data science model presentation

How to Present a Data Science Project (With Examples)

After passing a company’s take-home challenge, you might get asked to present your data science project to data scientists and the hiring manager. Presentations are high-pressure, especially if public speaking is not a strong skill for you.

Fortunately, making your data science presentation more engaging (and using it to land you the job) is a straightforward process.

In this article, we’ll discuss how to present a data science project and share tips to help you overcome these challenges and land your next data science job.

Data Science Presentations: Where to Start

Let’s first discuss the elements of your data science project that you will need to start preparing the presentation.

Learning the Purpose of the Project

For your own purposes, make sure you grasp the problem or question your project addresses. This helps set the stage for your audience, showing why your work matters. What might work for you is outlining the specific objectives you aim to achieve. This could include solving a particular business problem, making a prediction, or uncovering patterns in data.

Brainstorming the significance of the project’s outcome usually enables you to discuss its benefit to the business and community in the presentation. It also allows you to highlight the value of your findings. This might include cost savings, improved efficiency, new insights, or strategic advantages.

These are the things that the interviewers desire in data science project presentations, but candidates often overlook the details.

Identify Your Audience Type

To present an excellent data science project, you must first identify your audience type. While it may not be possible for you to know all about your audience, you should at least try to find out if your audience is familiar with data science concepts. And that information must influence how you present your findings. Consider who will benefit from your findings. Are they business executives, data scientists, or a general audience?

Tailor your content accordingly. For a technical audience, use more technical jargon, include detailed methodologies, and focus on the specifics of your data analysis. For others, simplify the language, avoid overly complex explanations, and focus on the implications and actionable insights. However, when in doubt, always embrace the simpler approach.

Focus on Relevance

You’ve already determined the purpose of your project; now, carefully incorporate it into the presentation by focusing on the relevance of your findings. Ensure your presentation aligns with the strategic goals or needs of the organization. Make sure it answers how your conclusions address the key issues or objectives and how they apply to real-world scenarios or business decisions. This helps in making your findings more relatable and impactful.

Furthermore, highlight the most relevant insights from your analysis. Emphasize the actionable takeaways for your audience. Use charts, graphs, and visualizations to make complex data more accessible and to highlight key points.

Questions Related to Your Project

You’ll likely be subjected to thorough Q&A during interview sessions regarding your data science presentations. Consider potential questions your audience might have regarding your methodology, data, or conclusions. Be ready to explain any aspects of your project that may be unclear or complex. This includes discussing limitations, assumptions, or alternative approaches.

Many data scientists overlook it, but fostering an environment where the audience feels comfortable asking questions can provide additional insights and demonstrate your expertise. Use questions as a feedback mechanism to gauge understanding and adjust your presentation if necessary.

What to Include in Data Science Presentations

Now that we’ve covered the foundational components of a successful data science project presentation, let’s discuss what your presentation should include:

Start by briefly stating the project’s objective, what are you going to cover, and its importance. Provide a roadmap for your presentation by outlining key sections to help your audience follow along and give a brief context about the industry or domain where the problem arises. This helps set the stage for why your project is relevant.

If you’re using PPTs (more on this later), a title slide, the purpose of the presentation, and a brief agenda should be discussed in the first few slides.

Problem Statement

Clearly describe the problem or question you are addressing. This should be specific and actionable. Explain why this problem is important and what impact solving it would have. This would help underscore the value of your data science project. The context and background of the problem statement should be clearly defined.

Moreover, state the objectives of your analysis. Discuss what you aim to achieve through your project. This can include solving a business problem, improving a process, or generating insights. An industry overview in the presentation often helps in better understanding the problem statement and your approach.

Data Source and Acquisition Methods

Thoroughly detail the sources of your data. Mention if they’re internal or external databases, APIs, or surveys. For technically savvy audiences, discuss whether the data is structured or unstructured. Explain why these sources were chosen and how they are relevant to your problem. Moreover, describe the methods used to collect the data. Was it through scraping, downloading, API calls, or manual entry?

Briefly outline any preprocessing steps taken to clean and prepare the data for analysis. Interviewers also love to know how you handled the missing values. Mention it just enough for them to ask about it, allowing you to showcase your data science knowledge.

Consider mentioning the initial insights you found while normalizing and transforming data. You could also attach a sample of the datasets in your presentation, especially when it comes to visual datasets.

Methodology and Model Selection

Methodology is critical when it comes to data science projects with source code . Explain the overall approach you took to address the problem. This might include exploratory data analysis, feature engineering, or hypothesis testing. Feel free to describe the models or algorithms used. Mention why you chose these particular models, any comparisons made, and the rationale behind your choices.

Furthermore, outline how you validated your models and which metrics you used to assess their performance (accuracy, precision, recall, F1 score). Let your interviewers know about any cross-validation or testing procedures used to ensure robustness and generalizability.

Results: Your Findings

For your data science interviewers, this is the most significant section of your presentation. Make it count by presenting the main findings of your analysis. Use clear visuals such as charts, graphs, and tables to illustrate the results. Highlight any significant insights or patterns discovered. This is where you make the data come alive and show its value.

If possible, visually generate a comparison of different models on the same dataset. Be sure to use ROC curves and AUC to solidify your arguments. Moreover, don’t forget to discuss the implications of your findings. Thoroughly discuss how they address the problem statement and may influence the business or the industry.

Don’t hesitate to include any unexpected results you found during the project. Present them in a compelling way to show the interviewers you genuinely worked on the project and found discrepancies.

Interpretation and Recommendation

Provide a detailed interpretation of the results in your data science project presentation. Discuss what they signify in the context of the problem, and relate your findings back to the real-world problem and the project’s objectives.

Be sure to offer specific recommendations that align with the interests of the company or the industry, and provide strategic advice, if applicable. Mention how the insights can be leveraged for better decision-making or workflow improvement.

Challenges and Limitations

Discuss any challenges or obstacles you faced during the project. This could include data quality issues, computational constraints, or unexpected findings.

Acknowledge the limitations of your analysis, including factors that impacted the accuracy or generalizability of your results. Likewise, mention any assumptions made during the analysis and how they might have affected the results.

Summarize the key points of your presentation. Reiterate the problem, findings, and recommendations, and provide any concluding thoughts or reflections on the project.

Introduce a call to action. Suggest the next steps or actions to be taken based on your findings. This might include implementing recommendations, conducting further research, or making strategic changes.

How to Present Your Data Science Project

Now we’ve come to the most anticipated part of the article, addressing something most beginner data scientists wonder about where to showcase their projects when applying for a company or presenting to interviewers. Let’s discuss:

DataLab is a great place to share your work because it lets you create interactive reports. You can include live code, charts, and explanations in one place, making it easy for others to see what you did and how. If you want to show off your coding skills and make your analysis look super polished, DataLab is a solid choice.

However, it mostly relies on the AI capabilities of the platform and allows very limited control over your projects.

GitHub is the go-to for code sharing and version control. It’s where a lot of developers and data scientists put their work. By posting your projects on GitHub, you can show off your code, documentation, and how you keep everything organized. Plus, having a well-managed GitHub profile can make you look professional and detail-oriented.

Kaggle is a bit like a playground for data scientists. It’s great for showcasing your skills through competitions and public notebooks. If you’ve tackled a tough dataset or participated in a challenge, Kaggle lets you share that with the community. It’s a cool way to get noticed and get feedback from other data science enthusiasts.

Kaggle also has a vast array of datasets to build your data science projects.

Personal Website and Slides

If you’re seeking freedom to present your work exactly how you want, personal websites and slides give you exactly that. A personal website is like your own online portfolio where you can show off detailed project descriptions, interactive demos, and more.

Slides, however, are perfect for summarizing your project in a neat, easy-to-follow format, especially useful for interviews or presentations. Many current presentation tools come equipped with AI capabilities to make the job easier for data scientists.

More Tips for a Data Science Project Presentation

As you build your presentation slides and rehearse, here are some of the best practices and tips to make your performance even stronger:

Keep it concise - Keep your presentation simple and to the point. You can’t show every step you took. Instead, keep it brief and to the point, focusing only on key details.

Choose your best visualizations - Images and charts make your presentation easier to follow and clearly display the impact/findings of your project. Include only vital information in the chart, and be sure to consider fonts, color theory, and other good practices of visualization design . A general rule of thumb : It should be clear to a layman what a chart is conveying.

Focus on the impact - If you’re presenting on a project from a previous job, show the impact it had using metrics. Increased revenue, reduced churn, customer acquisition, and other factors will illustrate how your work impacted the bottom line.

Include limitations - Every project has limitations and challenges. Although it might seem counterintuitive to talk about what went wrong, discussing limitations will make your presentation stronger. It shows you can identify potential flaws in reasoning and that you care about quality controls.

Talk through your decisions - Explain why you made the technical decisions you did. This will help the audience understand your approach, what factors lead to you making a certain decision, and how you personally use creative problem-solving.

Make it accessible - Explain the technical details of your project in layman’s terms. Examples and analogies can be helpful for audiences, and ideally, you should be able to explain an algorithm or complex data science technique in one or two sentences for a non-technical audience.

For the Presentation: Final Tips

Public speaking is nerve-wracking. But there are strategies you can take to calm your nerves and make the most of your presentation time. Here are public speaking tips for your data science presentation:

Make eye contact - Eye contact connects you with your audience and makes your presentation more engaging and impactful. One strategy: sustain eye contact with one person per thought. Be sure to practice this during your rehearsals.

Allow space for questions - Although there’s usually a Q&A at the end, questions can come up throughout. If you’re not sure if the audience has questions, take a pause and ask, “Does anyone have any questions?” Remember, you don’t want to talk AT them.

Avoid rushing - Focus on pacing. You should be talking at a normal conversational speed. Too fast, and you’ll end up losing the audience. Too slow, and you will bore them.

Breath, relax, and collect your thoughts - Before you begin, take some deep breaths. One strategy: reframe the focus from you (e.g., “What if I blow it?” ) to the audience ( “My focus is helping the audience understand and learn.” ).

The Bottom Line

Ensure that your presentation is tailored to the audience, is relevant, and provides actionable insights understandably and appealingly. Also, be prepared to handle post-presentation questions related or tangential to the project and your associated experience.

If you’re looking for more projects to tackle, we’ve got them at Interview Query:

• Python Projects for Your Resume
• Customer Churn Datasets and Projects
• Supply Chain Projects and Datasets
• Healthcare Data Science and ML Projects
• 90+ Free Datasets

September special: Business Transformation PPT Templates

Data Scientist Presentation Toolbox – 5 Handy PPT Templates to Use

• July 1, 2022
• IT, AI & Data Analytics , PowerPoint templates for download

A data scientist job requires not only specific technical IT skills but also soft skills, in particular presenting the work results to your peers or executives.

Therefore it is a handy shortcut to have a set of PowerPoint templates you can use to quickly create professional slides on the topic of data analysis or predictive modeling.

Let me present the selection of presentation blueprints I would recommend to senior or junior data scientists, analysts, or IT consultants to make their work easier.

Get any of the graphics presented here – click on the slide pictures to see and download the source illustration. Check the full collection of Data science PowerPoint templates here .

I used to work in an analytics consulting company for several years. My work included presenting data mining models and making tens of marketing materials explaining big data analytics solutions my company offered at that time.

The main challenge was to translate sophisticated predictive technology for business people not aware of all the technical details of various machine learning algorithms or data processing peculiarities. Using graphical diagrams and illustrations helped me a lot and I believe it can be useful also for you.

What do data scientists actually do?

The work of a data scientist requires a background in several fields, as this job involves:

• understanding the IT infrastructure: how data is stored, what is the database system, what’s the OLAP cube structure (see OLAP cube slide examples in this article), how data is updated, etc.
• the informatics theory: information processing, data structure types, effective data handling e.g. using SQL of visual tools
• applying advanced statistical techniques and mathematics to calculate predictions, correlations, and associations among data trends
• interpreting results  – need good communication skills to present your work well 

Tasks performed by data scientists start with collecting the data, preprocessing it, and exploring properties and main statistics. And just then data is used to create a predictive model. Such a model needs to be tested before being put into production, for example, to estimate a credit risk score.

All those tasks can benefit from using visuals to present them.

How to make a good presentation of Data Science concepts?

A presentation on a topic related to data science usually belongs to more complex ones. It is full of technical terms, data charts, and non-trivial statistical analysis correlation explanations. Therefore it’s important to keep in mind the following hints.

Have a clear presentation structure

Having a clear structure even before you start making slides is crucial. Write down what you want to present. I would recommend even doing it on paper first, as this supports creativity – more in this blog . 

If your presentation is a long one, it’s a good idea to show this structure on the agenda slide so your audience will get a vision of what the presentation will be about.

Define the audience of your data analytics talk

Before going deeper into designing a presentation, ask yourself a question: who is your audience? 

• Are they laics (students, new hires, journalists for example) who need an explanation of all technical terms used in data science? 
• Or is it a group of AI experts who are interested in details? 
• Or do you present to the executive board who wants to know the project cost and financial impact?

Write down the main message you want to communicate

After settling down the audience and structure, think about what’s the main message you want to convey. Is it a certain dependency? List of most important attributes? Performance of a scoring model? A quality measure of a forecasting system? Or a financial impact, or ROI of the machine learning system as a whole?

These are questions your audience can ask you as a data analyst, depending on who you are presenting to. Make sure this message is properly stated on a slide, in the beginning, at the end summary, or both. And then make sure all slides support this message, by presenting facts and supporting evidence by model quality statistics or operational model performance KPI values.

In the end, don’t forget to add a summary of data science project outcomes. Having a simple executive summary slide with main deliverables and effects can do the work.

Suggested PowerPoint templates for data scientist tasks

Here is a suggestion of several templates you can reuse for your analytical report or speech. You can use them as a whole presentation or just copy specific segments or graphics to your slides. 

Project Timeline and Gantt charts templates

In case you are presenting a project involving data analytics and modeling, you can use a template with calendar timeline diagrams graphics, to show how long the data science project takes. 

Using graphical flowcharts, you can clearly present your analytical project assumptions, and preparatory steps, which included the implementation phase and final project delivery.

Data description presentation icons 

For the initial analysis of available data, their structures, and types, there is a template with various data categories and analytical processes. For example, you can present the whole data mining process of designing a predictive model as a flowchart diagram with steps for each stage.

If you prefer another graphical style – here is a more elegant outline icon set:

Big Data concept presentation template

If you need to present the concept of Big Data, check diagrams explaining the definition of 4Vs or 3V characteristics of big data ideas, along with a few recognized definition slides. 

See more examples of slides explaining Big Data and learn how to make Big Data presentation s visually appealing.

Artificial Intelligence and Machine Learning 101 presentation

For explaining what’s the essence of Artificial Intelligence and Machine Learning, we have also a presentation template covering this topic, including the history of AI and its application areas list. Having an editable PowerPoint source you can expand or modify all those diagrams and lists, to fit your industry and experience. 

You may have many other topics to present – as data science is today present in various business areas. Ideas for other areas you can explore are here

• e-commerce dashboards 
• blockchain essence presentation
• digital transformation processes 

Feel free to explore those and reuse visualization ideas if they fit your work.

The main takeaways for a data scientist in making a presentation 

The job of a data scientist requires a variety of skills. Besides obvious hard skills, you also need to handle some level of soft communication skills. Making an engaging and clear presentation of your work is one of those essential skills. 

However, with the right approach and some experience you can handle this skill as well. Several presentation design tips to remember are: 

• having a clear presentation structure
• defining the audience and adjusting content to it
• expressing the main message properly e.g. with the help of diagram visuals.

I shared several PowerPoint slide examples and templates you can use to get you started.

For more inspiration, subscribe to our YouTube channel:

Resources: Data work-related PowerPoint Templates

If you are interested in checking data-related slides, see those pages:

• data science presentations templates
• IT & analytics presentations
• Universal diagrams and flowcharts

Using PowerPoint template format – with various data-related graphics ensures you can edit all content, and texts, expand diagrams, or replace icons as you need. A PPT format template is an easy self-service toolbox that can improve significantly your work. You can also import those slides to Google Slides or Keynote presentation software if that is a presentation tool you use.

infoDiagram Co-founder, Visual Communication Expert

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20+ Free PowerPoint and Google Slides Templates for Data Presentations

Graphs and diagrams are crucial in data presentations since they make complex information much more understandable . Imagine copying and pasting all 1,000 rows of data onto your slides and expecting your audience to understand it. It’s really hard, isn’t it?

Presenting your data analysis doesn’t have to be a struggle. These PowerPoint and Google Slides templates will significantly cut down your preparation time, allowing you to focus on ensuring the accuracy of your data analysis while we handle the design.

This article is divided into two sections: the first covers our free PowerPoint templates , and the second covers our free Google Slides templates . Oh, and in case you’re wondering, yes, you can use a PowerPoint template in Google Slides and vice versa .

PowerPoint Templates for Your Data Presentations

Let’s start with our data presentation templates in PowerPoint. 

As you may know, PowerPoint is one of the best presentation software programs available today. So, take advantage of all its features with our free templates! 

1. Playful Venn Diagram PowerPoint Template

Venn diagrams show the similarities and differences between 2 or more data sets. Your audience can tell if there’s anything familiar between them just by looking at the diagram.

Likewise, if you want to emphasize the differences between data sets, Venn diagrams are great for that purpose, too. Now, for this template pack, you’ve got 10 slides to choose from. Pick your favorite!

2. Graph, Diagram, and Data Sheet PowerPoint Template

Using graphics is the best way to create data presentations, and at 24Slides, we know that! 

If you’re looking for simple yet creative graphs, including a Gantt Chart in PowerPoint , this 5-slide template pack is perfect for you. Take a look at the previews and download the pack for free!

3. Generic Data-Driven PowerPoint Template

Here are more basic graphs for your presentation decks. This template can be used for many situations, including a job interview, a sales presentation, or even an academic one.

If you want to make the slides look even more unique, you can customize the background with some personal images.

4. Cockpit Chart PowerPoint Template

If you’re giving a high-level presentation to decision-makers who need to see complex data and proper analysis, then this free template pack is for you.

With this pack, each of the 9 slides brings a fresh example of charts and diagrams, ready to make your data come alive. Click on the title and pick the perfect one to captivate your audience!

5. Matrix Chart PowerPoint Template

A matrix chart allows you to compare and analyze different sets of data. You can use it to prove certain data sets are related. Plus, you can even show the strength of that relationship. 

Download our 8 matrix models for free now! 

6. Stair Diagram PowerPoint Template

Like their namesake, stair diagrams show steps or progression in data presentations. You can use good, old-fashioned bullet points, but it won’t be much fun. 

This template offers 10 stair diagrams; the screenshot above shows a steps stair diagram . Explore all of them for free!

7. Tables PowerPoint Template

Tables have been a staple in data visualization for a long time, and we believe they continue to be widely used today. Despite the evolution of various visualization tools and techniques, tables remain a fundamental way to present data clearly and effectively.

This template pack offers standard table slides as well as creative designs, including a subscription slide, a table with different symbols, and a matrix organizational structure. Choose your favorite based on your needs!

8. Flow Chart PowerPoint Template

Flowcharts are handy for documenting specific company procedures. They can even present the company hierarchy and who is responsible for certain tasks. 

Instead of verbally discussing processes, why not try using a flowchart? 

9. Financial Pie Graphs PowerPoint Template

Whether you’re presenting in front of the directors of your company or potential investors for your startup, these radial charts will help you get your point across. With a few clicks, you can customize these resources and make them your own!

This data visualization template includes 3 slides: a financial pie chart for comparison (shown above), a ring pie chart, and a doughnut pie chart slide.

10. Research and Development Data PowerPoint Template

Every successful startup needs a solid research and development (R&D) process, which can be lengthy and costly and often require external funding. 

This template pack is designed to help you create a concise, impactful presentation for potential investors. Remember, while design is important, your passion and persuasive skills will ultimately drive your success in a data presentation!

11. Sales Report PowerPoint Template

Our list of data presentation templates wouldn’t be complete without a sales report template in PowerPoint. 

This pack includes sales bar charts, line charts, radial charts, sales data visualization sections, and annual sales report slides. Everything you need in one presentation deck!

12. Data-Driven PowerPoint Template

This 9-slide template pack contains charts and diagrams for your business presentations or any project you lead. 

With its thoughtful design and diverse range of graphs, this template is perfect for most financial presentations. So, what are you waiting for? Check out our template pack now!

13. Block Chain Data PowerPoint Template

Cryptocurrency and blockchain are all the rage nowadays. Many people became millionaires overnight, but many more gambled and lost their entire life savings!

Don’t get left behind and explore more about digital currencies with our free template pack.

Google Slides Templates for Your Data Presentations

PowerPoint is awesome, but Google Slides is also a brilliant tool. If you haven’t used this platform, this is your signal to start doing so. Unlock the potential of your data with our free templates, crafted to transform your slides into stunning visual stories!

With Google Slides templates, there’s no need to download anything to your computer. Simply create an account on our Templates Repository and make a copy of the template. As you can imagine, editing it will be a breeze!

1. Corporate Data Presentation in Google Slides

Our Google Slides template provides essential charts for data presentation, including bar charts, pie charts, and line charts. 

The best part? Each chart is linked to a Google Sheets spreadsheet, giving you complete control over the data.

2. Life Cycle Diagram in Google Slides

A product’s life cycle—spanning from introduction to growth, maturity, and decline—directly influences your company's marketing and pricing strategies. So, you have to know how to monitor each stage.

This template pack includes a summary slide to introduce your objectives and guide the audience. It also features an area chart to visually represent product growth over time, helping to clarify the current stage. See it yourself by clicking on the title!

3. Playful Pie Chart in Google Slides

Unlike the other pie charts in this article, this one will be straightforward to use. You’ve got 8 pie chart slides to choose from, including 3D and 2D pie charts in Google Slides. 

Choose the ones that best convey your message, then edit and present them!

4. Dashboard Template in Google Slides

A dashboard slide can convey everything your audience needs in just one slide. While you can use separate slides for each chart, it won’t have the same impact as a dashboard (as you can see in the image). 

Dashboard templates are perfect for elevator pitches because they are highly eye-catching. Explore the designs we’ve prepared for you!

5. Waterfall Diagram Template in Google Slides

Waterfall charts are excellent for financial presentations, allowing you to show gains or losses over time. They are also helpful in demonstrating changes in cash flow or stock market performance. 

This template pack includes a waterfall performance comparison slide (pictured), a basic waterfall diagram, a project timeline slide, and more. Download all for free!

6. Playful Data-Driven Template in Google Slides

Do you think data presentation templates have to be serious? Think again! 

This 10-slide playful template is packed with various charts and graphs, including bar graphs, radar charts, waterfall statistics, treemaps, and more. Log in to our Template Repository to download this free Google Slides template.

7. Circle Diagrams in Google Slides

This template pack features 8 types of circle charts in Google Slides, including pie charts, timelines , cyclical processes, project management charts, and Venn diagrams. 

The design is both playful and professional, making it suitable for any audience!

8. Creative Data-Driven and Financial Charts in Google Slides

Number crunchers will love the clean design of these 7 data-driven slides. With ample white space and visually appealing graphics, it will help your audience grasp complex financial information. 

You only need to replace the placeholder content with your own information and practice your data presentation for the best results!

9. Graph, Diagram, and Data Sheet Presentation in Google Slides

This pack of 5 Google Slides templates includes a versatile collection of charts and diagrams, perfect for any presentation. 

Remember that each chart is fully customizable to meet your specific needs. Download this data visualization pack for free today!

10. SWOT Presentation Templates in Google Slides

Data visualization isn’t just for numbers; it also includes qualitative data. If you need to present a SWOT analysis, these templates are your go-to solution. 

With 8 pre-designed SWOT diagrams, you can easily create impactful presentations. Best of all, they’re free to download—what are you waiting for?

11. ICO Presentation Template in Google Slides

Planning to present an Initial Coin Offering (ICO) for your company or startup? 24Slides has you covered.

We’ve designed this data presentation template with the unpredictable nature of digital currencies in mind, featuring a chart that helps you clearly explain all the details to your audience.

12. Budget Presentation Template in Google Slides

Presenting a project’s budget doesn’t have to be boring!

This resource offers 8 different diagrams in Google Slides, making it easy to streamline your design process. Download our data visualization pack for free now! 

13. Financial Template Pack in Google Slides

You should know that effective financial management is crucial to every business’s success. So why not showcase that professionalism in your financial slides? 

Explore this final Google Slides template pack and impress your audience with professional and polished data slides!

I hope these 20+ free PowerPoint and Google Slides templates for data presentations are helpful for any project you have in mind. Our templates are designed to be visually attractive while maintaining a professional look. Follow us and stay tuned for all the content we’ve prepared for you!

Where can you find the best templates for FREE?

In 2024, it’s no mystery that there are various ways to optimize your time when designing presentations. One of the most effective methods is using pre-designed templates, and of course, 24Slides has its own repository.

When you enter our Template Repository , you’ll find data visualization templates, marketing templates, portfolio templates, planning templates, and much more!

It’s time to work smart, begin today .

If you like this content, you should check:

• Mastering the Art of Presenting Data in PowerPoint
• 20+ Self Introduction PowerPoint Templates: Download for free!   
• The Ultimate Brand Identity Presentation Guide [FREE PPT Template]
• How to Make a PowerPoint Template (Tutorial with Pictures!)   
• 11 Time-Saving PowerPoint Hacks for Creating Quick Presentations

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8 Tips for Creating a Compelling Presentation for Data Science

How to Create a Compelling Slide Deck

As data scientists or analysts, we spend countless hours perfecting our ability to analyze data, build machine learning models, and keep up with the latest technology trends. One skill, however, that everyone needs is the ability to create a compelling presentation.

Every Data Scientist, Analyst, and Data Engineer needs to get good at building a compelling presentation.

Here are tips and tricks I've gathered over 20 years of presenting to executives, customers, and peers. None of these tips are limited to Data Science and can be used by anyone creating a presentation; let's take a quick look at them.

Start With an Outline

Storytelling with situation, complication, resolution, the one minute per slide rule, the rule of three, write slide titles as outcomes, reading titles outloud, focus your audience's attention, creating compelling data visualizations.

Let's get started!

When starting a presentation, they often open PowerPoint, Keynote, or their tool of choice and start trying to build the deck immediately. This issue, of course, is that you're going to iterate 100 times on the slides and probably end up deleting most of what you've created.

A better way to approach this is to start with an outline , No different than you were taught in English 101. But don't think of this as an outline of the slides you want to create. Think of the outline as the story you're going to tell . There are two common ways to create an outline. The first is simply using ordered lists and structuring your outline by ideas or sections of your story (I'll cover the storytelling part next). If you're a more visual person, you can use a mind map and structure it similarly.

Once you have your outline set, creating the supporting content is a breeze.

A classic storytelling framework is the Hero's Journey . The general idea is that a hero goes on a journey and introduces some obstacles; finally, the hero can overcome that obstacle, and everyone lives happily ever after.

In our business presentations, we can use a similar framework called Situation-Complication-Resolution or SCR . I was first introduced to SCR through the book The Pyramid Principle by Barbara Minto, who popularized this method while working at McKinsey Consulting. The structure of this is perfect for creating a business story. It's a simple framework that keeps you organized in your structure, brings action-oriented results, and fits into the Rule-of-Three, which I'll cover later.

• Situation : Facts about the current state.
• Complication : Action is required based on the situation.
• Resolution : The action is taken or recommended to solve the complication.

Using our outline format, here is an overly simplified example of SCR. In practice, you would introduce more details as sub-items of the nodes. Utilizing SCR will help you create a clean, compelling story!

This one is simple and effective. Instead of creating your presentation and rehearsing the timing, consider that each major content slide will take one minute to present . If you have a 20-minute presentation, aim for 20 slides with content. You should not include section dividers, the cover, or a closing logo slide in your count. I've found over the years that, generally speaking, some will take longer, some will be shorter, but on average, they'll take about a minute.

Eliminate the stress from figuring out how much content you need to create.

Another guiding principle is the Rule of Three . The rule of three is simple: stick with three and only three items when building your structure, sections of your story, or the number of bullet points on your slide.

Apple has implemented this all over their presentation and product lines, and science has shown that our brain loves patterns and three is the minimum number needed to form a pattern .

Structuring your slides utilizing the rule of three will help your audience remember your content and simplify building the presentation. You might be tempted to think, "more information is better with four, six, or eight bullet points. No one will be able to follow all that, so cut it down to the three most impactful messages.

Another trick if you have slightly more information is to structure it three-by-three like the image below.

I often see the mistake of Data Scientists writing their slide titles describing what is on the slide vs. what the outcome or takeaway of the slide is. This simple practice you can get into will dramatically improve your presentation with little effort. Let's take a look at a couple of examples.

It is pretty clear with examples like this which is more compelling to your audience. Remember, your slide titles are the outcome of your slide!

There are plenty of articles that will tell you not to read your slides out loud. Reading your content directly from your slides is a sure-fire way to bore your audience and lose their attention.

However, I have one caveat to that rule; read your slide titles out loud .

According to Naegle :

Reading and verbal processing use the same cognitive channels—therefore, an audience member can either read the slide, listen to you, or do some part of both (each poorly) due to cognitive overload.

By reading just the tile and title only as you start each slide, the audience will be able to process the message much more easily than reading the written words and listening to you simultaneously. While this might feel uncomfortable initially, practice it with some colleagues and see for yourself!

For the rest of the slide, do not read the content, especially if you use a lot of bulleted or ordered lists. Reading all of your content can be monotonous, as mentioned above.

When you have more than just a single word or number on your slide (which can also be a really powerful practice), you can leverage techniques or attributes to focus your audience's attention on the most important words. These attention getters are known as preattentive attributes .

When your eyes and brain first see a slide, for the first fraction of a second, you are drawn to different elements that stand out. Items can be in bold , italics , or a different color or size . The fact that they are different from the main text is how you can focus your attention.

A great example of this is adapted from Stephen Few's Tapping into the Power of Visual Perception . When we look at the first block of text, it all tends to blend. If we were to ask you, "tell me how many number sevens there are," it would take a little time.

However, when you look at the second image where we've tapped into preattentive attributes of bold and color , we can see each seven.

This concept also directly applies to building data visualizations, which we'll cover next.

This section alone could warrant an entire article (or book) written about it. The good news is that the are great ones that already exist. I recommend two that you should get right now:

• Storytelling with Data by Cole Nussbaumer Knaflic
• Data Story by Nancy Duarte

Both of these books cover how to build a better visualization. Read these, study them, and refer to them each time you build a visual and a presentation.

Learning how to present your Data Science project results compellingly is one of the most critical skills you can learn. We covered how to start with an outline, utilizing storytelling frameworks to structure your presentation, the one-minute rule, and the rule of three. We also discussed how to form better titles by writing them as outcomes instead of subjects. We talked about two ways to focus your audience's attention: reading your titles aloud when presenting and tapping into preattentive attributes through methods like bold and color. Finally, we covered creating a compelling visualization of your data. Follow these as guidelines for your next presentation, and I'm confident you will be able to create a compelling presentation.

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Presentation Tips to Improve Your Data Science Communication Skills

In data science, communication is critical.

Of course, all data science work requires the technical skills to acquire your data, clean it, and perform your analysis. But as you're doing this, it’s also important to keep the why in mind. When you’re given a project, it’s worth stopping to ask yourself what value it has to the company, and where it fits into the larger picture.

Knowing the answers to those why questions is the first step in a process that’s as important as your actual analysis: communicating your findings to an audience of (usually) non-data scientists.

Data science communication is a topic Kristen Sosulski knows a lot about. She’s the Clinical Associate Professor of Information, Operations, and Management Sciences at New York University Stern School of Business, and she has essentially made a career out of teaching how to effectively communicate, both in academia and in business. She’s even written a book, Data Visualization Made Simple , about communicating data science results effectively with visualizations.

“Presenting and communicating your insights across an organization can be really, really powerful,” says Kristen.

So how can you approach communicating your models in a way that’s effective?

Relating The Problem

Let’s say you’ve built a model and have the opportunity to present your findings in front of a major decision-maker in the company. It’s your job to explain what the model means and the impact it could have on the business.

Kristen advocates starting by identifying the problem or challenge you’re addressing. Relate the problem to the interests of the audience, and help them understand the larger context. To get the audience on your side, ask questions before proposing your solution. For example:

Have you ever experienced this?

Have you ever observed that in our business?

This isn’t just a rhetorical technique, it’s a way of measuring what information your audience needs to understand the rest of your pitch. “If no one thinks this is a problem, then you have to start by introducing the problem, and then building the case for the problem,” says Kristen “You don't want to lose your audience by alienating them because they think this isn't a problem at all.”

Keep in mind that what seems like an obvious problem to you isn’t necessarily going to be obvious to your audience, particularly if you’ve spent the last few weeks with your head buried deep in data sets nobody else has seen yet. The problem you found in the data and are attempting to solve with your model could be something that nobody else is really aware of yet.

Once you’ve made the case for the problem itself, you can then present common solutions and why those aren’t the best, most effective fit.

“You want to create some type of suspense, but you're rooting all of this in a narrative,” says Kristen. “Starting with a problem, showing alternative solutions, and then you're ultimately going to reveal your solution.”

Communicating with Data

Although your pitch is often going to be primarily language-based (whether it’s a written report or a standup presentation at a meeting), representing your data visually is absolutely crucial to communicating its meaning with your audience. Very few people can look at a spreadsheet or table and draw quick, clear conclusions about what the data says. Anyone can compare the size of bars on a bar chart, or follow the trend on a line graph.

Data visualization is a crucial skill at every stage of the data science process, of course. “There are a lot of angles that you can take with visualization, and ways to look at it,” says Kristen. “You can look at it purely from the technical viewpoint, you can look at it from the exploratory viewpoint, like using visualization as a tool to explore your data.”

But it’s also critical for communication.

“I think about data visualization as something that we have in the toolkit to help people better understand our insights and our data,” says Kristen.

“Just on a human level, visualizations just allow us to perceive information a lot more clearly when they're well designed.”

When designing visuals for communication outside your own team, it’s important to keep your audience in mind. Your coworkers probably don’t have the context on your problem that your team has, and they may not have the technical knowledge, either. One of the biggest challenges of data science communication is tailoring your presentation to your audience's technical level and still getting your point across without overwhelming them (or patronizing them). 

A good trick for putting yourself in the shoes of a non-technical audience is thinking about the information you want reported to you when you’ve taken a car into the auto repair shop (assuming you’re not a car mechanic yourself). Generally, the most convincing mechanics are going to be the ones who can:

• Explain your problem in clear, simple terms.
• Show you the evidence the problem exists. ​
• Explain in clear, simple terms how the problem can be fixed.
• Give you a clear timeline and price for what the fix will cost.

You don’t want a 30-minute lecture on the factors that affect engine efficiency. You just want to be confident that you know what the problem really is and that the mechanic knows the best way to fix it.

This applies to communicating in data science, too, but now you’re the mechanic. When in doubt, the best approach is to keep it simple. Leaving in all of the details can be confusing and make your charts less readable, so include only what is necessary to communicate your point.

“Know that you don't have to show every data point at once, that you can slow it down. You can show a few data points at a time to help build your story and your narrative,” says Kristen.

Remember: you can always provide more information by answering questions if your coworkers feel they haven’t seen enough. But if you throw a series of complicated, difficult-to-read charts at them, you risk completely losing them, and that's difficult to undo.

Presentation Tips

Incorporating visualizations into a presentation is a bit of an art form, especially with highly technical data. To keep things simple and effective, Kristen suggests keeping a few guidelines in mind.

First: don’t force the chart to speak for itself. Make sure that you are taking the time to clearly explain what's shown on the screen. If you’re displaying data in a graph, only show one graph at a time, and explain what it’s showing and what it means in the broader context of the problem you’re addressing. You can also show where relationships exist, where outliers are, and how effective your model is compared to other models.

Pace is important, too.

“Don't go too fast, but this whole type of presentation shouldn't be more than 10 or 15 minutes,” says Kristen. “You want to make sure that you can do this type of pitch in a short period of time without overwhelming the audience with detail, but also being able to show the data clearly, and use the data as convincing evidence.”

Don’t be afraid to talk specifics. While you don’t want to overwhelm your audience with technical details, you do need to make sure you’ve included the details that are required to understand your presentation, and the charts they’ll be looking at. Are you talking about new leads generated over a period of hours, or years? Do the math for your audience. If you’re making a prediction, quantify it for them.

It also helps to direct the audience’s attention to certain visualizations. It can be tough correlating spoken word with visual data. If you’re talking about a particular section on your graph, point to it. Build your story from there.

Ultimately, you need to remember that communication is first and foremost a human interaction. “You’re the one sitting in front of the CEO, allow yourself to provide the explanations supported by the graphics, not the other way around.”

Data Science Communication Tools of the Trade

Of course, the first step in creating any presentation like this is actually creating the data visualizations. What you use to do that depends on your programming language of choice. “For me, my tool of choice is R and R Studio, and the various packages that go along with that, which are numerous,” says Kristen.

Python programmers also have a plethora of options for data visualization.

If you don’t know yet how you like visualizing data, Dataquest has interactive online courses on exploratory data visualization and storytelling with data viz in Python as well as a free course on data visualization in R . We also have a quick guide with some design tips that’ll help you make your charts easier to read.

Whatever tools you use, remember these basic tips for data science communication and you’ll have a better chance of nailing your next presentation:

• Start with the problem. Is this a problem your audience knows about already? If not, you’ll have to begin by establishing in clear terms that there is a problem.
• Have empathy for your audience and present them with the information they want in a format and in language they can understand.
• Illustrate your conclusions with data visualizations, but let your own explanation - not the charts - drive your presentation.
• Keep it simple, and leave out unnecessary detail in both your explanations and your charts. Don’t exceed 10 to 15 minutes for the whole presentation.

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Present Your Data Like a Pro

by Joel Schwartzberg

Summary .   

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.

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15 Data Science Documentation Best Practices

by Nick Hotz | Last updated Apr 5, 2024 | Project Management

Table of Contents

There’s no single data science project documentation recipe. Rather, your documentation needs will vary by project, team, organization, and industry.

And it’s not just about producing data science model documentation. Instead, think broader and ask – What do I need to document and why? 

Once you’ve thought this through and have goals in place, you can then set a repeatable plan for how to document a data science project.

Guiding Principles

Let’s start with three guiding principles.

1. Document with a purpose

Before you build out your documentation, ask:

• Who will consume this documentation?
• Why do they need this documentation?
• How would they like to consume documentation?

Think broadly and don’t take a “one-size-fits-all” approach. Rather, you should create various artifacts that best serve each set of stakeholder’s needs.

2. Prioritize deliverables over documentation

At first glance, this might seem like an odd best practice for effective documentation.

But think about it. Your goal isn’t deliverable world-class documentation. Rather you aspire to deliver valuable insights and modeling systems that improve your internal stakeholders, end-users, and broader society.

As such, don’t let the documentation drag you down. Time constraints will occur. When they crop up, don’t compromise the quality of your models and systems. But it is generally acceptable to let some quality slip in your documentation (particularly if shortly thereafter you clean it up). 

Prioritizing deliverables is a key tenet of the  Agile Manifesto

3. Keep it simple … but not too simple

Related to the above point, if you document more than what’s needed, you’re taking time away from your model. Indeed, as you read through each of these best practices, skip items that don’t meet your specific needs.

To accomplish this:

• Cut out the fat and remove any outdated/un-needed documentation.
• Avoid redundancy across documents whenever possible.
• Don’t document more than you have to.
• Avoid extensive upfront requirements gathering.
• Be flexible and update the documentation as you go through the life cycle of the project.

Yet on the other hand, if you are too light on your documentation, you’ll accrue technical debt with systems that you don’t understand how to maintain. Inefficiencies may slow down your pace, regulations, and policies might be inadvertently forgotten, and chaos may ensue.

In short, find the right balance.

Data Science Project Documentation

Keeping these principles in mind, let’s move on to documenting your plan.

4. Start your project with a clear purpose

One of the most frustrating and wasteful endeavors is to develop something that no one really needs. And yet, we all fall victim to this sometimes (I know I have, at least).

To help mitigate this risk, start any project with a clear purpose. To accomplish this:

• Document the customer’s business objectives.
• Define how your data science project will meet their needs.
• Set a vision for your project or product so that you can steer the team in the right direction.
• Define clear evaluation metrics so that you can objectively determine whether the project was successful.
• Conduct a cost-benefit analysis can help determine project go/no-go and prioritization against other potential projects.
• Document what you are not looking to accomplish (beyond your project scope).

5. Develop a sufficient upfront project plan

The project plan encompasses many of the previously stated items such as the vision and purpose. It will also be more comprehensive, defining items such as…

• Resources – Staffing, computing requirements, cloud services, software, etc.
• Milestones – Projected deliverables inclusive of their general deliverable dates
• Budget – How much effort will the project require? / How much funding can the project team spend?
• Risks and contingencies – What could go wrong? How will you mitigate these potential risks?

Don’t go overboard but plan enough upfront so that you can execute your project more efficiently. Generally, you should start with a solid understanding of the initial project work and desired end goal. Define key dependencies and risks that fall in between. Most of the middle of your project plan can just be placeholders that you’ll update as you proceed.

Also, don’t fall in love with your plan. It will be wrong. Accept that and be ready to flex the plan throughout the project to meet the evolving realities you encounter.

The project plan should scale with the size and complexity of a project. Even for a small, simple project, jotting down some basic bullet considerations and a process list could help you conceptualize your approach. Meanwhile, larger endeavors should have more comprehensive plans.

A related key artifact is a project or product roadmap . This maps out how each of your intended deliverables will evolve into and fulfill your desired product vision. A good roadmap is lightweight and fits nicely on a slide or webpage.

A  data science roadmap example  that you can customize and re-use

​​An accompanying artifact to the product roadmap is the product backlog. This is a key artifact that Scrum and Data Driven Scrum teams use to keep track of deliverable ideas. A preferred format for each backlog item is the user story format.

6. Consider a Data Science Design Document

An alternative view from the project plan is a data science design document which Vincent likens to a lighthouse that guides you toward a specific destination. He outlines a data science design document with:

• The minimum viable product
• Research and explorations
• Milestones and results

We’ve covered most of the concepts already. But the new one here to elaborate on is the M inimum V iable P roduct (MVP) which is the next version of your data science product that allows you to learn the most about the problem space with the least level of effort. This could be, for example, a one-time offline model that predicts a subset of the overall problem space. From there, you can extend this model to a broader set of use cases, and transition it into a model that runs on an ongoing basis.

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7. write data science user stories.

You should frequently generate ideas for delvierables and insights and deliver on those with the highest value relative to the expected effort. A great way to organize these ideas is in user stories which are short statements, often with some accompanying details such as acceptance criteria or links to more thorough requirements.

A typical user story format is stated from the lens of the stakeholder. It identifies who the stakeholder is, what they would like to receive, and why they would like deliverable.

Example Data Science User Story

As someone is denied a credit card I would like to receive a timely email that briefly describes the main reasons that I was denied So that I can understand the reason behind the denial

This format provides numerous benefits:

• User stories are easy-to-understand.
• User stories force you to look at deliverables from the lens of the stakeholders.
• The short nature of the stories helps facilitate prioritization and follow-up conversations.
• User stories shift the focus toward conversations among the stakeholders and data science team.

Note that user stories might remove some of the burdens of detailed documentation but they won’t replace it. For example, software testers will need to develop a library of test use cases. Legal might need detailed documentation to comply with regulations. And your project contract might include a service-level agreement.

Data Science Model Documentation

We’ve covered guiding principles and some documentation to support the project plan. Let’s now focus on the data science model documentation.​

8. Document the data

Proper data documentation can answer several questions such as:

• What data is being used for the model?
• Why was this data selected (and other data sets excluded)?
• How was the data obtained?
• What are known issues in the data?
• What does the data look like? (mean, median, mode, skewness, expected data volume, etc.)
• How did you alter the data (transformations, imputations, other data cleaning techniques applied, etc.)
• Where is the data located?
• How frequently is the data refreshed?
• Is the data usage compliant with user agreements, data privacy best practices, and relevant regulations? (if not, don’t use it)
• What security protections do you have data-at-rest and data-in-motion to ensure compliance and data privacy?

Data documentation will help in many ways. These last two questions help ensure that data is being used ethically and responsibly (Related: 10 Data Science Ethics Questions ). Moreover, the data issues, exploratory data analyses, and corrections can help you troubleshoot issues during the modeling phase. Even more broadly, the documentation will help others who might want to use the same data for future uses. A data dictionary is a great way to encourage data reuse and enforce data standards.

9. Document your experimental design

Moving on to something that is close to each data scientist’s heart – the scientific method. This core process runs through the cycle of making a hypothesis, running an experiment, and measuring the results. Most data science projects will likewise flow through these steps – often looping through them several times. Document each one of the following before running the experiment (perhaps as accompanying detail in a user story).

• Your testable hypothesis
• The assumptions you made
• The target variable
• The control/test split
• The validation set
• (if relevant) The experimentation time window

At the end of the experiment (and possibly at occasional checkpoints during it), document the results – both from a statistical and a business impact perspective. Use this information to guide the design of potential follow-up experiments and project work.

10. Document the algorithms

As part of your data science model documentation, you should document the algorithms used. A great practice is to also include techniques that you attempted but decided not to use. This will help you look back and keep track of the decisions you made. It will also help you share knowledge with and educate other members of your team. For many use cases, you might also want to document the biggest drivers for the model. Sometimes it’s even required by law. For example, credit card companies in the USA need to explain why an applicant was denied a credit card. In this scenario, you’ll need to detail why the model made each specific decision. Even if not legally required, documenting model drivers can help. For example, a retention team would like to know more than just the likelihood of a customer churning, but also why they might churn.

Supporting Systems Documentation

11. document the code.

How does your code work? You can clearly explain that just after you wrote it. But that might be tough a year from now when you tweak your data pipeline or retrain the model. It’ll even be tougher if you’re picking up someone’s else code who recently left your team. You should always comment your code to help build a maintainable codebase. For Python data science project documentation, use # to for single-line comments and “” for multi-line comments to clarify anything potentially ambiguous such as the purpose of a variable or a function. Wikis, README files, Word documents, or Google Docs can also be great ways to provide higher, project-level documentation. However, if you go this route, be sure to update these documents with any sizeable update to the codebase.

12. Document the infrastructure

If you’re delivering one-off analyses, you could skip this. But production-grade models will need it. In fact, per a Google research paper , the vast majority of code in a machine learning system stems from the supporting infrastructure. Infrastructure documentation will help with both preventative and correction system maintenance.

Preventative Maintenance: Software grows old. New security threats arise all the time. Models and data will drift. Documenting how to best maintain the system in advance will you keep it running smoothly. Consider documenting items such as…

• Cadence to check the model for re-training
• Calendar of key events like SSL certificate expiry or cloud resource budget planning cycles
• Software versions used
• How to scale your system to support increased product usage (could be planned in advance or automatic)

Corrective Maintenance: Your system will probably fail at some point. And if (when) it does, you’ll thank the person who put this documentation together for you so that you’re prepared with a response.

13. Build user documentation

Don’t forget your users! Rather, be sure they know how to use your system. If you have a user interface, a great practice is to put a help menu link in the upper right of your screen so that the user can navigate to find items such as:

• How do I control the visualization?
• What are the definitions for key measures and dimensions?
• When is the system available?
• Where do I report a bug or request a product feature?

Another common output for a model is via an API. In this case, write technical documentation so that the receiving-end engineers can build on top of your API. Include items such as definitions, endpoints, parameters, data formats, and response times.

Data Science Project Documentation Templates

14. grab a pre-built template.

There are a few templates that can help get you started.

• CRISP-DM: CRISP-DM is the most common data science life cycle and defines a series of documents you should develop throughout a data mining project. Warning – these documents tend toward a more traditional view of extensive documentation, and (given CRISP’s age) the CRISP-DM guide lacks modern deployment best practices. You can visit Patiegm’s Github page for a handy CRISP-DM documentation template .
• Microsoft’s TDSP: Microsoft takes a more modern documentation approach in its Team Data Science Process . The Charter  and  Exit Report  are particularly useful, even if you do not use TDSP.
• Model Cards: In a 2019 research paper , Google introduced the concept of a Model Card to set a vision for standardized and transparent model reporting. Visit withgoogle.com for an overview.
• Data Science Checklist: Checklists are a great way to identify what needs to be done and to track the status of each task. As such, consider our data science project checklist .

15. Build your own data science documentation template

The reality is that your project, team, and organizational needs will deviate from the above templates. As such, use these as starting points toward creating your own data science documentation templates.

Congrats! You made it to the end. But your work is just getting started. Remember that these data science project documentation best practices do not apply to all circumstances. And your situation will likely require some additional practices not mentioned here. So to review:

• Know your audience
• Keep it simple but not too simple
• Document your plan
• Document your model
• Document your system
• Build and customize your own templates

Best of luck and reach out if you have some additional pointers you found useful.

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Network analyses of emotion components: an exploratory application to the component process model of emotion

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• Published: 14 September 2024

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• Livia Sacchi   ORCID: orcid.org/0000-0002-6428-3789 1 &
• Elise Dan-Glauser 1  

Emotion is an episode involving changes in multiple components, specifically subjective feelings, physiological arousal, expressivity, and action tendencies, all these driven by appraisal processes. However, very few attempts have been made to comprehensively model emotion episodes from this full componential perspective, given the statistical and methodological complexity involved. Recently, network analyses have been proposed in the field of emotion and cognition as an innovative theoretical and statistical framework able to integrate several properties of emotions. We therefore addressed the call for more multi-componential evidence by modeling the network of a comprehensive list of emotion components drawn from the Component Process Model of Emotion. Five-hundred students were confronted with mildly ambiguous scenarios from everyday life, and reported on their situational appraisals and emotion responses. Network analyses were applied to the emotion components related to a positive and a negative scenario to explore 1) how the components organize themselves into networks and dimensions; 2) which components are the most central within networks and dimensions; and 3) the patterns of components relation between and within dimensions. A three-dimensional solution emerged in both scenarios. Additionally, some appraisals and responses appeared to be differentially relevant and related to each other in both scenarios, highlighting the importance of context in shaping the strength of emotion component relations. Overall, we enriched the field of affective science by exploring the connections between emotion components in three novel ways: by using network analyses, by integrating them into a multi-componential framework, and by providing context to our emotion components. Our results can also potentially inform applied research, where understanding the interconnections and the centrality of components could aid the personalization of interventions.

Avoid common mistakes on your manuscript.

In emotion research, it is generally accepted that an emotion has a componential nature: that is, what we call emotion is the byproduct of the interaction of several components, namely subjective evaluations, feelings, physiological arousal, expressivity, and action tendencies (Lange et al., 2020 ). Several emotion theories coexist, and differ in their conceptualizations of how and which of these components are most central. However, they converge on the necessity of an antecedent event for an emotion to occur – that is, a situation that will then be appraised (Scherer & Moors, 2019 ).

The Component Process Model (CPM) of emotion by Scherer ( 2009 ) has established itself as one of the most authoritative modern appraisal theories thanks to its dynamic and functional architecture. In the CPM, an emotion is a synchronized, multi-componential episode initiated by cognitive appraisal of an emotionally charged situation (e.g., an external event such as a friend not greeting back, or an internal event such as an upsetting memory). Compared to other emotion theories, the CPM assigns a special gate-keeper role to appraisal. Indeed, as a result of phylogenetic processes, appraisal is a highly sophisticated cognitive tool that allows us to navigate safely through complex and ambiguous situations. In other words, appraisals serve an evolutionary function, that the CPM has the merit of organizing into an increasingly differentiated, sequential architecture. For example, within a situational appraisal, early Stimulus Evaluation Checks (SECs) act as orienting responses to novelty, followed by evaluations of pleasantness, and of personal goal relevance. These first SECs fall under the functional category of Relevance detection, which is also found in non-human species and in simple organisms (Ellsworth & Scherer, 2003 ). It is theorized that if these basic checks are not present, an emotion episode cannot be elicited. Then, more “costly” cognitive checks are endorsed. The functional category of Implications regroups those checks that assess the personal consequences that might result from the situation, for example depending on who caused it, whether it is still conducive to personal goals, and what the probability of the desired outcome is. Then, the functional category of Coping Potential regroups those checks that determine the individual’s ability to cope with and/or adjust to the situation. Finally, the functional category of Normative Significance assesses the compatibility of the situation to personal and external norms. In the CPM, the result of this multilevel appraisal process causally leads to the differentiation and modification of emotion responses, i.e., the experiential (e.g., frustration), somatic (e.g., feeling hot), expressive (e.g., frowning), and motivational (e.g., repair instinct) components of an emotion episode. Finally, the event is represented centrally as nonverbal feelings, and the emergent emotion (e.g., sadness) is categorized and labeled (Fig. S1 ). Importantly, in the CPM, emotion components activation is theorized to have a recursive effect on the other components: that is, once an emotion episode is initiated, a dynamic update of the system takes place continuously, always with an adaptive function (Scherer, 2009 ).

Despite the agreement on the multi-componential nature of emotion episodes, virtually no attempts have been made to model them comprehensively under a full componential perspective, mainly for two reasons. The first concerns the overwhelming amount of research dedicated to the investigation of what is considered as the “real” outcome of an emotion episode, that is, a categorical emotion (e.g., guilt, pride). Early appraisal theory aimed to identify the fixed patterns of component activation that would lead to the experience of these prototypical emotions, usually by applying self-report measures in a deductive-semantic fashion (Gentsch et al., 2018 ). In such cases, participants are presented with an emotion term, followed by a list of emotion components to be matched to the emotion based on their beliefs and experience. This procedure has however been heavily criticized for eliciting culturally-based and/or stereotypical assumptions about emotions (Scherer & Moors, 2019 ). Modern appraisal theorists have also now acknowledged the pervasive “impurity” and complexity of the emotion experience, proving that the existence of pure emotions is the exception rather than the rule (Scherer & Meuleman, 2013 ; Scherer & Moors, 2019 ). This has led appraisal research to shift from the identification of categorical emotions as outcomes of the emotion chain to the study of the interconnections between the five components: however, paradigm shifts are slow to be implemented in practice. Indeed, the most prolific contemporary strand of appraisal research is known as bi-componential, that is, concerned with exploring relations between component pairs (Meuleman et al., 2019 ). Scherer and Moors ( 2019 ) recently provided a summary of such evidence: overall, novel and goal-relevant stimuli elicit pre-attentive appraisals, which are linked to automatic action tendencies such as approach or avoidance tendencies, depending on the stimuli valence. When a stimulus is negatively valenced and externally caused, or general control/power over the situation is high, action tendencies are more aggressive in nature. Regarding the relation between appraisal and physiological reactions, evidence suggests that novel and goal-relevant stimuli re-orient attention and induce physiological changes in parameters such as muscular tone, electrodermal and respiratory activity, as well as pupil dilation, with negative and positive valence inducing differential reactions. A higher vascular reactivity and sympathetic arousal have also been associated with low prospective control. Finally, fewer experimental evidence is available for the appraisal-expressivity relationship: the strongest results concern the intrinsic pleasantness appraisal affecting frowning (corrugator muscles activity) and smiling (zygomatic muscles activity), and the power and control appraisals affecting vocal expressivity (Scherer & Moors, 2019 ).

The second reason for the scarcity of multi-componential modeling is pragmatic: attempting to model a large number of components involves great statistical and methodological complexity (Meuleman et al., 2019 ). Examples of these multi-componential attempts are rare, but two are noteworthy for their innovation. Within the CPM framework, Meuleman et al. ( 2019 ) used machine learning algorithms to explore the relationships between 18 appraisals and the emotion responses factors. They found that factors within the same emotion component, as well as appraisals factors and response factors, were mostly uncorrelated. Only the appraisal of goal compatibility and of suddenness were strongly related to physiological and expressive responses, respectively. More recently, Lange et al. ( 2020 ) proposed the network approach to model emotion components, which postulates how, for instance, the emotion of anger actually emerges from the interaction of beliefs, motivations, expressive behaviors and bodily reactions. Inspired by the theoretical proposition of Lange et al. ( 2020 ), we believe that networks could similarly advance modern appraisal research by allowing a comprehensive exploration of multi-componential relations.

Applied to many scientific fields in the last decade, network modeling has also grown exponentially in psychology (Borsboom et al., 2021 ). Its widespread popularity across disciplines lies in its foundational assumption that phenomena between and within individuals– from human genes and mental health to relational or social media transactions– are dynamic, complex systems, and thus exhibit complex behavior (Barabási, 2012 ). Because systems entail a structural organization—a network– of their components, it is often fruitless to study their functioning in isolation (Barabási, 2012 ; Borsboom et al., 2021 ). This is especially true when components interact with each other to give life to a phenomenon, which in turn can influence the functioning of the same components through feedback loops – a property known as bi-directionality (Dalege et al., 2016 ; Lewis, 2005 ). The simplicity with which networks visually convey very complex relationships between system components is another reason why they have become so popular (Hevey, 2018 ). Components, called nodes, are connected via edges that convey the magnitude and direction of their association. For example, thicker and green (or blue) edges signal a strong and positive (excitatory) relation, while thinner and red edges a weak and negative (inhibitory) relation (Borsboom et al., 2021 ; Dalege et al., 2016 ). Moreover, nodes can a) be more important (central) to the network, by being more strongly connected to all the other nodes; b) cluster into communities– i.e., groups of more densely connected nodes; and c) bridge different communities (Borsboom et al., 2021 ).

In psychology, these network properties have led to important theoretical and empirical advances through the modeling of affects, cognitions, and behaviors. For instance, network approaches have allowed the subfield of clinical psychology to move away from the long-standing essentialist, biologically-based view of mental disorders, and to explore how syndromes may actually be the byproducts of causal and dynamic interconnections of symptoms, such as negative cognitive schemas (Bringmann et al., 2022 ; Robinaugh et al., 2020 ). For example, in a sample of American psychology college students, Collins et al. ( 2023 ) investigated the moderating influence of depressive symptoms on the network of negative self-schemas associated with fear of happiness. They found that more depressed students reported stronger and positive links between nodes representing avoidance and devaluation of positivity. Similarly, Tao et al. ( 2022 ) explored the association between anxiety, depression and sleep disturbance symptoms in a large convenience sample of Chinese university students. The authors found that the symptoms of guilt, irritability, restlessness, fear, and sleep disturbance bridged the three disorders, meaning that once these symptoms are activated, they would in turn activate the entire network. This knowledge opens the possibility of improving the whole mental health network by acting on a single symptom (Jones et al., 2021 ). In cognitive psychology, networks have allowed to easily visualize how and with what intensity nodes are connected (or not), as well as their centrality, across groups. For example, Neubeck et al. ( 2022b ) modelled cognitive performance components in young and old individuals and showed how the fluid intelligence component was more central, and the link between intelligence and working memory stronger, in the old group compared to the young one, while the accelerated attention component was most central in the latter. Similarly, Neubeck et al. ( 2022a ) found that self-regulation and executive control functions were more strongly interconnected in older than in younger individuals, possibly due to a stronger effect of cognitive decline on overall regulatory processes. In the field of emotion psychology, Mattsson et al. ( 2020 ) explored the interconnection of academic-related positive and negative emotions in a sample of 241 Finnish university students, highlighting how self-efficacy beliefs emerged as the most central node and therefore targetable. More recently, Lange and Zickfeld ( 2021 , 2023 ) confirmed the utility of the network approach by demonstrating that components are indeed shared between similarly valenced emotions, such as awe, admiration, and gratitude, guilt and shame, or awe and kama muta (“being moved”).

Strikingly, emotion episodes were already being discussed as complex systems of component interactions almost 20 years ago (Lewis, 2005 ; Sander et al., 2005 ). The CPM itself relies on dynamic system principles, in particular that of recursiveness (i.e., bi-directionality; Moors, 2022 ; Sander et al., 2005 ), according to which “the emotion process is considered as a continuously fluctuating pattern of change in several organismic subsystems that become integrated into coherence clusters and thus yields an extraordinarily large number of different emotional qualities” (Scherer, 2009 , p. 1320). The application of networks to the modeling of CPM emotion components is thus a natural step, if not the required step, to focus on the mechanisms underlying an emotion episode and move beyond standard emotion labels as outcomes (Scherer & Moors, 2019 ). Moreover, as suggested by Lange et al. ( 2020 ), the application of networks to emotion components and emotions has the potential to achieve the integration needed in emotion research, by serving as an alternative psychometric model to perhaps the most explicitly (and implicitly) applied one in the field: the reflective latent-variable model. Its theorization of what constitutes an emotion episode coincides with the lay notion of an unobserved (i.e., latent) construct, whose symptoms (i.e., indicators) are instead observable (Lange et al., 2020 ). These indicators are thus causally dependent on the latent variable and causally independent of each other, implying that: 1) an emotion episode is separable from its components; 2) these components have a fixed and universal pattern of activation that leads only to the experience of a particular emotion; and 3) they are correlated to the latent variable but not causally interacting between each other (Lange et al., 2020 ). However, empirical evidence contradicts the reflective latent-variable model of emotion: components are routinely manipulated to assess the target emotion (Mauss & Robinson, 2009 ); individuals vary in their situational appraisals, and in the intensity with which these appraisals affect emotion reactivity (Kuppens & Tong, 2010 ); appraisals exert a causal effect on other components of emotion (Meuleman et al., 2019 ), and some components are known to be more correlated than others (Lange et al., 2020 ; Scherer & Moors, 2019 ); and, finally, mixed emotions are the norm rather than the exception (Israel & Schönbrodt, 2021 ; Scherer & Meuleman, 2013 ).

Thus, inspired by the theoretical and methodological proposal by Lange et al. ( 2020 ), we aimed to explore the network of a comprehensive list of emotion components in slightly ambiguous, positive and negative daily life situations, without deductive prompt of emotion terms (Gentsch et al., 2018 ). This was done to explore what is referred to as an emotion episode in the CPM (Scherer & Moors, 2019 ). As noted in the findings reported above, bi-componential research points to few but stable relations between appraisal and emotion responses, while multi-componential evidence is so far sparse and heterogeneous. Therefore, we had several goals with this work.

Our first research question was to explore how the five emotion components organized themselves into a network and into dimensions. This would provide important information regarding the influence of context on component inter-connections and clustering. Emotion components in the CPM are theorized to be organized at a higher-order level, which comprises a four-factor structure of Valence, Arousal, Power, and Novelty (Fontaine et al., 2013 ). Recently, Fontaine et al. ( 2022 ) provided even more nuanced results concerning the relations between these four dimensions as negative and positive emotion terms turned out to be strongly distributed across a dimensional space consisting of the first two dimensions. The meaning of these terms was then further contextually refined by the dimensions of Power and Novelty. For example, the authors found a strong relation between Valence and Power dimensions in positive emotion terms, which did not hold for negative ones. The authors explained this finding by arguing that positive valence already captures substantial variance in power-related components (i.e., having power over a situation is generally perceived as positive). Based on this evidence, Fontaine et al. ( 2022 ) formulated specific predictions concerning the emergence of a distinct Power dimension, depending on the proportion of positive versus negative emotion terms. Translating their predictions to scenarios, we thus hypothesized that, in a positive one, appraisals belonging to the Coping Potential category would be more connected to or clustered with clearly valenced appraisals, such as the appraisal of pleasantness and of consequences, resulting in a blend of power and valence appraisals; and that, in a negative scenario, a Power dimension would emerge more clearly. Moreover, the authors show that when Novelty was higher, more Arousal and less Power were reported in emotion terms, respectively: we therefore hypothesized that the appraisals of suddenness, predictability, urgency, and immediateness, theoretically related to the higher-order Novelty dimension, would be more strongly associated to, or clustered with, either Arousal-related emotion components, or appraisals of Coping Potential, depending on the perceived situational novelty.

Our second research question concerned the assessment of components centrality: that is, we aimed at evaluating which component(s) appeared to be the most central (i.e., important) in the network and in the assigned dimensions. By estimating centrality indices, nodes that play a pivotal role in network activation and in their assigned dimensions can be identified. Given the strong evolutionary implication of the appraisals of pleasantness and of goal conduciveness in emotion emergence (Ellsworth & Scherer, 2003 ), we hypothesized that these will emerge as more central in the networks and in their assigned dimensions than other appraisals, regardless of the contextual valence. We further hypothesized that SECs related to the Coping Potential category will also emerge as central in the networks, given the theoretical and the empirical implications of these appraisals in valenced situations (Scherer, 2020 ; Scherer et al., 2022 ). Indeed, this hypothesis would also align with the finding by Mattsson et al. ( 2020 ) of self-efficacy beliefs emerging as the most central node in a network of positive and negative academic emotions.

Finally, our third research question concerned the formal testing of the between- and within-dimension component relations, following the recent contribution by Lange and Zickfeld ( 2023 ). This test permits to highlight the interrelation of components within the CPM. Given that previous research on emotion coherence has generally found stronger associations between elements within each component than across components (Lange et al., 2020 ; Mauss & Robinson, 2009 ), we hypothesized that emotion components within the same dimension would be more strongly connected to each other than across dimensions, an empirically proven property known as “small-world” (Borsboom et al., 2021 ; Dalege et al., 2016 ).

All in all, to the best of our knowledge, this is the first contextual application of network models within the CPM framework, aiming at providing complex modeling of specific emotion episodes.

Participants

We began by recruiting first-year psychology students at our host institution, who received compensation in the form of course credits. In a second round of recruitment via social media, we then extended the study to students at other Swiss educational institutions at the Bachelor, Master, and occasionally doctoral level, if deemed appropriate. These participants were rewarded with a voucher. Inclusion criteria were being between 18 and 45 years old, being in good health, and having sufficient proficiency in French. The former age inclusion criterion was dictated by the known physiological and hormonal changes occurring after the age of 45 (Crandall et al., 2023 ; McKinlay, 1996 ; Rymer & Morris, 2000 ). Exclusion criteria were medical treatment, regular use of drugs or medication, and diagnosis of a psychiatric disorder, as these factors are known to influence emotional and physiological processes at both the self-report and objective levels (Clark & Beck, 2010 ; Edgar et al., 2007 ; Kin et al., 2007 ; Wirth & Gaffey, 2013 ). Concerning sample size, guidelines for network models in psychology are still in their infancy (Hevey, 2018 ). However, a sample size of 250 for approximately 25 nodes is generally recommended based on simulations (Dalege et al., 2017 ). Given that several research questions were to be answered by this database, we aimed at the largest possible sample. The final sample consisted of 500 participants, of whom 212 (42.4%) were rewarded with vouchers and 288 (57.6%) with credits. In total, the sample included 412 females (83%) and had a mean age of 22.41 years (SD = 3.23), with 78% of the participants being native French speakers. The predominant educational level was bachelor’s degree (90% of the sample), with psychology being the most common subject (72% of the sample). The sample size obtained was considered adequate for network analyses.

As appraisals and emotion responses are specifically about situations, contextualization of our measures had to be performed. To explore emotion components, participants were thus administered four emotionally loaded scenarios (contexts) that were pre-tested in a pilot study. The first criterion for selecting the scenarios was that the scenario content had to be relevant to a student population. In the context of the CPM, emotionally charged autobiographical or written scenarios have been used extensively with student samples similar to ours (Gentsch et al., 2018 ; Pivetti et al., 2016 ; Scherer et al., 2022 ). The second selection criterion was that the scenario had to include some ambiguity in their formulation, as early pioneers in emotion research stated the important role of ambiguity in amplifying individual differences in appraisal processes (Lazarus & Folkman, 1984 ). Indeed, recent evidence shows that presenting stimuli with unambiguous valence increases the likelihood of obtaining floor or ceiling effects (Neta & Brock, 2021 ).

In the present work, analyses were conducted on the emotion components embedded in daily life situations. Specifically, out of the four scenarios, we employed the positive one, describing a birthday party - hereafter, Positive Scenario, adapted from Farrell et al. ( 2015 ) and Rohrbacher and Reinecke ( 2014 ) – and one of two negative scenarios, concerning social rejection. The scenario retained in the present work– hereafter, Social Rejection Scenario, adapted from Zimmer-Gembeck and Nesdale ( 2013 ) – reports an incident of ambiguous rejection around a group of close friends. Previous studies on emotion coherence have focused on situations that could activate the four emotion component systems, like anger or surprise situations (Evers et al., 2014 ; Reisenzein, 2000 ): we thus deemed this type of scenario appropriate to maximise a differentiated response in terms of valence and arousal, given the unexpectedness and negativity of the event. The Positive Scenario was tested for comparison purposes, as routinely done in affective science (e.g., Mauss & Robinson, 2009 ; Mauss et al., 2005 ). The other two scenarios, depicting an ambiguous, more active - overt - rejection incident and a neutral situation, are to be employed in a separate study on emotional processing and maladaptive personality, given the cognitive interpretation biases exhibited by individuals with pathological traits in these contexts (An et al., 2023 ; Grynberg et al., 2012 ; Priebe et al., 2022 ). Therefore, these two additional scenarios are not reported here. Nonetheless, the text of all scenarios, along with their corresponding French translations for the selected ones, are reported in supplementary Table S1 .

Within the CPM, the five emotion components (appraisal, physiological reaction, expressivity, experience, and action tendency) were operationalized using a psycholinguistic instrument called GRID (Fontaine et al., 2013 ). The GRID was originally designed to assess semantic profiles of emotions at a componential level with 142 features (i.e., items). Later, the GRID has been applied to emotionally charged situations, such as scenarios (Scherer, 2020 ; Schlegel & Scherer, 2018 ) and video-clips (Mohammadi & Vuilleumier, 2020 ). Due to its length, two shorter versions were derived from the GRID (Scherer et al., 2013 ): the CoreGRID (63 features) and the MiniGRID (14 features).

The GRID, and derivatively the CoreGRID and MiniGRID, are organized at a higher-order level, which comprises a four-factor structure, and a lower-order level (see Table  1 , “Higher Order Factor Assignment”, and “Lower Order Factor Assignment”; Fontaine et al., 2013 ). For the current project, as a trade-off between comprehensiveness and parsimony, we integrated the MiniGRID with the Appraisal component of the CoreGRID to have better coverage of appraisals categories and content.

Appraisal measures

As described in Scherer et al. ( 2013 ), 21 appraisals were derived from the French version of the Appraisal component of the CoreGRID instrument. Appraisals are categorized into the four main SEC functional categories of Relevance, Implications, Coping Potential, and Normative Significance (Fig. S1 ; Table  1 ). Participants rated each of the 21 items for each scenario on a 9-point scale ranging from 1 (not at all) to 9 (completely).

Emotion responses

Emotional reactivity was assessed using the French version of the MiniGRID instrument (Scherer et al., 2013 ), with two items tapping the feeling component, four tapping the physiological component, four tapping the expressive component, and two tapping the action tendency component (Table  1 ).

Other measures

For other projects, additional measures were administered which will not be discussed in depth as not part of the current study. Briefly, participants were asked to rate the intensity of nine categorical emotions experienced in the scenarios on a scale from 0 to 100, and to fill the following individual differences batteries: the Toronto Alexithymia Scale (TAS; Bagby et al., 1994 ), the Difficulties in Emotion Regulation Scale (DERS-F; Dan-Glauser & Scherer, 2013 ), the NEO Five-Factor Inventory (NEO-FFI; Costa & McCrae, 1992 ), the Personality Inventory for DSM-5 (PID-5; Maples et al., 2015 ), the 4-item Patient-Health Questionnaire (PHQ-4; Kroenke et al., 2009 ), the Berkeley Expressivity Questionnaire (BEQ; Gross & John, 1997 ); and the Positive and Negative Affective Schedule (PANAS; Watson et al., 1988 ).

The entire study was conducted on LimeSurvey ( https://www.limesurvey.org/fr ), an online survey platform accessible from smartphones and laptops. All data were anonymized. The study was approved by the Ethics Committee of the University of Lausanne (protocol number: C-SSP-042020-00001).

At the beginning of the online study, students were greeted and given general information about the content of the study. After signing the consent form, they answered general demographic questions. The study was divided in two parts, a scenario part, and a questionnaire part, which were randomized to avoid order effect. Before being confronted with the two scenarios, participants were given a brief instruction based on that of Smith and Lazarus ( 1993 ), encouraging them to imagine themselves in the scenario and to immerse themselves in the emotions, feelings, and thoughts they elicited. Each of the two scenarios started with a description of the scene over a few lines. For each scenario, participants had to answer the selected CoreGRID and MiniGRID items and complete the emotion category questions. At the end of the study, a detailed debriefing on the research questions was provided. The study lasted between 50 and 90 min.

Data processing

Analyses were performed in the R environment (R Development Core Team, 2020 ). For each of the two scenarios, we followed the same steps. Based on Cronbach's alpha calculations, the CoreGRID Appraisal component and MiniGRID items were reversed to obtain coherent response scores. We then transformed our data to ensure that the multivariate normality assumption was met (Epskamp et al., 2018 ). Deidentified data, R scripts for all analyses, and supplementary material - including code source and acknowledgments - can be found at our OSF link at https://osf.io/t9f43/.

Network and dimensionality estimation

To address our first research question, we endorsed the Exploratory Graph Analysis (EGA) framework (Golino & Christensen, 2024 ; Golino & Epskamp, 2017 ). Within this framework, we applied to our transformed data the standard psychometric network model - known as the Gaussian graphical model (GGM; Lauritzen, 1996 ) - in combination with a clustering algorithm– known as the Walktrap community detection algorithm (Pons & Latapy, 2005 ). The GGM estimates partial correlation coefficients that are plotted as edges connecting two nodes (Borsboom et al., 2021 ; Epskamp et al., 2018 ). Edge weights (connection strength) are depicted in the networks, along with their magnitude - thin or thick line - and direction - red for negative and green or blue for positive (Epskamp et al., 2018 ). GGM was used in conjunction with the extended Bayesian Information Criteria (EBIC; Chen & Chen, 2008 ) - graphic least absolute shrinkage and selection operator (lasso; Tibshirani, 1996 ) approach, which shrinks partial correlation coefficients to zero to retain only those truly different from zero (Epskamp et al., 2018 ). The Walktrap community detection algorithm allows the identification of dimensions – or communities – by grouping nodes that are more strongly interconnected in the network (Golino & Epskamp, 2017 ).

We then performed a variable redundancy check. Local dependence – i.e., strong correlations – among items can lead to network instability: we therefore applied Unique Variable Analysis (UVA; Christensen et al., 2023 ), an approach that detects highly correlated items. For the current work, UVA is particularly useful since the Appraisal component of the CoreGRID and the MiniGRID were designed as semantic emotion analysis tools, and high intercorrelations between the items are thus expected. UVA reports the extent to which nodes overlap and share nearly the same relationships with other nodes in terms of edge strength and positive/negative direction via a measure called weighted topological overlap (wTO; Christensen et al., 2023 ). Based on recent guidelines and implementations (Christensen et al., 2023 ; Maertens et al., 2023 ), we implemented a wTO threshold of 0.20, and for each pair of items flagged as redundant, we retained the one with the higher ratio of main network loadings to cross-loadings, to obtain higher dimension stability. Developed within the EGA framework, network loadings have been shown to be equivalent to factor analytic loadings, with values of 0.15, 0.25, and 0.35 indicating low, moderate, and high magnitude, respectively (Christensen & Golino, 2021b ). Indeed, as in factor analytic methods, items that cross-load heavily on dimensions other than the assigned one can lead to model misfit and instability (Christensen et al., 2023 ; Maertens et al., 2023 ).

After network structures and dimensions were retrieved in the empirical data, and redundant items removed, the stability and consistency of these dimensions was inspected with Bootstrapped EGA (bootEGA; Christensen & Golino, 2021a ). Briefly, it is important to inspect if the number of dimensions retrieved by bootEGA is a recurrent solution, or if other dimension solutions are also found. Notably, the more frequently a dimension solution is retrieved, the more stable it is. Perfect stability is reached when the dimension solution is found 100% of time in the bootstrapped replicated samples. An item stability plot is then run to visualize how items are loading on their respective dimensions, and to identify possibly unstable items. Item stability values below the threshold of 0.75 and with network loadings lower than 0.15 signal instability (Christensen et al., 2023 ; Maertens et al., 2023 ). It is recommended to remove such items. The dimensionality and structural consistency of the network is then reassessed in an iterative fashion, until an optimal and stable solution is found (Christensen & Golino, 2021a ).

Following this procedure, we were then able to robustly retrieve the underlying structural and dimensional organization of the CoreGRID Appraisals and MiniGRID components in both scenarios.

Network centrality indices estimation

To address our second research question, we followed the guidelines by Epskamp et al. ( 2018 ). We computed the centrality metrics of Node Strength and Expected Influence, and evaluated their stability. Centrality indices are measures of node importance and indicate which node plays a pivotal role in the network. Node Strength indicates how strongly a node is directly connected to all the other nodes in the network. Expected Influence centrality, on the other hand, is a measure of positive connectivity (Epskamp et al., 2018 ). The larger these parameters, the more influential a given node is in the network. To evaluate the stability of these aforementioned centrality indices, we applied the case-dropping subset bootstrap (Epskamp et al., 2018 ). This method verifies if centrality indices, after iteratively dropping a predefined percentage of cases (i.e., observations) from the dataset, are still stably correlated with the centrality indices of the original dataset. Their stability is measured by a parameter called the correlation-stability (CS) coefficient (Epskamp et al., 2018 ): values above 0.25 indicate acceptable stability, and values above 0.5 indicate optimal stability. Following Epskamp et al. ( 2018 ) guidelines, we also estimated the trustworthiness of edge weights via bootstrapped confidence intervals and via bootstrapped difference tests, which are reported in details in the Supplementary Material. Given that centrality indices are estimated in relation to the network and not to the retrieved dimensions, we also report the results from network loadings: the highest the network loading for a given node is, the most central this node is to its assigned dimension (Christensen & Golino, 2021b ).

Within-dimension and between-dimension mean edge weight comparison

To address our third goal, we followed the procedure recently outlined by Lange and Zickfeld ( 2023 ). Even though dimensionality estimation can provide a visual understanding of emotion components connections, a formal test is needed and was hence conducted. Specifically, the first formal test assesses if edges between the retrieved EGA dimensions are different from zero. This would confirm the utility of using networks to model emotion components: otherwise, emotion components would be perfectly independent and separable, which is against the CPM. The second test assesses if within-dimension edges are stronger than between-dimension edges, which would provide additional insight into coherence among the CPM emotion components. Bootstrapping techniques were used, in conjunction with an adapted version of an equivalence test based on the 95% bias-corrected and accelerated (BCa) confidence intervals and Holm correction for statistical significance testing. We refer the reader to the original publication and script by Lange and Zickfeld ( 2023 ) for further analytical details.

Further exploratory testing

Recently, in a multi-sample study, Schlegel and Scherer ( 2018 ) found an age effect on Emotion Knowledge, that is, the ability to understand and recognize the emotions of others from a componential perspective. Subjects were presented with the five emotion components described in the CPM and had to select those that best represented a given emotional episode. The authors found that emotion understanding increased with age until reaching a plateau in middle and late adulthood, with women scoring slightly higher on the construct. However, to the best of our knowledge, studies examining these demographic differences in age and gender on each and all the five CPM components are virtually absent. The only exception is the recent study by Young and Mikels ( 2020 ) who tested if age differences in the appraisal of personal, other- or circumstantial control over the consequences of ambiguous social and non-social situations emerged in a sample of 50 older adults (M Age  = 62.8; SD = 5.2) and 50 younger adults (M Age  = 22.8; SD = 2.1). Interestingly, older adults appraised situations higher in terms of personal control, and lower in terms of negativity (but similar in terms of positivity), compared to younger adults (Young & Mikels, 2020 ). Given this recent evidence, we deemed appropriate to control for the effects of age (above or below the median age in years; for a similar approach, see McCormick et al., 2023 ) on all CPM components through the metric invariance analyses with permutation tests developed by Jamison et al. ( 2022 ) in the EGA framework. For the sake of comprehensiveness, we also tested for metric invariance for incentive groups (Group 1 versus Group 2) and for gender. While the former test is not expected to yield significant results, gender differences may appear spuriously due to the unbalanced nature of our sample (83% females). We report these exploratory results in detail in the Supplementary Material retrievable at our OSF link. In both scenarios, metric invariance analyses on the CoreGRID Appraisal and MiniGRID items retained in the final EGA models showed no significant differences in network loadings for median age, sex and group belonging as the grouping variables (Table S7-S9 for the negative scenario and Table S10-12 for the positive scenario). Since none of these testings resulted significant, the variables age, gender and groups were not considered further in the modelling process.

The descriptive statistics of the untransformed variables after reversing the marked items are shown in Table  2 (see Table S2 in the Supplementary Material for the descriptive statistics of the transformed variables). For the sake of clarity, in the network analyses, an “S” and “P” prefixes were added to the appraisals and emotional reactivity items from Table  1 to distinguish between those belonging to the Social Rejection and to the Positive scenarios, respectively. The reader can thus refer to Table  1 for variables content.

Social rejection scenario

To answer our first research question regarding the Social Rejection Scenario, after applying the default EGA approach to all 33 transformed CoreGRID Appraisal and MiniGRID items, we first checked for local dependence issues. UVA identified three pairs of redundant items (see Table  1 for items content): SAC2 and SAC4 (wTO = 0.293); SRF1 and SRF2 (wTO = 0.400); and SRA2 and SRA3 (wTO = 0.505). The ratio of network loadings (main/cross-loadings) were as follows: SAC2 = Inf (i.e., perfect loading on assigned dimension) versus SAC4 = 54.289; SRF1 = 2.405 versus SRF2 = 1.712; SRA2 = 2.405 versus SRA3 = 6.416. Therefore, only SAC2, SRF1 and SRA3 were retained in the subsequent analyses.

To explore structural consistency and replicability of the dimensions emerging from these locally reduced data, bootEGA was then performed. The median number of dimensions found via bootEGA in the reduced dataset was 3, with acceptable confidence intervals (95% CI [1.41, 4.59]). However, their structural consistency was very low (0.390, 0.334, and 0.194 for dimension 1,2, and 3, respectively), with item stability indices varying between 25 and 100%, indicating overall instability (Fig. S2 , left panel): as recommended, we therefore removed items with items stability indices below 75% (Christensen & Golino, 2021a ), ending up with 23 nodes. We then repeated the bootEGA procedure, now obtaining satisfactory structural consistency (0.808, 0.952, and 0.996 for dimension 1,2, and 3, respectively) and item stability range (between 91 and 100%). Following existing guidelines, and to further strengthen the structural consistency of our dimensions, we did not retain items with network loadings lower than 0.15, as this denotes weak dimensional belonging (Christensen et al., 2023 ; Maertens et al., 2023 ). With this procedure, two appraisals were discarded: SAI7 (urgency) and SAI8 (personal agency).

The final reduced structure included 21 items from the CoreGRID and MiniGRID , and 90 non-zero edges. The median number of dimensions extracted by bootEGA was 3, with optimal confidence intervals (95% CI [2.55, 3.45]) and even better structural consistency (0.966, 0.972, and 0.984 for dimension 1,2, and 3, respectively) and item stability range (between 97 and 100%; Fig. S2 , right panel). Figure  1 shows the structural and dimensional organization of CPM emotion components in the Social Rejection Scenario.

Estimated network structure and dimensionality results for EGA for the final reduced data set, with unstable items removed. Items labels start with an “S”, denoting their belonging to the Social Rejection Scenario. Connection strength between nodes is represented by lines thickness. Red and green lines indicate negative and positive relations, respectively.

On dimension 1, labelled “Valence/Relevance”, loaded the following items: SAR2 (relevance of personal goal); SAR3 (unpleasantness); SAI2 (negative consequences); SAI4 (need for immediate response); SAC6 (inability to live with consequences); SAN1 (violation of socially accepted norms); SAN2 (violation of personal norms); SRF1 (intensity of emotions); and SRT1 (wanting to tackle the situation). On dimension 2, labelled “Unexpectedness/Coping”, loaded the following items: SAR1 (suddenness); SAI6 (unpredictability); SAC1 (uncontrollability); SAC2 (no control of consequences); SAC3 (no dominance); SAC4 (no power over consequences); SAC5 (powerlessness). Finally, on dimension 3, labelled “Arousal/Expressivity”, loaded the following MiniGRID items: SRA1 (felt weak limbs); SRA3 (breathing faster); SRA4 (sweating); SRE1 (dropped jaw); SRE3 (closed eyes); and SRE4 (speaking more loudly).

To answer our second research question, again focusing on the Social Rejection scenario, we computed the centrality indices of Strength and Expected Influence of the network (Fig.  2 ).

Centrality indices (z-scores) of the CoreGRID Appraisal Component and MiniGRID in the Social Rejection Scenario. Respective communities are indicated

We then investigated the stability of the centrality indices via the case-dropping subset bootstrap approach (Epskamp et al., 2018 ). The CS -coefficients of Strength ( CS (cor = 0.7) = 0.672), and Expected Influence ( CS (cor = 0.7) = 0.672) were all above the cutoff of 0.5. Overall, we can be confident about the interpretation of these centrality metrics (Fig. S3 ).

Results from the edge-weight bootstrapped confidence intervals and bootstrapped difference tests supported the findings that edges were stable, and that the strongest and weakest edges were significantly different from each other (see Figs. S4 , S5 , and Table S3 ). SAI2, SRF1, SRA3, SAC5, and SRA1 were therefore robustly confirmed to be central to the network, in order of magnitude (see Fig, S5 , bottom panels).

Table S4 reports the network loadings for the three dimensions in the Social Rejection Scenario. The results are quite similar to the centrality indices reported in Fig.  2 : SAI2 also emerged as the node with the highest network loading (0.39) within the Valence/Relevance dimension. While SRA1 emerged as the node with the highest network loading (0.39) within the Arousal/Expressivity dimension, SRA3 emerged as slightly more central to the entire network. Similarly, SAC1 emerged as the node with the highest network loading (0.35) within the Unexpectedness/Coping dimension: however, it was not the most central to the entire network, which appeared to be SAC5 instead.

Finally, to answer our third research question for the Social Rejection scenario, the bootstrapped analyses results following Lange and Zickfeld ( 2023 ) procedure are reported in Table  3 . Overall, the average edge between all dimension contrasts were statistically and significantly different from zero (at p  < 0.001 and p  < 0.01), meaning that dimensions were not independent from each other. This is visually evident from Fig.  1 from the dense interconnections between nodes across dimensions.

All the tests concerning the differences of average within- and between-dimension edges were significantly different from zero ( p  < 0.001), meaning that within-dimension edges were stronger than between-dimension edges, for each set of dimension comparisons.

Positive scenario

To answer our first research question regarding the Positive scenario, and after applying the default EGA approach to all 33 transformed CoreGRID Appraisal and MiniGRID items, we first checked for local dependence issues. UVA identified two pairs of redundant items (see Table  1 for item content): PRF1 and PRF2 (wTO = 0.505), as well as PRA2 and PRA3 (wTO = 0.466). The ratio of network loadings (main/cross-loadings) were as follows: PRF1 = 7.134 versus PRF2 = 6.221; PRA2 = 12.505 versus PRA3 = 2.151. Therefore, PRF1 and PRA2 were retained in the subsequent analyses.

To explore structural consistency and replicability of the dimensions emerging from these locally reduced data, bootEGA was then performed. The median number of dimensions found via bootEGA in the reduced dataset was 4, with acceptable confidence intervals (95% CI [2.06, 5.93]). However, their structural consistency was very low (0.282, 0.350, 0.436 and 0.950 for dimension 1, 2, 3, and 4, respectively), with the appearance of other residual dimensions. Item stability indices varied between 19 and 100%, indicating overall instability (Fig. S6 , left panel). As recommended, we thus removed items with item stability indices below 75% (Christensen & Golino, 2021a ), ending up with 18 nodes. We then repeated the bootEGA procedure, obtaining acceptable– but not satisfactory– structural consistency (0.524, 0.720, and 0.968 for dimension 1, 2, and 3, respectively) and item stability range (between 53 and 100%). Following existing guidelines, and to further strengthen the structural consistency of our dimensions, we did not retain items with network loadings lower than 0.15, as this denotes weak dimensional belonging (Christensen et al., 2023 ; Maertens et al., 2023 ). With this procedure, two appraisals were discarded: PAR2 (personal relevance) and PAR4 (other relevance).

The final reduced structure included 16 items from the CoreGRID Appraisal component and MiniGRID , and 58 non-zero edges. The median number of dimensions extracted by bootEGA was 3, with optimal confidence intervals (95% CI [2.79, 3.21]), and satisfactory structural consistency (0.984, 0.868, and 0.990 for dimension 1, 2, and 3, respectively) and item stability range (between 90 and 100%; Fig. S6 , right panel). Figure  3 shows the structural and dimensional organization of CPM emotion components in the Positive Scenario.

Estimated network structure and dimensionality results for EGA for the final reduced data set, with unstable items removed. Items labels start with an “P”, denoting their belonging to the Positive Scenario. Connection strength between nodes is represented by lines thickness. Red and green lines indicate negative and positive relations, respectively

On dimension 1, labelled “Self-Valence/Coping”, loaded the following items: PAR3 (pleasantness); PAI2 (reversed; original formulation: negative consequences); PAI9 (expectations confirmed); PAC5 (reversed; original formulation: powerless); PAC6 (reversed; original formulation: inability to live with consequences); and PAN2 (reversed; original formulation: violation of personal norms). On dimension 2, labelled “Other-Novelty/Relevance”, loaded: PAR1 (suddenness); PAI3 (reversed; original formulation: chance-caused); PAI4 (reversed; original formulation: need for immediate response); and PAI5 (reversed; original formulation: other-agency). On dimension 3, labelled “Emo-Reactivity”, loaded all the MiniGRID items: PRF1 (intensity of emotion state); PRA2 (heartbeat getting faster); PRA4 (sweating); PRE4 (speaking more loudly); PRT1 (wanted to tackle the situation), and PRT2 (wanted to sing and dance).

To answer our second research question, still on the Positive scenario, we computed the centrality indices of Strength and Expected Influence of the network (Fig.  4 ).

Centrality indices (z-scores) of the CoreGRID Appraisal Component and MiniGRID in the Positive Scenario. Respective communities are indicated

The CS -coefficients of Strength ( CS (cor = 0.7) = 0.672) and Expected Influence ( CS (cor = 0.7) = 0.672) were all above the cutoff of 0.5 (Fig. S7 ). Similarly to the Social Rejection scenario, results from the edge-weight bootstrapped confidence intervals and bootstrapped difference tests supported the findings that edges were stable, and that the strongest and weakest edges were significantly different from each other (see Figs. S8 , S9 and Table S5 ). PAR3 and PRF1were confirmed to be the most central to the entire network, followed by PRA2, PAI9, and PRT2, albeit less robustly (see Fig, S9, bottom panels).

Table S6 reports the network loadings for the three dimensions in the Positive Scenario. The results are quite similar to the centrality indices reported in Fig.  4 : PAR3 emerged as the node with the highest network loading (0.37) within the Self-Valence/Coping dimension. PRF1 emerged as the node with the second highest network loading (0.37) within the Emo-Reactivity dimension, preceded by PRA2, and it was the most central to the entire network. PAR1 emerged as the node with the highest network loading (0.43) within the Other-Novelty/Relevance dimension: however, it was not the most central to the entire network, possibly due to the small size of this dimension.

Finally, to answer our third research question regarding the Positive scenario, the bootstrapped analyses results following Lange and Zickfeld ( 2023 ) procedure are reported in Table  4 . Overall, the average edge between all dimension contrasts were statistically and significantly different from zero (at p  < 0.001 and p  < 0.01), meaning that dimensions were not independent from each other. This is visually evident from Fig.  3 from the dense interconnections between nodes across dimensions.

With this study, we aimed to contribute to the existing heterogeneous and sparse multi-componential literature on emotion components using network analysis.

Our first goal was to uncover the structural and dimensional organization of emotion components within the CPM framework in different contexts. Overall, we found densely interconnected networks, with nodes clustering into three dimensions in each scenario. Within their assigned dimensions, some appraisals and emotion responses were unstable and were removed from the models. This is consistent with a variable-set approach to appraisal theories (Fernando et al., 2017 ). Indeed, not all appraisals and emotion responses might be salient in all situations, as cues to make a certain appraisal might be missing. Moreover, an emotion state could be present without the need for a certain appraisal (Fernando et al., 2017 ). To the best of our knowledge, only one other study investigated the influence of context on the semantic meaning of emotion terms within the CPM. Gentsch et al. ( 2018 ) similarly found that appraisal was the least stable component when embedded in an achievement versus a generalised context. In our study, the three-dimensional structure differed in two interesting ways. First, whereas in the negative scenario the focus was on the subjects’ goals, needs, consequences, and coping, in the positive scenario, there was a clearly distinguished self- and other-oriented dimensions. Studies of the appraisal profiles of several positive emotions (Yih et al., 2020 ) have similarly shown the presence of an “other” orientation component. Second, the experiential and action tendency components loaded onto the “Valence/Relevance” and the “Emo-Reactivity” dimensions for the negative and positive scenarios, respectively. One explanation for this finding could lie in the scenario contents themselves. In other words, the negative context could push the responses towards a Valence/Relevance dimension given the unexpectedness feature of the scenario and the need to restore the situation by acting out, something that is not needed in the positive scenario. Similar to our results, Gentsch et al. ( 2018 ) also found that the experiential component qualitatively changed following appraisal changes depending on the context.

Additionally, we replicated the findings by Fontaine et al. ( 2022 ) by retrieving the two stable and transversal dimensions of Valence and Arousal in both scenarios. As further hypothesized, we found that a clearly separated Power dimension emerged in the negative scenario, which we labelled Unexpectedness/Coping, and which included the four Coping Potential appraisals. This Power dimension did not emerge in the positive scenario, where these types of appraisals were less numerous and clustered with Valence-related appraisals, again in line with Fontaine et al. ( 2022 ). We also found their hypothesized patterns of Novelty– Power– Arousal relations. In the positive scenario, the appraisal of immediateness (belonging to the Other-Novelty/Relevance dimension) was moderately and negatively connected to the Action Tendency (behavioral response) component item “Wanted to tackle the situation”. In the negative scenario, the appraisals of suddenness and unpredictability (belonging to the Unexpectedness/Coping dimension) were moderately and strongly connected to the appraisal of uncontrollability (belonging to the Unexpectedness/Coping dimension), respectively. In other words, the higher the Novelty, the lower the Power. In our networks, we found virtually no evidence for the Novelty-Arousal direct relation, only very marginally in the positive scenario, in the direction hypothesized by Fontaine et al. ( 2022 ). The appraisal of suddenness (belonging to the Self-Valence/Coping dimension) was negatively and very weakly correlated with arousal symptoms of sweating (belonging to the Emotion Reactivity dimension). Finally, we found evidence for the Power-Arousal relationship. In the positive scenario, the appraisal of power over the situation (belonging to the Self-Valence/Coping dimension) was negatively correlated with arousal symptoms of sweating (belonging to the Emo-Reactivity dimension). Similarly, in the negative scenario, the appraisal of powerlessness (belonging to the Unexpectedness/Coping dimension) was positively and weakly correlated with arousal symptoms of increased breathing (belonging to the Arousal/Expressivity dimension).

From the above results, a differentiated componential organization emerges as a function of the context, which was also confirmed by the centrality metrics. Indeed, our second goal was to identify the most important node(s) within each network among a truly context relevant pool of features, and within each dimension. Given the similarity of findings, we focus here on the Expected Influence parameter, given its extended use in network research (Robinaugh et al., 2016 ). In the negative scenario, the Expected Influence parameter reported that the appraisal of negative consequences, the current emotion intensity, the appraisal of powerlessness, as well as the autonomic responses of distress (i.e., feeling the limbs weak) and arousal (i.e., breathing faster) were the nodes that, when activated, were responsible for the subsequent activation of the whole network and activation persistence. Similarly, in the positive scenario, the appraisals of situational pleasantness and the intensity state were the nodes with the highest Expected Influence value. The differentiated component patterns tell an interesting story: while in both scenarios the experiential component plays an important role in the network, in a negative context, appraising its consequences and recruiting physical resources as in a fight or flight situation are more central than in the positive context, where the focus seems to be more on the “here and now” in terms of valence and feelings. These findings are consistent with an evolutionary perspective of appraisal, whose paramount goal is to ensure personal well-being in adverse conditions (Ellsworth & Scherer, 2003 ). Moreover, the fact that the appraisal of powerlessness emerged as one of the most central node in the negative scenario is consistent with the attention it has received in appraisal research as a plausible cause for the onset of emotion disorders (Mehu & Scherer, 2015 ). This has recently been shown to be the case when the appraisal of personal coping potential is chronically underestimated, leading to appraisal biases that can impact healthy affectivity in the long run (Scherer et al., 2022 ).

Inspired by the recent work of Lange and Zickfeld ( 2023 ), our third goal was to investigate the relations of emotion components between and within dimensions, and to test if they significantly differ from zero. Overall, we showed, visually and via formal testing, that features within the same emotion components (e.g., appraisal) were more connected to each other than across emotion components, a sign of emotion coherence (Lange et al., 2020 ). For example, within the same appraisal dimension, we found strong relations among valence-oriented features (i.e., appraisal of negative/positive consequences) and unpleasantness/pleasantness of situation. Similarly, within an emotion response dimension, we also found strong relations among emotion response components, such as the distress symptoms of limb weakness and sweating and the autonomic arousal feature of respiration acceleration in the Social Rejection Scenario, or the intensity of the emotion state, the action tendency of wanting to sing and dance, and the arousal response of heart beating faster in the Positive Scenario. This is consistent with Lange and Zickfeld ( 2021 ), who found that the powerlessness/coping potential-related items were more strongly interconnected than with other appraisal categories; and that physiological reactions items also showed thicker edges between them.

Interestingly, when considering the dimensional shift of the experiential component in the two scenarios (i.e., clustering with appraisal in the Social Rejection Scenario, and with emotion responses in the Positive Scenario), we witnessed something similar to Mauss et al. ( 2005 ). After administering an emotionally salient film clip, alternating amusing and sadness scenes, the authors found that the intensity of the experience of amusement correlated with the concordance of physiological and behavioural components, while this was not the case at higher levels of sadness experience. Mauss et al. ( 2005 ) argue that the intensity of the experience of sadness could be decoupled from the other emotion responses because of social pressure, requiring hence to be controlled. This rationale also appears to apply well to the findings in our negative scenario.

Focusing on the CPM, similarly to Meuleman et al. ( 2019 ), we found strong correlations between emotion response components, a sign of emotion coherence. For example, in the positive scenario, we replicated Meuleman et al. ( 2019 )’s positive correlations between the expressive response of “Spoke more loudly” and the action tendency responses of “Wanted to sing and dance” and “Wanted to tackle the situation”, as well as the arousal-related emotion responses of faster heartbeat and sweating, although with some differences in magnitude. Similarly, in the negative scenario, we replicated the positive correlation between the expressive responses of "Jaw drop" and “Spoke more loudly”, and between the latter and the action tendency response of “Wanted to tackle the situation”, and the arousal-related emotion response of sweating, although again with some differences in magnitude. We however also noticed several discrepancies. For example, concerning appraisal-emotion response relations, we found in our Social Rejection Scenario that the appraisal of personal goal relevance was only slightly positively associated with the action tendency component of “Wanting to tackle the situation”. The opposite is true for Meuleman et al. ( 2019 ). In our study, the appraisal of suddenness was not directly related to any emotion response variables, while in Meuleman et al. ( 2019 ) it was moderately and positively correlated with the expressivity factor of “Jaw drop”. Overall, we believe that the standing discrepancies between our results and previous componential literature may be due to the estimation of conditional dependencies, i.e., controlling for all other variables in the network, which may have led to weaker/absent correlations between certain nodes in our study. Another explanation could lie in the estimation of composite scores via principal component analysis in Meuleman et al. ( 2019 ), which might have led to more parsimonious but less nuanced models. Finally, we could argue that Lange and Zickfeld ( 2021 ) have a preponderance of feeling components at the expense of the other components.

From a theoretical standpoint, we provided evidence for the utility of a variable-set conceptualisation of multi-componential emotional episodes (Fernando et al., 2017 ). This approach has been recently proposed as an alternative to early approaches in appraisal theories focused on finding fixed and prototypical patterns of components (Moors, 2024 ). In a data-driven way, we showed that not all appraisals were indeed relevant to a specific context and emotional episode. In other words, we were able to identify the variability in appraisal-emotion response relations across situations (Fernando et al., 2017 ). Moreover, we provided evidence for the interconnection of a comprehensive spectrum of emotion components with advanced and refined analyses, urged within the CPM framework by Scherer and Moors ( 2019 ), extending beyond employing pairs of appraisals (e.g., pleasantness, relevance, and goal conduciveness; Aue & Scherer, 2008 ; Kreibig et al., 2012 ; van Reekum et al., 2004 ) or a limited number of appraisals (Menétrey et al., 2022 ), or appraisal clusters (Meuleman et al., 2019 ). With the present study, we showed how emotion components cluster and cohere differently in different contexts, contributing to a conversation in the field on the topic of emotion coherence which has been long debated (Constantinou et al., 2023 ; Gentsch et al., 2014 ; Sznycer & Cohen, 2021 ). Interestingly, in line with recent evidence (Lange, 2023 ; Lohani et al., 2018 ), we found stronger coherence of emotion components in a negatively salient context, marked by a denser network and a higher number of non-zero edges compared to a positively salient context, which generally speaking is also less researched upon.

From a practical standpoint, the knowledge produced can subsequently inform studies on real-life structural organisation of emotion components and their reciprocal influences (Fontaine et al., 2022 ; Scherer, 2019 ), spurring the field towards the application of networks to ecological momentary assessment of emotion components. This will honour the dynamic system approach roots of emotional episodes as theorised in the CPM (Lewis, 2005 ; Sander et al., 2005 ). More importantly though, we have confirmed the centrality in a negative context of the appraisal of powerlessness, which resonates with recent evidence on the role of the broader Coping Potential appraisal category in predicting the frequency of negative emotions and emotional disturbances (Mehu & Scherer, 2015 ; Scherer, 2020 , 2022 ). Urgency in addressing cognitive biases within this category in young people has thus been strongly vocalized in the field (Scherer et al., 2022 ), as affective disturbances appear to be potentially triggered or worsened by transitioning to university (Duffy et al., 2019 ). Thus, our findings can guide educators, university counsellors and psychologists in the tailoring of existing psychoeducational programs to specifically young students by promoting empowered appraisal along with the strengthening of coping skills (Anderson et al., 2024 ; Compas et al., 2017 ) in the face of daily, ambiguous social situations. Psychologists and university counsellors should also collaborate with policymakers in raising public awareness on mental health well-being in this young population, which appears to have increasingly worsened in the last decade (Arakelyan et al., 2023 ) and in securing a place for the aforementioned psychoeducational interventions in educational curricula across colleges and universities. In turn, policymakers should ensure the allocation of resources for professional developmental programs to train educators, counsellors and teachers, as well as for the optimal implementation and delivery of these interventions.

Finally, regarding the generalizability of our findings, although the validation and application of the GRID instrument have been carried out cross-culturally (Fontaine et al., 2013 , 2022 ), appraisal profiles of positive (Cong et al., 2022 ) and negative (Roseman et al., 1995 ) emotions appear to be modulated by cultural belonging. Thus, emotion components clusters and coherence could also differ across cultures (Lange et al., 2020 ; Mesquita & Ellsworth, 2001 ; Zickfeld et al., 2019 ). Moreover, the young age of our sample prevents generalization of our findings to older populations, as age differences in appraisal processes have been recently showed by Young and Mikels ( 2020 ), although in a small sample. Thus, future studies should tackle these empirical questions, and attempt replication of our findings in larger samples, diversified for culture of belonging and age.

Limitation and future directions

Our study has several limitations. First of all, as we found some evidence of items multi-dimensionality (i.e., item cross-loadings on other dimensions; see Tables S4 and S6), future studies could conduct hierarchical network analysis, a method recently proposed (Jiménez et al., 2023 ). This approach would allow disentangling the variance accounted for in the CoreGRID Appraisal component and MiniGRID items by the four identified higher order factors and replicating the lower-order factors, as in Fontaine et al. ( 2013 ), and explore in a more nuanced way the hypotheses set by Fontaine et al. ( 2022 ). Second, psychometric network analysis does not provide information about the degree of variable endorsement (Lange et al., 2020 ). Hence, we cannot claim that the edges connecting the emotion components in this study apply similarly to everyone. This will require further personalized evidence using techniques such as network comparison tests (van Borkulo et al., 2022 ) or moderated networks (Haslbeck et al., 2021 ). Moreover, network models cannot convey information about causal relationships between nodes as the edges are partial correlation coefficients. In other words, the directionality of effects between two nodes cannot be established (Lange & Zickfeld, 2021 ). Finally, as discussed above, we acknowledge the non-generalizability of our findings, given the employment of only two scenarios, and the exploratory modelling of CPM components, whose dynamic and sequentiality assumptions cannot be met in cross-correlational network models (Lange et al., 2020 ). However, we believe that our work can inspire future researchers to apply network models to emotion components embedded in more diverse contexts, with varying degrees of ambiguity and potentially inform further ecological momentary assessment studies of appraisals and emotional response in everyday life situations.

Overall, this study explored the relationships between emotion components in three novel ways: 1) by using networks, 2) by embedding these in a multi-componential framework, and 3) by providing context to emotion components. Our results can be informative for applied research, such as in educational settings, where understanding the interconnections and centrality of components could aid the personalization of interventions.

Data availability

De-identified data, analyses code and supplementary material are available at https://osf.io/t9f43/

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Acknowledgements

We are grateful to Professor Farrell and Professor Zimmer-Gembeck for sharing their scenarios with us. We are grateful to Professor Christensen for the insightful correspondence on network loadings.

This work was supported by a Swiss National Science Foundation Eccellenza Grant (no PCEFP1_186836) to E.D-G.

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Livia Sacchi: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing, Visualization. Elise Dan-Glauser: Conceptualization, Methodology, Writing, Visualization, Supervision, Project administration, Funding acquisition.

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Sacchi, L., Dan-Glauser, E. Network analyses of emotion components: an exploratory application to the component process model of emotion. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-06479-3

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DOI : https://doi.org/10.1007/s12144-024-06479-3

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