case study as a type of qualitative research

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The case study as a type of qualitative research

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Case Study – Methods, Examples and Guide

Table of Contents

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

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

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

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

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

Multiple-Case Study

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

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

Exploratory Case Study

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

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

Descriptive Case Study

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

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

Instrumental Case Study

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

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

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

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

Observations

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

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

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

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

How to conduct Case Study Research

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

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

Examples of Case Study

Here are some examples of case study research:

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

Application of Case Study

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

Business and Management

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

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

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

Social Sciences

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

Law and Ethics

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

Purpose of Case Study

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

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

Case studies can also serve other purposes, including:

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

Advantages of Case Study Research

There are several advantages of case study research, including:

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

Limitations of Case Study Research

There are several limitations of case study research, including:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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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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Methodology

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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Distinguishing case study as a research method from case reports as a publication type

The purpose of this editorial is to distinguish between case reports and case studies. In health, case reports are familiar ways of sharing events or efforts of intervening with single patients with previously unreported features. As a qualitative methodology, case study research encompasses a great deal more complexity than a typical case report and often incorporates multiple streams of data combined in creative ways. The depth and richness of case study description helps readers understand the case and whether findings might be applicable beyond that setting.

Single-institution descriptive reports of library activities are often labeled by their authors as “case studies.” By contrast, in health care, single patient retrospective descriptions are published as “case reports.” Both case reports and case studies are valuable to readers and provide a publication opportunity for authors. A previous editorial by Akers and Amos about improving case studies addresses issues that are more common to case reports; for example, not having a review of the literature or being anecdotal, not generalizable, and prone to various types of bias such as positive outcome bias [ 1 ]. However, case study research as a qualitative methodology is pursued for different purposes than generalizability. The authors’ purpose in this editorial is to clearly distinguish between case reports and case studies. We believe that this will assist authors in describing and designating the methodological approach of their publications and help readers appreciate the rigor of well-executed case study research.

Case reports often provide a first exploration of a phenomenon or an opportunity for a first publication by a trainee in the health professions. In health care, case reports are familiar ways of sharing events or efforts of intervening with single patients with previously unreported features. Another type of study categorized as a case report is an “N of 1” study or single-subject clinical trial, which considers an individual patient as the sole unit of observation in a study investigating the efficacy or side effect profiles of different interventions. Entire journals have evolved to publish case reports, which often rely on template structures with limited contextualization or discussion of previous cases. Examples that are indexed in MEDLINE include the American Journal of Case Reports , BMJ Case Reports, Journal of Medical Case Reports, and Journal of Radiology Case Reports . Similar publications appear in veterinary medicine and are indexed in CAB Abstracts, such as Case Reports in Veterinary Medicine and Veterinary Record Case Reports .

As a qualitative methodology, however, case study research encompasses a great deal more complexity than a typical case report and often incorporates multiple streams of data combined in creative ways. Distinctions include the investigator’s definitions and delimitations of the case being studied, the clarity of the role of the investigator, the rigor of gathering and combining evidence about the case, and the contextualization of the findings. Delimitation is a term from qualitative research about setting boundaries to scope the research in a useful way rather than describing the narrow scope as a limitation, as often appears in a discussion section. The depth and richness of description helps readers understand the situation and whether findings from the case are applicable to their settings.

CASE STUDY AS A RESEARCH METHODOLOGY

Case study as a qualitative methodology is an exploration of a time- and space-bound phenomenon. As qualitative research, case studies require much more from their authors who are acting as instruments within the inquiry process. In the case study methodology, a variety of methodological approaches may be employed to explain the complexity of the problem being studied [ 2 , 3 ].

Leading authors diverge in their definitions of case study, but a qualitative research text introduces case study as follows:

Case study research is defined as a qualitative approach in which the investigator explores a real-life, contemporary bounded system (a case) or multiple bound systems (cases) over time, through detailed, in-depth data collection involving multiple sources of information, and reports a case description and case themes. The unit of analysis in the case study might be multiple cases (a multisite study) or a single case (a within-site case study). [ 4 ]

Methodologists writing core texts on case study research include Yin [ 5 ], Stake [ 6 ], and Merriam [ 7 ]. The approaches of these three methodologists have been compared by Yazan, who focused on six areas of methodology: epistemology (beliefs about ways of knowing), definition of cases, design of case studies, and gathering, analysis, and validation of data [ 8 ]. For Yin, case study is a method of empirical inquiry appropriate to determining the “how and why” of phenomena and contributes to understanding phenomena in a holistic and real-life context [ 5 ]. Stake defines a case study as a “well-bounded, specific, complex, and functioning thing” [ 6 ], while Merriam views “the case as a thing, a single entity, a unit around which there are boundaries” [ 7 ].

Case studies are ways to explain, describe, or explore phenomena. Comments from a quantitative perspective about case studies lacking rigor and generalizability fail to consider the purpose of the case study and how what is learned from a case study is put into practice. Rigor in case studies comes from the research design and its components, which Yin outlines as (a) the study’s questions, (b) the study’s propositions, (c) the unit of analysis, (d) the logic linking the data to propositions, and (e) the criteria for interpreting the findings [ 5 ]. Case studies should also provide multiple sources of data, a case study database, and a clear chain of evidence among the questions asked, the data collected, and the conclusions drawn [ 5 ].

Sources of evidence for case studies include interviews, documentation, archival records, direct observations, participant-observation, and physical artifacts. One of the most important sources for data in qualitative case study research is the interview [ 2 , 3 ]. In addition to interviews, documents and archival records can be gathered to corroborate and enhance the findings of the study. To understand the phenomenon or the conditions that created it, direct observations can serve as another source of evidence and can be conducted throughout the study. These can include the use of formal and informal protocols as a participant inside the case or an external or passive observer outside of the case [ 5 ]. Lastly, physical artifacts can be observed and collected as a form of evidence. With these multiple potential sources of evidence, the study methodology includes gathering data, sense-making, and triangulating multiple streams of data. Figure 1 shows an example in which data used for the case started with a pilot study to provide additional context to guide more in-depth data collection and analysis with participants.

Key sources of data for a sample case study

VARIATIONS ON CASE STUDY METHODOLOGY

Case study methodology is evolving and regularly reinterpreted. Comparative or multiple case studies are used as a tool for synthesizing information across time and space to research the impact of policy and practice in various fields of social research [ 9 ]. Because case study research is in-depth and intensive, there have been efforts to simplify the method or select useful components of cases for focused analysis. Micro-case study is a term that is occasionally used to describe research on micro-level cases [ 10 ]. These are cases that occur in a brief time frame, occur in a confined setting, and are simple and straightforward in nature. A micro-level case describes a clear problem of interest. Reporting is very brief and about specific points. The lack of complexity in the case description makes obvious the “lesson” that is inherent in the case; although no definitive “solution” is necessarily forthcoming, making the case useful for discussion. A micro-case write-up can be distinguished from a case report by its focus on briefly reporting specific features of a case or cases to analyze or learn from those features.

DATABASE INDEXING OF CASE REPORTS AND CASE STUDIES

Disciplines such as education, psychology, sociology, political science, and social work regularly publish rich case studies that are relevant to particular areas of health librarianship. Case reports and case studies have been defined as publication types or subject terms by several databases that are relevant to librarian authors: MEDLINE, PsycINFO, CINAHL, and ERIC. Library, Information Science & Technology Abstracts (LISTA) does not have a subject term or publication type related to cases, despite many being included in the database. Whereas “Case Reports” are the main term used by MEDLINE’s Medical Subject Headings (MeSH) and PsycINFO’s thesaurus, CINAHL and ERIC use “Case Studies.”

Case reports in MEDLINE and PsycINFO focus on clinical case documentation. In MeSH, “Case Reports” as a publication type is specific to “clinical presentations that may be followed by evaluative studies that eventually lead to a diagnosis” [ 11 ]. “Case Histories,” “Case Studies,” and “Case Study” are all entry terms mapping to “Case Reports”; however, guidance to indexers suggests that “Case Reports” should not be applied to institutional case reports and refers to the heading “Organizational Case Studies,” which is defined as “descriptions and evaluations of specific health care organizations” [ 12 ].

PsycINFO’s subject term “Case Report” is “used in records discussing issues involved in the process of conducting exploratory studies of single or multiple clinical cases.” The Methodology index offers clinical and non-clinical entries. “Clinical Case Study” is defined as “case reports that include disorder, diagnosis, and clinical treatment for individuals with mental or medical illnesses,” whereas “Non-clinical Case Study” is a “document consisting of non-clinical or organizational case examples of the concepts being researched or studied. The setting is always non-clinical and does not include treatment-related environments” [ 13 ].

Both CINAHL and ERIC acknowledge the depth of analysis in case study methodology. The CINAHL scope note for the thesaurus term “Case Studies” distinguishes between the document and the methodology, though both use the same term: “a review of a particular condition, disease, or administrative problem. Also, a research method that involves an in-depth analysis of an individual, group, institution, or other social unit. For material that contains a case study, search for document type: case study.” The ERIC scope note for the thesaurus term “Case Studies” is simple: “detailed analyses, usually focusing on a particular problem of an individual, group, or organization” [ 14 ].

PUBLICATION OF CASE STUDY RESEARCH IN LIBRARIANSHIP

We call your attention to a few examples published as case studies in health sciences librarianship to consider how their characteristics fit with the preceding definitions of case reports or case study research. All present some characteristics of case study research, but their treatment of the research questions, richness of description, and analytic strategies vary in depth and, therefore, diverge at some level from the qualitative case study research approach. This divergence, particularly in richness of description and analysis, may have been constrained by the publication requirements.

As one example, a case study by Janke and Rush documented a time- and context-bound collaboration involving a librarian and a nursing faculty member [ 15 ]. Three objectives were stated: (1) describing their experience of working together on an interprofessional research team, (2) evaluating the value of the librarian role from librarian and faculty member perspectives, and (3) relating findings to existing literature. Elements that signal the qualitative nature of this case study are that the authors were the research participants and their use of the term “evaluation” is reflection on their experience. This reads like a case study that could have been enriched by including other types of data gathered from others engaging with this team to broaden the understanding of the collaboration.

As another example, the description of the academic context is one of the most salient components of the case study written by Clairoux et al., which had the objectives of (1) describing the library instruction offered and learning assessments used at a single health sciences library and (2) discussing the positive outcomes of instruction in that setting [ 16 ]. The authors focus on sharing what the institution has done more than explaining why this institution is an exemplar to explore a focused question or understand the phenomenon of library instruction. However, like a case study, the analysis brings together several streams of data including course attendance, online material page views, and some discussion of results from surveys. This paper reads somewhat in between an institutional case report and a case study.

The final example is a single author reporting on a personal experience of creating and executing the role of research informationist for a National Institutes of Health (NIH)–funded research team [ 17 ]. There is a thoughtful review of the informationist literature and detailed descriptions of the institutional context and the process of gaining access to and participating in the new role. However, the motivating question in the abstract does not seem to be fully addressed through analysis from either the reflective perspective of the author as the research participant or consideration of other streams of data from those involved in the informationist experience. The publication reads more like a case report about this informationist’s experience than a case study that explores the research informationist experience through the selection of this case.

All of these publications are well written and useful for their intended audiences, but in general, they are much shorter and much less rich in depth than case studies published in social sciences research. It may be that the authors have been constrained by word counts or page limits. For example, the submission category for Case Studies in the Journal of the Medical Library Association (JMLA) limited them to 3,000 words and defined them as “articles describing the process of developing, implementing, and evaluating a new service, program, or initiative, typically in a single institution or through a single collaborative effort” [ 18 ]. This definition’s focus on novelty and description sounds much more like the definition of case report than the in-depth, detailed investigation of a time- and space-bound problem that is often examined through case study research.

Problem-focused or question-driven case study research would benefit from the space provided for Original Investigations that employ any type of quantitative or qualitative method of analysis. One of the best examples in the JMLA of an in-depth multiple case study that was authored by a librarian who published the findings from her doctoral dissertation represented all the elements of a case study. In eight pages, she provided a theoretical basis for the research question, a pilot study, and a multiple case design, including integrated data from interviews and focus groups [ 19 ].

We have distinguished between case reports and case studies primarily to assist librarians who are new to research and critical appraisal of case study methodology to recognize the features that authors use to describe and designate the methodological approaches of their publications. For researchers who are new to case research methodology and are interested in learning more, Hancock and Algozzine provide a guide [ 20 ].

We hope that JMLA readers appreciate the rigor of well-executed case study research. We believe that distinguishing between descriptive case reports and analytic case studies in the journal’s submission categories will allow the depth of case study methodology to increase. We also hope that authors feel encouraged to pursue submitting relevant case studies or case reports for future publication.

Editor’s note: In response to this invited editorial, the Journal of the Medical Library Association will consider manuscripts employing rigorous qualitative case study methodology to be Original Investigations (fewer than 5,000 words), whereas manuscripts describing the process of developing, implementing, and assessing a new service, program, or initiative—typically in a single institution or through a single collaborative effort—will be considered to be Case Reports (formerly known as Case Studies; fewer than 3,000 words).

Qualitative research: methods and examples

Last updated

13 April 2023

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Qualitative research involves gathering and evaluating non-numerical information to comprehend concepts, perspectives, and experiences. It’s also helpful for obtaining in-depth insights into a certain subject or generating new research ideas. 

As a result, qualitative research is practical if you want to try anything new or produce new ideas.

There are various ways you can conduct qualitative research. In this article, you'll learn more about qualitative research methodologies, including when you should use them.

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• What is qualitative research?

Qualitative research is a broad term describing various research types that rely on asking open-ended questions. Qualitative research investigates “how” or “why” certain phenomena occur. It is about discovering the inherent nature of something.

The primary objective of qualitative research is to understand an individual's ideas, points of view, and feelings. In this way, collecting in-depth knowledge of a specific topic is possible. Knowing your audience's feelings about a particular subject is important for making reasonable research conclusions.

Unlike quantitative research , this approach does not involve collecting numerical, objective data for statistical analysis. Qualitative research is used extensively in education, sociology, health science, history, and anthropology.

• Types of qualitative research methodology

Typically, qualitative research aims at uncovering the attitudes and behavior of the target audience concerning a specific topic. For example,  “How would you describe your experience as a new Dovetail user?”

Some of the methods for conducting qualitative analysis include:

Focus groups

Hosting a focus group is a popular qualitative research method. It involves obtaining qualitative data from a limited sample of participants. In a moderated version of a focus group, the moderator asks participants a series of predefined questions. They aim to interact and build a group discussion that reveals their preferences, candid thoughts, and experiences.

Unmoderated, online focus groups are increasingly popular because they eliminate the need to interact with people face to face.

Focus groups can be more cost-effective than 1:1 interviews or studying a group in a natural setting and reporting one’s observations.

Focus groups make it possible to gather multiple points of view quickly and efficiently, making them an excellent choice for testing new concepts or conducting market research on a new product.

However, there are some potential drawbacks to this method. It may be unsuitable for sensitive or controversial topics. Participants might be reluctant to disclose their true feelings or respond falsely to conform to what they believe is the socially acceptable answer (known as response bias).

Case study research

A case study is an in-depth evaluation of a specific person, incident, organization, or society. This type of qualitative research has evolved into a broadly applied research method in education, law, business, and the social sciences.

Even though case study research may appear challenging to implement, it is one of the most direct research methods. It requires detailed analysis, broad-ranging data collection methodologies, and a degree of existing knowledge about the subject area under investigation.

Historical model

The historical approach is a distinct research method that deeply examines previous events to better understand the present and forecast future occurrences of the same phenomena. Its primary goal is to evaluate the impacts of history on the present and hence discover comparable patterns in the present to predict future outcomes.

Oral history

This qualitative data collection method involves gathering verbal testimonials from individuals about their personal experiences. It is widely used in historical disciplines to offer counterpoints to established historical facts and narratives. The most common methods of gathering oral history are audio recordings, analysis of auto-biographical text, videos, and interviews.

Qualitative observation

One of the most fundamental, oldest research methods, qualitative observation , is the process through which a researcher collects data using their senses of sight, smell, hearing, etc. It is used to observe the properties of the subject being studied. For example, “What does it look like?” As research methods go, it is subjective and depends on researchers’ first-hand experiences to obtain information, so it is prone to bias. However, it is an excellent way to start a broad line of inquiry like, “What is going on here?”

Record keeping and review

Record keeping uses existing documents and relevant data sources that can be employed for future studies. It is equivalent to visiting the library and going through publications or any other reference material to gather important facts that will likely be used in the research.

Grounded theory approach

The grounded theory approach is a commonly used research method employed across a variety of different studies. It offers a unique way to gather, interpret, and analyze. With this approach, data is gathered and analyzed simultaneously.  Existing analysis frames and codes are disregarded, and data is analyzed inductively, with new codes and frames generated from the research.

Ethnographic research

Ethnography  is a descriptive form of a qualitative study of people and their cultures. Its primary goal is to study people's behavior in their natural environment. This method necessitates that the researcher adapts to their target audience's setting. 

Thereby, you will be able to understand their motivation, lifestyle, ambitions, traditions, and culture in situ. But, the researcher must be prepared to deal with geographical constraints while collecting data i.e., audiences can’t be studied in a laboratory or research facility.

This study can last from a couple of days to several years. Thus, it is time-consuming and complicated, requiring you to have both the time to gather the relevant data as well as the expertise in analyzing, observing, and interpreting data to draw meaningful conclusions.

Narrative framework

A narrative framework is a qualitative research approach that relies on people's written text or visual images. It entails people analyzing these events or narratives to determine certain topics or issues. With this approach, you can understand how people represent themselves and their experiences to a larger audience.

Phenomenological approach

The phenomenological study seeks to investigate the experiences of a particular phenomenon within a group of individuals or communities. It analyzes a certain event through interviews with persons who have witnessed it to determine the connections between their views. Even though this method relies heavily on interviews, other data sources (recorded notes), and observations could be employed to enhance the findings.

• Qualitative research methods (tools)

Some of the instruments involved in qualitative research include:

Document research: Also known as document analysis because it involves evaluating written documents. These can include personal and non-personal materials like archives, policy publications, yearly reports, diaries, or letters.

Focus groups:  This is where a researcher poses questions and generates conversation among a group of people. The major goal of focus groups is to examine participants' experiences and knowledge, including research into how and why individuals act in various ways.

Secondary study: Involves acquiring existing information from texts, images, audio, or video recordings.

Observations:   This requires thorough field notes on everything you see, hear, or experience. Compared to reported conduct or opinion, this study method can assist you in getting insights into a specific situation and observable behaviors.

Structured interviews :  In this approach, you will directly engage people one-on-one. Interviews are ideal for learning about a person's subjective beliefs, motivations, and encounters.

Surveys:  This is when you distribute questionnaires containing open-ended questions

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• What are common examples of qualitative research?

Everyday examples of qualitative research include:

Conducting a demographic analysis of a business

For instance, suppose you own a business such as a grocery store (or any store) and believe it caters to a broad customer base, but after conducting a demographic analysis, you discover that most of your customers are men.

You could do 1:1 interviews with female customers to learn why they don't shop at your store.

In this case, interviewing potential female customers should clarify why they don't find your shop appealing. It could be because of the products you sell or a need for greater brand awareness, among other possible reasons.

Launching or testing a new product

Suppose you are the product manager at a SaaS company looking to introduce a new product. Focus groups can be an excellent way to determine whether your product is marketable.

In this instance, you could hold a focus group with a sample group drawn from your intended audience. The group will explore the product based on its new features while you ensure adequate data on how users react to the new features. The data you collect will be key to making sales and marketing decisions.

Conducting studies to explain buyers' behaviors

You can also use qualitative research to understand existing buyer behavior better. Marketers analyze historical information linked to their businesses and industries to see when purchasers buy more.

Qualitative research can help you determine when to target new clients and peak seasons to boost sales by investigating the reason behind these behaviors.

• Qualitative research: data collection

Data collection is gathering information on predetermined variables to gain appropriate answers, test hypotheses, and analyze results. Researchers will collect non-numerical data for qualitative data collection to obtain detailed explanations and draw conclusions.

To get valid findings and achieve a conclusion in qualitative research, researchers must collect comprehensive and multifaceted data.

Qualitative data is usually gathered through interviews or focus groups with videotapes or handwritten notes. If there are recordings, they are transcribed before the data analysis process. Researchers keep separate folders for the recordings acquired from each focus group when collecting qualitative research data to categorize the data.

• Qualitative research: data analysis

Qualitative data analysis is organizing, examining, and interpreting qualitative data. Its main objective is identifying trends and patterns, responding to research questions, and recommending actions based on the findings. Textual analysis is a popular method for analyzing qualitative data.

Textual analysis differs from other qualitative research approaches in that researchers consider the social circumstances of study participants to decode their words, behaviors, and broader meaning. 

Learn more about qualitative research data analysis software

• When to use qualitative research

Qualitative research is helpful in various situations, particularly when a researcher wants to capture accurate, in-depth insights. 

Here are some instances when qualitative research can be valuable:

Examining your product or service to improve your marketing approach

When researching market segments, demographics, and customer service teams

Identifying client language when you want to design a quantitative survey

When attempting to comprehend your or someone else's strengths and weaknesses

Assessing feelings and beliefs about societal and public policy matters

Collecting information about a business or product's perception

Analyzing your target audience's reactions to marketing efforts

When launching a new product or coming up with a new idea

When seeking to evaluate buyers' purchasing patterns

• Qualitative research methods vs. quantitative research methods

Qualitative research examines people's ideas and what influences their perception, whereas quantitative research draws conclusions based on numbers and measurements.

Qualitative research is descriptive, and its primary goal is to comprehensively understand people's attitudes, behaviors, and ideas.

In contrast, quantitative research is more restrictive because it relies on numerical data and analyzes statistical data to make decisions. This research method assists researchers in gaining an initial grasp of the subject, which deals with numbers. For instance, the number of customers likely to purchase your products or use your services.

What is the most important feature of qualitative research?

A distinguishing feature of qualitative research is that it’s conducted in a real-world setting instead of a simulated environment. The researcher is examining actual phenomena instead of experimenting with different variables to see what outcomes (data) might result.

Can I use qualitative and quantitative approaches together in a study?

Yes, combining qualitative and quantitative research approaches happens all the time and is known as mixed methods research. For example, you could study individuals’ perceived risk in a certain scenario, such as how people rate the safety or riskiness of a given neighborhood. Simultaneously, you could analyze historical data objectively, indicating how safe or dangerous that area has been in the last year. To get the most out of mixed-method research, it’s important to understand the pros and cons of each methodology, so you can create a thoughtfully designed study that will yield compelling results.

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Research Writing and Analysis

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• Trustworthiness of Qualitative Data
• Analysis and Coding Example- Qualitative Data
• Thematic Data Analysis in Qualitative Design
• Dissertation to Journal Article This link opens in a new window
• International Journal of Online Graduate Education (IJOGE) This link opens in a new window
• Journal of Research in Innovative Teaching & Learning (JRIT&L) This link opens in a new window

Writing a Case Study

What is a case study?

A Case study is: 

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

What are the different types of case studies?

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

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

Who are your case study participants?

What is triangulation ? 

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

How to write a Case Study?

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

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• Last Updated: Aug 30, 2024 8:27 AM
• URL: https://resources.nu.edu/researchtools

Qualitative Research in Psychology

Research Methods in Psychology

January 2023

This twelve-hour course on qualitative research in psychology begins by exploring the historical and cross-disciplinary foundations of the field, emphasizing philosophical underpinnings such as postpositivism, constructivism, transformative, and pragmatism. The course highlights the iterative, naturalistic, and contextual facets of qualitative research, focusing on trustworthiness criteria like credibility, transferability, dependability, and confirmability. It also addresses common critiques from a quantitative perspective and the concept of reflexivity, stressing the importance of the researcher’s role and biases.

Phenomenology, narrative inquiry, and constructivist grounded theory are explored next. Phenomenology captures the essence of human experiences through in-depth interviews, revealing nuances like maternal identity development. Narrative inquiry uses storytelling to uncover life nuances, such as the challenges faced by first-generation college students. Constructivist grounded theory develops theories based on participants’ experiences, explaining social processes like identity development and resilience-building. Interviewing techniques tailored to each tradition are explored, emphasizing the researcher’s role as the core instrument of data collection.

The course then explores ethnographic inquiry and case studies. Ethnography involves direct engagement in the setting of interest, uncovering cultural phenomena through various genres like classical, mainstream, public, and postmodern. Case studies investigate phenomena bounded by time and place, focusing on individuals, interventions, organizations, or systems.

Finally, the course examines qualitative data analysis and coding techniques. The iterative nature of qualitative research is emphasized, where data collection and analysis occur simultaneously, allowing for constant comparison and refinement of codes.

Learning objectives

• Describe the philosophical and interpretive foundations of qualitative research.
• Differentiate qualitative claims, methods, and analyses from quantitative claims, methods, and analyses.
• Explore, identify, and evaluate core qualitative traditions (phenomenology, narrative inquiry, constructivist grounded theory, ethnographic inquiry, and case study).
• Distinguish common methods (e.g., interviewing, focus groups) and analytical techniques (qualitative data analysis) used within and across core qualitative traditions.
• Begin thinking about your own qualitative study of a topic in psychology.

This program does not offer CE credit.

More in this series

A concepts-focused introduction to basic descriptive and inferential statistics

January 2023 On Demand Training

Principles of design and ethics for research in psychology

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Speaker 1: Welcome to this overview of qualitative research methods. This tutorial will help give you the big picture of qualitative research, and introduce key concepts that will help you determine if qualitative methods are appropriate for your project study. First, let's review what research, particularly educational research, is designed to do. Research is an organized, systematic, disciplined approach to answering questions about our observations and experiences in the world. It is a structured approach to gathering and interpreting information that allows us to understand, theorize about, or explain experience. What, then, is distinctive about qualitative research? Qualitative research focuses on generating meaning and understanding through rich description. It can be a particularly useful approach to studying educational problems that requires developing an understanding of complex social environments, and the meaning that people within those environments bring to their experience. Qualitative research differs from quantitative research in several ways. It typically addresses different problems, arises from a different philosophical view of the world, works to achieve different goals, and uses different methods and design. This table illustrates some of the key differences. Focus Qualitative research focuses on the quality of experience, trying to describe or understand the essence or nature of human experience. Quantitative research, on the other hand, focuses on more measurable factors, asking questions such as, how much, how many, or how frequently. Philosophical Roots Qualitative research integrates more subjective human experience, rather than purely objective external reality. It belongs to the school of constructivism or interpretivism. Quantitative research is based on positivism that holds that physical and social phenomena are independent of the observer, are fairly stable over time and place, and can be objectively observed and quantified. Goals of Investigation The goals of qualitative research are to understand, describe, discover meaning, or generate hypotheses or theory. Quantitative research aims to predict, control, confirm, and test hypotheses. Design Characteristics The designs used in these two types of research are suited to their goals. Qualitative research design is more flexible, evolving, and emergent, while quantitative research design is structured and predetermined. It should be emphasized that the flexible structure of qualitative research in no way suggests that it is less disciplined or easier to design or implement. Quite the contrary. Well-designed, valid, scholarly qualitative research has flexible structure and is designed and implemented with the same care and attention to detail as any well-designed, valid, and scholarly quantitative study. Data Collection In qualitative research, the researcher is the primary instrument, bringing his or her own perspectives to the selection and meaning of data. Quantitative research depends upon external instruments, such as tests, surveys, or other tools used to measure and quantify a particular phenomenon. Now that we have discussed the nature of qualitative research and the kinds and forms of qualitative data, it is easier to understand how qualitative research pursues its research goals. In its very earliest stages, qualitative research aims to explore. The goal is to identify patterns, themes, hunches, and initial models that provide an initial understanding of this phenomenon. Description is the heart of qualitative research. The essential characteristics of description is that it conveys information with the detail and specificity necessary to accurately convey the experience. Ultimately, qualitative research strives to produce meaningful interpretations of events and phenomena. With interpretation, the goal is to make sense of what goes on, to reach out for understanding or explanation. Through exploration, description, and interpretation, the qualitative researcher arrives at a complete understanding of a phenomenon in a particular setting or context. Case studies explore a program, an event, an activity, a process, or one or more individuals in depth. Grounded theory derives a general abstract theory of a process, action, or interaction grounded in the views of participants. Ethnography studies an intact cultural group in a natural setting over a prolonged period of time. Phenomenology identifies the essence of human experiences. Narrative approaches study the stories that individuals provide about their lives and experiences. Methodologies come out of different social sciences. For example, ethnography has its roots in anthropology, while grounded theory got its start in sociological research. Phenomenology is rooted in the philosophy of phenomenology. Case study can combine any number of qualitative and quantitative traditions and techniques in order to meet the specific needs of the research situation. Case study is perhaps the most flexible methodology, able to bend several traditions into a valid research design. Consequently, it is among the most widely used research methodologies, particularly for applied research. Qualitative data are typically obtained from sources such as interviews, focus groups, observations of real-life settings, and existing documents. One study may include data from one, several, or all of these sources. For example, a researcher studying a school environment might observe students as they work on daily tasks in the classroom, including students' reactions to the activities such as body language and facial expressions. A researcher might interview the teacher and students individually, or as part of a small group, about what they were thinking or feeling during the lessons. She may also examine documents such as student work samples and lesson plans to paint a holistic picture of the educational experience. As you plan your research study, you must create a justification for your data collection methods in order to explain why the methods you propose are the most appropriate and most effective way to understand the phenomenon or focus of your study. Before you collect any data for your study, you must receive approval from Walden's Institutional Review Board, or IRB. Visit Walden University's IRB website in order to make sure that you obtain the proper permissions to collect and use data. Qualitative data analysis follows three basic steps. First, the researcher prepares and organizes the data. This could include transcribing interview notes, organizing field notes from observations, or ensuring all documents to be included in the analysis are present and available. Second, the researcher reduces the data by identifying themes, coding data elements, and creating categories. Finally, just as quantitative data must be presented in tables or figures, qualitative data can be presented in narrative form, tables, or visual diagrams. In qualitative research, the data analysis process is flexible and designed to meet each study's needs, but also follows an established protocol and relies on rigorous methodological approaches. The processes of analysis and preparing results are not distinct steps, but are interrelated and often occur simultaneously. In qualitative research, validity is the extent to which the data and the interpretation of the data are credible. Qualitative researchers use different terms to refer to validity. Maxwell uses validity, Lincoln and Guba use trustworthiness, and Creswell uses validation. In addition, these authors use other related subterms. It is important for students to use one recognized author to define all relevant terms for validity. For instance, Lincoln and Guba use four additional terms to specify different aspects of trustworthiness – credibility, transferability, dependability, and confirmability. As with any research approach, the researcher must take steps to ensure the validity or accuracy of the research findings. In qualitative research, validity is the extent to which the data and the interpretation of the data are credible. Creswell identifies eight different strategies used by qualitative researchers to ensure the validity of their findings. Prolonged engagement and persistent data gathering ensure that the researcher does not draw conclusions based upon an isolated idiosyncratic experience with a phenomenon. Using rich, thick description ensures that a sufficient level of detail about the phenomenon studied is included so that others might draw the same or similar conclusions. Triangulation refers to using multiple data sources in order to build up a complete picture of a phenomenon. Member checking allows the researcher to present the study's findings or conclusions to the original participants so they can comment on whether they believe their perspectives are accurately portrayed. Presenting negative or discrepant information acknowledges observations or findings that run contrary to the study's key themes. Clarifying one's biases as a researcher similarly acknowledges those preconceptions or biases that will inevitably color the study's conclusions. Peer debriefing enlists the aid of a person other than the researcher to review the findings and ensure that they make sense. Finally, an external auditor is someone not familiar with the researcher or the study who can review the study's overall logic, coherence, and consistency. When considering whether a qualitative approach is right for your study, ask yourself the following questions. First, what kind of phenomenon are you planning to study? Is it related to some aspect of human experience that cannot be counted or expressed in numbers? Does it relate to subjective experience, cultural characteristics, personal perspective, idiosyncratic ideas, or comparisons of intangibles? Second, what do you want to know about the phenomenon? Can you find out what you want to know by immersing yourself in the environment in which you will study the phenomenon, by observing or talking to people within that environment, or by studying the materials they have created? Third, why are you doing the study? Are you interested in interpreting, generating meaning, and gaining a holistic view of a phenomenon, rather than in comparing, measuring, or quantifying a phenomenon? If you answered positively to these questions, qualitative research may be the right choice for your study. Qualitative research is a powerful method of studying the implicit as well as the explicit. It accomplishes this by focusing on personal perceptions of the world and the experiences of people as they construct the reality in which they live. Because of these characteristics, qualitative research can be a powerful tool for social change. As a Walden student, social change is a feature of every student capstone. Qualitative methods may help you meet this requirement. Once you have decided to embark upon the process of conducting a qualitative study, use the following steps to get started. First, review research studies that have been conducted on your topic to determine what methods and research traditions were used. Consider the strengths and weaknesses of the various research traditions, data collection methods, and data analysis methods. Next, review the literature on qualitative research methods. Every aspect of your research has a body of literature associated with it. Just as you would not confine yourself to your course textbooks for your review of research on your topic, you should not limit yourself to your course texts for your review of methodological literature. Read broadly and deeply from the scholarly literature to gain expertise in qualitative research. Additional self-paced tutorials have been developed on different methodologies and techniques associated with qualitative research. Make sure you complete all of the self-paced tutorials and review them as often as needed. You will then be prepared to complete a literature review of the specific methodologies and techniques you will use in your study. Thank you for watching.

• Open access
• Published: 04 September 2024

Insights into research activities of senior dental students in the Middle East: A multicenter preliminary study

• Mohammad S. Alrashdan 1 , 2 ,
• Abubaker Qutieshat 3 , 4 ,
• Mohamed El-Kishawi 5 ,
• Abdulghani Alarabi 6 ,
• Lina Khasawneh 7 &
• Sausan Al Kawas 1  

BMC Medical Education volume  24 , Article number:  967 ( 2024 ) Cite this article

Metrics details

Despite the increasing recognition of the importance of research in undergraduate dental education, limited studies have explored the nature of undergraduate research activities in dental schools in the Middle East region. This study aimed to evaluate the research experience of final year dental students from three dental schools in the Middle East.

A descriptive, cross-sectional study was conducted among final-year dental students from three institutions, namely Jordan University of Science and Technology, University of Sharjah (UAE), and Oman Dental College. Participants were asked about the nature and scope of their research projects, the processes involved in the research, and their perceived benefits of engaging in research.

A total of 369 respondents completed the questionnaire.  Cross-sectional studies represented the most common research type  (50.4%), with public health (29.3%) and dental education (27.9%) being the predominant domains. More than half of research proposals were developed via discussions with instructors (55.0%), and literature reviews primarily utilized PubMed (70.2%) and Google Scholar (68.5%). Regarding statistical analysis, it was usually carried out with instructor’s assistance (45.2%) or using specialized software (45.5%). The students typically concluded their projects with a manuscript (58.4%), finding the discussion section most challenging to write (42.0%). The research activity was considered highly beneficial, especially in terms of teamwork and communication skills, as well as data interpretation skills, with 74.1% of students reporting a positive impact on their research perspectives.

Conclusions

The research experience was generally positive among surveyed dental students. However, there is a need for more diversity in research domains, especially in qualitative studies, greater focus on guiding students in research activities s, especially in manuscript writing and publication. The outcomes of this study could provide valuable insights for dental schools seeking to improve their undergraduate research activities.

Peer Review reports

Introduction

The importance of research training for undergraduate dental students cannot be overstressed and many reports have thoroughly discussed the necessity of incorporating research components in the dental curricula [ 1 , 2 , 3 , 4 ]. A structured research training is crucial to ensure that dental graduates will adhere to evidence-based practices and policies in their future career and are able to critically appraise the overwhelming amount of dental and relevant medical literature so that only rigorous scientific outcomes are adopted. Furthermore, a sound research background is imperative for dental graduates to overcome some of the reported barriers to scientific evidence uptake. This includes the lack of familiarity or uncertain applicability and the lack of agreement with available evidence [ 5 ]. There is even evidence that engagement in research activities can improve the academic achievements of students [ 6 ]. Importantly, many accreditation bodies around the globe require a distinct research component with clear learning outcomes to be present in the curriculum of the dental schools [ 1 ].

Research projects and courses have become fundamental elements of modern biomedical education worldwide. The integration of research training in biomedical academic programs has evolved over the years, reflecting the growing recognition of research as a cornerstone of evidence-based practice [ 7 ]. Notwithstanding the numerous opportunities presented by the inclusion of research training in biomedical programs, it poses significant challenges such as limited resources, varying levels of student preparedness, and the need for faculty development in research mentorship [ 8 , 9 ]. Addressing these challenges is essential to maximize the benefits of research training and to ensure that all students can engage meaningfully in research activities.

While there are different models for incorporating research training into biomedical programs, including dentistry, almost all models share the common goals of equipping students with basic research skills and techniques, critical thinking training and undertaking research projects either as an elective or a summer training course, or more commonly as a compulsory course required for graduation [ 2 , 4 , 10 ].

Dental colleges in the Middle East region are not an exception and most of these colleges are continuously striving to update their curricula to improve the undergraduate research component and cultivate a research-oriented academic teaching environment. Despite these efforts, there remains a significant gap in our understanding of the nature and scope of student-led research in these institutions, the challenges they face, and the perceived benefits of their research experiences. Furthermore, a common approach in most studies in this domain is to confine data collection to a single center from a single country, which in turn limits the value of the outcomes. Therefore, it is of utmost importance to conduct studies with representative samples and preferably multiple institutions in order to address the existing knowledge gaps, to provide valuable insights that can inform future curricular improvements and to support the development of more effective research training programs in dental education across the region. Accordingly, this study was designed and conducted to elucidate some of these knowledge gaps.

The faculty of dentistry at Jordan University of Science and Technology (JUST) is the biggest in Jordan and adopts a five-year bachelor’s program in dental surgery (BDS). The faculty is home to more than 1600 undergraduate and 75 postgraduate students. The college of dental medicine at the University of Sharjah (UoS) is also the biggest in the UAE, with both undergraduate and postgraduate programs, local and international accreditation and follows a (1 + 5) program structure, whereby students need to finish a foundation year and then qualify for the five-year BDS program. Furthermore, the UoS dental college applies an integrated stream-based curriculum. Finally, Oman Dental College (ODC) is the sole dental school in Oman and represents an independent college that does not belong to a university body.

The aim of this study was to evaluate the research experience of final year dental students from three major dental schools in the Middle East, namely JUST from Jordan, UoS from the UAE, and ODC from Oman. Furthermore, the hypothesis of this study was that research activities conducted at dental schools has no perceived benefit for final year dental students.

The rationale for selecting these three dental schools stems from the diversity in the dental curriculum and program structure as well as the fact that final year BDS students are required to conduct a research project as a prerequisite for graduation in the three schools. Furthermore, the authors from these dental schools have a strong scholarly record and have been collaborating in a variety of academic and research activities.

Materials and methods

The current study is a population-based descriptive cross-sectional observational study. The study was conducted using an online self-administered questionnaire and targeted final-year dental students at three dental schools in the Middle East region: JUST from Jordan, UoS from the UAE, and ODC from Oman. The study took place in the period from January to June 2023.

For inclusion in the study, participants should have been final-year dental students at the three participating schools, have finished their research project and agreed to participate. Exclusion criteria included any students not in their final year, those who have not conducted or finished their research projects and those who refused to participate.

The study was approved by the institutional review board of JUST (Reference: 724–2022), the research ethics committee of the UoS (Reference: REC-22-02-22-3) as well as ODC (Reference: ODC-MA-2022-166). The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [ 11 ]. The checklist is available as a supplementary file.

Sample size determination was based on previous studies with a similar design and was further confirmed with a statistical formula. A close look at the relevant literature reveals that such studies were either targeting a single dental or medical school or multiple schools and the sample size generally ranged from 158 to 360 [ 4 , 8 , 9 , 10 , 12 ]. Furthermore, to confirm the sample size, the following 2-step formula for finite population sample size calculation was used [ 13 ]:

Wherein Z is the confidence level at 95% =1.96, P is the population proportion = 0.5, and E is the margin of error = 0.05. Based on this formula, the resultant initial sample size was 384.

Wherein n is the initial sample size = 384, N is the total population size (total number of final year dental students in the 3 schools) = 443. Based on this formula, the adjusted sample size was 206.

An online, self-administered questionnaire comprising 13 questions was designed to assess the research experience of final year dental students in the participating schools. The questionnaire was initially prepared by the first three authors and was then reviewed and approved by the other authors. The questionnaire was developed following an extensive review of relevant literature to identify the most critical aspects of research projects conducted at the dental or medical schools and the most common challenges experienced by students with regards to research project design, research components, attributes, analysis, interpretation, drafting, writing, and presentation of the final outcomes.

The questionnaire was then pretested for both face and content validity. Face validity was assessed by a pilot study that evaluated clarity, validity, and comprehensiveness in a small cohort of 30 students. Content validity was assessed by the authors, who are all experienced academics with remarkable research profiles and experience in supervising undergraduate and postgraduate research projects. The authors critically evaluated each item and made the necessary changes whenever required. Furthermore, Cronbach’s alpha was used to assess the internal consistency/ reliability of the questionnaire and the correlation between the questionnaire items was found to be 0.79. Thereafter, online invitations along with the questionnaire were sent out to a total of 443 students, 280 from JUST, 96 from UoS and 67 from ODC, which represented the total number of final year students at the three schools. A first reminder was sent 2 weeks later, and a second reminder was sent after another 2 weeks.

In addition to basic demographic details, the questionnaire comprised questions related to the type of study conducted, the scope of the research project, whether the research project was proposed by the students or the instructors or both, the literature review part of the project, the statistical analysis performed, the final presentation of the project, the writing up of the resultant manuscript if applicable, the perceived benefits of the research project and finally suggestions to improve the research component for future students.

The outcomes of the study were the students’ research experience in terms of research design, literature review, data collection, analysis, interpretation and presentation, students’ perceived benefits from research, students’ perspective towards research in their future career and students’ suggestions to improve their research experience.

The exposures were the educational and clinical experience of students, research supervision by mentors and faculty members, and participation in extracurricular activities, while the predictors were the academic performance of students, previous research experience and self-motivation.

The collected responses were entered into a Microsoft Excel spreadsheet and analyzed using SPSS Statistics software, version 20.0 (SPSS Inc., Chicago, IL, USA). Descriptive data were presented as frequencies and percentages. For this study, only descriptive statistics were carried out as the aim was not to compare and contrast the three schools but rather to provide an overview of the research activities at the participating dental schools.

The heatmap generated to represent the answers for question 11 (perceived benefits of the research activity) was created using Python programming language (Python 3.11) and the pandas, seaborn, and matplotlib libraries. The heatmap was customized to highlight the count and percentage of responses in each component, with the highest values shown in red and the lowest values shown in blue.

Potentially eligible participants in this study were all final year dental students at the three dental schools (443 students, 280 from JUST, 96 from UoS and 67 from ODC). All potentially eligible participants were confirmed to be eligible and were invited to participate in the study.

The total number of participants included in the study, i.e. the total number of students who completed the questionnaire and whose responses were analyzed, was 369 (223 from JUST, 80 from UoS and 66 from ODC). The overall response rate was 83.3% (79.6% from JUST, 83.3% from UoS and 98.5% from ODC).

The highest proportion of participants were from JUST ( n  = 223, 60.4%), followed by UoS ( n  = 80, 21.7%), and then ODC ( n  = 66, 17.9%). The majority of the participants were females ( n  = 296, 80.4%), while males represented a smaller proportion ( n  = 73, 19.6%). It is noteworthy that these proportions reflect the size of the cohorts in each college.

With regards to the type of study, half of final-year dental students in the 3 colleges participated in observational cross-sectional studies (i.e., population-based studies) ( n  = 186, 50.4%), while literature review projects were the second most common type ( n  = 83, 22.5%), followed by experimental studies ( n  = 55, 14.9%). Longitudinal studies randomized controlled trials, and other types of studies (e.g., qualitative studies, case reports) were less common, with ( n  = 5, 1.4%), ( n  = 10, 2.7%), and ( n  = 30, 8.1%) participation rates, respectively. Distribution of study types within each college is shown Fig.  1 .

Distribution in percent of study types within each college. JUST: Jordan University of Science and Technology, UOS: University of Sharjah, ODC: Oman Dental College

The most common scope of research projects among final-year dental students was in public health/health services ( n  = 108, 29.3%) followed by dental education/attitudes of students or faculty ( n  = 103, 27.9%) (Fig.  2 ). Biomaterials/dental materials ( n  = 62, 16.8%) and restorative dentistry ( n  = 41, 11.1%) were also popular research areas. Oral diagnostic sciences (oral medicine/oral pathology/oral radiology) ( n  = 28, 7.6%), oral surgery ( n  = 12, 3.2%) and other research areas ( n  = 15, 4.1%) were less common among the participants. Thirty-two students (8.7%) were engaged in more than one research project.

Percentages of the scope of research projects among final-year dental students. JUST: Jordan University of Science and Technology, UOS: University of Sharjah, ODC: Oman Dental College

The majority of research projects were proposed through a discussion and agreement between the students and the instructor (55.0%). Instructors proposed the topic for 36.6% of the research projects, while students proposed the topic for the remaining 8.4% of the projects.

Most dental students (79.1%) performed the literature review for their research projects using internet search engines. Material provided by the instructor was used for the literature review by 15.5% of the students, while 5.4% of the students did not perform a literature review. More than half of the students ( n  = 191, 51.7%) used multiple search engines in their literature search. The most popular search engines for literature review among dental students were PubMed (70.2% of cases) and Google Scholar (68.5% of cases). Scopus was used by 12.8% of students, while other search engines were used by 15.6% of students.

The majority of dental students ( n  = 276, 74.8%) did not utilize the university library to gain access to the required material for their research. In contrast, 93 students (25.2%) reported using the university library for this purpose.

Dental students performed statistical analysis in their projects primarily by receiving help from the instructor ( n  = 167, 45.2%) or using specialized software ( n  = 168, 45.5%). A smaller percentage of students ( n  = 34, 9.4%) consulted a professional statistician for assistance with statistical analysis. at the end of the research project, 58.4% of students ( n  = 215) presented their work in the form of a manuscript or scientific paper. Other methods of presenting the work included PowerPoint presentations ( n  = 80, 21.7%) and discussions with the instructor ( n  = 74, 19.8%).

For those students who prepared a manuscript at the conclusion of their project, the most difficult part of the writing-up was the discussion section ( n  = 155, 42.0%), followed by the methodology section ( n  = 120, 32.5%), a finding that was common across the three colleges. Fewer students found the introduction ( n  = 13, 3.6%) and conclusion ( n  = 10, 2.7%) sections to be challenging. Additionally, 71 students (19.2%) were not sure which part of the manuscript was the most difficult to prepare (Fig.  3 ).

Percentages of the most difficult part reported by dental students during the writing-up of their projects. JUST: Jordan University of Science and Technology, UOS: University of Sharjah, ODC: Oman Dental College

The dental students’ perceived benefits from the research activity were evaluated across seven components, including literature review skills, research design skills, data collection and interpretation, manuscript writing, publication, teamwork and effective communication, and engagement in continuing professional development.

The majority of students found the research activity to be beneficial or highly beneficial in most of the areas, with the highest ratings observed in teamwork and effective communication, where 33.5% rated it as beneficial and 32.7% rated it as highly beneficial. Similarly, in the area of data collection and interpretation, 33.0% rated it as beneficial and 27.5% rated it as highly beneficial. In the areas of literature review skills and research design skills, 28.6% and 34.0% of students rated the research activity as beneficial, while 25.3% and 22.7% rated it as highly beneficial, respectively. Students also perceived the research activity to be helpful for the manuscript writing, with 27.9% rating it as beneficial and 19.2% rating it as highly beneficial.

When it comes to publication, students’ perceptions were more variable, with 22.0% rating it as beneficial and 11.3% rating it as highly beneficial. A notable 29.9% rated it as neutral, and 17.9% reported no benefit. Finally, in terms of engaging in continuing professional development, 26.8% of students rated the research activity as beneficial and 26.2% rated it as highly beneficial (Fig.  4 ).

Heatmap of the dental students’ perceived benefits from the research activity

The research course’s impact on students’ perspectives towards being engaged in research activities or pursuing a research career after graduation was predominantly positive, wherein 274 students (74.1%) reported a positive impact on their research perspectives. However, 79 students (21.5%) felt that the course had no impact on their outlook towards research engagement or a research career. A small percentage of students ( n  = 16, 4.4%) indicated that the course had a negative impact on their perspective towards research activities or a research career after graduation.

Finally, when students were asked about their suggestions to improve research activities, they indicated the need for more training and orientation ( n  = 127, 34.6%) as well as to allow more time for students to finish their research projects ( n  = 87, 23.6%). Participation in competitions and more generous funding were believed to be less important factors to improve students` research experience ( n  = 78, 21.2% and n  = 63, 17.1%, respectively). Other factors such as external collaborations and engagement in research groups were even less important from the students` perspective (Fig.  5 ).

Precentages of dental students’ suggestions to improve research activities at their colleges

To the best of our knowledge, this report is the first to provide a comprehensive overview of the research experience of dental students from three leading dental colleges in the Middle East region, which is home to more than 50 dental schools according to the latest SCImago Institutions Ranking ® ( https://www.scimagoir.com ). The reasonable sample size and different curricular structure across the participating colleges enhanced the value of our findings not only for dental colleges in the Middle East, but also to any dental college seeking to improve and update its undergraduate research activities. However, it is noteworthy that since the study has included only three dental schools, the generalizability of the current findings would be limited, and the outcomes are preliminary in nature.

Cross-sectional (epidemiological) studies and literature reviews represented the most common types of research among our cohort of students, which can be attributed to the feasibility, shorter time and low cost required to conduct such research projects. On the contrary, longitudinal studies and randomized trials, both known to be time consuming and meticulous, were the least common types. These findings concur with previous reports, which demonstrated that epidemiological studies are popular among undergraduate research projects [ 4 , 10 ]. In a retrospective study, Nalliah et al. also demonstrated a remarkable increase in epidemiological research concurrent with a decline in the clinical research in dental students` projects over a period of 4 years [ 4 ]. However, literature reviews, whether systematic or scoping, were not as common in some dental schools as in our cohort. For instance, a report from Sweden showed that literature reviews accounted for less than 10% of total dental students` projects [ 14 ]. Overall, qualitative research was seldom performed among our cohort, which is in agreement with a general trend in dental research that has been linked to the low level of competence and experience of dental educators to train students in qualitative research, as this requires special training in social research [ 15 , 16 ].

In terms of the research topics, public health research, research in dental education and attitudinal research were the most prevalent among our respondents. In agreement with our results, research in health care appears common in dental students` projects [ 12 ]. In general, these research domains may reflect the underlying interests of the faculty supervisors, who, in our case, were actively engaged in the selection of the research topic for more than 90% of the projects. Other areas of research, such as clinical dentistry and basic dental research are also widely reported [ 4 , 10 , 14 , 17 ].

The selection of a research domain is a critical step in undergraduate research projects, and a systematic approach in identifying research gaps and selecting appropriate research topics is indispensable and should always be given an utmost attention by supervisors [ 18 ].

More than half of the projects in the current report were reasonably selected based on a discussion between the students and the supervisor, whereas 36% were selected by the supervisors. Otuyemi et al. reported that about half of undergraduate research topics in a Nigerian dental school were selected by students themselves, however, a significant proportion of these projects (20%) were subsequently modified by supervisors [ 19 ]. The autonomy in selecting the research topic was discussed in a Swedish report, which suggested that such approach can enhance the learning experience of students, their motivation and creativity [ 20 ]. Flexibility in selecting the research topic as well as the faculty supervisor, whenever feasible, should be offered to students in order to improve their research experience and gain better outcomes [ 12 ].

Pubmed and Google Scholar were the most widely used search engines for performing a literature review. This finding is consistent with recent reviews which classify these two search systems as the most commonly used ones in biomedical research despite some critical limitations [ 21 , 22 ]. It is noteworthy that students should be competent in critical appraisal of available literature to perform the literature review efficiently. Interestingly, only 25% of students used their respective university library`s access to the search engines, which means that most students retrieved only open access publications for their literature reviews, a finding that requires attention from faculty mentors to guide students to utilize the available library services to widen their accessibility to available literature.

Statistical analysis has classically been viewed as a perceived obstacle for undergraduate students to undertake research in general [ 23 , 24 ] and recent literature has highlighted the crucial need of biomedical students to develop necessary competencies in biostatistics during their studies [ 25 ]. One obvious advantage of conducting research in our cohort is that 45.5% of students used a specialized software to analyze their data, which means that they did have at least an overview of how data are processed and analyzed to reach their final results and inferences. Unfortunately, the remaining 54.5% of students were, partially or completely, dependent on the supervisor or a professional statistician for data analysis. It is noteworthy that the research projects were appropriately tailored to the undergraduate level, focusing on fundamental statistical analysis methods. Therefore, consulting a professional statistician for more complex analyses was done only if indicated, which explains the small percentage of students who consulted a professional statistician.

Over half of participating students (58.4%) prepared a manuscript at the end of their research projects and for these students, the discussion section was identified as the most challenging to prepare, followed by the methodology section. These findings can be explained by the students’ lack of knowledge and experience related to conducting and writing-up scientific research. The same was reported by Habib et al. who found dental students’ research knowledge to be less than that of medical students [ 26 ]. The skills of critical thinking and scientific writing are believed to be of paramount importance to biomedical students and several strategies have been proposed to enhance these skills especially for both English and non-English speaking students [ 27 , 28 , 29 ].

Dental students in the current study reported positive attitude towards research and found the research activity to be beneficial in several aspects of their education, with the most significant benefits in the areas of teamwork, effective communication, data collection and interpretation, literature review skills, and research design skills. Similar findings were reported by previous studies with most of participating students reporting a positive impact of their research experience [ 4 , 10 , 12 , 30 ]. Furthermore, 74% of students found that their research experience had a positive impact on their perspectives towards engagement in research in the future. This particular finding may be promising in resolving a general lack of interest in research by dental students, as shown in a previous report from one of the participating colleges in this study (JUST), which demonstrated that only 2% of students may consider a research career in the future [ 31 ].

Notably, only 11.3% of our students perceived their research experience as being highly beneficial with regards to publication. Students` attitudes towards publishing their research appear inconsistent in literature and ranges from highly positive rates in developed countries [ 4 ] to relatively low rates in developing countries [ 8 , 32 , 33 ]. This can be attributed to lack of motivation and poor training in scientific writing skills, a finding that has prompted researchers to propose strategies to tackle such a gap as mentioned in the previous section.

Finally, key suggestions by the students to improve the research experience were the provision of more training and orientation, more time to conduct the research, as well as participation in competitions and more funding opportunities. These findings are generally in agreement with previous studies which demonstrated that dental students perceived these factors as potential barriers to improving their research experience [ 8 , 10 , 17 , 30 , 34 ].

A major limitation of the current study is the inclusion of only three dental schools from the Middle East which my limit the generalizability and validity of the findings. Furthermore, the cross-sectional nature of the study would not allow definitive conclusions to be drawn as students’ perspectives were not evaluated before and after the research project. Potential confounders in the study include the socioeconomic status of the students, the teaching environment, previous research experience, and self-motivation. Moreover, potential sources of bias include variations in the available resources and funding to students’ projects and variations in the quality of supervision provided. Another potential source of bias is the non-response bias whereby students with low academic performance or those who were not motivated might not respond to the questionnaire. This potential source of bias was managed by sending multiple reminders to students and aiming for the highest response rate and largest sample size possible.

In conclusion, the current study evaluated the key aspects of dental students’ research experience at three dental colleges in the Middle East. While there were several perceived benefits, some aspects need further reinforcement and revision including the paucity of qualitative and clinical research, the need for more rigorous supervision from mentors with focus on scientific writing skills and research presentation opportunities. Within the limitations of the current study, these outcomes can help in designing future larger scale studies and provide valuable guidance for dental colleges to foster the research component in their curricula. Further studies with larger and more representative samples are required to validate these findings and to explore other relevant elements in undergraduate dental research activities.

Data availability

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

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Acknowledgements

The authors would like to acknowledge final year dental students at the three participating colleges for their time completing the questionnaire.

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M.A.: Conceptualization, data curation, project administration; supervision, validation, writing - original draft; writing - review and editing. A.Q: Conceptualization, data curation, project administration; writing - review and editing. M.E: Conceptualization, data curation, project administration; validation, writing - original draft; writing - review and editing. A.A.: data curation, writing - original draft; writing - review and editing. L.K.: Conceptualization, data curation, validation, writing - original draft; writing - review and editing. S.A: Conceptualization, writing - review and editing.

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Alrashdan, M.S., Qutieshat, A., El-Kishawi, M. et al. Insights into research activities of senior dental students in the Middle East: A multicenter preliminary study. BMC Med Educ 24 , 967 (2024). https://doi.org/10.1186/s12909-024-05955-5

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Evaluating the Capabilities of Generative AI Tools in Understanding Medical Papers: Qualitative Study

Authors of this article:

Original Paper

• Seyma Handan Akyon 1 , MD   ; 
• Fatih Cagatay Akyon 2, 3 , PhD   ; 
• Ahmet Sefa Camyar 4 , MD   ; 
• Fatih Hızlı 5 , MD   ; 
• Talha Sari 2, 6 , BSc   ; 
• Şamil Hızlı 7 , Prof Dr, MD  

1 Golpazari Family Health Center, Bilecik, Turkey

2 SafeVideo AI, San Francisco, CA, United States

3 Graduate School of Informatics, Middle East Technical University, Ankara, Turkey

4 Department of Internal Medicine, Ankara Etlik City Hospital, Ankara, Turkey

5 Faculty of Medicine, Ankara Yildirim Beyazit University, Ankara, Turkey

6 Department of Computer Science, Istanbul Technical University, Istanbul, Turkey

7 Department of Pediatric Gastroenterology, Children Hospital, Ankara Bilkent City Hospital, Ankara Yildirim Beyazit University, Ankara, Turkey

Corresponding Author:

Seyma Handan Akyon, MD

Golpazari Family Health Center

Istiklal Mahallesi Fevzi Cakmak Caddesi No:23 Golpazari

Bilecik, 11700

Phone: 90 5052568096

Email: [email protected]

Background: Reading medical papers is a challenging and time-consuming task for doctors, especially when the papers are long and complex. A tool that can help doctors efficiently process and understand medical papers is needed.

Objective: This study aims to critically assess and compare the comprehension capabilities of large language models (LLMs) in accurately and efficiently understanding medical research papers using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist, which provides a standardized framework for evaluating key elements of observational study.

Methods: The study is a methodological type of research. The study aims to evaluate the understanding capabilities of new generative artificial intelligence tools in medical papers. A novel benchmark pipeline processed 50 medical research papers from PubMed, comparing the answers of 6 LLMs (GPT-3.5-Turbo, GPT-4-0613, GPT-4-1106, PaLM 2, Claude v1, and Gemini Pro) to the benchmark established by expert medical professors. Fifteen questions, derived from the STROBE checklist, assessed LLMs’ understanding of different sections of a research paper.

Results: LLMs exhibited varying performance, with GPT-3.5-Turbo achieving the highest percentage of correct answers (n=3916, 66.9%), followed by GPT-4-1106 (n=3837, 65.6%), PaLM 2 (n=3632, 62.1%), Claude v1 (n=2887, 58.3%), Gemini Pro (n=2878, 49.2%), and GPT-4-0613 (n=2580, 44.1%). Statistical analysis revealed statistically significant differences between LLMs ( P <.001), with older models showing inconsistent performance compared to newer versions. LLMs showcased distinct performances for each question across different parts of a scholarly paper—with certain models like PaLM 2 and GPT-3.5 showing remarkable versatility and depth in understanding.

Conclusions: This study is the first to evaluate the performance of different LLMs in understanding medical papers using the retrieval augmented generation method. The findings highlight the potential of LLMs to enhance medical research by improving efficiency and facilitating evidence-based decision-making. Further research is needed to address limitations such as the influence of question formats, potential biases, and the rapid evolution of LLM models.

Introduction

Artificial intelligence (AI) has revolutionized numerous fields, including health care, with its potential to enhance patient outcomes, increase efficiency, and reduce costs [ 1 ]. AI devices are divided into 2 main categories. One category uses machine learning techniques to analyze structured data for medical applications, while the other category uses natural language processing methods to extract information from unstructured data, such as clinical notes, thereby improving the analysis of structured medical data [ 2 ]. A key development within natural language processing has been the emergence of large language models (LLMs), which are advanced systems trained on vast amounts of text data to generate human-like language and perform a variety of language-based tasks [ 3 ]. While deep learning models recognize patterns in data [ 4 ], LLMs are trained to predict the probability of a word sequence based on the context. By training on large amounts of text data, LLMs can generate new and plausible sequences of words that the mode has not previously observed [ 4 ]. ChatGPT, an advanced conversational AI technology developed by OpenAI in late 2022, is a general-purpose LLM [ 5 ]. GPT is part of a growing landscape of conversational AI products, with other notable examples including Llama (Meta), Jurassic (Ai21), Claude (Anthropic), Command (Cohere), Gemini (formerly known as Bard), PaLM, and Bard (Google) [ 5 ]. The potential of AI systems to enhance medical care and health outcomes is highly promising [ 6 ]. Therefore, it is essential to ensure that the creation of AI systems in health care adheres to the principles of trust and explainability. Evaluating the medical knowledge of AI systems compared to that of expert clinicians is a vital initial step to assess these qualities [ 5 , 7 , 8 ].

Reading medical papers is a challenging and time-consuming task for doctors, especially when the papers are long and complex. This poses a significant barrier to efficient knowledge acquisition and evidence-based decision-making in health care. There is a need for a tool that can help doctors to process and understand medical papers more efficiently and accurately. Although LLMs are promising in evaluating patients, diagnosis, and treatment processes [ 9 ], studies on reading academic papers are limited. LLMs can be directly questioned and can generate answers from their own memory [ 10 , 11 ]. This has been extensively studied in many papers. However, these pose the problem of artificial hallucinations, which are inaccurate outputs, in LLMs. The retrieval augmented generation (RAG) method, which intuitively addresses the knowledge gap by conditioning language models on relevant documents retrieved from an external knowledge source, can be used to overcome this issue [ 12 ].

The STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist provides a standardized framework for evaluating key elements of observational study and sufficient information for critical evaluation. These guidelines consist of 22 items that authors should adhere to before submitting their manuscripts for publication [ 13 - 15 ]. This study aims to address this gap by evaluating the comprehension capabilities of LLMs in accurately and efficiently understanding medical research papers. We use the STROBE checklist to assess LLMs’ ability to understand different sections of research papers. This study uses a novel benchmark pipeline that can process PubMed papers regardless of their length using various generative AI tools. This research will provide critical insights into the strengths and weaknesses of different LLMs in enhancing medical research paper comprehension. To overcome the problem of “artificial hallucinations,” we implement the RAG method. RAG involves providing the LLMs with a prompt that instructs them to answer while staying relevant to the given document, ensuring responses align with the provided information. The results of this study will provide valuable information for medical professionals, researchers, and developers seeking to leverage the potential of LLMs for improving medical literature comprehension and ultimately enhance patient care and research efficiency.

Design of Study

This study uses a methodological research design to evaluate the comprehension capabilities of generative AI tools using the STROBE checklist.

Paper Selection

We included the first 50 observational studies conducted within the past 5 years that were retrieved through an advanced search on PubMed on December 19, 2023, using “obesity” in the title as the search term. The included studies were limited to those written in English, available as free full text, and focusing specifically on human participants ( Figure 1 ). The papers included in the study were statistically examined in detail, and a total of 11 of them were excluded because they were not observational studies. The study was completed with 39 papers. A post hoc power analysis was conducted to assess the statistical power of our study based on the total correct responses across all repetitions. The analysis excluded GPT-4-1106 and GPT-3.5-Turbo-1106 due to their similar performance and the significant differences observed between other models. The power analysis, conducted using G*Power (version 3.1.9.7; Heinrich-Heine-Universität Düsseldorf), indicated that all analyses exceeded 95% power. Thus, the study was completed with the 39 selected papers, ensuring sufficient statistical power to detect meaningful differences in LLM performance.

Benchmark Development

This study used a novel benchmark pipeline to evaluate the understanding capabilities of LLMs when processing medical research papers. To establish a reference standard for evaluating the LLMs’ comprehension, we relied on the expertise of an experienced medical professor and an epidemiology expert doctor. The professor, with their extensive medical knowledge, was tasked with answering 15 questions derived from the STROBE checklist, designed to assess key elements of observational studies and cover different sections of a research paper ( Table 1 ). The epidemiology expert doctor, with their specialized knowledge in statistical analysis and epidemiological methods, provided verification and validation of the professor’s answers, ensuring the rigor of the benchmark. The combined expertise of both professionals provided a robust and reliable reference standard against which the LLMs’ responses were compared.

This list of 15 questions, 2 multiple-choice and 13 yes or no questions, has been prepared by selecting the STROBE checklist items that can be answered definitively and have clear, nonsubjective responses. Question 1, related to title and abstract, examines the LLMs’ ability to identify and understand research designs and terms that are commonly used, evaluating the model’s comprehension of the concise language typically used in titles and abstracts. Questions 2-8, related to methods, cover various aspects of the study’s methodology, from the type of observational study to the statistical analysis programs used. They test the model’s understanding of the detailed and technical language often found in this section. Questions 9-11, related to results, focus on the accuracy and completeness of reported results, such as participant numbers at each study stage and demographic characteristics. These questions gauge the LLMs’ capability to parse and summarize factual data. Questions 12-14, related to the discussion, involve summarizing key results, discussing limitations, and addressing the study’s generalizability. These questions assess the LLMs’ ability to engage with more interpretive and evaluative content, showcasing their understanding of research impacts and contexts. Question 15, related to funding, tests the LLMs’ attentiveness to specific yet crucial details that could influence the interpretation of research findings.

Development of Novel RAG-Based LLM Web Application

The methodology incorporated a novel web application specifically designed for this purpose to assess the understanding capabilities of generative AI tools in medical research papers ( Figure 2 ). To mitigate the problem of “artificial hallucinations” inherent to LLMs, this study implemented the RAG method, which involves using a web application to dissect PDF-format medical papers from PubMed into text chunks ready to be processed by various LLMs. This approach guides the LLMs to provide answers grounded in the provided information by supplying them with relevant text chunks retrieved from the target paper.

Benchmark Pipeline

The benchmark pipeline itself is designed to process PubMed papers of varying lengths and extract relevant information for analysis. This pipeline operates as follows:

• Paper retrieval: We retrieved 39 observational studies from PubMed using the search term “obesity” in the title.
• Text extraction and chunking: Each retrieved PubMed paper was converted to PDF format and then processed through our web application. The application extracts all text content from the paper and divides it into smaller text chunks of manageable size.
• Vector representation: Using the OpenAI text-ada-embedding-002 model, each text chunk was converted into a representation vector. These vectors capture the semantic meaning of the text chunks, allowing for efficient information retrieval.
• Vector database storage: The generated representation vectors were stored in a vector database (LanceDB in our case). This database allows for rapid searching and retrieval of the most relevant text chunks based on a given query.
• Query processing: When a query (question from the STROBE checklist) was posed to an LLM, our pipeline calculated the cosine similarities between the query’s representation vector and the vectors stored in the database. This identified the most relevant text chunks from the paper.
• RAG: The retrieved text chunks, along with the original query, were then combined and presented to the LLM. This approach, known as RAG, ensured that the LLM’s responses were grounded in the specific information present in the paper, mitigating the risk of hallucinations.
• Answer generation and evaluation: The LLM generated an answer to the query based on the provided text chunks. The accuracy of each LLM’s response was then evaluated by comparing it to the benchmark answers provided by a medical professor.

Using this benchmark pipeline, we compared the answers of the generative AI tools, such as GPT-3.5-Turbo-1106 (June 11th version), GPT-4-0613 (November 6th version), GPT-4-1106 (June 11th version), PaLM 2 (chat-bison), Claude v1, and Gemini Pro, with the benchmark in 15 questions for 39 medical research papers ( Table 2 ). In this study, 15 questions selected from the STROBE checklists were posed 10 times each for 39 papers to 6 different LLMs.

a The company does not explicitly state a cutoff date.

Access issues with Claude v1, specifically restrictions on its ability to process certain medical information, resulted in the exclusion of data from 6 papers, limiting the study’s scope to 33 papers. LLMs commonly provide a “knowledge-cutoff” date, indicating the point at which their training data ends and they may not have access to the most up-to-date information. With some LLMs, however, the company does not explicitly state a cutoff date. The explicitly stated cutoff dates are given in Table 2 , based on the publicly available information for each LLM.

A chatbot conversation begins when a user enters a query, often called a system prompt. The chatbot responds in natural language within a second, creating an interactive, conversation-like exchange. This is possible because the chatbot understands context. In addition to the RAG method, providing LLMs with well-designed system prompts that guide them to stay relevant to a given document can help generate responses that align with the provided information. We used the following system prompt for all LLMs:

You are an expert medical professor specialized in pediatric gastroenterology hepatology and nutrition, with a detailed understanding of various research methodologies, study types, ethical considerations, and statistical analysis procedures. Your task is to categorize research articles based on information provided in query prompts. There are multiple options for each question, and you must select the most appropriate one based on your expertise and the context of the research article presented in the query.

The language models used in this study rely on statistical models that incorporate random seeds to facilitate the generation of diverse outputs. However, the companies behind these LLMs do not offer a stable way to fix these seeds, meaning that a degree of randomness is inherent in their responses. To further control this randomness, we used the “temperature” parameter within the language models. This parameter allows for adjustment of the level of randomness, with a lower temperature setting generally producing more deterministic outputs. For this study, we opted for a low-temperature parameter setting of 0.1 to minimize the impact of randomness. Despite these efforts, complete elimination of randomness is not possible. To further mitigate its effects and enhance the consistency of our findings, we repeated each question 10 times for the same language model. By analyzing the responses across these 10 repetitions, we could determine the frequency of accurate and consistent answers. This approach helped to identify instances where the LLM’s responses were consistently aligned with the benchmark answers, highlighting areas of strength and consistency in comprehension.

Statistical Analysis

Each question was repeated 10 times in the same time period to obtain answers from multiple LLMs and ensure the consistency and reliability of responses. Consequently, the responses to the same question were analyzed to determine how many aligned with the benchmark, and the findings were examined. Only the answers that were correct and followed the instructions provided in the question text were considered “correct.” Ambiguous answers, evident mistakes, and responses with an excessive number of candidates were considered incorrect. The data were carefully examined, and the findings were documented and analyzed. Each inquiry and its response formed the basis of the analysis. Various descriptive statistical tests were used to assess the data presented as numbers and percentages. The Shapiro-Wilk test was used to assess the data’s normal distribution. The Kruskal-Wallis and Pearson chi-square tests were used in the statistical analysis. Type I error level was accepted as 5% in the analyses performed using the SPSS (version 29.0; IBM Corp).

Ethical Considerations

This study only used information that had already been published on the internet. Ethics approval is not required for this study since it did not involve any human or animal research participants. This study did not involve a clinical trial, as it focused on evaluating the capabilities of AI tools in understanding medical papers.

In this study, 15 questions selected from the STROBE checklists were posed 10 times each for 39 papers to 6 different LLMs. Access issues with Claude v1, specifically restrictions on its ability to process certain medical information, resulted in the exclusion of data from 6 papers, limiting the study’s scope to 33 papers. The percentage of correct answers for each LLM is shown in Table 3 , with GPT-3.5-Turbo achieving the highest rate (n=3916, 66.9%), followed by GPT-4-1106 (n=3837, 65.6%), PaLM 2 (n=3632, 62.1%), Claude v1 (n=2887, 58.3%), Gemini Pro (n=2878, 49.2%), and GPT-4-0613 (n=2580, 44.1%).

Each LLM was compared with another LLM that provided a lower percentage of correct answers. Statistical analysis using the Kruskal-Wallis test revealed statistically significant differences between the LLMs ( P <.001). The lowest correct answer percentage was provided by GPT-4-0613, at 44.1% (n=2580). Gemini Pro yielded 49.2% (n=2878) correct answers, significantly higher than GPT-4-0613 ( P <.001). Claude v1 yielded 58.3% (n=2887) correct answers, statistically significantly higher than Gemini Pro ( P <.001). PaLM 2 achieved 62.1% (n=3632) correct answers, significantly higher than Claude v1 ( P <.001). GPT-4-1106 achieved 65.6% (n=3837) correct answers, significantly higher than PaLM 2 ( P <.001). The difference between GPT-4-1106 and GPT-3.5-Turbo-1106 was not statistically significant ( P =.06). Of the 39 papers analyzed, 28 (71.8%) were published before the training data cutoff date for GPT-3.5-Turbo and GPT-4-0613, while all 39 (100%) papers were published before the cutoff date for GPT-4-1106. Explicit cutoff dates for the remaining LLMs (Claude, PaLM 2, and Gemini Pro) were not publicly available and therefore could not be assessed in this study. When all LLMs are collectively considered, the 3 questions receiving the highest percentage of correct answers were question 12 (n=4025, 68.3%), question 13 (n=3695, 62.8%), and question 10 (n=3565, 60.5%). Conversely, the 3 questions with the lowest percentage of correct responses were question 8 (n=1971, 33.5%), question 15 (n=2107, 35.8%), and question 1 (n=2147, 36.5%; Table 4 ).

The percentages of correct answers given by all LLMs for each question are depicted in Figure 3 . The median values for questions 7, 8, 9, 10, and 14 were similar across all LLMs, indicating a general consistency in performance for these specific areas of comprehension. However, significant differences were observed in the performance of different LLMs for other questions. The statistical tests used in this analysis were the Kruskal-Wallis test for comparing the medians of multiple groups and the chi-square test for comparing categorical data. For question 1, the fewest correct answers were provided by Claude (n=124, 24.8%) and Gemini Pro (n=197, 39.5%), while the most correct answers were provided by PaLM 2 (n=301, 60.3%; P =.01). In question 2, Claude v1 (n=366, 73.3%) achieved the highest median correct answer count (10.0, IQR 5.0-10.0), while Gemini Pro provided the fewest correct answers (n=237, 47.4%; P =.03). For question 3, GPT-3.5 (n=425, 85.1%) and PaLM 2 (n=434, 86.8%) had the highest median correct answer counts, while GPT-4-0613 (n=164, 32.8%) and Gemini Pro (n=189, 37.9%) had the lowest ( P <.001). In the fourth question, PaLM 2 (n=369, 73.8%), GPT-3.5 (n=293, 58.7%), and GPT-4-1106 (n=336, 67.2%) performed best, while GPT-4-0613 (n=187, 37.4%) showed the lowest performance ( P <.001). For questions 5 and 6, GPT-4-0613 (n=209, 41.8%) and Gemini Pro (n=186, 37.2%) provided fewer correct answers compared to the other LLMs ( P <.001 and P =.001, respectively). In question 11, GPT-4-1106 (n=406, 81.2%), Claude (n=347, 69.4%), and PaLM 2 (n=406, 81.2%) performed well, while Gemini Pro (n=264, 52.8%) had the fewest correct answers ( P =.001). For questions 12 and 13, all LLMs, except GPT-4-0613, performed well in these areas ( P <.001). In question 15, GPT-3.5 (n=368, 73.6%) showed the highest number of correct answers ( P <.001; Multimedia Appendix 1 ).

Principal Findings

AI can improve the data analysis and publication process in scientific research while also being used to generate medical papers [ 16 ]. Although these fraudulent papers may appear well-crafted, their semantic inaccuracies and errors can be detected by expert readers upon closer examination [ 11 , 17 ]. The impact of LLMs on health care is often discussed in terms of their ability to replace health professionals, but their significant impact on medical and research writing applications and limitations is often overlooked. Therefore, physicians involved in research need to be cautious and verify information when using LLMs. As their reliance can lead to ethical concerns and inaccuracies, the scientific community should be vigilant in ensuring the accuracy and reliability of AI tools by using them as aids rather than replacements, understanding their limitations and biases [ 10 , 18 ]. With millions of papers published annually, AI could generate summaries or recommendations, simplifying the process of gathering evidence and enabling researchers to grasp important aspects of scientific results more efficiently [ 18 ]. Moreover, there is limited research focused on assessing the comprehension of academic papers.

This study aimed to evaluate the ability of 6 different LLMs to understand medical research papers using the STROBE checklist. We used a novel benchmark pipeline that processed 39 PubMed papers, posing 15 questions derived from the STROBE checklist to each model. The benchmark was established using the answers provided by an experienced medical professor and validated by an epidemiologist, serving as a reference standard against which the LLMs’ responses were compared. To mitigate the problem of “artificial hallucinations” inherent to LLMs, our study implemented the RAG method, which involves using a web application to dissect PDF-format medical papers into text chunks and present them to the LLMs.

Our findings reveal significant variation in the performance of different LLMs, suggesting that LLMs are capable of understanding medical papers to varying degrees. While newer models like GPT-3.5-Turbo and GPT-4-1106 generally demonstrated better comprehension, GPT-3.5-Turbo outperformed even the more recent GPT-4-0613 in certain areas. This unexpected finding highlights the complexity of LLM performance, indicating that simple assumptions about newer models consistently outperforming older ones may not always hold true. The impact of training data cutoffs on LLM performance is a critical consideration in evaluating their ability to understand medical research [ 19 ]. While we were able to obtain explicitly stated cutoff dates for GPT-3.5-Turbo, GPT-4-1106, and GPT-4-0613, this information was not readily available for the remaining models. This lack of transparency regarding training data limits our ability to definitively assess the impact of knowledge cutoffs on model performance. The observation that all 39 papers were published before the cutoff date for GPT-4-1106, while only 28 papers were published before the cutoff date for GPT-3.5-Turbo and GPT-4-0613, suggests that the knowledge cutoff may play a role in the observed performance differences. GPT-4-1106, with a more recent knowledge cutoff, has access to a larger data set, potentially including information from more recently published research. This could contribute to its generally better performance compared to GPT-3.5-Turbo. However, it is important to note that GPT-3.5-Turbo still outperformed GPT-4-0613 in specific areas, even with a similar knowledge cutoff. This suggests that factors beyond training data (eg, the number of layers, the type of attention mechanism, or the use of transformers) and compression techniques (eg, quantization, pruning, or knowledge distillation) may also play a significant role in LLM performance. Future research should prioritize transparency regarding training data cutoffs and aim to standardize how LLMs communicate these crucial details to users.

This study evaluated the performance of various LLMs in accurately answering specific questions related to different sections of a scholarly paper: title and abstract, methods, results, discussion, and funding. The results shed light on which LLMs excel in specific areas of comprehension and information retrieval from academic texts. PaLM 2 (n=219, 60.3%) showed superior performance in question 1, identifying the study design from the title or abstract, suggesting enhanced capability in understanding and identifying specific terminologies. Claude (n=82, 24.8%) and Gemini Pro (n=154, 39.5%), however, lagged, indicating a potential area for improvement in terminology recognition and interpretation. Claude v1 (n=242, 73.3%) and PaLM 2 (n=295, 86.8%) exhibited strong capabilities in identifying methodological details, such as observational study types and settings or locations (questions 2-8). This suggests a robust understanding of complex methodological descriptions and the ability to distinguish between different study frameworks. For questions regarding the results section (questions 9-11), it is evident that models like GPT-4-1106 (n=317, 81.3%), Claude (n=229, 69.4%), and PaLM 2 (n=276, 81.2%) showed superior performance in providing correct answers related to the study participants’ demographic characteristics and the use of flowcharts. All LLMs except for GPT4-0613 (n=89, 22.8%) exhibited remarkable competence in summarizing key results, discussing limitations, and addressing the generalizability of the study (questions 12-14), which are critical aspects of the discussion section. GPT-3.5 (n=287, 73.6%) particularly excelled in identifying the mention of funding (question 15), indicating a nuanced understanding of acknowledgments and funding disclosures often nuanced and embedded toward the end of papers. Across the array of tested questions, both GPT-3.5 and PaLM 2 exhibit remarkable strengths in understanding and analyzing scholarly papers, with PaLM 2 generally showing a slight edge in versatility, especially in interpreting methodological details and study design. GPT-3.5, while strong in discussing study limitations, generalized findings, and funding details, indicates that improvements can be made in extracting complex methodological information. We observed that different models excelled in different areas, indicating that no single LLM currently demonstrates universal dominance in medical paper understanding. This suggests that factors like training data, model architecture, and question complexity influence performance, and further research is needed to understand the specific contributions of each factor.

Comparison to Prior Work

LLMs can be directly questioned and can generate answers from their own memory [ 11 ]. This has been extensively studied in many medical papers . According to a study, ChatGPT, an LLM, was evaluated on the United States Medical Licensing Examination. The results showed that GPT performed at or near the passing threshold for examinations without any specialized training, demonstrating a high level of concordance and insight in its explanations. These findings suggest that LLMs have the potential to aid in medical education and potentially assist with clinical decision-making [ 5 , 20 ]. Another study aimed to evaluate the knowledge level of GPT in medical education by assessing its performance in a multiple-choice question examination and its potential impact on the medical examination system. The results indicated that GPT achieved a satisfactory score in both basic and clinical medical sciences, highlighting its potential as an educational tool for medical students and faculties [ 21 ]. Furthermore, GPT offers information and aids health care professionals in diagnosing patients by analyzing symptoms and suggesting appropriate tests or treatments. However, advancements are required to ensure AI’s interpretability and practical implementation in clinical settings [ 8 ]. The study conducted in October 2023 explored the diagnostic capabilities of GPT-4V, an AI model, in complex clinical scenarios involving medical imaging and textual patient data. Results showed that GPT-4V had the highest diagnostic accuracy when provided with multimodal inputs, aligning with confirmed diagnoses in 80.6% of cases [ 22 ]. In another study, GPT-4 was instructed to address the case with multiple-choice questions followed by an unedited clinical case report that evaluated the effectiveness of the newly developed AI model GPT-4 in solving complex medical case challenges. GPT-4 correctly diagnosed 57% of the cases, outperforming 99.98% of human readers who were also tasked with the same challenge [ 23 ]. These studies highlight the potential of multimodal AI models like GPT-4 in clinical diagnostics, but further investigation is needed to uncover biases and limitations due to the model’s proprietary training data and architecture.

There are few studies in which LLMs are directly questioned, and their capacities to produce answers from their own memories are compared with each other and expert clinicians. In a study, GPT-3.5 and GPT-4 were compared to orthopedic residents in their performance on the American Board of Orthopaedic Surgery written examination, with residents scoring higher overall, and a subgroup analysis revealed that GPT-3.5 and GPT-4 outperformed residents in answering text-only questions, while residents scored higher in image interpretation questions. GPT-4 scored higher than GPT-3.5 [ 24 ]. A study aimed to evaluate and compare the recommendations provided by GPT-3 and GPT-4 with those of primary care physicians for the management of depressive episodes. The results showed that both GPT-3.5 and GPT-4 largely aligned with accepted guidelines for treating mild and severe depression while demonstrating a lack of gender or socioeconomic biases observed among primary care physicians. However, further research is needed to refine the AI recommendations for severe cases and address potential ethical concerns and risks associated with their use in clinical decision-making [ 25 ]. Another study assessed the accuracy and comprehensiveness of health information regarding urinary incontinence generated by various LLMs. By inputting selected questions into GPT-3.5, GPT-4, and Gemini, the researchers found that GPT-4 performed the best in terms of accuracy and comprehensiveness, surpassing GPT-3.5 and Gemini [ 26 ]. According to a study that evaluates the performance of 2 GPT models (GPT-3.5 and GPT-4) and human professionals in answering ophthalmology questions from the StatPearls question bank, GPT-4 outperformed both GPT-3.5 and human professionals on most ophthalmology questions, showing significant performance improvements and emphasizing the potential of advanced AI technology in the field of ophthalmology [ 27 ]. Some studies showed that GPT-4 is more proficient, as evidenced by scoring higher than GPT-3.5 in both multiple-choice dermatology examinations and non–multiple-choice cardiology heart failure questions from various sources and outperforming GPT-3.5 and Flan-PaLM 540B on medical competency assessments and benchmark data sets [ 28 - 30 ]. In a study conducted on the proficiency of various open-source and proprietary LLMs in the context of nephrology multiple-choice test-taking ability, it was found that their performance on 858 nephSAP questions ranged from 17.1% to 30.6%, with Claude 2 at 54.4% accuracy and GPT-4 at 73.3%, highlighting the potential for adaptation in medical training and patient care scenarios [ 31 ]. To our knowledge, this is the first study to assess the performance of evaluating medical papers and understanding the capabilities of different LLMs. The findings reveal that the performance of LLMs varies across different questions, with some LLMs showing superior understanding and answer accuracy in certain areas. Comparative analysis across different LLMs showcases a gradient of capabilities. The results revealed a hierarchical performance ranking as follows: GPT-4-1106 equals GPT-3.5-Turbo, which is superior to PaLM 2, followed by Claude v1, then Gemini Pro, and finally, GPT-4-0613. Similar to the literature review, GPT-4-1106 and GPT-3.5 showed improved accuracy and understanding compared to other LLMs. This mirrors wider literature trends, indicating LLMs’ rapid evolution and increasing sophistication in handling complex medical queries. Notably, GPT-3.5-Turbo showed better performance than GPT-4-0613, which may be counterintuitive, considering the tendency to assume newer iterations naturally perform better. This anomaly in performance between newer and older versions can be attributed to the application of compression techniques in developing new models to reduce computational costs. While these advancements make deploying LLMs more cost-effective and thus accessible, they can inadvertently compromise the performance of LLMs. The notable absence of responses from PaLM in certain instances, actually stemming from Google’s policy to restrict the use of its medical information, presents an intriguing case within the scope of our discussion. Despite these constraints, PaLM’s demonstrated high performance in other areas is both surprising and promising. This suggests that even when faced with limitations on accessing a vast repository of medical knowledge, PaLM’s underlying architecture and algorithms enable it to make effective use of the information it can access, showcasing the robust potential of LLMs in medical settings even under restricted conditions.

Strengths and Limitations

While LLMs can be directly questioned and generate answers from their own memory, as demonstrated in numerous studies above, this approach can lead to inaccuracies known as hallucinations. Hallucinations in LLMs have diverse origins, encompassing the entire spectrum of the capability acquisition process, with hallucinations primarily categorized into 3 aspects: training, inference, and data. Architecture flaws, exposure bias, and misalignment issues in both pretraining and alignment phases induce hallucinations. To address this challenge, our study used the RAG method, ensuring that the LLMs’ responses were grounded in factual information retrieved from the target paper. The RAG method intuitively addresses the knowledge gap by conditioning language models on relevant documents retrieved from an external knowledge source [ 12 , 32 ]. RAG provides the LLM with relevant text chunks extracted from the specific paper being analyzed. This ensures that the LLM’s responses are directly supported by the provided information, reducing the risk of hallucination. While a few studies have explored the use of RAG to compare LLMs, like the one demonstrating GPT-4’s improved accuracy with RAG for interpreting oncology guidelines [ 33 ], our study is the first to evaluate LLM comprehension of medical research papers using this method. This method conditions LLMs on relevant documents retrieved from an external knowledge source, ensuring their answers are grounded in factual information. The design of system prompts is crucial for LLMs, as it provides context, instructions, and formatting guidelines to ensure the desired output [ 34 ]. In this study, it is empirically determined that a foundational system and set of system prompts universally enhanced the response quality across all language models tested. This approach was designed to optimize the comprehension and summarization capabilities of each generative AI tool when processing medical research papers. The specific configuration of system settings and query structures we identified significantly contributed to improving the accuracy and relevance of the models’ answers. These optimized parameters were crucial in achieving a more standardized and reliable evaluation of each model’s ability to understand complex medical texts. While further research is needed to fully understand the effectiveness of RAG across different medical scenarios, our findings demonstrate its potential to enhance the reliability and accuracy of LLMs in medical research comprehension.

This study, while offering valuable insights, is subject to several limitations. The selection of 50 papers focused on obesity, and the use of a specific set of 15 STROBE-derived questions might not fully capture the breadth of medical research. Additionally, the reliance on binary and multiple-choice questions restricts the evaluation of LLMs’ ability to provide nuanced answers. The rapid evolution of LLMs means that the findings might not be applicable to future versions, and potential biases within the training data have not been systematically assessed. Furthermore, the study’s reliance on a single highly experienced medical professor as the benchmark, while evaluating, might limit the generalizability of the findings. A larger panel of experts with diverse areas of specialization might provide a more comprehensive reference standard for evaluating LLM performance. Further investigation with a wider scope and more advanced methodologies is needed to fully understand the potential of LLMs in medical research.

Future Directions

In conclusion, LLMs show promise for transforming medical research, potentially enhancing research efficiency and evidence-based decision-making. This study demonstrates that LLMs exhibit varying capabilities in understanding medical research papers. While newer models generally demonstrate better comprehension, no single LLM currently excels in all areas. This highlights the need for further research to understand the complex interplay of factors influencing LLM performance. Continued research is crucial to address these limitations and ensure the safe and effective integration of LLMs in health care, maximizing their benefits while mitigating risks.

Acknowledgments

The authors gratefully acknowledge Dr Hilal Duzel for her invaluable assistance in validating the reference standard used in this study. Dr Duzel’s expertise in epidemiology and statistical analysis ensured the accuracy and robustness of the benchmark against which the LLMs were evaluated. We would also like to thank Ahmet Hamza Dogan, a promising future engineer, for his contributions to the LLM analysis.

Conflicts of Interest

None declared.

Percentages of correct answers by large language models for each question.

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Abbreviations

Edited by A Castonguay; submitted 07.04.24; peer-reviewed by C Wang, S Mao, W Cui; comments to author 04.06.24; revised version received 16.06.24; accepted 05.07.24; published 04.09.24.

©Seyma Handan Akyon, Fatih Cagatay Akyon, Ahmet Sefa Camyar, Fatih Hızlı, Talha Sari, Şamil Hızlı. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 04.09.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.

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The case study as a type of qualitative research JOURNAL OF CONTEMPORARY EDUCATIONAL STUDIES 1/2013, 28-43

This article presents the case study as a type of qualitative research. Its aim is to give a detailed description of a case study-its definition, some classifications, and several advantages and disadvantages-in order to provide a better understanding of this widely used type of qualitative approac h. In comparison to other types of qualitative research, case studies have been little understood both from a methodological point of view, where disagreements exist about whether case studies should be considered a research method or a research type, and from a content point of view, where there are ambiguities regarding what should be considered a case or research subject. A great emphasis is placed on the disadvantages of case studies, where we try to refute some of the criticisms concerning case studies, particularly in comparison to quantitative research approaches.

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Nikhil Chandra Shil, FCMA

Nina Oktavia

Case study is believed as the widely used kind of research to view phenomena, despite of some critics on it concerning mostly on its data reliability, validity and subjectivity. This article therefore discusses some aspects of case study which are considered important to be recognized by novice researchers, especially about the way how to design and how to make sure the quality and reliability of the case. In addition, the case studying educational research also becomes the focus to be discussed, completed with some examples, to be able to open our mind to the plenty opportunities for case study in education.

Waleed Alabri

Journal of Interdisciplinary Studies in Education

Fernando Almeida

The case study is a widely used method in qualitative research. Although defining the case study can be simple, it is complex to develop its strategy. Furthermore, it is still often not considered to be a sufficiently robust research strategy in the education field because it does not offer well-defined and use well-structured protocols. One of the most frequent criticisms associated with the case study approach is its low validity and reliability. In this sense, this study aims to concisely explore the main difficulties inherent to the process of developing a case study, also attempting to suggest some practices that can increase its reliability, construct validity, internal and external validity. Qualitative research methodologies broadly describe a set of strategies and methods that have similar characteristics to each other. In a qualitative methodology, we have an interactive model of data collection

Thabit Alomari

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Dynamic Bayesian networks for spatiotemporal modeling and its uncertainty in tradeoffs and synergies of ecosystem services: a case study in the Tarim River Basin, China

• ORIGINAL PAPER
• Published: 02 September 2024

Cite this article

• Yang Hu 1 , 2 ,
• Jie Xue 2 , 3 , 4 ,
• Jianping Zhao 1 ,
• Xinlong Feng 1 ,
• Huaiwei Sun 5 ,
• Junhu Tang 6 &
• Jingjing Chang 2  

Ecosystem services (ESs) refer to the benefits that humans obtain from ecosystems. These services are subject to environmental changes and human interventions, which introduce a significant level of uncertainty. Traditional ES modeling approaches often employ Bayesian networks, but they fall short in capturing spatiotemporal dynamic change processes. To address this limitation, dynamic Bayesian networks (DBNs) have emerged as stochastic models capable of incorporating uncertainty and capturing dynamic changes. Consequently, DBNs have found increasing application in ES modeling. However, the structure and parameter learning of DBNs present complexities within the field of ES modeling. To mitigate the reliance on expert knowledge, this study proposes an algorithm for structure and parameter learning, integrating the InVEST (Integrated Valuation of Ecosystem Services and Trade-Offs) model with DBNs to develop a comprehensive understanding of the spatiotemporal dynamics and uncertainty of ESs in the Tarim River Basin, China from 2000 to 2020. The study further evaluates the tradeoffs and synergies among four key ecosystem services: water yield, habitat quality, sediment delivery ratio, and carbon storage and sequestration. The findings show that (1) the proposed structure learning and parameter learning algorithm for DBNs, including the hill-climb algorithm, linear analysis, the Markov blanket, and the EM algorithm, effectively address subjective factors that can influence model learning when dealing with uncertainty; (2) significant spatial heterogeneity is observed in the supply of ESs within the Tarim River Basin, with notable changes in habitat quality, water yield, and sediment delivery ratios occurring between 2000–2005, 2010–2015, and 2015–2020, respectively; (3) tradeoffs exist between water yield and habitat quality, as well as between soil conservation and carbon sequestration, while synergies are found among habitat quality, soil retention, and carbon sequestration. The land-use type emerges as the most influential factor affecting the tradeoffs and synergies of ESs. This study serves to validate the capacity of DBNs in addressing spatiotemporal dynamic changes and establishes an improved research methodology for ES modeling that considers uncertainty.

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Acknowledgements

This work was financially supported by National Natural Science Foundation of China (42071259), the Tianshan Talents Program of Xinjiang Uygur Autonomous Region (2022TSYCJU0002), the original innovation project of the basic frontier scientific research program, Chinese Academy of Sciences (ZDBS-LY-DQC031), the Natural Science Foundation of Xinjiang Uygur Autonomous Region (2021D01E01), the water system evolution and risk assessment in arid regions for original innovation project of institute (2023–2025), and the Outstanding Member of the Youth Innovation Promotion Association of the Chinese Academy of Sciences (2019430) (2024-2026). We are also grateful to three anonymous referees for their constructive comments in this manuscript.

This work was supported by National Natural Science Foundation of China (Grant number: 42071259).

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College of Mathematics and System Science, Xinjiang University, Urumqi, 830046, China

Yang Hu, Jianping Zhao & Xinlong Feng

State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, Xinjiang, China

Yang Hu, Jie Xue & Jingjing Chang

Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Cele, 848300, Xinjiang, China

University of Chinese Academy of Sciences, Beijing, 100049, China

School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

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College of Ecology and Environment, Xinjiang University, Urumqi, 830046, China

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Yang Hu: conceptualization, methodology, software, validation, formal analysis, writing—original draft. Jie Xue, Jianping Zhao, Xinlong Feng, and Huaiwei Sun: conceptualization, methodology, supervision, writing—review & editing. Junhu Tang and Jingjing Chang: data curation, visualization.

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Hu, Y., Xue, J., Zhao, J. et al. Dynamic Bayesian networks for spatiotemporal modeling and its uncertainty in tradeoffs and synergies of ecosystem services: a case study in the Tarim River Basin, China. Stoch Environ Res Risk Assess (2024). https://doi.org/10.1007/s00477-024-02805-0

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DOI : https://doi.org/10.1007/s00477-024-02805-0

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