been highlighted in previous studies.

This study encompasses a limited segment of the health research community. Consequently, future studies should meticulously assess and scrutinize diverse dimensions of research in this domain while also considering the perspectives of a wider array of individuals and specialties. Policies governing the use of this network and crucial elements contributing to its advancement should be established based on these evaluations.

The findings underscore the importance of expanding scientific evidence and research in the field of infectious disease. A strong network linking different departments, specialties, and experts from various disciplines is critical for rapid response. Creating such a network requires strategic partnerships with healthcare institutions and non-governmental sector entities while maintaining consistent communication through direct and digital interactions. This study identifies the facilitators and barriers to implementing a knowledge network specifically for infectious diseases. By carefully evaluating the effectiveness and practical applications of each aspect, we can significantly enhance the likelihood of successfully establishing, growing, and maintaining a network. It is essential to recognize and successfully navigate challenges that arise to guarantee a network’s successful establishment and ongoing functionality.

Data availability

The datasets formed and analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

We appreciate of Iran University of Medical Sciences for financial support with Grant NO: IUMS/SHMIS-1401-1-37-23099.

This work was supported by Partial financial from Iran University of Medical Sciences with Grant NO: IUMS/SHMIS-1401-1-37-23099.

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Ph.D in Medical Library & Information Science, School of Health Management & Information Sciences, Student Research Committee, Iran University of Medical Sciences, Tehran, Iran

Mina Mahami-Oskouei

Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran

Leila Nemati-Anaraki

Department of Medical Library and Information Science, School of Health Management and Medical Information Sciences , Iran University of Medical Sciences, Tehran, Iran

Sirous Panahi & Shadi Asadzandi

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All authors contributed to the study’s conception and design. The interviews were collected by [Mina Mahami-Oskouei]; implementation, and analysis by [Leila Nemati Anaraki], [Sirous Panahi], and [Shadi Asadzandi]. The first draft of the manuscript was written by [Mina Mahami-Oskouei]. Writing – review and editing final of the manuscript was written by [Mina Mahami-Oskouei], [Sirous Panahi], [Leila Nemati Anaraki], and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Leila Nemati-Anaraki .

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The study procedure was approved by the Medical Ethics Committee of Iran University of Medical Sciences [date: Mar 2022, ID: IR.IUMS.REC.1400.1082] As part of the doctoral dissertation “Developing a Conceptual Model for a Knowledge Network in the Infectious Disease Field in Iran”. All participants’ information was private and nameless; there was no personal information that could link the answers with any of the participants in the present study.

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Mahami-Oskouei, M., Nemati-Anaraki, L., Panahi, S. et al. Iranian experts’ perspectives on facilitators and barriers influencing the infectious disease knowledge network:  A qualitative study. BMC Health Serv Res 24 , 1040 (2024). https://doi.org/10.1186/s12913-024-11525-8

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Received : 23 March 2024

Accepted : 02 September 2024

Published : 07 September 2024

DOI : https://doi.org/10.1186/s12913-024-11525-8

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