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Blog Graphic Design

15+ Professional Case Study Examples [Design Tips + Templates]

By Alice Corner , Jan 12, 2023

Venngage case study examples

Have you ever bought something — within the last 10 years or so — without reading its reviews or without a recommendation or prior experience of using it?

If the answer is no — or at least, rarely — you get my point.

Positive reviews matter for selling to regular customers, and for B2B or SaaS businesses, detailed case studies are important too.

Wondering how to craft a compelling case study ? No worries—I’ve got you covered with 15 marketing case study templates , helpful tips, and examples to ensure your case study converts effectively.

Click to jump ahead:

  • What is a Case Study?

Business Case Study Examples

Simple case study examples.

  • Marketing Case Study Examples

Sales Case Study Examples

  • Case Study FAQs

What is a case study?

A case study is a research method to gain a better understanding of a subject or process. Case studies involve in-depth research into a given subject, in order to understand its functionality and successes.

In the context of a business, however, case studies take customer success stories and explore how they use your product to help them achieve their business goals.

Case Study Definition LinkedIn Post

As well as being valuable marketing tools , case studies are a good way to evaluate your product as it allows you to objectively examine how others are using it.

It’s also a good way to interview your customers about why they work with you.

Related: What is a Case Study? [+6 Types of Case Studies]

What is a marketing case study?

A marketing case study is a type of marketing where you use your existing customers as an example of what your product or services can achieve. You can also create case studies of internal, successful marketing projects.

Here’s an example of a marketing case study template:

marketing case study example

Whether you’re a B2B or B2C company, business case studies can be a powerful resource to help with your sales, marketing, and even internal departmental awareness.

Business and business management case studies should encompass strategic insights alongside anecdotal and qualitative findings, like in the business case study examples below.

Conduct a B2B case study by researching the company holistically

When it comes to writing a case study, make sure you approach the company holistically and analyze everything from their social media to their sales.

Think about every avenue your product or service has been of use to your case study company, and ask them about the impact this has had on their wider company goals.

Venngage orange marketing case study example

In business case study examples like the one above, we can see that the company has been thought about holistically simply by the use of icons.

By combining social media icons with icons that show in-person communication we know that this is a well-researched and thorough case study.

This case study report example could also be used within an annual or end-of-year report.

Highlight the key takeaway from your marketing case study

To create a compelling case study, identify the key takeaways from your research. Use catchy language to sum up this information in a sentence, and present this sentence at the top of your page.

This is “at a glance” information and it allows people to gain a top-level understanding of the content immediately. 

Purple SAAS Business Case Study Template

You can use a large, bold, contrasting font to help this information stand out from the page and provide interest.

Learn  how to choose fonts  effectively with our Venngage guide and once you’ve done that.

Upload your fonts and  brand colors  to Venngage using the  My Brand Kit  tool and see them automatically applied to your designs.

The heading is the ideal place to put the most impactful information, as this is the first thing that people will read.

In this example, the stat of “Increase[d] lead quality by 90%” is used as the header. It makes customers want to read more to find out how exactly lead quality was increased by such a massive amount.

Purple SAAS Business Case Study Template Header

If you’re conducting an in-person interview, you could highlight a direct quote or insight provided by your interview subject.

Pick out a catchy sentence or phrase, or the key piece of information your interview subject provided and use that as a way to draw a potential customer in.

Use charts to visualize data in your business case studies

Charts are an excellent way to visualize data and to bring statistics and information to life. Charts make information easier to understand and to illustrate trends or patterns.

Making charts is even easier with Venngage.

In this consulting case study example, we can see that a chart has been used to demonstrate the difference in lead value within the Lead Elves case study.

Adding a chart here helps break up the information and add visual value to the case study. 

Red SAAS Business Case Study Template

Using charts in your case study can also be useful if you’re creating a project management case study.

You could use a Gantt chart or a project timeline to show how you have managed the project successfully.

event marketing project management gantt chart example

Use direct quotes to build trust in your marketing case study

To add an extra layer of authenticity you can include a direct quote from your customer within your case study.

According to research from Nielsen , 92% of people will trust a recommendation from a peer and 70% trust recommendations even if they’re from somebody they don’t know.

Case study peer recommendation quote

So if you have a customer or client who can’t stop singing your praises, make sure you get a direct quote from them and include it in your case study.

You can either lift part of the conversation or interview, or you can specifically request a quote. Make sure to ask for permission before using the quote.

Contrast Lead Generation Business Case Study Template

This design uses a bright contrasting speech bubble to show that it includes a direct quote, and helps the quote stand out from the rest of the text.

This will help draw the customer’s attention directly to the quote, in turn influencing them to use your product or service.

Less is often more, and this is especially true when it comes to creating designs. Whilst you want to create a professional-looking, well-written and design case study – there’s no need to overcomplicate things.

These simple case study examples show that smart clean designs and informative content can be an effective way to showcase your successes.

Use colors and fonts to create a professional-looking case study

Business case studies shouldn’t be boring. In fact, they should be beautifully and professionally designed.

This means the normal rules of design apply. Use fonts, colors, and icons to create an interesting and visually appealing case study.

In this case study example, we can see how multiple fonts have been used to help differentiate between the headers and content, as well as complementary colors and eye-catching icons.

Blue Simple Business Case Study Template

Marketing case study examples

Marketing case studies are incredibly useful for showing your marketing successes. Every successful marketing campaign relies on influencing a consumer’s behavior, and a great case study can be a great way to spotlight your biggest wins.

In the marketing case study examples below, a variety of designs and techniques to create impactful and effective case studies.

Show off impressive results with a bold marketing case study

Case studies are meant to show off your successes, so make sure you feature your positive results prominently. Using bold and bright colors as well as contrasting shapes, large bold fonts, and simple icons is a great way to highlight your wins.

In well-written case study examples like the one below, the big wins are highlighted on the second page with a bright orange color and are highlighted in circles.

Making the important data stand out is especially important when attracting a prospective customer with marketing case studies.

Light simplebusiness case study template

Use a simple but clear layout in your case study

Using a simple layout in your case study can be incredibly effective, like in the example of a case study below.

Keeping a clean white background, and using slim lines to help separate the sections is an easy way to format your case study.

Making the information clear helps draw attention to the important results, and it helps improve the  accessibility of the design .

Business case study examples like this would sit nicely within a larger report, with a consistent layout throughout.

Modern lead Generaton Business Case Study Template

Use visuals and icons to create an engaging and branded business case study

Nobody wants to read pages and pages of text — and that’s why Venngage wants to help you communicate your ideas visually.

Using icons, graphics, photos, or patterns helps create a much more engaging design. 

With this Blue Cap case study icons, colors, and impactful pattern designs have been used to create an engaging design that catches your eye.

Social Media Business Case Study template

Use a monochromatic color palette to create a professional and clean case study

Let your research shine by using a monochromatic and minimalistic color palette.

By sticking to one color, and leaving lots of blank space you can ensure your design doesn’t distract a potential customer from your case study content.

Color combination examples

In this case study on Polygon Media, the design is simple and professional, and the layout allows the prospective customer to follow the flow of information.

The gradient effect on the left-hand column helps break up the white background and adds an interesting visual effect.

Gray Lead Generation Business Case Study Template

Did you know you can generate an accessible color palette with Venngage? Try our free accessible color palette generator today and create a case study that delivers and looks pleasant to the eye:

Venngage's accessible color palette generator

Add long term goals in your case study

When creating a case study it’s a great idea to look at both the short term and the long term goals of the company to gain the best understanding possible of the insights they provide.

Short-term goals will be what the company or person hopes to achieve in the next few months, and long-term goals are what the company hopes to achieve in the next few years.

Check out this modern pattern design example of a case study below:

Lead generation business case study template

In this case study example, the short and long-term goals are clearly distinguished by light blue boxes and placed side by side so that they are easy to compare.

Lead generation case study example short term goals

Use a strong introductory paragraph to outline the overall strategy and goals before outlining the specific short-term and long-term goals to help with clarity.

This strategy can also be handy when creating a consulting case study.

Use data to make concrete points about your sales and successes

When conducting any sort of research stats, facts, and figures are like gold dust (aka, really valuable).

Being able to quantify your findings is important to help understand the information fully. Saying sales increased 10% is much more effective than saying sales increased.

While sales dashboards generally tend it make it all about the numbers and charts, in sales case study examples, like this one, the key data and findings can be presented with icons. This contributes to the potential customer’s better understanding of the report.

They can clearly comprehend the information and it shows that the case study has been well researched.

Vibrant Content Marketing Case Study Template

Use emotive, persuasive, or action based language in your marketing case study

Create a compelling case study by using emotive, persuasive and action-based language when customizing your case study template.

Case study example pursuasive language

In this well-written case study example, we can see that phrases such as “Results that Speak Volumes” and “Drive Sales” have been used.

Using persuasive language like you would in a blog post. It helps inspire potential customers to take action now.

Bold Content Marketing Case Study Template

Keep your potential customers in mind when creating a customer case study for marketing

82% of marketers use case studies in their marketing  because it’s such an effective tool to help quickly gain customers’ trust and to showcase the potential of your product.

Why are case studies such an important tool in content marketing?

By writing a case study you’re telling potential customers that they can trust you because you’re showing them that other people do.

Not only that, but if you have a SaaS product, business case studies are a great way to show how other people are effectively using your product in their company.

In this case study, Network is demonstrating how their product has been used by Vortex Co. with great success; instantly showing other potential customers that their tool works and is worth using.

Teal Social Media Business Case Study Template

Related: 10+ Case Study Infographic Templates That Convert

Case studies are particularly effective as a sales technique.

A sales case study is like an extended customer testimonial, not only sharing opinions of your product – but showcasing the results you helped your customer achieve.

Make impactful statistics pop in your sales case study

Writing a case study doesn’t mean using text as the only medium for sharing results.

You should use icons to highlight areas of your research that are particularly interesting or relevant, like in this example of a case study:

Coral content marketing case study template.jpg

Icons are a great way to help summarize information quickly and can act as visual cues to help draw the customer’s attention to certain areas of the page.

In some of the business case study examples above, icons are used to represent the impressive areas of growth and are presented in a way that grabs your attention.

Use high contrast shapes and colors to draw attention to key information in your sales case study

Help the key information stand out within your case study by using high contrast shapes and colors.

Use a complementary or contrasting color, or use a shape such as a rectangle or a circle for maximum impact.

Blue case study example case growth

This design has used dark blue rectangles to help separate the information and make it easier to read.

Coupled with icons and strong statistics, this information stands out on the page and is easily digestible and retainable for a potential customer.

Blue Content Marketing Case Study Tempalte

Case Study Examples Summary

Once you have created your case study, it’s best practice to update your examples on a regular basis to include up-to-date statistics, data, and information.

You should update your business case study examples often if you are sharing them on your website .

It’s also important that your case study sits within your brand guidelines – find out how Venngage’s My Brand Kit tool can help you create consistently branded case study templates.

Case studies are important marketing tools – but they shouldn’t be the only tool in your toolbox. Content marketing is also a valuable way to earn consumer trust.

Case Study FAQ

Why should you write a case study.

Case studies are an effective marketing technique to engage potential customers and help build trust.

By producing case studies featuring your current clients or customers, you are showcasing how your tool or product can be used. You’re also showing that other people endorse your product.

In addition to being a good way to gather positive testimonials from existing customers , business case studies are good educational resources and can be shared amongst your company or team, and used as a reference for future projects.

How should you write a case study?

To create a great case study, you should think strategically. The first step, before starting your case study research, is to think about what you aim to learn or what you aim to prove.

You might be aiming to learn how a company makes sales or develops a new product. If this is the case, base your questions around this.

You can learn more about writing a case study  from our extensive guide.

Related: How to Present a Case Study like a Pro (With Examples)

Some good questions you could ask would be:

  • Why do you use our tool or service?
  • How often do you use our tool or service?
  • What does the process of using our product look like to you?
  • If our product didn’t exist, what would you be doing instead?
  • What is the number one benefit you’ve found from using our tool?

You might also enjoy:

  • 12 Essential Consulting Templates For Marketing, Planning and Branding
  • Best Marketing Strategies for Consultants and Freelancers in 2019 [Study + Infographic]

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  • Knowledge Base

Methodology

  • What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

  • Your overall research objectives and approach
  • Whether you’ll rely on primary research or secondary research
  • Your sampling methods or criteria for selecting subjects
  • Your data collection methods
  • The procedures you’ll follow to collect data
  • Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

  • Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

  • How much time do you have to collect data and write up the research?
  • Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
  • Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
  • Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

  • Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
  • Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

  • Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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case study research design example

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

  • How many participants do you need for an adequate sample size?
  • What inclusion and exclusion criteria will you use to identify eligible participants?
  • How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

  • The distribution of the data (e.g., the frequency of each score on a test)
  • The central tendency of the data (e.g., the mean to describe the average score)
  • The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

  • Make estimates about the population based on your sample data.
  • Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

  • Simple random sampling
  • Stratified sampling
  • Cluster sampling
  • Likert scales
  • Reproducibility

 Statistics

  • Null hypothesis
  • Statistical power
  • Probability distribution
  • Effect size
  • Poisson distribution

Research bias

  • Optimism bias
  • Cognitive bias
  • Implicit bias
  • Hawthorne effect
  • Anchoring bias
  • Explicit bias

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

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

Quantitative research designs can be divided into two main categories:

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

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

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

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

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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The case study approach

Sarah crowe.

1 Division of Primary Care, The University of Nottingham, Nottingham, UK

Kathrin Cresswell

2 Centre for Population Health Sciences, The University of Edinburgh, Edinburgh, UK

Ann Robertson

3 School of Health in Social Science, The University of Edinburgh, Edinburgh, UK

Anthony Avery

Aziz sheikh.

The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables ​ Tables1, 1 , ​ ,2, 2 , ​ ,3 3 and ​ and4) 4 ) and those of others to illustrate our discussion[ 3 - 7 ].

Example of a case study investigating the reasons for differences in recruitment rates of minority ethnic people in asthma research[ 3 ]

Example of a case study investigating the process of planning and implementing a service in Primary Care Organisations[ 4 ]

Example of a case study investigating the introduction of the electronic health records[ 5 ]

Example of a case study investigating the formal and informal ways students learn about patient safety[ 6 ]

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table ​ (Table5), 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Definitions of a case study

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table ​ (Table1), 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables ​ Tables2, 2 , ​ ,3 3 and ​ and4) 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 - 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table ​ (Table2) 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables ​ Tables2 2 and ​ and3, 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table ​ (Table4 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table ​ (Table6). 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

Example of epistemological approaches that may be used in case study research

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table ​ Table7 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

Example of a checklist for rating a case study proposal[ 8 ]

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table ​ (Table3), 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table ​ (Table1) 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table ​ Table3) 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 - 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table ​ (Table2 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table ​ (Table1 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table ​ (Table3 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table ​ (Table4 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table ​ Table3, 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table ​ (Table4), 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table ​ Table8 8 )[ 8 , 18 - 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table ​ (Table9 9 )[ 8 ].

Potential pitfalls and mitigating actions when undertaking case study research

Stake's checklist for assessing the quality of a case study report[ 8 ]

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

AS conceived this article. SC, KC and AR wrote this paper with GH, AA and AS all commenting on various drafts. SC and AS are guarantors.

Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1471-2288/11/100/prepub

Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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Organizing Your Social Sciences Research Assignments

  • Annotated Bibliography
  • Analyzing a Scholarly Journal Article
  • Group Presentations
  • Dealing with Nervousness
  • Using Visual Aids
  • Grading Someone Else's Paper
  • Types of Structured Group Activities
  • Group Project Survival Skills
  • Leading a Class Discussion
  • Multiple Book Review Essay
  • Reviewing Collected Works
  • Writing a Case Analysis Paper
  • Writing a Case Study
  • About Informed Consent
  • Writing Field Notes
  • Writing a Policy Memo
  • Writing a Reflective Paper
  • Writing a Research Proposal
  • Generative AI and Writing
  • Acknowledgments

A case study research paper examines a person, place, event, condition, phenomenon, or other type of subject of analysis in order to extrapolate  key themes and results that help predict future trends, illuminate previously hidden issues that can be applied to practice, and/or provide a means for understanding an important research problem with greater clarity. A case study research paper usually examines a single subject of analysis, but case study papers can also be designed as a comparative investigation that shows relationships between two or more subjects. The methods used to study a case can rest within a quantitative, qualitative, or mixed-method investigative paradigm.

Case Studies. Writing@CSU. Colorado State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010 ; “What is a Case Study?” In Swanborn, Peter G. Case Study Research: What, Why and How? London: SAGE, 2010.

How to Approach Writing a Case Study Research Paper

General information about how to choose a topic to investigate can be found under the " Choosing a Research Problem " tab in the Organizing Your Social Sciences Research Paper writing guide. Review this page because it may help you identify a subject of analysis that can be investigated using a case study design.

However, identifying a case to investigate involves more than choosing the research problem . A case study encompasses a problem contextualized around the application of in-depth analysis, interpretation, and discussion, often resulting in specific recommendations for action or for improving existing conditions. As Seawright and Gerring note, practical considerations such as time and access to information can influence case selection, but these issues should not be the sole factors used in describing the methodological justification for identifying a particular case to study. Given this, selecting a case includes considering the following:

  • The case represents an unusual or atypical example of a research problem that requires more in-depth analysis? Cases often represent a topic that rests on the fringes of prior investigations because the case may provide new ways of understanding the research problem. For example, if the research problem is to identify strategies to improve policies that support girl's access to secondary education in predominantly Muslim nations, you could consider using Azerbaijan as a case study rather than selecting a more obvious nation in the Middle East. Doing so may reveal important new insights into recommending how governments in other predominantly Muslim nations can formulate policies that support improved access to education for girls.
  • The case provides important insight or illuminate a previously hidden problem? In-depth analysis of a case can be based on the hypothesis that the case study will reveal trends or issues that have not been exposed in prior research or will reveal new and important implications for practice. For example, anecdotal evidence may suggest drug use among homeless veterans is related to their patterns of travel throughout the day. Assuming prior studies have not looked at individual travel choices as a way to study access to illicit drug use, a case study that observes a homeless veteran could reveal how issues of personal mobility choices facilitate regular access to illicit drugs. Note that it is important to conduct a thorough literature review to ensure that your assumption about the need to reveal new insights or previously hidden problems is valid and evidence-based.
  • The case challenges and offers a counter-point to prevailing assumptions? Over time, research on any given topic can fall into a trap of developing assumptions based on outdated studies that are still applied to new or changing conditions or the idea that something should simply be accepted as "common sense," even though the issue has not been thoroughly tested in current practice. A case study analysis may offer an opportunity to gather evidence that challenges prevailing assumptions about a research problem and provide a new set of recommendations applied to practice that have not been tested previously. For example, perhaps there has been a long practice among scholars to apply a particular theory in explaining the relationship between two subjects of analysis. Your case could challenge this assumption by applying an innovative theoretical framework [perhaps borrowed from another discipline] to explore whether this approach offers new ways of understanding the research problem. Taking a contrarian stance is one of the most important ways that new knowledge and understanding develops from existing literature.
  • The case provides an opportunity to pursue action leading to the resolution of a problem? Another way to think about choosing a case to study is to consider how the results from investigating a particular case may result in findings that reveal ways in which to resolve an existing or emerging problem. For example, studying the case of an unforeseen incident, such as a fatal accident at a railroad crossing, can reveal hidden issues that could be applied to preventative measures that contribute to reducing the chance of accidents in the future. In this example, a case study investigating the accident could lead to a better understanding of where to strategically locate additional signals at other railroad crossings so as to better warn drivers of an approaching train, particularly when visibility is hindered by heavy rain, fog, or at night.
  • The case offers a new direction in future research? A case study can be used as a tool for an exploratory investigation that highlights the need for further research about the problem. A case can be used when there are few studies that help predict an outcome or that establish a clear understanding about how best to proceed in addressing a problem. For example, after conducting a thorough literature review [very important!], you discover that little research exists showing the ways in which women contribute to promoting water conservation in rural communities of east central Africa. A case study of how women contribute to saving water in a rural village of Uganda can lay the foundation for understanding the need for more thorough research that documents how women in their roles as cooks and family caregivers think about water as a valuable resource within their community. This example of a case study could also point to the need for scholars to build new theoretical frameworks around the topic [e.g., applying feminist theories of work and family to the issue of water conservation].

Eisenhardt, Kathleen M. “Building Theories from Case Study Research.” Academy of Management Review 14 (October 1989): 532-550; Emmel, Nick. Sampling and Choosing Cases in Qualitative Research: A Realist Approach . Thousand Oaks, CA: SAGE Publications, 2013; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Seawright, Jason and John Gerring. "Case Selection Techniques in Case Study Research." Political Research Quarterly 61 (June 2008): 294-308.

Structure and Writing Style

The purpose of a paper in the social sciences designed around a case study is to thoroughly investigate a subject of analysis in order to reveal a new understanding about the research problem and, in so doing, contributing new knowledge to what is already known from previous studies. In applied social sciences disciplines [e.g., education, social work, public administration, etc.], case studies may also be used to reveal best practices, highlight key programs, or investigate interesting aspects of professional work.

In general, the structure of a case study research paper is not all that different from a standard college-level research paper. However, there are subtle differences you should be aware of. Here are the key elements to organizing and writing a case study research paper.

I.  Introduction

As with any research paper, your introduction should serve as a roadmap for your readers to ascertain the scope and purpose of your study . The introduction to a case study research paper, however, should not only describe the research problem and its significance, but you should also succinctly describe why the case is being used and how it relates to addressing the problem. The two elements should be linked. With this in mind, a good introduction answers these four questions:

  • What is being studied? Describe the research problem and describe the subject of analysis [the case] you have chosen to address the problem. Explain how they are linked and what elements of the case will help to expand knowledge and understanding about the problem.
  • Why is this topic important to investigate? Describe the significance of the research problem and state why a case study design and the subject of analysis that the paper is designed around is appropriate in addressing the problem.
  • What did we know about this topic before I did this study? Provide background that helps lead the reader into the more in-depth literature review to follow. If applicable, summarize prior case study research applied to the research problem and why it fails to adequately address the problem. Describe why your case will be useful. If no prior case studies have been used to address the research problem, explain why you have selected this subject of analysis.
  • How will this study advance new knowledge or new ways of understanding? Explain why your case study will be suitable in helping to expand knowledge and understanding about the research problem.

Each of these questions should be addressed in no more than a few paragraphs. Exceptions to this can be when you are addressing a complex research problem or subject of analysis that requires more in-depth background information.

II.  Literature Review

The literature review for a case study research paper is generally structured the same as it is for any college-level research paper. The difference, however, is that the literature review is focused on providing background information and  enabling historical interpretation of the subject of analysis in relation to the research problem the case is intended to address . This includes synthesizing studies that help to:

  • Place relevant works in the context of their contribution to understanding the case study being investigated . This would involve summarizing studies that have used a similar subject of analysis to investigate the research problem. If there is literature using the same or a very similar case to study, you need to explain why duplicating past research is important [e.g., conditions have changed; prior studies were conducted long ago, etc.].
  • Describe the relationship each work has to the others under consideration that informs the reader why this case is applicable . Your literature review should include a description of any works that support using the case to investigate the research problem and the underlying research questions.
  • Identify new ways to interpret prior research using the case study . If applicable, review any research that has examined the research problem using a different research design. Explain how your use of a case study design may reveal new knowledge or a new perspective or that can redirect research in an important new direction.
  • Resolve conflicts amongst seemingly contradictory previous studies . This refers to synthesizing any literature that points to unresolved issues of concern about the research problem and describing how the subject of analysis that forms the case study can help resolve these existing contradictions.
  • Point the way in fulfilling a need for additional research . Your review should examine any literature that lays a foundation for understanding why your case study design and the subject of analysis around which you have designed your study may reveal a new way of approaching the research problem or offer a perspective that points to the need for additional research.
  • Expose any gaps that exist in the literature that the case study could help to fill . Summarize any literature that not only shows how your subject of analysis contributes to understanding the research problem, but how your case contributes to a new way of understanding the problem that prior research has failed to do.
  • Locate your own research within the context of existing literature [very important!] . Collectively, your literature review should always place your case study within the larger domain of prior research about the problem. The overarching purpose of reviewing pertinent literature in a case study paper is to demonstrate that you have thoroughly identified and synthesized prior studies in relation to explaining the relevance of the case in addressing the research problem.

III.  Method

In this section, you explain why you selected a particular case [i.e., subject of analysis] and the strategy you used to identify and ultimately decide that your case was appropriate in addressing the research problem. The way you describe the methods used varies depending on the type of subject of analysis that constitutes your case study.

If your subject of analysis is an incident or event . In the social and behavioral sciences, the event or incident that represents the case to be studied is usually bounded by time and place, with a clear beginning and end and with an identifiable location or position relative to its surroundings. The subject of analysis can be a rare or critical event or it can focus on a typical or regular event. The purpose of studying a rare event is to illuminate new ways of thinking about the broader research problem or to test a hypothesis. Critical incident case studies must describe the method by which you identified the event and explain the process by which you determined the validity of this case to inform broader perspectives about the research problem or to reveal new findings. However, the event does not have to be a rare or uniquely significant to support new thinking about the research problem or to challenge an existing hypothesis. For example, Walo, Bull, and Breen conducted a case study to identify and evaluate the direct and indirect economic benefits and costs of a local sports event in the City of Lismore, New South Wales, Australia. The purpose of their study was to provide new insights from measuring the impact of a typical local sports event that prior studies could not measure well because they focused on large "mega-events." Whether the event is rare or not, the methods section should include an explanation of the following characteristics of the event: a) when did it take place; b) what were the underlying circumstances leading to the event; and, c) what were the consequences of the event in relation to the research problem.

If your subject of analysis is a person. Explain why you selected this particular individual to be studied and describe what experiences they have had that provide an opportunity to advance new understandings about the research problem. Mention any background about this person which might help the reader understand the significance of their experiences that make them worthy of study. This includes describing the relationships this person has had with other people, institutions, and/or events that support using them as the subject for a case study research paper. It is particularly important to differentiate the person as the subject of analysis from others and to succinctly explain how the person relates to examining the research problem [e.g., why is one politician in a particular local election used to show an increase in voter turnout from any other candidate running in the election]. Note that these issues apply to a specific group of people used as a case study unit of analysis [e.g., a classroom of students].

If your subject of analysis is a place. In general, a case study that investigates a place suggests a subject of analysis that is unique or special in some way and that this uniqueness can be used to build new understanding or knowledge about the research problem. A case study of a place must not only describe its various attributes relevant to the research problem [e.g., physical, social, historical, cultural, economic, political], but you must state the method by which you determined that this place will illuminate new understandings about the research problem. It is also important to articulate why a particular place as the case for study is being used if similar places also exist [i.e., if you are studying patterns of homeless encampments of veterans in open spaces, explain why you are studying Echo Park in Los Angeles rather than Griffith Park?]. If applicable, describe what type of human activity involving this place makes it a good choice to study [e.g., prior research suggests Echo Park has more homeless veterans].

If your subject of analysis is a phenomenon. A phenomenon refers to a fact, occurrence, or circumstance that can be studied or observed but with the cause or explanation to be in question. In this sense, a phenomenon that forms your subject of analysis can encompass anything that can be observed or presumed to exist but is not fully understood. In the social and behavioral sciences, the case usually focuses on human interaction within a complex physical, social, economic, cultural, or political system. For example, the phenomenon could be the observation that many vehicles used by ISIS fighters are small trucks with English language advertisements on them. The research problem could be that ISIS fighters are difficult to combat because they are highly mobile. The research questions could be how and by what means are these vehicles used by ISIS being supplied to the militants and how might supply lines to these vehicles be cut off? How might knowing the suppliers of these trucks reveal larger networks of collaborators and financial support? A case study of a phenomenon most often encompasses an in-depth analysis of a cause and effect that is grounded in an interactive relationship between people and their environment in some way.

NOTE:   The choice of the case or set of cases to study cannot appear random. Evidence that supports the method by which you identified and chose your subject of analysis should clearly support investigation of the research problem and linked to key findings from your literature review. Be sure to cite any studies that helped you determine that the case you chose was appropriate for examining the problem.

IV.  Discussion

The main elements of your discussion section are generally the same as any research paper, but centered around interpreting and drawing conclusions about the key findings from your analysis of the case study. Note that a general social sciences research paper may contain a separate section to report findings. However, in a paper designed around a case study, it is common to combine a description of the results with the discussion about their implications. The objectives of your discussion section should include the following:

Reiterate the Research Problem/State the Major Findings Briefly reiterate the research problem you are investigating and explain why the subject of analysis around which you designed the case study were used. You should then describe the findings revealed from your study of the case using direct, declarative, and succinct proclamation of the study results. Highlight any findings that were unexpected or especially profound.

Explain the Meaning of the Findings and Why They are Important Systematically explain the meaning of your case study findings and why you believe they are important. Begin this part of the section by repeating what you consider to be your most important or surprising finding first, then systematically review each finding. Be sure to thoroughly extrapolate what your analysis of the case can tell the reader about situations or conditions beyond the actual case that was studied while, at the same time, being careful not to misconstrue or conflate a finding that undermines the external validity of your conclusions.

Relate the Findings to Similar Studies No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your case study results to those found in other studies, particularly if questions raised from prior studies served as the motivation for choosing your subject of analysis. This is important because comparing and contrasting the findings of other studies helps support the overall importance of your results and it highlights how and in what ways your case study design and the subject of analysis differs from prior research about the topic.

Consider Alternative Explanations of the Findings Remember that the purpose of social science research is to discover and not to prove. When writing the discussion section, you should carefully consider all possible explanations revealed by the case study results, rather than just those that fit your hypothesis or prior assumptions and biases. Be alert to what the in-depth analysis of the case may reveal about the research problem, including offering a contrarian perspective to what scholars have stated in prior research if that is how the findings can be interpreted from your case.

Acknowledge the Study's Limitations You can state the study's limitations in the conclusion section of your paper but describing the limitations of your subject of analysis in the discussion section provides an opportunity to identify the limitations and explain why they are not significant. This part of the discussion section should also note any unanswered questions or issues your case study could not address. More detailed information about how to document any limitations to your research can be found here .

Suggest Areas for Further Research Although your case study may offer important insights about the research problem, there are likely additional questions related to the problem that remain unanswered or findings that unexpectedly revealed themselves as a result of your in-depth analysis of the case. Be sure that the recommendations for further research are linked to the research problem and that you explain why your recommendations are valid in other contexts and based on the original assumptions of your study.

V.  Conclusion

As with any research paper, you should summarize your conclusion in clear, simple language; emphasize how the findings from your case study differs from or supports prior research and why. Do not simply reiterate the discussion section. Provide a synthesis of key findings presented in the paper to show how these converge to address the research problem. If you haven't already done so in the discussion section, be sure to document the limitations of your case study and any need for further research.

The function of your paper's conclusion is to: 1) reiterate the main argument supported by the findings from your case study; 2) state clearly the context, background, and necessity of pursuing the research problem using a case study design in relation to an issue, controversy, or a gap found from reviewing the literature; and, 3) provide a place to persuasively and succinctly restate the significance of your research problem, given that the reader has now been presented with in-depth information about the topic.

Consider the following points to help ensure your conclusion is appropriate:

  • If the argument or purpose of your paper is complex, you may need to summarize these points for your reader.
  • If prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the conclusion of your paper to describe your main points and explain their significance.
  • Move from a detailed to a general level of consideration of the case study's findings that returns the topic to the context provided by the introduction or within a new context that emerges from your case study findings.

Note that, depending on the discipline you are writing in or the preferences of your professor, the concluding paragraph may contain your final reflections on the evidence presented as it applies to practice or on the essay's central research problem. However, the nature of being introspective about the subject of analysis you have investigated will depend on whether you are explicitly asked to express your observations in this way.

Problems to Avoid

Overgeneralization One of the goals of a case study is to lay a foundation for understanding broader trends and issues applied to similar circumstances. However, be careful when drawing conclusions from your case study. They must be evidence-based and grounded in the results of the study; otherwise, it is merely speculation. Looking at a prior example, it would be incorrect to state that a factor in improving girls access to education in Azerbaijan and the policy implications this may have for improving access in other Muslim nations is due to girls access to social media if there is no documentary evidence from your case study to indicate this. There may be anecdotal evidence that retention rates were better for girls who were engaged with social media, but this observation would only point to the need for further research and would not be a definitive finding if this was not a part of your original research agenda.

Failure to Document Limitations No case is going to reveal all that needs to be understood about a research problem. Therefore, just as you have to clearly state the limitations of a general research study , you must describe the specific limitations inherent in the subject of analysis. For example, the case of studying how women conceptualize the need for water conservation in a village in Uganda could have limited application in other cultural contexts or in areas where fresh water from rivers or lakes is plentiful and, therefore, conservation is understood more in terms of managing access rather than preserving access to a scarce resource.

Failure to Extrapolate All Possible Implications Just as you don't want to over-generalize from your case study findings, you also have to be thorough in the consideration of all possible outcomes or recommendations derived from your findings. If you do not, your reader may question the validity of your analysis, particularly if you failed to document an obvious outcome from your case study research. For example, in the case of studying the accident at the railroad crossing to evaluate where and what types of warning signals should be located, you failed to take into consideration speed limit signage as well as warning signals. When designing your case study, be sure you have thoroughly addressed all aspects of the problem and do not leave gaps in your analysis that leave the reader questioning the results.

Case Studies. Writing@CSU. Colorado State University; Gerring, John. Case Study Research: Principles and Practices . New York: Cambridge University Press, 2007; Merriam, Sharan B. Qualitative Research and Case Study Applications in Education . Rev. ed. San Francisco, CA: Jossey-Bass, 1998; Miller, Lisa L. “The Use of Case Studies in Law and Social Science Research.” Annual Review of Law and Social Science 14 (2018): TBD; Mills, Albert J., Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Putney, LeAnn Grogan. "Case Study." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE Publications, 2010), pp. 116-120; Simons, Helen. Case Study Research in Practice . London: SAGE Publications, 2009;  Kratochwill,  Thomas R. and Joel R. Levin, editors. Single-Case Research Design and Analysis: New Development for Psychology and Education .  Hilldsale, NJ: Lawrence Erlbaum Associates, 1992; Swanborn, Peter G. Case Study Research: What, Why and How? London : SAGE, 2010; Yin, Robert K. Case Study Research: Design and Methods . 6th edition. Los Angeles, CA, SAGE Publications, 2014; Walo, Maree, Adrian Bull, and Helen Breen. “Achieving Economic Benefits at Local Events: A Case Study of a Local Sports Event.” Festival Management and Event Tourism 4 (1996): 95-106.

Writing Tip

At Least Five Misconceptions about Case Study Research

Social science case studies are often perceived as limited in their ability to create new knowledge because they are not randomly selected and findings cannot be generalized to larger populations. Flyvbjerg examines five misunderstandings about case study research and systematically "corrects" each one. To quote, these are:

Misunderstanding 1 :  General, theoretical [context-independent] knowledge is more valuable than concrete, practical [context-dependent] knowledge. Misunderstanding 2 :  One cannot generalize on the basis of an individual case; therefore, the case study cannot contribute to scientific development. Misunderstanding 3 :  The case study is most useful for generating hypotheses; that is, in the first stage of a total research process, whereas other methods are more suitable for hypotheses testing and theory building. Misunderstanding 4 :  The case study contains a bias toward verification, that is, a tendency to confirm the researcher’s preconceived notions. Misunderstanding 5 :  It is often difficult to summarize and develop general propositions and theories on the basis of specific case studies [p. 221].

While writing your paper, think introspectively about how you addressed these misconceptions because to do so can help you strengthen the validity and reliability of your research by clarifying issues of case selection, the testing and challenging of existing assumptions, the interpretation of key findings, and the summation of case outcomes. Think of a case study research paper as a complete, in-depth narrative about the specific properties and key characteristics of your subject of analysis applied to the research problem.

Flyvbjerg, Bent. “Five Misunderstandings About Case-Study Research.” Qualitative Inquiry 12 (April 2006): 219-245.

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Case study design, using case study design in the applied doctoral experience (ade), applicability of case study design to applied problem of practice, case study design references.

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The field of qualitative research there are a number of research designs (also referred to as “traditions” or “genres”), including case study, phenomenology, narrative inquiry, action research, ethnography, grounded theory, as well as a number of critical genres including Feminist theory, indigenous research, critical race theory and cultural studies. The choice of research design is directly tied to and must be aligned with your research problem and purpose. As Bloomberg & Volpe (2019) explain:

Choice of research design is directly tied to research problem and purpose. As the researcher, you actively create the link among problem, purpose, and design through a process of reflecting on problem and purpose, focusing on researchable questions, and considering how to best address these questions. Thinking along these lines affords a research study methodological congruence (p. 38).

Case study is an in-depth exploration from multiple perspectives of a bounded social phenomenon, be this a social system such as a program, event, institution, organization, or community (Stake, 1995, 2005; Yin, 2018). Case study is employed across disciplines, including education, health care, social work, sociology, and organizational studies. The purpose is to generate understanding and deep insights to inform professional practice, policy development, and community or social action (Bloomberg 2018).

Yin (2018) and Stake (1995, 2005), two of the key proponents of case study methodology, use different terms to describe case studies. Yin categorizes case studies as exploratory or descriptive . The former is used to explore those situations in which the intervention being evaluated has no clear single set of outcomes. The latter is used to describe an intervention or phenomenon and the real-life context in which it occurred. Stake identifies case studies as intrinsic or instrumental , and he proposes that a primary distinction in designing case studies is between single and multiple (or collective) case study designs. A single case study may be an instrumental case study (research focuses on an issue or concern in one bounded case) or an intrinsic case study (the focus is on the case itself because the case presents a unique situation). A longitudinal case study design is chosen when the researcher seeks to examine the same single case at two or more different points in time or to capture trends over time. A multiple case study design is used when a researcher seeks to determine the prevalence or frequency of a particular phenomenon. This approach is useful when cases are used for purposes of a cross-case analysis in order to compare, contrast, and synthesize perspectives regarding the same issue. The focus is on the analysis of diverse cases to determine how these confirm the findings within or between cases, or call the findings into question.

Case study affords significant interaction with research participants, providing an in-depth picture of the phenomenon (Bloomberg & Volpe, 2019). Research is extensive, drawing on multiple methods of data collection, and involves multiple data sources. Triangulation is critical in attempting to obtain an in-depth understanding of the phenomenon under study and adds rigor, breadth, and depth to the study and provides corroborative evidence of the data obtained. Analysis of data can be holistic or embedded—that is, dealing with the whole or parts of the case (Yin, 2018). With multiple cases the typical analytic strategy is to provide detailed description of themes within each case (within-case analysis), followed by thematic analysis across cases (cross-case analysis), providing insights regarding how individual cases are comparable along important dimensions. Research culminates in the production of a detailed description of a setting and its participants, accompanied by an analysis of the data for themes or patterns (Stake, 1995, 2005; Yin, 2018). In addition to thick, rich description, the researcher’s interpretations, conclusions, and recommendations contribute to the reader’s overall understanding of the case study.

Analysis of findings should show that the researcher has attended to all the data, should address the most significant aspects of the case, and should demonstrate familiarity with the prevailing thinking and discourse about the topic. The goal of case study design (as with all qualitative designs) is not generalizability but rather transferability —that is, how (if at all) and in what ways understanding and knowledge can be applied in similar contexts and settings. The qualitative researcher attempts to address the issue of transferability by way of thick, rich description that will provide the basis for a case or cases to have relevance and potential application across a broader context.

Qualitative research methods ask the questions of "what" and "how" a phenomenon is understood in a real-life context (Bloomberg & Volpe, 2019). In the education field, qualitative research methods uncover educational experiences and practices because qualitative research allows the researcher to reveal new knowledge and understanding. Moreover, qualitative descriptive case studies describe, analyze and interpret events that explain the reasoning behind specific phenomena (Bloomberg, 2018). As such, case study design can be the foundation for a rigorous study within the Applied Doctoral Experience (ADE).

Case study design is an appropriate research design to consider when conceptualizing and conducting a dissertation research study that is based on an applied problem of practice with inherent real-life educational implications. Case study researchers study current, real-life cases that are in progress so that they can gather accurate information that is current. This fits well with the ADE program, as students are typically exploring a problem of practice. Because of the flexibility of the methods used, a descriptive design provides the researcher with the opportunity to choose data collection methods that are best suited to a practice-based research purpose, and can include individual interviews, focus groups, observation, surveys, and critical incident questionnaires. Methods are triangulated to contribute to the study’s trustworthiness. In selecting the set of data collection methods, it is important that the researcher carefully consider the alignment between research questions and the type of data that is needed to address these. Each data source is one piece of the “puzzle,” that contributes to the researcher’s holistic understanding of a phenomenon. The various strands of data are woven together holistically to promote a deeper understanding of the case and its application to an educationally-based problem of practice.

Research studies within the Applied Doctoral Experience (ADE) will be practical in nature and focus on problems and issues that inform educational practice.  Many of the types of studies that fall within the ADE framework are exploratory, and align with case study design. Case study design fits very well with applied problems related to educational practice, as the following set of examples illustrate:

Elementary Bilingual Education Teachers’ Self-Efficacy in Teaching English Language Learners: A Qualitative Case Study

The problem to be addressed in the proposed study is that some elementary bilingual education teachers’ beliefs about their lack of preparedness to teach the English language may negatively impact the language proficiency skills of Hispanic ELLs (Ernst-Slavit & Wenger, 2016; Fuchs et al., 2018; Hoque, 2016). The purpose of the proposed qualitative descriptive case study was to explore the perspectives and experiences of elementary bilingual education teachers regarding their perceived lack of preparedness to teach the English language and how this may impact the language proficiency of Hispanic ELLs.

Exploring Minority Teachers Experiences Pertaining to their Value in Education: A Single Case Study of Teachers in New York City

The problem is that minority K-12 teachers are underrepresented in the United States, with research indicating that school leaders and teachers in schools that are populated mainly by black students, staffed mostly by white teachers who may be unprepared to deal with biases and stereotypes that are ingrained in schools (Egalite, Kisida, & Winters, 2015; Milligan & Howley, 2015). The purpose of this qualitative exploratory single case study was to develop a clearer understanding of minority teachers’ experiences concerning the under-representation of minority K-12 teachers in urban school districts in the United States since there are so few of them.

Exploring the Impact of an Urban Teacher Residency Program on Teachers’ Cultural Intelligence: A Qualitative Case Study

The problem to be addressed by this case study is that teacher candidates often report being unprepared and ill-equipped to effectively educate culturally diverse students (Skepple, 2015; Beutel, 2018). The purpose of this study was to explore and gain an in-depth understanding of the perceived impact of an urban teacher residency program in urban Iowa on teachers’ cultural competence using the cultural intelligence (CQ) framework (Earley & Ang, 2003).

Qualitative Case Study that Explores Self-Efficacy and Mentorship on Women in Academic Administrative Leadership Roles

The problem was that female school-level administrators might be less likely to experience mentorship, thereby potentially decreasing their self-efficacy (Bing & Smith, 2019; Brown, 2020; Grant, 2021). The purpose of this case study was to determine to what extent female school-level administrators in the United States who had a mentor have a sense of self-efficacy and to examine the relationship between mentorship and self-efficacy.

Suburban Teacher and Administrator Perceptions of Culturally Responsive Teaching to Promote Connectedness in Students of Color: A Qualitative Case Study

The problem to be addressed in this study is the racial discrimination experienced by students of color in suburban schools and the resulting negative school experience (Jara & Bloomsbury, 2020; Jones, 2019; Kohli et al., 2017; Wandix-White, 2020). The purpose of this case study is to explore how culturally responsive practices can counteract systemic racism and discrimination in suburban schools thereby meeting the needs of students of color by creating positive learning experiences. 

As you can see, all of these studies were well suited to qualitative case study design. In each of these studies, the applied research problem and research purpose were clearly grounded in educational practice as well as directly aligned with qualitative case study methodology. In the Applied Doctoral Experience (ADE), you will be focused on addressing or resolving an educationally relevant research problem of practice. As such, your case study, with clear boundaries, will be one that centers on a real-life authentic problem in your field of practice that you believe is in need of resolution or improvement, and that the outcome thereof will be educationally valuable.

Bloomberg, L. D. (2018). Case study method. In B. B. Frey (Ed.), The SAGE Encyclopedia of educational research, measurement, and evaluation (pp. 237–239). SAGE. https://go.openathens.net/redirector/nu.edu?url=https%3A%2F%2Fmethods.sagepub.com%2FReference%2Fthe-sage-encyclopedia-of-educational-research-measurement-and-evaluation%2Fi4294.xml

Bloomberg, L. D. & Volpe, M. (2019). Completing your qualitative dissertation: A road map from beginning to end . (4th Ed.). SAGE.

Stake, R. E. (1995). The art of case study research. SAGE.

Stake, R. E. (2005). Qualitative case studies. In N. K. Denzin and Y. S. Lincoln (Eds.), The SAGE handbook of qualitative research (3rd ed., pp. 443–466). SAGE.

Yin, R. (2018). Case study research and applications: Designs and methods. SAGE.

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case study research design example

Case Study Research Design

The case study research design have evolved over the past few years as a useful tool for investigating trends and specific situations in many scientific disciplines.

This article is a part of the guide:

  • Research Designs
  • Quantitative and Qualitative Research
  • Literature Review
  • Quantitative Research Design
  • Descriptive Research

Browse Full Outline

  • 1 Research Designs
  • 2.1 Pilot Study
  • 2.2 Quantitative Research Design
  • 2.3 Qualitative Research Design
  • 2.4 Quantitative and Qualitative Research
  • 3.1 Case Study
  • 3.2 Naturalistic Observation
  • 3.3 Survey Research Design
  • 3.4 Observational Study
  • 4.1 Case-Control Study
  • 4.2 Cohort Study
  • 4.3 Longitudinal Study
  • 4.4 Cross Sectional Study
  • 4.5 Correlational Study
  • 5.1 Field Experiments
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  • 5.3 Identical Twins Study
  • 6.1 Experimental Design
  • 6.2 True Experimental Design
  • 6.3 Double Blind Experiment
  • 6.4 Factorial Design
  • 7.1 Literature Review
  • 7.2 Systematic Reviews
  • 7.3 Meta Analysis

The case study has been especially used in social science, psychology, anthropology and ecology.

This method of study is especially useful for trying to test theoretical models by using them in real world situations. For example, if an anthropologist were to live amongst a remote tribe, whilst their observations might produce no quantitative data, they are still useful to science.

case study research design example

What is a Case Study?

Basically, a case study is an in depth study of a particular situation rather than a sweeping statistical survey . It is a method used to narrow down a very broad field of research into one easily researchable topic.

Whilst it will not answer a question completely, it will give some indications and allow further elaboration and hypothesis creation on a subject.

The case study research design is also useful for testing whether scientific theories and models actually work in the real world. You may come out with a great computer model for describing how the ecosystem of a rock pool works but it is only by trying it out on a real life pool that you can see if it is a realistic simulation.

For psychologists, anthropologists and social scientists they have been regarded as a valid method of research for many years. Scientists are sometimes guilty of becoming bogged down in the general picture and it is sometimes important to understand specific cases and ensure a more holistic approach to research .

H.M.: An example of a study using the case study research design.

Case Study

The Argument for and Against the Case Study Research Design

Some argue that because a case study is such a narrow field that its results cannot be extrapolated to fit an entire question and that they show only one narrow example. On the other hand, it is argued that a case study provides more realistic responses than a purely statistical survey.

The truth probably lies between the two and it is probably best to try and synergize the two approaches. It is valid to conduct case studies but they should be tied in with more general statistical processes.

For example, a statistical survey might show how much time people spend talking on mobile phones, but it is case studies of a narrow group that will determine why this is so.

The other main thing to remember during case studies is their flexibility. Whilst a pure scientist is trying to prove or disprove a hypothesis , a case study might introduce new and unexpected results during its course, and lead to research taking new directions.

The argument between case study and statistical method also appears to be one of scale. Whilst many 'physical' scientists avoid case studies, for psychology, anthropology and ecology they are an essential tool. It is important to ensure that you realize that a case study cannot be generalized to fit a whole population or ecosystem.

Finally, one peripheral point is that, when informing others of your results, case studies make more interesting topics than purely statistical surveys, something that has been realized by teachers and magazine editors for many years. The general public has little interest in pages of statistical calculations but some well placed case studies can have a strong impact.

How to Design and Conduct a Case Study

The advantage of the case study research design is that you can focus on specific and interesting cases. This may be an attempt to test a theory with a typical case or it can be a specific topic that is of interest. Research should be thorough and note taking should be meticulous and systematic.

The first foundation of the case study is the subject and relevance. In a case study, you are deliberately trying to isolate a small study group, one individual case or one particular population.

For example, statistical analysis may have shown that birthrates in African countries are increasing. A case study on one or two specific countries becomes a powerful and focused tool for determining the social and economic pressures driving this.

In the design of a case study, it is important to plan and design how you are going to address the study and make sure that all collected data is relevant. Unlike a scientific report, there is no strict set of rules so the most important part is making sure that the study is focused and concise; otherwise you will end up having to wade through a lot of irrelevant information.

It is best if you make yourself a short list of 4 or 5 bullet points that you are going to try and address during the study. If you make sure that all research refers back to these then you will not be far wrong.

With a case study, even more than a questionnaire or survey , it is important to be passive in your research. You are much more of an observer than an experimenter and you must remember that, even in a multi-subject case, each case must be treated individually and then cross case conclusions can be drawn .

How to Analyze the Results

Analyzing results for a case study tends to be more opinion based than statistical methods. The usual idea is to try and collate your data into a manageable form and construct a narrative around it.

Use examples in your narrative whilst keeping things concise and interesting. It is useful to show some numerical data but remember that you are only trying to judge trends and not analyze every last piece of data. Constantly refer back to your bullet points so that you do not lose focus.

It is always a good idea to assume that a person reading your research may not possess a lot of knowledge of the subject so try to write accordingly.

In addition, unlike a scientific study which deals with facts, a case study is based on opinion and is very much designed to provoke reasoned debate. There really is no right or wrong answer in a case study.

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Writing A Case Study

Case Study Examples

Barbara P

Brilliant Case Study Examples and Templates For Your Help

15 min read

Published on: Jun 26, 2019

Last updated on: Nov 29, 2023

Case Study Examples

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It’s no surprise that writing a case study is one of the most challenging academic tasks for students. You’re definitely not alone here!

Most people don't realize that there are specific guidelines to follow when writing a case study. If you don't know where to start, it's easy to get overwhelmed and give up before you even begin.

Don't worry! Let us help you out!

We've collected over 25 free case study examples with solutions just for you. These samples with solutions will help you win over your panel and score high marks on your case studies.

So, what are you waiting for? Let's dive in and learn the secrets to writing a successful case study.

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An Overview of Case Studies

A case study is a research method used to study a particular individual, group, or situation in depth. It involves analyzing and interpreting data from a variety of sources to gain insight into the subject being studied. 

Case studies are often used in psychology, business, and education to explore complicated problems and find solutions. They usually have detailed descriptions of the subject, background info, and an analysis of the main issues.

The goal of a case study is to provide a comprehensive understanding of the subject. Typically, case studies can be divided into three parts, challenges, solutions, and results. 

Here is a case study sample PDF so you can have a clearer understanding of what a case study actually is:

Case Study Sample PDF

How to Write a Case Study Examples

Learn how to write a case study with the help of our comprehensive case study guide.

Case Study Examples for Students

Quite often, students are asked to present case studies in their academic journeys. The reason instructors assign case studies is for students to sharpen their critical analysis skills, understand how companies make profits, etc.

Below are some case study examples in research, suitable for students:

Case Study Example in Software Engineering

Qualitative Research Case Study Sample

Software Quality Assurance Case Study

Social Work Case Study Example

Ethical Case Study

Case Study Example PDF

These examples can guide you on how to structure and format your own case studies.

Struggling with formatting your case study? Check this case study format guide and perfect your document’s structure today.

Business Case Study Examples

A business case study examines a business’s specific challenge or goal and how it should be solved. Business case studies usually focus on several details related to the initial challenge and proposed solution. 

To help you out, here are some samples so you can create case studies that are related to businesses: 

Here are some more business case study examples:

Business Case Studies PDF

Business Case Studies Example

Typically, a business case study discovers one of your customer's stories and how you solved a problem for them. It allows your prospects to see how your solutions address their needs. 

Medical Case Study Examples

Medical case studies are an essential part of medical education. They help students to understand how to diagnose and treat patients. 

Here are some medical case study examples to help you.

Medical Case Study Example

Nursing Case Study Example

Want to understand the various types of case studies? Check out our types of case study blog to select the perfect type.

Psychology Case Study Examples 

Case studies are a great way of investigating individuals with psychological abnormalities. This is why it is a very common assignment in psychology courses. 

By examining all the aspects of your subject’s life, you discover the possible causes of exhibiting such behavior. 

For your help, here are some interesting psychology case study examples:

Psychology Case Study Example

Mental Health Case Study Example

Sales Case Study Examples

Case studies are important tools for sales teams’ performance improvement. By examining sales successes, teams can gain insights into effective strategies and create action plans to employ similar tactics.

By researching case studies of successful sales campaigns, sales teams can more accurately identify challenges and develop solutions.

Sales Case Study Example

Interview Case Study Examples

Interview case studies provide businesses with invaluable information. This data allows them to make informed decisions related to certain markets or subjects.

Interview Case Study Example

Marketing Case Study Examples

Marketing case studies are real-life stories that showcase how a business solves a problem. They typically discuss how a business achieves a goal using a specific marketing strategy or tactic.

They typically describe a challenge faced by a business, the solution implemented, and the results achieved.

This is a short sample marketing case study for you to get an idea of what an actual marketing case study looks like.

 Here are some more popular marketing studies that show how companies use case studies as a means of marketing and promotion:

“Chevrolet Discover the Unexpected” by Carol H. Williams

This case study explores Chevrolet's “ DTU Journalism Fellows ” program. The case study uses the initials “DTU” to generate interest and encourage readers to learn more. 

Multiple types of media, such as images and videos, are used to explain the challenges faced. The case study concludes with an overview of the achievements that were met.

Key points from the case study include:

  • Using a well-known brand name in the title can create interest.
  • Combining different media types, such as headings, images, and videos, can help engage readers and make the content more memorable.
  • Providing a summary of the key achievements at the end of the case study can help readers better understand the project's impact.

“The Met” by Fantasy

“ The Met ” by Fantasy is a fictional redesign of the Metropolitan Museum of Art in New York City, created by the design studio Fantasy. The case study clearly and simply showcases the museum's website redesign.

The Met emphasizes the website’s features and interface by showcasing each section of the interface individually, allowing the readers to concentrate on the significant elements.

For those who prefer text, each feature includes an objective description. The case study also includes a “Contact Us” call-to-action at the bottom of the page, inviting visitors to contact the company.

Key points from this “The Met” include:

  • Keeping the case study simple and clean can help readers focus on the most important aspects.
  • Presenting the features and solutions with a visual showcase can be more effective than writing a lot of text.
  • Including a clear call-to-action at the end of the case study can encourage visitors to contact the company for more information.

“Better Experiences for All” by Herman Miller

Herman Miller's minimalist approach to furniture design translates to their case study, “ Better Experiences for All ”, for a Dubai hospital. The page features a captivating video with closed-captioning and expandable text for accessibility.

The case study presents a wealth of information in a concise format, enabling users to grasp the complexities of the strategy with ease. It concludes with a client testimonial and a list of furniture items purchased from the brand.

Key points from the “Better Experiences” include:

  • Make sure your case study is user-friendly by including accessibility features like closed captioning and expandable text.
  • Include a list of products that were used in the project to guide potential customers.

“NetApp” by Evisort 

Evisort's case study on “ NetApp ” stands out for its informative and compelling approach. The study begins with a client-centric overview of NetApp, strategically directing attention to the client rather than the company or team involved.

The case study incorporates client quotes and explores NetApp’s challenges during COVID-19. Evisort showcases its value as a client partner by showing how its services supported NetApp through difficult times. 

  • Provide an overview of the company in the client’s words, and put focus on the customer. 
  • Highlight how your services can help clients during challenging times.
  • Make your case study accessible by providing it in various formats.

“Red Sox Season Campaign,” by CTP Boston

The “ Red Sox Season Campaign ” showcases a perfect blend of different media, such as video, text, and images. Upon visiting the page, the video plays automatically, there are videos of Red Sox players, their images, and print ads that can be enlarged with a click.

The page features an intuitive design and invites viewers to appreciate CTP's well-rounded campaign for Boston's beloved baseball team. There’s also a CTA that prompts viewers to learn how CTP can create a similar campaign for their brand.

Some key points to take away from the “Red Sox Season Campaign”: 

  • Including a variety of media such as video, images, and text can make your case study more engaging and compelling.
  • Include a call-to-action at the end of your study that encourages viewers to take the next step towards becoming a customer or prospect.

“Airbnb + Zendesk” by Zendesk

The case study by Zendesk, titled “ Airbnb + Zendesk : Building a powerful solution together,” showcases a true partnership between Airbnb and Zendesk. 

The article begins with an intriguing opening statement, “Halfway around the globe is a place to stay with your name on it. At least for a weekend,” and uses stunning images of beautiful Airbnb locations to captivate readers.

Instead of solely highlighting Zendesk's product, the case study is crafted to tell a good story and highlight Airbnb's service in detail. This strategy makes the case study more authentic and relatable.

Some key points to take away from this case study are:

  • Use client's offerings' images rather than just screenshots of your own product or service.
  • To begin the case study, it is recommended to include a distinct CTA. For instance, Zendesk presents two alternatives, namely to initiate a trial or seek a solution.

“Influencer Marketing” by Trend and WarbyParker

The case study "Influencer Marketing" by Trend and Warby Parker highlights the potential of influencer content marketing, even when working with a limited budget. 

The “Wearing Warby” campaign involved influencers wearing Warby Parker glasses during their daily activities, providing a glimpse of the brand's products in use. 

This strategy enhanced the brand's relatability with influencers' followers. While not detailing specific tactics, the case study effectively illustrates the impact of third-person case studies in showcasing campaign results.

Key points to take away from this case study are:

  • Influencer marketing can be effective even with a limited budget.
  • Showcasing products being used in everyday life can make a brand more approachable and relatable.
  • Third-person case studies can be useful in highlighting the success of a campaign.

Marketing Case Study Example

Marketing Case Study Template

Now that you have read multiple case study examples, hop on to our tips.

Tips to Write a Good Case Study

Here are some note-worthy tips to craft a winning case study 

  • Define the purpose of the case study This will help you to focus on the most important aspects of the case. The case study objective helps to ensure that your finished product is concise and to the point.
  • Choose a real-life example. One of the best ways to write a successful case study is to choose a real-life example. This will give your readers a chance to see how the concepts apply in a real-world setting.
  • Keep it brief. This means that you should only include information that is directly relevant to your topic and avoid adding unnecessary details.
  • Use strong evidence. To make your case study convincing, you will need to use strong evidence. This can include statistics, data from research studies, or quotes from experts in the field.
  • Edit and proofread your work. Before you submit your case study, be sure to edit and proofread your work carefully. This will help to ensure that there are no errors and that your paper is clear and concise.

There you go!

We’re sure that now you have secrets to writing a great case study at your fingertips! This blog teaches the key guidelines of various case studies with samples. So grab your pen and start crafting a winning case study right away!

Having said that, we do understand that some of you might be having a hard time writing compelling case studies.

But worry not! Our expert case study writing service is here to take all your case-writing blues away! 

With 100% thorough research guaranteed, our professional essay writing service can craft an amazing case study within 6 hours! 

So why delay? Let us help you shine in the eyes of your instructor!

Barbara P (Literature, Marketing)

Dr. Barbara is a highly experienced writer and author who holds a Ph.D. degree in public health from an Ivy League school. She has worked in the medical field for many years, conducting extensive research on various health topics. Her writing has been featured in several top-tier publications.

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  • Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

  • Provide new or unexpected insights into the subject
  • Challenge or complicate existing assumptions and theories
  • Propose practical courses of action to resolve a problem
  • Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

  • Exemplify a theory by showing how it explains the case under investigation
  • Expand on a theory by uncovering new concepts and ideas that need to be incorporated
  • Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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What is case study research?

Last updated

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Reviewed by

Cathy Heath

Suppose a company receives a spike in the number of customer complaints, or medical experts discover an outbreak of illness affecting children but are not quite sure of the reason. In both cases, carrying out a case study could be the best way to get answers.

Organization

Case studies can be carried out across different disciplines, including education, medicine, sociology, and business.

Most case studies employ qualitative methods, but quantitative methods can also be used. Researchers can then describe, compare, evaluate, and identify patterns or cause-and-effect relationships between the various variables under study. They can then use this knowledge to decide what action to take. 

Another thing to note is that case studies are generally singular in their focus. This means they narrow focus to a particular area, making them highly subjective. You cannot always generalize the results of a case study and apply them to a larger population. However, they are valuable tools to illustrate a principle or develop a thesis.

Analyze case study research

Dovetail streamlines case study research to help you uncover and share actionable insights

  • What are the different types of case study designs?

Researchers can choose from a variety of case study designs. The design they choose is dependent on what questions they need to answer, the context of the research environment, how much data they already have, and what resources are available.

Here are the common types of case study design:

Explanatory

An explanatory case study is an initial explanation of the how or why that is behind something. This design is commonly used when studying a real-life phenomenon or event. Once the organization understands the reasons behind a phenomenon, it can then make changes to enhance or eliminate the variables causing it. 

Here is an example: How is co-teaching implemented in elementary schools? The title for a case study of this subject could be “Case Study of the Implementation of Co-Teaching in Elementary Schools.”

Descriptive

An illustrative or descriptive case study helps researchers shed light on an unfamiliar object or subject after a period of time. The case study provides an in-depth review of the issue at hand and adds real-world examples in the area the researcher wants the audience to understand. 

The researcher makes no inferences or causal statements about the object or subject under review. This type of design is often used to understand cultural shifts.

Here is an example: How did people cope with the 2004 Indian Ocean Tsunami? This case study could be titled "A Case Study of the 2004 Indian Ocean Tsunami and its Effect on the Indonesian Population."

Exploratory

Exploratory research is also called a pilot case study. It is usually the first step within a larger research project, often relying on questionnaires and surveys . Researchers use exploratory research to help narrow down their focus, define parameters, draft a specific research question , and/or identify variables in a larger study. This research design usually covers a wider area than others, and focuses on the ‘what’ and ‘who’ of a topic.

Here is an example: How do nutrition and socialization in early childhood affect learning in children? The title of the exploratory study may be “Case Study of the Effects of Nutrition and Socialization on Learning in Early Childhood.”

An intrinsic case study is specifically designed to look at a unique and special phenomenon. At the start of the study, the researcher defines the phenomenon and the uniqueness that differentiates it from others. 

In this case, researchers do not attempt to generalize, compare, or challenge the existing assumptions. Instead, they explore the unique variables to enhance understanding. Here is an example: “Case Study of Volcanic Lightning.”

This design can also be identified as a cumulative case study. It uses information from past studies or observations of groups of people in certain settings as the foundation of the new study. Given that it takes multiple areas into account, it allows for greater generalization than a single case study. 

The researchers also get an in-depth look at a particular subject from different viewpoints.  Here is an example: “Case Study of how PTSD affected Vietnam and Gulf War Veterans Differently Due to Advances in Military Technology.”

Critical instance

A critical case study incorporates both explanatory and intrinsic study designs. It does not have predetermined purposes beyond an investigation of the said subject. It can be used for a deeper explanation of the cause-and-effect relationship. It can also be used to question a common assumption or myth. 

The findings can then be used further to generalize whether they would also apply in a different environment.  Here is an example: “What Effect Does Prolonged Use of Social Media Have on the Mind of American Youth?”

Instrumental

Instrumental research attempts to achieve goals beyond understanding the object at hand. Researchers explore a larger subject through different, separate studies and use the findings to understand its relationship to another subject. This type of design also provides insight into an issue or helps refine a theory. 

For example, you may want to determine if violent behavior in children predisposes them to crime later in life. The focus is on the relationship between children and violent behavior, and why certain children do become violent. Here is an example: “Violence Breeds Violence: Childhood Exposure and Participation in Adult Crime.”

Evaluation case study design is employed to research the effects of a program, policy, or intervention, and assess its effectiveness and impact on future decision-making. 

For example, you might want to see whether children learn times tables quicker through an educational game on their iPad versus a more teacher-led intervention. Here is an example: “An Investigation of the Impact of an iPad Multiplication Game for Primary School Children.” 

  • When do you use case studies?

Case studies are ideal when you want to gain a contextual, concrete, or in-depth understanding of a particular subject. It helps you understand the characteristics, implications, and meanings of the subject.

They are also an excellent choice for those writing a thesis or dissertation, as they help keep the project focused on a particular area when resources or time may be too limited to cover a wider one. You may have to conduct several case studies to explore different aspects of the subject in question and understand the problem.

  • What are the steps to follow when conducting a case study?

1. Select a case

Once you identify the problem at hand and come up with questions, identify the case you will focus on. The study can provide insights into the subject at hand, challenge existing assumptions, propose a course of action, and/or open up new areas for further research.

2. Create a theoretical framework

While you will be focusing on a specific detail, the case study design you choose should be linked to existing knowledge on the topic. This prevents it from becoming an isolated description and allows for enhancing the existing information. 

It may expand the current theory by bringing up new ideas or concepts, challenge established assumptions, or exemplify a theory by exploring how it answers the problem at hand. A theoretical framework starts with a literature review of the sources relevant to the topic in focus. This helps in identifying key concepts to guide analysis and interpretation.

3. Collect the data

Case studies are frequently supplemented with qualitative data such as observations, interviews, and a review of both primary and secondary sources such as official records, news articles, and photographs. There may also be quantitative data —this data assists in understanding the case thoroughly.

4. Analyze your case

The results of the research depend on the research design. Most case studies are structured with chapters or topic headings for easy explanation and presentation. Others may be written as narratives to allow researchers to explore various angles of the topic and analyze its meanings and implications.

In all areas, always give a detailed contextual understanding of the case and connect it to the existing theory and literature before discussing how it fits into your problem area.

  • What are some case study examples?

What are the best approaches for introducing our product into the Kenyan market?

How does the change in marketing strategy aid in increasing the sales volumes of product Y?

How can teachers enhance student participation in classrooms?

How does poverty affect literacy levels in children?

Case study topics

Case study of product marketing strategies in the Kenyan market

Case study of the effects of a marketing strategy change on product Y sales volumes

Case study of X school teachers that encourage active student participation in the classroom

Case study of the effects of poverty on literacy levels in children

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Qualitative study design: Case Studies

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In depth description of the experience of a single person, a family, a group, a community or an organisation.

An example of a qualitative case study is a life history which is the story of one specific person.  A case study may be done to highlight a specific issue by telling a story of one person or one group. 

  • Oral recording

Ability to explore and describe, in depth, an issue or event. 

Develop an understanding of health, illness and health care in context. 

Single case can be used to develop or disprove a theory. 

Can be used as a model or prototype .  

Limitations

Labour intensive and generates large diverse data sets which can be hard to manage. 

Case studies are seen by many as a weak methodology because they only look at one person or one specific group and aren’t as broad in their participant selection as other methodologies. 

Example questions

This methodology can be used to ask questions about a specific drug or treatment and its effects on an individual.

  • Does thalidomide cause birth defects?
  • Does exposure to a pesticide lead to cancer?

Example studies

  • Choi, T. S. T., Walker, K. Z., & Palermo, C. (2018). Diabetes management in a foreign land: A case study on Chinese Australians. Health & Social Care in the Community, 26(2), e225-e232. 
  • Reade, I., Rodgers, W., & Spriggs, K. (2008). New Ideas for High Performance Coaches: A Case Study of Knowledge Transfer in Sport Science.  International Journal of Sports Science & Coaching , 3(3), 335-354. 
  • Wingrove, K., Barbour, L., & Palermo, C. (2017). Exploring nutrition capacity in Australia's charitable food sector.  Nutrition & Dietetics , 74(5), 495-501. 
  • Green, J., & Thorogood, N. (2018). Qualitative methods for health research (4th ed.). London: SAGE. 
  • University of Missouri-St. Louis. Qualitative Research Designs. Retrieved from http://www.umsl.edu/~lindquists/qualdsgn.html   
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  • Last Updated: Oct 12, 2023 11:29 AM
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What Is a Case Study?

Weighing the pros and cons of this method of research

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

case study research design example

Cara Lustik is a fact-checker and copywriter.

case study research design example

Verywell / Colleen Tighe

  • Pros and Cons

What Types of Case Studies Are Out There?

Where do you find data for a case study, how do i write a psychology case study.

A case study is an in-depth study of one person, group, or event. In a case study, nearly every aspect of the subject's life and history is analyzed to seek patterns and causes of behavior. Case studies can be used in many different fields, including psychology, medicine, education, anthropology, political science, and social work.

The point of a case study is to learn as much as possible about an individual or group so that the information can be generalized to many others. Unfortunately, case studies tend to be highly subjective, and it is sometimes difficult to generalize results to a larger population.

While case studies focus on a single individual or group, they follow a format similar to other types of psychology writing. If you are writing a case study, we got you—here are some rules of APA format to reference.  

At a Glance

A case study, or an in-depth study of a person, group, or event, can be a useful research tool when used wisely. In many cases, case studies are best used in situations where it would be difficult or impossible for you to conduct an experiment. They are helpful for looking at unique situations and allow researchers to gather a lot of˜ information about a specific individual or group of people. However, it's important to be cautious of any bias we draw from them as they are highly subjective.

What Are the Benefits and Limitations of Case Studies?

A case study can have its strengths and weaknesses. Researchers must consider these pros and cons before deciding if this type of study is appropriate for their needs.

One of the greatest advantages of a case study is that it allows researchers to investigate things that are often difficult or impossible to replicate in a lab. Some other benefits of a case study:

  • Allows researchers to capture information on the 'how,' 'what,' and 'why,' of something that's implemented
  • Gives researchers the chance to collect information on why one strategy might be chosen over another
  • Permits researchers to develop hypotheses that can be explored in experimental research

On the other hand, a case study can have some drawbacks:

  • It cannot necessarily be generalized to the larger population
  • Cannot demonstrate cause and effect
  • It may not be scientifically rigorous
  • It can lead to bias

Researchers may choose to perform a case study if they want to explore a unique or recently discovered phenomenon. Through their insights, researchers develop additional ideas and study questions that might be explored in future studies.

It's important to remember that the insights from case studies cannot be used to determine cause-and-effect relationships between variables. However, case studies may be used to develop hypotheses that can then be addressed in experimental research.

Case Study Examples

There have been a number of notable case studies in the history of psychology. Much of  Freud's work and theories were developed through individual case studies. Some great examples of case studies in psychology include:

  • Anna O : Anna O. was a pseudonym of a woman named Bertha Pappenheim, a patient of a physician named Josef Breuer. While she was never a patient of Freud's, Freud and Breuer discussed her case extensively. The woman was experiencing symptoms of a condition that was then known as hysteria and found that talking about her problems helped relieve her symptoms. Her case played an important part in the development of talk therapy as an approach to mental health treatment.
  • Phineas Gage : Phineas Gage was a railroad employee who experienced a terrible accident in which an explosion sent a metal rod through his skull, damaging important portions of his brain. Gage recovered from his accident but was left with serious changes in both personality and behavior.
  • Genie : Genie was a young girl subjected to horrific abuse and isolation. The case study of Genie allowed researchers to study whether language learning was possible, even after missing critical periods for language development. Her case also served as an example of how scientific research may interfere with treatment and lead to further abuse of vulnerable individuals.

Such cases demonstrate how case research can be used to study things that researchers could not replicate in experimental settings. In Genie's case, her horrific abuse denied her the opportunity to learn a language at critical points in her development.

This is clearly not something researchers could ethically replicate, but conducting a case study on Genie allowed researchers to study phenomena that are otherwise impossible to reproduce.

There are a few different types of case studies that psychologists and other researchers might use:

  • Collective case studies : These involve studying a group of individuals. Researchers might study a group of people in a certain setting or look at an entire community. For example, psychologists might explore how access to resources in a community has affected the collective mental well-being of those who live there.
  • Descriptive case studies : These involve starting with a descriptive theory. The subjects are then observed, and the information gathered is compared to the pre-existing theory.
  • Explanatory case studies : These   are often used to do causal investigations. In other words, researchers are interested in looking at factors that may have caused certain things to occur.
  • Exploratory case studies : These are sometimes used as a prelude to further, more in-depth research. This allows researchers to gather more information before developing their research questions and hypotheses .
  • Instrumental case studies : These occur when the individual or group allows researchers to understand more than what is initially obvious to observers.
  • Intrinsic case studies : This type of case study is when the researcher has a personal interest in the case. Jean Piaget's observations of his own children are good examples of how an intrinsic case study can contribute to the development of a psychological theory.

The three main case study types often used are intrinsic, instrumental, and collective. Intrinsic case studies are useful for learning about unique cases. Instrumental case studies help look at an individual to learn more about a broader issue. A collective case study can be useful for looking at several cases simultaneously.

The type of case study that psychology researchers use depends on the unique characteristics of the situation and the case itself.

There are a number of different sources and methods that researchers can use to gather information about an individual or group. Six major sources that have been identified by researchers are:

  • Archival records : Census records, survey records, and name lists are examples of archival records.
  • Direct observation : This strategy involves observing the subject, often in a natural setting . While an individual observer is sometimes used, it is more common to utilize a group of observers.
  • Documents : Letters, newspaper articles, administrative records, etc., are the types of documents often used as sources.
  • Interviews : Interviews are one of the most important methods for gathering information in case studies. An interview can involve structured survey questions or more open-ended questions.
  • Participant observation : When the researcher serves as a participant in events and observes the actions and outcomes, it is called participant observation.
  • Physical artifacts : Tools, objects, instruments, and other artifacts are often observed during a direct observation of the subject.

If you have been directed to write a case study for a psychology course, be sure to check with your instructor for any specific guidelines you need to follow. If you are writing your case study for a professional publication, check with the publisher for their specific guidelines for submitting a case study.

Here is a general outline of what should be included in a case study.

Section 1: A Case History

This section will have the following structure and content:

Background information : The first section of your paper will present your client's background. Include factors such as age, gender, work, health status, family mental health history, family and social relationships, drug and alcohol history, life difficulties, goals, and coping skills and weaknesses.

Description of the presenting problem : In the next section of your case study, you will describe the problem or symptoms that the client presented with.

Describe any physical, emotional, or sensory symptoms reported by the client. Thoughts, feelings, and perceptions related to the symptoms should also be noted. Any screening or diagnostic assessments that are used should also be described in detail and all scores reported.

Your diagnosis : Provide your diagnosis and give the appropriate Diagnostic and Statistical Manual code. Explain how you reached your diagnosis, how the client's symptoms fit the diagnostic criteria for the disorder(s), or any possible difficulties in reaching a diagnosis.

Section 2: Treatment Plan

This portion of the paper will address the chosen treatment for the condition. This might also include the theoretical basis for the chosen treatment or any other evidence that might exist to support why this approach was chosen.

  • Cognitive behavioral approach : Explain how a cognitive behavioral therapist would approach treatment. Offer background information on cognitive behavioral therapy and describe the treatment sessions, client response, and outcome of this type of treatment. Make note of any difficulties or successes encountered by your client during treatment.
  • Humanistic approach : Describe a humanistic approach that could be used to treat your client, such as client-centered therapy . Provide information on the type of treatment you chose, the client's reaction to the treatment, and the end result of this approach. Explain why the treatment was successful or unsuccessful.
  • Psychoanalytic approach : Describe how a psychoanalytic therapist would view the client's problem. Provide some background on the psychoanalytic approach and cite relevant references. Explain how psychoanalytic therapy would be used to treat the client, how the client would respond to therapy, and the effectiveness of this treatment approach.
  • Pharmacological approach : If treatment primarily involves the use of medications, explain which medications were used and why. Provide background on the effectiveness of these medications and how monotherapy may compare with an approach that combines medications with therapy or other treatments.

This section of a case study should also include information about the treatment goals, process, and outcomes.

When you are writing a case study, you should also include a section where you discuss the case study itself, including the strengths and limitiations of the study. You should note how the findings of your case study might support previous research. 

In your discussion section, you should also describe some of the implications of your case study. What ideas or findings might require further exploration? How might researchers go about exploring some of these questions in additional studies?

Need More Tips?

Here are a few additional pointers to keep in mind when formatting your case study:

  • Never refer to the subject of your case study as "the client." Instead, use their name or a pseudonym.
  • Read examples of case studies to gain an idea about the style and format.
  • Remember to use APA format when citing references .

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach .  BMC Med Res Methodol . 2011;11:100.

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011 Jun 27;11:100. doi:10.1186/1471-2288-11-100

Gagnon, Yves-Chantal.  The Case Study as Research Method: A Practical Handbook . Canada, Chicago Review Press Incorporated DBA Independent Pub Group, 2010.

Yin, Robert K. Case Study Research and Applications: Design and Methods . United States, SAGE Publications, 2017.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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  • Open access
  • Published: 12 February 2024

Markers of imminent myocardial infarction

  • Stefan Gustafsson 1   na1 ,
  • Erik Lampa 1   na1 ,
  • Karin Jensevik Eriksson   ORCID: orcid.org/0000-0003-1946-7068 2 ,
  • Adam S. Butterworth   ORCID: orcid.org/0000-0002-6915-9015 3 , 4 , 5 , 6 ,
  • Sölve Elmståhl 7 ,
  • Gunnar Engström 7 ,
  • Kristian Hveem 8 , 9 ,
  • Mattias Johansson 10 ,
  • Arnulf Langhammer 8 , 11 ,
  • Lars Lind 1 ,
  • Kristi Läll 12 ,
  • Giovanna Masala 13 ,
  • Andres Metspalu 12 ,
  • Conchi Moreno-Iribas 14 , 15 ,
  • Peter M. Nilsson 7 ,
  • Markus Perola 16 ,
  • Birgit Simell 16 ,
  • Hemmo Sipsma 17 ,
  • Bjørn Olav Åsvold 8 , 9 , 18 ,
  • Erik Ingelsson 1 ,
  • Ulf Hammar 1 , 19 ,
  • Andrea Ganna 20 , 21 , 22 ,
  • Bodil Svennblad 2 , 23 ,
  • Tove Fall 1 , 19 &
  • Johan Sundström   ORCID: orcid.org/0000-0003-2247-8454 1 , 24  

Nature Cardiovascular Research volume  3 ,  pages 130–139 ( 2024 ) Cite this article

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  • Myocardial infarction
  • Prognostic markers

Myocardial infarction is a leading cause of death globally but is notoriously difficult to predict. We aimed to identify biomarkers of an imminent first myocardial infarction and design relevant prediction models. Here, we constructed a new case–cohort consortium of 2,018 persons without prior cardiovascular disease from six European cohorts, among whom 420 developed a first myocardial infarction within 6 months after the baseline blood draw. We analyzed 817 proteins and 1,025 metabolites in biobanked blood and 16 clinical variables. Forty-eight proteins, 43 metabolites, age, sex and systolic blood pressure were associated with the risk of an imminent first myocardial infarction. Brain natriuretic peptide was most consistently associated with the risk of imminent myocardial infarction. Using clinically readily available variables, we devised a prediction model for an imminent first myocardial infarction for clinical use in the general population, with good discriminatory performance and potential for motivating primary prevention efforts.

Despite declining age-standardized rates, myocardial infarction remains the leading and increasing cause of death globally 1 . Prevention of myocardial infarction is highly prioritized 2 , but the targeting of primary preventive efforts is hampered by inefficient means of identifying individuals at the highest risk for an imminent myocardial infarction (IMI). This could be partially explained by the inability of most risk prediction models to account for the highly dynamic nature of the period leading up to a myocardial infarction. For instance, traumatic events, such as a cancer diagnosis or loss of a spouse, markedly increase the risk of myocardial infarction 3 , 4 . In addition, the degree of stenosis in the culprit lesion in the coronary artery appears to increase in the months just before the myocardial infarction 5 . Nonetheless, to date, most biomarkers have been investigated over several years of follow-up because of a low number of individuals with a first myocardial infarction shortly after baseline in the general population. Hence, a large population-based study focusing on identifying biomarkers of an IMI is needed.

Primary prevention for asymptomatic risk factors over a long period is costly, and motivation among patients and providers is limited even for secondary prevention 6 . Risk prediction in the short term based on biomarkers of IMI might tilt the scales for prevention, as the knowledge of an increased risk of a first myocardial infarction within the ensuing few months might motivate patients and doctors to consider preventive strategies.

We hypothesized that circulating biomarkers of the dynamic biological processes that operate in the months preceding a myocardial infarction could be measured and used to assess risk. We tested this in a new nested case–cohort study and devised a prediction model for an imminent first myocardial infarction.

We assembled a new nested case–cohort study, the Markers of Imminent Myocardial Infarction (MIMI) study. The study includes initially cardiovascular disease-free individuals in six European general population-based cohorts who developed a myocardial infarction within the first 6 months after the baseline examination, with up to four cohort representatives per case (Fig. 1 and Supplementary Table 1 ). The case–cohort design allows for time-to-event analyses and derivation of accurate prediction models; it is also less prone to certain biases than the case–control design 7 . After exclusions, data of 2,018 individuals weighted to represent the full cohort of 169,053 persons were available for analysis (420 IMI cases and 1,598 subcohort representatives). Their characteristics at baseline are shown in Extended Data Table 1 .

figure 1

The distribution of MIMI participants across Europe is shown, with the participating countries and cohort centers indicated. Cases ( n  = 420) were initially sampled, and center-specific strata based on sex and median age were constructed. From each cohort center, up to four subcohort representatives were drawn for each case from the same stratum. A subcohort ( n  = 1,598) weighted to represent the total cohort ( N  = 169,053) based on the number of individuals in the age and sex strata in the total cohort was thus assembled. NA, not applicable.

Thereafter, we determined the levels of 817 proteins (some duplicates) and 1,025 metabolites in biobanked plasma samples from the cohort baseline examinations in a core laboratory and harmonized 16 clinical variables between the cohorts. We divided the study sample into a discovery sample (EpiHealth, Trøndelag Health Study (HUNT) and Lifelines; 70% of the sample) and an external validation sample (European Prospective Investigation into Cancer and Nutrition—Cardiovascular Disease (EPIC-CVD), Estonian Biobank study and Malmö Preventive Project (MFM); 30% of the sample). Considering the limited sample size of the study, we also performed an internal validation as an exploratory analysis by randomly splitting the study sample into a 70:30 discovery/validation sample, repeated in 100 random draws.

We investigated the associations of proteins, metabolites and clinical variables with the risk of a first myocardial infarction within 6 months after baseline using weighted, stratified Cox proportional-hazards regression models in the discovery sample. Biomarkers that passed multiple testing bounds (a Benjamini–Hochberg false discovery rate (FDR) of <0.05) were verified in the same models in the validation sample (this was done in the external and internal validation sets), with directionally consistent results at P  < 0.05 considered replicated.

In one-by-one models adjusting for technical covariates (season, storage time and plate; Fig. 2 ), 48 proteins, 43 metabolites and 3 clinical variables (age, sex and systolic blood pressure) were found to be associated with IMI after the discovery–validation process (Fig. 3 and Supplementary Table 2 ).

figure 2

The associations of 817 proteins, 1,025 metabolites and 16 clinical variables with the risk of a first myocardial infarction within 6 months in the full MIMI study, adjusted for technical covariates, are shown by biomarker category (clinical, metabolite or protein). HR relates to a doubling of the concentration of proteins and metabolites and a one-unit higher level of clinical biomarkers on their original scale (for example, years, mmol l −1 ). The top 25 biomarkers that passed external validation and ranked on how many internal validation splits the biomarker passed the replication criteria in the model adjusted for technical covariates in addition to the external validation are highlighted. a IL-6 and b KIM1 were measured on multiple Olink panels and tested in separate statistical tests. n  = 420 cases and 1,598 noncases.

figure 3

The top 25 biomarkers that passed external validation and ranked on how many internal validation splits the biomarker passed the replication criteria in the model adjusted for technical covariates in addition to the external validation are shown. Each predictor is represented by two rows, with the discovery result (blue) presented first and the validation result presented second (red). The results are sorted by predictor type (clinical, metabolite or protein) and effect size from the combined analysis of the discovery and validation samples. P value was calculated based on a 2 d.f. Wald test for metabolites analyzed using the missing indicator method (biomarker and missing indicator) and a 1 d.f. Wald test otherwise (biomarker only), two-sided in both cases. The 95% CI of the point estimate (log(HR)) was calculated for the biomarker only and might include 1 even if P  < 0.05 from the 2 d.f. (biomarker + indicator) Wald test. a IL-6 and b KIM1 values were determined from multiple Olink panels and tested in separate statistical tests. n  = 296 cases and 1,121 noncases in the discovery sample; n  = 124 cases and 477 noncases in the validation sample.

Thereafter, we investigated promising markers in models further adjusting for age and sex. Among them, brain natriuretic peptide (BNP) was the only biomarker with a borderline significant association with IMI (HR per doubling of BNP level (95% confidence interval (95% CI)) = 1.33 (1.15, 1.55), P  = 1.63 × 10 −4 , FDR = 0.11 in the discovery sample and 1.40 (1.00, 1.94), P  = 0.049 in the validation sample; Extended Data Fig. 2 ). BNP was the only biomarker with a suggestive association in the internal validation, passing the formal replication criteria in 22 of 100 random splits. By comparison, stem cell factor (SCF) and interleukin-6 (IL-6), biomarkers with a weaker support of an association, replicated in only 5 or 4 of 100 random splits. The cumulative hazard of IMI by fourths of BNP is shown in Extended Data Fig. 3 . The associations of BNP with IMI in sensitivity analyses excluding one cohort at a time and in a random-effects meta-analysis were similar, as shown in Extended Data Figs. 4 and 5 . For some of the 94 variables, we observed substantial between-cohort heterogeneity in the estimates when they were evaluated in a random-effects meta-analysis (Supplementary Table 3 ). The addition of interaction terms between sex and the biomarkers did not reveal any additional associations. Associations with IMI within 3 months (185 cases) were similar to those within 6 months (Extended Data Fig. 6 ).

In a model investigating the total effect of the BNP–IMI association (with a priori selected confounders, not mediators, according to Extended Data Fig. 1 ), adjusting for age, sex, weight, height, creatinine and systolic blood pressure, the association of BNP with IMI remained similar (HR (95% CI) = 1.34 (1.14, 1.57), P  = 3.12 × 10 −4 in the discovery sample and 1.51 (1.05, 2.18), P  = 0.028 in the validation sample; per doubling of BNP level).

We then investigated the association of the most promising marker, BNP, with the coronary artery calcium score (CACS) at a cardiac computer tomography examination in an external population-based cohort of 1,586 participants of the Swedish CArdioPulmonary bioImage Study (SCAPIS) who were free from self-reported cardiovascular disease. Here, a higher CACS was not notably associated with a higher BNP level (odds ratio (95% CI) = 1.14 (0.91, 1.42), P  = 0.25; per doubling of BNP level) in an ordinal regression model adjusting for the same covariates as in the total-effects model.

Finally, we investigated the possibility of developing a clinical risk prediction algorithm for a first IMI using clinically available variables and a weighted Cox ridge regression model. The prediction model achieved an internally validated C-index of 0.78, indicating a good ability to discriminate between IMI cases and noncases. When validating the model in the UK Biobank, a C-index of 0.82 was obtained, while a calibration plot showed some overestimation of 6-month IMI risks. As a comparison, the recalibrated SCORE2 achieved C-indexes of 0.77 (MIMI cohort) and 0.81 (UK Biobank) and overestimated the IMI risks in both samples (Extended Data Fig. 7 ). A nomogram based on the model is shown in Fig. 4 , with a worked example of its intended use displayed in Extended Data Fig. 8 and its cross-validated calibration presented in Extended Data Fig. 7 . An interactive web application is presented at miscore.org . Coefficients for predicting IMI from the model are shown in Supplementary Table 4 .

figure 4

A nomogram for predicting IMI risk based on the final clinical model is shown. Each variable value contributes points (ruler at the top) that are summed up and translated to the predicted risk of a myocardial infarction within 6 months (bottom two rulers). Equation, β coefficients, 6-month survival and mean variable values are provided in Supplementary Table 4 . A worked example is shown in Extended Data Fig. 8 . The model is also presented as an interactive web application at miscore.org.

No biomarkers improved risk prediction in a LASSO (least absolute shrinkage and selection operator) Cox regression model; the variable selection by the LASSO was unstable, with the 95% bootstrap CI on the model size being 0–128 variables. No biomarkers improved risk prediction in a random forest model using 2,000 trees; it also ranked BNP and N-terminal pro-BNP (NT-proBNP) at the top but with very large CIs (Supplementary Table 5 ).

We here set out to identify and test biomarkers and the predictability of an imminent first myocardial infarction using a new case–cohort consortium of individuals without prior cardiovascular disease and with biobanked blood samples. From more than 1,800 biomarkers, we identified 48 proteins, 43 metabolites and 3 clinical variables associated with the risk of an imminent first myocardial infarction independent of technical covariates. Further analyses revealed BNP as the only biomarker consistently associated with IMI risk. We also derived a prediction model to discriminate between subsequent cases and noncases. The IMI phenotype has rarely been studied prospectively in the general population and with a broad panel of biomarkers. The findings may have implications for both clinical primary prevention studies and further etiological studies.

In the current study, higher BNP levels in individuals without a known cardiovascular disease were linked to a higher risk of a first myocardial infarction within 6 months in several models. Cardiomyocytes produce BNP in response to strain 8 , and NT-proBNP measurement is a pillar of the clinical management of heart failure 9 but is not used in diagnosing myocardial infarction 10 . Diastolic dysfunction is an early feature of myocardial ischemia, and a higher BNP level in this context is likely underpinned by diastolic dysfunction caused by subclinical ischemia 11 in individuals with some degree of coronary stenosis. This is supported by the weak association of BNP and CACS observed herein, although the association should be interpreted carefully. The noncausal explanation is further supported by the noncausality suggested by Mendelian randomization studies (acknowledging that associations of genetically determined lifelong BNP levels with coronary disease may have limited relevance to a temporally boxed-in series of events): a genetic variant affecting the expression of the BNP gene ( NPPB , rs198389) is not associated with cardiovascular endpoints 12 or coronary artery disease 13 . The influence of chance on the finding is low, as NT-proBNP was also significantly associated with IMI in the discovery sample, with a borderline association in the validation sample (Extended Data Fig. 5 ). While BNP may hence reflect an underlying coronary artery disease, it did not add materially to a risk prediction model for IMI composed of more readily available biomarkers.

Several known mechanisms implicated in atherosclerosis and ischemia were represented among the other 94 biomarkers associated with an IMI in both the discovery and validation samples after adjusting for technical covariates, including inflammation (IL-6) 14 , extracellular matrix metabolism (WAP four-disulfide core domain protein 2 (WFDC2)) 15 , hypertrophy (adhesion G-protein-coupled receptor G1 (AGRG1)) 16 , apoptosis (triggering receptor expressed on myeloid cells 1 (TREM1), tumor necrosis factor receptor superfamily member 10B (TRAIL-R2)) and cell adhesion (AGRG1). We also observed associations with markers representing mechanisms less often implicated in coronary diseases, such as markers of kidney injury (kidney injury molecule 1 (KIM1)) 17 , appetite regulation (growth differentiation factor 15 (GDF15)) 18 , and an α-amino acid found in dietary supplements and associated with paracetamol use (pyroglutamine) 19 . While some associations may be causal, others, such as associations with levels of chitinase-3-like protein 1 (CHI3L1) 20 , pleiotrophin (PTN) or KIT, may more likely be responses to myocardial ischemia. These findings may accelerate further etiological studies of acute coronary events.

We here developed a prediction model for IMI in the general population. An imminent infarction is difficult to predict; the signals are weak, and we faced power limitations. The model achieved good discriminative ability, with acceptable calibration in the lower risk range. It is possible to transpose to other settings by entering the base hazards and variable means of those settings, for example, interactively at miscore.org . Given the increasing global burden of deaths from myocardial infarction, the importance of predicting them and increasing the individual motivation for preventing such deaths may be substantial; this can be tested in clinical trials.

The current study has several limitations. First, the use of multiple cohorts introduced heterogeneity. We addressed this at the sampling, biomarker analysis and statistical analysis stages, with the resulting limitation that the heterogeneity decreases statistical power. The strengths are the same as in other multicenter studies, including that only biomarkers with consistent importance in different settings are brought forward. Other study limitations are inherent to the uncertainty of ranking the top findings and the inability of one-by-one strategies to capture complex interrelationships. The instability of the variable importances from the random forest was unsurprising, as such methods are notoriously data hungry and require far larger datasets than classical modeling techniques 21 . While the studied markers are easily obtainable by a simple blood test or clinical assessment, a limitation is that a blood sample will not always capture tissue-specific processes. In addition, our study was limited to proteins and metabolites that remain stable in the freezer for many years. The biomarker analyses used herein are currently not available in clinical practice, and we lacked the clinically available and more precise immunoassay measurements of, for example, NT-proBNP and cardiac troponin; hence, imprecision in the proximity extension assay and ultra-high performance liquid chromatography–mass spectrometry (UPLC–MS) technologies may preclude definitive mechanistic insights and maximal clinical utility. Further, making causal assumptions is fundamentally challenging in a multimarker landscape where many causal pathways are unknown. Most markers could be potential mediators in pathways for known causes of myocardial infarction, including age and sex. Consequently, we provided models adjusted for technical covariates only and models with further biological covariate adjustment. Thus, some associations could be explained by confounding by, for example, age and sex. Notably, mediators of causal effects are also important to identify, with implications for prediction and use as treatment targets.

In conclusion, we identified biomarkers associated with the risk of an imminent first myocardial infarction, including BNP. Delineation of the distinct biological processes that operate in the months before the first myocardial infarction will be key to discovering prevention targets. We developed and validated a prediction model with a fair ability to discriminate between persons with and without risk of an imminent first myocardial infarction. Risk prediction in the short term may enhance the motivation of patients and doctors for primary prevention.

Study sample and outcome

The MIMI study sample draws biobanked blood and data from six European cohorts of the BBMRI-LPC (Biobanking and Biomolecular Research Infrastructure—Large Prospective Cohorts) collaboration 22 , as shown in Fig. 1 and Supplementary Table 1 . After sample size determination, we supplied each cohort with a standardized protocol (in which all definitions are described in detail) and an R script for selecting cohort representatives for the subcohort ( Supplementary Notes ).

Cohort participants with biobanked samples (at least 250 μl of plasma or serum; eventually, only plasma was included) and no previous clinical cardiovascular disease were eligible for inclusion in the present study. The exclusion criteria were previous clinical cardiovascular disease (defined as the presence at any time before baseline of any of the following: myocardial infarction, coronary procedure, heart failure, structural heart disease, tachyarrhythmias, stroke, thromboembolic disease and peripheral vascular disease) and renal failure.

Individuals with acute myocardial infarction (International Classification of Diseases, tenth revision (ICD-10), I21; ICD-9, 410.0–410.6 and 410.8) as the primary cause of hospitalization or death within 6 months after baseline were defined as IMI cases. We included both ST-elevation and non-ST-elevation myocardial infarctions; we encouraged efforts to include only type 1 myocardial infarctions by not counting cases with any of the following ICD codes in secondary positions: anemia (for example, ICD-10, D50–D64; ICD-9, 280–285), tachyarrhythmias (for example, ICD-10, I47–I49; ICD-9, 427), heart failure (for example, ICD-10, I50; ICD-9, 428), renal failure (for example, ICD-10, N17–N19; ICD-9, 584–586), chronic obstructive pulmonary disease (for example, ICD-10, J43–J44; ICD-9, 491, 492 and 496), sepsis and other severe infections (for example, ICD-10, A40–A41; ICD-9, 038), or hypertensive crises.

Up to four cohort representatives per available IMI case were randomly drawn from the full cohort to the subcohort in 50 strata based on sex, age (above/below median) and study center in a stratified case–cohort design 7 . All 420 IMI cases, and 1,598 subcohort representatives, were drawn from the full cohort of 169,053 participants, as summarized in Fig. 1 .

Clinical variables (age, sex, height, weight, waist circumference, systolic and diastolic blood pressure, triglycerides, high-density lipoprotein (HDL) cholesterol, non-HDL cholesterol, low-density lipoprotein (LDL) cholesterol, total cholesterol, glucose, diabetes status, highest education, smoking status, previous smoking exposure, alcohol intake and physical activity) were harmonized between the cohorts ( Supplementary Notes ). Non-HDL cholesterol was calculated as total cholesterol − HDL cholesterol. LDL levels were calculated using the extended Martin–Hopkins equation 23 .

All blood samples were randomized into appropriate measurement plates, stratified by cohort (with a similar number from each cohort on every plate), and aliquoted into the plates. Quality controls are summarized below and described in detail in the Supplementary Notes .

Protein measurements were done using the Olink proximity extension assay (Olink), a highly specific 92-plex immunoassay. Overall, 829 proteins across nine panels (cardiometabolic, cardiovascular II, cardiovascular III, development, immune response, inflammation, metabolism, oncology II and organ damage) were analyzed, including 804 unique proteins (considering overlap between panels). Relative protein values on a log 2 scale are reported, with each protein value normalized by plate by centering all plates at the same median, assuming random plate placement. Values below the assay’s lower limit of detection (LOD) were also included in the analyses.

Metabolites were analyzed using the UPLC–tandem MS (UPLC–MS/MS)-based Metabolon platform (Metabolon) by four different methods: reversed-phase UPLC–MS/MS with positive-mode electrospray ionization (early and late phase), reversed-phase UPLC–MS/MS with negative-mode electrospray ionization, and hydrophilic interaction LC/UPLC–MS/MS with negative-mode electrospray ionization. Overall, 1,135 metabolites were captured, including 925 with known identity and 210 with unknown identity. Relative metabolite levels were determined and normalized by analysis day. Metabolite levels were log 2 transformed, and nondetectable levels (<LOD or metabolite not present in the sample) were constant value imputed to a value below the minimum metabolite value (minimum/sqrt(2)).

Samples that did not satisfy the quality control criteria were initially excluded; exclusion filters were applied separately for the proteomics and metabolomics analyses, and only samples passing quality control for both analyses were included in the analysis set. For the proteomics analysis, samples with more than 50% of panels failing for technical reasons were excluded ( n excluded = 33). For the metabolomics analysis, samples were excluded because of low volume or detection of fewer metabolites than expected ( n excluded = 4). Consequently, samples for 420 cases and 1,598 subcohort representatives remained for analysis.

Next, biomarkers with an extremely high proportion of nondetectable or below-LOD measurements were excluded, with the same exclusion filters for proteins and metabolites. Biomarkers had to be detected in all six cohorts with at least 30 detectable values across all cohorts (~1.5% of the MIMI samples) or were otherwise excluded. Consequently, 817 proteins (some duplicates) and 1,025 metabolites were retained for analysis.

Statistical analysis

All analyses were done using R (version 4.1.1) 24 with the glmnet 25 , mice 26 , rms 27 , ranger 28 and survival 29 add-on packages.

One-by-one etiological analyses

In the discovery sample, the associations of all clinical variables (listed in Extended Data Table 1 ), proteins and metabolites with IMI were analyzed in separate weighted, stratified Cox proportional-hazards regression models adjusting for covariates, as described below. Inverse sampling probability weights (Borgan II) were applied to account for the case–cohort design in a stratified model, allowing for a different shape of the baseline hazard for each MIMI cohort (six levels) and using a robust variance estimator (Huber–White). Nonlinear relationships between continuous covariates (not including the biomarkers) and IMI were modeled using restricted cubic splines, and all factor variables were considered unordered.

Associations with an FDR (Benjamini–Hochberg) of <0.05 were taken forward to the validation sample, in which directionally consistent results with P  < 0.05 were considered replicated.

Cox proportional-hazards models adjusting for technical covariates (season, storage time and plate) were initially applied. Replicating biomarkers from the model adjusting for technical covariates were investigated in a model further adjusting for age and sex. A model allowing for an interaction between the biomarker and sex was further tested. Replicating biomarkers in the model adjusted for age and sex were then subjected to causal assumptions (Extended Data Fig. 1 ), and a bias-minimized model for each biomarker was investigated, estimating the total effects (including the effects of mediators).

Missingness and sensitivity analyses

Clinical variables with high missingness (previous smoking exposure, alcohol intake and physical activity) were not used in the analyses. Protein values below the LOD were included in the analyses; nondetectable metabolite levels were replaced with a constant value, and a missing indicator was added, as described below. The remaining missing values in the covariates were multiple imputed ( n imputations = 20) using chained equations including the outcome, clinical covariates and other variables correlated with the variable in the imputation model 30 . Regression results across imputed datasets were combined using Rubin’s rules 31 .

Interactions with sex were investigated by analyzing an interaction term for sex and each biomarker in models adjusting for technical covariates, age and sex. The interaction terms and all terms including the biomarker were tested using a multivariable chi-squared test with the same multiple-testing correction described above, requiring directionally consistent discovery and validation results.

The following secondary sensitivity analyses were included: random-effects inverse variance-weighted meta-analyses (DerSimonian–Laird) combining per-cohort results, leave-one-out analyses investigating the influence of single cohorts, complete-case analyses not imputing missing values in the clinical covariates, and analyses limiting the follow-up time to 3 months.

Simultaneous modeling and development of a prediction model

To attempt predicting this phenotype, we developed a prediction model for IMI using age, sex, anthropometric variables (height, weight and waist circumference), variables routinely collected in the laboratory (LDL cholesterol, HDL cholesterol, creatinine, glucose and triglycerides), systolic and diastolic blood pressure, smoking status (never, former or current) and education level. Regression coefficients were estimated using a weighted Cox ridge regression model, which shrinks coefficients toward zero using an L 2 penalty to accommodate overfitting. The strength of the penalty (lambda) was determined using tenfold cross-validation over a grid of 250 lambda values, repeated 100 times. The lambda selection was repeated in each imputed dataset, and the coefficients associated with the lambda giving the lowest cross-validated deviance were extracted. The final coefficient set was obtained by taking the median of the coefficients from each imputed dataset. A single-imputed dataset was used for validation and calibration. The C-index, which indicates a model’s ability to rank the risks, was determined using 100 repeats of tenfold cross-validation. A calibration curve was constructed using 100 repeats of tenfold cross-validation 32 . All modeling steps were repeated in each fold to assess the calibration accuracy objectively. The model containing only clinical variables was then reduced by approximating the linear predictor from the full model through stepwise regression. Predictions from the full model were used as the outcome in a linear model wherein variables were dropped sequentially until R 2  > 0.95. This yielded a highly parsimonious final model incorporating the main drivers of predictions. The prediction model was compared to SCORE2, a validated prediction model for the 10-year risk of cardiovascular disease developed using multiple European cohorts 33 . The 10-year survival probability and the covariate mean values used in the SCORE2 equations were replaced with the estimated 6-month survival probability and mean values from the current data to calculate the SCORE2-estimated 6-month cardiovascular disease risk 34 . Two additional external validations of the model were performed in the UK Biobank. First, all coefficients and covariate mean values in Supplementary Table 4 were used to validate the model. Second, the model was recalibrated using mean values and the estimated baseline risk from the UK Biobank cohort before validation.

To evaluate whether any biomarkers added to the clinical prediction model improve risk prediction, we used the linear predictor from the prediction model as an offset in a LASSO Cox regression model. Before model fitting, all proteins and metabolites were adjusted for technical variables. Briefly, each biomarker was used as the outcome variable in a regression model with all technical variables as covariates. The residuals from these models were used in place of the original biomarker values in the LASSO model. The LASSO model fitting was bootstrapped 250 times to investigate the stability of the variable selection.

As the biomarkers may have nonlinear associations with the outcome and interact with one another, and prior knowledge about nonlinearities and interactions among these variables is scarce, a random forest with 2,000 trees was fitted to the data as an exploratory analysis. Briefly, the random forest fits survival trees to bootstrap data samples using a random subset of the variables in each tree, handling interactions and nonlinearities naturally. A variable importance measure is associated with each variable and calculated based on the number of splits in which a variable is involved. The random forest was bootstrapped 250 times to obtain CIs for the variable importance measures.

Further analysis of relevant biomarkers

The associations of proteins detected using the Olink panels cardiovascular II and cardiovascular III with the CACS were available for testing in individuals free from cardiovascular disease (self-reported myocardial infarction, angina, coronary intervention, heart failure, atrial fibrillation, stroke and peripheral artery disease) for 1,586 participants at the Malmö or Uppsala centers of SCAPIS 35 . A higher CACS reflects a higher myocardial infarction risk. Proteins replicated in the primary MIMI analysis (BNP) were tested for an association with the CACS using an ordinal regression model adjusting for age, sex, body mass index, systolic blood pressure, creatinine, center, Olink plate, analysis date and season.

This study was approved by the Uppsala Ethics Authority (Dnr 2016/197). All Estonian Biobank participants signed a broad informed consent form. The study was carried out under ethical approval 258/M-21 from the research ethics committee of the University of Tartu and data release J08 from the Estonian Biobank. The Lifelines protocol was approved by the University Medical Center Groningen medical ethical committee under number 2007/152. The study was performed in accordance with the Declaration of Helsinki. The EpiHealth study was approved by the ethics committee of Uppsala University, and all participants provided informed written consent. The MFM was approved by the previous regional research committee in Lund, Sweden (2014/643), and all participants provided informed consent. Ethical review boards of the cohorts in EPIC-CVD approved the study protocol, and all participants provided written informed consent. Participation in the HUNT study was based on informed consent, and the Data Inspectorate and the Regional Ethics Committee for Medical Research in Norway approved the study.

Reporting summary

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

Data availability

Data supporting the findings of this study are provided in the article and related files. Raw data are not publicly available, as they contain sensitive personal information, but may be obtained from the original cohorts upon request, with varying processes, requirements and response times. For example, researchers can apply to use the Lifelines data used in this study; information on how to request access to Lifelines data and the conditions of use can be found at https://lifelines.nl/researcher/how-to-apply . Data accession codes for this study are described below.

Code availability

The prediction model equation is available at miscore.org . Code is available at https://github.com/stefan-gustafsson-work/mimi . All code used to analyze UK Biobank data is deposited at the UK Biobank repository. Questions about the analyses and code can be sent to the corresponding author.

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Acknowledgements

Transnational access to the large European prospective cohorts was provided by the BBMRI-LPC project funded by the European Commission’s Seventh Framework Programme (grant no. 313010, J.S.).

The Estonian Biobank study was supported by the European Union through the European Regional Development Fund (project no. 2014-2020.4.01.15-0012) and by institutional research funding IUT (IUT20-60) of the Estonian Ministry of Education and Research. We thank M. Alver (Estonian Biobank) for help with phenotype data.

The Trøndelag Health Study (HUNT) is a collaboration between the HUNT Research Centre (Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU), Trøndelag County Council, Central Norway Regional Health Authority, and the Norwegian Institute of Public Health.

The Lifelines initiative was made possible by a subsidy from the Dutch Ministry of Health, Welfare and Sport; the Dutch Ministry of Economic Affairs; the University Medical Center Groningen (UMCG), Groningen University; and the provinces in the north of the Netherlands (Drenthe, Friesland, Groningen). Lifelines is a multidisciplinary, prospective, population-based cohort study examining the health and health-related behaviors of 167,729 persons living in the north of the Netherlands in a unique three-generation design. The study used a broad range of investigative procedures in assessing the biomedical, sociodemographic, behavioral, physical and psychological factors that contribute to the health and disease of the general population, with a particular focus on multimorbidity and complex genetics.

EPIC-CVD was supported by funding from the European Commission Framework Programme 7 (HEALTH-F2-2012-279233), European Research Council (268834), Novartis, UK Medical Research Council (G0800270, MR/L003120/1), British Heart Foundation (SP/09/002, RG/13/13/30194, RG/18/13/33946), and National Institute for Health and Care Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The coordination of EPIC was financially supported by the International Agency for Research on Cancer (IARC) and the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC). The national cohorts were supported by the Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l’Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Federal Ministry of Education and Research (BMBF) (Germany); Associazione Italiana per la Ricerca sul Cancro (AIRC), Compagnia di SanPaolo and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (Netherlands); Health Research Fund (FIS)–Instituto de Salud Carlos III (ISCIII), regional governments (Andalucía, Asturias, Basque Country, Murcia and Navarra), Catalan Institute of Oncology (ICO) (Spain); Swedish Cancer Society, Swedish Research Council and county councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk, C8221/A29017 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk, MR/M012190/1 to EPIC-Oxford) (United Kingdom). We thank all EPIC participants and staff for their contribution to the study, the laboratory teams at the Medical Research Council Epidemiology Unit for sample management and at Cambridge Genomic Services for genotyping, S. Spackman for data management, and the team at the EPIC-CVD Coordinating Centre for study coordination and administration.

SCAPIS was supported by Hjärt-Lungfonden, the Knut and Alice Wallenberg Foundation, the Swedish Research Council and VINNOVA.

This research was conducted using the UK Biobank resource under application no. 52678.

Grants were received from the European Research Council (no. 801965), Swedish Research Council, VR (2019-01471) and Hjärt-Lungfonden (20190505) (T.F.).

Grants were received from AFA Försäkring (160266), the Swedish Research Council (2016-01065), Hjärt-Lungfonden (20160734) and A. Wiklöf (J.S.).

The computations were enabled by resources in projects sens2019006 and sens2020005 provided by the Swedish National Infrastructure for Computing (SNIC) at UPPMAX, partially funded by the Swedish Research Council through grant agreement no. 2018-05973.

The funding sources for the different cohorts and the sponsors of the current work did not have any part in the collection, analysis and interpretation of data or in the decision to submit the paper for publication. Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article, and they do not necessarily represent the decisions, policies or views of the International Agency for Research on Cancer/World Health Organization.

Open access funding provided by Uppsala University.

Author information

These authors contributed equally: Stefan Gustafsson, Erik Lampa.

Authors and Affiliations

Department of Medical Sciences, Uppsala University, Uppsala, Sweden

Stefan Gustafsson, Erik Lampa, Lars Lind, Erik Ingelsson, Ulf Hammar, Tove Fall & Johan Sundström

Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden

Karin Jensevik Eriksson & Bodil Svennblad

BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

Adam S. Butterworth

BHF Centre of Research Excellence, University of Cambridge, Cambridge, UK

NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK

HDR UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK

Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Sweden

Sölve Elmståhl, Gunnar Engström & Peter M. Nilsson

K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway

Kristian Hveem, Arnulf Langhammer & Bjørn Olav Åsvold

HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway

Kristian Hveem & Bjørn Olav Åsvold

Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France

Mattias Johansson

Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway

Arnulf Langhammer

Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia

Kristi Läll & Andres Metspalu

Clinical Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy

Giovanna Masala

Navarra Public Health Institute, Pamplona, Spain

Conchi Moreno-Iribas

IdiSNA, Navarra Institute for Health Research, Pamplona, Spain

Finnish Institute for Health and Welfare, Helsinki, Finland

Markus Perola & Birgit Simell

Lifelines Cohort Study, Groningen, Netherlands

Hemmo Sipsma

Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

Bjørn Olav Åsvold

Science for Life Laboratory, Uppsala University, Uppsala, Sweden

Ulf Hammar & Tove Fall

Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland

Andrea Ganna

Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Department of Surgical Sciences, Uppsala University, Uppsala, Sweden

Bodil Svennblad

The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia

Johan Sundström

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Contributions

J.S., S.G., E.L., E.I., U.H., A.G., B. Svennblad and T.F. were responsible for the study conception and design. S.G., E.L. and K.J.E. performed the statistical analyses. J.S. wrote the final draft together with S.G. and E.L. A.S.B., S.E., G.E., K.H., M.J., A.L., L.L., K.L., G.M., A.M., C.M.-I., P.M.N., M.P., B. Simell, H.S. and B.O.Å. were responsible for data acquisition for the different cohorts. All authors contributed to manuscript preparation and provided critical comments on the final manuscript. All authors had full access to all study data and had final responsibility for the decision to submit the paper for publication.

Corresponding author

Correspondence to Johan Sundström .

Ethics declarations

Competing interests.

The authors declare the following competing interests: A.S.B. reports grants outside this work (from AstraZeneca, Bayer, Biogen, BioMarin, Bioverativ, Novartis and Sanofi) and personal fees from Novartis. E.I. is now an employee at GlaxoSmithKline. S.G. is an employee of Sence Research AB. J.S. reports stock ownership in Anagram kommunikation AB and Symptoms Europe AB outside the submitted work. All other authors declare no competing interests.

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Extended data

Extended data fig. 1 causal assumptions for developing bias- minimized models..

Directional acyclic graph (DAG) showing the best-guess relationship between the exposure (BNP) and the outcome (IMI) and other factors influencing this relationship together with the expected direction.

Extended Data Fig. 2 Variables associated with risk of an imminent myocardial infarction, further adjusted for age and sex.

Scatter plot comparing the point estimate (log[HR]) and corresponding 95% confidence interval from models adjusting for technical covariates (x-axis) and models additionally adjusting for age and sex (y-axis) in the full MIMI study. The 95% C.I. is calculated for the biomarker only whereas the p-value in the tables is based on the 2 d.f. Wald test (two-sided) of biomarker and missing indicator (when used), hence the 95% C.I. might overlap with null for a p-value < 0.05. N=420 cases and 1598 non-cases.

Extended Data Fig. 3 Association of BNP with Risk of an Imminent Myocardial Infarction.

Kaplan–Meier graph of unadjusted cumulative hazard of an imminent myocardial infarction (IMI) by fourths (Q) of brain natriuretic peptide (BNP), weighted by sampling weights.

Extended Data Fig. 4 Leave-one-out analyses of the association of BNP with imminent myocardial infarction.

Model with BNP and technical covariates in leave-one-out analyses where one cohort was omitted at time.

Extended Data Fig. 5 Associations of BNP and NT-proBNP with imminent myocardial infarction in the individual cohorts.

Forest plot of the regression results in the model adjusting for technical covariates performed per cohort for all available BNP measurements (BNP and N-terminal pro form). Hazard ratio and corresponding 95% confidence interval is presented. NT-proBNP is measured on both the CVD2 (*) and Metabolism panel (**). Individual cohort sample sizes are given in Supplementary Table 1 .

Extended Data Fig. 6 Comparison of associations of biomarkers with risk of IMI within 3 vs 6 months.

Regression estimates from models with time to IMI within 3 vs 6 months as outcomes. MI, myocardial infarction.

Extended Data Fig. 7 Calibration of the prediction model.

a Internal calibration. Cross-validated calibration curves for predicted probabilities of an imminent myocardial infarction from the MIMI model (solid black line) and the SCORE2 model (dashed black line). b External calibration. Calibration curves for predicted probabilities of an imminent myocardial infarction from the original MIMI model (solid black line), the MIMI model recalibrated to the UKBB data (dotted black line), and the original SCORE2 model (dashed black line). The diagonal gray line in both panels is the line of ideal calibration where predicted probabilities match the observed fraction experiencing the event.

Extended Data Fig. 8

Worked example of nomogram use. A 78-year-old (73 points) smoking (13 points) low-educated (10 points) man (23 points) with diabetes (8 points), height 1.71 m (28 points), waist circumference 110 cm (22 points), LDL cholesterol 4.5 mmol/L (21 points) and HDL cholesterol 1.2 mmol/L (39 points) will have a total score of 73 + 13 + 10 + 23 + 8 + 28 + 22 + 21 + 39 = 237 points, corresponding to a 6- month risk of a first myocardial infarction of circa 1.58%. The model is also presented as an interactive web application on miscore.org.

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Gustafsson, S., Lampa, E., Jensevik Eriksson, K. et al. Markers of imminent myocardial infarction. Nat Cardiovasc Res 3 , 130–139 (2024). https://doi.org/10.1038/s44161-024-00422-2

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case study research design example

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