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How to Write a Problem Statement | Guide & Examples

Published on November 6, 2022 by Shona McCombes and Tegan George. Revised on November 28, 2022.

A problem statement is a concise and concrete summary of the research problem you seek to address. It should:

Table of contents

When should you write a problem statement, step 1: contextualize the problem, step 2: show why it matters, step 3: set your aims and objectives.

Problem statement example

Frequently asked questions about problem statements

There are various situations in which you might have to write a problem statement.

In the business world, writing a problem statement is often the first step in kicking off an improvement project. In this case, the problem statement is usually a stand-alone document.

In academic research, writing a problem statement can help you contextualize and understand the significance of your research problem. It is often several paragraphs long, and serves as the basis for your research proposal . Alternatively, it can be condensed into just a few sentences in your introduction .

A problem statement looks different depending on whether you’re dealing with a practical, real-world problem or a theoretical issue. Regardless, all problem statements follow a similar process.

The problem statement should frame your research problem, giving some background on what is already known.

Practical research problems

For practical research, focus on the concrete details of the situation:

Theoretical research problems

For theoretical research, think about the scientific, social, geographical and/or historical background:

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The problem statement should also address the relevance of the research. Why is it important that the problem is addressed?

Don’t worry, this doesn’t mean you have to do something groundbreaking or world-changing. It’s more important that the problem is researchable, feasible, and clearly addresses a relevant issue in your field.

Practical research is directly relevant to a specific problem that affects an organization, institution, social group, or society more broadly. To make it clear why your research problem matters, you can ask yourself:

Sometimes theoretical issues have clear practical consequences, but sometimes their relevance is less immediately obvious. To identify why the problem matters, ask:

Finally, the problem statement should frame how you intend to address the problem. Your goal here should not be to find a conclusive solution, but rather to propose more effective approaches to tackling or understanding it.

The research aim is the overall purpose of your research. It is generally written in the infinitive form:

The research objectives are the concrete steps you will take to achieve the aim:

The aims and objectives should lead directly to your research questions.

Learn how to formulate research questions

You can use these steps to write your own problem statement, like the example below.

Step 1: Contextualize the problem A family-owned shoe manufacturer has been in business in New England for several generations, employing thousands of local workers in a variety of roles, from assembly to supply-chain to customer service and retail. Employee tenure in the past always had an upward trend, with the average employee staying at the company for 10+ years. However, in the past decade, the trend has reversed, with some employees lasting only a few months, and others leaving abruptly after many years.

Step 2: Show why it matters As the perceived loyalty of their employees has long been a source of pride for the company, they employed an outside consultant firm to see why there was so much turnover. The firm focused on the new hires, concluding that a rival shoe company located in the next town offered higher hourly wages and better “perks”, such as pizza parties. They claimed this was what was leading employees to switch. However, to gain a fuller understanding of why the turnover persists even after the consultant study, in-depth qualitative research focused on long-term employees is also needed. Focusing on why established workers leave can help develop a more telling reason why turnover is so high, rather than just due to salaries. It can also potentially identify points of change or conflict in the company’s culture that may cause workers to leave.

Step 3: Set your aims and objectives This project aims to better understand why established workers choose to leave the company. Qualitative methods such as surveys and interviews will be conducted comparing the views of those who have worked 10+ years at the company and chose to stay, compared with those who chose to leave.

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

All research questions should be:

Writing Strong Research Questions

Research objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

Your research objectives indicate how you’ll try to address your research problem and should be specific:

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What Is a Case Study?

An in-depth study of one person, group, or event

Kendra Cherry, MS, is an author and educational consultant focused on helping students learn about psychology.

problem of case study research

Cara Lustik is a fact-checker and copywriter.

problem of case study research

Verywell / Colleen Tighe

Benefits and Limitations

Types of case studies, how to write a 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 various fields, including psychology, medicine, education, anthropology, political science, and social work.

The purpose 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, it is important to follow the rules of APA format .  

A case study can have both 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 to impossible to replicate in a lab. Some other benefits of a case study:

On the negative side, a case study:

Researchers may choose to perform a case study if they are interested in exploring a unique or recently discovered phenomenon. The insights gained from such research can help the researchers develop additional ideas and study questions that might be explored in future studies.

However, it is important to remember that the insights gained 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 the use of individual case studies. Some great examples of case studies in psychology include:

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 had denied her the opportunity to learn language at critical points in her development.

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

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

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 utilize depends on the unique characteristics of the situation as well as the case itself.

There are also different methods that can be used to conduct a case study, including prospective and retrospective case study methods.

Prospective case study methods are those in which an individual or group of people is observed in order to determine outcomes. For example, a group of individuals might be watched over an extended period of time to observe the progression of a particular disease.

Retrospective case study methods involve looking at historical information. For example, researchers might start with an outcome, such as a disease, and then work their way backward to look at information about the individual's life to determine risk factors that may have contributed to the onset of the illness.

Where to Find Data

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:

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.

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?

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

A Word From Verywell

Case studies can be a useful research tool, but they need to be used wisely. In many cases, they are best utilized in situations where conducting an experiment would be difficult or impossible. They are helpful for looking at unique situations and allow researchers to gather a great deal of information about a specific individual or group of people.

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 that you are required to follow. If you are writing your case study for professional publication, be sure to check with the publisher for their specific guidelines for submitting a case study.

Simply Psychology. Case Study Method .

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 Kendra Cherry, MS, is an author and educational consultant focused on helping students learn about psychology.

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Organizing Your Social Sciences Research Paper

A research problem is a definite or clear expression [statement] about an area of concern, a condition to be improved upon, a difficulty to be eliminated, or a troubling question that exists in scholarly literature, in theory, or within existing practice that points to a need for meaningful understanding and deliberate investigation. A research problem does not state how to do something, offer a vague or broad proposition, or present a value question.

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Guba, Egon G., and Yvonna S. Lincoln. “Competing Paradigms in Qualitative Research.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, editors. (Thousand Oaks, CA: Sage, 1994), pp. 105-117.

Importance of...

The purpose of a problem statement is to:

In the social sciences, the research problem establishes the means by which you must answer the "So What?" question. This declarative question refers to a research problem surviving the relevancy test [the quality of a measurement procedure that provides repeatability and accuracy]. Note that answering the "So What?" question requires a commitment on your part to not only show that you have reviewed the literature, but that you have thoroughly considered the significance of the research problem and its implications applied to creating new knowledge and understanding or informing practice.

To survive the "So What" question, problem statements should possess the following attributes:

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Brown, Perry J., Allen Dyer, and Ross S. Whaley. "Recreation Research—So What?" Journal of Leisure Research 5 (1973): 16-24; Castellanos, Susie. Critical Writing and Thinking. The Writing Center. Dean of the College. Brown University; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Selwyn, Neil. "‘So What?’…A Question that Every Journal Article Needs to Answer." Learning, Media, and Technology 39 (2014): 1-5.

Structure and Writing Style

I.  Types and Content

There are four general conceptualizations of a research problem in the social sciences:

A problem statement in the social sciences should contain :

NOTE :   A statement describing the research problem of your paper should not be viewed as a thesis statement that you may be familiar with from high school. Given the content listed above, a description of the research problem is usually a short paragraph in length.

II.  Sources of Problems for Investigation

The identification of a problem to study can be challenging, not because there's a lack of issues that could be investigated, but due to the challenge of formulating an academically relevant and researchable problem which is unique and does not simply duplicate the work of others. To facilitate how you might select a problem from which to build a research study, consider these sources of inspiration:

Deductions from Theory This relates to deductions made from social philosophy or generalizations embodied in life and in society that the researcher is familiar with. These deductions from human behavior are then placed within an empirical frame of reference through research. From a theory, the researcher can formulate a research problem or hypothesis stating the expected findings in certain empirical situations. The research asks the question: “What relationship between variables will be observed if theory aptly summarizes the state of affairs?” One can then design and carry out a systematic investigation to assess whether empirical data confirm or reject the hypothesis, and hence, the theory.

Interdisciplinary Perspectives Identifying a problem that forms the basis for a research study can come from academic movements and scholarship originating in disciplines outside of your primary area of study. This can be an intellectually stimulating exercise. A review of pertinent literature should include examining research from related disciplines that can reveal new avenues of exploration and analysis. An interdisciplinary approach to selecting a research problem offers an opportunity to construct a more comprehensive understanding of a very complex issue that any single discipline may be able to provide.

Interviewing Practitioners The identification of research problems about particular topics can arise from formal interviews or informal discussions with practitioners who provide insight into new directions for future research and how to make research findings more relevant to practice. Discussions with experts in the field, such as, teachers, social workers, health care providers, lawyers, business leaders, etc., offers the chance to identify practical, “real world” problems that may be understudied or ignored within academic circles. This approach also provides some practical knowledge which may help in the process of designing and conducting your study.

Personal Experience Don't undervalue your everyday experiences or encounters as worthwhile problems for investigation. Think critically about your own experiences and/or frustrations with an issue facing society or related to your community, your neighborhood, your family, or your personal life. This can be derived, for example, from deliberate observations of certain relationships for which there is no clear explanation or witnessing an event that appears harmful to a person or group or that is out of the ordinary.

Relevant Literature The selection of a research problem can be derived from a thorough review of pertinent research associated with your overall area of interest. This may reveal where gaps exist in understanding a topic or where an issue has been understudied. Research may be conducted to: 1) fill such gaps in knowledge; 2) evaluate if the methodologies employed in prior studies can be adapted to solve other problems; or, 3) determine if a similar study could be conducted in a different subject area or applied in a different context or to different study sample [i.e., different setting or different group of people]. Also, authors frequently conclude their studies by noting implications for further research; read the conclusion of pertinent studies because statements about further research can be a valuable source for identifying new problems to investigate. The fact that a researcher has identified a topic worthy of further exploration validates the fact it is worth pursuing.

III.  What Makes a Good Research Statement?

A good problem statement begins by introducing the broad area in which your research is centered, gradually leading the reader to the more specific issues you are investigating. The statement need not be lengthy, but a good research problem should incorporate the following features:

1.  Compelling Topic The problem chosen should be one that motivates you to address it but simple curiosity is not a good enough reason to pursue a research study because this does not indicate significance. The problem that you choose to explore must be important to you, but it must also be viewed as important by your readers and to a the larger academic and/or social community that could be impacted by the results of your study. 2.  Supports Multiple Perspectives The problem must be phrased in a way that avoids dichotomies and instead supports the generation and exploration of multiple perspectives. A general rule of thumb in the social sciences is that a good research problem is one that would generate a variety of viewpoints from a composite audience made up of reasonable people. 3.  Researchability This isn't a real word but it represents an important aspect of creating a good research statement. It seems a bit obvious, but you don't want to find yourself in the midst of investigating a complex research project and realize that you don't have enough prior research to draw from for your analysis. There's nothing inherently wrong with original research, but you must choose research problems that can be supported, in some way, by the resources available to you. If you are not sure if something is researchable, don't assume that it isn't if you don't find information right away--seek help from a librarian !

NOTE:   Do not confuse a research problem with a research topic. A topic is something to read and obtain information about, whereas a problem is something to be solved or framed as a question raised for inquiry, consideration, or solution, or explained as a source of perplexity, distress, or vexation. In short, a research topic is something to be understood; a research problem is something that needs to be investigated.

IV.  Asking Analytical Questions about the Research Problem

Research problems in the social and behavioral sciences are often analyzed around critical questions that must be investigated. These questions can be explicitly listed in the introduction [i.e., "This study addresses three research questions about women's psychological recovery from domestic abuse in multi-generational home settings..."], or, the questions are implied in the text as specific areas of study related to the research problem. Explicitly listing your research questions at the end of your introduction can help in designing a clear roadmap of what you plan to address in your study, whereas, implicitly integrating them into the text of the introduction allows you to create a more compelling narrative around the key issues under investigation. Either approach is appropriate.

The number of questions you attempt to address should be based on the complexity of the problem you are investigating and what areas of inquiry you find most critical to study. Practical considerations, such as, the length of the paper you are writing or the availability of resources to analyze the issue can also factor in how many questions to ask. In general, however, there should be no more than four research questions underpinning a single research problem.

Given this, well-developed analytical questions can focus on any of the following:

NOTE:   Questions of how and why concerning a research problem often require more analysis than questions about who, what, where, and when. You should still ask yourself these latter questions, however. Thinking introspectively about the who, what, where, and when of a research problem can help ensure that you have thoroughly considered all aspects of the problem under investigation and helps define the scope of the study in relation to the problem.

V.  Mistakes to Avoid

Beware of circular reasoning! Do not state the research problem as simply the absence of the thing you are suggesting. For example, if you propose the following, "The problem in this community is that there is no hospital," this only leads to a research problem where:

This is an example of a research problem that fails the "So What?" test . In this example, the problem does not reveal the relevance of why you are investigating the fact there is no hospital in the community [e.g., perhaps there's a hospital in the community ten miles away]; it does not elucidate the significance of why one should study the fact there is no hospital in the community [e.g., that hospital in the community ten miles away has no emergency room]; the research problem does not offer an intellectual pathway towards adding new knowledge or clarifying prior knowledge [e.g., the county in which there is no hospital already conducted a study about the need for a hospital, but it was conducted ten years ago]; and, the problem does not offer meaningful outcomes that lead to recommendations that can be generalized for other situations or that could suggest areas for further research [e.g., the challenges of building a new hospital serves as a case study for other communities].

Alvesson, Mats and Jörgen Sandberg. “Generating Research Questions Through Problematization.” Academy of Management Review 36 (April 2011): 247-271 ; Choosing and Refining Topics. [email protected] Colorado State University; D'Souza, Victor S. "Use of Induction and Deduction in Research in Social Sciences: An Illustration." Journal of the Indian Law Institute 24 (1982): 655-661; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); How to Write a Research Question. The Writing Center. George Mason University; Invention: Developing a Thesis Statement. The Reading/Writing Center. Hunter College; Problem Statements PowerPoint Presentation. The Writing Lab and The OWL. Purdue University; Procter, Margaret. Using Thesis Statements. University College Writing Centre. University of Toronto; Trochim, William M.K. Problem Formulation. Research Methods Knowledge Base. 2006; Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Walk, Kerry. Asking an Analytical Question. [Class handout or worksheet]. Princeton University; White, Patrick. Developing Research Questions: A Guide for Social Scientists . New York: Palgrave McMillan, 2009.

Home Blog Business Case Study: How to Write and Present It

Case Study: How to Write and Present It

Case Study: How to Write and Present It

Marketers, consultants, salespeople, and all other types of business managers often use case study analysis to highlight a success story, showing how an exciting problem can be or was addressed. But how do you create a compelling case study and then turn it into a memorable presentation? Get a lowdown from this post! 

What is a Case Study? 

Let’s start with this great case study definition by the University of South Caroline:

In the social sciences, the term case study refers to both a method of analysis and a specific research design for examining a problem, both of which can generalize findings across populations.

In simpler terms — a case study is an investigative research into a problem aimed at presenting or highlighting solution(s) to the analyzed issues.

A standard business case study provides insights into:

Case studies (also called case reports) are also used in clinical settings to analyze patient outcomes outside of the business realm. 

But this is a topic for another time. In this post, we’ll focus on teaching you how to write and present a business case, plus share several case study PowerPoint templates and design tips! 

Case Study Woman Doing Research PPT Template

Why Case Studies are a Popular Marketing Technique 

Besides presenting a solution to an internal issue, case studies are often used as a content marketing technique . According to a 2020 Content Marketing Institute report, 69% of B2B marketers use case studies as part of their marketing mix.

A case study informs the reader about a possible solution and soft-sells the results, which can be achieved with your help (e.g., by using your software or by partnering with your specialist). 

For the above purpose, case studies work like a charm. Per the same report: 

Moreover, case studies also help improve your brand’s credibility, especially in the current fake news landscape and dubious claims made without proper credits. 

Ultimately, case studies naturally help build up more compelling, relatable stories and showcase your product benefits through the prism of extra social proof, courtesy of the case study subject. 

Case Study Computer PPT Template

Popular Case Study Format Types

Most case studies come either as a slide deck or as a downloadable PDF document. 

Typically, you have several options to distribute your case study for maximum reach:

Case Study Example Google PPT Template

How to Write a Case Study: a 4-Step Framework

Once you decide on your case study format, the next step is collecting data and then translating it into a storyline. There are different case study methods and research approaches you can use to procure data. 

But let’s say you already have all your facts straight and need to organize them in a clean copy for your presentation deck. Here’s how you should do it. 

Business Case Study Example PPT Template

1. Identify the Problem 

Every compelling case study research starts with a problem statement definition. While in business settings, there’s no need to explain your methodology in-depth; you should still open your presentation with a quick problem recap slide.

Be sure to mention: 

The above information should nicely fit in several paragraphs or 2-3 case study template slides

2. Explain the Solution 

The bulk of your case study copy and presentation slides should focus on the provided solution(s). This is the time to speak at lengths about how the subject went from before to the glorious after. 

Here are some writing prompts to help you articulate this better:

This part may take the longest to write. Don’t rush it and reiterate several times. Sprinkle in some powerful words and catchphrases to make your copy more compelling.

3. Collect Testimonials 

Persuasive case studies feature the voice of customer (VoC) data — first-party testimonials and assessments of how well the solution work. These provide extra social proof and credibility to all the claims you are making. 

So plan and schedule interviews with your subjects to collect their input and testimonials. Also, design your case study interview questions in a way that lets you obtain the quantifiable result.

4. Package The Information in a Slide Deck

Once you have a rough first draft, try different business case templates and designs to see how these help structure all the available information. 

As a rule of thumb, try to keep one big idea per slide. If you are talking about a solution, first present the general bullet points. Then give each solution a separate slide where you’ll provide more context and perhaps share some quantifiable results.

For example, if you look at case study presentation examples from AWS like this one about Stripe , you’ll notice that the slide deck has few texts and really focuses on the big picture, while the speaker provides extra context.

Need some extra case study presentation design help? Download our Business Case Study PowerPoint template with 100% editable slides. 

Case Study Man With Giant Clipboard PPT Template

How to Do a Case Study Presentation: 3 Proven Tips

Your spoken presentation (and public speaking skills ) are equally if not more important than the case study copy and slide deck. To make a strong business case, follow these quick techniques. 

Focus on Telling a Great Story

A case study is a story of overcoming a challenge, achieving something grand. Your delivery should reflect that. Step away from the standard “features => benefits” sales formula. Instead, make your customer the hero of the study. Describe the road they went through and how you’ve helped them succeed. 

The premises of your story can be as simple as:

Based on the above, create a clear story arc. Show where your hero started. Then explain what type of a journey they went through. Inject some emotions in the mix to make your narrative more relatable and memorable. 

Experiment with Copywriting Formulas 

Copywriting is the art and science of organizing words into compelling and persuasive combinations which help readers retain the right ideas. 

To ensure that the audience retains the right takeaways from your case study presentation, you can try using some of the classic copywriting formulas to structure your delivery. These include:

Take an Emotion-Inducing Perspectives

The key to building a strong rapport with an audience is showing that you are one of them and fully understand what they are going through. 

One of the ways to build this connection is speaking from an emotion-inducing perspective. This is best illustrated with an example: 

In the second case, the wording prompts listeners to paint a mental picture from the perspective of the bank employees — a role you’d like them to relate to. By placing your audience in the right visual perspective, you can make them more receptive to your pitches. 

Case Study Medical Example PPT Template

Final Tip: Use Compelling Presentation Visuals

Our brain is wired to process images much faster than text. So when you are presenting a case study, always look for an opportunity to tie in some illustrations such as: 

Use icons to minimize the volume of texts. Also, opt for readable fonts which can look good in a smaller size too.

Finally, practice your case study presentation several times — solo and together with your team — to collect feedback and make last-moment refinements! 

1. Business Case Study PowerPoint Template

problem of case study research

To efficiently create a Business Case Study it’s important to ask all the right questions and document everything necessary, therefore this PowerPoint Template will provide all the sections you need. 

Use This Template

2. Medical Case Study PowerPoint Template

problem of case study research

3. Medical Infographics PowerPoint Templates

problem of case study research

4. Success Story PowerPoint Template

problem of case study research

5. Detective Research PowerPoint Template

problem of case study research

6. Animated Clinical Study PowerPoint Templates

problem of case study research

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What is a case study?

A case study is a type of research method. In case studies, the unit of analysis is a case . The case typically provides a detailed account of a situation that usually focuses on a conflict or complexity that one might encounter in the workplace.

This research guide will assist you in finding individual case studies, as well as providing information on designing case studies. If you need assistance locating information, please Ask a Librarian .


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Challenges regarding transition from case-based learning to problem-based learning: a qualitative study with student nurses.

problem of case study research

1. Introduction

2. material and methods, 2.2. research design, 2.3. study setting, 2.4. population and sampling, 2.5. data collection method, 2.6. data analysis, 2.7. ethical considerations, 3.1. theme 1.1: challenges regarding facilitation, 3.1.1. subtheme 1.1.1: adaptation challenges.

“The challenge that I faced during transition was to adapt from case-based to problem-based learning.” ( Participant U ).
“Well, my point supports the statement of student U. The main problem is that we struggled to adapt.” ( Participant Y ).
“With case-based learning, we were face to face with the lecturers. Lecturers guided us that is why we adapted very easily into case-based learning unlike in problem based learning.” ( Participant W ).

3.1.2. Subtheme 1.1.2: Group Work

“When we transit to problem-based learning, we were given problems to solve and we were working in groups. Some of the students did not want to participate, end up having to go to class unprepared, not being able to solve the given problems. So, the biggest problem was working in groups.” ( Participant W ).
“The only issue that we had is poor participation from some group members. You gather your own information and end up doing the other member’s part of the job delegated to him/her.” ( Participant D ).
“The groups are changing every year. In the beginning of the year, we expect to have challenges because we are not used to each other as we are all new in the group and it is a big challenge. But as time goes on it becomes better since we get used to each other as group members. We get to know strengths and weaknesses of each other and we are delegating task based on strength and weaknesses. It becomes much easier to work together in that way. But now next year is the same routine again where I will get a new group and have to start the process all over again.” ( Participant A1 ).

3.1.3. Subthemes 1.1.3: Information Search

“You have to go and look for information and sometimes you don’t even know the sites (database) to get information from. You find out that sometimes you didn’t get enough information and feedback from the lecturer and you don’t know if you are right or wrong.” ( Participant N ).
“Problem-based learning was a whole new experience on its own as we look for information ourselves which is a challenge.” ( Participant A5 ).

3.1.4. Subtheme 1.1.4: Workload and Insufficient Time for PBL Content

“It (PBL) needs you to be very flexible of which it comes with a lot of work and we don’t have much of time because we are doing practicals as well as theory.” ( Participant K ).
“With PBL we do a lot of different topics in a short period of time and we end up struggling remembering all the condition, we lose concentration.” ( Participant Q ).
“Another challenge is that the PBL workload is too much, as a student the work load is always a challenge but in this case it was too much to a point that we ended up doing the work for the sake of just submitting.” ( Participant S ).
“I don’t know if it comes with problem-based learning but I feel like our lecturers gives us less time to do the work. They will give us work on Friday and say we must submit on Tuesday.” ( Participant H ).

3.1.5. Subtheme 1.1.5: Lack of Proper Guidance from Lecturers

“Most of the work is being done by the student without the guidance of the lecturers, it’s more like you are your own lecturer.” ( Participant P ).
“Sometimes we do not get clarity in class, we are usually given a topic or condition and we go and prepare when we come back to present, there is usually no clarity as to say what you did there was wrong and what you did there was right, you should go fix here and there.” ( Participant A2 ).
“Personally the challenges that I face with transiting to problem-based learning (PBL) is that, we are given a problem and we have to go and find solutions ourselves, then we present it to the lecturers after that we are not being corrected or told that this and this is wrong, they are a learning issue if we ask questions.” ( Participant Q ).

3.1.6. Subtheme 1.1.6: Learning Issues

“In problem-based learning, we facilitate everything ourselves. So we end up having learning issues, then we go research about them then come back the next day and present the same thing. We are not facilitated properly or corrected by the module facilitator in most cases actually hence we end up having learning issues.” ( Participant U ).
“Regarding transition to problem-based learning, during our classes there can be something new that is raised and if we do not the answer in class, it becomes a learning issue for a very long time.” ( Participant A3 ).
“If we can’t find the correct information or answer for that learning issue it becomes a learning issue for about three weeks.” ( Participant H ).
“It can become a learning issue forever because none of us comes up with a direct answer or if we come up with the wrong answer during presentations then it’s going to be a learning issue until we come up with the right answer.” ( Participant A6 ).
“Adding on the learning issues, yes my colleagues are correct with this process of us getting learning issues every week instead of the lecturers correcting us-It is a problem because if we were supposed to finish one module in 10 weeks we end up taking longer to finish the module because of those learning issues.” ( Participant A1 ).

3.1.7. Subtheme 1.1.7: Online Problem-Based Learning

“I feel like it is the online learning that is challenging so I think since we are going back to contact learning I think it is going to be much better.” ( Participant P ).
“The online learning also contributed in us not really understanding the problem-based learning. Even now, we don’t fully understand problem-based learning, because we have been doing it online.” ( Participant W ).
“Sometimes you will find that we attend class while we are in our beds and you will fall asleep and when you wake up you did not hear anything that’s the problem. Because we are attending online in our own space, we will be sleeping and the lecturers are not even aware of that.” ( Participant J ).
“We are using online platforms such as google meetings and some people have connectivity problems. They will be having the correct answer but due to connectivity problem, they cannot give that answer then it becomes a learning issue.” ( Participant A1 ).
“Remember most of the lessons are conducted online and we have network issues, so sometimes when there is an assistance we experience connectivity issues and end up missing that segment of the lesson.” ( Participant A5 ).
“Most classes are online and there are connectivity issue sometimes. This makes it hard to voice out your opinion, so we can’t express ourselves like in a contact classes.” ( Participant P ).
“I did not have problems or challenges with transition from case-based learning to problem-based learning, so for me the issue was more of the online problem-based learning.” ( Participant S ).

3.1.8. Subtheme 1.1.8: Lack of Student Instructors (SI)

“it is not advise perse to the students but it’s a suggestion on what the lecturers maybe can do or what the school of nursing can implement, like for our ancillary modules we used to have SI. So maybe if they can introduce SI’s for third and fourth years whereby other students can actually conduct classes to explain further for those who needs further explanations. Like they are senior students so they are able to explain better to the junior students.” ( Participant G ).
“Sometimes I just wish like we had SI for certain modules, like other student who are doing different courses have SI to help them. So in this course we don’t have SI who can help us with studying like other programs in the university.” ( Participant A4 ).

3.2. Theme 1.2: Challenges Regarding Assessment

3.2.1. subtheme 1.2.1: lack of feedback.

“With lecturers in PBL we do not get correction or feedback.” ( Participant Q ).
“Sometimes you didn’t get enough feedback from the lecturer and you don’t know if you are right or wrong.” ( Participant N ).

3.2.2. Subtheme 1.2.2: Lack of Revision

“When we are busy with the learning issues, we can’t move to other topics, that way we have less time to finish that module for that semester. In that way, we don’t have enough revision time for exam.” ( Participant H ).
“ Adding on the learning issues, yes my colleagues are correct with this process of us getting learning issues every week instead of the lecturers correcting us. It is a problem because if we were supposed to finish one module in 10 weeks we end up taking longer to finish the module because of those learning issues. The duration of a module becomes longer, it can take up to 16 weeks or 17 weeks and we end up finishing late and it waste the time we should be using revising the content of that module in preparation to the upcoming tests or exams.” ( Participant A1 ).

3.3. Theme 1.3: Strategies to Overcome Challenges

3.3.1. subtheme 1.3.1: collaboration with classmates/peer-assisted learning.

“I will also ask the presentations of different groups and compile and study with it together with my own work because I can not only rely on my work to help me so that’s how I manage to cope.” ( Participant N ).
“Studying as a group also helps because discussing amongst ourselves also makes it easier.” ( Participant Q ).
“We as student decided to share the presentations and slides that we have with each other and we would discuss with each other or go into other platform like YouTube to watch videos that would give more information. It made it much better.” ( Participant S ).
“What helped was to consult with each other because you may find that some else understands the problem better than I do, then we would help each other that way.” ( Participant A6 ).
“Collaborated team work will assist in getting information that the lecturer will, accept at the end of the day.” ( Participant Y ).
“I will advise student to utilize good communication it helps a lot to communicate with others especially when you approaching exams it happens that there are some things that you don’t understand and my friend explains to me it is easy to remember what you friend said than remembering what the lecturer said because is intimidating.” ( Participant L ).
“What I did I studied was I put more effort and I also ask my fellow class mates to explain some things better to me, because sometimes it’s a bit difficult to approach the lectures so I prefer to ask my colleagues to help me where they understand.” ( Participant N ).
“To add on what student M was saying I think what also help to overcome the challenges is to get different presentation from different groups as some conditions have similar symptoms so by gathering all that information you will know what makes them different.” ( Participant K ).

3.3.2. Subtheme 1.3.2: Use of Relevant Study Materials Such as Articles, Prescribed Books, and Past Question Papers

“The right prescribes text books and also relying on online books for more information helped me to improve on my second semester.” ( Participant K ).
“So firstly, I will start with either google scholar and find information and after I will go to the library for additional information and more sources.” ( Participant W ).
“What helped me was going through past question papers to see how the lecturers are setting- that’s my other coping mechanism. So that one helped a lot.” ( Participant Q ).

3.3.3. Subtheme 1.3.3: Plan Study Time

“I did what all the other students did basically. I put more effort and also practice time management. I look on the dates for tests, placement and exams. It is also helps to note them down so that you plan study time for yourself thus give you time to prepare.” ( Participant M ).
“Prepare before time like before going to class, so to know the topics that will be done.” ( Participant P ).

3.3.4. Subtheme 1.3.4: Consultation with Lecturers

“If I feel like I have questions I usually go and consult with the module facilitator or the relevant person which we are referred to by the module facilitator to consult.” ( Participant U ).
“Well what I can advise others is that they should consult with the lectures because some of us failed to consult that is where we encounter problems they should consult more and also use the library they can use the scenarios on the question papers.” ( Participant N ).
“I also forget to add consultation the lectures give you feedback and show you what to do.” ( Participant P ).
“The consultations with the lecturers were helpful because they helped us identify the areas that were troubling us too much and we would try to work on them.” ( Participant A5 ).
“Attending class wholeheartedly, make notes and revise after each class in order to identify the areas where challenges are. After that consult with the lecturers with informed information because some lecturers like to ask what is it that you really don’t understand and you can’t say everything you have to be specific on what you don’t understand.” ( Participant M ).

4. Discussion

4.1. challenges regarding facilitation, 4.2. challenges regarding assessment, 4.3. strategies to overcome challenges, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

Share and Cite

Phage, R.J.; Molato, B.J.; Matsipane, M.J. Challenges Regarding Transition from Case-Based Learning to Problem-Based Learning: A Qualitative Study with Student Nurses. Nurs. Rep. 2023 , 13 , 389-403. https://doi.org/10.3390/nursrep13010036

Phage RJ, Molato BJ, Matsipane MJ. Challenges Regarding Transition from Case-Based Learning to Problem-Based Learning: A Qualitative Study with Student Nurses. Nursing Reports . 2023; 13(1):389-403. https://doi.org/10.3390/nursrep13010036

Phage, Ramoipei J., Boitumelo J. Molato, and Molekodi J. Matsipane. 2023. "Challenges Regarding Transition from Case-Based Learning to Problem-Based Learning: A Qualitative Study with Student Nurses" Nursing Reports 13, no. 1: 389-403. https://doi.org/10.3390/nursrep13010036

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Case Study Research – Everything You Wanted to Know

Case study research remains a controversial data collection approach. However, it is recognized widely in different social studies. That’s because it enables researchers to provide in-depth explanations of different social behaviors.

case study research

Perhaps, you’re wondering, what is a case study research? Maybe you want to know what it involves. Well, this is a research method that utilizes reports from past studies. It allows researchers to explore and understand complex issues using those reports. This research method may be considered robust, especially when researchers require holistic, in-depth investigation.

Most social sciences recognize case study research design and methods and their roles have become more prominent. This approach is used to research and write about topics in education, sociology, and community-based issues like drug addiction, poverty, and unemployment.

What is Case Study Research?

In social studies, the case study is a research method in which a phenomenon is investigated in its real-life context. It’s an empirical inquiry and research strategy that is based on an in-depth investigation of a group, event, or individual to explore the underlying principles causes.

Essentially, this study can be defined as an exploratory and descriptive analysis of a case. But, what is a case study in research? Well, a case can be anything that a researcher wants to investigate. This can include a person, a group, an event, a decision, a policy, period, institution, or any other system that can be studied historically.

Methods Used in Case Study Research

This type of research uses the same study methodology with other research types. But, the most common case study research method starts with the definition of a single case. It can also be a group comprising similar cases. These can be incorporated for a multiple-case study.

This is followed by a search to determine what is already known about the case or cases. This search can involve a review of grey literature, reports, and media content. This review plays a critical role in enabling the researchers to understand the case. It also informs researchers when it comes to developing case study research questions.

In most case studies, data is often qualitative, though not exclusively. Thus, researchers engage in case study qualitative research. When researchers use multiple cases, they analyze each case separately. Themes can arise from assertions or analysis about the entire case.

Case study research methodology can include: Personal interviews Archival records Psychometric tests Direct observation

Case studies are more in-depth when compared to observational research. That’s because they use several records or measures while focusing on a single subject. In some cases, a multiple-case design can be used. What’s more, a case study can be retrospective or prospective. A retrospective case study uses criteria to choose cases from historical records. Prospective case studies, on the other hand, uses established criteria while including extra cases as long as they meet the set criteria.

Because case studies use qualitative data like the one collected from interviews, they tend to be more liable. However, quantitative data and questionnaires can also be used. For instance, a case study can be used in clinical research to monitor and determine the effectiveness of treatment.

Types of Case Study Research

When you research case study, you explore causation to identify the underlying principles. But, they can’t be generalized to a larger population the way researchers do when conducting experimental research. They also can’t provide predictive power the same way correlational research can do. Rather, they provide extensive data that can be used to develop new hypotheses that can be used for further research. It can also be used to study rare conditions or events that are hard-to-study.

A case study research paper can fall into any of these categories:

People may define case study research differently based on these major types of this investigation. Nevertheless, it’s an intensive and systematic investigation of a group, community, individuals, or other units where the researchers examine in-depth data that relate to several variables.

Example of Case Study Research

Case study definition in research may vary. However, students should be keen to choose topics they are comfortable researching and writing about. Here are case study research question examples that be used for this kind of investigation.

Different data collection methods can be used to assess and understand each case separately. This can lead to a better understanding of the phenomenon under investigation. The goal of the case study research design is to provide a framework that can be used to evaluate and analyze complex issues. For instance, in the last two examples above, a case study can be used to shed light on the nursing practice as a holistic approach. It can also provide a perspective that will inform the nurses to give improved care to their patients.

How to Do Case Study Research

When writing a case study paper, follow these steps, suggested by our writing professionals :

In a nutshell, a case study entails collecting data that leads to a better understanding of a phenomenon. The methodology of a case study provides a framework that is used to analyze and evaluate more complex issues.

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

problem of case study research

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:

case study

Case Study Format

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

How to Write a Case Study

Let's discover how to write a case study.

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:


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:

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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.


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:

case study

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:

Problems to avoid:

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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:

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 writing service, and all of your worries will be gone.

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Problem Solving Case Studies Samples For Students

24 samples of this type

WowEssays.com paper writer service proudly presents to you a free database of Problem Solving Case Studies aimed to help struggling students tackle their writing challenges. In a practical sense, each Problem Solving Case Study sample presented here may be a guidebook that walks you through the important phases of the writing process and showcases how to develop an academic work that hits the mark. Besides, if you need more visionary help, these examples could give you a nudge toward a fresh Problem Solving Case Study topic or inspire a novice approach to a banal subject.

In case this is not enough to satisfy the thirst for effective writing help, you can request personalized assistance in the form of a model Case Study on Problem Solving crafted by an expert from scratch and tailored to your specific instructions. Be it a plain 2-page paper or an in-depth, lengthy piece, our writers specialized in Problem Solving and related topics will deliver it within the pre-set period. Buy cheap essays or research papers now!

Self-analysis Paper Case Study Examples

Analysis Section

Good Case Study On System Thinking: Strategic Planning

Free case study about reason for referral.

Alberto, age 55, was brought to the emergency department of a regional medical center by his brother-in-law. Alberto is pacing, demanding, agitated, and speaking vociferously. “I did not wish to come here! My brother-in-law is simply jealous and he is trying to make me appear like I am suffering from some sort of insanity!” Alberto’s treatment is financially subsidized by his brother in law. Alberto will undergo a maximum of 8 sessions at 2 hours each session. The session will start on June 25, 2014.

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Good Example Of Case Study On Management and Leadership

Management and leadership, case study on information systems in education, good utilitarianism case study example, free case study on the beach farm ltd, introduction.

Creating overall viable plan for the development of the company requires thorough consideration of the peculiarities of business, strategy-friendly organizational structure and the ways to enhance employees’ engagement level, as well as proper management tools. For the purposes of this assignment I will research into above-mentioned issues with respect to elaborating on the overall business strategy for the enterprise under study. To make the discussion more practical I will view the organization as a problem-solving mechanism (Coperrider&Srivastva 149), and identify issues and the way to solve them for each of the questions of the case study

Organization structure

Dell company case study, earlene and martha: case study examples, human observation project case study examples, early childhood:.

Observer: Code Name of Subject: “Pony Tails” Age of Subject: Three years old Location: Baby day care center Supervisor Signature: Physical Characteristics Height – One foot. Weight – Roughly eight kilogram’s. Subject has average weight compared to children her age. Bone structure is not fully visible at this age.

Proportions – “Pony tails” still has a large head to body proportion which is consistent with children her age.

Hearing – “Pony tails” passed the ball when requested by a fellow child. This was from approximately 20 feet away amidst playground high noise levels.

Vision – “Pony tails” could spot a small tennis ball that got lost in a flower bush from 10 feet away.

Management case studies questions, skills approach, case study on strategic management paper, communication politics case study examples, example of crisis theory and intervention case study, good case study on the od consultation style to be recommended in this situation would be: the pathfinder, a leadership case study.

Introduction This paper examines the manifestation of certain leadership traits in two different case study subjects and presents a comparative analysis between management and leadership and as to the effect on their effectiveness.

Closing Case: Strategy Implementation At Dell Computer Case Study Samples

- Use electronic and non-electronic sources to find out how Dell utilized its different kinds of organizational structures over time in capturing the following performance area: - Motivation / Goal Setting Process - Decision making/ Problem solving process - Conflict Resolution - Team building/ team process

Designation Dell utilized its different kinds of organizational structures over time in capturing motivation and goal setting process, decision making, conflict resolution and team building in the following manner.

Motivation / Goal Setting Process

Who should be considered for the promotion case studies example.

LT Brown should be considered for the spot promotion. This decision is based on leadership perspectives that are to be considered. Choosing between the two candidates LT Coffer and LT Brown is a process that will require CDR Smith to consider a number of aspects. This is because both candidates are equally qualified in terms of their experience, rank, and passion for higher responsibilities. In his book, Leadership: Theory and Practice Peter Northouse suggests a number of approaches that can be used to select leaders in such circumstances.

Factors that lead to the decision

Corporate responsibility case study examples, case study on everest team simulation, performance appraisal case study examples, lola’s performance evaluation, total quality management in toyota case study examples.

1) List the principles of TQM in Toyota and discuss how Toyota implements the principles. Provide examples in every point.

Case Study On Leadership and Organization

A case study on fridgecom.

Question 1: Using specific examples drawn from the case study, critically discuss different styles of leadership and potential behavioural responses.

Case Study On Managing People Information And Knowledge

Using specific examples drawn from the case study, critically discuss different styles of leadership and potential behavioural responses.

In today’s global economy, leadership and organizational behaviour are important factors that can determine the success of organizations. However, the methods used by leaders to influence employees for achieving goals are a matter of contention among researchers (Skansi, 2000). In answering this question, various leadership styles that motivate employees to increase productivity levels have been discussed. This answer attempts to focus on the various modes of leadership and potential behavioural responses to them in terms of motivation, communication, perception, and teamwork.

Understanding Leadership

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problem of case study research

Case Study Research Design

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

This article is a part of the guide:

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

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

problem of case study research

What is a Case Study?

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

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

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

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

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

Case Study

The Argument for and Against the Case Study Research Design

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

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

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

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

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

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

How to Design and Conduct a Case Study

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

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

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

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

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

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

How to Analyze the Results

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

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

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

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

Martyn Shuttleworth (Apr 1, 2008). Case Study Research Design. Retrieved Mar 06, 2023 from Explorable.com: https://explorable.com/case-study-research-design

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Research Method

Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

Case Study Research

Case study is a research method that involves an in-depth, detailed examination of a single unit, such as an individual, family, group, organization, community, or event. Case studies are usually conducted by sociologists, psychologists, historians, anthropologists, or researchers from other social science disciplines.

Case studies are used to provide a rich and detailed account of a particular social phenomenon. They are often used to generate new hypotheses or to test existing theories. In some cases, case studies are also used to evaluate programs or interventions.

Types of Case Study

There are three types of case study research:

Exploratory Case Studies

Descriptive case studies, explanatory case studies.

Exploratory case studies are conducted when little is known about a phenomenon. They are used to generate hypotheses and gather preliminary data.

Descriptive case studies describe a phenomenon in detail. They are used to develop an understanding of a complex issue.

Explanatory case studies explain why or how something happens. They are used to test theories and identify cause-and-effect relationships.

Case Study Data Collection Methods

There are a variety of case study data collection methods, including:


Interviews are perhaps the most common type of data collection in case studies. They allow researchers to collect detailed information about individuals’ experiences and perspectives.

Observations can also be useful in case studies, particularly if the researcher is interested in studying how people interact with their environment.

Document Analysis

Document analysis is another common data collection method in case studies; it involves examining documents such as policy records, media reports, and demographic data.

How to conduct Case Study Research

Conducting case study research is a complex process that requires both scientific and methodological rigor. Follow the steps below:

Advantages of Case Study Research

There are several advantages of using case study research.

Also see Focus Groups in Qualitative Research

Disadvantages of Case Study Research

There are also a number of drawbacks to using this approach.

About the author

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Muhammad Hassan

I am Muhammad Hassan, a Researcher, Academic Writer, Web Developer, and Android App Developer. I have worked in various industries and have gained a wealth of knowledge and experience. In my spare time, I enjoy writing blog posts and articles on a variety of Academic topics. I also like to stay up-to-date with the latest trends in the IT industry to share my knowledge with others through my writing.

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Case Study Research Method in Psychology

Saul Mcleod, PhD

Educator, Researcher

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

Saul Mcleod, Ph.D., is a qualified psychology teacher with over 18 years experience of working in further and higher education.

Learn about our Editorial Process

Olivia Guy-Evans

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.

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

What are Case Studies?

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 which are currently occurring in his or her everyday life.

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

Case studies are widely used in psychology, and amongst the best known were the ones carried out by Sigmund Freud, including Anna O and Little Hans .

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. Even today, case histories are one of the main methods of investigation in abnormal psychology and psychiatry.

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.

The procedure used in a case study means that the researcher provides a description of the behavior. This comes from interviews and other sources, such as observation.

The client also reports detail of events from his or her point of view. The researcher then writes up the information from both sources above as the case study and interprets the information.

The research may also continue for an extended period of time, so processes and developments can be studied as they happen.

Amongst the sources of data the psychologist is likely to turn to when carrying out a case study are observations of a person’s daily routine, unstructured interviews with the participant herself (and with people who know her), diaries, personal notes (e.g., letters, photographs, notes) or official document (e.g., case notes, clinical notes, appraisal reports).

The case study method often involves simply observing what happens to or reconstructing ‘the case history’ of a single participant or group of individuals (such as a school class or a specific social group), i.e., the idiographic approach .

The interview is also 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.

Most of this information is likely to be qualitative (i.e., verbal description rather than measurement), but the psychologist might collect numerical data as well.

The data collected can be analyzed using different theories (e.g., grounded theory, interpretative phenomenological analysis, text interpretation, e.g., thematic coding).

All the approaches mentioned here use preconceived categories in the analysis, and they are ideographic in their approach, i.e., they focus on the individual case without reference to a comparison group.

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.

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 ).


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 observer bias, 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.

How to reference this article:

McLeod, S. A. (2019, August 03). Case study method . Simply Psychology. simplypsychology.org/case-study.html

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.

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

Freud’s Case Studies

Little Hans – Freudian Case Study

H.M. Case Study

Anna O – Freudian Case Study

Genie Case Study – Curtiss (1977)

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Insight Learning: 10 Examples, Definition, Case Studies

insight learning examples definition and features

Insight learning is the sudden realization of a solution to a problem. It is the result of seeing the connection between variables in a situation that were not recognized previously.

Today, this is commonly referred to as the “A-ha” moment, often signified in emoji or image form by a lightbulb appearing over one’s head.

Examples of insight learning include coming to a realization when walking in the woods, coming to a sudden moment of clarity during an experiment, or overcoming a threshold concept in mathematics.

Insight Learning Definition and Properties

Wolfgang Köhler (1925; 1959) is the first modern scholar to identify insight learning during his studies with chimpanzees.

He was interested in studying their ability to problem solve and thus, presented them with various scenarios and observed their attempted solutions.

As quoted by Vonk et al. (2021),

“Köhler (1925/2019) defined insight as the awareness of functional relationships in a given situation and its rapid application to formulate a solution to the present situation” (p. 9).

According to Shettleworth (2012) insight learning has three general properties:

Insight Learning Examples

Case Studies and Research Basis

1. the mentality of apes (köhler, 1913).

In 1913, Wolfgang Köhler became the director of a research station in the Canary Islands. At that time in science, there was considerable debate as to whether animals were capable of intelligent problem solving, or just able to solve problems haphazardly or as a result of conditioning.   

Köhler presented various animals with a series of problems (fetching a fruit) that could only be resolved by overcoming an obstacle.

Time and time again, Köhler referred to a time delay between early failed attempts at solving the problem, and the eventual solution being found.

“After many failures, [Tschego] finally sits down quietly. But her eyes wander and soon fix on the little tree, which she had left lying a little way behind her, and all of a sudden, she seizes it quickly and surely, breaks off a branch, and immediately pulls the objective to her with it.” (Köhler, 1925, pp. 111–112)

The time delay is considered a key property of insight learning.

2. Darwin’s Thinking Path (Darwin, 1859)

Darwin is considered to be one of the greatest theorists in history. He developed insights into human evolution that still impact our thinking today. Darwin was as an avid walker and developed many aspects of his greatest insights while walking down his trail.

As Jeremy DeSilva explains:

“Darwin’s best thinking, however, was not done in his study. It was done outside, on a lowercase d –shaped path on the edge of his property. Darwin called it the Sandwalk. Today, it is known as Darwin’s thinking path.”

Walking clears the mind…lets it wander freely…and often leads to insights that would have never occurred if sitting at a desk under fluorescent lights.

Darwin’s often misunderstood survival of the fittest is illustrated in this quote:

“…those individuals whose functions are most out of equilibrium with the modified aggregate of external forces, will be those to die; and that those will survive whose functions happen to be most nearly in equilibrium with the modified aggregate of external forces.” (Darwin, 1859, p. 444)

3. Animal Insights (Goodall, 1990)

Before being offended, consider the problem presented to crows in the above video. Do you think you could solve it? Of course you could, and probably much faster than a crow. The purpose of this thought experiment, however, is to highlight the issue of what separates people from animals.

The more research conducted is in comparative psychology, the more difficult it becomes to define human beings in a way that completely precludes animals.

Over the years there have been many concepts proposed to distinguish humans from animals: social dynamics, communication, cooperative problem-solving, tool use, and…insight learning.

Each distinction has so far failed to pass the test of time.

As Jane Goodall (1990) states, a

“succession of experiments that, taken together, clearly prove that many intellectual abilities that had been thought unique to humans were actually present, though in a less highly developed form, in other, non-human beings” (p. 18).

The behaviorists saw the “A-ha” moment observed in animal problem-solving as a result of accumulated stimulus-response associations.

However, Wolfgang Köhler’s experiments led him to the conclusion that the chimpanzees and apes in his studies were engaged in cognitive processing. In fact, his characterizations of their problem-solving abilities would fit our definition today of insight learning.

4. Insight Discovery (Pearce et al., 2022)

As the world becomes more interconnected, cross-cultural dynamics and shared challenges become more prevalent. Therefore, conventional approaches and mindsets to multi-faceted issues and problem-solving are becoming less applicable.

Pearce et al. (2022) suggest that “particular expertise that can identify new connections between diverse knowledge fields is needed in order to integrate diverse perspectives…and develop novel solutions” (p. 1).

“Traditionally, institutions of higher education have been organized around providing students with the competences to succeed in individual disciplines rather than to have the capacity to solve problems in the real world” (p. 8).

The authors introduce an “insight discovery process” (IDP) as a path of breaking conventional mindsets and integrating more varied perspectives.

Over a 4-year period, a Transdisciplinary Lab (TdLab) was designed to tackle environmental issues. The program took place in Wislikofen Switzerland and included master’s students, Ph.D. candidates and postdoctoral researchers from 13 countries.

The researchers conclude that IDP can play a role in addressing sustainability and climate change, provided a means for “transformative learning,” and facilitates transdisciplinary collaboration.

Insight learning occurs after a delay in failed problem-solving attempts. The solution appears suddenly and is often referred to as an “A-ha” moment.

Although there are specific conditions for a solution to qualify as insight learning, the meaning of the term is sometimes defined more flexibly.

Researchers in non-Western countries may have slightly different definitions of the term. Others have devised unique educational programs with the goal of generating transdisciplinary perspectives on today’s most pressing challenges.

Insight learning also has an interesting history of debate among researchers in comparative psychology.

Some of the field’s greatest minds, including Edward Thorndike, Jane Goodall, and Wolfgang Köhler, conducted experiments and observations to better understand the nuances of both animal and human problem-solving.

Danek, A. H., Fraps, T., von Müller, A., Grothe, B., & Öllinger, M. (2014). It’s a kind of magic: What self-reports can reveal about the phenomenology of insight problem solving. Frontiers in Psychology, 5 , 1408. https://doi.org/10.3389/fpsyg.2014.01408

Darwin, C. (1859). On the origin of species . Oxford: Oxford University Press.

Goodall, J. (1990). Through a window . Boston: Houghton Mifflin.

Köhler, W. (1925/1959). The mentality of apes. (E. Winter, Trans.). New York, USA: Vintage Books.

Pearce, B.J., Deutsch, L., Fry, P., Marafatto, F. F., & Lieu, J. (2022) . Going beyond the AHA! moment: insight discovery for transdisciplinary research and learning. Humanities & Social Sciences Communications, 9, 123. https://doi.org/10.1057/s41599-022-01129-0

Povinelli, D., & Bering, J. (2002). The mentality of apes revisited. Current Directions in Psychological Science, 11 , 115-119. https://doi.org/10.1111/1467-8721.00181

Sahriana, N., Suminar, T., & Pranoto, Y. K. S. (2020). Development of maritime insight learning tools for ocean literacy in children aged 5-6 years old. Journal of Primary Education , 9 (5), 536-545.

Shettleworth, S. J. (2012). Do animals have insight, and what is insight anyway? Canadian Journal of Experimental Psychology / Revue canadienne de psychologie expérimentale, 66 (4), 217–226.

Thorpe, W. H. (1943). A type of insight learning in birds. British Birds , 37 , 29-31.

Vonk, J., Vincent, J., & O’Connor, V. (2021). It’s hard to be social alone: Cognitive complexity as transfer within and across domains. Comparative Cognition & Behavior Reviews, 16 , 1-35. https://doi.org/10.3819/CCBR.2021.160003


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This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

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A Quick Guide to Case Study

Published by Alvin Nicolas at August 14th, 2021 , Revised On February 9, 2023

A case study is a documented history and detailed analysis of a situation concerning organisations, industries, and markets.

A case study:

When to Use Case Study? 

You can use a case study in your research when:

You can consider a single case to gain in-depth knowledge about the subject, or you can choose multiple cases to know about various aspects of your  research problem .

What are the Aims of the Case Study?

Types of Case Studies

There are different types of case studies that can be categorised based on the purpose of the investigation.

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How to Conduct a Case Study?

Step1: select the case to investigate.

The first step is to select a case to conduct your investigation. You should remember the following points.

Step2: Formulate the Research Question

It’s necessary to  formulate a research question  to proceed with your case study. Most of the research questions begin with  how, why, what, or what can . 

You can also use a research statement instead of a research question to conduct your research which can be conditional or non-conditional. 

Step 3: Review of Literature

Once you formulate your research statement or question, you need to extensively  review the documentation about the existing discoveries related to your research question or statement.

Step 4: Choose the Precise Case to Use in Your Study.

You need to select a specific case or multiple cases related to your research. It would help if you treated each case individually while using multiple cases. The outcomes of each case can be used as contributors to the outcomes of the entire study.  You can select the following cases. 

Step 5: Select Data Collection and Analysis Techniques

You can choose both  qualitative or quantitative approaches  for  collecting the data . You can use  interviews ,  surveys , artifacts, documentation, newspapers, and photographs, etc. To avoid biased observation, you can triangulate  your research to provide different views of your case. Even if you are focusing on a single case, you need to observe various case angles. It would help if you constructed validity, internal and external validity, as well as reliability.

Example: Identifying the impacts of contaminated water on people’s health and the factors responsible for it. You need to gather the data using qualitative and quantitative approaches to understand the case in such cases.

Construct validity:  You should select the most suitable measurement tool for your research. 

Internal validity:   You should use various methodological tools to  triangulate  the data. Try different methods to study the same hypothesis.

External validity:  You need to effectively apply the data beyond the case’s circumstances to more general issues.

Reliability:   You need to be confident enough to formulate the new direction for future studies based on your findings.

Also Read:  Reliability and Validity

Step 6: Collect the Data.

Beware of the following when collecting data:

Step 7: Analyze the Data.

The research data identifies the relationship between the objects of study and the research questions or statements. You need to reconfirm the collected information and tabulate it correctly for better understanding. 

Step 8: Prepare the Report.

It’s essential to prepare a report for your case study. You can write your case study in the form of a scientific paper or thesis discussing its detail with supporting evidence. 

A case study can be represented by incorporating  quotations,  stories, anecdotes,  interview transcripts , etc., with empirical data in the result section. 

You can also write it in narrative styles using  textual analysis  or   discourse analysis . Your report should also include evidence from published literature, and you can put it in the discussion section.

Advantages and Disadvantages of Case Study

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This article provides the key advantages of primary research over secondary research so you can make an informed decision.

Content analysis is used to identify specific words, patterns, concepts, themes, phrases, or sentences within the content in the recorded communication.

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problem of case study research

A case study in Research

A case study in research is a strategy and empirical investigation which includes an analysis of the real phenomenon. Many students pursuing master’s or PhD have certain questions such as how and when to do a case study.

In this article, our professional by providing the guideline about a case study is resolving the queries of students.

A case study in Research

Before gaining knowledge about How to do case studies, you should first develop an understanding of what is a case study?

A  Research case study is a detailed survey on a specific topic or subject. It can also refer to as an in-depth investigation perform for analyzing a particular situation. The researcher mainly uses case study design for conducting research in the field of education, science clinical field, etc.

It mainly includes the Qualitative method , but sometimes also includes quantitative methods . You can utilize case study design in such a situation where you intend to describe and compare different aspects of the research problem.

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How to do case studies in Research?

The 4 steps of doing a case study in research are:

Step 1: Need to choose a specific case

After writing a problem statement and designing research questions , you need to select a particular case. A characteristic of a good case study is that

One important characteristic of the case study design is that it does not need any representative sample.  In the context of the case study research design , the researcher mainly emphasizes unusual and neglected research problems.

Step 2:Putting Together a Theoretical Framework

Research Case studies mainly emphasize providing great detail. While making a selection of cases you should ensure that it has some link with existing theory related to the field. The case study research design will help you in increasing your knowledge about a specific topic.

The main aim of the case study design is to:

Tip: You should conduct a literature review of sources relevant to topics and create a theoretical framework. It is a tactic that will help you in making sure that the case which you have selected has strong academic relevancy.  You should address key concepts and hypotheses for guiding analysis and interpretation.

Step 3: Information-gathering strategies

You can use several techniques for the collection of information about the topic. The different techniques which you can utilize for gathering information about the specific case are survey, interview, observation, etc.

These are a technique that you can utilize for accumulating primary information about the case. You can gather secondary data about the case by reading books, newspapers, articles, etc.

Mixed method case study example In the context of the case study on the establishment of wind farms in rural areas, the researcher needs to collect qualitative data about employment rates and revenue earned by companies. An investigator by conducting a survey could accumulate information about local people’s views about the establishment of farms in their region.  After that you can analyze the facts. The main objective of the case study to analyze the different aspects of case.

Step 3: Several methods for assessing the case that you can use

At the time of writing a case, you need to integrate different aspects which are essential to complete a picture. After that based on  the  type of research performed you need to report your finding.

In the case study, you need to write a separate section for methodology, discussion, and results.

You should write a case study in a narrative style. While writing a case study your aim should be to analyze the case from different aspects. Before start writing a case study, you should understand its meaning and importance.

The different techniques which you can utilize for analyzing the case include textual analysis, discourse analysis, etc.

While writing all types of cases you need to provide background detail of the case. After that, you need to link the same with existing literature and theory.

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When to do a case study?

A case study research design can be more suitable for gaining a deep understanding of something. It is by analyzing the different cases that researchers can identify important characteristics, implications, and meaning about whatever it may be they’re studying.

Making a selection of case studies for writing a thesis or dissertation is considering the best decision.

By utilizing the case study design you will be able to maintain the relevance of the research project.  It will also help you in managing the research project, especially at that time when you have insufficient time and resources for performing study on a large scale.

The researcher can use a complex case study design for an in-depth analysis of one subject. You can utilize multiple cases for making comparisons and providing illustrations of the specific research problems.

Example of case study

Why do we do research using case studies?

In exploratory research, case studies are frequently used. They may be able to assist us in generating fresh ideas (that might be tested by other methods). They are a useful tool for presenting theories and for demonstrating how different parts of a person’s life are connected.

Gaining an in-depth understanding of a subject can be difficult, especially when the problem is complex. A good way to get around this difficulty may be through utilizing case studies as they can examine individual cases and go into detail about them.

One technique that has been found useful for gathering information on these subjects is called content analysis; it involves developing a system by which one could measure how often certain words or phrases occur within different documents related to the study’s topic area.

It has been summarized above that while gaining in-depth knowledge about something might seem daunting at first glance, there are ways of overcoming this obstacle such as by using case studies and other techniques like content analyses where more detailed examinations can take place based on specific topics being

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Research Article

Applied machine learning to identify differential risk groups underlying externalizing and internalizing problem behaviors trajectories: A case study using a cohort of Asian American children

Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States of America

ORCID logo

Roles Formal analysis, Software, Validation, Writing – review & editing

Roles Data curation, Formal analysis, Methodology, Software, Visualization, Writing – review & editing

Roles Data curation, Resources, Writing – review & editing

Roles Conceptualization, Funding acquisition, Investigation, Resources, Supervision, Writing – review & editing


Fig 1

Internalizing and externalizing problems account for over 75% of the mental health burden in children and adolescents in the US, with higher burden among minority children. While complex interactions of multilevel factors are associated with these outcomes and may enable early identification of children in higher risk, prior research has been limited by data and application of traditional analysis methods. In this case example focused on Asian American children, we address the gap by applying data-driven statistical and machine learning methods to study clusters of mental health trajectories among children, investigate optimal predictions of children at high-risk cluster, and identify key early predictors.

Data from the US Early Childhood Longitudinal Study 2010–2011 were used. Multilevel information provided by children, families, teachers, schools, and care-providers were considered as predictors. Unsupervised machine learning algorithm was applied to identify groups of internalizing and externalizing problems trajectories. For prediction of high-risk group, ensemble algorithm, Superlearner, was implemented by combining several supervised machine learning algorithms. Performance of Superlearner and candidate algorithms, including logistic regression, was assessed using discrimination and calibration metrics via crossvalidation. Variable importance measures along with partial dependence plots were utilized to rank and visualize key predictors.

We found two clusters suggesting high- and low-risk groups for both externalizing and internalizing problems trajectories. While Superlearner had overall best discrimination performance, logistic regression had comparable performance for externalizing problems but worse for internalizing problems. Predictions from logistic regression were not well calibrated compared to those from Superlearner, however they were still better than few candidate algorithms. Important predictors identified were combination of test scores, child factors, teacher rated scores, and contextual factors, which showed non-linear associations with predicted probabilities.


We demonstrated the application of data-driven analytical approach to predict mental health outcomes among Asian American children. Findings from the cluster analysis can inform critical age for early intervention, while prediction analysis has potential to inform intervention programing prioritization decisions. However, to better understand external validity, replicability, and value of machine learning in broader mental health research, more studies applying similar analytical approach is needed.

Citation: Adhikari S, You S, Chen A, Cheng S, Huang K-Y (2023) Applied machine learning to identify differential risk groups underlying externalizing and internalizing problem behaviors trajectories: A case study using a cohort of Asian American children. PLoS ONE 18(3): e0282235. https://doi.org/10.1371/journal.pone.0282235

Editor: Guangyu Tong, Yale University, UNITED STATES

Received: July 20, 2022; Accepted: February 9, 2023; Published: March 3, 2023

Copyright: © 2023 Adhikari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study are from Public-use ECLS-K:2011 data files, which are available online at https://nces.ed.gov/ecls/dataproducts.asp . All the code used for the analysis are made available in GitHub ( https://github.com/shiying88/AAChildMentalHealth ).

Funding: Dr. Adhikari was supported by Johnson & Johnson Women in Stem Award. The project is supported by funding grants from the National Institute of Health, pilot award to Dr. Adhikari from the NYU Center for Asian American Health (U54MD000538), and 1R21MH124149-01A1 to Dr. Huang. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.


The burden of mental, neurological, and substance disorders account for 10–18% of the Global Burden of Disease [ 1 , 2 ], and half of the cases develop by age of 14 [ 3 , 4 ]. Internalizing problems (anxiety, depression) and externalizing problems (aggressive behavior, conduct difficulties) together account for over 75% of the total mental health burden in children and adolescents [ 4 , 5 ]. Mental health burden among children and adolescents from racial and ethnic minorities in the US is even higher (31–45%) [ 3 , 6 , 7 ]. However, knowledge related to the patterns of developmental trajectories during the critical childhood periods and factors that contribute to the disorders in different ethnic minorities remains limited. Although progress has been made in using a more comprehensive Health Disparities Framework to better inform mental health disparities research [ 8 ], analytical tools used in such research are still largely traditional, limiting further progress.

Most health disparities research has relied on data with small sample size using conventional regression approach that relies on strong modeling assumptions for analysis. However, such analytical approach can be limited when incorporating multidomain and multilevel risk factors simultaneously, leading to suboptimal models with less reliable results and limiting the generalizability and predictive ability of the findings, making them less actionable. Thus, to advance actionable mental health disparities research, application of modern data-driven statistical approaches and predictive models utilizing rich multidomain contextual and longitudinal data must be explored. Applications of data-driven analytical approaches can help us utilize predictive tools that are often more suitable for large datasets with many predictors, and may also inform personalized health intervention decisions and support better understanding of mental health developmental processes.

This paper addressed the gaps in mental health disparities and developmental psychopathology research by applying modern statistical and machine learning methods to study patterns of mental health trajectories from early childhood to early teen periods (kindergarten to Grade 5) and investigate key early predictors (i.e., individual, parental and contextual factors at kindergarten) of poor and optimal trajectories. We explored the feasibility and potential benefits, if any, of using big administrative data from a national longitudinal survey along with data-driven machine learning (unsupervised and supervised) algorithms in child and adolescent mental health research. Unsupervised machine learning algorithms can help identify underlying sub-groups in the trajectories of internalizing and externalizing problem behaviors, whereas supervised machine learning algorithms can optimize predictions by utilizing high dimensional predictors with minimal prior assumptions on the models and produce highly accurate and well-calibrated predictions.

Considering different ethnic populations’ unique living experiences and contexts, which may associate with different mechanisms toward disparities, this study focused on Asian American children and adolescents as a case example to demonstrate the application of machine learning in mental health disparities research. We focused on Asian Americans for three reasons. First, prior studies have documented that while Asian American children and adolescents were at greater risk for internalizing problems, they had similar risks for externalizing problems, relative to non-Asian US children and Non-US Asian children [ 6 , 9 ]. Approximately one third of Asian American children were at elevated risk for anxiety, somatization, and depressive problems, relative to 12–15% reported for the general population in the literature [ 6 , 9 ]. Second, a combination of cultural, individual, family, and school factors, which explained 17–39% of the variance in mental health problem development in Asian American children and adolescents, have been studied in the literature [ 10 – 13 ], including our prior work [ 6 , 14 , 15 ]. This study builds on prior work and considers multi-level factors and longitudinal mental health data simultaneously to document the added values in knowledge gain using machine learning. Third, since Asian-Americans are one of the fastest-growing ethnic minorities in the US, with a population of around 20 million [ 16 ], understanding the health status and service needs of this population is imperative to advance population health.

In this paper, we first investigated heterogeneity on the longitudinal trajectory of mental health problem behaviors among Asian American children and assess clusters of children in the high-risk category. Second, applying multi-dimensional risk factors data (individual, parental, and contextual factors) at kindergarten and ensemble of supervised machine learning algorithms, we evaluated prediction algorithms for identification of Asian American children at high-risk. We hypothesized that the machine learning algorithms will have better predictive performance than the traditional parametric logistic regression.

Materials and methods

Data source.

This study leveraged a rich publicly available national database from the “Early Childhood Longitudinal Study—Kindergarten Class” (ECLS-K) 2011 cohort [ 17 ]. The ECLS-K study, which was sponsored by the US Department of Education’s National Center for Education Statistics, followed a cohort of children from kindergarten through their elementary school years, using a multistage probability sampling design, to select a nationally representative sample of children attending kindergarten in the academic year 2010–2011. Approximately 18,200 children (and parents) throughout the country were recruited. Three primary methods of data collection, direct child assessment, parent interview, and teacher and school administrator questionnaires, were utilized. Current analysis was based on the public-use dataset, utilizing ECLS-K 2011 sub-cohort of 1,660 children with self-reported race of Asian or Native Hawaiian/Pacific Islander. Further details on data collection methodology are published elsewhere [ 17 ].

Externalizing and internalizing problem behaviors subscales.

The Social Rating Scale (SRS) adapted from the Social Skills Rating System (SSRS [ 18 ]) was used to assess the mental health status of children in a classroom, starting in kindergarten. The SRS is based on teacher ratings on a four-point scale, which includes subscales on (a) Externalizing Problem Behaviors; (b) Internalizing Problem Behaviors; (c) Self-Control; and (d) Interpersonal Skills. Teachers used a frequency scale (1–4) to rate how often a child displays a particular social skill or behavior (i.e., 1 = a student never exhibits this behavior; 4 = a student exhibits this behavior most of the time). Our outcome measures included two subscales, externalizing and internalizing problem behaviors, from the SRS collected over six years.

The externalizing problem behaviors measure acting out behaviors, such as arguing, fighting, showing anger, acting impulsively, and disturbing the classroom’s ongoing activities. Whereas, the internalizing problem behaviors measure whether a child appears anxious, lonely, sad, or has low self-esteem. Scores for the externalizing and internalizing problem behaviors sub-scales were reported in fall and spring periods of kindergarten through the second grade (2010–2013), and in the spring periods of third through fifth grades (2014–2016). Due to low response rates in the fall periods, only spring period measurements were considered in our analysis. When possible, fall scores were carried forward to impute missing spring scores in the same grade for both problem behaviors.


Race/ethnicity data were collected from parents’ self-report. While the information on subgroups within Asian American race was not available in the public-use dataset considered for this paper, restricted-use data included such information. The composition of race/ethnicity, which has been published elsewhere [ 19 ], suggests that the Asian race in ECLSK-2011 included seven subgroups within the Asian American community: Indian, Chinese, Hmong, Japanese, Korean, Vietnamese, and other Asian. Thus, the cohort of Asian Americans for this analysis consisted of diverse representative groups, also including Pacific Islanders and multiethnic Asians, irrespective of ethnicity [ 20 ].

Baseline predictors.

Multidomain/Multilevel contextual variables assessed at kindergartens were used as predictors. We selected representative variables, instead of an exhaustive list, based on the social determinants framework for the purpose of exploration and feasibility demonstration. Children, their families, teachers, schools and care providers provided information on children’s cognitive, social, emotional, and physical development characteristics, as well as culture, immigration, home environment, parenting, home educational activities, school environment and quality, and neighborhood characteristics.

Child level predictors included measures on child learning, health, physical and socioemotional wellbeing, disability status, and social-process skills from direct child assessment, and parent and teacher report data. The kindergarten direct child assessment measured reading, mathematics, and science knowledge and skills, and executive function. Teachers reported on approaches to learning scale, providing information on how often their students exhibited a selected set of learning behaviors. The kindergarten questionnaires also asked teachers to indicate how often their children exhibited certain social skills and behaviors related to inhibitory control and attentional focusing. The parents-reported social scales consisted of four subscales, self-control, social interaction, sad/lonely, and impulsive/overactive behaviors. Parents also reported on approaches to learning scale. The parent interview reported on family structure, family literacy practices, parental involvement in school, care arrangements, household composition, family income, parent education level, culture/immigration, and other demographic indicators. Neighborhood level measures included measures of community support and community violence.

A complete list of predictors and their descriptions are presented in Supplement 1. Measures with less than 30% missingness were included in the predictive analysis, resulting in a total of 24 predictors. Continuous predictors were standardized to have mean 0 and variance of 1. Mean imputation was considered for missing continuous variables. An explicit class of missing was introduced for each categorical variable to maintain sample size in the predictive analysis.

Statistical analysis

Longitudinal trajectory analysis..

To investigate heterogeneity in the trajectory patterns of mental health problems, we applied the latent class mixed effects models (LCMMs) [ 21 ], a type of unsupervised algorithm. LCMM, an extension of the linear mixed model or latent class analysis, is a flexible probabilistic approach to uncover underlying clusters of trajectories using longitudinal observations, and allows modeling of various types of data distribution and complex trajectory shapes. Measurements from six time points were used to identify clusters of outcome trajectories. Time was considered as a continuous variable and specified as a fixed effect. Random effects at the child level allowed for within child clustering over time. Multiple LCMMs with several link functions (linear, beta, and I-splines), as well as a linear model with a quadratic time trend were fitted to identify the best fitting model with an optimal number of clusters. To determine the optimal number of clusters and optimal link function, Bayesian information criterion (BIC) was used. Additionally, to avoid models with small class sizes (<5% of total sample), class membership size was also considered while selecting the optimal number of classes. The classes identified from LCMMs were considered as outcome categories in the predictive modeling. Similar to the trajectory analysis, we conducted prediction separately for the internalizing and externalizing problems behaviors.

Prediction model development.

To predict Asian American children at elevated risk for poor mental health development, we used SuperLearner [ 22 ] algorithm, a supervised machine learning approach. Different from the traditional parametric logistic regression model, SuperLearner is a non-parametric prediction method which is constructed as a combination (ensemble) of multiple independent candidate prediction algorithms. By creating an optimal weighted average of predictions from candidate algorithms, with the goal of maximizing area under the curve (AUC), Superlearner performs as well or better compared to the individual algorithms with respect to the loss function (AUC). We considered candidate algorithms that are commonly used for prediction and are suitable for data with a relatively large number of potentially correlated predictors. These candidate algorithms included a simple mean model which predicts the observed proportion of the target outcome class ( mean model ), a traditional logistic regression without penalty ( logistic regression ), a logistic regression with lasso penalty [ 23 ] ( lasso regression ), a logistic regression with group lasso penalty [ 24 ] ( group lasso regression ), a support vector machine with radial basis kernel [ 25 ] ( SVM ), and random forest [ 26 ] with nine different parameter settings.

In the mean learner, predicted probability is computed as the percentage of ones in the training data. The traditional logistic regression computes predicted probability of the outcome by fitting a generalized linear model on the training data using a logistic link function. Maximizing a likelihood in the logistic regression is equivalent to minimizing a negative likelihood loss function without any penalty. The lasso and group lasso regressions are extensions of the logistic regression model, such that a penalty is introduced in the loss function. The penalty, which is a function of a user provided hyperparameter, controls the model complexity by shrinking some of the individual coefficients (in lasso regression) or a group of related coefficients (in group lasso regression) to zero. The SVM is a flexible prediction algorithm in which the predictors are mapped into a higher dimensional space using a user defined kernel function to obtain an optimal prediction rule. Finally, the random forest is a tree-based algorithm that produces optimal prediction by aggregating decision trees. Number of trees and number of variables sampled at each split in a tree are the hyperparameters that need to be specified by a user in the random forest.

We selected the hyperparameters for the lasso and group lasso regressions via hyperparameter tuning. The grouping indicator for the group lasso penalty was specified by including the continuous predictors into separate groups and grouping categories from respective categorical predictors together into a group. We specified a radial basis kernel function with default parameter values for the SVM. We considered random forests with combinations of number of trees (ntree) and number of variables sampled at each split (mtry) as different candidate algorithms in the Superlearner. The ntree parameter ranged from 100 to 700 whereas the mtry option was set to 3, 5 and 7. Finally, using optimal fitted weights from the Superlearner, the predicted probability for each individual was computed. Further, as a biproduct of fitting candidate algorithms for creating Superlearner predictions, we were also able to obtain predicted probabilities from each individual algorithm. Thus, to assess prediction performance, we validated and compared predictions from Superlearner and the candidate algorithms.

Model validation and assessment of prediction performance.

problem of case study research

Variable importance measures and partial dependence plots.

Finally, separate random forest algorithms (with hyperparameters that optimized AUC) were fitted to the entire data to evaluate influential predictors with high variable importance for each outcome. Variable importance was assessed using the mean decrease in Gini index, which was rescaled to 0–100. The mean decrease in Gini index of a variable provides a measure of how often this variable was selected and its overall contribution to the prediction in a random forest. A high value of mean decrease in Gini index indicates high variable importance in the model.

Further to facilitate interpretation, we visualized the dependence of predicted probability on each of the top ten important variables using partial dependence plots [ 28 ]. The partial dependence plot shows the marginal effect of a variable on the predicted probability (on logit scale), and can provide insights on the direction of association.

Analyses were conducted in R (version 4.0.2) on MacOS using packages ‘lcmm’, ‘randomForest’, ‘elasticnet’, ‘gglasso’, ‘SuperLearner’, and ‘caret’. Software code developed for the analysis is available as open access in GitHub ( https://github.com/shiying88/AAChildMentalHealth ).

Asian American population characteristics

Children and parents’ demographic characteristics, reading scores at kindergarten, and contextual variables of the analytical cohort are summarized in S1 Table . Of the 1,660 Asian American children, 53% were female, 6.9% had disabilities, and 75% were in kindergarten for the first time while 4% reported repeating kindergarten and 21% did not report this information. Similarly, 48% of the families did not speak English as their primary language at home, 32% of parents had high-school or less than high-school education, 10% of the families made less than 20,000 household annual income, and 4.8% of the families were experiencing food insecurities.

Patterns of problem behavior trajectories

Children with measurements for less than two time points for either internalizing or externalizing problems scores were dropped from the analysis, resulting in a final analytical sample of 1,279 students. During kindergarten (time 0), the mean internalizing problem score was 1.41 (standard deviation; SD = 0.41) and the mean externalizing problem score was 1.49 (SD = 0.52). By fifth grade, the average internalizing problem score had increased to 1.50 (SD = 0.49) whereas the average externalizing problem score was 1.46 (SD = 0.50).

Figs 1 and 2 show trajectories of the scores for externalizing and internalizing problem behaviors with latent classes selected from the best fitting models. For both externalizing and internalizing problem behavior trajectories, a two-class solution was optimal. For each problem behavior trajectory, the latent classes identified from the LCMMs were labelled as high- and low-risk groups based on the relative group means of the observed scores among children within each class. The high-risk group for externalizing problem behavior consisted of 16.3%(n = 208) of the sample, with the average score of 2.24 (SD = 0.57). Whereas, the low-risk group had 83.7%(n = 1071) of the children with the overall average score of 1.37 (SD = 0.36). Further, the trajectories of scores for both high- and low-risk groups for externalizing problem were stationary over time. Class membership for internalizing problem behavior was more imbalanced across groups, with 6.9%(n = 88) of children in the high-risk group and 93.1%(n = 1191) of children in the low-risk group. In contrast to the externalizing problem behavior, the trajectory of the high-risk group for internalizing problem behavior displayed an increasing trend over time, with the average scores of 1.66 (SD = 0.55) in kindergarten and 2.44 (SD = 0.55) in fifth grade, respectively. Whereas, the low-risk group for the internalizing problem behavior displayed a stationary trajectory with an overall average score of 1.41 (SD = 0.39).


Right panel shows class mean with shaded region representing the bootstrapped 95% confidence band.



Left panel shows individual observed trajectories with dark line representing the mean for each latent class. Right panel shows class mean with shaded region representing the bootstrapped 95% confidence band.


Sample characteristics of the children in high- and low-risk classes for each of the problem behaviors are also reported in S1 Table . Descriptively, among those in high-risk subgroups for both problem behaviors, there were lower percent of females and higher percent of children with disability. Further, compared to the low-risk subgroups, high-risk subgroups reported higher math, reading and science scores, and scored lower on teacher rated sub-scales for interpersonal skills, self-control, inhibitory control and attention focus.

Prediction of children at high-risk of problem behaviors

Comparison of the performance metrics for the predictions from the Superlearner and individual candidate algorithms is shown in Fig 3 . For the externalizing problem behavior outcome, compared to the mean learner, all machine learning candidate algorithms had better predictive performance. While the AUCpr of mean learner was lower than the baseline (AUCpr = 0.161 vs 0.163), AUCpr for all other algorithms were relatively higher ranging from 0.42 to 0.48, and lasso was the best algorithm overall (AUCpr = 0.48) followed by SuperLearner (AUCpr = 0.47). The AUC, ranging between 0.75 and 0.78, ranked algorithms similarly, with lasso performing best (AUC = 0.78) followed by Superlearner (AUC = 0.77). Lasso regression also provided the best prediction in terms of balancing TPR and TNR (72% and 69%) with accuracy of 70% at optimal cutoff of 0.154. Superlearner had an accuracy of 63%, TPR of 82% and TNR of 60% at optimal cutoff of 0.123. Further, performance of logistic regression was comparable in terms of AUC (0.77), AUCpr (0.46) and TNR (69%), with slightly lower accuracy (69%) and TPR (69%).


Crossvalidated performance metrics at optimal threshold for predicting externalizing problem behavior (externalization; left panel) and internalizing problem behavior (internalization; right panel); using predictions from Superlearner ensemble as well as individual candidate algorithms. Individual candidate algorithms included mean learner (Mean), logistic regression (Logistic), lasso regression (Lasso), group lasso regression (Group Lasso,) random forest with different combinations of number of variables sampled at each split and number of trees, and support vector machine with radial basis kernel function (KSVM). ACU = area under the curve, AUCpr = area under the precision recall curve, TPR = true positive rate, TNR = true negative rate.


In predicting internalizing problem behavior, where the target high-risk class was rare, there was disagreement between AUCpr and AUC in identifying the best algorithm. All algorithms had an AUCpr higher than the baseline value of 0.069, ranging between 0.12 and 0.16, with the mean learner performing the worst. Lasso had the best AUCpr (0.164), followed by Superlearner (AUCpr = 0.158). Overall, AUC ranged between 0.62 (for logistic regression) and 0.67 (for Superlearner). Superlearner also had the overall best balance of TPR (60%) and TNR (69%) with an accuracy of 69% at the optimal cutoff of 0.072. Compared to other algorithms, logistic regression had lower AUC (0.62), AUCpr (0.12), accuracy (64%) and TPR (55%).

For predicting externalizing problem behavior, all algorithms except the mean learner and logistic regression had a good calibration ( S1 Fig ), with the best calibration for the ensemble Superlearner, indicating that the predicted risk scores were representative of the observed risk categories. However, most of the learners in the prediction of internalizing problem behavior were not well-calibrated, given that the outcome was rare. Superlearner ensemble was also comparably better calibrated for predicting internalizing problem behavior.

Finally, for computing variable importance for externalizing problem behavior outcome, among different parameter settings for the random forest algorithm, mtry of 7 and ntree of 700 with the optimized AUC of 0.763 were selected. The top ten important baseline predictors based on variable importance measures ( Fig 4 ) were combination of teacher-rated scores (inhibitory control, attentional focus and approach to learning), test scores (science score, reading score, math score), child’s age, BMI and social determinants including SES and community violence. For internalizing problem behavior outcome, the random forest algorithm with mtry of 5 and ntree 100 had optimized AUC of 0.656, and was selected for computing variable importance ( Fig 5 ). Similar to the externalizing problem behavior, the top ten important early childhood predictors for the internalizing problem behavior were test scores (reading, math and science), child-specific factors (age and BMI), teacher-rated scores on inhibitory control, approach to learning, attentional focus and social determinants (SES and community violence).


BMI = body mass index, SES = socioeconomic status.




Figs 6 and 7 show the partial dependence plots for the top ten variables identified by variable importance measure for externalizing and internalizing problem behaviors, respectively. For externalizing problem behavior, the patterns of associations with predicted probability of high-risk were similar for age and BMI. Age (in months) and BMI showed non-linear associations such that the values on the low and high end of the distribution of the variables had higher predicted risks than those in the center. Higher scores for approach to learning, attentional focus and inhibitory control were contributing to lower predicted risk. Finally, scores on reading math and science, as well as SES and community violence showed inverted-U associations with predicted risks. The shapes of the associations were similar for predicting increased risk of high internalizing problem behavior.


The y-axis represents the log odds of predicted probability for a fixed value of the variable of interest, conditional on all other variables (marginal effect). The x-axis represents the observed values of the variables scaled to have mean of zero and standard deviation of 1. BMI = body mass index, SES = socioeconomic status.




We explored several state-of-the-art data-driven analytical methods and assessed the value of using machine learning for health disparities research. As a case example, we focused on Asian American children’s mental health problems. We used big data from the US ECLS-K 2010–2011 longitudinal cohort and applied machine learning to study two specific research questions. We first utilized unsupervised machine learning to identify clusters of mental health developmental trajectories for childhood internalizing and externalizing problem behaviors. Next, we utilized supervised machine learning to identify children at high-risk of problem behavior development and related top-ranking predictors from early childhood.

Among the Asian American cohort considered in our study, the high-risk group we identified for externalizing problem behavior had a constant trajectory with the mean score of 2.24. Whereas, the high-risk group for internalizing problem behavior had a mean score of 1.66 in kindergarten which increased to 2.44 in grade five. However, the overall average in the entire ECLS-K sample, as reported by NCES [ 29 ], ranged between 1.61 and 1.78 for externalizing problem behavior, and between 1.47 and 1.69 for internalizing problem behavior, from kindergarten to grade five. Thus, even though clinical cutoffs for the externalizing and internalizing problem behaviors measures have not been established, the high-risk subgroups we identified using data-driven approach expressed on average higher mental health burden compared to the reported averages for the overall ECLSK-2011 sample. This finding is especially significant because poor mental health, including internalizing and externalizing problem behaviors, is associated with various behavioral problems among youth, including lower educational achievements and increased engagement in risky behaviors, and these problems often persist into adulthood [ 30 – 32 ].

Consistent with the literature, we found 10–20% of Asian American children were at high risk for externalizing problems [ 33 ], and the externalizing problems persisted or slightly reduced over time [ 34 , 35 ]. Similarly, consistent with the internalizing behavior literature based on teacher-reported data, compared to externalizing problems we found lower proportion of children (less than 10%) were at risk for internalizing problem. Literature has shown that the proportion of Asian American children with internalizing problems is often higher from parent-reported data (>20%), due to challenges in observing less expressive behaviors and symptoms by teachers [ 36 ]. We also observed that the internalizing problem emerged over time as children became more mature in cognitive and self-identity development with age [ 34 , 37 ]. The findings from the internalizing problem behavior trajectory analysis suggest that prior to 3 rd grade (around age 8) is the critical period for early intervention to prevent deterioration of internalizing problems in Asian American children. Further, similar analyses incorporating parent reported outcomes are needed to address existing mental health need of Asian American children.

In the prediction analysis, we compared several machine learning algorithms with the traditional logistic regression. We included child, parent, family, and social determinants at kindergarten as predictors. As our primary algorithm, we considered a flexible ensemble approach (Superlearner), which was constructed using predictions from multiple algorithms ranging from traditional logistic regression to more complex ones such as random forest and support vector machine. We evaluated individual algorithms as well as the Superlearner. Consistent with the literature [ 38 ], we demonstrated that data-driven machine learning algorithms such as Superlearner and lasso regression have the potential to predict mental health outcomes with acceptable to high AUC (>0.75 for externalizing and >0.67 for internalizing problem behaviors). Further, predictions from the Superlearner had the best calibration for both mental health outcomes, suggesting that predicted probabilities were more representative of the observed risk patterns. Our results demonstrate the feasibility and usefulness of using data-driven machine learning to identify and uncover predictors without pre-specifying functional form or transformations of covariates, when complex multilevel predictors are considered and when there are limited theoretical frameworks to guide decision making. However, these models are not yet suitable for clinical utilization given the sub-optimal accuracy and true positive rates, as well as limited external validity.

Based on the variable importance measures, we identified similar predictors for externalizing and internalizing problem behaviors. The most important predictors for both outcomes were school readiness/early learning competency (reading, math and science scores at kindergarten), child-specific factors (age and BMI), teacher-rated scores on inhibitory control, approach to learning, attentional focus, and social determinants of health (SES and community violence). Our findings are mostly consistent with the developmental literature that school readiness (including emotion regulation/control, early learning and foundational academic skills) and living contexts (i.e., poverty and community violence) are critical predictors for childhood problem behavior development [ 6 , 12 , 37 ]. We found Asian American children’s early school readiness, learning behavior, emotion and behavioral regulations are key contributing factors for growing or persistence of both internalizing and externalizing problems. We also found SES, community violence and cultural heritage are important for problem behaviors development. These findings further confirm the benefit of early intervention programs that target on school readiness, family SES needs, cultural identity, and neighborhood/community violence. However, somewhat unexpectedly, we found child BMI as an important predictor that is worth further investigation to better understand underlying mechanisms. Association between BMI and perceived weight with mental health among adolescents have been established, showing that lower and higher than normal BMI are associated with high mental health risk [ 39 ]. However, previous research has not focused on health differences in mental health problem development among young Asian American children [ 40 , 41 ]. Our findings inform new direction of mental health disparities research to better understand related experience in Asian American children.

This study contributes to mental health disparities research in several innovative ways. From the analytical perspective, our study demonstrates the value of applying the LCMM to uncover underlying patterns of trajectories, and supervised machine learning algorithms to identify predictors that have not been discovered in previous Asian American mental health research. From the health disparities evidence perspective, this study also contributes new evidence to guide future directions of psychopathology and intervention research for improving Asian American youths’ mental health. Specifically, our analysis uncovered a subgroup of Asian American children at an elevated risk of mental health disorders, as well as informed the critical age period for early intervention. The prediction modeling helped to identify children at risk and the important factors that need to be prioritized for early intervention.

Although this study demonstrates the feasibility of machine learning for health disparities research, there are few limitations that need to be considered. First, while machine learning methods are more flexible, can accommodate high number of covariates and require fewer model assumptions in terms of linearity and interactions, they often come with a cost of reduced interpretability. In our case study, the gain in performance from machine learning was only marginal compared to logistic regression. However, with a high number of correlated covariates, logistic regression often runs into stability issues and may not provide consistent results, which is not the case with machine learning algorithms. While we attempted to open the black box by looking at the variable importance metrics and partial dependence plots, we need to be careful not to make any causal interpretation from such results. Further, partial dependence plots are not suitable to visualize interaction and joint associations between multiple covariates and outcome of interest, and thus may not always reflect the full picture of dependence. Second, this study included a limited set of previously identified factors as predictors. For the exploration purpose, we did not include many parenting, school/classroom, and cultural/racial contextual factors that have also been identified in childhood mental health literature.

It is important to consider both traditional theory-guided epidemiological research and new machine learning methods to advance health disparities research. It is necessary to build on the current work and consider other parenting, family, cultural/racial contextual factors, time variant, developmental stages/ages, and dynamic influence of contextual factors on mental health trajectory development among Asian American children. While our study was focused on assessing longitudinal trajectory and predicting risk of persistent symptomatology over time to inform early intervention strategies, an alternative approach assessing high-risk children at kindergarten may provide additional insights. However, children who demonstrate persistent or increasing trajectory of high problem behaviors during their developmental years, beyond kindergarten, will benefit tremendously from early mental healthcare intervention. Thus, our approach provides a foundation for addressing current mental health crisis by first assessing trends of problem behaviors over the developmental years rather than at a static time point. Further, to better inform whether similar analytical approach developed in this study and findings can be replicated to other racial/ethnic groups, our long-term goal is to apply a similar methodology to other populations and develop a generalizable analytic framework for future mental health disparity research among different racial/ethnic groups.

This study demonstrates the feasibility and value of using machine learning for mental health research. We uncover new patterns and predictors that are important to Asian American children’s mental health. Several clinical implications can be drawn from our study. Findings from the cluster trajectory analysis suggest that critical age for early intervention for externalizing problem needs to be prior to kindergarten (before time 0 when the group difference is not established), and for internalizing problem needs to be between kindergarten and age 8 (time 0–4, during the time group difference starting to emerge). Findings from the prediction analysis also suggest potential benefit of applying evidence-based interventions that aim to promote early school readiness, culture identity, and reduce adversity of family SES and neighborhood/ community violence to improve mental health outcomes for Asian American children. Finally, our study suggests the need for additional epidemiological mechanism and causal pathway testing research for the predictors that have not been well studied to inform new strategies to improve mental health outcomes among Asian American children.

Supporting information

S1 table. summary of baseline characteristics of asian american children in the longitudinal cohort, overall and by latent cluster classes for internalizing problem behavior and externalizing problem behavior..


S2 Table. Summary of missing observations for continuous measures considered as candidate predictors.


S1 Fig. Calibration plots comparing observed and predicted risk scores within each decile of predicted risk interval for externalizing problem behavior outcome.


S2 Fig. Calibration plots comparing observed and predicted risk scores within each decile of predicted risk interval for internalizing problem behavior outcome.


Press Release

This study on vegetable protein market research includes identifying competitors, assessing their strengths and weaknesses, and doing a cagr analysis..

The MarketWatch News Department was not involved in the creation of this content.

Mar 06, 2023 (Prime PR Wire via Comtex) -- This “ Vegetable Protein Market " highlights key external drivers that affect the industry and assesses the current performance and revenue growth of the industry. This report helps to collect industry information for target audiences before commencing any advertising campaign.

Vegetable Protein Market Analysis and Report Coverage

This report is of 135 pages.

The global Vegetable Protein market size is projected to reach multi million by 2030, in comparision to 2021, at unexpected CAGR during 2023-2030 (Ask for Sample Report).

Get a Sample PDF of the Report - https://www.reliablebusinessinsights.com/enquiry/request-sample/958593

What is Vegetable Protein?

Vegetable protein is a type of protein that is derived from plants and is considered a healthier alternative to animal-based proteins. It can be found in various plant sources such as soybeans, chickpeas, lentils, peas, and quinoa. The growth of the vegetable protein market has been attributed to the increasing demand for plant-based diets and the rising health concerns among consumers. Additionally, the food industry is also exploring various plant-based protein sources as a sustainable and eco-friendly alternative to meat-based proteins. As a result, the vegetarian and vegan food industry has been experiencing significant growth, driving the demand for vegetable protein products.

Market Segmentation Analysis

Vegetable Protein Market Types:

1. Wheat Protein: It is a gluten-based protein commonly used in bakery and noodle products. It is used as a meat alternative in vegetarian meals.

2. Soy Protein Concentrate: It has a high protein content and is used in dairy alternatives, meat replacements, and nutrition products.

3. Soy Protein Isolate: It is a pure form of soy protein that has higher protein content than concentrates. It is mainly used in nutrition products.

4. Textured Soy Protein: It is a meat substitute made from defatted soy flour. It is used in vegetarian and vegan diets.

5. Pea Protein: It is a plant-based protein that is allergen-free and has high nutritional value. It is used in nutrition products and meat substitutes.

Vegetable Protein Market Applications:

1. Feed: Vegetable proteins are used as a source of protein in animal feed.

2. Bakery Food Ingredients: Wheat proteins are used in making bread and baked goods.

3. Nutrition Health Care Products: Soy and pea proteins are used in nutrition products like protein bars, powders, and shakes.

4. Meat Substitutes: Textured soy protein and pea protein are used as meat substitutes in vegetarian and vegan diets.

5. Beverage: Soy protein concentrate is used in making beverage products like soy milk and smoothies.

6. Others: Vegetable proteins are also used in snacks, sauces, and dressings.

Vegetable Protein Market Regional Analysis

Vegetable protein is commonly used in North America and Europe as a substitute for animal protein in various food products, including plant-based meat alternatives. In Asia-Pacific, vegetable protein is widely used in traditional dishes such as tofu and tempeh. In Latin America, it is commonly used in local cuisines including bean-based dishes. In the Middle East and Africa, it is mainly used in vegetarian dishes and soups.

Prominent Market Players of Vegetable Protein Market

{Explain Vegetable Protein by company - DuPont,ADM,CHS,Manildra Group,Roquette,Midwest Grain,CropEnergies,Tereos Syral,Showa Sangyo,Fuji Oil,Cargill,Cosucra,Nisshin Oillio,Tate & Lyle,World Food Processing,Topagri,Gushen Biological,Shansong Biological,Tianguan,Yuwang Group,Scents Holdings,Chinalotus,Goldensea Industry,Sinoglory Health Food,Shuangta Food,Harbin Hi-tech Soybean,Fiber Source Biological Engineering,Oriental Protein Tech,Wonderful Industrial Group Tianjing Plant Albumen. How these companies can help to grow Vegetable Protein Market. Answer it in less than 100 words}

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Vegetable Protein Market Dynamics ( Drivers, Restraints, Opportunity, Challenges)

Urbanization and lifestyle changes will be significant drivers of growth:

They are expanding consumer awareness of health issues, advancing consumer preferences, and profound lifestyle changes across the developing world, much like advanced nations are boosting demand for seasoned water. The Vegetable Protein market employs a methodology that includes including favorite fixings in their products to catch the attention of customers who focus on their prosperity, and well-being. These assistants support the growth of the Vegetable Protein Market, encourage contributions from organizations, and increase overall net revenue.


The Personalization Trend Will Offer Profitable Opportunities

Customization and personalization of beverages have emerged as vital opportunities that sellers are using to improve their offers on a global scale. In addition, the internet business sector, which has grown very popular among online consumers, offers businesses a chance.

Market Growth Will Be Compromised by Weakened Demand for the products

The number of obstacles that prevent customers from buying the products will be used to gauge the market's development. In the coming years, the global market for the Vegetable Protein may face serious risks due to the increasing availability of options.

The Vegetable Protein market research report contains the following TOC:

Read full TOC - https://www.reliablebusinessinsights.com/toc/958593#tableofcontents

The Impact of Covid-19 and Russia-Ukraine War on Vegetable Protein Market

The Post Covid-19 Pandemic and Russia-Ukraine War have had a significant impact on the Vegetable Protein market. With the pandemic leading to production and supply chain disruptions, many countries have turned towards self-sustenance and reduced import dependency. This has led to an increased demand for plant-based proteins, as they are more sustainable and can be produced domestically. Additionally, the ongoing Russia-Ukraine War has affected the agricultural sector in the region, leading to a reduced supply of animal protein products. This has further propelled the shift towards vegetable protein sources. Overall, the combined effects of the pandemic and conflict have accelerated the growth of the vegetable protein industry and have led to a greater focus on sustainability and diversification of protein sources.

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Market Segmentation 2023 - 2030:

The worldwide Vegetable Protein market is categorized by Product Type and Product Application.

In terms of Product Type, the Vegetable Protein market is segmented into:

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In terms of Product Application, the Vegetable Protein market is segmented into:

The available Vegetable Protein Market Players are listed by region as follows:

Research Methodology

When we speak of research methodology in the context of business, we are referring to the systematic process used by organizations to gather, examine, and interpret data in order to reach reliable conclusions. Employing a method of research that has been carefully considered allows businesses to assess market opportunities, spot trends, and determine the needs and preferences of their clients. One of the most common research methodologies in business is survey research, which collects information via surveys and questionnaires. Observational research, in which businesses gather data by attentively observing consumers or market trends, is another well-liked strategy. In order to find cause-and-effect relationships and evaluate the success of marketing strategies, businesses also conduct experimental research, which involves manipulating variables. An additional technique that is frequently employed is secondary research. Businesses use this technique to gather data from publicly available sources such as market reports, official statistics, and published studies. To ensure the validity and reliability of their research, businesses frequently use a variety of research methodologies, such as sampling, randomization, and control groups. For the analysis and interpretation of data, they also employ statistical techniques like regression analysis and hypothesis testing. A well-designed research methodology is crucial for businesses because it provides a methodical and strictly scientific approach to data collection and analysis.

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