case study science

The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

Applications for case study research, what is a good case study, process of case study design, benefits and limitations of case studies.

• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Case studies

Case studies are essential to qualitative research , offering a lens through which researchers can investigate complex phenomena within their real-life contexts. This chapter explores the concept, purpose, applications, examples, and types of case studies and provides guidance on how to conduct case study research effectively.

Whereas quantitative methods look at phenomena at scale, case study research looks at a concept or phenomenon in considerable detail. While analyzing a single case can help understand one perspective regarding the object of research inquiry, analyzing multiple cases can help obtain a more holistic sense of the topic or issue. Let's provide a basic definition of a case study, then explore its characteristics and role in the qualitative research process.

Definition of a case study

A case study in qualitative research is a strategy of inquiry that involves an in-depth investigation of a phenomenon within its real-world context. It provides researchers with the opportunity to acquire an in-depth understanding of intricate details that might not be as apparent or accessible through other methods of research. The specific case or cases being studied can be a single person, group, or organization – demarcating what constitutes a relevant case worth studying depends on the researcher and their research question .

Among qualitative research methods , a case study relies on multiple sources of evidence, such as documents, artifacts, interviews , or observations , to present a complete and nuanced understanding of the phenomenon under investigation. The objective is to illuminate the readers' understanding of the phenomenon beyond its abstract statistical or theoretical explanations.

Characteristics of case studies

Case studies typically possess a number of distinct characteristics that set them apart from other research methods. These characteristics include a focus on holistic description and explanation, flexibility in the design and data collection methods, reliance on multiple sources of evidence, and emphasis on the context in which the phenomenon occurs.

Furthermore, case studies can often involve a longitudinal examination of the case, meaning they study the case over a period of time. These characteristics allow case studies to yield comprehensive, in-depth, and richly contextualized insights about the phenomenon of interest.

The role of case studies in research

Case studies hold a unique position in the broader landscape of research methods aimed at theory development. They are instrumental when the primary research interest is to gain an intensive, detailed understanding of a phenomenon in its real-life context.

In addition, case studies can serve different purposes within research - they can be used for exploratory, descriptive, or explanatory purposes, depending on the research question and objectives. This flexibility and depth make case studies a valuable tool in the toolkit of qualitative researchers.

Remember, a well-conducted case study can offer a rich, insightful contribution to both academic and practical knowledge through theory development or theory verification, thus enhancing our understanding of complex phenomena in their real-world contexts.

What is the purpose of a case study?

Case study research aims for a more comprehensive understanding of phenomena, requiring various research methods to gather information for qualitative analysis . Ultimately, a case study can allow the researcher to gain insight into a particular object of inquiry and develop a theoretical framework relevant to the research inquiry.

Why use case studies in qualitative research?

Using case studies as a research strategy depends mainly on the nature of the research question and the researcher's access to the data.

Conducting case study research provides a level of detail and contextual richness that other research methods might not offer. They are beneficial when there's a need to understand complex social phenomena within their natural contexts.

The explanatory, exploratory, and descriptive roles of case studies

Case studies can take on various roles depending on the research objectives. They can be exploratory when the research aims to discover new phenomena or define new research questions; they are descriptive when the objective is to depict a phenomenon within its context in a detailed manner; and they can be explanatory if the goal is to understand specific relationships within the studied context. Thus, the versatility of case studies allows researchers to approach their topic from different angles, offering multiple ways to uncover and interpret the data .

The impact of case studies on knowledge development

Case studies play a significant role in knowledge development across various disciplines. Analysis of cases provides an avenue for researchers to explore phenomena within their context based on the collected data.

This can result in the production of rich, practical insights that can be instrumental in both theory-building and practice. Case studies allow researchers to delve into the intricacies and complexities of real-life situations, uncovering insights that might otherwise remain hidden.

Types of case studies

In qualitative research , a case study is not a one-size-fits-all approach. Depending on the nature of the research question and the specific objectives of the study, researchers might choose to use different types of case studies. These types differ in their focus, methodology, and the level of detail they provide about the phenomenon under investigation.

Understanding these types is crucial for selecting the most appropriate approach for your research project and effectively achieving your research goals. Let's briefly look at the main types of case studies.

Exploratory case studies

Exploratory case studies are typically conducted to develop a theory or framework around an understudied phenomenon. They can also serve as a precursor to a larger-scale research project. Exploratory case studies are useful when a researcher wants to identify the key issues or questions which can spur more extensive study or be used to develop propositions for further research. These case studies are characterized by flexibility, allowing researchers to explore various aspects of a phenomenon as they emerge, which can also form the foundation for subsequent studies.

Descriptive case studies

Descriptive case studies aim to provide a complete and accurate representation of a phenomenon or event within its context. These case studies are often based on an established theoretical framework, which guides how data is collected and analyzed. The researcher is concerned with describing the phenomenon in detail, as it occurs naturally, without trying to influence or manipulate it.

Explanatory case studies

Explanatory case studies are focused on explanation - they seek to clarify how or why certain phenomena occur. Often used in complex, real-life situations, they can be particularly valuable in clarifying causal relationships among concepts and understanding the interplay between different factors within a specific context.

Intrinsic, instrumental, and collective case studies

These three categories of case studies focus on the nature and purpose of the study. An intrinsic case study is conducted when a researcher has an inherent interest in the case itself. Instrumental case studies are employed when the case is used to provide insight into a particular issue or phenomenon. A collective case study, on the other hand, involves studying multiple cases simultaneously to investigate some general phenomena.

Each type of case study serves a different purpose and has its own strengths and challenges. The selection of the type should be guided by the research question and objectives, as well as the context and constraints of the research.

The flexibility, depth, and contextual richness offered by case studies make this approach an excellent research method for various fields of study. They enable researchers to investigate real-world phenomena within their specific contexts, capturing nuances that other research methods might miss. Across numerous fields, case studies provide valuable insights into complex issues.

Critical information systems research

Case studies provide a detailed understanding of the role and impact of information systems in different contexts. They offer a platform to explore how information systems are designed, implemented, and used and how they interact with various social, economic, and political factors. Case studies in this field often focus on examining the intricate relationship between technology, organizational processes, and user behavior, helping to uncover insights that can inform better system design and implementation.

Health research

Health research is another field where case studies are highly valuable. They offer a way to explore patient experiences, healthcare delivery processes, and the impact of various interventions in a real-world context.

Case studies can provide a deep understanding of a patient's journey, giving insights into the intricacies of disease progression, treatment effects, and the psychosocial aspects of health and illness.

Asthma research studies

Specifically within medical research, studies on asthma often employ case studies to explore the individual and environmental factors that influence asthma development, management, and outcomes. A case study can provide rich, detailed data about individual patients' experiences, from the triggers and symptoms they experience to the effectiveness of various management strategies. This can be crucial for developing patient-centered asthma care approaches.

Other fields

Apart from the fields mentioned, case studies are also extensively used in business and management research, education research, and political sciences, among many others. They provide an opportunity to delve into the intricacies of real-world situations, allowing for a comprehensive understanding of various phenomena.

Case studies, with their depth and contextual focus, offer unique insights across these varied fields. They allow researchers to illuminate the complexities of real-life situations, contributing to both theory and practice.

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Understanding the key elements of case study design is crucial for conducting rigorous and impactful case study research. A well-structured design guides the researcher through the process, ensuring that the study is methodologically sound and its findings are reliable and valid. The main elements of case study design include the research question , propositions, units of analysis, and the logic linking the data to the propositions.

The research question is the foundation of any research study. A good research question guides the direction of the study and informs the selection of the case, the methods of collecting data, and the analysis techniques. A well-formulated research question in case study research is typically clear, focused, and complex enough to merit further detailed examination of the relevant case(s).

Propositions

Propositions, though not necessary in every case study, provide a direction by stating what we might expect to find in the data collected. They guide how data is collected and analyzed by helping researchers focus on specific aspects of the case. They are particularly important in explanatory case studies, which seek to understand the relationships among concepts within the studied phenomenon.

Units of analysis

The unit of analysis refers to the case, or the main entity or entities that are being analyzed in the study. In case study research, the unit of analysis can be an individual, a group, an organization, a decision, an event, or even a time period. It's crucial to clearly define the unit of analysis, as it shapes the qualitative data analysis process by allowing the researcher to analyze a particular case and synthesize analysis across multiple case studies to draw conclusions.

Argumentation

This refers to the inferential model that allows researchers to draw conclusions from the data. The researcher needs to ensure that there is a clear link between the data, the propositions (if any), and the conclusions drawn. This argumentation is what enables the researcher to make valid and credible inferences about the phenomenon under study.

Understanding and carefully considering these elements in the design phase of a case study can significantly enhance the quality of the research. It can help ensure that the study is methodologically sound and its findings contribute meaningful insights about the case.

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Conducting a case study involves several steps, from defining the research question and selecting the case to collecting and analyzing data . This section outlines these key stages, providing a practical guide on how to conduct case study research.

Defining the research question

The first step in case study research is defining a clear, focused research question. This question should guide the entire research process, from case selection to analysis. It's crucial to ensure that the research question is suitable for a case study approach. Typically, such questions are exploratory or descriptive in nature and focus on understanding a phenomenon within its real-life context.

Selecting and defining the case

The selection of the case should be based on the research question and the objectives of the study. It involves choosing a unique example or a set of examples that provide rich, in-depth data about the phenomenon under investigation. After selecting the case, it's crucial to define it clearly, setting the boundaries of the case, including the time period and the specific context.

Previous research can help guide the case study design. When considering a case study, an example of a case could be taken from previous case study research and used to define cases in a new research inquiry. Considering recently published examples can help understand how to select and define cases effectively.

Developing a detailed case study protocol

A case study protocol outlines the procedures and general rules to be followed during the case study. This includes the data collection methods to be used, the sources of data, and the procedures for analysis. Having a detailed case study protocol ensures consistency and reliability in the study.

The protocol should also consider how to work with the people involved in the research context to grant the research team access to collecting data. As mentioned in previous sections of this guide, establishing rapport is an essential component of qualitative research as it shapes the overall potential for collecting and analyzing data.

Collecting data

Gathering data in case study research often involves multiple sources of evidence, including documents, archival records, interviews, observations, and physical artifacts. This allows for a comprehensive understanding of the case. The process for gathering data should be systematic and carefully documented to ensure the reliability and validity of the study.

Analyzing and interpreting data

The next step is analyzing the data. This involves organizing the data , categorizing it into themes or patterns , and interpreting these patterns to answer the research question. The analysis might also involve comparing the findings with prior research or theoretical propositions.

Writing the case study report

The final step is writing the case study report . This should provide a detailed description of the case, the data, the analysis process, and the findings. The report should be clear, organized, and carefully written to ensure that the reader can understand the case and the conclusions drawn from it.

Each of these steps is crucial in ensuring that the case study research is rigorous, reliable, and provides valuable insights about the case.

The type, depth, and quality of data in your study can significantly influence the validity and utility of the study. In case study research, data is usually collected from multiple sources to provide a comprehensive and nuanced understanding of the case. This section will outline the various methods of collecting data used in case study research and discuss considerations for ensuring the quality of the data.

Interviews are a common method of gathering data in case study research. They can provide rich, in-depth data about the perspectives, experiences, and interpretations of the individuals involved in the case. Interviews can be structured , semi-structured , or unstructured , depending on the research question and the degree of flexibility needed.

Observations

Observations involve the researcher observing the case in its natural setting, providing first-hand information about the case and its context. Observations can provide data that might not be revealed in interviews or documents, such as non-verbal cues or contextual information.

Documents and artifacts

Documents and archival records provide a valuable source of data in case study research. They can include reports, letters, memos, meeting minutes, email correspondence, and various public and private documents related to the case.

These records can provide historical context, corroborate evidence from other sources, and offer insights into the case that might not be apparent from interviews or observations.

Physical artifacts refer to any physical evidence related to the case, such as tools, products, or physical environments. These artifacts can provide tangible insights into the case, complementing the data gathered from other sources.

Ensuring the quality of data collection

Determining the quality of data in case study research requires careful planning and execution. It's crucial to ensure that the data is reliable, accurate, and relevant to the research question. This involves selecting appropriate methods of collecting data, properly training interviewers or observers, and systematically recording and storing the data. It also includes considering ethical issues related to collecting and handling data, such as obtaining informed consent and ensuring the privacy and confidentiality of the participants.

Data analysis

Analyzing case study research involves making sense of the rich, detailed data to answer the research question. This process can be challenging due to the volume and complexity of case study data. However, a systematic and rigorous approach to analysis can ensure that the findings are credible and meaningful. This section outlines the main steps and considerations in analyzing data in case study research.

Organizing the data

The first step in the analysis is organizing the data. This involves sorting the data into manageable sections, often according to the data source or the theme. This step can also involve transcribing interviews, digitizing physical artifacts, or organizing observational data.

Categorizing and coding the data

Once the data is organized, the next step is to categorize or code the data. This involves identifying common themes, patterns, or concepts in the data and assigning codes to relevant data segments. Coding can be done manually or with the help of software tools, and in either case, qualitative analysis software can greatly facilitate the entire coding process. Coding helps to reduce the data to a set of themes or categories that can be more easily analyzed.

Identifying patterns and themes

After coding the data, the researcher looks for patterns or themes in the coded data. This involves comparing and contrasting the codes and looking for relationships or patterns among them. The identified patterns and themes should help answer the research question.

Interpreting the data

Once patterns and themes have been identified, the next step is to interpret these findings. This involves explaining what the patterns or themes mean in the context of the research question and the case. This interpretation should be grounded in the data, but it can also involve drawing on theoretical concepts or prior research.

Verification of the data

The last step in the analysis is verification. This involves checking the accuracy and consistency of the analysis process and confirming that the findings are supported by the data. This can involve re-checking the original data, checking the consistency of codes, or seeking feedback from research participants or peers.

Like any research method , case study research has its strengths and limitations. Researchers must be aware of these, as they can influence the design, conduct, and interpretation of the study.

Understanding the strengths and limitations of case study research can also guide researchers in deciding whether this approach is suitable for their research question . This section outlines some of the key strengths and limitations of case study research.

Benefits include the following:

• Rich, detailed data: One of the main strengths of case study research is that it can generate rich, detailed data about the case. This can provide a deep understanding of the case and its context, which can be valuable in exploring complex phenomena.
• Flexibility: Case study research is flexible in terms of design , data collection , and analysis . A sufficient degree of flexibility allows the researcher to adapt the study according to the case and the emerging findings.
• Real-world context: Case study research involves studying the case in its real-world context, which can provide valuable insights into the interplay between the case and its context.
• Multiple sources of evidence: Case study research often involves collecting data from multiple sources , which can enhance the robustness and validity of the findings.

On the other hand, researchers should consider the following limitations:

• Generalizability: A common criticism of case study research is that its findings might not be generalizable to other cases due to the specificity and uniqueness of each case.
• Time and resource intensive: Case study research can be time and resource intensive due to the depth of the investigation and the amount of collected data.
• Complexity of analysis: The rich, detailed data generated in case study research can make analyzing the data challenging.
• Subjectivity: Given the nature of case study research, there may be a higher degree of subjectivity in interpreting the data , so researchers need to reflect on this and transparently convey to audiences how the research was conducted.

Being aware of these strengths and limitations can help researchers design and conduct case study research effectively and interpret and report the findings appropriately.

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Case Study Methods and Examples

By Janet Salmons, PhD Manager, Sage Research Methods Community

What is Case Study Methodology ?

Case studies in research are both unique and uniquely confusing. The term case study is confusing because the same term is used multiple ways. The term can refer to the methodology, that is, a system of frameworks used to design a study, or the methods used to conduct it. Or, case study can refer to a type of academic writing that typically delves into a problem, process, or situation.

Case study methodology can entail the study of one or more "cases," that could be described as instances, examples, or settings where the problem or phenomenon can be examined. The researcher is tasked with defining the parameters of the case, that is, what is included and excluded. This process is called bounding the case , or setting boundaries.

Case study can be combined with other methodologies, such as ethnography, grounded theory, or phenomenology. In such studies the research on the case uses another framework to further define the study and refine the approach.

Case study is also described as a method, given particular approaches used to collect and analyze data. Case study research is conducted by almost every social science discipline: business, education, sociology, psychology. Case study research, with its reliance on multiple sources, is also a natural choice for researchers interested in trans-, inter-, or cross-disciplinary studies.

The Encyclopedia of case study research provides an overview:

The purpose of case study research is twofold: (1) to provide descriptive information and (2) to suggest theoretical relevance. Rich description enables an in-depth or sharpened understanding of the case.

It is unique given one characteristic: case studies draw from more than one data source. Case studies are inherently multimodal or mixed methods because this they use either more than one form of data within a research paradigm, or more than one form of data from different paradigms.

A case study inquiry could include multiple types of data:

multiple forms of quantitative data sources, such as Big Data + a survey

multiple forms of qualitative data sources, such as interviews + observations + documents

multiple forms of quantitative and qualitative data sources, such as surveys + interviews

Case study methodology can be used to achieve different research purposes.

Robert Yin , methodologist most associated with case study research, differentiates between descriptive , exploratory and explanatory case studies:

Descriptive : A case study whose purpose is to describe a phenomenon. Explanatory : A case study whose purpose is to explain how or why some condition came to be, or why some sequence of events occurred or did not occur. Exploratory: A case study whose purpose is to identify the research questions or procedures to be used in a subsequent study.

Robert Yin’s book is a comprehensive guide for case study researchers!

You can read the preface and Chapter 1 of Yin's book here . See the open-access articles below for some published examples of qualitative, quantitative, and mixed methods case study research.

Mills, A. J., Durepos, G., & Wiebe, E. (2010).  Encyclopedia of case study research (Vols. 1-0). Thousand Oaks, CA: SAGE Publications, Inc. doi: 10.4135/9781412957397

Yin, R. K. (2018). Case study research and applications (6th ed.). Thousand Oaks: SAGE Publications.

Open-Access Articles Using Case Study Methodology

As you can see from this collection, case study methods are used in qualitative, quantitative and mixed methods research.

Ang, C.-S., Lee, K.-F., & Dipolog-Ubanan, G. F. (2019). Determinants of First-Year Student Identity and Satisfaction in Higher Education: A Quantitative Case Study. SAGE Open. https://doi.org/10.1177/2158244019846689

Abstract. First-year undergraduates’ expectations and experience of university and student engagement variables were investigated to determine how these perceptions influence their student identity and overall course satisfaction. Data collected from 554 first-year undergraduates at a large private university were analyzed. Participants were given the adapted version of the Melbourne Centre for the Study of Higher Education Survey to self-report their learning experience and engagement in the university community. The results showed that, in general, the students’ reasons of pursuing tertiary education were to open the door to career opportunities and skill development. Moreover, students’ views on their learning and university engagement were at the moderate level. In relation to student identity and overall student satisfaction, it is encouraging to state that their perceptions of studentship and course satisfaction were rather positive. After controlling for demographics, student engagement appeared to explain more variance in student identity, whereas students’ expectations and experience explained greater variance in students’ overall course satisfaction. Implications for practice, limitations, and recommendation of this study are addressed.

Baker, A. J. (2017). Algorithms to Assess Music Cities: Case Study—Melbourne as a Music Capital. SAGE Open. https://doi.org/10.1177/2158244017691801

Abstract. The global  Mastering of a Music City  report in 2015 notes that the concept of music cities has penetrated the global political vernacular because it delivers “significant economic, employment, cultural and social benefits.” This article highlights that no empirical study has combined all these values and offers a relevant and comprehensive definition of a music city. Drawing on industry research,1 the article assesses how mathematical flowcharts, such as Algorithm A (Economics), Algorithm B (Four T’s creative index), and Algorithm C (Heritage), have contributed to the definition of a music city. Taking Melbourne as a case study, it illustrates how Algorithms A and B are used as disputed evidence about whether the city is touted as Australia’s music capital. The article connects the three algorithms to an academic framework from musicology, urban studies, cultural economics, and sociology, and proposes a benchmark Algorithm D (Music Cities definition), which offers a more holistic assessment of music activity in any urban context. The article concludes by arguing that Algorithm D offers a much-needed definition of what comprises a music city because it builds on the popular political economy focus and includes the social importance of space and cultural practices.

Brown, K., & Mondon, A. (2020). Populism, the media, and the mainstreaming of the far right: The Guardian’s coverage of populism as a case study. Politics. https://doi.org/10.1177/0263395720955036

Abstract. Populism seems to define our current political age. The term is splashed across the headlines, brandished in political speeches and commentaries, and applied extensively in numerous academic publications and conferences. This pervasive usage, or populist hype, has serious implications for our understanding of the meaning of populism itself and for our interpretation of the phenomena to which it is applied. In particular, we argue that its common conflation with far-right politics, as well as its breadth of application to other phenomena, has contributed to the mainstreaming of the far right in three main ways: (1) agenda-setting power and deflection, (2) euphemisation and trivialisation, and (3) amplification. Through a mixed-methods approach to discourse analysis, this article uses  The Guardian  newspaper as a case study to explore the development of the populist hype and the detrimental effects of the logics that it has pushed in public discourse.

Droy, L. T., Goodwin, J., & O’Connor, H. (2020). Methodological Uncertainty and Multi-Strategy Analysis: Case Study of the Long-Term Effects of Government Sponsored Youth Training on Occupational Mobility. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 147–148(1–2), 200–230. https://doi.org/10.1177/0759106320939893

Abstract. Sociological practitioners often face considerable methodological uncertainty when undertaking a quantitative analysis. This methodological uncertainty encompasses both data construction (e.g. defining variables) and analysis (e.g. selecting and specifying a modelling procedure). Methodological uncertainty can lead to results that are fragile and arbitrary. Yet, many practitioners may be unaware of the potential scale of methodological uncertainty in quantitative analysis, and the recent emergence of techniques for addressing it. Recent proposals for ‘multi-strategy’ approaches seek to identify and manage methodological uncertainty in quantitative analysis. We present a case-study of a multi-strategy analysis, applied to the problem of estimating the long-term impact of 1980s UK government-sponsored youth training. We use this case study to further highlight the problem of cumulative methodological fragilities in applied quantitative sociology and to discuss and help develop multi-strategy analysis as a tool to address them.

Ebneyamini, S., & Sadeghi Moghadam, M. R. (2018). Toward Developing a Framework for Conducting Case Study Research .  International Journal of Qualitative Methods .  https://doi.org/10.1177/1609406918817954

Abstract. This article reviews the use of case study research for both practical and theoretical issues especially in management field with the emphasis on management of technology and innovation. Many researchers commented on the methodological issues of the case study research from their point of view thus, presenting a comprehensive framework was missing. We try representing a general framework with methodological and analytical perspective to design, develop, and conduct case study research. To test the coverage of our framework, we have analyzed articles in three major journals related to the management of technology and innovation to approve our framework. This study represents a general structure to guide, design, and fulfill a case study research with levels and steps necessary for researchers to use in their research.

Lai, D., & Roccu, R. (2019). Case study research and critical IR: the case for the extended case methodology. International Relations , 33 (1), 67-87. https://doi.org/10.1177/0047117818818243

Abstract. Discussions on case study methodology in International Relations (IR) have historically been dominated by positivist and neopositivist approaches. However, these are problematic for critical IR research, pointing to the need for a non-positivist case study methodology. To address this issue, this article introduces and adapts the extended case methodology as a critical, reflexivist approach to case study research, whereby the case is constructed through a dynamic interaction with theory, rather than selected, and knowledge is produced through extensions rather than generalisation. Insofar as it seeks to study the world in complex and non-linear terms, take context and positionality seriously, and generate explicitly political and emancipatory knowledge, the extended case methodology is consistent with the ontological and epistemological commitments of several critical IR approaches. Its potential is illustrated in the final part of the article with reference to researching the socioeconomic dimension of transitional justice in Bosnia and Herzegovina.

Lynch, R., Young, J. C., Boakye-Achampong, S., Jowaisas, C., Sam, J., & Norlander, B. (2020). Benefits of crowdsourcing for libraries: A case study from Africa . IFLA Journal. https://doi.org/10.1177/0340035220944940

Abstract. Many libraries in the Global South do not collect comprehensive data about themselves, which creates challenges in terms of local and international visibility. Crowdsourcing is an effective tool that engages the public to collect missing data, and it has proven to be particularly valuable in countries where governments collect little public data. Whereas crowdsourcing is often used within fields that have high levels of development funding, such as health, the authors believe that this approach would have many benefits for the library field as well. They present qualitative and quantitative evidence from 23 African countries involved in a crowdsourcing project to map libraries. The authors find benefits in terms of increased connections between stakeholders, capacity-building, and increased local visibility. These findings demonstrate the potential of crowdsourced approaches for tasks such as mapping to benefit libraries and similarly positioned institutions in the Global South in multifaceted ways.

Mason, W., Morris, K., Webb, C., Daniels, B., Featherstone, B., Bywaters, P., Mirza, N., Hooper, J., Brady, G., Bunting, L., & Scourfield, J. (2020). Toward Full Integration of Quantitative and Qualitative Methods in Case Study Research: Insights From Investigating Child Welfare Inequalities. Journal of Mixed Methods Research, 14 (2), 164-183. https://doi.org/10.1177/1558689819857972

Abstract. Delineation of the full integration of quantitative and qualitative methods throughout all stages of multisite mixed methods case study projects remains a gap in the methodological literature. This article offers advances to the field of mixed methods by detailing the application and integration of mixed methods throughout all stages of one such project; a study of child welfare inequalities. By offering a critical discussion of site selection and the management of confirmatory, expansionary and discordant data, this article contributes to the limited body of mixed methods exemplars specific to this field. We propose that our mixed methods approach provided distinctive insights into a complex social problem, offering expanded understandings of the relationship between poverty, child abuse, and neglect.

Rashid, Y., Rashid, A., Warraich, M. A., Sabir, S. S., & Waseem, A. (2019). Case Study Method: A Step-by-Step Guide for Business Researchers .  International Journal of Qualitative Methods .  https://doi.org/10.1177/1609406919862424

Abstract. Qualitative case study methodology enables researchers to conduct an in-depth exploration of intricate phenomena within some specific context. By keeping in mind research students, this article presents a systematic step-by-step guide to conduct a case study in the business discipline. Research students belonging to said discipline face issues in terms of clarity, selection, and operationalization of qualitative case study while doing their final dissertation. These issues often lead to confusion, wastage of valuable time, and wrong decisions that affect the overall outcome of the research. This article presents a checklist comprised of four phases, that is, foundation phase, prefield phase, field phase, and reporting phase. The objective of this article is to provide novice researchers with practical application of this checklist by linking all its four phases with the authors’ experiences and learning from recently conducted in-depth multiple case studies in the organizations of New Zealand. Rather than discussing case study in general, a targeted step-by-step plan with real-time research examples to conduct a case study is given.

VanWynsberghe, R., & Khan, S. (2007). Redefining Case Study. International Journal of Qualitative Methods, 80–94. https://doi.org/10.1177/160940690700600208

Abstract. In this paper the authors propose a more precise and encompassing definition of case study than is usually found. They support their definition by clarifying that case study is neither a method nor a methodology nor a research design as suggested by others. They use a case study prototype of their own design to propose common properties of case study and demonstrate how these properties support their definition. Next, they present several living myths about case study and refute them in relation to their definition. Finally, they discuss the interplay between the terms case study and unit of analysis to further delineate their definition of case study. The target audiences for this paper include case study researchers, research design and methods instructors, and graduate students interested in case study research.

More Sage Research Methods Community Posts about Case Study Research

Use research cases as the basis for individual or team activities that build skills.

Find an 10-step process for using research cases to teach methods with learning activities for individual students, teams, or small groups. (Or use the approach yourself!)

How do you decide which methodology fits your study? In this dialogue Linda Bloomberg and Janet Boberg explain the importance of a strategic approach to qualitative research design that stresses alignment with the purpose of the study.

Case study methods are used by researchers in many disciplines. Here are some open-access articles about multimodal qualitative or mixed methods designs that include both qualitative and quantitative elements.

Case study methodology is both unique, and uniquely confusing. It is unique given one characteristic: case studies draw from more than one data source.

What is case study methodology? It is unique given one characteristic: case studies draw from more than one data source. In this post find definitions and a collection of multidisciplinary examples.

Find discussion of case studies and published examples.

Istanbul as a regional computational social science hub

Experiments and quantitative research.

• Our Mission

Making Learning Relevant With Case Studies

The open-ended problems presented in case studies give students work that feels connected to their lives.

To prepare students for jobs that haven’t been created yet, we need to teach them how to be great problem solvers so that they’ll be ready for anything. One way to do this is by teaching content and skills using real-world case studies, a learning model that’s focused on reflection during the problem-solving process. It’s similar to project-based learning, but PBL is more focused on students creating a product.

Case studies have been used for years by businesses, law and medical schools, physicians on rounds, and artists critiquing work. Like other forms of problem-based learning, case studies can be accessible for every age group, both in one subject and in interdisciplinary work.

You can get started with case studies by tackling relatable questions like these with your students:

• How can we limit food waste in the cafeteria?
• How can we get our school to recycle and compost waste? (Or, if you want to be more complex, how can our school reduce its carbon footprint?)
• How can we improve school attendance?
• How can we reduce the number of people who get sick at school during cold and flu season?

Addressing questions like these leads students to identify topics they need to learn more about. In researching the first question, for example, students may see that they need to research food chains and nutrition. Students often ask, reasonably, why they need to learn something, or when they’ll use their knowledge in the future. Learning is most successful for students when the content and skills they’re studying are relevant, and case studies offer one way to create that sense of relevance.

Teaching With Case Studies

Ultimately, a case study is simply an interesting problem with many correct answers. What does case study work look like in classrooms? Teachers generally start by having students read the case or watch a video that summarizes the case. Students then work in small groups or individually to solve the case study. Teachers set milestones defining what students should accomplish to help them manage their time.

During the case study learning process, student assessment of learning should be focused on reflection. Arthur L. Costa and Bena Kallick’s Learning and Leading With Habits of Mind gives several examples of what this reflection can look like in a classroom: 

Journaling: At the end of each work period, have students write an entry summarizing what they worked on, what worked well, what didn’t, and why. Sentence starters and clear rubrics or guidelines will help students be successful. At the end of a case study project, as Costa and Kallick write, it’s helpful to have students “select significant learnings, envision how they could apply these learnings to future situations, and commit to an action plan to consciously modify their behaviors.”

Interviews: While working on a case study, students can interview each other about their progress and learning. Teachers can interview students individually or in small groups to assess their learning process and their progress.

Student discussion: Discussions can be unstructured—students can talk about what they worked on that day in a think-pair-share or as a full class—or structured, using Socratic seminars or fishbowl discussions. If your class is tackling a case study in small groups, create a second set of small groups with a representative from each of the case study groups so that the groups can share their learning.

4 Tips for Setting Up a Case Study

1. Identify a problem to investigate: This should be something accessible and relevant to students’ lives. The problem should also be challenging and complex enough to yield multiple solutions with many layers.

2. Give context: Think of this step as a movie preview or book summary. Hook the learners to help them understand just enough about the problem to want to learn more.

3. Have a clear rubric: Giving structure to your definition of quality group work and products will lead to stronger end products. You may be able to have your learners help build these definitions.

4. Provide structures for presenting solutions: The amount of scaffolding you build in depends on your students’ skill level and development. A case study product can be something like several pieces of evidence of students collaborating to solve the case study, and ultimately presenting their solution with a detailed slide deck or an essay—you can scaffold this by providing specified headings for the sections of the essay.

Problem-Based Teaching Resources

There are many high-quality, peer-reviewed resources that are open source and easily accessible online.

• The National Center for Case Study Teaching in Science at the University at Buffalo built an online collection of more than 800 cases that cover topics ranging from biochemistry to economics. There are resources for middle and high school students.
• Models of Excellence , a project maintained by EL Education and the Harvard Graduate School of Education, has examples of great problem- and project-based tasks—and corresponding exemplary student work—for grades pre-K to 12.
• The Interdisciplinary Journal of Problem-Based Learning at Purdue University is an open-source journal that publishes examples of problem-based learning in K–12 and post-secondary classrooms.
• The Tech Edvocate has a list of websites and tools related to problem-based learning.

In their book Problems as Possibilities , Linda Torp and Sara Sage write that at the elementary school level, students particularly appreciate how they feel that they are taken seriously when solving case studies. At the middle school level, “researchers stress the importance of relating middle school curriculum to issues of student concern and interest.” And high schoolers, they write, find the case study method “beneficial in preparing them for their future.”

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Case-based learning.

Case-based learning (CBL) is an established approach used across disciplines where students apply their knowledge to real-world scenarios, promoting higher levels of cognition (see Bloom’s Taxonomy ). In CBL classrooms, students typically work in groups on case studies, stories involving one or more characters and/or scenarios.  The cases present a disciplinary problem or problems for which students devise solutions under the guidance of the instructor. CBL has a strong history of successful implementation in medical, law, and business schools, and is increasingly used within undergraduate education, particularly within pre-professional majors and the sciences (Herreid, 1994). This method involves guided inquiry and is grounded in constructivism whereby students form new meanings by interacting with their knowledge and the environment (Lee, 2012).

There are a number of benefits to using CBL in the classroom. In a review of the literature, Williams (2005) describes how CBL: utilizes collaborative learning, facilitates the integration of learning, develops students’ intrinsic and extrinsic motivation to learn, encourages learner self-reflection and critical reflection, allows for scientific inquiry, integrates knowledge and practice, and supports the development of a variety of learning skills.

CBL has several defining characteristics, including versatility, storytelling power, and efficient self-guided learning.  In a systematic analysis of 104 articles in health professions education, CBL was found to be utilized in courses with less than 50 to over 1000 students (Thistlethwaite et al., 2012). In these classrooms, group sizes ranged from 1 to 30, with most consisting of 2 to 15 students.  Instructors varied in the proportion of time they implemented CBL in the classroom, ranging from one case spanning two hours of classroom time, to year-long case-based courses. These findings demonstrate that instructors use CBL in a variety of ways in their classrooms.

The stories that comprise the framework of case studies are also a key component to CBL’s effectiveness. Jonassen and Hernandez-Serrano (2002, p.66) describe how storytelling:

Is a method of negotiating and renegotiating meanings that allows us to enter into other’s realms of meaning through messages they utter in their stories,

Helps us find our place in a culture,

Allows us to explicate and to interpret, and

Facilitates the attainment of vicarious experience by helping us to distinguish the positive models to emulate from the negative model.

Neurochemically, listening to stories can activate oxytocin, a hormone that increases one’s sensitivity to social cues, resulting in more empathy, generosity, compassion and trustworthiness (Zak, 2013; Kosfeld et al., 2005). The stories within case studies serve as a means by which learners form new understandings through characters and/or scenarios.

CBL is often described in conjunction or in comparison with problem-based learning (PBL). While the lines are often confusingly blurred within the literature, in the most conservative of definitions, the features distinguishing the two approaches include that PBL involves open rather than guided inquiry, is less structured, and the instructor plays a more passive role. In PBL multiple solutions to the problem may exit, but the problem is often initially not well-defined. PBL also has a stronger emphasis on developing self-directed learning. The choice between implementing CBL versus PBL is highly dependent on the goals and context of the instruction.  For example, in a comparison of PBL and CBL approaches during a curricular shift at two medical schools, students and faculty preferred CBL to PBL (Srinivasan et al., 2007). Students perceived CBL to be a more efficient process and more clinically applicable. However, in another context, PBL might be the favored approach.

In a review of the effectiveness of CBL in health profession education, Thistlethwaite et al. (2012), found several benefits:

Students enjoyed the method and thought it enhanced their learning,

Instructors liked how CBL engaged students in learning,

CBL seemed to facilitate small group learning, but the authors could not distinguish between whether it was the case itself or the small group learning that occurred as facilitated by the case.

Other studies have also reported on the effectiveness of CBL in achieving learning outcomes (Bonney, 2015; Breslin, 2008; Herreid, 2013; Krain, 2016). These findings suggest that CBL is a vehicle of engagement for instruction, and facilitates an environment whereby students can construct knowledge.

Science – Students are given a scenario to which they apply their basic science knowledge and problem-solving skills to help them solve the case. One example within the biological sciences is two brothers who have a family history of a genetic illness. They each have mutations within a particular sequence in their DNA. Students work through the case and draw conclusions about the biological impacts of these mutations using basic science. Sample cases: You are Not the Mother of Your Children ; Organic Chemisty and Your Cellphone: Organic Light-Emitting Diodes ;   A Light on Physics: F-Number and Exposure Time

Medicine – Medical or pre-health students read about a patient presenting with specific symptoms. Students decide which questions are important to ask the patient in their medical history, how long they have experienced such symptoms, etc. The case unfolds and students use clinical reasoning, propose relevant tests, develop a differential diagnoses and a plan of treatment. Sample cases: The Case of the Crying Baby: Surgical vs. Medical Management ; The Plan: Ethics and Physician Assisted Suicide ; The Haemophilus Vaccine: A Victory for Immunologic Engineering

Public Health – A case study describes a pandemic of a deadly infectious disease. Students work through the case to identify Patient Zero, the person who was the first to spread the disease, and how that individual became infected.  Sample cases: The Protective Parent ; The Elusive Tuberculosis Case: The CDC and Andrew Speaker ; Credible Voice: WHO-Beijing and the SARS Crisis

Law – A case study presents a legal dilemma for which students use problem solving to decide the best way to advise and defend a client. Students are presented information that changes during the case.  Sample cases: Mortgage Crisis Call (abstract) ; The Case of the Unpaid Interns (abstract) ; Police-Community Dialogue (abstract)

Business – Students work on a case study that presents the history of a business success or failure. They apply business principles learned in the classroom and assess why the venture was successful or not. Sample cases: SELCO-Determining a path forward ; Project Masiluleke: Texting and Testing to Fight HIV/AIDS in South Africa ; Mayo Clinic: Design Thinking in Healthcare

Humanities - Students consider a case that presents a theater facing financial and management difficulties. They apply business and theater principles learned in the classroom to the case, working together to create solutions for the theater. Sample cases: David Geffen School of Drama

Recommendations

Finding and Writing Cases

Consider utilizing or adapting open access cases - The availability of open resources and databases containing cases that instructors can download makes this approach even more accessible in the classroom. Two examples of open databases are the Case Center on Public Leadership and Harvard Kennedy School (HKS) Case Program , which focus on government, leadership and public policy case studies.

• Consider writing original cases - In the event that an instructor is unable to find open access cases relevant to their course learning objectives, they may choose to write their own. See the following resources on case writing: Cooking with Betty Crocker: A Recipe for Case Writing ; The Way of Flesch: The Art of Writing Readable Cases ;   Twixt Fact and Fiction: A Case Writer’s Dilemma ; And All That Jazz: An Essay Extolling the Virtues of Writing Case Teaching Notes .

Implementing Cases

Take baby steps if new to CBL - While entire courses and curricula may involve case-based learning, instructors who desire to implement on a smaller-scale can integrate a single case into their class, and increase the number of cases utilized over time as desired.

Use cases in classes that are small, medium or large - Cases can be scaled to any course size. In large classes with stadium seating, students can work with peers nearby, while in small classes with more flexible seating arrangements, teams can move their chairs closer together. CBL can introduce more noise (and energy) in the classroom to which an instructor often quickly becomes accustomed. Further, students can be asked to work on cases outside of class, and wrap up discussion during the next class meeting.

Encourage collaborative work - Cases present an opportunity for students to work together to solve cases which the historical literature supports as beneficial to student learning (Bruffee, 1993). Allow students to work in groups to answer case questions.

Form diverse teams as feasible - When students work within diverse teams they can be exposed to a variety of perspectives that can help them solve the case. Depending on the context of the course, priorities, and the background information gathered about the students enrolled in the class, instructors may choose to organize student groups to allow for diversity in factors such as current course grades, gender, race/ethnicity, personality, among other items.  

Use stable teams as appropriate - If CBL is a large component of the course, a research-supported practice is to keep teams together long enough to go through the stages of group development: forming, storming, norming, performing and adjourning (Tuckman, 1965).

Walk around to guide groups - In CBL instructors serve as facilitators of student learning. Walking around allows the instructor to monitor student progress as well as identify and support any groups that may be struggling. Teaching assistants can also play a valuable role in supporting groups.

Interrupt strategically - Only every so often, for conversation in large group discussion of the case, especially when students appear confused on key concepts. An effective practice to help students meet case learning goals is to guide them as a whole group when the class is ready. This may include selecting a few student groups to present answers to discussion questions to the entire class, asking the class a question relevant to the case using polling software, and/or performing a mini-lesson on an area that appears to be confusing among students.  

Assess student learning in multiple ways - Students can be assessed informally by asking groups to report back answers to various case questions. This practice also helps students stay on task, and keeps them accountable. Cases can also be included on exams using related scenarios where students are asked to apply their knowledge.

Barrows HS. (1996). Problem-based learning in medicine and beyond: a brief overview. New Directions for Teaching and Learning, 68, 3-12.  

Bonney KM. (2015). Case Study Teaching Method Improves Student Performance and Perceptions of Learning Gains. Journal of Microbiology and Biology Education, 16(1): 21-28.

Breslin M, Buchanan, R. (2008) On the Case Study Method of Research and Teaching in Design.  Design Issues, 24(1), 36-40.

Bruffee KS. (1993). Collaborative learning: Higher education, interdependence, and authority of knowledge. Johns Hopkins University Press, Baltimore, MD.

Herreid CF. (2013). Start with a Story: The Case Study Method of Teaching College Science, edited by Clyde Freeman Herreid. Originally published in 2006 by the National Science Teachers Association (NSTA); reprinted by the National Center for Case Study Teaching in Science (NCCSTS) in 2013.

Herreid CH. (1994). Case studies in science: A novel method of science education. Journal of Research in Science Teaching, 23(4), 221–229.

Jonassen DH and Hernandez-Serrano J. (2002). Case-based reasoning and instructional design: Using stories to support problem solving. Educational Technology, Research and Development, 50(2), 65-77.  

Kosfeld M, Heinrichs M, Zak PJ, Fischbacher U, Fehr E. (2005). Oxytocin increases trust in humans. Nature, 435, 673-676.

Krain M. (2016) Putting the learning in case learning? The effects of case-based approaches on student knowledge, attitudes, and engagement. Journal on Excellence in College Teaching, 27(2), 131-153.

Lee V. (2012). What is Inquiry-Guided Learning?  New Directions for Learning, 129:5-14.

Nkhoma M, Sriratanaviriyakul N. (2017). Using case method to enrich students’ learning outcomes. Active Learning in Higher Education, 18(1):37-50.

Srinivasan et al. (2007). Comparing problem-based learning with case-based learning: Effects of a major curricular shift at two institutions. Academic Medicine, 82(1): 74-82.

Thistlethwaite JE et al. (2012). The effectiveness of case-based learning in health professional education. A BEME systematic review: BEME Guide No. 23.  Medical Teacher, 34, e421-e444.

Tuckman B. (1965). Development sequence in small groups. Psychological Bulletin, 63(6), 384-99.

Williams B. (2005). Case-based learning - a review of the literature: is there scope for this educational paradigm in prehospital education? Emerg Med, 22, 577-581.

Zak, PJ (2013). How Stories Change the Brain. Retrieved from: https://greatergood.berkeley.edu/article/item/how_stories_change_brain

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• Introduction

The case study creation process

Types of case studies, benefits and limitations.

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• Table Of Contents

case study , detailed description and assessment of a specific situation in the real world created for the purpose of deriving generalizations and other insights from it. A case study can be about an individual, a group of people, an organization, or an event, among other subjects.

By focusing on a specific subject in its natural setting, a case study can help improve understanding of the broader features and processes at work. Case studies are a research method used in multiple fields, including business, criminology , education , medicine and other forms of health care, anthropology , political science , psychology , and social work . Data in case studies can be both qualitative and quantitative. Unlike experiments, where researchers control and manipulate situations, case studies are considered to be “naturalistic” because subjects are studied in their natural context . ( See also natural experiment .)

The creation of a case study typically involves the following steps:

• The research question to be studied is defined, informed by existing literature and previous research. Researchers should clearly define the scope of the case, and they should compile a list of evidence to be collected as well as identify the nature of insights that they expect to gain from the case study.
• Once the case is identified, the research team is given access to the individual, organization, or situation being studied. Individuals are informed of risks associated with participation and must provide their consent , which may involve signing confidentiality or anonymity agreements.
• Researchers then collect evidence using multiple methods, which may include qualitative techniques, such as interviews, focus groups , and direct observations, as well as quantitative methods, such as surveys, questionnaires, and data audits. The collection procedures need to be well defined to ensure the relevance and accuracy of the evidence.
• The collected evidence is analyzed to come up with insights. Each data source must be reviewed carefully by itself and in the larger context of the case study so as to ensure continued relevance. At the same time, care must be taken not to force the analysis to fit (potentially preconceived) conclusions. While the eventual case study may serve as the basis for generalizations, these generalizations must be made cautiously to ensure that specific nuances are not lost in the averages.
• Finally, the case study is packaged for larger groups and publication. At this stage some information may be withheld, as in business case studies, to allow readers to draw their own conclusions. In scientific fields, the completed case study needs to be a coherent whole, with all findings and statistical relationships clearly documented.

Case studies have been used as a research method across multiple fields. They are particularly popular in the fields of law, business, and employee training; they typically focus on a problem that an individual or organization is facing. The situation is presented in considerable detail, often with supporting data, to discussion participants, who are asked to make recommendations that will solve the stated problem. The business case study as a method of instruction was made popular in the 1920s by instructors at Harvard Business School who adapted an approach used at Harvard Law School in which real-world cases were used in classroom discussions. Other business and law schools started compiling case studies as teaching aids for students. In a business school case study, students are not provided with the complete list of facts pertaining to the topic and are thus forced to discuss and compare their perspectives with those of their peers to recommend solutions.

In criminology , case studies typically focus on the lives of an individual or a group of individuals. These studies can provide particularly valuable insight into the personalities and motives of individual criminals, but they may suffer from a lack of objectivity on the part of the researchers (typically because of the researchers’ biases when working with people with a criminal history), and their findings may be difficult to generalize.

In sociology , the case-study method was developed by Frédéric Le Play in France during the 19th century. This approach involves a field worker staying with a family for a period of time, gathering data on the family members’ attitudes and interactions and on their income, expenditures, and physical possessions. Similar approaches have been used in anthropology . Such studies can sometimes continue for many years.

Case studies provide insight into situations that involve a specific entity or set of circumstances. They can be beneficial in helping to explain the causal relationships between quantitative indicators in a field of study, such as what drives a company’s market share. By introducing real-world examples, they also plunge the reader into an actual, concrete situation and make the concepts real rather than theoretical. They also help people study rare situations that they might not otherwise experience.

Because case studies are in a “naturalistic” environment , they are limited in terms of research design: researchers lack control over what they are studying, which means that the results often cannot be reproduced. Also, care must be taken to stay within the bounds of the research question on which the case study is focusing. Other limitations to case studies revolve around the data collected. It may be difficult, for instance, for researchers to organize the large volume of data that can emerge from the study, and their analysis of the data must be carefully thought through to produce scientifically valid insights. The research methodology used to generate these insights is as important as the insights themselves, for the latter need to be seen in the proper context. Taken out of context, they may lead to erroneous conclusions. Like all scientific studies, case studies need to be approached objectively; personal bias or opinion may skew the research methods as well as the results. ( See also confirmation bias .)

Business case studies in particular have been criticized for approaching a problem or situation from a narrow perspective. Students are expected to come up with solutions for a problem based on the data provided. However, in real life, the situation is typically reversed: business managers face a problem and must then look for data to help them solve it.

Case Studies in Science Education

A video library for k-8 science teachers: 25 half-hour video programs and guides.

These video case studies take science education reform to a personal level, where individual teachers struggle to make changes that matter. Follow Donna, Mike, Audrey, and other science teachers as they work to adopt one or more research-based interventions to improve science teaching and learning. Each case follows a single teacher over the course of a year and is divided into three modules: the teacher's background and the problem he or she chooses to address, the chosen approach and implementation, and the outcome with assessment by the teacher and his or her advisor. Average running time: 1/2 hour. Program guides and supporting materials (PDF) Program guides and supporting materials (Link)

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Many companies set emissions targets with great fanfare—and never meet them, says research by Shirley Lu and colleagues. But what if investors held businesses accountable for achieving their climate plans?

• 09 Jan 2024

Could Clean Hydrogen Become Affordable at Scale by 2030?

The cost to produce hydrogen could approach the $1-per-kilogram target set by US regulators by 2030, helping this cleaner energy source compete with fossil fuels, says research by Gunther Glenk and colleagues. But planned global investments in hydrogen production would need to come to fruition to reach full potential.

• 02 Jan 2024

Should Businesses Take a Stand on Societal Issues?

Should businesses take a stand for or against particular societal issues? And how should leaders determine when and how to engage on these sensitive matters? Harvard Business School Senior Lecturer Hubert Joly, who led the electronics retailer Best Buy for almost a decade, discusses examples of corporate leaders who had to determine whether and how to engage with humanitarian crises, geopolitical conflict, racial justice, climate change, and more in the case, “Deciding When to Engage on Societal Issues.”

10 Trends to Watch in 2024

Employees may seek new approaches to balance, even as leaders consider whether to bring more teams back to offices or make hybrid work even more flexible. These are just a few trends that Harvard Business School faculty members will be following during a year when staffing, climate, and inclusion will likely remain top of mind.

• 19 Sep 2023

What Chandrayaan-3 Says About India's Entrepreneurial Approach to Space

India reached an unexplored part of the moon despite its limited R&D funding compared with NASA and SpaceX. Tarun Khanna discusses the significance of the landing, and the country's advancements in data and digital technology.

• 12 Sep 2023
• What Do You Think?

Who Gets the Loudest Voice in DEI Decisions?

Business leaders are wrestling with how to manage their organizations' commitment to diversity, equity, and inclusion. If you were a CEO, which constituency would you consider most: your employees, customers, or investors? asks James Heskett. Open for comment; 0 Comments.

• 26 Jul 2023

STEM Needs More Women. Recruiters Often Keep Them Out

Tech companies and programs turn to recruiters to find top-notch candidates, but gender bias can creep in long before women even apply, according to research by Jacqueline Ng Lane and colleagues. She highlights several tactics to make the process more equitable.

• 18 Jul 2023

Will Global Demand for Oil Peak This Decade?

The International Energy Agency expects the world's oil demand to start to ebb in the coming years. However, Joseph Lassiter and Lauren Cohen say the outlook will likely be more complex, especially as poor and fast-growing regions seek energy sources for their economies.

• 28 Apr 2023

Sweden’s Northvolt Electric Battery Maker: A Startup with a Mission

In Stockholm, Sweden an upstart battery maker, Northvolt, is trying to recreate the value chain for European car manufacturers making the switch to EVs. With two founders from Tesla and two experienced financiers at the helm, the company seems bound for success. But can they partner with government, scale fast enough, and truly be part of the climate solution? Harvard Business School professor George Serafeim discusses what it takes to scale a business—the right people, in the right place, at the right time—with the aim of providing a climate solution in the case, “Northvolt, Building Batteries to Fight Climate Change.” As part of a new first-year MBA course at Harvard Business School, this case examines the central question: what is the social purpose of the firm?

• 18 Apr 2023

What Happens When Banks Ditch Coal: The Impact Is 'More Than Anyone Thought'

Bank divestment policies that target coal reduced carbon dioxide emissions, says research by Boris Vallée and Daniel Green. Could the finance industry do even more to confront climate change?

National Center for case study teaching in science

Student assessment, evaluating case discussion.

Business school case teachers do it all the time. It’s not uncommon for them to base the final course grade on 50% class participation. And this with 50-70 students in a class! This sends shudders up the spines of most science teachers. Yet, what's so tough about the concept? We are constantly making judgments about the verbal statements of our colleagues, politicians, and even administrators. Why can't we do it for classroom contributions?

Most of our discomfort comes from the subjective nature of the act, something that we scientists work hard to avoid in our work-a-day world. It may be that we are even predisposed to become scientists because we are looking for a structured and quantifiable world. Flowing from this subjective quandary is the fact that we feel we must be able to justify our grades to the students. We are decidedly uncomfortable if we can't show them the numbers. This is one of the reasons that multiple-choice questions have such appeal for some faculty.

But let’s take a look at how the business school people evaluate case discussion. Some of them try to do it in the classroom, making written notes even as the discussion unfolds, using a seating chart, and calling on perhaps 25 students in a period. As you might expect, this usually interferes with running an effective discussion. Other instructors tape-record the discussion and listen to it later in thoughtful contemplation. Most folks, however, sit down shortly after their classes with seating chart in hand and reflect on the discussion. They rank student contributions into categories of excellent, good, or bad, or they may use numbers to evaluate the students from 1 to 4 with 4 being excellent. They may give negative evaluations to people who weren’t prepared or were absent. These numbers are tallied up at the end of the semester to calculate the grade. And that’s as quantified as it gets.

I especially like mathematician/philosopher Blaise Pascal's view of evaluation: “We first distinguish grapes from among fruits, then Muscat grapes, then those from Condrieu, then from Desargues, then the particular graft. Is that all? Has a vine ever produced two bunches alike, and has any bunch produced two grapes alike?” “I have never judged anything in exactly the same way,” Pascal continues. “I cannot judge a work while doing it. I must do as painters do and stand back, but not too far. How far then? Guess ....”

Assignments

The simplest solution to case work evaluation is to forget classroom participation and grade everything on the basis of familiar criteria, say papers or presentations. This puts professors back in familiar territory. Even business and law school professors use this strategy as part of their grades. I’m all for this. In fact, I always ask for some written analysis in the form of journals, papers, and reports. Along with an exam, these are my sole bases for grades. I don’t lose sleep over evaluating class participation.

You can give any sort of exam in a case-based course, including multiple-choice, but doesn’t it make more sense to have at least part of the exam a case? If you have used cases all semester and trained students in case analysis, surely you should consider a case-based test. Too often we test on different things than we have taught.

Peer Evaluation

Some of the best case studies involve small group work and group projects. In fact, I strongly believe teaching cases this way is the most user-friendly for science faculty and the most rewarding for students. Nonetheless, even some aficionados of group work don’t like group projects. They say, how do you know who’s doing the work? Even if they ask for a group project, they argue against grading it. They rely strictly on individual marks for a final grade determination. I’m on the other side of the fence. I believe that great projects can come from teams, and if you don't grade the work, what is the incentive for participating? Moreover, employers report that most people are fired because they can’t get along with other people. Not all of us are naturally team players. Practice helps. So, I’m all for group work including teamwork during quizzes where groups almost invariably perform better than the best individuals. But we have to build in safeguards like peer evaluation.

“Social loafers” and “compulsive workhorses” exist in every class. When you form groups such as those in Problem-Based Learning (PBL) and Team Learning (the best ways to teach cases, in my judgment), you must set up a system to monitor the situation. In PBL it is common to have tutors who can make evaluations. Still, I believe it is essential to use peer evaluations. I use a method that I picked up from Larry Michaelsen in the School of Management at the University of Oklahoma.

At the beginning of every course I explain the use of these anonymous peer evaluations. I show students the form that they will fill out at the end of the semester ( Table 1 ). Then they will be asked to name their teammates and give each one the number of points that reflects their contributions to group projects throughout the course. Say the group has five team members then each person would have 40 points to give to the other four members of his team. If a student feels that everyone has contributed equally to the group projects, then he should give each teammate 10 points. Obviously, if everyone in the team feels the same way about everyone else, they all will get an average score of 10 points. Persons with an average of 10 points will receive 100% of the group score for any group project.

But suppose that things aren’t going well. Maybe John has not pulled his weight in the group projects and ends up with an average score of 8, and Sarah has done more than her share and receives a 12. What then? Well, John gets only 80% of any group grade and Sarah receives 120%.

There are some additional rules that I use. One is that a student cannot give anyone more than 15 points. This is to stop a student from saving his friend John by giving him 40 points. Another is that any student receiving an average of seven or less will fail my course. This is designed to stop a student from doing nothing in the group because he is simply trying to slip by with a barely passing grade and is willing to undermine the group effort. Here are some observations after many years of using peer evaluations:

• Most students are reasonable. Although they are inclined to be generous, most give scores between 8 and 12.
• Occasionally, I receive a set of scores where one isn’t consistent with the others. For example, a student may get a 10, 10, 11, and a 5. Obviously, something is amiss here. When this happens, I set the odd number aside and use the other scores for the average.
• About one group in five initially will have problems because one or two people are not participating adequately or are habitually late or absent. These problems can be corrected.
• It is essential that you give a practice peer evaluation about one-third or one-half of the way through the semester. The students fill these out and you tally them and give the students their average scores. You must carefully remind everyone what these numbers mean, and if they don't like the results, they must do something to improve their scores. I tell them that it is no use blaming their group members for their perceptions. They must fix things, perhaps by talking to the group and asking how to compensate for their previous weakness. Also, I will always speak privately to any student who is in danger. These practice evaluations almost always significantly improve the group performance. Tardiness virtually stops and attendance is at least 95%.

© 1999-2024 National Center for Case Study Teaching in Science, University at Buffalo. All Rights Reserved.

Organizing Your Social Sciences Research Paper: Writing a Case Study

• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Reading Research Effectively
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Multiple Book Review Essay
• Reviewing Collected Essays
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Bibliography

The term case study refers to both a method of analysis and a specific research design for examining a problem, both of which are used in most circumstances to generalize across populations. This tab focuses on the latter--how to design and organize a research paper in the social sciences that analyzes a specific case.

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

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

How to Approach Writing a Case Study Research Paper

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

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

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

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

Structure and Writing Style

The purpose of a paper in the social sciences designed around a case study is to thoroughly investigate a subject of analysis in order to reveal a new understanding about the research problem and, in so doing, contributing new knowledge to what is already known from previous studies. In applied social sciences disciplines [e.g., education, social work, public administration, etc.], case studies may also be used to reveal best practices, highlight key programs, or investigate interesting aspects of professional work. In general, the structure of a case study research paper is not all that different from a standard college-level research paper. However, there are subtle differences you should be aware of. Here are the key elements to organizing and writing a case study research paper.

I.  Introduction

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

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

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

II.  Literature Review

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

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

III.  Method

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

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

If your subject of analysis is a person. Explain why you selected this particular individual to be studied and describe what experience he or she has had that provides an opportunity to advance new understandings about the research problem. Mention any background about this person which might help the reader understand the significance of his/her experiences that make them worthy of study. This includes describing the relationships this person has had with other people, institutions, and/or events that support using him or her as the subject for a case study research paper. It is particularly important to differentiate the person as the subject of analysis from others and to succinctly explain how the person relates to examining the research problem.

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

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

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

IV.  Discussion

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

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

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

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

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

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

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

V.  Conclusion

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

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

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

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

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

Problems to Avoid

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

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

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

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

Writing Tip

At Least Five Misconceptions about Case Study Research

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

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

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

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

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Data Science Case Studies: Solved and Explained

Data science case studies solved and explained using python..

Aman Kharwal

Analytics Vidhya

Solving a Data Science case study means analyzing and solving a problem statement intensively. Solving case studies will help you show unique and amazing data science use cases in your portfolio. In this article, I’m going to introduce you to 3 data science case studies solved and explained using Python.

Data Science Case Studies

If you’ve learned data science by taking a course or certification program, you’re still not that close to finding a job easily. The most important point of your Data Science interview is to show how you can use your skills in real use cases. Below are 3 data science case studies that will help you understand how to analyze and solve a problem. All of the data science case studies mentioned below are solved and explained using Python.

Case Study 1: Text Emotions Detection

If you are one of them who is having an interest in natural language processing then this use case is for you. The idea is to train a machine learning model to generate emojis based on input text. Then this machine learning model can be used in training Artificial Intelligent Chatbots.

Use Case: A human can express his emotions in any form, such as the face, gestures, speech and text. The detection of text emotions is a content-based classification problem. Detecting a person’s emotions is a difficult task, but detecting the emotions using text written by a person is even more difficult as a human can express his emotions in any form.

Recognizing this type of emotion from a text written by a person plays an important role in applications such as chatbots, customer support forum, customer reviews etc. So you have to train a machine learning model that can identify the emotion of a text by presenting the most relevant emoji according to the input text.

Solution: Machine Learning Project on Text Emotions Detection .

Case Study 2: Hotel Recommendation System

A hotel recommendation system typically works on collaborative filtering that makes recommendations based on ratings given by other customers in the same category as the user looking for a product.

Use Case: We all plan trips and the first thing to do when planning a trip is finding a hotel. There are so many websites recommending the best hotel for our trip. A hotel recommendation system aims to predict which hotel a user is most likely to choose from among all hotels. So to build this type of system which will help the user to book the best hotel out of all the other hotels. We can do this using customer reviews.

For example, suppose you want to go on a business trip, so the hotel recommendation system should show you the hotels that other customers have rated best for business travel. It is therefore also our approach to build a recommendation system based on customer reviews and ratings. So use the ratings and reviews given by customers who belong to the same category as the user and build a hotel recommendation system.

Solution: Data Science Project on Hotel Recommendation System .

Case Study 3: Customer Personality Analysis

The analysis of customers is one of the most important roles that a data scientist has to do who is working at a product based company. So if you are someone who wants to join a product based company then this data science case study is best for you.

Use Case: Customer Personality Analysis is a detailed analysis of a company’s ideal customers. It helps a business to better understand its customers and makes it easier for them to modify products according to the specific needs, behaviours and concerns of different types of customers.

You have to do an analysis that should help a business to modify its product based on its target customers from different types of customer segments. For example, instead of spending money to market a new product to every customer in the company’s database, a company can analyze which customer segment is most likely to buy the product and then market the product only on that particular segment.

Solution: Data Science Project on Customer Personality Analysis .

So these three data science case studies are based on real-world problems, starting with the first; Text Emotions Detection, which is completely based on natural language processing and the machine learning model trained by you will be used in training an AI chatbot. The second use case; Hotel Recommendation System, is also based on NLP, but here you will understand how to generate recommendations using collaborative filtering. The last use case; customer personality analysis, is based on someone who wants to focus on the analysis part.

All these data science case studies are solved using Python, here are the resources where you will find these use cases solved and explained:

• Text Emotions Detection
• Hotel Recommendation System
• Customer Personality Analysis

I hope you liked this article on data science case studies solved and explained using the Python programming language. Feel free to ask your valuable questions in the comments section below.

Written by Aman Kharwal

I write stories behind the data📈 | instagram.com/amankharwal.official/

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Case Study and Passage Based Questions for Class 10 Science Chapter 1 Chemical Reactions and Equations

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• Reading Time: 11 Minutes

In CBSE Class 10 Science Paper, Students will have to answer some questions based on Assertion and Reason . There will be a few questions based on case studies and passage based as well. In that, a paragraph will be given, and then the MCQ questions based on it will be asked.

Here, we have provided case based/passage based questions for Class 10 Science Chapter 1 Chemical Reactions and Equations.

Table of Contents

Case Study/Passage Based Questions on Chemical Reactions and Equations

Case Study/Passage Based Questions

Question 1:

Corrosion is the phenomenon of deterioration of surface of metal in presence of air and moisture. It is a natural process and in the presence of a moist atmosphere, chemically active metals get corroded. This is an oxidation reaction. Rusting is the process where iron corrodes due to exposure to the atmosphere. The main circumstance of corrosion occurs with iron because it is a structural material in construction, bridges, buildings, rail transport, ships, etc. Aluminium is also an important structural metal, but even aluminium undergoes oxidation reactions. However, aluminium doesn’t corrode or oxidize as rapidly as its reactivity suggests. Copper (Cu) corrodes and forms a basic green carbonate.

(i) What is rusting?

(ii) Which two metals do not corrode easily?

(iii) Write the chemical name of the compound formed on corrosion of silver.

(iv) Corrosion is (a) a redox reaction (b) a reduction reaction (c) a displacement reaction (d) an oxidation reaction

Also read: Assertion Reason Questions for Class 10 Science Chapter 1 Chemical Reactions and Equations

Question 2:

Oxidation is the process of gaining of oxygen, or losing of hydrogen. Reduction is the process of losing of oxygen or gaining of hydrogen. The substance which undergoes oxidation is the reducing agent while the substance which undergoes reduction is known as the oxidising agent. Oxidation and reduction always take place together and these type of reactions are known as redox reactions. Some of the examples of redox reactions are given below:

(i) Give two examples of oxidation reaction from your everyday life.

(ii) Write the oxidising agent in the reaction III and VI.

(iii) Which of the following is an oxidising agent? (a) LiAlH 4 (b) Alkaline KMnO 4 (c) Acidified K 2 Cr 2 O 7 (d) Both (b) and (c)

(iv) Out of oxidation and reduction, which reaction takes place at anode?

Also read: Extra Questions for Class 10 Science Chapter 1 Chemical Reactions and Equations

Question 3:

A chemical reaction is a representation of chemical change in terms of symbols and formulae of reactants and products. There are various types of chemical reactions like combination, decomposition, displacement, double displacement, oxidation and reduction reactions. Reactions in which heat is released along with the formation of products are called exothermic chemical reactions. All combustion reactions are exothermic reactions.

(i) The chemical reaction in which a single substance breaks down into two or more simpler substances upon heating is known as (a) thermal decomposition reaction (b) photo decomposition reaction (c) electric decomposition reaction (d) both (a) and (c)

(ii) The massive force that pushes the rocket forward through space is generated due to the (a) combination reaction (b) decomposition reaction (c) displacement reaction (d) double displacement reaction

(iii) A white salt on heating decomposes to give brown fumes and yellow residue is left behind. The yellow residue left is of (a) lead nitrate (b) nitrogen oxide (c) lead oxide (d) oxygen gas

(iv) Which of the following reactions represents a combination reaction? (a) CaO (s) + H 2 O (l) → Ca(OH) 2 (aq) (b) CaCO 3 (s) → CaO (s) + CO 2 (g) (c) Zn(s) + CuSO 4 (aq) → ZnSO 4 (aq) + Cu(s) (d) 2FeSO 4 (s) → Fe 2 O 3 (s) +SO 2 (g) + SO 3 (g)

(v) Complete the following statements by choosing correct type of reaction for X and Y. Statement 1: The heating of lead nitrate is an example of ‘X’ reaction. Statement 2: The burning of magnesium is an example of ‘Y’ reaction. (a) X- Combination, Y- Decomposition (b) X- Decomposition, Y-Combination (c) X- Combination, Y-Displacement (d) X- Displacement, Y-Decomposition

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3 thoughts on “ Case Study and Passage Based Questions for Class 10 Science Chapter 1 Chemical Reactions and Equations ”

Good examples! But can you please available practical types and equations type of case based questions which we can read and learn an then they help us to solve the Boards examm. Pleaseeww🙂🙂🙂

would love to see more equation based questions. nevertheless, it proved quite useful in my revision!

after going through the above content child should develops ideas to answer based on knowledge acquired.

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Cautionary Tales: Ethics and Case Studies in Science

Ethical concerns are normally avoided in science classrooms in spite of the fact that many of our discoveries impinge directly on personal and societal values. We should not leave the ethical problems for another day, but deal with them using realistic case studies that challenge students at their ethical core. In this article we illustrate how case studies can be used to teach STEM students principles of ethics.

INTRODUCTION

Americans consider morality the most essential part of self ( 11 ).

This may be true of other cultures as well. All societies have elaborate rules of conduct that are often codified into law. Some of these imperatives seem hardwired. Human infants younger than a year and a half will look longer at visual displays showing violations of social rules ( 2 ). It is part of our primate heritage; individuals are punished if they stray far from acceptable behavior. Capuchin monkeys will reject a reward if they think they are being treated unfairly; they have a clear sense of right and wrong which depends on the social situation ( 3 ). Aesop would agree—he penned many a story where animals behaved badly and paid the penalty.

If morality and ethics are so central to our beings, what are our responsibilities as STEM educators to pass along the standards of society? And if we accept this challenge, what is the best way to instruct our youthful comrades in their quest for knowledge? I argue in this article that we should accept this obligation and that case study teaching is an ideal way to deliver the message.

Case-study teaching has a long and honorable lineage ( 4 ). In academic circles we find it used 100 years ago in Harvard Law School. The instructor would bring in a true criminal or civil case that had been adjudicated and conduct a class discussion with future lawyers, asking them to justify the rationale for the final decision—challenging them every step of the way. This provided students a real-world problem as part of their training for a real world ahead. The method was soon adopted by the Harvard Business School and various schools across the country, where it is now the standard. Medical schools have their own version of the method called Problem-Based Learning. Again the idea was to use real world problems to train physicians, but in this case students work in small groups to analyze patient problems and provide diagnoses. The idea of using similar strategies to teach basic sciences to undergraduates is largely due to the efforts of faculty at the University of Delaware and the National Center for Case Study Teaching in Science, where there are hundreds of cases now published http://sciencecases.lib.buffalo.edu/cs/ .

Research has shown that minorities and women undergraduates respond well to cases ( 5 , 8 ). Among this group, cases have been shown to increase students’ understanding of science by encouraging them to make connections between science concepts and situations they may encounter in their lives ( 7 ). In addition, the case method promotes the internalization of learning and the development of analytical and decision-making skills, as well as proficiency in oral communication and teamwork ( 6 ). The method, moreover, is a flexible teaching tool. Cases can take many different forms and be taught in many different ways, ranging from the classical discussion method used in business and law schools, to the arguably strongest approaches, Problem-Based Learning and the Interrupted Case Method, with their emphasis on small-group, cooperative learning strategies ( 4 ).

The method seems ideal for teaching ethics to STEM students. We have plenty of precedents to guide us. We have legal ethics, business ethics, medical ethics, bioethics, geoethics, environmental ethics, teaching ethics, research ethics, engineering ethics, and so on. And, of course, there are religious ethics, with each faith describing canons of behavior not to be breached. Some of them are commonly held community values, such as “thou shalt not steal, lie, or cheat.” Others are more specific, such as the research tenet, “thou should replicate experiments.” While some of these “rules” are so entrenched that they are tantamount to absolutes, others are more fragile and malleable; they are subject to the changing moral landscape. Policies about smoking in public places have rapidly shifted ( 12 ). Decrees against interracial marriage, once laws of the land, are now anachronisms, as are statues against same-sex marriage ( 1 , 10 ). Such shifts in the moral topography offer wonderful opportunities for case studies as they challenge students at their central core of beliefs. There are hundreds of these case studies now available for teachers in repositories such as the National Center for Case Study Teaching in Science ( http://sciencecases.lib.buffalo.edu ), where you can find moral dilemmas depicted in cases on evolution, genetic engineering, nutrition, euthanasia, cloning, and organic farming.

Case studies can be used to show students acceptable standards of behavior within a given profession—the do’s and don’ts—and the disastrous consequences that can occur if the rules are not obeyed. We learn of breaches of research ethics such as fraud, plagiarism, and sloppy book-keeping that ruin careers. We come to know cautionary tales, like Dr. Andrew Wakefield, who misrepresented the medical histories of 12 patients and claimed that his research results showed that vaccinations caused autism. He was eventually discredited and Britain stripped him of his medical license. Unfortunately, this sensational allegation has resulted in thousands of people refusing to have their children vaccinated, with a subsequent striking rise in measles.

In the past, these stories were neglected in the STEM classroom. Questions of right or wrong belonged elsewhere—in the home, in a philosophy class, in a church or tabernacle. In the science classroom we learned how to make petroleum, shoot rockets, synthesize drugs, manipulate DNA, and clone animals, not whether we should do so. Then came the Second World War. The academic community ran squarely into two striking examples of the deep entanglement of science and ethics. Suddenly, there was a public debate about whether Truman’s decision to drop the atom bomb on Japan with the loss of millions of lives was ethical. The sensational trials of generals and scientists implicated in the atrocities at the Nazi concentration camps came to light during the Nuremberg Trials and patient bills of rights were drafted. Today our IRB committees and other ethical bodies monitor our experimental protocols involving research into issues of genetic engineering, stem cell research, three-parent embryos, etc. So my argument is that we should not ignore these disputes in the science classroom; this is where the technology is coming from—the STEM laboratories and the people in charge.

This is especially true as scientists have gained technological expertise; we see more clearly than ever how science and technological decisions can wreak havoc in our lives. Think about science in the courtroom, the public policy decisions on health and insurance, the intrusion of listening devices and the tracking of our e-mails and phone calls, the science of warfare and the use of chemical weapons and drones, the use of chemical fertilizers and organic farming, and possible designer babies. Very little that we humans do is not filled with moral or ethical conundrums. No more should we eschew these quandaries in our classrooms. When we discuss DNA genomes, we should not only speak of how the technology can be used to track potential criminals, but also how it can lead to social and personal dilemmas when we identify parentage, plot evolutionary lineages, discover genetically modified food, and detect mutations that might lead to lethal disease and the loss of insurance. How better to deal with such contentious matters than to use case studies? Case studies are stories with an educational message, and as such they are perfect vehicles to integrate science with societal and policy issues. They are ideal because of their interdisciplinary nature. They deal with real issues that students will face in the future. And people love stories.

RESOURCES FOR ETHICS CASES

There are several STEM case repositories in the world; arguably the largest is the National Center for Case Study Teaching in Science, with over 500 case studies published over the past 25 years. Its greatest strength is in the fields of biology and health-related professions. Over 100 cases are catalogued as having ethical issues, ranging in suitability from middle school student classes to faculty seminars.

We seldom find pure instances of ethical transgressions, where issues of fraud, fabrication, or plagiarism are discussed. Rather, ethical issues are more apt to be a sidebar to the main thrust of a case concentrated on a health or environmental problem. And even in these cases, an individual may not be wrestling with problems involving societal standards. Instead, they grapple with whether it is prudent to make one decision versus another. It may be as simple as whether or not to have an operation or whether it is healthy to use drugs to lose weight.

Let me give you a flavor of the kinds of issues and cases that are available:

Personal dilemma

Often such cases involve medical issues, as we see in “A Right to Her Genes” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=316&id=316 ). In this true story, students examine the case of a woman, Michelle, with a family predisposition to cancer, who is considering genetic testing. The woman wishes to get some information to confirm this predisposition from a reluctant aunt so that she can better decide whether to remove her breasts and/or ovaries prophylactically. The aunt is illiterate and poor and had previously been estranged from the rest of the family. A genetic counselor is involved to help educate the aunt and hopefully obtain consent to get a DNA sample from her. Michelle must decide for herself what course of action she should take.

In “Spirituality and Health Care: A Request for Prayer” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=434&id=434 ), a fourth-year medical student making hospital rounds with an attending physician is asked by a family member of a patient to pray with her. The case allows medical students to explore issues related to patients’ religious beliefs as they think through how they might respond to different expectations and requests they may receive from patients and their families in their professional career.

Social ethics

These are cases where protagonists must decide how they will respond to evolving social standards. “SNPs and Snails and Puppy Dog Tails, and That’s What People Are Made Of” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=337&id=337 ) deals with questions of genome privacy. Students work together to research six lobbying groups’ views in this area and then present their insights before a mock meeting of a U.S. House of Representatives Subcommittee voting on the Genetic Information Nondiscrimination Act. In working through the case, students learn about single nucleotide polymorphisms, common molecular biology techniques, and current legislation governing genome privacy.

“A Case of Cheating?” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=399&id=399 ) involves two international students who are accused of cheating at the end of the semester, and the teacher must decide how to handle the accusation so that all students see that justice is done. The case raises cultural questions in the context of the use of peer evaluation and cooperative learning strategies.

Medical ethics

Patient rights are a common concern in medical cases, whether they are the central issue of the case or a sidebar to teaching students about a particular disease syndrome. It is the central theme of the infamous “Bad Blood” case involving black men in Tuskegee, Alabama, in the 1920s ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=371&id=371 ). They had contracted syphilis, and public health officials studying the progress of the debilitating disease originally did not have an effective treatment. Twenty years later, the antibiotic penicillin was discovered, yet treatment was withheld to maintain the integrity of the study, whose purpose was to follow the progression of the disease. The study was immediately stopped when this transgression was made public.

Often there are competing concerns, as when a person is confronted with a decision where their personal morality may be at odds with the decrees of a society or institution. “The Plan: Ethics and Physician Assisted Suicide” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=436&id=436 ) is based on an article published in 1991 in the New England Journal of Medicine in which Dr. Timothy E. Quill described his care for a patient suffering from acute leukemia, including how he prescribed a lethal dose of barbiturates knowing that the woman intended to commit suicide. As a consequence of the article’s publication, a grand jury was convened to consider a charge of manslaughter against Dr. Quill. Students read the case and then, as part of a classroom-simulated trial, discuss physician-assisted suicide in terms of fundamental medical ethics principles.

Research ethics

Courses in experimental design are frequently part of psychology curricula. They seldom are part of the typical undergraduate programs in other STEM fields, although there is an excellent resource in the text Research Ethics ( 9 ). Apparently, students in STEM disciplines are supposed to absorb the proper canons of behavior by observation and osmosis.

“A Rush to Judgment” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=250&id=250 ) deals with a typical psychological experiment, where a faculty professor is inattentive to his student assistants, one of whom is misrepresenting the results of an experiment. Another student is confronted with a moral dilemma of whether to report this infraction at a potential cost to herself. Involved in the case is a consideration of proper research protocol when dealing with human participants: informed consent, freedom from harm, freedom from coercion, anonymity, and confidentiality. Students are referred to the American Psychological Association's Ethical Principles of Psychologists and Code of Conduct.

“How a Cancer Trial Ended in Betrayal” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=233&id=233 ) begins with a quote from a news item.

Birmingham, Alabama —After Bob Lange spent 8 weeks rubbing an experimental cream, BCX-34, from a prominent biotech company BioCryst on the fiery patches on his body, researchers at the University of Alabama at Birmingham told him the drug was defeating the killer inside him. He felt grateful. “I believed it,” he recalls. “I actually thought I might be cured.” But it was a lie. The drug had no effect on Lange’s rare and potentially fatal skin cancer. And the two key people testing the drug knew it. Lange and 21 other patients were victims of fraud—a scheme made possible by the close tie between the university and the state’s most prominent biotech company. — The Baltimore Sun , June 24, 2001

The authors of this fascinating case state that the learning objectives are to learn the basics of scientific research in a clinical trial; to learn the principles of the scientific method; and to consider the ethical issues involved in clinical trials. Ethical potholes litter the road when universities travel with businesses, and millions of dollars and fame are at stake.

Socio-environmental ethics

Conflicting concerns are the norm when dealing with the environmental problems that beset our world. They not only involve scientific principles, but invariably policy and hurly burly politics as well.

“One Glass for Two People: A Case of Water Use Rights in the Eastern United States” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=603&id=603 ) focuses on the growing issue of water use. Approximately 1.3 million people in North and South Carolina depend on the Catawba-Wateree River for water and electricity. The river is also important for recreation and real estate development. To meet growing water demands, elected officials in Concord and Kannapolis, NC, petitioned their state government to approve an inter-basin transfer of 25 million gallons of water a day from the Catawba River. Other towns in North Carolina and South Carolina that are part of the Catawba-Wateree watershed fought this request for water transfer. For this exercise, students are divided into teams that take the role of different stakeholders trying to negotiate a settlement to this lawsuit. In the course of the debate, students address fundamental legal, ethical, and environmental questions about water use.

“Ecotourism: Who Benefits?” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=359&id=359 ) critically examines the costs and the benefits of visiting fragile, pristine, and relatively undisturbed natural areas. Although ecotourism has among its goals to provide funds for ecological conservation as well as economic benefit and empowerment to local communities, it can result in the exploitation of the natural resources (and communities) it seeks to protect. Students assess ecotourism in Costa Rica by considering the viewpoints of a displaced landowner, banana plantation worker, environmentalist, state official, U.S. trade representative, and national park employee.

Legal ethics

“The Slippery Slope of Litigating Geologic Hazards” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=385&id=385 ) is based on a lawsuit brought against the County of Los Angeles by homeowners suing over damage to their homes in the wake of the Portuguese Bend Landslide. It teaches students principles of landslide movement while illustrating the difficulties involved with litigation resulting from natural hazards. Students first read a newspaper article based on the actual events and then receive details about the geologic setting and landslide characteristics. They are then asked to evaluate the possible causes of the disaster and the responsibilities involved.

“The Sad But True Case of Earl Washington: DNA Analysis and the Criminal Justice System” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=725&id=725 ) recounts how, in 1983, Earl Washington “confessed” to a violent crime that he did not commit and was sentenced to death row. After spending 17 years in prison for something he did not do, Earl was released in 2001 after his innocence was proven through the use of modern DNA technology. The case guides students through the wrongful incarceration of Earl and explores the biological mechanisms behind DNA profiling and the ethical issues involved.

“Complexity in Conservation: The Legal and Ethical Case of a Bird-Eating Cat and its Human Killer” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=664&id=664 ) presents the true story of a Texas man who killed a cat that was killing piping plovers, a type of endangered bird species, and was prosecuted for it. In Texas, it is a crime to kill an animal that “belongs to another,” and there was evidence that another person was feeding the cat, which otherwise appeared to be feral. Students engage in a role-playing activity as jurors; they discuss the case and collectively decide whether the cat killer should be acquitted or convicted. This role-playing coupled with follow-up discussions helps students examine and articulate their own views on a controversial environmental issue and gain a better understanding about the complex interdisciplinary nature of conservation science and practice.

There are plenty of ethical issues in every science classroom to discuss; they are not in short supply. They are hovering around every scientific study that reaches the public eye. Pick any news item with science as its theme and there will be the central question that is often not spoken: should we be doing this research at all, not only because of its economic cost, but because of the social, environmental, or health costs? Surely this should be always a pivotal question in the minds of all citizens. It is sometimes asserted that scientific discovery cannot or should not be stopped—that all knowledge is good. But even if we accept that premise, it seems worthwhile to consider the consequences of our actions. Where else to start than in our classrooms?

Acknowledgments

This material is based upon work supported by the National Science Foundation (NSF) under Grant Nos. DUE-0341279, DUE-0618570, DUE-0920264, and DUE-1323355. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the NSF. The author declares that there are no conflicts of interest.

FOR EMPLOYERS

Top 10 real-world data science case studies.

Aditya Sharma

Aditya is a content writer with 5+ years of experience writing for various industries including Marketing, SaaS, B2B, IT, and Edtech among others. You can find him watching anime or playing games when he’s not writing.

Frequently Asked Questions

Real-world data science case studies differ significantly from academic examples. While academic exercises often feature clean, well-structured data and simplified scenarios, real-world projects tackle messy, diverse data sources with practical constraints and genuine business objectives. These case studies reflect the complexities data scientists face when translating data into actionable insights in the corporate world.

Real-world data science projects come with common challenges. Data quality issues, including missing or inaccurate data, can hinder analysis. Domain expertise gaps may result in misinterpretation of results. Resource constraints might limit project scope or access to necessary tools and talent. Ethical considerations, like privacy and bias, demand careful handling.

Lastly, as data and business needs evolve, data science projects must adapt and stay relevant, posing an ongoing challenge.

Real-world data science case studies play a crucial role in helping companies make informed decisions. By analyzing their own data, businesses gain valuable insights into customer behavior, market trends, and operational efficiencies.

These insights empower data-driven strategies, aiding in more effective resource allocation, product development, and marketing efforts. Ultimately, case studies bridge the gap between data science and business decision-making, enhancing a company's ability to thrive in a competitive landscape.

Key takeaways from these case studies for organizations include the importance of cultivating a data-driven culture that values evidence-based decision-making. Investing in robust data infrastructure is essential to support data initiatives. Collaborating closely between data scientists and domain experts ensures that insights align with business goals.

Finally, continuous monitoring and refinement of data solutions are critical for maintaining relevance and effectiveness in a dynamic business environment. Embracing these principles can lead to tangible benefits and sustainable success in real-world data science endeavors.

Data science is a powerful driver of innovation and problem-solving across diverse industries. By harnessing data, organizations can uncover hidden patterns, automate repetitive tasks, optimize operations, and make informed decisions.

In healthcare, for example, data-driven diagnostics and treatment plans improve patient outcomes. In finance, predictive analytics enhances risk management. In transportation, route optimization reduces costs and emissions. Data science empowers industries to innovate and solve complex challenges in ways that were previously unimaginable.

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Top 12 Data Science Case Studies: Across Various Industries

Home Blog Data Science Top 12 Data Science Case Studies: Across Various Industries

Data science has become popular in the last few years due to its successful application in making business decisions. Data scientists have been using data science techniques to solve challenging real-world issues in healthcare, agriculture, manufacturing, automotive, and many more. For this purpose, a data enthusiast needs to stay updated with the latest technological advancements in AI . An excellent way to achieve this is through reading industry data science case studies. I recommend checking out Data Science With Python course syllabus to start your data science journey. In this discussion, I will present some case studies to you that contain detailed and systematic data analysis of people, objects, or entities focusing on multiple factors present in the dataset. Aspiring and practising data scientists can motivate themselves to learn more about the sector, an alternative way of thinking, or methods to improve their organization based on comparable experiences. Almost every industry uses data science in some way. You can learn more about data science fundamentals in this data science course content . From my standpoint, data scientists may use it to spot fraudulent conduct in insurance claims. Automotive data scientists may use it to improve self-driving cars. In contrast, e-commerce data scientists can use it to add more personalization for their consumers—the possibilities are unlimited and unexplored. Let’s look at the top eight data science case studies in this article so you can understand how businesses from many sectors have benefitted from data science to boost productivity, revenues, and more. Read on to explore more or use the following links to go straight to the case study of your choice.

Examples of Data Science Case Studies

• Hospitality:  Airbnb focuses on growth by  analyzing  customer voice using data science.  Qantas uses predictive analytics to mitigate losses  
• Healthcare:  Novo Nordisk  is  Driving innovation with NLP.  AstraZeneca harnesses data for innovation in medicine  
• Covid 19:  Johnson and Johnson use s  d ata science  to fight the Pandemic  
• E-commerce:  Amazon uses data science to personalize shop p ing experiences and improve customer satisfaction  
• Supply chain management :  UPS optimizes supp l y chain with big data analytics
• Meteorology:  IMD leveraged data science to achieve a rec o rd 1.2m evacuation before cyclone ''Fani''  
• Entertainment Industry:  Netflix  u ses data science to personalize the content and improve recommendations.  Spotify uses big   data to deliver a rich user experience for online music streaming  
• Banking and Finance:  HDFC utilizes Big  D ata Analytics to increase income and enhance  the  banking experience  

Top 8 Data Science Case Studies  [For Various Industries]

1. data science in hospitality industry.

In the hospitality sector, data analytics assists hotels in better pricing strategies, customer analysis, brand marketing , tracking market trends, and many more.

Airbnb focuses on growth by analyzing customer voice using data science.  A famous example in this sector is the unicorn '' Airbnb '', a startup that focussed on data science early to grow and adapt to the market faster. This company witnessed a 43000 percent hypergrowth in as little as five years using data science. They included data science techniques to process the data, translate this data for better understanding the voice of the customer, and use the insights for decision making. They also scaled the approach to cover all aspects of the organization. Airbnb uses statistics to analyze and aggregate individual experiences to establish trends throughout the community. These analyzed trends using data science techniques impact their business choices while helping them grow further.  

Travel industry and data science

Predictive analytics benefits many parameters in the travel industry. These companies can use recommendation engines with data science to achieve higher personalization and improved user interactions. They can study and cross-sell products by recommending relevant products to drive sales and increase revenue. Data science is also employed in analyzing social media posts for sentiment analysis, bringing invaluable travel-related insights. Whether these views are positive, negative, or neutral can help these agencies understand the user demographics, the expected experiences by their target audiences, and so on. These insights are essential for developing aggressive pricing strategies to draw customers and provide better customization to customers in the travel packages and allied services. Travel agencies like Expedia and Booking.com use predictive analytics to create personalized recommendations, product development, and effective marketing of their products. Not just travel agencies but airlines also benefit from the same approach. Airlines frequently face losses due to flight cancellations, disruptions, and delays. Data science helps them identify patterns and predict possible bottlenecks, thereby effectively mitigating the losses and improving the overall customer traveling experience.  

How Qantas uses predictive analytics to mitigate losses  

Qantas , one of Australia's largest airlines, leverages data science to reduce losses caused due to flight delays, disruptions, and cancellations. They also use it to provide a better traveling experience for their customers by reducing the number and length of delays caused due to huge air traffic, weather conditions, or difficulties arising in operations. Back in 2016, when heavy storms badly struck Australia's east coast, only 15 out of 436 Qantas flights were cancelled due to their predictive analytics-based system against their competitor Virgin Australia, which witnessed 70 cancelled flights out of 320.  

2. Data Science in Healthcare

The  Healthcare sector  is immensely benefiting from the advancements in AI. Data science, especially in medical imaging, has been helping healthcare professionals come up with better diagnoses and effective treatments for patients. Similarly, several advanced healthcare analytics tools have been developed to generate clinical insights for improving patient care. These tools also assist in defining personalized medications for patients reducing operating costs for clinics and hospitals. Apart from medical imaging or computer vision,  Natural Language Processing (NLP)  is frequently used in the healthcare domain to study the published textual research data.     

A. Pharmaceutical

Driving innovation with NLP: Novo Nordisk.  Novo Nordisk  uses the Linguamatics NLP platform from internal and external data sources for text mining purposes that include scientific abstracts, patents, grants, news, tech transfer offices from universities worldwide, and more. These NLP queries run across sources for the key therapeutic areas of interest to the Novo Nordisk R&D community. Several NLP algorithms have been developed for the topics of safety, efficacy, randomized controlled trials, patient populations, dosing, and devices. Novo Nordisk employs a data pipeline to capitalize the tools' success on real-world data and uses interactive dashboards and cloud services to visualize this standardized structured information from the queries for exploring commercial effectiveness, market situations, potential, and gaps in the product documentation. Through data science, they are able to automate the process of generating insights, save time and provide better insights for evidence-based decision making.  

How AstraZeneca harnesses data for innovation in medicine.  AstraZeneca  is a globally known biotech company that leverages data using AI technology to discover and deliver newer effective medicines faster. Within their R&D teams, they are using AI to decode the big data to understand better diseases like cancer, respiratory disease, and heart, kidney, and metabolic diseases to be effectively treated. Using data science, they can identify new targets for innovative medications. In 2021, they selected the first two AI-generated drug targets collaborating with BenevolentAI in Chronic Kidney Disease and Idiopathic Pulmonary Fibrosis.   

Data science is also helping AstraZeneca redesign better clinical trials, achieve personalized medication strategies, and innovate the process of developing new medicines. Their Center for Genomics Research uses  data science and AI  to analyze around two million genomes by 2026. Apart from this, they are training their AI systems to check these images for disease and biomarkers for effective medicines for imaging purposes. This approach helps them analyze samples accurately and more effortlessly. Moreover, it can cut the analysis time by around 30%.   

AstraZeneca also utilizes AI and machine learning to optimize the process at different stages and minimize the overall time for the clinical trials by analyzing the clinical trial data. Summing up, they use data science to design smarter clinical trials, develop innovative medicines, improve drug development and patient care strategies, and many more.

C. Wearable Technology  

Wearable technology is a multi-billion-dollar industry. With an increasing awareness about fitness and nutrition, more individuals now prefer using fitness wearables to track their routines and lifestyle choices.  

Fitness wearables are convenient to use, assist users in tracking their health, and encourage them to lead a healthier lifestyle. The medical devices in this domain are beneficial since they help monitor the patient's condition and communicate in an emergency situation. The regularly used fitness trackers and smartwatches from renowned companies like Garmin, Apple, FitBit, etc., continuously collect physiological data of the individuals wearing them. These wearable providers offer user-friendly dashboards to their customers for analyzing and tracking progress in their fitness journey.

3. Covid 19 and Data Science

In the past two years of the Pandemic, the power of data science has been more evident than ever. Different  pharmaceutical companies  across the globe could synthesize Covid 19 vaccines by analyzing the data to understand the trends and patterns of the outbreak. Data science made it possible to track the virus in real-time, predict patterns, devise effective strategies to fight the Pandemic, and many more.  

How Johnson and Johnson uses data science to fight the Pandemic   

The  data science team  at  Johnson and Johnson  leverages real-time data to track the spread of the virus. They built a global surveillance dashboard (granulated to county level) that helps them track the Pandemic's progress, predict potential hotspots of the virus, and narrow down the likely place where they should test its investigational COVID-19 vaccine candidate. The team works with in-country experts to determine whether official numbers are accurate and find the most valid information about case numbers, hospitalizations, mortality and testing rates, social compliance, and local policies to populate this dashboard. The team also studies the data to build models that help the company identify groups of individuals at risk of getting affected by the virus and explore effective treatments to improve patient outcomes.

4. Data Science in E-commerce  

In the  e-commerce sector , big data analytics can assist in customer analysis, reduce operational costs, forecast trends for better sales, provide personalized shopping experiences to customers, and many more.  

Amazon uses data science to personalize shopping experiences and improve customer satisfaction.  Amazon  is a globally leading eCommerce platform that offers a wide range of online shopping services. Due to this, Amazon generates a massive amount of data that can be leveraged to understand consumer behavior and generate insights on competitors' strategies. Amazon uses its data to provide recommendations to its users on different products and services. With this approach, Amazon is able to persuade its consumers into buying and making additional sales. This approach works well for Amazon as it earns 35% of the revenue yearly with this technique. Additionally, Amazon collects consumer data for faster order tracking and better deliveries.     

Similarly, Amazon's virtual assistant, Alexa, can converse in different languages; uses speakers and a   camera to interact with the users. Amazon utilizes the audio commands from users to improve Alexa and deliver a better user experience. 

5. Data Science in Supply Chain Management

Predictive analytics and big data are driving innovation in the Supply chain domain. They offer greater visibility into the company operations, reduce costs and overheads, forecasting demands, predictive maintenance, product pricing, minimize supply chain interruptions, route optimization, fleet management , drive better performance, and more.     

Optimizing supply chain with big data analytics: UPS

UPS  is a renowned package delivery and supply chain management company. With thousands of packages being delivered every day, on average, a UPS driver makes about 100 deliveries each business day. On-time and safe package delivery are crucial to UPS's success. Hence, UPS offers an optimized navigation tool ''ORION'' (On-Road Integrated Optimization and Navigation), which uses highly advanced big data processing algorithms. This tool for UPS drivers provides route optimization concerning fuel, distance, and time. UPS utilizes supply chain data analysis in all aspects of its shipping process. Data about packages and deliveries are captured through radars and sensors. The deliveries and routes are optimized using big data systems. Overall, this approach has helped UPS save 1.6 million gallons of gasoline in transportation every year, significantly reducing delivery costs.    

6. Data Science in Meteorology

Weather prediction is an interesting  application of data science . Businesses like aviation, agriculture and farming, construction, consumer goods, sporting events, and many more are dependent on climatic conditions. The success of these businesses is closely tied to the weather, as decisions are made after considering the weather predictions from the meteorological department.   

Besides, weather forecasts are extremely helpful for individuals to manage their allergic conditions. One crucial application of weather forecasting is natural disaster prediction and risk management.  

Weather forecasts begin with a large amount of data collection related to the current environmental conditions (wind speed, temperature, humidity, clouds captured at a specific location and time) using sensors on IoT (Internet of Things) devices and satellite imagery. This gathered data is then analyzed using the understanding of atmospheric processes, and machine learning models are built to make predictions on upcoming weather conditions like rainfall or snow prediction. Although data science cannot help avoid natural calamities like floods, hurricanes, or forest fires. Tracking these natural phenomena well ahead of their arrival is beneficial. Such predictions allow governments sufficient time to take necessary steps and measures to ensure the safety of the population.  

IMD leveraged data science to achieve a record 1.2m evacuation before cyclone ''Fani''   

Most  d ata scientist’s responsibilities  rely on satellite images to make short-term forecasts, decide whether a forecast is correct, and validate models. Machine Learning is also used for pattern matching in this case. It can forecast future weather conditions if it recognizes a past pattern. When employing dependable equipment, sensor data is helpful to produce local forecasts about actual weather models. IMD used satellite pictures to study the low-pressure zones forming off the Odisha coast (India). In April 2019, thirteen days before cyclone ''Fani'' reached the area,  IMD  (India Meteorological Department) warned that a massive storm was underway, and the authorities began preparing for safety measures.  

It was one of the most powerful cyclones to strike India in the recent 20 years, and a record 1.2 million people were evacuated in less than 48 hours, thanks to the power of data science.   

7. Data Science in the Entertainment Industry

Due to the Pandemic, demand for OTT (Over-the-top) media platforms has grown significantly. People prefer watching movies and web series or listening to the music of their choice at leisure in the convenience of their homes. This sudden growth in demand has given rise to stiff competition. Every platform now uses data analytics in different capacities to provide better-personalized recommendations to its subscribers and improve user experience.   

How Netflix uses data science to personalize the content and improve recommendations  

Netflix  is an extremely popular internet television platform with streamable content offered in several languages and caters to various audiences. In 2006, when Netflix entered this media streaming market, they were interested in increasing the efficiency of their existing ''Cinematch'' platform by 10% and hence, offered a prize of $1 million to the winning team. This approach was successful as they found a solution developed by the BellKor team at the end of the competition that increased prediction accuracy by 10.06%. Over 200 work hours and an ensemble of 107 algorithms provided this result. These winning algorithms are now a part of the Netflix recommendation system.  

Netflix also employs Ranking Algorithms to generate personalized recommendations of movies and TV Shows appealing to its users.   

Spotify uses big data to deliver a rich user experience for online music streaming  

Personalized online music streaming is another area where data science is being used.  Spotify  is a well-known on-demand music service provider launched in 2008, which effectively leveraged big data to create personalized experiences for each user. It is a huge platform with more than 24 million subscribers and hosts a database of nearly 20million songs; they use the big data to offer a rich experience to its users. Spotify uses this big data and various algorithms to train machine learning models to provide personalized content. Spotify offers a "Discover Weekly" feature that generates a personalized playlist of fresh unheard songs matching the user's taste every week. Using the Spotify "Wrapped" feature, users get an overview of their most favorite or frequently listened songs during the entire year in December. Spotify also leverages the data to run targeted ads to grow its business. Thus, Spotify utilizes the user data, which is big data and some external data, to deliver a high-quality user experience.  

8. Data Science in Banking and Finance

Data science is extremely valuable in the Banking and  Finance industry . Several high priority aspects of Banking and Finance like credit risk modeling (possibility of repayment of a loan), fraud detection (detection of malicious or irregularities in transactional patterns using machine learning), identifying customer lifetime value (prediction of bank performance based on existing and potential customers), customer segmentation (customer profiling based on behavior and characteristics for personalization of offers and services). Finally, data science is also used in real-time predictive analytics (computational techniques to predict future events).    

How HDFC utilizes Big Data Analytics to increase revenues and enhance the banking experience    

One of the major private banks in India,  HDFC Bank , was an early adopter of AI. It started with Big Data analytics in 2004, intending to grow its revenue and understand its customers and markets better than its competitors. Back then, they were trendsetters by setting up an enterprise data warehouse in the bank to be able to track the differentiation to be given to customers based on their relationship value with HDFC Bank. Data science and analytics have been crucial in helping HDFC bank segregate its customers and offer customized personal or commercial banking services. The analytics engine and SaaS use have been assisting the HDFC bank in cross-selling relevant offers to its customers. Apart from the regular fraud prevention, it assists in keeping track of customer credit histories and has also been the reason for the speedy loan approvals offered by the bank.  

9. Data Science in Urban Planning and Smart Cities  

Data Science can help the dream of smart cities come true! Everything, from traffic flow to energy usage, can get optimized using data science techniques. You can use the data fetched from multiple sources to understand trends and plan urban living in a sorted manner.  

The significant data science case study is traffic management in Pune city. The city controls and modifies its traffic signals dynamically, tracking the traffic flow. Real-time data gets fetched from the signals through cameras or sensors installed. Based on this information, they do the traffic management. With this proactive approach, the traffic and congestion situation in the city gets managed, and the traffic flow becomes sorted. A similar case study is from Bhubaneswar, where the municipality has platforms for the people to give suggestions and actively participate in decision-making. The government goes through all the inputs provided before making any decisions, making rules or arranging things that their residents actually need.  

10. Data Science in Agricultural Yield Prediction   

Have you ever wondered how helpful it can be if you can predict your agricultural yield? That is exactly what data science is helping farmers with. They can get information about the number of crops they can produce in a given area based on different environmental factors and soil types. Using this information, the farmers can make informed decisions about their yield and benefit the buyers and themselves in multiple ways.  

Farmers across the globe and overseas use various data science techniques to understand multiple aspects of their farms and crops. A famous example of data science in the agricultural industry is the work done by Farmers Edge. It is a company in Canada that takes real-time images of farms across the globe and combines them with related data. The farmers use this data to make decisions relevant to their yield and improve their produce. Similarly, farmers in countries like Ireland use satellite-based information to ditch traditional methods and multiply their yield strategically.  

11. Data Science in the Transportation Industry   

Transportation keeps the world moving around. People and goods commute from one place to another for various purposes, and it is fair to say that the world will come to a standstill without efficient transportation. That is why it is crucial to keep the transportation industry in the most smoothly working pattern, and data science helps a lot in this. In the realm of technological progress, various devices such as traffic sensors, monitoring display systems, mobility management devices, and numerous others have emerged.  

Many cities have already adapted to the multi-modal transportation system. They use GPS trackers, geo-locations and CCTV cameras to monitor and manage their transportation system. Uber is the perfect case study to understand the use of data science in the transportation industry. They optimize their ride-sharing feature and track the delivery routes through data analysis. Their data science approach enabled them to serve more than 100 million users, making transportation easy and convenient. Moreover, they also use the data they fetch from users daily to offer cost-effective and quickly available rides.  

12. Data Science in the Environmental Industry    

Increasing pollution, global warming, climate changes and other poor environmental impacts have forced the world to pay attention to environmental industry. Multiple initiatives are being taken across the globe to preserve the environment and make the world a better place. Though the industry recognition and the efforts are in the initial stages, the impact is significant, and the growth is fast.  

The popular use of data science in the environmental industry is by NASA and other research organizations worldwide. NASA gets data related to the current climate conditions, and this data gets used to create remedial policies that can make a difference. Another way in which data science is actually helping researchers is they can predict natural disasters well before time and save or at least reduce the potential damage considerably. A similar case study is with the World Wildlife Fund. They use data science to track data related to deforestation and help reduce the illegal cutting of trees. Hence, it helps preserve the environment.  

Where to Find Full Data Science Case Studies?  

Data science is a highly evolving domain with many practical applications and a huge open community. Hence, the best way to keep updated with the latest trends in this domain is by reading case studies and technical articles. Usually, companies share their success stories of how data science helped them achieve their goals to showcase their potential and benefit the greater good. Such case studies are available online on the respective company websites and dedicated technology forums like Towards Data Science or Medium.  

Additionally, we can get some practical examples in recently published research papers and textbooks in data science.  

What Are the Skills Required for Data Scientists?  

Data scientists play an important role in the data science process as they are the ones who work on the data end to end. To be able to work on a data science case study, there are several skills required for data scientists like a good grasp of the fundamentals of data science, deep knowledge of statistics, excellent programming skills in Python or R, exposure to data manipulation and data analysis, ability to generate creative and compelling data visualizations, good knowledge of big data, machine learning and deep learning concepts for model building & deployment. Apart from these technical skills, data scientists also need to be good storytellers and should have an analytical mind with strong communication skills.    

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Conclusion  

These were some interesting  data science case studies  across different industries. There are many more domains where data science has exciting applications, like in the Education domain, where data can be utilized to monitor student and instructor performance, develop an innovative curriculum that is in sync with the industry expectations, etc.   

Almost all the companies looking to leverage the power of big data begin with a swot analysis to narrow down the problems they intend to solve with data science. Further, they need to assess their competitors to develop relevant data science tools and strategies to address the challenging issue. This approach allows them to differentiate themselves from their competitors and offer something unique to their customers.  

With data science, the companies have become smarter and more data-driven to bring about tremendous growth. Moreover, data science has made these organizations more sustainable. Thus, the utility of data science in several sectors is clearly visible, a lot is left to be explored, and more is yet to come. Nonetheless, data science will continue to boost the performance of organizations in this age of big data.  

Frequently Asked Questions (FAQs)

A case study in data science requires a systematic and organized approach for solving the problem. Generally, four main steps are needed to tackle every data science case study: 

• Defining the problem statement and strategy to solve it  
• Gather and pre-process the data by making relevant assumptions  
• Select tool and appropriate algorithms to build machine learning /deep learning models 
• Make predictions, accept the solutions based on evaluation metrics, and improve the model if necessary. 

Getting data for a case study starts with a reasonable understanding of the problem. This gives us clarity about what we expect the dataset to include. Finding relevant data for a case study requires some effort. Although it is possible to collect relevant data using traditional techniques like surveys and questionnaires, we can also find good quality data sets online on different platforms like Kaggle, UCI Machine Learning repository, Azure open data sets, Government open datasets, Google Public Datasets, Data World and so on.  

Data science projects involve multiple steps to process the data and bring valuable insights. A data science project includes different steps - defining the problem statement, gathering relevant data required to solve the problem, data pre-processing, data exploration & data analysis, algorithm selection, model building, model prediction, model optimization, and communicating the results through dashboards and reports.  

Devashree Madhugiri

Devashree holds an M.Eng degree in Information Technology from Germany and a background in Data Science. She likes working with statistics and discovering hidden insights in varied datasets to create stunning dashboards. She enjoys sharing her knowledge in AI by writing technical articles on various technological platforms. She loves traveling, reading fiction, solving Sudoku puzzles, and participating in coding competitions in her leisure time.

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Trust, more than knowledge, critical for acceptance of fully autonomous vehicles

While not yet on the market, fully autonomous vehicles are promoted as a way to make road travel dramatically safer, but a recent study found that knowing more about them did not improve people's perception of their risk. They needed to have more trust in them too.

This study adds to the evidence from other research that knowledge alone is not enough to sway people's attitudes toward complex technology and science, such as gene editing or climate change. In this case, Washington State University researchers found that trust in the autonomous vehicles' reliability and performance played the strongest role in improving perceptions of the technology's risk.

That may be critical to whether this technology will ever be realized, said Kathryn Robinson-Tay, lead author of the study published in the Journal of Risk Research .

"Autonomous vehicles are such consumer-oriented products. Whether they are used or not is really dependent on whether people will buy them," said Robinson-Tay, a doctoral student in WSU's Murrow College of Communication. "We found there was no significant relationship between people's knowledge and their risk perceptions of autonomous vehicles -- without the mediation of trust."

While some cars with autonomous features, like Tesla's adaptive cruise control, are on the roads now, fully driverless vehicles are not yet available. By some estimates, if they do become available, they could improve traffic safety by 90%. But that likely depends on their wide-adoption, and currently, perceptions of their safety are very low. A 2022 Pew Research poll showed 44% of Americans have a negative view of autonomous vehicles.

For this study, Robinson-Tay and her advising professor Wei Peng conducted a representative, cross-sectional survey of 323 adults in the U.S. using Census-based quotas for age, gender and race to ensure a diverse sample. The participants answered questions about their knowledge and perceptions of autonomous vehicles and their risk. While trust emerged as the most influential factor, people's desire to experience using fully autonomous vehicles also indirectly led to improved perceptions of risk.

The fact that fully autonomous vehicles are not yet available even to try out may be part of the problem, but their very autonomous nature may also hinder their acceptance, said Peng, a WSU communications researcher.

"It's basic psychology that people want to interact with the things they use. They want to control them through physical touch. With fully autonomous vehicles, you do not need to touch them, so people may feel they are very risky or unsafe," he said.

News reports about accidents with partially autonomous vehicles has also likely hurt perceptions, the researchers said.

"Accidents happen all the time on the road every day, but people tend to overestimate the risk of something that's new, or that they're less familiar with," Peng said.

Regardless, this study's findings point to the need to build trust with the public if fully autonomous vehicles are to ever take to the roads.

"Proponents should do their best to communicate the benefits, and the risks, of autonomous vehicles in an effort to increase both knowledge and trust," said Robinson-Tay. "It's really important to communicate as honestly as possible so people can have a balanced understanding of what they're exactly getting into with purchasing one."

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Materials provided by Washington State University . Original written by Sara Zaske. Note: Content may be edited for style and length.

Journal Reference :

• Kathryn Robinson-Tay, Wei Peng. The role of knowledge and trust in developing risk perceptions of autonomous vehicles: a moderated mediation model . Journal of Risk Research , 2024; 1 DOI: 10.1080/13669877.2024.2360923

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Urban resilience evaluation based on entropy-TOPSIS model: a case study of county-level cities in Ningxia, Northwest China

• Original Paper
• Published: 10 July 2024

Cite this article

• C. Miao 1 ,
• H. Chen 1 &
• M. Ding 2  

In the process of rapid urbanization, any unexpected event will harm the stability of the city. Strengthening urban resilience construction will effectively respond to unexpected events. Taking 22 counties (cities, districts) in Ningxia, Northwest China as the research object, 20 indicators are selected from four aspects of economic, social, infrastructure, and ecological resilience to construct an urban resilience evaluation index system. The entropy-TOPSIS model (consisting of the Entropy method and the Technique for Order Preference by Similarity to an Ideal Solution method) is used to comprehensively evaluate the resilience and comprehensive resilience of various dimensions of county-level cities. The results show that: (1) There is a significant spatial differentiation in the economic, social, infrastructure, and ecological resilience of county-level cities in Ningxia, resulting in a higher comprehensive resilience of cities in the northern region compared to those in the southern region, and comparatively high and high resilience is distributed in the central districts and counties of the northern and southern regions. (2) Low, comparatively low, medium, comparatively high, and high resilience cities account for 13.64%, 31.82%, 31.82%, 18.18% and 4.54% of the total number of county-level cities in Ningxia, respectively. Among them, Xingqing District has the highest comprehensive resilience and Jingyuan County has the lowest comprehensive resilience. The research findings can provide a basis for the dynamic assessment of resilience in county-level cities in Ningxia, the analysis of the impact of geological environment changes on urban resilience, and thus a theoretical and practical basis for the sustainable development of county-level cities in Ningxia and the enhancement of their ability to respond to emergencies.

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Acknowledgements

This work was supported by the Natural Science Foundation of Ningxia (Grant No. 2023AAC03344); the Scientific Research Project of Ningxia Education Department (Grant Nos. NYG2024179; NYG2022083); the Ningxia Excellent Talent Support Program (The sixth batch of Ningxia Youth Science and Technology Talent Recruitment Project in 2021); the National Natural Science Foundation of China (Grant No. 42371203), the National Natural Science Foundation of China Joint Fund Project (Grant No. U21A2032).

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Cheng Miao and Mengying Na contributed to the conception. Mengying Na organized the data and performed the formal analysis. Cheng Miao and Mengying Na wrote the manuscript. Hongxiang Chen and Mingtao Ding provided guidance, validation, and review and editing of the manuscript. All authors approved the submitted version of the manuscript.

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Miao, C., Na, M., Chen, H. et al. Urban resilience evaluation based on entropy-TOPSIS model: a case study of county-level cities in Ningxia, Northwest China. Int. J. Environ. Sci. Technol. (2024). https://doi.org/10.1007/s13762-024-05880-6

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Published : 10 July 2024

DOI : https://doi.org/10.1007/s13762-024-05880-6

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Share and Cite

Meynecke, J.-O.; Zigic, S.; Perez, L.; Dunn, R.J.K.; Benfer, N.; Gustafson, J.; Bosshard, S. Dead on the Beach? Predicting the Drift of Whale Remains Improves Management for Offshore Disposal. J. Mar. Sci. Eng. 2024 , 12 , 1156. https://doi.org/10.3390/jmse12071156

Meynecke J-O, Zigic S, Perez L, Dunn RJK, Benfer N, Gustafson J, Bosshard S. Dead on the Beach? Predicting the Drift of Whale Remains Improves Management for Offshore Disposal. Journal of Marine Science and Engineering . 2024; 12(7):1156. https://doi.org/10.3390/jmse12071156

Meynecke, Jan-Olaf, Sasha Zigic, Larissa Perez, Ryan J. K. Dunn, Nathan Benfer, Johan Gustafson, and Simone Bosshard. 2024. "Dead on the Beach? Predicting the Drift of Whale Remains Improves Management for Offshore Disposal" Journal of Marine Science and Engineering 12, no. 7: 1156. https://doi.org/10.3390/jmse12071156

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METHODS article

Selection of low impact development technical measures in the distribution area of expansive soil: a case study of hefei, china.

• 1 College of Environmental and Energy Engineering, Anhui Jianzhu University, Hefei, China
• 2 Institute of Remote Sensing and Geographic Information Systems, Anhui Jianzhu University, Hefei, China
• 3 Shucheng Zhonghe Real Estate Development Co., Ltd, Hefei, China
• 4 Anhui Eco-environmental Monitoring Center, Hefei, China

Expansive soils are widely distributed around the world. They have significant characteristics of both hygroscopic expansion and water-loss shrinkage, which have caused serious damage to road paving, construction of low-rise houses, and construction of slopes along the banks of rivers. Similarly, the implementation of low impact development measures can cause considerable difficulties in the distribution area of expansive soil. The entire urban area of Hefei is situated on expansive soil. Although Hefei city has developed a sponge city plan, it has not been carried out on a large scale for implementation of low impact development technical measures. Experimental studies have shown that exposed expansive soils produce fissures that run up and down during wet and dry cycles. These fissures are extremely unfavorable to the infiltration of surface runoff formed by short-term heavy rainfall. This is also one of the reasons for short-term rainfall in Hefei city, resulting in serious flooding in low-lying areas with a poor drainage system. At the same time, initial rainfall is ineffective in cleaning up surface source pollution. Therefore, we can enhance the characteristics of expansive soil, keep the expansive soil unexposed, and maintain a certain level of humidity. These approaches can play a better role in the control of rainfall runoff and surface source pollution. The characteristics of expansive soils can be enhanced by mixing them with weathered sand, a physical improvement, to meet the technical requirements for infiltration, interception, and purification. It is recommended to carefully select low impact development measures in the distribution area of expansive soil to avoid the occurrence of wasteful investment and poor results.

1 Introduction

Expansive soil is a kind of special clay widely distributed on the land surface. The clay particles in the soil are mainly composed of hydrophilic minerals, including montmorillonite, illite, and kaolinite. It is a cohesive soil with significant characteristics of water-absorption expansion and water-loss shrinkage. Because of its remarkable properties of expansion and contraction, it often causes serious damage to engineering construction in the expansive soil area. Expansive soil is widely distributed in more than 40 countries across the world ( Zhang, 2005 ).

The hazard and risk due to expansive soils is often overlooked when tabulating natural hazard risk, vulnerability, and resilience ( Bin Mostafiz et al., 2021 ). The randomly distributed fissures and repeated expansion and contraction deformation in the soil mass cause great harm to engineering construction, which is often referred to as disastrous soil in engineering ( Jones and Holtz, 1973 ). Worldwide, the annual economic losses caused by this are up to 15 billion U.S. dollars or more, with China being one of the most seriously affected countries, where the distribution of expansive soil is extremely wide, throughout Anhui, Guangxi, Yunnan, Hunan, Henan, and in more than 20 other provinces and autonomous regions ( Figure 1 ) ( Ye, 2010 ).

Figure 1 . China’s expansive soil regional distribution map ( Ye, 2010 ).

From 1959 to 1977, the United Kingdom, the United States, Romania, the former Soviet Union, and Japan have successively added provisions on expansive soil to the officially issued geotechnical and railway specifications and other documents ( Sun et al., 1995 ). In 1987, China officially issued the Technical Code for Buildings in Expansive Soil Regions ( GBJ 112-87 ), and in 2013, it was revised to the Technical Code for Buildings in Expansive Soil Regions ( GB 50112-2013 ). The current methods of dealing with expansive soils are mainly chemical modification, such as stabilization of expansive soils by mixing lime, cement, fly ash, sodium chloride, calcium chloride, and phosphoric acid. Among chemical modifications, the treatment of expansive soils with lime is the most common and effective method. It has been widely reported that the addition of an appropriate amount of lime to an expansive soil improves the workability of the soil by causing a reduction in the soil’s swelling potential ( Locat et al., 1990 ; Rao and Thyagaraj, 2003 ; Khattab et al., 2007 ; Al-Mukhtar et al., 2011 ; Al-Mukhtar et al., 2012 ; Obuzor et al., 2012 ).

Low impact development (LID) is an ecologically based stormwater management approach favoring soft engineering to manage rainfall on a site through a vegetative treatment network. The LID aims to sustain a site’s pre-development hydrologic regime by using techniques that infiltrate, filter, store, and evaporate stormwater runoff close to its source. Contrary to the conventional “pipe-and-pond” conveyance infrastructure that channels runoff elsewhere through pipes, catchment basins, and curbs and gutters, LID remediates polluted runoff through a network of distributed treatment landscapes ( Low Impact Development, 2010 ). In developed countries, such as the United States, Australia, and Germany, LID technology has been widely used in rainfall control and utilization projects, renovation of old towns, planning of new towns, and regional development design, and corresponding design standards have been formed. LID technology is the core idea of sponge city planning, which has been well-developed in China in recent years, and it is mainly used to manage urban flooding and to improve urban water treatment methods and water recycling capacity. Future research should include comprehensive and systematic research on the LID technology, research on the effectiveness of removal of difficult-to-detect pollutants, and research on the structure and materials in LID facilities.

In October 2014, Housing and Urban–Rural Development of the People’s Republic of China issued the Technical Guidelines for Sponge City Construction—Low Impact Development Stormwater System Construction ( Trial ), which clarifies that the total runoff control pathway includes reduction of infiltration and emission of rainwater and direct storage and utilization. LID technologies mainly include infiltration, storage, regulation, transfer and interception, and purification technologies ( Beijing University of Civil Engineering and Architecture et al., 2014 ).

The city of Hefei is located in the central-eastern part of China ( Figure 1 ), and the entire main urban area of Hefei is situated on top of expansive soils. Therefore, the study of both the nature of the expansive soils and their engineering treatment is very significant for the selection of technical measures for LID.

2 Main characteristics of expansive soils in the study area

2.1 main mineral composition of expansive soil.

According to the regional data, the expansive soils of the Upper-Pleistocene Xiashu Group are widely distributed throughout the urban area of Hefei. The expansive soils contain a large number of hydrophilic clay mineral components, of which 35%–43% are hydroclays, 4.6%–6.8% are kaolins, and 2%–2.7% are montmorillonites ( Anhui Engineering Investigation Institute, 2008 ). These mineral components have strong hydrophilicity and have significant characteristics of both water-absorbing expansion and water-losing contraction with change in water content. The change in soil volume, i.e., expansion or contraction, causes damage to engineering structures or slopes.

2.2 Water content of expansive soil

According to regional data, the natural water content of expansive soils widely distributed in the main city of Hefei is 21%–28% ( Anhui Engineering Investigation Institute, 2008 ). Relevant studies show that when the water content of the expansive soil body increases by 1%, the volume of the soil body can increase by 2%–5%. Dry expansive soil has a high swelling potential, and conversely, it has low swelling potential. The soil body has a dry and wet cycle and constantly develops soil fissures, which will have an impact on the LID measures. Therefore, the selection of LID measures should take into account the water content of the soil body of the expansive soil. The “ Sponge City Construction Technical Guidelines—Low impact Development of Stormwater System Construction ( Trial )" pointed out that when applying the permeable paving LID measures in the distribution area of expansive soil, necessary preventive and control measures should be taken to prevent the impact of geologic hazards of expansive soil deformation ( Shi et al., 2002 ; Xie, 2009 ; Beijing University of Civil Engineering and Architecture et al., 2014 ).

2.3 Free swelling ratio of expansive soil

According to the Technical Code for Buildings in Expansive Soil Regions ( GB50112-2013 ), the free swelling ratio of expansive soils should be calculated by the following formula ( Ministry of Housing and Urban-Rural Development of the People’s Republic of China, 2012 ):

where δ ef is the free swelling ratio of expansive soil (%), v w is the volume of the soil sample after swelling and stabilization in water (mL), and v 0 is the original volume of the soil sample (mL).

IS 1498 ( BUREAU OF INDIAN STANDARDS, 1970 ) gives a criterion to predict the expansivity of soils, based on the free swell index ( BUREAU OF INDIAN STANDARDS, 1977 ).

where V d is the sediment volume of 10 g of oven-dried soil passing a 425 μm sieve placed in a 100 mL graduated measuring jar containing distilled water and V k is the sediment volume of 10 g of oven-dried soil passing through a 425 μm sieve placed in a 100 mL graduated measuring jar containing kerosene.

Equations 1 and 2 show that the formula for calculating the free swelling ratio for expansive soil is the same for China and India, but the method of measurement is different. In this paper, the free expansion rate is determined based on the Chinese Technical Code for Buildings in Expansive Soil Regions ( GB50112-2013 ).

In May 2008, Anhui Engineering Investigation Institute completed collection of 50 soil samples ( Figure 2 ) and testing work in the Hefei city area and collected and utilized data of soil samples testing of 158 boreholes in geotechnical engineering survey projects in the Hefei city area ( Figure 2 ). The soil samples were extracted without disturbance.

Figure 2 . Map of the sampling sites in the distribution area of expansive soils in Hefei, China ( Anhui Engineering Investigation Institute, 2008 ).

The 30%–40% free swelling ratio is 28%. The 40%–50% free swelling ratio is 20%. The greater than 50% free swelling ratio is more than 50% ( Table 1 ). These indicated that clays in the Xiashu group of the Upper Pleistocene distributed in the area generally have a weak potential for expansion ( Figure 3 ) ( Anhui Engineering Investigation Institute, 2008 ).

Table 1 . Statistical table showing free swelling ratio results for soil samples in the Hefei urban area ( Anhui Engineering Investigation Institute, 2008 ).

Figure 3 . Area distribution of expansive soils in Hefei, China ( Anhui Engineering Investigation Institute, 2008 ).

2.4 Climate effects of expansive soils

2.4.1 humidity coefficient of expansive soils.

The humidity coefficient of expansive soil is the ratio of the minimum value of soil moisture content that may be reached at 1 m depth to its plastic limit value under the influence of natural climate.

According to the Technical Code for Buildings in Expansive Soil Regions ( GB50112-2013 ) ( Ministry of Housing and Urban-Rural Development of the People’s Republic of China, 2012 ), the humidity coefficient of the soil can be calculated according to the following formula based on the relevant meteorological data of Hefei:

where ψ w is the humidity coefficient of expansive soil, α is the ratio of the sum of the local evaporation power from September to February to the annual evaporation power, and c is the sum of the difference between the evaporation power and precipitation in the months, with dryness greater than 1.00 in the whole year (mm).

2.4.2 Climate-influenced layer and markedly climate-influenced layer of expansive soils

According to Equation 3 , the humidity coefficient ( ψ w ) of expansive soil is calculated as 0.868 ( Table 2 ). The thickness of the climate-influenced layer as calculated by the interpolation method is 3.34 m Table 3 . The thickness of the markedly climate-influenced layer is 1.50 m, calculated by multiplying the thickness of atmospheric influence by 0.45 ( Ministry of Housing and Urban-Rural Development of the People’s Republic of China, 2012 ). The deformation disaster of expansive soil will be more serious at this depth, i.e., the LID measures need to consider the climate-influenced layer of 3.34 m and the markedly climate-influenced layer of 1.50 m, which will directly affect the implementation of the LID measures.

Table 2 . Values of the humidity coefficient of expansive soils ( Ministry of Housing and Urban-Rural Development of the People’s Republic of China, 2012 ).

Table 3 . Climate-influenced layer thickness ( Ministry of Housing and Urban-Rural Development of the People’s Republic of China, 2012 ).

3 Test simulation systems and methods

3.1 test simulation system.

A laboratory small-scale multi-nozzle, under spray tank-type artificial rainfall simulation system was built. Spraying was controlled by controlling the switch of the solenoid valve. The pressure of the nozzles was controlled by a pipe pressure gauge, which ultimately enabled the simulation of different rainfall intensities ( Figures 4 , 5 ). The main equipment components of the laboratory artificial rainfall simulation system consisted of the expansive soil trough surrounded by rain curtains ( Figure 6A ), the expansive soil trough support frame ( Figure 6B ), the top wide-angle cone nozzle ( Figure 6C ), the pressure gauge and solenoid valve ( Figure 6D ), the control cabinet ( Figure 6E ), and the submersible pump ( Figure 6F ).

Figure 4 . Expansive soil trough layout plan.

Figure 5 . System diagram of the simulated rainfall device above the expansive soil trough.

Figure 6 . Main equipment components of the laboratory artificial rainfall simulation system.

The purpose of establishing this test simulation system was to study the infiltration capacity and runoff generation characteristics of expansive soils under different rainfall intensities. In addition, the test simulation system also studied the infiltration capacity of expansive soils and the characteristics of runoff generation under different levels of initial water content in expansive soils.

According to Chinese meteorology data, rainfall of 16 mm or more per hour is called “torrential rain.” Rainfall intensities of 0.25 mm/min, 0.44 mm/min, 0.57 mm/min, 0.67 mm/min, and 1.04 mm/min were designed for simulation tests in combination with historical rainfall statistics from Hefei meteorological stations.

3.2 Test methods

The test expansive soil was taken from the bare expansive soil excavated from the longitudinal section of the local newly repaired road in order to simulate the natural compaction of the real expansion soil body. The test fill thickness was 25 cm ( Figure 6A ). The expansion soil body was wetted by rainfall three times, with an interval of 3 days. Each time, the wetting was to be stopped by the production of streams, and it was covered with a rain cloth until the expansion soil body was sufficiently settled and sinked ( Figure 6A ). The surface soil was sampled and observed, and the average dry bulk weight was 1.41 g/cm 3 , which was close to the bulk weight of the expansive soil after natural consolidation.

Before the beginning of the test, the water content of the surface layer of the expansive soil body was maintained at 23% (±0.3%) to ensure that the water content of the soil body itself does not affect the generation time of rainfall runoff and runoff volume, respectively, in the rainfall intensity of 0.25 mm/min, 0.44 mm/min, 0.57 mm/min, 0.67 mm/min, and 1.04 mm/min; the surface of the expansive soil body was stimulated to produce the runoff process.

3.3 Test results

3.3.1 infiltration capacity of expansive soils.

It was found that at rainfall intensities of 0.25 mm/min and 0.44 mm/min, no runoff was generated from the surface of the expansive soil mass within 1 h. Under the rainfall intensities of 0.57 mm/min ( Figure 7A ), 0.67 mm/min ( Figure 7B ), and 1.04 mm/min ( Figure 7C ), the time to start generating runoff was 44 min, 30 min, and 17 min, respectively, and the time to reach the maximum runoff was 47 min ( Figure 7A ), 33 min ( Figure 7B ), and 19 min ( Figure 7C ), respectively. The time intervals between the start of generation of runoff and reaching the maximum level of runoff were 3 min, 3 min, and 2 min, respectively. The maximum runoff intervals are similar for rainfall intensities of 0.57 mm/min ( Figure 7A ), 0.67 mm/min ( Figure 7B ), and 1.04 mm/min ( Figure 7C ). The test results show that the exposed expansive soil has a rapid infiltration capacity and a short time interval to reach the maximum runoff volume under higher rainfall intensity, which is not very effective in reducing surface runoff.

Figure 7 . Runoff curves under different rainfall intensities.

Relevant studies have shown that with the continuation of rainfall, the infiltration potential gradient of expansive soils decreases after water absorption, and the surface cracks of land with expansive soils are closed due to soil expansion, resulting in a decrease in soil infiltration capacity. When encountering strong rainfall, due to the expansion, the cracks in the soil will be rapidly closed. This leads to a rapid decline in soil infiltration capacity ( Lei et al., 2020 ).

3.3.2 Initial water content of expansive soils

It was found that the simulated surface layer of expansive soil was operated under the initial water content of 21.6%, 24.8%, 26.3%, and 27.5%, and the time for starting generating of runoff was 49 min ( Figure 8A ), 31 min ( Figure 8B ), 22 min ( Figure 8C ), and 16 min ( Figure 8D ), respectively. The time taken for reaching the maximum runoff was 66 min ( Figure 8A ), 48 min ( Figure 8B ), 40 min ( Figure 8C ), and 33 min ( Figure 8D ), respectively. The time interval of arriving at maximum runoff was 17 min ( Figure 8A ), 17 min ( Figure 8B ), 18 min ( Figure 8C ), and 17 min ( Figure 8D ), respectively. It can be seen that the four simulated water content rates were similar to the natural water content of local expansive soil. The time interval for producing maximum runoff was similar as well, indicating that there is a certain effect of abatement of runoff generated by rainfall under the natural water content of expansive soils. This further demonstrates that maintaining the natural water content of expansive soils can reduce the occurrence of geologic hazards and surface runoff from rainfall.

Figure 8 . Runoff curves under different water contents.

4 Selection of LID technical measures

Low impact development technologies can generally be categorized into several types according to their main functions, such as infiltration, storage, regulation, transfer, interception, and purification.

In October 2014, the Technical Guidelines for Sponge City Construction—Construction of Low Impact Development Stormwater System ( Trial ) listed the relevant LID technical measures ( Table 4 ).

Table 4 . Comparison of low impact development facilities ( Beijing University of Civil Engineering and Architecture et al., 2014 ).

Some of the low impact development technical measures listed in Table 4 , such as infiltration, sewage interception, and purification, are not suitable to be selected in expansive soil development areas. If necessary, infiltration and decontamination measures can be selected after enhancement of expansive soil properties, but this implies an increase in the cost. This is a particular concern for our municipal, environmental, and landscape design engineers.

At present, the treatment for enhancing the properties of expansive soil roadbeds all over the world generally adopts the mixing of lime, cement, fly ash, and other mineral cementing materials. First of all, all the above materials are in powder form, and it is difficult to mix the expanded soil sufficiently. Second, the cost of improvement is high, and it also has a greater impact on the surrounding environment and the health of the construction workers ( Chen et al., 2006 ; Yang et al., 2014 ).

The characteristics of expansive soils can be enhanced by mixing them with weathered sand, which is a physical improvement. The weathered sand particles have a certain particle size, and adding it to the expansive soil can result in better mixing and homogeneous nature. Second, the surrounding environment and the health of the construction workers will face a smaller impact. In addition, the low price of weathered sand can vastly reduce the cost related to improvement. Therefore, the improvement strategies involving the mixing of weathered sand compared with the traditional means of improvement have the advantages of simple construction process, environmental protection, and low cost ( Chen et al., 2006 ; Yang et al., 2014 ).

Bouassida et al. (2022) proposed a new method for characterizing expansive clays after barometer test results on intact saturated clay specimens. Oedometer tests performed on a 53% thickness clay specimen overlaid by a 47% thickness of sand showed a significant reduction in the swelling pressure compared to that measured on a full expansive clay specimen. The mitigation solution reduced the swelling effect by placing a compacted granular layer as an interface between the expansive clay and the foundation ( Bouassida et al., 2022 ).

In summary, the mixing of weathered sand can effectively inhibit the expansion of expansive soil. This ensures the implementation of LID techniques.

5 Conclusion

The selection of the LID technical measures in the expansive soil distribution area cannot be generalized, and the following points should be paid attention to in particular:

To carry out the technical measures of infiltration, sewage interception, and purification, it is necessary to enhance the expansive soil properties. Weathered sand is easy to pick up and is inexpensive; hence, mixing of weathered sand in the improvement treatment of expansive soil should be prioritized, which is a means of physical improvement. It can meet the technical requirements of infiltration, sewage interception, and purification.

The depth of atmospheric influence and the depth of the acute layer of atmospheric influence directly affect the selection of the LID technical measures; for example, the depth of atmospheric influence in Hefei city is 3.34 m, and the depth of the acute layer of atmospheric influence is 1.5 m. When LID facilities are constructed, the adopted LID technical measures must take into account the impact of these two depths on the LID facilities. Otherwise, the expansive soils will cause damage to the LID facilities.

The continuous cycle of drying and wetting of expansive soil causes cracks to develop continuously, resulting in the surface cracks of expansive soil extending deeper. When encountering strong rainfall, the cracks close rapidly due to soil expansion, which leads to a rapid decrease in soil infiltration capacity. Therefore, maintaining the natural water content of the expansive soil can not only better inhibit the expansion of the soil body to produce cracks, which can reduce the frequency of expansion of the soil rise and fall, but also help reduce the surface runoff generated by rainfall.

The results of the test simulation system show that the exposed expansive soil has a fast infiltration capacity and a short time interval to reach the maximum runoff volume under larger rainfall intensity, which is not very effective in reducing surface runoff. In addition, maintaining the natural water content of expansive soils can reduce the occurrence of geologic hazards and surface runoff from rainfall.

Therefore, the natural water content of expansive soil can be maintained by avoiding exposing expansive soils to vegetative cover. Granular materials can be used to mitigate swelling when LID techniques such as infiltration, filtration, and water storage are used. More research studies must be carried out on granular materials to alleviate the swelling phenomenon in the distribution area of expansive soil so as to provide technical support for the upcoming Special Plan of Hefei City Sponge City .

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary Material; further inquiries can be directed to the corresponding author.

Author contributions

MH: conceptualization, funding acquisition, investigation, software, writing–original draft, and writing–review and editing. ZL: formal analysis, investigation, and writing–review and editing. RZ: data curation, project administration, and writing–original draft. YT: data curation, software, and writing–review and editing. Y-MS: writing–review and editing and conceptualization.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was funded by the Science and Technology Major Project for Water Pollution Control and Management (2014ZX07303003) and the Anhui Provincial Department of Education Quality Engineering Project (2020rcsfjd05).

Conflict of interest

Author RZ was employed by Shucheng Zhonghe Real Estate Development Co.Ltd.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2024.1417048/full#supplementary-material

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Keywords: low impact development, technical measure, expansive soil, distribution area, selection

Citation: Huang M, Liu Z, Zhang R, Tao Y and Sun Y-m (2024) Selection of low impact development technical measures in the distribution area of expansive soil: a case study of Hefei, China. Front. Environ. Sci. 12:1417048. doi: 10.3389/fenvs.2024.1417048

Received: 13 April 2024; Accepted: 13 June 2024; Published: 10 July 2024.

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Copyright © 2024 Huang, Liu, Zhang, Tao and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ming Huang, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.