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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

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

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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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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22 Case Study Research: In-Depth Understanding in Context

Helen Simons, School of Education, University of Southampton

• Published: 01 July 2014
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This chapter explores case study as a major approach to research and evaluation. After first noting various contexts in which case studies are commonly used, the chapter focuses on case study research directly Strengths and potential problematic issues are outlined and then key phases of the process. The chapter emphasizes how important it is to design the case, to collect and interpret data in ways that highlight the qualitative, to have an ethical practice that values multiple perspectives and political interests, and to report creatively to facilitate use in policy making and practice. Finally, it explores how to generalize from the single case. Concluding questions center on the need to think more imaginatively about design and the range of methods and forms of reporting requiredto persuade audiences to value qualitative ways of knowing in case study research.

Introduction

This chapter explores case study as a major approach to research and evaluation using primarily qualitative methods, as well as documentary sources, contemporaneous or historical. However, this is not the only way in which case study can be conceived. No one has a monopoly on the term. While sharing a focus on the singular in a particular context, case study has a wide variety of uses, not all associated with research. A case study, in common parlance, documents a particular situation or event in detail in a specific sociopolitical context. The particular can be a person, a classroom, an institution, a program, or a policy. Below I identify different ways in which case study is used before focusing on qualitative case study research in particular. However, first I wish to indicate how I came to advocate and practice this form of research. Origins, context, and opportunity often shape the research processes we endorse. It is helpful for the reader, I think, to know how I came to the perspective I hold.

The Beginnings

I first came to appreciate and enjoy the virtues of case study research when I entered the field of curriculum evaluation and research in the 1970s. The dominant research paradigm for educational research at that time was experimental or quasi- experimental, cost-benefit, or systems analysis, and the dominant curriculum model was aims and objectives ( House, 1993 ). The field was dominated, in effect, by a psychometric view of research in which quantitative methods were preeminent. But the innovative projects we were asked to evaluate (predominantly, but not exclusively, in the humanities) were not amenable to such methodologies. The projects were challenging to the status quo of institutions, involved people interpreting the policy and programs, were implemented differently in different contexts and regions, and had many unexpected effects.

We had no choice but to seek other ways to evaluate these complex programs, and case study was the methodology we found ourselves exploring, in order to understand how the projects were being implemented, why they had positive effects in some regions of the country and not others, and what the outcomes meant in different sociopolitical and cultural contexts. What better way to do this than to talk with people to see how they interpreted the “new” curriculum; to watch how teachers and students put it into practice; to document transactions, outcomes, and unexpected consequences; and to interpret all in the specific context of the case ( Simons, 1971 , 1987 , pp. 55–89). From this point on and in further studies, case study in educational research and evaluation came to be a major methodology for understanding complex educational and social programs. It also extended to other practice professions, such as nursing, health, and social care ( Zucker, 2001 ; Greenhalgh & Worrall, 1997 ; Shaw & Gould, 2001 ). For further details of the evolution of the case study approach and qualitative methodologies in evaluation, see House, 1993 , pp. 2–3; Greene, 2000 ; Simons, 2009 , pp. 14–18; Simons & McCormack, 2007 , pp. 292–311).

This was not exactly the beginning of case study, of course. It has a long history in many disciplines ( Simons, 1980; Ragin, 1992; Gomm, Hammersley, & Foster, 2004 ; Platt, 2007 ), many aspects of which form part of case study practice to this day. But its evolution in the context just described was a major move in the contemporary evolution of the logic of evaluative inquiry ( House, 1980 ). It also coincided with movement toward the qualitative in other disciplines, such as sociology and psychology. This was all part of what Denzin & Lincoln (1994) termed “a quiet methodological revolution” (p. ix) in qualitative inquiry that had been evolving over the course of forty years.

There is a further reason why I continue to advocate and practice case study research and evaluation to this day and that is my personal predilection for trying to understand and represent complexity, for puzzling through the ambiguities that exist in many contexts and programs and for presenting and negotiating different values and interests in fair and just ways.

Put more simply, I like interacting with people, listening to their stories, trials and tribulations—giving them a voice in understanding the contexts and projects with which they are involved, and finding ways to share these with a range of audiences. In other words, the move toward case study methodology described here suited my preference for how I learn.

Concepts and Purposes of Case Study

Before exploring case study as it has come to be established in educational research and evaluation over the past forty years, I wish to acknowledge other uses of case study. More often than not, these relate to purpose, and appropriately so in their different contexts, but many do not have a research intention. For a study to count as research, it would need to be a systematic investigation generating evidence that leads to “new” knowledge that is made public and open to scrutiny. There are many ways to conduct research stemming from different traditions and disciplines, but they all, in different ways, involve these characteristics.

Everyday Usage: Stories We Tell

The most common of these uses of case study is the everyday reference to a person, an anecdote or story illustrative of a particular incident, event, or experience of that person. It is often a short, reported account commonly seen in journalism but also in books exploring a phenomenon, such as recovery from serious accidents or tragedies, where the author chooses to illustrate the story or argument with a “lived” example. This is sometimes written by the author and sometimes by the person whose tale it is. “Let me share with you a story,” is a phrase frequently heard

The spirit behind this common usage and its power to connect can be seen in a report by Tim Adams of the London Olympics opening ceremony’s dramatization by Danny Boyle.

It was the point when we suddenly collectively wised up to the idea that what we are about to receive over the next two weeks was not only about “legacy collateral” and “targeted deliverables,” not about G4S failings and traffic lanes and branding opportunities, but about the second-by-second possibilities of human endeavour and spirit and communality, enacted in multiple places and all at the same time. Stories in other words. ( Adams, 2012 )

This was a collective story, of course, not an individual one, but it does convey some of the major characteristics of case study—that richness of detail, time, place, multiple happenings and experiences—that are also manifest in case study research, although carefully evidenced in the latter instance. We can see from this common usage how people have come to associate case study with story. I return to this thread in the reporting section.

Professions Individual Cases

In professional settings, in health and social care, case studies, often called case histories , are used to accurately record a person’s health or social care history and his or her current symptoms, experience, and treatment. These case histories include facts but also judgments and observations about the person’s reaction to situations or medication. Usually these are confidential. Not dissimilar is the detailed documentation of a case in law, often termed a case precedent when referred to in a court case to support an argument being made. However in law there is a difference in that such case precedents are publicly documented.

Case Studies in Teaching

Exemplars of practice.

In education, but also in health and social care training contexts, case studies have long been used as exemplars of practice. These are brief descriptions with some detail of a person or project’s experience in an area of practice. Though frequently reported accounts, they are based on a person’s experience and sometimes on previous research.

Case scenarios

Management studies are a further context in which case studies are often used. Here, the case is more like a scenario outlining a particular problem situation for the management student to resolve. These scenarios may be based on research but frequently are hypothetical situations used to raise issues for discussion and resolution. What distinguishes these case scenarios and the case exemplars in education from case study research is the intention to use them for teaching purposes.

Country Case Studies

Then there are case studies of programs, projects, and even countries, as in international development, where a whole-country study might be termed a case study or, in the context of the Organization for Economic Co-operation and Development (OECD), where an exploration is conducted of the state of the art of a subject, such as education or environmental science in one or several countries. This may be a contemporaneous study and/or what transpired in a program over a period of time. Such studies often do have a research base but frequently are reported accounts that do not detail the design, methodology, and analysis of the case, as a research case study would do, or report in ways that give readers a vicarious experience of what it was like to be there. Such case studies tend to be more knowledge and information-focused than experiential.

Case Study as History

Closer to a research context is case study as history—what transpired at a certain time in a certain place. This is likely to be supported by documentary evidence but not primary data gathering unless it is an oral history. In education, in the late 1970s, Stenhouse (1978) experimented with a case study archive. Using contemporaneous data gathering, primarily through interviewing, he envisaged this database, which he termed a “case record,” forming an archive from which different individuals,, at some later date, could write a “case study.” This approach uses case study as a documentary source to begin to generate a history of education, as the subtitle of Stenhouse’s 1978 paper indicates “Towards a contemporary history of education.”

Case Study Research

From here on, my focus is on case study research per se, adopting for this purpose the following definition:

Case study is an in-depth exploration from multiple perspectives of the complexity and uniqueness of a particular project, policy, institution or system in a “real-life” context. It is research based, inclusive of different methods and is evidence-led. ( Simons, 2009 , p. 21).

For further related definitions of case study, see Stake (1995) , Merriam (1998), and Chadderton & Torrance (2011) . And for definitions from a slightly different perspective, see Yin (2004) and Thomas (2011a) .

Not Defined by Method or Perspective

The inclusion of different methods in the definition quoted above definition signals that case study research is not defined by methodology or method. What defines case study is its singularity and the concept and boundary of the case. It is theoretically possible to conduct a case study using primarily quantitative data if this is the best way of providing evidence to inform the issues the case is exploring. It is equally possible to conduct case study that is mainly qualitative, to engage people with the experience of the case or to provide a rich portrayal of an event, project, or program.

Or one can design the case using mixed methods. This increases the options for learning from different ways of knowing and is sometimes preferred by stakeholders who believe it provides a firmer basis for informing policy. This is not necessarily the case but is beyond the scope of this chapter to explore. For further discussion of the complexities of mixing methods and the virtue of using qualitative methods and case study in a mixed method design, see Greene (2007) .

Case study research may also be conducted from different standpoints—realist, interpretivist, or constructivist, for example. My perspective falls within a constructivist, interpretivist framework. What interests me is how I and those in the case perceive and interpret what we find and how we construct or co-construct understandings of the case. This not only suits my predilection for how I see the world, but also my preferred phenomenological approach to interviewing and curiosity about people and how they act in social and professional life.

Qualitative Case Study Research

Qualitative case study research shares many characteristics with other forms of qualitative research, such as narrative, oral history, life history, ethnography, in-depth interview, and observational studies that utilize qualitative methods. However, its focus, purpose, and origins, in educational research at least, are a little different.

The focus is clearly the study of the singular. The purpose is to portray an in-depth view of the quality and complexity of social/educational programs or policies as they are implemented in specific sociopolitical contexts. What makes it qualitative is its emphasis on subjective ways of knowing, particularly the experiential, practical, and presentational rather than the propositional ( Heron, 1992 , 1999 ) to comprehend and communicate what transpired in the case.

Characteristic Features and Advantages

Case study research is not method dependent, as noted earlier, nor is it constrained by resources or time. Although it can be conducted over several years, which provides an opportunity to explore the process of change and explain how and why things happened, it can equally be carried out contemporaneously in a few days, weeks, or months. This flexibility is extremely useful in many contexts, particularly when a change in policy or unforeseen issues in the field require modifying the design.

Flexibility extends to reporting. The case can be written up in different lengths and forms to meet different audience needs and to maximize use (see the section on Reporting). Using the natural language of participants and familiar methods (like interview, observation, oral history) also enables participants to engage in the research process, thereby contributing significantly to the generation of knowledge of the case. As I have indicated elsewhere ( Simons, 2009 ), “This is both a political and epistemological point. It signals a potential shift in the power base of who controls knowledge and recognizes the importance of co-constructing perceived reality through the relationships and joint understandings we create in the field” (p. 23).

Possible Disadvantages

If one is an advocate, identifying advantages of a research approach is easier than pointing out its disadvantages, something detractors are quite keen to do anyway! But no approach is perfect, and here are some of the issues that often trouble people about case study research. The “sample of one” is an obvious issue that worries those convinced that only large samples can constitute valid research and especially if this is to inform policy. Understanding complexity in depth may not be a sufficient counterargument, and I suspect there is little point in trying to persuade otherwise For frequently, this perception is one of epistemological and methodological, if not ideological, preference.

However, there are some genuine concerns that many case researchers face: the difficulty of processing a mass of data; of “telling the truth” in contexts where people may be identifiable; personal involvement, when the researcher is the main instrument of data gathering; and writing reports that are data-based, yet readable in style and length. But one issue that concerns advocates and nonadvocates alike is how inferences are drawn from the single case.

Answers to some of these issues are covered in the sections that follow. Whether they convince may again be a question of preference. However, it is worth noting here that I do not think we should seek to justify these concerns in terms identified by other methodologies. Many of them are intrinsic to the nature and strength of qualitative case study research.

Subjectivity, for instance, both of participants and researcher is inevitable, as it is in many other qualitative methodologies. This is often the basis on which we act. Rather than see this as bias or something to counter, it is an intelligence that is essential to understanding and interpreting the experience of participants and stakeholders. Such subjectivity needs to be disciplined, of course, through procedures that examine both the validity of individuals’ representations of “their truth”, and demonstrate how the researcher took a reflexive approach to monitoring how his or her own values and predilections may have unduly influenced the data.

Types of Case Study

There are numerous types of case study, too many to categorize, I think, as there are overlaps between them. However, attempts have been made to do this and, for those who value typologies, I refer them to Bassey (1999) and, for a more extended typology, to Thomas (2011b) . A slightly different approach is taken by Gomm, Hammersley, and Foster (2004) in annotating the different emphases in major texts on case study. What I prefer to do here is to highlight a few familiar types to focus the discussion that follows on the practice of case study research.

Stake (1995) offers a threefold distinction that is helpful when it comes to practice, he says, because it influences the methods we choose to gather data (p. 4). He distinguishes between an intrinsic case study , one that is studied to learn about the particular case itself and an instrumental case study , in which we choose a case to gain insight into a particular issue (i.e., the case is instrumental to understanding something else; p. 3). The collective case study is what its name suggests: an extension of the instrumental to several cases.

Theory-led or theory-generated case study is similarly self-explanatory, the first starting from a specific theory that is tested through the case; the second constructing a theory through interpretation of data generated in the case. In other words, one ends rather than begins with a theory. In qualitative case study research, this is the more familiar route. The theory of the case becomes the argument or story you will tell.

Evaluation case study requires a slightly longer description as this is my context of practice, one which has influenced the way I conduct case study and what I choose to emphasize in this chapter. An evaluation case study has three essential features: to determine the value of the case, to include and balance different interests and values, and to report findings to a range of stakeholders in ways that they can use. The reasons for this may be found in the interlude that follows, which offers a brief characterization of the social and ethical practice of evaluation and why qualitative methods are so important in this practice.

Interlude: Social and Ethical Practice of Evaluation

Evaluation is a social practice that documents, portrays, and seeks to understand the value of a particular project, program, or policy. This can be determined by different evaluation methodologies, of course. But the value of qualitative case study is that it is possible to discern this value without decontextualizing the data. While the focus of the case is usually a project, program, policy, or some unit within, studies of key individuals, what I term case profiles , may be embedded within the overall case. In some instances, these profiles, or even shorter cameos of individuals, may be quite prominent. For it is through the perceptions, interpretations, and interactions of people that we learn how policies and programs are enacted ( Kushner, 2000 , p. 12). The program is still the main focus of analysis, but, in exploring how individuals play out their different roles in the program, we get closer to the actual experience and meaning of the program in practice.

Case study evaluation is often commissioned from an external source (government department or other agency) keen to know the worth of publicly funded programs and policies to inform future decision making. It needs to be responsive to issues or questions identified by stakeholders, who often have different values and interests in the expected outcomes and appreciate different perspectives of the program in action. The context also is often highly politicized, and interests can conflict. The task of the evaluator in such situations becomes one of including and balancing all interests and values in the program fairly and justly.

This is an inherently political process and requires an ethical practice that offers participants some protection over the personal data they give as part of the research and agreed audiences access to the findings, presented in ways they can understand. Negotiating what information becomes public can be quite difficult in singular settings where people are identifiable and intricate or problematic transactions have been documented. The consequences that ensue from making knowledge public that hitherto was private may be considerable for those in the case. It may also be difficult to portray some of the contextual detail that would enhance understanding for readers.

The ethical stance that underpins the case study research and evaluation I conduct stems from a theory of ethics that emphasizes the centrality of relationships in the specific context and the consequences for individuals, while remaining aware of the research imperative to publicly report. It is essentially an independent democratic process based on the concepts of fairness and justice, in which confidentiality, negotiation, and accessibility are key principles ( MacDonald, 1976 ; Simons, 2009 , pp. 96–111; and Simons 2010 ). The principles are translated into specific procedures to guide the collection, validation, and dissemination of data in the field. These include:

engaging participants and stakeholders in identifying issues to explore and sometimes also in interpreting the data;

documenting how different people interpret and value the program;

negotiating what data becomes public respecting both the individual’s “right to privacy” and the public’s “right to know”;

offering participants opportunities to check how their data are used in the context of reporting;

reporting in language and forms accessible to a wide range of audiences;

disseminating to audiences within and beyond the case.

For further discussion of the ethics of democratic case study evaluation and examples of their use in practice, see Simons (2000 , 2006 , 2009 , chapter 6, 2010 ).

Designing Case Study Research

Design issues in case study sometimes take second place to those of data gathering, the more exciting task perhaps in starting research. However, it is critical to consider the design at the outset, even if changes are required in practice due to the reality of what is encountered in the field. In this sense, the design of case study is emergent, rather than preordinate, shaped and reshaped as understanding of the significance of foreshadowed issues emerges and more are discovered.

Before entering the field, there are a myriad of planning issues to think about related to stakeholders, participants, and audiences. These include whose values matter, whether to engage them in data gathering and interpretation, the style of reporting appropriate for each, and the ethical guidelines that will underpin data collection and reporting. However, here I emphasize only three: the broad focus of the study, what the case is a case of, and framing questions/issues. These are steps often ignored in an enthusiasm to gather data, resulting in a case study that claims to be research but lacks the basic principles required for generation of valid, public knowledge.

Conceptualize the Topic

First, it is important that the topic of the research is conceptualized in a way that it can be researched (i.e., it is not too wide). This seems an obvious point to make, but failure to think through precisely what it is about your research topic you wish to investigate will have a knock-on effect on the framing of the case, data gathering, and interpretation and may lead, in some instances, to not gathering or analyzing data that actually informs the topic. Further conceptualization or reconceptualization may be necessary as the study proceeds, but it is critical to have a clear focus at the outset.

What Constitutes the Case

Second, I think it is important to decide what would constitute the case (i.e., what it is a case of) and where the boundaries of this lie. This often proves more difficult than first appears. And sometimes, partly because of the semifluid nature of the way the case evolves, it is only possible to finally establish what the case is a case of at the end. Nevertheless, it is useful to identify what the case and its boundaries are at the outset to help focus data collection while maintaining an awareness that these may shift. This is emergent design in action.

In deciding the boundary of the case, there are several factors to bear in mind. Is it bounded by an institution or a unit within an institution, by people within an institution, by region, or by project, program or policy,? If we take a school as an example, the case could be comprised of the principal, teachers, and students, or the boundary could be extended to the cleaners, the caretaker, the receptionist, people who often know a great deal about the subnorms and culture of the institution.

If the case is a policy or particular parameter of a policy, the considerations may be slightly different. People will still be paramount—those who generated the policy and those who implemented it—but there is likely also to be a political culture surrounding the policy that had an influence on the way the policy evolved. Would this be part of the case?

Whatever boundary is chosen, this may change in the course of conducting the study when issues arise that can only be understood by going to another level. What transpires in a classroom, for example, if this is the case, is often partly dependent on the support of the school leadership and culture of the institution and this, in turn, to some extent is dependent on what resources are allocated from the local education administration. Much like a series of Russian dolls, one context inside the other.

Unit of analysis

Thinking about what would constitute the unit of analysis— a classroom, an institution, a program, a region—may help in setting the boundaries of the case, and it will certainly help when it comes to analysis. But this is a slightly different issue from deciding what the case is a case of. Taking a health example, the case may be palliative care support, but the unit of analysis the palliative care ward or wards. If you took the palliative care ward as the unit of analysis this would be as much about how palliative care was exercised in this or that ward than issues about palliative care support in general. In other words, you would need to have specific information and context about how this ward was structured and managed to understand how palliative care was conducted in this particular ward. Here, as in the school example above, you would need to consider which of the many people who populate the ward form part of the case—nurses, interns, or doctors only, or does it extend to patients, cleaners, nurse aides, and medical students?

Framing Questions and Issues

The third most important consideration is how to frame the study, and you are likely to do this once you have selected the site or sites for study. There are at least four approaches. You could start with precise questions, foreshadowed issues ( Smith & Pohland, 1974 ), theories, or a program logic. To some extent, your choice will be dictated by the type of case you have chosen, but also by your personal preference for how to conduct it—in either a structured or open way.

Initial questions give structure; foreshadowed issues more freedom to explore. In qualitative case study, foreshadowed issues are more common, allowing scope for issues to change as the study evolves, guided by participants’ perspectives and events in the field. With this perspective, it is more likely that you will generate a theory of the case toward the end, through your interpretation and analysis.

If you are conducting an instrumental case study, staying close to the questions or foreshadowed issues is necessary to be sure you gain data that will illuminate the central focus of the study. This is critical if you are exploring issues across several cases, although it is possible to do a cross-case analysis from cases that have each followed a different route to discovering significant issues.

Opting to start with a theoretical framework provides a basis for formulating questions and issues, but it can also constrain the study to only those questions/issues that fit the framework. The same is true with using program logic to frame the case. This is an approach frequently adopted in evaluation case study where the evaluator, individually or with stakeholders, examines how the aims and objectives of the program relate to the activities designed to promote it and the outcomes and impacts expected. It provides direction, although it can lead to simply confirming what was anticipated, rather than documenting what transpired in the case.

Whichever approach you choose to frame the case, it is useful to think about the rationale or theory for each question and what methods would best enable you to gain an understanding of them. This will not only start a reflexive process of examining your choices—an important aspect of the process of data gathering and interpretation—it will also aid analysis and interpretation further down the track.

Methodology and Methods

Qualitative case study research, as already noted, appeals to subjective ways of knowing and to a primarily qualitative methodology, that captures experiential understanding ( Stake, 2010 , pp. 56–70). It follows that the main methods of data gathering to access this way of knowing will be qualitative. Interviewing, observation, and document analysis are the primary three, often supported by critical incidents, focus groups, cameos, vignettes, diaries/journals, and photographs. Before gathering any primary data, however, it is useful to search relevant existing sources (written or visual) to learn about the antecedents and context of a project, program, or policy as a backdrop to the case. This can sharpen framing questions, avoid unnecessary data gathering, and shorten the time needed in the field.

Given that there are excellent texts on qualitative methods (see, for example, Denzin & Lincoln, 1994 ; Seale, 1999 ; Silverman, 2000 , 2004 ), I will not discuss all potential relevant methods here, but simply focus on the qualities of the primary methods that are particularly appropriate for case study research.

Primary Qualitative Data Gathering Methods

Interviewing.

The most effective style of interviewing in qualitative case study research to gain in-depth data, document multiple perspectives and experiences and explore contested issues is the unstructured interview, active listening and open questioning are paramount, whatever prequestions or foreshadowed issues have been identified. This can include photographs—a useful starting point with certain cultural groups and the less articulate, to encourage them to tell their story through connecting or identifying with something in the image.

The flexibility of unstructured interviewing has three further advantages for understanding participants’ experiences. First, through questioning, probing, listening, and, above all, paying attention to the silences and what they mean, you can get closer to the meaning of participants’ experiences. It is not always what they say.

Second, unstructured interviewing is useful for engaging participants in the process of research. Instead of starting with questions and issues, invite participants to tell their stories or reflect on specific issues, to conduct their own self-evaluative interview, in fact. Not only will they contribute their particular perspective to the case, they will also learn about themselves, thereby making the process of research educative for them as well as for the audiences of the research.

Third, the open-endedness of this style of interviewing has the potential for creating a dialogue between participants and the researcher and between the researcher and the public, if enough of the dialogue is retained in the publication ( Bellah, Madsen, Sullivan, Swidler, & Tipton, 1985 ).

Observations

Observations in case study research are likely to be close-up descriptions of events, activities, and incidents that detail what happens in a particular context. They will record time, place, specific incidents, transactions, and dialogue, and note characteristics of the setting and of people in it without preconceived categories or judgment. No description is devoid of some judgment in selection, of course, but, on the whole, the intent is to describe the scene or event “as it is,” providing a rich, textured description to give readers a sense of what it was like to be there or provide a basis for later interpretation.

Take the following excerpt from a study of the West Bromwich Operatic Society. It is the first night of a new production, The Producers , by this amateur operatic society. This brief excerpt is from a much longer observation of the overture to the first evening’s performance, detailing exactly what the production is, where it is, and why there is such a tremendous sense of atmosphere and expectation surrounding the event. Space prevents including the whole observation, but I hope you can get a glimmer of the passion and excitement that precedes the performance:

Birmingham, late November, 2011, early evening.... Bars and restaurants spruce up for the evening’s trade. There is a chill in the air but the party season is just starting....

A few hundred yards away, past streaming traffic on Suffolk Street, Queensway, an audience is gathering at the New Alexandra Theatre. The foyer windows shine in the orange sodium night. Above each one is the rubric: WORLD CLASS THEATRE.

Inside the preparatory rituals are being observed; sweets chosen, interval drinks ordered and programmes bought. People swap news and titbits about the production.... The bubble of anticipation grows as the 5-minute warning sounds. People make their way to the auditorium. There have been so many nights like this in the past 110 years since a man named William Coutts invested £10,000 to build this palace of dreams.... So many fantasies have been played under this arch: melodramas and pantomimes, musicals and variety.... So many audiences, settling down in their tip-up seats, wanting to be transported away from work, from ordinariness and private troubles.... The dimming lights act like a mother’s hush. You could touch the silence. Boinnng! A spongy thump on a bass drum, and the horns pipe up that catchy, irrepressible, tasteless tune and already you’re singing under your breath, ‘Springtime for Hitler and Germany....’ The orchestra is out of sight in the pit. There’s just the velvet curtain to watch as your fingers tap along. What’s waiting behind? Then it starts it to move. Opening night.... It’s opening night! ( Matarasso, 2012 , pp. 1–2)

For another and different example—a narrative observation of an everyday but unique incident that details date, time, place, and experience—see Simons (2009 , p. 60).

Such naturalistic observations are also useful in contexts where we cannot understand what is going on through interviewing alone—in cultures with which we are less familiar or where key actors may not share our language or have difficulty expressing it. Careful description in these situations can help identify key issues, discover the norms and values that exist in the culture, and, if sufficiently detailed, allow others to cross corroborate what significance we draw from these observations. This last point is very important to avoid the danger in observation of ascribing motivations to people and meanings to transactions.

Finally, naturalistic observations are very important in highly politicized environments, often the case in commissioned evaluation case study, where individuals in interview may try to elude the “truth” or press on you that their view is the “right” view of the situation. In these contexts, naturalistic observations not only enable you to document interactions as you perceive them, but they also provide a cross-check on the veracity of information obtained in interviews.

Document analysis

Analysis of documents, as already intimated, is useful for establishing what historical antecedents might exist to provide a springboard for contemporaneous data gathering. In most cases, existing documents are also extremely pertinent for understanding the policy context.

In a national policy case study I conducted on a major curriculum change, the importance of preexisting documentation was brought home to me sharply when certain documentation initially proved elusive to obtain. It was difficult to believe that it did not exist, as the evolution of the innovation involved several parties who had not worked together before. There was bound, I thought, to be minuted meetings sharing progress and documentation of the “new” curriculum. In the absence of some crucial documents, I began to piece together the story through interviewing. Only there were gaps, and certain issues did not make sense.

It was only when I presented two versions of what I discerned had transpired in the development of this initiative in an interim report eighteen months into the study that things started to change. Subsequent to the meeting at which the report was presented, the “missing” documents started to appear. Suddenly found. What lay behind the “missing documents,” something I suspected from what certain individuals did and did not say in interview, was a major difference of view about how the innovation evolved, who was key in the process, and whose voice was more important in the context. Political differences, in other words, that some stakeholders were trying to keep from me. The emergence of the documents enabled me to finally produce an accurate and fair account.

This is an example of the importance of having access to all relevant documents relating to a program or policy in order to study it fairly. The other major way in which document analysis is useful in case study is for understanding the values, explicit and hidden, in policy and program documents and in the organization where the program or policy is implemented. Not to be ignored as documents are photographs, and these, too, can form the basis of a cultural and value analysis of an organization ( Prosser, 2000 ).

Creative artistic approaches

Increasingly, some case study researchers are employing creative approaches associated with the arts as a means of data gathering and analysis. Artistic approaches have often been used in representing findings, but less frequently in data gathering and interpretation ( Simons & McCormack, 2007 ). A major exception is the work of Richardson (1994) , who sees the very process of writing as an interpretative act, and of Cancienne and Snowber (2003) , who argue for movement as method.

The most familiar of these creative and artistic forms are written—narratives and short stories ( Clandinin & Connelly, 2000 ; Richardson, 1994 ; Sparkes, 2002 ), poems or poetic form ( Butler-Kisber, 2010 ; Duke, 2007 ; Richardson, 1997 ; Sparkes & Douglas, 2007 ), cameos of people, or vignettes of situations. These can be written by participants or by the researcher or developed in partnership. They can also be shared with participants to further interpret the data. But photographs also have a long history in qualitative research for presenting and constructing understanding ( Butler-Kisber, 2010 ; Collier, 1967 ; Prosser, 2000 ; Rugang, 2006 ; Walker, 1993 ).

Less common are other visual forms of gathering data, such as “draw and write” ( Sewell, 2011 ), artefacts, drawings, sketches, paintings, and collages, although all forms are now on the increase. For examples of the use of collage in data gathering, see Duke (2007) and Butler-Kisber (2010) , and for charcoal drawing, Elliott (2008) .

In qualitative inquiry broadly, these creative approaches are now quite common. And in the context of arts and health in particular (see, for example, Frank, 1997 ; Liamputtong & Rumbold, 2008 ; Spouse, 2000 ), we can see how artistic approaches illuminate in-depth understanding. However, in case study research to date, I think narrative forms have tended to be most prominent.

Finally, for capturing the quality and essence of peoples’ experience, nothing could be more revealing than a recording of their voices. Video diaries—self-evaluative portrayals by individuals of their perspectives, feelings, or experience of an event or situation—are a most potent way both of gaining understanding and communicating that to others. It is rather more difficult to gain access for observational videos, but they are useful for documentation and have the potential to engage participants and stakeholders in the interpretation.

Getting It All Together

Case study is so often associated with story or with a report of some event or program that it is easy to forget that much analysis and interpretation has gone on before we reach this point. In many case study reports, this process is hidden, leaving the reader with little evidence on which to assess the validity of the findings and having to trust the one who wrote the tale.

This section briefly outlines possibilities, first, for analyzing and interpreting data, and second, for how to communicate the findings to others. However it is useful to think of these together and indeed, at the start, because decisions about how you report may influence how you choose to make sense of the data. Your choice may also vary according to the context of the study—what is expected or acceptable—and your personal predilections, whether you prefer a more rational than intuitive mode of analysis, for example, or a formal or informal style of writing up that includes images, metaphor, narratives, or poetic forms.

Analyzing and Interpreting Data

When it comes to making sense of data, I make a distinction between analysis—a formal inductive process that seeks to explain—and interpretation, a more intuitive process that gains understanding and insight from a holistic grasp of data, although these may interact and overlap at different stages.

The process, whichever emphasis you choose, is one of reducing or transforming a large amount of data to themes that can encapsulate the overarching meaning in the data. This involves sorting, refining, and refocusing data until they make sense. It starts at the beginning with preliminary hunches, sometimes called “interpretative asides” or “working hypotheses,” later moving to themes, analytic propositions, or a theory of the case.

There are many ways to conduct this process. Two strategies often employed are concept mapping —a means of representing data visually to explore links between related concepts—and progressive focusing ( Parlett & Hamilton, 1976 ), the gradual reframing of initially identified issues into themes that are then further interpreted to generate findings. Each of these strategies tends to have three stages: initial sense making, identification of themes, and examination of patterns and relationships between them.

If taking a formal analytic approach to the task, the data would likely be broken down into segments or datasets (coded and categorized) and then reordered and explored for themes, patterns, and possible propositions. If adopting a more intuitive process, you might focus on identifying insights through metaphors and images, lateral thinking, or puzzling over paradoxes and ambiguities in the data, after first immersing yourself in the total dataset, reading and re-reading interview scripts, observations and field notes to get a sense of the whole. Trying out different forms of making sense through poetry, vignettes, cameos, narratives, collages, and drawing are further creative ways to interpret data, as are photographs taken in the case arranged to explain or tell the story of the case.

Reporting Case Study Research

Narrative structure and story.

As indicated in the introduction, telling a story is often associated with case study and some think this is what a case study is. In one sense, it is and, given that story is the natural way in which we learn ( Okri, 1997 ), it is a useful framework both for gathering data and for communicating case study findings. Not any story will do however. To count as research, it must be authentic, grounded in data, interpreted and analyzed to convey the meaning of the case.

There are several senses in which story is appropriate in qualitative case study: in capturing stories participants tell, in generating a narrative structure that makes sense of the case (i.e., the story you will tell), and in deciding how you communicate this narrative (i.e., in story form). If you choose a written story form (and advice here can be sought from Harrington (2003) and Caulley (2008) ), it needs to be clearly structured, well written, and contain only the detail that is necessary to give readers the vicarious experience of what it was like in the case. If the story is to be communicated in other ways, through, for example, audio or videotape, or computer or personal interaction, the same applies, substituting visual and interpersonal skill for written.

Matching forms of reporting to audience

The art of reporting is strongly connected to usability, so forms of reporting need to connect to the audiences we hope to inform: how they learn, what kind of evidence they value, and what kind of reporting maximizes the chances they will use the findings to promote policies and programs in the interests of beneficiaries. As Okri (1997) further reminds us, the writer only does half the work; the reader does the other (p. 41).

There may be other considerations as well: how open are commissioners to receiving stories of difficulties, as well as success stories? What might they need to hear beyond what is sought in the technical brief? And through what style of reporting would you try and persuade them? If conducting noncommissioned case study research, the scope for different forms of reporting is wider. In academia, for instance, many institutions these days accept creative and artistic forms of reporting when supported by supervisors and appreciated by examiners.

Styles of Reporting

The most obvious form of reporting is linear, often starting with a short executive summary and a brief description of focus and context, followed by methodology, the case study or thematic analysis, findings, and conclusions or implications. Conclusion-led reporting is similar in terms of its formality, but simply starts the other way around. From the conclusions drawn from the analyzed data, it works backward to tell the story through narrative, verbatim, and observational data of how these conclusions were reached. Both have a strong story line. The intent is analytic and explanatory.

Quite a different approach is to engage the reader in the experience and veracity of the case. Rather like constructing a portrait or editing a documentary film, this involves the sifting, constructing, re-ordering of frames, events and episodes to tell a coherent story primarily through interview excerpts, observations, vignettes, and critical incidents that depict what transpired in the case. Interpretation is indirect through the weaving of the data. The story can start at any point provided the underlying narrative structure is maintained to establish coherence ( House, 1980 , p. 116).

Different again, and from the other end of a continuum, is a highly interpretative account that may use similar ways of presenting data but weaves a story from the outset that is highly interpretative. Engaging metaphor, images, short stories, contradictions, paradoxes, and puzzles, it is invariably interesting to read and can be most persuasive. However, the evidence is less visible and therefore less open to alternative interpretations.

Even more persuasive is a case study that uses artistic forms to communicate the story of the case. Paintings, poetic form, drawings, photography, collage, and movement can all be adopted to report findings, whether the data was acquired using these forms or by other means. The arts-based inquiry movement ( Mullen & Finley, 2003 ) has contributed hugely to the validation and legitimation of artistic and creative ways of representing qualitative research findings. The journal Qualitative Inquiry contains many good examples, but see also Liamputtong & Rumbold (2008) . Such artistic forms of representation may not be for everyone or appropriate in some contexts, but they do have the power to engage an audience and the potential to facilitate use.

Generalization in Case Study Research

One of the potential limitations of case study often proposed is that it is impossible to generalize. This is not so. However, the way in which one generalizes from a case is different from that adopted in traditional forms of social science research that utilize large samples (randomly selected) and statistical procedures and which assume regularities in the social world that allow cause and effect to be determined. In this form of research inferences from data are stated as formal propositions that apply to all in the target population. See Donmoyer (1990) for an argument on the restricted nature of this form of generalization when considering single-case studies.

Making inferences from cases with a qualitative data set arises more from a process of interpretation in context, appealing to tacit and situated understanding for acceptance of their validity. Such inferences are possible where the context and experience of the case is richly described so the reader can recognize and connect with the events and experiences portrayed. There are two ways to examine how to reach these generalized understandings. One is to generalize from the case to other cases of a similar or dissimilar nature. The other is to see what we learn in-depth from the uniqueness of the single case itself.

Generalizing from the Single Case

A common approach to generalization and one most akin to a propositional form is cross-case generalization. In a collective or multi-site case study, each case is explored to see if issues that arise in one case also exist in other cases and what interconnecting themes there are between them. This kind of generalization has a degree of abstraction and potential for theorizing and is often welcomed by commissioners of research concerned that findings from the single case do not provide an adequate or “safe” basis for policy determination.

However, there are four additional ways to generalize from the single case, all of which draw more on tacit knowledge and recognition of context, although in different ways. In naturalistic generalization , first proposed by Stake (1978) , generalization is reached on the basis of recognition of similarities and differences to cases with which we are familiar. To enable such recognition, the case needs to feature rich description; people’s voices; and enough detail of time, place, and context to provide a vicarious experience to help readers discern what is similar and dissimilar to their own context ( Stake, 1978 ).

Situated generalization ( Simons, Kushner, Jones, & James, 2003 ) is close to the concept of naturalistic generalization in relying for its generality on retaining a connectedness with the context in which it first evolved. However, it has an extra dimension in a practice context. This notion of generalization was identified in an evaluation of a research project that engaged teachers in and with research. Here, in addition to the usual validity criteria to establish the warrant for the findings, the generalization was seen as dependable if trust existed between those who conducted the research (teachers, in this example) and those thinking about using it (other teachers). In other words, beyond the technical validity of the research, teachers considered using the findings in their own practice because they had confidence in those who generated them. This is a useful way to think about generalization if we wish research findings to improve professional practice.

The next two concepts of generalization— concept and process generalization —relate more to what you discover in making sense of the case. As you interpret and analyze, you begin to generate a theory of the case that makes sense of the whole. Concepts may be identified that make sense in the one case but have equal significance in other cases of a similar kind, even if the contexts are different.

It is the concept that generalizes, not the specific content or context. This may be similar to the process Donmoyer (2008) identifies of “intellectual generalization” (quoted by Butler-Kisber, 2010 , p. 15) to indicate the cognitive understanding one can gain from qualitative accounts even if settings are quite different.

The same is true for generalization of a process. It is possible to identify a significant process in one case (or several cases) that is transferable to other contexts, irrespective of the precise content and contexts of those other cases. An example here is the collaborative model for sustainable school self-evaluation I identified in researching school self-evaluation in a number of schools and countries ( Simons, 2002 ). Schools that successfully sustained school self-evaluation had an infrastructure that was collaborative at all stages of the evaluation process from design to conduct of the study, to analyzing the results and to reporting the findings. This ensured that the whole school was involved and that results were discussed and built into the ongoing development of school policies and practice. In other cases, different processes may be discovered that have applicability in a range of contexts. As with concept generalization, it is the process that generalizes not the substantive content or specific context.

Particularization

The forms of generalization discussed above are useful when we have to justify case study in a research or policy context. But the overarching justification for how we learn from case study is particularization —a rich portrayal of insights and understandings interpreted in the particular context. Several authors have made this point ( Stake, 1995 ; Flyvberg, 2006 ; Simons 2009 ). Stake puts it most sharply when he observes that “The real business of case study is particularization, not generalization” (p. 8), referring here to the main reason for studying the singular, which is to understand the uniqueness of the case itself.

My perspective (explored further in Simons, 1996 ; Simons, 2009 , p. 239; Simons & McCormack, 2007 ) is similar in that I believe the “real” strength of case study lies in the insights we gain from in-depth study of the particular. But I also argue for the universality of such insights—if we get it “right.” By which I mean that if we are able to capture and report the uniqueness, the essence, of the case in all its particularity and present this in a way we can all recognize, we will discover something of universal significance. This is something of a paradox. The more you learn in depth about the particularity of one person, situation, or context, the more likely you are to discover something universal. This process of reaching understanding has support both from the way in which many discoveries are made in science and in how we learn from artists, poets, and novelists, who reach us by communicating a recognizable truth about individuals, human relationships, and/or social contexts.

This concept of particularization is far from new, as the quotation from a preface to a book written in 1908 attests. Stephen Reynolds, the author of A Poor Man’s House , notes that the substance of the book was first recorded in a journal, kept for purposes of fiction, and in letters to one of his friends, but fiction proved an inappropriate medium. He felt that the life and the people were so much better than anything he could invent. The book therefore consists of the journal and letters drawn together to present a picture of a typical poor man’s house and life, much as we might draw together a range of data to present a case study. It is not the substance of the book that concerns us here but the methodological relevance to case study research. Reynolds notes that the conclusions expressed are tentative and possibly go beyond this man’s life, so he thought some explanation of the way he arrived at them was needed:

Educated people usually deal with the poor man’s life deductively; they reason from the general to the particular; and, starting with a theory, religious, philanthropic, political, or what not, they seek, and too easily find, among the millions of poor, specimens—very frequently abnormal—to illustrate their theories. With anything but human beings, that is an excellent method. Human beings, unfortunately, have individualities. They do what, theoretically, they ought not to do, and leave undone those things they ought to do. They are even said to possess souls—untrustworthy things beyond the reach of sociologists. The inductive method—reasoning from the particular to the general... should at least help to counterbalance the psychological superficiality of the deductive method. ( Reynolds, 1908 : preface) 1

Slightly overstated perhaps, but the point is well made. In our search for general laws, we not only lose sight of the uniqueness and humanity of individuals, but reduce them in the process, failing to present their experience in any “real” sense. What is astonishing about the quotation is that it was written over a century ago and yet many still argue today that you cannot generalize from the particular.

Going even further back, in 1798, Blake proclaimed that “To Generalize is to be an Idiot. To Particularize is the Alone Distinction of Merit.” In research, we may not wish to make such a strong distinction: these processes both have their uses in different kinds of research. But there is a major point here for the study of the particular that Wilson (2008) notes in commenting on Blake’s perception when he says: “Favouring the abstract over the concrete, one ‘sees all things only thro’ the narrow chinks of his cavern”’ (referring here to Blake’s The Marriage of Heaven and Hell [1793]; in Wilson, 2008 , p. 62). The danger Wilson is pointing to here is that abstraction relies heavily on what we know from our past understanding of things, and this may prevent us experiencing a concrete event directly or “apprehend[ing] a particular moment” ( Wilson, 2008 , p. 63).

Blake had a different mission, of course, than case researchers, and he was not himself free from abstractions, as Wilson points out, although he fought hard “to break through mental barriers to something unique and living” ( Wilson, 2008 , p. 65). It is this search for the “unique and living” and experiencing the “isness” of the particular that we should take from the Blake example to remind ourselves of the possibility of discovering something “new,” beyond our current understanding of the way things are.

Focusing on particularization does not diminish the usefulness of case study research for policy makers or practitioners. Grounded in recognizable experience, the potential is there to reach a range of audiences and to facilitate use of the findings. It may be more difficult for those who seek formal generalizations that seem to offer a safe basis for policy making to accept case study reports. However, particular stories often hold the key to why policies have or have not worked well in the past. It is not necessary to present long cases—a criticism frequently levelled—to demonstrate the story of the case. Such case stories can be most insightful for policy makers who, like many of us in everyday life, often draw inferences from a single instance or case, whatever the formal evidence presented. “I am reminded of the story of....”

The case for studying the particular to inform practice in professional contexts needs less persuasion because practitioners can recognize the content and context quite readily and make the inference to their own particular context ( Simons et al., 2003 ). In both sets of circumstances—policy and practice—it is more a question of whether the readers of our case research accept the validity of findings determined in this way, how they choose to learn, and our skill in telling the case study story.

Conclusion and Future Directions

In this chapter, I have presented an argument for case study research, making the case, in particular, for using qualitative methods to highlight what it is that qualitative case study research can bring to the study of social and educational programs. I outlined the various ways in which case study is commonly used before focusing directly on case study as a major mode of research inquiry, noting characteristics it shares with other qualitative methodologies, as well as itsdifference and the difficulties it is sometimes perceived to have. The chapter emphasizes the importance of thinking through what the case is, to be sure that the issues explored and the data generated do illuminate this case and not any other.

But there is still more to be done. In particular, I think we need to be more adventurous in how we craft and report the case. I suspect we may have been too cautious in the past in how we justified case study research, borrowing concepts from other disciplines and forms of educational research. More than 40 years on, it is time to take a greater risk—in demonstrating the intrinsic nature of case study and what it can offer to our understanding of human and social situations.

I have already drawn attention to the need to design the case, although this could be developed further to accentuate the uniqueness of the particular case. One way to do this is to feature individuals more in the design itself, not only to explore programs and policies through perspectives of key actors or groups and transactions between them, which to some extent happens already, but also to get them to characterize what makes the context unique. This is the reversal of many a design framework that starts with the logic of a program and takes forward the argument for personal evaluation ( Kushner, 2000 ), noted in the interlude on evaluation. Apart from this attention to design, there are three other issues I think we need to explore further: the warrant for creative methods in case study, more imaginative reporting; and how we learn from a study of the singular.

Warrant for More Creative Methods in Case Study Research

The promise that creative methods have for eliciting in-depth understanding and capturing the unusual, the idiosyncratic, the uniqueness of the case, was mentioned in the methods section. Yet, in case study research, particularly in program and policy contexts, we have few good examples of the use of artistic approaches for eliciting and interpreting data, although more, as acknowledged later, for presenting it. This may be because case study research is often conducted in academic or policy environments, where propositional ways of knowing are more valued.

Using creative and artistic forms in generating and interpreting case study data offers a form of evidence that acknowledges experiential understanding in illuminating the uniqueness of the case. The question is how to establish the warrant for this way of knowing and persuade others of its virtue. The answer is simple. By demonstrating the use of these methods in action, by arguing for a different form of validity that matches the intrinsic nature of the method, and, above all, by good examples.

Representing Findings to Engage Audiences in Learning

In evaluative and research policy contexts, where case study is often the main mode of inquiry or part of a broader study, case study reports often take a formal structure or sometimes, where the context is receptive, a portrayal or interpretative form. But, too often, the qualitative is an add-on to a story told by other means or reduced to issues in which the people who gave rise to the data are no longer seen. However, there are many ways to put them center stage.

Tell good stories and tell them well. Or, let key actors tell their own stories. Explore the different ways technology can help. Make video clips that demonstrate events in context, illustrate interactions between people, give voice to participants—show the reality of the program, in other words. Use graphics to summarize key issues and interactive, cartoon technology, as seen on some TED presentations, to summarize and visually show the complexity of the case. Video diaries were mentioned in the methods section: seeing individuals tell their tales directly is a powerful way of communicating, unhindered by “our” sense making. Tell photo stories. Let the photos convey the narrative, but make sure the structure of the narrative is evident to ensure coherence. These are just the beginnings. Those skilled in information technology could no doubt stretch our imagination further.

One problem and a further question concerns our audiences. Will they accept these modes of communication? Maybe not, in some contexts. However, there are three points I wish to leave you with. First, do not presume that they won’t. If people are fully present in the story and the complexity is not diminished, those reading, watching, or hearing about the case will get the message. If you are worried about how commissioners might respond, remember that they are no different from any other stakeholder or participant when it comes to how they learn from human experience. Witness the reference to Okri (1997) earlier about how we learn.

Second, when you detect that the context requires a more formal presentation of findings, respond according to expectation but also include elements of other forms of presentation. Nudge a little in the direction of creativity. Third, simply take a chance, that risk I spoke about earlier. Challenge the status quo. Find situations and contexts where you can fully represent the qualitative nature of the experience in the cases you study with creative forms of interpretation and representation. And let the audience decide.

Learning from a Study of the Singular

Finally, to return to the issue of “generalization” in case study that worries some audiences. I pointed out in the generalization section several ways in which it is possible to generalize from case study research, not in a formal propositional sense or from a case to a population, but by retaining a connection with the context in which the generalization first arose—that is, to realize in-depth understanding in context in different circumstances and situations. However, I also emphasized that, in many instances, it is particularization from which we learn. That is the point of the singular case study, and it is an art to perceive and craft the case in ways that we can.

Acknowledgments

Parts of this chapter build on ideas first explored in Simons, 2009 .

I am grateful to Bob Williams for pointing out the relevance of this quotation from Reynolds to remind us that “there is nothing new under the sun” and that we sometimes continue to engage endlessly in debates that have been well rehearsed before.

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How to use and assess qualitative research methods

Loraine busetto.

1 Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany

Wolfgang Wick

2 Clinical Cooperation Unit Neuro-Oncology, German Cancer Research Center, Heidelberg, Germany

Christoph Gumbinger

Associated data.

Not applicable.

This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 – 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 – 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. ​ Fig.2, 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 – 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 – 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 – 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table ​ Table1. 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Take-away-points

Acknowledgements

Abbreviations, authors’ contributions.

LB drafted the manuscript; WW and CG revised the manuscript; all authors approved the final versions.

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Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

• Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
• Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

• Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
• Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
• Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
• Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
• Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
• Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
• Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

• Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
• Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

• Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
• Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
• Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
• Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

• Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
• Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
• Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
• Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
• Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

• Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
• Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
• Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
• Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
• Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
• Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

• Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
• Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
• Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
• Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
• Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
• Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

• Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
• Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
• Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
• Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
• Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
• Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

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Chapter 8: Case study

Darshini Ayton

Learning outcomes

Upon completion of this chapter, you should be able to:

• Identify the key terms and concepts used in qualitative case study research.
• Discuss the advantages and disadvantages of qualitative case study research.

What is a case study?

The key concept in a case study is context .

In qualitative research, case studies provide in-depth accounts of events, relationships, experiences or processes. Stemming from the fields of evaluation, political science and law, the aim of a qualitative case study is to explore a phenomenon within the context of the case 1 and to answer how and why research questions. 2 The contextual conditions are relevant to the phenomenon under study and the contextual factors tend to lie with the case. 1 From the outset it is important (a) to determine who or what is your case – this can be a person, program, organisation or group, or a process – and (b) to articulate the phenomenon of interest.

An example of why context is important in understanding the phenomenon of interest is a study of health promotion action by local churches in Victoria, Australia. 3 The phenomenon under study was health promotion action, with 10 churches comprising the cases, which were mapped across the framework of health promotion approaches. 4 The contextual factors included church denomination (Baptist, Church of Christ, Uniting, Anglican, Catholic and Salvation Army), size (small, medium and large), location (rural and metropolitan), partnerships with external organisations (government, local schools and social welfare organisations) and theological orientation (traditional, modern or postmodern), to understand the phenomenon of health promotion action. Data collection took 12 months and involved interviews with 37 church leaders, 10 focus groups with volunteers, 17 instances of participant observation of church activities, including church services, youth events, food banks and community meals, and 12 documentary analyses of church websites, newsletters and annual reports. The case studies identified and illustrated how and why three different expressions of church – traditional, new modern and emerging – led to different levels and types of health promotion activities.

Three prominent qualitative case study methodologists, Robert Stake, Robert Yin and Sharan Merriams, have articulated different approaches to case studies and their underpinning philosophical and paradigmatic assumptions. Table 8 outlines these approaches, based on work by Yazan, 5 whose expanded table covers characteristics of case studies, data collection and analysis.

Table 8.1. Comparison of case study terms used by three key methodologists

Table 8.1 is derived from ‘Three Approaches to Case Study Methods in Education: Yin, Merriam, and Stake ‘  by Bedrettin Yazan,  licensed under CC BY-NC-SA 4.0. 5

There are several forms of qualitative case studies. 1,2

Discovery-led case studies, which:

• describe what is happening in the setting
• explore the key issues affecting people within the setting
• compare settings, to learn from the similarities and differences between them.

Theory-led case studies, which:

• explain the causes of events, processes or relationships within a setting
• illustrate how a particular theory applies to a real-life setting
• experiment with changes in the setting to test specific factors or variables.

Single and collective case studies, where: 2, 9

• the researcher wants to understand a unique phenomenon in detail– known as an intrinsic case study
• the researcher is seeking insight and understanding of a particular situation or phenomenon, known as an illustrative case study or instrumental case study.

In both intrinsic, instrumental and illustrative case studies, the exploration might take place within a single case. In contrast, a collective case study includes multiple individual cases, and the exploration occurs both within and between cases. Collective case studies may include comparative cases, whereby cases are sampled to provide points of comparison for either context or the phenomenon. Embedded case studies are increasingly common within multi-site, randomised controlled trials, where each of the study sites is considered a case.

Multiple forms of data collection and methods of analysis (e.g. thematic, content, framework and constant comparative analyses) can be employed, since case studies are characterised by the depth of knowledge they provide and their nuanced approaches to understanding phenomena within context. 2,5 This approach enables triangulation between data sources (interviews, focus groups, participant observations), researchers and theory. Refer to Chapter 19 for information about triangulation.

Advantages and disadvantages of qualitative case studies

Advantages of using a case study approach include the ability to explore the subtleties and intricacies of complex social situations, and the use of multiple data collection methods and data from multiple sources within the case, which enables rigour through triangulation. Collective case studies enable comparison and contrasting within and across cases.

However, it can be challenging to define the boundaries of the case and to gain appropriate access to the case for the ‘deep dive’ form of analysis. Participant observation, which is a common form of data collection, can lead to observer bias. Data collection can take a long time and may require lengthy times, resources and funding to conduct the study. 9

Table 8.2 provides an example of a single case study and of a collective case study.

Table 8.2. Examples of qualitative case studies

Qualitative case studies provide a study design with diverse methods to examine the contextual factors relevant to understanding the why and how of a phenomenon within a case. The design incorporates single case studies and collective cases, which can also be embedded within randomised controlled trials as a form of process evaluation.

• Creswell J, Hanson W, Clark Plano V et al.. Qualitative research designs: selection and implementation. Couns Psychol  2007;35(2):236-264. doi:10.1177/0011000006287390
• Crowe S, Cresswell K, Robertson A, et al. The case study approach. BMC Med Res Methodol . 2011;11:100. doi:10.1186/1471-2288-11-100
• Ayton D, Manderson L, Smith BJ et al. Health promotion in local churches in Victoria: an exploratory study. Health Soc Care Community . 2016;24(6):728-738. doi:10.1111/hsc.12258
• Keleher H, Murphy C. Understanding Health: A Determinants Approach . Oxford University Press; 2004.
• Yazan B. Three approaches to case study methods in education: Yin, Merriam, and Stake. The Qualitative Report . 2015;20(2):134-152. doi:10.46743/2160-3715/2015.2102
• Stake RE. The A rt of C ase S tudy R esearch . SAGE Publications; 1995.
• Yin RK. Case S tudy R esearch: Design and M ethods . SAGE Publications; 2002.
• Merriam SB. Qualitative R esearch and C ase S tudy A pplications in E ducation . Jossey-Boss; 1998.
• Kekeya J. Qualitative case study research design: the commonalities and differences between collective, intrinsic and instrumental case studies. Contemporary PNG Studies . 2021;36:28-37.
• Nayback-Beebe AM, Yoder LH. The lived experiences of a male survivor of intimate partner violence: a qualitative case study. Medsurg Nurs . 2012;21(2):89-95; quiz 96.
• Clack L, Zingg W, Saint S et al. Implementing infection prevention practices across European hospitals: an in-depth qualitative assessment. BMJ Qual Saf . 2018;27(10):771-780. doi:10.1136/bmjqs-2017-007675

Qualitative Research – a practical guide for health and social care researchers and practitioners Copyright © 2023 by Darshini Ayton is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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9 methodologies for a successful qualitative research assignment

Qualitative research is important in the educational and scientific domains. It enables a deeper understanding of phenomena, experiences, and context. Many researchers employ such research activities in the fields of history, sociology, and anthropology. For such researchers, learning quality analysis insights is crucial. This way, they can perform well throughout their research journey. Writing a qualitative research assignment is one such way to practice qualitative interpretations. When students address various qualitative questions in these projects, they become efficient in conducting these activities at a higher level, such as for a master’s or Ph.D. thesis.

The FormPlus highlights why researchers prefer qualitative research over quantitative research. It is faster, scientific, objective, focused, and acceptable. Researchers who don’t know what to expect from the research outcomes usually choose qualitative research. In this guide, we will discuss the top methodologies that students can employ while writing their qualitative research assignments. This way, you can write an appealing document that perfectly demonstrates your qualitative research skills.

However, being stressed with academic and daily life commitments, if you find it challenging to manage time exclusively for such projects, availing of assignment writing services can make it manageable. Instead of doing anything wrong in the hustle, get it done by the professionals specifically working to handle these academic write-ups. Now, let’s define quality research before we discuss the actual topic.

What is meant by qualitative research?

Quality research is a market research method that gathers data from conversational and open-ended communication. In simple words, it is about what people think and why they think so. It relates to the nature or standard of something rather than dealing with its quantity. Such researchers collect nonnumerical data to understand opinions, concepts, and ideas.

How do you write a qualitative research assignment? Top 9 methodologies

Writing an assignment requires your command of various tasks. Qualitative research assignment design involves research, writing, structuring, and providing citations of the resources used. Assignment writing plays a crucial role in upgrading your grades.

So, you must make it accurate and authentic. Write it with the utmost care without skipping any important aspects. Sometimes, it can be hard, but it becomes easy if you correctly use effective methodologies. This is why we have brought together some of the common methodologies you can use to write your qualitative research assignments.

1. Interviews

A qualitative interview is mostly used in projects that involve market research. In this study personal interaction is required to collect in-depth information of the participants. In qualitative research for assignment, consider the interview as a personal form of research agenda rather than a focused group study. A qualitative interview requires careful planning so that you can gather meaningful data.

Here are the simple steps to consider for its implementation in a qualitative research assignment:

• Define research objectives.
• Identify the target population.
• Obtain informed consent of participants.
• Make an interview guideline.
• Select a suitable location.
• Conduct the interview.
• Show respect for participant’s perspectives.
• Analyse the data.

2. Observation

In qualitative observation, the researcher gathers data from five senses: sight, hearing, touch, smell, and taste. It is a subject approach that depends on the sensory organ of the researcher. This method allows you to better understand the culture, process, and people under study. Some of its characteristics to consider for writing a qualitative research assignment include,

• It is a naturalistic inquiry of the participants in a natural environment.
• This approach is subjective and depends on the researcher’s observation.
• It does not seek a definite answer to a query.
• The researcher can recognise their own biases when compiling findings.

3. Questionnaires

In this type of survey, the researcher asks open-ended questions to participants. This way, they price the long written or typed document. In writing qualitative research assignments, these questions aim to reveal the participants’ narratives and experiences. Once you know what type of information you need, you can start curating your questionnaire form. The questions must be specific and clear enough that the participants can comprehend them.

Below are the main points that must be considered when creating qualitative research questionnaires.

• Avoid jargon and ambiguity in the questions.
• Each question should contribute to the research objectives.
• Use simple language.
• The questions should be neutral and unbiased.
• Be precise, as the complex questions can overwhelm the respondents.
• Always conduct a pilot test.
• Put yourself in the respondent’s shoes while asking questions.

4. Case Study

A case study is a detailed analysis of a person, place, thing, organisation, or phenomenon. This method is appropriate when you want to gain a contextual, concrete, and in-depth understanding of the real-world problem for writing your qualitative research assignment. This method is especially helpful when you need more time to conduct large-scale research activities.

The four crucial steps below can be followed up with this methodology.

• Select a case that has the potential to provide new and unexpected insights into the subject.
• Make a theoretical framework.
• Collect your data from various primary and secondary resources.
• Describe and analyse the case to provide a clear picture of the subject.

5. Focus Groups

Focused group research has some interesting properties. In this method, a planned interview is conducted within a small group. For this purpose, some of the participants are sampled from the study population to record data for writing a qualitative research assignment. Typically, a focused group has features like,

• At least four to ten participants must meet for up to two hours.
• There must be a facilitator who can guide the discussion by asking open-ended questions.
• The emphasis must be put on the group discussion rather than the discussion of the group members with the facilitator.
• The discussion should be recorded and transcribed by the researchers.

6. Ethnographic Research

It is the most in-depth research method that involves studying people in their natural environment. It requires the researcher to adopt the target audience environment. The environment can be anything from an organisation to a city or any remote location.

However, the geographical constraints can be a problem in this study. For students who are writing their qualitative research assignment, some of the features of ethnographic research to write in their document include,

• The researcher can get a more realistic picture of the study.
• It uncovers extremely valuable insights.
• Provides accurate predictions.
• You can extend the observation to create more in-depth data.
• You can interact with people within a particular context.

7. Record Keeping

This method is similar to going to the library to collect data from books. You consult various relayed books, note the important points, and take note of the referencing. So, the researcher uses already existing data rather than introducing new things in the field.

Later on, this data can be used to conduct new research. Yet, when faced with the vast resources available in your institution’s library, seeking assistance from UK-based assignment writing services is an excellent solution if you need help pinpointing the most relevant information for your topic. Proficient in data gathering and adept at structuring qualitative research assignments, these professionals can significantly elevate your academic results.

This method is mostly used by companies to understand a group of customers’ behaviour, characteristics, and motivation. It allows respondents to ask in-depth questions about their experience. In a business market, it helps you understand how your customers make decisions. The intent is to understand them at their level and make related changes in your setup. The researcher must ask generic and precise questions that have a clear purpose.

Consider the below examples of qualitative survey questions. It can be useful in recording data and writing qualitative research assignments.

• Why did you buy this skin care product?
• What is the overall narrative of this brand?
• How do you feel after buying this product?
• What sets this brand apart from others?
• How will this product fulfil your needs?
• What are the things that you expect from this brand to grant you?

9. Action Research

This method involves collaboration and empowerment of the participants. It is mostly appropriate for marginalised groups where there is no flexibility.

The primary characteristics of the action research that can be quoted in your qualitative research assignment include,

• It is action-oriented, and participants are actively involved in the research.
• There is a collaborative process between participants and researchers.
• The nature of action research is flexible to the changing situation.

However, the survey also accompanies some of the limitations, including,

• The researcher can misinterpret the open-ended questions.
• The data ownership between the researcher and participants needs to be negotiated.
• The ethical considerations must be kept.
• It is not considered a scientific method as it is fluid in data collection. Consequently, it may not attract the finding.

What is the difference between quantitative and qualitative research?

Both research types share the common aim of knowledge acquisition. In quantitative research, the use of numbers and objective measures is used. It seeks answers to questions like when and where.

On the other hand, in qualitative research, the researcher is concerned with subjective phenomena. Such data can’t be numerically measured. For example, you might conduct a survey to analyse how different people experience grief.

What are the 4 types of qualitative research?

There are various types of qualitative research. It may include,

● Phenomenological studies:

It examines the human experience via description provided by the people involved. These are the lived experiences of the people. It is usually used in research areas where little knowledge is known.

● Ethnographic studies:

It involves the analysis of data about cultural groups. In such analysis, the researcher mostly lives with different communities and becomes part of their culture to provide solid interpretations.

● Grounded theory studies:

In this qualitative approach, the researcher collects and analyses the data. Later on, a theory is developed that is grounded in the data. It used both inductive and deductive approaches for theory development.

● Historical studies:

It is concerned with the location, identification, evaluation, and synthesis of data from the past. These researchers are not concerned with discovering past events but with relating these events to the present happenings.

The Research Gate provides a flow chart illustrating various qualitative research methods.

What are The 7 characteristics of qualitative research?

The following are some of the distinct features of qualitative research. You can write about them in your qualitative research assignment, as they are collected from reliable sources.

• It can even capture the changing attitude within the target group.
• It is beyond the limitations associated with quantitative research
• It explains something that numbers alone can’t describe.
• It is a flexible approach to improve the outcomes.
• A researcher is not supposed to become more speculative about the results.
• This approach is more targeted.
• It keeps the cost of data collection down.

What are the advantages and disadvantages of qualitative research?

The pros of qualitative research can’t be denied. However, some cons are also associated with this research.

• Explore attitudes and behaviours in depth.
• It encourages discussions for better results.
• Generate descriptive data that can formulate new theories.
• The small sample size can be a problem.
• Bias in the sample collection.
• Lack of privacy if you are covering a sensitive topic.

Qualitative research assignment examples

The Afe Babalola University ePortal provides an example of a qualitative assignment. Here is the description of quality questions and related answers. You can get an idea about how to handle your quality research assignment project with this sample.

The questions asked in the paper are displayed below.

The Slide Team presents a template for further compressing other details, such as the qualitative research assignment template. You can use it to make your presentation look professional.

Writing a qualitative research assignment is crucial, especially if you want to engage in research activities for your master’s thesis. Most researchers choose this method because of the associated credibility and reliability of the results. In the above guide, we have discussed some of the prominent features of this method. All of the given data can help you in writing your assignments. We have discussed the benefits of each methodology and a brief account of how you can carry it.

However, even after going through this whole guideline, if the concepts of the Qualitative Research methods assignment seem ambiguous and you think you can’t write a good project, then ask professional to “ write my assignment .” These experts can consult the best sources for the data collection of your project. Consequently, they will deliver you the winning document that can stand out among other write-ups.

• Open access
• Published: 02 April 2022

A qualitative study of rural healthcare providers’ views of social, cultural, and programmatic barriers to healthcare access

• Nicholas C. Coombs 1 ,
• Duncan G. Campbell 2 &
• James Caringi 1  

BMC Health Services Research volume  22 , Article number:  438 ( 2022 ) Cite this article

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Ensuring access to healthcare is a complex, multi-dimensional health challenge. Since the inception of the coronavirus pandemic, this challenge is more pressing. Some dimensions of access are difficult to quantify, namely characteristics that influence healthcare services to be both acceptable and appropriate. These link to a patient’s acceptance of services that they are to receive and ensuring appropriate fit between services and a patient’s specific healthcare needs. These dimensions of access are particularly evident in rural health systems where additional structural barriers make accessing healthcare more difficult. Thus, it is important to examine healthcare access barriers in rural-specific areas to understand their origin and implications for resolution.

We used qualitative methods and a convenience sample of healthcare providers who currently practice in the rural US state of Montana. Our sample included 12 healthcare providers from diverse training backgrounds and specialties. All were decision-makers in the development or revision of patients’ treatment plans. Semi-structured interviews and content analysis were used to explore barriers–appropriateness and acceptability–to healthcare access in their patient populations. Our analysis was both deductive and inductive and focused on three analytic domains: cultural considerations, patient-provider communication, and provider-provider communication. Member checks ensured credibility and trustworthiness of our findings.

Five key themes emerged from analysis: 1) a friction exists between aspects of patients’ rural identities and healthcare systems; 2) facilitating access to healthcare requires application of and respect for cultural differences; 3) communication between healthcare providers is systematically fragmented; 4) time and resource constraints disproportionately harm rural health systems; and 5) profits are prioritized over addressing barriers to healthcare access in the US.

Conclusions

Inadequate access to healthcare is an issue in the US, particularly in rural areas. Rural healthcare consumers compose a hard-to-reach patient population. Too few providers exist to meet population health needs, and fragmented communication impairs rural health systems’ ability to function. These issues exacerbate the difficulty of ensuring acceptable and appropriate delivery of healthcare services, which compound all other barriers to healthcare access for rural residents. Each dimension of access must be monitored to improve patient experiences and outcomes for rural Americans.

Peer Review reports

Unequal access to healthcare services is an important element of health disparities in the United States [ 1 ], and there remains much about access that is not fully understood. The lack of understanding is attributable, in part, to the lack of uniformity in how access is defined and evaluated, and the extent to which access is often oversimplified in research [ 2 ]. Subsequently, attempts to address population-level barriers to healthcare access are insufficient, and access remains an unresolved, complex health challenge [ 3 , 4 , 5 ]. This paper presents a study that aims to explore some of the less well studied barriers to healthcare access, particularly those that influence healthcare acceptability and appropriateness.

In truth, healthcare access entails a complicated calculus that combines characteristics of individuals, their households, and their social and physical environments with characteristics of healthcare delivery systems, organizations, and healthcare providers. For one to fully ‘access’ healthcare, they must have the means to identify their healthcare needs and have available to them care providers and the facilities where they work. Further, patients must then reach, obtain, and use the healthcare services in order to have their healthcare needs fulfilled. Levesque and colleagues critically examined access conceptualizations in 2013 and synthesized all ways in which access to healthcare was previously characterized; Levesque et al. proposed five dimensions of access: approachability, acceptability, availability, affordability and appropriateness [ 2 ]. These refer to the ability to perceive, seek, reach, pay for, and engage in services, respectively.

According to Levesque et al.’s framework, the five dimensions combine to facilitate access to care or serve as barriers. Approachability indicates that people facing health needs understand that healthcare services exist and might be helpful. Acceptability represents whether patients see healthcare services as consistent or inconsistent with their own social and cultural values and worldviews. Availability indicates that healthcare services are reached both physically and in a timely manner. Affordability simplifies one’s capacity to pay for healthcare services without compromising basic necessities, and finally, appropriateness represents the fit between healthcare services and a patient’s specific healthcare needs [ 2 ]. This study focused on the acceptability and appropriateness dimensions of access.

Before the novel coronavirus (SARS-CoV-2; COVID-19) pandemic, approximately 13.3% of adults in the US did not have a usual source of healthcare [ 6 ]. Millions more did not utilize services regularly, and close to two-thirds reported that they would be debilitated by an unexpected medical bill [ 7 , 8 , 9 ]. Findings like these emphasized a fragility in the financial security of the American population [ 10 ]. These concerns were exacerbated by the pandemic when a sudden surge in unemployment increased un- and under-insurance rates [ 11 ]. Indeed, employer-sponsored insurance covers close to half of Americans’ total cost of illness [ 12 ]. Unemployment linked to COVID-19 cut off the lone outlet to healthcare access for many. Health-related financial concerns expanded beyond individuals, as healthcare organizations were unequipped to manage a simultaneous increase in demand for specialized healthcare services and a steep drop off for routine revenue-generating healthcare services [ 13 ]. These consequences of the COVID-19 pandemic all put additional, unexpected pressure on an already fragmented US healthcare system.

Other structural barriers to healthcare access exist in relation to the rural–urban divide. Less than 10% of US healthcare resources are located in rural areas where approximately 20% of the American population resides [ 14 ]. In a country with substantially fewer providers per capita compared to many other developed countries, persons in rural areas experience uniquely pressing healthcare provider shortages [ 15 , 16 ]. Rural inhabitants also tend to have lower household income, higher rates of un- or under-insurance, and more difficulty with travel to healthcare clinics than urban dwellers [ 17 ]. Subsequently, persons in rural communities use healthcare services at lower rates, and potentially preventable hospitalizations are more prevalent [ 18 ]. This disparity often leads rural residents to use services primarily for more urgent needs and less so for routine care [ 19 , 20 , 21 ].

The differences in how rural and urban healthcare systems function warranted a federal initiative to focus exclusively on rural health priorities and serve as counterpart to Healthy People objectives [ 22 ]. The rural determinants of health, a more specific expression of general social determinants, add issues of geography and topography to the well-documented social, economic and political factors that influence all Americans’ access to healthcare [ 23 ]. As a result, access is consistently regarded as a top priority in rural areas, and many research efforts have explored the intersection between access and rurality, namely within its less understood dimensions (acceptability and appropriateness) [ 22 ].

Acceptability-related barriers to care

Acceptability represents the dimension of healthcare access that affects a patient’s ability to seek healthcare, particularly linked to one’s professional values, norms and culture [ 2 ]. Access to health information is an influential factor for acceptable healthcare and is essential to promote and maintain a healthy population [ 24 ]. According to the Centers for Disease Control and Prevention, health literacy or a high ‘health IQ’ is the degree to which individuals have the ability to find, understand, and use information and services to inform health-related decisions and actions for themselves and others, which impacts healthcare use and system navigation [ 25 ]. The literature indicates that lower levels of health literacy contribute to health disparities among rural populations [ 26 , 27 , 28 ]. Evidence points to a need for effective health communication between healthcare organizations and patients to improve health literacy [ 24 ]. However, little research has been done in this area, particularly as it relates to technologically-based interventions to disseminate health information [ 29 ].

Stigma, an undesirable position of perceived diminished status in an individual’s social position, is another challenge that influences healthcare acceptability [ 30 ]. Those who may experience stigma fear negative social consequences in relation to care seeking. They are more likely to delay seeking care, especially among ethnic minority populations [ 31 , 32 ]. Social media presents opportunities for the dissemination of misleading medical information; this runs further risk for stigma [ 33 ]. Stigma is difficult to undo, but research has shown that developing a positive relationship with a healthcare provider or organization can work to reduce stigma among patients, thus promoting healthcare acceptability [ 34 ].

A provider’s attempts to engage patients and empower them to be active decision-makers regarding their treatment has also been shown to improve healthcare acceptability. One study found that patients with heart disease who completed a daily diary of weight and self-assessment of symptoms, per correspondence with their provider, had better care outcomes than those who did not [ 35 ]. Engaging with household family members and involved community healers also mitigates barriers to care, emphasizing the importance of a team-based approach that extends beyond those who typically provide healthcare services [ 36 , 37 ]. One study, for instance, explored how individuals closest to a pregnant woman affect the woman’s decision to seek maternity care; partners, female relatives, and community health-workers were among the most influential in promoting negative views, all of which reduced a woman’s likelihood to access care [ 38 ].

Appropriateness-related barriers to care

Appropriateness marks the dimension of healthcare access that affects a patient’s ability to engage, and according to Levesque et al., is of relevance once all other dimensions (the ability to perceive, seek, reach and pay for) are achieved [ 2 ]. The ability to engage in healthcare is influenced by a patient’s level of empowerment, adherence to information, and support received by their healthcare provider. Thus, barriers to healthcare access that relate to appropriateness are often those that indicate a breakdown in communication between a patient with their healthcare provider. Such breakdown can involve a patient experiencing miscommunication, confrontation, and/or a discrepancy between their provider’s goals and their own goals for healthcare. Appropriateness represents a dimension of healthcare access that is widely acknowledged as an area in need of improvement, which indicates a need to rethink how healthcare providers and organizations can adapt to serve the healthcare needs of their communities [ 39 ]. This is especially true for rural, ethnic minority populations, which disproportionately experience an abundance of other barriers to healthcare access. Culturally appropriate care is especially important for members of minority populations [ 40 , 41 , 42 ]. Ultimately, patients value a patient-provider relationship characterized by a welcoming, non-judgmental atmosphere [ 43 , 44 ]. In rural settings especially, level of trust and familiarity are common factors that affect service utilization [ 45 ]. Evidence suggests that kind treatment by a healthcare provider who promotes patient-centered care can have a greater overall effect on a patient’s experience than a provider’s degree of medical knowledge or use of modern equipment [ 46 ]. Of course, investing the time needed to nurture close and caring interpersonal connections is particularly difficult in under-resourced, time-pressured rural health systems [ 47 , 48 ].

The most effective way to evaluate access to healthcare largely depends on which dimensions are explored. For instance, a population-based survey can be used to measure the barrier of healthcare affordability. Survey questions can inquire directly about health insurance coverage, care-related financial burden, concern about healthcare costs, and the feared financial impacts of illness and/or disability. Many national organizations have employed such surveys to measure affordability-related barriers to healthcare. For example, a question may ask explicitly about financial concerns: ‘If you get sick or have an accident, how worried are you that you will not be able to pay your medical bills?’ [ 49 ]. Approachability and availability dimensions of access are also studied using quantitative analysis of survey questions, such as ‘Is there a place that you usually go to when you are sick or need advice about your health?’ or ‘Have you ever delayed getting medical care because you couldn’t get through on the telephone?’ In contrast, the remaining two dimensions–acceptability and appropriateness–require a qualitative approach, as the social and cultural factors that determine a patient’s likelihood of accepting aspects of the services that are to be received (acceptability) and the fit between those services and the patient’s specific healthcare needs (appropriateness) can be more abstract [ 50 , 51 ]. In social science, qualitative methods are appropriate to generate knowledge of what social events mean to individuals and how those individuals interact within them; these methods allow for an exploration of depth rather than breadth [ 52 , 53 ]. Qualitative methods, therefore, are appropriate tools for understanding the depth of healthcare providers’ experiences in the inherently social context of seeking and engaging in healthcare.

In sum, acceptability- and appropriateness-related barriers to healthcare access are multi-layered, complex and abundant. Ensuring access becomes even more challenging if structural barriers to access are factored in. In this study, we aimed to explore barriers to healthcare access among persons in Montana, a historically underserved, under-resourced, rural region of the US. Montana is the fourth largest and third least densely populated state in the country; more than 80% of Montana counties are classified as non-core (the lowest level of urban/rural classification), and over 90% are designated as health professional shortage areas [ 54 , 55 ]. Qualitative methods supported our inquiry to explore barriers to healthcare access related to acceptability and appropriateness.

Participants

Qualitative methods were utilized for this interpretive, exploratory study because knowledge regarding barriers to healthcare access within Montana’s rural health systems is limited. We chose Montana healthcare providers, rather than patients, as the population of interest so we may explore barriers to healthcare access from the perspective of those who serve many persons in rural settings. Inclusion criteria required study participants to provide direct healthcare to patients at least one-half of their time. We defined ‘provider’ as a healthcare organization employee with clinical decision-making power and the qualifications to develop or revise patients’ treatment plans. In an attempt to capture a group of providers with diverse experience, we included providers across several types and specialties. These included advanced practice registered nurses (APRNs), physicians (MDs and DOs), and physician assistants (PAs) who worked in critical care medicine, emergency medicine, family medicine, hospital medicine, internal medicine, pain medicine, palliative medicine, pediatrics, psychiatry, and urgent care medicine. We also included licensed clinical social workers (LCSWs) and clinical psychologists who specialize in behavioral healthcare provision.

Recruitment and Data Collection

We recruited participants via email using a snowball sampling approach [ 56 ]. We opted for this approach because of its effectiveness in time-pressured contexts, such as the COVID-19 pandemic, which has made healthcare provider populations hard to reach [ 57 ]. Considering additional constraints with the pandemic and the rural nature of Montana, interviews were administered virtually via Zoom video or telephone conferencing with Zoom’s audio recording function enabled. All interviews were conducted by the first author between January and September 2021. The average length of interviews was 50 min, ranging from 35 to 70 min. There were occasional challenges experienced during interviews (poor cell phone reception from participants, dropped calls), in which case the interviewer remained on the line until adequate communication was resumed. All interviews were included for analysis and transcribed verbatim into NVivo Version 12 software. All qualitative data were saved and stored on a password-protected University of Montana server. Hard-copy field notes were securely stored in a locked office on the university’s main campus.

Data analysis included a deductive followed by an inductive approach. This dual analysis adheres to Levesque’s framework for qualitative methods, which is discussed in the Definition of Analytic Domains sub-section below. Original synthesis of the literature informed the development of our initial deductive codebook. The deductive approach was derived from a theory-driven hypothesis, which consisted of synthesizing previous research findings regarding acceptability- and appropriateness-related barriers to care. Although the locations, patient populations and specific type of healthcare services varied by study in the existing literature, several recurring barriers to healthcare access were identified. We then operationalized three analytic domains based on these findings: cultural considerations, patient-provider communication, and provider-provider communication. These domains were chosen for two reasons: 1) the terms ‘culture’ and ‘communication’ were the most frequently documented characteristics across the studies examined, and 2) they each align closely with the acceptability and appropriateness dimensions of access to healthcare, respectively. In addition, ‘culture’ is included in the definition of acceptability and ‘communication’ is a quintessential aspect of appropriateness. These domains guided the deductive portion of our analysis, which facilitated the development of an interview guide used for this study.

Interviews were semi-structured to allow broad interpretations from participants and expand the open-ended characterization of study findings. Data were analyzed through a flexible coding approach proposed by Deterding and Waters [ 58 ]. Qualitative content analysis was used, a method particularly beneficial for analyzing large amounts of qualitative data collected through interviews that offers possibility of quantifying categories to identify emerging themes [ 52 , 59 ]. After fifty percent of data were analyzed, we used an inductive approach as a formative check and repeated until data saturation, or the point at which no new information was gathered in interviews [ 60 ]. At each point of inductive analysis, interview questions were added, removed, or revised in consideration of findings gathered [ 61 ]. The Standards for Reporting Qualitative Research (SRQR) was used for reporting all qualitative data for this study [ 62 ]. The first and third authors served as primary and secondary analysts of the qualitative data and collaborated to triangulate these findings. An audit approach was employed, which consisted of coding completed by the first author and then reviewed by the third author. After analyses were complete, member checks ensured credibility and trustworthiness of findings [ 63 ]. Member checks consisted of contacting each study participant to explain the study’s findings; one-third of participants responded and confirmed all findings. All study procedures were reviewed and approved by the Human Subjects Committee of the authors’ institution’s Institutional Review Board.

Definitions of Analytic Domains

Cultural considerations.

Western health systems often fail to consider aspects of patients’ cultural perspectives and histories. This can manifest in the form of a providers’ lack of cultural humility. Cultural humility is a process of preventing imposition of one’s worldview and cultural beliefs on others and recognizing that everyone’s conception of the world is valid. Humility cultivates sensitive approaches in treating patients [ 64 ]. A lack of cultural humility impedes the delivery of acceptable and appropriate healthcare [ 65 ], which can involve low empathy or respect for patients, or dismissal of culture and traditions as superstitions that interfere with standard treatments [ 66 , 67 ]. Ensuring cultural humility among all healthcare employees is a step toward optimal healthcare delivery. Cultural humility is often accomplished through training that can be tailored to particular cultural- or gender-specific populations [ 68 , 69 ]. Since cultural identities and humility have been marked as factors that can heavily influence patients’ access to care, cultural considerations composed our first analytic domain. To assess this domain, we asked participants how they address the unique needs of their patients, how they react when they observe a cultural behavior or attitude from a patient that may not directly align with their treatment plan, and if they have received any multicultural training or training on cultural considerations in their current role.

Patient-provider communication

Other barriers to healthcare access can be linked to ineffective patient-provider communication. Patients who do not feel involved in healthcare decisions are less likely to adhere to treatment recommendations [ 70 ]. Patients who experience communication difficulties with providers may feel coerced, which generates disempowerment and leads patients to employ more covert ways of engagement [ 71 , 72 ]. Language barriers can further compromise communication and hinder outcomes or patient progress [ 73 , 74 ]. Any miscommunication between a patient and provider can affect one’s access to healthcare, namely affecting appropriateness-related barriers. For these reasons, patient-provider communication composed our second analytic domain. We asked participants to highlight the challenges they experience when communicating with their patients, how those complications are addressed, and how communication strategies inform confidentiality in their practice. Confidentiality is a core ethical principle in healthcare, especially in rural areas that have smaller, interconnected patient populations [ 75 ].

Provider-Provider Communication

A patient’s journey through the healthcare system necessitates sufficient correspondence between patients, primary, and secondary providers after discharge and care encounters [ 76 ]. Inter-provider and patient-provider communication are areas of healthcare that are acknowledged to have some gaps. Inconsistent mechanisms for follow up communication with patients in primary care have been documented and emphasized as a concern among those with chronic illness who require close monitoring [ 68 , 77 ]. Similar inconsistencies exist between providers, which can lead to unclear care goals, extended hospital stays, and increased medical costs [ 78 ]. For these reasons, provider-provider communication composed our third analytic domain. We asked participants to describe the approaches they take to streamline communication after a patient’s hospital visit, the methods they use to ensure collaborative communication between primary or secondary providers, and where communication challenges exist.

Healthcare provider characteristics

Our sample included 12 providers: four in family medicine (1 MD, 1 DO, 1 PA & 1 APRN), three in pediatrics (2 MD with specialty in hospital medicine & 1 DO), three in palliative medicine (2 MDs & 1 APRN with specialty in wound care), one in critical care medicine (DO with specialty in pediatric pulmonology) and one in behavioral health (1 LCSW with specialty in trauma). Our participants averaged 9 years (range 2–15) as a healthcare provider; most reported more than 5 years in their current professional role. The diversity of participants extended to their patient populations as well, with each participant reporting a unique distribution of age, race and level of medical complexity among their patients. Most participants reported that a portion of their patients travel up to five hours, sometimes across county- or state-lines, to receive care.

Theme 1: A friction exists between aspects of patients’ rural identities and healthcare systems

Our participants comprised a collection of medical professions and reported variability among health-related reasons their patients seek care. However, most participants acknowledged similar characteristics that influence their patients’ challenges to healthcare access. These identified factors formed categories from which the first theme emerged. There exists a great deal of ‘rugged individualism’ among Montanans, which reflects a self-sufficient and self-reliant way of life. Stoicism marked a primary factor to characterize this quality. One participant explained:

True Montanans are difficult to treat medically because they tend to be a tough group. They don’t see doctors. They don’t want to go, and they don’t want to be sick. That’s an aspect of Montana that makes health culture a little bit difficult.

Another participant echoed this finding by stating:

The backwoods Montana range guy who has an identity of being strong and independent probably doesn’t seek out a lot of medical care or take a lot of medications. Their sense of vitality, independence and identity really come from being able to take care and rely on themselves. When that is threatened, that’s going to create a unique experience of illness.

Similar responses were shared by all twelve participants; stoicism seemed to be heavily embedded in many patient populations in Montana and serves as a key determinant of healthcare acceptability. There are additional factors, however, that may interact with stoicism but are multiply determined. Stigma is an example of this, presented in this context as one’s concern about judgement by the healthcare system. Respondents were openly critical of this perception of the healthcare system as it was widely discussed in interviews. One participant stated:

There is a real perception of a punitive nature in the medical community, particularly if I observe a health issue other than the primary reason for one’s hospital visit, whether that may be predicated on medical neglect, delay of care, or something that may warrant a report to social services. For many of the patients and families I see, it’s not a positive experience and one that is sometimes an uphill barrier that I work hard to circumnavigate.

Analysis of these factors suggest that low use of healthcare services may link to several characteristics, including access problems. Separately, a patient’s perceived stigma from healthcare providers may also impact a patient’s willingness to receive services. One participant put it best by stating

Sometimes, families assume that I didn’t want to see them because they will come in for follow up to meet with me but end up meeting with another provider, which is frustrating because I want to maintain patients on my panel but available time and resource occasionally limits me from doing so. It could be really hard adapting to those needs on the fly, but it’s an honest miss.

When a patient arrives for a healthcare visit and experiences this frustration, it may elicit a patient’s perceptions of neglect or disorganization. This ‘honest miss’ may, in turn, exacerbate other acceptable-related barriers to care.

Theme 2: Facilitating access to healthcare requires application of and respect for cultural differences

The biomedical model is the standard of care utilized in Western medicine [ 79 , 80 ]. However, the US comprises people with diverse social and cultural identities that may not directly align with Western conceptions of health and wellness. Approximately 11.5% of the Montana population falls within an ethnic minority group. 6.4% are of American Indian or Alaska Native origin, 0.5% are of Black or African American origin, 0.8% are of Asian origin and 3.8% are of multiple or other origins. [ 81 ]. Cultural insensitivity is acknowledged in health services research as an active deterrent for appropriate healthcare delivery [ 65 ]. Participants for this study were asked how they react when a patient brings up a cultural attitude or behavior that may impact the proposed treatment plan. Eight participants noted a necessity for humility when this occurs. One participant conceptualized this by stating:

When this happens, I learn about individuals and a way of life that is different to the way I grew up. There is a lot of beauty and health in a non-patriarchal, non-dominating, non-sexist framework, and when we can engage in such, it is really expansive for my own learning process.

The participants who expressed humility emphasized that it is best to work in tandem with their patient, congruently. Especially for those with contrasting worldviews, a provider and a patient working as a team poses an opportunity to develop trust. Without it, a patient can easily fall out of the system, further hindering their ability to access healthcare services in the future. One participant stated:

The approach that ends up being successful for a lot of patients is when we understand their modalities, and they have a sense we understand those things. We have to show understanding and they have to trust. From there, we can make recommendations to help get them there, not decisions for them to obey, rather views based on our experiences and understanding of medicine.

Curiosity was another reaction noted by a handful of participants. One participant said:

I believe patients and their caregivers can be engaged and loving in different ways that don’t always follow the prescribed approach in the ways I’ve been trained, but that doesn’t necessarily mean that they are detrimental. I love what I do, and I love learning new things or new approaches, but I also love being surprised. My style of medicine is not to predict peoples’ lives, rather to empower and support what makes life meaningful for them.

Participants mentioned several other characteristics that they use in practice to prevent cultural insensitivity and support a collaborative approach to healthcare. Table 1 lists these facilitating characteristics and quotes to explain the substance of their benefit.

Consensus among participants indicated that the use of these protective factors to promote cultural sensitivity and apply them in practice is not standardized. When asked, all but two participants said they had not received any culturally-based training since beginning their practice. Instead, they referred to developing skills through “on the job training” or “off the cuff learning.” The general way of medicine, one participant remarked, was to “throw you to the fire.” This suggested that use of standardized cultural humility training modules for healthcare providers was not common practice. Many attributed this to time constraints.

Individual efforts to gain culturally appropriate skills or enhance cultural humility were mentioned, however. For example, three participants reported that they attended medical conferences to discuss cultural challenges within medicine, one participant sought out cultural education within their organization, and another was invited by Native American community members to engage in traditional peace ceremonies. Participants described these additional efforts as uncommon and outside the parameters of a provider’s job responsibilities, as they require time commitments without compensation.

Additionally, eight participants said they share their personal contact information with patients so they may call them directly for medical needs. The conditions and frequency with which this is done was variable and more common among providers in specialized areas of medicine or those who described having a manageable patient panel. All who reported that they shared their personal contact information described it as an aspect of rural health service delivery that is atypical in other, non-rural healthcare systems.

Theme 3: Communication between healthcare providers is systematically fragmented

Healthcare is complex and multi-disciplinary, and patients’ treatment is rarely overseen by a single provider [ 82 ]. The array of provider types and specialties is vast, as is the range of responsibilities ascribed to providers. Thus, open communication among providers both within and between healthcare systems is vital for the success of collaborative healthcare [ 83 ]. Without effective communication achieved between healthcare providers, the appropriate delivery of healthcare services may be become compromised. Our participants noted that they face multiple challenges that complicate communication with other providers. Miscommunication between departments, often implicating the Emergency Department (ED), was a recurring point noted among participants. One participant who is a primary care physician said:

If one of my patients goes to the ER, I don’t always get the notes. They’re supposed to send them to the patient’s primary care doc. The same thing happens with general admissions, but again, I often find out from somebody else that my patient was admitted to the hospital.

This failure to communicate can negatively impact the patient, particularly if time sensitivity or medical complexity is essential to treatment. A patient’s primary care physician is the most accurate source of their medical history; without an effective way to obtain and synthesize a patient’s health information, there may be increased risk of medical error. One participant in a specialty field stated:

One of the biggest barriers I see is obtaining a concise description of a patient’s history and needs. You can imagine if you’re a mom and you’ve got a complicated kid. You head to the ER. The ER doc looks at you with really wide eyes, not knowing how to get information about your child that’s really important.

This concern was highlighted with a specific example from a different participant:

I have been unable to troubleshoot instances when I send people to the ER with a pretty clear indication for admission, and then they’re sent home. For instance, I had an older fellow with pretty severe chronic kidney disease. He presented to another practitioner in my office with shortness of breath and swelling and appeared to have newly onset decompensated heart failure. When I figured this out, I sent him to the ER, called and gave my report. The patient later came back for follow up to find out not only that they had not been admitted but they lost no weight with outpatient dialysis . I feel like a real opportunity was missed to try to optimize the care of the patient simply because there was poor communication between myself and the ER. This poor guy… He ended up going to the ER four times before he got admitted for COVID-19.

In some cases, communication breakdown was reported as the sole cause of a poor outcome. When communication is effective, each essential member of the healthcare team is engaged and collaborating with the same information. Some participants called this process ‘rounds’ when a regularly scheduled meeting is staged between a group of providers to ensure access to accurate patient information. Accurate communication may also help build trust and improve a patient’s experience. In contrast, ineffective communication can result in poor clarity regarding providers’ responsibilities or lost information. Appropriate delivery of healthcare considers the fit between providers and a patient’s specific healthcare needs; the factors noted here suggest that provider-provider miscommunication can adversely affect this dimension of healthcare access.

Another important mechanism of communication is the sharing of electronic medical records (EMRs), a process that continues to shift with technological advances. Innovation is still recent enough, however, for several of our study participants to be able to recall a time when paper charts were standard. Widespread adoption and embrace of the improvements inherent in electronic medical records expanded in the late 2000’s [ 84 ]. EMRs vastly improved the ability to retain, organize, safeguard, and transfer health information. Every participant highlighted EMRs at one point or another and often did so with an underlying sense of anger or frustration. Systematic issues and problems with EMRs were discussed. One participant provided historical context to such records:

Years back, the government aimed to buy an electronic medical record system, whichever was the best, and a number of companies created their own. Each were a reasonable system, so they all got their checks and now we have four completely separate operating systems that do not talk to each other. The idea was to make a router or some type of relay that can share information back and forth. There was no money in that though, so of course, no one did anything about it. Depending on what hospital, clinic or agency you work for, you will most likely work within one of these systems. It was a great idea; it just didn’t get finished.

Seven participants confirmed these points and their impacts on making coordination more difficult, relying on outdated communication strategies more often than not. Many noted this even occurs between facilities within the same city and in separate small metropolitan areas across the state. One participant said:

If my hospital decides to contract with one EMR and the hospital across town contracts with another, correspondence between these hospitals goes back to traditional faxing. As a provider, you’re just taking a ‘fingered crossed’ approach hoping that the fax worked, is picked up, was put in the appropriate inbox and was actually looked at. Information acquisition and making sure it’s timely are unforeseen between EMRs.

Participants reported an “astronomic” number of daily faxes and telephone calls to complete the communication EMRs were initially designed to handle. These challenges are even more burdensome if a patient moves from out of town or out of state; obtaining their medical records was repeatedly referred to as a “chore” so onerous that it often remains undone. Another recurring concern brought up by participants regarded accuracy within EMRs to lend a false sense of security. They are not frequently updated, not designed to be family-centered and not set up to do anything automatically. One participant highlighted these limitations by stating:

I was very proud of a change I made in our EMR system [EPIC], even though it was one I never should have had to make. I was getting very upset because I would find out from my nursing assistant who read the obituary that one of my patients had died. There was a real problem with the way the EMR was notifying PCP’s, so I got an EPIC-level automated notification built into our EMR so that any time a patient died, their status would be changed to deceased and a notification would be sent to their PCP. It’s just really awful to find out a week later that your patient died, especially when you know these people and their families really well. It’s not good care to have blind follow up.

Whether it be a physical or electronic miscommunication between healthcare providers, the appropriate delivery of healthcare can be called to question

Theme 4: Time and resource constraints disproportionately harm rural health systems

Several measures of system capacity suggest the healthcare system in the US is under-resourced. There are fewer physicians and hospital beds per capita compared to most comparable countries, and the growth of healthcare provider populations has stagnated over time [ 15 ]. Rural areas, in particular, are subject to resource limitations [ 16 ]. All participants discussed provider shortages in detail. They described how shortages impact time allocation in their day-to-day operations. Tasks like patient intakes, critical assessments, and recovering information from EMRs take time, of which most participants claimed to not have enough of. There was also a consensus in having inadequate time to spend on medically complex cases. Time pressures were reported to subsequently influence quality of care. One participant stated:

With the constant pace of medicine, time is not on your side. A provider cannot always participate in an enriching dialogue with their patients, so rather than listen and learn, we are often coerced into the mindset of ‘getting through’ this patient so we can move on. This echoes for patient education during discharge, making the whole process more arduous than it otherwise could be if time and resources were not as sparse.

Depending on provider type, specialty, and the size of patient panels, four participants said they have the luxury of extending patient visits to 40 + minutes. Any flexibility with patient visits was regarded as just that: a luxury. Very few providers described the ability to coordinate their schedules as such. This led some study participants to limit the number of patients they serve. One participant said:

We simply don’t have enough clinicians, which is a shame because these people are really skilled, exceptional, brilliant providers but are performing way below their capacity. Because of this, I have a smaller case load so I can engage in a level of care that I feel is in the best interest of my patients. Everything is a tradeoff. Time has to be sacrificed at one point or another. This compromise sets our system up to do ‘ok’ work, not great work.

Of course, managing an overly large number of patients with high complexity is challenging. Especially while enduring the burden of a persisting global pandemic, participants reflected that the general outlook of administering healthcare in the US is to “do more with less.” This often forces providers to delegate responsibilities, which participants noted has potential downsides. One participant described how delegating patient care can cause problems.

Very often will a patient schedule a follow up that needs to happen within a certain time frame, but I am unable to see them myself. So, they are then placed with one of my mid-level providers. However, if additional health issues are introduced, which often happens, there is a high-risk of bounce-back or need to return once again to the hospital. It’s an inefficient vetting process that falls to people who may not have specific training in the labs and imaging that are often included in follow up visits. Unfortunately, it’s a forlorn hope to have a primary care physician be able to attend all levels of a patient’s care.

Several participants described how time constraints stretch all healthcare staff thin and complicate patient care. This was particularly important among participants who reported having a patient panel exceeding 1000. There were some participants, however, who praised the relationships they have with their nurse practitioners and physician’s assistants and mark transparency as the most effective way to coordinate care. Collectively, these clinical relationships were built over long standing periods of time, a disadvantage to providers at the start of their medical career. All but one participant with over a decade of clinical experience mentioned the usefulness of these relationships. The factors discussed in Theme 4 are directly linked to the Availability dimension of access to healthcare. A patient’s ability to reach care is subject to the capacity of their healthcare provider(s). Additionally, further analysis suggests these factors also link to the Appropriateness dimension because the quality of patient-provider relationships may be negatively impacted if a provider’s time is compromised.

Theme 5: Profits are prioritized over addressing barriers to healthcare access in the US.

The US healthcare system functions partially for-profit in the public and private sectors. The federal government provides funding for national programs such as Medicare, but a majority of Americans access healthcare through private employer plans [ 85 ]. As a result, uninsurance rates influence healthcare access. Though the rate of the uninsured has dropped over the last decade through expansion of the Affordable Care Act, it remains above 8 percent [ 86 ]. Historically, there has been ethical criticism in the literature of a for-profit system as it is said to exacerbate healthcare disparities and constitute unfair competition against nonprofit institutions. Specifically, the US healthcare system treats healthcare as a commodity instead of a right, enables organizational controls that adversely affect patient-provider relationships, undermines medical education, and constitutes a medical-industrial complex that threatens influence on healthcare-related public policy [ 87 ]. Though unprompted by the interviewer, participants raised many of these concerns. One participant shared their views on how priorities stand in their practice:

A lot of the higher-ups in the healthcare system where I work see each patient visit as a number. It’s not that they don’t have the capacity to think beyond that, but that’s what their role is, making sure we’re profitable. That’s part of why our healthcare system in the US is as broken as it is. It’s accentuated focus on financially and capitalistically driven factors versus understanding all these other barriers to care.

Eight participants echoed a similar concept, that addressing barriers to healthcare access in their organizations is largely complicated because so much attention is directed on matters that have nothing to do with patients. A few other participants supported this by alluding to a “cherry-picking” process by which those at the top of the hierarchy devote their attention to the easiest tasks. One participant shared an experience where contrasting work demands between administrators and front-line clinical providers produces adverse effects:

We had a new administrator in our hospital. I had been really frustrated with the lack of cultural awareness and curiosity from our other leaders in the past, so I offered to meet and take them on a tour of the reservation. This was meant to introduce them to kids, families and Tribal leaders who live in the area and their interface with healthcare. They declined, which I thought was disappointing and eye-opening.

Analysis of these factors suggest that those who work directly with patients understand patient needs better than those who serve in management roles. This same participant went on to suggest an ulterior motive for a push towards telemedicine, as administrators primarily highlight the benefit of billing for virtual visits instead of the nature of the visits themselves.

This study explored barriers and facilitators to healthcare access from the perspective of rural healthcare providers in Montana. Our qualitative analysis uncovered five key themes: 1) a friction exists between aspects of patients’ rural identities and healthcare systems; 2) facilitating access to healthcare requires application of and respect for cultural differences; 3) communication between healthcare providers is systematically fragmented; 4) time and resource constraints disproportionately harm rural health systems; and 5) profits are prioritized over addressing barriers to healthcare access in the US. Themes 2 and 3 were directly supported by earlier qualitative studies that applied Levesque’s framework, specifically regarding healthcare providers’ poor interpersonal quality and lack of collaboration with other providers that are suspected to result from a lack of provider training [ 67 , 70 ]. This ties back to the importance of cultural humility, which many previous culture-based trainings have referred to as cultural competence. Cultural competence is achieved through a plethora of trainings designed to expose providers to different cultures’ beliefs and values but induces risk of stereotyping and stigmatizing a patient’s views. Therefore, cultural humility is the preferred idea, by which providers reflect and gain open-ended appreciation for a patient’s culture [ 88 ].

Implications for Practice

Perhaps the most substantial takeaway is how embedded rugged individualism is within rural patient populations and how difficult that makes the delivery of care in rural health systems. We heard from participants that stoicism and perceptions of stigma within the system contribute to this, but other resulting factors may be influential at the provider- and organizational-levels. Stoicism and perceived stigma both appear to arise, in part, from an understandable knowledge gap regarding the care system. For instance, healthcare providers understand the relations between primary and secondary care, but many patients may perceive both concepts as elements of a single healthcare system [ 89 ]. Any issue experienced by a patient when tasked to see both a primary and secondary provider may result in a patient becoming confused [ 90 ]. This may also overlap with our third theme, as a disjointed means of communication between healthcare providers can exacerbate patients’ negative experiences. One consideration to improve this is to incorporate telehealth programs into an existing referral framework to reduce unnecessary interfacility transfers; telehealth programs have proven effective in rural and remote settings [ 91 ].

In fact, telehealth has been rolled out in a variety of virtual platforms throughout its evolution, its innovation matched with continued technological advancement. Simply put, telehealth allows health service delivery from a distance; it allows knowledge and practice of clinical care to be in a different space than a patient. Because of this, a primary benefit of telehealth is its impact on improving patient-centered outcomes among those living in rural areas. For instance, text messaging technology improves early infant diagnosis, adherence to recommended diagnostic testing, and participant engagement in lifestyle change interventions [ 92 , 93 , 94 ]. More sophisticated interventions have found their way into smartphone-based technology, some of which are accessible even without an internet connection [ 95 , 96 ]. Internet accessibility is important because a number of study participants noted internet connectivity as a barrier for patients who live in low resource communities. Videoconferencing is another function of telehealth that has delivered a variety of health services, including those which are mental health-specific [ 97 ], and mobile health clinics have been used in rural, hard-to-reach settings to show the delivery of quality healthcare is both feasible and acceptable [ 98 , 99 , 100 ]. While telehealth has potential to reduce a number of healthcare access barriers, it may not always address the most pressing healthcare needs [ 101 ]. However, telehealth does serve as a viable, cost-effective alternative for rural populations with limited physical access to specialized services [ 102 ]. With time and resource limitations acknowledged as a key theme in our study, an emphasis on expanding telehealth services is encouraged as it will likely have significant involvement on advancing healthcare in the future, especially as the COVID-19 pandemic persists [ 103 ].

Implications for Policy

One could argue that most of the areas of fragmentation in the US healthcare system can be linked to the very philosophy on which it is based: an emphasis on profits as highest priority. Americans are, therefore, forced to navigate a health service system that does not work solely in their best interests. It is not surprising to observe lower rates of healthcare usage in rural areas, which may be a result from rural persons’ negative views of the US healthcare system or a perception that the system does not exist to support wellness. These perceptions may interact with ‘rugged individualism’ to squelch rural residents’ engagement in healthcare. Many of the providers we interviewed for this study appeared to understand this and strived to improve their patients’ experiences and outcomes. Though these efforts are admirable, they may not characterize all providers who serve in rural areas of the US. From a policy standpoint, it is important to recognize these expansive efforts from providers. If incentives were offered to encourage maximum efforts be made, it may lessen burden due to physician burnout and fatigue. Of course, there is no easy fix to the persisting limit of time and resources for providers, problems that require workforce expansion. Ultimately, though, the current structure of the US healthcare system is failing rural America and doing little to help the practice of rural healthcare providers.

Implications for Future Research

It is important for future health systems research efforts to consider issues that arise from both individual- and system-level access barriers and where the two intersect. Oftentimes, challenges that appear linked to a patient or provider may actually stem from an overarching system failure. If failures are critically and properly addressed, we may refine our understanding of what we can do in our professional spaces to improve care as practitioners, workforce developers, researchers and advocates. This qualitative study was exploratory in nature. It represents a step forward in knowledge generation regarding challenges in access to healthcare for rural Americans. Although mental health did not come up by design in this study, future efforts exploring barriers to healthcare access in rural systems should focus on access to mental healthcare. In many rural areas, Montana included, rates of suicide, substance use and other mental health disorders are highly prevalent. These characteristics should be part of the overall discussion of access to healthcare in rural areas. Optimally, barriers to healthcare access should continue to be explored through qualitative and mixed study designs to honor its multi-dimensional stature.

Strengths and Limitations

It is important to note first that this study interviewed healthcare providers instead of patients, which served as both a strength and limitation. Healthcare providers were able to draw on numerous patient-provider experiences, enabling an account of the aggregate which would have been impossible for a patient population. However, accounts of healthcare providers’ perceptions of barriers to healthcare access for their patients may differ from patients’ specific views. Future research should examine acceptability- and appropriateness-related barriers to healthcare access in patient populations. Second, study participants were recruited through convenience sampling methods, so results may be biased towards healthcare providers who are more invested in addressing barriers to healthcare access. Particularly, the providers interviewed for this study represented a subset who go beyond expectations of their job descriptions by engaging with their communities and spending additional uncompensated time with their patients. It is likely that a provider who exhibits these behavioral traits is more likely to participate in research aimed at addressing barriers to healthcare access. Third, the inability to conduct face-to-face interviews for our qualitative study may have posed an additional limitation. It is possible, for example, that in-person interviews might have resulted in increased rapport with study participants. Notwithstanding this possibility, the remote interview format was necessary to accommodate health risks to the ongoing COVID-19 pandemic. Ultimately, given our qualitative approach, results from our study cannot be generalizable to all rural providers’ views or other rural health systems. In addition, no causality can be inferred regarding the influence of aspects of rurality on access. The purpose of this exploratory qualitative study was to probe research questions for future efforts. We also acknowledge the authors’ roles in the research, also known as reflexivity. The first author was the only author who administered interviews and had no prior relationships with all but one study participant. Assumptions and pre-dispositions to interview content by the first author were regularly addressed throughout data analysis to maintain study integrity. This was achieved by conducting analysis by unique interview question, rather than by unique participant, and recoding the numerical order of participants for each question. Our commitment to rigorous qualitative methods was a strength for the study for multiple reasons. Conducting member checks with participants ensured trustworthiness of findings. Continuing data collection to data saturation ensured dependability of findings, which was achieved after 10 interviews and confirmed after 2 additional interviews. We further recognize the heterogeneity in our sample of participants, which helped generate variability in responses. To remain consistent with appropriate means of presenting results in qualitative research however, we shared minimal demographic information about our study participants to ensure confidentiality.

The divide between urban and rural health stretches beyond a disproportionate allocation of resources. Rural health systems serve a more complicated and hard-to-reach patient population. They lack sufficient numbers of providers to meet population health needs. These disparities impact collaboration between patients and providers as well as the delivery of acceptable and appropriate healthcare. The marker of rurality complicates the already cumbersome challenge of administering acceptable and appropriate healthcare and impediments stemming from rurality require continued monitoring to improve patient experiences and outcomes. Our qualitative study explored rural healthcare providers’ views on some of the social, cultural, and programmatic factors that influence access to healthcare among their patient populations. We identified five key themes: 1) a friction exists between aspects of patients’ rural identities and healthcare systems; 2) facilitating access to healthcare requires application of and respect for cultural differences; 3) communication between healthcare providers is systematically fragmented; 4) time and resource constraints disproportionately harm rural health systems; and 5) profits are prioritized over addressing barriers to healthcare access in the US. This study provides implications that may shift the landscape of a healthcare provider’s approach to delivering healthcare. Further exploration is required to understand the effects these characteristics have on measurable patient-centered outcomes in rural areas.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to individual privacy could be compromised but are available from the corresponding author on reasonable request.

Ethics approval and consent to participate.

All study procedures and methods were carried out in accordance with relevant guidelines and regulations from the World Medical Association Declaration of Helsinki. Ethics approval was given by exempt review from the Institutional Review Board (IRB) at the University of Montana (IRB Protocol No.: 186–20). Participants received oral and written information about the study prior to interview, which allowed them to provide informed consent for the interviews to be recorded and used for qualitative research purposes. No ethical concerns were experienced in this study pertaining to human subjects.

Consent for publication.

The participants consented to the publication of de-identified material from the interviews.

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Acknowledgements

This research was supported by the Center for Biomedical Research Excellence award (P20GM130418) from the National Institute of General Medical Sciences of the National Institute of Health. The first author was also supported by the University of Montana Burnham Population Health Fellowship. We would like to thank Dr. Christopher Dietrich, Dr. Jennifer Robohm and Dr. Eric Arzubi for their contributions on determining inclusion criteria for the healthcare provider population used for this study.

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

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The authors confirm contribution to the paper as follows: study conception and design: NC and JC; data collection: NC; analysis and interpretation of results: NC and JC; draft manuscript preparation: NC, DC and JC; and manuscript editing: NC, DC and JC. All authors reviewed the results and approved the final version of the manuscript.

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Coombs, N.C., Campbell, D.G. & Caringi, J. A qualitative study of rural healthcare providers’ views of social, cultural, and programmatic barriers to healthcare access. BMC Health Serv Res 22 , 438 (2022). https://doi.org/10.1186/s12913-022-07829-2

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Towards universal health coverage in Vietnam: a mixed-method case study of enrolling people with tuberculosis into social health insurance

• Rachel Forse   ORCID: orcid.org/0000-0002-0716-3342 1 , 2 ,
• Clara Akie Yoshino 2 ,
• Thanh Thi Nguyen 1 ,
• Thi Hoang Yen Phan 3 ,
• Luan N. Q. Vo 1 , 2 ,
• Andrew J. Codlin 1 , 2 ,
• Lan Nguyen 4 ,
• Chi Hoang 4 ,
• Lopa Basu 5 ,
• Minh Pham 5 ,
• Hoa Binh Nguyen 6 ,
• Luong Van Dinh 6 ,
• Maxine Caws 7 , 8 ,
• Tom Wingfield 2 , 7 ,
• Knut Lönnroth 2 &
• Kristi Sidney-Annerstedt 2  

Health Research Policy and Systems volume  22 , Article number:  40 ( 2024 ) Cite this article

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Vietnam’s primary mechanism of achieving sustainable funding for universal health coverage (UHC) and financial protection has been through its social health insurance (SHI) scheme. Steady progress towards access has been made and by 2020, over 90% of the population were enrolled in SHI. In 2022, as part of a larger transition towards the increased domestic financing of healthcare, tuberculosis (TB) services were integrated into SHI. This change required people with TB to use SHI for treatment at district-level facilities or to pay out of pocket for services. This study was conducted in preparation for this transition. It aimed to understand more about uninsured people with TB, assess the feasibility of enrolling them into SHI, and identify the barriers they faced in this process.

A mixed-method case study was conducted using a convergent parallel design between November 2018 and January 2022 in ten districts of Hanoi and Ho Chi Minh City, Vietnam. Quantitative data were collected through a pilot intervention that aimed to facilitate SHI enrollment for uninsured individuals with TB. Descriptive statistics were calculated. Qualitative interviews were conducted with 34 participants, who were purposively sampled for maximum variation. Qualitative data were analyzed through an inductive approach and themes were identified through framework analysis. Quantitative and qualitative data sources were triangulated.

We attempted to enroll 115 uninsured people with TB into SHI; 76.5% were able to enroll. On average, it took 34.5 days to obtain a SHI card and it cost USD 66 per household. The themes indicated that a lack of knowledge, high costs for annual premiums, and the household-based registration requirement were barriers to SHI enrollment. Participants indicated that alternative enrolment mechanisms and greater procedural flexibility, particularly for undocumented people, is required to achieve full population coverage with SHI in urban centers.

Conclusions

Significant addressable barriers to SHI enrolment for people affected by TB were identified. A quarter of individuals remained unable to enroll after receiving enhanced support due to lack of required documentation. The experience gained during this health financing transition is relevant for other middle-income countries as they address the provision of financial protection for the treatment of infectious diseases.

Peer Review reports

Contributing to universal health coverage (UHC) by improving access to fair and sustainable health financing, of which one mechanism is health insurance, has become a priority among low- and middle-income countries [ 1 , 2 ]. Many countries in the Asia Pacific region have made steady progress towards UHC coverage through sustained political commitments and fiscal policy aligned with their commitment [ 3 ]. By 2020, 27 countries had implemented a social health insurance (SHI) financing mechanism, which typically includes open enrollment for the full population along with partial or full subsidization of healthcare costs for vulnerable groups [ 4 ].

Vietnam’s first SHI scheme was piloted in 1989 and grew through successive pilots and expansions. In 2009 the national-level Health Insurance Law (HIL) went into effect, uniting the existing health insurance programs and schemes for the poor [ 5 ]. Amendments to the HIL effective in 2015 made SHI compulsory for all and pooled risk by re-structuring registration around the household unit [ 4 ]. A household in Vietnam is defined by inclusion in the ‘family book ’, the national system of family and address registration [ 6 ].

Access to SHI in Vietnam increased rapidly, principally through subsidization of premiums. Specific groups were enrolled automatically with full subsidy, including vulnerable populations (e.g., households classified as ‘poor’, children aged < 6, people aged > 80), pensioners and meritorious groups (e.g., veterans). Partial premium subsidization was also available for students, households classified as ‘near-poor’ and some farmers [ 7 ]. More than half of SHI members are entitled to 80% coverage with a 20% co-payment for services [ 8 ]. However, co-payments are reduced to 5% or are eliminated for subsidized groups (e.g., households classified as ‘poor’ and ‘near-poor’, children < 6) [ 4 ].

By 2020, Vietnam recorded a 91% national SHI coverage rate [ 7 ]. Those remaining uninsured mainly consisted of informally employed individuals [ 7 ]. Enrollment rates were highest among low- and high-income groups, leaving the so-called “missing middle” of uninsured [ 5 ].

Vietnam continues to transition to domestic financing of healthcare from donor financing by expanding the breadth of the national SHI. The Ministry of Health and Vietnam Social Security (VSS) have begun to close service gaps and integrate vertical health programs (e.g., those with stand-alone budget allocations and/or direct donor financing) into SHI financing [ 7 ]. The costs for antiretroviral therapy (ART) were transitioned from donor funding to SHI in 2019 [ 9 ], COVID-19 treatments were covered by SHI in 2020, and financing for tuberculosis (TB) care was fully transitioned to SHI in 2022 [ 7 ].

Until this financing transition, anti-TB medications and consultations were provided free of charge in the public sector, funded by a mixture of domestic and international funding [ 10 ]. While first-line TB medications were included in the SHI-reimbursable list of essential medicines, the government network of District TB Units (DTUs) were ineligible for registration with VSS, or reimbursement for services provided. Since July 2022, TB health facilities that met certain conditions could register with VSS and receive reimbursements for TB consultations, diagnostics and anti-TB medications [ 11 ]. The financing for drug-resistant (DR-)TB tests and medications remains largely unchanged, co-financed by the Global Fund and domestic budgets [ 12 ].

This transition of the TB financing model in Vietnam is a large undertaking as the country has the world’s 10th highest TB burden and the SHI benefits package is already considered to be generous, and the sustainability of the SHI fund is a concern [ 4 , 13 ] An estimated 169,000 individuals developed TB in 2021, and the disease killed approximately 14,200 [ 14 ]. A national costing survey of TB-affected households showed that 63% experienced catastrophic costs, spending ≥ 20% of their annual income on TB [ 10 ]. Many face food insecurity and cope with TB-related costs by taking loans, dissavings and informally borrowing money [ 10 , 15 , 16 ].

As Vietnam continues to expand SHI financing for the TB program, it is now vital for people with TB to have SHI. Those without SHI coverage will need to finance their care out of pocket (OOP) or purchase SHI and make co-payments for their care to be subsidized. For these reasons, it is important to understand why certain people with TB are uninsured, the feasibility of enrolling them in insurance when they begin treatment, and the challenges they may face with enrolling in SHI.

We conducted a convergent parallel mixed-method case study [ 17 ]. A case study was selected because it is well-suited to describe a complex issue in a real-life setting [ 18 ]. We used a naturalistic design with theoretical sampling of uninsured persons with TB using an interpretivist approach [ 19 ]. Mixed methods were selected to facilitate comparisons between quantitative and qualitative data and interpretation of the findings. An intervention, assisting TB-affected households to enroll in SHI, was conducted between November 2019 and January 2022, prior to the integration of the TB program into the SHI financing scheme. Quantitative data collection sought to answer questions regarding enrollment success rate, time to enrollment and cost of SHI enrollment for uninsured TB-affected households upon TB treatment initiation. The qualitative data explored barriers to SHI enrollment to explain and contextualize the quantitative findings. The quantitative and qualitative data were weighted equally [ 17 ].

Intervention description

A pilot intervention was conducted to facilitate SHI enrollment for people with TB in ten districts of Ha Noi and Ho Chi Minh City (HCMC). The standard process for first-time enrollment into SHI was mapped and costed from a household’s perspective (Additional file 1 ). Uninsured individuals were identified from the TB treatment register when they were enrolled in drug-susceptible (DS-)TB treatment at DTUs [ 20 ]. Study staff then attempted to facilitate enrollment of the person with TB and up to three household members into SHI.

SHI enrollment support included home visits by study staff to provide detailed information and counseling about the process of SHI enrollment, assistance with SHI application preparation including obtaining photocopies of all required documents, follow-up to obtain missing documentation within the household, accompaniment to the SHI office for application submission, and direct payment of the annual SHI premium for the household. For people who did not have the paperwork certifying temporary residence in Hanoi or Ho Chi Minh City, staff visited the local government office to obtain the information about the process for individual cases to obtain residency certificates and support participants with navigation of the bureaucracy. TB-affected people and their household members were also provided with a hotline number to call and receive support during working hours from the social workers who were employed by the study. Study staff attempted to facilitate the SHI enrollment process throughout the entire 6-month duration of DS-TB treatment. After a TB treatment outcome was recorded by the DTU, study staff stopped assisting with SHI enrollment and participants were recorded as ‘not enrolled in SHI’ in the study’s evaluation.

Quantitative methods

Case-level TB treatment notification data and SHI status were exported from VITIMES, the government-implemented electronic TB register for Vietnam, for all individuals who started TB treatment during the intervention period. The pilot intervention recruited participants from two TB treatment support projects (Project 1, n  = 59 and Project 2, n  = 56) [ 21 , 22 ] and tracked study forms housed in ONA.io. The sample size was determined by the availability of funding provided by the donor for treatment support service delivery, rather than to measure a specific end point of SHI enrollment. Descriptive statistics summarizing the enrollment cascade and turnaround time of enrollment were calculated using Stata v17 (Stata17 Corp, College Station, USA). To obtain the mean costs for household SHI enrollment, total direct costs for purchasing SHI were summed and divided by the total number of participants. Costs were captured in Vietnamese Dong (VND) and converted to United States Dollars (USD) using the exchange rate from the mid-point of the pilot intervention (1 June 2020) from OANDA.com.

Qualitative methods

Individuals were purposively sampled for maximum variation to ensure representation of all implementation areas and provide gender balance [ 23 ].The concept of information power guided the sample size [ 24 ]. Given the well-defined study aim, high quality in-depth responses from the participants and the authors’ expertise in the subject area, the sample size of 19 individual interviews and three focus group discussions was deemed appropriate. These were conducted in Ha Noi and HCMC. A total of 34 individuals participated in the interviews (Table  1 ).

They included 14 people enrolled in the pilot intervention, five community members who were non-beneficiaries of the treatment support intervention, 13 TB program staff from the national-, provincial- and district-levels and two study staff. Interviews were conducted at two time points: June 2019 and 2020. SHI enrollment barriers were collected as part of a qualitative study on the acceptability of providing cash transfers and SHI enrollment to adults with TB [ 25 ]. During the second round of interviews in 2020, study staff were included due to their in-depth knowledge of the challenges faced by TB-affected households when attempting to enroll in SHI and their ability to suggest programmatic-level solutions to these challenges. These interviews were conducted one-on-one, after the other interviews and focus groups had been conducted to reduce bias. The interviews were conducted at the National Lung Hospital, HCMC Provincial Lung Hospital, study office or DTUs. All interviews were conducted and transcribed in Vietnamese, translated into English, checked and finalized by a lead translator.

The interviews were analyzed through an inductive approach and themes were drawn through a framework analysis [ 26 ] to identify barriers to enrolling in SHI using Dedoose Version 7.0.23 (SocioCultural Research Consultants, Los Angeles, USA).

Data triangulation

Quantitative and qualitative data were collected in parallel. Triangulation of quantitative and qualitative data was conducted to synthesize findings and assess the level of agreement, convergence, and divergence from the findings generated by the different methods [ 17 ].

During the study, 5887 individuals were treated for DS-TB across the 10 intervention districts (Table  2 ). TB registers indicated that 2846 (48.3%) individuals were uninsured upon treatment initiation, or their SHI enrollment status was not recorded. Among 115 uninsured study participants, 88 (76.5%) were successfully enrolled in SHI before the end of their TB treatment. Among those, the household had an average of two members, resulting in a total of 206 individuals living in TB-affected households receiving SHI coverage through the pilot intervention.

The median time between DS-TB treatment initiation and SHI card issuance was 34.5 days (IQR 24–68): 11 days (IQR 5–23) between treatment initiation and pilot enrollment, 7 days (IQR 1–19.5) for SHI application preparation and submission, and 12 days (IQR 9–20) for application processing and SHI card provision.

The qualitative data showed that participants across all participant groups broadly understood that SHI is a system designed to prevent catastrophic OOP medical expenditure. As shown in Table  3 , National and provincial-level TB staff described SHI as a human right and spoke about achieving UHC as a nation; no other participant groups discussed SHI in this way. However, district-level doctors and intervention beneficiaries spoke in greater details about coverage and service gaps, and the practicalities of utilizing SHI. These participant groups expressed that when individuals purchase SHI only after a negative health event, such as a TB diagnosis, then the social safety net is unavailable to provide support until SHI coverage begins. Drawn from these views, the first theme indicated that the optimal time to purchase SHI is prior to a TB diagnosis.

One DTU staff member described how the standard processing time, or delays in processing SHI applications led to periods of high OOP expenditure:

“Unfortunately, claims are not immediately paid upon [SHI registration] submission. They may be handled in about 2 or 3 weeks, or even one month. That is why the insurance is not available at the time that they want to go for an examination and treat their condition using insurance.” (Female, District-level TB staff)

A complementary theme was that perceived lack of knowledge about SHI enrollment procedures prevents or delays enrollment. District-level TB doctors and program staff identified a lack of understanding and knowledge of the SHI enrollment process as a main contributor to lack of insurance or delays in obtaining coverage.

“Actually, for some people [with TB] who do not clearly understand the [enrollment] procedures… it will take a lot of time [to obtain SHI]. It also depends on the staff who handle the files at the commune; some staff are very enthusiastic and they help patients complete forms. There are cases [...] where they [people with TB] are required to fill in all information and write specific codes of each insurance card [from other family members] on a form. Meanwhile some people in their family work far from home and cannot send their insurance cards home in a timely manner.” (Female, program staff)

Participants tended to believe that individuals who lacked information about SHI made up the small minority of uninsured people in Vietnamese society. The above quote illustrated that the complicated administrative process prohibits enrollment; however, a factor potentially facilitating SHI enrollment may be the helpfulness of the person processing the SHI application.

The average cost per household to obtain SHI enrollment for one year (Table  2 ) was VND 1,503,313 (USD 65.52). (For detailed information on the costs of SHI enrollment, see Additional file 1 ). A third theme contextualized this finding and showed that SHI enrollment costs were perceived as prohibitively high for some. Cost was a greater challenge for lower income families, who did not meet the government’s criterion of households classified as ‘poor’ or ‘near-poor’, and were therefore ineligible for premium subsidies and SHI registration with lower co-payment rates. One DTU doctor reported that:

“We think that it is simple to buy health insurance cards, but that is only true for those who have sustainable income - when our income is much higher than the fee for buying health insurance. For some people, buying health insurance is a luxury.” (Male, District-level TB staff)

Twenty-seven people with TB (23.5%) were unable to obtain SHI coverage. The primary reason (70.4%) was missing documentation. In four instances (14.8%) a household member other than the person with TB refused to enroll in SHI. One individual (3.7%) died during the enrollment process. Three individuals (11.1%) did not enroll for other reasons.

SHI refusal by household members was not identified as a barrier to SHI enrollment in the qualitative data. However, a fourth theme confirmed the primary reason for non-enrollment by showing that some individuals do not possess the required documentation to obtain SHI, such as their identity card or ‘family book.’ [See Supplementary File] Even with six months of support from study staff, some TB-affected households were unable to gather the required documents for enrollment. The following quotation by an undocumented, elderly woman with TB illustrates the prolonged challenges she faced with obtaining formal employment, access to government services and SHI:

“I have had problems with my personal papers for a few decades and I cannot adjust my papers because I don’t have the money. […] I searched for my Identity Card and found out that I had lost it. Then I came back there [my hometown] to get the family book, to reissue my ID and to get my CV certified so I could join a company. I was very young at that time, just a little bit more than thirty years old, and I learned that I was cut from the family book.” (Female, pilot beneficiary)

To address challenges with documentation, one DTU officer in HCMC suggested that individuals who had never been insured required a change to the SHI registration requirements to ensure that everyone in Vietnam can access SHI:

“I think we should be flexible with these cases or we can find another way. Normally, the people who really need the support and the insurance or cash support, they are the people who have less information. […] We cannot have the same requirements for these people as for other people. Actually, for those who have [met] all conditions, they already have health insurance cards.” (Male, District-level TB staff)

Participants expressed that the uninsured had often not purchased SHI for a reason, and alternative registration procedures were needed to make SHI accessible for all. A fifth theme was identified indicating that current SHI enrollment procedures may prevent full population coverage.

Beyond the undocumented, some participants reported the enrollment mandate for the entire household (made under the Amendment to the HIL) for first-time enrollees was viewed as prohibitive of SHI coverage.

“Because in the old days, health insurance was sold individually for each person, but now it is sold to households, and many households do not have as good economic [situation]… so they can only afford to buy it for 50% or 60% of the household. Unskilled labor or low-income labor cannot afford to buy it for the whole family. That is to say, it is easier to buy it for each individual and it is difficult to buy for the whole family.” (Male, community member)

Though individual registration would make SHI more accessible to individuals with TB due to lower annual costs, household members with high vulnerability to TB would not be covered if policy promoted individual enrollment solely for TB.

This mixed-methods case study showed that by providing full subsidy and registration assistance, most uninsured people with TB could access SHI. However, the median time to insurance coverage meant that approximately 20% of a person’s DS-TB treatment duration remained uncovered by SHI despite successful enrollment. A substantial number of participants were unable to enroll in SHI and are likely to be perpetually locked out of SHI due to lack of personal documentation. Additional barriers to SHI enrollment were found to be lack of knowledge, the cost of obtaining coverage, and the household-based registration requirement.

The pilot intervention had dedicated staff who facilitated SHI application development and submission, yet it still took a median of 34.5 days for SHI coverage to take effect. In a context where this level of support is not available to all people with TB, it is likely that the turnaround time for SHI coverage is longer due to the complicated bureaucracy involved. This poses a major challenge, as TB-affected households incur the highest cost during the first two months of treatment [ 15 ]. One cost avoidance/mitigation strategy that people with a TB diagnosis may employ following the health financing transition is delaying TB treatment initiation until SHI coverage commences. This will likely lead to worse outcomes and sustained community transmission. The time between diagnosis and treatment should be rigorously monitored to ensure that this coping strategy is not employed, and alternative support should be made available to ensure that people diagnosed with TB are able to receive immediate treatment.

With the TB health financing transition, the uninsured will be asked to pay OOP for TB treatment and most insured individuals must co-pay for TB services which were previously provided free of cost. A national patient cost survey in 2018 found that 63% of TB-affected households experienced catastrophic costs under the previous health financing model [ 10 ]. There is a risk that the proportion of TB-affected households experiencing catastrophic costs could increase with the introduction of fees. This was not found to be the case for people living with HIV (PLHIV) when the costs of ART transitioned to SHI in Vietnam, but a new nationally representative TB costing survey is needed to assess this risk [ 9 ]. Several domestic solutions could ameliorate these challenges. As suggested for the Indian context, domestic revenues allocated by the Ministry of Finance to VSS could be increased to better support TB care [ 27 ]. VSS could also reclassify the category of TB disease and thus ensure that SHI paid for all diagnostics and drugs associated with TB treatment, without the need for a co-payment. A mid-term review of the Global Fund program in Vietnam has also called for a SHI package specifically designed to cover the OOP medical costs of TB care [ 28 ]. There are several potential mechanisms to prevent costs from falling on TB-affected households. A deeper investigation is needed to understand the fiscal space available within the Vietnamese government to cover such costs.

This case study showed that 23.5% of the uninsured people with TB were never able to enroll for the duration of their treatment, primarily due to lack of documentation. Specific provisions need to be made for the undocumented to receive free TB diagnosis, consultations, and medications through routine practice of the TB program. Multi- and bi-lateral funding mechanisms can also play a role in filling gaps by paying for TB tests for the uninsured, purchasing SHI for those diagnosed with TB, subsidizing or reimbursing OOP expenditure in the period before SHI coverage takes effect, and fully financing TB care for the undocumented. Furthermore, longer-term health system strengthening initiatives, such as creating a legal mechanism for the undocumented to obtain SHI, are likely needed to address the challenges faced by the 9% of the general population that remain uninsured. The ILO has called for “determining new strategies, which may include extension of state budget-funded subsidies to further support the participation of workers in the informal economy [ 7 ].” These forms of inclusive initiatives would solve the TB-specific challenges identified in this study and have a large positive impact on society.

We found that addressing the cost of SHI premiums and knowledge gaps in the enrollment procedures may improve SHI coverage. These findings mirror those following the transition of HIV financing to SHI in 2017. A study among PLHIV identified burdensome processes, lack of information about SHI registration procedures, and high SHI premium costs for a household as key barriers to SHI coverage [ 29 ]. However, a cluster randomized control trial which provided education, a 25% premium subsidy, or both to uninsured households found that these interventions had limited effects on SHI enrollment. Yet, “less healthy” individuals had higher SHI enrollment rates [ 30 ]. This suggests that people who have just received a TB diagnosis could be more receptive to interventions promoting SHI enrollment through premium subsidization and education. Vietnam’s National TB Program (NTP) has established a fund to subsidize SHI enrollment costs for TB-affected individuals. The size of the fund could be increased with additional support while access to the fund and the procedures for receiving support could be optimized [ 31 ]. Given the SHI transition, the NTP should also consider providing educational materials about the SHI enrollment process through the DTU network to uninsured persons with TB.

TB registers indicated that 52% of people starting TB treatment in the urban intervention districts had recorded SHI coverage. This rate is lower than other recent SHI coverage reports. A 2018–2022 DS-TB costing survey reported a SHI coverage of 70% [ 32 ], while in a DR-TB costing survey (2020–2022) it was 85% [ 16 ]. All available data sources indicate that SHI coverage among people with TB is lower than the general population, which is indicative of their socioeconomic vulnerability [ 33 ]. However, this large SHI coverage rate discrepancy may be explained by people with TB not revealing they had SHI coverage, or DTU staff could have also inconsistently recorded an individual’s SHI status in the paper TB registers since these data did not have much clinical relevance for TB treatment at the time. Now that DTUs receive financial reimbursements for the TB services from VSS, SHI coverage rates in treatment registers are likely to increase. Further research should be conducted to understand the national SHI coverage rate for people receiving TB treatment, along with the risk factors associated with being uninsured.

Limitations

This case study was conducted in the two largest cities of Vietnam and findings may not be representative of the entire country. Quantitative data were collected in a programmatic setting, and SHI coverage data for all individuals initiating TB treatment in the intervention areas appear to be underreported for reasons described above. Lastly, we were unable to collect SHI enrollment data from a control population, either prospectively during the pilot intervention or retrospectively during the pilot evaluation. As a result, we do not have information on the enrollment status or time to obtain SHI coverage among a population that did not receive assistance from the pilot intervention. However, given the substantial additional support provided by study staff for the enrollment process, we believe it is safe to assume that if left alone, TB-affected households would be slower in the enrollment process and likely enroll in lower rates.

Vietnam is viewed as a leader among Southeast Asian nations in its commitment and progress towards UHC. This mixed-methods case study illustrated the progress that Vietnam has made in its path to greater domestic financing of healthcare through SHI. This study is one of the first to examine the integration of TB services into SHI in Vietnam and define the challenges that people with TB face while attempting to gain access to financial protection after receiving a TB diagnosis. In order to make strides towards UHC in Vietnam and to close population coverage gaps, initiatives are required to specifically address the barriers faced by the uninsured. This study found that the majority of the uninsured were able to gain access to SHI through full subsidization of premiums, enrollment assistance and education. However, initiating TB care and SHI enrollment concomitantly left a significant portion of the 6-month TB treatment duration without financial protection. Additionally, a quarter of the uninsured with TB were unable to gain access to SHI during treatment, primarily due to a lack of documentation. There is great need for official mechanisms to be in place that enable those without sufficient state documents to access the TB program and to address the time-sensitive nature of providing effective financial protection during treatment of an infectious disease. These findings are relevant for other high TB burden, middle-income countries who are on a similar pathway for transitioning away from donor-financed TB programs to ones supported with a higher proportion of domestic resources.

Availability of data and materials

The quantitative dataset used and analyzed during the current study are available from the corresponding author on reasonable request. Seven anonymized transcripts of interviews with the people enrolled in the pilot intervention and non-beneficiaries have been uploaded to the following URL: https://doi.org/ https://doi.org/10.5281/zenodo.7736220 .

Abbreviations

Anti antiretroviral therapy

Drug resistant tuberculosis

Drug susceptible tuberculosis

District TB Unit

Ho Chi Minh City

Health Insurance Law

Human immunodeficiency virus

International Labour Organization

Interquartile range

National Tuberculosis Program

Out of pocket

People Living with HIV

Social Health Insurance

• Tuberculosis

Universal Health Coverage

United States Dollar

Vietnamese Dong

Vietnam Social Security

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Acknowledgements

The authors would like to acknowledge the contributions of Hoang Thi My Linh, Chu Thi Hoang Anh, Nguyen Khac Cuong, Nham Thi Yen Ngoc and Tran Thai Hiep for conducting qualitative interviews and assisting with SHI enrollment activities. Special thanks to Dr. Kerri Viney for providing insightful comments on an early draft of this manuscript; they greatly strengthened the final version. This work was graciously supported by the staff of Vietnam’s National TB Program, the Hanoi Lung Hospital, Pham Ngoc Thach Provincial TB Hospital and 10 District TB Units. Lastly, we would like to thank the interview participants who shared their time and insights.

Open access funding provided by Karolinska Institute. The European Commission's Horizon 2020 program supported the provision of SHI and all data collection in 2019 through the IMPACT-TB study under grant agreement number 733174. For the period of 2020–2022, support to implement the pilot and conduct the evaluation was made possible by the generous support of the American people through the USAID under award number 72044020FA00001. TW was supported by grants from: the Wellcome Trust, UK ( Seed Award, grant number 209075/Z/17/Z); the Department of Health and Social Care (DHSC), the Foreign, Commonwealth & Development Office (FCDO), the Medical Research Council (MRC) and Wellcome, UK (Joint Global Health Trials, MR/V004832/1); the Medical Research Council (Public Health Intervention Development Award “PHIND”, APP2293); and the Medical Research Foundation (Dorothy Temple Cross International Collaboration Research Grant, MRF-131–0006-RG-KHOS-C0942). KSA was supported by the ASPECT Trial funded the Swedish Research Council (2022-00727). The contents of this study are the responsibility of the listed authors, and do not necessarily reflect the views of USAID or the United States Government.

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This study was conceived of by RF, KSA, TTN, THYP, CAY, AJC, LNQV. The study was administered by RF, YP, TTN, AJC. Support from Vietnam’s National TB program was provided by HBN and LVD. The methodology was developed by RJ, CAY, KV, KL, KSA. The analysis was carried out by RF, CAY, TTN, and THYP. LNQV, AJC, TW, LN, CH, LB, MP, HBN, LVD, MC, KV, KL, and KSA supported the interpretation of findings. The first manuscript was written by RF. All co-authors reviewed and commented on the initial manuscript. The final manuscript was approved and reviewed by all authors.

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Ethics approval and consent to participate.

All study procedures were conducted in strict adherence to the Declaration of Helsinki. Ethical approvals were granted by the National Lung Hospital Institutional Review Board (114/19/CT-HĐKH-ĐĐ), the Pham Ngoc Thach Hospital Institutional Review Board (1225/PNT-HĐĐĐ) and Ha Noi University of Public Health Institutional Review Board (300/2020/YTCC-HD3). All participants provided written informed consent and individual-level data were pseudonymized prior to analysis.

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Informed written consent was obtained for all individuals who the study attempted to enroll in SHI, as part of the pilot intervention. It was also obtained for all individuals who participated in the qualitative interviews.

Competing interests

Ten of the authors received salary support from one of the funding agencies to implement the pilot interventions and their evaluation. Two of the authors were employed by United States Agency for International Development (USAID), which funded one of the two pilot interventions. They played no role in the design or implementation of the pilot interventions or their evaluation, but during the development of the manuscript, they provided their insights about the context of the results and Vietnam’s health financing transition as experts in the field.

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Mapping of procedures and costs for first-time enrollment into Vietnam's social health insurance scheme.

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Forse, R., Yoshino, C.A., Nguyen, T.T. et al. Towards universal health coverage in Vietnam: a mixed-method case study of enrolling people with tuberculosis into social health insurance. Health Res Policy Sys 22 , 40 (2024). https://doi.org/10.1186/s12961-024-01132-8

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Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis

• Imran Raza 1 ,
• Muhammad Hasan Jamal 1 ,
• Rizwan Qureshi 1 ,
• Abdul Karim Shahid 1 ,
• Angel Olider Rojas Vistorte 2 , 3 , 4 ,
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Scientific Reports volume  14 , Article number:  7635 ( 2024 ) Cite this article

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Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson’s patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson’s dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson’s disease analysis.

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Introduction

The advancement of technology has facilitated the accumulation of vast amounts of data from various sources such as databases, web repositories, and files, necessitating robust tools for analysis and decision-making 1 , 2 . Data mining, employing techniques such as support vector machine (SVM), decision trees, neural networks, clustering, fuzzy logic, and genetic algorithms, plays a pivotal role in extracting information and uncovering hidden patterns within the data 3 , 4 . However, the complexity of the data landscape, characterized by high dimensionality, heterogeneity, and non-traditional structures, renders the data mining process inherently challenging 5 , 6 . To tackle these challenges effectively, a combination of complementary and cooperative intelligent techniques, including SVM, fuzzy logic, probabilistic reasoning, genetic algorithms, and neural networks, has been advocated 7 , 8 .

Hybrid intelligent systems, amalgamating various intelligent techniques, have emerged as a promising approach to enhance the efficacy of data mining. Adaptive neuro-fuzzy inference systems (ANFIS) have laid the groundwork for intelligent systems in data mining techniques, providing a foundation for exploring complex data relationships 7 , 8 . Moreover, the theory of rough sets has found practical application in tasks such as attribute selection, data reduction, decision rule generation, and pattern extraction, contributing to the development of intelligent systems for knowledge discovery 7 , 8 . Extracting meaningful knowledge from hybrid data, which encompasses both categorical and numerical data, presents a significant challenge. Two predominant strategies have emerged to address this challenge 9 , 10 . The first strategy involves employing numerical data processing techniques such as Principal Component Analysis (PCA) 11 , 12 , Neural Networks 13 , 14 , 15 , 16 , and SVM 17 . However, this approach necessitates converting categorical data into numerical equivalents, leading to a loss of contextual meaning 18 , 19 . The second strategy leverages rough set theory alongside methods tailored for categorical data. Nonetheless, applying rough set theory to numerical data requires a discretization process, resulting in information loss 20 , 21 . Numerous hybrid data processing methods have been proposed, combining rough sets and fuzzy sets to handle uncertainty 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 . However, selecting an appropriate rough set model for a given dataset necessitates exploring the inherent relationships among existing models, presenting a challenge for users. The selection and utilization of an appropriate model in data mining thus demand qualitative and quantitative comparisons of existing hybrid data processing models.

This research endeavors to present a comprehensive analysis of hybrid data processing models, with a specific focus on those rooted in neighborhood rough sets (NRS). By investigating the inherent interconnections among these models, this study aims to elucidate their complex dynamics. To address the challenges posed by hybrid data, a novel hybrid model founded on NRS is introduced. This model enhances the efficiency of the data mining process without discretization mitigating information loss and ambiguity in data interpretation. Notably, the adaptability of the proposed model, particularly in adjusting the threshold value governing the neighborhood approximation space, ensures optimal performance aligned with dataset characteristics while maintaining high accuracy. A dedicated testbed tailored for Parkinson’s patients is developed to evaluate the real-world effectiveness of the proposed approach. Furthermore, a rigorous evaluation of the proposed model is conducted, encompassing both accuracy and overall effectiveness. Encouragingly, the results demonstrate that the proposed scheme surpasses alternative approaches, adeptly managing both numerical and categorical data through an adaptive framework.

The major contributions, listed below, collectively emphasize the innovative hybrid data processing model, the adaptive nature of its thresholding mechanism, and the empirical validation using a Parkinson’s patient testbed, underscoring the relevance and significance of the study’s findings.

Novel Hybrid Data Processing Model: This research introduces a novel hybrid data processing model based on NRS, preserving the practical meaning of both numerical and categorical data types. Unlike conventional methods, it minimizes information loss while optimizing interpretability. The proposed distance function combines Euclidean and Levenshtein distances with weighted calculations and dynamic selection mechanisms to enhance accuracy and realism in neighborhood approximation spaces.

Adaptive Thresholding Mechanism: Another key contribution is the integration of an adaptive thresholding mechanism within the hybrid model. This feature dynamically adjusts the threshold value based on dataset characteristics, ensuring optimal performance and yielding more accurate and contextually relevant results.

Empirical Validation through Parkinson’s Testbed: This research provides a dedicated testbed for analyzing behavioral data from Parkinson’s patients, allowing rigorous evaluation of the proposed hybrid data processing model. Utilizing real-world datasets enhances the model’s practical applicability and advances knowledge in medical data analysis and diagnosis.

The subsequent structure of the paper unfolds as follows: section “ Related work ” delves into the related work. The proposed model is introduced in section “ Adaptive neighborhood rough set model ”, Section “ Instrumentation ” underscores the instrumentation aspect, section “ Result and discussion ” unfolds the presentation of results and ensuing discussions, while section “ Conclusion and future work ” provides the concluding remarks for the paper. A list of notations used in this study is provided in Table  1 .

Related work

Rough set-based approaches have been utilized in various applications like bankruptcy prediction 42 , attribute/feature subset selection 43 , 44 , cancer prediction 45 , 46 , etc. In addition, recently, several innovative hybrid models have emerged, blending the realms of fuzzy logic and non-randomized systems (NRSs). One such development is presented by Yin et al. 47 , who introduce a parameterized hybrid fuzzy similarity relation. They apply this relation to the task of granulating multilabel data, subsequently extending it to the domain of multilabel learning. To construct a noise-tolerant multilabel fuzzy NRS model (NT-MLFNRS), they leverage the inclusion relationship between fuzzy neighborhood granules and fuzzy decisions. Building upon NT-MLFNRS, Yin et al. also devise a noise-resistant heuristic multilabel feature selection (NRFSFN) algorithm. To further enhance the efficiency of feature selection and address the complexities associated with handling large-scale multilabel datasets, they culminate their efforts by introducing an efficient extended version of NRFSFN known as ENFSFN.

Sang et al. 48 explore incremental feature selection methodologies, introducing a novel conditional entropy metric tailored for dynamic ordered data robustness. Their approach introduces the concept of a fuzzy dominance neighborhood rough set (FDNRS) and defines a conditional entropy metric with robustness, leveraging the FDNRS model. This metric serves as an evaluation criterion for features, and it is integrated into a heuristic feature selection algorithm. The resulting incremental feature selection algorithm is built upon this innovative model

Wang et al. 19 introduced the Fuzzy Rough Iterative Computational (FRIC) model, addressing challenges in hybrid information systems (HIS). Their framework includes a specialized distance function for object sets, enhancing object differentiation precision within HIS. Utilizing this function, they establish fuzzy symmetric relations among objects to formulate fuzzy rough approximations. Additionally, they introduce evaluation functions like fuzzy positive regions, dependency functions, and attribute importance functions to assess classification capabilities of attribute sets. They developed an attribute reduction algorithm tailored for hybrid data based on FRIC principles. This work contributes significantly to HIS analysis, providing a robust framework for data classification and feature selection in complex hybrid information systems.

Xu et al. 49 introduced a novel Fitting Fuzzy Rough Set (FRS) model enriched with relative dependency complement mutual information. This model addresses challenges related to data distribution and precision enhancement of fuzzy information granules. They utilized relative distance to mitigate the influence of data distribution on fuzzy similarity relationships and introduced a fitting fuzzy neighborhood radius optimized for enhancing the precision of fuzzy information granules. Within this model, the authors conducted a comprehensive analysis of information uncertainty, introducing definitions of relative complement information entropy and formulating a multiview uncertainty measure based on relative dependency complement mutual information. This work significantly advances our understanding of managing information uncertainty within FRS models, making a valuable contribution to computational modeling and data analysis.

Jiang et al. 50 presented an innovative approach for multiattribute decision-making (MADM) rooted in PROMETHEE II methodologies. Building upon the NRS model, they introduce two additional variants of covering-based variable precision fuzzy rough sets (CVPFRSs) by applying fuzzy logical operators, specifically type-I CVPFRSs and type-II CVPFRSs. In the context of MADM, their method entails the selection of medicines using an algorithm that leverages the identified features.

Qu et al. 51 introduced the concept of Adaptive Neighborhood Rough Sets (ANRSs), aiming for effective integration of feature separation and linkage with classification. They utilize the mRMR-based Feature Selection Algorithm (FSRMI), demonstrating outstanding performance across various selected datasets. However, it’s worth noting that FSRMI may not consistently outperform other algorithms on all datasets.

Xu et al. 52 introduced the Fuzzy Neighborhood Joint Entropy Model (FNSIJE) for feature selection, leveraging fuzzy neighborhood self-information measures and joint entropy to capture combined feature information. FNSIJE comprehensively analyzes the neighborhood decision system, considering noise, uncertainty, and ambiguity. To improve classification performance, the authors devised a new forward search method. Experimental results demonstrated the effectiveness of FNSIJE-KS, efficiently selecting fewer features for both low-dimensional UCI datasets and high-dimensional gene datasets while maintaining optimal classification performance. This approach advances feature selection techniques in machine learning and data analysis.

In 53 , the authors introduced a novel multi-label feature selection method utilizing fuzzy NRS to optimize classification performance in multi-label fuzzy neighborhood decision systems. By combining the NRS and FRS models a Multi-Label Fuzzy NRS model is introduced. They devised a fuzzy neighborhood approximation accuracy metric and crafted a hybrid metric integrating fuzzy neighborhood approximate accuracy with fuzzy neighborhood conditional entropy for attribute importance evaluation. Rigorous evaluation of their methods across ten diverse multi-label datasets showcased significant progress in multi-label feature selection techniques, promising enhanced classification performance in complex multi-label scenarios.

Sanget et al. 54 introduced the Fuzzy Dominance Neighborhood Rough Set (NRS) model for Interval-Valued Ordered Decision Systems (IvODS), along with a robust conditional entropy measure to assess monotonic consistency within IvODS. They also presented two incremental feature selection algorithms. Experimental results on nine publicly available datasets showcased the robustness of their proposed metric and the effectiveness and efficiency of the incremental algorithms, particularly in dynamic IvODS updates. This research significantly advances the application of fuzzy dominance NRS models in IvODS scenarios, providing valuable insights for data analysis and decision-making processes.

Zheng et al. 55 generalized the FRSs using axiomatic and constructive approaches. A pair of dual generalized fuzzy approximation operators is defined using arbitrary fuzzy relation in the constructive approach. Different classes of FRSs are characterized using different sets of axioms. The postulates governing fuzzy approximation operators ensure the presence of specific categories of fuzzy relations yielding identical operators. Using a generalized FRS model, Hu et al. 18 introduced an efficient algorithm for hybrid attribute reduction based on fuzzy relations constructing a forward greedy algorithm for hybrid attribute reduction resulting in optimal classification performance with lesser selected features and higher accuracy. Considering the similarity between two objects, Wang et al. 36 redefine fuzzy upper and lower approximations. The existing concepts of knowledge reduction are extending fuzzy environment resulting in a heuristic algorithm to learn fuzzy rules.

Gogoi et al. 56 use rough set theory for generating decision rules from inconsistent data. The proposed scheme uses indiscernibility relation to find inconsistencies in the data generating minimized and non-redundant rules using lower and upper approximations. The proposed scheme is based on the LEM2 algorithm 57 which performs the local covering option for generating minimum and non-redundant sets of classification rules and does not consider the global covering. The scheme is evaluated on a variety of data sets from the UCI Machine Learning Repository. All these data sets are either categorical or numerical having variable feature spaces. The proposed scheme performs consistently better for categorical data sets, as it is designed to handle inconsistencies in the data having at least one inconsistency. Results show that the proposed scheme generates minimized rule without reducing the feature space unlike other schemes, which compromise the feature space.

In 58 , the authors introduced a novel NRS model to address attribute reduction in noisy systems with heterogeneous attributes. This model extends traditional NRS by incorporating tolerance neighborhood relation and probabilistic theory, resulting in more comprehensive information granules. It evaluates the significance of heterogeneous attributes by considering neighborhood dependency and aims to maximize classification consistency within selected feature spaces. The feature space reduction algorithm employs an incremental approach, adding features while preserving maximal dependency in each round and halting when a new feature no longer increases dependency. This approach selects fewer features than other methods while achieving significantly improved classification performance, demonstrating its effectiveness in attribute reduction for noisy systems.

Zhu et al. 59 propose a fault tolerance scheme combining kernel method, NRS, and statistical features to adaptively select sensitive features. They employ a Gaussian kernel function with NRS to map fault data to a high-dimensional space. Their feature selection algorithm utilizes the hyper-sphere radius in high-dimensional feature space as the neighborhood value, selecting features based on significance measure regardless of the classification algorithm. A wrapper deploys a classification algorithm to evaluate selected features, choosing a subset for optimal classification. Experimental results demonstrate precise determination of the neighborhood value by mapping data into a high-dimensional space using the kernel function and hyper-sphere radius. This methodology proficiently selects sensitive fault features, diagnoses fault types, and identifies fault degrees in rolling bearing datasets.

A neighborhood covering a rough set model for the fuzziness of decision systems is proposed that solves the problem of hybrid decision systems having both fuzzy and numerical attributes 60 . The fuzzy neighborhood relation measures the indiscernibility relation and approximates the universe space using information granules, which deal with fuzzy attributes directly. The experimental results evaluate the influence of neighborhood operator size on the accuracy and attribute reduction of fuzzy neighborhood rough sets. The attribute reduction increases with the increase in the threshold size. A feature will not distinguish any samples and cannot reduce attributes if the neighborhood operator exceeds a certain value.

Hou et al. 61 applied NRS reduction techniques to cancer molecular classification, focusing on gene expression profiles. Their method introduced a novel perspective by using gene occurrence probability in selected gene subsets to indicate tumor classification efficacy. Unlike traditional methods, it integrated both Filters and Wrappers, enhancing classification performance while being computationally efficient. Additionally, they developed an ensemble classifier to improve accuracy and stability without overfitting. Experimental results showed the method achieved high prediction accuracy, identified potential cancer biomarkers, and demonstrated stability in performance.

Table  2 gives a comparison of existing rough set-based schemes for quantitative and qualitative analysis. The comparative parameters include handling hybrid data, generalized NRS, attribute reduction, classification, and accuracy rate. Most of the existing schemes do not handle hybrid data sets without discretization resulting in information loss and a lack of practical meanings. Another parameter to evaluate the effectiveness of the existing scheme is the ability to adapt the threshold value according to the given data sets. Most of the schemes do not adapt threshold values for neighborhood approximation space resulting in variable accuracy rates for different datasets. The end-user has to adjust the value of the threshold for different datasets without understanding its impact in terms of overfitting. Selecting a large threshold value will result in more global rules resulting in poor accuracy. There needs to be a mechanism to adaptively choose the value of the threshold considering both the global and local information without compromising on the accuracy rate. The schemes are also evaluated for their ability to attribute reduction using NRS. This can greatly improve processing time and accuracy by not considering insignificant attributes. The comparative analysis shows that most of the NRS-based existing schemes perform better than many other well-known schemes in terms of accuracy. Most of these schemes have a higher accuracy rate than CART, C4.5, and k NN. This makes the NRS-based schemes a choice for attribute reduction and classification.

Adaptive neighborhood rough set model

The detailed analysis of existing techniques highlights the need for a generalized NRS-based classification technique to handle both categorical and numerical data. The proposed NRS-based techniques not only handle the hybrid information granules but also dynamically select the threshold \(\delta \) producing optimal results with a high accuracy rate. The proposed scheme considers a hybrid tuple \(HIS=\langle U_h,\ Q_h,\ V,\ f \rangle \) , where \(U_h\) is nonempty set of hybrid records \(\{x_{h1},\ x_{h2},\ x_{h3},\ \ldots ,\ x_{hn}\}\) , \(Q_h=\left\{ q_{h1},\ q_{h2},\ \ q_{h3},\ \ldots \,\ q_{hn}\right\} \) is the non-empty set of hybrid features. \( V_{q_h}\) is the domain of attribute \(q_h\) and \(V=\ \cup _{q_h\in Q_h}V_{q_h}\) , and \(f=U_h\ x\ Q_h\rightarrow V\) is a total function such \(f\left( x_h,q_h\right) \in V_{q_h}\) for each \(q_h\in Q_h, x_h\in U_h\) , called information function. \(\langle U_h,\ Q_h,\ V,\ f\rangle \) is also known as a decision table if \(Q_h=C_h\cup D\) , where \(C_h\) is the set of hybrid condition attributes and D is the decision attribute.

A neighborhood relation N is calculated using this set of hybrid samples \(U_h\) creating the neighborhood approximation space \(\langle U_h,\ N\rangle \) which contains information granules \( \left\{ \delta ({x_h}_i)\big |{x_h}_i\in U_h\right\} \) based on some distance function \(\Delta \) . For an arbitrary sample \({x_h}_i\in U_h\) and \(B \subseteq C_h\) , the neighborhood \(\delta _B({x_h}_i)\) of \({x_h}_i\) in the subspace B is defined as \(\delta _B\left( {x_h}_i\right) =\{{x_h}_j\left| {x_h}_j\right. \in U_h,\ \Delta B(x_i,x_j) \le \delta \}\) . The scheme proposes a new hybrid distance function to handle both the categorical and numerical features in an approximation space.

The proposed distance function uses Euclidean distance for numerical features and Levenshtein distance for categorical features. The distance function also takes care of the significant features calculating weighted distance for both the categorical and numerical features. The proposed algorithm dynamically selects the distance function at the run time. The use of Levenshtein distance for categorical features provides precise distance for optimal neighborhood approximation space providing better results. Existing techniques add 1 to distance if two strings do not match in calculating the distance for categorical data and add 0 otherwise. This may not result in a realistic neighborhood approximation space.

The neighborhood size depends on the threshold \(\delta \) . The neighborhood will contain more samples if \(\delta \) is greater and results in more rules not considering the local information data. The accuracy rate of the NRS greatly depends on the selection of threshold values. The proposed scheme dynamically calculates the threshold value for any given dataset considering both local and global information. The threshold calculation formula is given below where \({min}_D\) is the minimum distance between the set of training samples and the test sample containing local information and \(R_D\) is the range of distance between the set of training samples and the test sample containing the global information.

The proposed scheme then calculates the lower and upper approximations given a neighborhood space \(\langle U_h, N\rangle \) for \(X \subseteq U_h\) , the lower and upper approximations of X are defined as:

Given a hybrid neighborhood decision table \(HNDT=\langle U_h,\ C_h\cup \ D, V, f\rangle \) , \(\{ X_{h1},X_{h2},\ \ldots ,\ X_{hN} \}\) are the sample hybrid subjects with decision 1 to N , \(\delta _B\left( x_{hi}\right) \) is the information granules generated by attributes \(B \subseteq C_h\) , then the lower and upper approximation is defined as:

and the boundary region of D is defined as:

The lower and upper approximation spaces are the set of rules, which are used to classify a test sample. A test sample forms its neighborhood using a lower approximation having all the rules with a distance less than a dynamically calculated threshold value. The majority voting is used in the neighborhood of a test sample to decide the class of a test sample. K-fold cross-validation is used to measure the accuracy of the proposed scheme where the value k is 10. The algorithm 1 of the proposed scheme has a time complexity of \(O(nm^{2})\) where n is the number of clients and m is the size of the categorial data.

Instrumentation

The proposed generalized rough set model has been rigorously assessed through the development of a testbed designed for the classification of Parkinson’s patients. It has also been subjected to testing using various standard datasets sourced from the University of California at Irvine machine learning data repository 63 . This research underscores the increasing significance of biomedical engineering in healthcare, particularly in light of the growing prevalence of Parkinson’s disease, which ranks as the second most common neurodegenerative condition, impacting over 1% of the population aged 65 and above 64 . The disease manifests through distinct motor symptoms like resting tremors, bradykinesia (slowness of movement), rigidity, and poor balance, with medication-related side effects such as wearing off and dyskinesias 65 .

In this study, to address the need for a reliable quantitative method for assessing motor complications in Parkinson’s patients, the data collection process involves utilizing a home-monitoring system equipped with wireless wearable sensors. These sensors were specifically deployed to closely monitor Parkinson’s patients with severe tremors in real time. It’s important to note that all patients involved in the study were clinically diagnosed with Parkinson’s disease. Additionally, before data collection, proper consent was obtained from each participant, and the study protocol was approved by the ethical committee of our university. The data collected from these sensors is then analyzed, yielding reliable quantitative information that can significantly aid clinical decision-making within both routine patient care and clinical trials of innovative treatments.

Testbed for Parkinson’s patients.

Figure  1 illustrates a real-time Testbed designed for monitoring Parkinson’s patients. This system utilizes a tri-axial accelerometer to capture three signals, one for each axis \((x,\ y,\ and\ z)\) , resulting in a total of 18 channels of data. The sensors employed in this setup employ ZigBee (IEEE 802.15.4 infrastructure) protocol to transmit data to a computer at a sampling rate of 62.5 Hz. To ensure synchronization of the transmitted signals, a transition protocol is applied. These data packets are received through the Serial Forwarder using the TinyOS platform ( http://www.tinyos.net ). The recorded acceleration data is represented as digital signals and can be visualized on an oscilloscope. The frequency domain data is obtained by applying the Fast Fourier Transform (FFT) to the signal, resulting in an ARFF file format that is then employed for classification purposes. The experimental flowchart is shown in Fig.  2 .

Experimental flowchart.

The real-time testbed includes various components to capture data using the Unified Parkinson’s Disease Rating Scale (UPDRS). TelosB MTM-CM5000-MSP and MTM-CM3000-MSP sensors are used to send and receive radio signals from the sensor to the PC. These sensors are based on an open-source TelosB/Tmote Sky platform, designed and developed by the University of California, Berkeley.

TelosB sensor uses the IEEE 802.15.4 wireless structure and the embedded sensors can measure temperature, relative humidity, and light. In CM3000, the USB connector is replaced with an ERNI connector that is compatible with interface modules. Also, the Hirose 51-pin connector makes this more versatile as it can be attachable to any sensor board family, and the coverage area is increased using SMA design by a 5dBi external antenna 66 . These components can be used for a variety of applications such as low-power Wireless Sensor Networks (WSN) platforms, network monitoring, and environment monitoring systems.

MTS-EX1000 sensor board is used for the amplification of the voltage/current value from the accelerometer. The EX1000 is an attachable board that supports the CMXXXX series of wireless sensors network Motes (Hirose 51-pin connector). The basic functionality of EX1000 is to connect the external sensors with CMXX00 communication modules to enhance the mote’s I/O capability and support different kinds of sensors based on the sensor type and its output signal. ADXL-345 Tri-accelerometer sensor is used to calculate body motion along x, y, and z-axis relative to gravity. It is a small, thin, low-power, 3-axis accelerometer that calculates high resolution (13-bit) measurements at up to ±16g. Its digital output, in 16-bit twos complement format, is accessible through either an SPI (3- or 4-wire) or I2C digital interface. A customized main circuit board is used having a programmed IC, registers, and transistors. Its basic functionality is to convert the digital data, accessed through the ADXL-345 sensor, into analog form and send it to MTS1000.

Result and discussion

The proposed generalized and ANRS is evaluated against different data sets taken from the machine learning data repository, at the University of California at Irvine. In addition to these common data sets, a real-time Testbed for Parkinson’s patients is also used to evaluate the proposed scheme. The hybrid data of 500 people was collected using the Testbed for Parkinson’s patients including 10 Parkinson’s patients, 20 people have abnormal and uncontrolled hand movements, and the rest of the samples were taken approximating the hand movements of Parkinson’s patients. The objective of this evaluation is to compare the accuracy rate of the proposed scheme with CART, k NN, and SVM having both simple and complex datasets containing numerical and hybrid features respectively. The results also demonstrate the selection of radius r for dynamically calculating the threshold value.

Table  3 provides the details of the datasets used for the evaluation of the proposed scheme including the training and test ratio used for evaluation in addition to data type, total number of instances, total feature, a feature considered for evaluation, and number of classes. The hybrid datasets are also selected to evaluate to performance of the proposed scheme against the hybrid feature space without discretization preventing information loss.

The accuracy of the NRS is greatly dependent on the threshold value. Most of the existing techniques do not dynamically adapt the threshold \(\delta \) value for different hybrid datasets. This results in the variant of NRS suitable for specific datasets with different threshold values. A specific threshold value may produce better results for one dataset and poor results for others requiring a more generic threshold value catering to different datasets with optimal results. The proposed scheme introduces an adaptable threshold calculation mechanism to achieve optimal results regardless of the datasets under evaluation. The radius value plays a pivotal role in forming a neighborhood, as the threshold values consider both the local and global information of the NRS to calculate neighborhood approximation space. Table  4 shows the accuracy rate having different values of the radius of the NRS. The proposed threshold mechanism provides better results for all datasets if the value of the radius is 0.002. Results also show that assigning no weight to the radius produces poor results, as it will then only consider the local information for the approximation space. Selecting other weights for radius may produce better results for one dataset but not for all datasets.

Table  5 presents the comparative analysis of the proposed scheme with k NN, Naive Bayes, and C45. The results show that the proposed scheme performs well against other well-known techniques for both the categorical and numerical features space. Naive Bayes and C45 also result in information loss, as these techniques cannot process the hybrid data. So the proposed scheme handles the hybrid data without compromising on the information completeness producing acceptable results. K-fold cross-validation is used to measure the accuracy of the proposed scheme. Each dataset is divided into 10 subsets to use one of the K subsets as the test set and the other K-1 subsets as training sets. Then the average accuracy of all K trials is computed with the advantage of having results regardless of the dataset division.

Conclusion and future work

This work evaluates the existing NRS-based scheme for handling hybrid data sets i.e. numerical and categorical features. The comparative analysis of existing NRS-based schemes shows that there is a need for a generic NRS-based approach to adapt the threshold selection forming neighborhood approximation space. A generalized and ANRS-based scheme is proposed to handle both the categorical and numerical features avoiding information loss and lack of practical meanings. The proposed scheme uses a Euclidean and Levenshtein distance to calculate the upper and lower approximation of NRS for numerical and categorical features respectively. Euclidean and Levenshtein distances have been modified to handle the impact of outliers in calculating the approximation spaces. The proposed scheme defines an adaptive threshold mechanism for calculating neighborhood approximation space regardless of the data set under consideration. A Testbed is developed for real-time behavioral analysis of Parkinson’s patients evaluating the effectiveness of the proposed scheme. The evaluation results show that the proposed scheme provides better accuracy than k NN, C4.5, and Naive Bayes for both the categorical and numerical feature space achieving 95% accuracy on the Parkinson’s dataset. The proposed scheme will be evaluated against the hybrid data set having more than two classes in future work. Additionally, in future work, we aim to explore the following areas; (i) conduct longitudinal studies to track the progression of Parkinson’s disease over time, allowing for a deeper understanding of how behavioral patterns evolve and how interventions may impact disease trajectory, (ii) explore the integration of additional data sources, such as genetic data, imaging studies, and environmental factors, to provide a more comprehensive understanding of Parkinson’s disease etiology and progression, (iii) validate our findings in larger and more diverse patient populations and investigate the feasibility of implementing our proposed approach in clinical settings to support healthcare providers in decision-making processes, (iv) investigate novel biomarkers or physiological signals that may provide additional insights into Parkinson’s disease progression and motor complications, potentially leading to the development of new diagnostic and monitoring tools, and (v) conduct patient-centered outcomes research to better understand the impact of Parkinson’s disease on patients’ quality of life, functional abilities, and overall well-being, with a focus on developing personalized treatment approaches.

Data availability

The datasets used in this study are publicly available at the following links:

Bupa 67 : https://doi.org/10.24432/C54G67 , Sonar 68 : https://doi.org/10.24432/C5T01Q , Mammographic Mass 69 : https://doi.org/10.24432/C53K6Z , Haberman’s Survival 70 : https://doi.org/10.24432/C5XK51 , Credit-g 71 : https://doi.org/10.24432/C5NC77 , Lymmography 73 : https://doi.org/10.24432/C54598 , Splice 74 : https://doi.org/10.24432/C5M888 , Optdigits 75 : https://doi.org/10.24432/C50P49 , Pendigits 76 : https://doi.org/10.1137/1.9781611972825.9 , Pageblocks 77 : https://doi.org/10.24432/C5J590 , Statlog 78 : https://doi.org/10.24432/C55887 , Magic04 79 : https://doi.org/10.1609/aaai.v29i1.9277 .

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Imran Raza, Muhammad Hasan Jamal, Rizwan Qureshi & Abdul Karim Shahid

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Imran Raza: Conceptualization, Formal analysis, Writing—original draft; Muhammad Hasan Jamal: Conceptualization, Data curation, Writing—original draft; Rizwan Qureshi: Data curation, Formal analysis, Methodology; Abdul Karim Shahid: Project administration, Software, Visualization; Angel Olider Rojas Vistorte: Funding acquisition, Investigation, Project administration; Md Abdus Samad: Investigation, Software, Resources; Imran Ashraf: Supervision, Validation, Writing —review and editing. All authors reviewed the manuscript and approved it.

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Raza, I., Jamal, M.H., Qureshi, R. et al. Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis. Sci Rep 14 , 7635 (2024). https://doi.org/10.1038/s41598-024-57547-4

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ORIGINAL RESEARCH article

Public perception of media social responsibility in developing countries: a case study of albania provisionally accepted.

• 1 Aleksandër Moisiu University, Albania

The final, formatted version of the article will be published soon.

This study delves into public perceptions of media social responsibility within the contemporary Albanian media landscape. Through a comprehensive analysis of various factors, the study identifies the prevailing principles that the public deems crucial for the media's social responsibility and how these principles can enhance the media's contribution to society. A structured questionnaire was used to capture a wide range of public perceptions, with 1,321 questionnaires filled out. These questionnaires were distributed using a face-to-face method across five major urban centers in Albania, ensuring a comprehensive and representative sample of public viewpoints. The distribution method employed a stratified sampling approach to ensure diverse representation across different demographic groups. Additionally, employing a mixed-methods approach, the research includes qualitative interviews with 20 influential stakeholders, including media directors, professors, analysts, and media researchers. Purposive sampling was utilized to select stakeholders representing various sectors of the media landscape. Rigorous measures were taken to mitigate data pollution, including thorough interviewer training and constant monitoring of data quality. An overarching thematic analysis was conducted to identify common themes and patterns across the qualitative interviews, complementing the quantitative findings. To gain further insights, we purposefully selected and conducted a focus group with 28 journalists from various media platforms. The sampling method for the focus group involved purposive sampling to ensure representation from diverse media backgrounds and experiences. Data collected from the focus group underwent thematic analysis to identify common themes and patterns, contributing to an overarching qualitative analysis. The empirical findings reveal that the media's social responsibility in Albania does not fully adhere to the expected standards encompassing all relevant principles. Internal dynamics within media organizations and external forces from politics, economics, and society collectively influence this shortfall. The study highlights the importance of considering public perceptions and expectations in shaping media's social responsibility, emphasizing the need for substantial improvements. In conclusion, this research not only provides practical insights for media practitioners but also offers valuable perspectives for policymakers.

Keywords: media social responsibility, public perception, contemporary media landscape, transparency and accountability, Media ethics

Received: 14 Nov 2023; Accepted: 03 Apr 2024.

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

* Correspondence: Dr. Blerina Gjerazi, Aleksandër Moisiu University, Durrës, 3001-3006, Albania

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