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• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, 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 analyze the case, other interesting articles.

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

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

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.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

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

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

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.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

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 analyze 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.

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

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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How to Write a Case Study | Examples & Methods

What is a case study?

A case study is a research approach that provides an in-depth examination of a particular phenomenon, event, organization, or individual. It involves analyzing and interpreting data to provide a comprehensive understanding of the subject under investigation. 

Case studies can be used in various disciplines, including business, social sciences, medicine ( clinical case report ), engineering, and education. The aim of a case study is to provide an in-depth exploration of a specific subject, often with the goal of generating new insights into the phenomena being studied.

When to write a case study

Case studies are often written to present the findings of an empirical investigation or to illustrate a particular point or theory. They are useful when researchers want to gain an in-depth understanding of a specific phenomenon or when they are interested in exploring new areas of inquiry. 

Case studies are also useful when the subject of the research is rare or when the research question is complex and requires an in-depth examination. A case study can be a good fit for a thesis or dissertation as well.

Case study examples

Below are some examples of case studies with their research questions:

These examples demonstrate the diversity of research questions and case studies that can be explored. From studying small businesses in Ghana to the ethical issues in supply chains, case studies can be used to explore a wide range of phenomena.

Outlying cases vs. representative cases

An outlying case stud y refers to a case that is unusual or deviates significantly from the norm. An example of an outlying case study could be a small, family-run bed and breakfast that was able to survive and even thrive during the COVID-19 pandemic, while other larger hotels struggled to stay afloat.

On the other hand, a representative case study refers to a case that is typical of the phenomenon being studied. An example of a representative case study could be a hotel chain that operates in multiple locations that faced significant challenges during the COVID-19 pandemic, such as reduced demand for hotel rooms, increased safety and health protocols, and supply chain disruptions. The hotel chain case could be representative of the broader hospitality industry during the pandemic, and thus provides an insight into the typical challenges that businesses in the industry faced.

Steps for Writing a Case Study

As with any academic paper, writing a case study requires careful preparation and research before a single word of the document is ever written. Follow these basic steps to ensure that you don’t miss any crucial details when composing your case study.

Step 1: Select a case to analyze

After you have developed your statement of the problem and research question , the first step in writing a case study is to select a case that is representative of the phenomenon being investigated or that provides an outlier. For example, if a researcher wants to explore the impact of COVID-19 on the hospitality industry, they could select a representative case, such as a hotel chain that operates in multiple locations, or an outlying case, such as a small bed and breakfast that was able to pivot their business model to survive during the pandemic. Selecting the appropriate case is critical in ensuring the research question is adequately explored.

Step 2: Create a theoretical framework

Theoretical frameworks are used to guide the analysis and interpretation of data in a case study. The framework should provide a clear explanation of the key concepts, variables, and relationships that are relevant to the research question. The theoretical framework can be drawn from existing literature, or the researcher can develop their own framework based on the data collected. The theoretical framework should be developed early in the research process to guide the data collection and analysis.

To give your case analysis a strong theoretical grounding, be sure to include a literature review of references and sources relating to your topic and develop a clear theoretical framework. Your case study does not simply stand on its own but interacts with other studies related to your topic. Your case study can do one of the following: 

• Demonstrate a theory by showing how it explains the case being investigated
• Broaden a theory by identifying additional concepts and ideas that can be incorporated to strengthen it
• Confront a theory via an outlier case that does not conform to established conclusions or assumptions

Step 3: Collect data for your case study

Data collection can involve a variety of research methods , including interviews, surveys, observations, and document analyses, and it can include both primary and secondary sources . It is essential to ensure that the data collected is relevant to the research question and that it is collected in a systematic and ethical manner. Data collection methods should be chosen based on the research question and the availability of data. It is essential to plan data collection carefully to ensure that the data collected is of high quality

Step 4: Describe the case and analyze the details

The final step is to describe the case in detail and analyze the data collected. This involves identifying patterns and themes that emerge from the data and drawing conclusions that are relevant to the research question. It is essential to ensure that the analysis is supported by the data and that any limitations or alternative explanations are acknowledged.

The manner in which you report your findings depends on the type of research you are doing. Some case studies are structured like a standard academic paper, with separate sections or chapters for the methods section , results section , and discussion section , while others are structured more like a standalone literature review.

Regardless of the topic you choose to pursue, writing a case study requires a systematic and rigorous approach to data collection and analysis. By following the steps outlined above and using examples from existing literature, researchers can create a comprehensive and insightful case study that contributes to the understanding of a particular phenomenon.

Preparing Your Case Study for Publication

After completing the draft of your case study, be sure to revise and edit your work for any mistakes, including grammatical errors , punctuation errors , spelling mistakes, and awkward sentence structure . Ensure that your case study is well-structured and that your arguments are well-supported with language that follows the conventions of academic writing .  To ensure your work is polished for style and free of errors, get English editing services from Wordvice, including our paper editing services and manuscript editing services . Let our academic subject experts enhance the style and flow of your academic work so you can submit your case study with confidence.

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Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

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How to write a case study — examples, templates, and tools

It’s a marketer’s job to communicate the effectiveness of a product or service to potential and current customers to convince them to buy and keep business moving. One of the best methods for doing this is to share success stories that are relatable to prospects and customers based on their pain points, experiences, and overall needs.

That’s where case studies come in. Case studies are an essential part of a content marketing plan. These in-depth stories of customer experiences are some of the most effective at demonstrating the value of a product or service. Yet many marketers don’t use them, whether because of their regimented formats or the process of customer involvement and approval.

A case study is a powerful tool for showcasing your hard work and the success your customer achieved. But writing a great case study can be difficult if you’ve never done it before or if it’s been a while. This guide will show you how to write an effective case study and provide real-world examples and templates that will keep readers engaged and support your business.

In this article, you’ll learn:

What is a case study?

How to write a case study, case study templates, case study examples, case study tools.

A case study is the detailed story of a customer’s experience with a product or service that demonstrates their success and often includes measurable outcomes. Case studies are used in a range of fields and for various reasons, from business to academic research. They’re especially impactful in marketing as brands work to convince and convert consumers with relatable, real-world stories of actual customer experiences.

The best case studies tell the story of a customer’s success, including the steps they took, the results they achieved, and the support they received from a brand along the way. To write a great case study, you need to:

• Celebrate the customer and make them — not a product or service — the star of the story.
• Craft the story with specific audiences or target segments in mind so that the story of one customer will be viewed as relatable and actionable for another customer.
• Write copy that is easy to read and engaging so that readers will gain the insights and messages intended.
• Follow a standardized format that includes all of the essentials a potential customer would find interesting and useful.
• Support all of the claims for success made in the story with data in the forms of hard numbers and customer statements.

Case studies are a type of review but more in depth, aiming to show — rather than just tell — the positive experiences that customers have with a brand. Notably, 89% of consumers read reviews before deciding to buy, and 79% view case study content as part of their purchasing process. When it comes to B2B sales, 52% of buyers rank case studies as an important part of their evaluation process.

Telling a brand story through the experience of a tried-and-true customer matters. The story is relatable to potential new customers as they imagine themselves in the shoes of the company or individual featured in the case study. Showcasing previous customers can help new ones see themselves engaging with your brand in the ways that are most meaningful to them.

Besides sharing the perspective of another customer, case studies stand out from other content marketing forms because they are based on evidence. Whether pulling from client testimonials or data-driven results, case studies tend to have more impact on new business because the story contains information that is both objective (data) and subjective (customer experience) — and the brand doesn’t sound too self-promotional.

Case studies are unique in that there’s a fairly standardized format for telling a customer’s story. But that doesn’t mean there isn’t room for creativity. It’s all about making sure that teams are clear on the goals for the case study — along with strategies for supporting content and channels — and understanding how the story fits within the framework of the company’s overall marketing goals.

Here are the basic steps to writing a good case study.

1. Identify your goal

Start by defining exactly who your case study will be designed to help. Case studies are about specific instances where a company works with a customer to achieve a goal. Identify which customers are likely to have these goals, as well as other needs the story should cover to appeal to them.

The answer is often found in one of the buyer personas that have been constructed as part of your larger marketing strategy. This can include anything from new leads generated by the marketing team to long-term customers that are being pressed for cross-sell opportunities. In all of these cases, demonstrating value through a relatable customer success story can be part of the solution to conversion.

2. Choose your client or subject

Who you highlight matters. Case studies tie brands together that might otherwise not cross paths. A writer will want to ensure that the highlighted customer aligns with their own company’s brand identity and offerings. Look for a customer with positive name recognition who has had great success with a product or service and is willing to be an advocate.

The client should also match up with the identified target audience. Whichever company or individual is selected should be a reflection of other potential customers who can see themselves in similar circumstances, having the same problems and possible solutions.

Some of the most compelling case studies feature customers who:

• Switch from one product or service to another while naming competitors that missed the mark.
• Experience measurable results that are relatable to others in a specific industry.
• Represent well-known brands and recognizable names that are likely to compel action.
• Advocate for a product or service as a champion and are well-versed in its advantages.

Whoever or whatever customer is selected, marketers must ensure they have the permission of the company involved before getting started. Some brands have strict review and approval procedures for any official marketing or promotional materials that include their name. Acquiring those approvals in advance will prevent any miscommunication or wasted effort if there is an issue with their legal or compliance teams.

3. Conduct research and compile data

Substantiating the claims made in a case study — either by the marketing team or customers themselves — adds validity to the story. To do this, include data and feedback from the client that defines what success looks like. This can be anything from demonstrating return on investment (ROI) to a specific metric the customer was striving to improve. Case studies should prove how an outcome was achieved and show tangible results that indicate to the customer that your solution is the right one.

This step could also include customer interviews. Make sure that the people being interviewed are key stakeholders in the purchase decision or deployment and use of the product or service that is being highlighted. Content writers should work off a set list of questions prepared in advance. It can be helpful to share these with the interviewees beforehand so they have time to consider and craft their responses. One of the best interview tactics to keep in mind is to ask questions where yes and no are not natural answers. This way, your subject will provide more open-ended responses that produce more meaningful content.

4. Choose the right format

There are a number of different ways to format a case study. Depending on what you hope to achieve, one style will be better than another. However, there are some common elements to include, such as:

• An engaging headline
• A subject and customer introduction
• The unique challenge or challenges the customer faced
• The solution the customer used to solve the problem
• The results achieved
• Data and statistics to back up claims of success
• A strong call to action (CTA) to engage with the vendor

It’s also important to note that while case studies are traditionally written as stories, they don’t have to be in a written format. Some companies choose to get more creative with their case studies and produce multimedia content, depending on their audience and objectives. Case study formats can include traditional print stories, interactive web or social content, data-heavy infographics, professionally shot videos, podcasts, and more.

5. Write your case study

We’ll go into more detail later about how exactly to write a case study, including templates and examples. Generally speaking, though, there are a few things to keep in mind when writing your case study.

• Be clear and concise. Readers want to get to the point of the story quickly and easily, and they’ll be looking to see themselves reflected in the story right from the start.
• Provide a big picture. Always make sure to explain who the client is, their goals, and how they achieved success in a short introduction to engage the reader.
• Construct a clear narrative. Stick to the story from the perspective of the customer and what they needed to solve instead of just listing product features or benefits.
• Leverage graphics. Incorporating infographics, charts, and sidebars can be a more engaging and eye-catching way to share key statistics and data in readable ways.
• Offer the right amount of detail. Most case studies are one or two pages with clear sections that a reader can skim to find the information most important to them.
• Include data to support claims. Show real results — both facts and figures and customer quotes — to demonstrate credibility and prove the solution works.

6. Promote your story

Marketers have a number of options for distribution of a freshly minted case study. Many brands choose to publish case studies on their website and post them on social media. This can help support SEO and organic content strategies while also boosting company credibility and trust as visitors see that other businesses have used the product or service.

Marketers are always looking for quality content they can use for lead generation. Consider offering a case study as gated content behind a form on a landing page or as an offer in an email message. One great way to do this is to summarize the content and tease the full story available for download after the user takes an action.

Sales teams can also leverage case studies, so be sure they are aware that the assets exist once they’re published. Especially when it comes to larger B2B sales, companies often ask for examples of similar customer challenges that have been solved.

Now that you’ve learned a bit about case studies and what they should include, you may be wondering how to start creating great customer story content. Here are a couple of templates you can use to structure your case study.

Template 1 — Challenge-solution-result format

• Start with an engaging title. This should be fewer than 70 characters long for SEO best practices. One of the best ways to approach the title is to include the customer’s name and a hint at the challenge they overcame in the end.
• Create an introduction. Lead with an explanation as to who the customer is, the need they had, and the opportunity they found with a specific product or solution. Writers can also suggest the success the customer experienced with the solution they chose.
• Present the challenge. This should be several paragraphs long and explain the problem the customer faced and the issues they were trying to solve. Details should tie into the company’s products and services naturally. This section needs to be the most relatable to the reader so they can picture themselves in a similar situation.
• Share the solution. Explain which product or service offered was the ideal fit for the customer and why. Feel free to delve into their experience setting up, purchasing, and onboarding the solution.
• Explain the results. Demonstrate the impact of the solution they chose by backing up their positive experience with data. Fill in with customer quotes and tangible, measurable results that show the effect of their choice.
• Ask for action. Include a CTA at the end of the case study that invites readers to reach out for more information, try a demo, or learn more — to nurture them further in the marketing pipeline. What you ask of the reader should tie directly into the goals that were established for the case study in the first place.

Template 2 — Data-driven format

• Start with an engaging title. Be sure to include a statistic or data point in the first 70 characters. Again, it’s best to include the customer’s name as part of the title.
• Create an overview. Share the customer’s background and a short version of the challenge they faced. Present the reason a particular product or service was chosen, and feel free to include quotes from the customer about their selection process.
• Present data point 1. Isolate the first metric that the customer used to define success and explain how the product or solution helped to achieve this goal. Provide data points and quotes to substantiate the claim that success was achieved.
• Present data point 2. Isolate the second metric that the customer used to define success and explain what the product or solution did to achieve this goal. Provide data points and quotes to substantiate the claim that success was achieved.
• Present data point 3. Isolate the final metric that the customer used to define success and explain what the product or solution did to achieve this goal. Provide data points and quotes to substantiate the claim that success was achieved.
• Summarize the results. Reiterate the fact that the customer was able to achieve success thanks to a specific product or service. Include quotes and statements that reflect customer satisfaction and suggest they plan to continue using the solution.
• Ask for action. Include a CTA at the end of the case study that asks readers to reach out for more information, try a demo, or learn more — to further nurture them in the marketing pipeline. Again, remember that this is where marketers can look to convert their content into action with the customer.

While templates are helpful, seeing a case study in action can also be a great way to learn. Here are some examples of how Adobe customers have experienced success.

Juniper Networks

One example is the Adobe and Juniper Networks case study , which puts the reader in the customer’s shoes. The beginning of the story quickly orients the reader so that they know exactly who the article is about and what they were trying to achieve. Solutions are outlined in a way that shows Adobe Experience Manager is the best choice and a natural fit for the customer. Along the way, quotes from the client are incorporated to help add validity to the statements. The results in the case study are conveyed with clear evidence of scale and volume using tangible data.

The story of Lenovo’s journey with Adobe is one that spans years of planning, implementation, and rollout. The Lenovo case study does a great job of consolidating all of this into a relatable journey that other enterprise organizations can see themselves taking, despite the project size. This case study also features descriptive headers and compelling visual elements that engage the reader and strengthen the content.

Tata Consulting

When it comes to using data to show customer results, this case study does an excellent job of conveying details and numbers in an easy-to-digest manner. Bullet points at the start break up the content while also helping the reader understand exactly what the case study will be about. Tata Consulting used Adobe to deliver elevated, engaging content experiences for a large telecommunications client of its own — an objective that’s relatable for a lot of companies.

Case studies are a vital tool for any marketing team as they enable you to demonstrate the value of your company’s products and services to others. They help marketers do their job and add credibility to a brand trying to promote its solutions by using the experiences and stories of real customers.

When you’re ready to get started with a case study:

• Think about a few goals you’d like to accomplish with your content.
• Make a list of successful clients that would be strong candidates for a case study.
• Reach out to the client to get their approval and conduct an interview.
• Gather the data to present an engaging and effective customer story.

Adobe can help

There are several Adobe products that can help you craft compelling case studies. Adobe Experience Platform helps you collect data and deliver great customer experiences across every channel. Once you’ve created your case studies, Experience Platform will help you deliver the right information to the right customer at the right time for maximum impact.

To learn more, watch the Adobe Experience Platform story .

Keep in mind that the best case studies are backed by data. That’s where Adobe Real-Time Customer Data Platform and Adobe Analytics come into play. With Real-Time CDP, you can gather the data you need to build a great case study and target specific customers to deliver the content to the right audience at the perfect moment.

Watch the Real-Time CDP overview video to learn more.

Finally, Adobe Analytics turns real-time data into real-time insights. It helps your business collect and synthesize data from multiple platforms to make more informed decisions and create the best case study possible.

Request a demo to learn more about Adobe Analytics.

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Designing and Conducting Case Studies

This guide examines case studies, a form of qualitative descriptive research that is used to look at individuals, a small group of participants, or a group as a whole. Researchers collect data about participants using participant and direct observations, interviews, protocols, tests, examinations of records, and collections of writing samples. Starting with a definition of the case study, the guide moves to a brief history of this research method. Using several well documented case studies, the guide then looks at applications and methods including data collection and analysis. A discussion of ways to handle validity, reliability, and generalizability follows, with special attention to case studies as they are applied to composition studies. Finally, this guide examines the strengths and weaknesses of case studies.

Definition and Overview

Case study refers to the collection and presentation of detailed information about a particular participant or small group, frequently including the accounts of subjects themselves. A form of qualitative descriptive research, the case study looks intensely at an individual or small participant pool, drawing conclusions only about that participant or group and only in that specific context. Researchers do not focus on the discovery of a universal, generalizable truth, nor do they typically look for cause-effect relationships; instead, emphasis is placed on exploration and description.

Case studies typically examine the interplay of all variables in order to provide as complete an understanding of an event or situation as possible. This type of comprehensive understanding is arrived at through a process known as thick description, which involves an in-depth description of the entity being evaluated, the circumstances under which it is used, the characteristics of the people involved in it, and the nature of the community in which it is located. Thick description also involves interpreting the meaning of demographic and descriptive data such as cultural norms and mores, community values, ingrained attitudes, and motives.

Unlike quantitative methods of research, like the survey, which focus on the questions of who, what, where, how much, and how many, and archival analysis, which often situates the participant in some form of historical context, case studies are the preferred strategy when how or why questions are asked. Likewise, they are the preferred method when the researcher has little control over the events, and when there is a contemporary focus within a real life context. In addition, unlike more specifically directed experiments, case studies require a problem that seeks a holistic understanding of the event or situation in question using inductive logic--reasoning from specific to more general terms.

In scholarly circles, case studies are frequently discussed within the context of qualitative research and naturalistic inquiry. Case studies are often referred to interchangeably with ethnography, field study, and participant observation. The underlying philosophical assumptions in the case are similar to these types of qualitative research because each takes place in a natural setting (such as a classroom, neighborhood, or private home), and strives for a more holistic interpretation of the event or situation under study.

Unlike more statistically-based studies which search for quantifiable data, the goal of a case study is to offer new variables and questions for further research. F.H. Giddings, a sociologist in the early part of the century, compares statistical methods to the case study on the basis that the former are concerned with the distribution of a particular trait, or a small number of traits, in a population, whereas the case study is concerned with the whole variety of traits to be found in a particular instance" (Hammersley 95).

Case studies are not a new form of research; naturalistic inquiry was the primary research tool until the development of the scientific method. The fields of sociology and anthropology are credited with the primary shaping of the concept as we know it today. However, case study research has drawn from a number of other areas as well: the clinical methods of doctors; the casework technique being developed by social workers; the methods of historians and anthropologists, plus the qualitative descriptions provided by quantitative researchers like LePlay; and, in the case of Robert Park, the techniques of newspaper reporters and novelists.

Park was an ex-newspaper reporter and editor who became very influential in developing sociological case studies at the University of Chicago in the 1920s. As a newspaper professional he coined the term "scientific" or "depth" reporting: the description of local events in a way that pointed to major social trends. Park viewed the sociologist as "merely a more accurate, responsible, and scientific reporter." Park stressed the variety and value of human experience. He believed that sociology sought to arrive at natural, but fluid, laws and generalizations in regard to human nature and society. These laws weren't static laws of the kind sought by many positivists and natural law theorists, but rather, they were laws of becoming--with a constant possibility of change. Park encouraged students to get out of the library, to quit looking at papers and books, and to view the constant experiment of human experience. He writes, "Go and sit in the lounges of the luxury hotels and on the doorsteps of the flophouses; sit on the Gold Coast settees and on the slum shakedowns; sit in the Orchestra Hall and in the Star and Garter Burlesque. In short, gentlemen [sic], go get the seats of your pants dirty in real research."

But over the years, case studies have drawn their share of criticism. In fact, the method had its detractors from the start. In the 1920s, the debate between pro-qualitative and pro-quantitative became quite heated. Case studies, when compared to statistics, were considered by many to be unscientific. From the 1930's on, the rise of positivism had a growing influence on quantitative methods in sociology. People wanted static, generalizable laws in science. The sociological positivists were looking for stable laws of social phenomena. They criticized case study research because it failed to provide evidence of inter subjective agreement. Also, they condemned it because of the few number of cases studied and that the under-standardized character of their descriptions made generalization impossible. By the 1950s, quantitative methods, in the form of survey research, had become the dominant sociological approach and case study had become a minority practice.

Educational Applications

The 1950's marked the dawning of a new era in case study research, namely that of the utilization of the case study as a teaching method. "Instituted at Harvard Business School in the 1950s as a primary method of teaching, cases have since been used in classrooms and lecture halls alike, either as part of a course of study or as the main focus of the course to which other teaching material is added" (Armisted 1984). The basic purpose of instituting the case method as a teaching strategy was "to transfer much of the responsibility for learning from the teacher on to the student, whose role, as a result, shifts away from passive absorption toward active construction" (Boehrer 1990). Through careful examination and discussion of various cases, "students learn to identify actual problems, to recognize key players and their agendas, and to become aware of those aspects of the situation that contribute to the problem" (Merseth 1991). In addition, students are encouraged to "generate their own analysis of the problems under consideration, to develop their own solutions, and to practically apply their own knowledge of theory to these problems" (Boyce 1993). Along the way, students also develop "the power to analyze and to master a tangled circumstance by identifying and delineating important factors; the ability to utilize ideas, to test them against facts, and to throw them into fresh combinations" (Merseth 1991).

In addition to the practical application and testing of scholarly knowledge, case discussions can also help students prepare for real-world problems, situations and crises by providing an approximation of various professional environments (i.e. classroom, board room, courtroom, or hospital). Thus, through the examination of specific cases, students are given the opportunity to work out their own professional issues through the trials, tribulations, experiences, and research findings of others. An obvious advantage to this mode of instruction is that it allows students the exposure to settings and contexts that they might not otherwise experience. For example, a student interested in studying the effects of poverty on minority secondary student's grade point averages and S.A.T. scores could access and analyze information from schools as geographically diverse as Los Angeles, New York City, Miami, and New Mexico without ever having to leave the classroom.

The case study method also incorporates the idea that students can learn from one another "by engaging with each other and with each other's ideas, by asserting something and then having it questioned, challenged and thrown back at them so that they can reflect on what they hear, and then refine what they say" (Boehrer 1990). In summary, students can direct their own learning by formulating questions and taking responsibility for the study.

Types and Design Concerns

Researchers use multiple methods and approaches to conduct case studies.

Types of Case Studies

Under the more generalized category of case study exist several subdivisions, each of which is custom selected for use depending upon the goals and/or objectives of the investigator. These types of case study include the following:

Illustrative Case Studies These are primarily descriptive studies. They typically utilize one or two instances of an event to show what a situation is like. Illustrative case studies serve primarily to make the unfamiliar familiar and to give readers a common language about the topic in question.

Exploratory (or pilot) Case Studies These are condensed case studies performed before implementing a large scale investigation. Their basic function is to help identify questions and select types of measurement prior to the main investigation. The primary pitfall of this type of study is that initial findings may seem convincing enough to be released prematurely as conclusions.

Cumulative Case Studies These serve to aggregate information from several sites collected at different times. The idea behind these studies is the collection of past studies will allow for greater generalization without additional cost or time being expended on new, possibly repetitive studies.

Critical Instance Case Studies These examine one or more sites for either the purpose of examining a situation of unique interest with little to no interest in generalizability, or to call into question or challenge a highly generalized or universal assertion. This method is useful for answering cause and effect questions.

Identifying a Theoretical Perspective

Much of the case study's design is inherently determined for researchers, depending on the field from which they are working. In composition studies, researchers are typically working from a qualitative, descriptive standpoint. In contrast, physicists will approach their research from a more quantitative perspective. Still, in designing the study, researchers need to make explicit the questions to be explored and the theoretical perspective from which they will approach the case. The three most commonly adopted theories are listed below:

Individual Theories These focus primarily on the individual development, cognitive behavior, personality, learning and disability, and interpersonal interactions of a particular subject.

Organizational Theories These focus on bureaucracies, institutions, organizational structure and functions, or excellence in organizational performance.

Social Theories These focus on urban development, group behavior, cultural institutions, or marketplace functions.

Two examples of case studies are used consistently throughout this chapter. The first, a study produced by Berkenkotter, Huckin, and Ackerman (1988), looks at a first year graduate student's initiation into an academic writing program. The study uses participant-observer and linguistic data collecting techniques to assess the student's knowledge of appropriate discourse conventions. Using the pseudonym Nate to refer to the subject, the study sought to illuminate the particular experience rather than to generalize about the experience of fledgling academic writers collectively.

For example, in Berkenkotter, Huckin, and Ackerman's (1988) study we are told that the researchers are interested in disciplinary communities. In the first paragraph, they ask what constitutes membership in a disciplinary community and how achieving membership might affect a writer's understanding and production of texts. In the third paragraph they state that researchers must negotiate their claims "within the context of his sub specialty's accepted knowledge and methodology." In the next paragraph they ask, "How is literacy acquired? What is the process through which novices gain community membership? And what factors either aid or hinder students learning the requisite linguistic behaviors?" This introductory section ends with a paragraph in which the study's authors claim that during the course of the study, the subject, Nate, successfully makes the transition from "skilled novice" to become an initiated member of the academic discourse community and that his texts exhibit linguistic changes which indicate this transition. In the next section the authors make explicit the sociolinguistic theoretical and methodological assumptions on which the study is based (1988). Thus the reader has a good understanding of the authors' theoretical background and purpose in conducting the study even before it is explicitly stated on the fourth page of the study. "Our purpose was to examine the effects of the educational context on one graduate student's production of texts as he wrote in different courses and for different faculty members over the academic year 1984-85." The goal of the study then, was to explore the idea that writers must be initiated into a writing community, and that this initiation will change the way one writes.

The second example is Janet Emig's (1971) study of the composing process of a group of twelfth graders. In this study, Emig seeks to answer the question of what happens to the self as a result educational stimuli in terms of academic writing. The case study used methods such as protocol analysis, tape-recorded interviews, and discourse analysis.

In the case of Janet Emig's (1971) study of the composing process of eight twelfth graders, four specific hypotheses were made:

• Twelfth grade writers engage in two modes of composing: reflexive and extensive.
• These differences can be ascertained and characterized through having the writers compose aloud their composition process.
• A set of implied stylistic principles governs the writing process.
• For twelfth grade writers, extensive writing occurs chiefly as a school-sponsored activity, or reflexive, as a self-sponsored activity.

In this study, the chief distinction is between the two dominant modes of composing among older, secondary school students. The distinctions are:

• The reflexive mode, which focuses on the writer's thoughts and feelings.
• The extensive mode, which focuses on conveying a message.

Emig also outlines the specific questions which guided the research in the opening pages of her Review of Literature , preceding the report.

Designing a Case Study

After considering the different sub categories of case study and identifying a theoretical perspective, researchers can begin to design their study. Research design is the string of logic that ultimately links the data to be collected and the conclusions to be drawn to the initial questions of the study. Typically, research designs deal with at least four problems:

• What questions to study
• What data are relevant
• What data to collect
• How to analyze that data

In other words, a research design is basically a blueprint for getting from the beginning to the end of a study. The beginning is an initial set of questions to be answered, and the end is some set of conclusions about those questions.

Because case studies are conducted on topics as diverse as Anglo-Saxon Literature (Thrane 1986) and AIDS prevention (Van Vugt 1994), it is virtually impossible to outline any strict or universal method or design for conducting the case study. However, Robert K. Yin (1993) does offer five basic components of a research design:

• A study's questions.
• A study's propositions (if any).
• A study's units of analysis.
• The logic that links the data to the propositions.
• The criteria for interpreting the findings.

In addition to these five basic components, Yin also stresses the importance of clearly articulating one's theoretical perspective, determining the goals of the study, selecting one's subject(s), selecting the appropriate method(s) of collecting data, and providing some considerations to the composition of the final report.

Conducting Case Studies

To obtain as complete a picture of the participant as possible, case study researchers can employ a variety of approaches and methods. These approaches, methods, and related issues are discussed in depth in this section.

Method: Single or Multi-modal?

To obtain as complete a picture of the participant as possible, case study researchers can employ a variety of methods. Some common methods include interviews , protocol analyses, field studies, and participant-observations. Emig (1971) chose to use several methods of data collection. Her sources included conversations with the students, protocol analysis, discrete observations of actual composition, writing samples from each student, and school records (Lauer and Asher 1988).

Berkenkotter, Huckin, and Ackerman (1988) collected data by observing classrooms, conducting faculty and student interviews, collecting self reports from the subject, and by looking at the subject's written work.

A study that was criticized for using a single method model was done by Flower and Hayes (1984). In this study that explores the ways in which writers use different forms of knowing to create space, the authors used only protocol analysis to gather data. The study came under heavy fire because of their decision to use only one method.

Participant Selection

Case studies can use one participant, or a small group of participants. However, it is important that the participant pool remain relatively small. The participants can represent a diverse cross section of society, but this isn't necessary.

For example, the Berkenkotter, Huckin, and Ackerman (1988) study looked at just one participant, Nate. By contrast, in Janet Emig's (1971) study of the composition process of twelfth graders, eight participants were selected representing a diverse cross section of the community, with volunteers from an all-white upper-middle-class suburban school, an all-black inner-city school, a racially mixed lower-middle-class school, an economically and racially mixed school, and a university school.

Often, a brief "case history" is done on the participants of the study in order to provide researchers with a clearer understanding of their participants, as well as some insight as to how their own personal histories might affect the outcome of the study. For instance, in Emig's study, the investigator had access to the school records of five of the participants, and to standardized test scores for the remaining three. Also made available to the researcher was the information that three of the eight students were selected as NCTE Achievement Award winners. These personal histories can be useful in later stages of the study when data are being analyzed and conclusions drawn.

Data Collection

There are six types of data collected in case studies:

• Archival records.
• Interviews.
• Direct observation.
• Participant observation.

In the field of composition research, these six sources might be:

• A writer's drafts.
• School records of student writers.
• Transcripts of interviews with a writer.
• Transcripts of conversations between writers (and protocols).
• Videotapes and notes from direct field observations.
• Hard copies of a writer's work on computer.

Depending on whether researchers have chosen to use a single or multi-modal approach for the case study, they may choose to collect data from one or any combination of these sources.

Protocols, that is, transcriptions of participants talking aloud about what they are doing as they do it, have been particularly common in composition case studies. For example, in Emig's (1971) study, the students were asked, in four different sessions, to give oral autobiographies of their writing experiences and to compose aloud three themes in the presence of a tape recorder and the investigator.

In some studies, only one method of data collection is conducted. For example, the Flower and Hayes (1981) report on the cognitive process theory of writing depends on protocol analysis alone. However, using multiple sources of evidence to increase the reliability and validity of the data can be advantageous.

Case studies are likely to be much more convincing and accurate if they are based on several different sources of information, following a corroborating mode. This conclusion is echoed among many composition researchers. For example, in her study of predrafting processes of high and low-apprehensive writers, Cynthia Selfe (1985) argues that because "methods of indirect observation provide only an incomplete reflection of the complex set of processes involved in composing, a combination of several such methods should be used to gather data in any one study." Thus, in this study, Selfe collected her data from protocols, observations of students role playing their writing processes, audio taped interviews with the students, and videotaped observations of the students in the process of composing.

It can be said then, that cross checking data from multiple sources can help provide a multidimensional profile of composing activities in a particular setting. Sharan Merriam (1985) suggests "checking, verifying, testing, probing, and confirming collected data as you go, arguing that this process will follow in a funnel-like design resulting in less data gathering in later phases of the study along with a congruent increase in analysis checking, verifying, and confirming."

It is important to note that in case studies, as in any qualitative descriptive research, while researchers begin their studies with one or several questions driving the inquiry (which influence the key factors the researcher will be looking for during data collection), a researcher may find new key factors emerging during data collection. These might be unexpected patterns or linguistic features which become evident only during the course of the research. While not bearing directly on the researcher's guiding questions, these variables may become the basis for new questions asked at the end of the report, thus linking to the possibility of further research.

Data Analysis

As the information is collected, researchers strive to make sense of their data. Generally, researchers interpret their data in one of two ways: holistically or through coding. Holistic analysis does not attempt to break the evidence into parts, but rather to draw conclusions based on the text as a whole. Flower and Hayes (1981), for example, make inferences from entire sections of their students' protocols, rather than searching through the transcripts to look for isolatable characteristics.

However, composition researchers commonly interpret their data by coding, that is by systematically searching data to identify and/or categorize specific observable actions or characteristics. These observable actions then become the key variables in the study. Sharan Merriam (1988) suggests seven analytic frameworks for the organization and presentation of data:

• The role of participants.
• The network analysis of formal and informal exchanges among groups.
• Historical.
• Thematical.
• Ritual and symbolism.
• Critical incidents that challenge or reinforce fundamental beliefs, practices, and values.

There are two purposes of these frameworks: to look for patterns among the data and to look for patterns that give meaning to the case study.

As stated above, while most researchers begin their case studies expecting to look for particular observable characteristics, it is not unusual for key variables to emerge during data collection. Typical variables coded in case studies of writers include pauses writers make in the production of a text, the use of specific linguistic units (such as nouns or verbs), and writing processes (planning, drafting, revising, and editing). In the Berkenkotter, Huckin, and Ackerman (1988) study, for example, researchers coded the participant's texts for use of connectives, discourse demonstratives, average sentence length, off-register words, use of the first person pronoun, and the ratio of definite articles to indefinite articles.

Since coding is inherently subjective, more than one coder is usually employed. In the Berkenkotter, Huckin, and Ackerman (1988) study, for example, three rhetoricians were employed to code the participant's texts for off-register phrases. The researchers established the agreement among the coders before concluding that the participant used fewer off-register words as the graduate program progressed.

Composing the Case Study Report

In the many forms it can take, "a case study is generically a story; it presents the concrete narrative detail of actual, or at least realistic events, it has a plot, exposition, characters, and sometimes even dialogue" (Boehrer 1990). Generally, case study reports are extensively descriptive, with "the most problematic issue often referred to as being the determination of the right combination of description and analysis" (1990). Typically, authors address each step of the research process, and attempt to give the reader as much context as possible for the decisions made in the research design and for the conclusions drawn.

This contextualization usually includes a detailed explanation of the researchers' theoretical positions, of how those theories drove the inquiry or led to the guiding research questions, of the participants' backgrounds, of the processes of data collection, of the training and limitations of the coders, along with a strong attempt to make connections between the data and the conclusions evident.

Although the Berkenkotter, Huckin, and Ackerman (1988) study does not, case study reports often include the reactions of the participants to the study or to the researchers' conclusions. Because case studies tend to be exploratory, most end with implications for further study. Here researchers may identify significant variables that emerged during the research and suggest studies related to these, or the authors may suggest further general questions that their case study generated.

For example, Emig's (1971) study concludes with a section dedicated solely to the topic of implications for further research, in which she suggests several means by which this particular study could have been improved, as well as questions and ideas raised by this study which other researchers might like to address, such as: is there a correlation between a certain personality and a certain composing process profile (e.g. is there a positive correlation between ego strength and persistence in revising)?

Also included in Emig's study is a section dedicated to implications for teaching, which outlines the pedagogical ramifications of the study's findings for teachers currently involved in high school writing programs.

Sharan Merriam (1985) also offers several suggestions for alternative presentations of data:

• Prepare specialized condensations for appropriate groups.
• Replace narrative sections with a series of answers to open-ended questions.
• Present "skimmer's" summaries at beginning of each section.
• Incorporate headlines that encapsulate information from text.
• Prepare analytic summaries with supporting data appendixes.
• Present data in colorful and/or unique graphic representations.

Issues of Validity and Reliability

Once key variables have been identified, they can be analyzed. Reliability becomes a key concern at this stage, and many case study researchers go to great lengths to ensure that their interpretations of the data will be both reliable and valid. Because issues of validity and reliability are an important part of any study in the social sciences, it is important to identify some ways of dealing with results.

Multi-modal case study researchers often balance the results of their coding with data from interviews or writer's reflections upon their own work. Consequently, the researchers' conclusions become highly contextualized. For example, in a case study which looked at the time spent in different stages of the writing process, Berkenkotter concluded that her participant, Donald Murray, spent more time planning his essays than in other writing stages. The report of this case study is followed by Murray's reply, wherein he agrees with some of Berkenkotter's conclusions and disagrees with others.

As is the case with other research methodologies, issues of external validity, construct validity, and reliability need to be carefully considered.

Commentary on Case Studies

Researchers often debate the relative merits of particular methods, among them case study. In this section, we comment on two key issues. To read the commentaries, choose any of the items below:

Strengths and Weaknesses of Case Studies

Most case study advocates point out that case studies produce much more detailed information than what is available through a statistical analysis. Advocates will also hold that while statistical methods might be able to deal with situations where behavior is homogeneous and routine, case studies are needed to deal with creativity, innovation, and context. Detractors argue that case studies are difficult to generalize because of inherent subjectivity and because they are based on qualitative subjective data, generalizable only to a particular context.

Flexibility

The case study approach is a comparatively flexible method of scientific research. Because its project designs seem to emphasize exploration rather than prescription or prediction, researchers are comparatively freer to discover and address issues as they arise in their experiments. In addition, the looser format of case studies allows researchers to begin with broad questions and narrow their focus as their experiment progresses rather than attempt to predict every possible outcome before the experiment is conducted.

Emphasis on Context

By seeking to understand as much as possible about a single subject or small group of subjects, case studies specialize in "deep data," or "thick description"--information based on particular contexts that can give research results a more human face. This emphasis can help bridge the gap between abstract research and concrete practice by allowing researchers to compare their firsthand observations with the quantitative results obtained through other methods of research.

Inherent Subjectivity

"The case study has long been stereotyped as the weak sibling among social science methods," and is often criticized as being too subjective and even pseudo-scientific. Likewise, "investigators who do case studies are often regarded as having deviated from their academic disciplines, and their investigations as having insufficient precision (that is, quantification), objectivity and rigor" (Yin 1989). Opponents cite opportunities for subjectivity in the implementation, presentation, and evaluation of case study research. The approach relies on personal interpretation of data and inferences. Results may not be generalizable, are difficult to test for validity, and rarely offer a problem-solving prescription. Simply put, relying on one or a few subjects as a basis for cognitive extrapolations runs the risk of inferring too much from what might be circumstance.

High Investment

Case studies can involve learning more about the subjects being tested than most researchers would care to know--their educational background, emotional background, perceptions of themselves and their surroundings, their likes, dislikes, and so on. Because of its emphasis on "deep data," the case study is out of reach for many large-scale research projects which look at a subject pool in the tens of thousands. A budget request of $10,000 to examine 200 subjects sounds more efficient than a similar request to examine four subjects.

Ethical Considerations

Researchers conducting case studies should consider certain ethical issues. For example, many educational case studies are often financed by people who have, either directly or indirectly, power over both those being studied and those conducting the investigation (1985). This conflict of interests can hinder the credibility of the study.

The personal integrity, sensitivity, and possible prejudices and/or biases of the investigators need to be taken into consideration as well. Personal biases can creep into how the research is conducted, alternative research methods used, and the preparation of surveys and questionnaires.

A common complaint in case study research is that investigators change direction during the course of the study unaware that their original research design was inadequate for the revised investigation. Thus, the researchers leave unknown gaps and biases in the study. To avoid this, researchers should report preliminary findings so that the likelihood of bias will be reduced.

Concerns about Reliability, Validity, and Generalizability

Merriam (1985) offers several suggestions for how case study researchers might actively combat the popular attacks on the validity, reliability, and generalizability of case studies:

• Prolong the Processes of Data Gathering on Site: This will help to insure the accuracy of the findings by providing the researcher with more concrete information upon which to formulate interpretations.
• Employ the Process of "Triangulation": Use a variety of data sources as opposed to relying solely upon one avenue of observation. One example of such a data check would be what McClintock, Brannon, and Maynard (1985) refer to as a "case cluster method," that is, when a single unit within a larger case is randomly sampled, and that data treated quantitatively." For instance, in Emig's (1971) study, the case cluster method was employed, singling out the productivity of a single student named Lynn. This cluster profile included an advanced case history of the subject, specific examination and analysis of individual compositions and protocols, and extensive interview sessions. The seven remaining students were then compared with the case of Lynn, to ascertain if there are any shared, or unique dimensions to the composing process engaged in by these eight students.
• Conduct Member Checks: Initiate and maintain an active corroboration on the interpretation of data between the researcher and those who provided the data. In other words, talk to your subjects.
• Collect Referential Materials: Complement the file of materials from the actual site with additional document support. For example, Emig (1971) supports her initial propositions with historical accounts by writers such as T.S. Eliot, James Joyce, and D.H. Lawrence. Emig also cites examples of theoretical research done with regards to the creative process, as well as examples of empirical research dealing with the writing of adolescents. Specific attention is then given to the four stages description of the composing process delineated by Helmoltz, Wallas, and Cowley, as it serves as the focal point in this study.
• Engage in Peer Consultation: Prior to composing the final draft of the report, researchers should consult with colleagues in order to establish validity through pooled judgment.

Although little can be done to combat challenges concerning the generalizability of case studies, "most writers suggest that qualitative research should be judged as credible and confirmable as opposed to valid and reliable" (Merriam 1985). Likewise, it has been argued that "rather than transplanting statistical, quantitative notions of generalizability and thus finding qualitative research inadequate, it makes more sense to develop an understanding of generalization that is congruent with the basic characteristics of qualitative inquiry" (1985). After all, criticizing the case study method for being ungeneralizable is comparable to criticizing a washing machine for not being able to tell the correct time. In other words, it is unjust to criticize a method for not being able to do something which it was never originally designed to do in the first place.

Annotated Bibliography

Armisted, C. (1984). How Useful are Case Studies. Training and Development Journal, 38 (2), 75-77.

This article looks at eight types of case studies, offers pros and cons of using case studies in the classroom, and gives suggestions for successfully writing and using case studies.

Bardovi-Harlig, K. (1997). Beyond Methods: Components of Second Language Teacher Education . New York: McGraw-Hill.

A compilation of various research essays which address issues of language teacher education. Essays included are: "Non-native reading research and theory" by Lee, "The case for Psycholinguistics" by VanPatten, and "Assessment and Second Language Teaching" by Gradman and Reed.

Bartlett, L. (1989). A Question of Good Judgment; Interpretation Theory and Qualitative Enquiry Address. 70th Annual Meeting of the American Educational Research Association. San Francisco.

Bartlett selected "quasi-historical" methodology, which focuses on the "truth" found in case records, as one that will provide "good judgments" in educational inquiry. He argues that although the method is not comprehensive, it can try to connect theory with practice.

Baydere, S. et. al. (1993). Multimedia conferencing as a tool for collaborative writing: a case study in Computer Supported Collaborative Writing. New York: Springer-Verlag.

The case study by Baydere et. al. is just one of the many essays in this book found in the series "Computer Supported Cooperative Work." Denley, Witefield and May explore similar issues in their essay, "A case study in task analysis for the design of a collaborative document production system."

Berkenkotter, C., Huckin, T., N., & Ackerman J. (1988). Conventions, Conversations, and the Writer: Case Study of a Student in a Rhetoric Ph.D. Program. Research in the Teaching of English, 22, 9-44.

The authors focused on how the writing of their subject, Nate or Ackerman, changed as he became more acquainted or familiar with his field's discourse community.

Berninger, V., W., and Gans, B., M. (1986). Language Profiles in Nonspeaking Individuals of Normal Intelligence with Severe Cerebral Palsy. Augmentative and Alternative Communication, 2, 45-50.

Argues that generalizations about language abilities in patients with severe cerebral palsy (CP) should be avoided. Standardized tests of different levels of processing oral language, of processing written language, and of producing written language were administered to 3 male participants (aged 9, 16, and 40 yrs).

Bockman, J., R., and Couture, B. (1984). The Case Method in Technical Communication: Theory and Models. Texas: Association of Teachers of Technical Writing.

Examines the study and teaching of technical writing, communication of technical information, and the case method in terms of those applications.

Boehrer, J. (1990). Teaching With Cases: Learning to Question. New Directions for Teaching and Learning, 42 41-57.

This article discusses the origins of the case method, looks at the question of what is a case, gives ideas about learning in case teaching, the purposes it can serve in the classroom, the ground rules for the case discussion, including the role of the question, and new directions for case teaching.

Bowman, W. R. (1993). Evaluating JTPA Programs for Economically Disadvantaged Adults: A Case Study of Utah and General Findings . Washington: National Commission for Employment Policy.

"To encourage state-level evaluations of JTPA, the Commission and the State of Utah co-sponsored this report on the effectiveness of JTPA Title II programs for adults in Utah. The technique used is non-experimental and the comparison group was selected from registrants with Utah's Employment Security. In a step-by-step approach, the report documents how non-experimental techniques can be applied and several specific technical issues can be addressed."

Boyce, A. (1993) The Case Study Approach for Pedagogists. Annual Meeting of the American Alliance for Health, Physical Education, Recreation and Dance. (Address). Washington DC.

This paper addresses how case studies 1) bridge the gap between teaching theory and application, 2) enable students to analyze problems and develop solutions for situations that will be encountered in the real world of teaching, and 3) helps students to evaluate the feasibility of alternatives and to understand the ramifications of a particular course of action.

Carson, J. (1993) The Case Study: Ideal Home of WAC Quantitative and Qualitative Data. Annual Meeting of the Conference on College Composition and Communication. (Address). San Diego.

"Increasingly, one of the most pressing questions for WAC advocates is how to keep [WAC] programs going in the face of numerous difficulties. Case histories offer the best chance for fashioning rhetorical arguments to keep WAC programs going because they offer the opportunity to provide a coherent narrative that contextualizes all documents and data, including what is generally considered scientific data. A case study of the WAC program, . . . at Robert Morris College in Pittsburgh demonstrates the advantages of this research method. Such studies are ideal homes for both naturalistic and positivistic data as well as both quantitative and qualitative information."

---. (1991). A Cognitive Process Theory of Writing. College Composition and Communication. 32. 365-87.

No abstract available.

Cromer, R. (1994) A Case Study of Dissociations Between Language and Cognition. Constraints on Language Acquisition: Studies of Atypical Children . Hillsdale: Lawrence Erlbaum Associates, 141-153.

Crossley, M. (1983) Case Study in Comparative and International Education: An Approach to Bridging the Theory-Practice Gap. Proceedings of the 11th Annual Conference of the Australian Comparative and International Education Society. Hamilton, NZ.

Case study research, as presented here, helps bridge the theory-practice gap in comparative and international research studies of education because it focuses on the practical, day-to-day context rather than on the national arena. The paper asserts that the case study method can be valuable at all levels of research, formation, and verification of theories in education.

Daillak, R., H., and Alkin, M., C. (1982). Qualitative Studies in Context: Reflections on the CSE Studies of Evaluation Use . California: EDRS

The report shows how the Center of the Study of Evaluation (CSE) applied qualitative techniques to a study of evaluation information use in local, Los Angeles schools. It critiques the effectiveness and the limitations of using case study, evaluation, field study, and user interview survey methodologies.

Davey, L. (1991). The Application of Case Study Evaluations. ERIC/TM Digest.

This article examines six types of case studies, the type of evaluation questions that can be answered, the functions served, some design features, and some pitfalls of the method.

Deutch, C. E. (1996). A course in research ethics for graduate students. College Teaching, 44, 2, 56-60.

This article describes a one-credit discussion course in research ethics for graduate students in biology. Case studies are focused on within the four parts of the course: 1) major issues, 2 )practical issues in scholarly work, 3) ownership of research results, and 4) training and personal decisions.

DeVoss, G. (1981). Ethics in Fieldwork Research. RIE 27p. (ERIC)

This article examines four of the ethical problems that can happen when conducting case study research: acquiring permission to do research, knowing when to stop digging, the pitfalls of doing collaborative research, and preserving the integrity of the participants.

Driscoll, A. (1985). Case Study of a Research Intervention: the University of Utah’s Collaborative Approach . San Francisco: Far West Library for Educational Research Development.

Paper presented at the annual meeting of the American Association of Colleges of Teacher Education, Denver, CO, March 1985. Offers information of in-service training, specifically case studies application.

Ellram, L. M. (1996). The Use of the Case Study Method in Logistics Research. Journal of Business Logistics, 17, 2, 93.

This article discusses the increased use of case study in business research, and the lack of understanding of when and how to use case study methodology in business.

Emig, J. (1971) The Composing Processes of Twelfth Graders . Urbana: NTCE.

This case study uses observation, tape recordings, writing samples, and school records to show that writing in reflexive and extensive situations caused different lengths of discourse and different clusterings of the components of the writing process.

Feagin, J. R. (1991). A Case For the Case Study . Chapel Hill: The University of North Carolina Press.

This book discusses the nature, characteristics, and basic methodological issues of the case study as a research method.

Feldman, H., Holland, A., & Keefe, K. (1989) Language Abilities after Left Hemisphere Brain Injury: A Case Study of Twins. Topics in Early Childhood Special Education, 9, 32-47.

"Describes the language abilities of 2 twin pairs in which 1 twin (the experimental) suffered brain injury to the left cerebral hemisphere around the time of birth and1 twin (the control) did not. One pair of twins was initially assessed at age 23 mo. and the other at about 30 mo.; they were subsequently evaluated in their homes 3 times at about 6-mo intervals."

Fidel, R. (1984). The Case Study Method: A Case Study. Library and Information Science Research, 6.

The article describes the use of case study methodology to systematically develop a model of online searching behavior in which study design is flexible, subject manner determines data gathering and analyses, and procedures adapt to the study's progressive change.

Flower, L., & Hayes, J. R. (1984). Images, Plans and Prose: The Representation of Meaning in Writing. Written Communication, 1, 120-160.

Explores the ways in which writers actually use different forms of knowing to create prose.

Frey, L. R. (1992). Interpreting Communication Research: A Case Study Approach Englewood Cliffs, N.J.: Prentice Hall.

The book discusses research methodologies in the Communication field. It focuses on how case studies bridge the gap between communication research, theory, and practice.

Gilbert, V. K. (1981). The Case Study as a Research Methodology: Difficulties and Advantages of Integrating the Positivistic, Phenomenological and Grounded Theory Approaches . The Annual Meeting of the Canadian Association for the Study of Educational Administration. (Address) Halifax, NS, Can.

This study on an innovative secondary school in England shows how a "low-profile" participant-observer case study was crucial to the initial observation, the testing of hypotheses, the interpretive approach, and the grounded theory.

Gilgun, J. F. (1994). A Case for Case Studies in Social Work Research. Social Work, 39, 4, 371-381.

This article defines case study research, presents guidelines for evaluation of case studies, and shows the relevance of case studies to social work research. It also looks at issues such as evaluation and interpretations of case studies.

Glennan, S. L., Sharp-Bittner, M. A. & Tullos, D. C. (1991). Augmentative and Alternative Communication Training with a Nonspeaking Adult: Lessons from MH. Augmentative and Alternative Communication, 7, 240-7.

"A response-guided case study documented changes in a nonspeaking 36-yr-old man's ability to communicate using 3 trained augmentative communication modes. . . . Data were collected in videotaped interaction sessions between the nonspeaking adult and a series of adult speaking."

Graves, D. (1981). An Examination of the Writing Processes of Seven Year Old Children. Research in the Teaching of English, 15, 113-134.

Hamel, J. (1993). Case Study Methods . Newbury Park: Sage. .

"In a most economical fashion, Hamel provides a practical guide for producing theoretically sharp and empirically sound sociological case studies. A central idea put forth by Hamel is that case studies must "locate the global in the local" thus making the careful selection of the research site the most critical decision in the analytic process."

Karthigesu, R. (1986, July). Television as a Tool for Nation-Building in the Third World: A Post-Colonial Pattern, Using Malaysia as a Case-Study. International Television Studies Conference. (Address). London, 10-12.

"The extent to which Television Malaysia, as a national mass media organization, has been able to play a role in nation building in the post-colonial period is . . . studied in two parts: how the choice of a model of nation building determines the character of the organization; and how the character of the organization influences the output of the organization."

Kenny, R. (1984). Making the Case for the Case Study. Journal of Curriculum Studies, 16, (1), 37-51.

The article looks at how and why the case study is justified as a viable and valuable approach to educational research and program evaluation.

Knirk, F. (1991). Case Materials: Research and Practice. Performance Improvement Quarterly, 4 (1 ), 73-81.

The article addresses the effectiveness of case studies, subject areas where case studies are commonly used, recent examples of their use, and case study design considerations.

Klos, D. (1976). Students as Case Writers. Teaching of Psychology, 3.2, 63-66.

This article reviews a course in which students gather data for an original case study of another person. The task requires the students to design the study, collect the data, write the narrative, and interpret the findings.

Leftwich, A. (1981). The Politics of Case Study: Problems of Innovation in University Education. Higher Education Review, 13.2, 38-64.

The article discusses the use of case studies as a teaching method. Emphasis is on the instructional materials, interdisciplinarity, and the complex relationships within the university that help or hinder the method.

Mabrito, M. (1991, Oct.). Electronic Mail as a Vehicle for Peer Response: Conversations of High and Low Apprehensive Writers. Written Communication, 509-32.

McCarthy, S., J. (1955). The Influence of Classroom Discourse on Student Texts: The Case of Ella . East Lansing: Institute for Research on Teaching.

A look at how students of color become marginalized within traditional classroom discourse. The essay follows the struggles of one black student: Ella.

Matsuhashi, A., ed. (1987). Writing in Real Time: Modeling Production Processes Norwood, NJ: Ablex Publishing Corporation.

Investigates how writers plan to produce discourse for different purposes to report, to generalize, and to persuade, as well as how writers plan for sentence level units of language. To learn about planning, an observational measure of pause time was used" (ERIC).

Merriam, S. B. (1985). The Case Study in Educational Research: A Review of Selected Literature. Journal of Educational Thought, 19.3, 204-17.

The article examines the characteristics of, philosophical assumptions underlying the case study, the mechanics of conducting a case study, and the concerns about the reliability, validity, and generalizability of the method.

---. (1988). Case Study Research in Education: A Qualitative Approach San Francisco: Jossey Bass.

Merry, S. E., & Milner, N. eds. (1993). The Possibility of Popular Justice: A Case Study of Community Mediation in the United States . Ann Arbor: U of Michigan.

". . . this volume presents a case study of one experiment in popular justice, the San Francisco Community Boards. This program has made an explicit claim to create an alternative justice, or new justice, in the midst of a society ordered by state law. The contributors to this volume explore the history and experience of the program and compare it to other versions of popular justice in the United States, Europe, and the Third World."

Merseth, K. K. (1991). The Case for Cases in Teacher Education. RIE. 42p. (ERIC).

This monograph argues that the case method of instruction offers unique potential for revitalizing the field of teacher education.

Michaels, S. (1987). Text and Context: A New Approach to the Study of Classroom Writing. Discourse Processes, 10, 321-346.

"This paper argues for and illustrates an approach to the study of writing that integrates ethnographic analysis of classroom interaction with linguistic analysis of written texts and teacher/student conversational exchanges. The approach is illustrated through a case study of writing in a single sixth grade classroom during a single writing assignment."

Milburn, G. (1995). Deciphering a Code or Unraveling a Riddle: A Case Study in the Application of a Humanistic Metaphor to the Reporting of Social Studies Teaching. Theory and Research in Education, 13.

This citation serves as an example of how case studies document learning procedures in a senior-level economics course.

Milley, J. E. (1979). An Investigation of Case Study as an Approach to Program Evaluation. 19th Annual Forum of the Association for Institutional Research. (Address). San Diego.

The case study method merged a narrative report focusing on the evaluator as participant-observer with document review, interview, content analysis, attitude questionnaire survey, and sociogram analysis. Milley argues that case study program evaluation has great potential for widespread use.

Minnis, J. R. (1985, Sept.). Ethnography, Case Study, Grounded Theory, and Distance Education Research. Distance Education, 6.2.

This article describes and defines the strengths and weaknesses of ethnography, case study, and grounded theory.

Nunan, D. (1992). Collaborative language learning and teaching . New York: Cambridge University Press.

Included in this series of essays is Peter Sturman’s "Team Teaching: a case study from Japan" and David Nunan’s own "Toward a collaborative approach to curriculum development: a case study."

Nystrand, M., ed. (1982). What Writers Know: The Language, Process, and Structure of Written Discourse . New York: Academic Press.

Owenby, P. H. (1992). Making Case Studies Come Alive. Training, 29, (1), 43-46. (ERIC)

This article provides tips for writing more effective case studies.

---. (1981). Pausing and Planning: The Tempo of Writer Discourse Production. Research in the Teaching of English, 15 (2),113-34.

Perl, S. (1979). The Composing Processes of Unskilled College Writers. Research in the Teaching of English, 13, 317-336.

"Summarizes a study of five unskilled college writers, focusing especially on one of the five, and discusses the findings in light of current pedagogical practice and research design."

Pilcher J. and A. Coffey. eds. (1996). Gender and Qualitative Research . Brookfield: Aldershot, Hants, England.

This book provides a series of essays which look at gender identity research, qualitative research and applications of case study to questions of gendered pedagogy.

Pirie, B. S. (1993). The Case of Morty: A Four Year Study. Gifted Education International, 9 (2), 105-109.

This case study describes a boy from kindergarten through third grade with above average intelligence but difficulty in learning to read, write, and spell.

Popkewitz, T. (1993). Changing Patterns of Power: Social Regulation and Teacher Education Reform. Albany: SUNY Press.

Popkewitz edits this series of essays that address case studies on educational change and the training of teachers. The essays vary in terms of discipline and scope. Also, several authors include case studies of educational practices in countries other than the United States.

---. (1984). The Predrafting Processes of Four High- and Four Low Apprehensive Writers. Research in the Teaching of English, 18, (1), 45-64.

Rasmussen, P. (1985, March) A Case Study on the Evaluation of Research at the Technical University of Denmark. International Journal of Institutional Management in Higher Education, 9 (1).

This is an example of a case study methodology used to evaluate the chemistry and chemical engineering departments at the University of Denmark.

Roth, K. J. (1986). Curriculum Materials, Teacher Talk, and Student Learning: Case Studies in Fifth-Grade Science Teaching . East Lansing: Institute for Research on Teaching.

Roth offers case studies on elementary teachers, elementary school teaching, science studies and teaching, and verbal learning.

Selfe, C. L. (1985). An Apprehensive Writer Composes. When a Writer Can't Write: Studies in Writer's Block and Other Composing-Process Problems . (pp. 83-95). Ed. Mike Rose. NMY: Guilford.

Smith-Lewis, M., R. and Ford, A. (1987). A User's Perspective on Augmentative Communication. Augmentative and Alternative Communication, 3, 12-7.

"During a series of in-depth interviews, a 25-yr-old woman with cerebral palsy who utilized augmentative communication reflected on the effectiveness of the devices designed for her during her school career."

St. Pierre, R., G. (1980, April). Follow Through: A Case Study in Metaevaluation Research . 64th Annual Meeting of the American Educational Research Association. (Address).

The three approaches to metaevaluation are evaluation of primary evaluations, integrative meta-analysis with combined primary evaluation results, and re-analysis of the raw data from a primary evaluation.

Stahler, T., M. (1996, Feb.) Early Field Experiences: A Model That Worked. ERIC.

"This case study of a field and theory class examines a model designed to provide meaningful field experiences for preservice teachers while remaining consistent with the instructor's beliefs about the role of teacher education in preparing teachers for the classroom."

Stake, R. E. (1995). The Art of Case Study Research. Thousand Oaks: Sage Publications.

This book examines case study research in education and case study methodology.

Stiegelbauer, S. (1984) Community, Context, and Co-curriculum: Situational Factors Influencing School Improvements in a Study of High Schools. Presented at the annual meeting of the American Educational Research Association, New Orleans, LA.

Discussion of several case studies: one looking at high school environments, another examining educational innovations.

Stolovitch, H. (1990). Case Study Method. Performance And Instruction, 29, (9), 35-37.

This article describes the case study method as a form of simulation and presents guidelines for their use in professional training situations.

Thaller, E. (1994). Bibliography for the Case Method: Using Case Studies in Teacher Education. RIE. 37 p.

This bibliography presents approximately 450 citations on the use of case studies in teacher education from 1921-1993.

Thrane, T. (1986). On Delimiting the Senses of Near-Synonyms in Historical Semantics: A Case Study of Adjectives of 'Moral Sufficiency' in the Old English Andreas. Linguistics Across Historical and Geographical Boundaries: In Honor of Jacek Fisiak on the Occasion of his Fiftieth Birthday . Berlin: Mouton de Gruyter.

United Nations. (1975). Food and Agriculture Organization. Report on the FAO/UNFPA Seminar on Methodology, Research and Country: Case Studies on Population, Employment and Productivity . Rome: United Nations.

This example case study shows how the methodology can be used in a demographic and psychographic evaluation. At the same time, it discusses the formation and instigation of the case study methodology itself.

Van Vugt, J. P., ed. (1994). Aids Prevention and Services: Community Based Research . Westport: Bergin and Garvey.

"This volume has been five years in the making. In the process, some of the policy applications called for have met with limited success, such as free needle exchange programs in a limited number of American cities, providing condoms to prison inmates, and advertisements that depict same-sex couples. Rather than dating our chapters that deal with such subjects, such policy applications are verifications of the type of research demonstrated here. Furthermore, they indicate the critical need to continue community based research in the various communities threatened by acquired immuno-deficiency syndrome (AIDS) . . . "

Welch, W., ed. (1981, May). Case Study Methodology in Educational Evaluation. Proceedings of the Minnesota Evaluation Conference. Minnesota. (Address).

The four papers in these proceedings provide a comprehensive picture of the rationale, methodology, strengths, and limitations of case studies.

Williams, G. (1987). The Case Method: An Approach to Teaching and Learning in Educational Administration. RIE, 31p.

This paper examines the viability of the case method as a teaching and learning strategy in instructional systems geared toward the training of personnel of the administration of various aspects of educational systems.

Yin, R. K. (1993). Advancing Rigorous Methodologies: A Review of 'Towards Rigor in Reviews of Multivocal Literatures.' Review of Educational Research, 61, (3).

"R. T. Ogawa and B. Malen's article does not meet its own recommended standards for rigorous testing and presentation of its own conclusions. Use of the exploratory case study to analyze multivocal literatures is not supported, and the claim of grounded theory to analyze multivocal literatures may be stronger."

---. (1989). Case Study Research: Design and Methods. London: Sage Publications Inc.

This book discusses in great detail, the entire design process of the case study, including entire chapters on collecting evidence, analyzing evidence, composing the case study report, and designing single and multiple case studies.

Related Links

Consider the following list of related Web sites for more information on the topic of case study research. Note: although many of the links cover the general category of qualitative research, all have sections that address issues of case studies.

• Sage Publications on Qualitative Methodology: Search here for a comprehensive list of new books being published about "Qualitative Methodology" http://www.sagepub.co.uk/
• The International Journal of Qualitative Studies in Education: An on-line journal "to enhance the theory and practice of qualitative research in education." On-line submissions are welcome. http://www.tandf.co.uk/journals/tf/09518398.html
• Qualitative Research Resources on the Internet: From syllabi to home pages to bibliographies. All links relate somehow to qualitative research. http://www.nova.edu/ssss/QR/qualres.html

Becker, Bronwyn, Patrick Dawson, Karen Devine, Carla Hannum, Steve Hill, Jon Leydens, Debbie Matuskevich, Carol Traver, & Mike Palmquist. (2005). Case Studies. Writing@CSU . Colorado State University. https://writing.colostate.edu/guides/guide.cfm?guideid=60

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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."

Cara Lustik is a fact-checker and copywriter.

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."

Research Writing and Analysis

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Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

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All You Wanted to Know About How to Write a Case Study

What do you study in your college? If you are a psychology, sociology, or anthropology student, we bet you might be familiar with what a case study is. This research method is used to study a certain person, group, or situation. In this guide from our dissertation writing service , you will learn how to write a case study professionally, from researching to citing sources properly. Also, we will explore different types of case studies and show you examples — so that you won’t have any other questions left.

What Is a Case Study?

A case study is a subcategory of research design which investigates problems and offers solutions. Case studies can range from academic research studies to corporate promotional tools trying to sell an idea—their scope is quite vast.

What Is the Difference Between a Research Paper and a Case Study?

While research papers turn the reader’s attention to a certain problem, case studies go even further. Case study guidelines require students to pay attention to details, examining issues closely and in-depth using different research methods. For example, case studies may be used to examine court cases if you study Law, or a patient's health history if you study Medicine. Case studies are also used in Marketing, which are thorough, empirically supported analysis of a good or service's performance. Well-designed case studies can be valuable for prospective customers as they can identify and solve the potential customers pain point.

Case studies involve a lot of storytelling – they usually examine particular cases for a person or a group of people. This method of research is very helpful, as it is very practical and can give a lot of hands-on information. Most commonly, the length of the case study is about 500-900 words, which is much less than the length of an average research paper.

The structure of a case study is very similar to storytelling. It has a protagonist or main character, which in your case is actually a problem you are trying to solve. You can use the system of 3 Acts to make it a compelling story. It should have an introduction, rising action, a climax where transformation occurs, falling action, and a solution.

Here is a rough formula for you to use in your case study:

Problem (Act I): > Solution (Act II) > Result (Act III) > Conclusion.

Types of Case Studies

The purpose of a case study is to provide detailed reports on an event, an institution, a place, future customers, or pretty much anything. There are a few common types of case study, but the type depends on the topic. The following are the most common domains where case studies are needed:

• Historical case studies are great to learn from. Historical events have a multitude of source info offering different perspectives. There are always modern parallels where these perspectives can be applied, compared, and thoroughly analyzed.
• Problem-oriented case studies are usually used for solving problems. These are often assigned as theoretical situations where you need to immerse yourself in the situation to examine it. Imagine you’re working for a startup and you’ve just noticed a significant flaw in your product’s design. Before taking it to the senior manager, you want to do a comprehensive study on the issue and provide solutions. On a greater scale, problem-oriented case studies are a vital part of relevant socio-economic discussions.
• Cumulative case studies collect information and offer comparisons. In business, case studies are often used to tell people about the value of a product.
• Critical case studies explore the causes and effects of a certain case.
• Illustrative case studies describe certain events, investigating outcomes and lessons learned.

Need a compelling case study? EssayPro has got you covered. Our experts are ready to provide you with detailed, insightful case studies that capture the essence of real-world scenarios. Elevate your academic work with our professional assistance.

Case Study Format

The case study format is typically made up of eight parts:

• Executive Summary. Explain what you will examine in the case study. Write an overview of the field you’re researching. Make a thesis statement and sum up the results of your observation in a maximum of 2 sentences.
• Background. Provide background information and the most relevant facts. Isolate the issues.
• Case Evaluation. Isolate the sections of the study you want to focus on. In it, explain why something is working or is not working.
• Proposed Solutions. Offer realistic ways to solve what isn’t working or how to improve its current condition. Explain why these solutions work by offering testable evidence.
• Conclusion. Summarize the main points from the case evaluations and proposed solutions. 6. Recommendations. Talk about the strategy that you should choose. Explain why this choice is the most appropriate.
• Implementation. Explain how to put the specific strategies into action.
• References. Provide all the citations.

How to Write a Case Study

Let's discover how to write a case study.

Setting Up the Research

When writing a case study, remember that research should always come first. Reading many different sources and analyzing other points of view will help you come up with more creative solutions. You can also conduct an actual interview to thoroughly investigate the customer story that you'll need for your case study. Including all of the necessary research, writing a case study may take some time. The research process involves doing the following:

• Define your objective. Explain the reason why you’re presenting your subject. Figure out where you will feature your case study; whether it is written, on video, shown as an infographic, streamed as a podcast, etc.
• Determine who will be the right candidate for your case study. Get permission, quotes, and other features that will make your case study effective. Get in touch with your candidate to see if they approve of being part of your work. Study that candidate’s situation and note down what caused it.
• Identify which various consequences could result from the situation. Follow these guidelines on how to start a case study: surf the net to find some general information you might find useful.
• Make a list of credible sources and examine them. Seek out important facts and highlight problems. Always write down your ideas and make sure to brainstorm.
• Focus on several key issues – why they exist, and how they impact your research subject. Think of several unique solutions. Draw from class discussions, readings, and personal experience. When writing a case study, focus on the best solution and explore it in depth. After having all your research in place, writing a case study will be easy. You may first want to check the rubric and criteria of your assignment for the correct case study structure.

Read Also: ' WHAT IS A CREDIBLE SOURCES ?'

Although your instructor might be looking at slightly different criteria, every case study rubric essentially has the same standards. Your professor will want you to exhibit 8 different outcomes:

• Correctly identify the concepts, theories, and practices in the discipline.
• Identify the relevant theories and principles associated with the particular study.
• Evaluate legal and ethical principles and apply them to your decision-making.
• Recognize the global importance and contribution of your case.
• Construct a coherent summary and explanation of the study.
• Demonstrate analytical and critical-thinking skills.
• Explain the interrelationships between the environment and nature.
• Integrate theory and practice of the discipline within the analysis.

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Case Study Outline

Let's look at the structure of an outline based on the issue of the alcoholic addiction of 30 people.

Introduction

• Statement of the issue: Alcoholism is a disease rather than a weakness of character.
• Presentation of the problem: Alcoholism is affecting more than 14 million people in the USA, which makes it the third most common mental illness there.
• Explanation of the terms: In the past, alcoholism was commonly referred to as alcohol dependence or alcohol addiction. Alcoholism is now the more severe stage of this addiction in the disorder spectrum.
• Hypotheses: Drinking in excess can lead to the use of other drugs.
• Importance of your story: How the information you present can help people with their addictions.
• Background of the story: Include an explanation of why you chose this topic.
• Presentation of analysis and data: Describe the criteria for choosing 30 candidates, the structure of the interview, and the outcomes.
• Strong argument 1: ex. X% of candidates dealing with anxiety and depression...
• Strong argument 2: ex. X amount of people started drinking by their mid-teens.
• Strong argument 3: ex. X% of respondents’ parents had issues with alcohol.
• Concluding statement: I have researched if alcoholism is a disease and found out that…
• Recommendations: Ways and actions for preventing alcohol use.

Writing a Case Study Draft

After you’ve done your case study research and written the outline, it’s time to focus on the draft. In a draft, you have to develop and write your case study by using: the data which you collected throughout the research, interviews, and the analysis processes that were undertaken. Follow these rules for the draft:

• Your draft should contain at least 4 sections: an introduction; a body where you should include background information, an explanation of why you decided to do this case study, and a presentation of your main findings; a conclusion where you present data; and references.
• In the introduction, you should set the pace very clearly. You can even raise a question or quote someone you interviewed in the research phase. It must provide adequate background information on the topic. The background may include analyses of previous studies on your topic. Include the aim of your case here as well. Think of it as a thesis statement. The aim must describe the purpose of your work—presenting the issues that you want to tackle. Include background information, such as photos or videos you used when doing the research.
• Describe your unique research process, whether it was through interviews, observations, academic journals, etc. The next point includes providing the results of your research. Tell the audience what you found out. Why is this important, and what could be learned from it? Discuss the real implications of the problem and its significance in the world.
• Include quotes and data (such as findings, percentages, and awards). This will add a personal touch and better credibility to the case you present. Explain what results you find during your interviews in regards to the problem and how it developed. Also, write about solutions which have already been proposed by other people who have already written about this case.
• At the end of your case study, you should offer possible solutions, but don’t worry about solving them yourself.

Use Data to Illustrate Key Points in Your Case Study

Even though your case study is a story, it should be based on evidence. Use as much data as possible to illustrate your point. Without the right data, your case study may appear weak and the readers may not be able to relate to your issue as much as they should. Let's see the examples from essay writing service :

‍ With data: Alcoholism is affecting more than 14 million people in the USA, which makes it the third most common mental illness there. Without data: A lot of people suffer from alcoholism in the United States.

Try to include as many credible sources as possible. You may have terms or sources that could be hard for other cultures to understand. If this is the case, you should include them in the appendix or Notes for the Instructor or Professor.

Finalizing the Draft: Checklist

After you finish drafting your case study, polish it up by answering these ‘ask yourself’ questions and think about how to end your case study:

• Check that you follow the correct case study format, also in regards to text formatting.
• Check that your work is consistent with its referencing and citation style.
• Micro-editing — check for grammar and spelling issues.
• Macro-editing — does ‘the big picture’ come across to the reader? Is there enough raw data, such as real-life examples or personal experiences? Have you made your data collection process completely transparent? Does your analysis provide a clear conclusion, allowing for further research and practice?

Problems to avoid:

• Overgeneralization – Do not go into further research that deviates from the main problem.
• Failure to Document Limitations – 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.
• 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.

How to Create a Title Page and Cite a Case Study

Let's see how to create an awesome title page.

Your title page depends on the prescribed citation format. The title page should include:

• A title that attracts some attention and describes your study
• The title should have the words “case study” in it
• The title should range between 5-9 words in length
• Your name and contact information
• Your finished paper should be only 500 to 1,500 words in length.With this type of assignment, write effectively and avoid fluff

Here is a template for the APA and MLA format title page:

There are some cases when you need to cite someone else's study in your own one – therefore, you need to master how to cite a case study. A case study is like a research paper when it comes to citations. You can cite it like you cite a book, depending on what style you need.

Citation Example in MLA ‍ Hill, Linda, Tarun Khanna, and Emily A. Stecker. HCL Technologies. Boston: Harvard Business Publishing, 2008. Print.

Citation Example in APA ‍ Hill, L., Khanna, T., & Stecker, E. A. (2008). HCL Technologies. Boston: Harvard Business Publishing.

Citation Example in Chicago Hill, Linda, Tarun Khanna, and Emily A. Stecker. HCL Technologies.

Case Study Examples

To give you an idea of a professional case study example, we gathered and linked some below.

Eastman Kodak Case Study

Case Study Example: Audi Trains Mexican Autoworkers in Germany

To conclude, a case study is one of the best methods of getting an overview of what happened to a person, a group, or a situation in practice. It allows you to have an in-depth glance at the real-life problems that businesses, healthcare industry, criminal justice, etc. may face. This insight helps us look at such situations in a different light. This is because we see scenarios that we otherwise would not, without necessarily being there. If you need custom essays , try our research paper writing services .

Get Help Form Qualified Writers

Crafting a case study is not easy. You might want to write one of high quality, but you don’t have the time or expertise. If you’re having trouble with your case study, help with essay request - we'll help. EssayPro writers have read and written countless case studies and are experts in endless disciplines. Request essay writing, editing, or proofreading assistance from our custom case study writing service , and all of your worries will be gone.

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What Is A Case Study?

How to cite a case study in apa, how to write a case study, related articles.

Case Study Research Method in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

 Famous Case Studies

• Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
• Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
• Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
• Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
• Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

• Studying the effectiveness of psychotherapy approaches with an individual patient
• Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
• Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

• Studying psychological development from birth through adolescence
• Cases of learning disabilities, autism spectrum disorders, ADHD
• Effects of trauma, abuse, deprivation on development

Types of Case Studies

• Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
• Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
• Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
• Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
• Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
• Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

• Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
• Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
• Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

• News/Media – News coverage of events related to the case study.
• Academic articles – Journal articles, dissertations etc. that discuss the case.
• Government reports – Official data and records related to the case context.
• Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

• Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
• Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
• School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

• Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
• Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

• Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
• Include client demographics like age and gender, information about social relationships, and mental health history.
• Describe all physical, emotional, and/or sensory symptoms reported by the client.
• Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
• Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
• Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

• Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
• Present the results of the intervention,including any quantitative or qualitative data collected.
• For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

• Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
• Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
• Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
• Offer clinical implications, and suggest future research directions.

5. Additional Items

• Thank specific assistants for writing support only. No patient acknowledgments.
• References should directly support any key claims or quotes included.
• Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
• Provides detailed (rich qualitative) information.
• Provides insight for further research.
• Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

• Lacking scientific rigor and providing little basis for generalization of results to the wider population.
• Researchers’ own subjective feelings may influence the case study (researcher bias).
• Difficult to replicate.
• Time-consuming and expensive.
• The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895).  Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head.  Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868).  Recovery from the Passage of an Iron Bar through the Head .  Publications of the Massachusetts Medical Society. 2  (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972).  Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

• Case Study Approach
• Case Study Method
• Enhancing the Quality of Case Studies in Health Services Research
• “We do things together” A case study of “couplehood” in dementia
• Using mixed methods for evaluating an integrative approach to cancer care: a case study

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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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• Published: 27 June 2011

The case study approach

• Sarah Crowe 1 ,
• Kathrin Cresswell 2 ,
• Ann Robertson 2 ,
• Guro Huby 3 ,
• Anthony Avery 1 &
• Aziz Sheikh 2  

BMC Medical Research Methodology volume  11 , Article number:  100 ( 2011 ) Cite this article

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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.

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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 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].

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 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.

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 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 2 , 3 and 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 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 2 and 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 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 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 ].

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 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 ].

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 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 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 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 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 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 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 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 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 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 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 9 )[ 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.

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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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Sarah Crowe & Anthony Avery

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

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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.

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Crowe, S., Cresswell, K., Robertson, A. et al. The case study approach. BMC Med Res Methodol 11 , 100 (2011). https://doi.org/10.1186/1471-2288-11-100

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Research Guides

Multiple Case Studies

Nadia Alqahtani and Pengtong Qu

Description

The case study approach is popular across disciplines in education, anthropology, sociology, psychology, medicine, law, and political science (Creswell, 2013). It is both a research method and a strategy (Creswell, 2013; Yin, 2017). In this type of research design, a case can be an individual, an event, or an entity, as determined by the research questions. There are two variants of the case study: the single-case study and the multiple-case study. The former design can be used to study and understand an unusual case, a critical case, a longitudinal case, or a revelatory case. On the other hand, a multiple-case study includes two or more cases or replications across the cases to investigate the same phenomena (Lewis-Beck, Bryman & Liao, 2003; Yin, 2017). …a multiple-case study includes two or more cases or replications across the cases to investigate the same phenomena

The difference between the single- and multiple-case study is the research design; however, they are within the same methodological framework (Yin, 2017). Multiple cases are selected so that “individual case studies either (a) predict similar results (a literal replication) or (b) predict contrasting results but for anticipatable reasons (a theoretical replication)” (p. 55). When the purpose of the study is to compare and replicate the findings, the multiple-case study produces more compelling evidence so that the study is considered more robust than the single-case study (Yin, 2017).

To write a multiple-case study, a summary of individual cases should be reported, and researchers need to draw cross-case conclusions and form a cross-case report (Yin, 2017). With evidence from multiple cases, researchers may have generalizable findings and develop theories (Lewis-Beck, Bryman & Liao, 2003).

Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches (3rd ed.). Los Angeles, CA: Sage.

Lewis-Beck, M., Bryman, A. E., & Liao, T. F. (2003). The Sage encyclopedia of social science research methods . Los Angeles, CA: Sage.

Yin, R. K. (2017). Case study research and applications: Design and methods . Los Angeles, CA: Sage.

Key Research Books and Articles on Multiple Case Study Methodology

Yin discusses how to decide if a case study should be used in research. Novice researchers can learn about research design, data collection, and data analysis of different types of case studies, as well as writing a case study report.

Chapter 2 introduces four major types of research design in case studies: holistic single-case design, embedded single-case design, holistic multiple-case design, and embedded multiple-case design. Novice researchers will learn about the definitions and characteristics of different designs. This chapter also teaches researchers how to examine and discuss the reliability and validity of the designs.

Creswell, J. W., & Poth, C. N. (2017). Qualitative inquiry and research design: Choosing among five approaches . Los Angeles, CA: Sage.

This book compares five different qualitative research designs: narrative research, phenomenology, grounded theory, ethnography, and case study. It compares the characteristics, data collection, data analysis and representation, validity, and writing-up procedures among five inquiry approaches using texts with tables. For each approach, the author introduced the definition, features, types, and procedures and contextualized these components in a study, which was conducted through the same method. Each chapter ends with a list of relevant readings of each inquiry approach.

This book invites readers to compare these five qualitative methods and see the value of each approach. Readers can consider which approach would serve for their research contexts and questions, as well as how to design their research and conduct the data analysis based on their choice of research method.

Günes, E., & Bahçivan, E. (2016). A multiple case study of preservice science teachers’ TPACK: Embedded in a comprehensive belief system. International Journal of Environmental and Science Education, 11 (15), 8040-8054.

In this article, the researchers showed the importance of using technological opportunities in improving the education process and how they enhanced the students’ learning in science education. The study examined the connection between “Technological Pedagogical Content Knowledge” (TPACK) and belief system in a science teaching context. The researchers used the multiple-case study to explore the effect of TPACK on the preservice science teachers’ (PST) beliefs on their TPACK level. The participants were three teachers with the low, medium, and high level of TPACK confidence. Content analysis was utilized to analyze the data, which were collected by individual semi-structured interviews with the participants about their lesson plans. The study first discussed each case, then compared features and relations across cases. The researchers found that there was a positive relationship between PST’s TPACK confidence and TPACK level; when PST had higher TPACK confidence, the participant had a higher competent TPACK level and vice versa.

Recent Dissertations Using Multiple Case Study Methodology

Milholland, E. S. (2015). A multiple case study of instructors utilizing Classroom Response Systems (CRS) to achieve pedagogical goals . Retrieved from ProQuest Dissertations & Theses Global. (Order Number 3706380)

The researcher of this study critiques the use of Classroom Responses Systems by five instructors who employed this program five years ago in their classrooms. The researcher conducted the multiple-case study methodology and categorized themes. He interviewed each instructor with questions about their initial pedagogical goals, the changes in pedagogy during teaching, and the teaching techniques individuals used while practicing the CRS. The researcher used the multiple-case study with five instructors. He found that all instructors changed their goals during employing CRS; they decided to reduce the time of lecturing and to spend more time engaging students in interactive activities. This study also demonstrated that CRS was useful for the instructors to achieve multiple learning goals; all the instructors provided examples of the positive aspect of implementing CRS in their classrooms.

Li, C. L. (2010). The emergence of fairy tale literacy: A multiple case study on promoting critical literacy of children through a juxtaposed reading of classic fairy tales and their contemporary disruptive variants . Retrieved from ProQuest Dissertations & Theses Global. (Order Number 3572104)

To explore how children’s development of critical literacy can be impacted by their reactions to fairy tales, the author conducted a multiple-case study with 4 cases, in which each child was a unit of analysis. Two Chinese immigrant children (a boy and a girl) and two American children (a boy and a girl) at the second or third grade were recruited in the study. The data were collected through interviews, discussions on fairy tales, and drawing pictures. The analysis was conducted within both individual cases and cross cases. Across four cases, the researcher found that the young children’s’ knowledge of traditional fairy tales was built upon mass-media based adaptations. The children believed that the representations on mass-media were the original stories, even though fairy tales are included in the elementary school curriculum. The author also found that introducing classic versions of fairy tales increased children’s knowledge in the genre’s origin, which would benefit their understanding of the genre. She argued that introducing fairy tales can be the first step to promote children’s development of critical literacy.

Asher, K. C. (2014). Mediating occupational socialization and occupational individuation in teacher education: A multiple case study of five elementary pre-service student teachers . Retrieved from ProQuest Dissertations & Theses Global. (Order Number 3671989)

This study portrayed five pre-service teachers’ teaching experience in their student teaching phase and explored how pre-service teachers mediate their occupational socialization with occupational individuation. The study used the multiple-case study design and recruited five pre-service teachers from a Midwestern university as five cases. Qualitative data were collected through interviews, classroom observations, and field notes. The author implemented the case study analysis and found five strategies that the participants used to mediate occupational socialization with occupational individuation. These strategies were: 1) hindering from practicing their beliefs, 2) mimicking the styles of supervising teachers, 3) teaching in the ways in alignment with school’s existing practice, 4) enacting their own ideas, and 5) integrating and balancing occupational socialization and occupational individuation. The study also provided recommendations and implications to policymakers and educators in teacher education so that pre-service teachers can be better supported.

Multiple Case Studies Copyright © 2019 by Nadia Alqahtani and Pengtong Qu is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Guidelines To Writing A Clinical Case Report

What is a clinical case report.

A case report is a detailed report of the symptoms, signs, diagnosis, treatment, and follow-up of an individual patient. Case reports usually describe an unusual or novel occurrence and as such, remain one of the cornerstones of medical progress and provide many new ideas in medicine. Some reports contain an extensive review of the relevant literature on the topic. The case report is a rapid short communication between busy clinicians who may not have time or resources to conduct large scale research.

WHAT ARE THE REASONS FOR PUBLISHING A CASE REPORT?

The most common reasons for publishing a case are the following: 1) an unexpected association between diseases or symptoms; 2) an unexpected event in the course observing or treating a patient; 3) findings that shed new light on the possible pathogenesis of a disease or an adverse effect; 4) unique or rare features of a disease; 5) unique therapeutic approaches; variation of anatomical structures.

Most journals publish case reports that deal with one or more of the following:

• Unusual observations
• Adverse response to therapies
• Unusual combination of conditions leading to confusion
• Illustration of a new theory
• Question regarding a current theory
• Personal impact.

STRUCTURE OF A CASE REPORT[ 1 , 2 ]

Different journals have slightly different formats for case reports. It is always a good idea to read some of the target jiurnals case reports to get a general idea of the sequence and format.

In general, all case reports include the following components: an abstract, an introduction, a case, and a discussion. Some journals might require literature review.

The abstract should summarize the case, the problem it addresses, and the message it conveys. Abstracts of case studies are usually very short, preferably not more than 150 words.

Introduction

The introduction gives a brief overview of the problem that the case addresses, citing relevant literature where necessary. The introduction generally ends with a single sentence describing the patient and the basic condition that he or she is suffering from.

This section provides the details of the case in the following order:

• Patient description
• Case history
• Physical examination results
• Results of pathological tests and other investigations
• Treatment plan
• Expected outcome of the treatment plan
• Actual outcome.

The author should ensure that all the relevant details are included and unnecessary ones excluded.

This is the most important part of the case report; the part that will convince the journal that the case is publication worthy. This section should start by expanding on what has been said in the introduction, focusing on why the case is noteworthy and the problem that it addresses.

This is followed by a summary of the existing literature on the topic. (If the journal specifies a separate section on literature review, it should be added before the Discussion). This part describes the existing theories and research findings on the key issue in the patient's condition. The review should narrow down to the source of confusion or the main challenge in the case.

Finally, the case report should be connected to the existing literature, mentioning the message that the case conveys. The author should explain whether this corroborates with or detracts from current beliefs about the problem and how this evidence can add value to future clinical practice.

A case report ends with a conclusion or with summary points, depending on the journal's specified format. This section should briefly give readers the key points covered in the case report. Here, the author can give suggestions and recommendations to clinicians, teachers, or researchers. Some journals do not want a separate section for the conclusion: it can then be the concluding paragraph of the Discussion section.

Notes on patient consent

Informed consent in an ethical requirement for most studies involving humans, so before you start writing your case report, take a written consent from the patient as all journals require that you provide it at the time of manuscript submission. In case the patient is a minor, parental consent is required. For adults who are unable to consent to investigation or treatment, consent of closest family members is required.

Patient anonymity is also an important requirement. Remember not to disclose any information that might reveal the identity of the patient. You need to be particularly careful with pictures, and ensure that pictures of the affected area do not reveal the identity of the patient.

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

Definition and Introduction

Case analysis is a problem-based teaching and learning method that involves critically analyzing complex scenarios within an organizational setting for the purpose of placing the student in a “real world” situation and applying reflection and critical thinking skills to contemplate appropriate solutions, decisions, or recommended courses of action. It is considered a more effective teaching technique than in-class role playing or simulation activities. The analytical process is often guided by questions provided by the instructor that ask students to contemplate relationships between the facts and critical incidents described in the case.

Cases generally include both descriptive and statistical elements and rely on students applying abductive reasoning to develop and argue for preferred or best outcomes [i.e., case scenarios rarely have a single correct or perfect answer based on the evidence provided]. Rather than emphasizing theories or concepts, case analysis assignments emphasize building a bridge of relevancy between abstract thinking and practical application and, by so doing, teaches the value of both within a specific area of professional practice.

Given this, the purpose of a case analysis paper is to present a structured and logically organized format for analyzing the case situation. It can be assigned to students individually or as a small group assignment and it may include an in-class presentation component. Case analysis is predominately taught in economics and business-related courses, but it is also a method of teaching and learning found in other applied social sciences disciplines, such as, social work, public relations, education, journalism, and public administration.

Ellet, William. The Case Study Handbook: A Student's Guide . Revised Edition. Boston, MA: Harvard Business School Publishing, 2018; Christoph Rasche and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Analysis . Writing Center, Baruch College; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

How to Approach Writing a Case Analysis Paper

The organization and structure of a case analysis paper can vary depending on the organizational setting, the situation, and how your professor wants you to approach the assignment. Nevertheless, preparing to write a case analysis paper involves several important steps. As Hawes notes, a case analysis assignment “...is useful in developing the ability to get to the heart of a problem, analyze it thoroughly, and to indicate the appropriate solution as well as how it should be implemented” [p.48]. This statement encapsulates how you should approach preparing to write a case analysis paper.

Before you begin to write your paper, consider the following analytical procedures:

• Review the case to get an overview of the situation . A case can be only a few pages in length, however, it is most often very lengthy and contains a significant amount of detailed background information and statistics, with multilayered descriptions of the scenario, the roles and behaviors of various stakeholder groups, and situational events. Therefore, a quick reading of the case will help you gain an overall sense of the situation and illuminate the types of issues and problems that you will need to address in your paper. If your professor has provided questions intended to help frame your analysis, use them to guide your initial reading of the case.
• Read the case thoroughly . After gaining a general overview of the case, carefully read the content again with the purpose of understanding key circumstances, events, and behaviors among stakeholder groups. Look for information or data that appears contradictory, extraneous, or misleading. At this point, you should be taking notes as you read because this will help you develop a general outline of your paper. The aim is to obtain a complete understanding of the situation so that you can begin contemplating tentative answers to any questions your professor has provided or, if they have not provided, developing answers to your own questions about the case scenario and its connection to the course readings,lectures, and class discussions.
• Determine key stakeholder groups, issues, and events and the relationships they all have to each other . As you analyze the content, pay particular attention to identifying individuals, groups, or organizations described in the case and identify evidence of any problems or issues of concern that impact the situation in a negative way. Other things to look for include identifying any assumptions being made by or about each stakeholder, potential biased explanations or actions, explicit demands or ultimatums , and the underlying concerns that motivate these behaviors among stakeholders. The goal at this stage is to develop a comprehensive understanding of the situational and behavioral dynamics of the case and the explicit and implicit consequences of each of these actions.
• Identify the core problems . The next step in most case analysis assignments is to discern what the core [i.e., most damaging, detrimental, injurious] problems are within the organizational setting and to determine their implications. The purpose at this stage of preparing to write your analysis paper is to distinguish between the symptoms of core problems and the core problems themselves and to decide which of these must be addressed immediately and which problems do not appear critical but may escalate over time. Identify evidence from the case to support your decisions by determining what information or data is essential to addressing the core problems and what information is not relevant or is misleading.
• Explore alternative solutions . As noted, case analysis scenarios rarely have only one correct answer. Therefore, it is important to keep in mind that the process of analyzing the case and diagnosing core problems, while based on evidence, is a subjective process open to various avenues of interpretation. This means that you must consider alternative solutions or courses of action by critically examining strengths and weaknesses, risk factors, and the differences between short and long-term solutions. For each possible solution or course of action, consider the consequences they may have related to their implementation and how these recommendations might lead to new problems. Also, consider thinking about your recommended solutions or courses of action in relation to issues of fairness, equity, and inclusion.
• Decide on a final set of recommendations . The last stage in preparing to write a case analysis paper is to assert an opinion or viewpoint about the recommendations needed to help resolve the core problems as you see them and to make a persuasive argument for supporting this point of view. Prepare a clear rationale for your recommendations based on examining each element of your analysis. Anticipate possible obstacles that could derail their implementation. Consider any counter-arguments that could be made concerning the validity of your recommended actions. Finally, describe a set of criteria and measurable indicators that could be applied to evaluating the effectiveness of your implementation plan.

Use these steps as the framework for writing your paper. Remember that the more detailed you are in taking notes as you critically examine each element of the case, the more information you will have to draw from when you begin to write. This will save you time.

NOTE : If the process of preparing to write a case analysis paper is assigned as a student group project, consider having each member of the group analyze a specific element of the case, including drafting answers to the corresponding questions used by your professor to frame the analysis. This will help make the analytical process more efficient and ensure that the distribution of work is equitable. This can also facilitate who is responsible for drafting each part of the final case analysis paper and, if applicable, the in-class presentation.

Framework for Case Analysis . College of Management. University of Massachusetts; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Rasche, Christoph and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Study Analysis . University of Arizona Global Campus Writing Center; Van Ness, Raymond K. A Guide to Case Analysis . School of Business. State University of New York, Albany; Writing a Case Analysis . Business School, University of New South Wales.

Structure and Writing Style

A case analysis paper should be detailed, concise, persuasive, clearly written, and professional in tone and in the use of language . As with other forms of college-level academic writing, declarative statements that convey information, provide a fact, or offer an explanation or any recommended courses of action should be based on evidence. If allowed by your professor, any external sources used to support your analysis, such as course readings, should be properly cited under a list of references. The organization and structure of case analysis papers can vary depending on your professor’s preferred format, but its structure generally follows the steps used for analyzing the case.

Introduction

The introduction should provide a succinct but thorough descriptive overview of the main facts, issues, and core problems of the case . The introduction should also include a brief summary of the most relevant details about the situation and organizational setting. This includes defining the theoretical framework or conceptual model on which any questions were used to frame your analysis.

Following the rules of most college-level research papers, the introduction should then inform the reader how the paper will be organized. This includes describing the major sections of the paper and the order in which they will be presented. Unless you are told to do so by your professor, you do not need to preview your final recommendations in the introduction. U nlike most college-level research papers , the introduction does not include a statement about the significance of your findings because a case analysis assignment does not involve contributing new knowledge about a research problem.

Background Analysis

Background analysis can vary depending on any guiding questions provided by your professor and the underlying concept or theory that the case is based upon. In general, however, this section of your paper should focus on:

• Providing an overarching analysis of problems identified from the case scenario, including identifying events that stakeholders find challenging or troublesome,
• Identifying assumptions made by each stakeholder and any apparent biases they may exhibit,
• Describing any demands or claims made by or forced upon key stakeholders, and
• Highlighting any issues of concern or complaints expressed by stakeholders in response to those demands or claims.

These aspects of the case are often in the form of behavioral responses expressed by individuals or groups within the organizational setting. However, note that problems in a case situation can also be reflected in data [or the lack thereof] and in the decision-making, operational, cultural, or institutional structure of the organization. Additionally, demands or claims can be either internal and external to the organization [e.g., a case analysis involving a president considering arms sales to Saudi Arabia could include managing internal demands from White House advisors as well as demands from members of Congress].

Throughout this section, present all relevant evidence from the case that supports your analysis. Do not simply claim there is a problem, an assumption, a demand, or a concern; tell the reader what part of the case informed how you identified these background elements.

Identification of Problems

In most case analysis assignments, there are problems, and then there are problems . Each problem can reflect a multitude of underlying symptoms that are detrimental to the interests of the organization. The purpose of identifying problems is to teach students how to differentiate between problems that vary in severity, impact, and relative importance. Given this, problems can be described in three general forms: those that must be addressed immediately, those that should be addressed but the impact is not severe, and those that do not require immediate attention and can be set aside for the time being.

All of the problems you identify from the case should be identified in this section of your paper, with a description based on evidence explaining the problem variances. If the assignment asks you to conduct research to further support your assessment of the problems, include this in your explanation. Remember to cite those sources in a list of references. Use specific evidence from the case and apply appropriate concepts, theories, and models discussed in class or in relevant course readings to highlight and explain the key problems [or problem] that you believe must be solved immediately and describe the underlying symptoms and why they are so critical.

Alternative Solutions

This section is where you provide specific, realistic, and evidence-based solutions to the problems you have identified and make recommendations about how to alleviate the underlying symptomatic conditions impacting the organizational setting. For each solution, you must explain why it was chosen and provide clear evidence to support your reasoning. This can include, for example, course readings and class discussions as well as research resources, such as, books, journal articles, research reports, or government documents. In some cases, your professor may encourage you to include personal, anecdotal experiences as evidence to support why you chose a particular solution or set of solutions. Using anecdotal evidence helps promote reflective thinking about the process of determining what qualifies as a core problem and relevant solution .

Throughout this part of the paper, keep in mind the entire array of problems that must be addressed and describe in detail the solutions that might be implemented to resolve these problems.

Recommended Courses of Action

In some case analysis assignments, your professor may ask you to combine the alternative solutions section with your recommended courses of action. However, it is important to know the difference between the two. A solution refers to the answer to a problem. A course of action refers to a procedure or deliberate sequence of activities adopted to proactively confront a situation, often in the context of accomplishing a goal. In this context, proposed courses of action are based on your analysis of alternative solutions. Your description and justification for pursuing each course of action should represent the overall plan for implementing your recommendations.

For each course of action, you need to explain the rationale for your recommendation in a way that confronts challenges, explains risks, and anticipates any counter-arguments from stakeholders. Do this by considering the strengths and weaknesses of each course of action framed in relation to how the action is expected to resolve the core problems presented, the possible ways the action may affect remaining problems, and how the recommended action will be perceived by each stakeholder.

In addition, you should describe the criteria needed to measure how well the implementation of these actions is working and explain which individuals or groups are responsible for ensuring your recommendations are successful. In addition, always consider the law of unintended consequences. Outline difficulties that may arise in implementing each course of action and describe how implementing the proposed courses of action [either individually or collectively] may lead to new problems [both large and small].

Throughout this section, you must consider the costs and benefits of recommending your courses of action in relation to uncertainties or missing information and the negative consequences of success.

The conclusion should be brief and introspective. Unlike a research paper, the conclusion in a case analysis paper does not include a summary of key findings and their significance, a statement about how the study contributed to existing knowledge, or indicate opportunities for future research.

Begin by synthesizing the core problems presented in the case and the relevance of your recommended solutions. This can include an explanation of what you have learned about the case in the context of your answers to the questions provided by your professor. The conclusion is also where you link what you learned from analyzing the case with the course readings or class discussions. This can further demonstrate your understanding of the relationships between the practical case situation and the theoretical and abstract content of assigned readings and other course content.

Problems to Avoid

The literature on case analysis assignments often includes examples of difficulties students have with applying methods of critical analysis and effectively reporting the results of their assessment of the situation. A common reason cited by scholars is that the application of this type of teaching and learning method is limited to applied fields of social and behavioral sciences and, as a result, writing a case analysis paper can be unfamiliar to most students entering college.

After you have drafted your paper, proofread the narrative flow and revise any of these common errors:

• Unnecessary detail in the background section . The background section should highlight the essential elements of the case based on your analysis. Focus on summarizing the facts and highlighting the key factors that become relevant in the other sections of the paper by eliminating any unnecessary information.
• Analysis relies too much on opinion . Your analysis is interpretive, but the narrative must be connected clearly to evidence from the case and any models and theories discussed in class or in course readings. Any positions or arguments you make should be supported by evidence.
• Analysis does not focus on the most important elements of the case . Your paper should provide a thorough overview of the case. However, the analysis should focus on providing evidence about what you identify are the key events, stakeholders, issues, and problems. Emphasize what you identify as the most critical aspects of the case to be developed throughout your analysis. Be thorough but succinct.
• Writing is too descriptive . A paper with too much descriptive information detracts from your analysis of the complexities of the case situation. Questions about what happened, where, when, and by whom should only be included as essential information leading to your examination of questions related to why, how, and for what purpose.
• Inadequate definition of a core problem and associated symptoms . A common error found in case analysis papers is recommending a solution or course of action without adequately defining or demonstrating that you understand the problem. Make sure you have clearly described the problem and its impact and scope within the organizational setting. Ensure that you have adequately described the root causes w hen describing the symptoms of the problem.
• Recommendations lack specificity . Identify any use of vague statements and indeterminate terminology, such as, “A particular experience” or “a large increase to the budget.” These statements cannot be measured and, as a result, there is no way to evaluate their successful implementation. Provide specific data and use direct language in describing recommended actions.
• Unrealistic, exaggerated, or unattainable recommendations . Review your recommendations to ensure that they are based on the situational facts of the case. Your recommended solutions and courses of action must be based on realistic assumptions and fit within the constraints of the situation. Also note that the case scenario has already happened, therefore, any speculation or arguments about what could have occurred if the circumstances were different should be revised or eliminated.

Bee, Lian Song et al. "Business Students' Perspectives on Case Method Coaching for Problem-Based Learning: Impacts on Student Engagement and Learning Performance in Higher Education." Education & Training 64 (2022): 416-432; The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Georgallis, Panikos and Kayleigh Bruijn. "Sustainability Teaching using Case-Based Debates." Journal of International Education in Business 15 (2022): 147-163; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Georgallis, Panikos, and Kayleigh Bruijn. "Sustainability Teaching Using Case-based Debates." Journal of International Education in Business 15 (2022): 147-163; .Dean,  Kathy Lund and Charles J. Fornaciari. "How to Create and Use Experiential Case-Based Exercises in a Management Classroom." Journal of Management Education 26 (October 2002): 586-603; Klebba, Joanne M. and Janet G. Hamilton. "Structured Case Analysis: Developing Critical Thinking Skills in a Marketing Case Course." Journal of Marketing Education 29 (August 2007): 132-137, 139; Klein, Norman. "The Case Discussion Method Revisited: Some Questions about Student Skills." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 30-32; Mukherjee, Arup. "Effective Use of In-Class Mini Case Analysis for Discovery Learning in an Undergraduate MIS Course." The Journal of Computer Information Systems 40 (Spring 2000): 15-23; Pessoa, Silviaet al. "Scaffolding the Case Analysis in an Organizational Behavior Course: Making Analytical Language Explicit." Journal of Management Education 46 (2022): 226-251: Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Schweitzer, Karen. "How to Write and Format a Business Case Study." ThoughtCo. https://www.thoughtco.com/how-to-write-and-format-a-business-case-study-466324 (accessed December 5, 2022); Reddy, C. D. "Teaching Research Methodology: Everything's a Case." Electronic Journal of Business Research Methods 18 (December 2020): 178-188; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

Writing Tip

Ca se Study and Case Analysis Are Not the Same!

Confusion often exists between what it means to write a paper that uses a case study research design and writing a paper that analyzes a case; they are two different types of approaches to learning in the social and behavioral sciences. Professors as well as educational researchers contribute to this confusion because they often use the term "case study" when describing the subject of analysis for a case analysis paper. But you are not studying a case for the purpose of generating a comprehensive, multi-faceted understanding of a research problem. R ather, you are critically analyzing a specific scenario to argue logically for recommended solutions and courses of action that lead to optimal outcomes applicable to professional practice.

To avoid any confusion, here are twelve characteristics that delineate the differences between writing a paper using the case study research method and writing a case analysis paper:

• Case study is a method of in-depth research and rigorous inquiry ; case analysis is a reliable method of teaching and learning . A case study is a modality of research that investigates a phenomenon for the purpose of creating new knowledge, solving a problem, or testing a hypothesis using empirical evidence derived from the case being studied. Often, the results are used to generalize about a larger population or within a wider context. The writing adheres to the traditional standards of a scholarly research study. A case analysis is a pedagogical tool used to teach students how to reflect and think critically about a practical, real-life problem in an organizational setting.
• The researcher is responsible for identifying the case to study; a case analysis is assigned by your professor . As the researcher, you choose the case study to investigate in support of obtaining new knowledge and understanding about the research problem. The case in a case analysis assignment is almost always provided, and sometimes written, by your professor and either given to every student in class to analyze individually or to a small group of students, or students select a case to analyze from a predetermined list.
• A case study is indeterminate and boundless; a case analysis is predetermined and confined . A case study can be almost anything [see item 9 below] as long as it relates directly to examining the research problem. This relationship is the only limit to what a researcher can choose as the subject of their case study. The content of a case analysis is determined by your professor and its parameters are well-defined and limited to elucidating insights of practical value applied to practice.
• Case study is fact-based and describes actual events or situations; case analysis can be entirely fictional or adapted from an actual situation . The entire content of a case study must be grounded in reality to be a valid subject of investigation in an empirical research study. A case analysis only needs to set the stage for critically examining a situation in practice and, therefore, can be entirely fictional or adapted, all or in-part, from an actual situation.
• Research using a case study method must adhere to principles of intellectual honesty and academic integrity; a case analysis scenario can include misleading or false information . A case study paper must report research objectively and factually to ensure that any findings are understood to be logically correct and trustworthy. A case analysis scenario may include misleading or false information intended to deliberately distract from the central issues of the case. The purpose is to teach students how to sort through conflicting or useless information in order to come up with the preferred solution. Any use of misleading or false information in academic research is considered unethical.
• Case study is linked to a research problem; case analysis is linked to a practical situation or scenario . In the social sciences, the subject of an investigation is most often framed as a problem that must be researched in order to generate new knowledge leading to a solution. Case analysis narratives are grounded in real life scenarios for the purpose of examining the realities of decision-making behavior and processes within organizational settings. A case analysis assignments include a problem or set of problems to be analyzed. However, the goal is centered around the act of identifying and evaluating courses of action leading to best possible outcomes.
• The purpose of a case study is to create new knowledge through research; the purpose of a case analysis is to teach new understanding . Case studies are a choice of methodological design intended to create new knowledge about resolving a research problem. A case analysis is a mode of teaching and learning intended to create new understanding and an awareness of uncertainty applied to practice through acts of critical thinking and reflection.
• A case study seeks to identify the best possible solution to a research problem; case analysis can have an indeterminate set of solutions or outcomes . Your role in studying a case is to discover the most logical, evidence-based ways to address a research problem. A case analysis assignment rarely has a single correct answer because one of the goals is to force students to confront the real life dynamics of uncertainly, ambiguity, and missing or conflicting information within professional practice. Under these conditions, a perfect outcome or solution almost never exists.
• Case study is unbounded and relies on gathering external information; case analysis is a self-contained subject of analysis . The scope of a case study chosen as a method of research is bounded. However, the researcher is free to gather whatever information and data is necessary to investigate its relevance to understanding the research problem. For a case analysis assignment, your professor will often ask you to examine solutions or recommended courses of action based solely on facts and information from the case.
• Case study can be a person, place, object, issue, event, condition, or phenomenon; a case analysis is a carefully constructed synopsis of events, situations, and behaviors . The research problem dictates the type of case being studied and, therefore, the design can encompass almost anything tangible as long as it fulfills the objective of generating new knowledge and understanding. A case analysis is in the form of a narrative containing descriptions of facts, situations, processes, rules, and behaviors within a particular setting and under a specific set of circumstances.
• Case study can represent an open-ended subject of inquiry; a case analysis is a narrative about something that has happened in the past . A case study is not restricted by time and can encompass an event or issue with no temporal limit or end. For example, the current war in Ukraine can be used as a case study of how medical personnel help civilians during a large military conflict, even though circumstances around this event are still evolving. A case analysis can be used to elicit critical thinking about current or future situations in practice, but the case itself is a narrative about something finite and that has taken place in the past.
• Multiple case studies can be used in a research study; case analysis involves examining a single scenario . Case study research can use two or more cases to examine a problem, often for the purpose of conducting a comparative investigation intended to discover hidden relationships, document emerging trends, or determine variations among different examples. A case analysis assignment typically describes a stand-alone, self-contained situation and any comparisons among cases are conducted during in-class discussions and/or student presentations.

The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Yin, Robert K. Case Study Research and Applications: Design and Methods . 6th edition. Thousand Oaks, CA: Sage, 2017; Crowe, Sarah et al. “The Case Study Approach.” BMC Medical Research Methodology 11 (2011):  doi: 10.1186/1471-2288-11-100; Yin, Robert K. Case Study Research: Design and Methods . 4th edition. Thousand Oaks, CA: Sage Publishing; 1994.

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Measuring adherence to AI ethics: a methodology for assessing adherence to ethical principles in the use case of AI-enabled credit scoring application

• Original Research
• Open access
• Published: 15 April 2024

Cite this article

You have full access to this open access article

• Maria Pokholkova   ORCID: orcid.org/0000-0002-6294-0669 1 ,
• Auxane Boch 1 ,
• Ellen Hohma 1 &
• Christoph Lütge 1  

This article discusses the critical need to find solutions for ethically assessing artificial intelligence systems, underlining the importance of ethical principles in designing, developing, and employing these systems to enhance their acceptance in society. In particular, measuring AI applications’ adherence to ethical principles is determined to be a major concern. This research proposes a methodology for measuring an application’s adherence to acknowledged ethical principles. The proposed concept is grounded in existing research on quantification, specifically, Expert Workshop, which serves as a foundation of this study. The suggested method is tested on the use case of AI-enabled Credit Scoring applications using the ethical principle of transparency as an example. AI development, AI Ethics, finance, and regulation experts were invited to a workshop. The study’s findings underscore the importance of ethical AI implementation and highlight benefits and limitations for measuring ethical adherence. A proposed methodology thus offers insights into a foundation for future AI ethics assessments within and outside the financial industry, promoting responsible AI practices and constructive dialogue.

Avoid common mistakes on your manuscript.

1 Introduction

The misuse of decision-making artificial intelligence (AI) systems leads to unintended consequences stemming from the computational techniques and AI infrastructure employed in their development. Footnote 1 According to Ayling and Chapman [ 7 ], who focus on the epistemic concerns of technologies, Footnote 2 misuse stems from traditional data harms: non-intentional harms that result in individuals’ problems associated with privacy violations, Footnote 3 discrimination, Footnote 4 and automatic consent. Footnote 5 Recently, a growing body of research addressed ethical compliance in the global AI landscape, encompassing Europe and beyond. Footnote 6 The authors also stress the need for practical tools that go beyond high-level ethical principles and focus on applying these principles in AI production and deployment, emphasizing the importance of addressing the “how” of applied ethics rather than just the “what.” Footnote 7 Those efforts are translated, for example, into frameworks Footnote 8 in the public domain (recruitment, education, enforcement), which aim to systematically ensure that the high-level principles are operationalised. Footnote 9 However, Attard-Frost et al. [ 6 ] state that few AI ethical guidelines focus on fairness, accountability, and transparency within technical systems. Footnote 10 Jobin et al. [ 34 ] note, importantly, that the public’s judgment tends to have a polarized view of AI algorithms, perceiving them as bad or good,at the same time, the ethical implications of AI technologies should be addressed on the level of design. That explains why assessing AI technology at every step of the AI lifecycle is important for tackling the problem of misuse.

Many frameworks, principles, protocols, and guidelines aim to evaluate the impact of AI technology and even provide standards for its quality in different domains. For instance, Value- Based Engineering (VBE), published by IEEE, prioritizes ethical considerations in designing AI systems. Footnote 11 Technical experts from the United States and China collaborate on AI technical standards globally. Footnote 12 However, governments struggle to cooperate on ethical AI standards, namely on the issues addressed, actors involved, and strategies used. Footnote 13 This fact postpones the establishment of global governance frameworks and results in a lack of interpretation of ethical AI rules and their operationalization for more detailed and concrete cases. This absence of a standardized approach to ethical AI has contributed to a global disparity in consumer trust in AI systems. Footnote 14 However, according to Omrani et al. [ 46 ], this trust can be enhanced by maximizing the technological features of AI systems. Hooks et al. [ 26 ] claim that the technological acceptance of various newly emerged AI-specific applications directly correlates to levels of trust in AI in general. Footnote 15 The research on trust in AI lacks in-depth examinations of specific AI cases and, specifically, the impact of the underlying trust factors on those cases. An analogy to this research problem can be illustrated with an example of research on evaluating the impact of Environment, Social, and Governance (ESG) reporting. Namely, research demonstrates that employing qualitative and quantitative methods effectively reveals the direct positive effect of ESG reporting on consumer trust in a company’s brand, product, and service. Footnote 16 Consistent methods for quantifying compliance with the declared principles and values of AI systems’ developers and deployers, including the mentioned high-level principles of ethics, become desirable and essential in establishing trust and promoting the adoption of AI systems.

Besides the challenges associated with integrating AI infrastructure within business organizations, which encompass both AI developed by businesses and AI employed within businesses, there are critical concerns related to ownership rights, cybersecurity, and data protection. Footnote 17 Moreover, those concerns should be tackled while achieving economic beneficence. According to the data collected from Western data sources, at least in Western literature, neither AI developers nor businesses using AI are obliged to follow jurisdictional rules or international AI principles. Footnote 18 Globally, the fragmented regulatory landscape, global variability, or variation of ethical considerations across countries, and the inherent complexity of AI raise the probability of emerging AI systems that risk harming humans. On national levels, a lack of a clear interpretation of political acts regarding the requirements for the ethics of AI increases the gap between public policy and the practice of using AI systems. Footnote 19 In these circumstances, assessing the level of trustworthiness in tools using AI systems seems, therefore, rather difficult due to organizational and structural risks. Footnote 20 In terms of businesses, the lack of proactive strategy for integrating AI ethics into the corporate structure is explained by the “wait- and-see” policies arising from uncertainty. Footnote 21 It is why the current state of “ethical governance” Footnote 22 is underdeveloped. At the same time, the ability to effectively control the ethicality of AI systems constitutes a competitive advantage of such businesses, as it improves overall product quality and consumer trust. Footnote 23

This paper seeks to contribute to the current research on AI ethics implementability by using the elicitations of experts, a technique already implemented to solve problems in political science, government, statistics, management science, and psychology. The elicitation of experts is asking a group of qualified individuals to express their opinions and judgments regarding uncertain events in terms of probabilities. According to the scientists who applied statistical methods to elicit expert knowledge, elicitation allows for incorporating subjective beliefs and opinions into probabilistic models. Despite the strong opinion that expert judgment cannot be quantified, and such a category as ethics cannot or should not be assessed quantitatively; statistical modeling in eliciting expert judgments proved effective in predicting complex physical phenomena. Footnote 24

Due to the rapid entry of AI systems into the market, there is a pressing need for a self-consistent assessment method that would allow us to decompose the ethical characteristics of AI products and assess them. This problem can be addressed by involving qualified experts who can not only select features upon which to quantitatively evaluate the components of the overall composition but also consider the collective contribution of each feature to the overall assessment. Expert Workshop (EW), one of the approaches that employ statistical modeling, namely weighted sums, in expert judgment elicitation, fulfills these requirements. Publishing results from EW could attract and engage a broader range of experts in establishing quantitative assessment criteria for AI systems. Ultimately, this effort seeks to improve both the quality of AI systems and the performance of the financial companies utilizing them.

Therefore, in this paper, EW will be used to quantify the expert judgment of compliance with an ethical principle by an AI system that will be used for testing. This study aims to create a digital image of one of the characteristics of AI ethics used in the financial sector using data from a selected group of qualified experts. This paper illustrates the discussion using a case study involving an Expert Workshop where experts proposed a system of numerical criteria to assess the compliance of the AI Credit Scoring system with the principle of transparency.

Considering this, the hypothesis of this research can be formulated as follows: “Quantitative assessment of the constituent elements of AI ethics can be carried out based on a generalized expert opinion using statistical modeling (weighted sums) in expert judgment elicitation.” Although our proposed metrics involve weighted sums of expert judgment, it is essential to recognize that this approach may have limitations compared to other potential methods. This paper will address these issues throughout the article, providing a comprehensive analysis of the strengths and weaknesses of the proposed methodology.

This paper will first provide a theoretical background defending the proposed methodology of the Expert Workshop for defining quantifiable measures of AI ethics principles. Subsequently, the outcomes of the proof of concept Expert Workshop will be presented, followed by a discussion on the usability and effectiveness of the proposed methodology.

2 Theoretical framework

2.1 literature review on ai ethics measurement techniques.

The analysis of AI ethics research can be presented in two categories: one that examines principles by comparing and classifying them without contextualizing them to a specific industry and another that analyzes techniques tailored to address the challenges related to AI ethics integration in particular sectors. An example of the first research category is the AI regulation strategy document, “Ethics Guidelines for Trustworthy AI,” designed and published on behalf of the European Commission’s AI High-Level Expert Group (AI HLEG). Footnote 25 The document is a non-obligatory framework within the European Union that implies the implementation of procedures into businesses that guarantee seven ethical principles: fairness, transparency, privacy, security, accountability, reliability, and safety. Footnote 26 Unlike other prominent high-level frameworks like the IEEE Global Initiative on Ethics Footnote 27 and the Montreal Declaration on Responsible AI, Footnote 28 AI HLEG focuses on the scope of Europe and promotes a regulatory approach that involves influencing regulatory bodies and policymakers when designing AI ethics implementation procedures. It comes to a second category of research encompassing an engineering approach of integrating ethics into the design of a concrete AI tool, Footnote 29 i.e., algorithmic decision-making. The research findings reveal that the ethical principles of fairness, transparency, and accountability are underrepresented in AI ethical business practices and, due to the disciplinary scope of ethics, are being replaced by speculative norms, i.e., corporate secrecy. Footnote 30 This suggests the need for more transparent methods to align AI ethics considerations with the business practices of organizations that deploy AI.

Understanding the difference between different quantification approaches that aim to assess the ethicality of AI systems is essential, as the optimal synergy between the most helpful assessment techniques is a must for strengthening the use of AI systems. For instance, literature distinguishes existing quantification frameworks that concern AI ethicality based on their efficacy, scope, focus, purpose, and manner in which they connect the cause and effect of the AI systems. Footnote 31 Among them are impact assessments, technology assessments, audits Footnote 32 tailored to an industry that involves AI ethics aspects, and design toolkits like value-centered design. Footnote 33 The impact assessments are also used for industry and business-specific purposes like achieving sustainable goals or ensuring stakeholder participation. Footnote 34 At the same time, there is no universally accepted rating of the most efficient or least efficient quantification methodologies for evaluating the integration of AI ethics principles into business practices. Given the evolving nature of the AI ethics field, adaptable and context-specific quantification methods play a valuable role. The method proposed in this paper aims to contribute to the ongoing dialogue and practical application of ethical principles in AI business contexts.

When defining the methods, AI research often focuses on qualitative evaluations of the adherence of AI systems to ethical principles or legal standards. Footnote 35 The assessment is also crucial for monitoring product quality, providing businesses with insights on enhancing product competitiveness in the market, and ensuring that consumers’ rights are respected. Footnote 36 Among different options for a comprehensive evaluation of the ethicality of AI systems are ethical audits and assessments, frameworks, and guidelines developed by international, Footnote 37 national, Footnote 38 and industry-led initiatives Footnote 39 using interdisciplinary approaches. However, some practical guidelines or systems are designed to measure AI systems’ ethical qualities, often proposed by ethical consultancies to ensure impartiality. For example, some publications evaluate AI systems’ ethicality using a labeling approach introduced by AI Ethics Impact Group, Footnote 40 tailored to a concrete tool’s specific context.

Another example is a TÜV SÜD AI Quality Framework Footnote 41 that proposes to measure risk for non- compliance to the legal framework for AI systems by calculating the severity of the AI ethical implication and the scope of the corresponding industry. These qualitative approaches are often regarded as practical decision-making tools with the potential to serve as monitoring tools for AI systems characteristics. They can be helpful to various stakeholders with different needs, including policymakers, regulators, and business owners.

The quantification allowed by those models is a competitive advantage that allows for control over the ethical quality of an AI system and, therefore, simplifies the organizations’ harmonization of AI standards that will be outlined in the EU AI Act. Footnote 42 However, due to high complexity, the frameworks mentioned above can also be considered complex for understanding and implementation. Hence, the current state of AI ethical quantification frameworks must offer compelling evaluation examples. The quality of these assessments remains unclear, shielded behind non-disclosure agreements (NDAs) and corporate confidentiality. Finally, there needs to be evidence that those frameworks consider the importance of bringing various stakeholders to a consensus on the definition of ethical assessment.

A quantitative assessment of ethics in general, as well as the AI ethics and AI application’s ethical adherence, is impossible due to the complexity and abstractness of these concepts as philosophical categories. At the same time, when using AI systems in practice, having a measurable level of trustworthiness as described in the HLEG [ 24 ] work and experts’ opinions on such devices is helpful. Undoubtedly, the factors of AI systems that characterize trustworthy AI, including AI used in the financial sector, include ethics and integrity. At this stage of development and practical use of AI systems, it seems appropriate to decompose the general concept of ethics into components, a quantitative assessment of each of which can be carried out using the elicitation of expert knowledge. Merging databases of evaluations of elements, taking into account their importance, into a single database will allow us to obtain an “image” of the ethics of AI and consider its level when assessing its trustworthiness. An Expert Workshop (EW), a seminar-style approach that combines individual and group-based techniques to address complex ethical challenges or any problematic situations, leveraging the collective expertise of participants, seems a suitable methodology in this case.

2.2 Reusing the concept of expert workshop for quantification of adherence to ethical principles

The Expert Workshop (EW) is a method of seminar conduction that proposes a strategy to understand and quantify complex phenomena by involving 10–25 professionals who are specialized in corresponding phenomena. Footnote 43 This method was developed and proposed in a doctoral dissertation of Tolkacheva, Footnote 44 whose idea was to systematize a set of well-known practices and techniques for problem-oriented training for specialists in the field of engineering and technology. Two workshops were conducted using the EW methodology to assess students’ adherence to Sustainable Development (SD) values Footnote 45 with Russian and international HEI (High Engineering Institutions) stakeholders, namely engineering students and educators. The experts were selected and invited to assess the engineering students’ Sustainable Development mindset formation level. The characteristics chosen by both groups of experts were quantitative, relative, and applicable to any university and its engineering students. The authors also categorized the participants into three distinct groups: the level of the university’s commitment to SD goals, SD mindset in the student community, and individuals’ adherence to SD values. All characteristics represented a percentage or a share of the entity (i.e., “average % of study time within engineering courses devoted to SD issues”).

Based on the expert assessment, it was found that the tested level of sustainability development (SD) mindset formation among engineering students in the investigated universities is low (73% of criteria). It suggests that 73% of the criteria used to evaluate the level of sustainability development (SD) mindset formation among engineering students have been met or fall into the “low” level category. At the same time, according to authors, Footnote 46 a comparison between the initial intuitive assessment and the subsequent quantitative assessment revealed that defining quantitative criteria and applying quantitative scales to the evaluation process led to a more comprehensive analysis; resulting in a more critical evaluation. Initially, the assessment indicated a much higher level of SD mindset formation, with 43.7–52.8% of responses suggesting a level above “low.” In contrast, the later evaluation showed no indication of a level above “low.”

The authors argue that a high-quality expert selection process for the EW is crucial for building a correct and comprehensive digital “image” of the problem. If a repeated EW is conducted, the probability of new evolving characteristics is close to zero. If the experts were selected correctly, it indicates that they are highly qualified, and the criteria they propose will likely align with or be similar to those suggested by other potential future experts. Nonetheless, with each successive EW conducted, the digital “image” of the phenomena becomes increasingly detailed. This process not only aids stakeholders in undergoing a conscious transformation but also fosters their inner motivation and understanding of the addressed transformation problem. A prerequisite of EW is the experts’ competence, not based on their position or level of qualifications; but on their experience, direct involvement, and knowledge of the practical aspects of the problem.

The process for conducting expert research follows a structured sequence of steps (see Fig. 1 ). The preparation starts with selecting a seminar topic, subject, and research problem; followed by formulating requirements for experts and finally inviting chosen participants. Once experts are selected, they are provided with the seminar’s goals.

The procedure of the expert workshop

The workshop begins with the qualitative phase, during which experts collaborate to adopt a definition, make assumptions, formulate the main question, and select a qualitative assessment scale. Individual surveys gather qualitative expert opinions, and expert teams nominate characteristics for quantitative assessment. In the qualitative phase, the moderation process plays a key role during the stage of characteristics nomination. The facilitator ensures that a consensus among the participants is reached. Moderated deliberation enhances the fairness of the characteristic selection and helps to conclude the discussion with the consent characteristic formulation among the participants. The moderator’s task is to guide the discussion and maximize the consensus process within the group, facilitate precise phrasing of the characteristics, and help the group select the most argumentable and informative characteristics. At the same time, experts define their opinions and formulate improved characteristics by participating in the discussion. During the consensus process, voting can help if the deliberation and definition of opinions take too much time. Additionally, if the new characteristics are formulated by all participants based on the ones proposed by groups, they are written down and used during voting.

The most informative characteristics are selected during a participants’ discussion, leading to the construction of a 5x5 matrix. This is followed by a quantitative phase of the workshop when criteria for the object’s condition are established, and the comparative level of information content for each criterion is determined. Finally, experts provide quantitative assessments, which are mathematically processed to construct a model describing the subject of research based on selected criteria and their contributions. This process guides the transition from problem formulation to quantification and model creation.

2.3 Quantitative assessment and calculation process in expert workshop methodology

First, the “Aggregated Quantified Assessment” of the researched quality of a subject is calculated by multiplying each of the Status Quo values corresponding to its values of Ratio of Importance. This assessment aims to provide a numerical value representing the level of subject quality. It uses relative values (KSQ1 to KSQ5) assigned to specific characteristics selected by experts to evaluate general quality (ranging from 0 to 1). Each characteristic’s value is weighted by a ratio of importance (ɣ1 to ɣ5), where the sum of these ratios equals 1. It results in a generalized quantitative assessment of the subject’s quality level, which adequately represents reality in the present moment according to the expert’s perception. It is calculated as follows:

where KSQ1 … KSQ5 are calculated relative values of characteristics selected by experts to assess the current level of adherence of AI Credit Scoring to a principle of transparency (0–1);

where ɣ1 … ɣ5—the ratio of importance, or relative assessment of the specific weight of the selected criteria, within (0–1).

The Quantitative Assessment of the Levels of Qualitative States (QALQS) is calculated with K i : a value of the criterion and ɣ i : the specific weight of the i th criterion. Different qualitative states of quality are defined as: “Critically Low,” “Low,” “Satisfactory,” “Good,” and “Excellent.” For each state, a formula is provided to calculate a numerical value based on the weighted sum of characteristic values ( K i ) using specific weights (ɣ i ). Equations ( 3 ), ( 4 ), ( 5 ), ( 6 ), and ( 7 ) represent different thresholds of quality states, and the calculated values help to classify the subject quality into one of these qualitative states. It is calculated as follows:

This calculation not only allows for a generalized quantified number for each of the states of quality but also accounts for the importance of each of the characteristics for the general quality of the subject.

The third calculated result, a Qualitative Expert Judgement (QEJ), is the result of the intuitive survey on the subject’s current state. A scale from 0 to 1 to represent the qualitative judgments obtained from the survey. These judgments are expressed as shares, indicating the percentage of respondents who selected each qualitative category (e.g., critically low, low, satisfactory, good, excellent). The exemplary question for the survey can be: “What is, according to your opinion, the current quality state of the subject X?” For coherence with the numeric framework, the survey’s answers are coded as E, and five E results, expressed as shares, match the quality states already used (critically low, low, satisfactory, good, excellent).

Finally, step four is a “Quantified Assessment of Average Collective Judgment of Experts,” or QAACJE is done by multiplying the values of QEJ with the generalised scale or each of the respective states of quality QALQS (Fig. 2 ).

Assessment of subject quality and quantitative evaluation by experts

The final result is calculated in Eq. ( 8 ):

This multiplication results in the formation of a new scale that unites both qualitative and quantitative perceptions of experts, and the summation of those values gives a number that summarizes the expert’s assessment of the subject’s quality. It is important to clarify that QAACJE is a valuable component of the Expert Workshop methodology, as it is a technique that enhances its flexibility and effectiveness in transforming opinions into quantifiable data. However, the method of weighted sums does not eliminate the subjectivity of expert opinions. The strengths and weaknesses of the EW procedure and its metrics will be discussed in the next chapter.

2.4 Exploring the nuances of expert workshop (EW)

According to Morgan [ 42 ], poorly done expert elicitation, when used for applied decision analysis, can discredit the whole approach and lead to useless or deceptive results. Moreover, the elicitation procedure should account for inherent biases and minimize them within the process and in the results. Therefore, some principles and interpretations of the methods and techniques and the concept might be necessary for the reproducibility of the EW methodology. Finally, the methodology’s weaknesses should be minimized by raising awareness about its shortcomings and explaining the measures of control over the method.

2.4.1 The principles of EW preparation and conduction

One of the most crucial steps in organizing an EW is the selection of experts. Experts are professionals of a specific field with accumulated knowledge and expertise, complemented by their deep understanding of the subject’s constraints and advantages. Therefore, selecting appropriate experts is based on three principles: qualifications in a particular field of research, a high level of engagement, and professional interest in finding a solution to the problematic situation addressed in the workshop. Organizers of the EW ensure that at least two of the principles should be satisfied when selecting experts:

The principle of Relevance is implemented through the study of publications, information about conferences, seminars, and other events that allow the identification of a pool of qualified researchers on the relevant issue who may subsequently be invited to participate in the EW. For example, the invited experts should have at least one publication on the subject of investigation in the last three years or a minimum of two years of work experience in the context of the subject.

The principle of Engagement is realized by inviting experienced individuals who have knowledge about the phenomenon studied in the EW from their professional activities or everyday lives. Often, the expert opinions of such individuals are no less valuable than those of qualified expert researchers.

The principle of Motivation : individuals show motivation to resolve the problem of the phenomenon under study. This is particularly important due to the necessity of collectively finding ways to resolve the researched problem during the EW seminar.

Regarding the principles of EW conduction, facilitators play a crucial role in maintaining neutrality towards the various perspectives of experts. This vital principle of EW conduction is to ensure that experts feel comfortable expressing their opinions without feeling pressured to adopt a dominant viewpoint and to create conditions for experts to express their ideas.

2.4.2 Comparison with other expert judgment elicitation methods

Several categories of methods can be distinguished among numerous publications on expert elicitation methods, or methods of gathering the insights and opinions of knowledgeable individuals in a particular field regarding uncertainty. Many are expert elicitation methods explicitly tailored to the public sector, Footnote 47 environmental science and risk assessment, Footnote 48 policy analysis, Footnote 49 etc. Even though some techniques declare their ability to assess phenomena, Footnote 50 it is unclear whether there is an effective comparable method to the EW that specializes in assessing the phenomena’s state.

In the context of Expert Judgment Elicitation (EJE) taxonomy, a categorization system that organizes various methods and approaches used in expert judgment elicitation, EW could be attributed to quantitative and qualitative methods that use fluent and numerate methods. Footnote 51 Fluent methods involve gathering qualitative or descriptive information from experts, which aim to capture the experts’ subjective insights, opinions, or experiences without quantifying them into numerical values. Numerate methods aim to provide more precise assessments and can include probability estimations. The concept of the EJE provides a foundation that describes the EW method, which combines direct and indirect elicitation and individual and consensus aggregation. Footnote 52 Therefore, according to the EJE taxonomy, EW is a mixed-method group elicitation approach that combines qualitative expert judgment methods with quantitative methods, mathematical methods, or a weighted factor method. Footnote 53

EW could be compared to the Delphi method, a consensus-building technique that uses questionnaires to collect participant data. Footnote 54 However, the Delphi method often uses the opinion of geographically dispersed experts. Footnote 55 Therefore, it builds on electronic and anonymous communication, which does not leave room for clarification when interpreting the results. In contrast, EW allows for collaborative face-to-face interactions among experts, facilitating the development of agreed-upon judgments and the selection of informative numerical criteria. Delphi consists of 3–4 iteration rounds in which experts must give their statements and then reassess them to reach a consensus at the end of the process. Compared to EW, Delphi’s method presents design vulnerabilities. Delphi’s method has no requirement of being present and engaged, and the method includes the obligation to grant participants a large block of time (i.e., 2 weeks). Footnote 56

Another method, the Analytic Hierarchy Process (AHP), is a mixed-method approach that uses pairwise comparisons to derive weighted comparisons. This method can be better compared to EW, as both EW and AHP use literate and numeric metrics. AHP is a practical decision-making method that divides complex problems into hierarchical structures, allowing for comparing elements and calculating weights based on expert judgments and the relationships between factors. Footnote 57 Specifically, the similarity with AHP is noted as both AHP and EW, apart from freeform methods, such as brainstorming, use scaling methods with discrete and continuous ratings (i.e., 0–1).

According to the literature review, EW is a unique method for gathering expert judgments and selecting informative numerical criteria. It is distinct from other established methods like AHP in its focus on assessing the state of a problem or phenomenon rather than choosing among alternatives. Thus far, evidence suggests that expert elicitation methods primarily address selecting options from multiple alternatives. While EW can be formally compared to AHP as they both employ quantitative metrics, the applicability of weighted sums metrics for expert judgment elicitation requires further research.

2.4.3 Accuracy and metrics (weighted sum method)

Human brains struggle to process large amounts of data or perform intricate statistical computations, Footnote 58 therefore, there is no technical possibility to validate the accuracy of the results obtained while eliciting expert judgment. Uncertainty must be accepted when judging the probability of events and the inherent cognitive biases. The EW demonstrates the aspect of statistical representation of the expert knowledge. It states that the accuracy and statistical significance of the results obtained depend on the level of competence and the number of experts involved. Further efforts to expand the amount of experts and raise the competence level of the experts involved would increase the accuracy of the obtained group result. However, there should be a limit of approximately 25 people to keep the discussion engaging and manageable.

The structure of the matrix approach exemplifies the necessity for a participation number limitation. The accuracy of the digital portrait of the investigated phenomenon depends on the number of selected characteristics for its description and the numerical criteria for evaluation. More characteristics mean a more detailed and accurate digital portrait of the phenomena; this also applies to the range of qualitative assessment scales. The Likert Scale is the qualitative scale used for the Expert Workshop; it proposes a scale of five states of the phenomena, providing standardization and accuracy across assessments. Footnote 59 At the same time, the greater the number of features and the wider the range of qualitative assessments, the more work the experts need to do during the seminar. The matrix approach (used to obtain a digital image of the investigated phenomena when using a 10 × 10 matrix) may allow for the exclusion of maximum and minimum values during statistical processing but significantly increases the duration of the seminar. A 5 × 5 matrix allows for conducting a workshop with 15–20 participants in 3.5–4.0 h. However, a 10 × 10 matrix would take at least 8 h. According to previous tests of the methodology and the experts’ feedback, an acceptable level of accuracy of the obtained digital image of the subject under investigation is achieved with a 5 × 5 matrix. Footnote 60

Apart from the quality of experts, the selection of metrics directly impacts the accuracy and reliability of the results obtained through expert judgment elicitation and helps to reduce bias. Multi-criteria problems are fundamentally more complex than single-criteria problems and require unique methods to find their solution. Footnote 61 The Expert Workshop aims to conduct a multicriteria assessment, for which the weighted sum method (WSM) is convenient. The Weighted Sum Method (WSM) is an approach used in decision-making that aggregates multiple criteria into a single composite criterion; typically represented as a weighted sum of the individual criteria. Footnote 62 Namely, calculations that are applicable for decision-making tasks in various scenarios; such as the selection of the best option or multiple best options, ordering all options by preference, and assessing the characteristics. However, solving multi-criteria problems, such as those in multi-criteria decision analysis (MCDA), requires significant effort to gather and process decision-makers’ preferences. This can be resource-intensive and time-consuming. Moreover, since MCDA relies on subjective decision-maker preferences, there are no objective solutions for comparison; posing challenges in evaluating results against benchmarks.

The simplicity of the 0–1 continuous scale used in the Expert Workshop quantification phase is beneficial because it allows for an intuitive assessment process. Despite the complexity of the studied problem, which involves ethical considerations; this approach streamlines the evaluation to make it easier for experts to provide their insights. In ethical quality assessment, where multiple factors and perspectives are at play, a simple system helps condense the core ideas of experts’ qualitative judgments into quantifiable measures to facilitate a clearer understanding of the overall ethical landscape. However, the WSM’s dependency on expert judgment, namely on their subjective opinion, can introduce potential biases or errors in the subject’s assessment. Footnote 63 At the same time, while being biased, the method allows for the quantification of subjectivity. This accounts for the direction in which the subjectivity of a specific group of experts is directed.

Understanding subjectivity could determine the stakeholders’ priorities in the topic of development.

2.4.4 Group subjectivity in expert judgment

In an Expert Workshop (EW) scenario, the subjective probability expressed by an expert reflects their personal belief; which is influenced by both formal evidence and informal knowledge or experiences. Despite being biased, subjective probability distributions (SPD) are more effective than other statistical methods when eliciting uncertain expert knowledge. Footnote 64 In a subjectivist or Bayesian perspective, individuals assess the likelihood of uncertain events or quantities based on their subjective judgments about the present or future state of the world and the underlying governing processes. Footnote 65 Regarding group elicitation, the subjectivity extends to the group dynamics in EW.

As a result, collective judgment is influenced by groupthink—when group members feel peer pressure to conform with a dominant opinion. This is reinforced by cultural stereotypes, such as the ultimate importance of group members’ opinions who outweigh others regarding their social status (age, gender, authority, and professional achievements) during the workshop. Another consideration during the process includes the moderator’s awareness of group dynamics, ensuring that each expert holds equal weight during the discussion.

At the same time, consensus building, an essential activity of Expert Workshop (EW) achieved due to its in-person setup, aims to reach an agreement through open dialogue, negotiation, and compromise among participants. Various methods and techniques employed in EW prioritize avoiding groupthink and achieving consensus. Revising the group work results during the consequent critical analysis of the whole group of experts contributes to a more precise understanding of elicitation concepts, notions, or elicitation questions. Notably, the multi-criteria decision analysis process helps to ensure that all relevant factors are named, formulated, and considered. The success of Expert Workshops hinges on balancing individual subjectivity, group dynamics, and effective consensus-building strategies to ensure accurate and reliable collective judgments.

2.4.5 Challenges associated with engaging experts

Contacting experts via email using organizers’ professional and personal networks, conducting “cold calls” using online databases and social media, and confirming the interest of experts can be challenging. It requires extensive effort to reach out to suitable experts and ensure their participation, as usually, it is difficult for a sufficient number of experts to be physically present on a specific date, time, and place. Additionally, hosting workshops in a fixed location may limit the involvement to only those who can physically attend. However, this limitation can be mitigated by coordinating workshops with other events that draw experts from diverse locations. This challenge is also mitigated when seeking a localized perspective on the investigation.

At the same time, the challenge remains to find experts that would fit the investigated subject. The methodology procedure is explained in the invitation, which allows experts to decide on participation. However, even with the detailed explanations in the invitation, some experts may not fully agree with the contextual definition provided by the organizers, which may lead to disagreements during the workshop. For instance, according to the past use cases of EW, Footnote 66 it is likely that at least one expert in the group refuses to accept the contextual definition proposed by an organizer. In this case, the moderator asks the expert to provide their definition. So far, there has not been a case where the contextual definition of the investigated subject of the EW has been formulated promptly by an expert.

2.4.6 Stakeholder dynamics

Another challenge associated with stakeholder dynamics occurs when the stakeholders play different roles in the process (i.e., seller and buyer, user and creator). Achieving consensus during the workshop can be challenging due to the risk of stakeholders leading polarized discussions. Moreover, briefing and moderation must mobilize the participants to ensure that experts remain engaged, thoughtful, and productive. However, it can be difficult for a moderator to manage the group due to the need to accommodate participants’ diverse backgrounds and preferences. Moreover, a moderator should be experienced and knowledgeable enough to coordinate smoothly and promptly during all stages of the EW; reacting appropriately to experts’ questions, comments, and objections. During the group work stage, where experts collaborate to formulate characteristics, the moderator must monitor multiple groups simultaneously; to ensure that discussions progress in the right direction. For example, developing quantifiable characteristics can challenge the participants. If a group encounters difficulties, the moderator should react proactively and provide guidance by naming correctly formulated characteristics. Several instructed moderators instead of one could simplify the moderator’s task in cases when an EW hosts a large number of experts or organizers who lack experience in workshop conduction.

Since the EW method relies more on qualitative assessments and expert consensus than complex mathematical models, it exclusively addresses real-life problems and practical challenges with complex, multifaceted contextual nuances, like the ethics of AI. Considering this article aims to find a suitable methodology for hands-on, functional, measurable characteristics of AI systems’ ethics, the preferred method would be interdisciplinary to provide diverse perspectives. In this context, the use case of credit scoring is selected as the AI system for testing as it holds significant relevance in the financial sector. Credit scoring systems are widely used in lending and financial decision-making processes; impacting individuals and businesses. Given the potential implications of biased or unethical AI algorithms in this domain, the choice of AI CS as the use case allows for a focused examination of a real-world, high-stakes application of AI ethics.

3 Procedure of the proof-of-concept workshop

3.1 the use case of credit scoring.

The integration of Artificial Financial Intelligence (AFI) into the operations of modern fintech companies and traditional financial organizations is a rapidly growing trend. Artificial Financial Intelligence (AFI) refers to AI techniques and technologies that automate financial processes while complementing existing human financial expertise. Footnote 67 Modern fintech companies and traditional financial organizations that aim to upscale their financial operations undergo a snowballing process of AFI integration Footnote 68 in their business models. One example of AFI is AI- enabled credit scoring (AI CS). This AI type of program automates and replicates aspects of human financial expertise through a combination of machine learning (ML), Footnote 69 a subcategory of AI algorithms and other AI techniques. From an economic and technical perspective, Credit Scoring (CS) using AI has brought forth a spectrum of applications, such as Machine Learning

(ML) and sophisticated Deep Learning (DL) methodologies like Neural Networks, known for their proficiency in deciphering complex data relationships. Footnote 70 The selection of a particular model hinges upon several critical factors, including the scale and quality of available data, the intricacy of credit decisions, and the specific requirements of lending institutions. Footnote 71 AI CS can use financial and non-financial data sources, including social media activity, textual data, and online behavioral patterns. The abovementioned AI models excel compared to humans in assessing the probability of loan repayment. Footnote 72 Lastly, AI CS can autonomously decide whether to grant individuals or entities a loan or other financial services.

The advantages and disadvantages of AI CS, including its potential for improved credit risk assessment, enhanced financial policies, and concerns about unconscious bias, have been extensively discussed in the recent literature on trustworthy AI. Footnote 73 From an academic perspective, AI CS is described as a powerful tool for financial inclusion, providing affordable service to vulnerable members of society through algorithmic decision-making that mimics intelligent human behavior. Footnote 74 At the same time, while AI CS demonstrates cost-efficiency compared to traditional human creditworthiness assessments and the potential for financial inclusion for borrowers, it also raises significant ethical concerns that need careful consideration Footnote 75 : potentially discriminative AI decisions, which are based on biased algorithms; lack of interpretation on how a decision was made. In the literature, some methods are described that allow for evaluating the AI CS effectiveness, predictability, and justification mechanisms underlying the tool’s decision. Footnote 76 Specifically, in the literature examining the economic angle of AI CS when analyzing artificial intelligence, more attention is paid to economic aspects, such as the accuracy of AI CS risk evaluations. Footnote 77 However, in several specific cases, using AI in the financial sector raises serious ethical issues that require careful consideration, such as compliance with fairness, accountability, and transparency. Footnote 78 Recently, many researchers have emphasized prioritizing the principles of fairness and transparency over other moral principles. Footnote 79 This fact suggests that the change of question from “what” to “how” in the applicability of AI ethics is especially relevant for the specific AI industry in finance.

Regarding the impact assessment of AI technologies in finance, solutions have been mentioned in academic and industry dimensions. Organizations and fintech companies across various industries, from technical equipment providers offering AI-powered platforms to financial consultancies involved in the entire AI CS production cycle, have devised their practices, guidelines, and metrics for adhering to ethical AI principles. These frameworks have been developed, for instance, by IBM (AI Fairness 360), Footnote 80 Ernst and Young (Trusted AI Framework), Footnote 81 and JP Morgan Chase (Explainable AI Centre of Excellence). Footnote 82 These initiatives have not been developed explicitly in response to the AI HLEG’s recommendations. However, they share a broader objective of promoting responsible and ethical AI practices. Frameworks are committed to addressing principles such as fairness, transparency, and accountability in the context of companies’ services and product characteristics. For instance, the IBM framework is declared to detect bias in the machine learning models used to train AFI and AI CS and remove them, as the reliance on biased algorithms potentially leads to unfair or discriminatory decisions. Such bias can manifest in various forms, including prioritizing certain user groups based on ethnicity, gender, or income. Even though AI CS application scenarios have employed a quantitative assessment to measure the fairness of decisions made by AI CS to account for the extent to which its decisions are fair regarding different demographic groups, there are no signs that those metrics are widely applicable. Footnote 83 For example, limited evidence suggests that these policy frameworks comprehensively address organizational challenges in ensuring AFI’s compliance with the ethical criteria established by the EU AI HLEG [ 24 ]. At the same time, considering the speed of AFI integration, there is a pressing need for an understandable, unified methodology that includes metrics that can evaluate adherence to the most challenging ethical concerns surrounding AFI. The need for transparency in AFI was also articulated by a preliminary survey conducted on the preparatory stage of an Expert Workshop: transparency was selected to be the second most important ethical principle for AFI after security. Therefore, the importance of transparency is underscored by the choice of this principle for a proof-of- concept workshop.

In summary, AI-enabled credit scoring (AI CS) is rapidly reshaping the financial industry, offering improved credit risk assessment while raising critical ethical concerns. Given the paper’s objective to demonstrate the feasibility of the Expert Workshop (EW) method in quantifying adherence to ethical principles, the selection of AI CS for the proof of concept is adequate: AI CS provides an ideal example of a combination of financial and technical aspects. Also, considering the necessity to decompose the general concept of ethics on its underlying principles and test them separately, it was decided to methodologically test the principle of transparency based on its definition of the AI HLEG framework. Furthermore, the choice of test principle was informed by the results of a preliminary survey conducted remotely among the invited experts before the EW venue. This survey demonstrated the overarching importance of the transparency principle in the AFI. Also, the interdisciplinarity of the AI CS use case matches the specificity of the Expert Workshop methodology that fosters an interdisciplinary approach for effectiveness in assessing ethical concerns in a specific domain.

3.2 Proof of concept workshop

3.2.1 participants.

The pre-selection of experts was done via search through social network engines, such as LinkedIn, the research of thematic forums, academic search platforms, and databases focused on AI in finance. The requirements for candidates were their professional employment in the AI financial sector. AI developers, ethicists, financial professionals, regulators, consumer advocates, and business and academia representatives who possess specific knowledge about the decision-making process of AI in finance were considered suitable candidates for the EW in AI CS. Additionally, experts with knowledge of decision-making in traditional finance and individuals who work with organizational risks for ethical AI implementation were welcomed. Of 55 invited experts, thirteen confirmed their participation in the Workshop: five women and eight men (Fig. 3 ).

Composition of Expert Workshop Participants

3.2.2 Preliminary survey on the ethical principles of AI systems in finance

The choice of testing the adherence of the AI CS system to only one most important ethical principle, rather than a set of principles, is explained by the structure of the Expert Workshop methodology that allows for the decomposition of complex phenomena into more minor elements. In the seminar context, assessing the ethical quality of AI systems in finance proves challenging due to the multifaceted nature of ethical principles involved in this domain. This implies that by focusing on one principle, one can thoroughly analyze and evaluate its application in the context of AI systems in finance. The purpose of the preliminary survey was explained by the necessity to define the most pressing ethical problem about the AI systems employed in the financial industry and test it using expert knowledge.

Selected candidates for the workshop were invited via email to vote on the most important ethical principle in AI in finance based on the five principles (transparency, fairness, privacy, security, and accountability). The survey question “Which two characteristics are the most important ones in AI systems when applied in the finance industry?” was answered by six people, and security scored four, the most considerable number of votes. Three votes were equally scored by fairness and transparency, whereas privacy scored two votes and accountability scored one vote. Considering the totality of the factors, such as the limited reachability to the experts with the necessary knowledge and the need to align the workshop research focus with the contextual knowledge of confirmed participants, as well as the prevailing literature discussions on AI ethics in AFI, transparency was chosen as the principle to be tested.

3.2.3 Expert workshop

As a first step, participants were presented with background research on the problems of the ethical aspects of AI Credit Scoring and the methodology of the Expert Workshop. Additionally, experts were presented with the preliminary survey results on the most important ethical principles and proposed using the contextual understanding of ethicality during the workshop. Also, they were asked to consent to the predefined definitions and conditions for the common usage in the context of the EW. Namely, participants had to agree with the validity of the following statements: “An AI CS is considered ethical if it has adhered to the principles of ethical AI, particularly transparency.” Also, “The ethicality of the AI Credit Scoring tool can be qualitatively assessed by measuring its adherence to the qualitative characteristics inherent to AI CS products.”

The first step in the Ethical Workshop (EW) also involved simplifying the concept of AI ethicality by equating it with transparency. This simplification was deliberately designed to enhance contextual comprehension of ethicality, and the reason for this simplification is rooted in the intricate nature of ethical considerations, which often originate from the realm of philosophy and are prone to individual and, consequently, biased interpretations. The introduction of these contextual definitions and conditions marked a pivotal initial stage in the methodology, aimed at testing both the hypothesis’s validity and the validity of the eventual results. Establishing these conditions is critical as it streamlines the intricate landscape of ethical considerations and fosters consensus among the workshop participants.

After agreeing on using contextual definitions in terms of the EW, experts were invited to individually share their opinion on the current state of AI Credit Scoring adherence to transparency using an online multiple-choice survey. Namely, they were given five types of answers: excellent, good, satisfactory, low, and critically low state to characterize the subject. The results were displayed in the form of a diagram for the attention of all experts specifically. The majority of experts considered the AI CS transparency to be low.

As a next step, the Expert Workshop (EW) participants were divided into teams, with two groups consisting of four experts each and one group comprising five experts. Each group was tasked to name five measurable characteristics that would allow for the qualitative assessment of the AI Credit Scoring tool’s transparency. For that, participants received handouts Footnote 84 providing a context for ideation of the characteristics of AI CS. Namely, participants were given an example of an abstract AI CS tool developed and tested in the EU and based on Artificial Neural Networks (ANN) models. Also, it was mentioned that the producer company claims that their tool expands access to capital and financial services for marginalized communities and uses financial and non-specified alternative data for decision-making when the client consents to disclose its data, as required to comply with GDPR.

Each group of experts was proposed to name such characteristics or features that should be evaluated on a scale from 0 to 1. After 15 characteristics were named in total (see Appendix B ) Footnote 85 participants were invited to participate in a quorum discussion with other groups to select five of the fifteen most relevant characteristics and formulate them to be scalable from 0 to 1. As a part of the process, group representatives had to defend their formulation of characteristics and consider criticism of other groups. This stage of the EW took the most significant share of the total duration of the EW. Specifically, all 13 participants were challenged to agree on the formulation of scalable characteristics, as their perspectives on what constitutes transparency factors for AI CS did not align.

With that selection, the five best characteristics were inserted into the matrix table in Microsoft Excel, Footnote 86 and the table was shared with all participants individually. Experts were asked to work individually, filling the matrix using their expert knowledge. Namely, participants were tasked with assigning values from a scale of 0 to 1 to evaluate five characteristics, a ratio indicating the importance of each characteristic, and a scale representing the status quo of the AI CS in transparency. The personal Excel table was shared with each participant via a weblink, allowing them to input the numbers they consider adequate and consistent with their expert knowledge. The values proposed by each expert were processed with thirteen Microsoft Excel Lists and connected to one standard matrix programmed to calculate the arithmetic mean of each criterion. Due to technical and organizational challenges, only ten participants could complete the matrix. As a result, the quantitative results for the group are based on the assessments provided by these ten experts, as defined in Fig. 4 .

Matrix of criteria for assessing the level of transparency of AI Credit Scoring (on a scale of 0–1)

4 Results of Quantitative Assessment of AI Credit Scoring Transparency

The first step involves calculating an Aggregated Quantified Assessment (AQA) of the transparency level of an AI Credit Scoring tool. Participants propose numerical criteria individually for this assessment. In the proof-of-concept workshop, experts opted not to provide AQA or quantitative assessments of the current state of specific AI Credit Scoring (AI CS) due to concerns about the accuracy of such assessments. Although it was their first attempt to evaluate transparency, the experts expressed confidence in their ability to assess AI CS competencies. Instead of quantifiable data, they offered their individual intuitive opinions and insights. In the second step, the Quantitative Assessment of the Levels of Qualitative States (QALQS) is calculated based on Eqs. ( 3 ), ( 4 ), ( 5 ), ( 6 ), and ( 7 ). The results show a critically low state of 0.26, a low state of 0.36, a satisfactory state of 0.48, a good state of 0.61, and an excellent state of 0.75 (refer to Appendix D for a detailed calculation). These results form a generalized scale of states, as illustrated in Fig. 4 .

Step three is a Qualitative Expert Judgement (QEJ) or Intuitive Survey Results (%) collected from 11 participants, resulting in a chart in Fig. 4 . The survey question was “What do you think is the current state of AI Credit Scoring adherence to the ethical principle of transparency”? The majority of experts evaluated the state of AI Credit Scoring transparency as low (six votes). In contrast, two experts evaluated the state as satisfactory, two as critically low, and one participant evaluated it as a good state (Fig. 5 ).

Intuitive survey results

Quantitative Assessment of the Average Collective Judgement of Experts (QAACJE), signifying the level of AI Credit Scoring transparency, is based on the results of expert judgments obtained from an intuitive survey and a matrix table. The process results in forming a new scale (E*S), which combines both qualitative and quantitative judgments of experts (see Fig. 4 ). For the tested group of experts, the average collective judgment resulted in a score of 0.38 (Fig. 6 ).

Assessment of AI CS transparency and quantitative evaluation by experts

5 Findings, feasibility of the method, limitations, and outlook

The Expert Workshop (EW) serves as a valuable tool for conducting an in-depth analysis of the transparency level of AI-based credit scoring systems. Before the study, a clear definition and a proposed assumption of AI Credit Scoring’s transparency concept were established for the expert group. The expert group then had to confirm their understanding of AI ethics concepts and transparency. The following key steps were taken during the study:

Experts assessed the problem emotionally using the proposed scale from excellent to critically low. This was expressed quantitatively due to the methodology.

The experts identified and selected the five most informative characteristics, which served as a basis for establishing criteria to assess the transparency of AI-based credit scoring systems.

Characteristics enabled experts to designate the appropriate criteria levels for qualitative assessments of critically low, low, satisfactory, and excellent for each of the selected characteristics.

By applying the criteria selected by experts to indicate states of transparency, a generalized scale was derived considering all five indicators and their respective importance ratios.

The generalized scale enabled a quantitative assessment of a generalized qualitative judgment from a specific group of experts.

The collaborative efforts from the selected group of experts yielded results that expressed the opinions and judgments of this group. As per the QALQS developed by the participating group of experts, the consensus among experts regarding the adherence of AI Credit Scoring (AI CS) to the ethical principle of transparency falls within the range of “low” (0.36) to “satisfactory” (0.48); leaning more towards the “low” end of the spectrum.

The quantified assessment allows for easy comparison between different subjects or situations, which is particularly important when evaluating AI systems; especially in assessing ethical qualities. Moreover, if an Expert Workshop is conducted periodically, QAACJE allows for monitoring of how the transparency changes of AI CS over time. This gives evaluators and stakeholders full disclosure during the subject evaluation process, as they can see the numeric values and understand how the assessment was reached. The quantitative aspect of the workshop methodology serves as a competitive advantage, enabling constructive dialogue among experts by translating theoretical considerations into practical activities.

Additionally, the five unique characteristics formulated by the expert group, along with the initial fifteen characteristics proposed by three different groups during the second phase of the Expert Workshop (EW), hold significant importance. These characteristics shed light on perspectives that sometimes clash due to variations in the background knowledge of AI system implementations. Differences in the granularity of the initially formulated characteristics became apparent. Achieving consensus on characteristic definitions proved challenging due to disagreements among participants regarding the actors responsible for AI credit scoring transparency. Concerns were raised about the transferability of these characteristics to different jurisdictional layouts, which is a crucial aspect when regulating AI within responsible business contexts. Experts also noted the absence of a real-life AI Credit Scoring example for evaluation and identified a need for contextual settings in a use case description.

5.1 Feasibility of the method

The prior research on the landscape of AI ethical assessment frameworks identified their abundance Footnote 87 and, at the same time, the need for practical, quantifiable methods to evaluate AI adherence to ethical principles. The complexities associated with assessing AI ethicality were highlighted, especially considering the intricate nature of ethical concepts and the often opaque quality of existing assessments due to corporate confidentiality and non-disclosure agreements. In these terms, integrating various stakeholders’ perspectives on ethical assessments were identified as critical aspects in addressing the practicability of ethical frameworks. Within this context, the feasibility of the Expert Workshop (EW) as a methodology to address these challenges was explored.

The study validated the feasibility of the EW methodology for assessing AI ethicality and shed light on the complexities and challenges involved in evaluating AI systems’ ethicality. Moreover, it became evident that the specific conditions characterizing a concrete AI Credit Scoring tool depend on various factors, including the host company’s corporate and business goals, industry conditions, market dynamics, and prevailing rules and regulations at a given point in time. This underscores the need for tailoring an Expert Workshop (EW) to the specific requirements of particular AI tools. Finally, the participation of experts who are deeply involved in developing specific AI systems and demonstrate strong motivation to ensure its competitive compliance with ethical AI standards is essential for conducting a high-quality assessment of such phenomena.

The Expert Workshop (EW) has demonstrated that its structured methodology provides valuable insights into the challenges and considerations associated with implementing responsible practices in business models utilizing AI. This conclusion is supported by the coherence observed across different results, emphasizing the methodology’s potential effectiveness in evaluating the ethicality of AI systems. It provides a systematic way of developing quantifiable attributes to evaluate ethical compliance with the trustworthiness principles of AI systems.

5.2 Limitations

The limitations of the Expert Workshop method stem from the lack of comparable methods for research in expert elicitation methods. The metric system of weighted sums allows for statistical representation, however it still has the potential to produce biased results. Moreover, it is crucial to consider that psychological phenomena like group thinking might cause inaccurate or biased responses, and consequently yield biased quantified results. The requirement that characteristics need to be expressed as shares limits the evaluation to quantifiable aspects, which may overlook non-quantifiable ethical considerations. The technical and organizational challenges led to the fact that 10 participants out of thirteen could complete the matrix, and this aspect negatively affected the accuracy of the group result. Also, some limitations happened due to the discrepancy between the fast development of AI technologies and the lack of widespread certainty among experts in the field. This discrepancy was made apparent by experts who refused to provide judgment due to hesitation from a lack of concrete knowledge. As AI becomes increasingly implemented, there is a potential for this discrepancy to be bridged through expanded research.

5.3 Outlook

This paper has demonstrated that the assessment of AI Credit Scoring via the Expert Workshop (EW) can be achieved by obtaining quantified general estimates of the problem based on the expert opinion of a group. A set of measures is advisable to conduct a quality expert assessment, namely:

Improved data collection on characteristics could be achieved by expanding the pool of experts in AFI and soliciting expert opinions from a broader range of individuals. For example, further distribution of questionnaires and formed scales could help to collect more data from other experts who could not attend the current EW. Footnote 88

Involving more types of stakeholders in the AI industry, such as policymakers, academics, start-up representatives, and public sector members, would be beneficial. Additionally, considering experts from other communities, cities, and countries would provide more diverse perspectives and enhance objectivity in problem-solving.

A given use case’s “image” quality could be accomplished by increasing the number of characteristics considered, leading to a more comprehensive understanding of specific issues and potential solutions.

Testing other important AI ethical principles outlined by AI HLEG in the context of Expert Workshops (EW) could enable comparisons between ethical qualities to deepen understanding of the relationship between the ethical principles.

Analyzing and comparing the results of multiple EWs could reveal similarities or differences in perceptions. Patterns that evolve from these comparisons could lead to the formation of a map that explores the perception of given use cases depending on a set of factors, such as stakeholder characteristics.

This approach could be instrumental in addressing the organizational challenges associated with implementing AI ethics. This is particularly relevant for rapidly evolving industries; where it can be challenging for self-identified experts to reach a consensus.

6 Conclusion

This study utilized the Expert Workshop (EW) methodology to define quantifiable adherence characteristics to ethical principles, focusing on transparency in AI-based credit scoring systems. Through a proof of concept EW, the study aimed to evaluate the effectiveness of the EW method in assessing the ethics of AI systems, particularly in the financial sector. Due to the type of expert elicitation method used, which provided relative estimates, numeric results were obtained through mathematical models. These results support the hypothesis that the Expert Workshop (EW) methodology is a viable approach for assessing the ethicality of AI systems.

Regarding the proof-of-concept results, experts’ subjective opinions indicate low transparency achievement in AI CS technology. Experts provided a tentative scale for quantifying the adherence of such tools to the transparency principle and revealed the concerns about transparency in AI CS technology. Despite the workshop design nuances and the inherent subjectivity of the weighted sum metric, the study exemplifies the effectiveness of the methodology in this domain. All in all, the results of the initial stage demonstrate the exemplary study of EW methodology usage for assessing AI ethics components. In the meantime, there is an identified potential for evaluating a variety of ethical principles through the methodology of EW and assessing the comparative importance of principles in the context of AI. The proposed methodology can serve as a foundational framework for developing an ethical principles map, offering innovative insights into the ethical landscape of AI systems.

Data availability

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

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CL, EH, AB and MP contributed to the conception and design of the overall research project and the conception and planning of the conducted workshop. MP, EH, CL and AB contributed to the preparation, realization, post-processing and analysis of the workshop. Further, MP, AB, EH and CL contributed to writing, revising and approving the manuscript. All authors contributed to the article and approved the submitted version.

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Ethical review and approval were not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Participants were made aware before participating in the workshop of the data collection and use for research purposes, as well as research questions and interests. All data have been anonymised in accord with the respect of participants’ privacy.

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1.1 Appendix A: Handout for Participants

1.2 Appendix B: 15 Characteristics

Share of relevant data points that were used in decision-making of AI CS that was

disclosed and explained to the customer.

Share of AI CS decisions that a credit analysis domain expert reviewed

Share of reviewed decisions by an AI CS, explanations on which were found satisfactory by a domain expert

Share of predictions correctly explained by a local interpretation method

Share of complaints/incidents asked on an AI CS decision after a customer asked for clarification on his/her decision

weight of data source and type

share of cases where human intervention was needed

share of (sensitive) features used

model metrics (accuracy, confidence level, fairness metrics)

number of different data sources/share of trustworthy data sources

Share documentation of relevant steps in the AI tool lifecycle (defined by standards and including post-hoc adjustments)

Share of cases for which output is reproducible within acceptable standards (defined by standards)

Share of group of users (reporting) understanding of the tool (UX research)

Share of known potential limitations presented to the public

Share of information about the system that is publically available (based on internal documentation)

1.3 Appendix C: Microsoft excel table

1.4 Appendix D: detailed calculations

The calculation of the Quantitative Assessment of the Levels of Qualitative States (QALQS) based on Equations ( 3 ), ( 4 ), ( 5 ), ( 6 ), and ( 7 ) is presented below.

1. Calculation of Step 2 (QALQS) with Eqs. ( 3 ), ( 4 ), ( 5 ), ( 6 ), ( 7 ).

critically low:

\((0.2*0.27)+(0.3*0.25)+(0.34*0.18)+(0.4*0.13)+(0.0*0.17)\) = 0.2554–0.26;

\((0.4*0.27)+(0.5*0.25)+(0.43*0.18)+(0.5*0.13)+(0.1*0.17)\) = 0.3581–0.36;

satisfactory:

\((0.5*0.27)+(0.6*0.25)+(0.52*0.18)+(0.7*0.13)+(0.1*0.17)\) = 0.4788–0.48;

\((0.6*0.27)+(0.7*0.25)+(0.62*0.18)+(0.8*0.13)+(0.2*0.17)\) =0.6136–0.61;

\((0.8*0.27)+(0.9*0.25)+(0.77*0.18)+(0.9*0.13)+(0.3*0.17)\) = 0.7528–0.75.

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Pokholkova, M., Boch, A., Hohma, E. et al. Measuring adherence to AI ethics: a methodology for assessing adherence to ethical principles in the use case of AI-enabled credit scoring application. AI Ethics (2024). https://doi.org/10.1007/s43681-024-00468-9

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Measuring Business Excellence

ISSN : 1368-3047

Article publication date: 1 April 2024

This study aims to present a methodology and a system to support the technical and managerial issues involved in anomaly detection within the reverse logistics process of an e-commerce company.

Design/methodology/approach

A case study approach is used to document the company’s experience, with interviews of key stakeholders and integration of obtained evidence with secondary data.

The paper presents an algorithm and a system to support a more efficient and smart management of reverse logistics based on a set of anticipatory actions, and continuous and automatic monitoring of returned goods. Improvements are described in terms of a number of key performance indicators.

Research limitations/implications

The analysis and the developed system need further applications and validations in other organizational contexts. However, the research presents a roadmap and a research agenda for the reverse logistics transformation in Industry 4.0, by also providing new insights to design a multidimensional performance dashboard for reverse logistics.

Practical implications

The paper describes a replicable experience and provides checklists for implementing similar initiatives in the domain of reverse logistics, in the aim to increase the company’s performance along four key complementary dimensions, i.e. time savings, accuracy, completeness of data analysis and interpretation and cost efficiency.

Originality/value

The main novelty of the study stays in carrying out a classification of anomalies by type and product category, with related causes, and in proposing operational recommendations, including process monitoring and control indicators that can be included to design a reverse logistics performance dashboard.

• Anomaly detection
• Case study: e-commerce
• Reverse logistics
• Supply chain management

Acknowledgements

This paper was developed with the financial support of Apulia region (Italy) in the framework of the project “A.LO.SOL.” – Advanced LOgistics SOLutions.

Elia, G. , Ghiani, G. , Manni, E. and Margherita, A. (2024), "A system for anomaly detection in reverse logistics: an application into an e-commerce company", Measuring Business Excellence , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/MBE-01-2024-0002

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• Published: 10 April 2024

Development of an index system for the scientific literacy of medical staff: a modified Delphi study in China

• Shuyu Liang 2   na1 ,
• Ziyan Zhai 2   na1 ,
• Xingmiao Feng 2 ,
• Xiaozhi Sun 1 ,
• Jingxuan Jiao 1 ,
• Yuan Gao 1   na2 &
• Kai Meng   ORCID: orcid.org/0000-0003-1467-7904 2 , 3   na2  

BMC Medical Education volume  24 , Article number:  397 ( 2024 ) Cite this article

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Metrics details

Scientific research activity in hospitals is important for promoting the development of clinical medicine, and the scientific literacy of medical staff plays an important role in improving the quality and competitiveness of hospital research. To date, no index system applicable to the scientific literacy of medical staff in China has been developed that can effectively evaluate and guide scientific literacy. This study aimed to establish an index system for the scientific literacy of medical staff in China and provide a reference for improving the evaluation of this system.

In this study, a preliminary indicator pool for the scientific literacy of medical staff was constructed through the nominal group technique ( n  = 16) with medical staff. Then, two rounds of Delphi expert consultation surveys ( n  = 20) were conducted with clinicians, and the indicators were screened, revised and supplemented using the boundary value method and expert opinions. Next, the hierarchical analysis method was utilized to determine the weights of the indicators and ultimately establish a scientific literacy indicator system for medical staff.

Following expert opinion, the index system for the scientific literacy of medical staff featuring 2 first-level indicators, 9 second-level indicators, and 38 third-level indicators was ultimately established, and the weights of the indicators were calculated. The two first-level indicators were research literacy and research ability, and the second-level indicators were research attitude (0.375), ability to identify problems (0.2038), basic literacy (0.1250), ability to implement projects (0.0843), research output capacity (0.0747), professional capacity (0.0735), data-processing capacity (0.0239), thesis-writing skills (0.0217), and ability to use literature (0.0181).

Conclusions

This study constructed a comprehensive scientific literacy index system that can assess medical staff's scientific literacy and serve as a reference for evaluating and improving their scientific literacy.

Peer Review reports

Due to the accelerated aging of the population and the growing global demand for healthcare in the wake of epidemics, there is an urgent need for medicine to provide greater support and protection. Medical scientific research is a critical element in promoting medical science and technological innovation, as well as improving clinical diagnosis and treatment techniques. It is the main driving force for the development of healthcare [ 1 ].

Medical personnel are highly compatible with clinical research. Due to their close interaction with patients, medical staff are better equipped to identify pertinent clinical research issues and actually implement clinical research projects [ 2 ]. Countries have created favorable conditions for the research and development of medical personnel by providing financial support, developing policies, and offering training courses [ 3 , 4 ]. However, some clinical studies have shown that the ability of most medical staff does not match current health needs and cannot meet the challenges posed by the twenty-first century [ 5 ]. It is clear that highly skilled professionals with scientific literacy are essential for national and social development [ 6 ]. Given the importance of scientific research in countries and hospitals, it is crucial to determine the level of scientific research literacy that medical personnel should possess and how to train them to acquire the necessary scientific research skills. These issues have significant practical implications.

Scientific literacy refers to an individual's ability to engage in science-related activities [ 7 ]. Some scholars suggest that the scientific literacy of medical personnel encompasses the fundamental qualities required for scientific research work, encompassing three facets: academic moral accomplishment, scientific research theory accomplishment, and scientific research ability accomplishment [ 8 ]. The existing research has focused primarily on the research capabilities of medical staff. According to Rillero, problem-solving skills, critical thinking, communication skills, and the ability to interpret data are the four core components of scientific literacy [ 9 ]. The ability to perform scientific research in nursing encompasses a range of abilities, including identifying problems, conducting literature reviews, designing and conducting scientific research, practicing scientific research, processing data, and writing papers [ 10 ]. Moule and Goodman proposed a framework of skills that research-literate nurses should possess, such as critical thinking capacity, analytical skills, searching skills, research critique skills, the ability to read and critically appraise research, and an awareness of ethical issues [ 11 ]. Several researchers have developed self-evaluation questionnaires to assess young researchers' scientific research and innovative abilities in the context of university-affiliated hospitals (UHAs) [ 12 ]. The relevant indicators include sensitivity to problems, sensitivity to cutting-edge knowledge, critical thinking, and other aspects. While these indicators cover many factors, they do not consider the issue of scientific research integrity in the medical field. The lack of detailed and targeted indicators, such as clinical resource collection ability and interdisciplinary cooperation ability, hinders the effective measurement of the current status of scientific literacy among medical staff [ 12 ]. In conclusion, the current research on the evaluation indicators of scientific literacy among medical personnel is incomplete, overlooking crucial humanistic characteristics, attitudes, and other moral literacy factors. Therefore, there is an urgent need to establish a comprehensive and systematic evaluation index to effectively assess the scientific literacy of medical staff.

Therefore, this study utilized a literature search and nominal group technique to screen the initial evaluation index and subsequently constructed an evaluation index system for medical staff's scientific research literacy utilizing the Delphi method. This index system would serve as a valuable tool for hospital managers, aiding them in the selection, evaluation, and training of scientific research talent. Additionally, this approach would enable medical personnel to identify their own areas of weakness and implement targeted improvement strategies.

Patient and public involvement

Patients and the public were not involved in this research.

Study design and participants

In this study, an initial evaluation index system was developed through a literature review and nominal group technique. Subsequently, a more comprehensive and scientific index system was constructed by combining qualitative and quantitative analysis utilizing the Delphi method to consult with experts. Finally, the hierarchical analysis method and the percentage weight method were employed to empower the index system.

The program used for this study is shown in Fig.  1 .

Study design. AHP, analytic hierarchy process

Establishing the preliminary indicator pool

Search process.

A literature search was performed in the China National Knowledge Infrastructure (CNKI), WanFang, PubMed, Web of Science and Scopus databases to collect the initial evaluation indicators. The time span ranged from the establishment of the database to July 2022. We used a combination of several MeSH terms in our searches:(("Medical Staff"[Mesh] OR "Nurses"[Mesh] OR "Physicians"[Mesh])) AND (("Literacy"[Mesh]) OR "Aptitude"[Mesh]). We also used several Title/Abstract searches, including keywords such as: Evaluation, scientific literacy, research ability.

The inclusion criteria were as follows: (1)The subjects were nurses, medicial staff and other personnel engaged in the medical industry; (2) Explore topics related to scientific literacy, such as research ability, and literature that can clarify the structure or dependency between indicators of scientific literacy; (3) Select articles published in countries such as China, the United States, the United Kingdom, Australia and Canada; (4) Research published in English or Chinese is considered to be eligible. The exclusion criteria are as follows: (1) indicators not applicable to medical staff; (2) Conference abstracts, case reports or review papers; (3) Articles with repeated descriptions; (4) There are no full-text articles or grey literature. A total of 78 articles were retrieved and 60 were retained after screening according to inclusion and exclusion criteria.

The research was conducted by two graduate students and two undergraduate students who participated in the literature search and screening. The entire research process was supervised and guided by one professor. All five members were from the fields of social medicine and health management. The professor was engaged in hospital management and health policy research for many years.

Nominal group technique

The nominal group technique was introduced at Hospital H in Beijing in July 2022. This hospital, with over 2,500 beds and 3,000 doctors, is a leading comprehensive medical center also known for its educational and research achievements, including numerous national research projects and awards.

The interview questions were based on the research question: What research literacy should medical staff have? 16 clinicians and nurses from Hospital H were divided into 2 equal groups and asked to provide their opinions on important aspects of research literacy based on their positions and experiences. Once all participants had shared their thoughts, similar responses were merged and polished. If anyone had further inputs after this, a second round of interviews was held until no new inputs were given. The entire meeting, including both rounds, was documented by researchers with audio recordings on a tape recorder.

Scientific literacy dimensions

Based on the search process, the research group extracted 58 tertiary indicators. To ensure the practicality and comprehensiveness of the indicators, the Nominal group technique was used on the basis of the literature search. Panelists summarized the entries shown in the interviews and merged similar content to obtain 32 third-level indicators. The indicators obtained from the literature search were compared. Several indicators with similar meanings, such as capture information ability, language expression ability, communication ability, and scientific research integrity, were merged. Additionally, the indicators obtained from the literature search, such as scientific research ethics, database use ability, feasibility and analysis ability, were added to the 15 indicators. A total of 47 third-level indicators were identified.

Fengling Dai and colleagues developed an innovation ability index system with six dimensions covering problem discovery, information retrieval, research design, practice, data analysis, and report writing, which represents the whole of innovative activity. Additionally, the system includes an innovation spirit index focusing on motivation, thinking, emotion, and will, reflecting the core of the innovation process in terms of competence [ 13 ]. Liao et al. evaluated the following five dimensions in their study on scientific research competence: literature processing, experimental manipulation, statistical analysis, manuscript production, and innovative project design [ 14 ]. Mohan claimed that scientific literacy consists of four core components: problem solving, critical thinking, communication skills, and the ability to interpret data [ 15 ].

This study structured scientific literacy into 2 primary indicators (research literacy and research competence) and 9 secondary indicators (basic qualifications, research ethics, research attitude, problem identification, literature use, professional capacity, subject implementation, data processing, thesis writing, and research output).

Using the Delphi method to develop an index system

Expert selection.

This study used the Delphi method to distribute expert consultation questionnaires online, allowing experts to exchange opinions anonymously to ensure that the findings were more desirable and scientific. No fixed number of experts is required for a Delphi study, but the more experts involved, the more stable the results will be [ 16 ]; this method generally includes 15 to 50 experts [ 17 ]. We selected clinicians from several tertiary hospitals in the Beijing area to serve as Delphi study consultants based on the following inclusion criteria: (1) they had a title of senior associate or above; (2) they had more than 10 years of work experience in the field of clinical scientific research, and (3) they were presiding over national scientific research projects. The exclusion criteria were as follows: (1) full-time scientific researchers, and (2) personnel in hospitals who were engaged only in management. To ensure that the selected experts were representative, this study selected 20 experts from 14 tertiary hospitals affiliated with Capital Medical University, Peking University, the Chinese Academy of Medical Sciences and the China Academy of Traditional Chinese Medicine according to the inclusion criteria; the hospitals featured an average of 1,231 beds each, and 9 hospitals were included among the 77 hospitals in the domestic comprehensive hospital ranking (Fudan Hospital Management Institute ranking). The experts represented various specialties and roles from different hospitals, including cardiology, neurosurgery, neurology, ear and throat surgery, head and neck surgery, radiology, imaging, infection, vascular interventional oncology, pediatrics, general practice, hematology, stomatology, nephrology, urology, and other related fields. This diverse group included physicians, nurses, managers, and vice presidents. The selected experts had extensive clinical experience, achieved numerous scientific research accomplishments and possessed profound knowledge and experience in clinical scientific research. This ensured the reliability of the consultation outcomes.

Design of the expert consultation questionnaire

The Delphi survey for experts included sections on their background, familiarity with the indicator system, system evaluation, and opinions. Experts rated indicators on importance, feasibility, and sensitivity using a 1–10 scale and their own familiarity with the indicators on a 1–5 scale. They also scored their judgment basis and impact on a 1–3 scale, considering theoretical analysis, work experience, peer understanding, and intuition. Two rounds of Delphi surveys were carried out via email with 20 experts to evaluate and suggest changes to the indicators. Statistical coefficients were calculated to validate the Delphi process. Feedback from the first round led to modifications and the inclusion of an AHP questionnaire for the second round. After the second round, indicators deemed less important were removed, and expert discussion finalized the indicator weights based on their relative importance scores. This resulted in the development of an index system for medical staff scientific literacy. The questionnaire is included in Additional file 1 (first round) and Additional file 2 (second round).

Using the boundary value method to screen the indicators

In this study, the boundary value method was utilized to screen the indicators of medical staff's scientific literacy, and the importance, feasibility, and sensitivity of each indicator were measured using the frequency of perfect scores, the arithmetic mean, and the coefficient of variation, respectively. When calculating the frequency of perfect scores and arithmetic means, the boundary value was set as "mean-SD," and indicators with scores higher than this value were retained. When calculating the coefficient of variation, the cutoff value was set to "mean + SD," and indicators with values below this threshold were retained.

The principles of indicator screening are as follows:

To evaluate the importance of the indicators, if none of the boundary values of the three statistics met the requirements, the indicators were deleted.

If an indicator has two aspects, importance, feasibility, or sensitivity, and each aspect has two or more boundary values that do not meet the requirements, then the indicator is deleted.

If all three boundary values for an indicator meet the requirements, the research group discusses the modification feedback from the experts and determines whether the indicator should be used.

The results of the two rounds of boundary values are shown in Table  1 .

Using the AHP to assign weights

After the second round of Delphi expert consultations, the analytic hierarchy process (AHP) was used to determine the weights of the two first-level indicators and the nine second-level indicators. The weights of the 37 third-level indicators were subsequently calculated via the percentage weight method. The AHP, developed by Saaty in the 1980s, is used to determine the priority and importance of elements constituting the decision-making hierarchy. It is based on multicriteria decision-making (MCDM) and determines the importance of decision-makers' judgments based on weights derived from pairwise comparisons between elements. In the AHP, pairwise comparisons are based on a comparative evaluation in which each element's weight in the lower tier is compared with that of other lower elements based on the element in the upper tier [ 18 ].

AHP analysis involves the following steps:

Step 1: Establish a final goal and list related elements to construct a hierarchy based on interrelated criteria.

Step 2: Perform a pairwise comparison for each layer to compare the weights of each element. Using a score from 1 to 9, which is the basic scale of the AHP, each pair is compared according to the expert’s judgment, and the importance is judged [ 19 , 20 ].

Yaahp software was employed to analyze data by creating a judgment matrix based on the experts' scores and hierarchical model. The index system weights were obtained by combining the experts' scores. The percentage weight method used experts' importance ratings from the second round to calculate weights, ranking indicators by importance, calculating their scores based on frequency of ranking, and determining weighting coefficients by dividing these scores by the total of all third-level indicators' scores. The third-level indicator weighting coefficients were then calculated by multiplying the coefficients [ 21 ].

Data analysis

Expert positivity coefficient.

The expert positivity coefficient is indicated by the effective recovery rate of the expert consultation questionnaire, which represents the level of expert positivity toward this consultation and determines the credibility and scientific validity of the questionnaire results. Generally, a questionnaire with an effective recovery rate of 70% is considered very good [ 22 ].

In this study, 20 questionnaires were distributed in both rounds of Delphi expert counseling, and all 20 were effectively recovered, resulting in a 100% effective recovery rate. Consequently, the experts provided positive feedback on the Delphi counseling.

Expert authority coefficient (CR)

The expert authority coefficient (Cr) is the arithmetic mean of the judgment coefficient (Ca) and the familiarity coefficient (Cs), namely, Cr =  \(\frac{({\text{Ca}}+{\text{Cs}})}{2}\) . The higher the degree of expert authority is, the greater the predictive accuracy of the indicator. A Cr ≥ 0.70 was considered to indicate an acceptable level of confidence [ 23 ]. Ca represents the basis on which the expert makes a judgment about the scenario in question, while Cs represents the expert's familiarity with the relevant problem [ 24 ].

Ca is calculated on the basis of experts' judgments of each indicator and the magnitude of its influence. In this study, experts used "practical experience (0.4), "theoretical analysis (0.3), "domestic and foreign peers (0.2)" and "intuition (0.1)" as the basis for judgment and assigned points according to the influence of each basis for judgment on the experts' judgment. Ca = 1 when the basis for judgment has a large influence on the experts, and Ca = 0.5 when the influence of the experts' judgment is at a medium level. When no influence on expert judgment was evident, Ca = 0 [ 25 ] (Table  2 ).

Cs refers to the degree to which the expert was familiar with the question. This study used the Likert scale method to score experts’ familiarity with the question on a scale ranging from 0 to 1 (1 = very familiar, 0.75 = more familiar, 0.5 = moderately familiar, 0.25 = less familiar, 0 = unfamiliar). The familiarity coefficient for each expert (the average familiarity for each indicator) was calculated. The average familiarity coefficient was subsequently computed [ 26 ].

The Cr value of the primary indicator in this study was 0.83, and the Cr value of the secondary indicator was 0.82 (> 0.7); hence, the results of the expert consultation were credible and accurate, as shown in Table  3 .

The degree of expert coordination is an important indicator used to judge the consistency among various experts regarding indicator scores. This study used the Kendall W coordination coefficient test to determine the degree of expert coordination. A higher Kendall W coefficient indicates a greater degree of expert coordination and greater consistency in expert opinion, and P  <  0.05 indicates that the difference is significant [ 26 ]. The results of the three-dimensional harmonization coefficient test for each indicator in the two rounds of the expert consultation questionnaire were valid ( p  <  0.01 ), emphasizing the consistency of the experts' scores. The values of the Kendall W coordination coefficients for both rounds are shown in Table  4 .

Basic information regarding the participants

The 20 Delphi experts who participated in this study were predominantly male (80.0%) rather than female (20.0%). In addition, the participants’ ages were mainly concentrated in the range of 41–50 years old (60.0%). The majority of the experts were doctors by profession (85.0%), and their education and titles were mainly doctoral degree (90.0%) and full senior level (17.0%). The experts also exhibited high academic achievement in their respective fields and had many years of working experience, with the majority having between 21 and 25 years of experience (40.0%) (Table  5 ).

Index screening

The boundary value method was applied to eliminate indicators, leading to the removal of 6 third-level indicators in the first round. One of these, the ability to use statistical software, was associated with a more significant second-level indicator involving data processing, which was kept after expert review. Six indicators were merged into three indicators due to duplication, and 5 third-level indicators were added, resulting in 2 primary indicators, 10 secondary indicators, and 43 third-level indicators.

In the second round of Delphi expert consultation, 5 third-level indicators were deleted, as shown in Additional file 3 , and only one third-level indicator, "Scientific spirit", remained under the secondary indicator "research attitude". The secondary indicator "Research attitude" was combined with "Research ethics" and the third-level indicator "Scientific spirit" was also considered part of "Research ethics". After expert discussion, these were merged into a new secondary indicator "Research attitude" with three third-level indicators: "Research ethics", "Research integrity", and "Scientific spirit". The final index system included two primary indicators, nine secondary indicators, and thirty-eight third-level indicators, as shown in Additional File 3 .

Final index system with weights

The weights of the two primary indexes, research literacy and research ability, were equal. This was determined using the hierarchical analysis method and the percentage weight method based on the results of the second round of Delphi expert consultation (Table  6 ). The primary indicator of research literacy encompasses the fundamental qualities and attitudes medical staff develop over time, including basic qualifications and approach to research. The primary indicator of research ability refers to medical professionals' capacity to conduct scientific research in new areas using suitable methods, as well as their skills needed for successful research using scientific methods.

In this study, the Delphi method was employed, and after two rounds of expert consultation, in accordance with the characteristics and scientific research requirements of medical staff in China, an index system for the scientific literacy of medical staff in China was constructed. The index system for medical staff's scientific literacy in this study consists of 2 first-level indicators, 9 second-level indicators, and 38 third-level indicators. Medical institutions at all levels can use this index system to scientifically assess medical staff's scientific literacy.

In 2014, the Joint Task Force for Clinical Trial Competency (JTF) published its Core Competency Framework [ 27 ]. The Framework focuses more on the capacity to conduct clinical research. These include principles such as clinical research and quality practices for drug clinical trials. However, this framework does not apply to the current evaluation of scientific literacy in hospitals. Because these indicators do not apply to all staff members, there is a lack of practical scientific research, such as information about the final paper output. Therefore, the experts who constructed the index system in this study came from different specialties, and the indicators can be better applied to scientific researchers in all fields. This approach not only addresses clinical researchers but also addresses the concerns of hospital managers, and the indicators are more applicable.

The weighted analysis showed that the primary indicators "research literacy" and "research ability" had the same weight (0.50) and were two important components of scientific literacy. Research ability is a direct reflection of scientific literacy and includes the ability to identify problems, the ability to use literature, professional capacity, subject implementation capacity, data-processing capacity, thesis-writing skills, and research output capacity. Only by mastering these skills can medical staff carry out scientific research activities more efficiently and smoothly. The ability to identify problems refers to the ability of medical staff to obtain insights into the frontiers of their discipline and to identify and ask insightful questions. Ratten claimed that only with keen insight and sufficient sensitivity to major scientific issues can we exploit the opportunities for innovation that may lead to breakthroughs [ 28 ]. Therefore, it is suggested that in the process of cultivating the scientific literacy of medical staff, the ability to identify problems, including divergent thinking, innovative sensitivity, and the ability to produce various solutions, should be improved. Furthermore, this study included three subentries of the secondary indicator "research attitude", namely, research ethics, research integrity, and scientific spirit. This is likely because improper scientific research behavior is still prevalent. A study conducted in the United States and Europe showed that the rate of scientific research misconduct was 2% [ 13 ]. A small survey conducted in Indian medical schools and hospitals revealed that 57% of the respondents knew that someone had modified or fabricated data for publication [ 28 ]. The weight of this index ranked first in the secondary indicators, indicating that scientific attitude is an important condition for improving research quality, relevance, and reliability. Countries and hospitals should develop, implement, and optimize policies and disciplinary measures to combat academic misconduct.

In addition, the third-level indicator "scheduling ability" under the second-level indicator "basic qualification" has a high weight, indicating that medical staff attach importance to management and distribution ability in the context of scientific research. Currently, hospitals face several problems, such as a shortage of medical personnel, excessive workload, and an increase in the number of management-related documents [ 29 , 30 ]. These factors result in time conflicts between daily responsibilities and scientific research tasks, thereby presenting significant obstacles to the allocation of sufficient time for scientific inquiry [ 31 ]. Effectively arranging clinical work and scientific research time is crucial to improving the overall efficiency of scientific research. In the earlier expert interviews, most medical staff believed that scientific research work must be combined with clinical work rather than focused only on scientific research. Having the ability to make overall arrangements is essential to solving these problems. The high weight given to the second-level index of 'subject implementation capacity', along with its associated third-level indicators, highlights the challenges faced by young medical staff in obtaining research subjects. Before implementing a project, researchers must thoroughly investigate, analyze, and compare various aspects of the research project, including its technical, economic, and engineering aspects. Moreover, potential financial and economic benefits, as well as social impacts, need to be predicted to determine the feasibility of the project and develop a research plan [ 32 ]. However, for most young medical staff in medical institutions, executing such a project can be challenging due to their limited scientific research experience [ 33 ]. A researcher who possesses these skills can truly carry out independent scientific research.

The weights of the second-level index "research output capacity" cannot be ignored. In Chinese hospitals, the ability to produce scientific research output plays a certain role in employees’ ability to obtain rewards such as high pay, and this ability is also used as a reference for performance appraisals [ 34 ]. The general scientific research performance evaluation includes the number of projects, scientific papers and monographs, scientific and technological achievements, and patents. In particular, the publication of papers is viewed as an indispensable aspect of performance appraisal by Chinese hospitals [ 35 ]. Specifically, scientific research papers are the carriers of scientific research achievements and academic research and thus constitute an important symbol of the level of medical development exhibited by medical research institutions; they are thus used as recognized and important indicators of scientific research output [ 36 ]. This situation is consistent with the weight evaluation results revealed by this study.

The results of this study are important for the training and management of the scientific research ability of medical personnel. First, the index system focuses not only on external characteristics such as scientific knowledge and skills but also on internal characteristics such as individual traits, motivation, and attitudes. Therefore, when building a research team and selecting and employing researchers, hospital managers can use the index system to comprehensively and systematically evaluate the situation of researchers, which is helpful for optimizing the allocation of a research team, learning from each other's strengths, and strengthening the strength of the research team. Second, this study integrates the content of existing research to obtain useful information through in-depth interviews with medical staff and constructs an evaluation index system based on Delphi expert consultation science, which comprehensively includes the evaluation of the whole process of scientific research activities. These findings can provide a basis for medical institutions to formulate scientific research training programs, help medical personnel master and improve scientific research knowledge and skills, and improve their working ability and quality. Moreover, the effectiveness of the training can also be evaluated according to the system.

In China, with the emergence of STEM rankings, hospitals pay more and more attention to the scientific research performance of medical personnel. Scientific literacy not only covers the abilities of medical personnel engaged in scientific research, but also reflects their professional quality in this field. Having high quality medical personnel often means that they have excellent scientific research ability, and their scientific research performance will naturally rise. In view of this,,medical institutions can define the meaning of third-level indicators and create Likert scales to survey medical staff. Based on the weights assigned to each indicator, comprehensive scores can be calculated to evaluate the level of scientific literacy among medical staff. Through detailed data analysis, they can not only reveal their shortcomings in scientific research ability and quality, but also provide a strong basis for subsequent training and promotion. Through targeted inspection, we can not only promote the comprehensive improvement of the ability of medical staff, but also promote the steady improvement of their scientific research performance, and inject new vitality into the scientific research cause of hospitals.

Limitations

This study has several limitations that need to be considered. First, the participants were only recruited from Beijing (a city in China), potentially lacking geographical diversity. We plan to select more outstanding experts from across the country to participate. Second, the index system may be more suitable for countries with medical systems similar to those of China. When applying this system in other countries, some modifications may be necessary based on the local context. Last, While this study has employed scientific methods to establish the indicator system, the index system has yet to be implemented on a large sample of medical staff. Therefore, the reliability and validity of the index system must be confirmed through further research. In conclusion, it is crucial to conduct further detailed exploration of the effectiveness and practical application of the index system in the future.

This study developed an evaluation index system using the Delphi method to assess the scientific literacy of medical staff in China. The system comprises two primary indicators, nine secondary indicators, and thirty-eight third-level indicators, with each index assigned a specific weight. The index system emphasizes the importance of both attitudes and abilities in the scientific research process for medical staff and incorporates more comprehensive evaluation indicators. In the current era of medical innovation, enhancing the scientific literacy of medical staff is crucial for enhancing the competitiveness of individuals, hospitals, and overall medical services in society. This evaluation index system is universally applicable and beneficial for countries with healthcare systems similar to those of China. This study can serve as a valuable reference for cultivating highly qualified and capable research personnel and enhancing the competitiveness of medical research.

Availability of data and materials

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

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Acknowledgements

The authors thank all who participated in the nominal group technique and two rounds of the Delphi study.

This study was supported by the National Natural Science Foundation of China (72074160) and the Natural Science Foundation Project of Beijing (9222004).

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Shuyu Liang and Ziyan Zhai contributed equally to this work and joint first authors.

Kai Meng and Yuan Gao contributed equally to this work and share corresponding author.

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Aerospace Center Hospital, No. 15 Yuquan Road, Haidian District, Beijing, 100049, China

Xiaozhi Sun, Jingxuan Jiao & Yuan Gao

School of Public Health, Capital Medical University, No.10 Xitoutiao, Youanmenwai Street, Fengtai District, Beijing, 100069, China

Shuyu Liang, Ziyan Zhai, Xingmiao Feng & Kai Meng

Beijing Tiantan Hospital, Capital Medical University, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China

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S.L. and Z.Z. contributed equally to this paper. S.L. took charge of the nominal group technique, data analysis, writing the first draft and revising the manuscript; Z.Z. was responsible for the Delphi survey, data analysis, and writing of the first draft of the manuscript; XF was responsible for the rigorous revision of Delphi methods; X.S. and J.J. were responsible for the questionnaire survey and data collection; Y.G. contributed to the questionnaire survey, organization of the nominal group interview, supervision, project administration and resources; and K.M. contributed to conceptualization, methodology, writing—review; editing, supervision, and project administration. All the authors read and approved the final manuscript.

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Correspondence to Yuan Gao or Kai Meng .

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Liang, S., Zhai, Z., Feng, X. et al. Development of an index system for the scientific literacy of medical staff: a modified Delphi study in China. BMC Med Educ 24 , 397 (2024). https://doi.org/10.1186/s12909-024-05350-0

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DOI : https://doi.org/10.1186/s12909-024-05350-0

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Life expectancy, long-term care demand and dynamic financing mechanism simulation: an empirical study of Zhejiang Pilot, China

• Xueying Xu 1 ,
• Yichao Li 2 &
• Hong Mi 2  

BMC Health Services Research volume  24 , Article number:  469 ( 2024 ) Cite this article

Metrics details

China has piloted Long-Term Care Insurance (LTCI) to address increasing care demand. However, many cities neglected adjusting LTCI premiums since the pilot, risking the long-term sustainability of LTCI. Therefore, using Zhejiang Province as a case, this study simulated mortality-adjusted long-term care demand and the balance of LTCI funds through dynamic financing mechanism under diverse life expectancy and disability scenarios.

Three-parameter log-quadratic model was used to estimate the mortality from 1990 to 2020. Mortality with predicted interval from 2020 to 2080 was projected by Lee-Carter method extended with rotation. Cohort-component projection model was used to simulate the number of older population with different degrees of disability. Disability data of the older people is sourced from China Health and Retirement Longitudinal Study 2018. The balance of LTCI fund was simulated by dynamic financing actuarial model.

Life expectancy of Zhejiang for male (female) is from 80.46 (84.66) years in 2020 to 89.39 [86.61, 91.74] (91.24 [88.90, 93.25]) years in 2080. The number of long-term care demand with severe disability in Zhejiang demonstrates an increasing trend from 285 [276, 295] thousand in 2023 to 1027 [634, 1657] thousand in 2080 under predicted mean of life expectancy. LTCI fund in Zhejiang will become accumulated surplus from 2024 to 2080 when annual premium growth rate is 5.25% [4.20%, 6.25%] under various disability scenarios, which is much higher than the annual growth of unit cost of long-term care services (2.25%). The accumulated balance of LTCI fund is sensitive with life expectancy.

Conclusions

Dynamic growth of LTCI premium is essential in dealing with current deficit around 2050 and realizing Zhejiang’s LTCI sustainability in the long-run. The importance of dynamic monitoring disability and mortality information is emphasized to respond immediately to the increase of premiums. LTCI should strike a balance between expanding coverage and controlling financing scale. This study provides implications for developing countries to establish or pilot LTCI schemes.

Peer Review reports

The lack of sufficient long-term care (LTC) for older individuals has become a pressing concern in both developed and developing countries with global population aging and increased longevity [ 1 ]. Although healthy life expectancy generally increased over last decades [ 2 ], the episode of disability in older people could have catastrophic impact on their household welfare [ 3 ]. Several developed countries, such as the Netherlands, Germany, and Japan, have established social long-term care insurance (LTCI) to address LTC demands of households with disabled older individuals. This approach proves more efficient in pooling disability risks than private LTCI [ 4 , 5 ]. Nonetheless, many developed countries had to reform their LTCI systems to deal with increasing aging population with LTC demands, often by raising premiums. Even though, these adjustments usually had time lags which affected the long-term sustainability of LTCI schemes. However, establishing social LTCI in developing countries proves more challenging than in developed countries because the lower income of residents restricts the financing capacity of LTCI. In addition, the lack of high-quality death registration and health survey data hinders optimizing LTCI systems design according to changing LTC demands, particularly in developing countries or small areas [ 6 ].

Massive evidence shows that there will be a steady and slow increase in life expectancy [ 7 , 8 , 9 ]. Evidence from developed countries shows that the long-term care needs increasing rapidly because of the increasing life expectancy [ 10 , 11 ]. The trend of the gap between life expectancy and healthy life expectancy is still inconclusive [ 12 ], which also affects the identification of LTC needs [ 13 ]. There is still mixed conclusion of disability and LTC demands trend in the future based on the three different assumptions of health transitions [ 14 , 15 , 16 ]. Whereas, there is less evidence regarding the assessment of LTC needs under different mortality scenarios. Zeng, et al. [ 17 ] calculated long-term care needs under different life expectancy scenarios, but the setting of life expectancy was relatively subjective. Besides, many studies in country-level controlled the impact of underreported mortality on the LTCI system by using modified mortality data [ 18 , 19 ], but few studies in the provincial level took that into consideration.

Most countries such as Germany and the Netherlands adopt a fixed percentage of income model to collect social LTCI premiums from individuals [ 20 ], and a few countries such as Singapore adopt a fixed amount premium model [ 21 ]. The premium of Germany LTCI has been 3.05% of gross income or 3.40% if individuals aged 23 and above without children since 2020 [ 22 ]. The Netherlands also has a tax-funded LTCI with the compulsory contribution of 9.65% of taxable income since 2017 [ 20 ]. In Singapore, fixed amount premium of LTCI is determined by the age of starting contribution and sex. The premium for a 30-year-old male (female) is around 200 (250) Singapore Dollars in 2020 [ 21 ], with an increase of 2% per year from 2020 to 2025 [ 23 ]. Financing parameters from both models should be adjusted regularly to ensure sustainability [ 24 , 25 ]. In China, both models are adopted in different LTCI pilot areas [ 26 ], but the areas that adopt the fixed amount of premium have not increased the premium level since the pilot, which affects long-term sustainability.

OECD countries will face high pressure of LTCI financing because of increasing average public LTC expenditures to 2.3% of GDP in 2040 for the future financing level of LTCI [ 27 ]. Therefore, an adjustment factor is suggested incorporated to simulate LTCI fund to reduce future financing pressure [ 22 ], but a higher short-term financing will bring greater resistance to reforms. Most simulation studies on China’s LTCI, based on fixed percentage of income model, demonstrated that LTCI financing will increase rapidly based on different disability scenarios [ 28 , 29 , 30 , 31 ]. Some studies also simulated LTCI financing based on fixed amount of premium model [ 32 , 33 ], but they did not consider its variation under different mortality scenarios. Only one study modified the mortality in a pilot city by using national mortality data when simulating the dynamic financing burden [ 34 ]. However, it only simulated to 2040 which did not cover plateau period of China’s aging.

China, as a developing country, pioneered social LTCI schemes in 2016. Local governments were granted significant autonomy, resulting in fragmented LTCI structures due to regional disparities in the pilot cities [ 35 ]. Thus it has become crucial to ensure the sustainability of China’s LTCI pilot areas. Zhejiang Province stands as a representative case among these pilot areas and its five cities (Tonglu, Ningbo, Jiaxing, Yiwu and Whenzhou) have piloted LTCI since 2017. Zhejiang has standardized disability assessments, coverage groups, benefit levels, and financing amounts of LTCI in province-level by 2022 [ 36 ]. It faces rapid aging ahead with high life expectancy in China. Notably, Zhejiang, one of the areas with fixed amount of premium of LTCI in China, has never increased its fixed premium since the pilot’s inception [ 36 ]. This lack of financing adjustment coupled with inflationary pressures strains Zhejiang’s LTCI fund. Zhejiang has capacities to facilitate LTCI operations through modified financing mechanism as the demonstration zone for the Initiative of Common Prosperity in China. Therefore, it can serve as a practical model for other developing countries establishing LTCI schemes to evaluate life expectancy and LTC demand parameters and guide its LTCI financing.

In summary, massive studies predict the LTC needs in developed countries and China. However, most of the studies on LTCI financing in China pilots overlook the potential death underreporting in census and uncertainty of mortality in projection period, which may misestimate the future LTC needs and financing pressure. In addition, current studies on the sustainability of China’s LTCI rarely involve the dynamic financing adjustment of fixed amount of premium model, and most studies do not cover the plateau period of China’s aging in the future, which may underestimate the financing level to achieve sustainable LTCI. Therefore, drawing from the Zhejiang Province case in China, this study proposes a dynamic financing mechanism to achieve a balance between sustainability and efficiency in social LTCI schemes, utilizing a simulation model with limited mortality and disability information. Our aim is to offer insights for developing countries to establish or pilot LTCI schemes. Three research questions will be addressed:

What is the long-term trend of life expectancy in Zhejiang from 1990 to 2080?

What extent of LTC demand will be reached among older people in Zhejiang from 2023 to 2080, with aging process?

What level of LTCI dynamic financing standards will achieve an actuarial equilibrium of the LTCI fund in Zhejiang, with rising life expectancy and LTC demand?

Data sources

For demographic data, the age-specific mortality and the population number by gender are from population census of Zhejiang Province in 1990, 2000, 2010 and 2020. The population census, which has been conducted once every 10 years since 1990, is a complete account of the entire population, mortality and fertility by age and sex in each census year and has the province-level representativeness of Zhejiang. Child mortality data is from Chinese Center for Disease Control and Prevention (CDC) in 1990–2013 [ 37 ], and official annual data of Zhejiang reported u p to 2020 [ 38 ]. Chinese CDC sorted and estimated under-5 mortality rates in China before 2013 with county-level and province-level representativeness, including data in Zhejiang. Data on the prevalence rate of disability of the older people is sourced from China Health and Retirement Longitudinal Study (CHARLS) in 2018. CHARLS is a national representative survey which covers a wide range of topics related to the adults aged 45 and above, including demographic information and health status. The national prevalence rate of disability by age and sex from CHARLS is used as a proxy for Zhejiang referring to existing research, due to lack of latest representative disability data in Zhejiang [ 39 ]. Older people are defined as those aged 60 and above based the statistical standards from World Health Organization [ 40 ], whose age groups are covered by CHARLS. The benefit criteria and financing criteria data is from the LTCI official regulations of pilot cities in Zhejiang [ 41 , 42 , 43 , 44 , 45 ]. Healthcare Consumer Price Index (CPI) from 2010 to 2020 in Zhejiang is from National Bureau of Statistics of China, covering the socio-economic indicators at province-level [ 46 ]. The change rate of total fertility of China from 2020 to 2080 is from World Population Prospects 2022 which forecasted fertility in country-level around the world [ 47 ].

Estimation of mortality pattern with three-parameter model life table approach

Model life tables methods are widely used in simulation of mortality for their effectiveness and accessibility to overcome the limited mortality information in developing countries [ 48 , 49 ]. Two-parameters log-quadratic model considering the child and adult mortality overcomes the shortage of Coale-Demeny and UN model life tables, among those model life tables methods [ 50 ]. Three-parameter log-quadratic model is designed on this to calculate the life table considering extra old-age mortality parameter with an adjustment of intercept with real census information [ 51 ]. It is so-called developing countries mortality database (DCMD) model which was adopted in the World Population Prospects 2019 since the three-parameter log-quadratic model life table was initially used in those developing countries without the high-quality mortality data [ 52 ]. The basic function of DCMD model is showed below:

This study used adjusted DCMD model to estimate the mortality in Zhejiang from 1990 to 2020 to make it usable for open population conditions. Child mortality ( \({\,}_{5}{q_0}\) ) is the first parameter of DCMD model, and adult mortality ( \({\,}_{{45}}{q_{15}}\) ) is the second parameter to be compared with estimated adult mortality ( \({\,}_{{45}}{\hat {q}_{15}}\) ) from two-parameter log-quadratic model with adjustment factor \(k\) . Specifically, child mortality by gender in consecutive years is estimated by sex ratio of child mortality in China [ 53 ]. Adult mortality in census years is calculated from census life table directly as the register completeness of adults’ death is higher in China [ 53 ]. Moreover, we averaged old-age mortality estimated from two-parameter log-quadratic model and from survival model for midpoint of old-age mortality between censuses (1995, 2005 and 2015) [ 51 ]. We averaged old-age mortality from two-parameter log-quadratic model and from census life table calculations for census years (1990, 2000, 2010 and 2020). The adjusted DCMD model was constructed on the incorporated old-age mortality. After that, the cubic hermite polynomial interpolation approach (pchip package in R) was adopted to estimate adult and old-age mortality from 1990 to 2020 [ 54 ]. The life table for consecutive years was estimated with DCMD model.

After that, Lee-Carter method extended with rotation (LC_ER) (mortcast package in R) was used to forecast the mortality up to 2080 [ 55 ], which provides critical death parameters to assess the LTCI demands in our case area. Since in low mortality countries, mortality decline is decelerating at younger ages and accelerating at older ages [ 56 ], the static assumption of mortality decline of traditional Lee-Carter model would be anomalous in long-term projection. LC_ER is a time-varying Lee-Carter model considering the changes of mortality decline between different age groups when modeling, which was widely recognized and adopted by World Population Prospects 2022 [ 57 , 58 ]. Therefore, potential LTCI demands change caused by changes in old-age mortality decline in long-term projections could be captured by LC_ER. The predicted mean of life expectancy would be set as the medium life expectancy scenario, and the lower and upper 95% predicted interval would be set as the low and high life expectancy scenarios.

Number of severe disabled older adults

LTCI beneficiaries refer to the severe disabled population according to the rules of LTCI in Zhejiang [ 36 ]. The study used the cohort-component projection (CCP) method to forecast the number of older population of Zhejiang from 2020 to 2080 [ 59 ]. The number of age-specific population by sex from Zhejiang population census 2020 was used as the base population of CCP model. Furthermore, the age-specific prevalence rate of disability from CHARLS 2018 was calculated. After that, the number of severe disabled older adults as the LTCI beneficiaries was calculated by multiplying age-specific older population and prevalence rate of disability. The basic project method is as follows:

\({\,}_{{x+1}}P_{x}^{{t+1}}\) represents the population of single age groups with the age of x to x  + 1 at the t  + 1 time. \(\left[ {{L^t}(x+1)/{L^t}(x)} \right]\) represents the survival ratio of age x to x  + 1 at t time. \(N{I^*}\) represents the net migration numbers in the corresponding age group from the t to t +  1 period, from other regions to Zhejiang.

Our estimated mortality will be used in CCP model. Since the total fertility of Zhejiang is lower than that of China, this study assumed that the total fertility of Zhejiang would start at 1.04 in 2020 based on Zhejiang population census [ 60 ]. Then, the future trend of Zhejiang’s total fertility would follow the United Nations’ estimated change rate of total fertility of China from 2020 to 2080 [ 47 ]. For net migration, The Census Survival Ratio Method was used to estimate the migration pattern based on the census data [ 61 ]. As one of the highest net in-migration provinces since 2010, Zhejiang will face the lower net in-migration intensity and be close to migration equilibrium in 2040 [ 62 ]. Based on this, it is assumed that the net migration rate in Zhejiang will experience a linear decrease and realize migration equilibrium by 2045.

Disability is defined as a difficulty in performing at least one of six Activities in Daily Living (ADL) [ 63 ], including bathing, dressing, eating, getting in/out of bed, using the toilet, and controlling urination and defecation in CHARLS. Then, mild disability is defined as having difficulty in 1–2 items of ADL, moderate disability as having difficulty in 3–4 items of ADL, and severe disability as having difficulty in at least 5 items of ADL [ 64 , 65 ]. Based on the discussion on the Disease Expansion, Disease Compression and Dynamic Equilibrium Theory [ 66 ], three different scenarios in changing disability were calculated [ 16 ]: a 0.8% annual decrease for age-specific prevalence rate of disability as the low disability scenario, the constant age-specific prevalence rate of disability as the middle disability scenario, and a 0.8% annual increase for age-specific prevalence rate of disability as the high disability scenario.

Dynamic financing actuarial model of social LTCI schemes

The study built a macro simulation model to further simulate the expenditure, financing and fund balance of LTCI based on the projection of severe disabled older population ( \(DisOP\) ) aged 60 and above and contribution population ( \(CP\) ) of LTCI aged 20 and above. The macro model is showed below:

In Formula (4), \(LTCE\) means LTC expenditures, \(HbdcCost\) , \(IcCost\) , \(HbdmcCost\) and \(NhcCost\) represent the unit cost of home-based daily living care (HBDC), institutional care, home-based daily living & medical care (HBDMC) and nursing hospital care per person per year, respectively. Among them, HBDC means that beneficiaries only receive formal daily living care services at home but without any medical care. HBDMC means that beneficiaries receive both formal daily living care services and professional medical care services at home. The difference of institutional care and nursing hospital care lies in that the former focuses more on daily living care, while the latter specializes in medical care. From 2023 to 2080, the unit cost of each type of LTC services is given an increase of 2.25% annually based on the average increase of healthcare CPI from 2010 to 2020. \(\alpha \) means the percentage of different types of LTC services utilization. Formula (5) describes the dynamic financing model and current balance of LTCI every year. \(premiu{m_{{t_0}}}\) is the fixed amount of premiums of LTCI in our base period. \(\lambda \) is annual growth of the amount of LTCI premiums. Formula (6) shows the accumulated balance of LTC fund which is determined by the current balance and the accumulated balance in previous period. \(\gamma \) is the interest rate of LTCI fund which represents the time value of the LTCI fund. Taking the inflation rate (2.25%) as a reference in the simulation process, we test the minimum value of \(\lambda \) that ensures a consistently positive accumulated balance in the LTCI fund up to 2080 across various disability scenarios.

Parameters of LTCI schemes in Zhejiang Province, China

The policies of LTCI schemes in five pilot cities in Zhejiang are sorted in Additional Table 1  (see Additional file 1 ) [ 41 ]. The LTCI schemes in Jiaxing City are representative among five pilot cities of LTCI in Zhejiang. Firstly, Jiaxing is the first city covering all employees and urban and rural residents equitably with the same benefits and premium since the adoption of LTCI (in 2017), which has navigated the reform of LTCI in Zhejiang. Secondly, LTCI benefits in Jiaxing are at the middle level among the five pilot cities, which is representative of average level in Zhejiang. The maximum benefits of HBDC in Jiaxing are lower than those in Yiwu and Wenzhou, and equal to those in Tonglu and Ningbo. Besides, the maximum benefits of institutional care are also lower than those in Yiwu, but higher than those in Tonglu and Ningbo. Overall, Jiaxing’s LTCI benefits stay average in Zhejiang. Thirdly, LTCI financing criteria in Jiaxing align with Ningbo and Tonglu (90 Chinese Yuan (CNY)/person/year), reflecting the standards across five cities. Therefore, this study adopted Jiaxing’s LTCI criteria as the parameters of LTCI simulations in Zhejiang. The unit costs of HBDC, institutional care, HBDMC and nursing hospital care are set at 1200 CNY/month, 2100 CNY/month, 1680 CNY/month and 1680 CNY/month in 2024 according to LTCI maximum benefits in Jiaxing (see Additional Table 1 , Additional file 1 ) [ 41 ]. The contributory group of LTCI is the group participating in social health insurance, whether retired or not. The LTCI financing parameter \(premiu{m_{{t_0}}}\) is based on a fixed amount of premiums in Jiaxing, of which the standard is 90 CNY/person/year [ 41 ].

Chinese government proposed a model of elderly care named “9073” model: 90% of older people receive home-based care, 7% receive community care and 3% receive institutional care [ 67 ]. “9073” model represents the prospects of China’s elderly care and is therefore suitable for the long-term simulation in this study [ 29 , 62 ]. Specifically, proportion of HBDC ( \({\alpha _{\text{1}}}\) ), institutional care ( \({\alpha _2}\) ), and combination of HBDMC and nursing hospital care ( \({\alpha _3}\) + \({\alpha _4}\) ) are set at 90%, 3% and 7%, respectively. Disabled older people can choose to receive HBDMC at home or receive nursing hospital care at medical institutions when facing medical care needs. It is free to choose the locations for these two LTC services, and it is quite similar to receiving community care in nature, as it also allows the option of receiving services at home or at community centers. Additionally, the LTCI benefits of these two LTC services in Jiaxing are equal. Therefore, we grouped them together when determining the beneficiaries’ choice of LTC services type ( \({\alpha _3}\) + \({\alpha _4}\) ). We set the interest rate of LTCI fund at 2.5% based on current interest rate of 5-year time deposit in China’s banks [ 68 ]. The sources of each parameter for simulation framework of the study are demonstrated in Additional Fig.  1 (see Additional File 1 ).

The mortality pattern and life expectancy of Zhejiang

The estimated mortality of Zhejiang from 1990 to 2020 is demonstrated in Fig.  1 based on adjusted DCMD model. Overall, the mortality for male and female presents a declining trend. Specially, the child mortality had a continued decline during the estimation period, but the adult mortality and old-age mortality had a slight increase between 1990 and 2000, then with a sharp decline between 2000 and 2020 afterwards.

We further predict the life expectancy at birth with 95% confidential interval under the LC_ER model from 2020 to 2080. The estimated and predicted life expectancy is demonstrated in Fig.  2 . Life expectancy of female had a stable increase from 1990 to 2020. While there was a slight decline of life expectancy of male from 1990 to 2000, then there was a rapid increase until 2020. The model results based on historical information show that life expectancy of both female and male will have an upward trend from 2020 to 2080. Besides, the gender difference in life expectancy will remain relatively stable in the future. In 2020, life expectancy was 80.46 years for male, 84.66 years for female. In 2080, the life expectancy will reach 89.39 [86.61, 91.74] years for male, 91.24 [88.90, 93.25] years for female. Besides, the age-specific rates of mortality decline of Zhejiang from 2021 to 2080 estimated by LC_ER are illustrated in Additional Fig.  2 (see Additional File 1 ).

Mortality pattern of Zhejiang in 1990–2020 based on adjusted DCMD model

Estimated and predicted life expectancy of Zhejiang in 1990–2080

The simulation of long-term care demand and expenditures in Zhejiang

Based on CCP method, the study has projected the number of older people and the number of severely disabled older people with different scenarios of disability in Zhejiang from 2020 to 2080 (shown in Table  1 ). It is illustrated that the population aged 60 and above in Zhejiang will firstly expand to around 2060 and then shrink until 2080. The number of older people with disabilities, especially those with severe disability, reflects the long-term care demand from a demographic perspective. We found that the number of older people with severe disability will continue to increase to 2080 under both medium and high disability scenarios. However, the number of older people with different degrees of disability will increase before 2060, and then decline slightly in the following 20 years under the low disability scenario. We also found that the number of severely and moderately disabled older people will be of little difference before 2050, which means that severe and moderate LTC demand is roughly equal.

Besides, the results of LTC demand under the high and low life expectancy scenarios are illustrated in Additional Table  2 and Additional Table  3 (see Additional file 1 ). It can be seen that Zhejiang Province will have a higher LTC demand under the scenario of higher life expectancy. The number of older people with severe disability under 95% upper interval of life expectancy in 2080 is 154 thousands higher than that under the predicted mean of life expectancy. And the number of older people with severe disability under 95% lower interval of life expectancy in 2080 is 169 thousands lower than that under the predicted mean of life expectancy. This result demonstrates the importance of mortality level prediction for assessing LTC demand.

Our study further calculated the LTCI expenditure paid by insurance fund every year from 2020 to 2080 to analyze the future long-term care demand in our case area from a financial perspective. The expenditure from LTCI illustrates an upward trend from 2023 to 2080 (see Fig. 3 ), with the higher price of long-term care services and increasing number of severe disabled older people. The LTCI expenditure is still increasing although there will be a slight decline in severe disabled older people under low disability scenario.

Projection of Long-term care insurance expenditure in Zhejiang, 2024–2080. Notes Results are based on the predicted mean of life expectancy

The simulation of LTCI fund under diverse disability and financing scenarios

The accumulated balance of LTCI fund from 2022 to 2080 is simulated on different dynamic financing growth scenarios in order to test how to make LTCI achieve actuarial balance in the long run. The accumulated balance and current balance of LTCI fund in Zhejiang are shown in Figs.  4 and 5 . When we set the annual premium growth rate at 2.25% which is equal to the average increase of healthcare CPI, there will be a deficit of current balance before 2028. As a result, the accumulated balance will become negative in 2032 under medium disability scenario, under high disability scenario in 2030 and under low disability scenario in 2036. This result shows that LTCI fund can only be sustainable within 12 years if the financing level grows at a low pace from 2024.

Accumulated balance of LTCI fund under different financing and disability scenarios. Notes Results are based on the predicted mean of life expectancy

Current balance of LTCI fund under different financing and disability scenarios. Notes Results are based on the predicted mean of life expectancy

The minimum annual premium growth is further tested to achieve the positive accumulated balance of LTCI fund under various scenarios from 2022 to 2080. We found that when the annual premium growth rate equals to 4.20%, LTCI fund will realize the long-term sustainable under low disability scenario, which means that the 4.20% financing growth standard is effective to make LTCI sustainable at a relatively low premium level under low disability scenario; however, it will still face the risk of the shortage of financing with 4.20% annual premium growth under the medium and high disability scenarios after 2039 and 2033.

Furthermore, the accumulate balance of LTCI fund remains at a moderate surplus and will not face a shortage until 2080 under the medium disability scenario when the annual premium growth rate equals 5.25%. Although the current balance of LTC fund will be negative in 2043 to 2058 under 5.25% annual premium growth (see Fig.  5 ), the accumulated surplus before 2042 and continuous interest will still realize the accumulated surplus of LTCI fund (5.83 billion CNY) in 2058. Overall, the annual premium growth rate at 5.25% is the best parameter choice if the age-specific prevalence rate of disability in Zhejiang Province is projected to remain stable. Finally, LTCI will be sustainable under all disability scenarios when the premium increases by 6.25% per year. However, this level will put a heavy payment burden on the residents, and there will be a large amount of fund redundancy if the disability does not continue to increase.

The simulation of LTCI fund under diverse life expectancy and financing scenarios

The impact of different life expectancy trend on the sustainability of LTCI schemes is further discussed. The simulation results of accumulated balance of LTCI fund under predicted mean, 95% upper confidential interval and lower confidential interval of life expectancy scenarios are demonstrated in Fig.  6 . It is learned that the sustainability of the LTCI fund will face a completely different situation in the long-term because of the difference trends in life expectancy even under the same disability level and financing level. Under the 5.25% annual premium growth rate and medium disability scenario, LTCI fund will become accumulated deficit under 95% upper interval of life expectancy after 2045. However, the LTCI fund will always remain in surplus before 2080 with the predicted mean or lower 95% interval of life expectancy. Therefore, the balance of LTCI fund is sensitive to life expectancy. In addition to affecting LTC expenditures when other conditions are the same, life expectancy is also related the total amount of financing by the number of contributors, thereby influencing the sustainability of LTCI fund.

Current and accumulated balance of LTCI under different life expectancy scenarios. Notes Results are based on the 5.25% annual premium growth rate scenario and medium disability scenario

This study shows two novel contributions to the existing literature. The first contribution is that we have found an important but often overlooked point that LTCI financing is sensitive to the variability of life expectancy in the long-term. In 2080, the 95% upper interval of the life expectancy in Zhejiang Province will be 2.01 years for female (2.35 years for male) higher than the predicted mean, and its cumulative impact will make LTCI unsustainable 35 years in advance. This finding shows that the accurate estimation of life expectancy is critical for assessing the sustainability of social insurance schemes like LTCI [ 69 , 70 ], and also reveals the significance of life expectancy analysis in this study, because health factors can be dynamically monitored through the evaluation and reimbursement records within the LTCI system [ 34 , 71 ], but life expectancy estimation will become difficult due to the lack of timely statistical data. Besides, the study also finds that LTCI financing is also sensitive to the variability of prevalence rate of disability in the long-term. Only 4.20% annual growth of premium can make Zhejiang’s LTCI sustainable under a disability compression assumption. However, the 6.25% annual growth of premium is necessary for Zhejiang’s LTCI sustainability under disability expansion assumption. The results are consistent with some existing research with various disability scenarios [ 28 , 72 ]. The overall incidence of disability will face a growing trend with population aging [ 17 ]. Therefore, proposing health promotion and postponing disability actions to reduce the incidence and duration of severe disability among older people will mitigate the pressure of LTCI funding [ 73 ].

The second contribution is that Zhejiang’s LTCI financing needs to grow at a relative high speed annually (5.25% under the medium scenario) to achieve sustainability in the long-term. It should be noticed that the LTCI financing parameters to achieve short-term and long-term fund equilibrium are different, and it is clear that long-term fund balance is a necessary condition to ensure the sustainability of the system [ 22 , 29 ]. If the accumulated surplus of the LTCI fund in Zhejiang Province before 2050 is used as a criterion for determining sustainability, as many studies have done [ 19 , 74 ], our results indicate that Zhejiang LTCI fund is projected to experience an accumulating deficit for over 20 years after 2050. Like Zhejiang, there are also several pilot cities in China that have adopted the fixed amount of premium model without premium adjustment [ 32 ]. LTCI funds in these regions will run the risk of accumulating deficits in the short term [ 43 ]. China and other countries adopting social LTCI need to adjust the scale of premium in a timely and dynamic manner to cope with the long-term LTCI financing pressure since China’s aging plateau will continue after 2060 [ 47 ].

Our simulation results can also be used as a reference for countries and regions that adopt a fixed percentage of income model of LTCI financing although we focus on the fixed amount model of LTCI financing. The study finds that LTCI premium in Zhejiang needs to increase by 5.25% per year to ensure sustainability to 2080 under the assumption of disability with dynamic equilibrium. However, the growth rate may exceed the income growth rate of some countries in the context of declining global economic growth [ 75 ]. Therefore, even those countries based on a fixed percentage of income model need adjust financing parameters dynamically [ 1 ]. In LTCI fund management, China and other countries can learn from Germany’s experience to deal with the long-term impact of population aging, which has established a demographic reserve fund which saves 0.1% of premium every year for payment in the future [ 25 ].

Reasonable coverage and benefits are also important factors to achieve sustainable LTCI. Like developed countries, the LTCI pilot cities in Zhejiang Province cover all urban and rural residents. However, most of the LTCI pilot cities in China only cover urban employees [ 35 ]. Therefore, the analysis of LTCI in Zhejiang Province in this paper provides implications for other LTCI pilot cities in China to expand the coverage and promote the equity of receiving LTC. Besides, it should be noted that this study only considers the older adults with severe disabilities according to the rules when estimating LTC needs in Zhejiang Province [ 36 ]. Whereas, it is not only the families of severely disabled groups that face the burden of long-term care [ 17 ]. Moderately disabled people in some developed countries and pilot cities in China are also covered by LTCI [ 76 , 77 ]. Even considering only severe disability, our simulation results show that only a high premium growth rate can make the system sustainable in the long run. Therefore, LTCI policymakers need to comprehensively weigh residents’ payment pressure and long-term care benefits, and make a balance between expanding coverage and increasing financing with the aim of protecting the most vulnerable groups.

This study has explored and built a long-term care insurance system that can be a reference for China and other developing countries to provide LTC services for the disabled older adults in the future. The strength of this study is that a more accurate life expectancy estimation based on the DCMD model is adopted when estimating dynamic financing of LTCI. However, this paper still has some limitations. Firstly, the paper only considers the activities of daily living when estimating the prevalence rate of disability of older people in Zhejiang Province, but does not consider cognitive function, perception and communication function due to the lack of data. Secondly, this study only considers the expenditure cost of LTC in the simulation analysis, but does not consider the operating cost of the LTCI system. Thirdly, this study only considers the total amount of financing for LTCI, but does not discuss the financing structure including individual contributions, government subsidies, and pooling funds. Finally, this study focuses only on the case in Zhejiang, but does not simulate the LTCI financing standard for actuarial equilibrium in other LTCI pilot areas in China.

In summary, this study estimates and predicts the mortality rate in Zhejiang Province from 1990 to 2080 through the DCMD model and LC model, and further evaluates the increasing LTC need in Zhejiang Province in the future. The LTCI dynamic financing in Zhejiang Province under different disability scenarios and life expectancy scenarios is simulated on the LTCI expenditure forecast results, and it is found that only by maintaining a relatively high level (5.25% under medium scenario) of premium growth can Zhejiang’s LTCI be sustainable in the long run. Our empirical case in Zhejiang offers implications for developing countries and LTCI pilot areas that lack high-quality mortality information to establish and dynamically optimize LTCI financing. Therefore, policy makers are called upon to assess the sustainability of LTCI from a long-term perspective, and regularly monitor changes in residents’ health and life expectancy to ensure that LTCI fund can meet LTCI expenditure and control the financing burden.

Data availability

In this study, all the data sources are publicly available. The data calculated in this study is available upon request to the corresponding author.

Abbreviations

Long-term care

• Long-term care insurance

China Health and Retirement Longitudinal Study

Center for Disease Control and Prevention

Consumer Price Index

Developing Country Mortality Database

Lee-Carter method extended with rotation

Cohort-component projection

Chinese Yuan

Home-based daily living care

Home-based daily living & medical care

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Acknowledgements

We would like to thank Professor Xiangming Fang from Zhejiang University, Professor Guangdi Chen from Zhejiang University and Chengxu Long from King’s College London for their constructive advice during the research process of the paper. We would also like to appreciate any comments from the 34th REVES meeting.

This work was supported by the Major Project of Zhejiang Provincial Natural Science Foundation of China (LD21G030001).

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Xu, X., Li, Y. & Mi, H. Life expectancy, long-term care demand and dynamic financing mechanism simulation: an empirical study of Zhejiang Pilot, China. BMC Health Serv Res 24 , 469 (2024). https://doi.org/10.1186/s12913-024-10875-7

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DOI : https://doi.org/10.1186/s12913-024-10875-7

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