case study survey research

Case Study vs. Survey

What's the difference.

Case studies and surveys are both research methods used in various fields to gather information and insights. However, they differ in their approach and purpose. A case study involves an in-depth analysis of a specific individual, group, or situation, aiming to understand the complexities and unique aspects of the subject. It often involves collecting qualitative data through interviews, observations, and document analysis. On the other hand, a survey is a structured data collection method that involves gathering information from a larger sample size through standardized questionnaires. Surveys are typically used to collect quantitative data and provide a broader perspective on a particular topic or population. While case studies provide rich and detailed information, surveys offer a more generalizable and statistical overview.

Further Detail

Introduction.

When conducting research, there are various methods available to gather data and analyze it. Two commonly used methods are case study and survey. Both approaches have their own unique attributes and can be valuable in different research contexts. In this article, we will explore the characteristics of case study and survey, highlighting their strengths and limitations.

A case study is an in-depth investigation of a particular individual, group, or phenomenon. It involves collecting detailed information about the subject of study through various sources such as interviews, observations, and document analysis. Case studies are often used in social sciences, psychology, and business research to gain a deep understanding of complex issues.

One of the key attributes of a case study is its ability to provide rich and detailed data. Researchers can gather extensive information about the subject, including their background, experiences, and perspectives. This depth of data allows for a comprehensive analysis and interpretation of the case, providing valuable insights into the phenomenon under investigation.

Furthermore, case studies are particularly useful when studying rare or unique cases. Since case studies focus on specific individuals or groups, they can shed light on situations that are not easily replicated or observed in larger populations. This makes case studies valuable in exploring complex and nuanced phenomena that may not be easily captured through other research methods.

However, it is important to note that case studies have certain limitations. Due to their in-depth nature, case studies are often time-consuming and resource-intensive. Researchers need to invest significant effort in data collection, analysis, and interpretation. Additionally, the findings of a case study may not be easily generalized to larger populations, as the focus is on a specific case rather than a representative sample.

Despite these limitations, case studies offer a unique opportunity to explore complex issues in real-life contexts. They provide a detailed understanding of individual experiences and can generate hypotheses for further research.

A survey is a research method that involves collecting data from a sample of individuals through a structured questionnaire or interview. Surveys are widely used in social sciences, market research, and public opinion studies to gather information about a larger population. They aim to provide a snapshot of people's opinions, attitudes, behaviors, or characteristics.

One of the main advantages of surveys is their ability to collect data from a large number of respondents. By reaching out to a representative sample, researchers can generalize the findings to a larger population. Surveys also allow for efficient data collection, as questionnaires can be distributed electronically or in person, making it easier to gather a wide range of responses in a relatively short period.

Moreover, surveys offer a structured approach to data collection, ensuring consistency in the questions asked and the response options provided. This allows for easy comparison and analysis of the data, making surveys suitable for quantitative research. Surveys can also be conducted anonymously, which can encourage respondents to provide honest and unbiased answers, particularly when sensitive topics are being explored.

However, surveys also have their limitations. One of the challenges is the potential for response bias. Respondents may provide inaccurate or socially desirable answers, leading to biased results. Additionally, surveys often rely on self-reported data, which may be subject to memory recall errors or misinterpretation of questions. Researchers need to carefully design the survey instrument and consider potential biases to ensure the validity and reliability of the data collected.

Furthermore, surveys may not capture the complexity and depth of individual experiences. They provide a snapshot of people's opinions or behaviors at a specific point in time, but may not uncover the underlying reasons or motivations behind those responses. Surveys also rely on predetermined response options, limiting the range of possible answers and potentially overlooking important nuances.

Case studies and surveys are both valuable research methods, each with its own strengths and limitations. Case studies offer in-depth insights into specific cases, providing rich and detailed data. They are particularly useful for exploring complex and unique phenomena. On the other hand, surveys allow for efficient data collection from a large number of respondents, enabling generalization to larger populations. They provide structured and quantifiable data, making them suitable for statistical analysis.

Ultimately, the choice between case study and survey depends on the research objectives, the nature of the research question, and the available resources. Researchers need to carefully consider the attributes of each method and select the most appropriate approach to gather and analyze data effectively.

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Case Study – Methods, Examples and Guide

Table of Contents

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

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

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

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

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

Multiple-Case Study

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

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

Exploratory Case Study

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

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

Descriptive Case Study

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

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

Instrumental Case Study

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

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

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

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

Observations

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

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

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

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

How to conduct Case Study Research

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

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

Examples of Case Study

Here are some examples of case study research:

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

Application of Case Study

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

Business and Management

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

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

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

Social Sciences

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

Law and Ethics

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

Purpose of Case Study

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

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

Case studies can also serve other purposes, including:

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

Advantages of Case Study Research

There are several advantages of case study research, including:

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

Limitations of Case Study Research

There are several limitations of case study research, including:

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

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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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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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Integrating case study and survey research methods: an example in information systems

• Original Article
• Published: 01 January 1994
• Volume 3 , pages 112–126, ( 1994 )

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• G.G. Gable 1  

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The case for combining research methods generally, and more specifically that for combining qualitative and quantitative methods, is strong. Yet, research designs that extensively integrate both fieldwork (e.g. case studies) and survey research are rare. Moreover, some journals tend tacitly to specialise by methodology thereby encouraging purity of method. The multi-method model of research, while not new, has not been appreciated. In this respect it is useful to describe its usage through example. By reference to a recently completed study of IS consultant engagement success factors this paper presents an analysis of the benefits of integrating case study and survey research methods. The emphasis is on the qualitative case study method and how it can complement more quantitative survey research. Benefits are demonstrated through specific examples from the reference study.

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Gable, G. Integrating case study and survey research methods: an example in information systems. Eur J Inf Syst 3 , 112–126 (1994). https://doi.org/10.1057/ejis.1994.12

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Organizing Your Social Sciences Research Assignments

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

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

How to Approach Writing a Case Study Research Paper

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

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

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

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

Structure and Writing Style

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

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

I.  Introduction

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

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

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

II.  Literature Review

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

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

III.  Method

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

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

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

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

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

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

IV.  Discussion

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

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

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

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

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

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

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

V.  Conclusion

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

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

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

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

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

Problems to Avoid

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

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

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

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

Writing Tip

At Least Five Misconceptions about Case Study Research

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

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

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

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

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• Doing Survey Research | A Step-by-Step Guide & Examples

Doing Survey Research | A Step-by-Step Guide & Examples

Published on 6 May 2022 by Shona McCombes . Revised on 10 October 2022.

Survey research means collecting information about a group of people by asking them questions and analysing the results. To conduct an effective survey, follow these six steps:

• Determine who will participate in the survey
• Decide the type of survey (mail, online, or in-person)
• Design the survey questions and layout
• Distribute the survey
• Analyse the responses
• Write up the results

Surveys are a flexible method of data collection that can be used in many different types of research .

Table of contents

What are surveys used for, step 1: define the population and sample, step 2: decide on the type of survey, step 3: design the survey questions, step 4: distribute the survey and collect responses, step 5: analyse the survey results, step 6: write up the survey results, frequently asked questions about surveys.

Surveys are used as a method of gathering data in many different fields. They are a good choice when you want to find out about the characteristics, preferences, opinions, or beliefs of a group of people.

Common uses of survey research include:

• Social research: Investigating the experiences and characteristics of different social groups
• Market research: Finding out what customers think about products, services, and companies
• Health research: Collecting data from patients about symptoms and treatments
• Politics: Measuring public opinion about parties and policies
• Psychology: Researching personality traits, preferences, and behaviours

Surveys can be used in both cross-sectional studies , where you collect data just once, and longitudinal studies , where you survey the same sample several times over an extended period.

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Before you start conducting survey research, you should already have a clear research question that defines what you want to find out. Based on this question, you need to determine exactly who you will target to participate in the survey.

Populations

The target population is the specific group of people that you want to find out about. This group can be very broad or relatively narrow. For example:

• The population of Brazil
• University students in the UK
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18 to 24
• British transgender women over the age of 50

Your survey should aim to produce results that can be generalised to the whole population. That means you need to carefully define exactly who you want to draw conclusions about.

It’s rarely possible to survey the entire population of your research – it would be very difficult to get a response from every person in Brazil or every university student in the UK. Instead, you will usually survey a sample from the population.

The sample size depends on how big the population is. You can use an online sample calculator to work out how many responses you need.

There are many sampling methods that allow you to generalise to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions.

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by post, online, or in person, and respondents fill it out themselves
• An interview , where the researcher asks a set of questions by phone or in person and records the responses

Which type you choose depends on the sample size and location, as well as the focus of the research.

Questionnaires

Sending out a paper survey by post is a common method of gathering demographic information (for example, in a government census of the population).

• You can easily access a large sample.
• You have some control over who is included in the sample (e.g., residents of a specific region).
• The response rate is often low.

Online surveys are a popular choice for students doing dissertation research , due to the low cost and flexibility of this method. There are many online tools available for constructing surveys, such as SurveyMonkey and Google Forms .

• You can quickly access a large sample without constraints on time or location.
• The data is easy to process and analyse.
• The anonymity and accessibility of online surveys mean you have less control over who responds.

If your research focuses on a specific location, you can distribute a written questionnaire to be completed by respondents on the spot. For example, you could approach the customers of a shopping centre or ask all students to complete a questionnaire at the end of a class.

• You can screen respondents to make sure only people in the target population are included in the sample.
• You can collect time- and location-specific data (e.g., the opinions of a shop’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations.

Oral interviews are a useful method for smaller sample sizes. They allow you to gather more in-depth information on people’s opinions and preferences. You can conduct interviews by phone or in person.

• You have personal contact with respondents, so you know exactly who will be included in the sample in advance.
• You can clarify questions and ask for follow-up information when necessary.
• The lack of anonymity may cause respondents to answer less honestly, and there is more risk of researcher bias.

Like questionnaires, interviews can be used to collect quantitative data : the researcher records each response as a category or rating and statistically analyses the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analysed individually to gain a richer understanding of their opinions and feelings.

Next, you need to decide which questions you will ask and how you will ask them. It’s important to consider:

• The type of questions
• The content of the questions
• The phrasing of the questions
• The ordering and layout of the survey

Open-ended vs closed-ended questions

There are two main forms of survey questions: open-ended and closed-ended. Many surveys use a combination of both.

Closed-ended questions give the respondent a predetermined set of answers to choose from. A closed-ended question can include:

• A binary answer (e.g., yes/no or agree/disagree )
• A scale (e.g., a Likert scale with five points ranging from strongly agree to strongly disagree )
• A list of options with a single answer possible (e.g., age categories)
• A list of options with multiple answers possible (e.g., leisure interests)

Closed-ended questions are best for quantitative research . They provide you with numerical data that can be statistically analysed to find patterns, trends, and correlations .

Open-ended questions are best for qualitative research. This type of question has no predetermined answers to choose from. Instead, the respondent answers in their own words.

Open questions are most common in interviews, but you can also use them in questionnaires. They are often useful as follow-up questions to ask for more detailed explanations of responses to the closed questions.

The content of the survey questions

To ensure the validity and reliability of your results, you need to carefully consider each question in the survey. All questions should be narrowly focused with enough context for the respondent to answer accurately. Avoid questions that are not directly relevant to the survey’s purpose.

When constructing closed-ended questions, ensure that the options cover all possibilities. If you include a list of options that isn’t exhaustive, you can add an ‘other’ field.

Phrasing the survey questions

In terms of language, the survey questions should be as clear and precise as possible. Tailor the questions to your target population, keeping in mind their level of knowledge of the topic.

Use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no bias towards one answer or another.

Ordering the survey questions

The questions should be arranged in a logical order. Start with easy, non-sensitive, closed-ended questions that will encourage the respondent to continue.

If the survey covers several different topics or themes, group together related questions. You can divide a questionnaire into sections to help respondents understand what is being asked in each part.

If a question refers back to or depends on the answer to a previous question, they should be placed directly next to one another.

Before you start, create a clear plan for where, when, how, and with whom you will conduct the survey. Determine in advance how many responses you require and how you will gain access to the sample.

When you are satisfied that you have created a strong research design suitable for answering your research questions, you can conduct the survey through your method of choice – by post, online, or in person.

There are many methods of analysing the results of your survey. First you have to process the data, usually with the help of a computer program to sort all the responses. You should also cleanse the data by removing incomplete or incorrectly completed responses.

If you asked open-ended questions, you will have to code the responses by assigning labels to each response and organising them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analysing interviews.

Statistical analysis is usually conducted using programs like SPSS or Stata. The same set of survey data can be subject to many analyses.

Finally, when you have collected and analysed all the necessary data, you will write it up as part of your thesis, dissertation , or research paper .

In the methodology section, you describe exactly how you conducted the survey. You should explain the types of questions you used, the sampling method, when and where the survey took place, and the response rate. You can include the full questionnaire as an appendix and refer to it in the text if relevant.

Then introduce the analysis by describing how you prepared the data and the statistical methods you used to analyse it. In the results section, you summarise the key results from your analysis.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

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2.2 Approaches to Research

Learning objectives.

By the end of this section, you will be able to:

• Describe the different research methods used by psychologists
• Discuss the strengths and weaknesses of case studies, naturalistic observation, surveys, and archival research
• Compare longitudinal and cross-sectional approaches to research
• Compare and contrast correlation and causation

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected. All of the methods described thus far are correlational in nature. This means that researchers can speak to important relationships that might exist between two or more variables of interest. However, correlational data cannot be used to make claims about cause-and-effect relationships.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in this chapter, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

Clinical or Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

Link to Learning

Watch this CBC video about Krista's and Tatiana's lives to learn more.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

Over time, it has become clear that while Krista and Tatiana share some sensory experiences and motor control, they remain two distinct individuals, which provides invaluable insight for researchers interested in the mind and the brain (Egnor, 2017).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a precious amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this chapter: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway ( Figure 2.7 ).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall , for example, spent nearly five decades observing the behavior of chimpanzees in Africa ( Figure 2.8 ). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

The greatest benefit of naturalistic observation is the validity , or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the chapter on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally ( Figure 2.9 ). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population. Generally, researchers will begin this process by calculating various measures of central tendency from the data they have collected. These measures provide an overall summary of what a typical response looks like. There are three measures of central tendency: mode, median, and mean. The mode is the most frequently occurring response, the median lies at the middle of a given data set, and the mean is the arithmetic average of all data points. Means tend to be most useful in conducting additional analyses like those described below; however, means are very sensitive to the effects of outliers, and so one must be aware of those effects when making assessments of what measures of central tendency tell us about a data set in question.

There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this chapter: People don't always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research . Archival research relies on looking at past records or data sets to look for interesting patterns or relationships.

For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and calculate how long it took them to complete their degrees, as well as course loads, grades, and extracurricular involvement. Archival research could provide important information about who is most likely to complete their education, and it could help identify important risk factors for struggling students ( Figure 2.10 ).

In comparing archival research to other research methods, there are several important distinctions. For one, the researcher employing archival research never directly interacts with research participants. Therefore, the investment of time and money to collect data is considerably less with archival research. Additionally, researchers have no control over what information was originally collected. Therefore, research questions have to be tailored so they can be answered within the structure of the existing data sets. There is also no guarantee of consistency between the records from one source to another, which might make comparing and contrasting different data sets problematic.

Longitudinal and Cross-Sectional Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again at age 40.

Another approach is cross-sectional research. In cross-sectional research , a researcher compares multiple segments of the population at the same time. Using the dietary habits example above, the researcher might directly compare different groups of people by age. Instead of studying a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old individuals. While cross-sectional research requires a shorter-term investment, it is also limited by differences that exist between the different generations (or cohorts) that have nothing to do with age per se, but rather reflect the social and cultural experiences of different generations of individuals that make them different from one another.

To illustrate this concept, consider the following survey findings. In recent years there has been significant growth in the popular support of same-sex marriage. Many studies on this topic break down survey participants into different age groups. In general, younger people are more supportive of same-sex marriage than are those who are older (Jones, 2013). Does this mean that as we age we become less open to the idea of same-sex marriage, or does this mean that older individuals have different perspectives because of the social climates in which they grew up? Longitudinal research is a powerful approach because the same individuals are involved in the research project over time, which means that the researchers need to be less concerned with differences among cohorts affecting the results of their study.

Often longitudinal studies are employed when researching various diseases in an effort to understand particular risk factors. Such studies often involve tens of thousands of individuals who are followed for several decades. Given the enormous number of people involved in these studies, researchers can feel confident that their findings can be generalized to the larger population. The Cancer Prevention Study-3 (CPS-3) is one of a series of longitudinal studies sponsored by the American Cancer Society aimed at determining predictive risk factors associated with cancer. When participants enter the study, they complete a survey about their lives and family histories, providing information on factors that might cause or prevent the development of cancer. Then every few years the participants receive additional surveys to complete. In the end, hundreds of thousands of participants will be tracked over 20 years to determine which of them develop cancer and which do not.

Clearly, this type of research is important and potentially very informative. For instance, earlier longitudinal studies sponsored by the American Cancer Society provided some of the first scientific demonstrations of the now well-established links between increased rates of cancer and smoking (American Cancer Society, n.d.) ( Figure 2.11 ).

As with any research strategy, longitudinal research is not without limitations. For one, these studies require an incredible time investment by the researcher and research participants. Given that some longitudinal studies take years, if not decades, to complete, the results will not be known for a considerable period of time. In addition to the time demands, these studies also require a substantial financial investment. Many researchers are unable to commit the resources necessary to see a longitudinal project through to the end.

Research participants must also be willing to continue their participation for an extended period of time, and this can be problematic. People move, get married and take new names, get ill, and eventually die. Even without significant life changes, some people may simply choose to discontinue their participation in the project. As a result, the attrition rates, or reduction in the number of research participants due to dropouts, in longitudinal studies are quite high and increase over the course of a project. For this reason, researchers using this approach typically recruit many participants fully expecting that a substantial number will drop out before the end. As the study progresses, they continually check whether the sample still represents the larger population, and make adjustments as necessary.

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Open Access

Peer-reviewed

Research Article

Open-source data pipeline for street-view images: A case study on community mobility during COVID-19 pandemic

Contributed equally to this work with: Matthew Martell, Nick Terry

Roles Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Industrial & Systems Engineering, University of Washington, Seattle, WA, United States of America

Roles Data curation, Formal analysis, Software

Roles Data curation, Software

Roles Conceptualization, Funding acquisition, Methodology, Project administration, Writing – review & editing

Affiliation Environmental & Occupational Health Sciences, University of Washington, Seattle, WA, United States of America

Affiliation Human Centered Design & Engineering, University of Washington, Seattle, WA, United States of America

Roles Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Writing – review & editing

Affiliation Civil & Environmental Engineering, University of Washington, Seattle, WA, United States of America

Roles Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing

• Matthew Martell, 
• Nick Terry, 
• Ribhu Sengupta, 
• Chris Salazar, 
• Nicole A. Errett, 
• Scott B. Miles, 
• Joseph Wartman, 
• Youngjun Choe

• Published: May 10, 2024
• https://doi.org/10.1371/journal.pone.0303180
• Reader Comments

Street View Images (SVI) are a common source of valuable data for researchers. Researchers have used SVI data for estimating pedestrian volumes, demographic surveillance, and to better understand built and natural environments in cityscapes. However, the most common source of publicly available SVI data is Google Street View. Google Street View images are collected infrequently, making temporal analysis challenging, especially in low population density areas. Our main contribution is the development of an open-source data pipeline for processing 360-degree video recorded from a car-mounted camera. The video data is used to generate SVIs, which then can be used as an input for longitudinal analysis. We demonstrate the use of the pipeline by collecting an SVI dataset over a 38-month longitudinal survey of Seattle, WA, USA during the COVID-19 pandemic. The output of our pipeline is validated through statistical analyses of pedestrian traffic in the images. We confirm known results in the literature and provide new insights into outdoor pedestrian traffic patterns. This study demonstrates the feasibility and value of collecting and using SVI for research purposes beyond what is possible with currently available SVI data. Our methods and dataset represent a first of its kind longitudinal collection and application of SVI data for research purposes. Limitations and future improvements to the data pipeline and case study are also discussed.

Citation: Martell M, Terry N, Sengupta R, Salazar C, Errett NA, Miles SB, et al. (2024) Open-source data pipeline for street-view images: A case study on community mobility during COVID-19 pandemic. PLoS ONE 19(5): e0303180. https://doi.org/10.1371/journal.pone.0303180

Editor: Ahmed Mancy Mosa, Al Mansour University College-Baghdad-Iraq, IRAQ

Received: January 26, 2024; Accepted: April 20, 2024; Published: May 10, 2024

Copyright: © 2024 Martell et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All images collected throughout this longitudinal study are available on mapillary.com under the username ‘uwrapid’. Full instructions and code on how to reproduce the data pipeline described in this paper are available at https://github.com/marte292/rapid-data-pipeline . The processed output necessary to reproduce the regression analyses in this paper are within the supporting files.

Funding: The U.S. National Science Foundation (Grant Number 2031119) provided financial support for this research. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF. Data was collected using instrumentation provided by NSF as part of the RAPID Facility, a component of the Natural Hazards Engineering Research Infrastructure, under Award No. CMMI: 2130997. There was no additional external funding received for this study.

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

Introduction

Street-level imagery is becoming an increasingly popular form of data for research [ 1 ]. Between 2009 and 2020, more than 200 publications utilized street-level imagery from corporate sources in urban research [ 1 ]. Out of all these sources, Google Street View’s Street View Images (SVI) were the most popular among academics [ 1 – 3 ]. Uses for SVI data include estimating demographics [ 4 ], evaluating the built environment [ 5 ], surveying plant species [ 6 ], measuring pedestrian volume [ 7 ], among many other applications [ 8 – 10 ].

While SVI data can provide many useful insights for researchers, it is not without its flaws. For corporate-collected images such as Google Street View, or Tencent Street View the availability of images depends on where the companies decide to collect data, while the accessibility of these images hinges on the companies’ data provision policies. For example, there is no Google Street View service in most parts of Africa. An alternative to corporate-collected images are crowdsourced SVI databases such as Mapillary [ 11 ]. These crowdsourced images sometimes may have better coverage or temporal resolution than Google Street View, at the cost of varying image quality, field of view, and positional accuracy [ 3 , 12 ]. Perhaps the largest challenge with SVI data is its temporal instability. Updates to these image datasets at specific locations are infrequent, especially in rural areas [ 1 , 13 , 14 ]. Additionally, images frequently are not collected at a consistent time of day, or season, even within the same city. These issues make existing SVI data unreliable for temporal studies.

Typically, temporal studies involving image data use images (or video) from fixed locations. This data is used to do things such as evaluate disaster recovery [ 15 ], monitor ecological change [ 16 ], or measure urban flooding [ 17 ]. Data from fixed cameras is also used to count people [ 18 ]. The challenge with these methods is that they are fixed-location. In order to collect spatial image data for these methods, frequently a large team is required to traverse areas on foot. This challenge, along with existing SVI data’s temporal issues, demonstrate the potential value of collecting longitudinal SVI data.

Our main contribution is demonstrating the feasibility of collecting longitudinal SVI data. We demonstrate this through the creation of a complete data pipeline for conducting pedestrian counts using car-based street-level imagery. The pipeline accepts raw video collected by the camera as an input and outputs a record of each pedestrian detection and their locations (latitude and longitude). This approach allows for analysis of mobility patterns with high spatial resolution and a short lag time. It alleviates the quality and field of view inconsistencies that come with crowdsourcing SVI data [ 3 , 12 ], generates data that is not corporately owned, eliminates the temporal instability challenge of both kinds of data [ 1 , 13 , 14 ], while still maintaining the advantages of SVI data over fixed-location methods [ 15 ].

Specifically, we use this pipeline to generate and analyze video from 37 video-collection runs in the city of Seattle, Washington, USA from May 2020 through July 2023. The video data was converted into over 4 million high-resolution images, with each data-collection run representing about 1.5 TB of image data. We used the images to create a record containing the location of each detected pedestrian, cross-referenced to the relevant GEOID [ 19 ]. To detect pedestrians in the still images, our pipeline leverages the state-of-the-art convolutional neural network, Pedestron [ 20 ]. We used the cascade_hrnet architecture benchmarked on the CrowdHuman data set [ 21 ]. Our methods and dataset represent a first of its kind longitudinal collection and application of SVI data for research purposes.

As a secondary contribution, we provide a case study based on the video data collected throughout the COVID-19 pandemic. We examine the effect of vaccine availability and local demographics on pedestrian detections, while accounting for weekly and yearly seasonality. Community mobility became a key metric during the height of the COVID-19 pandemic as government officials worked to halt the spread of the virus [ 22 , 23 ]. Two of the largest and most widely used data sets for community mobility during this time were the Google Community Mobility Reports [ 24 ] and Apple Mobility Trends Reports [ 25 ]. Researchers used this data to study the incidence of COVID-19 in the US [ 26 ] and the effectiveness of government lockdown policies [ 27 , 28 ], among other topics. Issues with these two data sets include mandatory opt-in, use of specific map applications, a lack of independent verification, and no long-term data availability guarantees [ 26 , 28 – 30 ]. Our findings demonstrate the utility of our data processing pipeline as an alternative for tracking community mobility over time and show the potential for its use in a variety of research domains.

Data collection

We collected our data as a part of the Seattle street-level imagery campaign, an ongoing series of video surveys for the purposes of documenting mobility throughout the COVID-19 pandemic. During each survey, a vehicle equipped with a 360° video camera is driven along a pre-defined route through Seattle while collecting video data and GPS metadata. The route incorporates broad neighborhood/area canvassing designed to collect data useful to multidisciplinary researchers as well as capital transects. Full details on the route design are available in Errett et al. [ 31 ]. The capital transects specifically target capitals (social, cultural, built, economic, and public health) which are theorized to be closely tied to community resilience [ 32 ]. Specific canvassing areas and capitals within Seattle were chosen to ensure a representative sample of the overall population of Seattle [ 31 ]. While the drivers try to make the surveys as consistent as possible, occasionally exogenous factors caused deviations from standard protocols. For example, during three of the surveys (05-29-2020, 06-18-2020, and 06-26-2020), protests over the murder of George Floyd caused parts of the survey route to be unnavigable.

After consulting with the University of Washington Human Subjects Division, it was determined that this study was not considered human subjects research and would not require IRB approval. The data we captured was people in public places, where they cannot expect personal privacy. As an added precaution, all data for this study was published through Mapillary [ 11 ], which automatically obscures faces.

Data processing pipeline

After video collection, the raw data is segmented into image data. The images are subsampled from video frames so that they are collected about every 4 meters. The images are then uploaded into the DesignSafe-CI Data Depot [ 33 ]. From DesignSafe, the images are transferred to the TACC Frontera high-performance computing cluster [ 34 ]. We completed all file transfers between the two services using Globus [ 35 ]. Without access to these services, or similar ones, the storage and computing requirements for this project would be intractable.

On Frontera, orthorectification is performed to the images, then pedestrian detection is performed on the orthorectified images. The orthorectification transforms the images from a single image in the equirectangular projection to two images in the rectilinear (gnomonic) projection [ 36 ]. Pedestrians are detected on each of the new images using a convolutional neural network (CNN) based on a pre-trained model from the Pedestron repository [ 20 ]. Our data represents a highly challenging detection task, as there is great variation in lighting, backgrounds, human poses, levels of occlusion and crowd density from image to image and run to run. The Cascade Mask R-CNN architecture in the Pedestron repository performed well on the CrowdHuman data set, representing a similar challenge to our data [ 21 ]. All testing and use of the CNN was performed using GPUs on the Frontera cluster. An example image after undergoing orthorectification and pedestrian detection is shown in Fig 1 .

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

The left image is an original 360° image from a data collection run. The image on the right is the right-hand side of the original image after orthorectification and pedestrian detection (both sides of the image are processed separately). There are two pedestrians that were detected by the algorithm (in red bounding boxes).

https://doi.org/10.1371/journal.pone.0303180.g001

Using one GPU node on Frontera, with four NVIDIA Quadro RTX 5000 GPUs, the entire process takes about 3 seconds per original 360° image. Given the 4 million images we collected, this takes about 3,300 hours of computing time. While this is not a small number, when running in parallel, the whole process can be completed in a manner of days. In comparison, a human taking 10s per orthorectified image to count all the pedestrians would take over 22,000 hours to complete the same task. File compression/decompression for file transfer also takes a substantial amount of time. Since we used DesignSafe as our main data storage platform, we had to transfer files to/from the Frontera supercomputer to perform our pedestrian detection. To avoid overloading the file transfer system, we compressed the images from each run into a tar file prior to transferring the files to Frontera. This file compression/decompression can take several hours per run, but can be performed in parallel with the detection algorithm since they are on different systems. After compression, file transfer using Globus [ 35 ] takes minutes.

In post-processing, the pipeline filters out low-confidence detections (defined as any detection with less than 80% confidence) and associates the remaining high-confidence detections to U.S. Cenus Bureau GEOIDs [ 19 ]. We arrived at this confidence level after tuning for the precision and recall of the CNN classifier. Specifically, the pipeline filters based on the output of the second to last layer of the CNN, known as a softmax layer . For a k –class classification problem, the softmax layer will output a k –dimensional probability vector, where each i th entry of the vector gives the probability that the original input to the CNN belongs to class i .

The final stage of post-processing is GEOID matching, where latitude and longitude metadata are cross-referenced to disjoint geographic regions (e.g. U.S. census tracts or block groups) and their respective GEOID codes. The cross-referencing code assumes the availability of shapefiles describing the geometry of the geographic regions. Aggregating the pedestrian detections according to U.S. Census Bureau GEOIDs [ 19 ] is necessary for analyses using sociodemographic data collected by the census. Additionally, the pedestrian detections can easily be cross-referenced with custom geometry defined using popular geographic information system software, such as the capitals data used in route construction and our analysis.

Following the GEOID matching step, the pedestrian detections data is written to a tabular format file (e.g. comma separated values). This file is an “analysis-ready” data product, in the sense that it is readable by most popular statistical analysis software (R, SPSS, Stata, etc.) and can be easily merged with other datasets using the GEOID column(s). A visual depiction of the entire pipeline is seen in Fig 2 . Full code and a manual for following our process is available at https://github.com/marte292/rapid-data-pipeline .

The parts of the flowchart in gray occur on NHERI DesignSafe-CI, while the right-hand part in blue is done on the Frontera cluster.

https://doi.org/10.1371/journal.pone.0303180.g002

Case study: Community mobility in Seattle during the COVID-19 pandemic

Data processing..

All analysis is performed using the Python programming language version 3.11 [ 37 ]. The initial data product as outlined in the previous section is a list of detections, alongside the date of collection, geolocation, and GEOID. We also utilized a similar list of the images themselves with the same features. The last dataset we utilized is the median household income data and racial demographic data from the 2019 American Community Survey (ACS) 5-year estimates. We aggregated the detections and image data for each data collection survey at the census tract level, then matched each census tract’s total number of detections and images to its respective demographic and income data.

We utilized the data from 36 of the 37 surveys, omitting data from 10-29-2020. A heavy rain event caused the survey to be stopped early due to poor video quality. For each survey, we divided the number of detections in each census tract by the number of images collected in the tract to create a normalized ‘detections per image’ metric. This is a necessary step as the number of images in each tract may change survey to survey due to circumstances outside our control, such as construction or community events altering the route.

The last step in data processing was to transform some of our data to be represented by categorical variables. The date of each survey was coded both as either a weekend or weekday, and by the season. The date was also coded as either being before, or after the date that vaccines became publicly available. Income data was coded to be one of 5 levels that were used during route design. These brackets were $48,274 and below, $48,275 to $80,819, $80,820, to $110,536, $110537 to $153,500, and $153,501 and above. Lastly, the proportion of the census tract’s population that identifies as non-white was coded as an indicator variable, with ‘1’ corresponding to areas that are 55.5% white or more. We determined this threshold using Jenk’s natural breaks optimization. This left us with a dataset of 3171 observations to be used for analysis. Each observation represented a census tract with a detections per image value, as well as values for each of the categorical variables defined above.

Initial regression analysis.

In addition to the above analysis, we subset the data by only looking at detections that occurred in an image with at least one other detection. Then we calculated detections per image again, and fit the above model again with the new response variable. This same process was followed for detections with at least two, three, and four other detections in the same image. The goal of these analyses was to see if there were different trends for larger groups of people when compared with the entire data set.

Data pipeline

Our main contribution, the open-source data pipeline, is publicly available on https://github.com/marte292/rapid-data-pipeline . The repository contains a process manual with step-by-step instructions on how to implement the data pipeline in Python [ 37 ]. The required Python libraries and system requirements are provided. Additionally, we provide enough code for future researchers to implement the pipeline on their own systems, with their own file structure. The pipeline is capable of processing terabytes of image data and outputting an analysis-ready data product in a matter of days (using high-performance computing, such as a single GPU node on Frontera, an academic supercomputer) with minimal human input.

Using data from the Seattle street-level imagery campaign, we calculated the number of detections per image across all data collection surveys. Fig 3 shows the detections per image for each survey, as well as the detections per image for the subset of detections sharing an image with at least 4 others. Fig 3 also displays the timestamp of COVID-19 vaccines becoming publicly available in Washington state.

As the survey dates are irregular, all dates are included in the figure. Please note that the axis for total detections per image does not start at 0. This was done purposefully to facilitate comparison between the trends of the two graphs.

https://doi.org/10.1371/journal.pone.0303180.g003

Fig 3 depicts the trends over time for detections per image and detections sharing an image with at least 4 others. While both graphs exhibit similar trends overall, notably after vaccine rollout the graph of detections sharing an image with at least 4 others exceeds the graph of detections per image in all cases. The spike in detections seen in June 2020 is due to the large scale protests of police brutality that took place in Seattle in the aftermath of George Floyd’s murder.

The full results of the linear regression model for total detections per image are displayed in Table 1 . They show that the season being summer is the only significant seasonal effect. Additionally, the income bracket is a significant predictor, with wealthier areas seeing less pedestrian traffic. Finally, a census tract having a population greater than 55.5% white is a significant positive predictor. All other variables are not significant, including vaccine availability.

The first three non-intercept terms represent indicator variables for the different seasons, with fall being the baseline. The Vaccine Available term represents a binary variable for whether the COVID-19 initial vaccination series was publicly available or not. Weekend is a binary variable for whether the data was collected on Saturday or Sunday. The four Income Bracket terms are indicator variables for the median income level of the census tract where the data was collected. The income brackets are defined in our methods. Lastly, the More than 55.5% White term is an indicator variable for if the census tract in question had a populace that is more than 55.5% White. Full documentation for the Python package used to make this output is available from the developers [ 38 ].

https://doi.org/10.1371/journal.pone.0303180.t001

For the regression models using a subset of data, the results are similar to the initial model. All models have the same significant predictors as the initial model. The model using the detections sharing an image with at least one other also had the weekend as a borderline significant, negative predictor. The models using detections sharing an image with at least 3 and 4 others had vaccine availability as a significant, positive predictor. The full results of the linear regression model for detections per image with at least 4 others are displayed in Table 2 , with all other regression models available in the supporting information.

Coefficients are defined the same as in Table 1 .

https://doi.org/10.1371/journal.pone.0303180.t002

Comparison to Google Community Mobility data

Given the ability to measure community mobility through pedestrian counts, there is potential value of our pipeline for social sciences and public health research [ 22 , 23 ]. At an individual level, higher physical activity is known to predict better physical [ 39 , 40 ] and mental health [ 41 – 43 ], and is associated with higher self-reported satisfaction and quality of life [ 44 , 45 ]. In an aggregate sense, mobility is theorized to be an intermediate variable through which socioeconomic deprivation affects vulnerability to infectious disease [ 46 , 47 ], resilience to disasters [ 48 ], and exposure to environmental hazards [ 49 ]. In light of this body of literature, we argue that the use of pedestrian counts to assess mobility could be a differentiating factor in researching social and health inequity. One extremely common source of mobility data during the COVID-19 Pandemic has been Google Community Mobility Reports [ 24 ] and Apple Mobility Trends Reports [ 25 ]. While there have been improvements in recent years [ 50 ], there are known representation and self-selection biases with existing mobility data captured by smartphones and other internet-based data collection methods [ 51 – 55 ].

Given the large number of publications using smartphone data as the foundation for their work, a natural question is how our data compares to smartphone mobility data. Comparison between our data set and the still publicly available Google Community Mobility Reports data can reveal some of the similarities and differences between the two data sets [ 24 ]. Google Community Mobility data is reported at the county level in the United States. Since Seattle is in King County, Washington, the King County data is what we use to draw the comparison.

Google Community Mobility data does not provide raw mobility numbers, but rather is reported as a percentage change from the five-week period of Jan 5–Feb 6, 2020. This data is collected from smartphones running the Android operating system with location history turned on, which is off by default. The data is baselined by day of the week, so data from a given Monday is compared to the median of the five Mondays in the baseline window to calculate a percent change. Additionally, it is unclear how exactly Google quantifies mobility. It is mentioned that it combines number of visitors to a location with amount of time spent in that location, but no specifics beyond that are provided.

Google mobility data is broken down into different categories. The category that most closely aligns with one of the categories used in our analysis is parks. Although Google’s data classifies parks as official national parks and not the general outdoors, it does not indicate how it accounts for city or state parks. Our own data for park locations is based on the City of Seattle’s official classifications.

Fig 4 shows a comparison of our detections per image data against Google Community Mobility data. Note that not all surveys are included because Google Community Mobility data stopped being provided on October 15, 2022. Overall, the trends between the two data sets are remarkably similar, lending further credibility to our data collection procedure. The more notable differences in the graph are from the months of November 2020 through August 2021, where the Google mobility data shows a larger drop followed by an increase in community mobility than was visible through our own data.

The Pearson correlation between the two data sets is 0.387. The Google Community mobility data is aggregated at King County, WA, while our data covers a survey route within Seattle, which belongs to King County. As the dates of surveys were irregular (e.g., due to weather conditions), all dates are included in the figure.

https://doi.org/10.1371/journal.pone.0303180.g004

One plausible explanation for this is the upwards sampling bias that occurs when using smartphone data [ 56 , 57 ]. Our data set captures anyone on the street, including individuals experiencing homelessness, who are less likely to have smartphones. This population was on the streets throughout the entirety of the pandemic, so they were consistently captured by our data collection efforts. This consistent baseline pedestrian count could lead to a lesser response to vaccine rollout and winter weather in our own data in comparison with Google’s. Additionally, there is a known income gap in both vaccination rates and smartphone ownership [ 58 , 59 ]. This gap could drive the increase in the Google Mobility data during vaccine rollout.

Implications, limitations, and extensions

Our results show that it is possible for researchers to collect and analyze longitudinal SVI data. The presented methods can be used to collect and process SVI data from 8 hours worth of video in a manner of days. This time will only further decrease with faster data processing infrastructure and methods. These methods will allow novel longitudinal SVI data to be collected for research in a variety of application areas.

The results of the case study also bear further discussion. We demonstrated expected relationships between seasonal effects like day of week and weather on pedestrian traffic. Additionally, we showed that pedestrian traffic is inversely proportional to income, a known result during the COVID-19 pandemic, as lower income households are constrained in their capacity to work from home or take time off of work [ 30 , 60 ]. Our results also showed that more white areas had higher on average pedestrian counts. This could be due to known trends, such as areas with larger non-white populations being more likely to stay home in response to government restrictions [ 61 ] and participate in other risk-reducing practices such as wearing a mask [ 62 ], or just due to local trends, as racial mobility trends tend to vary between cities [ 63 ]. These findings are consistent across all of our models, both looking at the entire data set, and the subsets examining pedestrians sharing an image. These results validate our method with respect to established literature, and provide a quantitative confirmation of results that had previously been found using cell phone data.

One new finding from our case study is that while overall pedestrian counts did not respond to vaccine availability, the subset of pedestrians who were in larger groups (4+ people in an image) did. Likely, the reason we did not see a response to the vaccine in the aggregate data is because our data only captures people who are outdoors. There is data that shows that outdoor pedestrian activity varied across cities, frequently increasing at recreation locations like trails, during the early days of the pandemic [ 64 , 65 ]. Given these increases at some locations, a return to ‘normal’ pedestrian traffic may not mean an increase, but rather a change in traffic patterns. Our data captures this by showing that there was a significant increase in larger groups of people after the vaccine became available. This implies that people were more willing to be near each other outdoors after they had been vaccinated.

While the data pipeline presented here does represent a method for generating a novel data product, there are implementation challenges worth further discussion. For data collection, in addition to the time required to drive the route limiting the places of interest the route could reach, there were also many tradeoffs that had to be made when designing the route itself [ 31 ]. Despite having our survey route carefully designed to assess a representative sample of the Seattle population, some bias in route design is unavoidable. Since the route design included data from the American Community Survey aggregated at the census tract level, there is an implicit assumption of spatial homogeneity of the population within each census tract. Such bias is a manifestation of the well-known modifiable areal unit problem [ 66 ]. Since the majority of the route was primarily based on locations of interest throughout the city, this concern is somewhat mitigated.

In terms of processing, the pre-trained model we used required a substantial amount of high-performance computing time, and at times the data product generated was so large as to be unwieldy. Given the challenge our data set represents, using a model designed to be generalizable is necessary to attain good detection results. As many state-of-the-art models perform substantially worse out of sample, we had to be careful to choose a model that was designed to perform well in this situation, at the cost of slower computing times [ 67 ]. Another unforeseen challenge was regular updates to the video camera’s software to process and segment the video data into images. Consistent image formatting was vital for the data processing pipeline to function, so regular quality checks are necessary to make sure the images are processed properly.

The data product created, pedestrian detections, has some limitations as well. First, our method only captures pedestrians who are outdoors and near enough to the street to be captured via camera. This means that our data set does not include people who are indoors at these locations of interest, or who are too far from the street to be seen by camera. While the changes over time in pedestrian traffic we observed are still meaningful, it is important to recognize they don’t capture everything. Similarly, our data cannot be interpreted as the actual number of pedestrians on the street. There is overlap in the image data, even when subset at 4 meter intervals and cropped during orthorectification. The orthorectified images only represent about 25% of the originals. However, this natural cropping is not enough to avoid the image overlap and further cropping would risk information loss. Pedestrians that appear in the foreground of one image may end up in the background of another. There are also several known instances of cyclists keeping relative pace with the street-view vehicle for several blocks, resulting in numerous detections. These issues are easy to circumvent in analysis by comparing the relative number of detections, although at the cost of interpretability.

Even with the above limitations, the data pipeline presented in this paper can be directly applied or adapted to be used in a number of contexts. Potential applications of longitudinal SVI data in assessing the built environment [ 14 ], broad urban research [ 1 , 3 , 68 ], and health research [ 8 ] have been well-documented, as the temporal instability of existing SVI data is discussed as a limitation in all of these fields. Beyond this, it is possible to estimate population demographics [ 4 ], and other neighborhood-level statistics [ 13 , 69 ] using SVI data. As our ability to quickly and accurately parse scenes using computer vision improves [ 70 ], potential application areas will only increase in number.

Another field where longitudinal SVI data could contribute a lot is disaster research. There is a substantial body of research dedicated to empirical methods for modeling various aspects of disaster recovery [ 71 ]. Our methods could be applied in this field to quantify recovery using pedestrian detections as a metric for community mobility, or another metric assessing the built environment as appropriate. Similar work has been done using repeat photography after Hurricane Katrina [ 15 ] but our methods represent a substantial increase in generated data, allowing for a wider range of analyses. Spatial video data collection for disaster reconnaissance has also been done [ 72 ], but involves manual assessment of the captured video. Our methods demonstrate that a fully-automated approach is possible, which would allow for more frequent data collection at a lower cost.

This article describes the creation of the first open-source SVI data pipeline for longitudinal analysis. Regression analysis based on the resulting longitudinal SVI data showed that pedestrian traffic patterns changed in response to the availability of the COVID-19 vaccine, thereby demonstrating the data pipeline’s usefulness in research and practice. In particular, we showed that there were statistically significant increases in groups of people in proximity to each other after the vaccine became publicly available. Our data also captured expected trends in pedestrian traffic based on annual seasonality and socioeconomic factors. Our results demonstrate the feasibility and value in collecting SVI data as part of a longitudinal study. Longitudinal SVI data is capable of providing valuable insights in a variety of fields of study. Future work includes applications of our methods in broader public health research, disaster research, and other fields of study that can benefit from longitudinal SVI data. Potential methodological directions include study-specific route design process improvements and newer pedestrian detection approaches, as further progress is made in this area.

Supporting information

S1 dataset. full dataset used for obtaining regression results presented in this paper..

https://doi.org/10.1371/journal.pone.0303180.s001

https://doi.org/10.1371/journal.pone.0303180.s002

Acknowledgments

The authors gratefully acknowledge DesignSafe and the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing the cyberinfrastructure that enabled the research results reported within this paper.

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• Open access
• Published: 07 May 2024

The impact of scheduling ketamine as an internationally controlled substance on anaesthesia care in Sub-Saharan Africa: a case study and key informant interviews

• Gaby I. Ooms 1 , 2 ,
• Mohammed A. Usman 3 , 4 ,
• Tim Reed 1 ,
• Hendrika A. van den Ham 2 &
• Aukje K. Mantel-Teeuwisse 2  

BMC Health Services Research volume  24 , Article number:  598 ( 2024 ) Cite this article

53 Accesses

Metrics details

Access to anaesthesia and surgical care is a major problem for people living in Sub-Saharan Africa. In this region, ketamine is critical for the provision of anaesthesia care. However, efforts to control ketamine internationally as a controlled substance may significantly impact its accessibility. This research therefore aims to estimate the importance of ketamine for anaesthesia and surgical care in Sub-Saharan Africa and assess the potential impact on access to ketamine if it were to be scheduled.

This research is a mixed-methods study, comprising of a cross-sectional survey at the hospital level in Rwanda, and key informant interviews with experts on anaesthesia care in Sub-Saharan Africa. Data on availability of four anaesthetic agents were collected from hospitals ( n  = 54) in Rwanda. Semi-structured interviews with 10 key informants were conducted, collecting information on the importance of ketamine, the potential impact of scheduling ketamine internationally, and opinions on misuse of ketamine. Interviews were transcribed verbatim and analysed using a thematic analysis approach.

The survey conducted in Rwanda found that availability of ketamine and propofol was comparable at around 80%, while thiopental and inhalational agents were available at only about half of the hospitals. Significant barriers impeding access to anaesthesia care were identified, including a general lack of attention given to the specialty by governments, a shortage of anaesthesiologists and migration of trained anaesthesiologists, and a scarcity of medicines and equipment. Ketamine was described as critical for the provision of anaesthesia care as a consequence of these barriers. Misuse of ketamine was not believed to be an issue by the informants.

Ketamine is critical for the provision of anaesthesia care in Sub-Saharan Africa, and its scheduling would have a significantly negative impact on its availability for anaesthesia care.

Peer Review reports

Introduction

Surgical care is defined by the Lancet Commission on Global Surgery as “the provision of operative, perioperative, and non-operative management; anaesthesia; and obstetric care for all surgical conditions” [ 1 ]. Surgical care is a cross-cutting field of care, and surgical procedures are essential in the treatment of communicable and non-communicable diseases, maternal, neonatal and nutritional disorders, and injuries [ 1 ]. It is estimated that conditions requiring surgery are responsible for around 30% of the global burden of disease, while access to safe, affordable and timely surgical and anaesthesia care is a major issue for more than 4.8 billion people worldwide [ 2 , 3 ]. This treatment gap is felt the most by people living in low- and middle-income countries (LMICs): an additional 143 million surgical procedures are needed in LMICs annually to avert preventable disability and deaths, and more than 77 million disability-adjusted life-years (DALYs) could be averted with adequate provision of basic surgical care [ 1 ]. Anaesthesia is a key component of surgical care.

Access to to timely, safe and affordable surgical and anaesthesia care is a major problem for people living in Sub-Saharan Africa (SSA), where it is beyond the reach of more than 95% of the population [ 3 ]. Lack of access to surgical and anaesthesia care in SSA is caused by a paucity of specialised healthcare workers, poor basic infrastructure, absence of surgical and anaesthesia equipment, and scarcity of essential medicines, including anaesthetic agents [ 4 ]. It is estimated that in the World Health Organization (WHO) Africa Region, there are on average 0.41 physician anaesthesia providers (PAPs) per 100,000 population. This number is far below the 10 PAPs per 100,000 population as recommended by the World Federation of Societies for Anaesthesiologists (WFSA) [ 5 ]. Research has shown that consistent access to electricity and running water remains problematic across SSA, and that availability of oxygen and functional anaesthetic machines is generally low [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Essential medicines, such as local or general anaesthetic agents, remain in low supply [ 6 , 9 , 17 , 19 , 20 , 21 ].

Due to the lack of PAPs, infrastructure, equipment, and essential medicines in much of SSA, surgical procedures often take place without adequate anaesthesia or pain management [ 22 ]. To alleviate the suffering of patients in these settings, hospitals have become reliant on ketamine. The WHO Model List of Essential Medicines lists ketamine for use as an anaesthetic [ 23 , 24 ]. Its use in low-resource settings is popular as ketamine does not depress respiratory function in patients while it increases blood pressure, and can therefore be used when access to airway equipment is lacking and monitoring of vital signs is challenging [ 22 , 23 ]. Because of these properties, ketamine can also be used by non-physician providers, if they have been appropriately trained [ 22 ].

Ketamine is misused as a recreational drug in high-income countries, especially in China, Hong Kong, Taiwan, and Japan, and more generally in East and Southeast Asia [ 4 , 23 , 25 ]. Because of this misuse, China has repeatedly submitted a request to schedule ketamine as a Schedule I drug under the Single Convention on Narcotic Drugs in 2006, 2012 and 2014, and submitted a request to have it scheduled as a schedule IV drug under the Convention on Psychotropic Substances in 2015 [ 23 , 26 , 27 , 28 , 29 ], see Box 1 for detailed information.

All four instances that scheduling was requested, the WHO Expert Committee on Drug Dependence (ECDD) declined, stating that ketamine poses no great global public health risk, while scheduling it would have a significant impact on medical care in LMICs and in emergency situations [ 25 , 26 , 27 ]. Subsequently, the United Nations Commission on Narcotic Drugs (CND) has not scheduled ketamine as a Schedule I drug in the Single Convention, or as a Schedule IV drug in the Convention on Psychotropic Substances [ 25 , 26 , 27 ]. However, it is likely that similar requests will be made in the future.

While the importance of ketamine for anaesthesia care has been discussed in an article in the Guardian and in editorials, no research has been undertaken in which anaesthesiologists from the field provide their insights into the issue [ 26 , 27 , 30 , 31 ]. This research therefore aims to estimate the importance of ketamine for anaesthesia and surgical care in SSA, and assess the potential impact on access to ketamine if it were to be scheduled, through a case study of essential anaesthesia commodities availability in Rwanda, and key informant interviews with experts from SSA.

Study design

This research is a mixed-methods study, comprising of a cross-sectional survey at the hospital level in Rwanda, and key informant interviews with experts on anaesthesia care in SSA. The survey on the availability of anaesthesia commodities was part of a larger project in Rwanda on access to essential medicines for the management and treatment of snakebites [ 32 ]. In this study, 34 commodities were surveyed, including four commodities that are used in anaesthesia care (ketamine, thiopental, inhalational agents, and propofol). The survey functioned as a case-study to gain insight into the availability of a range of anaesthesia commodities in a specific SSA country. Semi-structured interviews were conducted with key informants from the whole of SSA to gather a more generalised insight into the importance of ketamine for anaesthesia and surgical care in the entire region, given the situation in Rwanda may not be representative of the region.

Study participants and recruitment

In Rwanda, all general, non-specialised hospitals from the public and private sectors were selected for the survey. This included four private hospitals, and 51 public district-, provincial- and referral hospitals. The hospitals were contacted beforehand by email or telephone to schedule a study visit. One specialised hospital solely focussing on psychiatric care was excluded as were lower level health facilities, including health centres and health posts. These facilities were not included since they were not expected to have (most) anaesthetic agents.

Key stakeholders identified for participation in the interview component of this study were anaesthesiologists with expertise in anaesthesia care in SSA. They were identified and recruited through document desk review, the network of the WFSA and its national chapters, and the professional network of the researchers. Inclusion criteria for participation were: participants are 18 years or older, knowledgeable on anaesthesia care and ketamine use in SSA, and able to communicate in English. Participants were invited over email and provided with background information on the study. Multiple follow-up emails were sent in case of non-response.

Data collection

The WHO-WFSA International Standards for a Safe Practice of Anesthesia guided the selection of the general anaesthesia commodities [ 33 ]. Information on electricity, running water, and functional anaesthesia machines was also recorded. Data within the Rwandan hospitals was collected in February 2023. A mobile application, KoboCollect, was used for data collection. Data collectors received a two-day training from one of the authors (GIO), which included a field-test. Data collectors collected data in pairs and were supervised by an in-country lead investigator. Data on availability of the commodities was recorded only when they could be physically seen. A commodity was considered available if it was present at the hospital at the time of data collection. A photo was taken of each available, surveyed commodity as an additional validity measure.

A semi-structured key-informant interview guide was developed based on literature to guide the interviews (see Additional File 1 ). Questions focused on the contextual situation of anaesthesia care, including barriers to access, in the countries in which participants have work experience, their beliefs about ketamine and its relevance for anaesthesia care in these respective countries, and their perceived potential impact of ketamine scheduling on anaesthesia and surgical care in these contexts. We also sought the participants’ opinions on the level of misuse of ketamine in their countries, and about recommendations to safeguard access to anaesthesia care while at the same time preventing misuse of ketamine. Interviews were conducted by GIO from May to July 2023 with 10 participants. Nine interviews took place online through virtual meeting platform Zoom, and one interview was conducted via email, where the key informant responded to the questions in written form due to language barriers. Interviews lasted between 27 and 53 min. Interviews were recorded, and Zoom’s build-in automatic transcription setting was used.

Data management and analysis

Survey data were uploaded to the KoboToolbox server by the data collectors after completion, after which the data was downloaded into Microsoft Excel. The data was double-checked and cleaned by the researchers, and was analysed in Microsoft Excel using descriptive statistics. Availability across all hospitals was calculated as the proportion of hospitals where the commodity was present at the time of the survey.

The automatic, verbatim interview transcripts were checked by the researchers for errors and corrected when necessary after a consensus was reached. The interviews were analysed using a thematic analysis approach by one researcher (GIO), and consisted of coding text into predetermined themes, which were based on the interview topics.

Quality assurance

The qualitative component of this research was guided by the Consolidated Criteria for Reporting Qualitative Research (COREQ) framework (see Additional File 2 ) [ 34 ]. Triangulation occurred in two ways: informant triangulation through the inclusion of stakeholders from multiple countries, and data triangulation through the use of both quantitative and qualitative research methods. Transferability of the research is increased through a detailed description of the context of the research, the data collection, and data analysis.

Ethical considerations

In accordance with the Declaration of Helsinki, ethical approval for the hospital survey was granted by the University of Global Health Equity Institutional Review Board, approval number UGHE-IRB/2022/056, and by the Rwanda National Health Research Committee, approval number NHRC/2022/PROT/050. Ethical approval for the interviews was granted by the Ethics Review Board of the faculties of Science and Geosciences, Utrecht University, approval number S-23,008. Informed consent was given by all participants (see Additional File 3 ).

Availability of anaesthetic commodities in Rwandan hospitals

In total, 54 hospitals participated in this study. One hospital declined participation. The general anaesthetic with highest availability was propofol (81.5%), followed by ketamine (77.8%). Inhalational agents, such as halothane, isoflurane or sevoflurane were available at 53.7% of the hospitals, and thiopental at 44.4%. All hospitals had running water and electricity, and 90.7% had a functional anaesthetic machine.

Ketamine was indicated as the general anaesthetic agent most used by 23 hospitals (42.6%). Twenty hospitals (37.0%) indicated it was propofol, while the remaining ten medical professionals (18.5%) indicated it was halothane. Data was missing for one hospital. In the hospitals where ketamine was the most used anaesthetic agent, it was also the anaesthetic agent with the highest availability at 82.6% (see Fig.  1 ). In the 30 hospitals where other general anaesthetic agents were indicated to be most used, highest availability was found for propofol (90.0%, see Fig.  1 ).

Anaesthetic agents’ availability in hospitals, stratified by self-reported most used anaesthetic agent

Key informant interviews

Sixty-nine individuals or national anaesthesia societies were contacted for participation in the study, of which ten agreed to participate. Key informant characteristics are provided in Table  1 . Nine informants were knowledgeable about a country-specific context, while one informant (a WFSA member from Europe) had knowledge about the Sub-Saharan region in general.

Barriers to anaesthesia care

Multiple barriers to anaesthesia care were highlighted by the key informants. One of the main issues raised by all participants, was the lack of anaesthesiologists (Table  2 , Quote 1). The number of anaesthesiologists was said to be critically low, with all anaesthesiologists primarily located in urban locations, in the more specialised hospitals. The key informant from the Democratic Republic of Congo (DRC) sketched this situation (Table  2 , Quote 2). As a consequence, anaesthesia care is provided by non-physician providers, such as nurses and medical officers. However, eight of the key informants reported that these non-physician providers often had limited training in anaesthesia care, and do not have the skills or knowledge to provide more complex anaesthesia. This issue was highlighted by the key informant from Zambia (Table  2 , Quote 3). One key informant also reported that protocols are not followed in some locations when providing anaesthesia care.

Another issue raised by the two informants from South Africa and Zimbabwe, was migration of trained anaesthesiologists, both within the country and abroad. For example, anaesthesiologists moved towards the private sectors, as they are offered better wages and working conditions there (Table  2 , Quote 4). Crucially, the lack of medicines and equipment was also a significant barrier to anaesthesia care. Nine of the informants reported that the lack of medicines and equipment experienced in health facilities impedes the provision of anaesthesia care (Table  2 , Quote 5). The respondent from Zimbabwe mentioned that the government, as part of the National Surgical, Obstetrician and Anaesthesia Strategy is purchasing equipment to tackle this problem. In South Africa, the respondent shared that availability of medicines has improved and is not a major issue there.

Lastly, four informants specifically mentioned the lack of training opportunities and attention, and subsequently the lack of budget, given to anaesthesia care. The informant from Ethiopia referred to the government’s primary policy focus on prevention of infectious diseases, not on chronic diseases. The respondent from Namibia shared that only since 2018, doctors can train to become anaesthesiologists as part of the Namibian medical curriculum; before they needed to travel to other countries, such as South Africa, to study. In the Gambia there is no training available yet for anaesthesiologists. The informant from Zambia referred to the lack of attention among medical professionals and the public, as well as policy makers, as the main barrier to anaesthesia care (Table  2 , Quote 6).

Ketamine for anaesthesia care

Ketamine was described as critical for the provision of anaesthesia care in their respective countries by all of the key informants. Five of the informants reported that in more specialised hospitals, where anaesthesiologists provide anaesthesia care, propofol, also a non-controlled substance, was the preferred anaesthetic. However, ketamine is also commonly used in these hospitals, specifically for haemodynamically unstable patients, hypotensive patients, patients who are in shock, and as a sedative in paediatric patients, patients with asthma or patients on the intensive care unit (ICU). Ketamine is also used for pain management. Four of the informants also referred to shortages of anaesthetic agents, such as propofol, that occurred in the specialised hospitals, which made them reliant on ketamine (Table  3 , Quote 1).

One of the primary reasons given for the importance of ketamine by all of the informants, is that it can easily be used by non-physician providers, who provide the bulk of anaesthesia services, especially in rural areas (Table  3 , Quote 2). Informants shared that non-physician providers prefer to use ketamine as they are uncomfortable providing anaesthesia with alternatives because of potential side effects. Further, these providers often have only received a basic training in anaesthesia care and are not conversant with providing other anaesthetic agents (Table  3 , Quote 3). Related, in lower-level hospitals and in rural areas, a lack of equipment, such as anaesthetic machines, exacerbated the difficulties of providing anaesthesia, and increased the reliance on ketamine, as they were fearful of the adverse consequences, and the possibility of death, when using other anaesthetic agents (Table  3 , Quote 4). The informants from Somaliland and Nigeria raised the issue of affordability of medicines, and that next to ketamine being the most available anaesthetic agent, it was also the most affordable (Table  3 , Quote 5).

When the key informants were asked about the availability of anaesthetic agents in their respective countries as compared to the findings of the survey conducted in Rwanda, variations were reported. First, the informants emphasised it is difficult to report exact availabilities of the anaesthetic agents without conducting a similar survey. However, the informants shared that ketamine availability would be similar, or even higher, in their countries. Zambia was an exception, as the informant reported that for months preceding the interview, there had been critical shortages of ketamine (Table  3 , Quote 6). The informant did not know the reason for the shortages. Informants from the DRC, Ethiopia, Nigeria, Somaliland and Zambia shared that the availability of propofol would be (slightly) lower than in Rwanda, especially in rural hospitals, while the informants from the Gambia, Namibia and South Africa shared that it would be more or less similar.

Misuse of ketamine

None of the key informants reported that misuse of ketamine was a significant issue in their respective countries, as far as they were aware. Three informants offered anecdotal evidence of specific instances of misuse that they knew or had heard about, although the case reported by the informant from the DRC about a sickle cell patient misusing it for the treatment of vaso-occlusive crisis is strictly speaking not about misuse but unlicensed use. Two informants shared that there was some misuse of ketamine among medical professionals in their countries. The Zambian informant shared that a medical professional had died as a consequence of the misuse. The South African informant reported that while she was aware of medical professionals that had misused ketamine and this issue should not be overlooked, the balance between control and access should be kept in mind (Table  4 , Quote 1).

In all the other countries, the informants were unaware of misuse cases among medical professionals. Additionally, all of the informants shared that ketamine misuse among the general public was not an issue. The informant from Nigeria shared their opinion that ketamine may be misused among the internally displaced. Some of the informants also shared that if misuse is occurring in high-income countries, it might eventually also happen in their countries (Table  4 , Quote 2).

International scheduling of ketamine as a controlled substance

In three of the nine countries in which the informants work, ketamine is scheduled or regulated to some extent at the national level. In the Gambia, Namibia and South Africa, ketamine is stored in a locked cabinet, and medical professionals are required to request ketamine, and the release is signed off in a logbook by both the requesting medical professional as well as an in-charge nurse. However, in Namibia and South Africa informants shared that this procedure is not always followed as tightly as it might need to be (Table  4 , Quote 3). In the other countries, ketamine was not subject to additional, national control. Some of the informants from these countries could see the added value of having such controls at the national level for better stewardship (Table  4 , Quote 4).

If ketamine were to be scheduled as a controlled substance at the international level, it was believed it would negatively impact access in the informants’ respective countries, especially in the more rural locations. They all emphasised the critical importance of ketamine (Table  4 , Quote 5). Informants from Namibia, the Gambia and Somaliland also made the comparison to already controlled substances, fearing the availability of ketamine would decrease to similar levels (Table  4 , Quote 6). Next to the availability, some informants also raised concerns about increased costs of ketamine as a consequence of its scheduling, which would hamper access. In Zambia, where there is currently a shortage of ketamine, the informant shared their fears of this being the new reality. Lastly, one of the informants argued that LMICs and high-income countries should not be subjected to the same measures as they have very different resources available to them (Table  4 , Quote 7).

Recommendations to improve access to anaesthesia care

Recommendations made by the key informants to improve access to anaesthesia care were related to increasing attention and budgets for anaesthesia care, training and retention of anaesthesiologists and non-physician providers, improving availability of medicines and equipment, and decentralisation of care. For example, the key informant from Europe representing the global view argued that countries need to take responsibility and put resources into anaesthesia care (Table  5 , Quote 1). Similarly, the key informant from the Gambia argued for increasing the incentives to work in anaesthesia (Table  5 , Quote 2). In line with this, the informant from Ethiopia argued for better collaboration between medical professionals and the Ministry of Health to ensure the medicines provided are the ones needed. The informant from Zimbabwe highlighted that, while much can still be improved, in the last few years, more and more attention has been paid to anaesthesia care. The informant from Nigeria pointed to COVID-19 for the increased availability of equipment, but also stressed the need for better policies without waiting for another pandemic to occur (Table  5 , Quote 3). The importance of training of medical professionals was highlighted by the informant from Namibia (Table  5 , Quote 4), while the informant from South Africa added the need to find a way to retain their trained specialists, as many are leaving to work in high-income countries. Last, the same informant also emphasised the importance of decentralisation of care, in which anaesthesiologists should go to rural areas to treat patients, instead of patients travelling far to come to the specialised hospitals in the big cities (Table  5 , Quote 5).

This is a first-of-its kind research on the importance of ketamine as detailed by anaesthesiologists working in SSA. It also studied the availability of ketamine compared to other anaesthetic agents specifically in Rwandan hospitals. The interviews with the key informants from across SSA found that there were significant barriers impeding access to anaesthesia care, including a general lack of attention given to the speciality by governments, a shortage of anaesthesiologists and migration of trained anaesthesiologists, and a scarcity of medicines and equipment. Ketamine was described as critical for the provision of anaesthesia care as a consequence of these barriers, and its scheduling would have a significantly negative impact on the quality of anaesthesia care that can be provided. The survey conducted in Rwanda found that availability of ketamine and propofol was comparable at around 80%, while thiopental and inhalational agents such as halothane, isoflurane or sevoflurane were available at only about half of the hospitals.

These barriers to anaesthesia care identified in this study have been identified previously in different contexts, and this research supports those findings [ 4 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. When the key informants were asked whether the availability in their respective countries was comparable to the availability found in Rwanda, the responses were variable. This is in line with previous research studying the availability of anaesthetic agents [ 6 , 17 , 21 ]. For instance, a study from Liberia found that ketamine was available 76-100% of the time in 88% of surveyed facilities, and this was the case for propofol in only 46% of facilities [ 6 ]. Similarly, while anaesthesia using ketamine was available in 13 of 14 health facilities surveyed in Somalia, anaesthesia using inhalational agents was available at five of the facilities [ 17 ]. Further, all surveyed hospitals in Rwanda had running water and electricity. Previous studies in Nigeria and Somalia found that access to running water and electricity was not guaranteed; the study in Nigeria found that hospitals suffered daily power outages ranging from 10 to 22 h, and only 15% had running water [ 18 ]. In Somalia, 28% of surveyed health facilities never or only sometimes had access to running water, and only 50% had consistent access to electricity [ 17 ]. Last, in this study it was found that 90.7% of hospitals had a functional anaesthesia machine. In Tanzania, Nigeria and Somalia, 67%, 23% and 15% of hospitals, respectively, had a functional anaesthesia machine available [ 15 , 17 , 18 ].

The case study of Rwanda thus may not be representative of the availability in other countries in the region. However, this research has shown that even when other anaesthetic agents, such as propofol, are available, much of anaesthesia care is still provided using ketamine. This is due to the lack of trained anaesthesiologists, and the subsequent reliance on non-specialist anaesthesia providers, such as nurses and medical officers. These non-physician providers feel better prepared to provide anaesthesia using ketamine, as there are much fewer potential side-effects than the other agents. This has also been described elsewhere [ 27 , 35 ]. Further, also in more specialised hospitals where anaesthesiologists are present to provide anaesthesia care, key informants shared ketamine is still one of the main anaesthetic agents used due to shortages of propofol that occur. A study conducted in district hospitals in Malawi, Zambia and Tanzania reported similar findings, showing that anaesthesia care at the district level is provided only by non-physician anaesthesia providers, and that ketamine was widely used to mitigate shortages of other anaesthetic agents [ 8 ].

In this study, the key informants reported that, as far as they were aware, misuse of ketamine is not a significant issue in their respective countries. A few did provide anecdotal evidence of specific instances of misuse among medical professionals. However, all informants believed scheduling ketamine internationally as a controlled substance would have a negative impact on access to anaesthesia care, as its availability would likely decrease. This fear is not unsubstantiated, as multiple informants referred to the difficulties with accessing opioids in their countries. In line with this, while in Liberia and Ethiopia ketamine was (almost) always available in 88% and 100% of facilities, respectively, morphine was (almost) always available at only 35% and 27% of facilities, respectively [ 6 ]. Consequences of international scheduling are restrictions on production, manufacturing, importation, distribution and use of medicines, resulting in severely limited access to controlled medicines [ 36 ]. It is thus paramount that ketamine does not become a scheduled substance. Instead, to safeguard against potential ketamine misuse in their respective countries, key informants believed in strengthening prescribing and dispensing practices in the healthcare setting. In many countries, ketamine is still freely available for all healthcare workers. Limiting ketamine so it is only obtainable for those allowed to use it may prevent future misuse. In Namibia, for example, ketamine is a Schedule 3 substance, and subsequently needs to be locked away and can only be sold or provided by designated personnel, on the basis of a prescription. The amount sold or provided has to be recorded in a logbook or prescription book [ 37 ].

Limitations

While this is the first study collecting experts’ insights into the importance of ketamine for anaesthesia care in SSA, some limitations should be noted. In the survey conducted in Rwandan hospitals, no price or stock data was collected for the anaesthetic agents. This might have provided insights into the differences in costs between the different agents, and the availability over time. While the hospitals in Rwanda were contacted beforehand to schedule a visit for the survey, because the data collected for this study was part of a larger study on snakebites it is believed that hospitals could not have taken measures that might have changed the availability numbers. Further, while more than 60 individuals and national anaesthesia societies were contacted, only ten individuals agreed to participate. Of these, none were unfortunately from Rwanda and only two were from West Africa. Due to this low number of respondents, it is difficult to assess whether topical saturation was fully reached. However, after initial analysis of eight interviews, the subsequent analysis of the last two interviews did not yield new insights, indicating potential data saturation. This study thus gives a first, detailed insight into the importance of ketamine for anaesthesia care in SSA. Further research may be undertaken to tease out more detailed, contextual factors that may not have been caught in this study.

This study has shown that ketamine is a critical medicine for the provision of anaesthesia care in SSA, as this field faces barriers related to its workforce and availability of medicines and equipment. If accessibility of ketamine changes as a result of its international scheduling, millions of people’s access to safe anaesthesia and surgical care will be in danger. Countries should strengthen prescribing and dispensing practices in the healthcare setting. Further, concerted efforts should focus on improving anaesthesia care in SSA in general, so in the future there can be less of a reliance on ketamine. Governments should focus more of their attention on the speciality, allocating more budget, facilitating training of more anaesthesiologists and non-physician providers, improving availability of medicines and equipment, as well as focusing efforts on retaining their anaesthesia workforce.

Data availability

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

Abbreviations

Commission on Narcotic Drugs

Disability-adjusted life-years

Democratic Republic of Congo

Expert Committee on Drug Dependence

Intensive care unit

Low- and middle-income countries

Physician anaesthesia providers

Consolidated Criteria for Reporting Qualitative Research

Sub-Saharan Africa

World Federation of Societies for Anaesthesiologists

World Health Organization

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Acknowledgements

We would like to thank the key informants and hospitals for participating in this research.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Gaby I. Ooms & Tim Reed

Utrecht WHO Collaborating Centre for Pharmaceutical Policy and Regulation, Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, The Netherlands

Gaby I. Ooms, Hendrika A. van den Ham & Aukje K. Mantel-Teeuwisse

Federal University of Dutse, Dutse, Jigawa State, Nigeria

Mohammed A. Usman

Rasheed Shekoni Teaching Hospital, Dutse, Jigawa State, Nigeria

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GIO: conceptualisation, methodology, investigation, data curation, formal analysis, writing – original draft, visualisation; UAM: methodology, validation, writing – review and editing; HAvdH: conceptualisation, methodology, validation, writing – review and editing; AKM: conceptualisation, methodology, validation, writing – review and editing; TR: conceptualisation, methodology, validation, writing – review and editing.

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Correspondence to Gaby I. Ooms .

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In accordance with the Declaration of Helsinki, ethical approval for the hospital survey was granted by the University of Global Health Equity Institutional Review Board, approval number UGHE-IRB/2022/056, and by the Rwanda National Health Research Committee, approval number NHRC/2022/PROT/050. Ethical approval for the interviews was granted by the Ethics Review Board of the faculties of Science and Geosciences, Utrecht University, approval number S-23008. Informed consent was given by all participants.

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Ooms, G.I., Usman, M.A., Reed, T. et al. The impact of scheduling ketamine as an internationally controlled substance on anaesthesia care in Sub-Saharan Africa: a case study and key informant interviews. BMC Health Serv Res 24 , 598 (2024). https://doi.org/10.1186/s12913-024-11040-w

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Received : 16 November 2023

Accepted : 24 April 2024

Published : 07 May 2024

DOI : https://doi.org/10.1186/s12913-024-11040-w

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