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What is Field Research: Definition, Methods, Examples and Advantages

What is Field Research?

Field research is defined as a qualitative method of data collection that aims to observe, interact and understand people while they are in a natural environment. For example, nature conservationists observe behavior of animals in their natural surroundings and the way they react to certain scenarios. In the same way, social scientists conducting field research may conduct interviews or observe people from a distance to understand how they behave in a social environment and how they react to situations around them.

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Field research encompasses a diverse range of social research methods including direct observation, limited participation, analysis of documents and other information, informal interviews, surveys etc. Although field research is generally characterized as qualitative research, it often involves multiple aspects of quantitative research in it.

Field research typically begins in a specific setting although the end objective of the study is to observe and analyze the specific behavior of a subject in that setting. The cause and effect of a certain behavior, though, is tough to analyze due to presence of multiple variables in a natural environment. Most of the data collection is based not entirely on cause and effect but mostly on correlation. While field research looks for correlation, the small sample size makes it difficult to establish a causal relationship between two or more variables.

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Methods of Field Research

Field research is typically conducted in 5 distinctive methods. They are:

• Direct Observation

In this method, the data is collected via an observational method or subjects in a natural environment. In this method, the behavior or outcome of situation is not interfered in any way by the researcher. The advantage of direct observation is that it offers contextual data on people management , situations, interactions and the surroundings. This method of field research is widely used in a public setting or environment but not in a private environment as it raises an ethical dilemma.

• Participant Observation

In this method of field research, the researcher is deeply involved in the research process, not just purely as an observer, but also as a participant. This method too is conducted in a natural environment but the only difference is the researcher gets involved in the discussions and can mould the direction of the discussions. In this method, researchers live in a comfortable environment with the participants of the research design , to make them comfortable and open up to in-depth discussions.

• Ethnography

Ethnography is an expanded observation of social research and social perspective and the cultural values of an  entire social setting. In ethnography, entire communities are observed objectively. For example,  if a researcher would like to understand how an Amazon tribe lives their life and operates, he/she may chose to observe them or live amongst them and silently observe their day-to-day behavior.

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• Qualitative Interviews

Qualitative interviews are close-ended questions that are asked directly to the research subjects. The qualitative interviews could be either informal and conversational, semi-structured, standardized and open-ended or a mix of all the above three. This provides a wealth of data to the researcher that they can sort through. This also helps collect relational data. This method of field research can use a mix of one-on-one interviews, focus groups and text analysis .

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A case study research is an in-depth analysis of a person, situation or event. This method may look difficult to operate, however, it is one of the simplest ways of conducting research as it involves a deep dive and thorough understanding the data collection methods and inferring the data.

Steps in Conducting Field Research

Due to the nature of field research, the magnitude of timelines and costs involved, field research can be very tough to plan, implement and measure. Some basic steps in the management of field research are:

• Build the Right Team: To be able to conduct field research, having the right team is important. The role of the researcher and any ancillary team members is very important and defining the tasks they have to carry out with defined relevant milestones is important. It is important that the upper management too is vested in the field research for its success.
• Recruiting People for the Study: The success of the field research depends on the people that the study is being conducted on. Using sampling methods , it is important to derive the people that will be a part of the study.
• Data Collection Methodology: As spoken in length about above, data collection methods for field research are varied. They could be a mix of surveys, interviews, case studies and observation. All these methods have to be chalked out and the milestones for each method too have to be chalked out at the outset. For example, in the case of a survey, the survey design is important that it is created and tested even before the research begins.
• Site Visit: A site visit is important to the success of the field research and it is always conducted outside of traditional locations and in the actual natural environment of the respondent/s. Hence, planning a site visit alongwith the methods of data collection is important.
• Data Analysis: Analysis of the data that is collected is important to validate the premise of the field research and  decide the outcome of the field research.
• Communicating Results: Once the data is analyzed, it is important to communicate the results to the stakeholders of the research so that it could be actioned upon.

LEARN ABOUT: Research Process Steps

Field Research Notes

Keeping an ethnographic record is very important in conducting field research. Field notes make up one of the most important aspects of the ethnographic record. The process of field notes begins as the researcher is involved in the observational research process that is to be written down later.

Types of Field Research Notes

The four different kinds of field notes are:

• Job Notes: This method of taking notes is while the researcher is in the study. This could be in close proximity and in open sight with the subject in study. The notes here are short, concise and in condensed form that can be built on by the researcher later. Most researchers do not prefer this method though due to the fear of feeling that the respondent may not take them seriously.
• Field Notes Proper: These notes are to be expanded on immediately after the completion of events. The notes have to be detailed and the words have to be as close to possible as the subject being studied.
• Methodological Notes: These notes contain methods on the research methods used by the researcher, any new proposed research methods and the way to monitor their progress. Methodological notes can be kept with field notes or filed separately but they find their way to the end report of a study.
• Journals and Diaries: This method of field notes is an insight into the life of the researcher. This tracks all aspects of the researchers life and helps eliminate the Halo effect or any research bias that may have cropped up during the field research.

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Reasons to Conduct Field Research

Field research has been commonly used in the 20th century in the social sciences. But in general, it takes a lot of time to conduct and complete, is expensive and in a lot of cases invasive. So why then is this commonly used and is preferred by researchers to validate data? We look at 4 major reasons:

• Overcoming lack of data: Field research resolves the major issue of gaps in data. Very often, there is limited to no data about a topic in study, especially in a specific environment analysis . The research problem might be known or suspected but there is no way to validate this without primary research and data. Conducting field research helps not only plug-in gaps in data but collect supporting material and hence is a preferred research method of researchers.
• Understanding context of the study: In many cases, the data collected is adequate but field research is still conducted. This helps gain insight into the existing data. For example, if the data states that horses from a stable farm generally win races because the horses are pedigreed and the stable owner hires the best jockeys. But conducting field research can throw light into other factors that influence the success like quality of fodder and care provided and conducive weather conditions.
• Increasing the quality of data: Since this research method uses more than one tool to collect data, the data is of higher quality. Inferences can be made from the data collected and can be statistically analyzed via the triangulation of data.
• Collecting ancillary data: Field research puts the researchers in a position of localized thinking which opens them new lines of thinking. This can help collect data that the study didn’t account to collect.

LEARN ABOUT: Behavioral Research

Examples of Field Research

Some examples of field research are:

• Decipher social metrics in a slum Purely by using observational methods and in-depth interviews, researchers can be part of a community to understand the social metrics and social hierarchy of a slum. This study can also understand the financial independence and day-to-day operational nuances of a slum. The analysis of this data can provide an insight into how different a slum is from structured societies.
• U nderstand the impact of sports on a child’s development This method of field research takes multiple years to conduct and the sample size can be very large. The data analysis of this research provides insights into how the kids of different geographical locations and backgrounds respond to sports and the impact of sports on their all round development.
• Study animal migration patterns Field research is used extensively to study flora and fauna. A major use case is scientists monitoring and studying animal migration patterns with the change of seasons. Field research helps collect data across years and that helps draw conclusions about how to safely expedite the safe passage of animals.

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Advantages of Field Research

The advantages of field research are:

• It is conducted in a real-world and natural environment where there is no tampering of variables and the environment is not doctored.
• Due to the study being conducted in a comfortable environment, data can be collected even about ancillary topics.
• The researcher gains a deep understanding into the research subjects due to the proximity to them and hence the research is extensive, thorough and accurate.

Disadvantages of Field Research

The disadvantages of field research are:

• The studies are expensive and time-consuming and can take years to complete.
• It is very difficult for the researcher to distance themselves from a bias in the research study.
• The notes have to be exactly what the researcher says but the nomenclature is very tough to follow.
• It is an interpretive method and this is subjective and entirely dependent on the ability of the researcher.
• In this method, it is impossible to control external variables and this constantly alters the nature of the research.

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Methodology

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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What is case study research?

Last updated

8 February 2023

Reviewed by

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Suppose a company receives a spike in the number of customer complaints, or medical experts discover an outbreak of illness affecting children but are not quite sure of the reason. In both cases, carrying out a case study could be the best way to get answers.

Organization

Case studies can be carried out across different disciplines, including education, medicine, sociology, and business.

Most case studies employ qualitative methods, but quantitative methods can also be used. Researchers can then describe, compare, evaluate, and identify patterns or cause-and-effect relationships between the various variables under study. They can then use this knowledge to decide what action to take. 

Another thing to note is that case studies are generally singular in their focus. This means they narrow focus to a particular area, making them highly subjective. You cannot always generalize the results of a case study and apply them to a larger population. However, they are valuable tools to illustrate a principle or develop a thesis.

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• What are the different types of case study designs?

Researchers can choose from a variety of case study designs. The design they choose is dependent on what questions they need to answer, the context of the research environment, how much data they already have, and what resources are available.

Here are the common types of case study design:

Explanatory

An explanatory case study is an initial explanation of the how or why that is behind something. This design is commonly used when studying a real-life phenomenon or event. Once the organization understands the reasons behind a phenomenon, it can then make changes to enhance or eliminate the variables causing it. 

Here is an example: How is co-teaching implemented in elementary schools? The title for a case study of this subject could be “Case Study of the Implementation of Co-Teaching in Elementary Schools.”

Descriptive

An illustrative or descriptive case study helps researchers shed light on an unfamiliar object or subject after a period of time. The case study provides an in-depth review of the issue at hand and adds real-world examples in the area the researcher wants the audience to understand. 

The researcher makes no inferences or causal statements about the object or subject under review. This type of design is often used to understand cultural shifts.

Here is an example: How did people cope with the 2004 Indian Ocean Tsunami? This case study could be titled "A Case Study of the 2004 Indian Ocean Tsunami and its Effect on the Indonesian Population."

Exploratory

Exploratory research is also called a pilot case study. It is usually the first step within a larger research project, often relying on questionnaires and surveys . Researchers use exploratory research to help narrow down their focus, define parameters, draft a specific research question , and/or identify variables in a larger study. This research design usually covers a wider area than others, and focuses on the ‘what’ and ‘who’ of a topic.

Here is an example: How do nutrition and socialization in early childhood affect learning in children? The title of the exploratory study may be “Case Study of the Effects of Nutrition and Socialization on Learning in Early Childhood.”

An intrinsic case study is specifically designed to look at a unique and special phenomenon. At the start of the study, the researcher defines the phenomenon and the uniqueness that differentiates it from others. 

In this case, researchers do not attempt to generalize, compare, or challenge the existing assumptions. Instead, they explore the unique variables to enhance understanding. Here is an example: “Case Study of Volcanic Lightning.”

This design can also be identified as a cumulative case study. It uses information from past studies or observations of groups of people in certain settings as the foundation of the new study. Given that it takes multiple areas into account, it allows for greater generalization than a single case study. 

The researchers also get an in-depth look at a particular subject from different viewpoints.  Here is an example: “Case Study of how PTSD affected Vietnam and Gulf War Veterans Differently Due to Advances in Military Technology.”

Critical instance

A critical case study incorporates both explanatory and intrinsic study designs. It does not have predetermined purposes beyond an investigation of the said subject. It can be used for a deeper explanation of the cause-and-effect relationship. It can also be used to question a common assumption or myth. 

The findings can then be used further to generalize whether they would also apply in a different environment.  Here is an example: “What Effect Does Prolonged Use of Social Media Have on the Mind of American Youth?”

Instrumental

Instrumental research attempts to achieve goals beyond understanding the object at hand. Researchers explore a larger subject through different, separate studies and use the findings to understand its relationship to another subject. This type of design also provides insight into an issue or helps refine a theory. 

For example, you may want to determine if violent behavior in children predisposes them to crime later in life. The focus is on the relationship between children and violent behavior, and why certain children do become violent. Here is an example: “Violence Breeds Violence: Childhood Exposure and Participation in Adult Crime.”

Evaluation case study design is employed to research the effects of a program, policy, or intervention, and assess its effectiveness and impact on future decision-making. 

For example, you might want to see whether children learn times tables quicker through an educational game on their iPad versus a more teacher-led intervention. Here is an example: “An Investigation of the Impact of an iPad Multiplication Game for Primary School Children.” 

• When do you use case studies?

Case studies are ideal when you want to gain a contextual, concrete, or in-depth understanding of a particular subject. It helps you understand the characteristics, implications, and meanings of the subject.

They are also an excellent choice for those writing a thesis or dissertation, as they help keep the project focused on a particular area when resources or time may be too limited to cover a wider one. You may have to conduct several case studies to explore different aspects of the subject in question and understand the problem.

• What are the steps to follow when conducting a case study?

1. Select a case

Once you identify the problem at hand and come up with questions, identify the case you will focus on. The study can provide insights into the subject at hand, challenge existing assumptions, propose a course of action, and/or open up new areas for further research.

2. Create a theoretical framework

While you will be focusing on a specific detail, the case study design you choose should be linked to existing knowledge on the topic. This prevents it from becoming an isolated description and allows for enhancing the existing information. 

It may expand the current theory by bringing up new ideas or concepts, challenge established assumptions, or exemplify a theory by exploring how it answers the problem at hand. A theoretical framework starts with a literature review of the sources relevant to the topic in focus. This helps in identifying key concepts to guide analysis and interpretation.

3. Collect the data

Case studies are frequently supplemented with qualitative data such as observations, interviews, and a review of both primary and secondary sources such as official records, news articles, and photographs. There may also be quantitative data —this data assists in understanding the case thoroughly.

4. Analyze your case

The results of the research depend on the research design. Most case studies are structured with chapters or topic headings for easy explanation and presentation. Others may be written as narratives to allow researchers to explore various angles of the topic and analyze its meanings and implications.

In all areas, always give a detailed contextual understanding of the case and connect it to the existing theory and literature before discussing how it fits into your problem area.

• What are some case study examples?

What are the best approaches for introducing our product into the Kenyan market?

How does the change in marketing strategy aid in increasing the sales volumes of product Y?

How can teachers enhance student participation in classrooms?

How does poverty affect literacy levels in children?

Case study topics

Case study of product marketing strategies in the Kenyan market

Case study of the effects of a marketing strategy change on product Y sales volumes

Case study of X school teachers that encourage active student participation in the classroom

Case study of the effects of poverty on literacy levels in children

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• Knowledge Base
• Methodology
• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

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

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

Table of contents

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

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

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

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

Prevent plagiarism, run a free check.

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

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

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

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

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

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

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

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

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

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

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

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

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, January 30). Case Study | Definition, Examples & Methods. Scribbr. Retrieved 19 August 2024, from https://www.scribbr.co.uk/research-methods/case-studies/

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Shona McCombes

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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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Qualitative study design: Field research

• Qualitative study design
• Phenomenology
• Grounded theory
• Ethnography
• Narrative inquiry
• Action research
• Case Studies

Field research

• Focus groups
• Observation
• Surveys & questionnaires
• Study Designs Home

To understand attitudes, practices, roles, organisations, groups, or behaviours in their natural setting

In a way you have probably done field research before – when you’ve been in a doctor’s waiting room, or on an aeroplane. Field research is at its core about observing and participating in social behaviour and trying to understand it. Qualitative field research takes these natural skills and curiosities and refines them to address and answer a research question The “field” is vast, consisting of numerous people, activities, events, and words. When undertaking field research, the researcher needs to determine the exact activities or practices that are of interest to the researcher to answer their research question. Instead of the more artificial environment of an interview or survey, field research lets researchers observe subtle communications, cues, or other events that they may not have anticipated or even measured otherwise.

Field research is often referred to interchangeably as “participant observation”. Participant observation is a type of field research where the researcher is an active participant in the everyday life, habits, or beliefs of the field alongside members. An example of this might be where a researcher goes into a hospital and works alongside hospital staff. A contrast to this is “direct observation”, a type of field research where the researcher observes members in the field but doesn’t actively participate. An example might be a researcher who sits at a hospital cafeteria and observes staff who may not realize they’re being studied.

You may be wondering what the difference is between ethnography and field research. The two terms are often used interchangeably, so it can be a really blurred line! Ethnography is about making sense of culture – it’s about making a detailed overview of the social group and organising your information. Field research is going out into the field – so describing “how” you’re going to conduct research. Ethnographical research can be field research (as in, you’re studying the culture of a hospital by observing within the hospital), or field research can be ethnographic (you’re observing staff in a hospital to see how staff handle crisis intervention). It’s a fine line between the two, and even experienced researchers can be unsure of the difference (or even use the terms interchangeably, depending on discipline), so when in doubt, it is best to talk to your supervisor or an experienced researcher in this discipline

Different studies may benefit from different degrees of researcher involvement. Ultimately, the researcher needs to be sensitive to the impact their presence might have on the data and on participants – and also aware of any ethical requirements around this study type, such as informed consent, duties to report (such as if the researcher observes criminal activities), and confidentiality and privacy of participants.

Observation, unstructured interviews

• Allows for observation in a natural setting
• Picks up on subtle cues
• Allows in depth exploration which contributes to a full appreciation of what’s being studied, including “whys” around human behaviour

Limitations

• Requires a high degree of sensitivity by the researcher to the impact of the research and their presence on participants and on the data
• Risk of reactivity, where research subjects may alter their behaviour from what it would have been normally as a result of being studied
• Ethical considerations involved in insider research
• Possible loss of objectivity

Example questions

How do student nurses integrate their training into care provision at end-of-life?

Example studies

• Barber-Parker, E. (2002). Integrating patient teaching into bedside patient care: a participant-observation study of hospital nurses.  Patient Education and Counselling, 48 ( 2): 107-113  
• Shikuku, D., Milimo, B., Ayebare, E., Gisore, P., & Gorrette, N. (2018). Practice and outcomes of neonatal resuscitation for newborns with birth asphyxia at Kakamega County General Hospital, Kenya: a direct observation study, BMC Pediatrics, 18 (1), doi: 10.1186/s12887-018-1127-6  

Babbie, E. (2008). The basics of social research (4th ed). Belmont: Thomson Wadsworth  

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Article Contents

Introduction, what is fieldwork, purpose of fieldwork, physical safety, mental wellbeing and affect, ethical considerations, remote fieldwork, concluding thoughts, acknowledgments, funder information.

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Field Research: A Graduate Student's Guide

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Ezgi Irgil, Anne-Kathrin Kreft, Myunghee Lee, Charmaine N Willis, Kelebogile Zvobgo, Field Research: A Graduate Student's Guide, International Studies Review , Volume 23, Issue 4, December 2021, Pages 1495–1517, https://doi.org/10.1093/isr/viab023

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What is field research? Is it just for qualitative scholars? Must it be done in a foreign country? How much time in the field is “enough”? A lack of disciplinary consensus on what constitutes “field research” or “fieldwork” has left graduate students in political science underinformed and thus underequipped to leverage site-intensive research to address issues of interest and urgency across the subfields. Uneven training in Ph.D. programs has also left early-career researchers underprepared for the logistics of fieldwork, from developing networks and effective sampling strategies to building respondents’ trust, and related issues of funding, physical safety, mental health, research ethics, and crisis response. Based on the experience of five junior scholars, this paper offers answers to questions that graduate students puzzle over, often without the benefit of others’ “lessons learned.” This practical guide engages theory and praxis, in support of an epistemologically and methodologically pluralistic discipline.

¿Qué es la investigación de campo? ¿Es solo para académicos cualitativos? ¿Debe realizarse en un país extranjero? ¿Cuánto tiempo en el terreno es “suficiente”? La falta de consenso disciplinario con respecto a qué constituye la “investigación de campo” o el “trabajo de campo” ha causado que los estudiantes de posgrado en ciencias políticas estén poco informados y, por lo tanto, capacitados de manera insuficiente para aprovechar la investigación exhaustiva en el sitio con el objetivo de abordar los asuntos urgentes y de interés en los subcampos. La capacitación desigual en los programas de doctorado también ha provocado que los investigadores en las primeras etapas de su carrera estén poco preparados para la logística del trabajo de campo, desde desarrollar redes y estrategias de muestreo efectivas hasta generar la confianza de las personas que facilitan la información, y las cuestiones relacionadas con la financiación, la seguridad física, la salud mental, la ética de la investigación y la respuesta a las situaciones de crisis. Con base en la experiencia de cinco académicos novatos, este artículo ofrece respuestas a las preguntas que desconciertan a los estudiantes de posgrado, a menudo, sin el beneficio de las “lecciones aprendidas” de otras personas. Esta guía práctica incluye teoría y praxis, en apoyo de una disciplina pluralista desde el punto de vista epistemológico y metodológico.

En quoi consiste la recherche de terain ? Est-elle uniquement réservée aux chercheurs qualitatifs ? Doit-elle être effectuée dans un pays étranger ? Combien de temps faut-il passer sur le terrain pour que ce soit « suffisant » ? Le manque de consensus disciplinaire sur ce qui constitue une « recherche de terrain » ou un « travail de terrain » a laissé les étudiants diplômés en sciences politiques sous-informés et donc sous-équipés pour tirer parti des recherches de terrain intensives afin d'aborder les questions d'intérêt et d'urgence dans les sous-domaines. L'inégalité de formation des programmes de doctorat a mené à une préparation insuffisante des chercheurs en début de carrière à la logistique du travail de terrain, qu'il s'agisse du développement de réseaux et de stratégies d’échantillonnage efficaces, de l'acquisition de la confiance des personnes interrogées ou des questions de financement, de sécurité physique, de santé mentale, d’éthique de recherche et de réponse aux crises qui y sont associées. Cet article s'appuie sur l'expérience de cinq jeunes chercheurs pour proposer des réponses aux questions que les étudiants diplômés se posent, souvent sans bénéficier des « enseignements tirés » par les autres. Ce guide pratique engage théorie et pratique en soutien à une discipline épistémologiquement et méthodologiquement pluraliste.

Days before embarking on her first field research trip, a Ph.D. student worries about whether she will be able to collect the qualitative data that she needs for her dissertation. Despite sending dozens of emails, she has received only a handful of responses to her interview requests. She wonders if she will be able to gain more traction in-country. Meanwhile, in the midst of drafting her thesis proposal, an M.A. student speculates about the feasibility of his project, given a modest budget. Thousands of miles away from home, a postdoc is concerned about their safety, as protests erupt outside their window and state security forces descend into the streets.

These anecdotes provide a small glimpse into the concerns of early-career researchers undertaking significant projects with a field research component. Many of these fieldwork-related concerns arise from an unfortunate shortage in curricular offerings for qualitative and mixed-method research in political science graduate programs ( Emmons and Moravcsik 2020 ), 1 as well as the scarcity of instructional materials for qualitative and mixed-method research, relative to those available for quantitative research ( Elman, Kapiszewski, and Kirilova 2015 ; Kapiszewski, MacLean, and Read 2015 ; Mosley 2013 ). A recent survey among the leading United States Political Science programs in Comparative Politics and International Relations found that among graduate students who have carried out international fieldwork, 62 percent had not received any formal fieldwork training and only 20 percent felt very or mostly prepared for their fieldwork ( Schwartz and Cronin-Furman 2020 , 7–8). This shortfall in training and instruction means that many young researchers are underprepared for the logistics of fieldwork, from developing networks and effective sampling strategies to building respondents’ trust. In addition, there is a notable lack of preparation around issues of funding, physical safety, mental health, research ethics, and crisis response. This is troubling, as field research is highly valued and, in some parts of the field, it is all but expected, for instance in comparative politics.

Beyond subfield-specific expectations, research that leverages multiple types of data and methods, including fieldwork, is one of the ways that scholars throughout the discipline can more fully answer questions of interest and urgency. Indeed, multimethod work, a critical means by which scholars can parse and evaluate causal pathways, is on the rise ( Weller and Barnes 2016 ). The growing appearance of multimethod research in leading journals and university presses makes adequate training and preparation all the more significant ( Seawright 2016 ; Nexon 2019 ).

We are five political scientists interested in providing graduate students and other early-career researchers helpful resources for field research that we lacked when we first began our work. Each of us has recently completed or will soon complete a Ph.D. at a United States or Swedish university, though we come from many different national backgrounds. We have conducted field research in our home countries and abroad. From Colombia and Guatemala to the United States, from Europe to Turkey, and throughout East and Southeast Asia, we have spanned the globe to investigate civil society activism and transitional justice in post-violence societies, conflict-related sexual violence, social movements, authoritarianism and contentious politics, and the everyday politics and interactions between refugees and host-country citizens.

While some of us have studied in departments that offer strong training in field research methods, most of us have had to self-teach, learning through trial and error. Some of us have also been fortunate to participate in short courses and workshops hosted by universities such as the Consortium for Qualitative Research Methods and interdisciplinary institutions such as the Peace Research Institute Oslo. Recognizing that these opportunities are not available to or feasible for all, and hoping to ease the concerns of our more junior colleagues, we decided to compile our experiences and recommendations for first-time field researchers.

Our experiences in the field differ in several key respects, from the time we spent in the field to the locations we visited, and how we conducted our research. The diversity of our experiences, we hope, will help us reach and assist the broadest possible swath of graduate students interested in field research. Some of us have spent as little as ten days in a given country or as much as several months, in some instances visiting a given field site location just once and in other instances returning several times. At times, we have been able to plan weeks and months in advance. Other times, we have quickly arranged focus groups and impromptu interviews. Other times still, we have completed interviews virtually, when research participants were in remote locations or when we ourselves were unable to travel, of note during the coronavirus pandemic. We have worked in countries where we are fluent or have professional proficiency in the language, and in countries where we have relied on interpreters. We have worked in settings with precarious security as well as in locations that feel as comfortable as home. Our guide is not intended to be prescriptive or exhaustive. What we offer is a set of experience-based suggestions to be implemented as deemed relevant and appropriate by the researcher and their advisor(s).

In terms of the types of research and data sources and collection, we have conducted archival research, interviews, focus groups, and ethnographies with diplomats, bureaucrats, military personnel, ex-combatants, civil society advocates, survivors of political violence, refugees, and ordinary citizens. We have grappled with ethical dilemmas, chief among them how to get useful data for our research projects in ways that exceed the minimal standards of human subjects’ research evaluation panels. Relatedly, we have contemplated how to use our platforms to give back to the individuals and communities who have so generously lent us their time and knowledge, and shared with us their personal and sometimes harrowing stories.

Our target audience is first and foremost graduate students and early-career researchers who are interested in possibly conducting fieldwork but who either (1) do not know the full potential or value of fieldwork, (2) know the potential and value of fieldwork but think that it is excessively cost-prohibitive or otherwise infeasible, or (3) who have the interest, the will, and the means but not necessarily the know-how. We also hope that this resource will be of value to graduate programs, as they endeavor to better support students interested in or already conducting field research. Further, we target instructional faculty and graduate advisors (and other institutional gatekeepers like journal and book reviewers), to show that fieldwork does not have to be year-long, to give just one example. Instead, the length of time spent in the field is a function of the aims and scope of a given project. We also seek to formalize and normalize the idea of remote field research, whether conducted because of security concerns in conflict zones, for instance, or because of health and safety concerns, like the Covid-19 pandemic. Accordingly, researchers in the field for shorter stints or who conduct fieldwork remotely should not be penalized.

We note that several excellent resources on fieldwork such as the bibliography compiled by Advancing Conflict Research (2020) catalogue an impressive list of articles addressing questions such as ethics, safety, mental health, reflexivity, and methods. Further resources can be found about the positionality of the researcher in the field while engaging vulnerable communities, such as in the research field of migration ( Jacobsen and Landau 2003 ; Carling, Bivand Erdal, and Ezzati 2014 ; Nowicka and Cieslik 2014 ; Zapata-Barrero and Yalaz 2019 ). However, little has been written beyond conflict-affected contexts, fragile settings, and vulnerable communities. Moreover, as we consulted different texts and resources, we found no comprehensive guide to fieldwork explicitly written with graduate students in mind. It is this gap that we aim to fill.

In this paper, we address five general categories of questions that graduate students puzzle over, often without the benefit of others’ “lessons learned.” First, What is field research? Is it just for qualitative scholars? Must it be conducted in a foreign country? How much time in the field is “enough”? Second, What is the purpose of fieldwork? When does it make sense to travel to a field site to collect data? How can fieldwork data be used? Third, What are the nuts and bolts? How does one get ready and how can one optimize limited time and financial resources? Fourth, How does one conduct fieldwork safely? What should a researcher do to keep themselves, research assistants, and research subjects safe? What measures should they take to protect their mental health? Fifth, How does one conduct ethical, beneficent field research?

Finally, the Covid-19 pandemic has impressed upon the discipline the volatility of research projects centered around in-person fieldwork. Lockdowns and closed borders left researchers sequestered at home and unable to travel, forced others to cut short any trips already begun, and unexpectedly confined others still to their fieldwork sites. Other factors that may necessitate a (spontaneous) readjustment of planned field research include natural disasters, a deteriorating security situation in the field site, researcher illness, and unexpected changes in personal circumstances. We, therefore, conclude with a section on the promise and potential pitfalls of remote (or virtual) fieldwork. Throughout this guide, we engage theory and praxis to support an epistemologically and methodologically pluralistic discipline.

The concept of “fieldwork” is not well defined in political science. While several symposia discuss the “nuts and bolts” of conducting research in the field within the pages of political science journals, few ever define it ( Ortbals and Rincker 2009 ; Hsueh, Jensenius, and Newsome 2014 ). Defining the concept of fieldwork is important because assumptions about what it is and what it is not underpin any suggestions for conducting it. A lack of disciplinary consensus about what constitutes “fieldwork,” we believe, explains the lack of a unified definition. Below, we discuss three areas of current disagreement about what “fieldwork” is, including the purpose of fieldwork, where it occurs, and how long it should be. We follow this by offering our definition of fieldwork.

First, we find that many in the discipline view fieldwork as squarely in the domain of qualitative research, whether interpretivist or positivist. However, field research can also serve quantitative projects—for example, by providing crucial context, supporting triangulation, or illustrating causal mechanisms. For instance, Kreft (2019) elaborated her theory of women's civil society mobilization in response to conflict-related sexual violence based on interviews she carried out in Colombia. She then examined cross-national patterns through statistical analysis. Conversely, Willis's research on the United States military in East Asia began with quantitative data collection and analysis of protest events before turning to fieldwork to understand why protests occurred in some instances but not others. Researchers can also find quantifiable data in the field that is otherwise unavailable to them at home ( Read 2006 ; Chambers-Ju 2014 ; Jensenius 2014 ). Accordingly, fieldwork is not in the domain of any particular epistemology or methodology as its purpose is to acquire data for further information.

Second, comparative politics and international relations scholars often opine that fieldwork requires leaving the country in which one's institution is based. Instead, we propose that what matters most is the nature of the research project, not the locale. For instance, some of us in the international relations subfield have interviewed representatives of intergovernmental organizations (IGOs) and international nongovernmental organizations (INGOs), whose headquarters are generally located in Global North countries. For someone pursuing a Ph.D. in the United States and writing on transnational advocacy networks, interviews with INGO representatives in New York certainly count as fieldwork ( Zvobgo 2020 ). Similarly, a graduate student who returns to her home country to interview refugees and native citizens is conducting a field study as much as a researcher for whom the context is wholly foreign. Such interviews can provide necessary insights and information that would not have been gained otherwise—one of the key reasons researchers conduct fieldwork in the first place. In other instances, conducting any in-person research is simply not possible, due to financial constraints, safety concerns, or other reasons. For example, the Covid-19 pandemic has forced many researchers to shift their face-to-face research plans to remote data collection, either over the phone or virtually ( Howlett 2021 , 2). For some research projects, gathering data through remote methods may yield the same if not similar information than in-person research ( Howlett 2021 , 3–4). As Howlett (2021 , 11) notes, digital platforms may offer researchers the ability to “embed ourselves in other contexts from a distance” and glimpse into our subjects’ lives in ways similar to in-person research. By adopting a broader definition of fieldwork, researchers can be more flexible in getting access to data sources and interacting with research subjects.

Third, there is a tendency, especially among comparativists, to only count fieldwork that spans the better part of a year; even “surgical strike” field research entails one to three months, according to some scholars ( Ortbals and Rincker 2009 ; Weiss, Hicken, and Kuhonta 2017 ). The emphasis on spending as much time as possible in the field is likely due to ethnographic research traditions, reflected in classics such as James Scott's Weapons of the Weak , which entail year-long stints of research. However, we suggest that the appropriate amount of time in the field should be assessed on a project-by-project basis. Some studies require the researcher to be in the field for long periods; others do not. For example, Willis's research on the discourse around the United States’ military presence in overseas host communities has required months in the field. By contrast, Kreft only needed ten days in New York to carry out interviews with diplomats and United Nations staff, in a context with which she already had some familiarity from a prior internship. Likewise, Zvobgo spent a couple of weeks in her field research sites, conducting interviews with directors and managers of prominent human rights nongovernmental organizations. This population is not so large as to require a whole month or even a few months. This has also been the case for Irgil, as she had spent one month in the field site conducting interviews with ordinary citizens. The goal of the project was to acquire information on citizens’ perceptions of refugees. As we discuss in the next section, when deciding how long to spend in the field, scholars must consider the information their project requires and consider the practicalities of fieldwork, notably cost.

Thus, we highlight three essential points in fieldwork and offer a definition accordingly: fieldwork involves acquiring information, using any set of appropriate data collection techniques, for qualitative, quantitative, or experimental analysis through embedded research whose location and duration is dependent on the project. We argue that adopting such a definition of “fieldwork” is necessary to include the multitude of forms fieldwork can take, including remote methods, whose value and challenges the Covid-19 pandemic has impressed upon the discipline.

When does a researcher need to conduct fieldwork? Fieldwork can be effective for (1) data collection, (2) theory building, and (3) theory testing. First, when a researcher is interested in a research topic, yet they could not find an available and/or reliable data source for the topic, fieldwork could provide the researcher with plenty of options. Some research agendas can require researchers to visit archives to review historical documents. For example, Greitens (2016) visited national archives in the Philippines, South Korea, Taiwan, and the United States to find historical documents about the development of coercive institutions in past authoritarian governments for her book, Dictators and Their Secret Police . Also, newly declassified archival documents can open new possibilities for researchers to examine restricted topics. To illustrate, thanks to the newly released archival records of the Chinese Communist Party's communications, and exchange of visits with the European communist world, Sarotte (2012) was able to study the Party's decision to crack down on Tiananmen protesters, which had previously been deemed as an unstudiable topic due to the limited data.

Other research agendas can require researchers to conduct (semistructured) in-depth interviews to understand human behavior or a situation more closely, for example, by revealing the meanings of concepts for people and showing how people perceive the world. For example, O'Brien and Li (2005) conducted in-depth interviews with activists, elites, and villagers to understand how these actors interact with each other and what are the outcomes of the interaction in contentious movements in rural China. Through research, they revealed that protests have deeply influenced all these actors’ minds, a fact not directly observable without in-depth interviews.

Finally, data collection through fieldwork should not be confined to qualitative data ( Jensenius 2014 ). While some quantitative datasets can be easily compiled or accessed through use of the internet or contact with data-collection agencies, other datasets can only be built or obtained through relationships with “gatekeepers” such as government officials, and thus require researchers to visit the field ( Jensenius 2014 ). Researchers can even collect their own quantitative datasets by launching surveys or quantifying data contained in archives. In a nutshell, fieldwork will allow researchers to use different techniques to collect and access original/primary data sources, whether these are qualitative, quantitative, or experimental in nature, and regardless of the intended method of analysis. 2

But fieldwork is not just for data collection as such. Researchers can accomplish two other fundamental elements of the research process: theory building and theory testing. When a researcher finds a case where existing theories about a phenomenon do not provide plausible explanations, they can build a theory through fieldwork ( Geddes 2003 ). Lee's experience provides a good example. When studying the rise of a protest movement in South Korea for her dissertation, Lee applied commonly discussed social movement theories, grievances, political opportunity, resource mobilization, and repression, to explain the movement's eruption and found that these theories do not offer a convincing explanation for the protest movement. She then moved on to fieldwork and conducted interviews with the movement participants to understand their motivations. Finally, through those interviews, she offered an alternative theory that the protest participants’ collective identity shaped during the authoritarian past played a unifying factor and eventually led them to participate in the movement. Her example shows that theorization can take place through careful review and rigorous inference during fieldwork.

Moreover, researchers can test their theory through fieldwork. Quantitative observational data has limitations in revealing causal mechanisms ( Esarey 2017 ). Therefore, many political scientists turn their attention to conducting field experiments or lab-in-the-field experiments to reveal causality ( Druckman et al. 2006 ; Beath, Christia, and Enikolopov 2013 ; Finseraas and Kotsadam 2017 ), or to leveraging in-depth insights or historical records gained through qualitative or archival research in process-tracing ( Collier 2011 ; Ricks and Liu 2018 ). Surveys and survey experiments may also be useful tools to substantiate a theoretical story or test a theory ( Marston 2020 ). Of course, for most Ph.D. students, especially those not affiliated with more extensive research projects, some of these options will be financially prohibitive.

A central concern for graduate students, especially those working with a small budget and limited time, is optimizing time in the field and integrating remote work. We offer three pieces of advice: have a plan, build in flexibility, and be strategic, focusing on collecting data that are unavailable at home. We also discuss working with local translators or research assistants. Before we turn to these more practical issues arising during fieldwork, we address a no less important issue: funding.

The challenge of securing funds is often overlooked in discussions of what constitutes field research. Months- or year-long in-person research can be cost-prohibitive, something academic gatekeepers must consider when evaluating “what counts” and “what is enough.” Unlike their predecessors, many graduate students today have a significant amount of debt and little savings. 3 Additionally, if researchers are not able to procure funding, they have to pay out of pocket and possibly take on more debt. Not only is in-person fieldwork costly, but researchers may also have to forego working while they are in the field, making long stretches in the field infeasible for some.

For researchers whose fieldwork involves travelling to another location, procuring funding via grants, fellowships, or other sources is a necessity, regardless of how long one plans to be in the field. A good mantra for applying for research funding is “apply early and often” ( Kelsky 2015 , 110). Funding applications take a considerable amount of time to prepare, from writing research statements to requesting letters of recommendation. Even adapting one's materials for different applications takes time. Not only is the application process itself time-consuming, but the time between applying for and receiving funds, if successful, can be quite long, from several months to a year. For example, after defending her prospectus in May 2019, Willis began applying to funding sources for her dissertation, all of which had deadlines between June and September. She received notifications between November and January; however, funds from her successful applications were not available until March and April, almost a year later. 4 Accordingly, we recommend applying for funding as early as possible; this not only increases one's chances of hitting the ground running in the field, but the application process can also help clarify the goals and parameters of one's research.

Graduate students should also apply often for funding opportunities. There are different types of funding for fieldwork: some are larger, more competitive grants such as the National Science Foundation Political Science Doctoral Dissertation Improvement Grant in the United States, others, including sources through one's own institution, are smaller. Some countries, like Sweden, boast a plethora of smaller funding agencies that disburse grants of 20,000–30,0000 Swedish Kronor (approx. 2,500–3,500 U.S. dollars) to Ph.D. students in the social sciences. Listings of potential funding sources are often found on various websites including those belonging to universities, professional organizations (such as the American Political Science Association or the European Consortium for Political Research), and governmental institutions dealing with foreign affairs. Once you have identified fellowships and grants for which you and your project are a good match, we highly recommend soliciting information and advice from colleagues who have successfully applied for them. This can include asking them to share their applications with you, and if possible, to have them, another colleague or set of colleagues read through your project description and research plan (especially for bigger awards) to ensure that you have made the best possible case for why you should be selected. While both large and small pots of funding are worth applying for, many researchers end up funding their fieldwork through several small grants or fellowships. One small award may not be sufficient to fund the entirety of one's fieldwork, but several may. For example, Willis's fieldwork in Japan and South Korea was supported through fellowships within each country. Similarly, Irgil was able to conduct her fieldwork abroad through two different and relatively smaller grants by applying to them each year.

Of course, situations vary in different countries with respect to what kinds of grants from what kinds of funders are available. An essential part of preparing for fieldwork is researching the funding landscape well in advance, even as early as the start of the Ph.D. We encourage first-time field researchers to be aware that universities and departments may themselves not be aware of the full range of possible funds available, so it is always a good idea to do your own research and watch research-related social media channels. The amount of funding needed thereby depends on the nature of one's project and how long one intends to be in the field. As we elaborate in the next section, scholars should think carefully about their project goals, the data required to meet those goals, and the requisite time to attain them. For some projects, even a couple of weeks in the field is sufficient to get the needed information.

Preparing to Enter “the field”

It is important to prepare for the field as much as possible. What kind of preparations do researchers need? For someone conducting interviews with NGO representatives, this might involve identifying the largest possible pool of potential respondents, securing their contact information, sending them study invitation letters, finding a mutually agreeable time to meet, and pulling together short biographies for each interviewee in order to use your time together most effectively. If you plan to travel to conduct interviews, you should reach out to potential respondents roughly four to six weeks prior to your arrival. For individuals who do not respond, you can follow up one to two weeks before you arrive and, if needed, once more when you are there. This is still no guarantee for success, of course. For Kreft, contacting potential interviewees in Colombia initially proved more challenging than anticipated, as many of the people she targeted did not respond to her emails. It turned out that many Colombians have a preference for communicating via phone or, in particular, WhatsApp. Some of those who responded to her emails sent in advance of her field trip asked her to simply be in touch once she was in the country, to set up appointments on short notice. This made planning and arranging her interview schedule more complicated. Therefore, a general piece of advice is to research your target population's preferred communication channels and mediums in the field site if email requests yield no or few responses.

In general, we note for the reader that contacting potential research participants should come after one has designed an interview questionnaire (plus an informed consent protocol) and sought and received, where applicable, approval from institutional review boards (IRBs) or other ethical review procedures in place (both at one's home institution/in the country of the home institution as well as in the country where one plans to conduct research if travelling abroad). The most obvious advantage of having the interview questionnaire in place and having secured all necessary institutional approvals before you start contacting potential interviewees is that you have a clearer idea of the universe of individuals you would like to interview, and for what purpose. Therefore, it is better to start sooner rather than later and be mindful of “high seasons,” when institutional and ethical review boards are receiving, processing, and making decisions on numerous proposals. It may take a few months for them to issue approvals.

On the subject of ethics and review panels, we encourage you to consider talking openly and honestly with your supervisors and/or funders about the situations where a written consent form may not be suitable and might need to be replaced with “verbal consent.” For instance, doing fieldwork in politically unstable contexts, highly scrutinized environments, or vulnerable communities, like refugees, might create obstacles for the interviewees as well as the researcher. The literature discusses the dilemma in offering the interviewees anonymity and requesting signed written consent in addition to the emphasis on total confidentiality ( Jacobsen and Landau 2003 ; Mackenzie, McDowell, and Pittaway 2007 ; Saunders, Kitzinger, and Kitzinger 2015 ). Therefore, in those situations, the researcher might need to take the initiative on how to act while doing the interviews as rigorously as possible. In her fieldwork, Irgil faced this situation as the political context of Turkey did not guarantee that there would not be any adverse consequences for interviewees on both sides of her story: citizens of Turkey and Syrian refugees. Consequently, she took hand-written notes and asked interviewees for their verbal consent in a safe interview atmosphere. This is something respondents greatly appreciated ( Irgil 2020 ).

Ethical considerations, of course, also affect the research design itself, with ramifications for fieldwork. When Kreft began developing her Ph.D. proposal to study women's political and civil society mobilization in response to conflict-related sexual violence, she initially aimed to recruit interviewees from the universe of victims of this violence, to examine variation among those who did and those who did not mobilize politically. As a result of deeper engagement with the literature on researching conflict-related sexual violence, conversations with senior colleagues who had interviewed victims, and critical self-reflection of her status as a researcher (with no background in psychology or social work), she decided to change focus and shift toward representatives of civil society organizations and victims’ associations. This constituted a major reconfiguration of her research design, from one geared toward identifying the factors that drive mobilization of victims toward using insights from interviews to understand better how those mobilize perceive and “make sense” of conflict-related sexual violence. Needless to say, this required alterations to research strategies and interview guides, including reassessing her planned fieldwork. Kreft's primary consideration was not to cause harm to her research participants, particularly in the form of re-traumatization. She opted to speak only with those women who on account of their work are used to speaking about conflict-related sexual violence. In no instance did she inquire about interviewees’ personal experiences with sexual violence, although several brought this up on their own during the interviews.

Finally, if you are conducting research in another country where you have less-than-professional fluency in the language, pre-fieldwork planning should include hiring a translator or research assistant, for example, through an online hiring platform like Upwork, or a local university. Your national embassy or consulate is another option; many diplomatic offices have lists of individuals who they have previously contracted. More generally, establishing contact with a local university can be beneficial, either in the form of a visiting researcher arrangement, which grants access to research groups and facilities like libraries or informally contacting individual researchers. The latter may have valuable insights into the local context, contacts to potential research participants, and they may even be able to recommend translators or research assistants. Kreft, for example, hired local research assistants recommended by researchers at a Bogotá-based university and remunerated them equivalent to the salary they would have received as graduate research assistants at the university, while also covering necessary travel expenses. Irgil, on the other hand, established contacts with native citizens and Syrian gatekeepers, who are shop owners in the area where she conducted her research because she had the opportunity to visit the fieldwork site multiple times.

Depending on the research agenda, researchers may visit national archives, local government offices, etc. Before visiting, researchers should contact these facilities and make sure the materials that they need are accessible. For example, Lee visited the Ronald Reagan Presidential Library Archives to find the United States’ strategic evaluations on South Korea's dictator in the 1980s. Before her visit, she contacted librarians in the archives, telling them her visit plans and her research purpose. Librarians made suggestions on which categories she should start to review based on her research goal, and thus she was able to make a list of categories of the materials she needed, saving her a lot of her time.

Accessibility of and access to certain facilities/libraries can differ depending on locations/countries and types of facilities. Facilities in authoritarian countries might not be easily accessible to foreign researchers. Within democratic countries, some facilities are more restrictive than others. Situations like the pandemic or national holidays can also restrict accessibility. Therefore, researchers are well advised to do preliminary research on whether a certain facility opens during the time they visit and is accessible to researchers regardless of their citizenship status. Moreover, researchers must contact the staff of facilities to know whether identity verification is needed and if so, what kind of documents (photo I.D. or passport) should be exhibited.

Adapting to the Reality of the Field

Researchers need to be flexible because you may meet people you did not make appointments with, come across opportunities you did not expect, or stumble upon new ideas about collecting data in the field. These happenings will enrich your field experience and will ultimately be beneficial for your research. Similarly, researchers should not be discouraged by interviews that do not go according to plan; they present an opportunity to pursue relevant people who can provide an alternative path to your work. Note that planning ahead does not preclude fortuitous encounters or epiphanies. Rather, it provides a structure for them to happen.

If your fieldwork entails travelling abroad, you will also be able to recruit more interviewees once you arrive at your research site. In fact, you may have greater success in-country; not everyone is willing to respond to a cold email from an unknown researcher in a foreign country. In Irgil's fieldwork, she contacted store owners that are known in the area and who know the community. This eased her process of introduction into the community and recruiting interviewees. For Zvobgo, she had fewer than a dozen interviews scheduled when she travelled to Guatemala to study civil society activism and transitional justice since the internal armed conflict. But she was able to recruit additional participants in-country. Interviewees with whom she built a rapport connected her to other NGOs, government offices, and the United Nations country office, sometimes even making the call and scheduling interviews for her. Through snowball sampling, she was able to triple the number of participants. Likewise, snowball sampling was central to Kreft's recruitment of interview partners. Several of her interviewees connected her to highly relevant individuals she would never have been able to identify and contact based on web searches alone.

While in the field, you may nonetheless encounter obstacles that necessitate adjustments to your original plans. Once Kreft had arrived in Colombia, for example, it transpired quickly that carrying out in-person interviews in more remote/rural areas was near impossible given her means, as these were not easily accessible by bus/coach, further complicated by a complex security situation. Instead, she adjusted her research design and shifted her focus to the big cities, where most of the major civil society organizations are based. She complemented the in-person interviews carried out there with a smaller number of phone interviews with civil society activists in rural areas, and she was also able to meet a few activists operating in rural or otherwise inaccessible areas as they were visiting the major cities. The resulting focus on urban settings changed the kinds of generalizations she was able to make based on her fieldwork data and produced a somewhat different study than initially anticipated.

This also has been the case for Irgil, despite her prior arrangements with the Syrian gatekeepers, which required adjustments as in the case of Kreft. Irgil acquired research clearance one year before, during the interviews with native citizens, conducting the interviews with Syrian refugees. She also had her questionnaire ready based on the previously collected data and the media search she had conducted for over a year before travelling to the field site. As she was able to visit the field site multiple times, two months before conducting interviews with Syrian refugees, she developed a schedule with the Syrian gatekeepers and informants. Yet, once she was in the field, influenced by Turkey's recent political events and the policy of increasing control over Syrian refugees, half of the previously agreed informants changed their minds or did not want to participate in interviews. As Irgil was following the policies and the news related to Syrian refugees in Turkey closely, this did not come as that big of a surprise but challenged the previously developed strategy to recruit interviewees. Thus, she changed the strategy of finding interviewees in the field site, such as asking people, almost one by one, whether they would like to participate in the interview. Eventually, she could not find willing Syrian women refugees as she had planned, which resulted in a male-dominant sample. As researchers encounter such situations, it is essential to remind oneself that not everything can go according to plan, that “different” does not equate to “worse,” but that it is important to consider what changes to fieldwork data collection and sampling imply for the study's overall findings and the contribution it makes to the literature.

We should note that conducting interviews is very taxing—especially when opportunities multiply, as in Zvobgo's case. Depending on the project, each interview can take an hour, if not two or more. Hence, you should make a reasonable schedule: we recommend no more than two interviews per day. You do not want to have to cut off an interview because you need to rush to another one, whether the interviews are in-person or remote. And you do not want to be too exhausted to have a robust engagement with your respondent who is generously lending you their time. Limiting the number of interviews per day is also important to ensure that you can write comprehensive and meaningful fieldnotes, which becomes even more essential where it is not possible to audio-record your interviews. Also, be sure to remember to eat, stay hydrated, and try to get enough sleep.

Finally, whether to provide gifts or payments to the subject also requires adapting to the reality of the field. You must think about payments beforehand when you apply for IRB approval (or whatever other ethical review processes may be in place) since these applications usually contain questions about payments. Obviously, the first step is to carefully evaluate whether the gifts and payments provided can harm the subject or are likely to unduly affect the responses they will give in response to your questions. If that is not the case, you have to make payment decisions based on your budget, field situation, and difficulties in recruitment. Usually, payment of respondents is more common in survey research, whereas it is less common in interviews and focus groups.

Nevertheless, payment practices vary depending on the field and the target group. In some cases, it may become a custom to provide small gifts or payments when interviewing a certain group. In other cases, interviewees might be offended if they are provided with money. Therefore, knowing past practices and field situations is important. For example, Lee provided small coffee gift cards to one group while she did not to the other based on previous practices of other researchers. That is, for a particular group, it has become a custom for interviewers to pay interviewees. Sometimes, you may want to reimburse your subject's interview costs such as travel expenses and provide beverages and snacks during the conduct of research, as Kreft did when conducting focus groups in Colombia. To express your gratitude to your respondents, you can prepare small gifts such as your university memorabilia (e.g., notebooks and pens). Since past practices about payments can affect your interactions and interviews with a target group, you want to seek advice from your colleagues and other researchers who had experiences interacting with the target group. If you cannot find researchers who have this knowledge, you can search for published works on the target population to find if the authors share their interview experiences. You may also consider contacting the authors for advice before your interviews.

Researching Strategically

Distinguishing between things that can only be done in person at a particular site and things that can be accomplished later at home is vital. Prioritize the former over the latter. Lee's fieldwork experience serves as a good example. She studied a conservative protest movement called the Taegeukgi Rally in South Korea. She planned to conduct interviews with the rally participants to examine their motivations for participating. But she only had one month in South Korea. So, she focused on things that could only be done in the field: she went to the rally sites, she observed how protests proceeded, which tactics and chants were used, and she met participants and had some casual conversations with them. Then, she used the contacts she made while attending the rallies to create a social network to solicit interviews from ordinary protesters, her target population. She was able to recruit twenty-five interviewees through good rapport with the people she met. The actual interviews proceeded via phone after she returned to the United States. In a nutshell, we advise you not to be obsessed with finishing interviews in the field. Sometimes, it is more beneficial to use your time in the field to build relationships and networks.

Working With Assistants and Translators

A final consideration on logistics is working with research assistants or translators; it affects how you can carry out interviews, focus groups, etc. To what extent constant back-and-forth translation is necessary or advisable depends on the researcher's skills in the interview language and considerations about time and efficiency. For example, Kreft soon realized that she was generally able to follow along quite well during her interviews in Colombia. In order to avoid precious time being lost to translation, she had her research assistant follow the interview guide Kreft had developed, and interjected follow-up questions in Spanish or English (then to be translated) as they arose.

Irgil's and Zvobgo's interviews went a little differently. Irgil's Syrian refugee interviewees in Turkey were native Arabic speakers, and Zvobgo's interviewees in Guatemala were native Spanish speakers. Both Irgil and Zvobgo worked with research assistants. In Irgil's case, her assistant was a Syrian man, who was outside of the area. Meanwhile, Zvobgo's assistant was an undergraduate from her home institution with a Spanish language background. Irgil and Zvobgo began preparing their assistants a couple of months before entering the field, over Skype for Irgil and in-person for Zvobgo. They offered their assistants readings and other resources to provide them with the necessary background to work well. Both Irgil and Zvobgo's research assistants joined them in the interviews and actually did most of the speaking, introducing the principal investigator, explaining the research, and then asking the questions. In Zvobgo's case, interviewee responses were relayed via a professional interpreter whom she had also hired. After every interview, Irgil and Zvobgo and their respective assistants discussed the answers of the interviewees, potential improvements in phrasing, and elaborated on their hand-written interview notes. As a backup, Zvobgo, with the consent of her respondents, had accompanying audio recordings.

Researchers may carry out fieldwork in a country that is considerably less safe than what they are used to, a setting affected by conflict violence or high crime rates, for instance. Feelings of insecurity can be compounded by linguistic barriers, cultural particularities, and being far away from friends and family. Insecurity is also often gendered, differentially affecting women and raising the specter of unwanted sexual advances, street harassment, or even sexual assault ( Gifford and Hall-Clifford 2008 ; Mügge 2013 ). In a recent survey of Political Science graduate students in the United States, about half of those who had done fieldwork internationally reported having encountered safety issues in the field, (54 percent female, 47 percent male), and only 21 percent agreed that their Ph.D. programs had prepared them to carry out their fieldwork safely ( Schwartz and Cronin-Furman 2020 , 8–9).

Preventative measures scholars may adopt in an unsafe context may involve, at their most fundamental, adjustments to everyday routines and habits, restricting one's movements temporally and spatially. Reliance on gatekeepers may also necessitate adopting new strategies, such as a less vehement and cold rejection of unwanted sexual advances than one ordinarily would exhibit, as Mügge (2013) illustratively discusses. At the same time, a competitive academic job market, imperatives to collect novel and useful data, and harmful discourses surrounding dangerous fieldwork also, problematically, shape incentives for junior researchers to relax their own standards of what constitutes acceptable risk ( Gallien 2021 ).

Others have carefully collected a range of safety precautions that field researchers in fragile or conflict-affected settings may take before and during fieldwork ( Hilhorst et al. 2016 ). Therefore, we are more concise in our discussion of recommendations, focusing on the specific situations of graduate students. Apart from ensuring that supervisors and university administrators have the researcher's contact information in the field (and possibly also that of a local contact person), researchers can register with their country's embassy or foreign office and any crisis monitoring and prevention systems it has in place. That way, they will be informed of any possible unfolding emergencies and the authorities have a record of them being in the country.

It may also be advisable to set up more individualized safety protocols with one or two trusted individuals, such as friends, supervisors, or colleagues at home or in the fieldwork setting itself. The latter option makes sense in particular if one has an official affiliation with a local institution for the duration of the fieldwork, which is often advisable. Still, we would also recommend establishing relationships with local researchers in the absence of a formal affiliation. To keep others informed of her whereabouts, Kreft, for instance, made arrangements with her supervisors to be in touch via email at regular intervals to report on progress and wellbeing. This kept her supervisors in the loop, while an interruption in communication would have alerted them early if something were wrong. In addition, she announced planned trips to other parts of the country and granted her supervisors and a colleague at her home institution emergency reading access to her digital calendar. To most of her interviews, she was moreover accompanied by her local research assistant/translator. If the nature of the research, ethical considerations, and the safety situation allow, it might also be possible to bring a local friend along to interviews as an “assistant,” purely for safety reasons. This option needs to be carefully considered already in the planning stage and should, particularly in settings of fragility or if carrying out research on politically exposed individuals, be noted in any ethical and institutional review processes where these are required. Adequate compensation for such an assistant should be ensured. It may also be advisable to put in place an emergency plan, that is, choose emergency contacts back home and “in the field,” know whom to contact if something happens, and know how to get to the nearest hospital or clinic.

We would be remiss if we did not mention that, when in an unfamiliar context, one's safety radar may be misguided, so it is essential to listen to people who know the context. For example, locals can give advice on which means of transport are safe and which are not, a question that is of the utmost importance when traveling to appointments. For example, Kreft was warned that in Colombia regular taxis are often unsafe, especially if waved down in the streets, and that to get to her interviews safely, she should rely on a ride-share service. In one instance, a Colombian friend suggested that when there was no alternative to a regular taxi, Kreft should book through the app and share the order details, including the taxi registration number or license plate, with a friend. Likewise, sharing one's cell phone location with a trusted friend while traveling or when one feels unsafe may be a viable option. Finally, it is prudent to heed the safety recommendations and travel advisories provided by state authorities and embassies to determine when and where it is safe to travel. Especially if researchers have a responsibility not only for themselves but also for research assistants and research participants, safety must be a top priority.

This does not mean that a researcher should be careless in a context they know either. Of course, conducting fieldwork in a context that is known to the researcher offers many advantages. However, one should be prepared to encounter unwanted events too. For instance, Irgil has conducted fieldwork in her country of origin in a city she knows very well. Therefore, access to the site, moving around the site, and blending in has not been a problem; she also has the advantage of speaking the native language. Yet, she took notes of the streets she walked in, as she often returned from the field site after dark and thought she might get confused after a tiring day. She also established a closer relationship with two or three store owners in different parts of the field site if she needed something urgent, like running out of battery. Above all, one should always be aware of one's surroundings and use common sense. If something feels unsafe, chances are it is.

Fieldwork may negatively affect the researcher's mental health and mental wellbeing regardless of where one's “field” is, whether related to concerns about crime and insecurity, linguistic barriers, social isolation, or the practicalities of identifying, contacting and interviewing research participants. Coping with these different sources of stress can be both mentally and physically exhausting. Then there are the things you may hear, see and learn during the research itself, such as gruesome accounts of violence and suffering conveyed in interviews or archival documents one peruses. Kreft and Zvobgo have spoken with women victims of conflict-related sexual violence, who sometimes displayed strong emotions of pain and anger during the interviews. Likewise, Irgil and Willis have spoken with members of other vulnerable populations such as refugees and former sex workers ( Willis 2020 ).

Prior accounts ( Wood 2006 ; Loyle and Simoni 2017 ; Skjelsbæk 2018 ; Hummel and El Kurd 2020 ; Williamson et al. 2020 ; Schulz and Kreft 2021 ) show that it is natural for sensitive research and fieldwork challenges to affect or even (vicariously) traumatize the researcher. By removing researchers from their regular routines and support networks, fieldwork may also exacerbate existing mental health conditions ( Hummel and El Kurd 2020 ). Nonetheless, mental wellbeing is rarely incorporated into fieldwork courses and guidelines, where these exist at all. But even if you know to anticipate some sort of reaction, you rarely know what that reaction will be until you experience it. When researching sensitive or difficult topics, for example, reactions can include sadness, frustration, anger, fear, helplessness, and flashbacks to personal experiences of violence ( Williamson et al. 2020 ). For example, Kreft responded with episodic feelings of depression and both mental and physical exhaustion. But curiously, these reactions emerged most strongly after she had returned from fieldwork and in particular as she spent extended periods analyzing her interview data, reliving some of the more emotional scenes during the interviews and being confronted with accounts of (sexual) violence against women in a concentrated fashion. This is a crucial reminder that fieldwork does not end when one returns home; the after-effects may linger. Likewise, Zvobgo was physically and mentally drained upon her return from the field. Both Kreft and Zvobgo were unable to concentrate for long periods of time and experienced lower-than-normal levels of productivity for weeks afterward, patterns that formal and informal conversations with other scholars confirm to be common ( Schulz and Kreft 2021 ). Furthermore, the boundaries between “field” and “home” are blurred when conducting remote fieldwork ( Howlett 2021 , 11).

Nor are these adverse reactions limited to cases where the researcher has carried out the interviews themselves. Accounts of violence, pain, and suffering transported in reports, secondary literature, or other sources can evoke similar emotional stress, as Kreft experienced when engaging in a concentrated fashion with additional accounts of conflict-related sexual violence in Colombia and with the feminist literature on sexual and gender-based violence in the comfort of her Swedish office. This could also be applicable to Irgil's fieldwork as she interviewed refugees whose traumas have come out during the interviews or recall specific events triggered by the questions. Likewise, Lee has reviewed primary and secondary materials on North Korean defectors in the national archives and these materials contain violent, intense, emotional narratives.

Fortunately, there are several strategies to cope with and manage such adverse consequences. In a candid and insightful piece, other researchers have discussed the usefulness of distractions, sharing with colleagues, counseling, exercise, and, probably less advisable in the long term, comfort eating and drinking ( Williamson et al. 2020 ; see also Loyle and Simoni 2017 ; Hummel and El Kurd 2020 ). Our experiences largely tally with their observations. In this section, we explore some of these in more detail.

First, in the face of adverse consequences on your mental wellbeing, whether in the field or after your return, it is essential to be patient and generous with yourself. Negative effects on the researcher's mental wellbeing can hit in unexpected ways and at unexpected times. Even if you think that certain reactions are disproportionate or unwarranted at that specific moment, they may simply have been building up over a long time. They are legitimate. Second, the importance of taking breaks and finding distractions, whether that is exercise, socializing with friends, reading a good book, or watching a new series, cannot be overstated. It is easy to fall into a mode of thinking that you constantly have to be productive while you are “in the field,” to maximize your time. But as with all other areas in life, balance is key and rest is necessary. Taking your mind off your research and the research questions you puzzle over is also a good way to more fully soak up and appreciate the context in which you find yourself, in the case of in-person fieldwork, and about which you ultimately write.

Third, we cannot stress enough the importance of investing in social relations. Before going on fieldwork, researchers may want to consult others who have done it before them. Try to find (junior) scholars who have done fieldwork on similar kinds of topics or in the same country or countries you are planning to visit. Utilizing colleagues’ contacts and forging connections using social media are valuable strategies to expand your networks (in fact, this very paper is the result of a social media conversation and several of the authors have never met in person). Having been in the same situation before, most field researchers are, in our experience, generous with their time and advice. Before embarking on her first trip to Colombia, Kreft contacted other researchers in her immediate and extended network and received useful advice on questions such as how to move around Bogotá, whom to speak to, and how to find a research assistant. After completing her fieldwork, she has passed on her experiences to others who contacted her before their first fieldwork trip. Informal networks are, in the absence of more formalized fieldwork preparation, your best friend.

In the field, seeking the company of locals and of other researchers who are also doing fieldwork alleviates anxiety and makes fieldwork more enjoyable. Exchanging experiences, advice and potential interviewee contacts with peers can be extremely beneficial and make the many challenges inherent in fieldwork (on difficult topics) seem more manageable. While researchers conducting remote fieldwork may be physically isolated from other researchers, even connecting with others doing remote fieldwork may be comforting. And even when there are no precise solutions to be found, it is heartening or even cathartic to meet others who are in the same boat and with whom you can talk through your experiences. When Kreft shared some of her fieldwork-related struggles with another researcher she had just met in Bogotá and realized that they were encountering very similar challenges, it was like a weight was lifted off her shoulders. Similarly, peer support can help with readjustment after the fieldwork trip, even if it serves only to reassure you that a post-fieldwork dip in productivity and mental wellbeing is entirely natural. Bear in mind that certain challenges are part of the fieldwork experience and that they do not result from inadequacy on the part of the researcher.

Finally, we would like to stress a point made by Inger Skjelsbæk (2018 , 509) and which has not received sufficient attention: as a discipline, we need to take the question of researcher mental wellbeing more seriously—not only in graduate education, fieldwork preparation, and at conferences, but also in reflecting on how it affects the research process itself: “When strong emotions arise, through reading about, coding, or talking to people who have been impacted by [conflict-related sexual violence] (as victims or perpetrators), it may create a feeling of being unprofessional, nonscientific, and too subjective.”

We contend that this is a challenge not only for research on sensitive issues but also for fieldwork more generally. To what extent is it possible, and desirable, to uphold the image of the objective researcher during fieldwork, when we are at our foundation human beings? And going even further, how do the (anticipated) effects of our research on our wellbeing, and the safety precautions we take ( Gifford and Hall-Clifford 2008 ), affect the kinds of questions we ask, the kinds of places we visit and with whom we speak? How do they affect the methods we use and how we interpret our findings? An honest discussion of affective responses to our research in methods sections seems utopian, as emotionality in the research process continues to be silenced and relegated to the personal, often in gendered ways, which in turn is considered unconnected to the objective and scientific research process ( Jamar and Chappuis 2016 ). But as Gifford and Hall-Clifford (2008 , 26) aptly put it: “Graduate education should acknowledge the reality that fieldwork is scholarly but also intimately personal,” and we contend that the two shape each other. Therefore, we encourage political science as a discipline to reflect on researcher wellbeing and affective responses to fieldwork more carefully, and we see the need for methods courses that embrace a more holistic notion of the subjectivity of the researcher.

Interacting with people in the field is one of the most challenging yet rewarding parts of the work that we do, especially in comparison to impersonal, often tedious wrangling and analysis of quantitative data. Field researchers often make personal connections with their interviewees. Consequently, maintaining boundaries can be a bit tricky. Here, we recommend being honest with everyone with whom you interact without overstating the abilities of a researcher. This appears as a challenge in the field, particularly when you empathize with people and when they share profound parts of their lives with you for your research in addition to being “human subjects” ( Fujii 2012 ). For instance, when Irgil interviewed native citizens about the changes in their neighborhood following the arrival of Syrian refugees, many interviewees questioned what she would offer them in return for their participation. Irgil responded that her primary contribution would be her published work. She also noted, however, that academic papers can take a year, sometimes longer, to go through the peer-reviewed process and, once published, many studies have a limited audience. The Syrian refugees posed similar questions. Irgil responded not only with honesty but also, given this population's vulnerable status, she provided them contact information for NGOs with which they could connect if they needed help or answers to specific questions.

For her part, Zvobgo was very upfront with her interviewees about her role as a researcher: she recognized that she is not someone who is on the frontlines of the fight for human rights and transitional justice like they are. All she could/can do is use her platform to amplify their stories, bringing attention to their vital work through her future peer-reviewed publications. She also committed to sending them copies of the work, as electronic journal articles are often inaccessible due to paywalls and university press books are very expensive, especially for nonprofits. Interviewees were very receptive; some were even moved by the degree of self-awareness and the commitment to do right by them. In some cases, this prompted them to share even more, because they knew that the researcher was really there to listen and learn. This is something that junior scholars, and all scholars really, should always remember. We enter the field to be taught. Likewise, Kreft circulated among her interviewees Spanish-language versions of an academic article and a policy brief based on the fieldwork she had carried out in Colombia.

As researchers from the Global North, we recognize a possible power differential between us and our research subjects, and certainly an imbalance in power between the countries where we have been trained and some of the countries where we have done and continue to do field research, particularly in politically dynamic contexts ( Knott 2019 ). This is why we are so concerned with being open and transparent with everyone with whom we come into contact in the field and why we are committed to giving back to those who so generously lend us their time and knowledge. Knott (2019 , 148) summarizes this as “Reflexive openness is a form of transparency that is methodologically and ethically superior to providing access to data in its raw form, at least for qualitative data.”

We also recognize that academics, including in the social sciences and especially those hailing from countries in the Global North, have a long and troubled history of exploiting their power over others for the sake of their research—including failing to be upfront about their research goals, misrepresenting the on-the-ground realities of their field research sites (including remote fieldwork), and publishing essentializing, paternalistic, and damaging views and analyses of the people there. No one should build their career on the backs of others, least of all in a field concerned with the possession and exercise of power. Thus, it is highly crucial to acknowledge the power hierarchies between the researcher and the interviewees, and to reflect on them both in the field and beyond the field upon return.

A major challenge to conducting fieldwork is when researchers’ carefully planned designs do not go as planned due to unforeseen events outside of our control, such as pandemics, natural disasters, deteriorating security situations in the field, or even the researcher falling ill. As the Covid-19 pandemic has made painfully clear, researchers may face situations where in-person research is simply not possible. In some cases, researchers may be barred entry to their fieldwork site; in others, the ethical implications of entering the field greatly outweigh the importance of fieldwork. Such barriers to conducting in-person research require us to reconsider conventional notions of what constitutes fieldwork. Researchers may need to shift their data collection methods, for example, conducting interviews remotely instead of in person. Even while researchers are in the field, they may still need to carry out part of their interviews or surveys virtually or by phone. For example, Kreft (2020) carried out a small number of interviews remotely while she was based in Bogotá, because some of the women's civil society activists with whom she intended to speak were based in parts of the country that were difficult and/or dangerous to access.

Remote field research, which we define as the collection of data over the internet or over the phone where in-person fieldwork is not possible due to security, health or other risks, comes with its own sets of challenges. For one, there may be certain populations that researchers cannot reach remotely due to a lack of internet connectivity or technology such as cellphones and computers. In such instances, there will be a sampling bias toward individuals and groups that do have these resources, a point worth noting when scholars interpret their research findings. In the case of virtual research, the risk of online surveillance, hacking, or wiretapping may also produce reluctance on the part of interviewees to discuss sensitive issues that may compromise their safety. Researchers need to carefully consider how the use of digital technology may increase the risk to research participants and what changes to the research design and any interview guides this necessitates. In general, it is imperative that researchers reflect on how they can ethically use digital technology in their fieldwork ( Van Baalen 2018 ). Remote interviews may also be challenging to arrange for researchers who have not made connections in person with people in their community of interest.

Some of the serendipitous happenings we discussed earlier may also be less likely and snowball sampling more difficult. For example, in phone or virtual interviews, it is harder to build good rapport and trust with interviewees as compared to face-to-face interviews. Accordingly, researchers should be more careful in communicating with interviewees and creating a comfortable interview environment. Especially when dealing with sensitive topics, researchers may have to make several phone calls and sometimes have to open themselves to establishing trust with interviewees. Also, researchers must be careful in protecting interviewees in phone or virtual interviews when they deal with sensitive topics of countries interviewees reside in.

The inability to physically visit one's community of interest may also encourage scholars to critically reflect on how much time in the field is essential to completing their research and to consider creative, alternative means for accessing information to complete their projects. While data collection techniques such as face-to-face interviews and archival work in the field may be ideal in normal times, there exist other data sources that can provide comparably useful information. For example, in her research on the role of framing in the United States base politics, Willis found that social media accounts and websites yielded information useful to her project. Many archives across the world have also been digitized. Researchers may also consider crowdsourcing data from the field among their networks, as fellow academics tend to collect much more data in the field than they ever use in their published works. They may also elect to hire someone, perhaps a graduate student, in a city or a country where they cannot travel and have the individual access, scan, and send archival materials. This final suggestion may prove generally useful to researchers with limited time and financial resources.

Remote qualitative data collection techniques, while they will likely never be “the gold-standard,” also pose several advantages. These techniques may help researchers avoid some of the issues mentioned previously. Remote interviews, for example, are less time-consuming in terms of travel to the interview site ( Archibald et al. 2019 ). The implication is that researchers may have less fatigue from conducting interviews and/or may be able to conduct more interviews. For example, while Willis had little energy to do anything else after an in-person interview (or two) in a given day, she had much more energy after completing remote interviews. Second, remote fieldwork also helps researchers avoid potentially dangerous situations in the field mentioned previously. Lastly, remote fieldwork generally presents fewer financial barriers than in-person research ( Archibald et al. 2019 ). In that sense, considering remote qualitative data collection, a type of “fieldwork” may make fieldwork more accessible to a greater number of scholars.

Many of the substantive, methodological and practical challenges that arise during fieldwork can be anticipated. Proper preparation can help you hit the ground running once you enter your fieldwork destination, whether in-person or virtually. Nonetheless, there is no such thing as being perfectly prepared for the field. Some things will simply be beyond your control, and especially as a newcomer to field research, and you should be prepared for things to not go as planned. New questions will arise, interview participants may cancel appointments, and you might not get the answers you expected. Be ready to make adjustments to research plans, interview guides, or questionnaires. And, be mindful of your affective reactions to the overall fieldwork situation and be gentle with yourself.

We recommend approaching fieldwork as a learning experience as much as, or perhaps even more than, a data collection effort. This also applies to your research topic. While it is prudent always to exercise a healthy amount of skepticism about what people tell you and why, the participants in your research will likely have unique perspectives and knowledge that will challenge yours. Be an attentive listener and remember that they are experts of their own experiences.

We encourage more institutions to offer courses that cover field research preparation and planning, practical advice on safety and wellbeing, and discussion of ethics. Specifically, we align with Schwartz and Cronin-Furman's (2020 , 3) contention “that treating fieldwork preparation as the methodology will improve individual scholars’ experiences and research.” In this article, we outline a set of issue areas in which we think formal preparation is necessary, but we note that our discussion is by no means exhaustive. Formal fieldwork preparation should also extend beyond what we have covered in this article, such as issues of data security and preparing for nonqualitative fieldwork methods. We also note that field research is one area that has yet to be comprehensively addressed in conversations on diversity and equity in the political science discipline and the broader academic profession. In a recent article, Brielle Harbin (2021) begins to fill this gap by sharing her experiences conducting in-person election surveys as a Black woman in a conservative and predominantly white region of the United States and the challenges that she encountered. Beyond race and gender, citizenship, immigration status, one's Ph.D. institution and distance to the field also affect who is able to do what type of field research, where, and for how long. Future research should explore these and related questions in greater detail because limits on who is able to conduct field research constrict the sociological imagination of our field.

While Emmons and Moravcsik (2020) focus on leading Political Science Ph.D. programs in the United States, these trends likely obtain, both in lower ranked institutions in the broader United States as well as in graduate education throughout North America and Europe.

As all the authors have carried out qualitative fieldwork, this is the primary focus of this guide. This does not, however, mean that we exclude quantitative or experimental data collection from our definition of fieldwork.

There is great variation in graduate students’ financial situations, even in the Global North. For example, while higher education is tax-funded in most countries in Europe and Ph.D. students in countries such as Sweden, Norway, Denmark, the Netherlands, and Switzerland receive a comparatively generous full-time salary, healthcare and contributions to pension schemes, Ph.D. programs in other contexts like the United States and the United Kingdom have (high) enrollment fees and rely on scholarships, stipends, or departmental duties like teaching to (partially) offset these, while again others, such as Germany, are commonly financed by part-time (50 percent) employment at the university with tasks substantively unrelated to the dissertation. These different preconditions leave many Ph.D. students struggling financially and even incurring debt, while others are in a more comfortable financial position. Likewise, Ph.D. programs around the globe differ in structure, such as required coursework, duration and supervision relationships. Naturally, all of these factors have a bearing on the extent to which fieldwork is feasible. We acknowledge unequal preconditions across institutions and contexts, and trust that those Ph.D. students interested in pursuing fieldwork are best able to assess the structural and institutional context in which they operate and what this implies for how, when, and how long to carry out fieldwork.

In our experience, this is not only the general cycle for graduate students in North America, but also in Europe and likely elsewhere.

For helpful advice and feedback on earlier drafts, we wish to thank the editors and reviewers at International Studies Review , and Cassandra Emmons. We are also grateful to our interlocuters in Argentina, Canada, Colombia, Germany, Guatemala, Japan, Kenya, Norway, the Philippines, Sierra Leone, South Korea, Spain, Sweden, Turkey, the United Kingdom, and the United States, without whom this reflection on fieldwork would not have been possible. All authors contributed equally to this manuscript.

This material is based upon work supported by the Forskraftstiftelsen Theodor Adelswärds Minne, Knut and Alice Wallenberg Foundation(KAW 2013.0178), National Science Foundation Graduate Research Fellowship Program(DGE-1418060), Southeast Asia Research Group (Pre-Dissertation Fellowship), University at Albany (Initiatives for Women and the Benevolent Association), University of Missouri (John D. Bies International Travel Award Program and Kinder Institute on Constitutional Democracy), University of Southern California (Provost Fellowship in the Social Sciences), Vetenskapsrådet(Diarienummer 2019-06298), Wilhelm och Martina Lundgrens Vetenskapsfond(2016-1102; 2018-2272), and William & Mary (Global Research Institute Pre-doctoral Fellowship).

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Field Research explained

Field research: this article explains the concept of field research in a practical way. The article starts with the definition of this term, followed by a general explanation and some practical examples of field research. You will also find an explanation of the various methods and a step-by-step plan for conducting field research. Enjoy reading!

What is field research?

Field research, also known as fieldwork, is a method of collecting raw data outside of the lab, library, or usual workplace.

It involves observing and interacting with people, animals or phenomena in their natural environment to gain a deeper understanding of their behavior, social interactions and the dynamics of their environment. Read more about experimental research .

Field research methods vary by field. For example, biologists observe animals in their natural habitats, and social scientists conduct interviews and observations to study human societies.

The definition of Field research

Field research is a qualitative research method that focuses on observing and understanding individuals, groups, communities or society as a whole.

It aims to capture authentic and contextual data by immersing researchers in the environments they study.

Through direct observation and interaction with subjects, field researchers gain firsthand insight into their behaviour, beliefs, cultural practices and social structures.

It encompasses a wide variety of well-defined methods, including:

• Formal interviews
• Informal interviews

Direct observation

Participating observation.

• Collective discussions
• Analysis of personal documents
• Self-analysis
• Offline and online activities

Although this type of research is mainly qualitative, it can also contain quantitative aspects, depending on the research goals and methodologies applied.

History and the origin of Field research

Field research has a long history, especially within cultural anthropology . Anthropologists have made extensive use of field research to study different cultures, often focusing on so-called primitive cultures or cultural differences based on factors such as class.

The term “field” refers to defined areas of research, such as education, industrial environments or Amazon rainforests, where social research is conducted.

Influential figures in the early development of this type of research include Alfred Radcliffe-Brown and Bronisław Malinowski, who laid the foundations for future work in anthropology.

Field research versus laboratory research

Field research and laboratory research differ in their approach to data collection.

Field research takes place in natural environments, where researchers make direct observations and interact. It provides contextual data and insight into complex processes, but may be limited in establishing causal relationships.

On the other hand, laboratory research takes place in controlled environments, where variables are manipulated and repeatability is ensured.

It is well suited for testing hypotheses and obtaining accurate measurements, but may lack the complexity of natural environments.

Both approaches complement each other and the choice depends on the research questions and available resources .

Research Methods For Business Students Course A-Z guide to writing a rockstar Research Paper with a bulletproof Research Methodology!   More information

Methods for field research

Field research involves the use of various methods to collect valuable data and gain insight into the phenomena under investigation.

Here are some common methods applied in field research:

This method involves carefully observing people, animals, or events in their natural environment. By watching closely, researchers can study behaviors, interactions, and responses to specific situations without actively participating.

In this method, the researcher actively participates in the group, community, or environment under study. By participating in activities, having conversations and being involved in daily routines, researchers can develop a deep understanding of the social structures, norms and values, and the meaning attached to certain actions.

Qualitative interviews

This includes conducting interviews with individuals or groups to find out their perspectives, experiences and opinions. By asking open-ended questions and delving deeper into topics, researchers can gain insight into participants’ thoughts and feelings.

Data analysis of documents

In this method, documents, such as letters, diaries, reports, or other written materials, are analyzed to obtain information and insights. These documents can provide valuable context and provide a historical perspective on the issues examined.

Informal conversations

Sometimes having informal conversations with people in the research environment can yield useful information. These can be casual chats during breaks or informal gatherings where people talk freely about their experiences and perspectives.

The use of these different methods allows researchers to collect a wide range of data and develop an in-depth understanding of the social, cultural and behavioral aspects of the phenomena under study.

Case studies

Case studies are a useful approach in field research to gain in-depth insights into specific situations, groups or phenomena.

Step-by-step plan for conducting field research

Follow the steps below to get started conducting field research yourself.

Step 1: define your research goal

Determine the specific goal of your research. What do you want to discover, understand or observe? Clearly formulate your research question(s) and objective(s).

Step 2: design your research plan

Consider which methods and approaches are best suited to your research question. Consider the location, participants/population, data collection methods and time frame.

Step 3: Get permission

If necessary, obtain permission from relevant agencies, organizations or individuals to access the study site and collect data. Make sure you follow ethical guidelines and procedures.

Step 4: collect data

Go to the research site and start collecting data according to your research plan. This may include direct observation, interviews, surveys, participant observation or collection of documentation.

Step 5: Analyze and interpret your data

Evaluate and analyze the collected data . Identify patterns, themes or trends relevant to your research question. Interpretation of the data should be based on accurate and systematic analysis.

Step 6: draw conclusions and formulate results

Based on your analysis and interpretation, you come to conclusions that answer your research question . Formulate clear results and present them in a structured way .

Step 7: Report and share your findings

Write a research report describing the methodology, findings and conclusions. Share your results with the scientific community, stakeholders or the wider public through publications, presentations or other appropriate channels.

Step 8: Reflect and Evaluate your field research

Take the time to evaluate your research experience . What were the strengths and challenges of your research? What would you do differently in the future? Reflect on possible improvements and learning points for subsequent studies.

Examples of known field studies

Numerous interesting discoveries have been made while conducting research. Here are three examples of discoveries made while conducting this type of research:

New animal species

Field research has led to the discovery of several new animal species. For example, in 2018, during a field research expedition in the rainforests of Ecuador, researchers discovered a new species of tree frog.

This discovery highlighted the importance of field research in identifying biodiversity and understanding the ecological systems in which these species live.

Ecological changes

Field research has also helped identify ecological changes and understand their causes.

For example, by studying coral reefs in different parts of the world, scientists have found that coral bleaching, a consequence of climate change, is having a devastating effect on coral reef health.

It’s Your Turn

What do you think? Do you recognize the explanation about field research? Have you ever heard of this type of research before? Have you ever conducted this yourself? What do you think are the advantages compared to, for example, research in a laboratory? Do you have tips or other comments?

Share your experience and knowledge in the comments box below.

More information

• Barick, R. (2021). Research Methods For Business Students . Retrieved 02/16/2024 from Udemy.
• Burgess, R. G. (Ed.). (2003). Field Research: A sourcebook and field manual (Vol. 4) . Routledge.
• Burgess, R. G. (2002). In the field: An introduction to Field Research (Vol. 8) . Routledge.
• Edmondson, A. C., & McManus, S. E. (2007). Methodological fit in management Field Research . Academy of management review, 32(4), 1246-1264.
• McKinnon, J. (1988). Reliability and validity in Field Research: some strategies and tactics . Accounting, Auditing & Accountability Journal, 1(1), 34-54

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• Published: 27 June 2011

The case study approach

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

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

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The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

Peer Review reports

Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables 2 , 3 and 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 – 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables 2 and 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 – 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table 8 )[ 8 , 18 – 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table 9 )[ 8 ].

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

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We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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

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

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

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

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

CASE STUDY AS A RESEARCH METHODOLOGY

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

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

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

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

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

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

Key sources of data for a sample case study

VARIATIONS ON CASE STUDY METHODOLOGY

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

DATABASE INDEXING OF CASE REPORTS AND CASE STUDIES

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

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

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

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

PUBLICATION OF CASE STUDY RESEARCH IN LIBRARIANSHIP

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

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

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

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

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

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

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

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

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

Case Study Research

• First Online: 29 September 2022

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As a footnote to the previous chapter, there is such a beast known as the ethnographic case study. Ethnographic case study has found its way into this chapter rather than into the previous one because of grammatical considerations. Simply put, the “case study” part of the phrase is the noun (with “case” as an adjective defining what kind of study it is), while the “ethnographic” part of the phrase is an adjective defining the type of case study that is being conducted. As such, the case study becomes the methodology, while the ethnography part refers to a method, mode or approach relating to the development of the study.

The experiential account that we get from a case study or qualitative research of a similar vein is just so necessary. How things happen over time and the degree to which they are subject to personality and how they are only gradually perceived as tolerable or intolerable by the communities and the groups that are involved is so important. Robert Stake, University of Illinois, Urbana-Champaign

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A Case in Case Study Methodology

Christine Benedichte Meyer

Norwegian School of Economics and Business Administration

Meyer, C. B. (2001). A Case in Case Study Methodology. Field Methods 13 (4), 329-352.

The purpose of this article is to provide a comprehensive view of the case study process from the researcher’s perspective, emphasizing methodological considerations. As opposed to other qualitative or quantitative research strategies, such as grounded theory or surveys, there are virtually no specific requirements guiding case research. This is both the strength and the weakness of this approach. It is a strength because it allows tailoring the design and data collection procedures to the research questions. On the other hand, this approach has resulted in many poor case studies, leaving it open to criticism, especially from the quantitative field of research. This article argues that there is a particular need in case studies to be explicit about the methodological choices one makes. This implies discussing the wide range of decisions concerned with design requirements, data collection procedures, data analysis, and validity and reliability. The approach here is to illustrate these decisions through a particular case study of two mergers in the financial industry in Norway.

In the past few years, a number of books have been published that give useful guidance in conducting qualitative studies (Gummesson 1988; Cassell & Symon 1994; Miles & Huberman 1994; Creswell 1998; Flick 1998; Rossman & Rallis 1998; Bryman & Burgess 1999; Marshall & Rossman 1999; Denzin & Lincoln 2000). One approach often mentioned is the case study (Yin 1989). Case studies are widely used in organizational studies in the social science disciplines of sociology, industrial relations, and anthropology (Hartley 1994). Such a study consists of detailed investigation of one or more organizations, or groups within organizations, with a view to providing an analysis of the context and processes involved in the phenomenon under study.

As opposed to other qualitative or quantitative research strategies, such as grounded theory (Glaser and Strauss 1967) or surveys (Nachmias & Nachmias 1981), there are virtually no specific requirements guiding case research. Yin (1989) and Eisenhardt (1989) give useful insights into the case study as a research strategy, but leave most of the design decisions on the table. This is both the strength and the weakness of this approach. It is a strength because it allows tailoring the design and data collection procedures to the research questions. On the other hand, this approach has resulted in many poor case studies, leaving it open to criticism, especially from the quantitative field of research (Cook and Campbell 1979). The fact that the case study is a rather loose design implies that there are a number of choices that need to be addressed in a principled way.

Although case studies have become a common research strategy, the scope of methodology sections in articles published in journals is far too limited to give the readers a detailed and comprehensive view of the decisions taken in the particular studies, and, given the format of methodology sections, will remain so. The few books (Yin 1989, 1993; Hamel, Dufour, & Fortin 1993; Stake 1995) and book chapters on case studies (Hartley 1994; Silverman 2000) are, on the other hand, mainly normative and span a broad range of different kinds of case studies. One exception is Pettigrew (1990, 1992), who places the case study in the context of a research tradition (the Warwick process research).

Given the contextual nature of the case study and its strength in addressing contemporary phenomena in real-life contexts, I believe that there is a need for articles that provide a comprehensive overview of the case study process from the researcher’s perspective, emphasizing methodological considerations. This implies addressing the whole range of choices concerning specific design requirements, data collection procedures, data analysis, and validity and reliability.

WHY A CASE STUDY?

Case studies are tailor-made for exploring new processes or behaviors or ones that are little understood (Hartley 1994). Hence, the approach is particularly useful for responding to how and why questions about a contemporary set of events (Leonard-Barton 1990). Moreover, researchers have argued that certain kinds of information can be difficult or even impossible to tackle by means other than qualitative approaches such as the case study (Sykes 1990). Gummesson (1988:76) argues that an important advantage of case study research is the opportunity for a holistic view of the process: “The detailed observations entailed in the case study method enable us to study many different aspects, examine them in relation to each other, view the process within its total environment and also use the researchers’ capacity for ‘verstehen.’ ”

The contextual nature of the case study is illustrated in Yin’s (1993:59) definition of a case study as an empirical inquiry that “investigates a contemporary phenomenon within its real-life context and addresses a situation in which the boundaries between phenomenon and context are not clearly evident.”

The key difference between the case study and other qualitative designs such as grounded theory and ethnography (Glaser & Strauss 1967; Strauss & Corbin 1990; Gioia & Chittipeddi 1991) is that the case study is open to the use of theory or conceptual categories that guide the research and analysis of data. In contrast, grounded theory or ethnography presupposes that theoretical perspectives are grounded in and emerge from firsthand data. Hartley (1994) argues that without a theoretical framework, the researcher is in severe danger of providing description without meaning. Gummesson (1988) says that a lack of preunderstanding will cause the researcher to spend considerable time gathering basic information. This preunderstanding may arise from general knowledge such as theories, models, and concepts or from specific knowledge of institutional conditions and social patterns. According to Gummesson, the key is not to require researchers to have split but dual personalities: “Those who are able to balance on a razor’s edge using their pre-understanding without being its slave” (p. 58).

DESCRIPTION OF THE ILLUSTRATIVE STUDY

The study that will be used for illustrative purposes is a comparative and longitudinal case study of organizational integration in mergers and acquisitions taking place in Norway. The study had two purposes: (1) to identify contextual factors and features of integration that facilitated or impeded organizational integration, and (2) to study how the three dimensions of organizational integration (integration of tasks, unification of power, and integration of cultures and identities) interrelated and evolved over time. Examples of contextual factors were relative power, degree of friendliness, and economic climate. Integration features included factors such as participation, communication, and allocation of positions and functions.

Mergers and acquisitions are inherently complex. Researchers in the field have suggested that managers continuously underestimate the task of integrating the merging organizations in the postintegration process (Haspeslaph & Jemison 1991). The process of organizational integration can lead to sharp interorganizational conflict as the different top management styles, organizational and work unit cultures, systems, and other aspects of organizational life come into contact (Blake & Mounton 1985; Schweiger & Walsh 1990; Cartwright & Cooper 1993). Furthermore, cultural change in mergers and acquisitions is compounded by additional uncertainties, ambiguities, and stress inherent in the combination process (Buono & Bowditch 1989).

I focused on two combinations: one merger and one acquisition. The first case was a merger between two major Norwegian banks, Bergen Bank and DnC (to be named DnB), that started in the late 1980s. The second case was a study of a major acquisition in the insurance industry (i.e., Gjensidige’s acquisition of Forenede), that started in the early 1990s. Both combinations aimed to realize operational synergies though merging the two organizations into one entity. This implied disruption of organizational boundaries and threat to the existing power distribution and organizational cultures.

The study of integration processes in mergers and acquisitions illustrates the need to find a design that opens for exploration of sensitive issues such as power struggles between the two merging organizations. Furthermore, the inherent complexity in the integration process, involving integration of tasks, unification of power, and cultural integration stressed the need for in-depth study of the phenomenon over time. To understand the cultural integration process, the design also had to be linked to the past history of the two organizations.

DESIGN DECISIONS

In the introduction, I stressed that a case is a rather loose design that requires that a number of design choices be made. In this section, I go through the most important choices I faced in the study of organizational integration in mergers and acquisitions. These include: (1) selection of cases; (2) sampling time; (3) choosing business areas, divisions, and sites; and (4) selection of and choices regarding data collection procedures, interviews, documents, and observation.

Selection of Cases

There are several choices involved in selecting cases. First, there is the question of how many cases to include. Second, one must sample cases and decide on a unit of analysis. I will explore these issues subsequently.

Single or Multiple Cases

Case studies can involve single or multiple cases. The problem of single cases is limitations in generalizability and several information-processing biases (Eisenhardt 1989).

One way to respond to these biases is by applying a multi-case approach (Leonard-Barton 1990). Multiple cases augment external validity and help guard against observer biases. Moreover, multi-case sampling adds confidence to findings. By looking at a range of similar and contrasting cases, we can understand a single-case finding, grounding it by specifying how and where and, if possible, why it behaves as it does. (Miles & Huberman 1994)

Given these limitations of the single case study, it is desirable to include more than one case study in the study. However, the desire for depth and a pluralist perspective and tracking the cases over time implies that the number of cases must be fairly few. I chose two cases, which clearly does not support generalizability any more than does one case, but allows for comparison and contrast between the cases as well as a deeper and richer look at each case.

Originally, I planned to include a third case in the study. Due to changes in management during the initial integration process, my access to the case was limited and I left this case entirely. However, a positive side effect was that it allowed a deeper investigation of the two original cases and in hindsight turned out to be a good decision.

Sampling Cases

The logic of sampling cases is fundamentally different from statistical sampling. The logic in case studies involves theoretical sampling, in which the goal is to choose cases that are likely to replicate or extend the emergent theory or to fill theoretical categories and provide examples for polar types (Eisenhardt 1989). Hence, whereas quantitative sampling concerns itself with representativeness, qualitative sampling seeks information richness and selects the cases purposefully rather than randomly (Crabtree and Miller 1992).

The choice of cases was guided by George (1979) and Pettigrew’s (1990) recommendations. The aim was to find cases that matched the three dimensions in the dependent variable and provided variation in the contextual factors, thus representing polar cases.

To match the choice of outcome variable, organizational integration, I chose cases in which the purpose was to fully consolidate the merging parties’ operations. A full consolidation would imply considerable disruption in the organizational boundaries and would be expected to affect the task-related, political, and cultural features of the organizations. As for the contextual factors, the two cases varied in contextual factors such as relative power, friendliness, and economic climate. The DnB merger was a friendly combination between two equal partners in an unfriendly economic climate. Gjensidige’s acquisition of Forenede was, in contrast, an unfriendly and unbalanced acquisition in a friendly economic climate.

Unit of Analysis

Another way to respond to researchers’ and respondents’ biases is to have more than one unit of analysis in each case (Yin 1993). This implies that, in addition to developing contrasts between the cases, researchers can focus on contrasts within the cases (Hartley 1994). In case studies, there is a choice of a holistic or embedded design (Yin 1989). A holistic design examines the global nature of the phenomenon, whereas an embedded design also pays attention to subunit(s).

I used an embedded design to analyze the cases (i.e., within each case, I also gave attention to subunits and subprocesses). In both cases, I compared the combination processes in the various divisions and local networks. Moreover, I compared three distinct change processes in DnB: before the merger, during the initial combination, and two years after the merger. The overall and most important unit of analysis in the two cases was, however, the integration process.

Sampling Time

According to Pettigrew (1990), time sets a reference for what changes can be seen and how those changes are explained. When conducting a case study, there are several important issues to decide when sampling time. The first regards how many times data should be collected, while the second concerns when to enter the organizations. There is also a need to decide whether to collect data on a continuous basis or in distinct periods.

Number of data collections. I studied the process by collecting real time and retrospective data at two points in time, with one-and-a-half- and two-year intervals in the two cases. Collecting data twice had some interesting implications for the interpretations of the data. During the first data collection in the DnB study, for example, I collected retrospective data about the premerger and initial combination phase and real-time data about the second step in the combination process.

Although I gained a picture of how the employees experienced the second stage of the combination process, it was too early to assess the effects of this process at that stage. I entered the organization two years later and found interesting effects that I had not anticipated the first time. Moreover, it was interesting to observe how people’s attitudes toward the merger processes changed over time to be more positive and less emotional.

When to enter the organizations. It would be desirable to have had the opportunity to collect data in the precombination processes. However, researchers are rarely given access in this period due to secrecy. The emphasis in this study was to focus on the postcombination process. As such, the precombination events were classified as contextual factors. This implied that it was most important to collect real-time data after the parties had been given government approval to merge or acquire. What would have been desirable was to gain access earlier in the postcombination process. This was not possible because access had to be negotiated. Due to the change of CEO in the middle of the merger process and the need for renegotiating access, this took longer than expected.

Regarding the second case, I was restricted by the time frame of the study. In essence, I had to choose between entering the combination process as soon as governmental approval was given, or entering the organization at a later stage. In light of the previous studies in the field that have failed to go beyond the initial two years, and given the need to collect data about the cultural integration process, I chose the latter strategy. And I decided to enter the organizations at two distinct periods of time rather than on a continuous basis.

There were several reasons for this approach, some methodological and some practical. First, data collection on a continuous basis would have required use of extensive observation that I didn’t have access to, and getting access to two data collections in DnB was difficult in itself. Second, I had a stay abroad between the first and second data collection in Gjensidige. Collecting data on a continuous basis would probably have allowed for better mapping of the ongoing integration process, but the contrasts between the two different stages in the integration process that I wanted to elaborate would probably be more difficult to detect. In Table 1 I have listed the periods of time in which I collected data in the two combinations.

Sampling Business Areas, Divisions, and Sites

Even when the cases for a study have been chosen, it is often necessary to make further choices within each case to make the cases researchable. The most important criteria that set the boundaries for the study are importance or criticality, relevance, and representativeness. At the time of the data collection, my criteria for making these decisions were not as conscious as they may appear here. Rather, being restricted by time and my own capacity as a researcher, I had to limit the sites and act instinctively. In both cases, I decided to concentrate on the core businesses (criticality criterion) and left out the business units that were only mildly affected by the integration process (relevance criterion). In the choice of regional offices, I used the representativeness criterion as the number of offices widely exceeded the number of sites possible to study. In making these choices, I relied on key informants in the organizations.

SELECTION OF DATA COLLECTION PROCEDURES

The choice of data collection procedures should be guided by the research question and the choice of design. The case study approach typically combines data collection methods such as archives, interviews, questionnaires, and observations (Yin 1989). This triangulated methodology provides stronger substantiation of constructs and hypotheses. However, the choice of data collection methods is also subject to constraints in time, financial resources, and access.

I chose a combination of interviews, archives, and observation, with main emphasis on the first two. Conducting a survey was inappropriate due to the lack of established concepts and indicators. The reason for limited observation, on the other hand, was due to problems in obtaining access early in the study and time and resource constraints. In addition to choosing among several different data collection methods, there are a number of choices to be made for each individual method.

When relying on interviews as the primary data collection method, the issue of building trust between the researcher and the interviewees becomes very important. I addressed this issue by several means. First, I established a procedure of how to approach the interviewees. In most cases, I called them first, then sent out a letter explaining the key features of the project and outlining the broad issues to be addressed in the interview. In this letter, the support from the institution’s top management was also communicated. In most cases, the top management’s support of the project was an important prerequisite for the respondent’s input. Some interviewees did, however, fear that their input would be open to the top management without disguising the information source. Hence, it became important to communicate how I intended to use and store the information.

To establish trust, I also actively used my preunderstanding of the context in the first case and the phenomenon in the second case. As I built up an understanding of the cases, I used this information to gain confidence. The active use of my preunderstanding did, however, pose important challenges in not revealing too much of the research hypotheses and in balancing between asking open-ended questions and appearing knowledgeable.

There are two choices involved in conducting interviews. The first concerns the sampling of interviewees. The second is that you must decide on issues such as the structure of the interviews, use of tape recorder, and involvement of other researchers.

Sampling Interviewees

Following the desire for detailed knowledge of each case and for grasping different participant’s views the aim was, in line with Pettigrew (1990), to apply a pluralist view by describing and analyzing competing versions of reality as seen by actors in the combination processes.

I used four criteria for sampling informants. First, I drew informants from populations representing multiple perspectives. The first data collection in DnB was primarily focused on the top management level. Moreover, most middle managers in the first data collection were employed at the head offices, either in Bergen or Oslo. In the second data collection, I compensated for this skew by including eight local middle managers in the sample. The difference between the number of employees interviewed in DnB and Gjensidige was primarily due to the fact that Gjensidige has three unions, whereas DnB only has one. The distribution of interviewees is outlined in Table 2 .

The second criterion was to use multiple informants. According to Glick et al. (1990), an important advantage of using multiple informants is that the validity of information provided by one informant can be checked against that provided by other informants. Moreover, the validity of the data used by the researcher can be enhanced by resolving the discrepancies among different informants’ reports. Hence, I selected multiple respondents from each perspective.

Third, I focused on key informants who were expected to be knowledgeable about the combination process. These people included top management members, managers, and employees involved in the integration project. To validate the information from these informants, I also used a fourth criterion by selecting managers and employees who had been affected by the process but who were not involved in the project groups.

Structured versus unstructured. In line with the explorative nature of the study, the goal of the interviews was to see the research topic from the perspective of the interviewee, and to understand why he or she came to have this particular perspective. To meet this goal, King (1994:15) recommends that one have “a low degree of structure imposed on the interviewer, a preponderance of open questions, a focus on specific situations and action sequences in the world of the interviewee rather than abstractions and general opinions.” In line with these recommendations, the collection of primary data in this study consists of unstructured interviews.

Using tape recorders and involving other researchers. The majority of the interviews were tape-recorded, and I could thus concentrate fully on asking questions and responding to the interviewees’ answers. In the few interviews that were not tape-recorded, most of which were conducted in the first phase of the DnB-study, two researchers were present. This was useful as we were both able to discuss the interviews later and had feedback on the role of an interviewer.

In hindsight, however, I wish that these interviews had been tape-recorded to maintain the level of accuracy and richness of data. Hence, in the next phases of data collection, I tape-recorded all interviews, with two exceptions (people who strongly opposed the use of this device). All interviews that were tape-recorded were transcribed by me in full, which gave me closeness and a good grasp of the data.

When organizations merge or make acquisitions, there are often a vast number of documents to choose from to build up an understanding of what has happened and to use in the analyses. Furthermore, when firms make acquisitions or merge, they often hire external consultants, each of whom produces more documents. Due to time constraints, it is seldom possible to collect and analyze all these documents, and thus the researcher has to make a selection.

The choice of documentation was guided by my previous experience with merger and acquisition processes and the research question. Hence, obtaining information on the postintegration process was more important than gaining access to the due-diligence analysis. As I learned about the process, I obtained more documents on specific issues. I did not, however, gain access to all the documents I asked for, and, in some cases, documents had been lost or shredded.

The documents were helpful in a number of ways. First, and most important, they were used as inputs to the interview guide and saved me time, because I did not have to ask for facts in the interviews. They were also useful for tracing the history of the organizations and statements made by key people in the organizations. Third, the documents were helpful in counteracting the biases of the interviews. A list of the documents used in writing the cases is shown in Table 3 .

Observation

The major strength of direct observation is that it is unobtrusive and does not require direct interaction with participants (Adler and Adler 1994). Observation produces rigor when it is combined with other methods. When the researcher has access to group processes, direct observation can illuminate the discrepancies between what people said in the interviews and casual conversations and what they actually do (Pettigrew 1990).

As with interviews, there are a number of choices involved in conducting observations. Although I did some observations in the study, I used interviews as the key data collection source. Discussion in this article about observations will thus be somewhat limited. Nevertheless, I faced a number of choices in conducting observations, including type of observation, when to enter, how much observation to conduct, and which groups to observe.

The are four ways in which an observer may gather data: (1) the complete participant who operates covertly, concealing any intention to observe the setting; (2) the participant-as-observer, who forms relationships and participates in activities, but makes no secret of his or her intentions to observe events; (3) the observer-as-participant, who maintains only superficial contact with the people being studied; and (4) the complete observer, who merely stands back and eavesdrops on the proceedings (Waddington 1994).

In this study, I used the second and third ways of observing. The use of the participant-as-observer mode, on which much ethnographic research is based, was rather limited in the study. There were two reasons for this. First, I had limited time available for collecting data, and in my view interviews made more effective use of this limited time than extensive participant observation. Second, people were rather reluctant to let me observe these political and sensitive processes until they knew me better and felt I could be trusted. Indeed, I was dependent on starting the data collection before having built sufficient trust to observe key groups in the integration process. Nevertheless, Gjensidige allowed me to study two employee seminars to acquaint me with the organization. Here I admitted my role as an observer but participated fully in the activities. To achieve variation, I chose two seminars representing polar groups of employees.

As observer-as-participant, I attended a top management meeting at the end of the first data collection in Gjensidige and observed the respondents during interviews and in more informal meetings, such as lunches. All these observations gave me an opportunity to validate the data from the interviews. Observing the top management group was by far the most interesting and rewarding in terms of input.

Both DnB and Gjensidige started to open up for more extensive observation when I was about to finish the data collection. By then, I had built up the trust needed to undertake this approach. Unfortunately, this came a little late for me to take advantage of it.

DATA ANALYSIS

Published studies generally describe research sites and data-collection methods, but give little space to discuss the analysis (Eisenhardt 1989). Thus, one cannot follow how a researcher arrives at the final conclusions from a large volume of field notes (Miles and Huberman 1994).

In this study, I went through the stages by which the data were reduced and analyzed. This involved establishing the chronology, coding, writing up the data according to phases and themes, introducing organizational integration into the analysis, comparing the cases, and applying the theory. I will discuss these phases accordingly.

The first step in the analysis was to establish the chronology of the cases. To do this, I used internal and external documents. I wrote the chronologies up and included appendices in the final report.

The next step was to code the data into phases and themes reflecting the contextual factors and features of integration. For the interviews, this implied marking the text with a specific phase and a theme, and grouping the paragraphs on the same theme and phase together. I followed the same procedure in organizing the documents.

I then wrote up the cases using phases and themes to structure them. Before starting to write up the cases, I scanned the information on each theme, built up the facts and filled in with perceptions and reactions that were illustrative and representative of the data.

The documents were primarily useful in establishing the facts, but they also provided me with some perceptions and reactions that were validated in the interviews. The documents used included internal letters and newsletters as well as articles from the press. The interviews were less factual, as intended, and gave me input to assess perceptions and reactions. The limited observation was useful to validate the data from the interviews. The result of this step was two descriptive cases.

To make each case more analytical, I introduced the three dimensions of organizational integration—integration of tasks, unification of power, and cultural integration—into the analysis. This helped to focus the case and to develop a framework that could be used to compare the cases. The cases were thus structured according to phases, organizational integration, and themes reflecting the factors and features in the study.

I took all these steps to become more familiar with each case as an individual entity. According to Eisenhardt (1989:540), this is a process that “allows the unique patterns of each case to emerge before the investigators push to generalise patterns across cases. In addition it gives investigators a rich familiarity with each case which, in turn, accelerates cross-case comparison.”

The comparison between the cases constituted the next step in the analysis. Here, I used the categories from the case chapters, filled in the features and factors, and compared and contrasted the findings. The idea behind cross-case searching tactics is to force investigators to go beyond initial impressions, especially through the use of structural and diverse lenses on the data. These tactics improve the likelihood of accurate and reliable theory, that is, theory with a close fit to the data (Eisenhardt 1989).

As a result, I had a number of overall themes, concepts, and relationships that had emerged from the within-case analysis and cross-case comparisons. The next step was to compare these emergent findings with theory from the organizational field of mergers and acquisitions, as well as other relevant perspectives.

This method of generalization is known as analytical generalization. In this approach, a previously developed theory is used as a template with which to compare the empirical results of the case study (Yin 1989). This comparison of emergent concepts, theory, or hypotheses with the extant literature involves asking what it is similar to, what it contradicts, and why. The key to this process is to consider a broad range of theory (Eisenhardt 1989). On the whole, linking emergent theory to existent literature enhances the internal validity, generalizability, and theoretical level of theory-building from case research.

According to Eisenhardt (1989), examining literature that conflicts with the emergent literature is important for two reasons. First, the chance of neglecting conflicting findings is reduced. Second, “conflicting results forces researchers into a more creative, frame-breaking mode of thinking than they might otherwise be able to achieve” (p. 544). Similarly, Eisenhardt (1989) claims that literature discussing similar findings is important because it ties together underlying similarities in phenomena not normally associated with each other. The result is often a theory with a stronger internal validity, wider generalizability, and a higher conceptual level.

The analytical generalization in the study included exploring and developing the concepts and examining the relationships between the constructs. In carrying out this analytical generalization, I acted on Eisenhardt’s (1989) recommendation to use a broad range of theory. First, I compared and contrasted the findings with the organizational stream on mergers and acquisition literature. Then I discussed other relevant literatures, including strategic change, power and politics, social justice, and social identity theory to explore how these perspectives could contribute to the understanding of the findings. Finally, I discussed the findings that could not be explained either by the merger and acquisition literature or the four theoretical perspectives.

In every scientific study, questions are raised about whether the study is valid and reliable. The issues of validity and reliability in case studies are just as important as for more deductive designs, but the application is fundamentally different.

VALIDITY AND RELIABILITY

The problems of validity in qualitative studies are related to the fact that most qualitative researchers work alone in the field, they focus on the findings rather than describe how the results were reached, and they are limited in processing information (Miles and Huberman 1994).

Researchers writing about qualitative methods have questioned whether the same criteria can be used for qualitative and quantitative studies (Kirk & Miller 1986; Sykes 1990; Maxwell 1992). The problem with the validity criteria suggested in qualitative research is that there is little consistency across the articles as each author suggests a new set of criteria.

One approach in examining validity and reliability is to apply the criteria used in quantitative research. Hence, the criteria to be examined here are objectivity/intersubjectivity, construct validity, internal validity, external validity, and reliability.

Objectivity/Intersubjectivity

The basic issue of objectivity can be framed as one of relative neutrality and reasonable freedom from unacknowledged research biases (Miles & Huberman 1994). In a real-time longitudinal study, the researcher is in danger of losing objectivity and of becoming too involved with the organization, the people, and the process. Hence, Leonard-Barton (1990) claims that one may be perceived as, and may even become, an advocate rather than an observer.

According to King (1994), however, qualitative research, in seeking to describe and make sense of the world, does not require researchers to strive for objectivity and distance themselves from research participants. Indeed, to do so would make good qualitative research impossible, as the interviewer’s sensitivity to subjective aspects of his or her relationship with the interviewee is an essential part of the research process (King 1994:31).

This does not imply, however, that the issue of possible research bias can be ignored. It is just as important as in a structured quantitative interview that the findings are not simply the product of the researcher’s prejudices and prior experience. One way to guard against this bias is for the researcher to explicitly recognize his or her presuppositions and to make a conscious effort to set these aside in the analysis (Gummesson 1988). Furthermore, rival conclusions should be considered (Miles & Huberman 1994).

My experience from the first phase of the DnB study was that it was difficult to focus the questions and the analysis of the data when the research questions were too vague and broad. As such, developing a framework before collecting the data for the study was useful in guiding the collection and analysis of data. Nevertheless, it was important to be open-minded and receptive to new and surprising data. In the DnB study, for example, the positive effect of the reorganization process on the integration of cultures came as a complete surprise to me and thus needed further elaboration.

I also consciously searched for negative evidence and problems by interviewing outliers (Miles & Huberman 1994) and asking problem-oriented questions. In Gjensidige, the first interviews with the top management revealed a much more positive perception of the cultural integration process than I had expected. To explore whether this was a result of overreliance on elite informants, I continued posing problem-oriented questions to outliers and people at lower levels in the organization. Moreover, I told them about the DnB study to be explicit about my presuppositions.

Another important issue when assessing objectivity is whether other researchers can trace the interpretations made in the case studies, or what is called intersubjectivity. To deal with this issue, Miles & Huberman (1994) suggest that: (1) the study’s general methods and procedures should be described in detail, (2) one should be able to follow the process of analysis, (3) conclusions should be explicitly linked with exhibits of displayed data, and (4) the data from the study should be made available for reanalysis by others.

In response to these requirements, I described the study’s data collection procedures and processing in detail. Then, the primary data were displayed in the written report in the form of quotations and extracts from documents to support and illustrate the interpretations of the data. Because the study was written up in English, I included the Norwegian text in a separate appendix. Finally, all the primary data from the study were accessible for a small group of distinguished researchers.

Construct Validity

Construct validity refers to whether there is substantial evidence that the theoretical paradigm correctly corresponds to observation (Kirk & Miller 1986). In this form of validity, the issue is the legitimacy of the application of a given concept or theory to established facts.

The strength of qualitative research lies in the flexible and responsive interaction between the interviewer and the respondents (Sykes 1990). Thus, meaning can be probed, topics covered easily from a number of angles, and questions made clear for respondents. This is an advantage for exploring the concepts (construct or theoretical validity) and the relationships between them (internal validity). Similarly, Hakim (1987) says the great strength of qualitative research is the validity of data obtained because individuals are interviewed in sufficient detail for the results to be taken as true, correct, and believable reports of their views and experiences.

Construct validity can be strengthened by applying a longitudinal multicase approach, triangulation, and use of feedback loops. The advantage of applying a longitudinal approach is that one gets the opportunity to test sensitivity of construct measures to the passage of time. Leonard-Barton (1990), for example, found that one of her main constructs, communicability, varied across time and relative to different groups of users. Thus, the longitudinal study aided in defining the construct more precisely. By using more than one case study, one can validate stability of construct across situations (Leonard-Barton 1990). Since my study only consists of two case studies, the opportunity to test stability of constructs across cases is somewhat limited. However, the use of more than one unit of analysis helps to overcome this limitation.

Construct validity is strengthened by the use of multiple sources of evidence to build construct measures, which define the construct and distinguish it from other constructs. These multiple sources of evidence can include multiple viewpoints within and across the data sources. My study responds to these requirements in its sampling of interviewees and uses of multiple data sources.

Use of feedback loops implies returning to interviewees with interpretations and developing theory and actively seeking contradictions in data (Crabtree & Miller 1992; King 1994). In DnB, the written report had to be approved by the bank’s top management after the first data collection. Apart from one minor correction, the bank had no objections to the established facts. In their comments on my analysis, some of the top managers expressed the view that the political process had been overemphasized, and that the CEO’s role in initiating a strategic process was undervalued. Hence, an important objective in the second data collection was to explore these comments further. Moreover, the report was not as positive as the management had hoped for, and negotiations had to be conducted to publish the report. The result of these negotiations was that publication of the report was postponed one-and-a-half years.

The experiences from the first data collection in the DnB had some consequences. I was more cautious and brought up the problems of confidentiality and the need to publish at the outset of the Gjensidige study. Also, I had to struggle to get access to the DnB case for the second data collection and some of the information I asked for was not released. At Gjensidige, I sent a preliminary draft of the case chapter to the corporation’s top management for comments, in addition to having second interviews with a small number of people. Beside testing out the factual description, these sessions gave me the opportunity to test out the theoretical categories established as a result of the within-case analysis.

Internal Validity

Internal validity concerns the validity of the postulated relationships among the concepts. The main problem of internal validity as a criterion in qualitative research is that it is often not open to scrutiny. According to Sykes (1990), the researcher can always provide a plausible account and, with careful editing, may ensure its coherence. Recognition of this problem has led to calls for better documentation of the processes of data collection, the data itself, and the interpretative contribution of the researcher. The discussion of how I met these requirements was outlined in the section on objectivity/subjectivity above.

However, there are some advantages in using qualitative methods, too. First, the flexible and responsive methods of data collection allow cross-checking and amplification of information from individual units as it is generated. Respondents’ opinions and understandings can be thoroughly explored. The internal validity results from strategies that eliminate ambiguity and contradiction, filling in detail and establishing strong connections in data.

Second, the longitudinal study enables one to track cause and effect. Moreover, it can make one aware of intervening variables (Leonard-Barton 1990). Eisenhardt (1989:542) states, “Just as hypothesis testing research an apparent relationship may simply be a spurious correlation or may reflect the impact of some third variable on each of the other two. Therefore, it is important to discover the underlying reasons for why the relationship exists.”

Generalizability

According to Mitchell (1983), case studies are not based on statistical inference. Quite the contrary, the inferring process turns exclusively on the theoretically necessary links among the features in the case study. The validity of the extrapolation depends not on the typicality or representativeness of the case but on the cogency of the theoretical reasoning. Hartley (1994:225) claims, “The detailed knowledge of the organization and especially the knowledge about the processes underlying the behaviour and its context can help to specify the conditions under which behaviour can be expected to occur. In other words, the generalisation is about theoretical propositions not about populations.”

Generalizability is normally based on the assumption that this theory may be useful in making sense of similar persons or situations (Maxwell 1992). One way to increase the generalizability is to apply a multicase approach (Leonard-Barton 1990). The advantage of this approach is that one can replicate the findings from one case study to another. This replication logic is similar to that used on multiple experiments (Yin 1993).

Given the choice of two case studies, the generalizability criterion is not supported in this study. Through the discussion of my choices, I have tried to show that I had to strike a balance between the need for depth and mapping changes over time and the number of cases. In doing so, I deliberately chose to provide a deeper and richer look at each case, allowing the reader to make judgments about the applicability rather than making a case for generalizability.

Reliability

Reliability focuses on whether the process of the study is consistent and reasonably stable over time and across researchers and methods (Miles & Huberman 1994). In the context of qualitative research, reliability is concerned with two questions (Sykes 1990): Could the same study carried out by two researchers produce the same findings? and Could a study be repeated using the same researcher and respondents to yield the same findings?

The problem of reliability in qualitative research is that differences between replicated studies using different researchers are to be expected. However, while it may not be surprising that different researchers generate different findings and reach different conclusions, controlling for reliability may still be relevant. Kirk and Miller’s (1986:311) definition takes into account the particular relationship between the researcher’s orientation, the generation of data, and its interpretation:

For reliability to be calculated, it is incumbent on the scientific investigator to document his or her procedure. This must be accomplished at such a level of abstraction that the loci of decisions internal to the project are made apparent. The curious public deserves to know how the qualitative researcher prepares him or herself for the endeavour, and how the data is collected and analysed.

The study addresses these requirements by discussing my point of departure regarding experience and framework, the sampling and data collection procedures, and data analysis.

Case studies often lack academic rigor and are, as such, regarded as inferior to more rigorous methods where there are more specific guidelines for collecting and analyzing data. These criticisms stress that there is a need to be very explicit about the choices one makes and the need to justify them.

One reason why case studies are criticized may be that researchers disagree about the definition and the purpose of carrying out case studies. Case studies have been regarded as a design (Cook and Campbell 1979), as a qualitative methodology (Cassell and Symon 1994), as a particular data collection procedure (Andersen 1997), and as a research strategy (Yin 1989). Furthermore, the purpose for carrying out case studies is unclear. Some regard case studies as supplements to more rigorous qualitative studies to be carried out in the early stage of the research process; others claim that it can be used for multiple purposes and as a research strategy in its own right (Gummesson 1988; Yin 1989). Given this unclear status, researchers need to be very clear about their interpretation of the case study and the purpose of carrying out the study.

This article has taken Yin’s (1989) definition of the case study as a research strategy as a starting point and argued that the choice of the case study should be guided by the research question(s). In the illustrative study, I used a case study strategy because of a need to explore sensitive, ill-defined concepts in depth, over time, taking into account the context and history of the mergers and the existing knowledge about the phenomenon. However, the choice of a case study strategy extended rather than limited the number of decisions to be made. In Schramm’s (1971, cited in Yin 1989:22–23) words, “The essence of a case study, the central tendency among all types of case study, is that it tries to illuminate a decision or set of decisions, why they were taken, how they were implemented, and with what result.”

Hence, the purpose of this article has been to illustrate the wide range of decisions that need to be made in the context of a particular case study and to discuss the methodological considerations linked to these decisions. I argue that there is a particular need in case studies to be explicit about the methodological choices one makes and that these choices can be best illustrated through a case study of the case study strategy.

As in all case studies, however, there are limitations to the generalizability of using one particular case study for illustrative purposes. As such, the strength of linking the methodological considerations to a specific context and phenomenon also becomes a weakness. However, I would argue that the questions raised in this article are applicable to many case studies, but that the answers are very likely to vary. The design choices are shown in Table 4 . Hence, researchers choosing a longitudinal, comparative case study need to address the same set of questions with regard to design, data collection procedures, and analysis, but they are likely to come up with other conclusions, given their different research questions.

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Christine Benedichte Meyer is an associate professor in the Department of Strategy and Management in the Norwegian School of Economics and Business Administration, Bergen-Sandviken, Norway. Her research interests are mergers and acquisitions, strategic change, and qualitative research. Recent publications include: “Allocation Processes in Mergers and Acquisitions: An Organisational Justice Perspective” (British Journal of Management 2001) and “Motives for Acquisitions in the Norwegian Financial Industry” (CEMS Business Review 1997).

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White, R.E., Cooper, K. (2022). Case Study Research. In: Qualitative Research in the Post-Modern Era. Springer, Cham. https://doi.org/10.1007/978-3-030-85124-8_7

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Module 2: Sociological Research

Field research, learning outcomes.

• Explain the three types of field research: participant observation, ethnography, and case studies

The work of sociology rarely happens in limited, confined spaces. Sociologists seldom study subjects in their own offices or laboratories. Rather, sociologists go out into the world. They meet subjects where they live, work, and play. Field research refers to gathering primary data from a natural environment without doing a lab experiment or a survey. It is a research method suited to an interpretive framework rather than to the scientific method. To conduct field research, the sociologist must be willing to step into new environments and observe, participate, or experience those worlds. In field work, the sociologists, rather than the subjects, are the ones out of their element.

The researcher interacts with or observes a person or people and gathers data along the way. The key point in field research is that it takes place in the subject’s natural environment, whether it’s a coffee shop or tribal village, a homeless shelter or the DMV, a hospital, airport, mall, or beach resort.

Figure 1. Sociological researchers travel across countries and cultures to interact with and observe subjects in their natural environments. (Photo courtesy of IMLS Digital Collections and Content/flickr and Olympic National Park)

While field research often begins in a specific setting , the study’s purpose is to observe specific behaviors in that setting. Field work is optimal for observing how people behave. It is less useful, however, for understanding why they behave that way. You can’t really narrow down cause and effect when there are so many variables to be factored into a natural environment.

Many of the data gathered in field research are based not on cause and effect but on correlation. And while field research looks for correlation, its small sample size does not allow for establishing a causal relationship between two variables.

BeyoncÉ and LADY gaga as sociological subjects

Figure 2. Researchers have used surveys and participant observations to accumulate data on Lady Gaga and Beyonce as multifaceted performers. (Credit a: John Robert Chartlon/flickr, b: Kristopher Harris/flickr.)

Sociologists have studied Lady Gaga and Beyoncé and their impact on music, movies, social media, fan participation, and social equality. In their studies, researchers have used several research methods including secondary analysis, participant observation, and surveys from concert participants.

In their study, Click, Lee & Holiday (2013) interviewed 45 Lady Gaga fans who utilized social media to communicate with the artist. These fans viewed Lady Gaga as a mirror of themselves and a source of inspiration. Like her, they embrace not being a part of mainstream culture. Many of Lady Gaga’s fans are members of the LGBTQ community. They see the “song “Born This Way” as a rallying cry and answer her calls for “Paws Up” with a physical expression of solidarity—outstretched arms and fingers bent and curled to resemble monster claws.”

Sascha Buchanan (2019) made use of participant observation to study the relationship between two fan groups, that of Beyoncé and that of Rihanna. She observed award shows sponsored by iHeartRadio, MTV EMA, and BET that pit one group against another as they competed for Best Fan Army, Biggest Fans, and FANdemonium. Buchanan argues that the media thus sustains a myth of rivalry between the two most commercially successful Black women vocal artists.

Here, we will look at three types of field research: participant observation, ethnography, and the case study.

Participant Observation

In participant observation  research, a sociologist joins people and participates in a group’s routine activities for the purpose of observing them within that context. This method lets researchers experience a specific aspect of social life. A researcher might go to great lengths to get a firsthand look into a trend, institution, or behavior. Researchers temporarily put themselves into roles and record their observations. A researcher might work as a waitress in a diner, live as a homeless person for several weeks, or ride along with police officers as they patrol their regular beat.

Although these researchers try to blend in seamlessly with the population they study, they are still obligated to obtain IRB approval. In keeping with scholarly objectives, the purpose of their observation is different from simply “people watching” at one’s workplace, on the bus or train, or in a public space.

Figure 3.  Who is the sociologist in this photo? It’s impossible to tell! In participant observation, researchers immerse themselves in an environment for a time.  (Photo courtesy of zoetnet/flickr)

At the beginning of a field study, researchers might have a question: “What   really goes on in the kitchen of the most popular diner on campus?” or “What is it like to experience homelessness?” Participant observation is a useful method if the researcher wants to explore a certain environment from the inside.

Field researchers simply want to observe and learn. In such a setting, the researcher will be alert and open minded to whatever happens, recording all observations accurately. Soon, as patterns emerge, questions will become more specific, observations will lead to hypotheses, and hypotheses will guide the researcher in shaping data into results.

Some sociologists prefer not to alert people to their presence. The main advantage of covert participant observation is that it allows the researcher access to authentic, natural behaviors of a group’s members. The challenge, however, is gaining access to a setting without disrupting the pattern of others’ beha vior. Becoming an inside member of a group, organization, or subculture takes time and effort. Researchers must pretend to be something they are not. The process could involve role playing, making contacts, networking, or applying for a job. Whenever deception is involved in sociological research, it will be intensely scrutinized and may or may not be approved by an institutional IRB.  

Once inside a group, participation observation research can last months or even years. Sociologists have to balance the types of interpersonal relationships that arise from living and/or working with other people with objectivity as a researcher.  They must keep their purpose in mind and apply the sociological perspective. That way, they illuminate social patterns that are often unrecognized. Because information gathered during participant observation is mostly qualitative, rather than quantitative, the e nd results are often descriptive or interpretive. This type of research is well-suited to learning about the kinds of human behavior or social groups that are not known by the scientific community, who are particularly closed or secretive, or when one is attempting to understand societal structures, as we will see in the following example. 

Nickel and Dimed (2001, 2011)

Journalist Barbara Ehrenreich con ducted participation observation research for her book Nickel and Dimed . One day over lunch with her editor, Ehrenreich mentioned an idea. How can people exist on minimum-wage work? How do low-income workers get by? she wondered aloud. Someone should do a study. To her surprise, her editor responded, Why don’t you do it?

That’s how Ehrenreich found herself joining the ranks of the working class. For several months, she left her comfortable home and lived and worked among people who lacked, for the most part, higher education and marketable job skills. Undercover, she applied for and worked minimum wage jobs as a waitress, a cleaning woman, a nursing home aide, and a retail chain employee. During her participant observation, she used only her income from those jobs to pay for food, clothing, transportation, and shelter.

She discovered the obvious, that it’s almost impossible to get by on minimum wage service work. She also experienced and observed attitudes many middle and upper-class people never think about. She witnessed firsthand the treatment of working class employees. She saw the extreme measures people take to make ends meet and to survive. She described fellow employees who held two or three jobs, worked seven days a week, lived in cars, could not pay to treat chronic health conditions, got randomly fired, submitted to drug tests, and moved in and out of homeless shelters. She brought aspects of that life to light, describing difficult working conditions and the poor treatment that low-wage workers suffer.

Nickel and Dimed: On (Not) Getting By in America , the book she w rote upon her return to her real life as a well-paid writer, has been widely read and used in many college classrooms. The first edition was published in 2001 and a follow-up post-recession edition was published with updated information in 2011. 

Figure 4. Field research happens in real locations. What type of environment do work spaces foster? What would a sociologist discover after blending in? (Photo courtesy of drewzhrodague/flickr)

Ethnography

Ethnography is a type of social research that involves the extended observation of the social perspective and cultural values of an entire social setting. Ethnogra phies involve objective observation of an entire community, and they often involve participant observation as a research method.

British anthropologist Bronislaw Malinowski, who studied the Trobriand Islanders near Papua New Guinea during World War I, was one of the first anthropologists to engage with the communities they studied and he became known for this methodological contribution, which differed from the detached observations that took place from a distance (i.e., “on the verandas” or “armchair anthropology”). 

Although anthropologists had been doing ethnographic research longer, sociologists were doing ethnographic research in the 20th century, particularly in what became known as The Chicago School at the University of Chicago. William Foote Whyte’s  Street Corner Society:  The Social Structure of an Italian Slum  (1943) is a seminal work of urban ethnography and a “classic” sociological text. 

The heart of an ethnographic study focuses on how subjects view their own social standing and how they understand themselves in relation to a community. An ethnographic study might observe, for example, a small U.S. fishing town, an Inuit community, a village in Thailand, a Buddhist monastery, a private boarding school, or an amusement park. These places all have borders. People live, work, study, or vacation within those borders. People are there for a certain reason and therefore behave in certain ways and respect certain cultural norms. An ethnographer would commit to spending a predetermined amount of time studying every aspect of the chosen place, taking in as much as possible.

A sociologist studying a tribe in the Amazon might watch the way villagers go about their daily lives and then write a paper about it. To observe a spiritual retreat center, an ethnographer might attend as a guest for an extended stay, observe and record data, and collate the material into results.

The Making of Middletown: A Study in Modern U.S. Culture

In 1924, a young married couple named Robert and Helen Lynd undertook an unprecedented ethnography: to apply sociological methods to the study of one U.S. city in order to discover what “ordinary” people in the United States did and believed. Choosing Muncie, Indiana (population about 30,000), as their subject, they moved to the small town and lived there for eighteen months.

Ethnographers had been examining other cultures for decades—groups considered minority or outsider—like gangs, immigrants, and the poor. But no one had studied the so-called average American.

Recording interviews and using surveys to gather data, the Lynds did not sugarcoat or idealize U.S. life (PBS). They objectively stated what they observed. Researching existing sources, they compared Muncie in 1890 to the Muncie they observed in 1924. Most Muncie adults, they found, had grown up on farms but now lived in homes inside the city. From that discovery, the Lynds focused their study on the impact of industrialization and urbanization.

They observed that the workers of Muncie were divided into business class and working class groups. They defined business class as dealing with abstract concepts and symbols, while working class people used tools to create concrete objects. The two classes led different lives with different goals and hopes. However, the Lynds observed, mass production offered both classes the same amenities. Like wealthy families, the working class was now able to own radios, cars, washing machines, telephones, vacuum cleaners, and refrigerators. This was a newly emerging economic and material reality of the 1920s.

Figure 5. A classroom in Muncie, Indiana, in 1917, five years before John and Helen Lynd began researching this “typical” U.S. community. (Photo courtesy of Don O’Brien/flickr)

As the Lynds worked, they divided their manuscript into six sections: Getting a Living, Making a Home, Training the Young, Using Leisure, Engaging in Religious Practices, and Engaging in Community Activities. Each chapter included subsections such as “The Long Arm of the Job” and “Why Do They Work So Hard?” in the “Getting a Living” chapter.

When the study was completed, the Lynds encountered a big problem. The Rockefeller Foundation, which had commissioned the book, claimed it was useless and refused to publish it. The Lynds asked if they could seek a publisher themselves.

As it turned out, Middletown: A Study in Modern American Culture was not only published in 1929, but also became an instant bestseller, a status unheard of for a sociological study. The book sold out six printings in its first year of publication, and has never gone out of print (PBS).

Nothing like it had ever been done before. Middletown was reviewed on the front page of the New York Times . Readers in the 1920s and 1930s identified with the citizens of Muncie, Indiana, but they were equally fascinated by the sociological methods and the use of scientific data to define ordinary people in the United States. The book was proof that social data were important—and interesting—to the U.S. public.

Institutional Ethnography

Institutional ethnography is an extension of basic ethnographic research principles that focuses intentionally on everyday concrete social relationships. Developed by Canadian sociologist Dorothy E. Smith, institutional ethnography is often considered a feminist-inspired approach to social analysis and primarily considers women’s experiences within male-dominated societies and power structures. Smith’s work challenges sociology’s exclusion of women, both academically and in the study of women’s lives (Fenstermaker, n.d.).

Historically, social science research tended to objectify women and ignore their experiences except as viewed from a male perspective. Modern feminists note that describing women, and other marginalized groups, as subordinates helps those in authority maintain their own dominant positions (Social Sciences and Humanities Research Council of Canada, n.d.). Smith’s three major works explored what she called “the conceptual practices of power” (1990; cited in Fensternmaker, n.d.) and are still considered seminal works in feminist theory and ethnography.

Sometimes a researcher wants to study one specific person or event. A case study is an in-depth analysis of a single event, situation, or individual. To conduct a case study, a researcher examines existing sources like documents and archival records, conducts interviews, or engages in direct observation and even participant observation, if possible.

Researchers might use this method to study a single case of, for example, a foster child, drug lord, cancer patient, criminal, or rape victim. However, a major criticism of the case study method is that a developed study of a single case, while offering depth on a topic, does not provide broad enough evidence to form a generalized conclusion. In other words, it is difficult to make universal claims based on just one person, since one person does not verify a pattern. This is why most sociologists do not use case studies as a primary research method.

However, case studies are useful when the single case is unique. In these instances, a single case study can add tremendous knowledge to a certain discipline. For example, a feral child, also called a “wild child,” is one who grows up isolated from other human beings. Feral children grow up without social contact and language, which are elements crucial to a “civilized” child’s development. These children mimic the behaviors and movements of animals, and often invent their own language. There are only about one hundred cases of “feral children” in the world.

As you may imagine, a feral child is a subject of great interest to researchers. Feral children provide unique information about child development because they have grown up outside of the parameters of “normal” child socialization and language acquisition. And since there are very few feral children, the case study is the most appropriate method for researchers to use in studying the subject.

At age three, a Ukranian girl named Oxana Malaya suffered severe parental neglect. She lived in a shed with dogs, and she ate raw meat and scraps. Five years later, a neighbor called authorities and reported seeing a girl who ran on all fours, barking. Officials brought Oxana into society, where she was cared for and taught some human behaviors, but she never became fully socialized. She has been designated as unable to support herself and now lives in a mental institution (Grice 2011). Case studies like this offer a way for sociologists to collect data that may not be collectable by any other method.

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Case Study Research Method in Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

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On This Page:

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

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

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

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

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

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

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

 Famous Case Studies

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

Clinical Case Studies

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

Child Psychology Case Studies

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

Types of Case Studies

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

Where Do You Find Data for a Case Study?

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

1. Primary sources

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

2. Secondary sources

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

3. Archival records

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

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

4. Organizational records

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

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

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

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

How do I Write a Case Study in Psychology?

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

1. Introduction

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

2. Case Presentation

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

3. Management and Outcome

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

4. Discussion

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

5. Additional Items

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

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

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

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

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

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

Limitations

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Further Information

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

Certificates in Research For Social Action

Building field research capacity for social sector practitioners

PG Diplomas and Certificates

Campus Bengaluru

2.5 weeks / certificate

Batch Starting

6 January 2025

Azim Premji University’s Research for Social Action Certificates Programme aims to enhance the capacity of the social sector in doing field study, analysing the data and writing about it. While the social sector makes significant contributions to social change, it is equally necessary to build adequate capacities in the sector to leverage the power of research in forwarding and deepening the possibilities of social action. Social sector organisations and individuals can benefit from this programme, in doing a base line, endline, monitoring and evaluation, case study writing and data visualisation. Thus, the Research for Social Action Programme adapts research to everyday needs of the social sector organisations and individuals rather than for academic outcomes.

Key features of the programme

• The programme has three certificates, each for 2.5 weeks on campus, to be delivered as per the yearly calendar of the university. All courses in a certificate will run parallelly for 6 days a week. 
• Courses have class transactions, hands-on work, readings, presentations and some amount of field work. Each course has 2 or 3 graded assessments.
• Participants can enrol into any of these certificates, in any sequence. 
• The medium of instruction will be English.
• This programme can be continued alongside work, if the participant wishes to do so.

Who will benefit from the programme

The programme is designed for professionals with a minimum of 2 years of work experience from social sector organisations, social movements, thematic action networks, activist groups, and development media.

Practitioners involved in activities like quantitative and qualitative monitoring of programmes, project design, data collection, analysis, documentation, writing case studies, among others will benefit from this course. 

Facilities at the University

Accommodation will be provided at the University to all participants. This includes access to WiFi and the library (including remote access), cafeteria, academic and social events, indoor and outdoor games.

Certificates we offer

Research design and primary data collection | starts in january 2025.

This certificate provides the basic understanding and skills of what it takes to do a systematic field study in a social sector programme/​project context.

Ashok Kumar Sircar, Juhi Tyagi, Manjula M, Rajesh Joseph

Quantitative Analysis and Data Visualisation | starts in May 2025

This certificate, consisting of two courses, aims to build competencies in analysing quantitative data and its visual representation.

Participatory Approaches and Case Study Writing | starts in November 2025

This certificate, consisting of two courses, aims to build competencies in using qualitative evidence for analysis and social action.

Listen to our Alumni’s experience

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Selection Process - RSA

Step 1: Application form (Online) Step 2: Panel Interview or written test

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Each certificate fees – INR 20,000 (including GST) Accommodation fee – INR 9000 for 2.5 weeks Food and travel expenses are to be borne by the participant.

Note: GST rates are in line with Government guidelines and are subject to change.

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Three-dimensional subsurface pipe network survey and target identification using ground-penetrating radar: a case study at jilin jianzhu university campus.

1. Introduction

2. gpr field survey and data processing, 2.1. description of the test site in the jlju campus, 2.2. field data acquisition, 3.1. basic data processing and interpretation.

• Manhole covers (square or round) and the rainwater catch basin
• Cafeteria wall and the foundation below
• Electrical cables (with markers)
• Mouse cavities

3.2. Three-Dimensional Imaging and Common Attribute Analysis

3.3. time-varying centroid frequency attribute analysis, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Zhang, X.; Pei, J.; Liu, H.; You, Q.; Zhang, H.; Yao, L.; Song, Z. Three-Dimensional Subsurface Pipe Network Survey and Target Identification Using Ground-Penetrating Radar: A Case Study at Jilin Jianzhu University Campus. Appl. Sci. 2024 , 14 , 7293. https://doi.org/10.3390/app14167293

Zhang X, Pei J, Liu H, You Q, Zhang H, Yao L, Song Z. Three-Dimensional Subsurface Pipe Network Survey and Target Identification Using Ground-Penetrating Radar: A Case Study at Jilin Jianzhu University Campus. Applied Sciences . 2024; 14(16):7293. https://doi.org/10.3390/app14167293

Zhang, Xuebing, Junxuan Pei, Haotian Liu, Qin You, Hongfeng Zhang, Longxiang Yao, and Zhengchun Song. 2024. "Three-Dimensional Subsurface Pipe Network Survey and Target Identification Using Ground-Penetrating Radar: A Case Study at Jilin Jianzhu University Campus" Applied Sciences 14, no. 16: 7293. https://doi.org/10.3390/app14167293

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Estimating velocity distribution and flood discharge at river bridges using entropy theory – insights from computational fluid dynamics flow fields

Farhad bahmanpouri, tommaso lazzarin, silvia barbetta, tommaso moramarco, daniele p. viero.

Estimating the flow velocity and discharge in rivers is of particular interest for monitoring, modeling, and research purposes. Instruments for measuring water level and surface velocity are generally mounted on bridge decks, and this poses a challenge because the bridge structure, with piers and abutments, can perturb the flow field. The current research aims to investigate the applicability of entropy theory to estimate the velocity distribution and the discharge in the vicinity of river bridges. For this purpose, a computational fluid dynamics (CFD) model is used to obtain three-dimensional flow fields along a stretch of the Paglia River (central Italy), where a historical multi-arch bridge strongly affects flood flows. The input data for the entropy model include the cross-sectional bathymetry and the surface velocity provided by the numerical simulations. A total of 12 samples, including three different flow conditions at four cross-sections, one upstream and three downstream of the bridge, are considered. It is found that the entropy model can be reliably applied upstream of the bridge, also when forced with a single (i.e., the maximum) value of the surface velocity, with errors on total discharge below 13 % in the considered case. By contrast, downstream of the bridge, the wakes generated by the bridge piers strongly affect the velocity distribution, both in the spanwise and in the vertical directions and for very long distances. Here, notwithstanding the complex and multimodal spanwise distribution of flow velocity, the entropy model estimates the discharge with error lower than 8 % if forced with the river-wide distribution of the surface velocity. The present study has important implications for the optimal positioning of sensors and suggests the potential of using CFD modeling and entropy theory jointly to foster greater knowledge of river systems.

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Bahmanpouri, F., Lazzarin, T., Barbetta, S., Moramarco, T., and Viero, D. P.: Estimating velocity distribution and flood discharge at river bridges using entropy theory – insights from computational fluid dynamics flow fields, Hydrol. Earth Syst. Sci., 28, 3717–3737, https://doi.org/10.5194/hess-28-3717-2024, 2024.

Velocity and discharge measurements in rivers are fundamental for monitoring, modeling, and research purposes (Depetris, 2021; Di Baldassarre and Montanari, 2009; Dottori et al., 2013; Gore and Banning, 2017; Herschy, 2009). Unfortunately, measuring river discharge can be very challenging for different reasons, for example in the case of intermittent rivers typical of semi-arid regions, of flash floods in mountain areas, of flood flows involving wide floodplains, and of freshwater flows affected by saline tidal intrusions in estuaries. While monitoring river discharge on the ground has definite advantages (Fekete et al., 2012), the use of traditional methods such as current meters and acoustic Doppler current profilers (ADCPs) is generally expensive, time-consuming, and risky for operators, particularly during severe flow conditions, and such methods are not applicable in remote and inaccessible locations. Different techniques can be used to measure the surface velocity, also during severe flood conditions, including large-scale particle image velocimetry (LSPIV) (Eltner et al., 2020; Jodeau et al., 2008; Le Coz et al., 2010; Muste et al., 2011, 2014), space–time image velocimetry (STIV) (Fujita et al., 2007, 2019), infrared quantitative image velocimetry (Schweitzer and Cowen, 2021), and other methods based on the use of either terrestrial or autonomous aerial system sensors (Bandini et al., 2020, 2021; Herschy, 2009). Indirect methods have been proposed to estimate the flow discharge using these kinds of remotely sensed data (Bogning et al., 2018; Fekete and Vörösmarty, 2002; Spada et al., 2017; Vandaele et al., 2023; Zhang et al., 2019). The flow rate is generally obtained by applying suitable velocity coefficients to estimate the depth-averaged velocity or by integrating a hypothetical flow velocity distribution in the cross-sectional area. The key point is thus estimating the depth-averaged velocity, or its full cross-sectional distribution, starting from surface velocity data, a process whose reliability depends on the (un)evenness of the actual velocity distribution.

In natural rivers with large cross-sections, the streamwise velocity typically shows a logarithmic vertical distribution, mainly determined by the bottom roughness. According to field data, the maximum velocity is found just below the free surface and gradually decreases towards the bed (Franca et al., 2008; Guo, 2014). However, plenty of factors contribute to making the velocity distribution irregular. For instance, channel bends and deformed bathymetry produce large-scale secondary currents (Constantinescu et al., 2011; Lazzarin and Viero, 2023; Yang et al., 2012), and the presence of banks and of discontinuities of bed elevation in the spanwise directions can generate secondary currents of the second kind because of turbulence heterogeneity (Nikora and Roy, 2011; Proust and Nikora, 2020), which all increase the three-dimensionality of the flow field and alter the vertical and spanwise distributions of the flow velocity.

The presence of in-stream structures, such as bridges characterized by the presence of piers and/or of lateral abutments, can induce sudden variations of the flow field (Laursen, 1960, 1963) and complex three-dimensional turbulent structures (Ataie-Ashtiani and Aslani-Kordkandi, 2012; Chang et al., 2013; Lazzarin et al., 2024a; Salaheldin et al., 2004). Secondary currents in the cross-section transport low momentum fluid from lateral regions to the center of the channel and high-momentum fluid from the free surface toward the bed (Bonakdari et al., 2008; Nezu and Nakagawa, 1993; Yang et al., 2004). Coherent systems of vortices with horizontal (horseshoe vortex) or vertical axes (wake vortex) modify the velocity distribution (Kirkil and Constantinescu, 2015; Sumer et al., 1997). The wakes generated by in-stream obstacles and contractions can produce uneven spatial distributions of the water surface elevations close to the bridge and can propagate downstream of bridges, thus altering the cross-sectional velocity distribution for quite long distances (Briaud et al., 2009; Yang et al., 2021). Furthermore, because of particular bridge shape (e.g., arch-piers) and irregular cross-sections (e.g., compound sections), the flow field may show a marked dependence on the water depth and the flow rate.

Even though the above factors complicate estimation of the cross-sectional velocity distribution (and thus the flow discharge) based on surface velocity data in the vicinity of in-stream structures, it has to be observed that measuring instruments such as hydrometers, as well as radar sensors or cameras for estimating the surface velocity, are often mounted on bridge decks for convenience reasons. Notwithstanding the recommendation of installing height gauge at the upstream side of bridges (Meals and Dressing, 2008), measuring instruments are often located downstream of bridges, where the flow field unevenness is expected to further complicate the discharge estimation (Kästner et al., 2018). Besides the measurement of the flow discharge, knowing the flow field nearby bridges has additional practical implications; the flow velocity is the dominant parameter to study the local scour at bridge piers, which may cause bridge collapse during floods (Barbetta et al., 2017; Cheng et al., 2018; Federico et al., 2003; Khosronejad et al., 2012; Lu et al., 2022).

One of the most promising methods to estimate the cross-sectional velocity distribution from joint measures of water level and surface velocity is based on the concept of entropy. Researchers have widely applied this concept to predict the velocity distribution, flow discharge, and other relevant parameters of open-channel flows (Bahmanpouri et al., 2022b; Bonakdari et al., 2015; Chahrour et al., 2021; Chiu, 1989; Chiu et al., 2005; Chiu and Said, 1995; Ebtehaj et al., 2018; Moramarco et al., 2019; Moramarco and Singh, 2010; Singh et al., 2017; Sterling and Knight, 2002; Termini and Moramarco, 2017; Vyas et al., 2021). Recent applications of the entropic velocity distribution include the case of large meandering channels (Termini and Moramarco, 2020), the estimation of the depth-averaged velocity as a function of the aspect ratio (Abdolvandi et al., 2021), the confluence of the large Negro and Solimões rivers (Bahmanpouri et al. 2022a), and the regionalization of the entropy parameter (Ammari et al., 2022). One advantage of the entropy approach is providing the complete cross-sectional distribution of velocity, whereas other indirect methods for estimating flow discharge only compute the depth-averaged value from the surface velocities at subsections using a fixed reduction coefficient (e.g., Le Coz et al., 2010). Previous studies demonstrated the accuracy of the entropy method in undisturbed flow conditions and also in cases like confluences or low-curvature bends characterized by large-scale three-dimensional effects and secondary currents.

The present research is meant to investigate the predictive ability of entropy theory in estimating the velocity distribution, and hence the streamflow discharge, in the case of complex flow fields generated by the presence of bridges. The issue is of particular relevance because, as already noted, water levels and free-surface velocities are often measured by instruments mounted on bridges, where the flow–structure interaction can significantly disturb the flow field.

Considering that measuring the cross-sectional velocity distribution in the vicinity of bridges is practically unfeasible in flood conditions, in the present study a three-dimensional computational fluid dynamics (3D-CFD) model is used to obtain physics-based and high-resolution descriptions of the real flow field, for a sufficiently long river segment and for different values of the flow discharge. The CFD-computed surface velocity (either a single value or its river-wide distribution) is used as input for the entropy model, thus simulating the availability of suitable data provided by remote sense instruments. Then, the cross-sectional velocity distributions provided by the entropic model are benchmarked against those computed by the CFD model, which allows the reliability of the entropy model to be assessed. The exercise is repeated for different cross-sections, both upstream and downstream of the bridge, to investigate the pros and cons of different locations where estimating the discharge and thus to provide applicative guidelines. A reach of the Paglia River, in central Italy, is chosen as a relevant case study; here, a level gauge and a radar sensor for measuring the surface velocity are mounted on a historical multi-arch bridge, which produces strong flow–structure interactions.

The present analysis allows guidelines to be provided for the proper application of entropy theory and the optimal choice and positioning of measuring instruments, aimed at the reliable estimation of flow discharge in the vicinity of river bridges.

2.1  Field site

The Paglia River, in the central part of Italy (Fig. 1a), is a tributary of Tiber River, subject to severe flooding and high sediment transport. The reach of interest, near the town of Orvieto, is across the Adunata bridge (Fig. 1b) along the Paglia River (basin area of about 1200 km 2 , average discharge of 10 m 3  s −1 , flood discharge up to 2500 m 3  s −1 ). The Adunata bridge connects the settlements of Orvieto Scalo and Ciconia, as part of the Italian State Road no. 71 (Fig. 1c). It is a masonry multi-arch bridge, with five arches ending at four piers on the river bed. On the right-hand side, an abutment sustains the bridge and separates it from the floodplain; on the left-hand side, the bridge deck is supported by the main levee. Close to the bottom, the piers have a roughly elliptical shape with the major axis, aligned with the flow, 15 m long, and the minor axis, orthogonal to the flow, 5.7 m wide. At the bottom, each pier is sustained by an elliptical plinth, whose profile is 2.0 m larger than the pier. The center distance between the piers is 23.2 m. The pier width increases approaching the deck because of the arches; the deck width is approximatively 10 m.

Figure 1 (a)  Location of the field site; (b)  downstream view of the Adunata bridge on the Paglia River during normal flow condition (11 November 2021); (c)  digital terrain model (DTM) nearby the Adunata bridge (dotted line), with the domain of the 3D CFD model (black line); and (d)  location of the level gauge and of the radar sensor with the field of view (FOV) in an aerial image (© Google Earth, 2023).

The main thread of the flow is on the right-hand side of the river, and a large depositional area forms on the left-hand side just downstream of the bridge (Fig. 1b). The main channel axis is characterized by a significant curvature, bending to the left at the bridge section (Fig. 1c).

2.2  Available data

At the downstream side of the Adunata bridge, a water level gauge and a radar sensor for measuring the water surface velocity are located at the center of the first and second arch, respectively (Fig. 1d). The time resolution of both the sensors is 10 min. In addition, a number of flow rate measures and cross-sectional velocity distributions were provided by the Umbria Region Hydrological Service. The flow rate data were collected using a current meter by wading a few tens of meters downstream of the Adunata bridge in the period 2009–2011 (flow rate ranging between 3.3 and 14.3 m 3  s −1 ), and from the bridge in the period 1995–2010 (flow rate ranging between 16.8 and 147 m 3  s −1 ); additional flow rate data were collected using an acoustic Doppler current profiler (ADCP) some hundreds of meters upstream of the bridge in the period 2014–2019 (flow rate ranging between 0.37 and 45 m 3  s −1 ). The official rating curve for the Adunata bridge, provided by ARPA Lazio, is based on these measurements.

As detailed in the following sections, the rating curve derived from current meter and ADCP data, the water levels, and the free-surface velocity data collected by the sensors mounted on the Adunata bridge were used to validate the hydrodynamic numerical models (Sect. 2.3 and Appendix A). The cross-sectional velocity distributions measured with the current meter just downstream of the bridge were used to further assess the spatial variability of the entropy-based velocity distributions, as detailed in Sect. 3.1.

2.3  Numerical model

The commercial CFD software STAR-CCM + (Siemens) was used for the numerical simulations. It implements the finite-volume method to compute the flow field on unstructured, Cartesian computational grids. The software has been used and validated in several applications, including complex flows over deformed bathymetry, in the presence of obstacles (Chang et al., 2013; Kirkil et al., 2009; Lazzarin et al., 2023c, 2024b) and channel bends (Constantinescu et al., 2011, 2013; Koken et al., 2013). In the present application, the two-phase volume-of-fluid (VoF; Hirt and Nichols, 1981) method was used to track the water–air interface within the computational domain (Horna-Munoz and Constantinescu, 2018; Lazzarin et al., 2023b; Li and Zhang, 2022; Luo et al., 2018; Yoshimura and Fujita, 2020). STAR-CCM + was used to solve the Reynolds-averaged Navier–Stokes (RANS) equations, in which the stress tensor in the momentum equations is related to the mean flow quantities by adopting the Boussinesq approximation. The eddy viscosity,  μ T , was determined by solving transport equations for the turbulent kinetic energy,  k , and dissipation rate,  ε , according to the realizable k – ε turbulence model (Shih et al., 1995), suitable for large-scale complex flows in natural rivers (e.g., Horna-Munoz and Constantinescu, 2018).

The computational domain reproduced a ∼1100  m long reach of the Paglia River (Fig. 1c), centered at the Adunata bridge. The average size of the grid elements was 1.0 m. Starting 100 m upstream of the bridge and up to 300 m downstream of the bridge, the grid was refined using elements with average length of 0.5 m. To capture the near-wall boundary layer well, a prism layer refinement with three layers was used to reduce the wall-normal thickness of the grid cells close to solid boundaries (i.e., the riverbed and bridge structure). The final computational grid was made of ∼4  million elements. A rough-wall, no-slip condition was imposed at the solid boundaries by means of a wall function (roughness height of 0.1 m at the bottom, and of 0.01 m at the bridge surfaces). The upper boundary of the computational grid was treated as a symmetry plane (i.e., slip condition) for the airflow. The water elevation at the outlet (i.e., downstream section) was kept fixed in time by imposing a suitable hydrostatic-pressure distribution. The value of the downstream level, for each of the simulated scenarios, was derived from an auxiliary two-dimensional (2D), depth-averaged hydrodynamic model calibrated on available data; the 2DEF model has been used for this purpose (see Appendix A for details on the model and its calibration/verification). A constant-in-time, logarithmic velocity distribution was imposed as the upstream boundary condition for the water fraction. For the air fraction (upper part of the numerical domain), zero velocity and zero pressure were imposed at the inlet and at the outlet, respectively. The simulations were advanced in time with an implicit, first-order discretization, until steady-state conditions were reached.

The 3D-CFD model was validated by comparing the surface velocity computed by the model with that measured by the radar sensor located downstream of the bridge (see the yellow bullets in Fig. A2c and d).

2.4  Flood events considered in the study

Three different steady flow conditions have been simulated with the 3D-CFD model STAR-CCM + , which correspond to the peak flow conditions of flood events that occurred in 2012, 2019, and 2022, as provided by the rating curve for the Adunata bridge (Table 1). In all three of the flow conditions, water flowed in the main channel and over the sediment bars that are dry in the low-flow condition of Fig. 1b and d. During the most severe flood of 2012, water also flowed on the floodplains adjacent to the main river and caused the incipient pressurization of flow below the bridge arches. The preliminary simulation carried out with the 2DEF depth-averaged model showed that, at the peak of the 2012 flood event, 700 m 3  s −1 flowed through the floodplain, overflowing the bridge access roads, and 1800 m 3  s −1 flowed within the main channel; this last value was used in the 3D-CFD simulation, which considered only the main channel of the river. The flood events of 2019 and 2022, although being quite ordinary, were the largest floods that occurred after the installation of the radar sensor for the surface velocity (thus, surface velocity data were not available for the 2012 flood).

Table 1 Simulations performed in the present work. The value in brackets indicates the total discharge with consideration of the flow over floodplains, which is not considered in the 3D simulations.

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2.5  Entropy theory

Entropy theory deals with physical systems that may have a large number of states from a probabilistic point of view. The concept of entropy is used for statistical inference, to determine a probability distribution function when the available information is limited to some average quantities, defined as constraints such as mean and variance. For the application of entropy to streamflow measurements, the pioneer was Chiu (1987), who developed a probabilistic formulation of the cross-sectional velocity distribution in open channels, in which the expected value of the point velocity is determined by applying the maximum entropy principle (Chiu, 1987, 1988, 1989). Using this probabilistic formulation, the velocity distribution is given analytically as a function of the cross-sectional geometry; of the dimensionless entropy parameter,  M ; and possibly of the depth at which the maximum velocity occurs (the so-called dip,  h ). There is a one-to-one correspondence between  M and the ratio of mean to maximum velocity in the cross-section, which is defined as the entropic function,  ϕ ( M ) (Chiu, 1991). In general, for a given river site, the magnitude of  M and, in turn, of  ϕ ( M ) , mainly depends on hydraulic parameters such as roughness and hydraulic radius, whereas they are poorly affected by the flow discharge (Chiu and Murray, 1992; Moramarco and Singh, 2010). Moreover, ϕ ( M )  is consistently found to be nearly constant at different cross-sections through gauged river sites for different flow conditions (Moramarco and Singh, 2010; Ammari et al., 2022). This is because the value of ϕ ( M ) is associated with geometric and hydraulic characteristics that tend to vary smoothly within a river system (Ammari et al., 2022).

The estimation of cross-sectional velocity distribution,  U ( x , y ) , developed by Chiu (1989), was later simplified by Moramarco et al. (2004). Using this approach, one can divide the wet cross-sectional area into N v  verticals and determine the entropy-based velocity profile along each vertical as

where U  is the time-averaged velocity, U max ( x i )  is the maximum value of  U along the i th vertical, x i  is the distance of the i th sampled vertical from the left bank, h ( x i )  is the dip (i.e., the depth of  U max ( x i ) below the water surface), D ( x i )  the flow depth, and y  is the vertical distance from the bed. The relationship between the entropic parameter,  M , and the entropic function,  ϕ ( M ) , is (Chiu, 1989)

in which U m  and U MAX  are the average and maximum flow velocities within the entire cross-section. It is worth mentioning that U m  represents the expected value of velocity that can be different from the observed mean velocity (Marini and Fontana, 2020). These two values are quite similar in the case of wide rivers (aspect ratio larger than 5). In the present research, considering the large aspect ratio for all cross-sections (Table 2), this hypothesis is valid.

Table 2 Flow data for the cross-sections of Fig. 2 and the three considered flood events of Table 1. The values of the entropic function,  ϕ ( M ) , and parameter,  M , are obtained from the 3D-CFD velocity distributions and estimated according to Eq. (4).

Introducing the variable δ ( x i ) = D t ( x i ) / [ D ( x i ) - h ( x i ) ] , the velocity dip,  h ( x i ) , is estimated according to Yang et al. (2004) from the spanwise distribution of  δ ( x i ) , which is given as

in which x min  is the spanwise distance of the x i  vertical from the nearest bank. Note that h ( x i )=0 and δ ( x i )=1 when the maximum velocity occurs at the free surface.

In the case of gauged cross-sections,  ϕ ( M ) can be inferred from measured mean and maximum flow velocities (e.g., with ADCP). For ungauged sites,  ϕ ( M ) can be estimated as (Moramarco and Singh, 2010)

where y 0  is the vertical coordinate, taken from the bottom, where the velocity is zero; k  is the von Karman constant; R  is the hydraulic radius; n  is the Manning roughness; D  is the maximum water depth; and h  is computed with Eq. (3) at the thalweg, i.e., where the water depth is maximum. According to van Rijn (1982), y 0 =0.065 ξ d 90 , where d 90  is the 90th percentile for grain size and ξ  a parameter ranging from 1 to 10 (Ferro, 2003; Moramarco and Singh, 2010).

When only the surface velocities,  U surf ( x i ) , are available at a river site, then U m a x ( x i ) can be estimated as (Fulton and Ostrowski, 2008)

For the current research, the methodological steps to estimate the cross-sectional velocity distribution (and hence the flow discharge) using entropy theory are as follows. The input data are the river-wide velocity distribution at the free-surface,  U surf , provided by the 3D-CFD model. When only the maximum value of  U surf is used as input, corresponding to the hypothetical case in which only point-sensor data are available, the spanwise distribution of  U surf is obtained by applying either a parabolic or an elliptical spanwise distribution (Bahmanpouri et al., 2022a). The velocity dip is computed using Eq. (3). The cross-sectional velocity distribution is then obtained using an iterative procedure, in which p  denotes the iteration. At the first iteration, the entropic function, ϕ ( M ) p =1 , is computed with Eq. (4), and M p =1 is computed with Eq. (2). After computing the maximum velocity for each vertical, U max ( x i ) p =1 , with Eq. (5), Eq. (1) allows the entropic velocity distribution in the whole cross-section, U ( x i , y ) p = 1 , to be estimated. The following iteration starts by computing the average and the maximum flow velocities,  U m and  U MAX , from the velocity distribution obtained in the previous iteration and then ϕ ( M ) p = U m / U MAX , M p  using Eq. (2), U max ( x i ) p with Eq. (5), and the velocity distribution U ( x i , y ) p with Eq. (1). The iterative procedure continues until the difference ϕ ( M ) p – ϕ ( M ) p −1 becomes lower than 0.01. For more details, the reader is referred to Moramarco et al. (2017).

The comparison between the entropy-based and the CFD-derived velocity distributions was performed considering four cross-sections (Fig. 2), at a distance of 50 m upstream and 50, 100, and 200 m downstream of the bridge, and the three flood events of 2012, 2019, and 2022 (see Table 1). The sections just upstream and downstream of the bridge are located at a distance of about 0.45  B from the bridge, with B ∼110  m the width of the river at the bridge section. This is a short distance, relevant for the application given that the remote sensors for surface velocity (such as radar and large-scale particle image velocimetry (PIV)) have their field of view located some tens of meter upstream or downstream of the bridge. The sections far downstream are considered to assess how far the flow field is affected by the presence of the bridge.

Figure 2 Location of the Adunata bridge and of the four selected cross-sections (aerial image from © Google Earth 2023).

First, the study analyzed the variability of the entropy function,  ϕ ( M ) , at the four cross-sections, as derived from the cross-sectional velocity distributions provided by both the 3D-CFD model and the current meter measures (Sect. 3.1). Then, in applying the entropy model to estimate the cross-sectional velocity, two different procedures were considered. In the first one, the entropy model was forced with the river-wide distribution of the surface velocities computed by the 3D-CFD model (this is described in the following Sect. 3.2); in the second one, only the maximum value of the surface velocity computed by the 3D-CFD model was considered to be input for the entropy model (Sect. 3.3). The first procedure was applied to all the four cross-sections, whereas the latter was only applied to cross-sections 1 and 4, i.e., where the effects of the bridge piers are minimal and thus the spanwise velocity distribution is unimodal.

3.1  Variability of the entropy function

Some relevant parameters that characterize the flow field (e.g., aspect ratio, average and maximum velocity) at the selected cross-sections are presented in Table 2 for the peak flow condition of the three flood events. The values of the entropic function,  ϕ ( M ) CFD , were first computed as the ratio of average to maximum velocity within the cross-section provided by the 3D-CFD model. Then, assuming the site as being ungauged,  ϕ ( M ) Eq. (4) was estimated using Eq. (4) with d 90 =0.01  m (Pilbala et al., 2024) and a Manning parameter,  n , equal to 0.035 m - 1 / 3 s at the upstream ( −50  m) and far downstream sections ( +100  and +200  m) and equal to 0.055 m - 1 / 3 s just downstream of the bridge ( +50  m cross-section), where larger energy losses are expected because of the wakes generated by the bridge piers. The values of  ϕ ( M ) Eq. (4) , reported in Table 2 and corresponding with the points marked with dashed lines in Fig. 3b, were obtained using ξ =5 to compute  y 0 (Sect. 2.5 just after Eq. 4), and the gray band was obtained by varying  ξ in the range [1, 10]. Finally, the values of the entropic parameter associated with the different values of  ϕ ( M ) are computed using Eq. (2).

Figure 3 Entropic function  ϕ ( M ) , for the different simulated scenarios, as a function of the distance from the bridge (positive downstream), (a)  computed from the 3D-CFD flow fields and (b)  estimated with Eq. (4), where the lines refer to the average values, and the gray band is obtained by varying the reference height  y 0 in Eq. (4) within the expected range. Green circles refer to data derived from velocity distributions measured with the current meter just downstream of the bridge.

Since the entropic function is typically assumed to be constant for all flow conditions at a given cross-section, it is of interest to analyze its actual variation by exploiting the flow fields provided by the 3D-CFD model and to see the effectiveness of their first-guess estimates obtained using Eq. (4). The values of  ϕ ( M ) reported in Table 2 are plotted in Fig. 3 as a function of the downstream distance from the bridge. At the first cross-section downstream of the bridge (i.e., cross-section 2), although referring to different flow conditions, the values of the entropic function computed with the 3D-CFD and the current meter velocity distributions show the same magnitude, further confirming the reliability of the 3D-CFD model. The first-guess estimates of  ϕ ( M ) in Fig. 3b, although they have a marginal role on the entropy-based computations, show a similar trend to the 3D-CFD estimates (Fig. 3b), provided that the increased Manning parameter is used at the section just downstream of the bridge. The need to calibrate such an increased Manning parameter complicates efforts in the case of disturbed flows.

Figure 4 Flood event of 2019, cross-section 2 (50 m downstream of the bridge). Velocity distributions provided by (a)  the 3D-CFD model and (b)  the entropy model forced with the river-wide distribution of the free-surface velocity. Comparison of vertical distributions of velocity at 0.2  B   (c) , 0.5  B   (d) , and 0.8  B   (e) , where B  is the width of the cross-section.

For each flood event, at cross-sections 1 and 4, i.e., where the flow field is not characterized by the wakes generated by the bridge piers, the entropic function assumes similar values, which can be identified as “undisturbed” values. The variability of such undisturbed values of  ϕ ( M ) with the flow rate is relatively small, as all the values fall in the range 0.65 < ϕ ( M ) < 0.75 , in agreement with the range found by Bahmanpouri et al. (2022b) for similar European rivers. By contrast, at cross-sections 2 and 3, just downstream of the bridge, the values of  ϕ ( M ) are consistently reduced due to the effect of the bridge. In the largest flood event of 2012, which produced near-pressure flow conditions at the bridge with marked localized increasing of the flow velocity, ϕ ( M ) CFD  equals 0.415 at cross-section 2, corresponding to M CFD = - 1.03 . The low value of  ϕ ( M ) and the negative value of  M attest the markedly non-uniform distribution of the velocity (i.e., the maximum velocity in this cross-section is much higher than the average velocity). A sensible reduction is still present 100 m downstream of the bridge (cross-section 3). For the moderate peak flows of 2019 and 2022 events, the entropic function recovers undisturbed values already at cross-section 3, i.e., 100 m downstream of the bridge.

This first analysis suggests that assuming constant values of  ϕ ( M ) can be reasonable in undisturbed river reaches; however, in the case of irregular flow fields induced by the interaction with in-stream structures, the entropic function  ϕ ( M ) can vary with respect to undisturbed values, and, in addition, it can show significant variations with the flow rate.

Table 3 Comparison between 3D-CFD outputs and entropy-based estimations forced with the river-wide distribution of the free-surface velocity.

Figure 5 Flood event of 2019, cross-section 3 (100 m downstream of the bridge). Velocity distributions provided by (a)  the 3D-CFD model and (b)  the entropy model forced with the river-wide distribution of the free-surface velocity. Comparison of vertical distributions of velocity at 0.2  B   (c) , 0.5  B   (d) , and 0.8  B   (e) , where B  is the width of the cross-section.

Figure 6 Flood event of 2019, cross-section 4 (200 m downstream of the bridge). Velocity distributions provided by (a)  the 3D-CFD model and (b)  the entropy model forced with the river-wide distribution of the free-surface velocity. Comparison of vertical distributions of velocity at 0.2  B   (c) , 0.5  B   (d) , and 0.8  B   (e) , where B  is the width of the cross-section.

3.2  Entropy model forced with the river-wide profile of free-surface velocity

The efficacy of the entropy model is tested here for the case in which the surface velocity is known for all the width of the cross-section. This could be the case in which the river-wide surface velocity is estimated from imaging techniques (e.g., Eltner et al., 2020; Schweitzer and Cowen, 2021). The results, in terms of cross-sectional velocity distributions, are presented for brevity only for the intermediate peak flow of the 2019 flood event and for the most challenging cross-sections just downstream of the bridge, where the flow field is disturbed by the pier wakes. The same results, for the peak flows of 2012 and 2022 events, are provided in the Supplement.

Figure 7 Flood event of 2012, cross-section 1 (50 m upstream of the bridge). Cross-sectional velocity distribution computed with the 3D-CFD model  (a) . Entropy theory with parabolic spanwise velocity distribution  (b) and entropy theory with elliptic spanwise velocity distribution  (c) .

Figure 4 presents the cross-sectional velocity distribution 50 m downstream of the bridge (cross-section 2). As shown by the 3D-CFD flow field (Fig. 4a) and reflected in the low value of  ϕ ( M ) for this cross-section (Table 2 and Fig. 3), the effect of the piers is very strong, such that there is a clearly uneven distribution of the cross-sectional velocity because of the wakes developing downstream of the piers. Just downstream of the bridge, due to the presence of the bridge arches, the flow field provided by the 3D-CFD model is configured as a sort of partial orifice flow that increases the vertical uniformity of the velocity distribution compared to a uniform shear flow. Of course, the entropy model cannot capture such localized flow features, which entails some difference in the patchiness of the physics-based and the entropy velocity distributions (Fig. 4a–e). Despite that, using the river-wide distribution of the surface velocity provided by the CFD simulation as input, the entropy model can reliably capture the salient features of the cross-sectional velocity distribution. Figure 4c–e highlight the comparison of 3D-CFD and entropy flow velocities along three verticals located at 0.2, 0.5, and 0.8  B (where B  is the channel width). Compared to the results of the 3D-CFD model, the entropy approach underestimates the velocity close to the bed. Since the velocities and the volumetric fluxes are still relatively small near the bed, these discrepancies marginally affect the estimation of the section-averaged velocity and, consequently, of the total discharge (Table 3). The percentage error is larger (7.6 %) for the very high-flow condition of the 2012 event (see Supplement), due to the accentuation of orifice-flow conditions associated with the higher water levels.

Figure 8 Flood event of 2012, cross-section 1 (50 m upstream of the bridge). Spanwise distribution of the surface velocity  (a) and comparison of vertical distributions of velocity at 0.2  B   (b) , 0.5  B   (c) , and 0.8  B   (d) .

Figure 5 depicts the cross-sectional velocity distributions at a larger distance from the bridge, i.e., at cross-section 3, placed 100 m downstream of the bridge. The visual comparison with Fig. 4 suggests that the effects of the piers on the flow field are reduced because of the increased distance, and the cross-sectional distribution provided by the 3D-CFD model (Fig. 5a) appears to be more regular. The statistical analysis confirms that in this case the entropy model (Fig. 5b) is able to simulate the velocity profiles with a higher accuracy.

Figure 6 shows the cross-sectional velocity distributions of 3D-CFD and entropy models for cross-section 4, located 200 m downstream of the bridge. Compared to cross-section 3, the effect of the bridge piers is further reduced because of both the distance and the more compact shape of the cross-section. Since the effect of the bridge piers is minimum, the statistical analysis shows a better agreement of the entropy model results with the CFD-based data. Though areas with relatively high velocities are still visible in simulations with higher values of the discharge (i.e., events of 2012 and 2019), for the high-flow conditions of 2022, the effect of the bridge pier has completely vanished. Therefore, the lower the flow discharge, the lower the distance from the bridge to recover undisturbed flow conditions.

The results presented here show that, when the river-wide distribution of the free-surface velocity is provided, the entropy method provides good estimations of the cross-sectional velocity distribution even when the influence of bridge piers, and thus the unevenness of the flow field, is relevant. The main discrepancies are observed in low-velocity regions, which slightly affect the estimation of the flow discharge. Table 3 lists some statistics and error percentages for the depth-averaged velocity and discharge estimations for all cross-sections and the three events considered. The estimations provided by the entropy method are in good agreement with results of CFD model, both upstream and downstream of the Adunata bridge. Though the accuracy is slightly reduced downstream of the bridge, the results are also reliable in the vicinity of the structure (i.e., at cross-section 2), suggesting the applicability of the entropy model to estimate the flow discharges, even in the case of irregular distributions of the cross-sectional velocity, provided that the river-wide distribution of the surface velocity is used as input data.

Figure 9 Flood event of 2022, cross-section 4 (200 m downstream of the bridge). Cross-sectional velocity distribution computed with the 3D-CFD model  (a) . Entropy theory with parabolic spanwise velocity distribution and  (b) entropy theory with elliptic spanwise velocity distribution  (c) .

Figure 10 Flood event of 2022, cross-section 4 (200 m downstream of the bridge). Spanwise distribution of the surface velocity  (a) and comparison of vertical distributions of velocity at 0.2  B   (b) , 0.5  B   (c) , and 0.8  B   (d) .

3.3  Entropy model forced with a single value of free-surface velocity

In this section, the results are presented considering only a single value of the surface velocity as input for the entropy model, which corresponds to the maximum surface velocity predicted by the 3D-CFD model. Two different spanwise velocity distributions are enforced in the entropic model, namely a parabolic spanwise distribution (PSD) and an elliptic spanwise distribution (ESD). Of course, applying the entropy model using a unique value of the velocity is particularly sensitive to this value and supposes a unimodal velocity distribution in the spanwise direction. For this reason, this kind of approach cannot be used in the cross-sections immediately downstream of the bridge, where the spanwise velocity distribution is markedly irregular (see e.g., Fig. 4). Herein, the results are presented for cross-section 1, located 50 m upstream of the bridge for the high-flow condition of the 2012 event, and for cross-section 4, located 200 m downstream of the bridge, for the modest peak flow condition of the 2022 event, where the effect of bridge piers on the velocity distribution wears off in a shorter distance.

Figure 7 shows the distribution of the surface velocity based on the 3D-CFD outputs and both the PSD and ESD entropy models. The agreement of both the PSD and the ESD is generally good in the central and the right parts of the channel and less good in the left part of the channel. Here, due to the irregular bathymetry (i.e., gravel deposit), the 3D-CFD model predicts localized stagnation zones that cannot be captured by the entropy model based on a single value of the surface velocity. This is confirmed by Fig. 8, which shows the cross-sectional distribution of the surface velocity and three vertical profiles. In the perspective of estimating the flow discharge, the lateral discrepancies represent a minor limit, as the central part of the cross-sections conveys the largest part of the total discharge.

Table 4 Comparison between 3D-CFD and entropy-based outputs considering a single surface velocity.

Overall, the cross-sectional velocity distributions based on ESD seem more accurate than those based on the PSD: they provide similar results at the center of the channel, but the parabolic distribution generally underestimates the flow velocity close to the banks. Both cross-sectional and vertical distributions of the velocity profiles (Figs. 7a and 8c) highlight the existence of a velocity dip; i.e., the maximum velocity is below the water surface, particularly at the center of the channel. This is generally the consequence of secondary currents superposed on the main flow (Termini and Moramarco, 2020). Yang et al. (2004) and Moramarco et al. (2017) reported that for large aspect ratios of channel flow,  B / D , the dip phenomenon appears primarily near the sidewall region, whereas for relatively low aspect ratios ( B / D = 9.26 for cross-section 1) the velocity dip is generally located at the center of the channel (Bahmanpouri et al., 2022a, b; Kundu and Ghoshal, 2018; Moramarco et al., 2017; Termini and Moramarco, 2020). In this case, the 3D flow field from the CFD simulation shows that the dip depends on the counter-clockwise rotating secondary current generated by the upstream right-handed bend. Indeed, rotational inertia makes these curvature-induced helical flow structures propagate downstream for relatively long distances (Dominguez Ruben et al., 2021; Lazzarin and Viero, 2023; Thorne et al., 1985).

Figure 11 Flood event of 2022. Color map of the instantaneous surface velocities computed with the 3D-CFD model for the Paglia River at the Adunata bridge (aerial image from © Google Earth, 2023).

The velocity distribution at cross-section 4 (200 m downstream of the bridge) is presented in Fig. 9 for the moderate peak flow condition of the 2022 event. For this cross-section, in the 3D-CFD results (Fig. 9a), the maximum surface velocity is located on the left side of the channel, rather than at its center (this aspect is discussed in the following). Forced with the maximum water surface velocity, the entropy model reproduces the velocity field in the central part of the riverbed well. Larger discrepancies are instead observed in the lateral part of the cross-section, with the elliptic spanwise distribution (ESD) that performs slightly better than the parabolic (PSD), particularly in the right side. Figure 10 shows the cross-sectional distribution of the surface velocity and the velocity distribution along three verticals. In terms of cross-sectional average velocity and flow discharge, both the PSD and ESD produce error that are lower than 10 % (Table 4), larger than those obtained using the river-wide surface velocity as input for the entropy model.

A last point worth discussing regards the unusual cross-sectional distribution of flow velocity in Sect. 4 (Fig. 9a). The reason that the 3D-CFD model locates the maximum velocity on the left of the thalweg is the alternate vortex shedding occurring downstream of the bridge piers, which propagates beyond the last considered cross-section. This is evident in the map of instantaneous surface velocity of Fig. 11. This particular occurrence poses interesting questions on the application of the entropy model to estimate the flow discharge downstream of in-stream structures. First, the spanwise location of the maximum surface velocity is subject to a periodical shift, which prevents its correct detection by means of a fixed sensor with a small-size field of view, like the one mounted on the Adunata bridge. Secondly, marked time-varying flow fields, which occasionally (or periodically) deviate from nearly uniform flow conditions, can hardly be captured by any preset velocity distribution. To alleviate the problem, the periodic signal of surface velocity can be filtered, which is equivalent to looking at time-averaged modeled flow fields; this requires knowing the frequency of vortex shedding.

The results shown in this section confirm the general accuracy of the entropy model in predicting the cross-sectional velocity distributions. As expected, when using a single value of velocity in place of the river-wide distribution of surface velocity, the accuracy of the method slightly decreases. Provided that using a single velocity is beyond the scope of the method when the spanwise velocity distribution is markedly irregular, the entropy approach can still be forced with a single surface velocity and produce accurate results, when there is no evidence of strong disturbances of the flow. Indeed, such an approach cannot capture marked unevenness in the flow field, as shown in the case of the lateral low-velocity regions at cross-section 1 for the 2012 event (Fig. 7) and in the time-varying flow field of cross-section 4 for the 2022 event (Fig. 9).

The present study investigated the ability of the entropy-based method to estimate the cross-sectional distribution of velocity, as well as the associated river discharge, for different flow conditions in a representative case study. As sensors for continuous monitoring of water level and surface velocity are often mounted on bridges, we analyzed a stretch of the Paglia River where a multi-arch bridge with thick piers, hosting a level gauge and a radar sensor, strongly affects the flow field. A 3D-CFD model was set up to obtain reliable, physics-based velocity distributions at relevant cross-sections, both upstream and downstream of the bridge. The entropy model was then applied to reproduce this set of velocity distributions, using the bathymetric data and the CFD-computed surface velocity as input data.

As a first point, the study highlighted the potential of using accurate, physics-based 3D-CFD models to deepen the knowledge of rivers and, specifically, of theoretical methods for discharge estimation. Indeed, 3D-CFD models provide pictures of complex flow fields that are more complete than, e.g., ADCP measures, in terms of spatial and temporal distribution and, above all, valid for high-flow regimes, which typically prevent any direct measurement of the flow field beneath the free surface. This entails unexplored chances of outlining best practices in the use of simplified methods for continuous discharge monitoring and, as a consequence, to improve their accuracy.

According to the present analysis, the entropy model revealed remarkable skills in also reproducing disturbed and uneven flow fields when the river-wide distribution of the surface velocity is used as input data. This occurred also just downstream of the bridge, where the pier-induced wakes made the velocity distribution multimodal and extremely irregular, with error on discharge estimates lower than 8 %. The availability of innovative measuring techniques, able to collect river-wide surface velocity data at a relatively low cost, adds value to the present findings.

On the other side, the accuracy of the entropy model is reduced when only the maximum surface velocity is used as input data, so that the spanwise velocity distribution has to be assumed on a theoretical basis (e.g., parabolic or elliptical). While such a method is absolutely discouraged in the case of disturbed flow fields (e.g., downstream of in-stream structures), it still provides accurate estimates when the velocity field is sufficiently smooth.

As a final recommendation, measuring instruments and sensors for surface velocity become more effective when placed upstream of in-stream structures, i.e., where the flow field is only marginally affected by the structure and both the water surface elevation and the velocity distribution are far more regular.

A main limitation of the present methodological approach is that it relies in the assumption of a fixed bed in both the CFD analysis and the application of the entropic model. In natural rivers, bed scouring during severe flood events and the ensuing formation of local deposits, especially close to in-stream structures such as bridges, can alter the bathymetry and, in turn, the velocity distribution and the discharge estimates. In the case of a movable bed and in the absence of protection measures (e.g., riprap or bed sills), the uncertainty associated with the local bed mobility has to be evaluated with due care. Future research on more complex scenarios that still need a comprehensive assessment, and which could largely benefit from physics-based numerical modeling, will include the case of mobile beds and the analysis of stage-dependent variations of cross-sectional velocity distribution, particularly in the case of compound cross-sections that are typical of lowland natural rivers.

To impose the boundary conditions in the 3D-CFD model, a 2D depth-averaged model of a longer stretch of the Paglia River has been set up. We used the 2DEF finite-element model (Defina, 2003; Lazzarin et al., 2023a, 2024c; Viero, 2019; Viero et al., 2013, 2014), which solves a modified version of the shallow water equations (SWEs) that allow for a robust treatment of wetting and drying over irregular topographies (D'Alpaos and Defina, 2007; Defina, 2000). The SWEs are written as

in which h s  is the free surface elevation; t  is the time; ∇  and ∇⋅  denote the 2D gradient and divergence operators, respectively; q = ( q x ; q y ) is the depth-integrated velocity (i.e., the unit-width discharge); Y  is the equivalent water depth (i.e., the volume of water per unit area); η ( h s )  is a storativity coefficient to account for the wetted fraction of the domain; τ = ( τ x ; τ y )  is the bed shear stress, evaluated using the Gauckler–Strickler formula; ρ  is the water density; and R e  is the horizontal components of the Reynolds stresses, modeled according to the Boussinesq approximation. A mixed Eulerian–Lagrangian approach allows the total derivative of the flow velocity in the momentum equations to be evaluated using finite differences and a backward tracing technique based on the method of characteristics (Defina, 2003; Giraldo, 2003; Walters and Casulli, 1998). Then, the SWEs are solved with a finite-element method, based on triangular, unstructured grids. The model also allows 2D triangular elements to be coupled with 1D elements (either open or closed sections) to model the minor hydraulic network efficiently; other 1D elements are used to model particular devices, such as pumps and weirs (Martini et al., 2004). The model has been successfully used to simulate flows in various rivers (e.g., Mel et al., 2020a, b; Viero et al., 2019; Baldasso et al., 2023); its effectiveness have also been demonstrated in different research fields, such as lagoon and marine environments (e.g., Carniello et al., 2012; Pivato et al., 2020; Tognin et al., 2022; Viero and Defina, 2016).

In the present case, the computational mesh covered a stretch of the Paglia River about 7 km long, from 600 m upstream of the Adunata bridge to the confluence with the Tiber River, including floodable floodplains (Fig. A1). The average mesh size ranged from 10 m in the riverbed near the Adunata bridge to 30 m in the floodplains and far downstream of the Adunata bridge. The computational mesh included 61 000 triangular elements, 16 1D elements to simulate underpasses, and 4 1D weir elements to simulate the sill located 500 m downstream of the Adunata bridge.

Figure A1 Spatial extent of the 2D computational mesh (aerial image from World Imagery). The color map shows the bottom elevation of the grid elements derived from the lidar-based DTM.

The inflow hydrographs, prescribed at the upstream mesh inlet, were derived from water levels measured at the Adunata bridge using the associated rating curve. At the outlet, an arbitrary rating curve was applied as the downstream boundary condition; a sensitivity analysis showed that, because of the distance from the Adunata bridge, this boundary condition did not produce any perceivable effect in the water levels simulated at the study site.

Figure A2 Observed (red) and predicted (blue) water levels at the Adunata bridge gauging station for the flood events of 2019  (a) and 2022  (b) . Observed and predicted water velocity for the flood events of 2019  (c) and 2022  (d) .

Different Gauckler–Strickler coefficients were assigned to the different parts of the domain (e.g., floodplains and densely vegetated areas) based on the soil cover. The value assigned to the main riverbed were calibrated to match the time series of the water levels measured at the Adunata bridge gauging station for the 2019 flood event (Fig. A2a), and, for the most severe flood event that occurred in 2012, the model results were also checked in terms of extent of flooded areas. The minor flood of 2022 was used to verify the model (Fig. A2b). Finally, the depth-averaged velocity just downstream of the Adunata bridge was compared with the free-surface velocity measured by the radar sensor. Due to the use of a coarse grid and to the depth-average assumption, the 2D model underpredicted the measured water surface systematically (Fig. A2c and d); however, using an amplification factor of 1.7 (gray dots in Fig. A2c and d), the predicted values were quite similar to the measured ones.

Data are available on request from the authors.

The supplement related to this article is available online at:  https://doi.org/10.5194/hess-28-3717-2024-supplement .

Conceptualization: FB, TL, SB, TM, DPV. Formal analysis: FB and TL. Funding acquisition: TM. Investigation: FB and TL. Methodology: FB, TL, SB, TM, DPV. Project administration: SB, TM, DPV. Software: FB, TL, TM, DPV. Supervision: SB, TM, DPV. Visualization: FB, TL, DPV. Writing (original draft preparation): FB. Writing (review and editing): TL, SB, TM, DPV.

The contact author has declared that none of the authors has any competing interests.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

The authors acknowledge the assistance of Luigi di Micco, Shiva Rezazadeh, and Marco Dionigi.

This study was supported by the Italian National Research Program PRIN 2017 (project no. 2017SEB7Z8), “IntEractions between hydrodyNamics and bioTic communities in fluvial Ecosystems: advancement in the knowledge and undeRstanding of PRocesses and ecosystem sustainability by the development of novel technologieS with fIeld monitoriNg and laboratory testing (ENTERPRISING)”. Tommaso Lazzarin is sponsored by a scholarship provided by the CARIPARO Foundation.

This paper was edited by Alberto Guadagnini and reviewed by Gustavo Marini and two anonymous referees.

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• Introduction
• Material and methods
• Results and discussions
• Conclusions
• Data availability
• Author contributions
• Competing interests
• Acknowledgements
• Financial support
• Review statement