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Understanding Data Presentations (Guide + Examples)

In this age of overwhelming information, the skill to effectively convey data has become extremely valuable. Initiating a discussion on data presentation types involves thoughtful consideration of the nature of your data and the message you aim to convey. Different types of visualizations serve distinct purposes. Whether you’re dealing with how to develop a report or simply trying to communicate complex information, how you present data influences how well your audience understands and engages with it. This extensive guide leads you through the different ways of data presentation.

Table of Contents

What is a Data Presentation?

What should a data presentation include, line graphs, treemap chart, scatter plot, how to choose a data presentation type, recommended data presentation templates, common mistakes done in data presentation.

A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable. This process requires a series of tools, such as charts, graphs, tables, infographics, dashboards, and so on, supported by concise textual explanations to improve understanding and boost retention rate.

Data presentations require us to cull data in a format that allows the presenter to highlight trends, patterns, and insights so that the audience can act upon the shared information. In a few words, the goal of data presentations is to enable viewers to grasp complicated concepts or trends quickly, facilitating informed decision-making or deeper analysis.

Data presentations go beyond the mere usage of graphical elements. Seasoned presenters encompass visuals with the art of data storytelling , so the speech skillfully connects the points through a narrative that resonates with the audience. Depending on the purpose – inspire, persuade, inform, support decision-making processes, etc. – is the data presentation format that is better suited to help us in this journey.

To nail your upcoming data presentation, ensure to count with the following elements:

• Clear Objectives: Understand the intent of your presentation before selecting the graphical layout and metaphors to make content easier to grasp.
• Engaging introduction: Use a powerful hook from the get-go. For instance, you can ask a big question or present a problem that your data will answer. Take a look at our guide on how to start a presentation for tips & insights.
• Structured Narrative: Your data presentation must tell a coherent story. This means a beginning where you present the context, a middle section in which you present the data, and an ending that uses a call-to-action. Check our guide on presentation structure for further information.
• Visual Elements: These are the charts, graphs, and other elements of visual communication we ought to use to present data. This article will cover one by one the different types of data representation methods we can use, and provide further guidance on choosing between them.
• Insights and Analysis: This is not just showcasing a graph and letting people get an idea about it. A proper data presentation includes the interpretation of that data, the reason why it’s included, and why it matters to your research.
• Conclusion & CTA: Ending your presentation with a call to action is necessary. Whether you intend to wow your audience into acquiring your services, inspire them to change the world, or whatever the purpose of your presentation, there must be a stage in which you convey all that you shared and show the path to staying in touch. Plan ahead whether you want to use a thank-you slide, a video presentation, or which method is apt and tailored to the kind of presentation you deliver.
• Q&A Session: After your speech is concluded, allocate 3-5 minutes for the audience to raise any questions about the information you disclosed. This is an extra chance to establish your authority on the topic. Check our guide on questions and answer sessions in presentations here.

Bar charts are a graphical representation of data using rectangular bars to show quantities or frequencies in an established category. They make it easy for readers to spot patterns or trends. Bar charts can be horizontal or vertical, although the vertical format is commonly known as a column chart. They display categorical, discrete, or continuous variables grouped in class intervals [1] . They include an axis and a set of labeled bars horizontally or vertically. These bars represent the frequencies of variable values or the values themselves. Numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

Real-Life Application of Bar Charts

Let’s say a sales manager is presenting sales to their audience. Using a bar chart, he follows these steps.

Step 1: Selecting Data

The first step is to identify the specific data you will present to your audience.

The sales manager has highlighted these products for the presentation.

• Product A: Men’s Shoes
• Product B: Women’s Apparel
• Product C: Electronics
• Product D: Home Decor

Step 2: Choosing Orientation

Opt for a vertical layout for simplicity. Vertical bar charts help compare different categories in case there are not too many categories [1] . They can also help show different trends. A vertical bar chart is used where each bar represents one of the four chosen products. After plotting the data, it is seen that the height of each bar directly represents the sales performance of the respective product.

It is visible that the tallest bar (Electronics – Product C) is showing the highest sales. However, the shorter bars (Women’s Apparel – Product B and Home Decor – Product D) need attention. It indicates areas that require further analysis or strategies for improvement.

Step 3: Colorful Insights

Different colors are used to differentiate each product. It is essential to show a color-coded chart where the audience can distinguish between products.

• Men’s Shoes (Product A): Yellow
• Women’s Apparel (Product B): Orange
• Electronics (Product C): Violet
• Home Decor (Product D): Blue

Bar charts are straightforward and easily understandable for presenting data. They are versatile when comparing products or any categorical data [2] . Bar charts adapt seamlessly to retail scenarios. Despite that, bar charts have a few shortcomings. They cannot illustrate data trends over time. Besides, overloading the chart with numerous products can lead to visual clutter, diminishing its effectiveness.

For more information, check our collection of bar chart templates for PowerPoint .

Line graphs help illustrate data trends, progressions, or fluctuations by connecting a series of data points called ‘markers’ with straight line segments. This provides a straightforward representation of how values change [5] . Their versatility makes them invaluable for scenarios requiring a visual understanding of continuous data. In addition, line graphs are also useful for comparing multiple datasets over the same timeline. Using multiple line graphs allows us to compare more than one data set. They simplify complex information so the audience can quickly grasp the ups and downs of values. From tracking stock prices to analyzing experimental results, you can use line graphs to show how data changes over a continuous timeline. They show trends with simplicity and clarity.

Real-life Application of Line Graphs

To understand line graphs thoroughly, we will use a real case. Imagine you’re a financial analyst presenting a tech company’s monthly sales for a licensed product over the past year. Investors want insights into sales behavior by month, how market trends may have influenced sales performance and reception to the new pricing strategy. To present data via a line graph, you will complete these steps.

First, you need to gather the data. In this case, your data will be the sales numbers. For example:

• January: $45,000
• February: $55,000
• March: $45,000
• April: $60,000
• May: $ 70,000
• June: $65,000
• July: $62,000
• August: $68,000
• September: $81,000
• October: $76,000
• November: $87,000
• December: $91,000

After choosing the data, the next step is to select the orientation. Like bar charts, you can use vertical or horizontal line graphs. However, we want to keep this simple, so we will keep the timeline (x-axis) horizontal while the sales numbers (y-axis) vertical.

Step 3: Connecting Trends

After adding the data to your preferred software, you will plot a line graph. In the graph, each month’s sales are represented by data points connected by a line.

Step 4: Adding Clarity with Color

If there are multiple lines, you can also add colors to highlight each one, making it easier to follow.

Line graphs excel at visually presenting trends over time. These presentation aids identify patterns, like upward or downward trends. However, too many data points can clutter the graph, making it harder to interpret. Line graphs work best with continuous data but are not suitable for categories.

For more information, check our collection of line chart templates for PowerPoint and our article about how to make a presentation graph .

A data dashboard is a visual tool for analyzing information. Different graphs, charts, and tables are consolidated in a layout to showcase the information required to achieve one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs). You don’t make new visuals in the dashboard; instead, you use it to display visuals you’ve already made in worksheets [3] .

Keeping the number of visuals on a dashboard to three or four is recommended. Adding too many can make it hard to see the main points [4]. Dashboards can be used for business analytics to analyze sales, revenue, and marketing metrics at a time. They are also used in the manufacturing industry, as they allow users to grasp the entire production scenario at the moment while tracking the core KPIs for each line.

Real-Life Application of a Dashboard

Consider a project manager presenting a software development project’s progress to a tech company’s leadership team. He follows the following steps.

Step 1: Defining Key Metrics

To effectively communicate the project’s status, identify key metrics such as completion status, budget, and bug resolution rates. Then, choose measurable metrics aligned with project objectives.

Step 2: Choosing Visualization Widgets

After finalizing the data, presentation aids that align with each metric are selected. For this project, the project manager chooses a progress bar for the completion status and uses bar charts for budget allocation. Likewise, he implements line charts for bug resolution rates.

Step 3: Dashboard Layout

Key metrics are prominently placed in the dashboard for easy visibility, and the manager ensures that it appears clean and organized.

Dashboards provide a comprehensive view of key project metrics. Users can interact with data, customize views, and drill down for detailed analysis. However, creating an effective dashboard requires careful planning to avoid clutter. Besides, dashboards rely on the availability and accuracy of underlying data sources.

For more information, check our article on how to design a dashboard presentation , and discover our collection of dashboard PowerPoint templates .

Treemap charts represent hierarchical data structured in a series of nested rectangles [6] . As each branch of the ‘tree’ is given a rectangle, smaller tiles can be seen representing sub-branches, meaning elements on a lower hierarchical level than the parent rectangle. Each one of those rectangular nodes is built by representing an area proportional to the specified data dimension.

Treemaps are useful for visualizing large datasets in compact space. It is easy to identify patterns, such as which categories are dominant. Common applications of the treemap chart are seen in the IT industry, such as resource allocation, disk space management, website analytics, etc. Also, they can be used in multiple industries like healthcare data analysis, market share across different product categories, or even in finance to visualize portfolios.

Real-Life Application of a Treemap Chart

Let’s consider a financial scenario where a financial team wants to represent the budget allocation of a company. There is a hierarchy in the process, so it is helpful to use a treemap chart. In the chart, the top-level rectangle could represent the total budget, and it would be subdivided into smaller rectangles, each denoting a specific department. Further subdivisions within these smaller rectangles might represent individual projects or cost categories.

Step 1: Define Your Data Hierarchy

While presenting data on the budget allocation, start by outlining the hierarchical structure. The sequence will be like the overall budget at the top, followed by departments, projects within each department, and finally, individual cost categories for each project.

• Top-level rectangle: Total Budget
• Second-level rectangles: Departments (Engineering, Marketing, Sales)
• Third-level rectangles: Projects within each department
• Fourth-level rectangles: Cost categories for each project (Personnel, Marketing Expenses, Equipment)

Step 2: Choose a Suitable Tool

It’s time to select a data visualization tool supporting Treemaps. Popular choices include Tableau, Microsoft Power BI, PowerPoint, or even coding with libraries like D3.js. It is vital to ensure that the chosen tool provides customization options for colors, labels, and hierarchical structures.

Here, the team uses PowerPoint for this guide because of its user-friendly interface and robust Treemap capabilities.

Step 3: Make a Treemap Chart with PowerPoint

After opening the PowerPoint presentation, they chose “SmartArt” to form the chart. The SmartArt Graphic window has a “Hierarchy” category on the left.  Here, you will see multiple options. You can choose any layout that resembles a Treemap. The “Table Hierarchy” or “Organization Chart” options can be adapted. The team selects the Table Hierarchy as it looks close to a Treemap.

Step 5: Input Your Data

After that, a new window will open with a basic structure. They add the data one by one by clicking on the text boxes. They start with the top-level rectangle, representing the total budget.  

Step 6: Customize the Treemap

By clicking on each shape, they customize its color, size, and label. At the same time, they can adjust the font size, style, and color of labels by using the options in the “Format” tab in PowerPoint. Using different colors for each level enhances the visual difference.

Treemaps excel at illustrating hierarchical structures. These charts make it easy to understand relationships and dependencies. They efficiently use space, compactly displaying a large amount of data, reducing the need for excessive scrolling or navigation. Additionally, using colors enhances the understanding of data by representing different variables or categories.

In some cases, treemaps might become complex, especially with deep hierarchies.  It becomes challenging for some users to interpret the chart. At the same time, displaying detailed information within each rectangle might be constrained by space. It potentially limits the amount of data that can be shown clearly. Without proper labeling and color coding, there’s a risk of misinterpretation.

A heatmap is a data visualization tool that uses color coding to represent values across a two-dimensional surface. In these, colors replace numbers to indicate the magnitude of each cell. This color-shaded matrix display is valuable for summarizing and understanding data sets with a glance [7] . The intensity of the color corresponds to the value it represents, making it easy to identify patterns, trends, and variations in the data.

As a tool, heatmaps help businesses analyze website interactions, revealing user behavior patterns and preferences to enhance overall user experience. In addition, companies use heatmaps to assess content engagement, identifying popular sections and areas of improvement for more effective communication. They excel at highlighting patterns and trends in large datasets, making it easy to identify areas of interest.

We can implement heatmaps to express multiple data types, such as numerical values, percentages, or even categorical data. Heatmaps help us easily spot areas with lots of activity, making them helpful in figuring out clusters [8] . When making these maps, it is important to pick colors carefully. The colors need to show the differences between groups or levels of something. And it is good to use colors that people with colorblindness can easily see.

Check our detailed guide on how to create a heatmap here. Also discover our collection of heatmap PowerPoint templates .

Pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, making it easy to visualize the contribution of each component to the total.

The size of the pie charts is influenced by the value of data points within each pie. The total of all data points in a pie determines its size. The pie with the highest data points appears as the largest, whereas the others are proportionally smaller. However, you can present all pies of the same size if proportional representation is not required [9] . Sometimes, pie charts are difficult to read, or additional information is required. A variation of this tool can be used instead, known as the donut chart , which has the same structure but a blank center, creating a ring shape. Presenters can add extra information, and the ring shape helps to declutter the graph.

Pie charts are used in business to show percentage distribution, compare relative sizes of categories, or present straightforward data sets where visualizing ratios is essential.

Real-Life Application of Pie Charts

Consider a scenario where you want to represent the distribution of the data. Each slice of the pie chart would represent a different category, and the size of each slice would indicate the percentage of the total portion allocated to that category.

Step 1: Define Your Data Structure

Imagine you are presenting the distribution of a project budget among different expense categories.

• Column A: Expense Categories (Personnel, Equipment, Marketing, Miscellaneous)
• Column B: Budget Amounts ($40,000, $30,000, $20,000, $10,000) Column B represents the values of your categories in Column A.

Step 2: Insert a Pie Chart

Using any of the accessible tools, you can create a pie chart. The most convenient tools for forming a pie chart in a presentation are presentation tools such as PowerPoint or Google Slides.  You will notice that the pie chart assigns each expense category a percentage of the total budget by dividing it by the total budget.

For instance:

• Personnel: $40,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 40%
• Equipment: $30,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 30%
• Marketing: $20,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 20%
• Miscellaneous: $10,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 10%

You can make a chart out of this or just pull out the pie chart from the data.

3D pie charts and 3D donut charts are quite popular among the audience. They stand out as visual elements in any presentation slide, so let’s take a look at how our pie chart example would look in 3D pie chart format.

Step 03: Results Interpretation

The pie chart visually illustrates the distribution of the project budget among different expense categories. Personnel constitutes the largest portion at 40%, followed by equipment at 30%, marketing at 20%, and miscellaneous at 10%. This breakdown provides a clear overview of where the project funds are allocated, which helps in informed decision-making and resource management. It is evident that personnel are a significant investment, emphasizing their importance in the overall project budget.

Pie charts provide a straightforward way to represent proportions and percentages. They are easy to understand, even for individuals with limited data analysis experience. These charts work well for small datasets with a limited number of categories.

However, a pie chart can become cluttered and less effective in situations with many categories. Accurate interpretation may be challenging, especially when dealing with slight differences in slice sizes. In addition, these charts are static and do not effectively convey trends over time.

For more information, check our collection of pie chart templates for PowerPoint .

Histograms present the distribution of numerical variables. Unlike a bar chart that records each unique response separately, histograms organize numeric responses into bins and show the frequency of reactions within each bin [10] . The x-axis of a histogram shows the range of values for a numeric variable. At the same time, the y-axis indicates the relative frequencies (percentage of the total counts) for that range of values.

Whenever you want to understand the distribution of your data, check which values are more common, or identify outliers, histograms are your go-to. Think of them as a spotlight on the story your data is telling. A histogram can provide a quick and insightful overview if you’re curious about exam scores, sales figures, or any numerical data distribution.

Real-Life Application of a Histogram

In the histogram data analysis presentation example, imagine an instructor analyzing a class’s grades to identify the most common score range. A histogram could effectively display the distribution. It will show whether most students scored in the average range or if there are significant outliers.

Step 1: Gather Data

He begins by gathering the data. The scores of each student in class are gathered to analyze exam scores.

After arranging the scores in ascending order, bin ranges are set.

Step 2: Define Bins

Bins are like categories that group similar values. Think of them as buckets that organize your data. The presenter decides how wide each bin should be based on the range of the values. For instance, the instructor sets the bin ranges based on score intervals: 60-69, 70-79, 80-89, and 90-100.

Step 3: Count Frequency

Now, he counts how many data points fall into each bin. This step is crucial because it tells you how often specific ranges of values occur. The result is the frequency distribution, showing the occurrences of each group.

Here, the instructor counts the number of students in each category.

• 60-69: 1 student (Kate)
• 70-79: 4 students (David, Emma, Grace, Jack)
• 80-89: 7 students (Alice, Bob, Frank, Isabel, Liam, Mia, Noah)
• 90-100: 3 students (Clara, Henry, Olivia)

Step 4: Create the Histogram

It’s time to turn the data into a visual representation. Draw a bar for each bin on a graph. The width of the bar should correspond to the range of the bin, and the height should correspond to the frequency.  To make your histogram understandable, label the X and Y axes.

In this case, the X-axis should represent the bins (e.g., test score ranges), and the Y-axis represents the frequency.

The histogram of the class grades reveals insightful patterns in the distribution. Most students, with seven students, fall within the 80-89 score range. The histogram provides a clear visualization of the class’s performance. It showcases a concentration of grades in the upper-middle range with few outliers at both ends. This analysis helps in understanding the overall academic standing of the class. It also identifies the areas for potential improvement or recognition.

Thus, histograms provide a clear visual representation of data distribution. They are easy to interpret, even for those without a statistical background. They apply to various types of data, including continuous and discrete variables. One weak point is that histograms do not capture detailed patterns in students’ data, with seven compared to other visualization methods.

A scatter plot is a graphical representation of the relationship between two variables. It consists of individual data points on a two-dimensional plane. This plane plots one variable on the x-axis and the other on the y-axis. Each point represents a unique observation. It visualizes patterns, trends, or correlations between the two variables.

Scatter plots are also effective in revealing the strength and direction of relationships. They identify outliers and assess the overall distribution of data points. The points’ dispersion and clustering reflect the relationship’s nature, whether it is positive, negative, or lacks a discernible pattern. In business, scatter plots assess relationships between variables such as marketing cost and sales revenue. They help present data correlations and decision-making.

Real-Life Application of Scatter Plot

A group of scientists is conducting a study on the relationship between daily hours of screen time and sleep quality. After reviewing the data, they managed to create this table to help them build a scatter plot graph:

In the provided example, the x-axis represents Daily Hours of Screen Time, and the y-axis represents the Sleep Quality Rating.

The scientists observe a negative correlation between the amount of screen time and the quality of sleep. This is consistent with their hypothesis that blue light, especially before bedtime, has a significant impact on sleep quality and metabolic processes.

There are a few things to remember when using a scatter plot. Even when a scatter diagram indicates a relationship, it doesn’t mean one variable affects the other. A third factor can influence both variables. The more the plot resembles a straight line, the stronger the relationship is perceived [11] . If it suggests no ties, the observed pattern might be due to random fluctuations in data. When the scatter diagram depicts no correlation, whether the data might be stratified is worth considering.

Choosing the appropriate data presentation type is crucial when making a presentation . Understanding the nature of your data and the message you intend to convey will guide this selection process. For instance, when showcasing quantitative relationships, scatter plots become instrumental in revealing correlations between variables. If the focus is on emphasizing parts of a whole, pie charts offer a concise display of proportions. Histograms, on the other hand, prove valuable for illustrating distributions and frequency patterns. 

Bar charts provide a clear visual comparison of different categories. Likewise, line charts excel in showcasing trends over time, while tables are ideal for detailed data examination. Starting a presentation on data presentation types involves evaluating the specific information you want to communicate and selecting the format that aligns with your message. This ensures clarity and resonance with your audience from the beginning of your presentation.

1. Fact Sheet Dashboard for Data Presentation

Convey all the data you need to present in this one-pager format, an ideal solution tailored for users looking for presentation aids. Global maps, donut chats, column graphs, and text neatly arranged in a clean layout presented in light and dark themes.

Use This Template

2. 3D Column Chart Infographic PPT Template

Represent column charts in a highly visual 3D format with this PPT template. A creative way to present data, this template is entirely editable, and we can craft either a one-page infographic or a series of slides explaining what we intend to disclose point by point.

3. Data Circles Infographic PowerPoint Template

An alternative to the pie chart and donut chart diagrams, this template features a series of curved shapes with bubble callouts as ways of presenting data. Expand the information for each arch in the text placeholder areas.

4. Colorful Metrics Dashboard for Data Presentation

This versatile dashboard template helps us in the presentation of the data by offering several graphs and methods to convert numbers into graphics. Implement it for e-commerce projects, financial projections, project development, and more.

5. Animated Data Presentation Tools for PowerPoint & Google Slides

A slide deck filled with most of the tools mentioned in this article, from bar charts, column charts, treemap graphs, pie charts, histogram, etc. Animated effects make each slide look dynamic when sharing data with stakeholders.

6. Statistics Waffle Charts PPT Template for Data Presentations

This PPT template helps us how to present data beyond the typical pie chart representation. It is widely used for demographics, so it’s a great fit for marketing teams, data science professionals, HR personnel, and more.

7. Data Presentation Dashboard Template for Google Slides

A compendium of tools in dashboard format featuring line graphs, bar charts, column charts, and neatly arranged placeholder text areas. 

8. Weather Dashboard for Data Presentation

Share weather data for agricultural presentation topics, environmental studies, or any kind of presentation that requires a highly visual layout for weather forecasting on a single day. Two color themes are available.

9. Social Media Marketing Dashboard Data Presentation Template

Intended for marketing professionals, this dashboard template for data presentation is a tool for presenting data analytics from social media channels. Two slide layouts featuring line graphs and column charts.

10. Project Management Summary Dashboard Template

A tool crafted for project managers to deliver highly visual reports on a project’s completion, the profits it delivered for the company, and expenses/time required to execute it. 4 different color layouts are available.

11. Profit & Loss Dashboard for PowerPoint and Google Slides

A must-have for finance professionals. This typical profit & loss dashboard includes progress bars, donut charts, column charts, line graphs, and everything that’s required to deliver a comprehensive report about a company’s financial situation.

Overwhelming visuals

One of the mistakes related to using data-presenting methods is including too much data or using overly complex visualizations. They can confuse the audience and dilute the key message.

Inappropriate chart types

Choosing the wrong type of chart for the data at hand can lead to misinterpretation. For example, using a pie chart for data that doesn’t represent parts of a whole is not right.

Lack of context

Failing to provide context or sufficient labeling can make it challenging for the audience to understand the significance of the presented data.

Inconsistency in design

Using inconsistent design elements and color schemes across different visualizations can create confusion and visual disarray.

Failure to provide details

Simply presenting raw data without offering clear insights or takeaways can leave the audience without a meaningful conclusion.

Lack of focus

Not having a clear focus on the key message or main takeaway can result in a presentation that lacks a central theme.

Visual accessibility issues

Overlooking the visual accessibility of charts and graphs can exclude certain audience members who may have difficulty interpreting visual information.

In order to avoid these mistakes in data presentation, presenters can benefit from using presentation templates . These templates provide a structured framework. They ensure consistency, clarity, and an aesthetically pleasing design, enhancing data communication’s overall impact.

Understanding and choosing data presentation types are pivotal in effective communication. Each method serves a unique purpose, so selecting the appropriate one depends on the nature of the data and the message to be conveyed. The diverse array of presentation types offers versatility in visually representing information, from bar charts showing values to pie charts illustrating proportions. 

Using the proper method enhances clarity, engages the audience, and ensures that data sets are not just presented but comprehensively understood. By appreciating the strengths and limitations of different presentation types, communicators can tailor their approach to convey information accurately, developing a deeper connection between data and audience understanding.

[1] Government of Canada, S.C. (2021) 5 Data Visualization 5.2 Bar Chart , 5.2 Bar chart .  https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch9/bargraph-diagrammeabarres/5214818-eng.htm

[2] Kosslyn, S.M., 1989. Understanding charts and graphs. Applied cognitive psychology, 3(3), pp.185-225. https://apps.dtic.mil/sti/pdfs/ADA183409.pdf

[3] Creating a Dashboard . https://it.tufts.edu/book/export/html/1870

[4] https://www.goldenwestcollege.edu/research/data-and-more/data-dashboards/index.html

[5] https://www.mit.edu/course/21/21.guide/grf-line.htm

[6] Jadeja, M. and Shah, K., 2015, January. Tree-Map: A Visualization Tool for Large Data. In GSB@ SIGIR (pp. 9-13). https://ceur-ws.org/Vol-1393/gsb15proceedings.pdf#page=15

[7] Heat Maps and Quilt Plots. https://www.publichealth.columbia.edu/research/population-health-methods/heat-maps-and-quilt-plots

[8] EIU QGIS WORKSHOP. https://www.eiu.edu/qgisworkshop/heatmaps.php

[9] About Pie Charts.  https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c8.htm

[10] Histograms. https://sites.utexas.edu/sos/guided/descriptive/numericaldd/descriptiven2/histogram/ [11] https://asq.org/quality-resources/scatter-diagram

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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

• JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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10 Methods of Data Presentation with 5 Great Tips to Practice, Best in 2024

Leah Nguyen • 05 April, 2024 • 17 min read

There are different ways of presenting data, so which one is suited you the most? You can end deathly boring and ineffective data presentation right now with our 10 methods of data presentation . Check out the examples from each technique!

Have you ever presented a data report to your boss/coworkers/teachers thinking it was super dope like you’re some cyber hacker living in the Matrix, but all they saw was a pile of static numbers that seemed pointless and didn’t make sense to them?

Understanding digits is rigid . Making people from non-analytical backgrounds understand those digits is even more challenging.

How can you clear up those confusing numbers in the types of presentation that have the flawless clarity of a diamond? So, let’s check out best way to present data. 💎

Table of Contents

• What are Methods of Data Presentations?
• #1 – Tabular

#3 – Pie chart

#4 – bar chart, #5 – histogram, #6 – line graph, #7 – pictogram graph, #8 – radar chart, #9 – heat map, #10 – scatter plot.

• 5 Mistakes to Avoid
• Best Method of Data Presentation

Frequently Asked Questions

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What are Methods of Data Presentation?

The term ’data presentation’ relates to the way you present data in a way that makes even the most clueless person in the room understand. 

Some say it’s witchcraft (you’re manipulating the numbers in some ways), but we’ll just say it’s the power of turning dry, hard numbers or digits into a visual showcase that is easy for people to digest.

Presenting data correctly can help your audience understand complicated processes, identify trends, and instantly pinpoint whatever is going on without exhausting their brains.

Good data presentation helps…

• Make informed decisions and arrive at positive outcomes . If you see the sales of your product steadily increase throughout the years, it’s best to keep milking it or start turning it into a bunch of spin-offs (shoutout to Star Wars👀).
• Reduce the time spent processing data . Humans can digest information graphically 60,000 times faster than in the form of text. Grant them the power of skimming through a decade of data in minutes with some extra spicy graphs and charts.
• Communicate the results clearly . Data does not lie. They’re based on factual evidence and therefore if anyone keeps whining that you might be wrong, slap them with some hard data to keep their mouths shut.
• Add to or expand the current research . You can see what areas need improvement, as well as what details often go unnoticed while surfing through those little lines, dots or icons that appear on the data board.

Methods of Data Presentation and Examples

Imagine you have a delicious pepperoni, extra-cheese pizza. You can decide to cut it into the classic 8 triangle slices, the party style 12 square slices, or get creative and abstract on those slices. 

There are various ways for cutting a pizza and you get the same variety with how you present your data. In this section, we will bring you the 10 ways to slice a pizza – we mean to present your data – that will make your company’s most important asset as clear as day. Let’s dive into 10 ways to present data efficiently.

#1 – Tabular 

Among various types of data presentation, tabular is the most fundamental method, with data presented in rows and columns. Excel or Google Sheets would qualify for the job. Nothing fancy.

This is an example of a tabular presentation of data on Google Sheets. Each row and column has an attribute (year, region, revenue, etc.), and you can do a custom format to see the change in revenue throughout the year.

When presenting data as text, all you do is write your findings down in paragraphs and bullet points, and that’s it. A piece of cake to you, a tough nut to crack for whoever has to go through all of the reading to get to the point.

• 65% of email users worldwide access their email via a mobile device.
• Emails that are optimised for mobile generate 15% higher click-through rates.
• 56% of brands using emojis in their email subject lines had a higher open rate.

(Source: CustomerThermometer )

All the above quotes present statistical information in textual form. Since not many people like going through a wall of texts, you’ll have to figure out another route when deciding to use this method, such as breaking the data down into short, clear statements, or even as catchy puns if you’ve got the time to think of them.

A pie chart (or a ‘donut chart’ if you stick a hole in the middle of it) is a circle divided into slices that show the relative sizes of data within a whole. If you’re using it to show percentages, make sure all the slices add up to 100%.

The pie chart is a familiar face at every party and is usually recognised by most people. However, one setback of using this method is our eyes sometimes can’t identify the differences in slices of a circle, and it’s nearly impossible to compare similar slices from two different pie charts, making them the villains in the eyes of data analysts.

Bonus example: A literal ‘pie’ chart! 🥧

The bar chart is a chart that presents a bunch of items from the same category, usually in the form of rectangular bars that are placed at an equal distance from each other. Their heights or lengths depict the values they represent.

They can be as simple as this:

Or more complex and detailed like this example of presentation of data. Contributing to an effective statistic presentation, this one is a grouped bar chart that not only allows you to compare categories but also the groups within them as well.

Similar in appearance to the bar chart but the rectangular bars in histograms don’t often have the gap like their counterparts.

Instead of measuring categories like weather preferences or favourite films as a bar chart does, a histogram only measures things that can be put into numbers.

Teachers can use presentation graphs like a histogram to see which score group most of the students fall into, like in this example above.

Recordings to ways of displaying data, we shouldn’t overlook the effectiveness of line graphs. Line graphs are represented by a group of data points joined together by a straight line. There can be one or more lines to compare how several related things change over time. 

On a line chart’s horizontal axis, you usually have text labels, dates or years, while the vertical axis usually represents the quantity (e.g.: budget, temperature or percentage).

A pictogram graph uses pictures or icons relating to the main topic to visualise a small dataset. The fun combination of colours and illustrations makes it a frequent use at schools.

Pictograms are a breath of fresh air if you want to stay away from the monotonous line chart or bar chart for a while. However, they can present a very limited amount of data and sometimes they are only there for displays and do not represent real statistics.

If presenting five or more variables in the form of a bar chart is too stuffy then you should try using a radar chart, which is one of the most creative ways to present data.

Radar charts show data in terms of how they compare to each other starting from the same point. Some also call them ‘spider charts’ because each aspect combined looks like a spider web.

Radar charts can be a great use for parents who’d like to compare their child’s grades with their peers to lower their self-esteem. You can see that each angular represents a subject with a score value ranging from 0 to 100. Each student’s score across 5 subjects is highlighted in a different colour.

If you think that this method of data presentation somehow feels familiar, then you’ve probably encountered one while playing Pokémon .

A heat map represents data density in colours. The bigger the number, the more colour intense that data will be represented.

Most U.S citizens would be familiar with this data presentation method in geography. For elections, many news outlets assign a specific colour code to a state, with blue representing one candidate and red representing the other. The shade of either blue or red in each state shows the strength of the overall vote in that state.

Another great thing you can use a heat map for is to map what visitors to your site click on. The more a particular section is clicked the ‘hotter’ the colour will turn, from blue to bright yellow to red.

If you present your data in dots instead of chunky bars, you’ll have a scatter plot. 

A scatter plot is a grid with several inputs showing the relationship between two variables. It’s good at collecting seemingly random data and revealing some telling trends.

For example, in this graph, each dot shows the average daily temperature versus the number of beach visitors across several days. You can see that the dots get higher as the temperature increases, so it’s likely that hotter weather leads to more visitors.

5 Data Presentation Mistakes to Avoid

#1 – assume your audience understands what the numbers represent.

You may know all the behind-the-scenes of your data since you’ve worked with them for weeks, but your audience doesn’t.

Showing without telling only invites more and more questions from your audience, as they have to constantly make sense of your data, wasting the time of both sides as a result.

While showing your data presentations, you should tell them what the data are about before hitting them with waves of numbers first. You can use interactive activities such as polls , word clouds , online quiz and Q&A sections , combined with icebreaker games , to assess their understanding of the data and address any confusion beforehand.

#2 – Use the wrong type of chart

Charts such as pie charts must have a total of 100% so if your numbers accumulate to 193% like this example below, you’re definitely doing it wrong.

Before making a chart, ask yourself: what do I want to accomplish with my data? Do you want to see the relationship between the data sets, show the up and down trends of your data, or see how segments of one thing make up a whole?

Remember, clarity always comes first. Some data visualisations may look cool, but if they don’t fit your data, steer clear of them. 

#3 – Make it 3D

3D is a fascinating graphical presentation example. The third dimension is cool, but full of risks.

Can you see what’s behind those red bars? Because we can’t either. You may think that 3D charts add more depth to the design, but they can create false perceptions as our eyes see 3D objects closer and bigger than they appear, not to mention they cannot be seen from multiple angles.

#4 – Use different types of charts to compare contents in the same category

This is like comparing a fish to a monkey. Your audience won’t be able to identify the differences and make an appropriate correlation between the two data sets. 

Next time, stick to one type of data presentation only. Avoid the temptation of trying various data visualisation methods in one go and make your data as accessible as possible.

#5 – Bombard the audience with too much information

The goal of data presentation is to make complex topics much easier to understand, and if you’re bringing too much information to the table, you’re missing the point.

The more information you give, the more time it will take for your audience to process it all. If you want to make your data understandable and give your audience a chance to remember it, keep the information within it to an absolute minimum. You should set your session with open-ended questions , to avoid dead-communication!

What are the Best Methods of Data Presentation?

Finally, which is the best way to present data?

The answer is…

There is none 😄 Each type of presentation has its own strengths and weaknesses and the one you choose greatly depends on what you’re trying to do. 

For example:

• Go for a scatter plot if you’re exploring the relationship between different data values, like seeing whether the sales of ice cream go up because of the temperature or because people are just getting more hungry and greedy each day?
• Go for a line graph if you want to mark a trend over time. 
• Go for a heat map if you like some fancy visualisation of the changes in a geographical location, or to see your visitors’ behaviour on your website.
• Go for a pie chart (especially in 3D) if you want to be shunned by others because it was never a good idea👇

What is chart presentation?

A chart presentation is a way of presenting data or information using visual aids such as charts, graphs, and diagrams. The purpose of a chart presentation is to make complex information more accessible and understandable for the audience.

When can I use charts for presentation?

Charts can be used to compare data, show trends over time, highlight patterns, and simplify complex information.

Why should use charts for presentation?

You should use charts to ensure your contents and visual look clean, as they are the visual representative, provide clarity, simplicity, comparison, contrast and super time-saving!

What are the 4 graphical methods of presenting data?

Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Leah Nguyen

Words that convert, stories that stick. I turn complex ideas into engaging narratives - helping audiences learn, remember, and take action.

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10 Tips for Presenting Data

Big data. Analytics. Data science. Businesses are clamoring to use data to get a competitive edge, but all the data in the world won’t help if your stakeholders can’t understand, or if their eyes glaze over as you present your incredibly insightful analysis . This post outlines my top ten tips for presenting data.

It’s worth noting that these tips are tool agnostic—whether you use Data Studio, Domo, Tableau or another data viz tool, the principles are the same. However, don’t assume your vendors are in lock-step with data visualization best practices! Vendor defaults frequently violate key principles of data visualization, so it’s up to the analyst to put these principles in practice.

Here are my 10 tips for presenting data:

• Recognize that presentation matters
• Don’t scare people with numbers
• Maximize the data pixel ratio
• Save 3D for the movies
• Friends don’t let friends use pie charts
• Choose the appropriate chart
• Don’t mix chart types for no reason
• Don’t use axes to mislead
• Never rely solely on color
• Use color with intention

1) Recognize That Presentation Matters

The first step to presenting data is to understand that how you present data matters . It’s common for analysts to feel they’re not being heard by stakeholders, or that their analysis or recommendations never generate action. The problem is, if you’re not communicating data clearly for business users, it’s really easy for them to tune out.

Analysts may ask, “But I’m so busy with the actual work of putting together these reports. Why should I take the time to ‘make it pretty’?”

Because it’s not about “making things pretty.” It’s about making your data understandable.

My very first boss in Analytics told me, “As an analyst, you are an information architect.” It’s so true. Our job is to take a mass of information and architect it in such a way that people can easily comprehend it.

Take these two visuals. The infographic style shows Top 10 Salaries at Google. The first one is certainly “prettier.” However, the visual is pretty meaningless, and you have to actually read the information to understand any of it. (That defeats the purpose of a data viz!)

Pretty, but not helpful

On the flip side, the simpler (but far less pretty) visualization makes it very easy to see:

• Which job category pays the most
• Which pays the least
• Which has the greatest range of salaries
• Which roles have similar ranges

It’s not about pretty. When it comes to presenting data clearly, “informative” is more important than “beautiful.”

Just as we optimize our digital experiences, our analyses must be optimized to how people perceive and process information. You can think of this as a three-step process:

• Information passes through the Visual Sensory Register . This is pre-attentive processing—it’s what we process before we’re even aware we’re doing so. Certain things will stand out to us, objects may get unconsciously grouped together.
• From there, information passes to Short Term Memory. This is a limited capacity system, and information not considered “useful” will be discarded. We will only retain 3-9 “chunks” of visual information. However, a “chunk” can be defined differently based on how information is grouped. For example, we might be able to remember 3-9 letters. But, we could also remember 3-9 words, or 3-9 song lyrics! Your goal, therefore, is to present information in such a way that people can easily “chunk” information, to allow greater retention through short-term memory. (For example, a table of data ensures the numbers themselves can’t possibly all be retained, but a chart that shows our conversion rate trending down may be retained as one chunk of information—“trending down.”)
• From short-term memory, information is passed to Long-Term Memory. The goal here is to retain meaningful information—but not the precise details.

2) Don’t Scare People with Numbers

Analysts like numbers. Not everybody does! Many of your stakeholders may feel overwhelmed by numbers, data, charts. But when presenting data, there are little things you can do to make numbers immediately more “friendly.”

Simple formatting

Don’t make people count zeros in numbers! (e.g. 1000000 vs. 100,000,000).

Skip unnecessary decimals

How many decimals are “necessary” depends on the range of your values. If your values range from 2 to 90 percent, you don’t need two decimals places.

But on the flip side, if you have numbers that are really close (for example, all values are within a few percent of each other) it’s important to include decimal places.

Too often, this comes from confusing “precision” with “accuracy.” Just because you are more precise (in including more decimal places) doesn’t make your data more accurate. It just gives the illusion of it.

Right align numbers

Always right-align columns of numbers. This is the default in many solutions, but not always. What it allows for is your data to form a “quasi bar chart” where people can easily scan for the biggest number, by the number of characters. This can be harder to do if you center-align.

3) Maximize the Data-Pixel Ratio

The Data-Pixel Ratio originally stems from Edward Tufte’s “Data-Ink Ratio”, later renamed the “Data-Pixel Ratio” by Stephen Few. The more complicated explanation (with an equation, GAH!) is:

A simpler way of thinking of it: Your pixels (or ink) should be used for data display, and not for fluff or decoration. (I like to explain that I’m just really stingy with printer ink—so, I don’t want to print a ton of wasted decorations.)

Here are some quick transformations to maximize the data-pixel ratio:

Avoid repeating information

For example, if you include the word “Region” in the column header, there’s no need to repeat the word in each cell within the column. You don’t even need to repeat the dollar sign. Once we know the column is in dollars, we know all the values are too.

Avoid repeating information when presenting data

For bar and column charts:

• Remove borders (that Excel loves to put in by default, and Google Sheets still doesn’t let you remove them, grumble grumble.)
• Display information horizontally. Choosing a bar over a column chart can make the axis easier to read.
• Condense axes, to show values “in Millions” or “in K”, rather than unnecessarily repeating zeros (“,000”)

For line charts:

• Remove unnecessary legends. If you only have one series in a line chart, the title will explain what the chart is—a legend is duplicated information.
• Grey (or even remove) grid lines. While sometimes grid lines can be useful to help users track across to see the value on the y-axis, the lines don’t need to be heavy to guide the eyes (and certainly not as visually important as the data).

4) Save 3D for the Movies

These two charts have the same information. In the top left one, you can see at a glance that the bar is slightly above $150,000. In the bottom one, you can “kind of sort of tell” that it’s at $150,000, but you have to work much harder to figure that out. With a 3D chart you’re adding an extra cognitive step, where someone has to think about what they’re looking at.

And don't even get me started on this one:

However, I’ll concede: there is an exception to every rule. When is 3D okay? When it does a better job telling the story , and isn’t just there to make it “snazzy.” For example, take this recent chart from the 2016 election: 3D adds a critical element of information, that a 2D version would miss.

5) Friends Don’t Let Friends Use Pie Charts

It’s easy to hate on pie charts (and yet, every vendor is excited to announce that they have ZOMG EXPLODING DONUT CHARTS! just added in their recent release).

However, there are some justified reasons for the backlash against the use (and especially, the overuse) of pie charts when presenting data:

• We aren’t as good at judging the relative differences in area or circles, versus lines . For example, if we look at a line, we’re more easily able to say “that line is about a third bigger.”We are not adept at doing this same thing with area or circles, so often a bar or column chart is simply easier for us to process.
• They’re used incorrectly . Pie charts are intended to show “parts of a whole”, so a pie chart that adds up to more than 100% is a misuse of the visualization.
• They have too many pieces . Perhaps they do add up to 100%, but there’s little a pie chart like this will do to help you understand the data.

With that understood, if you feel you must use pie charts, the following stipulations apply:

• The pie chart shouldn’t represent more than three items.
• The data has to represent parts of a whole (aka, the pieces must add to 100%).
• You can only use one. As soon as you need to compare data (for example, three series across multiple years) then pie charts are a no-go. Instead, go for a stacked bar chart.

Like 3D, pie charts are acceptable when they are the best possible way for presenting data and getting your message across. This is an example of where, hands-down, a pie chart is the right visualization:

6) Choose the Appropriate Chart for Presenting Data

A chart should be carefully chosen, to convey the message you want someone to take from your data presentation. For example, are you trying to show that the United States and India’s average order value are similar? Or that India’s revenue is trending up more quickly? Or that Asia is twice the rest of the world?

For a more comprehensive guide, check out Extreme Presentation’s Chart Chooser. But in the meantime, here is a quick version for some commonly used charts:

Line charts

Use line charts to demonstrate trends. If there are important things that happened, you can also highlight specific point

Bar or column charts

Bar or column charts should be used to emphasize the differences between things.

If you don’t have much space, you might consider using sparklines for presenting data trends. Sparklines are a small chart contained within a single cell of a table. (You can also choose to use bar charts within your data table.)

Here are some resources on how to build sparklines into the different data viz platforms:

Google Sheets

7) Don’t Mix Chart Types for No Reason

I repeat. Don’t mix chart types for no reason . Presenting data sets together should tell a story or reveal insights together, that isn’t possible if left apart. Unfortunately, far too many charts involving cramming multiple data series on them is purely to conserve the space of adding another chart. The problem is, as soon as you put those two series of data together, your end users are going to assume there’s a connection between them (and waste valuable brain power trying to figure out what it is).

Below are good and bad examples of mixing chart types when presenting data. On the first, we have a column and line chart together, because we’re trying to demonstrate that the two metrics trend similarly. Together they are telling a story, that they wouldn’t tell on two separate charts.

The second, however, is an example of “just trying to fit two series onto a chart.”

For the second chart, a better option for presenting the data might be to have two side-by-side bar or column charts.

8) Don’t Use Axes to Mislead

“If you torture the data long enough, it will confess to anything” – Ronald Coase

One easy way to mislead readers is to change the axes of your data. Doing so quickly magnifies what might be small differences, and can distort the story your data is telling you. For example, starting the axis at 155,000 makes the differences between the highs and lows look more dramatic.

In the next example, the line chart doesn’t actually correspond to the axis! (Did you know 8.6 is more than 8.8?!)

The most truthful option is to always start your axes at zero. But sometimes, we need to show differences in metrics that don’t shift much over time. (For example, our conversion rate might range between 1.0% and 1.3% from month to month.) In that case, my recommendation would be to show the more truthful axis starting at zero, but provide a second view of the chart (a “zoomed in view”, so to speak) that shows a smaller range on the axis, so you can see the month-to-month change.

9) Never Rely Solely on Color When Presenting Data

Color is commonly used as a way to differentiate “good” vs. “bad” results, or “above” or “below” target. The problem is, about ten percent of the population is colorblind! And it’s not just red/green colorblind (though that’s the most common). There are many other kinds of colorblindness. As a result, ten percent of your stakeholders may actually not be comprehending your color scheme. (Not to mention, all black and white printers are “colorblind.”)

That doesn’t mean you can’t use any red or green (it can be an easily understood color scheme) when presenting data. But you do have to check that your data visualization is understandable by those with colorblindness, or if someone prints your document in black and white.

Additionally, there are also differences in how colors are perceived in different cultures. (For example, red means “death” in some cultures.) If you are distributing your data presentation globally, this is an additional factor to be conscious of.

10) Use Color with Intention

In the below chart, the colors are completely meaningless. (Or, as I like to call it, “rainbow barf.”)

Being careful with color also means using it consistently. If you are using multiple charts with the same values, you have to keep the colors consistent. Consider the tax on someone’s interpretation of your visualization if they constantly have to think “Okay, Facebook is blue on this chart, but it’s green on this other one.” Not only are you making them think really hard to do those comparisons, but more likely, they’re going to draw an incorrect conclusion.

So be thoughtful with how you use color! A good option can be to use brand colors. These are typically well-understood uses of color (for example, Facebook is blue, YouTube is red.) This may help readers understand the chart more intuitively.

(Data Studio only recently added a feature where you can keep the colors of data consistent across charts!)

Another user-friendly method of using color intentionally is to match your series color to your axis (where you have a dual-axis chart). This makes it very easy for a user to understand which series relates to which axis, without much thought.

Bonus Tip 11. Dashboards Should Follow The Above Data Visualization Rules

So, what about dashboards? Dashboards should follow all the same basic rules of presenting data, plus one important rule:

“A dashboard is a visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance.” -Stephen Few (Emphasis added.)

Key phrase: “on a single screen.” If you are expecting someone to look at your dashboard, and make connections between different data points, you are relying on their short-term memory. (Which, as discussed before, is a limited-capacity system.) So, dashboards must follow all the same data viz rules, but additionally, to be called a “dashboard”, it must be one page/screen/view. (So, that 8 page report is not a “dashboard”! You can have longer “reports”, but to truly be considered a “dashboard”, they must fit into one view.)

I hope these tips for presenting data have been useful! If you’re interested in learning more, these are some books I’d recommend checking out:

The Wall Street Journal Guide to Information Graphics

Information Dashboard Design

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Blog Data Visualization 10 Data Presentation Examples For Strategic Communication

10 Data Presentation Examples For Strategic Communication

Written by: Krystle Wong Sep 28, 2023

Knowing how to present data is like having a superpower. 

Data presentation today is no longer just about numbers on a screen; it’s storytelling with a purpose. It’s about captivating your audience, making complex stuff look simple and inspiring action. 

To help turn your data into stories that stick, influence decisions and make an impact, check out Venngage’s free chart maker or follow me on a tour into the world of data storytelling along with data presentation templates that work across different fields, from business boardrooms to the classroom and beyond. Keep scrolling to learn more! 

Click to jump ahead:

10 Essential data presentation examples + methods you should know

What should be included in a data presentation, what are some common mistakes to avoid when presenting data, faqs on data presentation examples, transform your message with impactful data storytelling.

Data presentation is a vital skill in today’s information-driven world. Whether you’re in business, academia, or simply want to convey information effectively, knowing the different ways of presenting data is crucial. For impactful data storytelling, consider these essential data presentation methods:

1. Bar graph

Ideal for comparing data across categories or showing trends over time.

Bar graphs, also known as bar charts are workhorses of data presentation. They’re like the Swiss Army knives of visualization methods because they can be used to compare data in different categories or display data changes over time. 

In a bar chart, categories are displayed on the x-axis and the corresponding values are represented by the height of the bars on the y-axis. 

It’s a straightforward and effective way to showcase raw data, making it a staple in business reports, academic presentations and beyond.

Make sure your bar charts are concise with easy-to-read labels. Whether your bars go up or sideways, keep it simple by not overloading with too many categories.

2. Line graph

Great for displaying trends and variations in data points over time or continuous variables.

Line charts or line graphs are your go-to when you want to visualize trends and variations in data sets over time.

One of the best quantitative data presentation examples, they work exceptionally well for showing continuous data, such as sales projections over the last couple of years or supply and demand fluctuations. 

The x-axis represents time or a continuous variable and the y-axis represents the data values. By connecting the data points with lines, you can easily spot trends and fluctuations.

A tip when presenting data with line charts is to minimize the lines and not make it too crowded. Highlight the big changes, put on some labels and give it a catchy title.

3. Pie chart

Useful for illustrating parts of a whole, such as percentages or proportions.

Pie charts are perfect for showing how a whole is divided into parts. They’re commonly used to represent percentages or proportions and are great for presenting survey results that involve demographic data. 

Each “slice” of the pie represents a portion of the whole and the size of each slice corresponds to its share of the total. 

While pie charts are handy for illustrating simple distributions, they can become confusing when dealing with too many categories or when the differences in proportions are subtle.

Don’t get too carried away with slices — label those slices with percentages or values so people know what’s what and consider using a legend for more categories.

4. Scatter plot

Effective for showing the relationship between two variables and identifying correlations.

Scatter plots are all about exploring relationships between two variables. They’re great for uncovering correlations, trends or patterns in data. 

In a scatter plot, every data point appears as a dot on the chart, with one variable marked on the horizontal x-axis and the other on the vertical y-axis.

By examining the scatter of points, you can discern the nature of the relationship between the variables, whether it’s positive, negative or no correlation at all.

If you’re using scatter plots to reveal relationships between two variables, be sure to add trendlines or regression analysis when appropriate to clarify patterns. Label data points selectively or provide tooltips for detailed information.

5. Histogram

Best for visualizing the distribution and frequency of a single variable.

Histograms are your choice when you want to understand the distribution and frequency of a single variable. 

They divide the data into “bins” or intervals and the height of each bar represents the frequency or count of data points falling into that interval. 

Histograms are excellent for helping to identify trends in data distributions, such as peaks, gaps or skewness.

Here’s something to take note of — ensure that your histogram bins are appropriately sized to capture meaningful data patterns. Using clear axis labels and titles can also help explain the distribution of the data effectively.

6. Stacked bar chart

Useful for showing how different components contribute to a whole over multiple categories.

Stacked bar charts are a handy choice when you want to illustrate how different components contribute to a whole across multiple categories. 

Each bar represents a category and the bars are divided into segments to show the contribution of various components within each category. 

This method is ideal for highlighting both the individual and collective significance of each component, making it a valuable tool for comparative analysis.

Stacked bar charts are like data sandwiches—label each layer so people know what’s what. Keep the order logical and don’t forget the paintbrush for snazzy colors. Here’s a data analysis presentation example on writers’ productivity using stacked bar charts:

7. Area chart

Similar to line charts but with the area below the lines filled, making them suitable for showing cumulative data.

Area charts are close cousins of line charts but come with a twist. 

Imagine plotting the sales of a product over several months. In an area chart, the space between the line and the x-axis is filled, providing a visual representation of the cumulative total. 

This makes it easy to see how values stack up over time, making area charts a valuable tool for tracking trends in data.

For area charts, use them to visualize cumulative data and trends, but avoid overcrowding the chart. Add labels, especially at significant points and make sure the area under the lines is filled with a visually appealing color gradient.

8. Tabular presentation

Presenting data in rows and columns, often used for precise data values and comparisons.

Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points. 

A table is invaluable for showcasing detailed data, facilitating comparisons and presenting numerical information that needs to be exact. They’re commonly used in reports, spreadsheets and academic papers.

When presenting tabular data, organize it neatly with clear headers and appropriate column widths. Highlight important data points or patterns using shading or font formatting for better readability.

9. Textual data

Utilizing written or descriptive content to explain or complement data, such as annotations or explanatory text.

Textual data presentation may not involve charts or graphs, but it’s one of the most used qualitative data presentation examples. 

It involves using written content to provide context, explanations or annotations alongside data visuals. Think of it as the narrative that guides your audience through the data. 

Well-crafted textual data can make complex information more accessible and help your audience understand the significance of the numbers and visuals.

Textual data is your chance to tell a story. Break down complex information into bullet points or short paragraphs and use headings to guide the reader’s attention.

10. Pictogram

Using simple icons or images to represent data is especially useful for conveying information in a visually intuitive manner.

Pictograms are all about harnessing the power of images to convey data in an easy-to-understand way. 

Instead of using numbers or complex graphs, you use simple icons or images to represent data points. 

For instance, you could use a thumbs up emoji to illustrate customer satisfaction levels, where each face represents a different level of satisfaction. 

Pictograms are great for conveying data visually, so choose symbols that are easy to interpret and relevant to the data. Use consistent scaling and a legend to explain the symbols’ meanings, ensuring clarity in your presentation.

Looking for more data presentation ideas? Use the Venngage graph maker or browse through our gallery of chart templates to pick a template and get started! 

A comprehensive data presentation should include several key elements to effectively convey information and insights to your audience. Here’s a list of what should be included in a data presentation:

1. Title and objective

• Begin with a clear and informative title that sets the context for your presentation.
• State the primary objective or purpose of the presentation to provide a clear focus.

2. Key data points

• Present the most essential data points or findings that align with your objective.
• Use charts, graphical presentations or visuals to illustrate these key points for better comprehension.

3. Context and significance

• Provide a brief overview of the context in which the data was collected and why it’s significant.
• Explain how the data relates to the larger picture or the problem you’re addressing.

4. Key takeaways

• Summarize the main insights or conclusions that can be drawn from the data.
• Highlight the key takeaways that the audience should remember.

5. Visuals and charts

• Use clear and appropriate visual aids to complement the data.
• Ensure that visuals are easy to understand and support your narrative.

6. Implications or actions

• Discuss the practical implications of the data or any recommended actions.
• If applicable, outline next steps or decisions that should be taken based on the data.

7. Q&A and discussion

• Allocate time for questions and open discussion to engage the audience.
• Address queries and provide additional insights or context as needed.

Presenting data is a crucial skill in various professional fields, from business to academia and beyond. To ensure your data presentations hit the mark, here are some common mistakes that you should steer clear of:

Overloading with data

Presenting too much data at once can overwhelm your audience. Focus on the key points and relevant information to keep the presentation concise and focused. Here are some free data visualization tools you can use to convey data in an engaging and impactful way. 

Assuming everyone’s on the same page

It’s easy to assume that your audience understands as much about the topic as you do. But this can lead to either dumbing things down too much or diving into a bunch of jargon that leaves folks scratching their heads. Take a beat to figure out where your audience is coming from and tailor your presentation accordingly.

Misleading visuals

Using misleading visuals, such as distorted scales or inappropriate chart types can distort the data’s meaning. Pick the right data infographics and understandable charts to ensure that your visual representations accurately reflect the data.

Not providing context

Data without context is like a puzzle piece with no picture on it. Without proper context, data may be meaningless or misinterpreted. Explain the background, methodology and significance of the data.

Not citing sources properly

Neglecting to cite sources and provide citations for your data can erode its credibility. Always attribute data to its source and utilize reliable sources for your presentation.

Not telling a story

Avoid simply presenting numbers. If your presentation lacks a clear, engaging story that takes your audience on a journey from the beginning (setting the scene) through the middle (data analysis) to the end (the big insights and recommendations), you’re likely to lose their interest.

Infographics are great for storytelling because they mix cool visuals with short and sweet text to explain complicated stuff in a fun and easy way. Create one with Venngage’s free infographic maker to create a memorable story that your audience will remember.

Ignoring data quality

Presenting data without first checking its quality and accuracy can lead to misinformation. Validate and clean your data before presenting it.

Simplify your visuals

Fancy charts might look cool, but if they confuse people, what’s the point? Go for the simplest visual that gets your message across. Having a dilemma between presenting data with infographics v.s data design? This article on the difference between data design and infographics might help you out. 

Missing the emotional connection

Data isn’t just about numbers; it’s about people and real-life situations. Don’t forget to sprinkle in some human touch, whether it’s through relatable stories, examples or showing how the data impacts real lives.

Skipping the actionable insights

At the end of the day, your audience wants to know what they should do with all the data. If you don’t wrap up with clear, actionable insights or recommendations, you’re leaving them hanging. Always finish up with practical takeaways and the next steps.

Can you provide some data presentation examples for business reports?

Business reports often benefit from data presentation through bar charts showing sales trends over time, pie charts displaying market share,or tables presenting financial performance metrics like revenue and profit margins.

What are some creative data presentation examples for academic presentations?

Creative data presentation ideas for academic presentations include using statistical infographics to illustrate research findings and statistical data, incorporating storytelling techniques to engage the audience or utilizing heat maps to visualize data patterns.

What are the key considerations when choosing the right data presentation format?

When choosing a chart format , consider factors like data complexity, audience expertise and the message you want to convey. Options include charts (e.g., bar, line, pie), tables, heat maps, data visualization infographics and interactive dashboards.

Knowing the type of data visualization that best serves your data is just half the battle. Here are some best practices for data visualization to make sure that the final output is optimized. 

How can I choose the right data presentation method for my data?

To select the right data presentation method, start by defining your presentation’s purpose and audience. Then, match your data type (e.g., quantitative, qualitative) with suitable visualization techniques (e.g., histograms, word clouds) and choose an appropriate presentation format (e.g., slide deck, report, live demo).

For more presentation ideas , check out this guide on how to make a good presentation or use a presentation software to simplify the process.  

How can I make my data presentations more engaging and informative?

To enhance data presentations, use compelling narratives, relatable examples and fun data infographics that simplify complex data. Encourage audience interaction, offer actionable insights and incorporate storytelling elements to engage and inform effectively.

The opening of your presentation holds immense power in setting the stage for your audience. To design a presentation and convey your data in an engaging and informative, try out Venngage’s free presentation maker to pick the right presentation design for your audience and topic. 

What is the difference between data visualization and data presentation?

Data presentation typically involves conveying data reports and insights to an audience, often using visuals like charts and graphs. Data visualization , on the other hand, focuses on creating those visual representations of data to facilitate understanding and analysis. 

Now that you’ve learned a thing or two about how to use these methods of data presentation to tell a compelling data story , it’s time to take these strategies and make them your own. 

But here’s the deal: these aren’t just one-size-fits-all solutions. Remember that each example we’ve uncovered here is not a rigid template but a source of inspiration. It’s all about making your audience go, “Wow, I get it now!”

Think of your data presentations as your canvas – it’s where you paint your story, convey meaningful insights and make real change happen. 

So, go forth, present your data with confidence and purpose and watch as your strategic influence grows, one compelling presentation at a time.

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Qualitative Data Analysis

23 Presenting the Results of Qualitative Analysis

Mikaila Mariel Lemonik Arthur

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

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

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

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

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

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

Audience and Voice

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

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

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

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

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

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

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

Making Data Come Alive

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

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

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

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

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

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

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

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

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

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

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

The Genre of Research Writing

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

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

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

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

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

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

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

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

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

Concluding Your Work

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

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

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

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

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

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

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

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

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

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

Skills for Learning : Research Skills

Data analysis is an ongoing process that should occur throughout your research project. Suitable data-analysis methods must be selected when you write your research proposal. The nature of your data (i.e. quantitative or qualitative) will be influenced by your research design and purpose. The data will also influence the analysis methods selected.

We run interactive workshops to help you develop skills related to doing research, such as data analysis, writing literature reviews and preparing for dissertations. Find out more on the Skills for Learning Workshops page.

We have online academic skills modules within MyBeckett for all levels of university study. These modules will help your academic development and support your success at LBU. You can work through the modules at your own pace, revisiting them as required. Find out more from our FAQ What academic skills modules are available?

Quantitative data analysis

Broadly speaking, 'statistics' refers to methods, tools and techniques used to collect, organise and interpret data. The goal of statistics is to gain understanding from data. Therefore, you need to know how to:

• Produce data – for example, by handing out a questionnaire or doing an experiment.
• Organise, summarise, present and analyse data.
• Draw valid conclusions from findings.

There are a number of statistical methods you can use to analyse data. Choosing an appropriate statistical method should follow naturally, however, from your research design. Therefore, you should think about data analysis at the early stages of your study design. You may need to consult a statistician for help with this.

Tips for working with statistical data

• Plan so that the data you get has a good chance of successfully tackling the research problem. This will involve reading literature on your subject, as well as on what makes a good study.
• To reach useful conclusions, you need to reduce uncertainties or 'noise'. Thus, you will need a sufficiently large data sample. A large sample will improve precision. However, this must be balanced against the 'costs' (time and money) of collection.
• Consider the logistics. Will there be problems in obtaining sufficient high-quality data? Think about accuracy, trustworthiness and completeness.
• Statistics are based on random samples. Consider whether your sample will be suited to this sort of analysis. Might there be biases to think about?
• How will you deal with missing values (any data that is not recorded for some reason)? These can result from gaps in a record or whole records being missed out.
• When analysing data, start by looking at each variable separately. Conduct initial/exploratory data analysis using graphical displays. Do this before looking at variables in conjunction or anything more complicated. This process can help locate errors in the data and also gives you a 'feel' for the data.
• Look out for patterns of 'missingness'. They are likely to alert you if there’s a problem. If the 'missingness' is not random, then it will have an impact on the results.
• Be vigilant and think through what you are doing at all times. Think critically. Statistics are not just mathematical tricks that a computer sorts out. Rather, analysing statistical data is a process that the human mind must interpret!

Top tips! Try inventing or generating the sort of data you might get and see if you can analyse it. Make sure that your process works before gathering actual data. Think what the output of an analytic procedure will look like before doing it for real.

(Note: it is actually difficult to generate realistic data. There are fraud-detection methods in place to identify data that has been fabricated. So, remember to get rid of your practice data before analysing the real stuff!)

Statistical software packages

Software packages can be used to analyse and present data. The most widely used ones are SPSS and NVivo.

SPSS is a statistical-analysis and data-management package for quantitative data analysis. Click on ‘ How do I install SPSS? ’ to learn how to download SPSS to your personal device. SPSS can perform a wide variety of statistical procedures. Some examples are:

• Data management (i.e. creating subsets of data or transforming data).
• Summarising, describing or presenting data (i.e. mean, median and frequency).
• Looking at the distribution of data (i.e. standard deviation).
• Comparing groups for significant differences using parametric (i.e. t-test) and non-parametric (i.e. Chi-square) tests.
• Identifying significant relationships between variables (i.e. correlation).

NVivo can be used for qualitative data analysis. It is suitable for use with a wide range of methodologies. Click on ‘ How do I access NVivo ’ to learn how to download NVivo to your personal device. NVivo supports grounded theory, survey data, case studies, focus groups, phenomenology, field research and action research.

• Process data such as interview transcripts, literature or media extracts, and historical documents.
• Code data on screen and explore all coding and documents interactively.
• Rearrange, restructure, extend and edit text, coding and coding relationships.
• Search imported text for words, phrases or patterns, and automatically code the results.

Qualitative data analysis

Miles and Huberman (1994) point out that there are diverse approaches to qualitative research and analysis. They suggest, however, that it is possible to identify 'a fairly classic set of analytic moves arranged in sequence'. This involves:

• Affixing codes to a set of field notes drawn from observation or interviews.
• Noting reflections or other remarks in the margins.
• Sorting/sifting through these materials to identify: a) similar phrases, relationships between variables, patterns and themes and b) distinct differences between subgroups and common sequences.
• Isolating these patterns/processes and commonalties/differences. Then, taking them out to the field in the next wave of data collection.
• Highlighting generalisations and relating them to your original research themes.
• Taking the generalisations and analysing them in relation to theoretical perspectives.

        (Miles and Huberman, 1994.)

Patterns and generalisations are usually arrived at through a process of analytic induction (see above points 5 and 6). Qualitative analysis rarely involves statistical analysis of relationships between variables. Qualitative analysis aims to gain in-depth understanding of concepts, opinions or experiences.

Presenting information

There are a number of different ways of presenting and communicating information. The particular format you use is dependent upon the type of data generated from the methods you have employed.

Here are some appropriate ways of presenting information for different types of data:

Bar charts: These   may be useful for comparing relative sizes. However, they tend to use a large amount of ink to display a relatively small amount of information. Consider a simple line chart as an alternative.

Pie charts: These have the benefit of indicating that the data must add up to 100%. However, they make it difficult for viewers to distinguish relative sizes, especially if two slices have a difference of less than 10%.

Other examples of presenting data in graphical form include line charts and  scatter plots .

Qualitative data is more likely to be presented in text form. For example, using quotations from interviews or field diaries.

• Plan ahead, thinking carefully about how you will analyse and present your data.
• Think through possible restrictions to resources you may encounter and plan accordingly.
• Find out about the different IT packages available for analysing your data and select the most appropriate.
• If necessary, allow time to attend an introductory course on a particular computer package. You can book SPSS and NVivo workshops via MyHub .
• Code your data appropriately, assigning conceptual or numerical codes as suitable.
• Organise your data so it can be analysed and presented easily.
• Choose the most suitable way of presenting your information, according to the type of data collected. This will allow your information to be understood and interpreted better.

Primary, secondary and tertiary sources

Information sources are sometimes categorised as primary, secondary or tertiary sources depending on whether or not they are ‘original’ materials or data. For some research projects, you may need to use primary sources as well as secondary or tertiary sources. However the distinction between primary and secondary sources is not always clear and depends on the context. For example, a newspaper article might usually be categorised as a secondary source. But it could also be regarded as a primary source if it were an article giving a first-hand account of a historical event written close to the time it occurred.

• Primary sources
• Secondary sources
• Tertiary sources
• Grey literature

Primary sources are original sources of information that provide first-hand accounts of what is being experienced or researched. They enable you to get as close to the actual event or research as possible. They are useful for getting the most contemporary information about a topic.

Examples include diary entries, newspaper articles, census data, journal articles with original reports of research, letters, email or other correspondence, original manuscripts and archives, interviews, research data and reports, statistics, autobiographies, exhibitions, films, and artists' writings.

Some information will be available on an Open Access basis, freely accessible online. However, many academic sources are paywalled, and you may need to login as a Leeds Beckett student to access them. Where Leeds Beckett does not have access to a source, you can use our  Request It! Service .

Secondary sources interpret, evaluate or analyse primary sources. They're useful for providing background information on a topic, or for looking back at an event from a current perspective. The majority of your literature searching will probably be done to find secondary sources on your topic.

Examples include journal articles which review or interpret original findings, popular magazine articles commenting on more serious research, textbooks and biographies.

The term tertiary sources isn't used a great deal. There's overlap between what might be considered a secondary source and a tertiary source. One definition is that a tertiary source brings together secondary sources.

Examples include almanacs, fact books, bibliographies, dictionaries and encyclopaedias, directories, indexes and abstracts. They can be useful for introductory information or an overview of a topic in the early stages of research.

Depending on your subject of study, grey literature may be another source you need to use. Grey literature includes technical or research reports, theses and dissertations, conference papers, government documents, white papers, and so on.

Artificial intelligence tools

Before using any generative artificial intelligence or paraphrasing tools in your assessments, you should check if this is permitted on your course.

If their use is permitted on your course, you must  acknowledge any use of generative artificial intelligence tools  such as ChatGPT or paraphrasing tools (e.g., Grammarly, Quillbot, etc.), even if you have only used them to generate ideas for your assessments or for proofreading.

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Key Steps in Data Presentation: Editing, Coding, and Transcribing

Table of Contents

Have you ever wondered how the raw data from countless surveys, experiments, and studies is transformed into clear, understandable formats that we see in research papers and articles? The answer lies in the fundamental processes of editing , coding , and transcribing data. Let’s take a deep dive into these critical steps that ensure the accuracy, consistency, and homogeneity of data presented in research.

Editing: The First Line of Defense for Quality Data

Editting serves as the quality control phase in data presentation. It involves scrutinizing the raw data collected to correct errors and ensure that it aligns with the research objectives. Think of editing as the gatekeeper that ensures only the most accurate and relevant data passes through for further analysis.

Types of Editing

• Field Editing : Performed by the individuals collecting the data to immediately identify and rectify obvious errors.
• Central Editing : Conducted after the data collection phase, often at a central location by a specialized team of editors.

Editing for Accuracy and Consistency

Editing is not just about correcting typos or filling missed fields. It also involves checking the data for internal consistency. For example, if a respondent mentions being unemployed but also states their job title, this discrepancy must be resolved.

Tools and Techniques for Effective Editing

Editors use a range of tools from simple checklists to sophisticated software that can automate the detection of errors. Techniques such as imputation (filling missing data based on logical rules) can also be part of the editing arsenal.

Coding: Assigning Meaning to Raw Data

Once the data is edited, coding comes into play. Coding is the translation of raw data into a form that can be processed by statistical software. This is where responses such as “Strongly Agree” or “Very Satisfied” are converted into numerical values that can be analyzed quantitatively.

Developing a Coding Scheme

The foundation of effective coding is a well-planned coding scheme. This includes deciding on the categories and assigning a unique code to each. The scheme should be exhaustive and mutually exclusive, ensuring that every piece of data fits into one and only one code.

Challenges in Coding Open-Ended Responses

Coding close-ended questions is straightforward, but open-ended responses pose a challenge. They require thematic analysis to identify common themes and subthemes, which are then coded.

Software Tools to Assist with Coding

Software like NVivo and ATLAS\.ti aids researchers in coding, especially with qualitative data. These tools provide features that help in organizing, coding, and retrieving data efficiently.

Transcribing: From Spoken Words to Written Text

Transcription is the process of converting spoken words, often from interviews or focus group discussions, into written text. It is crucial for qualitative analysis, as it makes non-numerical data accessible for thorough examination.

Types of Transcription

• Verbatim Transcription : Captures every word, pause, and emotion, often used when the manner of speech is as important as the content.
• Intelligent Transcription : Focuses on the content, omitting filler words and correcting grammatical errors for clarity.

Ensuring Anonymity and Ethical Considerations

Transcribers must often anonymize data to protect respondents’ identities, replacing names with pseudonyms or codes. They also need to be aware of ethical considerations, ensuring that the transcription process honors the integrity of the participant’s words.

Technology in Transcription

While manual transcription ensures a high level of accuracy, it is labor-intensive. Speech\-to\-text software can speed up the process but may require post-transcription editing to correct errors and ensure the text accurately reflects the spoken word.

The Interconnectedness of Editing, Coding, and Transcribing

It’s essential to understand that these processes are interconnected. Effective editing ensures that coding is smoother, as there are fewer errors to contend with. Similarly, well-done transcriptions make for more accessible data that can be edited and coded with greater ease.

The meticulous processes of editing, coding, and transcribing are the unsung heroes in the world of research. They work behind the scenes to ensure that the data we rely on for making decisions, whether in academia, business, or healthcare, is accurate and reliable. By understanding these processes, we gain a greater appreciation for the integrity and hard work that goes into presenting research data.

What do you think? How do you see the role of these processes influencing the reliability of research findings? Can you think of a time when data presentation might have significantly altered the interpretation of research results?

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Research Methodologies & Methods

1 Logic of Inquiry in Social Research

• A Science of Society
• Comte’s Ideas on the Nature of Sociology
• Observation in Social Sciences
• Logical Understanding of Social Reality

2 Empirical Approach

• Empirical Approach
• Rules of Data Collection
• Cultural Relativism
• Problems Encountered in Data Collection
• Difference between Common Sense and Science
• What is Ethical?
• What is Normal?
• Understanding the Data Collected
• Managing Diversities in Social Research
• Problematising the Object of Study
• Conclusion: Return to Good Old Empirical Approach

3 Diverse Logic of Theory Building

• Concern with Theory in Sociology
• Concepts: Basic Elements of Theories
• Why Do We Need Theory?
• Hypothesis Description and Experimentation
• Controlled Experiment
• Designing an Experiment
• How to Test a Hypothesis
• Sensitivity to Alternative Explanations
• Rival Hypothesis Construction
• The Use and Scope of Social Science Theory
• Theory Building and Researcher’s Values

4 Theoretical Analysis

• Premises of Evolutionary and Functional Theories
• Critique of Evolutionary and Functional Theories
• Turning away from Functionalism
• What after Functionalism
• Post-modernism
• Trends other than Post-modernism

5 Issues of Epistemology

• Some Major Concerns of Epistemology
• Rationalism
• Phenomenology: Bracketing Experience

6 Philosophy of Social Science

• Foundations of Science
• Science, Modernity, and Sociology
• Rethinking Science
• Crisis in Foundation

7 Positivism and its Critique

• Heroic Science and Origin of Positivism
• Early Positivism
• Consolidation of Positivism
• Critiques of Positivism

8 Hermeneutics

• Methodological Disputes in the Social Sciences
• Tracing the History of Hermeneutics
• Hermeneutics and Sociology
• Philosophical Hermeneutics
• The Hermeneutics of Suspicion
• Phenomenology and Hermeneutics

9 Comparative Method

• Relationship with Common Sense; Interrogating Ideological Location
• The Historical Context
• Elements of the Comparative Approach

10 Feminist Approach

• Features of the Feminist Method
• Feminist Methods adopt the Reflexive Stance
• Feminist Discourse in India

11 Participatory Method

• Delineation of Key Features

12 Types of Research

• Basic and Applied Research
• Descriptive and Analytical Research
• Empirical and Exploratory Research
• Quantitative and Qualitative Research
• Explanatory (Causal) and Longitudinal Research
• Experimental and Evaluative Research
• Participatory Action Research

13 Methods of Research

• Evolutionary Method
• Comparative Method
• Historical Method
• Personal Documents

14 Elements of Research Design

• Structuring the Research Process

15 Sampling Methods and Estimation of Sample Size

• Classification of Sampling Methods
• Sample Size

16 Measures of Central Tendency

• Relationship between Mean, Mode, and Median
• Choosing a Measure of Central Tendency

17 Measures of Dispersion and Variability

• The Variance
• The Standard Deviation
• Coefficient of Variation

18 Statistical Inference- Tests of Hypothesis

• Statistical Inference
• Tests of Significance

19 Correlation and Regression

• Correlation
• Method of Calculating Correlation of Ungrouped Data
• Method Of Calculating Correlation Of Grouped Data

20 Survey Method

• Rationale of Survey Research Method
• History of Survey Research
• Defining Survey Research
• Sampling and Survey Techniques
• Operationalising Survey Research Tools
• Advantages and Weaknesses of Survey Research

21 Survey Design

• Preliminary Considerations
• Stages / Phases in Survey Research
• Formulation of Research Question
• Survey Research Designs
• Sampling Design

22 Survey Instrumentation

• Techniques/Instruments for Data Collection
• Questionnaire Construction
• Issues in Designing a Survey Instrument

23 Survey Execution and Data Analysis

• Problems and Issues in Executing Survey Research
• Data Analysis
• Ethical Issues in Survey Research

24 Field Research – I

• History of Field Research
• Ethnography
• Theme Selection
• Gaining Entry in the Field
• Key Informants
• Participant Observation

25 Field Research – II

• Interview its Types and Process
• Feminist and Postmodernist Perspectives on Interviewing
• Narrative Analysis
• Interpretation
• Case Study and its Types
• Life Histories
• Oral History
• PRA and RRA Techniques

26 Reliability, Validity and Triangulation

• Concepts of Reliability and Validity
• Three Types of “Reliability”
• Working Towards Reliability
• Procedural Validity
• Field Research as a Validity Check
• Method Appropriate Criteria
• Triangulation
• Ethical Considerations in Qualitative Research

27 Qualitative Data Formatting and Processing

• Qualitative Data Processing and Analysis
• Description
• Classification
• Making Connections
• Theoretical Coding
• Qualitative Content Analysis

28 Writing up Qualitative Data

• Problems of Writing Up
• Grasp and Then Render
• “Writing Down” and “Writing Up”
• Write Early
• Writing Styles
• First Draft

29 Using Internet and Word Processor

• What is Internet and How Does it Work?
• Internet Services
• Searching on the Web: Search Engines
• Accessing and Using Online Information
• Online Journals and Texts
• Statistical Reference Sites
• Data Sources
• Uses of E-mail Services in Research

30 Using SPSS for Data Analysis Contents

• Introduction
• Starting and Exiting SPSS
• Creating a Data File
• Univariate Analysis
• Bivariate Analysis

31 Using SPSS in Report Writing

• Why to Use SPSS
• Working with SPSS Output
• Copying SPSS Output to MS Word Document

32 Tabulation and Graphic Presentation- Case Studies

• Structure for Presentation of Research Findings
• Data Presentation: Editing, Coding, and Transcribing
• Case Studies
• Qualitative Data Analysis and Presentation through Software
• Types of ICT used for Research

33 Guidelines to Research Project Assignment

• Overview of Research Methodologies and Methods (MSO 002)
• Research Project Objectives
• Preparation for Research Project
• Stages of the Research Project
• Supervision During the Research Project
• Submission of Research Project
• Methodology for Evaluating Research Project

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A Guide To The Methods, Benefits & Problems of The Interpretation of Data

Table of Contents

1) What Is Data Interpretation?

2) How To Interpret Data?

3) Why Data Interpretation Is Important?

4) Data Interpretation Skills

5) Data Analysis & Interpretation Problems

6) Data Interpretation Techniques & Methods

7) The Use of Dashboards For Data Interpretation

8) Business Data Interpretation Examples

Data analysis and interpretation have now taken center stage with the advent of the digital age… and the sheer amount of data can be frightening. In fact, a Digital Universe study found that the total data supply in 2012 was 2.8 trillion gigabytes! Based on that amount of data alone, it is clear the calling card of any successful enterprise in today’s global world will be the ability to analyze complex data, produce actionable insights, and adapt to new market needs… all at the speed of thought.

Business dashboards are the digital age tools for big data. Capable of displaying key performance indicators (KPIs) for both quantitative and qualitative data analyses, they are ideal for making the fast-paced and data-driven market decisions that push today’s industry leaders to sustainable success. Through the art of streamlined visual communication, data dashboards permit businesses to engage in real-time and informed decision-making and are key instruments in data interpretation. First of all, let’s find a definition to understand what lies behind this practice.

What Is Data Interpretation?

Data interpretation refers to the process of using diverse analytical methods to review data and arrive at relevant conclusions. The interpretation of data helps researchers to categorize, manipulate, and summarize the information in order to answer critical questions.

The importance of data interpretation is evident, and this is why it needs to be done properly. Data is very likely to arrive from multiple sources and has a tendency to enter the analysis process with haphazard ordering. Data analysis tends to be extremely subjective. That is to say, the nature and goal of interpretation will vary from business to business, likely correlating to the type of data being analyzed. While there are several types of processes that are implemented based on the nature of individual data, the two broadest and most common categories are “quantitative and qualitative analysis.”

Yet, before any serious data interpretation inquiry can begin, it should be understood that visual presentations of data findings are irrelevant unless a sound decision is made regarding measurement scales. Before any serious data analysis can begin, the measurement scale must be decided for the data as this will have a long-term impact on data interpretation ROI. The varying scales include:

• Nominal Scale: non-numeric categories that cannot be ranked or compared quantitatively. Variables are exclusive and exhaustive.
• Ordinal Scale: exclusive categories that are exclusive and exhaustive but with a logical order. Quality ratings and agreement ratings are examples of ordinal scales (i.e., good, very good, fair, etc., OR agree, strongly agree, disagree, etc.).
• Interval: a measurement scale where data is grouped into categories with orderly and equal distances between the categories. There is always an arbitrary zero point.
• Ratio: contains features of all three.

For a more in-depth review of scales of measurement, read our article on data analysis questions . Once measurement scales have been selected, it is time to select which of the two broad interpretation processes will best suit your data needs. Let’s take a closer look at those specific methods and possible data interpretation problems.

How To Interpret Data? Top Methods & Techniques

When interpreting data, an analyst must try to discern the differences between correlation, causation, and coincidences, as well as many other biases – but he also has to consider all the factors involved that may have led to a result. There are various data interpretation types and methods one can use to achieve this.

The interpretation of data is designed to help people make sense of numerical data that has been collected, analyzed, and presented. Having a baseline method for interpreting data will provide your analyst teams with a structure and consistent foundation. Indeed, if several departments have different approaches to interpreting the same data while sharing the same goals, some mismatched objectives can result. Disparate methods will lead to duplicated efforts, inconsistent solutions, wasted energy, and inevitably – time and money. In this part, we will look at the two main methods of interpretation of data: qualitative and quantitative analysis.

Qualitative Data Interpretation

Qualitative data analysis can be summed up in one word – categorical. With this type of analysis, data is not described through numerical values or patterns but through the use of descriptive context (i.e., text). Typically, narrative data is gathered by employing a wide variety of person-to-person techniques. These techniques include:

• Observations: detailing behavioral patterns that occur within an observation group. These patterns could be the amount of time spent in an activity, the type of activity, and the method of communication employed.
• Focus groups: Group people and ask them relevant questions to generate a collaborative discussion about a research topic.
• Secondary Research: much like how patterns of behavior can be observed, various types of documentation resources can be coded and divided based on the type of material they contain.
• Interviews: one of the best collection methods for narrative data. Inquiry responses can be grouped by theme, topic, or category. The interview approach allows for highly focused data segmentation.

A key difference between qualitative and quantitative analysis is clearly noticeable in the interpretation stage. The first one is widely open to interpretation and must be “coded” so as to facilitate the grouping and labeling of data into identifiable themes. As person-to-person data collection techniques can often result in disputes pertaining to proper analysis, qualitative data analysis is often summarized through three basic principles: notice things, collect things, and think about things.

After qualitative data has been collected through transcripts, questionnaires, audio and video recordings, or the researcher’s notes, it is time to interpret it. For that purpose, there are some common methods used by researchers and analysts.

• Content analysis : As its name suggests, this is a research method used to identify frequencies and recurring words, subjects, and concepts in image, video, or audio content. It transforms qualitative information into quantitative data to help discover trends and conclusions that will later support important research or business decisions. This method is often used by marketers to understand brand sentiment from the mouths of customers themselves. Through that, they can extract valuable information to improve their products and services. It is recommended to use content analytics tools for this method as manually performing it is very time-consuming and can lead to human error or subjectivity issues. Having a clear goal in mind before diving into it is another great practice for avoiding getting lost in the fog.  
• Thematic analysis: This method focuses on analyzing qualitative data, such as interview transcripts, survey questions, and others, to identify common patterns and separate the data into different groups according to found similarities or themes. For example, imagine you want to analyze what customers think about your restaurant. For this purpose, you do a thematic analysis on 1000 reviews and find common themes such as “fresh food”, “cold food”, “small portions”, “friendly staff”, etc. With those recurring themes in hand, you can extract conclusions about what could be improved or enhanced based on your customer’s experiences. Since this technique is more exploratory, be open to changing your research questions or goals as you go. 
• Narrative analysis: A bit more specific and complicated than the two previous methods, it is used to analyze stories and discover their meaning. These stories can be extracted from testimonials, case studies, and interviews, as these formats give people more space to tell their experiences. Given that collecting this kind of data is harder and more time-consuming, sample sizes for narrative analysis are usually smaller, which makes it harder to reproduce its findings. However, it is still a valuable technique for understanding customers' preferences and mindsets.  
• Discourse analysis : This method is used to draw the meaning of any type of visual, written, or symbolic language in relation to a social, political, cultural, or historical context. It is used to understand how context can affect how language is carried out and understood. For example, if you are doing research on power dynamics, using discourse analysis to analyze a conversation between a janitor and a CEO and draw conclusions about their responses based on the context and your research questions is a great use case for this technique. That said, like all methods in this section, discourse analytics is time-consuming as the data needs to be analyzed until no new insights emerge.  
• Grounded theory analysis : The grounded theory approach aims to create or discover a new theory by carefully testing and evaluating the data available. Unlike all other qualitative approaches on this list, grounded theory helps extract conclusions and hypotheses from the data instead of going into the analysis with a defined hypothesis. This method is very popular amongst researchers, analysts, and marketers as the results are completely data-backed, providing a factual explanation of any scenario. It is often used when researching a completely new topic or with little knowledge as this space to start from the ground up. 

Quantitative Data Interpretation

If quantitative data interpretation could be summed up in one word (and it really can’t), that word would be “numerical.” There are few certainties when it comes to data analysis, but you can be sure that if the research you are engaging in has no numbers involved, it is not quantitative research, as this analysis refers to a set of processes by which numerical data is analyzed. More often than not, it involves the use of statistical modeling such as standard deviation, mean, and median. Let’s quickly review the most common statistical terms:

• Mean: A mean represents a numerical average for a set of responses. When dealing with a data set (or multiple data sets), a mean will represent the central value of a specific set of numbers. It is the sum of the values divided by the number of values within the data set. Other terms that can be used to describe the concept are arithmetic mean, average, and mathematical expectation.
• Standard deviation: This is another statistical term commonly used in quantitative analysis. Standard deviation reveals the distribution of the responses around the mean. It describes the degree of consistency within the responses; together with the mean, it provides insight into data sets.
• Frequency distribution: This is a measurement gauging the rate of a response appearance within a data set. When using a survey, for example, frequency distribution, it can determine the number of times a specific ordinal scale response appears (i.e., agree, strongly agree, disagree, etc.). Frequency distribution is extremely keen in determining the degree of consensus among data points.

Typically, quantitative data is measured by visually presenting correlation tests between two or more variables of significance. Different processes can be used together or separately, and comparisons can be made to ultimately arrive at a conclusion. Other signature interpretation processes of quantitative data include:

• Regression analysis: Essentially, it uses historical data to understand the relationship between a dependent variable and one or more independent variables. Knowing which variables are related and how they developed in the past allows you to anticipate possible outcomes and make better decisions going forward. For example, if you want to predict your sales for next month, you can use regression to understand what factors will affect them, such as products on sale and the launch of a new campaign, among many others. 
• Cohort analysis: This method identifies groups of users who share common characteristics during a particular time period. In a business scenario, cohort analysis is commonly used to understand customer behaviors. For example, a cohort could be all users who have signed up for a free trial on a given day. An analysis would be carried out to see how these users behave, what actions they carry out, and how their behavior differs from other user groups.
• Predictive analysis: As its name suggests, the predictive method aims to predict future developments by analyzing historical and current data. Powered by technologies such as artificial intelligence and machine learning, predictive analytics practices enable businesses to identify patterns or potential issues and plan informed strategies in advance.
• Prescriptive analysis: Also powered by predictions, the prescriptive method uses techniques such as graph analysis, complex event processing, and neural networks, among others, to try to unravel the effect that future decisions will have in order to adjust them before they are actually made. This helps businesses to develop responsive, practical business strategies.
• Conjoint analysis: Typically applied to survey analysis, the conjoint approach is used to analyze how individuals value different attributes of a product or service. This helps researchers and businesses to define pricing, product features, packaging, and many other attributes. A common use is menu-based conjoint analysis, in which individuals are given a “menu” of options from which they can build their ideal concept or product. Through this, analysts can understand which attributes they would pick above others and drive conclusions.
• Cluster analysis: Last but not least, the cluster is a method used to group objects into categories. Since there is no target variable when using cluster analysis, it is a useful method to find hidden trends and patterns in the data. In a business context, clustering is used for audience segmentation to create targeted experiences. In market research, it is often used to identify age groups, geographical information, and earnings, among others.

Now that we have seen how to interpret data, let's move on and ask ourselves some questions: What are some of the benefits of data interpretation? Why do all industries engage in data research and analysis? These are basic questions, but they often don’t receive adequate attention.

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Why Data Interpretation Is Important

The purpose of collection and interpretation is to acquire useful and usable information and to make the most informed decisions possible. From businesses to newlyweds researching their first home, data collection and interpretation provide limitless benefits for a wide range of institutions and individuals.

Data analysis and interpretation, regardless of the method and qualitative/quantitative status, may include the following characteristics:

• Data identification and explanation
• Comparing and contrasting data
• Identification of data outliers
• Future predictions

Data analysis and interpretation, in the end, help improve processes and identify problems. It is difficult to grow and make dependable improvements without, at the very least, minimal data collection and interpretation. What is the keyword? Dependable. Vague ideas regarding performance enhancement exist within all institutions and industries. Yet, without proper research and analysis, an idea is likely to remain in a stagnant state forever (i.e., minimal growth). So… what are a few of the business benefits of digital age data analysis and interpretation? Let’s take a look!

1) Informed decision-making: A decision is only as good as the knowledge that formed it. Informed data decision-making can potentially set industry leaders apart from the rest of the market pack. Studies have shown that companies in the top third of their industries are, on average, 5% more productive and 6% more profitable when implementing informed data decision-making processes. Most decisive actions will arise only after a problem has been identified or a goal defined. Data analysis should include identification, thesis development, and data collection, followed by data communication.

If institutions only follow that simple order, one that we should all be familiar with from grade school science fairs, then they will be able to solve issues as they emerge in real-time. Informed decision-making has a tendency to be cyclical. This means there is really no end, and eventually, new questions and conditions arise within the process that need to be studied further. The monitoring of data results will inevitably return the process to the start with new data and sights.

2) Anticipating needs with trends identification: data insights provide knowledge, and knowledge is power. The insights obtained from market and consumer data analyses have the ability to set trends for peers within similar market segments. A perfect example of how data analytics can impact trend prediction is evidenced in the music identification application Shazam . The application allows users to upload an audio clip of a song they like but can’t seem to identify. Users make 15 million song identifications a day. With this data, Shazam has been instrumental in predicting future popular artists.

When industry trends are identified, they can then serve a greater industry purpose. For example, the insights from Shazam’s monitoring benefits not only Shazam in understanding how to meet consumer needs but also grant music executives and record label companies an insight into the pop-culture scene of the day. Data gathering and interpretation processes can allow for industry-wide climate prediction and result in greater revenue streams across the market. For this reason, all institutions should follow the basic data cycle of collection, interpretation, decision-making, and monitoring.

3) Cost efficiency: Proper implementation of analytics processes can provide businesses with profound cost advantages within their industries. A recent data study performed by Deloitte vividly demonstrates this in finding that data analysis ROI is driven by efficient cost reductions. Often, this benefit is overlooked because making money is typically viewed as “sexier” than saving money. Yet, sound data analyses have the ability to alert management to cost-reduction opportunities without any significant exertion of effort on the part of human capital.

A great example of the potential for cost efficiency through data analysis is Intel. Prior to 2012, Intel would conduct over 19,000 manufacturing function tests on their chips before they could be deemed acceptable for release. To cut costs and reduce test time, Intel implemented predictive data analyses. By using historical and current data, Intel now avoids testing each chip 19,000 times by focusing on specific and individual chip tests. After its implementation in 2012, Intel saved over $3 million in manufacturing costs. Cost reduction may not be as “sexy” as data profit, but as Intel proves, it is a benefit of data analysis that should not be neglected.

4) Clear foresight: companies that collect and analyze their data gain better knowledge about themselves, their processes, and their performance. They can identify performance challenges when they arise and take action to overcome them. Data interpretation through visual representations lets them process their findings faster and make better-informed decisions on the company's future.

Key Data Interpretation Skills You Should Have

Just like any other process, data interpretation and analysis require researchers or analysts to have some key skills to be able to perform successfully. It is not enough just to apply some methods and tools to the data; the person who is managing it needs to be objective and have a data-driven mind, among other skills. 

It is a common misconception to think that the required skills are mostly number-related. While data interpretation is heavily analytically driven, it also requires communication and narrative skills, as the results of the analysis need to be presented in a way that is easy to understand for all types of audiences. 

Luckily, with the rise of self-service tools and AI-driven technologies, data interpretation is no longer segregated for analysts only. However, the topic still remains a big challenge for businesses that make big investments in data and tools to support it, as the interpretation skills required are still lacking. It is worthless to put massive amounts of money into extracting information if you are not going to be able to interpret what that information is telling you. For that reason, below we list the top 5 data interpretation skills your employees or researchers should have to extract the maximum potential from the data. 

• Data Literacy: The first and most important skill to have is data literacy. This means having the ability to understand, work, and communicate with data. It involves knowing the types of data sources, methods, and ethical implications of using them. In research, this skill is often a given. However, in a business context, there might be many employees who are not comfortable with data. The issue is the interpretation of data can not be solely responsible for the data team, as it is not sustainable in the long run. Experts advise business leaders to carefully assess the literacy level across their workforce and implement training instances to ensure everyone can interpret their data. 
• Data Tools: The data interpretation and analysis process involves using various tools to collect, clean, store, and analyze the data. The complexity of the tools varies depending on the type of data and the analysis goals. Going from simple ones like Excel to more complex ones like databases, such as SQL, or programming languages, such as R or Python. It also involves visual analytics tools to bring the data to life through the use of graphs and charts. Managing these tools is a fundamental skill as they make the process faster and more efficient. As mentioned before, most modern solutions are now self-service, enabling less technical users to use them without problem.
• Critical Thinking: Another very important skill is to have critical thinking. Data hides a range of conclusions, trends, and patterns that must be discovered. It is not just about comparing numbers; it is about putting a story together based on multiple factors that will lead to a conclusion. Therefore, having the ability to look further from what is right in front of you is an invaluable skill for data interpretation. 
• Data Ethics: In the information age, being aware of the legal and ethical responsibilities that come with the use of data is of utmost importance. In short, data ethics involves respecting the privacy and confidentiality of data subjects, as well as ensuring accuracy and transparency for data usage. It requires the analyzer or researcher to be completely objective with its interpretation to avoid any biases or discrimination. Many countries have already implemented regulations regarding the use of data, including the GDPR or the ACM Code Of Ethics. Awareness of these regulations and responsibilities is a fundamental skill that anyone working in data interpretation should have. 
• Domain Knowledge: Another skill that is considered important when interpreting data is to have domain knowledge. As mentioned before, data hides valuable insights that need to be uncovered. To do so, the analyst needs to know about the industry or domain from which the information is coming and use that knowledge to explore it and put it into a broader context. This is especially valuable in a business context, where most departments are now analyzing data independently with the help of a live dashboard instead of relying on the IT department, which can often overlook some aspects due to a lack of expertise in the topic. 

Common Data Analysis And Interpretation Problems

The oft-repeated mantra of those who fear data advancements in the digital age is “big data equals big trouble.” While that statement is not accurate, it is safe to say that certain data interpretation problems or “pitfalls” exist and can occur when analyzing data, especially at the speed of thought. Let’s identify some of the most common data misinterpretation risks and shed some light on how they can be avoided:

1) Correlation mistaken for causation: our first misinterpretation of data refers to the tendency of data analysts to mix the cause of a phenomenon with correlation. It is the assumption that because two actions occurred together, one caused the other. This is inaccurate, as actions can occur together, absent a cause-and-effect relationship.

• Digital age example: assuming that increased revenue results from increased social media followers… there might be a definitive correlation between the two, especially with today’s multi-channel purchasing experiences. But that does not mean an increase in followers is the direct cause of increased revenue. There could be both a common cause and an indirect causality.
• Remedy: attempt to eliminate the variable you believe to be causing the phenomenon.

2) Confirmation bias: our second problem is data interpretation bias. It occurs when you have a theory or hypothesis in mind but are intent on only discovering data patterns that support it while rejecting those that do not.

• Digital age example: your boss asks you to analyze the success of a recent multi-platform social media marketing campaign. While analyzing the potential data variables from the campaign (one that you ran and believe performed well), you see that the share rate for Facebook posts was great, while the share rate for Twitter Tweets was not. Using only Facebook posts to prove your hypothesis that the campaign was successful would be a perfect manifestation of confirmation bias.
• Remedy: as this pitfall is often based on subjective desires, one remedy would be to analyze data with a team of objective individuals. If this is not possible, another solution is to resist the urge to make a conclusion before data exploration has been completed. Remember to always try to disprove a hypothesis, not prove it.

3) Irrelevant data: the third data misinterpretation pitfall is especially important in the digital age. As large data is no longer centrally stored and as it continues to be analyzed at the speed of thought, it is inevitable that analysts will focus on data that is irrelevant to the problem they are trying to correct.

• Digital age example: in attempting to gauge the success of an email lead generation campaign, you notice that the number of homepage views directly resulting from the campaign increased, but the number of monthly newsletter subscribers did not. Based on the number of homepage views, you decide the campaign was a success when really it generated zero leads.
• Remedy: proactively and clearly frame any data analysis variables and KPIs prior to engaging in a data review. If the metric you use to measure the success of a lead generation campaign is newsletter subscribers, there is no need to review the number of homepage visits. Be sure to focus on the data variable that answers your question or solves your problem and not on irrelevant data.

4) Truncating an Axes: When creating a graph to start interpreting the results of your analysis, it is important to keep the axes truthful and avoid generating misleading visualizations. Starting the axes in a value that doesn’t portray the actual truth about the data can lead to false conclusions. 

• Digital age example: In the image below, we can see a graph from Fox News in which the Y-axes start at 34%, making it seem that the difference between 35% and 39.6% is way higher than it actually is. This could lead to a misinterpretation of the tax rate changes. 

* Source : www.venngage.com *

• Remedy: Be careful with how your data is visualized. Be respectful and realistic with axes to avoid misinterpretation of your data. See below how the Fox News chart looks when using the correct axis values. This chart was created with datapine's modern online data visualization tool.

5) (Small) sample size: Another common problem is using a small sample size. Logically, the bigger the sample size, the more accurate and reliable the results. However, this also depends on the size of the effect of the study. For example, the sample size in a survey about the quality of education will not be the same as for one about people doing outdoor sports in a specific area. 

• Digital age example: Imagine you ask 30 people a question, and 29 answer “yes,” resulting in 95% of the total. Now imagine you ask the same question to 1000, and 950 of them answer “yes,” which is again 95%. While these percentages might look the same, they certainly do not mean the same thing, as a 30-person sample size is not a significant number to establish a truthful conclusion. 
• Remedy: Researchers say that in order to determine the correct sample size to get truthful and meaningful results, it is necessary to define a margin of error that will represent the maximum amount they want the results to deviate from the statistical mean. Paired with this, they need to define a confidence level that should be between 90 and 99%. With these two values in hand, researchers can calculate an accurate sample size for their studies.

6) Reliability, subjectivity, and generalizability : When performing qualitative analysis, researchers must consider practical and theoretical limitations when interpreting the data. In some cases, this type of research can be considered unreliable because of uncontrolled factors that might or might not affect the results. This is paired with the fact that the researcher has a primary role in the interpretation process, meaning he or she decides what is relevant and what is not, and as we know, interpretations can be very subjective.

Generalizability is also an issue that researchers face when dealing with qualitative analysis. As mentioned in the point about having a small sample size, it is difficult to draw conclusions that are 100% representative because the results might be biased or unrepresentative of a wider population. 

While these factors are mostly present in qualitative research, they can also affect the quantitative analysis. For example, when choosing which KPIs to portray and how to portray them, analysts can also be biased and represent them in a way that benefits their analysis.

• Digital age example: Biased questions in a survey are a great example of reliability and subjectivity issues. Imagine you are sending a survey to your clients to see how satisfied they are with your customer service with this question: “How amazing was your experience with our customer service team?”. Here, we can see that this question clearly influences the response of the individual by putting the word “amazing” on it. 
• Remedy: A solution to avoid these issues is to keep your research honest and neutral. Keep the wording of the questions as objective as possible. For example: “On a scale of 1-10, how satisfied were you with our customer service team?”. This does not lead the respondent to any specific answer, meaning the results of your survey will be reliable. 

Data Interpretation Best Practices & Tips

Data analysis and interpretation are critical to developing sound conclusions and making better-informed decisions. As we have seen with this article, there is an art and science to the interpretation of data. To help you with this purpose, we will list a few relevant techniques, methods, and tricks you can implement for a successful data management process. 

As mentioned at the beginning of this post, the first step to interpreting data in a successful way is to identify the type of analysis you will perform and apply the methods respectively. Clearly differentiate between qualitative (observe, document, and interview notice, collect and think about things) and quantitative analysis (you lead research with a lot of numerical data to be analyzed through various statistical methods). 

1) Ask the right data interpretation questions

The first data interpretation technique is to define a clear baseline for your work. This can be done by answering some critical questions that will serve as a useful guideline to start. Some of them include: what are the goals and objectives of my analysis? What type of data interpretation method will I use? Who will use this data in the future? And most importantly, what general question am I trying to answer?

Once all this information has been defined, you will be ready for the next step: collecting your data. 

2) Collect and assimilate your data

Now that a clear baseline has been established, it is time to collect the information you will use. Always remember that your methods for data collection will vary depending on what type of analysis method you use, which can be qualitative or quantitative. Based on that, relying on professional online data analysis tools to facilitate the process is a great practice in this regard, as manually collecting and assessing raw data is not only very time-consuming and expensive but is also at risk of errors and subjectivity. 

Once your data is collected, you need to carefully assess it to understand if the quality is appropriate to be used during a study. This means, is the sample size big enough? Were the procedures used to collect the data implemented correctly? Is the date range from the data correct? If coming from an external source, is it a trusted and objective one? 

With all the needed information in hand, you are ready to start the interpretation process, but first, you need to visualize your data. 

3) Use the right data visualization type 

Data visualizations such as business graphs , charts, and tables are fundamental to successfully interpreting data. This is because data visualization via interactive charts and graphs makes the information more understandable and accessible. As you might be aware, there are different types of visualizations you can use, but not all of them are suitable for any analysis purpose. Using the wrong graph can lead to misinterpretation of your data, so it’s very important to carefully pick the right visual for it. Let’s look at some use cases of common data visualizations. 

• Bar chart: One of the most used chart types, the bar chart uses rectangular bars to show the relationship between 2 or more variables. There are different types of bar charts for different interpretations, including the horizontal bar chart, column bar chart, and stacked bar chart. 
• Line chart: Most commonly used to show trends, acceleration or decelerations, and volatility, the line chart aims to show how data changes over a period of time, for example, sales over a year. A few tips to keep this chart ready for interpretation are not using many variables that can overcrowd the graph and keeping your axis scale close to the highest data point to avoid making the information hard to read. 
• Pie chart: Although it doesn’t do a lot in terms of analysis due to its uncomplex nature, pie charts are widely used to show the proportional composition of a variable. Visually speaking, showing a percentage in a bar chart is way more complicated than showing it in a pie chart. However, this also depends on the number of variables you are comparing. If your pie chart needs to be divided into 10 portions, then it is better to use a bar chart instead. 
• Tables: While they are not a specific type of chart, tables are widely used when interpreting data. Tables are especially useful when you want to portray data in its raw format. They give you the freedom to easily look up or compare individual values while also displaying grand totals. 

With the use of data visualizations becoming more and more critical for businesses’ analytical success, many tools have emerged to help users visualize their data in a cohesive and interactive way. One of the most popular ones is the use of BI dashboards . These visual tools provide a centralized view of various graphs and charts that paint a bigger picture of a topic. We will discuss the power of dashboards for an efficient data interpretation practice in the next portion of this post. If you want to learn more about different types of graphs and charts , take a look at our complete guide on the topic. 

4) Start interpreting 

After the tedious preparation part, you can start extracting conclusions from your data. As mentioned many times throughout the post, the way you decide to interpret the data will solely depend on the methods you initially decided to use. If you had initial research questions or hypotheses, then you should look for ways to prove their validity. If you are going into the data with no defined hypothesis, then start looking for relationships and patterns that will allow you to extract valuable conclusions from the information. 

During the process of interpretation, stay curious and creative, dig into the data, and determine if there are any other critical questions that should be asked. If any new questions arise, you need to assess if you have the necessary information to answer them. Being able to identify if you need to dedicate more time and resources to the research is a very important step. No matter if you are studying customer behaviors or a new cancer treatment, the findings from your analysis may dictate important decisions in the future. Therefore, taking the time to really assess the information is key. For that purpose, data interpretation software proves to be very useful.

5) Keep your interpretation objective

As mentioned above, objectivity is one of the most important data interpretation skills but also one of the hardest. Being the person closest to the investigation, it is easy to become subjective when looking for answers in the data. A good way to stay objective is to show the information related to the study to other people, for example, research partners or even the people who will use your findings once they are done. This can help avoid confirmation bias and any reliability issues with your interpretation. 

Remember, using a visualization tool such as a modern dashboard will make the interpretation process way easier and more efficient as the data can be navigated and manipulated in an easy and organized way. And not just that, using a dashboard tool to present your findings to a specific audience will make the information easier to understand and the presentation way more engaging thanks to the visual nature of these tools. 

6) Mark your findings and draw conclusions

Findings are the observations you extracted from your data. They are the facts that will help you drive deeper conclusions about your research. For example, findings can be trends and patterns you found during your interpretation process. To put your findings into perspective, you can compare them with other resources that use similar methods and use them as benchmarks.

Reflect on your own thinking and reasoning and be aware of the many pitfalls data analysis and interpretation carry—correlation versus causation, subjective bias, false information, inaccurate data, etc. Once you are comfortable with interpreting the data, you will be ready to develop conclusions, see if your initial questions were answered, and suggest recommendations based on them.

Interpretation of Data: The Use of Dashboards Bridging The Gap

As we have seen, quantitative and qualitative methods are distinct types of data interpretation and analysis. Both offer a varying degree of return on investment (ROI) regarding data investigation, testing, and decision-making. But how do you mix the two and prevent a data disconnect? The answer is professional data dashboards. 

For a few years now, dashboards have become invaluable tools to visualize and interpret data. These tools offer a centralized and interactive view of data and provide the perfect environment for exploration and extracting valuable conclusions. They bridge the quantitative and qualitative information gap by unifying all the data in one place with the help of stunning visuals. 

Not only that, but these powerful tools offer a large list of benefits, and we will discuss some of them below. 

1) Connecting and blending data. With today’s pace of innovation, it is no longer feasible (nor desirable) to have bulk data centrally located. As businesses continue to globalize and borders continue to dissolve, it will become increasingly important for businesses to possess the capability to run diverse data analyses absent the limitations of location. Data dashboards decentralize data without compromising on the necessary speed of thought while blending both quantitative and qualitative data. Whether you want to measure customer trends or organizational performance, you now have the capability to do both without the need for a singular selection.

2) Mobile Data. Related to the notion of “connected and blended data” is that of mobile data. In today’s digital world, employees are spending less time at their desks and simultaneously increasing production. This is made possible because mobile solutions for analytical tools are no longer standalone. Today, mobile analysis applications seamlessly integrate with everyday business tools. In turn, both quantitative and qualitative data are now available on-demand where they’re needed, when they’re needed, and how they’re needed via interactive online dashboards .

3) Visualization. Data dashboards merge the data gap between qualitative and quantitative data interpretation methods through the science of visualization. Dashboard solutions come “out of the box” and are well-equipped to create easy-to-understand data demonstrations. Modern online data visualization tools provide a variety of color and filter patterns, encourage user interaction, and are engineered to help enhance future trend predictability. All of these visual characteristics make for an easy transition among data methods – you only need to find the right types of data visualization to tell your data story the best way possible.

4) Collaboration. Whether in a business environment or a research project, collaboration is key in data interpretation and analysis. Dashboards are online tools that can be easily shared through a password-protected URL or automated email. Through them, users can collaborate and communicate through the data in an efficient way. Eliminating the need for infinite files with lost updates. Tools such as datapine offer real-time updates, meaning your dashboards will update on their own as soon as new information is available.  

Examples Of Data Interpretation In Business

To give you an idea of how a dashboard can fulfill the need to bridge quantitative and qualitative analysis and help in understanding how to interpret data in research thanks to visualization, below, we will discuss three valuable examples to put their value into perspective.

1. Customer Satisfaction Dashboard 

This market research dashboard brings together both qualitative and quantitative data that are knowledgeably analyzed and visualized in a meaningful way that everyone can understand, thus empowering any viewer to interpret it. Let’s explore it below. 

**click to enlarge**

The value of this template lies in its highly visual nature. As mentioned earlier, visuals make the interpretation process way easier and more efficient. Having critical pieces of data represented with colorful and interactive icons and graphs makes it possible to uncover insights at a glance. For example, the colors green, yellow, and red on the charts for the NPS and the customer effort score allow us to conclude that most respondents are satisfied with this brand with a short glance. A further dive into the line chart below can help us dive deeper into this conclusion, as we can see both metrics developed positively in the past 6 months. 

The bottom part of the template provides visually stunning representations of different satisfaction scores for quality, pricing, design, and service. By looking at these, we can conclude that, overall, customers are satisfied with this company in most areas. 

2. Brand Analysis Dashboard

Next, in our list of data interpretation examples, we have a template that shows the answers to a survey on awareness for Brand D. The sample size is listed on top to get a perspective of the data, which is represented using interactive charts and graphs. 

When interpreting information, context is key to understanding it correctly. For that reason, the dashboard starts by offering insights into the demographics of the surveyed audience. In general, we can see ages and gender are diverse. Therefore, we can conclude these brands are not targeting customers from a specified demographic, an important aspect to put the surveyed answers into perspective. 

Looking at the awareness portion, we can see that brand B is the most popular one, with brand D coming second on both questions. This means brand D is not doing wrong, but there is still room for improvement compared to brand B. To see where brand D could improve, the researcher could go into the bottom part of the dashboard and consult the answers for branding themes and celebrity analysis. These are important as they give clear insight into what people and messages the audience associates with brand D. This is an opportunity to exploit these topics in different ways and achieve growth and success. 

3. Product Innovation Dashboard 

Our third and last dashboard example shows the answers to a survey on product innovation for a technology company. Just like the previous templates, the interactive and visual nature of the dashboard makes it the perfect tool to interpret data efficiently and effectively. 

Starting from right to left, we first get a list of the top 5 products by purchase intention. This information lets us understand if the product being evaluated resembles what the audience already intends to purchase. It is a great starting point to see how customers would respond to the new product. This information can be complemented with other key metrics displayed in the dashboard. For example, the usage and purchase intention track how the market would receive the product and if they would purchase it, respectively. Interpreting these values as positive or negative will depend on the company and its expectations regarding the survey. 

Complementing these metrics, we have the willingness to pay. Arguably, one of the most important metrics to define pricing strategies. Here, we can see that most respondents think the suggested price is a good value for money. Therefore, we can interpret that the product would sell for that price. 

To see more data analysis and interpretation examples for different industries and functions, visit our library of business dashboards .

To Conclude…

As we reach the end of this insightful post about data interpretation and analysis, we hope you have a clear understanding of the topic. We've covered the definition and given some examples and methods to perform a successful interpretation process.

The importance of data interpretation is undeniable. Dashboards not only bridge the information gap between traditional data interpretation methods and technology, but they can help remedy and prevent the major pitfalls of the process. As a digital age solution, they combine the best of the past and the present to allow for informed decision-making with maximum data interpretation ROI.

To start visualizing your insights in a meaningful and actionable way, test our online reporting software for free with our 14-day trial !

Data Collection, Presentation and Analysis

• First Online: 25 May 2023

Cite this chapter

• Uche M. Mbanaso 4 ,
• Lucienne Abrahams 5 &
• Kennedy Chinedu Okafor 6  

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This chapter covers the topics of data collection, data presentation and data analysis. It gives attention to data collection for studies based on experiments, on data derived from existing published or unpublished data sets, on observation, on simulation and digital twins, on surveys, on interviews and on focus group discussions. One of the interesting features of this chapter is the section dealing with using measurement scales in quantitative research, including nominal scales, ordinal scales, interval scales and ratio scales. It explains key facets of qualitative research including ethical clearance requirements. The chapter discusses the importance of data visualization as key to effective presentation of data, including tabular forms, graphical forms and visual charts such as those generated by Atlas.ti analytical software.

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Mbanaso, U.M., Abrahams, L., Okafor, K.C. (2023). Data Collection, Presentation and Analysis. In: Research Techniques for Computer Science, Information Systems and Cybersecurity. Springer, Cham. https://doi.org/10.1007/978-3-031-30031-8_7

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Quantitative Data Analysis 101

The lingo, methods and techniques, explained simply.

By: Derek Jansen (MBA)  and Kerryn Warren (PhD) | December 2020

Quantitative data analysis is one of those things that often strikes fear in students. It’s totally understandable – quantitative analysis is a complex topic, full of daunting lingo , like medians, modes, correlation and regression. Suddenly we’re all wishing we’d paid a little more attention in math class…

The good news is that while quantitative data analysis is a mammoth topic, gaining a working understanding of the basics isn’t that hard , even for those of us who avoid numbers and math . In this post, we’ll break quantitative analysis down into simple , bite-sized chunks so you can approach your research with confidence.

Overview: Quantitative Data Analysis 101

• What (exactly) is quantitative data analysis?
• When to use quantitative analysis
• How quantitative analysis works

The two “branches” of quantitative analysis

• Descriptive statistics 101
• Inferential statistics 101
• How to choose the right quantitative methods
• Recap & summary

What is quantitative data analysis?

Despite being a mouthful, quantitative data analysis simply means analysing data that is numbers-based – or data that can be easily “converted” into numbers without losing any meaning.

For example, category-based variables like gender, ethnicity, or native language could all be “converted” into numbers without losing meaning – for example, English could equal 1, French 2, etc.

This contrasts against qualitative data analysis, where the focus is on words, phrases and expressions that can’t be reduced to numbers. If you’re interested in learning about qualitative analysis, check out our post and video here .

What is quantitative analysis used for?

Quantitative analysis is generally used for three purposes.

• Firstly, it’s used to measure differences between groups . For example, the popularity of different clothing colours or brands.
• Secondly, it’s used to assess relationships between variables . For example, the relationship between weather temperature and voter turnout.
• And third, it’s used to test hypotheses in a scientifically rigorous way. For example, a hypothesis about the impact of a certain vaccine.

Again, this contrasts with qualitative analysis , which can be used to analyse people’s perceptions and feelings about an event or situation. In other words, things that can’t be reduced to numbers.

How does quantitative analysis work?

Well, since quantitative data analysis is all about analysing numbers , it’s no surprise that it involves statistics . Statistical analysis methods form the engine that powers quantitative analysis, and these methods can vary from pretty basic calculations (for example, averages and medians) to more sophisticated analyses (for example, correlations and regressions).

Sounds like gibberish? Don’t worry. We’ll explain all of that in this post. Importantly, you don’t need to be a statistician or math wiz to pull off a good quantitative analysis. We’ll break down all the technical mumbo jumbo in this post.

Need a helping hand?

As I mentioned, quantitative analysis is powered by statistical analysis methods . There are two main “branches” of statistical methods that are used – descriptive statistics and inferential statistics . In your research, you might only use descriptive statistics, or you might use a mix of both , depending on what you’re trying to figure out. In other words, depending on your research questions, aims and objectives . I’ll explain how to choose your methods later.

So, what are descriptive and inferential statistics?

Well, before I can explain that, we need to take a quick detour to explain some lingo. To understand the difference between these two branches of statistics, you need to understand two important words. These words are population and sample .

First up, population . In statistics, the population is the entire group of people (or animals or organisations or whatever) that you’re interested in researching. For example, if you were interested in researching Tesla owners in the US, then the population would be all Tesla owners in the US.

However, it’s extremely unlikely that you’re going to be able to interview or survey every single Tesla owner in the US. Realistically, you’ll likely only get access to a few hundred, or maybe a few thousand owners using an online survey. This smaller group of accessible people whose data you actually collect is called your sample .

So, to recap – the population is the entire group of people you’re interested in, and the sample is the subset of the population that you can actually get access to. In other words, the population is the full chocolate cake , whereas the sample is a slice of that cake.

So, why is this sample-population thing important?

Well, descriptive statistics focus on describing the sample , while inferential statistics aim to make predictions about the population, based on the findings within the sample. In other words, we use one group of statistical methods – descriptive statistics – to investigate the slice of cake, and another group of methods – inferential statistics – to draw conclusions about the entire cake. There I go with the cake analogy again…

With that out the way, let’s take a closer look at each of these branches in more detail.

Branch 1: Descriptive Statistics

Descriptive statistics serve a simple but critically important role in your research – to describe your data set – hence the name. In other words, they help you understand the details of your sample . Unlike inferential statistics (which we’ll get to soon), descriptive statistics don’t aim to make inferences or predictions about the entire population – they’re purely interested in the details of your specific sample .

When you’re writing up your analysis, descriptive statistics are the first set of stats you’ll cover, before moving on to inferential statistics. But, that said, depending on your research objectives and research questions , they may be the only type of statistics you use. We’ll explore that a little later.

So, what kind of statistics are usually covered in this section?

Some common statistical tests used in this branch include the following:

• Mean – this is simply the mathematical average of a range of numbers.
• Median – this is the midpoint in a range of numbers when the numbers are arranged in numerical order. If the data set makes up an odd number, then the median is the number right in the middle of the set. If the data set makes up an even number, then the median is the midpoint between the two middle numbers.
• Mode – this is simply the most commonly occurring number in the data set.
• In cases where most of the numbers are quite close to the average, the standard deviation will be relatively low.
• Conversely, in cases where the numbers are scattered all over the place, the standard deviation will be relatively high.
• Skewness . As the name suggests, skewness indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph, or do they skew to the left or right?

Feeling a bit confused? Let’s look at a practical example using a small data set.

On the left-hand side is the data set. This details the bodyweight of a sample of 10 people. On the right-hand side, we have the descriptive statistics. Let’s take a look at each of them.

First, we can see that the mean weight is 72.4 kilograms. In other words, the average weight across the sample is 72.4 kilograms. Straightforward.

Next, we can see that the median is very similar to the mean (the average). This suggests that this data set has a reasonably symmetrical distribution (in other words, a relatively smooth, centred distribution of weights, clustered towards the centre).

In terms of the mode , there is no mode in this data set. This is because each number is present only once and so there cannot be a “most common number”. If there were two people who were both 65 kilograms, for example, then the mode would be 65.

Next up is the standard deviation . 10.6 indicates that there’s quite a wide spread of numbers. We can see this quite easily by looking at the numbers themselves, which range from 55 to 90, which is quite a stretch from the mean of 72.4.

And lastly, the skewness of -0.2 tells us that the data is very slightly negatively skewed. This makes sense since the mean and the median are slightly different.

As you can see, these descriptive statistics give us some useful insight into the data set. Of course, this is a very small data set (only 10 records), so we can’t read into these statistics too much. Also, keep in mind that this is not a list of all possible descriptive statistics – just the most common ones.

But why do all of these numbers matter?

While these descriptive statistics are all fairly basic, they’re important for a few reasons:

• Firstly, they help you get both a macro and micro-level view of your data. In other words, they help you understand both the big picture and the finer details.
• Secondly, they help you spot potential errors in the data – for example, if an average is way higher than you’d expect, or responses to a question are highly varied, this can act as a warning sign that you need to double-check the data.
• And lastly, these descriptive statistics help inform which inferential statistical techniques you can use, as those techniques depend on the skewness (in other words, the symmetry and normality) of the data.

Simply put, descriptive statistics are really important , even though the statistical techniques used are fairly basic. All too often at Grad Coach, we see students skimming over the descriptives in their eagerness to get to the more exciting inferential methods, and then landing up with some very flawed results.

Don’t be a sucker – give your descriptive statistics the love and attention they deserve!

Branch 2: Inferential Statistics

As I mentioned, while descriptive statistics are all about the details of your specific data set – your sample – inferential statistics aim to make inferences about the population . In other words, you’ll use inferential statistics to make predictions about what you’d expect to find in the full population.

What kind of predictions, you ask? Well, there are two common types of predictions that researchers try to make using inferential stats:

• Firstly, predictions about differences between groups – for example, height differences between children grouped by their favourite meal or gender.
• And secondly, relationships between variables – for example, the relationship between body weight and the number of hours a week a person does yoga.

In other words, inferential statistics (when done correctly), allow you to connect the dots and make predictions about what you expect to see in the real world population, based on what you observe in your sample data. For this reason, inferential statistics are used for hypothesis testing – in other words, to test hypotheses that predict changes or differences.

Of course, when you’re working with inferential statistics, the composition of your sample is really important. In other words, if your sample doesn’t accurately represent the population you’re researching, then your findings won’t necessarily be very useful.

For example, if your population of interest is a mix of 50% male and 50% female , but your sample is 80% male , you can’t make inferences about the population based on your sample, since it’s not representative. This area of statistics is called sampling, but we won’t go down that rabbit hole here (it’s a deep one!) – we’ll save that for another post .

What statistics are usually used in this branch?

There are many, many different statistical analysis methods within the inferential branch and it’d be impossible for us to discuss them all here. So we’ll just take a look at some of the most common inferential statistical methods so that you have a solid starting point.

First up are T-Tests . T-tests compare the means (the averages) of two groups of data to assess whether they’re statistically significantly different. In other words, do they have significantly different means, standard deviations and skewness.

This type of testing is very useful for understanding just how similar or different two groups of data are. For example, you might want to compare the mean blood pressure between two groups of people – one that has taken a new medication and one that hasn’t – to assess whether they are significantly different.

Kicking things up a level, we have ANOVA, which stands for “analysis of variance”. This test is similar to a T-test in that it compares the means of various groups, but ANOVA allows you to analyse multiple groups , not just two groups So it’s basically a t-test on steroids…

Next, we have correlation analysis . This type of analysis assesses the relationship between two variables. In other words, if one variable increases, does the other variable also increase, decrease or stay the same. For example, if the average temperature goes up, do average ice creams sales increase too? We’d expect some sort of relationship between these two variables intuitively , but correlation analysis allows us to measure that relationship scientifically .

Lastly, we have regression analysis – this is quite similar to correlation in that it assesses the relationship between variables, but it goes a step further to understand cause and effect between variables, not just whether they move together. In other words, does the one variable actually cause the other one to move, or do they just happen to move together naturally thanks to another force? Just because two variables correlate doesn’t necessarily mean that one causes the other.

Stats overload…

I hear you. To make this all a little more tangible, let’s take a look at an example of a correlation in action.

Here’s a scatter plot demonstrating the correlation (relationship) between weight and height. Intuitively, we’d expect there to be some relationship between these two variables, which is what we see in this scatter plot. In other words, the results tend to cluster together in a diagonal line from bottom left to top right.

As I mentioned, these are are just a handful of inferential techniques – there are many, many more. Importantly, each statistical method has its own assumptions and limitations .

For example, some methods only work with normally distributed (parametric) data, while other methods are designed specifically for non-parametric data. And that’s exactly why descriptive statistics are so important – they’re the first step to knowing which inferential techniques you can and can’t use.

How to choose the right analysis method

To choose the right statistical methods, you need to think about two important factors :

• The type of quantitative data you have (specifically, level of measurement and the shape of the data). And,
• Your research questions and hypotheses

Let’s take a closer look at each of these.

Factor 1 – Data type

The first thing you need to consider is the type of data you’ve collected (or the type of data you will collect). By data types, I’m referring to the four levels of measurement – namely, nominal, ordinal, interval and ratio. If you’re not familiar with this lingo, check out the video below.

Why does this matter?

Well, because different statistical methods and techniques require different types of data. This is one of the “assumptions” I mentioned earlier – every method has its assumptions regarding the type of data.

For example, some techniques work with categorical data (for example, yes/no type questions, or gender or ethnicity), while others work with continuous numerical data (for example, age, weight or income) – and, of course, some work with multiple data types.

If you try to use a statistical method that doesn’t support the data type you have, your results will be largely meaningless . So, make sure that you have a clear understanding of what types of data you’ve collected (or will collect). Once you have this, you can then check which statistical methods would support your data types here .

If you haven’t collected your data yet, you can work in reverse and look at which statistical method would give you the most useful insights, and then design your data collection strategy to collect the correct data types.

Another important factor to consider is the shape of your data . Specifically, does it have a normal distribution (in other words, is it a bell-shaped curve, centred in the middle) or is it very skewed to the left or the right? Again, different statistical techniques work for different shapes of data – some are designed for symmetrical data while others are designed for skewed data.

This is another reminder of why descriptive statistics are so important – they tell you all about the shape of your data.

Factor 2: Your research questions

The next thing you need to consider is your specific research questions, as well as your hypotheses (if you have some). The nature of your research questions and research hypotheses will heavily influence which statistical methods and techniques you should use.

If you’re just interested in understanding the attributes of your sample (as opposed to the entire population), then descriptive statistics are probably all you need. For example, if you just want to assess the means (averages) and medians (centre points) of variables in a group of people.

On the other hand, if you aim to understand differences between groups or relationships between variables and to infer or predict outcomes in the population, then you’ll likely need both descriptive statistics and inferential statistics.

So, it’s really important to get very clear about your research aims and research questions, as well your hypotheses – before you start looking at which statistical techniques to use.

Never shoehorn a specific statistical technique into your research just because you like it or have some experience with it. Your choice of methods must align with all the factors we’ve covered here.

Time to recap…

You’re still with me? That’s impressive. We’ve covered a lot of ground here, so let’s recap on the key points:

• Quantitative data analysis is all about  analysing number-based data  (which includes categorical and numerical data) using various statistical techniques.
• The two main  branches  of statistics are  descriptive statistics  and  inferential statistics . Descriptives describe your sample, whereas inferentials make predictions about what you’ll find in the population.
• Common  descriptive statistical methods include  mean  (average),  median , standard  deviation  and  skewness .
• Common  inferential statistical methods include  t-tests ,  ANOVA ,  correlation  and  regression  analysis.
• To choose the right statistical methods and techniques, you need to consider the  type of data you’re working with , as well as your  research questions  and hypotheses.

Psst... there’s more!

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

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Home » Data Analysis – Process, Methods and Types

Data Analysis – Process, Methods and Types

Table of Contents

Data Analysis

Definition:

Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets. The ultimate aim of data analysis is to convert raw data into actionable insights that can inform business decisions, scientific research, and other endeavors.

Data Analysis Process

The following are step-by-step guides to the data analysis process:

Define the Problem

The first step in data analysis is to clearly define the problem or question that needs to be answered. This involves identifying the purpose of the analysis, the data required, and the intended outcome.

Collect the Data

The next step is to collect the relevant data from various sources. This may involve collecting data from surveys, databases, or other sources. It is important to ensure that the data collected is accurate, complete, and relevant to the problem being analyzed.

Clean and Organize the Data

Once the data has been collected, it needs to be cleaned and organized. This involves removing any errors or inconsistencies in the data, filling in missing values, and ensuring that the data is in a format that can be easily analyzed.

Analyze the Data

The next step is to analyze the data using various statistical and analytical techniques. This may involve identifying patterns in the data, conducting statistical tests, or using machine learning algorithms to identify trends and insights.

Interpret the Results

After analyzing the data, the next step is to interpret the results. This involves drawing conclusions based on the analysis and identifying any significant findings or trends.

Communicate the Findings

Once the results have been interpreted, they need to be communicated to stakeholders. This may involve creating reports, visualizations, or presentations to effectively communicate the findings and recommendations.

Take Action

The final step in the data analysis process is to take action based on the findings. This may involve implementing new policies or procedures, making strategic decisions, or taking other actions based on the insights gained from the analysis.

Types of Data Analysis

Types of Data Analysis are as follows:

Descriptive Analysis

This type of analysis involves summarizing and describing the main characteristics of a dataset, such as the mean, median, mode, standard deviation, and range.

Inferential Analysis

This type of analysis involves making inferences about a population based on a sample. Inferential analysis can help determine whether a certain relationship or pattern observed in a sample is likely to be present in the entire population.

Diagnostic Analysis

This type of analysis involves identifying and diagnosing problems or issues within a dataset. Diagnostic analysis can help identify outliers, errors, missing data, or other anomalies in the dataset.

Predictive Analysis

This type of analysis involves using statistical models and algorithms to predict future outcomes or trends based on historical data. Predictive analysis can help businesses and organizations make informed decisions about the future.

Prescriptive Analysis

This type of analysis involves recommending a course of action based on the results of previous analyses. Prescriptive analysis can help organizations make data-driven decisions about how to optimize their operations, products, or services.

Exploratory Analysis

This type of analysis involves exploring the relationships and patterns within a dataset to identify new insights and trends. Exploratory analysis is often used in the early stages of research or data analysis to generate hypotheses and identify areas for further investigation.

Data Analysis Methods

Data Analysis Methods are as follows:

Statistical Analysis

This method involves the use of mathematical models and statistical tools to analyze and interpret data. It includes measures of central tendency, correlation analysis, regression analysis, hypothesis testing, and more.

Machine Learning

This method involves the use of algorithms to identify patterns and relationships in data. It includes supervised and unsupervised learning, classification, clustering, and predictive modeling.

Data Mining

This method involves using statistical and machine learning techniques to extract information and insights from large and complex datasets.

Text Analysis

This method involves using natural language processing (NLP) techniques to analyze and interpret text data. It includes sentiment analysis, topic modeling, and entity recognition.

Network Analysis

This method involves analyzing the relationships and connections between entities in a network, such as social networks or computer networks. It includes social network analysis and graph theory.

Time Series Analysis

This method involves analyzing data collected over time to identify patterns and trends. It includes forecasting, decomposition, and smoothing techniques.

Spatial Analysis

This method involves analyzing geographic data to identify spatial patterns and relationships. It includes spatial statistics, spatial regression, and geospatial data visualization.

Data Visualization

This method involves using graphs, charts, and other visual representations to help communicate the findings of the analysis. It includes scatter plots, bar charts, heat maps, and interactive dashboards.

Qualitative Analysis

This method involves analyzing non-numeric data such as interviews, observations, and open-ended survey responses. It includes thematic analysis, content analysis, and grounded theory.

Multi-criteria Decision Analysis

This method involves analyzing multiple criteria and objectives to support decision-making. It includes techniques such as the analytical hierarchy process, TOPSIS, and ELECTRE.

Data Analysis Tools

There are various data analysis tools available that can help with different aspects of data analysis. Below is a list of some commonly used data analysis tools:

• Microsoft Excel: A widely used spreadsheet program that allows for data organization, analysis, and visualization.
• SQL : A programming language used to manage and manipulate relational databases.
• R : An open-source programming language and software environment for statistical computing and graphics.
• Python : A general-purpose programming language that is widely used in data analysis and machine learning.
• Tableau : A data visualization software that allows for interactive and dynamic visualizations of data.
• SAS : A statistical analysis software used for data management, analysis, and reporting.
• SPSS : A statistical analysis software used for data analysis, reporting, and modeling.
• Matlab : A numerical computing software that is widely used in scientific research and engineering.
• RapidMiner : A data science platform that offers a wide range of data analysis and machine learning tools.

Applications of Data Analysis

Data analysis has numerous applications across various fields. Below are some examples of how data analysis is used in different fields:

• Business : Data analysis is used to gain insights into customer behavior, market trends, and financial performance. This includes customer segmentation, sales forecasting, and market research.
• Healthcare : Data analysis is used to identify patterns and trends in patient data, improve patient outcomes, and optimize healthcare operations. This includes clinical decision support, disease surveillance, and healthcare cost analysis.
• Education : Data analysis is used to measure student performance, evaluate teaching effectiveness, and improve educational programs. This includes assessment analytics, learning analytics, and program evaluation.
• Finance : Data analysis is used to monitor and evaluate financial performance, identify risks, and make investment decisions. This includes risk management, portfolio optimization, and fraud detection.
• Government : Data analysis is used to inform policy-making, improve public services, and enhance public safety. This includes crime analysis, disaster response planning, and social welfare program evaluation.
• Sports : Data analysis is used to gain insights into athlete performance, improve team strategy, and enhance fan engagement. This includes player evaluation, scouting analysis, and game strategy optimization.
• Marketing : Data analysis is used to measure the effectiveness of marketing campaigns, understand customer behavior, and develop targeted marketing strategies. This includes customer segmentation, marketing attribution analysis, and social media analytics.
• Environmental science : Data analysis is used to monitor and evaluate environmental conditions, assess the impact of human activities on the environment, and develop environmental policies. This includes climate modeling, ecological forecasting, and pollution monitoring.

When to Use Data Analysis

Data analysis is useful when you need to extract meaningful insights and information from large and complex datasets. It is a crucial step in the decision-making process, as it helps you understand the underlying patterns and relationships within the data, and identify potential areas for improvement or opportunities for growth.

Here are some specific scenarios where data analysis can be particularly helpful:

• Problem-solving : When you encounter a problem or challenge, data analysis can help you identify the root cause and develop effective solutions.
• Optimization : Data analysis can help you optimize processes, products, or services to increase efficiency, reduce costs, and improve overall performance.
• Prediction: Data analysis can help you make predictions about future trends or outcomes, which can inform strategic planning and decision-making.
• Performance evaluation : Data analysis can help you evaluate the performance of a process, product, or service to identify areas for improvement and potential opportunities for growth.
• Risk assessment : Data analysis can help you assess and mitigate risks, whether it is financial, operational, or related to safety.
• Market research : Data analysis can help you understand customer behavior and preferences, identify market trends, and develop effective marketing strategies.
• Quality control: Data analysis can help you ensure product quality and customer satisfaction by identifying and addressing quality issues.

Purpose of Data Analysis

The primary purposes of data analysis can be summarized as follows:

• To gain insights: Data analysis allows you to identify patterns and trends in data, which can provide valuable insights into the underlying factors that influence a particular phenomenon or process.
• To inform decision-making: Data analysis can help you make informed decisions based on the information that is available. By analyzing data, you can identify potential risks, opportunities, and solutions to problems.
• To improve performance: Data analysis can help you optimize processes, products, or services by identifying areas for improvement and potential opportunities for growth.
• To measure progress: Data analysis can help you measure progress towards a specific goal or objective, allowing you to track performance over time and adjust your strategies accordingly.
• To identify new opportunities: Data analysis can help you identify new opportunities for growth and innovation by identifying patterns and trends that may not have been visible before.

Examples of Data Analysis

Some Examples of Data Analysis are as follows:

• Social Media Monitoring: Companies use data analysis to monitor social media activity in real-time to understand their brand reputation, identify potential customer issues, and track competitors. By analyzing social media data, businesses can make informed decisions on product development, marketing strategies, and customer service.
• Financial Trading: Financial traders use data analysis to make real-time decisions about buying and selling stocks, bonds, and other financial instruments. By analyzing real-time market data, traders can identify trends and patterns that help them make informed investment decisions.
• Traffic Monitoring : Cities use data analysis to monitor traffic patterns and make real-time decisions about traffic management. By analyzing data from traffic cameras, sensors, and other sources, cities can identify congestion hotspots and make changes to improve traffic flow.
• Healthcare Monitoring: Healthcare providers use data analysis to monitor patient health in real-time. By analyzing data from wearable devices, electronic health records, and other sources, healthcare providers can identify potential health issues and provide timely interventions.
• Online Advertising: Online advertisers use data analysis to make real-time decisions about advertising campaigns. By analyzing data on user behavior and ad performance, advertisers can make adjustments to their campaigns to improve their effectiveness.
• Sports Analysis : Sports teams use data analysis to make real-time decisions about strategy and player performance. By analyzing data on player movement, ball position, and other variables, coaches can make informed decisions about substitutions, game strategy, and training regimens.
• Energy Management : Energy companies use data analysis to monitor energy consumption in real-time. By analyzing data on energy usage patterns, companies can identify opportunities to reduce energy consumption and improve efficiency.

Characteristics of Data Analysis

Characteristics of Data Analysis are as follows:

• Objective : Data analysis should be objective and based on empirical evidence, rather than subjective assumptions or opinions.
• Systematic : Data analysis should follow a systematic approach, using established methods and procedures for collecting, cleaning, and analyzing data.
• Accurate : Data analysis should produce accurate results, free from errors and bias. Data should be validated and verified to ensure its quality.
• Relevant : Data analysis should be relevant to the research question or problem being addressed. It should focus on the data that is most useful for answering the research question or solving the problem.
• Comprehensive : Data analysis should be comprehensive and consider all relevant factors that may affect the research question or problem.
• Timely : Data analysis should be conducted in a timely manner, so that the results are available when they are needed.
• Reproducible : Data analysis should be reproducible, meaning that other researchers should be able to replicate the analysis using the same data and methods.
• Communicable : Data analysis should be communicated clearly and effectively to stakeholders and other interested parties. The results should be presented in a way that is understandable and useful for decision-making.

Advantages of Data Analysis

Advantages of Data Analysis are as follows:

• Better decision-making: Data analysis helps in making informed decisions based on facts and evidence, rather than intuition or guesswork.
• Improved efficiency: Data analysis can identify inefficiencies and bottlenecks in business processes, allowing organizations to optimize their operations and reduce costs.
• Increased accuracy: Data analysis helps to reduce errors and bias, providing more accurate and reliable information.
• Better customer service: Data analysis can help organizations understand their customers better, allowing them to provide better customer service and improve customer satisfaction.
• Competitive advantage: Data analysis can provide organizations with insights into their competitors, allowing them to identify areas where they can gain a competitive advantage.
• Identification of trends and patterns : Data analysis can identify trends and patterns in data that may not be immediately apparent, helping organizations to make predictions and plan for the future.
• Improved risk management : Data analysis can help organizations identify potential risks and take proactive steps to mitigate them.
• Innovation: Data analysis can inspire innovation and new ideas by revealing new opportunities or previously unknown correlations in data.

Limitations of Data Analysis

• Data quality: The quality of data can impact the accuracy and reliability of analysis results. If data is incomplete, inconsistent, or outdated, the analysis may not provide meaningful insights.
• Limited scope: Data analysis is limited by the scope of the data available. If data is incomplete or does not capture all relevant factors, the analysis may not provide a complete picture.
• Human error : Data analysis is often conducted by humans, and errors can occur in data collection, cleaning, and analysis.
• Cost : Data analysis can be expensive, requiring specialized tools, software, and expertise.
• Time-consuming : Data analysis can be time-consuming, especially when working with large datasets or conducting complex analyses.
• Overreliance on data: Data analysis should be complemented with human intuition and expertise. Overreliance on data can lead to a lack of creativity and innovation.
• Privacy concerns: Data analysis can raise privacy concerns if personal or sensitive information is used without proper consent or security measures.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Blood pressure medication and acute kidney injury after intracerebral haemorrhage: an analysis of the ATACH-II trial

Andrew m naidech.

1 Neurology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA

Hanyin Wang

2 Health Services Integrated Program, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA

Meghan Hutch

Julianne murphy, james paparello.

3 Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA

Philip Bath

4 Nottingham Clinical Trials Unit, University of Nottingham, Nottingham, Illinois, UK

Anand Srivastava

5 Department of Medicine, University of Illinois, Chicago, Illinois, USA

6 Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA

Associated Data

Data are available in a public, open access repository. Data are available from the NINDS Clinical Trials Archive on request from the NIH, at https://www.ninds.nih.gov/current-research/research-funded-ninds/clinical-research/archived-clinical-research-datasets .

Acute blood pressure (BP) reduction is standard of care after acute intracerebral haemorrhage (ICH). More acute BP reduction is associated with acute kidney injury (AKI). It is not known if the choice of antihypertensive medications affects the risk of AKI.

We analysed data from the ATACH-II clinical trial. AKI was defined by the Kidney Disease: Improving Global Outcomes criteria. We analysed antihypertensive medication from two sources. The first was a case report form that specified the use of labetalol, diltiazem, urapidil or other. We tested the hypothesis that the secondary medication was associated with AKI with χ 2 test. Second, we tested the hypotheses the dosage of diltiazem was associated with AKI using Mann-Whitney U test.

AKI occurred in 109 of 1000 patients (10.9%). A higher proportion of patients with AKI received diltiazem after nicardipine (12 (29%) vs 21 (12%), p=0.03). The 95%ile (90%–99% ile) of administered diltiazem was 18 (0–130) mg in patients with AKI vs 0 (0–30) mg in patients without AKI (p=0.002). There was no apparent confounding by indication for diltiazem use.

Conclusions

The use of diltiazem, and more diltiazem, was associated with AKI in patients with acute ICH.

WHAT IS ALREADY KNOWN ON THIS TOPIC

• Acute kidney injury (AKI) is related to blood pressure reduction and worsens outcomes.

WHAT THIS STUDY ADDS

• Diltiazem after nicardipine increases the risk of AKI.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

• Protocols might specify combinations of antihypertensive medication that minimise the risk of AKI.

Introduction

Intracerebral haemorrhage (ICH), spontaneous bleeding into brain tissue, is frequently disabling or deadly. Most patients who present with ICH have elevated blood pressure (BP), 1 2 a risk factor for haematoma expansion, growth of the intracranial haematoma on repeated CT scans. Acute BP reduction is considered standard of care to reduce haematoma expansion, disability and death in patients with acute ICH. 1–5

Acute kidney injury (AKI) complicates the management of acute ICH. AKI is independently associated with more dependence or death at follow-up. 6 More acute BP reduction after acute ICH increases the risk of AKI. 4 However, it is not known if the choice of antihypertensive medication is associated with AKI. We tested the hypothesis that the choice of antihypertensive medication and dosage is associated with AKI in patients with acute ICH.

Materials and methods

We used data from the Antihypertensive Treatment of Acute Cerebral Haemorrhage (ATACH)-II clinical trial ( {"type":"clinical-trial","attrs":{"text":"NCT01176565","term_id":"NCT01176565"}} NCT01176565 ), 7 which randomised 1000 patients worldwide with acute ICH and systolic BP>180 mm Hg to a goal systolic BP of 120 mm Hg (intensive acute BP reduction) or 140 mm Hg (standard acute BP reduction) in a 1:1 ratio. 4 The protocol is available online and has been previously published. 4 Data were obtained from the NIH. Outcome assessment was blinded, although BP goals were not blinded. Nicardipine was the preferred first-line antihypertensive medication. Data are available online from the NIH.

We defined AKI as per the Kidney Disease: Improving Global Outcomes (KDIGO) criteria by serum creatinine values. 8 Incident AKI was defined as ≥0.3 mg/dL increase in baseline creatinine level over any 48-hour period during the first 7 days in the ICU or an creatinine level at least 1.5 times the baseline creatinine level within 7 days. Twenty-eight patients (2.8%) did not have two creatinine values and so the presence of AKI could not be determined—to be conservative, they were considered to have no AKI. None of these 28 patients without two creatinine values had a renal adverse event documented, supporting the adjudication that these patients did not have AKI.

We compared continuous data with analysis of variance, non-normally distributed values with a Mann-Whitney U test (expressed as median (Q1–Q3)), and frequency of categorical variables with χ 2 test as appropriate. Calculations were performed with standard statistical software (R V.4, RStudio V.1.4, R Foundation for Statistical Computing, Boston, Massachusetts, USA).

The data set contained 1000 patients, evenly assigned to goal systolic BP 120 or 140 mm Hg. Overall, 109 patients (10.9%) had AKI. There were 90 patients with KDIGO stage 1, 15 patients with KDIGO stage 2 and 4 patients with KDIGO stage 3 (most severe AKI).

Antihypertensive medications were associated with AKI. Antihypertensive medications in addition to nicardipine were generally needed only in patients randomised to intensive BP treatment. Patients with AKI were more likely to receive diltiazem as a secondary agent to nicardipine and to have type 2 diabetes ( table 1 ).

Antihypertensive medications and outcomes stratified by acute kidney injury

Data are N(%) or median (Q1–Q3).

We confirmed higher doses of diltiazem were administered in patients with AKI. Most patients received no diltiazem, so differences were at the extremes. The 95%ile (90%–99% ile) was 18 (0–130) mg in patients with AKI vs 0 (0–30) mg in patients without AKI (p=0.002).

We examined data from a large, prospective, randomised, clinical trial of patients with acute ICH. Different trials have allowed local clinicians wide latitude to choose antihypertensive medications. 2 These data associate diltiazem after nicardipine with an increased likelihood of AKI compared with other antihypertensive medications. 6 There was no evidence of confounding by an indication for diltiazem (eg, atrial fibrillation was uncommon). The consistent results between the summary of the use of diltiazem in a case report form and the analysis of dosages of administered medications is reassuring because the use of diltiazem was independently documented twice, reducing the potential of a chance finding. These data suggest that diltiazem should not be used as a second-line medication when nicardipine is inadequate for acute BP reduction in patients with acute ICH.

The mechanism for diltiazem’s association with AKI is not clear from these data. Acute BP reduction and vasodilators are likely to reduce kidney blood flow. Doppler of kidney blood flow 9 might determine the magnitude of kidney blood flow associated with AKI. It is possible that kidney blood flow can be manipulated with different antihypertensive medications or different BP goals. Routine CT angiography typically does not lead to AKI in patients with acute stroke. 10 Thus, imaging is unlikely to explain our findings, and there is no evidence that CT angiography confounds our results. In other acute conditions (eg, sepsis), a variety of potential mechanisms exist for AKI. Sepsis was rare in this cohort, and would not explain our results. Type 2 diabetes was more common in patients with AKI; however, this affected too few patients to drive our results overall. Potential explanations include diltiazem’s inhibition of cytochrome P450, which could be synergistic with the effects of intravenous nicardipine. Although the use of labetalol was not different between patients with AKI or not, diltiazem (a calcium antagonist) could also potentiate bradycardia when used concurrently with labetalol (a beta/alpha antagonist).

There are limitations to these data. These data are from a trial that is relatively large for ICH, but not by the standards of other conditions. However, the results are in line with other clinical trials that have found some harm from antihypertensive medication. 11 ATACH-II was the last trial in patients with ICH to specify a BP goal below 140 mm Hg; the incidence of AKI may be lower with less intensive BP goals or general practice. The variety of antihypertensive medications used makes interpretation more challenging. We attempted to minimise the likelihood of a chance finding by comparing two sources of documentation of antihypertension medication use, a summary choice on a case report form and cleaned data from all medication administration dosages.

In sum, we found that diltiazem was associated with AKI in patients with acute ICH, particularly those randomised to intensive BP reduction. Future research could determine mechanisms by which acute BP reduction leads to AKI. Minimising the occurrence of AKI could improve the efficacy and safety of acute BP reduction, the only treatment administered to most patients with acute ICH.

Acknowledgments

We gratefully acknowledge James Grotta, MD, for review of the manuscript.

Contributors: AMN and JM performed the analysis. AMN obtained the data from NIH and wrote the paper. MH, HW and YL revised the paper for analysis. JP and AS edited the manuscript regarding kidney injury. PB edited the manuscript. AMN acts as guarantor.

Funding: This work was supported in part by R01 NS110779 from the US National Institutes of Health.

Competing interests: None declared.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data availability statement

Ethics statements, patient consent for publication.

Not applicable.

Ethics approval

No IRB review was necessary (and thus no number was assigned) because the use of a publicly available data set of deidentified data fell outside the board’s guidelines as human subjects research.

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• About Adverse Childhood Experiences
• Risk and Protective Factors
• Program: Essentials for Childhood: Preventing Adverse Childhood Experiences through Data to Action
• Adverse childhood experiences can have long-term impacts on health, opportunity and well-being.
• Adverse childhood experiences are common and some groups experience them more than others.

What are adverse childhood experiences?

Adverse childhood experiences, or ACEs, are potentially traumatic events that occur in childhood (0-17 years). Examples include: 1

• Experiencing violence, abuse, or neglect.
• Witnessing violence in the home or community.
• Having a family member attempt or die by suicide.

Also included are aspects of the child’s environment that can undermine their sense of safety, stability, and bonding. Examples can include growing up in a household with: 1

• Substance use problems.
• Mental health problems.
• Instability due to parental separation.
• Instability due to household members being in jail or prison.

The examples above are not a complete list of adverse experiences. Many other traumatic experiences could impact health and well-being. This can include not having enough food to eat, experiencing homelessness or unstable housing, or experiencing discrimination. 2 3 4 5 6

Quick facts and stats

ACEs are common. About 64% of adults in the United States reported they had experienced at least one type of ACE before age 18. Nearly one in six (17.3%) adults reported they had experienced four or more types of ACEs. 7

Preventing ACEs could potentially reduce many health conditions. Estimates show up to 1.9 million heart disease cases and 21 million depression cases potentially could have been avoided by preventing ACEs. 1

Some people are at greater risk of experiencing one or more ACEs than others. While all children are at risk of ACEs, numerous studies show inequities in such experiences. These inequalities are linked to the historical, social, and economic environments in which some families live. 5 6 ACEs were highest among females, non-Hispanic American Indian or Alaska Native adults, and adults who are unemployed or unable to work. 7

ACEs are costly. ACEs-related health consequences cost an estimated economic burden of $748 billion annually in Bermuda, Canada, and the United States. 8

ACEs can have lasting effects on health and well-being in childhood and life opportunities well into adulthood. 9 Life opportunities include things like education and job potential. These experiences can increase the risks of injury, sexually transmitted infections, and involvement in sex trafficking. They can also increase risks for maternal and child health problems including teen pregnancy, pregnancy complications, and fetal death. Also included are a range of chronic diseases and leading causes of death, such as cancer, diabetes, heart disease, and suicide. 1 10 11 12 13 14 15 16 17

ACEs and associated social determinants of health, such as living in under-resourced or racially segregated neighborhoods, can cause toxic stress. Toxic stress, or extended or prolonged stress, from ACEs can negatively affect children’s brain development, immune systems, and stress-response systems. These changes can affect children’s attention, decision-making, and learning. 18

Children growing up with toxic stress may have difficulty forming healthy and stable relationships. They may also have unstable work histories as adults and struggle with finances, jobs, and depression throughout life. 18 These effects can also be passed on to their own children. 19 20 21 Some children may face further exposure to toxic stress from historical and ongoing traumas. These historical and ongoing traumas refer to experiences of racial discrimination or the impacts of poverty resulting from limited educational and economic opportunities. 1 6

Adverse childhood experiences can be prevented. Certain factors may increase or decrease the risk of experiencing adverse childhood experiences.

Preventing adverse childhood experiences requires understanding and addressing the factors that put people at risk for or protect them from violence.

Creating safe, stable, nurturing relationships and environments for all children can prevent ACEs and help all children reach their full potential. We all have a role to play.

• Merrick MT, Ford DC, Ports KA, et al. Vital Signs: Estimated Proportion of Adult Health Problems Attributable to Adverse Childhood Experiences and Implications for Prevention — 25 States, 2015–2017. MMWR Morb Mortal Wkly Rep 2019;68:999-1005. DOI: http://dx.doi.org/10.15585/mmwr.mm6844e1 .
• Cain KS, Meyer SC, Cummer E, Patel KK, Casacchia NJ, Montez K, Palakshappa D, Brown CL. Association of Food Insecurity with Mental Health Outcomes in Parents and Children. Science Direct. 2022; 22:7; 1105-1114. DOI: https://doi.org/10.1016/j.acap.2022.04.010 .
• Smith-Grant J, Kilmer G, Brener N, Robin L, Underwood M. Risk Behaviors and Experiences Among Youth Experiencing Homelessness—Youth Risk Behavior Survey, 23 U.S. States and 11 Local School Districts. Journal of Community Health. 2022; 47: 324-333.
• Experiencing discrimination: Early Childhood Adversity, Toxic Stress, and the Impacts of Racism on the Foundations of Health | Annual Review of Public Health ( annualreviews.org).
• Sedlak A, Mettenburg J, Basena M, et al. Fourth national incidence study of child abuse and neglect (NIS-4): Report to Congress. Executive Summary. Washington, DC: U.S. Department of Health an Human Services, Administration for Children and Families.; 2010.
• Font S, Maguire-Jack K. Pathways from childhood abuse and other adversities to adult health risks: The role of adult socioeconomic conditions. Child Abuse Negl. 2016;51:390-399.
• Swedo EA, Aslam MV, Dahlberg LL, et al. Prevalence of Adverse Childhood Experiences Among U.S. Adults — Behavioral Risk Factor Surveillance System, 2011–2020. MMWR Morb Mortal Wkly Rep 2023;72:707–715. DOI: http://dx.doi.org/10.15585/mmwr.mm7226a2 .
• Bellis, MA, et al. Life Course Health Consequences and Associated Annual Costs of Adverse Childhood Experiences Across Europe and North America: A Systematic Review and Meta-Analysis. Lancet Public Health 2019.
• Adverse Childhood Experiences During the COVID-19 Pandemic and Associations with Poor Mental Health and Suicidal Behaviors Among High School Students — Adolescent Behaviors and Experiences Survey, United States, January–June 2021 | MMWR
• Hillis SD, Anda RF, Dube SR, Felitti VJ, Marchbanks PA, Marks JS. The association between adverse childhood experiences and adolescent pregnancy, long-term psychosocial consequences, and fetal death. Pediatrics. 2004 Feb;113(2):320-7.
• Miller ES, Fleming O, Ekpe EE, Grobman WA, Heard-Garris N. Association Between Adverse Childhood Experiences and Adverse Pregnancy Outcomes. Obstetrics & Gynecology . 2021;138(5):770-776. https://doi.org/10.1097/AOG.0000000000004570 .
• Sulaiman S, Premji SS, Tavangar F, et al. Total Adverse Childhood Experiences and Preterm Birth: A Systematic Review. Matern Child Health J . 2021;25(10):1581-1594. https://doi.org/10.1007/s10995-021-03176-6 .
• Ciciolla L, Shreffler KM, Tiemeyer S. Maternal Childhood Adversity as a Risk for Perinatal Complications and NICU Hospitalization. Journal of Pediatric Psychology . 2021;46(7):801-813. https://doi.org/10.1093/jpepsy/jsab027 .
• Mersky JP, Lee CP. Adverse childhood experiences and poor birth outcomes in a diverse, low-income sample. BMC pregnancy and childbirth. 2019;19(1). https://doi.org/10.1186/s12884-019-2560-8.
• Reid JA, Baglivio MT, Piquero AR, Greenwald MA, Epps N. No youth left behind to human trafficking: Exploring profiles of risk. American journal of orthopsychiatry. 2019;89(6):704.
• Diamond-Welch B, Kosloski AE. Adverse childhood experiences and propensity to participate in the commercialized sex market. Child Abuse & Neglect. 2020 Jun 1;104:104468.
• Shonkoff, J. P., Garner, A. S., Committee on Psychosocial Aspects of Child and Family Health, Committee on Early Childhood, Adoption, and Dependent Care, & Section on Developmental and Behavioral Pediatrics (2012). The lifelong effects of early childhood adversity and toxic stress. Pediatrics, 129(1), e232–e246. https://doi.org/10.1542/peds.2011-2663
• Narayan AJ, Kalstabakken AW, Labella MH, Nerenberg LS, Monn AR, Masten AS. Intergenerational continuity of adverse childhood experiences in homeless families: unpacking exposure to maltreatment versus family dysfunction. Am J Orthopsych. 2017;87(1):3. https://doi.org/10.1037/ort0000133.
• Schofield TJ, Donnellan MB, Merrick MT, Ports KA, Klevens J, Leeb R. Intergenerational continuity in adverse childhood experiences and rural community environments. Am J Public Health. 2018;108(9):1148-1152. https://doi.org/10.2105/AJPH.2018.304598.
• Schofield TJ, Lee RD, Merrick MT. Safe, stable, nurturing relationships as a moderator of intergenerational continuity of child maltreatment: a meta-analysis. J Adolesc Health. 2013;53(4 Suppl):S32-38. https://doi.org/10.1016/j.jadohealth.2013.05.004 .

Adverse Childhood Experiences (ACEs)

ACEs can have a tremendous impact on lifelong health and opportunity. CDC works to understand ACEs and prevent them.

• Open access
• Published: 14 May 2024

Evaluation of the feasibility of a midwifery educator continuous professional development (CPD) programme in Kenya and Nigeria: a mixed methods study

• Duncan N. Shikuku 1 , 2 ,
• Hauwa Mohammed 3 ,
• Lydia Mwanzia 4 ,
• Alice Norah Ladur 2 ,
• Peter Nandikove 5 ,
• Alphonce Uyara 6 ,
• Catherine Waigwe 7 ,
• Lucy Nyaga 1 ,
• Issak Bashir 8 ,
• Eunice Ndirangu 9 ,
• Carol Bedwell 2 ,
• Sarah Bar-Zeev 10 &
• Charles Ameh 2 , 11 , 12  

BMC Medical Education volume  24 , Article number:  534 ( 2024 ) Cite this article

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Midwifery education is under-invested in developing countries with limited opportunities for midwifery educators to improve/maintain their core professional competencies. To improve the quality of midwifery education and capacity for educators to update their competencies, a blended midwifery educator-specific continuous professional development (CPD) programme was designed with key stakeholders. This study evaluated the feasibility of this programme in Kenya and Nigeria.

This was a mixed methods intervention study using a concurrent nested design. 120 randomly selected midwifery educators from 81 pre-service training institutions were recruited. Educators completed four self-directed online learning (SDL) modules and three-day practical training of the blended CPD programme on teaching methods (theory and clinical skills), assessments, effective feedback and digital innovations in teaching and learning. Pre- and post-training knowledge using multiple choice questions in SDL; confidence (on a 0–4 Likert scale) and practical skills in preparing a teaching a plan and microteaching (against a checklist) were measured. Differences in knowledge, confidence and skills were analysed. Participants’ reaction to the programme (relevance and satisfaction assessed on a 0–4 Likert scale, what they liked and challenges) were collected. Key informant interviews with nursing and midwifery councils and institutions’ managers were conducted. Thematic framework analysis was conducted for qualitative data.

116 (96.7%) and 108 (90%) educators completed the SDL and practical components respectively. Mean knowledge scores in SDL modules improved from 52.4% (± 10.4) to 80.4% (± 8.1), preparing teaching plan median scores improved from 63.6% (IQR 45.5) to 81.8% (IQR 27.3), and confidence in applying selected pedagogy skills improved from 2.7 to 3.7, p  < 0.001. Participants rated the SDL and practical components of the programme high for relevance and satisfaction (median, 4 out of 4 for both). After training, 51.4% and 57.9% of the participants scored 75% or higher in preparing teaching plans and microteaching assessments. Country, training institution type or educator characteristics had no significant associations with overall competence in preparing teaching plans and microteaching ( p  > 0.05). Qualitatively, educators found the programme educative, flexible, convenient, motivating, and interactive for learning. Internet connectivity, computer technology, costs and time constraints were potential challenges to completing the programme.

The programme was feasible and effective in improving the knowledge and skills of educators for effective teaching/learning. For successful roll-out, policy framework for mandatory midwifery educator specific CPD programme is needed.

Peer Review reports

Introduction

Quality midwifery education underpins the provision of quality midwifery care and is vital for the health and well-being of women, infants, and families [ 1 ]. The recent State of the World’s Midwifery report (SoWMy) (2021) indicates that urgent investments are needed in midwifery, especially quality midwifery education, to improve health outcomes for women and neonates. Despite evidence to support midwifery, midwifery education and training is grossly underfunded in low- and middle-income countries (LMICs) with variation in the quality, content and duration of content between and within countries [ 2 ]. Barriers to achieving quality education are: inadequate content, lack of learning and teaching materials, insufficient and poorly trained educators and weak regulation, midwifery educators having no connection with clinical practice or opportunities for updating their knowledge or skills competencies [ 3 , 4 ].

The WHO, UNFPA, UNICEF and the International Confederation of Midwives’ (ICM) seven-step action plan to strengthen quality midwifery education, and ICM’s four pillars for midwives to achieve their potential emphasize strengthening midwifery faculty to teach students as a key priority [ 4 , 5 ]. Consequently, ICM recommends that (i) at least 50% of midwifery education curriculum should be practise-based with opportunities for clinical experience, (ii) midwifery faculty should use fair, valid and reliable formative and summative assessment methods to measure student performance and progress in learning and (iii) midwifery programmes have sufficient and up-to-date teaching and learning resources and technical support for virtual/distance learning to meet programme needs [ 6 ]. To achieve this, WHO’s Midwifery Educator Core Competencies and ICM’s Global Standards for Midwifery Education provide core competencies that midwifery educators must possess for effective practice [ 6 , 7 ]. The WHO’s global midwifery educator survey in 2018–2019 reported that fewer than half of the educators (46%) were trained or accredited as educators [ 5 ]. Educators are important determinants of quality graduates from midwifery programmes [ 7 ]. However, the survey identified that none of the educators felt confident in all of WHO’s midwifery educator core competencies [ 5 ]. Further evidence shows that many midwifery educators are more confident with theoretical classroom teaching than clinical teaching despite advances in teaching methods and have low confidence in facilitating online/virtual teaching and learning [ 4 , 8 , 9 ]. To remain competent, design and deliver competency-based curriculum and strengthen midwifery practice, ICM and WHO emphasize that midwifery faculty should engage in ongoing professional development as a midwifery practitioner, teacher/lecturer and leader [ 6 , 10 , 11 ]. However in many settings there is inadequate provision or access to faculty development opportunities [ 12 ].

Continuous professional development (CPD)

Continuous professional development has been defined as the means by which members of the profession maintain, improve and broaden their knowledge, expertise, and competence, and develop the personal and professional qualities required throughout their professional lives [ 13 ]. This can be achieved through multiple formal educational pathways based on the ICM Global Standards for Midwifery Education whilst incorporating the ICM Essential Competencies for Basic Midwifery Practice [ 6 , 14 ]. There are formal CPD activities where there is structured learning that often follows set curricula, usually approved by independent accreditation services or informal CPD that is usually self-directed learning. Participating in accredited CPD programmes is beneficial to the profession. A requirement of regular CPD renewal by a country to maintain licensure ensures an up-to-date, relevant nursing and midwifery workforce [ 15 ] and increases the legitimacy of CPD [ 16 ]. Structured learning (direct or distant), mandatory training, attending workshops and conferences, accredited college/university courses and trainings, research and peer review activities are opportunities for CPD [ 17 ]. Importantly, these CPD programmes are essential for safe, competent and effective practice that is essential to the universal health coverage (UHC) & maternal and newborn health SDGs agenda particularly in developing countries [ 18 , 19 ].

Whilst regulatory bodies and employers in many countries have requirements for midwives to complete CPD programmes and activities, these programmes and supporting activities are found to be ineffective if CPD is irrelevant to the practitioners’ practice setting, attended only because of monetary or non-monetary benefits, geared towards improving a skill for which there is no demonstrated need, and taken only to meet regulatory requirements rather than to close a competency gap [ 20 ]. In most LMICs, midwifery licensure is permanent, without obligation to demonstrate ongoing education or competence [ 15 ]. Consequently, CPD processes are not in place, and if in place, not fully utilised. A systematic review on CPD status in WHO regional office for Africa member states reported that nurses and midwives are required to attend formalised programmes delivered face-to-face or online, but only16 out of 46 (34.7%) member states had mandatory CPD programmes [ 15 ]. This underscores the need for designing regulator approved midwifery educator CPD programmes to improve the quality of midwifery education in LMICs.

Modes and approaches for delivery of CPD

Face-to-face contact is a common mode of delivery of CPD although mHealth is an emerging platform that increases access, particularly to nurses and midwives in rural areas [ 12 , 21 ]. Emerging platforms and organisations such as World Continuing Education Alliance (WCEA) offer mHealth learning opportunities in LMICs for skilled health personnel to access CPD resources that can improve health care provider knowledge and skills and potentially positively impact healthcare outcomes [ 22 ]. Although there is evidence of capacity building initiatives and CPD for midwifery educators in LMICs [ 23 ], these have been largely delivered as part of long duration (2-year) fellowship programmes and led by international organisations. In addition, these programmes have largely focused on curriculum design, leadership, management, research, project management and programme evaluation skills in health professions education with little on teaching and learning approaches and assessment for educators [ 24 , 25 , 26 ]. Successful CPD initiatives should be (i) accredited by the national regulatory bodies (Nursing and Midwifery Councils); (ii) multifaceted and provide different types of formal and informal learning opportunities and support; (iii) combine theory and clinical practice to develop the knowledge, skills and attitudes and (iv) must be adapted to fit the local context in which participants work and teach to ensure local ownership and sustainability of the initiatives [ 16 ].

Short competency-based blended trainings for educators improve their competence and confidence in delivering the quality midwifery teaching. However, systems for regular updates to sustain the competencies are lacking [ 27 , 28 ]. Evidence on effectiveness of the available CPD initiatives is limited. Even where these initiatives have been evaluated, this has largely focused on the outcomes of the programmes and little attention on the feasibility and sustainability of such programmes in low-resourced settings [ 24 , 25 , 29 ]. As part of global investments to improve the quality of midwifery education and training, Liverpool School of Tropical Medicine (LSTM) in collaboration with the UNFPA Headquarters Global Midwifery Programme and Kenya midwifery educators developed a blended midwifery educator CPD programme (described in detail in the methods section). The CPD programme modules in this programme are aligned to the WHO’s midwifery educators’ core competencies [ 7 ] and ICM essential competencies for midwifery practice [ 14 ]. The programme is also aligned to the nursing and midwifery practice national regulatory requirements of Nursing and Midwifery Councils in LMICs such as Kenya and Nigeria, and relevant national policy [ 30 , 31 , 32 ].This programme aimed at sustaining and improving the educators’ competencies in delivery of their teaching, assessments, mentoring and feedback to students. To promote uptake, there is need to test the relevance and practicability of the CPD programme. Feasibility studies are used to determine whether an intervention is appropriate for further testing, relevant and sustainable in answering the question – Can it work [ 33 ]? The key focus of these studies are acceptability of the intervention, resources and ability to manage and implement intervention (availability, requirements, sustainability), practicality, adaptation, integration into the system, limited efficacy testing of the intervention in controlled settings and preliminary evaluation of participant responses to the intervention [ 33 , 34 , 35 ].

This study evaluated the feasibility of the LSTM/UNFPA midwifery educator CPD programme using the Kirkpatrick’s model for evaluating training programmes [ 36 ]. This model is an effective tool with four levels for evaluating training programmes. Level 1 (Participants’ reaction to the programme experience) helps to understand how satisfying, engaging and relevant participants find the experience. Level 2 (Learning) measures the changes in knowledge, skills and confidence after training. Level 3 (Behaviour) measures the degree to which participants apply what they learned during training when they are back on job and this can be immediately and several months after the training. This level is critical as it can also reveal where participants might need help to transfer learning during the training to practice afterwards. Level 4 (Results) measures the degree to which targeted outcomes occur because of training. In this study, participants’ reaction to the programme – satisfaction and relevance of the programme to meeting their needs (level 1) and change in knowledge, confidence and skills after the CPD programme (level 2) were assessed. Also, user perspectives and barriers to implementing the CPD programme were explored.

Study design

This was a mixed methods intervention study using a concurrent nested/embedded/convergent design conducted in Kenya and Nigeria in May and June 2023. This was designed to evaluate the feasibility of the midwifery educator CPD programme. The goal was to obtain different but complementary data to better understand the CPD programme with the data collected from the same participants or similar target populations [ 37 ].

The quantitative component of the evaluation used a quasi-experimental pre-post and post-test only designs to evaluate the effectiveness of the blended CPD programme intervention among midwifery educators from mid-level training colleges and universities from the two countries. Pre and post evaluation of knowledge (online self-directed component) and skills (developing a teaching plan during the face-to-face component) was performed. Post intervention evaluation on programme satisfaction, relevance of CPD programme and microteaching sessions for educators was conducted.

The qualitative component of the evaluation included open-ended written responses from the midwifery educators and master trainers to describe what worked well (enablers), challenges/barriers experienced in the blended programme and key recommendations for improvement were collected. In addition, key informant interviews with the key stakeholders (nursing and midwifery councils and the national heads of training institutions) were conducted. Data on challenges anticipated in the scale up of the programme and measures to promote sustainability, access and uptake of the programme were collected from both educators and key stakeholders.

A mixed methods design was used for its strengths in (i) collecting the two types of data (quantitative and qualitative) simultaneously, during a single data collection phase, (ii) provided the study with the advantages of both quantitative and qualitative data and (iii) helped gain perspectives and contextual experiences from the different types of data or from different levels (educators, master trainers, heads of training institutions and nursing and midwifery councils) within the study [ 38 , 39 ].

The study was conducted in Kenya and Nigeria. Kenya has over 121 mid-level training colleges and universities offering nursing and midwifery training while Nigeria has about 300. Due to the vastness in Nigeria, representative government-owned nursing and midwifery training institutions were randomly selected from each of the six geo-political zones in the country and the Federal Capital Territory. Mid-level training colleges offer the integrated nursing and midwifery training at diploma level while universities offer integrated nursing and midwifery training at bachelor/master degree level in the two countries (three universities in Kenya offer midwifery training at bachelor level). All nurse-midwives and midwives trained at both levels are expected to possess ICM competencies to care for the woman and newborn. Midwifery educators in Kenya and Nigeria are required to have at least advanced diploma qualifications although years of clinical experience are not specified.

It is a mandatory requirement of the Nursing and Midwifery Councils for nurse/midwives and midwifery educators in both countries to demonstrate evidence of CPD for renewal of practising license in both countries [ 40 , 41 ]. A minimum of 20 CPD points (equivalent to 20 credit hours) is recommended annually for Kenya and 60 credit hours for Nigeria every three years. However, there are no specific midwifery educator CPD that incorporated both face-to-face and online modes of delivery, available for Kenya and Nigeria and indeed for many countries in the region. Nursing and midwifery educators are registered and licensed to practice nursing and midwifery while those from other disciplines who teach in the midwifery programme are qualified in the content they teach.

Study sites

In Kenya, a set of two mid-level colleges (Nairobi and Kakamega Kenya Medical Training Colleges (KMTCs) and two universities (Nairobi and Moi Universities), based on the geographical distribution of the training institutions were identified as CPD Centres of Excellence (COEs)/hubs. In Nigeria, two midwifery schools (Centre of Excellence for Midwifery and Medical Education, College of Nursing and Midwifery, Illorin, Kwara State and Centre of Excellence for Midwifery and Medical Education, School of Nursing Gwagwalada, Abuja, FCT) were identified. These centres were equipped with teaching and EmONC training equipment for the practical components of the CPD programme. The centres were selected based on the availability of spacious training labs/classes specific for skills training and storage of equipment and an emergency obstetrics and newborn care (EmONC) master trainer among the educators in the institution. They were designated as host centres for the capacity strengthening of educators in EmONC and teaching skills.

Intervention

Nursing and midwifery educators accessed and completed 20 h of free, self-directed online modules on the WCEA portal and face-to-face practical sessions in the CPD centres of excellence.

The design of the midwifery educator CPD programme

The design of the CPD modules was informed by the existing gap for professional development for midwifery educators in Kenya and other LMICs and the need for regular updates in knowledge and skills competencies in delivery of teaching [ 9 , 15 , 23 , 28 ]. Liverpool School of Tropical Medicine led the overall design of the nursing and midwifery educator CPD programme (see Fig.  1 for summarised steps taken in the design of the blended programme).

This was a two-part blended programme with a 20-hour self-directed online learning component (accessible through the WCEA platform at no cost) and a 3-day face-to-face component designed to cover theoretical and practical skills components respectively. The 20-hour self-directed online component had four 5-hour modules on reflection practice, teaching/learning theories and methods, student assessments and effective feedback and mentoring. These modules had pretest and post-test questions and were interactive with short videos, short quizzes within modules, links for further directed reading and resources to promote active learning. This online component is also available on the WCEA platform as a resource for other nurses and midwifery educators across the globe ( https://wcea.education/2022/05/05/midwifery-educator-cpd-programme/ ).

Practical aspects of competency-based teaching pedagogy, clinical teaching skills including selected EmONC skills, giving effective feedback, applying digital innovations in teaching and learning for educators and critical thinking and appraisal were delivered through a 3-day residential face-to-face component in designated CPD centres of excellence. Specific skills included: planning and preparing teaching sessions (lesson plans), teaching practical skills methodologies (lecture, simulation, scenario and role plays), selected EmONC skills, managing teaching and learning sessions, assessing students, providing effective feedback and mentoring and use of online applications such as Mentimeter and Kahoot in formative classroom assessment of learning. Selected EmONC skills delivered were shoulder dystocia, breech delivery, assisted vaginal delivery (vacuum assisted birth), managing hypovolemic shock and pre-eclampsia/eclampsia and newborn resuscitation. These were designed to reinforce the competencies of educators in using contemporary teaching pedagogies. The goal was to combine theory and practical aspects of effective teaching as well as provide high quality, evidence-based learning environment and support for students in midwifery education [ 4 ]. These modules integrated the ICM essential competencies for midwifery practice to provide a high quality, evidence-based learning environment for midwifery students. The pre and post tests form part of the CPD programme as a standard assessment of the educators.

As part of the design, this programme was piloted among 60 midwifery educators and regulators from 16 countries across Africa at the UNFPA funded Alliance to Improve Midwifery Education (AIME) Africa regional workshop in Nairobi in November 2022. They accessed and completed the self-directed online modules on the WCEA platform, participated in selected practical sessions, self-evaluated the programme and provided useful feedback for strengthening the modules.

The Nursing and Midwifery Councils of Kenya and Nigeria host the online CPD courses from individual or organisation entities on the WCEA portal. In addition, the Nursing Council of Kenya provides opportunities for self-reporting for various CPD events including accredited online CPD activities/programmes, skill development workshops, attending conferences and seminars, in-service short courses, practice-based research projects (as learner, principal investigator, principal author, or co-author) among others. In Nigeria, a certificate of attendance for Mandatory Continuing Professional Development Programme (MCPDP) is required as evidence for CPD during license renewal. However, the accredited CPD programmes specific for midwifery educators are not available in both countries and Africa region [ 15 , 42 ].

Midwifery educator CPD programme design stages

Participants and sample size

Bowen and colleagues suggest that many feasibility studies are designed to test an intervention in a limited way and such tests may be conducted in a convenience sample, with intermediate rather than final outcomes, with shorter follow-up periods, or with limited statistical power [ 34 ].

A convenience random sample across the two countries was used. Sample size calculations were performed using the formula for estimation of a proportion: a 95% confidence interval for estimation of a proportion can be estimated using the formula: \(p\pm 1.96\sqrt{\frac{\text{p}(1-\text{p})}{n}}\) The margin of error (d) is the second term in the equation. For calculation of the percentage change in competence detectable Stata’s power paired proportion function was used.

To achieve the desired level of low margin of error of 5% and a 90% power (value of proportion) to detect competence change after the training, a sample of 120 participants was required. Using the same sample to assess competence before and after training, so that the improvement in percentage competent can be derived and 2.5% are assessed as competent prior to training but not after training (regress), a 90% power would give a 12% improvement change in competence after the training.

A random sample of 120 educators (60 each from Kenya & Nigeria; 30 each from mid-level training colleges and universities) were invited to participate via an email invitation in the two components of the CPD programme (Fig.  2 ). Importantly, only participants who completed the self-directed online modules were eligible to progress to the face-to-face practical component.

Flow of participants in the CPD programme (SDL = self-directed online learning; F2F = face-to-face practical)

For qualitative interviews, eight key informant interviews were planned with a representative each from the Nursing and Midwifery Councils, mid-level training institutions’ management, university and midwifery associations in both countries. Interviews obtained data related to challenges anticipated in the scale up of the programme and measures to promote sustainability, access and uptake of the programme.

Participant recruitment

Only nursing and midwifery educators registered and licensed by the Nursing and Midwifery Councils were eligible and participated. This was because they can access the WCEA website with the self-directed online programme via the Nursing and Midwifery Councils’ websites, only accessible to registered and licensed nurses and midwives.

The recruitment process was facilitated through the central college management headquarters (for mid-level training colleges’ educators) and Nursing and Midwifery Councils (for university participants). Training institutions’ heads of nursing and midwifery departments were requested to share the contact details of all educators teaching midwifery modules, particularly the antepartum, intrapartum, postpartum and newborn care modules in the two countries. A list of 166 midwifery educators from 81 universities and mid-level training colleges was obtained through the Heads of the Department in the institutions.

The research lead, with the assistance by the co-investigator from Nigeria then randomly sampled 120 educators based on institution type and region for representativeness across the countries. Following the selection of participants, the two investigators shared the electronic detailed participant study information sheet and consent form to the potential participants one week before the start of the self-directed online modules. Clear guidance and emphasis on the conduct of the two-part program including completing the mandatory four self-directed online modules was provided. Due to the large number of eligible participants, the recruitment and consenting process was closed after reaching the first 30 participants consenting per institution type and region, with 1–2 educators per institution randomly recruited. This allowed as many institutions to be represented across the country as possible. Participants received a study information sheet and an auto-generated copy of the electronic consent form completed in their emails. Other opportunities for participating in the two-part programme were provided as appropriate for those who missed out. Only those who completed the four online modules were invited for the practical component. A WhatsApp community group for the recruited participants was formed for clarifications about the study, troubleshooting on challenges with online access and completion of the modules before and during the programme.

Self-directed online component

Upon consenting, the contact details of the educators from each level were shared with WCEA program director for generation of a unique identification code to access the self-directed online modules on the WCEA portal. Educators completed their baseline characteristics (demographic and academic) in the online platform just before the modules. Each self-directed online module was estimated to be completed in five hours. Only after completing a module was the participant allowed to progress to the next module. The modules were available for participants to complete at their own time/schedule. An autogenerated certificate of completion with the participant’s post-completion score was awarded as evidence of completing a module. Participants completed a set of 20 similar pretest and posttest multiple choice questions in each module for knowledge check. A dedicated staff from WCEA actively provided technical support for educators to register, access and complete the online modules. At the end of each module, participants completed a self-evaluation on a 5-point Likert scale for satisfaction (0 = very unsatisfied, 1 = unsatisfied, 2 = neutral, 3 = satisfied and 4 = very satisfied) and relevance of the modules (0 = very irrelevant, 1 = irrelevant, 2 = neutral, 3 = relevant and 4 = very relevant). This provided participants’ reactions to the different components of the modules on whether they met the individual educator’s development needs. In addition, participants responded to the open-ended questions at the end of the modules. These were on what they liked about the modules, challenges encountered in completing the modules and suggestions for improvement of the modules. A maximum period of two weeks was given for educators to complete the modules before progressing to the practical component.

Practical component

The practical component was delivered by a pool of 18 master trainers who received a 1-day orientation from the research lead before the training. The master trainers were a blend of experienced midwifery and obstetrics faculty in teaching and clinical practice actively engaged in facilitating EmONC trainings selected from Kenya and Nigeria. Four of these master trainers from Kenya participated in the delivery of both sets of trainings in Kenya and Nigeria.

Only educator participants who completed the self-directed online modules and certified were invited to participate in a 3-day residential practical component. Two separate classes were trained (mid-level and university level educators) per country by the same group of eight master trainers. The sessions were delivered through short interactive lectures; small group and plenary discussions; skills demonstrations/simulations and scenario teaching in small breakout groups; role plays and debrief sessions. Sessions on digital innovations in teaching and learning were live practical sessions with every participant using own laptop. Nursing and Midwifery Councils representatives and training institutions’ managers were invited to participate in both components of the programme.

Participant costs for participating in the two-part CPD programme were fully sponsored by the study. These were internet data for completing the self-directed online component and residential costs – transport, accommodation, and meals during the practical component.

Data collection

Self-directed online knowledge pretests and post-tests results, self-rated measures of satisfaction and relevance of the modules including what they liked about the modules, challenges encountered in accessing and completing the modules and suggestions for improvement data was extracted from the WCEA platform in Microsoft Excel.

On day 1 of the practical component, participants using their personal computers developed a teaching plan. On the last day (day 3), participants prepared a teaching plan and powerpoint presentation for the microteaching sessions. No teaching plan template from the trainers was provided to the participants before the training. However, they used formats from their institutions if available. A standard teaching plan template was provided at the end of the training.

The group of master trainers and participants were divided into groups for the microteaching sessions which formed part of the formative assessment. Each participant delivered a powerpoint presentation on a topic of interest (covered in the teaching plan) to the small group of 13–15 participants. This was followed by a structured session of constructive feedback that started with a self-reflection and assessment. This was followed by peer supportive and constructive feedback from the audience participants and faculty/master trainers identifying areas of effective practice and opportunities for further development. Each microteaching session lasted 10–15 min. Each of the microteaching session presentation and teaching plan were evaluated against a pre-determined electronic checklist by two designated faculty members independently during/immediately after the microteaching session. The checklist was adapted from LSTM’s microteaching assessment of the United Kingdom’s Higher Education Academy (HEA)’s Leading in Global Health Teaching (LIGHT) programme. The evaluation included preparing a teaching plan, managing a teaching and learning session using multiple interactive activities, designing and conducting formative assessments for learning using digital/online platforms, and giving effective feedback and critical appraisal. The master trainers received an orientation training on the scoring checklist by the lead researcher/corresponding author.

Self-rated confidence in different teaching pedagogy skills were evaluated before (on day 1) and after (day 3) the training on a 5-point Likert scale (0 = not at all confident, 1 = slightly confident, 2 = somewhat confident, 3 = quite confident and 4 = very confident). A satisfaction and relevance of practical component evaluation on a 5-point Likert scale was completed by the participants on an online designed form on day 3 after the microteaching sessions of the practical component. This form also had a similar qualitative survey with open-ended questions on what they liked about the practical component, challenges encountered in completing the practical component and suggestions for improvement of the component.

Using a semi-structured interview guide, six qualitative key informant interviews, each lasting about 30–45 min, were conducted by the lead researcher with the Nursing and Midwifery Councils focal persons and training institutions’ managers. These were audio recorded in English, anonymized, and deleted after transcription. These interviews were aimed at getting their perspectives on the programme design, anticipated barriers/enablers with the CPD programme and strategies for promoting uptake of the CPD programme. These interviews were considered adequate due to their information power (indicating that the more information the sample holds, relevant for the actual study, the lower amount of participants is needed) [ 43 ] and upon obtaining data saturation, considered the cornerstone of rigor in qualitative research [ 44 , 45 ].

Assessment of outcomes

Participants’ reaction to the programme (satisfaction and relevance) (Kirkpatrick level 1) was tested using the self-rated 5-point Likert scales. Change in knowledge, confidence and skills (Kirkpatrick level 2) was tested as follows: knowledge through 20 pretest and post-test multiple choice questions per module in the self-directed online modules; confidence in applying different pedagogy skills through the self-rated 5-point Likert scale; and teaching skills through the observed microteaching sessions using a checklist.

Reliability and validity of the data collection tools

The internal consistency (a measure of the reliability, generalizability or reproducibility of a test) of the Likert scales/tools assessing the relevance of the online and practical modules and satisfaction of educators with the two blended modules were tested using the Cronbach’s alpha statistic. The Cronbach’s alpha statistics for the four Likert scales/tools ranged from 0.835 to 0.928, all indicating acceptably good to excellent level of reliability [ 46 ]. Validity (which refers to the accuracy of a measure) of the Likert scales were tested using the Pearson correlation coefficient statistic. Obtained correlation values were compared to the critical values and p-values reported at 95% confidence intervals. All the scales were valid with obtained Pearson correlation coefficients reported − 0.1946, which were all greater than the critical values ( p  < 0.001) [ 46 ]. The semi-structured interview guides for the qualitative interviews with the training institutions’ managers and midwifery councils (regulators) were developed and reviewed by expert study team members with experience in qualitative research.

Data management and analysis

Data from the online/electronic tools was extracted in Microsoft Excel and exported to SPSS version 28 for cleaning and analysis. Normality of data was tested using the Kolmogorov-Smirnov test suitable for samples above 50. Proportions of educator characteristics in the two countries were calculated. Differences between the educator characteristics in the two countries were tested using chi-square tests (and Fishers-exact test for cells with counts of less than 5).

For self-rated relevance of CPD programme components and satisfaction with the programme on the 0–4 Likert scales, descriptive statistics were calculated (median scores and proportions). Results are presented as bar graphs and tables. Cronbach alpha and Pearson correlation coefficients were used to test the reliability and validity of the test items respectively.

Change in knowledge in online modules, confidence in pedagogy skills and preparing teaching plans among educators was assessed by comparing pre-training scores and post-training scores. Descriptive statistics are reported based on normality of data. Differences in the scores were analysed using the Wilcoxon signed ranks tests, a non-parametric equivalent of the paired t-test. Differences between educators scores in microteaching by country and institution type were performed by Mann-Whitney U test. Level of competence demonstrated in the teaching plan and microteaching skill was defined as the percentage of the desired characteristics present in the teaching plan and microteaching session, set at 75% and above. The proportion of participants that achieved the desired level of competence in their teaching plan and microteaching skill was calculated. Binary logistic regression models were used to assess for the strengths of associations between individual educator and institutional characteristics (age, gender, qualifications, length of time as educator, training institution and country) and the overall dichotomised competent score (proportion achieved competence in teaching plan and microteaching skills). P-values less than 0.05 at 95% confidence interval were considered statistically significant.

Preparation for qualitative data analysis involved a rigorous process of transcription of recorded interviews with key informants. In addition, online free text responses by midwifery educators on what worked well, challenges encountered, and recommendations were extracted in Microsoft Excel format and exported to Microsoft Word for data reduction (coding) and theme development. Qualitative data was analysed using thematic framework analysis by Braun and Clarke (2006) as it provides clear steps to follow, is flexible and uses a very structured process and enables transparency and team working [ 47 ]. Due to the small number of transcripts, computer assisted coding in Microsoft Word using the margin and comments tool were used. The six steps by Braun and Clarke in thematic analysis were conducted: (i) familiarising oneself with the data through transcription and reading transcripts, looking for recurring issues/inconsistencies and, identifying possible categories and sub-categories of data; (ii) generating initial codes – both deductive (using topic guides/research questions) and inductive coding (recurrent views, phrases, patterns from the data) was conducted for transparency; (iii) searching for themes by collating initial codes into potential sub-themes/themes; (iv) reviewing themes by generating a thematic map (code book) of the analysis; (v) defining and naming themes (ongoing analysis to refine the specifics of each sub-theme/theme, and the overall story the analysis tells); and (vi) writing findings/producing a report. Confidentiality was maintained by using pseudonyms for participant identification in the study. Trustworthiness was achieved by (i) respondent validation/check during the interviews for accurate data interpretation; (ii) using a criterion for thematic analysis; (iii) returning to the data repeatedly to check for accuracy in interpretation; (iv) quality checks and discussions with the study team with expertise in mixed methods research [ 39 , 47 ].

Integration of findings used the parallel-databases variant and are synthesised in the discussion section. In this common approach, two parallel strands of data are collected and analysed independently and are only brought together during interpretation. The two sets of independent results are then synthesized or compared during the discussion [ 39 ].

Quantitative findings

Midwifery educators’ characteristics.

A total of 116 (96.7%) and 108 (90.0%) educators from 81 institutions completed the self-directed online learning and practical component respectively from the two countries. There were no significant differences between countries in educators’ qualifications, when last taught a midwifery class and whether attended any CPD training in the preceding year before the study ( p  > 0.05). Overall, only 28.7% of the educators had a midwifery related CPD training in the preceding year before the study. Midwifery educator characteristics are outlined below (Table  1 ).

Change in knowledge

This was assessed in each of the four self-directed online modules. The results from ranked scores based on Wilcoxon signed ranks test showed significant improvements in educators’ knowledge in all the four online modules completed ( p  < 0.001). The highest mean score improvement was observed in students’ assessment module, 48.1% (SD ± 15.1) to 85.2% (SD ± 15.7), a 37.1% improvement. Improvements in knowledge in the other modules were as follows: reflective practice (27.6%), mentoring and giving effective feedback (27.4%) and teaching methods (19.2%). Overall knowledge score for all modules improved from 52.4% (SD ± 10.4) to 80.4 (SD ± 8.1), p  < 0.001 (Table  2 ).

Relevance of self-directed online modules

The internal consistency of each of the four modules was tested with Cronbach’s alpha. The overall Cronbach’s alpha for the four items was 0.837, a good and acceptable level of reliability. All the four modules assessed were valid with calculated Pearson correlation coefficient values greater than the critical value of 0.1946 ( p  < 0.001) at 95% confidence interval.

Educators from the two countries, on a scale of 0–4 rated the online modules as very relevant with a median score of 4 out of 4 (IQR 0) for each of the four modules: reflective practice, teaching methods, students’ assessments and mentoring and giving effective feedback. There were no ratings of 0, 1 and 2 for all the modules (Fig.  3 ).

Educators’ ratings of the relevance of self-directed online modules

Satisfaction with the self-directed online modules

The internal consistency of each of the eight items was tested with Cronbach’s alpha. The overall Cronbach’s alpha for the eight items was 0.928, an excellent level of reliability. All the eight items assessed were valid with their obtained Pearson correlation coefficient values greater than the critical value of 0.1946 ( p  < 0.001) at 95% confidence interval.

Each of the eight items rated on satisfaction had a median score of 4 out of 4 (IQR 0). Over 80% of the educators were very satisfied with the online modules’ content as presented in a logical format and informative. Also, the modules helped them to learn something new, updated their knowledge and the materials were useful and valuable for their practice. Over 70% were very satisfied with the modules as they helped them refresh their knowledge and skills with the links and activities embedded in the modules useful in adding to their learning. None of the educators were dissatisfied (rated 0 or 1) with the online modules (Table  3 ).

Change in confidence in different pedagogy skills

The internal consistency of each of the eight items assessed was tested with Cronbach’s alpha using the baseline data. The overall Cronbach’s alpha for the eight items was 0.893, a good level of reliability. All the eight items assessed were valid with their obtained Pearson correlation coefficient values greater than the critical value of 0.1946 ( p  < 0.001) at 95% confidence interval.

Changes in confidence before and after the training were compared using the Wilcoxon signed rank test, a parametric equivalent of the paired t-test when data is not normally distributed. The mean score of self-rated confidence of educators on a scale of 0–4 for all the eight skills significantly improved after the training from 2.73 (SD ± 0.68) to 3.74 (SD ± 0.34) ( p  < 0.001). Mean confidence was highest in facilitating a lecture (3.23, SD ± 0.8) and lowest on using digital innovations (Mentimeter) in formative assessment of teaching/learning (1.75, SD ± 1.15) before the training. These improved significantly after the training to 3.84 (SD ± 0.41) for facilitating a lecture and 3.50 (SD ± 0.63) for using digital innovations (Mentimeter) in formative assessment of teaching/learning, p  < 0.001. The mean confidence of educators was largely average before the training and significantly improved after the training in six skills ( p  < 0.001). These were designing learning outcomes using measurable Bloom’s taxonomy verbs, preparing a teaching plan, identifying relevant resources to enhance learning, facilitating a scenario teaching, facilitating a practical simulation/demonstration and giving effective feedback for learning (Table  4 ).

Preparing a teaching plan and microteaching skills

The overall median score in preparing a teaching plan was 63.6% (IQR 45.5) before the training and improved significantly to 81.8% (IQR 27.3) after the training, p  < 0.001. The median scores differed significantly by country before and after the training. Before the training, Kenyan educators had higher median scores (72.7%, IQR 27.3) compared to Nigeria counterparts (54.5%, IQR 36.4), p  < 0.001. After the training, Kenyan educators had significantly higher median scores (81.2%, IQR 18.2) than Nigerian counterparts (72.7%, IQR 18.2), p  = 0.024. However, there were no significant differences in the median scores between the training institutions before and after the training, p  > 0.05. For microteaching, the overall median score was 76.5% (IQR 29.4). There were no significant differences between countries and training institutions in the microteaching scores, p  > 0.05. Kenya educators (82.4%, IQR 29.4) had slightly higher scores than Nigeria (76.5%, IQR 29.4), p  = 0.78. Mid-level educators (79.4%, IQR 29.4) had slightly higher scores than university educators (76.5%, IQR 28.7), p  = 0.515 (Table  5 ).

The inter-rater reliability/agreement of the eight pairs of assessors in both countries were assessed by Cohen Kappa statistic. The Kappa statistics for the eight pairs ranged between 0.806 and 0.917, p  < 0.001, showing near perfect agreement between the pairs of assessors.

Association between independent educator and institutional characteristics and the microteaching skill scores

Categorised skills scores (≥ 75% mean score as competent) showed that 55 (51.4%) and 62 (57.9%) of the educators scored 75% or higher in the teaching plan preparation and microteaching skill assessments respectively. Logistic regression analysis showed that educator’s country, age, gender, qualifications, training institution type and length as educator were not significantly associated with the overall categorised teaching plan or microteaching scores ( p  > 0.05).

Relevance of the practical component

The internal consistency of each of the six skills items was tested with Cronbach’s alpha. The overall Cronbach’s alpha for the six items was 0.866, a good level of reliability. All the six skills items assessed were valid with their obtained Pearson correlation coefficient values greater than the critical value of 0.1946 ( p  < 0.001) at 95% confidence interval.

On a self-rating Likert scale of 0–4, the median score for each of the six skills assessed and trained was 4 out of a maximum of 4, indicating that the educators found the different pedagogy skills very relevant after the training. Over 80% of the educators rated the sessions on teaching plan (85.2%), scenario teaching (87.0%), simulation/demonstration teaching (82.4%) and giving effective feedback (85.2%) as very relevant. Over three-quarters (77.8%) of the educators rated the sessions on lecture teaching and use of digital innovations (Mentimeter) in assessment as very relevant (Fig.  4 ).

Relevance of the practical components

Satisfaction with the practical component

The internal consistency of each of the six skills items was tested with Cronbach’s alpha. The overall Cronbach’s alpha for the six items was 0.835, a good level of reliability. All the six skills items assessed were valid with their obtained Pearson correlation coefficient values greater than the critical value of 0.1946 ( p  < 0.001) at 95% confidence interval.

On a self-rating Likert scale of 0–4, the median score for each of the six skills assessed was 4 out of a maximum of 4, indicating that educators were very satisfied with the practical skills sessions. Over 70% of the educators were very satisfied with the sessions on giving effective feedback (79.6%), lecture teaching (75.9%), scenario and simulation teaching (73.1% each). Two-thirds of the educators (67.6%) were very satisfied with the digital innovations in teaching (use of Mentimeter) for formative assessment in teaching and learning. All educators were satisfied with the preparing of teaching plan in teaching and learning with the majority (63.0%) as very satisfied while the remaining 37.0% satisfied. None of the educators were dissatisfied with the practical component of the training (Fig.  5 ).

Satisfaction with practical skills

Qualitative findings

What educators liked about the self-directed online modules.

Educators from both levels and countries had similar views on the online component. These are broadly summarised under the sub-themes: (i) educative and relevant for practice, (ii) flexible and convenient learning and (iii) motivating, interesting and interactive.

Educative and relevant for practice

Educators reported the online modules as educative and informative and, improved their knowledge in teaching, assessments, reflective practice and providing effective feedback to students to promote learning as well as increasing their self-confidence and critical thinking skills. Besides, educators found the modules valuable and relevant for their professional growth and practice.

“The modules were well organized, they were relevant to my practice and met my expectations” university midwifery educator, Kenya. “The materials are very rich with current information to guide. Very informative & valuable to my professional growth” university midwifery educator, Nigeria.

Flexible and convenient learning

Educators reported that they could access and complete the online modules at their flexible and convenient time. This flexibility enhanced and stimulated them to complete the informative modules at their comfort times either at home or office without disruption to their schedules.

“(The modules) gave me ample time to read at my own pace and time without any hurry to understand the content well. They were well organised. Also, flexibility of learning and the access to materials was excellent” university midwifery educator, Kenya. “It is flexible and convenient. It empowers the learner to take ownership of the learning process. Learning is personalized” mid-level training college midwifery educator, Nigeria.

Motivating, interesting and interactive

Educators reported that the online modules were well structured, motivating, interesting and had components that promoted interaction for learning. For example, pretests, various quizzes within the modules and posttest questions and the added specific short extra reading segments promoted interaction and learning.

“The intermittent assessment questions. It helped maintain my focus” university midwifery educator, Nigeria . “Very interactive. They were very informative and extra reading assignments complemented the content” university midwifery educator, Kenya .

Challenges encountered with the self-directed online learning modules

Four sub-themes emerged that summarised the challenges experienced by midwifery educators in the two countries to access and complete the self-directed online modules. These are (i) network/internet connectivity, (ii) technology challenges, (iii) electricity power supply and power outages and, (iv) time constraints.

Network/internet connectivity

Network and internet connectivity difficulties and fluctuations was the commonest reported challenge in completing the self-directed online modules by educators from both countries. This affected the access, progress, downloading extra resources embedded within the modules and completing the integrated evaluations within the modules.

“Accessing the modules, problem with submitting forms and exams, had network problem” mid-level training college midwifery educator, Nigeria . “I kept going offline and I would have to restart every time. They were too internet dependent” university midwifery educator, Kenya.

Technology challenges

Technological challenges were observed as well as reported among educators from both countries. These ranged from poor access to emails due to forgotten email addresses, usernames or passwords, difficult access and navigation through the online modules, completing the matching questions that required dragging items, completing the evaluations and downloading certificates after completion of the modules.

“I am not very good with ICT, so I had issues using my laptop” mid-level training college midwifery educator, Nigeria. “Accessibility was difficult. I had to restart the process a number of times. The modules would sometimes take you back more than 20 slides which delayed the completion rate” university midwifery educator, Kenya.

Electricity power supply interruptions and fluctuations

Power interruptions, fluctuations and outages especially in Nigeria were cited as a challenge to complete the online modules. This delayed the completion of the modules as electric power was critical to access and complete the modules on either WCEA app on mobile phones or computers.

“The modules should not start from beginning whenever there is interrupted power supply” MLC midwifery educator, Nigeria. “Network failure due to interrupted power supply” university midwifery educator, Nigeria.

Time constraints

Although educators commented the flexibility with which to complete the online modules, time to complete the online modules was also cited as a challenge in both countries.

“It requires a lot of time, this is a challenge because I am also involved with other activities at the place of work which require my attention” university midwifery educator, Kenya.

What educators liked about the practical component

Educators written feedback on what they liked about the practical component of the CPD programme was categorised into the four sub-themes: new knowledge and relevant for practice; improved knowledge, skills and confidence to teach; enhanced participatory and active learning; individualised support in learning.

New knowledge and relevant for practice

The practical component provided new learning particularly on the use of digital platforms (Mentimeter and Kahoot) for formative assessment to evaluate learning during classroom teaching. In their integrated teaching using both online and face-to-face delivery, use of technology (Mentimeter and Kahoot) in classroom assessment was not a common practice as most of them had not heard about the available online platforms. They found Mentimeter (and Kahoot) to be interesting resources for formative assessments in class to facilitate teaching and learning. The techniques of giving effective feedback using the sandwich and ‘stop, start, continue’ methods were viewed to promote interaction between the educator and the learner for effective learning. Educators also acknowledged new knowledge and skills updates on EmONC relevant for their practice.

“Giving feedback, innovation of the online formative assessment, the teaching plan. I wish we would adapt them for daily application rather than the traditional teacher centered one.” Mid-level training college educator, Kenya . “(I liked) Everything, especially the technological innovations for assessment” Mid-level training college educator, Nigeria .

Improved knowledge, skills and confidence to teach

Educators reported that the practical sessions were interactive and engaging with good combination of theory and practice which facilitated learning. They reported that participating in the practical component enabled them to update and improve their knowledge, skills and confidence in planning and delivering theoretical and practical teaching using multiple methods. Similar improvements were reported on preparing and conducting students’ assessments and giving effective feedback to promote learning. On use of technology in formative assessments, the interactive practical sessions boosted the confidence of educators in using Mentimeter (and Kahoot) online platforms during classroom teaching.

“It helped build my confidence, had hands on practice on clinical skills and teaching skills, learnt about outdated practices and current evidence based clinical and teaching skills.” Mid-level training college educator, Nigeria . “They were very interesting especially the scenarios and skills. I was able to enhance my practical skills and technology in evaluating learning.” University midwifery educator, Kenya .

Enhanced participatory and active learning

The practical component complemented the self-directed online learning for educators. They highly commented and benefitted from the hands-on opportunities to actively engage through return demonstrations during the practical programme. This component also enabled them to brainstorm and contribute actively during the sessions. They highlighted that the practical component enhanced and reinforced learning through active participation in demonstrations, questions, group discussions and plenary sessions.

“This face-to-face module provided me with the opportunity to brainstorm with other educators, facilitators and resource persons. This will enhance my teaching skills.” Mid-level training college midwifery educator, Nigeria . “Interaction with facilitators who could clarify points that I had earlier not understood, interaction with other participants and was also able to learn from them.” University midwifery educator, Kenya .

Individualised support in learning

Educators received individualised peer support and learning during the practical component. They had opportunities within the small breakout groups for peer learning and one-to-one support from the facilitators to update and learn new knowledge and skills.

“A chance to get immediate feedback was availed by the presenters.” University midwifery educator, Kenya . “Facilitators were well informed and gave learners opportunity for return demonstration and support.” Mid-level training college midwifery educator, Kenya .

Challenges encountered with the practical component

Key challenges reported by the mixed group of educators and master trainers across the two countries include: inadequate time, computer technology challenges and poor internet connectivity for practical components.

Inadequate time

Although small breakout sessions were utilised to provide each educator with an opportunity to practice the skills, it was commonly reported that time was inadequate for skills demonstrations and return demonstrations by all educators. This was especially for areas educators had inadequate knowledge and new skills that were observed thus adequate time for teaching and repeat demonstrations for mastery was required. Similar observations were made by the master trainers who felt that some educators had never encountered some of the basic EmONC skills demonstrated or never practised and thus required a longer duration for familiarisation and practice.

“Time was short hence not enough to return demo” Mid-level training college midwifery educator, Kenya . “Some of the things were new and required more time for demonstration and practice.” Mid-level training college midwifery educator, Nigeria .

Computer technology challenges and poor internet connectivity for practical components

Some educators encountered technical difficulties in using computers during the practical component. In some cases, this was compounded by poor network/internet connectivity. This delayed completion of practical components requiring the use of computers including pretests, preparing teaching plans and presentations, post-tests and classroom demonstrations using digital innovations in teaching and learning. However, assistance was provided by the trainers as appropriate to those who needed technical support.

“(There were) technical challenges with use of computers for few participants.” Master trainer, Nigeria . “Slow internet can hinder smooth flow of sessions.” Master trainer, Kenya .

Key areas for additional support

For quality education and training, master trainers generally recommended that all educators should be trained and regularly supported in the basic EmONC course to strengthen their competencies for effective teaching of EmONC skills. Further support in computer technology use including basics in navigation around windows/programmes, formatting in Microsoft Office Word and Powerpoint, literature searching, and referencing were other critical components to be strengthened.

Perspectives from training institutions managers and midwifery regulators

Measures to ensure midwifery educators take specific cpds that have been designed to improve their teaching competencies.

Key informant interviews with the pre-service training institutions’ managers and nursing and midwifery councils from the two countries were conducted and revealed key strategies outlined below that should ensure access and completion of the blended CPD programme specific for educators’ teaching competencies.

Awareness creation, integrating programme into policy and performance appraisal

The aspect of online CPD was highlighted as a new concept in Nigeria. Due to this novelty, the country was reluctant to accredit many online CPD programmes for in-service and pre-service nursing and midwifery personnel. However, the regulatory Nursing and Midwifery Council of Nigeria had established monitoring mechanisms to evaluate its uptake to meet the definition of CPD and is still work in progress.

“For the online, it’s actually a relatively new concept, in fact because of monitoring and evaluation, we have struggled with accepting online CPDs… So, we’re struggling on how to develop a guideline for online CPDs. So, we’re now starting with the WCEA. So far, only the WCEA has that approval to provide CPD…We said let’s look at how this works out before we can extend it to other providers.” Nursing and Midwifery Council, Nigeria .

Both countries emphasized the need to create awareness of the CPD programme for midwifery educators and a policy framework for CPD. Regulators emphasized the need to have the CPD programme as mandatory for all midwifery educators through a policy directive. They suggested that the blended CPD programme should form a mandatory specified proportion of the content addressing their specific competencies. Besides, the training institution recommended that the programme should form part of the educator’s performance appraisal on a regular basis. Active monitoring systems were suggested to be in place to ensure compliance of participation and completion to acquire specific relevant competencies in pedagogy.

“…Ensure that educators take the particular modules before license renewal. Tie modules that are related to midwifery education to the educators and make them mandatory. Yes, we make it as a matter of policy that you should be taking these courses over and over again.” Nursing and Midwifery Council, Nigeria .

It was strongly suggested that attaching incentives as motivators to completing the programme would attract educators to complete the CPD programme. These incentives include certification, recognition for participation in curriculum reviews, national examination setting, facilitating national examinations, promotion and service and eligibility as trainers of trainers to colleagues.

“You attach a course, one training, you cannot guarantee that these courses will be taken. So we find a way to attach something to it. You must have evidence that you attended these programs. So once you attach something like that, they will all flock because there is an incentive to it. Because we say, as an educator, before you go after every examination to examine students, you must have taken these courses.” Nursing and Midwifery Council, Nigeria .

Internet connectivity

Training institutions’ managers suggested investments in internet connectivity for training institutions to support educators access and complete the self-directed online programme. This was also highlighted as a critical challenge for the online component by the educators in both countries.

“The issues of internet connectivity and I think we need to be proactive about it so that we have a way to constantly bring it to the forefront especially in our policies. But connectivity would be a major area to look at as people are using their money.” Mid-level training college manager, Kenya .

Anticipated challenges in the scale-up of the CPD programme

Key challenges anticipated in the roll-out and scale-up of the blended CPD programme were identified as inadequate skills of the educators in the use of information and communication technology during the practical component (including preparation of powerpoint presentations and completing tasks using a computer), and participant costs to attend the practical component (including participants’ residential costs and investments in proctor technology for ensuring academic integrity and monitoring and evaluation tool for educators’ compliance.) It was also emphasized that due to low remuneration of the educators, additional costs from their pocket to undertake the CPD could be a limiting factor for the intended faculty development initiatives. Other challenges included maintaining quality and academic integrity of the programme, potential bias in the selection of educators to attend future CPD programmes that is based on pre-existing relationships and ensuring an adequate pool of in-country trainers of trainers with midwifery competencies to deliver the practical component of the CPD programme.

There were strong suggestions that personal commitment by educators was required for personal and professional development. There were observations that educators sometimes completed the professional development programmes purely for relicensing and not necessarily for professional development. Regulators and institutional managers emphasized that educators need to understand the value of continuous professional development and create time to participate in the targeted CPD programmes to improve their competencies.

“We do advise our nurses, or we continue to inform them that taking these courses shouldn’t be tied to license renewal. It shouldn’t be tied to licence expiration or renewal of licences. You should continue to take these courses to develop yourself and not waiting until your licence expired before you take the courses. Yes, we actually try as much as possible to dissociate the renewal of licences with these courses.” Nursing and Midwifery Council, Nigeria .

Key results

Our study evaluated the feasibility of what the authors believe to be the first blended programme with online and face-to-face learning available in Africa, as a tool to reach midwifery educators in both urban and rural low-resource areas. In addition, our study is in line to an important call by WHO, UNFPA, UNICEF and ICM for an effective midwifery educator with formal preparation for teaching and engages in ongoing development as a midwifery practitioner, teacher/lecturer and leader [ 6 , 7 ]. Consequently, our intervention is part of investments for improving and strengthening the capacity of midwifery educators for quality and competent midwifery workforce as recommended by multiple global reports [ 4 , 5 , 11 ] and other publications [ 12 , 15 , 23 , 42 ]. Our study findings showed that the midwifery educators were very satisfied with the blended CPD programme. Educators rated the programme as highly relevant, educative, flexible, interesting and interactive, improved their knowledge, confidence and practical skills in their professional competencies for practice. Use of digital technology in teaching and students’ assessment was found to be an effective and innovative approach in facilitating teaching and learning. Key challenges experienced by educators included deficiencies in computer technology use, internet/network connectivity for online components, time constraints to complete the blended programme and isolated electric power outages and fluctuations which affected completion of the self-directed online components. Costs for participating and completing the programme, motivation, investments in information and communication technology, quality assurance and academic integrity were highlighted as critical components for the scale-up of the programme by institutional managers and training regulators. Establishment of a policy framework for educators to complete mandatory specific and relevant CPD was recommended for a successful roll-out in the countries.

Interpretation of our findings

Our study findings demonstrated that educators found the theoretical and practical content educative, informative and relevant to their practice. Recent evidence showed that midwifery educators had no/limited connection with clinical practice or opportunities for updating their knowledge or skills [ 15 , 42 ]. This underscores the value and importance of regular opportunities of CPD specific for educators to improve their professional competencies. It has provided these educators with a flexible educational model that allows them to continue working while developing their professional practice.

The use of a blended programme was beneficial as educators’ needs were met. It provided opportunities for educators to reflect, critically think, internalise and complement what was learned in the self-directed online component during the practical phase. This approach has been considered a means to adequately prepare midwifery faculty and improving national midwifery programmes in low-resource and remote settings [ 48 , 49 ]. Use of self-directed online platforms has emerged as a key strategy to improve access to CPD with flexibility and convenience as educators take responsibility for their own learning. Evidence suggests that the flexibility of net-based learning offers the midwifery educators a new and effective educational opportunity that they previously did not have [ 50 , 51 ]. A practical – based learning is important in pre-service education settings where the capacity of midwifery educators needs to be strengthened [ 52 , 53 ]. However, without continuous regular training, the midwives’ competence deteriorate and this in turn threaten the quality of pre-service midwifery education [ 52 , 54 ]. Implementation of this flexible blended educational model allows educators to continue working while developing their professional practice.

The quality of educators is an important factor affecting the quality of graduates from midwifery programmes to provide quality maternal and newborn health services [ 7 ]. Evidence suggests that many midwifery educators are more confident with theoretical classroom teaching than clinical practice teaching and that they also struggle to maintain their own midwifery clinical skills [ 4 , 5 ]. Our findings showed that the programme was effective, and educators improved their knowledge, confidence and skills in teaching, students’ assessment, effective feedback, reflective practice, mentoring and use of digital innovations in teaching and assessments. Our findings are similar to other related models of capacity building midwifery educators in other developing countries [ 24 , 50 , 53 , 55 , 56 , 57 ]. It is expected that educators will apply the learning in their planning for teaching, delivery of interactive and stimulatory teaching, monitoring learning through formative and summative assessments and mentoring their students into competent midwives. This is a pathway for accelerating the achievement of maternal and newborn health SDGs, universal health coverage, ending preventable maternal mortalities and every newborn action plan targets.

The value for CPD on educators’ knowledge, confidence and skills has been demonstrated with opportunities for improvement. Specific CPD targeted to relevant professional competencies is beneficial to the profession, quality of graduates for maternal and newborn health care and global targets. However, further investments in strengthening capacity of educators in EmONC skills and information and communication technology for effective teaching and learning is mandatory. Related challenges with individual technical capacity, technological deficiencies and infrastructure to support the technological advancement have been reported in other studies that have used a blended learning approach [ 58 ]. Resource constraints – financial and infrastructural (e.g. computers) as well as internet access are key challenges to participation in CPD activities especially the self-directed learning [ 16 ]. Designing self-directed modules that can be accessed and completed offline will increase access especially in poorly connected settings with electric power and network coverage.

Strengths and limitations

This study assessed the feasibility a blended midwifery educator CPD programme in low resource settings. This was conducted in a multi-country and multi-site context which provided opportunities for learning across the two countries, two levels of training institutions and specific in-country experiences [ 20 ]. The study served to improve awareness of the availability of the CPD programme so that (1) regulators can ensure that midwifery educators take this as part of mandatory CPD required for relicensing and (2) training institutions can plan to support their educators access/participate in the practical components of the programme after the study. It is a mandatory requirement of the Nursing and Midwifery Councils of Kenya and Nigeria for nurse/midwives and midwifery educators to demonstrate evidence of CPD for renewal of practising license [ 40 , 41 ]. The use of mixed methods research design with multiple evaluations was relevant to address the aims and objectives of the study and ensure methodological rigour, depth and scientific validity as recommended for good practice in designing pilot studies [ 37 , 38 ]. This also enhanced triangulation of findings and enabled the capturing of broad perspectives important in strengthening sustainable implementation of the blended CPD programme [ 39 ]. Preliminary findings were disseminated to participant stakeholders from Kenya and Nigeria at the knowledge management and learning event in Nairobi. This approach enhanced the credibility and trustworthiness of the final findings reported. We believe our study findings from different participants using multiple data collection methods are robust, transparent and trustworthy for generalization to other contexts [ 38 ].The self-directed learning component of the blended CPD programme is hosted on the WCEA platform which is accessible to healthcare professionals in over 60 countries in Africa, Asia and Middle East and accredited for continuous professional development (59). Although our sample size was small, it is sufficient, geographically representative for training institutions across the countries and acceptable for feasibility studies [ 34 ].

The additional cost analysis of implementing the blended midwifery educator CPD programme is relevant and key to the uptake, scale-up and sustainability of the programme but this was not conducted due to limited funding. Different CPD programme funding models exist. In Nigeria, educators are required to meet the costs for accessing and completing the CPD programme components, while in Kenya the cost of accessing the online component is minimal (internet access costs only) and the face-to-face component has to be funded. The cost of implementing the programme should be explored in future studies and optional models for sustainable funding explored with stakeholders.

Implications

Our findings show demand for the CPD programme. Regular continuous professional development could help to bridge the gap between theory and practice and improve the quality of teaching by midwifery educators. A blended CPD programme is effective in improving the teaching and clinical skills of midwifery educators and increasing their confidence in effective teaching. However, midwifery educators require motivation and close support (individual capacity, time, technological infrastructure and policy) if the blended CPD approach is to be mandatory and successfully implemented in resource limited settings. Besides, regular quality assurance modalities including review of content, monitoring and evaluation of uptake of the CPD programme should be undertaken to ensure that updated and relevant content is available.

For quality CPD programmes, hands-on teaching is more effective than didactic classroom teaching and should be used when feasible to transfer clinical skills. Distance education models (self-directed learning) in combination with short residential training and mentoring should be embraced to strengthen capacity strengthening of midwifery educators; and CPD programmes must consider the local context in which participants work and teach [ 16 , 23 ]. Evidence has shown that knowledge and clinical skills are retained for up to 12 months after training [ 54 ]. Taking the CPD programme annually will potentially maintain/improve knowledge, skills and practice by midwifery educators for quality teaching and learning leading to a competent midwifery workforce.

For quality midwifery education and practice, educators need contact with clinical practice to strengthen classroom teaching [ 6 , 7 ]. This will promote and enable students to acquire the skills, knowledge, and behaviours essential to become autonomous midwifery practitioners. Therefore, demonstrating relevant practical clinical CPD should be included in midwifery educator CPD policy. In addition, a business case by the CPD hubs on the sustainability of the face-to-face practical components in the centres is necessary. Stakeholder engagement on cost and sustainability are required as key policy components for the scale-up of the blended midwifery educator CPD programme for impact.

The blended CPD programme was relevant, acceptable and feasible to implement. Midwifery educators reacted positively to its content as they were very satisfied with the modules meeting their needs and rated the content as relevant to their practice. The programme also improved their knowledge, confidence and skills in teaching, students’ assessments and providing effective feedback for learning and using digital/technological innovations for effective teaching and learning. Investments in information and communication technology, quality assurance and academic integrity were highlighted as critical components for the scale-up of the programme. For successful and mandatory implementation of the specific midwifery educator CPD programme to enhance practice, a policy framework by midwifery training regulators is required by countries.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to the confidentiality of the data but are available from the corresponding author on request.

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Acknowledgements

The study was made possible through the financial support of the Johnson and Johnson Foundation for the three-year “Design, implementation and evaluation of Nursing/Midwifery CPD Educator Programme in Kenya” (2021 – 2023) and the Alliance to Improve Midwifery Education through UNFPA Headquarters. Special acknowledgement to nursing and midwifery educators from mid-level training colleges and universities in Kenya and Nigeria, Ministries of Health, Nursing Council of Kenya, Nursing and Midwifery Council of Nigeria, KMTC headquarters management who participated in the study. Also, we specially appreciate the World Continuing Education Alliance for the dedicated support with the online modules and expert trainers who participated in the delivery of the face-to-face training component: Aisha Hassan, Dr. Mojisola Ojibara, Dr. Eniola Risikat Kadir, Aminat Titi Kadir, Benson Milimo, Esther Ounza, Marthar Opisa, Millicent Kabiru, Sylvia Kimutai, Dr. Joyce Jebet, Dr. Steve Karangau, Dr. Moses Lagat and Dr. Evans Ogoti. Gratitude to Boslam Adacha and Roselynne Githinji for their dedicated support with data preparation for analysis and Dr. Sarah White for her statistical analysis expert guidance and support. Thank you also to Geeta Lal at UNFPA Headquarters. Lastly, the authors would like to acknowledge the special technical and logistical support provided by the LSTM – Kenya team (Onesmus Maina, Martin Eyinda, David Ndakalu, Diana Bitta, Esther Wekesa and Evans Koitaba) and LSTM Nigeria team (Dr. Michael Adeyemi and Deborah Charles) during the trainings.

The study was funded by the Johnson and Johnson Foundation as part of the three-year “Design, implementation and evaluation of Nursing/Midwifery CPD Educator Programme in Kenya” and the Alliance to Improve Midwifery Education through UNFPA. The Johnson and Johnson Foundation were not involved in the research – study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

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Contributions

DNS, SBZ and CA conceived the idea and designed the study protocol; DNS designed the online data collection tools/checklists/assessments, performed data extraction, cleaning, analysis and interpretation of the results, drafted the primary manuscript, reviewed and prepared it for publication; DNS, HM, LM, PN and AU conducted the training intervention, collected data and reviewed the drafts and final manuscript; AL participated in the design of the study, qualitative data analysis, interpretation of findings and reviewed draft manuscripts; CW, LN, IB, EN, CB and SBZ participated in the design of the study procedures and substantively reviewed the drafts and final manuscript. CA reviewed study procedures, data collection tools, provided oversight in investigation, analysis, interpretation and substantively reviewed the manuscript drafts. SBZ and CA obtained funding for the study. All the authors read and approved the final manuscript.

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Correspondence to Duncan N. Shikuku .

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Ethics review and approvals were obtained from Liverpool School of Tropical Medicine’s Research Ethics Committee (LSTM REC No. 23 − 004) and in-country ethical approvals from Kenya (MTRH/MU – IREC FAN 0004383; NACOSTI License No: NACOSTI/P/23/25498) and Nigeria (NHREC Approval Number NHREC/01/01/2007- 31/03/2023). Participation in the study was strictly voluntary and did not form part of the educator’s performance appraisals. Not taking part in the study did not disadvantage some educators who consented but missed out. Informed electronic and written consent was obtained from all participants. Unique participant codes were used for identification and all the data collection tools/forms and datasets were de-identified with no participant identifying information. All interviews were conducted at the offices of the respective stakeholders maintaining privacy during data collection process.

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Shikuku, D.N., Mohammed, H., Mwanzia, L. et al. Evaluation of the feasibility of a midwifery educator continuous professional development (CPD) programme in Kenya and Nigeria: a mixed methods study. BMC Med Educ 24 , 534 (2024). https://doi.org/10.1186/s12909-024-05524-w

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Predicting outcome after aneurysmal subarachnoid hemorrhage by exploitation of signal complexity: a prospective two-center cohort study

• Stefan Yu Bögli 1 ,
• Ihsane Olakorede 1 ,
• Michael Veldeman 3 ,
• Erta Beqiri 1 ,
• Miriam Weiss 3 , 4 ,
• Gerrit Alexander Schubert 3 , 4 ,
• Jan Folkard Willms 2 ,
• Emanuela Keller 2 &
• Peter Smielewski 1  

Critical Care volume  28 , Article number:  163 ( 2024 ) Cite this article

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Metrics details

Signal complexity (i.e. entropy) describes the level of order within a system. Low physiological signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular system leading to (or reflecting) autoregulation failure. Aneurysmal subarachnoid hemorrhage (aSAH) is followed by a cascade of complex systemic and cerebral sequelae. In aSAH, the value of entropy has not been established yet.

aSAH patients from 2 prospective cohorts (Zurich—derivation cohort, Aachen—validation cohort) were included. Multiscale Entropy (MSE) was estimated for arterial blood pressure, intracranial pressure, heart rate, and their derivatives, and compared to dichotomized (1–4 vs. 5–8) or ordinal outcome (GOSE—extended Glasgow Outcome Scale) at 12 months using uni- and multivariable (adjusted for age, World Federation of Neurological Surgeons grade, modified Fisher (mFisher) grade, delayed cerebral infarction), and ordinal methods (proportional odds logistic regression/sliding dichotomy). The multivariable logistic regression models were validated internally using bootstrapping and externally by assessing the calibration and discrimination.

A total of 330 (derivation: 241, validation: 89) aSAH patients were analyzed. Decreasing MSE was associated with a higher likelihood of unfavorable outcome independent of covariates and analysis method. The multivariable adjusted logistic regression models were well calibrated and only showed a slight decrease in discrimination when assessed in the validation cohort. The ordinal analysis revealed its effect to be linear. MSE remained valid when adjusting the outcome definition against the initial severity.

Conclusions

MSE metrics and thereby complexity of physiological signals are independent, internally and externally valid predictors of 12-month outcome. Incorporating high-frequency physiological data as part of clinical outcome prediction may enable precise, individualized outcome prediction. The results of this study warrant further investigation into the cause of the resulting complexity as well as its association to important and potentially preventable complications including vasospasm and delayed cerebral ischemia.

Introduction

Aneurysmal subarachnoid hemorrhage (aSAH) remains a serious disease with often poor prognosis even after successful securing of the aneurysm [ 1 ]. Patients who survive the initial hemorrhage remain at risk for developing secondary brain injury, such as delayed cerebral ischemia (DCI) [ 2 ]. DCI is a major cause of death and disability after aSAH [ 3 ]. It is the consequence of complex interactions of neuronal activity, cerebral and systemic hemodynamics, and feedback mechanisms—neurovascular (un)coupling, cerebral autoregulation, and CO 2 reactivity [ 2 ]. Dynamic changes of multiple interacting factors including cerebral vasospasm [ 4 ], inflammatory markers [ 5 ], oxygenation [ 6 ], blood pressure, and cardiac output [ 7 ] precede DCI occurrence. The paramount goal of neurocritical care is to predict, counteract or even prevent these secondary injuries to improve patients’ outcome. Consequently, the acute period following the hemorrhage is accompanied by extensive multimodal monitoring within a neurocritical care unit (NCCU) environment. The monitoring comprises aspects of cerebral physiology and hemodynamics (incl. intracranial pressure (ICP), cerebral perfusion pressure (CPP)) integrated with systemic physiological parameters (arterial blood pressure, cardiac output, heart rate (HR), oxygenation, and ventilation) [ 8 ].

Signal complexity (i.e. entropy) describes the level of apparent disorder within a system. Low signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular feedback/regulating system leading to (or reflecting) autoregulation failure [ 9 , 10 , 11 , 12 ] This, in turn, leaves the brain susceptible to secondary injury. Physiological systems are regulated by multiple, interacting, mechanisms leading to dynamically changing biosignals across different temporal scales. 14 Multiscale entropy (MSE), a version of signal complexity, estimates sample entropy over a range of increasingly downsampled (i.e. averaged) data [ 13 , 14 ] In comparison to sample entropy of a single scale MSE has the following benefits: 1. It allows for the evaluation of complex physiological systems that operate across different time scales; 2. It suppresses the impact of noise on the resulting metric. In 2012 Lu et al. described the association between decreased ICP signal complexity and unfavorable outcome after traumatic brain injury [ 9 ]. Zeiler et al. validated the concepts presented in a large multi-center cohort and extended the description to include other biosignals [ 15 ]. In aSAH, a metric related to signal complexity, heart rate variability, has shown, to a degree, an association with complications and unfavorable outcome [ 16 , 17 , 18 ]. However, other patho-physiological states such as sepsis decrease heart rate variability, whereby its use in clinical practice for prediction of specific complications has remained limited. We aimed to exploit the abundance of monitoring data acquired from each patient within an NCCU environment to assess the potential use of MSE as an outcome predictor after aSAH.

Materials and methods

The study was approved by the local ethics committees Zurich and Aachen and was in accordance with the ethical standards laid down in the 2013 Declaration of Helsinki for research involving human subjects. Informed consent was received before inclusion by the patient or their legal medical representative. Data from two prospective observational cohorts (University Hospital Zurich, Switzerland; the Rheinisch-Westfälische Technische Hochschule Aachen, Germany) was analyzed. The Zurich cohort was used as the derivation cohort to establish models and analyses, while the Aachen cohort was used for external validation.

Study population

For the Zurich cohort a total of 244 consecutively admitted adult patients with aSAH were recruited as part of the ICU Cockpit Prospective Cohort Study between 2016 and 2022. All of these received multimodal monitoring data acquisition and were consequently evaluated for inclusion. For the Aachen cohort a total of 316 consecutively admitted adult patients with aSAH were collected as part of a prospective cohort between 2014 and 2021. 102 of these received multimodal monitoring data acquisition and were consequently evaluated for inclusion. Inclusion criteria were: 1. aSAH due to an angiography confirmed ruptured aneurysm; 2. Admission to the NCCU and recording of high-resolution monitoring data. The only exclusion criterion was loss to follow up with missing 12-month outcome. Patients at both centers were treated according to the guidelines of the Neurocritical Care Society, American Heart Association guidelines, and the respective standard therapies of the two centers [ 19 , 20 ].

Data acquisition

The following relevant clinical data were prospectively included in the respective databases: Demographics, World Federation of Neurological Surgeons scale (WFNS)[ 21 ], modified Fisher Score (mFisher) [ 22 ], clinical course incl. aneurysm occlusion modality, occurrence of angiographic vasospasm (defined as narrowing of the vessels in neuroimaging independent of clinical symptoms), delayed cerebral infarction (DCI—infarction within neuroimaging not present on imaging performed within 24–48 h after aneurysm occlusion, and not attributable to other causes [ 23 ]), and outcome at 12 months (represented by the Glasgow Outcome Scale Extended—GOSE [ 24 ]). WFNS was evaluated after neurological resuscitation (i.e. after insertion of EVD and/or hematoma evacuation). In either center outcome was assessed during routine outpatient follow-up consultations or by contacting the patient, their next of kin, or caregiver by telephone in a structured interview. Physiological high-resolution data (at least 100 Hz—BP, ICP, HR) was collected in Zurich (Moberg Component Neuromonitoring Systems (CNS)—Moberg Research Inc, PA, USA) and Aachen (MPR2 logO Datalogger (Raumedic, Helmbrechts, Germany) or, after July 2018, Moberg Component Neuromonitoring Systems (CNS)—Moberg Research Inc, PA, USA). The data acquisition was started after admission to the respective NCCUs (after neurological resuscitation and generally after securing of the aneurysm) and stopped either when the patient was discharged to the ward or if invasive monitoring was deemed unnecessary.

Data preprocessing

The high-resolution (i.e. waveform) monitoring data from either center was transformed into an HDF5 format for streamlined analysis of the different formats. NCCU high-resolution waveform data contains, without exception, artifacts which are not representative of the patients’ physiology. Thus, raw waveform data was preprocessed using ICM + ® (Cambridge Enterprise Ltd, Cambridge, United Kingdom). Data was curated to remove artifacts using both manual and automated methods. The manual methods were applied to remove sections with arterial line failure (continuous reduction of the arterial blood pressure amplitude followed by flushing) and sections with manipulation or opening of the external ventricular drain (EVD—high frequency artefacts with or without sudden changes of ICP level). Automated methods for cleaning of arterial blood pressure were removal of pressure below 0 or above 300 mmHg and removal of sections with pulse amplitude of less than 15 mmHg. Automated methods for cleaning of ICP included removal of values below − 20 or above 200 mmHg, removal of sections with low amplitude (< 0.04 mmHg) corresponding to noise or EVD opening, and removal of values with a 95% Spectral edge frequency above 10 Hz (high-frequency noise). Only the remaining data (termed artifact-free) is used for further processing mitigating the effect of artificial, non-physiological sections.

Data was then processed to acquire 10 s averages of mean arterial blood pressure (ABP), systolic blood pressure (SBP), diastolic blood pressure (DBP), ICP, ICP amplitude (AMP), CPP (difference between ABP and ICP), and HR. Averaging, in effect, allowed for the removal of cardiac and respiratory components.

Multiscale entropy analysis

MSE was calculated as previously described based on the estimation of sample entropy [ 13 ]. Sample entropy describes the probability that matching sequences of length m will exhibit the same behavior (i.e. will also match) when extended by one point. It is estimated as the negative natural logarithm of the ratio between the number of m  + 1 length patterns to the corresponding m length patterns [ 25 ]. We estimated sample entropy using m = 2 and a tolerance of 0.15. MSE describes the process of calculating sample entropy over different time scales. A total of 20 scales starting from 1 up to 20 (produced by averaging based coarse graining i.e. Step 1—no averaging, step 2—averaging of 2 consecutive samples … step 20—averaging of 20 consecutive samples) covering the range of slow waves was used. MSE is the resulting area under the curve (AUC) of the plotted sample entropies. Higher values represent higher signal entropy/complexity. MSE was calculated for each of the 10 s biosignals resulting in the metrics MSE abp, MSE sbp, MSE dbp, MSE cpp, MSE hr, MSE icp, MSE amp.

Statistical analysis

Statistical analysis was performed in R Studio (R version 4.3.2— https://www.r-project.org/ —packages used: rstatix, pROC, boot, rms, MASS, ResourceSelection, predtools, brant ).

Descriptive variables are reported as counts/percentages or mean ± standard deviation. Distribution of the different continuous variables was assessed using the Shapiro–Wilk test. Equality of variances was tested using the Bartlett test or the Levene test. Different statistical methods were explored to assess the association between MSE and outcome. Both univariable as well as multivariable analysis (covariates: age, WFNS, mFisher, and occurrence of DCI) were performed. A significance level of p  < 0.05 was set due to the exploratory nature of the study and the different tests used for exploration. The Bonferroni corrected adjusted significance level would be p  = 0.00089.

Univariable: First the different MSE variables were compared to outcome as dichotomized by GOSE (1–4 vs. 5–8) using independent t-tests. To assess the overall diagnostic performance of the different MSE metrics, ROC curves (receiver operating characteristic curves) were plotted and evaluated by calculating the AUC (overall diagnostic performance) and its confidence interval (CI), and by estimating the optimal threshold (based on the Youden Index) to assess sensitivity, specificity, positive/negative predictive values, and accuracy. MSE metrics were then plotted against outcome as grouped into Dead/Vegetative (GOSE 1–2), Severe Disability (GOSE 3–4), Moderate Disability (GOSE 5–6), and Good Recovery (GOSE 7–8) and evaluated by analysis of variance (ANOVA).

Multivariable: Covariate adjusted logistic regression models were built with dichotomized GOSE (1–4 vs. 5–8) as endpoint to assess the independence of the MSE metrics as predictors of outcome. Effect of the metrics on model performance was described using the odds ratio (OR) including its CI. Diagnostic performance of the models was assessed using the AUC, the Nagelkerke R 2 (R 2 ), and the Brier Score. The effect of MSE metric inclusion was evaluated using the DeLong’s test comparing the different AUCs to a base model without the inclusion of MSE metrics. The established models were validated both internally as well as externally. Internal validation was performed by bootstrapping (1000 replications with replacement). During this process prediction models were derived from each bootstrap sample and applied to both the bootstrap and the original dataset allowing for the estimation of optimism (i.e. the difference between the AUC/R 2 /Brier scores of the results derived from the original vs. the different bootstrapping data sets). External validity was assessed by: 1. Evaluating the calibration (agreement between predicted and observed outcome described using its intercept and slope and assessed using the Hosmer–Lemeshow-goodness-of-fit test) 2. Evaluating the discrimination (AUC) when applying the derivation-dataset-based model to the validation cohort.

Ordinal multivariable: Due to the ordinal nature of the outcome score we additionally performed a proportional odds logistic regression and a sliding dichotomy analysis. Both, proportional odds logistic regression as well as sliding dichotomy allow for exploiting the range of the outcome scale by providing either the assessment of OR across different cutoffs or the assessment of baseline adjusted outcome definitions thereby increasing statistical power [ 26 ]. Proportional odds logistic regression adjusted for covariates was applied to the same scales as described above with moving cutoffs (Dead/Vegetative vs. Severe Disability, Severe Disability vs. Moderate Disability, Moderate Disability vs. Good Recovery) to assess the common odds ratio. The proportional odds assumption was tested using the Brant-Wald test. Lastly a sliding dichotomy approach was used to assess the importance of MSE metrics for a baseline severity adjusted outcome definition. For each patient, based on the baseline covariates (age, WFNS, mFisher score, and occurrence of DCI), a prognostic risk probability for unfavorable outcome was estimated. The resulting scores were then divided into 3 prognostic groups of roughly equal size corresponding to low, intermediate, and high likelihood of unfavorable outcome. For each prognostic group a separate cutoff was defined to dichotomize outcome into favorable and unfavorable, with the adjusted favorable outcome classified as:

GOSE 7–8: for the group with low likelihood for unfavorable outcome,

GOSE 5–8: for the group with intermediate likelihood for unfavorable outcome

GOSE 3–8: for the group with high likelihood for unfavorable outcome.

The resulting baseline severity adjusted outcome variable was then assessed against the MSE metrics using logistic regression. For both methods bootstrapping was applied for internal validation and to acquire the CI.

Secondary analysis

Three additional secondary analyses were performed to assess further aspects associated with the metric MSE based on the most promising metrics found. First: To assess, whether early outcome prediction using MSE is feasible, a secondary analysis was performed including only data acquired within the first 48 h after NCCU admission. Second: To evaluate whether MSE was associated with specific clinical aspects of the disease, values were assessed against clinical events. For this purpose, the following additional clinical parameters were extracted from the electronic patient records (occurrence of rebleeding, global cerebral edema, brain herniation, and seizures) and evaluated using t-tests. The raw metrics (ABP, HR, ICP) were assessed against the derived MSE metric to reveal possible intercorrelations. Third: The stability of the metric was assessed by evaluating the change when considering longer amounts of data within one patient (between 1 and 24 h) as well as when comparing the results of the metrics to the duration of the measurement in the whole cohort.

Patient characteristics and high-resolution data availability

Derivation cohort: 241 patients were included as part of the derivation cohort (3 were excluded due to loss to follow up). ABP/HR data was available in all patients, ICP data was available in 150 (62%) patients. The following amount of artefact free data was available: ABP—239 h/patient (total of 57′257 monitoring hours), HR—267 h/patient (total of 63′955 monitoring hours), ICP—205 h/patient (total of 30′778 monitoring hours). Validation cohort: 89 Patients were included as part of the derivation cohort (13 were excluded due to loss to follow up). ABP/HR data was available in 101 (99%), and ICP data was available in 73 (72%) patients. The following amount of artefact free data was available: ABP/HR—268 h/patient (total of 23′553 monitoring hours), ICP—290 h/patient (total of 21′169 monitoring hours). The median time between the initial hemorrhage and start of multimodality monitoring was 18 h in the derivation and 31 h in the validation cohort. The distributions of available data of the derivation and validation cohort can be found in the supplement including overall lengths of datasets as well as the density with respect to the timing from the initial hemorrhage (Additional file 1 : A). The highest density of data was available between day 3 and 14 after the initial hemorrhage in either center. Overall descriptions of physiology metrics can be found in the supplement (Additional file 1 : B). As this was a cohort undergoing active treatment, ICP within either cohort was mostly below 20 mmHg and ABP was around 90–100 mmHg. The clinical characteristics of the derivation and validation cohort can be found in Table  1 . The outcome at 12 months (assessed using GOSE) is shown in Fig.  1 .

Glasgow Outcome Scale Extended (GOSE) at 12 months ( A —derivation cohort; B —validation cohort)

Univariable analysis

In a first step, the different MSE metrics were evaluated against dichotomized outcome (GOSE 1–4 vs. GOSE 5–8). Overall, there was a difference between the outcome groups irrespective of the MSE metric. The specific p -values were: MSE abp (9.15 e-16), MSE sbp (3.81 e-19), MSE dbp (7.17 e-13), MSE cpp (1.77 e-6), MSE hr (6.65 e-19) MSE icp (4.70 e-10), MSE amp (6.72 e-6). The respective data is shown in form of boxplots in Fig.  2 panel A. The predictive value of the different metrics (ROC curves) is shown in Fig.  2 panel B. The specific AUC (CI) can be found in Table  2 . AUC ranged between 0.71 and 0.83. The highest values were found for MSE hr (AUC 0.83 (0.78–0.89)) and MSE sbp (AUC 0.82 (0.77–0.87)). The Youden Index was established for each MSE metric and used to calculate related metrics and accuracy (Table  2 ). The accuracy of the metrics was between 68% (MSE amp) and 77% (MSE hr) when using the Youden Index as a cutoff. To assess the MSE metrics against a higher granularity of outcome, they were then plotted against Dead/Vegetative, Severe Disability, Moderate Disability, and Good Recovery (Fig.  3 ). The respective p -values can be found in Table  3 . Overall, there were monotonic decreases of MSE with higher values found in more favorable outcomes.

MSE vs. Dichotomized Outcome. Panel A. The different MSE metrics are shown using boxplots comparing unfavourable (GOSE 1–4; pink) and favourable (5–8; green) outcome. An independent t-test was used for statistical analysis. Significant differences are shown using asterisks (*** =  p  < 0.001). The specific p -values were: MSE abo (9.15 e-16), MSE sbp (3.81 e-19), MSE dbp (7.17 e-13), MSE cpp (1.77 e-6), MSE hr (6.65 e-19) MSE icp (4.70 e-10), MSE amp (6.72 e-6). Panel B shows the corresponding ROC curves describing the predictive value of the different MSE scores

MSE vs. Ordinal Outcome. The different MSE metrics are shown using boxplots grouped by ordinal outcome: Dead/Vegetative (GOSE 1–2), Severe Disability (GOSE 3–4), Moderate Disability (GOSE 5–6), and Good Recovery (GOSE 7–8). The color coding ranges from intense pink (dead/vegetative) to intense green (good recovery). The respective p-values of the performed ANOVA can be found in Table  3

Multivariable analysis

To assess the independence of MSE metrics when corrected for covariates, multivariable logistic regression models were built. The results describing adjusted effect of the metric (OR), as well as the overall model performance (AUC, Nagelkerke R 2 , Brier Score) are shown in Table  4 (top panel). All MSE metrics remained independently associated with outcome with OR between 0.78 (MSE sbp) and 0.86 (MSE amp). AUCs ranged between 0.79 and 0.87 and R 2 between 0.32 and 0.51. Overall MSE sbp and MSE hr showed the highest effect and discriminatory value. MSE abp ( p  = 0.0068), MSE sbp ( p  = 0.0028), MSE dbp ( p  = 0.024), MSE cpp ( p  = 0.032), MSE hr ( p  = 0.003), MSE icp ( p  = 0.004), MSE amp ( p  = 0.029) all increased the AUC when compared to the model excluding the MSE metrics.

To assess internal validity, optimism-corrected performance estimates (AUC, Nagelkerke R 2 , and Brier Scores) were established using bootstrapping and are shown in Table  4 (middle panel). AUC optimism was at most 0.01 and R 2 optimism was between 0.01 and 0.02. To assess external validity, the models were applied to the validation cohort describing discriminatory performance (AUC) and calibration using the Hosmer–Lemeshow-goodness-of-fit test and the calibration intercepts and slopes (Table  4 , bottom panel). AUCs of the models when applied to the validation cohort were between 0.72 and 0.80. The Hosmer–Lemeshow-goodness-of-fit test found good model fits (test statistics were non-significant). The calibration slope was between 0.79 and 0.88 with the intercept being close to 0 in all cases.

Ordinal multivariable analysis

To assess the effect of MSE on the outcome in form of an ordinal scale, a proportional odds logistic regression model adjusted for covariates was produced. The proportional odds assumptions were met for all MSE metrics and the common OR and p -values of the proportional odds regression are shown in Table  5 . Common OR ranged between 0.79 and 0.88. Lastly, a sliding dichotomy approach was used to estimate the added value of MSE metrics when outcome was dichotomized based on individualized outcome prediction. The OR and p-values of the sliding dichotomy approach can be found in Table  5 . Overall, OR ranged between 0.82 and 0.91.

Different further aspects of MSE were evaluated within the secondary analysis. Firstly, to evaluate the potential for early outcome prediction, MSE based on only the data acquired within the first 48 h after NCCU admission was evaluated. MSE sbp, MSE hr, and MSE icp all remained associated with outcome both when considered within univariable as well as multivariable and ordinal analyses (Additional file 1 : C). After correction for the confounders (age, WFNS, mFisher, and occurrence of DCI) the OR of MSE sbp, MSE hr, and MSE icp were 0.87 (0.81–0.94), 0.86 (0.81–0.92), and 0.88 (0.83–0.95) per 1 step increase respectively. Overall, the models showed good discrimination with AUCs of 0.81 (0.75–0.85), 0.83 (0.77–0.87), 0.78 (0.70–0.85) for the multivariable logistic regression models including MSE sbp, MSE hr, and MSE icp respectively. The additional analyses can be found in Additional file 1 : C.

In a second step, the MSE metrics MSE sbp, MSE hr, and MSE icp were evaluated against different clinical aspects and events (Additional file 1 : D). Higher WFNS grade was associated with a decrease in all MSE metrics (MSE sbp: 25.4 ± 4.5 vs. 21.7 ± 4.2, p  < 0.001; MSE hr: 24.6 ± 4.8 vs. 20.0 ± 5.1, p  < 0.001; MSE icp: 21 ± 7 vs. 16 ± 6, p  < 0.001 for low vs. high WFNS respectively). While no MSE metric was associated with mFisher, MSE icp was higher in patients who received coiling (19 ± 7) as compared to clipping (16 ± 6, p  = 0.003). Conditions associated with or resulting from high ICP (cerebral edema, brain herniation) were associated with decreases in all three MSE metrics. Rebleeding and hydrocephalus on the other hand were only associated with a decrease in MSE sbp (rebleeding: 23.9 ± 4.6 vs. 20.0 ± 5.8, p  = 0.008; hydrocephalus: 25.2 ± 5.1 vs. 22.4 ± 4.3, p  < 0.001) and MSE hr (rebleeding: 23.1 ± 5.5 vs. 19.2 ± 6.7, p  = 0.032; hydrocephalus: 24.1 ± 5.8 vs. 21.0 ± 5.2, p  < 0.001) but not MSE icp. Similarly, DCI was associated with a decrease in MSE sbp (23.7 ± 5.0 vs. 22.5 ± 4.1, p  = 0.039) and MSE hr (22.8 ± 5.6 vs. 20.2 ± 5.3, p  < 0.001), but not MSE icp. The additional results can be found Additional file 1 : D.

Lastly, the stability of MSE was assess by evaluating the change when including longer amounts of data as well as when comparing the results of the metrics to the duration of the measurement (Additional file 1 : E). Starting from 3 to 6 h, stable MSE values could be found. There was no difference in absolute value of MSE compared to the duration of the recording when considering all patients.

Entropy, and in particular its multiscale version, MSE, builds on the previously described concept that physiological systems are regulated by multiple, interacting, mechanisms that collectively result in dynamically changing, irregular, fluctuations of biosignals across different temporal scales [ 14 ]. Lower MSE represents higher regularity of a system. While a “stable”, “regular” system would at first glance seemingly give the impression of being ‘healthy’, in reality, such a system is more rigid, with impaired capacity to counteract ever-present, random environmental triggers. The disease course of aSAH includes a variety of pathological processes necessitating continuous and rapid adjustments to equilibrate the system [ 27 ]. Patients that survive the initial hemorrhage remain at risk for developing secondary brain injury due to numerous pathophysiological cascades and complications. As part of the early brain injury, due to the initial hemorrhage, ICP rises either immediately (due to the volume of the bleeding itself [ 28 ]) or with a certain delay (i.e. resulting hydrocephalus [ 29 ] and/or brain edema). In the worst-case scenario, either one can lead to a relevant reduction in CPP. If the system fails to counteract this decrease in cerebral perfusion, this may lead to transient cerebral hypoxia or, ultimately, even infarction. Various injury cascades (upregulation of inflammatory pathways [ 30 ], coagulopathy [ 31 ]) further damage the system in case of cerebral hypoxia. A non-reactive system, represented consequently by low MSE, has been associated with higher pressure reactivity index (PRx) values supporting the notion that MSE represents the activity level of the physiological regulation systems, including cerebral blood flow autoregulation [ 9 , 15 ]. While both metrics clearly share similar mechanisms, to equate MSE with cerebrovascular reactivity (as assessed using PRx) would be an oversimplification, considering Lu et al. also showed that PRx loses its predictive value when MSE is added to multivariable regression models for outcome prediction after traumatic brain injury [ 9 ].

The main driver of secondary brain injury in aSAH is DCI [ 3 ]. Although, DCI is the consequence of complex interacting pathophysiological sequelae, a well described and potentially reversible cause is vasospasm, which describes the narrowing of cerebral vessels [ 4 ]. Depending on the severity of such narrowing, ischemia or even infarction can occur. In aSAH, intact cerebral autoregulation is essential to counteract such dynamic reductions of vessel diameter by automatically and immediately increasing CPP. Autoregulation failure is detrimental, as symptomatic treatment (using vasoactive medications or intra-arterial spasmolysis) can only be initiated with a significant delay.

In addition to cerebral complications, aSAH leads to various systemic and most prominently cardiac complications [ 32 , 33 ]. Both, myocardial ischemia and neurogenic stunned myocardium are found after aSAH leading to wall motion abnormalities and consequently reduced cardiac output. In the worst case, cardiac complications coincide with (or cause) pulmonary edema leading to further impairment of cardiac function and oxygenation [ 34 , 35 , 36 ]. Sufficient cardiac output is necessary to counteract impaired perfusion due to vasospasm. Cardiac output guided therapy is beneficial for managing cerebral oxygenation in patients with vasospasm [ 7 , 37 ]. Myocardial injury might indeed be one cause of decreased entropy with previous reports describing good discrimination when assessing heart rate variability after aSAH and diagnosis of neurocardiogenic injury [ 17 ]. To date, the most commonly evaluated complexity related metric in aSAH is heart rate variability due to its simple determination based on short electrocardiograms [ 18 ]. Considering these results, it is not surprising that MSE hr was a predictor of outcome. However, variability and entropy cannot be used interchangeably due to the large number of metrics used for description some of which depend on distribution while others depend on specific patterns.

In aSAH, due to the complex nature and course of disease, NCCU monitoring plays a pivotal role for guiding treatment. Neurocritical care bioinformatics allow for the acquisition, integration, and synchronization of the various biosignals within the same environment, thereby permitting exploitation of advanced data-driven methods for guiding treatment and outcome prediction [ 38 ]. Yet, computational methods remain underutilized and are generally not readily available for direct bedside implementation. Commonly, single (at times arithmetic mean or worse, single snapshot) targets are used for guiding treatment, ignoring the potential benefits of complex integration. The results of this study underline the potential of advanced analytical tools for improving the understanding of the complex pathophysiology of multimodal monitoring dynamics after aSAH. However, relevant limitations exist.

Limitations

Although this study included over 300 patients from 2 centres, they were all treated at highly specialized, high resource centers with relatively high patient volumes. On a similar note, patients underwent active treatment throughout their NCCU stay. It is unclear if, and to what extent interventions and complications are associated with changes in MSE. This is underlined by the results showing changes in MSE depending on various events. Either aspect, however, implies that the biosignals acquired do not represent the “natural” course of the disease, but patients undergoing active treatment. Due to the exploratory nature of this study, we did not explore time-trends of MSE, thus we cannot comment on the patterns of variability of MSE over the course of the NCCU stay (e.g. depending on intervention, medication or similar) or whether there were specific turning points (e.g. refractory ICP increase, DCI etc.). Overall, many complications and disease aspects were associated with changes in MSE and it is likely that the resulting metric represents a composite of various aspects. It is important to note that while the results were adjusted for the relevant and known outcome predictors age, clinical and imaging severity, and the important complication DCI, many other clinical variables are of importance when predicting outcome. To date, no multimodal monitoring based metric alone can or should replace clinical examination. However, metrics such as MSE should be seen as complementary allowing for additional physiology information.

This study provides the first description of MSE as an outcome predictor after aSAH. MSE metrics and thereby complexity of physiological signals are independent, internally and externally valid predictors of 12 month outcome after aSAH. MSE decreases monotonically with worse outcomes and remains a valid outcome predictor when adjusting the outcome definition to the initial severity of disease and age. The promising results of this study warrant further investigation into the cause of the resulting complexity as well as its association with important and potentially preventable complications (i.e. vasospasm and DCI). Of particular importance will be the assessment of time-trends and the evaluation of intraindividual episodes of decreased entropy and their association to specific events. Promising targets for such analysis are MSE sbp and MSE hr since neither requires continuous neuromonitoring and can therefore be applied to the whole aSAH population.

Data availability

The processed data is available upon reasonable request by the corresponding author.

Abbreviations

Aneurysmal subarachnoid hemorrhage

Mean arterial blood pressure

Intracranial pressure amplitude

Area under the curve

Confidence interval

Cerebral perfusion pressure

Diastolic blood pressure

Delayed cerebral ischemia

Glasgow outcome scale extended

Intracranial pressure

Modified Fisher scale

• Multiscale entropy

Neurocritical care unit

Negative predictive value

Positive predictive value

Receiver operating characteristic

Systolic blood pressure

World Federation of Neurosurgical Societies grading scale

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Acknowledgements

We wish to thank all the patients, family members and staff that participated in the study.

This project was made possible by the generous funding from the Swiss National Science Foundation (Grant Number: 210839) received by Stefan Yu Bögli. Erta Beqiri is supported by the Medical Research Council (Grant No.: MR N013433-1) and by the Gates Cambridge Scholarship.

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Stefan Yu Bögli, Ihsane Olakorede, Erta Beqiri & Peter Smielewski

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Department of Neurosurgery, RWTH Aachen University, Aachen, Germany

Michael Veldeman, Miriam Weiss & Gerrit Alexander Schubert

Department of Neurosurgery, Cantonal Hospital Aarau, Aarau, Switzerland

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Contributions

The study was conceived by SYB. SYB, EK, PS contributed to the study design. Data collection was performed by SYB, MV, MW, GAS, JFW and EK. Data processing and formal analysis were performed by SYB and IO. SYB wrote the first draft of the manuscript. All authors commented and revised the manuscript and all authors read and approved the final manuscript.

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Correspondence to Stefan Yu Bögli .

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The study was approved by the local ethics committees Zurich and Aachen and was in accordance with the ethical standards laid down in the 2013 Declaration of Helsinki for research involving human subjects. Informed consent was received before inclusion by the patient or their legal medical representative.

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PS declares financial interest in part of the licensing fees for ICM + , used in this study for data analysis. The other authors declare no financial or non-financial conflicts of interest.

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Supplementary Information

Additional file 1..

Supplementary data describing data coverage (A), physiological metrics (B), and the results of the secondary analyses (C-E).

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Identification and verification of a novel signature that combines cuproptosis-related genes with ferroptosis-related genes in osteoarthritis using bioinformatics analysis and experimental validation

• Baoqiang He 1 , 2   na1 ,
• Yehui Liao 1   na1 ,
• Minghao Tian 1 ,
• Chao Tang 1 ,
• Qiang Tang 1 ,
• Wenyang Zhou 1 ,
• Yebo Leng 1 , 3 &
• Dejun Zhong 1 , 2  

Arthritis Research & Therapy volume  26 , Article number:  100 ( 2024 ) Cite this article

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Exploring the pathogenesis of osteoarthritis (OA) is important for its prevention, diagnosis, and treatment. Therefore, we aimed to construct novel signature genes (c-FRGs) combining cuproptosis-related genes (CRGs) with ferroptosis-related genes (FRGs) to explore the pathogenesis of OA and aid in its treatment.

Materials and methods

Differentially expressed c-FRGs (c-FDEGs) were obtained using R software. Enrichment analysis was performed and a protein–protein interaction (PPI) network was constructed based on these c-FDEGs. Then, seven hub genes were screened. Three machine learning methods and verification experiments were used to identify four signature biomarkers from c-FDEGs, after which gene set enrichment analysis, gene set variation analysis, single-sample gene set enrichment analysis, immune function analysis, drug prediction, and ceRNA network analysis were performed based on these signature biomarkers. Subsequently, a disease model of OA was constructed using these biomarkers and validated on the GSE82107 dataset. Finally, we analyzed the distribution of the expression of these c-FDEGs in various cell populations.

A total of 63 FRGs were found to be closely associated with 11 CRGs, and 40 c-FDEGs were identified. Bioenrichment analysis showed that they were mainly associated with inflammation, external cellular stimulation, and autophagy. CDKN1A, FZD7, GABARAPL2, and SLC39A14 were identified as OA signature biomarkers, and their corresponding miRNAs and lncRNAs were predicted. Finally, scRNA-seq data analysis showed that the differentially expressed c-FRGs had significantly different expression distributions across the cell populations.

Four genes, namely CDKN1A, FZD7, GABARAPL2, and SLC39A14, are excellent biomarkers and prospective therapeutic targets for OA.

Introduction

As a degenerative disease that is difficult to reverse, osteoarthritis (OA) is often accompanied by joint pain, stiffness, joint swelling, restricted movement, and joint deformity, all of which seriously affect daily life activities. The structural changes in OA mainly involve the articular cartilage, subchondral bone, ligaments, capsule, synovium, and periarticular muscles [ 1 ]. The prevalence of OA is steadily rising due to the aging population and the obesity epidemic [ 1 ], and it has placed a significant burden on society [ 2 ]. Currently, the main treatments for OA remain nonsteroidal anti-inflammatory drugs (NSAIDs), pain medications, and joint replacement surgery. However, these treatments cannot reduce the incidence of the early stages of the disease [ 3 ], prevent further cartilage degeneration, or promote cartilage regeneration [ 4 ]. Therefore, further understanding of the pathophysiological mechanisms of OA could aid in the development of additional approaches for more effective diagnosis and treatment.

Ferroptosis is a specific type of programmed cell death driven by iron-dependent lipid peroxidation characterized by an abnormal accumulation of lipid reactive oxygen species (ROS) [ 5 , 6 ]. This programmed cell death was first reported and named by Dixon in 2012 [ 7 ]. Many studies have demonstrated that ferroptosis and the development of OA are closely related [ 8 , 9 , 10 , 11 ], and ferroptosis-related genes (FRGs) can help in the diagnosis of OA, as well as in predicting the immune status of patients with OA [ 12 , 13 ].

Copper is an indispensable trace element involved in a wide range of biological reactions. A small study reported elevated plasma and synovial copper concentrations in patients with OA compared with healthy controls [ 14 ], and another study also found that elevated levels of copper were associated with an increased risk of OA [ 15 ]. When the oxidizing capacity of copper ions in the body exceeds the antioxidant capacity of the body, joints can be destroyed [ 16 ]. Cuproptosis is a novel form of programmed cell death during which copper binds directly to the fatty acylated components of the tricarboxylic acid (TCA) cycle, thereby leading to an increase in toxic proteins and ultimately to cell death [ 17 ]. Ferroptosis is an iron-dependent programmed cell death caused by lipid peroxidation and the massive accumulation of reactive oxygen radicals[ 7 ]. Furthermore, copper and iron are closely related; copper is essential for iron absorption, meaning that copper deficiency or overload can impair the balance of iron metabolism [ 18 ]. When the balance of iron metabolism is disturbed, lipid peroxidation and oxidative stress may be induced, which in turn leads to ferroptosis and alters the expression of FRGs [ 19 , 20 , 21 ]. However, it has not yet been reported whether new signature genes (c-FRGs) combining cuproptosis-related genes (CRGs) with FRGs are beneficial for the diagnosis and treatment of OA.

In this study, we explored and analyzed the immune characteristics and biological functions of c-FRGs in patients with OA. In addition, we screened key ferroptosis-related biomarkers associated with cuproptosis in OA, constructed ceRNA networks, and predicted potential drugs for OA treatment. Our results suggest that c-FRGs may play an important role in the pathophysiological process of OA and provide new directions and ideas for OA research.

Data collection

The US National Center for Biotechnology Information (NCBI) gene expression omnibus (GEO) is the world's largest international public repository of high-throughput molecular information. Using “osteoarthritis” as a search term, the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ) was searched for appropriate datasets, and four datasets that met the study requirements were downloaded. These four datasets were GSE55235, GSE169077, GSE55457, and GSE55584, and the chip type was Affymetrix Human Genome U133a. We eventually obtained 25 normal human synovial samples and 32 OA synovial samples from the four datasets as samples for the follow-up study. To assess the accuracy of the analysis, the GSE82107 dataset was used as validation sets. In addition, the FRGs and CRGs were obtained from the published literature [ 6 ] and the FerrDb website ( http://www.zhounan.org/ferrdb/ ).

Extraction of c-FRGs and obtaining differentially expressed c-FRGs

Inter-batch differences between the four groups (GSE55235, GSE169077, GSE55457, and GSE55584) were eliminated using “affy” packet merging and the “sva” packet. We performed a Pearson correlation analysis of CRGs with FRGs to obtain particular FRGs (c-FRGs) that were highly correlated with CRGs (|r| > 0.5, adj. p value < 0.05). Differentially expressed genes (DEGs) and differentially expressed c-FRGs (c-FDEGs) were obtained using the “limma” package ( p value < 0.05).

Function enrichment analysis and protein–protein interaction (PPI) networks

To acquire disease-related biological functions and signaling pathways, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of c-FDEGs were performed. GO enrichment analysis was used to describe the molecular functions (MF), cellular components (CC), and biological processes (BP) involved in the target genes ( p -value < 0.05). KEGG analysis was used to systematically analyze gene functions and to link genomic information and functional information ( p -value < 0.05). The results of the gene set enrichment analysis (GSEA), GO enrichment analysis, and KEGG pathway enrichment analysis of the c-FDEGs were visualized using the “ClusterProfiler” package in R. GSEA was based on the gene set (h. all. v7. 5. 1. symbols. gmt), which was downloaded from MSigDB ( https://www.gsea-msigdb.org/gsea/msigdb/index.jsp ). The STRING database is used for searching interactions between known proteins and for predicting interactions between proteins and is one of the most data-rich and widely used databases for studying protein interactions. Protein interaction analysis was performed on all c-FDEGs through the STRING website ( https://string-db.org/ ) and visualized using Cytoscape software. The degree values of the c-FDEGs were calculated using the cytoHubba plugin, and the top seven genes were used as hub genes.

Acquisition and validation of biomarkers

In this research, we used three machine learning algorithms: support vector machine recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO) regression analysis, and random forest analysis (RF). First, we used the “e1071” R package for SVM-RFE analysis. Subsequently, the “glmnet” package was used to perform LASSO regression analysis. In addition, RF was conducted adopting the “randomForest” package, and genes with importance > 1 were retained. The crossover genes obtained by these three methods were regarded as prospective biomarkers for OA.

Construction and validation of disease model (nomogram)

In addition, a nomogram based on characteristic biomarkers was structured using the “rms” R package. Receiver operating characteristic (ROC) analysis was performed on the biomarkers and the obtained models, and the area under the curve (AUC) values were calculated with the “pROC” package to assess the diagnostic efficacy of the potential biomarkers. In addition, the four biomarkers and the obtained disease nomogram were validated on the GSE82107 validation set.

Collection of clinical samples

Synovial tissue collection and all experimental procedures were approved by the Institutional Review Board of the Affiliated Hospital of Southwest Medical University (KY2023293) in accordance with the guidelines of the Chinese Health Sciences Administration, and written informed consent was obtained from the donors. Synovial tissue from the suprapatellar bursa was collected as OA synovial samples and normal control samples, respectively, from patients who met the American College of Rheumatology criteria for the diagnosis of primary symptomatic knee OA (n=6; men: 3, women: 3; age: 55-70 years) and from patients who underwent trauma-related lower extremity amputation but did not have osteoarthritis or rheumatoid arthritis (n=6; men: 4, women: 2; age: 50-67 years). All samples were collected within two hours of arthroplasty or lower limb amputation and were divided into two portions for subsequent immunofluorescence staining and western blot experiments, respectively.

Immunofluorescence staining

Mid-sagittal sections (4-μm thick) of paraffin-embedded clinical synovial specimens were incubated for 1 hour at room temperature, after which the slides were closed with 10% bovine serum (Solarbio, Beijing, China) for 1 hour at room temperature and then incubated with primary antibodies for 16 hours at 4°C. The fluorescent dye was incubated for 1 hour at room temperature, and the slides were subsequently sealed with DAPI Sealer (Thermo Fisher Scientific, Waltham, MA, USA).

Western blot analysis

Protein lysates were extracted from synovial tissue samples and lysed with RIPA buffer to extract the total protein. After conducting a BCA protein assay (Beyotime, Shanghai, China), 5 × sample buffer (Servicebio, Wuhan, China) was added to the protein lysates. Equal amounts of lysates were then separated through SDS-PAGE and transferred to a 0.22-um PVDF microporous membrane (Merck Millipore, Burlington, MA, USA). Next, the membrane was sealed with 5% skimmed milk and incubated with the primary antibody for 16 hours at 4°C, after which the membrane was incubated with the secondary antibody for 60 minutes at room temperature. Target protein bands were visualized using FDbio-Dura ECL (Merck Millipore, Burlington, MA, USA). The antibodies used for immunofluorescence and western blot in this study were as follows: rabbit anti-FZD7 (Cat. #: DF8657, 1:1,000; AFFBIOTECH, USA), rabbit anti-SLC39A14 (ZIP14) (Cat. #: 26540-1-AP, 1:1,000, Proteintech, Rosemont, IL, USA), rabbit anti-CDKN1A (p21) (Cat. #: 2947T, 1:1,000, Cell Signaling Technology, Danvers, MA, USA), rabbit anti-GABARAPL2 (Cat. #: 14256T, 1:1,000, Cell Signaling Technology), anti-GAPDH (Cat. #: 60004 -1-Ig, 1:1,000, Proteintech, USA), and species-matched HRP-conjugated secondary antibody (Cat. #: SA00001-1, 1:1,000; Proteintech, USA).

ssGSEA, GSEA, and GSVA for differentially expressed c-FRGs

The gene set (h.all.v2022.1.Hs.symbols.gmt), a collection of 50 symbolic gene sets for humans, was downloaded from MSigDB ( https://www.gsea-msigdb.org/gsea/msigdb/index.jsp ). The 50 symbolic human gene set scores were calculated for each sample using single-sample GSEA (ssGSEA), and differential scores were obtained for the non-OA and OA groups. The “corrplot” package was used to perform correlation analysis between biomarkers and ssGSEA gene sets. Next, GSEA and gene set variation analysis (GSVA) were performed for the four biomarkers, the seven hub genes, and the remaining 29 differentially expressed c-FRGs.

Prediction of therapeutic drugs

The gene–drug interaction database (DGIDB, http://www.dgidb.org ) [ 22 ] can help researchers annotate known pharmacogenetic interactions and potential drug accessibility–related genes. In this research, we used DGIdb to filter potential drugs targeted to biomarkers so as to identify new therapeutic targets. The obtained drug prediction results were also imported into Cytoscape (v3.9.1) software for visualization.

Construction of ceRNA network

The miRanda, TargetScan, and miRDB databases are authoritative databases used for predicting miRNA–target gene regulatory relationships, and spongeScan is a web tool designed for sequence-based complementary detection of miRNA-binding elements in lncRNA sequences. Biomarkers of common mRNA–miRNA interactions were identified in miRanda ( http://www.microrna.org/microrna/home.do ), TargetScan ( http://www.targetscan.org ), and miRDB ( https://mirdb.org ). miRNA–lncRNA interactions were obtained from Spongescan ( http://spongescan.rc.ufl.edu ). These interactions were imported into Cytoscape to construct the ceRNA network.

Immune infiltration analysis

To better understand the changes that occur in the immune system of patients with OA, the “CIBERSORT” R package was used to describe the basic expression of 22 immune cell subtypes. Next, we analyzed the correlation between potential biomarkers, hub genes, and the 22 immune cell types.

scRNA‑seq analysis

The OA synovial scRNA-seq data (GSE152805) from three patients were obtained from the GEO database and analyzed using the "Seurat" software package. To ensure high quality of the data, we removed low-quality cells (cells with <200 or >10,000 detected genes, >10% of mitochondrial genes, or <300 or >30,000 expressed genes) and low-expressed genes (any gene expressed in less than three cells). We used the "NormalizeData" function to normalize the gene expression of the included cells and performed principal component analysis (PCA) using the top 2000 highly variable genes to extract the top 12 principal components (PCs), which were retained for further analysis using the "FindVariableFeatures" function. To perform unsupervised and unbiased clustering of cell subpopulations, the "FindNeighbors," "FindClusters" (resolution = 0.6), and "RunUMAP" functions were applied. Each cell cluster was manually annotated according to the cell-specific marker genes. These marker genes were obtained from previously published literature[ 23 , 24 ] and from the CellMarker website ( http://xteam.xbio.top/CellMarker/ ). Finally, we used CellChat (1.6.1) for the inference and analysis of cell–cell communication.

Figure 1 describes the entire flow of the study.

A graphical flowchart of the study design

Extracting c-FRGs and obtaining differentially expressed c-FRGs

After merging the GSE55235, GSE169077, GSE55457, and GSE55584 datasets (Table 1 ), the newly produced gene expression matrices were subjected to normalization and presented as bidimensional PCA plots prior to and after processing (Fig. 2 a and b), indicating that the final sample data obtained were plausible. A total of 63 FRGs were found to be closely associated with 11 CRGs (Fig. 2 e, Supplementary Table 1 ). A total of 4167 DEGs were determined and identified (Fig. 2 c). There were a total of 40 c-FDEGs, including 13 upregulated genes and 27 downregulated genes (Fig. 2 d, Supplementary Table 2 ). The correlations between the 40 c-FDEGs are shown in Supplementary Figure 1 . The expression patterns of the 40 c-FDEGs are visualized in the heatmap (Fig. 2 f).

Extraction of particular ferroptosis-related genes (c-FRGs) and obtainment of differentially expressed c-FRGs (c-FDEGs). a, b Two-dimensional PCA cluster plot of GSE55235, GSE169077, GSE55457, and GSE55584 datasets before and after normalization. c Volcano plot of DEGs. Red spots represent upregulated genes and green spots represent downregulated genes. d Overall expression landscape of c-FRGs in osteoarthritis (OA). * P < 0.05; ** P < 0.01; *** P < 0. 001. OA represents the OA group and Normal represents the normal control group. e Extraction of c-FDEGs. f  Heatmap of c-FDEGs. The redder the color, the higher the expression; conversely, the bluer the color, the lower the expression

Function enrichment analysis

Understanding the signaling pathways, biological processes, and interrelationships involved in c-FDEGs is of great importance in revealing the pathogenesis of OA. GO enrichment analysis showed that c-FDEGs were significantly enriched in the regulation of the inflammatory response (BP), the positive regulation of cellular catabolic process (BP), the autophagosome membrane (CC), the recycling endosome (CC), and NF-κB binding (MF) (Fig. 3 a, Supplementary Table 3 ). KEGG pathway analysis showed that these c-FDEGs were mainly involved in the IL-17 signaling pathway, NOD-like receptor signaling pathway, HIF-1 signaling pathway, and TNF signaling pathway (Fig. 3 b, Supplementary Table 4 ). GSEA suggested that the development of OA may be associated with hypoxia, MYC targets v2, the P53 pathway, the inflammatory response, TNFα signaling via NF-κB, the interferon-α response, and peroxisome (Fig. 3 c and d).

Functional analyses: ( a ) Gene Ontology (GO) enrichment analysis showed that the 40 c-FDEGs were significantly enriched in the regulation of the inflammatory response, the positive regulation of cellular catabolic process, the autophagosome membrane, the recycling endosome, and NF-κB binding. b Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that these c-FDEGs were mainly involved in the IL-17 signaling pathway, NOD-like receptor signaling pathway, HIF-1 signaling pathway, and TNF signaling pathway. c Gene set enrichment analysis (GSEA) in the normal control group and (d) GSEA in the OA group based on the core set of 50 human genes suggested that the development of OA may be associated with hypoxia, MYC targets v2, the P53 pathway, the inflammatory response, TNFα signaling via NF-κB, the interferon-α response, and peroxisome

Building PPI networks

The String database is a database that can be used to retrieve interactions between known and predicted proteins. To explore the interactions between each c-FDEG, all of the abovementioned 40 c-FDEGs were imported into the STRING database. The PPI network of c-FDEGs after deleting isolated c-FDEGs and adding the six related CRGs (without CDKN2A) is shown in Fig. 4 a. The cytoHubba plugin in Cytoscape software was used to calculate the degree values (degrees) of the top seven genes (IL6, IL1B, RELA, PTGS2, EGFR, CDKN2A, and SOCS1) as the PPI network’s hub genes (Fig. 4 b).

Protein–protein interaction (PPI) network and core gene screening. a PPI network constructed from 40 c-FDEGs; red triangles represent c-FDEGs, green triangles represent CRGs that are closely related to them, and the correlation between c-FDEGs and CRGs is indicated by dashed lines. b The top seven core gene interaction networks calculated using the cytoHubba plugin: the darker the color, the more powerful the critical degree

Machine learning algorithm–based biomarker screening for patients with OA

In this study, 40 c-FDEGs were further analyzed for potential biomarkers associated with OA using multiple machine learning methods. SVM-RFE analysis showed that the model containing 24 genes had the best accuracy (Fig. 5 a). LASSO regression analysis showed that the model was able to accurately predict OA when λ was equal to 12. Thus, the LASSO regression model generated 12 candidate genes (Fig. 5 b). We retained the candidate biomarkers with RF results importance > 1 (Fig. 5 c). Lastly, the results of these three methods were integrated, and CDKN1A, FZD7, GABARAPL2, and SLC39A14 were identified as the final potential biomarkers for OA (Fig. 5 d).

Machine learning-based potential biomarker screening. a SVM-RFE model with the optimal error rate when the number of signature genes was 58. b LASSO regression model. c Random forest model and the top 20 genes in terms of importance. d The final biomarkers screened using three machine learning algorithms

Experimental validation of four biomarkers

To validate the results of the bioinformatics analysis, we collected OA samples (n=6) and normal group samples (n=6), respectively, and performed western blot analysis and immunofluorescence staining (Fig. 6 ). Both results were consistent with the bioinformatics analysis, i.e., higher expression of FZD7 and GABARAPL2 and lower expression of CDKN1A (p21) and SLC39A14 (ZIP14) in the OA group compared with the normal group.

Experimental validation of four biomarkers. a Representative immunofluorescence staining images of the four biomarker proteins (p21, FZD7, GABARAPL2, and ZIP14) in the normal and OA groups, with nuclei stained blue with 4’,6-diamidino-2-phenylindole. Scale bar = 25 µm. b Semi-quantitative analysis of mean fluorescence intensity of the four biomarker proteins in the normal and OA groups ( n = 6). (c, d) Representative western blotting and statistical comparisons of the four biomarker proteins in the normal and OA groups ( n = 6). * p < 0.05, ** p < 0.01, all by independent samples t-test

To better capture the function of the four biomarkers in OA, GSEA, GSVA, and ssGSEA were conducted on each of the above biomarkers (Fig. 7 ). The ssGSEA showed that the OA group was significantly enriched in Notch signaling, interferon alpha (IFN-α) response, the Wnt/β-catenin pathway, bile acid metabolism, and peroxisome, while the non-OA group was mainly enriched in TNFα signaling via NF-κB, hypoxia, MYC targets v2, the P53 pathway, the inflammatory response, PI3K AKT mTOR signaling, and IL6 JAK STAT3 signaling (Fig. 7 i). Correlation analysis showed that CDKN1A and SLC39A14 were significantly positively correlated with the gene sets of hypoxia, TNF-α signaling via NF-κB, the P53 pathway, and mTORC1 signaling. Meanwhile, GABARAPL2 and FZD7 showed significant negative correlations with the gene sets of TNF-α signaling via NF-κB, PI3K AKT mTOR signaling, and mTORC1 signaling (Fig. 7 j). The single-gene GSEA results for the seven hub genes are shown in Supplementary Figure 2 (a–g). The remaining 29 differentially expressed c-FRGs are shown in Supplementary Figure 3 .

GSEA, GSVA, and ssGSEA results of four potential biomarkers. a–d Single-gene GSEA-KEGG pathway analysis of four potential biomarkers. We show the top six pathways with the smallest p -value. e–h High- and low-expression groups based on the expression levels of each potential biomarker combined with gene set variation analysis (GSVA). Red means the pathway is significantly upregulated, green means the pathway is significantly downregulated, and gray means the pathway is not statistically significant. i ssGSEA of OA and normal controls based on the h.all.v7.5.1.symbols.gmt gene set. * P < 0.05; ** P < 0.01; *** P < 0. 001. Treat represents the OA group, and control represents the normal group. (j) Correlation of four biomarkers with 50 human symbolic gene sets from the h.all.v7.5.1.symbols.gmt gene set

Using the above four biomarkers, a disease nomogram was constructed. The AUC values of the individual genes CDKN1A, FZD7, GABARAPL2, and SLC39A4 were 0.931, 0.879, 0.989, and 0.850, respectively, all of which were greater than 0.85 (Fig. 8 a), further indicating that the above genes had good diagnostic ability (Fig. 8 b). The AUC value of this model was 0. 996, which was significantly greater than the AUC value of individual biomarkers, indicating that this model had good diagnostic value (Fig. 8 c and d). To verify whether the above model is diagnostically meaningful, validation was performed on the GSE8207 dataset. The results showed that the AUC values of the four biomarkers were all greater than 0.7, and the AUC value of the model was 1 for the validation set (Fig. 8 f). These results indicate that CDKN1A, FZD7, GABARAPL2, and SLC39A4 are effective disease biomarkers for OA and that the model has high diagnostic efficacy.

Validation of four biomarkers. a ROC analysis of the four biomarkers. b ROC analysis of the disease model constructed from the four biomarkers. c, d Nomograms based on the disease model: we obtained the corresponding scores for each genetic variable, drew a vertical line above the “points” axis, summed the scores of all predictor variables, found the final value on the “total score” axis, and then drew a straight line on the “probability” axis to determine the patient’s risk of osteoarthritis. e, f Validation of the disease model and four biomarkers on the GSE82107 validation dataset

Construction of drug prediction network and lncRNA–miRNA–mRNA network

The corresponding drug prediction network was constructed using the database based on the four biomarkers (Supplementary Figure 4 a). The predicted drugs were celecoxib, paclitaxel, carboplatin, acetaminophen, vantictumab, and nortriptyline. Based on the competitive endogenous RNA hypothesis, an lncRNA–miRNA–mRNA competitive endogenous RNA (ceRNA) network was constructed to explore the function of lncRNA as an miRNA sponge in OA. We obtained 150 target miRNAs based on these biomarkers. Then, 48 lncRNAs were obtained based on these miRNA predictions. The four biomarkers with predicted miRNAs and lncRNAs were introduced into Cytoscape, and constituted a ceRNA network containing 48 lncRNA nodes, 150 miRNA nodes, 4 hub gene nodes, and 198 edges (Supplementary Figure 4 b).

The immune microenvironment plays an important role in the progression of OA. Therefore, with the help of CIBERSORT, we summarized the differences in immune infiltration by immune cell subpopulations between OA samples and non-OA tissues (Fig 9 a). The OA samples contained a higher proportion of memory B cells, M0 macrophages, M2 macrophages, and resting mast cells than the control group, as well as a lower proportion of resting CD4 memory T cells and activated mast cells. Correlation analysis showed that activated mast cells showed positive correlations with PTGS2, IL6, and IL1B, and the correlation between activated mast cells and PTGS2 was the highest (0. 686) (Fig. 9 b). There were positive correlations between IL1B, PTGS2, and M1 macrophages, resting CD4 memory T cells and PTGS2, and regulatory T cells (Tregs) and RELA. There were significant negative correlations between follicular helper T cells and RELA, as well as between plasma cells and SLC39A14 (Fig. 9 c and d).

Results of immune infiltration by CIBERSORTx. a Bar plot showing the composition of 22 types of immune cells. b Box plot presenting the difference of immune infiltration of 22 types of immune cells. Treat represents the OA group, and Control represents the normal group. c Heatmap showing the correlation between seven hub genes and 22 types of immune cells in osteoarthritis. d Correlation between the four biomarkers and 22 types of immune cells in osteoarthritis

Single‑cell analysis

The scRNA-seq data from three OA synovial samples were obtained from the GSE152805 dataset. After initial quality control, we finally retained 10,194 cells for cell annotation (Supplementary Figure 5 ). The top 2000 highly variable genes were selected for further analysis (Supplementary Figure 5 b). We used the "RunPCA" function to reduce the dimensionality and obtained 14 clusters (Supplementary Figures 6 d and e); the first five DEGs of each cluster are shown in Supplementary Table 5 . Later, we performed cellular annotation using marker genes and annotated seven cell populations: fibroblasts (77.7%), macrophages (8.8%), dendritic cells (DCs) (3.6%), endothelial cells (ECs) (3.5%), smooth muscle cells (SMCs) (3.4%), T cells (1.8%), and mast cells (1.2%) (Fig. 10 a). Next, we performed differential gene expression analysis on these seven cell populations to verify the accuracy of the cell annotation (Fig. 10 b). Figures 10 c and d show the distribution and expression of seven hub genes and four biomarker genes in different cell populations. We found that 11 c-FRGs were significantly different in macrophages, DCs, mast cells, and NK cells. For example, IL1B, PTGS2, and SLC39A4 were significantly highly expressed in some cells, whereas they were significantly less expressed, or even absent, in other cells. We used CellChat to identify differentially overexpressed ligands and receptors for each cell population. In total, 254 significant ligand–receptor pairs were detected, which were further classified into 62 signaling pathways (Table 2 ). We found that the immune cells interacted weakly with each other; however, the non-immune cells had extensive communication interactions with other cells and were involved in various paracrine and autocrine signaling interactions (Fig. 10 e to g).

Analysis of single-cell RNA sequencing data from three OA synovial samples. a UMAP plot of scRNA-seq showing unsupervised clusters colored according to putative cell types among a total of 10,194 cells in OA synovial samples. The percentages of total acquired cells were as follows: 77.7% fibroblasts, 8.8% macrophages, 3.6% dendritic cells (DCs), 3.5% endothelial cells (ECs), 3.4% smooth muscle cells (SMCs), 1.8% T cells, and 1.2% mast cells. b Heatmap depicting the expression levels of the top five marker genes among seven detected cell clusters. c, d UMAP plots and violin plots showing the expression of the selected seven hub c-FRGs and four potential biomarkers for each cell type. e Interaction net count plot of OA synovial cells. The thicker the line, the greater the number of interactions. f Interaction weight plot of synovial cells. The thicker the line, the stronger the interaction weights/strength between the two cell types. g Detailed network of cell–cell interactions among seven cell subsets

Copper is an irreplaceable trace metal element that participates in a variety of biological processes. When copper ions accumulate in excess, they eventually lead to cell death, and this new form of programmed cell death is known as cuproptosis [ 17 ]. A recent report has demonstrated that copper levels are significantly higher in the serum and synovial tissue of patients with OA than in controls [ 14 ]. Evidence from several studies suggests that the development of OA is closely related to ferroptosis in articular cartilage and synovium [ 25 , 26 , 27 , 28 , 29 ], and that OA can be treated to some extent by modulation of ferroptosis [ 29 , 30 ]. Additionally, previous studies have reported that copper and iron levels are closely correlated with each other in patients with OA [ 14 , 15 , 31 ].

In this study, we identified transcriptional alterations and expression of c-FRGs based on the GSE55235, GSE169077, GSE55457, and GSE55584 datasets. Forty c-FDEGs were identified in 63 c-FRGs. GO enrichment analysis showed that these 40 c-FDEGs were mainly associated with the inflammatory response, cellular response to external stimulus, and autophagy. The KEGG enrichment analysis showed that these genes were highly enriched mainly in the IL-17 signaling pathway, NOD-like receptor signaling pathway, HIF-1 signaling pathway, and TNFα signaling pathway. For both OA and non-OA groups, GSEA and ssGSEA showed that OA was mainly associated with the enrichments in Notch signaling, adipogenesis, xenobiotic metabolism, fatty acid metabolism, peroxisome, TNFα signaling via NF-κB, the inflammatory response, PI3K AKT mTOR signaling, and IL6 JAK STAT3 signaling. This indicates that the mechanism of OA development is closely related to fatty acid metabolism, the inflammatory response, immune regulation, and cell adhesion.

We analyzed the PPI results using the cytoHubba plugin in Cytoscape, revealing seven key c-FDEGs, including IL6, IL1B, RELA, PTGS2, EGFR, CDKN2A, and SOCS1. GSEA and GSVA of the seven genes revealed that IL6, IL1B, RELA, PTGS2, SOCS1, and EGFR were closely associated with inflammation, immune regulation, extracellular matrix, and cell adhesion pathways in OA, which is consistent with previous findings [ 32 , 33 ]. Interestingly, we also found that they were closely associated with lipid metabolism and fatty acid metabolism in OA. Considering that increased iron accumulation, free radical production, fatty acid supply, and increased lipid peroxidation are key to the induction of ferroptosis [ 5 , 6 , 7 ], it is possible that they affect the development of OA by regulating lipid metabolism and fatty acid metabolism, which affects ferroptosis; however, this needs to be further investigated.

Notably, CDKN2A acts as both a cuproptosis-related gene and a ferroptosis-related gene simultaneously. CDKN2A is often considered an important gene in cellular senescence and aging [ 34 ], and it is used as a molecular marker of cellular senescence [ 35 ]. Our study showed that CDKN2A expression was higher in patients with OA, suggesting that CDKN2A may contribute to the development of OA by affecting cellular senescence and thereby promoting the development of OA.

This is the first study to use the new signature genes combining CRGs with FRGs to reveal the pathogenesis of OA and aid in its treatment. We executed three machine learning algorithms using the 40 c-FDEGs mentioned above and eventually identified four biomarkers: CDKN1A, FZD7, GABARAPL2, and SLC39A14.

Frizzled7 (FZD7) is known to be a receptor of the Wnt pathway. Fzl receptors are usually classified as belonging to the G protein receptor family and are rich in cysteine, which can directly interact with Wnt proteins and thus activate downstream responses [ 36 , 37 , 38 ]. Numerous studies have shown that excessive upregulation or downregulation of Wnt signaling pathways in OA may lead to cartilage damage and ultimately accelerate the progression of OA. Therefore, it is necessary and important to maintain a balance in the biological activity of Wnt-related pathways [ 39 , 40 , 41 ]. In the present study, FZD7 was significantly increased in the OA group compared with the non-OA group. Therefore, we speculate that an excess of FZD7 may lead to the abnormal activation of Wnt-related pathways and ultimately accelerate the development of OA.

ZIP14 (SLC39A14) is a metal transporter [ 42 ] that affects the metabolic balance of zinc, manganese, iron, copper, and other metals [ 43 ]. For example, ZIP14 can transport non-transferrin-bound iron (NTBI) [ 44 ] and ZIP14 can transport cadmium and manganese through metal/bicarbonate symbiotic activity [ 45 ]. It has been shown that OA is closely related to the metabolic balance of metals such as iron, copper, and manganese [ 14 , 15 , 31 , 46 , 47 , 48 ]. In this study, we found that ZIP14 was greatly reduced in the OA group compared with the non-OA group. Furthermore, scRNA-seq analysis showed that the distribution of SLC39A14 in OA patients varied significantly among cell populations, with low or even no expression in some cells, which is likely to disrupt the metal metabolic balance in the joints and eventually cause the accumulation of metals such as iron and copper. Therefore, SLC39A14 (ZIP14) may be a very important therapeutic target for OA treatment in the future.

ssGSEA showed that CDKN1A significantly positively correlated with TNF-α signaling via NF-κB, the TGF-β signaling pathway, hypoxia, the P53 pathway, apoptosis, mTORC1 signaling, and other gene sets, suggesting that CDKN1A may affect OA by regulating inflammation, apoptosis, and hypoxia. Although both the CDKN1A and GABARAPL2 genes have been reported previously [ 49 , 50 , 51 , 52 ], their relationship with ferroptosis and cuproptosis in OA is not yet known. This suggests that these genes may be targets not only for immunotherapy, inflammation, and autophagy but also for the treatment of cuproptosis and ferroptosis in OA. Notably, we found that melphalan, paclitaxel, vinblastine, and vantictumab may serve as potential drugs for the treatment of OA. Previous studies have reported that they act therapeutically by regulating CDKN1A or FZD7 [ 53 , 54 , 55 ], thus affecting processes such as the cell cycle, cell proliferation, and apoptosis, which also validates our prediction. We then constructed a disease model of OA based on these four biomarkers that could significantly improve our ability to recognize OA at an early stage. Thus, our findings suggest that CDKN1A, FZD7, GABARAPL2, and SLC39A14 are excellent disease biomarkers and potential therapeutic targets for OA, and the disease model constructed based on them has good diagnostic efficacy.

Recently, an increasing number of studies have shown that immune cell infiltration is essential for OA onset and development and cartilage repair [ 56 , 57 , 58 ]. Our study showed a close relationship between the seven hub genes and immune cells. Notably, there were significant positive correlations of PTGS2, IL6, and IL1B with M1 macrophages and activated mast cells. Previous studies have demonstrated that the activation of macrophages and mast cells may significantly accelerate the progression of OA [ 58 , 59 , 60 ]. Therefore, we speculate that PTGS2, IL6, and IL1B may influence the onset and progression of OA by regulating these cells. Interestingly, scRNA-seq analysis further revealed that PTGS2 was significantly highly expressed in mast cells, leading us to speculate that PTGC2 may influence the progression of OA by regulating the activation of mast cells and thus the progression of OA. Surprisingly, we found weak interactions between immune cells in the synovial tissue of patients with OA, whereas there were complex communication networks between immune and non-immune cells (fibroblasts, SMCs, and ECs). These hypotheses and questions require more studies to reveal intricate interrelationships between these c-FRGs, immune cells, and OA.

In addition, we found that C10orf91 could regulate CDKN1A and SLC39A14 by regulating hsa-miR-149-3p, hsa-miR-423-5p, hsa-miR-31-5p, and hsa-miR-30b-3p. Both hsa-miR-513a-3p and has-miR-548c-3p can regulate both CDKN1A and GABARAPL2; however, no related study has been reported yet, so this needs to be further investigated and validated in the future.

This study was conducted mainly using bioinformatics analysis, and despite the combination of scRNA-seq analysis and the use of powerful machine learning algorithms, such as RF and SVM-RFE, there are still some limitations to our study. First, the small sample size of the analysis may have led to inaccuracies in the determination of hub genes, CIBERSORT analysis, and single-cell analysis. Second, although the disease model nomogram was well validated, the data was obtained retrospectively from public databases, meaning that inherent selection bias may have affected their accuracy. In addition, while our data can show the correlation between OA and immune cells, they cannot reveal causality. Extensive prospective studies, as well as complementary in vivo and in vitro experimental studies, are necessary to validate the accuracy of potential therapeutic targets and biomarkers.

Conclusions

Our study showed that four genes—CDKN1A, FZD7, GABARAPL2, and SLC39A14—are good disease biomarkers and potential therapeutic targets for OA. Our study provides a theoretical basis and research direction for understanding the role of c-FRGs in the pathophysiological process and for potential therapeutic intervention in OA.

Availability of data and materials

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

Abbreviations

• Osteoarthritis

Nonsteroidal anti-inflammatory drugs

Reactive oxygen species

Ferroptosis-related genes

Tricarboxylic acid

Cuproptosis-related genes

The new signature genes combining cuproptosis-related genes (CRGs) with ferroptosis-related genes (FRGs)

National Center for Biotechnology Information

Gene expression omnibus

Differentially expressed genes

Differentially expressed c-FRGs

Gene Ontology

Kyoto Encyclopedia of Genes and Genomes

Gene set enrichment analysis

Support vector machine recursive feature elimination

Random forest analysis

Least absolute shrinkage and selection operator

Receiver operating characteristic

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Acknowledgments

This study was a re-analysis based on published data from the GEO database. We would like to thank the GEO database for sharing the data.

This study was supported by Sichuan Medical Association (No. S17075, Q22008, Q21005), the Sichuan Science and Technology Program(No. 24NSFSC2177), the Science and Technology Strategic Cooperation Project between the People's Government of Luzhou City and Southwest Medical University (No. 2020LZXNYDJ22), the Doctoral Research Initiation Fund of Affiliated Hospital of Southwest Medical University (No. 22155), and Sichuan Student Innovation and Entrepreneurship Training Program Project (No. S202010632174).

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Baoqiang He and Yehui Liao are contributed equally.

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Department of Orthopedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taping Street, Lu Zhou City, China

Baoqiang He, Yehui Liao, Minghao Tian, Chao Tang, Qiang Tang, Fei Ma, Wenyang Zhou, Yebo Leng & Dejun Zhong

Southwest Medical University, Lu Zhou City, China

Baoqiang He & Dejun Zhong

Meishan Tianfu New Area People’s Hospital, Meishan City, China

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HBQ, LYB, LYH and ZDJ designed the study. Data analysis was performed by HBQ, TC, TQ and MF. HBQ, TMH and ZWY carried out the experiments. HBQ, LYB, and ZDJ wrote the first draft. ZDJ critically revised the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yebo Leng or Dejun Zhong .

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Synovial tissue collection and all experimental procedures were approved by the Institutional Review Board of the Affiliated Hospital of Southwest Medical University (KY2023293) in accordance with the guidelines of the Chinese Health Sciences Administration, and written informed consent was obtained from the donors.

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He, B., Liao, Y., Tian, M. et al. Identification and verification of a novel signature that combines cuproptosis-related genes with ferroptosis-related genes in osteoarthritis using bioinformatics analysis and experimental validation. Arthritis Res Ther 26 , 100 (2024). https://doi.org/10.1186/s13075-024-03328-3

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• Optimal timing for the Modified Early Warning Score for prediction of short-term critical illness in the acute care chain: a prospective observational study
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• http://orcid.org/0000-0002-9568-0138 Lars Ingmar Veldhuis 1 , 2 ,
• Merijn Kuit 1 ,
• Liza Karim 1 ,
• Milan L Ridderikhof 3 ,
• Prabath WB Nanayakkara 4 ,
• Jeroen Ludikhuize 5 , 6
• 1 Emergency Department , Amsterdam UMC Locatie AMC , Amsterdam , The Netherlands
• 2 Department of Anaesthesiology , Amsterdam UMC Locatie AMC , Amsterdam , The Netherlands
• 3 Emergency Medicine , Amsterdam UMC - Locatie AMC , Amsterdam , The Netherlands
• 4 Section Acute Medicine, Department of Internal Medicine , Amsterdam Universitair Medische Centra , Amsterdam , The Netherlands
• 5 Department of Internal Medicine , Amsterdam UMC Locatie VUmc , Amsterdam , The Netherlands
• 6 Department of Intensive Care , Haga Hospital , Den Haag , The Netherlands
• Correspondence to Lars Ingmar Veldhuis, Emergency Department, Amsterdam UMC Locatie AMC, Amsterdam, 1105 AZ, The Netherlands; l.i.veldhuis{at}amsterdamumc.nl

Introduction The Modified Early Warning Score (MEWS) is an effective tool to identify patients in the acute care chain who are likely to deteriorate. Although it is increasingly being implemented in the ED, the optimal moment to use the MEWS is unknown. This study aimed to determine at what moment in the acute care chain MEWS has the highest accuracy in predicting critical illness.

Methods Adult patients brought by ambulance to the ED at both locations of the Amsterdam UMC, a level 1 trauma centre, were prospectively included between 11 March and 28 October 2021. MEWS was calculated using vital parameters measured prehospital, at ED presentation, 1 hour and 3 hours thereafter, imputing for missing temperature and/or consciousness, as these values were expected not to deviate. Critical illness was defined as requiring intensive care unit admission, myocardial infarction or death within 72 hours after ED presentation. Accuracy in predicting critical illness was assessed using the area under the receiver operating characteristics curve (AUROC).

Results Of the 790 included patients, critical illness occurred in 90 (11.4%). MEWS based on vital parameters at ED presentation had the highest performance in predicting critical illness with an AUROC of 0.73 (95% CI 0.67 to 0.79) but did not significantly differ compared with other moments. Patients with an increasing MEWS over time are significantly more likely to become critical ill compared with patients with an improving MEWS.

Conclusion The performance of MEWS is moderate in predicting critical illness using vital parameters measured surrounding ED admission. However, an increase of MEWS during ED admission is correlated with the development of critical illness. Therefore, early recognition of deteriorating patients at the ED may be achieved by frequent MEWS calculation. Further studies should investigate the effect of continuous monitoring of these patients at the ED.

• emergency department
• emergency care systems
• care systems
• critical care

Data availability statement

Data are available upon reasonable request.

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

https://doi.org/10.1136/emermed-2022-212733

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WHAT IS ALREADY KNOWN ON THIS TOPIC

The Modified Early Warning Score (MEWS) is an effective tool to identify deteriorating patients in the acute care chain who might deteriorate.

Although it is increasingly being implemented, the optimal timing for assessing the MEWS is unknown.

WHAT THIS STUDY ADDS

This prospective multicentre study included 790 patients and found that MEWS measured at ED presentation had the highest accuracy in predicting the development of critical illness. However, the performance is moderate and not significantly better compared to MEWS based at other moments in the acute care chain.

However, an increase in MEWS during the ED encounter is highly correlated with the development of critical illness.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

As clinical deterioration and subsequent development of critical illness is highly correlated with an increase of MEWS during the ED stay, we suggest further investigation on the value of continuous monitoring of these patients at the ED

Introduction

Early recognition of the deteriorating patient is of vital importance to reduce the occurrence of serious adverse events (SAEs) including cardiopulmonary arrests, (delayed) intensive care unit (ICU) admissions and death. Prior research indicates that up to 80% of deteriorating patients show physiological abnormalities up to 24 hours before the event. 1–4 Track and trigger systems, including the Early Warning Score (EWS) were developed to recognise the early signs of deterioration. These scoring systems are relatively simple models using the patients’ vital parameters to assess the degree of illness of the patient.

In general, the higher the EWS, the more likely it is that a patient is clinically deteriorating and subsequently becomes critically ill. 5 This use of an EWS has proven to be efficient for detecting deteriorating patients on the wards. 6 When a deteriorating patient is identified, the Medical Emergency Team can be consulted, and more appropriate care can be provided. The implementation of EWS-based systems can lead to a reduction in SAEs and reduced time to ICU admission in deteriorating patients. 7

As the EWS-based system has been shown to be effective in general wards, the model has been increasingly implemented in other aspects of acute care, that is, the prehospital and ED settings. 8–11 Several studies suggest that EWS can be useful in the entire acute care chain. Prior studies showed a MEWS performance in the ED setting of area under the receiver operating characteristics curve (AUROC) 0.65. 12

However, it is unclear what moment in the acute care chain has the highest accuracy in predicting deterioration.

Timely interventions such as administration of antibiotics, and fluid challenges strongly affect vital parameters and overall survival. 13 These interventions may stabilise the patient and prevent further deterioration, which influences the EWS.

The primary aim of this study was to determine at which time point, from the first moment of contact with the EMS to admission to a nursing ward, an EWS is most accurate in detecting a deteriorating patient. Although the National EWS is generally slightly more accurate compared with the Modified Early Warning Score (MEWS), 14 we studied the performance of MEWS, as this is the tool regularly used in the Netherlands.

Study design and population

This was a prospective observational multicentre study, conducted at a university hospital, serving as a level 1 trauma centre with two locations. All adult patients (18 years and older) brought by ambulance to one of these two centres between 11 March and 28 October 2021, were included. Interhospital transfers and patients receiving prehospital cardiopulmonary resuscitation were excluded. Participants gave informed consent before taking part.

Data collection

Data were collected by a researcher present during EMS presentation between 10:00 hours and 18:00 hours on workdays, as during this period most ambulances arrive at the EDs of both centres. As we recorded data up to 3 hours after ED presentation, data were obtained until 21:00 hours. Patient characteristics, including vital parameters measured at four time points were collected on paper forms: prehospital (recorded by the ambulance); at ED admission (±15 min); at 1 hour (±15 min); and at 3 hours (±30 min) after ED arrival. Three-day outcome was obtained from the electronic patient records. All obtained data were processed using a standardised data worksheet. Collected data were anonymously processed using an online data collection system (Castor eClinical Data Management).

Endpoints and definitions

The primary outcome was the performance of MEWS in predicting critical illness for all four time points during which data were collected.

Secondary outcome was the association between the MEWS over time (ie, increase of MEWS 1 hour after ED admission compared with prehospital MEWS) and subsequent development of critical illness.

Critical illness was defined as mortality; ICU admission and/or myocardial infarction (as concluded by a cardiologist) all within 3 days after ED presentation.

Primary and secondary outcome was assessed by investigating the electronic medical records on day 4 after the initial ED admission. MEWS was thereafter calculated using the vital parameters at each time point; see figure 1 for thresholds of the MEWS.

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Modified Early Warning Score.

Missing data

Previous studies have shown that the temperature and level of consciousness of patients generally remain constant from transportation by EMS to arrival at the ED. 13 Therefore, in any cases where the temperature or level of consciousness of a patient was recorded prehospitally but missing at admission or vice versa, the recorded values for these parameters were used. MEWS was then calculated if a minimum of four out of six vital parameters was available with the one or two missing parameters considered normal. In choosing this method we acted on the assumption that diverging vital parameters would have been registered by the ED nurse. If more than two vital parameters were missing for a certain point in time, the MEWS at that time was not calculated. Patients for whom the MEWS could not be calculated were excluded from analysis for that specific point in time.

Sensitivity analysis

Model performance was tested after excluding patients with SARS-CoV-2 infection, as patients with COVID-19 are known to have relatively stable vital parameters despite being critically ill (as compared with patients without COVID-19). 15

Primary and secondary outcomes

The primary outcome was the performance of MEWS at different periods of time using the outcomes of developing critical illness (as defined above). The secondary outcome was whether an increase in MEWS over time was associated with becoming critically ill.

Statistical analysis

Descriptive and statistical analysis was performed using SPSS V.22.0 (SPSS, Chicago, Illinois, USA). Non-normally distributed continuous variables were described as medians with IQRs and were compared with the Mann-Whitney U test. Categorical variables were described as numbers and percentages and were compared by Pearson’s χ 2 test. The primary outcome was expressed as the AUROC of the MEWS for each time point. Also, for each MEWS between 0 and 5, sensitivity and specificity were calculated.

Using the AUROC derived from MEWS at the different time points, superiority in performance was assessed using the method of Hanley and McNeil. 16 In general, the AUROC is characterised using standard terms, where AUROC 0.6–0.7 is considered a poor testing method, 0.7–0.8 is considered moderate, 0.8–0.9 is good and a test with an AUROC >0.9 is considered an excellent method.

A χ 2 test was used to test whether an increase of MEWS over time had a higher incidence of becoming critically ill compared with a decreased or stable MEWS.

Sample size calculation

For the sample size calculation, the previously reported performance (AUROC 0.65) of MEWS in the ED was used. 12 For the primary outcome (the moment with the highest AUROC of MEWS) based on a 95% CI, 80% power and a 0.1 difference in MEWS, 114 patients were needed to test for statistically significant difference. These calculations were performed in nQuery tool for design of trials, link https://www.statsols.com/nquery .

Patient and public involvement

Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Of the 790 patients included in this study, critical illness occurred in 90 patients (11.4%). Prehospital alert calls to the ED were made significantly more often for critically ill patients (88.9% vs 69.4%, p<0.001). Additionally, these patients were assessed more often in either the resuscitation or trauma bay ( table 1 ).

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Patient characteristics

Of the 90 critically ill patients, 41 patients were directly admitted from the ED to the ICU, 16 patients were initially admitted to the ward then went to the ICU, 15 died and 17 had a myocardial infarction, all within 72 hours after ED presentation. Prior to imputing for missing values, the number of complete MEWS values was limited ( table 2 ). After imputing for missing values, the most complete moment of measurements was at ED arrival (94.8%).

Complete MEWS before and after imputing

Primary outcome

MEWS based on vital parameters measured at ED admission had the highest performance with an AUROC of 0.726 ( table 3 , figure 2 ). MEWS based on vital parameters measured 1 hour and 3 hours after ED admission had lower performance ( table 4 ). The performance of MEWS measured at ED admission was not significantly superior compared with the other time points in predicting critical illness.

AUROCs for the prediction of critical illness within 72 hours

Receiver operating characteristics (ROC) curves for prediction of critical illness within 72 hours.

Sensitivity and specificity for cut-off points of MEWS

Of the 790 patients, 82 had a proven SARS-CoV-2 infection. Excluding patients with a proven SARS-CoV-2 infection did not lead to a significant improvement of MEWS accuracy in predicting critical illness ( table 3 ).

In addition, sensitivity and specificity were calculated for each threshold ( table 4 ). For the MEWS measured at ED admission using a cut-off value of 3, sensitivity was 64.0% (95% CI 60.5% to 67.4%) and specificity was 70.1% (95% CI 66.7% to 73.3%).

Secondary outcome

To estimate the influence of a change over time in MEWS (delta MEWS) on outcome, a χ 2 test was performed. An increase in MEWS between the MEWS measured prehospitally and 1 hour after ED admission had an incidence of 25.7% of critical illness, while stable or decreasing MEWS had an incidence of 7.5%. This difference was significantly different (p<0.05) (see table 5 ).

Changes in MEWS during admission and the development of critical illness

While many studies focus on the performance of EWS in either the prehospital or ED setting, little is known about the best timing to use it in the acute care chain. 8 17 18 Therefore, this prospective multicentre study was performed to attempt to direct clinical practice to the best moment in the acute care chain to measure MEWS to identify subsequent development of critical illness in patients brought to the ED by ambulance. Although MEWS calculated based at presentation had the highest accuracy in predicting the development of critical illness, an AUROC of 0.726 was not significantly superior to MEWS measured prehospitally or 1 hour or 3 hours after ED presentation. Also, excluding patients with proven SARS-CoV-2 infection did not lead to an improvement in model performance. While the performance of MEWS found in this study in predicting critical illness is moderate, this was consistent with other studies. 19

Our secondary outcome was to test the correlation between an increase of MEWS over time and the development of critical illness. Prior studies suggest that the trend of MEWS during the first hours of ED presentation may identify clinically deteriorating patients better compared with a single MEWS calculation. 5 10 20 21 Our results indicate that an increase of MEWS between prehospital and at 1 hour after ED admission was significantly correlated with the development of critical illness, p=0.005. Therefore, we suggest that patients with an increasing MEWS during ED stay should be more intensively monitored and early consultation with the ICU consultant may be justifiable.

Limitations

The study has several limitations which may reduce the generalisability of our data and have most likely influenced our results. First, the study ran during the summer months, so season-specific diseases may have occurred. Furthermore, there was a high percentage of missing data for calculating MEWS. We have excluded patients from analysis if two vital parameters other than temperature or mental status were missing. Also, we only included patients arriving between 10:00 hours and 18:00 hours potentially leading to selection bias. To improve the quality and clinical relevance of the data, future studies should also include cases where MEWS is found to be above the cut-off point, even if there are missing variables. Additionally, it is possible that the data were not missing at random. When a patient has normal vital signs during the first check, their vitals usually do not get monitored as frequently as when a patient initially has abnormal vital signs. Therefore, only including cases with known MEWS at all time points can cause a distorted view of the predictive performance of EWS in the ED, since there is a probability that patients with abnormal vital signs are disproportionately over-represented. It is important to record the full vital parameters set needed to calculate MEWS in clinical practice.

Clinical implication

Implementation of a single standard time point for measurement of MEWS in the prehospital setting or ED is clinically not useful due to its moderate performance. However, patients with an increase of MEWS over time is highly correlated with the development of critical illness. Implementing standard repeated measurements in the acute care chain may result in better prediction of which patients are likely to become critically ill.

In conclusion, MEWS based on vital parameters measured at ED presentation has the highest accuracy in predicting the development of critical illness. However, performance is moderate and not significantly better compared with MEWS measured at other moments in the acute care chain. However, an increase in MEWS during the encounter is highly correlated with the development of critical illness. We, therefore, conclude that it would be valuable to assess MEWS over time, rather than only at a single moment.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants. The Medical Ethics Committee of both locations of Amsterdam UMC waived ethics approval for this study (Waiver: W-19_480 # 19.554). Participants gave informed consent to participate in the study before taking part.

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Handling editor Kirsty Challen

Contributors LIV and MK: planning, conceptualisation, methodology, data curation and writing original draft. LK: data curation. MLR, PWBN and JL: important intellectual content and guarantor of the article.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

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