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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 Coding 101

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

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

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

Overview: Qualitative Data Coding

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

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

What is qualitative data coding?

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

“Pigeons attacked me and stole my sandwich.”

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

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

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

What are the different types of coding?

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

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

Deductive coding 101

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

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

“What foods do college students eat the most?”

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

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

Inductive coding 101 

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

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

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

A little bit of both… hybrid coding approaches

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

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

Need a helping hand?

How to code qualitative data

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

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

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

Step 1 – Initial coding

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

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

What pets do you have?

I have an alpaca and three dogs.

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

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

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

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

How to decide which codes to use

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

• In vivo coding 

Process coding

• Open coding

Descriptive coding

Structural coding.

• Value coding

Let’s take a look at each of these:

In vivo coding

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Values coding

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

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

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

Step 2 – Line by line coding

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

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

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

Moving from coding to analysis

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

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

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

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

Code categorisation

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

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

Theme identification

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

Tips & tricks for quality coding

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

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

32 Comments

I appreciated the valuable information provided to accomplish the various stages of the inductive and inductive coding process. However, I would have been extremely satisfied to be appraised of the SPECIFIC STEPS to follow for: 1. Deductive coding related to the phenomenon and its features to generate the codes, categories, and themes. 2. Inductive coding related to using (a) Initial (b) Axial, and (c) Thematic procedures using transcribe data from the research questions

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You might also want to consult our other sections on  Planning your research ,  Quantitative research  and  Writing up research , and check out the Additional resources .

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Tips for a qualitative dissertation

Veronika Williams

17 October 2017

Tips for students

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

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

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

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

2) Reflect on your role

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

3)  Don’t forget the theory

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

4) Think about depth rather than breadth

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

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

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

6) Move beyond the descriptive

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

7) It’s not just about the average experience

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

8) Bounce ideas

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

9) Be selective

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

10) Don’t panic!

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

Related course:

Qualitative research methods.

Short Course

Frequently asked questions

How do you analyze qualitative data.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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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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Presenting and Evaluating Qualitative Research

The purpose of this paper is to help authors to think about ways to present qualitative research papers in the American Journal of Pharmaceutical Education . It also discusses methods for reviewers to assess the rigour, quality, and usefulness of qualitative research. Examples of different ways to present data from interviews, observations, and focus groups are included. The paper concludes with guidance for publishing qualitative research and a checklist for authors and reviewers.

INTRODUCTION

Policy and practice decisions, including those in education, increasingly are informed by findings from qualitative as well as quantitative research. Qualitative research is useful to policymakers because it often describes the settings in which policies will be implemented. Qualitative research is also useful to both pharmacy practitioners and pharmacy academics who are involved in researching educational issues in both universities and practice and in developing teaching and learning.

Qualitative research involves the collection, analysis, and interpretation of data that are not easily reduced to numbers. These data relate to the social world and the concepts and behaviors of people within it. Qualitative research can be found in all social sciences and in the applied fields that derive from them, for example, research in health services, nursing, and pharmacy. 1 It looks at X in terms of how X varies in different circumstances rather than how big is X or how many Xs are there? 2 Textbooks often subdivide research into qualitative and quantitative approaches, furthering the common assumption that there are fundamental differences between the 2 approaches. With pharmacy educators who have been trained in the natural and clinical sciences, there is often a tendency to embrace quantitative research, perhaps due to familiarity. A growing consensus is emerging that sees both qualitative and quantitative approaches as useful to answering research questions and understanding the world. Increasingly mixed methods research is being carried out where the researcher explicitly combines the quantitative and qualitative aspects of the study. 3 , 4

Like healthcare, education involves complex human interactions that can rarely be studied or explained in simple terms. Complex educational situations demand complex understanding; thus, the scope of educational research can be extended by the use of qualitative methods. Qualitative research can sometimes provide a better understanding of the nature of educational problems and thus add to insights into teaching and learning in a number of contexts. For example, at the University of Nottingham, we conducted in-depth interviews with pharmacists to determine their perceptions of continuing professional development and who had influenced their learning. We also have used a case study approach using observation of practice and in-depth interviews to explore physiotherapists' views of influences on their leaning in practice. We have conducted in-depth interviews with a variety of stakeholders in Malawi, Africa, to explore the issues surrounding pharmacy academic capacity building. A colleague has interviewed and conducted focus groups with students to explore cultural issues as part of a joint Nottingham-Malaysia pharmacy degree program. Another colleague has interviewed pharmacists and patients regarding their expectations before and after clinic appointments and then observed pharmacist-patient communication in clinics and assessed it using the Calgary Cambridge model in order to develop recommendations for communication skills training. 5 We have also performed documentary analysis on curriculum data to compare pharmacist and nurse supplementary prescribing courses in the United Kingdom.

It is important to choose the most appropriate methods for what is being investigated. Qualitative research is not appropriate to answer every research question and researchers need to think carefully about their objectives. Do they wish to study a particular phenomenon in depth (eg, students' perceptions of studying in a different culture)? Or are they more interested in making standardized comparisons and accounting for variance (eg, examining differences in examination grades after changing the way the content of a module is taught). Clearly a quantitative approach would be more appropriate in the last example. As with any research project, a clear research objective has to be identified to know which methods should be applied.

Types of qualitative data include:

• Audio recordings and transcripts from in-depth or semi-structured interviews
• Structured interview questionnaires containing substantial open comments including a substantial number of responses to open comment items.
• Audio recordings and transcripts from focus group sessions.
• Field notes (notes taken by the researcher while in the field [setting] being studied)
• Video recordings (eg, lecture delivery, class assignments, laboratory performance)
• Case study notes
• Documents (reports, meeting minutes, e-mails)
• Diaries, video diaries
• Observation notes
• Press clippings
• Photographs

RIGOUR IN QUALITATIVE RESEARCH

Qualitative research is often criticized as biased, small scale, anecdotal, and/or lacking rigor; however, when it is carried out properly it is unbiased, in depth, valid, reliable, credible and rigorous. In qualitative research, there needs to be a way of assessing the “extent to which claims are supported by convincing evidence.” 1 Although the terms reliability and validity traditionally have been associated with quantitative research, increasingly they are being seen as important concepts in qualitative research as well. Examining the data for reliability and validity assesses both the objectivity and credibility of the research. Validity relates to the honesty and genuineness of the research data, while reliability relates to the reproducibility and stability of the data.

The validity of research findings refers to the extent to which the findings are an accurate representation of the phenomena they are intended to represent. The reliability of a study refers to the reproducibility of the findings. Validity can be substantiated by a number of techniques including triangulation use of contradictory evidence, respondent validation, and constant comparison. Triangulation is using 2 or more methods to study the same phenomenon. Contradictory evidence, often known as deviant cases, must be sought out, examined, and accounted for in the analysis to ensure that researcher bias does not interfere with or alter their perception of the data and any insights offered. Respondent validation, which is allowing participants to read through the data and analyses and provide feedback on the researchers' interpretations of their responses, provides researchers with a method of checking for inconsistencies, challenges the researchers' assumptions, and provides them with an opportunity to re-analyze their data. The use of constant comparison means that one piece of data (for example, an interview) is compared with previous data and not considered on its own, enabling researchers to treat the data as a whole rather than fragmenting it. Constant comparison also enables the researcher to identify emerging/unanticipated themes within the research project.

STRENGTHS AND LIMITATIONS OF QUALITATIVE RESEARCH

Qualitative researchers have been criticized for overusing interviews and focus groups at the expense of other methods such as ethnography, observation, documentary analysis, case studies, and conversational analysis. Qualitative research has numerous strengths when properly conducted.

Strengths of Qualitative Research

• Issues can be examined in detail and in depth.
• Interviews are not restricted to specific questions and can be guided/redirected by the researcher in real time.
• The research framework and direction can be quickly revised as new information emerges.
• The data based on human experience that is obtained is powerful and sometimes more compelling than quantitative data.
• Subtleties and complexities about the research subjects and/or topic are discovered that are often missed by more positivistic enquiries.
• Data usually are collected from a few cases or individuals so findings cannot be generalized to a larger population. Findings can however be transferable to another setting.

Limitations of Qualitative Research

• Research quality is heavily dependent on the individual skills of the researcher and more easily influenced by the researcher's personal biases and idiosyncrasies.
• Rigor is more difficult to maintain, assess, and demonstrate.
• The volume of data makes analysis and interpretation time consuming.
• It is sometimes not as well understood and accepted as quantitative research within the scientific community
• The researcher's presence during data gathering, which is often unavoidable in qualitative research, can affect the subjects' responses.
• Issues of anonymity and confidentiality can present problems when presenting findings
• Findings can be more difficult and time consuming to characterize in a visual way.

PRESENTATION OF QUALITATIVE RESEARCH FINDINGS

The following extracts are examples of how qualitative data might be presented:

Data From an Interview.

The following is an example of how to present and discuss a quote from an interview.

The researcher should select quotes that are poignant and/or most representative of the research findings. Including large portions of an interview in a research paper is not necessary and often tedious for the reader. The setting and speakers should be established in the text at the end of the quote.

The student describes how he had used deep learning in a dispensing module. He was able to draw on learning from a previous module, “I found that while using the e learning programme I was able to apply the knowledge and skills that I had gained in last year's diseases and goals of treatment module.” (interviewee 22, male)

This is an excerpt from an article on curriculum reform that used interviews 5 :

The first question was, “Without the accreditation mandate, how much of this curriculum reform would have been attempted?” According to respondents, accreditation played a significant role in prompting the broad-based curricular change, and their comments revealed a nuanced view. Most indicated that the change would likely have occurred even without the mandate from the accreditation process: “It reflects where the profession wants to be … training a professional who wants to take on more responsibility.” However, they also commented that “if it were not mandated, it could have been a very difficult road.” Or it “would have happened, but much later.” The change would more likely have been incremental, “evolutionary,” or far more limited in its scope. “Accreditation tipped the balance” was the way one person phrased it. “Nobody got serious until the accrediting body said it would no longer accredit programs that did not change.”

Data From Observations

The following example is some data taken from observation of pharmacist patient consultations using the Calgary Cambridge guide. 6 , 7 The data are first presented and a discussion follows:

Pharmacist: We will soon be starting a stop smoking clinic. Patient: Is the interview over now? Pharmacist: No this is part of it. (Laughs) You can't tell me to bog off (sic) yet. (pause) We will be starting a stop smoking service here, Patient: Yes. Pharmacist: with one-to-one and we will be able to help you or try to help you. If you want it. In this example, the pharmacist has picked up from the patient's reaction to the stop smoking clinic that she is not receptive to advice about giving up smoking at this time; in fact she would rather end the consultation. The pharmacist draws on his prior relationship with the patient and makes use of a joke to lighten the tone. He feels his message is important enough to persevere but he presents the information in a succinct and non-pressurised way. His final comment of “If you want it” is important as this makes it clear that he is not putting any pressure on the patient to take up this offer. This extract shows that some patient cues were picked up, and appropriately dealt with, but this was not the case in all examples.

Data From Focus Groups

This excerpt from a study involving 11 focus groups illustrates how findings are presented using representative quotes from focus group participants. 8

Those pharmacists who were initially familiar with CPD endorsed the model for their peers, and suggested it had made a meaningful difference in the way they viewed their own practice. In virtually all focus groups sessions, pharmacists familiar with and supportive of the CPD paradigm had worked in collaborative practice environments such as hospital pharmacy practice. For these pharmacists, the major advantage of CPD was the linking of workplace learning with continuous education. One pharmacist stated, “It's amazing how much I have to learn every day, when I work as a pharmacist. With [the learning portfolio] it helps to show how much learning we all do, every day. It's kind of satisfying to look it over and see how much you accomplish.” Within many of the learning portfolio-sharing sessions, debates emerged regarding the true value of traditional continuing education and its outcome in changing an individual's practice. While participants appreciated the opportunity for social and professional networking inherent in some forms of traditional CE, most eventually conceded that the academic value of most CE programming was limited by the lack of a systematic process for following-up and implementing new learning in the workplace. “Well it's nice to go to these [continuing education] events, but really, I don't know how useful they are. You go, you sit, you listen, but then, well I at least forget.”

The following is an extract from a focus group (conducted by the author) with first-year pharmacy students about community placements. It illustrates how focus groups provide a chance for participants to discuss issues on which they might disagree.

Interviewer: So you are saying that you would prefer health related placements? Student 1: Not exactly so long as I could be developing my communication skill. Student 2: Yes but I still think the more health related the placement is the more I'll gain from it. Student 3: I disagree because other people related skills are useful and you may learn those from taking part in a community project like building a garden. Interviewer: So would you prefer a mixture of health and non health related community placements?

GUIDANCE FOR PUBLISHING QUALITATIVE RESEARCH

Qualitative research is becoming increasingly accepted and published in pharmacy and medical journals. Some journals and publishers have guidelines for presenting qualitative research, for example, the British Medical Journal 9 and Biomedcentral . 10 Medical Education published a useful series of articles on qualitative research. 11 Some of the important issues that should be considered by authors, reviewers and editors when publishing qualitative research are discussed below.

Introduction.

A good introduction provides a brief overview of the manuscript, including the research question and a statement justifying the research question and the reasons for using qualitative research methods. This section also should provide background information, including relevant literature from pharmacy, medicine, and other health professions, as well as literature from the field of education that addresses similar issues. Any specific educational or research terminology used in the manuscript should be defined in the introduction.

The methods section should clearly state and justify why the particular method, for example, face to face semistructured interviews, was chosen. The method should be outlined and illustrated with examples such as the interview questions, focusing exercises, observation criteria, etc. The criteria for selecting the study participants should then be explained and justified. The way in which the participants were recruited and by whom also must be stated. A brief explanation/description should be included of those who were invited to participate but chose not to. It is important to consider “fair dealing,” ie, whether the research design explicitly incorporates a wide range of different perspectives so that the viewpoint of 1 group is never presented as if it represents the sole truth about any situation. The process by which ethical and or research/institutional governance approval was obtained should be described and cited.

The study sample and the research setting should be described. Sampling differs between qualitative and quantitative studies. In quantitative survey studies, it is important to select probability samples so that statistics can be used to provide generalizations to the population from which the sample was drawn. Qualitative research necessitates having a small sample because of the detailed and intensive work required for the study. So sample sizes are not calculated using mathematical rules and probability statistics are not applied. Instead qualitative researchers should describe their sample in terms of characteristics and relevance to the wider population. Purposive sampling is common in qualitative research. Particular individuals are chosen with characteristics relevant to the study who are thought will be most informative. Purposive sampling also may be used to produce maximum variation within a sample. Participants being chosen based for example, on year of study, gender, place of work, etc. Representative samples also may be used, for example, 20 students from each of 6 schools of pharmacy. Convenience samples involve the researcher choosing those who are either most accessible or most willing to take part. This may be fine for exploratory studies; however, this form of sampling may be biased and unrepresentative of the population in question. Theoretical sampling uses insights gained from previous research to inform sample selection for a new study. The method for gaining informed consent from the participants should be described, as well as how anonymity and confidentiality of subjects were guaranteed. The method of recording, eg, audio or video recording, should be noted, along with procedures used for transcribing the data.

Data Analysis.

A description of how the data were analyzed also should be included. Was computer-aided qualitative data analysis software such as NVivo (QSR International, Cambridge, MA) used? Arrival at “data saturation” or the end of data collection should then be described and justified. A good rule when considering how much information to include is that readers should have been given enough information to be able to carry out similar research themselves.

One of the strengths of qualitative research is the recognition that data must always be understood in relation to the context of their production. 1 The analytical approach taken should be described in detail and theoretically justified in light of the research question. If the analysis was repeated by more than 1 researcher to ensure reliability or trustworthiness, this should be stated and methods of resolving any disagreements clearly described. Some researchers ask participants to check the data. If this was done, it should be fully discussed in the paper.

An adequate account of how the findings were produced should be included A description of how the themes and concepts were derived from the data also should be included. Was an inductive or deductive process used? The analysis should not be limited to just those issues that the researcher thinks are important, anticipated themes, but also consider issues that participants raised, ie, emergent themes. Qualitative researchers must be open regarding the data analysis and provide evidence of their thinking, for example, were alternative explanations for the data considered and dismissed, and if so, why were they dismissed? It also is important to present outlying or negative/deviant cases that did not fit with the central interpretation.

The interpretation should usually be grounded in interviewees or respondents' contributions and may be semi-quantified, if this is possible or appropriate, for example, “Half of the respondents said …” “The majority said …” “Three said…” Readers should be presented with data that enable them to “see what the researcher is talking about.” 1 Sufficient data should be presented to allow the reader to clearly see the relationship between the data and the interpretation of the data. Qualitative data conventionally are presented by using illustrative quotes. Quotes are “raw data” and should be compiled and analyzed, not just listed. There should be an explanation of how the quotes were chosen and how they are labeled. For example, have pseudonyms been given to each respondent or are the respondents identified using codes, and if so, how? It is important for the reader to be able to see that a range of participants have contributed to the data and that not all the quotes are drawn from 1 or 2 individuals. There is a tendency for authors to overuse quotes and for papers to be dominated by a series of long quotes with little analysis or discussion. This should be avoided.

Participants do not always state the truth and may say what they think the interviewer wishes to hear. A good qualitative researcher should not only examine what people say but also consider how they structured their responses and how they talked about the subject being discussed, for example, the person's emotions, tone, nonverbal communication, etc. If the research was triangulated with other qualitative or quantitative data, this should be discussed.

Discussion.

The findings should be presented in the context of any similar previous research and or theories. A discussion of the existing literature and how this present research contributes to the area should be included. A consideration must also be made about how transferrable the research would be to other settings. Any particular strengths and limitations of the research also should be discussed. It is common practice to include some discussion within the results section of qualitative research and follow with a concluding discussion.

The author also should reflect on their own influence on the data, including a consideration of how the researcher(s) may have introduced bias to the results. The researcher should critically examine their own influence on the design and development of the research, as well as on data collection and interpretation of the data, eg, were they an experienced teacher who researched teaching methods? If so, they should discuss how this might have influenced their interpretation of the results.

Conclusion.

The conclusion should summarize the main findings from the study and emphasize what the study adds to knowledge in the area being studied. Mays and Pope suggest the researcher ask the following 3 questions to determine whether the conclusions of a qualitative study are valid 12 : How well does this analysis explain why people behave in the way they do? How comprehensible would this explanation be to a thoughtful participant in the setting? How well does the explanation cohere with what we already know?

CHECKLIST FOR QUALITATIVE PAPERS

This paper establishes criteria for judging the quality of qualitative research. It provides guidance for authors and reviewers to prepare and review qualitative research papers for the American Journal of Pharmaceutical Education . A checklist is provided in Appendix 1 to assist both authors and reviewers of qualitative data.

ACKNOWLEDGEMENTS

Thank you to the 3 reviewers whose ideas helped me to shape this paper.

Appendix 1. Checklist for authors and reviewers of qualitative research.

Introduction

• □ Research question is clearly stated.
• □ Research question is justified and related to the existing knowledge base (empirical research, theory, policy).
• □ Any specific research or educational terminology used later in manuscript is defined.
• □ The process by which ethical and or research/institutional governance approval was obtained is described and cited.
• □ Reason for choosing particular research method is stated.
• □ Criteria for selecting study participants are explained and justified.
• □ Recruitment methods are explicitly stated.
• □ Details of who chose not to participate and why are given.
• □ Study sample and research setting used are described.
• □ Method for gaining informed consent from the participants is described.
• □ Maintenance/Preservation of subject anonymity and confidentiality is described.
• □ Method of recording data (eg, audio or video recording) and procedures for transcribing data are described.
• □ Methods are outlined and examples given (eg, interview guide).
• □ Decision to stop data collection is described and justified.
• □ Data analysis and verification are described, including by whom they were performed.
• □ Methods for identifying/extrapolating themes and concepts from the data are discussed.
• □ Sufficient data are presented to allow a reader to assess whether or not the interpretation is supported by the data.
• □ Outlying or negative/deviant cases that do not fit with the central interpretation are presented.
• □ Transferability of research findings to other settings is discussed.
• □ Findings are presented in the context of any similar previous research and social theories.
• □ Discussion often is incorporated into the results in qualitative papers.
• □ A discussion of the existing literature and how this present research contributes to the area is included.
• □ Any particular strengths and limitations of the research are discussed.
• □ Reflection of the influence of the researcher(s) on the data, including a consideration of how the researcher(s) may have introduced bias to the results is included.

Conclusions

• □ The conclusion states the main finings of the study and emphasizes what the study adds to knowledge in the subject area.

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Trustworthiness of Qualitative Data

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As noted in the dissertation template for qualitative studies, the section directly following the Chapter 4 introduction is to be labeled Trustworthiness of the Data, and in this section, qualitative researchers are required to articulate evidence of four primary criteria to ensure trustworthiness of the final study data set:

Credibility (e.g., triangulation, member checks)

Credibility of qualitative data can be assured through multiple perspectives throughout data collection to ensure data are appropriate. This may be done through data, investigator, or theoretical triangulation; participant validation or member checks; or the rigorous techniques used to gather the data.

Transferability (e.g., the extent to which the findings are generalizable to other situations)

Generalizability is not expected in qualitative research, so transferability of qualitative data assures the study findings are applicable to similar settings or individuals. Transferability can be demonstrated by clear assumptions and contextual inferences of the research setting and participants.

Dependability (e.g., an in-depth description of the methodology and design to allow the study to be repeated)

Dependability of the qualitative data is demonstrated through assurances that the findings were established despite any changes within the research setting or participants during data collection. Again, rigorous data collection techniques and procedures can assure dependability of the final data set.

Confirmability (e.g., the steps to ensure that the data and findings are not due to participant and/or researcher bias)

Confirmability of qualitative data is assured when data are checked and rechecked throughout data collection and analysis to ensure results would likely be repeatable by others. This can be documented by a clear coding schema that identifies the codes and patterns identified in analyses. Finally, a data audit prior to analysis can also ensure dependability.

For more information on these criteria, visit the Sage Research Methods database in the NU Library: https://resources.nu.edu/sagerm

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How to Use Creative Data Visualization Techniques for Easy Comprehension of Qualitative Research

“A picture is worth a thousand words!”—an adage used so often stands true even whilst reporting your research data. Research studies with overwhelming data can perhaps be difficult to comprehend by some readers or can even be time-consuming. While presenting quantitative research data becomes easier with the help of graphs, pie charts, etc. researchers face an undeniable challenge whilst presenting qualitative research data. In this article, we will elaborate on effectively presenting qualitative research using data visualization techniques .

Table of Contents

What is Data Visualization?

Data visualization is the process of converting textual information into graphical and illustrative representations. It is imperative to think beyond numbers to get a holistic and comprehensive understanding of research data. Hence, this technique is adopted to help presenters communicate relevant research data in a way that’s easy for the viewer to interpret and draw conclusions.

What Is the Importance of Data Visualization in Qualitative Research?

According to the form in which the data is collected and expressed, it is broadly divided into qualitative data and quantitative data. Quantitative data expresses the size or quantity of data in a countable integer. Unlike quantitative data, qualitative data cannot be expressed in continuous integer values; it refers to data values ​​described in the non-numeric form related to subjects, places, things, events, activities, or concepts.

What Are the Advantages of Good Data Visualization Techniques?

Excellent data visualization techniques have several benefits:

• Human eyes are often drawn to patterns and colors. Moreover, in this age of Big Data , visualization can be considered an asset to quickly and easily comprehend large amounts of data generated in a research study.
• Enables viewers to recognize emerging trends and accelerate their response time on the basis of what is seen and assimilated.
• Illustrations make it easier to identify correlated parameters.
• Allows the presenter to narrate a story whilst helping the viewer understand the data and draw conclusions from it.
• As humans can process visual images better than texts, data visualization techniques enable viewers to remember them for a longer time.

Different Types of Data Visualization Techniques in Qualitative Research

Here are several data visualization techniques for presenting qualitative data for better comprehension of research data.

1. Word Clouds

• Word Clouds is a type of data visualization technique which helps in visualizing one-word descriptions.
• It is a single image composing multiple words associated with a particular text or subject.
• The size of each word indicates its importance or frequency in the data.
• Wordle and Tagxedo are two majorly used tools to create word clouds.

2. Graphic Timelines

• Graphic timelines are created to present regular text-based timelines with pictorial illustrations or diagrams, photos, and other images.
• It visually displays a series of events in chronological order on a timescale.
• Furthermore, showcasing timelines in a graphical manner makes it easier to understand critical milestones in a study.

3. Icons Beside Descriptions

• Rather than writing long descriptive paragraphs, including resembling icons beside brief and concise points enable quick and easy comprehension.

4. Heat Map

• Using a heat map as a data visualization technique better displays differences in data with color variations.
• The intensity and frequency of data is well addressed with the help of these color codes.
• However, a clear legend must be mentioned alongside the heat map to correctly interpret a heat map.
• Additionally, it also helps identify trends in data.

5. Mind Map

• A mind map helps explain concepts and ideas linked to a central idea.
• Allows visual structuring of ideas without overwhelming the viewer with large amounts of text.
• These can be used to present graphical abstracts

Do’s and Don’ts of Data Visualization Techniques

It perhaps is not easy to visualize qualitative data and make it recognizable and comprehensible to viewers at a glance. However, well-visualized qualitative data can be very useful in order to clearly convey the key points to readers and listeners in presentations.

Are you struggling with ways to display your qualitative data? Which data visualization techniques have you used before? Let us know about your experience in the comments section below!

nicely explained

None. And I want to use it from now.

Would it be ideal or suggested to use these techniques to display qualitative data in a thesis perhaps?

Using data visualization techniques in a qualitative research thesis can help convey your findings in a more engaging and comprehensible manner. Here’s a brief overview of how to incorporate data visualization in such a thesis:

Select Relevant Visualizations: Identify the types of data you have (e.g., textual, audio, visual) and the appropriate visualization techniques that can represent your qualitative data effectively. Common options include word clouds, charts, graphs, timelines, and thematic maps.

Data Preparation: Ensure your qualitative data is well-organized and coded appropriately. This might involve using qualitative analysis software like NVivo or Atlas.ti to tag and categorize data.

Create Visualizations: Generate visualizations that illustrate key themes, patterns, or trends within your qualitative data. For example: Word clouds can highlight frequently occurring terms or concepts. Bar charts or histograms can show the distribution of specific themes or categories. Timeline visualizations can help display chronological trends. Concept maps can illustrate the relationships between different concepts or ideas.

Integrate Visualizations into Your Thesis: Incorporate these visualizations within your thesis to complement your narrative. Place them strategically to support your arguments or findings. Include clear and concise captions and labels for each visualization, providing context and explaining their significance.

Interpretation: In the text of your thesis, interpret the visualizations. Explain what patterns or insights they reveal about your qualitative data. Offer meaningful insights and connections between the visuals and your research questions or hypotheses.

Maintain Consistency: Maintain a consistent style and formatting for your visualizations throughout the thesis. This ensures clarity and professionalism.

Ethical Considerations: If your qualitative research involves sensitive or personal data, consider ethical guidelines and privacy concerns when presenting visualizations. Anonymize or protect sensitive information as needed.

Review and Refinement: Before finalizing your thesis, review the visualizations for accuracy and clarity. Seek feedback from peers or advisors to ensure they effectively convey your qualitative findings.

Appendices: If you have a large number of visualizations or detailed data, consider placing some in appendices. This keeps the main body of your thesis uncluttered while providing interested readers with supplementary information.

Cite Sources: If you use specific software or tools to create your visualizations, acknowledge and cite them appropriately in your thesis.

Hope you find this helpful. Happy Learning!

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Qualitative Tools

• AtlasTi All-in-one Research Software. ATLAS.ti - Discover insights for your research projects. QDA and mixed methods. UX research.Term paper, thesis, dissertation.
• Dedoose Dedoose is a web application for mixed methods research developed by academics from UCLA, with support from the William T. Grant Foundation, and is the successor to EthnoNotes. Dedoose is an alternative to other qualitative data analysis software, explicitly aimed at facilitating rigorous mixed methods research.
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“Because people don’t know what it is, they don’t really know it exists” : a qualitative study of postgraduate medical educators’ perceptions of dyscalculia

• Laura Josephine Cheetham 1  

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

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

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

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

Conclusions

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

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

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

Context for medical education

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

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

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

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

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

Educational challenges, learning needs and local context

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

Identifying attitudes and understanding

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

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

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

Consequently, the research question and aims below were constructed.

Research question:

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

Research aims:

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

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

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

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

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

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

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

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

Inclusion criteria were:

Primary research or review.

International or UK-based research reported in English.

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

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

Exclusion criteria were:

Literature published in non-English languages.

Opinion and commentary articles.

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

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

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

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

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

Ethical considerations

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

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

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

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

Reflexivity

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

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

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

Summary of themes and subthemes generated

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Experience shapes educator knowledge, understanding and attitudes

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

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

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

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

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

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

Greater educator support and training regarding dyscalculia is needed

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

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

Coping strategies are important to better understand

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

Identification is key but not fully enabling

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

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

The PGME clinical context may limit support

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

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

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

Uncertainty regarding reasonable adjustments need addressing

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

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

Limitations

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

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

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

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

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

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

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Attention Deficit and Hyperactivity Disorder

Doctors in Training

General Medical Council

Higher Education Institution

Health Education and Improvement Wales

Postgraduate Medical Education

Professional Support Unit

Reasonable Adjustment

Reflexive Thematic Analysis

Specific Learning Difference

United Kingdom

Zone of Proximal Development

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Acknowledgements

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

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

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

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Correspondence to Laura Josephine Cheetham .

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

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Research participants gave written and verbal consent for the contents of their interviews to be analysed and reported as part of this study.

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The authors declare no competing interests.

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

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

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Received : 27 November 2023

Accepted : 14 August 2024

Published : 20 August 2024

DOI : https://doi.org/10.1186/s12909-024-05912-2

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