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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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Qualitative Research : Definition

Qualitative research is the naturalistic study of social meanings and processes, using interviews, observations, and the analysis of texts and images.  In contrast to quantitative researchers, whose statistical methods enable broad generalizations about populations (for example, comparisons of the percentages of U.S. demographic groups who vote in particular ways), qualitative researchers use in-depth studies of the social world to analyze how and why groups think and act in particular ways (for instance, case studies of the experiences that shape political views).   

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Home » Qualitative Research – Methods, Analysis Types and Guide

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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• Knowledge Base
• Methodology
• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on 4 April 2022 by Pritha Bhandari . Revised on 30 January 2023.

Qualitative research involves collecting and analysing 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 analysing 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, and history.

• 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 organisation?
• 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, 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 emphasise different aims and perspectives.

Prevent plagiarism, run a free check.

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 analysing 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 organise 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 categorise 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 analysing qualitative data. Although these methods share similar processes, they emphasise 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.

Researchers must consider practical and theoretical limitations in analysing 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 analysing 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 generalisability

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

• Labour-intensive

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

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

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 organisation 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 organisations.

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

• Prepare and organise 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 .

Cite this Scribbr article

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Bhandari, P. (2023, January 30). What Is Qualitative Research? | Methods & Examples. Scribbr. Retrieved 9 April 2024, from https://www.scribbr.co.uk/research-methods/introduction-to-qualitative-research/

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Chapter 1. Introduction

“Science is in danger, and for that reason it is becoming dangerous” -Pierre Bourdieu, Science of Science and Reflexivity

Why an Open Access Textbook on Qualitative Research Methods?

I have been teaching qualitative research methods to both undergraduates and graduate students for many years.  Although there are some excellent textbooks out there, they are often costly, and none of them, to my mind, properly introduces qualitative research methods to the beginning student (whether undergraduate or graduate student).  In contrast, this open-access textbook is designed as a (free) true introduction to the subject, with helpful, practical pointers on how to conduct research and how to access more advanced instruction.  

Textbooks are typically arranged in one of two ways: (1) by technique (each chapter covers one method used in qualitative research); or (2) by process (chapters advance from research design through publication).  But both of these approaches are necessary for the beginner student.  This textbook will have sections dedicated to the process as well as the techniques of qualitative research.  This is a true “comprehensive” book for the beginning student.  In addition to covering techniques of data collection and data analysis, it provides a road map of how to get started and how to keep going and where to go for advanced instruction.  It covers aspects of research design and research communication as well as methods employed.  Along the way, it includes examples from many different disciplines in the social sciences.

The primary goal has been to create a useful, accessible, engaging textbook for use across many disciplines.  And, let’s face it.  Textbooks can be boring.  I hope readers find this to be a little different.  I have tried to write in a practical and forthright manner, with many lively examples and references to good and intellectually creative qualitative research.  Woven throughout the text are short textual asides (in colored textboxes) by professional (academic) qualitative researchers in various disciplines.  These short accounts by practitioners should help inspire students.  So, let’s begin!

What is Research?

When we use the word research , what exactly do we mean by that?  This is one of those words that everyone thinks they understand, but it is worth beginning this textbook with a short explanation.  We use the term to refer to “empirical research,” which is actually a historically specific approach to understanding the world around us.  Think about how you know things about the world. [1] You might know your mother loves you because she’s told you she does.  Or because that is what “mothers” do by tradition.  Or you might know because you’ve looked for evidence that she does, like taking care of you when you are sick or reading to you in bed or working two jobs so you can have the things you need to do OK in life.  Maybe it seems churlish to look for evidence; you just take it “on faith” that you are loved.

Only one of the above comes close to what we mean by research.  Empirical research is research (investigation) based on evidence.  Conclusions can then be drawn from observable data.  This observable data can also be “tested” or checked.  If the data cannot be tested, that is a good indication that we are not doing research.  Note that we can never “prove” conclusively, through observable data, that our mothers love us.  We might have some “disconfirming evidence” (that time she didn’t show up to your graduation, for example) that could push you to question an original hypothesis , but no amount of “confirming evidence” will ever allow us to say with 100% certainty, “my mother loves me.”  Faith and tradition and authority work differently.  Our knowledge can be 100% certain using each of those alternative methods of knowledge, but our certainty in those cases will not be based on facts or evidence.

For many periods of history, those in power have been nervous about “science” because it uses evidence and facts as the primary source of understanding the world, and facts can be at odds with what power or authority or tradition want you to believe.  That is why I say that scientific empirical research is a historically specific approach to understand the world.  You are in college or university now partly to learn how to engage in this historically specific approach.

In the sixteenth and seventeenth centuries in Europe, there was a newfound respect for empirical research, some of which was seriously challenging to the established church.  Using observations and testing them, scientists found that the earth was not at the center of the universe, for example, but rather that it was but one planet of many which circled the sun. [2]   For the next two centuries, the science of astronomy, physics, biology, and chemistry emerged and became disciplines taught in universities.  All used the scientific method of observation and testing to advance knowledge.  Knowledge about people , however, and social institutions, however, was still left to faith, tradition, and authority.  Historians and philosophers and poets wrote about the human condition, but none of them used research to do so. [3]

It was not until the nineteenth century that “social science” really emerged, using the scientific method (empirical observation) to understand people and social institutions.  New fields of sociology, economics, political science, and anthropology emerged.  The first sociologists, people like Auguste Comte and Karl Marx, sought specifically to apply the scientific method of research to understand society, Engels famously claiming that Marx had done for the social world what Darwin did for the natural world, tracings its laws of development.  Today we tend to take for granted the naturalness of science here, but it is actually a pretty recent and radical development.

To return to the question, “does your mother love you?”  Well, this is actually not really how a researcher would frame the question, as it is too specific to your case.  It doesn’t tell us much about the world at large, even if it does tell us something about you and your relationship with your mother.  A social science researcher might ask, “do mothers love their children?”  Or maybe they would be more interested in how this loving relationship might change over time (e.g., “do mothers love their children more now than they did in the 18th century when so many children died before reaching adulthood?”) or perhaps they might be interested in measuring quality of love across cultures or time periods, or even establishing “what love looks like” using the mother/child relationship as a site of exploration.  All of these make good research questions because we can use observable data to answer them.

What is Qualitative Research?

“All we know is how to learn. How to study, how to listen, how to talk, how to tell.  If we don’t tell the world, we don’t know the world.  We’re lost in it, we die.” -Ursula LeGuin, The Telling

At its simplest, qualitative research is research about the social world that does not use numbers in its analyses.  All those who fear statistics can breathe a sigh of relief – there are no mathematical formulae or regression models in this book! But this definition is less about what qualitative research can be and more about what it is not.  To be honest, any simple statement will fail to capture the power and depth of qualitative research.  One way of contrasting qualitative research to quantitative research is to note that the focus of qualitative research is less about explaining and predicting relationships between variables and more about understanding the social world.  To use our mother love example, the question about “what love looks like” is a good question for the qualitative researcher while all questions measuring love or comparing incidences of love (both of which require measurement) are good questions for quantitative researchers. Patton writes,

Qualitative data describe.  They take us, as readers, into the time and place of the observation so that we know what it was like to have been there.  They capture and communicate someone else’s experience of the world in his or her own words.  Qualitative data tell a story. ( Patton 2002:47 )

Qualitative researchers are asking different questions about the world than their quantitative colleagues.  Even when researchers are employed in “mixed methods” research ( both quantitative and qualitative), they are using different methods to address different questions of the study.  I do a lot of research about first-generation and working-college college students.  Where a quantitative researcher might ask, how many first-generation college students graduate from college within four years? Or does first-generation college status predict high student debt loads?  A qualitative researcher might ask, how does the college experience differ for first-generation college students?  What is it like to carry a lot of debt, and how does this impact the ability to complete college on time?  Both sets of questions are important, but they can only be answered using specific tools tailored to those questions.  For the former, you need large numbers to make adequate comparisons.  For the latter, you need to talk to people, find out what they are thinking and feeling, and try to inhabit their shoes for a little while so you can make sense of their experiences and beliefs.

Examples of Qualitative Research

You have probably seen examples of qualitative research before, but you might not have paid particular attention to how they were produced or realized that the accounts you were reading were the result of hours, months, even years of research “in the field.”  A good qualitative researcher will present the product of their hours of work in such a way that it seems natural, even obvious, to the reader.  Because we are trying to convey what it is like answers, qualitative research is often presented as stories – stories about how people live their lives, go to work, raise their children, interact with one another.  In some ways, this can seem like reading particularly insightful novels.  But, unlike novels, there are very specific rules and guidelines that qualitative researchers follow to ensure that the “story” they are telling is accurate , a truthful rendition of what life is like for the people being studied.  Most of this textbook will be spent conveying those rules and guidelines.  Let’s take a look, first, however, at three examples of what the end product looks like.  I have chosen these three examples to showcase very different approaches to qualitative research, and I will return to these five examples throughout the book.  They were all published as whole books (not chapters or articles), and they are worth the long read, if you have the time.  I will also provide some information on how these books came to be and the length of time it takes to get them into book version.  It is important you know about this process, and the rest of this textbook will help explain why it takes so long to conduct good qualitative research!

Example 1 : The End Game (ethnography + interviews)

Corey Abramson is a sociologist who teaches at the University of Arizona.   In 2015 he published The End Game: How Inequality Shapes our Final Years ( 2015 ). This book was based on the research he did for his dissertation at the University of California-Berkeley in 2012.  Actually, the dissertation was completed in 2012 but the work that was produced that took several years.  The dissertation was entitled, “This is How We Live, This is How We Die: Social Stratification, Aging, and Health in Urban America” ( 2012 ).  You can see how the book version, which was written for a more general audience, has a more engaging sound to it, but that the dissertation version, which is what academic faculty read and evaluate, has a more descriptive title.  You can read the title and know that this is a study about aging and health and that the focus is going to be inequality and that the context (place) is going to be “urban America.”  It’s a study about “how” people do something – in this case, how they deal with aging and death.  This is the very first sentence of the dissertation, “From our first breath in the hospital to the day we die, we live in a society characterized by unequal opportunities for maintaining health and taking care of ourselves when ill.  These disparities reflect persistent racial, socio-economic, and gender-based inequalities and contribute to their persistence over time” ( 1 ).  What follows is a truthful account of how that is so.

Cory Abramson spent three years conducting his research in four different urban neighborhoods.  We call the type of research he conducted “comparative ethnographic” because he designed his study to compare groups of seniors as they went about their everyday business.  It’s comparative because he is comparing different groups (based on race, class, gender) and ethnographic because he is studying the culture/way of life of a group. [4]   He had an educated guess, rooted in what previous research had shown and what social theory would suggest, that people’s experiences of aging differ by race, class, and gender.  So, he set up a research design that would allow him to observe differences.  He chose two primarily middle-class (one was racially diverse and the other was predominantly White) and two primarily poor neighborhoods (one was racially diverse and the other was predominantly African American).  He hung out in senior centers and other places seniors congregated, watched them as they took the bus to get prescriptions filled, sat in doctor’s offices with them, and listened to their conversations with each other.  He also conducted more formal conversations, what we call in-depth interviews, with sixty seniors from each of the four neighborhoods.  As with a lot of fieldwork , as he got closer to the people involved, he both expanded and deepened his reach –

By the end of the project, I expanded my pool of general observations to include various settings frequented by seniors: apartment building common rooms, doctors’ offices, emergency rooms, pharmacies, senior centers, bars, parks, corner stores, shopping centers, pool halls, hair salons, coffee shops, and discount stores. Over the course of the three years of fieldwork, I observed hundreds of elders, and developed close relationships with a number of them. ( 2012:10 )

When Abramson rewrote the dissertation for a general audience and published his book in 2015, it got a lot of attention.  It is a beautifully written book and it provided insight into a common human experience that we surprisingly know very little about.  It won the Outstanding Publication Award by the American Sociological Association Section on Aging and the Life Course and was featured in the New York Times .  The book was about aging, and specifically how inequality shapes the aging process, but it was also about much more than that.  It helped show how inequality affects people’s everyday lives.  For example, by observing the difficulties the poor had in setting up appointments and getting to them using public transportation and then being made to wait to see a doctor, sometimes in standing-room-only situations, when they are unwell, and then being treated dismissively by hospital staff, Abramson allowed readers to feel the material reality of being poor in the US.  Comparing these examples with seniors with adequate supplemental insurance who have the resources to hire car services or have others assist them in arranging care when they need it, jolts the reader to understand and appreciate the difference money makes in the lives and circumstances of us all, and in a way that is different than simply reading a statistic (“80% of the poor do not keep regular doctor’s appointments”) does.  Qualitative research can reach into spaces and places that often go unexamined and then reports back to the rest of us what it is like in those spaces and places.

Example 2: Racing for Innocence (Interviews + Content Analysis + Fictional Stories)

Jennifer Pierce is a Professor of American Studies at the University of Minnesota.  Trained as a sociologist, she has written a number of books about gender, race, and power.  Her very first book, Gender Trials: Emotional Lives in Contemporary Law Firms, published in 1995, is a brilliant look at gender dynamics within two law firms.  Pierce was a participant observer, working as a paralegal, and she observed how female lawyers and female paralegals struggled to obtain parity with their male colleagues.

Fifteen years later, she reexamined the context of the law firm to include an examination of racial dynamics, particularly how elite white men working in these spaces created and maintained a culture that made it difficult for both female attorneys and attorneys of color to thrive. Her book, Racing for Innocence: Whiteness, Gender, and the Backlash Against Affirmative Action , published in 2012, is an interesting and creative blending of interviews with attorneys, content analyses of popular films during this period, and fictional accounts of racial discrimination and sexual harassment.  The law firm she chose to study had come under an affirmative action order and was in the process of implementing equitable policies and programs.  She wanted to understand how recipients of white privilege (the elite white male attorneys) come to deny the role they play in reproducing inequality.  Through interviews with attorneys who were present both before and during the affirmative action order, she creates a historical record of the “bad behavior” that necessitated new policies and procedures, but also, and more importantly , probed the participants ’ understanding of this behavior.  It should come as no surprise that most (but not all) of the white male attorneys saw little need for change, and that almost everyone else had accounts that were different if not sometimes downright harrowing.

I’ve used Pierce’s book in my qualitative research methods courses as an example of an interesting blend of techniques and presentation styles.  My students often have a very difficult time with the fictional accounts she includes.  But they serve an important communicative purpose here.  They are her attempts at presenting “both sides” to an objective reality – something happens (Pierce writes this something so it is very clear what it is), and the two participants to the thing that happened have very different understandings of what this means.  By including these stories, Pierce presents one of her key findings – people remember things differently and these different memories tend to support their own ideological positions.  I wonder what Pierce would have written had she studied the murder of George Floyd or the storming of the US Capitol on January 6 or any number of other historic events whose observers and participants record very different happenings.

This is not to say that qualitative researchers write fictional accounts.  In fact, the use of fiction in our work remains controversial.  When used, it must be clearly identified as a presentation device, as Pierce did.  I include Racing for Innocence here as an example of the multiple uses of methods and techniques and the way that these work together to produce better understandings by us, the readers, of what Pierce studied.  We readers come away with a better grasp of how and why advantaged people understate their own involvement in situations and structures that advantage them.  This is normal human behavior , in other words.  This case may have been about elite white men in law firms, but the general insights here can be transposed to other settings.  Indeed, Pierce argues that more research needs to be done about the role elites play in the reproduction of inequality in the workplace in general.

Example 3: Amplified Advantage (Mixed Methods: Survey Interviews + Focus Groups + Archives)

The final example comes from my own work with college students, particularly the ways in which class background affects the experience of college and outcomes for graduates.  I include it here as an example of mixed methods, and for the use of supplementary archival research.  I’ve done a lot of research over the years on first-generation, low-income, and working-class college students.  I am curious (and skeptical) about the possibility of social mobility today, particularly with the rising cost of college and growing inequality in general.  As one of the few people in my family to go to college, I didn’t grow up with a lot of examples of what college was like or how to make the most of it.  And when I entered graduate school, I realized with dismay that there were very few people like me there.  I worried about becoming too different from my family and friends back home.  And I wasn’t at all sure that I would ever be able to pay back the huge load of debt I was taking on.  And so I wrote my dissertation and first two books about working-class college students.  These books focused on experiences in college and the difficulties of navigating between family and school ( Hurst 2010a, 2012 ).  But even after all that research, I kept coming back to wondering if working-class students who made it through college had an equal chance at finding good jobs and happy lives,

What happens to students after college?  Do working-class students fare as well as their peers?  I knew from my own experience that barriers continued through graduate school and beyond, and that my debtload was higher than that of my peers, constraining some of the choices I made when I graduated.  To answer these questions, I designed a study of students attending small liberal arts colleges, the type of college that tried to equalize the experience of students by requiring all students to live on campus and offering small classes with lots of interaction with faculty.  These private colleges tend to have more money and resources so they can provide financial aid to low-income students.  They also attract some very wealthy students.  Because they enroll students across the class spectrum, I would be able to draw comparisons.  I ended up spending about four years collecting data, both a survey of more than 2000 students (which formed the basis for quantitative analyses) and qualitative data collection (interviews, focus groups, archival research, and participant observation).  This is what we call a “mixed methods” approach because we use both quantitative and qualitative data.  The survey gave me a large enough number of students that I could make comparisons of the how many kind, and to be able to say with some authority that there were in fact significant differences in experience and outcome by class (e.g., wealthier students earned more money and had little debt; working-class students often found jobs that were not in their chosen careers and were very affected by debt, upper-middle-class students were more likely to go to graduate school).  But the survey analyses could not explain why these differences existed.  For that, I needed to talk to people and ask them about their motivations and aspirations.  I needed to understand their perceptions of the world, and it is very hard to do this through a survey.

By interviewing students and recent graduates, I was able to discern particular patterns and pathways through college and beyond.  Specifically, I identified three versions of gameplay.  Upper-middle-class students, whose parents were themselves professionals (academics, lawyers, managers of non-profits), saw college as the first stage of their education and took classes and declared majors that would prepare them for graduate school.  They also spent a lot of time building their resumes, taking advantage of opportunities to help professors with their research, or study abroad.  This helped them gain admission to highly-ranked graduate schools and interesting jobs in the public sector.  In contrast, upper-class students, whose parents were wealthy and more likely to be engaged in business (as CEOs or other high-level directors), prioritized building social capital.  They did this by joining fraternities and sororities and playing club sports.  This helped them when they graduated as they called on friends and parents of friends to find them well-paying jobs.  Finally, low-income, first-generation, and working-class students were often adrift.  They took the classes that were recommended to them but without the knowledge of how to connect them to life beyond college.  They spent time working and studying rather than partying or building their resumes.  All three sets of students thought they were “doing college” the right way, the way that one was supposed to do college.   But these three versions of gameplay led to distinct outcomes that advantaged some students over others.  I titled my work “Amplified Advantage” to highlight this process.

These three examples, Cory Abramson’s The End Game , Jennifer Peirce’s Racing for Innocence, and my own Amplified Advantage, demonstrate the range of approaches and tools available to the qualitative researcher.  They also help explain why qualitative research is so important.  Numbers can tell us some things about the world, but they cannot get at the hearts and minds, motivations and beliefs of the people who make up the social worlds we inhabit.  For that, we need tools that allow us to listen and make sense of what people tell us and show us.  That is what good qualitative research offers us.

How Is This Book Organized?

This textbook is organized as a comprehensive introduction to the use of qualitative research methods.  The first half covers general topics (e.g., approaches to qualitative research, ethics) and research design (necessary steps for building a successful qualitative research study).  The second half reviews various data collection and data analysis techniques.  Of course, building a successful qualitative research study requires some knowledge of data collection and data analysis so the chapters in the first half and the chapters in the second half should be read in conversation with each other.  That said, each chapter can be read on its own for assistance with a particular narrow topic.  In addition to the chapters, a helpful glossary can be found in the back of the book.  Rummage around in the text as needed.

Chapter Descriptions

Chapter 2 provides an overview of the Research Design Process.  How does one begin a study? What is an appropriate research question?  How is the study to be done – with what methods ?  Involving what people and sites?  Although qualitative research studies can and often do change and develop over the course of data collection, it is important to have a good idea of what the aims and goals of your study are at the outset and a good plan of how to achieve those aims and goals.  Chapter 2 provides a road map of the process.

Chapter 3 describes and explains various ways of knowing the (social) world.  What is it possible for us to know about how other people think or why they behave the way they do?  What does it mean to say something is a “fact” or that it is “well-known” and understood?  Qualitative researchers are particularly interested in these questions because of the types of research questions we are interested in answering (the how questions rather than the how many questions of quantitative research).  Qualitative researchers have adopted various epistemological approaches.  Chapter 3 will explore these approaches, highlighting interpretivist approaches that acknowledge the subjective aspect of reality – in other words, reality and knowledge are not objective but rather influenced by (interpreted through) people.

Chapter 4 focuses on the practical matter of developing a research question and finding the right approach to data collection.  In any given study (think of Cory Abramson’s study of aging, for example), there may be years of collected data, thousands of observations , hundreds of pages of notes to read and review and make sense of.  If all you had was a general interest area (“aging”), it would be very difficult, nearly impossible, to make sense of all of that data.  The research question provides a helpful lens to refine and clarify (and simplify) everything you find and collect.  For that reason, it is important to pull out that lens (articulate the research question) before you get started.  In the case of the aging study, Cory Abramson was interested in how inequalities affected understandings and responses to aging.  It is for this reason he designed a study that would allow him to compare different groups of seniors (some middle-class, some poor).  Inevitably, he saw much more in the three years in the field than what made it into his book (or dissertation), but he was able to narrow down the complexity of the social world to provide us with this rich account linked to the original research question.  Developing a good research question is thus crucial to effective design and a successful outcome.  Chapter 4 will provide pointers on how to do this.  Chapter 4 also provides an overview of general approaches taken to doing qualitative research and various “traditions of inquiry.”

Chapter 5 explores sampling .  After you have developed a research question and have a general idea of how you will collect data (Observations?  Interviews?), how do you go about actually finding people and sites to study?  Although there is no “correct number” of people to interview , the sample should follow the research question and research design.  Unlike quantitative research, qualitative research involves nonprobability sampling.  Chapter 5 explains why this is so and what qualities instead make a good sample for qualitative research.

Chapter 6 addresses the importance of reflexivity in qualitative research.  Related to epistemological issues of how we know anything about the social world, qualitative researchers understand that we the researchers can never be truly neutral or outside the study we are conducting.  As observers, we see things that make sense to us and may entirely miss what is either too obvious to note or too different to comprehend.  As interviewers, as much as we would like to ask questions neutrally and remain in the background, interviews are a form of conversation, and the persons we interview are responding to us .  Therefore, it is important to reflect upon our social positions and the knowledges and expectations we bring to our work and to work through any blind spots that we may have.  Chapter 6 provides some examples of reflexivity in practice and exercises for thinking through one’s own biases.

Chapter 7 is a very important chapter and should not be overlooked.  As a practical matter, it should also be read closely with chapters 6 and 8.  Because qualitative researchers deal with people and the social world, it is imperative they develop and adhere to a strong ethical code for conducting research in a way that does not harm.  There are legal requirements and guidelines for doing so (see chapter 8), but these requirements should not be considered synonymous with the ethical code required of us.   Each researcher must constantly interrogate every aspect of their research, from research question to design to sample through analysis and presentation, to ensure that a minimum of harm (ideally, zero harm) is caused.  Because each research project is unique, the standards of care for each study are unique.  Part of being a professional researcher is carrying this code in one’s heart, being constantly attentive to what is required under particular circumstances.  Chapter 7 provides various research scenarios and asks readers to weigh in on the suitability and appropriateness of the research.  If done in a class setting, it will become obvious fairly quickly that there are often no absolutely correct answers, as different people find different aspects of the scenarios of greatest importance.  Minimizing the harm in one area may require possible harm in another.  Being attentive to all the ethical aspects of one’s research and making the best judgments one can, clearly and consciously, is an integral part of being a good researcher.

Chapter 8 , best to be read in conjunction with chapter 7, explains the role and importance of Institutional Review Boards (IRBs) .  Under federal guidelines, an IRB is an appropriately constituted group that has been formally designated to review and monitor research involving human subjects .  Every institution that receives funding from the federal government has an IRB.  IRBs have the authority to approve, require modifications to (to secure approval), or disapprove research.  This group review serves an important role in the protection of the rights and welfare of human research subjects.  Chapter 8 reviews the history of IRBs and the work they do but also argues that IRBs’ review of qualitative research is often both over-inclusive and under-inclusive.  Some aspects of qualitative research are not well understood by IRBs, given that they were developed to prevent abuses in biomedical research.  Thus, it is important not to rely on IRBs to identify all the potential ethical issues that emerge in our research (see chapter 7).

Chapter 9 provides help for getting started on formulating a research question based on gaps in the pre-existing literature.  Research is conducted as part of a community, even if particular studies are done by single individuals (or small teams).  What any of us finds and reports back becomes part of a much larger body of knowledge.  Thus, it is important that we look at the larger body of knowledge before we actually start our bit to see how we can best contribute.  When I first began interviewing working-class college students, there was only one other similar study I could find, and it hadn’t been published (it was a dissertation of students from poor backgrounds).  But there had been a lot published by professors who had grown up working class and made it through college despite the odds.  These accounts by “working-class academics” became an important inspiration for my study and helped me frame the questions I asked the students I interviewed.  Chapter 9 will provide some pointers on how to search for relevant literature and how to use this to refine your research question.

Chapter 10 serves as a bridge between the two parts of the textbook, by introducing techniques of data collection.  Qualitative research is often characterized by the form of data collection – for example, an ethnographic study is one that employs primarily observational data collection for the purpose of documenting and presenting a particular culture or ethnos.  Techniques can be effectively combined, depending on the research question and the aims and goals of the study.   Chapter 10 provides a general overview of all the various techniques and how they can be combined.

The second part of the textbook moves into the doing part of qualitative research once the research question has been articulated and the study designed.  Chapters 11 through 17 cover various data collection techniques and approaches.  Chapters 18 and 19 provide a very simple overview of basic data analysis.  Chapter 20 covers communication of the data to various audiences, and in various formats.

Chapter 11 begins our overview of data collection techniques with a focus on interviewing , the true heart of qualitative research.  This technique can serve as the primary and exclusive form of data collection, or it can be used to supplement other forms (observation, archival).  An interview is distinct from a survey, where questions are asked in a specific order and often with a range of predetermined responses available.  Interviews can be conversational and unstructured or, more conventionally, semistructured , where a general set of interview questions “guides” the conversation.  Chapter 11 covers the basics of interviews: how to create interview guides, how many people to interview, where to conduct the interview, what to watch out for (how to prepare against things going wrong), and how to get the most out of your interviews.

Chapter 12 covers an important variant of interviewing, the focus group.  Focus groups are semistructured interviews with a group of people moderated by a facilitator (the researcher or researcher’s assistant).  Focus groups explicitly use group interaction to assist in the data collection.  They are best used to collect data on a specific topic that is non-personal and shared among the group.  For example, asking a group of college students about a common experience such as taking classes by remote delivery during the pandemic year of 2020.  Chapter 12 covers the basics of focus groups: when to use them, how to create interview guides for them, and how to run them effectively.

Chapter 13 moves away from interviewing to the second major form of data collection unique to qualitative researchers – observation .  Qualitative research that employs observation can best be understood as falling on a continuum of “fly on the wall” observation (e.g., observing how strangers interact in a doctor’s waiting room) to “participant” observation, where the researcher is also an active participant of the activity being observed.  For example, an activist in the Black Lives Matter movement might want to study the movement, using her inside position to gain access to observe key meetings and interactions.  Chapter  13 covers the basics of participant observation studies: advantages and disadvantages, gaining access, ethical concerns related to insider/outsider status and entanglement, and recording techniques.

Chapter 14 takes a closer look at “deep ethnography” – immersion in the field of a particularly long duration for the purpose of gaining a deeper understanding and appreciation of a particular culture or social world.  Clifford Geertz called this “deep hanging out.”  Whereas participant observation is often combined with semistructured interview techniques, deep ethnography’s commitment to “living the life” or experiencing the situation as it really is demands more conversational and natural interactions with people.  These interactions and conversations may take place over months or even years.  As can be expected, there are some costs to this technique, as well as some very large rewards when done competently.  Chapter 14 provides some examples of deep ethnographies that will inspire some beginning researchers and intimidate others.

Chapter 15 moves in the opposite direction of deep ethnography, a technique that is the least positivist of all those discussed here, to mixed methods , a set of techniques that is arguably the most positivist .  A mixed methods approach combines both qualitative data collection and quantitative data collection, commonly by combining a survey that is analyzed statistically (e.g., cross-tabs or regression analyses of large number probability samples) with semi-structured interviews.  Although it is somewhat unconventional to discuss mixed methods in textbooks on qualitative research, I think it is important to recognize this often-employed approach here.  There are several advantages and some disadvantages to taking this route.  Chapter 16 will describe those advantages and disadvantages and provide some particular guidance on how to design a mixed methods study for maximum effectiveness.

Chapter 16 covers data collection that does not involve live human subjects at all – archival and historical research (chapter 17 will also cover data that does not involve interacting with human subjects).  Sometimes people are unavailable to us, either because they do not wish to be interviewed or observed (as is the case with many “elites”) or because they are too far away, in both place and time.  Fortunately, humans leave many traces and we can often answer questions we have by examining those traces.  Special collections and archives can be goldmines for social science research.  This chapter will explain how to access these places, for what purposes, and how to begin to make sense of what you find.

Chapter 17 covers another data collection area that does not involve face-to-face interaction with humans: content analysis .  Although content analysis may be understood more properly as a data analysis technique, the term is often used for the entire approach, which will be the case here.  Content analysis involves interpreting meaning from a body of text.  This body of text might be something found in historical records (see chapter 16) or something collected by the researcher, as in the case of comment posts on a popular blog post.  I once used the stories told by student loan debtors on the website studentloanjustice.org as the content I analyzed.  Content analysis is particularly useful when attempting to define and understand prevalent stories or communication about a topic of interest.  In other words, when we are less interested in what particular people (our defined sample) are doing or believing and more interested in what general narratives exist about a particular topic or issue.  This chapter will explore different approaches to content analysis and provide helpful tips on how to collect data, how to turn that data into codes for analysis, and how to go about presenting what is found through analysis.

Where chapter 17 has pushed us towards data analysis, chapters 18 and 19 are all about what to do with the data collected, whether that data be in the form of interview transcripts or fieldnotes from observations.  Chapter 18 introduces the basics of coding , the iterative process of assigning meaning to the data in order to both simplify and identify patterns.  What is a code and how does it work?  What are the different ways of coding data, and when should you use them?  What is a codebook, and why do you need one?  What does the process of data analysis look like?

Chapter 19 goes further into detail on codes and how to use them, particularly the later stages of coding in which our codes are refined, simplified, combined, and organized.  These later rounds of coding are essential to getting the most out of the data we’ve collected.  As students are often overwhelmed with the amount of data (a corpus of interview transcripts typically runs into the hundreds of pages; fieldnotes can easily top that), this chapter will also address time management and provide suggestions for dealing with chaos and reminders that feeling overwhelmed at the analysis stage is part of the process.  By the end of the chapter, you should understand how “findings” are actually found.

The book concludes with a chapter dedicated to the effective presentation of data results.  Chapter 20 covers the many ways that researchers communicate their studies to various audiences (academic, personal, political), what elements must be included in these various publications, and the hallmarks of excellent qualitative research that various audiences will be expecting.  Because qualitative researchers are motivated by understanding and conveying meaning , effective communication is not only an essential skill but a fundamental facet of the entire research project.  Ethnographers must be able to convey a certain sense of verisimilitude , the appearance of true reality.  Those employing interviews must faithfully depict the key meanings of the people they interviewed in a way that rings true to those people, even if the end result surprises them.  And all researchers must strive for clarity in their publications so that various audiences can understand what was found and why it is important.

The book concludes with a short chapter ( chapter 21 ) discussing the value of qualitative research. At the very end of this book, you will find a glossary of terms. I recommend you make frequent use of the glossary and add to each entry as you find examples. Although the entries are meant to be simple and clear, you may also want to paraphrase the definition—make it “make sense” to you, in other words. In addition to the standard reference list (all works cited here), you will find various recommendations for further reading at the end of many chapters. Some of these recommendations will be examples of excellent qualitative research, indicated with an asterisk (*) at the end of the entry. As they say, a picture is worth a thousand words. A good example of qualitative research can teach you more about conducting research than any textbook can (this one included). I highly recommend you select one to three examples from these lists and read them along with the textbook.

A final note on the choice of examples – you will note that many of the examples used in the text come from research on college students.  This is for two reasons.  First, as most of my research falls in this area, I am most familiar with this literature and have contacts with those who do research here and can call upon them to share their stories with you.  Second, and more importantly, my hope is that this textbook reaches a wide audience of beginning researchers who study widely and deeply across the range of what can be known about the social world (from marine resources management to public policy to nursing to political science to sexuality studies and beyond).  It is sometimes difficult to find examples that speak to all those research interests, however. A focus on college students is something that all readers can understand and, hopefully, appreciate, as we are all now or have been at some point a college student.

Recommended Reading: Other Qualitative Research Textbooks

I’ve included a brief list of some of my favorite qualitative research textbooks and guidebooks if you need more than what you will find in this introductory text.  For each, I’ve also indicated if these are for “beginning” or “advanced” (graduate-level) readers.  Many of these books have several editions that do not significantly vary; the edition recommended is merely the edition I have used in teaching and to whose page numbers any specific references made in the text agree.

Barbour, Rosaline. 2014. Introducing Qualitative Research: A Student’s Guide. Thousand Oaks, CA: SAGE.  A good introduction to qualitative research, with abundant examples (often from the discipline of health care) and clear definitions.  Includes quick summaries at the ends of each chapter.  However, some US students might find the British context distracting and can be a bit advanced in some places.  Beginning .

Bloomberg, Linda Dale, and Marie F. Volpe. 2012. Completing Your Qualitative Dissertation . 2nd ed. Thousand Oaks, CA: SAGE.  Specifically designed to guide graduate students through the research process. Advanced .

Creswell, John W., and Cheryl Poth. 2018 Qualitative Inquiry and Research Design: Choosing among Five Traditions .  4th ed. Thousand Oaks, CA: SAGE.  This is a classic and one of the go-to books I used myself as a graduate student.  One of the best things about this text is its clear presentation of five distinct traditions in qualitative research.  Despite the title, this reasonably sized book is about more than research design, including both data analysis and how to write about qualitative research.  Advanced .

Lareau, Annette. 2021. Listening to People: A Practical Guide to Interviewing, Participant Observation, Data Analysis, and Writing It All Up .  Chicago: University of Chicago Press. A readable and personal account of conducting qualitative research by an eminent sociologist, with a heavy emphasis on the kinds of participant-observation research conducted by the author.  Despite its reader-friendliness, this is really a book targeted to graduate students learning the craft.  Advanced .

Lune, Howard, and Bruce L. Berg. 2018. 9th edition.  Qualitative Research Methods for the Social Sciences.  Pearson . Although a good introduction to qualitative methods, the authors favor symbolic interactionist and dramaturgical approaches, which limits the appeal primarily to sociologists.  Beginning .

Marshall, Catherine, and Gretchen B. Rossman. 2016. 6th edition. Designing Qualitative Research. Thousand Oaks, CA: SAGE.  Very readable and accessible guide to research design by two educational scholars.  Although the presentation is sometimes fairly dry, personal vignettes and illustrations enliven the text.  Beginning .

Maxwell, Joseph A. 2013. Qualitative Research Design: An Interactive Approach .  3rd ed. Thousand Oaks, CA: SAGE. A short and accessible introduction to qualitative research design, particularly helpful for graduate students contemplating theses and dissertations. This has been a standard textbook in my graduate-level courses for years.  Advanced .

Patton, Michael Quinn. 2002. Qualitative Research and Evaluation Methods . Thousand Oaks, CA: SAGE.  This is a comprehensive text that served as my “go-to” reference when I was a graduate student.  It is particularly helpful for those involved in program evaluation and other forms of evaluation studies and uses examples from a wide range of disciplines.  Advanced .

Rubin, Ashley T. 2021. Rocking Qualitative Social Science: An Irreverent Guide to Rigorous Research. Stanford : Stanford University Press.  A delightful and personal read.  Rubin uses rock climbing as an extended metaphor for learning how to conduct qualitative research.  A bit slanted toward ethnographic and archival methods of data collection, with frequent examples from her own studies in criminology. Beginning .

Weis, Lois, and Michelle Fine. 2000. Speed Bumps: A Student-Friendly Guide to Qualitative Research . New York: Teachers College Press.  Readable and accessibly written in a quasi-conversational style.  Particularly strong in its discussion of ethical issues throughout the qualitative research process.  Not comprehensive, however, and very much tied to ethnographic research.  Although designed for graduate students, this is a recommended read for students of all levels.  Beginning .

Patton’s Ten Suggestions for Doing Qualitative Research

The following ten suggestions were made by Michael Quinn Patton in his massive textbooks Qualitative Research and Evaluations Methods . This book is highly recommended for those of you who want more than an introduction to qualitative methods. It is the book I relied on heavily when I was a graduate student, although it is much easier to “dip into” when necessary than to read through as a whole. Patton is asked for “just one bit of advice” for a graduate student considering using qualitative research methods for their dissertation.  Here are his top ten responses, in short form, heavily paraphrased, and with additional comments and emphases from me:

• Make sure that a qualitative approach fits the research question. The following are the kinds of questions that call out for qualitative methods or where qualitative methods are particularly appropriate: questions about people’s experiences or how they make sense of those experiences; studying a person in their natural environment; researching a phenomenon so unknown that it would be impossible to study it with standardized instruments or other forms of quantitative data collection.
• Study qualitative research by going to the original sources for the design and analysis appropriate to the particular approach you want to take (e.g., read Glaser and Straus if you are using grounded theory )
• Find a dissertation adviser who understands or at least who will support your use of qualitative research methods. You are asking for trouble if your entire committee is populated by quantitative researchers, even if they are all very knowledgeable about the subject or focus of your study (maybe even more so if they are!)
• Really work on design. Doing qualitative research effectively takes a lot of planning.  Even if things are more flexible than in quantitative research, a good design is absolutely essential when starting out.
• Practice data collection techniques, particularly interviewing and observing. There is definitely a set of learned skills here!  Do not expect your first interview to be perfect.  You will continue to grow as a researcher the more interviews you conduct, and you will probably come to understand yourself a bit more in the process, too.  This is not easy, despite what others who don’t work with qualitative methods may assume (and tell you!)
• Have a plan for analysis before you begin data collection. This is often a requirement in IRB protocols , although you can get away with writing something fairly simple.  And even if you are taking an approach, such as grounded theory, that pushes you to remain fairly open-minded during the data collection process, you still want to know what you will be doing with all the data collected – creating a codebook? Writing analytical memos? Comparing cases?  Having a plan in hand will also help prevent you from collecting too much extraneous data.
• Be prepared to confront controversies both within the qualitative research community and between qualitative research and quantitative research. Don’t be naïve about this – qualitative research, particularly some approaches, will be derided by many more “positivist” researchers and audiences.  For example, is an “n” of 1 really sufficient?  Yes!  But not everyone will agree.
• Do not make the mistake of using qualitative research methods because someone told you it was easier, or because you are intimidated by the math required of statistical analyses. Qualitative research is difficult in its own way (and many would claim much more time-consuming than quantitative research).  Do it because you are convinced it is right for your goals, aims, and research questions.
• Find a good support network. This could be a research mentor, or it could be a group of friends or colleagues who are also using qualitative research, or it could be just someone who will listen to you work through all of the issues you will confront out in the field and during the writing process.  Even though qualitative research often involves human subjects, it can be pretty lonely.  A lot of times you will feel like you are working without a net.  You have to create one for yourself.  Take care of yourself.
• And, finally, in the words of Patton, “Prepare to be changed. Looking deeply at other people’s lives will force you to look deeply at yourself.”
• We will actually spend an entire chapter ( chapter 3 ) looking at this question in much more detail! ↵
• Note that this might have been news to Europeans at the time, but many other societies around the world had also come to this conclusion through observation.  There is often a tendency to equate “the scientific revolution” with the European world in which it took place, but this is somewhat misleading. ↵
• Historians are a special case here.  Historians have scrupulously and rigorously investigated the social world, but not for the purpose of understanding general laws about how things work, which is the point of scientific empirical research.  History is often referred to as an idiographic field of study, meaning that it studies things that happened or are happening in themselves and not for general observations or conclusions. ↵
• Don’t worry, we’ll spend more time later in this book unpacking the meaning of ethnography and other terms that are important here.  Note the available glossary ↵

An approach to research that is “multimethod in focus, involving an interpretative, naturalistic approach to its subject matter.  This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them.  Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives." ( Denzin and Lincoln 2005:2 ). Contrast with quantitative research .

In contrast to methodology, methods are more simply the practices and tools used to collect and analyze data.  Examples of common methods in qualitative research are interviews , observations , and documentary analysis .  One’s methodology should connect to one’s choice of methods, of course, but they are distinguishable terms.  See also methodology .

A proposed explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation.  The positing of a hypothesis is often the first step in quantitative research but not in qualitative research.  Even when qualitative researchers offer possible explanations in advance of conducting research, they will tend to not use the word “hypothesis” as it conjures up the kind of positivist research they are not conducting.

The foundational question to be addressed by the research study.  This will form the anchor of the research design, collection, and analysis.  Note that in qualitative research, the research question may, and probably will, alter or develop during the course of the research.

An approach to research that collects and analyzes numerical data for the purpose of finding patterns and averages, making predictions, testing causal relationships, and generalizing results to wider populations.  Contrast with qualitative research .

Data collection that takes place in real-world settings, referred to as “the field;” a key component of much Grounded Theory and ethnographic research.  Patton ( 2002 ) calls fieldwork “the central activity of qualitative inquiry” where “‘going into the field’ means having direct and personal contact with people under study in their own environments – getting close to people and situations being studied to personally understand the realities of minutiae of daily life” (48).

The people who are the subjects of a qualitative study.  In interview-based studies, they may be the respondents to the interviewer; for purposes of IRBs, they are often referred to as the human subjects of the research.

The branch of philosophy concerned with knowledge.  For researchers, it is important to recognize and adopt one of the many distinguishing epistemological perspectives as part of our understanding of what questions research can address or fully answer.  See, e.g., constructivism , subjectivism, and  objectivism .

An approach that refutes the possibility of neutrality in social science research.  All research is “guided by a set of beliefs and feelings about the world and how it should be understood and studied” (Denzin and Lincoln 2005: 13).  In contrast to positivism , interpretivism recognizes the social constructedness of reality, and researchers adopting this approach focus on capturing interpretations and understandings people have about the world rather than “the world” as it is (which is a chimera).

The cluster of data-collection tools and techniques that involve observing interactions between people, the behaviors, and practices of individuals (sometimes in contrast to what they say about how they act and behave), and cultures in context.  Observational methods are the key tools employed by ethnographers and Grounded Theory .

Research based on data collected and analyzed by the research (in contrast to secondary “library” research).

The process of selecting people or other units of analysis to represent a larger population. In quantitative research, this representation is taken quite literally, as statistically representative.  In qualitative research, in contrast, sample selection is often made based on potential to generate insight about a particular topic or phenomenon.

A method of data collection in which the researcher asks the participant questions; the answers to these questions are often recorded and transcribed verbatim. There are many different kinds of interviews - see also semistructured interview , structured interview , and unstructured interview .

The specific group of individuals that you will collect data from.  Contrast population.

The practice of being conscious of and reflective upon one’s own social location and presence when conducting research.  Because qualitative research often requires interaction with live humans, failing to take into account how one’s presence and prior expectations and social location affect the data collected and how analyzed may limit the reliability of the findings.  This remains true even when dealing with historical archives and other content.  Who we are matters when asking questions about how people experience the world because we, too, are a part of that world.

The science and practice of right conduct; in research, it is also the delineation of moral obligations towards research participants, communities to which we belong, and communities in which we conduct our research.

An administrative body established to protect the rights and welfare of human research subjects recruited to participate in research activities conducted under the auspices of the institution with which it is affiliated. The IRB is charged with the responsibility of reviewing all research involving human participants. The IRB is concerned with protecting the welfare, rights, and privacy of human subjects. The IRB has the authority to approve, disapprove, monitor, and require modifications in all research activities that fall within its jurisdiction as specified by both the federal regulations and institutional policy.

Research, according to US federal guidelines, that involves “a living individual about whom an investigator (whether professional or student) conducting research:  (1) Obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or  (2) Obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens.”

One of the primary methodological traditions of inquiry in qualitative research, ethnography is the study of a group or group culture, largely through observational fieldwork supplemented by interviews. It is a form of fieldwork that may include participant-observation data collection. See chapter 14 for a discussion of deep ethnography. 

A form of interview that follows a standard guide of questions asked, although the order of the questions may change to match the particular needs of each individual interview subject, and probing “follow-up” questions are often added during the course of the interview.  The semi-structured interview is the primary form of interviewing used by qualitative researchers in the social sciences.  It is sometimes referred to as an “in-depth” interview.  See also interview and  interview guide .

A method of observational data collection taking place in a natural setting; a form of fieldwork .  The term encompasses a continuum of relative participation by the researcher (from full participant to “fly-on-the-wall” observer).  This is also sometimes referred to as ethnography , although the latter is characterized by a greater focus on the culture under observation.

A research design that employs both quantitative and qualitative methods, as in the case of a survey supplemented by interviews.

An epistemological perspective that posits the existence of reality through sensory experience similar to empiricism but goes further in denying any non-sensory basis of thought or consciousness.  In the social sciences, the term has roots in the proto-sociologist August Comte, who believed he could discern “laws” of society similar to the laws of natural science (e.g., gravity).  The term has come to mean the kinds of measurable and verifiable science conducted by quantitative researchers and is thus used pejoratively by some qualitative researchers interested in interpretation, consciousness, and human understanding.  Calling someone a “positivist” is often intended as an insult.  See also empiricism and objectivism.

A place or collection containing records, documents, or other materials of historical interest; most universities have an archive of material related to the university’s history, as well as other “special collections” that may be of interest to members of the community.

A method of both data collection and data analysis in which a given content (textual, visual, graphic) is examined systematically and rigorously to identify meanings, themes, patterns and assumptions.  Qualitative content analysis (QCA) is concerned with gathering and interpreting an existing body of material.    

A word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data (Saldaña 2021:5).

Usually a verbatim written record of an interview or focus group discussion.

The primary form of data for fieldwork , participant observation , and ethnography .  These notes, taken by the researcher either during the course of fieldwork or at day’s end, should include as many details as possible on what was observed and what was said.  They should include clear identifiers of date, time, setting, and names (or identifying characteristics) of participants.

The process of labeling and organizing qualitative data to identify different themes and the relationships between them; a way of simplifying data to allow better management and retrieval of key themes and illustrative passages.  See coding frame and  codebook.

A methodological tradition of inquiry and approach to analyzing qualitative data in which theories emerge from a rigorous and systematic process of induction.  This approach was pioneered by the sociologists Glaser and Strauss (1967).  The elements of theory generated from comparative analysis of data are, first, conceptual categories and their properties and, second, hypotheses or generalized relations among the categories and their properties – “The constant comparing of many groups draws the [researcher’s] attention to their many similarities and differences.  Considering these leads [the researcher] to generate abstract categories and their properties, which, since they emerge from the data, will clearly be important to a theory explaining the kind of behavior under observation.” (36).

A detailed description of any proposed research that involves human subjects for review by IRB.  The protocol serves as the recipe for the conduct of the research activity.  It includes the scientific rationale to justify the conduct of the study, the information necessary to conduct the study, the plan for managing and analyzing the data, and a discussion of the research ethical issues relevant to the research.  Protocols for qualitative research often include interview guides, all documents related to recruitment, informed consent forms, very clear guidelines on the safekeeping of materials collected, and plans for de-identifying transcripts or other data that include personal identifying information.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

Quantitative and Qualitative Research

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What is qualitative research?

Qualitative research is a process of naturalistic inquiry that seeks an in-depth understanding of social phenomena within their natural setting. It focuses on the "why" rather than the "what" of social phenomena and relies on the direct experiences of human beings as meaning-making agents in their every day lives. Rather than by logical and statistical procedures, qualitative researchers use multiple systems of inquiry for the study of human phenomena including biography, case study, historical analysis, discourse analysis, ethnography, grounded theory, and phenomenology.

University of Utah College of Nursing, (n.d.). What is qualitative research? [Guide] Retrieved from  https://nursing.utah.edu/research/qualitative-research/what-is-qualitative-research.php#what 

The following video will explain the fundamentals of qualitative research.

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Qualitative research: methods and examples

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13 April 2023

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

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

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

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• What is qualitative research?

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

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

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

• Types of qualitative research methodology

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

Some of the methods for conducting qualitative analysis include:

Focus groups

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

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

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

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

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

Case study research

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

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

Historical model

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

Oral history

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

Qualitative observation

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

Record keeping and review

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

Grounded theory approach

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

Ethnographic research

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

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

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

Narrative framework

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

Phenomenological approach

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

• Qualitative research methods (tools)

Some of the instruments involved in qualitative research include:

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

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

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

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

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

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

• What are common examples of qualitative research?

Everyday examples of qualitative research include:

Conducting a demographic analysis of a business

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

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

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

Launching or testing a new product

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

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

Conducting studies to explain buyers' behaviors

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

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

• Qualitative research: data collection

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

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

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

• Qualitative research: data analysis

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

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

Learn more about qualitative research data analysis software

• When to use qualitative research

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

Here are some instances when qualitative research can be valuable:

Examining your product or service to improve your marketing approach

When researching market segments, demographics, and customer service teams

Identifying client language when you want to design a quantitative survey

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

Assessing feelings and beliefs about societal and public policy matters

Collecting information about a business or product's perception

Analyzing your target audience's reactions to marketing efforts

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

When seeking to evaluate buyers' purchasing patterns

• Qualitative research methods vs. quantitative research methods

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

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

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

What is the most important feature of qualitative research?

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

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

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

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• Open access
• Published: 27 May 2020

How to use and assess qualitative research methods

• Loraine Busetto   ORCID: orcid.org/0000-0002-9228-7875 1 ,
• Wolfgang Wick 1 , 2 &
• Christoph Gumbinger 1  

Neurological Research and Practice volume  2 , Article number:  14 ( 2020 ) Cite this article

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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 , 8 , 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 , 10 , 11 , 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. 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 , 25 , 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 , 32 , 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 , 38 , 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 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.

Availability of data and materials

Not applicable.

Abbreviations

Endovascular treatment

Randomised Controlled Trial

Standard Operating Procedure

Standards for Reporting Qualitative Research

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Busetto, L., Wick, W. & Gumbinger, C. How to use and assess qualitative research methods. Neurol. Res. Pract. 2 , 14 (2020). https://doi.org/10.1186/s42466-020-00059-z

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Qualitative Research

What is qualitative research.

Qualitative research is the methodology researchers use to gain deep contextual understandings of users via non-numerical means and direct observations. Researchers focus on smaller user samples—e.g., in interviews—to reveal data such as user attitudes, behaviors and hidden factors: insights which guide better designs.

“ There are also unknown unknowns, things we don’t know we don’t know.” — Donald Rumsfeld, Former U.S. Secretary of Defense

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See how you can use qualitative research to expose hidden truths about users and iteratively shape better products.

Qualitative Research Focuses on the “Why”

Qualitative research is a subset of user experience (UX) research and user research . By doing qualitative research, you aim to gain narrowly focused but rich information about why users feel and think the ways they do. Unlike its more statistics-oriented “counterpart”, quantitative research , qualitative research can help expose hidden truths about your users’ motivations, hopes, needs, pain points and more to help you keep your project’s focus on track throughout development. UX design professionals do qualitative research typically from early on in projects because—since the insights they reveal can alter product development dramatically—they can prevent costly design errors from arising later. Compare and contrast qualitative with quantitative research here:

Qualitative research

Quantitative Research

You Aim to Determine

The “why” – to get behind how users approach their problems in their world

The “what”, “where” & “when” of the users’ needs & problems – to help keep your project’s focus on track during development

Loosely structured (e.g., contextual inquiries) – to learn why users behave how they do & explore their opinions

Highly structured (e.g., surveys) – to gather data about what users do & find patterns in large user groups

Number of Representative Users

Often around 5

Ideally 30+

Level of Contact with Users

More direct & less remote (e.g., usability testing to examine users’ stress levels when they use your design)

Less direct & more remote (e.g., analytics)

Statistically

You need to take great care with handling non-numerical data (e.g., opinions), as your own opinions might influence findings

Reliable – given enough test users

Regarding care with opinions, it’s easy to be subjective about qualitative data, which isn’t as comprehensively analyzable as quantitative data. That’s why design teams also apply quantitative research methods, to reinforce the “why” with the “what”.

Qualitative Research Methods You Can Use to Get Behind Your Users

You have a choice of many methods to help gain the clearest insights into your users’ world – which you might want to complement with quantitative research methods. In iterative processes such as user-centered design , you/your design team would use quantitative research to spot design problems, discover the reasons for these with qualitative research, make changes and then test your improved design on users again. The best method/s to pick will depend on the stage of your project and your objectives. Here are some:

Diary studies – You ask users to document their activities, interactions, etc. over a defined period. This empowers users to deliver context-rich information. Although such studies can be subjective—since users will inevitably be influenced by in-the-moment human issues and their emotions—they’re helpful tools to access generally authentic information.

Structured – You ask users specific questions and analyze their responses with other users’.

Semi-structured – You have a more free-flowing conversation with users, but still follow a prepared script loosely.

Ethnographic – You interview users in their own environment to appreciate how they perform tasks and view aspects of tasks.

Usability testing

Moderated – In-person testing in, e.g., a lab.

Unmoderated – Users complete tests remotely: e.g., through a video call.

Guerrilla – “Down-the-hall”/“down-and-dirty” testing on a small group of random users or colleagues.

User observation – You watch users get to grips with your design and note their actions, words and reactions as they attempt to perform tasks.

Qualitative research can be more or less structured depending on the method.

Qualitative Research – How to Get Reliable Results

Some helpful points to remember are:

Participants – Select a number of test users carefully (typically around 5). Observe the finer points such as body language. Remember the difference between what they do and what they say they do.

Moderated vs. unmoderated – You can obtain the richest data from moderated studies, but these can involve considerable time and practice. You can usually conduct unmoderated studies more quickly and cheaply, but you should plan these carefully to ensure instructions are clear, etc.

Types of questions – You’ll learn far more by asking open-ended questions. Avoid leading users’ answers – ask about their experience during, say, the “search for deals” process rather than how easy it was. Try to frame questions so users respond honestly: i.e., so they don’t withhold grievances about their experience because they don’t want to seem impolite. Distorted feedback may also arise in guerrilla testing, as test users may be reluctant to sound negative or to discuss fine details if they lack time.

Location – Think how where users are might affect their performance and responses. If, for example, users’ tasks involve running or traveling on a train, select the appropriate method (e.g., diary studies for them to record aspects of their experience in the environment of a train carriage and the many factors impacting it).

Overall, no single research method can help you answer all your questions. Nevertheless, The Nielsen Norman Group advise that if you only conduct one kind of user research, you should pick qualitative usability testing, since a small sample size can yield many cost- and project-saving insights. Always treat users and their data ethically. Finally, remember the importance of complementing qualitative methods with quantitative ones: You gain insights from the former; you test those using the latter.

Learn More about Qualitative Research

Take our course on User Research to see how to get the most from qualitative research.

Read about the numerous considerations for qualitative research in this in-depth piece.

This blog discusses the importance of qualitative research , with tips.

Explore additional insights into qualitative research here .

Literature on Qualitative Research

Here’s the entire UX literature on Qualitative Research by the Interaction Design Foundation, collated in one place:

Learn more about Qualitative Research

Take a deep dive into Qualitative Research with our course User Research – Methods and Best Practices .

How do you plan to design a product or service that your users will love , if you don't know what they want in the first place? As a user experience designer, you shouldn't leave it to chance to design something outstanding; you should make the effort to understand your users and build on that knowledge from the outset. User research is the way to do this, and it can therefore be thought of as the largest part of user experience design .

In fact, user research is often the first step of a UX design process—after all, you cannot begin to design a product or service without first understanding what your users want! As you gain the skills required, and learn about the best practices in user research, you’ll get first-hand knowledge of your users and be able to design the optimal product—one that’s truly relevant for your users and, subsequently, outperforms your competitors’ .

This course will give you insights into the most essential qualitative research methods around and will teach you how to put them into practice in your design work. You’ll also have the opportunity to embark on three practical projects where you can apply what you’ve learned to carry out user research in the real world . You’ll learn details about how to plan user research projects and fit them into your own work processes in a way that maximizes the impact your research can have on your designs. On top of that, you’ll gain practice with different methods that will help you analyze the results of your research and communicate your findings to your clients and stakeholders—workshops, user journeys and personas, just to name a few!

By the end of the course, you’ll have not only a Course Certificate but also three case studies to add to your portfolio. And remember, a portfolio with engaging case studies is invaluable if you are looking to break into a career in UX design or user research!

We believe you should learn from the best, so we’ve gathered a team of experts to help teach this course alongside our own course instructors. That means you’ll meet a new instructor in each of the lessons on research methods who is an expert in their field—we hope you enjoy what they have in store for you!

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Qualitative Research Methods: Types, Analysis + Examples

Qualitative research is based on the disciplines of social sciences like psychology, sociology, and anthropology. Therefore, the qualitative research methods allow for in-depth and further probing and questioning of respondents based on their responses. The interviewer/researcher also tries to understand their motivation and feelings. Understanding how your audience makes decisions can help derive conclusions in market research.

What is qualitative research?

Qualitative research is defined as a market research method that focuses on obtaining data through open-ended and conversational communication .

This method is about “what” people think and “why” they think so. For example, consider a convenience store looking to improve its patronage. A systematic observation concludes that more men are visiting this store. One good method to determine why women were not visiting the store is conducting an in-depth interview method with potential customers.

For example, after successfully interviewing female customers and visiting nearby stores and malls, the researchers selected participants through random sampling . As a result, it was discovered that the store didn’t have enough items for women.

So fewer women were visiting the store, which was understood only by personally interacting with them and understanding why they didn’t visit the store because there were more male products than female ones.

Gather research insights

Types of qualitative research methods with examples

Qualitative research methods are designed in a manner that helps reveal the behavior and perception of a target audience with reference to a particular topic. There are different types of qualitative research methods, such as in-depth interviews, focus groups, ethnographic research, content analysis, and case study research that are usually used.

The results of qualitative methods are more descriptive, and the inferences can be drawn quite easily from the obtained data .

Qualitative research methods originated in the social and behavioral research sciences. Today, our world is more complicated, and it is difficult to understand what people think and perceive. Online research methods make it easier to understand that as it is a more communicative and descriptive analysis .

The following are the qualitative research methods that are frequently used. Also, read about qualitative research examples :

1. One-on-one interview

Conducting in-depth interviews is one of the most common qualitative research methods. It is a personal interview that is carried out with one respondent at a time. This is purely a conversational method and invites opportunities to get details in depth from the respondent.

One of the advantages of this method is that it provides a great opportunity to gather precise data about what people believe and their motivations . If the researcher is well experienced, asking the right questions can help him/her collect meaningful data. If they should need more information, the researchers should ask such follow-up questions that will help them collect more information.

These interviews can be performed face-to-face or on the phone and usually can last between half an hour to two hours or even more. When the in-depth interview is conducted face to face, it gives a better opportunity to read the respondents’ body language and match the responses.

2. Focus groups

A focus group is also a commonly used qualitative research method used in data collection. A focus group usually includes a limited number of respondents (6-10) from within your target market.

The main aim of the focus group is to find answers to the “why, ” “what,” and “how” questions. One advantage of focus groups is you don’t necessarily need to interact with the group in person. Nowadays, focus groups can be sent an online survey on various devices, and responses can be collected at the click of a button.

Focus groups are an expensive method as compared to other online qualitative research methods. Typically, they are used to explain complex processes. This method is very useful for market research on new products and testing new concepts.

3. Ethnographic research

Ethnographic research is the most in-depth observational research method that studies people in their naturally occurring environment.

This method requires the researchers to adapt to the target audiences’ environments, which could be anywhere from an organization to a city or any remote location. Here, geographical constraints can be an issue while collecting data.

This research design aims to understand the cultures, challenges, motivations, and settings that occur. Instead of relying on interviews and discussions, you experience the natural settings firsthand.

This type of research method can last from a few days to a few years, as it involves in-depth observation and collecting data on those grounds. It’s a challenging and time-consuming method and solely depends on the researcher’s expertise to analyze, observe, and infer the data.

4. Case study research

T he case study method has evolved over the past few years and developed into a valuable quality research method. As the name suggests, it is used for explaining an organization or an entity.

This type of research method is used within a number of areas like education, social sciences, and similar. This method may look difficult to operate; however , it is one of the simplest ways of conducting research as it involves a deep dive and thorough understanding of the data collection methods and inferring the data.

5. Record keeping

This method makes use of the already existing reliable documents and similar sources of information as the data source. This data can be used in new research. This is similar to going to a library. There, one can go over books and other reference material to collect relevant data that can likely be used in the research.

6. Process of observation

Qualitative Observation is a process of research that uses subjective methodologies to gather systematic information or data. Since the focus on qualitative observation is the research process of using subjective methodologies to gather information or data. Qualitative observation is primarily used to equate quality differences.

Qualitative observation deals with the 5 major sensory organs and their functioning – sight, smell, touch, taste, and hearing. This doesn’t involve measurements or numbers but instead characteristics.

Explore Insightfully Contextual Inquiry in Qualitative Research

Qualitative research: data collection and analysis

A. qualitative data collection.

Qualitative data collection allows collecting data that is non-numeric and helps us to explore how decisions are made and provide us with detailed insight. For reaching such conclusions the data that is collected should be holistic, rich, and nuanced and findings to emerge through careful analysis.

• Whatever method a researcher chooses for collecting qualitative data, one aspect is very clear the process will generate a large amount of data. In addition to the variety of methods available, there are also different methods of collecting and recording the data.

For example, if the qualitative data is collected through a focus group or one-to-one discussion, there will be handwritten notes or video recorded tapes. If there are recording they should be transcribed and before the process of data analysis can begin.

• As a rough guide, it can take a seasoned researcher 8-10 hours to transcribe the recordings of an interview, which can generate roughly 20-30 pages of dialogues. Many researchers also like to maintain separate folders to maintain the recording collected from the different focus group. This helps them compartmentalize the data collected.
• In case there are running notes taken, which are also known as field notes, they are helpful in maintaining comments, environmental contexts, environmental analysis , nonverbal cues etc. These filed notes are helpful and can be compared while transcribing audio recorded data. Such notes are usually informal but should be secured in a similar manner as the video recordings or the audio tapes.

B. Qualitative data analysis

Qualitative data analysis such as notes, videos, audio recordings images, and text documents. One of the most used methods for qualitative data analysis is text analysis.

Text analysis is a  data analysis method that is distinctly different from all other qualitative research methods, where researchers analyze the social life of the participants in the research study and decode the words, actions, etc. 

There are images also that are used in this research study and the researchers analyze the context in which the images are used and draw inferences from them. In the last decade, text analysis through what is shared on social media platforms has gained supreme popularity.

Characteristics of qualitative research methods

• Qualitative research methods usually collect data at the sight, where the participants are experiencing issues or research problems . These are real-time data and rarely bring the participants out of the geographic locations to collect information.
• Qualitative researchers typically gather multiple forms of data, such as interviews, observations, and documents, rather than rely on a single data source .
• This type of research method works towards solving complex issues by breaking down into meaningful inferences, that is easily readable and understood by all.
• Since it’s a more communicative method, people can build their trust on the researcher and the information thus obtained is raw and unadulterated.

Qualitative research method case study

Let’s take the example of a bookstore owner who is looking for ways to improve their sales and customer outreach. An online community of members who were loyal patrons of the bookstore were interviewed and related questions were asked and the questions were answered by them.

At the end of the interview, it was realized that most of the books in the stores were suitable for adults and there were not enough options for children or teenagers.

By conducting this qualitative research the bookstore owner realized what the shortcomings were and what were the feelings of the readers. Through this research now the bookstore owner can now keep books for different age categories and can improve his sales and customer outreach.

Such qualitative research method examples can serve as the basis to indulge in further quantitative research , which provides remedies.

When to use qualitative research

Researchers make use of qualitative research techniques when they need to capture accurate, in-depth insights. It is very useful to capture “factual data”. Here are some examples of when to use qualitative research.

• Developing a new product or generating an idea.
• Studying your product/brand or service to strengthen your marketing strategy.
• To understand your strengths and weaknesses.
• Understanding purchase behavior.
• To study the reactions of your audience to marketing campaigns and other communications.
• Exploring market demographics, segments, and customer care groups.
• Gathering perception data of a brand, company, or product.

LEARN ABOUT: Steps in Qualitative Research

Qualitative research methods vs quantitative research methods

The basic differences between qualitative research methods and quantitative research methods are simple and straightforward. They differ in:

• Their analytical objectives
• Types of questions asked
• Types of data collection instruments
• Forms of data they produce
• Degree of flexibility

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Qualitative Research: Characteristics, Design, Methods & Examples

Lauren McCall

MSc Health Psychology Graduate

MSc, Health Psychology, University of Nottingham

Lauren obtained an MSc in Health Psychology from The University of Nottingham with a distinction classification.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

“Not everything that can be counted counts, and not everything that counts can be counted“ (Albert Einstein)

Qualitative research is a process used for the systematic collection, analysis, and interpretation of non-numerical data (Punch, 2013). 

Qualitative research can be used to: (i) gain deep contextual understandings of the subjective social reality of individuals and (ii) to answer questions about experience and meaning from the participant’s perspective (Hammarberg et al., 2016).

Unlike quantitative research, which focuses on gathering and analyzing numerical data for statistical analysis, qualitative research focuses on thematic and contextual information.

Characteristics of Qualitative Research 

Reality is socially constructed.

Qualitative research aims to understand how participants make meaning of their experiences – individually or in social contexts. It assumes there is no objective reality and that the social world is interpreted (Yilmaz, 2013). 

The primacy of subject matter 

The primary aim of qualitative research is to understand the perspectives, experiences, and beliefs of individuals who have experienced the phenomenon selected for research rather than the average experiences of groups of people (Minichiello, 1990).

Variables are complex, interwoven, and difficult to measure

Factors such as experiences, behaviors, and attitudes are complex and interwoven, so they cannot be reduced to isolated variables , making them difficult to measure quantitatively.

However, a qualitative approach enables participants to describe what, why, or how they were thinking/ feeling during a phenomenon being studied (Yilmaz, 2013). 

Emic (insider’s point of view)

The phenomenon being studied is centered on the participants’ point of view (Minichiello, 1990).

Emic is used to describe how participants interact, communicate, and behave in the context of the research setting (Scarduzio, 2017).

Why Conduct Qualitative Research? 

In order to gain a deeper understanding of how people experience the world, individuals are studied in their natural setting. This enables the researcher to understand a phenomenon close to how participants experience it. 

Qualitative research allows researchers to gain an in-depth understanding, which is difficult to attain using quantitative methods. 

An in-depth understanding is attained since qualitative techniques allow participants to freely disclose their experiences, thoughts, and feelings without constraint (Tenny et al., 2022). 

This helps to further investigate and understand quantitative data by discovering reasons for the outcome of a study – answering the why question behind statistics. 

The exploratory nature of qualitative research helps to generate hypotheses that can then be tested quantitatively (Busetto et al., 2020).

To design hypotheses, theory must be researched using qualitative methods to find out what is important in order to begin research. 

For example, by conducting interviews or focus groups with key stakeholders to discover what is important to them. 

Examples of qualitative research questions include: 

• How does stress influence young adults’ behavior?
• What factors influence students’ school attendance rates in developed countries?
• How do adults interpret binge drinking in the UK?
• What are the psychological impacts of cervical cancer screening in women?
• How can mental health lessons be integrated into the school curriculum? 

Collecting Qualitative Data

There are four main research design methods used to collect qualitative data: observations, interviews,  focus groups, and ethnography.

Observations

This method involves watching and recording phenomena as they occur in nature. Observation can be divided into two types: participant and non-participant observation.

In participant observation, the researcher actively participates in the situation/events being observed.

In non-participant observation, the researcher is not an active part of the observation and tries not to influence the behaviors they are observing (Busetto et al., 2020). 

Observations can be covert (participants are unaware that a researcher is observing them) or overt (participants are aware of the researcher’s presence and know they are being observed).

However, awareness of an observer’s presence may influence participants’ behavior. 

Interviews give researchers a window into the world of a participant by seeking their account of an event, situation, or phenomenon. They are usually conducted on a one-to-one basis and can be distinguished according to the level at which they are structured (Punch, 2013). 

Structured interviews involve predetermined questions and sequences to ensure replicability and comparability. However, they are unable to explore emerging issues.

Informal interviews consist of spontaneous, casual conversations which are closer to the truth of a phenomenon. However, information is gathered using quick notes made by the researcher and is therefore subject to recall bias. 

Semi-structured interviews have a flexible structure, phrasing, and placement so emerging issues can be explored (Denny & Weckesser, 2022).

The use of probing questions and clarification can lead to a detailed understanding, but semi-structured interviews can be time-consuming and subject to interviewer bias. 

Focus groups 

Similar to interviews, focus groups elicit a rich and detailed account of an experience. However, focus groups are more dynamic since participants with shared characteristics construct this account together (Denny & Weckesser, 2022).

A shared narrative is built between participants to capture a group experience shaped by a shared context. 

The researcher takes on the role of a moderator, who will establish ground rules and guide the discussion by following a topic guide to focus the group discussions.

Typically, focus groups have 4-10 participants as a discussion can be difficult to facilitate with more than this, and this number allows everyone the time to speak.

Ethnography

Ethnography is a methodology used to study a group of people’s behaviors and social interactions in their environment (Reeves et al., 2008).

Data are collected using methods such as observations, field notes, or structured/ unstructured interviews.

The aim of ethnography is to provide detailed, holistic insights into people’s behavior and perspectives within their natural setting. In order to achieve this, researchers immerse themselves in a community or organization. 

Due to the flexibility and real-world focus of ethnography, researchers are able to gather an in-depth, nuanced understanding of people’s experiences, knowledge and perspectives that are influenced by culture and society.

In order to develop a representative picture of a particular culture/ context, researchers must conduct extensive field work. 

This can be time-consuming as researchers may need to immerse themselves into a community/ culture for a few days, or possibly a few years.

Qualitative Data Analysis Methods

Different methods can be used for analyzing qualitative data. The researcher chooses based on the objectives of their study. 

The researcher plays a key role in the interpretation of data, making decisions about the coding, theming, decontextualizing, and recontextualizing of data (Starks & Trinidad, 2007). 

Grounded theory

Grounded theory is a qualitative method specifically designed to inductively generate theory from data. It was developed by Glaser and Strauss in 1967 (Glaser & Strauss, 2017).

 This methodology aims to develop theories (rather than test hypotheses) that explain a social process, action, or interaction (Petty et al., 2012). To inform the developing theory, data collection and analysis run simultaneously. 

There are three key types of coding used in grounded theory: initial (open), intermediate (axial), and advanced (selective) coding. 

Throughout the analysis, memos should be created to document methodological and theoretical ideas about the data. Data should be collected and analyzed until data saturation is reached and a theory is developed. 

Content analysis

Content analysis was first used in the early twentieth century to analyze textual materials such as newspapers and political speeches.

Content analysis is a research method used to identify and analyze the presence and patterns of themes, concepts, or words in data (Vaismoradi et al., 2013). 

This research method can be used to analyze data in different formats, which can be written, oral, or visual. 

The goal of content analysis is to develop themes that capture the underlying meanings of data (Schreier, 2012). 

Qualitative content analysis can be used to validate existing theories, support the development of new models and theories, and provide in-depth descriptions of particular settings or experiences.

The following six steps provide a guideline for how to conduct qualitative content analysis.

• Define a Research Question : To start content analysis, a clear research question should be developed.
• Identify and Collect Data : Establish the inclusion criteria for your data. Find the relevant sources to analyze.
• Define the Unit or Theme of Analysis : Categorize the content into themes. Themes can be a word, phrase, or sentence.
• Develop Rules for Coding your Data : Define a set of coding rules to ensure that all data are coded consistently.
• Code the Data : Follow the coding rules to categorize data into themes.
• Analyze the Results and Draw Conclusions : Examine the data to identify patterns and draw conclusions in relation to your research question.

Discourse analysis

Discourse analysis is a research method used to study written/ spoken language in relation to its social context (Wood & Kroger, 2000).

In discourse analysis, the researcher interprets details of language materials and the context in which it is situated.

Discourse analysis aims to understand the functions of language (how language is used in real life) and how meaning is conveyed by language in different contexts. Researchers use discourse analysis to investigate social groups and how language is used to achieve specific communication goals.

Different methods of discourse analysis can be used depending on the aims and objectives of a study. However, the following steps provide a guideline on how to conduct discourse analysis.

• Define the Research Question : Develop a relevant research question to frame the analysis.
• Gather Data and Establish the Context : Collect research materials (e.g., interview transcripts, documents). Gather factual details and review the literature to construct a theory about the social and historical context of your study.
• Analyze the Content : Closely examine various components of the text, such as the vocabulary, sentences, paragraphs, and structure of the text. Identify patterns relevant to the research question to create codes, then group these into themes.
• Review the Results : Reflect on the findings to examine the function of the language, and the meaning and context of the discourse. 

Thematic analysis

Thematic analysis is a method used to identify, interpret, and report patterns in data, such as commonalities or contrasts. 

Although the origin of thematic analysis can be traced back to the early twentieth century, understanding and clarity of thematic analysis is attributed to Braun and Clarke (2006).

Thematic analysis aims to develop themes (patterns of meaning) across a dataset to address a research question. 

In thematic analysis, qualitative data is gathered using techniques such as interviews, focus groups, and questionnaires. Audio recordings are transcribed. The dataset is then explored and interpreted by a researcher to identify patterns. 

This occurs through the rigorous process of data familiarisation, coding, theme development, and revision. These identified patterns provide a summary of the dataset and can be used to address a research question.

Themes are developed by exploring the implicit and explicit meanings within the data. Two different approaches are used to generate themes: inductive and deductive. 

An inductive approach allows themes to emerge from the data. In contrast, a deductive approach uses existing theories or knowledge to apply preconceived ideas to the data.

Phases of Thematic Analysis

Braun and Clarke (2006) provide a guide of the six phases of thematic analysis. These phases can be applied flexibly to fit research questions and data. 

Template analysis

Template analysis refers to a specific method of thematic analysis which uses hierarchical coding (Brooks et al., 2014).

Template analysis is used to analyze textual data, for example, interview transcripts or open-ended responses on a written questionnaire.

To conduct template analysis, a coding template must be developed (usually from a subset of the data) and subsequently revised and refined. This template represents the themes identified by researchers as important in the dataset. 

Codes are ordered hierarchically within the template, with the highest-level codes demonstrating overarching themes in the data and lower-level codes representing constituent themes with a narrower focus.

A guideline for the main procedural steps for conducting template analysis is outlined below.

• Familiarization with the Data : Read (and reread) the dataset in full. Engage, reflect, and take notes on data that may be relevant to the research question.
• Preliminary Coding : Identify initial codes using guidance from the a priori codes, identified before the analysis as likely to be beneficial and relevant to the analysis.
• Organize Themes : Organize themes into meaningful clusters. Consider the relationships between the themes both within and between clusters.
• Produce an Initial Template : Develop an initial template. This may be based on a subset of the data.
• Apply and Develop the Template : Apply the initial template to further data and make any necessary modifications. Refinements of the template may include adding themes, removing themes, or changing the scope/title of themes. 
• Finalize Template : Finalize the template, then apply it to the entire dataset. 

Frame analysis

Frame analysis is a comparative form of thematic analysis which systematically analyzes data using a matrix output.

Ritchie and Spencer (1994) developed this set of techniques to analyze qualitative data in applied policy research. Frame analysis aims to generate theory from data.

Frame analysis encourages researchers to organize and manage their data using summarization.

This results in a flexible and unique matrix output, in which individual participants (or cases) are represented by rows and themes are represented by columns. 

Each intersecting cell is used to summarize findings relating to the corresponding participant and theme.

Frame analysis has five distinct phases which are interrelated, forming a methodical and rigorous framework.

• Familiarization with the Data : Familiarize yourself with all the transcripts. Immerse yourself in the details of each transcript and start to note recurring themes.
• Develop a Theoretical Framework : Identify recurrent/ important themes and add them to a chart. Provide a framework/ structure for the analysis.
• Indexing : Apply the framework systematically to the entire study data.
• Summarize Data in Analytical Framework : Reduce the data into brief summaries of participants’ accounts.
• Mapping and Interpretation : Compare themes and subthemes and check against the original transcripts. Group the data into categories and provide an explanation for them.

Preventing Bias in Qualitative Research

To evaluate qualitative studies, the CASP (Critical Appraisal Skills Programme) checklist for qualitative studies can be used to ensure all aspects of a study have been considered (CASP, 2018).

The quality of research can be enhanced and assessed using criteria such as checklists, reflexivity, co-coding, and member-checking. 

Co-coding 

Relying on only one researcher to interpret rich and complex data may risk key insights and alternative viewpoints being missed. Therefore, coding is often performed by multiple researchers.

A common strategy must be defined at the beginning of the coding process  (Busetto et al., 2020). This includes establishing a useful coding list and finding a common definition of individual codes.

Transcripts are initially coded independently by researchers and then compared and consolidated to minimize error or bias and to bring confirmation of findings. 

Member checking

Member checking (or respondent validation) involves checking back with participants to see if the research resonates with their experiences (Russell & Gregory, 2003).

Data can be returned to participants after data collection or when results are first available. For example, participants may be provided with their interview transcript and asked to verify whether this is a complete and accurate representation of their views.

Participants may then clarify or elaborate on their responses to ensure they align with their views (Shenton, 2004).

This feedback becomes part of data collection and ensures accurate descriptions/ interpretations of phenomena (Mays & Pope, 2000). 

Reflexivity in qualitative research

Reflexivity typically involves examining your own judgments, practices, and belief systems during data collection and analysis. It aims to identify any personal beliefs which may affect the research. 

Reflexivity is essential in qualitative research to ensure methodological transparency and complete reporting. This enables readers to understand how the interaction between the researcher and participant shapes the data.

Depending on the research question and population being researched, factors that need to be considered include the experience of the researcher, how the contact was established and maintained, age, gender, and ethnicity.

These details are important because, in qualitative research, the researcher is a dynamic part of the research process and actively influences the outcome of the research (Boeije, 2014). 

Reflexivity Example

Who you are and your characteristics influence how you collect and analyze data. Here is an example of a reflexivity statement for research on smoking. I am a 30-year-old white female from a middle-class background. I live in the southwest of England and have been educated to master’s level. I have been involved in two research projects on oral health. I have never smoked, but I have witnessed how smoking can cause ill health from my volunteering in a smoking cessation clinic. My research aspirations are to help to develop interventions to help smokers quit.

Establishing Trustworthiness in Qualitative Research

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability.

Credibility in Qualitative Research

Credibility refers to how accurately the results represent the reality and viewpoints of the participants.

To establish credibility in research, participants’ views and the researcher’s representation of their views need to align (Tobin & Begley, 2004).

To increase the credibility of findings, researchers may use data source triangulation, investigator triangulation, peer debriefing, or member checking (Lincoln & Guba, 1985). 

Transferability in Qualitative Research

Transferability refers to how generalizable the findings are: whether the findings may be applied to another context, setting, or group (Tobin & Begley, 2004).

Transferability can be enhanced by giving thorough and in-depth descriptions of the research setting, sample, and methods (Nowell et al., 2017). 

Dependability in Qualitative Research

Dependability is the extent to which the study could be replicated under similar conditions and the findings would be consistent.

Researchers can establish dependability using methods such as audit trails so readers can see the research process is logical and traceable (Koch, 1994).

Confirmability in Qualitative Research

Confirmability is concerned with establishing that there is a clear link between the researcher’s interpretations/ findings and the data.

Researchers can achieve confirmability by demonstrating how conclusions and interpretations were arrived at (Nowell et al., 2017).

This enables readers to understand the reasoning behind the decisions made. 

Audit Trails in Qualitative Research

An audit trail provides evidence of the decisions made by the researcher regarding theory, research design, and data collection, as well as the steps they have chosen to manage, analyze, and report data. 

The researcher must provide a clear rationale to demonstrate how conclusions were reached in their study.

A clear description of the research path must be provided to enable readers to trace through the researcher’s logic (Halpren, 1983).

Researchers should maintain records of the raw data, field notes, transcripts, and a reflective journal in order to provide a clear audit trail. 

Discovery of unexpected data

Open-ended questions in qualitative research mean the researcher can probe an interview topic and enable the participant to elaborate on responses in an unrestricted manner.

This allows unexpected data to emerge, which can lead to further research into that topic. 

Flexibility

Data collection and analysis can be modified and adapted to take the research in a different direction if new ideas or patterns emerge in the data.

This enables researchers to investigate new opportunities while firmly maintaining their research goals. 

Naturalistic settings

The behaviors of participants are recorded in real-world settings. Studies that use real-world settings have high ecological validity since participants behave more authentically. 

Limitations

Time-consuming .

Qualitative research results in large amounts of data which often need to be transcribed and analyzed manually.

Even when software is used, transcription can be inaccurate, and using software for analysis can result in many codes which need to be condensed into themes. 

Subjectivity 

The researcher has an integral role in collecting and interpreting qualitative data. Therefore, the conclusions reached are from their perspective and experience.

Consequently, interpretations of data from another researcher may vary greatly. 

Limited generalizability

The aim of qualitative research is to provide a detailed, contextualized understanding of an aspect of the human experience from a relatively small sample size.

Despite rigorous analysis procedures, conclusions drawn cannot be generalized to the wider population since data may be biased or unrepresentative.

Therefore, results are only applicable to a small group of the population. 

Extraneous variables

Qualitative research is often conducted in real-world settings. This may cause results to be unreliable since extraneous variables may affect the data, for example:

• Situational variables : different environmental conditions may influence participants’ behavior in a study. The random variation in factors (such as noise or lighting) may be difficult to control in real-world settings.
• Participant characteristics : this includes any characteristics that may influence how a participant answers/ behaves in a study. This may include a participant’s mood, gender, age, ethnicity, sexual identity, IQ, etc.
• Experimenter effect : experimenter effect refers to how a researcher’s unintentional influence can change the outcome of a study. This occurs when (i) their interactions with participants unintentionally change participants’ behaviors or (ii) due to errors in observation, interpretation, or analysis. 

What sample size should qualitative research be?

The sample size for qualitative studies has been recommended to include a minimum of 12 participants to reach data saturation (Braun, 2013).

Are surveys qualitative or quantitative?

Surveys can be used to gather information from a sample qualitatively or quantitatively. Qualitative surveys use open-ended questions to gather detailed information from a large sample using free text responses.

The use of open-ended questions allows for unrestricted responses where participants use their own words, enabling the collection of more in-depth information than closed-ended questions.

In contrast, quantitative surveys consist of closed-ended questions with multiple-choice answer options. Quantitative surveys are ideal to gather a statistical representation of a population.

What are the ethical considerations of qualitative research?

Before conducting a study, you must think about any risks that could occur and take steps to prevent them. Participant Protection : Researchers must protect participants from physical and mental harm. This means you must not embarrass, frighten, offend, or harm participants. Transparency : Researchers are obligated to clearly communicate how they will collect, store, analyze, use, and share the data. Confidentiality : You need to consider how to maintain the confidentiality and anonymity of participants’ data.

What is triangulation in qualitative research?

Triangulation refers to the use of several approaches in a study to comprehensively understand phenomena. This method helps to increase the validity and credibility of research findings. 

Types of triangulation include method triangulation (using multiple methods to gather data); investigator triangulation (multiple researchers for collecting/ analyzing data), theory triangulation (comparing several theoretical perspectives to explain a phenomenon), and data source triangulation (using data from various times, locations, and people; Carter et al., 2014).

Why is qualitative research important?

Qualitative research allows researchers to describe and explain the social world. The exploratory nature of qualitative research helps to generate hypotheses that can then be tested quantitatively.

In qualitative research, participants are able to express their thoughts, experiences, and feelings without constraint.

Additionally, researchers are able to follow up on participants’ answers in real-time, generating valuable discussion around a topic. This enables researchers to gain a nuanced understanding of phenomena which is difficult to attain using quantitative methods.

What is coding data in qualitative research?

Coding data is a qualitative data analysis strategy in which a section of text is assigned with a label that describes its content.

These labels may be words or phrases which represent important (and recurring) patterns in the data.

This process enables researchers to identify related content across the dataset. Codes can then be used to group similar types of data to generate themes.

What is the difference between qualitative and quantitative research?

Qualitative research involves the collection and analysis of non-numerical data in order to understand experiences and meanings from the participant’s perspective.

This can provide rich, in-depth insights on complicated phenomena. Qualitative data may be collected using interviews, focus groups, or observations.

In contrast, quantitative research involves the collection and analysis of numerical data to measure the frequency, magnitude, or relationships of variables. This can provide objective and reliable evidence that can be generalized to the wider population.

Quantitative data may be collected using closed-ended questionnaires or experiments.

What is trustworthiness in qualitative research?

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability. 

Credibility refers to how accurately the results represent the reality and viewpoints of the participants. Transferability refers to whether the findings may be applied to another context, setting, or group.

Dependability is the extent to which the findings are consistent and reliable. Confirmability refers to the objectivity of findings (not influenced by the bias or assumptions of researchers).

What is data saturation in qualitative research?

Data saturation is a methodological principle used to guide the sample size of a qualitative research study.

Data saturation is proposed as a necessary methodological component in qualitative research (Saunders et al., 2018) as it is a vital criterion for discontinuing data collection and/or analysis. 

The intention of data saturation is to find “no new data, no new themes, no new coding, and ability to replicate the study” (Guest et al., 2006). Therefore, enough data has been gathered to make conclusions.

Why is sampling in qualitative research important?

In quantitative research, large sample sizes are used to provide statistically significant quantitative estimates.

This is because quantitative research aims to provide generalizable conclusions that represent populations.

However, the aim of sampling in qualitative research is to gather data that will help the researcher understand the depth, complexity, variation, or context of a phenomenon. The small sample sizes in qualitative studies support the depth of case-oriented analysis.

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What is Qualitative in Research

• Review Essay
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• Published: 28 October 2021
• Volume 44 , pages 599–608, ( 2021 )

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In this text we respond and elaborate on the four comments addressing our original article. In that piece we define qualitative research as an “iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied.” In light of the comments, we identify three positions in relation to our contribution: (1) to not define qualitative research; (2) to work with one definition for each study or approach of “qualitative research” which is predominantly left implicit; (3) to systematically define qualitative research. This article elaborates on these positions and argues that a definition is a point of departure for researchers, including those reflecting on, or researching, the fields of qualitative and quantitative research. The proposed definition can be used both as a standard of evaluation as well as a catalyst for discussions on how to evaluate and innovate different styles of work.

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What is Qualitative in Qualitative Research

Patrik Aspers & Ugo Corte

What is “Qualitative” in Qualitative Research? Why the Answer Does not Matter but the Question is Important

Mario L. Small

Unsettling Definitions of Qualitative Research

Japonica Brown-Saracino

Avoid common mistakes on your manuscript.

The editors of Qualitative Sociology have given us the opportunity not only to receive comments by a group of particularly qualified scholars who engage with our text in a constructive fashion, but also to reply, and thereby to clarify our position. We have read the four essays that comment on our article What is qualitative in qualitative research (Aspers and Corte 2019 ) with great interest. Japonica Brown-Saracino, Paul Lichterman, Jennifer Reich, and Mario Luis Small agree that what we do is new. We are grateful for the engagement that the four commenters show with our text.

Our article is based on a standard approach: we pose a question drawing on our personal experiences and knowledge of the field, make systematic selections from existing literature, identify, collect and analyze data, read key texts closely, make interpretations, move between theory and evidence to connect them, and ultimately present a definition: “ qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied” (Aspers and Corte 2019 , 139) . We acknowledge that there are different qualitative characteristics of research, meaning that we do not merely operate with a binary code of qualitative versus non-qualitative research. Our definition is an attempt to make a new distinction that clarifies what is qualitative in qualitative research and which is useful to the scientific community. Consequently, our work is in line with the definition that we have proposed.

Given the interest that our contribution has already generated, it is reasonable to argue that the new distinction we put forth is also significant . As researchers we make claims about significance, but it is always the audience—other scientists—who decide whether the contribution is significant or not. Iteration means that one goes back and forth between theory and evidence, and improved understanding refers to the epistemic gains of a study. To achieve this improved understanding by pursuing qualitative research, it is necessary that one gets close to the empirical material. When these four components are combined, we speak of qualitative research.

The four commentators welcome our text, which does not imply that they agree with all of the arguments we advance. In what follows, we single out some of the most important critiques we received and provide a reply aiming to push the conversation about qualitative research forward.

Why a Definition?

We appreciate that all critics have engaged closely with our definition. One main point of convergence between them is that one should not try to define qualitative research. Small ( Forthcoming ) asks rhetorically: “Is producing a single definition a good idea?” He justifies his concern by pointing out that the term is used to describe both different practices (different kinds of studies) and three elements (types of data; data collection, and analysis). Similarly, both Brown-Saracino ( Forthcoming ) and Lichterman, ( Forthcoming ) argue that not only there is no single entity called qualitative research—a view that we share, but instead, that definitions change over time. For Small, producing a single definition for a field as diverse as sociology, or the social sciences for that matter, is restrictive, a point which is also, albeit differently, shared by Brown-Saracino. Brown-Saracino asserts that our endeavor “might calcify boundaries, stifle innovation, and prevent recognition of areas of common ground across areas that many of us have long assumed to be disparate.” Hence, one should not define what is qualitative, because definitions may harm development. Both Small and Brown-Saracino say that we are drawing boundaries between qualitative and quantitative approaches and overstate differences between them. Yet, part of our intent was the opposite: to build bridges between different approaches by arguing that the ‘qualitative’ feature of research pertains both quantitative and qualitative methodologies, which may use and even combine different methods.

In light of these comments we need to elaborate our argument. Moreover, it is important not to maintain hard lines that may lead to scientific tribalism. Nonetheless, the critique of our—or any other definition of qualitative research—typically implies that there is something “there,” but that we have not captured it correctly with our definition. Thus, the critique that we should not define qualitative research comes with an implicit contradiction. If all agree that there is something called “qualitative research,” even if it is only something that is not quantitative, this still presumes that there is something called “qualitative.” Had we done research on any other topic it would probably have been requested by reviewers to define what we are talking about. The same criteria should apply also when we turn the researcher’s gaze on to our own practice.

Moreover, it is doubtful that our commentators would claim that qualitative research can be “anything,” as the more Dadaistic interpretation by Paul Feyerabend ( 1976 ) would have it. But without referring to the realist view of Karl Popper ( 1963 , 232–3) and his ideas of verisimilitude (i.e., that we get close to the truth) we have tried to spell out what we see as an account of the phenomenology of “qualitative.” We identify three positions in relation to the issue of definition of qualitative research:

We should not define qualitative research.

We can work with one definition for each study or approach of “qualitative research,” which is predominantly left implicit.

We can try to systematically define qualitative research.

Obviously, we have embraced and practiced position 3 in reaction to the current state of the field which is largely dominated by position 2--namely that what is qualitative research is open to a large variety of “definitions.” The critical points of our commentators explicitly or implicitly argue in favor of position 1, or perhaps position 2. Our claim that a definition can help researchers sort good from less good research has triggered criticism. Below, we elaborate on this issue.

We maintain that a definition is a valid starting point useful for junior scholars to learn more about what is qualitative and what is quantitative, and for more advanced researchers it may feature as a point of departure to make improvements, for instance, in clarifying their epistemological positions and goals. But we could have done a better job in clarifying our position. Nonetheless, we contend that change and improvement at this late stage of development in social sciences is partially related to and dependent upon pushing against or building upon clear benchmarks, such as the definition that we have formulated. We acknowledge that “definitions might evolve or diversify over time,” as Brown-Saracino suggests. Still, surely social scientists can keep two things in mind at the same time: an existing definition may be useful, but new research may change it. This becomes evident if one applies our definition to the definition itself: our definition is not immune to work that leads to new qualitative distinctions! Having said this, we are happy to see that all four comments profit from getting in close contact with the definition. This means that our definition and the article offer the reader an opportunity to think with (Fine and Corte 2022 ) or, as Small writes, “forces the reader to think.” We believe that both in principle and in practice, we all agree that clarity and definitions are scientific virtues.

What can a Definition Enable?

While we agree with several points in Small’s essay, we disagree on others. Our underlying assumption is that we can build on existing knowledge, albeit not in the way positivism envisioned it. It follows that work which is primarily descriptive, evocative, political, or generally aimed at social change may entail new knowledge, but it does not fit well within the frame within which we operate in this piece. The existence of different kinds of work, each of which relies on different standards of evaluation—which are often unclear and consequential, especially to graduate students and junior scholars (see Corte and Irwin 2017 )—brings us to another point highlighted by both Small and Lichterman: can the definition be used to differentiate good from lesser good kinds of work?

Small argues that while our article promises to develop a standard of evaluation, it fails to do so. We agree: our definition does not specify the exact criteria of what is good and what is poor research. Our definition demarcates qualitative research from non-qualitative by spelling out the qualitative elements of research, which advances a criterion of evaluation. In addition, there is definitely research that meets the characteristics of being qualitative, but that is uninteresting, irrelevant, or essentially useless (see Alvesson et al. 2017 on “gap spotting,” for instance). What is good or not good research  is to be decided in an ongoing scientific discussion led by those who actively contribute to the development of a field. A definition, nonetheless, can serve as a point of reference to evaluate scholarly work, and it can also serve as a guideline to demarcate what is qualitative from what it is not.

A Good Definition?

Even if one accepts that there should be a definition of qualitative research, and thinks that such a definition could be useful, it does not follow that one must accept our definition. Small identifies what he sees a paradox in our text, namely that we both speak of qualitative research in general and of qualitative elements in different research activities. The term qualitative, as we note and as Small specifies, is used to describe different things: from small n studies to studies of organizations, states, or other units conceptualized as case studies and analyzed quantitatively as well as qualitatively. We are grateful for this observation, which is correct. We failed to properly address this issue in the original text.

As we discuss in the article, the elements used in our definitions (distinctions, process, closeness, and improved understanding) are present in all kinds of research, even quantitative. Perhaps the title of our article should have been: “What is Qualitative in Research?” Our position is that only when all the elements of the definition are applied can one speak of qualitative research. Hence, the first order constructs (i.e., the constructs the actors in the field have made) (Aspers 2009 ) of, for example, “qualitative observations,” may indeed refer to observations that make qualitative distinction in the Aristotelian sense on which we rely. Still, if these qualitative observations are commensurated with a ratio-scale (i.e., get reduced to numbers) this research can no longer be called “qualitative.” It is for this reason that we say that, to refer to first order constructs, “quantitative” research processes entail “qualitative” elements. This research is, as it were, partially qualitative, but it is not, taken together, qualitative research. Brown-Saracino raises a similar point in relation to her own and others works that combine “qualitative” and “quantitative” research. We do not think that one is inherently better, yet we agree with the general idea that qualitative research is particularly useful in identifying research questions and formulating theories (distinctions) that, at a later point should, when possible, be tested quantitatively on larger samples (cf. Small 2005 ). It is our hope that, with our clarification above, it will be easier for researchers to understand what one is and what one is not doing. We also hope that our study will stimulate further dialogue and collaboration between researchers who primarily work within different traditions.

Small wonders if a researcher who tries to replicate a “qualitative” study (according to our definition) is doing qualitative research. The person is certainly doing research, and some elements are likely conducted in a qualitative fashion according to our definition, for example if the method of in-depth fieldwork is employed. But regardless of the method used, and regardless of whether the person finds new things, if the result is binary coded as either confirming or disconfirming existing research, qualitative research is not being conducted because no new distinction is offered. Imagine the same study being replicated for the 20 th time. Surely the researcher must use the same “qualitative” methods (to use the first order construct). It may even excite a large academic audience, but it would not count as qualitative research according to our definition. Our definition requires both that the research process has made use of all its elements, but it also requires the acceptance by the audience. Having said this, in practice, it is more likely that such a study would also report new distinctions that are acknowledged by an audience. If such a study is reviewed and published, these are additional indicators that the new distinctions are considered significant, at least to some extent: how much research space it opens up, and how much it helps other researchers continue the discussion by formulating their own questions and making their own claims (Collins 1998 , 31), whether by agreeing with it by applying it, by refining it (Snow et al. 2003 ), or by disagreeing and identifying new ways forward. There are two key characteristics that make a contribution relevant: newness and usefulness (Csikszentmihalyi 1996 ), both of which are related to the established state of knowledge within a field. Relatedly, Small asks: “Is newness enough? What does a new distinction that does not improve understanding look like?” There are also other indicators that demarcate whether a contribution is significant and to what extent. Some of these indicators include the number of citations a piece of work generates, the reputation of the journal or press where the work is published, and how widely the contribution is used—for instance, across specializations within the same discipline, or across different fields (i.e., different ways of valuation and evaluation) (Aspers and Beckert 2011 ) of scientific output. In principle, if a contribution ends up being used in an area where it would have unlikely been used, then one may further argue for its significance.

As it is implicit in our work when we talk about distinctions, we refer to theory building, albeit appreciating different conceptualizations and uses of the term theory (Abend 2008 ) and ways to achieve it (e.g., Zerubavel 2020 ). Brown-Saracino writes that our project may hold “the unintended consequence of limiting exploratory research designs and methodological innovations.” While we cannot predict the impact of our research, we are certainly in favor of experimentation and different styles of work. In line with David Snow, Calvin Morrill and Leon Anderson ( 2003 , 184), we argue that many qualitative researchers start their projects being underprepared in theory and theory development, oftentimes with the goal of describing, and leaving alone the black box of theory, or postponing it to later phases of the project. Our definition, along with the work by those authors and others on theory development, can be one way to heighten the chances researchers can make distinctions and develop theory.

Lichterman argues that we are not giving enough weight to interpretation and that we should relate more strongly to the larger project of the Geistenwissenschaften . We agree that interpretation is a key element in qualitative research, and we draw on Hans-Georg Gadamer ( 1988 ) who refined the idea of the hermeneutic circle.

Another critique, raised by Reich ( Forthcoming ), is that positionality is a key element of qualitative research. That in working towards a definition, we have “overlooked much of the methodological writings and contributions of women, scholars of color, and queer scholars” that could have enriched our definition, especially regarding “getting closer to the phenomenon studied.” Surely, the way we have searched for and included references means that we have ‘excluded’ the vast majority of research and researchers who do qualitative work. However, we have not included texts by some authors in our sample based on any specific characteristics or according to any specific position. This critique is valid only if Reich shows more explicitly what this inclusion would add to our definition.

Though we agree with much of what Reich says, for example about the role of bodies and reflexivity in ethnographic work, the idea of positionality as a normative notion is problematic. At least since Gadamer wrote in the early 1960s (1988), it is clear that there are no interpretations ‘from nowhere.’ Who one is cannot be bracketed in an interpretation of what has occurred. The scientific value of this more identity- and positionality-oriented research that accounts also of the positionality of the interpreter, is essentially already well acknowledged. Reflection is not just something that qualitative researcher do; it is a general aspect of research. Ethnographic researchers may need certain skills to get close and understand the phenomenon they study, yet they also need to maintain distance. As Fine and Hallett write: “The ethnographic stranger is uniquely positioned to be a broker in connecting the field with the academy, bringing the site into theory and, perhaps, permitting the academy to consider joint action with previously distant actors” (Fine and Hallett 2014 , 195). Moreover, Brown-Saracino illustrates well what it means to get close, and we too see that ethnography, in various forms and ways, is useful as other qualitative activities. Though ethnographic research cannot be quantitative, qualitative work is broader than solely ethnographic research. Furthermore, reflexivity is not something that one has to do when doing qualitative research, but something one does as a researcher.

Reich’s second point is more important. The claim is that if the standpoint-oriented argument is completely accepted, it will most likely violate what we see as the essence of research. We warned in our article that qualitative research may be treated as less scientific than quantitative within academia, but also in the general public, if too many in academia claim to be doing “qualitative research” while they are in fact telling stories, engaging in activism, or writing like journalists. Such approaches are extra problematic if only some people with certain characteristics are viewed as the only legitimate producers of certain types of knowledge. If these tendencies are fueled, it is not merely the definition of “qualitative” that is at stake, but what the great majority see as research in general. Science cannot reach “The Truth,” but if one gives up the idea communal and universal nature of scientific knowledge production and even a pragmatic notion of truth, much of its value and rationale of science as an independent sphere in society is lost (Merton 1973 ; Weber 1985 ). Ralf Dahrendorf framed this form of publicness by writing that: “Science is always a concert, a contrapuntal chorus of the many who are engaged in it. Insofar as truth exists at all, it exists not as a possession of the individual scholar, but as the net result of scientific interchange” (1968, 242–3). The issue of knowledge is a serious matter, but it is also another debate which relates to social sciences being low consensus fields (Collins 1994 ; Fuchs 1992 ; Parker and Corte 2017 , 276) in which the proliferation of journals and lack of agreement about common definitions, research methods, and interpretations of data contributes to knowledge fragmentation. To abandon the idea of community may also cause confusion, and piecemeal contributions while affording academics a means to communicate with a restricted in-group who speak their own small language and share their views among others of the same tribe, but without neither the risk nor possibility of gaining general public recognition. In contrast, we see knowledge as something public, that, ideal-typically, “can be seen and heard by everybody” (Arendt 1988 , 50), reflecting a pragmatic consensual approach to knowledge, but with this argument we are way beyond the theme of our article.

Our concern with qualitative research was triggered by the external critique of what is qualitative research and current debates in social science. Our definition, which deliberately tries to avoid making the use of a specific method or technique the essence of qualitative, can be used as a point of reference. In all the replies by Brown-Saracino, Lichterman, Reich, and Small, several examples of practices that are in line with our definition are given. Thus, the definition can be used to understand the practice of research, but it would also allow researchers to deliberately deviate from it and develop it. We are happy to see that all commentators have used our definition to move further, and in this pragmatic way the definition has already proved its value.

New research should be devoted to delineating standards and measures of evaluation for different kinds of work such as the those we have identified above: theoretical, descriptive, evocative, political, or aimed at social change (see Brady and Collier 2004; Ragin et al. 2004 ; Van Maanen 2011 ). And those standards could respectively be based upon scientific or stylistic advancement and social and societal impact. Footnote 1 Different work should be evaluated in relation to their respective canons, goals, and audiences, and there is certainly much to gain from learning from other perspectives. Relatedly, being fully aware of the research logics of both qualitative and quantitative traditions (Small 2005 ) is also an advantage for improving both of them and to spur further collaboration. Bringing further clarity on these points will ultimately improve different traditions, foster creativity potentially leading to innovative projects, and be useful both to younger researchers and established scholars.

The last two terms refer to whether the impacts are more micro as related to agency, or macro, as related to structural changes. An example of the latter kind is Matthew Desmond’s Eviction (2016) having substantial societal impact on public policy discussions, raising and researching a broader range of housing issues in the US. A case of the former is Arlie Hochchild’s studies on emotional labor of women in the workplace (1983) and her more recent book on the alienation of white, working-class Americans (2016).

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Acknowledgements

The authors are grateful for comments by Gary Alan Fine, Jukka Gronow, and John Parker.

Open access funding provided by University of St. Gallen. The research reported here is funded by University of St. Gallen, Switzerland and University of Stavanger, Norway.

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Aspers, P., Corte, U. What is Qualitative in Research. Qual Sociol 44 , 599–608 (2021). https://doi.org/10.1007/s11133-021-09497-w

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Articulate the specific aims and objectives of your research project with clarity. These should be in harmony with your research question and provide a structured framework for your investigation, ultimately aligning with your overarching business goals.

• Research Methods

Select the most appropriate research tools and statistical methods to address your question effectively. This may include a variety of qualitative and quantitative approaches to ensure comprehensive analysis.

• Quantitative methods (e.g., surveys, experiments)
• Qualitative methods (e.g., interviews, focus groups)
• Mixed methods (combining quantitative and qualitative approaches)
• Access to databases, archives, or special collections
• Specialized equipment or software
• Funding for travel, materials, or participant compensation
• Assistance from research assistants, librarians, or subject matter experts
• Participant Recruitment

If your research involves human subjects, develop a strategic plan for recruiting participants. Consider factors such as the inclusion of diverse ethnic groups and the use of user interviews to gather rich, qualitative data.

• Target population and sample size
• Inclusion and exclusion criteria
• Recruitment strategies (e.g., flyers, social media, snowball sampling)
• Informed consent procedures
• Instruments or tools for gathering data (e.g., questionnaires, interview guides)
• Data storage and management protocols
• Statistical or qualitative analysis techniques
• Software or tools for data analysis (e.g., SPSS, NVivo)

Create a realistic project strategy for your research project, breaking it down into manageable stages or milestones. Consider factors such as resource availability and potential bottlenecks.

• Literature review and background research
• IRB approval (if applicable)
• Participant recruitment and data collection
• Data analysis and interpretation
• Writing and revising your findings
• Dissemination of results (e.g., presentations, publications)

By developing a comprehensive research plan, incorporating key research process steps, you'll be better equipped to anticipate challenges, allocate resources effectively, and ensure the integrity and rigor of your research process. Remember to remain flexible and adaptable to navigate unexpected obstacles or opportunities that may arise.

Step 5: Conduct the Research

With your research plan in place, it's time to dive into the data collection phase. As you conduct your research, adhere to the established research process steps to ensure the integrity and quality of your findings.

Conduct your research in accordance with federal regulations, state laws, institutional SOPs, and policies. Familiarize yourself with the IRB-approved protocol and follow it diligently, as part of the essential research process steps.

• Roles and Responsibilities

Understand and adhere to the roles and responsibilities of the principal investigator and other research team members. Maintain open communication lines with all stakeholders, including the sponsor and IRB, to foster cross-functional collaboration.

• Data Management

Develop and maintain an effective system for data collection and storage, utilizing advanced research tools. Ensure that each member of the research team has seamless access to the most up-to-date documents, including the informed consent document, protocol, and case report forms.

• Quality Assurance

Implement comprehensive quality assurance measures to verify that the study adheres strictly to the IRB-approved protocol, institutional policy, and all required regulations. Confirm that all study activities are executed as planned and that any deviations are addressed with precision and appropriateness.

• Participant Eligibility

As part of the essential research process steps, verify that potential study subjects meet all eligibility criteria and none of the ineligibility criteria before advancing with the research.

To maintain the highest standards of academic integrity and ethical conduct:

• Conduct research with unwavering honesty in all facets, including experimental design, data generation, and analysis, as well as the publication of results, as these are critical research process steps.
• Maintain a climate conducive to conducting research in strict accordance with good research practices, ensuring each step of the research process is meticulously observed.
• Provide appropriate supervision and training for researchers.
• Encourage open discussion of ideas and the widest dissemination of results possible.
• Keep clear and accurate records of research methods and results.
• Exercise a duty of care to all those involved in the research.

When collecting and assimilating data:

• Use professional online data analysis tools to streamline the process.
• Use metadata for context
• Assign codes or labels to facilitate grouping or comparison
• Convert data into different formats or scales for compatibility
• Organize documents in both the study participant and investigator's study regulatory files, creating a central repository for easy access and reference, as this organization is a pivotal step in the research process.

By adhering to these guidelines and upholding a commitment to ethical and rigorous research practices, you'll be well-equipped to conduct your research effectively and contribute meaningful insights to your field of study, thereby enhancing the integrity of the research process steps.

Step 6: Analyze and Interpret Data

Embarking on the research process steps, once you have gathered your research data, the subsequent critical phase is to delve into analysis and interpretation. This stage demands a meticulous examination of the data, spotting trends, and forging insightful conclusions that directly respond to your research question. Reflect on these tactics for a robust approach to data analysis and interpretation:

• Organize and Clean Your Data

A pivotal aspect of the research process steps is to start by structuring your data in an orderly and coherent fashion. This organizational task may encompass:

• Creating a spreadsheet or database to store your data
• Assigning codes or labels to facilitate grouping or comparison
• Cleaning the data by removing any errors, inconsistencies, or missing values
• Converting data into different formats or scales for compatibility
• Calculating measures of central tendency (mean, median, mode)
• Determining measures of variability (range, standard deviation)
• Creating frequency tables or histograms to visualize the distribution of your data
• Identifying any outliers or unusual patterns in your data
• Perform Inferential Analysis

Integral to the research process steps, you might engage in inferential analysis to evaluate hypotheses or extrapolate findings to a broader demographic, contingent on your research design and query. This analytical step may include:

• Selecting appropriate statistical tests (e.g., t-tests, ANOVA, regression analysis)
• As part of the research process steps, establishing a significance threshold (e.g., p < 0.05) is essential to gauge the likelihood of your results being a random occurrence rather than a significant finding.
• Interpreting the results of your statistical tests in the context of your research question
• Considering the practical significance of your findings, in addition to statistical significance

When interpreting your data, it's essential to:

• Look for relationships, patterns, and trends in your data
• Consider alternative explanations for your findings
• Acknowledge any limitations or potential biases in your research design or data collection
• Leverage data visualization techniques such as graphs, charts, and infographics to articulate your research findings with clarity and impact, thereby enhancing the communicative value of your data.
• Seek feedback from peers, mentors, or subject matter experts to validate your interpretations

It's important to recognize that data interpretation is a cyclical process that hinges on critical thinking, inventiveness, and the readiness to refine your conclusions with emerging insights. By tackling data analysis and interpretation with diligence and openness, you're setting the stage to derive meaningful and justifiable inferences from your research, in line with the research process steps.

Step 7: Present the Findings

After meticulous analysis and interpretation of your research findings, as dictated by the research process steps, the moment arrives to disseminate your insights. Effectively presenting your research is key to captivating your audience and conveying the importance of your findings. Employ these strategies to create an engaging and persuasive presentation:

• Organize Your Findings : 

Use the PEEL method to structure your presentation:

• Point: Clearly state your main argument or finding
• Evidence: Present the data and analysis that support your point
• Explanation: Provide context and interpret the significance of your evidence
• Link: Connect your findings to the broader research question or field
• Tailor Your Message

Understanding your audience is crucial to effective communication. When presenting your research, it's important to tailor your message to their background, interests, and level of expertise, effectively employing user personas to guide your approach.

• Use clear, concise language and explain technical terms
• Highlight what makes your research unique and impactful
• Craft a compelling narrative with a clear structure and hook
• Share the big picture, emphasizing the significance of your findings
• Engage Your Audience : Make your presentation enjoyable and memorable by incorporating creative elements:
• Use visual aids, such as tables, charts, and graphs, to communicate your findings effectively
• To vividly convey your research journey, consider employing storytelling techniques, such as UX comics or storyboards, which can make complex information more accessible and engaging.
• Injecting humor and personality into your presentation can be a powerful tool for communication. Utilize funny messages or GIFs to lighten the mood, breaking up tension and refocusing attention, thereby enhancing the effectiveness of humor in communication.

By adhering to these strategies, you'll be well-prepared to present your research findings in a manner that's both clear and captivating. Ensure you follow research process steps such as citing your sources accurately and discussing the broader implications of your work, providing actionable recommendations, and delineating the subsequent phases for integrating your findings into broader practice or policy frameworks.

The research process is an intricate journey that demands meticulous planning, steadfast execution, and incisive analysis. By adhering to the fundamental research process steps outlined in this guide, from pinpointing your topic to showcasing your findings, you're setting yourself up for conducting research that's both effective and influential. Keep in mind that the research journey is iterative, often necessitating revisits to certain stages as fresh insights surface or unforeseen challenges emerge.

As you commence your research journey, seize the chance to contribute novel insights to your field and forge a positive global impact. By tackling your research with curiosity, integrity, and a dedication to excellence, you're paving the way towards attaining your research aspirations and making a substantial difference with your work, all while following the critical research process steps.

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• 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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Introduction to qualitative research methods – Part I

Shagufta bhangu.

Department of Global Health and Social Medicine, King's College London, London, United Kingdom

Fabien Provost

Carlo caduff.

Qualitative research methods are widely used in the social sciences and the humanities, but they can also complement quantitative approaches used in clinical research. In this article, we discuss the key features and contributions of qualitative research methods.

INTRODUCTION

Qualitative research methods refer to techniques of investigation that rely on nonstatistical and nonnumerical methods of data collection, analysis, and evidence production. Qualitative research techniques provide a lens for learning about nonquantifiable phenomena such as people's experiences, languages, histories, and cultures. In this article, we describe the strengths and role of qualitative research methods and how these can be employed in clinical research.

Although frequently employed in the social sciences and humanities, qualitative research methods can complement clinical research. These techniques can contribute to a better understanding of the social, cultural, political, and economic dimensions of health and illness. Social scientists and scholars in the humanities rely on a wide range of methods, including interviews, surveys, participant observation, focus groups, oral history, and archival research to examine both structural conditions and lived experience [ Figure 1 ]. Such research can not only provide robust and reliable data but can also humanize and add richness to our understanding of the ways in which people in different parts of the world perceive and experience illness and how they interact with medical institutions, systems, and therapeutics.

Examples of qualitative research techniques

Qualitative research methods should not be seen as tools that can be applied independently of theory. It is important for these tools to be based on more than just method. In their research, social scientists and scholars in the humanities emphasize social theory. Departing from a reductionist psychological model of individual behavior that often blames people for their illness, social theory focuses on relations – disease happens not simply in people but between people. This type of theoretically informed and empirically grounded research thus examines not just patients but interactions between a wide range of actors (e.g., patients, family members, friends, neighbors, local politicians, medical practitioners at all levels, and from many systems of medicine, researchers, policymakers) to give voice to the lived experiences, motivations, and constraints of all those who are touched by disease.

PHILOSOPHICAL FOUNDATIONS OF QUALITATIVE RESEARCH METHODS

In identifying the factors that contribute to the occurrence and persistence of a phenomenon, it is paramount that we begin by asking the question: what do we know about this reality? How have we come to know this reality? These two processes, which we can refer to as the “what” question and the “how” question, are the two that all scientists (natural and social) grapple with in their research. We refer to these as the ontological and epistemological questions a research study must address. Together, they help us create a suitable methodology for any research study[ 1 ] [ Figure 2 ]. Therefore, as with quantitative methods, there must be a justifiable and logical method for understanding the world even for qualitative methods. By engaging with these two dimensions, the ontological and the epistemological, we open a path for learning that moves away from commonsensical understandings of the world, and the perpetuation of stereotypes and toward robust scientific knowledge production.

Developing a research methodology

Every discipline has a distinct research philosophy and way of viewing the world and conducting research. Philosophers and historians of science have extensively studied how these divisions and specializations have emerged over centuries.[ 1 , 2 , 3 ] The most important distinction between quantitative and qualitative research techniques lies in the nature of the data they study and analyze. While the former focus on statistical, numerical, and quantitative aspects of phenomena and employ the same in data collection and analysis, qualitative techniques focus on humanistic, descriptive, and qualitative aspects of phenomena.[ 4 ]

For the findings of any research study to be reliable, they must employ the appropriate research techniques that are uniquely tailored to the phenomena under investigation. To do so, researchers must choose techniques based on their specific research questions and understand the strengths and limitations of the different tools available to them. Since clinical work lies at the intersection of both natural and social phenomena, it means that it must study both: biological and physiological phenomena (natural, quantitative, and objective phenomena) and behavioral and cultural phenomena (social, qualitative, and subjective phenomena). Therefore, clinical researchers can gain from both sets of techniques in their efforts to produce medical knowledge and bring forth scientifically informed change.

KEY FEATURES AND CONTRIBUTIONS OF QUALITATIVE RESEARCH METHODS

In this section, we discuss the key features and contributions of qualitative research methods [ Figure 3 ]. We describe the specific strengths and limitations of these techniques and discuss how they can be deployed in scientific investigations.

Key features of qualitative research methods

One of the most important contributions of qualitative research methods is that they provide rigorous, theoretically sound, and rational techniques for the analysis of subjective, nebulous, and difficult-to-pin-down phenomena. We are aware, for example, of the role that social factors play in health care but find it hard to qualify and quantify these in our research studies. Often, we find researchers basing their arguments on “common sense,” developing research studies based on assumptions about the people that are studied. Such commonsensical assumptions are perhaps among the greatest impediments to knowledge production. For example, in trying to understand stigma, surveys often make assumptions about its reasons and frequently associate it with vague and general common sense notions of “fear” and “lack of information.” While these may be at work, to make such assumptions based on commonsensical understandings, and without conducting research inhibit us from exploring the multiple social factors that are at work under the guise of stigma.

In unpacking commonsensical understandings and researching experiences, relationships, and other phenomena, qualitative researchers are assisted by their methodological commitment to open-ended research. By open-ended research, we mean that these techniques take on an unbiased and exploratory approach in which learnings from the field and from research participants, are recorded and analyzed to learn about the world.[ 5 ] This orientation is made possible by qualitative research techniques that are particularly effective in learning about specific social, cultural, economic, and political milieus.

Second, qualitative research methods equip us in studying complex phenomena. Qualitative research methods provide scientific tools for exploring and identifying the numerous contributing factors to an occurrence. Rather than establishing one or the other factor as more important, qualitative methods are open-ended, inductive (ground-up), and empirical. They allow us to understand the object of our analysis from multiple vantage points and in its dispersion and caution against predetermined notions of the object of inquiry. They encourage researchers instead to discover a reality that is not yet given, fixed, and predetermined by the methods that are used and the hypotheses that underlie the study.

Once the multiple factors at work in a phenomenon have been identified, we can employ quantitative techniques and embark on processes of measurement, establish patterns and regularities, and analyze the causal and correlated factors at work through statistical techniques. For example, a doctor may observe that there is a high patient drop-out in treatment. Before carrying out a study which relies on quantitative techniques, qualitative research methods such as conversation analysis, interviews, surveys, or even focus group discussions may prove more effective in learning about all the factors that are contributing to patient default. After identifying the multiple, intersecting factors, quantitative techniques can be deployed to measure each of these factors through techniques such as correlational or regression analyses. Here, the use of quantitative techniques without identifying the diverse factors influencing patient decisions would be premature. Qualitative techniques thus have a key role to play in investigations of complex realities and in conducting rich exploratory studies while embracing rigorous and philosophically grounded methodologies.

Third, apart from subjective, nebulous, and complex phenomena, qualitative research techniques are also effective in making sense of irrational, illogical, and emotional phenomena. These play an important role in understanding logics at work among patients, their families, and societies. Qualitative research techniques are aided by their ability to shift focus away from the individual as a unit of analysis to the larger social, cultural, political, economic, and structural forces at work in health. As health-care practitioners and researchers focused on biological, physiological, disease and therapeutic processes, sociocultural, political, and economic conditions are often peripheral or ignored in day-to-day clinical work. However, it is within these latter processes that both health-care practices and patient lives are entrenched. Qualitative researchers are particularly adept at identifying the structural conditions such as the social, cultural, political, local, and economic conditions which contribute to health care and experiences of disease and illness.

For example, the decision to delay treatment by a patient may be understood as an irrational choice impacting his/her chances of survival, but the same may be a result of the patient treating their child's education as a financial priority over his/her own health. While this appears as an “emotional” choice, qualitative researchers try to understand the social and cultural factors that structure, inform, and justify such choices. Rather than assuming that it is an irrational choice, qualitative researchers try to understand the norms and logical grounds on which the patient is making this decision. By foregrounding such logics, stories, fears, and desires, qualitative research expands our analytic precision in learning about complex social worlds, recognizing reasons for medical successes and failures, and interrogating our assumptions about human behavior. These in turn can prove useful in arriving at conclusive, actionable findings which can inform institutional and public health policies and have a very important role to play in any change and transformation we may wish to bring to the societies in which we work.

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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: 23 September 2023

Educational interventions targeting pregnant women to optimise the use of caesarean section: What are the essential elements? A qualitative comparative analysis

• Rana Islamiah Zahroh   ORCID: orcid.org/0000-0001-7831-2336 1 ,
• Katy Sutcliffe   ORCID: orcid.org/0000-0002-5469-8649 2 ,
• Dylan Kneale   ORCID: orcid.org/0000-0002-7016-978X 2 ,
• Martha Vazquez Corona   ORCID: orcid.org/0000-0003-2061-9540 1 ,
• Ana Pilar Betrán   ORCID: orcid.org/0000-0002-5631-5883 3 ,
• Newton Opiyo   ORCID: orcid.org/0000-0003-2709-3609 3 ,
• Caroline S. E. Homer   ORCID: orcid.org/0000-0002-7454-3011 4 &
• Meghan A. Bohren   ORCID: orcid.org/0000-0002-4179-4682 1  

BMC Public Health volume  23 , Article number:  1851 ( 2023 ) Cite this article

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Caesarean section (CS) rates are increasing globally, posing risks to women and babies. To reduce CS, educational interventions targeting pregnant women have been implemented globally, however, their effectiveness is varied. To optimise benefits of these interventions, it is important to understand which intervention components influence success. In this study, we aimed to identify essential intervention components that lead to successful implementation of interventions focusing on pregnant women to optimise CS use.

We re-analysed existing systematic reviews that were used to develop and update WHO guidelines on non-clinical interventions to optimise CS. To identify if certain combinations of intervention components (e.g., how the intervention was delivered, and contextual characteristics) are associated with successful implementation, we conducted a Qualitative Comparative Analysis (QCA). We defined successful interventions as interventions that were able to reduce CS rates. We included 36 papers, comprising 17 CS intervention studies and an additional 19 sibling studies (e.g., secondary analyses, process evaluations) reporting on these interventions to identify intervention components. We conducted QCA in six stages: 1) Identifying conditions and calibrating the data; 2) Constructing truth tables, 3) Checking quality of truth tables; 4) Identifying parsimonious configurations through Boolean minimization; 5) Checking quality of the solution; 6) Interpretation of solutions. We used existing published qualitative evidence synthesis to develop potential theories driving intervention success.

We found successful interventions were those that leveraged social or peer support through group-based intervention delivery, provided communication materials to women, encouraged emotional support by partner or family participation, and gave women opportunities to interact with health providers. Unsuccessful interventions were characterised by the absence of at least two of these components.

We identified four key essential intervention components which can lead to successful interventions targeting women to reduce CS. These four components are 1) group-based delivery, 2) provision of IEC materials, 3) partner or family member involvement, and 4) opportunity for women to interact with health providers. Maternal health services and hospitals aiming to better prepare women for vaginal birth and reduce CS can consider including the identified components to optimise health and well-being benefits for the woman and baby.

Peer Review reports

Introduction

In recent years, caesarean section (CS) rates have increased globally [ 1 , 2 , 3 , 4 ]. CS can be a life-saving procedure when vaginal birth is not possible; however, it comes with higher risks both in the short- and long-term for women and babies [ 1 , 5 ]. Women with CS have increased risks of surgical complications, complications in future pregnancies, subfertility, bowel obstruction, and chronic pain [ 5 , 6 , 7 , 8 ]. Similarly, babies born through CS have increased risks of hypoglycaemia, respiratory problems, allergies and altered immunity [ 9 , 10 , 11 ]. At a population level, CS rates exceeding 15% are unlikely to reduce mortality rates [ 1 , 12 ]. Despite these risks, an analysis across 154 countries reported a global average CS rate of 21.1% in 2018, projected to increase to 28.5% by 2030 [ 3 ].

There are many reasons for the increasing CS rates, and these vary between and within countries. Increasingly, non-clinical factors across different societal dimensions and stakeholders (e.g. women and communities, health providers, and health systems) are contributing to this increase [ 13 , 14 , 15 , 16 , 17 ]. Women may prefer CS over vaginal birth due to fear of labour or vaginal birth, previous negative experience of childbirth, perceived increased risks of vaginal birth, beliefs about an auspicious or convenient day of birth, or beliefs that caesarean section is safer, quick, and painless compared to vaginal birth [ 13 , 14 , 15 ].

Interventions targeting pregnant women to reduce CS have been implemented globally. A Cochrane intervention review synthesized evidence from non-clinical interventions targeting pregnant women and family, providers, and health systems to reduce unnecessary CS, and identified 15 interventions targeting women [ 18 ]. Interventions targeting women primarily focused on improving women’s knowledge around birth, improving women’s ability to cope during labour, and decreasing women’s stress related to labour through childbirth education, and decision aids for women with previous CS [ 18 ]. These types of interventions aim to reduce the concerns of pregnant women and their partners around childbirth, and prepare them for vaginal birth.

The effectiveness of interventions targeting women in reducing CS is mixed [ 18 , 19 ]. Plausible explanations for this limited success include the multifactorial nature of the factors driving increases in CS, as well as the contextual characteristics of the interventions, which may include the study environment, participant characteristics, intensity of exposure to the intervention and method of implementation. Understanding which intervention components are essential influencers of the success of the interventions is conducive to optimising benefits. This study used a Qualitative Comparative Analysis (QCA) approach to re-analyse evidence from existing systematic reviews to identify essential intervention components that lead to the successful implementation of non-clinical interventions focusing on pregnant women to optimise the use of CS. Updating and re-analysing existing systematic reviews using new analytical frameworks may help to explore the heterogeneity in effects and ascertain why some studies appear to be effective while others are not.

Data sources, case selection, and defining outcomes

Developing a logic model.

We developed a logic model to guide our understanding of different pathways and intervention components potentially leading to successful implementation (Additional file 1 ). The logic model was developed based on published qualitative evidence syntheses and systematic reviews [ 18 , 20 , 21 , 22 , 23 , 24 ]. The logic model depicts the desired outcome of reduced CS rates in low-risk women (at the time of admission for birth, these women are typically represented by Robson groups 1–4 [ 25 ] and are women with term, cephalic, singleton pregnancies without a previous CS) and works backwards to understand what inputs and processes are needed to achieve the desired outcome. Our logic model shows multiple pathways to success and highlights the interactions between different levels of factors (women, providers, societal, health system) (Additional file 1 ). Based on the logic model, we have separated our QCA into two clusters of interventions: 1) interventions targeting women, and 2) interventions targeting health providers. The results of analysis on interventions targeting health providers have been published elsewhere [ 26 ]. The logic model was also used to inform the potential important components that influence success.

Identifying data sources and selecting cases

We re-analysed the systematic reviews which were used to inform the development and update of World Health Organization (WHO) guidelines. In 2018, WHO issued global guidance on non-clinical interventions to reduce unnecessary CS, with interventions designed to target three different levels or stakeholders: women, health providers, and health systems [ 27 ]. As part of the guideline recommendations, a series of systematic reviews about CS interventions were conducted: 1) a Cochrane intervention review of effectiveness by Chen et al. (2018) [ 18 ] and 2) three qualitative evidence syntheses exploring key stakeholder perspectives and experiences of interventions focusing on women and communities, health professionals, and health organisations, facilities and systems by Kingdon et al. (2018) [ 20 , 21 , 22 ]. Later on, Opiyo and colleagues (2020) published a scoping review of financial and regulatory interventions to optimise the use of CS [ 23 ].

Therefore, the primary data sources of this QCA are the intervention studies included in Chen et al. (2018) [ 18 ] and Opiyo et al. (2020) [ 23 ]. We used these two systematic reviews as not only they are comprehensive, but they were also used to inform the WHO guidelines development. A single intervention study is referred to as a “case”. Eligible cases were intervention studies focusing on pregnant women and aimed to reduce or optimise the use of CS. No restrictions on study design were imposed in the QCA. Therefore, we also assessed the eligibility of intervention studies excluded from Chen et al. (2018) [ 18 ] and Opiyo et al. (2020) [ 23 ] due to ineligible study designs (such as cohort study, uncontrolled before and after study, interrupted time series with fewer than three data points), as these studies could potentially show other pathways to successful implementation. We complemented these intervention studies with additional intervention studies published since the last review updates in 2018 and 2020, to include intervention studies that are likely to meet the review inclusion criteria for future review updates. No further search was conducted as QCA is suitable for medium-N cases, approximately around 10–50 cases, and inclusion of more studies may threaten study rigour [ 28 ].

Once eligible studies were selected, we searched for their ‘sibling studies’. Sibling studies are studies linked to the included intervention studies, such as formative research or process evaluations which may have been published separately. Sibling studies can provide valuable additional information about study context, intervention components, and implementation outcomes (e.g. acceptability, fidelity, adherence, dosage), which may not be well described in a single article about intervention effectiveness. We searched for sibling studies using the following steps: 1) reference list search of the intervention studies included in Chen et al. (2018) [ 18 ] and Opiyo et al. (2020) [ 23 ], 2) reference list search of the qualitative studies included in Kingdon et al. (2018) reviews [ 20 , 21 , 22 ]; and 3) forward reference search of the intervention studies (through “Cited by” function) in Scopus and Web of Science. Sibling studies were included if they included any information on intervention components or implementation outcomes, regardless of the methodology used. One author conducted the study screening independently (RIZ), and 10% of the screening was double-checked by a second author (MAB). Disagreements during screening were discussed until consensus, and with the rest of the author team if needed.

Defining outcomes

We assessed all outcomes related to the mode of birth in the studies included in the Chen et al. (2018) [ 18 ] and Opiyo et al. (2020) [ 23 ] reviews. Based on the consistency of outcome reporting, we selected “overall CS rate” as the primary outcome of interest due to its presence across studies. We planned to rank the rate ratio across these studies to select the 10 most successful and unsuccessful intervention studies. However, due to heterogeneity in how CS outcomes were reported across studies (e.g. odds ratios, rate ratios, percentages across different intervention stages), the final categorisation of successful or unsuccessful interventions is based on whether the CS rate decreased, based on the precision of the confidence interval or p-value (successful, coded as 1), or CS rate increased or did not change (unsuccessful, coded as 0).

Assessing risk of bias in intervention studies

All intervention studies eligible for inclusion were assessed for risk of bias. All studies included in Chen et al. (2018) and Opiyo et al. (2020) already had risk of bias assessed and reported [ 18 , 23 ], and we used these assessments. Additional intervention studies outside the included studies on these reviews were assessed using the same tools depending on the type of evidence (two randomized controlled trials and one uncontrolled before and after study), and details of the risk of bias assessment results can be found in Additional file 2 . We excluded studies with a high risk of bias to ensure that the analysis was based on high-quality studies and to enhance the ability of researchers to develop deep case knowledge by limiting the overall number of studies.

Qualitative comparative analysis (QCA)

QCA was first developed and used in political sciences and has since been extended to systematic reviews of complex health interventions [ 24 , 29 , 30 , 31 ]. Despite the term “qualitative”, QCA is not a typical qualitative analysis, and is often conceptualised as a methodology that bridges qualitative and quantitative methodologies based on its process, data used and theoretical standpoint [ 24 ]. Here, QCA is used to identify if certain configurations or combinations of intervention components (e.g. participants, types of interventions, contextual characteristics, and intervention delivery) are associated with the desired outcome [ 31 ]. These intervention components are referred to as “conditions” in the QCA methodology. Whilst statistical synthesis methods may be used to examine intervention heterogeneity in systematic reviews, such as meta-regression, QCA is a particularly suitable method to understand complex interventions like those aiming to optimise CS, as it allows for multiple overlapping pathways to causality [ 31 ]. Moreover, QCA allows the exploration of different combinations of conditions, rather than relying on a single condition leading to intervention effectiveness [ 31 ]. Although meta-regression allows for the assessment of multiple conditions, a sufficient number of studies may not be available to conduct the analysis. In complex interventions, such as interventions aiming to optimise the use of CS, single condition or standard meta-analysis may be less likely to yield usable and nuanced information about what intervention components are more or less likely to yield success [ 31 ].

QCA uses ‘set theory’ to systematically compare characteristics of the cases (e.g. intervention in the case of systematic reviews) in relation to the outcomes [ 31 , 32 ]. This means QCA compares the characteristics of the successful ‘cases’ (e.g. interventions that are effective) to those unsuccessful ‘cases’ (e.g. interventions that are not effective). The comparison is conducted using a scoring system based on ‘set membership’ [ 31 , 32 ]. In this scoring, conditions and outcomes are coded based on the extent to which a certain feature is present or absent to form set membership scores [ 31 , 32 ]. There are two scoring systems in QCA: 1) crisp set QCA (csQCA) and 2) fuzzy set QCA (fsQCA). csQCA assigns binary scores of 0 (“fully out” to set membership for cases with certain conditions) and 1 (“fully in” to set membership for cases with certain conditions), while fsQCA assigns ordinal scoring of conditions and outcomes, permitting partial membership scores between 0 and 1 [ 31 , 32 ]. For example, using fsQCA we may assign a five-level scoring system (0, 0.33, 0.5, 0.67, 1), where 0.33 would indicate “more out” than “in” to the set of membership, and 0.67 would indicate “more in” than “out”, and 0.5 would indicate ambiguity (i.e. a lack of information about whether a case was “in” or “out”) [ 31 , 32 ]. In our analysis, we used the combination of both csQCA and fsQCA to calibrate our data. This approach was necessary because some conditions were better suited to binary options using csQCA, while others were more complex, depending on the distribution of cases, and required fsQCA to capture the necessary information. In our final analysis, however, the conditions run on the final analysis were all using the csQCA scoring system.

Two relationships can be investigated using QCA [ 24 , 31 ]. First, if all instances of successful interventions share the same condition(s), this suggests these features are ‘necessary’ to trigger successful outcomes [ 24 , 31 ]. Second, if all instances of a particular condition are associated with successful interventions, this suggests these conditions are ‘sufficient’ for triggering successful outcomes [ 24 , 31 ]. In this QCA, we were interested to explore the relationship of sufficiency: that is, to assess the various combinations of intervention components that can trigger successful outcomes. We were interested in sufficiency because our logic model (explained further below) highlighted the multiple pathways that can lead to a CS and different interventions that may optimise the use of CS along those pathways, which suggested that it would be unlikely for all successful interventions to share the same conditions. We calculated the degree of sufficiency using consistency measures, which evaluate the frequency in which conditions are present when the desired outcome is achieved [ 31 , 32 ]. The conditions with a consistency score of at least 0.8 were considered sufficient in triggering successful interventions [ 31 , 32 ]. At present, there is no tool available for reporting guidelines in the re-analysis of systematic reviews using QCA, however, CARU-QCA is currently being developed for this purpose [ 33 ]. QCA was conducted using R programming software with a package developed by Thiem & Duşa (2013) and QCA with R guidebook [ 32 ]. QCA was conducted in six stages based on Thomas et al. (2014) [ 31 ] and explained below.

QCA stage 1: Identifying conditions, building data tables and calibration

We used a deductive and inductive process to determine the potential conditions (intervention components) that may trigger successful implementation. Conditions were first derived deductively using the developed logic model (Additional file 1 ). We then added additional conditions inductively using Intervention Component Analysis from the intervention studies [ 34 ], and qualitative evidence (“view”) synthesis [ 22 ] using Melendez-Torres’s (2018) approach [ 35 ]. Intervention Component Analysis is a methodological approach that examines factors affecting implementation through reflections from the trialist, which is typically presented in the discussion section of a published trial [ 34 ]. Examples of conditions identified in the Intervention Component Analysis include using an individualised approach, interaction with health providers, policies that encourage CS and acknowledgement of women’s previous birth experiences. After consolidating or merging similar conditions, a total of 52 conditions were selected and extracted from each included intervention and analysed in this QCA (Details of conditions and definitions generated for this study can be found in Additional files 3 and 4 ). We adapted the coding framework from Harris et al. (2019) [ 24 ] by adapting coding rules and six domains that were used, to organize the 52 conditions and make more sense of the data. These six domains are broadly classified as 1) context and participants, 2) intervention design, 3) program content, 4) method of engagement, 5) health system factors, and 6) process outcomes.

One author (RIZ) extracted data relevant to the conditions for each included study into a data table, which was then double-reviewed by two other authors (MVC, MAB). The data table is a matrix in which each case is represented in a row, and columns are used to represent the conditions. Following data extraction, calibration rules using either csQCA or fsQCA (e.g. group-based intervention delivery condition: yes = 1 (present), no = 0 (absent)) were developed through consultation with all authors. We developed a table listing the conditions and rules of coding the conditions, by either direct or transformational assignment of quantitative and qualitative data [ 24 , 32 ] (Additional file 3 depicts the calibration rules). The data tables were then calibrated by applying scores, to explore the extent to which interventions have ‘set membership’ with the outcome or conditions of interest. During this iterative process, the calibration criteria were explicitly defined, emerging from the literature and the cases themselves. It is important to note, that maximum ambiguity is typically scored as 0.5 in QCA, however, we decided it would be more appropriate to assume that if a condition was not reported it was unlikely to be a feature of the intervention, so we treated not reported as “absence” that is we coded it 0.

QCA stage 2: Constructing truth tables

Truth tables are an analytical tool used in QCA to analyse associations between configurations of conditions and outcomes. Whereas the data table represents individual cases (rows) and individual conditions (columns) – the truth table synthesises this data to examine configurations – with each row representing a different configuration of the conditions. The columns indicate a) which conditions are featured in the configuration in that row, b) how many of the cases are represented by that configuration, and c) their association with the outcome.

We first constructed the truth tables based on context and participants, intervention designs, program content, and method of engagement; however, no configurations to trigger successful interventions were observed. Instead, we observed limited diversity, meaning there were many instances in which the configurations were unsupported by cases, likely due to the presence of too many conditions in the truth tables. We used the learning from these truth tables to return to the literature to explore potential explanatory theories about what conditions are important from the perspectives of participants and trialists to trigger successful interventions (adhering to the ‘utilisation of view’ perspective [ 35 ]). Through this process, we found that women and communities liked to learn new information about childbirth, and desired emotional support from partners and health providers while learning [ 22 ]. They also appreciated educational interventions that provide opportunities for discussion and dialogue with health providers and align with current clinical practice and advice from health providers [ 22 ]. Therefore, three models of truth tables were iteratively constructed and developed based on three important hypothesised theories about how the interventions should be delivered: 1) how birth information was provided to women, 2) emotional support was provided to women (including interactions between women and providers), and 3) a consolidated model examining the interactions of important conditions identified from model 1 and 2. We also conducted a sub-analysis of interventions targeting both women and health providers or systems (‘multi-target interventions’). This sub-analysis was conducted to explore if similar conditions were observed in triggering successful interventions in multi-target interventions, among the components for women only. Table 1 presents the list of truth tables that were iteratively constructed and refined.

QCA stage 3: Checking quality of truth tables

We iteratively developed and improved the quality of truth tables by checking the configurations of successful and unsuccessful interventions, as recommended by Thomas et al. (2014) [ 31 ]. This includes by assessing the number of studies clustering to each configuration, and exploring the presence of any contradictory results between successful and unsuccessful interventions. We found contradictory configurations across the five truth tables, which were resolved by considering the theoretical perspectives and iteratively refining the truth tables.

QCA stage 4: Identifying parsimonious configurations through Boolean minimization

Once we determined that the truth tables were suitable for further analysis, we used Boolean minimisation to explore pathways resulting in successful intervention through the configurations of different conditions [ 31 ]. We simplified the “complex solution” of the pathways to a “parsimonious solution” and an “intermediate solution” by incorporating logical remainders (configurations where no cases were observed) [ 36 ].

QCA stage 5: Checking the quality of the solution

We presented the intermediate solution as the final solution instead of the most parsimonious solution, as it is most closely aligned with the underlying theory. We checked consistency and coverage scores to assess if the pathways identified were sufficient to trigger success. We also checked the intermediate solution by negating the outcome to see if it predicts the observed solutions.

QCA stage 6: Interpretation of solutions

We iteratively interpreted the results of the findings through discussions among the QCA team. This reflexive approach ensured that the results of the analysis considered the perspectives from the literature discourse, methodological approach, and that the results were coherent with the current understanding of the phenomenon.

Overview of included studies

Out of 79 intervention studies assessed by Chen et al. (2018) [ 18 ] and Opiyo et al. (2020) [ 23 ], 17 intervention studies targeted women and are included, comprising 11 interventions targeting only women [ 37 , 38 , 39 , 40 , 41 , 42 , 43 ] and six interventions targeting both women and health providers or systems [ 44 , 45 , 46 , 47 , 48 , 49 ]. From 17 included studies, 19 sibling studies were identified [ 43 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 ]. Thus, a total of 36 papers from 17 intervention studies are included in this QCA (See Fig.  1 : PRISMA Flowchart).

PRISMA flowchart. *Sibling studies: studies that were conducted in the same settings, participants, and timeframe; **Intervention components: information on intervention input, activities, and outputs, including intervention context and other characteristics

The 11 interventions targeting women comprised of five successful interventions [ 37 , 68 , 69 , 70 , 71 ] and six unsuccessful interventions [ 37 , 38 , 39 , 40 , 41 , 42 , 43 ] in reducing CS. Sixteen sibling studies were identified, from five out of 11 included interventions [ 37 , 41 , 43 , 70 , 71 ]. Included studies were conducted in six countries across North America (2 from Canada [ 38 ] and 1 from United States of America [ 71 ]), Asia–Pacific (1 from Australia [ 41 ]), 5 from Iran [ 39 , 40 , 68 , 69 , 70 ]), Europe (2 from Finland [ 37 , 42 ], 1 from United Kingdom [ 43 ]). Six studies were conducted in high-income countries, while five studies were conducted in upper-middle-income countries (all from Iran). All 11 studies targeted women, with three studies also explicitly targeting women’s partners [ 68 , 69 , 71 ]. One study delivering psychoeducation allowed women to bring any family members to accompany them during the intervention but did not specifically target partners [ 37 ]. All 11 studies delivered childbirth education, with four delivering general antenatal education [ 38 , 40 , 68 , 69 ], six delivering psychoeducation [ 37 , 39 , 41 , 42 , 70 , 71 ], and one implementing decision aids [ 43 ]. All studies were included in Chen et al. (2018), and some risks of bias were identified [ 18 ] (Additional file 2).

The multi-target interventions consisted of five successful interventions [ 44 , 45 , 46 , 47 , 48 ] and one unsuccessful intervention [ 49 ]. Sibling studies were only identified from one study [ 48 ]. The interventions were delivered in five countries across: South America (1 from Brazil [ 46 ]), Asia–Pacific (4 from China [ 44 , 45 , 47 , 49 ]), Europe (1 from Italy [ 48 ], 1 from Ireland [ 48 ], and 1 from Germany [ 48 ]). Three studies were conducted in high-income countries and five studies in upper middle-income countries. The multi-target interventions targeted women, health providers and health organisations. For this analysis, however, we only consider the components of the intervention that targeted women, which was typically childbirth education. One study came from Chen et al. (2018) [ 18 ] and was graded as having some concerns [ 47 ], two studies from Opiyo et al. (2020) [ 23 ] were graded as having no serious concerns [ 45 , 46 ], and three studies are newly published studies assessed as low [ 44 ] and some concerns about risk of bias [ 48 , 49 ] Table 2 and 3 show characteristics of included studies.

The childbirth education interventions included information about mode of birth, birth process, mental health and coping strategies, pain relief methods, and partners’ roles in birth. Most interventions were delivered in group settings, and only in three studies they were delivered on a one-to-one basis [ 38 , 41 , 42 ]. Only one study explicitly stated that the intervention was individualised to a woman’s unique needs and experiences [ 38 ].

Overall, there was limited theory used to design interventions among the included studies: less than half of interventions (7/17) explicitly used theory in designing the intervention. Among the seven interventions that used theory in intervention development, the theories included the health promotion-disease prevention framework [ 38 ], midwifery counselling framework [ 41 ], cognitive behavioural therapy [ 42 ], Ost’s applied relaxation [ 70 ], conceptual model of parenting [ 71 ], attachment and social cognitive theories [ 37 ], and healthcare improvement scale-up framework [ 46 ]. The remaining 10 studies only relied on previously published studies to design the interventions. We identified very limited process evaluation or implementation outcome evidence related to the included interventions, which is a limitation of the field of CS and clinical interventions more broadly.

• Qualitative comparative analysis

Model 1 – How birth information was provided to women

Model 1 is constructed based on the finding from Kingdon et al. (2018) [ 22 ] that women and communities enjoy learning new birth information, as it opens up new ways of thinking about vaginal birth and CS. Learning new information allows them to understand better the benefits and risks of CS and vaginal births, as well as increase their knowledge about CS [ 22 ].

We used four conditions in constructing model 1 truth table: 1) the provision of information, education, and communication (IEC) materials on what to expect during labour and birth, 2) type of education delivered (antenatal education or psychoeducation), and 3) group-based intervention delivery. We explored this model considering other conditions, such as type of information provided (e.g. information about mode of birth including birth process, mental health and coping strategies, pain relief), delivery technique (e.g. didactic, practical) and frequency and duration of intervention delivery; however these additional conditions did not result in configurations.

Of 16 possible configurations, we identified seven configurations (Table 4 ). The first two row shows perfect consistency of configurations (inclusion = 1) in five studies [ 37 , 68 , 69 , 70 , 71 ] in which all conditions are present, except antenatal education or psychoeducation. The remaining configurations are unsuccessful interventions. Interestingly, when either IEC materials or group-based intervention delivery are present (but not both), implementation is likely to be unsuccessful (rows 3–7).

Boolean minimisation identified two intermediate pathways to successful interventions (Fig.  2 ). The two pathways are similar, except for one condition: type of education. The antenatal education or psychoeducation materials is the content tailored to the type of women they target. Therefore, from the two pathways, we can see that the presence of distribution of IEC materials on birth information and group-based intervention delivery of either antenatal education to the general population of women (e.g. not groups of women with specific risks or conditions) or psychoeducation to women with fear of birth trigger successful interventions. From this solution, we can see that the successful interventions are consistently characterised by the presence of both IEC materials and group-based intervention delivery.

Intermediate pathways from model 1 that trigger successful interventions targeting pregnant women to optimise CS. In QCA, asterisk (*) denotes an ‘AND’ relationship; Inclusion score (InclS), also known as consistency, indicates the degree to which the evidence is consistent with the hypothesis that there is sufficient relation between the configuration and the outcome; Proportional Reduction in Inconsistency (PRI) refers to the extent in which a configuration is sufficient in triggering successful outcome as well as the negation of the outcome; Coverage score (CovS) refers to percentage of cases in which the configuration is valid

Model 2 – Emotional support was provided to women

Model 2 was constructed based on the theory that women desire emotional support alongside the communication of information about childbirth [ 22 ]. This includes emotional support from husbands or partners, health professional, or doulas [ 22 ]. Furthermore, Kingdon et al. (2018) describe the importance of two-way conversation and dialogue between women and providers during pregnancy care, particularly to ensure the opportunity for discussion [ 22 ]. Interventions may generate more questions than they answered, creating the need and desire of women to have more dialogue with health professionals [ 22 ]. Women considered intervention content to be most useful when it complements clinical care, is consistent with advice from health professionals and provides a basis for more informed, meaningful dialogue between women and care providers [ 22 ].

Based on this underlying theory, we constructed model 3 truth table by considering three conditions representative of providing emotional support to women, including partner or family member involvement, group-based intervention delivery which provide social or peer support to women, and opportunity for women to interact with health providers. Of 8 possible configurations, we identified six configurations (Table 5 ). The first three rows represent successful interventions with perfect consistency (inclusion = 1). The first row shows successful interventions with all conditions present. The second and third row shows successful interventions with all conditions except partner or family member involvement or interaction with health providers. The remaining rows represent unsuccessful interventions, where at least two conditions are absent.

Boolean minimisation identified two intermediate pathways to successful interventions (Fig.  3 ). In the first pathway, the partner or family members involvement and group-based intervention delivery enable successful interventions. In the second pathway, however, when partner or family members are not involved, successful interventions can happen only when interaction with health providers is included alongside group-based intervention. From these two pathways, we can see that group-based intervention, involvement of partner and family member, and opportunity for women to interact with providers seem to be important in driving intervention success.

Intermediate pathways from model 2 that trigger successful interventions targeting pregnant women to optimise CS. In QCA, asterisk (*) denotes an ‘AND’ relationship; Inclusion score (InclS), also known as consistency, indicates the degree to which the evidence is consistent with the hypothesis that there is sufficient relation between the configuration and the outcome; Proportional Reduction in Inconsistency (PRI) refers to the extent in which a configuration is sufficient in triggering successful outcome as well as the negation of the outcome; Coverage score (CovS) refers to percentage of cases in which the configuration is valid

Consolidated model – Essential conditions to prompt successful interventions focusing on women

Using the identified important conditions observed in models 1 and 2, we constructed a consolidated model to examine the final essential conditions which could prompt successful educational interventions targeting women. We merged and tested four conditions: the provision of IEC materials on what to expect during labour and birth, group-based intervention delivery, partner or family member involvement, and opportunity for interaction between women and health providers.

Of the 16 possible configurations, we identified six configurations (Table 6 ). The first three rows show configurations resulting in successful interventions with perfect consistency (inclusion = 1). The first row shows successful interventions with all conditions present; the second and third rows show successful interventions with all conditions present except interaction with health providers or partner or family member involvement. The remaining three rows are configurations of unsuccessful interventions, missing at least two conditions, including the consistent absence of partner or family member involvement.

Boolean minimisation identified two intermediate pathways to successful intervention (Fig.  4 ). The first pathway shows that the opportunity for women to interact with health providers, provision of IEC materials, and group-based intervention delivery prompts successful interventions. The second pathway, however, shows that when there is no opportunity for women to interact with health providers, it is important to have partner or family member involvement alongside group-based intervention delivery and provision of IEC materials. These two pathways suggest that the delivery of educational interventions accompanied by provision of IEC materials and presence of emotional support for women during the intervention is important to trigger successful interventions. These pathways also emphasise that emotional support for women during the intervention can come from either partner, family member, or health provider. For the consolidated model, we did not simplify the solution further, as the intermediate solution is more theoretically sound compared to the most parsimonious solution.

Intermediate pathways from consolidated model that trigger successful interventions targeting pregnant women to optimise CS.  In QCA, asterisk (*) denotes an ‘AND’ relationship; Inclusion score (InclS), also known as consistency, indicates the degree to which the evidence is consistent with the hypothesis that there is sufficient relation between the configuration and the outcome; Proportional Reduction in Inconsistency (PRI) refers to the extent in which a configuration is sufficient in triggering successful outcome as well as the negation of the outcome; Coverage score (CovS) refers to percentage of cases in which the configuration is valid.

Sub-analysis – Interventions targeting both women and health providers or systems

In this sub-analysis, we run the important conditions identified from the consolidated model, added condition of multi-target intervention, and applied it to 17 interventions: 11 interventions targeting women, and six interventions targeting both women and health providers or systems (multi-target interventions).

Of 32 possible configurations, we identified eight configurations (Table 7 ). The first four rows show configurations with successful interventions with perfect consistency (inclusion = 1). The first row is where all the multi-target interventions are clustered, except the unsuccessful intervention Zhang (2020) [ 49 ], and where all the conditions are present. All the conditions in the second to fourth rows are present, except multi-target interventions (all rows), interaction with health providers (third row) and partner and family member involvement (fourth row). The remaining rows are all configurations to unsuccessful interventions, where at least three conditions are missing, except row 8, which is a single case row. This case is the only multi-target intervention that is unsuccessful and in which partner or family members were not involved.

The Boolean minimisation identified two intermediate pathways (Fig.  5 ). The first pathway shows that partner or family involvement, provision of IEC materials, and group-based intervention delivery prompt successful interventions. The first pathway is comprised of all five successful multi-target interventions [ 44 , 45 , 46 , 47 , 48 ] and four of 11 interventions targeting only women [ 37 , 68 , 69 , 71 ]. The second pathway shows that when multi-target interventions are absent, but when interaction with health providers is present, alongside provision of IEC materials and group-based intervention delivery, it prompts successful interventions (3/11 interventions targeting women only [ 37 , 69 , 70 ]). The first pathway shows that there are successful configurations with and without multi-target interventions. Therefore, similar to the interventions targeting women, when implementing multi-target interventions, intervention components targeting women are more likely to be successful when partners or family members are involved, interventions are implemented through group-based intervention delivery, IEC materials were provided, and there is an opportunity for women to interact with health providers.

Intermediate pathways from multi-target interventions sub-analysis that trigger successful interventions targeting pregnant women to optimise CS. In QCA, asterisk (*) denotes an ‘AND’ relationship; Inclusion score (InclS), also known as consistency, indicates the degree to which the evidence is consistent with the hypothesis that there is sufficient relation between the configuration and the outcome; Proportional Reduction in Inconsistency (PRI) refers to the extent in which a configuration is sufficient in triggering successful outcome as well as the negation of the outcome; Coverage score (CovS) refers to percentage of cases in which the configuration is valid

To summarise, there are four essential intervention components which trigger successful educational interventions focusing on pregnant women to reduce CS, this includes 1) group-based intervention delivery, 2) provision of IEC materials on what to expect during labour and birth, 3) partner or family member involvement on the intervention, and 4) opportunity for women to interact with health providers. These conditions do not work in siloed or independently but instead work jointly as parts of configurations to enable successful interventions.

Our extensive QCA identified configurations of essential intervention components which are sufficient to trigger successful interventions to optimised CS. Educational interventions focusing on women were successful by: 1) leveraging social or peer support through group-based intervention delivery, 2) improving women’s knowledge and awareness of what to expect during labour and birth, 3) ensuring women have emotional support through partner or family participation in the intervention, and 4) providing opportunities for women to interact with health providers. We found that the absence of two or more of the above characteristics in an intervention result in unsuccessful interventions. Unlike our logic model, which predicted engagement strategies (i.e. intensity, frequency, technique, recruitment, incentives) to be essential to intervention success, we found that “support” seems to be central in maximising benefits of interventions targeting women.

Group-based intervention delivery is present across all four truth tables and eight pathways leading to successful intervention implementation, suggesting that group-based intervention delivery is an essential component of interventions targeting women. Despite this, we cannot conclude that group-based intervention delivery is a necessary condition, as there may be other pathways not captured in this QCA. The importance of group-based intervention delivery may be due to the group setting providing women with a sense of confidence through peer support and engagement. In group-based interventions, women may feel more confident when learning with others and peer support may motivate women. Furthermore, all group-based interventions in our included studies are conducted at health facilities, which may provide women with more confidence that information is aligned with clinical recommendations. Evidence on benefits of group-based interventions involving women who are pregnant has been demonstrated previously [ 72 , 73 ]. Women reported that group-based interventions reduce their feelings of isolation, provide access to group support, and allow opportunities for them to share their experiences [ 72 , 74 , 75 , 76 ]. This is aligned with social support theory, in which social support through a group or social environment may provide women with feelings of reassurance, compassion, reduce feelings of uncertainty, increase sense of control, access to new contacts to solve problems, and provision of instrumental support, which eventually influence positive health behaviours [ 72 , 77 ]. Women may resolve their uncertainties around mode of birth by sharing their concerns with others and learning at the same time how others cope with it. These findings are consistent with the benefits associated with group-based antenatal care, which is recommended by WHO [ 78 , 79 ].

Kingdon et al. (2018) reported that women and communities liked learning new birth information, as it opens new ways of thinking about vaginal birth and CS, and educates about benefits of different modes of birth, including risks of CS. Our QCA is aligned with this finding where provision of information about birth through education delivery leads to successful interventions but with certain caveats. That is, provision of birth information should be accompanied by IEC materials and through group-based intervention delivery. There is not enough information to distinguish what type of IEC materials lead to successful intervention; however, it is important to note that the format of the IEC materials (such as paper-based or mobile application) may affect success. More work is needed to understand how women and families react to format of IEC materials; for example, will paper-based IEC materials be relegated over more modern methods of reaching women with information through digital applications? The QUALI-DEC (Quality decision-making (QUALI-DEC) by women and healthcare providers for appropriate use of caesarean section) study is currently implementing a decision-analysis tool to help women make an informed decision on preferred mode of birth using both a paper-based and mobile application that may shed some light on this [ 80 ].

Previous research has shown that women who participated in interventions aiming to reduce CS desired emotional support (from partners, doulas or health providers) alongside the communication about childbirth [ 22 ]. Our QCA is aligned with this finding in which emotional support from partners or family members is highly influential in leading to successful interventions. Partner involvement in maternity care has been extensively studied and has been demonstrated to improve maternal health care utilisation and outcomes [ 81 ]. Both women and their partners perceived that partner involvement is crucial as it facilitates men to learn directly from providers, thus promoting shared decision-making among women and partners and enabling partners to reinforce adherence to any beneficial suggestions [ 82 , 83 , 84 , 85 , 86 ]. Partners provide psychosocial support to women, for example through being present during pregnancy and the childbirth process, as well as instrumental support, which includes supporting women financially [ 82 , 83 , 84 ]. Despite the benefits of partner involvement, partner's participation in maternity care is still low [ 82 ], as reflected in this study where only four out of 11 included interventions on this study involved partner or family member involvement. Reasons for this low participation, which include unequal gender norms and limited health system capability [ 82 , 84 , 85 , 86 ], should be explored and addressed to ensure the benefits of the interventions.

Furthermore, our QCA demonstrates the importance of interaction with health providers to trigger successful interventions. The interaction of women with providers in CS decision-making, however, is on a “nexus of power, trust, and risk”, where it may be beneficial but can also reinforce the structural oppression of women [ 13 ]. A recent study on patient-provider interaction in CS decision-making concluded that the interaction between providers who are risk-averse, and women who are cautious about their pregnancies in the health system results in discouragement of vaginal births [ 87 ]. However, this decision could be averted by meaningful communication between women and providers where CS risks and benefits are communicated in an environment where vaginal birth is encouraged [ 87 ]. Furthermore, the reasons women desire interaction with providers can come from opposite directions. Some women see providers as the most trusted and knowledgeable source, in which women can trust the judgement and ensure that the information learned is reliable and evidenced-based [ 22 ]. On the other hand, some women may have scepticism towards providers where women understand that providers’ preference may negatively influence their preferred mode of birth [ 22 ]. Therefore, adequate, two-way interaction is important for women to build a good rapport with providers.

It is also important to note that we have limited evidence (3/17 intervention studies) involving women with previous CS. Vaginal birth after previous CS (VBAC) can be a safe and positive experience for some women, but there are also potential risks depending on their obstetric history [ 88 , 89 , 90 ]. Davis (2020) found that women were motivated to have VBAC due to negative experiences of CS, such as the difficult recovery, and that health providers' roles served as pivotal drivers in motivating women towards VBAC [ 91 ]. Other than this, VBAC also requires giving birth in a suitably staffed and equipped maternity unit, with staff trained on VBAC, equipment for labour monitoring, and resources for emergency CS if needed [ 89 , 90 ]. There is comparatively less research conducted on VBAC and trial of labour after CS [ 88 ]. Therefore, more work is needed to explore if there are potentially different pathways that lead to successful intervention implementation for women with previous CS. It may be more likely that interventions targeting various stakeholders are more crucial in this group of women. For example, both education for women and partners or families, as well as training to upskill health providers might be needed to support VBAC.

Strength and limitations

We found many included studies had poor reporting of the interventions, including the general intervention components (e.g. presence of policies that may support interventions) and process evaluation components, which is reflective of the historical approach to reporting trial data. This poor reporting means we could not engage further in the interventions and thus may have missed important conditions that were not reported. However, we have attempted to compensate for limited process evaluation components by identifying all relevant sibling studies that could contribute to a better understanding of context. Furthermore, there are no studies conducted in low-income countries, despite rapidly increasing CS rates in these settings. Lastly, we were not able to conduct more nuanced analyses about CS, such as exploring how CS interventions impacted changes to emergency versus elective CS, VBAC, or instrumental birth, due to an insufficient number of studies and heterogeneity in outcome measurements. Therefore, it is important to note that we are not necessarily measuring the optimal outcome of interest—reducing unnecessary CS. However, it is unlikely that these non-clinical interventions will interfere with a decision of CS based on clinical indications.

Despite these limitations, this is the first study aiming to understand how certain interventions can be successful in targeting women to optimise CS use. We used the QCA approach and new analytical frameworks to re-analyse existing systematic review evidence to generate new knowledge. We ensure robustness through the use of a logic model and worked backwards in understanding what aspects are different in the intervention across different outcomes. The use of QCA and qualitative evidence synthesis ensured that the results are theory-driven, incorporate participants’ perspectives into the analysis, and explored iteratively to find the appropriate configurations, reducing the risk of data fishing. Lastly, this QCA extends the understanding of effectiveness review conducted by Chen et al. (2018) [ 18 ] by explaining the potential intervention components which may influence heterogeneity.

Implications for practice and research

To aid researchers and health providers to reduce CS in their contexts and designing educational interventions targeting women during pregnancy, we have developed a checklist of key components or questions to consider when designing the interventions that may help lead to successful implementation:

Is the intervention delivered in a group setting?

Are IEC materials on what to expect during labour and birth disseminated to women?

Are women’s partners or families involved in the intervention?

Do women have opportunities to interact with health providers?

We have used this checklist to explore the extent to which the included interventions in our QCA include these components using a matrix model (Fig.  6 ).

Matrix model assessing the extent to which the included intervention studies have essential intervention components identified in the QCA

Additionally, future research on interventions to optimise the use of CS should report the intervention components implemented, including process outcomes such as fidelity, attrition, contextual factors (e.g. policies, details of how the intervention is delivered), and stakeholder factors (e.g. women’s perceptions and satisfaction). These factors are important in not just evaluating whether the intervention is successful or not, but also in exploring why similar interventions can work in one but not in another context. There is also a need for more intervention studies implementing VBAC to reduce CS, to understand how involving women with previous CS may result in successful interventions. Furthermore, more studies understanding impact of the interventions targeting women in LMICs are needed.

This QCA illustrates crucial intervention components and potential pathways that can trigger successful educational interventions to optimise CS, focusing on pregnant women. The following intervention components are found to be sufficient in triggering successful outcomes: 1) group-based delivery, 2) provision of IEC materials, 3) partner or family member involvement, and 4) opportunity for women to interact with health providers. These intervention components do not work in siloed or independently but instead work jointly as parts of configurations to enable successful interventions. Researchers, trialists, hospitals, or other institutions and stakeholders planning interventions focusing on pregnant women can consider including these components to ensure benefits. More studies understanding impact of the interventions targeting women to optimise CS are needed from LMICs. Researchers should clearly describe and report intervention components in trials, and consider how process evaluations can help explain why trials were successful or not. More robust trial reporting and process evaluations can help to better understand mechanisms of action and why interventions may work in one context yet not another.

Availability of data and materials

Additional information files have been provided and more data may be provided upon request to [email protected].

Abbreviations

Coverage score

• Caesarean section

Crisp set qualitative comparative analysis

Fuzzy set qualitative comparative analysis

Information, education, and communication

Inclusion score

Low- and middle-income countries

Proportional reduction in inconsistency

Quality decision-making by women and healthcare providers for appropriate use of caesarean section

Vaginal birth after previous caesarean section

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Acknowledgements

We extend our thanks to Jim Berryman (Brownless Medical Library, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne) for his help in refining the search strategy for sibling studies.

This research was made possible with the support of UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), a co-sponsored programme executed by the World Health Organization (WHO). RIZ is supported by Melbourne Research Scholarship and Human Rights Scholarship from The University of Melbourne. CSEH is supported by a National Health and Medical Research Council (NHMRC) Principal Research Fellowship. MAB’s time is supported by an Australian Research Council Discovery Early Career Researcher Award (DE200100264) and a Dame Kate Campbell Fellowship (University of Melbourne Faculty of Medicine, Dentistry, and Health Sciences). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The contents of this publication are the responsibility of the authors and do not reflect the views of the UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), World Health Organization.

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Correspondence to Rana Islamiah Zahroh .

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Supplementary Information

Additional file 1..

Logic model in optimizing CS use.

Additional file 2.

Risk of bias assessments.

Additional file 3.

Coding framework and calibration rules.

Additional file 4.

Coding framework as applied to each intervention (data table).

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Zahroh, R.I., Sutcliffe, K., Kneale, D. et al. Educational interventions targeting pregnant women to optimise the use of caesarean section: What are the essential elements? A qualitative comparative analysis. BMC Public Health 23 , 1851 (2023). https://doi.org/10.1186/s12889-023-16718-0

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DOI : https://doi.org/10.1186/s12889-023-16718-0

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Corporate activities that influence population health: A scoping review and qualitative synthesis to develop the HEALTH-CORP typology

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Introduction: The concept of the commercial determinants of health (CDH) is used to study the actions (and associated structures) of commercial entities that influence population health and health equity. The aim of this study was to develop a typology that describes the diverse set of activities through which corporations influence population health and health equity across industries. Methods: We conducted a scoping review of articles using CDH terms (n=116) that discuss corporate activities that can influence population health and health equity across 16 industries. We used the qualitative constant comparison method to build a typology called the Corporate Influences on Population Health (HEALTH-CORP) typology. Results: The HEALTH-CORP typology identifies 70 corporate activities that can influence health across industries and categorizes them into seven domains of corporate influence (e.g., political practices, employment practices). We present a model that situates these domains based on their proximity to health outcomes and identify five population groups (e.g., workers, local communities) to consider when evaluating corporate health impacts. Discussion: The HEALTH-CORP typology facilitates an understanding of the diverse set of corporate activities that can influence population health and the population groups affected by these activities. We discuss the utility of these contributions in terms of identifying interventions to address the CDH and advancing efforts to measure and monitor the CDH. We also leverage our findings to identify key gaps in CDH literature and suggest avenues for future research.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

Raquel Burgess was supported by a Doctoral Foreign Study Award provided by the Canadian Institutes of Health Research at the time this research was conducted. Funding was provided by the Yale School of Public Health and the Yale Graduate Student Assembly to present this work at the American Public Health Association Annual Meeting in 2022.

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

The data for this study are published academic articles which are available from the respective publishers (see Supplementary Material, Appendix 2 for the characteristics of included articles). In addition, we uploaded the following files to Open Science Framework (DOI 10.17605/OSF.IO/TG9S7) to support data availability: 1) a .csv file containing a list of the articles that underwent title and abstract screening in our study and the respective screening decisions that were assigned, and 2) .ris files containing the citations to the respective articles and the assigned screening decisions, which can be uploaded into a reference manager. Interested parties can contact the corresponding author for additional information.

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